From a566c1c1a7f1cfc0401e9506acdb06d361528f2a Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Mon, 10 Jan 2022 11:27:04 +0800 Subject: [PATCH 01/41] Update fused embedding modelzoo perf benchmark --- .../features/GPUFusedEmbedding/.gitignore | 4 +- .../features/GPUFusedEmbedding/DLRM/README.md | 6 +- .../features/GPUFusedEmbedding/DLRM/train.py | 314 ++++++++++-------- .../GPUFusedEmbedding/DeepFM/README.md | 6 +- .../features/GPUFusedEmbedding/WDL/README.md | 4 +- 5 files changed, 185 insertions(+), 149 deletions(-) diff --git a/modelzoo/features/GPUFusedEmbedding/.gitignore b/modelzoo/features/GPUFusedEmbedding/.gitignore index 32a3fccc355..69ebd9d97be 100644 --- a/modelzoo/features/GPUFusedEmbedding/.gitignore +++ b/modelzoo/features/GPUFusedEmbedding/.gitignore @@ -1,3 +1,5 @@ *.csv */result/model_* -record.py \ No newline at end of file +record.py +*.sh +*.nsys-rep \ No newline at end of file diff --git a/modelzoo/features/GPUFusedEmbedding/DLRM/README.md b/modelzoo/features/GPUFusedEmbedding/DLRM/README.md index 75d22a4ed67..39c79aab534 100644 --- a/modelzoo/features/GPUFusedEmbedding/DLRM/README.md +++ b/modelzoo/features/GPUFusedEmbedding/DLRM/README.md @@ -16,6 +16,6 @@ On A100-80GB-PCIE GPU, with 8 cores AMD EPYC 7232P CPU @ 3.20GHz. Average of 500 | | Avg Time per Iteration | | ------- | ---------------------- | -| Unfused | 37.15 ms | -| Fused | 31.43 ms | -| SpeedUp | 1.18x | +| Unfused | 20.78 ms | +| Fused | 17.41 ms | +| SpeedUp | 1.19x | diff --git a/modelzoo/features/GPUFusedEmbedding/DLRM/train.py b/modelzoo/features/GPUFusedEmbedding/DLRM/train.py index 043021524c3..b78b9a8c911 100644 --- a/modelzoo/features/GPUFusedEmbedding/DLRM/train.py +++ b/modelzoo/features/GPUFusedEmbedding/DLRM/train.py @@ -82,9 +82,8 @@ def parse_csv(value): def build_feature_cols(): - wide_column = [] - deep_column = [] - fm_column = [] + dense_column = [] + sparse_column = [] for column_name in FEATURE_COLUMNS: if column_name in CATEGORICAL_COLUMNS: categorical_column = tf.feature_column.categorical_column_with_hash_bucket( @@ -93,46 +92,38 @@ def build_feature_cols(): hash_bucket_size=10000, dtype=tf.string) - categorical_embedding_column = tf.feature_column.embedding_column( - categorical_column, dimension=16, combiner='mean', - do_fusion=True) - - wide_column.append(categorical_embedding_column) - deep_column.append(categorical_embedding_column) - fm_column.append(categorical_embedding_column) + sparse_column.append( + tf.feature_column.embedding_column(categorical_column, + dimension=16, + combiner='mean', + do_fusion=True)) else: column = tf.feature_column.numeric_column(column_name, shape=(1, )) - wide_column.append(column) - deep_column.append(column) + dense_column.append(column) - return wide_column, fm_column, deep_column + return dense_column, sparse_column -class DeepFM(): +class DLRM(): def __init__(self, - wide_column=None, - fm_column=None, - deep_column=None, - dnn_hidden_units=[1024, 256, 32], - final_hidden_units=[128, 64], - optimizer_type='adam', - learning_rate=0.001, + dense_column=None, + sparse_column=None, + mlp_bot=[512, 256, 64, 16], + mlp_top=[512, 256], + learning_rate=0.1, inputs=None, - use_bn=True, + interaction_op='dot', bf16=False, input_layer_partitioner=None, dense_layer_partitioner=None): if not inputs: raise ValueError('Dataset is not defined.') - self.wide_column = wide_column - self.deep_column = deep_column - self.fm_column = fm_column - if not wide_column or not fm_column or not deep_column: - raise ValueError( - 'Wide column, FM column or Deep column is not defined.') - self.dnn_hidden_units = dnn_hidden_units - self.final_hidden_units = final_hidden_units - self.optimizer_type = optimizer_type + self.dense_column = dense_column + self.sparse_column = sparse_column + if not dense_column or not sparse_column: + raise ValueError('Dense column or sparse column is not defined.') + self.mlp_bot = mlp_bot + self.mlp_top = mlp_top self.learning_rate = learning_rate self.input_layer_partitioner = input_layer_partitioner self.dense_layer_partitioner = dense_layer_partitioner @@ -141,118 +132,166 @@ def __init__(self, self.label = inputs[1] self.bf16 = bf16 - self._is_training = True - self.use_bn = use_bn + self.interaction_op = interaction_op + if self.interaction_op not in ['dot', 'cat']: + print("Invaild interaction op, must be 'dot' or 'cat'.") + sys.exit() self.predict = self.prediction() with tf.name_scope('head'): self.train_op, self.loss = self.optimizer() - self.acc, self.acc_op = tf.metrics.accuracy(labels=self.label, - predictions=self.predict) + self.acc, self.acc_op = tf.metrics.accuracy( + labels=self.label, predictions=self.predict) self.auc, self.auc_op = tf.metrics.auc(labels=self.label, - predictions=self.predict, - num_thresholds=1000) + predictions=self.predict, + num_thresholds=1000) tf.summary.scalar('eval_acc', self.acc) tf.summary.scalar('eval_auc', self.auc) - def dnn(self, dnn_input, dnn_hidden_units=None, layer_name=''): - for layer_id, num_hidden_units in enumerate(dnn_hidden_units): - with tf.variable_scope(layer_name + "_%d" % layer_id, - partitioner=self.dense_layer_partitioner, - reuse=tf.AUTO_REUSE) as dnn_layer_scope: - dnn_input = tf.layers.dense(dnn_input, - units=num_hidden_units, - activation=tf.nn.relu, - name=dnn_layer_scope) - if self.use_bn: - dnn_input = tf.layers.batch_normalization( - dnn_input, training=self._is_training, trainable=True) - add_layer_summary(dnn_input, dnn_layer_scope.name) - - return dnn_input + def dot_op(self, features): + batch_size = tf.shape(features)[0] + matrixdot = tf.matmul(features, features, transpose_b=True) + feature_dim = matrixdot.shape[-1] - def prediction(self): - # input features - with tf.variable_scope('input_layer', - partitioner=self.input_layer_partitioner, - reuse=tf.AUTO_REUSE): - - fm_cols = {} - wide_input = tf.feature_column.input_layer( - self.feature, self.wide_column, cols_to_output_tensors=fm_cols) - fm_input = tf.stack([fm_cols[cols] for cols in self.fm_column], 1) - dnn_input = tf.feature_column.input_layer(self.feature, - self.deep_column) + ones_mat = tf.ones_like(matrixdot) + lower_tri_mat = ones_mat - tf.linalg.band_part(ones_mat, 0, -1) + lower_tri_mask = tf.cast(lower_tri_mat, tf.bool) + result = tf.boolean_mask(matrixdot, lower_tri_mask) + output_dim = feature_dim * (feature_dim - 1) // 2 - if self.bf16: - wide_input = tf.cast(wide_input, dtype=tf.bfloat16) - fm_input = tf.cast(fm_input, dtype=tf.bfloat16) - dnn_input = tf.cast(dnn_input, dtype=tf.bfloat16) + return tf.reshape(result, (batch_size, output_dim)) - # DNN part + def prediction(self): + # input dense feature and embedding of sparse features + with tf.variable_scope('input_layer', reuse=tf.AUTO_REUSE): + with tf.variable_scope('dense_input_layer', + partitioner=self.input_layer_partitioner, + reuse=tf.AUTO_REUSE): + dense_inputs = tf.feature_column.input_layer( + self.feature, self.dense_column) + with tf.variable_scope('sparse_input_layer', reuse=tf.AUTO_REUSE): + column_tensors = {} + sparse_inputs = tf.feature_column.input_layer( + self.feature, + self.sparse_column, + cols_to_output_tensors=column_tensors) + + # MLP behind dense inputs if self.bf16: - with tf.variable_scope('dnn').keep_weights(): - dnn_output = self.dnn(dnn_input, self.dnn_hidden_units, - 'dnn_layer') + dense_inputs = tf.cast(dense_inputs, dtype=tf.bfloat16) + with tf.variable_scope('mlp_bot_layer', + partitioner=self.dense_layer_partitioner, + reuse=tf.AUTO_REUSE).keep_weights(): + for layer_id, num_hidden_units in enumerate(self.mlp_bot): + with tf.variable_scope( + "mlp_bot_hiddenlayer_%d" % layer_id, + reuse=tf.AUTO_REUSE) as mlp_bot_hidden_layer_scope: + dense_inputs = tf.layers.dense( + dense_inputs, + units=num_hidden_units, + activation=tf.nn.relu, + name=mlp_bot_hidden_layer_scope) + add_layer_summary(dense_inputs, + mlp_bot_hidden_layer_scope.name) + dense_inputs = tf.cast(dense_inputs, dtype=tf.float32) else: - with tf.variable_scope('dnn'): - dnn_output = self.dnn(dnn_input, self.dnn_hidden_units, - 'dnn_layer') - - # linear / fisrt order part - with tf.variable_scope('linear', reuse=tf.AUTO_REUSE) as linear: - linear_output = tf.reduce_sum(wide_input, axis=1, keepdims=True) - - # FM second order part - with tf.variable_scope('fm', reuse=tf.AUTO_REUSE) as fm: - sum_square = tf.square(tf.reduce_sum(fm_input, axis=1)) - square_sum = tf.reduce_sum(tf.square(fm_input), axis=1) - fm_output = 0.5 * tf.subtract(sum_square, square_sum) - - # Final dnn layer - all_input = tf.concat([dnn_output, linear_output, fm_output], 1) + with tf.variable_scope('mlp_bot_layer', + partitioner=self.dense_layer_partitioner, + reuse=tf.AUTO_REUSE): + for layer_id, num_hidden_units in enumerate(self.mlp_bot): + with tf.variable_scope( + "mlp_bot_hiddenlayer_%d" % layer_id, + reuse=tf.AUTO_REUSE) as mlp_bot_hidden_layer_scope: + dense_inputs = tf.layers.dense( + dense_inputs, + units=num_hidden_units, + activation=tf.nn.relu, + name=mlp_bot_hidden_layer_scope) + add_layer_summary(dense_inputs, + mlp_bot_hidden_layer_scope.name) + #interaction_op + if self.interaction_op == 'dot': + # dot op + with tf.variable_scope('Op_dot_layer', reuse=tf.AUTO_REUSE): + net = [dense_inputs] + for cols in self.sparse_column: + net.append(column_tensors[cols]) + net = tf.stack(net, axis=1) + net = self.dot_op(net) + net = tf.concat([dense_inputs, net], 1) + elif self.interaction_op == 'cat': + net = tf.concat([dense_inputs, sparse_inputs], 1) + + # top MLP before output if self.bf16: - with tf.variable_scope('final_dnn').keep_weights(): - net = self.dnn(all_input, self.final_hidden_units, 'final_dnn') + net = tf.cast(net, dtype=tf.bfloat16) + with tf.variable_scope('mlp_top_layer', + partitioner=self.dense_layer_partitioner, + reuse=tf.AUTO_REUSE).keep_weights(): + for layer_id, num_hidden_units in enumerate(self.mlp_top): + with tf.variable_scope( + "mlp_top_hiddenlayer_%d" % layer_id, + reuse=tf.AUTO_REUSE) as mlp_top_hidden_layer_scope: + net = tf.layers.dense(net, + units=num_hidden_units, + activation=tf.nn.relu, + name=mlp_top_hidden_layer_scope) + + add_layer_summary(net, mlp_top_hidden_layer_scope.name) + + with tf.variable_scope( + "mlp_top_hiddenlayer_last", + reuse=tf.AUTO_REUSE) as mlp_top_hidden_layer_scope: + net = tf.layers.dense(net, + units=1, + activation=None, + name=mlp_top_hidden_layer_scope) + + # net = tf.cast(net, dtype=tf.float32) + net = tf.math.sigmoid(net) + + add_layer_summary(net, mlp_top_hidden_layer_scope.name) + net = tf.cast(net, dtype=tf.float32) else: - with tf.variable_scope('final_dnn'): - net = self.dnn(all_input, self.final_hidden_units, 'final_dnn') - - net = tf.layers.dense(net, units=1) - net = tf.math.sigmoid(net) + with tf.variable_scope('mlp_top_layer', + partitioner=self.dense_layer_partitioner, + reuse=tf.AUTO_REUSE): + for layer_id, num_hidden_units in enumerate(self.mlp_top): + with tf.variable_scope( + "mlp_top_hiddenlayer_%d" % layer_id, + reuse=tf.AUTO_REUSE) as mlp_top_hidden_layer_scope: + net = tf.layers.dense(net, + units=num_hidden_units, + activation=tf.nn.relu, + name=mlp_top_hidden_layer_scope) + + add_layer_summary(net, mlp_top_hidden_layer_scope.name) + + with tf.variable_scope( + "mlp_top_hiddenlayer_last", + reuse=tf.AUTO_REUSE) as mlp_top_hidden_layer_scope: + net = tf.layers.dense(net, + units=1, + activation=None, + name=mlp_top_hidden_layer_scope) + net = tf.math.sigmoid(net) + + add_layer_summary(net, mlp_top_hidden_layer_scope.name) return net def optimizer(self): - loss_func = tf.losses.mean_squared_error + bce_loss_func = tf.keras.losses.BinaryCrossentropy() self.predict = tf.squeeze(self.predict) - loss = tf.math.reduce_mean(loss_func(self.label, self.predict)) + loss = tf.math.reduce_mean(bce_loss_func(self.label, self.predict)) tf.summary.scalar('loss', loss) self.global_step = tf.train.get_or_create_global_step() - if self.optimizer_type == 'adam': - optimizer = tf.train.AdamOptimizer( - learning_rate=self.learning_rate, - beta1=0.9, - beta2=0.999, - epsilon=1e-8) - elif self.optimizer_type == 'adagrad': - optimizer = tf.train.AdagradOptimizer( - learning_rate=self.learning_rate, - initial_accumulator_value=1e-8) - elif self.optimizer_type == 'adamasync': - optimizer = tf.train.AdamAsyncOptimizer( - learning_rate=self.learning_rate, - beta1=0.9, - beta2=0.999, - epsilon=1e-8) - else: - raise ValueError('Optimzier type error.') + optimizer = tf.train.GradientDescentOptimizer( + learning_rate=self.learning_rate) - update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) - with tf.control_dependencies(update_ops): - train_op = optimizer.minimize(loss, global_step=self.global_step) + train_op = optimizer.minimize(loss, global_step=self.global_step) return train_op, loss @@ -282,14 +321,10 @@ def get_arg_parser(): help='Dropout regularization for deep model', type=float, default=0.0) - parser.add_argument("--optimizer", - type=str, - choices=["adam", "adagrad", "adamasync"], - default="adam") parser.add_argument('--learning_rate', help='Learning rate for model', type=float, - default=0.001) + default=0.1) parser.add_argument('--save_steps', help='set the number of steps on saving checkpoints', type=int, @@ -308,6 +343,10 @@ def get_arg_parser(): help='number of steps on saving timeline. Default 0', type=int, default=0) + parser.add_argument("--interaction_op", + type=str, + choices=["dot", "cat"], + default="cat") parser.add_argument("--protocol", type=str, choices=["grpc", "grpc++", "star_server"], @@ -371,12 +410,12 @@ def main(tf_config=None, server=None): # set directory path model_dir = os.path.join(args.output_dir, - 'model_DeepFM_' + str(int(time.time()))) + 'model_DLRM_' + str(int(time.time()))) checkpoint_dir = args.checkpoint if args.checkpoint else model_dir print("Saving model checkpoints to " + checkpoint_dir) # create data pipline - wide_column, fm_column, deep_column = build_feature_cols() + dense_column, sparse_column = build_feature_cols() train_dataset = generate_input_data(train_file, batch_size, no_of_epochs) test_dataset = generate_input_data(test_file, batch_size, 1) @@ -399,25 +438,23 @@ def main(tf_config=None, server=None): 10) if args.dense_layer_partitioner else None # create model - model = DeepFM(wide_column=wide_column, - fm_column=fm_column, - deep_column=deep_column, - optimizer_type=args.optimizer, - learning_rate=args.learning_rate, - bf16=args.bf16, - inputs=next_element, - input_layer_partitioner=input_layer_partitioner, - dense_layer_partitioner=dense_layer_partitioner) + model = DLRM(dense_column=dense_column, + sparse_column=sparse_column, + learning_rate=args.learning_rate, + bf16=args.bf16, + interaction_op=args.interaction_op, + inputs=next_element, + input_layer_partitioner=input_layer_partitioner, + dense_layer_partitioner=dense_layer_partitioner) sess_config = tf.ConfigProto() if args.inter: sess_config.inter_op_parallelism_threads = args.inter if args.intra: sess_config.intra_op_parallelism_threads = args.intra - hooks = [] + hooks = [] if tf_config: - print('train steps : %d' % train_steps) hooks.append(tf.train.StopAtStepHook(last_step=train_steps)) hooks.append( tf.train.LoggingTensorHook( @@ -455,7 +492,6 @@ def main(tf_config=None, server=None): # train model sess.run(train_init_op) - model._is_training = True start = time.perf_counter() for _in in range(0, train_steps): @@ -467,7 +503,7 @@ def main(tf_config=None, server=None): checkpoint_path = saver.save(sess, save_path=os.path.join( checkpoint_dir, - 'DeepFM-checkpoint'), + 'dlrm-checkpoint'), global_step=_in) print("Save checkpoint to %s" % checkpoint_path) elif (args.timeline > 0 and _in % args.timeline == 0): @@ -504,8 +540,6 @@ def main(tf_config=None, server=None): sess.run(test_init_op) sess.run(tf.local_variables_initializer()) - model._is_training = False - for _in in range(1, test_steps + 1): if (_in != test_steps): sess.run( diff --git a/modelzoo/features/GPUFusedEmbedding/DeepFM/README.md b/modelzoo/features/GPUFusedEmbedding/DeepFM/README.md index 5c371128aa7..edb3dc54d6a 100644 --- a/modelzoo/features/GPUFusedEmbedding/DeepFM/README.md +++ b/modelzoo/features/GPUFusedEmbedding/DeepFM/README.md @@ -16,6 +16,6 @@ On A100-80GB-PCIE GPU, with 8 cores AMD EPYC 7232P CPU @ 3.20GHz. Average of 500 | | Avg Time per Iteration | | ------- | ---------------------- | -| Unfused | 37.70 ms | -| Fused | 31.08 ms | -| SpeedUp | 1.21x | +| Unfused | 37.24 ms | +| Fused | 30.98 ms | +| SpeedUp | 1.20x | diff --git a/modelzoo/features/GPUFusedEmbedding/WDL/README.md b/modelzoo/features/GPUFusedEmbedding/WDL/README.md index 6a8384bfbed..df91b6533ad 100755 --- a/modelzoo/features/GPUFusedEmbedding/WDL/README.md +++ b/modelzoo/features/GPUFusedEmbedding/WDL/README.md @@ -17,6 +17,6 @@ On A100-80GB-PCIE GPU, with 8 cores AMD EPYC 7232P CPU @ 3.20GHz. Average of 500 | | Avg Time per Iteration | | ------- | ---------------------- | -| Unfused | 36.42 ms | -| Fused | 34.57 ms | +| Unfused | 36.38 ms | +| Fused | 34.52 ms | | SpeedUp | 1.05x | \ No newline at end of file From 45679183c4999cb7617929bfeb403514874abbfb Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Mon, 10 Jan 2022 15:07:14 +0800 Subject: [PATCH 02/41] Update perf benchmark under docs --- docs/Fused-Embedding.md | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) diff --git a/docs/Fused-Embedding.md b/docs/Fused-Embedding.md index 5a552d7aa62..f1a2a9eacd5 100644 --- a/docs/Fused-Embedding.md +++ b/docs/Fused-Embedding.md @@ -141,5 +141,34 @@ def fused_embedding_lookup_sparse(params, 4. **FusedEmbeddingSparsePostLookUpGrad** 负责 FusedEmbeddingSparsePostLookUp 的反向梯度计算。 +## 性能对比 +在 modelzoo 中，对比了一些 model 在 unfused 以及 fused embedding 情况下性能提升（5000个 iteration 平均结果） +Machine: +8 cores AMD EPYC 7232P CPU @ 3.20GHz. +A100-80GB-PCIE GPU + +DLRM Model: + +| | Avg Time per Iteration | +| ------- | ---------------------- | +| Unfused | 20.78 ms | +| Fused | 17.41 ms | +| SpeedUp | 1.19x | + +DeepFM Model: + +| | Avg Time per Iteration | +| ------- | ---------------------- | +| Unfused | 37.24 ms | +| Fused | 30.98 ms | +| SpeedUp | 1.20x | + +WDL Model: + +| | Avg Time per Iteration | +| ------- | ---------------------- | +| Unfused | 36.38 ms | +| Fused | 34.52 ms | +| SpeedUp | 1.05x | From fe129e89fd31238a35a36058fbc3ed2242c7b025 Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Fri, 14 Jan 2022 19:19:48 +0800 Subject: [PATCH 03/41] Add nvtx to fused embedding ops --- tensorflow/core/kernels/BUILD | 4 ++-- .../fused_embedding/fused_embedding_post_ops_gpus.cu.cc | 5 +++++ .../fused_embedding/fused_embedding_pre_ops_gpus.cu.cc | 3 +++ 3 files changed, 10 insertions(+), 2 deletions(-) diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD index b976dc19cd1..261cb081382 100644 --- a/tensorflow/core/kernels/BUILD +++ b/tensorflow/core/kernels/BUILD @@ -5296,8 +5296,8 @@ tf_kernel_library( "fused_embedding/fused_embedding_pre_ops_gpus.cu.cc", "fused_embedding/fused_embedding_post_ops_gpus.cu.cc" ], - deps = ["//third_party/eigen3"] + DYNAMIC_DEPS + - if_cuda(["@cub_archive//:cub", ":fused_embedding_common_cuh"]), + deps = ["//third_party/eigen3", "//tensorflow/core/profiler:nvtx_utils"] + + DYNAMIC_DEPS + if_cuda(["@cub_archive//:cub", ":fused_embedding_common_cuh"]), ) tf_kernel_library( diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc b/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc index 54713357088..efbbde8ec81 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc @@ -9,6 +9,7 @@ #define EIGEN_USE_GPU #include "tensorflow/core/kernels/fused_embedding/fused_embedding_common.cu.h" +#include "tensorflow/core/profiler/nvtx_utils.h" #include "tensorflow/core/util/gpu_kernel_helper.h" #include "third_party/cub/thread/thread_operators.cuh" @@ -137,6 +138,8 @@ class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { OP_REQUIRES_OK(ctx, ctx->input("row_empty_and_invalid_flags", &row_empty_and_invalid_flags)); + nvtx::ScopedRangeIfEnabled nvtx_range(this); + const int64_t emb_vec_size = emb_shards[0].shape().dim_size(1); const int64_t batch_size = dense_shape_tensor->flat().data()[0]; @@ -259,6 +262,8 @@ class FusedEmbeddingSparsePostLookUpGradGPU : public OpKernel { OpOutputList grad_shards; OP_REQUIRES_OK(ctx, ctx->output_list("grad_shards", &grad_shards)); + nvtx::ScopedRangeIfEnabled nvtx_range(this); + const int64_t batch_size = top_grad_tensor->shape().dim_size(0); const int64_t emb_vec_size = emb_shards[0].shape().dim_size(1); diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc b/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc index 72f1eeb711f..9d3eb981cbc 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc @@ -9,6 +9,7 @@ #define EIGEN_USE_GPU #include "tensorflow/core/kernels/fused_embedding/fused_embedding_common.cu.h" +#include "tensorflow/core/profiler/nvtx_utils.h" #include "tensorflow/core/util/gpu_kernel_helper.h" #include "third_party/cub/device/device_radix_sort.cuh" #include "third_party/cub/device/device_select.cuh" @@ -153,6 +154,8 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { const int64_t default_id = default_id_ >= 0 ? default_id_ : 0; const int linear_mapping_threads = 128; + nvtx::ScopedRangeIfEnabled nvtx_range(this); + // 1. bind inputs Tensor const* values_tensor = nullptr; OP_REQUIRES_OK(ctx, ctx->input("sp_values", &values_tensor)); From 13906e442b2162b31e45259723200e530ab5eac3 Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Thu, 20 Jan 2022 17:42:19 +0800 Subject: [PATCH 04/41] refactor a little big --- tensorflow/core/kernels/BUILD | 19 +- .../fused_embedding/fused_embedding.cu.h | 156 ++++++++ .../fused_embedding_common.cu.h | 80 ---- .../fused_embedding_gpu_kernels.cu.cc | 366 ++++++++++++++++++ .../fused_embedding_local_ops_gpu.cu.cc | 9 +- .../fused_embedding_post_ops_gpus.cu.cc | 232 ++++------- .../fused_embedding_pre_ops_gpus.cu.cc | 349 +++++------------ .../fused_embedding_local_ops_test.cc | 5 - .../fused_embedding_post_grad_ops_test.cc | 1 - .../fused_embedding_post_ops_test.cc | 1 - .../fused_embedding_pre_ops_test.cc | 4 +- 11 files changed, 706 insertions(+), 516 deletions(-) create mode 100644 tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h delete mode 100644 tensorflow/core/kernels/fused_embedding/fused_embedding_common.cu.h create mode 100644 tensorflow/core/kernels/fused_embedding/fused_embedding_gpu_kernels.cu.cc rename tensorflow/core/kernels/fused_embedding/{ => tests}/fused_embedding_local_ops_test.cc (98%) rename tensorflow/core/kernels/fused_embedding/{ => tests}/fused_embedding_post_grad_ops_test.cc (99%) rename tensorflow/core/kernels/fused_embedding/{ => tests}/fused_embedding_post_ops_test.cc (99%) rename tensorflow/core/kernels/fused_embedding/{ => tests}/fused_embedding_pre_ops_test.cc (99%) diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD index 261cb081382..1cb5b076c65 100644 --- a/tensorflow/core/kernels/BUILD +++ b/tensorflow/core/kernels/BUILD @@ -1948,10 +1948,10 @@ tf_cuda_cc_test( tf_cuda_cc_test( name = "fused_embedding_ops_test", size = "small", - srcs = ["fused_embedding/fused_embedding_local_ops_test.cc", - "fused_embedding/fused_embedding_pre_ops_test.cc", - "fused_embedding/fused_embedding_post_ops_test.cc", - "fused_embedding/fused_embedding_post_grad_ops_test.cc"], + srcs = ["fused_embedding/tests/fused_embedding_local_ops_test.cc", + "fused_embedding/tests/fused_embedding_pre_ops_test.cc", + "fused_embedding/tests/fused_embedding_post_ops_test.cc", + "fused_embedding/tests/fused_embedding_post_grad_ops_test.cc"], tags = tf_cuda_tests_tags(), deps = [ ":fused_embedding_ops", @@ -1960,10 +1960,8 @@ tf_cuda_cc_test( "//tensorflow/cc:cc_ops", "//tensorflow/core:core_cpu", "//tensorflow/core:framework", - "//tensorflow/core:framework_internal", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", - "//tensorflow/core:tensorflow", "//tensorflow/core:test", "//tensorflow/core:test_main", "//tensorflow/core:testlib", @@ -5285,8 +5283,8 @@ tf_kernel_library( ) tf_cuda_library( - name = "fused_embedding_common_cuh", - hdrs = ["fused_embedding/fused_embedding_common.cu.h"], + name = "fused_embedding_cuh", + hdrs = ["fused_embedding/fused_embedding.cu.h"], ) tf_kernel_library( @@ -5294,10 +5292,11 @@ tf_kernel_library( gpu_srcs = [ "fused_embedding/fused_embedding_local_ops_gpu.cu.cc", "fused_embedding/fused_embedding_pre_ops_gpus.cu.cc", - "fused_embedding/fused_embedding_post_ops_gpus.cu.cc" + "fused_embedding/fused_embedding_post_ops_gpus.cu.cc", + "fused_embedding/fused_embedding_gpu_kernels.cu.cc" ], deps = ["//third_party/eigen3", "//tensorflow/core/profiler:nvtx_utils"] + - DYNAMIC_DEPS + if_cuda(["@cub_archive//:cub", ":fused_embedding_common_cuh"]), + DYNAMIC_DEPS + if_cuda(["@cub_archive//:cub", "fused_embedding_cuh"]), ) tf_kernel_library( diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h b/tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h new file mode 100644 index 00000000000..1dfaf43dd3f --- /dev/null +++ b/tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h @@ -0,0 +1,156 @@ +#ifndef TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_FUSED_EMBEDDING_CU_H_ +#define TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_FUSED_EMBEDDING_CU_H_ + +#if GOOGLE_CUDA + +#define EIGEN_USE_GPU + +#include "tensorflow/core/framework/op_kernel.h" + +#define CK_CUDA_THROW_(x) \ + do { \ + cudaError_t retval = (x); \ + if (retval != cudaSuccess) { \ + throw std::runtime_error(std::string("Runtime error: ") + \ + (cudaGetErrorString(retval)) + " " + __FILE__ + \ + ":" + std::to_string(__LINE__) + " \n"); \ + } \ + } while (0) + +namespace tensorflow { + +namespace fused_embedding { + +template +inline T* data_p_with_type(Tensor& t) { + return reinterpret_cast(t.data()); +} + +template +inline T* data_p_with_type(const Tensor& t) { + return reinterpret_cast(t.data()); +} + +template +inline T* data_p_with_type(Tensor* t) { + return reinterpret_cast(t->data()); +} + +template +inline T* data_p_with_type(const Tensor* t) { + return reinterpret_cast(t->data()); +} + +struct IndicePair { + int64_t row_in_batch; + int64_t entry_in_column; +}; + +// ===================== combiner ====================== // + +enum Combiner { Mean, Sum, Sqrtn }; + +template +__forceinline__ __device__ float Combine(const float in, const int feature_num); + +template <> +__forceinline__ __device__ float Combine(const float in, + const int feature_num) { + return in / sqrtf(feature_num); +} + +template <> +__forceinline__ __device__ float Combine(const float in, + const int feature_num) { + return in / feature_num; +} + +template <> +__forceinline__ __device__ float Combine(const float in, + const int feature_num) { + return in; +} + +template +__forceinline__ __device__ float CombineGrad(const float grad, + const int feature_num); + +template <> +__forceinline__ __device__ float CombineGrad(const float grad, + const int feature_num) { + return grad / sqrtf(feature_num); +} + +template <> +__forceinline__ __device__ float CombineGrad(const float grad, + const int feature_num) { + return grad / feature_num; +} + +template <> +__forceinline__ __device__ float CombineGrad(const float grad, + const int feature_num) { + return grad; +} + +// ================================================== // + +// ================== computations ================== // + +using GPUDevice = Eigen::GpuDevice; + +void InitFlagsToOneInt4(const GPUDevice& d, int length, int* flags); + +void DetectInvalid(const GPUDevice& d, const int64_t* values, const int64_t nnz, + int* invalid_id_flag); + +void FusedMultiFunctional(const GPUDevice& d, const IndicePair* indices, + const int64_t* values, const int64_t nnz, + const int64_t batch_size, const bool prune_invalid_id, + const int64_t default_id, int* row_emptiness_flag, + int* invalid_id_flag, IndicePair* tmp_indices_buffer, + int64_t* values_extended); + +void CalcElementsOffsetPerPartition(const GPUDevice& d, int num_partitions, + const int64_t* values_sorted, + int64_t* partition_sizes_accumulate, + int64_t* elements_offset_per_partition, + int nnz); + +void GatherAndConvertToSubPartition(const GPUDevice& d, + const int64_t* sub_values_sorted, + int64_t* sub_partitioned_values, + const int64_t partition_start_base, + const int64_t partition_size); + +void SumUpEmbeddingShard(const GPUDevice& d, const size_t sub_nnz, + const float* emb_shard, + const int64_t* partitioned_indice, float* emb_vectors, + int* feature_nums, const float max_norm, + const int emb_vec_size); + +template +void ApplyCombiner(const GPUDevice& d, const int batch_size, + const int emb_vec_size, float* emb_vectors, + const int* row_emptiness_flag, const bool set_empty_row_zero, + const int* feature_nums); + +template +void DistributeGradToShard(const GPUDevice& d, const float* top_grad, + const float* emb_shard, + const int64_t* partitioned_indice, + const int* feature_nums, + const int* row_emptiness_flag, + const bool set_empty_row_zero, float* grad_shard, + const int64_t sub_nnz, const int64_t emb_vec_size, + const float max_norm); + +// ================================================== // + +} // namespace fused_embedding + +} // namespace tensorflow + +#endif // GOOGLE_CUDA + +#endif // TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_FUSED_EMBEDDING_CU_H_ \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_common.cu.h b/tensorflow/core/kernels/fused_embedding/fused_embedding_common.cu.h deleted file mode 100644 index eff9e7c0782..00000000000 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_common.cu.h +++ /dev/null @@ -1,80 +0,0 @@ -#ifndef TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_FUSED_EMBEDDING_COMMON_CU_H_ -#define TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_FUSED_EMBEDDING_COMMON_CU_H_ - -#if GOOGLE_CUDA - -#define CK_CUDA_THROW_(x) \ - do { \ - cudaError_t retval = (x); \ - if (retval != cudaSuccess) { \ - throw std::runtime_error(std::string("Runtime error: ") + \ - (cudaGetErrorString(retval)) + " " + __FILE__ + \ - ":" + std::to_string(__LINE__) + " \n"); \ - } \ - } while (0) - -namespace tensorflow { - -namespace { - -inline int CalcBlocksLinearMapping(const int problem_size, const int threads) { - return problem_size % threads == 0 ? (problem_size / threads) - : (problem_size / threads + 1); -} - -struct IndicePair { - int64_t row_in_batch; - int64_t entry_in_column; -}; - -enum Combiner { Mean, Sum, Sqrtn }; - -template -__forceinline__ __device__ float Combine(const float in, const int feature_num); - -template <> -__forceinline__ __device__ float Combine(const float in, - const int feature_num) { - return in / sqrtf(feature_num); -} - -template <> -__forceinline__ __device__ float Combine(const float in, - const int feature_num) { - return in / feature_num; -} - -template <> -__forceinline__ __device__ float Combine(const float in, - const int feature_num) { - return in; -} - -template -__forceinline__ __device__ float CombineGrad(const float grad, - const int feature_num); - -template <> -__forceinline__ __device__ float CombineGrad(const float grad, - const int feature_num) { - return grad / sqrtf(feature_num); -} - -template <> -__forceinline__ __device__ float CombineGrad(const float grad, - const int feature_num) { - return grad / feature_num; -} - -template <> -__forceinline__ __device__ float CombineGrad(const float grad, - const int feature_num) { - return grad; -} -} // namespace - -} // namespace tensorflow - -#endif // GOOGLE_CUDA - -#endif // TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_FUSED_EMBEDDING_COMMON_CU_H_ \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_gpu_kernels.cu.cc b/tensorflow/core/kernels/fused_embedding/fused_embedding_gpu_kernels.cu.cc new file mode 100644 index 00000000000..d94e063880e --- /dev/null +++ b/tensorflow/core/kernels/fused_embedding/fused_embedding_gpu_kernels.cu.cc @@ -0,0 +1,366 @@ + + +#if GOOGLE_CUDA + +#define EIGEN_USE_GPU + +#include "tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h" +#include "tensorflow/core/platform/stream_executor.h" +#include "tensorflow/core/util/gpu_kernel_helper.h" + +namespace tensorflow { + +namespace fused_embedding { + +using GPUDevice = Eigen::GpuDevice; + +#define LINER_MAPPING_THREADS 128 + +inline int CalcBlocksLinearMapping(const int problem_size, const int threads) { + return problem_size % threads == 0 ? (problem_size / threads) + : (problem_size / threads + 1); +} + +__global__ void InitFlagsToOneInt4Kernel(int length, int* flags) { + int offset = blockIdx.x * blockDim.x + threadIdx.x; + if (4 * offset + 3 < length) { + *((int4*)(flags + 4 * offset)) = make_int4(1, 1, 1, 1); + } else if (4 * offset < length) { + for (int i = 0; i < length - 4 * offset; i++) { + flags[4 * offset + i] = 1; + } + } +} + +void InitFlagsToOneInt4(const GPUDevice& d, int length, int* flags) { + const int threads = LINER_MAPPING_THREADS; + const int blocks = CalcBlocksLinearMapping(length, threads * 4); + TF_CHECK_OK(GpuLaunchKernel(InitFlagsToOneInt4Kernel, blocks, threads, 0, + d.stream(), length, flags)); +} + +__global__ void DetectInvalidKernel(const int64_t* values, const int64_t nnz, + int* invalid_id_flag) { + const int offset = blockIdx.x * blockDim.x + threadIdx.x; + if (offset < nnz) { + const int64_t value = values[offset]; + if (value < 0) { + atomicAnd(invalid_id_flag + offset, 0); + } + } +} + +void DetectInvalid(const GPUDevice& d, const int64_t* values, const int64_t nnz, + int* invalid_id_flag) { + const int threads = LINER_MAPPING_THREADS; + const int blocks = CalcBlocksLinearMapping(nnz, threads); + TF_CHECK_OK(GpuLaunchKernel(DetectInvalidKernel, blocks, threads, 0, + d.stream(), values, nnz, invalid_id_flag)); +} + +__global__ void FusedMultiFunctionalKernel( + const IndicePair* indices, const int64_t* values, const int64_t nnz, + const int64_t batch_size, const bool prune_invalid_id, + const int64_t default_id, int* row_emptiness_flag, int* invalid_id_flag, + IndicePair* tmp_indices_buffer, int64_t* values_extended) { + // This kernel will do many things together + // 1. The first part of threads will do job 1(DetectRowEmptiness), others will + // do job2(InitBatchRowsBuffer) + // 2. Do job3 (set values extended to default id) + + const int offset = blockIdx.x * blockDim.x + threadIdx.x; + if (offset < nnz) { + // do DetectRowEmptiness + if (prune_invalid_id) { + const int64_t value = values[offset]; + if (value < 0) { + // invalid, set invalid_id_flag + atomicAnd(invalid_id_flag + offset, 0); + } else { + // valid, set row_emptiness_flag + const int64_t row_in_batch = indices[offset].row_in_batch; + atomicAnd(row_emptiness_flag + row_in_batch, 0); + } + } else { + // set row_emptiness_flag + const int64_t row_in_batch = indices[offset].row_in_batch; + atomicAnd(row_emptiness_flag + row_in_batch, 0); + } + } else { + // do InitBatchRowsBuffer + const int other_offset = offset - nnz; + if (other_offset < batch_size) { + tmp_indices_buffer[other_offset].row_in_batch = other_offset; + // always set entry id to 0; + tmp_indices_buffer[other_offset].entry_in_column = 0; + } + } + + // set values extended to default id + if (2 * offset + 1 < nnz + batch_size) { + longlong2 l2 = make_longlong2(default_id, default_id); + *((longlong2*)(values_extended + 2 * offset)) = l2; + } else if (2 * offset < nnz + batch_size) { + values_extended[2 * offset] = default_id; + } +} + +void FusedMultiFunctional(const GPUDevice& d, const IndicePair* indices, + const int64_t* values, const int64_t nnz, + const int64_t batch_size, const bool prune_invalid_id, + const int64_t default_id, int* row_emptiness_flag, + int* invalid_id_flag, IndicePair* tmp_indices_buffer, + int64_t* values_extended) { + const int threads = LINER_MAPPING_THREADS; + const int blocks = CalcBlocksLinearMapping(nnz + batch_size, threads); + TF_CHECK_OK(GpuLaunchKernel( + FusedMultiFunctionalKernel, blocks, threads, 0, d.stream(), indices, + values, nnz, batch_size, prune_invalid_id, default_id, row_emptiness_flag, + invalid_id_flag, tmp_indices_buffer, values_extended)); +} + +__global__ void CalcElementsOffsetPerPartitionKernel( + const int64_t* values_sorted, int64_t* partition_sizes_accumulate, + int64_t* elements_offset_per_partition, int nnz) { + // dichotomy + const int64_t target = partition_sizes_accumulate[blockIdx.x]; + int roof = nnz; + int floor = 0; + + int pos = (roof + floor) / 2; + while (1) { + if (pos == 0) { + pos = -1; + break; + } else if (pos == nnz - 1) { + break; + } + int64_t value = values_sorted[pos]; + int64_t value_plus_1 = values_sorted[pos + 1]; + if (value < target && value_plus_1 >= target) { + break; + } + if (value < target) { + floor = pos; + } else { + roof = pos; + } + pos = (roof + floor) / 2; + } + elements_offset_per_partition[blockIdx.x] = int64_t(pos + 1); +} + +void CalcElementsOffsetPerPartition(const GPUDevice& d, int num_partitions, + const int64_t* values_sorted, + int64_t* partition_sizes_accumulate, + int64_t* elements_offset_per_partition, + int nnz) { + const int blocks = num_partitions; + const int threads = 1; + + TF_CHECK_OK(GpuLaunchKernel(CalcElementsOffsetPerPartitionKernel, blocks, + threads, 0, d.stream(), values_sorted, + partition_sizes_accumulate, + elements_offset_per_partition, nnz)); +} + +__global__ void GatherAndConvertToSubPartitionKernel( + const int64_t* sub_values_sorted, int64_t* sub_partitioned_values, + const int64_t partition_start_base, const int64_t partition_size) { + const int t_offset = blockIdx.x * blockDim.x + threadIdx.x; + if (t_offset < partition_size) { + int64_t value = sub_values_sorted[t_offset]; + // rebase value to it's corresponding sub partition + value = value - partition_start_base; + sub_partitioned_values[t_offset] = value; + } +} + +void GatherAndConvertToSubPartition(const GPUDevice& d, + const int64_t* sub_values_sorted, + int64_t* sub_partitioned_values, + const int64_t partition_start_base, + const int64_t partition_size) { + const int threads = LINER_MAPPING_THREADS; + int blocks = CalcBlocksLinearMapping(partition_size, threads); + + TF_CHECK_OK(GpuLaunchKernel(GatherAndConvertToSubPartitionKernel, blocks, + threads, 0, d.stream(), sub_values_sorted, + sub_partitioned_values, partition_start_base, + partition_size)); +} + +__global__ void SumUpEmbeddingShardKernel(const float* emb_shard, + const int64_t* partitioned_indice, + float* emb_vectors, int* feature_nums, + const float max_norm, + const int emb_vec_size) { + __shared__ float l2_sum[1]; + + const int64_t row_in_batch = partitioned_indice[2 * blockIdx.x]; + float emb_element = emb_shard[blockIdx.x * emb_vec_size + threadIdx.x]; + if (max_norm >= 0.0f) { + if (threadIdx.x == 0) { + l2_sum[0] = 0.0f; + } + __syncthreads(); + atomicAdd(l2_sum, emb_element * emb_element); + __syncthreads(); + float l2_norm = sqrtf(l2_sum[0]); + if (l2_norm > max_norm) { + emb_element *= max_norm / l2_norm; + } + } + + atomicAdd(emb_vectors + row_in_batch * emb_vec_size + threadIdx.x, + emb_element); + + if (threadIdx.x == 0) { + atomicAdd(feature_nums + row_in_batch, 1); + } +} + +void SumUpEmbeddingShard(const GPUDevice& d, const size_t sub_nnz, + const float* emb_shard, + const int64_t* partitioned_indice, float* emb_vectors, + int* feature_nums, const float max_norm, + const int emb_vec_size) { + const int blocks = sub_nnz; + const int threads = emb_vec_size; + TF_CHECK_OK(GpuLaunchKernel( + SumUpEmbeddingShardKernel, blocks, threads, 0, d.stream(), emb_shard, + partitioned_indice, emb_vectors, feature_nums, max_norm, emb_vec_size)); +} + +template +__global__ void ApplyCombinerKernel(float* emb_vectors, + const int* row_emptiness_flag, + const bool set_empty_row_zero, + const int* feature_nums) { + const int offset = blockIdx.x * blockDim.x + threadIdx.x; + if (set_empty_row_zero) { + if (row_emptiness_flag[blockIdx.x]) { + emb_vectors[offset] = 0.0f; + return; + } + } + const int feature_num = feature_nums[blockIdx.x]; + const float emb_element = emb_vectors[offset]; + emb_vectors[offset] = Combine(emb_element, feature_num); +} + +template +void ApplyCombiner(const GPUDevice& d, const int batch_size, + const int emb_vec_size, float* emb_vectors, + const int* row_emptiness_flag, const bool set_empty_row_zero, + const int* feature_nums) { + const int blocks = batch_size; + const int threads = emb_vec_size; + TF_CHECK_OK(GpuLaunchKernel(ApplyCombinerKernel, blocks, threads, 0, + d.stream(), emb_vectors, row_emptiness_flag, + set_empty_row_zero, feature_nums)); +} + +template void ApplyCombiner(const GPUDevice& d, const int batch_size, + const int emb_vec_size, float* emb_vectors, + const int* row_emptiness_flag, + const bool set_empty_row_zero, + const int* feature_nums); +template void ApplyCombiner(const GPUDevice& d, const int batch_size, + const int emb_vec_size, float* emb_vectors, + const int* row_emptiness_flag, + const bool set_empty_row_zero, + const int* feature_nums); +template void ApplyCombiner(const GPUDevice& d, const int batch_size, + const int emb_vec_size, float* emb_vectors, + const int* row_emptiness_flag, + const bool set_empty_row_zero, + const int* feature_nums); + +template +__global__ void DistributeGradToShardKernel( + const float* top_grad, const float* emb_shard, + const int64_t* partitioned_indice, const int* feature_nums, + const int* row_emptiness_flag, const bool set_empty_row_zero, + float* grad_shard, const int64_t sub_nnz, const int64_t emb_vec_size, + const float max_norm) { + __shared__ int64_t row_in_batch_shared[1]; + __shared__ int feature_num_shared[1]; + __shared__ float l2_sum[1]; + int64_t row_in_batch; + if (threadIdx.x == 0) { + row_in_batch = partitioned_indice[2 * blockIdx.x]; + row_in_batch_shared[0] = row_in_batch; + feature_num_shared[0] = feature_nums[row_in_batch]; + } + __syncthreads(); + row_in_batch = row_in_batch_shared[0]; + const int feature_num = feature_num_shared[0]; + if (set_empty_row_zero) { + if (row_emptiness_flag[row_in_batch]) { + grad_shard[blockIdx.x * emb_vec_size + threadIdx.x] = 0.0f; + return; + } + } + float grad = top_grad[row_in_batch * emb_vec_size + threadIdx.x]; + grad = CombineGrad(grad, feature_num); + if (max_norm >= 0.0f) { + const float emb_element = + emb_shard[blockIdx.x * emb_vec_size + threadIdx.x]; + if (threadIdx.x == 0) { + l2_sum[0] = 0.0f; + } + __syncthreads(); + atomicAdd(l2_sum, emb_element * emb_element); + __syncthreads(); + float l2_norm = sqrtf(l2_sum[0]); + if (l2_norm > max_norm) { + grad *= max_norm / l2_norm; + } + } + grad_shard[blockIdx.x * emb_vec_size + threadIdx.x] = grad; +} + +template +void DistributeGradToShard(const GPUDevice& d, const float* top_grad, + const float* emb_shard, + const int64_t* partitioned_indice, + const int* feature_nums, + const int* row_emptiness_flag, + const bool set_empty_row_zero, float* grad_shard, + const int64_t sub_nnz, const int64_t emb_vec_size, + const float max_norm) { + const int blocks = sub_nnz; + const int threads = emb_vec_size; + TF_CHECK_OK(GpuLaunchKernel( + DistributeGradToShardKernel, blocks, threads, 0, d.stream(), + top_grad, emb_shard, partitioned_indice, feature_nums, row_emptiness_flag, + set_empty_row_zero, grad_shard, sub_nnz, emb_vec_size, max_norm)); +} + +template void DistributeGradToShard( + const GPUDevice& d, const float* top_grad, const float* emb_shard, + const int64_t* partitioned_indice, const int* feature_nums, + const int* row_emptiness_flag, const bool set_empty_row_zero, + float* grad_shard, const int64_t sub_nnz, const int64_t emb_vec_size, + const float max_norm); + +template void DistributeGradToShard( + const GPUDevice& d, const float* top_grad, const float* emb_shard, + const int64_t* partitioned_indice, const int* feature_nums, + const int* row_emptiness_flag, const bool set_empty_row_zero, + float* grad_shard, const int64_t sub_nnz, const int64_t emb_vec_size, + const float max_norm); + +template void DistributeGradToShard( + const GPUDevice& d, const float* top_grad, const float* emb_shard, + const int64_t* partitioned_indice, const int* feature_nums, + const int* row_emptiness_flag, const bool set_empty_row_zero, + float* grad_shard, const int64_t sub_nnz, const int64_t emb_vec_size, + const float max_norm); + +} // namespace fused_embedding + +} // namespace tensorflow + +#endif // GOOGLE_CUDA \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_local_ops_gpu.cu.cc b/tensorflow/core/kernels/fused_embedding/fused_embedding_local_ops_gpu.cu.cc index 266b960a32c..ff4ba43f1dc 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_local_ops_gpu.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/fused_embedding_local_ops_gpu.cu.cc @@ -7,13 +7,13 @@ #define EIGEN_USE_GPU -#include "tensorflow/core/kernels/fused_embedding/fused_embedding_common.cu.h" +#include "tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h" #include "tensorflow/core/util/gpu_kernel_helper.h" namespace tensorflow { using GPUDevice = Eigen::GpuDevice; -namespace { +namespace fused_embedding { __global__ void SetToIntMaxSTG128(int* values_offset, const int batch_size) { const int thread_offset = 4 * (blockIdx.x * blockDim.x + threadIdx.x); @@ -121,7 +121,7 @@ __global__ void DoEmbeddingGrad(const float* top_grad, } } -} // namespace +} // namespace fused_embedding class FusedEmbeddingLocalSparseLookUpGPU : public OpKernel { public: @@ -132,6 +132,8 @@ class FusedEmbeddingLocalSparseLookUpGPU : public OpKernel { } void Compute(OpKernelContext* ctx) override { + using namespace fused_embedding; + auto stream = ctx->eigen_device().stream(); Tensor const* values_tensor = nullptr; @@ -235,6 +237,7 @@ class FusedEmbeddingLocalSparseLookUpGradGPU : public OpKernel { } void Compute(OpKernelContext* ctx) override { + using namespace fused_embedding; auto stream = ctx->eigen_device().stream(); Tensor const* top_grad_tensor = nullptr; diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc b/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc index efbbde8ec81..684bec3c3c6 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc @@ -8,7 +8,7 @@ #define EIGEN_USE_GPU -#include "tensorflow/core/kernels/fused_embedding/fused_embedding_common.cu.h" +#include "tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h" #include "tensorflow/core/profiler/nvtx_utils.h" #include "tensorflow/core/util/gpu_kernel_helper.h" #include "third_party/cub/thread/thread_operators.cuh" @@ -16,98 +16,6 @@ namespace tensorflow { using GPUDevice = Eigen::GpuDevice; -namespace { -__global__ void SumUpEmbeddingShard(const float* emb_shard, - const int64_t* partitioned_indice, - float* emb_vectors, int* feature_nums, - const float max_norm, - const int emb_vec_size) { - __shared__ float l2_sum[1]; - - const int64_t row_in_batch = partitioned_indice[2 * blockIdx.x]; - float emb_element = emb_shard[blockIdx.x * emb_vec_size + threadIdx.x]; - if (max_norm >= 0.0f) { - if (threadIdx.x == 0) { - l2_sum[0] = 0.0f; - } - __syncthreads(); - atomicAdd(l2_sum, emb_element * emb_element); - __syncthreads(); - float l2_norm = sqrtf(l2_sum[0]); - if (l2_norm > max_norm) { - emb_element *= max_norm / l2_norm; - } - } - - atomicAdd(emb_vectors + row_in_batch * emb_vec_size + threadIdx.x, - emb_element); - - if (threadIdx.x == 0) { - atomicAdd(feature_nums + row_in_batch, 1); - } -} - -template -__global__ void ApplyCombiner(float* emb_vectors, const int* row_emptiness_flag, - const bool set_empty_row_zero, - const int* feature_nums) { - const int offset = blockIdx.x * blockDim.x + threadIdx.x; - if (set_empty_row_zero) { - if (row_emptiness_flag[blockIdx.x]) { - emb_vectors[offset] = 0.0f; - return; - } - } - const int feature_num = feature_nums[blockIdx.x]; - const float emb_element = emb_vectors[offset]; - emb_vectors[offset] = Combine(emb_element, feature_num); -} - -template -__global__ void DistributeGradToShard( - const float* top_grad, const float* emb_shard, - const int64_t* partitioned_indice, const int* feature_nums, - const int* row_emptiness_flag, const bool set_empty_row_zero, - float* grad_shard, const int64_t sub_nnz, const int64_t emb_vec_size, - const float max_norm) { - __shared__ int64_t row_in_batch_shared[1]; - __shared__ int feature_num_shared[1]; - __shared__ float l2_sum[1]; - int64_t row_in_batch; - if (threadIdx.x == 0) { - row_in_batch = partitioned_indice[2 * blockIdx.x]; - row_in_batch_shared[0] = row_in_batch; - feature_num_shared[0] = feature_nums[row_in_batch]; - } - __syncthreads(); - row_in_batch = row_in_batch_shared[0]; - const int feature_num = feature_num_shared[0]; - if (set_empty_row_zero) { - if (row_emptiness_flag[row_in_batch]) { - grad_shard[blockIdx.x * emb_vec_size + threadIdx.x] = 0.0f; - return; - } - } - float grad = top_grad[row_in_batch * emb_vec_size + threadIdx.x]; - grad = CombineGrad(grad, feature_num); - if (max_norm >= 0.0f) { - const float emb_element = - emb_shard[blockIdx.x * emb_vec_size + threadIdx.x]; - if (threadIdx.x == 0) { - l2_sum[0] = 0.0f; - } - __syncthreads(); - atomicAdd(l2_sum, emb_element * emb_element); - __syncthreads(); - float l2_norm = sqrtf(l2_sum[0]); - if (l2_norm > max_norm) { - grad *= max_norm / l2_norm; - } - } - grad_shard[blockIdx.x * emb_vec_size + threadIdx.x] = grad; -} -} // namespace - class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { public: explicit FusedEmbeddingSparsePostLookUpGPU(OpKernelConstruction* ctx) @@ -122,7 +30,8 @@ class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { } void Compute(OpKernelContext* ctx) override { - auto stream = ctx->eigen_device().stream(); + using namespace fused_embedding; + auto device = ctx->eigen_device(); OpInputList emb_shards; OP_REQUIRES_OK(ctx, ctx->input_list("emb_shards", &emb_shards)); @@ -143,7 +52,7 @@ class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { const int64_t emb_vec_size = emb_shards[0].shape().dim_size(1); const int64_t batch_size = dense_shape_tensor->flat().data()[0]; - // 1. sum up emb values from different entries and dump into output + // = 1. sum up emb values from differententries and dump into output = // Tensor* emb_vectors_tensor = nullptr; OP_REQUIRES_OK( ctx, ctx->allocate_output(0, TensorShape({batch_size, emb_vec_size}), @@ -155,7 +64,8 @@ class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { // emb_vectors_tensor->NumElements() * sizeof(float)); cudaMemsetAsync(emb_vectors_tensor->flat().data(), 0x0, - sizeof(float) * emb_vectors_tensor->NumElements(), stream); + sizeof(float) * emb_vectors_tensor->NumElements(), + device.stream()); Tensor* feature_nums; OP_REQUIRES_OK( @@ -166,7 +76,7 @@ class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { // stream->ThenMemZero(&feature_nums_wrapper, // feature_nums.NumElements() * sizeof(int)); cudaMemsetAsync(feature_nums->flat().data(), 0x0, - sizeof(int) * feature_nums->NumElements(), stream); + sizeof(int) * feature_nums->NumElements(), device.stream()); for (int i = 0; i < num_partitions_; i++) { const size_t sub_nnz = emb_shards[i].shape().dim_size(0); @@ -174,43 +84,39 @@ class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { ctx, sub_nnz == partitioned_indices[i].shape().dim_size(0), errors::InvalidArgument( "emb_shard and partitioned_indice dosn't have the same length")); - - { - const int blocks = sub_nnz; - const int threads = emb_vec_size; - SumUpEmbeddingShard<<>>( - emb_shards[i].flat().data(), - reinterpret_cast( - partitioned_indices[i].flat().data()), - emb_vectors_tensor->flat().data(), - feature_nums->flat().data(), max_norm_, emb_vec_size); - CK_CUDA_THROW_(cudaGetLastError()); - } + SumUpEmbeddingShard( + device, sub_nnz, data_p_with_type(emb_shards[i]), + data_p_with_type(partitioned_indices[i]), + data_p_with_type(emb_vectors_tensor), + data_p_with_type(feature_nums), max_norm_, emb_vec_size); + CK_CUDA_THROW_(cudaGetLastError()); } const bool set_empty_row_zero = default_id_ >= 0; - // 2. combiner - { - const int blocks = batch_size; - const int threads = emb_vec_size; - if (combiner_ == "sqrtn") { - ApplyCombiner<<>>( - emb_vectors_tensor->flat().data(), - row_empty_and_invalid_flags->flat().data(), set_empty_row_zero, - feature_nums->flat().data()); - } else if (combiner_ == "mean") { - ApplyCombiner<<>>( - emb_vectors_tensor->flat().data(), - row_empty_and_invalid_flags->flat().data(), set_empty_row_zero, - feature_nums->flat().data()); - } else { - ApplyCombiner<<>>( - emb_vectors_tensor->flat().data(), - row_empty_and_invalid_flags->flat().data(), set_empty_row_zero, - feature_nums->flat().data()); - } - CK_CUDA_THROW_(cudaGetLastError()); + // ================================================================ // + + // ========================= 2. combiner ========================== // + if (combiner_ == "sqrtn") { + ApplyCombiner(device, batch_size, emb_vec_size, + data_p_with_type(emb_vectors_tensor), + data_p_with_type(row_empty_and_invalid_flags), + set_empty_row_zero, + data_p_with_type(feature_nums)); + } else if (combiner_ == "mean") { + ApplyCombiner(device, batch_size, emb_vec_size, + data_p_with_type(emb_vectors_tensor), + data_p_with_type(row_empty_and_invalid_flags), + set_empty_row_zero, + data_p_with_type(feature_nums)); + } else { + ApplyCombiner(device, batch_size, emb_vec_size, + data_p_with_type(emb_vectors_tensor), + data_p_with_type(row_empty_and_invalid_flags), + set_empty_row_zero, + data_p_with_type(feature_nums)); } + CK_CUDA_THROW_(cudaGetLastError()); + // ================================================================ // } private: @@ -240,7 +146,8 @@ class FusedEmbeddingSparsePostLookUpGradGPU : public OpKernel { } void Compute(OpKernelContext* ctx) override { - auto stream = ctx->eigen_device().stream(); + using namespace fused_embedding; + auto device = ctx->eigen_device(); Tensor const* top_grad_tensor = nullptr; OP_REQUIRES_OK(ctx, ctx->input("top_grad", &top_grad_tensor)); @@ -277,42 +184,35 @@ class FusedEmbeddingSparsePostLookUpGradGPU : public OpKernel { ctx, grad_shards.allocate(i, TensorShape({sub_nnz, emb_vec_size}), &grad_shard)); - { - const int blocks = sub_nnz; - const int threads = emb_vec_size; - if (combiner_ == "sqrtn") { - DistributeGradToShard<<>>( - top_grad_tensor->flat().data(), - emb_shards[i].flat().data(), - reinterpret_cast( - partitioned_indices[i].flat().data()), - feature_nums->flat().data(), - row_empty_and_invalid_flags->flat().data(), - set_empty_row_zero, grad_shard->flat().data(), sub_nnz, - emb_vec_size, max_norm_); - } else if (combiner_ == "mean") { - DistributeGradToShard<<>>( - top_grad_tensor->flat().data(), - emb_shards[i].flat().data(), - reinterpret_cast( - partitioned_indices[i].flat().data()), - feature_nums->flat().data(), - row_empty_and_invalid_flags->flat().data(), - set_empty_row_zero, grad_shard->flat().data(), sub_nnz, - emb_vec_size, max_norm_); - } else { - DistributeGradToShard<<>>( - top_grad_tensor->flat().data(), - emb_shards[i].flat().data(), - reinterpret_cast( - partitioned_indices[i].flat().data()), - feature_nums->flat().data(), - row_empty_and_invalid_flags->flat().data(), - set_empty_row_zero, grad_shard->flat().data(), sub_nnz, - emb_vec_size, max_norm_); - } - CK_CUDA_THROW_(cudaGetLastError()); + if (combiner_ == "sqrtn") { + DistributeGradToShard( + device, data_p_with_type(top_grad_tensor), + data_p_with_type(emb_shards[i]), + data_p_with_type(partitioned_indices[i]), + data_p_with_type(feature_nums), + data_p_with_type(row_empty_and_invalid_flags), + set_empty_row_zero, data_p_with_type(grad_shard), sub_nnz, + emb_vec_size, max_norm_); + } else if (combiner_ == "mean") { + DistributeGradToShard( + device, data_p_with_type(top_grad_tensor), + data_p_with_type(emb_shards[i]), + data_p_with_type(partitioned_indices[i]), + data_p_with_type(feature_nums), + data_p_with_type(row_empty_and_invalid_flags), + set_empty_row_zero, data_p_with_type(grad_shard), sub_nnz, + emb_vec_size, max_norm_); + } else { + DistributeGradToShard( + device, data_p_with_type(top_grad_tensor), + data_p_with_type(emb_shards[i]), + data_p_with_type(partitioned_indices[i]), + data_p_with_type(feature_nums), + data_p_with_type(row_empty_and_invalid_flags), + set_empty_row_zero, data_p_with_type(grad_shard), sub_nnz, + emb_vec_size, max_norm_); } + CK_CUDA_THROW_(cudaGetLastError()); } } diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc b/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc index 9d3eb981cbc..203b8a63876 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc @@ -8,7 +8,7 @@ #define EIGEN_USE_GPU -#include "tensorflow/core/kernels/fused_embedding/fused_embedding_common.cu.h" +#include "tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h" #include "tensorflow/core/profiler/nvtx_utils.h" #include "tensorflow/core/util/gpu_kernel_helper.h" #include "third_party/cub/device/device_radix_sort.cuh" @@ -19,122 +19,6 @@ namespace tensorflow { using GPUDevice = Eigen::GpuDevice; -namespace { - -__global__ void InitFlagsToOneInt4(int length, int* flags) { - int offset = blockIdx.x * blockDim.x + threadIdx.x; - if (4 * offset + 3 < length) { - *((int4*)(flags + 4 * offset)) = make_int4(1, 1, 1, 1); - } else if (4 * offset < length) { - for (int i = 0; i < length - 4 * offset; i++) { - flags[4 * offset + i] = 1; - } - } -} - -__global__ void FusedMultiFunctionalKernel( - const IndicePair* indices, const int64_t* values, const int64_t nnz, - const int64_t batch_size, const bool prune_invalid_id, - const int64_t default_id, int* row_emptiness_flag, int* invalid_id_flag, - IndicePair* tmp_indices_buffer, int64_t* values_extended) { - // This kernel will do many things together - // 1. The first part of threads will do job 1(DetectRowEmptiness), others will - // do job2(InitBatchRowsBuffer) - // 2. Do job3 (set values extended to default id) - - const int offset = blockIdx.x * blockDim.x + threadIdx.x; - if (offset < nnz) { - // do DetectRowEmptiness - if (prune_invalid_id) { - const int64_t value = values[offset]; - if (value < 0) { - // invalid, set invalid_id_flag - atomicAnd(invalid_id_flag + offset, 0); - } else { - // valid, set row_emptiness_flag - const int64_t row_in_batch = indices[offset].row_in_batch; - atomicAnd(row_emptiness_flag + row_in_batch, 0); - } - } else { - // set row_emptiness_flag - const int64_t row_in_batch = indices[offset].row_in_batch; - atomicAnd(row_emptiness_flag + row_in_batch, 0); - } - } else { - // do InitBatchRowsBuffer - const int other_offset = offset - nnz; - if (other_offset < batch_size) { - tmp_indices_buffer[other_offset].row_in_batch = other_offset; - // always set entry id to 0; - tmp_indices_buffer[other_offset].entry_in_column = 0; - } - } - - // set values extended to default id - if (2 * offset + 1 < nnz + batch_size) { - longlong2 l2 = make_longlong2(default_id, default_id); - *((longlong2*)(values_extended + 2 * offset)) = l2; - } else if (2 * offset < nnz + batch_size) { - values_extended[2 * offset] = default_id; - } -} - -__global__ void DetectInvalid(const int64_t* values, const int64_t nnz, - int* invalid_id_flag) { - const int offset = blockIdx.x * blockDim.x + threadIdx.x; - if (offset < nnz) { - const int64_t value = values[offset]; - if (value < 0) { - atomicAnd(invalid_id_flag + offset, 0); - } - } -} - -__global__ void CalcElementsOffsetPerPartition( - const int64_t* values_sorted, int64_t* partition_sizes_accumulate, - int64_t* elements_offset_per_partition, int nnz) { - // dichotomy - const int64_t target = partition_sizes_accumulate[blockIdx.x]; - int roof = nnz; - int floor = 0; - - int pos = (roof + floor) / 2; - while (1) { - if (pos == 0) { - pos = -1; - break; - } else if (pos == nnz - 1) { - break; - } - int64_t value = values_sorted[pos]; - int64_t value_plus_1 = values_sorted[pos + 1]; - if (value < target && value_plus_1 >= target) { - break; - } - if (value < target) { - floor = pos; - } else { - roof = pos; - } - pos = (roof + floor) / 2; - } - elements_offset_per_partition[blockIdx.x] = int64_t(pos + 1); -} - -__global__ void GatherAndConvertToSubPartition( - const int64_t* sub_values_sorted, int64_t* sub_partitioned_values, - const int64_t partition_start_base, const int64_t partition_size) { - const int t_offset = blockIdx.x * blockDim.x + threadIdx.x; - if (t_offset < partition_size) { - int64_t value = sub_values_sorted[t_offset]; - // rebase value to it's corresponding sub partition - value = value - partition_start_base; - sub_partitioned_values[t_offset] = value; - } -} - -} // namespace - class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { public: explicit FusedEmbeddingSparsePreLookUpGPU(OpKernelConstruction* ctx) @@ -149,14 +33,15 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { } void Compute(OpKernelContext* ctx) override { - auto stream = ctx->eigen_device().stream(); + using namespace fused_embedding; + auto device = ctx->eigen_device(); const int64_t default_id = default_id_ >= 0 ? default_id_ : 0; const int linear_mapping_threads = 128; nvtx::ScopedRangeIfEnabled nvtx_range(this); - // 1. bind inputs + // ================ 1. bind inputs ================ // Tensor const* values_tensor = nullptr; OP_REQUIRES_OK(ctx, ctx->input("sp_values", &values_tensor)); const int64_t nnz = values_tensor->shape().dim_size(0); @@ -182,32 +67,25 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { partition_sizes_accumulate_.back(); partition_sizes_accumulate_.push_back(accu); } + // =============================================== // - // 2. allocate cub tmp storage + // ========= 2. allocate cub tmp storage ========= // Tensor cub_temp_storage; size_t max_cub_bytes = 0; size_t temp_storage_bytes = 0; - if (num_partitions_ > 1) { - cub::DeviceRadixSort::SortPairs( - (void*)nullptr, temp_storage_bytes, (int64_t*)nullptr, - (int64_t*)nullptr, (IndicePair*)nullptr, (IndicePair*)nullptr, - int(nnz + batch_size), 0, sizeof(int64_t) * 8, stream); - max_cub_bytes = temp_storage_bytes > max_cub_bytes ? temp_storage_bytes - : max_cub_bytes; - } - if (fill_empty_row_ || prune_invalid_id_) { cub::DeviceSelect::Flagged(nullptr, temp_storage_bytes, (int64_t*)nullptr, (int*)nullptr, (int64_t*)nullptr, - (int*)nullptr, nnz, stream); + (int*)nullptr, nnz, device.stream()); max_cub_bytes = temp_storage_bytes > max_cub_bytes ? temp_storage_bytes : max_cub_bytes; - cub::DeviceSelect::Flagged( - (void*)nullptr, temp_storage_bytes, (IndicePair*)nullptr, - (int*)nullptr, (IndicePair*)nullptr, (int*)nullptr, nnz, stream); + cub::DeviceSelect::Flagged((void*)nullptr, temp_storage_bytes, + (IndicePair*)nullptr, (int*)nullptr, + (IndicePair*)nullptr, (int*)nullptr, nnz, + device.stream()); max_cub_bytes = temp_storage_bytes > max_cub_bytes ? temp_storage_bytes : max_cub_bytes; @@ -216,18 +94,28 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { cub::DeviceSelect::Flagged((void*)nullptr, temp_storage_bytes, (IndicePair*)nullptr, (int*)nullptr, (IndicePair*)nullptr, (int*)nullptr, - batch_size, stream); + batch_size, device.stream()); max_cub_bytes = temp_storage_bytes > max_cub_bytes ? temp_storage_bytes : max_cub_bytes; } } + if (num_partitions_ > 1) { + cub::DeviceRadixSort::SortPairs( + (void*)nullptr, temp_storage_bytes, (int64_t*)nullptr, + (int64_t*)nullptr, (IndicePair*)nullptr, (IndicePair*)nullptr, + int(nnz + batch_size), 0, sizeof(int64_t) * 8, device.stream()); + max_cub_bytes = temp_storage_bytes > max_cub_bytes ? temp_storage_bytes + : max_cub_bytes; + } + OP_REQUIRES_OK( ctx, ctx->allocate_temp( DT_INT8, TensorShape({static_cast(max_cub_bytes)}), &cub_temp_storage)); + // =============================================== // - // 3. fill_empty_row, prune, if avaliable. + // === 3. fill_empty_row, prune, if avaliable. === // Tensor values_extended; Tensor indices_extended; Tensor tmp_indices_buffer; @@ -252,102 +140,78 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { OP_REQUIRES_OK( ctx, ctx->allocate_temp(DT_INT32, TensorShape{1}, &selected_num_d)); - { - const int threads = linear_mapping_threads; - const int blocks = - CalcBlocksLinearMapping(batch_size + nnz, threads * 4); - InitFlagsToOneInt4<<>>( - batch_size + nnz, all_flags->flat().data()); - CK_CUDA_THROW_(cudaGetLastError()); - } + InitFlagsToOneInt4(device, batch_size + nnz, + all_flags->flat().data()); + CK_CUDA_THROW_(cudaGetLastError()); // 3.1 set flags, init tmp_indices_buffer etc. if (fill_empty_row_) { - { - const int threads = linear_mapping_threads; - const int blocks = CalcBlocksLinearMapping(nnz + batch_size, threads); - FusedMultiFunctionalKernel<<>>( - reinterpret_cast( - indices_tensor->flat().data()), - reinterpret_cast( - values_tensor->flat().data()), - nnz, batch_size, prune_invalid_id_, default_id, - all_flags->flat().data(), - all_flags->flat().data() + batch_size, - reinterpret_cast( - tmp_indices_buffer.flat().data()), - reinterpret_cast(values_extended.flat().data())); - CK_CUDA_THROW_(cudaGetLastError()); - } + FusedMultiFunctional( + device, data_p_with_type(indices_tensor), + data_p_with_type(values_tensor), nnz, batch_size, + prune_invalid_id_, default_id, + data_p_with_type(all_flags), + data_p_with_type(all_flags) + batch_size, + data_p_with_type(tmp_indices_buffer), + data_p_with_type(values_extended)); + CK_CUDA_THROW_(cudaGetLastError()); + } else if (prune_invalid_id_) { - { - const int threads = linear_mapping_threads; - const int blocks = CalcBlocksLinearMapping(nnz, threads); - DetectInvalid<<>>( - reinterpret_cast( - values_tensor->flat().data()), - nnz, all_flags->flat().data() + batch_size); - CK_CUDA_THROW_(cudaGetLastError()); - } + DetectInvalid(device, data_p_with_type(values_tensor), + nnz, data_p_with_type(all_flags) + batch_size); + CK_CUDA_THROW_(cudaGetLastError()); } // 3.2 select copy valid id, select copy empty row indices - cudaError_t cuda_ret = cudaSuccess; - cuda_ret = cub::DeviceSelect::Flagged( - cub_temp_storage.flat().data(), max_cub_bytes, - reinterpret_cast(values_tensor->flat().data()), - (const int*)(all_flags->flat().data() + batch_size), - reinterpret_cast(values_extended.flat().data()), - selected_num_d.flat().data(), int(nnz), stream); + cub::DeviceSelect::Flagged( + data_p_with_type(cub_temp_storage), max_cub_bytes, + data_p_with_type(values_tensor), + data_p_with_type(all_flags) + batch_size, + data_p_with_type(values_extended), + data_p_with_type(selected_num_d), int(nnz), device.stream()); CK_CUDA_THROW_(cudaGetLastError()); cub::DeviceSelect::Flagged( - cub_temp_storage.flat().data(), max_cub_bytes, - reinterpret_cast( - indices_tensor->flat().data()), - all_flags->flat().data() + batch_size, - reinterpret_cast(indices_extended.flat().data()), - selected_num_d.flat().data(), nnz, stream); + data_p_with_type(cub_temp_storage), max_cub_bytes, + data_p_with_type(indices_tensor), + data_p_with_type(all_flags) + batch_size, + data_p_with_type(indices_extended), + data_p_with_type(selected_num_d), nnz, device.stream()); if (prune_invalid_id_) { int selected_num; - cudaMemcpyAsync(&selected_num, selected_num_d.flat().data(), - sizeof(int), cudaMemcpyDeviceToHost, stream); - cudaStreamSynchronize(stream); + cudaMemcpyAsync(&selected_num, data_p_with_type(selected_num_d), + sizeof(int), cudaMemcpyDeviceToHost, device.stream()); + cudaStreamSynchronize(device.stream()); new_nnz = selected_num; } if (fill_empty_row_) { cub::DeviceSelect::Flagged( - cub_temp_storage.flat().data(), max_cub_bytes, - reinterpret_cast( - tmp_indices_buffer.flat().data()), - all_flags->flat().data(), - reinterpret_cast( - indices_extended.flat().data()) + - new_nnz, - selected_num_d.flat().data(), batch_size, stream); + data_p_with_type(cub_temp_storage), max_cub_bytes, + data_p_with_type(tmp_indices_buffer), + data_p_with_type(all_flags), + data_p_with_type(indices_extended) + new_nnz, + data_p_with_type(selected_num_d), batch_size, device.stream()); CK_CUDA_THROW_(cudaGetLastError()); int selected_num; - cudaMemcpyAsync(&selected_num, selected_num_d.flat().data(), - sizeof(int), cudaMemcpyDeviceToHost, stream); - cudaStreamSynchronize(stream); + cudaMemcpyAsync(&selected_num, data_p_with_type(selected_num_d), + sizeof(int), cudaMemcpyDeviceToHost, device.stream()); + cudaStreamSynchronize(device.stream()); new_nnz += selected_num; } } + // =============================================== // // 3.5 set the correct pointer - const int64_t* values_in = (fill_empty_row_ || prune_invalid_id_) - ? reinterpret_cast( - values_extended.flat().data()) - : reinterpret_cast( - values_tensor->flat().data()); + const int64_t* values_in = + (fill_empty_row_ || prune_invalid_id_) + ? data_p_with_type(values_extended) + : data_p_with_type(values_tensor); const IndicePair* indices_in = (fill_empty_row_ || prune_invalid_id_) - ? reinterpret_cast( - indices_extended.flat().data()) - : reinterpret_cast( - indices_tensor->flat().data()); + ? data_p_with_type(indices_extended) + : data_p_with_type(indices_tensor); OpOutputList partitioned_values; OP_REQUIRES_OK(ctx, @@ -369,12 +233,12 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { partitioned_indices.allocate( 0, TensorShape({static_cast(new_nnz), 2}), &pi_out)); - cudaMemcpyAsync(pv_out->flat().data(), values_in, + cudaMemcpyAsync(data_p_with_type(pv_out), values_in, sizeof(int64_t) * new_nnz, cudaMemcpyDeviceToDevice, - stream); - cudaMemcpyAsync(pi_out->flat().data(), indices_in, + device.stream()); + cudaMemcpyAsync(data_p_with_type(pi_out), indices_in, sizeof(IndicePair) * new_nnz, cudaMemcpyDeviceToDevice, - stream); + device.stream()); } else { // multi-partitions case, calcaulate indices and split them. @@ -386,11 +250,10 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { &indices_sorted)); cub::DeviceRadixSort::SortPairs( - cub_temp_storage.flat().data(), max_cub_bytes, values_in, - reinterpret_cast(values_sorted.flat().data()), - indices_in, - reinterpret_cast(indices_sorted.flat().data()), - int(new_nnz), 0, sizeof(int64_t) * 8, stream); + data_p_with_type(cub_temp_storage), max_cub_bytes, values_in, + data_p_with_type(values_sorted), indices_in, + data_p_with_type(indices_sorted), int(new_nnz), 0, + sizeof(int64_t) * 8, device.stream()); CK_CUDA_THROW_(cudaGetLastError()); // 4.1 calculate how many elements for each @@ -401,10 +264,10 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { ctx->allocate_temp( DT_INT64, TensorShape({static_cast(num_partitions_)}), &partition_sizes_accumulate)); - cudaMemcpyAsync(partition_sizes_accumulate.flat().data(), + cudaMemcpyAsync(data_p_with_type(partition_sizes_accumulate), partition_sizes_accumulate_.data(), num_partitions_ * sizeof(int64_t), cudaMemcpyHostToDevice, - stream); + device.stream()); Tensor elements_offset_per_partition; OP_REQUIRES_OK( @@ -413,37 +276,32 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { DT_INT64, TensorShape({static_cast(num_partitions_)}), &elements_offset_per_partition)); - { - const int blocks = num_partitions_; - const int threads = 1; - CalcElementsOffsetPerPartition<<>>( - reinterpret_cast( - values_sorted.flat().data()), - reinterpret_cast( - partition_sizes_accumulate.flat().data()), - reinterpret_cast( - elements_offset_per_partition.flat().data()), - int(new_nnz)); - CK_CUDA_THROW_(cudaGetLastError()); - } + CalcElementsOffsetPerPartition( + device, num_partitions_, + data_p_with_type(values_sorted), + data_p_with_type(partition_sizes_accumulate), + data_p_with_type(elements_offset_per_partition), + int(new_nnz)); + CK_CUDA_THROW_(cudaGetLastError()); elements_offset_per_partition_.clear(); elements_offset_per_partition_.resize(num_partitions_); - // stream_executor::DeviceMemoryBase + // device.stream()_executor::DeviceMemoryBase // elements_offset_per_partition_wrapped( // elements_offset_per_partition.flat().data(), // num_partitions_); - // stream->ThenMemcpy(elements_offset_per_partition_.data(), + // device.stream()->ThenMemcpy(elements_offset_per_partition_.data(), // elements_offset_per_partition_wrapped, // num_partitions_ * // sizeof(int64_t)); - // stream->BlockHostUntilDone(); + // device.stream()->BlockHostUntilDone(); cudaMemcpyAsync(elements_offset_per_partition_.data(), - elements_offset_per_partition.flat().data(), + data_p_with_type(elements_offset_per_partition), num_partitions_ * sizeof(int64_t), cudaMemcpyDeviceToHost, - stream); - cudaStreamSynchronize(stream); + device.stream()); + cudaStreamSynchronize(device.stream()); + // 4.2 set output int64_t sub_start_offset = 0; @@ -463,40 +321,37 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { if (size > 0) { // some partition does not have any // element that falls in it - const int threads = linear_mapping_threads; - int blocks = CalcBlocksLinearMapping(size, threads); - const int partition_start_base = i == 0 ? 0 : partition_sizes_accumulate_[i - 1]; - GatherAndConvertToSubPartition<<>>( - reinterpret_cast( - values_sorted.flat().data()) + - sub_start_offset, - reinterpret_cast( - sub_partitioned_values->flat().data()), + + GatherAndConvertToSubPartition( + device, + data_p_with_type(values_sorted) + sub_start_offset, + data_p_with_type(sub_partitioned_values), partition_start_base, size); CK_CUDA_THROW_(cudaGetLastError()); - // stream_executor::DeviceMemoryBase + // device.stream()_executor::DeviceMemoryBase // sub_indices_sorted_wrapped( // reinterpret_cast(indices_sorted.flat().data()) // + // partition_start_base, // size * sizeof(IndicePair)); - // stream_executor::DeviceMemoryBase + // device.stream()_executor::DeviceMemoryBase // sub_indices_out_wrapped( // reinterpret_cast( // sub_partitioned_indices.flat().data()), // size * sizeof(IndicePair)); - // stream->ThenMemcpy(&sub_indices_out_wrapped, + // device.stream()->ThenMemcpy(&sub_indices_out_wrapped, // sub_indices_sorted_wrapped, // size * 2 * // sizeof(int64_t)); cudaMemcpyAsync( sub_partitioned_indices->flat().data(), indices_sorted.flat().data() + 2 * sub_start_offset, - size * 2 * sizeof(int64_t), cudaMemcpyDeviceToDevice, stream); + size * 2 * sizeof(int64_t), cudaMemcpyDeviceToDevice, + device.stream()); } sub_start_offset = elements_offset_per_partition_[i]; } diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_local_ops_test.cc b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_local_ops_test.cc similarity index 98% rename from tensorflow/core/kernels/fused_embedding/fused_embedding_local_ops_test.cc rename to tensorflow/core/kernels/fused_embedding/tests/fused_embedding_local_ops_test.cc index f22a7d5f8af..b441c8640cc 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_local_ops_test.cc +++ b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_local_ops_test.cc @@ -13,16 +13,11 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/cc/ops/const_op.h" -#include "tensorflow/cc/ops/image_ops.h" -#include "tensorflow/cc/ops/nn_ops.h" -#include "tensorflow/cc/ops/standard_ops.h" #include "tensorflow/core/common_runtime/kernel_benchmark_testlib.h" #include "tensorflow/core/framework/fake_input.h" #include "tensorflow/core/framework/node_def_builder.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/types.h" -#include "tensorflow/core/kernels/conv_ops_gpu.h" #include "tensorflow/core/kernels/ops_testutil.h" #include "tensorflow/core/kernels/ops_util.h" #include "tensorflow/core/platform/test.h" diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_post_grad_ops_test.cc b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_grad_ops_test.cc similarity index 99% rename from tensorflow/core/kernels/fused_embedding/fused_embedding_post_grad_ops_test.cc rename to tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_grad_ops_test.cc index acef29612fc..5335c7ea3de 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_post_grad_ops_test.cc +++ b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_grad_ops_test.cc @@ -18,7 +18,6 @@ limitations under the License. #include "tensorflow/core/framework/node_def_builder.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/types.h" -#include "tensorflow/core/kernels/conv_ops_gpu.h" #include "tensorflow/core/kernels/ops_testutil.h" #include "tensorflow/core/kernels/ops_util.h" #include "tensorflow/core/platform/test.h" diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_test.cc b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc similarity index 99% rename from tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_test.cc rename to tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc index 3321f3ff677..959ee948882 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_test.cc +++ b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc @@ -18,7 +18,6 @@ limitations under the License. #include "tensorflow/core/framework/node_def_builder.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/types.h" -#include "tensorflow/core/kernels/conv_ops_gpu.h" #include "tensorflow/core/kernels/ops_testutil.h" #include "tensorflow/core/kernels/ops_util.h" #include "tensorflow/core/platform/test.h" diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_test.cc b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_pre_ops_test.cc similarity index 99% rename from tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_test.cc rename to tensorflow/core/kernels/fused_embedding/tests/fused_embedding_pre_ops_test.cc index e960330406c..6f0213b2664 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_test.cc +++ b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_pre_ops_test.cc @@ -13,8 +13,6 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include - #include "tensorflow/core/common_runtime/kernel_benchmark_testlib.h" #include "tensorflow/core/framework/fake_input.h" #include "tensorflow/core/framework/node_def_builder.h" @@ -59,6 +57,7 @@ class FusedEmbeddingSparsePreLookUpOpTest : public OpsTestBase { }; TEST_F(FusedEmbeddingSparsePreLookUpOpTest, Partition3_Int64) { + printf("hello?\n"); MakeOpAndSetDevice(Device::GPU, 3, false, false, -1); // partition_shapes 0 AddInputFromArray(TensorShape({2}), {6, 16}); @@ -97,7 +96,6 @@ TEST_F(FusedEmbeddingSparsePreLookUpOpTest, Partition3_Int64) { test::FillValues(&expected_indices, {12, 12, 13, 0}); test::ExpectTensorEqual(expected_indices, *GetOutput(4)); } - { Tensor expected_values(allocator(), DT_INT64, TensorShape({4})); test::FillValues(&expected_values, {2, 3, 5, 6}); From 95a08ff0ba85b2b8c04ff1f8c574762bf15dd03b Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Thu, 27 Jan 2022 16:59:52 +0800 Subject: [PATCH 05/41] Update: add unique sub op and use cu event to sync --- tensorflow/core/kernels/BUILD | 12 +- .../fused_embedding_pre_ops_gpus.cu.cc | 16 +- .../fused_embedding/unique_gpu/unique.cu.cc | 553 ++++++++++++++++++ .../fused_embedding/unique_gpu/unique.cu.h | 0 4 files changed, 571 insertions(+), 10 deletions(-) create mode 100644 tensorflow/core/kernels/fused_embedding/unique_gpu/unique.cu.cc create mode 100644 tensorflow/core/kernels/fused_embedding/unique_gpu/unique.cu.h diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD index 1cb5b076c65..35c5134551f 100644 --- a/tensorflow/core/kernels/BUILD +++ b/tensorflow/core/kernels/BUILD @@ -5293,10 +5293,14 @@ tf_kernel_library( "fused_embedding/fused_embedding_local_ops_gpu.cu.cc", "fused_embedding/fused_embedding_pre_ops_gpus.cu.cc", "fused_embedding/fused_embedding_post_ops_gpus.cu.cc", - "fused_embedding/fused_embedding_gpu_kernels.cu.cc" - ], - deps = ["//third_party/eigen3", "//tensorflow/core/profiler:nvtx_utils"] + - DYNAMIC_DEPS + if_cuda(["@cub_archive//:cub", "fused_embedding_cuh"]), + "fused_embedding/fused_embedding_gpu_kernels.cu.cc", + "fused_embedding/unique_gpu/unique.cu.cc" + ], + deps = ["//third_party/eigen3", + "//tensorflow/core/profiler:nvtx_utils"] + + DYNAMIC_DEPS + + if_cuda(["@cub_archive//:cub", + "fused_embedding_cuh"]), ) tf_kernel_library( diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc b/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc index 203b8a63876..dc66239d2f5 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc @@ -67,6 +67,9 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { partition_sizes_accumulate_.back(); partition_sizes_accumulate_.push_back(accu); } + + cudaEvent_t memcpy_event; + cudaEventCreateWithFlags(&memcpy_event, cudaEventDisableTiming); // =============================================== // // ========= 2. allocate cub tmp storage ========= // @@ -149,8 +152,7 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { FusedMultiFunctional( device, data_p_with_type(indices_tensor), data_p_with_type(values_tensor), nnz, batch_size, - prune_invalid_id_, default_id, - data_p_with_type(all_flags), + prune_invalid_id_, default_id, data_p_with_type(all_flags), data_p_with_type(all_flags) + batch_size, data_p_with_type(tmp_indices_buffer), data_p_with_type(values_extended)); @@ -182,7 +184,8 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { int selected_num; cudaMemcpyAsync(&selected_num, data_p_with_type(selected_num_d), sizeof(int), cudaMemcpyDeviceToHost, device.stream()); - cudaStreamSynchronize(device.stream()); + cudaEventRecord(memcpy_event, device.stream()); + cudaEventSynchronize(memcpy_event); new_nnz = selected_num; } @@ -197,7 +200,8 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { int selected_num; cudaMemcpyAsync(&selected_num, data_p_with_type(selected_num_d), sizeof(int), cudaMemcpyDeviceToHost, device.stream()); - cudaStreamSynchronize(device.stream()); + cudaEventRecord(memcpy_event, device.stream()); + cudaEventSynchronize(memcpy_event); new_nnz += selected_num; } } @@ -300,8 +304,8 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { data_p_with_type(elements_offset_per_partition), num_partitions_ * sizeof(int64_t), cudaMemcpyDeviceToHost, device.stream()); - cudaStreamSynchronize(device.stream()); - + cudaEventRecord(memcpy_event, device.stream()); + cudaEventSynchronize(memcpy_event); // 4.2 set output int64_t sub_start_offset = 0; diff --git a/tensorflow/core/kernels/fused_embedding/unique_gpu/unique.cu.cc b/tensorflow/core/kernels/fused_embedding/unique_gpu/unique.cu.cc new file mode 100644 index 00000000000..1163e732673 --- /dev/null +++ b/tensorflow/core/kernels/fused_embedding/unique_gpu/unique.cu.cc @@ -0,0 +1,553 @@ +#if GOOGLE_CUDA + +#define EIGEN_USE_GPU + +#include + +#include "tensorflow/core/common_runtime/gpu/gpu_event_mgr.h" +#include "tensorflow/core/lib/core/bits.h" +#include "tensorflow/core/util/gpu_kernel_helper.h" +#include "tensorflow/stream_executor/cuda/cuda_activation.h" + +namespace tensorflow { + +typedef Eigen::GpuDevice GPUDevice; + +namespace { + +// Returns true iff index is at the end of a segment (which is equivalent to the +// beginning of the next segment). +template +struct SegmentIndicatorFunctor { + const T* __restrict__ sorted_input_ptr_; + SegmentIndicatorFunctor(const T* sorted_input_ptr) + : sorted_input_ptr_(sorted_input_ptr) {} + __device__ bool operator()(const TIndex& i) const { + return i > 0 && sorted_input_ptr_[i] != sorted_input_ptr_[i - 1]; + } +}; + +template +__global__ void ExtractFirstOccurrenceIndicesKernel( + int64 input_size, int64 uniq_size, + const TIndex* __restrict__ sorted_input_inds, + const TIndex* __restrict__ sorted_input_unique_ids, + TIndex* __restrict__ unique_input_inds, TIndex* __restrict__ segment_ends) { + GPU_1D_KERNEL_LOOP(i, input_size) { + TIndex sorted_input_unique_id = sorted_input_unique_ids[i]; + if (i == 0 || sorted_input_unique_id != sorted_input_unique_ids[i - 1]) { + unique_input_inds[sorted_input_unique_id] = sorted_input_inds[i]; + if (segment_ends) { + if (i == 0) { + // First thread writes the last element. + segment_ends[uniq_size - 1] = input_size; + } else { + segment_ends[sorted_input_unique_id - 1] = i; + } + } + } + } +} + +// Scatters the index of the first occurrence of each unique input value to +// unique_input_inds. +// If segment_ends is not nullptr, it is filled with the end index of each +// unique value's range in the sorted input (the last element is always set +// to input_size). +template +Status ExtractFirstOccurrenceIndices(const GPUDevice& d, int64 input_size, + int64 uniq_size, + const TIndex* sorted_input_inds, + const TIndex* sorted_input_unique_ids, + TIndex* unique_input_inds, + TIndex* segment_ends) { + CHECK_GT(input_size, 0); // Crash OK + GpuLaunchConfig config = GetGpuLaunchConfig( + input_size, d, &ExtractFirstOccurrenceIndicesKernel, + /*dynamic_shared_memory_size=*/0, /*block_size_limit=*/0); + return GpuLaunchKernel(ExtractFirstOccurrenceIndicesKernel, + config.block_count, config.thread_per_block, 0, + d.stream(), input_size, uniq_size, sorted_input_inds, + sorted_input_unique_ids, unique_input_inds, + segment_ends); +} + +template +__global__ void GatherOutputsAndInvertPermutationKernel( + int64 uniq_size, const T* __restrict__ input, + const TIndex* __restrict__ sorted_unique_input_inds, + const TIndex* __restrict__ sorted_unique_perm, + const TIndex* __restrict__ segment_ends, T* __restrict__ output, + TIndex* __restrict__ inv_sorted_unique_perm, TIndex* __restrict__ count) { + GPU_1D_KERNEL_LOOP(i, uniq_size) { + output[i] = input[sorted_unique_input_inds[i]]; + auto j = sorted_unique_perm[i]; + inv_sorted_unique_perm[j] = i; + if (count) { + TIndex beg = j == 0 ? 0 : segment_ends[j - 1]; + TIndex end = segment_ends[j]; + count[i] = end - beg; + } + } +} + +// Gathers input values using sorted_unique_input_inds, and inverts the +// permutation specified by sorted_unique_perm. +template +Status GatherOutputsAndInvertPermutation(const GPUDevice& d, int64 uniq_size, + const T* input, + const TIndex* sorted_unique_input_inds, + const TIndex* sorted_unique_perm, + const TIndex* segment_ends, T* output, + TIndex* inv_sorted_unique_perm, + TIndex* count) { + if (uniq_size == 0) return Status::OK(); + GpuLaunchConfig config = GetGpuLaunchConfig( + uniq_size, d, &GatherOutputsAndInvertPermutationKernel, + /*dynamic_shared_memory_size=*/0, /*block_size_limit=*/0); + return GpuLaunchKernel(GatherOutputsAndInvertPermutationKernel, + config.block_count, config.thread_per_block, 0, + d.stream(), uniq_size, input, sorted_unique_input_inds, + sorted_unique_perm, segment_ends, output, + inv_sorted_unique_perm, count); +} + +template +__global__ void LookupAndScatterUniqueIdsKernel( + int64 input_size, const TIndex* sorted_input_inds, + const TIndex* __restrict__ sorted_input_unique_ids, + const TIndex* __restrict__ inv_sorted_unique_perm, + TIndex* __restrict__ idx) { + GPU_1D_KERNEL_LOOP(i, input_size) { + idx[sorted_input_inds[i]] = + inv_sorted_unique_perm[sorted_input_unique_ids[i]]; + } +} + +// Maps the values of sorted_input_unique_ids and scatters them to idx using +// sorted_input_inds. +template +Status LookupAndScatterUniqueIds(const GPUDevice& d, int64 input_size, + const TIndex* sorted_input_inds, + const TIndex* sorted_input_unique_ids, + const TIndex* inv_sorted_unique_perm, + TIndex* idx) { + CHECK_GT(input_size, 0); // Crash OK + GpuLaunchConfig config = GetGpuLaunchConfig( + input_size, d, &LookupAndScatterUniqueIdsKernel, + /*dynamic_shared_memory_size=*/0, /*block_size_limit=*/0); + return GpuLaunchKernel(LookupAndScatterUniqueIdsKernel, + config.block_count, config.thread_per_block, 0, + d.stream(), input_size, sorted_input_inds, + sorted_input_unique_ids, inv_sorted_unique_perm, idx); +} + +template +__global__ void RangeInitKernel(const T start, const T delta, const T size, + T* out) { + GPU_1D_KERNEL_LOOP(i, size) { out[i] = start + i * delta; } +} + +// Initialize out with range start, start + delta, start + 2 * delta, ... +template +Status RangeInit(const Eigen::GpuDevice& d, const T start, const T delta, + const T size, T* out) { + if (size == 0) return Status::OK(); + GpuLaunchConfig config = GetGpuLaunchConfig(size, d); + return GpuLaunchKernel(RangeInitKernel, config.block_count, + config.thread_per_block, 0, d.stream(), start, delta, + size, out); +} + +// Computes keys_out = sorted(keys_in), and indices_out = argsort(keys_in). +// If keys_out is not required, it can be set to nullptr. +// If indices_in is nullptr, the range of input indices [0, size) will be used. +template +Status GpuRadixSort(OpKernelContext* context, int size, const Tkey* keys_in, + Tkey* keys_out, // Optional + const Tindex* indices_in, // Optional + Tindex* indices_out, int num_bits = sizeof(Tkey) * 8) { + if (size == 0) return Status::OK(); + // Allocate temporary inputs/outputs if necessary. + Tensor tmp_indices_in; + if (!indices_in) { + TF_RETURN_IF_ERROR(context->allocate_temp( + DataTypeToEnum::value, TensorShape({size}), &tmp_indices_in)); + Tindex* mutable_indices_in = tmp_indices_in.flat().data(); + indices_in = mutable_indices_in; + const Eigen::GpuDevice& device = context->eigen_device(); + // Initialize indices_in to the input index range. + TF_RETURN_IF_ERROR(RangeInit(device, Tindex(0), Tindex(1), Tindex(size), + mutable_indices_in)); + } + Tensor tmp_keys_out; + if (!keys_out) { + TF_RETURN_IF_ERROR(context->allocate_temp( + DataTypeToEnum::value, TensorShape({size}), &tmp_keys_out)); + keys_out = tmp_keys_out.flat().data(); + } + // Determine temporary device storage requirements. + Tensor temp_storage; + size_t temp_storage_bytes = 0; + const auto& cu_stream = GetGpuStream(context); + auto err = cub::DeviceRadixSort::SortPairs(nullptr, temp_storage_bytes, + keys_in, keys_out, indices_in, + indices_out, size, /*begin_bit=*/0, + /*end_bit=*/num_bits, cu_stream); + if (err != 0) { + return errors::Internal( + "Failed to launch gpuprim::DeviceRadixSort::SortPairs to calculate " + "temp_storage_bytes, status: ", + cudaGetErrorString(err)); + } + // Allocate temporary storage. + TF_RETURN_IF_ERROR(context->allocate_temp( + DT_INT8, TensorShape({static_cast(temp_storage_bytes)}), + &temp_storage)); + // Sort indices by keys. + err = cub::DeviceRadixSort::SortPairs( + temp_storage.flat().data(), temp_storage_bytes, keys_in, keys_out, + indices_in, indices_out, size, + /*begin_bit=*/0, /*end_bit=*/num_bits, cu_stream); + if (err != 0) { + return errors::Internal( + "Failed to launch gpuprim::DeviceRadixSort::SortPairs, " + "temp_storage_bytes: ", + temp_storage_bytes, "status: ", cudaGetErrorString(err)); + } + return Status::OK(); +} + +template +Status GpuInclusivePrefixSum(OpKernelContext* context, int size, + InputIteratorT input, OutputIteratorT output) { + static_assert( + !std::is_same::type, + bool>::value, + "GpuInclusivePrefixSum does not work correct with booleans, please use " + "TransformInputIterator to explicitly cast to an integer."); + if (size == 0) return Status::OK(); + const auto& cu_stream = GetGpuStream(context); + size_t temp_storage_bytes; + auto err = cub::DeviceScan::InclusiveSum(nullptr, temp_storage_bytes, input, + output, size, cu_stream); + if (err != 0) { + return errors::Internal( + "Failed to launch gpuprim::DeviceScan::InclusiveSum to calculate " + "temp_storage_bytes, status: ", + cudaGetErrorString(err)); + } + Tensor temp_storage; + TF_RETURN_IF_ERROR(context->allocate_temp( + DT_INT8, TensorShape({static_cast(temp_storage_bytes)}), + &temp_storage)); + err = cub::DeviceScan::InclusiveSum(temp_storage.flat().data(), + temp_storage_bytes, input, output, size, + cu_stream); + if (err != 0) { + return errors::Internal( + "Failed to launch gpuprim::DeviceScan::InclusiveSum, " + "temp_storage_bytes: ", + temp_storage_bytes, ", status: ", cudaGetErrorString(err)); + } + return Status::OK(); +} + +// Helper class to allocate scratch memory and keep track of debug info. +// Mostly a thin wrapper around Tensor & allocate_temp. +template +class ScratchSpace { + public: + ScratchSpace(OpKernelContext* context, int64 size, bool on_host) + : ScratchSpace(context, TensorShape({size}), "", on_host) {} + + ScratchSpace(OpKernelContext* context, int64 size, const string& debug_info, + bool on_host) + : ScratchSpace(context, TensorShape({size}), debug_info, on_host) {} + + ScratchSpace(OpKernelContext* context, const TensorShape& shape, + const string& debug_info, bool on_host) + : context_(context), debug_info_(debug_info), on_host_(on_host) { + AllocatorAttributes alloc_attr; + if (on_host) { + // Allocate pinned memory on the host to avoid unnecessary + // synchronization. + alloc_attr.set_on_host(true); + alloc_attr.set_gpu_compatible(true); + } + TF_CHECK_OK(context->allocate_temp(DataTypeToEnum::value, shape, + &scratch_tensor_, alloc_attr)); + } + + virtual ~ScratchSpace() {} + + Scalar* mutable_data() { + return scratch_tensor_.template flat().data(); + } + const Scalar* data() const { + return scratch_tensor_.template flat().data(); + } + Scalar& operator()(int64 i) { + return scratch_tensor_.template flat()(i); + } + const Scalar& operator()(int64 i) const { + return scratch_tensor_.template flat()(i); + } + int64 bytes() const { return scratch_tensor_.TotalBytes(); } + int64 size() const { return scratch_tensor_.NumElements(); } + const string& debug_info() const { return debug_info_; } + + Tensor& tensor() { return scratch_tensor_; } + const Tensor& tensor() const { return scratch_tensor_; } + + // Returns true if this ScratchSpace is in host memory. + bool on_host() const { return on_host_; } + + protected: + OpKernelContext* context() const { return context_; } + + private: + OpKernelContext* context_; // not owned + const string debug_info_; + const bool on_host_; + Tensor scratch_tensor_; +}; + +template +void AllocateTemp(OpKernelContext* context, int64 size, Tensor* tensor, + U** tensor_data) { + OP_REQUIRES_OK(context, context->allocate_temp(DataTypeToEnum::value, + TensorShape({size}), tensor)); + *tensor_data = tensor->flat().data(); +} + +} // namespace + +namespace unique_gpu { + +template +struct UniqueSubOpGPU { + // Not really a TensorFlow Op + void operator()(OpKernelContext* context, const Tensor& input, + bool has_count_output, Tensor* unique_keys_out, + Tensor* unique_idxs_out, Tensor* unique_counts_out) { + OP_REQUIRES(context, + input.NumElements() <= std::numeric_limits::max(), + errors::InvalidArgument( + "unique does not support input tensors larger than ", + std::numeric_limits::max(), " elements")); + + OP_REQUIRES(context, TensorShapeUtils::IsVector(input.shape()), + errors::InvalidArgument("unique expects a 1D vector.")); + + se::Stream* stream = context->op_device_context()->stream(); + OP_REQUIRES(context, stream, errors::Internal("No GPU stream available.")); + + cudaEvent_t memcpy_event; + cudaEventCreateWithFlags(&memcpy_event, cudaEventDisableTiming); + + int64 input_size = input.NumElements(); + if (input_size == 0) { + // Early exit for trivial case. + Tensor** temp; + AllocateTemp(context, 0, unique_keys_out, temp); + AllocateTemp(context, 0, unique_idxs_out, temp); + if (has_count_output) { + AllocateTemp(context, 0, unique_counts_out, temp); + } + return; + } + + // The algorithm implemented here is as follows: + // input = [3, 5, 3, 4, 1, 4, 9, 8, 6, 3, 5, 7, 8, 8, 4, 6, 4, 2, 5, 6] + // 1) Sort the input to group equal values together in segments. + // sorted_input, sorted_input_inds = sort(input) + // sorted_input: + // [1, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 8, 8, 8, 9] + // sorted_input_inds: + // [4, 17, 0, 2, 9, 3, 5, 14, 16, 1, 10, 18, 8, 15, 19, 11, 7, 12, 13, 6] + // 2) Identify the boundaries between segments and use prefix sum to + // compute the unique ID for each sorted value. + // sorted_input_unique_ids = prefix_sum(indicator(sorted_input)) + // indicator(sorted_input): + // [0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1] + // sorted_input_unique_ids: + // [0, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 7, 7, 7, 8] + // 3) Extract the input index of the first occurrence of each unique value. + // If counts are required, also extract the end index of each segment. + // unique_input_inds[sorted_input_unique_ids] = + // sorted_input_inds (@ indicator) + // segment_ends[sorted_input_unique_ids[i] - 1] = i (@ indicator) + // unique_input_inds: [4, 17, 0, 3, 1, 8, 11, 7, 6] + // segment_ends: [1, 2, 5, 9, 12, 15, 16, 19, 20] + // 4) Sort the extracted unique input indices to put them in order of + // first appearance. + // sorted_unique_input_inds, sorted_unique_perm = + // sort(unique_input_inds) + // sorted_unique_input_inds: [0, 1, 3, 4, 6, 7, 8, 11, 17] + // sorted_unique_perm: [2, 4, 3, 0, 8, 7, 5, 6, 1] + // 5) Gather the sorted unique input values to produce output, and invert + // the second sort permutation to produce an inverse ID mapping. If + // counts are required, also take the adjacent difference between + // segment_ends indices to produce counts. + // output = input[sorted_unique_input_inds] + // inv_sorted_unique_perm[sorted_unique_perm[i]] = i + // counts = adjacent_difference(segment_ends) + // output: [3, 5, 4, 1, 9, 8, 6, 7, 2] + // inv_sorted_unique_perm: [3, 8, 0, 2, 1, 6, 7, 5, 4] + // counts: [3, 3, 4, 1, 1, 3, 3, 1, 1] + // 6) Look up unique IDs via the inverse ID mapping and scatter them using + // the original sort permutation to produce the indices output. + // idx[sorted_input_inds] = + // inv_sorted_unique_perm[sorted_input_unique_ids] + // idx: [0, 1, 0, 2, 3, 2, 4, 5, 6, 0, 1, 7, 5, 5, 2, 6, 2, 8, 1, 6] + + Tensor sorted_input_inds; + TIndex* sorted_input_inds_ptr = nullptr; + AllocateTemp(context, input_size, &sorted_input_inds, + &sorted_input_inds_ptr); + if (!context->status().ok()) return; + + Tensor sorted_input; + T* sorted_input_ptr = nullptr; + AllocateTemp(context, input_size, &sorted_input, &sorted_input_ptr); + if (!context->status().ok()) return; + + const T* input_ptr = input.flat().data(); + OP_REQUIRES_OK( + context, + GpuRadixSort(context, input_size, /*keys_in=*/input_ptr, + /*keys_out=*/sorted_input_ptr, + /*indices_in=*/static_cast(nullptr), + /*indices_out=*/sorted_input_inds_ptr)); + + // Create a fancy input iterator to indicate segment boundaries. + cub::TransformInputIterator, + cub::CountingInputIterator> + segment_indicator_iter(0, {sorted_input_ptr}); + + Tensor sorted_input_unique_ids; + TIndex* sorted_input_unique_ids_ptr = nullptr; + AllocateTemp(context, input_size, &sorted_input_unique_ids, + &sorted_input_unique_ids_ptr); + if (!context->status().ok()) return; + + OP_REQUIRES_OK(context, GpuInclusivePrefixSum(context, input_size, + segment_indicator_iter, + sorted_input_unique_ids_ptr)); + + // Copy the last element of sorted_input_unique_ids back to the host to + // obtain uniq_size. + ScratchSpace last_idx_host(context, 1, /*on_host=*/true); + OP_REQUIRES_OK( + context, + stream + ->ThenMemcpy(last_idx_host.mutable_data(), + se::DeviceMemoryBase( + const_cast(sorted_input_unique_ids_ptr) + + (input_size - 1), + sizeof(*last_idx_host.data())), + sizeof(*last_idx_host.data())) + .ok()); + + const GPUDevice& device = context->eigen_gpu_device(); + cudaEventRecord(memcpy_event, device.stream()); + cudaEventSynchronize(memcpy_event); + int64 uniq_size = (*last_idx_host.data()) + 1; + + se::cuda::ScopedActivateExecutorContext scoped_activation{ + context->op_device_context()->stream()->parent()}; + + Tensor unique_input_inds; + TIndex* unique_input_inds_ptr = nullptr; + AllocateTemp(context, uniq_size, &unique_input_inds, + &unique_input_inds_ptr); + if (!context->status().ok()) return; + + Tensor segment_ends; + TIndex* segment_ends_ptr = nullptr; + if (has_count_output) { + AllocateTemp(context, uniq_size, &segment_ends, &segment_ends_ptr); + if (!context->status().ok()) return; + } + + OP_REQUIRES_OK(context, + ExtractFirstOccurrenceIndices( + device, input_size, uniq_size, sorted_input_inds_ptr, + sorted_input_unique_ids_ptr, unique_input_inds_ptr, + segment_ends_ptr)); + + Tensor sorted_unique_input_inds; + TIndex* sorted_unique_input_inds_ptr = nullptr; + AllocateTemp(context, uniq_size, &sorted_unique_input_inds, + &sorted_unique_input_inds_ptr); + if (!context->status().ok()) return; + + Tensor sorted_unique_perm; + TIndex* sorted_unique_perm_ptr = nullptr; + AllocateTemp(context, uniq_size, &sorted_unique_perm, + &sorted_unique_perm_ptr); + if (!context->status().ok()) return; + + // Sort by input index so that output is in order of appearance. + OP_REQUIRES_OK( + context, + GpuRadixSort(context, uniq_size, + /*keys_in=*/unique_input_inds_ptr, + /*keys_out=*/sorted_unique_input_inds_ptr, + /*indices_in=*/static_cast(nullptr), + /*indices_out=*/sorted_unique_perm_ptr, + /*num_bits=*/Log2Ceiling(input_size))); + + // Free temporary tensor that is no longer needed. + unique_input_inds = Tensor(); + unique_input_inds_ptr = nullptr; + + // output 0 unique_keys_out + T* output_ptr = nullptr; + AllocateTemp(context, uniq_size, unique_keys_out, &output_ptr); + + Tensor inv_sorted_unique_perm; + TIndex* inv_sorted_unique_perm_ptr = nullptr; + AllocateTemp(context, uniq_size, &inv_sorted_unique_perm, + &inv_sorted_unique_perm_ptr); + if (!context->status().ok()) return; + + // output 2 unique_counts_out + TIndex* count_ptr = nullptr; + if (has_count_output) { + AllocateTemp(context, uniq_size, unique_counts_out, &count_ptr); + } + + // Compute output and counts (if necessary). + OP_REQUIRES_OK( + context, GatherOutputsAndInvertPermutation( + device, uniq_size, input_ptr, sorted_unique_input_inds_ptr, + sorted_unique_perm_ptr, segment_ends_ptr, output_ptr, + inv_sorted_unique_perm_ptr, count_ptr)); + + // Free temporary tensors that are no longer needed. + sorted_unique_perm = Tensor(); + sorted_unique_perm_ptr = nullptr; + sorted_unique_input_inds = Tensor(); + sorted_unique_input_inds_ptr = nullptr; + segment_ends = Tensor(); + segment_ends_ptr = nullptr; + + TIndex* idx_ptr = nullptr; + // output 1 unique_idxs_out + AllocateTemp(context, input_size, unique_idxs_out, &idx_ptr); + + // Compute indices output. + OP_REQUIRES_OK(context, LookupAndScatterUniqueIds( + device, input_size, sorted_input_inds_ptr, + sorted_input_unique_ids_ptr, + inv_sorted_unique_perm_ptr, idx_ptr)); + } +}; + +} // namespace unique_gpu + +} // namespace tensorflow + +#endif // GOOGLE_CUDA \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/unique_gpu/unique.cu.h b/tensorflow/core/kernels/fused_embedding/unique_gpu/unique.cu.h new file mode 100644 index 00000000000..e69de29bb2d From f2ca5ea737043fd425a209190bf653e322aae508 Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Wed, 9 Feb 2022 19:00:36 +0800 Subject: [PATCH 06/41] Minor change --- tensorflow/core/kernels/BUILD | 8 +- .../fused_embedding/fused_embedding.cu.h | 101 --- .../fused_embedding_local_ops_gpu.cu.cc | 1 + .../fused_embedding_post_ops_gpus.cu.cc | 31 +- .../fused_embedding_pre_ops_gpus.cu.cc | 1 + .../kernels.cu.cc} | 2 + .../gpu_functions/kernels.cu.h | 114 ++++ .../gpu_functions/partition_select.cu.cc | 136 ++++ .../gpu_functions/partition_select.cu.h | 22 + .../gpu_functions/unique.cu.cc | 642 ++++++++++++++++++ .../fused_embedding/gpu_functions/unique.cu.h | 28 + .../fused_embedding/unique_gpu/unique.cu.cc | 553 --------------- .../fused_embedding/unique_gpu/unique.cu.h | 0 tensorflow/python/ops/embedding_ops.py | 1 + 14 files changed, 968 insertions(+), 672 deletions(-) rename tensorflow/core/kernels/fused_embedding/{fused_embedding_gpu_kernels.cu.cc => gpu_functions/kernels.cu.cc} (99%) create mode 100644 tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h create mode 100644 tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc create mode 100644 tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.h create mode 100644 tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc create mode 100644 tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h delete mode 100644 tensorflow/core/kernels/fused_embedding/unique_gpu/unique.cu.cc delete mode 100644 tensorflow/core/kernels/fused_embedding/unique_gpu/unique.cu.h diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD index 35c5134551f..4d15a3b4cff 100644 --- a/tensorflow/core/kernels/BUILD +++ b/tensorflow/core/kernels/BUILD @@ -5284,7 +5284,9 @@ tf_kernel_library( tf_cuda_library( name = "fused_embedding_cuh", - hdrs = ["fused_embedding/fused_embedding.cu.h"], + hdrs = ["fused_embedding/fused_embedding.cu.h", + "fused_embedding/gpu_functions/kernels.cu.h", + "fused_embedding/gpu_functions/unique.cu.h"], ) tf_kernel_library( @@ -5293,8 +5295,8 @@ tf_kernel_library( "fused_embedding/fused_embedding_local_ops_gpu.cu.cc", "fused_embedding/fused_embedding_pre_ops_gpus.cu.cc", "fused_embedding/fused_embedding_post_ops_gpus.cu.cc", - "fused_embedding/fused_embedding_gpu_kernels.cu.cc", - "fused_embedding/unique_gpu/unique.cu.cc" + "fused_embedding/gpu_functions/kernels.cu.cc", + "fused_embedding/gpu_functions/unique.cu.cc" ], deps = ["//third_party/eigen3", "//tensorflow/core/profiler:nvtx_utils"] + diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h b/tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h index 1dfaf43dd3f..9f334b11b45 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h +++ b/tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h @@ -46,107 +46,6 @@ struct IndicePair { int64_t entry_in_column; }; -// ===================== combiner ====================== // - -enum Combiner { Mean, Sum, Sqrtn }; - -template -__forceinline__ __device__ float Combine(const float in, const int feature_num); - -template <> -__forceinline__ __device__ float Combine(const float in, - const int feature_num) { - return in / sqrtf(feature_num); -} - -template <> -__forceinline__ __device__ float Combine(const float in, - const int feature_num) { - return in / feature_num; -} - -template <> -__forceinline__ __device__ float Combine(const float in, - const int feature_num) { - return in; -} - -template -__forceinline__ __device__ float CombineGrad(const float grad, - const int feature_num); - -template <> -__forceinline__ __device__ float CombineGrad(const float grad, - const int feature_num) { - return grad / sqrtf(feature_num); -} - -template <> -__forceinline__ __device__ float CombineGrad(const float grad, - const int feature_num) { - return grad / feature_num; -} - -template <> -__forceinline__ __device__ float CombineGrad(const float grad, - const int feature_num) { - return grad; -} - -// ================================================== // - -// ================== computations ================== // - -using GPUDevice = Eigen::GpuDevice; - -void InitFlagsToOneInt4(const GPUDevice& d, int length, int* flags); - -void DetectInvalid(const GPUDevice& d, const int64_t* values, const int64_t nnz, - int* invalid_id_flag); - -void FusedMultiFunctional(const GPUDevice& d, const IndicePair* indices, - const int64_t* values, const int64_t nnz, - const int64_t batch_size, const bool prune_invalid_id, - const int64_t default_id, int* row_emptiness_flag, - int* invalid_id_flag, IndicePair* tmp_indices_buffer, - int64_t* values_extended); - -void CalcElementsOffsetPerPartition(const GPUDevice& d, int num_partitions, - const int64_t* values_sorted, - int64_t* partition_sizes_accumulate, - int64_t* elements_offset_per_partition, - int nnz); - -void GatherAndConvertToSubPartition(const GPUDevice& d, - const int64_t* sub_values_sorted, - int64_t* sub_partitioned_values, - const int64_t partition_start_base, - const int64_t partition_size); - -void SumUpEmbeddingShard(const GPUDevice& d, const size_t sub_nnz, - const float* emb_shard, - const int64_t* partitioned_indice, float* emb_vectors, - int* feature_nums, const float max_norm, - const int emb_vec_size); - -template -void ApplyCombiner(const GPUDevice& d, const int batch_size, - const int emb_vec_size, float* emb_vectors, - const int* row_emptiness_flag, const bool set_empty_row_zero, - const int* feature_nums); - -template -void DistributeGradToShard(const GPUDevice& d, const float* top_grad, - const float* emb_shard, - const int64_t* partitioned_indice, - const int* feature_nums, - const int* row_emptiness_flag, - const bool set_empty_row_zero, float* grad_shard, - const int64_t sub_nnz, const int64_t emb_vec_size, - const float max_norm); - -// ================================================== // - } // namespace fused_embedding } // namespace tensorflow diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_local_ops_gpu.cu.cc b/tensorflow/core/kernels/fused_embedding/fused_embedding_local_ops_gpu.cu.cc index ff4ba43f1dc..e776c08d82d 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_local_ops_gpu.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/fused_embedding_local_ops_gpu.cu.cc @@ -8,6 +8,7 @@ #define EIGEN_USE_GPU #include "tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h" +#include "tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h" #include "tensorflow/core/util/gpu_kernel_helper.h" namespace tensorflow { diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc b/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc index 684bec3c3c6..75859430d91 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc @@ -9,6 +9,7 @@ #define EIGEN_USE_GPU #include "tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h" +#include "tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h" #include "tensorflow/core/profiler/nvtx_utils.h" #include "tensorflow/core/util/gpu_kernel_helper.h" #include "third_party/cub/thread/thread_operators.cuh" @@ -97,23 +98,23 @@ class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { // ========================= 2. combiner ========================== // if (combiner_ == "sqrtn") { - ApplyCombiner(device, batch_size, emb_vec_size, - data_p_with_type(emb_vectors_tensor), - data_p_with_type(row_empty_and_invalid_flags), - set_empty_row_zero, - data_p_with_type(feature_nums)); + ApplyCombiner( + device, batch_size, emb_vec_size, + data_p_with_type(emb_vectors_tensor), + data_p_with_type(row_empty_and_invalid_flags), + set_empty_row_zero, data_p_with_type(feature_nums)); } else if (combiner_ == "mean") { - ApplyCombiner(device, batch_size, emb_vec_size, - data_p_with_type(emb_vectors_tensor), - data_p_with_type(row_empty_and_invalid_flags), - set_empty_row_zero, - data_p_with_type(feature_nums)); + ApplyCombiner( + device, batch_size, emb_vec_size, + data_p_with_type(emb_vectors_tensor), + data_p_with_type(row_empty_and_invalid_flags), + set_empty_row_zero, data_p_with_type(feature_nums)); } else { - ApplyCombiner(device, batch_size, emb_vec_size, - data_p_with_type(emb_vectors_tensor), - data_p_with_type(row_empty_and_invalid_flags), - set_empty_row_zero, - data_p_with_type(feature_nums)); + ApplyCombiner( + device, batch_size, emb_vec_size, + data_p_with_type(emb_vectors_tensor), + data_p_with_type(row_empty_and_invalid_flags), + set_empty_row_zero, data_p_with_type(feature_nums)); } CK_CUDA_THROW_(cudaGetLastError()); // ================================================================ // diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc b/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc index dc66239d2f5..b777dd1a0a1 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc @@ -9,6 +9,7 @@ #define EIGEN_USE_GPU #include "tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h" +#include "tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h" #include "tensorflow/core/profiler/nvtx_utils.h" #include "tensorflow/core/util/gpu_kernel_helper.h" #include "third_party/cub/device/device_radix_sort.cuh" diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_gpu_kernels.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.cc similarity index 99% rename from tensorflow/core/kernels/fused_embedding/fused_embedding_gpu_kernels.cu.cc rename to tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.cc index d94e063880e..da78a5d2e23 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_gpu_kernels.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.cc @@ -4,6 +4,8 @@ #define EIGEN_USE_GPU +#include "tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h" + #include "tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h" #include "tensorflow/core/platform/stream_executor.h" #include "tensorflow/core/util/gpu_kernel_helper.h" diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h b/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h new file mode 100644 index 00000000000..ae5e1844fce --- /dev/null +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h @@ -0,0 +1,114 @@ +#ifndef TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_KERNELS_CU_H_ +#define TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_KERNELS_CU_H_ + +#if GOOGLE_CUDA + +#define EIGEN_USE_GPU + +#include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h" + +namespace tensorflow { + +namespace fused_embedding { + +enum Combiner { Mean, Sum, Sqrtn }; + +template +__forceinline__ __device__ float Combine(const float in, const int feature_num); + +template <> +__forceinline__ __device__ float Combine(const float in, + const int feature_num) { + return in / sqrtf(feature_num); +} + +template <> +__forceinline__ __device__ float Combine(const float in, + const int feature_num) { + return in / feature_num; +} + +template <> +__forceinline__ __device__ float Combine(const float in, + const int feature_num) { + return in; +} + +template +__forceinline__ __device__ float CombineGrad(const float grad, + const int feature_num); + +template <> +__forceinline__ __device__ float CombineGrad(const float grad, + const int feature_num) { + return grad / sqrtf(feature_num); +} + +template <> +__forceinline__ __device__ float CombineGrad(const float grad, + const int feature_num) { + return grad / feature_num; +} + +template <> +__forceinline__ __device__ float CombineGrad(const float grad, + const int feature_num) { + return grad; +} + +using GPUDevice = Eigen::GpuDevice; + +void InitFlagsToOneInt4(const GPUDevice& d, int length, int* flags); + +void DetectInvalid(const GPUDevice& d, const int64_t* values, const int64_t nnz, + int* invalid_id_flag); + +void FusedMultiFunctional(const GPUDevice& d, const IndicePair* indices, + const int64_t* values, const int64_t nnz, + const int64_t batch_size, const bool prune_invalid_id, + const int64_t default_id, int* row_emptiness_flag, + int* invalid_id_flag, IndicePair* tmp_indices_buffer, + int64_t* values_extended); + +void CalcElementsOffsetPerPartition(const GPUDevice& d, int num_partitions, + const int64_t* values_sorted, + int64_t* partition_sizes_accumulate, + int64_t* elements_offset_per_partition, + int nnz); + +void GatherAndConvertToSubPartition(const GPUDevice& d, + const int64_t* sub_values_sorted, + int64_t* sub_partitioned_values, + const int64_t partition_start_base, + const int64_t partition_size); + +void SumUpEmbeddingShard(const GPUDevice& d, const size_t sub_nnz, + const float* emb_shard, + const int64_t* partitioned_indice, float* emb_vectors, + int* feature_nums, const float max_norm, + const int emb_vec_size); + +template +void ApplyCombiner(const GPUDevice& d, const int batch_size, + const int emb_vec_size, float* emb_vectors, + const int* row_emptiness_flag, const bool set_empty_row_zero, + const int* feature_nums); + +template +void DistributeGradToShard(const GPUDevice& d, const float* top_grad, + const float* emb_shard, + const int64_t* partitioned_indice, + const int* feature_nums, + const int* row_emptiness_flag, + const bool set_empty_row_zero, float* grad_shard, + const int64_t sub_nnz, const int64_t emb_vec_size, + const float max_norm); + +} // namespace fused_embedding + +} // namespace tensorflow + +#endif // GOOGLE_CUDA + +#endif // TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_KERNELS_CU_H_ \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc new file mode 100644 index 00000000000..d6f7c367919 --- /dev/null +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc @@ -0,0 +1,136 @@ +#if GOOGLE_CUDA + +#define EIGEN_USE_GPU +#include "tensorflow/core/kernels/fused_embedding/gpu_sub_ops/partition_select.cu.h" + +#include + +#include "tensorflow/core/common_runtime/gpu/gpu_event_mgr.h" +#include "tensorflow/core/lib/core/bits.h" +#include "tensorflow/core/util/gpu_kernel_helper.h" +#include "tensorflow/stream_executor/cuda/cuda_activation.h" + +namespace tensorflow { + +typedef Eigen::GpuDevice GPUDevice; + +namespace { + +// A macro helper to declare SelectScan. Need to define following macros before +// using this: KernelName, PartitionArgs, PassPartitionArgs +#define DeclareSelectScan \ + template \ + Status SelectScanDiv(const GPUDevice& d, const T* keys, PartitionArgs, \ + const const int64 num_partitions, const int length, \ + unsigned int* predicates, unsigned int* counters) { \ + if (length == 0) return Status::OK(); \ + const int64 threads = 32; \ + const int64 blocks = \ + length % 1024 == 0 ? (length / 1024) : (length / 1024 + 1); \ + return GpuLaunchKernel(KernelName, blocks, threads, 0, \ + d.stream(), keys, PassPartitionArgs, \ + int64 num_partitions, length, predicates, \ + counters); \ + } + +// A macro helper to declare SelectScanKernels, because they just have only +// a little bit differences. Need to define following macros before using this: +// KernelName, PartitionArgs, PartitionConditionLoadCodeBlock, +// PartitionSelectCodeBlock +#define DeclareSelectScanKernel \ + template \ + __global__ void KernelName( \ + const T* keys, PartitionArgs, const int64 num_partitions, \ + const int length, unsigned int* predicates, unsigned int* counters) { \ + int g_tid = blockIdx.x * blockDim.x + threadIdx.x; \ + int lnid = threadIdx.x % 32; \ + if (g_tid >= (length >> 5)) return; \ + int g_warp_id = g_tid >> 5; \ + unsigned int mask; \ + unsigned int cnt; \ + \ + for (int j = 0; i < num_partitions; j++) { \ + PartitionConditionLoadCodeBlock; \ + \ +#pragma unroll for (int i = 0; i < 32; i++) { \ + int selected; \ + int load_offset = (g_warp_id << 10) + (i << 5) + lnid; \ + if (load_offset < length) { \ + T key = keys[load_offset]; \ + PartitionSelectCodeBlock; \ + } else { \ + selected = 0; \ + } \ + \ + mask = __ballot(selected); \ + if (lnid == 0) predicates[(g_warp_id << 5)] = mask; \ + if (lnid == i) cnt = __popc(mask); \ + } \ +#pragma unroll for (int offset = 16; offset > 0; offset >>= 1) cnt += \ + __shfl_down(cnt, offset); \ + \ + if (lnid == 0) counter[g_warp_id] = cnt; \ + } \ + } + +#define KernelName SelectScanDivKernel +#define PartitionArgs const TIndex* accu_div +#define PassPartitionArgs accu_div +#define PartitionConditionLoadCodeBlock \ + TIndex lower_bound = j > 0 ? accu_div[j - 1] : 0; \ + TIndex upper_bound = accu_div[j]; +#define PartitionSelectCodeBlock \ + selected = int(key >= lower_bound && key < upper_bound); + +DeclareSelectScanKernel +DeclareSelectScan + +/* backup +template +__global__ void SelectScanDivKernel(const T* keys, const TIndex* accu_div, + const int64 num_partitions, + const int length, unsigned int* predicates, + unsigned int* counters) { + int g_tid = blockIdx.x * blockDim.x + threadIdx.x; // global tid + int lnid = threadIdx.x % 32; + if (g_tid >= (length >> 5)) return; + int g_warp_id = g_tid >> 5; + unsigned int mask; + unsigned int cnt; + + for (int j = 0; i < num_partitions; j++) { + TIndex lower_bound = j > 0 ? accu_div[j - 1] : 0; + TIndex upper_bound = accu_div[j]; + +#pragma unroll + for (int i = 0; i < 32; i++) { + int selected; + int load_offset = (g_warp_id << 10) + (i << 5) + lnid; + if (load_offset < length) { + T key = keys[load_offset]; + selected = int(key >= lower_bound && key < upper_bound); + } else { + selected = 0; + } + + mask = __ballot(selected); + if (lnid == 0) predicates[(g_warp_id << 5)] = mask; + if (lnid == i) cnt = __popc(mask); + } + +#pragma unroll + for (int offset = 16; offset > 0; offset >>= 1) + cnt += __shfl_down(cnt, offset); + + if (lnid == 0) counter[g_warp_id] = cnt; + } +} +*/ + +} // namespace + +namespace fused_embedding {} // namespace fused_embedding + +} // namespace tensorflow + +#endif // GOOGLE_CUDA \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.h b/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.h new file mode 100644 index 00000000000..51198d79cf5 --- /dev/null +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.h @@ -0,0 +1,22 @@ +#ifndef TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_PARTITION_SELECT_H_ +#define TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_PARTITION_SELECT_H_ + +#if GOOGLE_CUDA + +#define EIGEN_USE_GPU + +#include "tensorflow/core/framework/op_kernel.h" + +namespace tensorflow { + +namespace fused_embedding { + +enum PartitionStrategy { DIV }; + +} // namespace fused_embedding + +} // namespace tensorflow + +#endif // GOOGLE_CUDA + +#endif // TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_PARTITION_SELECT_H_ \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc new file mode 100644 index 00000000000..64d0d029f80 --- /dev/null +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc @@ -0,0 +1,642 @@ +#if GOOGLE_CUDA + +#define EIGEN_USE_GPU + +#include "tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h" + +#include + +#include "tensorflow/core/common_runtime/gpu/gpu_event_mgr.h" +#include "tensorflow/core/lib/core/bits.h" +#include "tensorflow/core/util/gpu_kernel_helper.h" +#include "tensorflow/stream_executor/cuda/cuda_activation.h" + +namespace tensorflow { + +typedef Eigen::GpuDevice GPUDevice; + +namespace { + +// Returns true iff index is at the end of a segment (which is equivalent to the +// beginning of the next segment). +template +struct SegmentIndicatorFunctor { + const T* __restrict__ sorted_input_ptr_; + SegmentIndicatorFunctor(const T* sorted_input_ptr) + : sorted_input_ptr_(sorted_input_ptr) {} + __device__ bool operator()(const TIndex& i) const { + return i > 0 && sorted_input_ptr_[i] != sorted_input_ptr_[i - 1]; + } +}; + +template +__global__ void ExtractFirstOccurrenceIndicesKernel( + int64 input_size, int64 uniq_size, + const TIndex* __restrict__ sorted_input_inds, + const TIndex* __restrict__ sorted_input_unique_ids, + TIndex* __restrict__ unique_input_inds, TIndex* __restrict__ segment_ends) { + GPU_1D_KERNEL_LOOP(i, input_size) { + TIndex sorted_input_unique_id = sorted_input_unique_ids[i]; + if (i == 0 || sorted_input_unique_id != sorted_input_unique_ids[i - 1]) { + unique_input_inds[sorted_input_unique_id] = sorted_input_inds[i]; + if (segment_ends) { + if (i == 0) { + // First thread writes the last element. + segment_ends[uniq_size - 1] = input_size; + } else { + segment_ends[sorted_input_unique_id - 1] = i; + } + } + } + } +} + +// Scatters the index of the first occurrence of each unique input value to +// unique_input_inds. +// If segment_ends is not nullptr, it is filled with the end index of each +// unique value's range in the sorted input (the last element is always set +// to input_size). +template +Status ExtractFirstOccurrenceIndices(const GPUDevice& d, int64 input_size, + int64 uniq_size, + const TIndex* sorted_input_inds, + const TIndex* sorted_input_unique_ids, + TIndex* unique_input_inds, + TIndex* segment_ends) { + CHECK_GT(input_size, 0); // Crash OK + GpuLaunchConfig config = GetGpuLaunchConfig( + input_size, d, &ExtractFirstOccurrenceIndicesKernel, + /*dynamic_shared_memory_size=*/0, /*block_size_limit=*/0); + return GpuLaunchKernel(ExtractFirstOccurrenceIndicesKernel, + config.block_count, config.thread_per_block, 0, + d.stream(), input_size, uniq_size, sorted_input_inds, + sorted_input_unique_ids, unique_input_inds, + segment_ends); +} + +template +__global__ void GatherOutputsAndInvertPermutationKernel( + int64 uniq_size, const T* __restrict__ input, + const TIndex* __restrict__ sorted_unique_input_inds, + const TIndex* __restrict__ sorted_unique_perm, + const TIndex* __restrict__ segment_ends, T* __restrict__ output, + TIndex* __restrict__ inv_sorted_unique_perm, TIndex* __restrict__ count) { + GPU_1D_KERNEL_LOOP(i, uniq_size) { + output[i] = input[sorted_unique_input_inds[i]]; + auto j = sorted_unique_perm[i]; + inv_sorted_unique_perm[j] = i; + if (count) { + TIndex beg = j == 0 ? 0 : segment_ends[j - 1]; + TIndex end = segment_ends[j]; + count[i] = end - beg; + } + } +} + +// Gathers input values using sorted_unique_input_inds, and inverts the +// permutation specified by sorted_unique_perm. +template +Status GatherOutputsAndInvertPermutation(const GPUDevice& d, int64 uniq_size, + const T* input, + const TIndex* sorted_unique_input_inds, + const TIndex* sorted_unique_perm, + const TIndex* segment_ends, T* output, + TIndex* inv_sorted_unique_perm, + TIndex* count) { + if (uniq_size == 0) return Status::OK(); + GpuLaunchConfig config = GetGpuLaunchConfig( + uniq_size, d, &GatherOutputsAndInvertPermutationKernel, + /*dynamic_shared_memory_size=*/0, /*block_size_limit=*/0); + return GpuLaunchKernel(GatherOutputsAndInvertPermutationKernel, + config.block_count, config.thread_per_block, 0, + d.stream(), uniq_size, input, sorted_unique_input_inds, + sorted_unique_perm, segment_ends, output, + inv_sorted_unique_perm, count); +} + +template +__global__ void LookupAndScatterUniqueIdsKernel( + int64 input_size, const TIndex* sorted_input_inds, + const TIndex* __restrict__ sorted_input_unique_ids, + const TIndex* __restrict__ inv_sorted_unique_perm, + TIndex* __restrict__ idx) { + GPU_1D_KERNEL_LOOP(i, input_size) { + idx[sorted_input_inds[i]] = + inv_sorted_unique_perm[sorted_input_unique_ids[i]]; + } +} + +// Maps the values of sorted_input_unique_ids and scatters them to idx using +// sorted_input_inds. +template +Status LookupAndScatterUniqueIds(const GPUDevice& d, int64 input_size, + const TIndex* sorted_input_inds, + const TIndex* sorted_input_unique_ids, + const TIndex* inv_sorted_unique_perm, + TIndex* idx) { + CHECK_GT(input_size, 0); // Crash OK + GpuLaunchConfig config = GetGpuLaunchConfig( + input_size, d, &LookupAndScatterUniqueIdsKernel, + /*dynamic_shared_memory_size=*/0, /*block_size_limit=*/0); + return GpuLaunchKernel(LookupAndScatterUniqueIdsKernel, + config.block_count, config.thread_per_block, 0, + d.stream(), input_size, sorted_input_inds, + sorted_input_unique_ids, inv_sorted_unique_perm, idx); +} + +template +__global__ void RangeInitKernel(const T start, const T delta, const T size, + T* out) { + GPU_1D_KERNEL_LOOP(i, size) { out[i] = start + i * delta; } +} + +// Initialize out with range start, start + delta, start + 2 * delta, ... +template +Status RangeInit(const Eigen::GpuDevice& d, const T start, const T delta, + const T size, T* out) { + if (size == 0) return Status::OK(); + GpuLaunchConfig config = GetGpuLaunchConfig(size, d); + return GpuLaunchKernel(RangeInitKernel, config.block_count, + config.thread_per_block, 0, d.stream(), start, delta, + size, out); +} + +template +__global__ void GetIdxOfInputToUniqueKernel( + const TIndex* count_ptr, const TIndex* segment_ends_ptr, + const TIndex* sorted_unique_perm_ptr, const TIndex* count_prefix_sum_ptr, + const TIndex* sorted_input_inds_ptr, TIndex* idx_of_input_to_unique_ptr) { + const int target_unique_key_idx = blockIdx.x; + const TIndex unique_key_count = count_ptr[target_unique_key_idx]; + int offset_of_sorted_input_inds = + segment_ends_ptr[sorted_unique_perm_ptr[target_unique_key_idx]] - + unique_key_count; + for (int j = threadIdx.x; j < unique_key_count; j += blockDim.x) { + int result_offset = count_prefix_sum_ptr[target_unique_key_idx] + j; + idx_of_input_to_unique_ptr[result_offset] = + sorted_input_inds_ptr[offset_of_sorted_input_inds + j]; + } +} + +template +Status GetIdxOfInputToUnique(const Eigen::GpuDevice& d, const int64 uniq_size, + const TIndex* count_ptr, + const TIndex* segment_ends_ptr, + const TIndex* sorted_unique_perm_ptr, + const TIndex* count_prefix_sum_ptr, + const TIndex* sorted_input_inds_ptr, + TIndex* idx_of_input_to_unique_ptr) { + if (uniq_size == 0) return Status::OK(); + const int64 blocks = uniq_size; + const int64 threads = 32; + return GpuLaunchKernel(GetIdxOfInputToUniqueKernel, blocks, threads, + 0, d.stream(), count_ptr, segment_ends_ptr, + sorted_unique_perm_ptr, sorted_unique_perm_ptr, + sorted_input_inds_ptr, idx_of_input_to_unique_ptr); +} + +// Computes keys_out = sorted(keys_in), and indices_out = argsort(keys_in). +// If keys_out is not required, it can be set to nullptr. +// If indices_in is nullptr, the range of input indices [0, size) will be +// used. +template +Status GpuRadixSort(OpKernelContext* context, int size, const Tkey* keys_in, + Tkey* keys_out, // Optional + const Tindex* indices_in, // Optional + Tindex* indices_out, int num_bits = sizeof(Tkey) * 8) { + if (size == 0) return Status::OK(); + // Allocate temporary inputs/outputs if necessary. + Tensor tmp_indices_in; + if (!indices_in) { + TF_RETURN_IF_ERROR(context->allocate_temp( + DataTypeToEnum::value, TensorShape({size}), &tmp_indices_in)); + Tindex* mutable_indices_in = tmp_indices_in.flat().data(); + indices_in = mutable_indices_in; + const Eigen::GpuDevice& device = context->eigen_device(); + // Initialize indices_in to the input index range. + TF_RETURN_IF_ERROR(RangeInit(device, Tindex(0), Tindex(1), Tindex(size), + mutable_indices_in)); + } + Tensor tmp_keys_out; + if (!keys_out) { + TF_RETURN_IF_ERROR(context->allocate_temp( + DataTypeToEnum::value, TensorShape({size}), &tmp_keys_out)); + keys_out = tmp_keys_out.flat().data(); + } + // Determine temporary device storage requirements. + Tensor temp_storage; + size_t temp_storage_bytes = 0; + const auto& cu_stream = GetGpuStream(context); + auto err = cub::DeviceRadixSort::SortPairs(nullptr, temp_storage_bytes, + keys_in, keys_out, indices_in, + indices_out, size, /*begin_bit=*/0, + /*end_bit=*/num_bits, cu_stream); + if (err != 0) { + return errors::Internal( + "Failed to launch gpuprim::DeviceRadixSort::SortPairs to calculate " + "temp_storage_bytes, status: ", + cudaGetErrorString(err)); + } + // Allocate temporary storage. + TF_RETURN_IF_ERROR(context->allocate_temp( + DT_INT8, TensorShape({static_cast(temp_storage_bytes)}), + &temp_storage)); + // Sort indices by keys. + err = cub::DeviceRadixSort::SortPairs( + temp_storage.flat().data(), temp_storage_bytes, keys_in, keys_out, + indices_in, indices_out, size, + /*begin_bit=*/0, /*end_bit=*/num_bits, cu_stream); + if (err != 0) { + return errors::Internal( + "Failed to launch gpuprim::DeviceRadixSort::SortPairs, " + "temp_storage_bytes: ", + temp_storage_bytes, "status: ", cudaGetErrorString(err)); + } + return Status::OK(); +} + +enum PrefixSumType { inclusive, exclusive }; + +template +Status GpuPrefixSum(OpKernelContext* context, int size, + PrefixSumType prefix_sum_type, InputIteratorT input, + OutputIteratorT output) { + static_assert( + !std::is_same::type, + bool>::value, + "GpuInclusivePrefixSum does not work correct with booleans, please use " + "TransformInputIterator to explicitly cast to an integer."); + if (size == 0) return Status::OK(); + const auto& cu_stream = GetGpuStream(context); + size_t temp_storage_bytes; + + cudaError_t err; + if (prefix_sum_type == inclusive) { + err = cub::DeviceScan::InclusiveSum(nullptr, temp_storage_bytes, input, + output, size, cu_stream); + } else { + // exclusive + err = cub::DeviceScan::ExclusiveSum(nullptr, temp_storage_bytes, input, + output, size, cu_stream); + } + + if (err != 0) { + return errors::Internal( + "Failed to launch gpuprim::DeviceScan::(Ex)InclusiveSum to calculate " + "temp_storage_bytes, status: ", + cudaGetErrorString(err)); + } + Tensor temp_storage; + TF_RETURN_IF_ERROR(context->allocate_temp( + DT_INT8, TensorShape({static_cast(temp_storage_bytes)}), + &temp_storage)); + + if (prefix_sum_type == inclusive) { + err = cub::DeviceScan::InclusiveSum(temp_storage.flat().data(), + temp_storage_bytes, input, output, size, + cu_stream); + } else { + err = cub::DeviceScan::ExclusiveSum(temp_storage.flat().data(), + temp_storage_bytes, input, output, size, + cu_stream); + } + if (err != 0) { + return errors::Internal( + "Failed to launch gpuprim::DeviceScan::(Ex)InclusiveSum, " + "temp_storage_bytes: ", + temp_storage_bytes, ", status: ", cudaGetErrorString(err)); + } + return Status::OK(); +} + +// Helper class to allocate scratch memory and keep track of debug info. +// Mostly a thin wrapper around Tensor & allocate_temp. +template +class ScratchSpace { + public: + ScratchSpace(OpKernelContext* context, int64 size, bool on_host) + : ScratchSpace(context, TensorShape({size}), "", on_host) {} + + ScratchSpace(OpKernelContext* context, int64 size, const string& debug_info, + bool on_host) + : ScratchSpace(context, TensorShape({size}), debug_info, on_host) {} + + ScratchSpace(OpKernelContext* context, const TensorShape& shape, + const string& debug_info, bool on_host) + : context_(context), debug_info_(debug_info), on_host_(on_host) { + AllocatorAttributes alloc_attr; + if (on_host) { + // Allocate pinned memory on the host to avoid unnecessary + // synchronization. + alloc_attr.set_on_host(true); + alloc_attr.set_gpu_compatible(true); + } + TF_CHECK_OK(context->allocate_temp(DataTypeToEnum::value, shape, + &scratch_tensor_, alloc_attr)); + } + + virtual ~ScratchSpace() {} + + Scalar* mutable_data() { + return scratch_tensor_.template flat().data(); + } + const Scalar* data() const { + return scratch_tensor_.template flat().data(); + } + Scalar& operator()(int64 i) { + return scratch_tensor_.template flat()(i); + } + const Scalar& operator()(int64 i) const { + return scratch_tensor_.template flat()(i); + } + int64 bytes() const { return scratch_tensor_.TotalBytes(); } + int64 size() const { return scratch_tensor_.NumElements(); } + const string& debug_info() const { return debug_info_; } + + Tensor& tensor() { return scratch_tensor_; } + const Tensor& tensor() const { return scratch_tensor_; } + + // Returns true if this ScratchSpace is in host memory. + bool on_host() const { return on_host_; } + + protected: + OpKernelContext* context() const { return context_; } + + private: + OpKernelContext* context_; // not owned + const string debug_info_; + const bool on_host_; + Tensor scratch_tensor_; +}; + +template +void AllocateTemp(OpKernelContext* context, int64 size, Tensor* tensor, + U** tensor_data) { + OP_REQUIRES_OK(context, context->allocate_temp(DataTypeToEnum::value, + TensorShape({size}), tensor)); + *tensor_data = tensor->flat().data(); +} + +} // namespace + +namespace fused_embedding { + +// Not really a TensorFlow Op and output "idx_of_input_to_unique" is +// not the same as output "idx" in TensorFlow 2.x's UniqueWithCounts operator +template +void UniqueWithCountsGPU(OpKernelContext* context, const Tensor& input, + Tensor* unique_keys_out, + Tensor* idx_of_input_to_unique, + Tensor* unique_counts_out) { + OP_REQUIRES(context, input.NumElements() <= std::numeric_limits::max(), + errors::InvalidArgument( + "unique does not support input tensors larger than ", + std::numeric_limits::max(), " elements")); + + OP_REQUIRES(context, TensorShapeUtils::IsVector(input.shape()), + errors::InvalidArgument("unique expects a 1D vector.")); + + se::Stream* stream = context->op_device_context()->stream(); + OP_REQUIRES(context, stream, errors::Internal("No GPU stream available.")); + + cudaEvent_t memcpy_event; + cudaEventCreateWithFlags(&memcpy_event, cudaEventDisableTiming); + + int64 input_size = input.NumElements(); + if (input_size == 0) { + // Early exit for trivial case. + Tensor** temp; + AllocateTemp(context, 0, unique_keys_out, temp); + AllocateTemp(context, 0, idx_of_input_to_unique, temp); + AllocateTemp(context, 0, unique_counts_out, temp); + return; + } + + // The algorithm implemented here is as follows: + // input = [3, 5, 3, 4, 1, 4, 9, 8, 6, 3, 5, 7, 8, 8, 4, 6, 4, 2, 5, 6] + // 1) Sort the input to group equal values together in segments. + // sorted_input, sorted_input_inds = sort(input) + // sorted_input: + // [1, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 8, 8, 8, 9] + // sorted_input_inds: + // [4, 17, 0, 2, 9, 3, 5, 14, 16, 1, 10, 18, 8, 15, 19, 11, 7, 12, 13, 6] + // 2) Identify the boundaries between segments and use prefix sum to + // compute the unique ID for each sorted value. + // sorted_input_unique_ids = prefix_sum(indicator(sorted_input)) + // indicator(sorted_input): + // [0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1] + // sorted_input_unique_ids: + // [0, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 7, 7, 7, 8] + // 3) Extract the input index of the first occurrence of each unique value. + // If counts are required, also extract the end index of each segment. + // unique_input_inds[sorted_input_unique_ids] = + // sorted_input_inds (@ indicator) + // segment_ends[sorted_input_unique_ids[i] - 1] = i (@ indicator) + // unique_input_inds: [4, 17, 0, 3, 1, 8, 11, 7, 6] + // segment_ends: [1, 2, 5, 9, 12, 15, 16, 19, 20] + // 4) Sort the extracted unique input indices to put them in order of + // first appearance. + // sorted_unique_input_inds, sorted_unique_perm = + // sort(unique_input_inds) + // sorted_unique_input_inds: [0, 1, 3, 4, 6, 7, 8, 11, 17] + // sorted_unique_perm: [2, 4, 3, 0, 8, 7, 5, 6, 1] + // 5) Gather the sorted unique input values to produce output, and invert + // the second sort permutation to produce an inverse ID mapping. If + // counts are required, also take the adjacent difference between + // segment_ends indices to produce counts. + // output = input[sorted_unique_input_inds] + // inv_sorted_unique_perm[sorted_unique_perm[i]] = i + // counts = adjacent_difference(segment_ends) + // output: [3, 5, 4, 1, 9, 8, 6, 7, 2] + // inv_sorted_unique_perm: [3, 8, 0, 2, 1, 6, 7, 5, 4] + // counts: [3, 3, 4, 1, 1, 3, 3, 1, 1] + // 6) calculate prefix sum of counts + // epsc = exclusive_prefix_sum_of_counts = [3, 6, 10, 11, 12, 15, 18, 19, + // 20] 7) Use custom kernel to calculate the mapping between output and + // corresponding indices of the input. + // pseudo code of this kernel: + // suii = sorted_unique_input_inds; + // sup = sorted_unique_perm; + // epsc = exclusive_prefix_sum_of_counts; + // GetIdxOfInputToUnique kernel + // blocks = len(suii) + // threads = 32 or 64 or else + // each block i: + // target_count = counts[i] + // offset = segment_ends[sup[i]] - target_count + // for each thread j : (j <= target_count) + // idx_of_input_to_unique_ptr[epsc[i] + j] = sorted_input_inds[offset + // + j] + // idx_of_input_to_unique_ptr: [0, 2, 9, 1, 10, 18, 3, 5, 14, 16, 4, 6, 7, + // 12, 13, 8, 15, 19, 11, 17] + + /* REVISE: below not necessary + // 6) Look up unique IDs via the inverse ID mapping and scatter them + using + // the original sort permutation to produce the indices output. + // idx[sorted_input_inds] = + // inv_sorted_unique_perm[sorted_input_unique_ids] + // idx: [0, 1, 0, 2, 3, 2, 4, 5, 6, 0, 1, 7, 5, 5, 2, 6, 2, 8, 1, 6] + */ + + Tensor sorted_input_inds; + TIndex* sorted_input_inds_ptr = nullptr; + AllocateTemp(context, input_size, &sorted_input_inds, &sorted_input_inds_ptr); + if (!context->status().ok()) return; + + Tensor sorted_input; + T* sorted_input_ptr = nullptr; + AllocateTemp(context, input_size, &sorted_input, &sorted_input_ptr); + if (!context->status().ok()) return; + + const T* input_ptr = input.flat().data(); + OP_REQUIRES_OK( + context, GpuRadixSort(context, input_size, /*keys_in=*/input_ptr, + /*keys_out=*/sorted_input_ptr, + /*indices_in=*/static_cast(nullptr), + /*indices_out=*/sorted_input_inds_ptr)); + + // Create a fancy input iterator to indicate segment boundaries. + cub::TransformInputIterator, + cub::CountingInputIterator> + segment_indicator_iter(0, {sorted_input_ptr}); + + Tensor sorted_input_unique_ids; + TIndex* sorted_input_unique_ids_ptr = nullptr; + AllocateTemp(context, input_size, &sorted_input_unique_ids, + &sorted_input_unique_ids_ptr); + if (!context->status().ok()) return; + + OP_REQUIRES_OK(context, GpuPrefixSum(context, input_size, inclusive, + segment_indicator_iter, + sorted_input_unique_ids_ptr)); + + // Copy the last element of sorted_input_unique_ids back to the host to + // obtain uniq_size. + ScratchSpace last_idx_host(context, 1, /*on_host=*/true); + OP_REQUIRES_OK( + context, + stream + ->ThenMemcpy(last_idx_host.mutable_data(), + se::DeviceMemoryBase( + const_cast(sorted_input_unique_ids_ptr) + + (input_size - 1), + sizeof(*last_idx_host.data())), + sizeof(*last_idx_host.data())) + .ok()); + + const GPUDevice& device = context->eigen_gpu_device(); + cudaEventRecord(memcpy_event, device.stream()); + cudaEventSynchronize(memcpy_event); + int64 uniq_size = (*last_idx_host.data()) + 1; + + se::cuda::ScopedActivateExecutorContext scoped_activation{ + context->op_device_context()->stream()->parent()}; + + Tensor unique_input_inds; + TIndex* unique_input_inds_ptr = nullptr; + AllocateTemp(context, uniq_size, &unique_input_inds, &unique_input_inds_ptr); + if (!context->status().ok()) return; + + Tensor segment_ends; + TIndex* segment_ends_ptr = nullptr; + AllocateTemp(context, uniq_size, &segment_ends, &segment_ends_ptr); + if (!context->status().ok()) return; + + OP_REQUIRES_OK(context, + ExtractFirstOccurrenceIndices( + device, input_size, uniq_size, sorted_input_inds_ptr, + sorted_input_unique_ids_ptr, unique_input_inds_ptr, + segment_ends_ptr)); + + Tensor sorted_unique_input_inds; + TIndex* sorted_unique_input_inds_ptr = nullptr; + AllocateTemp(context, uniq_size, &sorted_unique_input_inds, + &sorted_unique_input_inds_ptr); + if (!context->status().ok()) return; + + Tensor sorted_unique_perm; + TIndex* sorted_unique_perm_ptr = nullptr; + AllocateTemp(context, uniq_size, &sorted_unique_perm, + &sorted_unique_perm_ptr); + if (!context->status().ok()) return; + + // Sort by input index so that output is in order of appearance. + OP_REQUIRES_OK( + context, GpuRadixSort(context, uniq_size, + /*keys_in=*/unique_input_inds_ptr, + /*keys_out=*/sorted_unique_input_inds_ptr, + /*indices_in=*/static_cast(nullptr), + /*indices_out=*/sorted_unique_perm_ptr, + /*num_bits=*/Log2Ceiling(input_size))); + + // Free temporary tensor that is no longer needed. + unique_input_inds = Tensor(); + unique_input_inds_ptr = nullptr; + + // output 0 unique_keys_out + T* output_ptr = nullptr; + AllocateTemp(context, uniq_size, unique_keys_out, &output_ptr); + + Tensor inv_sorted_unique_perm; + TIndex* inv_sorted_unique_perm_ptr = nullptr; + AllocateTemp(context, uniq_size, &inv_sorted_unique_perm, + &inv_sorted_unique_perm_ptr); + if (!context->status().ok()) return; + + // output 2 unique_counts_out + TIndex* count_ptr = nullptr; + AllocateTemp(context, uniq_size, unique_counts_out, &count_ptr); + + // Compute output and counts (if necessary). + OP_REQUIRES_OK(context, + GatherOutputsAndInvertPermutation( + device, uniq_size, input_ptr, sorted_unique_input_inds_ptr, + sorted_unique_perm_ptr, segment_ends_ptr, output_ptr, + inv_sorted_unique_perm_ptr, count_ptr)); + + // Free temporary tensors that are no longer needed. + sorted_unique_perm = Tensor(); + sorted_unique_perm_ptr = nullptr; + sorted_unique_input_inds = Tensor(); + sorted_unique_input_inds_ptr = nullptr; + segment_ends = Tensor(); + segment_ends_ptr = nullptr; + + // Compute prefix sum of counts + TIndex* count_prefix_sum_ptr = nullptr; + Tensor count_prefix_sum; + AllocateTemp(context, uniq_size, &count_prefix_sum, &count_prefix_sum_ptr); + + OP_REQUIRES_OK(context, GpuPrefixSum(context, uniq_size, exclusive, count_ptr, + count_prefix_sum_ptr)); + + // GetIdxOfInputToUnique kernel to calculate the mapping between output and + // corresponding indices of the input + TIndex* idx_of_input_to_unique_ptr; + AllocateTemp(context, input_size, idx_of_input_to_unique, + &idx_of_input_to_unique_ptr); + OP_REQUIRES_OK( + context, + GetIdxOfInputToUnique(device, uniq_size, count_ptr, segment_ends_ptr, + sorted_unique_perm_ptr, count_prefix_sum_ptr, + sorted_input_inds_ptr, idx_of_input_to_unique_ptr)); + + /* REVISE: idx out is not needed for now + TIndex* idx_ptr = nullptr; + // output 1 unique_idxs_out + AllocateTemp(context, input_size, unique_idxs_out, &idx_ptr); + + // Compute indices output. + OP_REQUIRES_OK(context, LookupAndScatterUniqueIds( + device, input_size, sorted_input_inds_ptr, + sorted_input_unique_ids_ptr, + inv_sorted_unique_perm_ptr, idx_ptr)); + */ +} + +} // namespace fused_embedding + +} // namespace tensorflow + +#endif // GOOGLE_CUDA \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h new file mode 100644 index 00000000000..fb461da84b3 --- /dev/null +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h @@ -0,0 +1,28 @@ +#ifndef TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_UNIQUE_CU_H_ +#define TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_UNIQUE_CU_H_ + +#if GOOGLE_CUDA + +#define EIGEN_USE_GPU + +#include "tensorflow/core/framework/op_kernel.h" + +namespace tensorflow { + +namespace fused_embedding { + +// Not really a TensorFlow Op and output "idx_of_input_to_unique" is +// not the same as output "idx" in TensorFlow 2.x's UniqueWithCounts operator +template +void UniqueWithCountsGPU(OpKernelContext* context, const Tensor& input, + Tensor* unique_keys_out, + Tensor* idx_of_input_to_unique, + Tensor* unique_counts_out); + +} // namespace fused_embedding + +} // namespace tensorflow + +#endif // GOOGLE_CUDA + +#endif // TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_UNIQUE_CU_H_ \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/unique_gpu/unique.cu.cc b/tensorflow/core/kernels/fused_embedding/unique_gpu/unique.cu.cc deleted file mode 100644 index 1163e732673..00000000000 --- a/tensorflow/core/kernels/fused_embedding/unique_gpu/unique.cu.cc +++ /dev/null @@ -1,553 +0,0 @@ -#if GOOGLE_CUDA - -#define EIGEN_USE_GPU - -#include - -#include "tensorflow/core/common_runtime/gpu/gpu_event_mgr.h" -#include "tensorflow/core/lib/core/bits.h" -#include "tensorflow/core/util/gpu_kernel_helper.h" -#include "tensorflow/stream_executor/cuda/cuda_activation.h" - -namespace tensorflow { - -typedef Eigen::GpuDevice GPUDevice; - -namespace { - -// Returns true iff index is at the end of a segment (which is equivalent to the -// beginning of the next segment). -template -struct SegmentIndicatorFunctor { - const T* __restrict__ sorted_input_ptr_; - SegmentIndicatorFunctor(const T* sorted_input_ptr) - : sorted_input_ptr_(sorted_input_ptr) {} - __device__ bool operator()(const TIndex& i) const { - return i > 0 && sorted_input_ptr_[i] != sorted_input_ptr_[i - 1]; - } -}; - -template -__global__ void ExtractFirstOccurrenceIndicesKernel( - int64 input_size, int64 uniq_size, - const TIndex* __restrict__ sorted_input_inds, - const TIndex* __restrict__ sorted_input_unique_ids, - TIndex* __restrict__ unique_input_inds, TIndex* __restrict__ segment_ends) { - GPU_1D_KERNEL_LOOP(i, input_size) { - TIndex sorted_input_unique_id = sorted_input_unique_ids[i]; - if (i == 0 || sorted_input_unique_id != sorted_input_unique_ids[i - 1]) { - unique_input_inds[sorted_input_unique_id] = sorted_input_inds[i]; - if (segment_ends) { - if (i == 0) { - // First thread writes the last element. - segment_ends[uniq_size - 1] = input_size; - } else { - segment_ends[sorted_input_unique_id - 1] = i; - } - } - } - } -} - -// Scatters the index of the first occurrence of each unique input value to -// unique_input_inds. -// If segment_ends is not nullptr, it is filled with the end index of each -// unique value's range in the sorted input (the last element is always set -// to input_size). -template -Status ExtractFirstOccurrenceIndices(const GPUDevice& d, int64 input_size, - int64 uniq_size, - const TIndex* sorted_input_inds, - const TIndex* sorted_input_unique_ids, - TIndex* unique_input_inds, - TIndex* segment_ends) { - CHECK_GT(input_size, 0); // Crash OK - GpuLaunchConfig config = GetGpuLaunchConfig( - input_size, d, &ExtractFirstOccurrenceIndicesKernel, - /*dynamic_shared_memory_size=*/0, /*block_size_limit=*/0); - return GpuLaunchKernel(ExtractFirstOccurrenceIndicesKernel, - config.block_count, config.thread_per_block, 0, - d.stream(), input_size, uniq_size, sorted_input_inds, - sorted_input_unique_ids, unique_input_inds, - segment_ends); -} - -template -__global__ void GatherOutputsAndInvertPermutationKernel( - int64 uniq_size, const T* __restrict__ input, - const TIndex* __restrict__ sorted_unique_input_inds, - const TIndex* __restrict__ sorted_unique_perm, - const TIndex* __restrict__ segment_ends, T* __restrict__ output, - TIndex* __restrict__ inv_sorted_unique_perm, TIndex* __restrict__ count) { - GPU_1D_KERNEL_LOOP(i, uniq_size) { - output[i] = input[sorted_unique_input_inds[i]]; - auto j = sorted_unique_perm[i]; - inv_sorted_unique_perm[j] = i; - if (count) { - TIndex beg = j == 0 ? 0 : segment_ends[j - 1]; - TIndex end = segment_ends[j]; - count[i] = end - beg; - } - } -} - -// Gathers input values using sorted_unique_input_inds, and inverts the -// permutation specified by sorted_unique_perm. -template -Status GatherOutputsAndInvertPermutation(const GPUDevice& d, int64 uniq_size, - const T* input, - const TIndex* sorted_unique_input_inds, - const TIndex* sorted_unique_perm, - const TIndex* segment_ends, T* output, - TIndex* inv_sorted_unique_perm, - TIndex* count) { - if (uniq_size == 0) return Status::OK(); - GpuLaunchConfig config = GetGpuLaunchConfig( - uniq_size, d, &GatherOutputsAndInvertPermutationKernel, - /*dynamic_shared_memory_size=*/0, /*block_size_limit=*/0); - return GpuLaunchKernel(GatherOutputsAndInvertPermutationKernel, - config.block_count, config.thread_per_block, 0, - d.stream(), uniq_size, input, sorted_unique_input_inds, - sorted_unique_perm, segment_ends, output, - inv_sorted_unique_perm, count); -} - -template -__global__ void LookupAndScatterUniqueIdsKernel( - int64 input_size, const TIndex* sorted_input_inds, - const TIndex* __restrict__ sorted_input_unique_ids, - const TIndex* __restrict__ inv_sorted_unique_perm, - TIndex* __restrict__ idx) { - GPU_1D_KERNEL_LOOP(i, input_size) { - idx[sorted_input_inds[i]] = - inv_sorted_unique_perm[sorted_input_unique_ids[i]]; - } -} - -// Maps the values of sorted_input_unique_ids and scatters them to idx using -// sorted_input_inds. -template -Status LookupAndScatterUniqueIds(const GPUDevice& d, int64 input_size, - const TIndex* sorted_input_inds, - const TIndex* sorted_input_unique_ids, - const TIndex* inv_sorted_unique_perm, - TIndex* idx) { - CHECK_GT(input_size, 0); // Crash OK - GpuLaunchConfig config = GetGpuLaunchConfig( - input_size, d, &LookupAndScatterUniqueIdsKernel, - /*dynamic_shared_memory_size=*/0, /*block_size_limit=*/0); - return GpuLaunchKernel(LookupAndScatterUniqueIdsKernel, - config.block_count, config.thread_per_block, 0, - d.stream(), input_size, sorted_input_inds, - sorted_input_unique_ids, inv_sorted_unique_perm, idx); -} - -template -__global__ void RangeInitKernel(const T start, const T delta, const T size, - T* out) { - GPU_1D_KERNEL_LOOP(i, size) { out[i] = start + i * delta; } -} - -// Initialize out with range start, start + delta, start + 2 * delta, ... -template -Status RangeInit(const Eigen::GpuDevice& d, const T start, const T delta, - const T size, T* out) { - if (size == 0) return Status::OK(); - GpuLaunchConfig config = GetGpuLaunchConfig(size, d); - return GpuLaunchKernel(RangeInitKernel, config.block_count, - config.thread_per_block, 0, d.stream(), start, delta, - size, out); -} - -// Computes keys_out = sorted(keys_in), and indices_out = argsort(keys_in). -// If keys_out is not required, it can be set to nullptr. -// If indices_in is nullptr, the range of input indices [0, size) will be used. -template -Status GpuRadixSort(OpKernelContext* context, int size, const Tkey* keys_in, - Tkey* keys_out, // Optional - const Tindex* indices_in, // Optional - Tindex* indices_out, int num_bits = sizeof(Tkey) * 8) { - if (size == 0) return Status::OK(); - // Allocate temporary inputs/outputs if necessary. - Tensor tmp_indices_in; - if (!indices_in) { - TF_RETURN_IF_ERROR(context->allocate_temp( - DataTypeToEnum::value, TensorShape({size}), &tmp_indices_in)); - Tindex* mutable_indices_in = tmp_indices_in.flat().data(); - indices_in = mutable_indices_in; - const Eigen::GpuDevice& device = context->eigen_device(); - // Initialize indices_in to the input index range. - TF_RETURN_IF_ERROR(RangeInit(device, Tindex(0), Tindex(1), Tindex(size), - mutable_indices_in)); - } - Tensor tmp_keys_out; - if (!keys_out) { - TF_RETURN_IF_ERROR(context->allocate_temp( - DataTypeToEnum::value, TensorShape({size}), &tmp_keys_out)); - keys_out = tmp_keys_out.flat().data(); - } - // Determine temporary device storage requirements. - Tensor temp_storage; - size_t temp_storage_bytes = 0; - const auto& cu_stream = GetGpuStream(context); - auto err = cub::DeviceRadixSort::SortPairs(nullptr, temp_storage_bytes, - keys_in, keys_out, indices_in, - indices_out, size, /*begin_bit=*/0, - /*end_bit=*/num_bits, cu_stream); - if (err != 0) { - return errors::Internal( - "Failed to launch gpuprim::DeviceRadixSort::SortPairs to calculate " - "temp_storage_bytes, status: ", - cudaGetErrorString(err)); - } - // Allocate temporary storage. - TF_RETURN_IF_ERROR(context->allocate_temp( - DT_INT8, TensorShape({static_cast(temp_storage_bytes)}), - &temp_storage)); - // Sort indices by keys. - err = cub::DeviceRadixSort::SortPairs( - temp_storage.flat().data(), temp_storage_bytes, keys_in, keys_out, - indices_in, indices_out, size, - /*begin_bit=*/0, /*end_bit=*/num_bits, cu_stream); - if (err != 0) { - return errors::Internal( - "Failed to launch gpuprim::DeviceRadixSort::SortPairs, " - "temp_storage_bytes: ", - temp_storage_bytes, "status: ", cudaGetErrorString(err)); - } - return Status::OK(); -} - -template -Status GpuInclusivePrefixSum(OpKernelContext* context, int size, - InputIteratorT input, OutputIteratorT output) { - static_assert( - !std::is_same::type, - bool>::value, - "GpuInclusivePrefixSum does not work correct with booleans, please use " - "TransformInputIterator to explicitly cast to an integer."); - if (size == 0) return Status::OK(); - const auto& cu_stream = GetGpuStream(context); - size_t temp_storage_bytes; - auto err = cub::DeviceScan::InclusiveSum(nullptr, temp_storage_bytes, input, - output, size, cu_stream); - if (err != 0) { - return errors::Internal( - "Failed to launch gpuprim::DeviceScan::InclusiveSum to calculate " - "temp_storage_bytes, status: ", - cudaGetErrorString(err)); - } - Tensor temp_storage; - TF_RETURN_IF_ERROR(context->allocate_temp( - DT_INT8, TensorShape({static_cast(temp_storage_bytes)}), - &temp_storage)); - err = cub::DeviceScan::InclusiveSum(temp_storage.flat().data(), - temp_storage_bytes, input, output, size, - cu_stream); - if (err != 0) { - return errors::Internal( - "Failed to launch gpuprim::DeviceScan::InclusiveSum, " - "temp_storage_bytes: ", - temp_storage_bytes, ", status: ", cudaGetErrorString(err)); - } - return Status::OK(); -} - -// Helper class to allocate scratch memory and keep track of debug info. -// Mostly a thin wrapper around Tensor & allocate_temp. -template -class ScratchSpace { - public: - ScratchSpace(OpKernelContext* context, int64 size, bool on_host) - : ScratchSpace(context, TensorShape({size}), "", on_host) {} - - ScratchSpace(OpKernelContext* context, int64 size, const string& debug_info, - bool on_host) - : ScratchSpace(context, TensorShape({size}), debug_info, on_host) {} - - ScratchSpace(OpKernelContext* context, const TensorShape& shape, - const string& debug_info, bool on_host) - : context_(context), debug_info_(debug_info), on_host_(on_host) { - AllocatorAttributes alloc_attr; - if (on_host) { - // Allocate pinned memory on the host to avoid unnecessary - // synchronization. - alloc_attr.set_on_host(true); - alloc_attr.set_gpu_compatible(true); - } - TF_CHECK_OK(context->allocate_temp(DataTypeToEnum::value, shape, - &scratch_tensor_, alloc_attr)); - } - - virtual ~ScratchSpace() {} - - Scalar* mutable_data() { - return scratch_tensor_.template flat().data(); - } - const Scalar* data() const { - return scratch_tensor_.template flat().data(); - } - Scalar& operator()(int64 i) { - return scratch_tensor_.template flat()(i); - } - const Scalar& operator()(int64 i) const { - return scratch_tensor_.template flat()(i); - } - int64 bytes() const { return scratch_tensor_.TotalBytes(); } - int64 size() const { return scratch_tensor_.NumElements(); } - const string& debug_info() const { return debug_info_; } - - Tensor& tensor() { return scratch_tensor_; } - const Tensor& tensor() const { return scratch_tensor_; } - - // Returns true if this ScratchSpace is in host memory. - bool on_host() const { return on_host_; } - - protected: - OpKernelContext* context() const { return context_; } - - private: - OpKernelContext* context_; // not owned - const string debug_info_; - const bool on_host_; - Tensor scratch_tensor_; -}; - -template -void AllocateTemp(OpKernelContext* context, int64 size, Tensor* tensor, - U** tensor_data) { - OP_REQUIRES_OK(context, context->allocate_temp(DataTypeToEnum::value, - TensorShape({size}), tensor)); - *tensor_data = tensor->flat().data(); -} - -} // namespace - -namespace unique_gpu { - -template -struct UniqueSubOpGPU { - // Not really a TensorFlow Op - void operator()(OpKernelContext* context, const Tensor& input, - bool has_count_output, Tensor* unique_keys_out, - Tensor* unique_idxs_out, Tensor* unique_counts_out) { - OP_REQUIRES(context, - input.NumElements() <= std::numeric_limits::max(), - errors::InvalidArgument( - "unique does not support input tensors larger than ", - std::numeric_limits::max(), " elements")); - - OP_REQUIRES(context, TensorShapeUtils::IsVector(input.shape()), - errors::InvalidArgument("unique expects a 1D vector.")); - - se::Stream* stream = context->op_device_context()->stream(); - OP_REQUIRES(context, stream, errors::Internal("No GPU stream available.")); - - cudaEvent_t memcpy_event; - cudaEventCreateWithFlags(&memcpy_event, cudaEventDisableTiming); - - int64 input_size = input.NumElements(); - if (input_size == 0) { - // Early exit for trivial case. - Tensor** temp; - AllocateTemp(context, 0, unique_keys_out, temp); - AllocateTemp(context, 0, unique_idxs_out, temp); - if (has_count_output) { - AllocateTemp(context, 0, unique_counts_out, temp); - } - return; - } - - // The algorithm implemented here is as follows: - // input = [3, 5, 3, 4, 1, 4, 9, 8, 6, 3, 5, 7, 8, 8, 4, 6, 4, 2, 5, 6] - // 1) Sort the input to group equal values together in segments. - // sorted_input, sorted_input_inds = sort(input) - // sorted_input: - // [1, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 8, 8, 8, 9] - // sorted_input_inds: - // [4, 17, 0, 2, 9, 3, 5, 14, 16, 1, 10, 18, 8, 15, 19, 11, 7, 12, 13, 6] - // 2) Identify the boundaries between segments and use prefix sum to - // compute the unique ID for each sorted value. - // sorted_input_unique_ids = prefix_sum(indicator(sorted_input)) - // indicator(sorted_input): - // [0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1] - // sorted_input_unique_ids: - // [0, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 7, 7, 7, 8] - // 3) Extract the input index of the first occurrence of each unique value. - // If counts are required, also extract the end index of each segment. - // unique_input_inds[sorted_input_unique_ids] = - // sorted_input_inds (@ indicator) - // segment_ends[sorted_input_unique_ids[i] - 1] = i (@ indicator) - // unique_input_inds: [4, 17, 0, 3, 1, 8, 11, 7, 6] - // segment_ends: [1, 2, 5, 9, 12, 15, 16, 19, 20] - // 4) Sort the extracted unique input indices to put them in order of - // first appearance. - // sorted_unique_input_inds, sorted_unique_perm = - // sort(unique_input_inds) - // sorted_unique_input_inds: [0, 1, 3, 4, 6, 7, 8, 11, 17] - // sorted_unique_perm: [2, 4, 3, 0, 8, 7, 5, 6, 1] - // 5) Gather the sorted unique input values to produce output, and invert - // the second sort permutation to produce an inverse ID mapping. If - // counts are required, also take the adjacent difference between - // segment_ends indices to produce counts. - // output = input[sorted_unique_input_inds] - // inv_sorted_unique_perm[sorted_unique_perm[i]] = i - // counts = adjacent_difference(segment_ends) - // output: [3, 5, 4, 1, 9, 8, 6, 7, 2] - // inv_sorted_unique_perm: [3, 8, 0, 2, 1, 6, 7, 5, 4] - // counts: [3, 3, 4, 1, 1, 3, 3, 1, 1] - // 6) Look up unique IDs via the inverse ID mapping and scatter them using - // the original sort permutation to produce the indices output. - // idx[sorted_input_inds] = - // inv_sorted_unique_perm[sorted_input_unique_ids] - // idx: [0, 1, 0, 2, 3, 2, 4, 5, 6, 0, 1, 7, 5, 5, 2, 6, 2, 8, 1, 6] - - Tensor sorted_input_inds; - TIndex* sorted_input_inds_ptr = nullptr; - AllocateTemp(context, input_size, &sorted_input_inds, - &sorted_input_inds_ptr); - if (!context->status().ok()) return; - - Tensor sorted_input; - T* sorted_input_ptr = nullptr; - AllocateTemp(context, input_size, &sorted_input, &sorted_input_ptr); - if (!context->status().ok()) return; - - const T* input_ptr = input.flat().data(); - OP_REQUIRES_OK( - context, - GpuRadixSort(context, input_size, /*keys_in=*/input_ptr, - /*keys_out=*/sorted_input_ptr, - /*indices_in=*/static_cast(nullptr), - /*indices_out=*/sorted_input_inds_ptr)); - - // Create a fancy input iterator to indicate segment boundaries. - cub::TransformInputIterator, - cub::CountingInputIterator> - segment_indicator_iter(0, {sorted_input_ptr}); - - Tensor sorted_input_unique_ids; - TIndex* sorted_input_unique_ids_ptr = nullptr; - AllocateTemp(context, input_size, &sorted_input_unique_ids, - &sorted_input_unique_ids_ptr); - if (!context->status().ok()) return; - - OP_REQUIRES_OK(context, GpuInclusivePrefixSum(context, input_size, - segment_indicator_iter, - sorted_input_unique_ids_ptr)); - - // Copy the last element of sorted_input_unique_ids back to the host to - // obtain uniq_size. - ScratchSpace last_idx_host(context, 1, /*on_host=*/true); - OP_REQUIRES_OK( - context, - stream - ->ThenMemcpy(last_idx_host.mutable_data(), - se::DeviceMemoryBase( - const_cast(sorted_input_unique_ids_ptr) + - (input_size - 1), - sizeof(*last_idx_host.data())), - sizeof(*last_idx_host.data())) - .ok()); - - const GPUDevice& device = context->eigen_gpu_device(); - cudaEventRecord(memcpy_event, device.stream()); - cudaEventSynchronize(memcpy_event); - int64 uniq_size = (*last_idx_host.data()) + 1; - - se::cuda::ScopedActivateExecutorContext scoped_activation{ - context->op_device_context()->stream()->parent()}; - - Tensor unique_input_inds; - TIndex* unique_input_inds_ptr = nullptr; - AllocateTemp(context, uniq_size, &unique_input_inds, - &unique_input_inds_ptr); - if (!context->status().ok()) return; - - Tensor segment_ends; - TIndex* segment_ends_ptr = nullptr; - if (has_count_output) { - AllocateTemp(context, uniq_size, &segment_ends, &segment_ends_ptr); - if (!context->status().ok()) return; - } - - OP_REQUIRES_OK(context, - ExtractFirstOccurrenceIndices( - device, input_size, uniq_size, sorted_input_inds_ptr, - sorted_input_unique_ids_ptr, unique_input_inds_ptr, - segment_ends_ptr)); - - Tensor sorted_unique_input_inds; - TIndex* sorted_unique_input_inds_ptr = nullptr; - AllocateTemp(context, uniq_size, &sorted_unique_input_inds, - &sorted_unique_input_inds_ptr); - if (!context->status().ok()) return; - - Tensor sorted_unique_perm; - TIndex* sorted_unique_perm_ptr = nullptr; - AllocateTemp(context, uniq_size, &sorted_unique_perm, - &sorted_unique_perm_ptr); - if (!context->status().ok()) return; - - // Sort by input index so that output is in order of appearance. - OP_REQUIRES_OK( - context, - GpuRadixSort(context, uniq_size, - /*keys_in=*/unique_input_inds_ptr, - /*keys_out=*/sorted_unique_input_inds_ptr, - /*indices_in=*/static_cast(nullptr), - /*indices_out=*/sorted_unique_perm_ptr, - /*num_bits=*/Log2Ceiling(input_size))); - - // Free temporary tensor that is no longer needed. - unique_input_inds = Tensor(); - unique_input_inds_ptr = nullptr; - - // output 0 unique_keys_out - T* output_ptr = nullptr; - AllocateTemp(context, uniq_size, unique_keys_out, &output_ptr); - - Tensor inv_sorted_unique_perm; - TIndex* inv_sorted_unique_perm_ptr = nullptr; - AllocateTemp(context, uniq_size, &inv_sorted_unique_perm, - &inv_sorted_unique_perm_ptr); - if (!context->status().ok()) return; - - // output 2 unique_counts_out - TIndex* count_ptr = nullptr; - if (has_count_output) { - AllocateTemp(context, uniq_size, unique_counts_out, &count_ptr); - } - - // Compute output and counts (if necessary). - OP_REQUIRES_OK( - context, GatherOutputsAndInvertPermutation( - device, uniq_size, input_ptr, sorted_unique_input_inds_ptr, - sorted_unique_perm_ptr, segment_ends_ptr, output_ptr, - inv_sorted_unique_perm_ptr, count_ptr)); - - // Free temporary tensors that are no longer needed. - sorted_unique_perm = Tensor(); - sorted_unique_perm_ptr = nullptr; - sorted_unique_input_inds = Tensor(); - sorted_unique_input_inds_ptr = nullptr; - segment_ends = Tensor(); - segment_ends_ptr = nullptr; - - TIndex* idx_ptr = nullptr; - // output 1 unique_idxs_out - AllocateTemp(context, input_size, unique_idxs_out, &idx_ptr); - - // Compute indices output. - OP_REQUIRES_OK(context, LookupAndScatterUniqueIds( - device, input_size, sorted_input_inds_ptr, - sorted_input_unique_ids_ptr, - inv_sorted_unique_perm_ptr, idx_ptr)); - } -}; - -} // namespace unique_gpu - -} // namespace tensorflow - -#endif // GOOGLE_CUDA \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/unique_gpu/unique.cu.h b/tensorflow/core/kernels/fused_embedding/unique_gpu/unique.cu.h deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/tensorflow/python/ops/embedding_ops.py b/tensorflow/python/ops/embedding_ops.py index be98f67c735..71e7c2c261f 100644 --- a/tensorflow/python/ops/embedding_ops.py +++ b/tensorflow/python/ops/embedding_ops.py @@ -271,6 +271,7 @@ def _embedding_lookup_and_transform(params, embs = [] pids_nozero = array_ops.boolean_mask(pids, math_ops.greater_equal(pblocknums , 1)) pblocknums_nozero = array_ops.boolean_mask(pblocknums, math_ops.greater_equal(pblocknums, 1)) + import pdb;pdb.set_trace() for i in range(params[p].blocknum()): with ops.colocate_with(params[p]._ev_list[i]): evids = array_ops.boolean_mask(pids_nozero, math_ops.greater_equal(pblocknums_nozero, i + 1)) From 1cf4c57904205dc7c5eb59da4a523d540034f708 Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Thu, 10 Feb 2022 19:41:58 +0800 Subject: [PATCH 07/41] minor update --- tensorflow/core/kernels/BUILD | 6 +- .../gpu_functions/partition_select.cu.cc | 233 ++++++++++-------- .../gpu_functions/partition_select.cu.h | 6 +- .../gpu_functions/unique.cu.cc | 3 +- 4 files changed, 132 insertions(+), 116 deletions(-) diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD index 4d15a3b4cff..e2fcd787d75 100644 --- a/tensorflow/core/kernels/BUILD +++ b/tensorflow/core/kernels/BUILD @@ -5286,7 +5286,8 @@ tf_cuda_library( name = "fused_embedding_cuh", hdrs = ["fused_embedding/fused_embedding.cu.h", "fused_embedding/gpu_functions/kernels.cu.h", - "fused_embedding/gpu_functions/unique.cu.h"], + "fused_embedding/gpu_functions/unique.cu.h", + "fused_embedding/gpu_functions/partition_select.cu.h"], ) tf_kernel_library( @@ -5296,7 +5297,8 @@ tf_kernel_library( "fused_embedding/fused_embedding_pre_ops_gpus.cu.cc", "fused_embedding/fused_embedding_post_ops_gpus.cu.cc", "fused_embedding/gpu_functions/kernels.cu.cc", - "fused_embedding/gpu_functions/unique.cu.cc" + "fused_embedding/gpu_functions/unique.cu.cc", + "fused_embedding/gpu_functions/partition_select.cu.cc", ], deps = ["//third_party/eigen3", "//tensorflow/core/profiler:nvtx_utils"] + diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc index d6f7c367919..aa312ad9a31 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc @@ -1,11 +1,14 @@ #if GOOGLE_CUDA #define EIGEN_USE_GPU -#include "tensorflow/core/kernels/fused_embedding/gpu_sub_ops/partition_select.cu.h" +#include "tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.h" #include +#include +#include #include "tensorflow/core/common_runtime/gpu/gpu_event_mgr.h" +#include "tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h" #include "tensorflow/core/lib/core/bits.h" #include "tensorflow/core/util/gpu_kernel_helper.h" #include "tensorflow/stream_executor/cuda/cuda_activation.h" @@ -14,122 +17,138 @@ namespace tensorflow { typedef Eigen::GpuDevice GPUDevice; -namespace { - -// A macro helper to declare SelectScan. Need to define following macros before -// using this: KernelName, PartitionArgs, PassPartitionArgs -#define DeclareSelectScan \ - template \ - Status SelectScanDiv(const GPUDevice& d, const T* keys, PartitionArgs, \ - const const int64 num_partitions, const int length, \ - unsigned int* predicates, unsigned int* counters) { \ - if (length == 0) return Status::OK(); \ - const int64 threads = 32; \ - const int64 blocks = \ - length % 1024 == 0 ? (length / 1024) : (length / 1024 + 1); \ - return GpuLaunchKernel(KernelName, blocks, threads, 0, \ - d.stream(), keys, PassPartitionArgs, \ - int64 num_partitions, length, predicates, \ - counters); \ - } +namespace fused_embedding { // A macro helper to declare SelectScanKernels, because they just have only // a little bit differences. Need to define following macros before using this: -// KernelName, PartitionArgs, PartitionConditionLoadCodeBlock, +// KernelName, SelectScanKernelArgs, PartitionConditionLoadCodeBlock, // PartitionSelectCodeBlock -#define DeclareSelectScanKernel \ - template \ - __global__ void KernelName( \ - const T* keys, PartitionArgs, const int64 num_partitions, \ - const int length, unsigned int* predicates, unsigned int* counters) { \ - int g_tid = blockIdx.x * blockDim.x + threadIdx.x; \ - int lnid = threadIdx.x % 32; \ - if (g_tid >= (length >> 5)) return; \ - int g_warp_id = g_tid >> 5; \ - unsigned int mask; \ - unsigned int cnt; \ - \ - for (int j = 0; i < num_partitions; j++) { \ - PartitionConditionLoadCodeBlock; \ - \ -#pragma unroll for (int i = 0; i < 32; i++) { \ - int selected; \ - int load_offset = (g_warp_id << 10) + (i << 5) + lnid; \ - if (load_offset < length) { \ - T key = keys[load_offset]; \ - PartitionSelectCodeBlock; \ - } else { \ - selected = 0; \ - } \ - \ - mask = __ballot(selected); \ - if (lnid == 0) predicates[(g_warp_id << 5)] = mask; \ - if (lnid == i) cnt = __popc(mask); \ - } \ -#pragma unroll for (int offset = 16; offset > 0; offset >>= 1) cnt += \ - __shfl_down(cnt, offset); \ - \ - if (lnid == 0) counter[g_warp_id] = cnt; \ - } \ +#define DeclareSelectScanKernel \ + template \ + __global__ void KernelName( \ + const T* keys, SelectScanKernelArgs, const int64 num_partitions, \ + const int64 length, const int64 predicates_length, \ + const int64 counters_length, unsigned int* predicates, \ + unsigned int* counters) { \ + int g_tid = blockIdx.x * blockDim.x + threadIdx.x; \ + int lnid = threadIdx.x % 32; \ + if (g_tid >= (length >> 5)) return; \ + int g_warp_id = g_tid >> 5; \ + unsigned int mask; \ + unsigned int cnt; \ + \ + for (int j = 0; j < num_partitions; j++) { \ + PartitionConditionLoadCodeBlock; \ + _Pragma("unroll") for (int i = 0; i < 32; i++) { \ + int selected; \ + int load_offset = (g_warp_id << 10) + (i << 5) + lnid; \ + if (load_offset < length) { \ + T key = keys[load_offset]; \ + PartitionSelectCodeBlock; \ + } else { \ + selected = 0; \ + } \ + \ + mask = __ballot(selected); \ + if (lnid == 0) predicates[(g_warp_id << 5)] = mask; \ + if (lnid == i) cnt = __popc(mask); \ + } \ + _Pragma("unroll") for (int offset = 16; offset > 0; offset >>= 1) { \ + cnt += __shfl_down(cnt, offset); \ + } \ + \ + if (lnid == 0) counters[g_warp_id] = cnt; \ + } \ + } + +// A macro helper to declare DefinePartitionSelect. Need to define following +// macros before using this: Name, KernelName, PartitionSelectArgs, +// PartitionSelectPassArgs +#define DeclarePartitionSelect \ + template \ + void Name(OpKernelContext* ctx, const Tensor& keys, PartitionSelectArgs, \ + const int64 num_partitions) { \ + OP_REQUIRES(ctx, keys.dims() == 1, \ + errors::InvalidArgument("Tensor keys must ranks 1")); \ + const GPUDevice& device = ctx->eigen_gpu_device(); \ + const int64 length = keys.shape().dim_size(0); \ + cudaEvent_t memcpy_event; \ + cudaEventCreateWithFlags(&memcpy_event, cudaEventDisableTiming); \ + Tensor predicates; \ + Tensor counters; \ + const int64 select_scan_warps = \ + length % 1024 == 0 ? (length / 1024) : (length / 1024 + 1); \ + const int64 counters_length = select_scan_warps; \ + const int64 predicates_length = select_scan_warps * 32; \ + { \ + const int64 threads = 32; \ + const int64 blocks = select_scan_warps; \ + OP_REQUIRES_OK( \ + ctx, ctx->allocate_temp( \ + DT_UINT32, TensorShape{predicates_length * num_partitions}, \ + &predicates)); \ + \ + OP_REQUIRES_OK( \ + ctx, ctx->allocate_temp( \ + DT_UINT32, TensorShape{counters_length * num_partitions}, \ + &counters)); \ + OP_REQUIRES_OK( \ + ctx, GpuLaunchKernel(KernelName, blocks, threads, 0, \ + device.stream(), data_p_with_type(keys), \ + PartitionSelectPassArgs, num_partitions, \ + length, predicates_length, counters_length, \ + data_p_with_type(predicates), \ + data_p_with_type(counters))); \ + } \ + Tensor selected_sums; \ + OP_REQUIRES_OK(ctx, \ + ctx->allocate_temp(DT_UINT32, TensorShape{num_partitions}, \ + &selected_sums)); \ + size_t cub_tmp_storage_bytes; \ + Tensor cub_tmp_storage; \ + cub::DeviceReduce::Sum(nullptr, cub_tmp_storage_bytes, \ + data_p_with_type(counters), \ + data_p_with_type(selected_sums), \ + counters_length, device.stream()); \ + OP_REQUIRES_OK( \ + ctx, ctx->allocate_temp(DT_INT8, TensorShape{cub_tmp_storage_bytes}, \ + &cub_tmp_storage)); \ + cub::DeviceReduce::Sum(data_p_with_type(cub_tmp_storage), \ + cub_tmp_storage_bytes, \ + data_p_with_type(counters), \ + data_p_with_type(selected_sums), \ + counters_length, device.stream()); \ + for (int i = 0; i < num_partitions; i++) { \ + cub::DeviceReduce::Sum( \ + data_p_with_type(cub_tmp_storage), cub_tmp_storage_bytes, \ + data_p_with_type(counters) + i * counters_length, \ + data_p_with_type(selected_sums) + i, counters_length, \ + device.stream()); \ + } \ + std::vector selected_sums_host; \ + selected_sums_host.resize(num_partitions); \ + cudaMemcpyAsync(selected_sums_host.data(), \ + data_p_with_type(selected_sums), \ + sizeof(unsigned int) * num_partitions, \ + cudaMemcpyDeviceToHost, device.stream()); \ + cudaEventRecord(memcpy_event, device.stream()); \ + cudaEventSynchronize(memcpy_event); \ } +#define Name PartitionSelectDiv +#define PartitionSelectArgs const Tensor& accu_div +#define PartitionSelectPassArgs data_p_with_type(accu_div) #define KernelName SelectScanDivKernel -#define PartitionArgs const TIndex* accu_div -#define PassPartitionArgs accu_div -#define PartitionConditionLoadCodeBlock \ - TIndex lower_bound = j > 0 ? accu_div[j - 1] : 0; \ - TIndex upper_bound = accu_div[j]; +#define SelectScanKernelArgs const int64_t* accu_div +#define PartitionConditionLoadCodeBlock \ + int64_t lower_bound = j > 0 ? accu_div[j - 1] : 0; \ + int64_t upper_bound = accu_div[j]; #define PartitionSelectCodeBlock \ selected = int(key >= lower_bound && key < upper_bound); +DeclareSelectScanKernel; +DeclarePartitionSelect; -DeclareSelectScanKernel -DeclareSelectScan - -/* backup -template -__global__ void SelectScanDivKernel(const T* keys, const TIndex* accu_div, - const int64 num_partitions, - const int length, unsigned int* predicates, - unsigned int* counters) { - int g_tid = blockIdx.x * blockDim.x + threadIdx.x; // global tid - int lnid = threadIdx.x % 32; - if (g_tid >= (length >> 5)) return; - int g_warp_id = g_tid >> 5; - unsigned int mask; - unsigned int cnt; - - for (int j = 0; i < num_partitions; j++) { - TIndex lower_bound = j > 0 ? accu_div[j - 1] : 0; - TIndex upper_bound = accu_div[j]; - -#pragma unroll - for (int i = 0; i < 32; i++) { - int selected; - int load_offset = (g_warp_id << 10) + (i << 5) + lnid; - if (load_offset < length) { - T key = keys[load_offset]; - selected = int(key >= lower_bound && key < upper_bound); - } else { - selected = 0; - } - - mask = __ballot(selected); - if (lnid == 0) predicates[(g_warp_id << 5)] = mask; - if (lnid == i) cnt = __popc(mask); - } - -#pragma unroll - for (int offset = 16; offset > 0; offset >>= 1) - cnt += __shfl_down(cnt, offset); - - if (lnid == 0) counter[g_warp_id] = cnt; - } -} -*/ - -} // namespace - -namespace fused_embedding {} // namespace fused_embedding +} // namespace fused_embedding } // namespace tensorflow diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.h b/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.h index 51198d79cf5..0ee94542180 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.h +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.h @@ -9,11 +9,7 @@ namespace tensorflow { -namespace fused_embedding { - -enum PartitionStrategy { DIV }; - -} // namespace fused_embedding +namespace fused_embedding {} // namespace fused_embedding } // namespace tensorflow diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc index 64d0d029f80..7874d4ae99b 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc @@ -381,8 +381,7 @@ void AllocateTemp(OpKernelContext* context, int64 size, Tensor* tensor, namespace fused_embedding { -// Not really a TensorFlow Op and output "idx_of_input_to_unique" is -// not the same as output "idx" in TensorFlow 2.x's UniqueWithCounts operator +// Not the same for output "idx" compared to TensorFlow 2.x's UniqueWithCounts operator template void UniqueWithCountsGPU(OpKernelContext* context, const Tensor& input, Tensor* unique_keys_out, From 22e936e4bce6a78169d0ea6c0a7b4c6313eeee19 Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Fri, 11 Feb 2022 19:16:27 +0800 Subject: [PATCH 08/41] minor update --- .../gpu_functions/partition_select.cu.cc | 197 ++++++++++++------ 1 file changed, 137 insertions(+), 60 deletions(-) diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc index aa312ad9a31..cc8351e7fbc 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc @@ -21,53 +21,102 @@ namespace fused_embedding { // A macro helper to declare SelectScanKernels, because they just have only // a little bit differences. Need to define following macros before using this: -// KernelName, SelectScanKernelArgs, PartitionConditionLoadCodeBlock, -// PartitionSelectCodeBlock +// SelectScanKernelName, SelectScanKernelArgs, SelectScanKernelLoadCodeBlock, +// SelectScanKernelEvalCodeBlock #define DeclareSelectScanKernel \ template \ - __global__ void KernelName( \ - const T* keys, SelectScanKernelArgs, const int64 num_partitions, \ - const int64 length, const int64 predicates_length, \ - const int64 counters_length, unsigned int* predicates, \ + __global__ void SelectScanKernelName( \ + const T* keys, SelectScanKernelArgs, const int64_t num_partitions, \ + const int64_t length, const int64_t predicates_length, \ + const int64_t counters_length, unsigned int* predicates, \ unsigned int* counters) { \ int g_tid = blockIdx.x * blockDim.x + threadIdx.x; \ int lnid = threadIdx.x % 32; \ - if (g_tid >= (length >> 5)) return; \ int g_warp_id = g_tid >> 5; \ unsigned int mask; \ unsigned int cnt; \ \ for (int j = 0; j < num_partitions; j++) { \ - PartitionConditionLoadCodeBlock; \ + SelectScanKernelLoadCodeBlock; \ _Pragma("unroll") for (int i = 0; i < 32; i++) { \ int selected; \ int load_offset = (g_warp_id << 10) + (i << 5) + lnid; \ if (load_offset < length) { \ T key = keys[load_offset]; \ - PartitionSelectCodeBlock; \ + SelectScanKernelEvalCodeBlock; \ } else { \ selected = 0; \ } \ \ mask = __ballot(selected); \ - if (lnid == 0) predicates[(g_warp_id << 5)] = mask; \ + if (lnid == 0) \ + predicates[j * predicates_length + (g_warp_id << 5)] = mask; \ if (lnid == i) cnt = __popc(mask); \ } \ _Pragma("unroll") for (int offset = 16; offset > 0; offset >>= 1) { \ cnt += __shfl_down(cnt, offset); \ } \ \ - if (lnid == 0) counters[g_warp_id] = cnt; \ + if (lnid == 0) counters[j * counters_length + g_warp_id] = cnt; \ } \ } -// A macro helper to declare DefinePartitionSelect. Need to define following -// macros before using this: Name, KernelName, PartitionSelectArgs, -// PartitionSelectPassArgs -#define DeclarePartitionSelect \ +#define DeclareSelectKernel \ template \ - void Name(OpKernelContext* ctx, const Tensor& keys, PartitionSelectArgs, \ - const int64 num_partitions) { \ + __global__ void SelectKernelName( \ + const T* keys, const TIndex* indices, SelectKernelArgs, \ + unsigned int* predicates, unsigned int* inclusive_sum_counters, \ + const void** output_ptrs, const int64_t num_partitions, \ + const int64_t length, const int64_t predicates_length, \ + const int64_t counters_length, T* selected_keys, \ + TIndex* selected_indices) { \ + const int g_tid = blockIdx.x * blockDim.x + threadIdx.x; \ + const int lnid = threadIdx.x % 32; \ + const int g_warp_id = g_tid >> 5; \ + unsigned int predmask; \ + unsigned int cnt; \ + for (int j = 0; j < num_partitions; j++) { \ + SelectKernelLoadCodeBlock; \ + T* keys_output_ptr = (T*)(output_ptrs[2 * j]); \ + TIndex* indices_output_ptr = (TIndex*)(output_ptrs[2 * j + 1]); \ + predmask = predicates[j * predicates_length + g_warp_id << 5 + lnid]; \ + cnt = __popc(predmask); \ + _Pragma("unroll") for (int offset = 1; offset < 32; offset <<= 1) { \ + int n = __shfl_up(cnt, offset); \ + if (lnid >= offset) cnt += n; \ + } \ + unsigned int global_index = 0; \ + if (g_warp_id > 0) global_index = inclusive_sum_counters[g_warp_id - 1]; \ + _Pragma("unroll") for (int i = 0; i < 32; i++) { \ + unsigned int mask = __shfl(predmask, i); \ + unsigned int sub_group_index = 0; \ + if (i > 0) sub_group_index = __shfl(cnt, i - 1); \ + if (mask & (1 << lnid)) { \ + int load_offset = (g_warp_id << 10) + (i << 5) + lnid; \ + T* key = \ + keys[load_offset]; /* Will not cause out of boundry access, \ + because mask will be 0 for this thread*/ \ + TIndex* indice = indices[load_offset]; \ + SelectKernelRecalcKeyCodeBlock; \ + int output_offset = \ + global_index + sub_group_index + \ + __popc(mask & ((unsigned int)(1) << lnid - (unsigned int)(1))); \ + keys_output_ptr[output_offset] = new_key; \ + indices_output_ptr[output_offset] = indice; \ + } \ + } \ + } \ + } + +// A macro helper to declare DefinePartitionSelect. Need to define following +// macros before using this: SelectScanKernelName, +// SelectArgs, SelectScanPassArgs +#define DeclareSelect \ + template \ + void SelectName(OpKernelContext* ctx, const Tensor& keys, SelectArgs, \ + const int64 num_partitions, \ + const OpOutputList& selected_keys, \ + const OpOutputList& selected_indices) { \ OP_REQUIRES(ctx, keys.dims() == 1, \ errors::InvalidArgument("Tensor keys must ranks 1")); \ const GPUDevice& device = ctx->eigen_gpu_device(); \ @@ -80,73 +129,101 @@ namespace fused_embedding { length % 1024 == 0 ? (length / 1024) : (length / 1024 + 1); \ const int64 counters_length = select_scan_warps; \ const int64 predicates_length = select_scan_warps * 32; \ + OP_REQUIRES_OK( \ + ctx, ctx->allocate_temp(DT_UINT32, \ + TensorShape{counters_length * num_partitions}, \ + &counters)); \ + OP_REQUIRES_OK( \ + ctx, ctx->allocate_temp( \ + DT_UINT32, TensorShape{predicates_length * num_partitions}, \ + &predicates)); \ + \ { \ const int64 threads = 32; \ const int64 blocks = select_scan_warps; \ OP_REQUIRES_OK( \ - ctx, ctx->allocate_temp( \ - DT_UINT32, TensorShape{predicates_length * num_partitions}, \ - &predicates)); \ - \ - OP_REQUIRES_OK( \ - ctx, ctx->allocate_temp( \ - DT_UINT32, TensorShape{counters_length * num_partitions}, \ - &counters)); \ - OP_REQUIRES_OK( \ - ctx, GpuLaunchKernel(KernelName, blocks, threads, 0, \ + ctx, GpuLaunchKernel(SelectScanKernelName, blocks, threads, 0, \ device.stream(), data_p_with_type(keys), \ - PartitionSelectPassArgs, num_partitions, \ - length, predicates_length, counters_length, \ + SelectScanPassArgs, num_partitions, length, \ + predicates_length, counters_length, \ data_p_with_type(predicates), \ data_p_with_type(counters))); \ } \ - Tensor selected_sums; \ - OP_REQUIRES_OK(ctx, \ - ctx->allocate_temp(DT_UINT32, TensorShape{num_partitions}, \ - &selected_sums)); \ + Tensor inclusive_sum_counters; \ + OP_REQUIRES_OK( \ + ctx, ctx->allocate_temp(DT_UINT32, \ + TensorShape{counters_length * num_partitions}, \ + &inclusive_sum_counters)); \ size_t cub_tmp_storage_bytes; \ Tensor cub_tmp_storage; \ - cub::DeviceReduce::Sum(nullptr, cub_tmp_storage_bytes, \ - data_p_with_type(counters), \ - data_p_with_type(selected_sums), \ - counters_length, device.stream()); \ + cub::DeviceScan::InclusiveSum( \ + nullptr, cub_tmp_storage_bytes, \ + data_p_with_type(counters), \ + data_p_with_type(inclusive_sum_counters), \ + counters_length, device.stream()); \ OP_REQUIRES_OK( \ - ctx, ctx->allocate_temp(DT_INT8, TensorShape{cub_tmp_storage_bytes}, \ - &cub_tmp_storage)); \ - cub::DeviceReduce::Sum(data_p_with_type(cub_tmp_storage), \ - cub_tmp_storage_bytes, \ - data_p_with_type(counters), \ - data_p_with_type(selected_sums), \ - counters_length, device.stream()); \ + ctx, \ + ctx->allocate_temp(DT_INT8, TensorShape{int64(cub_tmp_storage_bytes)}, \ + &cub_tmp_storage)); \ for (int i = 0; i < num_partitions; i++) { \ - cub::DeviceReduce::Sum( \ + cub::DeviceScan::InclusiveSum( \ data_p_with_type(cub_tmp_storage), cub_tmp_storage_bytes, \ data_p_with_type(counters) + i * counters_length, \ - data_p_with_type(selected_sums) + i, counters_length, \ - device.stream()); \ + data_p_with_type(inclusive_sum_counters) + \ + i * counters_length, \ + counters_length, device.stream()); \ } \ - std::vector selected_sums_host; \ - selected_sums_host.resize(num_partitions); \ - cudaMemcpyAsync(selected_sums_host.data(), \ - data_p_with_type(selected_sums), \ - sizeof(unsigned int) * num_partitions, \ - cudaMemcpyDeviceToHost, device.stream()); \ + std::vector selected_nums_host; \ + selected_nums_host.resize(num_partitions); \ + cudaMemcpy2DAsync( \ + selected_nums_host.data(), 1 * sizeof(unsigned int), \ + data_p_with_type(inclusive_sum_counters) + counters_length - 1, \ + counters_length * sizeof(unsigned int), 1 * sizeof(unsigned int), \ + num_partitions, cudaMemcpyDeviceToHost, device.stream()); \ cudaEventRecord(memcpy_event, device.stream()); \ cudaEventSynchronize(memcpy_event); \ + \ + std::vector output_ptrs_host; \ + output_ptrs_host.resize(2 * num_partitions); \ + for (int i = 0; i < num_partitions; i++) { \ + Tensor* tmp_out; \ + OP_REQUIRES_OK( \ + ctx, selected_keys.allocate( \ + i, TensorShape({int64(selected_nums_host[i])}), &tmp_out)); \ + selected_keys_ptrs_host[2 * i] = tmp_out; \ + OP_REQUIRES_OK( \ + ctx, selected_indices.allocate( \ + i, TensorShape({int64(selected_nums_host[i])}), &tmp_out)); \ + selected_indices_ptrs_host[2 * i + 1] = tmp_out; \ + } \ + Tensor output_ptrs; \ + OP_REQUIRES_OK(ctx, ctx->allocate_temp( \ + UINT64, TensorShape{int64(2 * num_partitions)}, \ + &output_ptrs)); \ + cudaMemcpyAsync(data_p_with_type(output_ptrs), \ + output_ptrs_host.data(), \ + 2 * num_partitions * sizeof(size_t), \ + cudaMemcpyHostToDevice, device.stream()); \ } -#define Name PartitionSelectDiv -#define PartitionSelectArgs const Tensor& accu_div -#define PartitionSelectPassArgs data_p_with_type(accu_div) -#define KernelName SelectScanDivKernel +#define SelectName PartitionSelectDiv +#define SelectArgs const Tensor& accu_div +#define SelectScanPassArgs data_p_with_type(accu_div) +#define SelectScanKernelName SelectScanDivKernel1024 #define SelectScanKernelArgs const int64_t* accu_div -#define PartitionConditionLoadCodeBlock \ +#define SelectScanKernelLoadCodeBlock \ int64_t lower_bound = j > 0 ? accu_div[j - 1] : 0; \ int64_t upper_bound = accu_div[j]; -#define PartitionSelectCodeBlock \ +#define SelectScanKernelEvalCodeBlock \ selected = int(key >= lower_bound && key < upper_bound); +#define SelectKernelArgs SelectScanKernelArgs +#define SelectKernelLoadCodeBlock \ + int64_t lower_bound = j > 0 ? accu_div[j - 1] : 0; +#define SelectKernelRecalcKeyCodeBlock T new_key = key - lower_bound; + DeclareSelectScanKernel; -DeclarePartitionSelect; +DeclareSelectScan; +DeclareSelectKernel; } // namespace fused_embedding From 3fab0e8eef2b044a3a9cdb931885d7e81ee9b6e3 Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Mon, 14 Feb 2022 14:12:26 +0800 Subject: [PATCH 09/41] Update: minor change --- .../gpu_functions/partition_select.cu.cc | 45 +++++++++++++------ 1 file changed, 31 insertions(+), 14 deletions(-) diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc index cc8351e7fbc..59793a8ce21 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc @@ -66,10 +66,9 @@ namespace fused_embedding { __global__ void SelectKernelName( \ const T* keys, const TIndex* indices, SelectKernelArgs, \ unsigned int* predicates, unsigned int* inclusive_sum_counters, \ - const void** output_ptrs, const int64_t num_partitions, \ - const int64_t length, const int64_t predicates_length, \ - const int64_t counters_length, T* selected_keys, \ - TIndex* selected_indices) { \ + const int64_t num_partitions, const int64_t length, \ + const int64_t predicates_length, const int64_t counters_length, \ + const void** output_ptrs) { \ const int g_tid = blockIdx.x * blockDim.x + threadIdx.x; \ const int lnid = threadIdx.x % 32; \ const int g_warp_id = g_tid >> 5; \ @@ -112,11 +111,11 @@ namespace fused_embedding { // macros before using this: SelectScanKernelName, // SelectArgs, SelectScanPassArgs #define DeclareSelect \ - template \ - void SelectName(OpKernelContext* ctx, const Tensor& keys, SelectArgs, \ - const int64 num_partitions, \ - const OpOutputList& selected_keys, \ - const OpOutputList& selected_indices) { \ + template \ + void SelectName(OpKernelContext* ctx, const Tensor& keys, \ + const Tensor& indices, SelectArgs, \ + const int64 num_partitions, OpOutputList& selected_keys, \ + OpOutputList& selected_indices) { \ OP_REQUIRES(ctx, keys.dims() == 1, \ errors::InvalidArgument("Tensor keys must ranks 1")); \ const GPUDevice& device = ctx->eigen_gpu_device(); \ @@ -190,40 +189,58 @@ namespace fused_embedding { OP_REQUIRES_OK( \ ctx, selected_keys.allocate( \ i, TensorShape({int64(selected_nums_host[i])}), &tmp_out)); \ - selected_keys_ptrs_host[2 * i] = tmp_out; \ + output_ptrs_host[2 * i] = data_p_with_type(tmp_out); \ OP_REQUIRES_OK( \ ctx, selected_indices.allocate( \ i, TensorShape({int64(selected_nums_host[i])}), &tmp_out)); \ - selected_indices_ptrs_host[2 * i + 1] = tmp_out; \ + output_ptrs_host[2 * i + 1] = data_p_with_type(tmp_out); \ } \ Tensor output_ptrs; \ OP_REQUIRES_OK(ctx, ctx->allocate_temp( \ - UINT64, TensorShape{int64(2 * num_partitions)}, \ + DT_UINT64, TensorShape{int64(2 * num_partitions)}, \ &output_ptrs)); \ cudaMemcpyAsync(data_p_with_type(output_ptrs), \ output_ptrs_host.data(), \ 2 * num_partitions * sizeof(size_t), \ cudaMemcpyHostToDevice, device.stream()); \ + { \ + const int64 threads = 32; \ + const int64 blocks = select_scan_warps; \ + OP_REQUIRES_OK( \ + ctx, GpuLaunchKernel( \ + SelectKernelName, blocks, threads, 0, \ + device.stream(), data_p_with_type(keys), \ + data_p_with_type(indices), SelectPassArgs, \ + data_p_with_type(predicates), \ + data_p_with_type(inclusive_sum_counters), \ + num_partitions, length, predicates_length, counters_length, \ + data_p_with_type(output_ptrs))); \ + } \ } #define SelectName PartitionSelectDiv #define SelectArgs const Tensor& accu_div #define SelectScanPassArgs data_p_with_type(accu_div) -#define SelectScanKernelName SelectScanDivKernel1024 +#define SelectPassArgs SelectScanPassArgs + +#define SelectScanKernelName SelectScanDivKernel #define SelectScanKernelArgs const int64_t* accu_div #define SelectScanKernelLoadCodeBlock \ int64_t lower_bound = j > 0 ? accu_div[j - 1] : 0; \ int64_t upper_bound = accu_div[j]; #define SelectScanKernelEvalCodeBlock \ selected = int(key >= lower_bound && key < upper_bound); + +#define SelectKernelName SelectDivKernel #define SelectKernelArgs SelectScanKernelArgs + #define SelectKernelLoadCodeBlock \ int64_t lower_bound = j > 0 ? accu_div[j - 1] : 0; #define SelectKernelRecalcKeyCodeBlock T new_key = key - lower_bound; DeclareSelectScanKernel; -DeclareSelectScan; DeclareSelectKernel; +DeclareSelect; } // namespace fused_embedding From d9765a50b36b91aa2a698fe3d4d846e1d851438b Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Mon, 14 Feb 2022 19:56:36 +0800 Subject: [PATCH 10/41] temp update --- .../fused_embedding_pre_ops_gpus.cu.cc | 59 +++++++++++++++---- .../gpu_functions/kernels.cu.cc | 16 +++++ .../gpu_functions/kernels.cu.h | 3 + .../gpu_functions/partition_select.cu.h | 9 ++- .../gpu_functions/unique.cu.cc | 59 +++++++++++-------- .../fused_embedding/gpu_functions/unique.cu.h | 10 ++-- 6 files changed, 115 insertions(+), 41 deletions(-) diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc b/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc index b777dd1a0a1..0b79832d335 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc @@ -10,6 +10,8 @@ #include "tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h" #include "tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h" +#include "tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.h" +#include "tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h" #include "tensorflow/core/profiler/nvtx_utils.h" #include "tensorflow/core/util/gpu_kernel_helper.h" #include "third_party/cub/device/device_radix_sort.cuh" @@ -104,15 +106,6 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { } } - if (num_partitions_ > 1) { - cub::DeviceRadixSort::SortPairs( - (void*)nullptr, temp_storage_bytes, (int64_t*)nullptr, - (int64_t*)nullptr, (IndicePair*)nullptr, (IndicePair*)nullptr, - int(nnz + batch_size), 0, sizeof(int64_t) * 8, device.stream()); - max_cub_bytes = temp_storage_bytes > max_cub_bytes ? temp_storage_bytes - : max_cub_bytes; - } - OP_REQUIRES_OK( ctx, ctx->allocate_temp( DT_INT8, TensorShape({static_cast(max_cub_bytes)}), @@ -128,7 +121,7 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { int new_nnz = nnz; OP_REQUIRES_OK( - ctx, ctx->allocate_output(2 * num_partitions_, + ctx, ctx->allocate_output("row_empty_and_invalid_flags", TensorShape{batch_size + nnz}, &all_flags)); if (fill_empty_row_ || prune_invalid_id_) { @@ -218,6 +211,52 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { ? data_p_with_type(indices_extended) : data_p_with_type(indices_tensor); + // 4. unique + Tensor unique_keys_out; + // actually below 4 will be allocate_output inside UniqueWithCountsGPU. + // So it's even okay to pass nullptr for these 4 to UniqueWithCountsGPU + Tensor *unique_idxs_out, *unique_counts_out, *idx_of_input_to_unique_out, + *unique_offsets_out; + UniqueWithCountsGPU( + ctx, values_in, + ((fill_empty_row_ || prune_invalid_id_) ? values_extended + : values_tensor), + &unique_keys_out, unique_idxs_out, unique_counts_out, + idx_of_input_to_unique, unique_offsets_out); + + int64 uniq_size = unique_keys_out.dim_size(0); + + // 5. partition select + OpOutputList partitioned_values; + OP_REQUIRES_OK(ctx, + ctx->output_list("partitioned_values", &partitioned_values)); + OpOutputList partition_permutations; + OP_REQUIRES_OK(ctx, ctx->output_list("partition_permutations", + &partition_permutations)); + + if (num_partitions_ == 1) { + // simply copy + // attention, need to allocate_output here + Tensor* partitioned_value; + partitioned_values.allocate(0, + TensorShape({uniq_size}, &partitioned_value)); + cudaMemcpyAsync(data_p_with_type(partitioned_values[0]), + data_p_with_type(unique_keys_out), + sizeof(int64_t) * uniq_size, cudaMemcpyDeviceToDevice, + device.stream()); + Tensor* partitioned_permutation; + partition_permutations.allocate( + 0, TensorShape({uniq_size}, &partitioned_permutation)); + RangeInit(uniq_size, data_p_with_type(partitioned_permutation)); + } else { + Tensor partition_permute_init; + OP_REQUIRES_OK(ctx, ctx->allocate_temp(DT_INT64, TensorShape({uniq_size}), + &partition_permute_init)); + + RangeInit(uniq_size, data_p_with_type(partition_permute_init)); + if (parti) // TBD continue from here 2022.2.14 + } + OpOutputList partitioned_values; OP_REQUIRES_OK(ctx, ctx->output_list("partitioned_values", &partitioned_values)); diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.cc index da78a5d2e23..34380f1a753 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.cc @@ -166,6 +166,22 @@ void CalcElementsOffsetPerPartition(const GPUDevice& d, int num_partitions, elements_offset_per_partition, nnz)); } +template +__global__ void RangeInitKernel(const int64_t length, T* out) { + int64_t idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < length) { + out[idx] = T(idx); + } +} + +template +void RangeInit(const int64_t length, T* out) { + const int threads = LINER_MAPPING_THREADS; + const int blocks = CalcBlocksLinearMapping(length, threads); + TF_CHECK_OK(GpuLaunchKernel(RangeInitKernel, blocks, threads, 0, + d.stream(), length, out)); +} + __global__ void GatherAndConvertToSubPartitionKernel( const int64_t* sub_values_sorted, int64_t* sub_partitioned_values, const int64_t partition_start_base, const int64_t partition_size) { diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h b/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h index ae5e1844fce..9f480cf343b 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h @@ -83,6 +83,9 @@ void GatherAndConvertToSubPartition(const GPUDevice& d, const int64_t partition_start_base, const int64_t partition_size); +template +void RangeInit(const int64_t length, T* out); + void SumUpEmbeddingShard(const GPUDevice& d, const size_t sub_nnz, const float* emb_shard, const int64_t* partitioned_indice, float* emb_vectors, diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.h b/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.h index 0ee94542180..7c120d3e211 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.h +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.h @@ -9,7 +9,14 @@ namespace tensorflow { -namespace fused_embedding {} // namespace fused_embedding +namespace fused_embedding { +template +void PartitionSelectDiv(OpKernelContext* ctx, const Tensor& keys, + const Tensor& indices, const Tensor& accu_div, + const int64 num_partitions, OpOutputList& selected_keys, + OpOutputList& selected_indices); + +} // namespace fused_embedding } // namespace tensorflow diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc index 7874d4ae99b..11cbad6ad51 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc @@ -381,12 +381,13 @@ void AllocateTemp(OpKernelContext* context, int64 size, Tensor* tensor, namespace fused_embedding { -// Not the same for output "idx" compared to TensorFlow 2.x's UniqueWithCounts operator +// Extention to TensorFlow 2.x's UniqueWithCount operator template void UniqueWithCountsGPU(OpKernelContext* context, const Tensor& input, - Tensor* unique_keys_out, - Tensor* idx_of_input_to_unique, - Tensor* unique_counts_out) { + Tensor* unique_keys, Tensor* unique_idxs_out, + Tensor* unique_counts_out, + Tensor* idx_of_input_to_unique_out, + Tensor* unique_offsets_out) { OP_REQUIRES(context, input.NumElements() <= std::numeric_limits::max(), errors::InvalidArgument( "unique does not support input tensors larger than ", @@ -405,9 +406,12 @@ void UniqueWithCountsGPU(OpKernelContext* context, const Tensor& input, if (input_size == 0) { // Early exit for trivial case. Tensor** temp; - AllocateTemp(context, 0, unique_keys_out, temp); - AllocateTemp(context, 0, idx_of_input_to_unique, temp); - AllocateTemp(context, 0, unique_counts_out, temp); + OP_REQUIRES_OK( + ctx, ctx->allocate_output("unique_idxs", TensorShape({0}), &temp)); + OP_REQUIRES_OK( + ctx, ctx->allocate_output("unique_counts", TensorShape({0}), &temp)); + OP_REQUIRES_OK(ctx, ctx->allocate_output("idx_of_input_to_unique", + TensorShape({0}), &temp)); return; } @@ -573,9 +577,9 @@ void UniqueWithCountsGPU(OpKernelContext* context, const Tensor& input, unique_input_inds = Tensor(); unique_input_inds_ptr = nullptr; - // output 0 unique_keys_out + // output 0 unique_keys T* output_ptr = nullptr; - AllocateTemp(context, uniq_size, unique_keys_out, &output_ptr); + AllocateTemp(context, uniq_size, unique_keys, &output_ptr); Tensor inv_sorted_unique_perm; TIndex* inv_sorted_unique_perm_ptr = nullptr; @@ -584,8 +588,10 @@ void UniqueWithCountsGPU(OpKernelContext* context, const Tensor& input, if (!context->status().ok()) return; // output 2 unique_counts_out - TIndex* count_ptr = nullptr; - AllocateTemp(context, uniq_size, unique_counts_out, &count_ptr); + OP_REQUIRES_OK(ctx, + ctx->allocate_output("unique_counts", TensorShape({uniq_size}), + &unique_counts_out)); + TIndex* count_ptr = unique_counts_out->flat().data(); // Compute output and counts (if necessary). OP_REQUIRES_OK(context, @@ -595,43 +601,46 @@ void UniqueWithCountsGPU(OpKernelContext* context, const Tensor& input, inv_sorted_unique_perm_ptr, count_ptr)); // Free temporary tensors that are no longer needed. - sorted_unique_perm = Tensor(); - sorted_unique_perm_ptr = nullptr; sorted_unique_input_inds = Tensor(); sorted_unique_input_inds_ptr = nullptr; - segment_ends = Tensor(); - segment_ends_ptr = nullptr; // Compute prefix sum of counts - TIndex* count_prefix_sum_ptr = nullptr; - Tensor count_prefix_sum; - AllocateTemp(context, uniq_size, &count_prefix_sum, &count_prefix_sum_ptr); + // also output 4 unique_offsets_out + OP_REQUIRES_OK( + ctx, ctx->allocate_output("unique_offsets", TensorShape({uniq_size}), + &unique_offsets_out)); + TIndex* count_prefix_sum_ptr = unique_offsets_out->flat.data(); OP_REQUIRES_OK(context, GpuPrefixSum(context, uniq_size, exclusive, count_ptr, count_prefix_sum_ptr)); // GetIdxOfInputToUnique kernel to calculate the mapping between output and // corresponding indices of the input - TIndex* idx_of_input_to_unique_ptr; - AllocateTemp(context, input_size, idx_of_input_to_unique, - &idx_of_input_to_unique_ptr); + + // output 3 idx_of_input_to_unique_out + OP_REQUIRES_OK(ctx, ctx->allocate_output("idx_of_input_to_unique", + TensorShape({uniq_size}), + &idx_of_input_to_unique_out)); + TIndex* idx_of_input_to_unique_ptr = + idx_of_input_to_unique_out->flat().data(); + OP_REQUIRES_OK( context, GetIdxOfInputToUnique(device, uniq_size, count_ptr, segment_ends_ptr, sorted_unique_perm_ptr, count_prefix_sum_ptr, sorted_input_inds_ptr, idx_of_input_to_unique_ptr)); - /* REVISE: idx out is not needed for now - TIndex* idx_ptr = nullptr; // output 1 unique_idxs_out - AllocateTemp(context, input_size, unique_idxs_out, &idx_ptr); + OP_REQUIRES_OK(ctx, + ctx->allocate_output("unique_idxs", TensorShape({input_size}), + &unique_idxs_out)); + TIndex* idx_ptr = unique_idxs_out->flat().data(); // Compute indices output. OP_REQUIRES_OK(context, LookupAndScatterUniqueIds( device, input_size, sorted_input_inds_ptr, sorted_input_unique_ids_ptr, inv_sorted_unique_perm_ptr, idx_ptr)); - */ } } // namespace fused_embedding diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h index fb461da84b3..96703ec59bd 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h @@ -11,13 +11,13 @@ namespace tensorflow { namespace fused_embedding { -// Not really a TensorFlow Op and output "idx_of_input_to_unique" is -// not the same as output "idx" in TensorFlow 2.x's UniqueWithCounts operator +// Extention to TensorFlow 2.x's UniqueWithCounts operator template void UniqueWithCountsGPU(OpKernelContext* context, const Tensor& input, - Tensor* unique_keys_out, - Tensor* idx_of_input_to_unique, - Tensor* unique_counts_out); + Tensor* unique_keys, Tensor* unique_idxs_out, + Tensor* unique_counts_out, + Tensor* idx_of_input_to_unique_out, + Tensor* unique_offsets_out) } // namespace fused_embedding From 3b00778f88984ed842ab3b46c727a5f17c882633 Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Tue, 15 Feb 2022 17:22:06 +0800 Subject: [PATCH 11/41] kernel impl compile pass --- .../fused_embedding_pre_ops_gpus.cu.cc | 222 ++++-------------- .../gpu_functions/kernels.cu.cc | 5 +- .../gpu_functions/kernels.cu.h | 2 +- .../gpu_functions/partition_select.cu.cc | 85 +++---- .../gpu_functions/unique.cu.cc | 65 ++--- .../fused_embedding/gpu_functions/unique.cu.h | 4 +- 6 files changed, 135 insertions(+), 248 deletions(-) diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc b/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc index 0b79832d335..3ac4338a3d5 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc @@ -30,6 +30,8 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { OP_REQUIRES_OK(ctx, ctx->GetAttr("partition_axis", &partition_axis_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("fill_empty_row", &fill_empty_row_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("prune_invalid_id", &prune_invalid_id_)); + OP_REQUIRES_OK(ctx, + ctx->GetAttr("partition_strategy", &partition_strategy_)); int temp_default_id; OP_REQUIRES_OK(ctx, ctx->GetAttr("default_id", &temp_default_id)); default_id_ = int64_t(temp_default_id); @@ -40,7 +42,6 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { auto device = ctx->eigen_device(); const int64_t default_id = default_id_ >= 0 ? default_id_ : 0; - const int linear_mapping_threads = 128; nvtx::ScopedRangeIfEnabled nvtx_range(this); @@ -56,21 +57,6 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { OP_REQUIRES_OK(ctx, ctx->input("sp_dense_shape", &dense_shape)); const int64_t batch_size = dense_shape->flat().data()[0]; - OpInputList partition_shapes; - OP_REQUIRES_OK(ctx, ctx->input_list("partition_shapes", &partition_shapes)); - - partition_sizes_accumulate_.clear(); - for (const Tensor& shape : partition_shapes) { - OP_REQUIRES(ctx, shape.dims() <= 2, - errors::InvalidArgument( - "input partition_shapes must all less than rank 2")); - const int64_t accu = partition_sizes_accumulate_.empty() - ? shape.flat().data()[0] - : shape.flat().data()[0] + - partition_sizes_accumulate_.back(); - partition_sizes_accumulate_.push_back(accu); - } - cudaEvent_t memcpy_event; cudaEventCreateWithFlags(&memcpy_event, cudaEventDisableTiming); // =============================================== // @@ -217,12 +203,12 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { // So it's even okay to pass nullptr for these 4 to UniqueWithCountsGPU Tensor *unique_idxs_out, *unique_counts_out, *idx_of_input_to_unique_out, *unique_offsets_out; - UniqueWithCountsGPU( - ctx, values_in, - ((fill_empty_row_ || prune_invalid_id_) ? values_extended + UniqueWithCountsGPU( + ctx, + ((fill_empty_row_ || prune_invalid_id_) ? &values_extended : values_tensor), &unique_keys_out, unique_idxs_out, unique_counts_out, - idx_of_input_to_unique, unique_offsets_out); + idx_of_input_to_unique_out, unique_offsets_out); int64 uniq_size = unique_keys_out.dim_size(0); @@ -235,172 +221,57 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { &partition_permutations)); if (num_partitions_ == 1) { - // simply copy - // attention, need to allocate_output here + // Simply copy Tensor* partitioned_value; - partitioned_values.allocate(0, - TensorShape({uniq_size}, &partitioned_value)); - cudaMemcpyAsync(data_p_with_type(partitioned_values[0]), - data_p_with_type(unique_keys_out), + partitioned_values.allocate(0, TensorShape({uniq_size}), + &partitioned_value); + cudaMemcpyAsync(data_p_with_type(partitioned_values[0]), + data_p_with_type(unique_keys_out), sizeof(int64_t) * uniq_size, cudaMemcpyDeviceToDevice, device.stream()); + // Need to allocate_output here Tensor* partitioned_permutation; - partition_permutations.allocate( - 0, TensorShape({uniq_size}, &partitioned_permutation)); - RangeInit(uniq_size, data_p_with_type(partitioned_permutation)); + partition_permutations.allocate(0, TensorShape({uniq_size}), + &partitioned_permutation); + RangeInit(device, uniq_size, + data_p_with_type(partitioned_permutation)); } else { Tensor partition_permute_init; OP_REQUIRES_OK(ctx, ctx->allocate_temp(DT_INT64, TensorShape({uniq_size}), &partition_permute_init)); - RangeInit(uniq_size, data_p_with_type(partition_permute_init)); - if (parti) // TBD continue from here 2022.2.14 - } - - OpOutputList partitioned_values; - OP_REQUIRES_OK(ctx, - ctx->output_list("partitioned_values", &partitioned_values)); - OpOutputList partitioned_indices; - OP_REQUIRES_OK( - ctx, ctx->output_list("partitioned_indices", &partitioned_indices)); - - // 4. set output - if (num_partitions_ == 1) { - // single partition case, just directly copy - Tensor* pv_out; - OP_REQUIRES_OK( - ctx, partitioned_values.allocate( - 0, TensorShape({static_cast(new_nnz)}), &pv_out)); - Tensor* pi_out; - OP_REQUIRES_OK( - ctx, - partitioned_indices.allocate( - 0, TensorShape({static_cast(new_nnz), 2}), &pi_out)); - - cudaMemcpyAsync(data_p_with_type(pv_out), values_in, - sizeof(int64_t) * new_nnz, cudaMemcpyDeviceToDevice, - device.stream()); - cudaMemcpyAsync(data_p_with_type(pi_out), indices_in, - sizeof(IndicePair) * new_nnz, cudaMemcpyDeviceToDevice, - device.stream()); - - } else { - // multi-partitions case, calcaulate indices and split them. - Tensor values_sorted; - OP_REQUIRES_OK(ctx, ctx->allocate_temp(DT_INT64, TensorShape{new_nnz}, - &values_sorted)); - Tensor indices_sorted; - OP_REQUIRES_OK(ctx, ctx->allocate_temp(DT_INT64, TensorShape{new_nnz, 2}, - &indices_sorted)); - - cub::DeviceRadixSort::SortPairs( - data_p_with_type(cub_temp_storage), max_cub_bytes, values_in, - data_p_with_type(values_sorted), indices_in, - data_p_with_type(indices_sorted), int(new_nnz), 0, - sizeof(int64_t) * 8, device.stream()); - CK_CUDA_THROW_(cudaGetLastError()); - - // 4.1 calculate how many elements for each - // partition - Tensor partition_sizes_accumulate; - OP_REQUIRES_OK( - ctx, - ctx->allocate_temp( - DT_INT64, TensorShape({static_cast(num_partitions_)}), - &partition_sizes_accumulate)); - cudaMemcpyAsync(data_p_with_type(partition_sizes_accumulate), - partition_sizes_accumulate_.data(), - num_partitions_ * sizeof(int64_t), cudaMemcpyHostToDevice, - device.stream()); - - Tensor elements_offset_per_partition; - OP_REQUIRES_OK( - ctx, - ctx->allocate_temp( - DT_INT64, TensorShape({static_cast(num_partitions_)}), - &elements_offset_per_partition)); - - CalcElementsOffsetPerPartition( - device, num_partitions_, - data_p_with_type(values_sorted), - data_p_with_type(partition_sizes_accumulate), - data_p_with_type(elements_offset_per_partition), - int(new_nnz)); - CK_CUDA_THROW_(cudaGetLastError()); - - elements_offset_per_partition_.clear(); - elements_offset_per_partition_.resize(num_partitions_); - // device.stream()_executor::DeviceMemoryBase - // elements_offset_per_partition_wrapped( - // elements_offset_per_partition.flat().data(), - // num_partitions_); - // device.stream()->ThenMemcpy(elements_offset_per_partition_.data(), - // elements_offset_per_partition_wrapped, - // num_partitions_ * - // sizeof(int64_t)); - // device.stream()->BlockHostUntilDone(); - - cudaMemcpyAsync(elements_offset_per_partition_.data(), - data_p_with_type(elements_offset_per_partition), - num_partitions_ * sizeof(int64_t), cudaMemcpyDeviceToHost, - device.stream()); - cudaEventRecord(memcpy_event, device.stream()); - cudaEventSynchronize(memcpy_event); - - // 4.2 set output - int64_t sub_start_offset = 0; - for (int i = 0; i < num_partitions_; i++) { - int64_t size = elements_offset_per_partition_[i] - sub_start_offset; - - Tensor* sub_partitioned_values; - OP_REQUIRES_OK(ctx, partitioned_values.allocate( - i, TensorShape({static_cast(size)}), - &sub_partitioned_values)); - - Tensor* sub_partitioned_indices; - OP_REQUIRES_OK(ctx, partitioned_indices.allocate( - i, TensorShape({static_cast(size), 2}), - &sub_partitioned_indices)); - - if (size > 0) { - // some partition does not have any - // element that falls in it - const int partition_start_base = - i == 0 ? 0 : partition_sizes_accumulate_[i - 1]; - - GatherAndConvertToSubPartition( - device, - data_p_with_type(values_sorted) + sub_start_offset, - data_p_with_type(sub_partitioned_values), - partition_start_base, size); - - CK_CUDA_THROW_(cudaGetLastError()); - - // device.stream()_executor::DeviceMemoryBase - // sub_indices_sorted_wrapped( - // reinterpret_cast(indices_sorted.flat().data()) - // + - // partition_start_base, - // size * sizeof(IndicePair)); - // device.stream()_executor::DeviceMemoryBase - // sub_indices_out_wrapped( - // reinterpret_cast( - // sub_partitioned_indices.flat().data()), - // size * sizeof(IndicePair)); - // device.stream()->ThenMemcpy(&sub_indices_out_wrapped, - // sub_indices_sorted_wrapped, - // size * 2 * - // sizeof(int64_t)); - cudaMemcpyAsync( - sub_partitioned_indices->flat().data(), - indices_sorted.flat().data() + 2 * sub_start_offset, - size * 2 * sizeof(int64_t), cudaMemcpyDeviceToDevice, - device.stream()); + RangeInit(device, uniq_size, + data_p_with_type(partition_permute_init)); + if (partition_strategy_ == "div") { + OpInputList partition_shapes; + OP_REQUIRES_OK(ctx, + ctx->input_list("partition_shapes", &partition_shapes)); + + std::vector accu_div_host; + accu_div_host.resize(num_partitions_); + for (int i = 0; i < partition_shapes.size(); i++) { + OP_REQUIRES(ctx, partition_shapes[i].dims() <= 2, + errors::InvalidArgument( + "input partition_shapes must all less than rank 2")); + const int64_t div = partition_shapes[i].flat().data()[0]; + accu_div_host[i] = i > 0 ? div : accu_div_host[i - 1] + div; } - sub_start_offset = elements_offset_per_partition_[i]; + + Tensor accu_div; + OP_REQUIRES_OK( + ctx, + ctx->allocate_temp( + DT_INT64, TensorShape({static_cast(num_partitions_)}), + &accu_div)); + cudaMemcpyAsync(data_p_with_type(accu_div), accu_div_host.data(), + num_partitions_ * sizeof(int64_t), + cudaMemcpyHostToDevice, device.stream()); + + PartitionSelectDiv( + ctx, unique_keys_out, partition_permute_init, accu_div, + num_partitions_, partitioned_values, partition_permutations); } } - // Op kernel execution done } private: @@ -408,9 +279,8 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { int partition_axis_; bool fill_empty_row_; bool prune_invalid_id_; + std::string partition_strategy_; int64_t default_id_; - std::vector partition_sizes_accumulate_; - std::vector elements_offset_per_partition_; }; REGISTER_KERNEL_BUILDER(Name("FusedEmbeddingSparsePreLookUp") diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.cc index 34380f1a753..63195d48912 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.cc @@ -175,13 +175,16 @@ __global__ void RangeInitKernel(const int64_t length, T* out) { } template -void RangeInit(const int64_t length, T* out) { +void RangeInit(const GPUDevice& d, const int64_t length, T* out) { const int threads = LINER_MAPPING_THREADS; const int blocks = CalcBlocksLinearMapping(length, threads); TF_CHECK_OK(GpuLaunchKernel(RangeInitKernel, blocks, threads, 0, d.stream(), length, out)); } +template void RangeInit(const GPUDevice& d, const int64_t length, + int64_t* out); + __global__ void GatherAndConvertToSubPartitionKernel( const int64_t* sub_values_sorted, int64_t* sub_partitioned_values, const int64_t partition_start_base, const int64_t partition_size) { diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h b/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h index 9f480cf343b..045a825f7d1 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h @@ -84,7 +84,7 @@ void GatherAndConvertToSubPartition(const GPUDevice& d, const int64_t partition_size); template -void RangeInit(const int64_t length, T* out); +void RangeInit(const GPUDevice& d, const int64_t length, T* out); void SumUpEmbeddingShard(const GPUDevice& d, const size_t sub_nnz, const float* emb_shard, diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc index 59793a8ce21..7de332d6541 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc @@ -26,9 +26,9 @@ namespace fused_embedding { #define DeclareSelectScanKernel \ template \ __global__ void SelectScanKernelName( \ - const T* keys, SelectScanKernelArgs, const int64_t num_partitions, \ - const int64_t length, const int64_t predicates_length, \ - const int64_t counters_length, unsigned int* predicates, \ + const T* keys, SelectScanKernelArgs, const int64 num_partitions, \ + const int64 length, const int64 predicates_length, \ + const int64 counters_length, unsigned int* predicates, \ unsigned int* counters) { \ int g_tid = blockIdx.x * blockDim.x + threadIdx.x; \ int lnid = threadIdx.x % 32; \ @@ -48,13 +48,13 @@ namespace fused_embedding { selected = 0; \ } \ \ - mask = __ballot(selected); \ + mask = __ballot_sync(0xffffffff, selected); \ if (lnid == 0) \ predicates[j * predicates_length + (g_warp_id << 5)] = mask; \ if (lnid == i) cnt = __popc(mask); \ } \ _Pragma("unroll") for (int offset = 16; offset > 0; offset >>= 1) { \ - cnt += __shfl_down(cnt, offset); \ + cnt += __shfl_down_sync(0xffffffff, cnt, offset); \ } \ \ if (lnid == 0) counters[j * counters_length + g_warp_id] = cnt; \ @@ -66,9 +66,9 @@ namespace fused_embedding { __global__ void SelectKernelName( \ const T* keys, const TIndex* indices, SelectKernelArgs, \ unsigned int* predicates, unsigned int* inclusive_sum_counters, \ - const int64_t num_partitions, const int64_t length, \ - const int64_t predicates_length, const int64_t counters_length, \ - const void** output_ptrs) { \ + const int64 num_partitions, const int64 length, \ + const int64 predicates_length, const int64 counters_length, \ + void** output_ptrs) { \ const int g_tid = blockIdx.x * blockDim.x + threadIdx.x; \ const int lnid = threadIdx.x % 32; \ const int g_warp_id = g_tid >> 5; \ @@ -81,21 +81,21 @@ namespace fused_embedding { predmask = predicates[j * predicates_length + g_warp_id << 5 + lnid]; \ cnt = __popc(predmask); \ _Pragma("unroll") for (int offset = 1; offset < 32; offset <<= 1) { \ - int n = __shfl_up(cnt, offset); \ + int n = __shfl_up_sync(0xffffffff, cnt, offset); \ if (lnid >= offset) cnt += n; \ } \ unsigned int global_index = 0; \ if (g_warp_id > 0) global_index = inclusive_sum_counters[g_warp_id - 1]; \ _Pragma("unroll") for (int i = 0; i < 32; i++) { \ - unsigned int mask = __shfl(predmask, i); \ + unsigned int mask = __shfl_sync(0xffffffff, predmask, i); \ unsigned int sub_group_index = 0; \ - if (i > 0) sub_group_index = __shfl(cnt, i - 1); \ + if (i > 0) sub_group_index = __shfl_sync(0xffffffff, cnt, i - 1); \ if (mask & (1 << lnid)) { \ int load_offset = (g_warp_id << 10) + (i << 5) + lnid; \ - T* key = \ + T key = \ keys[load_offset]; /* Will not cause out of boundry access, \ because mask will be 0 for this thread*/ \ - TIndex* indice = indices[load_offset]; \ + TIndex indice = indices[load_offset]; \ SelectKernelRecalcKeyCodeBlock; \ int output_offset = \ global_index + sub_group_index + \ @@ -142,11 +142,11 @@ namespace fused_embedding { const int64 blocks = select_scan_warps; \ OP_REQUIRES_OK( \ ctx, GpuLaunchKernel(SelectScanKernelName, blocks, threads, 0, \ - device.stream(), data_p_with_type(keys), \ + device.stream(), data_p_with_type(keys), \ SelectScanPassArgs, num_partitions, length, \ predicates_length, counters_length, \ - data_p_with_type(predicates), \ - data_p_with_type(counters))); \ + data_p_with_type(predicates), \ + data_p_with_type(counters))); \ } \ Tensor inclusive_sum_counters; \ OP_REQUIRES_OK( \ @@ -157,8 +157,8 @@ namespace fused_embedding { Tensor cub_tmp_storage; \ cub::DeviceScan::InclusiveSum( \ nullptr, cub_tmp_storage_bytes, \ - data_p_with_type(counters), \ - data_p_with_type(inclusive_sum_counters), \ + data_p_with_type(counters), \ + data_p_with_type(inclusive_sum_counters), \ counters_length, device.stream()); \ OP_REQUIRES_OK( \ ctx, \ @@ -166,19 +166,20 @@ namespace fused_embedding { &cub_tmp_storage)); \ for (int i = 0; i < num_partitions; i++) { \ cub::DeviceScan::InclusiveSum( \ - data_p_with_type(cub_tmp_storage), cub_tmp_storage_bytes, \ - data_p_with_type(counters) + i * counters_length, \ - data_p_with_type(inclusive_sum_counters) + \ + data_p_with_type(cub_tmp_storage), cub_tmp_storage_bytes, \ + data_p_with_type(counters) + i * counters_length, \ + data_p_with_type(inclusive_sum_counters) + \ i * counters_length, \ counters_length, device.stream()); \ } \ std::vector selected_nums_host; \ selected_nums_host.resize(num_partitions); \ - cudaMemcpy2DAsync( \ - selected_nums_host.data(), 1 * sizeof(unsigned int), \ - data_p_with_type(inclusive_sum_counters) + counters_length - 1, \ - counters_length * sizeof(unsigned int), 1 * sizeof(unsigned int), \ - num_partitions, cudaMemcpyDeviceToHost, device.stream()); \ + cudaMemcpy2DAsync(selected_nums_host.data(), 1 * sizeof(unsigned int), \ + data_p_with_type(inclusive_sum_counters) + \ + counters_length - 1, \ + counters_length * sizeof(unsigned int), \ + 1 * sizeof(unsigned int), num_partitions, \ + cudaMemcpyDeviceToHost, device.stream()); \ cudaEventRecord(memcpy_event, device.stream()); \ cudaEventSynchronize(memcpy_event); \ \ @@ -189,17 +190,17 @@ namespace fused_embedding { OP_REQUIRES_OK( \ ctx, selected_keys.allocate( \ i, TensorShape({int64(selected_nums_host[i])}), &tmp_out)); \ - output_ptrs_host[2 * i] = data_p_with_type(tmp_out); \ + output_ptrs_host[2 * i] = data_p_with_type(tmp_out); \ OP_REQUIRES_OK( \ ctx, selected_indices.allocate( \ i, TensorShape({int64(selected_nums_host[i])}), &tmp_out)); \ - output_ptrs_host[2 * i + 1] = data_p_with_type(tmp_out); \ + output_ptrs_host[2 * i + 1] = data_p_with_type(tmp_out); \ } \ Tensor output_ptrs; \ OP_REQUIRES_OK(ctx, ctx->allocate_temp( \ DT_UINT64, TensorShape{int64(2 * num_partitions)}, \ &output_ptrs)); \ - cudaMemcpyAsync(data_p_with_type(output_ptrs), \ + cudaMemcpyAsync(data_p_with_type(output_ptrs), \ output_ptrs_host.data(), \ 2 * num_partitions * sizeof(size_t), \ cudaMemcpyHostToDevice, device.stream()); \ @@ -209,25 +210,25 @@ namespace fused_embedding { OP_REQUIRES_OK( \ ctx, GpuLaunchKernel( \ SelectKernelName, blocks, threads, 0, \ - device.stream(), data_p_with_type(keys), \ - data_p_with_type(indices), SelectPassArgs, \ - data_p_with_type(predicates), \ - data_p_with_type(inclusive_sum_counters), \ + device.stream(), data_p_with_type(keys), \ + data_p_with_type(indices), SelectPassArgs, \ + data_p_with_type(predicates), \ + data_p_with_type(inclusive_sum_counters), \ num_partitions, length, predicates_length, counters_length, \ - data_p_with_type(output_ptrs))); \ + data_p_with_type(output_ptrs))); \ } \ } #define SelectName PartitionSelectDiv #define SelectArgs const Tensor& accu_div -#define SelectScanPassArgs data_p_with_type(accu_div) +#define SelectScanPassArgs data_p_with_type(accu_div) #define SelectPassArgs SelectScanPassArgs #define SelectScanKernelName SelectScanDivKernel -#define SelectScanKernelArgs const int64_t* accu_div -#define SelectScanKernelLoadCodeBlock \ - int64_t lower_bound = j > 0 ? accu_div[j - 1] : 0; \ - int64_t upper_bound = accu_div[j]; +#define SelectScanKernelArgs const int64* accu_div +#define SelectScanKernelLoadCodeBlock \ + int64 lower_bound = j > 0 ? accu_div[j - 1] : 0; \ + int64 upper_bound = accu_div[j]; #define SelectScanKernelEvalCodeBlock \ selected = int(key >= lower_bound && key < upper_bound); @@ -235,12 +236,16 @@ namespace fused_embedding { #define SelectKernelArgs SelectScanKernelArgs #define SelectKernelLoadCodeBlock \ - int64_t lower_bound = j > 0 ? accu_div[j - 1] : 0; + int64 lower_bound = j > 0 ? accu_div[j - 1] : 0; #define SelectKernelRecalcKeyCodeBlock T new_key = key - lower_bound; DeclareSelectScanKernel; DeclareSelectKernel; DeclareSelect; +template void PartitionSelectDiv( + OpKernelContext* ctx, const Tensor& keys, const Tensor& indices, + const Tensor& accu_div, const int64 num_partitions, + OpOutputList& selected_keys, OpOutputList& selected_indices); } // namespace fused_embedding diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc index 11cbad6ad51..c285e2d498e 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc @@ -383,17 +383,18 @@ namespace fused_embedding { // Extention to TensorFlow 2.x's UniqueWithCount operator template -void UniqueWithCountsGPU(OpKernelContext* context, const Tensor& input, +void UniqueWithCountsGPU(OpKernelContext* context, const Tensor* input, Tensor* unique_keys, Tensor* unique_idxs_out, Tensor* unique_counts_out, Tensor* idx_of_input_to_unique_out, Tensor* unique_offsets_out) { - OP_REQUIRES(context, input.NumElements() <= std::numeric_limits::max(), + OP_REQUIRES(context, + input->NumElements() <= std::numeric_limits::max(), errors::InvalidArgument( "unique does not support input tensors larger than ", std::numeric_limits::max(), " elements")); - OP_REQUIRES(context, TensorShapeUtils::IsVector(input.shape()), + OP_REQUIRES(context, TensorShapeUtils::IsVector(input->shape()), errors::InvalidArgument("unique expects a 1D vector.")); se::Stream* stream = context->op_device_context()->stream(); @@ -402,16 +403,16 @@ void UniqueWithCountsGPU(OpKernelContext* context, const Tensor& input, cudaEvent_t memcpy_event; cudaEventCreateWithFlags(&memcpy_event, cudaEventDisableTiming); - int64 input_size = input.NumElements(); + int64 input_size = input->NumElements(); if (input_size == 0) { // Early exit for trivial case. - Tensor** temp; - OP_REQUIRES_OK( - ctx, ctx->allocate_output("unique_idxs", TensorShape({0}), &temp)); - OP_REQUIRES_OK( - ctx, ctx->allocate_output("unique_counts", TensorShape({0}), &temp)); - OP_REQUIRES_OK(ctx, ctx->allocate_output("idx_of_input_to_unique", - TensorShape({0}), &temp)); + Tensor* temp; + OP_REQUIRES_OK(context, context->allocate_output("unique_idxs", + TensorShape({0}), &temp)); + OP_REQUIRES_OK(context, context->allocate_output("unique_counts", + TensorShape({0}), &temp)); + OP_REQUIRES_OK(context, context->allocate_output("idx_of_input_to_unique", + TensorShape({0}), &temp)); return; } @@ -492,7 +493,7 @@ void UniqueWithCountsGPU(OpKernelContext* context, const Tensor& input, AllocateTemp(context, input_size, &sorted_input, &sorted_input_ptr); if (!context->status().ok()) return; - const T* input_ptr = input.flat().data(); + const T* input_ptr = input->flat().data(); OP_REQUIRES_OK( context, GpuRadixSort(context, input_size, /*keys_in=*/input_ptr, /*keys_out=*/sorted_input_ptr, @@ -517,8 +518,7 @@ void UniqueWithCountsGPU(OpKernelContext* context, const Tensor& input, // Copy the last element of sorted_input_unique_ids back to the host to // obtain uniq_size. ScratchSpace last_idx_host(context, 1, /*on_host=*/true); - OP_REQUIRES_OK( - context, + auto status = stream ->ThenMemcpy(last_idx_host.mutable_data(), se::DeviceMemoryBase( @@ -526,7 +526,10 @@ void UniqueWithCountsGPU(OpKernelContext* context, const Tensor& input, (input_size - 1), sizeof(*last_idx_host.data())), sizeof(*last_idx_host.data())) - .ok()); + .ok(); + if (!status) { + context->SetStatus(errors::Internal("Copying device-to-host failed.")); + } const GPUDevice& device = context->eigen_gpu_device(); cudaEventRecord(memcpy_event, device.stream()); @@ -588,9 +591,9 @@ void UniqueWithCountsGPU(OpKernelContext* context, const Tensor& input, if (!context->status().ok()) return; // output 2 unique_counts_out - OP_REQUIRES_OK(ctx, - ctx->allocate_output("unique_counts", TensorShape({uniq_size}), - &unique_counts_out)); + OP_REQUIRES_OK(context, context->allocate_output("unique_counts", + TensorShape({uniq_size}), + &unique_counts_out)); TIndex* count_ptr = unique_counts_out->flat().data(); // Compute output and counts (if necessary). @@ -606,10 +609,10 @@ void UniqueWithCountsGPU(OpKernelContext* context, const Tensor& input, // Compute prefix sum of counts // also output 4 unique_offsets_out - OP_REQUIRES_OK( - ctx, ctx->allocate_output("unique_offsets", TensorShape({uniq_size}), - &unique_offsets_out)); - TIndex* count_prefix_sum_ptr = unique_offsets_out->flat.data(); + OP_REQUIRES_OK(context, context->allocate_output("unique_offsets", + TensorShape({uniq_size}), + &unique_offsets_out)); + TIndex* count_prefix_sum_ptr = unique_offsets_out->flat().data(); OP_REQUIRES_OK(context, GpuPrefixSum(context, uniq_size, exclusive, count_ptr, count_prefix_sum_ptr)); @@ -618,9 +621,10 @@ void UniqueWithCountsGPU(OpKernelContext* context, const Tensor& input, // corresponding indices of the input // output 3 idx_of_input_to_unique_out - OP_REQUIRES_OK(ctx, ctx->allocate_output("idx_of_input_to_unique", - TensorShape({uniq_size}), - &idx_of_input_to_unique_out)); + OP_REQUIRES_OK(context, + context->allocate_output("idx_of_input_to_unique", + TensorShape({uniq_size}), + &idx_of_input_to_unique_out)); TIndex* idx_of_input_to_unique_ptr = idx_of_input_to_unique_out->flat().data(); @@ -631,9 +635,9 @@ void UniqueWithCountsGPU(OpKernelContext* context, const Tensor& input, sorted_input_inds_ptr, idx_of_input_to_unique_ptr)); // output 1 unique_idxs_out - OP_REQUIRES_OK(ctx, - ctx->allocate_output("unique_idxs", TensorShape({input_size}), - &unique_idxs_out)); + OP_REQUIRES_OK( + context, context->allocate_output( + "unique_idxs", TensorShape({input_size}), &unique_idxs_out)); TIndex* idx_ptr = unique_idxs_out->flat().data(); // Compute indices output. @@ -643,6 +647,11 @@ void UniqueWithCountsGPU(OpKernelContext* context, const Tensor& input, inv_sorted_unique_perm_ptr, idx_ptr)); } +template void UniqueWithCountsGPU( + OpKernelContext* context, const Tensor* input, Tensor* unique_keys, + Tensor* unique_idxs_out, Tensor* unique_counts_out, + Tensor* idx_of_input_to_unique_out, Tensor* unique_offsets_out); + } // namespace fused_embedding } // namespace tensorflow diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h index 96703ec59bd..f2e7586c4bd 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h @@ -13,11 +13,11 @@ namespace fused_embedding { // Extention to TensorFlow 2.x's UniqueWithCounts operator template -void UniqueWithCountsGPU(OpKernelContext* context, const Tensor& input, +void UniqueWithCountsGPU(OpKernelContext* context, const Tensor* input, Tensor* unique_keys, Tensor* unique_idxs_out, Tensor* unique_counts_out, Tensor* idx_of_input_to_unique_out, - Tensor* unique_offsets_out) + Tensor* unique_offsets_out); } // namespace fused_embedding From 7589dc1e1e33a581d0bae2c92222ab29d2cec449 Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Tue, 22 Feb 2022 15:19:03 +0800 Subject: [PATCH 12/41] Update: pre-lookup ready. Unit tests all passed --- .../fused_embedding/fused_embedding.cu.h | 135 ++++++ .../fused_embedding_pre_ops_gpus.cu.cc | 15 +- .../gpu_functions/partition_select.cu.cc | 86 ++-- .../gpu_functions/unique.cu.cc | 69 ++- .../fused_embedding/gpu_functions/unique.cu.h | 4 +- .../tests/fused_embedding_pre_ops_test.cc | 411 +++++++++++------- tensorflow/core/ops/fused_embedding_ops.cc | 95 ++-- 7 files changed, 520 insertions(+), 295 deletions(-) diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h b/tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h index 9f334b11b45..77a19b5dca5 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h +++ b/tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h @@ -50,6 +50,141 @@ struct IndicePair { } // namespace tensorflow + +// ================================================================================================= +#include +#include +#include +#include +#include +#include +template +void printGPUTensorHelper(const T* src, + const std::vector& dims, + const std::vector& max_num_to_print, + bool if_exit = false) +{ + int element_num = 1; + int dim_num = (int)dims.size(); + + printf("Tensor size: ("); + for (int i = 0; i < dim_num; i++) { + element_num *= dims[i]; + printf("%d, ", dims[i]); + } + printf(")\n"); + T* host_buffer = new T[element_num]; + cudaMemcpy((void*)host_buffer, (void*)src, sizeof(T) * element_num, cudaMemcpyDeviceToHost); + + std::vector strides(dim_num, 1); + for (int i = dim_num - 2; i >= 0; i--) { + strides[i] = strides[i + 1] * dims[i + 1]; + printf("%d ", strides[i]); + } + + cudaDeviceSynchronize(); + + printf("================== start printing tensor =====================\n"); + + std::function recursive_print; + recursive_print = + [&recursive_print, host_buffer, &dims = dims, &max_num_to_print = max_num_to_print, &strides = strides]( + int current_dim, int current_dim_offset) -> void { + if (current_dim < dims.size() - 1) { + for (int i = 0; i < current_dim; i++) { + printf(" "); + } + printf("[ dim %d\n", current_dim); + + if (2 * max_num_to_print[current_dim] >= dims[current_dim]) { + for (int i = 0; i < dims[current_dim]; i++) { + recursive_print(current_dim + 1, current_dim_offset + i * strides[current_dim]); + } + } + else { + for (int i = 0; i < max_num_to_print[current_dim]; i++) { + recursive_print(current_dim + 1, current_dim_offset + i * strides[current_dim]); + } + for (int i = 0; i < current_dim; i++) { + printf(" "); + } + printf(" ......\n"); + for (int i = dims[current_dim] - max_num_to_print[current_dim]; i < dims[current_dim]; i++) { + recursive_print(current_dim + 1, current_dim_offset + i * strides[current_dim]); + } + } + for (int i = 0; i < current_dim; i++) { + printf(" "); + } + printf("]\n"); + } + else { + for (int i = 0; i < current_dim; i++) { + printf(" "); + } + printf("[ "); + printf("dim %d, offset: %d, ", current_dim, current_dim_offset); + if (2 * max_num_to_print[current_dim] >= dims[current_dim]) { + for (int i = 0; i < dims[current_dim]; i++) { + printf("%s ", std::to_string(host_buffer[current_dim_offset + i * strides[current_dim]]).c_str()); + } + } + else { + for (int i = 0; i < max_num_to_print[current_dim]; i++) { + printf("%s ", std::to_string(host_buffer[current_dim_offset + i * strides[current_dim]]).c_str()); + } + printf("...... "); + for (int i = dims[current_dim] - max_num_to_print[current_dim]; i < dims[current_dim]; i++) { + printf("%s ", std::to_string(host_buffer[current_dim_offset + i * strides[current_dim]]).c_str()); + } + } + printf("]\n"); + } + }; + + recursive_print(0, 0); + + printf("\n================== stop =====================\n"); + delete[] host_buffer; + + if (if_exit) { + exit(0); + } +}; + +#include +#include +template +void dumpGPUTensorHelper(const T* src, const std::vector& dims, const std::string& out_file, bool if_exit = true) +{ + int element_num = 1; + int dim_num = (int)dims.size(); + + printf("Tensor size: ("); + for (int i = 0; i < dim_num; i++) { + element_num *= dims[i]; + printf("%d, ", dims[i]); + } + printf(")\n"); + T* host_buffer = new T[element_num]; + cudaMemcpy((void*)host_buffer, (void*)src, sizeof(T) * element_num, cudaMemcpyDeviceToHost); + + std::ofstream file(out_file, std::ios::binary); + if (file.is_open()) { + file.write((const char*)host_buffer, sizeof(T) * element_num); + file.close(); + std::cout << "Dumped tensor to " << out_file << std::endl; + } + else { + std::cout << "Unable to open " << out_file << std::endl; + } + + if (if_exit) { + exit(0); + } +} + + #endif // GOOGLE_CUDA #endif // TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_FUSED_EMBEDDING_CU_H_ \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc b/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc index 3ac4338a3d5..f5529e6830c 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc @@ -118,7 +118,7 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { ctx, ctx->allocate_temp(DT_INT64, TensorShape{2 * (nnz + batch_size)}, &indices_extended)); OP_REQUIRES_OK(ctx, - ctx->allocate_temp(DT_INT64, TensorShape{2 * batch_size}, + ctx->allocate_temp(DT_INT64, TensorShape{batch_size, 2}, &tmp_indices_buffer)); OP_REQUIRES_OK( ctx, ctx->allocate_temp(DT_INT32, TensorShape{1}, &selected_num_d)); @@ -159,6 +159,7 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { data_p_with_type(all_flags) + batch_size, data_p_with_type(indices_extended), data_p_with_type(selected_num_d), nnz, device.stream()); + CK_CUDA_THROW_(cudaGetLastError()); if (prune_invalid_id_) { int selected_num; @@ -197,6 +198,14 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { ? data_p_with_type(indices_extended) : data_p_with_type(indices_tensor); + Tensor* indices_before_unique; + OP_REQUIRES_OK(ctx, ctx->allocate_output("indices_before_unique", + TensorShape{new_nnz, 2}, + &indices_before_unique)); + cudaMemcpyAsync(data_p_with_type(indices_before_unique), indices_in, + sizeof(IndicePair) * new_nnz, cudaMemcpyDeviceToDevice, + device.stream()); + // 4. unique Tensor unique_keys_out; // actually below 4 will be allocate_output inside UniqueWithCountsGPU. @@ -207,7 +216,7 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { ctx, ((fill_empty_row_ || prune_invalid_id_) ? &values_extended : values_tensor), - &unique_keys_out, unique_idxs_out, unique_counts_out, + new_nnz, &unique_keys_out, unique_idxs_out, unique_counts_out, idx_of_input_to_unique_out, unique_offsets_out); int64 uniq_size = unique_keys_out.dim_size(0); @@ -254,7 +263,7 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { errors::InvalidArgument( "input partition_shapes must all less than rank 2")); const int64_t div = partition_shapes[i].flat().data()[0]; - accu_div_host[i] = i > 0 ? div : accu_div_host[i - 1] + div; + accu_div_host[i] = i == 0 ? div : accu_div_host[i - 1] + div; } Tensor accu_div; diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc index 7de332d6541..b656af285b1 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc @@ -20,45 +20,43 @@ typedef Eigen::GpuDevice GPUDevice; namespace fused_embedding { // A macro helper to declare SelectScanKernels, because they just have only -// a little bit differences. Need to define following macros before using this: -// SelectScanKernelName, SelectScanKernelArgs, SelectScanKernelLoadCodeBlock, -// SelectScanKernelEvalCodeBlock -#define DeclareSelectScanKernel \ - template \ - __global__ void SelectScanKernelName( \ - const T* keys, SelectScanKernelArgs, const int64 num_partitions, \ - const int64 length, const int64 predicates_length, \ - const int64 counters_length, unsigned int* predicates, \ - unsigned int* counters) { \ - int g_tid = blockIdx.x * blockDim.x + threadIdx.x; \ - int lnid = threadIdx.x % 32; \ - int g_warp_id = g_tid >> 5; \ - unsigned int mask; \ - unsigned int cnt; \ - \ - for (int j = 0; j < num_partitions; j++) { \ - SelectScanKernelLoadCodeBlock; \ - _Pragma("unroll") for (int i = 0; i < 32; i++) { \ - int selected; \ - int load_offset = (g_warp_id << 10) + (i << 5) + lnid; \ - if (load_offset < length) { \ - T key = keys[load_offset]; \ - SelectScanKernelEvalCodeBlock; \ - } else { \ - selected = 0; \ - } \ - \ - mask = __ballot_sync(0xffffffff, selected); \ - if (lnid == 0) \ - predicates[j * predicates_length + (g_warp_id << 5)] = mask; \ - if (lnid == i) cnt = __popc(mask); \ - } \ - _Pragma("unroll") for (int offset = 16; offset > 0; offset >>= 1) { \ - cnt += __shfl_down_sync(0xffffffff, cnt, offset); \ - } \ - \ - if (lnid == 0) counters[j * counters_length + g_warp_id] = cnt; \ - } \ +// a little bit differences. Need to define macros +#define DeclareSelectScanKernel \ + template \ + __global__ void SelectScanKernelName( \ + const T* keys, SelectScanKernelArgs, const int64 num_partitions, \ + const int64 length, const int64 predicates_length, \ + const int64 counters_length, unsigned int* predicates, \ + unsigned int* counters) { \ + int g_tid = blockIdx.x * blockDim.x + threadIdx.x; \ + int lnid = threadIdx.x % 32; \ + int g_warp_id = g_tid >> 5; \ + unsigned int mask; \ + unsigned int cnt; \ + \ + for (int j = 0; j < num_partitions; j++) { \ + SelectScanKernelLoadCodeBlock; \ + _Pragma("unroll") for (int i = 0; i < 32; i++) { \ + int selected; \ + int load_offset = (g_warp_id << 10) + (i << 5) + lnid; \ + if (load_offset < length) { \ + T key = keys[load_offset]; \ + SelectScanKernelEvalCodeBlock; \ + } else { \ + selected = 0; \ + } \ + \ + mask = __ballot_sync(0xffffffff, selected); \ + if (lnid == 0) \ + predicates[j * predicates_length + (g_warp_id << 5) + i] = mask; \ + if (lnid == i) cnt = __popc(mask); \ + } \ + _Pragma("unroll") for (int offset = 16; offset > 0; offset >>= 1) { \ + cnt += __shfl_down_sync(0xffffffff, cnt, offset); \ + } \ + \ + if (lnid == 0) counters[j * counters_length + g_warp_id] = cnt; \ + } \ } #define DeclareSelectKernel \ @@ -78,7 +76,7 @@ namespace fused_embedding { SelectKernelLoadCodeBlock; \ T* keys_output_ptr = (T*)(output_ptrs[2 * j]); \ TIndex* indices_output_ptr = (TIndex*)(output_ptrs[2 * j + 1]); \ - predmask = predicates[j * predicates_length + g_warp_id << 5 + lnid]; \ + predmask = predicates[j * predicates_length + (g_warp_id << 5) + lnid]; \ cnt = __popc(predmask); \ _Pragma("unroll") for (int offset = 1; offset < 32; offset <<= 1) { \ int n = __shfl_up_sync(0xffffffff, cnt, offset); \ @@ -97,9 +95,9 @@ namespace fused_embedding { because mask will be 0 for this thread*/ \ TIndex indice = indices[load_offset]; \ SelectKernelRecalcKeyCodeBlock; \ - int output_offset = \ - global_index + sub_group_index + \ - __popc(mask & ((unsigned int)(1) << lnid - (unsigned int)(1))); \ + int output_offset = global_index + sub_group_index + \ + __popc(mask & (((unsigned int)(1) << lnid) - \ + (unsigned int)(1))); \ keys_output_ptr[output_offset] = new_key; \ indices_output_ptr[output_offset] = indice; \ } \ @@ -200,7 +198,7 @@ namespace fused_embedding { OP_REQUIRES_OK(ctx, ctx->allocate_temp( \ DT_UINT64, TensorShape{int64(2 * num_partitions)}, \ &output_ptrs)); \ - cudaMemcpyAsync(data_p_with_type(output_ptrs), \ + cudaMemcpyAsync(data_p_with_type(output_ptrs), \ output_ptrs_host.data(), \ 2 * num_partitions * sizeof(size_t), \ cudaMemcpyHostToDevice, device.stream()); \ diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc index c285e2d498e..8974b07cb1f 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc @@ -7,6 +7,7 @@ #include #include "tensorflow/core/common_runtime/gpu/gpu_event_mgr.h" +#include "tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h" #include "tensorflow/core/lib/core/bits.h" #include "tensorflow/core/util/gpu_kernel_helper.h" #include "tensorflow/stream_executor/cuda/cuda_activation.h" @@ -164,17 +165,17 @@ Status RangeInit(const Eigen::GpuDevice& d, const T start, const T delta, template __global__ void GetIdxOfInputToUniqueKernel( const TIndex* count_ptr, const TIndex* segment_ends_ptr, - const TIndex* sorted_unique_perm_ptr, const TIndex* count_prefix_sum_ptr, - const TIndex* sorted_input_inds_ptr, TIndex* idx_of_input_to_unique_ptr) { + const TIndex* sorted_unique_perm_ptr, const TIndex* sorted_input_inds_ptr, + const int64* count_prefix_sum_ptr, TIndex* idx_of_input_to_unique_ptr) { const int target_unique_key_idx = blockIdx.x; const TIndex unique_key_count = count_ptr[target_unique_key_idx]; - int offset_of_sorted_input_inds = - segment_ends_ptr[sorted_unique_perm_ptr[target_unique_key_idx]] - - unique_key_count; - for (int j = threadIdx.x; j < unique_key_count; j += blockDim.x) { - int result_offset = count_prefix_sum_ptr[target_unique_key_idx] + j; - idx_of_input_to_unique_ptr[result_offset] = - sorted_input_inds_ptr[offset_of_sorted_input_inds + j]; + const TIndex permute = sorted_unique_perm_ptr[target_unique_key_idx]; + const TIndex offset_of_sorted_input_inds = + segment_ends_ptr[permute] - unique_key_count; + int64 offset_of_result = count_prefix_sum_ptr[target_unique_key_idx]; + for (int i = threadIdx.x; i < unique_key_count; i += blockDim.x) { + idx_of_input_to_unique_ptr[offset_of_result + i] = + sorted_input_inds_ptr[offset_of_sorted_input_inds + i]; } } @@ -183,16 +184,16 @@ Status GetIdxOfInputToUnique(const Eigen::GpuDevice& d, const int64 uniq_size, const TIndex* count_ptr, const TIndex* segment_ends_ptr, const TIndex* sorted_unique_perm_ptr, - const TIndex* count_prefix_sum_ptr, const TIndex* sorted_input_inds_ptr, + const int64* count_prefix_sum_ptr, TIndex* idx_of_input_to_unique_ptr) { if (uniq_size == 0) return Status::OK(); const int64 blocks = uniq_size; const int64 threads = 32; return GpuLaunchKernel(GetIdxOfInputToUniqueKernel, blocks, threads, 0, d.stream(), count_ptr, segment_ends_ptr, - sorted_unique_perm_ptr, sorted_unique_perm_ptr, - sorted_input_inds_ptr, idx_of_input_to_unique_ptr); + sorted_unique_perm_ptr, sorted_input_inds_ptr, + count_prefix_sum_ptr, idx_of_input_to_unique_ptr); } // Computes keys_out = sorted(keys_in), and indices_out = argsort(keys_in). @@ -384,12 +385,11 @@ namespace fused_embedding { // Extention to TensorFlow 2.x's UniqueWithCount operator template void UniqueWithCountsGPU(OpKernelContext* context, const Tensor* input, - Tensor* unique_keys, Tensor* unique_idxs_out, - Tensor* unique_counts_out, + const int64 input_size, Tensor* unique_keys, + Tensor* unique_idxs_out, Tensor* unique_counts_out, Tensor* idx_of_input_to_unique_out, Tensor* unique_offsets_out) { - OP_REQUIRES(context, - input->NumElements() <= std::numeric_limits::max(), + OP_REQUIRES(context, input_size <= std::numeric_limits::max(), errors::InvalidArgument( "unique does not support input tensors larger than ", std::numeric_limits::max(), " elements")); @@ -403,7 +403,6 @@ void UniqueWithCountsGPU(OpKernelContext* context, const Tensor* input, cudaEvent_t memcpy_event; cudaEventCreateWithFlags(&memcpy_event, cudaEventDisableTiming); - int64 input_size = input->NumElements(); if (input_size == 0) { // Early exit for trivial case. Tensor* temp; @@ -413,6 +412,8 @@ void UniqueWithCountsGPU(OpKernelContext* context, const Tensor* input, TensorShape({0}), &temp)); OP_REQUIRES_OK(context, context->allocate_output("idx_of_input_to_unique", TensorShape({0}), &temp)); + OP_REQUIRES_OK(context, context->allocate_output("unique_offsets", + TensorShape({0}), &temp)); return; } @@ -456,32 +457,16 @@ void UniqueWithCountsGPU(OpKernelContext* context, const Tensor* input, // counts: [3, 3, 4, 1, 1, 3, 3, 1, 1] // 6) calculate prefix sum of counts // epsc = exclusive_prefix_sum_of_counts = [3, 6, 10, 11, 12, 15, 18, 19, - // 20] 7) Use custom kernel to calculate the mapping between output and + // 20] + // 7) Use custom kernel to calculate the mapping between output and // corresponding indices of the input. - // pseudo code of this kernel: - // suii = sorted_unique_input_inds; - // sup = sorted_unique_perm; - // epsc = exclusive_prefix_sum_of_counts; - // GetIdxOfInputToUnique kernel - // blocks = len(suii) - // threads = 32 or 64 or else - // each block i: - // target_count = counts[i] - // offset = segment_ends[sup[i]] - target_count - // for each thread j : (j <= target_count) - // idx_of_input_to_unique_ptr[epsc[i] + j] = sorted_input_inds[offset - // + j] // idx_of_input_to_unique_ptr: [0, 2, 9, 1, 10, 18, 3, 5, 14, 16, 4, 6, 7, // 12, 13, 8, 15, 19, 11, 17] - - /* REVISE: below not necessary - // 6) Look up unique IDs via the inverse ID mapping and scatter them - using - // the original sort permutation to produce the indices output. + // 8) Look up unique IDs via the inverse ID mapping and scatter them + // using the original sort permutation to produce the indices output. // idx[sorted_input_inds] = // inv_sorted_unique_perm[sorted_input_unique_ids] // idx: [0, 1, 0, 2, 3, 2, 4, 5, 6, 0, 1, 7, 5, 5, 2, 6, 2, 8, 1, 6] - */ Tensor sorted_input_inds; TIndex* sorted_input_inds_ptr = nullptr; @@ -623,7 +608,7 @@ void UniqueWithCountsGPU(OpKernelContext* context, const Tensor* input, // output 3 idx_of_input_to_unique_out OP_REQUIRES_OK(context, context->allocate_output("idx_of_input_to_unique", - TensorShape({uniq_size}), + TensorShape({input_size}), &idx_of_input_to_unique_out)); TIndex* idx_of_input_to_unique_ptr = idx_of_input_to_unique_out->flat().data(); @@ -631,8 +616,8 @@ void UniqueWithCountsGPU(OpKernelContext* context, const Tensor* input, OP_REQUIRES_OK( context, GetIdxOfInputToUnique(device, uniq_size, count_ptr, segment_ends_ptr, - sorted_unique_perm_ptr, count_prefix_sum_ptr, - sorted_input_inds_ptr, idx_of_input_to_unique_ptr)); + sorted_unique_perm_ptr, sorted_input_inds_ptr, + count_prefix_sum_ptr, idx_of_input_to_unique_ptr)); // output 1 unique_idxs_out OP_REQUIRES_OK( @@ -648,8 +633,8 @@ void UniqueWithCountsGPU(OpKernelContext* context, const Tensor* input, } template void UniqueWithCountsGPU( - OpKernelContext* context, const Tensor* input, Tensor* unique_keys, - Tensor* unique_idxs_out, Tensor* unique_counts_out, + OpKernelContext* context, const Tensor* input, const int64 input_size, + Tensor* unique_keys, Tensor* unique_idxs_out, Tensor* unique_counts_out, Tensor* idx_of_input_to_unique_out, Tensor* unique_offsets_out); } // namespace fused_embedding diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h index f2e7586c4bd..74412ce4a5c 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h @@ -14,8 +14,8 @@ namespace fused_embedding { // Extention to TensorFlow 2.x's UniqueWithCounts operator template void UniqueWithCountsGPU(OpKernelContext* context, const Tensor* input, - Tensor* unique_keys, Tensor* unique_idxs_out, - Tensor* unique_counts_out, + const int64 input_size, Tensor* unique_keys, + Tensor* unique_idxs_out, Tensor* unique_counts_out, Tensor* idx_of_input_to_unique_out, Tensor* unique_offsets_out); diff --git a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_pre_ops_test.cc b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_pre_ops_test.cc index 6f0213b2664..500df957fa9 100644 --- a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_pre_ops_test.cc +++ b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_pre_ops_test.cc @@ -13,6 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include + #include "tensorflow/core/common_runtime/kernel_benchmark_testlib.h" #include "tensorflow/core/framework/fake_input.h" #include "tensorflow/core/framework/node_def_builder.h" @@ -33,7 +35,8 @@ class FusedEmbeddingSparsePreLookUpOpTest : public OpsTestBase { protected: void MakeOpAndSetDevice(Device device, const int num_partitions, const bool fill_empty_row, - const bool prune_invalid_id, const int default_id) { + const bool prune_invalid_id, const int default_id, + const std::string& partition_strategy) { if (device == Device::GPU) { SetDevice(DEVICE_GPU, std::unique_ptr(DeviceFactory::NewDevice( @@ -47,6 +50,7 @@ class FusedEmbeddingSparsePreLookUpOpTest : public OpsTestBase { .Attr("fill_empty_row", fill_empty_row) .Attr("prune_invalid_id", prune_invalid_id) .Attr("default_id", default_id) + .Attr("partition_strategy", partition_strategy) .Input(FakeInput(num_partitions, DT_INT64)) .Input(FakeInput(DT_INT64)) .Input(FakeInput(DT_INT64)) @@ -56,9 +60,8 @@ class FusedEmbeddingSparsePreLookUpOpTest : public OpsTestBase { } }; -TEST_F(FusedEmbeddingSparsePreLookUpOpTest, Partition3_Int64) { - printf("hello?\n"); - MakeOpAndSetDevice(Device::GPU, 3, false, false, -1); +TEST_F(FusedEmbeddingSparsePreLookUpOpTest, OnlyTestPartition3Div) { + MakeOpAndSetDevice(Device::GPU, 3, false, false, -1, std::string("div")); // partition_shapes 0 AddInputFromArray(TensorShape({2}), {6, 16}); // partition_shapes 1 @@ -77,90 +80,146 @@ TEST_F(FusedEmbeddingSparsePreLookUpOpTest, Partition3_Int64) { TF_ASSERT_OK(RunOpKernel()); TF_EXPECT_OK(device_->Sync()); + // partitioned_values and partition_permutations 0 { - Tensor expected_values(allocator(), DT_INT64, TensorShape({6})); - test::FillValues(&expected_values, {0, 1, 3, 5, 5, 5}); + Tensor expected_values(allocator(), DT_INT64, TensorShape({4})); + test::FillValues(&expected_values, {1, 5, 3, 0}); test::ExpectTensorEqual(expected_values, *GetOutput(0)); - Tensor expected_indices(allocator(), DT_INT64, TensorShape({6, 2})); - test::FillValues(&expected_indices, - {11, 6, 2, 3, 1, 6, 4, 6, 7, 9, 11, 8}); - test::ExpectTensorEqual(expected_indices, *GetOutput(3)); + Tensor expected_permutes(allocator(), DT_INT64, TensorShape({4})); + test::FillValues(&expected_permutes, {0, 1, 2, 7}); + test::ExpectTensorEqual(expected_permutes, *GetOutput(3)); } - + // partitioned_values and partition_permutations 1 { Tensor expected_values(allocator(), DT_INT64, TensorShape({2})); test::FillValues(&expected_values, {0, 1}); test::ExpectTensorEqual(expected_values, *GetOutput(1)); - Tensor expected_indices(allocator(), DT_INT64, TensorShape({2, 2})); - test::FillValues(&expected_indices, {12, 12, 13, 0}); - test::ExpectTensorEqual(expected_indices, *GetOutput(4)); + + Tensor expected_permutes(allocator(), DT_INT64, TensorShape({2})); + test::FillValues(&expected_permutes, {3, 9}); + test::ExpectTensorEqual(expected_permutes, *GetOutput(4)); } + // partitioned_values and partition_permutations 2 { Tensor expected_values(allocator(), DT_INT64, TensorShape({4})); - test::FillValues(&expected_values, {2, 3, 5, 6}); + test::FillValues(&expected_values, {3, 5, 6, 2}); test::ExpectTensorEqual(expected_values, *GetOutput(2)); - Tensor expected_indices(allocator(), DT_INT64, TensorShape({4, 2})); - test::FillValues(&expected_indices, {12, 13, 12, 12, 11, 5, 15, 0}); - test::ExpectTensorEqual(expected_indices, *GetOutput(5)); + Tensor expected_permutes(allocator(), DT_INT64, TensorShape({4})); + test::FillValues(&expected_permutes, {4, 5, 6, 8}); + test::ExpectTensorEqual(expected_permutes, *GetOutput(5)); } + + // indices_before_unique + Tensor expected_indices_before_unique(allocator(), DT_INT64, + TensorShape({12, 2})); + test::FillValues(&expected_indices_before_unique, + {2, 3, 4, 6, 1, 6, 12, 12, 12, 12, 11, 5, + 15, 0, 11, 6, 7, 9, 11, 8, 12, 13, 13, 0}); + test::ExpectTensorEqual(expected_indices_before_unique, *GetOutput(7)); + + // unique_idxs + Tensor expected_unique_idxs(allocator(), DT_INT64, TensorShape({12})); + test::FillValues(&expected_unique_idxs, + {0, 1, 2, 3, 4, 5, 6, 7, 1, 1, 8, 9}); + test::ExpectTensorEqual(expected_unique_idxs, *GetOutput(8)); + + // unique_counts + Tensor expected_unique_counts(allocator(), DT_INT64, TensorShape({10})); + test::FillValues(&expected_unique_counts, + {1, 3, 1, 1, 1, 1, 1, 1, 1, 1}); + test::ExpectTensorEqual(expected_unique_counts, *GetOutput(9)); + + // idx_of_input_to_unique + Tensor expected_idx_of_input_to_unique(allocator(), DT_INT64, + TensorShape({12})); + test::FillValues(&expected_idx_of_input_to_unique, + {0, 1, 8, 9, 2, 3, 4, 5, 6, 7, 10, 11}); + test::ExpectTensorEqual(expected_idx_of_input_to_unique, + *GetOutput(10)); + + // unique_offsets + Tensor expected_unique_offsets(allocator(), DT_INT64, TensorShape({10})); + test::FillValues(&expected_unique_offsets, + {0, 1, 4, 5, 6, 7, 8, 9, 10, 11}); + test::ExpectTensorEqual(expected_unique_offsets, *GetOutput(11)); } -TEST_F(FusedEmbeddingSparsePreLookUpOpTest, Partition2_Fill_Empty) { - MakeOpAndSetDevice(Device::GPU, 2, true, false, -1); +TEST_F(FusedEmbeddingSparsePreLookUpOpTest, OnlyTestUnique) { + MakeOpAndSetDevice(Device::GPU, 1, false, false, -1, std::string("div")); // partition_shapes 0 - AddInputFromArray(TensorShape({2}), {5, 8}); - // partition_shapes 1 - AddInputFromArray(TensorShape({2}), {5, 8}); - + AddInputFromArray(TensorShape({2}), {20, 8}); // sp_values - AddInputFromArray(TensorShape({10}), - {0, 4, 3, -2, 5, -3, -4, 9, -6, 2}); - - // sp_indices + AddInputFromArray(TensorShape({20}), {3, 5, 3, 4, 1, 4, 9, 8, 6, 3, + 5, 7, 8, 8, 4, 6, 4, 2, 5, 6}); + // sp_indices, whatever the content AddInputFromArray( - TensorShape({10, 2}), - {0, 0, 0, 4, 1, 2, 3, 0, 3, 4, 4, 0, 5, 2, 6, 0, 6, 1, 6, 7}); - + TensorShape({20, 2}), + {3, 5, 3, 4, 1, 4, 9, 8, 6, 3, 5, 7, 8, 8, 4, 6, 4, 2, 5, 6, + 3, 5, 3, 4, 1, 4, 9, 8, 6, 3, 5, 7, 8, 8, 4, 6, 4, 2, 5, 6}); // sp_dense_shape - AddInputFromArray(TensorShape({2}), {7, 8}); + AddInputFromArray(TensorShape({2}), {16, 16}); TF_ASSERT_OK(RunOpKernel()); TF_EXPECT_OK(device_->Sync()); + // partitioned_values and partition_permutations 0 { Tensor expected_values(allocator(), DT_INT64, TensorShape({9})); - test::FillValues(&expected_values, {-6, -4, -3, -2, 0, 0, 2, 3, 4}); + test::FillValues(&expected_values, {3, 5, 4, 1, 9, 8, 6, 7, 2}); test::ExpectTensorEqual(expected_values, *GetOutput(0)); - Tensor expected_indices(allocator(), DT_INT64, TensorShape({9, 2})); - test::FillValues(&expected_indices, {6, 1, 5, 2, 4, 0, 3, 0, 0, 0, 2, - 0, 6, 7, 1, 2, 0, 4}); - test::ExpectTensorEqual(expected_indices, *GetOutput(2)); + Tensor expected_permutes(allocator(), DT_INT64, TensorShape({9})); + test::FillValues(&expected_permutes, {0, 1, 2, 3, 4, 5, 6, 7, 8}); + test::ExpectTensorEqual(expected_permutes, *GetOutput(1)); } - { - Tensor expected_values(allocator(), DT_INT64, TensorShape({2})); - test::FillValues(&expected_values, {0, 4}); - test::ExpectTensorEqual(expected_values, *GetOutput(1)); - Tensor expected_indices(allocator(), DT_INT64, TensorShape({2, 2})); - test::FillValues(&expected_indices, {3, 4, 6, 0}); - test::ExpectTensorEqual(expected_indices, *GetOutput(3)); - } + // indices_before_unique + Tensor expected_indices_before_unique(allocator(), DT_INT64, + TensorShape({20, 2})); + test::FillValues( + &expected_indices_before_unique, + {3, 5, 3, 4, 1, 4, 9, 8, 6, 3, 5, 7, 8, 8, 4, 6, 4, 2, 5, 6, + 3, 5, 3, 4, 1, 4, 9, 8, 6, 3, 5, 7, 8, 8, 4, 6, 4, 2, 5, 6}); + test::ExpectTensorEqual(expected_indices_before_unique, *GetOutput(3)); + + // unique_idxs + Tensor expected_unique_idxs(allocator(), DT_INT64, TensorShape({20})); + test::FillValues( + &expected_unique_idxs, + {0, 1, 0, 2, 3, 2, 4, 5, 6, 0, 1, 7, 5, 5, 2, 6, 2, 8, 1, 6}); + test::ExpectTensorEqual(expected_unique_idxs, *GetOutput(4)); + + // unique_counts + Tensor expected_unique_counts(allocator(), DT_INT64, TensorShape({9})); + test::FillValues(&expected_unique_counts, {3, 3, 4, 1, 1, 3, 3, 1, 1}); + test::ExpectTensorEqual(expected_unique_counts, *GetOutput(5)); + + // idx_of_input_to_unique + Tensor expected_idx_of_input_to_unique(allocator(), DT_INT64, + TensorShape({20})); + test::FillValues( + &expected_idx_of_input_to_unique, + {0, 2, 9, 1, 10, 18, 3, 5, 14, 16, 4, 6, 7, 12, 13, 8, 15, 19, 11, 17}); + test::ExpectTensorEqual(expected_idx_of_input_to_unique, + *GetOutput(6)); + + // unique_offsets + Tensor expected_unique_offsets(allocator(), DT_INT64, TensorShape({9})); + test::FillValues(&expected_unique_offsets, + {0, 3, 6, 10, 11, 12, 15, 18, 19}); + test::ExpectTensorEqual(expected_unique_offsets, *GetOutput(7)); } -TEST_F(FusedEmbeddingSparsePreLookUpOpTest, - Partition2_Fill_Empty_Prune_Invalid) { - MakeOpAndSetDevice(Device::GPU, 2, true, true, -1); +TEST_F(FusedEmbeddingSparsePreLookUpOpTest, OnlyTestFillEmpty) { + MakeOpAndSetDevice(Device::GPU, 1, true, false, -1, std::string("div")); // partition_shapes 0 - AddInputFromArray(TensorShape({2}), {5, 8}); - // partition_shapes 1 - AddInputFromArray(TensorShape({2}), {5, 8}); + AddInputFromArray(TensorShape({2}), {99999, 8}); // sp_values AddInputFromArray(TensorShape({10}), - {0, 4, 3, -2, 5, -3, -4, 9, -6, 2}); + {10, 4, 3, 2, 5, 13, 14, 9, 6, 1}); // sp_indices AddInputFromArray( @@ -173,38 +232,26 @@ TEST_F(FusedEmbeddingSparsePreLookUpOpTest, TF_ASSERT_OK(RunOpKernel()); TF_EXPECT_OK(device_->Sync()); - { - Tensor expected_values(allocator(), DT_INT64, TensorShape({7})); - test::FillValues(&expected_values, {0, 0, 0, 0, 2, 3, 4}); - test::ExpectTensorEqual(expected_values, *GetOutput(0)); - - Tensor expected_indices(allocator(), DT_INT64, TensorShape({7, 2})); - test::FillValues(&expected_indices, - {0, 0, 2, 0, 4, 0, 5, 0, 6, 7, 1, 2, 0, 4}); - test::ExpectTensorEqual(expected_indices, *GetOutput(2)); - } - - { - Tensor expected_values(allocator(), DT_INT64, TensorShape({2})); - test::FillValues(&expected_values, {0, 4}); - test::ExpectTensorEqual(expected_values, *GetOutput(1)); - Tensor expected_indices(allocator(), DT_INT64, TensorShape({2, 2})); - test::FillValues(&expected_indices, {3, 4, 6, 0}); - test::ExpectTensorEqual(expected_indices, *GetOutput(3)); - } + Tensor expected_values(allocator(), DT_INT64, TensorShape({11})); + test::FillValues(&expected_values, + {10, 4, 3, 2, 5, 13, 14, 9, 6, 1, 0}); + test::ExpectTensorEqual(expected_values, *GetOutput(0)); + + Tensor expected_indices_before_unique(allocator(), DT_INT64, + TensorShape({11, 2})); + test::FillValues( + &expected_indices_before_unique, + {0, 0, 0, 4, 1, 2, 3, 0, 3, 4, 4, 0, 5, 2, 6, 0, 6, 1, 6, 7, 2, 0}); + test::ExpectTensorEqual(expected_indices_before_unique, *GetOutput(3)); } -TEST_F(FusedEmbeddingSparsePreLookUpOpTest, - Partition2_Fill_Empty_Prune_Invalid_Default_7) { - MakeOpAndSetDevice(Device::GPU, 2, true, true, 7); +TEST_F(FusedEmbeddingSparsePreLookUpOpTest, OnlyTestPruneInvalid) { + MakeOpAndSetDevice(Device::GPU, 1, false, true, -1, std::string("div")); // partition_shapes 0 - AddInputFromArray(TensorShape({2}), {5, 8}); - // partition_shapes 1 - AddInputFromArray(TensorShape({2}), {5, 8}); - + AddInputFromArray(TensorShape({2}), {20, 8}); // sp_values AddInputFromArray(TensorShape({10}), - {0, 4, 3, -2, 5, -3, -4, 9, -6, 2}); + {10, 4, -3, 2, 5, 13, 14, -9, 6, 1}); // sp_indices AddInputFromArray( @@ -217,37 +264,24 @@ TEST_F(FusedEmbeddingSparsePreLookUpOpTest, TF_ASSERT_OK(RunOpKernel()); TF_EXPECT_OK(device_->Sync()); - { - Tensor expected_values(allocator(), DT_INT64, TensorShape({4})); - test::FillValues(&expected_values, {0, 2, 3, 4}); - test::ExpectTensorEqual(expected_values, *GetOutput(0)); + Tensor expected_values(allocator(), DT_INT64, TensorShape({8})); + test::FillValues(&expected_values, {10, 4, 2, 5, 13, 14, 6, 1}); + test::ExpectTensorEqual(expected_values, *GetOutput(0)); - Tensor expected_indices(allocator(), DT_INT64, TensorShape({4, 2})); - test::FillValues(&expected_indices, {0, 0, 6, 7, 1, 2, 0, 4}); - test::ExpectTensorEqual(expected_indices, *GetOutput(2)); - } - - { - Tensor expected_values(allocator(), DT_INT64, TensorShape({5})); - test::FillValues(&expected_values, {0, 2, 2, 2, 4}); - test::ExpectTensorEqual(expected_values, *GetOutput(1)); - Tensor expected_indices(allocator(), DT_INT64, TensorShape({5, 2})); - test::FillValues(&expected_indices, {3, 4, 2, 0, 4, 0, 5, 0, 6, 0}); - test::ExpectTensorEqual(expected_indices, *GetOutput(3)); - } + Tensor expected_indices_before_unique(allocator(), DT_INT64, + TensorShape({8, 2})); + test::FillValues(&expected_indices_before_unique, + {0, 0, 0, 4, 3, 0, 3, 4, 4, 0, 5, 2, 6, 1, 6, 7}); + test::ExpectTensorEqual(expected_indices_before_unique, *GetOutput(3)); } -TEST_F(FusedEmbeddingSparsePreLookUpOpTest, - Partition2_Prune_Invalid_Default_3) { - MakeOpAndSetDevice(Device::GPU, 2, false, true, 3); +TEST_F(FusedEmbeddingSparsePreLookUpOpTest, PruneInvalidAndFillEmptyDeault7) { + MakeOpAndSetDevice(Device::GPU, 1, true, true, 7, std::string("div")); // partition_shapes 0 - AddInputFromArray(TensorShape({2}), {5, 8}); - // partition_shapes 1 - AddInputFromArray(TensorShape({2}), {5, 8}); - + AddInputFromArray(TensorShape({2}), {20, 8}); // sp_values AddInputFromArray(TensorShape({10}), - {0, 4, 3, -2, 5, -3, -4, 9, -6, 2}); + {10, 4, 3, 2, 5, 13, 14, -9, 6, 1}); // sp_indices AddInputFromArray( @@ -260,90 +294,137 @@ TEST_F(FusedEmbeddingSparsePreLookUpOpTest, TF_ASSERT_OK(RunOpKernel()); TF_EXPECT_OK(device_->Sync()); - { - Tensor expected_values(allocator(), DT_INT64, TensorShape({4})); - test::FillValues(&expected_values, {0, 2, 3, 4}); - test::ExpectTensorEqual(expected_values, *GetOutput(0)); - - Tensor expected_indices(allocator(), DT_INT64, TensorShape({4, 2})); - test::FillValues(&expected_indices, {0, 0, 6, 7, 1, 2, 0, 4}); - test::ExpectTensorEqual(expected_indices, *GetOutput(2)); - } + Tensor expected_values(allocator(), DT_INT64, TensorShape({10})); + test::FillValues(&expected_values, {10, 4, 3, 2, 5, 13, 14, 6, 1, 7}); + test::ExpectTensorEqual(expected_values, *GetOutput(0)); - { - Tensor expected_values(allocator(), DT_INT64, TensorShape({2})); - test::FillValues(&expected_values, {0, 4}); - test::ExpectTensorEqual(expected_values, *GetOutput(1)); - Tensor expected_indices(allocator(), DT_INT64, TensorShape({2, 2})); - test::FillValues(&expected_indices, {3, 4, 6, 0}); - test::ExpectTensorEqual(expected_indices, *GetOutput(3)); - } + Tensor expected_indices_before_unique(allocator(), DT_INT64, + TensorShape({10, 2})); + test::FillValues( + &expected_indices_before_unique, + {0, 0, 0, 4, 1, 2, 3, 0, 3, 4, 4, 0, 5, 2, 6, 1, 6, 7, 2, 0}); + test::ExpectTensorEqual(expected_indices_before_unique, *GetOutput(3)); } -TEST_F(FusedEmbeddingSparsePreLookUpOpTest, Partition1) { - MakeOpAndSetDevice(Device::GPU, 1, false, false, -1); +TEST_F(FusedEmbeddingSparsePreLookUpOpTest, TestEverthingDiv) { + MakeOpAndSetDevice(Device::GPU, 4, true, true, 3, std::string("div")); // partition_shapes 0 - AddInputFromArray(TensorShape({2}), {10, 8}); + AddInputFromArray(TensorShape({2}), {3, 16}); + // partition_shapes 1 + AddInputFromArray(TensorShape({2}), {4, 16}); + // partition_shapes 2 + AddInputFromArray(TensorShape({2}), {6, 16}); + // partition_shapes 3 + AddInputFromArray(TensorShape({2}), {5, 16}); - // sp_values - AddInputFromArray(TensorShape({10}), - {0, 4, 3, -2, 5, -3, -4, 9, -6, 2}); + // for div, the parition interval is 0-2, 3-6, 7-12, 13-17 + // sp_values + AddInputFromArray(TensorShape({16}), {-2, 1, 1, 3, -2, 4, 10, 2, 6, 14, + 12, 16, -1, -100, 12, 6}); // sp_indices AddInputFromArray( - TensorShape({10, 2}), - {0, 0, 0, 4, 1, 2, 3, 0, 3, 4, 4, 0, 5, 2, 6, 0, 6, 1, 6, 7}); - + TensorShape({16, 2}), + {0, 3, 0, 4, 2, 1, 3, 2, 3, 6, 5, 1, 5, 4, 6, 7, + 8, 3, 9, 4, 11, 2, 11, 3, 11, 5, 12, 3, 13, 4, 13, 7}); // sp_dense_shape - AddInputFromArray(TensorShape({2}), {7, 8}); + AddInputFromArray(TensorShape({2}), {14, 16}); TF_ASSERT_OK(RunOpKernel()); TF_EXPECT_OK(device_->Sync()); - { - Tensor expected_values(allocator(), DT_INT64, TensorShape({10})); - test::FillValues(&expected_values, - {0, 4, 3, -2, 5, -3, -4, 9, -6, 2}); - test::ExpectTensorEqual(expected_values, *GetOutput(0)); + /* + after fill_empty and prune_invalid: + 1, 1, 3, 4, 10, 2, 6, 14, 12, 16, 12, 6, 3, 3, 3, 3, 3 + --------------------------------------------------------- + 0| 2| 3| 5| 5| 6| 8| 9| 11| 11| 13| 13| 1| 4| 7| 10| 12 + 4| 1| 2| 1| 4| 7| 3| 4| 2| 3| 4| 7| 0| 0| 0| 0| 0 + --------------------------------------------------------- + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12,13,14, 15, 16 - Tensor expected_indices(allocator(), DT_INT64, TensorShape({10, 2})); - test::FillValues(&expected_indices, {0, 0, 0, 4, 1, 2, 3, 0, 3, 4, - 4, 0, 5, 2, 6, 0, 6, 1, 6, 7}); - test::ExpectTensorEqual(expected_indices, *GetOutput(1)); - } -} -TEST_F(FusedEmbeddingSparsePreLookUpOpTest, - Partition1_Fill_Empty_Prune_Invalid_Default_3) { - MakeOpAndSetDevice(Device::GPU, 1, true, true, 3); - // partition_shapes 0 - AddInputFromArray(TensorShape({2}), {10, 8}); + after unique: + 1, 3, 4, 10, 2, 6, 14 ,12 ,16 + ----------------------------- + 0, 1, 2, 3, 4, 5, 6, 7, 8 + */ - // sp_values - AddInputFromArray(TensorShape({10}), - {0, 4, 3, -2, 5, -3, -4, 9, -6, 2}); + // partitioned_values and partition_permutations 0 + { + Tensor expected_values(allocator(), DT_INT64, TensorShape({2})); + test::FillValues(&expected_values, {1, 2}); + test::ExpectTensorEqual(expected_values, *GetOutput(0)); - // sp_indices - AddInputFromArray( - TensorShape({10, 2}), - {0, 0, 0, 4, 1, 2, 3, 0, 3, 4, 4, 0, 5, 2, 6, 0, 6, 1, 6, 7}); + Tensor expected_permutes(allocator(), DT_INT64, TensorShape({2})); + test::FillValues(&expected_permutes, {0, 4}); + test::ExpectTensorEqual(expected_permutes, *GetOutput(4)); + } + // partitioned_values and partition_permutations 1 + { + Tensor expected_values(allocator(), DT_INT64, TensorShape({3})); + test::FillValues(&expected_values, {0, 1, 3}); + test::ExpectTensorEqual(expected_values, *GetOutput(1)); - // sp_dense_shape - AddInputFromArray(TensorShape({2}), {7, 8}); + Tensor expected_permutes(allocator(), DT_INT64, TensorShape({3})); + test::FillValues(&expected_permutes, {1, 2, 5}); + test::ExpectTensorEqual(expected_permutes, *GetOutput(5)); + } - TF_ASSERT_OK(RunOpKernel()); - TF_EXPECT_OK(device_->Sync()); + // partitioned_values and partition_permutations 2 + { + Tensor expected_values(allocator(), DT_INT64, TensorShape({2})); + test::FillValues(&expected_values, {3, 5}); + test::ExpectTensorEqual(expected_values, *GetOutput(2)); + Tensor expected_permutes(allocator(), DT_INT64, TensorShape({2})); + test::FillValues(&expected_permutes, {3, 7}); + test::ExpectTensorEqual(expected_permutes, *GetOutput(6)); + } + // partitioned_values and partition_permutations 3 { - Tensor expected_values(allocator(), DT_INT64, TensorShape({9})); - test::FillValues(&expected_values, {0, 4, 3, 5, 9, 2, 3, 3, 3}); - test::ExpectTensorEqual(expected_values, *GetOutput(0)); + Tensor expected_values(allocator(), DT_INT64, TensorShape({2})); + test::FillValues(&expected_values, {1, 3}); + test::ExpectTensorEqual(expected_values, *GetOutput(3)); - Tensor expected_indices(allocator(), DT_INT64, TensorShape({9, 2})); - test::FillValues(&expected_indices, {0, 0, 0, 4, 1, 2, 3, 4, 6, 0, 6, - 7, 2, 0, 4, 0, 5, 0}); - test::ExpectTensorEqual(expected_indices, *GetOutput(1)); + Tensor expected_permutes(allocator(), DT_INT64, TensorShape({2})); + test::FillValues(&expected_permutes, {6, 8}); + test::ExpectTensorEqual(expected_permutes, *GetOutput(7)); } + + // indices_before_unique + Tensor expected_indices_before_unique(allocator(), DT_INT64, + TensorShape({17, 2})); + test::FillValues( + &expected_indices_before_unique, + {0, 4, 2, 1, 3, 2, 5, 1, 5, 4, 6, 7, 8, 3, 9, 4, 11, + 2, 11, 3, 13, 4, 13, 7, 1, 0, 4, 0, 7, 0, 10, 0, 12, 0}); + test::ExpectTensorEqual(expected_indices_before_unique, *GetOutput(9)); + + // unique_idxs + Tensor expected_unique_idxs(allocator(), DT_INT64, TensorShape({17})); + test::FillValues(&expected_unique_idxs, + {0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 7, 5, 1, 1, 1, 1, 1}); + test::ExpectTensorEqual(expected_unique_idxs, *GetOutput(10)); + + // unique_counts + Tensor expected_unique_counts(allocator(), DT_INT64, TensorShape({9})); + test::FillValues(&expected_unique_counts, {2, 6, 1, 1, 1, 2, 1, 2, 1}); + test::ExpectTensorEqual(expected_unique_counts, *GetOutput(11)); + + // idx_of_input_to_unique + Tensor expected_idx_of_input_to_unique(allocator(), DT_INT64, + TensorShape({17})); + test::FillValues( + &expected_idx_of_input_to_unique, + {0, 1, 2, 12, 13, 14, 15, 16, 3, 4, 5, 6, 11, 7, 8, 10, 9}); + test::ExpectTensorEqual(expected_idx_of_input_to_unique, + *GetOutput(12)); + + // unique_offsets + Tensor expected_unique_offsets(allocator(), DT_INT64, TensorShape({9})); + test::FillValues(&expected_unique_offsets, + {0, 2, 8, 9, 10, 11, 13, 14, 16}); + test::ExpectTensorEqual(expected_unique_offsets, *GetOutput(13)); } } // namespace diff --git a/tensorflow/core/ops/fused_embedding_ops.cc b/tensorflow/core/ops/fused_embedding_ops.cc index ebf178381c7..ade6b7b746e 100644 --- a/tensorflow/core/ops/fused_embedding_ops.cc +++ b/tensorflow/core/ops/fused_embedding_ops.cc @@ -36,8 +36,10 @@ REGISTER_OP("FusedEmbeddingLocalSparseLookUp") return Status::OK(); }); // .Doc(R"doc( -// FusedEmbedding ops that performs a local embedding lookup. The process will perform embedding vector copying from emb_variable. -// The input is usually a SparseTensor. The output sp_values_offset is reserved for gradient calculation. +// FusedEmbedding ops that performs a local embedding lookup. The process will +// perform embedding vector copying from emb_variable. The input is usually a +// SparseTensor. The output sp_values_offset is reserved for gradient +// calculation. // )doc"); REGISTER_OP("FusedEmbeddingLocalSparseLookUpGrad") @@ -58,8 +60,8 @@ REGISTER_OP("FusedEmbeddingLocalSparseLookUpGrad") }); // .Doc(R"doc( -// The gradient ops for FusedEmbeddingLocalSparseLookUp. sp_values_offset from the forward op -// need to be passed to this grad op as input. +// The gradient ops for FusedEmbeddingLocalSparseLookUp. sp_values_offset from +// the forward op need to be passed to this grad op as input. // )doc"); REGISTER_OP("FusedEmbeddingSparsePreLookUp") @@ -70,13 +72,19 @@ REGISTER_OP("FusedEmbeddingSparsePreLookUp") .Attr("fill_empty_row: bool = false") .Attr("prune_invalid_id: bool = false") .Attr("default_id: int = -1") + .Attr("partition_strategy : {'div'}") .Input("partition_shapes: num_partitions * int64") .Input("sp_values: int64") .Input("sp_indices: int64") .Input("sp_dense_shape: int64") .Output("partitioned_values: num_partitions * int64") - .Output("partitioned_indices: num_partitions * int64") + .Output("partition_permutations: num_partitions * int64") .Output("row_empty_and_invalid_flags: int32") + .Output("indices_before_unique: int64") + .Output("unique_idxs: int64") + .Output("unique_counts: int64") + .Output("idx_of_input_to_unique: int64") + .Output("unique_offsets: int64") .SetShapeFn([](InferenceContext* ctx) { int num_partitions; TF_RETURN_IF_ERROR(ctx->GetAttr("num_partitions", &num_partitions)); @@ -84,45 +92,53 @@ REGISTER_OP("FusedEmbeddingSparsePreLookUp") TF_RETURN_IF_ERROR(ctx->GetAttr("partition_axis", &partition_axis)); ShapeHandle unused; - // sp_values - TF_RETURN_IF_ERROR(ctx->WithRank(ctx->input(num_partitions), 1, &unused)); - // sp_indices - TF_RETURN_IF_ERROR( - ctx->WithRank(ctx->input(num_partitions + 1), 2, &unused)); + std::vector unused_list; + + ctx->input("sp_values", &unused_list); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); + + ctx->input("sp_indices", &unused_list); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 2, &unused)); DimensionHandle unused_dim; TF_RETURN_IF_ERROR(ctx->WithValue(ctx->Dim(unused, 1), 2, &unused_dim)); - // sp_dense_shape - TF_RETURN_IF_ERROR( - ctx->WithRank(ctx->input(num_partitions + 2), 1, &unused)); - // partition_shapes + ctx->input("sp_dense_shape", &unused_list); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); + + ctx->input("partition_shapes", &unused_list); for (int i = 0; i < num_partitions; i++) { - ShapeHandle partition_shape; - TF_RETURN_IF_ERROR(ctx->WithRank(ctx->input(i), 1, &partition_shape)); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[i], 1, &unused)); TF_RETURN_IF_ERROR( - ctx->WithValue(ctx->NumElements(partition_shape), 2, &unused_dim)); - - ShapeHandle values_result_shape, indices_result_shape; - if (int(partition_axis) == 0) { - values_result_shape = ctx->MakeShape({ctx->UnknownDim()}); - indices_result_shape = ctx->MakeShape({ctx->UnknownDim(), 2}); - } else { - return errors::InvalidArgument("partition_axis > 0 not implemented!"); - } - ctx->set_output(i, values_result_shape); - ctx->set_output(i + num_partitions, indices_result_shape); + ctx->WithValue(ctx->NumElements(unused), 2, &unused_dim)); } - ctx->set_output(2 * num_partitions, ctx->MakeShape({ctx->UnknownDim()})); + + unused_list.clear(); + unused_list.resize(num_partitions); + for (int i = 0; i < num_partitions; i++) { + unused_list[i] = ctx->MakeShape({ctx->UnknownDim()}); + } + ctx->set_output("partitioned_values", unused_list); + ctx->set_output("partition_permutations", unused_list); + + unused_list.clear(); + unused_list.resize(1); + unused_list[0] = ctx->MakeShape({ctx->UnknownDim(), 2}); + ctx->set_output("indices_before_unique", unused_list); + unused_list[0] = ctx->MakeShape({ctx->UnknownDim()}); + ctx->set_output("unique_idxs", unused_list); + ctx->set_output("unique_counts", unused_list); + ctx->set_output("unique_offsets", unused_list); return Status::OK(); }); // .Doc(R"doc( -// A fused embedding op, usually using for partitioned and distriuted embedding variables. -// FusedEmbeddingSparsePreLookUp, FusedEmbeddingSparsePostLookUp should be used together. -// This op will first read the partition pattern of embedding variables through partition_shapes, -// then sort, re-calculate and assign the embedding indices to the corresponding partition. Several Gather ops -// usually should be appended after this op to gather embedding shards from multiple partitioned embedding -// variables. This op has no gradient function. +// A fused embedding op, usually using for partitioned and distriuted embedding +// variables. FusedEmbeddingSparsePreLookUp, FusedEmbeddingSparsePostLookUp +// should be used together. This op will first read the partition pattern of +// embedding variables through partition_shapes, then sort, re-calculate and +// assign the embedding indices to the corresponding partition. Several Gather +// ops usually should be appended after this op to gather embedding shards from +// multiple partitioned embedding variables. This op has no gradient function. // )doc"); REGISTER_OP("FusedEmbeddingSparsePostLookUp") @@ -177,11 +193,12 @@ REGISTER_OP("FusedEmbeddingSparsePostLookUp") }); // .Doc(R"doc( -// A fused embedding op, usually using for partitioned and distriuted embedding variables. -// FusedEmbeddingSparsePreLookUp, FusedEmbeddingSparsePostLookUp should be used together. -// There should be several Gather ops before this op. The Gather ops gather embedding shards from -// embedding variable and this op glue them together, then apply combiner and max_morm according to -// embedding indices. +// A fused embedding op, usually using for partitioned and distriuted embedding +// variables. FusedEmbeddingSparsePreLookUp, FusedEmbeddingSparsePostLookUp +// should be used together. There should be several Gather ops before this op. +// The Gather ops gather embedding shards from embedding variable and this op +// glue them together, then apply combiner and max_morm according to embedding +// indices. // )doc"); REGISTER_OP("FusedEmbeddingSparsePostLookUpGrad") From d65cec19e69948cd1902b50dc53a3cb5621349c4 Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Tue, 1 Mar 2022 14:35:27 +0800 Subject: [PATCH 13/41] postlookup grad done except for unit test --- .../fused_embedding_post_ops_gpus.cu.cc | 113 +++++++--- .../gpu_functions/kernels.cu.cc | 195 +++++++++--------- .../gpu_functions/kernels.cu.h | 33 +-- .../fused_embedding_post_grad_ops_test.cc | 40 +++- .../tests/fused_embedding_post_ops_test.cc | 146 +++++++++++-- tensorflow/core/ops/fused_embedding_ops.cc | 134 ++++++++---- 6 files changed, 452 insertions(+), 209 deletions(-) diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc b/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc index 75859430d91..eb227414e47 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc @@ -37,9 +37,9 @@ class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { OpInputList emb_shards; OP_REQUIRES_OK(ctx, ctx->input_list("emb_shards", &emb_shards)); - OpInputList partitioned_indices; - OP_REQUIRES_OK( - ctx, ctx->input_list("partitioned_indices", &partitioned_indices)); + OpInputList partition_permutations; + OP_REQUIRES_OK(ctx, ctx->input_list("partition_permutations", + &partition_permutations)); Tensor const* dense_shape_tensor = nullptr; OP_REQUIRES_OK(ctx, ctx->input("sp_dense_shape", &dense_shape_tensor)); @@ -48,6 +48,20 @@ class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { OP_REQUIRES_OK(ctx, ctx->input("row_empty_and_invalid_flags", &row_empty_and_invalid_flags)); + Tensor const* indices_before_unique = nullptr; + OP_REQUIRES_OK(ctx, + ctx->input("indices_before_unique", &indices_before_unique)); + + Tensor const* unique_counts = nullptr; + OP_REQUIRES_OK(ctx, ctx->input("unique_counts", &unique_counts)); + + Tensor const* idx_of_input_to_unique = nullptr; + OP_REQUIRES_OK( + ctx, ctx->input("idx_of_input_to_unique", &idx_of_input_to_unique)); + + Tensor const* unique_offsets = nullptr; + OP_REQUIRES_OK(ctx, ctx->input("unique_offsets", &unique_offsets)); + nvtx::ScopedRangeIfEnabled nvtx_range(this); const int64_t emb_vec_size = emb_shards[0].shape().dim_size(1); @@ -80,41 +94,46 @@ class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { sizeof(int) * feature_nums->NumElements(), device.stream()); for (int i = 0; i < num_partitions_; i++) { - const size_t sub_nnz = emb_shards[i].shape().dim_size(0); - OP_REQUIRES( - ctx, sub_nnz == partitioned_indices[i].shape().dim_size(0), - errors::InvalidArgument( - "emb_shard and partitioned_indice dosn't have the same length")); + const size_t shard_len = emb_shards[i].shape().dim_size(0); + OP_REQUIRES(ctx, + shard_len == partition_permutations[i].shape().dim_size(0), + errors::InvalidArgument( + "emb_shard and partition_permutations doesn't " + "have the same length")); SumUpEmbeddingShard( - device, sub_nnz, data_p_with_type(emb_shards[i]), - data_p_with_type(partitioned_indices[i]), - data_p_with_type(emb_vectors_tensor), - data_p_with_type(feature_nums), max_norm_, emb_vec_size); + device, shard_len, data_p_with_type(emb_shards[i]), + data_p_with_type(partition_permutations[i]), + data_p_with_type(indices_before_unique), + data_p_with_type(unique_counts), + data_p_with_type(idx_of_input_to_unique), + data_p_with_type(unique_offsets), max_norm_, + emb_vec_size, data_p_with_type(emb_vectors_tensor), + data_p_with_type(feature_nums)); CK_CUDA_THROW_(cudaGetLastError()); } - const bool set_empty_row_zero = default_id_ >= 0; + const bool set_empty_row_zero = default_id_ < 0; // ================================================================ // // ========================= 2. combiner ========================== // if (combiner_ == "sqrtn") { ApplyCombiner( device, batch_size, emb_vec_size, - data_p_with_type(emb_vectors_tensor), data_p_with_type(row_empty_and_invalid_flags), - set_empty_row_zero, data_p_with_type(feature_nums)); + set_empty_row_zero, data_p_with_type(feature_nums), + data_p_with_type(emb_vectors_tensor)); } else if (combiner_ == "mean") { ApplyCombiner( device, batch_size, emb_vec_size, - data_p_with_type(emb_vectors_tensor), data_p_with_type(row_empty_and_invalid_flags), - set_empty_row_zero, data_p_with_type(feature_nums)); + set_empty_row_zero, data_p_with_type(feature_nums), + data_p_with_type(emb_vectors_tensor)); } else { ApplyCombiner( device, batch_size, emb_vec_size, - data_p_with_type(emb_vectors_tensor), data_p_with_type(row_empty_and_invalid_flags), - set_empty_row_zero, data_p_with_type(feature_nums)); + set_empty_row_zero, data_p_with_type(feature_nums), + data_p_with_type(emb_vectors_tensor)); } CK_CUDA_THROW_(cudaGetLastError()); // ================================================================ // @@ -156,13 +175,27 @@ class FusedEmbeddingSparsePostLookUpGradGPU : public OpKernel { OpInputList emb_shards; OP_REQUIRES_OK(ctx, ctx->input_list("emb_shards", &emb_shards)); - OpInputList partitioned_indices; - OP_REQUIRES_OK( - ctx, ctx->input_list("partitioned_indices", &partitioned_indices)); + OpInputList partition_permutations; + OP_REQUIRES_OK(ctx, ctx->input_list("partition_permutations", + &partition_permutations)); Tensor const* feature_nums = nullptr; OP_REQUIRES_OK(ctx, ctx->input("feature_nums", &feature_nums)); + Tensor const* indices_before_unique = nullptr; + OP_REQUIRES_OK(ctx, + ctx->input("indices_before_unique", &indices_before_unique)); + + Tensor const* unique_counts = nullptr; + OP_REQUIRES_OK(ctx, ctx->input("unique_counts", &unique_counts)); + + Tensor const* idx_of_input_to_unique = nullptr; + OP_REQUIRES_OK( + ctx, ctx->input("idx_of_input_to_unique", &idx_of_input_to_unique)); + + Tensor const* unique_offsets = nullptr; + OP_REQUIRES_OK(ctx, ctx->input("unique_offsets", &unique_offsets)); + Tensor const* row_empty_and_invalid_flags = nullptr; OP_REQUIRES_OK(ctx, ctx->input("row_empty_and_invalid_flags", &row_empty_and_invalid_flags)); @@ -175,43 +208,55 @@ class FusedEmbeddingSparsePostLookUpGradGPU : public OpKernel { const int64_t batch_size = top_grad_tensor->shape().dim_size(0); const int64_t emb_vec_size = emb_shards[0].shape().dim_size(1); - const bool set_empty_row_zero = default_id_ >= 0; + const bool set_empty_row_zero = default_id_ < 0; for (int i = 0; i < num_partitions_; i++) { - const int64_t sub_nnz = partitioned_indices[i].shape().dim_size(0); + const int64_t shard_len = partition_permutations[i].shape().dim_size(0); Tensor* grad_shard; OP_REQUIRES_OK( - ctx, grad_shards.allocate(i, TensorShape({sub_nnz, emb_vec_size}), + ctx, grad_shards.allocate(i, TensorShape({shard_len, emb_vec_size}), &grad_shard)); if (combiner_ == "sqrtn") { DistributeGradToShard( device, data_p_with_type(top_grad_tensor), data_p_with_type(emb_shards[i]), - data_p_with_type(partitioned_indices[i]), + data_p_with_type(partition_permutations[i]), + data_p_with_type(indices_before_unique), + data_p_with_type(unique_counts), + data_p_with_type(idx_of_input_to_unique), + data_p_with_type(unique_offsets), shard_len, + emb_vec_size, max_norm_, set_empty_row_zero, data_p_with_type(feature_nums), data_p_with_type(row_empty_and_invalid_flags), - set_empty_row_zero, data_p_with_type(grad_shard), sub_nnz, - emb_vec_size, max_norm_); + data_p_with_type(grad_shard)); } else if (combiner_ == "mean") { DistributeGradToShard( device, data_p_with_type(top_grad_tensor), data_p_with_type(emb_shards[i]), - data_p_with_type(partitioned_indices[i]), + data_p_with_type(partition_permutations[i]), + data_p_with_type(indices_before_unique), + data_p_with_type(unique_counts), + data_p_with_type(idx_of_input_to_unique), + data_p_with_type(unique_offsets), shard_len, + emb_vec_size, max_norm_, set_empty_row_zero, data_p_with_type(feature_nums), data_p_with_type(row_empty_and_invalid_flags), - set_empty_row_zero, data_p_with_type(grad_shard), sub_nnz, - emb_vec_size, max_norm_); + data_p_with_type(grad_shard)); } else { DistributeGradToShard( device, data_p_with_type(top_grad_tensor), data_p_with_type(emb_shards[i]), - data_p_with_type(partitioned_indices[i]), + data_p_with_type(partition_permutations[i]), + data_p_with_type(indices_before_unique), + data_p_with_type(unique_counts), + data_p_with_type(idx_of_input_to_unique), + data_p_with_type(unique_offsets), shard_len, + emb_vec_size, max_norm_, set_empty_row_zero, data_p_with_type(feature_nums), data_p_with_type(row_empty_and_invalid_flags), - set_empty_row_zero, data_p_with_type(grad_shard), sub_nnz, - emb_vec_size, max_norm_); + data_p_with_type(grad_shard)); } CK_CUDA_THROW_(cudaGetLastError()); } diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.cc index 63195d48912..071c1cc4f62 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.cc @@ -211,20 +211,20 @@ void GatherAndConvertToSubPartition(const GPUDevice& d, partition_size)); } -__global__ void SumUpEmbeddingShardKernel(const float* emb_shard, - const int64_t* partitioned_indice, - float* emb_vectors, int* feature_nums, - const float max_norm, - const int emb_vec_size) { +__global__ void SumUpEmbeddingShardKernel( + const float* emb_shard, const int64_t* partition_permutations, + const int64_t* indices_before_unique, const int64_t* unique_counts, + const int64_t* idx_of_input_to_unique, const int64_t* unique_offsets, + const float max_norm, const int emb_vec_size, float* emb_vectors, + int* feature_nums) { __shared__ float l2_sum[1]; + const int64_t partition_permute = partition_permutations[blockIdx.x]; + const int64_t ioitu_offset = unique_offsets[partition_permute]; + const int64_t counts = unique_counts[partition_permute]; - const int64_t row_in_batch = partitioned_indice[2 * blockIdx.x]; float emb_element = emb_shard[blockIdx.x * emb_vec_size + threadIdx.x]; if (max_norm >= 0.0f) { - if (threadIdx.x == 0) { - l2_sum[0] = 0.0f; - } - __syncthreads(); + l2_sum[0] = 0.0f; atomicAdd(l2_sum, emb_element * emb_element); __syncthreads(); float l2_norm = sqrtf(l2_sum[0]); @@ -233,152 +233,163 @@ __global__ void SumUpEmbeddingShardKernel(const float* emb_shard, } } - atomicAdd(emb_vectors + row_in_batch * emb_vec_size + threadIdx.x, - emb_element); - - if (threadIdx.x == 0) { - atomicAdd(feature_nums + row_in_batch, 1); + for (int64_t i = 0; i < counts; i++) { + const int64_t ioitu = idx_of_input_to_unique[ioitu_offset + i]; + const int64_t row_in_batch = indices_before_unique[2 * ioitu]; + atomicAdd(emb_vectors + row_in_batch * emb_vec_size + threadIdx.x, + emb_element); + if (threadIdx.x == 0) { + atomicAdd(feature_nums + row_in_batch, 1); + } } } -void SumUpEmbeddingShard(const GPUDevice& d, const size_t sub_nnz, - const float* emb_shard, - const int64_t* partitioned_indice, float* emb_vectors, - int* feature_nums, const float max_norm, - const int emb_vec_size) { - const int blocks = sub_nnz; +void SumUpEmbeddingShard( + const GPUDevice& d, const size_t shard_len, const float* emb_shard, + const int64_t* partition_permutations, const int64_t* indices_before_unique, + const int64_t* unique_counts, const int64_t* idx_of_input_to_unique, + const int64_t* unique_offsets, const float max_norm, const int emb_vec_size, + float* emb_vectors, int* feature_nums) { + const int blocks = shard_len; const int threads = emb_vec_size; - TF_CHECK_OK(GpuLaunchKernel( - SumUpEmbeddingShardKernel, blocks, threads, 0, d.stream(), emb_shard, - partitioned_indice, emb_vectors, feature_nums, max_norm, emb_vec_size)); + TF_CHECK_OK(GpuLaunchKernel(SumUpEmbeddingShardKernel, blocks, threads, 0, + d.stream(), emb_shard, partition_permutations, + indices_before_unique, unique_counts, + idx_of_input_to_unique, unique_offsets, max_norm, + emb_vec_size, emb_vectors, feature_nums)); } template -__global__ void ApplyCombinerKernel(float* emb_vectors, - const int* row_emptiness_flag, +__global__ void ApplyCombinerKernel(const int* row_emptiness_flag, const bool set_empty_row_zero, - const int* feature_nums) { + int* feature_nums, float* emb_vectors) { const int offset = blockIdx.x * blockDim.x + threadIdx.x; + const int feature_num = feature_nums[blockIdx.x]; if (set_empty_row_zero) { if (row_emptiness_flag[blockIdx.x]) { + feature_nums[blockIdx.x] = 0; emb_vectors[offset] = 0.0f; return; } } - const int feature_num = feature_nums[blockIdx.x]; const float emb_element = emb_vectors[offset]; emb_vectors[offset] = Combine(emb_element, feature_num); } template void ApplyCombiner(const GPUDevice& d, const int batch_size, - const int emb_vec_size, float* emb_vectors, - const int* row_emptiness_flag, const bool set_empty_row_zero, - const int* feature_nums) { + const int emb_vec_size, const int* row_emptiness_flag, + const bool set_empty_row_zero, int* feature_nums, + float* emb_vectors) { const int blocks = batch_size; const int threads = emb_vec_size; TF_CHECK_OK(GpuLaunchKernel(ApplyCombinerKernel, blocks, threads, 0, - d.stream(), emb_vectors, row_emptiness_flag, - set_empty_row_zero, feature_nums)); + d.stream(), row_emptiness_flag, + set_empty_row_zero, feature_nums, emb_vectors)); } template void ApplyCombiner(const GPUDevice& d, const int batch_size, - const int emb_vec_size, float* emb_vectors, + const int emb_vec_size, const int* row_emptiness_flag, const bool set_empty_row_zero, - const int* feature_nums); + int* feature_nums, float* emb_vectors); template void ApplyCombiner(const GPUDevice& d, const int batch_size, - const int emb_vec_size, float* emb_vectors, + const int emb_vec_size, const int* row_emptiness_flag, const bool set_empty_row_zero, - const int* feature_nums); + int* feature_nums, float* emb_vectors); template void ApplyCombiner(const GPUDevice& d, const int batch_size, - const int emb_vec_size, float* emb_vectors, + const int emb_vec_size, const int* row_emptiness_flag, const bool set_empty_row_zero, - const int* feature_nums); + int* feature_nums, float* emb_vectors); template __global__ void DistributeGradToShardKernel( const float* top_grad, const float* emb_shard, - const int64_t* partitioned_indice, const int* feature_nums, - const int* row_emptiness_flag, const bool set_empty_row_zero, - float* grad_shard, const int64_t sub_nnz, const int64_t emb_vec_size, - const float max_norm) { - __shared__ int64_t row_in_batch_shared[1]; - __shared__ int feature_num_shared[1]; + const int64_t* partition_permutations, const int64_t* indices_before_unique, + const int64_t* unique_counts, const int64_t* idx_of_input_to_unique, + const int64_t* unique_offsets, const int64_t shard_len, + const int64_t emb_vec_size, const float max_norm, + const bool set_empty_row_zero, const int* feature_nums, + const int* row_emptiness_flag, float* grad_shard) { __shared__ float l2_sum[1]; - int64_t row_in_batch; - if (threadIdx.x == 0) { - row_in_batch = partitioned_indice[2 * blockIdx.x]; - row_in_batch_shared[0] = row_in_batch; - feature_num_shared[0] = feature_nums[row_in_batch]; - } - __syncthreads(); - row_in_batch = row_in_batch_shared[0]; - const int feature_num = feature_num_shared[0]; - if (set_empty_row_zero) { - if (row_emptiness_flag[row_in_batch]) { - grad_shard[blockIdx.x * emb_vec_size + threadIdx.x] = 0.0f; - return; - } - } - float grad = top_grad[row_in_batch * emb_vec_size + threadIdx.x]; - grad = CombineGrad(grad, feature_num); + + const int64_t partition_permute = partition_permutations[blockIdx.x]; + const int64_t ioitu_offset = unique_offsets[partition_permute]; + const int64_t counts = unique_counts[partition_permute]; + float l2_norm = -1.0f; + if (max_norm >= 0.0f) { const float emb_element = emb_shard[blockIdx.x * emb_vec_size + threadIdx.x]; - if (threadIdx.x == 0) { - l2_sum[0] = 0.0f; - } - __syncthreads(); + l2_sum[0] = 0.0f; atomicAdd(l2_sum, emb_element * emb_element); __syncthreads(); - float l2_norm = sqrtf(l2_sum[0]); - if (l2_norm > max_norm) { + l2_norm = sqrtf(l2_sum[0]); + } + + float grad_accu = 0.0f; + for (int64_t i = 0; i < counts; i++) { + const int64_t ioitu = idx_of_input_to_unique[ioitu_offset + i]; + const int64_t row_in_batch = indices_before_unique[2 * ioitu]; + float grad = top_grad[row_in_batch * emb_vec_size + threadIdx.x]; + const int feature_num = feature_nums[row_in_batch]; + grad = CombineGrad(grad, feature_num); + if (max_norm >= 0.0f && l2_norm > max_norm) { grad *= max_norm / l2_norm; } + grad_accu += grad; } - grad_shard[blockIdx.x * emb_vec_size + threadIdx.x] = grad; + + grad_shard[blockIdx.x * emb_vec_size + threadIdx.x] = grad_accu; } template -void DistributeGradToShard(const GPUDevice& d, const float* top_grad, - const float* emb_shard, - const int64_t* partitioned_indice, - const int* feature_nums, - const int* row_emptiness_flag, - const bool set_empty_row_zero, float* grad_shard, - const int64_t sub_nnz, const int64_t emb_vec_size, - const float max_norm) { - const int blocks = sub_nnz; +void DistributeGradToShard( + const GPUDevice& d, const float* top_grad, const float* emb_shard, + const int64_t* partition_permutations, const int64_t* indices_before_unique, + const int64_t* unique_counts, const int64_t* idx_of_input_to_unique, + const int64_t* unique_offsets, const int64_t shard_len, + const int64_t emb_vec_size, const float max_norm, + const bool set_empty_row_zero, const int* feature_nums, + const int* row_emptiness_flag, float* grad_shard) { + const int blocks = shard_len; const int threads = emb_vec_size; TF_CHECK_OK(GpuLaunchKernel( DistributeGradToShardKernel, blocks, threads, 0, d.stream(), - top_grad, emb_shard, partitioned_indice, feature_nums, row_emptiness_flag, - set_empty_row_zero, grad_shard, sub_nnz, emb_vec_size, max_norm)); + top_grad, emb_shard, partition_permutations, indices_before_unique, + unique_counts, idx_of_input_to_unique, unique_offsets, shard_len, + emb_vec_size, max_norm, set_empty_row_zero, feature_nums, + row_emptiness_flag, grad_shard)); } template void DistributeGradToShard( const GPUDevice& d, const float* top_grad, const float* emb_shard, - const int64_t* partitioned_indice, const int* feature_nums, - const int* row_emptiness_flag, const bool set_empty_row_zero, - float* grad_shard, const int64_t sub_nnz, const int64_t emb_vec_size, - const float max_norm); + const int64_t* partition_permutations, const int64_t* indices_before_unique, + const int64_t* unique_counts, const int64_t* idx_of_input_to_unique, + const int64_t* unique_offsets, const int64_t shard_len, + const int64_t emb_vec_size, const float max_norm, + const bool set_empty_row_zero, const int* feature_nums, + const int* row_emptiness_flag, float* grad_shard); template void DistributeGradToShard( const GPUDevice& d, const float* top_grad, const float* emb_shard, - const int64_t* partitioned_indice, const int* feature_nums, - const int* row_emptiness_flag, const bool set_empty_row_zero, - float* grad_shard, const int64_t sub_nnz, const int64_t emb_vec_size, - const float max_norm); + const int64_t* partition_permutations, const int64_t* indices_before_unique, + const int64_t* unique_counts, const int64_t* idx_of_input_to_unique, + const int64_t* unique_offsets, const int64_t shard_len, + const int64_t emb_vec_size, const float max_norm, + const bool set_empty_row_zero, const int* feature_nums, + const int* row_emptiness_flag, float* grad_shard); template void DistributeGradToShard( const GPUDevice& d, const float* top_grad, const float* emb_shard, - const int64_t* partitioned_indice, const int* feature_nums, - const int* row_emptiness_flag, const bool set_empty_row_zero, - float* grad_shard, const int64_t sub_nnz, const int64_t emb_vec_size, - const float max_norm); + const int64_t* partition_permutations, const int64_t* indices_before_unique, + const int64_t* unique_counts, const int64_t* idx_of_input_to_unique, + const int64_t* unique_offsets, const int64_t shard_len, + const int64_t emb_vec_size, const float max_norm, + const bool set_empty_row_zero, const int* feature_nums, + const int* row_emptiness_flag, float* grad_shard); } // namespace fused_embedding diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h b/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h index 045a825f7d1..818428bb1d1 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h @@ -86,27 +86,28 @@ void GatherAndConvertToSubPartition(const GPUDevice& d, template void RangeInit(const GPUDevice& d, const int64_t length, T* out); -void SumUpEmbeddingShard(const GPUDevice& d, const size_t sub_nnz, - const float* emb_shard, - const int64_t* partitioned_indice, float* emb_vectors, - int* feature_nums, const float max_norm, - const int emb_vec_size); +void SumUpEmbeddingShard( + const GPUDevice& d, const size_t shard_len, const float* emb_shard, + const int64_t* partition_permutations, const int64_t* indices_before_unique, + const int64_t* unique_counts, const int64_t* idx_of_input_to_unique, + const int64_t* unique_offsets, const float max_norm, const int emb_vec_size, + float* emb_vectors, int* feature_nums); template void ApplyCombiner(const GPUDevice& d, const int batch_size, - const int emb_vec_size, float* emb_vectors, - const int* row_emptiness_flag, const bool set_empty_row_zero, - const int* feature_nums); + const int emb_vec_size, const int* row_emptiness_flag, + const bool set_empty_row_zero, int* feature_nums, + float* emb_vectors); template -void DistributeGradToShard(const GPUDevice& d, const float* top_grad, - const float* emb_shard, - const int64_t* partitioned_indice, - const int* feature_nums, - const int* row_emptiness_flag, - const bool set_empty_row_zero, float* grad_shard, - const int64_t sub_nnz, const int64_t emb_vec_size, - const float max_norm); +void DistributeGradToShard( + const GPUDevice& d, const float* top_grad, const float* emb_shard, + const int64_t* partition_permutations, const int64_t* indices_before_unique, + const int64_t* unique_counts, const int64_t* idx_of_input_to_unique, + const int64_t* unique_offsets, const int64_t shard_len, + const int64_t emb_vec_size, const float max_norm, + const bool set_empty_row_zero, const int* feature_nums, + const int* row_emptiness_flag, float* grad_shard); } // namespace fused_embedding diff --git a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_grad_ops_test.cc b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_grad_ops_test.cc index 5335c7ea3de..14ca4dbf041 100644 --- a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_grad_ops_test.cc +++ b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_grad_ops_test.cc @@ -49,9 +49,13 @@ class FusedEmbeddingSparsePostLookUpGradOpTest : public OpsTestBase { .Attr("max_norm", max_norm) .Attr("default_id", default_id) .Input(FakeInput(dtype)) - .Input(FakeInput(dtype)) - .Input(FakeInput(DT_INT64)) + .Input(FakeInput(num_partitions, dtype)) + .Input(FakeInput(num_partitions, DT_INT64)) .Input(FakeInput(DT_INT32)) + .Input(FakeInput(DT_INT64)) + .Input(FakeInput(DT_INT64)) + .Input(FakeInput(DT_INT64)) + .Input(FakeInput(DT_INT64)) .Input(FakeInput(DT_INT32)) .Finalize(node_def())); TF_EXPECT_OK(InitOp()); @@ -59,7 +63,7 @@ class FusedEmbeddingSparsePostLookUpGradOpTest : public OpsTestBase { }; TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, - Partition2_Mean_MaxNorm100_Float) { + Partition2MeanMaxNorm100Float) { const int nnz = 10; const int batch_size = 4; const int emb_vector_dim = 8; @@ -74,13 +78,14 @@ TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0}); - // emb_shards + // emb_shards 0 AddInputFromArray( TensorShape({6, emb_vector_dim}), {8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0}); + // emb_shards 1 AddInputFromArray( TensorShape({4, emb_vector_dim}), {56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, @@ -88,16 +93,29 @@ TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, 96.0, 97.0, 98.0, 99.0, 100.0, 101.0, 102.0, 103.0, 120.0, 121.0, 122.0, 123.0, 124.0, 125.0, 126.0, 127.0}); - // sp_values: 3, 1, 4, 5, 7, 3, 12, 12, 15, 4 - // partitioned_values: 1, 3, 3, 4, 4, 5 and 7, 12, 12, 15 - // partitioned_indices - AddInputFromArray(TensorShape({6, 2}), - {0, 5, 0, 1, 2, 1, 1, 2, 3, 6, 1, 1}); - AddInputFromArray(TensorShape({4, 2}), {1, 7, 2, 4, 2, 7, 3, 0}); + // partition_permutations 0 + AddInputFromArray(TensorShape({6}), {0, 2, 4, 6, 8, 9}); + + // partition_permutations 1 + AddInputFromArray(TensorShape({4}), {1, 3, 5, 7}); // feature_nums AddInputFromArray(TensorShape({batch_size}), {2, 3, 3, 2}); + // indices_before_unique + AddInputFromArray( + TensorShape({nnz, 2}), + {0, 5, 1, 7, 0, 1, 2, 4, 2, 1, 2, 7, 1, 2, 3, 0, 3, 6, 1, 1}); + + // unique_counts + AddInputFromArray(TensorShape({nnz}), {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}); + + // idx_of_input_to_unique + AddInputFromArray(TensorShape({nnz}), {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}); + + // unique_offsets + AddInputFromArray(TensorShape({nnz}), {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}); + // row_empty_and_invalid_flags AddInputFromArray(TensorShape({batch_size + nnz}), {0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}); @@ -138,6 +156,7 @@ TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, } } +/* TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, Partition2_SUM_Float_No_Default) { const int nnz = 3; @@ -237,6 +256,7 @@ TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, test::ExpectTensorNear(grad_shards_2, *GetOutput(1), 1e-4); } } +*/ } // namespace } // namespace tensorflow \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc index 959ee948882..4f272888706 100644 --- a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc +++ b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc @@ -50,15 +50,19 @@ class FusedEmbeddingSparsePostLookUpOpTest : public OpsTestBase { .Input(FakeInput(num_partitions, dtype)) .Input(FakeInput(num_partitions, DT_INT64)) .Input(FakeInput(DT_INT64)) + .Input(FakeInput(DT_INT64)) .Input(FakeInput(DT_INT32)) .Input(FakeInput(DT_INT64)) + .Input(FakeInput(DT_INT64)) + .Input(FakeInput(DT_INT64)) + .Input(FakeInput(DT_INT64)) .Finalize(node_def())); TF_EXPECT_OK(InitOp()); } }; TEST_F(FusedEmbeddingSparsePostLookUpOpTest, - Partition3_Sqrtn_MaxNorm200_Float) { + OnlyPartition3CombinerSqrtnMaxNorm200) { const int nnz = 10; const int batch_size = 4; const int emb_vector_dim = 8; @@ -66,7 +70,7 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, MakeOpAndSetDevice(Device::GPU, 3, DT_FLOAT, "sqrtn", 200.0, -1); - // emb_shards + // emb_shards 0 AddInputFromArray( TensorShape({6, emb_vector_dim}), { @@ -76,27 +80,42 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, 38.0, 39.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, }); + // emb_shards 1 AddInputFromArray(TensorShape({1, emb_vector_dim}), {56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0}); + // emb_shards 2 AddInputFromArray( TensorShape({3, emb_vector_dim}), {96.0, 97.0, 98.0, 99.0, 100.0, 101.0, 102.0, 103.0, 96.0, 97.0, 98.0, 99.0, 100.0, 101.0, 102.0, 103.0, 120.0, 121.0, 122.0, 123.0, 124.0, 125.0, 126.0, 127.0}); - // partitioned_indices - AddInputFromArray(TensorShape({6, 2}), - {0, 5, 0, 1, 2, 1, 1, 2, 3, 6, 1, 1}); - AddInputFromArray(TensorShape({1, 2}), {1, 7}); - AddInputFromArray(TensorShape({3, 2}), {2, 4, 2, 7, 3, 0}); + // partition_permutations + AddInputFromArray(TensorShape({6}), {0, 1, 2, 3, 4, 5}); + AddInputFromArray(TensorShape({1}), {6}); + AddInputFromArray(TensorShape({3}), {7, 8, 9}); // sp_dense_shape AddInputFromArray(TensorShape({2}), {batch_size, entries}); + // indices_before_unique + AddInputFromArray( + TensorShape({nnz, 2}), + {0, 5, 0, 1, 2, 1, 1, 2, 3, 6, 1, 1, 1, 7, 2, 4, 2, 7, 3, 0}); + // row_empty_and_invalid_flags AddInputFromArray(TensorShape({batch_size + nnz}), {0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}); + // unique_counts + AddInputFromArray(TensorShape({nnz}), {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}); + + // idx_of_input_to_unique + AddInputFromArray(TensorShape({nnz}), {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}); + + // unique_offsets + AddInputFromArray(TensorShape({nnz}), {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}); + TF_ASSERT_OK(RunOpKernel()); TF_EXPECT_OK(device_->Sync()); @@ -122,7 +141,7 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, } } -TEST_F(FusedEmbeddingSparsePostLookUpOpTest, Partition2_Sum_No_Default) { +TEST_F(FusedEmbeddingSparsePostLookUpOpTest, OnlyPartition2Sum) { const int nnz = 3; const int batch_size = 3; const int emb_vector_dim = 4; @@ -130,22 +149,35 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, Partition2_Sum_No_Default) { MakeOpAndSetDevice(Device::GPU, 2, DT_FLOAT, "sum", -1.0, -1); - // emb_shards + // emb_shards 0 AddInputFromArray(TensorShape({2, emb_vector_dim}), {1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0}); + // emb_shards 1 AddInputFromArray(TensorShape({2, emb_vector_dim}), {10.0, 10.0, 10.0, 10.0, 13.0, 13.0, 13.0, 13.0}); - // partitioned_indices - AddInputFromArray(TensorShape({2, 2}), {0, 0, 0, 5}); - AddInputFromArray(TensorShape({2, 2}), {1, 4, 2, 0}); + // partition_permutations + AddInputFromArray(TensorShape({2}), {1, 2}); + AddInputFromArray(TensorShape({2}), {3, 0}); // sp_dense_shape AddInputFromArray(TensorShape({2}), {batch_size, entries}); + // indices_before_unique + AddInputFromArray(TensorShape({nnz + 1, 2}), {2, 0, 0, 0, 0, 5, 1, 4}); + // row_empty_and_invalid_flags AddInputFromArray(TensorShape({batch_size + nnz}), {0, 0, 1, 1, 1, 1}); + // unique_counts + AddInputFromArray(TensorShape({nnz + 1}), {1, 1, 1, 1}); + + // idx_of_input_to_unique + AddInputFromArray(TensorShape({nnz + 1}), {0, 1, 2, 3}); + + // unique_offsets + AddInputFromArray(TensorShape({nnz + 1}), {0, 1, 2, 3}); + TF_ASSERT_OK(RunOpKernel()); TF_EXPECT_OK(device_->Sync()); @@ -154,41 +186,54 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, Partition2_Sum_No_Default) { TensorShape({batch_size, emb_vector_dim})); test::FillValues( &expected_emb_vectors, - {3.0, 3.0, 3.0, 3.0, 10.0, 10.0, 10.0, 10.0, 13.0, 13.0, 13.0, 13.0}); + {3.0, 3.0, 3.0, 3.0, 10.0, 10.0, 10.0, 10.0, 0.0, 0.0, 0.0, 0.0}); test::ExpectTensorNear(expected_emb_vectors, *GetOutput(0), 1e-4); } { Tensor feature_nums_expected(allocator(), DT_INT32, TensorShape({batch_size})); - test::FillValues(&feature_nums_expected, {2, 1, 1}); + test::FillValues(&feature_nums_expected, {2, 1, 0}); test::ExpectTensorEqual(feature_nums_expected, *GetOutput(1)); } } -TEST_F(FusedEmbeddingSparsePostLookUpOpTest, Partition2_Sum_Default_0) { +TEST_F(FusedEmbeddingSparsePostLookUpOpTest, OnlyPartition2SumDefault2) { const int nnz = 3; const int batch_size = 3; const int emb_vector_dim = 4; const int entries = 8; - MakeOpAndSetDevice(Device::GPU, 2, DT_FLOAT, "sum", -1.0, 0); + MakeOpAndSetDevice(Device::GPU, 2, DT_FLOAT, "sum", -1.0, 2); - // emb_shards + // emb_shards 0 AddInputFromArray(TensorShape({2, emb_vector_dim}), {1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0}); + // emb_shards 1 AddInputFromArray(TensorShape({2, emb_vector_dim}), {10.0, 10.0, 10.0, 10.0, 13.0, 13.0, 13.0, 13.0}); - // partitioned_indices - AddInputFromArray(TensorShape({2, 2}), {0, 0, 0, 5}); - AddInputFromArray(TensorShape({2, 2}), {1, 4, 2, 0}); + // partition_permutations + AddInputFromArray(TensorShape({2}), {1, 2}); + AddInputFromArray(TensorShape({2}), {3, 0}); // sp_dense_shape AddInputFromArray(TensorShape({2}), {batch_size, entries}); + // indices_before_unique + AddInputFromArray(TensorShape({nnz + 1, 2}), {2, 0, 0, 0, 0, 5, 1, 4}); + // row_empty_and_invalid_flags AddInputFromArray(TensorShape({batch_size + nnz}), {0, 0, 1, 1, 1, 1}); + // unique_counts + AddInputFromArray(TensorShape({nnz + 1}), {1, 1, 1, 1}); + + // idx_of_input_to_unique + AddInputFromArray(TensorShape({nnz + 1}), {0, 1, 2, 3}); + + // unique_offsets + AddInputFromArray(TensorShape({nnz + 1}), {0, 1, 2, 3}); + TF_ASSERT_OK(RunOpKernel()); TF_EXPECT_OK(device_->Sync()); @@ -197,7 +242,7 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, Partition2_Sum_Default_0) { TensorShape({batch_size, emb_vector_dim})); test::FillValues( &expected_emb_vectors, - {3.0, 3.0, 3.0, 3.0, 10.0, 10.0, 10.0, 10.0, 0.0, 0.0, 0.0, 0.0}); + {3.0, 3.0, 3.0, 3.0, 10.0, 10.0, 10.0, 10.0, 13.0, 13.0, 13.0, 13.0}); test::ExpectTensorNear(expected_emb_vectors, *GetOutput(0), 1e-4); } { @@ -208,5 +253,64 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, Partition2_Sum_Default_0) { } } +TEST_F(FusedEmbeddingSparsePostLookUpOpTest, OnlyPartition2MeanDefault2Unique) { + const int nnz = 7; + const int batch_size = 5; + const int emb_vector_dim = 2; + const int entries = 4; + + MakeOpAndSetDevice(Device::GPU, 2, DT_FLOAT, "mean", -1.0, 2); + + // emb_shards 0 + AddInputFromArray(TensorShape({2, emb_vector_dim}), + {1.0, 1.0, 2.0, 2.0}); + // emb_shards 1 + AddInputFromArray(TensorShape({2, emb_vector_dim}), + {3.0, 3.0, 4.0, 4.0}); + + // partition_permutations + AddInputFromArray(TensorShape({2}), {3, 1}); + AddInputFromArray(TensorShape({2}), {0, 2}); + + // sp_dense_shape + AddInputFromArray(TensorShape({2}), {batch_size, entries}); + + // indices_before_unique + AddInputFromArray(TensorShape({nnz + 1, 2}), + {0, 1, 0, 2, 1, 0, 1, 1, 3, 0, 3, 1, 4, 0, 2, 0}); + + // row_empty_and_invalid_flags + AddInputFromArray(TensorShape({batch_size + nnz}), + {0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1}); + + // unique_counts + AddInputFromArray(TensorShape({4}), {2, 2, 1, 3}); + + // idx_of_input_to_unique + AddInputFromArray(TensorShape({nnz + 1}), {0, 3, 1, 2, 4, 5, 7, 6}); + + // unique_offsets + AddInputFromArray(TensorShape({4}), {0, 2, 4, 5}); + + TF_ASSERT_OK(RunOpKernel()); + TF_EXPECT_OK(device_->Sync()); + + // {3 + 2, 3 + 2, 3 + 2, 3 + 2, 1, 1, 4 + 1, 4 + 1, 1, 1} + // {2, 2, 1, 2, 1} + + { + Tensor expected_emb_vectors(allocator(), DT_FLOAT, + TensorShape({batch_size, emb_vector_dim})); + test::FillValues(&expected_emb_vectors, + {2.5, 2.5, 2.5, 2.5, 1.0, 1.0, 2.5, 2.5, 1.0, 1.0}); + test::ExpectTensorNear(expected_emb_vectors, *GetOutput(0), 1e-4); + } + { + Tensor feature_nums_expected(allocator(), DT_INT32, + TensorShape({batch_size})); + test::FillValues(&feature_nums_expected, {2, 2, 1, 2, 1}); + test::ExpectTensorEqual(feature_nums_expected, *GetOutput(1)); + } +} } // namespace } // namespace tensorflow \ No newline at end of file diff --git a/tensorflow/core/ops/fused_embedding_ops.cc b/tensorflow/core/ops/fused_embedding_ops.cc index ade6b7b746e..095f1c2e6e4 100644 --- a/tensorflow/core/ops/fused_embedding_ops.cc +++ b/tensorflow/core/ops/fused_embedding_ops.cc @@ -151,9 +151,13 @@ REGISTER_OP("FusedEmbeddingSparsePostLookUp") .Attr("combiner: {'sqrtn', 'mean', 'sum'}") .Attr("max_norm: float = -1.0") .Input("emb_shards: num_partitions * T") - .Input("partitioned_indices: num_partitions * int64") + .Input("partition_permutations: num_partitions * int64") .Input("sp_dense_shape: int64") + .Input("indices_before_unique: int64") .Input("row_empty_and_invalid_flags: int32") + .Input("unique_counts: int64") + .Input("idx_of_input_to_unique: int64") + .Input("unique_offsets: int64") .Input( "partitioned_values: num_partitions * int64") // only for backward use. // actually directly port @@ -165,30 +169,63 @@ REGISTER_OP("FusedEmbeddingSparsePostLookUp") int num_partitions; TF_RETURN_IF_ERROR(ctx->GetAttr("num_partitions", &num_partitions)); + std::vector unused_list; + ShapeHandle unused; + DimensionHandle unused_dim; + + // emb_shards + ctx->input("emb_shards", &unused_list); ShapeHandle first_emb_shard_shape; TF_RETURN_IF_ERROR( - ctx->WithRank(ctx->input(0), 2, &first_emb_shard_shape)); + ctx->WithRank(unused_list[0], 2, &first_emb_shard_shape)); + DimensionHandle emb_vec_size_dim = ctx->Dim(first_emb_shard_shape, 1); + int64 emb_vec_size = ctx->Value(emb_vec_size_dim); - ShapeHandle unused; for (int i = 0; i < num_partitions; i++) { - // emb_shards - TF_RETURN_IF_ERROR(ctx->WithRank(ctx->input(i), 2, &unused)); - // partitioned_indices + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[i], 2, &unused)); TF_RETURN_IF_ERROR( - ctx->WithRank(ctx->input(i + num_partitions), 2, &unused)); - DimensionHandle unused_dim; - TF_RETURN_IF_ERROR(ctx->WithValue(ctx->Dim(unused, 1), 2, &unused_dim)); + ctx->WithValue(ctx->Dim(unused, 1), emb_vec_size, &unused_dim)); + } + + // partition_permutations + ctx->input("partition_permutations", &unused_list); + for (int i = 0; i < num_partitions; i++) { + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[i], 1, &unused)); } + // sp_dense_shape - TF_RETURN_IF_ERROR( - ctx->WithRank(ctx->input(2 * num_partitions), 1, &unused)); + ctx->input("sp_dense_shape", &unused_list); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); + + // indices_before_unique + ctx->input("indices_before_unique", &unused_list); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); + // row_empty_and_invalid_flags - TF_RETURN_IF_ERROR( - ctx->WithRank(ctx->input(2 * num_partitions + 1), 1, &unused)); + ctx->input("row_empty_and_invalid_flags", &unused_list); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); - DimensionHandle emb_vec_size_dim = ctx->Dim(first_emb_shard_shape, 1); - ctx->set_output(0, ctx->MakeShape({ctx->UnknownDim(), emb_vec_size_dim})); - ctx->set_output(1, ctx->MakeShape({ctx->UnknownDim()})); + // unique_counts + ctx->input("unique_counts", &unused_list); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); + + // idx_of_input_to_unique + ctx->input("idx_of_input_to_unique", &unused_list); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); + + // unique_offsets + ctx->input("unique_offsets", &unused_list); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); + + // emb_vectors + unused_list.clear(); + unused_list.resize(1); + unused_list[0] = ctx->MakeShape({ctx->UnknownDim(), emb_vec_size_dim}); + ctx->set_output("emb_vectors", unused_list); + + // feature_nums + unused_list[0] = ctx->MakeShape({ctx->UnknownDim()}); + ctx->set_output("feature_nums", unused_list); return Status::OK(); }); @@ -212,43 +249,68 @@ REGISTER_OP("FusedEmbeddingSparsePostLookUpGrad") .Attr("max_norm: float = -1.0") .Input("top_grad: T") .Input("emb_shards: num_partitions * T") - .Input("partitioned_indices: num_partitions * int64") + .Input("partition_permutations: num_partitions * int64") .Input("feature_nums: int32") + .Input("indices_before_unique: int64") + .Input("unique_counts: int64") + .Input("idx_of_input_to_unique: int64") + .Input("unique_offsets: int64") .Input("row_empty_and_invalid_flags: int32") .Output("grad_shards: num_partitions * T") .SetShapeFn([](InferenceContext* ctx) { int num_partitions; TF_RETURN_IF_ERROR(ctx->GetAttr("num_partitions", &num_partitions)); + std::vector unused_list; ShapeHandle unused; - ShapeHandle top_grad_shape; + DimensionHandle unused_dim; + ctx->input("top_grad", &unused_list); // top_grad - TF_RETURN_IF_ERROR(ctx->WithRank(ctx->input(0), 2, &top_grad_shape)); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 2, &unused)); + DimensionHandle emb_vec_size_dim = ctx->Dim(unused, 1); + // emb_shards - for (int i = 1; i < num_partitions + 1; i++) { - TF_RETURN_IF_ERROR(ctx->WithRank(ctx->input(i), 2, &unused)); + ctx->input("emb_shards", &unused_list); + for (int i = 0; i < num_partitions; i++) { + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[i], 2, &unused)); } - // partitioned_indices - for (int i = num_partitions + 1; i < 2 * num_partitions + 1; i++) { - TF_RETURN_IF_ERROR(ctx->WithRank(ctx->input(i), 2, &unused)); - DimensionHandle unused_dim; - TF_RETURN_IF_ERROR(ctx->WithValue(ctx->Dim(unused, 1), 2, &unused_dim)); + // partition_permutations + ctx->input("partition_permutations", &unused_list); + for (int i = 0; i < num_partitions; i++) { + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[i], 1, &unused)); } + // feature_nums - TF_RETURN_IF_ERROR( - ctx->WithRank(ctx->input(2 * num_partitions + 1), 1, &unused)); + ctx->input("feature_nums", &unused_list); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); + + // indices_before_unique + ctx->input("indices_before_unique", &unused_list); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 2, &unused)); + + // unique_counts + ctx->input("unique_counts", &unused_list); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); + + // idx_of_input_to_unique + ctx->input("idx_of_input_to_unique", &unused_list); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); + + // unique_offsets + ctx->input("unique_offsets", &unused_list); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); + // row_empty_and_invalid_flags - TF_RETURN_IF_ERROR( - ctx->WithRank(ctx->input(2 * num_partitions + 2), 1, &unused)); + ctx->input("row_empty_and_invalid_flags", &unused_list); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); - DimensionHandle emb_vec_size_dim = ctx->Dim(top_grad_shape, 1); + // grad_shards + unused_list.clear(); + unused_list.resize(1); + unused_list[0] = ctx->MakeShape({ctx->UnknownDim(), emb_vec_size_dim}); + ctx->set_output("grad_shards", unused_list); - ShapeHandle output_shape = - ctx->MakeShape({ctx->UnknownDim(), emb_vec_size_dim}); - for (int i = 0; i < num_partitions; i++) { - ctx->set_output(i, output_shape); - } return Status::OK(); }); From cca0b38132d782a86319aae61fc20ec7038f5a81 Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Thu, 10 Mar 2022 17:38:32 +0800 Subject: [PATCH 14/41] Unit test passed --- .../fused_embedding_post_ops_gpus.cu.cc | 8 +- .../gpu_functions/kernels.cu.cc | 4 + .../fused_embedding_post_grad_ops_test.cc | 176 ++++++++++++------ .../tests/fused_embedding_post_ops_test.cc | 11 +- tensorflow/core/ops/fused_embedding_ops.cc | 2 + tensorflow/python/ops/embedding_ops.py | 1 - 6 files changed, 140 insertions(+), 62 deletions(-) diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc b/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc index eb227414e47..f9927d72d89 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc @@ -25,6 +25,7 @@ class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { OP_REQUIRES_OK(ctx, ctx->GetAttr("partition_axis", &partition_axis_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("combiner", &combiner_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("max_norm", &max_norm_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("fill_empty_row", &fill_empty_row_)); int temp_default_id; OP_REQUIRES_OK(ctx, ctx->GetAttr("default_id", &temp_default_id)); default_id_ = int64_t(temp_default_id); @@ -112,7 +113,7 @@ class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { CK_CUDA_THROW_(cudaGetLastError()); } - const bool set_empty_row_zero = default_id_ < 0; + const bool set_empty_row_zero = default_id_ < 0 && fill_empty_row_; // ================================================================ // // ========================= 2. combiner ========================== // @@ -144,6 +145,7 @@ class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { int partition_axis_; std::string combiner_; float max_norm_; + bool fill_empty_row_; int64_t default_id_; }; @@ -160,6 +162,7 @@ class FusedEmbeddingSparsePostLookUpGradGPU : public OpKernel { OP_REQUIRES_OK(ctx, ctx->GetAttr("partition_axis", &partition_axis_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("combiner", &combiner_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("max_norm", &max_norm_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("fill_empty_row", &fill_empty_row_)); int temp_default_id; OP_REQUIRES_OK(ctx, ctx->GetAttr("default_id", &temp_default_id)); default_id_ = int64_t(temp_default_id); @@ -208,7 +211,7 @@ class FusedEmbeddingSparsePostLookUpGradGPU : public OpKernel { const int64_t batch_size = top_grad_tensor->shape().dim_size(0); const int64_t emb_vec_size = emb_shards[0].shape().dim_size(1); - const bool set_empty_row_zero = default_id_ < 0; + const bool set_empty_row_zero = default_id_ < 0 && fill_empty_row_; for (int i = 0; i < num_partitions_; i++) { const int64_t shard_len = partition_permutations[i].shape().dim_size(0); @@ -267,6 +270,7 @@ class FusedEmbeddingSparsePostLookUpGradGPU : public OpKernel { int partition_axis_; std::string combiner_; float max_norm_; + bool fill_empty_row_; int64_t default_id_; }; diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.cc index 071c1cc4f62..0a7dc9000fa 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.cc @@ -333,6 +333,10 @@ __global__ void DistributeGradToShardKernel( for (int64_t i = 0; i < counts; i++) { const int64_t ioitu = idx_of_input_to_unique[ioitu_offset + i]; const int64_t row_in_batch = indices_before_unique[2 * ioitu]; + if (set_empty_row_zero && row_emptiness_flag[row_in_batch]) { + continue; + } + float grad = top_grad[row_in_batch * emb_vec_size + threadIdx.x]; const int feature_num = feature_nums[row_in_batch]; grad = CombineGrad(grad, feature_num); diff --git a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_grad_ops_test.cc b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_grad_ops_test.cc index 14ca4dbf041..629b7ca5598 100644 --- a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_grad_ops_test.cc +++ b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_grad_ops_test.cc @@ -33,7 +33,7 @@ class FusedEmbeddingSparsePostLookUpGradOpTest : public OpsTestBase { protected: void MakeOpAndSetDevice(Device device, int num_partitions, DataType dtype, const std::string& combiner, const float max_norm, - const int default_id) { + const bool fill_empty_row, const int default_id) { if (device == Device::GPU) { SetDevice(DEVICE_GPU, std::unique_ptr(DeviceFactory::NewDevice( @@ -47,6 +47,7 @@ class FusedEmbeddingSparsePostLookUpGradOpTest : public OpsTestBase { .Attr("partition_axis", 0) .Attr("combiner", combiner) .Attr("max_norm", max_norm) + .Attr("fill_empty_row", fill_empty_row) .Attr("default_id", default_id) .Input(FakeInput(dtype)) .Input(FakeInput(num_partitions, dtype)) @@ -62,14 +63,13 @@ class FusedEmbeddingSparsePostLookUpGradOpTest : public OpsTestBase { } }; -TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, - Partition2MeanMaxNorm100Float) { +TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, Partition2MeanMaxNorm100) { const int nnz = 10; const int batch_size = 4; const int emb_vector_dim = 8; const int entries = 8; - MakeOpAndSetDevice(Device::GPU, 2, DT_FLOAT, "mean", 100.0, -1); + MakeOpAndSetDevice(Device::GPU, 2, DT_FLOAT, "mean", 100.0, false, -1); // top_grad AddInputFromArray( @@ -156,107 +156,175 @@ TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, } } -/* -TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, - Partition2_SUM_Float_No_Default) { - const int nnz = 3; - const int batch_size = 3; - const int emb_vector_dim = 4; +TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, Partition2SUMUnique) { + const int nnz = 6; + const int batch_size = 4; + const int emb_vector_dim = 1; const int entries = 8; - MakeOpAndSetDevice(Device::GPU, 2, DT_FLOAT, "sum", -1.0, -1); + MakeOpAndSetDevice(Device::GPU, 2, DT_FLOAT, "sum", -1.0, true, -1); // top_grad - AddInputFromArray( - TensorShape({batch_size, emb_vector_dim}), - {1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0}); + AddInputFromArray(TensorShape({batch_size, emb_vector_dim}), + {1.0, 2.0, 3.0, 4.0}); + + // emb_shards 0 + AddInputFromArray(TensorShape({3, emb_vector_dim}), {4.0, 5.0, 6.0}); + // emb_shards 1 + AddInputFromArray(TensorShape({2, emb_vector_dim}), {6.0, 7.0}); - // emb_shards - AddInputFromArray(TensorShape({2, emb_vector_dim}), - {8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0}); - AddInputFromArray(TensorShape({2, emb_vector_dim}), - {56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0}); + // partition_permutations 0 + AddInputFromArray(TensorShape({3}), {2, 4, 1}); - // partitioned_indices - AddInputFromArray(TensorShape({2, 2}), {0, 0, 0, 5}); - AddInputFromArray(TensorShape({2, 2}), {1, 4, 2, 0}); + // partition_permutations 1 + AddInputFromArray(TensorShape({2}), {3, 0}); // feature_nums - AddInputFromArray(TensorShape({batch_size}), {2, 1, 1}); + AddInputFromArray(TensorShape({batch_size}), {2, 2, 1, 2}); + + // values after fill empty: 1, 1, 2, 3, 4, 2, 0 + // after unique 1, 2, 3, 4, 0 + + // indices_before_unique + AddInputFromArray(TensorShape({nnz + 1, 2}), + {0, 1, 0, 3, 1, 2, 1, 3, 3, 2, 3, 6, 2, 0}); + + // unique_counts + AddInputFromArray(TensorShape({5}), {2, 2, 1, 1, 1}); + + // idx_of_input_to_unique + AddInputFromArray(TensorShape({nnz + 1}), {0, 1, 2, 5, 3, 4, 6}); + + // unique_offsets + AddInputFromArray(TensorShape({5}), {0, 2, 4, 5, 6}); // row_empty_and_invalid_flags - AddInputFromArray(TensorShape({batch_size + nnz}), {0, 0, 1, 1, 1, 1}); + AddInputFromArray(TensorShape({batch_size + nnz}), + {0, 0, 1, 0, 1, 1, 1, 1, 1, 1}); TF_ASSERT_OK(RunOpKernel()); TF_EXPECT_OK(device_->Sync()); { + /* + permute 2 -> unique_counts: 1, unique_offsets: 4 -> + idx_of_input_to_unique: 3 -> batch: 1, grad: 2.0 + + permute 4 -> unique_counts: 1, unique_offsets: 6 -> + idx_of_input_to_unique: 6 -> batch: 2: grad: 0.0, because fill_empty row + + permute 1 -> unique_counts: 2, unique_offsets: 2 + -> idx_of_input_to_unique: 2 -> batch: 1 -> grad : 2.0 + -> idx_of_input_to_unique: 5 -> batch: 3 -> grad : 4.0 + sum grad: 6.0 + */ Tensor grad_shards_1(allocator(), DT_FLOAT, - TensorShape({2, emb_vector_dim})); - test::FillValues(&grad_shards_1, - {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0}); + TensorShape({3, emb_vector_dim})); + test::FillValues(&grad_shards_1, {2.0, 0.0, 6.0}); test::ExpectTensorNear(grad_shards_1, *GetOutput(0), 1e-4); } { + /* + permute 3 -> unique_counts: 1, unique_offsets: 5 -> + idx_of_input_to_unique: 4 -> batch: 3 -> grad: 4.0 + + permute 0 -> unique_counts: 2, unique_offsets: 0 + -> idx_of_input_to_unique 0 -> batch: 0 -> grad: 1.0 + -> idx_of_input_to_unique 1 -> batch: 0 -> grad: 1.0 + sum grad: 2.0 + */ Tensor grad_shards_2(allocator(), DT_FLOAT, TensorShape({2, emb_vector_dim})); - test::FillValues(&grad_shards_2, - {2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0}); + test::FillValues(&grad_shards_2, {4.0, 2.0}); test::ExpectTensorNear(grad_shards_2, *GetOutput(1), 1e-4); } } -TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, - Partition2_SUM_Float_Default_0) { - const int nnz = 3; - const int batch_size = 3; - const int emb_vector_dim = 4; +TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, Partition2SUMUniqueDefault4) { + const int nnz = 6; + const int batch_size = 4; + const int emb_vector_dim = 1; const int entries = 8; - MakeOpAndSetDevice(Device::GPU, 2, DT_FLOAT, "sum", -1.0, 0); + MakeOpAndSetDevice(Device::GPU, 2, DT_FLOAT, "sum", -1.0, true, 4); // top_grad - AddInputFromArray( - TensorShape({batch_size, emb_vector_dim}), - {1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0}); + AddInputFromArray(TensorShape({batch_size, emb_vector_dim}), + {1.0, 2.0, 3.0, 4.0}); + + // emb_shards 0 + AddInputFromArray(TensorShape({3, emb_vector_dim}), {4.0, 5.0, 6.0}); + // emb_shards 1 + AddInputFromArray(TensorShape({2, emb_vector_dim}), {6.0, 7.0}); - // emb_shards - AddInputFromArray(TensorShape({2, emb_vector_dim}), - {8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0}); - AddInputFromArray(TensorShape({2, emb_vector_dim}), - {56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0}); + // partition_permutations 0 + AddInputFromArray(TensorShape({3}), {2, 4, 1}); - // partitioned_indices - AddInputFromArray(TensorShape({2, 2}), {0, 0, 0, 5}); - AddInputFromArray(TensorShape({2, 2}), {1, 4, 2, 0}); + // partition_permutations 1 + AddInputFromArray(TensorShape({2}), {3, 0}); // feature_nums - AddInputFromArray(TensorShape({batch_size}), {2, 1, 1}); + AddInputFromArray(TensorShape({batch_size}), {2, 2, 1, 2}); + + // values after fill empty: 1, 1, 2, 3, 4, 2, 0 + // after unique 1, 2, 3, 4, 0 + + // indices_before_unique + AddInputFromArray(TensorShape({nnz + 1, 2}), + {0, 1, 0, 3, 1, 2, 1, 3, 3, 2, 3, 6, 2, 0}); + + // unique_counts + AddInputFromArray(TensorShape({5}), {2, 2, 1, 1, 1}); + + // idx_of_input_to_unique + AddInputFromArray(TensorShape({nnz + 1}), {0, 1, 2, 5, 3, 4, 6}); + + // unique_offsets + AddInputFromArray(TensorShape({5}), {0, 2, 4, 5, 6}); // row_empty_and_invalid_flags - AddInputFromArray(TensorShape({batch_size + nnz}), {0, 0, 1, 1, 1, 1}); + AddInputFromArray(TensorShape({batch_size + nnz}), + {0, 0, 1, 0, 1, 1, 1, 1, 1, 1}); TF_ASSERT_OK(RunOpKernel()); TF_EXPECT_OK(device_->Sync()); { + /* + permute 2 -> unique_counts: 1, unique_offsets: 4 -> + idx_of_input_to_unique: 3 -> batch: 1, grad: 2.0 + + permute 4 -> unique_counts: 1, unique_offsets: 6 -> + idx_of_input_to_unique: 6 -> batch: 2: grad: 3.0 + + permute 1 -> unique_counts: 2, unique_offsets: 2 + -> idx_of_input_to_unique: 2 -> batch: 1 -> grad : 2.0 + -> idx_of_input_to_unique: 5 -> batch: 3 -> grad : 4.0 + sum grad: 6.0 + */ Tensor grad_shards_1(allocator(), DT_FLOAT, - TensorShape({2, emb_vector_dim})); - test::FillValues(&grad_shards_1, - {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0}); + TensorShape({3, emb_vector_dim})); + test::FillValues(&grad_shards_1, {2.0, 3.0, 6.0}); test::ExpectTensorNear(grad_shards_1, *GetOutput(0), 1e-4); } { + /* + permute 3 -> unique_counts: 1, unique_offsets: 5 -> + idx_of_input_to_unique: 4 -> batch: 3 -> grad: 4.0 + + permute 0 -> unique_counts: 2, unique_offsets: 0 + -> idx_of_input_to_unique 0 -> batch: 0 -> grad: 1.0 + -> idx_of_input_to_unique 1 -> batch: 0 -> grad: 1.0 + sum grad: 2.0 + */ Tensor grad_shards_2(allocator(), DT_FLOAT, TensorShape({2, emb_vector_dim})); - test::FillValues(&grad_shards_2, - {2.0, 2.0, 2.0, 2.0, 0.0, 0.0, 0.0, 0.0}); + test::FillValues(&grad_shards_2, {4.0, 2.0}); test::ExpectTensorNear(grad_shards_2, *GetOutput(1), 1e-4); } } -*/ } // namespace } // namespace tensorflow \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc index 4f272888706..720187760b4 100644 --- a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc +++ b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc @@ -32,7 +32,7 @@ class FusedEmbeddingSparsePostLookUpOpTest : public OpsTestBase { protected: void MakeOpAndSetDevice(Device device, int num_partitions, DataType dtype, const std::string& combiner, const float max_norm, - const int default_id) { + const bool fill_empty_row, const int default_id) { if (device == Device::GPU) { SetDevice(DEVICE_GPU, std::unique_ptr(DeviceFactory::NewDevice( @@ -46,6 +46,7 @@ class FusedEmbeddingSparsePostLookUpOpTest : public OpsTestBase { .Attr("partition_axis", 0) .Attr("combiner", combiner) .Attr("max_norm", max_norm) + .Attr("fill_empty_row", fill_empty_row) .Attr("default_id", default_id) .Input(FakeInput(num_partitions, dtype)) .Input(FakeInput(num_partitions, DT_INT64)) @@ -68,7 +69,7 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, const int emb_vector_dim = 8; const int entries = 8; - MakeOpAndSetDevice(Device::GPU, 3, DT_FLOAT, "sqrtn", 200.0, -1); + MakeOpAndSetDevice(Device::GPU, 3, DT_FLOAT, "sqrtn", 200.0, false, -1); // emb_shards 0 AddInputFromArray( @@ -147,7 +148,7 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, OnlyPartition2Sum) { const int emb_vector_dim = 4; const int entries = 8; - MakeOpAndSetDevice(Device::GPU, 2, DT_FLOAT, "sum", -1.0, -1); + MakeOpAndSetDevice(Device::GPU, 2, DT_FLOAT, "sum", -1.0, true, -1); // emb_shards 0 AddInputFromArray(TensorShape({2, emb_vector_dim}), @@ -203,7 +204,7 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, OnlyPartition2SumDefault2) { const int emb_vector_dim = 4; const int entries = 8; - MakeOpAndSetDevice(Device::GPU, 2, DT_FLOAT, "sum", -1.0, 2); + MakeOpAndSetDevice(Device::GPU, 2, DT_FLOAT, "sum", -1.0, true, 2); // emb_shards 0 AddInputFromArray(TensorShape({2, emb_vector_dim}), @@ -259,7 +260,7 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, OnlyPartition2MeanDefault2Unique) { const int emb_vector_dim = 2; const int entries = 4; - MakeOpAndSetDevice(Device::GPU, 2, DT_FLOAT, "mean", -1.0, 2); + MakeOpAndSetDevice(Device::GPU, 2, DT_FLOAT, "mean", -1.0, true, 2); // emb_shards 0 AddInputFromArray(TensorShape({2, emb_vector_dim}), diff --git a/tensorflow/core/ops/fused_embedding_ops.cc b/tensorflow/core/ops/fused_embedding_ops.cc index 095f1c2e6e4..46396bce4bf 100644 --- a/tensorflow/core/ops/fused_embedding_ops.cc +++ b/tensorflow/core/ops/fused_embedding_ops.cc @@ -144,6 +144,7 @@ REGISTER_OP("FusedEmbeddingSparsePreLookUp") REGISTER_OP("FusedEmbeddingSparsePostLookUp") .Attr("T : {float32}") .Attr("num_partitions: int >= 1 = 1") + .Attr("fill_empty_row: bool = false") .Attr("default_id: int = -1") .Attr("partition_axis: int >= 0 = 0") // for now only support = 0, // will consider support = 1 @@ -241,6 +242,7 @@ REGISTER_OP("FusedEmbeddingSparsePostLookUp") REGISTER_OP("FusedEmbeddingSparsePostLookUpGrad") .Attr("T : {float32}") .Attr("num_partitions: int >= 1 = 1") + .Attr("fill_empty_row: bool = false") .Attr("partition_axis: int >= 0 = 0") // for now only support = 0, // will consider support = 1 // if necessary diff --git a/tensorflow/python/ops/embedding_ops.py b/tensorflow/python/ops/embedding_ops.py index 71e7c2c261f..be98f67c735 100644 --- a/tensorflow/python/ops/embedding_ops.py +++ b/tensorflow/python/ops/embedding_ops.py @@ -271,7 +271,6 @@ def _embedding_lookup_and_transform(params, embs = [] pids_nozero = array_ops.boolean_mask(pids, math_ops.greater_equal(pblocknums , 1)) pblocknums_nozero = array_ops.boolean_mask(pblocknums, math_ops.greater_equal(pblocknums, 1)) - import pdb;pdb.set_trace() for i in range(params[p].blocknum()): with ops.colocate_with(params[p]._ev_list[i]): evids = array_ops.boolean_mask(pids_nozero, math_ops.greater_equal(pblocknums_nozero, i + 1)) From 279a6de774da09764ff9698e40dfa4f7f5f4c550 Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Thu, 10 Mar 2022 19:15:40 +0800 Subject: [PATCH 15/41] python api works right --- tensorflow/core/ops/fused_embedding_ops.cc | 9 +- tensorflow/python/ops/fused_embedding_ops.py | 86 +++++++++++++------- 2 files changed, 59 insertions(+), 36 deletions(-) diff --git a/tensorflow/core/ops/fused_embedding_ops.cc b/tensorflow/core/ops/fused_embedding_ops.cc index 46396bce4bf..7385852d4b4 100644 --- a/tensorflow/core/ops/fused_embedding_ops.cc +++ b/tensorflow/core/ops/fused_embedding_ops.cc @@ -125,8 +125,10 @@ REGISTER_OP("FusedEmbeddingSparsePreLookUp") unused_list[0] = ctx->MakeShape({ctx->UnknownDim(), 2}); ctx->set_output("indices_before_unique", unused_list); unused_list[0] = ctx->MakeShape({ctx->UnknownDim()}); + ctx->set_output("row_empty_and_invalid_flags", unused_list); ctx->set_output("unique_idxs", unused_list); ctx->set_output("unique_counts", unused_list); + ctx->set_output("idx_of_input_to_unique", unused_list); ctx->set_output("unique_offsets", unused_list); return Status::OK(); @@ -159,11 +161,6 @@ REGISTER_OP("FusedEmbeddingSparsePostLookUp") .Input("unique_counts: int64") .Input("idx_of_input_to_unique: int64") .Input("unique_offsets: int64") - .Input( - "partitioned_values: num_partitions * int64") // only for backward use. - // actually directly port - // to python grad op - // output .Output("emb_vectors: T") .Output("feature_nums: int32") .SetShapeFn([](InferenceContext* ctx) { @@ -200,7 +197,7 @@ REGISTER_OP("FusedEmbeddingSparsePostLookUp") // indices_before_unique ctx->input("indices_before_unique", &unused_list); - TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 2, &unused)); // row_empty_and_invalid_flags ctx->input("row_empty_and_invalid_flags", &unused_list); diff --git a/tensorflow/python/ops/fused_embedding_ops.py b/tensorflow/python/ops/fused_embedding_ops.py index 5ce8bb4db55..5eeaa524ec6 100644 --- a/tensorflow/python/ops/fused_embedding_ops.py +++ b/tensorflow/python/ops/fused_embedding_ops.py @@ -8,7 +8,6 @@ from tensorflow.python.ops import variables from tensorflow.python.ops import array_ops from tensorflow.python.framework import sparse_tensor -from tensorflow.python.ops import gen_fused_embedding_ops from tensorflow.python.ops.kv_variable_ops import EmbeddingVariable from tensorflow.python.ops.gen_fused_embedding_ops import fused_embedding_sparse_pre_look_up from tensorflow.python.ops.gen_fused_embedding_ops import fused_embedding_sparse_post_look_up @@ -26,6 +25,7 @@ def fused_embedding_lookup_sparse(params, max_norm=None, default_id=None, prune_invalid_ids=False, + fill_empty_row=True, blocknums=None): if sparse_weights is not None: raise ValueError("sparse_weights is not supported yet") @@ -35,12 +35,12 @@ def fused_embedding_lookup_sparse(params, raise ValueError("{} is not supported yet. Currently only support {}".format( partition_strategy, valid_partition_strategy)) - if default_id is not None and type(default_id) is not int: - raise ValueError("default_id must be a integer!") - if blocknums is not None: raise ValueError("Using blocknums for DynamicEmbeddingVariable is not supported yet") + if default_id is not None and type(default_id) is not int: + raise ValueError("default_id must be a integer!") + partition_nums = len(params) if type(params[0]) is EmbeddingVariable: @@ -53,46 +53,72 @@ def fused_embedding_lookup_sparse(params, with ops.name_scope(name, "fused_embedding_lookup_sparse", params + [sp_ids]) as name: - partitioned_values, partitioned_indices, \ - row_empty_and_invalid_flags = fused_embedding_sparse_pre_look_up( - partition_shapes=partition_shapes, - sp_values=sp_ids.values, - sp_indices=sp_ids.indices, - sp_dense_shape=sp_ids.dense_shape, - fill_empty_row=True, - default_id=default_id, - prune_invalid_id=bool(prune_invalid_ids) - ) + prelookup_out = fused_embedding_sparse_pre_look_up( + partition_shapes=partition_shapes, + sp_values=sp_ids.values, + sp_indices=sp_ids.indices, + sp_dense_shape=sp_ids.dense_shape, + fill_empty_row=fill_empty_row, + default_id=default_id, + prune_invalid_id=bool(prune_invalid_ids), + partition_strategy=partition_strategy) + + partitioned_values = prelookup_out[0] + partition_permutations = prelookup_out[1] + row_empty_and_invalid_flags = prelookup_out[2] + indices_before_unique = prelookup_out[3] + unique_idxs = prelookup_out[4] + unique_counts = prelookup_out[5] + idx_of_input_to_unique = prelookup_out[6] + unique_offsets = prelookup_out[7] + emb_shards = [] for i in range(partition_nums): - param = params[i] - sub_partition_values = partitioned_values[i] - with ops.colocate_with(param): - shard = array_ops.gather(param, sub_partition_values) + with ops.colocate_with(params[i]): + shard = array_ops.gather(params[i], partitioned_values[i]) emb_shards.append(shard) + emb_vectors, _ = fused_embedding_sparse_post_look_up( - emb_shards=emb_shards, partitioned_indices=partitioned_indices, + emb_shards=emb_shards, partition_permutations=partition_permutations, sp_dense_shape=sp_ids.dense_shape, + indices_before_unique=indices_before_unique, row_empty_and_invalid_flags=row_empty_and_invalid_flags, - partitioned_values=partitioned_values, - combiner=combiner, max_norm=max_norm, default_id=default_id + unique_counts=unique_counts, + idx_of_input_to_unique=idx_of_input_to_unique, + unique_offsets=unique_offsets, + combiner=combiner, max_norm=max_norm, fill_empty_row=fill_empty_row, + default_id=default_id ) + return emb_vectors @ops.RegisterGradient("FusedEmbeddingSparsePostLookUp") -def fused_embedding_sparse_post_look_up_grad(op, top_grad_emb_vec, _): +def fused_embedding_sparse_post_look_up_gradient(op, top_grad_emb_vec, _): num_partitions = op.get_attr("num_partitions") + combiner = op.get_attr("combiner") + max_norm = op.get_attr("max_norm") + fill_empty_row = op.get_attr("fill_empty_row") + default_id = op.get_attr("default_id") + emb_shards = [op.inputs[i] for i in range(0, num_partitions)] - partitioned_indices = [op.inputs[i] for i in range(num_partitions, 2 * num_partitions)] + partition_permutations = [op.inputs[i] for i in range(num_partitions, 2 * num_partitions)] + indices_before_unique = op.inputs[2 * num_partitions + 1] + row_empty_and_invalid_flags = op.inputs[2 * num_partitions + 2] + unique_counts = op.inputs[2 * num_partitions + 3] + idx_of_input_to_unique = op.inputs[2 * num_partitions + 4] + unique_offsets = op.inputs[2 * num_partitions + 5] + feature_nums = op.outputs[1] - row_empty_and_invalid_flags = op.inputs[2 * num_partitions + 1] - grad_shards = gen_fused_embedding_ops.fused_embedding_sparse_post_look_up_grad( + grad_shards = fused_embedding_sparse_post_look_up_grad( top_grad=top_grad_emb_vec, emb_shards=emb_shards, - partitioned_indices=partitioned_indices, - feature_nums=feature_nums, row_empty_and_invalid_flags=row_empty_and_invalid_flags, - combiner=op.get_attr("combiner"), max_norm=op.get_attr("max_norm"), - default_id=op.get_attr("default_id") + partition_permutations=partition_permutations, + feature_nums=feature_nums, indices_before_unique=indices_before_unique, + unique_counts=unique_counts, idx_of_input_to_unique=idx_of_input_to_unique, + unique_offsets=unique_offsets, row_empty_and_invalid_flags=row_empty_and_invalid_flags, + combiner=combiner, max_norm=max_norm, + fill_empty_row=fill_empty_row, + default_id=default_id ) - return grad_shards + [None for _ in range(0, 2 * num_partitions + 2)] + return grad_shards + [None for _ in range(len(op.inputs) - num_partitions)] From 6569c30b6ae836e4a223166086e4734072964a42 Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Tue, 15 Mar 2022 14:23:52 +0800 Subject: [PATCH 16/41] Update: try to optimize --- .../fused_embedding_pre_ops_gpus.cu.cc | 16 ++++++++-------- .../fused_embedding/gpu_functions/unique.cu.cc | 14 ++++++++------ .../fused_embedding/gpu_functions/unique.cu.h | 5 +++-- .../tests/fused_embedding_post_ops_test.cc | 1 - tensorflow/python/ops/fused_embedding_ops.py | 1 + 5 files changed, 20 insertions(+), 17 deletions(-) diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc b/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc index f5529e6830c..598da926708 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc @@ -35,6 +35,7 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { int temp_default_id; OP_REQUIRES_OK(ctx, ctx->GetAttr("default_id", &temp_default_id)); default_id_ = int64_t(temp_default_id); + cudaEventCreateWithFlags(&memcpy_event_, cudaEventDisableTiming); } void Compute(OpKernelContext* ctx) override { @@ -57,8 +58,6 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { OP_REQUIRES_OK(ctx, ctx->input("sp_dense_shape", &dense_shape)); const int64_t batch_size = dense_shape->flat().data()[0]; - cudaEvent_t memcpy_event; - cudaEventCreateWithFlags(&memcpy_event, cudaEventDisableTiming); // =============================================== // // ========= 2. allocate cub tmp storage ========= // @@ -165,8 +164,8 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { int selected_num; cudaMemcpyAsync(&selected_num, data_p_with_type(selected_num_d), sizeof(int), cudaMemcpyDeviceToHost, device.stream()); - cudaEventRecord(memcpy_event, device.stream()); - cudaEventSynchronize(memcpy_event); + cudaEventRecord(memcpy_event_, device.stream()); + cudaEventSynchronize(memcpy_event_); new_nnz = selected_num; } @@ -181,8 +180,8 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { int selected_num; cudaMemcpyAsync(&selected_num, data_p_with_type(selected_num_d), sizeof(int), cudaMemcpyDeviceToHost, device.stream()); - cudaEventRecord(memcpy_event, device.stream()); - cudaEventSynchronize(memcpy_event); + cudaEventRecord(memcpy_event_, device.stream()); + cudaEventSynchronize(memcpy_event_); new_nnz += selected_num; } } @@ -216,8 +215,8 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { ctx, ((fill_empty_row_ || prune_invalid_id_) ? &values_extended : values_tensor), - new_nnz, &unique_keys_out, unique_idxs_out, unique_counts_out, - idx_of_input_to_unique_out, unique_offsets_out); + new_nnz, memcpy_event_, &unique_keys_out, unique_idxs_out, + unique_counts_out, idx_of_input_to_unique_out, unique_offsets_out); int64 uniq_size = unique_keys_out.dim_size(0); @@ -290,6 +289,7 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { bool prune_invalid_id_; std::string partition_strategy_; int64_t default_id_; + cudaEvent_t memcpy_event_; }; REGISTER_KERNEL_BUILDER(Name("FusedEmbeddingSparsePreLookUp") diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc index 8974b07cb1f..bcac8c10de5 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc @@ -385,8 +385,9 @@ namespace fused_embedding { // Extention to TensorFlow 2.x's UniqueWithCount operator template void UniqueWithCountsGPU(OpKernelContext* context, const Tensor* input, - const int64 input_size, Tensor* unique_keys, - Tensor* unique_idxs_out, Tensor* unique_counts_out, + const int64 input_size, cudaEvent_t memcpy_event, + Tensor* unique_keys, Tensor* unique_idxs_out, + Tensor* unique_counts_out, Tensor* idx_of_input_to_unique_out, Tensor* unique_offsets_out) { OP_REQUIRES(context, input_size <= std::numeric_limits::max(), @@ -400,8 +401,8 @@ void UniqueWithCountsGPU(OpKernelContext* context, const Tensor* input, se::Stream* stream = context->op_device_context()->stream(); OP_REQUIRES(context, stream, errors::Internal("No GPU stream available.")); - cudaEvent_t memcpy_event; - cudaEventCreateWithFlags(&memcpy_event, cudaEventDisableTiming); + // cudaEvent_t memcpy_event; + // cudaEventCreateWithFlags(&memcpy_event, cudaEventDisableTiming); if (input_size == 0) { // Early exit for trivial case. @@ -634,8 +635,9 @@ void UniqueWithCountsGPU(OpKernelContext* context, const Tensor* input, template void UniqueWithCountsGPU( OpKernelContext* context, const Tensor* input, const int64 input_size, - Tensor* unique_keys, Tensor* unique_idxs_out, Tensor* unique_counts_out, - Tensor* idx_of_input_to_unique_out, Tensor* unique_offsets_out); + cudaEvent_t memcpy_event, Tensor* unique_keys, Tensor* unique_idxs_out, + Tensor* unique_counts_out, Tensor* idx_of_input_to_unique_out, + Tensor* unique_offsets_out); } // namespace fused_embedding diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h index 74412ce4a5c..ce0cffe9edc 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h +++ b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h @@ -14,8 +14,9 @@ namespace fused_embedding { // Extention to TensorFlow 2.x's UniqueWithCounts operator template void UniqueWithCountsGPU(OpKernelContext* context, const Tensor* input, - const int64 input_size, Tensor* unique_keys, - Tensor* unique_idxs_out, Tensor* unique_counts_out, + const int64 input_size, cudaEvent_t memcpy_event, + Tensor* unique_keys, Tensor* unique_idxs_out, + Tensor* unique_counts_out, Tensor* idx_of_input_to_unique_out, Tensor* unique_offsets_out); diff --git a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc index 720187760b4..e46e8a7c796 100644 --- a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc +++ b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc @@ -56,7 +56,6 @@ class FusedEmbeddingSparsePostLookUpOpTest : public OpsTestBase { .Input(FakeInput(DT_INT64)) .Input(FakeInput(DT_INT64)) .Input(FakeInput(DT_INT64)) - .Input(FakeInput(DT_INT64)) .Finalize(node_def())); TF_EXPECT_OK(InitOp()); } diff --git a/tensorflow/python/ops/fused_embedding_ops.py b/tensorflow/python/ops/fused_embedding_ops.py index 5eeaa524ec6..3199cc134d7 100644 --- a/tensorflow/python/ops/fused_embedding_ops.py +++ b/tensorflow/python/ops/fused_embedding_ops.py @@ -27,6 +27,7 @@ def fused_embedding_lookup_sparse(params, prune_invalid_ids=False, fill_empty_row=True, blocknums=None): + if sparse_weights is not None: raise ValueError("sparse_weights is not supported yet") From 6c29295c0934aa0a5ac4b189e62134a506115dc2 Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Tue, 15 Mar 2022 19:52:28 +0800 Subject: [PATCH 17/41] Split pre_embedding_lookup --- tensorflow/core/kernels/BUILD | 23 +- .../fused_embedding/fused_embedding.cu.h | 190 ------- .../fused_embedding_local_ops_gpu.cu.cc | 319 ----------- .../kernels/fused_embedding/gpu/common.cu.h | 200 +++++++ .../functions}/kernels.cu.cc | 4 +- .../functions}/kernels.cu.h | 1 - .../functions}/partition_select.cu.cc | 12 +- .../functions}/partition_select.cu.h | 2 +- .../fused_embedding_post_ops_gpus.cu.cc | 4 +- .../gpu/partition_with_permutation_ops.cu.cc | 119 ++++ ...une_invalid_and_fill_empty_rows_ops.cu.cc} | 144 ++--- .../unique_with_multi_output_ops.cu.cc} | 535 +++++++++--------- .../fused_embedding/gpu_functions/unique.cu.h | 29 - .../tests/fused_embedding_local_ops_test.cc | 405 ------------- tensorflow/core/ops/fused_embedding_ops.cc | 150 ++--- 15 files changed, 717 insertions(+), 1420 deletions(-) delete mode 100644 tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h delete mode 100644 tensorflow/core/kernels/fused_embedding/fused_embedding_local_ops_gpu.cu.cc create mode 100644 tensorflow/core/kernels/fused_embedding/gpu/common.cu.h rename tensorflow/core/kernels/fused_embedding/{gpu_functions => gpu/functions}/kernels.cu.cc (99%) rename tensorflow/core/kernels/fused_embedding/{gpu_functions => gpu/functions}/kernels.cu.h (98%) rename tensorflow/core/kernels/fused_embedding/{gpu_functions => gpu/functions}/partition_select.cu.cc (97%) rename tensorflow/core/kernels/fused_embedding/{gpu_functions => gpu/functions}/partition_select.cu.h (91%) rename tensorflow/core/kernels/fused_embedding/{ => gpu}/fused_embedding_post_ops_gpus.cu.cc (98%) create mode 100644 tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc rename tensorflow/core/kernels/fused_embedding/{fused_embedding_pre_ops_gpus.cu.cc => gpu/prune_invalid_and_fill_empty_rows_ops.cu.cc} (59%) rename tensorflow/core/kernels/fused_embedding/{gpu_functions/unique.cu.cc => gpu/unique_with_multi_output_ops.cu.cc} (57%) delete mode 100644 tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h delete mode 100644 tensorflow/core/kernels/fused_embedding/tests/fused_embedding_local_ops_test.cc diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD index e2fcd787d75..e26707b66aa 100644 --- a/tensorflow/core/kernels/BUILD +++ b/tensorflow/core/kernels/BUILD @@ -1948,9 +1948,7 @@ tf_cuda_cc_test( tf_cuda_cc_test( name = "fused_embedding_ops_test", size = "small", - srcs = ["fused_embedding/tests/fused_embedding_local_ops_test.cc", - "fused_embedding/tests/fused_embedding_pre_ops_test.cc", - "fused_embedding/tests/fused_embedding_post_ops_test.cc", + srcs = ["fused_embedding/tests/fused_embedding_post_ops_test.cc", "fused_embedding/tests/fused_embedding_post_grad_ops_test.cc"], tags = tf_cuda_tests_tags(), deps = [ @@ -5284,21 +5282,20 @@ tf_kernel_library( tf_cuda_library( name = "fused_embedding_cuh", - hdrs = ["fused_embedding/fused_embedding.cu.h", - "fused_embedding/gpu_functions/kernels.cu.h", - "fused_embedding/gpu_functions/unique.cu.h", - "fused_embedding/gpu_functions/partition_select.cu.h"], + hdrs = ["fused_embedding/gpu/common.cu.h", + "fused_embedding/gpu/functions/kernels.cu.h", + "fused_embedding/gpu/functions/partition_select.cu.h"], ) tf_kernel_library( name = "fused_embedding_ops", gpu_srcs = [ - "fused_embedding/fused_embedding_local_ops_gpu.cu.cc", - "fused_embedding/fused_embedding_pre_ops_gpus.cu.cc", - "fused_embedding/fused_embedding_post_ops_gpus.cu.cc", - "fused_embedding/gpu_functions/kernels.cu.cc", - "fused_embedding/gpu_functions/unique.cu.cc", - "fused_embedding/gpu_functions/partition_select.cu.cc", + "fused_embedding/gpu/fused_embedding_post_ops_gpus.cu.cc", + "fused_embedding/gpu/partition_with_permutation_ops.cu.cc", + "fused_embedding/gpu/prune_invalid_and_fill_empty_rows_ops.cu.cc", + "fused_embedding/gpu/unique_with_multi_output_ops.cu.cc", + "fused_embedding/gpu/functions/kernels.cu.cc", + "fused_embedding/gpu/functions/partition_select.cu.cc", ], deps = ["//third_party/eigen3", "//tensorflow/core/profiler:nvtx_utils"] + diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h b/tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h deleted file mode 100644 index 77a19b5dca5..00000000000 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h +++ /dev/null @@ -1,190 +0,0 @@ -#ifndef TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_FUSED_EMBEDDING_CU_H_ -#define TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_FUSED_EMBEDDING_CU_H_ - -#if GOOGLE_CUDA - -#define EIGEN_USE_GPU - -#include "tensorflow/core/framework/op_kernel.h" - -#define CK_CUDA_THROW_(x) \ - do { \ - cudaError_t retval = (x); \ - if (retval != cudaSuccess) { \ - throw std::runtime_error(std::string("Runtime error: ") + \ - (cudaGetErrorString(retval)) + " " + __FILE__ + \ - ":" + std::to_string(__LINE__) + " \n"); \ - } \ - } while (0) - -namespace tensorflow { - -namespace fused_embedding { - -template -inline T* data_p_with_type(Tensor& t) { - return reinterpret_cast(t.data()); -} - -template -inline T* data_p_with_type(const Tensor& t) { - return reinterpret_cast(t.data()); -} - -template -inline T* data_p_with_type(Tensor* t) { - return reinterpret_cast(t->data()); -} - -template -inline T* data_p_with_type(const Tensor* t) { - return reinterpret_cast(t->data()); -} - -struct IndicePair { - int64_t row_in_batch; - int64_t entry_in_column; -}; - -} // namespace fused_embedding - -} // namespace tensorflow - - -// ================================================================================================= -#include -#include -#include -#include -#include -#include -template -void printGPUTensorHelper(const T* src, - const std::vector& dims, - const std::vector& max_num_to_print, - bool if_exit = false) -{ - int element_num = 1; - int dim_num = (int)dims.size(); - - printf("Tensor size: ("); - for (int i = 0; i < dim_num; i++) { - element_num *= dims[i]; - printf("%d, ", dims[i]); - } - printf(")\n"); - T* host_buffer = new T[element_num]; - cudaMemcpy((void*)host_buffer, (void*)src, sizeof(T) * element_num, cudaMemcpyDeviceToHost); - - std::vector strides(dim_num, 1); - for (int i = dim_num - 2; i >= 0; i--) { - strides[i] = strides[i + 1] * dims[i + 1]; - printf("%d ", strides[i]); - } - - cudaDeviceSynchronize(); - - printf("================== start printing tensor =====================\n"); - - std::function recursive_print; - recursive_print = - [&recursive_print, host_buffer, &dims = dims, &max_num_to_print = max_num_to_print, &strides = strides]( - int current_dim, int current_dim_offset) -> void { - if (current_dim < dims.size() - 1) { - for (int i = 0; i < current_dim; i++) { - printf(" "); - } - printf("[ dim %d\n", current_dim); - - if (2 * max_num_to_print[current_dim] >= dims[current_dim]) { - for (int i = 0; i < dims[current_dim]; i++) { - recursive_print(current_dim + 1, current_dim_offset + i * strides[current_dim]); - } - } - else { - for (int i = 0; i < max_num_to_print[current_dim]; i++) { - recursive_print(current_dim + 1, current_dim_offset + i * strides[current_dim]); - } - for (int i = 0; i < current_dim; i++) { - printf(" "); - } - printf(" ......\n"); - for (int i = dims[current_dim] - max_num_to_print[current_dim]; i < dims[current_dim]; i++) { - recursive_print(current_dim + 1, current_dim_offset + i * strides[current_dim]); - } - } - for (int i = 0; i < current_dim; i++) { - printf(" "); - } - printf("]\n"); - } - else { - for (int i = 0; i < current_dim; i++) { - printf(" "); - } - printf("[ "); - printf("dim %d, offset: %d, ", current_dim, current_dim_offset); - if (2 * max_num_to_print[current_dim] >= dims[current_dim]) { - for (int i = 0; i < dims[current_dim]; i++) { - printf("%s ", std::to_string(host_buffer[current_dim_offset + i * strides[current_dim]]).c_str()); - } - } - else { - for (int i = 0; i < max_num_to_print[current_dim]; i++) { - printf("%s ", std::to_string(host_buffer[current_dim_offset + i * strides[current_dim]]).c_str()); - } - printf("...... "); - for (int i = dims[current_dim] - max_num_to_print[current_dim]; i < dims[current_dim]; i++) { - printf("%s ", std::to_string(host_buffer[current_dim_offset + i * strides[current_dim]]).c_str()); - } - } - printf("]\n"); - } - }; - - recursive_print(0, 0); - - printf("\n================== stop =====================\n"); - delete[] host_buffer; - - if (if_exit) { - exit(0); - } -}; - -#include -#include -template -void dumpGPUTensorHelper(const T* src, const std::vector& dims, const std::string& out_file, bool if_exit = true) -{ - int element_num = 1; - int dim_num = (int)dims.size(); - - printf("Tensor size: ("); - for (int i = 0; i < dim_num; i++) { - element_num *= dims[i]; - printf("%d, ", dims[i]); - } - printf(")\n"); - T* host_buffer = new T[element_num]; - cudaMemcpy((void*)host_buffer, (void*)src, sizeof(T) * element_num, cudaMemcpyDeviceToHost); - - std::ofstream file(out_file, std::ios::binary); - if (file.is_open()) { - file.write((const char*)host_buffer, sizeof(T) * element_num); - file.close(); - std::cout << "Dumped tensor to " << out_file << std::endl; - } - else { - std::cout << "Unable to open " << out_file << std::endl; - } - - if (if_exit) { - exit(0); - } -} - - -#endif // GOOGLE_CUDA - -#endif // TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_FUSED_EMBEDDING_CU_H_ \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_local_ops_gpu.cu.cc b/tensorflow/core/kernels/fused_embedding/fused_embedding_local_ops_gpu.cu.cc deleted file mode 100644 index e776c08d82d..00000000000 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_local_ops_gpu.cu.cc +++ /dev/null @@ -1,319 +0,0 @@ -#include -#include - -#include "tensorflow/core/framework/op_kernel.h" - -#if GOOGLE_CUDA - -#define EIGEN_USE_GPU - -#include "tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h" -#include "tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h" -#include "tensorflow/core/util/gpu_kernel_helper.h" - -namespace tensorflow { -using GPUDevice = Eigen::GpuDevice; - -namespace fused_embedding { - -__global__ void SetToIntMaxSTG128(int* values_offset, const int batch_size) { - const int thread_offset = 4 * (blockIdx.x * blockDim.x + threadIdx.x); - const int int_max = 0x7fffffff; - if (thread_offset + 4 < batch_size) { - int4 four = make_int4(int_max, int_max, int_max, int_max); - *((int4*)(values_offset + thread_offset)) = four; - } else if (thread_offset < batch_size) { - for (int i = thread_offset; i < batch_size; i++) { - values_offset[i] = int_max; - } - } -} - -__global__ void CalcPerElementRowInBatchValuesOffset(const int64_t* indices, - int* values_offset, - const int64_t nnz) { - const int thread_offset = blockIdx.x * blockDim.x + threadIdx.x; - if (thread_offset < int(nnz)) { - const int64_t element_row = indices[2 * thread_offset]; - atomicMin(values_offset + int(element_row), thread_offset); - } -} - -template -__global__ void EmbeddingLookUp(const float* emb_variable, - const int64_t* values, const int* values_offset, - float* embedding_vector, const float max_norm, - const int emb_vec_size, - const int64_t batch_size, const int64_t nnz) { - __shared__ float l2_sum[1]; - - int value_offset = values_offset[blockIdx.x]; - int feature_num; - if (blockIdx.x == int(batch_size) - 1) { - feature_num = int(nnz) - value_offset; - } else { - feature_num = values_offset[blockIdx.x + 1] - value_offset; - } - float out = 0.0f; - for (int i = 0; i < feature_num; i++) { - float emb_element = - emb_variable[int(values[value_offset + i]) * emb_vec_size + - threadIdx.x]; - if (max_norm >= 0.0f) { - // calc l2 norm of this emb row(per block) and compare with max_norm. - // if greater than max_norm, then clip every element with factor - // max_norm / l2norm - if (threadIdx.x == 0) { - l2_sum[0] = 0.0f; - } - __syncthreads(); - atomicAdd(l2_sum, emb_element * emb_element); - __syncthreads(); - float l2_norm = sqrtf(l2_sum[0]); - if (l2_norm > max_norm) { - emb_element *= max_norm / l2_norm; - } - } - out += emb_element; - } - - // combine - out = Combine(out, feature_num); - - // store the embedding vector - embedding_vector[blockIdx.x * emb_vec_size + threadIdx.x] = out; -} - -template -__global__ void DoEmbeddingGrad(const float* top_grad, - const float* emb_variable, - const int64_t* values, const int* values_offset, - float* grad_values, const float max_norm, - const int emb_vec_size, - const int64_t batch_size, const int64_t nnz) { - __shared__ float l2_sum[1]; - const int value_offset = values_offset[blockIdx.x]; - int feature_num; - if (blockIdx.x == int(batch_size) - 1) { - feature_num = int(nnz) - value_offset; - } else { - feature_num = values_offset[blockIdx.x + 1] - value_offset; - } - float grad = top_grad[blockIdx.x * emb_vec_size + threadIdx.x]; - grad = CombineGrad(grad, feature_num); - for (int i = 0; i < feature_num; i++) { - float grad_i = grad; - if (max_norm > 0.0f) { - float emb_element = - emb_variable[int(values[value_offset + i]) * emb_vec_size + - threadIdx.x]; - if (threadIdx.x == 0) { - l2_sum[0] = 0.0f; - } - __syncthreads(); - atomicAdd(l2_sum, emb_element * emb_element); - __syncthreads(); - float l2_norm = sqrtf(l2_sum[0]); - if (l2_norm > max_norm) { - grad_i *= max_norm / l2_norm; - } - } - grad_values[(value_offset + i) * emb_vec_size + threadIdx.x] = grad_i; - } -} - -} // namespace fused_embedding - -class FusedEmbeddingLocalSparseLookUpGPU : public OpKernel { - public: - explicit FusedEmbeddingLocalSparseLookUpGPU(OpKernelConstruction* ctx) - : OpKernel(ctx) { - OP_REQUIRES_OK(ctx, ctx->GetAttr("combiner", &combiner_)); - OP_REQUIRES_OK(ctx, ctx->GetAttr("max_norm", &max_norm_)); - } - - void Compute(OpKernelContext* ctx) override { - using namespace fused_embedding; - - auto stream = ctx->eigen_device().stream(); - - Tensor const* values_tensor = nullptr; - OP_REQUIRES_OK(ctx, ctx->input("sp_values", &values_tensor)); - Tensor const* indices_tensor = nullptr; - OP_REQUIRES_OK(ctx, ctx->input("sp_indices", &indices_tensor)); - Tensor const* dense_shape_tensor = nullptr; - OP_REQUIRES_OK(ctx, ctx->input("sp_dense_shape", &dense_shape_tensor)); - Tensor const* emb_variable_tensor = nullptr; - OP_REQUIRES_OK(ctx, ctx->input("emb_variable", &emb_variable_tensor)); - - auto dense_shape = dense_shape_tensor->flat().data(); - const size_t batch_size = dense_shape[0]; - const int64 nnz = indices_tensor->shape().dim_size(0); - const int64 emb_vec_size = emb_variable_tensor->shape().dim_size(1); - - TensorShape emb_vectors_tensor_shape; - - emb_vectors_tensor_shape = TensorShape( - std::vector({static_cast(batch_size), emb_vec_size})); - Tensor* emb_vectors_tensor = nullptr; - // allocate output - OP_REQUIRES_OK(ctx, ctx->allocate_output(0, emb_vectors_tensor_shape, - &emb_vectors_tensor)); - - // allocate offset tensor - TensorShape values_offset_tensor_shape = - TensorShape(std::vector({static_cast(batch_size)})); - - Tensor* values_offset_tensor = nullptr; - OP_REQUIRES_OK(ctx, ctx->allocate_output(1, values_offset_tensor_shape, - &values_offset_tensor)); - - { - const int threads = 1024; - int blocks = batch_size / threads; - blocks = batch_size % threads == 0 ? blocks : blocks + 1; - SetToIntMaxSTG128<<>>( - values_offset_tensor->flat().data(), int(batch_size)); - } - { - const int threads = 1024; - int blocks = nnz % threads == 0 ? (nnz / threads) : (nnz / threads + 1); - - // calculate values offset - CalcPerElementRowInBatchValuesOffset<<>>( - reinterpret_cast( - indices_tensor->flat().data()), - values_offset_tensor->flat().data(), nnz); - } - { - const int blocks = int(batch_size); - const int threads = int(emb_vec_size); - if (combiner_ == "sqrtn") { - EmbeddingLookUp<<>>( - reinterpret_cast( - emb_variable_tensor->flat().data()), - reinterpret_cast( - values_tensor->flat().data()), - values_offset_tensor->flat().data(), - reinterpret_cast(emb_vectors_tensor->flat().data()), - max_norm_, int(emb_vec_size), batch_size, nnz); - } else if (combiner_ == "mean") { - EmbeddingLookUp<<>>( - reinterpret_cast( - emb_variable_tensor->flat().data()), - reinterpret_cast( - values_tensor->flat().data()), - values_offset_tensor->flat().data(), - reinterpret_cast(emb_vectors_tensor->flat().data()), - max_norm_, int(emb_vec_size), batch_size, nnz); - } else { - EmbeddingLookUp<<>>( - reinterpret_cast( - emb_variable_tensor->flat().data()), - reinterpret_cast( - values_tensor->flat().data()), - values_offset_tensor->flat().data(), - reinterpret_cast(emb_vectors_tensor->flat().data()), - max_norm_, int(emb_vec_size), batch_size, nnz); - } - } - } - - private: - std::string combiner_; - float max_norm_; -}; - -REGISTER_KERNEL_BUILDER(Name("FusedEmbeddingLocalSparseLookUp") - .Device(DEVICE_GPU) - .HostMemory("sp_dense_shape"), - FusedEmbeddingLocalSparseLookUpGPU); - -class FusedEmbeddingLocalSparseLookUpGradGPU : public OpKernel { - public: - explicit FusedEmbeddingLocalSparseLookUpGradGPU(OpKernelConstruction* ctx) - : OpKernel(ctx) { - OP_REQUIRES_OK(ctx, ctx->GetAttr("combiner", &combiner_)); - OP_REQUIRES_OK(ctx, ctx->GetAttr("max_norm", &max_norm_)); - } - - void Compute(OpKernelContext* ctx) override { - using namespace fused_embedding; - auto stream = ctx->eigen_device().stream(); - - Tensor const* top_grad_tensor = nullptr; - OP_REQUIRES_OK(ctx, ctx->input("top_grad", &top_grad_tensor)); - - Tensor const* emb_variable_tensor = nullptr; - OP_REQUIRES_OK(ctx, ctx->input("emb_variable", &emb_variable_tensor)); - Tensor const* values_tensor = nullptr; - OP_REQUIRES_OK(ctx, ctx->input("sp_values", &values_tensor)); - Tensor const* values_offset_tensor = nullptr; - OP_REQUIRES_OK(ctx, ctx->input("sp_values_offset", &values_offset_tensor)); - - const int64 emb_vec_size = top_grad_tensor->shape().dim_size(1); - const int64 batch_size = top_grad_tensor->shape().dim_size(0); - const int64 nnz = values_tensor->shape().dim_size(0); - - Tensor* grad_emb_weight_sp_values_tensor; - TensorShape grad_emb_weight_sp_values_tensor_shape = - TensorShape(std::vector({nnz, emb_vec_size})); - OP_REQUIRES_OK( - ctx, ctx->allocate_output(0, grad_emb_weight_sp_values_tensor_shape, - &grad_emb_weight_sp_values_tensor)); - - { - const int blocks = int(batch_size); - const int threads = int(emb_vec_size); - - if (combiner_ == "sqrtn") { - DoEmbeddingGrad<<>>( - reinterpret_cast( - top_grad_tensor->flat().data()), - reinterpret_cast( - emb_variable_tensor->flat().data()), - reinterpret_cast( - values_tensor->flat().data()), - values_offset_tensor->flat().data(), - reinterpret_cast( - grad_emb_weight_sp_values_tensor->flat().data()), - max_norm_, emb_vec_size, batch_size, nnz); - } else if (combiner_ == "mean") { - DoEmbeddingGrad<<>>( - reinterpret_cast( - top_grad_tensor->flat().data()), - reinterpret_cast( - emb_variable_tensor->flat().data()), - reinterpret_cast( - values_tensor->flat().data()), - values_offset_tensor->flat().data(), - reinterpret_cast( - grad_emb_weight_sp_values_tensor->flat().data()), - max_norm_, emb_vec_size, batch_size, nnz); - } else { - DoEmbeddingGrad<<>>( - reinterpret_cast( - top_grad_tensor->flat().data()), - reinterpret_cast( - emb_variable_tensor->flat().data()), - reinterpret_cast( - values_tensor->flat().data()), - values_offset_tensor->flat().data(), - reinterpret_cast( - grad_emb_weight_sp_values_tensor->flat().data()), - max_norm_, emb_vec_size, batch_size, nnz); - } - } - } - - private: - float max_norm_; - std::string combiner_; -}; - -REGISTER_KERNEL_BUILDER( - Name("FusedEmbeddingLocalSparseLookUpGrad").Device(DEVICE_GPU), - FusedEmbeddingLocalSparseLookUpGradGPU); - -} // namespace tensorflow -#endif // GOOGLE_CUDA \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/gpu/common.cu.h b/tensorflow/core/kernels/fused_embedding/gpu/common.cu.h new file mode 100644 index 00000000000..e302063c979 --- /dev/null +++ b/tensorflow/core/kernels/fused_embedding/gpu/common.cu.h @@ -0,0 +1,200 @@ +#ifndef TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_COMMON_CU_H_ +#define TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_COMMON_CU_H_ + +#if GOOGLE_CUDA + +#define EIGEN_USE_GPU + +#include "tensorflow/core/framework/op_kernel.h" + +#define CK_CUDA_THROW_(x) \ + do { \ + cudaError_t retval = (x); \ + if (retval != cudaSuccess) { \ + throw std::runtime_error(std::string("Runtime error: ") + \ + (cudaGetErrorString(retval)) + " " + __FILE__ + \ + ":" + std::to_string(__LINE__) + " \n"); \ + } \ + } while (0) + +namespace tensorflow { + +namespace fused_embedding { + +template +inline T* data_p_with_type(Tensor& t) { + return reinterpret_cast(t.data()); +} + +template +inline T* data_p_with_type(const Tensor& t) { + return reinterpret_cast(t.data()); +} + +template +inline T* data_p_with_type(Tensor* t) { + return reinterpret_cast(t->data()); +} + +template +inline T* data_p_with_type(const Tensor* t) { + return reinterpret_cast(t->data()); +} + +struct IndicePair { + int64_t row_in_batch; + int64_t entry_in_column; +}; + +} // namespace fused_embedding + +} // namespace tensorflow + +// ================================================================================================= +#include +#include + +#include +#include +#include +#include +template +void printGPUTensorHelper(const T* src, const std::vector& dims, + const std::vector& max_num_to_print, + bool if_exit = false) { + int element_num = 1; + int dim_num = (int)dims.size(); + + printf("Tensor size: ("); + for (int i = 0; i < dim_num; i++) { + element_num *= dims[i]; + printf("%d, ", dims[i]); + } + printf(")\n"); + T* host_buffer = new T[element_num]; + cudaMemcpy((void*)host_buffer, (void*)src, sizeof(T) * element_num, + cudaMemcpyDeviceToHost); + + std::vector strides(dim_num, 1); + for (int i = dim_num - 2; i >= 0; i--) { + strides[i] = strides[i + 1] * dims[i + 1]; + printf("%d ", strides[i]); + } + + cudaDeviceSynchronize(); + + printf("================== start printing tensor =====================\n"); + + std::function recursive_print; + recursive_print = [&recursive_print, host_buffer, &dims = dims, + &max_num_to_print = max_num_to_print, &strides = strides]( + int current_dim, int current_dim_offset) -> void { + if (current_dim < dims.size() - 1) { + for (int i = 0; i < current_dim; i++) { + printf(" "); + } + printf("[ dim %d\n", current_dim); + + if (2 * max_num_to_print[current_dim] >= dims[current_dim]) { + for (int i = 0; i < dims[current_dim]; i++) { + recursive_print(current_dim + 1, + current_dim_offset + i * strides[current_dim]); + } + } else { + for (int i = 0; i < max_num_to_print[current_dim]; i++) { + recursive_print(current_dim + 1, + current_dim_offset + i * strides[current_dim]); + } + for (int i = 0; i < current_dim; i++) { + printf(" "); + } + printf(" ......\n"); + for (int i = dims[current_dim] - max_num_to_print[current_dim]; + i < dims[current_dim]; i++) { + recursive_print(current_dim + 1, + current_dim_offset + i * strides[current_dim]); + } + } + for (int i = 0; i < current_dim; i++) { + printf(" "); + } + printf("]\n"); + } else { + for (int i = 0; i < current_dim; i++) { + printf(" "); + } + printf("[ "); + printf("dim %d, offset: %d, ", current_dim, current_dim_offset); + if (2 * max_num_to_print[current_dim] >= dims[current_dim]) { + for (int i = 0; i < dims[current_dim]; i++) { + printf("%s ", + std::to_string( + host_buffer[current_dim_offset + i * strides[current_dim]]) + .c_str()); + } + } else { + for (int i = 0; i < max_num_to_print[current_dim]; i++) { + printf("%s ", + std::to_string( + host_buffer[current_dim_offset + i * strides[current_dim]]) + .c_str()); + } + printf("...... "); + for (int i = dims[current_dim] - max_num_to_print[current_dim]; + i < dims[current_dim]; i++) { + printf("%s ", + std::to_string( + host_buffer[current_dim_offset + i * strides[current_dim]]) + .c_str()); + } + } + printf("]\n"); + } + }; + + recursive_print(0, 0); + + printf("\n================== stop =====================\n"); + delete[] host_buffer; + + if (if_exit) { + exit(0); + } +}; + +#include + +#include +template +void dumpGPUTensorHelper(const T* src, const std::vector& dims, + const std::string& out_file, bool if_exit = true) { + int element_num = 1; + int dim_num = (int)dims.size(); + + printf("Tensor size: ("); + for (int i = 0; i < dim_num; i++) { + element_num *= dims[i]; + printf("%d, ", dims[i]); + } + printf(")\n"); + T* host_buffer = new T[element_num]; + cudaMemcpy((void*)host_buffer, (void*)src, sizeof(T) * element_num, + cudaMemcpyDeviceToHost); + + std::ofstream file(out_file, std::ios::binary); + if (file.is_open()) { + file.write((const char*)host_buffer, sizeof(T) * element_num); + file.close(); + std::cout << "Dumped tensor to " << out_file << std::endl; + } else { + std::cout << "Unable to open " << out_file << std::endl; + } + + if (if_exit) { + exit(0); + } +} + +#endif // GOOGLE_CUDA + +#endif // TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_COMMON_CU_H_ \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.cc similarity index 99% rename from tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.cc rename to tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.cc index 0a7dc9000fa..5fb6a698844 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.cc @@ -4,9 +4,9 @@ #define EIGEN_USE_GPU -#include "tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h" +#include "tensorflow/core/kernels/fused_embedding/gpu/common.cu.h" +#include "tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.h" -#include "tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h" #include "tensorflow/core/platform/stream_executor.h" #include "tensorflow/core/util/gpu_kernel_helper.h" diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h b/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.h similarity index 98% rename from tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h rename to tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.h index 818428bb1d1..0f13c0f5f06 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h +++ b/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.h @@ -6,7 +6,6 @@ #define EIGEN_USE_GPU #include "tensorflow/core/framework/op_kernel.h" -#include "tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h" namespace tensorflow { diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc similarity index 97% rename from tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc rename to tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc index b656af285b1..94f7c87a882 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc @@ -1,14 +1,14 @@ #if GOOGLE_CUDA #define EIGEN_USE_GPU -#include "tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.h" +#include "tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h" #include #include #include #include "tensorflow/core/common_runtime/gpu/gpu_event_mgr.h" -#include "tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h" +#include "tensorflow/core/kernels/fused_embedding/gpu/common.cu.h" #include "tensorflow/core/lib/core/bits.h" #include "tensorflow/core/util/gpu_kernel_helper.h" #include "tensorflow/stream_executor/cuda/cuda_activation.h" @@ -110,14 +110,14 @@ namespace fused_embedding { // SelectArgs, SelectScanPassArgs #define DeclareSelect \ template \ - void SelectName(OpKernelContext* ctx, const Tensor& keys, \ + void SelectName(OpKernelContext* ctx, const Tensor* keys, \ const Tensor& indices, SelectArgs, \ const int64 num_partitions, OpOutputList& selected_keys, \ OpOutputList& selected_indices) { \ - OP_REQUIRES(ctx, keys.dims() == 1, \ + OP_REQUIRES(ctx, keys->dims() == 1, \ errors::InvalidArgument("Tensor keys must ranks 1")); \ const GPUDevice& device = ctx->eigen_gpu_device(); \ - const int64 length = keys.shape().dim_size(0); \ + const int64 length = keys->shape().dim_size(0); \ cudaEvent_t memcpy_event; \ cudaEventCreateWithFlags(&memcpy_event, cudaEventDisableTiming); \ Tensor predicates; \ @@ -241,7 +241,7 @@ DeclareSelectScanKernel; DeclareSelectKernel; DeclareSelect; template void PartitionSelectDiv( - OpKernelContext* ctx, const Tensor& keys, const Tensor& indices, + OpKernelContext* ctx, const Tensor* keys, const Tensor& indices, const Tensor& accu_div, const int64 num_partitions, OpOutputList& selected_keys, OpOutputList& selected_indices); diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.h b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h similarity index 91% rename from tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.h rename to tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h index 7c120d3e211..b5a74023946 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.h +++ b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h @@ -11,7 +11,7 @@ namespace tensorflow { namespace fused_embedding { template -void PartitionSelectDiv(OpKernelContext* ctx, const Tensor& keys, +void PartitionSelectDiv(OpKernelContext* ctx, const Tensor* keys, const Tensor& indices, const Tensor& accu_div, const int64 num_partitions, OpOutputList& selected_keys, OpOutputList& selected_indices); diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/fused_embedding_post_ops_gpus.cu.cc similarity index 98% rename from tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc rename to tensorflow/core/kernels/fused_embedding/gpu/fused_embedding_post_ops_gpus.cu.cc index f9927d72d89..5e5033f35a2 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_post_ops_gpus.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/fused_embedding_post_ops_gpus.cu.cc @@ -8,8 +8,8 @@ #define EIGEN_USE_GPU -#include "tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h" -#include "tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h" +#include "tensorflow/core/kernels/fused_embedding/gpu/common.cu.h" +#include "tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.h" #include "tensorflow/core/profiler/nvtx_utils.h" #include "tensorflow/core/util/gpu_kernel_helper.h" #include "third_party/cub/thread/thread_operators.cuh" diff --git a/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc new file mode 100644 index 00000000000..c6b8fc10e08 --- /dev/null +++ b/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc @@ -0,0 +1,119 @@ +#include +#include +#include + +#include "tensorflow/core/framework/op_kernel.h" + +#if GOOGLE_CUDA + +#define EIGEN_USE_GPU + +#include "tensorflow/core/kernels/fused_embedding/gpu/common.cu.h" +#include "tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.h" +#include "tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h" +#include "tensorflow/core/profiler/nvtx_utils.h" +#include "tensorflow/core/util/gpu_kernel_helper.h" +#include "third_party/cub/device/device_radix_sort.cuh" +#include "third_party/cub/device/device_select.cuh" +#include "third_party/cub/iterator/constant_input_iterator.cuh" +#include "third_party/cub/thread/thread_operators.cuh" + +namespace tensorflow { +using GPUDevice = Eigen::GpuDevice; + +class PartitionWithPermutationGPU : public OpKernel { + public: + explicit PartitionWithPermutationGPU(OpKernelConstruction* ctx) + : OpKernel(ctx) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("num_partitions", &num_partitions_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("partition_axis", &partition_axis_)); + OP_REQUIRES_OK(ctx, + ctx->GetAttr("partition_strategy", &partition_strategy_)); + } + + void Compute(OpKernelContext* ctx) override { + using namespace fused_embedding; + auto device = ctx->eigen_device(); + + nvtx::ScopedRangeIfEnabled nvtx_range(this); + + OpOutputList partitioned_values; + OP_REQUIRES_OK(ctx, + ctx->output_list("partitioned_values", &partitioned_values)); + OpOutputList partition_permutations; + OP_REQUIRES_OK(ctx, ctx->output_list("partition_permutations", + &partition_permutations)); + + Tensor const* input = nullptr; + OP_REQUIRES_OK(ctx, ctx->input("input", &input)); + const int64 input_size = input->NumElements(); + + if (num_partitions_ == 1) { + // Simply copy + Tensor* partitioned_value; + partitioned_values.allocate(0, TensorShape({input_size}), + &partitioned_value); + cudaMemcpyAsync(data_p_with_type(partitioned_values[0]), + data_p_with_type(input), + sizeof(int64_t) * input_size, cudaMemcpyDeviceToDevice, + device.stream()); + // Need to allocate_output here + Tensor* partitioned_permutation; + partition_permutations.allocate(0, TensorShape({input_size}), + &partitioned_permutation); + RangeInit(device, input_size, + data_p_with_type(partitioned_permutation)); + } else { + Tensor partition_permute_init; + OP_REQUIRES_OK(ctx, + ctx->allocate_temp(DT_INT64, TensorShape({input_size}), + &partition_permute_init)); + + RangeInit(device, input_size, + data_p_with_type(partition_permute_init)); + if (partition_strategy_ == "div") { + OpInputList partition_shapes; + OP_REQUIRES_OK(ctx, + ctx->input_list("partition_shapes", &partition_shapes)); + + std::vector accu_div_host; + accu_div_host.resize(num_partitions_); + for (int i = 0; i < partition_shapes.size(); i++) { + OP_REQUIRES(ctx, partition_shapes[i].dims() <= 2, + errors::InvalidArgument( + "input partition_shapes must all less than rank 2")); + const int64_t div = partition_shapes[i].flat().data()[0]; + accu_div_host[i] = i == 0 ? div : accu_div_host[i - 1] + div; + } + + Tensor accu_div; + OP_REQUIRES_OK( + ctx, + ctx->allocate_temp( + DT_INT64, TensorShape({static_cast(num_partitions_)}), + &accu_div)); + cudaMemcpyAsync(data_p_with_type(accu_div), accu_div_host.data(), + num_partitions_ * sizeof(int64_t), + cudaMemcpyHostToDevice, device.stream()); + + PartitionSelectDiv( + ctx, input, partition_permute_init, accu_div, + num_partitions_, partitioned_values, partition_permutations); + } + } + } + + private: + int num_partitions_; + int partition_axis_; + std::string partition_strategy_; + cudaEvent_t memcpy_event_; +}; + +REGISTER_KERNEL_BUILDER(Name("PruneInvalidAndFillEmptyRows") + .Device(DEVICE_GPU) + .HostMemory("sp_dense_shape"), + PartitionWithPermutationGPU); +} // namespace tensorflow + +#endif // GOOGLE_CUDA \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/prune_invalid_and_fill_empty_rows_ops.cu.cc similarity index 59% rename from tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc rename to tensorflow/core/kernels/fused_embedding/gpu/prune_invalid_and_fill_empty_rows_ops.cu.cc index 598da926708..2a0d8e74718 100644 --- a/tensorflow/core/kernels/fused_embedding/fused_embedding_pre_ops_gpus.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/prune_invalid_and_fill_empty_rows_ops.cu.cc @@ -8,10 +8,8 @@ #define EIGEN_USE_GPU -#include "tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h" -#include "tensorflow/core/kernels/fused_embedding/gpu_functions/kernels.cu.h" -#include "tensorflow/core/kernels/fused_embedding/gpu_functions/partition_select.cu.h" -#include "tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h" +#include "tensorflow/core/kernels/fused_embedding/gpu/common.cu.h" +#include "tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.h" #include "tensorflow/core/profiler/nvtx_utils.h" #include "tensorflow/core/util/gpu_kernel_helper.h" #include "third_party/cub/device/device_radix_sort.cuh" @@ -22,16 +20,12 @@ namespace tensorflow { using GPUDevice = Eigen::GpuDevice; -class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { +class PruneInvalidAndFillEmptyRowsGPU : public OpKernel { public: - explicit FusedEmbeddingSparsePreLookUpGPU(OpKernelConstruction* ctx) + explicit PruneInvalidAndFillEmptyRowsGPU(OpKernelConstruction* ctx) : OpKernel(ctx) { - OP_REQUIRES_OK(ctx, ctx->GetAttr("num_partitions", &num_partitions_)); - OP_REQUIRES_OK(ctx, ctx->GetAttr("partition_axis", &partition_axis_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("fill_empty_row", &fill_empty_row_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("prune_invalid_id", &prune_invalid_id_)); - OP_REQUIRES_OK(ctx, - ctx->GetAttr("partition_strategy", &partition_strategy_)); int temp_default_id; OP_REQUIRES_OK(ctx, ctx->GetAttr("default_id", &temp_default_id)); default_id_ = int64_t(temp_default_id); @@ -101,13 +95,13 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { Tensor values_extended; Tensor indices_extended; Tensor tmp_indices_buffer; - Tensor* all_flags; + Tensor* row_empty_and_invalid_flags; Tensor selected_num_d; int new_nnz = nnz; - OP_REQUIRES_OK( - ctx, ctx->allocate_output("row_empty_and_invalid_flags", - TensorShape{batch_size + nnz}, &all_flags)); + OP_REQUIRES_OK(ctx, ctx->allocate_output("row_empty_and_invalid_flags", + TensorShape{batch_size + nnz}, + &row_empty_and_invalid_flags)); if (fill_empty_row_ || prune_invalid_id_) { OP_REQUIRES_OK(ctx, @@ -123,7 +117,7 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { ctx, ctx->allocate_temp(DT_INT32, TensorShape{1}, &selected_num_d)); InitFlagsToOneInt4(device, batch_size + nnz, - all_flags->flat().data()); + row_empty_and_invalid_flags->flat().data()); CK_CUDA_THROW_(cudaGetLastError()); // 3.1 set flags, init tmp_indices_buffer etc. @@ -131,15 +125,17 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { FusedMultiFunctional( device, data_p_with_type(indices_tensor), data_p_with_type(values_tensor), nnz, batch_size, - prune_invalid_id_, default_id, data_p_with_type(all_flags), - data_p_with_type(all_flags) + batch_size, + prune_invalid_id_, default_id, + data_p_with_type(row_empty_and_invalid_flags), + data_p_with_type(row_empty_and_invalid_flags) + batch_size, data_p_with_type(tmp_indices_buffer), data_p_with_type(values_extended)); CK_CUDA_THROW_(cudaGetLastError()); } else if (prune_invalid_id_) { - DetectInvalid(device, data_p_with_type(values_tensor), - nnz, data_p_with_type(all_flags) + batch_size); + DetectInvalid( + device, data_p_with_type(values_tensor), nnz, + data_p_with_type(row_empty_and_invalid_flags) + batch_size); CK_CUDA_THROW_(cudaGetLastError()); } // 3.2 select copy valid id, select copy empty row indices @@ -147,7 +143,7 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { cub::DeviceSelect::Flagged( data_p_with_type(cub_temp_storage), max_cub_bytes, data_p_with_type(values_tensor), - data_p_with_type(all_flags) + batch_size, + data_p_with_type(row_empty_and_invalid_flags) + batch_size, data_p_with_type(values_extended), data_p_with_type(selected_num_d), int(nnz), device.stream()); CK_CUDA_THROW_(cudaGetLastError()); @@ -155,7 +151,7 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { cub::DeviceSelect::Flagged( data_p_with_type(cub_temp_storage), max_cub_bytes, data_p_with_type(indices_tensor), - data_p_with_type(all_flags) + batch_size, + data_p_with_type(row_empty_and_invalid_flags) + batch_size, data_p_with_type(indices_extended), data_p_with_type(selected_num_d), nnz, device.stream()); CK_CUDA_THROW_(cudaGetLastError()); @@ -173,7 +169,7 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { cub::DeviceSelect::Flagged( data_p_with_type(cub_temp_storage), max_cub_bytes, data_p_with_type(tmp_indices_buffer), - data_p_with_type(all_flags), + data_p_with_type(row_empty_and_invalid_flags), data_p_with_type(indices_extended) + new_nnz, data_p_with_type(selected_num_d), batch_size, device.stream()); CK_CUDA_THROW_(cudaGetLastError()); @@ -197,106 +193,36 @@ class FusedEmbeddingSparsePreLookUpGPU : public OpKernel { ? data_p_with_type(indices_extended) : data_p_with_type(indices_tensor); - Tensor* indices_before_unique; - OP_REQUIRES_OK(ctx, ctx->allocate_output("indices_before_unique", - TensorShape{new_nnz, 2}, - &indices_before_unique)); - cudaMemcpyAsync(data_p_with_type(indices_before_unique), indices_in, - sizeof(IndicePair) * new_nnz, cudaMemcpyDeviceToDevice, + Tensor* sp_values_out; + OP_REQUIRES_OK(ctx, + ctx->allocate_output("sp_values_out", TensorShape{new_nnz}, + &sp_values_out)); + + Tensor* sp_indices_out; + OP_REQUIRES_OK( + ctx, ctx->allocate_output("sp_indices_out", TensorShape{new_nnz, 2}, + &sp_indices_out)); + + cudaMemcpyAsync(data_p_with_type(sp_values_out), values_in, + sizeof(int64) * new_nnz, cudaMemcpyDeviceToDevice, device.stream()); - // 4. unique - Tensor unique_keys_out; - // actually below 4 will be allocate_output inside UniqueWithCountsGPU. - // So it's even okay to pass nullptr for these 4 to UniqueWithCountsGPU - Tensor *unique_idxs_out, *unique_counts_out, *idx_of_input_to_unique_out, - *unique_offsets_out; - UniqueWithCountsGPU( - ctx, - ((fill_empty_row_ || prune_invalid_id_) ? &values_extended - : values_tensor), - new_nnz, memcpy_event_, &unique_keys_out, unique_idxs_out, - unique_counts_out, idx_of_input_to_unique_out, unique_offsets_out); - - int64 uniq_size = unique_keys_out.dim_size(0); - - // 5. partition select - OpOutputList partitioned_values; - OP_REQUIRES_OK(ctx, - ctx->output_list("partitioned_values", &partitioned_values)); - OpOutputList partition_permutations; - OP_REQUIRES_OK(ctx, ctx->output_list("partition_permutations", - &partition_permutations)); - - if (num_partitions_ == 1) { - // Simply copy - Tensor* partitioned_value; - partitioned_values.allocate(0, TensorShape({uniq_size}), - &partitioned_value); - cudaMemcpyAsync(data_p_with_type(partitioned_values[0]), - data_p_with_type(unique_keys_out), - sizeof(int64_t) * uniq_size, cudaMemcpyDeviceToDevice, - device.stream()); - // Need to allocate_output here - Tensor* partitioned_permutation; - partition_permutations.allocate(0, TensorShape({uniq_size}), - &partitioned_permutation); - RangeInit(device, uniq_size, - data_p_with_type(partitioned_permutation)); - } else { - Tensor partition_permute_init; - OP_REQUIRES_OK(ctx, ctx->allocate_temp(DT_INT64, TensorShape({uniq_size}), - &partition_permute_init)); - - RangeInit(device, uniq_size, - data_p_with_type(partition_permute_init)); - if (partition_strategy_ == "div") { - OpInputList partition_shapes; - OP_REQUIRES_OK(ctx, - ctx->input_list("partition_shapes", &partition_shapes)); - - std::vector accu_div_host; - accu_div_host.resize(num_partitions_); - for (int i = 0; i < partition_shapes.size(); i++) { - OP_REQUIRES(ctx, partition_shapes[i].dims() <= 2, - errors::InvalidArgument( - "input partition_shapes must all less than rank 2")); - const int64_t div = partition_shapes[i].flat().data()[0]; - accu_div_host[i] = i == 0 ? div : accu_div_host[i - 1] + div; - } - - Tensor accu_div; - OP_REQUIRES_OK( - ctx, - ctx->allocate_temp( - DT_INT64, TensorShape({static_cast(num_partitions_)}), - &accu_div)); - cudaMemcpyAsync(data_p_with_type(accu_div), accu_div_host.data(), - num_partitions_ * sizeof(int64_t), - cudaMemcpyHostToDevice, device.stream()); - - PartitionSelectDiv( - ctx, unique_keys_out, partition_permute_init, accu_div, - num_partitions_, partitioned_values, partition_permutations); - } - } + cudaMemcpyAsync(data_p_with_type(sp_indices_out), indices_in, + sizeof(IndicePair) * new_nnz, cudaMemcpyDeviceToDevice, + device.stream()); } private: - int num_partitions_; - int partition_axis_; bool fill_empty_row_; bool prune_invalid_id_; - std::string partition_strategy_; int64_t default_id_; cudaEvent_t memcpy_event_; }; -REGISTER_KERNEL_BUILDER(Name("FusedEmbeddingSparsePreLookUp") +REGISTER_KERNEL_BUILDER(Name("PruneInvalidAndFillEmptyRows") .Device(DEVICE_GPU) - .HostMemory("partition_shapes") .HostMemory("sp_dense_shape"), - FusedEmbeddingSparsePreLookUpGPU); + PruneInvalidAndFillEmptyRowsGPU); } // namespace tensorflow #endif // GOOGLE_CUDA \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/unique_with_multi_output_ops.cu.cc similarity index 57% rename from tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc rename to tensorflow/core/kernels/fused_embedding/gpu/unique_with_multi_output_ops.cu.cc index bcac8c10de5..354e01ef320 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/unique_with_multi_output_ops.cu.cc @@ -2,13 +2,12 @@ #define EIGEN_USE_GPU -#include "tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h" - #include #include "tensorflow/core/common_runtime/gpu/gpu_event_mgr.h" -#include "tensorflow/core/kernels/fused_embedding/fused_embedding.cu.h" +#include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/lib/core/bits.h" +#include "tensorflow/core/profiler/nvtx_utils.h" #include "tensorflow/core/util/gpu_kernel_helper.h" #include "tensorflow/stream_executor/cuda/cuda_activation.h" @@ -166,13 +165,13 @@ template __global__ void GetIdxOfInputToUniqueKernel( const TIndex* count_ptr, const TIndex* segment_ends_ptr, const TIndex* sorted_unique_perm_ptr, const TIndex* sorted_input_inds_ptr, - const int64* count_prefix_sum_ptr, TIndex* idx_of_input_to_unique_ptr) { + const TIndex* count_prefix_sum_ptr, TIndex* idx_of_input_to_unique_ptr) { const int target_unique_key_idx = blockIdx.x; const TIndex unique_key_count = count_ptr[target_unique_key_idx]; const TIndex permute = sorted_unique_perm_ptr[target_unique_key_idx]; const TIndex offset_of_sorted_input_inds = segment_ends_ptr[permute] - unique_key_count; - int64 offset_of_result = count_prefix_sum_ptr[target_unique_key_idx]; + TIndex offset_of_result = count_prefix_sum_ptr[target_unique_key_idx]; for (int i = threadIdx.x; i < unique_key_count; i += blockDim.x) { idx_of_input_to_unique_ptr[offset_of_result + i] = sorted_input_inds_ptr[offset_of_sorted_input_inds + i]; @@ -185,7 +184,7 @@ Status GetIdxOfInputToUnique(const Eigen::GpuDevice& d, const int64 uniq_size, const TIndex* segment_ends_ptr, const TIndex* sorted_unique_perm_ptr, const TIndex* sorted_input_inds_ptr, - const int64* count_prefix_sum_ptr, + const TIndex* count_prefix_sum_ptr, TIndex* idx_of_input_to_unique_ptr) { if (uniq_size == 0) return Status::OK(); const int64 blocks = uniq_size; @@ -380,266 +379,292 @@ void AllocateTemp(OpKernelContext* context, int64 size, Tensor* tensor, } // namespace -namespace fused_embedding { - // Extention to TensorFlow 2.x's UniqueWithCount operator template -void UniqueWithCountsGPU(OpKernelContext* context, const Tensor* input, - const int64 input_size, cudaEvent_t memcpy_event, - Tensor* unique_keys, Tensor* unique_idxs_out, - Tensor* unique_counts_out, - Tensor* idx_of_input_to_unique_out, - Tensor* unique_offsets_out) { - OP_REQUIRES(context, input_size <= std::numeric_limits::max(), - errors::InvalidArgument( - "unique does not support input tensors larger than ", - std::numeric_limits::max(), " elements")); - - OP_REQUIRES(context, TensorShapeUtils::IsVector(input->shape()), - errors::InvalidArgument("unique expects a 1D vector.")); - - se::Stream* stream = context->op_device_context()->stream(); - OP_REQUIRES(context, stream, errors::Internal("No GPU stream available.")); - - // cudaEvent_t memcpy_event; - // cudaEventCreateWithFlags(&memcpy_event, cudaEventDisableTiming); - - if (input_size == 0) { - // Early exit for trivial case. - Tensor* temp; - OP_REQUIRES_OK(context, context->allocate_output("unique_idxs", - TensorShape({0}), &temp)); - OP_REQUIRES_OK(context, context->allocate_output("unique_counts", - TensorShape({0}), &temp)); - OP_REQUIRES_OK(context, context->allocate_output("idx_of_input_to_unique", - TensorShape({0}), &temp)); - OP_REQUIRES_OK(context, context->allocate_output("unique_offsets", - TensorShape({0}), &temp)); - return; +class UniqueWithMultiOutputGPU : public OpKernel { + public: + explicit UniqueWithMultiOutputGPU(OpKernelConstruction* context) + : OpKernel(context) { + cudaEventCreateWithFlags(&memcpy_event_, cudaEventDisableTiming); } - // The algorithm implemented here is as follows: - // input = [3, 5, 3, 4, 1, 4, 9, 8, 6, 3, 5, 7, 8, 8, 4, 6, 4, 2, 5, 6] - // 1) Sort the input to group equal values together in segments. - // sorted_input, sorted_input_inds = sort(input) - // sorted_input: - // [1, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 8, 8, 8, 9] - // sorted_input_inds: - // [4, 17, 0, 2, 9, 3, 5, 14, 16, 1, 10, 18, 8, 15, 19, 11, 7, 12, 13, 6] - // 2) Identify the boundaries between segments and use prefix sum to - // compute the unique ID for each sorted value. - // sorted_input_unique_ids = prefix_sum(indicator(sorted_input)) - // indicator(sorted_input): - // [0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1] - // sorted_input_unique_ids: - // [0, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 7, 7, 7, 8] - // 3) Extract the input index of the first occurrence of each unique value. - // If counts are required, also extract the end index of each segment. - // unique_input_inds[sorted_input_unique_ids] = - // sorted_input_inds (@ indicator) - // segment_ends[sorted_input_unique_ids[i] - 1] = i (@ indicator) - // unique_input_inds: [4, 17, 0, 3, 1, 8, 11, 7, 6] - // segment_ends: [1, 2, 5, 9, 12, 15, 16, 19, 20] - // 4) Sort the extracted unique input indices to put them in order of - // first appearance. - // sorted_unique_input_inds, sorted_unique_perm = - // sort(unique_input_inds) - // sorted_unique_input_inds: [0, 1, 3, 4, 6, 7, 8, 11, 17] - // sorted_unique_perm: [2, 4, 3, 0, 8, 7, 5, 6, 1] - // 5) Gather the sorted unique input values to produce output, and invert - // the second sort permutation to produce an inverse ID mapping. If - // counts are required, also take the adjacent difference between - // segment_ends indices to produce counts. - // output = input[sorted_unique_input_inds] - // inv_sorted_unique_perm[sorted_unique_perm[i]] = i - // counts = adjacent_difference(segment_ends) - // output: [3, 5, 4, 1, 9, 8, 6, 7, 2] - // inv_sorted_unique_perm: [3, 8, 0, 2, 1, 6, 7, 5, 4] - // counts: [3, 3, 4, 1, 1, 3, 3, 1, 1] - // 6) calculate prefix sum of counts - // epsc = exclusive_prefix_sum_of_counts = [3, 6, 10, 11, 12, 15, 18, 19, - // 20] - // 7) Use custom kernel to calculate the mapping between output and - // corresponding indices of the input. - // idx_of_input_to_unique_ptr: [0, 2, 9, 1, 10, 18, 3, 5, 14, 16, 4, 6, 7, - // 12, 13, 8, 15, 19, 11, 17] - // 8) Look up unique IDs via the inverse ID mapping and scatter them - // using the original sort permutation to produce the indices output. - // idx[sorted_input_inds] = - // inv_sorted_unique_perm[sorted_input_unique_ids] - // idx: [0, 1, 0, 2, 3, 2, 4, 5, 6, 0, 1, 7, 5, 5, 2, 6, 2, 8, 1, 6] - - Tensor sorted_input_inds; - TIndex* sorted_input_inds_ptr = nullptr; - AllocateTemp(context, input_size, &sorted_input_inds, &sorted_input_inds_ptr); - if (!context->status().ok()) return; - - Tensor sorted_input; - T* sorted_input_ptr = nullptr; - AllocateTemp(context, input_size, &sorted_input, &sorted_input_ptr); - if (!context->status().ok()) return; - - const T* input_ptr = input->flat().data(); - OP_REQUIRES_OK( - context, GpuRadixSort(context, input_size, /*keys_in=*/input_ptr, - /*keys_out=*/sorted_input_ptr, - /*indices_in=*/static_cast(nullptr), - /*indices_out=*/sorted_input_inds_ptr)); - - // Create a fancy input iterator to indicate segment boundaries. - cub::TransformInputIterator, - cub::CountingInputIterator> - segment_indicator_iter(0, {sorted_input_ptr}); - - Tensor sorted_input_unique_ids; - TIndex* sorted_input_unique_ids_ptr = nullptr; - AllocateTemp(context, input_size, &sorted_input_unique_ids, - &sorted_input_unique_ids_ptr); - if (!context->status().ok()) return; - - OP_REQUIRES_OK(context, GpuPrefixSum(context, input_size, inclusive, - segment_indicator_iter, - sorted_input_unique_ids_ptr)); - - // Copy the last element of sorted_input_unique_ids back to the host to - // obtain uniq_size. - ScratchSpace last_idx_host(context, 1, /*on_host=*/true); - auto status = - stream - ->ThenMemcpy(last_idx_host.mutable_data(), - se::DeviceMemoryBase( - const_cast(sorted_input_unique_ids_ptr) + - (input_size - 1), - sizeof(*last_idx_host.data())), - sizeof(*last_idx_host.data())) - .ok(); - if (!status) { - context->SetStatus(errors::Internal("Copying device-to-host failed.")); - } + void Compute(OpKernelContext* context) override { + Tensor const* input = nullptr; + OP_REQUIRES_OK(context, context->input("input", &input)); + + const int64 input_size = input->NumElements(); + + nvtx::ScopedRangeIfEnabled nvtx_range(this); + + OP_REQUIRES(context, input_size <= std::numeric_limits::max(), + errors::InvalidArgument( + "unique does not support input tensors larger than ", + std::numeric_limits::max(), " elements")); + + OP_REQUIRES(context, TensorShapeUtils::IsVector(input->shape()), + errors::InvalidArgument("unique expects a 1D vector.")); + + se::Stream* stream = context->op_device_context()->stream(); + OP_REQUIRES(context, stream, errors::Internal("No GPU stream available.")); + + if (input_size == 0) { + // Early exit for trivial case. + Tensor* temp; + OP_REQUIRES_OK(context, context->allocate_output( + "unique_keys", TensorShape({0}), &temp)); + OP_REQUIRES_OK(context, context->allocate_output( + "unique_idxs", TensorShape({0}), &temp)); + OP_REQUIRES_OK(context, context->allocate_output( + "unique_counts", TensorShape({0}), &temp)); + OP_REQUIRES_OK(context, + context->allocate_output("idx_of_input_to_unique", + TensorShape({0}), &temp)); + OP_REQUIRES_OK(context, context->allocate_output( + "unique_offsets", TensorShape({0}), &temp)); + return; + } + + // The algorithm implemented here is as follows: + // input = [3, 5, 3, 4, 1, 4, 9, 8, 6, 3, 5, 7, 8, 8, 4, 6, 4, 2, 5, 6] + // 1) Sort the input to group equal values together in segments. + // sorted_input, sorted_input_inds = sort(input) + // sorted_input: + // [1, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 8, 8, 8, 9] + // sorted_input_inds: + // [4, 17, 0, 2, 9, 3, 5, 14, 16, 1, 10, 18, 8, 15, 19, 11, 7, 12, 13, 6] + // 2) Identify the boundaries between segments and use prefix sum to + // compute the unique ID for each sorted value. + // sorted_input_unique_ids = prefix_sum(indicator(sorted_input)) + // indicator(sorted_input): + // [0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1] + // sorted_input_unique_ids: + // [0, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 7, 7, 7, 8] + // 3) Extract the input index of the first occurrence of each unique value. + // If counts are required, also extract the end index of each segment. + // unique_input_inds[sorted_input_unique_ids] = + // sorted_input_inds (@ indicator) + // segment_ends[sorted_input_unique_ids[i] - 1] = i (@ indicator) + // unique_input_inds: [4, 17, 0, 3, 1, 8, 11, 7, 6] + // segment_ends: [1, 2, 5, 9, 12, 15, 16, 19, 20] + // 4) Sort the extracted unique input indices to put them in order of + // first appearance. + // sorted_unique_input_inds, sorted_unique_perm = + // sort(unique_input_inds) + // sorted_unique_input_inds: [0, 1, 3, 4, 6, 7, 8, 11, 17] + // sorted_unique_perm: [2, 4, 3, 0, 8, 7, 5, 6, 1] + // 5) Gather the sorted unique input values to produce output, and invert + // the second sort permutation to produce an inverse ID mapping. If + // counts are required, also take the adjacent difference between + // segment_ends indices to produce counts. + // output = input[sorted_unique_input_inds] + // inv_sorted_unique_perm[sorted_unique_perm[i]] = i + // counts = adjacent_difference(segment_ends) + // output: [3, 5, 4, 1, 9, 8, 6, 7, 2] + // inv_sorted_unique_perm: [3, 8, 0, 2, 1, 6, 7, 5, 4] + // counts: [3, 3, 4, 1, 1, 3, 3, 1, 1] + // 6) calculate prefix sum of counts + // epsc = exclusive_prefix_sum_of_counts = [3, 6, 10, 11, 12, 15, 18, 19, + // 20] + // 7) Use custom kernel to calculate the mapping between output and + // corresponding indices of the input. + // idx_of_input_to_unique_ptr: [0, 2, 9, 1, 10, 18, 3, 5, 14, 16, 4, 6, 7, + // 12, 13, 8, 15, 19, 11, 17] + // 8) Look up unique IDs via the inverse ID mapping and scatter them + // using the original sort permutation to produce the indices output. + // idx[sorted_input_inds] = + // inv_sorted_unique_perm[sorted_input_unique_ids] + // idx: [0, 1, 0, 2, 3, 2, 4, 5, 6, 0, 1, 7, 5, 5, 2, 6, 2, 8, 1, 6] + + Tensor sorted_input_inds; + TIndex* sorted_input_inds_ptr = nullptr; + AllocateTemp(context, input_size, &sorted_input_inds, + &sorted_input_inds_ptr); + if (!context->status().ok()) return; + + Tensor sorted_input; + T* sorted_input_ptr = nullptr; + AllocateTemp(context, input_size, &sorted_input, &sorted_input_ptr); + if (!context->status().ok()) return; + + const T* input_ptr = input->flat().data(); + OP_REQUIRES_OK( + context, + GpuRadixSort(context, input_size, /*keys_in=*/input_ptr, + /*keys_out=*/sorted_input_ptr, + /*indices_in=*/static_cast(nullptr), + /*indices_out=*/sorted_input_inds_ptr)); + + // Create a fancy input iterator to indicate segment boundaries. + cub::TransformInputIterator, + cub::CountingInputIterator> + segment_indicator_iter(0, {sorted_input_ptr}); + + Tensor sorted_input_unique_ids; + TIndex* sorted_input_unique_ids_ptr = nullptr; + AllocateTemp(context, input_size, &sorted_input_unique_ids, + &sorted_input_unique_ids_ptr); + if (!context->status().ok()) return; + + OP_REQUIRES_OK(context, GpuPrefixSum(context, input_size, inclusive, + segment_indicator_iter, + sorted_input_unique_ids_ptr)); + + // Copy the last element of sorted_input_unique_ids back to the host to + // obtain uniq_size. + ScratchSpace last_idx_host(context, 1, /*on_host=*/true); + auto status = + stream + ->ThenMemcpy(last_idx_host.mutable_data(), + se::DeviceMemoryBase( + const_cast(sorted_input_unique_ids_ptr) + + (input_size - 1), + sizeof(*last_idx_host.data())), + sizeof(*last_idx_host.data())) + .ok(); + if (!status) { + context->SetStatus(errors::Internal("Copying device-to-host failed.")); + } + + const GPUDevice& device = context->eigen_gpu_device(); + cudaEventRecord(memcpy_event_, device.stream()); + cudaEventSynchronize(memcpy_event_); + int64 uniq_size = (*last_idx_host.data()) + 1; + + se::cuda::ScopedActivateExecutorContext scoped_activation{ + context->op_device_context()->stream()->parent()}; + + Tensor unique_input_inds; + TIndex* unique_input_inds_ptr = nullptr; + AllocateTemp(context, uniq_size, &unique_input_inds, + &unique_input_inds_ptr); + if (!context->status().ok()) return; + + Tensor segment_ends; + TIndex* segment_ends_ptr = nullptr; + AllocateTemp(context, uniq_size, &segment_ends, &segment_ends_ptr); + if (!context->status().ok()) return; + + OP_REQUIRES_OK(context, + ExtractFirstOccurrenceIndices( + device, input_size, uniq_size, sorted_input_inds_ptr, + sorted_input_unique_ids_ptr, unique_input_inds_ptr, + segment_ends_ptr)); + + Tensor sorted_unique_input_inds; + TIndex* sorted_unique_input_inds_ptr = nullptr; + AllocateTemp(context, uniq_size, &sorted_unique_input_inds, + &sorted_unique_input_inds_ptr); + if (!context->status().ok()) return; + + Tensor sorted_unique_perm; + TIndex* sorted_unique_perm_ptr = nullptr; + AllocateTemp(context, uniq_size, &sorted_unique_perm, + &sorted_unique_perm_ptr); + if (!context->status().ok()) return; + + // Sort by input index so that output is in order of appearance. + OP_REQUIRES_OK( + context, + GpuRadixSort(context, uniq_size, + /*keys_in=*/unique_input_inds_ptr, + /*keys_out=*/sorted_unique_input_inds_ptr, + /*indices_in=*/static_cast(nullptr), + /*indices_out=*/sorted_unique_perm_ptr, + /*num_bits=*/Log2Ceiling(input_size))); + + // Free temporary tensor that is no longer needed. + unique_input_inds = Tensor(); + unique_input_inds_ptr = nullptr; + + // output 0 unique_keys + Tensor* unique_keys; + OP_REQUIRES_OK(context, + context->allocate_output( + "unique_keys", TensorShape({uniq_size}), &unique_keys)); + T* output_ptr = unique_keys->flat().data(); + + Tensor inv_sorted_unique_perm; + TIndex* inv_sorted_unique_perm_ptr = nullptr; + AllocateTemp(context, uniq_size, &inv_sorted_unique_perm, + &inv_sorted_unique_perm_ptr); + if (!context->status().ok()) return; + + // output 2 unique_counts + Tensor* unique_counts; + OP_REQUIRES_OK(context, context->allocate_output("unique_counts", + TensorShape({uniq_size}), + &unique_counts)); + TIndex* count_ptr = unique_counts->flat().data(); - const GPUDevice& device = context->eigen_gpu_device(); - cudaEventRecord(memcpy_event, device.stream()); - cudaEventSynchronize(memcpy_event); - int64 uniq_size = (*last_idx_host.data()) + 1; - - se::cuda::ScopedActivateExecutorContext scoped_activation{ - context->op_device_context()->stream()->parent()}; - - Tensor unique_input_inds; - TIndex* unique_input_inds_ptr = nullptr; - AllocateTemp(context, uniq_size, &unique_input_inds, &unique_input_inds_ptr); - if (!context->status().ok()) return; - - Tensor segment_ends; - TIndex* segment_ends_ptr = nullptr; - AllocateTemp(context, uniq_size, &segment_ends, &segment_ends_ptr); - if (!context->status().ok()) return; - - OP_REQUIRES_OK(context, - ExtractFirstOccurrenceIndices( - device, input_size, uniq_size, sorted_input_inds_ptr, - sorted_input_unique_ids_ptr, unique_input_inds_ptr, - segment_ends_ptr)); - - Tensor sorted_unique_input_inds; - TIndex* sorted_unique_input_inds_ptr = nullptr; - AllocateTemp(context, uniq_size, &sorted_unique_input_inds, - &sorted_unique_input_inds_ptr); - if (!context->status().ok()) return; - - Tensor sorted_unique_perm; - TIndex* sorted_unique_perm_ptr = nullptr; - AllocateTemp(context, uniq_size, &sorted_unique_perm, - &sorted_unique_perm_ptr); - if (!context->status().ok()) return; - - // Sort by input index so that output is in order of appearance. - OP_REQUIRES_OK( - context, GpuRadixSort(context, uniq_size, - /*keys_in=*/unique_input_inds_ptr, - /*keys_out=*/sorted_unique_input_inds_ptr, - /*indices_in=*/static_cast(nullptr), - /*indices_out=*/sorted_unique_perm_ptr, - /*num_bits=*/Log2Ceiling(input_size))); - - // Free temporary tensor that is no longer needed. - unique_input_inds = Tensor(); - unique_input_inds_ptr = nullptr; - - // output 0 unique_keys - T* output_ptr = nullptr; - AllocateTemp(context, uniq_size, unique_keys, &output_ptr); - - Tensor inv_sorted_unique_perm; - TIndex* inv_sorted_unique_perm_ptr = nullptr; - AllocateTemp(context, uniq_size, &inv_sorted_unique_perm, - &inv_sorted_unique_perm_ptr); - if (!context->status().ok()) return; - - // output 2 unique_counts_out - OP_REQUIRES_OK(context, context->allocate_output("unique_counts", - TensorShape({uniq_size}), - &unique_counts_out)); - TIndex* count_ptr = unique_counts_out->flat().data(); - - // Compute output and counts (if necessary). - OP_REQUIRES_OK(context, - GatherOutputsAndInvertPermutation( + // Compute output and counts (if necessary). + OP_REQUIRES_OK( + context, GatherOutputsAndInvertPermutation( device, uniq_size, input_ptr, sorted_unique_input_inds_ptr, sorted_unique_perm_ptr, segment_ends_ptr, output_ptr, inv_sorted_unique_perm_ptr, count_ptr)); - // Free temporary tensors that are no longer needed. - sorted_unique_input_inds = Tensor(); - sorted_unique_input_inds_ptr = nullptr; - - // Compute prefix sum of counts - // also output 4 unique_offsets_out - OP_REQUIRES_OK(context, context->allocate_output("unique_offsets", - TensorShape({uniq_size}), - &unique_offsets_out)); - TIndex* count_prefix_sum_ptr = unique_offsets_out->flat().data(); - - OP_REQUIRES_OK(context, GpuPrefixSum(context, uniq_size, exclusive, count_ptr, - count_prefix_sum_ptr)); - - // GetIdxOfInputToUnique kernel to calculate the mapping between output and - // corresponding indices of the input - - // output 3 idx_of_input_to_unique_out - OP_REQUIRES_OK(context, - context->allocate_output("idx_of_input_to_unique", - TensorShape({input_size}), - &idx_of_input_to_unique_out)); - TIndex* idx_of_input_to_unique_ptr = - idx_of_input_to_unique_out->flat().data(); - - OP_REQUIRES_OK( - context, - GetIdxOfInputToUnique(device, uniq_size, count_ptr, segment_ends_ptr, - sorted_unique_perm_ptr, sorted_input_inds_ptr, - count_prefix_sum_ptr, idx_of_input_to_unique_ptr)); - - // output 1 unique_idxs_out - OP_REQUIRES_OK( - context, context->allocate_output( - "unique_idxs", TensorShape({input_size}), &unique_idxs_out)); - TIndex* idx_ptr = unique_idxs_out->flat().data(); - - // Compute indices output. - OP_REQUIRES_OK(context, LookupAndScatterUniqueIds( - device, input_size, sorted_input_inds_ptr, - sorted_input_unique_ids_ptr, - inv_sorted_unique_perm_ptr, idx_ptr)); -} + // Free temporary tensors that are no longer needed. + sorted_unique_input_inds = Tensor(); + sorted_unique_input_inds_ptr = nullptr; + + // Compute prefix sum of counts + // also output 4 unique_offsets + Tensor* unique_offsets; + OP_REQUIRES_OK(context, context->allocate_output("unique_offsets", + TensorShape({uniq_size}), + &unique_offsets)); + TIndex* count_prefix_sum_ptr = unique_offsets->flat().data(); + + OP_REQUIRES_OK(context, GpuPrefixSum(context, uniq_size, exclusive, + count_ptr, count_prefix_sum_ptr)); + + // GetIdxOfInputToUnique kernel to calculate the mapping between output and + // corresponding indices of the input -template void UniqueWithCountsGPU( - OpKernelContext* context, const Tensor* input, const int64 input_size, - cudaEvent_t memcpy_event, Tensor* unique_keys, Tensor* unique_idxs_out, - Tensor* unique_counts_out, Tensor* idx_of_input_to_unique_out, - Tensor* unique_offsets_out); + // output 3 idx_of_input_to_unique + Tensor* idx_of_input_to_unique; + OP_REQUIRES_OK(context, context->allocate_output("idx_of_input_to_unique", + TensorShape({input_size}), + &idx_of_input_to_unique)); + TIndex* idx_of_input_to_unique_ptr = + idx_of_input_to_unique->flat().data(); + + OP_REQUIRES_OK(context, + GetIdxOfInputToUnique( + device, uniq_size, count_ptr, segment_ends_ptr, + sorted_unique_perm_ptr, sorted_input_inds_ptr, + count_prefix_sum_ptr, idx_of_input_to_unique_ptr)); + + // output 1 unique_idxs_out + Tensor* unique_idxs; + OP_REQUIRES_OK(context, + context->allocate_output( + "unique_idxs", TensorShape({input_size}), &unique_idxs)); + TIndex* idx_ptr = unique_idxs->flat().data(); + + // Compute indices output. + OP_REQUIRES_OK(context, LookupAndScatterUniqueIds( + device, input_size, sorted_input_inds_ptr, + sorted_input_unique_ids_ptr, + inv_sorted_unique_perm_ptr, idx_ptr)); + } + +private: + cudaEvent_t memcpy_event_; + +}; -} // namespace fused_embedding +#define REGISTER_GPU(T, Tindices) \ + REGISTER_KERNEL_BUILDER(Name("UniqueWithMultiOutput") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("T") \ + .TypeConstraint("out_idx"), \ + UniqueWithMultiOutputGPU); + +REGISTER_GPU(int32, int32); +REGISTER_GPU(int64, int32); +REGISTER_GPU(int32, int64); +REGISTER_GPU(int64, int64); } // namespace tensorflow diff --git a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h b/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h deleted file mode 100644 index ce0cffe9edc..00000000000 --- a/tensorflow/core/kernels/fused_embedding/gpu_functions/unique.cu.h +++ /dev/null @@ -1,29 +0,0 @@ -#ifndef TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_UNIQUE_CU_H_ -#define TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_UNIQUE_CU_H_ - -#if GOOGLE_CUDA - -#define EIGEN_USE_GPU - -#include "tensorflow/core/framework/op_kernel.h" - -namespace tensorflow { - -namespace fused_embedding { - -// Extention to TensorFlow 2.x's UniqueWithCounts operator -template -void UniqueWithCountsGPU(OpKernelContext* context, const Tensor* input, - const int64 input_size, cudaEvent_t memcpy_event, - Tensor* unique_keys, Tensor* unique_idxs_out, - Tensor* unique_counts_out, - Tensor* idx_of_input_to_unique_out, - Tensor* unique_offsets_out); - -} // namespace fused_embedding - -} // namespace tensorflow - -#endif // GOOGLE_CUDA - -#endif // TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_UNIQUE_CU_H_ \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_local_ops_test.cc b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_local_ops_test.cc deleted file mode 100644 index b441c8640cc..00000000000 --- a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_local_ops_test.cc +++ /dev/null @@ -1,405 +0,0 @@ -/* Copyright 2015 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include "tensorflow/core/common_runtime/kernel_benchmark_testlib.h" -#include "tensorflow/core/framework/fake_input.h" -#include "tensorflow/core/framework/node_def_builder.h" -#include "tensorflow/core/framework/tensor.h" -#include "tensorflow/core/framework/types.h" -#include "tensorflow/core/kernels/ops_testutil.h" -#include "tensorflow/core/kernels/ops_util.h" -#include "tensorflow/core/platform/test.h" -#include "tensorflow/core/platform/test_benchmark.h" -#include "tensorflow/core/public/session.h" - -namespace tensorflow { -namespace { - -enum class Device { CPU, GPU }; - -enum TestCase { Sqrtn, Mean, Sum, SqrtnAndMaxNorm200, MeanAndMaxNorm100 }; - -template -void get_node_attr_from_test_case(string& combiner_str, float& max_norm) { - if (test_case == Sqrtn) { - combiner_str = "sqrtn"; - max_norm = -1.0f; - } else if (test_case == Mean) { - combiner_str = "mean"; - max_norm = -1.0f; - } else if (test_case == Sum) { - combiner_str = "sum"; - max_norm = -1.0f; - } else if (test_case == SqrtnAndMaxNorm200) { - combiner_str = "sqrtn"; - max_norm = 200.0f; - } else if (test_case == MeanAndMaxNorm100) { - combiner_str = "mean"; - max_norm = 100.0f; - } -} - -template -void fill_emb_vector_expected(Tensor* expected); - -template <> -void fill_emb_vector_expected(Tensor* expected) { - test::FillValues( - expected, {22.627416610717773, 24.0416316986084, 25.45584487915039, - 26.870058059692383, 28.284271240234375, 29.698484420776367, - 31.112699508666992, 32.526912689208984, 73.90083312988281, - 75.63288879394531, 77.36493682861328, 79.09698486328125, - 80.82904052734375, 82.56108856201172, 84.29314422607422, - 86.02519226074219, 124.70765686035156, 126.43971252441406, - 128.17176818847656, 129.90380859375, 131.6358642578125, - 133.367919921875, 135.09996032714844, 136.83201599121094, - 107.48023223876953, 108.89444732666016, 110.30866241455078, - 111.72286987304688, 113.1370849609375, 114.55130004882812, - 115.96551513671875, 117.37973022460938}); -} - -template <> -void fill_emb_vector_expected(Tensor* expected) { - test::FillValues( - expected, {16.00000000000000, 17.00000000000000, 18.00000000000000, - 19.00000000000000, 20.00000000000000, 21.00000000000000, - 22.00000000000000, 23.00000000000000, 42.66666793823242, - 43.66666793823242, 44.66666793823242, 45.66666793823242, - 46.66666793823242, 47.66666793823242, 48.66666793823242, - 49.66666793823242, 72.00000000000000, 73.00000000000000, - 74.00000000000000, 75.00000000000000, 76.00000000000000, - 77.00000000000000, 78.00000000000000, 79.00000000000000, - 76.00000000000000, 77.00000000000000, 78.00000000000000, - 79.00000000000000, 80.00000000000000, 81.00000000000000, - 82.00000000000000, 83.00000000000000}); -} - -template <> -void fill_emb_vector_expected(Tensor* expected) { - test::FillValues( - expected, {32.0, 34.0, 36.0, 38.0, 40.0, 42.0, 44.0, 46.0, - 128.0, 131.0, 134.0, 137.0, 140.0, 143.0, 146.0, 149.0, - 216.0, 219.0, 222.0, 225.0, 228.0, 231.0, 234.0, 237.0, - 152.0, 154.0, 156.0, 158.0, 160.0, 162.0, 164.0, 166.0}); -} - -template <> -void fill_emb_vector_expected(Tensor* expected) { - test::FillValues( - expected, - {22.62741661, 24.04163170, 25.45584488, 26.87005806, 28.28427124, - 29.69848442, 31.11269951, 32.52691269, 73.90083313, 75.63288879, - 77.36493683, 79.09698486, 80.82904053, 82.56108856, 84.29314423, - 86.02519226, 92.61308289, 94.01081848, 95.40855408, 96.80628204, - 98.20401764, 99.60175323, 100.99948120, 102.39721680, 71.20205688, - 72.31395721, 73.42584991, 74.53774261, 75.64963531, 76.76153564, - 77.87342834, 78.98532867}); -} - -class FusedEmbeddingLocalSparseLookUpOpTest : public OpsTestBase { - protected: - template - void Run(Device device) { - if (device == Device::GPU) { - SetDevice(DEVICE_GPU, - std::unique_ptr(DeviceFactory::NewDevice( - "GPU", {}, "/job:a/replica:0/task:0"))); - } - DataType dtype = DataTypeToEnum::value; - std::string combiner_str; - float max_norm; - - get_node_attr_from_test_case(combiner_str, max_norm); - - TF_EXPECT_OK(NodeDefBuilder("fused_embedding_local_sparse_look_up", - "FusedEmbeddingLocalSparseLookUp") - .Input(FakeInput(DT_INT64)) - .Input(FakeInput(DT_INT64)) - .Input(FakeInput(DT_INT64)) - .Input(FakeInput(dtype)) - .Attr("T", dtype) - .Attr("combiner", combiner_str) - .Attr("max_norm", max_norm) - .Finalize(node_def())); - TF_EXPECT_OK(InitOp()); - - const int nnz = 10; - const int batch_size = 4; - const int emb_vector_dim = 8; - const int entries = 8; - const int bucket_size = 16; - - Tensor sp_values(DT_INT64, {nnz}); - Tensor sp_indices(DT_INT64, {nnz, 2}); - Tensor sp_dense_shape(DT_INT64, {2}); - Tensor emb_variable(dtype, {bucket_size, emb_vector_dim}); - - test::FillValues(&sp_values, {3, 1, 4, 5, 7, 3, 12, 12, 15, 4}); - test::FillValues(&sp_indices, {0, 1, 0, 5, 1, 2, 1, 1, 1, 7, - 2, 1, 2, 4, 2, 7, 3, 0, 3, 6}); - test::FillValues(&sp_dense_shape, {batch_size, entries}); - test::FillValues( - &emb_variable, - {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, - 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, - 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, - 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, - 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, - 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, - 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, - 70.0, 71.0, 72.0, 73.0, 74.0, 75.0, 76.0, 77.0, 78.0, 79.0, - 80.0, 81.0, 82.0, 83.0, 84.0, 85.0, 86.0, 87.0, 88.0, 89.0, - 90.0, 91.0, 92.0, 93.0, 94.0, 95.0, 96.0, 97.0, 98.0, 99.0, - 100.0, 101.0, 102.0, 103.0, 104.0, 105.0, 106.0, 107.0, 108.0, 109.0, - 110.0, 111.0, 112.0, 113.0, 114.0, 115.0, 116.0, 117.0, 118.0, 119.0, - 120.0, 121.0, 122.0, 123.0, 124.0, 125.0, 126.0, 127.0}); - - AddInputFromArray(sp_values.shape(), sp_values.flat()); - AddInputFromArray(sp_indices.shape(), sp_indices.flat()); - AddInputFromArray(sp_dense_shape.shape(), - sp_dense_shape.flat()); - AddInputFromArray(emb_variable.shape(), emb_variable.flat()); - - TF_ASSERT_OK(RunOpKernel()); - - Tensor emb_vector_expected(dtype, {batch_size, emb_vector_dim}); - Tensor sp_values_offset_expected(DT_INT32, {batch_size}); - fill_emb_vector_expected(&emb_vector_expected); - test::FillValues(&sp_values_offset_expected, {0, 2, 5, 8}); - - const Tensor& emb_vector = *GetOutput(0); - const Tensor& values_offset = *GetOutput(1); - TF_EXPECT_OK(device_->Sync()); - - test::ExpectTensorNear(emb_vector_expected, emb_vector, 1e-4); - test::ExpectTensorEqual(sp_values_offset_expected, values_offset); - } -}; - -template -void fill_grad_expected(Tensor* expected); - -template <> -void fill_grad_expected(Tensor* expected) { - test::FillValues( - expected, {0.000000000000000, 0.7071067690849304, 1.4142135381698608, - 2.1213204860687256, 2.8284270763397217, 3.535533905029297, - 4.242640972137451, 4.949747562408447, 0.000000000000000, - 0.7071067690849304, 1.4142135381698608, 2.1213204860687256, - 2.8284270763397217, 3.535533905029297, 4.242640972137451, - 4.949747562408447, 4.618802070617676, 5.196152687072754, - 5.773502826690674, 6.350852966308594, 6.928203582763672, - 7.505553722381592, 8.082903861999512, 8.66025447845459, - 4.618802070617676, 5.196152687072754, 5.773502826690674, - 6.350852966308594, 6.928203582763672, 7.505553722381592, - 8.082903861999512, 8.66025447845459, 4.618802070617676, - 5.196152687072754, 5.773502826690674, 6.350852966308594, - 6.928203582763672, 7.505553722381592, 8.082903861999512, - 8.66025447845459, 9.237604141235352, 9.81495475769043, - 10.392305374145508, 10.96965503692627, 11.547005653381348, - 12.124356269836426, 12.701705932617188, 13.279056549072266, - 9.237604141235352, 9.81495475769043, 10.392305374145508, - 10.96965503692627, 11.547005653381348, 12.124356269836426, - 12.701705932617188, 13.279056549072266, 9.237604141235352, - 9.81495475769043, 10.392305374145508, 10.96965503692627, - 11.547005653381348, 12.124356269836426, 12.701705932617188, - 13.279056549072266, 16.970563888549805, 17.677669525146484, - 18.384777069091797, 19.091882705688477, 19.79899024963379, - 20.5060977935791, 21.21320343017578, 21.920310974121094, - 16.970563888549805, 17.677669525146484, 18.384777069091797, - 19.091882705688477, 19.79899024963379, 20.5060977935791, - 21.21320343017578, 21.920310974121094}); -} - -template <> -void fill_grad_expected(Tensor* expected) { - test::FillValues( - expected, {0.000000000000000, 0.500000000000000, 1.000000000000000, - 1.500000000000000, 2.000000000000000, 2.500000000000000, - 3.000000000000000, 3.500000000000000, 0.000000000000000, - 0.500000000000000, 1.000000000000000, 1.500000000000000, - 2.000000000000000, 2.500000000000000, 3.000000000000000, - 3.500000000000000, 2.6666667461395264, 3.000000000000000, - 3.3333332538604736, 3.6666667461395264, 4.000000000000000, - 4.333333492279053, 4.666666507720947, 5.000000000000000, - 2.6666667461395264, 3.000000000000000, 3.3333332538604736, - 3.6666667461395264, 4.000000000000000, 4.333333492279053, - 4.666666507720947, 5.000000000000000, 2.6666667461395264, - 3.000000000000000, 3.3333332538604736, 3.6666667461395264, - 4.000000000000000, 4.333333492279053, 4.666666507720947, - 5.000000000000000, 5.333333492279053, 5.666666507720947, - 6.000000000000000, 6.333333492279053, 6.666666507720947, - 7.000000000000000, 7.333333492279053, 7.666666507720947, - 5.333333492279053, 5.666666507720947, 6.000000000000000, - 6.333333492279053, 6.666666507720947, 7.000000000000000, - 7.333333492279053, 7.666666507720947, 5.333333492279053, - 5.666666507720947, 6.000000000000000, 6.333333492279053, - 6.666666507720947, 7.000000000000000, 7.333333492279053, - 7.666666507720947, 12.000000000000000, 12.500000000000000, - 13.000000000000000, 13.500000000000000, 14.000000000000000, - 14.500000000000000, 15.000000000000000, 15.500000000000000, - 12.000000000000000, 12.500000000000000, 13.000000000000000, - 13.500000000000000, 14.000000000000000, 14.500000000000000, - 15.000000000000000, 15.500000000000000}); -} - -template <> -void fill_grad_expected(Tensor* expected) { - test::FillValues( - expected, - {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 0.0, 1.0, 2.0, 3.0, - 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, - 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 8.0, 9.0, 10.0, 11.0, - 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, - 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 16.0, 17.0, 18.0, 19.0, - 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, - 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0}); -} - -template <> -void fill_grad_expected(Tensor* expected) { - test::FillValues( - expected, - {0.00000000, 0.50000000, 1.00000000, 1.50000000, 2.00000000, 2.50000000, 3.00000000, 3.50000000, - 0.00000000, 0.50000000, 1.00000000, 1.50000000, 2.00000000, 2.50000000, 3.00000000, 3.50000000, - 2.65028572, 2.98157120, 3.31285667, 3.64414287, 3.97542834, 4.30671406, 4.63799953, 4.96928549, - 2.16437674, 2.43492365, 2.70547056, 2.97601795, 3.24656487, 3.51711202, 3.78765893, 4.05820608, - 1.58337951, 1.78130186, 1.97922409, 2.17714667, 2.37506914, 2.57299161, 2.77091384, 2.96883631, - 5.33333349, 5.66666651, 6.00000000, 6.33333349, 6.66666651, 7.00000000, 7.33333349, 7.66666651, - 1.89459133, 2.01300311, 2.13141513, 2.24982715, 2.36823893, 2.48665094, 2.60506320, 2.72347474, - 1.89459133, 2.01300311, 2.13141513, 2.24982715, 2.36823893, 2.48665094, 2.60506320, 2.72347474, - 3.43474555, 3.57786012, 3.72097445, 3.86408877, 4.00720310, 4.15031767, 4.29343224, 4.43654633, - 11.92628479, 12.42321396, 12.92014217, 13.41707039, 13.91399956, 14.41092777, 14.90785599, 15.40478516}); -} - -class FusedEmbeddingLocalSparseLookUpGradOpTest : public OpsTestBase { - protected: - template - void Run(Device device) { - if (device == Device::GPU) { - SetDevice(DEVICE_GPU, - std::unique_ptr(DeviceFactory::NewDevice( - "GPU", {}, "/job:a/replica:0/task:0"))); - } - DataType dtype = DataTypeToEnum::value; - std::string combiner_str; - float max_norm; - get_node_attr_from_test_case(combiner_str, max_norm); - - TF_EXPECT_OK(NodeDefBuilder("fused_embedding_local_sparse_look_up_grad", - "FusedEmbeddingLocalSparseLookUpGrad") - .Input(FakeInput(dtype)) - .Input(FakeInput(dtype)) - .Input(FakeInput(DT_INT64)) - .Input(FakeInput(DT_INT32)) - .Attr("T", dtype) - .Attr("combiner", combiner_str) - .Attr("max_norm", max_norm) - .Finalize(node_def())); - TF_EXPECT_OK(InitOp()); - - const int nnz = 10; - const int batch_size = 4; - const int emb_vector_dim = 8; - const int bucket_size = 16; - - Tensor top_grad(dtype, {batch_size, emb_vector_dim}); - Tensor emb_variable(dtype, {bucket_size, emb_vector_dim}); - Tensor sp_values(DT_INT64, {nnz}); - Tensor sp_values_offset(DT_INT32, {batch_size}); - - test::FillValues( - &top_grad, - {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, - 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, - 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0}); - test::FillValues( - &emb_variable, - {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, - 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, - 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, - 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, - 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, - 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, - 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, - 70.0, 71.0, 72.0, 73.0, 74.0, 75.0, 76.0, 77.0, 78.0, 79.0, - 80.0, 81.0, 82.0, 83.0, 84.0, 85.0, 86.0, 87.0, 88.0, 89.0, - 90.0, 91.0, 92.0, 93.0, 94.0, 95.0, 96.0, 97.0, 98.0, 99.0, - 100.0, 101.0, 102.0, 103.0, 104.0, 105.0, 106.0, 107.0, 108.0, 109.0, - 110.0, 111.0, 112.0, 113.0, 114.0, 115.0, 116.0, 117.0, 118.0, 119.0, - 120.0, 121.0, 122.0, 123.0, 124.0, 125.0, 126.0, 127.0}); - test::FillValues(&sp_values, {3, 1, 4, 5, 7, 3, 12, 12, 15, 4}); - test::FillValues(&sp_values_offset, {0, 2, 5, 8}); - - AddInputFromArray(top_grad.shape(), top_grad.flat()); - AddInputFromArray(emb_variable.shape(), emb_variable.flat()); - AddInputFromArray(sp_values.shape(), sp_values.flat()); - AddInputFromArray(sp_values_offset.shape(), - sp_values_offset.flat()); - - TF_ASSERT_OK(RunOpKernel()); - - Tensor grad_expected(dtype, {nnz, emb_vector_dim}); - fill_grad_expected(&grad_expected); - - const Tensor& grad = *GetOutput(0); - TF_EXPECT_OK(device_->Sync()); - - test::ExpectTensorNear(grad_expected, grad, 1e-4); - } -}; - -#ifdef GOOGLE_CUDA -TEST_F(FusedEmbeddingLocalSparseLookUpOpTest, EmbeddingLocalSparseLookUpFloatSqrtnGpu) { - Run(Device::GPU); -} - -TEST_F(FusedEmbeddingLocalSparseLookUpOpTest, EmbeddingLocalSparseLookUpFloatMeanGpu) { - Run(Device::GPU); -} - -TEST_F(FusedEmbeddingLocalSparseLookUpOpTest, EmbeddingLocalSparseLookUpFloatSumGpu) { - Run(Device::GPU); -} - -TEST_F(FusedEmbeddingLocalSparseLookUpOpTest, - EmbeddingLocalSparseLookUpFloatSqrtnAndMaxNorm200Gpu) { - Run(Device::GPU); -} - -TEST_F(FusedEmbeddingLocalSparseLookUpGradOpTest, - EmbeddingLocalSparseLookUpGradFloatGpu) { - Run(Device::GPU); -} - -TEST_F(FusedEmbeddingLocalSparseLookUpGradOpTest, - EmbeddingLocalSparseLookUpGradFloatMeanGpu) { - Run(Device::GPU); -} - -TEST_F(FusedEmbeddingLocalSparseLookUpGradOpTest, - EmbeddingLocalSparseLookUpGradFloatSumGpu) { - Run(Device::GPU); -} - -TEST_F(FusedEmbeddingLocalSparseLookUpGradOpTest, - EmbeddingLocalSparseLookUpGradFloatMeanAndMaxNorm100Gpu) { - Run(Device::GPU); -} - -#endif - -} // namespace -} // namespace tensorflow \ No newline at end of file diff --git a/tensorflow/core/ops/fused_embedding_ops.cc b/tensorflow/core/ops/fused_embedding_ops.cc index 7385852d4b4..867cac62c41 100644 --- a/tensorflow/core/ops/fused_embedding_ops.cc +++ b/tensorflow/core/ops/fused_embedding_ops.cc @@ -9,100 +9,94 @@ using shape_inference::DimensionHandle; using shape_inference::InferenceContext; using shape_inference::ShapeHandle; -REGISTER_OP("FusedEmbeddingLocalSparseLookUp") - .Attr("T: {float32}") - .Attr("combiner: {'sqrtn', 'mean', 'sum'}") - .Attr("max_norm: float = -1.0") +REGISTER_OP("PruneInvalidAndFillEmptyRows") + .Attr("fill_empty_row: bool = false") + .Attr("prune_invalid_id: bool = false") + .Attr("default_id: int = -1") .Input("sp_values: int64") .Input("sp_indices: int64") .Input("sp_dense_shape: int64") - .Input("emb_variable: T") - .Output("emb_vectors: T") - .Output("sp_values_offset: int32") + .Output("sp_values_out: int64") + .Output("sp_indices_out: int64") + .Output("row_empty_and_invalid_flags: int32") .SetShapeFn([](InferenceContext* ctx) { - ShapeHandle temp; - TF_RETURN_IF_ERROR(ctx->WithRank(ctx->input(0), 1, &temp)); - TF_RETURN_IF_ERROR(ctx->WithRank(ctx->input(1), 2, &temp)); - TF_RETURN_IF_ERROR(ctx->WithRank(ctx->input(2), 1, &temp)); - ShapeHandle emb_var_shape; - TF_RETURN_IF_ERROR(ctx->WithRank(ctx->input(3), 2, &emb_var_shape)); + ShapeHandle unused; + std::vector unused_list; - DimensionHandle emb_vec_size_dim = ctx->Dim(emb_var_shape, 1); - DimensionHandle batch_dim = ctx->UnknownDim(); + ctx->input("sp_values", &unused_list); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); - ShapeHandle output_shape = ctx->MakeShape({batch_dim, emb_vec_size_dim}); - ctx->set_output(0, output_shape); + ctx->input("sp_indices", &unused_list); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 2, &unused)); + DimensionHandle unused_dim; + TF_RETURN_IF_ERROR(ctx->WithValue(ctx->Dim(unused, 1), 2, &unused_dim)); + + ctx->input("sp_dense_shape", &unused_list); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); + + unused_list.clear(); + unused_list.resize(1); + unused_list[0] = ctx->MakeShape({ctx->UnknownDim()}); + ctx->set_output("sp_values_out", unused_list); + ctx->set_output("row_empty_flags", unused_list); + unused_list[0] = ctx->MakeShape({ctx->UnknownDim(), 2}); + ctx->set_output("sp_indices_out", unused_list); return Status::OK(); }); -// .Doc(R"doc( -// FusedEmbedding ops that performs a local embedding lookup. The process will -// perform embedding vector copying from emb_variable. The input is usually a -// SparseTensor. The output sp_values_offset is reserved for gradient -// calculation. -// )doc"); -REGISTER_OP("FusedEmbeddingLocalSparseLookUpGrad") - .Attr("T: {float32}") - .Attr("combiner: {'sqrtn', 'mean', 'sum'}") - .Attr("max_norm: float = -1.0") - .Input("top_grad: T") - .Input("emb_variable: T") - .Input("sp_values: int64") - .Input("sp_values_offset: int32") - .Output("grad_emb_weight_sp_values: T") +REGISTER_OP("UniqueWithMultiOutput") + .Input("input: T") + .Output("unique_keys: T") + .Output("unique_idxs: out_idx") + .Output("unique_counts: out_idx") + .Output("idx_of_input_to_unique: out_idx") + .Output("unique_offsets: out_idx") + .Attr("T: type") + .Attr("out_idx: {int32, int64} = DT_INT32") .SetShapeFn([](InferenceContext* ctx) { - ShapeHandle top_grad_shape; - TF_RETURN_IF_ERROR(ctx->WithRank(ctx->input(0), 2, &top_grad_shape)); - DimensionHandle emb_vec_size_dim = ctx->Dim(top_grad_shape, 1); - ctx->set_output(0, ctx->MakeShape({ctx->UnknownDim(), emb_vec_size_dim})); + ShapeHandle unused; + std::vector unused_list; + + ctx->input("input", &unused_list); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); + + unused_list.clear(); + unused_list.resize(1); + unused_list[0] = ctx->MakeShape({ctx->UnknownDim()}); + + ctx->set_output("unique_keys", unused_list); + ctx->set_output("unique_idxs", unused_list); + ctx->set_output("unique_counts", unused_list); + ctx->set_output("idx_of_input_to_unique", unused_list); + ctx->set_output("unique_offsets", unused_list); + return Status::OK(); }); -// .Doc(R"doc( -// The gradient ops for FusedEmbeddingLocalSparseLookUp. sp_values_offset from -// the forward op need to be passed to this grad op as input. -// )doc"); - -REGISTER_OP("FusedEmbeddingSparsePreLookUp") +REGISTER_OP("PartitionWithPermutation") .Attr("num_partitions: int >= 1 = 1") .Attr("partition_axis: int >= 0 = 0") // for now only support = 0, // will consider support = 1 // if necessary - .Attr("fill_empty_row: bool = false") - .Attr("prune_invalid_id: bool = false") - .Attr("default_id: int = -1") .Attr("partition_strategy : {'div'}") + .Input("input: int64") .Input("partition_shapes: num_partitions * int64") - .Input("sp_values: int64") - .Input("sp_indices: int64") - .Input("sp_dense_shape: int64") .Output("partitioned_values: num_partitions * int64") .Output("partition_permutations: num_partitions * int64") - .Output("row_empty_and_invalid_flags: int32") - .Output("indices_before_unique: int64") - .Output("unique_idxs: int64") - .Output("unique_counts: int64") - .Output("idx_of_input_to_unique: int64") - .Output("unique_offsets: int64") + .Attr("T: type") + .Attr("out_idx: {int32, int64} = DT_INT32") .SetShapeFn([](InferenceContext* ctx) { + ShapeHandle unused; + std::vector unused_list; + DimensionHandle unused_dim; + int num_partitions; TF_RETURN_IF_ERROR(ctx->GetAttr("num_partitions", &num_partitions)); int partition_axis; TF_RETURN_IF_ERROR(ctx->GetAttr("partition_axis", &partition_axis)); - ShapeHandle unused; - std::vector unused_list; - - ctx->input("sp_values", &unused_list); - TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); - - ctx->input("sp_indices", &unused_list); - TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 2, &unused)); - DimensionHandle unused_dim; - TF_RETURN_IF_ERROR(ctx->WithValue(ctx->Dim(unused, 1), 2, &unused_dim)); - - ctx->input("sp_dense_shape", &unused_list); + ctx->input("input", &unused_list); TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); ctx->input("partition_shapes", &unused_list); @@ -117,31 +111,11 @@ REGISTER_OP("FusedEmbeddingSparsePreLookUp") for (int i = 0; i < num_partitions; i++) { unused_list[i] = ctx->MakeShape({ctx->UnknownDim()}); } - ctx->set_output("partitioned_values", unused_list); + ctx->set_output("partitioned", unused_list); ctx->set_output("partition_permutations", unused_list); - unused_list.clear(); - unused_list.resize(1); - unused_list[0] = ctx->MakeShape({ctx->UnknownDim(), 2}); - ctx->set_output("indices_before_unique", unused_list); - unused_list[0] = ctx->MakeShape({ctx->UnknownDim()}); - ctx->set_output("row_empty_and_invalid_flags", unused_list); - ctx->set_output("unique_idxs", unused_list); - ctx->set_output("unique_counts", unused_list); - ctx->set_output("idx_of_input_to_unique", unused_list); - ctx->set_output("unique_offsets", unused_list); - return Status::OK(); }); -// .Doc(R"doc( -// A fused embedding op, usually using for partitioned and distriuted embedding -// variables. FusedEmbeddingSparsePreLookUp, FusedEmbeddingSparsePostLookUp -// should be used together. This op will first read the partition pattern of -// embedding variables through partition_shapes, then sort, re-calculate and -// assign the embedding indices to the corresponding partition. Several Gather -// ops usually should be appended after this op to gather embedding shards from -// multiple partitioned embedding variables. This op has no gradient function. -// )doc"); REGISTER_OP("FusedEmbeddingSparsePostLookUp") .Attr("T : {float32}") @@ -229,7 +203,7 @@ REGISTER_OP("FusedEmbeddingSparsePostLookUp") // .Doc(R"doc( // A fused embedding op, usually using for partitioned and distriuted embedding -// variables. FusedEmbeddingSparsePreLookUp, FusedEmbeddingSparsePostLookUp +// variables. FusedEmbeddingSparse`LookUp, FusedEmbeddingSparsePostLookUp // should be used together. There should be several Gather ops before this op. // The Gather ops gather embedding shards from embedding variable and this op // glue them together, then apply combiner and max_morm according to embedding From 84209bdefe98384900e2968facfee6304fc495db Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Wed, 16 Mar 2022 15:51:17 +0800 Subject: [PATCH 18/41] Update: python a[i --- .../gpu/partition_with_permutation_ops.cu.cc | 8 ++- tensorflow/core/ops/fused_embedding_ops.cc | 4 +- tensorflow/python/ops/fused_embedding_ops.py | 52 +++++++++++-------- 3 files changed, 35 insertions(+), 29 deletions(-) diff --git a/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc index c6b8fc10e08..88cbc4629f9 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc @@ -97,8 +97,8 @@ class PartitionWithPermutationGPU : public OpKernel { cudaMemcpyHostToDevice, device.stream()); PartitionSelectDiv( - ctx, input, partition_permute_init, accu_div, - num_partitions_, partitioned_values, partition_permutations); + ctx, input, partition_permute_init, accu_div, num_partitions_, + partitioned_values, partition_permutations); } } } @@ -110,9 +110,7 @@ class PartitionWithPermutationGPU : public OpKernel { cudaEvent_t memcpy_event_; }; -REGISTER_KERNEL_BUILDER(Name("PruneInvalidAndFillEmptyRows") - .Device(DEVICE_GPU) - .HostMemory("sp_dense_shape"), +REGISTER_KERNEL_BUILDER(Name("PartitionWithPermutation").Device(DEVICE_GPU), PartitionWithPermutationGPU); } // namespace tensorflow diff --git a/tensorflow/core/ops/fused_embedding_ops.cc b/tensorflow/core/ops/fused_embedding_ops.cc index 867cac62c41..8521e099339 100644 --- a/tensorflow/core/ops/fused_embedding_ops.cc +++ b/tensorflow/core/ops/fused_embedding_ops.cc @@ -84,8 +84,6 @@ REGISTER_OP("PartitionWithPermutation") .Input("partition_shapes: num_partitions * int64") .Output("partitioned_values: num_partitions * int64") .Output("partition_permutations: num_partitions * int64") - .Attr("T: type") - .Attr("out_idx: {int32, int64} = DT_INT32") .SetShapeFn([](InferenceContext* ctx) { ShapeHandle unused; std::vector unused_list; @@ -111,7 +109,7 @@ REGISTER_OP("PartitionWithPermutation") for (int i = 0; i < num_partitions; i++) { unused_list[i] = ctx->MakeShape({ctx->UnknownDim()}); } - ctx->set_output("partitioned", unused_list); + ctx->set_output("partitioned_values", unused_list); ctx->set_output("partition_permutations", unused_list); return Status::OK(); diff --git a/tensorflow/python/ops/fused_embedding_ops.py b/tensorflow/python/ops/fused_embedding_ops.py index 3199cc134d7..36967bbe7d8 100644 --- a/tensorflow/python/ops/fused_embedding_ops.py +++ b/tensorflow/python/ops/fused_embedding_ops.py @@ -2,14 +2,16 @@ from __future__ import division from __future__ import print_function -import tensorflow +import tensorflow as tf from tensorflow.python.framework.constant_op import constant from tensorflow.python.framework import ops from tensorflow.python.ops import variables from tensorflow.python.ops import array_ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops.kv_variable_ops import EmbeddingVariable -from tensorflow.python.ops.gen_fused_embedding_ops import fused_embedding_sparse_pre_look_up +from tensorflow.python.ops.gen_fused_embedding_ops import prune_invalid_and_fill_empty_rows +from tensorflow.python.ops.gen_fused_embedding_ops import unique_with_multi_output +from tensorflow.python.ops.gen_fused_embedding_ops import partition_with_permutation from tensorflow.python.ops.gen_fused_embedding_ops import fused_embedding_sparse_post_look_up from tensorflow.python.ops.gen_fused_embedding_ops import fused_embedding_sparse_post_look_up_grad from tensorflow.python.util.tf_export import tf_export @@ -48,30 +50,38 @@ def fused_embedding_lookup_sparse(params, if partition_nums != 1: raise ValueError("For EmbeddingVariable, do not support partition now") # fake shape for now. TBD change in the future - partition_shapes = [constant([1, 1], dtype=tensorflow.int64)] + partition_shapes = [constant([1, 1], dtype=tf.int64)] else: partition_shapes = [w.shape for w in params] with ops.name_scope(name, "fused_embedding_lookup_sparse", params + [sp_ids]) as name: - prelookup_out = fused_embedding_sparse_pre_look_up( - partition_shapes=partition_shapes, - sp_values=sp_ids.values, - sp_indices=sp_ids.indices, - sp_dense_shape=sp_ids.dense_shape, + + sp_values = sp_ids.values + sp_indices = sp_ids.indices + sp_dense_shape = sp_ids.dense_shape + prune_invalid_id = bool(prune_invalid_ids) + + if prune_invalid_ids or fill_empty_row: + sp_values, sp_indices, row_empty_and_invalid_flags = prune_invalid_and_fill_empty_rows( fill_empty_row=fill_empty_row, + prune_invalid_id=prune_invalid_id, default_id=default_id, - prune_invalid_id=bool(prune_invalid_ids), - partition_strategy=partition_strategy) - - partitioned_values = prelookup_out[0] - partition_permutations = prelookup_out[1] - row_empty_and_invalid_flags = prelookup_out[2] - indices_before_unique = prelookup_out[3] - unique_idxs = prelookup_out[4] - unique_counts = prelookup_out[5] - idx_of_input_to_unique = prelookup_out[6] - unique_offsets = prelookup_out[7] + sp_values=sp_values, + sp_indices=sp_indices, + sp_dense_shape=sp_dense_shape, + ) + + unique_keys, unique_idxs, unique_counts, idx_of_input_to_unique, unique_offsets = unique_with_multi_output( + input=sp_values, + out_idx=tf.int64, + ) + + partitioned_values, partition_permutations = partition_with_permutation( + partition_strategy=partition_strategy, + input=unique_keys, + partition_shapes=partition_shapes + ) emb_shards = [] for i in range(partition_nums): @@ -81,8 +91,8 @@ def fused_embedding_lookup_sparse(params, emb_vectors, _ = fused_embedding_sparse_post_look_up( emb_shards=emb_shards, partition_permutations=partition_permutations, - sp_dense_shape=sp_ids.dense_shape, - indices_before_unique=indices_before_unique, + sp_dense_shape=sp_dense_shape, + indices_before_unique=sp_indices, row_empty_and_invalid_flags=row_empty_and_invalid_flags, unique_counts=unique_counts, idx_of_input_to_unique=idx_of_input_to_unique, From 572c7d83d16ce061118961a94549bc9ce46e3db0 Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Thu, 17 Mar 2022 21:41:34 +0800 Subject: [PATCH 19/41] Update: modifying partition_select op --- .../gpu/functions/hash_functions.cu.h | 145 ++++++++++ .../gpu/functions/partition_select.cu.h | 6 +- .../gpu/functions/partition_select_new.cu.h | 252 ++++++++++++++++++ .../gpu/partition_with_permutation_ops.cu.cc | 85 +++--- .../gpu/unique_with_count_hash.ops.cu.cc | 0 tensorflow/core/ops/fused_embedding_ops.cc | 30 +-- 6 files changed, 447 insertions(+), 71 deletions(-) create mode 100644 tensorflow/core/kernels/fused_embedding/gpu/functions/hash_functions.cu.h create mode 100644 tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select_new.cu.h create mode 100644 tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_hash.ops.cu.cc diff --git a/tensorflow/core/kernels/fused_embedding/gpu/functions/hash_functions.cu.h b/tensorflow/core/kernels/fused_embedding/gpu/functions/hash_functions.cu.h new file mode 100644 index 00000000000..d2c6f248ee7 --- /dev/null +++ b/tensorflow/core/kernels/fused_embedding/gpu/functions/hash_functions.cu.h @@ -0,0 +1,145 @@ +/* + * Copyright (c) 2017, NVIDIA CORPORATION. + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_HASH_FUNCTIONS_CU_H_ +#define TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_HASH_FUNCTIONS_CU_H_ + + +//MurmurHash3_32 implementation from https://github.com/aappleby/smhasher/blob/master/src/MurmurHash3.cpp +//----------------------------------------------------------------------------- +// MurmurHash3 was written by Austin Appleby, and is placed in the public +// domain. The author hereby disclaims copyright to this source code. +// Note - The x86 and x64 versions do _not_ produce the same results, as the +// algorithms are optimized for their respective platforms. You can still +// compile and run any of them on any platform, but your performance with the +// non-native version will be less than optimal. +template +struct MurmurHash3_32 +{ + + using argument_type = Key; + using result_type = uint32_t; + + /*__forceinline__ + __host__ __device__ + MurmurHash3_32() : m_seed( 0 ) {}*/ + + __forceinline__ + __host__ __device__ static uint32_t rotl32( uint32_t x, int8_t r ) + { + return (x << r) | (x >> (32 - r)); + } + + __forceinline__ + __host__ __device__ static uint32_t fmix32( uint32_t h ) + { + h ^= h >> 16; + h *= 0x85ebca6b; + h ^= h >> 13; + h *= 0xc2b2ae35; + h ^= h >> 16; + return h; + } + + /* --------------------------------------------------------------------------*/ + /** + * @Synopsis Combines two hash values into a new single hash value. Called + * repeatedly to create a hash value from several variables. + * Taken from the Boost hash_combine function + * https://www.boost.org/doc/libs/1_35_0/doc/html/boost/hash_combine_id241013.html + * + * @Param lhs The first hash value to combine + * @Param rhs The second hash value to combine + * + * @Returns A hash value that intelligently combines the lhs and rhs hash values + */ + /* ----------------------------------------------------------------------------*/ + __host__ __device__ static result_type hash_combine(result_type lhs, result_type rhs) + { + result_type combined{lhs}; + + combined ^= rhs + 0x9e3779b9 + (combined << 6) + (combined >> 2); + + return combined; + } + + __forceinline__ + __host__ __device__ static result_type hash(const Key& key) + { + constexpr int len = sizeof(argument_type); + const uint8_t * const data = (const uint8_t*)&key; + constexpr int nblocks = len / 4; + uint32_t h1 = m_seed; + constexpr uint32_t c1 = 0xcc9e2d51; + constexpr uint32_t c2 = 0x1b873593; + //---------- + // body + const uint32_t * const blocks = (const uint32_t *)(data + nblocks*4); + for(int i = -nblocks; i; i++) + { + uint32_t k1 = blocks[i];//getblock32(blocks,i); + k1 *= c1; + k1 = rotl32(k1,15); + k1 *= c2; + h1 ^= k1; + h1 = rotl32(h1,13); + h1 = h1*5+0xe6546b64; + } + //---------- + // tail + const uint8_t * tail = (const uint8_t*)(data + nblocks*4); + uint32_t k1 = 0; + switch(len & 3) + { + case 3: k1 ^= tail[2] << 16; + case 2: k1 ^= tail[1] << 8; + case 1: k1 ^= tail[0]; + k1 *= c1; k1 = rotl32(k1,15); k1 *= c2; h1 ^= k1; + }; + //---------- + // finalization + h1 ^= len; + h1 = fmix32(h1); + return h1; + } + +}; + +template +struct Fix_Hash{ + + using result_type = index_type; + + __forceinline__ + __host__ __device__ static index_type hash(const key_type& key) + { + return result; + } + +}; + +template +struct Mod_Hash{ + + __forceinline__ + __host__ __device__ static result_type hash(const key_type& key) + { + return (result_type)key; + } + +}; + +#endif // TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_HASH_FUNCTIONS_CU_H_ diff --git a/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h index b5a74023946..cdb1da21adb 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h +++ b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h @@ -1,5 +1,5 @@ -#ifndef TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_PARTITION_SELECT_H_ -#define TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_PARTITION_SELECT_H_ +#ifndef TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_PARTITION_SELECT_CU_H_ +#define TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_PARTITION_SELECT_CU_H_ #if GOOGLE_CUDA @@ -22,4 +22,4 @@ void PartitionSelectDiv(OpKernelContext* ctx, const Tensor* keys, #endif // GOOGLE_CUDA -#endif // TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_PARTITION_SELECT_H_ \ No newline at end of file +#endif // TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_PARTITION_SELECT_CU_H_ \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select_new.cu.h b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select_new.cu.h new file mode 100644 index 00000000000..364477ad006 --- /dev/null +++ b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select_new.cu.h @@ -0,0 +1,252 @@ +#if GOOGLE_CUDA + +#define EIGEN_USE_GPU +#include +#include +#include + +#include "tensorflow/core/common_runtime/gpu/gpu_event_mgr.h" +#include "tensorflow/core/kernels/fused_embedding/gpu/common.cu.h" +#include "tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h" +#include "tensorflow/core/lib/core/bits.h" +#include "tensorflow/core/util/gpu_kernel_helper.h" +#include "tensorflow/stream_executor/cuda/cuda_activation.h" + +namespace tensorflow { + +typedef Eigen::GpuDevice GPUDevice; + +namespace fused_embedding { + +// A macro helper to declare SelectScanKernels, because they just have only +// a little bit differences. Need to define macros +#define DeclareSelectScanKernel \ + template \ + __global__ void SelectScanKernelName( \ + const T* keys, SelectScanKernelArgs, const int64 num_partitions, \ + const int64 length, const int64 predicates_length, \ + const int64 counters_length, unsigned int* predicates, \ + unsigned int* accumulate_counters) { \ + __shared__ unsigned int warp_cnt_sum[1]; \ + int lnid = threadIdx.x % 32; \ + if (lnid == 0) warp_cnt_sum[0] = 0; /* init shared mem of sum */ \ + const int warp_iteration = WarpWorkload / 32; \ + const int select_scan_warps = length % WarpWorkload == 0 \ + ? (length / WarpWorkload) \ + : (length / WarpWorkload + 1); \ + const int partition_id = blockIdx.x / select_scan_warps; \ + int warp_id_in_partition = blockIdx.x % select_scan_warps; \ + unsigned int mask; \ + unsigned int cnt; \ + SelectScanKernelLoadCodeBlock; \ + _Pragma("unroll") for (int i = 0; i < warp_iteration; i++) { \ + int selected; \ + int load_offset = warp_id_in_partition * WarpWorkload + i * 32 + lnid; \ + if (load_offset < length) { \ + T key = keys[load_offset]; \ + SelectScanKernelEvalCodeBlock; \ + } else { \ + selected = 0; \ + } \ + mask = __ballot_sync(0xffffffff, selected); \ + if (lnid == 0) \ + predicates[partition_id * predicates_length + \ + warp_id_in_partition * (WarpWorkload / 32) + i] = mask; \ + cnt = __popc(mask); \ + if (lnid == 0) atomicAdd(warp_cnt_sum, cnt); \ + } \ + /* use different threads in warp to accumulate to different location of \ + * accumulate_counters, this will do a prefix sum on global mem */ \ + const int counters_slots_need_accumulate = \ + select_scan_warps - warp_id_in_partition; \ + const unsigned int warp_cnt = warp_cnt_sum[0]; \ + for (int i = 0; i < counters_slots_need_accumulate; i += 32) { \ + if (i + lnid < counters_slots_need_accumulate) { \ + atomicAdd(accumulate_counters + partition_id * counters_length + \ + warp_id_in_partition + lnid, \ + warp_cnt); \ + } \ + } \ + } + +#define DeclareSelectKernel \ + template \ + __global__ void SelectKernelName( \ + const T* keys, SelectKernelArgs, unsigned int* predicates, \ + unsigned int* accumulate_counters, const int64 num_partitions, \ + const int64 length, const int64 predicates_length, \ + const int64 counters_length, void** output_ptrs, TIndex* permutation) { \ + const int lnid = threadIdx.x % 32; \ + const int warp_iteration = WarpWorkload / 32; \ + const int select_scan_warps = length % WarpWorkload == 0 \ + ? (length / WarpWorkload) \ + : (length / WarpWorkload + 1); \ + const int partition_id = blockIdx.x / select_scan_warps; \ + int warp_id_in_partition = blockIdx.x % select_scan_warps; \ + unsigned int predmask = 0; \ + unsigned int cnt = 0; \ + SelectKernelLoadCodeBlock; \ + T* keys_output_ptr = (T*)(output_ptrs[partition_id]); \ + if (lnid < warp_iteration) { \ + predmask = \ + predicates[partition_id * predicates_length + \ + (warp_id_in_partition * (WarpWorkload / 32)) + lnid]; \ + cnt = __popc(predmask); \ + } \ + _Pragma("unroll") for (int offset = 1; offset < warp_iteration; \ + offset <<= 1) { \ + /* prefix sum */ \ + int n = __shfl_up_sync(0xffffffff, cnt, offset); \ + if (lnid >= offset) cnt += n; \ + } \ + unsigned int global_index = 0; \ + if (warp_id_in_partition > 0) \ + global_index = accumulate_counters[partition_id * counters_length + \ + warp_id_in_partition - 1]; \ + _Pragma("unroll") for (int i = 0; i < warp_iteration; i++) { \ + unsigned int mask = __shfl_sync(0xffffffff, predmask, i); \ + unsigned int inner_warp_index = 0; \ + if (i > 0) inner_warp_index = __shfl_sync(0xffffffff, cnt, i - 1); \ + if (mask & (1 << lnid)) { \ + int load_offset = warp_id_in_partition * WarpWorkload + i * 32 + lnid; \ + T key = keys[load_offset]; /* Will not cause out of boundry access, \ + because mask will be 0 for this thread*/ \ + SelectKernelRecalcKeyCodeBlock; \ + int output_offset = \ + global_index + inner_warp_index + \ + __popc(mask & (((unsigned int)(1) << lnid) - (unsigned int)(1))); \ + keys_output_ptr[output_offset] = new_key; \ + permutation[2 * load_offset] = partition_id; \ + permutation[2 * load_offset + 1] = (TIndex)output_offset; \ + } \ + } \ + } + +// A macro helper to declare DefinePartitionSelect. Need to define following +// macros before using this: SelectScanKernelName, +// SelectArgs, SelectScanPassArgs +#define DeclareSelect \ + template \ + void SelectName(OpKernelContext* ctx, const Tensor* keys, SelectArgs, \ + const int64 num_partitions, cudaEvent_t memcpy_event, \ + OpOutputList& selected_keys, Tensor* permutation) { \ + OP_REQUIRES(ctx, keys->dims() == 1, \ + errors::InvalidArgument("Tensor keys must ranks 1")); \ + OP_REQUIRES(ctx, \ + WarpWorkload >= 32 && \ + (WarpWorkload && !(WarpWorkload & (WarpWorkload - 1))), \ + errors::InvalidArgument( \ + "WarpWorkload must be larger than warp size " \ + "32 and is exponential of 2, 32, 64, 128, i.e.")); \ + const GPUDevice& device = ctx->eigen_gpu_device(); \ + const int64 length = keys->NumElements(); \ + const int64 warp_iteration = WarpWorkload / 32; \ + Tensor predicates; \ + Tensor accumulate_counters; \ + const int64 select_scan_warps = length % WarpWorkload == 0 \ + ? (length / WarpWorkload) \ + : (length / WarpWorkload + 1); \ + const int64 counters_length = select_scan_warps; \ + const int64 predicates_length = select_scan_warps * warp_iteration; \ + OP_REQUIRES_OK( \ + ctx, ctx->allocate_temp(DT_UINT32, \ + TensorShape{counters_length * num_partitions}, \ + &accumulate_counters)); \ + cudaMemsetAsync(data_p_with_type(accumulate_counters), 0, \ + accumulate_counters.NumElements() * sizeof(unsigned int), \ + device.stream()); \ + OP_REQUIRES_OK( \ + ctx, ctx->allocate_temp( \ + DT_UINT32, TensorShape{predicates_length * num_partitions}, \ + &predicates)); \ + \ + { \ + const int64 threads = 32; \ + const int64 blocks = select_scan_warps * num_partitions; \ + OP_REQUIRES_OK( \ + ctx, \ + GpuLaunchKernel( \ + SelectScanKernelName, blocks, threads, 0, \ + device.stream(), data_p_with_type(keys), SelectScanPassArgs, \ + num_partitions, length, predicates_length, counters_length, \ + data_p_with_type(predicates), \ + data_p_with_type(accumulate_counters))); \ + } \ + std::vector selected_nums_host; \ + selected_nums_host.resize(num_partitions); \ + /* copy the last element(which is the sum of previous) with stride */ \ + cudaMemcpy2DAsync(selected_nums_host.data(), 1 * sizeof(unsigned int), \ + data_p_with_type(inclusive_sum_counters) + \ + counters_length - 1, \ + counters_length * sizeof(unsigned int), \ + 1 * sizeof(unsigned int), num_partitions, \ + cudaMemcpyDeviceToHost, device.stream()); \ + cudaEventRecord(memcpy_event, device.stream()); \ + cudaEventSynchronize(memcpy_event); \ + \ + std::vector output_ptrs_host; \ + output_ptrs_host.resize(num_partitions); \ + for (int i = 0; i < num_partitions; i++) { \ + Tensor* tmp_out; \ + OP_REQUIRES_OK( \ + ctx, selected_keys.allocate( \ + i, TensorShape({int64(selected_nums_host[i])}), &tmp_out)); \ + output_ptrs_host[i] = data_p_with_type(tmp_out); \ + } \ + Tensor output_ptrs; \ + OP_REQUIRES_OK( \ + ctx, ctx->allocate_temp(DT_UINT64, TensorShape{int64(num_partitions)}, \ + &output_ptrs)); \ + cudaMemcpyAsync(data_p_with_type(output_ptrs), \ + output_ptrs_host.data(), num_partitions * sizeof(size_t), \ + cudaMemcpyHostToDevice, device.stream()); \ + { \ + const int64 threads = 32; \ + const int64 blocks = select_scan_warps * num_partitions; \ + OP_REQUIRES_OK( \ + ctx, GpuLaunchKernel( \ + SelectKernelName, blocks, threads, \ + 0, device.stream(), data_p_with_type(keys), \ + SelectPassArgs, data_p_with_type(predicates), \ + data_p_with_type(accumulate_counters), \ + num_partitions, length, predicates_length, counters_length, \ + data_p_with_type(output_ptrs), \ + data_p_with_type(permutation))); \ + } \ + } + +#define SelectName PartitionSelectDiv +#define SelectArgs const Tensor& accu_div +#define SelectScanPassArgs data_p_with_type(accu_div) +#define SelectPassArgs SelectScanPassArgs + +#define SelectScanKernelName SelectScanDivKernel +#define SelectScanKernelArgs const int64* accu_div +#define SelectScanKernelLoadCodeBlock \ + int64 lower_bound = partition_id > 0 ? accu_div[partition_id - 1] : 0; \ + int64 upper_bound = accu_div[partition_id]; +#define SelectScanKernelEvalCodeBlock \ + selected = int(key >= lower_bound && key < upper_bound); + +#define SelectKernelName SelectDivKernel +#define SelectKernelArgs SelectScanKernelArgs + +#define SelectKernelLoadCodeBlock \ + int64 lower_bound = j > 0 ? accu_div[j - 1] : 0; +#define SelectKernelRecalcKeyCodeBlock T new_key = key - lower_bound; + +DeclareSelectScanKernel; +DeclareSelectKernel; +DeclareSelect; +template void PartitionSelectDiv(OpKernelContext* ctx, + const Tensor* keys, + const Tensor& accu_div, + const int64 num_partitions, + OpOutputList& selected_keys, + Tensor* permutation); + +} // namespace fused_embedding + +} // namespace tensorflow + +#endif // GOOGLE_CUDA \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc index 88cbc4629f9..4b83debbc3b 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc @@ -29,6 +29,7 @@ class PartitionWithPermutationGPU : public OpKernel { OP_REQUIRES_OK(ctx, ctx->GetAttr("partition_axis", &partition_axis_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("partition_strategy", &partition_strategy_)); + cudaEventCreateWithFlags(&memcpy_event_, cudaEventDisableTiming); } void Compute(OpKernelContext* ctx) override { @@ -40,66 +41,44 @@ class PartitionWithPermutationGPU : public OpKernel { OpOutputList partitioned_values; OP_REQUIRES_OK(ctx, ctx->output_list("partitioned_values", &partitioned_values)); - OpOutputList partition_permutations; - OP_REQUIRES_OK(ctx, ctx->output_list("partition_permutations", - &partition_permutations)); Tensor const* input = nullptr; OP_REQUIRES_OK(ctx, ctx->input("input", &input)); const int64 input_size = input->NumElements(); - if (num_partitions_ == 1) { - // Simply copy - Tensor* partitioned_value; - partitioned_values.allocate(0, TensorShape({input_size}), - &partitioned_value); - cudaMemcpyAsync(data_p_with_type(partitioned_values[0]), - data_p_with_type(input), - sizeof(int64_t) * input_size, cudaMemcpyDeviceToDevice, - device.stream()); - // Need to allocate_output here - Tensor* partitioned_permutation; - partition_permutations.allocate(0, TensorShape({input_size}), - &partitioned_permutation); - RangeInit(device, input_size, - data_p_with_type(partitioned_permutation)); - } else { - Tensor partition_permute_init; + Tensor* partition_permutation; + OP_REQUIRES_OK(ctx, ctx->allocate_output("partition_permutation", + TensorShape{input_size, 2}, + &partition_permutation)); + + if (partition_strategy_ == "div") { + OpInputList partition_shapes; OP_REQUIRES_OK(ctx, - ctx->allocate_temp(DT_INT64, TensorShape({input_size}), - &partition_permute_init)); - - RangeInit(device, input_size, - data_p_with_type(partition_permute_init)); - if (partition_strategy_ == "div") { - OpInputList partition_shapes; - OP_REQUIRES_OK(ctx, - ctx->input_list("partition_shapes", &partition_shapes)); - - std::vector accu_div_host; - accu_div_host.resize(num_partitions_); - for (int i = 0; i < partition_shapes.size(); i++) { - OP_REQUIRES(ctx, partition_shapes[i].dims() <= 2, - errors::InvalidArgument( - "input partition_shapes must all less than rank 2")); - const int64_t div = partition_shapes[i].flat().data()[0]; - accu_div_host[i] = i == 0 ? div : accu_div_host[i - 1] + div; - } - - Tensor accu_div; - OP_REQUIRES_OK( - ctx, - ctx->allocate_temp( - DT_INT64, TensorShape({static_cast(num_partitions_)}), - &accu_div)); - cudaMemcpyAsync(data_p_with_type(accu_div), accu_div_host.data(), - num_partitions_ * sizeof(int64_t), - cudaMemcpyHostToDevice, device.stream()); - - PartitionSelectDiv( - ctx, input, partition_permute_init, accu_div, num_partitions_, - partitioned_values, partition_permutations); + ctx->input_list("partition_shapes", &partition_shapes)); + + std::vector accu_div_host; + accu_div_host.resize(num_partitions_); + for (int i = 0; i < partition_shapes.size(); i++) { + OP_REQUIRES(ctx, partition_shapes[i].dims() == 2, + errors::InvalidArgument( + "input partition_shapes must all equal to rank 2")); + const int64_t div = partition_shapes[i].flat().data()[0]; + accu_div_host[i] = i == 0 ? div : accu_div_host[i - 1] + div; } + + Tensor accu_div; + OP_REQUIRES_OK( + ctx, + ctx->allocate_temp( + DT_INT64, TensorShape({static_cast(num_partitions_)}), + &accu_div)); + cudaMemcpyAsync(data_p_with_type(accu_div), accu_div_host.data(), + num_partitions_ * sizeof(int64_t), cudaMemcpyHostToDevice, + device.stream()); + + PartitionSelectDiv( + ctx, input, partition_permute_init, accu_div, num_partitions_, + partitioned_values, partition_permutations); } } diff --git a/tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_hash.ops.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_hash.ops.cu.cc new file mode 100644 index 00000000000..e69de29bb2d diff --git a/tensorflow/core/ops/fused_embedding_ops.cc b/tensorflow/core/ops/fused_embedding_ops.cc index 8521e099339..8507aa3c10c 100644 --- a/tensorflow/core/ops/fused_embedding_ops.cc +++ b/tensorflow/core/ops/fused_embedding_ops.cc @@ -75,7 +75,7 @@ REGISTER_OP("UniqueWithMultiOutput") }); REGISTER_OP("PartitionWithPermutation") - .Attr("num_partitions: int >= 1 = 1") + .Attr("num_partitions: int > 1 = 2") .Attr("partition_axis: int >= 0 = 0") // for now only support = 0, // will consider support = 1 // if necessary @@ -83,7 +83,7 @@ REGISTER_OP("PartitionWithPermutation") .Input("input: int64") .Input("partition_shapes: num_partitions * int64") .Output("partitioned_values: num_partitions * int64") - .Output("partition_permutations: num_partitions * int64") + .Output("partition_permutation: int64") .SetShapeFn([](InferenceContext* ctx) { ShapeHandle unused; std::vector unused_list; @@ -110,7 +110,11 @@ REGISTER_OP("PartitionWithPermutation") unused_list[i] = ctx->MakeShape({ctx->UnknownDim()}); } ctx->set_output("partitioned_values", unused_list); - ctx->set_output("partition_permutations", unused_list); + + unused_list.clear(); + unused_list.resize(1); + unused_list[0] = ctx->MakeShape({ctx->UnknownDim()} + ctx->set_output("partition_permutation", unused_list); return Status::OK(); }); @@ -126,7 +130,7 @@ REGISTER_OP("FusedEmbeddingSparsePostLookUp") .Attr("combiner: {'sqrtn', 'mean', 'sum'}") .Attr("max_norm: float = -1.0") .Input("emb_shards: num_partitions * T") - .Input("partition_permutations: num_partitions * int64") + .Input("partition_permutation: int64") .Input("sp_dense_shape: int64") .Input("indices_before_unique: int64") .Input("row_empty_and_invalid_flags: int32") @@ -157,11 +161,9 @@ REGISTER_OP("FusedEmbeddingSparsePostLookUp") ctx->WithValue(ctx->Dim(unused, 1), emb_vec_size, &unused_dim)); } - // partition_permutations - ctx->input("partition_permutations", &unused_list); - for (int i = 0; i < num_partitions; i++) { - TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[i], 1, &unused)); - } + // partition_permutation + ctx->input("partition_permutation", &unused_list); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); // sp_dense_shape ctx->input("sp_dense_shape", &unused_list); @@ -220,7 +222,7 @@ REGISTER_OP("FusedEmbeddingSparsePostLookUpGrad") .Attr("max_norm: float = -1.0") .Input("top_grad: T") .Input("emb_shards: num_partitions * T") - .Input("partition_permutations: num_partitions * int64") + .Input("partition_permutation: int64") .Input("feature_nums: int32") .Input("indices_before_unique: int64") .Input("unique_counts: int64") @@ -246,11 +248,9 @@ REGISTER_OP("FusedEmbeddingSparsePostLookUpGrad") for (int i = 0; i < num_partitions; i++) { TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[i], 2, &unused)); } - // partition_permutations - ctx->input("partition_permutations", &unused_list); - for (int i = 0; i < num_partitions; i++) { - TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[i], 1, &unused)); - } + // partition_permutation + ctx->input("partition_permutation", &unused_list); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); // feature_nums ctx->input("feature_nums", &unused_list); From c449de389706b3196a4209b1be3c77905e9f5d2d Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Fri, 18 Mar 2022 10:24:23 +0800 Subject: [PATCH 20/41] Update: modify partition_select --- .../gpu/functions/partition_select.cu.cc | 311 ++++++++++-------- .../gpu/functions/partition_select.cu.h | 7 +- .../gpu/functions/partition_select_new.cu.h | 252 -------------- 3 files changed, 169 insertions(+), 401 deletions(-) delete mode 100644 tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select_new.cu.h diff --git a/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc index 94f7c87a882..0777ddd1f69 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc @@ -1,14 +1,13 @@ #if GOOGLE_CUDA #define EIGEN_USE_GPU -#include "tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h" - #include #include #include #include "tensorflow/core/common_runtime/gpu/gpu_event_mgr.h" #include "tensorflow/core/kernels/fused_embedding/gpu/common.cu.h" +#include "tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h" #include "tensorflow/core/lib/core/bits.h" #include "tensorflow/core/util/gpu_kernel_helper.h" #include "tensorflow/stream_executor/cuda/cuda_activation.h" @@ -21,86 +20,104 @@ namespace fused_embedding { // A macro helper to declare SelectScanKernels, because they just have only // a little bit differences. Need to define macros -#define DeclareSelectScanKernel \ - template \ - __global__ void SelectScanKernelName( \ - const T* keys, SelectScanKernelArgs, const int64 num_partitions, \ - const int64 length, const int64 predicates_length, \ - const int64 counters_length, unsigned int* predicates, \ - unsigned int* counters) { \ - int g_tid = blockIdx.x * blockDim.x + threadIdx.x; \ - int lnid = threadIdx.x % 32; \ - int g_warp_id = g_tid >> 5; \ - unsigned int mask; \ - unsigned int cnt; \ - \ - for (int j = 0; j < num_partitions; j++) { \ - SelectScanKernelLoadCodeBlock; \ - _Pragma("unroll") for (int i = 0; i < 32; i++) { \ - int selected; \ - int load_offset = (g_warp_id << 10) + (i << 5) + lnid; \ - if (load_offset < length) { \ - T key = keys[load_offset]; \ - SelectScanKernelEvalCodeBlock; \ - } else { \ - selected = 0; \ - } \ - \ - mask = __ballot_sync(0xffffffff, selected); \ - if (lnid == 0) \ - predicates[j * predicates_length + (g_warp_id << 5) + i] = mask; \ - if (lnid == i) cnt = __popc(mask); \ - } \ - _Pragma("unroll") for (int offset = 16; offset > 0; offset >>= 1) { \ - cnt += __shfl_down_sync(0xffffffff, cnt, offset); \ - } \ - \ - if (lnid == 0) counters[j * counters_length + g_warp_id] = cnt; \ - } \ +#define DeclareSelectScanKernel \ + template \ + __global__ void SelectScanKernelName( \ + const T* keys, SelectScanKernelArgs, const int64 num_partitions, \ + const int64 length, const int64 predicates_length, \ + const int64 counters_length, unsigned int* predicates, \ + unsigned int* accumulate_counters) { \ + __shared__ unsigned int warp_cnt_sum[1]; \ + int lnid = threadIdx.x % 32; \ + if (lnid == 0) warp_cnt_sum[0] = 0; /* init shared mem of sum */ \ + const int warp_iteration = WarpWorkload / 32; \ + const int select_scan_warps = length % WarpWorkload == 0 \ + ? (length / WarpWorkload) \ + : (length / WarpWorkload + 1); \ + const int partition_id = blockIdx.x / select_scan_warps; \ + int warp_id_in_partition = blockIdx.x % select_scan_warps; \ + unsigned int mask; \ + unsigned int cnt; \ + SelectScanKernelLoadCodeBlock; \ + _Pragma("unroll") for (int i = 0; i < warp_iteration; i++) { \ + int selected; \ + int load_offset = warp_id_in_partition * WarpWorkload + i * 32 + lnid; \ + if (load_offset < length) { \ + T key = keys[load_offset]; \ + SelectScanKernelEvalCodeBlock; \ + } else { \ + selected = 0; \ + } \ + mask = __ballot_sync(0xffffffff, selected); \ + if (lnid == 0) \ + predicates[partition_id * predicates_length + \ + warp_id_in_partition * (WarpWorkload / 32) + i] = mask; \ + cnt = __popc(mask); \ + if (lnid == 0) atomicAdd(warp_cnt_sum, cnt); \ + } \ + /* use different threads in warp to accumulate to different location of \ + * accumulate_counters, this will do a prefix sum on global mem */ \ + const int counters_slots_need_accumulate = \ + select_scan_warps - warp_id_in_partition; \ + const unsigned int warp_cnt = warp_cnt_sum[0]; \ + for (int i = 0; i < counters_slots_need_accumulate; i += 32) { \ + if (i + lnid < counters_slots_need_accumulate) { \ + atomicAdd(accumulate_counters + partition_id * counters_length + \ + warp_id_in_partition + lnid, \ + warp_cnt); \ + } \ + } \ } #define DeclareSelectKernel \ - template \ + template \ __global__ void SelectKernelName( \ - const T* keys, const TIndex* indices, SelectKernelArgs, \ - unsigned int* predicates, unsigned int* inclusive_sum_counters, \ - const int64 num_partitions, const int64 length, \ - const int64 predicates_length, const int64 counters_length, \ - void** output_ptrs) { \ - const int g_tid = blockIdx.x * blockDim.x + threadIdx.x; \ + const T* keys, SelectKernelArgs, unsigned int* predicates, \ + unsigned int* accumulate_counters, const int64 num_partitions, \ + const int64 length, const int64 predicates_length, \ + const int64 counters_length, void** output_ptrs, TIndex* permutation) { \ const int lnid = threadIdx.x % 32; \ - const int g_warp_id = g_tid >> 5; \ - unsigned int predmask; \ - unsigned int cnt; \ - for (int j = 0; j < num_partitions; j++) { \ - SelectKernelLoadCodeBlock; \ - T* keys_output_ptr = (T*)(output_ptrs[2 * j]); \ - TIndex* indices_output_ptr = (TIndex*)(output_ptrs[2 * j + 1]); \ - predmask = predicates[j * predicates_length + (g_warp_id << 5) + lnid]; \ + const int warp_iteration = WarpWorkload / 32; \ + const int select_scan_warps = length % WarpWorkload == 0 \ + ? (length / WarpWorkload) \ + : (length / WarpWorkload + 1); \ + const int partition_id = blockIdx.x / select_scan_warps; \ + int warp_id_in_partition = blockIdx.x % select_scan_warps; \ + unsigned int predmask = 0; \ + unsigned int cnt = 0; \ + SelectKernelLoadCodeBlock; \ + T* keys_output_ptr = (T*)(output_ptrs[partition_id]); \ + if (lnid < warp_iteration) { \ + predmask = \ + predicates[partition_id * predicates_length + \ + (warp_id_in_partition * (WarpWorkload / 32)) + lnid]; \ cnt = __popc(predmask); \ - _Pragma("unroll") for (int offset = 1; offset < 32; offset <<= 1) { \ - int n = __shfl_up_sync(0xffffffff, cnt, offset); \ - if (lnid >= offset) cnt += n; \ - } \ - unsigned int global_index = 0; \ - if (g_warp_id > 0) global_index = inclusive_sum_counters[g_warp_id - 1]; \ - _Pragma("unroll") for (int i = 0; i < 32; i++) { \ - unsigned int mask = __shfl_sync(0xffffffff, predmask, i); \ - unsigned int sub_group_index = 0; \ - if (i > 0) sub_group_index = __shfl_sync(0xffffffff, cnt, i - 1); \ - if (mask & (1 << lnid)) { \ - int load_offset = (g_warp_id << 10) + (i << 5) + lnid; \ - T key = \ - keys[load_offset]; /* Will not cause out of boundry access, \ - because mask will be 0 for this thread*/ \ - TIndex indice = indices[load_offset]; \ - SelectKernelRecalcKeyCodeBlock; \ - int output_offset = global_index + sub_group_index + \ - __popc(mask & (((unsigned int)(1) << lnid) - \ - (unsigned int)(1))); \ - keys_output_ptr[output_offset] = new_key; \ - indices_output_ptr[output_offset] = indice; \ - } \ + } \ + _Pragma("unroll") for (int offset = 1; offset < warp_iteration; \ + offset <<= 1) { \ + /* prefix sum */ \ + int n = __shfl_up_sync(0xffffffff, cnt, offset); \ + if (lnid >= offset) cnt += n; \ + } \ + unsigned int global_index = 0; \ + if (warp_id_in_partition > 0) \ + global_index = accumulate_counters[partition_id * counters_length + \ + warp_id_in_partition - 1]; \ + _Pragma("unroll") for (int i = 0; i < warp_iteration; i++) { \ + unsigned int mask = __shfl_sync(0xffffffff, predmask, i); \ + unsigned int inner_warp_index = 0; \ + if (i > 0) inner_warp_index = __shfl_sync(0xffffffff, cnt, i - 1); \ + if (mask & (1 << lnid)) { \ + int load_offset = warp_id_in_partition * WarpWorkload + i * 32 + lnid; \ + T key = keys[load_offset]; /* Will not cause out of boundry access, \ + because mask will be 0 for this thread*/ \ + SelectKernelRecalcKeyCodeBlock; \ + int output_offset = \ + global_index + inner_warp_index + \ + __popc(mask & (((unsigned int)(1) << lnid) - (unsigned int)(1))); \ + keys_output_ptr[output_offset] = new_key; \ + permutation[2 * load_offset] = partition_id; \ + permutation[2 * load_offset + 1] = (TIndex)output_offset; \ } \ } \ } @@ -109,27 +126,35 @@ namespace fused_embedding { // macros before using this: SelectScanKernelName, // SelectArgs, SelectScanPassArgs #define DeclareSelect \ - template \ - void SelectName(OpKernelContext* ctx, const Tensor* keys, \ - const Tensor& indices, SelectArgs, \ - const int64 num_partitions, OpOutputList& selected_keys, \ - OpOutputList& selected_indices) { \ + template \ + void SelectName(OpKernelContext* ctx, const Tensor* keys, SelectArgs, \ + const int64 num_partitions, cudaEvent_t memcpy_event, \ + OpOutputList& selected_keys, Tensor* permutation) { \ OP_REQUIRES(ctx, keys->dims() == 1, \ errors::InvalidArgument("Tensor keys must ranks 1")); \ + OP_REQUIRES(ctx, \ + WarpWorkload >= 32 && \ + (WarpWorkload && !(WarpWorkload & (WarpWorkload - 1))), \ + errors::InvalidArgument( \ + "WarpWorkload must be larger than warp size " \ + "32 and is exponential of 2, 32, 64, 128, i.e.")); \ const GPUDevice& device = ctx->eigen_gpu_device(); \ - const int64 length = keys->shape().dim_size(0); \ - cudaEvent_t memcpy_event; \ - cudaEventCreateWithFlags(&memcpy_event, cudaEventDisableTiming); \ + const int64 length = keys->NumElements(); \ + const int64 warp_iteration = WarpWorkload / 32; \ Tensor predicates; \ - Tensor counters; \ - const int64 select_scan_warps = \ - length % 1024 == 0 ? (length / 1024) : (length / 1024 + 1); \ + Tensor accumulate_counters; \ + const int64 select_scan_warps = length % WarpWorkload == 0 \ + ? (length / WarpWorkload) \ + : (length / WarpWorkload + 1); \ const int64 counters_length = select_scan_warps; \ - const int64 predicates_length = select_scan_warps * 32; \ + const int64 predicates_length = select_scan_warps * warp_iteration; \ OP_REQUIRES_OK( \ ctx, ctx->allocate_temp(DT_UINT32, \ TensorShape{counters_length * num_partitions}, \ - &counters)); \ + &accumulate_counters)); \ + cudaMemsetAsync(data_p_with_type(accumulate_counters), 0, \ + accumulate_counters.NumElements() * sizeof(unsigned int), \ + device.stream()); \ OP_REQUIRES_OK( \ ctx, ctx->allocate_temp( \ DT_UINT32, TensorShape{predicates_length * num_partitions}, \ @@ -137,41 +162,19 @@ namespace fused_embedding { \ { \ const int64 threads = 32; \ - const int64 blocks = select_scan_warps; \ + const int64 blocks = select_scan_warps * num_partitions; \ OP_REQUIRES_OK( \ - ctx, GpuLaunchKernel(SelectScanKernelName, blocks, threads, 0, \ - device.stream(), data_p_with_type(keys), \ - SelectScanPassArgs, num_partitions, length, \ - predicates_length, counters_length, \ - data_p_with_type(predicates), \ - data_p_with_type(counters))); \ - } \ - Tensor inclusive_sum_counters; \ - OP_REQUIRES_OK( \ - ctx, ctx->allocate_temp(DT_UINT32, \ - TensorShape{counters_length * num_partitions}, \ - &inclusive_sum_counters)); \ - size_t cub_tmp_storage_bytes; \ - Tensor cub_tmp_storage; \ - cub::DeviceScan::InclusiveSum( \ - nullptr, cub_tmp_storage_bytes, \ - data_p_with_type(counters), \ - data_p_with_type(inclusive_sum_counters), \ - counters_length, device.stream()); \ - OP_REQUIRES_OK( \ - ctx, \ - ctx->allocate_temp(DT_INT8, TensorShape{int64(cub_tmp_storage_bytes)}, \ - &cub_tmp_storage)); \ - for (int i = 0; i < num_partitions; i++) { \ - cub::DeviceScan::InclusiveSum( \ - data_p_with_type(cub_tmp_storage), cub_tmp_storage_bytes, \ - data_p_with_type(counters) + i * counters_length, \ - data_p_with_type(inclusive_sum_counters) + \ - i * counters_length, \ - counters_length, device.stream()); \ + ctx, \ + GpuLaunchKernel( \ + SelectScanKernelName, blocks, threads, 0, \ + device.stream(), data_p_with_type(keys), SelectScanPassArgs, \ + num_partitions, length, predicates_length, counters_length, \ + data_p_with_type(predicates), \ + data_p_with_type(accumulate_counters))); \ } \ std::vector selected_nums_host; \ selected_nums_host.resize(num_partitions); \ + /* copy the last element(which is the sum of previous) with stride */ \ cudaMemcpy2DAsync(selected_nums_host.data(), 1 * sizeof(unsigned int), \ data_p_with_type(inclusive_sum_counters) + \ counters_length - 1, \ @@ -182,38 +185,33 @@ namespace fused_embedding { cudaEventSynchronize(memcpy_event); \ \ std::vector output_ptrs_host; \ - output_ptrs_host.resize(2 * num_partitions); \ + output_ptrs_host.resize(num_partitions); \ for (int i = 0; i < num_partitions; i++) { \ Tensor* tmp_out; \ OP_REQUIRES_OK( \ ctx, selected_keys.allocate( \ i, TensorShape({int64(selected_nums_host[i])}), &tmp_out)); \ - output_ptrs_host[2 * i] = data_p_with_type(tmp_out); \ - OP_REQUIRES_OK( \ - ctx, selected_indices.allocate( \ - i, TensorShape({int64(selected_nums_host[i])}), &tmp_out)); \ - output_ptrs_host[2 * i + 1] = data_p_with_type(tmp_out); \ + output_ptrs_host[i] = data_p_with_type(tmp_out); \ } \ Tensor output_ptrs; \ - OP_REQUIRES_OK(ctx, ctx->allocate_temp( \ - DT_UINT64, TensorShape{int64(2 * num_partitions)}, \ - &output_ptrs)); \ + OP_REQUIRES_OK( \ + ctx, ctx->allocate_temp(DT_UINT64, TensorShape{int64(num_partitions)}, \ + &output_ptrs)); \ cudaMemcpyAsync(data_p_with_type(output_ptrs), \ - output_ptrs_host.data(), \ - 2 * num_partitions * sizeof(size_t), \ + output_ptrs_host.data(), num_partitions * sizeof(size_t), \ cudaMemcpyHostToDevice, device.stream()); \ { \ const int64 threads = 32; \ - const int64 blocks = select_scan_warps; \ + const int64 blocks = select_scan_warps * num_partitions; \ OP_REQUIRES_OK( \ ctx, GpuLaunchKernel( \ - SelectKernelName, blocks, threads, 0, \ - device.stream(), data_p_with_type(keys), \ - data_p_with_type(indices), SelectPassArgs, \ - data_p_with_type(predicates), \ - data_p_with_type(inclusive_sum_counters), \ + SelectKernelName, blocks, threads, \ + 0, device.stream(), data_p_with_type(keys), \ + SelectPassArgs, data_p_with_type(predicates), \ + data_p_with_type(accumulate_counters), \ num_partitions, length, predicates_length, counters_length, \ - data_p_with_type(output_ptrs))); \ + data_p_with_type(output_ptrs), \ + data_p_with_type(permutation))); \ } \ } @@ -224,9 +222,9 @@ namespace fused_embedding { #define SelectScanKernelName SelectScanDivKernel #define SelectScanKernelArgs const int64* accu_div -#define SelectScanKernelLoadCodeBlock \ - int64 lower_bound = j > 0 ? accu_div[j - 1] : 0; \ - int64 upper_bound = accu_div[j]; +#define SelectScanKernelLoadCodeBlock \ + int64 lower_bound = partition_id > 0 ? accu_div[partition_id - 1] : 0; \ + int64 upper_bound = accu_div[partition_id]; #define SelectScanKernelEvalCodeBlock \ selected = int(key >= lower_bound && key < upper_bound); @@ -240,10 +238,33 @@ namespace fused_embedding { DeclareSelectScanKernel; DeclareSelectKernel; DeclareSelect; -template void PartitionSelectDiv( - OpKernelContext* ctx, const Tensor* keys, const Tensor& indices, - const Tensor& accu_div, const int64 num_partitions, - OpOutputList& selected_keys, OpOutputList& selected_indices); +template void PartitionSelectDiv(OpKernelContext* ctx, + const Tensor* keys, + const Tensor& accu_div, + const int64 num_partitions, + OpOutputList& selected_keys, + Tensor* permutation); + +template void PartitionSelectDiv(OpKernelContext* ctx, + const Tensor* keys, + const Tensor& accu_div, + const int64 num_partitions, + OpOutputList& selected_keys, + Tensor* permutation); + +template void PartitionSelectDiv(OpKernelContext* ctx, + const Tensor* keys, + const Tensor& accu_div, + const int64 num_partitions, + OpOutputList& selected_keys, + Tensor* permutation); + +template void PartitionSelectDiv(OpKernelContext* ctx, + const Tensor* keys, + const Tensor& accu_div, + const int64 num_partitions, + OpOutputList& selected_keys, + Tensor* permutation); } // namespace fused_embedding diff --git a/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h index cdb1da21adb..f4f76743c29 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h +++ b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h @@ -10,11 +10,10 @@ namespace tensorflow { namespace fused_embedding { -template +template void PartitionSelectDiv(OpKernelContext* ctx, const Tensor* keys, - const Tensor& indices, const Tensor& accu_div, - const int64 num_partitions, OpOutputList& selected_keys, - OpOutputList& selected_indices); + const Tensor& accu_div, const int64 num_partitions, + OpOutputList& selected_keys, Tensor* permutation); } // namespace fused_embedding diff --git a/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select_new.cu.h b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select_new.cu.h deleted file mode 100644 index 364477ad006..00000000000 --- a/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select_new.cu.h +++ /dev/null @@ -1,252 +0,0 @@ -#if GOOGLE_CUDA - -#define EIGEN_USE_GPU -#include -#include -#include - -#include "tensorflow/core/common_runtime/gpu/gpu_event_mgr.h" -#include "tensorflow/core/kernels/fused_embedding/gpu/common.cu.h" -#include "tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h" -#include "tensorflow/core/lib/core/bits.h" -#include "tensorflow/core/util/gpu_kernel_helper.h" -#include "tensorflow/stream_executor/cuda/cuda_activation.h" - -namespace tensorflow { - -typedef Eigen::GpuDevice GPUDevice; - -namespace fused_embedding { - -// A macro helper to declare SelectScanKernels, because they just have only -// a little bit differences. Need to define macros -#define DeclareSelectScanKernel \ - template \ - __global__ void SelectScanKernelName( \ - const T* keys, SelectScanKernelArgs, const int64 num_partitions, \ - const int64 length, const int64 predicates_length, \ - const int64 counters_length, unsigned int* predicates, \ - unsigned int* accumulate_counters) { \ - __shared__ unsigned int warp_cnt_sum[1]; \ - int lnid = threadIdx.x % 32; \ - if (lnid == 0) warp_cnt_sum[0] = 0; /* init shared mem of sum */ \ - const int warp_iteration = WarpWorkload / 32; \ - const int select_scan_warps = length % WarpWorkload == 0 \ - ? (length / WarpWorkload) \ - : (length / WarpWorkload + 1); \ - const int partition_id = blockIdx.x / select_scan_warps; \ - int warp_id_in_partition = blockIdx.x % select_scan_warps; \ - unsigned int mask; \ - unsigned int cnt; \ - SelectScanKernelLoadCodeBlock; \ - _Pragma("unroll") for (int i = 0; i < warp_iteration; i++) { \ - int selected; \ - int load_offset = warp_id_in_partition * WarpWorkload + i * 32 + lnid; \ - if (load_offset < length) { \ - T key = keys[load_offset]; \ - SelectScanKernelEvalCodeBlock; \ - } else { \ - selected = 0; \ - } \ - mask = __ballot_sync(0xffffffff, selected); \ - if (lnid == 0) \ - predicates[partition_id * predicates_length + \ - warp_id_in_partition * (WarpWorkload / 32) + i] = mask; \ - cnt = __popc(mask); \ - if (lnid == 0) atomicAdd(warp_cnt_sum, cnt); \ - } \ - /* use different threads in warp to accumulate to different location of \ - * accumulate_counters, this will do a prefix sum on global mem */ \ - const int counters_slots_need_accumulate = \ - select_scan_warps - warp_id_in_partition; \ - const unsigned int warp_cnt = warp_cnt_sum[0]; \ - for (int i = 0; i < counters_slots_need_accumulate; i += 32) { \ - if (i + lnid < counters_slots_need_accumulate) { \ - atomicAdd(accumulate_counters + partition_id * counters_length + \ - warp_id_in_partition + lnid, \ - warp_cnt); \ - } \ - } \ - } - -#define DeclareSelectKernel \ - template \ - __global__ void SelectKernelName( \ - const T* keys, SelectKernelArgs, unsigned int* predicates, \ - unsigned int* accumulate_counters, const int64 num_partitions, \ - const int64 length, const int64 predicates_length, \ - const int64 counters_length, void** output_ptrs, TIndex* permutation) { \ - const int lnid = threadIdx.x % 32; \ - const int warp_iteration = WarpWorkload / 32; \ - const int select_scan_warps = length % WarpWorkload == 0 \ - ? (length / WarpWorkload) \ - : (length / WarpWorkload + 1); \ - const int partition_id = blockIdx.x / select_scan_warps; \ - int warp_id_in_partition = blockIdx.x % select_scan_warps; \ - unsigned int predmask = 0; \ - unsigned int cnt = 0; \ - SelectKernelLoadCodeBlock; \ - T* keys_output_ptr = (T*)(output_ptrs[partition_id]); \ - if (lnid < warp_iteration) { \ - predmask = \ - predicates[partition_id * predicates_length + \ - (warp_id_in_partition * (WarpWorkload / 32)) + lnid]; \ - cnt = __popc(predmask); \ - } \ - _Pragma("unroll") for (int offset = 1; offset < warp_iteration; \ - offset <<= 1) { \ - /* prefix sum */ \ - int n = __shfl_up_sync(0xffffffff, cnt, offset); \ - if (lnid >= offset) cnt += n; \ - } \ - unsigned int global_index = 0; \ - if (warp_id_in_partition > 0) \ - global_index = accumulate_counters[partition_id * counters_length + \ - warp_id_in_partition - 1]; \ - _Pragma("unroll") for (int i = 0; i < warp_iteration; i++) { \ - unsigned int mask = __shfl_sync(0xffffffff, predmask, i); \ - unsigned int inner_warp_index = 0; \ - if (i > 0) inner_warp_index = __shfl_sync(0xffffffff, cnt, i - 1); \ - if (mask & (1 << lnid)) { \ - int load_offset = warp_id_in_partition * WarpWorkload + i * 32 + lnid; \ - T key = keys[load_offset]; /* Will not cause out of boundry access, \ - because mask will be 0 for this thread*/ \ - SelectKernelRecalcKeyCodeBlock; \ - int output_offset = \ - global_index + inner_warp_index + \ - __popc(mask & (((unsigned int)(1) << lnid) - (unsigned int)(1))); \ - keys_output_ptr[output_offset] = new_key; \ - permutation[2 * load_offset] = partition_id; \ - permutation[2 * load_offset + 1] = (TIndex)output_offset; \ - } \ - } \ - } - -// A macro helper to declare DefinePartitionSelect. Need to define following -// macros before using this: SelectScanKernelName, -// SelectArgs, SelectScanPassArgs -#define DeclareSelect \ - template \ - void SelectName(OpKernelContext* ctx, const Tensor* keys, SelectArgs, \ - const int64 num_partitions, cudaEvent_t memcpy_event, \ - OpOutputList& selected_keys, Tensor* permutation) { \ - OP_REQUIRES(ctx, keys->dims() == 1, \ - errors::InvalidArgument("Tensor keys must ranks 1")); \ - OP_REQUIRES(ctx, \ - WarpWorkload >= 32 && \ - (WarpWorkload && !(WarpWorkload & (WarpWorkload - 1))), \ - errors::InvalidArgument( \ - "WarpWorkload must be larger than warp size " \ - "32 and is exponential of 2, 32, 64, 128, i.e.")); \ - const GPUDevice& device = ctx->eigen_gpu_device(); \ - const int64 length = keys->NumElements(); \ - const int64 warp_iteration = WarpWorkload / 32; \ - Tensor predicates; \ - Tensor accumulate_counters; \ - const int64 select_scan_warps = length % WarpWorkload == 0 \ - ? (length / WarpWorkload) \ - : (length / WarpWorkload + 1); \ - const int64 counters_length = select_scan_warps; \ - const int64 predicates_length = select_scan_warps * warp_iteration; \ - OP_REQUIRES_OK( \ - ctx, ctx->allocate_temp(DT_UINT32, \ - TensorShape{counters_length * num_partitions}, \ - &accumulate_counters)); \ - cudaMemsetAsync(data_p_with_type(accumulate_counters), 0, \ - accumulate_counters.NumElements() * sizeof(unsigned int), \ - device.stream()); \ - OP_REQUIRES_OK( \ - ctx, ctx->allocate_temp( \ - DT_UINT32, TensorShape{predicates_length * num_partitions}, \ - &predicates)); \ - \ - { \ - const int64 threads = 32; \ - const int64 blocks = select_scan_warps * num_partitions; \ - OP_REQUIRES_OK( \ - ctx, \ - GpuLaunchKernel( \ - SelectScanKernelName, blocks, threads, 0, \ - device.stream(), data_p_with_type(keys), SelectScanPassArgs, \ - num_partitions, length, predicates_length, counters_length, \ - data_p_with_type(predicates), \ - data_p_with_type(accumulate_counters))); \ - } \ - std::vector selected_nums_host; \ - selected_nums_host.resize(num_partitions); \ - /* copy the last element(which is the sum of previous) with stride */ \ - cudaMemcpy2DAsync(selected_nums_host.data(), 1 * sizeof(unsigned int), \ - data_p_with_type(inclusive_sum_counters) + \ - counters_length - 1, \ - counters_length * sizeof(unsigned int), \ - 1 * sizeof(unsigned int), num_partitions, \ - cudaMemcpyDeviceToHost, device.stream()); \ - cudaEventRecord(memcpy_event, device.stream()); \ - cudaEventSynchronize(memcpy_event); \ - \ - std::vector output_ptrs_host; \ - output_ptrs_host.resize(num_partitions); \ - for (int i = 0; i < num_partitions; i++) { \ - Tensor* tmp_out; \ - OP_REQUIRES_OK( \ - ctx, selected_keys.allocate( \ - i, TensorShape({int64(selected_nums_host[i])}), &tmp_out)); \ - output_ptrs_host[i] = data_p_with_type(tmp_out); \ - } \ - Tensor output_ptrs; \ - OP_REQUIRES_OK( \ - ctx, ctx->allocate_temp(DT_UINT64, TensorShape{int64(num_partitions)}, \ - &output_ptrs)); \ - cudaMemcpyAsync(data_p_with_type(output_ptrs), \ - output_ptrs_host.data(), num_partitions * sizeof(size_t), \ - cudaMemcpyHostToDevice, device.stream()); \ - { \ - const int64 threads = 32; \ - const int64 blocks = select_scan_warps * num_partitions; \ - OP_REQUIRES_OK( \ - ctx, GpuLaunchKernel( \ - SelectKernelName, blocks, threads, \ - 0, device.stream(), data_p_with_type(keys), \ - SelectPassArgs, data_p_with_type(predicates), \ - data_p_with_type(accumulate_counters), \ - num_partitions, length, predicates_length, counters_length, \ - data_p_with_type(output_ptrs), \ - data_p_with_type(permutation))); \ - } \ - } - -#define SelectName PartitionSelectDiv -#define SelectArgs const Tensor& accu_div -#define SelectScanPassArgs data_p_with_type(accu_div) -#define SelectPassArgs SelectScanPassArgs - -#define SelectScanKernelName SelectScanDivKernel -#define SelectScanKernelArgs const int64* accu_div -#define SelectScanKernelLoadCodeBlock \ - int64 lower_bound = partition_id > 0 ? accu_div[partition_id - 1] : 0; \ - int64 upper_bound = accu_div[partition_id]; -#define SelectScanKernelEvalCodeBlock \ - selected = int(key >= lower_bound && key < upper_bound); - -#define SelectKernelName SelectDivKernel -#define SelectKernelArgs SelectScanKernelArgs - -#define SelectKernelLoadCodeBlock \ - int64 lower_bound = j > 0 ? accu_div[j - 1] : 0; -#define SelectKernelRecalcKeyCodeBlock T new_key = key - lower_bound; - -DeclareSelectScanKernel; -DeclareSelectKernel; -DeclareSelect; -template void PartitionSelectDiv(OpKernelContext* ctx, - const Tensor* keys, - const Tensor& accu_div, - const int64 num_partitions, - OpOutputList& selected_keys, - Tensor* permutation); - -} // namespace fused_embedding - -} // namespace tensorflow - -#endif // GOOGLE_CUDA \ No newline at end of file From 22506c52a13eb87207335ffa774ee2fc3fa8d94f Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Fri, 18 Mar 2022 17:43:15 +0800 Subject: [PATCH 21/41] 3rd version. Code modifying complete. No compile yet --- .../gpu/functions/hash_functions.cu.h | 225 +++--- .../gpu/functions/kernels.cu.cc | 352 +++++---- .../gpu/functions/kernels.cu.h | 52 +- .../gpu/functions/partition_select.cu.cc | 24 +- .../gpu/fused_embedding_post_ops_gpus.cu.cc | 227 +++--- .../gpu/partition_with_permutation_ops.cu.cc | 24 +- .../gpu/unique_with_count_gpu.ops.cu.cc | 365 ++++++++++ .../gpu/unique_with_count_hash.ops.cu.cc | 0 .../gpu/unique_with_multi_output_ops.cu.cc | 671 ------------------ tensorflow/core/ops/fused_embedding_ops.cc | 76 +- 10 files changed, 907 insertions(+), 1109 deletions(-) create mode 100644 tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_gpu.ops.cu.cc delete mode 100644 tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_hash.ops.cu.cc delete mode 100644 tensorflow/core/kernels/fused_embedding/gpu/unique_with_multi_output_ops.cu.cc diff --git a/tensorflow/core/kernels/fused_embedding/gpu/functions/hash_functions.cu.h b/tensorflow/core/kernels/fused_embedding/gpu/functions/hash_functions.cu.h index d2c6f248ee7..9ea9586aa08 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/functions/hash_functions.cu.h +++ b/tensorflow/core/kernels/fused_embedding/gpu/functions/hash_functions.cu.h @@ -17,129 +17,128 @@ #ifndef TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_HASH_FUNCTIONS_CU_H_ #define TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_HASH_FUNCTIONS_CU_H_ - -//MurmurHash3_32 implementation from https://github.com/aappleby/smhasher/blob/master/src/MurmurHash3.cpp +// MurmurHash3_32 implementation from +// https://github.com/aappleby/smhasher/blob/master/src/MurmurHash3.cpp //----------------------------------------------------------------------------- -// MurmurHash3 was written by Austin Appleby, and is placed in the public -// domain. The author hereby disclaims copyright to this source code. -// Note - The x86 and x64 versions do _not_ produce the same results, as the -// algorithms are optimized for their respective platforms. You can still -// compile and run any of them on any platform, but your performance with the -// non-native version will be less than optimal. +// MurmurHash3 was written by Austin Appleby, and is placed in the public +// domain. The author hereby disclaims copyright to this source code. +// Note - The x86 and x64 versions do _not_ produce the same results, as the +// algorithms are optimized for their respective platforms. You can still +// compile and run any of them on any platform, but your performance with the +// non-native version will be less than optimal. + +namespace tensorflow { + +namespace fused_embedding { + template -struct MurmurHash3_32 -{ - - using argument_type = Key; - using result_type = uint32_t; - - /*__forceinline__ - __host__ __device__ - MurmurHash3_32() : m_seed( 0 ) {}*/ - - __forceinline__ - __host__ __device__ static uint32_t rotl32( uint32_t x, int8_t r ) - { - return (x << r) | (x >> (32 - r)); - } - - __forceinline__ - __host__ __device__ static uint32_t fmix32( uint32_t h ) - { - h ^= h >> 16; - h *= 0x85ebca6b; - h ^= h >> 13; - h *= 0xc2b2ae35; - h ^= h >> 16; - return h; - } - - /* --------------------------------------------------------------------------*/ - /** - * @Synopsis Combines two hash values into a new single hash value. Called - * repeatedly to create a hash value from several variables. - * Taken from the Boost hash_combine function - * https://www.boost.org/doc/libs/1_35_0/doc/html/boost/hash_combine_id241013.html - * - * @Param lhs The first hash value to combine - * @Param rhs The second hash value to combine - * - * @Returns A hash value that intelligently combines the lhs and rhs hash values - */ - /* ----------------------------------------------------------------------------*/ - __host__ __device__ static result_type hash_combine(result_type lhs, result_type rhs) - { - result_type combined{lhs}; - - combined ^= rhs + 0x9e3779b9 + (combined << 6) + (combined >> 2); - - return combined; - } - - __forceinline__ - __host__ __device__ static result_type hash(const Key& key) - { - constexpr int len = sizeof(argument_type); - const uint8_t * const data = (const uint8_t*)&key; - constexpr int nblocks = len / 4; - uint32_t h1 = m_seed; - constexpr uint32_t c1 = 0xcc9e2d51; - constexpr uint32_t c2 = 0x1b873593; - //---------- - // body - const uint32_t * const blocks = (const uint32_t *)(data + nblocks*4); - for(int i = -nblocks; i; i++) - { - uint32_t k1 = blocks[i];//getblock32(blocks,i); - k1 *= c1; - k1 = rotl32(k1,15); - k1 *= c2; - h1 ^= k1; - h1 = rotl32(h1,13); - h1 = h1*5+0xe6546b64; - } - //---------- - // tail - const uint8_t * tail = (const uint8_t*)(data + nblocks*4); - uint32_t k1 = 0; - switch(len & 3) - { - case 3: k1 ^= tail[2] << 16; - case 2: k1 ^= tail[1] << 8; - case 1: k1 ^= tail[0]; - k1 *= c1; k1 = rotl32(k1,15); k1 *= c2; h1 ^= k1; - }; - //---------- - // finalization - h1 ^= len; - h1 = fmix32(h1); - return h1; - } +struct MurmurHash3_32 { + using argument_type = Key; + using result_type = uint32_t; -}; + /*__forceinline__ + __host__ __device__ + MurmurHash3_32() : m_seed( 0 ) {}*/ -template -struct Fix_Hash{ + __forceinline__ __host__ __device__ static uint32_t rotl32(uint32_t x, + int8_t r) { + return (x << r) | (x >> (32 - r)); + } - using result_type = index_type; + __forceinline__ __host__ __device__ static uint32_t fmix32(uint32_t h) { + h ^= h >> 16; + h *= 0x85ebca6b; + h ^= h >> 13; + h *= 0xc2b2ae35; + h ^= h >> 16; + return h; + } - __forceinline__ - __host__ __device__ static index_type hash(const key_type& key) - { - return result; - } + /* --------------------------------------------------------------------------*/ + /** + * @Synopsis Combines two hash values into a new single hash value. Called + * repeatedly to create a hash value from several variables. + * Taken from the Boost hash_combine function + * https://www.boost.org/doc/libs/1_35_0/doc/html/boost/hash_combine_id241013.html + * + * @Param lhs The first hash value to combine + * @Param rhs The second hash value to combine + * + * @Returns A hash value that intelligently combines the lhs and rhs hash + * values + */ + /* ----------------------------------------------------------------------------*/ + __host__ __device__ static result_type hash_combine(result_type lhs, + result_type rhs) { + result_type combined{lhs}; -}; + combined ^= rhs + 0x9e3779b9 + (combined << 6) + (combined >> 2); -template -struct Mod_Hash{ + return combined; + } - __forceinline__ - __host__ __device__ static result_type hash(const key_type& key) - { - return (result_type)key; + __forceinline__ __host__ __device__ static result_type hash(const Key& key) { + constexpr int len = sizeof(argument_type); + const uint8_t* const data = (const uint8_t*)&key; + constexpr int nblocks = len / 4; + uint32_t h1 = m_seed; + constexpr uint32_t c1 = 0xcc9e2d51; + constexpr uint32_t c2 = 0x1b873593; + //---------- + // body + const uint32_t* const blocks = (const uint32_t*)(data + nblocks * 4); + for (int i = -nblocks; i; i++) { + uint32_t k1 = blocks[i]; // getblock32(blocks,i); + k1 *= c1; + k1 = rotl32(k1, 15); + k1 *= c2; + h1 ^= k1; + h1 = rotl32(h1, 13); + h1 = h1 * 5 + 0xe6546b64; } + //---------- + // tail + const uint8_t* tail = (const uint8_t*)(data + nblocks * 4); + uint32_t k1 = 0; + switch (len & 3) { + case 3: + k1 ^= tail[2] << 16; + case 2: + k1 ^= tail[1] << 8; + case 1: + k1 ^= tail[0]; + k1 *= c1; + k1 = rotl32(k1, 15); + k1 *= c2; + h1 ^= k1; + }; + //---------- + // finalization + h1 ^= len; + h1 = fmix32(h1); + return h1; + } +}; +template +struct Fix_Hash { + using result_type = index_type; + + __forceinline__ __host__ __device__ static index_type hash( + const key_type& key) { + return result; + } +}; + +template +struct Mod_Hash { + __forceinline__ __host__ __device__ static result_type hash( + const key_type& key) { + return (result_type)key; + } }; -#endif // TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_HASH_FUNCTIONS_CU_H_ +} // namespace fused_embedding +} // namespace tensorflow + +#endif // TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_HASH_FUNCTIONS_CU_H_ diff --git a/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.cc index 5fb6a698844..9ee24359122 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.cc @@ -4,9 +4,9 @@ #define EIGEN_USE_GPU -#include "tensorflow/core/kernels/fused_embedding/gpu/common.cu.h" #include "tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.h" +#include "tensorflow/core/kernels/fused_embedding/gpu/common.cu.h" #include "tensorflow/core/platform/stream_executor.h" #include "tensorflow/core/util/gpu_kernel_helper.h" @@ -121,51 +121,6 @@ void FusedMultiFunctional(const GPUDevice& d, const IndicePair* indices, invalid_id_flag, tmp_indices_buffer, values_extended)); } -__global__ void CalcElementsOffsetPerPartitionKernel( - const int64_t* values_sorted, int64_t* partition_sizes_accumulate, - int64_t* elements_offset_per_partition, int nnz) { - // dichotomy - const int64_t target = partition_sizes_accumulate[blockIdx.x]; - int roof = nnz; - int floor = 0; - - int pos = (roof + floor) / 2; - while (1) { - if (pos == 0) { - pos = -1; - break; - } else if (pos == nnz - 1) { - break; - } - int64_t value = values_sorted[pos]; - int64_t value_plus_1 = values_sorted[pos + 1]; - if (value < target && value_plus_1 >= target) { - break; - } - if (value < target) { - floor = pos; - } else { - roof = pos; - } - pos = (roof + floor) / 2; - } - elements_offset_per_partition[blockIdx.x] = int64_t(pos + 1); -} - -void CalcElementsOffsetPerPartition(const GPUDevice& d, int num_partitions, - const int64_t* values_sorted, - int64_t* partition_sizes_accumulate, - int64_t* elements_offset_per_partition, - int nnz) { - const int blocks = num_partitions; - const int threads = 1; - - TF_CHECK_OK(GpuLaunchKernel(CalcElementsOffsetPerPartitionKernel, blocks, - threads, 0, d.stream(), values_sorted, - partition_sizes_accumulate, - elements_offset_per_partition, nnz)); -} - template __global__ void RangeInitKernel(const int64_t length, T* out) { int64_t idx = blockIdx.x * blockDim.x + threadIdx.x; @@ -185,78 +140,104 @@ void RangeInit(const GPUDevice& d, const int64_t length, T* out) { template void RangeInit(const GPUDevice& d, const int64_t length, int64_t* out); -__global__ void GatherAndConvertToSubPartitionKernel( - const int64_t* sub_values_sorted, int64_t* sub_partitioned_values, - const int64_t partition_start_base, const int64_t partition_size) { - const int t_offset = blockIdx.x * blockDim.x + threadIdx.x; - if (t_offset < partition_size) { - int64_t value = sub_values_sorted[t_offset]; - // rebase value to it's corresponding sub partition - value = value - partition_start_base; - sub_partitioned_values[t_offset] = value; +__global__ void SumUpEmbeddingShardSinglePartitionKernel( + const float* emb_shard, const int64_t* indices_before_unique, + const int* unique_idxs, const int nnz, const float max_norm, + const int emb_vec_size, float* emb_vectors, int* feature_nums) { + __shared__ float l2_sum[1]; + + if (blockIdx.x < nnz) { + const int64_t row_in_batch = indices_before_unique[2 * blockIdx.x]; + const int unique_id = unique_idxs[blockIdx.x]; + float emb_element = emb_shard[unique_id * emb_vec_size + threadIdx.x]; + if (max_norm >= 0.0f) { + if (threadIdx.x == 0) { + l2_sum[0] = 0.0f; + } + __syncthreads(); + atomicAdd(l2_sum, emb_element * emb_element); + __syncthreads(); + float l2_norm = sqrtf(l2_sum[0]); + if (l2_norm > max_norm) { + emb_element *= max_norm / l2_norm; + } + } + + atomicAdd(emb_vectors + row_in_batch * emb_vec_size + threadIdx.x, + emb_element); + + if (threadIdx.x == 0) { + atomicAdd(feature_nums + row_in_batch, 1); + } } } -void GatherAndConvertToSubPartition(const GPUDevice& d, - const int64_t* sub_values_sorted, - int64_t* sub_partitioned_values, - const int64_t partition_start_base, - const int64_t partition_size) { - const int threads = LINER_MAPPING_THREADS; - int blocks = CalcBlocksLinearMapping(partition_size, threads); - - TF_CHECK_OK(GpuLaunchKernel(GatherAndConvertToSubPartitionKernel, blocks, - threads, 0, d.stream(), sub_values_sorted, - sub_partitioned_values, partition_start_base, - partition_size)); +void SumUpEmbeddingShardSinglePartition(const GPUDevice& d, + const float* emb_shard, + const int64_t* indices_before_unique, + const int* unique_idxs, const int nnz, + const float max_norm, + const int emb_vec_size, + float* emb_vectors, int* feature_nums) { + const int blocks = nnz; + const int threads = emb_vec_size; + TF_CHECK_OK(GpuLaunchKernel(SumUpEmbeddingShardSinglePartitionKernel, blocks, + threads, 0, d.stream(), emb_shard, + indices_before_unique, unique_idxs, nnz, max_norm, + emb_vec_size, emb_vectors, feature_nums)); } -__global__ void SumUpEmbeddingShardKernel( - const float* emb_shard, const int64_t* partition_permutations, - const int64_t* indices_before_unique, const int64_t* unique_counts, - const int64_t* idx_of_input_to_unique, const int64_t* unique_offsets, +__global__ void SumUpEmbeddingShardMultiPartitionKernel( + const void* const* emb_shard_ptrs, const int* partition_permutation, + const int64_t* indices_before_unique, const int* unique_idxs, const int nnz, const float max_norm, const int emb_vec_size, float* emb_vectors, int* feature_nums) { __shared__ float l2_sum[1]; - const int64_t partition_permute = partition_permutations[blockIdx.x]; - const int64_t ioitu_offset = unique_offsets[partition_permute]; - const int64_t counts = unique_counts[partition_permute]; - - float emb_element = emb_shard[blockIdx.x * emb_vec_size + threadIdx.x]; - if (max_norm >= 0.0f) { - l2_sum[0] = 0.0f; - atomicAdd(l2_sum, emb_element * emb_element); - __syncthreads(); - float l2_norm = sqrtf(l2_sum[0]); - if (l2_norm > max_norm) { - emb_element *= max_norm / l2_norm; + + if (blockIdx.x < nnz) { + const int64_t row_in_batch = indices_before_unique[2 * blockIdx.x]; + const int unique_id = unique_idxs[blockIdx.x]; + const int partition_id = partition_permutation[2 * unique_id]; + const int64_t offset_in_partition = + partition_permutation[2 * unique_id + 1]; + + float emb_element = + ((const float*)(emb_shard_ptrs[partition_id]))[offset_in_partition * + emb_vec_size + + threadIdx.x]; + if (max_norm >= 0.0f) { + if (threadIdx.x == 0) { + l2_sum[0] = 0.0f; + } + __syncthreads(); + atomicAdd(l2_sum, emb_element * emb_element); + __syncthreads(); + float l2_norm = sqrtf(l2_sum[0]); + if (l2_norm > max_norm) { + emb_element *= max_norm / l2_norm; + } } - } - for (int64_t i = 0; i < counts; i++) { - const int64_t ioitu = idx_of_input_to_unique[ioitu_offset + i]; - const int64_t row_in_batch = indices_before_unique[2 * ioitu]; atomicAdd(emb_vectors + row_in_batch * emb_vec_size + threadIdx.x, emb_element); + if (threadIdx.x == 0) { atomicAdd(feature_nums + row_in_batch, 1); } } } -void SumUpEmbeddingShard( - const GPUDevice& d, const size_t shard_len, const float* emb_shard, - const int64_t* partition_permutations, const int64_t* indices_before_unique, - const int64_t* unique_counts, const int64_t* idx_of_input_to_unique, - const int64_t* unique_offsets, const float max_norm, const int emb_vec_size, - float* emb_vectors, int* feature_nums) { - const int blocks = shard_len; +void SumUpEmbeddingShardMultiPartition( + const GPUDevice& d, const void* const* emb_shard_ptrs, + const int* partition_permutation, const int64_t* indices_before_unique, + const int* unique_idxs, const int nnz, const float max_norm, + const int emb_vec_size, float* emb_vectors, int* feature_nums) { + const int blocks = nnz; const int threads = emb_vec_size; - TF_CHECK_OK(GpuLaunchKernel(SumUpEmbeddingShardKernel, blocks, threads, 0, - d.stream(), emb_shard, partition_permutations, - indices_before_unique, unique_counts, - idx_of_input_to_unique, unique_offsets, max_norm, - emb_vec_size, emb_vectors, feature_nums)); + TF_CHECK_OK(GpuLaunchKernel( + SumUpEmbeddingShardMultiPartitionKernel, blocks, threads, 0, d.stream(), + emb_shard_ptrs, partition_permutatio, indices_before_unique, unique_idxs, + nnz, max_norm, emb_vec_size, emb_vectors, feature_nums)); } template @@ -305,36 +286,32 @@ template void ApplyCombiner(const GPUDevice& d, const int batch_size, int* feature_nums, float* emb_vectors); template -__global__ void DistributeGradToShardKernel( +__global__ void DistributeGradToShardSinglePartitionKernel( const float* top_grad, const float* emb_shard, - const int64_t* partition_permutations, const int64_t* indices_before_unique, - const int64_t* unique_counts, const int64_t* idx_of_input_to_unique, - const int64_t* unique_offsets, const int64_t shard_len, - const int64_t emb_vec_size, const float max_norm, + const int64_t* indices_before_unique, const int64_t* unique_idxs, + const int nnz, const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, const int* feature_nums, const int* row_emptiness_flag, float* grad_shard) { __shared__ float l2_sum[1]; - - const int64_t partition_permute = partition_permutations[blockIdx.x]; - const int64_t ioitu_offset = unique_offsets[partition_permute]; - const int64_t counts = unique_counts[partition_permute]; float l2_norm = -1.0f; - if (max_norm >= 0.0f) { - const float emb_element = - emb_shard[blockIdx.x * emb_vec_size + threadIdx.x]; - l2_sum[0] = 0.0f; - atomicAdd(l2_sum, emb_element * emb_element); - __syncthreads(); - l2_norm = sqrtf(l2_sum[0]); - } + if (blockIdx.x < nnz) { + const int64_t row_in_batch = indices_before_unique[2 * blockIdx.x]; + const int unique_id = unique_idxs[blockIdx.x]; - float grad_accu = 0.0f; - for (int64_t i = 0; i < counts; i++) { - const int64_t ioitu = idx_of_input_to_unique[ioitu_offset + i]; - const int64_t row_in_batch = indices_before_unique[2 * ioitu]; if (set_empty_row_zero && row_emptiness_flag[row_in_batch]) { - continue; + return; + } + if (max_norm >= 0.0f) { + const float emb_element = + emb_shard[unique_id * emb_vec_size + threadIdx.x]; + if (threadIdx.x == 0) { + l2_sum[0] = 0.0f; + } + __syncthreads(); + atomicAdd(l2_sum, emb_element * emb_element); + __syncthreads(); + l2_norm = sqrtf(l2_sum[0]); } float grad = top_grad[row_in_batch * emb_vec_size + threadIdx.x]; @@ -343,58 +320,137 @@ __global__ void DistributeGradToShardKernel( if (max_norm >= 0.0f && l2_norm > max_norm) { grad *= max_norm / l2_norm; } - grad_accu += grad; - } - grad_shard[blockIdx.x * emb_vec_size + threadIdx.x] = grad_accu; + grad_shard[unique_id * emb_vec_size + threadIdx.x] = grad; + } } template -void DistributeGradToShard( +void DistributeGradToShardSinglePartition( const GPUDevice& d, const float* top_grad, const float* emb_shard, - const int64_t* partition_permutations, const int64_t* indices_before_unique, - const int64_t* unique_counts, const int64_t* idx_of_input_to_unique, - const int64_t* unique_offsets, const int64_t shard_len, - const int64_t emb_vec_size, const float max_norm, + const int64_t* indices_before_unique, const int64_t* unique_idxs, + const int nnz, const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, const int* feature_nums, const int* row_emptiness_flag, float* grad_shard) { - const int blocks = shard_len; + const int blocks = nnz; const int threads = emb_vec_size; TF_CHECK_OK(GpuLaunchKernel( - DistributeGradToShardKernel, blocks, threads, 0, d.stream(), - top_grad, emb_shard, partition_permutations, indices_before_unique, - unique_counts, idx_of_input_to_unique, unique_offsets, shard_len, + DistributeGradToShardSinglePartitionKernel, blocks, threads, 0, + d.stream(), top_grad, emb_shard, indices_before_unique, unique_idxs, nnz, emb_vec_size, max_norm, set_empty_row_zero, feature_nums, row_emptiness_flag, grad_shard)); } -template void DistributeGradToShard( +template void DistributeGradToShardSinglePartition( const GPUDevice& d, const float* top_grad, const float* emb_shard, - const int64_t* partition_permutations, const int64_t* indices_before_unique, - const int64_t* unique_counts, const int64_t* idx_of_input_to_unique, - const int64_t* unique_offsets, const int64_t shard_len, - const int64_t emb_vec_size, const float max_norm, + const int64_t* indices_before_unique, const int64_t* unique_idxs, + const int nnz, const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, const int* feature_nums, const int* row_emptiness_flag, float* grad_shard); -template void DistributeGradToShard( +template void DistributeGradToShardSinglePartition( const GPUDevice& d, const float* top_grad, const float* emb_shard, - const int64_t* partition_permutations, const int64_t* indices_before_unique, - const int64_t* unique_counts, const int64_t* idx_of_input_to_unique, - const int64_t* unique_offsets, const int64_t shard_len, - const int64_t emb_vec_size, const float max_norm, + const int64_t* indices_before_unique, const int64_t* unique_idxs, + const int nnz, const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, const int* feature_nums, const int* row_emptiness_flag, float* grad_shard); -template void DistributeGradToShard( +template void DistributeGradToShardSinglePartition( const GPUDevice& d, const float* top_grad, const float* emb_shard, - const int64_t* partition_permutations, const int64_t* indices_before_unique, - const int64_t* unique_counts, const int64_t* idx_of_input_to_unique, - const int64_t* unique_offsets, const int64_t shard_len, - const int64_t emb_vec_size, const float max_norm, + const int64_t* indices_before_unique, const int64_t* unique_idxs, + const int nnz, const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, const int* feature_nums, const int* row_emptiness_flag, float* grad_shard); +template +__global__ void DistributeGradToShardMultiPartitionKernel( + const float* top_grad, const void* const* emb_shard_ptrs, + const int* partition_permutation, const int64_t* indices_before_unique, + const int64_t* unique_idxs, const int nnz, const int emb_vec_size, + const float max_norm, const bool set_empty_row_zero, + const int* feature_nums, const int* row_emptiness_flag, + void** grad_shard_ptrs) { + __shared__ float l2_sum[1]; + float l2_norm = -1.0f; + + if (blockIdx.x < nnz) { + const int64_t row_in_batch = indices_before_unique[2 * blockIdx.x]; + if (set_empty_row_zero && row_emptiness_flag[row_in_batch]) { + return; + } + const int unique_id = unique_idxs[blockIdx.x]; + const int partition_id = partition_permutation[2 * unique_id]; + const int64_t offset_in_partition = + partition_permutation[2 * unique_id + 1]; + + if (max_norm >= 0.0f) { + float emb_element = + ((const float*)(emb_shard_ptrs[partition_id]))[offset_in_partition * + emb_vec_size + + threadIdx.x]; + if (threadIdx.x == 0) { + l2_sum[0] = 0.0f; + } + __syncthreads(); + atomicAdd(l2_sum, emb_element * emb_element); + __syncthreads(); + l2_norm = sqrtf(l2_sum[0]); + } + + float grad = top_grad[row_in_batch * emb_vec_size + threadIdx.x]; + const int feature_num = feature_nums[row_in_batch]; + grad = CombineGrad(grad, feature_num); + if (max_norm >= 0.0f && l2_norm > max_norm) { + grad *= max_norm / l2_norm; + } + + atomicAdd(((float*)(grad_shard_ptrs[partition_id])) + + offset_in_partition * emb_vec_size + threadIdx.x, + grad); + } +} + +template +void DistributeGradToShardMultiPartition( + const GPUDevice& d, const float* top_grad, + const void* const* emb_shard_ptrs, const int* partition_permutation, + const int64_t* indices_before_unique, const int64_t* unique_idxs, + const int nnz, const int emb_vec_size, const float max_norm, + const bool set_empty_row_zero, const int* feature_nums, + const int* row_emptiness_flag, void** grad_shard_ptrs) { + const int blocks = nnz; + const int threads = emb_vec_size; + TF_CHECK_OK(GpuLaunchKernel( + DistributeGradToShardMultiPartitionKernel, blocks, threads, 0, + d.stream(), top_grad, emb_shard_ptrs, partition_permutation, + indices_before_unique, unique_idxs, nnz, emb_vec_size, max_norm, + set_empty_row_zero, feature_nums, row_emptiness_flag, grad_shard_ptrs)); +} + +template void DistributeGradToShardMultiPartition( + const GPUDevice& d, const float* top_grad, + const void* const* emb_shard_ptrs, const int* partition_permutation, + const int64_t* indices_before_unique, const int64_t* unique_idxs, + const int nnz, const int emb_vec_size, const float max_norm, + const bool set_empty_row_zero, const int* feature_nums, + const int* row_emptiness_flag, void** grad_shard_ptrs); + +template void DistributeGradToShardMultiPartition( + const GPUDevice& d, const float* top_grad, + const void* const* emb_shard_ptrs, const int* partition_permutation, + const int64_t* indices_before_unique, const int64_t* unique_idxs, + const int nnz, const int emb_vec_size, const float max_norm, + const bool set_empty_row_zero, const int* feature_nums, + const int* row_emptiness_flag, void** grad_shard_ptrs); + +template void DistributeGradToShardMultiPartition( + const GPUDevice& d, const float* top_grad, + const void* const* emb_shard_ptrs, const int* partition_permutation, + const int64_t* indices_before_unique, const int64_t* unique_idxs, + const int nnz, const int emb_vec_size, const float max_norm, + const bool set_empty_row_zero, const int* feature_nums, + const int* row_emptiness_flag, void** grad_shard_ptrs); + } // namespace fused_embedding } // namespace tensorflow diff --git a/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.h b/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.h index 0f13c0f5f06..a92b7c02747 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.h +++ b/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.h @@ -11,6 +11,8 @@ namespace tensorflow { namespace fused_embedding { +using GPUDevice = Eigen::GpuDevice; + enum Combiner { Mean, Sum, Sqrtn }; template @@ -56,8 +58,6 @@ __forceinline__ __device__ float CombineGrad(const float grad, return grad; } -using GPUDevice = Eigen::GpuDevice; - void InitFlagsToOneInt4(const GPUDevice& d, int length, int* flags); void DetectInvalid(const GPUDevice& d, const int64_t* values, const int64_t nnz, @@ -70,27 +70,22 @@ void FusedMultiFunctional(const GPUDevice& d, const IndicePair* indices, int* invalid_id_flag, IndicePair* tmp_indices_buffer, int64_t* values_extended); -void CalcElementsOffsetPerPartition(const GPUDevice& d, int num_partitions, - const int64_t* values_sorted, - int64_t* partition_sizes_accumulate, - int64_t* elements_offset_per_partition, - int nnz); - -void GatherAndConvertToSubPartition(const GPUDevice& d, - const int64_t* sub_values_sorted, - int64_t* sub_partitioned_values, - const int64_t partition_start_base, - const int64_t partition_size); - template void RangeInit(const GPUDevice& d, const int64_t length, T* out); -void SumUpEmbeddingShard( - const GPUDevice& d, const size_t shard_len, const float* emb_shard, - const int64_t* partition_permutations, const int64_t* indices_before_unique, - const int64_t* unique_counts, const int64_t* idx_of_input_to_unique, - const int64_t* unique_offsets, const float max_norm, const int emb_vec_size, - float* emb_vectors, int* feature_nums); +void SumUpEmbeddingShardSinglePartition(const GPUDevice& d, + const float* emb_shard, + const int64_t* indices_before_unique, + const int* unique_idxs, const int nnz, + const float max_norm, + const int emb_vec_size, + float* emb_vectors, int* feature_nums); + +void SumUpEmbeddingShardMultiPartition( + const GPUDevice& d, const void* const* emb_shard_ptrs, + const int* partition_permutation, const int64_t* indices_before_unique, + const int* unique_idxs, const int nnz, const float max_norm, + const int emb_vec_size, float* emb_vectors, int* feature_nums); template void ApplyCombiner(const GPUDevice& d, const int batch_size, @@ -99,15 +94,22 @@ void ApplyCombiner(const GPUDevice& d, const int batch_size, float* emb_vectors); template -void DistributeGradToShard( +void DistributeGradToShardSinglePartition( const GPUDevice& d, const float* top_grad, const float* emb_shard, - const int64_t* partition_permutations, const int64_t* indices_before_unique, - const int64_t* unique_counts, const int64_t* idx_of_input_to_unique, - const int64_t* unique_offsets, const int64_t shard_len, - const int64_t emb_vec_size, const float max_norm, + const int64_t* indices_before_unique, const int64_t* unique_idxs, + const int nnz, const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, const int* feature_nums, const int* row_emptiness_flag, float* grad_shard); +template +void DistributeGradToShardMultiPartition( + const GPUDevice& d, const float* top_grad, + const void* const* emb_shard_ptrs, const int* partition_permutation, + const int64_t* indices_before_unique, const int64_t* unique_idxs, + const int nnz, const int emb_vec_size, const float max_norm, + const bool set_empty_row_zero, const int* feature_nums, + const int* row_emptiness_flag, void** grad_shard_ptrs); + } // namespace fused_embedding } // namespace tensorflow diff --git a/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc index 0777ddd1f69..57bea48eed5 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc @@ -1,13 +1,14 @@ #if GOOGLE_CUDA #define EIGEN_USE_GPU +#include "tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h" + #include #include #include #include "tensorflow/core/common_runtime/gpu/gpu_event_mgr.h" #include "tensorflow/core/kernels/fused_embedding/gpu/common.cu.h" -#include "tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h" #include "tensorflow/core/lib/core/bits.h" #include "tensorflow/core/util/gpu_kernel_helper.h" #include "tensorflow/stream_executor/cuda/cuda_activation.h" @@ -132,12 +133,13 @@ namespace fused_embedding { OpOutputList& selected_keys, Tensor* permutation) { \ OP_REQUIRES(ctx, keys->dims() == 1, \ errors::InvalidArgument("Tensor keys must ranks 1")); \ - OP_REQUIRES(ctx, \ - WarpWorkload >= 32 && \ - (WarpWorkload && !(WarpWorkload & (WarpWorkload - 1))), \ - errors::InvalidArgument( \ - "WarpWorkload must be larger than warp size " \ - "32 and is exponential of 2, 32, 64, 128, i.e.")); \ + OP_REQUIRES( \ + ctx, \ + WarpWorkload >= 32 && WarpWorkload <= 1024 && \ + (WarpWorkload && !(WarpWorkload & (WarpWorkload - 1))), \ + errors::InvalidArgument( \ + "WarpWorkload must be larger than warp size and less than 1024 " \ + "32 and is exponential of 2, 32, 64, 128, i.e.")); \ const GPUDevice& device = ctx->eigen_gpu_device(); \ const int64 length = keys->NumElements(); \ const int64 warp_iteration = WarpWorkload / 32; \ @@ -238,6 +240,14 @@ namespace fused_embedding { DeclareSelectScanKernel; DeclareSelectKernel; DeclareSelect; + +template void PartitionSelectDiv(OpKernelContext* ctx, + const Tensor* keys, + const Tensor& accu_div, + const int64 num_partitions, + OpOutputList& selected_keys, + Tensor* permutation); + template void PartitionSelectDiv(OpKernelContext* ctx, const Tensor* keys, const Tensor& accu_div, diff --git a/tensorflow/core/kernels/fused_embedding/gpu/fused_embedding_post_ops_gpus.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/fused_embedding_post_ops_gpus.cu.cc index 5e5033f35a2..3e26a8af71b 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/fused_embedding_post_ops_gpus.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/fused_embedding_post_ops_gpus.cu.cc @@ -38,9 +38,9 @@ class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { OpInputList emb_shards; OP_REQUIRES_OK(ctx, ctx->input_list("emb_shards", &emb_shards)); - OpInputList partition_permutations; - OP_REQUIRES_OK(ctx, ctx->input_list("partition_permutations", - &partition_permutations)); + Tensor const* partition_permutation = nullptr; + OP_REQUIRES_OK(ctx, + ctx->input("partition_permutation", &partition_permutation)); Tensor const* dense_shape_tensor = nullptr; OP_REQUIRES_OK(ctx, ctx->input("sp_dense_shape", &dense_shape_tensor)); @@ -54,66 +54,66 @@ class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { ctx->input("indices_before_unique", &indices_before_unique)); Tensor const* unique_counts = nullptr; - OP_REQUIRES_OK(ctx, ctx->input("unique_counts", &unique_counts)); - - Tensor const* idx_of_input_to_unique = nullptr; - OP_REQUIRES_OK( - ctx, ctx->input("idx_of_input_to_unique", &idx_of_input_to_unique)); - - Tensor const* unique_offsets = nullptr; - OP_REQUIRES_OK(ctx, ctx->input("unique_offsets", &unique_offsets)); + OP_REQUIRES_OK(ctx, ctx->input("unique_idxs", &unique_counts)); nvtx::ScopedRangeIfEnabled nvtx_range(this); - const int64_t emb_vec_size = emb_shards[0].shape().dim_size(1); - const int64_t batch_size = dense_shape_tensor->flat().data()[0]; + const int emb_vec_size = emb_shards[0].shape().dim_size(1); + const int batch_size = dense_shape_tensor->flat().data()[0]; + const int nnz = indices_before_unique->shape().dim_size(0); + const bool set_empty_row_zero = default_id_ < 0 && fill_empty_row_; - // = 1. sum up emb values from differententries and dump into output = // + // = 1. sum up emb values from emb_shards and dump into output = // Tensor* emb_vectors_tensor = nullptr; - OP_REQUIRES_OK( - ctx, ctx->allocate_output(0, TensorShape({batch_size, emb_vec_size}), - &emb_vectors_tensor)); - // stream_executor::DeviceMemoryBase emb_vectors_wrapper( - // emb_vectors_tensor.flat().data(), - // emb_vectors_tensor->NumElements() * sizeof(float)); - // stream->ThenMemZero(&emb_vectors_wrapper, - // emb_vectors_tensor->NumElements() * sizeof(float)); - + OP_REQUIRES_OK(ctx, ctx->allocate_output("emb_vectors", + TensorShape({int64(batch_size), + int64(emb_vec_size)}), + &emb_vectors_tensor)); cudaMemsetAsync(emb_vectors_tensor->flat().data(), 0x0, sizeof(float) * emb_vectors_tensor->NumElements(), device.stream()); Tensor* feature_nums; - OP_REQUIRES_OK( - ctx, ctx->allocate_output(1, TensorShape({batch_size}), &feature_nums)); - // stream_executor::DeviceMemoryBase feature_nums_wrapper( - // feature_nums.flat().data(), - // feature_nums.NumElements() * sizeof(int)); - // stream->ThenMemZero(&feature_nums_wrapper, - // feature_nums.NumElements() * sizeof(int)); + OP_REQUIRES_OK(ctx, ctx->allocate_output("feature_nums", + TensorShape({int64(batch_size)}), + &feature_nums)); cudaMemsetAsync(feature_nums->flat().data(), 0x0, sizeof(int) * feature_nums->NumElements(), device.stream()); - for (int i = 0; i < num_partitions_; i++) { - const size_t shard_len = emb_shards[i].shape().dim_size(0); - OP_REQUIRES(ctx, - shard_len == partition_permutations[i].shape().dim_size(0), - errors::InvalidArgument( - "emb_shard and partition_permutations doesn't " - "have the same length")); - SumUpEmbeddingShard( - device, shard_len, data_p_with_type(emb_shards[i]), - data_p_with_type(partition_permutations[i]), + Tensor* emb_shard_ptrs; + OP_REQUIRES_OK(ctx, + ctx->allocate_output("emb_shard_ptrs", + TensorShape({int64(num_partitions_)}), + &emb_shard_ptrs)); + + if (num_partitions_ == 1) { + SumUpEmbeddingShardSinglePartition( + device, data_p_with_type(emb_shards[0]), data_p_with_type(indices_before_unique), - data_p_with_type(unique_counts), - data_p_with_type(idx_of_input_to_unique), - data_p_with_type(unique_offsets), max_norm_, + data_p_with_type(unique_idxs), nnz, max_norm_, + emb_vec_size, data_p_with_type(emb_vectors_tensor), + data_p_with_type(feature_nums)); + } else { + std::vector emb_shard_ptrs_host; + emb_shard_ptrs_host.resize(num_partitions_); + for (int i = 0; i < num_partitions_; i++) { + emb_shard_ptrs_host[i] = data_p_with_type(emb_shards[i]); + } + + cudaMemcpyAsync(data_p_with_type(emb_shard_ptrs), + emb_shard_ptrs_host.data(), + num_partitions * sizeof(size_t), cudaMemcpyHostToDevice, + device.stream()); + + SumUpEmbeddingShardMultiPartition( + device, data_p_with_type(emb_shard_ptrs), + data_p_with_type(partition_permutation), + data_p_with_type(indices_before_unique), + data_p_with_type(unique_idxs), nnz, max_norm_, emb_vec_size, data_p_with_type(emb_vectors_tensor), data_p_with_type(feature_nums)); - CK_CUDA_THROW_(cudaGetLastError()); } - const bool set_empty_row_zero = default_id_ < 0 && fill_empty_row_; // ================================================================ // // ========================= 2. combiner ========================== // @@ -136,7 +136,6 @@ class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { set_empty_row_zero, data_p_with_type(feature_nums), data_p_with_type(emb_vectors_tensor)); } - CK_CUDA_THROW_(cudaGetLastError()); // ================================================================ // } @@ -178,9 +177,12 @@ class FusedEmbeddingSparsePostLookUpGradGPU : public OpKernel { OpInputList emb_shards; OP_REQUIRES_OK(ctx, ctx->input_list("emb_shards", &emb_shards)); - OpInputList partition_permutations; - OP_REQUIRES_OK(ctx, ctx->input_list("partition_permutations", - &partition_permutations)); + Tensor const* emb_shard_ptrs = nullptr; + OP_REQUIRES_OK(ctx, ctx->input("emb_shard_ptrs", &emb_shard_ptrs)); + + Tensor const* partition_permutation = nullptr; + OP_REQUIRES_OK(ctx, + ctx->input("partition_permutation", &partition_permutation)); Tensor const* feature_nums = nullptr; OP_REQUIRES_OK(ctx, ctx->input("feature_nums", &feature_nums)); @@ -189,15 +191,8 @@ class FusedEmbeddingSparsePostLookUpGradGPU : public OpKernel { OP_REQUIRES_OK(ctx, ctx->input("indices_before_unique", &indices_before_unique)); - Tensor const* unique_counts = nullptr; - OP_REQUIRES_OK(ctx, ctx->input("unique_counts", &unique_counts)); - - Tensor const* idx_of_input_to_unique = nullptr; - OP_REQUIRES_OK( - ctx, ctx->input("idx_of_input_to_unique", &idx_of_input_to_unique)); - - Tensor const* unique_offsets = nullptr; - OP_REQUIRES_OK(ctx, ctx->input("unique_offsets", &unique_offsets)); + Tensor const* unique_idxs = nullptr; + OP_REQUIRES_OK(ctx, ctx->input("unique_idxs", &unique_idxs)); Tensor const* row_empty_and_invalid_flags = nullptr; OP_REQUIRES_OK(ctx, ctx->input("row_empty_and_invalid_flags", @@ -208,60 +203,98 @@ class FusedEmbeddingSparsePostLookUpGradGPU : public OpKernel { nvtx::ScopedRangeIfEnabled nvtx_range(this); - const int64_t batch_size = top_grad_tensor->shape().dim_size(0); - const int64_t emb_vec_size = emb_shards[0].shape().dim_size(1); - + const int batch_size = top_grad_tensor->shape().dim_size(0); + const int emb_vec_size = emb_shards[0].shape().dim_size(1); + const int nnz = indices_before_unique->shape().dim_size(0); const bool set_empty_row_zero = default_id_ < 0 && fill_empty_row_; + std::vector grad_shard_ptrs_host; + grad_shard_ptrs_host.resize(num_partitions_); for (int i = 0; i < num_partitions_; i++) { - const int64_t shard_len = partition_permutations[i].shape().dim_size(0); - - Tensor* grad_shard; - OP_REQUIRES_OK( - ctx, grad_shards.allocate(i, TensorShape({shard_len, emb_vec_size}), - &grad_shard)); + Tensor* grad_out; + grad_shards.allocate(i, emb_shards[i].shape(), &grad_out); + grad_shard_ptrs_host[i] = data_p_with_type(grad_out); + cudaMemsetAsync(data_p_with_type(grad_out), 0x0, + sizeof(float) * grad_out->NumElements(), device.stream()); + } - if (combiner_ == "sqrtn") { - DistributeGradToShard( + if (num_partitions_ == 1) { + if (combiner_ == 'mean') { + DistributeGradToShardSinglePartition( device, data_p_with_type(top_grad_tensor), - data_p_with_type(emb_shards[i]), - data_p_with_type(partition_permutations[i]), + data_p_with_type(emb_shards[0]), data_p_with_type(indices_before_unique), - data_p_with_type(unique_counts), - data_p_with_type(idx_of_input_to_unique), - data_p_with_type(unique_offsets), shard_len, - emb_vec_size, max_norm_, set_empty_row_zero, + data_p_with_type(unique_idx), nnz, emb_vec_size, + max_norm_, set_empty_row_zero, data_p_with_type(feature_nums), - data_p_with_type(row_empty_and_invalid_flags), - data_p_with_type(grad_shard)); - } else if (combiner_ == "mean") { - DistributeGradToShard( + data_p_with_type(row_emptiness_flag), + data_p_with_type(grad_shards[0])); + } else if (combiner_ == 'sqrt') { + DistributeGradToShardSinglePartition( device, data_p_with_type(top_grad_tensor), - data_p_with_type(emb_shards[i]), - data_p_with_type(partition_permutations[i]), + data_p_with_type(emb_shards[0]), data_p_with_type(indices_before_unique), - data_p_with_type(unique_counts), - data_p_with_type(idx_of_input_to_unique), - data_p_with_type(unique_offsets), shard_len, - emb_vec_size, max_norm_, set_empty_row_zero, + data_p_with_type(unique_idx), nnz, emb_vec_size, + max_norm_, set_empty_row_zero, data_p_with_type(feature_nums), - data_p_with_type(row_empty_and_invalid_flags), - data_p_with_type(grad_shard)); + data_p_with_type(row_emptiness_flag), + data_p_with_type(grad_shards[0])); } else { - DistributeGradToShard( + DistributeGradToShardSinglePartition( device, data_p_with_type(top_grad_tensor), - data_p_with_type(emb_shards[i]), - data_p_with_type(partition_permutations[i]), + data_p_with_type(emb_shards[0]), data_p_with_type(indices_before_unique), - data_p_with_type(unique_counts), - data_p_with_type(idx_of_input_to_unique), - data_p_with_type(unique_offsets), shard_len, - emb_vec_size, max_norm_, set_empty_row_zero, + data_p_with_type(unique_idx), nnz, emb_vec_size, + max_norm_, set_empty_row_zero, + data_p_with_type(feature_nums), + data_p_with_type(row_emptiness_flag), + data_p_with_type(grad_shards[0])); + } + + } else { + Tensor grad_shard_ptrs; + OP_REQUIRES_OK(ctx, ctx->allocate_temp( + DT_UINT64, TensorShape({int64(num_partitions_)}), + &grad_shard_ptrs)); + cudaMemcpyAsync(data_p_with_type(grad_shard_ptrs), + data_p_with_type(grad_shard_ptrs_host.data()), + num_partitions_ * sizeof(size_t), cudaMemcpyHostToDevice, + device.stream()); + + if (combiner_ == 'mean') { + DistributeGradToShardMultiPartition( + device, data_p_with_type(top_grad_tensor), + data_p_with_type(emb_shard_ptrs), + data_p_with_type(partition_permutation) + data_p_with_type(indices_before_unique), + data_p_with_type(unique_idx), nnz, emb_vec_size, + max_norm_, set_empty_row_zero, + data_p_with_type(feature_nums), + data_p_with_type(row_emptiness_flag), + data_p_with_type(grad_shard_ptrs)); + } else if (combiner_ == 'sqrt') { + DistributeGradToShardMultiPartition( + device, data_p_with_type(top_grad_tensor), + data_p_with_type(emb_shard_ptrs), + data_p_with_type(partition_permutation) + data_p_with_type(indices_before_unique), + data_p_with_type(unique_idx), nnz, emb_vec_size, + max_norm_, set_empty_row_zero, + data_p_with_type(feature_nums), + data_p_with_type(row_emptiness_flag), + data_p_with_type(grad_shard_ptrs)); + } else { + DistributeGradToShardMultiPartition( + device, data_p_with_type(top_grad_tensor), + data_p_with_type(emb_shard_ptrs), + data_p_with_type(partition_permutation) + data_p_with_type(indices_before_unique), + data_p_with_type(unique_idx), nnz, emb_vec_size, + max_norm_, set_empty_row_zero, data_p_with_type(feature_nums), - data_p_with_type(row_empty_and_invalid_flags), - data_p_with_type(grad_shard)); + data_p_with_type(row_emptiness_flag), + data_p_with_type(grad_shard_ptrs)); } - CK_CUDA_THROW_(cudaGetLastError()); } } diff --git a/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc index 4b83debbc3b..1a945c737c3 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc @@ -76,9 +76,27 @@ class PartitionWithPermutationGPU : public OpKernel { num_partitions_ * sizeof(int64_t), cudaMemcpyHostToDevice, device.stream()); - PartitionSelectDiv( - ctx, input, partition_permute_init, accu_div, num_partitions_, - partitioned_values, partition_permutations); + if (input_size < 512) { + PartitionSelectDiv(ctx, input, accu_div, + num_partitions_, partitioned_values, + partition_permutation); + } else if (input_size < 1024) { + PartitionSelectDiv(ctx, input, accu_div, + num_partitions_, partitioned_values, + partition_permutation); + } else if (input_size < 2048) { + PartitionSelectDiv(ctx, input, accu_div, + num_partitions_, partitioned_values, + partition_permutation); + } else if (input_size < 4096) { + PartitionSelectDiv(ctx, input, accu_div, + num_partitions_, partitioned_values, + partition_permutation); + } else { + PartitionSelectDiv( + ctx, input, accu_div, num_partitions_, partitioned_values, + partition_permutation); + } } } diff --git a/tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_gpu.ops.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_gpu.ops.cu.cc new file mode 100644 index 00000000000..426f92bae74 --- /dev/null +++ b/tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_gpu.ops.cu.cc @@ -0,0 +1,365 @@ +#include +#include +#include + +#include "tensorflow/core/framework/op_kernel.h" + +#if GOOGLE_CUDA + +#define EIGEN_USE_GPU + +#include "tensorflow/core/kernels/fused_embedding/gpu/common.cu.h" +#include "tensorflow/core/kernels/fused_embedding/gpu/functions/hash_functions.cu.h" +#include "tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.h" +#include "tensorflow/core/profiler/nvtx_utils.h" +#include "tensorflow/core/util/gpu_kernel_helper.h" +#include "third_party/cub/device/device_radix_sort.cuh" +#include "third_party/cub/device/device_select.cuh" +#include "third_party/cub/iterator/constant_input_iterator.cuh" +#include "third_party/cub/thread/thread_operators.cuh" + +namespace tensorflow { +using GPUDevice = Eigen::GpuDevice; + +namespace gpu_unique_with_counts { + +using MurmurHash3_32 = fused_embedding::MurmurHash3_32 + +const static block_size = 64; + +// Overload CUDA atomic for other 64bit unsinged/signed integer type +__forceinline__ __device__ long atomicAdd(long* address, long val) { + return (long)atomicAdd((unsigned long long*)address, (unsigned long long)val); +} + +__forceinline__ __device__ long long atomicAdd(long long* address, + long long val) { + return (long long)atomicAdd((unsigned long long*)address, + (unsigned long long)val); +} + +__forceinline__ __device__ unsigned long atomicAdd(unsigned long* address, + unsigned long val) { + return (unsigned long)atomicAdd((unsigned long long*)address, + (unsigned long long)val); +} + +__forceinline__ __device__ long atomicCAS(long* address, long compare, + long val) { + return (long)atomicCAS((unsigned long long*)address, + (unsigned long long)compare, (unsigned long long)val); +} + +__forceinline__ __device__ long long atomicCAS(long long* address, + long long compare, + long long val) { + return (long long)atomicCAS((unsigned long long*)address, + (unsigned long long)compare, + (unsigned long long)val); +} + +__forceinline__ __device__ unsigned long atomicCAS(unsigned long* address, + unsigned long compare, + unsigned long val) { + return (unsigned long)atomicCAS((unsigned long long*)address, + (unsigned long long)compare, + (unsigned long long)val); +} + +template +__global__ void InitKernel(KeyType* keys, CounterType* vals, + CounterType* counts, CounterType* counter, + const size_t capacity, const KeyType empty_key, + const CounterType empty_val, + const CounterType empty_counts, + const CounterType init_counter_val) { + const size_t idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < capacity) { + // Simply store every element a unused pair + keys[idx] = empty_key; + vals[idx] = empty_val; + counts[idx] = empty_counts; + } + if (idx == 0) { + counter[idx] = init_counter_val; + } +} + +template +void Init(const GPUDevice& d, KeyType* keys, CounterType* vals, + CounterType* counts, CounterType* counter, const size_t capacity, + const KeyType empty_key, const CounterType empty_val, + const CounterType empty_counts, const CounterType init_counter_val) { + const int threads = block_size; + const int blocks = (capacity - 1) / block_size + 1; + TF_CHECK_OK(GpuLaunchKernel( + InitKernel, blocks, threads, 0, d.stream(), keys, + vals, counts, counter, capacity, KeyType empty_key, CounterType empty_val, + CounterType empty_counts, CounterType init_counter_val)); +} + +template +__global__ void GetSizeKernel(const KeyType* keys, const size_t capacity, + size_t* d_size, const KeyType empty_key) { + /* Per block accumulator */ + __shared__ size_t block_acc; + + const size_t idx = blockIdx.x * blockDim.x + threadIdx.x; + + /* Initialize */ + if (threadIdx.x == 0) { + block_acc = 0; + } + __syncthreads(); + + /* Whether the slot mapping to the current thread is empty? do nothing : + * Atomically add to counter */ + if (idx < capacity) { + if (keys[idx] != empty_key) { + atomicAdd(&block_acc, 1); + } + } + __syncthreads(); + + /* Atomically reduce block counter to global conuter */ + if (threadIdx.x == 0) { + atomicAdd(d_size, block_acc); + } +} + +template +void GetSize(const GPUDevice& d, const KeyType* keys, const size_t capacity, + size_t* d_size, const KeyType empty_key) { + const int threads = block_size; + const int blocks = (capacity - 1) / block_size + 1; + TF_CHECK_OK(GpuLaunchKernel(GetSizeKernel, blocks, threads, 0, + d.stream(), keys, capacity, d_size, empty_key)); +} + +template > +__global__ void GetInsertKernel(const KeyType* d_key, CounterType* d_val, + const size_t len, KeyType* keys, + CounterType* vals, CounterType* counts, + const size_t capacity, + CounterType* d_global_counter, + const KeyType empty_key, + const CounterType empty_val) { + const size_t idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < len) { + KeyType target_key = d_key[idx]; + size_t hash_index = hasher::hash(target_key) % capacity; + size_t counter = 0; + while (true) { + // Have searched all the slot in the hashtable, but all slots in the + // hashtable are occupied by other keys + if (counter >= capacity) { + assert(false && "error: unique op fails: hashtable is full"); + } + // Try to set the key for the current slot to target key + const KeyType old_key = + atomicCAS(keys + hash_index, empty_key, target_key); + volatile CounterType& target_val_pos = vals[hash_index]; + if (empty_key == old_key) { + CounterType result_val; + result_val = atomicAdd(d_global_counter, 1); + d_val[idx] = result_val; + target_val_pos = result_val; + atomicAdd(counts + hash_index, 1); + break; + } else if (target_key == old_key) { + while (target_val_pos == empty_val) + ; + d_val[idx] = target_val_pos; + atomicAdd(counts + hash_index, 1); + break; + } + counter++; + hash_index = (hash_index + 1) % capacity; + } + } +} + +template > +void GetInsert(const GPUDevice& d, const KeyType* d_key, CounterType* d_val, + const size_t input_size, KeyType* keys, CounterType* vals, + CounterType* counts, const size_t capacity, + CounterType* d_global_counter, const KeyType empty_key, + const CounterType empty_val) { + const int threads = block_size; + const int blocks = (input_size - 1) / block_size + 1; + TF_CHECK_OK(GpuLaunchKernel( + GetInsertGetInsertKernel, blocks, threads, + 0, d.stream(), d_key, d_val, input_size, keys, vals, counts, capacity, + d_global_counter, empty_key, empty_val)); +} + +template +__global__ void DumpKernel(KeyType* d_key, CounterType* d_counts, + const KeyType* keys, const CounterType* vals, + const CounterType* counts, const size_t offset, + const size_t capacity, size_t* d_dump_counter, + const KeyType empty_key) { + /* Per block accumulator */ + __shared__ size_t block_acc; + + const size_t idx = blockIdx.x * blockDim.x + threadIdx.x; + + /* Initialize */ + if (threadIdx.x == 0) { + block_acc = 0; + } + __syncthreads(); + + KeyType read_key; + CounterType read_val; + CounterType read_count; + bool valid_slot = false; + // Each thread gather the key and value from slot assigned to them. + if (idx < capacity) { + read_key = keys[offset + idx]; + if (read_key != empty_key) { + valid_slot = true; + atomicAdd(&block_acc, 1); + read_val = vals[offset + idx]; + read_count = counts[offset + idx]; + } + } + __syncthreads(); + + // Each block accumulate the dump count to global counter + if (threadIdx.x == 0) { + atomicAdd(d_dump_counter, block_acc); + } + + // Each thread store one slot's data back to global memory, d_dump_counter + // is how many slots in total dumped. + if (valid_slot) { + d_key[read_val] = read_key; + d_counts[read_val] = read_count; + } +} + +template +void Dump(const GPUDevice& d, KeyType* d_key, CounterType* d_counts, + const KeyType* keys, const CounterType* vals, + const CounterType* counts, const size_t offset, const size_t capacity, + size_t* d_dump_counter, const KeyType empty_key) { + const int threads = block_size; + const int blocks = (capacity - 1) / block_size + 1; + TF_CHECK_OK(GpuLaunchKernel(DumpKernel, blocks, threads, + 0, d.stream(), d_key, d_counts, keys, vals, + counts, offset, capacity, d_dump_counter, + empty_key)); +} + +} // namespace gpu_unique_with_counts + +template +class UniqueWithCountsGPU : public OpKernel { + public: + explicit UniqueWithCountsGPU(OpKernelConstruction* ctx) : OpKernel(ctx) { + cudaEventCreateWithFlags(&memcpy_event_, cudaEventDisableTiming); + } + + void Compute(OpKernelContext* ctx) override { + using namespace fused_embedding; + auto device = ctx->eigen_device(); + + nvtx::ScopedRangeIfEnabled nvtx_range(this); + + Tensor const* input = nullptr; + OP_REQUIRES_OK(ctx, ctx->input("input", &input)); + + const size_t input_size = input->NumElements(); + const KeyType empty_key = std::numeric_limits::max(); + const CounterType empty_val = std::numeric_limits::max(); + const CounterType empty_counts = 0; + const CounterType init_counter_val = 0; + const float load_factor = 1.3; + const size_t capacity = input_size * load_factor; + + Tensor keys_storage; + OP_REQUIRES_OK( + ctx, ctx->allocate_temp(DataTypeToEnum::value, + TensorShape({int64(capacity)}), &keys_storage)); + + Tensor vals_storage; + OP_REQUIRES_OK( + ctx, ctx->allocate_temp(DataTypeToEnum::value, + TensorShape({int64(capacity)}), &vals_storage)); + + Tensor counts_storage; + OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum::value, + TensorShape({int64(capacity)}), + &counts_storage)); + + Tensor counter; + OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum::value, + TensorShape({int64(1)}), &counter)); + + Tensor dump_counter; + OP_REQUIRES_OK(ctx, ctx->allocate_temp(DT_INT64, TensorShape({int64(1)}), + &dump_counter)); + + Tensor* unique_idxs; + OP_REQUIRES_OK( + ctx, ctx->allocate_output("unique_idxs", TensorShape({input_size}), + &unique_idxs)); + + Init(device, data_p_with_type(keys_storage), + data_p_with_type(vals_storage), + data_p_with_type(counts_storage), + data_p_with_type(counter), capacity, empty_key, empty_val, + empty_counts, init_counter_val); + + GetInsert(device, data_p_with_type(input), + data_p_with_type(unique_idxs), input_size, + data_p_with_type(keys_storage), + data_p_with_type(vals_storage), + data_p_with_type(counts_storage), capacity, + data_p_with_type(counter), empty_key, empty_val); + + CounterType uniq_size; + cudaMemcpyAsync(&uniq_size, data_p_with_type(counter), + sizeof(counter), cudaMemcpyDeviceToHost, device.stream()); + cudaEventRecord(memcpy_event_, device.stream()); + cudaEventSynchronize(memcpy_event_); + + Tensor* unique_keys; + OP_REQUIRES_OK( + ctx, ctx->allocate_output( + "unique_keys", TensorShape({int64(uniq_size)}), &unique_keys)); + + Tensor* unique_counts; + OP_REQUIRES_OK(ctx, ctx->allocate_output("unique_counts", + TensorShape({int64(uniq_size)}), + &unique_counts)); + + Dump(device, data_p_with_type(unique_keys), + data_p_with_type(unique_counts), + data_p_with_type(keys_storage), + data_p_with_type(vals_storage), + data_p_with_type(counter), 0, capacity, + data_p_with_type(dump_counter), empty_key); + } + + private: + cudaEvent_t memcpy_event_; +}; + +#define REGISTER_GPU(KeyType, CounterType) \ + REGISTER_KERNEL_BUILDER(Name("UniqueWithCountsGPU") \ + .Device(DEVICE_GPU) \ + .TypeConstraint("KeyType") \ + .TypeConstraint("CounterType"), \ + UniqueWithCountsGPU); + +REGISTER_GPU(int32, int32); +REGISTER_GPU(int64, int32); +REGISTER_GPU(int32, int64); +REGISTER_GPU(int64, int64); +} // namespace tensorflow + +#endif // GOOGLE_CUDA \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_hash.ops.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_hash.ops.cu.cc deleted file mode 100644 index e69de29bb2d..00000000000 diff --git a/tensorflow/core/kernels/fused_embedding/gpu/unique_with_multi_output_ops.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/unique_with_multi_output_ops.cu.cc deleted file mode 100644 index 354e01ef320..00000000000 --- a/tensorflow/core/kernels/fused_embedding/gpu/unique_with_multi_output_ops.cu.cc +++ /dev/null @@ -1,671 +0,0 @@ -#if GOOGLE_CUDA - -#define EIGEN_USE_GPU - -#include - -#include "tensorflow/core/common_runtime/gpu/gpu_event_mgr.h" -#include "tensorflow/core/framework/op_kernel.h" -#include "tensorflow/core/lib/core/bits.h" -#include "tensorflow/core/profiler/nvtx_utils.h" -#include "tensorflow/core/util/gpu_kernel_helper.h" -#include "tensorflow/stream_executor/cuda/cuda_activation.h" - -namespace tensorflow { - -typedef Eigen::GpuDevice GPUDevice; - -namespace { - -// Returns true iff index is at the end of a segment (which is equivalent to the -// beginning of the next segment). -template -struct SegmentIndicatorFunctor { - const T* __restrict__ sorted_input_ptr_; - SegmentIndicatorFunctor(const T* sorted_input_ptr) - : sorted_input_ptr_(sorted_input_ptr) {} - __device__ bool operator()(const TIndex& i) const { - return i > 0 && sorted_input_ptr_[i] != sorted_input_ptr_[i - 1]; - } -}; - -template -__global__ void ExtractFirstOccurrenceIndicesKernel( - int64 input_size, int64 uniq_size, - const TIndex* __restrict__ sorted_input_inds, - const TIndex* __restrict__ sorted_input_unique_ids, - TIndex* __restrict__ unique_input_inds, TIndex* __restrict__ segment_ends) { - GPU_1D_KERNEL_LOOP(i, input_size) { - TIndex sorted_input_unique_id = sorted_input_unique_ids[i]; - if (i == 0 || sorted_input_unique_id != sorted_input_unique_ids[i - 1]) { - unique_input_inds[sorted_input_unique_id] = sorted_input_inds[i]; - if (segment_ends) { - if (i == 0) { - // First thread writes the last element. - segment_ends[uniq_size - 1] = input_size; - } else { - segment_ends[sorted_input_unique_id - 1] = i; - } - } - } - } -} - -// Scatters the index of the first occurrence of each unique input value to -// unique_input_inds. -// If segment_ends is not nullptr, it is filled with the end index of each -// unique value's range in the sorted input (the last element is always set -// to input_size). -template -Status ExtractFirstOccurrenceIndices(const GPUDevice& d, int64 input_size, - int64 uniq_size, - const TIndex* sorted_input_inds, - const TIndex* sorted_input_unique_ids, - TIndex* unique_input_inds, - TIndex* segment_ends) { - CHECK_GT(input_size, 0); // Crash OK - GpuLaunchConfig config = GetGpuLaunchConfig( - input_size, d, &ExtractFirstOccurrenceIndicesKernel, - /*dynamic_shared_memory_size=*/0, /*block_size_limit=*/0); - return GpuLaunchKernel(ExtractFirstOccurrenceIndicesKernel, - config.block_count, config.thread_per_block, 0, - d.stream(), input_size, uniq_size, sorted_input_inds, - sorted_input_unique_ids, unique_input_inds, - segment_ends); -} - -template -__global__ void GatherOutputsAndInvertPermutationKernel( - int64 uniq_size, const T* __restrict__ input, - const TIndex* __restrict__ sorted_unique_input_inds, - const TIndex* __restrict__ sorted_unique_perm, - const TIndex* __restrict__ segment_ends, T* __restrict__ output, - TIndex* __restrict__ inv_sorted_unique_perm, TIndex* __restrict__ count) { - GPU_1D_KERNEL_LOOP(i, uniq_size) { - output[i] = input[sorted_unique_input_inds[i]]; - auto j = sorted_unique_perm[i]; - inv_sorted_unique_perm[j] = i; - if (count) { - TIndex beg = j == 0 ? 0 : segment_ends[j - 1]; - TIndex end = segment_ends[j]; - count[i] = end - beg; - } - } -} - -// Gathers input values using sorted_unique_input_inds, and inverts the -// permutation specified by sorted_unique_perm. -template -Status GatherOutputsAndInvertPermutation(const GPUDevice& d, int64 uniq_size, - const T* input, - const TIndex* sorted_unique_input_inds, - const TIndex* sorted_unique_perm, - const TIndex* segment_ends, T* output, - TIndex* inv_sorted_unique_perm, - TIndex* count) { - if (uniq_size == 0) return Status::OK(); - GpuLaunchConfig config = GetGpuLaunchConfig( - uniq_size, d, &GatherOutputsAndInvertPermutationKernel, - /*dynamic_shared_memory_size=*/0, /*block_size_limit=*/0); - return GpuLaunchKernel(GatherOutputsAndInvertPermutationKernel, - config.block_count, config.thread_per_block, 0, - d.stream(), uniq_size, input, sorted_unique_input_inds, - sorted_unique_perm, segment_ends, output, - inv_sorted_unique_perm, count); -} - -template -__global__ void LookupAndScatterUniqueIdsKernel( - int64 input_size, const TIndex* sorted_input_inds, - const TIndex* __restrict__ sorted_input_unique_ids, - const TIndex* __restrict__ inv_sorted_unique_perm, - TIndex* __restrict__ idx) { - GPU_1D_KERNEL_LOOP(i, input_size) { - idx[sorted_input_inds[i]] = - inv_sorted_unique_perm[sorted_input_unique_ids[i]]; - } -} - -// Maps the values of sorted_input_unique_ids and scatters them to idx using -// sorted_input_inds. -template -Status LookupAndScatterUniqueIds(const GPUDevice& d, int64 input_size, - const TIndex* sorted_input_inds, - const TIndex* sorted_input_unique_ids, - const TIndex* inv_sorted_unique_perm, - TIndex* idx) { - CHECK_GT(input_size, 0); // Crash OK - GpuLaunchConfig config = GetGpuLaunchConfig( - input_size, d, &LookupAndScatterUniqueIdsKernel, - /*dynamic_shared_memory_size=*/0, /*block_size_limit=*/0); - return GpuLaunchKernel(LookupAndScatterUniqueIdsKernel, - config.block_count, config.thread_per_block, 0, - d.stream(), input_size, sorted_input_inds, - sorted_input_unique_ids, inv_sorted_unique_perm, idx); -} - -template -__global__ void RangeInitKernel(const T start, const T delta, const T size, - T* out) { - GPU_1D_KERNEL_LOOP(i, size) { out[i] = start + i * delta; } -} - -// Initialize out with range start, start + delta, start + 2 * delta, ... -template -Status RangeInit(const Eigen::GpuDevice& d, const T start, const T delta, - const T size, T* out) { - if (size == 0) return Status::OK(); - GpuLaunchConfig config = GetGpuLaunchConfig(size, d); - return GpuLaunchKernel(RangeInitKernel, config.block_count, - config.thread_per_block, 0, d.stream(), start, delta, - size, out); -} - -template -__global__ void GetIdxOfInputToUniqueKernel( - const TIndex* count_ptr, const TIndex* segment_ends_ptr, - const TIndex* sorted_unique_perm_ptr, const TIndex* sorted_input_inds_ptr, - const TIndex* count_prefix_sum_ptr, TIndex* idx_of_input_to_unique_ptr) { - const int target_unique_key_idx = blockIdx.x; - const TIndex unique_key_count = count_ptr[target_unique_key_idx]; - const TIndex permute = sorted_unique_perm_ptr[target_unique_key_idx]; - const TIndex offset_of_sorted_input_inds = - segment_ends_ptr[permute] - unique_key_count; - TIndex offset_of_result = count_prefix_sum_ptr[target_unique_key_idx]; - for (int i = threadIdx.x; i < unique_key_count; i += blockDim.x) { - idx_of_input_to_unique_ptr[offset_of_result + i] = - sorted_input_inds_ptr[offset_of_sorted_input_inds + i]; - } -} - -template -Status GetIdxOfInputToUnique(const Eigen::GpuDevice& d, const int64 uniq_size, - const TIndex* count_ptr, - const TIndex* segment_ends_ptr, - const TIndex* sorted_unique_perm_ptr, - const TIndex* sorted_input_inds_ptr, - const TIndex* count_prefix_sum_ptr, - TIndex* idx_of_input_to_unique_ptr) { - if (uniq_size == 0) return Status::OK(); - const int64 blocks = uniq_size; - const int64 threads = 32; - return GpuLaunchKernel(GetIdxOfInputToUniqueKernel, blocks, threads, - 0, d.stream(), count_ptr, segment_ends_ptr, - sorted_unique_perm_ptr, sorted_input_inds_ptr, - count_prefix_sum_ptr, idx_of_input_to_unique_ptr); -} - -// Computes keys_out = sorted(keys_in), and indices_out = argsort(keys_in). -// If keys_out is not required, it can be set to nullptr. -// If indices_in is nullptr, the range of input indices [0, size) will be -// used. -template -Status GpuRadixSort(OpKernelContext* context, int size, const Tkey* keys_in, - Tkey* keys_out, // Optional - const Tindex* indices_in, // Optional - Tindex* indices_out, int num_bits = sizeof(Tkey) * 8) { - if (size == 0) return Status::OK(); - // Allocate temporary inputs/outputs if necessary. - Tensor tmp_indices_in; - if (!indices_in) { - TF_RETURN_IF_ERROR(context->allocate_temp( - DataTypeToEnum::value, TensorShape({size}), &tmp_indices_in)); - Tindex* mutable_indices_in = tmp_indices_in.flat().data(); - indices_in = mutable_indices_in; - const Eigen::GpuDevice& device = context->eigen_device(); - // Initialize indices_in to the input index range. - TF_RETURN_IF_ERROR(RangeInit(device, Tindex(0), Tindex(1), Tindex(size), - mutable_indices_in)); - } - Tensor tmp_keys_out; - if (!keys_out) { - TF_RETURN_IF_ERROR(context->allocate_temp( - DataTypeToEnum::value, TensorShape({size}), &tmp_keys_out)); - keys_out = tmp_keys_out.flat().data(); - } - // Determine temporary device storage requirements. - Tensor temp_storage; - size_t temp_storage_bytes = 0; - const auto& cu_stream = GetGpuStream(context); - auto err = cub::DeviceRadixSort::SortPairs(nullptr, temp_storage_bytes, - keys_in, keys_out, indices_in, - indices_out, size, /*begin_bit=*/0, - /*end_bit=*/num_bits, cu_stream); - if (err != 0) { - return errors::Internal( - "Failed to launch gpuprim::DeviceRadixSort::SortPairs to calculate " - "temp_storage_bytes, status: ", - cudaGetErrorString(err)); - } - // Allocate temporary storage. - TF_RETURN_IF_ERROR(context->allocate_temp( - DT_INT8, TensorShape({static_cast(temp_storage_bytes)}), - &temp_storage)); - // Sort indices by keys. - err = cub::DeviceRadixSort::SortPairs( - temp_storage.flat().data(), temp_storage_bytes, keys_in, keys_out, - indices_in, indices_out, size, - /*begin_bit=*/0, /*end_bit=*/num_bits, cu_stream); - if (err != 0) { - return errors::Internal( - "Failed to launch gpuprim::DeviceRadixSort::SortPairs, " - "temp_storage_bytes: ", - temp_storage_bytes, "status: ", cudaGetErrorString(err)); - } - return Status::OK(); -} - -enum PrefixSumType { inclusive, exclusive }; - -template -Status GpuPrefixSum(OpKernelContext* context, int size, - PrefixSumType prefix_sum_type, InputIteratorT input, - OutputIteratorT output) { - static_assert( - !std::is_same::type, - bool>::value, - "GpuInclusivePrefixSum does not work correct with booleans, please use " - "TransformInputIterator to explicitly cast to an integer."); - if (size == 0) return Status::OK(); - const auto& cu_stream = GetGpuStream(context); - size_t temp_storage_bytes; - - cudaError_t err; - if (prefix_sum_type == inclusive) { - err = cub::DeviceScan::InclusiveSum(nullptr, temp_storage_bytes, input, - output, size, cu_stream); - } else { - // exclusive - err = cub::DeviceScan::ExclusiveSum(nullptr, temp_storage_bytes, input, - output, size, cu_stream); - } - - if (err != 0) { - return errors::Internal( - "Failed to launch gpuprim::DeviceScan::(Ex)InclusiveSum to calculate " - "temp_storage_bytes, status: ", - cudaGetErrorString(err)); - } - Tensor temp_storage; - TF_RETURN_IF_ERROR(context->allocate_temp( - DT_INT8, TensorShape({static_cast(temp_storage_bytes)}), - &temp_storage)); - - if (prefix_sum_type == inclusive) { - err = cub::DeviceScan::InclusiveSum(temp_storage.flat().data(), - temp_storage_bytes, input, output, size, - cu_stream); - } else { - err = cub::DeviceScan::ExclusiveSum(temp_storage.flat().data(), - temp_storage_bytes, input, output, size, - cu_stream); - } - if (err != 0) { - return errors::Internal( - "Failed to launch gpuprim::DeviceScan::(Ex)InclusiveSum, " - "temp_storage_bytes: ", - temp_storage_bytes, ", status: ", cudaGetErrorString(err)); - } - return Status::OK(); -} - -// Helper class to allocate scratch memory and keep track of debug info. -// Mostly a thin wrapper around Tensor & allocate_temp. -template -class ScratchSpace { - public: - ScratchSpace(OpKernelContext* context, int64 size, bool on_host) - : ScratchSpace(context, TensorShape({size}), "", on_host) {} - - ScratchSpace(OpKernelContext* context, int64 size, const string& debug_info, - bool on_host) - : ScratchSpace(context, TensorShape({size}), debug_info, on_host) {} - - ScratchSpace(OpKernelContext* context, const TensorShape& shape, - const string& debug_info, bool on_host) - : context_(context), debug_info_(debug_info), on_host_(on_host) { - AllocatorAttributes alloc_attr; - if (on_host) { - // Allocate pinned memory on the host to avoid unnecessary - // synchronization. - alloc_attr.set_on_host(true); - alloc_attr.set_gpu_compatible(true); - } - TF_CHECK_OK(context->allocate_temp(DataTypeToEnum::value, shape, - &scratch_tensor_, alloc_attr)); - } - - virtual ~ScratchSpace() {} - - Scalar* mutable_data() { - return scratch_tensor_.template flat().data(); - } - const Scalar* data() const { - return scratch_tensor_.template flat().data(); - } - Scalar& operator()(int64 i) { - return scratch_tensor_.template flat()(i); - } - const Scalar& operator()(int64 i) const { - return scratch_tensor_.template flat()(i); - } - int64 bytes() const { return scratch_tensor_.TotalBytes(); } - int64 size() const { return scratch_tensor_.NumElements(); } - const string& debug_info() const { return debug_info_; } - - Tensor& tensor() { return scratch_tensor_; } - const Tensor& tensor() const { return scratch_tensor_; } - - // Returns true if this ScratchSpace is in host memory. - bool on_host() const { return on_host_; } - - protected: - OpKernelContext* context() const { return context_; } - - private: - OpKernelContext* context_; // not owned - const string debug_info_; - const bool on_host_; - Tensor scratch_tensor_; -}; - -template -void AllocateTemp(OpKernelContext* context, int64 size, Tensor* tensor, - U** tensor_data) { - OP_REQUIRES_OK(context, context->allocate_temp(DataTypeToEnum::value, - TensorShape({size}), tensor)); - *tensor_data = tensor->flat().data(); -} - -} // namespace - -// Extention to TensorFlow 2.x's UniqueWithCount operator -template -class UniqueWithMultiOutputGPU : public OpKernel { - public: - explicit UniqueWithMultiOutputGPU(OpKernelConstruction* context) - : OpKernel(context) { - cudaEventCreateWithFlags(&memcpy_event_, cudaEventDisableTiming); - } - - void Compute(OpKernelContext* context) override { - Tensor const* input = nullptr; - OP_REQUIRES_OK(context, context->input("input", &input)); - - const int64 input_size = input->NumElements(); - - nvtx::ScopedRangeIfEnabled nvtx_range(this); - - OP_REQUIRES(context, input_size <= std::numeric_limits::max(), - errors::InvalidArgument( - "unique does not support input tensors larger than ", - std::numeric_limits::max(), " elements")); - - OP_REQUIRES(context, TensorShapeUtils::IsVector(input->shape()), - errors::InvalidArgument("unique expects a 1D vector.")); - - se::Stream* stream = context->op_device_context()->stream(); - OP_REQUIRES(context, stream, errors::Internal("No GPU stream available.")); - - if (input_size == 0) { - // Early exit for trivial case. - Tensor* temp; - OP_REQUIRES_OK(context, context->allocate_output( - "unique_keys", TensorShape({0}), &temp)); - OP_REQUIRES_OK(context, context->allocate_output( - "unique_idxs", TensorShape({0}), &temp)); - OP_REQUIRES_OK(context, context->allocate_output( - "unique_counts", TensorShape({0}), &temp)); - OP_REQUIRES_OK(context, - context->allocate_output("idx_of_input_to_unique", - TensorShape({0}), &temp)); - OP_REQUIRES_OK(context, context->allocate_output( - "unique_offsets", TensorShape({0}), &temp)); - return; - } - - // The algorithm implemented here is as follows: - // input = [3, 5, 3, 4, 1, 4, 9, 8, 6, 3, 5, 7, 8, 8, 4, 6, 4, 2, 5, 6] - // 1) Sort the input to group equal values together in segments. - // sorted_input, sorted_input_inds = sort(input) - // sorted_input: - // [1, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 8, 8, 8, 9] - // sorted_input_inds: - // [4, 17, 0, 2, 9, 3, 5, 14, 16, 1, 10, 18, 8, 15, 19, 11, 7, 12, 13, 6] - // 2) Identify the boundaries between segments and use prefix sum to - // compute the unique ID for each sorted value. - // sorted_input_unique_ids = prefix_sum(indicator(sorted_input)) - // indicator(sorted_input): - // [0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1] - // sorted_input_unique_ids: - // [0, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 7, 7, 7, 8] - // 3) Extract the input index of the first occurrence of each unique value. - // If counts are required, also extract the end index of each segment. - // unique_input_inds[sorted_input_unique_ids] = - // sorted_input_inds (@ indicator) - // segment_ends[sorted_input_unique_ids[i] - 1] = i (@ indicator) - // unique_input_inds: [4, 17, 0, 3, 1, 8, 11, 7, 6] - // segment_ends: [1, 2, 5, 9, 12, 15, 16, 19, 20] - // 4) Sort the extracted unique input indices to put them in order of - // first appearance. - // sorted_unique_input_inds, sorted_unique_perm = - // sort(unique_input_inds) - // sorted_unique_input_inds: [0, 1, 3, 4, 6, 7, 8, 11, 17] - // sorted_unique_perm: [2, 4, 3, 0, 8, 7, 5, 6, 1] - // 5) Gather the sorted unique input values to produce output, and invert - // the second sort permutation to produce an inverse ID mapping. If - // counts are required, also take the adjacent difference between - // segment_ends indices to produce counts. - // output = input[sorted_unique_input_inds] - // inv_sorted_unique_perm[sorted_unique_perm[i]] = i - // counts = adjacent_difference(segment_ends) - // output: [3, 5, 4, 1, 9, 8, 6, 7, 2] - // inv_sorted_unique_perm: [3, 8, 0, 2, 1, 6, 7, 5, 4] - // counts: [3, 3, 4, 1, 1, 3, 3, 1, 1] - // 6) calculate prefix sum of counts - // epsc = exclusive_prefix_sum_of_counts = [3, 6, 10, 11, 12, 15, 18, 19, - // 20] - // 7) Use custom kernel to calculate the mapping between output and - // corresponding indices of the input. - // idx_of_input_to_unique_ptr: [0, 2, 9, 1, 10, 18, 3, 5, 14, 16, 4, 6, 7, - // 12, 13, 8, 15, 19, 11, 17] - // 8) Look up unique IDs via the inverse ID mapping and scatter them - // using the original sort permutation to produce the indices output. - // idx[sorted_input_inds] = - // inv_sorted_unique_perm[sorted_input_unique_ids] - // idx: [0, 1, 0, 2, 3, 2, 4, 5, 6, 0, 1, 7, 5, 5, 2, 6, 2, 8, 1, 6] - - Tensor sorted_input_inds; - TIndex* sorted_input_inds_ptr = nullptr; - AllocateTemp(context, input_size, &sorted_input_inds, - &sorted_input_inds_ptr); - if (!context->status().ok()) return; - - Tensor sorted_input; - T* sorted_input_ptr = nullptr; - AllocateTemp(context, input_size, &sorted_input, &sorted_input_ptr); - if (!context->status().ok()) return; - - const T* input_ptr = input->flat().data(); - OP_REQUIRES_OK( - context, - GpuRadixSort(context, input_size, /*keys_in=*/input_ptr, - /*keys_out=*/sorted_input_ptr, - /*indices_in=*/static_cast(nullptr), - /*indices_out=*/sorted_input_inds_ptr)); - - // Create a fancy input iterator to indicate segment boundaries. - cub::TransformInputIterator, - cub::CountingInputIterator> - segment_indicator_iter(0, {sorted_input_ptr}); - - Tensor sorted_input_unique_ids; - TIndex* sorted_input_unique_ids_ptr = nullptr; - AllocateTemp(context, input_size, &sorted_input_unique_ids, - &sorted_input_unique_ids_ptr); - if (!context->status().ok()) return; - - OP_REQUIRES_OK(context, GpuPrefixSum(context, input_size, inclusive, - segment_indicator_iter, - sorted_input_unique_ids_ptr)); - - // Copy the last element of sorted_input_unique_ids back to the host to - // obtain uniq_size. - ScratchSpace last_idx_host(context, 1, /*on_host=*/true); - auto status = - stream - ->ThenMemcpy(last_idx_host.mutable_data(), - se::DeviceMemoryBase( - const_cast(sorted_input_unique_ids_ptr) + - (input_size - 1), - sizeof(*last_idx_host.data())), - sizeof(*last_idx_host.data())) - .ok(); - if (!status) { - context->SetStatus(errors::Internal("Copying device-to-host failed.")); - } - - const GPUDevice& device = context->eigen_gpu_device(); - cudaEventRecord(memcpy_event_, device.stream()); - cudaEventSynchronize(memcpy_event_); - int64 uniq_size = (*last_idx_host.data()) + 1; - - se::cuda::ScopedActivateExecutorContext scoped_activation{ - context->op_device_context()->stream()->parent()}; - - Tensor unique_input_inds; - TIndex* unique_input_inds_ptr = nullptr; - AllocateTemp(context, uniq_size, &unique_input_inds, - &unique_input_inds_ptr); - if (!context->status().ok()) return; - - Tensor segment_ends; - TIndex* segment_ends_ptr = nullptr; - AllocateTemp(context, uniq_size, &segment_ends, &segment_ends_ptr); - if (!context->status().ok()) return; - - OP_REQUIRES_OK(context, - ExtractFirstOccurrenceIndices( - device, input_size, uniq_size, sorted_input_inds_ptr, - sorted_input_unique_ids_ptr, unique_input_inds_ptr, - segment_ends_ptr)); - - Tensor sorted_unique_input_inds; - TIndex* sorted_unique_input_inds_ptr = nullptr; - AllocateTemp(context, uniq_size, &sorted_unique_input_inds, - &sorted_unique_input_inds_ptr); - if (!context->status().ok()) return; - - Tensor sorted_unique_perm; - TIndex* sorted_unique_perm_ptr = nullptr; - AllocateTemp(context, uniq_size, &sorted_unique_perm, - &sorted_unique_perm_ptr); - if (!context->status().ok()) return; - - // Sort by input index so that output is in order of appearance. - OP_REQUIRES_OK( - context, - GpuRadixSort(context, uniq_size, - /*keys_in=*/unique_input_inds_ptr, - /*keys_out=*/sorted_unique_input_inds_ptr, - /*indices_in=*/static_cast(nullptr), - /*indices_out=*/sorted_unique_perm_ptr, - /*num_bits=*/Log2Ceiling(input_size))); - - // Free temporary tensor that is no longer needed. - unique_input_inds = Tensor(); - unique_input_inds_ptr = nullptr; - - // output 0 unique_keys - Tensor* unique_keys; - OP_REQUIRES_OK(context, - context->allocate_output( - "unique_keys", TensorShape({uniq_size}), &unique_keys)); - T* output_ptr = unique_keys->flat().data(); - - Tensor inv_sorted_unique_perm; - TIndex* inv_sorted_unique_perm_ptr = nullptr; - AllocateTemp(context, uniq_size, &inv_sorted_unique_perm, - &inv_sorted_unique_perm_ptr); - if (!context->status().ok()) return; - - // output 2 unique_counts - Tensor* unique_counts; - OP_REQUIRES_OK(context, context->allocate_output("unique_counts", - TensorShape({uniq_size}), - &unique_counts)); - TIndex* count_ptr = unique_counts->flat().data(); - - // Compute output and counts (if necessary). - OP_REQUIRES_OK( - context, GatherOutputsAndInvertPermutation( - device, uniq_size, input_ptr, sorted_unique_input_inds_ptr, - sorted_unique_perm_ptr, segment_ends_ptr, output_ptr, - inv_sorted_unique_perm_ptr, count_ptr)); - - // Free temporary tensors that are no longer needed. - sorted_unique_input_inds = Tensor(); - sorted_unique_input_inds_ptr = nullptr; - - // Compute prefix sum of counts - // also output 4 unique_offsets - Tensor* unique_offsets; - OP_REQUIRES_OK(context, context->allocate_output("unique_offsets", - TensorShape({uniq_size}), - &unique_offsets)); - TIndex* count_prefix_sum_ptr = unique_offsets->flat().data(); - - OP_REQUIRES_OK(context, GpuPrefixSum(context, uniq_size, exclusive, - count_ptr, count_prefix_sum_ptr)); - - // GetIdxOfInputToUnique kernel to calculate the mapping between output and - // corresponding indices of the input - - // output 3 idx_of_input_to_unique - Tensor* idx_of_input_to_unique; - OP_REQUIRES_OK(context, context->allocate_output("idx_of_input_to_unique", - TensorShape({input_size}), - &idx_of_input_to_unique)); - TIndex* idx_of_input_to_unique_ptr = - idx_of_input_to_unique->flat().data(); - - OP_REQUIRES_OK(context, - GetIdxOfInputToUnique( - device, uniq_size, count_ptr, segment_ends_ptr, - sorted_unique_perm_ptr, sorted_input_inds_ptr, - count_prefix_sum_ptr, idx_of_input_to_unique_ptr)); - - // output 1 unique_idxs_out - Tensor* unique_idxs; - OP_REQUIRES_OK(context, - context->allocate_output( - "unique_idxs", TensorShape({input_size}), &unique_idxs)); - TIndex* idx_ptr = unique_idxs->flat().data(); - - // Compute indices output. - OP_REQUIRES_OK(context, LookupAndScatterUniqueIds( - device, input_size, sorted_input_inds_ptr, - sorted_input_unique_ids_ptr, - inv_sorted_unique_perm_ptr, idx_ptr)); - } - -private: - cudaEvent_t memcpy_event_; - -}; - -#define REGISTER_GPU(T, Tindices) \ - REGISTER_KERNEL_BUILDER(Name("UniqueWithMultiOutput") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("T") \ - .TypeConstraint("out_idx"), \ - UniqueWithMultiOutputGPU); - -REGISTER_GPU(int32, int32); -REGISTER_GPU(int64, int32); -REGISTER_GPU(int32, int64); -REGISTER_GPU(int64, int64); - -} // namespace tensorflow - -#endif // GOOGLE_CUDA \ No newline at end of file diff --git a/tensorflow/core/ops/fused_embedding_ops.cc b/tensorflow/core/ops/fused_embedding_ops.cc index 8507aa3c10c..752a34badf9 100644 --- a/tensorflow/core/ops/fused_embedding_ops.cc +++ b/tensorflow/core/ops/fused_embedding_ops.cc @@ -45,15 +45,14 @@ REGISTER_OP("PruneInvalidAndFillEmptyRows") return Status::OK(); }); -REGISTER_OP("UniqueWithMultiOutput") - .Input("input: T") - .Output("unique_keys: T") - .Output("unique_idxs: out_idx") - .Output("unique_counts: out_idx") - .Output("idx_of_input_to_unique: out_idx") - .Output("unique_offsets: out_idx") - .Attr("T: type") - .Attr("out_idx: {int32, int64} = DT_INT32") +REGISTER_OP("UniqueWithCountsGPU") + .Attr("KeyType: {int32, int64} = DT_INT64") + .Attr("CounterType: {int32, int64} = DT_INT32") + .Input("input: KeyType") + .Output("unique_keys: KeyType") + .Output("unique_idxs: CounterType") + .Output("unique_counts: CounterType") + .SetShapeFn([](InferenceContext* ctx) { ShapeHandle unused; std::vector unused_list; @@ -68,22 +67,18 @@ REGISTER_OP("UniqueWithMultiOutput") ctx->set_output("unique_keys", unused_list); ctx->set_output("unique_idxs", unused_list); ctx->set_output("unique_counts", unused_list); - ctx->set_output("idx_of_input_to_unique", unused_list); - ctx->set_output("unique_offsets", unused_list); return Status::OK(); }); REGISTER_OP("PartitionWithPermutation") .Attr("num_partitions: int > 1 = 2") - .Attr("partition_axis: int >= 0 = 0") // for now only support = 0, - // will consider support = 1 - // if necessary + .Attr("partition_axis: int >= 0 = 0") .Attr("partition_strategy : {'div'}") .Input("input: int64") .Input("partition_shapes: num_partitions * int64") .Output("partitioned_values: num_partitions * int64") - .Output("partition_permutation: int64") + .Output("partition_permutation: int32") .SetShapeFn([](InferenceContext* ctx) { ShapeHandle unused; std::vector unused_list; @@ -124,21 +119,18 @@ REGISTER_OP("FusedEmbeddingSparsePostLookUp") .Attr("num_partitions: int >= 1 = 1") .Attr("fill_empty_row: bool = false") .Attr("default_id: int = -1") - .Attr("partition_axis: int >= 0 = 0") // for now only support = 0, - // will consider support = 1 - // if necessary + .Attr("partition_axis: int >= 0 = 0") .Attr("combiner: {'sqrtn', 'mean', 'sum'}") .Attr("max_norm: float = -1.0") .Input("emb_shards: num_partitions * T") - .Input("partition_permutation: int64") + .Input("partition_permutation: int32") .Input("sp_dense_shape: int64") .Input("indices_before_unique: int64") .Input("row_empty_and_invalid_flags: int32") - .Input("unique_counts: int64") - .Input("idx_of_input_to_unique: int64") - .Input("unique_offsets: int64") + .Input("unique_idxs: int32") .Output("emb_vectors: T") .Output("feature_nums: int32") + .Output("emb_shard_ptrs: uint64") .SetShapeFn([](InferenceContext* ctx) { int num_partitions; TF_RETURN_IF_ERROR(ctx->GetAttr("num_partitions", &num_partitions)); @@ -178,15 +170,7 @@ REGISTER_OP("FusedEmbeddingSparsePostLookUp") TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); // unique_counts - ctx->input("unique_counts", &unused_list); - TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); - - // idx_of_input_to_unique - ctx->input("idx_of_input_to_unique", &unused_list); - TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); - - // unique_offsets - ctx->input("unique_offsets", &unused_list); + ctx->input("unique_idxs", &unused_list); TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); // emb_vectors @@ -198,6 +182,10 @@ REGISTER_OP("FusedEmbeddingSparsePostLookUp") // feature_nums unused_list[0] = ctx->MakeShape({ctx->UnknownDim()}); ctx->set_output("feature_nums", unused_list); + + // emb_shard_ptrs + unused_list[0] = ctx->MakeShape({num_partitions}); + ctx->set_output("emb_shard_ptrs", unused_list); return Status::OK(); }); @@ -222,12 +210,11 @@ REGISTER_OP("FusedEmbeddingSparsePostLookUpGrad") .Attr("max_norm: float = -1.0") .Input("top_grad: T") .Input("emb_shards: num_partitions * T") - .Input("partition_permutation: int64") + .Input("emb_shard_ptrs: uint64") + .Input("partition_permutation: int32") .Input("feature_nums: int32") .Input("indices_before_unique: int64") - .Input("unique_counts: int64") - .Input("idx_of_input_to_unique: int64") - .Input("unique_offsets: int64") + .Input("unique_idxs: int32") .Input("row_empty_and_invalid_flags: int32") .Output("grad_shards: num_partitions * T") .SetShapeFn([](InferenceContext* ctx) { @@ -248,6 +235,13 @@ REGISTER_OP("FusedEmbeddingSparsePostLookUpGrad") for (int i = 0; i < num_partitions; i++) { TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[i], 2, &unused)); } + + // emb_shard_ptrs + ctx->input("emb_shard_ptrs", &unused_list); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); + TF_RETURN_IF_ERROR( + ctx->WithValue(ctx->Dim(unused, 0), num_partitions, &unused_dim)); + // partition_permutation ctx->input("partition_permutation", &unused_list); TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); @@ -260,16 +254,8 @@ REGISTER_OP("FusedEmbeddingSparsePostLookUpGrad") ctx->input("indices_before_unique", &unused_list); TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 2, &unused)); - // unique_counts - ctx->input("unique_counts", &unused_list); - TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); - - // idx_of_input_to_unique - ctx->input("idx_of_input_to_unique", &unused_list); - TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); - - // unique_offsets - ctx->input("unique_offsets", &unused_list); + // unique_idxs + ctx->input("unique_idxs", &unused_list); TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); // row_empty_and_invalid_flags From 85fc9562000c8316edeb65f4b338e8e39a67515f Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Fri, 18 Mar 2022 19:48:49 +0800 Subject: [PATCH 22/41] Update: compilee pass --- tensorflow/core/kernels/BUILD | 3 +- .../gpu/functions/hash_functions.cu.h | 4 +- .../gpu/functions/kernels.cu.cc | 24 ++--- .../gpu/functions/kernels.cu.h | 5 +- .../gpu/functions/partition_select.cu.cc | 54 +++++------- .../gpu/functions/partition_select.cu.h | 3 +- .../gpu/fused_embedding_post_ops_gpus.cu.cc | 58 ++++++------- .../gpu/partition_with_permutation_ops.cu.cc | 28 +++--- .../gpu/unique_with_count_gpu.ops.cu.cc | 87 +++++++++++-------- tensorflow/core/ops/fused_embedding_ops.cc | 4 +- tensorflow/python/ops/fused_embedding_ops.py | 64 +++++++------- 11 files changed, 173 insertions(+), 161 deletions(-) diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD index e26707b66aa..cea35f4f087 100644 --- a/tensorflow/core/kernels/BUILD +++ b/tensorflow/core/kernels/BUILD @@ -5283,6 +5283,7 @@ tf_kernel_library( tf_cuda_library( name = "fused_embedding_cuh", hdrs = ["fused_embedding/gpu/common.cu.h", + "fused_embedding/gpu/functions/hash_functions.cu.h", "fused_embedding/gpu/functions/kernels.cu.h", "fused_embedding/gpu/functions/partition_select.cu.h"], ) @@ -5293,7 +5294,7 @@ tf_kernel_library( "fused_embedding/gpu/fused_embedding_post_ops_gpus.cu.cc", "fused_embedding/gpu/partition_with_permutation_ops.cu.cc", "fused_embedding/gpu/prune_invalid_and_fill_empty_rows_ops.cu.cc", - "fused_embedding/gpu/unique_with_multi_output_ops.cu.cc", + "fused_embedding/gpu/unique_with_count_gpu.ops.cu.cc", "fused_embedding/gpu/functions/kernels.cu.cc", "fused_embedding/gpu/functions/partition_select.cu.cc", ], diff --git a/tensorflow/core/kernels/fused_embedding/gpu/functions/hash_functions.cu.h b/tensorflow/core/kernels/fused_embedding/gpu/functions/hash_functions.cu.h index 9ea9586aa08..b41d90d8bbc 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/functions/hash_functions.cu.h +++ b/tensorflow/core/kernels/fused_embedding/gpu/functions/hash_functions.cu.h @@ -29,7 +29,7 @@ namespace tensorflow { -namespace fused_embedding { +namespace gpu_unique_with_counts { template struct MurmurHash3_32 { @@ -138,7 +138,7 @@ struct Mod_Hash { } }; -} // namespace fused_embedding +} // namespace gpu_unique_with_counts } // namespace tensorflow #endif // TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_FUNCTIONS_HASH_FUNCTIONS_CU_H_ diff --git a/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.cc index 9ee24359122..9ea81585251 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.cc @@ -236,7 +236,7 @@ void SumUpEmbeddingShardMultiPartition( const int threads = emb_vec_size; TF_CHECK_OK(GpuLaunchKernel( SumUpEmbeddingShardMultiPartitionKernel, blocks, threads, 0, d.stream(), - emb_shard_ptrs, partition_permutatio, indices_before_unique, unique_idxs, + emb_shard_ptrs, partition_permutation, indices_before_unique, unique_idxs, nnz, max_norm, emb_vec_size, emb_vectors, feature_nums)); } @@ -288,7 +288,7 @@ template void ApplyCombiner(const GPUDevice& d, const int batch_size, template __global__ void DistributeGradToShardSinglePartitionKernel( const float* top_grad, const float* emb_shard, - const int64_t* indices_before_unique, const int64_t* unique_idxs, + const int64_t* indices_before_unique, const int* unique_idxs, const int nnz, const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, const int* feature_nums, const int* row_emptiness_flag, float* grad_shard) { @@ -328,7 +328,7 @@ __global__ void DistributeGradToShardSinglePartitionKernel( template void DistributeGradToShardSinglePartition( const GPUDevice& d, const float* top_grad, const float* emb_shard, - const int64_t* indices_before_unique, const int64_t* unique_idxs, + const int64_t* indices_before_unique, const int* unique_idxs, const int nnz, const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, const int* feature_nums, const int* row_emptiness_flag, float* grad_shard) { @@ -343,21 +343,21 @@ void DistributeGradToShardSinglePartition( template void DistributeGradToShardSinglePartition( const GPUDevice& d, const float* top_grad, const float* emb_shard, - const int64_t* indices_before_unique, const int64_t* unique_idxs, + const int64_t* indices_before_unique, const int* unique_idxs, const int nnz, const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, const int* feature_nums, const int* row_emptiness_flag, float* grad_shard); template void DistributeGradToShardSinglePartition( const GPUDevice& d, const float* top_grad, const float* emb_shard, - const int64_t* indices_before_unique, const int64_t* unique_idxs, + const int64_t* indices_before_unique, const int* unique_idxs, const int nnz, const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, const int* feature_nums, const int* row_emptiness_flag, float* grad_shard); template void DistributeGradToShardSinglePartition( const GPUDevice& d, const float* top_grad, const float* emb_shard, - const int64_t* indices_before_unique, const int64_t* unique_idxs, + const int64_t* indices_before_unique, const int* unique_idxs, const int nnz, const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, const int* feature_nums, const int* row_emptiness_flag, float* grad_shard); @@ -366,7 +366,7 @@ template __global__ void DistributeGradToShardMultiPartitionKernel( const float* top_grad, const void* const* emb_shard_ptrs, const int* partition_permutation, const int64_t* indices_before_unique, - const int64_t* unique_idxs, const int nnz, const int emb_vec_size, + const int* unique_idxs, const int nnz, const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, const int* feature_nums, const int* row_emptiness_flag, void** grad_shard_ptrs) { @@ -414,7 +414,7 @@ template void DistributeGradToShardMultiPartition( const GPUDevice& d, const float* top_grad, const void* const* emb_shard_ptrs, const int* partition_permutation, - const int64_t* indices_before_unique, const int64_t* unique_idxs, + const int64_t* indices_before_unique, const int* unique_idxs, const int nnz, const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, const int* feature_nums, const int* row_emptiness_flag, void** grad_shard_ptrs) { @@ -430,7 +430,7 @@ void DistributeGradToShardMultiPartition( template void DistributeGradToShardMultiPartition( const GPUDevice& d, const float* top_grad, const void* const* emb_shard_ptrs, const int* partition_permutation, - const int64_t* indices_before_unique, const int64_t* unique_idxs, + const int64_t* indices_before_unique, const int* unique_idxs, const int nnz, const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, const int* feature_nums, const int* row_emptiness_flag, void** grad_shard_ptrs); @@ -438,15 +438,15 @@ template void DistributeGradToShardMultiPartition( template void DistributeGradToShardMultiPartition( const GPUDevice& d, const float* top_grad, const void* const* emb_shard_ptrs, const int* partition_permutation, - const int64_t* indices_before_unique, const int64_t* unique_idxs, + const int64_t* indices_before_unique, const int* unique_idxs, const int nnz, const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, const int* feature_nums, const int* row_emptiness_flag, void** grad_shard_ptrs); -template void DistributeGradToShardMultiPartition( +template void DistributeGradToShardMultiPartition( const GPUDevice& d, const float* top_grad, const void* const* emb_shard_ptrs, const int* partition_permutation, - const int64_t* indices_before_unique, const int64_t* unique_idxs, + const int64_t* indices_before_unique, const int* unique_idxs, const int nnz, const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, const int* feature_nums, const int* row_emptiness_flag, void** grad_shard_ptrs); diff --git a/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.h b/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.h index a92b7c02747..83894ed9a35 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.h +++ b/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.h @@ -6,6 +6,7 @@ #define EIGEN_USE_GPU #include "tensorflow/core/framework/op_kernel.h" +#include "tensorflow/core/kernels/fused_embedding/gpu/common.cu.h" namespace tensorflow { @@ -96,7 +97,7 @@ void ApplyCombiner(const GPUDevice& d, const int batch_size, template void DistributeGradToShardSinglePartition( const GPUDevice& d, const float* top_grad, const float* emb_shard, - const int64_t* indices_before_unique, const int64_t* unique_idxs, + const int64_t* indices_before_unique, const int* unique_idxs, const int nnz, const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, const int* feature_nums, const int* row_emptiness_flag, float* grad_shard); @@ -105,7 +106,7 @@ template void DistributeGradToShardMultiPartition( const GPUDevice& d, const float* top_grad, const void* const* emb_shard_ptrs, const int* partition_permutation, - const int64_t* indices_before_unique, const int64_t* unique_idxs, + const int64_t* indices_before_unique, const int* unique_idxs, const int nnz, const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, const int* feature_nums, const int* row_emptiness_flag, void** grad_shard_ptrs); diff --git a/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc index 57bea48eed5..80951a7c34e 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc @@ -178,7 +178,7 @@ namespace fused_embedding { selected_nums_host.resize(num_partitions); \ /* copy the last element(which is the sum of previous) with stride */ \ cudaMemcpy2DAsync(selected_nums_host.data(), 1 * sizeof(unsigned int), \ - data_p_with_type(inclusive_sum_counters) + \ + data_p_with_type(accumulate_counters) + \ counters_length - 1, \ counters_length * sizeof(unsigned int), \ 1 * sizeof(unsigned int), num_partitions, \ @@ -234,47 +234,37 @@ namespace fused_embedding { #define SelectKernelArgs SelectScanKernelArgs #define SelectKernelLoadCodeBlock \ - int64 lower_bound = j > 0 ? accu_div[j - 1] : 0; + int64 lower_bound = partition_id > 0 ? accu_div[partition_id - 1] : 0; #define SelectKernelRecalcKeyCodeBlock T new_key = key - lower_bound; DeclareSelectScanKernel; DeclareSelectKernel; DeclareSelect; -template void PartitionSelectDiv(OpKernelContext* ctx, - const Tensor* keys, - const Tensor& accu_div, - const int64 num_partitions, - OpOutputList& selected_keys, - Tensor* permutation); +template void PartitionSelectDiv( + OpKernelContext* ctx, const Tensor* keys, const Tensor& accu_div, + const int64 num_partitions, cudaEvent_t memcpy_event, + OpOutputList& selected_keys, Tensor* permutation); -template void PartitionSelectDiv(OpKernelContext* ctx, - const Tensor* keys, - const Tensor& accu_div, - const int64 num_partitions, - OpOutputList& selected_keys, - Tensor* permutation); +template void PartitionSelectDiv( + OpKernelContext* ctx, const Tensor* keys, const Tensor& accu_div, + const int64 num_partitions, cudaEvent_t memcpy_event, + OpOutputList& selected_keys, Tensor* permutation); -template void PartitionSelectDiv(OpKernelContext* ctx, - const Tensor* keys, - const Tensor& accu_div, - const int64 num_partitions, - OpOutputList& selected_keys, - Tensor* permutation); +template void PartitionSelectDiv( + OpKernelContext* ctx, const Tensor* keys, const Tensor& accu_div, + const int64 num_partitions, cudaEvent_t memcpy_event, + OpOutputList& selected_keys, Tensor* permutation); -template void PartitionSelectDiv(OpKernelContext* ctx, - const Tensor* keys, - const Tensor& accu_div, - const int64 num_partitions, - OpOutputList& selected_keys, - Tensor* permutation); +template void PartitionSelectDiv( + OpKernelContext* ctx, const Tensor* keys, const Tensor& accu_div, + const int64 num_partitions, cudaEvent_t memcpy_event, + OpOutputList& selected_keys, Tensor* permutation); -template void PartitionSelectDiv(OpKernelContext* ctx, - const Tensor* keys, - const Tensor& accu_div, - const int64 num_partitions, - OpOutputList& selected_keys, - Tensor* permutation); +template void PartitionSelectDiv( + OpKernelContext* ctx, const Tensor* keys, const Tensor& accu_div, + const int64 num_partitions, cudaEvent_t memcpy_event, + OpOutputList& selected_keys, Tensor* permutation); } // namespace fused_embedding diff --git a/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h index f4f76743c29..882970ca14c 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h +++ b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h @@ -13,7 +13,8 @@ namespace fused_embedding { template void PartitionSelectDiv(OpKernelContext* ctx, const Tensor* keys, const Tensor& accu_div, const int64 num_partitions, - OpOutputList& selected_keys, Tensor* permutation); + cudaEvent_t memcpy_event, OpOutputList& selected_keys, + Tensor* permutation); } // namespace fused_embedding diff --git a/tensorflow/core/kernels/fused_embedding/gpu/fused_embedding_post_ops_gpus.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/fused_embedding_post_ops_gpus.cu.cc index 3e26a8af71b..8980ed73803 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/fused_embedding_post_ops_gpus.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/fused_embedding_post_ops_gpus.cu.cc @@ -53,8 +53,8 @@ class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { OP_REQUIRES_OK(ctx, ctx->input("indices_before_unique", &indices_before_unique)); - Tensor const* unique_counts = nullptr; - OP_REQUIRES_OK(ctx, ctx->input("unique_idxs", &unique_counts)); + Tensor const* unique_idxs = nullptr; + OP_REQUIRES_OK(ctx, ctx->input("unique_idxs", &unique_idxs)); nvtx::ScopedRangeIfEnabled nvtx_range(this); @@ -102,7 +102,7 @@ class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { cudaMemcpyAsync(data_p_with_type(emb_shard_ptrs), emb_shard_ptrs_host.data(), - num_partitions * sizeof(size_t), cudaMemcpyHostToDevice, + num_partitions_ * sizeof(size_t), cudaMemcpyHostToDevice, device.stream()); SumUpEmbeddingShardMultiPartition( @@ -208,7 +208,7 @@ class FusedEmbeddingSparsePostLookUpGradGPU : public OpKernel { const int nnz = indices_before_unique->shape().dim_size(0); const bool set_empty_row_zero = default_id_ < 0 && fill_empty_row_; - std::vector grad_shard_ptrs_host; + std::vector grad_shard_ptrs_host; grad_shard_ptrs_host.resize(num_partitions_); for (int i = 0; i < num_partitions_; i++) { Tensor* grad_out; @@ -219,35 +219,35 @@ class FusedEmbeddingSparsePostLookUpGradGPU : public OpKernel { } if (num_partitions_ == 1) { - if (combiner_ == 'mean') { + if (combiner_ == "mean") { DistributeGradToShardSinglePartition( device, data_p_with_type(top_grad_tensor), data_p_with_type(emb_shards[0]), data_p_with_type(indices_before_unique), - data_p_with_type(unique_idx), nnz, emb_vec_size, + data_p_with_type(unique_idxs), nnz, emb_vec_size, max_norm_, set_empty_row_zero, data_p_with_type(feature_nums), - data_p_with_type(row_emptiness_flag), + data_p_with_type(row_empty_and_invalid_flags), data_p_with_type(grad_shards[0])); - } else if (combiner_ == 'sqrt') { - DistributeGradToShardSinglePartition( + } else if (combiner_ == "sqrt") { + DistributeGradToShardSinglePartition( device, data_p_with_type(top_grad_tensor), data_p_with_type(emb_shards[0]), data_p_with_type(indices_before_unique), - data_p_with_type(unique_idx), nnz, emb_vec_size, + data_p_with_type(unique_idxs), nnz, emb_vec_size, max_norm_, set_empty_row_zero, data_p_with_type(feature_nums), - data_p_with_type(row_emptiness_flag), + data_p_with_type(row_empty_and_invalid_flags), data_p_with_type(grad_shards[0])); } else { DistributeGradToShardSinglePartition( device, data_p_with_type(top_grad_tensor), data_p_with_type(emb_shards[0]), data_p_with_type(indices_before_unique), - data_p_with_type(unique_idx), nnz, emb_vec_size, + data_p_with_type(unique_idxs), nnz, emb_vec_size, max_norm_, set_empty_row_zero, data_p_with_type(feature_nums), - data_p_with_type(row_emptiness_flag), + data_p_with_type(row_empty_and_invalid_flags), data_p_with_type(grad_shards[0])); } @@ -257,42 +257,42 @@ class FusedEmbeddingSparsePostLookUpGradGPU : public OpKernel { DT_UINT64, TensorShape({int64(num_partitions_)}), &grad_shard_ptrs)); cudaMemcpyAsync(data_p_with_type(grad_shard_ptrs), - data_p_with_type(grad_shard_ptrs_host.data()), + grad_shard_ptrs_host.data(), num_partitions_ * sizeof(size_t), cudaMemcpyHostToDevice, device.stream()); - if (combiner_ == 'mean') { + if (combiner_ == "mean") { DistributeGradToShardMultiPartition( device, data_p_with_type(top_grad_tensor), data_p_with_type(emb_shard_ptrs), - data_p_with_type(partition_permutation) - data_p_with_type(indices_before_unique), - data_p_with_type(unique_idx), nnz, emb_vec_size, + data_p_with_type(partition_permutation), + data_p_with_type(indices_before_unique), + data_p_with_type(unique_idxs), nnz, emb_vec_size, max_norm_, set_empty_row_zero, data_p_with_type(feature_nums), - data_p_with_type(row_emptiness_flag), + data_p_with_type(row_empty_and_invalid_flags), data_p_with_type(grad_shard_ptrs)); - } else if (combiner_ == 'sqrt') { - DistributeGradToShardMultiPartition( + } else if (combiner_ == "sqrt") { + DistributeGradToShardMultiPartition( device, data_p_with_type(top_grad_tensor), data_p_with_type(emb_shard_ptrs), - data_p_with_type(partition_permutation) - data_p_with_type(indices_before_unique), - data_p_with_type(unique_idx), nnz, emb_vec_size, + data_p_with_type(partition_permutation), + data_p_with_type(indices_before_unique), + data_p_with_type(unique_idxs), nnz, emb_vec_size, max_norm_, set_empty_row_zero, data_p_with_type(feature_nums), - data_p_with_type(row_emptiness_flag), + data_p_with_type(row_empty_and_invalid_flags), data_p_with_type(grad_shard_ptrs)); } else { DistributeGradToShardMultiPartition( device, data_p_with_type(top_grad_tensor), data_p_with_type(emb_shard_ptrs), - data_p_with_type(partition_permutation) - data_p_with_type(indices_before_unique), - data_p_with_type(unique_idx), nnz, emb_vec_size, + data_p_with_type(partition_permutation), + data_p_with_type(indices_before_unique), + data_p_with_type(unique_idxs), nnz, emb_vec_size, max_norm_, set_empty_row_zero, data_p_with_type(feature_nums), - data_p_with_type(row_emptiness_flag), + data_p_with_type(row_empty_and_invalid_flags), data_p_with_type(grad_shard_ptrs)); } } diff --git a/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc index 1a945c737c3..929e67c926f 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc @@ -77,25 +77,25 @@ class PartitionWithPermutationGPU : public OpKernel { device.stream()); if (input_size < 512) { - PartitionSelectDiv(ctx, input, accu_div, - num_partitions_, partitioned_values, - partition_permutation); + PartitionSelectDiv( + ctx, input, accu_div, num_partitions_, memcpy_event_, + partitioned_values, partition_permutation); } else if (input_size < 1024) { - PartitionSelectDiv(ctx, input, accu_div, - num_partitions_, partitioned_values, - partition_permutation); + PartitionSelectDiv( + ctx, input, accu_div, num_partitions_, memcpy_event_, + partitioned_values, partition_permutation); } else if (input_size < 2048) { - PartitionSelectDiv(ctx, input, accu_div, - num_partitions_, partitioned_values, - partition_permutation); + PartitionSelectDiv( + ctx, input, accu_div, num_partitions_, memcpy_event_, + partitioned_values, partition_permutation); } else if (input_size < 4096) { - PartitionSelectDiv(ctx, input, accu_div, - num_partitions_, partitioned_values, - partition_permutation); + PartitionSelectDiv( + ctx, input, accu_div, num_partitions_, memcpy_event_, + partitioned_values, partition_permutation); } else { PartitionSelectDiv( - ctx, input, accu_div, num_partitions_, partitioned_values, - partition_permutation); + ctx, input, accu_div, num_partitions_, memcpy_event_, + partitioned_values, partition_permutation); } } } diff --git a/tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_gpu.ops.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_gpu.ops.cu.cc index 426f92bae74..dc496842a06 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_gpu.ops.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_gpu.ops.cu.cc @@ -18,15 +18,6 @@ #include "third_party/cub/iterator/constant_input_iterator.cuh" #include "third_party/cub/thread/thread_operators.cuh" -namespace tensorflow { -using GPUDevice = Eigen::GpuDevice; - -namespace gpu_unique_with_counts { - -using MurmurHash3_32 = fused_embedding::MurmurHash3_32 - -const static block_size = 64; - // Overload CUDA atomic for other 64bit unsinged/signed integer type __forceinline__ __device__ long atomicAdd(long* address, long val) { return (long)atomicAdd((unsigned long long*)address, (unsigned long long)val); @@ -66,6 +57,13 @@ __forceinline__ __device__ unsigned long atomicCAS(unsigned long* address, (unsigned long long)val); } +namespace tensorflow { +using GPUDevice = Eigen::GpuDevice; + +namespace gpu_unique_with_counts { + +const static int block_size = 64; + template __global__ void InitKernel(KeyType* keys, CounterType* vals, CounterType* counts, CounterType* counter, @@ -92,10 +90,10 @@ void Init(const GPUDevice& d, KeyType* keys, CounterType* vals, const CounterType empty_counts, const CounterType init_counter_val) { const int threads = block_size; const int blocks = (capacity - 1) / block_size + 1; - TF_CHECK_OK(GpuLaunchKernel( - InitKernel, blocks, threads, 0, d.stream(), keys, - vals, counts, counter, capacity, KeyType empty_key, CounterType empty_val, - CounterType empty_counts, CounterType init_counter_val)); + TF_CHECK_OK(GpuLaunchKernel(InitKernel, blocks, threads, + 0, d.stream(), keys, vals, counts, counter, + capacity, empty_key, empty_val, empty_counts, + init_counter_val)); } template @@ -143,7 +141,7 @@ __global__ void GetInsertKernel(const KeyType* d_key, CounterType* d_val, CounterType* vals, CounterType* counts, const size_t capacity, CounterType* d_global_counter, - const KeyType empty_key, + KeyType empty_key, const CounterType empty_val) { const size_t idx = blockIdx.x * blockDim.x + threadIdx.x; if (idx < len) { @@ -185,14 +183,14 @@ template , blocks, threads, - 0, d.stream(), d_key, d_val, input_size, keys, vals, counts, capacity, - d_global_counter, empty_key, empty_val)); + TF_CHECK_OK(GpuLaunchKernel(GetInsertKernel, + blocks, threads, 0, d.stream(), d_key, d_val, + input_size, keys, vals, counts, capacity, + d_global_counter, empty_key, empty_val)); } template @@ -264,7 +262,9 @@ class UniqueWithCountsGPU : public OpKernel { } void Compute(OpKernelContext* ctx) override { - using namespace fused_embedding; + using namespace gpu_unique_with_counts; + using fused_embedding::data_p_with_type; + auto device = ctx->eigen_device(); nvtx::ScopedRangeIfEnabled nvtx_range(this); @@ -304,14 +304,14 @@ class UniqueWithCountsGPU : public OpKernel { &dump_counter)); Tensor* unique_idxs; - OP_REQUIRES_OK( - ctx, ctx->allocate_output("unique_idxs", TensorShape({input_size}), - &unique_idxs)); + OP_REQUIRES_OK(ctx, ctx->allocate_output("unique_idxs", + TensorShape({int64(input_size)}), + &unique_idxs)); Init(device, data_p_with_type(keys_storage), - data_p_with_type(vals_storage), - data_p_with_type(counts_storage), - data_p_with_type(counter), capacity, empty_key, empty_val, + data_p_with_type(vals_storage), + data_p_with_type(counts_storage), + data_p_with_type(counter), capacity, empty_key, empty_val, empty_counts, init_counter_val); GetInsert(device, data_p_with_type(input), @@ -349,17 +349,30 @@ class UniqueWithCountsGPU : public OpKernel { cudaEvent_t memcpy_event_; }; -#define REGISTER_GPU(KeyType, CounterType) \ - REGISTER_KERNEL_BUILDER(Name("UniqueWithCountsGPU") \ - .Device(DEVICE_GPU) \ - .TypeConstraint("KeyType") \ - .TypeConstraint("CounterType"), \ - UniqueWithCountsGPU); - -REGISTER_GPU(int32, int32); -REGISTER_GPU(int64, int32); -REGISTER_GPU(int32, int64); -REGISTER_GPU(int64, int64); +REGISTER_KERNEL_BUILDER(Name("UniqueWithCountsGPU") + .Device(DEVICE_GPU) + .TypeConstraint("KeyType") + .TypeConstraint("CounterType"), + UniqueWithCountsGPU); + +REGISTER_KERNEL_BUILDER(Name("UniqueWithCountsGPU") + .Device(DEVICE_GPU) + .TypeConstraint("KeyType") + .TypeConstraint("CounterType"), + UniqueWithCountsGPU); + +REGISTER_KERNEL_BUILDER(Name("UniqueWithCountsGPU") + .Device(DEVICE_GPU) + .TypeConstraint("KeyType") + .TypeConstraint("CounterType"), + UniqueWithCountsGPU); + +REGISTER_KERNEL_BUILDER(Name("UniqueWithCountsGPU") + .Device(DEVICE_GPU) + .TypeConstraint("KeyType") + .TypeConstraint("CounterType"), + UniqueWithCountsGPU); + } // namespace tensorflow #endif // GOOGLE_CUDA \ No newline at end of file diff --git a/tensorflow/core/ops/fused_embedding_ops.cc b/tensorflow/core/ops/fused_embedding_ops.cc index 752a34badf9..b3f3fc3d565 100644 --- a/tensorflow/core/ops/fused_embedding_ops.cc +++ b/tensorflow/core/ops/fused_embedding_ops.cc @@ -72,7 +72,7 @@ REGISTER_OP("UniqueWithCountsGPU") }); REGISTER_OP("PartitionWithPermutation") - .Attr("num_partitions: int > 1 = 2") + .Attr("num_partitions: int >= 2 = 2") .Attr("partition_axis: int >= 0 = 0") .Attr("partition_strategy : {'div'}") .Input("input: int64") @@ -108,7 +108,7 @@ REGISTER_OP("PartitionWithPermutation") unused_list.clear(); unused_list.resize(1); - unused_list[0] = ctx->MakeShape({ctx->UnknownDim()} + unused_list[0] = ctx->MakeShape({ctx->UnknownDim()}); ctx->set_output("partition_permutation", unused_list); return Status::OK(); diff --git a/tensorflow/python/ops/fused_embedding_ops.py b/tensorflow/python/ops/fused_embedding_ops.py index 36967bbe7d8..3e32e99344d 100644 --- a/tensorflow/python/ops/fused_embedding_ops.py +++ b/tensorflow/python/ops/fused_embedding_ops.py @@ -2,6 +2,8 @@ from __future__ import division from __future__ import print_function +from numpy import dtype + import tensorflow as tf from tensorflow.python.framework.constant_op import constant from tensorflow.python.framework import ops @@ -10,7 +12,7 @@ from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops.kv_variable_ops import EmbeddingVariable from tensorflow.python.ops.gen_fused_embedding_ops import prune_invalid_and_fill_empty_rows -from tensorflow.python.ops.gen_fused_embedding_ops import unique_with_multi_output +from tensorflow.python.ops.gen_fused_embedding_ops import unique_with_counts_gpu from tensorflow.python.ops.gen_fused_embedding_ops import partition_with_permutation from tensorflow.python.ops.gen_fused_embedding_ops import fused_embedding_sparse_post_look_up from tensorflow.python.ops.gen_fused_embedding_ops import fused_embedding_sparse_post_look_up_grad @@ -71,17 +73,23 @@ def fused_embedding_lookup_sparse(params, sp_indices=sp_indices, sp_dense_shape=sp_dense_shape, ) + else: + row_empty_and_invalid_flags = tf.constant([0, ], dtype=tf.int32) # dummy - unique_keys, unique_idxs, unique_counts, idx_of_input_to_unique, unique_offsets = unique_with_multi_output( + unique_keys, unique_idxs, unique_counts = unique_with_counts_gpu( input=sp_values, out_idx=tf.int64, ) - partitioned_values, partition_permutations = partition_with_permutation( - partition_strategy=partition_strategy, - input=unique_keys, - partition_shapes=partition_shapes - ) + if partition_nums > 1: + partitioned_values, partition_permutation = partition_with_permutation( + partition_strategy=partition_strategy, + input=unique_keys, + partition_shapes=partition_shapes + ) + else: + partitioned_values = [unique_keys] + partition_permutation = [tf.constant([0, ], dtype=tf.int32)] # dummy emb_shards = [] for i in range(partition_nums): @@ -89,23 +97,22 @@ def fused_embedding_lookup_sparse(params, shard = array_ops.gather(params[i], partitioned_values[i]) emb_shards.append(shard) - emb_vectors, _ = fused_embedding_sparse_post_look_up( - emb_shards=emb_shards, partition_permutations=partition_permutations, + emb_vectors, _, _ = fused_embedding_sparse_post_look_up( + fill_empty_row=fill_empty_row, default_id=default_id, + combiner=combiner, max_norm=max_norm, + emb_shards=emb_shards, partition_permutations=partition_permutation, sp_dense_shape=sp_dense_shape, indices_before_unique=sp_indices, row_empty_and_invalid_flags=row_empty_and_invalid_flags, - unique_counts=unique_counts, - idx_of_input_to_unique=idx_of_input_to_unique, - unique_offsets=unique_offsets, - combiner=combiner, max_norm=max_norm, fill_empty_row=fill_empty_row, - default_id=default_id + unique_idxs=unique_idxs, ) return emb_vectors @ops.RegisterGradient("FusedEmbeddingSparsePostLookUp") -def fused_embedding_sparse_post_look_up_gradient(op, top_grad_emb_vec, _): +def fused_embedding_sparse_post_look_up_gradient(op, top_grad, + uesless_grad_1, uesless_grad_2): num_partitions = op.get_attr("num_partitions") combiner = op.get_attr("combiner") max_norm = op.get_attr("max_norm") @@ -113,23 +120,22 @@ def fused_embedding_sparse_post_look_up_gradient(op, top_grad_emb_vec, _): default_id = op.get_attr("default_id") emb_shards = [op.inputs[i] for i in range(0, num_partitions)] - partition_permutations = [op.inputs[i] for i in range(num_partitions, 2 * num_partitions)] - indices_before_unique = op.inputs[2 * num_partitions + 1] - row_empty_and_invalid_flags = op.inputs[2 * num_partitions + 2] - unique_counts = op.inputs[2 * num_partitions + 3] - idx_of_input_to_unique = op.inputs[2 * num_partitions + 4] - unique_offsets = op.inputs[2 * num_partitions + 5] + partition_permutation = op.inputs[num_partitions] + # sp_dense_shape = op.inputs[num_partitions + 1] + indices_before_unique = op.inputs[num_partitions + 2] + row_empty_and_invalid_flags = op.inputs[num_partitions + 3] + unique_idxs = op.inputs[num_partitions + 4] feature_nums = op.outputs[1] + emb_shard_ptrs = op.outputs[2] grad_shards = fused_embedding_sparse_post_look_up_grad( - top_grad=top_grad_emb_vec, emb_shards=emb_shards, - partition_permutations=partition_permutations, - feature_nums=feature_nums, indices_before_unique=indices_before_unique, - unique_counts=unique_counts, idx_of_input_to_unique=idx_of_input_to_unique, - unique_offsets=unique_offsets, row_empty_and_invalid_flags=row_empty_and_invalid_flags, + fill_empty_row=fill_empty_row, default_id=default_id, combiner=combiner, max_norm=max_norm, - fill_empty_row=fill_empty_row, - default_id=default_id - ) + top_grad=top_grad, emb_shards=emb_shards, + emb_shard_ptrs=emb_shard_ptrs, + partition_permutation=partition_permutation, + feature_nums=feature_nums, indices_before_unique=indices_before_unique, + unique_idxs=unique_idxs, row_empty_and_invalid_flags=row_empty_and_invalid_flags) + return grad_shards + [None for _ in range(len(op.inputs) - num_partitions)] From 04d4e604ea7970b13b51c4ed9f973f446268e5e3 Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Sat, 19 Mar 2022 11:49:20 +0800 Subject: [PATCH 23/41] add more partition strategies --- .../gpu/functions/partition_select.cu.cc | 123 +++++++++++++++--- .../gpu/functions/partition_select.cu.h | 10 ++ .../gpu/partition_with_permutation_ops.cu.cc | 44 +++++++ tensorflow/core/ops/fused_embedding_ops.cc | 2 +- tensorflow/python/ops/fused_embedding_ops.py | 10 +- 5 files changed, 165 insertions(+), 24 deletions(-) diff --git a/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc index 80951a7c34e..f423fb701c3 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc @@ -19,12 +19,12 @@ typedef Eigen::GpuDevice GPUDevice; namespace fused_embedding { -// A macro helper to declare SelectScanKernels, because they just have only -// a little bit differences. Need to define macros +// A macro helper to declare SelectScanKernel, because they just have only +// a little bit differences. Need to define macros before using this #define DeclareSelectScanKernel \ template \ __global__ void SelectScanKernelName( \ - const T* keys, SelectScanKernelArgs, const int64 num_partitions, \ + const T* keys, SelectScanKernelArgs const int64 num_partitions, \ const int64 length, const int64 predicates_length, \ const int64 counters_length, unsigned int* predicates, \ unsigned int* accumulate_counters) { \ @@ -70,10 +70,12 @@ namespace fused_embedding { } \ } +// A macro helper to declare SelectKernel, because they just have only +// a little bit differences. Need to define macros before using this #define DeclareSelectKernel \ template \ __global__ void SelectKernelName( \ - const T* keys, SelectKernelArgs, unsigned int* predicates, \ + const T* keys, SelectKernelArgs unsigned int* predicates, \ unsigned int* accumulate_counters, const int64 num_partitions, \ const int64 length, const int64 predicates_length, \ const int64 counters_length, void** output_ptrs, TIndex* permutation) { \ @@ -123,14 +125,14 @@ namespace fused_embedding { } \ } -// A macro helper to declare DefinePartitionSelect. Need to define following -// macros before using this: SelectScanKernelName, -// SelectArgs, SelectScanPassArgs +// A macro helper to declare DefinePartitionSelect. Need to define +// macros before using this #define DeclareSelect \ template \ - void SelectName(OpKernelContext* ctx, const Tensor* keys, SelectArgs, \ - const int64 num_partitions, cudaEvent_t memcpy_event, \ - OpOutputList& selected_keys, Tensor* permutation) { \ + void SelectName(OpKernelContext* ctx, const Tensor* keys, \ + SelectArgs const int64 num_partitions, \ + cudaEvent_t memcpy_event, OpOutputList& selected_keys, \ + Tensor* permutation) { \ OP_REQUIRES(ctx, keys->dims() == 1, \ errors::InvalidArgument("Tensor keys must ranks 1")); \ OP_REQUIRES( \ @@ -169,9 +171,9 @@ namespace fused_embedding { ctx, \ GpuLaunchKernel( \ SelectScanKernelName, blocks, threads, 0, \ - device.stream(), data_p_with_type(keys), SelectScanPassArgs, \ - num_partitions, length, predicates_length, counters_length, \ - data_p_with_type(predicates), \ + device.stream(), data_p_with_type(keys), \ + SelectScanPassArgs num_partitions, length, predicates_length, \ + counters_length, data_p_with_type(predicates), \ data_p_with_type(accumulate_counters))); \ } \ std::vector selected_nums_host; \ @@ -209,7 +211,7 @@ namespace fused_embedding { ctx, GpuLaunchKernel( \ SelectKernelName, blocks, threads, \ 0, device.stream(), data_p_with_type(keys), \ - SelectPassArgs, data_p_with_type(predicates), \ + SelectPassArgs data_p_with_type(predicates), \ data_p_with_type(accumulate_counters), \ num_partitions, length, predicates_length, counters_length, \ data_p_with_type(output_ptrs), \ @@ -217,13 +219,14 @@ namespace fused_embedding { } \ } +// =============== Div Selection =============== // #define SelectName PartitionSelectDiv -#define SelectArgs const Tensor& accu_div -#define SelectScanPassArgs data_p_with_type(accu_div) +#define SelectArgs const Tensor &accu_div, +#define SelectScanPassArgs data_p_with_type(accu_div), #define SelectPassArgs SelectScanPassArgs #define SelectScanKernelName SelectScanDivKernel -#define SelectScanKernelArgs const int64* accu_div +#define SelectScanKernelArgs const int64 *accu_div, #define SelectScanKernelLoadCodeBlock \ int64 lower_bound = partition_id > 0 ? accu_div[partition_id - 1] : 0; \ int64 upper_bound = accu_div[partition_id]; @@ -266,6 +269,92 @@ template void PartitionSelectDiv( const int64 num_partitions, cudaEvent_t memcpy_event, OpOutputList& selected_keys, Tensor* permutation); +// =============== Mod Selection =============== // + +#define SelectName PartitionSelectMod +#define SelectArgs +#define SelectScanPassArgs +#define SelectPassArgs SelectScanPassArgs + +#define SelectScanKernelName SelectScanModKernel +#define SelectScanKernelArgs +#define SelectScanKernelLoadCodeBlock +#define SelectScanKernelEvalCodeBlock \ + selected = int(key % num_partitions == partition_id); + +#define SelectKernelName SelectModKernel +#define SelectKernelArgs SelectScanKernelArgs + +#define SelectKernelLoadCodeBlock +#define SelectKernelRecalcKeyCodeBlock T new_key = key / num_partitions; + +DeclareSelectScanKernel; +DeclareSelectKernel; +DeclareSelect; + +template void PartitionSelectMod( + OpKernelContext* ctx, const Tensor* keys, const int64 num_partitions, + cudaEvent_t memcpy_event, OpOutputList& selected_keys, Tensor* permutation); + +template void PartitionSelectMod( + OpKernelContext* ctx, const Tensor* keys, const int64 num_partitions, + cudaEvent_t memcpy_event, OpOutputList& selected_keys, Tensor* permutation); + +template void PartitionSelectMod( + OpKernelContext* ctx, const Tensor* keys, const int64 num_partitions, + cudaEvent_t memcpy_event, OpOutputList& selected_keys, Tensor* permutation); + +template void PartitionSelectMod( + OpKernelContext* ctx, const Tensor* keys, const int64 num_partitions, + cudaEvent_t memcpy_event, OpOutputList& selected_keys, Tensor* permutation); + +template void PartitionSelectMod( + OpKernelContext* ctx, const Tensor* keys, const int64 num_partitions, + cudaEvent_t memcpy_event, OpOutputList& selected_keys, Tensor* permutation); + +// =============== Mod EV Selection =============== // + +#define SelectName PartitionSelectModEV +#define SelectArgs +#define SelectScanPassArgs +#define SelectPassArgs SelectScanPassArgs + +#define SelectScanKernelName SelectScanModEVKernel +#define SelectScanKernelArgs +#define SelectScanKernelLoadCodeBlock +#define SelectScanKernelEvalCodeBlock \ + selected = int(key % 1000 % num_partitions == partition_id); + +#define SelectKernelName SelectModEVKernel +#define SelectKernelArgs SelectScanKernelArgs + +#define SelectKernelLoadCodeBlock +#define SelectKernelRecalcKeyCodeBlock T new_key = key; + +DeclareSelectScanKernel; +DeclareSelectKernel; +DeclareSelect; + +template void PartitionSelectModEV( + OpKernelContext* ctx, const Tensor* keys, const int64 num_partitions, + cudaEvent_t memcpy_event, OpOutputList& selected_keys, Tensor* permutation); + +template void PartitionSelectModEV( + OpKernelContext* ctx, const Tensor* keys, const int64 num_partitions, + cudaEvent_t memcpy_event, OpOutputList& selected_keys, Tensor* permutation); + +template void PartitionSelectModEV( + OpKernelContext* ctx, const Tensor* keys, const int64 num_partitions, + cudaEvent_t memcpy_event, OpOutputList& selected_keys, Tensor* permutation); + +template void PartitionSelectModEV( + OpKernelContext* ctx, const Tensor* keys, const int64 num_partitions, + cudaEvent_t memcpy_event, OpOutputList& selected_keys, Tensor* permutation); + +template void PartitionSelectModEV( + OpKernelContext* ctx, const Tensor* keys, const int64 num_partitions, + cudaEvent_t memcpy_event, OpOutputList& selected_keys, Tensor* permutation); + } // namespace fused_embedding } // namespace tensorflow diff --git a/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h index 882970ca14c..0f67a3fd15b 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h +++ b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.h @@ -16,6 +16,16 @@ void PartitionSelectDiv(OpKernelContext* ctx, const Tensor* keys, cudaEvent_t memcpy_event, OpOutputList& selected_keys, Tensor* permutation); +template +void PartitionSelectMod(OpKernelContext* ctx, const Tensor* keys, + const int64 num_partitions, cudaEvent_t memcpy_event, + OpOutputList& selected_keys, Tensor* permutation); + +template +void PartitionSelectModEV(OpKernelContext* ctx, const Tensor* keys, + const int64 num_partitions, cudaEvent_t memcpy_event, + OpOutputList& selected_keys, Tensor* permutation); + } // namespace fused_embedding } // namespace tensorflow diff --git a/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc index 929e67c926f..a36fcb9537c 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc @@ -97,6 +97,50 @@ class PartitionWithPermutationGPU : public OpKernel { ctx, input, accu_div, num_partitions_, memcpy_event_, partitioned_values, partition_permutation); } + } else if (partition_strategy_ == "mod") { + if (input_size < 512) { + PartitionSelectMod(ctx, input, num_partitions_, + memcpy_event_, partitioned_values, + partition_permutation); + } else if (input_size < 1024) { + PartitionSelectMod(ctx, input, num_partitions_, + memcpy_event_, partitioned_values, + partition_permutation); + } else if (input_size < 2048) { + PartitionSelectMod(ctx, input, num_partitions_, + memcpy_event_, partitioned_values, + partition_permutation); + } else if (input_size < 4096) { + PartitionSelectMod(ctx, input, num_partitions_, + memcpy_event_, partitioned_values, + partition_permutation); + } else { + PartitionSelectMod(ctx, input, num_partitions_, + memcpy_event_, partitioned_values, + partition_permutation); + } + } else if (partition_strategy_ == "mod_ev") { + if (input_size < 512) { + PartitionSelectModEV(ctx, input, num_partitions_, + memcpy_event_, partitioned_values, + partition_permutation); + } else if (input_size < 1024) { + PartitionSelectModEV(ctx, input, num_partitions_, + memcpy_event_, partitioned_values, + partition_permutation); + } else if (input_size < 2048) { + PartitionSelectModEV(ctx, input, num_partitions_, + memcpy_event_, partitioned_values, + partition_permutation); + } else if (input_size < 4096) { + PartitionSelectModEV(ctx, input, num_partitions_, + memcpy_event_, partitioned_values, + partition_permutation); + } else { + PartitionSelectModEV( + ctx, input, num_partitions_, memcpy_event_, partitioned_values, + partition_permutation); + } } } diff --git a/tensorflow/core/ops/fused_embedding_ops.cc b/tensorflow/core/ops/fused_embedding_ops.cc index b3f3fc3d565..fef2a72baee 100644 --- a/tensorflow/core/ops/fused_embedding_ops.cc +++ b/tensorflow/core/ops/fused_embedding_ops.cc @@ -74,7 +74,7 @@ REGISTER_OP("UniqueWithCountsGPU") REGISTER_OP("PartitionWithPermutation") .Attr("num_partitions: int >= 2 = 2") .Attr("partition_axis: int >= 0 = 0") - .Attr("partition_strategy : {'div'}") + .Attr("partition_strategy : {'div', 'mod', 'mod_ev'}") .Input("input: int64") .Input("partition_shapes: num_partitions * int64") .Output("partitioned_values: num_partitions * int64") diff --git a/tensorflow/python/ops/fused_embedding_ops.py b/tensorflow/python/ops/fused_embedding_ops.py index 3e32e99344d..1d44719eba4 100644 --- a/tensorflow/python/ops/fused_embedding_ops.py +++ b/tensorflow/python/ops/fused_embedding_ops.py @@ -35,7 +35,7 @@ def fused_embedding_lookup_sparse(params, if sparse_weights is not None: raise ValueError("sparse_weights is not supported yet") - valid_partition_strategy = ['div'] + valid_partition_strategy = ['div', 'mod', 'mod_ev'] if partition_strategy not in valid_partition_strategy: raise ValueError("{} is not supported yet. Currently only support {}".format( partition_strategy, valid_partition_strategy)) @@ -49,10 +49,8 @@ def fused_embedding_lookup_sparse(params, partition_nums = len(params) if type(params[0]) is EmbeddingVariable: - if partition_nums != 1: - raise ValueError("For EmbeddingVariable, do not support partition now") - # fake shape for now. TBD change in the future - partition_shapes = [constant([1, 1], dtype=tf.int64)] + partition_strategy = 'mod_ev' + partition_shapes = [constant([1, 1], dtype=tf.int64) for _ in params] # dummy else: partition_shapes = [w.shape for w in params] @@ -94,7 +92,7 @@ def fused_embedding_lookup_sparse(params, emb_shards = [] for i in range(partition_nums): with ops.colocate_with(params[i]): - shard = array_ops.gather(params[i], partitioned_values[i]) + shard = array_ops.gather(params[i], partitioned_values[i], counts=unique_counts) emb_shards.append(shard) emb_vectors, _, _ = fused_embedding_sparse_post_look_up( From 241e08606b730d61d736854be0123e8a5225d2cc Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Sat, 19 Mar 2022 12:25:56 +0800 Subject: [PATCH 24/41] Add one test --- tensorflow/core/kernels/BUILD | 3 +- ...ne_invalid_and_fill_empty_rows_ops_test.cc | 175 ++++++++++++++++++ 2 files changed, 176 insertions(+), 2 deletions(-) create mode 100644 tensorflow/core/kernels/fused_embedding/tests/prune_invalid_and_fill_empty_rows_ops_test.cc diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD index cea35f4f087..c3ebf244ce4 100644 --- a/tensorflow/core/kernels/BUILD +++ b/tensorflow/core/kernels/BUILD @@ -1948,8 +1948,7 @@ tf_cuda_cc_test( tf_cuda_cc_test( name = "fused_embedding_ops_test", size = "small", - srcs = ["fused_embedding/tests/fused_embedding_post_ops_test.cc", - "fused_embedding/tests/fused_embedding_post_grad_ops_test.cc"], + srcs = ["fused_embedding/tests/prune_invalid_and_fill_empty_rows_ops_test.cc"], tags = tf_cuda_tests_tags(), deps = [ ":fused_embedding_ops", diff --git a/tensorflow/core/kernels/fused_embedding/tests/prune_invalid_and_fill_empty_rows_ops_test.cc b/tensorflow/core/kernels/fused_embedding/tests/prune_invalid_and_fill_empty_rows_ops_test.cc new file mode 100644 index 00000000000..d9cd112f8b4 --- /dev/null +++ b/tensorflow/core/kernels/fused_embedding/tests/prune_invalid_and_fill_empty_rows_ops_test.cc @@ -0,0 +1,175 @@ +#include + +#include "tensorflow/core/common_runtime/kernel_benchmark_testlib.h" +#include "tensorflow/core/framework/fake_input.h" +#include "tensorflow/core/framework/node_def_builder.h" +#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/types.h" +#include "tensorflow/core/kernels/ops_testutil.h" +#include "tensorflow/core/kernels/ops_util.h" +#include "tensorflow/core/platform/test.h" +#include "tensorflow/core/platform/test_benchmark.h" +#include "tensorflow/core/public/session.h" + +namespace tensorflow { +namespace { + +enum class Device { GPU }; + +class PruneInvalidAndFillEmptyRowsOpTest : public OpsTestBase { + protected: + void MakeOpAndSetDevice(Device device, const bool fill_empty_row, + const bool prune_invalid_id, const int default_id) { + if (device == Device::GPU) { + SetDevice(DEVICE_GPU, + std::unique_ptr(DeviceFactory::NewDevice( + "GPU", {}, "/job:a/replica:0/task:0"))); + } + + TF_EXPECT_OK(NodeDefBuilder("prune_invalid_and_fill_empty_rows", + "PruneInvalidAndFillEmptyRows") + .Attr("fill_empty_row", fill_empty_row) + .Attr("prune_invalid_id", prune_invalid_id) + .Attr("default_id", default_id) + .Input(FakeInput(DT_INT64)) + .Input(FakeInput(DT_INT64)) + .Input(FakeInput(DT_INT64)) + .Finalize(node_def())); + TF_EXPECT_OK(InitOp()); + } +}; + +TEST_F(PruneInvalidAndFillEmptyRowsOpTest, NothingHappend) { + MakeOpAndSetDevice(Device::GPU, false, false, -1); + // sp_values + AddInputFromArray(TensorShape({12}), + {1, 5, 3, 6, 12, 14, 15, 0, 5, 5, 11, 7}); + // sp_indices + AddInputFromArray(TensorShape({12, 2}), + {2, 3, 4, 6, 1, 6, 12, 12, 12, 12, 11, 5, + 15, 0, 11, 6, 7, 9, 11, 8, 12, 13, 13, 0}); + // sp_dense_shape + AddInputFromArray(TensorShape({2}), {16, 16}); + + TF_ASSERT_OK(RunOpKernel()); + TF_EXPECT_OK(device_->Sync()); + // sp_values_out + { + Tensor expected(allocator(), DT_INT64, TensorShape({12})); + test::FillValues(&expected, + {1, 5, 3, 6, 12, 14, 15, 0, 5, 5, 11, 7}); + test::ExpectTensorEqual(expected, *GetOutput(0)); + } + // sp_indices_out + { + Tensor expected(allocator(), DT_INT64, TensorShape({12, 2})); + test::FillValues(&expected, + {2, 3, 4, 6, 1, 6, 12, 12, 12, 12, 11, 5, + 15, 0, 11, 6, 7, 9, 11, 8, 12, 13, 13, 0}); + test::ExpectTensorEqual(expected, *GetOutput(1)); + } +} + +TEST_F(PruneInvalidAndFillEmptyRowsOpTest, PruneWithAllValid) { + MakeOpAndSetDevice(Device::GPU, false, true, -1); + // sp_values + AddInputFromArray(TensorShape({12}), + {1, 5, 3, 6, 12, 14, 15, 0, 5, 5, 11, 7}); + // sp_indices + AddInputFromArray(TensorShape({12, 2}), + {2, 3, 4, 6, 1, 6, 12, 12, 12, 12, 11, 5, + 15, 0, 11, 6, 7, 9, 11, 8, 12, 13, 13, 0}); + // sp_dense_shape + AddInputFromArray(TensorShape({2}), {16, 16}); + + TF_ASSERT_OK(RunOpKernel()); + TF_EXPECT_OK(device_->Sync()); + // sp_values_out + { + Tensor expected(allocator(), DT_INT64, TensorShape({12})); + test::FillValues(&expected, + {1, 5, 3, 6, 12, 14, 15, 0, 5, 5, 11, 7}); + test::ExpectTensorEqual(expected, *GetOutput(0)); + } + // sp_indices_out + { + Tensor expected(allocator(), DT_INT64, TensorShape({12, 2})); + test::FillValues(&expected, + {2, 3, 4, 6, 1, 6, 12, 12, 12, 12, 11, 5, + 15, 0, 11, 6, 7, 9, 11, 8, 12, 13, 13, 0}); + test::ExpectTensorEqual(expected, *GetOutput(1)); + } + // row_empty_and_invalid_flags + { + Tensor expected(allocator(), DT_INT32, TensorShape({28})); + test::FillValues(&expected, + {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}); + test::ExpectTensorEqual(expected, *GetOutput(2)); + } +} + +TEST_F(PruneInvalidAndFillEmptyRowsOpTest, FillEmptyRows) { + MakeOpAndSetDevice(Device::GPU, true, false, -1); + // sp_values + AddInputFromArray(TensorShape({4}), {1, 5, 3, 6}); + // sp_indices + AddInputFromArray(TensorShape({4, 2}), {0, 1, 0, 2, 2, 3, 3, 1}); + // sp_dense_shape + AddInputFromArray(TensorShape({2}), {4, 8}); + + TF_ASSERT_OK(RunOpKernel()); + TF_EXPECT_OK(device_->Sync()); + // sp_values_out + { + Tensor expected(allocator(), DT_INT64, TensorShape({5})); + test::FillValues(&expected, {1, 5, 3, 6, 0}); + test::ExpectTensorEqual(expected, *GetOutput(0)); + } + // sp_indices_out + { + Tensor expected(allocator(), DT_INT64, TensorShape({5, 2})); + test::FillValues(&expected, {0, 1, 0, 2, 2, 3, 3, 1, 1, 0}); + test::ExpectTensorEqual(expected, *GetOutput(1)); + } + // row_empty_and_invalid_flags + { + Tensor expected(allocator(), DT_INT32, TensorShape({8})); + test::FillValues(&expected, {0, 1, 0, 0, 1, 1, 1, 1}); + test::ExpectTensorEqual(expected, *GetOutput(2)); + } +} + +TEST_F(PruneInvalidAndFillEmptyRowsOpTest, PruneAndFillEmptyRowsWithDefaultId) { + MakeOpAndSetDevice(Device::GPU, true, true, 8); + // sp_values + AddInputFromArray(TensorShape({4}), {1, 5, 3, -5}); + // sp_indices + AddInputFromArray(TensorShape({4, 2}), {0, 1, 0, 2, 2, 3, 3, 1}); + // sp_dense_shape + AddInputFromArray(TensorShape({2}), {4, 8}); + + TF_ASSERT_OK(RunOpKernel()); + TF_EXPECT_OK(device_->Sync()); + // sp_values_out + { + Tensor expected(allocator(), DT_INT64, TensorShape({5})); + test::FillValues(&expected, {1, 5, 3, 8, 8}); + test::ExpectTensorEqual(expected, *GetOutput(0)); + } + // sp_indices_out + { + Tensor expected(allocator(), DT_INT64, TensorShape({5, 2})); + test::FillValues(&expected, {0, 1, 0, 2, 2, 3, 1, 0, 3, 0}); + test::ExpectTensorEqual(expected, *GetOutput(1)); + } + // row_empty_and_invalid_flags + { + Tensor expected(allocator(), DT_INT32, TensorShape({8})); + test::FillValues(&expected, {0, 1, 0, 1, 1, 1, 1, 0}); + test::ExpectTensorEqual(expected, *GetOutput(2)); + } +} + +} // namespace +} // namespace tensorflow \ No newline at end of file From 75a3bd49537eb363dd5134b181f8e216133ee5e5 Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Sat, 19 Mar 2022 20:15:35 +0800 Subject: [PATCH 25/41] Add more unit tests --- tensorflow/core/kernels/BUILD | 4 +- .../kernels/fused_embedding/gpu/common.cu.h | 2 + .../gpu/functions/partition_select.cu.cc | 30 ++-- .../gpu/fused_embedding_post_ops_gpus.cu.cc | 32 ++-- .../gpu/partition_with_permutation_ops.cu.cc | 8 +- ...rune_invalid_and_fill_empty_rows_ops.cu.cc | 30 ++-- .../gpu/unique_with_count_gpu.ops.cu.cc | 11 +- .../partition_with_permutation_ops_test.cu.cc | 151 ++++++++++++++++++ .../tests/unique_with_count_gpu_ops_test.cc | 105 ++++++++++++ 9 files changed, 316 insertions(+), 57 deletions(-) create mode 100644 tensorflow/core/kernels/fused_embedding/tests/partition_with_permutation_ops_test.cu.cc create mode 100644 tensorflow/core/kernels/fused_embedding/tests/unique_with_count_gpu_ops_test.cc diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD index c3ebf244ce4..2cd5700095c 100644 --- a/tensorflow/core/kernels/BUILD +++ b/tensorflow/core/kernels/BUILD @@ -1948,7 +1948,9 @@ tf_cuda_cc_test( tf_cuda_cc_test( name = "fused_embedding_ops_test", size = "small", - srcs = ["fused_embedding/tests/prune_invalid_and_fill_empty_rows_ops_test.cc"], + srcs = ["fused_embedding/tests/prune_invalid_and_fill_empty_rows_ops_test.cc", + "fused_embedding/tests/unique_with_count_gpu_ops_test.cc", + "fused_embedding/tests/partition_with_permutation_ops_test.cu.cc"], tags = tf_cuda_tests_tags(), deps = [ ":fused_embedding_ops", diff --git a/tensorflow/core/kernels/fused_embedding/gpu/common.cu.h b/tensorflow/core/kernels/fused_embedding/gpu/common.cu.h index e302063c979..dd1bd5fd1e1 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/common.cu.h +++ b/tensorflow/core/kernels/fused_embedding/gpu/common.cu.h @@ -65,6 +65,8 @@ void printGPUTensorHelper(const T* src, const std::vector& dims, int element_num = 1; int dim_num = (int)dims.size(); + cudaDeviceSynchronize(); + printf("Tensor size: ("); for (int i = 0; i < dim_num; i++) { element_num *= dims[i]; diff --git a/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc index f423fb701c3..5131f3ddb28 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/functions/partition_select.cu.cc @@ -156,9 +156,10 @@ namespace fused_embedding { ctx, ctx->allocate_temp(DT_UINT32, \ TensorShape{counters_length * num_partitions}, \ &accumulate_counters)); \ - cudaMemsetAsync(data_p_with_type(accumulate_counters), 0, \ - accumulate_counters.NumElements() * sizeof(unsigned int), \ - device.stream()); \ + CK_CUDA_THROW_(cudaMemsetAsync( \ + data_p_with_type(accumulate_counters), 0, \ + accumulate_counters.NumElements() * sizeof(unsigned int), \ + device.stream())); \ OP_REQUIRES_OK( \ ctx, ctx->allocate_temp( \ DT_UINT32, TensorShape{predicates_length * num_partitions}, \ @@ -179,14 +180,14 @@ namespace fused_embedding { std::vector selected_nums_host; \ selected_nums_host.resize(num_partitions); \ /* copy the last element(which is the sum of previous) with stride */ \ - cudaMemcpy2DAsync(selected_nums_host.data(), 1 * sizeof(unsigned int), \ - data_p_with_type(accumulate_counters) + \ - counters_length - 1, \ - counters_length * sizeof(unsigned int), \ - 1 * sizeof(unsigned int), num_partitions, \ - cudaMemcpyDeviceToHost, device.stream()); \ - cudaEventRecord(memcpy_event, device.stream()); \ - cudaEventSynchronize(memcpy_event); \ + CK_CUDA_THROW_(cudaMemcpy2DAsync( \ + selected_nums_host.data(), 1 * sizeof(unsigned int), \ + data_p_with_type(accumulate_counters) + \ + counters_length - 1, \ + counters_length * sizeof(unsigned int), 1 * sizeof(unsigned int), \ + num_partitions, cudaMemcpyDeviceToHost, device.stream())); \ + CK_CUDA_THROW_(cudaEventRecord(memcpy_event, device.stream())); \ + CK_CUDA_THROW_(cudaEventSynchronize(memcpy_event)); \ \ std::vector output_ptrs_host; \ output_ptrs_host.resize(num_partitions); \ @@ -201,9 +202,10 @@ namespace fused_embedding { OP_REQUIRES_OK( \ ctx, ctx->allocate_temp(DT_UINT64, TensorShape{int64(num_partitions)}, \ &output_ptrs)); \ - cudaMemcpyAsync(data_p_with_type(output_ptrs), \ - output_ptrs_host.data(), num_partitions * sizeof(size_t), \ - cudaMemcpyHostToDevice, device.stream()); \ + CK_CUDA_THROW_(cudaMemcpyAsync(data_p_with_type(output_ptrs), \ + output_ptrs_host.data(), \ + num_partitions * sizeof(size_t), \ + cudaMemcpyHostToDevice, device.stream())); \ { \ const int64 threads = 32; \ const int64 blocks = select_scan_warps * num_partitions; \ diff --git a/tensorflow/core/kernels/fused_embedding/gpu/fused_embedding_post_ops_gpus.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/fused_embedding_post_ops_gpus.cu.cc index 8980ed73803..fe0a1fe6bcc 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/fused_embedding_post_ops_gpus.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/fused_embedding_post_ops_gpus.cu.cc @@ -69,16 +69,17 @@ class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { TensorShape({int64(batch_size), int64(emb_vec_size)}), &emb_vectors_tensor)); - cudaMemsetAsync(emb_vectors_tensor->flat().data(), 0x0, - sizeof(float) * emb_vectors_tensor->NumElements(), - device.stream()); + CK_CUDA_THROW_(cudaMemsetAsync( + emb_vectors_tensor->flat().data(), 0x0, + sizeof(float) * emb_vectors_tensor->NumElements(), device.stream())); Tensor* feature_nums; OP_REQUIRES_OK(ctx, ctx->allocate_output("feature_nums", TensorShape({int64(batch_size)}), &feature_nums)); - cudaMemsetAsync(feature_nums->flat().data(), 0x0, - sizeof(int) * feature_nums->NumElements(), device.stream()); + CK_CUDA_THROW_(cudaMemsetAsync(feature_nums->flat().data(), 0x0, + sizeof(int) * feature_nums->NumElements(), + device.stream())); Tensor* emb_shard_ptrs; OP_REQUIRES_OK(ctx, @@ -100,10 +101,10 @@ class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { emb_shard_ptrs_host[i] = data_p_with_type(emb_shards[i]); } - cudaMemcpyAsync(data_p_with_type(emb_shard_ptrs), - emb_shard_ptrs_host.data(), - num_partitions_ * sizeof(size_t), cudaMemcpyHostToDevice, - device.stream()); + CK_CUDA_THROW_(cudaMemcpyAsync(data_p_with_type(emb_shard_ptrs), + emb_shard_ptrs_host.data(), + num_partitions_ * sizeof(size_t), + cudaMemcpyHostToDevice, device.stream())); SumUpEmbeddingShardMultiPartition( device, data_p_with_type(emb_shard_ptrs), @@ -214,8 +215,9 @@ class FusedEmbeddingSparsePostLookUpGradGPU : public OpKernel { Tensor* grad_out; grad_shards.allocate(i, emb_shards[i].shape(), &grad_out); grad_shard_ptrs_host[i] = data_p_with_type(grad_out); - cudaMemsetAsync(data_p_with_type(grad_out), 0x0, - sizeof(float) * grad_out->NumElements(), device.stream()); + CK_CUDA_THROW_(cudaMemsetAsync(data_p_with_type(grad_out), 0x0, + sizeof(float) * grad_out->NumElements(), + device.stream())); } if (num_partitions_ == 1) { @@ -256,10 +258,10 @@ class FusedEmbeddingSparsePostLookUpGradGPU : public OpKernel { OP_REQUIRES_OK(ctx, ctx->allocate_temp( DT_UINT64, TensorShape({int64(num_partitions_)}), &grad_shard_ptrs)); - cudaMemcpyAsync(data_p_with_type(grad_shard_ptrs), - grad_shard_ptrs_host.data(), - num_partitions_ * sizeof(size_t), cudaMemcpyHostToDevice, - device.stream()); + CK_CUDA_THROW_(cudaMemcpyAsync(data_p_with_type(grad_shard_ptrs), + grad_shard_ptrs_host.data(), + num_partitions_ * sizeof(size_t), + cudaMemcpyHostToDevice, device.stream())); if (combiner_ == "mean") { DistributeGradToShardMultiPartition( diff --git a/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc index a36fcb9537c..878932dfed1 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc @@ -59,9 +59,9 @@ class PartitionWithPermutationGPU : public OpKernel { std::vector accu_div_host; accu_div_host.resize(num_partitions_); for (int i = 0; i < partition_shapes.size(); i++) { - OP_REQUIRES(ctx, partition_shapes[i].dims() == 2, + OP_REQUIRES(ctx, partition_shapes[i].NumElements() == 2, errors::InvalidArgument( - "input partition_shapes must all equal to rank 2")); + "input partition_shapes must all have 2 elements")); const int64_t div = partition_shapes[i].flat().data()[0]; accu_div_host[i] = i == 0 ? div : accu_div_host[i - 1] + div; } @@ -72,9 +72,9 @@ class PartitionWithPermutationGPU : public OpKernel { ctx->allocate_temp( DT_INT64, TensorShape({static_cast(num_partitions_)}), &accu_div)); - cudaMemcpyAsync(data_p_with_type(accu_div), accu_div_host.data(), + CK_CUDA_THROW_(cudaMemcpyAsync(data_p_with_type(accu_div), accu_div_host.data(), num_partitions_ * sizeof(int64_t), cudaMemcpyHostToDevice, - device.stream()); + device.stream())); if (input_size < 512) { PartitionSelectDiv( diff --git a/tensorflow/core/kernels/fused_embedding/gpu/prune_invalid_and_fill_empty_rows_ops.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/prune_invalid_and_fill_empty_rows_ops.cu.cc index 2a0d8e74718..c597e45333c 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/prune_invalid_and_fill_empty_rows_ops.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/prune_invalid_and_fill_empty_rows_ops.cu.cc @@ -118,7 +118,6 @@ class PruneInvalidAndFillEmptyRowsGPU : public OpKernel { InitFlagsToOneInt4(device, batch_size + nnz, row_empty_and_invalid_flags->flat().data()); - CK_CUDA_THROW_(cudaGetLastError()); // 3.1 set flags, init tmp_indices_buffer etc. if (fill_empty_row_) { @@ -130,13 +129,11 @@ class PruneInvalidAndFillEmptyRowsGPU : public OpKernel { data_p_with_type(row_empty_and_invalid_flags) + batch_size, data_p_with_type(tmp_indices_buffer), data_p_with_type(values_extended)); - CK_CUDA_THROW_(cudaGetLastError()); } else if (prune_invalid_id_) { DetectInvalid( device, data_p_with_type(values_tensor), nnz, data_p_with_type(row_empty_and_invalid_flags) + batch_size); - CK_CUDA_THROW_(cudaGetLastError()); } // 3.2 select copy valid id, select copy empty row indices @@ -146,7 +143,6 @@ class PruneInvalidAndFillEmptyRowsGPU : public OpKernel { data_p_with_type(row_empty_and_invalid_flags) + batch_size, data_p_with_type(values_extended), data_p_with_type(selected_num_d), int(nnz), device.stream()); - CK_CUDA_THROW_(cudaGetLastError()); cub::DeviceSelect::Flagged( data_p_with_type(cub_temp_storage), max_cub_bytes, @@ -154,14 +150,13 @@ class PruneInvalidAndFillEmptyRowsGPU : public OpKernel { data_p_with_type(row_empty_and_invalid_flags) + batch_size, data_p_with_type(indices_extended), data_p_with_type(selected_num_d), nnz, device.stream()); - CK_CUDA_THROW_(cudaGetLastError()); if (prune_invalid_id_) { int selected_num; - cudaMemcpyAsync(&selected_num, data_p_with_type(selected_num_d), - sizeof(int), cudaMemcpyDeviceToHost, device.stream()); - cudaEventRecord(memcpy_event_, device.stream()); - cudaEventSynchronize(memcpy_event_); + CK_CUDA_THROW_(cudaMemcpyAsync(&selected_num, data_p_with_type(selected_num_d), + sizeof(int), cudaMemcpyDeviceToHost, device.stream())); + CK_CUDA_THROW_(cudaEventRecord(memcpy_event_, device.stream())); + CK_CUDA_THROW_(cudaEventSynchronize(memcpy_event_)); new_nnz = selected_num; } @@ -172,12 +167,11 @@ class PruneInvalidAndFillEmptyRowsGPU : public OpKernel { data_p_with_type(row_empty_and_invalid_flags), data_p_with_type(indices_extended) + new_nnz, data_p_with_type(selected_num_d), batch_size, device.stream()); - CK_CUDA_THROW_(cudaGetLastError()); int selected_num; - cudaMemcpyAsync(&selected_num, data_p_with_type(selected_num_d), - sizeof(int), cudaMemcpyDeviceToHost, device.stream()); - cudaEventRecord(memcpy_event_, device.stream()); - cudaEventSynchronize(memcpy_event_); + CK_CUDA_THROW_(cudaMemcpyAsync(&selected_num, data_p_with_type(selected_num_d), + sizeof(int), cudaMemcpyDeviceToHost, device.stream())); + CK_CUDA_THROW_(cudaEventRecord(memcpy_event_, device.stream())); + CK_CUDA_THROW_(cudaEventSynchronize(memcpy_event_)); new_nnz += selected_num; } } @@ -203,13 +197,13 @@ class PruneInvalidAndFillEmptyRowsGPU : public OpKernel { ctx, ctx->allocate_output("sp_indices_out", TensorShape{new_nnz, 2}, &sp_indices_out)); - cudaMemcpyAsync(data_p_with_type(sp_values_out), values_in, + CK_CUDA_THROW_(cudaMemcpyAsync(data_p_with_type(sp_values_out), values_in, sizeof(int64) * new_nnz, cudaMemcpyDeviceToDevice, - device.stream()); + device.stream())); - cudaMemcpyAsync(data_p_with_type(sp_indices_out), indices_in, + CK_CUDA_THROW_(cudaMemcpyAsync(data_p_with_type(sp_indices_out), indices_in, sizeof(IndicePair) * new_nnz, cudaMemcpyDeviceToDevice, - device.stream()); + device.stream())); } private: diff --git a/tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_gpu.ops.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_gpu.ops.cu.cc index dc496842a06..890a106024e 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_gpu.ops.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_gpu.ops.cu.cc @@ -322,10 +322,11 @@ class UniqueWithCountsGPU : public OpKernel { data_p_with_type(counter), empty_key, empty_val); CounterType uniq_size; - cudaMemcpyAsync(&uniq_size, data_p_with_type(counter), - sizeof(counter), cudaMemcpyDeviceToHost, device.stream()); - cudaEventRecord(memcpy_event_, device.stream()); - cudaEventSynchronize(memcpy_event_); + CK_CUDA_THROW_(cudaMemcpyAsync( + &uniq_size, data_p_with_type(counter), sizeof(CounterType), + cudaMemcpyDeviceToHost, device.stream())); + CK_CUDA_THROW_(cudaEventRecord(memcpy_event_, device.stream())); + CK_CUDA_THROW_(cudaEventSynchronize(memcpy_event_)); Tensor* unique_keys; OP_REQUIRES_OK( @@ -341,7 +342,7 @@ class UniqueWithCountsGPU : public OpKernel { data_p_with_type(unique_counts), data_p_with_type(keys_storage), data_p_with_type(vals_storage), - data_p_with_type(counter), 0, capacity, + data_p_with_type(counts_storage), 0, capacity, data_p_with_type(dump_counter), empty_key); } diff --git a/tensorflow/core/kernels/fused_embedding/tests/partition_with_permutation_ops_test.cu.cc b/tensorflow/core/kernels/fused_embedding/tests/partition_with_permutation_ops_test.cu.cc new file mode 100644 index 00000000000..ed7952e2faa --- /dev/null +++ b/tensorflow/core/kernels/fused_embedding/tests/partition_with_permutation_ops_test.cu.cc @@ -0,0 +1,151 @@ +#include + +#include "tensorflow/core/common_runtime/kernel_benchmark_testlib.h" +#include "tensorflow/core/framework/fake_input.h" +#include "tensorflow/core/framework/node_def_builder.h" +#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/types.h" +#include "tensorflow/core/kernels/ops_testutil.h" +#include "tensorflow/core/kernels/ops_util.h" +#include "tensorflow/core/platform/test.h" +#include "tensorflow/core/platform/test_benchmark.h" +#include "tensorflow/core/public/session.h" + +namespace tensorflow { +namespace { + +enum class Device { GPU }; + +class PartitionWithPermutationOpTest : public OpsTestBase { + protected: + void MakeOpAndSetDevice(Device device, const int num_partitions, + const std::string& partition_strategy) { + if (device == Device::GPU) { + SetDevice(DEVICE_GPU, + std::unique_ptr(DeviceFactory::NewDevice( + "GPU", {}, "/job:a/replica:0/task:0"))); + } + + TF_EXPECT_OK( + NodeDefBuilder("partition_with_permutation", "PartitionWithPermutation") + .Attr("num_partitions", num_partitions) + .Attr("partition_axis", 0) + .Attr("partition_strategy", partition_strategy) + .Input(FakeInput(DT_INT64)) + .Input(FakeInput(num_partitions, DT_INT64)) + .Finalize(node_def())); + TF_EXPECT_OK(InitOp()); + } +}; + +TEST_F(PartitionWithPermutationOpTest, Partition3Div) { + MakeOpAndSetDevice(Device::GPU, 3, std::string("div")); + // sp_values + AddInputFromArray(TensorShape({12}), + {1, 5, 3, 6, 12, 14, 15, 0, 5, 5, 11, 7}); + // partition_shapes 0 + AddInputFromArray(TensorShape({2}), {6, 16}); + // partition_shapes 1 + AddInputFromArray(TensorShape({2}), {3, 16}); + // partition_shapes 2 + AddInputFromArray(TensorShape({2}), {7, 16}); + + TF_ASSERT_OK(RunOpKernel()); + TF_EXPECT_OK(device_->Sync()); + // partitioned_values 0 + { + Tensor expected(allocator(), DT_INT64, TensorShape({6})); + test::FillValues(&expected, {1, 5, 3, 0, 5, 5}); + test::ExpectTensorEqual(expected, *GetOutput(0)); + } + // partitioned_values 1 + { + Tensor expected(allocator(), DT_INT64, TensorShape({2})); + test::FillValues(&expected, {6 - 6, 7 - 6}); + test::ExpectTensorEqual(expected, *GetOutput(1)); + } + // partitioned_values 2 + { + Tensor expected(allocator(), DT_INT64, TensorShape({4})); + test::FillValues(&expected, {12 - 9, 14 - 9, 15 - 9, 11 - 9}); + test::ExpectTensorEqual(expected, *GetOutput(2)); + } + + // partition_permutation + { + Tensor expected(allocator(), DT_INT32, TensorShape({12, 2})); + test::FillValues(&expected, {0, 0, 0, 1, 0, 2, 1, 0, 2, 0, 2, 1, + 2, 2, 0, 3, 0, 4, 0, 5, 2, 3, 1, 1}); + test::ExpectTensorEqual(expected, *GetOutput(3)); + } +} + +TEST_F(PartitionWithPermutationOpTest, Partition2Mod) { + MakeOpAndSetDevice(Device::GPU, 2, std::string("mod")); + // sp_values + AddInputFromArray(TensorShape({12}), + {1, 5, 3, 6, 12, 14, 15, 0, 5, 5, 11, 7}); + // partition_shapes 0 + AddInputFromArray(TensorShape({2}), {6, 16}); + // partition_shapes 1 + AddInputFromArray(TensorShape({2}), {6, 16}); + + TF_ASSERT_OK(RunOpKernel()); + TF_EXPECT_OK(device_->Sync()); + // partitioned_values 0 + { + Tensor expected(allocator(), DT_INT64, TensorShape({4})); + test::FillValues(&expected, {6 / 2, 12 / 2, 14 / 2, 0 / 2}); + test::ExpectTensorEqual(expected, *GetOutput(0)); + } + // partitioned_values 1 + { + Tensor expected(allocator(), DT_INT64, TensorShape({8})); + test::FillValues( + &expected, {1 / 2, 5 / 2, 3 / 2, 15 / 2, 5 / 2, 5 / 2, 11 / 2, 7 / 2}); + test::ExpectTensorEqual(expected, *GetOutput(1)); + } + + // partition_permutation + { + Tensor expected(allocator(), DT_INT32, TensorShape({12, 2})); + test::FillValues(&expected, {1, 0, 1, 1, 1, 2, 0, 0, 0, 1, 0, 2, + 1, 3, 0, 3, 1, 4, 1, 5, 1, 6, 1, 7}); + test::ExpectTensorEqual(expected, *GetOutput(2)); + } +} + +TEST_F(PartitionWithPermutationOpTest, Partition2ModEV) { + MakeOpAndSetDevice(Device::GPU, 2, std::string("mod_ev")); + // sp_values + AddInputFromArray(TensorShape({6}), {5, 28, 1003, 2004, 1834, 17833}); + // partition_shapes 0 + AddInputFromArray(TensorShape({2}), {10000, 8}); + // partition_shapes 1 + AddInputFromArray(TensorShape({2}), {10000, 8}); + + TF_ASSERT_OK(RunOpKernel()); + TF_EXPECT_OK(device_->Sync()); + // partitioned_values 0 + { + Tensor expected(allocator(), DT_INT64, TensorShape({3})); + test::FillValues(&expected, {28, 2004, 1834}); + test::ExpectTensorEqual(expected, *GetOutput(0)); + } + // partitioned_values 1 + { + Tensor expected(allocator(), DT_INT64, TensorShape({3})); + test::FillValues(&expected, {5, 1003, 17833}); + test::ExpectTensorEqual(expected, *GetOutput(1)); + } + + // partition_permutation + { + Tensor expected(allocator(), DT_INT32, TensorShape({6, 2})); + test::FillValues(&expected, {1, 0, 0, 0, 1, 1, 0, 1, 0, 2, 1, 2}); + test::ExpectTensorEqual(expected, *GetOutput(2)); + } +} + +} // namespace +} // namespace tensorflow diff --git a/tensorflow/core/kernels/fused_embedding/tests/unique_with_count_gpu_ops_test.cc b/tensorflow/core/kernels/fused_embedding/tests/unique_with_count_gpu_ops_test.cc new file mode 100644 index 00000000000..24d79d5a97d --- /dev/null +++ b/tensorflow/core/kernels/fused_embedding/tests/unique_with_count_gpu_ops_test.cc @@ -0,0 +1,105 @@ +#include + +#include "tensorflow/core/common_runtime/kernel_benchmark_testlib.h" +#include "tensorflow/core/framework/fake_input.h" +#include "tensorflow/core/framework/node_def_builder.h" +#include "tensorflow/core/framework/tensor.h" +#include "tensorflow/core/framework/types.h" +#include "tensorflow/core/kernels/ops_testutil.h" +#include "tensorflow/core/kernels/ops_util.h" +#include "tensorflow/core/platform/test.h" +#include "tensorflow/core/platform/test_benchmark.h" +#include "tensorflow/core/public/session.h" + +namespace tensorflow { +namespace { + +enum class Device { GPU }; + +class UniqueWithCountsGPUOpTest : public OpsTestBase { + protected: + void MakeOpAndSetDevice(Device device) { + if (device == Device::GPU) { + SetDevice(DEVICE_GPU, + std::unique_ptr(DeviceFactory::NewDevice( + "GPU", {}, "/job:a/replica:0/task:0"))); + } + + TF_EXPECT_OK(NodeDefBuilder("unique_with_counts_gpu", "UniqueWithCountsGPU") + .Attr("CounterType", DT_INT32) + .Input(FakeInput(DT_INT64)) + .Finalize(node_def())); + TF_EXPECT_OK(InitOp()); + } +}; + +TEST_F(UniqueWithCountsGPUOpTest, UniqueWithCount) { + MakeOpAndSetDevice(Device::GPU); + const int input_size = 20; + const int uniq_size = 14; + // input + AddInputFromArray( + TensorShape({input_size}), + {1, 3, 2, 1, 4, 5, 6, 5, 7, 8, 1, 9, 10, 2, 13, 15, 17, 13, 12, 8}); + + TF_ASSERT_OK(RunOpKernel()); + TF_EXPECT_OK(device_->Sync()); + + std::vector input = {1, 3, 2, 1, 4, 5, 6, 5, 7, 8, + 1, 9, 10, 2, 13, 15, 17, 13, 12, 8}; + + std::vector unique_keys; + std::vector expected_unique_keys = {1, 2, 3, 4, 5, 6, 7, + 8, 9, 10, 12, 13, 15, 17}; + + std::vector expected_unique_counts = {3, 2, 1, 1, 2, 1, 1, + 2, 1, 1, 1, 2, 1, 1}; + + auto unique_keys_tensor = GetOutput(0); + auto unique_idxs_tensor = GetOutput(1); + auto unique_counts_tenosr = GetOutput(2); + + // unique_idxs + { + test::internal::Expector::Equal(unique_idxs_tensor->dim_size(0), + input_size); + for (int i = 0; i < input_size; i++) { + test::internal::Expector::Equal( + unique_keys_tensor->flat() + .data()[unique_idxs_tensor->flat().data()[i]], + input[i]); + } + } + // unique_counts + { + for (int i = 0; i < uniq_size; i++) { + const int count = expected_unique_counts[i]; + const int64 expected_key = expected_unique_keys[i]; + for (int j = 0; j < uniq_size; j++) { + if (unique_keys_tensor->flat().data()[j] == expected_key) { + test::internal::Expector::Equal( + unique_counts_tenosr->flat().data()[j], count); + } + } + } + } + + // test unique_keys + { + test::internal::Expector::Equal(unique_keys_tensor->dim_size(0), + uniq_size); + for (int i = 0; i < uniq_size; i++) { + unique_keys.push_back(unique_keys_tensor->flat().data()[i]); + } + + std::sort(unique_keys.begin(), unique_keys.end()); + + for (int i = 0; i < uniq_size; i++) { + test::internal::Expector::Equal(unique_keys[i], + expected_unique_keys[i]); + } + } +} + +} // namespace +} // namespace tensorflow \ No newline at end of file From b7380150ead83cde8948f0e52f19d697a8daf7cc Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Sat, 19 Mar 2022 21:21:04 +0800 Subject: [PATCH 26/41] post op ut passed --- tensorflow/core/kernels/BUILD | 3 +- .../tests/fused_embedding_post_ops_test.cc | 141 ++++-- .../tests/fused_embedding_pre_ops_test.cc | 431 ------------------ 3 files changed, 93 insertions(+), 482 deletions(-) delete mode 100644 tensorflow/core/kernels/fused_embedding/tests/fused_embedding_pre_ops_test.cc diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD index 2cd5700095c..00497b7b19a 100644 --- a/tensorflow/core/kernels/BUILD +++ b/tensorflow/core/kernels/BUILD @@ -1950,7 +1950,8 @@ tf_cuda_cc_test( size = "small", srcs = ["fused_embedding/tests/prune_invalid_and_fill_empty_rows_ops_test.cc", "fused_embedding/tests/unique_with_count_gpu_ops_test.cc", - "fused_embedding/tests/partition_with_permutation_ops_test.cu.cc"], + "fused_embedding/tests/partition_with_permutation_ops_test.cu.cc", + "fused_embedding/tests/fused_embedding_post_ops_test.cc"], tags = tf_cuda_tests_tags(), deps = [ ":fused_embedding_ops", diff --git a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc index e46e8a7c796..1b3b3f25087 100644 --- a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc +++ b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc @@ -27,7 +27,7 @@ limitations under the License. namespace tensorflow { namespace { -enum class Device { CPU, GPU }; +enum class Device { GPU }; class FusedEmbeddingSparsePostLookUpOpTest : public OpsTestBase { protected: void MakeOpAndSetDevice(Device device, int num_partitions, DataType dtype, @@ -49,20 +49,18 @@ class FusedEmbeddingSparsePostLookUpOpTest : public OpsTestBase { .Attr("fill_empty_row", fill_empty_row) .Attr("default_id", default_id) .Input(FakeInput(num_partitions, dtype)) - .Input(FakeInput(num_partitions, DT_INT64)) - .Input(FakeInput(DT_INT64)) - .Input(FakeInput(DT_INT64)) .Input(FakeInput(DT_INT32)) .Input(FakeInput(DT_INT64)) .Input(FakeInput(DT_INT64)) - .Input(FakeInput(DT_INT64)) + .Input(FakeInput(DT_INT32)) + .Input(FakeInput(DT_INT32)) .Finalize(node_def())); TF_EXPECT_OK(InitOp()); } }; TEST_F(FusedEmbeddingSparsePostLookUpOpTest, - OnlyPartition3CombinerSqrtnMaxNorm200) { + Partition3CombinerSqrtnMaxNorm200) { const int nnz = 10; const int batch_size = 4; const int emb_vector_dim = 8; @@ -90,10 +88,9 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, 96.0, 97.0, 98.0, 99.0, 100.0, 101.0, 102.0, 103.0, 120.0, 121.0, 122.0, 123.0, 124.0, 125.0, 126.0, 127.0}); - // partition_permutations - AddInputFromArray(TensorShape({6}), {0, 1, 2, 3, 4, 5}); - AddInputFromArray(TensorShape({1}), {6}); - AddInputFromArray(TensorShape({3}), {7, 8, 9}); + // partition_permutation + AddInputFromArray(TensorShape({nnz, 2}), {0, 0, 0, 1, 0, 2, 0, 3, 0, 4, + 0, 5, 1, 0, 2, 0, 2, 1, 2, 2}); // sp_dense_shape AddInputFromArray(TensorShape({2}), {batch_size, entries}); @@ -107,14 +104,8 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, AddInputFromArray(TensorShape({batch_size + nnz}), {0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}); - // unique_counts - AddInputFromArray(TensorShape({nnz}), {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}); - - // idx_of_input_to_unique - AddInputFromArray(TensorShape({nnz}), {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}); - - // unique_offsets - AddInputFromArray(TensorShape({nnz}), {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}); + // unique_idxs + AddInputFromArray(TensorShape({nnz}), {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}); TF_ASSERT_OK(RunOpKernel()); TF_EXPECT_OK(device_->Sync()); @@ -137,11 +128,11 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, Tensor feature_nums_expected(allocator(), DT_INT32, TensorShape({batch_size})); test::FillValues(&feature_nums_expected, {2, 3, 3, 2}); - test::ExpectTensorEqual(feature_nums_expected, *GetOutput(1)); + test::ExpectTensorEqual(feature_nums_expected, *GetOutput(1)); } } -TEST_F(FusedEmbeddingSparsePostLookUpOpTest, OnlyPartition2Sum) { +TEST_F(FusedEmbeddingSparsePostLookUpOpTest, Partition2SumFillEmpty) { const int nnz = 3; const int batch_size = 3; const int emb_vector_dim = 4; @@ -156,9 +147,8 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, OnlyPartition2Sum) { AddInputFromArray(TensorShape({2, emb_vector_dim}), {10.0, 10.0, 10.0, 10.0, 13.0, 13.0, 13.0, 13.0}); - // partition_permutations - AddInputFromArray(TensorShape({2}), {1, 2}); - AddInputFromArray(TensorShape({2}), {3, 0}); + // partition_permutation + AddInputFromArray(TensorShape({nnz + 1, 2}), {1, 1, 0, 0, 0, 1, 1, 0}); // sp_dense_shape AddInputFromArray(TensorShape({2}), {batch_size, entries}); @@ -169,14 +159,8 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, OnlyPartition2Sum) { // row_empty_and_invalid_flags AddInputFromArray(TensorShape({batch_size + nnz}), {0, 0, 1, 1, 1, 1}); - // unique_counts - AddInputFromArray(TensorShape({nnz + 1}), {1, 1, 1, 1}); - - // idx_of_input_to_unique - AddInputFromArray(TensorShape({nnz + 1}), {0, 1, 2, 3}); - - // unique_offsets - AddInputFromArray(TensorShape({nnz + 1}), {0, 1, 2, 3}); + // unique_idxs + AddInputFromArray(TensorShape({nnz + 1}), {0, 1, 2, 3}); TF_ASSERT_OK(RunOpKernel()); TF_EXPECT_OK(device_->Sync()); @@ -197,7 +181,7 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, OnlyPartition2Sum) { } } -TEST_F(FusedEmbeddingSparsePostLookUpOpTest, OnlyPartition2SumDefault2) { +TEST_F(FusedEmbeddingSparsePostLookUpOpTest, Partition2SumFillEmptyDefault2) { const int nnz = 3; const int batch_size = 3; const int emb_vector_dim = 4; @@ -212,9 +196,8 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, OnlyPartition2SumDefault2) { AddInputFromArray(TensorShape({2, emb_vector_dim}), {10.0, 10.0, 10.0, 10.0, 13.0, 13.0, 13.0, 13.0}); - // partition_permutations - AddInputFromArray(TensorShape({2}), {1, 2}); - AddInputFromArray(TensorShape({2}), {3, 0}); + // partition_permutation + AddInputFromArray(TensorShape({nnz + 1, 2}), {1, 1, 0, 0, 0, 1, 1, 0}); // sp_dense_shape AddInputFromArray(TensorShape({2}), {batch_size, entries}); @@ -225,14 +208,8 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, OnlyPartition2SumDefault2) { // row_empty_and_invalid_flags AddInputFromArray(TensorShape({batch_size + nnz}), {0, 0, 1, 1, 1, 1}); - // unique_counts - AddInputFromArray(TensorShape({nnz + 1}), {1, 1, 1, 1}); - - // idx_of_input_to_unique - AddInputFromArray(TensorShape({nnz + 1}), {0, 1, 2, 3}); - - // unique_offsets - AddInputFromArray(TensorShape({nnz + 1}), {0, 1, 2, 3}); + // unique_idxs + AddInputFromArray(TensorShape({nnz + 1}), {0, 1, 2, 3}); TF_ASSERT_OK(RunOpKernel()); TF_EXPECT_OK(device_->Sync()); @@ -253,7 +230,8 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, OnlyPartition2SumDefault2) { } } -TEST_F(FusedEmbeddingSparsePostLookUpOpTest, OnlyPartition2MeanDefault2Unique) { +TEST_F(FusedEmbeddingSparsePostLookUpOpTest, + Partition2MeanFillEmptyDefault2Unique) { const int nnz = 7; const int batch_size = 5; const int emb_vector_dim = 2; @@ -268,9 +246,8 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, OnlyPartition2MeanDefault2Unique) { AddInputFromArray(TensorShape({2, emb_vector_dim}), {3.0, 3.0, 4.0, 4.0}); - // partition_permutations - AddInputFromArray(TensorShape({2}), {3, 1}); - AddInputFromArray(TensorShape({2}), {0, 2}); + // partition_permutation + AddInputFromArray(TensorShape({4, 2}), {1, 0, 0, 1, 1, 1, 0, 0}); // sp_dense_shape AddInputFromArray(TensorShape({2}), {batch_size, entries}); @@ -283,14 +260,17 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, OnlyPartition2MeanDefault2Unique) { AddInputFromArray(TensorShape({batch_size + nnz}), {0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1}); + // unique_idxs + AddInputFromArray(TensorShape({nnz + 1}), {0, 1, 1, 0, 2, 3, 3, 3}); + // unique_counts - AddInputFromArray(TensorShape({4}), {2, 2, 1, 3}); + // AddInputFromArray(TensorShape({4}), {2, 2, 1, 3}); // idx_of_input_to_unique - AddInputFromArray(TensorShape({nnz + 1}), {0, 3, 1, 2, 4, 5, 7, 6}); + // AddInputFromArray(TensorShape({nnz + 1}), {0, 3, 1, 2, 4, 5, 7, 6}); // unique_offsets - AddInputFromArray(TensorShape({4}), {0, 2, 4, 5}); + // AddInputFromArray(TensorShape({4}), {0, 2, 4, 5}); TF_ASSERT_OK(RunOpKernel()); TF_EXPECT_OK(device_->Sync()); @@ -312,5 +292,66 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, OnlyPartition2MeanDefault2Unique) { test::ExpectTensorEqual(feature_nums_expected, *GetOutput(1)); } } + +TEST_F(FusedEmbeddingSparsePostLookUpOpTest, + SinglePartitionMeanFillEmptyDefault2Unique) { + const int nnz = 7; + const int batch_size = 5; + const int emb_vector_dim = 2; + const int entries = 4; + + MakeOpAndSetDevice(Device::GPU, 1, DT_FLOAT, "mean", -1.0, true, 2); + + // emb_shards 0 + AddInputFromArray(TensorShape({4, emb_vector_dim}), + {3.0, 3.0, 2.0, 2.0, 4.0, 4.0, 1.0, 1.0}); + + // partition_permutation, whatever, will not use this + AddInputFromArray(TensorShape({1, 1}), {1}); + + // sp_dense_shape + AddInputFromArray(TensorShape({2}), {batch_size, entries}); + + // indices_before_unique + AddInputFromArray(TensorShape({nnz + 1, 2}), + {0, 1, 0, 2, 1, 0, 1, 1, 3, 0, 3, 1, 4, 0, 2, 0}); + + // row_empty_and_invalid_flags + AddInputFromArray(TensorShape({batch_size + nnz}), + {0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1}); + + // unique_idxs + AddInputFromArray(TensorShape({nnz + 1}), {0, 1, 1, 0, 2, 3, 3, 3}); + + // unique_counts + // AddInputFromArray(TensorShape({4}), {2, 2, 1, 3}); + + // idx_of_input_to_unique + // AddInputFromArray(TensorShape({nnz + 1}), {0, 3, 1, 2, 4, 5, 7, 6}); + + // unique_offsets + // AddInputFromArray(TensorShape({4}), {0, 2, 4, 5}); + + TF_ASSERT_OK(RunOpKernel()); + TF_EXPECT_OK(device_->Sync()); + + // {3 + 2, 3 + 2, 3 + 2, 3 + 2, 1, 1, 4 + 1, 4 + 1, 1, 1} + // {2, 2, 1, 2, 1} + + { + Tensor expected_emb_vectors(allocator(), DT_FLOAT, + TensorShape({batch_size, emb_vector_dim})); + test::FillValues(&expected_emb_vectors, + {2.5, 2.5, 2.5, 2.5, 1.0, 1.0, 2.5, 2.5, 1.0, 1.0}); + test::ExpectTensorNear(expected_emb_vectors, *GetOutput(0), 1e-4); + } + { + Tensor feature_nums_expected(allocator(), DT_INT32, + TensorShape({batch_size})); + test::FillValues(&feature_nums_expected, {2, 2, 1, 2, 1}); + test::ExpectTensorEqual(feature_nums_expected, *GetOutput(1)); + } +} + } // namespace } // namespace tensorflow \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_pre_ops_test.cc b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_pre_ops_test.cc deleted file mode 100644 index 500df957fa9..00000000000 --- a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_pre_ops_test.cc +++ /dev/null @@ -1,431 +0,0 @@ -/* Copyright 2015 The TensorFlow Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. -==============================================================================*/ - -#include - -#include "tensorflow/core/common_runtime/kernel_benchmark_testlib.h" -#include "tensorflow/core/framework/fake_input.h" -#include "tensorflow/core/framework/node_def_builder.h" -#include "tensorflow/core/framework/tensor.h" -#include "tensorflow/core/framework/types.h" -#include "tensorflow/core/kernels/ops_testutil.h" -#include "tensorflow/core/kernels/ops_util.h" -#include "tensorflow/core/platform/test.h" -#include "tensorflow/core/platform/test_benchmark.h" -#include "tensorflow/core/public/session.h" - -namespace tensorflow { -namespace { - -enum class Device { CPU, GPU }; - -class FusedEmbeddingSparsePreLookUpOpTest : public OpsTestBase { - protected: - void MakeOpAndSetDevice(Device device, const int num_partitions, - const bool fill_empty_row, - const bool prune_invalid_id, const int default_id, - const std::string& partition_strategy) { - if (device == Device::GPU) { - SetDevice(DEVICE_GPU, - std::unique_ptr(DeviceFactory::NewDevice( - "GPU", {}, "/job:a/replica:0/task:0"))); - } - - TF_EXPECT_OK(NodeDefBuilder("fused_embedding_sparse_pre_look_up", - "FusedEmbeddingSparsePreLookUp") - .Attr("num_partitions", num_partitions) - .Attr("partition_axis", 0) - .Attr("fill_empty_row", fill_empty_row) - .Attr("prune_invalid_id", prune_invalid_id) - .Attr("default_id", default_id) - .Attr("partition_strategy", partition_strategy) - .Input(FakeInput(num_partitions, DT_INT64)) - .Input(FakeInput(DT_INT64)) - .Input(FakeInput(DT_INT64)) - .Input(FakeInput(DT_INT64)) - .Finalize(node_def())); - TF_EXPECT_OK(InitOp()); - } -}; - -TEST_F(FusedEmbeddingSparsePreLookUpOpTest, OnlyTestPartition3Div) { - MakeOpAndSetDevice(Device::GPU, 3, false, false, -1, std::string("div")); - // partition_shapes 0 - AddInputFromArray(TensorShape({2}), {6, 16}); - // partition_shapes 1 - AddInputFromArray(TensorShape({2}), {3, 16}); - // partition_shapes 2 - AddInputFromArray(TensorShape({2}), {7, 16}); - // sp_values - AddInputFromArray(TensorShape({12}), - {1, 5, 3, 6, 12, 14, 15, 0, 5, 5, 11, 7}); - // sp_indices - AddInputFromArray(TensorShape({12, 2}), - {2, 3, 4, 6, 1, 6, 12, 12, 12, 12, 11, 5, - 15, 0, 11, 6, 7, 9, 11, 8, 12, 13, 13, 0}); - // sp_dense_shape - AddInputFromArray(TensorShape({2}), {16, 16}); - - TF_ASSERT_OK(RunOpKernel()); - TF_EXPECT_OK(device_->Sync()); - // partitioned_values and partition_permutations 0 - { - Tensor expected_values(allocator(), DT_INT64, TensorShape({4})); - test::FillValues(&expected_values, {1, 5, 3, 0}); - test::ExpectTensorEqual(expected_values, *GetOutput(0)); - - Tensor expected_permutes(allocator(), DT_INT64, TensorShape({4})); - test::FillValues(&expected_permutes, {0, 1, 2, 7}); - test::ExpectTensorEqual(expected_permutes, *GetOutput(3)); - } - // partitioned_values and partition_permutations 1 - { - Tensor expected_values(allocator(), DT_INT64, TensorShape({2})); - test::FillValues(&expected_values, {0, 1}); - test::ExpectTensorEqual(expected_values, *GetOutput(1)); - - Tensor expected_permutes(allocator(), DT_INT64, TensorShape({2})); - test::FillValues(&expected_permutes, {3, 9}); - test::ExpectTensorEqual(expected_permutes, *GetOutput(4)); - } - // partitioned_values and partition_permutations 2 - { - Tensor expected_values(allocator(), DT_INT64, TensorShape({4})); - test::FillValues(&expected_values, {3, 5, 6, 2}); - test::ExpectTensorEqual(expected_values, *GetOutput(2)); - - Tensor expected_permutes(allocator(), DT_INT64, TensorShape({4})); - test::FillValues(&expected_permutes, {4, 5, 6, 8}); - test::ExpectTensorEqual(expected_permutes, *GetOutput(5)); - } - - // indices_before_unique - Tensor expected_indices_before_unique(allocator(), DT_INT64, - TensorShape({12, 2})); - test::FillValues(&expected_indices_before_unique, - {2, 3, 4, 6, 1, 6, 12, 12, 12, 12, 11, 5, - 15, 0, 11, 6, 7, 9, 11, 8, 12, 13, 13, 0}); - test::ExpectTensorEqual(expected_indices_before_unique, *GetOutput(7)); - - // unique_idxs - Tensor expected_unique_idxs(allocator(), DT_INT64, TensorShape({12})); - test::FillValues(&expected_unique_idxs, - {0, 1, 2, 3, 4, 5, 6, 7, 1, 1, 8, 9}); - test::ExpectTensorEqual(expected_unique_idxs, *GetOutput(8)); - - // unique_counts - Tensor expected_unique_counts(allocator(), DT_INT64, TensorShape({10})); - test::FillValues(&expected_unique_counts, - {1, 3, 1, 1, 1, 1, 1, 1, 1, 1}); - test::ExpectTensorEqual(expected_unique_counts, *GetOutput(9)); - - // idx_of_input_to_unique - Tensor expected_idx_of_input_to_unique(allocator(), DT_INT64, - TensorShape({12})); - test::FillValues(&expected_idx_of_input_to_unique, - {0, 1, 8, 9, 2, 3, 4, 5, 6, 7, 10, 11}); - test::ExpectTensorEqual(expected_idx_of_input_to_unique, - *GetOutput(10)); - - // unique_offsets - Tensor expected_unique_offsets(allocator(), DT_INT64, TensorShape({10})); - test::FillValues(&expected_unique_offsets, - {0, 1, 4, 5, 6, 7, 8, 9, 10, 11}); - test::ExpectTensorEqual(expected_unique_offsets, *GetOutput(11)); -} - -TEST_F(FusedEmbeddingSparsePreLookUpOpTest, OnlyTestUnique) { - MakeOpAndSetDevice(Device::GPU, 1, false, false, -1, std::string("div")); - // partition_shapes 0 - AddInputFromArray(TensorShape({2}), {20, 8}); - // sp_values - AddInputFromArray(TensorShape({20}), {3, 5, 3, 4, 1, 4, 9, 8, 6, 3, - 5, 7, 8, 8, 4, 6, 4, 2, 5, 6}); - // sp_indices, whatever the content - AddInputFromArray( - TensorShape({20, 2}), - {3, 5, 3, 4, 1, 4, 9, 8, 6, 3, 5, 7, 8, 8, 4, 6, 4, 2, 5, 6, - 3, 5, 3, 4, 1, 4, 9, 8, 6, 3, 5, 7, 8, 8, 4, 6, 4, 2, 5, 6}); - // sp_dense_shape - AddInputFromArray(TensorShape({2}), {16, 16}); - - TF_ASSERT_OK(RunOpKernel()); - TF_EXPECT_OK(device_->Sync()); - - // partitioned_values and partition_permutations 0 - { - Tensor expected_values(allocator(), DT_INT64, TensorShape({9})); - test::FillValues(&expected_values, {3, 5, 4, 1, 9, 8, 6, 7, 2}); - test::ExpectTensorEqual(expected_values, *GetOutput(0)); - - Tensor expected_permutes(allocator(), DT_INT64, TensorShape({9})); - test::FillValues(&expected_permutes, {0, 1, 2, 3, 4, 5, 6, 7, 8}); - test::ExpectTensorEqual(expected_permutes, *GetOutput(1)); - } - - // indices_before_unique - Tensor expected_indices_before_unique(allocator(), DT_INT64, - TensorShape({20, 2})); - test::FillValues( - &expected_indices_before_unique, - {3, 5, 3, 4, 1, 4, 9, 8, 6, 3, 5, 7, 8, 8, 4, 6, 4, 2, 5, 6, - 3, 5, 3, 4, 1, 4, 9, 8, 6, 3, 5, 7, 8, 8, 4, 6, 4, 2, 5, 6}); - test::ExpectTensorEqual(expected_indices_before_unique, *GetOutput(3)); - - // unique_idxs - Tensor expected_unique_idxs(allocator(), DT_INT64, TensorShape({20})); - test::FillValues( - &expected_unique_idxs, - {0, 1, 0, 2, 3, 2, 4, 5, 6, 0, 1, 7, 5, 5, 2, 6, 2, 8, 1, 6}); - test::ExpectTensorEqual(expected_unique_idxs, *GetOutput(4)); - - // unique_counts - Tensor expected_unique_counts(allocator(), DT_INT64, TensorShape({9})); - test::FillValues(&expected_unique_counts, {3, 3, 4, 1, 1, 3, 3, 1, 1}); - test::ExpectTensorEqual(expected_unique_counts, *GetOutput(5)); - - // idx_of_input_to_unique - Tensor expected_idx_of_input_to_unique(allocator(), DT_INT64, - TensorShape({20})); - test::FillValues( - &expected_idx_of_input_to_unique, - {0, 2, 9, 1, 10, 18, 3, 5, 14, 16, 4, 6, 7, 12, 13, 8, 15, 19, 11, 17}); - test::ExpectTensorEqual(expected_idx_of_input_to_unique, - *GetOutput(6)); - - // unique_offsets - Tensor expected_unique_offsets(allocator(), DT_INT64, TensorShape({9})); - test::FillValues(&expected_unique_offsets, - {0, 3, 6, 10, 11, 12, 15, 18, 19}); - test::ExpectTensorEqual(expected_unique_offsets, *GetOutput(7)); -} - -TEST_F(FusedEmbeddingSparsePreLookUpOpTest, OnlyTestFillEmpty) { - MakeOpAndSetDevice(Device::GPU, 1, true, false, -1, std::string("div")); - // partition_shapes 0 - AddInputFromArray(TensorShape({2}), {99999, 8}); - - // sp_values - AddInputFromArray(TensorShape({10}), - {10, 4, 3, 2, 5, 13, 14, 9, 6, 1}); - - // sp_indices - AddInputFromArray( - TensorShape({10, 2}), - {0, 0, 0, 4, 1, 2, 3, 0, 3, 4, 4, 0, 5, 2, 6, 0, 6, 1, 6, 7}); - - // sp_dense_shape - AddInputFromArray(TensorShape({2}), {7, 8}); - - TF_ASSERT_OK(RunOpKernel()); - TF_EXPECT_OK(device_->Sync()); - - Tensor expected_values(allocator(), DT_INT64, TensorShape({11})); - test::FillValues(&expected_values, - {10, 4, 3, 2, 5, 13, 14, 9, 6, 1, 0}); - test::ExpectTensorEqual(expected_values, *GetOutput(0)); - - Tensor expected_indices_before_unique(allocator(), DT_INT64, - TensorShape({11, 2})); - test::FillValues( - &expected_indices_before_unique, - {0, 0, 0, 4, 1, 2, 3, 0, 3, 4, 4, 0, 5, 2, 6, 0, 6, 1, 6, 7, 2, 0}); - test::ExpectTensorEqual(expected_indices_before_unique, *GetOutput(3)); -} - -TEST_F(FusedEmbeddingSparsePreLookUpOpTest, OnlyTestPruneInvalid) { - MakeOpAndSetDevice(Device::GPU, 1, false, true, -1, std::string("div")); - // partition_shapes 0 - AddInputFromArray(TensorShape({2}), {20, 8}); - // sp_values - AddInputFromArray(TensorShape({10}), - {10, 4, -3, 2, 5, 13, 14, -9, 6, 1}); - - // sp_indices - AddInputFromArray( - TensorShape({10, 2}), - {0, 0, 0, 4, 1, 2, 3, 0, 3, 4, 4, 0, 5, 2, 6, 0, 6, 1, 6, 7}); - - // sp_dense_shape - AddInputFromArray(TensorShape({2}), {7, 8}); - - TF_ASSERT_OK(RunOpKernel()); - TF_EXPECT_OK(device_->Sync()); - - Tensor expected_values(allocator(), DT_INT64, TensorShape({8})); - test::FillValues(&expected_values, {10, 4, 2, 5, 13, 14, 6, 1}); - test::ExpectTensorEqual(expected_values, *GetOutput(0)); - - Tensor expected_indices_before_unique(allocator(), DT_INT64, - TensorShape({8, 2})); - test::FillValues(&expected_indices_before_unique, - {0, 0, 0, 4, 3, 0, 3, 4, 4, 0, 5, 2, 6, 1, 6, 7}); - test::ExpectTensorEqual(expected_indices_before_unique, *GetOutput(3)); -} - -TEST_F(FusedEmbeddingSparsePreLookUpOpTest, PruneInvalidAndFillEmptyDeault7) { - MakeOpAndSetDevice(Device::GPU, 1, true, true, 7, std::string("div")); - // partition_shapes 0 - AddInputFromArray(TensorShape({2}), {20, 8}); - // sp_values - AddInputFromArray(TensorShape({10}), - {10, 4, 3, 2, 5, 13, 14, -9, 6, 1}); - - // sp_indices - AddInputFromArray( - TensorShape({10, 2}), - {0, 0, 0, 4, 1, 2, 3, 0, 3, 4, 4, 0, 5, 2, 6, 0, 6, 1, 6, 7}); - - // sp_dense_shape - AddInputFromArray(TensorShape({2}), {7, 8}); - - TF_ASSERT_OK(RunOpKernel()); - TF_EXPECT_OK(device_->Sync()); - - Tensor expected_values(allocator(), DT_INT64, TensorShape({10})); - test::FillValues(&expected_values, {10, 4, 3, 2, 5, 13, 14, 6, 1, 7}); - test::ExpectTensorEqual(expected_values, *GetOutput(0)); - - Tensor expected_indices_before_unique(allocator(), DT_INT64, - TensorShape({10, 2})); - test::FillValues( - &expected_indices_before_unique, - {0, 0, 0, 4, 1, 2, 3, 0, 3, 4, 4, 0, 5, 2, 6, 1, 6, 7, 2, 0}); - test::ExpectTensorEqual(expected_indices_before_unique, *GetOutput(3)); -} - -TEST_F(FusedEmbeddingSparsePreLookUpOpTest, TestEverthingDiv) { - MakeOpAndSetDevice(Device::GPU, 4, true, true, 3, std::string("div")); - // partition_shapes 0 - AddInputFromArray(TensorShape({2}), {3, 16}); - // partition_shapes 1 - AddInputFromArray(TensorShape({2}), {4, 16}); - // partition_shapes 2 - AddInputFromArray(TensorShape({2}), {6, 16}); - // partition_shapes 3 - AddInputFromArray(TensorShape({2}), {5, 16}); - - // for div, the parition interval is 0-2, 3-6, 7-12, 13-17 - - // sp_values - AddInputFromArray(TensorShape({16}), {-2, 1, 1, 3, -2, 4, 10, 2, 6, 14, - 12, 16, -1, -100, 12, 6}); - // sp_indices - AddInputFromArray( - TensorShape({16, 2}), - {0, 3, 0, 4, 2, 1, 3, 2, 3, 6, 5, 1, 5, 4, 6, 7, - 8, 3, 9, 4, 11, 2, 11, 3, 11, 5, 12, 3, 13, 4, 13, 7}); - // sp_dense_shape - AddInputFromArray(TensorShape({2}), {14, 16}); - - TF_ASSERT_OK(RunOpKernel()); - TF_EXPECT_OK(device_->Sync()); - - /* - after fill_empty and prune_invalid: - 1, 1, 3, 4, 10, 2, 6, 14, 12, 16, 12, 6, 3, 3, 3, 3, 3 - --------------------------------------------------------- - 0| 2| 3| 5| 5| 6| 8| 9| 11| 11| 13| 13| 1| 4| 7| 10| 12 - 4| 1| 2| 1| 4| 7| 3| 4| 2| 3| 4| 7| 0| 0| 0| 0| 0 - --------------------------------------------------------- - 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12,13,14, 15, 16 - - - after unique: - 1, 3, 4, 10, 2, 6, 14 ,12 ,16 - ----------------------------- - 0, 1, 2, 3, 4, 5, 6, 7, 8 - */ - - // partitioned_values and partition_permutations 0 - { - Tensor expected_values(allocator(), DT_INT64, TensorShape({2})); - test::FillValues(&expected_values, {1, 2}); - test::ExpectTensorEqual(expected_values, *GetOutput(0)); - - Tensor expected_permutes(allocator(), DT_INT64, TensorShape({2})); - test::FillValues(&expected_permutes, {0, 4}); - test::ExpectTensorEqual(expected_permutes, *GetOutput(4)); - } - // partitioned_values and partition_permutations 1 - { - Tensor expected_values(allocator(), DT_INT64, TensorShape({3})); - test::FillValues(&expected_values, {0, 1, 3}); - test::ExpectTensorEqual(expected_values, *GetOutput(1)); - - Tensor expected_permutes(allocator(), DT_INT64, TensorShape({3})); - test::FillValues(&expected_permutes, {1, 2, 5}); - test::ExpectTensorEqual(expected_permutes, *GetOutput(5)); - } - - // partitioned_values and partition_permutations 2 - { - Tensor expected_values(allocator(), DT_INT64, TensorShape({2})); - test::FillValues(&expected_values, {3, 5}); - test::ExpectTensorEqual(expected_values, *GetOutput(2)); - - Tensor expected_permutes(allocator(), DT_INT64, TensorShape({2})); - test::FillValues(&expected_permutes, {3, 7}); - test::ExpectTensorEqual(expected_permutes, *GetOutput(6)); - } - // partitioned_values and partition_permutations 3 - { - Tensor expected_values(allocator(), DT_INT64, TensorShape({2})); - test::FillValues(&expected_values, {1, 3}); - test::ExpectTensorEqual(expected_values, *GetOutput(3)); - - Tensor expected_permutes(allocator(), DT_INT64, TensorShape({2})); - test::FillValues(&expected_permutes, {6, 8}); - test::ExpectTensorEqual(expected_permutes, *GetOutput(7)); - } - - // indices_before_unique - Tensor expected_indices_before_unique(allocator(), DT_INT64, - TensorShape({17, 2})); - test::FillValues( - &expected_indices_before_unique, - {0, 4, 2, 1, 3, 2, 5, 1, 5, 4, 6, 7, 8, 3, 9, 4, 11, - 2, 11, 3, 13, 4, 13, 7, 1, 0, 4, 0, 7, 0, 10, 0, 12, 0}); - test::ExpectTensorEqual(expected_indices_before_unique, *GetOutput(9)); - - // unique_idxs - Tensor expected_unique_idxs(allocator(), DT_INT64, TensorShape({17})); - test::FillValues(&expected_unique_idxs, - {0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 7, 5, 1, 1, 1, 1, 1}); - test::ExpectTensorEqual(expected_unique_idxs, *GetOutput(10)); - - // unique_counts - Tensor expected_unique_counts(allocator(), DT_INT64, TensorShape({9})); - test::FillValues(&expected_unique_counts, {2, 6, 1, 1, 1, 2, 1, 2, 1}); - test::ExpectTensorEqual(expected_unique_counts, *GetOutput(11)); - - // idx_of_input_to_unique - Tensor expected_idx_of_input_to_unique(allocator(), DT_INT64, - TensorShape({17})); - test::FillValues( - &expected_idx_of_input_to_unique, - {0, 1, 2, 12, 13, 14, 15, 16, 3, 4, 5, 6, 11, 7, 8, 10, 9}); - test::ExpectTensorEqual(expected_idx_of_input_to_unique, - *GetOutput(12)); - - // unique_offsets - Tensor expected_unique_offsets(allocator(), DT_INT64, TensorShape({9})); - test::FillValues(&expected_unique_offsets, - {0, 2, 8, 9, 10, 11, 13, 14, 16}); - test::ExpectTensorEqual(expected_unique_offsets, *GetOutput(13)); -} - -} // namespace -} // namespace tensorflow \ No newline at end of file From 9ce8da1c307cf9a8c2df257d034835834cec4560 Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Sat, 19 Mar 2022 23:31:54 +0800 Subject: [PATCH 27/41] ut all passed --- tensorflow/core/kernels/BUILD | 3 +- .../gpu/functions/kernels.cu.cc | 74 ++++----- .../fused_embedding_post_grad_ops_test.cc | 150 +++++++++++++----- .../tests/fused_embedding_post_ops_test.cc | 9 -- 4 files changed, 145 insertions(+), 91 deletions(-) diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD index 00497b7b19a..8620bdb53a5 100644 --- a/tensorflow/core/kernels/BUILD +++ b/tensorflow/core/kernels/BUILD @@ -1951,7 +1951,8 @@ tf_cuda_cc_test( srcs = ["fused_embedding/tests/prune_invalid_and_fill_empty_rows_ops_test.cc", "fused_embedding/tests/unique_with_count_gpu_ops_test.cc", "fused_embedding/tests/partition_with_permutation_ops_test.cu.cc", - "fused_embedding/tests/fused_embedding_post_ops_test.cc"], + "fused_embedding/tests/fused_embedding_post_ops_test.cc", + "fused_embedding/tests/fused_embedding_post_grad_ops_test.cc"], tags = tf_cuda_tests_tags(), deps = [ ":fused_embedding_ops", diff --git a/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.cc index 9ea81585251..207ab344a36 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.cc @@ -288,20 +288,20 @@ template void ApplyCombiner(const GPUDevice& d, const int batch_size, template __global__ void DistributeGradToShardSinglePartitionKernel( const float* top_grad, const float* emb_shard, - const int64_t* indices_before_unique, const int* unique_idxs, - const int nnz, const int emb_vec_size, const float max_norm, - const bool set_empty_row_zero, const int* feature_nums, - const int* row_emptiness_flag, float* grad_shard) { + const int64_t* indices_before_unique, const int* unique_idxs, const int nnz, + const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, + const int* feature_nums, const int* row_emptiness_flag, float* grad_shard) { __shared__ float l2_sum[1]; float l2_norm = -1.0f; if (blockIdx.x < nnz) { const int64_t row_in_batch = indices_before_unique[2 * blockIdx.x]; - const int unique_id = unique_idxs[blockIdx.x]; - if (set_empty_row_zero && row_emptiness_flag[row_in_batch]) { return; } + + const int unique_id = unique_idxs[blockIdx.x]; + if (max_norm >= 0.0f) { const float emb_element = emb_shard[unique_id * emb_vec_size + threadIdx.x]; @@ -321,17 +321,16 @@ __global__ void DistributeGradToShardSinglePartitionKernel( grad *= max_norm / l2_norm; } - grad_shard[unique_id * emb_vec_size + threadIdx.x] = grad; + atomicAdd(grad_shard + unique_id * emb_vec_size + threadIdx.x, grad); } } template void DistributeGradToShardSinglePartition( const GPUDevice& d, const float* top_grad, const float* emb_shard, - const int64_t* indices_before_unique, const int* unique_idxs, - const int nnz, const int emb_vec_size, const float max_norm, - const bool set_empty_row_zero, const int* feature_nums, - const int* row_emptiness_flag, float* grad_shard) { + const int64_t* indices_before_unique, const int* unique_idxs, const int nnz, + const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, + const int* feature_nums, const int* row_emptiness_flag, float* grad_shard) { const int blocks = nnz; const int threads = emb_vec_size; TF_CHECK_OK(GpuLaunchKernel( @@ -343,24 +342,21 @@ void DistributeGradToShardSinglePartition( template void DistributeGradToShardSinglePartition( const GPUDevice& d, const float* top_grad, const float* emb_shard, - const int64_t* indices_before_unique, const int* unique_idxs, - const int nnz, const int emb_vec_size, const float max_norm, - const bool set_empty_row_zero, const int* feature_nums, - const int* row_emptiness_flag, float* grad_shard); + const int64_t* indices_before_unique, const int* unique_idxs, const int nnz, + const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, + const int* feature_nums, const int* row_emptiness_flag, float* grad_shard); template void DistributeGradToShardSinglePartition( const GPUDevice& d, const float* top_grad, const float* emb_shard, - const int64_t* indices_before_unique, const int* unique_idxs, - const int nnz, const int emb_vec_size, const float max_norm, - const bool set_empty_row_zero, const int* feature_nums, - const int* row_emptiness_flag, float* grad_shard); + const int64_t* indices_before_unique, const int* unique_idxs, const int nnz, + const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, + const int* feature_nums, const int* row_emptiness_flag, float* grad_shard); template void DistributeGradToShardSinglePartition( const GPUDevice& d, const float* top_grad, const float* emb_shard, - const int64_t* indices_before_unique, const int* unique_idxs, - const int nnz, const int emb_vec_size, const float max_norm, - const bool set_empty_row_zero, const int* feature_nums, - const int* row_emptiness_flag, float* grad_shard); + const int64_t* indices_before_unique, const int* unique_idxs, const int nnz, + const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, + const int* feature_nums, const int* row_emptiness_flag, float* grad_shard); template __global__ void DistributeGradToShardMultiPartitionKernel( @@ -414,10 +410,10 @@ template void DistributeGradToShardMultiPartition( const GPUDevice& d, const float* top_grad, const void* const* emb_shard_ptrs, const int* partition_permutation, - const int64_t* indices_before_unique, const int* unique_idxs, - const int nnz, const int emb_vec_size, const float max_norm, - const bool set_empty_row_zero, const int* feature_nums, - const int* row_emptiness_flag, void** grad_shard_ptrs) { + const int64_t* indices_before_unique, const int* unique_idxs, const int nnz, + const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, + const int* feature_nums, const int* row_emptiness_flag, + void** grad_shard_ptrs) { const int blocks = nnz; const int threads = emb_vec_size; TF_CHECK_OK(GpuLaunchKernel( @@ -430,26 +426,26 @@ void DistributeGradToShardMultiPartition( template void DistributeGradToShardMultiPartition( const GPUDevice& d, const float* top_grad, const void* const* emb_shard_ptrs, const int* partition_permutation, - const int64_t* indices_before_unique, const int* unique_idxs, - const int nnz, const int emb_vec_size, const float max_norm, - const bool set_empty_row_zero, const int* feature_nums, - const int* row_emptiness_flag, void** grad_shard_ptrs); + const int64_t* indices_before_unique, const int* unique_idxs, const int nnz, + const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, + const int* feature_nums, const int* row_emptiness_flag, + void** grad_shard_ptrs); template void DistributeGradToShardMultiPartition( const GPUDevice& d, const float* top_grad, const void* const* emb_shard_ptrs, const int* partition_permutation, - const int64_t* indices_before_unique, const int* unique_idxs, - const int nnz, const int emb_vec_size, const float max_norm, - const bool set_empty_row_zero, const int* feature_nums, - const int* row_emptiness_flag, void** grad_shard_ptrs); + const int64_t* indices_before_unique, const int* unique_idxs, const int nnz, + const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, + const int* feature_nums, const int* row_emptiness_flag, + void** grad_shard_ptrs); template void DistributeGradToShardMultiPartition( const GPUDevice& d, const float* top_grad, const void* const* emb_shard_ptrs, const int* partition_permutation, - const int64_t* indices_before_unique, const int* unique_idxs, - const int nnz, const int emb_vec_size, const float max_norm, - const bool set_empty_row_zero, const int* feature_nums, - const int* row_emptiness_flag, void** grad_shard_ptrs); + const int64_t* indices_before_unique, const int* unique_idxs, const int nnz, + const int emb_vec_size, const float max_norm, const bool set_empty_row_zero, + const int* feature_nums, const int* row_emptiness_flag, + void** grad_shard_ptrs); } // namespace fused_embedding diff --git a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_grad_ops_test.cc b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_grad_ops_test.cc index 629b7ca5598..f2233c6666c 100644 --- a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_grad_ops_test.cc +++ b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_grad_ops_test.cc @@ -27,7 +27,7 @@ limitations under the License. namespace tensorflow { namespace { -enum class Device { CPU, GPU }; +enum class Device { GPU }; class FusedEmbeddingSparsePostLookUpGradOpTest : public OpsTestBase { protected: @@ -51,13 +51,12 @@ class FusedEmbeddingSparsePostLookUpGradOpTest : public OpsTestBase { .Attr("default_id", default_id) .Input(FakeInput(dtype)) .Input(FakeInput(num_partitions, dtype)) - .Input(FakeInput(num_partitions, DT_INT64)) + .Input(FakeInput(DT_UINT64)) + .Input(FakeInput(DT_INT32)) .Input(FakeInput(DT_INT32)) - .Input(FakeInput(DT_INT64)) - .Input(FakeInput(DT_INT64)) - .Input(FakeInput(DT_INT64)) .Input(FakeInput(DT_INT64)) .Input(FakeInput(DT_INT32)) + .Input(FakeInput(DT_INT32)) .Finalize(node_def())); TF_EXPECT_OK(InitOp()); } @@ -85,6 +84,16 @@ TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, Partition2MeanMaxNorm100) { 28.0, 29.0, 30.0, 31.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0}); + + // make same input to dump to emb_shard_ptrs + Tensor emb_shards_0(allocator(), DT_FLOAT, TensorShape({6, emb_vector_dim})); + test::FillValues( + &emb_shards_0, + {8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 24.0, 25.0, 26.0, 27.0, + 28.0, 29.0, 30.0, 31.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, + 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 32.0, 33.0, 34.0, 35.0, + 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0}); + // emb_shards 1 AddInputFromArray( TensorShape({4, emb_vector_dim}), @@ -93,11 +102,22 @@ TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, Partition2MeanMaxNorm100) { 96.0, 97.0, 98.0, 99.0, 100.0, 101.0, 102.0, 103.0, 120.0, 121.0, 122.0, 123.0, 124.0, 125.0, 126.0, 127.0}); - // partition_permutations 0 - AddInputFromArray(TensorShape({6}), {0, 2, 4, 6, 8, 9}); + // make same input to dump to emb_shard_ptrs + Tensor emb_shards_1(allocator(), DT_FLOAT, TensorShape({4, emb_vector_dim})); + test::FillValues( + &emb_shards_1, {56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, + 96.0, 97.0, 98.0, 99.0, 100.0, 101.0, 102.0, 103.0, + 96.0, 97.0, 98.0, 99.0, 100.0, 101.0, 102.0, 103.0, + 120.0, 121.0, 122.0, 123.0, 124.0, 125.0, 126.0, 127.0}); - // partition_permutations 1 - AddInputFromArray(TensorShape({4}), {1, 3, 5, 7}); + // emb_shard_ptrs + AddInputFromArray(TensorShape({2}), + {reinterpret_cast(emb_shards_0.data()), + reinterpret_cast(emb_shards_1.data())}); + + // partition_permutation + AddInputFromArray(TensorShape({10, 2}), {0, 0, 1, 0, 0, 1, 1, 1, 0, 2, + 1, 2, 0, 3, 1, 3, 0, 4, 0, 5}); // feature_nums AddInputFromArray(TensorShape({batch_size}), {2, 3, 3, 2}); @@ -107,14 +127,8 @@ TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, Partition2MeanMaxNorm100) { TensorShape({nnz, 2}), {0, 5, 1, 7, 0, 1, 2, 4, 2, 1, 2, 7, 1, 2, 3, 0, 3, 6, 1, 1}); - // unique_counts - AddInputFromArray(TensorShape({nnz}), {1, 1, 1, 1, 1, 1, 1, 1, 1, 1}); - - // idx_of_input_to_unique - AddInputFromArray(TensorShape({nnz}), {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}); - - // unique_offsets - AddInputFromArray(TensorShape({nnz}), {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}); + // unique_idxs + AddInputFromArray(TensorShape({nnz}), {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}); // row_empty_and_invalid_flags AddInputFromArray(TensorShape({batch_size + nnz}), @@ -170,14 +184,22 @@ TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, Partition2SUMUnique) { // emb_shards 0 AddInputFromArray(TensorShape({3, emb_vector_dim}), {4.0, 5.0, 6.0}); + // make same input to dump to emb_shard_ptrs + Tensor emb_shards_0(allocator(), DT_FLOAT, TensorShape({3, emb_vector_dim})); + test::FillValues(&emb_shards_0, {4.0, 5.0, 6.0}); + // emb_shards 1 AddInputFromArray(TensorShape({2, emb_vector_dim}), {6.0, 7.0}); + Tensor emb_shards_1(allocator(), DT_FLOAT, TensorShape({2, emb_vector_dim})); + test::FillValues(&emb_shards_1, {6.0, 7.0}); - // partition_permutations 0 - AddInputFromArray(TensorShape({3}), {2, 4, 1}); + // emb_shard_ptrs + AddInputFromArray(TensorShape({2}), + {reinterpret_cast(emb_shards_0.data()), + reinterpret_cast(emb_shards_1.data())}); - // partition_permutations 1 - AddInputFromArray(TensorShape({2}), {3, 0}); + // partition_permutation + AddInputFromArray(TensorShape({5, 2}), {1, 1, 0, 2, 0, 0, 1, 0, 0, 1}); // feature_nums AddInputFromArray(TensorShape({batch_size}), {2, 2, 1, 2}); @@ -189,14 +211,8 @@ TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, Partition2SUMUnique) { AddInputFromArray(TensorShape({nnz + 1, 2}), {0, 1, 0, 3, 1, 2, 1, 3, 3, 2, 3, 6, 2, 0}); - // unique_counts - AddInputFromArray(TensorShape({5}), {2, 2, 1, 1, 1}); - - // idx_of_input_to_unique - AddInputFromArray(TensorShape({nnz + 1}), {0, 1, 2, 5, 3, 4, 6}); - - // unique_offsets - AddInputFromArray(TensorShape({5}), {0, 2, 4, 5, 6}); + // unique_idxs + AddInputFromArray(TensorShape({nnz + 1}), {0, 0, 1, 2, 3, 1, 4}); // row_empty_and_invalid_flags AddInputFromArray(TensorShape({batch_size + nnz}), @@ -211,7 +227,8 @@ TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, Partition2SUMUnique) { idx_of_input_to_unique: 3 -> batch: 1, grad: 2.0 permute 4 -> unique_counts: 1, unique_offsets: 6 -> - idx_of_input_to_unique: 6 -> batch: 2: grad: 0.0, because fill_empty row + idx_of_input_to_unique: 6 -> batch: 2: grad: 0.0, because fill_empty + row permute 1 -> unique_counts: 2, unique_offsets: 2 -> idx_of_input_to_unique: 2 -> batch: 1 -> grad : 2.0 @@ -255,14 +272,22 @@ TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, Partition2SUMUniqueDefault4) { // emb_shards 0 AddInputFromArray(TensorShape({3, emb_vector_dim}), {4.0, 5.0, 6.0}); + // make same input to dump to emb_shard_ptrs + Tensor emb_shards_0(allocator(), DT_FLOAT, TensorShape({3, emb_vector_dim})); + test::FillValues(&emb_shards_0, {4.0, 5.0, 6.0}); + // emb_shards 1 AddInputFromArray(TensorShape({2, emb_vector_dim}), {6.0, 7.0}); + Tensor emb_shards_1(allocator(), DT_FLOAT, TensorShape({2, emb_vector_dim})); + test::FillValues(&emb_shards_1, {6.0, 7.0}); - // partition_permutations 0 - AddInputFromArray(TensorShape({3}), {2, 4, 1}); + // emb_shard_ptrs + AddInputFromArray(TensorShape({2}), + {reinterpret_cast(emb_shards_0.data()), + reinterpret_cast(emb_shards_1.data())}); - // partition_permutations 1 - AddInputFromArray(TensorShape({2}), {3, 0}); + // partition_permutation + AddInputFromArray(TensorShape({5, 2}), {1, 1, 0, 2, 0, 0, 1, 0, 0, 1}); // feature_nums AddInputFromArray(TensorShape({batch_size}), {2, 2, 1, 2}); @@ -274,14 +299,8 @@ TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, Partition2SUMUniqueDefault4) { AddInputFromArray(TensorShape({nnz + 1, 2}), {0, 1, 0, 3, 1, 2, 1, 3, 3, 2, 3, 6, 2, 0}); - // unique_counts - AddInputFromArray(TensorShape({5}), {2, 2, 1, 1, 1}); - - // idx_of_input_to_unique - AddInputFromArray(TensorShape({nnz + 1}), {0, 1, 2, 5, 3, 4, 6}); - - // unique_offsets - AddInputFromArray(TensorShape({5}), {0, 2, 4, 5, 6}); + // unique_idxs + AddInputFromArray(TensorShape({nnz + 1}), {0, 0, 1, 2, 3, 1, 4}); // row_empty_and_invalid_flags AddInputFromArray(TensorShape({batch_size + nnz}), @@ -326,5 +345,52 @@ TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, Partition2SUMUniqueDefault4) { } } +TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, SinglePartitionSUMUnique) { + const int nnz = 6; + const int batch_size = 4; + const int emb_vector_dim = 1; + const int entries = 8; + + MakeOpAndSetDevice(Device::GPU, 1, DT_FLOAT, "sum", -1.0, true, -1); + + // top_grad + AddInputFromArray(TensorShape({batch_size, emb_vector_dim}), + {1.0, 2.0, 3.0, 4.0}); + + // emb_shards 0 + AddInputFromArray(TensorShape({5, emb_vector_dim}), + {7.0, 6.0, 4.0, 6.0, 5.0}); + + // emb_shard_ptrs, whatever, will not be used + AddInputFromArray(TensorShape({1}), {0}); + + // partition_permutation + AddInputFromArray(TensorShape({5, 2}), {1, 1, 0, 2, 0, 0, 1, 0, 0, 1}); + + // feature_nums + AddInputFromArray(TensorShape({batch_size}), {2, 2, 1, 2}); + + // values after fill empty: 1, 1, 2, 3, 4, 2, 0 + // after unique 1, 2, 3, 4, 0 + + // indices_before_unique + AddInputFromArray(TensorShape({nnz + 1, 2}), + {0, 1, 0, 3, 1, 2, 1, 3, 3, 2, 3, 6, 2, 0}); + + // unique_idxs + AddInputFromArray(TensorShape({nnz + 1}), {0, 0, 1, 2, 3, 1, 4}); + + // row_empty_and_invalid_flags + AddInputFromArray(TensorShape({batch_size + nnz}), + {0, 0, 1, 0, 1, 1, 1, 1, 1, 1}); + + TF_ASSERT_OK(RunOpKernel()); + TF_EXPECT_OK(device_->Sync()); + + Tensor grad_shards_0(allocator(), DT_FLOAT, TensorShape({5, emb_vector_dim})); + test::FillValues(&grad_shards_0, {2.0, 6.0, 2.0, 4.0, 0.0}); + test::ExpectTensorNear(grad_shards_0, *GetOutput(0), 1e-4); +} + } // namespace } // namespace tensorflow \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc index 1b3b3f25087..e40cdda3214 100644 --- a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc +++ b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc @@ -323,15 +323,6 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, // unique_idxs AddInputFromArray(TensorShape({nnz + 1}), {0, 1, 1, 0, 2, 3, 3, 3}); - // unique_counts - // AddInputFromArray(TensorShape({4}), {2, 2, 1, 3}); - - // idx_of_input_to_unique - // AddInputFromArray(TensorShape({nnz + 1}), {0, 3, 1, 2, 4, 5, 7, 6}); - - // unique_offsets - // AddInputFromArray(TensorShape({4}), {0, 2, 4, 5}); - TF_ASSERT_OK(RunOpKernel()); TF_EXPECT_OK(device_->Sync()); From cf96164cfd1b505b182037326e72e5ece3561f66 Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Sun, 20 Mar 2022 00:40:05 +0800 Subject: [PATCH 28/41] optimize prune and fill moore --- ...rune_invalid_and_fill_empty_rows_ops.cu.cc | 129 +++++++++++------- tensorflow/python/ops/fused_embedding_ops.py | 4 + 2 files changed, 85 insertions(+), 48 deletions(-) diff --git a/tensorflow/core/kernels/fused_embedding/gpu/prune_invalid_and_fill_empty_rows_ops.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/prune_invalid_and_fill_empty_rows_ops.cu.cc index c597e45333c..63e279d11f7 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/prune_invalid_and_fill_empty_rows_ops.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/prune_invalid_and_fill_empty_rows_ops.cu.cc @@ -8,6 +8,9 @@ #define EIGEN_USE_GPU +#include +#include + #include "tensorflow/core/kernels/fused_embedding/gpu/common.cu.h" #include "tensorflow/core/kernels/fused_embedding/gpu/functions/kernels.cu.h" #include "tensorflow/core/profiler/nvtx_utils.h" @@ -54,26 +57,56 @@ class PruneInvalidAndFillEmptyRowsGPU : public OpKernel { // =============================================== // - // ========= 2. allocate cub tmp storage ========= // + // =============================================== // + + // === 2. fill_empty_row, prune, if avaliable. === // + Tensor values_extended; + Tensor indices_extended; + Tensor tmp_indices_buffer; + Tensor* row_empty_and_invalid_flags; + Tensor selected_num_d; + int new_nnz = nnz; + Tensor cub_temp_storage; size_t max_cub_bytes = 0; size_t temp_storage_bytes = 0; + OP_REQUIRES_OK(ctx, ctx->allocate_output("row_empty_and_invalid_flags", + TensorShape{batch_size + nnz}, + &row_empty_and_invalid_flags)); + if (fill_empty_row_ || prune_invalid_id_) { - cub::DeviceSelect::Flagged(nullptr, temp_storage_bytes, (int64_t*)nullptr, - (int*)nullptr, (int64_t*)nullptr, - (int*)nullptr, nnz, device.stream()); + // create cub temp storage + + cub::DeviceSelect::Flagged( + nullptr, temp_storage_bytes, + thrust::make_zip_iterator(thrust::make_tuple( + data_p_with_type(values_tensor), + data_p_with_type(indices_tensor))), + (int*)nullptr, + thrust::make_zip_iterator(thrust::make_tuple( + data_p_with_type(values_extended), + data_p_with_type(indices_extended))), + (int*)nullptr, int(nnz), device.stream()); max_cub_bytes = temp_storage_bytes > max_cub_bytes ? temp_storage_bytes : max_cub_bytes; - cub::DeviceSelect::Flagged((void*)nullptr, temp_storage_bytes, - (IndicePair*)nullptr, (int*)nullptr, - (IndicePair*)nullptr, (int*)nullptr, nnz, - device.stream()); + // cub::DeviceSelect::Flagged(nullptr, temp_storage_bytes, + // (int64_t*)nullptr, + // (int*)nullptr, (int64_t*)nullptr, + // (int*)nullptr, nnz, device.stream()); - max_cub_bytes = temp_storage_bytes > max_cub_bytes ? temp_storage_bytes - : max_cub_bytes; + // max_cub_bytes = temp_storage_bytes > max_cub_bytes ? temp_storage_bytes + // : max_cub_bytes; + + // cub::DeviceSelect::Flagged((void*)nullptr, temp_storage_bytes, + // (IndicePair*)nullptr, (int*)nullptr, + // (IndicePair*)nullptr, (int*)nullptr, nnz, + // device.stream()); + + // max_cub_bytes = temp_storage_bytes > max_cub_bytes ? temp_storage_bytes + // : max_cub_bytes; if (fill_empty_row_) { cub::DeviceSelect::Flagged((void*)nullptr, temp_storage_bytes, @@ -83,27 +116,14 @@ class PruneInvalidAndFillEmptyRowsGPU : public OpKernel { max_cub_bytes = temp_storage_bytes > max_cub_bytes ? temp_storage_bytes : max_cub_bytes; } - } - - OP_REQUIRES_OK( - ctx, ctx->allocate_temp( - DT_INT8, TensorShape({static_cast(max_cub_bytes)}), - &cub_temp_storage)); - // =============================================== // - // === 3. fill_empty_row, prune, if avaliable. === // - Tensor values_extended; - Tensor indices_extended; - Tensor tmp_indices_buffer; - Tensor* row_empty_and_invalid_flags; - Tensor selected_num_d; - int new_nnz = nnz; + OP_REQUIRES_OK( + ctx, ctx->allocate_temp( + DT_INT8, TensorShape({static_cast(max_cub_bytes)}), + &cub_temp_storage)); - OP_REQUIRES_OK(ctx, ctx->allocate_output("row_empty_and_invalid_flags", - TensorShape{batch_size + nnz}, - &row_empty_and_invalid_flags)); + // allocate temp - if (fill_empty_row_ || prune_invalid_id_) { OP_REQUIRES_OK(ctx, ctx->allocate_temp(DT_INT64, TensorShape{nnz + batch_size}, &values_extended)); @@ -119,7 +139,7 @@ class PruneInvalidAndFillEmptyRowsGPU : public OpKernel { InitFlagsToOneInt4(device, batch_size + nnz, row_empty_and_invalid_flags->flat().data()); - // 3.1 set flags, init tmp_indices_buffer etc. + // set flags, init tmp_indices_buffer etc. if (fill_empty_row_) { FusedMultiFunctional( device, data_p_with_type(indices_tensor), @@ -135,26 +155,38 @@ class PruneInvalidAndFillEmptyRowsGPU : public OpKernel { device, data_p_with_type(values_tensor), nnz, data_p_with_type(row_empty_and_invalid_flags) + batch_size); } - // 3.2 select copy valid id, select copy empty row indices + // select copy valid id, select copy empty row indices cub::DeviceSelect::Flagged( data_p_with_type(cub_temp_storage), max_cub_bytes, - data_p_with_type(values_tensor), + thrust::make_zip_iterator(thrust::make_tuple( + data_p_with_type(values_tensor), + data_p_with_type(indices_tensor))), data_p_with_type(row_empty_and_invalid_flags) + batch_size, - data_p_with_type(values_extended), + thrust::make_zip_iterator(thrust::make_tuple( + data_p_with_type(values_extended), + data_p_with_type(indices_extended))), data_p_with_type(selected_num_d), int(nnz), device.stream()); - cub::DeviceSelect::Flagged( - data_p_with_type(cub_temp_storage), max_cub_bytes, - data_p_with_type(indices_tensor), - data_p_with_type(row_empty_and_invalid_flags) + batch_size, - data_p_with_type(indices_extended), - data_p_with_type(selected_num_d), nnz, device.stream()); + // cub::DeviceSelect::Flagged( + // data_p_with_type(cub_temp_storage), max_cub_bytes, + // data_p_with_type(values_tensor), + // data_p_with_type(row_empty_and_invalid_flags) + + // batch_size, data_p_with_type(values_extended), + // data_p_with_type(selected_num_d), int(nnz), device.stream()); + + // cub::DeviceSelect::Flagged( + // data_p_with_type(cub_temp_storage), max_cub_bytes, + // data_p_with_type(indices_tensor), + // data_p_with_type(row_empty_and_invalid_flags) + batch_size, + // data_p_with_type(indices_extended), + // data_p_with_type(selected_num_d), nnz, device.stream()); if (prune_invalid_id_) { int selected_num; - CK_CUDA_THROW_(cudaMemcpyAsync(&selected_num, data_p_with_type(selected_num_d), - sizeof(int), cudaMemcpyDeviceToHost, device.stream())); + CK_CUDA_THROW_(cudaMemcpyAsync( + &selected_num, data_p_with_type(selected_num_d), sizeof(int), + cudaMemcpyDeviceToHost, device.stream())); CK_CUDA_THROW_(cudaEventRecord(memcpy_event_, device.stream())); CK_CUDA_THROW_(cudaEventSynchronize(memcpy_event_)); new_nnz = selected_num; @@ -168,8 +200,9 @@ class PruneInvalidAndFillEmptyRowsGPU : public OpKernel { data_p_with_type(indices_extended) + new_nnz, data_p_with_type(selected_num_d), batch_size, device.stream()); int selected_num; - CK_CUDA_THROW_(cudaMemcpyAsync(&selected_num, data_p_with_type(selected_num_d), - sizeof(int), cudaMemcpyDeviceToHost, device.stream())); + CK_CUDA_THROW_(cudaMemcpyAsync( + &selected_num, data_p_with_type(selected_num_d), sizeof(int), + cudaMemcpyDeviceToHost, device.stream())); CK_CUDA_THROW_(cudaEventRecord(memcpy_event_, device.stream())); CK_CUDA_THROW_(cudaEventSynchronize(memcpy_event_)); new_nnz += selected_num; @@ -197,13 +230,13 @@ class PruneInvalidAndFillEmptyRowsGPU : public OpKernel { ctx, ctx->allocate_output("sp_indices_out", TensorShape{new_nnz, 2}, &sp_indices_out)); - CK_CUDA_THROW_(cudaMemcpyAsync(data_p_with_type(sp_values_out), values_in, - sizeof(int64) * new_nnz, cudaMemcpyDeviceToDevice, - device.stream())); + CK_CUDA_THROW_(cudaMemcpyAsync(data_p_with_type(sp_values_out), + values_in, sizeof(int64) * new_nnz, + cudaMemcpyDeviceToDevice, device.stream())); - CK_CUDA_THROW_(cudaMemcpyAsync(data_p_with_type(sp_indices_out), indices_in, - sizeof(IndicePair) * new_nnz, cudaMemcpyDeviceToDevice, - device.stream())); + CK_CUDA_THROW_(cudaMemcpyAsync(data_p_with_type(sp_indices_out), + indices_in, sizeof(IndicePair) * new_nnz, + cudaMemcpyDeviceToDevice, device.stream())); } private: diff --git a/tensorflow/python/ops/fused_embedding_ops.py b/tensorflow/python/ops/fused_embedding_ops.py index 1d44719eba4..9bce2d8e429 100644 --- a/tensorflow/python/ops/fused_embedding_ops.py +++ b/tensorflow/python/ops/fused_embedding_ops.py @@ -46,6 +46,10 @@ def fused_embedding_lookup_sparse(params, if default_id is not None and type(default_id) is not int: raise ValueError("default_id must be a integer!") + params_white_list = [EmbeddingVariable, tf.Variable] + if any([type(param) not in params_white_list for param in params]): + raise ValueError("Currently fused embedding only support: {}".format(params_white_list)) + partition_nums = len(params) if type(params[0]) is EmbeddingVariable: From 99cd77e3066df19b5640c3e80be7870dc7a4b4d8 Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Sun, 20 Mar 2022 18:42:39 +0800 Subject: [PATCH 29/41] Minor fixed --- .../kernels/fused_embedding/gpu/common.cu.h | 147 ------------------ tensorflow/core/ops/fused_embedding_ops.cc | 4 +- tensorflow/python/ops/fused_embedding_ops.py | 12 +- 3 files changed, 8 insertions(+), 155 deletions(-) diff --git a/tensorflow/core/kernels/fused_embedding/gpu/common.cu.h b/tensorflow/core/kernels/fused_embedding/gpu/common.cu.h index dd1bd5fd1e1..5a96fa15893 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/common.cu.h +++ b/tensorflow/core/kernels/fused_embedding/gpu/common.cu.h @@ -50,153 +50,6 @@ struct IndicePair { } // namespace tensorflow -// ================================================================================================= -#include -#include - -#include -#include -#include -#include -template -void printGPUTensorHelper(const T* src, const std::vector& dims, - const std::vector& max_num_to_print, - bool if_exit = false) { - int element_num = 1; - int dim_num = (int)dims.size(); - - cudaDeviceSynchronize(); - - printf("Tensor size: ("); - for (int i = 0; i < dim_num; i++) { - element_num *= dims[i]; - printf("%d, ", dims[i]); - } - printf(")\n"); - T* host_buffer = new T[element_num]; - cudaMemcpy((void*)host_buffer, (void*)src, sizeof(T) * element_num, - cudaMemcpyDeviceToHost); - - std::vector strides(dim_num, 1); - for (int i = dim_num - 2; i >= 0; i--) { - strides[i] = strides[i + 1] * dims[i + 1]; - printf("%d ", strides[i]); - } - - cudaDeviceSynchronize(); - - printf("================== start printing tensor =====================\n"); - - std::function recursive_print; - recursive_print = [&recursive_print, host_buffer, &dims = dims, - &max_num_to_print = max_num_to_print, &strides = strides]( - int current_dim, int current_dim_offset) -> void { - if (current_dim < dims.size() - 1) { - for (int i = 0; i < current_dim; i++) { - printf(" "); - } - printf("[ dim %d\n", current_dim); - - if (2 * max_num_to_print[current_dim] >= dims[current_dim]) { - for (int i = 0; i < dims[current_dim]; i++) { - recursive_print(current_dim + 1, - current_dim_offset + i * strides[current_dim]); - } - } else { - for (int i = 0; i < max_num_to_print[current_dim]; i++) { - recursive_print(current_dim + 1, - current_dim_offset + i * strides[current_dim]); - } - for (int i = 0; i < current_dim; i++) { - printf(" "); - } - printf(" ......\n"); - for (int i = dims[current_dim] - max_num_to_print[current_dim]; - i < dims[current_dim]; i++) { - recursive_print(current_dim + 1, - current_dim_offset + i * strides[current_dim]); - } - } - for (int i = 0; i < current_dim; i++) { - printf(" "); - } - printf("]\n"); - } else { - for (int i = 0; i < current_dim; i++) { - printf(" "); - } - printf("[ "); - printf("dim %d, offset: %d, ", current_dim, current_dim_offset); - if (2 * max_num_to_print[current_dim] >= dims[current_dim]) { - for (int i = 0; i < dims[current_dim]; i++) { - printf("%s ", - std::to_string( - host_buffer[current_dim_offset + i * strides[current_dim]]) - .c_str()); - } - } else { - for (int i = 0; i < max_num_to_print[current_dim]; i++) { - printf("%s ", - std::to_string( - host_buffer[current_dim_offset + i * strides[current_dim]]) - .c_str()); - } - printf("...... "); - for (int i = dims[current_dim] - max_num_to_print[current_dim]; - i < dims[current_dim]; i++) { - printf("%s ", - std::to_string( - host_buffer[current_dim_offset + i * strides[current_dim]]) - .c_str()); - } - } - printf("]\n"); - } - }; - - recursive_print(0, 0); - - printf("\n================== stop =====================\n"); - delete[] host_buffer; - - if (if_exit) { - exit(0); - } -}; - -#include - -#include -template -void dumpGPUTensorHelper(const T* src, const std::vector& dims, - const std::string& out_file, bool if_exit = true) { - int element_num = 1; - int dim_num = (int)dims.size(); - - printf("Tensor size: ("); - for (int i = 0; i < dim_num; i++) { - element_num *= dims[i]; - printf("%d, ", dims[i]); - } - printf(")\n"); - T* host_buffer = new T[element_num]; - cudaMemcpy((void*)host_buffer, (void*)src, sizeof(T) * element_num, - cudaMemcpyDeviceToHost); - - std::ofstream file(out_file, std::ios::binary); - if (file.is_open()) { - file.write((const char*)host_buffer, sizeof(T) * element_num); - file.close(); - std::cout << "Dumped tensor to " << out_file << std::endl; - } else { - std::cout << "Unable to open " << out_file << std::endl; - } - - if (if_exit) { - exit(0); - } -} - #endif // GOOGLE_CUDA #endif // TENSORFLOW_CORE_KERNELS_FUSED_EMBEDDING_GPU_COMMON_CU_H_ \ No newline at end of file diff --git a/tensorflow/core/ops/fused_embedding_ops.cc b/tensorflow/core/ops/fused_embedding_ops.cc index fef2a72baee..a962f7fb2a9 100644 --- a/tensorflow/core/ops/fused_embedding_ops.cc +++ b/tensorflow/core/ops/fused_embedding_ops.cc @@ -155,7 +155,7 @@ REGISTER_OP("FusedEmbeddingSparsePostLookUp") // partition_permutation ctx->input("partition_permutation", &unused_list); - TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 2, &unused)); // sp_dense_shape ctx->input("sp_dense_shape", &unused_list); @@ -244,7 +244,7 @@ REGISTER_OP("FusedEmbeddingSparsePostLookUpGrad") // partition_permutation ctx->input("partition_permutation", &unused_list); - TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 1, &unused)); + TF_RETURN_IF_ERROR(ctx->WithRank(unused_list[0], 2, &unused)); // feature_nums ctx->input("feature_nums", &unused_list); diff --git a/tensorflow/python/ops/fused_embedding_ops.py b/tensorflow/python/ops/fused_embedding_ops.py index 9bce2d8e429..bfdc802f870 100644 --- a/tensorflow/python/ops/fused_embedding_ops.py +++ b/tensorflow/python/ops/fused_embedding_ops.py @@ -46,7 +46,7 @@ def fused_embedding_lookup_sparse(params, if default_id is not None and type(default_id) is not int: raise ValueError("default_id must be a integer!") - params_white_list = [EmbeddingVariable, tf.Variable] + params_white_list = [EmbeddingVariable, tf.Tensor] if any([type(param) not in params_white_list for param in params]): raise ValueError("Currently fused embedding only support: {}".format(params_white_list)) @@ -76,11 +76,11 @@ def fused_embedding_lookup_sparse(params, sp_dense_shape=sp_dense_shape, ) else: - row_empty_and_invalid_flags = tf.constant([0, ], dtype=tf.int32) # dummy + row_empty_and_invalid_flags = tf.constant(0, shape=(1, ), dtype=tf.int32) # dummy unique_keys, unique_idxs, unique_counts = unique_with_counts_gpu( input=sp_values, - out_idx=tf.int64, + CounterType=tf.int32, ) if partition_nums > 1: @@ -91,7 +91,7 @@ def fused_embedding_lookup_sparse(params, ) else: partitioned_values = [unique_keys] - partition_permutation = [tf.constant([0, ], dtype=tf.int32)] # dummy + partition_permutation = tf.constant(0, shape=(1, 1), dtype=tf.int32) # dummy emb_shards = [] for i in range(partition_nums): @@ -102,11 +102,11 @@ def fused_embedding_lookup_sparse(params, emb_vectors, _, _ = fused_embedding_sparse_post_look_up( fill_empty_row=fill_empty_row, default_id=default_id, combiner=combiner, max_norm=max_norm, - emb_shards=emb_shards, partition_permutations=partition_permutation, + emb_shards=emb_shards, partition_permutation=partition_permutation, sp_dense_shape=sp_dense_shape, indices_before_unique=sp_indices, row_empty_and_invalid_flags=row_empty_and_invalid_flags, - unique_idxs=unique_idxs, + unique_idxs=unique_idxs ) return emb_vectors From e9115c36f7ab7c9174cda629aed4cc38200a6f6c Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Tue, 22 Mar 2022 10:51:43 +0800 Subject: [PATCH 30/41] Update: fix bug and update perf number for modelzoo --- modelzoo/features/GPUFusedEmbedding/DLRM/README.md | 9 ++++----- modelzoo/features/GPUFusedEmbedding/DeepFM/README.md | 9 ++++----- modelzoo/features/GPUFusedEmbedding/WDL/README.md | 9 ++++----- .../gpu/partition_with_permutation_ops.cu.cc | 11 +++++++---- tensorflow/core/ops/fused_embedding_ops.cc | 2 +- 5 files changed, 20 insertions(+), 20 deletions(-) diff --git a/modelzoo/features/GPUFusedEmbedding/DLRM/README.md b/modelzoo/features/GPUFusedEmbedding/DLRM/README.md index 39c79aab534..8a4b4223dad 100644 --- a/modelzoo/features/GPUFusedEmbedding/DLRM/README.md +++ b/modelzoo/features/GPUFusedEmbedding/DLRM/README.md @@ -14,8 +14,7 @@ categorical_embedding_column = tf.feature_column.embedding_column( On A100-80GB-PCIE GPU, with 8 cores AMD EPYC 7232P CPU @ 3.20GHz. Average of 5000 iterations. The perf boost: -| | Avg Time per Iteration | -| ------- | ---------------------- | -| Unfused | 20.78 ms | -| Fused | 17.41 ms | -| SpeedUp | 1.19x | +| | Unfused | Fused | Speedup | +| ---------------------------- | ------- | +| Step Time, Batch Size = 512 | 19.98ms | 14.81ms | 1.34x | +| Step Time, Batch Size = 4096 | 37.82ms | 28.82ms | 1.31x | diff --git a/modelzoo/features/GPUFusedEmbedding/DeepFM/README.md b/modelzoo/features/GPUFusedEmbedding/DeepFM/README.md index edb3dc54d6a..55667923e15 100644 --- a/modelzoo/features/GPUFusedEmbedding/DeepFM/README.md +++ b/modelzoo/features/GPUFusedEmbedding/DeepFM/README.md @@ -14,8 +14,7 @@ categorical_embedding_column = tf.feature_column.embedding_column( On A100-80GB-PCIE GPU, with 8 cores AMD EPYC 7232P CPU @ 3.20GHz. Average of 5000 iterations. The perf boost: -| | Avg Time per Iteration | -| ------- | ---------------------- | -| Unfused | 37.24 ms | -| Fused | 30.98 ms | -| SpeedUp | 1.20x | +| | Unfused | Fused | Speedup | +| ---------------------------- | ------- | +| Step Time, Batch Size = 512 | 31.2ms | 24.1ms | 1.29x | +| Step Time, Batch Size = 4096 | 57.1ms | 44.0ms | 1.29x | diff --git a/modelzoo/features/GPUFusedEmbedding/WDL/README.md b/modelzoo/features/GPUFusedEmbedding/WDL/README.md index df91b6533ad..bd8365472ef 100755 --- a/modelzoo/features/GPUFusedEmbedding/WDL/README.md +++ b/modelzoo/features/GPUFusedEmbedding/WDL/README.md @@ -15,8 +15,7 @@ deep_columns.append(tf.feature_column.embedding_column( On A100-80GB-PCIE GPU, with 8 cores AMD EPYC 7232P CPU @ 3.20GHz. Average of 5000 iterations. The perf boost: -| | Avg Time per Iteration | -| ------- | ---------------------- | -| Unfused | 36.38 ms | -| Fused | 34.52 ms | -| SpeedUp | 1.05x | \ No newline at end of file +| | Unfused | Fused | Speedup | +| ---------------------------- | ------- | +| Step Time, Batch Size = 512 | 41.3ms | 38.4ms | 1.07x | +| Step Time, Batch Size = 4096 | 75.1ms | 66.5ms | 1.12x | \ No newline at end of file diff --git a/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc index 878932dfed1..18d002315be 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/partition_with_permutation_ops.cu.cc @@ -72,9 +72,10 @@ class PartitionWithPermutationGPU : public OpKernel { ctx->allocate_temp( DT_INT64, TensorShape({static_cast(num_partitions_)}), &accu_div)); - CK_CUDA_THROW_(cudaMemcpyAsync(data_p_with_type(accu_div), accu_div_host.data(), - num_partitions_ * sizeof(int64_t), cudaMemcpyHostToDevice, - device.stream())); + CK_CUDA_THROW_(cudaMemcpyAsync(data_p_with_type(accu_div), + accu_div_host.data(), + num_partitions_ * sizeof(int64_t), + cudaMemcpyHostToDevice, device.stream())); if (input_size < 512) { PartitionSelectDiv( @@ -151,7 +152,9 @@ class PartitionWithPermutationGPU : public OpKernel { cudaEvent_t memcpy_event_; }; -REGISTER_KERNEL_BUILDER(Name("PartitionWithPermutation").Device(DEVICE_GPU), +REGISTER_KERNEL_BUILDER(Name("PartitionWithPermutation") + .Device(DEVICE_GPU) + .HostMemory("partition_shapes"), PartitionWithPermutationGPU); } // namespace tensorflow diff --git a/tensorflow/core/ops/fused_embedding_ops.cc b/tensorflow/core/ops/fused_embedding_ops.cc index a962f7fb2a9..13d89f85353 100644 --- a/tensorflow/core/ops/fused_embedding_ops.cc +++ b/tensorflow/core/ops/fused_embedding_ops.cc @@ -108,7 +108,7 @@ REGISTER_OP("PartitionWithPermutation") unused_list.clear(); unused_list.resize(1); - unused_list[0] = ctx->MakeShape({ctx->UnknownDim()}); + unused_list[0] = ctx->MakeShape({ctx->UnknownDim(), 2}); ctx->set_output("partition_permutation", unused_list); return Status::OK(); From d59c9ce22958af7c049f510f403ecf6dee922f9a Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Wed, 30 Mar 2022 14:49:25 +0800 Subject: [PATCH 31/41] update api def and golden --- ..._def_FusedEmbeddingLocalSparseLookUp.pbtxt | 3 -- ..._FusedEmbeddingLocalSparseLookUpGrad.pbtxt | 3 -- ...pi_def_FusedEmbeddingSparsePreLookUp.pbtxt | 3 -- .../api_def_PartitionWithPermutation.pbtxt | 3 ++ ...api_def_PruneInvalidAndFillEmptyRows.pbtxt | 3 ++ .../api_def_UniqueWithCountsGPU.pbtxt | 3 ++ .../tools/api/golden/v1/tensorflow.nn.pbtxt | 2 +- .../tools/api/golden/v1/tensorflow.pbtxt | 32 +++++++++---------- .../api/golden/v1/tensorflow.raw_ops.pbtxt | 32 +++++++++---------- .../tools/api/golden/v2/tensorflow.pbtxt | 32 +++++++++---------- .../api/golden/v2/tensorflow.raw_ops.pbtxt | 32 +++++++++---------- 11 files changed, 74 insertions(+), 74 deletions(-) delete mode 100644 tensorflow/core/api_def/base_api/api_def_FusedEmbeddingLocalSparseLookUp.pbtxt delete mode 100644 tensorflow/core/api_def/base_api/api_def_FusedEmbeddingLocalSparseLookUpGrad.pbtxt delete mode 100644 tensorflow/core/api_def/base_api/api_def_FusedEmbeddingSparsePreLookUp.pbtxt create mode 100644 tensorflow/core/api_def/base_api/api_def_PartitionWithPermutation.pbtxt create mode 100644 tensorflow/core/api_def/base_api/api_def_PruneInvalidAndFillEmptyRows.pbtxt create mode 100644 tensorflow/core/api_def/base_api/api_def_UniqueWithCountsGPU.pbtxt diff --git a/tensorflow/core/api_def/base_api/api_def_FusedEmbeddingLocalSparseLookUp.pbtxt b/tensorflow/core/api_def/base_api/api_def_FusedEmbeddingLocalSparseLookUp.pbtxt deleted file mode 100644 index 109f271e105..00000000000 --- a/tensorflow/core/api_def/base_api/api_def_FusedEmbeddingLocalSparseLookUp.pbtxt +++ /dev/null @@ -1,3 +0,0 @@ -op { - graph_op_name: "FusedEmbeddingLocalSparseLookUp" -} diff --git a/tensorflow/core/api_def/base_api/api_def_FusedEmbeddingLocalSparseLookUpGrad.pbtxt b/tensorflow/core/api_def/base_api/api_def_FusedEmbeddingLocalSparseLookUpGrad.pbtxt deleted file mode 100644 index 58e275b0a55..00000000000 --- a/tensorflow/core/api_def/base_api/api_def_FusedEmbeddingLocalSparseLookUpGrad.pbtxt +++ /dev/null @@ -1,3 +0,0 @@ -op { - graph_op_name: "FusedEmbeddingLocalSparseLookUpGrad" -} diff --git a/tensorflow/core/api_def/base_api/api_def_FusedEmbeddingSparsePreLookUp.pbtxt b/tensorflow/core/api_def/base_api/api_def_FusedEmbeddingSparsePreLookUp.pbtxt deleted file mode 100644 index aca7f31e296..00000000000 --- a/tensorflow/core/api_def/base_api/api_def_FusedEmbeddingSparsePreLookUp.pbtxt +++ /dev/null @@ -1,3 +0,0 @@ -op { - graph_op_name: "FusedEmbeddingSparsePreLookUp" -} diff --git a/tensorflow/core/api_def/base_api/api_def_PartitionWithPermutation.pbtxt b/tensorflow/core/api_def/base_api/api_def_PartitionWithPermutation.pbtxt new file mode 100644 index 00000000000..c28c7e4a1ce --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_PartitionWithPermutation.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "PartitionWithPermutation" +} diff --git a/tensorflow/core/api_def/base_api/api_def_PruneInvalidAndFillEmptyRows.pbtxt b/tensorflow/core/api_def/base_api/api_def_PruneInvalidAndFillEmptyRows.pbtxt new file mode 100644 index 00000000000..b541397ffbc --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_PruneInvalidAndFillEmptyRows.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "PruneInvalidAndFillEmptyRows" +} diff --git a/tensorflow/core/api_def/base_api/api_def_UniqueWithCountsGPU.pbtxt b/tensorflow/core/api_def/base_api/api_def_UniqueWithCountsGPU.pbtxt new file mode 100644 index 00000000000..70bd4feaf1e --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_UniqueWithCountsGPU.pbtxt @@ -0,0 +1,3 @@ +op { + graph_op_name: "UniqueWithCountsGPU" +} diff --git a/tensorflow/tools/api/golden/v1/tensorflow.nn.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.nn.pbtxt index 0c5946d9d49..1779baddc60 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.nn.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.nn.pbtxt @@ -222,7 +222,7 @@ tf_module { } member_method { name: "fused_embedding_lookup_sparse" - argspec: "args=[\'params\', \'sp_ids\', \'sparse_weights\', \'partition_strategy\', \'name\', \'combiner\', \'max_norm\', \'default_id\', \'prune_invalid_ids\', \'blocknums\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'False\', \'None\'], " + argspec: "args=[\'params\', \'sp_ids\', \'sparse_weights\', \'partition_strategy\', \'name\', \'combiner\', \'max_norm\', \'default_id\', \'prune_invalid_ids\', \'fill_empty_row\', \'blocknums\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'False\', \'True\', \'None\'], " } member_method { name: "gelu" diff --git a/tensorflow/tools/api/golden/v1/tensorflow.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.pbtxt index da4c86943bd..1ba25f86cde 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.pbtxt @@ -1476,25 +1476,13 @@ tf_module { name: "function" argspec: "args=[\'func\', \'input_signature\', \'autograph\', \'experimental_autograph_options\', \'experimental_relax_shapes\', \'experimental_compile\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'True\', \'None\', \'False\', \'None\'], " } - member_method { - name: "fused_embedding_local_sparse_look_up" - argspec: "args=[\'sp_values\', \'sp_indices\', \'sp_dense_shape\', \'emb_variable\', \'combiner\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'None\'], " - } - member_method { - name: "fused_embedding_local_sparse_look_up_grad" - argspec: "args=[\'top_grad\', \'emb_variable\', \'sp_values\', \'sp_values_offset\', \'combiner\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'None\'], " - } member_method { name: "fused_embedding_sparse_post_look_up" - argspec: "args=[\'emb_shards\', \'partitioned_indices\', \'sp_dense_shape\', \'row_empty_and_invalid_flags\', \'partitioned_values\', \'combiner\', \'default_id\', \'partition_axis\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'0\', \'-1\', \'None\'], " + argspec: "args=[\'emb_shards\', \'partition_permutation\', \'sp_dense_shape\', \'indices_before_unique\', \'row_empty_and_invalid_flags\', \'unique_idxs\', \'combiner\', \'fill_empty_row\', \'default_id\', \'partition_axis\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'-1\', \'0\', \'-1\', \'None\'], " } member_method { name: "fused_embedding_sparse_post_look_up_grad" - argspec: "args=[\'top_grad\', \'emb_shards\', \'partitioned_indices\', \'feature_nums\', \'row_empty_and_invalid_flags\', \'combiner\', \'partition_axis\', \'default_id\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'-1\', \'-1\', \'None\'], " - } - member_method { - name: "fused_embedding_sparse_pre_look_up" - argspec: "args=[\'partition_shapes\', \'sp_values\', \'sp_indices\', \'sp_dense_shape\', \'partition_axis\', \'fill_empty_row\', \'prune_invalid_id\', \'default_id\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'False\', \'False\', \'-1\', \'None\'], " + argspec: "args=[\'top_grad\', \'emb_shards\', \'emb_shard_ptrs\', \'partition_permutation\', \'feature_nums\', \'indices_before_unique\', \'unique_idxs\', \'row_empty_and_invalid_flags\', \'combiner\', \'fill_empty_row\', \'partition_axis\', \'default_id\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'0\', \'-1\', \'-1\', \'None\'], " } member_method { name: "gather" @@ -1730,7 +1718,7 @@ tf_module { } member_method { name: "initialize_kv_variable_op" - argspec: "args=[\'resource_self\', \'resource_primary\', \'value\', \'empty_key\', \'slotnum\', \'shape\', \'counter_type\', \'initial_num_buckets\', \'max_load_factor\', \'steps_to_live\', \'ht_type\', \'emb_index\', \'block_num\', \'slot_index\', \'ht_partition_num\', \'filter_freq\', \'max_freq\', \'max_element_size\', \'false_positive_probability\', \'l2_weight_threshold\', \'layout\', \'storage_type\', \'storage_path\', \'storage_size\', \'default_value_dim\', \'name\'], varargs=None, keywords=None, defaults=[\'131072\', \'0.8\', \'0\', \'\', \'0\', \'1\', \'0\', \'1000\', \'0\', \'999999\', \'0\', \'-1\', \'-1\', \'normal\', \'1\', \'.\', \'0\', \'4096\', \'None\'], " + argspec: "args=[\'resource_self\', \'resource_primary\', \'value\', \'empty_key\', \'shape\', \'counter_type\', \'slot_num\', \'initial_num_buckets\', \'max_load_factor\', \'steps_to_live\', \'ht_type\', \'emb_index\', \'block_num\', \'slot_index\', \'ht_partition_num\', \'filter_freq\', \'max_freq\', \'max_element_size\', \'false_positive_probability\', \'l2_weight_threshold\', \'layout\', \'storage_type\', \'storage_path\', \'storage_size\', \'default_value_dim\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'131072\', \'0.8\', \'0\', \'\', \'0\', \'1\', \'0\', \'1000\', \'0\', \'999999\', \'0\', \'-1\', \'-1\', \'normal\', \'1\', \'.\', \'0\', \'4096\', \'None\'], " } member_method { name: "initialize_local_variables" @@ -1842,7 +1830,7 @@ tf_module { } member_method { name: "kv_resource_import_v2" - argspec: "args=[\'prefix\', \'resource_self\', \'resource_primary\', \'value\', \'tensor_names\', \'empty_key\', \'slotnum\', \'shape\', \'counter_type\', \'emb_index\', \'slot_index\', \'slot_num\', \'block_num\', \'steps_to_live\', \'partition_id\', \'partition_num\', \'ht_type\', \'filter_freq\', \'ht_partition_num\', \'max_element_size\', \'false_positive_probability\', \'l2_weight_threshold\', \'layout\', \'max_freq\', \'storage_type\', \'storage_path\', \'storage_size\', \'default_value_dim\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'0\', \'0\', \'1\', \'0\', \'0\', \'1\', \'\', \'0\', \'1000\', \'0\', \'-1\', \'-1\', \'normal\', \'999999\', \'1\', \'.\', \'0\', \'4096\', \'None\'], " + argspec: "args=[\'prefix\', \'resource_self\', \'resource_primary\', \'value\', \'tensor_names\', \'empty_key\', \'shape\', \'counter_type\', \'slot_num\', \'emb_index\', \'slot_index\', \'block_num\', \'steps_to_live\', \'partition_id\', \'partition_num\', \'ht_type\', \'filter_freq\', \'ht_partition_num\', \'max_element_size\', \'false_positive_probability\', \'l2_weight_threshold\', \'layout\', \'max_freq\', \'storage_type\', \'storage_path\', \'storage_size\', \'default_value_dim\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'0\', \'0\', \'1\', \'0\', \'0\', \'1\', \'\', \'0\', \'1000\', \'0\', \'-1\', \'-1\', \'normal\', \'999999\', \'1\', \'.\', \'0\', \'4096\', \'None\'], " } member_method { name: "kv_resource_incr_import" @@ -2148,6 +2136,10 @@ tf_module { name: "parse_tensor" argspec: "args=[\'serialized\', \'out_type\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "partition_with_permutation" + argspec: "args=[\'input\', \'partition_shapes\', \'partition_strategy\', \'partition_axis\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'None\'], " + } member_method { name: "placeholder" argspec: "args=[\'dtype\', \'shape\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " @@ -2172,6 +2164,10 @@ tf_module { name: "print" argspec: "args=[], varargs=inputs, keywords=kwargs, defaults=None" } + member_method { + name: "prune_invalid_and_fill_empty_rows" + argspec: "args=[\'sp_values\', \'sp_indices\', \'sp_dense_shape\', \'fill_empty_row\', \'prune_invalid_id\', \'default_id\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'-1\', \'None\'], " + } member_method { name: "py_func" argspec: "args=[\'func\', \'inp\', \'Tout\', \'stateful\', \'name\'], varargs=None, keywords=None, defaults=[\'True\', \'None\'], " @@ -3000,6 +2996,10 @@ tf_module { name: "unique_with_counts" argspec: "args=[\'x\', \'out_idx\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\'], " } + member_method { + name: "unique_with_counts_gpu" + argspec: "args=[\'input\', \'CounterType\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\'], " + } member_method { name: "unravel_index" argspec: "args=[\'indices\', \'dims\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " diff --git a/tensorflow/tools/api/golden/v1/tensorflow.raw_ops.pbtxt b/tensorflow/tools/api/golden/v1/tensorflow.raw_ops.pbtxt index 5d254678bea..dc5b0f40519 100644 --- a/tensorflow/tools/api/golden/v1/tensorflow.raw_ops.pbtxt +++ b/tensorflow/tools/api/golden/v1/tensorflow.raw_ops.pbtxt @@ -1612,25 +1612,13 @@ tf_module { name: "FusedBatchNormV3" argspec: "args=[\'x\', \'scale\', \'offset\', \'mean\', \'variance\', \'epsilon\', \'exponential_avg_factor\', \'data_format\', \'is_training\', \'name\'], varargs=None, keywords=None, defaults=[\'0.0001\', \'1\', \'NHWC\', \'True\', \'None\'], " } - member_method { - name: "FusedEmbeddingLocalSparseLookUp" - argspec: "args=[\'sp_values\', \'sp_indices\', \'sp_dense_shape\', \'emb_variable\', \'combiner\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'None\'], " - } - member_method { - name: "FusedEmbeddingLocalSparseLookUpGrad" - argspec: "args=[\'top_grad\', \'emb_variable\', \'sp_values\', \'sp_values_offset\', \'combiner\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'None\'], " - } member_method { name: "FusedEmbeddingSparsePostLookUp" - argspec: "args=[\'emb_shards\', \'partitioned_indices\', \'sp_dense_shape\', \'row_empty_and_invalid_flags\', \'partitioned_values\', \'combiner\', \'default_id\', \'partition_axis\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'0\', \'-1\', \'None\'], " + argspec: "args=[\'emb_shards\', \'partition_permutation\', \'sp_dense_shape\', \'indices_before_unique\', \'row_empty_and_invalid_flags\', \'unique_idxs\', \'combiner\', \'fill_empty_row\', \'default_id\', \'partition_axis\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'-1\', \'0\', \'-1\', \'None\'], " } member_method { name: "FusedEmbeddingSparsePostLookUpGrad" - argspec: "args=[\'top_grad\', \'emb_shards\', \'partitioned_indices\', \'feature_nums\', \'row_empty_and_invalid_flags\', \'combiner\', \'partition_axis\', \'default_id\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'-1\', \'-1\', \'None\'], " - } - member_method { - name: "FusedEmbeddingSparsePreLookUp" - argspec: "args=[\'partition_shapes\', \'sp_values\', \'sp_indices\', \'sp_dense_shape\', \'partition_axis\', \'fill_empty_row\', \'prune_invalid_id\', \'default_id\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'False\', \'False\', \'-1\', \'None\'], " + argspec: "args=[\'top_grad\', \'emb_shards\', \'emb_shard_ptrs\', \'partition_permutation\', \'feature_nums\', \'indices_before_unique\', \'unique_idxs\', \'row_empty_and_invalid_flags\', \'combiner\', \'fill_empty_row\', \'partition_axis\', \'default_id\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'0\', \'-1\', \'-1\', \'None\'], " } member_method { name: "FusedPadConv2D" @@ -1942,7 +1930,7 @@ tf_module { } member_method { name: "InitializeKvVariableOp" - argspec: "args=[\'resource_self\', \'resource_primary\', \'value\', \'empty_key\', \'slotnum\', \'shape\', \'counter_type\', \'initial_num_buckets\', \'max_load_factor\', \'steps_to_live\', \'ht_type\', \'emb_index\', \'block_num\', \'slot_index\', \'ht_partition_num\', \'filter_freq\', \'max_freq\', \'max_element_size\', \'false_positive_probability\', \'l2_weight_threshold\', \'layout\', \'storage_type\', \'storage_path\', \'storage_size\', \'default_value_dim\', \'name\'], varargs=None, keywords=None, defaults=[\'131072\', \'0.8\', \'0\', \'\', \'0\', \'1\', \'0\', \'1000\', \'0\', \'999999\', \'0\', \'-1\', \'-1\', \'normal\', \'1\', \'.\', \'0\', \'4096\', \'None\'], " + argspec: "args=[\'resource_self\', \'resource_primary\', \'value\', \'empty_key\', \'shape\', \'counter_type\', \'slot_num\', \'initial_num_buckets\', \'max_load_factor\', \'steps_to_live\', \'ht_type\', \'emb_index\', \'block_num\', \'slot_index\', \'ht_partition_num\', \'filter_freq\', \'max_freq\', \'max_element_size\', \'false_positive_probability\', \'l2_weight_threshold\', \'layout\', \'storage_type\', \'storage_path\', \'storage_size\', \'default_value_dim\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'131072\', \'0.8\', \'0\', \'\', \'0\', \'1\', \'0\', \'1000\', \'0\', \'999999\', \'0\', \'-1\', \'-1\', \'normal\', \'1\', \'.\', \'0\', \'4096\', \'None\'], " } member_method { name: "InitializeTable" @@ -2078,7 +2066,7 @@ tf_module { } member_method { name: "KvResourceImportV2" - argspec: "args=[\'prefix\', \'resource_self\', \'resource_primary\', \'value\', \'tensor_names\', \'empty_key\', \'slotnum\', \'shape\', \'counter_type\', \'emb_index\', \'slot_index\', \'slot_num\', \'block_num\', \'steps_to_live\', \'partition_id\', \'partition_num\', \'ht_type\', \'filter_freq\', \'ht_partition_num\', \'max_element_size\', \'false_positive_probability\', \'l2_weight_threshold\', \'layout\', \'max_freq\', \'storage_type\', \'storage_path\', \'storage_size\', \'default_value_dim\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'0\', \'0\', \'1\', \'0\', \'0\', \'1\', \'\', \'0\', \'1000\', \'0\', \'-1\', \'-1\', \'normal\', \'999999\', \'1\', \'.\', \'0\', \'4096\', \'None\'], " + argspec: "args=[\'prefix\', \'resource_self\', \'resource_primary\', \'value\', \'tensor_names\', \'empty_key\', \'shape\', \'counter_type\', \'slot_num\', \'emb_index\', \'slot_index\', \'block_num\', \'steps_to_live\', \'partition_id\', \'partition_num\', \'ht_type\', \'filter_freq\', \'ht_partition_num\', \'max_element_size\', \'false_positive_probability\', \'l2_weight_threshold\', \'layout\', \'max_freq\', \'storage_type\', \'storage_path\', \'storage_size\', \'default_value_dim\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'0\', \'0\', \'1\', \'0\', \'0\', \'1\', \'\', \'0\', \'1000\', \'0\', \'-1\', \'-1\', \'normal\', \'999999\', \'1\', \'.\', \'0\', \'4096\', \'None\'], " } member_method { name: "KvResourceIncrImport" @@ -2864,6 +2852,10 @@ tf_module { name: "ParseTensor" argspec: "args=[\'serialized\', \'out_type\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "PartitionWithPermutation" + argspec: "args=[\'input\', \'partition_shapes\', \'partition_strategy\', \'partition_axis\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'None\'], " + } member_method { name: "PartitionedCall" argspec: "args=[\'args\', \'Tout\', \'f\', \'config\', \'config_proto\', \'executor_type\', \'name\'], varargs=None, keywords=None, defaults=[\'\', \'\', \'\', \'None\'], " @@ -2932,6 +2924,10 @@ tf_module { name: "Prod" argspec: "args=[\'input\', \'axis\', \'keep_dims\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " } + member_method { + name: "PruneInvalidAndFillEmptyRows" + argspec: "args=[\'sp_values\', \'sp_indices\', \'sp_dense_shape\', \'fill_empty_row\', \'prune_invalid_id\', \'default_id\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'-1\', \'None\'], " + } member_method { name: "PyFunc" argspec: "args=[\'input\', \'token\', \'Tout\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " @@ -5168,6 +5164,10 @@ tf_module { name: "UniqueWithCounts" argspec: "args=[\'x\', \'out_idx\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\'], " } + member_method { + name: "UniqueWithCountsGPU" + argspec: "args=[\'input\', \'CounterType\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\'], " + } member_method { name: "UniqueWithCountsV2" argspec: "args=[\'x\', \'axis\', \'out_idx\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\'], " diff --git a/tensorflow/tools/api/golden/v2/tensorflow.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.pbtxt index 906bdbf53d2..8a10fc7ddfe 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.pbtxt @@ -752,25 +752,13 @@ tf_module { name: "function" argspec: "args=[\'func\', \'input_signature\', \'autograph\', \'experimental_autograph_options\', \'experimental_relax_shapes\', \'experimental_compile\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'True\', \'None\', \'False\', \'None\'], " } - member_method { - name: "fused_embedding_local_sparse_look_up" - argspec: "args=[\'sp_values\', \'sp_indices\', \'sp_dense_shape\', \'emb_variable\', \'combiner\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'None\'], " - } - member_method { - name: "fused_embedding_local_sparse_look_up_grad" - argspec: "args=[\'top_grad\', \'emb_variable\', \'sp_values\', \'sp_values_offset\', \'combiner\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'None\'], " - } member_method { name: "fused_embedding_sparse_post_look_up" - argspec: "args=[\'emb_shards\', \'partitioned_indices\', \'sp_dense_shape\', \'row_empty_and_invalid_flags\', \'partitioned_values\', \'combiner\', \'default_id\', \'partition_axis\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'0\', \'-1\', \'None\'], " + argspec: "args=[\'emb_shards\', \'partition_permutation\', \'sp_dense_shape\', \'indices_before_unique\', \'row_empty_and_invalid_flags\', \'unique_idxs\', \'combiner\', \'fill_empty_row\', \'default_id\', \'partition_axis\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'-1\', \'0\', \'-1\', \'None\'], " } member_method { name: "fused_embedding_sparse_post_look_up_grad" - argspec: "args=[\'top_grad\', \'emb_shards\', \'partitioned_indices\', \'feature_nums\', \'row_empty_and_invalid_flags\', \'combiner\', \'partition_axis\', \'default_id\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'-1\', \'-1\', \'None\'], " - } - member_method { - name: "fused_embedding_sparse_pre_look_up" - argspec: "args=[\'partition_shapes\', \'sp_values\', \'sp_indices\', \'sp_dense_shape\', \'partition_axis\', \'fill_empty_row\', \'prune_invalid_id\', \'default_id\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'False\', \'False\', \'-1\', \'None\'], " + argspec: "args=[\'top_grad\', \'emb_shards\', \'emb_shard_ptrs\', \'partition_permutation\', \'feature_nums\', \'indices_before_unique\', \'unique_idxs\', \'row_empty_and_invalid_flags\', \'combiner\', \'fill_empty_row\', \'partition_axis\', \'default_id\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'0\', \'-1\', \'-1\', \'None\'], " } member_method { name: "gather" @@ -906,7 +894,7 @@ tf_module { } member_method { name: "initialize_kv_variable_op" - argspec: "args=[\'resource_self\', \'resource_primary\', \'value\', \'empty_key\', \'slotnum\', \'shape\', \'counter_type\', \'initial_num_buckets\', \'max_load_factor\', \'steps_to_live\', \'ht_type\', \'emb_index\', \'block_num\', \'slot_index\', \'ht_partition_num\', \'filter_freq\', \'max_freq\', \'max_element_size\', \'false_positive_probability\', \'l2_weight_threshold\', \'layout\', \'storage_type\', \'storage_path\', \'storage_size\', \'default_value_dim\', \'name\'], varargs=None, keywords=None, defaults=[\'131072\', \'0.8\', \'0\', \'\', \'0\', \'1\', \'0\', \'1000\', \'0\', \'999999\', \'0\', \'-1\', \'-1\', \'normal\', \'1\', \'.\', \'0\', \'4096\', \'None\'], " + argspec: "args=[\'resource_self\', \'resource_primary\', \'value\', \'empty_key\', \'shape\', \'counter_type\', \'slot_num\', \'initial_num_buckets\', \'max_load_factor\', \'steps_to_live\', \'ht_type\', \'emb_index\', \'block_num\', \'slot_index\', \'ht_partition_num\', \'filter_freq\', \'max_freq\', \'max_element_size\', \'false_positive_probability\', \'l2_weight_threshold\', \'layout\', \'storage_type\', \'storage_path\', \'storage_size\', \'default_value_dim\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'131072\', \'0.8\', \'0\', \'\', \'0\', \'1\', \'0\', \'1000\', \'0\', \'999999\', \'0\', \'-1\', \'-1\', \'normal\', \'1\', \'.\', \'0\', \'4096\', \'None\'], " } member_method { name: "io_kafka_dataset" @@ -978,7 +966,7 @@ tf_module { } member_method { name: "kv_resource_import_v2" - argspec: "args=[\'prefix\', \'resource_self\', \'resource_primary\', \'value\', \'tensor_names\', \'empty_key\', \'slotnum\', \'shape\', \'counter_type\', \'emb_index\', \'slot_index\', \'slot_num\', \'block_num\', \'steps_to_live\', \'partition_id\', \'partition_num\', \'ht_type\', \'filter_freq\', \'ht_partition_num\', \'max_element_size\', \'false_positive_probability\', \'l2_weight_threshold\', \'layout\', \'max_freq\', \'storage_type\', \'storage_path\', \'storage_size\', \'default_value_dim\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'0\', \'0\', \'1\', \'0\', \'0\', \'1\', \'\', \'0\', \'1000\', \'0\', \'-1\', \'-1\', \'normal\', \'999999\', \'1\', \'.\', \'0\', \'4096\', \'None\'], " + argspec: "args=[\'prefix\', \'resource_self\', \'resource_primary\', \'value\', \'tensor_names\', \'empty_key\', \'shape\', \'counter_type\', \'slot_num\', \'emb_index\', \'slot_index\', \'block_num\', \'steps_to_live\', \'partition_id\', \'partition_num\', \'ht_type\', \'filter_freq\', \'ht_partition_num\', \'max_element_size\', \'false_positive_probability\', \'l2_weight_threshold\', \'layout\', \'max_freq\', \'storage_type\', \'storage_path\', \'storage_size\', \'default_value_dim\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'0\', \'0\', \'1\', \'0\', \'0\', \'1\', \'\', \'0\', \'1000\', \'0\', \'-1\', \'-1\', \'normal\', \'999999\', \'1\', \'.\', \'0\', \'4096\', \'None\'], " } member_method { name: "kv_resource_incr_import" @@ -1148,6 +1136,10 @@ tf_module { name: "parallel_stack" argspec: "args=[\'values\', \'name\'], varargs=None, keywords=None, defaults=[\'parallel_stack\'], " } + member_method { + name: "partition_with_permutation" + argspec: "args=[\'input\', \'partition_shapes\', \'partition_strategy\', \'partition_axis\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'None\'], " + } member_method { name: "pow" argspec: "args=[\'x\', \'y\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " @@ -1156,6 +1148,10 @@ tf_module { name: "print" argspec: "args=[], varargs=inputs, keywords=kwargs, defaults=None" } + member_method { + name: "prune_invalid_and_fill_empty_rows" + argspec: "args=[\'sp_values\', \'sp_indices\', \'sp_dense_shape\', \'fill_empty_row\', \'prune_invalid_id\', \'default_id\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'-1\', \'None\'], " + } member_method { name: "py_function" argspec: "args=[\'func\', \'inp\', \'Tout\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " @@ -1592,6 +1588,10 @@ tf_module { name: "unique_with_counts" argspec: "args=[\'x\', \'out_idx\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\'], " } + member_method { + name: "unique_with_counts_gpu" + argspec: "args=[\'input\', \'CounterType\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\'], " + } member_method { name: "unravel_index" argspec: "args=[\'indices\', \'dims\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " diff --git a/tensorflow/tools/api/golden/v2/tensorflow.raw_ops.pbtxt b/tensorflow/tools/api/golden/v2/tensorflow.raw_ops.pbtxt index 5d254678bea..dc5b0f40519 100644 --- a/tensorflow/tools/api/golden/v2/tensorflow.raw_ops.pbtxt +++ b/tensorflow/tools/api/golden/v2/tensorflow.raw_ops.pbtxt @@ -1612,25 +1612,13 @@ tf_module { name: "FusedBatchNormV3" argspec: "args=[\'x\', \'scale\', \'offset\', \'mean\', \'variance\', \'epsilon\', \'exponential_avg_factor\', \'data_format\', \'is_training\', \'name\'], varargs=None, keywords=None, defaults=[\'0.0001\', \'1\', \'NHWC\', \'True\', \'None\'], " } - member_method { - name: "FusedEmbeddingLocalSparseLookUp" - argspec: "args=[\'sp_values\', \'sp_indices\', \'sp_dense_shape\', \'emb_variable\', \'combiner\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'None\'], " - } - member_method { - name: "FusedEmbeddingLocalSparseLookUpGrad" - argspec: "args=[\'top_grad\', \'emb_variable\', \'sp_values\', \'sp_values_offset\', \'combiner\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'None\'], " - } member_method { name: "FusedEmbeddingSparsePostLookUp" - argspec: "args=[\'emb_shards\', \'partitioned_indices\', \'sp_dense_shape\', \'row_empty_and_invalid_flags\', \'partitioned_values\', \'combiner\', \'default_id\', \'partition_axis\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'-1\', \'0\', \'-1\', \'None\'], " + argspec: "args=[\'emb_shards\', \'partition_permutation\', \'sp_dense_shape\', \'indices_before_unique\', \'row_empty_and_invalid_flags\', \'unique_idxs\', \'combiner\', \'fill_empty_row\', \'default_id\', \'partition_axis\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'-1\', \'0\', \'-1\', \'None\'], " } member_method { name: "FusedEmbeddingSparsePostLookUpGrad" - argspec: "args=[\'top_grad\', \'emb_shards\', \'partitioned_indices\', \'feature_nums\', \'row_empty_and_invalid_flags\', \'combiner\', \'partition_axis\', \'default_id\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'-1\', \'-1\', \'None\'], " - } - member_method { - name: "FusedEmbeddingSparsePreLookUp" - argspec: "args=[\'partition_shapes\', \'sp_values\', \'sp_indices\', \'sp_dense_shape\', \'partition_axis\', \'fill_empty_row\', \'prune_invalid_id\', \'default_id\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'False\', \'False\', \'-1\', \'None\'], " + argspec: "args=[\'top_grad\', \'emb_shards\', \'emb_shard_ptrs\', \'partition_permutation\', \'feature_nums\', \'indices_before_unique\', \'unique_idxs\', \'row_empty_and_invalid_flags\', \'combiner\', \'fill_empty_row\', \'partition_axis\', \'default_id\', \'max_norm\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'0\', \'-1\', \'-1\', \'None\'], " } member_method { name: "FusedPadConv2D" @@ -1942,7 +1930,7 @@ tf_module { } member_method { name: "InitializeKvVariableOp" - argspec: "args=[\'resource_self\', \'resource_primary\', \'value\', \'empty_key\', \'slotnum\', \'shape\', \'counter_type\', \'initial_num_buckets\', \'max_load_factor\', \'steps_to_live\', \'ht_type\', \'emb_index\', \'block_num\', \'slot_index\', \'ht_partition_num\', \'filter_freq\', \'max_freq\', \'max_element_size\', \'false_positive_probability\', \'l2_weight_threshold\', \'layout\', \'storage_type\', \'storage_path\', \'storage_size\', \'default_value_dim\', \'name\'], varargs=None, keywords=None, defaults=[\'131072\', \'0.8\', \'0\', \'\', \'0\', \'1\', \'0\', \'1000\', \'0\', \'999999\', \'0\', \'-1\', \'-1\', \'normal\', \'1\', \'.\', \'0\', \'4096\', \'None\'], " + argspec: "args=[\'resource_self\', \'resource_primary\', \'value\', \'empty_key\', \'shape\', \'counter_type\', \'slot_num\', \'initial_num_buckets\', \'max_load_factor\', \'steps_to_live\', \'ht_type\', \'emb_index\', \'block_num\', \'slot_index\', \'ht_partition_num\', \'filter_freq\', \'max_freq\', \'max_element_size\', \'false_positive_probability\', \'l2_weight_threshold\', \'layout\', \'storage_type\', \'storage_path\', \'storage_size\', \'default_value_dim\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'131072\', \'0.8\', \'0\', \'\', \'0\', \'1\', \'0\', \'1000\', \'0\', \'999999\', \'0\', \'-1\', \'-1\', \'normal\', \'1\', \'.\', \'0\', \'4096\', \'None\'], " } member_method { name: "InitializeTable" @@ -2078,7 +2066,7 @@ tf_module { } member_method { name: "KvResourceImportV2" - argspec: "args=[\'prefix\', \'resource_self\', \'resource_primary\', \'value\', \'tensor_names\', \'empty_key\', \'slotnum\', \'shape\', \'counter_type\', \'emb_index\', \'slot_index\', \'slot_num\', \'block_num\', \'steps_to_live\', \'partition_id\', \'partition_num\', \'ht_type\', \'filter_freq\', \'ht_partition_num\', \'max_element_size\', \'false_positive_probability\', \'l2_weight_threshold\', \'layout\', \'max_freq\', \'storage_type\', \'storage_path\', \'storage_size\', \'default_value_dim\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'0\', \'0\', \'1\', \'0\', \'0\', \'1\', \'\', \'0\', \'1000\', \'0\', \'-1\', \'-1\', \'normal\', \'999999\', \'1\', \'.\', \'0\', \'4096\', \'None\'], " + argspec: "args=[\'prefix\', \'resource_self\', \'resource_primary\', \'value\', \'tensor_names\', \'empty_key\', \'shape\', \'counter_type\', \'slot_num\', \'emb_index\', \'slot_index\', \'block_num\', \'steps_to_live\', \'partition_id\', \'partition_num\', \'ht_type\', \'filter_freq\', \'ht_partition_num\', \'max_element_size\', \'false_positive_probability\', \'l2_weight_threshold\', \'layout\', \'max_freq\', \'storage_type\', \'storage_path\', \'storage_size\', \'default_value_dim\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'0\', \'0\', \'1\', \'0\', \'0\', \'1\', \'\', \'0\', \'1000\', \'0\', \'-1\', \'-1\', \'normal\', \'999999\', \'1\', \'.\', \'0\', \'4096\', \'None\'], " } member_method { name: "KvResourceIncrImport" @@ -2864,6 +2852,10 @@ tf_module { name: "ParseTensor" argspec: "args=[\'serialized\', \'out_type\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " } + member_method { + name: "PartitionWithPermutation" + argspec: "args=[\'input\', \'partition_shapes\', \'partition_strategy\', \'partition_axis\', \'name\'], varargs=None, keywords=None, defaults=[\'0\', \'None\'], " + } member_method { name: "PartitionedCall" argspec: "args=[\'args\', \'Tout\', \'f\', \'config\', \'config_proto\', \'executor_type\', \'name\'], varargs=None, keywords=None, defaults=[\'\', \'\', \'\', \'None\'], " @@ -2932,6 +2924,10 @@ tf_module { name: "Prod" argspec: "args=[\'input\', \'axis\', \'keep_dims\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'None\'], " } + member_method { + name: "PruneInvalidAndFillEmptyRows" + argspec: "args=[\'sp_values\', \'sp_indices\', \'sp_dense_shape\', \'fill_empty_row\', \'prune_invalid_id\', \'default_id\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'-1\', \'None\'], " + } member_method { name: "PyFunc" argspec: "args=[\'input\', \'token\', \'Tout\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " @@ -5168,6 +5164,10 @@ tf_module { name: "UniqueWithCounts" argspec: "args=[\'x\', \'out_idx\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\'], " } + member_method { + name: "UniqueWithCountsGPU" + argspec: "args=[\'input\', \'CounterType\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\'], " + } member_method { name: "UniqueWithCountsV2" argspec: "args=[\'x\', \'axis\', \'out_idx\', \'name\'], varargs=None, keywords=None, defaults=[\"\", \'None\'], " From 3ecff7fc3189c3e826a69c0a11c3bd2d2f4a79f5 Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Thu, 7 Apr 2022 15:38:25 +0800 Subject: [PATCH 32/41] Update doc --- docs/Fused-Embedding.md | 94 ++++++++---------- docs/Fused-Embedding/img_1.png | Bin 160091 -> 0 bytes .../GPUFusedEmbedding/DLRM/result/README.md | 2 - 3 files changed, 41 insertions(+), 55 deletions(-) delete mode 100644 docs/Fused-Embedding/img_1.png delete mode 100644 modelzoo/features/GPUFusedEmbedding/DLRM/result/README.md diff --git a/docs/Fused-Embedding.md b/docs/Fused-Embedding.md index f1a2a9eacd5..25e3150421f 100644 --- a/docs/Fused-Embedding.md +++ b/docs/Fused-Embedding.md @@ -2,24 +2,31 @@ ## 介绍 -DeepRec 及 TensorFlow 原生的 embedding lookup 相关 API，如 safe_embedding_lookup_sparse，会创建比较多的 op，因此在 GPU 上执行时容易出现 kernel launch bound 的问题。因此，Embedding子图Fusion功能提供了一组接口，并提供了一组fusion ops，通过Fusion的Op，减少需要 launch 的 kernel 数量，并提供高性能的实现，达到在 GPU 上加速执行的目的。 +DeepRec 及 TensorFlow 原生的 embedding lookup 相关 API，如 safe_embedding_lookup_sparse，会创建比较多的 op，因此在 GPU 上执行时容易出现 kernel launch bound 的问题，且部分 op 只有 CPU 实现，速度相对较慢。因此，Embedding子图Fusion功能提供了一组接口，并提供了一组fusion ops，通过Fusion的Op，减少需要 launch 的 kernel 数量，并提供高性能的实现，达到在 GPU 上加速执行的目的。 + + ## FeatureColumn接口 + 用户 FeatureColumn作为接口。embedding_column 会返回一个 EmbeddingColumn 的类实例，常用的 EmbeddingColumn 有： -1. `tensorflow/python/feature_column/feature_column_v2.py` 的 `EmbeddingColumn` -1. `tensorflow/contrib/layers/python/layers/feature_column.py` 的 `_EmbeddingColumn` +1. `tensorflow/python/feature_column/feature_column_v2.py` 的 `EmbeddingColumn` +2. `tensorflow/contrib/layers/python/layers/feature_column.py` 的 `_EmbeddingColumn` 然后一般会通过 `tf.feature_column.input_layer` 或 `tf.feature_column_ops.input_from_feature_columns` 等高级接口，将此实例传入，建立 lookup 相关计算图。 因此，Embedding子图Fusion功能给上述的 `EmbeddingColumn` 类都添加了 `do_fusion` 属性，默认为 `False`，用户在使用时，可以显示的设置为 `True`，让 embedding lookup 过程使用 fused ops。 如下： + +a. tf.feature_column.embedding_column + ```python import tensorflow as tf from tensorflow.python.framework import ops -columns = tf.feature_column.categorical_column_with_embedding("col_emb", dtype=tf.dtypes.int64) -W = tf.feature_column.embedding_column(categorical_column=columns, +column = tf.feature_column.categorical_column_with_embedding("col_emb", dtype=tf.dtypes.int64) +W = tf.feature_column.embedding_column( + categorical_column=column, dimension=3, initializer=tf.ones_initializer(tf.dtypes.float32), do_fusion=True) @@ -27,7 +34,7 @@ W = tf.feature_column.embedding_column(categorical_column=columns, ids={} ids["col_emb"] = tf.SparseTensor(indices=[[0,0],[1,1],[2,2],[3,3],[4,4]], values=tf.cast([1,2,3,4,5], tf.dtypes.int64), dense_shape=[5, 4]) -# 传入设置了 use_fused_lookup 的 EmbeddingColumn 实例 +# 传入设置了 do_fusion 的 EmbeddingColumn 实例 emb = tf.feature_column.input_layer(ids, [W]) fun = tf.multiply(emb, 2.0, name='multiply') loss = tf.reduce_sum(fun, name='reduce_sum') @@ -43,6 +50,9 @@ with tf.Session() as sess: print(sess.run([emb, train_op,loss])) print(sess.run([emb, train_op,loss])) ``` + +b. tf.contrib.layers.python.layers.feature_column.embedding_column + ```python import tensorflow as tf from tensorflow.python.framework import ops @@ -90,9 +100,12 @@ def fused_safe_embedding_lookup_sparse(embedding_weights, prune=True): ``` 此接口与 DeepRec 的 `safe_embedding_lookup_sparse` 接口功能是一致的。因此参数不再赘述，可查看相关文档 + + ## fused_embedding_lookup_sparse接口 通过 `nn.fused_embedding_lookup_sparse` ```python +@tf_export(v1=["nn.fused_embedding_lookup_sparse"]) def fused_embedding_lookup_sparse(params, sp_ids, sparse_weights=None, @@ -102,9 +115,9 @@ def fused_embedding_lookup_sparse(params, max_norm=None, default_id=None, prune_invalid_ids=False, + fill_empty_row=True, blocknums=None): ``` - - `params`: List，可以含有单个的 embedding tensor 或是被 partition 过的 embedding tensors。embedding tensors 的 rank 须都为 2。 - `sp_ids`: SparseTenor，其 values 为需要查找的 id。indices 的 rank 须为 2。dense_shape 的 rank 须为 1，元素个数为 2。 - `sparse_weights`: sparse_ids 的 values 的权重。 @@ -112,63 +125,38 @@ def fused_embedding_lookup_sparse(params, - `name`: 此 operation 的名称。 - `combiner`: entry 维度进行 combine 的策略。 - `max_norm`: 如果不为 None, 则对每个 embedding vector 都计算 l2，然后对于超过 max_norm 值的进行 normalization。 -- `default_id`: 对于 empty 的 row，填充 default_id。如果 default_id 为 None, 则默认填充 0。 +- `default_id`: 若 `fill_empty_row=True`, 则对于 empty 的 row，填充 default_id。如果 default_id 为 None, 则默认填充 0。 +- `fill_empty_row`: 是否对 sparse_ids 进行空行填充，结合 `default_id` 使用。 - `prune_invalid_ids`: 是否对 sparse_ids 去除非法值(id < 0)。 - `blocknums`: DynamicEmbeddingVariable 使用的参数。 + + ## 注意事项 1. 目前 Embedding子图Fusion当前支持 Nvidia GPU 上执行。相应的 `tf.Variable` 和 `EmbeddingVariable` 及其他算子可以在 CPU 上。其中CPU版本的Embedding Fusion子图功能正在代码开发中。 -1. 目前不支持设置权重 `sparse_weights`。 -1. partition_strategy 目前只支持 div ，且在 axis = 0 上对 embedding tensor 做切分。且如果 embedding tensor 是 EmbeddingVariable 的话，目前只能是单个完整的 ev，还不支持对 ev 进行 partition 的查找模式。 -1. 目前不支持动态弹性维度、Multi-Hash Variable、AdaptiveEmbedding功能，后续会逐步支持。 -## Op 介绍及计算图 -新增了 Fused Embedding 相关算子: - -1. FusedEmbeddingSparsePreLookUp -2. FusedEmbeddingSparsePostLookUp -3. FusedEmbeddingSparsePostLookUpGrad +2. 目前不支持设置权重 `sparse_weights`。 +3. 目前不支持动态弹性维度、Multi-Hash Variable、AdaptiveEmbedding功能，后续会逐步支持。 +## Op 介绍及计算图 +新增了 Fused Embedding 相关算子: -以底层级接口 `fused_embedding_lookup_sparse` 为例，调用之后会创建如下的计算图： -![img_1.png](Fused-Embedding/img_1.png) +1. PruneInvalidAndFillEmptyRows +2. UniqueWithCountsGPU +3. PartitionWithPermutation +4. FusedEmbeddingSparsePostLookUp +5. FusedEmbeddingSparsePostLookUpGrad -1. **FusedEmbeddingSparsePreLookUp** 主要负责 fill empty row, prune invalid id, 以及根据 partition_strategy 对 sp_ids 的 values 和 indices 进行划分。 -2. **tf.Gather** 与 **EmbeddingVariable** 或 **tf.Variable** 在同一个 device 上，在 partition 的情况下可能有多份，在不同的 device 上(分布式)。它负责接受 PreEmbedding 划分过的 values 和 indices，进行实际的 embedding vector 查找。 -3. **FusedEmbeddingSparsePostLookUp** 则负责将 embedding vector 从各个 parition 上收集回来，然后进行 combiner 及 max_norm 等相关操作。 +以底层级接口 `fused_embedding_lookup_sparse` 为例，调用之后会依照下列顺序创建计算图: -4. **FusedEmbeddingSparsePostLookUpGrad** 负责 FusedEmbeddingSparsePostLookUp 的反向梯度计算。 +1. PruneInvalidAndFillEmptyRows 负责去除非法值及填充空行 +2. UniqueWithCountsGPU 负责对 sparse_ids 进行 unique 操作，在多机多卡的情况下可以减少通信量 +3. PartitionWithPermutation 在需要对 sparse_ids 进行 partition 时候，会创建此算子，按照不同的策略进行 partition +4. **tf.Gather** 与 **EmbeddingVariable** 或 **tf.Variable** 在同一个 device 上，在 partition 的情况下可能有多份，在不同的 device 上(分布式)。它进行实际的 embedding vector 查找。 +5. **FusedEmbeddingSparsePostLookUp** 则负责将 embedding vector 从各个 parition 上收集回来，然后进行 combiner 及 max_norm 等相关操作。 +6. **FusedEmbeddingSparsePostLookUpGrad** 负责 FusedEmbeddingSparsePostLookUp 的反向梯度计算。 ## 性能对比 -在 modelzoo 中，对比了一些 model 在 unfused 以及 fused embedding 情况下性能提升（5000个 iteration 平均结果） - -Machine: -8 cores AMD EPYC 7232P CPU @ 3.20GHz. - -A100-80GB-PCIE GPU - -DLRM Model: - -| | Avg Time per Iteration | -| ------- | ---------------------- | -| Unfused | 20.78 ms | -| Fused | 17.41 ms | -| SpeedUp | 1.19x | - -DeepFM Model: - -| | Avg Time per Iteration | -| ------- | ---------------------- | -| Unfused | 37.24 ms | -| Fused | 30.98 ms | -| SpeedUp | 1.20x | - -WDL Model: - -| | Avg Time per Iteration | -| ------- | ---------------------- | -| Unfused | 36.38 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z!`Ex%p+$a^c$U>97Z)#jr}W8A(q9@FZNS~>>=yLw<4j>W8x6bSprscYu%@ zH@Y(12+B+7vK^FSfpP1b=uu>g0>B+lky@BU)GK zyZ1QO>-!@~)$aUg11ll>Vw`QLd4z_SSQS`GaPA;l@G| zDI}*j#CdGrB^M6R?HnzBx$r~ou>0GhyopF8ik^`jB3OsU{IT<(qa;;hfcIU14?08;0H&{DTM`TswR$d>e z>sY9Lahr}f0#>T*wQ7KNI;%!YI;$<&o>7YMWpTlfXQrocZ91<{TtBvNLu^F2efVmB z?e({KUF}@N-<>R{zuN7=(;v>?c=@m5BWeathN+M{VWUEA2y%Hx?}b)loV$4FU)>M- z^iXXFh7NEV+>|8RBj`<>N0V0aCA&*24OUOehUyq>jxN>Y3_sM=!f(T)AY_7Z+2>Z=Edd zGT=&=*(4ow80F>jy=3%QXYgHhjZeTuBKuYQ=GIIJIUH&(?-1PGo1&G@5B%0);xXWt zJ-5L`d(R?iE;AC)@s~ zQM@3Z{{MZT_-pktWim7V9YFbya@gMgFMCJz{>1 Date: Thu, 21 Apr 2022 17:24:14 +0800 Subject: [PATCH 33/41] Ajust interface to V2 --- docs/Fused-Embedding.md | 18 +-- .../layers/python/layers/embedding_ops.py | 1 + tensorflow/core/kernels/BUILD | 10 +- ...=> fused_embedding_post_v2_ops_gpus.cu.cc} | 16 +- ...s.cu.cc => unique_with_count_v3_ops.cu.cc} | 20 +-- ... fused_embedding_post_v2_grad_ops_test.cc} | 14 +- ...cc => fused_embedding_post_v2_ops_test.cc} | 16 +- ...st.cc => unique_with_count_v3_ops_test.cc} | 6 +- tensorflow/core/ops/fused_embedding_ops.cc | 6 +- tensorflow/python/BUILD | 3 +- tensorflow/python/ops/fused_embedding_ops.py | 126 ++++++---------- .../python/ops/fused_embedding_ops_v2.py | 140 ++++++++++++++++++ 12 files changed, 238 insertions(+), 138 deletions(-) rename tensorflow/core/kernels/fused_embedding/gpu/{fused_embedding_post_ops_gpus.cu.cc => fused_embedding_post_v2_ops_gpus.cu.cc} (96%) rename tensorflow/core/kernels/fused_embedding/gpu/{unique_with_count_gpu.ops.cu.cc => unique_with_count_v3_ops.cu.cc} (96%) rename tensorflow/core/kernels/fused_embedding/tests/{fused_embedding_post_grad_ops_test.cc => fused_embedding_post_v2_grad_ops_test.cc} (96%) rename tensorflow/core/kernels/fused_embedding/tests/{fused_embedding_post_ops_test.cc => fused_embedding_post_v2_ops_test.cc} (96%) rename tensorflow/core/kernels/fused_embedding/tests/{unique_with_count_gpu_ops_test.cc => unique_with_count_v3_ops_test.cc} (94%) create mode 100644 tensorflow/python/ops/fused_embedding_ops_v2.py diff --git a/docs/Fused-Embedding.md b/docs/Fused-Embedding.md index 25e3150421f..f9e521e10b4 100644 --- a/docs/Fused-Embedding.md +++ b/docs/Fused-Embedding.md @@ -120,7 +120,7 @@ def fused_embedding_lookup_sparse(params, ``` - `params`: List，可以含有单个的 embedding tensor 或是被 partition 过的 embedding tensors。embedding tensors 的 rank 须都为 2。 - `sp_ids`: SparseTenor，其 values 为需要查找的 id。indices 的 rank 须为 2。dense_shape 的 rank 须为 1，元素个数为 2。 -- `sparse_weights`: sparse_ids 的 values 的权重。 +- `sparse_weights`: sparse_ids 的 values 的权重。目前还暂不支持。 - `partition_strategy`: embedding tensor 的 partition 策略。 - `name`: 此 operation 的名称。 - `combiner`: entry 维度进行 combine 的策略。 @@ -133,26 +133,24 @@ def fused_embedding_lookup_sparse(params, ## 注意事项 -1. 目前 Embedding子图Fusion当前支持 Nvidia GPU 上执行。相应的 `tf.Variable` 和 `EmbeddingVariable` 及其他算子可以在 CPU 上。其中CPU版本的Embedding Fusion子图功能正在代码开发中。 -2. 目前不支持设置权重 `sparse_weights`。 -3. 目前不支持动态弹性维度、Multi-Hash Variable、AdaptiveEmbedding功能，后续会逐步支持。 +1. 目前不支持动态弹性维度、Multi-Hash Variable、AdaptiveEmbedding功能，后续会逐步支持。 ## Op 介绍及计算图 -新增了 Fused Embedding 相关算子: +新增了 Fused Embedding V2 相关算子: 1. PruneInvalidAndFillEmptyRows -2. UniqueWithCountsGPU +2. UniqueWithCountsV3 3. PartitionWithPermutation -4. FusedEmbeddingSparsePostLookUp -5. FusedEmbeddingSparsePostLookUpGrad +4. FusedEmbeddingSparsePostLookUpV2 +5. FusedEmbeddingSparsePostLookUpV3Grad -以底层级接口 `fused_embedding_lookup_sparse` 为例，调用之后会依照下列顺序创建计算图: +以底层级接口 `fused_embedding_lookup_sparse_v2` 为例，调用之后会依照下列顺序创建计算图: 1. PruneInvalidAndFillEmptyRows 负责去除非法值及填充空行 -2. UniqueWithCountsGPU 负责对 sparse_ids 进行 unique 操作，在多机多卡的情况下可以减少通信量 +2. UniqueWithCountsV2 负责对 sparse_ids 进行 unique 操作，在多机多卡的情况下可以减少通信量 3. PartitionWithPermutation 在需要对 sparse_ids 进行 partition 时候，会创建此算子，按照不同的策略进行 partition 4. **tf.Gather** 与 **EmbeddingVariable** 或 **tf.Variable** 在同一个 device 上，在 partition 的情况下可能有多份，在不同的 device 上(分布式)。它进行实际的 embedding vector 查找。 5. **FusedEmbeddingSparsePostLookUp** 则负责将 embedding vector 从各个 parition 上收集回来，然后进行 combiner 及 max_norm 等相关操作。 diff --git a/tensorflow/contrib/layers/python/layers/embedding_ops.py b/tensorflow/contrib/layers/python/layers/embedding_ops.py index 34ea1401952..62b94564865 100644 --- a/tensorflow/contrib/layers/python/layers/embedding_ops.py +++ b/tensorflow/contrib/layers/python/layers/embedding_ops.py @@ -39,6 +39,7 @@ from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.ops import fused_embedding_ops +from tensorflow.python.ops import fused_embedding_ops_v2 __all__ = [ "safe_embedding_lookup_sparse", "scattered_embedding_lookup", diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD index 8620bdb53a5..96230c8b812 100644 --- a/tensorflow/core/kernels/BUILD +++ b/tensorflow/core/kernels/BUILD @@ -1949,10 +1949,10 @@ tf_cuda_cc_test( name = "fused_embedding_ops_test", size = "small", srcs = ["fused_embedding/tests/prune_invalid_and_fill_empty_rows_ops_test.cc", - "fused_embedding/tests/unique_with_count_gpu_ops_test.cc", + "fused_embedding/tests/unique_with_count_v3_ops_test.cc", "fused_embedding/tests/partition_with_permutation_ops_test.cu.cc", - "fused_embedding/tests/fused_embedding_post_ops_test.cc", - "fused_embedding/tests/fused_embedding_post_grad_ops_test.cc"], + "fused_embedding/tests/fused_embedding_post_v2_ops_test.cc", + "fused_embedding/tests/fused_embedding_post_v2_grad_ops_test.cc"], tags = tf_cuda_tests_tags(), deps = [ ":fused_embedding_ops", @@ -5294,10 +5294,10 @@ tf_cuda_library( tf_kernel_library( name = "fused_embedding_ops", gpu_srcs = [ - "fused_embedding/gpu/fused_embedding_post_ops_gpus.cu.cc", + "fused_embedding/gpu/fused_embedding_post_v2_ops_gpus.cu.cc", "fused_embedding/gpu/partition_with_permutation_ops.cu.cc", "fused_embedding/gpu/prune_invalid_and_fill_empty_rows_ops.cu.cc", - "fused_embedding/gpu/unique_with_count_gpu.ops.cu.cc", + "fused_embedding/gpu/unique_with_count_v3_ops.cu.cc", "fused_embedding/gpu/functions/kernels.cu.cc", "fused_embedding/gpu/functions/partition_select.cu.cc", ], diff --git a/tensorflow/core/kernels/fused_embedding/gpu/fused_embedding_post_ops_gpus.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/fused_embedding_post_v2_ops_gpus.cu.cc similarity index 96% rename from tensorflow/core/kernels/fused_embedding/gpu/fused_embedding_post_ops_gpus.cu.cc rename to tensorflow/core/kernels/fused_embedding/gpu/fused_embedding_post_v2_ops_gpus.cu.cc index fe0a1fe6bcc..f1b426102dc 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/fused_embedding_post_ops_gpus.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/fused_embedding_post_v2_ops_gpus.cu.cc @@ -17,9 +17,9 @@ namespace tensorflow { using GPUDevice = Eigen::GpuDevice; -class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { +class FusedEmbeddingSparsePostLookUpV2GPU : public OpKernel { public: - explicit FusedEmbeddingSparsePostLookUpGPU(OpKernelConstruction* ctx) + explicit FusedEmbeddingSparsePostLookUpV2GPU(OpKernelConstruction* ctx) : OpKernel(ctx) { OP_REQUIRES_OK(ctx, ctx->GetAttr("num_partitions", &num_partitions_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("partition_axis", &partition_axis_)); @@ -149,14 +149,14 @@ class FusedEmbeddingSparsePostLookUpGPU : public OpKernel { int64_t default_id_; }; -REGISTER_KERNEL_BUILDER(Name("FusedEmbeddingSparsePostLookUp") +REGISTER_KERNEL_BUILDER(Name("FusedEmbeddingSparsePostLookUpV2") .Device(DEVICE_GPU) .HostMemory("sp_dense_shape"), - FusedEmbeddingSparsePostLookUpGPU); + FusedEmbeddingSparsePostLookUpV2GPU); -class FusedEmbeddingSparsePostLookUpGradGPU : public OpKernel { +class FusedEmbeddingSparsePostLookUpV2GradGPU : public OpKernel { public: - explicit FusedEmbeddingSparsePostLookUpGradGPU(OpKernelConstruction* ctx) + explicit FusedEmbeddingSparsePostLookUpV2GradGPU(OpKernelConstruction* ctx) : OpKernel(ctx) { OP_REQUIRES_OK(ctx, ctx->GetAttr("num_partitions", &num_partitions_)); OP_REQUIRES_OK(ctx, ctx->GetAttr("partition_axis", &partition_axis_)); @@ -310,8 +310,8 @@ class FusedEmbeddingSparsePostLookUpGradGPU : public OpKernel { }; REGISTER_KERNEL_BUILDER( - Name("FusedEmbeddingSparsePostLookUpGrad").Device(DEVICE_GPU), - FusedEmbeddingSparsePostLookUpGradGPU); + Name("FusedEmbeddingSparsePostLookUpV2Grad").Device(DEVICE_GPU), + FusedEmbeddingSparsePostLookUpV2GradGPU); } // namespace tensorflow diff --git a/tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_gpu.ops.cu.cc b/tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_v3_ops.cu.cc similarity index 96% rename from tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_gpu.ops.cu.cc rename to tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_v3_ops.cu.cc index 890a106024e..ed75189624d 100644 --- a/tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_gpu.ops.cu.cc +++ b/tensorflow/core/kernels/fused_embedding/gpu/unique_with_count_v3_ops.cu.cc @@ -255,9 +255,9 @@ void Dump(const GPUDevice& d, KeyType* d_key, CounterType* d_counts, } // namespace gpu_unique_with_counts template -class UniqueWithCountsGPU : public OpKernel { +class UniqueWithCountsV3 : public OpKernel { public: - explicit UniqueWithCountsGPU(OpKernelConstruction* ctx) : OpKernel(ctx) { + explicit UniqueWithCountsV3(OpKernelConstruction* ctx) : OpKernel(ctx) { cudaEventCreateWithFlags(&memcpy_event_, cudaEventDisableTiming); } @@ -350,29 +350,29 @@ class UniqueWithCountsGPU : public OpKernel { cudaEvent_t memcpy_event_; }; -REGISTER_KERNEL_BUILDER(Name("UniqueWithCountsGPU") +REGISTER_KERNEL_BUILDER(Name("UniqueWithCountsV3") .Device(DEVICE_GPU) .TypeConstraint("KeyType") .TypeConstraint("CounterType"), - UniqueWithCountsGPU); + UniqueWithCountsV3); -REGISTER_KERNEL_BUILDER(Name("UniqueWithCountsGPU") +REGISTER_KERNEL_BUILDER(Name("UniqueWithCountsV3") .Device(DEVICE_GPU) .TypeConstraint("KeyType") .TypeConstraint("CounterType"), - UniqueWithCountsGPU); + UniqueWithCountsV3); -REGISTER_KERNEL_BUILDER(Name("UniqueWithCountsGPU") +REGISTER_KERNEL_BUILDER(Name("UniqueWithCountsV3") .Device(DEVICE_GPU) .TypeConstraint("KeyType") .TypeConstraint("CounterType"), - UniqueWithCountsGPU); + UniqueWithCountsV3); -REGISTER_KERNEL_BUILDER(Name("UniqueWithCountsGPU") +REGISTER_KERNEL_BUILDER(Name("UniqueWithCountsV3") .Device(DEVICE_GPU) .TypeConstraint("KeyType") .TypeConstraint("CounterType"), - UniqueWithCountsGPU); + UniqueWithCountsV3); } // namespace tensorflow diff --git a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_grad_ops_test.cc b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_v2_grad_ops_test.cc similarity index 96% rename from tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_grad_ops_test.cc rename to tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_v2_grad_ops_test.cc index f2233c6666c..2d8b070b7bb 100644 --- a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_grad_ops_test.cc +++ b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_v2_grad_ops_test.cc @@ -29,7 +29,7 @@ namespace { enum class Device { GPU }; -class FusedEmbeddingSparsePostLookUpGradOpTest : public OpsTestBase { +class FusedEmbeddingSparsePostLookUpV2GradOpTest : public OpsTestBase { protected: void MakeOpAndSetDevice(Device device, int num_partitions, DataType dtype, const std::string& combiner, const float max_norm, @@ -40,8 +40,8 @@ class FusedEmbeddingSparsePostLookUpGradOpTest : public OpsTestBase { "GPU", {}, "/job:a/replica:0/task:0"))); } - TF_EXPECT_OK(NodeDefBuilder("fused_embedding__sparse_post_look_up_grad", - "FusedEmbeddingSparsePostLookUpGrad") + TF_EXPECT_OK(NodeDefBuilder("fused_embedding_sparse_post_look_up_v2_grad", + "FusedEmbeddingSparsePostLookUpV2Grad") .Attr("T", dtype) .Attr("num_partitions", num_partitions) .Attr("partition_axis", 0) @@ -62,7 +62,7 @@ class FusedEmbeddingSparsePostLookUpGradOpTest : public OpsTestBase { } }; -TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, Partition2MeanMaxNorm100) { +TEST_F(FusedEmbeddingSparsePostLookUpV2GradOpTest, Partition2MeanMaxNorm100) { const int nnz = 10; const int batch_size = 4; const int emb_vector_dim = 8; @@ -170,7 +170,7 @@ TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, Partition2MeanMaxNorm100) { } } -TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, Partition2SUMUnique) { +TEST_F(FusedEmbeddingSparsePostLookUpV2GradOpTest, Partition2SUMUnique) { const int nnz = 6; const int batch_size = 4; const int emb_vector_dim = 1; @@ -258,7 +258,7 @@ TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, Partition2SUMUnique) { } } -TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, Partition2SUMUniqueDefault4) { +TEST_F(FusedEmbeddingSparsePostLookUpV2GradOpTest, Partition2SUMUniqueDefault4) { const int nnz = 6; const int batch_size = 4; const int emb_vector_dim = 1; @@ -345,7 +345,7 @@ TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, Partition2SUMUniqueDefault4) { } } -TEST_F(FusedEmbeddingSparsePostLookUpGradOpTest, SinglePartitionSUMUnique) { +TEST_F(FusedEmbeddingSparsePostLookUpV2GradOpTest, SinglePartitionSUMUnique) { const int nnz = 6; const int batch_size = 4; const int emb_vector_dim = 1; diff --git a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_v2_ops_test.cc similarity index 96% rename from tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc rename to tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_v2_ops_test.cc index e40cdda3214..423ef6b7bb8 100644 --- a/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_ops_test.cc +++ b/tensorflow/core/kernels/fused_embedding/tests/fused_embedding_post_v2_ops_test.cc @@ -28,7 +28,7 @@ namespace tensorflow { namespace { enum class Device { GPU }; -class FusedEmbeddingSparsePostLookUpOpTest : public OpsTestBase { +class FusedEmbeddingSparsePostLookUpV2OpTest : public OpsTestBase { protected: void MakeOpAndSetDevice(Device device, int num_partitions, DataType dtype, const std::string& combiner, const float max_norm, @@ -39,8 +39,8 @@ class FusedEmbeddingSparsePostLookUpOpTest : public OpsTestBase { "GPU", {}, "/job:a/replica:0/task:0"))); } - TF_EXPECT_OK(NodeDefBuilder("fused_embedding_sparse_post_look_up", - "FusedEmbeddingSparsePostLookUp") + TF_EXPECT_OK(NodeDefBuilder("fused_embedding_sparse_post_look_up_v2", + "FusedEmbeddingSparsePostLookUpV2") .Attr("T", dtype) .Attr("num_partitions", num_partitions) .Attr("partition_axis", 0) @@ -59,7 +59,7 @@ class FusedEmbeddingSparsePostLookUpOpTest : public OpsTestBase { } }; -TEST_F(FusedEmbeddingSparsePostLookUpOpTest, +TEST_F(FusedEmbeddingSparsePostLookUpV2OpTest, Partition3CombinerSqrtnMaxNorm200) { const int nnz = 10; const int batch_size = 4; @@ -132,7 +132,7 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, } } -TEST_F(FusedEmbeddingSparsePostLookUpOpTest, Partition2SumFillEmpty) { +TEST_F(FusedEmbeddingSparsePostLookUpV2OpTest, Partition2SumFillEmpty) { const int nnz = 3; const int batch_size = 3; const int emb_vector_dim = 4; @@ -181,7 +181,7 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, Partition2SumFillEmpty) { } } -TEST_F(FusedEmbeddingSparsePostLookUpOpTest, Partition2SumFillEmptyDefault2) { +TEST_F(FusedEmbeddingSparsePostLookUpV2OpTest, Partition2SumFillEmptyDefault2) { const int nnz = 3; const int batch_size = 3; const int emb_vector_dim = 4; @@ -230,7 +230,7 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, Partition2SumFillEmptyDefault2) { } } -TEST_F(FusedEmbeddingSparsePostLookUpOpTest, +TEST_F(FusedEmbeddingSparsePostLookUpV2OpTest, Partition2MeanFillEmptyDefault2Unique) { const int nnz = 7; const int batch_size = 5; @@ -293,7 +293,7 @@ TEST_F(FusedEmbeddingSparsePostLookUpOpTest, } } -TEST_F(FusedEmbeddingSparsePostLookUpOpTest, +TEST_F(FusedEmbeddingSparsePostLookUpV2OpTest, SinglePartitionMeanFillEmptyDefault2Unique) { const int nnz = 7; const int batch_size = 5; diff --git a/tensorflow/core/kernels/fused_embedding/tests/unique_with_count_gpu_ops_test.cc b/tensorflow/core/kernels/fused_embedding/tests/unique_with_count_v3_ops_test.cc similarity index 94% rename from tensorflow/core/kernels/fused_embedding/tests/unique_with_count_gpu_ops_test.cc rename to tensorflow/core/kernels/fused_embedding/tests/unique_with_count_v3_ops_test.cc index 24d79d5a97d..2efae49656b 100644 --- a/tensorflow/core/kernels/fused_embedding/tests/unique_with_count_gpu_ops_test.cc +++ b/tensorflow/core/kernels/fused_embedding/tests/unique_with_count_v3_ops_test.cc @@ -16,7 +16,7 @@ namespace { enum class Device { GPU }; -class UniqueWithCountsGPUOpTest : public OpsTestBase { +class UniqueWithCountsV3OpTest : public OpsTestBase { protected: void MakeOpAndSetDevice(Device device) { if (device == Device::GPU) { @@ -25,7 +25,7 @@ class UniqueWithCountsGPUOpTest : public OpsTestBase { "GPU", {}, "/job:a/replica:0/task:0"))); } - TF_EXPECT_OK(NodeDefBuilder("unique_with_counts_gpu", "UniqueWithCountsGPU") + TF_EXPECT_OK(NodeDefBuilder("unique_with_counts_v3", "UniqueWithCountsV3") .Attr("CounterType", DT_INT32) .Input(FakeInput(DT_INT64)) .Finalize(node_def())); @@ -33,7 +33,7 @@ class UniqueWithCountsGPUOpTest : public OpsTestBase { } }; -TEST_F(UniqueWithCountsGPUOpTest, UniqueWithCount) { +TEST_F(UniqueWithCountsV3OpTest, UniqueWithCount) { MakeOpAndSetDevice(Device::GPU); const int input_size = 20; const int uniq_size = 14; diff --git a/tensorflow/core/ops/fused_embedding_ops.cc b/tensorflow/core/ops/fused_embedding_ops.cc index 13d89f85353..41531607515 100644 --- a/tensorflow/core/ops/fused_embedding_ops.cc +++ b/tensorflow/core/ops/fused_embedding_ops.cc @@ -45,7 +45,7 @@ REGISTER_OP("PruneInvalidAndFillEmptyRows") return Status::OK(); }); -REGISTER_OP("UniqueWithCountsGPU") +REGISTER_OP("UniqueWithCountsV3") .Attr("KeyType: {int32, int64} = DT_INT64") .Attr("CounterType: {int32, int64} = DT_INT32") .Input("input: KeyType") @@ -114,7 +114,7 @@ REGISTER_OP("PartitionWithPermutation") return Status::OK(); }); -REGISTER_OP("FusedEmbeddingSparsePostLookUp") +REGISTER_OP("FusedEmbeddingSparsePostLookUpV2") .Attr("T : {float32}") .Attr("num_partitions: int >= 1 = 1") .Attr("fill_empty_row: bool = false") @@ -198,7 +198,7 @@ REGISTER_OP("FusedEmbeddingSparsePostLookUp") // indices. // )doc"); -REGISTER_OP("FusedEmbeddingSparsePostLookUpGrad") +REGISTER_OP("FusedEmbeddingSparsePostLookUpV2Grad") .Attr("T : {float32}") .Attr("num_partitions: int >= 1 = 1") .Attr("fill_empty_row: bool = false") diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index d385988ffca..fcf498e47dc 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -2086,7 +2086,8 @@ tf_gen_op_wrapper_private_py( py_library( name = "fused_embedding_ops", - srcs = ["ops/fused_embedding_ops.py"], + srcs = ["ops/fused_embedding_ops.py", + "ops/fused_embedding_ops_v2.py"], srcs_version = "PY2AND3", deps = [ ":fused_embedding_ops_gen", diff --git a/tensorflow/python/ops/fused_embedding_ops.py b/tensorflow/python/ops/fused_embedding_ops.py index bfdc802f870..361e71aeb34 100644 --- a/tensorflow/python/ops/fused_embedding_ops.py +++ b/tensorflow/python/ops/fused_embedding_ops.py @@ -2,18 +2,16 @@ from __future__ import division from __future__ import print_function -from numpy import dtype - -import tensorflow as tf +import tensorflow from tensorflow.python.framework.constant_op import constant +from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import variables from tensorflow.python.ops import array_ops from tensorflow.python.framework import sparse_tensor +from tensorflow.python.ops import gen_fused_embedding_ops from tensorflow.python.ops.kv_variable_ops import EmbeddingVariable -from tensorflow.python.ops.gen_fused_embedding_ops import prune_invalid_and_fill_empty_rows -from tensorflow.python.ops.gen_fused_embedding_ops import unique_with_counts_gpu -from tensorflow.python.ops.gen_fused_embedding_ops import partition_with_permutation +from tensorflow.python.ops.gen_fused_embedding_ops import fused_embedding_sparse_pre_look_up from tensorflow.python.ops.gen_fused_embedding_ops import fused_embedding_sparse_post_look_up from tensorflow.python.ops.gen_fused_embedding_ops import fused_embedding_sparse_post_look_up_grad from tensorflow.python.util.tf_export import tf_export @@ -29,115 +27,77 @@ def fused_embedding_lookup_sparse(params, max_norm=None, default_id=None, prune_invalid_ids=False, - fill_empty_row=True, blocknums=None): - if sparse_weights is not None: raise ValueError("sparse_weights is not supported yet") - valid_partition_strategy = ['div', 'mod', 'mod_ev'] + valid_partition_strategy = ['div'] if partition_strategy not in valid_partition_strategy: raise ValueError("{} is not supported yet. Currently only support {}".format( partition_strategy, valid_partition_strategy)) - if blocknums is not None: - raise ValueError("Using blocknums for DynamicEmbeddingVariable is not supported yet") - if default_id is not None and type(default_id) is not int: raise ValueError("default_id must be a integer!") - params_white_list = [EmbeddingVariable, tf.Tensor] - if any([type(param) not in params_white_list for param in params]): - raise ValueError("Currently fused embedding only support: {}".format(params_white_list)) + if blocknums is not None: + raise ValueError("Using blocknums for DynamicEmbeddingVariable is not supported yet") partition_nums = len(params) if type(params[0]) is EmbeddingVariable: - partition_strategy = 'mod_ev' - partition_shapes = [constant([1, 1], dtype=tf.int64) for _ in params] # dummy + if partition_nums != 1: + raise ValueError("For EmbeddingVariable, do not support partition now") + # fake shape for now. TBD change in the future + partition_shapes = [constant([1, 1], dtype=dtypes.int64)] else: partition_shapes = [w.shape for w in params] with ops.name_scope(name, "fused_embedding_lookup_sparse", params + [sp_ids]) as name: - - sp_values = sp_ids.values - sp_indices = sp_ids.indices - sp_dense_shape = sp_ids.dense_shape - prune_invalid_id = bool(prune_invalid_ids) - - if prune_invalid_ids or fill_empty_row: - sp_values, sp_indices, row_empty_and_invalid_flags = prune_invalid_and_fill_empty_rows( - fill_empty_row=fill_empty_row, - prune_invalid_id=prune_invalid_id, - default_id=default_id, - sp_values=sp_values, - sp_indices=sp_indices, - sp_dense_shape=sp_dense_shape, + partitioned_values, partitioned_indices, \ + row_empty_and_invalid_flags = fused_embedding_sparse_pre_look_up( + partition_shapes=partition_shapes, + sp_values=sp_ids.values, + sp_indices=sp_ids.indices, + sp_dense_shape=sp_ids.dense_shape, + fill_empty_row=True, + default_id=default_id, + prune_invalid_id=bool(prune_invalid_ids) ) - else: - row_empty_and_invalid_flags = tf.constant(0, shape=(1, ), dtype=tf.int32) # dummy - unique_keys, unique_idxs, unique_counts = unique_with_counts_gpu( - input=sp_values, - CounterType=tf.int32, - ) - - if partition_nums > 1: - partitioned_values, partition_permutation = partition_with_permutation( - partition_strategy=partition_strategy, - input=unique_keys, - partition_shapes=partition_shapes - ) - else: - partitioned_values = [unique_keys] - partition_permutation = tf.constant(0, shape=(1, 1), dtype=tf.int32) # dummy + # fixme(marvin): ple align the meaning between pre & post op. + default_id = 0 if default_id is None else default_id emb_shards = [] for i in range(partition_nums): - with ops.colocate_with(params[i]): - shard = array_ops.gather(params[i], partitioned_values[i], counts=unique_counts) + param = params[i] + sub_partition_values = partitioned_values[i] + with ops.colocate_with(param): + shard = array_ops.gather(param, sub_partition_values) emb_shards.append(shard) - - emb_vectors, _, _ = fused_embedding_sparse_post_look_up( - fill_empty_row=fill_empty_row, default_id=default_id, - combiner=combiner, max_norm=max_norm, - emb_shards=emb_shards, partition_permutation=partition_permutation, - sp_dense_shape=sp_dense_shape, - indices_before_unique=sp_indices, + emb_vectors, _ = fused_embedding_sparse_post_look_up( + emb_shards=emb_shards, partitioned_indices=partitioned_indices, + sp_dense_shape=sp_ids.dense_shape, row_empty_and_invalid_flags=row_empty_and_invalid_flags, - unique_idxs=unique_idxs + partitioned_values=partitioned_values, + combiner=combiner, max_norm=max_norm, default_id=default_id ) - return emb_vectors @ops.RegisterGradient("FusedEmbeddingSparsePostLookUp") -def fused_embedding_sparse_post_look_up_gradient(op, top_grad, - uesless_grad_1, uesless_grad_2): +def fused_embedding_sparse_post_look_up_grad(op, top_grad_emb_vec, _): num_partitions = op.get_attr("num_partitions") - combiner = op.get_attr("combiner") - max_norm = op.get_attr("max_norm") - fill_empty_row = op.get_attr("fill_empty_row") - default_id = op.get_attr("default_id") - emb_shards = [op.inputs[i] for i in range(0, num_partitions)] - partition_permutation = op.inputs[num_partitions] - # sp_dense_shape = op.inputs[num_partitions + 1] - indices_before_unique = op.inputs[num_partitions + 2] - row_empty_and_invalid_flags = op.inputs[num_partitions + 3] - unique_idxs = op.inputs[num_partitions + 4] - + partitioned_indices = [op.inputs[i] for i in range(num_partitions, 2 * num_partitions)] feature_nums = op.outputs[1] - emb_shard_ptrs = op.outputs[2] - - grad_shards = fused_embedding_sparse_post_look_up_grad( - fill_empty_row=fill_empty_row, default_id=default_id, - combiner=combiner, max_norm=max_norm, - top_grad=top_grad, emb_shards=emb_shards, - emb_shard_ptrs=emb_shard_ptrs, - partition_permutation=partition_permutation, - feature_nums=feature_nums, indices_before_unique=indices_before_unique, - unique_idxs=unique_idxs, row_empty_and_invalid_flags=row_empty_and_invalid_flags) - - return grad_shards + [None for _ in range(len(op.inputs) - num_partitions)] + row_empty_and_invalid_flags = op.inputs[2 * num_partitions + 1] + + grad_shards = gen_fused_embedding_ops.fused_embedding_sparse_post_look_up_grad( + top_grad=top_grad_emb_vec, emb_shards=emb_shards, + partitioned_indices=partitioned_indices, + feature_nums=feature_nums, row_empty_and_invalid_flags=row_empty_and_invalid_flags, + combiner=op.get_attr("combiner"), max_norm=op.get_attr("max_norm"), + default_id=op.get_attr("default_id") + ) + return grad_shards + [None for _ in range(0, 2 * num_partitions + 2)] diff --git a/tensorflow/python/ops/fused_embedding_ops_v2.py b/tensorflow/python/ops/fused_embedding_ops_v2.py new file mode 100644 index 00000000000..cbe39d7f659 --- /dev/null +++ b/tensorflow/python/ops/fused_embedding_ops_v2.py @@ -0,0 +1,140 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.framework.constant_op import constant +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.framework import sparse_tensor +from tensorflow.python.ops.kv_variable_ops import EmbeddingVariable +from tensorflow.python.ops.gen_fused_embedding_ops import prune_invalid_and_fill_empty_rows +from tensorflow.python.ops.gen_fused_embedding_ops import unique_with_counts_v3 +from tensorflow.python.ops.gen_fused_embedding_ops import partition_with_permutation +from tensorflow.python.ops.gen_fused_embedding_ops import fused_embedding_sparse_post_look_up_v2 +from tensorflow.python.ops.gen_fused_embedding_ops import fused_embedding_sparse_post_look_up_v2_grad +from tensorflow.python.util.tf_export import tf_export + + +@tf_export(v1=["nn.fused_embedding_lookup_sparse_v2"]) +def fused_embedding_lookup_sparse_v2(params, + sp_ids, + sparse_weights=None, + partition_strategy=None, + name=None, + combiner=None, + max_norm=None, + default_id=None, + prune_invalid_ids=False, + fill_empty_row=True, + blocknums=None): + + if sparse_weights is not None: + raise ValueError("sparse_weights is not supported yet") + + valid_partition_strategy = ['div', 'mod', 'mod_ev'] + if partition_strategy not in valid_partition_strategy: + raise ValueError("{} is not supported yet. Currently only support {}".format( + partition_strategy, valid_partition_strategy)) + + if blocknums is not None: + raise ValueError("Using blocknums for DynamicEmbeddingVariable is not supported yet") + + if default_id is not None and type(default_id) is not int: + raise ValueError("default_id must be a integer!") + + params_white_list = [EmbeddingVariable, ops.Tensor] + if any([type(param) not in params_white_list for param in params]): + raise ValueError("Currently fused embedding only support: {}".format(params_white_list)) + + partition_nums = len(params) + + if type(params[0]) is EmbeddingVariable: + partition_strategy = 'mod_ev' + partition_shapes = [constant([1, 1], dtype=dtypes.int64) for _ in params] # dummy + else: + partition_shapes = [w.shape for w in params] + + with ops.name_scope(name, "fused_embedding_lookup_sparse", + params + [sp_ids]) as name: + + sp_values = sp_ids.values + sp_indices = sp_ids.indices + sp_dense_shape = sp_ids.dense_shape + prune_invalid_id = bool(prune_invalid_ids) + + if prune_invalid_ids or fill_empty_row: + sp_values, sp_indices, row_empty_and_invalid_flags = prune_invalid_and_fill_empty_rows( + fill_empty_row=fill_empty_row, + prune_invalid_id=prune_invalid_id, + default_id=default_id, + sp_values=sp_values, + sp_indices=sp_indices, + sp_dense_shape=sp_dense_shape, + ) + else: + row_empty_and_invalid_flags = constant(0, shape=(1, ), dtype=dtypes.int32) # dummy + + unique_keys, unique_idxs, unique_counts = unique_with_counts_v3( + input=sp_values, + CounterType=dtypes.int32, + ) + + if partition_nums > 1: + partitioned_values, partition_permutation = partition_with_permutation( + partition_strategy=partition_strategy, + input=unique_keys, + partition_shapes=partition_shapes + ) + else: + partitioned_values = [unique_keys] + partition_permutation = constant(0, shape=(1, 1), dtype=dtypes.int32) # dummy + + emb_shards = [] + for i in range(partition_nums): + with ops.colocate_with(params[i]): + shard = array_ops.gather(params[i], partitioned_values[i], counts=unique_counts) + emb_shards.append(shard) + + emb_vectors, _, _ = fused_embedding_sparse_post_look_up_v2( + fill_empty_row=fill_empty_row, default_id=default_id, + combiner=combiner, max_norm=max_norm, + emb_shards=emb_shards, partition_permutation=partition_permutation, + sp_dense_shape=sp_dense_shape, + indices_before_unique=sp_indices, + row_empty_and_invalid_flags=row_empty_and_invalid_flags, + unique_idxs=unique_idxs + ) + + return emb_vectors + + +@ops.RegisterGradient("FusedEmbeddingSparsePostLookUpV2") +def fused_embedding_sparse_post_look_up_v2_gradient(op, top_grad, + uesless_grad_1, uesless_grad_2): + num_partitions = op.get_attr("num_partitions") + combiner = op.get_attr("combiner") + max_norm = op.get_attr("max_norm") + fill_empty_row = op.get_attr("fill_empty_row") + default_id = op.get_attr("default_id") + + emb_shards = [op.inputs[i] for i in range(0, num_partitions)] + partition_permutation = op.inputs[num_partitions] + # sp_dense_shape = op.inputs[num_partitions + 1] + indices_before_unique = op.inputs[num_partitions + 2] + row_empty_and_invalid_flags = op.inputs[num_partitions + 3] + unique_idxs = op.inputs[num_partitions + 4] + + feature_nums = op.outputs[1] + emb_shard_ptrs = op.outputs[2] + + grad_shards = fused_embedding_sparse_post_look_up_v2_grad( + fill_empty_row=fill_empty_row, default_id=default_id, + combiner=combiner, max_norm=max_norm, + top_grad=top_grad, emb_shards=emb_shards, + emb_shard_ptrs=emb_shard_ptrs, + partition_permutation=partition_permutation, + feature_nums=feature_nums, indices_before_unique=indices_before_unique, + unique_idxs=unique_idxs, row_empty_and_invalid_flags=row_empty_and_invalid_flags) + + return grad_shards + [None for _ in range(len(op.inputs) - num_partitions)] From 901fa8afe3685e984302b6793bfbfee17e61876b Mon Sep 17 00:00:00 2001 From: Randy Wang Date: Tue, 26 Apr 2022 16:22:59 +0800 Subject: [PATCH 34/41] Make embedding fusion v1 and v2 compatible --- docs/Fused-Embedding.md | 61 ++++++++++++++---- .../features/GPUFusedEmbedding/DLRM/README.md | 2 +- .../GPUFusedEmbedding/DLRM/dlrm.tar.gz | Bin 0 -> 17493480 bytes .../features/GPUFusedEmbedding/DLRM/train.py | 2 +- .../GPUFusedEmbedding/DeepFM/README.md | 2 +- .../GPUFusedEmbedding/DeepFM/train.py | 2 +- .../features/GPUFusedEmbedding/WDL/README.md | 2 +- .../features/GPUFusedEmbedding/WDL/train.py | 2 +- .../layers/python/layers/embedding_ops.py | 44 +++++++++---- .../layers/python/layers/feature_column.py | 11 ++-- .../python/layers/feature_column_ops.py | 3 - ...def_FusedEmbeddingSparsePostLookUpV2.pbtxt | 3 + ...FusedEmbeddingSparsePostLookUpV2Grad.pbtxt | 3 + ...pi_def_FusedEmbeddingSparsePreLookUp.pbtxt | 3 + .../api_def_UniqueWithCountsGPU.pbtxt | 3 - .../base_api/api_def_UniqueWithCountsV3.pbtxt | 3 + tensorflow/core/kernels/BUILD | 12 ++-- .../fused_embedding_post_v2_grad_ops_test.cc | 0 .../tests/fused_embedding_post_v2_ops_test.cc | 0 .../partition_with_permutation_ops_test.cc} | 0 ...ne_invalid_and_fill_empty_rows_ops_test.cc | 0 .../tests/unique_with_count_v3_ops_test.cc | 0 .../feature_column/feature_column_v2.py | 7 +- .../feature_column/feature_column_v2_test.py | 4 +- tensorflow/python/ops/embedding_ops.py | 42 ++++++++---- tensorflow/python/tpu/feature_column_v2.py | 2 +- .../golden/v1/tensorflow.feature_column.pbtxt | 2 +- .../tools/api/golden/v1/tensorflow.nn.pbtxt | 4 ++ .../tools/api/golden/v1/tensorflow.pbtxt | 24 ++++++- .../api/golden/v1/tensorflow.raw_ops.pbtxt | 22 +++++-- .../golden/v2/tensorflow.feature_column.pbtxt | 2 +- .../tools/api/golden/v2/tensorflow.pbtxt | 24 ++++++- .../api/golden/v2/tensorflow.raw_ops.pbtxt | 22 +++++-- 33 files changed, 230 insertions(+), 83 deletions(-) create mode 100644 modelzoo/features/GPUFusedEmbedding/DLRM/dlrm.tar.gz create mode 100644 tensorflow/core/api_def/base_api/api_def_FusedEmbeddingSparsePostLookUpV2.pbtxt create mode 100644 tensorflow/core/api_def/base_api/api_def_FusedEmbeddingSparsePostLookUpV2Grad.pbtxt create mode 100644 tensorflow/core/api_def/base_api/api_def_FusedEmbeddingSparsePreLookUp.pbtxt delete mode 100644 tensorflow/core/api_def/base_api/api_def_UniqueWithCountsGPU.pbtxt create mode 100644 tensorflow/core/api_def/base_api/api_def_UniqueWithCountsV3.pbtxt rename tensorflow/core/kernels/fused_embedding/{ => gpu}/tests/fused_embedding_post_v2_grad_ops_test.cc (100%) rename tensorflow/core/kernels/fused_embedding/{ => gpu}/tests/fused_embedding_post_v2_ops_test.cc (100%) rename tensorflow/core/kernels/fused_embedding/{tests/partition_with_permutation_ops_test.cu.cc => gpu/tests/partition_with_permutation_ops_test.cc} (100%) rename tensorflow/core/kernels/fused_embedding/{ => gpu}/tests/prune_invalid_and_fill_empty_rows_ops_test.cc (100%) rename tensorflow/core/kernels/fused_embedding/{ => gpu}/tests/unique_with_count_v3_ops_test.cc (100%) diff --git a/docs/Fused-Embedding.md b/docs/Fused-Embedding.md index f9e521e10b4..8578eac799b 100644 --- a/docs/Fused-Embedding.md +++ b/docs/Fused-Embedding.md @@ -2,7 +2,7 @@ ## 介绍 -DeepRec 及 TensorFlow 原生的 embedding lookup 相关 API，如 safe_embedding_lookup_sparse，会创建比较多的 op，因此在 GPU 上执行时容易出现 kernel launch bound 的问题，且部分 op 只有 CPU 实现，速度相对较慢。因此，Embedding子图Fusion功能提供了一组接口，并提供了一组fusion ops，通过Fusion的Op，减少需要 launch 的 kernel 数量，并提供高性能的实现，达到在 GPU 上加速执行的目的。 +DeepRec 及 TensorFlow 原生的 embedding lookup 相关 API，如 safe_embedding_lookup_sparse，会创建比较多的 op，因此在 GPU 上执行时容易出现 kernel launch bound 的问题，且部分 op 只有 CPU 实现，速度相对较慢。因此，Embedding子图Fusion功能提供了一组接口，并提供了一组fusion ops，通过Fusion的Op，减少需要 launch 的 kernel 数量，并提供高性能的实现，达到加速执行的目的。 ## FeatureColumn接口 @@ -13,7 +13,7 @@ DeepRec 及 TensorFlow 原生的 embedding lookup 相关 API，如 safe_embeddin 2. `tensorflow/contrib/layers/python/layers/feature_column.py` 的 `_EmbeddingColumn` 然后一般会通过 `tf.feature_column.input_layer` 或 `tf.feature_column_ops.input_from_feature_columns` 等高级接口，将此实例传入，建立 lookup 相关计算图。 -因此，Embedding子图Fusion功能给上述的 `EmbeddingColumn` 类都添加了 `do_fusion` 属性，默认为 `False`，用户在使用时，可以显示的设置为 `True`，让 embedding lookup 过程使用 fused ops。 +因此，Embedding子图Fusion功能给上述的 `EmbeddingColumn` 类都添加了 `do_fusion` 属性，默认为 None，用户在使用时，可以显示的设置为 `'v1', 'v2'` 这样的 fusion 版本，让 embedding lookup 过程使用 fused ops。 如下： @@ -29,7 +29,7 @@ W = tf.feature_column.embedding_column( categorical_column=column, dimension=3, initializer=tf.ones_initializer(tf.dtypes.float32), - do_fusion=True) + do_fusion='v2') ids={} ids["col_emb"] = tf.SparseTensor(indices=[[0,0],[1,1],[2,2],[3,3],[4,4]], values=tf.cast([1,2,3,4,5], tf.dtypes.int64), dense_shape=[5, 4]) @@ -64,7 +64,7 @@ columns = feature_column.sparse_column_with_embedding(column_name="col_emb", dty W = feature_column.embedding_column(sparse_id_column=columns, dimension=3, initializer=tf.ones_initializer(tf.dtypes.float32), - do_fusion=True) + do_fusion='v2') ids={} @@ -97,12 +97,17 @@ def fused_safe_embedding_lookup_sparse(embedding_weights, name=None, partition_strategy="div", max_norm=None, - prune=True): + prune=True, + blocknums=None, + fusion_version='v2'): ``` 此接口与 DeepRec 的 `safe_embedding_lookup_sparse` 接口功能是一致的。因此参数不再赘述，可查看相关文档 ## fused_embedding_lookup_sparse接口 + +### 使用 v1 版本 + 通过 `nn.fused_embedding_lookup_sparse` ```python @tf_export(v1=["nn.fused_embedding_lookup_sparse"]) @@ -118,6 +123,27 @@ def fused_embedding_lookup_sparse(params, fill_empty_row=True, blocknums=None): ``` + +### 使用 v2 版本 + +通过 `nn.fused_embedding_lookup_sparse_v2` +```python +@tf_export(v1=["nn.fused_embedding_lookup_sparse_v2"]) +def fused_embedding_lookup_sparse_v2(params, + sp_ids, + sparse_weights=None, + partition_strategy=None, + name=None, + combiner=None, + max_norm=None, + default_id=None, + prune_invalid_ids=False, + fill_empty_row=True, + blocknums=None): +``` + +### 参数说明 + - `params`: List，可以含有单个的 embedding tensor 或是被 partition 过的 embedding tensors。embedding tensors 的 rank 须都为 2。 - `sp_ids`: SparseTenor，其 values 为需要查找的 id。indices 的 rank 须为 2。dense_shape 的 rank 须为 1，元素个数为 2。 - `sparse_weights`: sparse_ids 的 values 的权重。目前还暂不支持。 @@ -131,13 +157,28 @@ def fused_embedding_lookup_sparse(params, - `blocknums`: DynamicEmbeddingVariable 使用的参数。 +`'v1'` 目前为 CPU fusion 实现，`'v2'` 目前为 GPU fusion 实现。请根据需要相应选择。 + ## 注意事项 1. 目前不支持动态弹性维度、Multi-Hash Variable、AdaptiveEmbedding功能，后续会逐步支持。 +2. 使用 v2 GPU fusion 时，可以考虑 `tf.ConfigProto(inter_op_parallelism_threads=1)`，测试发现在 embedding 数量较多的情况下，`inter_op_parallelism_threads=1` 可以避免一些 Schedule 的 overhead，更高的提速。 + + +## Op 介绍 + +### Fused Embedding V1 相关算子: +1. FusedEmbeddingSparsePreLookUp +2. FusedEmbeddingSparsePostLookUp +3. FusedEmbeddingSparsePostLookUpGrad -## Op 介绍及计算图 -新增了 Fused Embedding V2 相关算子: +FusedEmbeddingSparsePreLookUp 主要负责 fill empty row, prune invalid id, 以及根据 partition_strategy 对 sp_ids 的 values 和 indices 进行划分。 +tf.Gather 与 EmbeddingVariable 或 tf.Variable 在同一个 device 上，在 partition 的情况下可能有多份，在不同的 device 上(分布式)。它负责接受 PreEmbedding 划分过的 values 和 indices，进行实际的 embedding vector 查找。 +FusedEmbeddingSparsePostLookUp 则负责将 embedding vector 从各个 parition 上收集回来，然后进行 combiner 及 max_norm 等相关操作。 +FusedEmbeddingSparsePostLookUpGrad 负责 FusedEmbeddingSparsePostLookUp 的反向梯度计算。 + +### Fused Embedding V2 相关算子: 1. PruneInvalidAndFillEmptyRows 2. UniqueWithCountsV3 @@ -145,9 +186,7 @@ def fused_embedding_lookup_sparse(params, 4. FusedEmbeddingSparsePostLookUpV2 5. FusedEmbeddingSparsePostLookUpV3Grad - - -以底层级接口 `fused_embedding_lookup_sparse_v2` 为例，调用之后会依照下列顺序创建计算图: +调用 `fused_embedding_lookup_sparse_v2` 之后会依照下列顺序创建计算图: 1. PruneInvalidAndFillEmptyRows 负责去除非法值及填充空行 2. UniqueWithCountsV2 负责对 sparse_ids 进行 unique 操作，在多机多卡的情况下可以减少通信量 @@ -157,4 +196,4 @@ def fused_embedding_lookup_sparse(params, 6. **FusedEmbeddingSparsePostLookUpGrad** 负责 FusedEmbeddingSparsePostLookUp 的反向梯度计算。 ## 性能对比 -见 `modelzoo/features/GPUFusedEmbedding` 下的测试数据 \ No newline at end of file +v2 算子 GPU 相关，见 `modelzoo/features/GPUFusedEmbedding` 下的测试数据 \ No newline at end of file diff --git a/modelzoo/features/GPUFusedEmbedding/DLRM/README.md b/modelzoo/features/GPUFusedEmbedding/DLRM/README.md index 8a4b4223dad..51737830d0f 100644 --- a/modelzoo/features/GPUFusedEmbedding/DLRM/README.md +++ b/modelzoo/features/GPUFusedEmbedding/DLRM/README.md @@ -7,7 +7,7 @@ The only difference is that this model use GPU Fused Embedding to acclerate the ```python categorical_embedding_column = tf.feature_column.embedding_column( categorical_column, dimension=16, combiner='mean', - do_fusion=True) + do_fusion='v2') ``` ## Benchmark diff --git a/modelzoo/features/GPUFusedEmbedding/DLRM/dlrm.tar.gz 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