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PaddleDetection
提交 25e070ec 编写于 作者: T tensor-tang
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Merge remote-tracking branch 'ups/develop' into fea/jit/vadd

上级 cb4083b9 ea8984c9
展开全部 隐藏空白更改
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Showing with 800 addition and 981 deletion
paddle/fluid/framework/threadpool.cc
浏览文件 @ 25e070ec
......@@ -57,10 +57,10 @@ ThreadPool::ThreadPool(int num_threads) : running_(true) {
ThreadPool::~ThreadPool() {
{
// notify all threads to stop running
std::lock_guard<std::mutex> l(mutex_);
std::unique_lock<std::mutex> l(mutex_);
running_ = false;
scheduled_.notify_all();
}
scheduled_.notify_all();
for (auto& t : threads_) {
t->join();
......@@ -70,19 +70,25 @@ ThreadPool::~ThreadPool() {
void ThreadPool::TaskLoop() {
while (true) {
std::unique_lock<std::mutex> lock(mutex_);
Task task;
scheduled_.wait(
lock, [this] { return !this->tasks_.empty() || !this->running_; });
{
std::unique_lock<std::mutex> lock(mutex_);
scheduled_.wait(
lock, [this] { return !this->tasks_.empty() || !this->running_; });
if (!running_ || tasks_.empty()) {
return;
}
if (!running_ && tasks_.empty()) {
return;
}
if (tasks_.empty()) {
PADDLE_THROW("This thread has no task to Run");
}
// pop a task from the task queue
auto task = std::move(tasks_.front());
tasks_.pop();
lock.unlock();
// pop a task from the task queue
task = std::move(tasks_.front());
tasks_.pop();
}
// run the task
task();
......
paddle/fluid/framework/threadpool.h
浏览文件 @ 25e070ec
......@@ -58,7 +58,7 @@ class ThreadPool {
~ThreadPool();
// Run pushes a function to the task queue and returns a std::future
// object. To wait for the completion of the task, call
// object. To wait for the completion of the task, call
// std::future::wait().
template <typename Callback>
std::future<void> Run(Callback fn) {
......@@ -69,7 +69,6 @@ class ThreadPool {
template <typename Callback>
std::future<std::unique_ptr<platform::EnforceNotMet>> RunAndGetException(
Callback fn) {
std::unique_lock<std::mutex> lock(mutex_);
Task task([fn]() -> std::unique_ptr<platform::EnforceNotMet> {
try {
fn();
......@@ -84,7 +83,13 @@ class ThreadPool {
return nullptr;
});
std::future<std::unique_ptr<platform::EnforceNotMet>> f = task.get_future();
tasks_.push(std::move(task));
{
std::unique_lock<std::mutex> lock(mutex_);
if (!running_) {
PADDLE_THROW("enqueue on stopped ThreadPool");
}
tasks_.push(std::move(task));
}
scheduled_.notify_one();
return f;
}
......
paddle/fluid/operators/activation_op.cu
浏览文件 @ 25e070ec
......@@ -26,6 +26,8 @@ namespace plat = paddle::platform;
act_type##_grad, ops::ActivationGradKernel<plat::CUDADeviceContext, \
ops::grad_functor<float>>, \
ops::ActivationGradKernel<plat::CUDADeviceContext, \
ops::grad_functor<double>>);
ops::grad_functor<double>>, \
ops::ActivationGradKernel<plat::CUDADeviceContext, \
ops::grad_functor<plat::float16>>);
FOR_EACH_KERNEL_FUNCTOR(REGISTER_ACTIVATION_CUDA_KERNEL);
paddle/fluid/operators/activation_op.h
浏览文件 @ 25e070ec
......@@ -333,8 +333,7 @@ struct SqrtGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
const Out out_conj = Eigen::numext::conj(out);
dx.device(d) = static_cast<T>(0.5) * dout / out_conj;
dx.device(d) = static_cast<T>(0.5) * dout / out;
}
};
......@@ -740,7 +739,7 @@ struct PowGradFunctor : public BaseActivationFunctor<T> {
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout * static_cast<T>(factor) *
x.pow(static_cast<T>(factor - static_cast<T>(1)));
x.pow(static_cast<T>(factor) - static_cast<T>(1));
}
};
......
paddle/fluid/operators/batch_norm_op.cu.cc
浏览文件 @ 25e070ec
......@@ -219,8 +219,8 @@ class BatchNormGradKernel<platform::CUDADeviceContext, T>
auto *d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
d_x->mutable_data<T>(ctx.GetPlace());
d_scale->mutable_data<T>(ctx.GetPlace());
d_bias->mutable_data<T>(ctx.GetPlace());
d_scale->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());
d_bias->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());
auto &dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
if ((N * H * W * D) == 1) {
......@@ -272,8 +272,10 @@ class BatchNormGradKernel<platform::CUDADeviceContext, T>
const auto *saved_mean = ctx.Input<Tensor>("SavedMean");
const auto *saved_var = ctx.Input<Tensor>("SavedVariance");
const void *saved_mean_data = saved_mean->template data<T>();
const void *saved_var_data = saved_var->template data<T>();
const void *saved_mean_data =
saved_mean->template data<BatchNormParamType<T>>();
const void *saved_var_data =
saved_var->template data<BatchNormParamType<T>>();
CUDNN_ENFORCE(platform::dynload::cudnnBatchNormalizationBackward(
dev_ctx.cudnn_handle(), mode_, CudnnDataType<T>::kOne(),
......@@ -281,10 +283,10 @@ class BatchNormGradKernel<platform::CUDADeviceContext, T>
CudnnDataType<T>::kZero(), data_desc_, x->template data<T>(),
data_desc_, d_y->template data<T>(), data_desc_,
d_x->template mutable_data<T>(ctx.GetPlace()), bn_param_desc_,
scale->template data<T>(),
d_scale->template mutable_data<T>(ctx.GetPlace()),
d_bias->template mutable_data<T>(ctx.GetPlace()), epsilon,
saved_mean_data, saved_var_data));
scale->template data<BatchNormParamType<T>>(),
d_scale->template mutable_data<BatchNormParamType<T>>(ctx.GetPlace()),
d_bias->template mutable_data<BatchNormParamType<T>>(ctx.GetPlace()),
epsilon, saved_mean_data, saved_var_data));
// clean when exit.
CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(data_desc_));
......@@ -304,4 +306,5 @@ REGISTER_OP_CUDA_KERNEL(
ops::BatchNormKernel<plat::CUDADeviceContext, plat::float16>);
REGISTER_OP_CUDA_KERNEL(
batch_norm_grad, ops::BatchNormGradKernel<plat::CUDADeviceContext, float>,
ops::BatchNormGradKernel<plat::CUDADeviceContext, double>);
ops::BatchNormGradKernel<plat::CUDADeviceContext, double>,
ops::BatchNormGradKernel<plat::CUDADeviceContext, plat::float16>);
paddle/fluid/operators/conv_cudnn_op.cu.cc
浏览文件 @ 25e070ec
......@@ -143,9 +143,11 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
cudnn_conv_desc, CUDNN_TENSOR_OP_MATH));
// Currently tensor core is only enabled using this algo
algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
VLOG(5) << "use cudnn_tensor_op_math";
} else {
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
cudnn_conv_desc, CUDNN_DEFAULT_MATH));
VLOG(5) << "NOT use cudnn_tensor_op_math";
}
#endif
......@@ -361,7 +363,8 @@ REGISTER_OP_KERNEL(conv2d, CUDNN, plat::CUDAPlace,
paddle::operators::CUDNNConvOpKernel<plat::float16>);
REGISTER_OP_KERNEL(conv2d_grad, CUDNN, plat::CUDAPlace,
paddle::operators::CUDNNConvGradOpKernel<float>,
paddle::operators::CUDNNConvGradOpKernel<double>);
paddle::operators::CUDNNConvGradOpKernel<double>,
paddle::operators::CUDNNConvGradOpKernel<plat::float16>);
REGISTER_OP_KERNEL(conv3d, CUDNN, plat::CUDAPlace,
paddle::operators::CUDNNConvOpKernel<float>,
......
paddle/fluid/operators/cross_entropy_op.cu
浏览文件 @ 25e070ec
......@@ -13,12 +13,17 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/cross_entropy_op.h"
#include "paddle/fluid/platform/float16.h"
namespace plat = paddle::platform;
namespace ops = paddle::operators;
using CUDACtx = paddle::platform::CUDADeviceContext;
REGISTER_OP_CUDA_KERNEL(cross_entropy,
ops::CrossEntropyOpKernel<CUDACtx, float>,
ops::CrossEntropyOpKernel<CUDACtx, double>);
REGISTER_OP_CUDA_KERNEL(cross_entropy_grad,
ops::CrossEntropyGradientOpKernel<CUDACtx, float>,
ops::CrossEntropyGradientOpKernel<CUDACtx, double>);
ops::CrossEntropyOpKernel<CUDACtx, double>,
ops::CrossEntropyOpKernel<CUDACtx, plat::float16>);
REGISTER_OP_CUDA_KERNEL(
cross_entropy_grad, ops::CrossEntropyGradientOpKernel<CUDACtx, float>,
ops::CrossEntropyGradientOpKernel<CUDACtx, double>,
ops::CrossEntropyGradientOpKernel<CUDACtx, plat::float16>);
paddle/fluid/operators/elementwise_add_op.cu
浏览文件 @ 25e070ec
......@@ -30,4 +30,5 @@ REGISTER_OP_CUDA_KERNEL(
ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, float>,
ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, double>,
ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, int>,
ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, int64_t>);
ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, int64_t>,
ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, plat::float16>);
paddle/fluid/operators/elementwise_op_function.h
浏览文件 @ 25e070ec
......@@ -365,7 +365,7 @@ static __global__ void ElemwiseGradBroadcast1CUDAKernel(
int j = blockIdx.x;
int i = threadIdx.x;
int tid = threadIdx.x;
T val = 0;
T val(0);
do {
int x_offset = i * w + j;
......@@ -433,7 +433,7 @@ static __global__ void ElemwiseGradBroadcast2CUDAKernel(
int tid = threadIdx.x;
int j = blockIdx.x;
T val = 0;
T val(0);
int ttid = tid;
while (true) {
......
paddle/fluid/operators/math/cross_entropy.cu
浏览文件 @ 25e070ec
......@@ -21,6 +21,16 @@ namespace operators {
namespace math {
namespace {
__device__ __forceinline__ float real_log(float x) { return logf(x); }
__device__ __forceinline__ double real_log(double x) { return log(x); }
__device__ __forceinline__ platform::float16 real_log(
const platform::float16& val) {
return static_cast<platform::float16>(hlog(static_cast<half>(val)));
}
template <typename T>
__global__ void CrossEntropyKernel(T* Y, const T* X, const int64_t* label,
const int N, const int D,
......@@ -29,8 +39,8 @@ __global__ void CrossEntropyKernel(T* Y, const T* X, const int64_t* label,
i += blockDim.x * gridDim.x) {
PADDLE_ASSERT(label[i] >= 0 && label[i] < D || label[i] == ignore_index);
Y[i] = ignore_index == label[i]
? 0
: -math::TolerableValue<T>()(log(X[i * D + label[i]]));
? static_cast<T>(0)
: -math::TolerableValue<T>()(real_log(X[i * D + label[i]]));
}
}
......@@ -38,12 +48,12 @@ template <typename T>
__global__ void SoftCrossEntropyKernel(T* Y, const T* X, const T* label,
const int class_num) {
int tid = threadIdx.x;
T val = 0;
T val(0);
int idx = blockIdx.x * class_num + tid;
int end = blockIdx.x * class_num + class_num;
for (; idx < end; idx += blockDim.x) {
val += math::TolerableValue<T>()(std::log(X[idx])) * label[idx];
val += math::TolerableValue<T>()(real_log(X[idx])) * label[idx];
}
val = paddle::platform::reduceSum(val, tid, blockDim.x);
......@@ -53,8 +63,6 @@ __global__ void SoftCrossEntropyKernel(T* Y, const T* X, const T* label,
}
} // namespace
using Tensor = framework::Tensor;
template <typename T>
class CrossEntropyFunctor<platform::CUDADeviceContext, T> {
public:
......@@ -89,6 +97,8 @@ class CrossEntropyFunctor<platform::CUDADeviceContext, T> {
template class CrossEntropyFunctor<platform::CUDADeviceContext, float>;
template class CrossEntropyFunctor<platform::CUDADeviceContext, double>;
template class CrossEntropyFunctor<platform::CUDADeviceContext,
platform::float16>;
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/math/cross_entropy.h
浏览文件 @ 25e070ec
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <limits>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/platform/hostdevice.h"
......@@ -33,6 +34,26 @@ struct TolerableValue {
}
};
// NOTE(dzh): float16 value clip behave different.
// 1. Our ValueClipping has a hardcore threshold 1e20
// for float number. 1e20 will resulting in overflow in float16.
// 2. float16 should expose the the real number overflow to python.
// because mixed-training depends the inf/nan value to determine
// if the scale value will be adjusted.
// Also. In standard implementation of cross entropy, other
// framework not has the ValueClipping.
template <>
struct TolerableValue<platform::float16> {
HOSTDEVICE platform::float16 operator()(const platform::float16& x) const {
if (platform::isfinite(x))
return x;
else if (x > static_cast<platform::float16>(0))
return std::numeric_limits<platform::float16>::max();
else
return std::numeric_limits<platform::float16>::min();
}
};
template <typename DeviceContext, typename T>
class CrossEntropyFunctor {
public:
......
paddle/fluid/operators/math/selected_rows_functor.cu
浏览文件 @ 25e070ec
......@@ -18,6 +18,7 @@ limitations under the License. */
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/selected_rows_functor.h"
#include "paddle/fluid/platform/cuda_primitives.h"
#include "paddle/fluid/platform/float16.h"
namespace paddle {
namespace operators {
......@@ -118,7 +119,7 @@ struct SelectedRowsAddTensor<platform::CUDADeviceContext, T> {
auto* out_data = output->data<T>();
SetConstant<platform::CUDADeviceContext, T> functor;
functor(context, output, 0.0);
functor(context, output, static_cast<T>(0));
const int block_size = 256;
dim3 threads(block_size, 1);
......@@ -136,6 +137,9 @@ struct SelectedRowsAddTensor<platform::CUDADeviceContext, T> {
template struct SelectedRowsAddTensor<platform::CUDADeviceContext, float>;
template struct SelectedRowsAddTensor<platform::CUDADeviceContext, double>;
template struct SelectedRowsAdd<platform::CUDADeviceContext, platform::float16>;
template struct SelectedRowsAddTensor<platform::CUDADeviceContext,
platform::float16>;
template <typename T>
struct SelectedRowsAddTo<platform::CUDADeviceContext, T> {
......@@ -175,6 +179,8 @@ template struct SelectedRowsAddTo<platform::CUDADeviceContext, float>;
template struct SelectedRowsAddTo<platform::CUDADeviceContext, double>;
template struct SelectedRowsAddTo<platform::CUDADeviceContext, int>;
template struct SelectedRowsAddTo<platform::CUDADeviceContext, int64_t>;
template struct SelectedRowsAddTo<platform::CUDADeviceContext,
platform::float16>;
namespace {
template <typename T, int block_size>
......@@ -227,6 +233,8 @@ template struct SelectedRowsAddToTensor<platform::CUDADeviceContext, float>;
template struct SelectedRowsAddToTensor<platform::CUDADeviceContext, double>;
template struct SelectedRowsAddToTensor<platform::CUDADeviceContext, int>;
template struct SelectedRowsAddToTensor<platform::CUDADeviceContext, int64_t>;
template struct SelectedRowsAddToTensor<platform::CUDADeviceContext,
platform::float16>;
namespace scatter {
......@@ -287,7 +295,7 @@ struct MergeAdd<platform::CUDADeviceContext, T> {
context.GetPlace());
math::SetConstant<platform::CUDADeviceContext, T> constant_functor;
constant_functor(context, out.mutable_value(), 0.0);
constant_functor(context, out.mutable_value(), static_cast<T>(0));
auto* out_data = out.mutable_value()->data<T>();
auto* input_data = input.value().data<T>();
......@@ -347,7 +355,7 @@ struct MergeAdd<platform::CUDADeviceContext, T> {
context.GetPlace());
math::SetConstant<platform::CUDADeviceContext, T> constant_functor;
constant_functor(context, out.mutable_value(), 0.0);
constant_functor(context, out.mutable_value(), static_cast<T>(0));
auto* out_data = out.mutable_value()->data<T>();
......@@ -374,6 +382,7 @@ template struct MergeAdd<platform::CUDADeviceContext, float>;
template struct MergeAdd<platform::CUDADeviceContext, double>;
template struct MergeAdd<platform::CUDADeviceContext, int>;
template struct MergeAdd<platform::CUDADeviceContext, int64_t>;
template struct MergeAdd<platform::CUDADeviceContext, platform::float16>;
template <typename T, int block_size>
__global__ void UpdateToTensorKernel(const T* selected_rows,
......
paddle/fluid/operators/math/softmax.cu
浏览文件 @ 25e070ec
......@@ -96,12 +96,15 @@ template class SoftmaxCUDNNFunctor<float>;
template class SoftmaxCUDNNFunctor<double>;
template class SoftmaxGradCUDNNFunctor<float>;
template class SoftmaxGradCUDNNFunctor<double>;
template class SoftmaxGradCUDNNFunctor<platform::float16>;
template class SoftmaxFunctor<platform::CUDADeviceContext, platform::float16>;
template class SoftmaxFunctor<platform::CUDADeviceContext, float>;
template class SoftmaxFunctor<platform::CUDADeviceContext, double>;
template class SoftmaxGradFunctor<platform::CUDADeviceContext, float>;
template class SoftmaxGradFunctor<platform::CUDADeviceContext, double>;
template class SoftmaxGradFunctor<platform::CUDADeviceContext,
platform::float16>;
} // namespace math
} // namespace operators
......
paddle/fluid/operators/mean_op.cu
浏览文件 @ 25e070ec
......@@ -15,11 +15,15 @@ limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/fluid/operators/mean_op.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
mean, ops::MeanKernel<paddle::platform::CUDADeviceContext, float>,
ops::MeanKernel<paddle::platform::CUDADeviceContext, double>);
ops::MeanKernel<paddle::platform::CUDADeviceContext, double>,
ops::MeanKernel<paddle::platform::CUDADeviceContext, plat::float16>);
REGISTER_OP_CUDA_KERNEL(
mean_grad, ops::MeanGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::MeanGradKernel<paddle::platform::CUDADeviceContext, double>);
ops::MeanGradKernel<paddle::platform::CUDADeviceContext, double>,
ops::MeanGradKernel<paddle::platform::CUDADeviceContext, plat::float16>);
paddle/fluid/operators/mean_op.h
浏览文件 @ 25e070ec
......@@ -55,8 +55,7 @@ class MeanGradKernel : public framework::OpKernel<T> {
IG->mutable_data<T>(context.GetPlace());
T ig_size = static_cast<T>(IG->numel());
Eigen::DSizes<int, 1> bcast(ig_size);
Eigen::DSizes<int, 1> bcast(static_cast<int>(ig_size));
EigenVector<T>::Flatten(*IG).device(
*context.template device_context<DeviceContext>().eigen_device()) =
(EigenVector<T>::From(*OG) / ig_size).broadcast(bcast);
......
paddle/fluid/operators/mul_op.cu.cc
浏览文件 @ 25e070ec
......@@ -20,6 +20,7 @@ namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(mul, ops::MulKernel<plat::CUDADeviceContext, float>,
ops::MulKernel<plat::CUDADeviceContext, double>,
ops::MulKernel<plat::CUDADeviceContext, plat::float16>);
REGISTER_OP_CUDA_KERNEL(mul_grad,
ops::MulGradKernel<plat::CUDADeviceContext, float>,
ops::MulGradKernel<plat::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
mul_grad, ops::MulGradKernel<plat::CUDADeviceContext, float>,
ops::MulGradKernel<plat::CUDADeviceContext, double>,
ops::MulGradKernel<plat::CUDADeviceContext, plat::float16>);
paddle/fluid/operators/pool_cudnn_op.cu.cc
浏览文件 @ 25e070ec
......@@ -178,7 +178,8 @@ REGISTER_OP_KERNEL(pool2d, CUDNN, plat::CUDAPlace,
ops::PoolCUDNNOpKernel<plat::float16>);
REGISTER_OP_KERNEL(pool2d_grad, CUDNN, plat::CUDAPlace,
ops::PoolCUDNNGradOpKernel<float>,
ops::PoolCUDNNGradOpKernel<double>);
ops::PoolCUDNNGradOpKernel<double>,
ops::PoolCUDNNGradOpKernel<plat::float16>);
REGISTER_OP_KERNEL(pool3d, CUDNN, plat::CUDAPlace,
ops::PoolCUDNNOpKernel<float>,
......
paddle/fluid/operators/scale_op.cu
浏览文件 @ 25e070ec
......@@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/scale_op.h"
#include "paddle/fluid/platform/float16.h"
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
scale,
......@@ -20,4 +22,6 @@ REGISTER_OP_CUDA_KERNEL(
paddle::operators::ScaleKernel<paddle::platform::CUDADeviceContext, double>,
paddle::operators::ScaleKernel<paddle::platform::CUDADeviceContext, int>,
paddle::operators::ScaleKernel<paddle::platform::CUDADeviceContext,
int64_t>);
int64_t>,
paddle::operators::ScaleKernel<paddle::platform::CUDADeviceContext,
plat::float16>);
paddle/fluid/operators/softmax_cudnn_op.cu.cc
浏览文件 @ 25e070ec
......@@ -80,4 +80,5 @@ REGISTER_OP_KERNEL(softmax, CUDNN, plat::CUDAPlace,
ops::SoftmaxCUDNNKernel<plat::float16>);
REGISTER_OP_KERNEL(softmax_grad, CUDNN, plat::CUDAPlace,
ops::SoftmaxGradCUDNNKernel<float>,
ops::SoftmaxGradCUDNNKernel<double>);
ops::SoftmaxGradCUDNNKernel<double>,
ops::SoftmaxGradCUDNNKernel<plat::float16>);
paddle/fluid/operators/softmax_op.cu.cc
浏览文件 @ 25e070ec
......@@ -23,4 +23,5 @@ REGISTER_OP_CUDA_KERNEL(
ops::SoftmaxKernel<plat::CUDADeviceContext, plat::float16>);
REGISTER_OP_CUDA_KERNEL(
softmax_grad, ops::SoftmaxGradKernel<plat::CUDADeviceContext, float>,
ops::SoftmaxGradKernel<plat::CUDADeviceContext, double>);
ops::SoftmaxGradKernel<plat::CUDADeviceContext, double>,
ops::SoftmaxGradKernel<plat::CUDADeviceContext, plat::float16>);
paddle/fluid/operators/sum_op.cu
浏览文件 @ 25e070ec
......@@ -11,10 +11,13 @@ limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/fluid/operators/sum_op.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
sum, ops::SumKernel<paddle::platform::CUDADeviceContext, float>,
ops::SumKernel<paddle::platform::CUDADeviceContext, double>,
ops::SumKernel<paddle::platform::CUDADeviceContext, int>,
ops::SumKernel<paddle::platform::CUDADeviceContext, int64_t>);
ops::SumKernel<paddle::platform::CUDADeviceContext, int64_t>,
ops::SumKernel<paddle::platform::CUDADeviceContext, plat::float16>);
paddle/fluid/operators/sum_op.h
浏览文件 @ 25e070ec
......@@ -61,7 +61,7 @@ class SumKernel : public framework::OpKernel<T> {
if (start != 2) {
math::SetConstant<DeviceContext, T> constant_functor;
constant_functor(context.template device_context<DeviceContext>(),
out, 0.0);
out, static_cast<T>(0));
}
}
......
python/paddle/fluid/io.py
浏览文件 @ 25e070ec
......@@ -65,7 +65,7 @@ def is_persistable(var):
Examples:
.. code-block:: python
param = fluid.default_main_program().global_block().var('fc.w')
param = fluid.default_main_program().global_block().var('fc.b')
res = fluid.io.is_persistable(param)
"""
if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
......@@ -625,8 +625,13 @@ def save_inference_model(dirname,
main_program._distributed_lookup_table,
main_program._endpoints)
if not os.path.isdir(dirname):
# when a pserver and a trainer running on the same machine, mkdir may conflict
try:
os.makedirs(dirname)
except OSError as e:
if e.errno != errno.EEXIST:
raise
if model_filename is not None:
model_basename = os.path.basename(model_filename)
else:
......
python/paddle/fluid/recordio_writer.py
浏览文件 @ 25e070ec
......@@ -41,9 +41,6 @@ def convert_reader_to_recordio_file(
"""
Convert a Python Reader to a recordio file.
Please see :ref:`api_guide_python_reader` and :ref:`api_guide_reader_op` for
details.
Examples:
>>> import paddle.fluid as fluid
......
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @ 25e070ec
......@@ -54,14 +54,6 @@ def get_numeric_gradient(place,
def product(dim):
return six.moves.reduce(lambda a, b: a * b, dim, 1)
def get_output():
sum = []
op.run(scope, place)
for output_name in output_names:
sum.append(
np.array(scope.find_var(output_name).get_tensor()).mean())
return np.array(sum).sum() / len(output_names)
tensor_to_check = scope.find_var(input_to_check).get_tensor()
tensor_size = product(tensor_to_check.shape())
tensor_to_check_dtype = tensor_to_check._dtype()
......@@ -77,6 +69,15 @@ def get_numeric_gradient(place,
raise ValueError("Not supported data type " + str(
tensor_to_check_dtype))
def get_output():
sum = []
op.run(scope, place)
for output_name in output_names:
sum.append(
np.array(scope.find_var(output_name).get_tensor()).astype(
tensor_to_check_dtype).mean())
return tensor_to_check_dtype(np.array(sum).sum() / len(output_names))
gradient_flat = np.zeros(shape=(tensor_size, ), dtype=tensor_to_check_dtype)
def __get_elem__(tensor, i):
......
python/paddle/fluid/tests/unittests/test_conv2d_op.py
浏览文件 @ 25e070ec
......@@ -223,106 +223,81 @@ class TestWithInput1x1Filter1x1(TestConv2dOp):
#----------------Conv2dCUDNN----------------
class TestCUDNN(TestConv2dOp):
def init_kernel_type(self):
self.use_cudnn = True
class TestFP16CUDNN(TestConv2dOp):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=2e-2)
def create_test_cudnn_class(parent, cls_name):
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestCUDNNCase(parent):
def init_kernel_type(self):
self.use_cudnn = True
cls_name = "{0}".format(cls_name)
TestCUDNNCase.__name__ = cls_name
globals()[cls_name] = TestCUDNNCase
class TestCUDNNWithPad(TestWithPad):
def init_kernel_type(self):
self.use_cudnn = True
class TestFP16CUDNNWithPad(TestWithPad):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=2e-2)
class TestCUDNNWithStride(TestWithStride):
def init_kernel_type(self):
self.use_cudnn = True
class TestFP16CUDNNWithStride(TestWithStride):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=2e-2)
create_test_cudnn_class(TestConv2dOp, "TestPool2DCUDNNOp")
create_test_cudnn_class(TestWithPad, "TestPool2DCUDNNOpCase1")
create_test_cudnn_class(TestWithStride, "TestPool2DCUDNNOpCase2")
create_test_cudnn_class(TestWithGroup, "TestPool2DCUDNNOpCase3")
create_test_cudnn_class(TestWith1x1, "TestPool2DCUDNNOpCase4")
create_test_cudnn_class(TestWithInput1x1Filter1x1, "TestPool2DCUDNNOpCase4")
class TestCUDNNWithGroup(TestWithGroup):
def init_kernel_type(self):
self.use_cudnn = True
class TestFP16CUDNNWithGroup(TestWithGroup):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=2e-2)
#----------------Conv2dCUDNN----------------
class TestCUDNNWith1x1(TestWith1x1):
def init_kernel_type(self):
self.use_cudnn = True
def create_test_cudnn_fp16_class(parent, cls_name, grad_check=True):
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestConv2DCUDNNFp16(parent):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
class TestFP16CUDNNWith1x1(TestWith1x1):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=2e-2)
def test_check_output(self):
if core.is_compiled_with_cuda():
def test_check_grad_no_filter(self):
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=2e-2)
class TestCUDNNWithInput1x1Filter1x1(TestWithInput1x1Filter1x1):
def init_kernel_type(self):
self.use_cudnn = True
class TestFP16CUDNNWithInput1x1Filter1x1(TestWithInput1x1Filter1x1):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
if core.is_float16_supported(place) and grad_check:
self.check_grad_with_place(
place, ['Input'],
'Output',
max_relative_error=0.02,
no_grad_set=set(['Filter']))
def test_check_grad_no_input(self):
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=2e-2)
if core.is_float16_supported(place) and grad_check:
self.check_grad_with_place(
place, ['Filter'],
'Output',
max_relative_error=0.02,
no_grad_set=set(['Input']))
cls_name = "{0}".format(cls_name)
TestConv2DCUDNNFp16.__name__ = cls_name
globals()[cls_name] = TestConv2DCUDNNFp16
create_test_cudnn_fp16_class(
TestConv2dOp, "TestPool2DCUDNNFp16Op", grad_check=False)
create_test_cudnn_fp16_class(
TestWithPad, "TestPool2DCUDNNFp16OpCase1", grad_check=False)
create_test_cudnn_fp16_class(
TestWithStride, "TestPool2DCUDNNFp16OpCase2", grad_check=False)
create_test_cudnn_fp16_class(
TestWithGroup, "TestPool2DCUDNNFp16OpCase3", grad_check=False)
create_test_cudnn_fp16_class(
TestWith1x1, "TestPool2DCUDNNFp16OpCase4", grad_check=False)
create_test_cudnn_fp16_class(
TestWithInput1x1Filter1x1, "TestPool2DCUDNNFp16OpCase4", grad_check=False)
# -------TestDepthwiseConv
class TestDepthwiseConv(TestConv2dOp):
......
python/paddle/fluid/tests/unittests/test_cross_entropy_op.py
浏览文件 @ 25e070ec
......@@ -16,28 +16,58 @@ from __future__ import print_function
import unittest
import numpy as np
import paddle.fluid.core as core
from op_test import OpTest, randomize_probability
class TestCrossEntropyOp1(OpTest):
class TestCrossEntropyOp(OpTest):
"""Test cross-entropy with discrete one-hot labels.
"""
def setUp(self):
self.op_type = "cross_entropy"
batch_size = 30
class_num = 10
self.soft_label = False
self.ignore_index = -100
self.dtype = np.float64
self.batch_size = 30
self.class_num = 10
self.init_dtype_type()
self.init_attr_type()
self.init_bs_class_num()
self.init_x()
self.init_label()
self.get_cross_entropy()
self.inputs = {"X": self.x, "Label": self.label}
self.outputs = {"Y": self.cross_entropy}
self.attrs = {
"soft_label": self.soft_label,
"ignore_index": self.ignore_index
}
def init_x(self):
self.x = randomize_probability(
self.batch_size, self.class_num, dtype=self.dtype)
def init_label(self):
self.label = np.random.randint(
0, self.class_num, (self.batch_size, 1), dtype="int64")
def get_cross_entropy(self):
self.cross_entropy = np.asmatrix(
[[-np.log(self.x[i][self.label[i][0]])]
for i in range(self.x.shape[0])],
dtype="float64")
X = randomize_probability(batch_size, class_num, dtype='float64')
def init_attr_type(self):
pass
label = np.random.randint(0, class_num, (batch_size, 1), dtype="int64")
cross_entropy = np.asmatrix(
[[-np.log(X[i][label[i][0]])] for i in range(X.shape[0])],
dtype="float64")
def init_dtype_type(self):
pass
self.inputs = {"X": X, "Label": label}
self.outputs = {"Y": cross_entropy}
self.attrs = {"soft_label": False}
def init_bs_class_num(self):
pass
def test_check_output(self):
self.check_output()
......@@ -46,197 +76,231 @@ class TestCrossEntropyOp1(OpTest):
self.check_grad(["X"], "Y", numeric_grad_delta=0.001)
class TestCrossEntropyOp2(OpTest):
class TestCrossEntropyOp2(TestCrossEntropyOp):
"""Test cross-entropy with vectorized soft labels.
"""
def setUp(self):
self.op_type = "cross_entropy"
batch_size = 5
class_num = 37
def init_label(self):
self.label = np.random.uniform(
0.1, 1.0, [self.batch_size, self.class_num]).astype(self.dtype)
self.label /= self.label.sum(axis=1, keepdims=True)
X = randomize_probability(batch_size, class_num)
label = np.random.uniform(0.1, 1.0,
[batch_size, class_num]).astype("float32")
label /= label.sum(axis=1, keepdims=True)
cross_entropy = (-label * np.log(X)).sum(
axis=1, keepdims=True).astype("float32")
def get_cross_entropy(self):
self.cross_entropy = (-self.label * np.log(self.x)).sum(
axis=1, keepdims=True).astype(self.dtype)
self.inputs = {"X": X, "Label": label}
self.outputs = {"Y": cross_entropy}
self.attrs = {"soft_label": True}
def init_attr_type(self):
self.soft_label = True
def test_check_output(self):
self.check_output()
def init_dtype_type(self):
self.dtype = np.float32
def init_bs_class_num(self):
self.batch_size = 5
self.class_num = 37
def test_check_grad(self):
self.check_grad(
["X"], "Y", max_relative_error=0.05, numeric_grad_delta=0.001)
class TestCrossEntropyOp3(OpTest):
class TestCrossEntropyOp3(TestCrossEntropyOp):
"""Test cross-entropy with vectorized one-hot representation of labels.
"""
def setUp(self):
self.op_type = "cross_entropy"
batch_size = 5
class_num = 17
def init_label(self):
self.label_index = np.random.randint(0, self.class_num,
(self.batch_size))
self.label = np.zeros(self.x.shape).astype(self.dtype)
self.label[np.arange(self.batch_size), self.label_index] = 1
X = randomize_probability(batch_size, class_num)
label_index = np.random.randint(
0, class_num, (batch_size), dtype="int32")
label = np.zeros(X.shape)
label[np.arange(batch_size), label_index] = 1
def get_cross_entropy(self):
self.cross_entropy = np.asmatrix(
[[-np.log(self.x[i][self.label_index[i]])]
for i in range(self.x.shape[0])]).astype(self.dtype)
cross_entropy = np.asmatrix(
[[-np.log(X[i][label_index[i]])] for i in range(X.shape[0])],
dtype="float32")
cross_entropy2 = (-label * np.log(X)).sum(
axis=1, keepdims=True).astype("float32")
def init_attr_type(self):
self.soft_label = True
self.inputs = {"X": X, "Label": label.astype(np.float32)}
self.outputs = {"Y": cross_entropy}
self.attrs = {"soft_label": True}
def init_dtype_type(self):
self.dtype = np.float32
def test_check_output(self):
self.check_output()
def init_bs_class_num(self):
self.batch_size = 5
self.class_num = 17
def test_check_grad(self):
self.check_grad(
["X"], "Y", max_relative_error=0.05, numeric_grad_delta=0.001)
class TestCrossEntropyOp4(OpTest):
class TestCrossEntropyOp4(TestCrossEntropyOp):
"""Test high rank tensor cross-entropy with discrete one-hot labels.
"""
def setUp(self):
self.op_type = "cross_entropy"
shape = [10, 2, 4]
ins_num = np.prod(np.array(shape))
class_num = 10
def init_x(self):
self.shape = [10, 2, 4]
self.ins_num = np.prod(np.array(self.shape))
self.X_2d = randomize_probability(self.ins_num,
self.class_num).astype(self.dtype)
self.x = self.X_2d.reshape(self.shape + [self.class_num])
X_2d = randomize_probability(ins_num, class_num, dtype='float64')
def init_label(self):
self.label_2d = np.random.randint(
0, self.class_num, (self.ins_num, 1), dtype="int64")
self.label = self.label_2d.reshape(self.shape + [1])
label_2d = np.random.randint(0, class_num, (ins_num, 1), dtype="int64")
def get_cross_entropy(self):
cross_entropy_2d = np.asmatrix(
[[-np.log(X_2d[i][label_2d[i][0]])] for i in range(X_2d.shape[0])],
dtype="float64")
[[-np.log(self.X_2d[i][self.label_2d[i][0]])]
for i in range(self.X_2d.shape[0])]).astype(self.dtype)
self.cross_entropy = np.array(cross_entropy_2d).reshape(self.shape +
[1])
X = X_2d.reshape(shape + [class_num])
label = label_2d.reshape(shape + [1])
cross_entropy = np.array(cross_entropy_2d).reshape(shape + [1])
def init_attr_type(self):
self.soft_label = False
self.inputs = {"X": X, "Label": label}
self.outputs = {"Y": cross_entropy}
self.attrs = {"soft_label": False}
def test_check_output(self):
self.check_output()
def init_dtype_type(self):
self.dtype = np.float64
def test_check_grad(self):
self.check_grad(["X"], "Y", numeric_grad_delta=0.001)
def init_bs_class_num(self):
self.class_num = 10
class TestCrossEntropyOp5(OpTest):
class TestCrossEntropyOp5(TestCrossEntropyOp):
"""Test high rank tensor cross-entropy with vectorized soft labels.
"""
def setUp(self):
self.op_type = "cross_entropy"
shape = [4, 3]
ins_num = np.prod(np.array(shape))
class_num = 37
def init_x(self):
self.shape = [4, 3]
self.ins_num = np.prod(np.array(self.shape))
self.X_2d = randomize_probability(self.ins_num,
self.class_num).astype(self.dtype)
self.x = self.X_2d.reshape(self.shape + [self.class_num])
X_2d = randomize_probability(ins_num, class_num)
label_2d = np.random.uniform(0.1, 1.0,
[ins_num, class_num]).astype("float32")
label_2d /= label_2d.sum(axis=1, keepdims=True)
cross_entropy_2d = (-label_2d * np.log(X_2d)).sum(
axis=1, keepdims=True).astype("float32")
def init_label(self):
self.label_2d = np.random.uniform(
0.1, 1.0, [self.ins_num, self.class_num]).astype(self.dtype)
self.label_2d /= self.label_2d.sum(axis=1, keepdims=True)
self.label = self.label_2d.reshape(self.shape + [self.class_num])
X = X_2d.reshape(shape + [class_num])
label = label_2d.reshape(shape + [class_num])
cross_entropy = np.array(cross_entropy_2d).reshape(shape + [1])
def get_cross_entropy(self):
cross_entropy_2d = (-self.label_2d * np.log(self.X_2d)).sum(
axis=1, keepdims=True).astype(self.dtype)
self.cross_entropy = np.array(cross_entropy_2d).reshape(self.shape +
[1])
self.inputs = {"X": X, "Label": label}
self.outputs = {"Y": cross_entropy}
self.attrs = {"soft_label": True}
def init_attr_type(self):
self.soft_label = True
def test_check_output(self):
self.check_output()
def init_dtype_type(self):
self.dtype = np.float32
def init_bs_class_num(self):
self.class_num = 37
def test_check_grad(self):
self.check_grad(
["X"], "Y", max_relative_error=0.05, numeric_grad_delta=0.001)
class TestCrossEntropyOp6(OpTest):
class TestCrossEntropyOp6(TestCrossEntropyOp):
"""Test high rank tensor cross-entropy with vectorized one-hot representation of labels.
"""
def setUp(self):
self.op_type = "cross_entropy"
shape = [4, 3, 2]
ins_num = np.prod(np.array(shape))
class_num = 17
X_2d = randomize_probability(ins_num, class_num)
label_index_2d = np.random.randint(
0, class_num, (ins_num), dtype="int32")
label_2d = np.zeros(X_2d.shape)
label_2d[np.arange(ins_num), label_index_2d] = 1
def init_x(self):
self.shape = [4, 3, 2]
self.ins_num = np.prod(np.array(self.shape))
self.X_2d = randomize_probability(self.ins_num,
self.class_num).astype(self.dtype)
self.x = self.X_2d.reshape(self.shape + [self.class_num])
def init_label(self):
self.label_index_2d = np.random.randint(
0, self.class_num, (self.ins_num), dtype="int64")
label_2d = np.zeros(self.X_2d.shape)
label_2d[np.arange(self.ins_num), self.label_index_2d] = 1
self.label = label_2d.reshape(self.shape + [self.class_num]).astype(
self.dtype)
def get_cross_entropy(self):
cross_entropy_2d = np.asmatrix(
[[-np.log(X_2d[i][label_index_2d[i]])]
for i in range(X_2d.shape[0])],
dtype="float32")
[[-np.log(self.X_2d[i][self.label_index_2d[i]])]
for i in range(self.X_2d.shape[0])])
self.cross_entropy = np.array(cross_entropy_2d).reshape(
self.shape + [1]).astype(self.dtype)
X = X_2d.reshape(shape + [class_num])
label = label_2d.reshape(shape + [class_num])
cross_entropy = np.array(cross_entropy_2d).reshape(shape + [1])
def init_attr_type(self):
self.soft_label = True
self.inputs = {"X": X, "Label": label.astype(np.float32)}
self.outputs = {"Y": cross_entropy}
self.attrs = {"soft_label": True}
def init_dtype_type(self):
self.dtype = np.float32
def test_check_output(self):
self.check_output()
def init_bs_class_num(self):
self.class_num = 17
def test_check_grad(self):
self.check_grad(
["X"], "Y", max_relative_error=0.05, numeric_grad_delta=0.001)
class TestCrossEntropyOp7(OpTest):
class TestCrossEntropyOp7(TestCrossEntropyOp):
"""Test cross-entropy with ignore index.
"""
def setUp(self):
self.op_type = "cross_entropy"
batch_size = 30
class_num = 10
ignore_index = 3
X = randomize_probability(batch_size, class_num, dtype='float64')
label = np.random.randint(0, class_num, (batch_size, 1), dtype="int64")
cross_entropy = np.asmatrix(
[[-np.log(X[i][label[i][0]])]
if label[i][0] != ignore_index else [0]
for i in range(X.shape[0])],
dtype="float64")
self.inputs = {"X": X, "Label": label}
self.outputs = {"Y": cross_entropy}
self.attrs = {"soft_label": False, "ignore_index": ignore_index}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Y", numeric_grad_delta=0.001)
def init_label(self):
self.label = np.random.randint(
0, self.class_num, (self.batch_size, 1), dtype="int64")
def get_cross_entropy(self):
self.cross_entropy = np.asmatrix(
[[-np.log(self.x[i][self.label[i][0]])]
if self.label[i][0] != self.ignore_index else [0]
for i in range(self.x.shape[0])]).astype(self.dtype)
def init_attr_type(self):
self.soft_label = False
self.ignore_index = 3
def init_dtype_type(self):
self.dtype = np.float64
def init_bs_class_num(self):
self.batch_size = 30
self.class_num = 10
# Add Fp16 test
def create_test_class(parent, cls_name):
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestCrossEntropyFP16Op(parent):
def init_dtype_type(self):
return np.float16
def test_check_output(self):
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=2e-1)
def test_check_grad(self):
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_grad_with_place(
place, ['X'], 'Y', max_relative_error=0.9)
cls_name = "{0}".format(cls_name)
TestCrossEntropyFP16Op.__name__ = cls_name
globals()[cls_name] = TestCrossEntropyFP16Op
create_test_class(TestCrossEntropyOp, "TestCrossEntropyF16Op")
#create_test_class(TestCrossEntropyOp2, "TestCrossEntropyF16Op2")
create_test_class(TestCrossEntropyOp3, "TestCrossEntropyF16Op3")
create_test_class(TestCrossEntropyOp4, "TestCrossEntropyF16Op4")
#create_test_class(TestCrossEntropyOp5, "TestCrossEntropyF16Op5")
create_test_class(TestCrossEntropyOp6, "TestCrossEntropyF16Op6")
create_test_class(TestCrossEntropyOp7, "TestCrossEntropyF16Op7")
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_mean_op.py
浏览文件 @ 25e070ec
......@@ -17,14 +17,20 @@ from __future__ import print_function
import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid.core as core
class TestMeanOp(OpTest):
def setUp(self):
self.op_type = "mean"
self.inputs = {'X': np.random.random((10, 10)).astype("float32")}
self.dtype = np.float32
self.init_dtype_type()
self.inputs = {'X': np.random.random((10, 10)).astype(self.dtype)}
self.outputs = {'Out': np.mean(self.inputs["X"])}
def init_dtype_type(self):
pass
def test_check_output(self):
self.check_output()
......@@ -32,5 +38,23 @@ class TestMeanOp(OpTest):
self.check_grad(['X'], 'Out')
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestFP16MeanOp(TestMeanOp):
def init_dtype_type(self):
self.dtype = np.float16
def test_check_output(self):
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=2e-3)
def test_checkout_grad(self):
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_grad_with_place(
place, ['X'], 'Out', max_relative_error=0.8)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_mul_op.py
浏览文件 @ 25e070ec
......@@ -23,12 +23,17 @@ from op_test import OpTest
class TestMulOp(OpTest):
def setUp(self):
self.op_type = "mul"
self.dtype = np.float32
self.init_dtype_type()
self.inputs = {
'X': np.random.random((2, 5)).astype("float32"),
'Y': np.random.random((5, 3)).astype("float32")
'X': np.random.random((2, 5)).astype(self.dtype),
'Y': np.random.random((5, 3)).astype(self.dtype)
}
self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
def init_dtype_type(self):
pass
def test_check_output(self):
self.check_output()
......@@ -47,9 +52,11 @@ class TestMulOp(OpTest):
class TestMulOp2(OpTest):
def setUp(self):
self.op_type = "mul"
self.dtype = np.float32
self.init_dtype_type()
self.inputs = {
'X': np.random.random((3, 4, 4, 3)).astype("float32"),
'Y': np.random.random((2, 6, 1, 2, 3)).astype("float32")
'X': np.random.random((3, 4, 4, 3)).astype(self.dtype),
'Y': np.random.random((2, 6, 1, 2, 3)).astype(self.dtype)
}
self.attrs = {
'x_num_col_dims': 2,
......@@ -60,6 +67,9 @@ class TestMulOp2(OpTest):
result = result.reshape(3, 4, 1, 2, 3)
self.outputs = {'Out': result}
def init_dtype_type(self):
pass
def test_check_output(self):
self.check_output()
......@@ -75,40 +85,76 @@ class TestMulOp2(OpTest):
['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y'))
class TestFP16MulOp1(OpTest):
def setUp(self):
self.op_type = "mul"
x = np.random.random((3, 5)).astype("float16")
y = np.random.random((5, 4)).astype("float16")
self.inputs = {'X': x.view(np.float16), 'Y': y.view(np.float16)}
self.outputs = {'Out': np.dot(x, y)}
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestFP16MulOp1(TestMulOp):
def init_dtype_type(self):
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=1e-1)
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=1e-1)
def test_check_grad_normal(self):
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_grad_with_place(
place, ['X', 'Y'], 'Out', max_relative_error=0.5)
class TestFP16MulOp2(OpTest):
def setUp(self):
self.op_type = "mul"
x = np.random.random((3, 4, 4, 3)).astype("float16")
y = np.random.random((2, 6, 1, 2, 3)).astype("float16")
self.inputs = {'X': x.view(np.float16), 'Y': y.view(np.float16)}
self.attrs = {
'x_num_col_dims': 2,
'y_num_col_dims': 2,
}
result = np.dot(x.reshape(3 * 4, 4 * 3), y.reshape(2 * 6, 1 * 2 * 3))
result = result.reshape(3, 4, 1, 2, 3)
self.outputs = {'Out': result}
def test_check_grad_ingore_x(self):
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_grad_with_place(
place, ['Y'],
'Out',
max_relative_error=0.5,
no_grad_set=set("X"))
def test_check_grad_ingore_y(self):
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_grad_with_place(
place, ['X'],
'Out',
max_relative_error=0.5,
no_grad_set=set('Y'))
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestFP16MulOp2(TestMulOp2):
def init_dtype_type(self):
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=2e-1)
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=2e-1)
def test_check_grad_normal(self):
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_grad_with_place(
place, ['X', 'Y'], 'Out', max_relative_error=0.9)
def test_check_grad_ingore_x(self):
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_grad_with_place(
place, ['Y'],
'Out',
max_relative_error=0.5,
no_grad_set=set("X"))
def test_check_grad_ingore_y(self):
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_grad_with_place(
place, ['X'],
'Out',
max_relative_error=0.9,
no_grad_set=set('Y'))
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/test_pool2d_mkldnn_op.py
浏览文件 @ 25e070ec
......@@ -15,10 +15,10 @@
from __future__ import print_function
import unittest
from test_pool2d_op import TestPool2d_Op, TestCase1, TestCase2, TestCase3, TestCase4, TestCase5
from test_pool2d_op import TestPool2D_Op, TestCase1, TestCase2, TestCase3, TestCase4, TestCase5
class TestMKLDNNCase1(TestPool2d_Op):
class TestMKLDNNCase1(TestPool2D_Op):
def init_kernel_type(self):
self.use_mkldnn = True
......
python/paddle/fluid/tests/unittests/test_pool2d_op.py
浏览文件 @ 25e070ec
......@@ -81,7 +81,7 @@ def avg_pool2D_forward_naive(x,
return out
class TestPool2d_Op(OpTest):
class TestPool2D_Op(OpTest):
def setUp(self):
self.op_type = "pool2d"
self.use_cudnn = False
......@@ -160,7 +160,7 @@ class TestPool2d_Op(OpTest):
self.exclusive = True
class TestCase1(TestPool2d_Op):
class TestCase1(TestPool2D_Op):
def init_test_case(self):
self.shape = [2, 3, 7, 7]
self.ksize = [3, 3]
......@@ -175,7 +175,7 @@ class TestCase1(TestPool2d_Op):
self.global_pool = False
class TestCase2(TestPool2d_Op):
class TestCase2(TestPool2D_Op):
def init_test_case(self):
self.shape = [2, 3, 7, 7]
self.ksize = [3, 3]
......@@ -190,7 +190,7 @@ class TestCase2(TestPool2d_Op):
self.global_pool = False
class TestCase3(TestPool2d_Op):
class TestCase3(TestPool2D_Op):
def init_pool_type(self):
self.pool_type = "max"
self.pool2D_forward_naive = max_pool2D_forward_naive
......@@ -208,127 +208,98 @@ class TestCase5(TestCase2):
self.pool2D_forward_naive = max_pool2D_forward_naive
#--------------------test pool2d--------------------
class TestCUDNNCase1(TestPool2d_Op):
def init_kernel_type(self):
self.use_cudnn = True
#--------------------test pool2d cudnn--------------------
class TestFP16CUDNNCase1(TestPool2d_Op):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def create_test_cudnn_class(parent):
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestCUDNNCase(parent):
def init_kernel_type(self):
self.use_cudnn = True
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=1e-3)
cls_name = "{0}_{1}".format(parent.__name__, "CUDNNOp")
TestCUDNNCase.__name__ = cls_name
globals()[cls_name] = TestCUDNNCase
class TestCUDNNCase2(TestCase1):
def init_kernel_type(self):
self.use_cudnn = True
create_test_cudnn_class(TestPool2D_Op)
create_test_cudnn_class(TestCase1)
create_test_cudnn_class(TestCase2)
create_test_cudnn_class(TestCase3)
create_test_cudnn_class(TestCase4)
create_test_cudnn_class(TestCase5)
#--------------------test pool2d cudnn_fp16--------------------
class TestFP16CUDNNCase2(TestCase1):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=1e-3)
def create_test_cudnn_fp16_class(parent, check_grad=True):
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestCUDNNFp16Case(parent):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=1e-3)
class TestCUDNNCase3(TestCase2):
def init_kernel_type(self):
self.use_cudnn = True
class TestFP16CUDNNCase3(TestCase2):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
def test_check_grad(self):
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=1e-3)
if core.is_float16_supported(
place) and self.pool_type != "max" and check_grad:
self.check_grad_with_place(
place, set(['X']), 'Out', max_relative_error=0.07)
cls_name = "{0}_{1}".format(parent.__name__, "CUDNNFp16Op")
TestCUDNNFp16Case.__name__ = cls_name
globals()[cls_name] = TestCUDNNFp16Case
class TestCUDNNCase4(TestCase3):
def init_kernel_type(self):
self.use_cudnn = True
create_test_cudnn_fp16_class(TestPool2D_Op)
create_test_cudnn_fp16_class(TestCase1, check_grad=False)
create_test_cudnn_fp16_class(TestCase2)
create_test_cudnn_fp16_class(TestCase3)
create_test_cudnn_fp16_class(TestCase4)
create_test_cudnn_fp16_class(TestCase5)
class TestFP16CUDNNCase4(TestCase3):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
#--------------------test pool2d use ceil mode--------------------
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=1e-3)
def create_test_cudnn_use_ceil_class(parent):
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestPool2DUseCeilCase(parent):
def init_kernel_type(self):
self.use_cudnn = True
class TestCUDNNCase5(TestCase4):
def init_kernel_type(self):
self.use_cudnn = True
class TestFP16CUDNNCase5(TestCase4):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=1e-3)
class TestCUDNNCase6(TestCase5):
def init_kernel_type(self):
self.use_cudnn = True
def init_ceil_mode(self):
self.ceil_mode = True
class TestFP16CUDNNCase6(TestCase5):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
cls_name = "{0}_{1}".format(parent.__name__, "CUDNNOpCeilMode")
TestPool2DUseCeilCase.__name__ = cls_name
globals()[cls_name] = TestPool2DUseCeilCase
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=1e-3)
create_test_cudnn_use_ceil_class(TestPool2D_Op)
create_test_cudnn_use_ceil_class(TestCase1)
class TestCeilModeCase1(TestCUDNNCase1):
def init_ceil_mode(self):
self.ceil_mode = True
def create_test_use_ceil_class(parent):
class TestPool2DUseCeilCase(parent):
def init_ceil_mode(self):
self.ceil_mode = True
class TestCeilModeCase2(TestCUDNNCase2):
def init_ceil_mode(self):
self.ceil_mode = True
cls_name = "{0}_{1}".format(parent.__name__, "CeilModeCast")
TestPool2DUseCeilCase.__name__ = cls_name
globals()[cls_name] = TestPool2DUseCeilCase
class TestCeilModeCase3(TestCase1):
def init_ceil_mode(self):
self.ceil_mode = True
class TestCeilModeCase4(TestCase2):
def init_ceil_mode(self):
self.ceil_mode = True
create_test_use_ceil_class(TestCase1)
create_test_use_ceil_class(TestCase2)
class TestAvgInclude(TestCase2):
......@@ -336,7 +307,10 @@ class TestAvgInclude(TestCase2):
self.exclusive = False
class TestCUDNNAvgInclude(TestCUDNNCase3):
class TestCUDNNAvgInclude(TestCase2):
def init_kernel_type(self):
self.use_cudnn = True
def init_exclusive(self):
self.exclusive = False
......
python/paddle/fluid/tests/unittests/test_scale_op.py
浏览文件 @ 25e070ec
......@@ -24,9 +24,16 @@ from paddle.fluid.op import Operator
class TestScaleOp(OpTest):
def setUp(self):
self.op_type = "scale"
self.inputs = {'X': np.random.random((10, 10)).astype("float32")}
self.dtype = np.float32
self.init_dtype_type()
self.inputs = {'X': np.random.random((10, 10)).astype(self.dtype)}
self.attrs = {'scale': -2.3}
self.outputs = {'Out': self.inputs['X'] * self.attrs['scale']}
self.outputs = {
'Out': self.inputs['X'] * self.dtype(self.attrs['scale'])
}
def init_dtype_type(self):
pass
def test_check_output(self):
self.check_output()
......@@ -36,9 +43,15 @@ class TestScaleOp(OpTest):
class TestScaleOpSelectedRows(unittest.TestCase):
def init_dtype_type(self):
pass
def check_with_place(self, place, in_name, out_name):
scope = core.Scope()
self.dtype = np.float32
self.init_dtype_type()
# create and initialize Grad Variable
in_height = 10
in_rows = [0, 4, 7]
......@@ -49,7 +62,7 @@ class TestScaleOpSelectedRows(unittest.TestCase):
in_selected_rows.set_height(in_height)
in_selected_rows.set_rows(in_rows)
in_array = np.random.random(
(len(in_rows), in_row_numel)).astype("float32")
(len(in_rows), in_row_numel)).astype(self.dtype)
in_tensor = in_selected_rows.get_tensor()
in_tensor.set(in_array, place)
......@@ -87,5 +100,41 @@ class TestScaleOpSelectedRows(unittest.TestCase):
self.check_with_place(place, 'in', 'in')
# Add FP16 test
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestScaleFp16Op(TestScaleOp):
def init_dtype_type(self):
self.dtype = np.float16
def test_check_output(self):
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=0.002)
def test_check_grad(self):
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_grad_with_place(
place, ["X"], "Out", max_relative_error=0.05)
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestScaleFp16OpSelectedRows(TestScaleOpSelectedRows):
def init_dtype_type(self):
self.dtype = np.float16
def test_scale_selected_rows(self):
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_with_place(place, 'in', 'out')
def test_scale_selected_rows_inplace(self):
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_with_place(place, 'in', 'in')
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_softmax_op.py
浏览文件 @ 25e070ec
......@@ -62,12 +62,11 @@ class TestSoftmaxOp(OpTest):
self.check_output()
def test_check_grad(self):
if self.dtype == np.float16:
return
if self.use_cudnn:
if self.use_cudnn or self.dtype == np.float16:
place = core.CUDAPlace(0)
self.check_grad_with_place(
place, ["X"], "Out", max_relative_error=0.01)
if core.is_float16_supported(place):
self.check_grad_with_place(
place, ["X"], "Out", max_relative_error=0.01)
else:
self.check_grad(["X"], "Out", max_relative_error=0.01)
......@@ -103,10 +102,23 @@ class TestSoftmaxFP16Op(TestSoftmaxOp):
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=1e-3)
# FIXME: If the x_shape is [10, 10], gradient failed.
def test_check_grad(self):
pass
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestSoftmaxFP16Op2(TestSoftmaxFP16Op):
class TestSoftmaxFP16Op2(TestSoftmaxOp):
def init_kernel_type(self):
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=1e-3)
def get_x_shape(self):
return [2, 3, 4, 5]
......
python/paddle/fluid/tests/unittests/test_sum_op.py
浏览文件 @ 25e070ec
......@@ -24,16 +24,20 @@ from paddle.fluid.op import Operator
class TestSumOp(OpTest):
def setUp(self):
self.op_type = "sum"
self.init_kernel_type()
self.use_mkldnn = False
self.init_kernel_type()
x0 = np.random.random((3, 4)).astype('float32')
x1 = np.random.random((3, 4)).astype('float32')
x2 = np.random.random((3, 4)).astype('float32')
x0 = np.random.random((3, 4)).astype(self.dtype)
x1 = np.random.random((3, 4)).astype(self.dtype)
x2 = np.random.random((3, 4)).astype(self.dtype)
self.inputs = {"X": [("x0", x0), ("x1", x1), ("x2", x2)]}
y = x0 + x1 + x2
self.outputs = {'Out': y}
self.attrs = {'use_mkldnn': self.use_mkldnn}
def init_kernel_type(self):
self.dtype = np.float32
def test_check_output(self):
self.check_output()
......@@ -59,8 +63,11 @@ class TestSelectedRowsSumOp(OpTest):
self.check_input_and_optput(core.Scope(), place, inplace, False, False,
False)
def init_kernel_type(self):
self.dtype = np.float32
def _get_array(self, row_num, row_numel):
array = np.ones((row_num, row_numel)).astype("float32")
array = np.ones((row_num, row_numel)).astype(self.dtype)
for i in range(row_num):
array[i] *= i
return array
......@@ -129,5 +136,36 @@ class TestSelectedRowsSumOp(OpTest):
self.check_with_place(place, inplace)
class TestFP16SumOp(TestSumOp):
def init_kernel_type(self):
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=2e-2)
# FIXME: Because of the precision fp16, max_relative_error
# should be 0.15 here.
def test_check_grad(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_grad(['x0'], 'Out', max_relative_error=0.15)
class TestFP16SelectedRowsSumOp(TestSelectedRowsSumOp):
def init_kernel_type(self):
self.dtype = np.float16
def test_w_is_selected_rows(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
for inplace in [True, False]:
self.check_with_place(place, inplace)
if __name__ == "__main__":
unittest.main()
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