Merge pull request #14950 from colourful-tree/develop
add teacher student sigmoid lossrevert-15207-remove_op_handle_lock_and_fix_var
colourful-tree
6 years ago
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GitHub
commit
d5a8909131
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "paddle/fluid/operators/teacher_student_sigmoid_loss_op.h"
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#include "paddle/fluid/operators/math/math_function.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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class TeacherStudentSigmoidLossOp : 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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PADDLE_ENFORCE_EQ(x_dims.size(), 2UL, "Input(X)'s rank should be 2.");
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PADDLE_ENFORCE_EQ(label_dims.size(), 2UL,
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"Input(Label)'s rank should be 2.");
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PADDLE_ENFORCE_EQ(x_dims[0], label_dims[0],
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"The 1st dimension of Input(X) and Input(Label) should "
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"be equal.");
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PADDLE_ENFORCE_EQ(label_dims[1], 1UL,
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"The 2nd dimension of "
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"Input(Label) should be 1.");
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ctx->SetOutputDim("Y", {x_dims[0], 1});
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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
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// teacher_student_sigmoid_loss
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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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};
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class TeacherStudentSigmoidLossGradientOp
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: 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->HasInput(framework::GradVarName("Y")),
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"Input(Y@GRAD) should 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 = ctx->GetInputDim("X");
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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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PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
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PADDLE_ENFORCE_EQ(dy_dims.size(), 2, "Input(Y@Grad)'s rank should be 2.");
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PADDLE_ENFORCE_EQ(label_dims.size(), 2, "Input(Label)'s rank should be 2.");
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PADDLE_ENFORCE_EQ(x_dims[0], label_dims[0],
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"The 1st dimension of Input(X) and Input(Label) should "
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"be equal.");
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PADDLE_ENFORCE_EQ(x_dims[0], dy_dims[0],
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"The 1st dimension of Input(X) and Input(Y@Grad) should "
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"be equal.");
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PADDLE_ENFORCE_EQ(dy_dims[1], 1,
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"The 2nd dimension of Input(Y@Grad) should be 1.");
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PADDLE_ENFORCE_EQ(label_dims[1], 1,
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"When Attr(soft_label) == false, the 2nd dimension of "
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"Input(Label) should be 1.");
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ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
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ctx->ShareLoD("X", 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
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// teacher_student_sigmoid_loss
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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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};
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class TeacherStudentSigmoidLossOpMaker
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: public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X",
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"(Tensor, default Tensor<float>), a 2-D tensor with shape [N x 1],"
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" where N is the batch size and D is the output. "
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"This input is a probability computed by the previous operator, "
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"which is almost always the result of a softmax operator.");
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AddInput("Label",
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"(Tensor), the ground truth which is a 2-D tensor. "
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"Label is a Tensor<float> with shape [N x 1]. ");
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AddOutput("Y",
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"(Tensor, default Tensor<float>), a 2-D tensor with shape "
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"[N x 1]. The teacher student sigmoid loss.");
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AddAttr<float>(
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"soft_max_up_bound",
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"fp32, if input > soft_max_up_bound, will be bound, default 15.0")
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.SetDefault(15.0);
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AddAttr<float>(
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"soft_max_lower_bound",
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"fp32, if input < soft_max_lower_bound, will be bound, default -15.0")
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.SetDefault(-15.0);
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AddComment(R"DOC(
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TeacherStudentSigmoidLoss Operator.
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It's similarity to SigmoidCrossEntropyWithLogits Operator. The difference is that
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we add another label(z') to original.
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loss = max(x, 0) - x * z + log(1 + exp(-abs(x))) + max(x, 0) - x * z' + log(1 + exp(-abs(x)))
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z is click or not
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z' is teacher value
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label = {-2, -1, [0, 2]}
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when z' is not exist, clk = 0 : label = -2;
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when z' is not exist, clk = 1 : label = -1;
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when z' is exist , clk = 0 : label = 0 + z';
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when z' is exist , clk = 1 : label = 1 + z';
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)DOC");
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OPERATOR(teacher_student_sigmoid_loss,
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ops::TeacherStudentSigmoidLossOp,
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ops::TeacherStudentSigmoidLossOpMaker,
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paddle::framework::DefaultGradOpDescMaker<true>);
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REGISTER_OPERATOR(teacher_student_sigmoid_loss_grad,
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ops::TeacherStudentSigmoidLossGradientOp);
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REGISTER_OP_CPU_KERNEL(teacher_student_sigmoid_loss,
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ops::TeacherStudentSigmoidLossOpKernel<float>,
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ops::TeacherStudentSigmoidLossOpKernel<double>);
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REGISTER_OP_CPU_KERNEL(teacher_student_sigmoid_loss_grad,
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ops::TeacherStudentSigmoidLossGradOpKernel<float>,
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ops::TeacherStudentSigmoidLossGradOpKernel<double>);
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@ -0,0 +1,118 @@
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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/framework/eigen.h"
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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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using Tensor = framework::Tensor;
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template <typename T>
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class TeacherStudentSigmoidLossOpKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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Tensor* y = context.Output<Tensor>("Y");
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const Tensor* x = context.Input<Tensor>("X");
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const Tensor* labels = context.Input<Tensor>("Label");
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T* y_data = y->mutable_data<T>(context.GetPlace());
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const T* x_data = x->data<T>();
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const T* label_data = labels->data<T>();
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int64_t batch_size = x->dims()[0];
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// loss = max(x, 0) - x * z + log(1 + exp(-abs(x))) + max(x, 0) - x * z' +
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// log(1 + exp(-abs(x)))
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// z is click or not
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// z' is value q of feed_fine
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// label = {-2, -1, [0, 2]}
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// when z' is not exist, clk = 0 : label = -2;
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// when z' is not exist, clk = 1 : label = -1;
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// when z' is exist , clk = 0 : label = 0 + z';
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// when z' is exist , clk = 1 : label = 1 + z';
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for (int i = 0; i < batch_size; ++i) {
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if (label_data[i] < -1.0) {
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y_data[i] = (x_data[i] > 0 ? x_data[i] : 0.0) +
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log(1.0 + exp(-fabs(x_data[i])));
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} else if (label_data[i] < 0.0) {
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y_data[i] = (x_data[i] > 0 ? x_data[i] : 0.0) - x_data[i] +
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log(1.0 + exp(-fabs(x_data[i])));
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} else if (label_data[i] < 1.0) {
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y_data[i] = (x_data[i] > 0 ? x_data[i] : 0.0) +
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log(1.0 + exp(-fabs(x_data[i]))) +
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(x_data[i] > 0 ? x_data[i] : 0.0) -
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x_data[i] * label_data[i] +
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log(1.0 + exp(-fabs(x_data[i])));
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} else {
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y_data[i] = (x_data[i] > 0 ? x_data[i] : 0.0) - x_data[i] +
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log(1.0 + exp(-fabs(x_data[i]))) +
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(x_data[i] > 0 ? x_data[i] : 0.0) -
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x_data[i] * (label_data[i] - 1.0) +
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log(1.0 + exp(-fabs(x_data[i])));
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}
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}
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}
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};
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template <typename T>
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class TeacherStudentSigmoidLossGradOpKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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const Tensor* x = context.Input<Tensor>("X");
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const T* x_data = x->data<T>();
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Tensor* dx = context.Output<Tensor>(framework::GradVarName("X"));
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T* dx_data = dx->mutable_data<T>(context.GetPlace());
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const Tensor* labels = context.Input<Tensor>("Label");
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const T* label_data = labels->data<T>();
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T soft_max_up_bound =
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static_cast<T>(context.Attr<float>("soft_max_up_bound"));
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T soft_max_lower_bound =
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static_cast<T>(context.Attr<float>("soft_max_lower_bound"));
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int64_t batch_size = x->dims()[0];
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const framework::Tensor* dOut =
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context.Input<framework::Tensor>(framework::GradVarName("Y"));
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const T* dout_data = dOut->data<T>();
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for (int i = 0; i < batch_size; ++i) {
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T sum_val = x_data[i];
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if (sum_val > soft_max_up_bound) {
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sum_val = soft_max_up_bound;
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} else {
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if (sum_val < soft_max_lower_bound) {
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sum_val = soft_max_lower_bound;
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}
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}
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T pred = 1.0 / (1.0 + exp(-sum_val));
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if (label_data[i] < -1.0) {
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dx_data[i] = 0.0 - pred;
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} else if (label_data[i] < 0.0) {
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dx_data[i] = 1.0 - pred;
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} else {
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dx_data[i] = label_data[i] - 2.0 * pred;
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}
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if (sum_val >= soft_max_up_bound || sum_val <= soft_max_lower_bound) {
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dx_data[i] = 0;
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}
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dx_data[i] *= dout_data[i] * -1;
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}
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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 +1,59 @@
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# Copyright (c) 2018 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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import numpy as np
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from math import log
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from math import exp
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from op_test import OpTest
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from scipy.special import logit
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from scipy.special import expit
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import unittest
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class TestTeacherStudentSigmoidLossOp(OpTest):
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"""
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Test teacher_student_sigmoid_loss with discrete one-hot labels.
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"""
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def setUp(self):
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self.op_type = "teacher_student_sigmoid_loss"
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batch_size = 16
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num_classes = 1
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self.inputs = {
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'X': logit(
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np.random.uniform(0, 1, (batch_size, num_classes))
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.astype("float32")),
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'Label': np.random.uniform(0, 2, (batch_size, num_classes))
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.astype("float32")
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}
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outs = []
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for index, label in enumerate(self.inputs["Label"]):
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x = self.inputs["X"][index]
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if label < -1.0:
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outs.append(max(x, 0.0) + log(1.0 + exp(-abs(x))))
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elif label < 0.0:
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outs.append(max(x, 0.0) - x + log(1.0 + exp(-abs(x))))
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elif label < 1.0:
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outs.append(max(x, 0.0) + log(1.0 + exp(-abs(x))) + \
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max(x, 0.0) - x * label + log(1.0 + exp(-abs(x))))
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else:
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outs.append(max(x, 0.0) - x + log(1.0 + exp(-abs(x))) + \
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max(x, 0.0) - x * (label - 1.0) + log(1.0 + exp(-abs(x))))
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self.outputs = {'Y': np.array(outs)}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(["X"], "Y", numeric_grad_delta=0.005)
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