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未验证 提交 95710a33 编写于 作者: Q Qinghe JING 提交者： GitHub
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Merge branch 'develop' into fix_hdfs_download

上级 164f12fa fef94eac
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Showing with 3651 addition and 1033 deletion
paddle/fluid/framework/device_worker.h
浏览文件 @ 95710a33
......@@ -441,6 +441,7 @@ class SectionWorker : public DeviceWorker {
skip_vars_ = skip_vars;
}
static void ResetBatchId() { batch_id_ = 0; }
static void ResetThreadCompletedFlag() { threads_completed = false; }
static std::atomic<int> cpu_id_;
......
paddle/fluid/framework/distributed_strategy.proto
浏览文件 @ 95710a33
......@@ -41,6 +41,11 @@ message LocalSGDConfig {
optional int32 begin_step = 2 [ default = 1 ];
}
message AdaptiveLocalSGDConfig {
optional int32 init_k_steps = 1 [ default = 1 ];
optional int32 begin_step = 2 [ default = 1 ];
}
message GradientMergeConfig {
optional int32 k_steps = 1 [ default = 1 ];
optional bool avg = 2 [ default = true ];
......@@ -121,6 +126,7 @@ message DistributedStrategy {
optional bool cudnn_exhaustive_search = 21 [ default = true ];
optional int32 conv_workspace_size_limit = 22 [ default = 4000 ];
optional bool cudnn_batchnorm_spatial_persistent = 23 [ default = true ];
optional bool adaptive_localsgd = 24 [ default = false ];
optional RecomputeConfig recompute_configs = 101;
optional AMPConfig amp_configs = 102;
......@@ -131,6 +137,7 @@ message DistributedStrategy {
optional AsyncConfig a_sync_configs = 107;
optional LarsConfig lars_configs = 108;
optional LambConfig lamb_configs = 109;
optional AdaptiveLocalSGDConfig adaptive_localsgd_configs = 110;
optional BuildStrategy build_strategy = 201;
optional ExecutionStrategy execution_strategy = 202;
}
......
paddle/fluid/framework/ir/embedding_eltwise_layernorm_fuse_pass.cc
浏览文件 @ 95710a33
......@@ -19,6 +19,7 @@
#include <vector>
#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_version_registry.h"
namespace paddle {
namespace framework {
......@@ -334,3 +335,8 @@ void EmbeddingEltwiseLayerNormFusePass::ApplyImpl(Graph* graph) const {
REGISTER_PASS(embedding_eltwise_layernorm_fuse_pass,
paddle::framework::ir::EmbeddingEltwiseLayerNormFusePass);
REGISTER_PASS_CAPABILITY(embedding_eltwise_layernorm_fuse_pass)
.AddCombination(
paddle::framework::compatible::OpVersionComparatorCombination()
.EQ("lookup_table", 0)
.EQ("elementweise_add", 0));
paddle/fluid/framework/ir/embedding_eltwise_layernorm_fuse_pass_tester.cc
浏览文件 @ 95710a33
......@@ -16,12 +16,13 @@ limitations under the License. */
#include <gtest/gtest.h>
#include "paddle/fluid/framework/ir/pass_tester_helper.h"
#include "paddle/fluid/framework/op_version_registry.h"
namespace paddle {
namespace framework {
namespace ir {
TEST(SkipLayerNormFusePass, basic) {
TEST(EmbeddingElewiseLayernormFusePass, basic) {
// inputs operator output
// --------------------------------------------------------------------
// (x, y) elementwise_add -> elementwise_out
......@@ -91,6 +92,12 @@ TEST(SkipLayerNormFusePass, basic) {
"The number of fusion nodes does not meet expectations after fuse"));
}
TEST(EmbeddingElewiseLayernormFusePass, pass_op_version_check) {
ASSERT_TRUE(
paddle::framework::compatible::PassVersionCheckerRegistrar::GetInstance()
.IsPassCompatible("embedding_eltwise_layernorm_fuse_pass"));
}
} // namespace ir
} // namespace framework
} // namespace paddle
......
paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass.cc
浏览文件 @ 95710a33
......@@ -17,6 +17,7 @@
#include <string>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_version_registry.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
......@@ -84,6 +85,19 @@ void ConvBiasFusePass::ApplyImpl(ir::Graph* graph) const {
VLOG(3) << "do not perform " + type() + "+bias fuse";
return;
}
if (conv->Op()->HasAttr("dilations")) {
auto dilations =
BOOST_GET_CONST(std::vector<int>, conv->Op()->GetAttr("dilations"));
for (const auto& d : dilations) {
if (d != 1) {
LOG(WARNING)
<< "dilation conv not supported in MKLDNN, fuse not apply "
<< "and set conv attribute use_mkldnn = false";
conv->Op()->SetAttr("use_mkldnn", false);
return;
}
}
}
auto* eltwise_bias_tensor =
scope->FindVar(eltwise_bias->Name())->GetMutable<LoDTensor>();
......@@ -151,3 +165,8 @@ REGISTER_PASS(conv_transpose_bias_mkldnn_fuse_pass,
paddle::framework::ir::Conv2DTransposeBiasFusePass);
REGISTER_PASS(conv3d_bias_mkldnn_fuse_pass,
paddle::framework::ir::Conv3DBiasFusePass);
REGISTER_PASS_CAPABILITY(conv_bias_mkldnn_fuse_pass)
.AddCombination(
paddle::framework::compatible::OpVersionComparatorCombination()
.EQ("conv2d", 0)
.EQ("elementwise_add", 0));
paddle/fluid/framework/ir/mkldnn/conv_bias_mkldnn_fuse_pass_tester.cc
浏览文件 @ 95710a33
......@@ -18,6 +18,7 @@
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/framework/op_proto_maker.h"
#include "paddle/fluid/framework/op_version_registry.h"
#include "paddle/fluid/imperative/type_defs.h"
namespace paddle {
......@@ -149,6 +150,12 @@ TEST(ConvBiasFusePass, conv2d_transpose) {
ASSERT_EQ(pass.type(), std::string("conv2d_transpose"));
}
TEST(ConvBiasFusePass, pass_op_version_check) {
ASSERT_TRUE(
paddle::framework::compatible::PassVersionCheckerRegistrar::GetInstance()
.IsPassCompatible("conv_bias_mkldnn_fuse_pass"));
}
} // namespace ir
} // namespace framework
} // namespace paddle
......
paddle/fluid/framework/ir/mkldnn/conv_elementwise_add_mkldnn_fuse_pass.cc
浏览文件 @ 95710a33
......@@ -19,6 +19,7 @@
#include <memory>
#include <tuple>
#include "paddle/fluid/framework/ir/graph_traits.h"
#include "paddle/fluid/framework/op_version_registry.h"
namespace paddle {
namespace framework {
......@@ -341,3 +342,8 @@ void ResidualConnectionMKLDNNFusePass::ApplyImpl(graph_ptr graph) const {
REGISTER_PASS(conv_elementwise_add_mkldnn_fuse_pass,
paddle::framework::ir::ResidualConnectionMKLDNNFusePass);
REGISTER_PASS_CAPABILITY(conv_elementwise_add_mkldnn_fuse_pass)
.AddCombination(
paddle::framework::compatible::OpVersionComparatorCombination()
.EQ("conv2d", 0)
.EQ("elementwise_add", 0));
paddle/fluid/framework/ir/mkldnn/conv_elementwise_add_mkldnn_fuse_pass_tester.cc
浏览文件 @ 95710a33
......@@ -17,6 +17,7 @@
#include "paddle/fluid/framework/ir/graph_traits.h"
#include "paddle/fluid/framework/ir/mkldnn/conv_elementwise_add_mkldnn_fuse_pass.h"
#include "paddle/fluid/framework/op_version_registry.h"
namespace paddle {
namespace framework {
......@@ -267,6 +268,12 @@ TEST(ConvElementwiseAddMKLDNNFusePass, NoFusion) {
AssertOpsCount(graph, 2, 1);
}
TEST(ConvElementwiseAddMKLDNNFusePass, pass_op_version_check) {
ASSERT_TRUE(
paddle::framework::compatible::PassVersionCheckerRegistrar::GetInstance()
.IsPassCompatible("conv_elementwise_add_mkldnn_fuse_pass"));
}
} // namespace ir
} // namespace framework
} // namespace paddle
......
paddle/fluid/framework/ir/mkldnn/depthwise_conv_mkldnn_pass.cc
浏览文件 @ 95710a33
......@@ -14,6 +14,7 @@ limitations under the License. */
#include "paddle/fluid/framework/ir/mkldnn/depthwise_conv_mkldnn_pass.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
#include "paddle/fluid/framework/op_version_registry.h"
namespace paddle {
namespace framework {
......@@ -57,3 +58,7 @@ void DepthwiseConvMKLDNNPass::ApplyImpl(ir::Graph* graph) const {
REGISTER_PASS(depthwise_conv_mkldnn_pass,
paddle::framework::ir::DepthwiseConvMKLDNNPass);
REGISTER_PASS_CAPABILITY(depthwise_conv_mkldnn_pass)
.AddCombination(
paddle::framework::compatible::OpVersionComparatorCombination().EQ(
"depthwise_conv2d", 0));
paddle/fluid/framework/ir/mkldnn/depthwise_conv_mkldnn_pass_tester.cc
浏览文件 @ 95710a33
......@@ -16,6 +16,8 @@
#include <gtest/gtest.h>
#include "paddle/fluid/framework/op_version_registry.h"
namespace paddle {
namespace framework {
namespace ir {
......@@ -70,6 +72,12 @@ ProgramDesc BuildProgramDesc() {
return prog;
}
TEST(DepthwiseConvMKLDNNPass, pass_op_version_check) {
ASSERT_TRUE(
paddle::framework::compatible::PassVersionCheckerRegistrar::GetInstance()
.IsPassCompatible("depthwise_conv_mkldnn_pass"));
}
TEST(DepthwiseConvMKLDNNPass, basic) {
auto prog = BuildProgramDesc();
......
paddle/fluid/framework/ir/multihead_matmul_fuse_pass.cc
浏览文件 @ 95710a33
......@@ -19,6 +19,7 @@
#include <vector>
#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_version_registry.h"
#include "paddle/fluid/platform/errors.h"
namespace paddle {
......@@ -707,3 +708,13 @@ REGISTER_PASS(multihead_matmul_fuse_pass,
REGISTER_PASS(multihead_matmul_fuse_pass_v2,
paddle::framework::ir::MultiHeadMatmulV2FusePass);
REGISTER_PASS_CAPABILITY(multihead_matmul_fuse_pass_v2)
.AddCombination(
paddle::framework::compatible::OpVersionComparatorCombination()
.EQ("mul", 0)
.EQ("elementwise_add", 0)
.EQ("reshape2", 0)
.EQ("transpose2", 0)
.EQ("scale", 0)
.EQ("matmul", 0)
.EQ("softmax", 0));
paddle/fluid/framework/ir/multihead_matmul_fuse_pass_tester.cc
浏览文件 @ 95710a33
......@@ -12,6 +12,7 @@ limitations under the License. */
#include "paddle/fluid/framework/ir/multihead_matmul_fuse_pass.h" // NOLINT
#include <gtest/gtest.h>
#include "paddle/fluid/framework/ir/pass_tester_helper.h"
#include "paddle/fluid/framework/op_version_registry.h"
namespace paddle {
namespace framework {
......@@ -133,6 +134,12 @@ TEST(MultiHeadMatmulFusePass, basic) {
num_fused_nodes_after));
}
TEST(MultiHeadMatmulFusePass, pass_op_version_check) {
ASSERT_TRUE(
paddle::framework::compatible::PassVersionCheckerRegistrar::GetInstance()
.IsPassCompatible("multihead_matmul_fuse_pass_v2"));
}
} // namespace ir
} // namespace framework
} // namespace paddle
......
paddle/fluid/framework/ir/skip_layernorm_fuse_pass.cc
浏览文件 @ 95710a33
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <unordered_set>
#include <vector>
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
#include "paddle/fluid/framework/op_version_registry.h"
namespace paddle {
namespace framework {
......@@ -180,3 +181,8 @@ void SkipLayerNormFusePass::ApplyImpl(ir::Graph *graph) const {
REGISTER_PASS(skip_layernorm_fuse_pass,
paddle::framework::ir::SkipLayerNormFusePass);
REGISTER_PASS_CAPABILITY(skip_layernorm_fuse_pass)
.AddCombination(
paddle::framework::compatible::OpVersionComparatorCombination()
.EQ("elementwise_add", 0)
.EQ("layer_norm", 0));
paddle/fluid/framework/ir/skip_layernorm_fuse_pass_tester.cc
浏览文件 @ 95710a33
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <gtest/gtest.h>
#include "paddle/fluid/framework/ir/pass_tester_helper.h"
#include "paddle/fluid/framework/op_version_registry.h"
namespace paddle {
namespace framework {
......@@ -54,6 +55,12 @@ TEST(SkipLayerNormFusePass, basic) {
"The number of fusion nodes does not meet expectations after fuse"));
}
TEST(SkipLayerNormFusePass, pass_op_version_check) {
ASSERT_TRUE(
paddle::framework::compatible::PassVersionCheckerRegistrar::GetInstance()
.IsPassCompatible("skip_layernorm_fuse_pass"));
}
} // namespace ir
} // namespace framework
} // namespace paddle
......
paddle/fluid/framework/pipeline_trainer.cc
浏览文件 @ 95710a33
......@@ -251,6 +251,7 @@ void PipelineTrainer::Finalize() {
}
root_scope_->DropKids();
SectionWorker::ResetBatchId();
SectionWorker::ResetThreadCompletedFlag();
}
Scope* PipelineTrainer::GetWorkerScope(int thread_id) {
......
paddle/fluid/framework/section_worker.cc
浏览文件 @ 95710a33
......@@ -196,7 +196,6 @@ void SectionWorker::TrainFiles() {
if (threads_completed) {
VLOG(3) << "thread " << thread_id_ << " completed.";
lk.unlock();
threads_completed = false;
return;
}
lk.unlock();
......@@ -459,7 +458,6 @@ void SectionWorker::TrainFilesWithProfiler() {
<< ", mean_time: " << op_total_time[i] / op_count[i];
}
VLOG(0) << "================================";
threads_completed = false;
return;
}
lk.unlock();
......
paddle/fluid/inference/tests/api/CMakeLists.txt
浏览文件 @ 95710a33
......@@ -9,7 +9,8 @@ if(WITH_GPU AND TENSORRT_FOUND)
endif()
function(download_data install_dir data_file)
if (NOT EXISTS ${install_dir}/${data_file})
string(REGEX MATCH "[^/\\]+$" file_name ${data_file})
if (NOT EXISTS ${install_dir}/${file_name})
inference_download_and_uncompress(${install_dir} ${INFERENCE_URL} ${data_file})
endif()
endfunction()
......
paddle/fluid/operators/average_accumulates_op.h
浏览文件 @ 95710a33
......@@ -54,9 +54,13 @@ class AverageAccumulatesKernel : public framework::OpKernel<T> {
float average_window = ctx.Attr<float>("average_window");
int64_t max_average_window = ctx.Attr<int64_t>("max_average_window");
int64_t min_average_window = ctx.Attr<int64_t>("min_average_window");
PADDLE_ENFORCE_LE(min_average_window, max_average_window,
"min_average_window shouldn't be larger than "
"max_average_window");
PADDLE_ENFORCE_LE(
min_average_window, max_average_window,
platform::errors::InvalidArgument(
"The min_average_window > "
"max_average_window is not right, min_average_window is %ld, "
"max_average_window is %ld.",
min_average_window, max_average_window));
// Get inputs
auto* param = ctx.Input<Tensor>("param");
......
paddle/fluid/operators/distributed_ops/fake_init_op.cc
浏览文件 @ 95710a33
......@@ -43,9 +43,9 @@ class FakeInitOp : public framework::OperatorBase {
tensor = out_var.GetMutable<framework::SelectedRows>()->mutable_value();
tensor->Resize(framework::make_ddim(Attr<std::vector<int64_t>>("shape")));
} else {
PADDLE_THROW(
PADDLE_THROW(platform::errors::InvalidArgument(
"fake init op's output only"
"supports SelectedRows and LoDTensor");
"supports SelectedRows and LoDTensor"));
}
}
};
......
paddle/fluid/operators/distributed_ops/listen_and_serv_op.cc
浏览文件 @ 95710a33
......@@ -134,7 +134,10 @@ void ListenAndServOp::RunSyncLoop(
auto optimize_blocks =
Attr<std::vector<framework::BlockDesc *>>(kOptimizeBlocks);
PADDLE_ENFORCE_GE(num_blocks, 2,
"server program should have at least 2 blocks");
platform::errors::PreconditionNotMet(
"Invalid number of blocks in server program. Expected "
"equal or greater than 2. Recieved %zu",
num_blocks));
// Prepare all the server block
std::vector<int> optimize_blocks_list;
......@@ -218,7 +221,8 @@ void ListenAndServOp::ResetReceivedVars(framework::Scope *recv_scope,
VLOG(3) << "reset sparse var: " << varname;
var->GetMutable<framework::SelectedRows>()->mutable_rows()->clear();
} else {
PADDLE_THROW("The type of sparse var should be SelectedRows");
PADDLE_THROW(platform::errors::PreconditionNotMet(
"The type of sparse var should be SelectedRows"));
}
}
if (UNLIKELY(reset_all)) {
......@@ -235,7 +239,8 @@ void ListenAndServOp::ResetReceivedVars(framework::Scope *recv_scope,
math::set_constant(*dev_ctx, var->GetMutable<framework::Tensor>(),
static_cast<float>(0));
} else {
PADDLE_THROW("The type of dense var should be in [LoDTensor, Tensor]");
PADDLE_THROW(platform::errors::PreconditionNotMet(
"The type of dense var should be in [LoDTensor, Tensor]"));
}
}
}
......@@ -254,8 +259,15 @@ void ListenAndServOp::RunAsyncLoop(framework::Executor *executor,
std::vector<std::string> pieces;
split(grad_and_id, ':', &pieces);
VLOG(3) << "after split, key = " << pieces[0] << ", id=" << pieces[1];
PADDLE_ENFORCE_EQ(pieces.size(), 2);
PADDLE_ENFORCE_EQ(out_map->count(pieces[0]), 0);
PADDLE_ENFORCE_EQ(pieces.size(), 2,
platform::errors::PreconditionNotMet(
"Invalid format of grad_and_id argument. "
"Expected \"grad:block_id\". Recieved %s",
grad_and_id.c_str()));
PADDLE_ENFORCE_EQ(out_map->count(pieces[0]), 0,
platform::errors::AlreadyExists(
"The gradient name %s has already existed in out_map",
pieces[0].c_str()));
int block_id = std::stoi(pieces[1]);
(*out_map)[pieces[0]] = block_id;
......@@ -267,7 +279,10 @@ void ListenAndServOp::RunAsyncLoop(framework::Executor *executor,
size_t num_blocks = program->Size();
PADDLE_ENFORCE_GE(num_blocks, 2,
"server program should have at least 2 blocks");
platform::errors::PreconditionNotMet(
"Invalid number of blocks in server program. Expected "
"equal or greater than 2. Recieved %zu",
num_blocks));
std::vector<int> block_list;
for (size_t blkid = 1; blkid < num_blocks; ++blkid) {
block_list.push_back(blkid);
......@@ -342,9 +357,9 @@ void ListenAndServOp::CacheVarsType(const std::vector<std::string> &varnames,
var->IsType<framework::Tensor>()) {
dense_vars_.push_back(varname);
} else {
PADDLE_THROW(
PADDLE_THROW(platform::errors::PreconditionNotMet(
"The type of received var should be in [SelectedRows, LoDTensor, "
"Tensor].");
"Tensor]."));
}
}
}
......@@ -450,7 +465,12 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
split(prefetch_var_name_and_id, ':', &pieces);
VLOG(3) << "after split, prefetch_var = " << pieces[0]
<< ", id=" << pieces[1];
PADDLE_ENFORCE_EQ(pieces.size(), 2);
PADDLE_ENFORCE_EQ(
pieces.size(), 2,
platform::errors::PreconditionNotMet(
"Invalid format of prefetch_var_name_and_id argument. "
"Expected \"xxx:xxx\". Recieved %s",
prefetch_var_name_and_id.c_str()));
int block_id = std::stoi(pieces[1]);
prefetch_block_id_list.push_back(block_id);
......@@ -476,7 +496,12 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
sparse_grad_name_to_param_name_str) {
std::vector<std::string> pieces;
split(sparse_grad_name_and_param_name, ':', &pieces);
PADDLE_ENFORCE_EQ(pieces.size(), 2);
PADDLE_ENFORCE_EQ(
pieces.size(), 2,
platform::errors::PreconditionNotMet(
"Invalid format of sparse_grad_name_and_param_name argument. "
"Expected \"xxx:xxx\". Recieved %s",
sparse_grad_name_and_param_name.c_str()));
VLOG(3) << "after split, sparse_grad_name = " << pieces[0]
<< ", param_name = " << pieces[1];
sparse_grad_name_to_param_name[pieces[0]] = pieces[1];
......
paddle/fluid/operators/elementwise/elementwise_floordiv_op.h
浏览文件 @ 95710a33
......@@ -61,8 +61,15 @@ void elementwise_floor_div(const framework::ExecutionContext &ctx,
const framework::Tensor *x,
const framework::Tensor *y, framework::Tensor *z) {
int axis = ctx.Attr<int>("axis");
ElementwiseComputeEx<FloorDivFunctor<T>, DeviceContext, T>(
ctx, x, y, axis, FloorDivFunctor<T>(), z);
auto x_dims = x->dims();
auto y_dims = y->dims();
if (x_dims.size() >= y_dims.size()) {
ElementwiseComputeEx<FloorDivFunctor<T>, DeviceContext, T>(
ctx, x, y, axis, FloorDivFunctor<T>(), z);
} else {
ElementwiseComputeEx<InverseFloorDivFunctor<T>, DeviceContext, T>(
ctx, x, y, axis, InverseFloorDivFunctor<T>(), z);
}
}
template <typename DeviceContext, typename T>
......
paddle/fluid/operators/empty_op.cc
浏览文件 @ 95710a33
......@@ -55,31 +55,38 @@ class EmptyOp : public framework::OperatorWithKernel {
OP_INOUT_CHECK(context->HasOutput("Out"), "Output", "Out", "empty");
if (context->HasInput("ShapeTensor")) {
auto dims = context->GetInputDim("ShapeTensor");
auto shape_dims = context->GetInputDim("ShapeTensor");
int num_ele = 1;
for (int i = 0; i < dims.size(); ++i) {
num_ele *= dims[i];
for (int i = 0; i < shape_dims.size(); ++i) {
num_ele *= shape_dims[i];
}
context->SetOutputDim("Out", framework::make_ddim({num_ele}));
auto vec_dims = std::vector<int>(num_ele, -1);
context->SetOutputDim("Out", framework::make_ddim(vec_dims));
} else if (context->HasInputs("ShapeTensorList")) {
std::vector<int> out_dims;
auto dims_list = context->GetInputsDim("ShapeTensorList");
for (size_t i = 0; i < dims_list.size(); ++i) {
auto& dims = dims_list[i];
PADDLE_ENFORCE_EQ(
dims, framework::make_ddim({1}),
"ShapeError: The shape of Tensor in list must be [1]. "
"But received the shape "
"is [%s]",
dims);
out_dims.push_back(dims[0]);
PADDLE_ENFORCE_EQ(dims, framework::make_ddim({1}),
platform::errors::InvalidArgument(
"The shape of Tensor in list must be [1]. "
"But received the shape is [%s]",
dims));
out_dims.push_back(-1);
}
context->SetOutputDim("Out", framework::make_ddim(out_dims));
} else {
auto& shape = context->Attrs().Get<std::vector<int64_t>>("shape");
for (size_t i = 0; i < shape.size(); ++i) {
PADDLE_ENFORCE_GE(
shape[i], 0,
platform::errors::InvalidArgument(
"Each value of attribute 'shape' is expected to be no less "
"than 0. But recieved: shape[%u] = %d; shape = [%s].",
i, shape[i], framework::make_ddim(shape)));
}
context->SetOutputDim("Out", framework::make_ddim(shape));
}
}
......
paddle/fluid/operators/math/beam_search.cc
浏览文件 @ 95710a33
......@@ -87,7 +87,10 @@ class BeamSearchFunctor<platform::CPUDeviceContext, T> {
lod[0].assign(high_level.begin(), high_level.end());
lod[1].assign(low_level.begin(), low_level.end());
if (!framework::CheckLoD(lod)) {
PADDLE_THROW("lod %s is not right", framework::LoDToString(lod));
PADDLE_THROW(platform::errors::InvalidArgument(
"lod %s is not right in"
" beam_search, please check your code.",
framework::LoDToString(lod)));
}
selected_ids->set_lod(lod);
selected_scores->set_lod(lod);
......
paddle/fluid/operators/math/beam_search.cu
浏览文件 @ 95710a33
......@@ -400,7 +400,10 @@ class BeamSearchFunctor<platform::CUDADeviceContext, T> {
context.Wait();
if (!framework::CheckLoD(selected_lod)) {
PADDLE_THROW("lod %s is not right", framework::LoDToString(selected_lod));
PADDLE_THROW(platform::errors::InvalidArgument(
"lod %s is not right in"
" beam_search, please check your code.",
framework::LoDToString(selected_lod)));
}
selected_ids->set_lod(selected_lod);
......
paddle/fluid/operators/math/blas.cc
浏览文件 @ 95710a33
......@@ -20,7 +20,11 @@ namespace operators {
namespace math {
MatDescriptor CreateMatrixDescriptor(const framework::DDim &tensor_dim,
int num_flatten_cols, bool trans) {
PADDLE_ENFORCE_GT(tensor_dim.size(), 1);
PADDLE_ENFORCE_GT(
tensor_dim.size(), 1,
platform::errors::InvalidArgument("The tensor dim size should be greater "
"than 1, but reveived dim size is %d",
tensor_dim.size()));
MatDescriptor retv;
if (num_flatten_cols > 1) {
auto flatten_dim = framework::flatten_to_2d(tensor_dim, num_flatten_cols);
......
paddle/fluid/operators/math/blas_impl.cu.h
浏览文件 @ 95710a33
......@@ -60,7 +60,8 @@ struct CUBlas<float> {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cublasSgemmStridedBatched(args...));
#else
PADDLE_THROW("SgemmStridedBatched is not supported on cuda <= 7.5");
PADDLE_THROW(platform::errors::Unimplemented(
"SgemmStridedBatched is not supported on cuda <= 7.5"));
#endif
}
......@@ -85,7 +86,8 @@ struct CUBlas<float> {
beta, C, Ctype, ldc));
});
#else
PADDLE_THROW("cublasSgemmEx is supported on cuda >= 8.0");
PADDLE_THROW(platform::errors::Unimplemented(
"cublasSgemmEx is not supported on cuda <= 7.5"));
#endif
}
......@@ -146,13 +148,15 @@ struct CUBlas<double> {
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cublasDgemmStridedBatched(args...));
#else
PADDLE_THROW("DgemmStridedBatched is not supported on cuda <= 7.5");
PADDLE_THROW(platform::errors::Unimplemented(
"DgemmStridedBatched is not supported on cuda <= 7.5"));
#endif
}
template <typename... ARGS>
static void GEMM_EX(ARGS... args) {
PADDLE_THROW("Currently there are not cublasDgemmEx.");
PADDLE_THROW(platform::errors::Unimplemented(
"Currently there are not cublasDgemmEx."));
}
template <typename... ARGS>
......@@ -216,7 +220,8 @@ struct CUBlas<platform::float16> {
reinterpret_cast<const __half *>(beta), reinterpret_cast<__half *>(C),
ldc, strideC, batchCount));
#else
PADDLE_THROW("HgemmStridedBatched is not supported on cuda <= 7.5");
PADDLE_THROW(platform::errors::Unimplemented(
"HgemmStridedBatched is not supported on cuda <= 7.5"));
#endif
}
......@@ -247,7 +252,8 @@ struct CUBlas<platform::float16> {
beta, C, Ctype, ldc, computeType, algo));
});
#else
PADDLE_THROW("cublasGemmEx is supported on cuda >= 8.0");
PADDLE_THROW(platform::errors::Unimplemented(
"cublasGemmEx is not supported on cuda <= 7.5"));
#endif
}
};
......@@ -302,8 +308,12 @@ inline void Blas<platform::CUDADeviceContext>::GEMM(
(transB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
// TODO(kexinzhao): add processing code for compute capability < 53 case
PADDLE_ENFORCE_GE(context_.GetComputeCapability(), 53,
"cublas fp16 gemm requires GPU compute capability >= 53");
PADDLE_ENFORCE_GE(
context_.GetComputeCapability(), 53,
platform::errors::InvalidArgument(
"cublas fp16 gemm requires GPU compute capability >= 53,"
"but received %d",
context_.GetComputeCapability()));
float h_alpha = static_cast<float>(alpha);
float h_beta = static_cast<float>(beta);
......
paddle/fluid/operators/math/blas_impl.h
浏览文件 @ 95710a33
......@@ -29,7 +29,8 @@ template <>
struct CBlas<int8_t> {
template <typename... ARGS>
static void VCOPY(ARGS... args) {
PADDLE_THROW("Blas VCOPY don't support int8_t");
PADDLE_THROW(platform::errors::Unimplemented(
"Blas VCOPY do not supported on CPU, please check your code"));
}
};
......@@ -347,22 +348,47 @@ struct CBlas<double> {
template <>
struct CBlas<platform::float16> {
static void GEMM(...) { PADDLE_THROW("float16 GEMM not supported on CPU"); }
static void GEMM(...) {
PADDLE_THROW(platform::errors::Unimplemented(
"float16 GEMM not supported on CPU, please check your code"));
}
static void SMM_GEMM(...) {
PADDLE_THROW("float16 SMM_GEMM not supported on CPU");
PADDLE_THROW(platform::errors::Unimplemented(
"float16 SMM_GEMM not supported on CPU, please check your code"));
}
static void VMUL(...) { PADDLE_THROW("float16 VMUL not supported on CPU"); }
static void VEXP(...) { PADDLE_THROW("float16 VEXP not supported on CPU"); }
static void VSQUARE(...) {
PADDLE_THROW("float16 VSQUARE not supported on CPU");
static void VMUL(...) {
PADDLE_THROW(platform::errors::Unimplemented(
"float16 VMUL not supported on CPU, please check your code"));
}
static void VPOW(...) { PADDLE_THROW("float16 VPOW not supported on CPU"); }
static void DOT(...) { PADDLE_THROW("float16 DOT not supported on CPU"); };
static void SCAL(...) { PADDLE_THROW("float16 SCAL not supported on CPU"); };
static void ASUM(...) { PADDLE_THROW("float16 ASUM not supported on CPU"); };
static void VEXP(...) {
PADDLE_THROW(platform::errors::Unimplemented(
"float16 VEXP not supported on CPU, please check your code"));
}
static void VSQUARE(...) {
PADDLE_THROW(platform::errors::Unimplemented(
"float16 VSQUARE not supported on CPU, please check your code"));
}
static void VPOW(...) {
PADDLE_THROW(platform::errors::Unimplemented(
"float16 VPOW not supported on CPU, please check your code"));
}
static void DOT(...) {
PADDLE_THROW(platform::errors::Unimplemented(
"float16 DOT not supported on CPU, please check your code"));
};
static void SCAL(...) {
PADDLE_THROW(platform::errors::Unimplemented(
"float16 SCAL not supported on CPU, please check your code"));
};
static void ASUM(...) {
PADDLE_THROW(platform::errors::Unimplemented(
"float16 ASUM not supported on CPU, please check your code"));
};
#ifdef PADDLE_WITH_MKLML
static void GEMM_BATCH(...) {
PADDLE_THROW("float16 GEMM_BATCH not supported on CPU");
PADDLE_THROW(platform::errors::Unimplemented(
"float16 GEMM_BATCH not supported on CPU, please check your code"));
}
#endif
};
......@@ -446,11 +472,18 @@ void Blas<DeviceContext>::MatMul(const framework::Tensor &mat_a, bool trans_a,
auto dim_a = mat_a.dims();
auto dim_b = mat_b.dims();
auto dim_out = mat_out->dims();
PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() == 2,
"The input and output of matmul be matrix");
PADDLE_ENFORCE(
mat_a.place() == mat_b.place() && mat_a.place() == mat_out->place(),
"The places of matrices must be same");
PADDLE_ENFORCE_EQ(
dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() == 2, true,
platform::errors::InvalidArgument(
"The input and output of matmul should be matrix, the dim size must "
"be 2,"
"but received dim size input_a:%d, input_b:%d, output:%d",
dim_a.size(), dim_b.size(), dim_out.size()));
PADDLE_ENFORCE_EQ(
mat_a.place() == mat_b.place() && mat_a.place() == mat_out->place(), true,
platform::errors::InvalidArgument("The places of matrices in the matmul "
"should be same, please check your "
"code."));
int M = dim_out[0];
int N = dim_out[1];
......@@ -715,7 +748,13 @@ void Blas<platform::CPUDeviceContext>::BatchedGEMMWithHead(
}
} else {
PADDLE_ENFORCE_EQ(W1, H2);
PADDLE_ENFORCE_EQ(
W1, H2,
platform::errors::InvalidArgument(
"The fisrt matrix width should be same as second matrix height,"
"but received fisrt matrix width %d"
", second matrix height %d",
W1, H2));
int ldc = W2 * head_number;
int sub_width = W1 / head_number;
......@@ -785,7 +824,14 @@ void Blas<DeviceContext>::MatMul(const framework::Tensor &mat_a,
const framework::Tensor &mat_b,
const MatDescriptor &dim_b, T alpha,
framework::Tensor *mat_out, T beta) const {
PADDLE_ENFORCE_EQ(dim_a.width_, dim_b.height_);
PADDLE_ENFORCE_EQ(
dim_a.width_, dim_b.height_,
platform::errors::InvalidArgument(
"The fisrt matrix width should be same as second matrix height,"
"but received fisrt matrix width %d"
", second matrix height %d",
dim_a.width_, dim_b.height_));
CBLAS_TRANSPOSE transA = !dim_a.trans_ ? CblasNoTrans : CblasTrans;
CBLAS_TRANSPOSE transB = !dim_b.trans_ ? CblasNoTrans : CblasTrans;
if (dim_a.batch_size_ == 0 && dim_b.batch_size_ == 0) {
......@@ -793,12 +839,14 @@ void Blas<DeviceContext>::MatMul(const framework::Tensor &mat_a,
dim_a.width_, alpha, mat_a.data<T>(),
mat_b.data<T>(), beta, mat_out->data<T>());
} else {
PADDLE_ENFORCE(dim_a.batch_size_ == dim_b.batch_size_ ||
dim_a.batch_size_ == 0 || dim_b.batch_size_ == 0,
"dim_a.batch_size should be equal to dim_b.batch_size, or "
"one of dim_a.batch_size and dim_b.batch_size should be 0. "
"But got dim_a.batch_size = %d, dim_b.batch_size = %d.",
dim_a.batch_size_, dim_b.batch_size_);
PADDLE_ENFORCE_EQ(
dim_a.batch_size_ == dim_b.batch_size_ || dim_a.batch_size_ == 0 ||
dim_b.batch_size_ == 0,
true, platform::errors::InvalidArgument(
"dim_a.batch_size should be equal to dim_b.batch_size, or "
"one of dim_a.batch_size and dim_b.batch_size should be 0. "
"But got dim_a.batch_size = %d, dim_b.batch_size = %d.",
dim_a.batch_size_, dim_b.batch_size_));
this->template BatchedGEMM<T>(
transA, transB, dim_a.height_, dim_b.width_, dim_a.width_, alpha,
mat_a.data<T>(), mat_b.data<T>(), beta, mat_out->data<T>(),
......@@ -834,15 +882,42 @@ void Blas<DeviceContext>::MatMulWithHead(const framework::Tensor &mat_a,
int head_number,
framework::Tensor *mat_out, T beta,
bool mat_b_split_vertical) const {
PADDLE_ENFORCE_EQ(dim_a.width_ % head_number, 0);
PADDLE_ENFORCE_GE(head_number, 1);
PADDLE_ENFORCE_LE(head_number, dim_a.width_);
PADDLE_ENFORCE_EQ(
dim_a.width_ % head_number, 0,
platform::errors::InvalidArgument(
"The first input width must be some times the head number"
"but received first input width %d"
", head_number %d",
dim_a.width_, head_number));
PADDLE_ENFORCE_GE(head_number, 1,
platform::errors::InvalidArgument(
"The head number should be greater equal 1,"
"but received head number %d",
head_number));
PADDLE_ENFORCE_LE(
head_number, dim_a.width_,
platform::errors::InvalidArgument(
"The head number should be less equal first input width,"
"but received first input width %d"
", head_number %d",
dim_a.width_, head_number));
CBLAS_TRANSPOSE transA = !dim_a.trans_ ? CblasNoTrans : CblasTrans;
CBLAS_TRANSPOSE transB = !dim_b.trans_ ? CblasNoTrans : CblasTrans;
if (mat_b_split_vertical) {
PADDLE_ENFORCE_EQ(dim_b.height_, dim_a.width_ / head_number);
PADDLE_ENFORCE_EQ(dim_b.width_ % head_number, 0);
PADDLE_ENFORCE_EQ(
dim_b.height_, dim_a.width_ / head_number,
platform::errors::InvalidArgument(
"The second input height should be equal than first input width,"
"but received second input height %d, first input width %d",
dim_b.height_, dim_a.width_ / head_number));
PADDLE_ENFORCE_EQ(
dim_a.width_ % head_number, 0,
platform::errors::InvalidArgument(
"The second input width should be some times the head number"
"but received second input width %d"
", head_number %d",
dim_b.width_, head_number));
}
if (dim_a.batch_size_ == 0 && dim_b.batch_size_ == 0) {
......@@ -888,9 +963,16 @@ void Blas<DeviceContext>::MatMulWithHead(const framework::Tensor &mat_a,
mat_out->data<T>() + sub_matC_offset, ldc);
}
} else {
PADDLE_ENFORCE_EQ((dim_a.batch_size_ == dim_b.batch_size_ ||
dim_a.batch_size_ == 0 || dim_b.batch_size_ == 0),
true);
PADDLE_ENFORCE_EQ(
(dim_a.batch_size_ == dim_b.batch_size_ || dim_a.batch_size_ == 0 ||
dim_b.batch_size_ == 0),
true,
platform::errors::InvalidArgument(
"The first input batch size should be equal than second input,"
"either two input batch size is 0, but received first input batch "
"size"
" %d, second input batch size %d",
dim_a.batch_size_, dim_b.batch_size_));
this->template BatchedGEMMWithHead<T>(
transA, transB, dim_a.width_, dim_a.height_, dim_b.width_,
......
paddle/fluid/operators/math/math_function.cc
浏览文件 @ 95710a33
......@@ -22,10 +22,12 @@ limitations under the License. */
#include <cblas.h>
#endif
#include <utility>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/operators/math/math_function_impl.h"
#include "paddle/fluid/platform/float16.h"
#include "unsupported/Eigen/CXX11/Tensor"
namespace paddle {
namespace operators {
......@@ -63,6 +65,55 @@ DEFINE_CPU_TRANS(4);
DEFINE_CPU_TRANS(5);
DEFINE_CPU_TRANS(6);
template <typename T>
struct TransposeNormal<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& context,
const framework::Tensor& in, framework::Tensor* out,
const std::vector<int>& axis) {
const int rank = axis.size();
auto in_stride = framework::stride(in.dims());
auto out_stride = framework::stride(out->dims());
const T* in_ptr = in.data<T>();
T* out_ptr = out->data<T>();
auto transpose_helper = [&](int64_t beg, int64_t end) {
for (int64_t out_idx = beg; out_idx < end; ++out_idx) {
int64_t in_idx = 0;
int64_t tmp_idx = out_idx;
// calculate the input index
for (int i = 0; i < rank; ++i) {
const int64_t coordinate = tmp_idx / out_stride[i];
tmp_idx -= coordinate * out_stride[i];
in_idx += coordinate * in_stride[axis[i]];
}
out_ptr[out_idx] = in_ptr[in_idx];
}
};
double cost_per_iteration =
rank * (Eigen::TensorOpCost::DivCost<int64_t>() +
2 * Eigen::TensorOpCost::MulCost<int64_t>() +
2 * Eigen::TensorOpCost::AddCost<int64_t>());
Eigen::TensorOpCost cost(sizeof(T), sizeof(T), cost_per_iteration);
auto* cpu_device = context.eigen_pool_device();
cpu_device->parallelFor(out->numel(), cost, std::move(transpose_helper));
}
};
// define transpose normal
#define DEFINE_CPU_TRANS_NORMAL(TYPE) \
template struct TransposeNormal<platform::CPUDeviceContext, TYPE>
DEFINE_CPU_TRANS_NORMAL(platform::float16);
DEFINE_CPU_TRANS_NORMAL(platform::bfloat16);
DEFINE_CPU_TRANS_NORMAL(float);
DEFINE_CPU_TRANS_NORMAL(double);
DEFINE_CPU_TRANS_NORMAL(int);
DEFINE_CPU_TRANS_NORMAL(int64_t);
DEFINE_CPU_TRANS_NORMAL(bool);
DEFINE_CPU_TRANS_NORMAL(int16_t);
DEFINE_CPU_TRANS_NORMAL(uint8_t);
DEFINE_CPU_TRANS_NORMAL(int8_t);
struct TensorSetConstantCPU {
TensorSetConstantCPU(framework::Tensor* tensor, float value)
: tensor_(tensor), value_(value) {}
......
paddle/fluid/operators/math/math_function.cu
浏览文件 @ 95710a33
......@@ -11,8 +11,11 @@ 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 <algorithm>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/memory/malloc.h"
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/math_function_impl.h"
......@@ -23,6 +26,7 @@ namespace operators {
namespace math {
using float16 = paddle::platform::float16;
using bfloat16 = paddle::platform::bfloat16;
template struct SetConstant<platform::CUDADeviceContext, platform::float16>;
template struct SetConstant<platform::CUDADeviceContext, float>;
......@@ -31,12 +35,13 @@ template struct SetConstant<platform::CUDADeviceContext, int>;
template struct SetConstant<platform::CUDADeviceContext, int64_t>;
template struct SetConstant<platform::CUDADeviceContext, bool>;
#define DEFINE_GPU_TRANS(RANK) \
template struct Transpose<platform::CUDADeviceContext, float, RANK>; \
template struct Transpose<platform::CUDADeviceContext, double, RANK>; \
template struct Transpose<platform::CUDADeviceContext, float16, RANK>; \
template struct Transpose<platform::CUDADeviceContext, int8_t, RANK>; \
template struct Transpose<platform::CUDADeviceContext, int32_t, RANK>; \
#define DEFINE_GPU_TRANS(RANK) \
template struct Transpose<platform::CUDADeviceContext, float, RANK>; \
template struct Transpose<platform::CUDADeviceContext, double, RANK>; \
template struct Transpose<platform::CUDADeviceContext, float16, RANK>; \
template struct Transpose<platform::CUDADeviceContext, bfloat16, RANK>; \
template struct Transpose<platform::CUDADeviceContext, int8_t, RANK>; \
template struct Transpose<platform::CUDADeviceContext, int32_t, RANK>; \
template struct Transpose<platform::CUDADeviceContext, int64_t, RANK>;
DEFINE_GPU_TRANS(1);
......@@ -46,6 +51,88 @@ DEFINE_GPU_TRANS(4);
DEFINE_GPU_TRANS(5);
DEFINE_GPU_TRANS(6);
#define REINTERPRET(T, DST_PTR, SRC_PTR) \
T* DST_PTR = reinterpret_cast<T*>(SRC_PTR)
template <typename T>
__global__ void TransposeNormalKernel(const T* in_ptr, T* out_ptr,
int64_t element,
const int64_t* in_stride_ptr,
const int64_t* out_stride_ptr,
const int64_t* axis_ptr, int rank) {
CUDA_KERNEL_LOOP(out_idx, element) {
int64_t in_idx = 0;
int64_t tmp_idx = out_idx;
for (int i = 0; i < rank; ++i) {
const int64_t coordinate = tmp_idx / out_stride_ptr[i];
tmp_idx -= coordinate * out_stride_ptr[i];
in_idx += coordinate * in_stride_ptr[axis_ptr[i]];
}
out_ptr[out_idx] = in_ptr[in_idx];
}
}
template <typename T>
struct TransposeNormal<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& context,
const framework::Tensor& in, framework::Tensor* out,
const std::vector<int>& axis) {
const int rank = axis.size();
auto in_stride = framework::stride(in.dims());
auto out_stride = framework::stride(out->dims());
auto* in_ptr = in.data<T>();
auto* out_ptr = out->data<T>();
// copy in_stride, out_stride, axis to gpu device
const platform::CUDAPlace& cuda_place =
BOOST_GET_CONST(platform::CUDAPlace, context.GetPlace());
platform::CPUPlace cpu_place = platform::CPUPlace();
size_t size = 3 * rank * sizeof(int64_t);
auto cpu_buf_holder = memory::AllocShared(cpu_place, size);
auto cuda_buf_holder = memory::AllocShared(cuda_place, size);
REINTERPRET(int64_t, cpu_buf, cpu_buf_holder->ptr());
REINTERPRET(int64_t, cuda_buf, cuda_buf_holder->ptr());
for (int i = 0; i < rank; ++i) {
cpu_buf[i] = in_stride[i];
cpu_buf[rank + i] = out_stride[i];
cpu_buf[2 * rank + i] = axis[i];
}
memory::Copy(cuda_place, cuda_buf, cpu_place, cpu_buf, size,
context.stream());
REINTERPRET(const int64_t, in_stride_ptr, cuda_buf);
REINTERPRET(const int64_t, out_stride_ptr, cuda_buf + rank);
REINTERPRET(const int64_t, axis_ptr, cuda_buf + 2 * rank);
const int MAX_BLOCK_DIM = context.GetMaxThreadsPerBlock();
const int MAX_GRID_DIM =
context.GetMaxPhysicalThreadCount() / MAX_BLOCK_DIM;
int64_t elements = in.numel();
int block_size = (elements >= MAX_BLOCK_DIM)
? MAX_BLOCK_DIM
: (1 << static_cast<int>(std::log2(elements)));
int grid_size = elements / block_size;
grid_size = (grid_size >= MAX_GRID_DIM) ? MAX_GRID_DIM : grid_size;
TransposeNormalKernel<T><<<grid_size, block_size, 0, context.stream()>>>(
in_ptr, out_ptr, elements, in_stride_ptr, out_stride_ptr, axis_ptr,
rank);
}
};
// define transpose normal
#define DEFINE_GPU_TRANS_NORMAL(TYPE) \
template struct TransposeNormal<platform::CUDADeviceContext, TYPE>
DEFINE_GPU_TRANS_NORMAL(float16);
DEFINE_GPU_TRANS_NORMAL(bfloat16);
DEFINE_GPU_TRANS_NORMAL(float);
DEFINE_GPU_TRANS_NORMAL(double);
DEFINE_GPU_TRANS_NORMAL(int);
DEFINE_GPU_TRANS_NORMAL(int64_t);
DEFINE_GPU_TRANS_NORMAL(bool);
DEFINE_GPU_TRANS_NORMAL(int16_t);
DEFINE_GPU_TRANS_NORMAL(uint8_t);
DEFINE_GPU_TRANS_NORMAL(int8_t);
struct TensorSetConstantGPU {
TensorSetConstantGPU(const platform::DeviceContext& context,
framework::Tensor* tensor, float value)
......
paddle/fluid/operators/math/math_function.h
浏览文件 @ 95710a33
......@@ -26,6 +26,14 @@ limitations under the License. */
namespace paddle {
namespace operators {
namespace math {
template <typename DeviceContext, typename T>
struct TransposeNormal {
// for dims >= 7 situation
void operator()(const DeviceContext& context, const framework::Tensor& in,
framework::Tensor* out, const std::vector<int>& axis);
};
template <typename DeviceContext, typename T, int Rank>
struct Transpose {
void operator()(const DeviceContext& context, const framework::Tensor& in,
......
paddle/fluid/operators/math/math_function_test.cc
浏览文件 @ 95710a33
......@@ -226,8 +226,8 @@ TEST(math_funciton, set_constant) {
for (int64_t i = 0; i < t.numel(); ++i) {
PADDLE_ENFORCE_EQ(10, t.data<int>()[i],
paddle::platform::errors::InvalidArgument(
"Each value of input"
"tensor should be 10, but received %d.",
"Each value of input tensor should be 10, "
"but received %d.",
t.data<int>()[i]));
}
delete ctx;
......
paddle/fluid/operators/math/sampler.h
浏览文件 @ 95710a33
......@@ -33,10 +33,10 @@ namespace math {
class Sampler {
public:
explicit Sampler(int64_t range, unsigned int seed = 0UL) : range_(range) {
PADDLE_ENFORCE_GT(range, 0, platform::errors::InvalidArgument(
"Range should be"
" greater than 0, but recevied %d.",
range));
PADDLE_ENFORCE_GT(
range, 0,
platform::errors::InvalidArgument(
"Range should be greater than 0, but recevied %d.", range));
if (seed == 0) {
std::random_device r;
seed_ = r();
......
paddle/fluid/operators/math/vol2col.cc
浏览文件 @ 95710a33
......@@ -34,16 +34,15 @@ class Vol2ColFunctor<platform::CPUDeviceContext, T> {
const std::vector<int>& strides,
const std::vector<int>& paddings, framework::Tensor* col,
const DataLayout data_layout) const {
PADDLE_ENFORCE_EQ(
vol.dims().size(), 4,
platform::errors::InvalidArgument("The dimension of"
" vol should be 4, but received %d.",
vol.dims().size()));
PADDLE_ENFORCE_EQ(
col->dims().size(), 7,
platform::errors::InvalidArgument("The dimension of"
"col should be 7, but received %d.",
col->dims().size()));
PADDLE_ENFORCE_EQ(vol.dims().size(), 4,
platform::errors::InvalidArgument(
"The dimension of vol should be 4, but received %d.",
vol.dims().size()));
PADDLE_ENFORCE_EQ(col->dims().size(), 7,
platform::errors::InvalidArgument(
"The dimension of col should be 7, but received %d.",
col->dims().size()));
int input_channels =
(data_layout != DataLayout::kNHWC ? vol.dims()[0] : vol.dims()[3]);
......@@ -152,16 +151,15 @@ class Col2VolFunctor<platform::CPUDeviceContext, T> {
const std::vector<int>& strides,
const std::vector<int>& paddings, framework::Tensor* vol,
const DataLayout data_layout) const {
PADDLE_ENFORCE_EQ(
vol->dims().size(), 4,
platform::errors::InvalidArgument("The dimension of vol"
" should be 4, but received %d.",
vol->dims().size()));
PADDLE_ENFORCE_EQ(
col.dims().size(), 7,
platform::errors::InvalidArgument("The dimension of col"
" should be 7, but received %d.",
col.dims().size()));
PADDLE_ENFORCE_EQ(vol->dims().size(), 4,
platform::errors::InvalidArgument(
"The dimension of vol should be 4, but received %d.",
vol->dims().size()));
PADDLE_ENFORCE_EQ(col.dims().size(), 7,
platform::errors::InvalidArgument(
"The dimension of col should be 7, but received %d.",
col.dims().size()));
int input_channels =
(data_layout != DataLayout::kNHWC ? vol->dims()[0] : vol->dims()[3]);
......@@ -192,29 +190,29 @@ class Col2VolFunctor<platform::CPUDeviceContext, T> {
((dilations[0] * (filter_depth - 1) + 1))) /
strides[0] +
1;
PADDLE_ENFORCE_EQ(input_depth_tmp, output_depth,
platform::errors::InvalidArgument(
"input_depth(%d)"
" and output_depth(%d) are mismatching.",
input_depth_tmp, output_depth));
PADDLE_ENFORCE_EQ(
input_depth_tmp, output_depth,
platform::errors::InvalidArgument(
"input_depth(%d) and output_depth(%d) are mismatching.",
input_depth_tmp, output_depth));
auto input_height_tmp = (input_height + pad_h_up + pad_h_down -
((dilations[1] * (filter_height - 1) + 1))) /
strides[1] +
1;
PADDLE_ENFORCE_EQ(input_height_tmp, output_height,
platform::errors::InvalidArgument(
"input_height(%d)"
" and output_height(%d) are mismatching.",
input_height_tmp, output_height));
PADDLE_ENFORCE_EQ(
input_height_tmp, output_height,
platform::errors::InvalidArgument(
"input_height(%d) and output_height(%d) are mismatching.",
input_height_tmp, output_height));
auto input_width_tmp = (input_width + pad_w_left + pad_w_right -
((dilations[2] * (filter_width - 1) + 1))) /
strides[2] +
1;
PADDLE_ENFORCE_EQ(input_width_tmp, output_width,
platform::errors::InvalidArgument(
"input_width(%d)"
" and output_width(%d) are mismatching.",
input_width_tmp, output_width));
PADDLE_ENFORCE_EQ(
input_width_tmp, output_width,
platform::errors::InvalidArgument(
"input_width(%d) and output_width(%d) are mismatching.",
input_width_tmp, output_width));
T* vol_data = vol->data<T>();
const T* col_data = col.data<T>();
......
paddle/fluid/operators/math/vol2col.cu
浏览文件 @ 95710a33
......@@ -90,16 +90,14 @@ class Vol2ColFunctor<platform::CUDADeviceContext, T> {
const std::vector<int>& strides,
const std::vector<int>& paddings, framework::Tensor* col,
const DataLayout data_layout) const {
PADDLE_ENFORCE_EQ(
vol.dims().size(), 4,
platform::errors::InvalidArgument("The dimension of"
" vol should be 4, but received %d.",
vol.dims().size()));
PADDLE_ENFORCE_EQ(
col->dims().size(), 7,
platform::errors::InvalidArgument("The dimension of"
"col should be 7, but received %d.",
col->dims().size()));
PADDLE_ENFORCE_EQ(vol.dims().size(), 4,
platform::errors::InvalidArgument(
"The dimension of vol should be 4, but received %d.",
vol.dims().size()));
PADDLE_ENFORCE_EQ(col->dims().size(), 7,
platform::errors::InvalidArgument(
"The dimension of col should be 7, but received %d.",
col->dims().size()));
int input_channels =
(data_layout != DataLayout::kNHWC ? vol.dims()[0] : vol.dims()[3]);
......@@ -253,16 +251,14 @@ class Col2VolFunctor<platform::CUDADeviceContext, T> {
const std::vector<int>& strides,
const std::vector<int>& paddings, framework::Tensor* vol,
const DataLayout data_layout) const {
PADDLE_ENFORCE_EQ(
vol->dims().size(), 4,
platform::errors::InvalidArgument("The dimension of vol"
" should be 4, but received %d.",
vol->dims().size()));
PADDLE_ENFORCE_EQ(
col.dims().size(), 7,
platform::errors::InvalidArgument("The dimension of col"
" should be 7, but received %d.",
col.dims().size()));
PADDLE_ENFORCE_EQ(vol->dims().size(), 4,
platform::errors::InvalidArgument(
"The dimension of vol should be 4, but received %d.",
vol->dims().size()));
PADDLE_ENFORCE_EQ(col.dims().size(), 7,
platform::errors::InvalidArgument(
"The dimension of col should be 7, but received %d.",
col.dims().size()));
int input_channels =
(data_layout != DataLayout::kNHWC ? vol->dims()[0] : vol->dims()[3]);
......@@ -291,29 +287,29 @@ class Col2VolFunctor<platform::CUDADeviceContext, T> {
((dilations[0] * (filter_depth - 1) + 1))) /
strides[0] +
1;
PADDLE_ENFORCE_EQ(input_depth_tmp, output_depth,
platform::errors::InvalidArgument(
"input_depth(%d)"
" and output_depth(%d) are mismatching.",
input_depth_tmp, output_depth));
PADDLE_ENFORCE_EQ(
input_depth_tmp, output_depth,
platform::errors::InvalidArgument(
"input_depth(%d) and output_depth(%d) are mismatching.",
input_depth_tmp, output_depth));
auto input_height_tmp = (input_height + pad_h_up + pad_h_down -
((dilations[1] * (filter_height - 1) + 1))) /
strides[1] +
1;
PADDLE_ENFORCE_EQ(input_height_tmp, output_height,
platform::errors::InvalidArgument(
"input_height(%d)"
" and output_height(%d) are mismatching.",
input_height_tmp, output_height));
PADDLE_ENFORCE_EQ(
input_height_tmp, output_height,
platform::errors::InvalidArgument(
"input_height(%d) and output_height(%d) are mismatching.",
input_height_tmp, output_height));
auto input_width_tmp = (input_width + pad_w_left + pad_w_right -
((dilations[2] * (filter_width - 1) + 1))) /
strides[2] +
1;
PADDLE_ENFORCE_EQ(input_width_tmp, output_width,
platform::errors::InvalidArgument(
"input_width(%d)"
" and output_width(%d) are mismatching.",
input_width_tmp, output_width));
PADDLE_ENFORCE_EQ(
input_width_tmp, output_width,
platform::errors::InvalidArgument(
"input_width(%d) and output_width(%d) are mismatching.",
input_width_tmp, output_width));
int num_kernels = input_channels * input_depth * input_height * input_width;
......
paddle/fluid/operators/mkldnn/concat_mkldnn_op.cc
浏览文件 @ 95710a33
......@@ -86,8 +86,10 @@ class ConcatPrimitiveFactory {
concat CreateConcatPrimitive(const concat::primitive_desc& concat_pd,
Tensor* output, platform::CPUPlace place,
const mkldnn::engine& mkldnn_engine) {
dst_mem = mkldnn::memory(concat_pd.dst_desc(), mkldnn_engine,
output->mutable_data<T>(place));
dst_mem = mkldnn::memory(
concat_pd.dst_desc(), mkldnn_engine,
output->mutable_data<T>(place, concat_pd.dst_desc().get_size()));
return concat(concat_pd);
}
......@@ -193,7 +195,9 @@ class ConcatMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
prim_creator.SetSrcDataHandleByIndex(
*srcs, i, to_void_cast<T>(multi_input[i]->data<T>()));
}
prim_creator.SetDstDataHandle(*dst_mem, output->mutable_data<T>(place));
prim_creator.SetDstDataHandle(
*dst_mem,
output->mutable_data<T>(place, concat_pd->dst_desc().get_size()));
}
mkldnn::stream astream(mkldnn_engine);
......
paddle/fluid/operators/reduce_ops/reduce_op.h
浏览文件 @ 95710a33
......@@ -18,9 +18,10 @@ limitations under the License. */
#include <set>
#include <string>
#include <vector>
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/operators/cast_op.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/reduce_ops/reduce_op_function.h"
namespace paddle {
......@@ -34,6 +35,110 @@ namespace operators {
}
using Tensor = framework::Tensor;
using DDim = framework::DDim;
inline void GetShuffledDim(const DDim& src_dims, DDim* dst_dims,
const std::vector<int>& reduced_dims,
std::vector<int>* perm_axis) {
// check if it's a reduced dim
std::vector<bool> src_dims_check(src_dims.size(), false);
size_t src_size = src_dims.size();
size_t reduce_size = reduced_dims.size();
for (size_t i = 0; i < reduce_size; ++i) {
dst_dims->at(src_size - reduce_size + i) = src_dims[reduced_dims[i]];
(*perm_axis)[src_size - reduce_size + i] = reduced_dims[i];
src_dims_check[reduced_dims[i]] = true;
}
size_t offset = 0;
for (size_t i = 0; i < src_dims_check.size(); ++i) {
bool is_reduced = src_dims_check[i];
if (!is_reduced) {
(*perm_axis)[offset] = i;
dst_dims->at(offset++) = src_dims[i];
}
}
}
template <typename DeviceContext, typename OutT>
void GetShuffledInput(const framework::ExecutionContext& context,
const Tensor* input, Tensor* shuffled_input,
const std::vector<int>& dims) {
DDim shuffled_dims(input->dims());
std::vector<int> perm_axis(input->dims().size());
GetShuffledDim(input->dims(), &shuffled_dims, dims, &perm_axis);
shuffled_input->Resize(shuffled_dims);
shuffled_input->mutable_data<OutT>(context.GetPlace());
math::TransposeNormal<DeviceContext, OutT> trans;
trans(context.template device_context<DeviceContext>(), *input,
shuffled_input, perm_axis);
}
inline void GetOriginDimFromShuffled(const DDim& src_dim,
const std::vector<int>& dims,
std::vector<int>* origin_dim) {
DDim shuffled_dims(src_dim);
size_t n = src_dim.size();
std::vector<int> perm_axis(n);
GetShuffledDim(src_dim, &shuffled_dims, dims, &perm_axis);
for (size_t i = 0; i < n; ++i) {
(*origin_dim)[perm_axis[i]] = i;
}
}
template <typename DeviceContext, typename OutT, typename Functor>
void HandleLargeDim(const framework::ExecutionContext& context,
const Tensor* input, Tensor* output,
const std::vector<int>& dims, bool keep_dim) {
// shuffle the reduced dim to the end
Tensor shuffled_input;
GetShuffledInput<DeviceContext, OutT>(context, input, &shuffled_input, dims);
// transpose to 2D tensor whose shape is {unreduced, reduced}.
const int64_t unreduced = output->numel();
const int64_t reduced = shuffled_input.numel() / unreduced;
shuffled_input.Resize({unreduced, reduced});
DDim output_dim = output->dims();
output->Resize({unreduced});
ReduceFunctor<DeviceContext, OutT, 2, 1, Functor>(
context.template device_context<DeviceContext>(), shuffled_input, output,
{1}, keep_dim);
output->Resize(output_dim);
}
template <typename DeviceContext, typename T, typename Functor>
void HandleLargeDimGrad(const framework::ExecutionContext& context,
const framework::Tensor* x,
const framework::Tensor* out,
const framework::Tensor* dout, framework::Tensor* dx,
const std::vector<int>& dims) {
const int64_t unreduced = out->numel();
const int64_t reduced = x->numel() / unreduced;
DDim out_dim(out->dims());
DDim x_dim(x->dims());
// transpose and reshape X
Tensor shuffled_x;
GetShuffledInput<DeviceContext, T>(context, x, &shuffled_x, dims);
DDim shuffled_dim = shuffled_x.dims();
shuffled_x.Resize({unreduced, reduced});
// reshape dX {unreduced, reduced}
dx->Resize({unreduced, reduced});
ReduceGradFunctor<DeviceContext, T, 2, Functor>(
context.template device_context<DeviceContext>(), shuffled_x, *out, *dout,
dx, {1});
// transpose dX
std::vector<int> origin_axis(x_dim.size());
GetOriginDimFromShuffled(x_dim, dims, &origin_axis);
Tensor dx_tmp;
framework::TensorCopy(*dx, context.GetPlace(), &dx_tmp);
dx_tmp.Resize(shuffled_dim);
dx->Resize(x_dim);
math::TransposeNormal<DeviceContext, T> trans;
trans(context.template device_context<DeviceContext>(), dx_tmp, dx,
origin_axis);
}
template <typename DeviceContext, typename T, typename Functor>
struct ReduceKernelFunctor {
......@@ -69,22 +174,27 @@ struct ReduceKernelFunctor {
} else {
int ndim = input->dims().size();
int rdim = dims.size();
HANDLE_DIM(6, 5);
HANDLE_DIM(6, 4);
HANDLE_DIM(6, 3);
HANDLE_DIM(6, 2);
HANDLE_DIM(6, 1);
HANDLE_DIM(5, 4);
HANDLE_DIM(5, 3);
HANDLE_DIM(5, 2);
HANDLE_DIM(5, 1);
HANDLE_DIM(4, 3);
HANDLE_DIM(4, 2);
HANDLE_DIM(4, 1);
HANDLE_DIM(3, 2);
HANDLE_DIM(3, 1);
HANDLE_DIM(2, 1);
HANDLE_DIM(1, 1);
if (ndim > 6) {
HandleLargeDim<DeviceContext, OutT, Functor>(context, input, output,
dims, keep_dim);
} else {
HANDLE_DIM(6, 5);
HANDLE_DIM(6, 4);
HANDLE_DIM(6, 3);
HANDLE_DIM(6, 2);
HANDLE_DIM(6, 1);
HANDLE_DIM(5, 4);
HANDLE_DIM(5, 3);
HANDLE_DIM(5, 2);
HANDLE_DIM(5, 1);
HANDLE_DIM(4, 3);
HANDLE_DIM(4, 2);
HANDLE_DIM(4, 1);
HANDLE_DIM(3, 2);
HANDLE_DIM(3, 1);
HANDLE_DIM(2, 1);
HANDLE_DIM(1, 1);
}
}
}
};
......@@ -137,7 +247,6 @@ class ReduceKernel : public framework::OpKernel<T> {
}
}
};
template <typename DeviceContext, typename OutT, typename Functor>
class BoolReduceKernel : public framework::OpKernel<OutT> {
public:
......@@ -175,22 +284,27 @@ class BoolReduceKernel : public framework::OpKernel<OutT> {
int ndim = input->dims().size();
int rdim = dims.size();
// comments for accelerating compiling temporarily.
// HANDLE_DIM(6, 5);
// HANDLE_DIM(6, 4);
// HANDLE_DIM(6, 3);
// HANDLE_DIM(6, 2);
// HANDLE_DIM(6, 1);
// HANDLE_DIM(5, 4);
// HANDLE_DIM(5, 3);
// HANDLE_DIM(5, 2);
// HANDLE_DIM(5, 1);
HANDLE_DIM(4, 3);
HANDLE_DIM(4, 2);
HANDLE_DIM(4, 1);
HANDLE_DIM(3, 2);
HANDLE_DIM(3, 1);
HANDLE_DIM(2, 1);
HANDLE_DIM(1, 1);
if (ndim > 6) {
HandleLargeDim<DeviceContext, OutT, Functor>(context, input, output,
dims, keep_dim);
} else {
HANDLE_DIM(6, 5);
HANDLE_DIM(6, 4);
HANDLE_DIM(6, 3);
HANDLE_DIM(6, 2);
HANDLE_DIM(6, 1);
HANDLE_DIM(5, 4);
HANDLE_DIM(5, 3);
HANDLE_DIM(5, 2);
HANDLE_DIM(5, 1);
HANDLE_DIM(4, 3);
HANDLE_DIM(4, 2);
HANDLE_DIM(4, 1);
HANDLE_DIM(3, 2);
HANDLE_DIM(3, 1);
HANDLE_DIM(2, 1);
HANDLE_DIM(1, 1);
}
}
}
};
......@@ -279,6 +393,10 @@ class ReduceGradKernel : public framework::OpKernel<T> {
context.template device_context<DeviceContext>(), *input0,
*input1, *input2, output, dims);
break;
default:
HandleLargeDimGrad<DeviceContext, T, Functor>(context, input0, input1,
input2, output, dims);
break;
}
}
}
......@@ -313,12 +431,6 @@ class ReduceOp : public framework::OperatorWithKernel {
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "ReduceOp");
auto x_dims = ctx->GetInputDim("X");
auto x_rank = x_dims.size();
PADDLE_ENFORCE_LE(x_rank, 6,
platform::errors::InvalidArgument(
"The input tensor X's dimensions of ReduceOp "
"should be less equal than 6. But received X's "
"dimensions = %d, X's shape = [%s].",
x_rank, x_dims));
auto dims = ctx->Attrs().Get<std::vector<int>>("dim");
PADDLE_ENFORCE_GT(dims.size(), 0,
platform::errors::InvalidArgument(
......@@ -402,11 +514,6 @@ class ReduceGradOp : public framework::OperatorWithKernel {
"Out@GRAD", "ReduceOp");
auto x_dims = ctx->GetInputDim("X");
auto x_rank = x_dims.size();
PADDLE_ENFORCE_LE(x_rank, 6,
platform::errors::InvalidArgument(
"Tensors with rank at most 6 are supported by "
"ReduceOp. Received tensor with rank %d.",
x_rank));
auto dims = ctx->Attrs().Get<std::vector<int>>("dim");
for (size_t i = 0; i < dims.size(); ++i) {
PADDLE_ENFORCE_LT(dims[i], x_rank,
......
paddle/fluid/operators/shape_op.cc
浏览文件 @ 95710a33
......@@ -68,6 +68,6 @@ REGISTER_OPERATOR(
shape, ops::ShapeOp, ops::ShapeOpMaker,
paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
REGISTER_OP_CPU_KERNEL(shape, ops::ShapeKernel<int>, ops::ShapeKernel<int32_t>,
REGISTER_OP_CPU_KERNEL(shape, ops::ShapeKernel<bool>, ops::ShapeKernel<int>,
ops::ShapeKernel<int64_t>, ops::ShapeKernel<float>,
ops::ShapeKernel<double>);
paddle/fluid/operators/shape_op.cu
浏览文件 @ 95710a33
......@@ -15,8 +15,8 @@ limitations under the License. */
#include "paddle/fluid/operators/shape_op.h"
REGISTER_OP_CUDA_KERNEL(
shape, paddle::operators::ShapeKernel<int>,
paddle::operators::ShapeKernel<int32_t>,
shape, paddle::operators::ShapeKernel<bool>,
paddle::operators::ShapeKernel<int>,
paddle::operators::ShapeKernel<int64_t>,
paddle::operators::ShapeKernel<float>,
paddle::operators::ShapeKernel<double>,
......
paddle/fluid/operators/transpose_op.h
浏览文件 @ 95710a33
......@@ -53,10 +53,9 @@ inline void TransCompute(const int dim, const DeviceContext& dev_ctx,
trans6(dev_ctx, in, out, axis);
break;
default:
PADDLE_THROW(platform::errors::InvalidArgument(
"Tensors with rank at most 6 are supported"
", but received input tensor's rank is %d,",
dim));
// for dim >= 7 situation
math::TransposeNormal<DeviceContext, T> trans_normal;
trans_normal(dev_ctx, in, out, axis);
}
}
......
paddle/fluid/platform/device_context.cc
浏览文件 @ 95710a33
......@@ -12,6 +12,7 @@ limitations under the License. */
#include "paddle/fluid/platform/device_context.h"
#include <set>
#include <string>
#include <thread> //NOLINT
#include <unordered_set>
#include <vector>
......@@ -23,6 +24,7 @@ limitations under the License. */
#endif
#include "glog/logging.h"
#include "unsupported/Eigen/CXX11/ThreadPool"
namespace paddle {
namespace memory {
......@@ -131,16 +133,31 @@ DeviceContextPool::DeviceContextPool(
CPUDeviceContext::CPUDeviceContext() {
eigen_device_.reset(new Eigen::DefaultDevice());
InitPoolDevice();
}
CPUDeviceContext::CPUDeviceContext(CPUPlace place) : place_(place) {
eigen_device_.reset(new Eigen::DefaultDevice());
InitPoolDevice();
}
void CPUDeviceContext::InitPoolDevice() {
using EigenEnv = Eigen::StlThreadEnvironment;
using EigenThreadPool = Eigen::ThreadPoolTempl<EigenEnv>;
int num_threads = std::thread::hardware_concurrency();
eigen_threadpool_.reset(new EigenThreadPool(num_threads));
eigen_pool_device_.reset(
new Eigen::ThreadPoolDevice(eigen_threadpool_.get(), num_threads));
}
Eigen::DefaultDevice* CPUDeviceContext::eigen_device() const {
return eigen_device_.get();
}
Eigen::ThreadPoolDevice* CPUDeviceContext::eigen_pool_device() const {
return eigen_pool_device_.get();
}
Place CPUDeviceContext::GetPlace() const { return place_; }
#ifdef PADDLE_WITH_XPU
......
paddle/fluid/platform/device_context.h
浏览文件 @ 95710a33
......@@ -41,6 +41,7 @@ limitations under the License. */
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/stream/cuda_stream.h"
#endif
#define EIGEN_USE_THREADS
#include "unsupported/Eigen/CXX11/Tensor"
#ifdef PADDLE_WITH_XPU
......@@ -65,11 +66,17 @@ class CPUDeviceContext : public DeviceContext {
Eigen::DefaultDevice* eigen_device() const;
Eigen::ThreadPoolDevice* eigen_pool_device() const;
Place GetPlace() const override;
inline void InitPoolDevice();
private:
CPUPlace place_;
std::unique_ptr<Eigen::DefaultDevice> eigen_device_;
std::unique_ptr<Eigen::ThreadPoolDevice> eigen_pool_device_;
std::unique_ptr<Eigen::ThreadPool> eigen_threadpool_;
};
template <typename Place>
......
paddle/scripts/paddle_build.sh
浏览文件 @ 95710a33
......@@ -621,6 +621,7 @@ function generate_upstream_develop_api_spec() {
git checkout -b develop_base_pr upstream/$BRANCH
cmake_gen $1
build $2
cp ${PADDLE_ROOT}/python/requirements.txt /tmp
git checkout $cur_branch
generate_api_spec "$1" "DEV"
......@@ -641,7 +642,12 @@ function generate_api_spec() {
cd ${PADDLE_ROOT}/build/.check_api_workspace
virtualenv .${spec_kind}_env
source .${spec_kind}_env/bin/activate
pip install -r ${PADDLE_ROOT}/python/requirements.txt
if [ "$spec_kind" == "DEV" ]; then
pip install -r /tmp/requirements.txt
else
pip install -r ${PADDLE_ROOT}/python/requirements.txt
fi
pip --no-cache-dir install ${PADDLE_ROOT}/build/python/dist/*whl
spec_path=${PADDLE_ROOT}/paddle/fluid/API_${spec_kind}.spec
python ${PADDLE_ROOT}/tools/print_signatures.py paddle > $spec_path
......@@ -930,6 +936,10 @@ function parallel_test_base_gpu() {
EOF
set +x
precison_cases=""
if [ ${PRECISION_TEST:-OFF} == "ON" ]; then
precision_cases=`python $PADDLE_ROOT/tools/get_pr_ut.py`
fi
EXIT_CODE=0;
test_cases=$(ctest -N -V) # get all test cases
exclusive_tests='' # cases list which would be run exclusively
......@@ -959,10 +969,23 @@ set +x
echo $testcase" will only run at night."
continue
fi
if [ ${PRECISION_TEST:-OFF} == "ON" ] && [[ "$precision_cases" != "" ]]; then
will_test="false"
for case in $precision_cases; do
if [[ $testcase == $case ]]; then
will_test="true"
break
fi
done
if [[ $will_test == "false" ]]; then
echo $testcase" won't run in PRECISION_TEST mode."
continue
fi
fi
if [[ "$is_multicard" == "" ]]; then
# trick: treat all test case with prefix "test_dist" as dist case, and would run on 2 GPUs
read is_multicard <<< $(echo "$testcase"|grep -oEi "test_dist")
read is_multicard <<< $(echo "$testcase"|grep -oEi "test_dist_")
fi
if [[ "$is_exclusive" != "" ]]; then
......@@ -1077,8 +1100,6 @@ set +x
done
fi
if [[ "$EXIT_CODE" != "0" ]]; then
if [[ "$failed_test_lists" == "" ]]; then
echo "========================================"
......
python/paddle/__init__.py
浏览文件 @ 95710a33
......@@ -77,6 +77,7 @@ from .tensor.creation import triu #DEFINE_ALIAS
from .tensor.creation import tril #DEFINE_ALIAS
from .tensor.creation import meshgrid #DEFINE_ALIAS
from .tensor.creation import empty #DEFINE_ALIAS
from .tensor.creation import empty_like #DEFINE_ALIAS
from .tensor.linalg import matmul #DEFINE_ALIAS
from .tensor.linalg import dot #DEFINE_ALIAS
# from .tensor.linalg import einsum #DEFINE_ALIAS
......
python/paddle/distributed/fleet/base/distributed_strategy.py
浏览文件 @ 95710a33
......@@ -728,6 +728,63 @@ class DistributedStrategy(object):
"localsgd_configs")
assign_configs_value(self.strategy.localsgd_configs, configs)
@property
def adaptive_localsgd(self):
"""
Indicating whether we are using Adaptive Local SGD training. Default Value: False
For more details, please refer to `Adaptive Communication Strategies to Achieve
the Best Error-Runtime Trade-off in Local-Update SGD <https://arxiv.org/pdf/1810.08313.pdf>`_.
Examples:
.. code-block:: python
import paddle.distributed.fleet as fleet
strategy = fleet.DistributedStrategy()
strategy.adaptive_localsgd = True # by default this is false
"""
return self.strategy.localsgd
@adaptive_localsgd.setter
@is_strict_auto
def adaptive_localsgd(self, flag):
if isinstance(flag, bool):
self.strategy.localsgd = flag
else:
print("WARNING: adaptive_localsgd should have value of bool type")
@property
def adaptive_localsgd_configs(self):
"""
Set AdaptiveLocalSGD training configurations. AdaptiveLocalSGD has a configurable
setting that can be configured through a dict.
**Notes**:
init_k_steps(int) The initial steps for training before adaptive localsgd.
Then, the adaptive localsgd method will modify init_k_steps automatically.
Default 1.
begin_step(int) The step of begining training by adaptive localsgd. Default 1.
Examples:
.. code-block:: python
import paddle.distributed.fleet as fleet
strategy = fleet.DistributedStrategy()
strategy.adaptive_localsgd = True
strategy.adaptive_localsgd_configs = {"init_k_steps": 1,
"begin_step": 30}
"""
return get_msg_dict(self.strategy.adaptive_localsgd_configs)
@adaptive_localsgd_configs.setter
@is_strict_auto
def adaptive_localsgd_configs(self, configs):
check_configs_key(self.strategy.adaptive_localsgd_configs, configs,
"adaptive_localsgd_configs")
assign_configs_value(self.strategy.adaptive_localsgd_configs, configs)
@property
def dgc(self):
"""
......
python/paddle/distributed/fleet/base/fleet_base.py
浏览文件 @ 95710a33
......@@ -611,25 +611,31 @@ class Fleet(object):
@dygraph_only
def distributed_model(self, model):
"""
Return dygraph distributed data parallel model (Layer)
Only work in dygraph mode
Return distributed data parallel model (Only work in dygraph mode)
Args:
model (Layer): the user-defind model which inherits Layer.
Returns:
distributed data parallel model which inherits Layer.
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
from paddle.distributed import fleet
class LinearNet(nn.Layer):
def __init__(self):
super(LinearNet, self).__init__()
self._linear1 = nn.Linear(10, 10)
self._linear2 = nn.Linear(10, 1)
import paddle
import paddle.nn as nn
from paddle.distributed import fleet
class LinearNet(nn.Layer):
def __init__(self):
super(LinearNet, self).__init__()
self._linear1 = nn.Linear(10, 10)
self._linear2 = nn.Linear(10, 1)
def forward(self, x):
return self._linear2(self._linear1(x))
def forward(self, x):
return self._linear2(self._linear1(x))
def train():
# 1. enable dynamic mode
paddle.disable_static()
......@@ -661,8 +667,7 @@ class Fleet(object):
adam.step()
adam.clear_grad()
if __name__ == '__main__':
paddle.distributed.spawn(train)
"""
assert model is not None
self.model = paddle.DataParallel(model)
......@@ -672,29 +677,30 @@ class Fleet(object):
def state_dict(self):
"""
Get state dict information from optimizer.
Only work in dygraph mode
(Only work in dygraph mode)
Returns:
state_dict(dict) : dict contains all the Tensor used by optimizer
Examples:
.. code-block:: python
import numpy as np
import paddle
from paddle.distributed import fleet
paddle.disable_static()
fleet.init(is_collective=True)
import numpy as np
import paddle
from paddle.distributed import fleet
paddle.disable_static()
fleet.init(is_collective=True)
value = np.arange(26).reshape(2, 13).astype("float32")
a = paddle.fluid.dygraph.to_variable(value)
value = np.arange(26).reshape(2, 13).astype("float32")
a = paddle.fluid.dygraph.to_variable(value)
layer = paddle.nn.Linear(13, 5)
adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
layer = paddle.nn.Linear(13, 5)
adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
state_dict = adam.state_dict()
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
state_dict = adam.state_dict()
"""
# imitate target optimizer retrieval
return self.user_defined_optimizer.state_dict()
......@@ -703,34 +709,36 @@ class Fleet(object):
def set_state_dict(self, state_dict):
"""
Load optimizer state dict.
Only work in dygraph mode
(Only work in dygraph mode)
Args:
state_dict(dict) : Dict contains all the Tensor needed by optimizer
Returns: None
Returns:
None
Examples:
.. code-block:: python
import numpy as np
import paddle
from paddle.distributed import fleet
paddle.disable_static()
fleet.init(is_collective=True)
import numpy as np
import paddle
from paddle.distributed import fleet
paddle.disable_static()
fleet.init(is_collective=True)
value = np.arange(26).reshape(2, 13).astype("float32")
a = paddle.fluid.dygraph.to_variable(value)
value = np.arange(26).reshape(2, 13).astype("float32")
a = paddle.fluid.dygraph.to_variable(value)
layer = paddle.nn.Linear(13, 5)
adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
layer = paddle.nn.Linear(13, 5)
adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
state_dict = adam.state_dict()
paddle.framework.save(state_dict, "paddle_dy")
para_state_dict, opti_state_dict = paddle.framework.load( "paddle_dy")
adam.set_state_dict(opti_state_dict)
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
state_dict = adam.state_dict()
paddle.framework.save(state_dict, "paddle_dy")
para_state_dict, opti_state_dict = paddle.framework.load( "paddle_dy")
adam.set_state_dict(opti_state_dict)
"""
# imitate target optimizer retrieval
return self.user_defined_optimizer.set_state_dict(state_dict)
......@@ -739,42 +747,44 @@ class Fleet(object):
def set_lr(self, value):
"""
Set the value of the learning rate manually in the optimizer.
Only work in dygraph mode
(Only work in dygraph mode)
Args:
value (float|Tensor): the value of learning rate
Returns: None
Returns:
None
Examples:
.. code-block:: python
import numpy as np
import paddle
from paddle.distributed import fleet
paddle.disable_static()
fleet.init(is_collective=True)
import numpy as np
import paddle
from paddle.distributed import fleet
value = np.arange(26).reshape(2, 13).astype("float32")
a = paddle.fluid.dygraph.to_variable(value)
paddle.disable_static()
fleet.init(is_collective=True)
layer = paddle.nn.Linear(13, 5)
adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
value = np.arange(26).reshape(2, 13).astype("float32")
a = paddle.fluid.dygraph.to_variable(value)
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
layer = paddle.nn.Linear(13, 5)
adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
for i in range(5):
adam.set_lr(lr_list[i])
lr = adam.get_lr()
print("current lr is {}".format(lr))
# Print:
# current lr is 0.2
# current lr is 0.3
# current lr is 0.4
# current lr is 0.5
# current lr is 0.6
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
lr_list = [0.2, 0.3, 0.4, 0.5, 0.6]
for i in range(5):
adam.set_lr(lr_list[i])
lr = adam.get_lr()
print("current lr is {}".format(lr))
# Print:
# current lr is 0.2
# current lr is 0.3
# current lr is 0.4
# current lr is 0.5
# current lr is 0.6
"""
# imitate target optimizer retrieval
return self.user_defined_optimizer.set_lr(value)
......@@ -783,31 +793,32 @@ class Fleet(object):
def get_lr(self):
"""
Get current step learning rate.
Only work in dygraph mode
(Only work in dygraph mode)
Returns:
float: The learning rate of the current step.
Examples:
.. code-block:: python
import numpy as np
import paddle
from paddle.distributed import fleet
paddle.disable_static()
fleet.init(is_collective=True)
import numpy as np
import paddle
from paddle.distributed import fleet
paddle.disable_static()
fleet.init(is_collective=True)
value = np.arange(26).reshape(2, 13).astype("float32")
a = paddle.fluid.dygraph.to_variable(value)
value = np.arange(26).reshape(2, 13).astype("float32")
a = paddle.fluid.dygraph.to_variable(value)
layer = paddle.nn.Linear(13, 5)
adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
layer = paddle.nn.Linear(13, 5)
adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=layer.parameters())
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
adam = fleet.distributed_optimizer(adam)
dp_layer = fleet.distributed_model(layer)
lr = adam.get_lr()
print(lr) # 0.01
lr = adam.get_lr()
print(lr) # 0.01
"""
# imitate target optimizer retrieval
return self.user_defined_optimizer.get_lr()
......@@ -816,27 +827,27 @@ class Fleet(object):
def step(self):
"""
Execute the optimizer once.
Only work in dygraph mode
(Only work in dygraph mode)
Returns: None
Returns:
None
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
from paddle.distributed import fleet
import paddle
import paddle.nn as nn
from paddle.distributed import fleet
class LinearNet(nn.Layer):
def __init__(self):
super(LinearNet, self).__init__()
self._linear1 = nn.Linear(10, 10)
self._linear2 = nn.Linear(10, 1)
class LinearNet(nn.Layer):
def __init__(self):
super(LinearNet, self).__init__()
self._linear1 = nn.Linear(10, 10)
self._linear2 = nn.Linear(10, 1)
def forward(self, x):
return self._linear2(self._linear1(x))
def forward(self, x):
return self._linear2(self._linear1(x))
def train():
# 1. enable dynamic mode
paddle.disable_static()
......@@ -868,8 +879,6 @@ class Fleet(object):
adam.step()
adam.clear_grad()
if __name__ == '__main__':
paddle.distributed.spawn(train)
"""
# imitate target optimizer retrieval
......@@ -878,28 +887,28 @@ class Fleet(object):
@dygraph_only
def clear_grad(self):
"""
Execute the optimizer once.
Only work in dygraph mode
Clear the gradients of all optimized parameters for model.
(Only work in dygraph mode)
Returns: None
Returns:
None
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
from paddle.distributed import fleet
import paddle
import paddle.nn as nn
from paddle.distributed import fleet
class LinearNet(nn.Layer):
def __init__(self):
super(LinearNet, self).__init__()
self._linear1 = nn.Linear(10, 10)
self._linear2 = nn.Linear(10, 1)
class LinearNet(nn.Layer):
def __init__(self):
super(LinearNet, self).__init__()
self._linear1 = nn.Linear(10, 10)
self._linear2 = nn.Linear(10, 1)
def forward(self, x):
return self._linear2(self._linear1(x))
def forward(self, x):
return self._linear2(self._linear1(x))
def train():
# 1. enable dynamic mode
paddle.disable_static()
......@@ -931,8 +940,6 @@ class Fleet(object):
adam.step()
adam.clear_grad()
if __name__ == '__main__':
paddle.distributed.spawn(train)
"""
# imitate target optimizer retrieval
return self.user_defined_optimizer.clear_grad()
......
python/paddle/distributed/fleet/base/role_maker.py
浏览文件 @ 95710a33
......@@ -637,7 +637,7 @@ class PaddleCloudRoleMaker(RoleMakerBase):
return "lo"
def __start_kv_server(self, http_server_d, size_d):
from paddle.distributed.fleet.utils import KVServer
from paddle.distributed.fleet.utils.http_server import KVServer
http_server = KVServer(int(self._http_ip_port[1]), size_d)
http_server.start()
wait_seconds = 5
......@@ -651,6 +651,7 @@ class UserDefinedRoleMaker(PaddleCloudRoleMaker):
def __init__(self, is_collective=False, init_gloo=False, **kwargs):
super(UserDefinedRoleMaker, self).__init__(
is_collective=is_collective, init_gloo=init_gloo, **kwargs)
self._init_gloo = init_gloo
def _user_defined_ps_env(self):
self._server_endpoints = self._kwargs.get("server_endpoints")
......
python/paddle/distributed/fleet/base/util_factory.py
浏览文件 @ 95710a33
......@@ -16,20 +16,18 @@
"""basic collective operations in python"""
"""remote file system"""
__all__ = ['UtilBase']
import numpy as np
import os
import subprocess
from paddle.fluid import core
from collections import OrderedDict
import paddle.fluid as fluid
from google.protobuf import text_format
from paddle.fluid import debugger
from paddle.fluid.framework import Program
from paddle.fluid.proto import framework_pb2
from ..utils.fs import FS, LocalFS, HDFSClient
from paddle.fluid.proto import framework_pb2
from paddle.fluid.framework import Program
from paddle.fluid import debugger
from google.protobuf import text_format
import paddle.fluid as fluid
from collections import OrderedDict
from paddle.fluid import core
import subprocess
import os
import numpy as np
__all__ = ['UtilBase']
class UtilFactory(object):
......@@ -53,7 +51,7 @@ class UtilBase(object):
def _set_role_maker(self, role_maker):
self.role_maker = role_maker
def set_file_system(self, fs_client):
def _set_file_system(self, fs_client):
assert isinstance(
fs_client, FS
), "fs_client must be the instance of paddle.distributed.fleet.utils.FS"
......@@ -87,36 +85,183 @@ class UtilBase(object):
return _comm_world
def all_reduce(self, input, mode, comm_world="worker"):
"""
All reduce `input` between specified collection. This is a distributed API.
Args:
input (list|numpy.array): The input variable to do all_reduce between specified collection.
mode (str): "sum" or "min" or "max".
comm_world (str, optional): Collection used to execute all_reduce operation. Supported collections incude `worker` , `server` and `all` . The default is `worker` .
Returns:
output(Numpy.array|None): A numpy array with the same shape as the `input` .
Examples:
.. code-block:: python
# Save the following code in `train.py` , and then execute the command `fleetrun --server_num 2 --worker_num 2 train.py` .
from paddle.distributed.fleet.base.util_factory import fleet_util
import paddle.distributed.fleet as fleet
from paddle.distributed.fleet import PaddleCloudRoleMaker
import sys
import numpy as np
def train():
role = PaddleCloudRoleMaker(
is_collective=False,
init_gloo=True,
path="./tmp_gloo")
fleet.init(role)
fleet_util._set_role_maker(role)
if fleet.is_server():
input = [1, 2]
output = fleet_util.all_reduce(input, "sum", "server")
print(output)
# [2, 4]
elif fleet.is_worker():
input = np.array([3, 4])
output = fleet_util.all_reduce(input, "sum", "worker")
print(output)
# [6, 8]
output = fleet_util.all_reduce(input, "sum", "all")
print(output)
# [8, 12]
if __name__ == "__main__":
train()
"""
_comm_world = self.__check_comm_world(comm_world)
return self.role_maker._all_reduce(_comm_world, input, mode)
def barrier(self, comm_world="worker"):
"""
Barrier between specified collection.
Args:
comm_world (str, optional): Collection used to execute barrier operation. Supported collections incude `worker` , `server` and `all` . The default is `worker` .
Examples:
.. code-block:: python
# Save the following code in `train.py` , and then execute the command `fleetrun --server_num 2 --worker_num 2 train.py` .
from paddle.distributed.fleet.base.util_factory import fleet_util
import paddle.distributed.fleet as fleet
from paddle.distributed.fleet import PaddleCloudRoleMaker
import sys
def train():
role = PaddleCloudRoleMaker(
is_collective=False,
init_gloo=True,
path="./tmp_gloo")
fleet.init(role)
fleet_util._set_role_maker(role)
if fleet.is_server():
fleet_util.barrier("server")
print("all server arrive here")
elif fleet.is_worker():
fleet_util.barrier("worker")
print("all server arrive here")
fleet_util.barrier("all")
print("all servers and workers arrive here")
if __name__ == "__main__":
train()
"""
_comm_world = self.__check_comm_world(comm_world)
self.role_maker._barrier(_comm_world)
def all_gather(self, input, comm_world="worker"):
"""
All gather `input` between specified collection.
Args:
input (Int|Float): The input variable to do all_gather between specified collection.
comm_world (str, optional): Collection used to execute all_reduce operation. Supported collections incude `worker` , `server` and `all` . The default is `worker` .
Returns:
output (List): A list of gathered values.
Examples:
.. code-block:: python
# Save the following code in `train.py` , and then execute the command `fleetrun --server_num 2 --worker_num 2 train.py` .
from paddle.distributed.fleet.base.util_factory import fleet_util
import paddle.distributed.fleet as fleet
from paddle.distributed.fleet import PaddleCloudRoleMaker
import sys
def train():
role = PaddleCloudRoleMaker(
is_collective=False,
init_gloo=True,
path="./tmp_gloo")
fleet.init(role)
fleet_util._set_role_maker(role)
if fleet.is_server():
input = fleet.server_index()
output = fleet_util.all_gather(input, "server")
print(output)
# output = [0, 1]
elif fleet.is_worker():
input = fleet.worker_index()
output = fleet_util.all_gather(input, "worker")
# output = [0, 1]
print(output)
output = fleet_util.all_gather(input, "all")
print(output)
# output = [0, 1, 0, 1]
if __name__ == "__main__":
train()
"""
_comm_world = self.__check_comm_world(comm_world)
return self.role_maker._all_gather(_comm_world, input)
def broadcast(self):
def _broadcast(self):
pass
def scatter(self):
def _scatter(self):
pass
def get_file_shard(self, files):
"""
split files before distributed training,
example 1: files is [a, b, c ,d, e] and trainer_num = 2, then trainer
0 gets [a, b, c] and trainer 1 gets [d, e].
example 2: files is [a, b], and trainer_num = 3, then trainer 0 gets
[a], trainer 1 gets [b], trainer 2 gets []
Split files before distributed training, and return filelist assigned to the current trainer.
.. code-block:: text
example 1: files is [a, b, c ,d, e] and trainer_num = 2, then trainer
0 gets [a, b, c] and trainer 1 gets [d, e].
example 2: files is [a, b], and trainer_num = 3, then trainer 0 gets
[a], trainer 1 gets [b], trainer 2 gets []
Args:
files(list): file list need to be read.
files(list): File list need to be read.
Returns:
list: files belongs to this worker.
List: Files belong to this worker.
Examples:
.. code-block:: python
from paddle.distributed.fleet.base.util_factory import fleet_util
import paddle.distributed.fleet.base.role_maker as role_maker
role = role_maker.UserDefinedRoleMaker(
is_collective=False,
init_gloo=False,
current_id=0,
role=role_maker.Role.WORKER,
worker_endpoints=["127.0.0.1:6003", "127.0.0.1:6004"],
server_endpoints=["127.0.0.1:6001", "127.0.0.1:6002"])
fleet_util._set_role_maker(role)
files = fleet_util.get_file_shard(["file1", "file2", "file3"])
# files = ["file1", "file2"]
"""
if not isinstance(files, list):
raise TypeError("files should be a list of file need to be read.")
......@@ -140,6 +285,30 @@ class UtilBase(object):
return trainer_files[trainer_id]
def print_on_rank(self, message, rank_id):
"""
Woker of rank `rank_id` print some message.
Args:
message(str): Log to be printed.
rank_id(int): trainer id.
Examples:
.. code-block:: python
from paddle.distributed.fleet.base.util_factory import fleet_util
import paddle.distributed.fleet.base.role_maker as role_maker
role = role_maker.UserDefinedRoleMaker(
is_collective=False,
init_gloo=False,
current_id=0,
role=role_maker.Role.WORKER,
worker_endpoints=["127.0.0.1:6003", "127.0.0.1:6004"],
server_endpoints=["127.0.0.1:6001", "127.0.0.1:6002"])
fleet_util._set_role_maker(role)
fleet_util.print_on_rank("I'm worker 0", 0)
"""
if self.role_maker.worker_index() != rank_id:
return
print(message)
......@@ -297,7 +466,7 @@ class UtilBase(object):
with fluid.scope_guard(scope):
inference_program, feed_target_names, fetch_targets = \
fluid.io.load_inference_model(config.dump_model_dir, exe, model_filename=model_filename,
params_filename=config.save_params_filename)
params_filename=config.save_params_filename)
# check program vars and saved vars shape
orig_para_shape = {
......
python/paddle/distributed/fleet/launch.py
浏览文件 @ 95710a33
......@@ -87,7 +87,7 @@ def _parse_args():
see: http://www.paddlepaddle.org/documentation/docs/zh/1.6/user_guides/howto/training/cluster_howto.html#permalink-8--nccl2-
''')
#Optional arguments for the launch helper
# Optional arguments for the launch helper
parser.add_argument(
"--ips",
type=str,
......@@ -115,7 +115,7 @@ see: http://www.paddlepaddle.org/documentation/docs/zh/1.6/user_guides/howto/tra
default="log",
help="The path for each process's log.If it's not set, the log will printed to default pipe."
)
#positional
# positional
parser.add_argument(
"training_script",
type=str,
......@@ -124,7 +124,7 @@ see: http://www.paddlepaddle.org/documentation/docs/zh/1.6/user_guides/howto/tra
"followed by all the arguments for the "
"training script")
#rest from the training program
# rest from the training program
parser.add_argument('training_script_args', nargs=REMAINDER)
return parser.parse_args()
......@@ -138,7 +138,7 @@ def get_cluster_from_args(args, gpus):
# node_ip = args.node_ip
assert node_ip in node_ips, "Can't find your local ip {%s} in node_ips: {%s}" \
% (node_ip, node_ips)
% (node_ip, node_ips)
node_rank = node_ips.index(node_ip)
logger.debug("parsed from args: node_ips:{} node_ip:{} node_rank:{}".format(
......@@ -280,7 +280,7 @@ def launch_ps(args):
_, current_node_ip = get_host_name_ip()
assert current_node_ip in node_ips, "Can't find your local ip {%s} in args.servers and args.workers ips: {%s}" \
% (current_node_ip, node_ips)
% (current_node_ip, node_ips)
node_rank = node_ips.index(current_node_ip)
logger.debug(
"parsed from args: node_ips:{} current_node_ip:{} node_rank:{}, server_ports:{}".
......@@ -323,10 +323,12 @@ def launch_ps(args):
for idx, cur_server in enumerate(pod.servers):
proc_env = {
"PADDLE_PSERVERS_IP_PORT_LIST": server_endpoints,
"PADDLE_TRAINER_ENDPOINTS": worker_endpoints,
"PADDLE_PORT": cur_server.endpoint.split(":")[1],
"TRAINING_ROLE": "PSERVER",
"PADDLE_TRAINERS_NUM": str(worker_num),
"POD_IP": cur_server.endpoint.split(":")[0]
"POD_IP": cur_server.endpoint.split(":")[0],
"PADDLE_WITH_GLOO": "1"
}
current_env.update(proc_env)
......@@ -365,7 +367,8 @@ def launch_ps(args):
"PADDLE_TRAINER_ENDPOINTS": worker_endpoints,
"PADDLE_TRAINERS_NUM": str(worker_num),
"TRAINING_ROLE": "TRAINER",
"PADDLE_TRAINER_ID": str(cur_worker.rank)
"PADDLE_TRAINER_ID": str(cur_worker.rank),
"PADDLE_WITH_GLOO": "1"
}
current_env.update(proc_env)
......@@ -430,7 +433,11 @@ def launch():
co_arg for co_arg in collective_args
if co_arg in " ".join(sys.argv[1:-1])
]
cuda_device_num = fluid.core.get_cuda_device_count()
if fluid.core.is_compiled_with_cuda():
cuda_device_num = fluid.core.get_cuda_device_count()
else:
cuda_device_num = 0
if len(has_ps_args) > 0 or cuda_device_num == 0:
logger.info(
"Run parameter-sever cpu mode. pserver arguments:{}, cuda count:{}".
......
python/paddle/distributed/fleet/meta_optimizers/__init__.py
浏览文件 @ 95710a33
......@@ -18,6 +18,7 @@ from .graph_execution_optimizer import GraphExecutionOptimizer
from .parameter_server_optimizer import ParameterServerOptimizer
from .pipeline_optimizer import PipelineOptimizer
from .localsgd_optimizer import LocalSGDOptimizer
from .localsgd_optimizer import AdaptiveLocalSGDOptimizer
from .lars_optimizer import LarsOptimizer
from .parameter_server_graph_optimizer import ParameterServerGraphOptimizer
from .dgc_optimizer import DGCOptimizer
......
python/paddle/distributed/fleet/meta_optimizers/amp_optimizer.py
浏览文件 @ 95710a33
......@@ -22,9 +22,13 @@ class AMPOptimizer(MetaOptimizerBase):
self.amp_opt = None
# we do not allow meta optimizer to be inner optimizer currently
self.meta_optimizers_white_list = [
"LarsOptimizer", "LambOptimizer", "RecomputeOptimizer",
"LocalSGDOptimizer", "GradientMergeOptimizer",
"GraphExecutionOptimizer"
"LarsOptimizer",
"LambOptimizer",
"RecomputeOptimizer",
"LocalSGDOptimizer",
"GradientMergeOptimizer",
"GraphExecutionOptimizer",
"AdaptiveLocalSGDOptimizer",
]
self.meta_optimizers_black_list = ["DGCOptimizer"]
......
python/paddle/distributed/fleet/meta_optimizers/localsgd_optimizer.py
浏览文件 @ 95710a33
......@@ -25,7 +25,10 @@ class LocalSGDOptimizer(MetaOptimizerBase):
super(LocalSGDOptimizer, self).__init__(optimizer)
self.inner_opt = optimizer
self.meta_optimizers_white_list = []
self.meta_optimizers_black_list = ["GraphExecutionOptimizer"]
self.meta_optimizers_black_list = [
"GraphExecutionOptimizer",
"AdaptiveLocalSGDOptimizer",
]
self.snapshot_key = '@SNAPSHOT'
def _can_apply(self):
......@@ -186,3 +189,252 @@ class LocalSGDOptimizer(MetaOptimizerBase):
layers.cond(step > begin_step, begin_localsgd, communicate)
return minimized
class AdaptiveLocalSGDOptimizer(MetaOptimizerBase):
def __init__(self, optimizer):
super(AdaptiveLocalSGDOptimizer, self).__init__(optimizer)
self.inner_opt = optimizer
self.meta_optimizers_white_list = []
self.meta_optimizers_black_list = [
"GraphExecutionOptimizer", "LocalSGDOptimizer"
]
self.snapshot_key = '@SNAPSHOT'
def _can_apply(self):
if not self.role_maker._is_collective:
return False
if not self.user_defined_strategy.adaptive_localsgd:
return False
if self.role_maker.worker_num() <= 1:
return False
return isinstance(self.inner_opt, paddle.optimizer.momentum.Momentum) \
or isinstance(self.inner_opt, paddle.fluid.optimizer.Momentum) \
or isinstance(self.inner_opt, paddle.optimizer.sgd.SGD) \
or isinstance(self.inner_opt, paddle.fluid.optimizer.SGD)
def _disable_strategy(self, dist_strategy):
dist_strategy.adaptive_localsgd = False
dist_strategy.adaptive_localsgd_configs = {}
def _enable_strategy(self, dist_strategy, context):
dist_strategy.adaptive_localsgd = True
dist_strategy.adaptive_localsgd_configs = {
"init_k_steps": 1,
"begin_step": 1
}
def snapshot_name(self, param_name):
return param_name + self.snapshot_key
def create_snapshot_vars(self, program):
block = program.global_block()
non_dist_params = []
for param in block.iter_parameters():
if not param.is_distributed:
non_dist_params.append(param)
p2s = []
for param in non_dist_params:
snapshot = block.create_var(
name=self.snapshot_name(param.name),
shape=param.shape,
persistable=True,
stop_gradient=True,
dtype=param.dtype)
p2s.append([param, snapshot])
return p2s
def init_snapshot_vars(self, startup_program, param2snapshot):
with program_guard(startup_program):
for param, snapshot in param2snapshot:
layers.assign(param, snapshot)
def _generate_avg_loss(self, program_block, loss, avg_loss):
program_block.append_op(
type='c_allreduce_sum',
inputs={'X': [loss]},
outputs={'Out': [avg_loss]},
attrs={
'ring_id': 0,
OP_ROLE_KEY: OpRole.Optimize,
'use_calc_stream': True
})
program_block.append_op(
type='c_sync_calc_stream',
inputs={'X': [avg_loss]},
outputs={'Out': [avg_loss]},
attrs={OP_ROLE_KEY: OpRole.Optimize})
program_block.append_op(
type='scale',
inputs={'X': [avg_loss]},
outputs={'Out': [avg_loss]},
attrs={
'scale': 1.0 / self.role_maker.worker_num(),
OP_ROLE_KEY: OpRole.Optimize
})
def minimize_impl(self,
loss,
startup_program=None,
parameter_list=None,
no_grad_set=None):
minimized = self.inner_opt.minimize(
loss, startup_program=startup_program)
init_k_steps = self.user_defined_strategy.adaptive_localsgd_configs[
'init_k_steps']
begin_step_value = self.user_defined_strategy.adaptive_localsgd_configs[
'begin_step']
if startup_program is None:
startup_program = default_startup_program()
main_block = loss.block
self.nrings = 2
collective_helper = CollectiveHelper(self.role_maker, self.nrings)
collective_helper.update_startup_program(startup_program)
p2s = self.create_snapshot_vars(startup_program)
self.init_snapshot_vars(startup_program, p2s)
p2s = self.create_snapshot_vars(main_block.program)
with program_guard(main_block.program, startup_program):
step = layers.autoincreased_step_counter(begin=1)
k_steps = layers.create_global_var(
name="k_steps",
shape=[1],
value=int(init_k_steps),
dtype='int64',
persistable=True)
begin_step = layers.create_global_var(
name="begin_step",
shape=[1],
value=int(begin_step_value),
dtype='int64',
persistable=True)
last_step = layers.create_global_var(
name="last_step",
shape=[1],
value=int(0),
dtype='int64',
persistable=True)
avg_loss = layers.create_global_var(
name="avg_loss",
shape=[1],
value=float(0),
dtype=loss.dtype,
persistable=True)
lr_0 = layers.create_global_var(
name="lr_0",
shape=[1],
value=float(0),
dtype='float32',
persistable=True)
loss_0 = layers.create_global_var(
name="loss_0",
shape=[1],
value=float(0),
dtype='float32',
persistable=True)
global_lr = self.inner_opt._global_learning_rate()
def initialize():
self._generate_avg_loss(main_block, loss, avg_loss)
layers.assign(avg_loss, loss_0)
layers.assign(global_lr, lr_0)
layers.cond(step == 1, initialize)
def communicate():
sub_block = default_main_program().current_block()
ring_id = -1
for param, snapshot in p2s:
sub_block.append_op(
type='elementwise_sub',
inputs={'X': [snapshot],
'Y': [param]},
outputs={'Out': [param]},
attrs={OP_ROLE_KEY: OpRole.Optimize})
sub_block.append_op(
type='c_sync_calc_stream',
inputs={'X': param},
outputs={'Out': param},
attrs={OP_ROLE_KEY: OpRole.Optimize})
ring_id = (ring_id + 1) % self.nrings
sub_block.append_op(
type='c_allreduce_sum',
inputs={'X': [param]},
outputs={'Out': [param]},
attrs={
'ring_id': ring_id,
OP_ROLE_KEY: OpRole.Optimize
})
for ring_id in range(self.nrings):
sub_block.append_op(
type='c_sync_comm_stream',
inputs={'X': param},
outputs={'Out': param},
attrs={
'ring_id': ring_id,
OP_ROLE_KEY: OpRole.Optimize
})
for param, snapshot in p2s:
sub_block.append_op(
type='scale',
inputs={'X': [param]},
outputs={'Out': [param]},
attrs={
'scale': 1.0 / self.role_maker.worker_num(),
OP_ROLE_KEY: OpRole.Optimize
})
sub_block.append_op(
type='elementwise_sub',
inputs={'X': [snapshot],
'Y': [param]},
outputs={'Out': [param]},
attrs={OP_ROLE_KEY: OpRole.Optimize})
sub_block.append_op(
type='assign',
inputs={'X': [param]},
outputs={'Out': [snapshot]},
attrs={OP_ROLE_KEY: OpRole.Optimize})
layers.assign(step, last_step)
def communicate_avg_loss():
communicate()
self._generate_avg_loss(main_block, loss, avg_loss)
next_local_steps = layers.cast(
layers.ceil(
layers.sqrt(lr_0 * avg_loss / (global_lr * loss_0) *
float(init_k_steps))),
dtype='int64')
max_local_steps = layers.fill_constant(
shape=[1], dtype='int64', value=16)
min_local_steps = layers.fill_constant(
shape=[1], dtype='int64', value=1)
next_local_steps = layers.elementwise_min(next_local_steps,
max_local_steps)
next_local_steps = layers.elementwise_max(next_local_steps,
min_local_steps)
layers.assign(next_local_steps, k_steps)
def begin_localsgd():
layers.cond(step - last_step == k_steps, communicate_avg_loss)
layers.cond(step > begin_step, begin_localsgd, communicate)
return minimized
python/paddle/distributed/fleet/utils/__init__.py
浏览文件 @ 95710a33
......@@ -11,8 +11,3 @@
# 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.
from .fs import *
from .http_server import KVHandler, KVHTTPServer, KVServer
#__all__ = ['KVHandler', 'KVHTTPServer', 'KVServer'] + fs.__all__
python/paddle/fluid/contrib/slim/quantization/imperative/qat.py
浏览文件 @ 95710a33
......@@ -67,6 +67,7 @@ class ImperativeQuantAware(object):
Examples:
.. code-block:: python
import paddle
from paddle.fluid.contrib.slim.quantization \
import ImperativeQuantAware
from paddle.vision.models \
......@@ -86,13 +87,12 @@ class ImperativeQuantAware(object):
# ...
# Save quant model for the inference.
imperative_qat.save_quantized_model(
dirname="./resnet50_qat",
model=model,
input_shape=[(3, 224, 224)],
input_dtype=['float32'],
feed=[0],
fetch=[0])
paddle.jit.save(
layer=model,
model_path="./resnet50_qat",
input_spec=[
paddle.static.InputSpec(
shape=[None, 3, 224, 224], dtype='float32')])
"""
super(ImperativeQuantAware, self).__init__()
self._weight_bits = weight_bits
......@@ -148,75 +148,6 @@ class ImperativeQuantAware(object):
quant_layer = self._get_quantized_counterpart(layer)
setattr(obj, target, quant_layer)
def save_quantized_model(self,
dirname,
model,
input_shape,
input_dtype,
feed,
fetch,
append_batch_size=True):
"""
Save the quantized model for the inference.
Args:
dirname (str): the directory to save the quantized model.
model(fluid.dygraph.Layer): the quantized model to be saved.
input_shape(list[tuple(int)]): The shape value for each input,
e.g. [(3, 224, 224)].
input_dtype(list[str]): The dtype value for each input,
e.g. ['float32'].
feed(list[int]): the indices of the input variables of the
imperative functions which will be saved as input variables in
inference model.
fetch(list[int]): the indices of the returned variable of the
imperative functions which will be saved as output variables in
inference model.
append_batch_size(bool, optional):
If true, it prepends an extra axis to the input_shape, meanwhile,
the input_shape shouldn't contain the batch size dimension.
Otherwise, it just uses the input_shape. Default True.
Returns:
None
"""
assert isinstance(
input_shape, list), "The parameter `input_shape` shoubld be a list."
assert isinstance(
input_dtype, list), "The parameter `input_dtype` shoubld be a list."
assert isinstance(feed, list), "The parameter `feed` shoubld be a list."
assert isinstance(fetch,
list), "The parameter `fetch` shoubld be a list."
assert len(input_shape) == len(
input_dtype
), "The length of input_shape should be equal to input_dtype's."
assert len(input_dtype) == len(
feed), "The length of input_shape should be equal to feed's."
with dygraph.guard():
model.eval()
input_vars = []
for i, (shape, dtype) in enumerate(zip(input_shape, input_dtype)):
if append_batch_size:
shape = [None] + list(shape)
# Note(Aurelius84): need a elegant way to name this.
in_spec = paddle.static.InputSpec(shape, dtype, 'feed_%d' % i)
input_vars.append(in_spec)
# use `declarative` to convert dygraph into static program
model.forward = dygraph.jit.declarative(
model.forward, input_spec=input_vars)
outputs = model.forward.concrete_program.outputs
input_spec = [input_vars[i] for i in feed]
configs = dygraph.jit.SaveLoadConfig()
configs.separate_params = True
if not isinstance(outputs, (tuple, list)):
outputs = [outputs]
configs.output_spec = [outputs[i] for i in fetch]
dygraph.jit.save(
layer=model,
model_path=dirname,
input_spec=input_spec,
configs=configs)
def _get_quantized_counterpart(self, layer):
quant_layers = tuple(self._quant_layers_map.values())
quantized_counterpart = tuple('Quantized' + k
......
python/paddle/fluid/contrib/slim/tests/test_imperative_qat.py
浏览文件 @ 95710a33
......@@ -221,7 +221,7 @@ class TestImperativeQat(unittest.TestCase):
model_dict = lenet.state_dict()
fluid.save_dygraph(model_dict, "save_temp")
# test the correctness of `save_quantized_model`
# test the correctness of `paddle.jit.save`
data = next(test_reader())
test_data = np.array([x[0].reshape(1, 28, 28)
for x in data]).astype('float32')
......@@ -231,13 +231,14 @@ class TestImperativeQat(unittest.TestCase):
# save inference quantized model
path = "./mnist_infer_model"
imperative_qat.save_quantized_model(
dirname=path,
model=lenet,
input_shape=[(1, 28, 28)],
input_dtype=['float32'],
feed=[0],
fetch=[0])
paddle.jit.save(
layer=lenet,
model_path=path,
input_spec=[
paddle.static.InputSpec(
shape=[None, 1, 28, 28], dtype='float32')
])
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
......@@ -245,7 +246,10 @@ class TestImperativeQat(unittest.TestCase):
exe = fluid.Executor(place)
[inference_program, feed_target_names, fetch_targets] = (
fluid.io.load_inference_model(
dirname=path, executor=exe))
dirname=path,
executor=exe,
model_filename="__model__",
params_filename="__variables__"))
after_save, = exe.run(inference_program,
feed={feed_target_names[0]: test_data},
fetch_list=fetch_targets)
......@@ -332,13 +336,13 @@ class TestImperativeQat(unittest.TestCase):
if batch_id % 100 == 0:
_logger.info('{}: {}'.format('loss', avg_loss.numpy()))
imperative_qat.save_quantized_model(
dirname="./dynamic_mnist",
model=lenet,
input_shape=[(1, 28, 28)],
input_dtype=['float32'],
feed=[0],
fetch=[0])
paddle.jit.save(
layer=lenet,
model_path="./dynamic_mnist",
input_spec=[
paddle.static.InputSpec(
shape=[None, 1, 28, 28], dtype='float32')
])
# static graph train
_logger.info(
......
python/paddle/fluid/core.py
浏览文件 @ 95710a33
......@@ -39,6 +39,11 @@ try:
third_lib_path = current_path + os.sep + '..' + os.sep + 'libs'
os.environ['path'] = third_lib_path + ';' + os.environ['path']
sys.path.insert(0, third_lib_path)
# Note: from python3.8, PATH will not take effect
# https://github.com/python/cpython/pull/12302
# Use add_dll_directory to specify dll resolution path
if sys.version_info[:2] >= (3, 8):
os.add_dll_directory(third_lib_path)
except ImportError as e:
from .. import compat as cpt
......
python/paddle/fluid/dygraph/base.py
浏览文件 @ 95710a33
......@@ -23,7 +23,6 @@ from paddle.fluid import framework
from paddle.fluid.multiprocess_utils import CleanupFuncRegistrar
from .tracer import Tracer
import logging
import objgraph
from ..data_feeder import convert_dtype
import warnings
......@@ -368,24 +367,6 @@ def guard(place=None):
yield
def _print_debug_msg(parameter_list, limit=5, is_test=False):
if not core._is_dygraph_debug_enabled():
logging.warn(
'Debug mode is not enabled. Please set FLAGS_dygraph_debug=1 to enable debug'
)
return
unique_name_size = len(framework.unique_name.generator.ids)
tracer_var_size = len(parameter_list)
alive_cpp_var_size = len(core.VarBase._alive_vars())
if not is_test:
logging.warn(
'unique_name num: {}, tracer vars num: {}, alive cpp vars num: {}'
.format(unique_name_size, tracer_var_size, alive_cpp_var_size))
objgraph.show_growth(limit=limit)
else:
return unique_name_size, tracer_var_size, alive_cpp_var_size
@framework.dygraph_only
def grad(outputs,
inputs,
......
python/paddle/fluid/dygraph/checkpoint.py
浏览文件 @ 95710a33
......@@ -195,58 +195,11 @@ def load_dygraph(model_path, config=None):
params_file_path = model_prefix + ".pdparams"
opti_file_path = model_prefix + ".pdopt"
# deal with argument `configs`
configs = config
if configs is None:
configs = SaveLoadConfig()
if not os.path.exists(params_file_path) and not os.path.exists(
opti_file_path):
# Load state dict by `jit.save/io.save_inference_model` save format
# NOTE(chenweihang): [ Compatibility of save_inference_model save format ]
# The model saved by `save_inference_model` does not completely correspond to
# the information required by the `state_dict` under the dygraph.
# `save_inference_model` not save structured name, we need to remind
# the user to configure the `use_structured_name` argument when `set_state_dict`
# NOTE(chenweihang): `jit.save` doesn't save optimizer state
# 1. check model path
if not os.path.isdir(model_prefix):
raise ValueError("Model saved directory '%s' is not exists." %
model_prefix)
# deal with argument `config`
if config is None:
config = SaveLoadConfig()
# 2. load program desc & construct _ProgramHolder
programs = _construct_program_holders(model_path,
configs.model_filename)
# 3. load layer parameters & buffers
# NOTE: using fluid.dygraph.guard() here will cause import error in py2
with guard():
persistable_var_dict = _construct_params_and_buffers(
model_prefix,
programs,
configs.separate_params,
configs.params_filename,
append_suffix=False)
# 4. construct state_dict
para_dict = dict()
for var_name in persistable_var_dict:
para_dict[var_name] = persistable_var_dict[var_name].numpy()
# if __variables.info__ exists, we can recover structured_name
var_info_path = os.path.join(model_prefix, EXTRA_VAR_INFO_FILENAME)
if os.path.exists(var_info_path):
with open(var_info_path, 'rb') as f:
extra_var_info = pickle.load(f)
structured_para_dict = dict()
for var_name in para_dict:
structured_name = extra_var_info[var_name].get(
'structured_name', None)
assert structured_name is not None, "Cannot find saved variable (%s)'s structured name in saved model." % var_name
structured_para_dict[structured_name] = para_dict[var_name]
para_dict = structured_para_dict
else:
if os.path.exists(params_file_path) or os.path.exists(opti_file_path):
# Load state dict by `save_dygraph` save format
para_dict = {}
if os.path.exists(params_file_path):
......@@ -254,12 +207,103 @@ def load_dygraph(model_path, config=None):
para_dict = pickle.load(f) if six.PY2 else pickle.load(
f, encoding='latin1')
if not configs.keep_name_table and "StructuredToParameterName@@" in para_dict:
if not config.keep_name_table and "StructuredToParameterName@@" in para_dict:
del para_dict["StructuredToParameterName@@"]
if os.path.exists(opti_file_path):
with open(opti_file_path, 'rb') as f:
opti_dict = pickle.load(f) if six.PY2 else pickle.load(
f, encoding='latin1')
else:
# check model path
if not os.path.isdir(model_prefix):
raise ValueError("Model saved directory '%s' is not exists." %
model_prefix)
# check whether model file exists
if config.model_filename is None:
model_filename = '__model__'
else:
model_filename = config.model_filename
model_file_path = os.path.join(model_path, model_filename)
if os.path.exists(model_file_path):
# Load state dict by `jit.save/io.save_inference_model` save format
# NOTE(chenweihang): [ Compatibility of save_inference_model save format ]
# The model saved by `save_inference_model` does not completely correspond to
# the information required by the `state_dict` under the dygraph.
# `save_inference_model` not save structured name, we need to remind
# the user to configure the `use_structured_name` argument when `set_state_dict`
# NOTE(chenweihang): `jit.save` doesn't save optimizer state
# 1. load program desc & construct _ProgramHolder
programs = _construct_program_holders(model_path,
config.model_filename)
# 2. load layer parameters & buffers
# NOTE: using fluid.dygraph.guard() here will cause import error in py2
with guard():
persistable_var_dict = _construct_params_and_buffers(
model_prefix,
programs,
config.separate_params,
config.params_filename,
append_suffix=False)
# 3. construct state_dict
para_dict = dict()
for var_name in persistable_var_dict:
para_dict[var_name] = persistable_var_dict[var_name].numpy()
# if __variables.info__ exists, we can recover structured_name
var_info_path = os.path.join(model_prefix,
EXTRA_VAR_INFO_FILENAME)
if os.path.exists(var_info_path):
with open(var_info_path, 'rb') as f:
extra_var_info = pickle.load(f)
structured_para_dict = dict()
for var_name in para_dict:
structured_name = extra_var_info[var_name].get(
'structured_name', None)
assert structured_name is not None, "Cannot find saved variable (%s)'s structured name in saved model." % var_name
structured_para_dict[structured_name] = para_dict[
var_name]
para_dict = structured_para_dict
else:
# load state dict by `io.save_params/persistables` save format
# TODO(chenweihang): [ Now only supports loading parameters seperately ]
# If users save all parameters as one file, the [ variable.name -> variable ]
# mapping info will lost, so users need to give variable list, but users build
# variable list in dygraph mode is difficult, we recommend users to use
# paddle.io.load_program_state in this case
# Try to load all the files in the directory in VarBase format,
# the file name is used as the name of VarBase
load_var_list = []
# 1. load file names
var_name_list = []
for root, _, files in os.walk(model_path):
for filename in files:
file_path = os.path.join(root, filename)
tmp_var_name = os.path.relpath(file_path, model_path)
var_name = tmp_var_name.replace("\\", "/")
var_name_list.append(var_name)
# 2. create and load VarBase
with guard():
for name in var_name_list:
new_var = _varbase_creator(name=name, persistable=True)
_dygraph_tracer().trace_op(
type='load',
inputs={},
outputs={'Out': new_var},
attrs={'file_path': os.path.join(model_path, name)})
load_var_list.append(new_var)
# 3. construct state_dict
para_dict = dict()
for var in load_var_list:
para_dict[var.name] = var.numpy()
return para_dict, opti_dict
python/paddle/fluid/dygraph/dygraph_to_static/ast_transformer.py
浏览文件 @ 95710a33
......@@ -60,7 +60,7 @@ class DygraphToStaticAst(gast.NodeTransformer):
def transfer_from_node_type(self, node_wrapper):
translator_logger = logging_utils.TranslatorLogger()
translator_logger.log(
1, " Source code: \n{}".format(ast_to_source_code(self.root)))
1, "Source code: \n{}".format(ast_to_source_code(self.root)))
# Generic transformation
self.visit(node_wrapper.node)
......
python/paddle/fluid/dygraph/dygraph_to_static/function_spec.py
浏览文件 @ 95710a33
......@@ -12,17 +12,18 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import six
import inspect
import numpy as np
import collections
import paddle
from paddle.fluid import core
from paddle.fluid.dygraph import layers
from paddle.fluid.layers.utils import flatten
from paddle.fluid.layers.utils import pack_sequence_as
from paddle.fluid.dygraph.base import switch_to_static_graph
from paddle.fluid.dygraph.dygraph_to_static import logging_utils
from paddle.fluid.dygraph.dygraph_to_static.utils import parse_arg_and_kwargs
from paddle.fluid.dygraph.dygraph_to_static.utils import type_name
from paddle.fluid.dygraph.dygraph_to_static.utils import func_to_source_code
......@@ -291,7 +292,7 @@ def convert_to_input_spec(inputs, input_spec):
if len(inputs) > len(input_spec):
for rest_input in inputs[len(input_spec):]:
if isinstance(rest_input, (core.VarBase, np.ndarray)):
logging.warning(
logging_utils.warn(
"The inputs constain `{}` without specificing InputSpec, its shape and dtype will be treated immutable. "
"Please specific InputSpec information in `@declarative` if you expect them as mutable inputs.".
format(type_name(rest_input)))
......
python/paddle/fluid/dygraph/dygraph_to_static/logging_utils.py
浏览文件 @ 95710a33
......@@ -26,6 +26,8 @@ CODE_LEVEL_ENV_NAME = 'TRANSLATOR_CODE_LEVEL'
DEFAULT_VERBOSITY = -1
DEFAULT_CODE_LEVEL = -1
LOG_AllTransformer = 100
def synchronized(func):
def wrapper(*args, **kwargs):
......@@ -53,10 +55,15 @@ class TranslatorLogger(object):
return
self._initialized = True
self.logger_name = "Dynamic-to-Static"
self._logger = log_helper.get_logger(
__name__, 1, fmt='%(asctime)s-%(levelname)s: %(message)s')
self.logger_name,
1,
fmt='%(asctime)s %(name)s %(levelname)s: %(message)s')
self._verbosity_level = None
self._transformed_code_level = None
self._need_to_echo_log_to_stdout = None
self._need_to_echo_code_to_stdout = None
@property
def logger(self):
......@@ -86,6 +93,28 @@ class TranslatorLogger(object):
self.check_level(level)
self._transformed_code_level = level
@property
def need_to_echo_log_to_stdout(self):
if self._need_to_echo_log_to_stdout is not None:
return self._need_to_echo_log_to_stdout
return False
@need_to_echo_log_to_stdout.setter
def need_to_echo_log_to_stdout(self, log_to_stdout):
assert isinstance(log_to_stdout, (bool, type(None)))
self._need_to_echo_log_to_stdout = log_to_stdout
@property
def need_to_echo_code_to_stdout(self):
if self._need_to_echo_code_to_stdout is not None:
return self._need_to_echo_code_to_stdout
return False
@need_to_echo_code_to_stdout.setter
def need_to_echo_code_to_stdout(self, code_to_stdout):
assert isinstance(code_to_stdout, (bool, type(None)))
self._need_to_echo_code_to_stdout = code_to_stdout
def check_level(self, level):
if isinstance(level, (six.integer_types, type(None))):
rv = level
......@@ -110,34 +139,56 @@ class TranslatorLogger(object):
def error(self, msg, *args, **kwargs):
self.logger.error(msg, *args, **kwargs)
if self.need_to_echo_log_to_stdout:
self._output_to_stdout('ERROR: ' + msg, *args)
def warn(self, msg, *args, **kwargs):
self.logger.warn(msg, *args, **kwargs)
self.logger.warning(msg, *args, **kwargs)
if self.need_to_echo_log_to_stdout:
self._output_to_stdout('WARNING: ' + msg, *args)
def log(self, level, msg, *args, **kwargs):
if self.has_verbosity(level):
self.logger.log(level, msg, *args, **kwargs)
msg_with_level = '(Level {}) {}'.format(level, msg)
self.logger.info(msg_with_level, *args, **kwargs)
if self.need_to_echo_log_to_stdout:
self._output_to_stdout('INFO: ' + msg_with_level, *args)
def log_transformed_code(self, level, ast_node, transformer_name, *args,
**kwargs):
if self.has_code_level(level):
source_code = ast_to_source_code(ast_node)
header_msg = "After the level {} ast transformer: '{}', the transformed code:\n"\
.format(level, transformer_name)
if level == LOG_AllTransformer:
header_msg = "After the last level ast transformer: '{}', the transformed code:\n" \
.format(transformer_name)
else:
header_msg = "After the level {} ast transformer: '{}', the transformed code:\n"\
.format(level, transformer_name)
msg = header_msg + source_code
self.logger.info(msg, *args, **kwargs)
if self.need_to_echo_code_to_stdout:
self._output_to_stdout('INFO: ' + msg, *args)
def _output_to_stdout(self, msg, *args):
msg = self.logger_name + ' ' + msg
print(msg % args)
_TRANSLATOR_LOGGER = TranslatorLogger()
def set_verbosity(level=0):
def set_verbosity(level=0, also_to_stdout=False):
"""
Sets the verbosity level of log for dygraph to static graph.
Sets the verbosity level of log for dygraph to static graph. Logs can be output to stdout by setting `also_to_stdout`.
There are two means to set the logging verbosity:
1. Call function `set_verbosity`
2. Set environment variable `TRANSLATOR_VERBOSITY`
1. Call function `set_verbosity`
2. Set environment variable `TRANSLATOR_VERBOSITY`
**Note**:
`set_verbosity` has a higher priority than the environment variable.
......@@ -145,6 +196,7 @@ def set_verbosity(level=0):
Args:
level(int): The verbosity level. The larger value idicates more verbosity.
The default value is 0, which means no logging.
also_to_stdout(bool): Whether to also output log messages to `sys.stdout`.
Examples:
.. code-block:: python
......@@ -159,27 +211,30 @@ def set_verbosity(level=0):
# The verbosity level is now 3, but it has no effect because it has a lower priority than `set_verbosity`
"""
_TRANSLATOR_LOGGER.verbosity_level = level
_TRANSLATOR_LOGGER.need_to_echo_log_to_stdout = also_to_stdout
def get_verbosity():
return _TRANSLATOR_LOGGER.verbosity_level
LOG_AllTransformer = 100
def set_code_level(level=LOG_AllTransformer):
def set_code_level(level=LOG_AllTransformer, also_to_stdout=False):
"""
Sets the level to print code from specific level of Ast Transformer.
Sets the level to print code from specific level Ast Transformer. Code can be output to stdout by setting `also_to_stdout`.
There are two means to set the code level:
1. Call function `set_code_level`
2. Set environment variable `TRANSLATOR_CODE_LEVEL`
1. Call function `set_code_level`
2. Set environment variable `TRANSLATOR_CODE_LEVEL`
**Note**:
`set_code_level` has a higher priority than the environment variable.
Args:
level(int): The level to print code. Default is 100, which means to print the code after all AST Transformers.
also_to_stdout(bool): Whether to also output code to `sys.stdout`.
Examples:
.. code-block:: python
......@@ -195,6 +250,7 @@ def set_code_level(level=LOG_AllTransformer):
"""
_TRANSLATOR_LOGGER.transformed_code_level = level
_TRANSLATOR_LOGGER.need_to_echo_code_to_stdout = also_to_stdout
def get_code_level():
......
python/paddle/fluid/dygraph/dygraph_to_static/partial_program.py
浏览文件 @ 95710a33
......@@ -14,21 +14,17 @@
from __future__ import print_function
import numpy as np
import logging
import six
from paddle.fluid import log_helper
from paddle.fluid import framework, backward, core
from paddle.fluid.dygraph import layers
from paddle.fluid.dygraph.base import switch_to_static_graph
from paddle.fluid.dygraph.dygraph_to_static import logging_utils
from paddle.fluid.dygraph.dygraph_to_static.return_transformer import RETURN_NO_VALUE_MAGIC_NUM
from paddle.fluid.layers.utils import flatten
from paddle.fluid.layers.utils import pack_sequence_as
import paddle.compat as cpt
_logger = log_helper.get_logger(
__name__, logging.WARNING, fmt='%(asctime)s-%(levelname)s: %(message)s')
class NestSequence(object):
"""
......@@ -72,7 +68,7 @@ class NestSequence(object):
if not isinstance(var, (framework.Variable, core.VarBase)):
warning_types.add(type(var))
if warning_types:
_logger.warning(
logging_utils.warn(
"Output of traced function contains non-tensor type values: {}. "
"Currently, We don't support to update them while training and will return "
"what we first saw. Please try to return them as tensor.".
......
python/paddle/fluid/dygraph/dygraph_to_static/print_transformer.py
浏览文件 @ 95710a33
......@@ -15,14 +15,8 @@
from __future__ import print_function
import gast
import logging
from paddle.fluid import log_helper
from paddle.fluid.dygraph.dygraph_to_static.static_analysis import AstNodeWrapper, NodeVarType, StaticAnalysisVisitor
from paddle.fluid.dygraph.dygraph_to_static.utils import ast_to_source_code
_logger = log_helper.get_logger(
__name__, logging.WARNING, fmt='%(asctime)s-%(levelname)s: %(message)s')
from paddle.fluid.dygraph.dygraph_to_static.static_analysis import AstNodeWrapper, StaticAnalysisVisitor
class PrintTransformer(gast.NodeTransformer):
......
python/paddle/fluid/dygraph/dygraph_to_static/program_translator.py
浏览文件 @ 95710a33
......@@ -13,17 +13,15 @@
# limitations under the License.
from __future__ import print_function
import gast
import collections
import logging
import gast
import inspect
import six
import textwrap
import threading
import warnings
import weakref
import gast
from paddle.fluid import framework
from paddle.fluid import in_dygraph_mode
from paddle.fluid.dygraph import layers
......@@ -246,7 +244,7 @@ class StaticLayer(object):
self._function_spec = FunctionSpec(function, input_spec)
self._program_cache = ProgramCache()
self._descriptor_cache = weakref.WeakKeyDictionary()
# Note: Hold a reference to ProgramTranslator for switching `enable_declarative`.
# Note: Hold a reference to ProgramTranslator for switching `enable_to_static`.
self._program_trans = ProgramTranslator()
def __get__(self, instance, owner):
......@@ -299,16 +297,17 @@ class StaticLayer(object):
"""
# 1. call dygraph function directly if not enable `declarative`
if not self._program_trans.enable_declarative:
if not self._program_trans.enable_to_static:
logging_utils.warn(
"The decorator '@paddle.jit.to_static' does NOT work when setting ProgramTranslator.enable=False. "
"We will just return dygraph output.")
"The decorator '@paddle.jit.to_static' does NOT work when setting ProgramTranslator.enable to False. "
"We will just return dygraph output. If you would like to get static graph output, please call API "
"ProgramTranslator.enable(True)")
return self._call_dygraph_function(*args, **kwargs)
if not in_dygraph_mode() and self._program_trans.enable_declarative:
if not in_dygraph_mode():
raise RuntimeError(
"Failed to run the callable object {} decorated by '@paddle.jit.to_static', "
"because it does NOT in dynamic mode. Please disable the static mode to enter dynamic mode with the "
"because it is NOT in dynamic mode. Please disable the static mode to enter dynamic mode with the "
"following API: paddle.disable_static().".format(
self.dygraph_function))
......@@ -450,7 +449,7 @@ class StaticLayer(object):
format(self._function_spec))
# If more than one programs have been cached, return the recent converted program by default.
elif cached_program_len > 1:
logging.warning(
logging_utils.warn(
"Current {} has more than one cached programs: {}, the last traced progam will be return by default.".
format(self._function_spec, cached_program_len))
......@@ -631,7 +630,7 @@ class ProgramCache(object):
# Note: raise warnings if number of traced program is more than `max_tracing_count`
current_tracing_count = len(self._caches)
if current_tracing_count > MAX_TRACED_PROGRAM_COUNT:
logging.warning(
logging_utils.warn(
"Current traced program number: {} > `max_tracing_count`:{}. Too much cached programs will bring expensive overhead. "
"The reason may be: (1) passing tensors with different shapes, (2) passing python objects instead of tensors.".
format(current_tracing_count, MAX_TRACED_PROGRAM_COUNT))
......@@ -723,15 +722,15 @@ class ProgramTranslator(object):
return
self._initialized = True
self._program_cache = ProgramCache()
self.enable_declarative = True
self.enable_to_static = True
def enable(self, enable_declarative):
def enable(self, enable_to_static):
"""
Enable or disable the converting from imperative to declarative by
ProgramTranslator globally.
Args:
enable_declarative (bool): True or False to enable or disable declarative.
enable_to_static (bool): True or False to enable or disable declarative.
Returns:
None.
......@@ -760,9 +759,9 @@ class ProgramTranslator(object):
print(func(x).numpy()) # [[2. 2.]]
"""
check_type(enable_declarative, "enable_declarative", bool,
check_type(enable_to_static, "enable_to_static", bool,
"ProgramTranslator.enable")
self.enable_declarative = enable_declarative
self.enable_to_static = enable_to_static
def get_output(self, dygraph_func, *args, **kwargs):
"""
......@@ -803,10 +802,13 @@ class ProgramTranslator(object):
assert callable(
dygraph_func
), "Input dygraph_func is not a callable in ProgramTranslator.get_output"
if not self.enable_declarative:
warnings.warn(
"The ProgramTranslator.get_output doesn't work when setting ProgramTranslator.enable = False. "
"We will just return dygraph output.")
if not self.enable_to_static:
logging_utils.warn(
"The ProgramTranslator.get_output doesn't work when setting ProgramTranslator.enable to False. "
"We will just return dygraph output. "
"Please call ProgramTranslator.enable(True) if you would like to get static output."
)
return dygraph_func(*args, **kwargs)
try:
function_spec = FunctionSpec(dygraph_func)
......@@ -876,10 +878,12 @@ class ProgramTranslator(object):
assert callable(
dygraph_func
), "Input dygraph_func is not a callable in ProgramTranslator.get_func"
if not self.enable_declarative:
warnings.warn(
"The ProgramTranslator.get_func doesn't work when setting ProgramTranslator.enable=False. We will "
"just return dygraph output.")
if not self.enable_to_static:
logging_utils.warn(
"The ProgramTranslator.get_func doesn't work when setting ProgramTranslator.enable to False. We will "
"just return dygraph output. Please call ProgramTranslator.enable(True) if you would like to get static output."
)
return dygraph_func
static_func = convert_to_static(dygraph_func)
......@@ -929,10 +933,13 @@ class ProgramTranslator(object):
assert callable(
dygraph_func
), "Input dygraph_func is not a callable in ProgramTranslator.get_program"
if not self.enable_declarative:
warnings.warn(
"The ProgramTranslator.get_program doesn't work when setting ProgramTranslator.enable=False."
"We will just return dygraph output.")
if not self.enable_to_static:
logging_utils.warn(
"The ProgramTranslator.get_program doesn't work when setting ProgramTranslator.enable to False."
"We will just return dygraph output. "
"Please call ProgramTranslator.enable(True) if you would like to get static output."
)
return dygraph_func(*args, **kwargs)
function_spec = FunctionSpec(dygraph_func)
......
python/paddle/fluid/dygraph/jit.py
浏览文件 @ 95710a33
......@@ -26,6 +26,7 @@ from paddle.fluid import core
from paddle.fluid.compiler import BuildStrategy, CompiledProgram, ExecutionStrategy
from paddle.fluid.data_feeder import check_type
from paddle.fluid.dygraph.base import program_desc_tracing_guard, switch_to_static_graph
from paddle.fluid.dygraph.dygraph_to_static import logging_utils
from paddle.fluid.dygraph.dygraph_to_static.logging_utils import set_code_level, set_verbosity
from paddle.fluid.dygraph.dygraph_to_static.program_translator import ProgramTranslator, StaticLayer, unwrap_decorators
from paddle.fluid.dygraph.io import EXTRA_VAR_INFO_FILENAME, VARIABLE_FILENAME, TranslatedLayer
......@@ -119,8 +120,8 @@ def _dygraph_to_static_func_(dygraph_func):
# TODO: remove this decorator after we finalize training API
def __impl__(*args, **kwargs):
program_translator = ProgramTranslator()
if in_dygraph_mode() or not program_translator.enable_declarative:
warnings.warn(
if in_dygraph_mode() or not program_translator.enable_to_static:
logging_utils.warn(
"The decorator 'dygraph_to_static_func' doesn't work in "
"dygraph mode or set ProgramTranslator.enable to False. "
"We will just return dygraph output.")
......@@ -215,7 +216,7 @@ def declarative(function=None, input_spec=None):
if isinstance(function, Layer):
if isinstance(function.forward, StaticLayer):
class_name = function.__class__.__name__
warnings.warn(
logging_utils.warn(
"`{}.forward` has already been decorated somewhere. It will be redecorated to replace previous one.".
format(class_name))
function.forward = decorated(function.forward)
......@@ -832,9 +833,9 @@ def save(layer, model_path, input_spec=None, config=None):
# 1. input check
prog_translator = ProgramTranslator()
if not prog_translator.enable:
if not prog_translator.enable_to_static:
raise RuntimeError(
"The paddle.jit.save doesn't work when setting ProgramTranslator.enable=False."
"The paddle.jit.save doesn't work when setting ProgramTranslator.enable to False."
)
if not isinstance(layer, Layer):
raise TypeError(
......
python/paddle/fluid/incubate/checkpoint/auto_checkpoint.py
浏览文件 @ 95710a33
......@@ -98,7 +98,7 @@ class AutoCheckpointChecker(object):
self._fs_cache = os.getenv("PADDLE_EDL_FS_CACHE", ".cache")
self._save_checkpoint_inter = int(
os.getenv("PADDLE_EDL_SAVE_CHECKPOINT_INTER", "900")) #s
os.getenv("PADDLE_EDL_SAVE_CHECKPOINT_INTER", "900")) # s
if not self._ce_test:
assert len(self._hdfs_home) > 3 and \
......@@ -132,7 +132,7 @@ class AutoCheckpointChecker(object):
if in_dygraph_mode():
return False
return self._run_env is not None and \
return self._run_env is not None and \
self._platform is not None and \
self._job_id is not None and \
self._hdfs_home is not None and \
......
python/paddle/fluid/incubate/fleet/utils/fleet_util.py
浏览文件 @ 95710a33
......@@ -26,8 +26,7 @@ import paddle.fluid as fluid
from paddle.fluid.log_helper import get_logger
from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet as fleet_pslib
from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet as fleet_transpiler
from . import hdfs
from .hdfs import *
from paddle.distributed.fleet.utils.fs import LocalFS, HDFSClient
from . import utils
__all__ = ["FleetUtil"]
......
python/paddle/fluid/incubate/fleet/utils/fs.py 已删除 100644 → 0
浏览文件 @ 164f12fa
# 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.
import os
import sys
import subprocess
import multiprocessing
from datetime import datetime
import re
import copy
import errno
import time
import logging
import abc
from pathlib import PurePosixPath, Path
import shutil
__all__ = ['FS', 'LocalFS']
class ExecuteError(Exception):
pass
class FSFileExistsError(Exception):
pass
class FSFileNotExistsError(Exception):
pass
class FSTimeOut(Exception):
pass
class FSShellCmdAborted(ExecuteError):
pass
class FS(object):
@abc.abstractmethod
def ls_dir(self, fs_path):
raise NotImplementedError
@abc.abstractmethod
def is_file(self, fs_path):
raise NotImplementedError
@abc.abstractmethod
def is_dir(self, fs_path):
raise NotImplementedError
@abc.abstractmethod
def is_exist(self, fs_path):
raise NotImplementedError
@abc.abstractmethod
def upload(self, local_path, fs_path):
raise NotImplementedError
@abc.abstractmethod
def download(self, fs_path, local_path):
raise NotImplementedError
@abc.abstractmethod
def mkdirs(self, fs_path):
raise NotImplementedError
@abc.abstractmethod
def delete(self, fs_path):
raise NotImplementedError
@abc.abstractmethod
def need_upload_download(self):
raise NotImplementedError
@abc.abstractmethod
def rename(self, fs_src_path, fs_dst_path):
raise NotImplementedError
@abc.abstractmethod
def mv(self, fs_src_path, fs_dst_path, overwrite=False, test_exists=False):
raise NotImplementedError
@abc.abstractmethod
def upload_dir(self, local_dir, dest_dir):
raise NotImplementedError
@abc.abstractmethod
def list_dirs(self, fs_path):
raise NotImplementedError
@abc.abstractmethod
def touch(self, fs_path, exist_ok=True):
raise NotImplementedError
class LocalFS(FS):
def ls_dir(self, fs_path):
return [f for f in os.listdir(fs_path)]
def mkdirs(self, fs_path):
assert not os.path.isfile(fs_path), "{} is already a file".format(
fs_path)
os.system("mkdir -p {}".format(fs_path))
def rename(self, fs_src_path, fs_dst_path):
os.rename(fs_src_path, fs_dst_path)
def _rmr(self, fs_path):
shutil.rmtree(fs_path)
def _rm(self, fs_path):
os.remove(fs_path)
def delete(self, fs_path):
if not self.is_exist(fs_path):
return
if os.path.isfile(fs_path):
return self._rm(fs_path)
return self._rmr(fs_path)
def need_upload_download(self):
return False
def is_file(self, fs_path):
return os.path.isfile(fs_path)
def is_dir(self, fs_path):
return os.path.isdir(fs_path)
def is_exist(self, fs_path):
return os.path.exists(fs_path)
def touch(self, fs_path, exist_ok=True):
if self.is_exist(fs_path):
if exist_ok:
return
raise FSFileExistsError
return Path(fs_path).touch(exist_ok=True)
def mv(self, src_path, dst_path, overwrite=False, test_exists=False):
if not self.is_exist(src_path):
raise FSFileNotExistsError
if overwrite and self.is_exist(dst_path):
self.delete(dst_path)
if self.is_exist(dst_path):
raise FSFileExistsError
return self.rename(src_path, dst_path)
def list_dirs(self, fs_path):
"""
list directory under fs_path, and only give the pure name, not include the fs_path
"""
if not self.is_exist(fs_path):
return []
dirs = [
f for f in os.listdir(fs_path) if os.path.isdir(fs_path + "/" + f)
]
return dirs
python/paddle/fluid/layers/nn.py
浏览文件 @ 95710a33
......@@ -11229,7 +11229,7 @@ def shape(input):
input.shape = [3, 2]
Args:
input (Variable): The input can be N-D Tensor or SelectedRows with data type float16, float32, float64, int32, int64.
input (Variable): The input can be N-D Tensor or SelectedRows with data type bool, float16, float32, float64, int32, int64.
If input variable is type of SelectedRows, returns the shape of it's inner tensor.
Returns:
......@@ -11253,8 +11253,8 @@ def shape(input):
print(res) # [array([ 3, 100, 100], dtype=int32)]
"""
check_variable_and_dtype(
input, 'input', ['float16', 'float32', 'float64', 'int32', 'int64'],
'shape')
input, 'input',
['bool', 'float16', 'float32', 'float64', 'int32', 'int64'], 'shape')
helper = LayerHelper('shape', **locals())
out = helper.create_variable_for_type_inference(dtype='int32')
helper.append_op(
......
python/paddle/fluid/optimizer.py
浏览文件 @ 95710a33
......@@ -3570,8 +3570,10 @@ class ExponentialMovingAverage(object):
# bias correction
with layers.control_flow.Switch() as switch:
with switch.case(global_step > 0):
layers.assign(output=ema, input=ema / (1.0 - decay_pow))
layers.assign(input=ema, output=param)
layers.assign(
output=param, input=ema / (1.0 - decay_pow))
with switch.default():
layers.assign(output=param, input=ema)
self.restore_program = Program()
block = self.restore_program.global_block()
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ 95710a33
......@@ -4,6 +4,7 @@ set(GC_ENVS FLAGS_eager_delete_tensor_gb=0.0 FLAGS_fast_eager_deletion_mode=1 FL
set(dist_ENVS http_proxy="" https_proxy="")
file(GLOB DIST_TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_dist_*.py")
list(REMOVE_ITEM DIST_TEST_OPS "test_dist_op")
if(NOT WITH_NCCL)
list(REMOVE_ITEM DIST_TEST_OPS "test_dist_mnist_dgc_nccl")
endif()
......@@ -102,7 +103,6 @@ if(WIN32)
endif()
LIST(REMOVE_ITEM TEST_OPS test_fleet_rolemaker_new)
LIST(REMOVE_ITEM TEST_OPS test_auto_checkpoint)
LIST(REMOVE_ITEM TEST_OPS test_auto_checkpoint1)
LIST(REMOVE_ITEM TEST_OPS test_auto_checkpoint2)
......@@ -326,7 +326,6 @@ list(REMOVE_ITEM TEST_OPS test_basic_gru_api)
list(REMOVE_ITEM TEST_OPS test_basic_gru_unit_op)
list(REMOVE_ITEM TEST_OPS test_basic_lstm_api)
list(REMOVE_ITEM TEST_OPS test_basic_lstm_unit_op)
list(REMOVE_ITEM TEST_OPS test_imperative_debug_string)
list(REMOVE_ITEM TEST_OPS test_fuse_bn_act_pass)
list(REMOVE_ITEM TEST_OPS test_imperative_static_runner_mnist)
list(REMOVE_ITEM TEST_OPS test_imperative_static_runner_while)
......@@ -416,7 +415,6 @@ py_test_modules(test_imperative_ocr_attention_model MODULES test_imperative_ocr_
py_test_modules(test_install_check MODULES test_install_check ENVS
FLAGS_cudnn_deterministic=1 SERIAL)
set_tests_properties(test_install_check PROPERTIES LABELS "RUN_TYPE=DIST")
py_test_modules(test_imperative_debug_string MODULES test_imperative_debug_string ENVS FLAGS_dygraph_debug=1)
py_test_modules(test_imperative_static_runner_mnist MODULES test_imperative_static_runner_mnist ENVS
FLAGS_cudnn_deterministic=1)
py_test_modules(test_imperative_static_runner_while MODULES test_imperative_static_runner_while ENVS
......@@ -465,8 +463,8 @@ if(WITH_DISTRIBUTE)
#py_test_modules(test_fleet_auto MODULES test_fleet_auto ENVS ${dist_ENVS})
if(NOT WIN32)
py_test_modules(test_fleet_localsgd_meta_optimizer MODULES test_fleet_localsgd_meta_optimizer ENVS ${dist_ENVS})
#py_test_modules(test_fleet_lars_meta_optimizer MODULES test_fleet_lars_meta_optimizer ENVS ${dist_ENVS})
#py_test_modules(test_fleet_lamb_meta_optimizer MODULES test_fleet_lamb_meta_optimizer ENVS ${dist_ENVS})
py_test_modules(test_fleet_lars_meta_optimizer MODULES test_fleet_lars_meta_optimizer ENVS ${dist_ENVS})
py_test_modules(test_fleet_lamb_meta_optimizer MODULES test_fleet_lamb_meta_optimizer ENVS ${dist_ENVS})
endif(NOT WIN32)
endif(NOT APPLE)
if(WITH_DGC)
......@@ -560,7 +558,7 @@ endif()
set_tests_properties(test_parallel_executor_test_while_train test_parallel_executor_mnist
test_parallel_executor_feed_persistable_var
test_buffer_shared_memory_reuse_pass_and_fuse_optimization_op_pass
test_data_norm_op test_imperative_using_non_zero_gpu
test_data_norm_op
test_dataloader_keep_order
test_dataloader_unkeep_order
test_parallel_executor_fetch_isolated_var
......
python/paddle/fluid/tests/unittests/auto_checkpoint_utils.py
浏览文件 @ 95710a33
......@@ -20,8 +20,7 @@ from paddle.fluid.incubate.fleet.collective import CollectiveOptimizer, fleet
import os
import sys
from paddle.fluid.incubate.fleet.utils.fs import LocalFS
from paddle.fluid.incubate.fleet.utils.hdfs import HDFSClient
from paddle.distributed.fleet.utils.fs import LocalFS, HDFSClient
import paddle.fluid.incubate.checkpoint.auto_checkpoint as acp
from paddle.fluid.incubate.checkpoint.checkpoint_saver import PaddleModel
from paddle.fluid.framework import program_guard
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_logging_utils.py
浏览文件 @ 95710a33
......@@ -56,8 +56,30 @@ class TestLoggingUtils(unittest.TestCase):
with self.assertRaises(TypeError):
paddle.jit.set_verbosity(3.3)
def test_code_level(self):
def test_also_to_stdout(self):
logging_utils._TRANSLATOR_LOGGER.need_to_echo_log_to_stdout = None
self.assertEqual(
logging_utils._TRANSLATOR_LOGGER.need_to_echo_log_to_stdout, False)
paddle.jit.set_verbosity(also_to_stdout=False)
self.assertEqual(
logging_utils._TRANSLATOR_LOGGER.need_to_echo_log_to_stdout, False)
logging_utils._TRANSLATOR_LOGGER.need_to_echo_node_to_stdout = None
self.assertEqual(
logging_utils._TRANSLATOR_LOGGER.need_to_echo_code_to_stdout, False)
paddle.jit.set_code_level(also_to_stdout=True)
self.assertEqual(
logging_utils._TRANSLATOR_LOGGER.need_to_echo_code_to_stdout, True)
with self.assertRaises(AssertionError):
paddle.jit.set_verbosity(also_to_stdout=1)
with self.assertRaises(AssertionError):
paddle.jit.set_code_level(also_to_stdout=1)
def test_set_code_level(self):
paddle.jit.set_code_level(None)
os.environ[logging_utils.CODE_LEVEL_ENV_NAME] = '2'
self.assertEqual(logging_utils.get_code_level(), 2)
......@@ -71,7 +93,25 @@ class TestLoggingUtils(unittest.TestCase):
with self.assertRaises(TypeError):
paddle.jit.set_code_level(3.3)
def test_log(self):
def test_log_api(self):
# test api for CI Converage
logging_utils.set_verbosity(1, True)
logging_utils.warn("warn")
logging_utils.error("error")
logging_utils.log(1, "log level 1")
logging_utils.log(2, "log level 2")
source_code = "x = 3"
ast_code = gast.parse(source_code)
logging_utils.set_code_level(1, True)
logging_utils.log_transformed_code(1, ast_code, "TestTransformer")
logging_utils.set_code_level(logging_utils.LOG_AllTransformer, True)
logging_utils.log_transformed_code(logging_utils.LOG_AllTransformer,
ast_code, "TestTransformer")
def test_log_message(self):
stream = io.BytesIO() if six.PY2 else io.StringIO()
log = self.translator_logger.logger
stdout_handler = logging.StreamHandler(stream)
......@@ -84,13 +124,14 @@ class TestLoggingUtils(unittest.TestCase):
if six.PY3:
with mock.patch.object(sys, 'stdout', stream):
logging_utils.set_verbosity(1, False)
logging_utils.warn(warn_msg)
logging_utils.error(error_msg)
self.translator_logger.verbosity_level = 1
logging_utils.log(1, log_msg_1)
logging_utils.log(2, log_msg_2)
result_msg = '\n'.join([warn_msg, error_msg, log_msg_1, ""])
result_msg = '\n'.join(
[warn_msg, error_msg, "(Level 1) " + log_msg_1, ""])
self.assertEqual(result_msg, stream.getvalue())
def test_log_transformed_code(self):
......
python/paddle/fluid/tests/unittests/hdfs_test_utils.py
浏览文件 @ 95710a33
......@@ -19,7 +19,7 @@ from paddle.fluid.incubate.fleet.collective import CollectiveOptimizer, fleet
import os
import sys
from paddle.distributed.fleet.utils import LocalFS, HDFSClient, FSTimeOut, FSFileExistsError, FSFileNotExistsError
from paddle.distributed.fleet.utils.fs import LocalFS, HDFSClient, FSTimeOut, FSFileExistsError, FSFileNotExistsError
java_home = os.environ["JAVA_HOME"]
......
python/paddle/fluid/tests/unittests/ir/inference/test_conv_bias_mkldnn_fuse_pass.py 0 → 100644
浏览文件 @ 95710a33
# Copyright (c) 2020 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.
from __future__ import print_function
import unittest
import numpy as np
from inference_pass_test import InferencePassTest
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.core import AnalysisConfig
"""Test for fusion of conv and bias."""
#padding SAME
class ConvBiasMkldnnFusePassTest(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 3, 100, 100], dtype="float32")
param_attr = fluid.ParamAttr(
initializer=fluid.initializer.Xavier(uniform=False),
learning_rate=0.001)
conv_out = fluid.layers.conv2d(
input=data,
num_filters=3,
filter_size=3,
padding="SAME",
bias_attr=param_attr)
self.feeds = {
"data": np.random.random((1, 3, 100, 100)).astype("float32")
}
self.fetch_list = [conv_out]
self.enable_mkldnn = True
def test_check_output(self):
use_gpu = False
self.check_output_with_option(use_gpu)
#padding VALID
class ConvBiasMkldnnFusePassTest1(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 3, 100, 100], dtype="float32")
param_attr = fluid.ParamAttr(
initializer=fluid.initializer.Xavier(uniform=False),
learning_rate=0.001)
conv_out = fluid.layers.conv2d(
input=data,
num_filters=3,
filter_size=3,
padding="VALID",
bias_attr=param_attr)
self.feeds = {
"data": np.random.random((1, 3, 100, 100)).astype("float32")
}
self.fetch_list = [conv_out]
self.enable_mkldnn = True
def test_check_output(self):
use_gpu = False
self.check_output_with_option(use_gpu)
#padding number
class ConvBiasMkldnnFusePassTest2(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 3, 100, 100], dtype="float32")
param_attr = fluid.ParamAttr(
initializer=fluid.initializer.Xavier(uniform=False),
learning_rate=0.001)
conv_out = fluid.layers.conv2d(
input=data,
num_filters=3,
filter_size=3,
padding=[2, 4, 6, 8],
bias_attr=param_attr)
self.feeds = {
"data": np.random.random((1, 3, 100, 100)).astype("float32")
}
self.fetch_list = [conv_out]
self.enable_mkldnn = True
def test_check_output(self):
use_gpu = False
self.check_output_with_option(use_gpu)
#dilation not supported yet, just print warning log and does not fuse
class ConvBiasMkldnnFusePassTest3(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 3, 100, 100], dtype="float32")
param_attr = fluid.ParamAttr(
initializer=fluid.initializer.Xavier(uniform=False),
learning_rate=0.001)
conv_out = fluid.layers.conv2d(
input=data,
num_filters=3,
filter_size=3,
padding="VALID",
dilation=2,
groups=3,
bias_attr=param_attr,
use_cudnn=False,
act="softmax",
data_format="NCHW")
self.feeds = {
"data": np.random.random((1, 3, 100, 100)).astype("float32")
}
self.fetch_list = [conv_out]
self.enable_mkldnn = True
def test_check_output(self):
use_gpu = False
self.check_output_with_option(use_gpu)
#all conv params except for dilation
class ConvBiasMkldnnFusePassTest4(InferencePassTest):
def setUp(self):
with fluid.program_guard(self.main_program, self.startup_program):
data = fluid.data(
name="data", shape=[-1, 3, 100, 100], dtype="float32")
param_attr = fluid.ParamAttr(
initializer=fluid.initializer.Xavier(uniform=False),
learning_rate=0.001)
conv_out = fluid.layers.conv2d(
input=data,
num_filters=3,
filter_size=3,
padding="VALID",
groups=3,
bias_attr=param_attr,
use_cudnn=False,
act="softmax",
data_format="NCHW")
self.feeds = {
"data": np.random.random((1, 3, 100, 100)).astype("float32")
}
self.fetch_list = [conv_out]
self.enable_mkldnn = True
def test_check_output(self):
use_gpu = False
self.check_output_with_option(use_gpu)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_auto_checkpoint_dist_basic.py
浏览文件 @ 95710a33
......@@ -67,13 +67,13 @@ class AutoCheckpointTestDist(AutoCheckPointACLBase):
save_dir = "./run_save_0"
fs.delete(save_dir)
#basic
# basic
exe, main_prog, startup_prog = self._generate()
compiled, data_loader, optimizer, loss, image, label = \
self._init_env(exe, main_prog, startup_prog, minimize=False)
#fleet
# fleet
os.environ["TRAINING_ROLE"] = "TRAINER"
os.environ["PADDLE_TRAINER_ID"] = "0"
os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:6070"
......
python/paddle/fluid/tests/unittests/test_checkpoint_saver.py
浏览文件 @ 95710a33
......@@ -21,8 +21,7 @@ from paddle.fluid.incubate.checkpoint.checkpoint_saver import CheckpointSaver
import os
import sys
from paddle.fluid.incubate.fleet.utils.fs import LocalFS
from paddle.fluid.incubate.fleet.utils.hdfs import HDFSClient
from paddle.distributed.fleet.utils.fs import LocalFS, HDFSClient
from paddle.fluid.incubate.checkpoint.checkpoint_saver import CheckpointSaver
......
python/paddle/fluid/tests/unittests/test_cross_entropy_loss.py
浏览文件 @ 95710a33
......@@ -26,7 +26,7 @@ def stable_softmax(x):
return exps / np.sum(exps)
def log_softmax(x, axis=-1):
def log_softmax(x, axis=1):
softmax_out = np.apply_along_axis(stable_softmax, axis, x)
return np.log(softmax_out)
......
python/paddle/fluid/tests/unittests/test_device_guard.py
浏览文件 @ 95710a33
......@@ -33,6 +33,14 @@ def execute(main_program, startup_program):
exe.run(main_program)
def get_vaild_warning_num(warning, w):
num = 0
for i in range(len(w)):
if warning in str(w[i].message):
num += 1
return num
class TestDeviceGuard(unittest.TestCase):
def test_device_guard(self):
main_program = fluid.Program()
......@@ -133,7 +141,10 @@ class TestDeviceGuard(unittest.TestCase):
i = fluid.layers.increment(x=i, value=1, in_place=True)
fluid.layers.less_than(x=i, y=loop_len, cond=cond)
assert len(w) == 1
warning = "The Op(while) is not support to set device."
warning_num = get_vaild_warning_num(warning, w)
assert warning_num == 1
all_ops = main_program.global_block().ops
device_attr_name = core.op_proto_and_checker_maker.kOpDeviceAttrName()
for op in all_ops:
......@@ -169,7 +180,10 @@ class TestDeviceGuard(unittest.TestCase):
shape=[1], value=4.0, dtype='float32')
result = fluid.layers.less_than(x=x, y=y, force_cpu=False)
assert len(w) == 2
warning = "\'device_guard\' has higher priority when they are used at the same time."
warning_num = get_vaild_warning_num(warning, w)
assert warning_num == 2
all_ops = main_program.global_block().ops
device_attr_name = core.op_proto_and_checker_maker.kOpDeviceAttrName()
for op in all_ops:
......
python/paddle/fluid/tests/unittests/test_elementwise_floordiv_op.py
浏览文件 @ 95710a33
......@@ -67,6 +67,13 @@ class TestElementwiseModOp_scalar(TestElementwiseModOp):
self.out = np.floor_divide(self.x, self.y)
class TestElementwiseModOpInverse(TestElementwiseModOp):
def init_input_output(self):
self.x = np.random.uniform(0, 10000, [10]).astype(self.dtype)
self.y = np.random.uniform(0, 1000, [10, 10]).astype(self.dtype)
self.out = np.floor_divide(self.x, self.y)
class TestFloorDivideOp(unittest.TestCase):
def test_name(self):
with fluid.program_guard(fluid.Program()):
......
python/paddle/fluid/tests/unittests/test_empty_like_op.py 0 → 100644
浏览文件 @ 95710a33
# Copyright (c) 2020 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.
from __future__ import print_function
import unittest
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.data_feeder import convert_dtype
import paddle.fluid.core as core
from paddle.static import program_guard, Program
class TestEmptyLikeAPICommon(unittest.TestCase):
def __check_out__(self, out):
data_type = convert_dtype(out.dtype)
self.assertEqual(data_type, self.dst_dtype,
'dtype should be %s, but get %s' %
(self.dst_dtype, data_type))
shape = out.shape
self.assertTupleEqual(shape, self.dst_shape,
'shape should be %s, but get %s' %
(self.dst_shape, shape))
if data_type in ['float32', 'float64', 'int32', 'int64']:
max_value = np.nanmax(out)
min_value = np.nanmin(out)
always_non_full_zero = max_value > min_value
always_full_zero = max_value == 0.0 and min_value == 0.0
self.assertTrue(always_full_zero or always_non_full_zero,
'always_full_zero or always_non_full_zero.')
elif data_type in ['bool']:
total_num = out.size
true_num = np.sum(out == True)
false_num = np.sum(out == False)
self.assertTrue(total_num == true_num + false_num,
'The value should always be True or False.')
else:
self.assertTrue(False, 'invalid data type')
class TestEmptyLikeAPI(TestEmptyLikeAPICommon):
def setUp(self):
self.init_config()
def test_dygraph_api_out(self):
paddle.disable_static()
out = paddle.empty_like(self.x, self.dtype)
self.__check_out__(out.numpy())
paddle.enable_static()
def init_config(self):
self.x = np.random.random((200, 3)).astype("float32")
self.dtype = self.x.dtype
self.dst_shape = self.x.shape
self.dst_dtype = self.dtype
class TestEmptyLikeAPI2(TestEmptyLikeAPI):
def init_config(self):
self.x = np.random.random((200, 3)).astype("float64")
self.dtype = self.x.dtype
self.dst_shape = self.x.shape
self.dst_dtype = self.dtype
class TestEmptyLikeAPI3(TestEmptyLikeAPI):
def init_config(self):
self.x = np.random.random((200, 3)).astype("int")
self.dtype = self.x.dtype
self.dst_shape = self.x.shape
self.dst_dtype = self.dtype
class TestEmptyLikeAPI4(TestEmptyLikeAPI):
def init_config(self):
self.x = np.random.random((200, 3)).astype("int64")
self.dtype = self.x.dtype
self.dst_shape = self.x.shape
self.dst_dtype = self.dtype
class TestEmptyLikeAPI5(TestEmptyLikeAPI):
def init_config(self):
self.x = np.random.random((200, 3)).astype("bool")
self.dtype = self.x.dtype
self.dst_shape = self.x.shape
self.dst_dtype = self.dtype
class TestEmptyLikeAPI6(TestEmptyLikeAPI):
def init_config(self):
self.x = np.random.random((200, 3)).astype("float64")
self.dtype = "float32"
self.dst_shape = self.x.shape
self.dst_dtype = self.dtype
class TestEmptyLikeAPI7(TestEmptyLikeAPI):
def init_config(self):
self.x = np.random.random((200, 3)).astype("int")
self.dtype = "float32"
self.dst_shape = self.x.shape
self.dst_dtype = self.dtype
class TestEmptyLikeAPI8(TestEmptyLikeAPI):
def init_config(self):
self.x = np.random.random((200, 3)).astype("int64")
self.dtype = "float32"
self.dst_shape = self.x.shape
self.dst_dtype = self.dtype
class TestEmptyLikeAPI9(TestEmptyLikeAPI):
def init_config(self):
self.x = np.random.random((200, 3)).astype("bool")
self.dtype = "float32"
self.dst_shape = self.x.shape
self.dst_dtype = self.dtype
class TestEmptyLikeAPI10(TestEmptyLikeAPI):
def init_config(self):
self.x = np.random.random((200, 3)).astype("float32")
self.dtype = "bool"
self.dst_shape = self.x.shape
self.dst_dtype = self.dtype
class TestEmptyLikeAPI_Static(TestEmptyLikeAPICommon):
def setUp(self):
self.init_config()
def test_static_graph(self):
dtype = 'float32'
train_program = Program()
startup_program = Program()
with program_guard(train_program, startup_program):
x = np.random.random(self.x_shape).astype(dtype)
data_x = paddle.static.data(
'x', shape=self.data_x_shape, dtype=dtype)
out = paddle.empty_like(data_x)
place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda(
) else paddle.CPUPlace()
exe = paddle.static.Executor(place)
res = exe.run(train_program, feed={'x': x}, fetch_list=[out])
self.dst_dtype = dtype
self.dst_shape = x.shape
self.__check_out__(res[0])
def init_config(self):
self.x_shape = (200, 3)
self.data_x_shape = [200, 3]
class TestEmptyLikeAPI_Static2(TestEmptyLikeAPI_Static):
def init_config(self):
self.x_shape = (3, 200, 3)
self.data_x_shape = [-1, 200, 3]
class TestEmptyError(unittest.TestCase):
def test_attr(self):
def test_dtype():
x = np.random.random((200, 3)).astype("float64")
dtype = 'uint8'
result = paddle.empty_like(x, dtype=dtype)
self.assertRaises(TypeError, test_dtype)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_fleet_checkpoint.py
浏览文件 @ 95710a33
......@@ -21,8 +21,7 @@ from paddle.fluid.incubate.checkpoint.checkpoint_saver import CheckpointSaver
import os
import sys
from paddle.fluid.incubate.fleet.utils.fs import LocalFS
from paddle.fluid.incubate.fleet.utils.hdfs import HDFSClient
from paddle.distributed.fleet.utils.fs import LocalFS, HDFSClient
from paddle.fluid.incubate.checkpoint.checkpoint_saver import CheckpointSaver
......
python/paddle/fluid/tests/unittests/test_fleet_distributed_strategy.py
浏览文件 @ 95710a33
......@@ -86,6 +86,13 @@ class TestStrategyConfig(unittest.TestCase):
self.assertEqual(strategy.localsgd_configs["k_steps"], 4)
self.assertEqual(strategy.localsgd_configs["begin_step"], 120)
def test_adaptive_localsgd_configs(self):
strategy = paddle.distributed.fleet.DistributedStrategy()
configs = {"init_k_steps": 1, "begin_step": 120}
strategy.adaptive_localsgd_configs = configs
self.assertEqual(strategy.adaptive_localsgd_configs["init_k_steps"], 1)
self.assertEqual(strategy.adaptive_localsgd_configs["begin_step"], 120)
def test_dgc(self):
strategy = paddle.distributed.fleet.DistributedStrategy()
strategy.dgc = True
......
python/paddle/hapi/model.py
浏览文件 @ 95710a33
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