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Modified the Kernel policy. When the compute is NHWC #48563

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Dec 9, 2022
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Modified the Kernel policy. When the compute is NHWC #48563

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AnnaTrainingG Nov 30, 2022
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AnnaTrainingG Nov 30, 2022
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47 changes: 34 additions & 13 deletions paddle/phi/kernels/gpu/batch_norm_grad_kernel.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -858,15 +858,20 @@ void BatchNormGradRawKernel(const Context &ctx,
// ctx.GetPlace()),
// epsilon, saved_mean_data, saved_var_data));
#else
// CUDNN only support small batch size
// const size_t CUDNN_PER_ACTIVATION_THRESHOLD = 131070;
const size_t CUDNN_PER_ACTIVATION_THRESHOLD = 10240;
const size_t CUDNN_SPATIAL_THRESHOLD = 880801;
const bool use_native_kernel =
((x_dims.size() == 2 && N >= CUDNN_PER_ACTIVATION_THRESHOLD) ||
(x_dims.size() == 3 && N >= CUDNN_SPATIAL_THRESHOLD));
if (use_native_kernel) {
if (x_dims.size() == 2) {
}
// CUDNN only support small batch size
// const size_t CUDNN_PER_ACTIVATION_THRESHOLD = 131070;
const size_t CUDNN_PER_ACTIVATION_THRESHOLD = 10240;
const size_t CUDNN_SPATIAL_THRESHOLD = 880801;
bool use_native_nhwc =
d_x ? (x_dims.size() == 4 && compute_format == DataLayout::kNHWC)
: false;
const bool use_native_kernel =
((x_dims.size() == 2 && N >= CUDNN_PER_ACTIVATION_THRESHOLD) ||
(x_dims.size() == 3 && N >= CUDNN_SPATIAL_THRESHOLD));
if (use_native_nhwc || (d_x && d_scale && d_bias)) {
if (use_native_kernel || use_native_nhwc) {
if (x_dims.size() == 2 || use_native_nhwc) {
dim3 block;
dim3 grid;
const int block_size = 512;
Expand Down Expand Up @@ -937,6 +942,21 @@ void BatchNormGradRawKernel(const Context &ctx,
flag_ptr);
}
// 2. reduce_sum(x, dy, mean) => dscale, dbias
BatchNormParamType<T> *dscale = nullptr;
BatchNormParamType<T> *dbias = nullptr;
bool with_scale = false;
if (d_scale && d_bias) {
dscale = ctx.template Alloc<BatchNormParamType<T>>(d_scale);
dbias = ctx.template Alloc<BatchNormParamType<T>>(d_bias);
} else {
DenseTensor dscale_mem =
phi::Empty<BatchNormParamType<T>, Context>(ctx, {C});
DenseTensor dbias_mem =
phi::Empty<BatchNormParamType<T>, Context>(ctx, {C});
dscale = dscale_mem.data<BatchNormParamType<T>>();
dbias = dbias_mem.data<BatchNormParamType<T>>();
}

BNBackward2DChannelLastStage2<T, block_size>
<<<grid, block, 0, ctx.stream()>>>(
transformed_d_y.template data<T>(),
Expand All @@ -948,8 +968,8 @@ void BatchNormGradRawKernel(const Context &ctx,
H * W * D,
epsilon,
block_data_ptr,
ctx.template Alloc<BatchNormParamType<T>>(d_scale),
ctx.template Alloc<BatchNormParamType<T>>(d_bias),
dscale,
dbias,
flag_ptr);

// 3. elementwise_mul(scale, mean, inv_var, dy, dscale, dbias) => dx
Expand All @@ -958,8 +978,8 @@ void BatchNormGradRawKernel(const Context &ctx,
transformed_d_y.template data<T>(),
transformed_x.template data<T>(),
scale.template data<BatchNormParamType<T>>(),
d_scale->data<BatchNormParamType<T>>(),
d_bias->data<BatchNormParamType<T>>(),
dscale,
dbias,
mean_ptr,
variance_ptr,
C,
Expand Down Expand Up @@ -1169,6 +1189,7 @@ void BatchNormGradRawKernel(const Context &ctx,
paddle::platform::dynload::cudnnDestroyTensorDescriptor(
bn_param_desc_));
#endif

} else {
const auto *running_mean = mean.get_ptr();
const auto *running_var = variance.get_ptr();
Expand Down