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提交 b2943530 编写于 作者: D Dang Qingqing
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into map_api

上级 80ee069b 3cab25a5
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Showing with 1059 addition and 824 deletion
paddle/fluid/framework/framework.proto
浏览文件 @ b2943530
......@@ -80,7 +80,6 @@ message OpProto {
optional bool duplicable = 3 [ default = false ];
optional bool intermediate = 4 [ default = false ];
optional bool dispensable = 5 [ default = false ];
optional string reuse = 6;
}
// AttrProto describes the C++ type Attribute.
......
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ b2943530
......@@ -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 @ b2943530
......@@ -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 @ b2943530
......@@ -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 @ b2943530
......@@ -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/op_proto_maker.cc
浏览文件 @ b2943530
......@@ -21,7 +21,6 @@ namespace framework {
void OpProtoAndCheckerMaker::Validate() {
validated_ = true;
CheckNoDuplicatedInOutAttrs();
CheckReuseVars();
}
OpProtoAndCheckerMaker::VariableBuilder OpProtoAndCheckerMaker::AddInput(
......@@ -40,40 +39,6 @@ OpProtoAndCheckerMaker::VariableBuilder OpProtoAndCheckerMaker::AddOutput(
return OpProtoAndCheckerMaker::VariableBuilder{output};
}
void OpProtoAndCheckerMaker::Reuse(const std::string& name,
const std::string& reused_name) {
bool found = false;
proto::OpProto::Var* var;
for (auto& var : proto_->inputs()) {
if (var.name() == reused_name) {
found = true;
break;
}
}
PADDLE_ENFORCE(found == true,
"Input/Output name: %s reused_name: %s, one of them is not "
"exists or not matched.",
name, reused_name);
found = false;
for (int i = 0; i < proto_->outputs().size(); ++i) {
var = proto_->mutable_outputs()->Mutable(i);
if (var->name() == name) {
PADDLE_ENFORCE(!var->has_reuse(),
"Output(%s) has been set reused var of %s", name,
var->reuse());
found = true;
var->set_reuse(reused_name);
break;
}
}
PADDLE_ENFORCE(found == true,
"Input/Output name: %s reused_name: %s, one of them is not "
"exists or not matched.",
name, reused_name);
}
void OpProtoAndCheckerMaker::CheckNoDuplicatedInOutAttrs() {
std::unordered_set<std::string> names;
auto checker = [&](const std::string& name) {
......@@ -91,24 +56,6 @@ void OpProtoAndCheckerMaker::CheckNoDuplicatedInOutAttrs() {
}
}
void OpProtoAndCheckerMaker::CheckReuseVars() {
std::unordered_set<std::string> names;
for (auto& input : proto_->inputs()) {
names.insert(input.name());
}
auto checker = [&](const std::string& name, const std::string& reused) {
PADDLE_ENFORCE(
names.count(reused),
"Output [%s] reuse Input [%s], but the input is not registered.", name,
reused);
};
for (auto& output : proto_->outputs()) {
if (output.has_reuse()) {
checker(output.name(), output.reuse());
}
}
}
void OpProtoAndCheckerMaker::operator()(proto::OpProto* proto,
OpAttrChecker* attr_checker) {
proto_ = proto;
......
paddle/fluid/framework/op_proto_maker.h
浏览文件 @ b2943530
......@@ -14,8 +14,6 @@ limitations under the License. */
#pragma once
#include <string>
#include <unordered_set>
#include "glog/logging.h"
#include "paddle/fluid/framework/attribute.h"
#include "paddle/fluid/framework/framework.pb.h"
......@@ -73,11 +71,6 @@ class OpProtoAndCheckerMaker {
var_->set_dispensable(true);
return *this;
}
VariableBuilder &Reuse(const std::string &name) {
var_->set_reuse(name);
return *this;
}
};
VariableBuilder AddInput(const std::string &name, const std::string &comment);
......@@ -85,8 +78,6 @@ class OpProtoAndCheckerMaker {
VariableBuilder AddOutput(const std::string &name,
const std::string &comment);
void Reuse(const std::string &name, const std::string &reused_name);
template <typename T>
TypedAttrChecker<T> &AddAttr(const std::string &name,
const std::string &comment,
......@@ -105,8 +96,6 @@ class OpProtoAndCheckerMaker {
void CheckNoDuplicatedInOutAttrs();
void Validate();
void CheckReuseVars();
proto::OpProto *proto_;
OpAttrChecker *op_checker_;
bool validated_{false};
......
paddle/fluid/framework/op_proto_maker_test.cc
浏览文件 @ b2943530
......@@ -47,120 +47,3 @@ TEST(ProtoMaker, DuplicatedInOut) {
ASSERT_THROW(proto_maker(&op_proto, &op_checker),
paddle::platform::EnforceNotMet);
}
class TestInplaceProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "input of test op");
AddOutput("XOut", "output of test op").Reuse("X");
}
};
class TestInplaceProtoMaker2
: public paddle::framework::OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "input of test op");
AddOutput("XOut", "output of test op").Reuse("X");
AddOutput("NoOut", "output of test op").Reuse("NotExists");
}
};
TEST(ProtoMaker, InplaceOutput) {
paddle::framework::proto::OpProto op_proto, op_proto2;
paddle::framework::OpAttrChecker op_checker;
TestInplaceProtoMaker proto_maker;
TestInplaceProtoMaker2 proto_maker2;
proto_maker(&op_proto, &op_checker);
ASSERT_THROW(proto_maker2(&op_proto2, &op_checker),
paddle::platform::EnforceNotMet);
}
// normal reuse
class TestReuseProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "input of test op");
AddInput("Y", "input of test op");
AddOutput("Out", "output of test op");
AddOutput("XOut", "output of test op");
// avoid destructor exception.
// Validate();
TestReuse();
}
virtual void TestReuse() {}
};
// test duplicate reuse error
class TestReuseProtoMaker2 : public TestReuseProtoMaker {
public:
void TestReuse() {
Reuse("Out", "X");
Reuse("Out", "Y");
}
};
// NotExists Input
class TestReuseProtoMaker3 : public TestReuseProtoMaker {
public:
void TestReuse() {
Reuse("Out", "NotExists");
Reuse("XOut", "X");
}
};
// NotExists Output
class TestReuseProtoMaker4 : public TestReuseProtoMaker {
public:
void TestReuse() { Reuse("NotExists", "X"); }
};
TEST(ProtoMaker, Reuse) {
paddle::framework::proto::OpProto op_proto;
paddle::framework::OpAttrChecker op_checker;
TestReuseProtoMaker proto_maker;
proto_maker(&op_proto, &op_checker);
}
// NOTE(dzhwinter):
// There is a Fatal CHECK on base class destructor, which will call abort inside
// instead of
// throw an exception. If we throw an exception in Make(), we will trigger the
// CHECK and terminate the tests.
//
// I had tried to replace the default CHECK with a exception, however, it's
// still not supported by glog.
// the details:
// https://github.com/google/glog/issues/249
// https://github.com/facebookresearch/TensorComprehensions/issues/351
/*
TEST(ProtoMaker, ReuseWithException) {
paddle::framework::proto::OpProto op_proto2, op_proto3, op_proto4;
paddle::framework::OpAttrChecker op_checker;
TestReuseProtoMaker2 proto_maker2;
TestReuseProtoMaker3 proto_maker3;
TestReuseProtoMaker4 proto_maker4;
EXPECT_THROW(proto_maker2(&op_proto2, &op_checker),
paddle::platform::EnforceNotMet);
EXPECT_THROW(proto_maker3(&op_proto3, &op_checker),
paddle::platform::EnforceNotMet);
EXPECT_THROW(proto_maker4(&op_proto4, &op_checker),
paddle::platform::EnforceNotMet);
}
void FailureFunction() {
throw std::runtime_error("Check failed in destructor.");
// return 0;
}
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
google::InstallFailureFunction(&FailureFunction);
return RUN_ALL_TESTS();
}
*/
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ b2943530
......@@ -156,13 +156,11 @@ ParallelExecutor::ParallelExecutor(
params, member_->local_scopes_, member_->use_cuda_);
#endif
if (VLOG_IS_ON(5)) {
// If the loss_var_name is given, the number of graph should be only one.
if (loss_var_name.size()) {
PADDLE_ENFORCE_EQ(ir::GraphNum(*graph), 1,
"The number of graph should be only one");
}
}
if (exec_strategy.type_ == ExecutionStrategy::kDefault) {
member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
......
paddle/fluid/framework/program_desc_test.cc
浏览文件 @ b2943530
......@@ -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 @ b2943530
......@@ -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/api/demo_ci/run.sh
浏览文件 @ b2943530
......@@ -21,7 +21,7 @@ else
fi
USE_TENSORRT=OFF
if [ [-d"$TENSORRT_INCLUDE_DIR"] -a [-d"$TENSORRT_LIB_DIR"] ]; then
if [ -d "$TENSORRT_INCLUDE_DIR" -a -d "$TENSORRT_LIB_DIR" ]; then
USE_TENSORRT=ON
fi
......
paddle/fluid/inference/tensorrt/convert/pool2d_op.cc
浏览文件 @ b2943530
......@@ -42,16 +42,22 @@ class Pool2dOpConverter : public OpConverter {
boost::get<std::vector<int>>(op_desc.GetAttr("strides"));
std::vector<int> paddings =
boost::get<std::vector<int>>(op_desc.GetAttr("paddings"));
bool ceil_mode = boost::get<bool>(op_desc.GetAttr("ceil_mode"));
nvinfer1::DimsHW nv_ksize(ksize[0], ksize[1]);
if (global_pooling == true) {
nvinfer1::Dims input_shape = input1->getDimensions();
int nbDims = input_shape.nbDims;
nvinfer1::DimsHW nv_ksize(ksize[0], ksize[1]);
nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
if (global_pooling == true) {
nv_ksize.d[0] = input_shape.d[nbDims - 2];
nv_ksize.d[1] = input_shape.d[nbDims - 1];
nv_strides.h() = 1;
nv_strides.w() = 1;
nv_paddings.h() = 0;
nv_paddings.w() = 0;
}
const nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
const nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
PADDLE_ENFORCE_EQ(input1->getDimensions().nbDims, 3UL);
......@@ -64,6 +70,36 @@ class Pool2dOpConverter : public OpConverter {
PADDLE_THROW("TensorRT unsupported pooling type!");
}
if (ceil_mode) {
nvinfer1::DimsHW pre_pad(0, 0);
nvinfer1::DimsHW post_pad(0, 0);
int input_height = input_shape.d[nbDims - 2];
int input_width = input_shape.d[nbDims - 1];
int floor_h_output_size =
(input_height - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
int ceil_h_output_size =
(input_height - ksize[0] + 2 * paddings[0] + strides[0] - 1) /
strides[0] +
1;
int floor_w_output_size =
(input_width - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
int ceil_w_output_size =
(input_width - ksize[1] + 2 * paddings[1] + strides[1] - 1) /
strides[1] +
1;
if (floor_h_output_size != ceil_h_output_size) {
post_pad.h() = strides[0] - 1;
}
if (floor_w_output_size != ceil_w_output_size) {
post_pad.w() = strides[1] - 1;
}
auto* layer = TRT_ENGINE_ADD_LAYER(
engine_, Padding, *const_cast<nvinfer1::ITensor*>(input1), pre_pad,
post_pad);
input1 = layer->getOutput(0);
}
auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Pooling,
*const_cast<nvinfer1::ITensor*>(input1),
nv_pool_type, nv_ksize);
......
paddle/fluid/inference/tensorrt/convert/test_pool2d_op.cc
浏览文件 @ b2943530
......@@ -20,18 +20,20 @@ namespace paddle {
namespace inference {
namespace tensorrt {
void test_pool2d(bool global_pooling) {
void test_pool2d(bool global_pooling, bool ceil_mode) {
framework::Scope scope;
std::unordered_set<std::string> parameters;
TRTConvertValidation validator(5, parameters, scope, 1 << 15);
// The ITensor's Dims should not contain the batch size.
// So, the ITensor's Dims of input and output should be C * H * W.
validator.DeclInputVar("pool2d-X", nvinfer1::Dims3(3, 4, 4));
validator.DeclInputVar("pool2d-X", nvinfer1::Dims3(3, 13, 14));
if (global_pooling)
validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 1, 1));
else if (ceil_mode)
validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 6, 7));
else
validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 2, 2));
validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 6, 6));
// Prepare Op description
framework::OpDesc desc;
......@@ -39,7 +41,7 @@ void test_pool2d(bool global_pooling) {
desc.SetInput("X", {"pool2d-X"});
desc.SetOutput("Out", {"pool2d-Out"});
std::vector<int> ksize({2, 2});
std::vector<int> ksize({3, 3});
std::vector<int> strides({2, 2});
std::vector<int> paddings({0, 0});
std::string pooling_t = "max";
......@@ -49,6 +51,7 @@ void test_pool2d(bool global_pooling) {
desc.SetAttr("strides", strides);
desc.SetAttr("paddings", paddings);
desc.SetAttr("global_pooling", global_pooling);
desc.SetAttr("ceil_mode", ceil_mode);
LOG(INFO) << "set OP";
validator.SetOp(*desc.Proto());
......@@ -57,9 +60,10 @@ void test_pool2d(bool global_pooling) {
validator.Execute(3);
}
TEST(Pool2dOpConverter, normal) { test_pool2d(false); }
TEST(Pool2dOpConverter, normal) { test_pool2d(false, false); }
TEST(Pool2dOpConverter, test_global_pooling) { test_pool2d(true, false); }
TEST(Pool2dOpConverter, test_global_pooling) { test_pool2d(true); }
TEST(Pool2dOpConverter, test_ceil_mode) { test_pool2d(false, true); }
} // namespace tensorrt
} // namespace inference
......
paddle/fluid/inference/tests/api/analyzer_resnet50_tester.cc
浏览文件 @ b2943530
......@@ -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 @ b2943530
......@@ -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 @ b2943530
......@@ -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/activation_op.cc
浏览文件 @ b2943530
......@@ -28,7 +28,7 @@ using paddle::framework::Tensor;
public: \
void Make() override { \
AddInput("X", "Input of " #OP_NAME " operator"); \
AddOutput("Out", "Output of " #OP_NAME " operator").Reuse("X"); \
AddOutput("Out", "Output of " #OP_NAME " operator"); \
AddAttr<bool>("use_mkldnn", \
"(bool, default false) Only used in mkldnn kernel") \
.SetDefault(false); \
......
paddle/fluid/operators/adam_op.cc
浏览文件 @ b2943530
......@@ -92,9 +92,9 @@ class AdamOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Beta1Pow", "(Tensor) Input beta1 power accumulator");
AddInput("Beta2Pow", "(Tensor) Input beta2 power accumulator");
AddOutput("ParamOut", "(Tensor) Output parameter").Reuse("Param");
AddOutput("Moment1Out", "(Tensor) Output first moment").Reuse("Moment1");
AddOutput("Moment2Out", "(Tensor) Output second moment").Reuse("Moment2");
AddOutput("ParamOut", "(Tensor) Output parameter");
AddOutput("Moment1Out", "(Tensor) Output first moment");
AddOutput("Moment2Out", "(Tensor) Output second moment");
AddAttr<float>("beta1",
"(float, default 0.9) "
......
paddle/fluid/operators/array_to_lod_tensor_op.cc
浏览文件 @ b2943530
......@@ -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/batch_norm_op.cc
浏览文件 @ b2943530
......@@ -135,15 +135,13 @@ class BatchNormOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Variance",
"The global variance (for training) "
"or estimated Variance (for testing)");
AddOutput("Y", "result after normalization").Reuse("X");
AddOutput("Y", "result after normalization");
AddOutput("MeanOut",
"Share memory with Mean. "
"Store the global mean when training")
.Reuse("Mean");
"Store the global mean when training");
AddOutput("VarianceOut",
"Share memory with Variance. "
"Store the global Variance when training")
.Reuse("Variance");
"Store the global Variance when training");
AddOutput("SavedMean",
"Mean of the current mini batch, "
"will apply to output when training")
......
paddle/fluid/operators/concat_op.h
浏览文件 @ b2943530
......@@ -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/conv_op.cc
浏览文件 @ b2943530
......@@ -130,8 +130,7 @@ void Conv2DOpMaker::Make() {
.AsDispensable();
AddOutput("Output",
"(Tensor) The output tensor of convolution operator. "
"The format of output tensor is also NCHW.")
.Reuse("Input");
"The format of output tensor is also NCHW.");
AddInput("ResidualData",
"(Tensor) Tensor with residual data "
"to which convolution output will be added."
......@@ -238,8 +237,7 @@ void Conv3DOpMaker::Make() {
"input image channels divided by the groups.");
AddOutput("Output",
"(Tensor) The output tensor of convolution operator."
"The format of output tensor is also NCDHW.")
.Reuse("Input");
"The format of output tensor is also NCDHW.");
AddAttr<std::vector<int>>("strides",
"(vector<int>, default:{1, 1, 1}), the "
"strides(d_stride, h_stride, w_stride) of "
......
paddle/fluid/operators/detection/generate_proposal_labels_op.cc
浏览文件 @ b2943530
......@@ -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/detection/rpn_target_assign_op.cc
浏览文件 @ b2943530
......@@ -52,6 +52,9 @@ class RpnTargetAssignOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(
ctx->HasOutput("TargetBBox"),
"Output(TargetBBox) of RpnTargetAssignOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("BBoxInsideWeight"),
"Output(BBoxInsideWeight) of RpnTargetAssignOp should not be null");
auto anchor_dims = ctx->GetInputDim("Anchor");
auto gt_boxes_dims = ctx->GetInputDim("GtBoxes");
......@@ -68,6 +71,7 @@ class RpnTargetAssignOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("ScoreIndex", {-1});
ctx->SetOutputDim("TargetLabel", {-1, 1});
ctx->SetOutputDim("TargetBBox", {-1, 4});
ctx->SetOutputDim("BBoxInsideWeight", {-1, 4});
}
protected:
......@@ -169,6 +173,7 @@ void ScoreAssign(const T* anchor_by_gt_overlap_data,
const float rpn_positive_overlap,
const float rpn_negative_overlap, std::vector<int>* fg_inds,
std::vector<int>* bg_inds, std::vector<int>* tgt_lbl,
std::vector<int>* fg_fake, std::vector<T>* bbox_inside_weight,
std::minstd_rand engine, bool use_random) {
float epsilon = 0.00001;
int anchor_num = anchor_to_gt_max.dims()[0];
......@@ -201,12 +206,12 @@ void ScoreAssign(const T* anchor_by_gt_overlap_data,
// Reservoir Sampling
int fg_num = static_cast<int>(rpn_fg_fraction * rpn_batch_size_per_im);
ReservoirSampling(fg_num, &fg_inds_fake, engine, use_random);
fg_num = static_cast<int>(fg_inds_fake.size());
for (int64_t i = 0; i < fg_num; ++i) {
int fg_fake_num = static_cast<int>(fg_inds_fake.size());
for (int64_t i = 0; i < fg_fake_num; ++i) {
target_label[fg_inds_fake[i]] = 1;
}
int bg_num = rpn_batch_size_per_im - fg_num;
int bg_num = rpn_batch_size_per_im - fg_fake_num;
for (int64_t i = 0; i < anchor_num; ++i) {
if (anchor_to_gt_max_data[i] < rpn_negative_overlap) {
bg_inds_fake.push_back(i);
......@@ -214,12 +219,28 @@ void ScoreAssign(const T* anchor_by_gt_overlap_data,
}
ReservoirSampling(bg_num, &bg_inds_fake, engine, use_random);
bg_num = static_cast<int>(bg_inds_fake.size());
int fake_num = 0;
for (int64_t i = 0; i < bg_num; ++i) {
// fg fake found
if (target_label[bg_inds_fake[i]] == 1) {
fake_num++;
fg_fake->emplace_back(fg_inds_fake[0]);
for (int j = 0; j < 4; ++j) {
bbox_inside_weight->emplace_back(T(0.));
}
}
target_label[bg_inds_fake[i]] = 0;
}
for (int64_t i = 0; i < (fg_fake_num - fake_num) * 4; ++i) {
bbox_inside_weight->emplace_back(T(1.));
}
for (int64_t i = 0; i < anchor_num; ++i) {
if (target_label[i] == 1) fg_inds->emplace_back(i);
if (target_label[i] == 1) {
fg_inds->emplace_back(i);
fg_fake->emplace_back(i);
}
if (target_label[i] == 0) bg_inds->emplace_back(i);
}
fg_num = fg_inds->size();
......@@ -248,7 +269,8 @@ std::vector<Tensor> SampleRpnFgBgGt(const platform::CPUDeviceContext& ctx,
std::vector<int> bg_inds;
std::vector<int> gt_inds;
std::vector<int> tgt_lbl;
std::vector<int> fg_fake;
std::vector<T> bbox_inside_weight;
// Calculate the max IoU between anchors and gt boxes
// Map from anchor to gt box that has highest overlap
auto place = ctx.GetPlace();
......@@ -275,32 +297,37 @@ std::vector<Tensor> SampleRpnFgBgGt(const platform::CPUDeviceContext& ctx,
// Follow the Faster RCNN's implementation
ScoreAssign(anchor_by_gt_overlap_data, anchor_to_gt_max, gt_to_anchor_max,
rpn_batch_size_per_im, rpn_fg_fraction, rpn_positive_overlap,
rpn_negative_overlap, &fg_inds, &bg_inds, &tgt_lbl, engine,
use_random);
rpn_negative_overlap, &fg_inds, &bg_inds, &tgt_lbl, &fg_fake,
&bbox_inside_weight, engine, use_random);
int fg_num = fg_inds.size();
int bg_num = bg_inds.size();
gt_inds.reserve(fg_num);
for (int i = 0; i < fg_num; ++i) {
gt_inds.emplace_back(argmax[fg_inds[i]]);
int fg_fake_num = fg_fake.size();
gt_inds.reserve(fg_fake_num);
for (int i = 0; i < fg_fake_num; ++i) {
gt_inds.emplace_back(argmax[fg_fake[i]]);
}
Tensor loc_index_t, score_index_t, tgt_lbl_t, gt_inds_t;
int* loc_index_data = loc_index_t.mutable_data<int>({fg_num}, place);
Tensor loc_index_t, score_index_t, tgt_lbl_t, gt_inds_t, bbox_inside_weight_t;
int* loc_index_data = loc_index_t.mutable_data<int>({fg_fake_num}, place);
int* score_index_data =
score_index_t.mutable_data<int>({fg_num + bg_num}, place);
int* tgt_lbl_data = tgt_lbl_t.mutable_data<int>({fg_num + bg_num}, place);
int* gt_inds_data = gt_inds_t.mutable_data<int>({fg_num}, place);
std::copy(fg_inds.begin(), fg_inds.end(), loc_index_data);
int* gt_inds_data = gt_inds_t.mutable_data<int>({fg_fake_num}, place);
T* bbox_inside_weight_data =
bbox_inside_weight_t.mutable_data<T>({fg_fake_num, 4}, place);
std::copy(fg_fake.begin(), fg_fake.end(), loc_index_data);
std::copy(fg_inds.begin(), fg_inds.end(), score_index_data);
std::copy(bg_inds.begin(), bg_inds.end(), score_index_data + fg_num);
std::copy(tgt_lbl.begin(), tgt_lbl.end(), tgt_lbl_data);
std::copy(gt_inds.begin(), gt_inds.end(), gt_inds_data);
std::copy(bbox_inside_weight.begin(), bbox_inside_weight.end(),
bbox_inside_weight_data);
std::vector<Tensor> loc_score_tgtlbl_gt;
loc_score_tgtlbl_gt.emplace_back(loc_index_t);
loc_score_tgtlbl_gt.emplace_back(score_index_t);
loc_score_tgtlbl_gt.emplace_back(tgt_lbl_t);
loc_score_tgtlbl_gt.emplace_back(gt_inds_t);
loc_score_tgtlbl_gt.emplace_back(bbox_inside_weight_t);
return loc_score_tgtlbl_gt;
}
......@@ -318,6 +345,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
auto* score_index = context.Output<LoDTensor>("ScoreIndex");
auto* tgt_bbox = context.Output<LoDTensor>("TargetBBox");
auto* tgt_lbl = context.Output<LoDTensor>("TargetLabel");
auto* bbox_inside_weight = context.Output<LoDTensor>("BBoxInsideWeight");
PADDLE_ENFORCE_EQ(gt_boxes->lod().size(), 1UL,
"RpnTargetAssignOp gt_boxes needs 1 level of LoD");
......@@ -340,7 +368,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
score_index->mutable_data<int>({max_num}, place);
tgt_bbox->mutable_data<T>({max_num, 4}, place);
tgt_lbl->mutable_data<int>({max_num, 1}, place);
bbox_inside_weight->mutable_data<T>({max_num, 4}, place);
auto& dev_ctx = context.device_context<platform::CPUDeviceContext>();
std::random_device rnd;
......@@ -394,6 +422,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
Tensor sampled_score_index = loc_score_tgtlbl_gt[1];
Tensor sampled_tgtlbl = loc_score_tgtlbl_gt[2];
Tensor sampled_gt_index = loc_score_tgtlbl_gt[3];
Tensor sampled_bbox_inside_weight = loc_score_tgtlbl_gt[4];
int loc_num = sampled_loc_index.dims()[0];
int score_num = sampled_score_index.dims()[0];
......@@ -432,6 +461,8 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
AppendRpns<int>(score_index, total_score_num, &sampled_score_index_unmap);
AppendRpns<T>(tgt_bbox, total_loc_num * 4, &sampled_tgt_bbox);
AppendRpns<int>(tgt_lbl, total_score_num, &sampled_tgtlbl);
AppendRpns<T>(bbox_inside_weight, total_loc_num * 4,
&sampled_bbox_inside_weight);
total_loc_num += loc_num;
total_score_num += score_num;
......@@ -448,10 +479,12 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
score_index->set_lod(loc_score);
tgt_bbox->set_lod(lod_loc);
tgt_lbl->set_lod(loc_score);
bbox_inside_weight->set_lod(lod_loc);
loc_index->Resize({total_loc_num});
score_index->Resize({total_score_num});
tgt_bbox->Resize({total_loc_num, 4});
tgt_lbl->Resize({total_score_num, 1});
bbox_inside_weight->Resize({total_loc_num, 4});
}
};
......@@ -514,6 +547,9 @@ class RpnTargetAssignOpMaker : public framework::OpProtoAndCheckerMaker {
"TargetLabel",
"(Tensor<int>), The target labels of each anchor with shape "
"[F + B, 1], F and B are sampled foreground and backgroud number.");
AddOutput("BBoxInsideWeight",
"(Tensor), The bbox inside weight with shape "
"[F, 4], F is the sampled foreground number.");
AddComment(R"DOC(
This operator can be, for a given set of ground truth bboxes and the
anchors, to assign classification and regression targets to each prediction.
......
paddle/fluid/operators/elementwise_op.h
浏览文件 @ b2943530
......@@ -80,8 +80,6 @@ class ElementwiseOpMaker : public framework::OpProtoAndCheckerMaker {
void Make() final {
AddInput("X", "(Tensor), The first input tensor of elementwise op.");
AddInput("Y", "(Tensor), The second input tensor of elementwise op.");
// AddOutput("SavedShape", "(Tensor), save X, Y shape for grad to save
// memory.").AsIntermediate();
AddOutput("Out", "The output of elementwise op.");
AddAttr<int>("axis",
"(int, default -1). The start dimension index "
......@@ -129,13 +127,11 @@ But the output only shares the LoD information with the input $X$.
)DOC",
GetName(), GetEquation()));
SetReuse();
}
protected:
virtual std::string GetName() const = 0;
virtual std::string GetEquation() const = 0;
virtual void SetReuse() {}
};
class ElementwiseOpGrad : public framework::OperatorWithKernel {
......@@ -269,7 +265,6 @@ class ElemwiseGradKernel : public framework::OpKernel<T> {
protected: \
virtual std::string GetName() const { return op_name; } \
virtual std::string GetEquation() const { return equation; } \
virtual void SetReuse() { Reuse(__VA_ARGS__); } \
}; \
REGISTER_OPERATOR(op_type, ::paddle::operators::ElementwiseOp, \
__ElemwiseOp##op_type##Maker__, \
......
paddle/fluid/operators/fusion_gru_op.cc
浏览文件 @ b2943530
......@@ -16,10 +16,9 @@ limitations under the License. */
#include <cstring> // for memcpy
#include <string>
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/cpu_vec.h"
#include "paddle/fluid/operators/math/fc_compute.h"
#include "paddle/fluid/operators/math/jit_kernel.h"
#include "paddle/fluid/operators/math/sequence2batch.h"
#include "paddle/fluid/platform/cpu_info.h"
namespace paddle {
namespace operators {
......@@ -174,58 +173,44 @@ class FusionGRUKernel : public framework::OpKernel<T> {
}
}
#define INIT_VEC_FUNC \
std::function<void(const int, const T *, T *)> act_gate, act_state; \
std::function<void(const int, const T*, const T*, const T*, T*)> cross; \
auto& act_gate_str = ctx.Attr<std::string>("gate_activation"); \
auto& act_state_str = ctx.Attr<std::string>("activation"); \
if (platform::jit::MayIUse(platform::jit::avx)) { \
math::VecActivations<T, platform::jit::avx> act_functor; \
act_gate = act_functor(act_gate_str); \
act_state = act_functor(act_state_str); \
cross = math::vec_cross<T, platform::jit::avx>; \
} else { \
math::VecActivations<T, platform::jit::isa_any> act_functor; \
act_gate = act_functor(act_gate_str); \
act_state = act_functor(act_state_str); \
cross = math::vec_cross<T, platform::jit::isa_any>; \
}
#define INIT_BASE_INPUT_OUTPUT \
auto* h0 = ctx.Input<Tensor>("H0"); \
auto* wx = ctx.Input<Tensor>("WeightX"); \
#define INIT_BASE_DEFINES \
auto* x = ctx.Input<LoDTensor>("X"); \
auto* wh = ctx.Input<Tensor>("WeightH"); \
auto* bias = ctx.Input<Tensor>("Bias"); \
auto* xx = ctx.Output<LoDTensor>("XX"); \
auto* hidden_out = ctx.Output<LoDTensor>("Hidden"); \
bool is_reverse = ctx.Attr<bool>("is_reverse");
#define INIT_BASE_SIZES \
auto x_lod = x->lod(); \
auto x_dims = x->dims(); /* T x M*/ \
auto wh_dims = wh->dims(); /* D x 3D*/ \
const int total_T = x_dims[0]; \
const int D3 = wh_dims[1]
#define INIT_OTHER_DEFINES \
auto* h0 = ctx.Input<Tensor>("H0"); \
auto* wx = ctx.Input<Tensor>("WeightX"); \
auto* bias = ctx.Input<Tensor>("Bias"); \
auto* hidden_out = ctx.Output<LoDTensor>("Hidden"); \
bool is_reverse = ctx.Attr<bool>("is_reverse"); \
const int M = x_dims[1]; \
const int D = wh_dims[0]; \
const int D3 = wh_dims[1]; \
const int D2 = D * 2;
const int D2 = D * 2; \
const auto& ker = math::jitkernel::KernelPool::Instance() \
.template Get<math::jitkernel::GRUKernel<T>, \
const std::string&, const std::string&>( \
ctx.Attr<std::string>("gate_activation"), \
ctx.Attr<std::string>("activation"), D); \
const T* x_data = x->data<T>(); \
const T* wx_data = wx->data<T>(); \
const T* wh_data = wh->data<T>(); \
auto place = ctx.GetPlace(); \
T* xx_data = xx->mutable_data<T>(place)
void SeqCompute(const framework::ExecutionContext& ctx) const {
using DeviceContext = paddle::platform::CPUDeviceContext;
auto* x = ctx.Input<LoDTensor>("X");
INIT_BASE_INPUT_OUTPUT
INIT_BASE_SIZES
INIT_VEC_FUNC
auto x_lod = x->lod();
INIT_BASE_DEFINES;
INIT_OTHER_DEFINES;
const int N = x_lod[0].size() - 1;
const T* x_data = x->data<T>();
const T* h0_data = h0 ? h0->data<T>() : nullptr;
const T* wx_data = wx->data<T>();
const T* wh_data = wh->data<T>();
const T* wh_state_data = wh_data + D * D2;
T* xx_data = xx->mutable_data<T>(ctx.GetPlace());
T* hidden_out_data = hidden_out->mutable_data<T>(ctx.GetPlace());
T* hidden_out_data = hidden_out->mutable_data<T>(place);
auto blas = math::GetBlas<DeviceContext, T>(ctx);
math::FCCompute<DeviceContext, T>(blas, total_T, D3, M, x_data, wx_data,
xx_data,
......@@ -252,14 +237,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
if (h0_data) {
prev_hidden_data = h0_data + bid * D;
} else {
// W: {W_update, W_reset; W_state}
// update gate
act_gate(D, xx_data, xx_data);
// state gate
act_state(D, xx_data + D2, xx_data + D2);
// out = a*b
blas.VMUL(D, xx_data, xx_data + D2, hidden_out_data);
// save prev
ker->ComputeH1(xx_data, hidden_out_data);
prev_hidden_data = hidden_out_data;
tstart = 1;
move_step();
......@@ -269,17 +247,12 @@ class FusionGRUKernel : public framework::OpKernel<T> {
blas.GEMM(CblasNoTrans, CblasNoTrans, 1, D2, D, static_cast<T>(1),
prev_hidden_data, D, wh_data, D2, static_cast<T>(1), xx_data,
D3);
act_gate(D2, xx_data, xx_data);
// rt = rt*ht_1 inplace result
blas.VMUL(D, prev_hidden_data, xx_data + D, hidden_out_data);
ker->ComputeHtPart1(xx_data, prev_hidden_data, hidden_out_data);
// gemm rt * Ws
blas.GEMM(CblasNoTrans, CblasNoTrans, 1, D, D, static_cast<T>(1),
hidden_out_data, D, wh_state_data, D, static_cast<T>(1),
xx_data + D2, D3);
act_state(D, xx_data + D2, xx_data + D2);
// out = zt*ht~ + (1-zt)*ht_1
cross(D, xx_data, xx_data + D2, prev_hidden_data, hidden_out_data);
ker->ComputeHtPart2(xx_data, prev_hidden_data, hidden_out_data);
// save prev
prev_hidden_data = hidden_out_data;
move_step();
......@@ -289,28 +262,19 @@ class FusionGRUKernel : public framework::OpKernel<T> {
void BatchCompute(const framework::ExecutionContext& ctx) const {
using DeviceContext = paddle::platform::CPUDeviceContext;
auto* x = ctx.Input<LoDTensor>("X");
INIT_BASE_INPUT_OUTPUT
INIT_BASE_SIZES
if (x->lod()[0].size() == 2) {
INIT_BASE_DEFINES;
if (x_lod[0].size() == 2) {
xx->Resize({total_T, D3});
SeqCompute(ctx);
return;
}
INIT_VEC_FUNC
INIT_OTHER_DEFINES;
auto* reordered_h0 = ctx.Output<Tensor>("ReorderedH0");
auto* batched_input = ctx.Output<LoDTensor>("BatchedInput");
auto* batched_out = ctx.Output<LoDTensor>("BatchedOut");
const T* x_data = x->data<T>();
const T* wx_data = wx->data<T>();
const T* wh_data = wh->data<T>();
T* xx_data = xx->mutable_data<T>(ctx.GetPlace());
T* batched_input_data = batched_input->mutable_data<T>(ctx.GetPlace());
T* batched_out_data = batched_out->mutable_data<T>(ctx.GetPlace());
hidden_out->mutable_data<T>(ctx.GetPlace());
T* batched_input_data = batched_input->mutable_data<T>(place);
T* batched_out_data = batched_out->mutable_data<T>(place);
hidden_out->mutable_data<T>(place);
auto& dev_ctx = ctx.template device_context<DeviceContext>();
auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
math::LoDTensor2BatchFunctor<DeviceContext, T> to_batch;
......@@ -336,7 +300,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
T* prev_hidden_data = nullptr;
if (h0) {
// reorder h0
T* reordered_h0_data = reordered_h0->mutable_data<T>(ctx.GetPlace());
T* reordered_h0_data = reordered_h0->mutable_data<T>(place);
const T* h0_data = h0->data<T>();
prev_hidden_data = reordered_h0_data;
size_t sz = sizeof(T) * D;
......@@ -350,12 +314,7 @@ class FusionGRUKernel : public framework::OpKernel<T> {
T* cur_out_data = batched_out_data;
// W: {W_update, W_reset; W_state}
for (int i = 0; i < max_bs; ++i) {
// update gate
act_gate(D, cur_in_data, cur_in_data);
// state gate
act_state(D, cur_in_data + D2, cur_in_data + D2);
// out = a*b
blas.VMUL(D, cur_in_data, cur_in_data + D2, cur_out_data);
ker->ComputeH1(cur_in_data, cur_out_data);
// add offset
cur_in_data += D3;
cur_out_data += D;
......@@ -380,10 +339,8 @@ class FusionGRUKernel : public framework::OpKernel<T> {
T* cur_out_data = batched_out_data;
T* cur_prev_hidden_data = prev_hidden_data;
for (int i = 0; i < cur_bs; ++i) {
act_gate(D2, cur_batched_data, cur_batched_data);
// rt = rt*ht_1 inplace result
blas.VMUL(D, cur_prev_hidden_data, cur_batched_data + D, cur_out_data);
ker->ComputeHtPart1(cur_batched_data, cur_prev_hidden_data,
cur_out_data);
cur_batched_data += D3;
cur_prev_hidden_data += D;
cur_out_data += D;
......@@ -397,12 +354,8 @@ class FusionGRUKernel : public framework::OpKernel<T> {
cur_prev_hidden_data = prev_hidden_data;
for (int i = 0; i < cur_bs; ++i) {
// ht~ = act_state(...)
act_state(D, cur_batched_data + D2, cur_batched_data + D2);
// out = zt*ht~ + (1-zt)*ht_1
cross(D, cur_batched_data, cur_batched_data + D2, cur_prev_hidden_data,
ker->ComputeHtPart2(cur_batched_data, cur_prev_hidden_data,
cur_out_data);
cur_batched_data += D3;
cur_prev_hidden_data += D;
cur_out_data += D;
......@@ -416,9 +369,8 @@ class FusionGRUKernel : public framework::OpKernel<T> {
batched_out->set_lod(batched_lod);
to_seq(dev_ctx, *batched_out, hidden_out);
}
#undef INIT_VEC_FUNC
#undef INIT_BASE_SIZES
#undef INIT_BASE_INPUT_OUTPUT
#undef INIT_OTHER_DEFINES
#undef INIT_BASE_DEFINES
};
} // namespace operators
......
paddle/fluid/operators/lod_tensor_to_array_op.cc
浏览文件 @ b2943530
......@@ -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 @ b2943530
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)
......@@ -68,13 +68,14 @@ cc_test(selected_rows_functor_test SRCS selected_rows_functor_test.cc DEPS selec
cc_test(im2col_test SRCS im2col_test.cc DEPS im2col)
cc_test(vol2col_test SRCS vol2col_test.cc DEPS vol2col)
cc_test(sequence_padding_test SRCS sequence_padding_test.cc DEPS sequence_padding)
cc_test(sequence_pooling_test SRCS sequence_pooling_test.cc DEPS sequence_pooling)
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
SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_rnn.cc
DEPS cpu_info cblas)
cc_test(jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel)
paddle/fluid/operators/math/concat.cc → paddle/fluid/operators/math/concat_and_split.cc
浏览文件 @ b2943530
......@@ -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 @ b2943530
......@@ -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 @ b2943530
......@@ -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 @ b2943530
......@@ -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/math/jit_kernel.h
浏览文件 @ b2943530
......@@ -142,6 +142,15 @@ class LSTMKernel : public Kernel {
const T *wp_data = nullptr) const = 0;
};
template <typename T>
class GRUKernel : public Kernel {
public:
// compute h1 without h0
virtual void ComputeH1(T *gates, T *ht) const = 0;
virtual void ComputeHtPart1(T *gates, const T *ht_1, T *ht) const = 0;
virtual void ComputeHtPart2(T *gates, const T *ht_1, T *ht) const = 0;
};
} // namespace jitkernel
} // namespace math
} // namespace operators
......
paddle/fluid/operators/math/jit_kernel_lstm.cc → paddle/fluid/operators/math/jit_kernel_rnn.cc
浏览文件 @ b2943530
......@@ -136,6 +136,23 @@ static std::shared_ptr<const VActKernel<T>> GetActKernel(
return nullptr;
}
#ifdef __AVX__
template <jit::cpu_isa_t isa>
static std::unique_ptr<AVXAct> GetAVXAct(const std::string& type) {
if (type == "sigmoid") {
return std::unique_ptr<AVXAct>(new AVXActImpl<kSigmoid, isa>());
} else if (type == "relu") {
return std::unique_ptr<AVXAct>(new AVXActImpl<kRelu, isa>());
} else if (type == "tanh") {
return std::unique_ptr<AVXAct>(new AVXActImpl<kTanh, isa>());
} else if (type == "identity" || type == "") {
return std::unique_ptr<AVXAct>(new AVXActImpl<kIdentity, isa>());
}
PADDLE_THROW("Not support type: %s", type);
return nullptr;
}
#endif
/* LSTM JitKernel */
template <typename T, jit::cpu_isa_t isa, jit_block>
class LSTMKernelImpl : public LSTMKernel<T> {
......@@ -198,21 +215,9 @@ class LSTMKernelImpl : public LSTMKernel<T> {
const std::string& act_gate, const std::string& act_cand, \
const std::string& act_cell, int d) \
: LSTMKernel<float>() { \
auto GetAVXAct = [&](const std::string& type) -> std::unique_ptr<AVXAct> { \
if (type == "sigmoid") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kSigmoid, isa>()); \
} else if (type == "relu") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kRelu, isa>()); \
} else if (type == "tanh") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kTanh, isa>()); \
} else if (type == "identity" || type == "") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kIdentity, isa>()); \
} \
PADDLE_THROW("Not support type: %s", type); \
}; \
avx_act_gate_ = GetAVXAct(act_gate); \
avx_act_cand_ = GetAVXAct(act_cand); \
avx_act_cell_ = GetAVXAct(act_cell); \
avx_act_gate_ = GetAVXAct<isa>(act_gate); \
avx_act_cand_ = GetAVXAct<isa>(act_cand); \
avx_act_cell_ = GetAVXAct<isa>(act_cell); \
} \
template <> \
void LSTMKernelImpl<float, isa, kEQ8>::ComputeCtHt( \
......@@ -354,6 +359,126 @@ REGISTER_JITKERNEL_ARGS(lstm, LSTMKernel, JITKERNEL_DECLARE_LSTM,
#undef JITKERNEL_DECLARE_LSTM
#undef JITKERNEL_KEY_LSTM
#undef JITKERNEL_NEW_LSTM_IMPL
/* GRU JitKernel */
template <typename T, jit::cpu_isa_t isa, jit_block>
class GRUKernelImpl : public GRUKernel<T> {
public:
explicit GRUKernelImpl(const std::string& act_gate,
const std::string& act_state, int d)
: GRUKernel<T>() {
d_ = d;
d2_ = d * 2;
act_gate_d2_ = GetActKernel<T>(act_gate, d2_);
act_gate_d_ = GetActKernel<T>(act_gate, d);
act_state_d_ = GetActKernel<T>(act_state, d);
vmul_d_ = KernelPool::Instance().template Get<VMulKernel<T>>(d);
}
void ComputeH1(T* gates, T* ht) const override {
act_gate_d_->Compute(gates, gates);
act_state_d_->Compute(gates + d2_, gates + d2_);
vmul_d_->Compute(gates, gates + d2_, ht);
}
void ComputeHtPart1(T* gates, const T* ht_1, T* ht) const override {
// W: {W_update, W_reset; W_state}
act_gate_d2_->Compute(gates, gates);
vmul_d_->Compute(ht_1, gates + d_, ht);
}
void ComputeHtPart2(T* gates, const T* ht_1, T* ht) const override {
T* y = gates + d2_;
act_state_d_->Compute(y, y);
// out = zt*ht~ + (1-zt)*ht_1
for (int i = 0; i < d_; ++i) {
ht[i] = gates[i] * y[i] + (static_cast<T>(1) - gates[i]) * ht_1[i];
}
}
private:
int d_, d2_;
std::shared_ptr<const VActKernel<T>> act_gate_d2_, act_gate_d_, act_state_d_;
std::shared_ptr<const VMulKernel<T>> vmul_d_;
#ifdef __AVX__
std::unique_ptr<const AVXAct> avx_act_gate_, avx_act_state_;
#endif
};
#define INTRI8_FLOAT(isa) \
template <> \
GRUKernelImpl<float, isa, kEQ8>::GRUKernelImpl( \
const std::string& act_gate, const std::string& act_state, int d) \
: GRUKernel<float>() { \
avx_act_gate_ = GetAVXAct<isa>(act_gate); \
avx_act_state_ = GetAVXAct<isa>(act_state); \
} \
template <> \
void GRUKernelImpl<float, isa, kEQ8>::ComputeH1(float* gates, float* ht) \
const { \
__m256 u, s; \
/* W: {W_update, W_reset; W_state} */ \
u = _mm256_loadu_ps(gates); \
s = _mm256_loadu_ps(gates + 16); \
s = _mm256_mul_ps(avx_act_gate_->Compute(u), avx_act_state_->Compute(s)); \
_mm256_storeu_ps(ht, s); \
} \
template <> \
void GRUKernelImpl<float, isa, kEQ8>::ComputeHtPart1( \
float* gates, const float* ht_1, float* ht) const { \
/* not exactly equal the any implementation */ \
__m256 r, ht0; \
r = _mm256_loadu_ps(gates + 8); \
ht0 = _mm256_loadu_ps(ht_1); \
r = _mm256_mul_ps(avx_act_gate_->Compute(r), ht0); \
_mm256_storeu_ps(ht, r); \
} \
template <> \
void GRUKernelImpl<float, isa, kEQ8>::ComputeHtPart2( \
float* gates, const float* ht_1, float* ht) const { \
/* not exactly equal the any implementation */ \
__m256 u, s, ht0; \
u = _mm256_loadu_ps(gates); \
s = _mm256_loadu_ps(gates + 16); \
ht0 = _mm256_loadu_ps(ht_1); \
u = avx_act_gate_->Compute(u); \
s = _mm256_mul_ps(u, avx_act_state_->Compute(s)); \
u = _mm256_sub_ps(_mm256_set1_ps(1.f), u); \
u = _mm256_mul_ps(u, ht0); \
u = _mm256_add_ps(s, u); \
_mm256_storeu_ps(ht, u); \
}
#ifdef __AVX__
INTRI8_FLOAT(jit::avx);
#endif
#ifdef __AVX2__
INTRI8_FLOAT(jit::avx2);
#endif
#ifdef __AVX512F__
INTRI8_FLOAT(jit::avx512f);
#endif
#define JITKERNEL_DECLARE_GRU(ker_class, ker_dtype) \
template <> \
std::shared_ptr<const GRUKernel<ker_dtype>> KernelPool::Get< \
GRUKernel<ker_dtype>, const std::string&, const std::string&, int>( \
const std::string& act_gate, const std::string& act_state, int d)
#define JITKERNEL_KEY_GRU(ker_key, dtype_key) \
#ker_key #dtype_key + std::to_string(d) + act_gate + act_state
#define JITKERNEL_NEW_GRU_IMPL(ker, dtype, isa, k) \
p = std::dynamic_pointer_cast<ker<dtype>>( \
std::make_shared<ker##Impl<dtype, isa, k>>(act_gate, act_state, d));
REGISTER_JITKERNEL_ARGS(gru, GRUKernel, JITKERNEL_DECLARE_GRU,
JITKERNEL_KEY_GRU, JITKERNEL_NEW_GRU_IMPL);
#undef INTRI8_FLOAT
#undef JITKERNEL_NEW_GRU_IMPL
#undef JITKERNEL_KEY_GRU
#undef JITKERNEL_DECLARE_GRU
} // namespace jitkernel
} // namespace math
} // namespace operators
......
paddle/fluid/operators/math/sequence_pooling.cc
浏览文件 @ b2943530
......@@ -157,6 +157,31 @@ class FirstSeqPoolFunctor {
}
};
template <typename T>
class SumSeqPoolGradFunctor {
public:
void operator()(const platform::CPUDeviceContext& context,
const framework::Tensor& out_grad,
framework::LoDTensor* in_grad) {
auto lod = in_grad->lod()[0];
int64_t out_w = out_grad.numel() / out_grad.dims()[0];
int64_t in_w = in_grad->numel() / in_grad->dims()[0];
PADDLE_ENFORCE(in_w == out_w);
const T* out_g_data = out_grad.data<T>();
T* in_g_data = in_grad->mutable_data<T>(context.GetPlace());
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
int64_t h = static_cast<int64_t>(lod[i + 1] - lod[i]);
int64_t in_offset = lod[i] * in_w;
const T* out_pos = out_g_data + i * out_w;
T* in_pos = in_g_data + in_offset;
for (int r = 0; r != h; ++r) {
blas.VCOPY(in_w, out_pos, in_pos + r * in_w);
}
}
}
};
template <typename T>
class SequencePoolFunctor<platform::CPUDeviceContext, T> {
public:
......@@ -231,9 +256,15 @@ class SequencePoolGradFunctor<platform::CPUDeviceContext, T> {
math::SetConstant<platform::CPUDeviceContext, T> functor;
functor(context, in_grad, 0);
}
if (pooltype == "SUM") {
math::SumSeqPoolGradFunctor<T> sum_pool_grad;
sum_pool_grad(context, out_grad, in_grad);
return;
}
auto lod = in_grad->lod()[0];
auto& place = *context.eigen_device();
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
auto in_g_t = in_grad->Slice(static_cast<int>(lod[i]),
static_cast<int>(lod[i + 1]));
......@@ -247,12 +278,6 @@ class SequencePoolGradFunctor<platform::CPUDeviceContext, T> {
if (pooltype == "AVERAGE") {
in_g_e.device(place) = (out_g_e / static_cast<T>(h)).broadcast(bcast);
} else if (pooltype == "SUM") {
const T* out_g_data = out_g_t.data<T>();
T* in_g_data = in_g_t.mutable_data<T>(context.GetPlace());
for (int r = 0; r != h; ++r) {
blas.VCOPY(w, out_g_data, in_g_data + r * w);
}
} else if (pooltype == "SQRT") {
in_g_e.device(place) =
(out_g_e / std::sqrt(static_cast<T>(h))).broadcast(bcast);
......
paddle/fluid/operators/math/sequence_pooling_test.cc 0 → 100644
浏览文件 @ b2943530
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/math/sequence_pooling.h"
#include <gtest/gtest.h>
#include <vector>
template <typename DeviceContext, typename Place, typename T>
void TestSequencePoolingSum(const paddle::framework::LoD& lod) {
paddle::framework::LoDTensor cpu_out_grad;
paddle::framework::LoDTensor cpu_in_grad;
paddle::framework::LoDTensor out_grad;
paddle::framework::LoDTensor in_grad;
const size_t second_dim = 128u;
// construct out_grad's tensor in cpu
const size_t out_first_dim = lod[0].size() - 1;
auto out_dims = paddle::framework::make_ddim(
{static_cast<int64_t>(out_first_dim), static_cast<int64_t>(second_dim)});
cpu_out_grad.mutable_data<T>(out_dims, paddle::platform::CPUPlace());
for (int64_t i = 0; i < cpu_out_grad.numel(); ++i) {
cpu_out_grad.data<T>()[i] = static_cast<T>(i);
}
// copy to dst out_grad
auto* place = new Place();
DeviceContext* context = new DeviceContext(*place);
if (paddle::platform::is_cpu_place(*place)) {
out_grad = cpu_out_grad;
} else {
TensorCopySync(cpu_out_grad, *place, &out_grad);
}
// construct in_grad
in_grad.set_lod(lod);
auto in_dims = paddle::framework::make_ddim(
{static_cast<int64_t>(lod[0].back()), static_cast<int64_t>(second_dim)});
in_grad.mutable_data<T>(in_dims, context->GetPlace());
// check tensor contruction result
PADDLE_ENFORCE_EQ(in_grad.dims().size(), out_grad.dims().size());
for (int64_t i = 1; i < out_grad.dims().size(); ++i) {
PADDLE_ENFORCE_EQ(in_grad.dims()[i], out_grad.dims()[i]);
}
// call functor
paddle::operators::math::SequencePoolGradFunctor<DeviceContext, T>()(
*context, "SUM", out_grad, &in_grad);
if (paddle::platform::is_cpu_place(*place)) {
cpu_in_grad = in_grad;
} else {
TensorCopySync(in_grad, paddle::platform::CPUPlace(), &cpu_in_grad);
cpu_in_grad.set_lod(in_grad.lod());
}
EXPECT_EQ(in_grad.numel(), lod[0].back() * second_dim);
EXPECT_EQ(in_grad.lod(), lod);
if (paddle::platform::is_cpu_place(*place)) {
for (int64_t i = 0; i < in_grad.lod()[0].size() - 1; ++i) {
int64_t begin = in_grad.lod()[0][i];
int64_t end = in_grad.lod()[0][i + 1];
paddle::framework::Tensor tmp = in_grad.Slice(begin, end);
for (int64_t j = 0; j != tmp.numel() / second_dim; ++j) {
for (int64_t m = 0; m != second_dim; ++m) {
EXPECT_EQ(tmp.data<T>()[m + j * second_dim],
out_grad.data<T>()[m + i * second_dim]);
}
}
}
} else {
for (int64_t i = 0; i < cpu_in_grad.lod()[0].size() - 1; ++i) {
int64_t begin = cpu_in_grad.lod()[0][i];
int64_t end = cpu_in_grad.lod()[0][i + 1];
paddle::framework::Tensor tmp = cpu_in_grad.Slice(begin, end);
for (int64_t j = 0; j != tmp.numel() / second_dim; ++j) {
for (int64_t m = 0; m != second_dim; ++m) {
EXPECT_EQ(tmp.data<T>()[m + j * second_dim],
cpu_out_grad.data<T>()[m + i * second_dim]);
}
}
}
}
delete place;
delete context;
}
TEST(SequencePoolingGrad, CPU_SUM) {
paddle::framework::LoD lod1;
lod1.push_back(std::vector<size_t>{0, 10});
TestSequencePoolingSum<paddle::platform::CPUDeviceContext,
paddle::platform::CPUPlace, float>(lod1);
paddle::framework::LoD lod2;
lod2.push_back(std::vector<size_t>{0, 2, 7, 10});
TestSequencePoolingSum<paddle::platform::CPUDeviceContext,
paddle::platform::CPUPlace, float>(lod2);
}
#ifdef PADDLE_WITH_CUDA
TEST(SequencePoolingGrad, CUDA_SUM) {
paddle::framework::LoD lod1;
lod1.push_back(std::vector<size_t>{0, 10});
TestSequencePoolingSum<paddle::platform::CUDADeviceContext,
paddle::platform::CUDAPlace, float>(lod1);
paddle::framework::LoD lod2;
lod2.push_back(std::vector<size_t>{0, 2, 7, 10});
TestSequencePoolingSum<paddle::platform::CUDADeviceContext,
paddle::platform::CUDAPlace, float>(lod2);
}
#endif
paddle/fluid/operators/mean_op.cc
浏览文件 @ b2943530
......@@ -34,7 +34,7 @@ class MeanOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(Tensor) The input of mean op");
AddOutput("Out", "(Tensor) The output of mean op").Reuse("X");
AddOutput("Out", "(Tensor) The output of mean op");
AddComment(R"DOC(
Mean Operator calculates the mean of all elements in X.
......
paddle/fluid/operators/pool_op.cc
浏览文件 @ b2943530
......@@ -151,8 +151,7 @@ void Pool2dOpMaker::Make() {
"The format of output tensor is also NCHW, "
"where N is batch size, C is the number of channels, "
"H is the height of the feature, "
"and W is the width of the feature.")
.Reuse("X");
"and W is the width of the feature.");
AddAttr<std::string>("pooling_type",
"(string), pooling type, can be \"max\" for max-pooling "
......@@ -252,8 +251,7 @@ void Pool3dOpMaker::Make() {
"The format of output tensor is also NCDHW, "
"where N is batch size, C is "
"the number of channels, and D, H and W is the depth, height and "
"width of the feature, respectively.")
.Reuse("X");
"width of the feature, respectively.");
AddAttr<std::string>("pooling_type",
"(string) Pooling type, can be \"max\" for max-pooling "
......
paddle/fluid/operators/reader/reader_blocking_queue_test.cc
浏览文件 @ b2943530
......@@ -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 @ b2943530
......@@ -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/sgd_op.cc
浏览文件 @ b2943530
......@@ -77,8 +77,7 @@ class SGDOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Grad", "(Tensor or SelectedRows) Input gradient");
AddOutput("ParamOut",
"(Tensor or SelectedRows, same with Param) "
"Output parameter, should share the same memory with Param")
.Reuse("Param");
"Output parameter, should share the same memory with Param");
AddComment(R"DOC(
SGD operator
......
paddle/fluid/operators/softmax_op.cc
浏览文件 @ b2943530
......@@ -80,8 +80,7 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("X",
"The input tensor of softmax, "
"whose last dimension is the input_feature_dimensions.");
AddOutput("Out", "The normalized values with the same shape as X.")
.Reuse("X");
AddOutput("Out", "The normalized values with the same shape as X.");
AddAttr<bool>(
"use_cudnn",
"(bool, default false) Only used in cudnn kernel, need install cudnn")
......
paddle/fluid/operators/split_op.cc
浏览文件 @ b2943530
......@@ -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 @ b2943530
......@@ -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 @ b2943530
......@@ -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
paddle/fluid/operators/sum_op.cc
浏览文件 @ b2943530
......@@ -132,7 +132,7 @@ class SumOpMaker : public framework::OpProtoAndCheckerMaker {
void Make() override {
AddInput("X", "(vector<Tensor>) The input tensors of sum operator.")
.AsDuplicable();
AddOutput("Out", "(Tensor) The output tensor of sum operator.").Reuse("X");
AddOutput("Out", "(Tensor) The output tensor of sum operator.");
AddAttr<bool>("use_mkldnn",
"(bool, default false) Only used in mkldnn kernel")
.SetDefault(false);
......
paddle/fluid/operators/top_k_op.cc
浏览文件 @ b2943530
......@@ -50,7 +50,7 @@ class TopkOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(Tensor) The input of Topk op");
AddOutput("Out", "(Tensor) The output tensor of Topk op").Reuse("X");
AddOutput("Out", "(Tensor) The output tensor of Topk op");
AddOutput("Indices", "(Tensor) The indices of Topk elements of input");
AddComment(R"DOC(
Top K operator
......
paddle/fluid/operators/top_k_op.cu
浏览文件 @ b2943530
......@@ -262,31 +262,31 @@ __global__ void KeMatrixTopK(T* output, int output_stride, int64_t* indices,
const T* src, int lds, int dim, int k,
int grid_dim, int num) {
__shared__ Pair<T> sh_topk[BlockSize];
__shared__ int maxid[BlockSize / 2];
const int tid = threadIdx.x;
const int warp = threadIdx.x / 32;
const int bid = blockIdx.x;
for (int i = bid; i < num; i += grid_dim) {
output += i * output_stride;
indices += i * k;
int top_num = k;
__shared__ int maxid[BlockSize / 2];
T* out = output + i * output_stride;
int64_t* inds = indices + i * k;
Pair<T> topk[MaxLength];
int beam = MaxLength;
Pair<T> max;
bool is_empty = false;
bool firststep = true;
for (int k = 0; k < MaxLength; k++) {
topk[k].set(-INFINITY, -1);
for (int j = 0; j < MaxLength; j++) {
topk[j].set(-INFINITY, -1);
}
while (k) {
while (top_num) {
ThreadGetTopK<T, MaxLength, BlockSize>(
topk, &beam, k, src + i * lds, &firststep, &is_empty, &max, dim, tid);
sh_topk[tid] = topk[0];
BlockReduce<T, MaxLength, BlockSize>(sh_topk, maxid, topk, &output,
&indices, &beam, &k, tid, warp);
BlockReduce<T, MaxLength, BlockSize>(sh_topk, maxid, topk, &out, &inds,
&beam, &top_num, tid, warp);
}
}
}
......@@ -327,13 +327,15 @@ class TopkOpCUDAKernel : public framework::OpKernel<T> {
size_t k = static_cast<int>(ctx.Attr<int>("k"));
const T* input_data = input->data<T>();
T* output_data = output->mutable_data<T>(ctx.GetPlace());
// FIXME(typhoonzero): data is always converted to type T?
int64_t* indices_data = indices->mutable_data<int64_t>(ctx.GetPlace());
size_t input_height = input->dims()[0];
size_t input_width = input->dims()[1];
framework::DDim inputdims = input->dims();
const size_t input_height = framework::product(
framework::slice_ddim(inputdims, 0, inputdims.size() - 1));
const size_t input_width = inputdims[inputdims.size() - 1];
if (k > input_width) k = input_width;
// NOTE: pass lds and dim same to input width.
......@@ -342,14 +344,12 @@ class TopkOpCUDAKernel : public framework::OpKernel<T> {
const int kMaxHeight = 2048;
int gridx = input_height < kMaxHeight ? input_height : kMaxHeight;
auto& dev_ctx = ctx.cuda_device_context();
switch (GetDesiredBlockDim(input_width)) {
FIXED_BLOCK_DIM(
KeMatrixTopK<T, 5,
kBlockDim><<<gridx, kBlockDim, 0, dev_ctx.stream()>>>(
output_data, output->dims()[1], indices_data, input_data,
input_width, input_width, static_cast<int>(k), gridx,
input_height));
output_data, k, indices_data, input_data, input_width,
input_width, static_cast<int>(k), gridx, input_height));
default:
PADDLE_THROW("Error");
}
......
paddle/fluid/operators/top_k_op.h
浏览文件 @ b2943530
......@@ -34,7 +34,6 @@ class TopkKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
// Get the top k elements of each row of input tensor
// FIXME: only deal with matrix(2d tensor).
auto* input = ctx.Input<Tensor>("X");
auto* output = ctx.Output<Tensor>("Out");
auto* indices = ctx.Output<Tensor>("Indices");
......@@ -44,8 +43,6 @@ class TopkKernel : public framework::OpKernel<T> {
T* output_data = output->mutable_data<T>(ctx.GetPlace());
int64_t* indices_data = indices->mutable_data<int64_t>(ctx.GetPlace());
auto eg_input = EigenMatrix<T>::From(*input);
// reshape input to a flattern matrix(like flat_inner_dims)
framework::DDim inputdims = input->dims();
const size_t row = framework::product(
......@@ -53,7 +50,7 @@ class TopkKernel : public framework::OpKernel<T> {
const size_t col = inputdims[inputdims.size() - 1];
Eigen::DSizes<int, 2> flat2dims(row, col);
// NOTE: eigen shape doesn't affect paddle tensor.
eg_input.reshape(flat2dims);
auto eg_input = EigenMatrix<T>::Reshape(*input, inputdims.size() - 1);
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for
......
python/paddle/fluid/layer_helper.py
浏览文件 @ b2943530
......@@ -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 @ b2943530
......@@ -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 @ b2943530
......@@ -116,8 +116,8 @@ def rpn_target_assign(bbox_pred,
Returns:
tuple:
A tuple(predicted_scores, predicted_location, target_label,
target_bbox) is returned. The predicted_scores and
predicted_location is the predicted result of the RPN.
target_bbox, bbox_inside_weight) is returned. The predicted_scores
and predicted_location is the predicted result of the RPN.
The target_label and target_bbox is the ground truth,
respectively. The predicted_location is a 2D Tensor with shape
[F, 4], and the shape of target_bbox is same as the shape of
......@@ -126,6 +126,8 @@ def rpn_target_assign(bbox_pred,
[F + B, 1], and the shape of target_label is same as the shape
of the predicted_scores, B is the number of the background
anchors, the F and B is depends on the input of this operator.
Bbox_inside_weight represents whether the predicted loc is fake_fg
or not and the shape is [F, 4].
Examples:
.. code-block:: python
......@@ -138,7 +140,7 @@ def rpn_target_assign(bbox_pred,
append_batch_size=False, dtype='float32')
gt_boxes = layers.data(name='gt_boxes', shape=[10, 4],
append_batch_size=False, dtype='float32')
loc_pred, score_pred, loc_target, score_target =
loc_pred, score_pred, loc_target, score_target, bbox_inside_weight =
fluid.layers.rpn_target_assign(bbox_pred=bbox_pred,
cls_logits=cls_logits,
anchor_box=anchor_box,
......@@ -147,10 +149,13 @@ 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)
bbox_inside_weight = helper.create_variable_for_type_inference(
dtype=anchor_box.dtype)
helper.append_op(
type="rpn_target_assign",
inputs={
......@@ -163,7 +168,8 @@ def rpn_target_assign(bbox_pred,
'LocationIndex': loc_index,
'ScoreIndex': score_index,
'TargetLabel': target_label,
'TargetBBox': target_bbox
'TargetBBox': target_bbox,
'BBoxInsideWeight': bbox_inside_weight
},
attrs={
'rpn_batch_size_per_im': rpn_batch_size_per_im,
......@@ -178,13 +184,14 @@ def rpn_target_assign(bbox_pred,
score_index.stop_gradient = True
target_label.stop_gradient = True
target_bbox.stop_gradient = True
bbox_inside_weight.stop_gradient = True
cls_logits = nn.reshape(x=cls_logits, shape=(-1, 1))
bbox_pred = nn.reshape(x=bbox_pred, shape=(-1, 4))
predicted_cls_logits = nn.gather(cls_logits, score_index)
predicted_bbox_pred = nn.gather(bbox_pred, loc_index)
return predicted_cls_logits, predicted_bbox_pred, target_label, target_bbox
return predicted_cls_logits, predicted_bbox_pred, target_label, target_bbox, bbox_inside_weight
def detection_output(loc,
......@@ -282,7 +289,8 @@ 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 +322,7 @@ 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 +359,8 @@ 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 +391,7 @@ 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 +459,7 @@ 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 +566,9 @@ 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 +654,8 @@ 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 +826,10 @@ 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 +1009,8 @@ 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 +1348,8 @@ 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 +1395,7 @@ 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 +1429,15 @@ 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 +1519,10 @@ 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 @ b2943530
......@@ -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 @ b2943530
......@@ -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 @ b2943530
......@@ -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 @ b2943530
此差异已折叠。
python/paddle/fluid/layers/tensor.py
浏览文件 @ b2943530
......@@ -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 @ b2943530
......@@ -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 @ b2943530
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/test_detection.py
浏览文件 @ b2943530
......@@ -301,7 +301,7 @@ class TestRpnTargetAssign(unittest.TestCase):
dtype='float32',
lod_level=1,
append_batch_size=False)
pred_scores, pred_loc, tgt_lbl, tgt_bbox = layers.rpn_target_assign(
pred_scores, pred_loc, tgt_lbl, tgt_bbox, bbox_inside_weight = layers.rpn_target_assign(
bbox_pred=bbox_pred,
cls_logits=cls_logits,
anchor_box=anchor_box,
......@@ -313,15 +313,18 @@ class TestRpnTargetAssign(unittest.TestCase):
rpn_straddle_thresh=0.0,
rpn_fg_fraction=0.5,
rpn_positive_overlap=0.7,
rpn_negative_overlap=0.3)
rpn_negative_overlap=0.3,
use_random=False)
self.assertIsNotNone(pred_scores)
self.assertIsNotNone(pred_loc)
self.assertIsNotNone(tgt_lbl)
self.assertIsNotNone(tgt_bbox)
self.assertIsNotNone(bbox_inside_weight)
assert pred_scores.shape[1] == 1
assert pred_loc.shape[1] == 4
assert pred_loc.shape[1] == tgt_bbox.shape[1]
print(str(program))
class TestGenerateProposals(unittest.TestCase):
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ b2943530
......@@ -78,9 +78,9 @@ if(WITH_DISTRIBUTE)
set_tests_properties(test_dist_word2vec PROPERTIES TIMEOUT 200)
py_test_modules(test_dist_se_resnext MODULES test_dist_se_resnext)
set_tests_properties(test_dist_se_resnext PROPERTIES TIMEOUT 1000)
# TODO: fix this test
#py_test_modules(test_dist_transformer MODULES test_dist_transformer)
#set_tests_properties(test_dist_transformer PROPERTIES TIMEOUT 1000)
py_test_modules(test_dist_transformer MODULES test_dist_transformer)
set_tests_properties(test_dist_transformer PROPERTIES TIMEOUT 1000)
endif(NOT APPLE)
py_test_modules(test_dist_transpiler MODULES test_dist_transpiler)
endif()
......
python/paddle/fluid/tests/unittests/dist_transformer.py
浏览文件 @ b2943530
......@@ -35,7 +35,7 @@ import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
from paddle.fluid import core
from test_dist_base import TestDistRunnerBase, runtime_main
from test_dist_base import TestDistRunnerBase, runtime_main, RUN_STEP
import paddle.compat as cpt
from paddle.compat import long_type
......@@ -562,18 +562,12 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
for pass_id in six.moves.xrange(TrainTaskConfig.pass_num):
pass_start_time = time.time()
for batch_id, data in enumerate(train_data()):
if batch_id >= 5:
if batch_id >= RUN_STEP:
break
feed_list = []
total_num_token = 0
#if TrainTaskConfig.local:
# lr_rate = lr_scheduler.update_learning_rate()
#for place_id, data_buffer in enumerate(
# split_data(
# data, num_part=dev_count)):
if TrainTaskConfig.local:
lr_rate = lr_scheduler.update_learning_rate()
......@@ -619,7 +613,6 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
init = True
# Validate and save the model for inference.
if batch_id == 0 or batch_id == 4:
if TrainTaskConfig.val_file_pattern is not None:
val_avg_cost, val_ppl = test()
print("[%f]" % val_avg_cost)
......@@ -1701,7 +1694,7 @@ class DistTransformer2x2(TestDistRunnerBase):
def run_trainer(self, args):
TrainTaskConfig.use_gpu = args.use_cuda
sum_cost, avg_cost, predict, token_num, local_lr_scheduler = get_model(
sum_cost, avg_cost, predict, token_num, local_lr_scheduler, test_program = get_model(
args.is_dist, not args.sync_mode)
if args.is_dist:
......
python/paddle/fluid/tests/unittests/test_dist_mnist.py
浏览文件 @ b2943530
......@@ -40,7 +40,8 @@ class TestDistMnistAsync(TestDistBase):
self._sync_mode = False
self._use_reduce = False
def test_dist_train(self):
# FIXME(typhoonzero): fix async mode test later
def no_test_dist_train(self):
self.check_with_place("dist_mnist.py", delta=200)
......
python/paddle/fluid/tests/unittests/test_dist_se_resnext.py
浏览文件 @ b2943530
......@@ -40,7 +40,8 @@ class TestDistSeResneXt2x2Async(TestDistBase):
self._sync_mode = False
self._use_reader_alloc = False
def test_dist_train(self):
#FIXME(typhoonzero): fix async mode later
def no_test_dist_train(self):
self.check_with_place("dist_se_resnext.py", delta=100)
......
python/paddle/fluid/tests/unittests/test_dist_simnet_bow.py
浏览文件 @ b2943530
......@@ -42,7 +42,8 @@ class TestDistSimnetBow2x2DenseAsync(TestDistBase):
self._sync_mode = False
self._enforce_place = "CPU"
def test_simnet_bow(self):
#FIXME(typhoonzero): fix async tests later
def no_test_simnet_bow(self):
need_envs = {
"IS_DISTRIBUTED": '0',
"IS_SPARSE": '0',
......@@ -78,7 +79,8 @@ class TestDistSimnetBow2x2SparseAsync(TestDistBase):
self._sync_mode = False
self._enforce_place = "CPU"
def test_simnet_bow(self):
#FIXME(typhoonzero): fix async tests later
def no_test_simnet_bow(self):
need_envs = {
"IS_DISTRIBUTED": '0',
"IS_SPARSE": '1',
......
python/paddle/fluid/tests/unittests/test_dist_transformer.py
浏览文件 @ b2943530
......@@ -61,7 +61,8 @@ class TestDistTransformer2x2Sync(TestDistBase):
def test_dist_train(self):
download_files()
self.check_with_place("dist_transformer.py", delta=1e-5)
self.check_with_place(
"dist_transformer.py", delta=1e-5, check_error_log=False)
class TestDistTransformer2x2Async(TestDistBase):
......@@ -70,7 +71,8 @@ class TestDistTransformer2x2Async(TestDistBase):
def test_dist_train(self):
download_files()
self.check_with_place("dist_transformer.py", delta=1.0)
self.check_with_place(
"dist_transformer.py", delta=1.0, check_error_log=False)
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/test_fusion_gru_op.py
浏览文件 @ b2943530
......@@ -125,6 +125,12 @@ class TestFusionGRUOpMD2(TestFusionGRUOp):
self.D = 8
class TestFusionGRUOpMD3(TestFusionGRUOp):
def set_confs(self):
self.M = 17
self.D = 15
class TestFusionGRUOpBS1(TestFusionGRUOp):
def set_confs(self):
self.lod = [[3]]
......
python/paddle/fluid/tests/unittests/test_rpn_target_assign_op.py
浏览文件 @ b2943530
......@@ -50,8 +50,10 @@ def rpn_target_assign(anchor_by_gt_overlap,
fg_inds, size=(len(fg_inds) - num_fg), replace=False)
else:
disable_inds = fg_inds[num_fg:]
labels[disable_inds] = -1
fg_inds = np.where(labels == 1)[0]
bbox_inside_weight = np.zeros((len(fg_inds), 4), dtype=np.float32)
num_bg = rpn_batch_size_per_im - np.sum(labels == 1)
bg_inds = np.where(anchor_to_gt_max < rpn_negative_overlap)[0]
......@@ -59,18 +61,27 @@ def rpn_target_assign(anchor_by_gt_overlap,
enable_inds = bg_inds[np.random.randint(len(bg_inds), size=num_bg)]
else:
enable_inds = bg_inds[:num_bg]
fg_fake_inds = np.array([], np.int32)
fg_value = np.array([fg_inds[0]], np.int32)
fake_num = 0
for bg_id in enable_inds:
if bg_id in fg_inds:
fake_num += 1
fg_fake_inds = np.hstack([fg_fake_inds, fg_value])
labels[enable_inds] = 0
bbox_inside_weight[fake_num:, :] = 1
fg_inds = np.where(labels == 1)[0]
bg_inds = np.where(labels == 0)[0]
loc_index = fg_inds
score_index = np.hstack((fg_inds, bg_inds))
loc_index = np.hstack([fg_fake_inds, fg_inds])
score_index = np.hstack([fg_inds, bg_inds])
labels = labels[score_index]
assert not np.any(labels == -1), "Wrong labels with -1"
gt_inds = anchor_to_gt_argmax[fg_inds]
gt_inds = anchor_to_gt_argmax[loc_index]
return loc_index, score_index, labels, gt_inds
return loc_index, score_index, labels, gt_inds, bbox_inside_weight
def get_anchor(n, c, h, w):
......@@ -123,9 +134,12 @@ def rpn_target_assign_in_python(all_anchors,
gt_boxes_slice = gt_boxes_slice[not_crowd_inds]
iou = _bbox_overlaps(inside_anchors, gt_boxes_slice)
loc_inds, score_inds, labels, gt_inds = rpn_target_assign(
iou, rpn_batch_size_per_im, rpn_positive_overlap,
rpn_negative_overlap, rpn_fg_fraction, use_random)
loc_inds, score_inds, labels, gt_inds, bbox_inside_weight = \
rpn_target_assign(iou, rpn_batch_size_per_im,
rpn_positive_overlap,
rpn_negative_overlap,
rpn_fg_fraction,
use_random)
# unmap to all anchor
loc_inds = inds_inside[loc_inds]
score_inds = inds_inside[score_inds]
......@@ -139,6 +153,7 @@ def rpn_target_assign_in_python(all_anchors,
score_indexes = score_inds
tgt_labels = labels
tgt_bboxes = box_deltas
bbox_inside_weights = bbox_inside_weight
else:
loc_indexes = np.concatenate(
[loc_indexes, loc_inds + i * anchor_num])
......@@ -146,8 +161,10 @@ def rpn_target_assign_in_python(all_anchors,
[score_indexes, score_inds + i * anchor_num])
tgt_labels = np.concatenate([tgt_labels, labels])
tgt_bboxes = np.vstack([tgt_bboxes, box_deltas])
bbox_inside_weights = np.vstack([bbox_inside_weights, \
bbox_inside_weight])
return loc_indexes, score_indexes, tgt_bboxes, tgt_labels
return loc_indexes, score_indexes, tgt_bboxes, tgt_labels, bbox_inside_weights
class TestRpnTargetAssignOp(OpTest):
......@@ -182,9 +199,11 @@ class TestRpnTargetAssignOp(OpTest):
rpn_fg_fraction = 0.5
use_random = False
loc_index, score_index, tgt_bbox, labels = rpn_target_assign_in_python(
all_anchors, gt_boxes, is_crowd, im_info, lod, rpn_straddle_thresh,
rpn_batch_size_per_im, rpn_positive_overlap, rpn_negative_overlap,
loc_index, score_index, tgt_bbox, labels, bbox_inside_weights = \
rpn_target_assign_in_python(all_anchors, gt_boxes, is_crowd,
im_info, lod, rpn_straddle_thresh,
rpn_batch_size_per_im, rpn_positive_overlap,
rpn_negative_overlap,
rpn_fg_fraction, use_random)
labels = labels[:, np.newaxis]
......@@ -207,7 +226,8 @@ class TestRpnTargetAssignOp(OpTest):
'LocationIndex': loc_index.astype('int32'),
'ScoreIndex': score_index.astype('int32'),
'TargetBBox': tgt_bbox.astype('float32'),
'TargetLabel': labels.astype('int32')
'TargetLabel': labels.astype('int32'),
'BBoxInsideWeight': bbox_inside_weights.astype('float32')
}
def test_check_output(self):
......
python/paddle/fluid/tests/unittests/test_slice_var.py
浏览文件 @ b2943530
......@@ -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)
......
python/paddle/fluid/tests/unittests/test_top_k_op.py
浏览文件 @ b2943530
......@@ -21,22 +21,27 @@ from op_test import OpTest
class TestTopkOp(OpTest):
def setUp(self):
self.set_args()
self.op_type = "top_k"
k = 1
input = np.random.random((32, 84)).astype("float32")
output = np.ndarray((32, k))
indices = np.ndarray((32, k)).astype("int64")
k = self.top_k
input = np.random.random((self.row, k)).astype("float32")
output = np.ndarray((self.row, k))
indices = np.ndarray((self.row, k)).astype("int64")
self.inputs = {'X': input}
self.attrs = {'k': k}
for rowid in range(32):
for rowid in range(self.row):
row = input[rowid]
output[rowid] = np.sort(row)[-k:]
indices[rowid] = row.argsort()[-k:]
output[rowid] = np.sort(row)[::-1][:k]
indices[rowid] = row.argsort()[::-1][:k]
self.outputs = {'Out': output, 'Indices': indices}
def set_args(self):
self.row = 32
self.top_k = 1
def test_check_output(self):
self.check_output()
......@@ -50,14 +55,39 @@ class TestTopkOp3d(OpTest):
output = np.ndarray((64, k))
indices = np.ndarray((64, k)).astype("int64")
# FIXME: should use 'X': input for a 3d input
self.inputs = {'X': input_flat_2d}
self.inputs = {'X': input}
self.attrs = {'k': k}
for rowid in range(64):
row = input_flat_2d[rowid]
output[rowid] = np.sort(row)[-k:]
indices[rowid] = row.argsort()[-k:]
output[rowid] = np.sort(row)[::-1][:k]
indices[rowid] = row.argsort()[::-1][:k]
self.outputs = {
'Out': output.reshape((32, 2, k)),
'Indices': indices.reshape((32, 2, k))
}
def test_check_output(self):
self.check_output()
class TestTopkOp2(OpTest):
def setUp(self):
self.op_type = "top_k"
k = 1
m = 2056
input = np.random.random((m, 84)).astype("float32")
output = np.ndarray((m, k))
indices = np.ndarray((m, k)).astype("int64")
self.inputs = {'X': input}
self.attrs = {'k': k}
for rowid in range(m):
row = input[rowid]
output[rowid] = -np.sort(-row)[:k]
indices[rowid] = (-row).argsort()[:k]
self.outputs = {'Out': output, 'Indices': indices}
......@@ -65,5 +95,17 @@ class TestTopkOp3d(OpTest):
self.check_output()
class TestTopkOp3(TestTopkOp):
def set_args(self):
self.row = 2056
self.top_k = 3
class TestTopkOp4(TestTopkOp):
def set_args(self):
self.row = 40000
self.top_k = 1
if __name__ == "__main__":
unittest.main()
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