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未验证 提交 f33763e3 编写于 作者: Z zyfncg 提交者： GitHub
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Move apis(digamma, dist, dot) from legacy_api.yaml to api.yaml (#43956) 

* move standard apis to api.yaml

* revert erfinv

* delete dot_op.h

* fix dot

* rerun ci
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Showing with 141 addition and 656 deletion
paddle/fluid/operators/digamma_op.cc 已删除 100644 → 0
浏览文件 @ 56ddd7c2
/* Copyright (c) 2021 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. */
#include "paddle/fluid/operators/digamma_op.h"
namespace paddle {
namespace operators {
class DigammaOp : public framework::OperatorWithKernel {
public:
DigammaOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorWithKernel(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "Digamma");
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "Digamma");
auto in_dims = ctx->GetInputDim("X");
ctx->SetOutputDim("Out", in_dims);
ctx->ShareLoD("X", "Out");
}
};
class DigammaOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(Tensor), The input tensor of digamma operator.");
AddOutput("Out", "(Tensor), The output tensor of digamma operator.");
AddComment(R"DOC(
Digamma Operator.
This operator is used to perform elementwise digamma for input $X$.
$$out = \Psi(x) = \frac{ \Gamma^{'}(x) }{ \Gamma(x) }$$
)DOC");
}
};
class DigammaGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")),
"Input",
"Out@Grad",
"DigammaGrad");
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "DigammaGrad");
OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")),
"Output",
"X@Grad",
"DigammaGrad");
auto dout_dims = ctx->GetInputDim(framework::GradVarName("Out"));
ctx->SetOutputDim(framework::GradVarName("X"), dout_dims);
ctx->ShareLoD(framework::GradVarName("Out"), framework::GradVarName("X"));
}
};
template <typename T>
class DigammaGradOpMaker : public framework::SingleGradOpMaker<T> {
public:
using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
void Apply(GradOpPtr<T> retv) const override {
retv->SetType("digamma_grad");
retv->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
retv->SetInput("X", this->Input("X"));
retv->SetAttrMap(this->Attrs());
retv->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(digamma,
ops::DigammaOp,
ops::DigammaOpMaker,
ops::DigammaGradOpMaker<paddle::framework::OpDesc>,
ops::DigammaGradOpMaker<paddle::imperative::OpBase>);
REGISTER_OPERATOR(digamma_grad, ops::DigammaGradOp);
paddle/fluid/operators/digamma_op.h 已删除 100644 → 0
浏览文件 @ 56ddd7c2
/* Copyright (c) 2021 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. */
#pragma once
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
paddle/fluid/operators/dist_op.cc 已删除 100644 → 0
浏览文件 @ 56ddd7c2
// Copyright (c) 2020 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.
#include <string>
#include <vector>
#include "paddle/fluid/framework/infershape_utils.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/phi/core/infermeta_utils.h"
#include "paddle/phi/infermeta/binary.h"
namespace paddle {
namespace operators {
class DistOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "Dist");
OP_INOUT_CHECK(ctx->HasInput("Y"), "Input", "Y", "Dist");
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "Dist");
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_NE(phi::product(x_dims),
0,
platform::errors::InvalidArgument(
"The Input(X) has not been initialized properly. The "
"shape of Input(X) = [%s].",
x_dims));
PADDLE_ENFORCE_NE(phi::product(y_dims),
0,
platform::errors::InvalidArgument(
"The Input(Y) has not been initialized properly. The "
"shape of Input(Y) = [%s].",
y_dims));
ctx->SetOutputDim("Out", {1});
}
};
class DistOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "The input Tensor of Dist Op.");
AddInput("Y", "The Right-hand-side input Tensor of Dist Op.");
AddOutput("Out",
"The output of Dist Op, "
"which is the p-norm of (X - Y)");
AddAttr<float>("p", "the norm to be computed.").SetDefault(2.0f);
AddComment(R"DOC(
Dist Operator.
Given two tensors X and Y, compute Lp-norm of (X-Y). It is not a norm in a strict sense,
only as a measure of distance. The shapes of X and Y must be broadcastable. Where, Z = X - Y,
When p = 0, defining $0^0 = 0$, the zero-norm of Z is simply the number of non-zero elements of z.
$$
||Z||_{0} = \lim_{p \rightarrow 0} \sum_{i=1}^{m} |z_i|^p
$$
When p = inf, the inf-norm of Z is the maximum element of Z.
$$
||Z||_\infty=\max_i |z_i|
$$
When p = -inf, the negative-inf-norm of Z is the minimum element of Z.
$$
||Z||_{-\infty}=\min_i |z_i|
$$
Otherwise, the p-norm of Z follows the formula,
$$
||Z||_{p} = (\sum_{i=i}^{m} |z_i|^p)^{1/p}
$$
)DOC");
}
};
class DistOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
if (ctx->HasOutput(framework::GradVarName("X"))) {
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
}
if (ctx->HasOutput(framework::GradVarName("Y"))) {
ctx->SetOutputDim(framework::GradVarName("Y"), y_dims);
}
}
};
template <typename T>
class DistGradOpMaker : public framework::SingleGradOpMaker<T> {
public:
using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
protected:
void Apply(GradOpPtr<T> op) const override {
op->SetType(this->ForwardOpType() + "_grad");
op->SetInput("X", this->Input("X"));
op->SetInput("Y", this->Input("Y"));
op->SetInput("Out", this->Output("Out"));
op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
op->SetOutput(framework::GradVarName("Y"), this->InputGrad("Y"));
op->SetAttrMap(this->Attrs());
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
DECLARE_INFER_SHAPE_FUNCTOR(dist,
DistInferShapeFunctor,
PD_INFER_META(phi::DistInferMeta));
REGISTER_OPERATOR(dist,
ops::DistOp,
ops::DistOpMaker,
ops::DistGradOpMaker<paddle::framework::OpDesc>,
ops::DistGradOpMaker<paddle::imperative::OpBase>,
DistInferShapeFunctor);
REGISTER_OPERATOR(dist_grad, ops::DistOpGrad);
paddle/fluid/operators/dot_op.cc 已删除 100644 → 0
浏览文件 @ 56ddd7c2
// Copyright (c) 2020 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.
#include "paddle/fluid/operators/dot_op.h"
#include "paddle/fluid/framework/infershape_utils.h"
#include "paddle/phi/core/infermeta_utils.h"
#include "paddle/phi/infermeta/binary.h"
namespace paddle {
namespace operators {
class DotOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace());
}
};
class DotOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() final {
AddInput("X", "(Tensor) The first input tensor. ");
AddInput("Y", "(Tensor) The second input tensor. ");
AddOutput("Out", "(Tensor) The result tensor.");
AddComment("");
}
};
class DotGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE_EQ(
true,
ctx->HasInput("X"),
platform::errors::PreconditionNotMet("Input(X) should not be null."));
PADDLE_ENFORCE_EQ(
true,
ctx->HasInput("Y"),
platform::errors::PreconditionNotMet("Input(Y) should not be null."));
PADDLE_ENFORCE_EQ(true,
ctx->HasInput(framework::GradVarName("Out")),
platform::errors::PreconditionNotMet(
"Input(Out@GRAD) should not be null."));
auto x_grad_name = framework::GradVarName("X");
auto y_grad_name = framework::GradVarName("Y");
if (ctx->HasOutput(x_grad_name)) {
ctx->ShareDim("X", /*->*/ x_grad_name);
ctx->ShareLoD("X", /*->*/ x_grad_name);
}
if (ctx->HasOutput(y_grad_name)) {
ctx->ShareDim("Y", /*->*/ y_grad_name);
ctx->ShareLoD("Y", /*->*/ y_grad_name);
}
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(OperatorWithKernel::IndicateVarDataType(
ctx, framework::GradVarName("Out")),
ctx.GetPlace());
}
};
template <typename T>
class DotOpGradMaker : public framework::SingleGradOpMaker<T> {
public:
using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
protected:
void Apply(GradOpPtr<T> op) const override {
op->SetType("dot_grad");
op->SetInput("X", this->Input("X"));
op->SetInput("Y", this->Input("Y"));
op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
op->SetAttrMap(this->Attrs());
op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
op->SetOutput(framework::GradVarName("Y"), this->InputGrad("Y"));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
DECLARE_INFER_SHAPE_FUNCTOR(dot,
DotInferShapeFunctor,
PD_INFER_META(phi::DotInferMeta));
REGISTER_OPERATOR(dot,
ops::DotOp,
ops::DotOpMaker,
ops::DotOpGradMaker<paddle::framework::OpDesc>,
ops::DotOpGradMaker<paddle::imperative::OpBase>,
DotInferShapeFunctor);
REGISTER_OPERATOR(dot_grad, ops::DotGradOp);
REGISTER_OP_CPU_KERNEL(
dot,
ops::DotKernel<paddle::platform::CPUDeviceContext, float>,
ops::DotKernel<paddle::platform::CPUDeviceContext, double>,
ops::DotKernel<paddle::platform::CPUDeviceContext, int>,
ops::DotKernel<paddle::platform::CPUDeviceContext, int64_t>,
ops::DotKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex<float>>,
ops::DotKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex<double>>);
REGISTER_OP_CPU_KERNEL(
dot_grad,
ops::DotGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::DotGradKernel<paddle::platform::CPUDeviceContext, double>,
ops::DotGradKernel<paddle::platform::CPUDeviceContext, int>,
ops::DotGradKernel<paddle::platform::CPUDeviceContext, int64_t>,
ops::DotGradKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex<float>>,
ops::DotGradKernel<paddle::platform::CPUDeviceContext,
paddle::platform::complex<double>>);
paddle/fluid/operators/dot_op.cu 已删除 100644 → 0
浏览文件 @ 56ddd7c2
// Copyright (c) 2020 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.
#include "paddle/fluid/operators/dot_op.h"
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
dot,
ops::DotKernel<plat::CUDADeviceContext, float>,
ops::DotKernel<plat::CUDADeviceContext, double>,
ops::DotKernel<plat::CUDADeviceContext, int>,
ops::DotKernel<plat::CUDADeviceContext, int64_t>,
ops::DotKernel<plat::CUDADeviceContext, paddle::platform::complex<float>>,
ops::DotKernel<plat::CUDADeviceContext, paddle::platform::complex<double>>);
REGISTER_OP_CUDA_KERNEL(dot_grad,
ops::DotGradKernel<plat::CUDADeviceContext, float>,
ops::DotGradKernel<plat::CUDADeviceContext, double>,
ops::DotGradKernel<plat::CUDADeviceContext, int>,
ops::DotGradKernel<plat::CUDADeviceContext, int64_t>,
ops::DotGradKernel<plat::CUDADeviceContext,
paddle::platform::complex<float>>,
ops::DotGradKernel<plat::CUDADeviceContext,
paddle::platform::complex<double>>);
paddle/fluid/operators/dot_op.h 已删除 100644 → 0
浏览文件 @ 56ddd7c2
// Copyright (c) 2020 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.
#pragma once
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/for_range.h"
#include "paddle/phi/kernels/funcs/complex_functors.h"
// only can include the headers in paddle/phi/api dirs
#include "paddle/phi/api/lib/utils/tensor_utils.h"
#include "paddle/phi/kernels/dot_grad_kernel.h"
#include "paddle/phi/kernels/dot_kernel.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
// See Note [ Why still keep the original kernel implementation? ]
template <typename DeviceContext, typename T>
class DotKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<Tensor>("X");
auto* y = ctx.Input<Tensor>("Y");
auto* out = ctx.Output<Tensor>("Out");
auto& dev_ctx = ctx.device_context<DeviceContext>();
out->mutable_data<T>(x->place());
// call new kernel
phi::DotKernel<
T,
typename paddle::framework::ConvertToPhiContext<DeviceContext>::TYPE>(
static_cast<const typename paddle::framework::ConvertToPhiContext<
DeviceContext>::TYPE&>(dev_ctx),
*x,
*y,
out);
}
};
template <typename DeviceContext, typename T>
class DotGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* tensor_x = ctx.Input<Tensor>("X");
auto* tensor_y = ctx.Input<Tensor>("Y");
auto* tensor_dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto* tensor_dx = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* tensor_dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
if (tensor_dx) tensor_dx->mutable_data<T>(ctx.GetPlace());
if (tensor_dy) tensor_dy->mutable_data<T>(ctx.GetPlace());
auto& dev_ctx = ctx.device_context<DeviceContext>();
// call new kernel
phi::DotGradKernel<T>(
static_cast<const typename paddle::framework::ConvertToPhiContext<
DeviceContext>::TYPE&>(dev_ctx),
*tensor_x,
*tensor_y,
*tensor_dout,
tensor_dx,
tensor_dy);
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/matmul_v2_op.h
浏览文件 @ f33763e3
......@@ -21,7 +21,6 @@ limitations under the License. */
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/dot_op.h"
#include "paddle/fluid/operators/reduce_ops/reduce_sum_op.h"
#include "paddle/phi/kernels/funcs/blas/blas.h"
#include "paddle/phi/kernels/funcs/complex_functors.h"
......
paddle/phi/kernels/digamma_kernel.h
浏览文件 @ f33763e3
......@@ -18,6 +18,13 @@
namespace phi {
/**
* @brief This kernrel is used to perform elementwise digamma for x.
* $$out = \Psi(x) = \frac{ \Gamma^{'}(x) }{ \Gamma(x) }$$
* @param ctx device context
* @param x the input tensor of digamma
* @param out the output tensor of digamma
*/
template <typename T, typename Context>
void DigammaKernel(const Context& ctx, const DenseTensor& x, DenseTensor* out);
......
paddle/phi/kernels/dist_kernel.h
浏览文件 @ f33763e3
......@@ -18,6 +18,37 @@
namespace phi {
/**
* @brief Given two tensors x and y, compute Lp-norm of (x-y).
* It is not a norm in a strict sense, only as a measure of distance.
* The shapes of x and y must be broadcastable. Where, z = x - y,
*
* When p = 0, defining $0^0 = 0$, the zero-norm of z is simply
* the number of non-zero elements of z.
* $$
* ||z||_{0} = \lim_{p \rightarrow 0} \sum_{i=1}^{m} |z_i|^p
* $$
*
* When p = inf, the inf-norm of z is the maximum element of z.
* $$
* ||z||_\infty=\max_i |z_i|
* $$
*
* When p = -inf, the negative-inf-norm of z is the minimum element of z.
* $$
* ||z||_{-\infty}=\min_i |z_i|
* $$
*
* Otherwise, the p-norm of z follows the formula,
* $$
* ||z||_{p} = (\sum_{i=i}^{m} |z_i|^p)^{1/p}
* $$
* @param ctx device context
* @param x the input Tensor of Dist
* @param y the Right-hand-side input Tensor of Dist
* @param p the norm to be computed
* @param out the output of Dist, which is the p-norm of (x - y)
*/
template <typename T, typename Context>
void DistKernel(const Context& dev_ctx,
const DenseTensor& x,
......
paddle/phi/kernels/erfinv_kernel.h
浏览文件 @ f33763e3
......@@ -18,6 +18,18 @@
namespace phi {
/**
* @brief This kernel is used to compute inverse error function of x.
*
* The equation is:
* $$erfinv(x) = {ndtri({x \over 2} + 0.5)} \over {\sqrt{2}}$$
*
* The input `x` can carry the LoD (Level of Details) information,
* or not. And the output shares the LoD information with `x`
* @param ctx device context
* @param x the input tensor of erfinv
* @param out the output tensor of erfinv
*/
template <typename T, typename Context>
void ErfinvKernel(const Context& ctx, const DenseTensor& x, DenseTensor* out);
......
paddle/phi/ops/compat/digamma_sig.cc 已删除 100644 → 0
浏览文件 @ 56ddd7c2
/* Copyright (c) 2022 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. */
#include "paddle/phi/core/compat/op_utils.h"
namespace phi {
KernelSignature DigammaGradOpArgumentMapping(
const ArgumentMappingContext& ctx) {
return KernelSignature("digamma_grad", {"X", "Out@GRAD"}, {}, {"X@GRAD"});
}
} // namespace phi
PD_REGISTER_ARG_MAPPING_FN(digamma_grad, phi::DigammaGradOpArgumentMapping);
paddle/phi/ops/compat/dist_sig.cc 已删除 100644 → 0
浏览文件 @ 56ddd7c2
/* Copyright (c) 2022 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. */
#include "paddle/phi/core/compat/op_utils.h"
namespace phi {
KernelSignature DistGradOpArgumentMapping(const ArgumentMappingContext& ctx) {
return KernelSignature(
"dist_grad", {"X", "Y", "Out", "Out@GRAD"}, {"p"}, {"X@GRAD", "Y@GRAD"});
}
} // namespace phi
PD_REGISTER_ARG_MAPPING_FN(dist_grad, phi::DistGradOpArgumentMapping);
paddle/phi/ops/compat/dot_sig.cc 已删除 100644 → 0
浏览文件 @ 56ddd7c2
/* Copyright (c) 2022 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. */
#include "paddle/phi/core/compat/op_utils.h"
namespace phi {
KernelSignature DotGradOpArgumentMapping(const ArgumentMappingContext& ctx) {
return KernelSignature(
"dot_grad", {"X", "Y", "Out@GRAD"}, {}, {"X@GRAD", "Y@GRAD"});
}
} // namespace phi
PD_REGISTER_ARG_MAPPING_FN(dot_grad, phi::DotGradOpArgumentMapping);
python/paddle/fluid/tests/unittests/test_dot_op.py
浏览文件 @ f33763e3
......@@ -27,6 +27,7 @@ class DotOp(OpTest):
def setUp(self):
self.op_type = "dot"
self.python_api = paddle.dot
self.init_dtype()
self.init_input_output()
......@@ -38,34 +39,43 @@ class DotOp(OpTest):
self.attrs = {}
def test_check_output(self):
self.check_output()
self.check_output(check_eager=True)
def test_check_grad_normal(self):
if core.is_compiled_with_rocm():
self.check_grad(
['X', 'Y'],
'Out',
user_defined_grads=[self.inputs['Y'], self.inputs['X']])
user_defined_grads=[self.inputs['Y'], self.inputs['X']],
check_eager=True)
else:
self.check_grad(['X', 'Y'], 'Out')
self.check_grad(['X', 'Y'], 'Out', check_eager=True)
def test_check_grad_ingore_x(self):
if core.is_compiled_with_rocm():
self.check_grad(['Y'],
'Out',
no_grad_set=set("X"),
user_defined_grads=[self.inputs['X']])
user_defined_grads=[self.inputs['X']],
check_eager=True)
else:
self.check_grad(['Y'], 'Out', no_grad_set=set("X"))
self.check_grad(['Y'],
'Out',
no_grad_set=set("X"),
check_eager=True)
def test_check_grad_ingore_y(self):
if core.is_compiled_with_rocm():
self.check_grad(['X'],
'Out',
no_grad_set=set('Y'),
user_defined_grads=[self.inputs['Y']])
user_defined_grads=[self.inputs['Y']],
check_eager=True)
else:
self.check_grad(['X'], 'Out', no_grad_set=set('Y'))
self.check_grad(['X'],
'Out',
no_grad_set=set('Y'),
check_eager=True)
def init_input_output(self):
self.x = np.random.uniform(0.1, 1, [121]).astype(self.dtype)
......@@ -137,6 +147,7 @@ class TestComplexDotOp(OpTest):
def setUp(self):
self.op_type = "dot"
self.python_api = paddle.dot
self.init_base_dtype()
self.init_input_output()
self.init_grad_input_output()
......@@ -164,27 +175,30 @@ class TestComplexDotOp(OpTest):
self.grad_y = self.grad_out * np.conj(self.x)
def test_check_output(self):
self.check_output()
self.check_output(check_eager=True)
def test_check_grad_normal(self):
self.check_grad(['X', 'Y'],
'Out',
user_defined_grads=[self.grad_x, self.grad_y],
user_defined_grad_outputs=[self.grad_out])
user_defined_grad_outputs=[self.grad_out],
check_eager=True)
def test_check_grad_ingore_x(self):
self.check_grad(['Y'],
'Out',
no_grad_set=set("X"),
user_defined_grads=[self.grad_y],
user_defined_grad_outputs=[self.grad_out])
user_defined_grad_outputs=[self.grad_out],
check_eager=True)
def test_check_grad_ingore_y(self):
self.check_grad(['X'],
'Out',
no_grad_set=set('Y'),
user_defined_grads=[self.grad_x],
user_defined_grad_outputs=[self.grad_out])
user_defined_grad_outputs=[self.grad_out],
check_eager=True)
class TestComplexDotOp2D(OpTest):
......
python/paddle/tensor/linalg.py
浏览文件 @ f33763e3
......@@ -1017,11 +1017,12 @@ def dot(x, y, name=None):
print(z)
"""
if in_dygraph_mode():
return _C_ops.final_state_dot(x, y)
if _in_legacy_dygraph():
return _C_ops.dot(x, y)
op_type = 'dot'
# skip var type check in dygraph mode to improve efficiency
if paddle.in_dynamic_mode():
op = getattr(_C_ops, op_type)
return op(x, y)
assert x is not None, 'x cannot be None in {}'.format(op_type)
assert y is not None, 'y cannot be None in {}'.format(op_type)
......
python/paddle/utils/code_gen/api.yaml
浏览文件 @ f33763e3
......@@ -52,6 +52,34 @@
func : diagonal
backward : diagonal_grad
- api : digamma
args : (Tensor x)
output : Tensor
infer_meta :
func : UnchangedInferMeta
kernel :
func : digamma
backward : digamma_grad
- api : dist
args : (Tensor x, Tensor y, float p = 2.0)
output : Tensor
infer_meta :
func : DistInferMeta
kernel :
func : dist
backward : dist_grad
- api : dot
args : (Tensor x, Tensor y)
output : Tensor
infer_meta :
func : DotInferMeta
kernel :
func : dot
data_type : x
backward : dot_grad
- api : erf
args : (Tensor x)
output : Tensor
......
python/paddle/utils/code_gen/backward.yaml
浏览文件 @ f33763e3
......@@ -51,6 +51,37 @@
data_type : out_grad
no_need_buffer : x
- backward_api : digamma_grad
forward : digamma (Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : digamma_grad
- backward_api : dist_grad
forward : dist (Tensor x, Tensor y, float p) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out, Tensor out_grad, float p)
output : Tensor(x_grad), Tensor(y_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param : [x, y]
kernel :
func : dist_grad
- backward_api : dot_grad
forward : dot (Tensor x, Tensor y) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out_grad)
output : Tensor(x_grad), Tensor(y_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param : [x, y]
kernel :
func : dot_grad
data_type : out_grad
- backward_api : erf_grad
forward : erf (Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
......
python/paddle/utils/code_gen/legacy_api.yaml
浏览文件 @ f33763e3
......@@ -497,24 +497,6 @@
kernel :
func : diag
- api : digamma
args : (Tensor x)
output : Tensor
infer_meta :
func : UnchangedInferMeta
kernel :
func : digamma
backward : digamma_grad
- api : dist
args : (Tensor x, Tensor y, float p)
output : Tensor
infer_meta :
func : DistInferMeta
kernel :
func : dist
backward : dist_grad
- api : divide
args : (Tensor x, Tensor y)
output : Tensor
......@@ -524,14 +506,6 @@
func : divide
backward : divide_grad
- api : dot
args : (Tensor x, Tensor y)
output : Tensor
infer_meta :
func : DotInferMeta
kernel :
func : dot
- api : dropout
args : (Tensor x, Tensor seed_tensor, float p, bool is_test, str mode, int seed, bool fix_seed)
output : Tensor(out), Tensor(mask)
......@@ -629,14 +603,14 @@
kernel :
func : equal_all
# erfinv
- api : erfinv
args : (Tensor x)
output : Tensor
output : Tensor(out)
infer_meta :
func : UnchangedInferMeta
kernel :
func : erfinv
inplace : (x -> out)
backward : erfinv_grad
# exp
......
python/paddle/utils/code_gen/legacy_backward.yaml
浏览文件 @ f33763e3
......@@ -498,26 +498,6 @@
kernel :
func : determinant_grad
- backward_api : digamma_grad
forward : digamma (Tensor x) -> Tensor(out)
args : (Tensor x, Tensor out_grad)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : digamma_grad
- backward_api : dist_grad
forward : dist (Tensor x, Tensor y, float p) -> Tensor(out)
args : (Tensor x, Tensor y, Tensor out, Tensor out_grad, float p)
output : Tensor(x_grad), Tensor(y_grad)
infer_meta :
func : GeneralBinaryGradInferMeta
param : [x, y]
kernel :
func : dist_grad
- backward_api : divide_double_grad
forward : divide_grad (Tensor x, Tensor y, Tensor out, Tensor grad_out, int axis = -1) -> Tensor(grad_x), Tensor(grad_y)
args : (Tensor y, Tensor out, Tensor grad_x, Tensor grad_x_grad, Tensor grad_y_grad, int axis = -1)
......
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