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提交 8e8e8e66 编写于 作者: J JiabinYang
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into add_reorg_op

上级 6259dba5 6b4056bb
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Showing with 388 addition and 343 deletion
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ 8e8e8e66
......@@ -42,12 +42,10 @@ if(WITH_MKLDNN)
pass_library(mkldnn_placement_pass base)
pass_library(conv_bias_mkldnn_fuse_pass inference)
pass_library(conv_relu_mkldnn_fuse_pass inference)
pass_library(conv_elementwise_add_mkldnn_fuse_pass inference)
endif()
cc_library(fuse_elewise_add_act_pass SRCS fuse_elewise_add_act_pass.cc DEPS pass graph_pattern_detector )
if(WITH_MKLDNN)
pass_library(conv_elementwise_add_mkldnn_fuse_pass inference)
endif()
set(GLOB_PASS_LIB ${PASS_LIBRARY} CACHE INTERNAL "Global PASS library")
......
paddle/fluid/framework/ir/graph_helper_test.cc
浏览文件 @ 8e8e8e66
......@@ -200,15 +200,15 @@ TEST(GraphHelperTest, GraphNum) {
Graph g(prog);
BuildZeroGraph(&g);
ASSERT_EQ(GraphNum(g), 0);
ASSERT_EQ(GraphNum(g), 0UL);
Graph g2(prog);
BuildOneGraph(&g2);
ASSERT_EQ(GraphNum(g2), 1);
ASSERT_EQ(GraphNum(g2), 1UL);
Graph g3(prog);
BuildTwoGraphs(&g3);
ASSERT_EQ(GraphNum(g3), 2);
ASSERT_EQ(GraphNum(g3), 2UL);
}
} // namespace ir
......
paddle/fluid/framework/ir/graph_test.cc
浏览文件 @ 8e8e8e66
......@@ -124,7 +124,7 @@ TEST(GraphTest, Basic) {
ASSERT_EQ(n->outputs.size(), 0UL);
}
}
ASSERT_EQ(nodes.size(), 5);
ASSERT_EQ(nodes.size(), 5UL);
}
TEST(GraphTest, WriteAfterRead) {
......
paddle/fluid/framework/op_desc.cc
浏览文件 @ 8e8e8e66
......@@ -515,20 +515,14 @@ void OpDesc::InferShape(const BlockDesc &block) const {
}
void OpDesc::InferVarType(BlockDesc *block) const {
// There are a few places that var type can be set.
// When VarDesc is created, default set to LOD_TENSOR.
// When output variable is created, default is defaut set to LOD_TENSOR.
// We limit here to be the only place that operator defines its customized
// var type inference. Hence, we don't do any "default" setting here.
auto &info = OpInfoMap::Instance().Get(this->Type());
if (info.infer_var_type_) {
info.infer_var_type_(*this, block);
} else {
// all output type is LoDTensor by default
VLOG(10) << this->Type()
<< " has not registered InferVarType. Set output variables to "
"LOD_TENSOR";
for (auto &out_pair : this->outputs_) {
for (auto &out_var_name : out_pair.second) {
block->FindRecursiveOrCreateVar(out_var_name)
.SetType(proto::VarType::LOD_TENSOR);
}
}
}
}
......
paddle/fluid/framework/program_desc_test.cc
浏览文件 @ 8e8e8e66
......@@ -103,7 +103,7 @@ TEST(ProgramDesc, copy_ctor) {
ASSERT_EQ(1, op->GetBlockAttrId("sub_block"));
found_sub_block = true;
ASSERT_EQ(2, op->GetBlocksAttrIds("sub_blocks").size());
ASSERT_EQ(2UL, op->GetBlocksAttrIds("sub_blocks").size());
found_sub_blocks = true;
}
}
......
paddle/fluid/framework/reader_test.cc
浏览文件 @ 8e8e8e66
......@@ -40,7 +40,7 @@ TEST(READER, decorate_chain) {
auto endpoints = root->GetEndPoints();
ASSERT_EQ(endpoints.size(), 2U);
ASSERT_NE(endpoints.count(end_point1.get()), 0UL);
ASSERT_NE(endpoints.count(end_point2.get()), 0);
ASSERT_NE(endpoints.count(end_point2.get()), 0UL);
}
{
......
paddle/fluid/inference/tests/api/analyzer_resnet50_tester.cc
浏览文件 @ 8e8e8e66
......@@ -71,7 +71,7 @@ void profile(bool use_mkldnn = false) {
}
TEST(Analyzer_resnet50, profile) { profile(); }
#ifndef PADDLE_WITH_MKLDNN
#ifdef PADDLE_WITH_MKLDNN
TEST(Analyzer_resnet50, profile_mkldnn) { profile(true /* use_mkldnn */); }
#endif
......
paddle/fluid/inference/tests/api/tester_helper.h
浏览文件 @ 8e8e8e66
......@@ -50,7 +50,7 @@ void CompareResult(const std::vector<PaddleTensor> &outputs,
auto &ref_out = ref_outputs[i];
size_t size = VecReduceToInt(out.shape);
size_t ref_size = VecReduceToInt(ref_out.shape);
EXPECT_GT(size, 0);
EXPECT_GT(size, 0UL);
EXPECT_EQ(size, ref_size);
EXPECT_EQ(out.dtype, ref_out.dtype);
switch (out.dtype) {
......
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ 8e8e8e66
......@@ -284,10 +284,10 @@ op_library(max_sequence_len_op DEPS lod_rank_table)
op_library(sequence_conv_op DEPS context_project)
op_library(sequence_pool_op DEPS sequence_pooling)
if (NOT WIN32)
op_library(lstm_op DEPS sequence2batch lstm_compute)
op_library(hierarchical_sigmoid_op DEPS matrix_bit_code)
op_library(lstmp_op DEPS sequence2batch lstm_compute)
op_library(gru_op DEPS sequence2batch gru_compute)
op_library(lstm_op DEPS sequence2batch lstm_compute)
op_library(hierarchical_sigmoid_op DEPS matrix_bit_code)
op_library(lstmp_op DEPS sequence2batch lstm_compute)
op_library(gru_op DEPS sequence2batch gru_compute)
endif(NOT WIN32)
op_library(recurrent_op DEPS executor)
op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale)
......@@ -316,7 +316,7 @@ op_library(save_op DEPS lod_tensor)
op_library(load_op DEPS lod_tensor)
op_library(save_combine_op DEPS lod_tensor)
op_library(load_combine_op DEPS lod_tensor)
op_library(concat_op DEPS concat)
op_library(concat_op DEPS concat_and_split)
list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
......@@ -348,6 +348,6 @@ cc_test(strided_memcpy_test SRCS strided_memcpy_test.cc DEPS tensor memory)
cc_test(save_load_op_test SRCS save_load_op_test.cc DEPS save_op load_op)
cc_test(save_load_combine_op_test SRCS save_load_combine_op_test.cc DEPS save_combine_op load_combine_op)
if(NOT WIN32)
nv_test(nccl_op_test SRCS nccl_op_test.cu.cc DEPS nccl_op gpu_info device_context)
nv_test(nccl_op_test SRCS nccl_op_test.cu.cc DEPS nccl_op gpu_info device_context)
endif()
nv_test(dropout_op_test SRCS dropout_op_test.cc DEPS dropout_op tensor)
paddle/fluid/operators/array_to_lod_tensor_op.cc
浏览文件 @ 8e8e8e66
......@@ -11,7 +11,7 @@ distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <paddle/fluid/operators/math/concat.h>
#include <paddle/fluid/operators/math/concat_and_split.h>
#include <numeric>
#include "paddle/fluid/framework/lod_rank_table.h"
......
paddle/fluid/operators/concat_op.h
浏览文件 @ 8e8e8e66
......@@ -17,7 +17,7 @@ limitations under the License. */
#include <utility>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/concat.h"
#include "paddle/fluid/operators/math/concat_and_split.h"
#include "paddle/fluid/operators/strided_memcpy.h"
namespace paddle {
......@@ -89,28 +89,16 @@ class ConcatGradKernel : public framework::OpKernel<T> {
outputs.push_back(nullptr);
}
}
auto& dev_ctx = ctx.template device_context<DeviceContext>();
// Sometimes direct copies will be faster, this maybe need deeply analysis.
if (axis == 0 && outs.size() < 10) {
size_t input_offset = 0;
const auto in_stride = framework::stride_numel(out_grad->dims());
for (size_t i = 0; i < outs.size(); ++i) {
auto out_stride = framework::stride_numel(ins[i]->dims());
auto* out = outputs[i];
if (out != nullptr) {
StridedNumelCopyWithAxis<T>(
ctx.device_context(), axis, out->data<T>(), out_stride,
out_grad->data<T>() + input_offset, in_stride, out_stride[axis]);
}
input_offset += out_stride[axis];
}
std::vector<const framework::Tensor*> ref_shape;
ref_shape.insert(ref_shape.begin(), ins.begin(), ins.end());
StridedMemcpyWithAxis0<T>(dev_ctx, *out_grad, ref_shape, &outputs);
} else {
auto& dev_ctx = ctx.template device_context<DeviceContext>();
paddle::operators::math::ConcatGradFunctor<DeviceContext, T>
concat_grad_functor;
concat_grad_functor(dev_ctx, *out_grad,
ctx.MultiInput<framework::Tensor>("X"),
math::SplitFunctor<DeviceContext, T> split_functor;
split_functor(dev_ctx, *out_grad, ctx.MultiInput<framework::Tensor>("X"),
static_cast<int>(axis), &outputs);
}
}
......
paddle/fluid/operators/detection/generate_proposal_labels_op.cc
浏览文件 @ 8e8e8e66
......@@ -16,7 +16,7 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detection/bbox_util.h"
#include "paddle/fluid/operators/gather.h"
#include "paddle/fluid/operators/math/concat.h"
#include "paddle/fluid/operators/math/concat_and_split.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
......
paddle/fluid/operators/lod_tensor_to_array_op.cc
浏览文件 @ 8e8e8e66
......@@ -17,7 +17,7 @@ limitations under the License. */
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/math/concat.h"
#include "paddle/fluid/operators/math/concat_and_split.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/port.h"
......@@ -79,7 +79,7 @@ struct LoDTensorToArrayFunctor : public boost::static_visitor<void> {
template <typename DeviceContext>
template <typename T>
void LoDTensorToArrayFunctorImpl<DeviceContext>::apply() {
math::ConcatGradFunctor<DeviceContext, T> func;
math::SplitFunctor<DeviceContext, T> func;
func(*dev_ctx_, prev_functor_->input_, prev_functor_->ref_inputs_, 0,
&prev_functor_->outputs_);
}
......
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @ 8e8e8e66
if (NOT WIN32)
add_subdirectory(detail)
add_subdirectory(detail)
endif(NOT WIN32)
function(math_library TARGET)
......@@ -35,7 +35,7 @@ function(math_library TARGET)
endfunction()
# please add new math_library in alphabetical order
math_library(concat)
math_library(concat_and_split)
math_library(context_project DEPS im2col math_function)
math_library(cross_entropy)
math_library(cos_sim_functor)
......@@ -43,8 +43,8 @@ math_library(depthwise_conv)
math_library(im2col)
if (NOT WIN32) # windows do not support avx functions yet.
math_library(gru_compute DEPS activation_functions math_function)
math_library(lstm_compute DEPS activation_functions)
math_library(gru_compute DEPS activation_functions math_function)
math_library(lstm_compute DEPS activation_functions)
endif (NOT WIN32)
cc_library(blas SRCS blas.cc DEPS cblas framework_proto device_context)
......@@ -58,7 +58,7 @@ math_library(sequence_pooling DEPS math_function)
math_library(sequence_scale)
math_library(softmax DEPS math_function)
if (NOT WIN32)
math_library(matrix_bit_code)
math_library(matrix_bit_code)
endif (NOT WIN32)
math_library(unpooling)
math_library(vol2col)
......@@ -72,7 +72,7 @@ if(WITH_GPU)
nv_test(math_function_gpu_test SRCS math_function_test.cu DEPS math_function)
nv_test(selected_rows_functor_gpu_test SRCS selected_rows_functor_test.cu DEPS selected_rows_functor math_function)
endif()
cc_test(concat_test SRCS concat_test.cc DEPS concat)
cc_test(concat_test SRCS concat_test.cc DEPS concat_and_split)
cc_test(cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info)
cc_library(jit_kernel
SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_lstm.cc
......
paddle/fluid/operators/math/concat.cc → paddle/fluid/operators/math/concat_and_split.cc
浏览文件 @ 8e8e8e66
......@@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/math/concat.h"
#include "paddle/fluid/operators/math/concat_and_split.h"
#include <vector>
namespace paddle {
......@@ -67,7 +67,7 @@ class ConcatFunctor<platform::CPUDeviceContext, T> {
* each dimension must be the same, except the axis dimension.
*/
template <typename T>
class ConcatGradFunctor<platform::CPUDeviceContext, T> {
class SplitFunctor<platform::CPUDeviceContext, T> {
public:
void operator()(const platform::CPUDeviceContext& context,
const framework::Tensor& input,
......@@ -111,7 +111,7 @@ class ConcatGradFunctor<platform::CPUDeviceContext, T> {
};
#define DEFINE_FUNCTOR(type) \
template class ConcatFunctor<platform::CPUDeviceContext, type>; \
template class ConcatGradFunctor<platform::CPUDeviceContext, type>;
template class SplitFunctor<platform::CPUDeviceContext, type>;
FOR_ALL_TYPES(DEFINE_FUNCTOR);
......
paddle/fluid/operators/math/concat.cu → paddle/fluid/operators/math/concat_and_split.cu
浏览文件 @ 8e8e8e66
......@@ -15,7 +15,7 @@ limitations under the License. */
#include <algorithm>
#include <vector>
#include "paddle/fluid/framework/mixed_vector.h"
#include "paddle/fluid/operators/math/concat.h"
#include "paddle/fluid/operators/math/concat_and_split.h"
#include "paddle/fluid/platform/cuda_primitives.h"
#include "paddle/fluid/platform/float16.h"
......@@ -24,7 +24,7 @@ namespace operators {
namespace math {
template <typename T>
__global__ void KernelConcat(T** inputs, const int* input_cols, int col_size,
__global__ void ConcatKernel(T** inputs, const int* input_cols, int col_size,
const int output_rows, const int output_cols,
T* output) {
int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
......@@ -50,7 +50,7 @@ __global__ void KernelConcat(T** inputs, const int* input_cols, int col_size,
}
template <typename T>
__global__ void KernelConcat(T** inputs_data, const int fixed_in_col,
__global__ void ConcatKernel(T** inputs_data, const int fixed_in_col,
const int out_rows, const int out_cols,
T* output_data) {
int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
......@@ -67,7 +67,7 @@ __global__ void KernelConcat(T** inputs_data, const int fixed_in_col,
}
template <typename T>
__global__ void KernelConcatGrad(const T* input_data, const int in_row,
__global__ void SplitKernel(const T* input_data, const int in_row,
const int in_col, const int* out_cols,
int out_cols_size, T** outputs_data) {
int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
......@@ -94,7 +94,7 @@ __global__ void KernelConcatGrad(const T* input_data, const int in_row,
}
template <typename T>
__global__ void KernelConcatGrad(const T* input_data, const int in_row,
__global__ void SplitKernel(const T* input_data, const int in_row,
const int in_col, const int fixed_out_col,
T** outputs_data) {
int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
......@@ -170,11 +170,11 @@ class ConcatFunctor<platform::CUDADeviceContext, T> {
dim3 grid_size = dim3(grid_cols, grid_rows, 1);
if (sameShape) {
KernelConcat<<<grid_size, block_size, 0, context.stream()>>>(
ConcatKernel<<<grid_size, block_size, 0, context.stream()>>>(
dev_ins_data, in_col, out_row, out_col, output->data<T>());
} else {
const int* dev_ins_col_data = inputs_col.CUDAData(context.GetPlace());
KernelConcat<<<grid_size, block_size, 0, context.stream()>>>(
ConcatKernel<<<grid_size, block_size, 0, context.stream()>>>(
dev_ins_data, dev_ins_col_data, static_cast<int>(inputs_col.size()),
out_row, out_col, output->data<T>());
}
......@@ -189,7 +189,7 @@ class ConcatFunctor<platform::CUDADeviceContext, T> {
* each dimension must be the same, except the axis dimension.
*/
template <typename T>
class ConcatGradFunctor<platform::CUDADeviceContext, T> {
class SplitFunctor<platform::CUDADeviceContext, T> {
public:
void operator()(const platform::CUDADeviceContext& context,
const framework::Tensor& input,
......@@ -248,11 +248,11 @@ class ConcatGradFunctor<platform::CUDADeviceContext, T> {
dim3 grid_size = dim3(grid_cols, grid_rows, 1);
if (sameShape) {
KernelConcatGrad<<<grid_size, block_size, 0, context.stream()>>>(
SplitKernel<<<grid_size, block_size, 0, context.stream()>>>(
input.data<T>(), in_row, in_col, out0_col, dev_out_gpu_data);
} else {
const int* dev_outs_col_data = outputs_cols.CUDAData(context.GetPlace());
KernelConcatGrad<<<grid_size, block_size, 0, context.stream()>>>(
SplitKernel<<<grid_size, block_size, 0, context.stream()>>>(
input.data<T>(), in_row, in_col, dev_outs_col_data,
static_cast<int>(outputs_cols.size()), dev_out_gpu_data);
}
......@@ -264,7 +264,7 @@ class ConcatGradFunctor<platform::CUDADeviceContext, T> {
#define DEFINE_FUNCTOR(type) \
template class ConcatFunctor<platform::CUDADeviceContext, type>; \
template class ConcatGradFunctor<platform::CUDADeviceContext, type>
template class SplitFunctor<platform::CUDADeviceContext, type>
FOR_ALL_TYPES(DEFINE_FUNCTOR);
......
paddle/fluid/operators/math/concat.h → paddle/fluid/operators/math/concat_and_split.h
浏览文件 @ 8e8e8e66
......@@ -54,7 +54,7 @@ class ConcatFunctor {
* Output[1] = [[5,6]]
*/
template <typename DeviceContext, typename T>
class ConcatGradFunctor {
class SplitFunctor {
public:
void operator()(const DeviceContext& context, const framework::Tensor& input,
const std::vector<const framework::Tensor*>& ref_inputs,
......
paddle/fluid/operators/math/concat_test.cc
浏览文件 @ 8e8e8e66
......@@ -12,10 +12,10 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/math/concat.h"
#include <gtest/gtest.h>
#include <vector>
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/operators/math/concat_and_split.h"
template <typename DeviceContext, typename Place>
void testConcat() {
......
paddle/fluid/operators/reader/reader_blocking_queue_test.cc
浏览文件 @ 8e8e8e66
......@@ -237,7 +237,7 @@ TEST(BlockingQueue, speed_test_mode) {
}
for (size_t i = 0; i < queue_size; ++i) {
q2.Receive(&b);
EXPECT_EQ(b, 0);
EXPECT_EQ(b, 0UL);
}
EXPECT_EQ(q2.Size(), queue_size);
}
paddle/fluid/operators/sequence_concat_op.h
浏览文件 @ 8e8e8e66
......@@ -17,7 +17,7 @@
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/math/concat.h"
#include "paddle/fluid/operators/math/concat_and_split.h"
namespace paddle {
namespace operators {
......@@ -106,7 +106,7 @@ class SeqConcatGradKernel : public framework::OpKernel<T> {
}
}
math::ConcatGradFunctor<DeviceContext, T> functor;
math::SplitFunctor<DeviceContext, T> functor;
std::vector<const framework::Tensor *> sliced_x_ptr;
std::vector<framework::Tensor *> sliced_dx_ptr;
for (auto &x : sliced_x) {
......
paddle/fluid/operators/split_op.cc
浏览文件 @ 8e8e8e66
......@@ -111,11 +111,10 @@ Example:
} // namespace paddle
namespace ops = paddle::operators;
USE_CPU_ONLY_OP(concat);
REGISTER_OPERATOR(split, ops::SplitOp, ops::SplitOpMaker, ops::SplitGradMaker);
REGISTER_OP_CPU_KERNEL(split,
ops::SplitOpKernel<paddle::platform::CPUPlace, double>,
ops::SplitOpKernel<paddle::platform::CPUPlace, float>,
ops::SplitOpKernel<paddle::platform::CPUPlace, int64_t>,
ops::SplitOpKernel<paddle::platform::CPUPlace, int>);
REGISTER_OP_CPU_KERNEL(
split, ops::SplitOpKernel<paddle::platform::CPUDeviceContext, double>,
ops::SplitOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::SplitOpKernel<paddle::platform::CPUDeviceContext, int64_t>,
ops::SplitOpKernel<paddle::platform::CPUDeviceContext, int>);
paddle/fluid/operators/split_op.h
浏览文件 @ 8e8e8e66
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <chrono> // NOLINT
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/concat_and_split.h"
#include "paddle/fluid/operators/strided_memcpy.h"
namespace paddle {
......@@ -28,18 +29,22 @@ class SplitOpKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<framework::Tensor>("X");
auto outs = ctx.MultiOutput<framework::Tensor>("Out");
auto in_stride = framework::stride_numel(in->dims());
int64_t axis = static_cast<int64_t>(ctx.Attr<int>("axis"));
int axis = ctx.Attr<int>("axis");
auto place = ctx.GetPlace();
size_t input_offset = 0;
for (auto& out : outs) {
out->mutable_data<T>(ctx.GetPlace());
auto out_stride = framework::stride_numel(out->dims());
StridedNumelCopyWithAxis<T>(ctx.device_context(), axis, out->data<T>(),
out_stride, in->data<T>() + input_offset,
in_stride, out_stride[axis]);
input_offset += out_stride[axis];
std::vector<const framework::Tensor*> shape_refer;
for (size_t j = 0; j < outs.size(); ++j) {
outs[j]->mutable_data<T>(ctx.GetPlace());
shape_refer.emplace_back(outs[j]);
}
auto& dev_ctx = ctx.template device_context<DeviceContext>();
// Sometimes direct copies will be faster, this maybe need deeply analysis.
if (axis == 0 && outs.size() < 10) {
StridedMemcpyWithAxis0<T>(dev_ctx, *in, shape_refer, &outs);
} else {
math::SplitFunctor<DeviceContext, T> functor;
functor(dev_ctx, *in, shape_refer, axis, &outs);
}
}
};
......
paddle/fluid/operators/strided_memcpy.h
浏览文件 @ 8e8e8e66
......@@ -13,8 +13,9 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <vector>
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/detail/strided_memcpy.h"
namespace paddle {
namespace operators {
......@@ -98,5 +99,26 @@ inline void StridedNumelCopyWithAxis(const platform::DeviceContext& ctx,
}
}
template <typename T>
inline void StridedMemcpyWithAxis0(
const platform::DeviceContext& dev_ctx, const framework::Tensor& input,
const std::vector<const framework::Tensor*>& shape_refer,
std::vector<framework::Tensor*>* outputs) {
const framework::DDim in_stride = stride_numel(input.dims());
const int axis = 0;
size_t input_offset = 0;
for (size_t i = 0; i < outputs->size(); ++i) {
auto out_stride = stride_numel(shape_refer[i]->dims());
auto out = outputs->at(i);
if (out != nullptr) {
StridedNumelCopyWithAxis<T>(dev_ctx, axis, out->data<T>(), out_stride,
input.data<T>() + input_offset, in_stride,
out_stride[axis]);
}
input_offset += out_stride[axis];
}
}
} // namespace operators
} // namespace paddle
python/paddle/fluid/layer_helper.py
浏览文件 @ 8e8e8e66
......@@ -324,10 +324,19 @@ class LayerHelper(object):
raise ValueError("no Parameter name %s found" % name)
return param
def create_tmp_variable(self, dtype, stop_gradient=False):
def create_variable_for_type_inference(self, dtype, stop_gradient=False):
"""Create a temporary variable that should be type inferred layer.
Note:
The default type will be set to LOD_TENSOR. However, when
the var is used as operator output, its type will be updated
based on operator's `VarTypeInference` implementation in
infer_var_type.
"""
return self.main_program.current_block().create_var(
name=unique_name.generate(".".join([self.name, 'tmp'])),
dtype=dtype,
type=core.VarDesc.VarType.LOD_TENSOR,
persistable=False,
stop_gradient=stop_gradient)
......@@ -388,7 +397,7 @@ class LayerHelper(object):
b = self.create_parameter(
attr=bias_attr, shape=size, dtype=input_var.dtype, is_bias=True)
tmp = self.create_tmp_variable(dtype=input_var.dtype)
tmp = self.create_variable_for_type_inference(dtype=input_var.dtype)
self.append_op(
type='elementwise_add',
inputs={'X': [input_var],
......@@ -414,7 +423,7 @@ class LayerHelper(object):
tmp = input_var
# NOTE(dzhwinter): some activation support inplace compution.
if not core.IsInplace(act_type):
tmp = self.create_tmp_variable(dtype=input_var.dtype)
tmp = self.create_variable_for_type_inference(dtype=input_var.dtype)
self.append_op(
type=act_type,
inputs={"X": [input_var]},
......
python/paddle/fluid/layers/control_flow.py
浏览文件 @ 8e8e8e66
......@@ -80,8 +80,8 @@ def split_lod_tensor(input, mask, level=0):
"""
helper = LayerHelper('split_lod_tensor', **locals())
out_true = helper.create_tmp_variable(dtype=input.dtype)
out_false = helper.create_tmp_variable(dtype=input.dtype)
out_true = helper.create_variable_for_type_inference(dtype=input.dtype)
out_false = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type='split_lod_tensor',
inputs={
......@@ -131,7 +131,7 @@ def merge_lod_tensor(in_true, in_false, x, mask, level=0):
in_true=out_true, in_false=out_false, mask=y, x=x, level=level)
"""
helper = LayerHelper('merge_lod_tensor', **locals())
out = helper.create_tmp_variable(dtype=in_true.dtype)
out = helper.create_variable_for_type_inference(dtype=in_true.dtype)
helper.append_op(
type='merge_lod_tensor',
inputs={'X': x,
......@@ -524,7 +524,7 @@ class StaticRNN(object):
if not isinstance(o, Variable):
raise TypeError("step output takes a Variable")
tmp_o = self.helper.create_tmp_variable(dtype=o.dtype)
tmp_o = self.helper.create_variable_for_type_inference(dtype=o.dtype)
self.helper.append_op(
type='rnn_memory_helper',
inputs={'X': [o]},
......@@ -606,7 +606,8 @@ class StaticRNN(object):
pre_memories.append(mem.pre_mem.name)
mem_var = rnn_block.var(mem.mem.name)
assert isinstance(mem_var, Variable)
new_mem = self.helper.create_tmp_variable(dtype=mem_var.dtype)
new_mem = self.helper.create_variable_for_type_inference(
dtype=mem_var.dtype)
rnn_block.append_op(
type='rnn_memory_helper',
......@@ -813,7 +814,7 @@ def max_sequence_len(rank_table):
${out_comment}.
"""
helper = LayerHelper("max_seqence_len", **locals())
res = helper.create_tmp_variable(dtype="int64")
res = helper.create_variable_for_type_inference(dtype="int64")
helper.append_op(
type="max_sequence_len",
inputs={"RankTable": rank_table},
......@@ -884,7 +885,7 @@ def array_to_lod_tensor(x, table):
lod_tensor = fluid.layers.array_to_lod_tensor(array, table)
"""
helper = LayerHelper("array_to_lod_tensor", **locals())
tmp = helper.create_tmp_variable(dtype=x.dtype)
tmp = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type="array_to_lod_tensor",
inputs={'X': x,
......@@ -915,7 +916,7 @@ def increment(x, value=1.0, in_place=True):
"""
helper = LayerHelper("increment", **locals())
if not in_place:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = x
helper.append_op(
......@@ -1012,7 +1013,7 @@ def less_than(x, y, force_cpu=None, cond=None, **ignored):
"""
helper = LayerHelper("less_than", **locals())
if cond is None:
cond = helper.create_tmp_variable(dtype='bool')
cond = helper.create_variable_for_type_inference(dtype='bool')
cond.stop_gradient = True
attrs = dict()
......@@ -1051,7 +1052,7 @@ def equal(x, y, cond=None, **ignored):
"""
helper = LayerHelper("equal", **locals())
if cond is None:
cond = helper.create_tmp_variable(dtype='bool')
cond = helper.create_variable_for_type_inference(dtype='bool')
cond.stop_gradient = True
helper.append_op(
......@@ -1098,7 +1099,7 @@ def array_read(array, i):
array,
Variable) or array.type != core.VarDesc.VarType.LOD_TENSOR_ARRAY:
raise TypeError("array should be tensor array vairable")
out = helper.create_tmp_variable(dtype=array.dtype)
out = helper.create_variable_for_type_inference(dtype=array.dtype)
helper.append_op(
type='read_from_array',
inputs={'X': [array],
......@@ -1133,7 +1134,7 @@ def shrink_memory(x, i, table):
usage.
"""
helper = LayerHelper('shrink_memory', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='shrink_rnn_memory',
inputs={'X': [x],
......@@ -1170,7 +1171,7 @@ def array_length(array):
"""
helper = LayerHelper('array_length', **locals())
tmp = helper.create_tmp_variable(dtype='int64')
tmp = helper.create_variable_for_type_inference(dtype='int64')
tmp.stop_gradient = True
helper.append_op(
type='lod_array_length', inputs={'X': [array]}, outputs={'Out': [tmp]})
......@@ -1590,7 +1591,7 @@ class DynamicRNN(object):
self.mem_dict = dict()
self.output_array = []
self.outputs = []
self.cond = self.helper.create_tmp_variable(dtype='bool')
self.cond = self.helper.create_variable_for_type_inference(dtype='bool')
self.cond.stop_gradient = False
self.while_op = While(self.cond)
self.input_array = []
......@@ -1924,7 +1925,7 @@ def reorder_lod_tensor_by_rank(x, rank_table):
helper.is_instance('x', Variable)
helper.is_instance('rank_table', Variable)
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='reorder_lod_tensor_by_rank',
inputs={'X': [x],
......@@ -1958,7 +1959,7 @@ def is_empty(x, cond=None, **ignored):
"""
helper = LayerHelper("is_empty", **locals())
if cond is None:
cond = helper.create_tmp_variable(dtype='bool')
cond = helper.create_variable_for_type_inference(dtype='bool')
cond.stop_gradient = True
elif not isinstance(cond, Variable):
raise TypeError("cond takes a variable")
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 8e8e8e66
......@@ -147,10 +147,11 @@ def rpn_target_assign(bbox_pred,
helper = LayerHelper('rpn_target_assign', **locals())
# Assign target label to anchors
loc_index = helper.create_tmp_variable(dtype='int32')
score_index = helper.create_tmp_variable(dtype='int32')
target_label = helper.create_tmp_variable(dtype='int32')
target_bbox = helper.create_tmp_variable(dtype=anchor_box.dtype)
loc_index = helper.create_variable_for_type_inference(dtype='int32')
score_index = helper.create_variable_for_type_inference(dtype='int32')
target_label = helper.create_variable_for_type_inference(dtype='int32')
target_bbox = helper.create_variable_for_type_inference(
dtype=anchor_box.dtype)
helper.append_op(
type="rpn_target_assign",
inputs={
......@@ -282,7 +283,8 @@ def detection_output(loc,
scores = nn.reshape(x=scores, shape=compile_shape, actual_shape=run_shape)
scores = nn.transpose(scores, perm=[0, 2, 1])
scores.stop_gradient = True
nmsed_outs = helper.create_tmp_variable(dtype=decoded_box.dtype)
nmsed_outs = helper.create_variable_for_type_inference(
dtype=decoded_box.dtype)
helper.append_op(
type="multiclass_nms",
inputs={'Scores': scores,
......@@ -314,7 +316,7 @@ def iou_similarity(x, y, name=None):
"""
helper = LayerHelper("iou_similarity", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......@@ -351,7 +353,8 @@ def box_coder(prior_box,
helper = LayerHelper("box_coder", **locals())
if name is None:
output_box = helper.create_tmp_variable(dtype=prior_box.dtype)
output_box = helper.create_variable_for_type_inference(
dtype=prior_box.dtype)
else:
output_box = helper.create_variable(
name=name, dtype=prior_box.dtype, persistable=False)
......@@ -382,7 +385,7 @@ def polygon_box_transform(input, name=None):
"""
helper = LayerHelper("polygon_box_transform", **locals())
if name is None:
output = helper.create_tmp_variable(dtype=input.dtype)
output = helper.create_variable_for_type_inference(dtype=input.dtype)
else:
output = helper.create_variable(
name=name, dtype=prior_box.input, persistable=False)
......@@ -450,7 +453,7 @@ def detection_map(detect_res,
helper = LayerHelper("detection_map", **locals())
def __create_var(type):
return helper.create_tmp_variable(dtype=type)
return helper.create_variable_for_type_inference(dtype=type)
map_out = __create_var('float32')
accum_pos_count_out = out_states[0] if out_states else __create_var('int32')
......@@ -557,8 +560,9 @@ def bipartite_match(dist_matrix,
>>> matched_indices, matched_dist = fluid.layers.bipartite_match(iou)
"""
helper = LayerHelper('bipartite_match', **locals())
match_indices = helper.create_tmp_variable(dtype='int32')
match_distance = helper.create_tmp_variable(dtype=dist_matrix.dtype)
match_indices = helper.create_variable_for_type_inference(dtype='int32')
match_distance = helper.create_variable_for_type_inference(
dtype=dist_matrix.dtype)
helper.append_op(
type='bipartite_match',
inputs={'DistMat': dist_matrix},
......@@ -644,8 +648,8 @@ def target_assign(input,
gt, matched_indices, mismatch_value=0)
"""
helper = LayerHelper('target_assign', **locals())
out = helper.create_tmp_variable(dtype=input.dtype)
out_weight = helper.create_tmp_variable(dtype='float32')
out = helper.create_variable_for_type_inference(dtype=input.dtype)
out_weight = helper.create_variable_for_type_inference(dtype='float32')
helper.append_op(
type='target_assign',
inputs={
......@@ -816,9 +820,10 @@ def ssd_loss(location,
conf_loss = nn.reshape(
x=conf_loss, shape=(num, num_prior), actual_shape=actual_shape)
conf_loss.stop_gradient = True
neg_indices = helper.create_tmp_variable(dtype='int32')
neg_indices = helper.create_variable_for_type_inference(dtype='int32')
dtype = matched_indices.dtype
updated_matched_indices = helper.create_tmp_variable(dtype=dtype)
updated_matched_indices = helper.create_variable_for_type_inference(
dtype=dtype)
helper.append_op(
type='mine_hard_examples',
inputs={
......@@ -998,8 +1003,8 @@ def prior_box(input,
max_sizes = [max_sizes]
attrs['max_sizes'] = max_sizes
box = helper.create_tmp_variable(dtype)
var = helper.create_tmp_variable(dtype)
box = helper.create_variable_for_type_inference(dtype)
var = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="prior_box",
inputs={"Input": input,
......@@ -1337,8 +1342,8 @@ def anchor_generator(input,
'offset': offset
}
anchor = helper.create_tmp_variable(dtype)
var = helper.create_tmp_variable(dtype)
anchor = helper.create_variable_for_type_inference(dtype)
var = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="anchor_generator",
inputs={"Input": input},
......@@ -1384,7 +1389,7 @@ def roi_perspective_transform(input,
"""
helper = LayerHelper('roi_perspective_transform', **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="roi_perspective_transform",
inputs={"X": input,
......@@ -1418,11 +1423,15 @@ def generate_proposal_labels(rpn_rois,
helper = LayerHelper('generate_proposal_labels', **locals())
rois = helper.create_tmp_variable(dtype=rpn_rois.dtype)
labels_int32 = helper.create_tmp_variable(dtype=gt_classes.dtype)
bbox_targets = helper.create_tmp_variable(dtype=rpn_rois.dtype)
bbox_inside_weights = helper.create_tmp_variable(dtype=rpn_rois.dtype)
bbox_outside_weights = helper.create_tmp_variable(dtype=rpn_rois.dtype)
rois = helper.create_variable_for_type_inference(dtype=rpn_rois.dtype)
labels_int32 = helper.create_variable_for_type_inference(
dtype=gt_classes.dtype)
bbox_targets = helper.create_variable_for_type_inference(
dtype=rpn_rois.dtype)
bbox_inside_weights = helper.create_variable_for_type_inference(
dtype=rpn_rois.dtype)
bbox_outside_weights = helper.create_variable_for_type_inference(
dtype=rpn_rois.dtype)
helper.append_op(
type="generate_proposal_labels",
......@@ -1504,8 +1513,10 @@ def generate_proposals(scores,
"""
helper = LayerHelper('generate_proposals', **locals())
rpn_rois = helper.create_tmp_variable(dtype=bbox_deltas.dtype)
rpn_roi_probs = helper.create_tmp_variable(dtype=scores.dtype)
rpn_rois = helper.create_variable_for_type_inference(
dtype=bbox_deltas.dtype)
rpn_roi_probs = helper.create_variable_for_type_inference(
dtype=scores.dtype)
helper.append_op(
type="generate_proposals",
inputs={
......
python/paddle/fluid/layers/io.py
浏览文件 @ 8e8e8e66
......@@ -954,7 +954,7 @@ def read_file(reader):
"""
helper = LayerHelper('read_file')
out = [
helper.create_tmp_variable(
helper.create_variable_for_type_inference(
stop_gradient=True, dtype='float32')
for _ in range(len(reader.desc.shapes()))
]
......
python/paddle/fluid/layers/layer_function_generator.py
浏览文件 @ 8e8e8e66
......@@ -202,10 +202,12 @@ def generate_layer_fn(op_type):
out_var = out[0] if (isinstance(out, list) or
isinstance(out, tuple)) else out
else:
out_var = helper.create_tmp_variable(dtype=dtype)
out_var = helper.create_variable_for_type_inference(dtype=dtype)
outputs[o_name] = [out_var]
for name in intermediate_output_names:
outputs[name] = [helper.create_tmp_variable(dtype=dtype)]
outputs[name] = [
helper.create_variable_for_type_inference(dtype=dtype)
]
helper.append_op(
type=op_type, inputs=inputs, outputs=outputs, attrs=kwargs)
return helper.append_activation(out_var)
......@@ -229,7 +231,7 @@ def generate_layer_fn_noattr(op_type):
def func(x, name=None):
helper = LayerHelper(op_type, **locals())
output = helper.create_tmp_variable(dtype=x.dtype)
output = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type=op_type, inputs={"X": x}, outputs={"Out": output})
return output
......
python/paddle/fluid/layers/metric_op.py
浏览文件 @ 8e8e8e66
......@@ -58,11 +58,11 @@ def accuracy(input, label, k=1, correct=None, total=None):
"""
helper = LayerHelper("accuracy", **locals())
topk_out, topk_indices = nn.topk(input, k=k)
acc_out = helper.create_tmp_variable(dtype="float32")
acc_out = helper.create_variable_for_type_inference(dtype="float32")
if correct is None:
correct = helper.create_tmp_variable(dtype="int64")
correct = helper.create_variable_for_type_inference(dtype="int64")
if total is None:
total = helper.create_tmp_variable(dtype="int64")
total = helper.create_variable_for_type_inference(dtype="int64")
helper.append_op(
type="accuracy",
inputs={
......@@ -124,8 +124,8 @@ def auc(input,
auc_out=fluid.layers.auc(input=prediction, label=label)
"""
helper = LayerHelper("auc", **locals())
auc_out = helper.create_tmp_variable(dtype="float64")
batch_auc_out = helper.create_tmp_variable(dtype="float64")
auc_out = helper.create_variable_for_type_inference(dtype="float64")
batch_auc_out = helper.create_variable_for_type_inference(dtype="float64")
# make tp, tn, fp, fn persistable, so that can accumulate all batches.
# for batch auc
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 8e8e8e66
......@@ -243,7 +243,7 @@ def fc(input,
w = helper.create_parameter(
attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False)
tmp = helper.create_tmp_variable(dtype)
tmp = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="mul",
inputs={"X": input_var,
......@@ -256,7 +256,7 @@ def fc(input,
if len(mul_results) == 1:
pre_bias = mul_results[0]
else:
pre_bias = helper.create_tmp_variable(dtype)
pre_bias = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="sum",
inputs={"X": mul_results},
......@@ -315,7 +315,7 @@ def embedding(input,
helper = LayerHelper('embedding', **locals())
w = helper.create_parameter(
attr=helper.param_attr, shape=size, dtype=dtype, is_bias=False)
tmp = helper.create_tmp_variable(dtype)
tmp = helper.create_variable_for_type_inference(dtype)
padding_idx = -1 if padding_idx is None else padding_idx if padding_idx >= 0 else (
size[0] + padding_idx)
helper.append_op(
......@@ -419,10 +419,10 @@ def dynamic_lstm(input,
bias = helper.create_parameter(
attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
hidden = helper.create_tmp_variable(dtype)
cell = helper.create_tmp_variable(dtype)
batch_gate = helper.create_tmp_variable(dtype)
batch_cell_pre_act = helper.create_tmp_variable(dtype)
hidden = helper.create_variable_for_type_inference(dtype)
cell = helper.create_variable_for_type_inference(dtype)
batch_gate = helper.create_variable_for_type_inference(dtype)
batch_cell_pre_act = helper.create_variable_for_type_inference(dtype)
inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
batch_size = input.shape[0]
if h_0:
......@@ -622,12 +622,12 @@ def dynamic_lstmp(input,
bias = helper.create_parameter(
attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
projection = helper.create_tmp_variable(dtype)
cell = helper.create_tmp_variable(dtype)
ordered_proj0 = helper.create_tmp_variable(dtype)
batch_hidden = helper.create_tmp_variable(dtype)
batch_gate = helper.create_tmp_variable(dtype)
batch_cell_pre_act = helper.create_tmp_variable(dtype)
projection = helper.create_variable_for_type_inference(dtype)
cell = helper.create_variable_for_type_inference(dtype)
ordered_proj0 = helper.create_variable_for_type_inference(dtype)
batch_hidden = helper.create_variable_for_type_inference(dtype)
batch_gate = helper.create_variable_for_type_inference(dtype)
batch_cell_pre_act = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='lstmp',
......@@ -752,10 +752,10 @@ def dynamic_gru(input,
), 'The shape of h0 should be(batch_size, %d)' % size
inputs['H0'] = h_0
hidden = helper.create_tmp_variable(dtype)
batch_gate = helper.create_tmp_variable(dtype)
batch_reset_hidden_prev = helper.create_tmp_variable(dtype)
batch_hidden = helper.create_tmp_variable(dtype)
hidden = helper.create_variable_for_type_inference(dtype)
batch_gate = helper.create_variable_for_type_inference(dtype)
batch_reset_hidden_prev = helper.create_variable_for_type_inference(dtype)
batch_hidden = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='gru',
......@@ -845,9 +845,9 @@ def gru_unit(input,
weight = helper.create_parameter(
attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
gate = helper.create_tmp_variable(dtype)
reset_hidden_pre = helper.create_tmp_variable(dtype)
updated_hidden = helper.create_tmp_variable(dtype)
gate = helper.create_variable_for_type_inference(dtype)
reset_hidden_pre = helper.create_variable_for_type_inference(dtype)
updated_hidden = helper.create_variable_for_type_inference(dtype)
inputs = {'Input': input, 'HiddenPrev': hidden, 'Weight': weight}
# create bias
if helper.bias_attr:
......@@ -897,10 +897,14 @@ def linear_chain_crf(input, label, param_attr=None):
attr=helper.param_attr,
shape=[size + 2, size],
dtype=helper.input_dtype())
alpha = helper.create_tmp_variable(dtype=helper.input_dtype())
emission_exps = helper.create_tmp_variable(dtype=helper.input_dtype())
transition_exps = helper.create_tmp_variable(dtype=helper.input_dtype())
log_likelihood = helper.create_tmp_variable(dtype=helper.input_dtype())
alpha = helper.create_variable_for_type_inference(
dtype=helper.input_dtype())
emission_exps = helper.create_variable_for_type_inference(
dtype=helper.input_dtype())
transition_exps = helper.create_variable_for_type_inference(
dtype=helper.input_dtype())
log_likelihood = helper.create_variable_for_type_inference(
dtype=helper.input_dtype())
helper.append_op(
type='linear_chain_crf',
inputs={"Emission": [input],
......@@ -939,7 +943,8 @@ def crf_decoding(input, param_attr, label=None):
"""
helper = LayerHelper('crf_decoding', **locals())
transition = helper.get_parameter(param_attr.name)
viterbi_path = helper.create_tmp_variable(dtype=helper.input_dtype())
viterbi_path = helper.create_variable_for_type_inference(
dtype=helper.input_dtype())
helper.append_op(
type='crf_decoding',
inputs={"Emission": [input],
......@@ -963,9 +968,9 @@ def cos_sim(X, Y):
Variable: the output of cosine(X, Y).
"""
helper = LayerHelper('cos_sim', **locals())
out = helper.create_tmp_variable(dtype=X.dtype)
xnorm = helper.create_tmp_variable(dtype=X.dtype)
ynorm = helper.create_tmp_variable(dtype=X.dtype)
out = helper.create_variable_for_type_inference(dtype=X.dtype)
xnorm = helper.create_variable_for_type_inference(dtype=X.dtype)
ynorm = helper.create_variable_for_type_inference(dtype=X.dtype)
helper.append_op(
type='cos_sim',
inputs={'X': [X],
......@@ -1009,8 +1014,9 @@ def dropout(x, dropout_prob, is_test=False, seed=None, name=None):
"""
helper = LayerHelper('dropout', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
mask = helper.create_tmp_variable(dtype=x.dtype, stop_gradient=True)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
mask = helper.create_variable_for_type_inference(
dtype=x.dtype, stop_gradient=True)
if (seed is None or seed == 0) and helper.main_program.random_seed != 0:
seed = helper.main_program.random_seed
......@@ -1095,7 +1101,7 @@ def cross_entropy(input, label, soft_label=False, ignore_index=-100):
cost = fluid.layers.cross_entropy(input=predict, label=label)
"""
helper = LayerHelper('cross_entropy', **locals())
out = helper.create_tmp_variable(dtype=input.dtype)
out = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type='cross_entropy',
inputs={'X': [input],
......@@ -1142,14 +1148,14 @@ def square_error_cost(input, label):
"""
helper = LayerHelper('square_error_cost', **locals())
minus_out = helper.create_tmp_variable(dtype=input.dtype)
minus_out = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type='elementwise_sub',
inputs={'X': [input],
'Y': [label]},
outputs={'Out': [minus_out]})
square_out = helper.create_tmp_variable(dtype=input.dtype)
square_out = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type='square', inputs={'X': [minus_out]},
outputs={'Out': [square_out]})
......@@ -1255,12 +1261,13 @@ def chunk_eval(input,
helper = LayerHelper("chunk_eval", **locals())
# prepare output
precision = helper.create_tmp_variable(dtype="float32")
recall = helper.create_tmp_variable(dtype="float32")
f1_score = helper.create_tmp_variable(dtype="float32")
num_infer_chunks = helper.create_tmp_variable(dtype="int64")
num_label_chunks = helper.create_tmp_variable(dtype="int64")
num_correct_chunks = helper.create_tmp_variable(dtype="int64")
precision = helper.create_variable_for_type_inference(dtype="float32")
recall = helper.create_variable_for_type_inference(dtype="float32")
f1_score = helper.create_variable_for_type_inference(dtype="float32")
num_infer_chunks = helper.create_variable_for_type_inference(dtype="int64")
num_label_chunks = helper.create_variable_for_type_inference(dtype="int64")
num_correct_chunks = helper.create_variable_for_type_inference(
dtype="int64")
helper.append_op(
type="chunk_eval",
......@@ -1327,7 +1334,7 @@ def sequence_conv(input,
filter_shape = [filter_size * input.shape[1], num_filters]
filter_param = helper.create_parameter(
attr=helper.param_attr, shape=filter_shape, dtype=dtype)
pre_bias = helper.create_tmp_variable(dtype)
pre_bias = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='sequence_conv',
......@@ -1383,7 +1390,7 @@ def sequence_softmax(input, use_cudnn=False, name=None):
"""
helper = LayerHelper('sequence_softmax', **locals())
dtype = helper.input_dtype()
softmax_out = helper.create_tmp_variable(dtype)
softmax_out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="sequence_softmax",
inputs={"X": input},
......@@ -1437,7 +1444,7 @@ def softmax(input, use_cudnn=True, name=None):
"""
helper = LayerHelper('softmax', **locals())
dtype = helper.input_dtype()
softmax_out = helper.create_tmp_variable(dtype)
softmax_out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="softmax",
inputs={"X": input},
......@@ -1600,7 +1607,7 @@ def conv2d(input,
dtype=dtype,
default_initializer=_get_default_param_initializer())
pre_bias = helper.create_tmp_variable(dtype)
pre_bias = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type=l_type,
......@@ -1771,7 +1778,7 @@ def conv3d(input,
dtype=dtype,
default_initializer=_get_default_param_initializer())
pre_bias = helper.create_tmp_variable(dtype)
pre_bias = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type=l_type,
......@@ -1850,8 +1857,8 @@ def sequence_pool(input, pool_type):
"""
helper = LayerHelper('sequence_pool', **locals())
dtype = helper.input_dtype()
pool_out = helper.create_tmp_variable(dtype)
max_index = helper.create_tmp_variable(dtype)
pool_out = helper.create_variable_for_type_inference(dtype)
max_index = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="sequence_pool",
......@@ -1887,7 +1894,7 @@ def sequence_concat(input, name=None):
out = fluid.layers.sequence_concat(input=[seq1, seq2, seq3])
"""
helper = LayerHelper('sequence_concat', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
helper.append_op(
type='sequence_concat', inputs={'X': input}, outputs={'Out': [out]})
return out
......@@ -2014,7 +2021,7 @@ def sequence_slice(input, offset, length, name=None):
"""
helper = LayerHelper("sequence_slice", **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
offset.stop_gradient = True
length.stop_gradient = True
......@@ -2100,7 +2107,7 @@ def pool2d(input,
helper = LayerHelper(l_type, **locals())
dtype = helper.input_dtype()
pool_out = helper.create_tmp_variable(dtype)
pool_out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type=l_type,
......@@ -2168,7 +2175,7 @@ def pool3d(input,
l_type = "pool3d"
helper = LayerHelper(l_type, **locals())
dtype = helper.input_dtype()
pool_out = helper.create_tmp_variable(dtype)
pool_out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type=l_type,
......@@ -2311,10 +2318,13 @@ def batch_norm(input,
mean_out = mean
# variance and variance out share the same memory
variance_out = variance
saved_mean = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
saved_variance = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
saved_mean = helper.create_variable_for_type_inference(
dtype=dtype, stop_gradient=True)
saved_variance = helper.create_variable_for_type_inference(
dtype=dtype, stop_gradient=True)
batch_norm_out = input if in_place else helper.create_tmp_variable(dtype)
batch_norm_out = input if in_place else helper.create_variable_for_type_inference(
dtype)
helper.append_op(
type="batch_norm",
......@@ -2431,9 +2441,11 @@ def layer_norm(input,
inputs['Bias'] = bias
# create output
mean_out = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
variance_out = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
layer_norm_out = helper.create_tmp_variable(dtype)
mean_out = helper.create_variable_for_type_inference(
dtype=dtype, stop_gradient=True)
variance_out = helper.create_variable_for_type_inference(
dtype=dtype, stop_gradient=True)
layer_norm_out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="layer_norm",
......@@ -2620,7 +2632,7 @@ def conv2d_transpose(input,
img_filter = helper.create_parameter(
dtype=input.dtype, shape=filter_shape, attr=helper.param_attr)
pre_bias = helper.create_tmp_variable(dtype=input.dtype)
pre_bias = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type=op_type,
inputs={'Input': [input],
......@@ -2798,7 +2810,7 @@ def conv3d_transpose(input,
img_filter = helper.create_parameter(
dtype=input.dtype, shape=filter_shape, attr=helper.param_attr)
pre_bias = helper.create_tmp_variable(dtype=input.dtype)
pre_bias = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type=l_type,
inputs={'Input': [input],
......@@ -2877,7 +2889,7 @@ def sequence_expand(x, y, ref_level=-1, name=None):
"""
helper = LayerHelper('sequence_expand', input=x, **locals())
dtype = helper.input_dtype()
tmp = helper.create_tmp_variable(dtype)
tmp = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='sequence_expand',
inputs={'X': x,
......@@ -2943,7 +2955,7 @@ def sequence_expand_as(x, y, name=None):
"""
helper = LayerHelper('sequence_expand_as', input=x, **locals())
dtype = helper.input_dtype()
tmp = helper.create_tmp_variable(dtype)
tmp = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='sequence_expand_as',
inputs={'X': x,
......@@ -2988,8 +3000,8 @@ def sequence_pad(x, pad_value, maxlen=None, name=None):
helper = LayerHelper('sequence_pad', input=x, **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
length = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
length = helper.create_variable_for_type_inference(dtype)
pad_value.stop_gradient = True
length.stop_gradient = True
......@@ -3054,7 +3066,7 @@ def sequence_unpad(x, length, name=None):
helper = LayerHelper('sequence_unpad', input=x, **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
length.stop_gradient = True
......@@ -3153,8 +3165,9 @@ def beam_search(pre_ids,
score_type = scores.dtype
id_type = ids.dtype
selected_scores = helper.create_tmp_variable(dtype=score_type)
selected_ids = helper.create_tmp_variable(dtype=id_type)
selected_scores = helper.create_variable_for_type_inference(
dtype=score_type)
selected_ids = helper.create_variable_for_type_inference(dtype=id_type)
helper.append_op(
type='beam_search',
......@@ -3211,8 +3224,8 @@ def beam_search_decode(ids, scores, beam_size, end_id, name=None):
ids, scores, beam_size=5, end_id=0)
"""
helper = LayerHelper('beam_search_decode', **locals())
sentence_ids = helper.create_tmp_variable(dtype=ids.dtype)
sentence_scores = helper.create_tmp_variable(dtype=ids.dtype)
sentence_ids = helper.create_variable_for_type_inference(dtype=ids.dtype)
sentence_scores = helper.create_variable_for_type_inference(dtype=ids.dtype)
helper.append_op(
type="beam_search_decode",
......@@ -3342,8 +3355,8 @@ def lstm_unit(x_t,
param_attr=param_attr,
bias_attr=bias_attr)
dtype = x_t.dtype
c = helper.create_tmp_variable(dtype)
h = helper.create_tmp_variable(dtype)
c = helper.create_variable_for_type_inference(dtype)
h = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='lstm_unit',
......@@ -3397,7 +3410,7 @@ def reduce_sum(input, dim=None, keep_dim=False, name=None):
"""
helper = LayerHelper('reduce_sum', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
if dim is not None and not isinstance(dim, list):
dim = [dim]
helper.append_op(
......@@ -3454,7 +3467,7 @@ def reduce_mean(input, dim=None, keep_dim=False, name=None):
fluid.layers.reduce_mean(x, dim=[0, 1]) # [4.0, 5.0]
"""
helper = LayerHelper('reduce_mean', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
if dim is not None and not isinstance(dim, list):
dim = [dim]
helper.append_op(
......@@ -3509,7 +3522,7 @@ def reduce_max(input, dim=None, keep_dim=False, name=None):
fluid.layers.reduce_max(x, dim=[0, 1]) # [7.0, 8.0]
"""
helper = LayerHelper('reduce_max', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
if dim is not None and not isinstance(dim, list):
dim = [dim]
helper.append_op(
......@@ -3564,7 +3577,7 @@ def reduce_min(input, dim=None, keep_dim=False, name=None):
fluid.layers.reduce_min(x, dim=[0, 1]) # [1.0, 2.0]
"""
helper = LayerHelper('reduce_min', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
if dim is not None and not isinstance(dim, list):
dim = [dim]
helper.append_op(
......@@ -3620,7 +3633,7 @@ def reduce_prod(input, dim=None, keep_dim=False, name=None):
fluid.layers.reduce_prod(x, dim=[0, 1]) # [105.0, 384.0]
"""
helper = LayerHelper('reduce_prod', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
if dim is not None and not isinstance(dim, list):
dim = [dim]
helper.append_op(
......@@ -3680,7 +3693,7 @@ def split(input, num_or_sections, dim=-1, name=None):
dim], 'len(num_or_sections) must not be more than input.shape[dim].'
num = len(num_or_sections)
outs = [
helper.create_tmp_variable(dtype=helper.input_dtype())
helper.create_variable_for_type_inference(dtype=helper.input_dtype())
for i in range(num)
]
helper.append_op(
......@@ -3737,8 +3750,8 @@ def l2_normalize(x, axis, epsilon=1e-12, name=None):
axis = 0
helper = LayerHelper("l2_normalize", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
norm = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
norm = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type="norm",
inputs={"X": x},
......@@ -3847,7 +3860,7 @@ def matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None):
__check_input(x, y)
helper = LayerHelper('matmul', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='matmul',
inputs={'X': x,
......@@ -3918,8 +3931,8 @@ def topk(input, k, name=None):
top5_values, top5_indices = layers.topk(input, k=5)
"""
helper = LayerHelper("top_k", **locals())
values = helper.create_tmp_variable(dtype=input.dtype)
indices = helper.create_tmp_variable(dtype="int64")
values = helper.create_variable_for_type_inference(dtype=input.dtype)
indices = helper.create_variable_for_type_inference(dtype="int64")
helper.append_op(
type="top_k",
inputs={"X": [input]},
......@@ -3977,8 +3990,8 @@ def edit_distance(input, label, normalized=True, ignored_tokens=None):
# remove some tokens from input and labels
if ignored_tokens is not None and len(ignored_tokens) > 0:
erased_input = helper.create_tmp_variable(dtype="int64")
erased_label = helper.create_tmp_variable(dtype="int64")
erased_input = helper.create_variable_for_type_inference(dtype="int64")
erased_label = helper.create_variable_for_type_inference(dtype="int64")
helper.append_op(
type="sequence_erase",
......@@ -3995,8 +4008,8 @@ def edit_distance(input, label, normalized=True, ignored_tokens=None):
label = erased_label
# edit distance op
edit_distance_out = helper.create_tmp_variable(dtype="int64")
sequence_num = helper.create_tmp_variable(dtype="int64")
edit_distance_out = helper.create_variable_for_type_inference(dtype="int64")
sequence_num = helper.create_variable_for_type_inference(dtype="int64")
helper.append_op(
type="edit_distance",
inputs={"Hyps": [input],
......@@ -4071,7 +4084,7 @@ def ctc_greedy_decoder(input, blank, name=None):
_, topk_indices = topk(input, k=1)
# ctc align op
ctc_out = helper.create_tmp_variable(dtype="int64")
ctc_out = helper.create_variable_for_type_inference(dtype="int64")
helper.append_op(
type="ctc_align",
inputs={"Input": [topk_indices]},
......@@ -4121,8 +4134,8 @@ def warpctc(input, label, blank=0, norm_by_times=False):
"""
helper = LayerHelper('warpctc', **locals())
loss_out = helper.create_tmp_variable(dtype=input.dtype)
grad_out = helper.create_tmp_variable(dtype=input.dtype)
loss_out = helper.create_variable_for_type_inference(dtype=input.dtype)
grad_out = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type='warpctc',
inputs={'Logits': [input],
......@@ -4183,7 +4196,7 @@ def sequence_reshape(input, new_dim):
x_reshaped = fluid.layers.sequence_reshape(input=x, new_dim=10)
"""
helper = LayerHelper('sequence_reshape', **locals())
out = helper.create_tmp_variable(helper.input_dtype())
out = helper.create_variable_for_type_inference(helper.input_dtype())
helper.append_op(
type='sequence_reshape',
inputs={'X': [input]},
......@@ -4280,9 +4293,9 @@ def nce(input,
is_bias=True,
dtype=input.dtype)
inputs['Bias'] = b
cost = helper.create_tmp_variable(dtype=input.dtype)
sample_logits = helper.create_tmp_variable(dtype=input.dtype)
sample_labels = helper.create_tmp_variable(dtype=label.dtype)
cost = helper.create_variable_for_type_inference(dtype=input.dtype)
sample_logits = helper.create_variable_for_type_inference(dtype=input.dtype)
sample_labels = helper.create_variable_for_type_inference(dtype=label.dtype)
if num_neg_samples is None:
num_neg_samples = 10
......@@ -4358,8 +4371,8 @@ def hsigmoid(input,
helper = LayerHelper('hierarchical_sigmoid', **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
pre_out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
pre_out = helper.create_variable_for_type_inference(dtype)
dim = input.shape[1]
if num_classes < 2:
raise ValueError("num_classes must not be less than 2.")
......@@ -4419,8 +4432,8 @@ def transpose(x, perm, name=None):
(idx, perm[idx], len(x.shape)))
helper = LayerHelper('transpose', **locals())
out = helper.create_tmp_variable(x.dtype)
x_shape = helper.create_tmp_variable(x.dtype)
out = helper.create_variable_for_type_inference(x.dtype)
x_shape = helper.create_variable_for_type_inference(x.dtype)
helper.append_op(
type='transpose2',
inputs={'X': [x]},
......@@ -4562,7 +4575,7 @@ def im2sequence(input,
inputs["Y"] = input_image_size
attrs["out_stride"] = out_stride
helper = LayerHelper('im2sequence', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
helper.append_op(
type='im2sequence', inputs=inputs, outputs={'Out': out}, attrs=attrs)
return out
......@@ -4595,7 +4608,7 @@ def row_conv(input, future_context_size, param_attr=None, act=None):
filter_shape = [future_context_size + 1, input.shape[1]]
filter_param = helper.create_parameter(
attr=helper.param_attr, shape=filter_shape, dtype=dtype)
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='row_conv',
inputs={'X': [input],
......@@ -4628,7 +4641,7 @@ def multiplex(inputs, index):
raise ValueError("inputs should be a list object and contains at least "
"2 elements.")
out = helper.create_tmp_variable(inputs[0].dtype)
out = helper.create_variable_for_type_inference(inputs[0].dtype)
helper.append_op(
type='multiplex',
inputs={'X': inputs,
......@@ -4699,8 +4712,8 @@ def softmax_with_cross_entropy(logits,
logits=fc, label=label)
"""
helper = LayerHelper('softmax_with_cross_entropy', **locals())
softmax = helper.create_tmp_variable(dtype=logits.dtype)
loss = helper.create_tmp_variable(dtype=logits.dtype)
softmax = helper.create_variable_for_type_inference(dtype=logits.dtype)
loss = helper.create_variable_for_type_inference(dtype=logits.dtype)
helper.append_op(
type='softmax_with_cross_entropy',
inputs={'Logits': logits,
......@@ -4750,8 +4763,8 @@ def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
"""
helper = LayerHelper('smooth_l1_loss', **locals())
diff = helper.create_tmp_variable(dtype=x.dtype)
loss = helper.create_tmp_variable(dtype=x.dtype)
diff = helper.create_variable_for_type_inference(dtype=x.dtype)
loss = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='smooth_l1_loss',
inputs={
......@@ -4784,7 +4797,7 @@ def one_hot(input, depth):
one_hot_label = layers.one_hot(input=label, depth=10)
"""
helper = LayerHelper("one_hot", **locals())
one_hot_out = helper.create_tmp_variable(dtype='float32')
one_hot_out = helper.create_variable_for_type_inference(dtype='float32')
helper.append_op(
type="one_hot",
inputs={'X': input},
......@@ -4926,8 +4939,8 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
"except one unknown dimension.")
helper = LayerHelper("reshape2", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
x_shape = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
x_shape = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type="reshape2",
inputs=inputs,
......@@ -4976,8 +4989,8 @@ def squeeze(input, axes, name=None):
y = layers.sequeeze(input=x, axes=[1])
"""
helper = LayerHelper("squeeze", **locals())
out = helper.create_tmp_variable(dtype=input.dtype)
x_shape = helper.create_tmp_variable(dtype=input.dtype)
out = helper.create_variable_for_type_inference(dtype=input.dtype)
x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type="squeeze2",
inputs={"X": input},
......@@ -5013,8 +5026,8 @@ def unsqueeze(input, axes, name=None):
y = layers.unsequeeze(input=x, axes=[1])
"""
helper = LayerHelper("unsqueeze", **locals())
out = helper.create_tmp_variable(dtype=input.dtype)
x_shape = helper.create_tmp_variable(dtype=input.dtype)
out = helper.create_variable_for_type_inference(dtype=input.dtype)
x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type="unsqueeze2",
inputs={"X": input},
......@@ -5104,7 +5117,7 @@ def lod_reset(x, y=None, target_lod=None):
out = layers.lod_reset(x=x, y=y)
"""
helper = LayerHelper("lod_reset", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
if y is not None:
helper.append_op(
type="lod_reset", inputs={'X': x,
......@@ -5173,8 +5186,9 @@ def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None):
"dims of input must be 4(not %d), and it's order must be NCHW" %
(dims))
mid_out = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
lrn_out = helper.create_tmp_variable(dtype)
mid_out = helper.create_variable_for_type_inference(
dtype=dtype, stop_gradient=True)
lrn_out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="lrn",
inputs={"X": input},
......@@ -5239,7 +5253,7 @@ def pad(x, paddings, pad_value=0., name=None):
"""
helper = LayerHelper('pad', input=x, **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='pad',
inputs={'X': x},
......@@ -5319,7 +5333,7 @@ def pad_constant_like(x, y, pad_value=0., name=None):
"""
helper = LayerHelper('pad_constant_like', input=x, **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='pad_constant_like',
inputs={'X': x,
......@@ -5384,7 +5398,7 @@ def label_smooth(label,
raise ValueError("The value of epsilon must be between 0 and 1.")
helper = LayerHelper("label_smooth", **locals())
label.stop_gradient = True
smooth_label = helper.create_tmp_variable(dtype)
smooth_label = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="label_smooth",
inputs={"X": label,
......@@ -5416,8 +5430,8 @@ def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
"""
helper = LayerHelper('roi_pool', **locals())
dtype = helper.input_dtype()
pool_out = helper.create_tmp_variable(dtype)
argmaxes = helper.create_tmp_variable(dtype='int32')
pool_out = helper.create_variable_for_type_inference(dtype)
argmaxes = helper.create_variable_for_type_inference(dtype='int32')
helper.append_op(
type="roi_pool",
inputs={"X": input,
......@@ -5465,7 +5479,7 @@ def roi_align(input,
"""
helper = LayerHelper('roi_align', **locals())
dtype = helper.input_dtype()
align_out = helper.create_tmp_variable(dtype)
align_out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="roi_align",
inputs={"X": input,
......@@ -5590,7 +5604,7 @@ def image_resize(input,
out_h = int(input.shape[2] * scale)
out_w = int(input.shape[3] * scale)
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type=resample_methods[resample],
inputs=inputs,
......@@ -5699,7 +5713,7 @@ def gather(input, index):
"""
helper = LayerHelper('gather', **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="gather",
inputs={"X": input,
......@@ -5739,7 +5753,7 @@ def scatter(input, index, updates, name=None):
"""
helper = LayerHelper('scatter', **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="scatter",
inputs={"X": input,
......@@ -5799,7 +5813,7 @@ def sequence_scatter(input, index, updates, name=None):
"""
helper = LayerHelper('sequence_scatter', **locals())
dtype = helper.input_dtype()
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="sequence_scatter",
inputs={"X": input,
......@@ -5829,7 +5843,7 @@ def random_crop(x, shape, seed=None):
"""
helper = LayerHelper("random_crop", **locals())
dtype = x.dtype
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
if seed is None:
seed = np.random.randint(-65536, 65536)
op_attrs = {"shape": shape}
......@@ -5875,7 +5889,7 @@ def log(x, name=None):
"""
helper = LayerHelper('log', **locals())
dtype = helper.input_dtype(input_param_name='x')
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(type="log", inputs={"X": x}, outputs={"Out": out})
return out
......@@ -5906,7 +5920,7 @@ def relu(x, name=None):
"""
helper = LayerHelper('relu', **locals())
dtype = helper.input_dtype(input_param_name='x')
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(type="relu", inputs={"X": x}, outputs={"Out": out})
return out
......@@ -5945,9 +5959,9 @@ def mean_iou(input, label, num_classes):
"""
helper = LayerHelper('mean_iou', **locals())
dtype = helper.input_dtype()
out_mean_iou = helper.create_tmp_variable(dtype='float32')
out_wrong = helper.create_tmp_variable(dtype='int32')
out_correct = helper.create_tmp_variable(dtype='int32')
out_mean_iou = helper.create_variable_for_type_inference(dtype='float32')
out_wrong = helper.create_variable_for_type_inference(dtype='int32')
out_correct = helper.create_variable_for_type_inference(dtype='int32')
helper.append_op(
type="mean_iou",
inputs={"Predictions": input,
......@@ -6039,7 +6053,7 @@ def crop(x, shape=None, offsets=None, name=None):
if offsets is None:
offsets = [0] * len(x.shape)
out = helper.create_tmp_variable(x.dtype)
out = helper.create_variable_for_type_inference(x.dtype)
ipts = {'X': x}
attrs = {}
if isinstance(shape, Variable):
......@@ -6119,7 +6133,7 @@ def rank_loss(label, left, right, name=None):
if not (isinstance(right, Variable)):
raise ValueError("The right should be a Variable")
out = helper.create_tmp_variable("float32")
out = helper.create_variable_for_type_inference("float32")
helper.append_op(
type='rank_loss',
......@@ -6165,8 +6179,8 @@ def margin_rank_loss(label, left, right, margin=0.1, name=None):
raise ValueError("The left should be a Variable.")
if not isinstance(right, Variable):
raise ValueError("The right should be a Variable.")
out = helper.create_tmp_variable(left.dtype)
act = helper.create_tmp_variable(left.dtype)
out = helper.create_variable_for_type_inference(left.dtype)
act = helper.create_variable_for_type_inference(left.dtype)
helper.append_op(
type='margin_rank_loss',
inputs={"Label": label,
......@@ -6251,7 +6265,7 @@ def pad2d(input,
helper = LayerHelper('pad2d', **locals())
dtype = helper.input_dtype(input_param_name='input')
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='pad2d',
inputs={'X': input},
......@@ -6280,7 +6294,7 @@ def elu(x, alpha=1.0, name=None):
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('elu', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='elu',
inputs={'X': x},
......@@ -6303,7 +6317,7 @@ def relu6(x, threshold=6.0, name=None):
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('relu6', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='relu6',
inputs={'X': x},
......@@ -6326,7 +6340,7 @@ def pow(x, factor=1.0, name=None):
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('pow', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='pow',
inputs={'X': x},
......@@ -6350,7 +6364,7 @@ def stanh(x, scale_a=2.0 / 3.0, scale_b=1.7159, name=None):
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('stanh', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='stanh',
inputs={'X': x},
......@@ -6375,7 +6389,7 @@ def hard_sigmoid(x, slope=0.2, offset=0.5, name=None):
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('hard_sigmoid', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='hard_sigmoid',
inputs={'X': x},
......@@ -6399,7 +6413,7 @@ def swish(x, beta=1.0, name=None):
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('swish', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='swish',
inputs={'X': x},
......@@ -6451,7 +6465,7 @@ def prelu(x, mode, param_attr=None, name=None):
dtype='float32',
is_bias=False,
default_initializer=Constant(1.0))
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="prelu",
inputs={"X": x,
......@@ -6475,7 +6489,7 @@ def brelu(x, t_min=0.0, t_max=24.0, name=None):
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('brelu', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='brelu',
inputs={'X': x},
......@@ -6498,7 +6512,7 @@ def leaky_relu(x, alpha=0.02, name=None):
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('leaky_relu', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='leaky_relu',
inputs={'X': x},
......@@ -6520,7 +6534,7 @@ def soft_relu(x, threshold=40.0, name=None):
output(${out_type}): ${out_comment}
"""
helper = LayerHelper('soft_relu', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='soft_relu',
inputs={'X': x},
......@@ -6587,8 +6601,8 @@ def flatten(x, axis=1, name=None):
if not (isinstance(axis, int)) or axis > len(x.shape) or axis < 0:
raise ValueError("The axis should be a int, and in range [0, rank(x)]")
out = helper.create_tmp_variable(x.dtype)
x_shape = helper.create_tmp_variable(x.dtype)
out = helper.create_variable_for_type_inference(x.dtype)
x_shape = helper.create_variable_for_type_inference(x.dtype)
helper.append_op(
type='flatten2',
inputs={"X": x},
......@@ -6634,7 +6648,8 @@ def sequence_enumerate(input, win_size, pad_value=0, name=None):
out = fluid.layers.sequence_enumerate(input=x, win_size=3, pad_value=0)
"""
helper = LayerHelper('sequence_enumerate', **locals())
out = helper.create_tmp_variable(helper.input_dtype(), stop_gradient=True)
out = helper.create_variable_for_type_inference(
helper.input_dtype(), stop_gradient=True)
helper.append_op(
type='sequence_enumerate',
inputs={'X': input},
......@@ -6674,9 +6689,9 @@ def sequence_mask(x, maxlen=None, dtype='int64', name=None):
helper = LayerHelper('sequence_mask', **locals())
if name is None:
out = helper.create_tmp_variable(dtype=dtype)
out = helper.create_variable_for_type_inference(dtype=dtype)
else:
out = helper.create_tmp_variable(dtype=dtype, name=name)
out = helper.create_variable_for_type_inference(dtype=dtype, name=name)
helper.append_op(
type='sequence_mask',
......@@ -6719,7 +6734,7 @@ def stack(x, axis=0):
if not isinstance(x, list) and not isinstance(x, tuple):
x = [x]
out = helper.create_tmp_variable(x[0].dtype)
out = helper.create_variable_for_type_inference(x[0].dtype)
helper.append_op(
type='stack', inputs={'X': x}, outputs={'Y': out},
attrs={'axis': axis})
......@@ -6757,7 +6772,7 @@ def unstack(x, axis=0, num=None):
outs = []
for _ in num:
outs.append(helper.create_tmp_variable(x.dtype))
outs.append(helper.create_variable_for_type_inference(x.dtype))
helper.append_op(
type='unstack',
......@@ -6809,7 +6824,7 @@ def expand(x, expand_times, name=None):
"""
helper = LayerHelper('expand', input=x, **locals())
dtype = helper.input_dtype(input_param_name='x')
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='expand',
inputs={'X': x},
......@@ -6848,7 +6863,7 @@ def uniform_random_batch_size_like(input,
"""
helper = LayerHelper('uniform_random_batch_size_like', **locals())
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
c_dtype = convert_np_dtype_to_dtype_(dtype)
helper.append_op(
type='uniform_random_batch_size_like',
......@@ -6885,7 +6900,7 @@ def gaussian_random(shape, mean=0.0, std=1.0, seed=0, dtype='float32'):
"""
helper = LayerHelper('gaussian_random', **locals())
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
c_dtype = convert_np_dtype_to_dtype_(dtype)
helper.append_op(
type='gaussian_random',
......@@ -6920,7 +6935,7 @@ def sampling_id(x, min=0.0, max=1.0, seed=0, dtype='float32'):
"""
helper = LayerHelper('sampling_id', **locals())
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='sampling_id',
inputs={'X': x},
......@@ -6959,7 +6974,7 @@ def gaussian_random_batch_size_like(input,
"""
helper = LayerHelper('gaussian_random_batch_size_like', **locals())
out = helper.create_tmp_variable(dtype)
out = helper.create_variable_for_type_inference(dtype)
c_dtype = convert_np_dtype_to_dtype_(dtype)
helper.append_op(
type='gaussian_random_batch_size_like',
......@@ -6991,7 +7006,8 @@ def sum(x):
"""
helper = LayerHelper('sum', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype('x'))
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype('x'))
helper.append_op(
type='sum',
inputs={'X': x},
......@@ -7018,7 +7034,8 @@ def slice(input, axes, starts, ends):
"""
helper = LayerHelper('slice', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype('input'))
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype('input'))
helper.append_op(
type='slice',
inputs={'Input': input},
......@@ -7044,7 +7061,8 @@ def shape(input):
"""
helper = LayerHelper('shape', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype('input'))
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype('input'))
helper.append_op(
type='shape', inputs={'Input': input}, outputs={'Out': out})
......@@ -7061,7 +7079,7 @@ def _elementwise_op(helper):
use_mkldnn = helper.kwargs.get('use_mkldnn', False)
name = helper.kwargs.get('name', None)
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......@@ -7095,7 +7113,7 @@ def scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None):
helper = LayerHelper('scale', **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......@@ -7161,7 +7179,7 @@ def _logical_op(op_name, x, y, out=None, name=None, binary_op=True):
if out is None:
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......@@ -7269,7 +7287,7 @@ def clip(x, min, max, name=None):
helper = LayerHelper("clip", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......@@ -7301,7 +7319,7 @@ def clip_by_norm(x, max_norm, name=None):
helper = LayerHelper("clip_by_norm", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......@@ -7331,7 +7349,7 @@ def mean(x, name=None):
helper = LayerHelper("mean", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......@@ -7361,7 +7379,7 @@ def mul(x, y, x_num_col_dims=1, y_num_col_dims=1, name=None):
helper = LayerHelper("mul", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......@@ -7395,7 +7413,7 @@ def sigmoid_cross_entropy_with_logits(x, label, name=None):
helper = LayerHelper("sigmoid_cross_entropy_with_logits", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......@@ -7425,7 +7443,7 @@ def maxout(x, groups, name=None):
helper = LayerHelper("maxout", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......@@ -7515,7 +7533,7 @@ def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None):
helper = LayerHelper("affine_channel", **locals())
if name is None:
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
......
python/paddle/fluid/layers/tensor.py
浏览文件 @ 8e8e8e66
......@@ -152,7 +152,7 @@ def cast(x, dtype):
result = fluid.layers.cast(x=data, dtype='float64')
"""
helper = LayerHelper('cast', **locals())
out = helper.create_tmp_variable(dtype=dtype)
out = helper.create_variable_for_type_inference(dtype=dtype)
helper.append_op(
type='cast',
inputs={'X': [x]},
......@@ -184,7 +184,7 @@ def concat(input, axis=0, name=None):
out = fluid.layers.concat(input=[Efirst, Esecond, Ethird, Efourth])
"""
helper = LayerHelper('concat', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
helper.append_op(
type='concat',
inputs={'X': input},
......@@ -221,7 +221,8 @@ def sums(input, out=None):
"""
helper = LayerHelper('sum', **locals())
if out is None:
out = helper.create_tmp_variable(dtype=helper.input_dtype())
out = helper.create_variable_for_type_inference(
dtype=helper.input_dtype())
helper.append_op(
type='sum',
inputs={'X': input},
......@@ -252,7 +253,7 @@ def assign(input, output=None):
"""
helper = LayerHelper('assign', **locals())
if output is None:
output = helper.create_tmp_variable(dtype=input.dtype)
output = helper.create_variable_for_type_inference(dtype=input.dtype)
if isinstance(input, Variable):
helper.append_op(
type='assign', inputs={'X': [input]}, outputs={'Out': [output]})
......@@ -311,7 +312,7 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None):
helper = LayerHelper("fill_constant", **locals())
if out is None:
out = helper.create_tmp_variable(dtype=dtype)
out = helper.create_variable_for_type_inference(dtype=dtype)
helper.append_op(
type='fill_constant',
inputs={},
......@@ -358,7 +359,7 @@ def fill_constant_batch_size_like(input,
${out_comment}.
"""
helper = LayerHelper("fill_constant_batch_size_like", **locals())
out = helper.create_tmp_variable(dtype=dtype)
out = helper.create_variable_for_type_inference(dtype=dtype)
helper.append_op(
type='fill_constant_batch_size_like',
inputs={'Input': input},
......@@ -396,7 +397,7 @@ def argmin(x, axis=0):
out = fluid.layers.argmin(x=in, axis=-1)
"""
helper = LayerHelper("arg_min", **locals())
out = helper.create_tmp_variable(VarDesc.VarType.INT64)
out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)
helper.append_op(
type='arg_min',
inputs={'X': x},
......@@ -427,7 +428,7 @@ def argmax(x, axis=0):
out = fluid.layers.argmax(x=in, axis=-1)
"""
helper = LayerHelper("arg_max", **locals())
out = helper.create_tmp_variable(VarDesc.VarType.INT64)
out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)
helper.append_op(
type='arg_max',
inputs={'X': x},
......@@ -477,8 +478,10 @@ def argsort(input, axis=-1, name=None):
out, indices = fluid.layers.argsort(input, axis=0)
"""
helper = LayerHelper("argsort", **locals())
out = helper.create_tmp_variable(dtype=input.dtype, stop_gradient=True)
ids = helper.create_tmp_variable(VarDesc.VarType.INT64, stop_gradient=True)
out = helper.create_variable_for_type_inference(
dtype=input.dtype, stop_gradient=True)
ids = helper.create_variable_for_type_inference(
VarDesc.VarType.INT64, stop_gradient=True)
helper.append_op(
type='argsort',
inputs={'X': input},
......@@ -562,7 +565,7 @@ def reverse(x, axis):
if isinstance(axis, int):
axis = [axis]
helper = LayerHelper("reverse", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='reverse',
inputs={'Input': x},
......@@ -654,7 +657,7 @@ def has_inf(x):
Variable: The tensor variable storing the output, only a bool value.
"""
helper = LayerHelper("isinf", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type="isinf", inputs={"X": x}, outputs={"Out": out})
return out
......@@ -670,7 +673,7 @@ def has_nan(x):
Variable: The tensor variable storing the output, only a bool value.
"""
helper = LayerHelper("isnan", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type="isnan", inputs={"X": x}, outputs={"Out": out})
return out
......@@ -687,6 +690,6 @@ def isfinite(x):
Variable: The tensor variable storing the output, contains a bool value.
"""
helper = LayerHelper("isfinite", **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type="isfinite", inputs={"X": x}, outputs={"Out": out})
return out
python/paddle/fluid/regularizer.py
浏览文件 @ 8e8e8e66
......@@ -151,7 +151,7 @@ class L2DecayRegularizer(WeightDecayRegularizer):
decay = block.create_var(
dtype="float32",
shape=param.shape,
type=core.VarDesc.VarType.SELECTED_ROWS)
type=core.VarDesc.VarType.LOD_TENSOR)
block.append_op(
type='extract_rows', inputs={'X': grad}, outputs={'Out': idx})
block.append_op(
......@@ -228,7 +228,7 @@ class L1DecayRegularizer(WeightDecayRegularizer):
decay = block.create_var(
dtype="float32",
shape=param.shape,
type=core.VarDesc.VarType.SELECTED_ROWS)
type=core.VarDesc.VarType.LOD_TENSOR)
block.append_op(
type='extract_rows', inputs={'X': grad}, outputs={'Out': idx})
block.append_op(
......
python/paddle/fluid/tests/CMakeLists.txt
浏览文件 @ 8e8e8e66
if(NOT APPLE)
set(PYTHON_TESTS_DIR ${CMAKE_CURRENT_BINARY_DIR} CACHE PATH "python tests directory")
else()
set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests)
endif(NOT APPLE)
set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests CACHE INTERNAL "python tests directory")
file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
......
python/paddle/fluid/tests/unittests/test_slice_var.py
浏览文件 @ 8e8e8e66
......@@ -30,7 +30,6 @@ class TestSliceVar(unittest.TestCase):
var = program.global_block().create_var(
name=str(random.randint(10000, 99999)),
persistable=True,
# dtype=core.VarDesc.VarType.LOD_TENSOR,
shape=shape)
var_list.append(var)
blocks = slice_variable(var_list, 10, min_size)
......
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