提交 29cb8563 编写于 作者: Y Yiqun Liu 提交者: GitHub

Merge pull request #4144 from lcy-seso/softmax_with_cross_entropy_op

Softmax with cross entropy op.
......@@ -88,10 +88,14 @@ add_subdirectory(math)
set(DEPS_OPS
recurrent_op
cond_op)
cond_op
cross_entropy_op
softmax_with_cross_entropy_op)
op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS framework_proto tensor net_op)
op_library(cond_op SRCS cond_op.cc DEPS framework_proto tensor operator net_op)
op_library(cross_entropy_op DEPS cross_entropy_function)
op_library(softmax_with_cross_entropy_op DEPS cross_entropy_function softmax_function)
list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
foreach(src ${GENERAL_OPS})
......
......@@ -12,62 +12,12 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/op_registry.h"
#include "paddle/operators/cross_entropy_op.h"
#include "paddle/platform/assert.h"
#include "paddle/platform/hostdevice.h"
namespace paddle {
namespace operators {
template <typename T>
__global__ void CrossEntropyKernel(T* Y, const T* X, const int* label,
const int N, const int D) {
// TOOD(qingqing) define CUDA_1D_KERNEL_LOOP macro in a common file.
// CUDA_1D_KERNEL_LOOP(i, N) {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
i += blockDim.x * gridDim.x) {
PADDLE_ASSERT(label[i] >= 0 && label[i] < D);
Y[i] = -TolerableValue<T>()(log(X[i * D + label[i]]));
}
}
template <typename T>
__device__ __forceinline__ T sum_single_warp(T val) {
val += __shfl_down(val, 16);
val += __shfl_down(val, 8);
val += __shfl_down(val, 4);
val += __shfl_down(val, 2);
val += __shfl_down(val, 1);
return val;
}
template <typename T>
__global__ void SoftCrossEntropyKernel(T* Y, const T* X, const T* label,
const int class_num) {
int tid = threadIdx.x;
extern __shared__ T d_sum[];
d_sum[tid] = 0;
int cur_idx = tid;
int next_idx = blockIdx.x * class_num + tid;
while (cur_idx < class_num) {
d_sum[tid] += TolerableValue<T>()(std::log(X[next_idx])) * label[next_idx];
next_idx += blockDim.x;
cur_idx += blockDim.x;
}
__syncthreads();
for (unsigned int stride = blockDim.x >> 1; stride >= 32; stride >>= 1) {
if (tid < stride) d_sum[tid] += d_sum[tid + stride];
__syncthreads();
}
T val = d_sum[tid];
val = sum_single_warp<T>(val);
if (tid == 0) Y[blockIdx.x] = -val;
}
namespace {
// TODO(qingqing): make zero setting a common function.
template <typename T>
__global__ void Zero(T* X, const int N) {
......@@ -100,6 +50,7 @@ __global__ void SoftCrossEntropyGradientKernel(T* dX, const T* dY, const T* X,
dX[ids] = -label[ids] * dY[row_ids] / X[ids];
}
}
} // namespace
template <typename T>
class CrossEntropyOpCUDAKernel : public framework::OpKernel {
......@@ -107,36 +58,13 @@ class CrossEntropyOpCUDAKernel : public framework::OpKernel {
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
"This kernel only runs on GPU device.");
const Tensor* x = ctx.Input<Tensor>("X");
const Tensor* label = ctx.Input<Tensor>("Label");
Tensor* y = ctx.Output<Tensor>("Y");
y->mutable_data<T>(ctx.GetPlace());
const T* x_data = x->data<T>();
T* y_data = y->mutable_data<T>(ctx.GetPlace());
int batch_size = x->dims()[0];
int class_num = x->dims()[1];
if (ctx.Attr<bool>("softLabel")) {
auto* label_data = ctx.Input<Tensor>("Label")->data<T>();
int block = class_num > 512 ? 512 : pow(2, int(std::log2(class_num)));
SoftCrossEntropyKernel<
T><<<batch_size, block, block * sizeof(T),
reinterpret_cast<const platform::CUDADeviceContext&>(
ctx.device_context())
.stream()>>>(y_data, x_data, label_data, class_num);
} else {
auto* label_data = ctx.Input<Tensor>("Label")->data<int>();
int block = 512;
int grid = (batch_size + block - 1) / block;
CrossEntropyKernel<T><<<
grid, block, 0, reinterpret_cast<const platform::CUDADeviceContext&>(
ctx.device_context())
.stream()>>>(y_data, x_data, label_data,
batch_size, class_num);
}
math::CrossEntropyFunctor<platform::GPUPlace, T>()(
ctx, y, x, label, ctx.Attr<bool>("softLabel"));
}
};
......@@ -150,6 +78,7 @@ class CrossEntropyGradientOpCUDAKernel : public framework::OpKernel {
const Tensor* x = ctx.Input<Tensor>("X");
const Tensor* label = ctx.Input<Tensor>("Label");
Tensor* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
dx->mutable_data<T>(ctx.GetPlace());
const T* dy_data =
ctx.Input<Tensor>(framework::GradVarName("Y"))->data<T>();
......
......@@ -15,7 +15,7 @@ limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/platform/hostdevice.h"
#include "paddle/operators/math/cross_entropy.h"
namespace paddle {
namespace operators {
......@@ -25,18 +25,6 @@ template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename T>
struct TolerableValue {
HOSTDEVICE T operator()(const T& x) const {
PADDLE_ASSERT(std::is_floating_point<T>::value);
const T kApproInf = 1e20;
if (x == INFINITY) return kApproInf;
if (x == -INFINITY) return -kApproInf;
return x;
}
};
template <typename T>
class CrossEntropyOpKernel : public framework::OpKernel {
public:
......@@ -46,28 +34,10 @@ class CrossEntropyOpKernel : public framework::OpKernel {
const Tensor* x = ctx.Input<Tensor>("X");
const Tensor* labels = ctx.Input<Tensor>("Label");
Tensor* y = ctx.Output<Tensor>("Y");
T* y_data = y->mutable_data<T>(ctx.GetPlace());
const int batch_size = x->dims()[0];
if (ctx.Attr<bool>("softLabel")) {
auto prob = EigenMatrix<T>::From(*x);
auto lbl_mat = EigenMatrix<T>::From(*labels);
auto loss = EigenMatrix<T>::From(*y);
y->mutable_data<T>(ctx.GetPlace());
loss.device(ctx.GetEigenDevice<platform::CPUPlace>()) =
-((lbl_mat * prob.log().unaryExpr(TolerableValue<T>()))
.sum(Eigen::DSizes<int, 1>(1))
.reshape(Eigen::DSizes<int, 2>(batch_size, 1)));
} else {
const int class_num = x->dims()[1];
const T* x_data = x->data<T>();
const int* label_data = labels->data<int>();
for (int i = 0; i < batch_size; ++i) {
int index = i * class_num + label_data[i];
y_data[i] = -TolerableValue<T>()(std::log(x_data[index]));
}
}
math::CrossEntropyFunctor<platform::CPUPlace, T>()(
ctx, y, x, labels, ctx.Attr<bool>("softLabel"));
}
};
......
if(WITH_GPU)
nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc
im2col.cu DEPS cblas device_context)
im2col.cu DEPS cblas device_context operator)
nv_library(softmax_function SRCS softmax.cc softmax.cu
DEPS operator)
nv_library(cross_entropy_function SRCS cross_entropy.cc cross_entropy.cu
DEPS operator)
else()
cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context)
cc_library(math_function SRCS math_function.cc im2col.cc
DEPS cblas device_context operator)
cc_library(softmax_function SRCS softmax.cc DEPS operator)
cc_library(cross_entropy_function SRCS cross_entropy.cc DEPS operator)
endif()
nv_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
......
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/math/cross_entropy.h"
namespace paddle {
namespace operators {
namespace math {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename T>
class CrossEntropyFunctor<platform::CPUPlace, T> {
public:
void operator()(const framework::ExecutionContext& ctx,
framework::Tensor* out, const framework::Tensor* prob,
const framework::Tensor* labels, const bool softLabel) {
const int batch_size = prob->dims()[0];
if (softLabel) {
auto in = EigenMatrix<T>::From(*prob);
auto lbl = EigenMatrix<T>::From(*labels);
auto loss = EigenMatrix<T>::From(*out);
loss.device(ctx.GetEigenDevice<platform::CPUPlace>()) =
-((lbl * in.log().unaryExpr(math::TolerableValue<T>()))
.sum(Eigen::DSizes<int, 1>(1))
.reshape(Eigen::DSizes<int, 2>(batch_size, 1)));
} else {
const int class_num = prob->dims()[1];
const T* prob_data = prob->data<T>();
T* loss_data = out->data<T>();
const int* label_data = labels->data<int>();
for (int i = 0; i < batch_size; ++i) {
int index = i * class_num + label_data[i];
loss_data[i] = -math::TolerableValue<T>()(std::log(prob_data[index]));
}
}
}
};
template class CrossEntropyFunctor<platform::CPUPlace, float>;
} // namespace math
} // namespace operators
} // namespace paddle
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/math/cross_entropy.h"
namespace paddle {
namespace operators {
namespace math {
namespace {
template <typename T>
__global__ void CrossEntropyKernel(T* Y, const T* X, const int* label,
const int N, const int D) {
// TOOD(qingqing) define CUDA_1D_KERNEL_LOOP macro in a common file.
// CUDA_1D_KERNEL_LOOP(i, N) {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
i += blockDim.x * gridDim.x) {
PADDLE_ASSERT(label[i] >= 0 && label[i] < D);
Y[i] = -math::TolerableValue<T>()(log(X[i * D + label[i]]));
}
}
template <typename T>
__device__ __forceinline__ T sum_single_warp(T val) {
val += __shfl_down(val, 16);
val += __shfl_down(val, 8);
val += __shfl_down(val, 4);
val += __shfl_down(val, 2);
val += __shfl_down(val, 1);
return val;
}
template <typename T>
__global__ void SoftCrossEntropyKernel(T* Y, const T* X, const T* label,
const int class_num) {
int tid = threadIdx.x;
extern __shared__ T d_sum[];
d_sum[tid] = 0;
int cur_idx = tid;
int next_idx = blockIdx.x * class_num + tid;
while (cur_idx < class_num) {
d_sum[tid] +=
math::TolerableValue<T>()(std::log(X[next_idx])) * label[next_idx];
next_idx += blockDim.x;
cur_idx += blockDim.x;
}
__syncthreads();
for (unsigned int stride = blockDim.x >> 1; stride >= 32; stride >>= 1) {
if (tid < stride) d_sum[tid] += d_sum[tid + stride];
__syncthreads();
}
T val = d_sum[tid];
val = sum_single_warp<T>(val);
if (tid == 0) Y[blockIdx.x] = -val;
}
} // namespace
using Tensor = framework::Tensor;
template <typename T>
class CrossEntropyFunctor<platform::GPUPlace, T> {
public:
void operator()(const framework::ExecutionContext& ctx,
framework::Tensor* out, const framework::Tensor* prob,
const framework::Tensor* labels, bool softLabel) {
const T* prob_data = prob->data<T>();
T* loss_data = out->mutable_data<T>(ctx.GetPlace());
int batch_size = prob->dims()[0];
int class_num = prob->dims()[1];
if (softLabel) {
const T* label_data = labels->data<T>();
int block = class_num > 512 ? 512 : pow(2, int(std::log2(class_num)));
SoftCrossEntropyKernel<
T><<<batch_size, block, block * sizeof(T),
reinterpret_cast<const platform::CUDADeviceContext&>(
ctx.device_context())
.stream()>>>(loss_data, prob_data, label_data, class_num);
} else {
const int* label_data = labels->data<int>();
int block = 512;
int grid = (batch_size + block - 1) / block;
CrossEntropyKernel<T><<<
grid, block, 0, reinterpret_cast<const platform::CUDADeviceContext&>(
ctx.device_context())
.stream()>>>(loss_data, prob_data, label_data,
batch_size, class_num);
}
}
};
template class CrossEntropyFunctor<platform::GPUPlace, float>;
} // namespace math
} // namespace operators
} // namespace paddle
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/framework/eigen.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/hostdevice.h"
namespace paddle {
namespace operators {
namespace math {
template <typename T>
struct TolerableValue {
HOSTDEVICE T operator()(const T& x) const {
PADDLE_ASSERT(std::is_floating_point<T>::value);
const T kApproInf = 1e20;
if (x == INFINITY) return kApproInf;
if (x == -INFINITY) return -kApproInf;
return x;
}
};
template <typename Place, typename T>
class CrossEntropyFunctor {
public:
// (TODO caoying) it is much better to use DeviceContext as the first
// parameter.
void operator()(const framework::ExecutionContext& context,
framework::Tensor* out, const framework::Tensor* prob,
const framework::Tensor* labels, const bool softLabel);
};
} // namespace math
} // namespace operators
} // namespace paddle
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/math/softmax.h"
namespace paddle {
namespace operators {
namespace math {
template class SoftmaxFunctor<platform::GPUPlace, float>;
} // namespace math
} // namespace operators
} // namespace paddle
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#define EIGEN_USE_GPU
#include "paddle/operators/math/softmax.h"
namespace paddle {
namespace operators {
namespace math {
template class SoftmaxFunctor<platform::GPUPlace, float>;
} // namespace math
} // namespace operators
} // namespace paddle
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/framework/eigen.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/tensor.h"
namespace paddle {
namespace operators {
namespace math {
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename T>
struct ValueClip {
HOSTDEVICE T operator()(const T& x) const {
const T kThreshold = -64.;
return x < kThreshold ? kThreshold : x;
}
};
template <typename Place, typename T>
class SoftmaxFunctor {
public:
void operator()(const framework::ExecutionContext& context,
const framework::Tensor* X, framework::Tensor* Y) {
auto logits = EigenMatrix<T>::From(*X);
auto softmax = EigenMatrix<T>::From(*Y);
const int kBatchDim = 0;
const int kClassDim = 1;
const int batch_size = logits.dimension(kBatchDim);
const int num_classes = logits.dimension(kClassDim);
Eigen::DSizes<int, 1> along_class(kClassDim);
Eigen::DSizes<int, 2> batch_by_one(batch_size, 1);
Eigen::DSizes<int, 2> one_by_class(1, num_classes);
auto shifted_logits = (logits -
logits.maximum(along_class)
.eval()
.reshape(batch_by_one)
.broadcast(one_by_class))
.unaryExpr(ValueClip<T>());
softmax.device(context.GetEigenDevice<Place>()) = shifted_logits.exp();
softmax.device(context.GetEigenDevice<Place>()) =
(softmax *
softmax.sum(along_class)
.inverse()
.eval()
.reshape(batch_by_one)
.broadcast(one_by_class));
}
};
} // namespace math
} // namespace operators
} // namespace paddle
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/softmax.h"
namespace paddle {
namespace operators {
......@@ -30,36 +31,11 @@ class SoftmaxKernel : public framework::OpKernel {
void Compute(const framework::ExecutionContext& context) const override {
auto X = context.Input<Tensor>("X");
auto Y = context.Output<Tensor>("Y");
Y->mutable_data<T>(context.GetPlace());
auto logits = EigenMatrix<T>::From(*X);
auto softmax = EigenMatrix<T>::From(*Y);
const int kBatchDim = 0;
const int kClassDim = 1;
const int batch_size = logits.dimension(kBatchDim);
const int num_classes = logits.dimension(kClassDim);
Eigen::DSizes<int, 1> along_class(kClassDim);
Eigen::DSizes<int, 2> batch_by_one(batch_size, 1);
Eigen::DSizes<int, 2> one_by_class(1, num_classes);
auto shifted_logits = (logits -
logits.maximum(along_class)
.eval()
.reshape(batch_by_one)
.broadcast(one_by_class));
softmax.device(context.GetEigenDevice<Place>()) = shifted_logits.exp();
// allocate memory on device.
Y->mutable_data<T>(context.GetPlace());
softmax.device(context.GetEigenDevice<Place>()) =
(softmax *
softmax.sum(along_class)
.inverse()
.eval()
.reshape(batch_by_one)
.broadcast(one_by_class));
math::SoftmaxFunctor<Place, T>()(context, X, Y);
}
};
......@@ -67,8 +43,6 @@ template <typename Place, typename T>
class SoftmaxGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
std::shared_ptr<Tensor> scale_ = std::make_shared<Tensor>();
auto Y = context.Input<Tensor>("Y");
auto dY = context.Input<Tensor>(framework::GradVarName("Y"));
auto dX = context.Output<Tensor>(framework::GradVarName("X"));
......
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/softmax_with_cross_entropy_op.h"
namespace paddle {
namespace operators {
class SoftmaxWithCrossEntropyOpMaker
: public framework::OpProtoAndCheckerMaker {
public:
SoftmaxWithCrossEntropyOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Logits",
"(Tensor, default: Tensor<float>), The unscaled log probabilities "
"which is a 2-D tensor with shape [N x K]. N is the batch_size, "
"and K is the class number.")
.NotInGradient();
AddInput(
"Label",
"(Tensor, default: Tensor<int>), The ground truth which is a 2-D "
"tensor. "
"If softLable is set to 0, Label is a Tensor<int> with shape [N x 1]. "
"If softLable is set to 1, Label is a Tensor<float/double> "
"with shape [N x K].");
AddOutput(
"Softmax",
"(Tensor, default: Tensor<float>), A 2-D tensor with shape [N x K]. "
"The outputs value of softmax activation by given the input batch, "
"which will be used in backward calculation.")
.AsIntermediate();
AddOutput("Loss",
"(Tensor, default: Tensor<float>), A 2-D tensor. The cross "
"entropy loss with shape [N x 1].");
AddAttr<bool>(
"softLabel",
"(bool, default: false), A flag to indicate whether to interpretate "
"the given labels as soft labels.")
.SetDefault(false);
AddComment(R"DOC(
Cross entropy loss with softmax are used as the output layer extensively. This
operator computes the softmax normalized values for each row of the input
tensor, after which cross-entropy loss is then computed. This provides a more
numerically stable gradient.
Because this operators performs a softmax on logits internally, it expects
unscaled logits. Please do not call this op with the output of softmax operator,
which will produce incorrect results.
This operators expects mutually exclusive hard labels, each sample in a batch
is in exactly one class with probabilities 1. Each sample in the batch with one
and only one label.
Equation:
1) hard label (one-hot label)
Loss_j = -\text{Logit}_{Label_j} + \log\left(\sum_{i=0}^{K}\exp(\text{Logit}_i)\right), j = 1, ..., K
2) soft label (a distribution over all classes)
Loss_j = -\sum_{i=0}^{K}\text{Label}_i\left(\text{Logit}_i-\log\left(\sum_{i=0}^{K}\exp(\text{Logit}_i)\right)\right), j = 1,...,K
)DOC");
}
};
class SoftmaxWithCrossEntropyOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Logits"),
"Input(Logits) should be not null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
"Input(Label) should be not null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Softmax"),
"Output(Softmax) should be not null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Loss"),
"Output(Loss) should be not null.");
const Tensor* logits = ctx.Input<Tensor>("Logits");
const Tensor* labels = ctx.Input<Tensor>("Label");
PADDLE_ENFORCE_EQ(
logits->dims().size(), 2UL,
"The input of softmax_with_cross_entropy should be a 2-D tensor.");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("Label")->dims().size(), 2UL,
"The labels should be a 2-D tensor.");
if (ctx.Attr<bool>("softLabel")) {
PADDLE_ENFORCE_EQ(logits->dims()[1], labels->dims()[1],
"If Attr(softLabel) == true, the 2nd dimension of "
"Input(X) and Input(Label) should be equal.");
} else {
PADDLE_ENFORCE_EQ(labels->dims()[1], 1UL,
"If Attr(softLabel) == false, the 2nd dimension of "
"Input(Label) should be 1.");
}
ctx.Output<framework::Tensor>("Softmax")->Resize(logits->dims());
ctx.Output<framework::Tensor>("Loss")->Resize({logits->dims()[0], 1});
ctx.ShareLoD("Logits", /*->*/ "Softmax");
ctx.ShareLoD("Logits", /*->*/ "Loss");
}
};
class SoftmaxWithCrossEntropyOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Loss")),
"Input(Loss@Grad) should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Softmax"),
"Input(Softmax) should be not null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
"Input(Label) should be not null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar(framework::GradVarName("Logits")),
"Output(Logits@Grad) should be not null.");
const Tensor* softmax = ctx.Input<Tensor>("Softmax");
const Tensor* labels = ctx.Input<Tensor>("Label");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("Label")->dims().size(), 2UL,
"The labels should be a 2-D tensor.");
if (ctx.Attr<bool>("softLabel")) {
PADDLE_ENFORCE_EQ(softmax->dims()[1], labels->dims()[1],
"When Attr(softLabel) == true, the 2nd dimension of "
"Input(X) and Input(Label) should be equal.");
} else {
PADDLE_ENFORCE_EQ(labels->dims()[1], 1UL,
"When Attr(softLabel) == false, the 2nd dimension of "
"Input(Label) should be 1.");
}
ctx.Output<framework::LoDTensor>(framework::GradVarName("Logits"))
->Resize(ctx.Input<Tensor>("Softmax")->dims());
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(softmax_with_cross_entropy, ops::SoftmaxWithCrossEntropyOp,
ops::SoftmaxWithCrossEntropyOpMaker,
softmax_with_cross_entropy_grad,
ops::SoftmaxWithCrossEntropyOpGrad);
REGISTER_OP_CPU_KERNEL(softmax_with_cross_entropy,
ops::SoftmaxWithCrossEntropyKernel<float>);
REGISTER_OP_CPU_KERNEL(softmax_with_cross_entropy_grad,
ops::SoftmaxWithCrossEntropyGradKernel<float>);
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#define EIGEN_USE_GPU
#include "paddle/operators/softmax_with_cross_entropy_op.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
namespace {
template <typename T>
__global__ void CrossEntropyGrad(T* out_grad, const T* in_grad,
const int* labels, const int batch_size,
const int class_num) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
int sample_idx = tid / class_num;
if (tid < batch_size * class_num) out_grad[tid] *= in_grad[sample_idx];
__syncthreads();
if (tid < batch_size) {
PADDLE_ASSERT(labels[sample_idx] >= 0 && labels[sample_idx] < class_num);
out_grad[tid * class_num + labels[tid]] -= 1.;
}
}
template <typename T>
__global__ void SoftCrossEntropyGradientKernel(T* logit_grad,
const T* loss_grad,
const T* labels,
const int batch_size,
const int class_num) {
int ids = blockIdx.x * blockDim.x + threadIdx.x;
if (ids < batch_size * class_num) {
int row_ids = ids / class_num;
logit_grad[ids] = logit_grad[ids] * loss_grad[row_ids] - labels[ids];
}
}
} // namespace
template <typename T>
class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
PADDLE_ENFORCE(platform::is_gpu_place(context.GetPlace()),
"This kernel only runs on GPU device.");
const Tensor* logits = context.Input<Tensor>("Logits");
const Tensor* labels = context.Input<Tensor>("Label");
Tensor* softmax = context.Output<Tensor>("Softmax");
Tensor* loss = context.Output<Tensor>("Loss");
softmax->mutable_data<T>(context.GetPlace());
loss->mutable_data<T>(context.GetPlace());
math::SoftmaxFunctor<platform::GPUPlace, T>()(context, logits, softmax);
math::CrossEntropyFunctor<platform::GPUPlace, T>()(
context, loss, softmax, labels, context.Attr<bool>("softLabel"));
}
};
template <typename T>
class SoftmaxWithCrossEntropyGradCUDAKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
PADDLE_ENFORCE(platform::is_gpu_place(context.GetPlace()),
"This kernel only runs on GPU device.");
const Tensor* labels = context.Input<Tensor>("Label");
const T* loss_grad_data =
context.Input<Tensor>(framework::GradVarName("Loss"))->data<T>();
Tensor* logit_grad =
context.Output<Tensor>(framework::GradVarName("Logits"));
logit_grad->ShareDataWith<T>(*context.Input<Tensor>("Softmax"));
T* logit_grad_data = logit_grad->data<T>();
const int batch_size = logit_grad->dims()[0];
const int class_num = logit_grad->dims()[1];
int block = 512;
int grid = (batch_size * class_num + block - 1) / block;
if (context.Attr<bool>("softLabel")) {
const T* label_data = labels->data<T>();
SoftCrossEntropyGradientKernel<T><<<
grid, block, 0, reinterpret_cast<const platform::CUDADeviceContext&>(
context.device_context())
.stream()>>>(logit_grad_data, loss_grad_data,
label_data, batch_size, class_num);
} else {
const int* label_data = labels->data<int>();
CrossEntropyGrad<T><<<
grid, block, 0, reinterpret_cast<const platform::CUDADeviceContext&>(
context.device_context())
.stream()>>>(logit_grad_data, loss_grad_data,
label_data, batch_size, class_num);
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(softmax_with_cross_entropy,
ops::SoftmaxWithCrossEntropyCUDAKernel<float>);
REGISTER_OP_GPU_KERNEL(softmax_with_cross_entropy_grad,
ops::SoftmaxWithCrossEntropyGradCUDAKernel<float>);
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/cross_entropy.h"
#include "paddle/operators/math/softmax.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename T>
class SoftmaxWithCrossEntropyKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
PADDLE_ENFORCE(platform::is_cpu_place(context.GetPlace()),
"This kernel only runs on CPU.");
const Tensor* logits = context.Input<Tensor>("Logits");
const Tensor* labels = context.Input<Tensor>("Label");
Tensor* softmax = context.Output<Tensor>("Softmax");
Tensor* loss = context.Output<Tensor>("Loss");
softmax->mutable_data<T>(context.GetPlace());
loss->mutable_data<T>(context.GetPlace());
math::SoftmaxFunctor<platform::CPUPlace, T>()(context, logits, softmax);
math::CrossEntropyFunctor<platform::CPUPlace, T>()(
context, loss, softmax, labels, context.Attr<bool>("softLabel"));
}
};
template <typename T>
class SoftmaxWithCrossEntropyGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
const Tensor* out_grad =
context.Input<Tensor>(framework::GradVarName("Loss"));
const Tensor* labels = context.Input<Tensor>("Label");
Tensor* logit_grad =
context.Output<Tensor>(framework::GradVarName("Logits"));
logit_grad->ShareDataWith<T>(*context.Input<Tensor>("Softmax"));
const int class_num = logit_grad->dims()[1];
if (context.Attr<bool>("softLabel")) {
auto out_grad_mat = EigenMatrix<T>::From(*out_grad);
auto logit_grad_mat = EigenMatrix<T>::From(*logit_grad);
auto lbl_mat = EigenMatrix<T>::From(*labels);
logit_grad_mat.device(context.GetEigenDevice<platform::CPUPlace>()) =
logit_grad_mat *
out_grad_mat.broadcast(Eigen::DSizes<int, 2>(1, class_num)) -
lbl_mat;
} else {
const int batch_size = logit_grad->dims()[0];
const int* label_data = labels->data<int>();
const T* out_grad_data = out_grad->data<T>();
T* logit_grad_data = logit_grad->data<T>();
for (int i = 0; i < batch_size; ++i) {
int index = i * class_num + label_data[i];
logit_grad_data[index] =
(out_grad_data[i] * logit_grad_data[index] - 1.);
}
}
}
};
} // namespace operators
} // namespace paddle
......@@ -177,7 +177,7 @@ def get_gradient(scope, op, inputs, outputs, grad_name, place,
class OpTest(unittest.TestCase):
def check_output_with_place(self, place):
def check_output_with_place(self, place, atol):
self.scope = core.Scope()
op_inputs = self.inputs if hasattr(self, "inputs") else dict()
op_outputs = self.outputs if hasattr(self, "outputs") else dict()
......@@ -206,22 +206,23 @@ class OpTest(unittest.TestCase):
self.scope.find_var(sub_out_name).get_tensor())
self.assertTrue(
np.allclose(
actual, expect, atol=1e-05),
"output name: " + out_name + " has diff")
actual, expect, atol=atol),
"output name: " + out_name + " has diff.")
else:
actual = np.array(self.scope.find_var(out_name).get_tensor())
expect = self.outputs[out_name]
self.assertTrue(
np.allclose(
actual, expect, atol=1e-05),
"output name: " + out_name + " has diff")
actual, expect, atol=atol),
"output name: " + out_name + " has diff.")
def check_output(self):
def check_output(self, atol=1e-5):
places = [core.CPUPlace()]
if core.is_compile_gpu():
places.append(core.GPUPlace(0))
for place in places:
self.check_output_with_place(place)
self.check_output_with_place(place, atol)
def __assert_is_close(self, numeric_grads, analytic_grads, names,
max_relative_error, msg_prefix):
......@@ -235,9 +236,10 @@ class OpTest(unittest.TestCase):
def err_msg():
offset = np.argmax(diff_mat > max_relative_error)
return "%s Variable %s max gradient diff %f over limit %f, the first " \
"error element is %d" % (
msg_prefix, name, max_diff, max_relative_error, offset)
return ("%s Variable %s max gradient diff %f over limit %f, "
"the first error element is %d") % (
msg_prefix, name, max_diff, max_relative_error,
offset)
self.assertLessEqual(max_diff, max_relative_error, err_msg())
......
......@@ -5,7 +5,7 @@ from op_test import OpTest
def stable_softmax(x):
"""Compute the softmax of vector x in a numerically stable way."""
shiftx = x - np.max(x)
shiftx = x - np.max(x).clip(-64.)
exps = np.exp(shiftx)
return exps / np.sum(exps)
......
import unittest
import numpy as np
from op_test import OpTest
from test_softmax_op import stable_softmax
class TestSoftmaxWithCrossEntropyOp(OpTest):
"""
Test softmax with cross entropy operator with discreate one-hot labels.
"""
def setUp(self):
self.op_type = "softmax_with_cross_entropy"
batch_size = 3
class_num = 37
logits = np.random.uniform(0.1, 1.0,
[batch_size, class_num]).astype("float32")
softmax = np.apply_along_axis(stable_softmax, 1, logits)
labels = np.random.randint(0, class_num, [batch_size, 1], dtype="int32")
cross_entropy = np.asmatrix(
[[-np.log(softmax[i][labels[i][0]])]
for i in range(softmax.shape[0])],
dtype="float32")
self.inputs = {"Logits": logits, "Label": labels}
self.outputs = {"Softmax": softmax, "Loss": cross_entropy}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["Logits"], "Loss", max_relative_error=0.05)
class TestSoftmaxWithCrossEntropyOp2(OpTest):
"""
Test softmax with cross entropy operator with soft labels.
"""
def setUp(self):
self.op_type = "softmax_with_cross_entropy"
batch_size = 2
class_num = 17
logits = np.random.uniform(0.1, 1.0,
[batch_size, class_num]).astype("float32")
softmax = np.apply_along_axis(stable_softmax, 1, logits)
labels = np.random.uniform(0.1, 1.0,
[batch_size, class_num]).astype("float32")
labels /= np.sum(labels, axis=1, keepdims=True)
cross_entropy = (-labels * np.log(softmax)).sum(
axis=1, keepdims=True).astype("float32")
self.inputs = {"Logits": logits, "Label": labels}
self.outputs = {"Softmax": softmax, "Loss": cross_entropy}
self.attrs = {"softLabel": True}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["Logits"], "Loss", max_relative_error=0.05)
if __name__ == "__main__":
unittest.main()
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