sneaxiy
6 years ago
parent
cfd012e2cb
commit
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// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include <string>
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#include <unordered_map>
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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class CrossEntropyOpBase : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
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PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
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PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) should be not null.");
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auto x_dims = ctx->GetInputDim("X");
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auto label_dims = ctx->GetInputDim("Label");
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int rank = x_dims.size();
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PADDLE_ENFORCE_EQ(rank, label_dims.size(),
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"Input(X) and Input(Label) shall have the same rank.");
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bool check = true;
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if ((!ctx->IsRuntime()) && (framework::product(x_dims) <= 0 ||
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framework::product(label_dims) <= 0)) {
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check = false;
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}
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if (check) {
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PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1),
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framework::slice_ddim(label_dims, 0, rank - 1),
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"Input(X) and Input(Label) shall have the same shape "
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"except the last dimension.");
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}
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if (IsSoftLabel(ctx)) {
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if (check) {
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PADDLE_ENFORCE_EQ(x_dims[rank - 1], label_dims[rank - 1],
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"If Attr(soft_label) == true, the last dimension of "
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"Input(X) and Input(Label) should be equal.");
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}
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} else {
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PADDLE_ENFORCE_EQ(label_dims[rank - 1], 1UL,
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"If Attr(softLabel) == false, the last dimension of "
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"Input(Label) should be 1.");
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}
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auto y_dims = x_dims;
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y_dims[rank - 1] = 1;
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ctx->SetOutputDim("Y", y_dims);
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ctx->ShareLoD("X", /*->*/ "Y");
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}
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protected:
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// Explicitly set that the data type of computation kernel of cross_entropy
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// is determined by its input "X".
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
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ctx.device_context());
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}
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virtual bool IsSoftLabel(framework::InferShapeContext* ctx) const {
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return ctx->Attrs().Get<bool>("soft_label");
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}
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};
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class CrossEntropyOpInferVarType
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: public framework::PassInDtypeAndVarTypeToOutput {
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protected:
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std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
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const override {
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return std::unordered_map<std::string, std::string>{{"X", /*->*/ "Y"}};
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}
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};
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class CrossEntropyGradientOpBase : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const {
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PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")),
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"Input(Y@GRAD) shoudl be not null.");
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PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
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"Output(X@GRAD) should be not null.");
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auto x_dims = GetXDim(ctx);
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auto label_dims = ctx->GetInputDim("Label");
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auto dy_dims = ctx->GetInputDim(framework::GradVarName("Y"));
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int rank = x_dims.size();
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PADDLE_ENFORCE_EQ(dy_dims.size(), rank,
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"Input(Y@Grad) and Input(X) should have the same rank.");
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PADDLE_ENFORCE_EQ(label_dims.size(), rank,
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"Input(Label) and Input(X) should have the same rank.");
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bool check = true;
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if ((!ctx->IsRuntime()) && (framework::product(x_dims) <= 0 ||
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framework::product(label_dims) <= 0)) {
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check = false;
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}
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if (check) {
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PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1),
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framework::slice_ddim(label_dims, 0, rank - 1),
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"The Input(X) and Input(Label) should have the same "
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"shape except the last dimension.");
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PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1),
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framework::slice_ddim(dy_dims, 0, rank - 1),
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"The Input(X) and Input(Y@Grad) should have the same "
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"shape except the last dimension.");
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}
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if (IsSoftLabel(ctx)) {
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if (check) {
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PADDLE_ENFORCE_EQ(
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x_dims[rank - 1], label_dims[rank - 1],
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"When Attr(soft_label) == true, the last dimension of "
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"Input(X) and Input(Label) should be equal.");
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}
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} else {
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PADDLE_ENFORCE_EQ(label_dims[rank - 1], 1,
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"When Attr(soft_label) == false, the last dimension of "
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"Input(Label) should be 1.");
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}
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ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
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PADDLE_ENFORCE_EQ(dy_dims[rank - 1], 1,
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"The last dimension of Input(Y@Grad) should be 1.");
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ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
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ctx->ShareLoD(VarNameWithXLoD(), framework::GradVarName("X"));
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}
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protected:
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// Explicitly set that the data type of computation kernel of cross_entropy
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// is determined by its input "X".
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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return framework::OpKernelType(
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ctx.Input<Tensor>(framework::GradVarName("Y"))->type(),
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ctx.device_context());
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}
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virtual framework::DDim GetXDim(framework::InferShapeContext* ctx) const {
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return ctx->GetInputDim("X");
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}
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virtual const char* VarNameWithXLoD() const { return "X"; }
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virtual bool IsSoftLabel(framework::InferShapeContext* ctx) const {
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return ctx->Attrs().Get<bool>("soft_label");
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}
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};
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} // namespace operators
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} // namespace paddle
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@ -0,0 +1,42 @@
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// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include "paddle/fluid/platform/float16.h"
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#include "paddle/fluid/platform/hostdevice.h"
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#include "math.h" // NOLINT
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namespace paddle {
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namespace operators {
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inline HOSTDEVICE platform::float16 real_exp(platform::float16 x) {
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return static_cast<platform::float16>(::expf(static_cast<float>(x)));
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}
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inline HOSTDEVICE float real_exp(float x) { return ::expf(x); }
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inline HOSTDEVICE double real_exp(double x) { return ::exp(x); }
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inline HOSTDEVICE platform::float16 real_log(platform::float16 x) {
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return static_cast<platform::float16>(::logf(static_cast<float>(x)));
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}
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inline HOSTDEVICE float real_log(float x) { return ::logf(x); }
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inline HOSTDEVICE double real_log(double x) { return ::log(x); }
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} // namespace operators
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} // namespace paddle
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