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Support dropout backward in eval mode #35122

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Merged
guoshengCS merged 4 commits into from
Aug 26, 2021
Merged

Support dropout backward in eval mode #35122

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954086f
Support dropout backward in eval mode
smallv0221 Aug 24, 2021
ade37cb
add downscale case
smallv0221 Aug 25, 2021
072efb2
minor fix
smallv0221 Aug 25, 2021
8129d86
minor fix
smallv0221 Aug 26, 2021
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4 changes: 0 additions & 4 deletions paddle/fluid/operators/dropout_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -117,10 +117,6 @@ class DropoutOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;

void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE_EQ(ctx->Attrs().Get<bool>("is_test"), false,
platform::errors::InvalidArgument(
"GradOp is only callable when is_test is false"));

OP_INOUT_CHECK(ctx->HasInput("Mask"), "Input", "Mask", "DropoutGrad");
OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
framework::GradVarName("Out"), "DropoutGrad");
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56 changes: 30 additions & 26 deletions paddle/fluid/operators/dropout_op.h
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Expand Up @@ -160,50 +160,54 @@ template <typename DeviceContext, typename T>
class DropoutGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
PADDLE_ENFORCE_EQ(!context.Attr<bool>("is_test"), true,
platform::errors::PreconditionNotMet(
"GradOp is only callable when is_test is false"));

auto* grad_x = context.Output<Tensor>(framework::GradVarName("X"));
auto* grad_y = context.Input<Tensor>(framework::GradVarName("Out"));
auto* mask = context.Input<Tensor>("Mask");
grad_x->mutable_data<T>(context.GetPlace());
auto size = grad_x->numel();

auto M = EigenVector<uint8_t>::Flatten(*mask);
auto dX = EigenVector<T>::Flatten(*grad_x);
auto dY = EigenVector<T>::Flatten(*grad_y);

auto& place =
*context.template device_context<DeviceContext>().eigen_device();
auto& dropout_implementation =
context.Attr<std::string>("dropout_implementation");
if (dropout_implementation == "upscale_in_train") {
float dropout_prob = context.Attr<float>("dropout_prob");
if (dropout_prob == 1.0f) {
dX.device(place) = static_cast<T>(0) * dY;
if (context.Attr<bool>("is_test") == true) {
if (dropout_implementation == "upscale_in_train") {
dX.device(place) = static_cast<T>(1) * dY;
} else {
int vec_size = VectorizedSize<T>(grad_y->data<T>());
if (platform::is_gpu_place(context.GetPlace()) && vec_size == 4 &&
size % 4 == 0) {
float dropout_prob = context.Attr<float>("dropout_prob");
dX.device(place) = dY * static_cast<T>(1.0f - dropout_prob);
}
} else {
auto M = EigenVector<uint8_t>::Flatten(*mask);
if (dropout_implementation == "upscale_in_train") {
float dropout_prob = context.Attr<float>("dropout_prob");
if (dropout_prob == 1.0f) {
dX.device(place) = static_cast<T>(0) * dY;
} else {
int vec_size = VectorizedSize<T>(grad_y->data<T>());
if (platform::is_gpu_place(context.GetPlace()) && vec_size == 4 &&
size % 4 == 0) {
#if defined(__NVCC__) || defined(__HIPCC__)
auto factor = static_cast<T>(1.0f / (1.0f - dropout_prob));
auto stream = context.cuda_device_context().stream();
platform::GpuLaunchConfig config = platform::GetGpuLaunchConfig1D(
context.cuda_device_context(), size);
DropoutGradCUDAKernel<
T, uint8_t,
4><<<config.block_per_grid, config.thread_per_block, 0, stream>>>(
grad_y->data<T>(), mask->data<uint8_t>(), factor, size,
grad_x->data<T>());
auto factor = static_cast<T>(1.0f / (1.0f - dropout_prob));
auto stream = context.cuda_device_context().stream();
platform::GpuLaunchConfig config = platform::GetGpuLaunchConfig1D(
context.cuda_device_context(), size);
DropoutGradCUDAKernel<T, uint8_t, 4><<<
config.block_per_grid, config.thread_per_block, 0, stream>>>(
grad_y->data<T>(), mask->data<uint8_t>(), factor, size,
grad_x->data<T>());
#endif
} else {
dX.device(place) =
dY * M.cast<T>() / static_cast<T>(1.0f - dropout_prob);
} else {
dX.device(place) =
dY * M.cast<T>() / static_cast<T>(1.0f - dropout_prob);
}
}
} else {
dX.device(place) = dY * M.cast<T>();
}
} else {
dX.device(place) = dY * M.cast<T>();
}
}
};
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