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Revert "[Phi] Move mul op kernel into phi (PaddlePaddle#40833)"
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -47,4 +47,4 @@ TEST(MulOpConverter, main) { | |
| } // namespace inference | ||
| } // namespace paddle | ||
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| USE_OP_ITSELF(mul); | ||
| USE_OP(mul); | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,31 @@ | ||
| /* Copyright (c) 2016 PaddlePaddle 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. */ | ||
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| #include "paddle/fluid/operators/mul_op.h" | ||
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| #include "paddle/fluid/platform/float16.h" | ||
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| namespace ops = paddle::operators; | ||
| namespace plat = paddle::platform; | ||
| REGISTER_OP_CUDA_KERNEL(mul, ops::MulKernel<plat::CUDADeviceContext, float>, | ||
| ops::MulKernel<plat::CUDADeviceContext, double>, | ||
| ops::MulKernel<plat::CUDADeviceContext, plat::float16>); | ||
| REGISTER_OP_CUDA_KERNEL( | ||
| mul_grad, ops::MulGradKernel<plat::CUDADeviceContext, float>, | ||
| ops::MulGradKernel<plat::CUDADeviceContext, double>, | ||
| ops::MulGradKernel<plat::CUDADeviceContext, plat::float16>); | ||
| REGISTER_OP_CUDA_KERNEL( | ||
| mul_grad_grad, | ||
| ops::MulDoubleGradKernel<paddle::platform::CUDADeviceContext, float>, | ||
| ops::MulDoubleGradKernel<paddle::platform::CUDADeviceContext, double>); |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,207 @@ | ||
| /* Copyright (c) 2016 PaddlePaddle 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. */ | ||
|
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| #pragma once | ||
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| #include "paddle/fluid/framework/op_registry.h" | ||
| #include "paddle/fluid/framework/operator.h" | ||
| #include "paddle/phi/kernels/funcs/blas/blas.h" | ||
| #include "paddle/phi/kernels/funcs/math_function.h" | ||
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| namespace paddle { | ||
| namespace operators { | ||
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| using Tensor = framework::Tensor; | ||
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| constexpr int kMULMKLDNNINT8 = 1; | ||
| constexpr int kMULMKLDNNFP32 = 2; | ||
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| template <typename DeviceContext, typename T> | ||
| class MulKernel : public framework::OpKernel<T> { | ||
| public: | ||
| void Compute(const framework::ExecutionContext& context) const override { | ||
| const Tensor* x = context.Input<Tensor>("X"); | ||
| const Tensor* y = context.Input<Tensor>("Y"); | ||
| Tensor* z = context.Output<Tensor>("Out"); | ||
| const Tensor x_matrix = | ||
| x->dims().size() > 2 | ||
| ? framework::ReshapeToMatrix( | ||
| *x, context.template Attr<int>("x_num_col_dims")) | ||
| : *x; | ||
| const Tensor y_matrix = | ||
| y->dims().size() > 2 | ||
| ? framework::ReshapeToMatrix( | ||
| *y, context.template Attr<int>("y_num_col_dims")) | ||
| : *y; | ||
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| z->mutable_data<T>(context.GetPlace()); | ||
| auto z_dim = z->dims(); | ||
| if (z_dim.size() != 2) { | ||
| z->Resize({x_matrix.dims()[0], y_matrix.dims()[1]}); | ||
| } | ||
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| auto blas = phi::funcs::GetBlas<DeviceContext, T>(context); | ||
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| blas.MatMul(x_matrix, y_matrix, z); | ||
| if (z_dim.size() != 2) { | ||
| z->Resize(z_dim); | ||
| } | ||
| } | ||
| }; | ||
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| template <typename DeviceContext, typename T> | ||
| class MulGradKernel : public framework::OpKernel<T> { | ||
| public: | ||
| void Compute(const framework::ExecutionContext& ctx) const override { | ||
| int x_num_col_dims = ctx.template Attr<int>("x_num_col_dims"); | ||
| int y_num_col_dims = ctx.template Attr<int>("y_num_col_dims"); | ||
| auto* x = ctx.Input<framework::LoDTensor>("X"); | ||
| auto* y = ctx.Input<framework::LoDTensor>("Y"); | ||
| auto x_matrix = x->dims().size() > 2 | ||
| ? framework::ReshapeToMatrix(*x, x_num_col_dims) | ||
| : static_cast<const Tensor&>(*x); | ||
| auto y_matrix = y->dims().size() > 2 | ||
| ? framework::ReshapeToMatrix(*y, y_num_col_dims) | ||
| : static_cast<const Tensor&>(*y); | ||
| auto* dout = ctx.Input<framework::LoDTensor>(framework::GradVarName("Out")); | ||
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| Tensor dout_mat; | ||
| dout_mat.ShareDataWith(*dout); | ||
| dout_mat.Resize({phi::flatten_to_2d(x->dims(), x_num_col_dims)[0], | ||
| phi::flatten_to_2d(y->dims(), y_num_col_dims)[1]}); | ||
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| auto* dx = ctx.Output<framework::LoDTensor>(framework::GradVarName("X")); | ||
| auto* dy = ctx.Output<framework::LoDTensor>(framework::GradVarName("Y")); | ||
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| if (dx != nullptr) { | ||
| dx->set_lod(x->lod()); | ||
| } | ||
| if (dy != nullptr) { | ||
| dy->set_lod(y->lod()); | ||
| } | ||
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| auto& dev_ctx = ctx.template device_context<DeviceContext>(); | ||
| auto blas = phi::funcs::GetBlas<DeviceContext, T>(dev_ctx); | ||
| if (dx) { | ||
| dx->mutable_data<T>(ctx.GetPlace()); | ||
| Tensor dx_matrix = dx->dims().size() > 2 | ||
| ? framework::ReshapeToMatrix(*dx, x_num_col_dims) | ||
| : *dx; | ||
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| // dx = dout * y'. dx: M x K, dout : M x N, y : K x N | ||
| blas.MatMul(dout_mat, false, y_matrix, true, &dx_matrix); | ||
| } | ||
| if (dy) { | ||
| dy->mutable_data<T>(ctx.GetPlace()); | ||
| Tensor dy_matrix = dy->dims().size() > 2 | ||
| ? framework::ReshapeToMatrix(*dy, y_num_col_dims) | ||
| : *dy; | ||
| // dy = x' * dout. dy K x N, dout : M x N, x : M x K | ||
| blas.MatMul(x_matrix, true, dout_mat, false, &dy_matrix); | ||
| } | ||
| } | ||
| }; | ||
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| template <typename DeviceContext, typename T> | ||
| class MulDoubleGradKernel : public framework::OpKernel<T> { | ||
| public: | ||
| void Compute(const framework::ExecutionContext& ctx) const override { | ||
| int x_num_col_dims = ctx.template Attr<int>("x_num_col_dims"); | ||
| int y_num_col_dims = ctx.template Attr<int>("y_num_col_dims"); | ||
| auto* x = ctx.Input<framework::LoDTensor>("X"); | ||
| auto* y = ctx.Input<framework::LoDTensor>("Y"); | ||
| auto x_mat = x->dims().size() > 2 | ||
| ? framework::ReshapeToMatrix(*x, x_num_col_dims) | ||
| : static_cast<const Tensor&>(*x); | ||
| auto y_mat = y->dims().size() > 2 | ||
| ? framework::ReshapeToMatrix(*y, y_num_col_dims) | ||
| : static_cast<const Tensor&>(*y); | ||
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| const int m = phi::flatten_to_2d(x->dims(), x_num_col_dims)[0]; | ||
| const int n = phi::flatten_to_2d(y->dims(), y_num_col_dims)[1]; | ||
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| auto* dout = ctx.Input<framework::LoDTensor>("DOut"); | ||
| Tensor dout_mat; | ||
| dout_mat.ShareDataWith(*dout); | ||
| dout_mat.Resize({m, n}); | ||
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| auto* ddx = ctx.Input<framework::LoDTensor>("DDX"); | ||
| auto* ddy = ctx.Input<framework::LoDTensor>("DDY"); | ||
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| auto* dx = ctx.Output<framework::LoDTensor>("DX"); | ||
| auto* dy = ctx.Output<framework::LoDTensor>("DY"); | ||
| auto* ddout = ctx.Output<framework::LoDTensor>("DDOut"); | ||
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| Tensor ddout_mat; | ||
| if (ddout) { | ||
| ddout->set_lod(dout->lod()); | ||
| // allocate and reshape ddout | ||
| ddout->mutable_data<T>(ctx.GetPlace()); | ||
| ddout_mat.ShareDataWith(*ddout); | ||
| ddout_mat.Resize({m, n}); | ||
| } | ||
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| auto& dev_ctx = ctx.template device_context<DeviceContext>(); | ||
| auto blas = phi::funcs::GetBlas<DeviceContext, T>(dev_ctx); | ||
| // a flag to specify whether ddout value has been set, if flag | ||
| // is false, MatMul beta should be 0 to set ddout, if flag is | ||
| // true, MatMul beta should be 1 to add result to ddout. | ||
| bool ddout_flag = false; | ||
| if (ddx) { | ||
| auto ddx_mat = ddx->dims().size() > 2 | ||
| ? framework::ReshapeToMatrix(*ddx, x_num_col_dims) | ||
| : static_cast<const Tensor&>(*ddx); | ||
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| // dy = ddx' * dout. dy : K x M, ddx' : K x M, dout : M x N | ||
| if (dy) { | ||
| dy->set_lod(y->lod()); | ||
| // allocate and reshape dy | ||
| dy->mutable_data<T>(ctx.GetPlace()); | ||
| Tensor dy_mat = dy->dims().size() > 2 | ||
| ? framework::ReshapeToMatrix(*dy, y_num_col_dims) | ||
| : *dy; | ||
| blas.MatMul(ddx_mat, true, dout_mat, false, &dy_mat); | ||
| } | ||
| // ddout1 = ddx * y. ddx : M x K, y : K x N, ddout1 : M x N | ||
| if (ddout) { | ||
| blas.MatMul(ddx_mat, false, y_mat, false, static_cast<T>(1.0), | ||
| &ddout_mat, static_cast<T>(ddout_flag)); | ||
| ddout_flag = true; | ||
| } | ||
| } | ||
| if (ddy) { | ||
| auto ddy_mat = ddy->dims().size() > 2 | ||
| ? framework::ReshapeToMatrix(*ddy, y_num_col_dims) | ||
| : static_cast<const Tensor&>(*ddy); | ||
| // dx = dout * ddy'. dout : M x N, ddy' : N x K, dx : M x K | ||
| if (dx) { | ||
| dx->set_lod(x->lod()); | ||
| // allocate and reshape dx | ||
| dx->mutable_data<T>(ctx.GetPlace()); | ||
| Tensor dx_mat = dx->dims().size() > 2 | ||
| ? framework::ReshapeToMatrix(*dx, x_num_col_dims) | ||
| : *dx; | ||
| blas.MatMul(dout_mat, false, ddy_mat, true, &dx_mat); | ||
| } | ||
| // ddout2 = x * ddy. x : M x K, ddy : K x N, ddout2 : M x N | ||
| if (ddout) { | ||
| blas.MatMul(x_mat, false, ddy_mat, false, static_cast<T>(1.0), | ||
| &ddout_mat, static_cast<T>(ddout_flag)); | ||
| } | ||
| } | ||
| } | ||
| }; | ||
|
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| } // namespace operators | ||
| } // namespace paddle |
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