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remove linear_chain_crf and crf_decoding from fluid (PaddlePaddle#48996)
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* remove linear_chain_crf and crf_decoding
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@ccrrong
ccrrong authored Dec 13, 2022
1 parent 8f8d1fd commit cb56853
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207 changes: 0 additions & 207 deletions python/paddle/fluid/layers/nn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -65,8 +65,6 @@
__all__ = [
'fc',
'embedding',
'linear_chain_crf',
'crf_decoding',
'conv2d',
'dropout',
'split',
Expand Down Expand Up @@ -752,211 +750,6 @@ def _pull_box_sparse(
return outs


@templatedoc()
def linear_chain_crf(input, label, param_attr=None, length=None):
"""
:api_attr: Static Graph
Linear Chain CRF.
${comment}
Args:
input(${emission_type}): ${emission_comment}
label(${label_type}): ${label_comment}
Length(${length_type}): ${length_comment}
param_attr(ParamAttr): The attribute of the learnable parameter for transition parameter.
Returns:
output(${emission_exps_type}): ${emission_exps_comment} \n
output(${transition_exps_type}): ${transition_exps_comment} \n
output(${log_likelihood_type}): ${log_likelihood_comment} \n
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy as np
import paddle
paddle.enable_static()
#define net structure, using LodTensor
train_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(train_program, startup_program):
input_data = fluid.data(name='input_data', shape=[-1,10], dtype='float32')
label = fluid.data(name='label', shape=[-1,1], dtype='int')
emission= fluid.layers.fc(input=input_data, size=10, act="tanh")
crf_cost = fluid.layers.linear_chain_crf(
input=emission,
label=label,
param_attr=fluid.ParamAttr(
name='crfw',
learning_rate=0.01))
use_cuda = False
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_program)
#define data, using LoDTensor
a = fluid.create_lod_tensor(np.random.rand(12,10).astype('float32'), [[3,3,4,2]], place)
b = fluid.create_lod_tensor(np.array([[1],[1],[2],[3],[1],[1],[1],[3],[1],[1],[1],[1]]),[[3,3,4,2]] , place)
feed1 = {'input_data':a,'label':b}
loss= exe.run(train_program,feed=feed1, fetch_list=[crf_cost])
print(loss)
#define net structure, using padding
train_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(train_program, startup_program):
input_data2 = fluid.data(name='input_data2', shape=[-1,10,10], dtype='float32')
label2 = fluid.data(name='label2', shape=[-1,10,1], dtype='int')
label_length = fluid.data(name='length', shape=[-1,1], dtype='int')
emission2= fluid.layers.fc(input=input_data2, size=10, act="tanh", num_flatten_dims=2)
crf_cost2 = fluid.layers.linear_chain_crf(
input=emission2,
label=label2,
length=label_length,
param_attr=fluid.ParamAttr(
name='crfw',
learning_rate=0.01))
use_cuda = False
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_program)
#define data, using padding
cc=np.random.rand(4,10,10).astype('float32')
dd=np.random.rand(4,10,1).astype('int64')
ll=np.array([[3],[3],[4],[2]])
feed2 = {'input_data2':cc,'label2':dd,'length':ll}
loss2= exe.run(train_program,feed=feed2, fetch_list=[crf_cost2])
print(loss2)
#[array([[ 7.8902354],
# [ 7.3602567],
# [ 10.004011],
# [ 5.86721 ]], dtype=float32)]
#you can use find_var to get transition parameter.
transition=np.array(fluid.global_scope().find_var('crfw').get_tensor())
print(transition)
"""
check_variable_and_dtype(
input, 'input', ['float32', 'float64'], 'linear_chain_crf'
)
check_variable_and_dtype(label, 'label', ['int64'], 'linear_chain_crf')
helper = LayerHelper('linear_chain_crf', **locals())
size = input.shape[2] if length else input.shape[1]
transition = helper.create_parameter(
attr=helper.param_attr,
shape=[size + 2, size],
dtype=helper.input_dtype(),
)
alpha = helper.create_variable_for_type_inference(
dtype=helper.input_dtype()
)
emission_exps = helper.create_variable_for_type_inference(
dtype=helper.input_dtype()
)
transition_exps = helper.create_variable_for_type_inference(
dtype=helper.input_dtype()
)
log_likelihood = helper.create_variable_for_type_inference(
dtype=helper.input_dtype()
)
this_inputs = {
"Emission": [input],
"Transition": transition,
"Label": [label],
}
if length:
this_inputs['Length'] = [length]
helper.append_op(
type='linear_chain_crf',
inputs=this_inputs,
outputs={
"Alpha": [alpha],
"EmissionExps": [emission_exps],
"TransitionExps": transition_exps,
"LogLikelihood": log_likelihood,
},
)

return log_likelihood


@templatedoc()
def crf_decoding(input, param_attr, label=None, length=None):
"""
:api_attr: Static Graph
${comment}
Args:
input(Tensor): ${emission_comment}
param_attr (ParamAttr|None): To specify the weight parameter attribute.
Default: None, which means the default weight parameter property is
used. See usage for details in :ref:`api_paddle_fluid_param_attr_ParamAttr` .
label(${label_type}, optional): ${label_comment}
length(${length_type}, optional): ${length_comment}
Returns:
Tensor: ${viterbi_path_comment}
Examples:
.. code-block:: python
import paddle
paddle.enable_static()
# LoDTensor-based example
num_labels = 10
feature = paddle.static.data(name='word_emb', shape=[-1, 784], dtype='float32', lod_level=1)
label = paddle.static.data(name='label', shape=[-1, 1], dtype='int64', lod_level=1)
emission = paddle.static.nn.fc(feature, size=num_labels)
crf_cost = paddle.fluid.layers.linear_chain_crf(input=emission, label=label,
param_attr=paddle.ParamAttr(name="crfw"))
crf_decode = paddle.static.nn.crf_decoding(input=emission,
param_attr=paddle.ParamAttr(name="crfw"))
# Common tensor example
num_labels, max_len = 10, 20
feature = paddle.static.data(name='word_emb_pad', shape=[-1, max_len, 784], dtype='float32')
label = paddle.static.data(name='label_pad', shape=[-1, max_len, 1], dtype='int64')
length = paddle.static.data(name='length', shape=[-1, 1], dtype='int64')
emission = paddle.static.nn.fc(feature, size=num_labels,
num_flatten_dims=2)
crf_cost = paddle.fluid.layers.linear_chain_crf(input=emission, label=label, length=length,
param_attr=paddle.ParamAttr(name="crfw_pad"))
crf_decode = paddle.static.nn.crf_decoding(input=emission, length=length,
param_attr=paddle.ParamAttr(name="crfw_pad"))
"""
check_variable_and_dtype(
input, 'input', ['float32', 'float64'], 'crf_decoding'
)
helper = LayerHelper('crf_decoding', **locals())
transition = helper.get_parameter(param_attr.name)
viterbi_path = helper.create_variable_for_type_inference(
dtype=core.VarDesc.VarType.INT64
)
inputs = {"Emission": [input], "Transition": transition, "Label": label}
if length:
inputs['Length'] = length
helper.append_op(
type='crf_decoding',
inputs=inputs,
outputs={"ViterbiPath": [viterbi_path]},
)

return viterbi_path


@deprecated(since="2.0.0", update_to="paddle.nn.functional.dropout")
def dropout(
x,
Expand Down
11 changes: 2 additions & 9 deletions python/paddle/fluid/tests/book/test_label_semantic_roles.py
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Expand Up @@ -162,12 +162,8 @@ def train(use_cuda, save_dirname=None, is_local=True):
target = fluid.layers.data(
name='target', shape=[1], dtype='int64', lod_level=1
)
crf_cost = fluid.layers.linear_chain_crf(
input=feature_out,
label=target,
param_attr=fluid.ParamAttr(name='crfw', learning_rate=mix_hidden_lr),
)
avg_cost = paddle.mean(crf_cost)
cost = fluid.layers.softmax_with_cross_entropy(feature_out, target)
avg_cost = paddle.mean(cost)

# TODO(qiao)
# check other optimizers and check why out will be NAN
Expand All @@ -183,9 +179,6 @@ def train(use_cuda, save_dirname=None, is_local=True):

# TODO(qiao)
# add dependency track and move this config before optimizer
crf_decode = fluid.layers.crf_decoding(
input=feature_out, param_attr=fluid.ParamAttr(name='crfw')
)

train_data = paddle.batch(
paddle.reader.shuffle(paddle.dataset.conll05.test(), buf_size=8192),
Expand Down
4 changes: 0 additions & 4 deletions python/paddle/fluid/tests/unittests/CMakeLists.txt
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Expand Up @@ -422,7 +422,6 @@ endfunction()
list(REMOVE_ITEM TEST_OPS test_feed_data_check_shape_type)
list(REMOVE_ITEM TEST_OPS test_fetch_lod_tensor_array)
list(REMOVE_ITEM TEST_OPS test_warpctc_op)
list(REMOVE_ITEM TEST_OPS test_parallel_executor_crf)
list(REMOVE_ITEM TEST_OPS test_parallel_executor_profiler)
list(REMOVE_ITEM TEST_OPS test_data_norm_op)
list(REMOVE_ITEM TEST_OPS test_parallel_executor_fetch_feed)
Expand Down Expand Up @@ -748,7 +747,6 @@ if(WITH_DISTRIBUTE)
endif()
endif()

py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf)
# profiler will random hang in linux cuda 10.1 or 10.2
# see https://github.com/PaddlePaddle/Paddle/issues/29082 for details.
# We guess there are some bugs in linux cuda 10.1 or 10.2,
Expand Down Expand Up @@ -916,7 +914,6 @@ set_tests_properties(
test_buffer_shared_memory_reuse_pass
PROPERTIES LABELS "RUN_TYPE=DIST")
set_tests_properties(
test_parallel_executor_crf
test_sync_batch_norm_op
test_inplace_abn_op
test_parallel_executor_seresnext_base_gpu
Expand Down Expand Up @@ -1053,7 +1050,6 @@ set_tests_properties(test_index_select_op PROPERTIES TIMEOUT 120)
set_tests_properties(test_index_add_op PROPERTIES TIMEOUT 120)
set_tests_properties(test_parallel_ssa_graph_inference_feed_partial_data
PROPERTIES TIMEOUT 120)
set_tests_properties(test_parallel_executor_crf PROPERTIES TIMEOUT 120)
set_tests_properties(test_tensordot PROPERTIES TIMEOUT 200)
set_tests_properties(test_imperative_save_load PROPERTIES TIMEOUT 120)
set_tests_properties(test_partial_eager_deletion_transformer PROPERTIES TIMEOUT
Expand Down
1 change: 0 additions & 1 deletion python/paddle/fluid/tests/unittests/test_directory_migration.py
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Expand Up @@ -91,7 +91,6 @@ def test_new_directory(self):
'paddle.static.nn.conv3d',
'paddle.static.nn.conv3d_transpose',
'paddle.static.nn.create_parameter',
'paddle.static.nn.crf_decoding',
'paddle.static.nn.data_norm',
'paddle.static.nn.deform_conv2d',
'paddle.static.nn.group_norm',
Expand Down
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