提交 a412826c 编写于 作者: Z Zhen Wang

Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into nlp-dam

......@@ -62,13 +62,11 @@ option(WITH_DISTRIBUTE "Compile with distributed support" OFF)
option(USE_EIGEN_FOR_BLAS "Use matrix multiplication in Eigen" OFF)
option(EIGEN_USE_THREADS "Compile with multi-threaded Eigen" OFF)
option(WITH_ARM_FP16 "Use half precision support on armv8.2-a cpu" OFF)
option(WITH_FAST_BUNDLE_TEST "Bundle tests that can be run in a single process together to reduce launch overhead" OFF)
option(WITH_CONTRIB "Compile the third-party contributation" OFF)
option(REPLACE_ENFORCE_GLOG "Replace PADDLE_ENFORCE with glog/CHECK for better debug." OFF)
option(WITH_ANAKIN "Compile with Anakin library" OFF)
option(WITH_GRPC "Use grpc as the default rpc framework" ${WITH_DISTRIBUTE})
option(WITH_BRPC_RDMA "Use brpc rdma as the rpc protocal" OFF)
option(WITH_INFERENCE "Compile fluid inference library" ON)
option(ON_INFER "Turn on inference optimization." OFF)
option(WITH_INFERENCE_API_TEST "Test fluid inference high-level api interface" OFF)
option(WITH_SYSTEM_BLAS "Use system blas library" OFF)
......
......@@ -2,8 +2,8 @@
[![Build Status](https://travis-ci.org/PaddlePaddle/Paddle.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/Paddle)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://paddlepaddle.org/documentation/docs/en/1.0/getstarted/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://paddlepaddle.org/documentation/docs/zh/1.0/beginners_guide/index.html)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://paddlepaddle.org/documentation/docs/en/1.1/getstarted/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://paddlepaddle.org/documentation/docs/zh/1.1/beginners_guide/index.html)
[![Release](https://img.shields.io/github/release/PaddlePaddle/Paddle.svg)](https://github.com/PaddlePaddle/Paddle/releases)
[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
......@@ -19,7 +19,7 @@ Our vision is to enable deep learning for everyone via PaddlePaddle.
Please refer to our [release announcement](https://github.com/PaddlePaddle/Paddle/releases) to track the latest feature of PaddlePaddle.
### Latest PaddlePaddle Release: [Fluid 1.0.1](https://github.com/PaddlePaddle/Paddle/tree/release/1.0.0)
### Latest PaddlePaddle Release: [Fluid 1.1.0](https://github.com/PaddlePaddle/Paddle/tree/release/1.1)
### Install Latest Stable Release:
```
# Linux CPU
......@@ -27,9 +27,9 @@ pip install paddlepaddle
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu
# Linux GPU cuda8cudnn7
pip install paddlepaddle-gpu==1.0.1.post87
pip install paddlepaddle-gpu==1.1.0.post87
# Linux GPU cuda8cudnn5
pip install paddlepaddle-gpu==1.0.1.post85
pip install paddlepaddle-gpu==1.1.0.post85
# For installation on other platform, refer to http://paddlepaddle.org/
```
......@@ -76,26 +76,26 @@ pip install paddlepaddle-gpu==1.0.1.post85
## Installation
It is recommended to read [this doc](http://paddlepaddle.org/documentation/docs/zh/1.0/beginners_guide/index.html) on our website.
It is recommended to read [this doc](http://paddlepaddle.org/documentation/docs/zh/1.1/beginners_guide/index.html) on our website.
## Documentation
We provide [English](http://paddlepaddle.org/documentation/docs/en/1.0.0/getstarted/index_en.html) and
[Chinese](http://paddlepaddle.org/documentation/docs/zh/1.0/beginners_guide/index.html) documentation.
We provide [English](http://paddlepaddle.org/documentation/docs/en/1.1/getstarted/index_en.html) and
[Chinese](http://paddlepaddle.org/documentation/docs/zh/1.1/beginners_guide/index.html) documentation.
- [Deep Learning 101](https://github.com/PaddlePaddle/book)
You might want to start from this online interactive book that can run in a Jupyter Notebook.
- [Distributed Training](http://paddlepaddle.org/documentation/docs/zh/1.0/user_guides/howto/training/cluster_howto.html)
- [Distributed Training](http://paddlepaddle.org/documentation/docs/zh/1.1/user_guides/howto/training/cluster_howto.html)
You can run distributed training jobs on MPI clusters.
- [Python API](http://paddlepaddle.org/documentation/api/zh/1.0/fluid.html)
- [Python API](http://paddlepaddle.org/documentation/api/zh/1.1/fluid.html)
Our new API enables much shorter programs.
- [How to Contribute](http://paddlepaddle.org/documentation/docs/zh/1.0/advanced_usage/development/contribute_to_paddle.html)
- [How to Contribute](http://paddlepaddle.org/documentation/docs/zh/1.1/advanced_usage/development/contribute_to_paddle.html)
We appreciate your contributions!
......
......@@ -64,11 +64,11 @@ paddle.fluid.layers.chunk_eval ArgSpec(args=['input', 'label', 'chunk_scheme', '
paddle.fluid.layers.sequence_conv ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'bias_attr', 'param_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(3, 1, None, None, None, None, None))
paddle.fluid.layers.conv2d ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None))
paddle.fluid.layers.conv3d ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None))
paddle.fluid.layers.sequence_pool ArgSpec(args=['input', 'pool_type'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.sequence_pool ArgSpec(args=['input', 'pool_type', 'is_test'], varargs=None, keywords=None, defaults=(False,))
paddle.fluid.layers.sequence_softmax ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None))
paddle.fluid.layers.softmax ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(True, None))
paddle.fluid.layers.pool2d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None))
paddle.fluid.layers.pool3d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None))
paddle.fluid.layers.pool2d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True))
paddle.fluid.layers.pool3d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True))
paddle.fluid.layers.batch_norm ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, None, None, None, False, False))
paddle.fluid.layers.beam_search_decode ArgSpec(args=['ids', 'scores', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.conv2d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None))
......@@ -103,7 +103,7 @@ paddle.fluid.layers.beam_search ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 's
paddle.fluid.layers.row_conv ArgSpec(args=['input', 'future_context_size', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.multiplex ArgSpec(args=['inputs', 'index'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.layer_norm ArgSpec(args=['input', 'scale', 'shift', 'begin_norm_axis', 'epsilon', 'param_attr', 'bias_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(True, True, 1, 1e-05, None, None, None, None))
paddle.fluid.layers.softmax_with_cross_entropy ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index'], varargs=None, keywords=None, defaults=(False, -100))
paddle.fluid.layers.softmax_with_cross_entropy ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index', 'numeric_stable_mode'], varargs=None, keywords=None, defaults=(False, -100, False))
paddle.fluid.layers.smooth_l1 ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.one_hot ArgSpec(args=['input', 'depth'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.autoincreased_step_counter ArgSpec(args=['counter_name', 'begin', 'step'], varargs=None, keywords=None, defaults=(None, 1, 1))
......@@ -174,9 +174,11 @@ paddle.fluid.layers.mean ArgSpec(args=['x', 'name'], varargs=None, keywords=None
paddle.fluid.layers.mul ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None))
paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.maxout ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.affine_grid ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sequence_reverse ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.affine_channel ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None))
paddle.fluid.layers.hash ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None))
paddle.fluid.layers.grid_sampler ArgSpec(args=['x', 'grid', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.log_loss ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None))
paddle.fluid.layers.add_position_encoding ArgSpec(args=['input', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
......
......@@ -9,8 +9,6 @@ add_subdirectory(pybind)
add_subdirectory(recordio)
endif(NOT WIN32)
if(WITH_INFERENCE)
# NOTE: please add subdirectory inference at last.
add_subdirectory(inference)
add_subdirectory(train)
endif()
# NOTE: please add subdirectory inference at last.
add_subdirectory(inference)
add_subdirectory(train)
......@@ -35,13 +35,15 @@ if(WITH_GPU)
all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle graph graph_helper pass)
endif()
cc_library(sequential_execution_pass SRCS sequential_execution_pass.cc DEPS graph graph_helper pass)
cc_library(multi_devices_graph_pass SRCS multi_devices_graph_pass.cc DEPS multi_devices_helper computation_op_handle
scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle fused_broadcast_op_handle)
if(WITH_GPU)
cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS graph framework_proto reference_count_pass)
cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS graph framework_proto reference_count_pass sequential_execution_pass)
else()
cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS graph framework_proto)
cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS graph framework_proto sequential_execution_pass)
endif()
cc_library(threaded_ssa_graph_executor SRCS threaded_ssa_graph_executor.cc DEPS fetch_op_handle ssa_graph_executor scope
......@@ -56,6 +58,7 @@ cc_library(scope_buffered_ssa_graph_executor SRCS scope_buffered_ssa_graph_execu
# device_context reduce_op_handle )
cc_library(fast_threaded_ssa_graph_executor SRCS fast_threaded_ssa_graph_executor.cc
DEPS fetch_op_handle ssa_graph_executor scope simple_threadpool device_context)
cc_test(fused_broadcast_op_test SRCS fused_broadcast_op_handle_test.cc DEPS fused_broadcast_op_handle)
cc_library(build_strategy SRCS build_strategy.cc DEPS
graph_viz_pass multi_devices_graph_pass
......
......@@ -34,7 +34,7 @@ AllReduceOpHandle::AllReduceOpHandle(ir::Node *node,
nccl_ctxs_(ctxs) {
if (nccl_ctxs_) {
for (auto &p : places_) {
this->dev_ctxes_[p] = nccl_ctxs_->DevCtx(p);
this->SetDeviceContext(p, nccl_ctxs_->DevCtx(p));
}
}
}
......@@ -46,7 +46,7 @@ AllReduceOpHandle::AllReduceOpHandle(ir::Node *node,
#endif
void AllReduceOpHandle::RunImpl() {
platform::RecordEvent record_event(Name(), dev_ctxes_.begin()->second);
platform::RecordEvent record_event(Name(), dev_ctxes_.cbegin()->second);
if (NoDummyInputSize() == 1) {
return; // No need to all reduce when GPU count = 1;
......@@ -127,7 +127,7 @@ void AllReduceOpHandle::RunImpl() {
*local_scopes_[i]->FindVar(kLocalExecScopeName)->Get<Scope *>();
auto &p = places_[i];
auto *var = scope.FindVar(out_var_handles[i]->name_);
auto *dev_ctx = dev_ctxes_[p];
auto *dev_ctx = dev_ctxes_.at(p);
RunAndRecordEvent(p, [&trg, var, dev_ctx, p] {
auto &tensor_gpu = *var->GetMutable<framework::LoDTensor>();
......
......@@ -44,7 +44,8 @@ struct BroadcastOpHandle : public OpHandleBase {
nccl_ctxs_(nccl_ctxs) {
if (nccl_ctxs_) {
for (auto &p_ctx : nccl_ctxs_->contexts_) {
dev_ctxes_[platform::CUDAPlace(p_ctx.first)] = p_ctx.second.ctx_.get();
this->SetDeviceContext(platform::CUDAPlace(p_ctx.first),
p_ctx.second.ctx_.get());
}
}
}
......
......@@ -12,232 +12,12 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/broadcast_op_handle.h"
#include "gtest/gtest.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/framework/details/broadcast_op_handle_test.h"
namespace paddle {
namespace framework {
namespace details {
namespace f = paddle::framework;
namespace p = paddle::platform;
// test data amount
const f::DDim kDims = {20, 20};
struct TestBroadcastOpHandle {
std::vector<std::unique_ptr<p::DeviceContext>> ctxs_;
std::vector<Scope*> local_scopes_;
std::vector<Scope*> param_scopes_;
Scope g_scope_;
std::unique_ptr<OpHandleBase> op_handle_;
std::vector<std::unique_ptr<VarHandleBase>> vars_;
std::vector<p::Place> gpu_list_;
bool use_gpu_;
#ifdef PADDLE_WITH_CUDA
std::unique_ptr<platform::NCCLContextMap> nccl_ctxs_;
#endif
void WaitAll() {
for (size_t j = 0; j < ctxs_.size(); ++j) {
ctxs_[j]->Wait();
}
#ifdef PADDLE_WITH_CUDA
if (nccl_ctxs_) {
nccl_ctxs_->WaitAll();
}
#endif
}
void InitCtxOnGpu(bool use_gpu) {
use_gpu_ = use_gpu;
if (use_gpu_) {
#ifdef PADDLE_WITH_CUDA
int count = p::GetCUDADeviceCount();
if (count <= 1) {
LOG(WARNING) << "Cannot test multi-gpu Broadcast, because the CUDA "
"device count is "
<< count;
exit(0);
}
for (int i = 0; i < count; ++i) {
auto p = p::CUDAPlace(i);
gpu_list_.push_back(p);
ctxs_.emplace_back(new p::CUDADeviceContext(p));
}
nccl_ctxs_.reset(new platform::NCCLContextMap(gpu_list_));
#else
PADDLE_THROW("CUDA is not support.");
#endif
} else {
int count = 8;
for (int i = 0; i < count; ++i) {
auto p = p::CPUPlace();
gpu_list_.push_back(p);
ctxs_.emplace_back(new p::CPUDeviceContext(p));
}
#ifdef PADDLE_WITH_CUDA
nccl_ctxs_.reset(nullptr);
#endif
}
}
void InitBroadcastOp(size_t input_scope_idx) {
for (size_t j = 0; j < gpu_list_.size(); ++j) {
local_scopes_.push_back(&(g_scope_.NewScope()));
Scope& local_scope = local_scopes_.back()->NewScope();
*local_scopes_.back()
->Var(details::kLocalExecScopeName)
->GetMutable<Scope*>() = &local_scope;
local_scope.Var("out");
param_scopes_.emplace_back(&local_scope);
}
param_scopes_[input_scope_idx]->Var("input");
std::unique_ptr<ir::Node> n =
ir::CreateNodeForTest("node0", ir::Node::Type::kOperation);
if (use_gpu_) {
#ifdef PADDLE_WITH_CUDA
op_handle_.reset(new BroadcastOpHandle(n.get(), local_scopes_, gpu_list_,
nccl_ctxs_.get()));
#else
PADDLE_THROW("CUDA is not support.");
#endif
} else {
#ifdef PADDLE_WITH_CUDA
op_handle_.reset(new BroadcastOpHandle(n.get(), local_scopes_, gpu_list_,
nccl_ctxs_.get()));
#else
op_handle_.reset(
new BroadcastOpHandle(n.get(), local_scopes_, gpu_list_));
#endif
}
std::unique_ptr<ir::Node> v =
ir::CreateNodeForTest("node1", ir::Node::Type::kVariable);
auto* in_var_handle = new VarHandle(v.get(), 1, input_scope_idx, "input",
gpu_list_[input_scope_idx]);
vars_.emplace_back(in_var_handle);
op_handle_->AddInput(in_var_handle);
// add dummy var
std::unique_ptr<ir::Node> v2 =
ir::CreateNodeForTest("node2", ir::Node::Type::kVariable);
vars_.emplace_back(new DummyVarHandle(v2.get()));
DummyVarHandle* dummy_var_handle =
static_cast<DummyVarHandle*>(vars_.back().get());
dummy_var_handle->ClearGeneratedOp();
op_handle_->AddInput(dummy_var_handle);
for (size_t j = 0; j < gpu_list_.size(); ++j) {
if (!use_gpu_) {
op_handle_->SetDeviceContext(gpu_list_[j], ctxs_[j].get());
}
std::unique_ptr<ir::Node> v3 =
ir::CreateNodeForTest("node3", ir::Node::Type::kVariable);
VarHandle* out_var_handle =
new VarHandle(v3.get(), 2, j, "out", gpu_list_[j]);
vars_.emplace_back(out_var_handle);
op_handle_->AddOutput(out_var_handle);
}
// add dummy var
std::unique_ptr<ir::Node> v4 =
ir::CreateNodeForTest("node4", ir::Node::Type::kVariable);
vars_.emplace_back(new DummyVarHandle(v4.get()));
DummyVarHandle* out_dummy_var_handle =
static_cast<DummyVarHandle*>(vars_.back().get());
out_dummy_var_handle->ClearGeneratedOp();
op_handle_->AddOutput(out_dummy_var_handle);
}
void TestBroadcastLodTensor(size_t input_scope_idx) {
auto in_var = param_scopes_[input_scope_idx]->FindVar("input");
PADDLE_ENFORCE_NOT_NULL(in_var);
auto in_lod_tensor = in_var->GetMutable<f::LoDTensor>();
in_lod_tensor->mutable_data<float>(kDims, gpu_list_[input_scope_idx]);
std::vector<float> send_vector(static_cast<size_t>(f::product(kDims)));
for (size_t k = 0; k < send_vector.size(); ++k) {
send_vector[k] = k;
}
f::LoD lod{{0, 10, 20}};
paddle::framework::TensorFromVector<float>(
send_vector, *(ctxs_[input_scope_idx]), in_lod_tensor);
in_lod_tensor->set_lod(lod);
in_lod_tensor->Resize(kDims);
op_handle_->Run(false);
WaitAll();
p::CPUPlace cpu_place;
for (size_t j = 0; j < gpu_list_.size(); ++j) {
auto out_var = param_scopes_[j]->FindVar("out");
PADDLE_ENFORCE_NOT_NULL(out_var);
auto out_tensor = out_var->Get<f::LoDTensor>();
PADDLE_ENFORCE_EQ(out_tensor.lod(), lod, "lod is not equal.");
f::Tensor result_tensor;
f::TensorCopySync(out_tensor, cpu_place, &result_tensor);
float* ct = result_tensor.mutable_data<float>(cpu_place);
for (int64_t i = 0; i < f::product(kDims); ++i) {
ASSERT_NEAR(ct[i], send_vector[i], 1e-5);
}
}
}
void TestBroadcastSelectedRows(size_t input_scope_idx) {
auto in_var = param_scopes_[input_scope_idx]->FindVar("input");
PADDLE_ENFORCE_NOT_NULL(in_var);
auto in_selected_rows = in_var->GetMutable<f::SelectedRows>();
auto value = in_selected_rows->mutable_value();
value->mutable_data<float>(kDims, gpu_list_[input_scope_idx]);
int height = static_cast<int>(kDims[0]) * 2;
std::vector<int64_t> rows{0, 1, 2, 3, 3, 0, 14, 7, 3, 1,
2, 4, 6, 3, 1, 1, 1, 1, 3, 7};
in_selected_rows->set_height(height);
in_selected_rows->set_rows(rows);
std::vector<float> send_vector(static_cast<size_t>(f::product(kDims)));
for (size_t k = 0; k < send_vector.size(); ++k) {
send_vector[k] = k;
}
paddle::framework::TensorFromVector<float>(
send_vector, *(ctxs_[input_scope_idx]), value);
op_handle_->Run(false);
WaitAll();
p::CPUPlace cpu_place;
for (size_t j = 0; j < gpu_list_.size(); ++j) {
auto out_var = param_scopes_[j]->FindVar("out");
PADDLE_ENFORCE_NOT_NULL(out_var);
auto& out_select_rows = out_var->Get<f::SelectedRows>();
auto rt = out_select_rows.value();
PADDLE_ENFORCE_EQ(out_select_rows.height(), height,
"height is not equal.");
for (size_t k = 0; k < out_select_rows.rows().size(); ++k) {
PADDLE_ENFORCE_EQ(out_select_rows.rows()[k], rows[k]);
}
f::Tensor result_tensor;
f::TensorCopySync(rt, cpu_place, &result_tensor);
float* ct = result_tensor.data<float>();
for (int64_t i = 0; i < f::product(kDims); ++i) {
ASSERT_NEAR(ct[i], send_vector[i], 1e-5);
}
}
}
};
TEST(BroadcastTester, TestCPUBroadcastTestLodTensor) {
TestBroadcastOpHandle test_op;
size_t input_scope_idx = 0;
......
// 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.
#pragma once
#include <string>
#include <vector>
#include "gtest/gtest.h"
#include "paddle/fluid/framework/details/broadcast_op_handle.h"
#include "paddle/fluid/platform/device_context.h"
namespace paddle {
namespace framework {
namespace details {
namespace f = paddle::framework;
namespace p = paddle::platform;
// test data amount
const f::DDim kDims = {20, 20};
struct TestBroadcastOpHandle {
std::vector<std::unique_ptr<p::DeviceContext>> ctxs_;
std::vector<Scope*> local_scopes_;
std::vector<Scope*> param_scopes_;
Scope g_scope_;
std::unique_ptr<OpHandleBase> op_handle_;
std::vector<std::unique_ptr<VarHandleBase>> vars_;
std::vector<p::Place> place_list_;
bool use_gpu_;
#ifdef PADDLE_WITH_CUDA
std::unique_ptr<platform::NCCLContextMap> nccl_ctxs_;
#endif
void WaitAll() {
for (size_t j = 0; j < ctxs_.size(); ++j) {
ctxs_[j]->Wait();
}
#ifdef PADDLE_WITH_CUDA
if (nccl_ctxs_) {
nccl_ctxs_->WaitAll();
}
#endif
}
void InitCtxOnGpu(bool use_gpu) {
use_gpu_ = use_gpu;
if (use_gpu_) {
#ifdef PADDLE_WITH_CUDA
int count = p::GetCUDADeviceCount();
if (count <= 1) {
LOG(WARNING) << "Cannot test multi-gpu Broadcast, because the CUDA "
"device count is "
<< count;
exit(0);
}
for (int i = 0; i < count; ++i) {
auto p = p::CUDAPlace(i);
place_list_.push_back(p);
ctxs_.emplace_back(new p::CUDADeviceContext(p));
}
nccl_ctxs_.reset(new platform::NCCLContextMap(place_list_));
#else
PADDLE_THROW("CUDA is not support.");
#endif
} else {
int count = 8;
for (int i = 0; i < count; ++i) {
auto p = p::CPUPlace();
place_list_.push_back(p);
ctxs_.emplace_back(new p::CPUDeviceContext(p));
}
#ifdef PADDLE_WITH_CUDA
nccl_ctxs_.reset(nullptr);
#endif
}
}
void InitBroadcastOp(size_t input_scope_idx) {
for (size_t j = 0; j < place_list_.size(); ++j) {
local_scopes_.push_back(&(g_scope_.NewScope()));
Scope& local_scope = local_scopes_.back()->NewScope();
*local_scopes_.back()
->Var(details::kLocalExecScopeName)
->GetMutable<Scope*>() = &local_scope;
local_scope.Var("out");
param_scopes_.emplace_back(&local_scope);
}
param_scopes_[input_scope_idx]->Var("input");
std::unique_ptr<ir::Node> n =
ir::CreateNodeForTest("node0", ir::Node::Type::kOperation);
if (use_gpu_) {
#ifdef PADDLE_WITH_CUDA
op_handle_.reset(new BroadcastOpHandle(n.get(), local_scopes_,
place_list_, nccl_ctxs_.get()));
#else
PADDLE_THROW("CUDA is not support.");
#endif
} else {
#ifdef PADDLE_WITH_CUDA
op_handle_.reset(new BroadcastOpHandle(n.get(), local_scopes_,
place_list_, nccl_ctxs_.get()));
#else
op_handle_.reset(
new BroadcastOpHandle(n.get(), local_scopes_, place_list_));
#endif
}
std::unique_ptr<ir::Node> v =
ir::CreateNodeForTest("node1", ir::Node::Type::kVariable);
auto* in_var_handle = new VarHandle(v.get(), 1, input_scope_idx, "input",
place_list_[input_scope_idx]);
vars_.emplace_back(in_var_handle);
op_handle_->AddInput(in_var_handle);
// add dummy var
std::unique_ptr<ir::Node> v2 =
ir::CreateNodeForTest("node2", ir::Node::Type::kVariable);
vars_.emplace_back(new DummyVarHandle(v2.get()));
DummyVarHandle* dummy_var_handle =
static_cast<DummyVarHandle*>(vars_.back().get());
dummy_var_handle->ClearGeneratedOp();
op_handle_->AddInput(dummy_var_handle);
for (size_t j = 0; j < place_list_.size(); ++j) {
if (!use_gpu_) {
op_handle_->SetDeviceContext(place_list_[j], ctxs_[j].get());
}
std::unique_ptr<ir::Node> v3 =
ir::CreateNodeForTest("node3", ir::Node::Type::kVariable);
VarHandle* out_var_handle =
new VarHandle(v3.get(), 2, j, "out", place_list_[j]);
vars_.emplace_back(out_var_handle);
op_handle_->AddOutput(out_var_handle);
}
// add dummy var
std::unique_ptr<ir::Node> v4 =
ir::CreateNodeForTest("node4", ir::Node::Type::kVariable);
vars_.emplace_back(new DummyVarHandle(v4.get()));
DummyVarHandle* out_dummy_var_handle =
static_cast<DummyVarHandle*>(vars_.back().get());
out_dummy_var_handle->ClearGeneratedOp();
op_handle_->AddOutput(out_dummy_var_handle);
}
std::vector<float> InitLoDTensor(const std::string& varname,
size_t input_scope_idx, const f::LoD& lod,
float val_scalar = 0.0) {
auto var = param_scopes_[input_scope_idx]->FindVar(varname);
PADDLE_ENFORCE_NOT_NULL(var);
auto lod_tensor = var->GetMutable<f::LoDTensor>();
std::vector<float> send_vector(static_cast<size_t>(f::product(kDims)));
for (size_t k = 0; k < send_vector.size(); ++k) {
send_vector[k] = k + val_scalar;
}
paddle::framework::TensorFromVector<float>(
send_vector, *(ctxs_[input_scope_idx]), lod_tensor);
lod_tensor->set_lod(lod);
lod_tensor->Resize(kDims);
return send_vector;
}
std::vector<float> InitSelectedRows(const std::string& varname,
size_t input_scope_idx,
const std::vector<int64_t>& rows,
int height, float value_scalar = 0.0) {
std::vector<float> send_vector(static_cast<size_t>(f::product(kDims)));
for (size_t k = 0; k < send_vector.size(); ++k) {
send_vector[k] = k + value_scalar;
}
auto var = param_scopes_[input_scope_idx]->FindVar(varname);
PADDLE_ENFORCE_NOT_NULL(var);
auto selected_rows = var->GetMutable<f::SelectedRows>();
auto value = selected_rows->mutable_value();
value->mutable_data<float>(kDims, place_list_[input_scope_idx]);
selected_rows->set_height(height);
selected_rows->set_rows(rows);
paddle::framework::TensorFromVector<float>(
send_vector, *(ctxs_[input_scope_idx]), value);
return send_vector;
}
void SelectedRowsEqual(const std::string& varname, int input_scope_idx,
const std::vector<float>& send_vector,
const std::vector<int64_t>& rows, int height) {
auto var = param_scopes_[input_scope_idx]->FindVar(varname);
PADDLE_ENFORCE_NOT_NULL(var);
auto& selected_rows = var->Get<f::SelectedRows>();
auto rt = selected_rows.value();
PADDLE_ENFORCE_EQ(selected_rows.height(), height, "height is not equal.");
for (size_t k = 0; k < selected_rows.rows().size(); ++k) {
PADDLE_ENFORCE_EQ(selected_rows.rows()[k], rows[k]);
}
p::CPUPlace cpu_place;
f::Tensor result_tensor;
f::TensorCopySync(rt, cpu_place, &result_tensor);
float* ct = result_tensor.data<float>();
for (int64_t i = 0; i < f::product(kDims); ++i) {
ASSERT_NEAR(ct[i], send_vector[i], 1e-5);
}
}
void LoDTensorEqual(const std::string& varname,
const std::vector<float>& send_vec, const f::LoD& lod,
framework::Scope* scope) {
p::CPUPlace cpu_place;
auto var = scope->FindVar(varname);
PADDLE_ENFORCE_NOT_NULL(var);
auto tensor = var->Get<f::LoDTensor>();
PADDLE_ENFORCE_EQ(tensor.lod(), lod, "lod is not equal.");
f::Tensor result_tensor;
f::TensorCopySync(tensor, cpu_place, &result_tensor);
float* ct = result_tensor.mutable_data<float>(cpu_place);
for (int64_t k = 0; k < f::product(kDims); ++k) {
ASSERT_NEAR(ct[k], send_vec[k], 1e-5);
}
}
void TestBroadcastLodTensor(size_t input_scope_idx) {
f::LoD lod{{0, 10, 20}};
auto send_vector = InitLoDTensor("input", input_scope_idx, lod);
op_handle_->Run(false);
WaitAll();
for (size_t j = 0; j < place_list_.size(); ++j) {
LoDTensorEqual("out", send_vector, lod, param_scopes_[j]);
}
}
void TestBroadcastSelectedRows(size_t input_scope_idx) {
std::vector<int64_t> rows{0, 1, 2, 3, 3, 0, 14, 7, 3, 1,
2, 4, 6, 3, 1, 1, 1, 1, 3, 7};
int height = static_cast<int>(kDims[0] * 2);
auto send_vector = InitSelectedRows("input", input_scope_idx, rows, height);
op_handle_->Run(false);
WaitAll();
for (size_t j = 0; j < place_list_.size(); ++j) {
SelectedRowsEqual("out", input_scope_idx, send_vector, rows, height);
}
}
};
} // namespace details
} // namespace framework
} // namespace paddle
......@@ -16,6 +16,7 @@ limitations under the License. */
#include "paddle/fluid/framework/details/multi_devices_graph_check_pass.h"
#include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
#include "paddle/fluid/framework/details/sequential_execution_pass.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_viz_pass.h"
......@@ -27,6 +28,10 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
public:
explicit ParallelExecutorPassBuilder(const BuildStrategy &strategy)
: ir::PassBuilder(), strategy_(strategy) {
if (strategy_.enable_sequential_execution_) {
AppendPass("sequential_execution_pass");
}
// Add a graph viz pass to record a graph.
if (!strategy_.debug_graphviz_path_.empty()) {
auto viz_pass = AppendPass("graph_viz_pass");
......@@ -110,6 +115,11 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
pass->Erase("nccl_ctxs");
pass->SetNotOwned<platform::NCCLContextMap>("nccl_ctxs", nctx);
#endif
} else if (pass->Type() == "sequential_execution_pass") {
pass->Erase(kAllOpDescs);
pass->Set<const std::vector<OpDesc *>>(
kAllOpDescs,
new std::vector<OpDesc *>(main_program.Block(0).AllOps()));
}
graph = pass->Apply(std::move(graph));
}
......@@ -125,3 +135,4 @@ USE_PASS(multi_batch_merge_pass);
USE_PASS(multi_devices_pass);
USE_PASS(multi_devices_check_pass);
USE_PASS(multi_devices_print_pass);
USE_PASS(sequential_execution_pass);
......@@ -69,6 +69,8 @@ struct BuildStrategy {
bool enable_data_balance_{false};
bool enable_sequential_execution_{false};
bool fuse_broadcast_op_{false};
// User normally doesn't need to call this API.
......
......@@ -37,7 +37,7 @@ void ComputationOpHandle::RunImpl() {
bool ComputationOpHandle::NeedWait(VarHandleBase *in_var) {
bool need_wait =
in_var && in_var->GeneratedOp() &&
in_var->GeneratedOp()->DeviceContext(place_) != dev_ctxes_[place_];
in_var->GeneratedOp()->DeviceContext(place_) != dev_ctxes_.at(place_);
return need_wait;
}
......
......@@ -28,7 +28,7 @@ DataBalanceOpHandle::DataBalanceOpHandle(
: OpHandleBase(node), local_scopes_(local_scopes), places_(places) {
if (ctxs) {
for (auto &p : places_) {
this->dev_ctxes_[p] = ctxs->DevCtx(p);
this->SetDeviceContext(p, ctxs->DevCtx(p));
}
}
}
......@@ -89,8 +89,8 @@ void DataBalanceOpHandle::RunImpl() {
PADDLE_ENFORCE_GT(places_.size(), 1,
"Data balance can only be enabled when the number of "
"places to run larger than 1.");
auto in_var_handles = DynamicCast<VarHandle>(inputs_);
auto out_var_handles = DynamicCast<VarHandle>(outputs_);
auto in_var_handles = DynamicCast<VarHandle>(this->Inputs());
auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
PADDLE_ENFORCE(in_var_handles.size() % places_.size() == 0);
PADDLE_ENFORCE_EQ(
in_var_handles.size(), out_var_handles.size(),
......
......@@ -92,13 +92,13 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run(
size_t num_complete = 0;
remaining_ = 0;
BlockingQueue<size_t> complete_q;
auto complete_q = std::make_shared<BlockingQueue<size_t>>();
for (auto op : bootstrap_ops_) {
RunOpAsync(op_deps.get(), op, &complete_q);
RunOpAsync(op_deps.get(), op, complete_q);
}
while (num_complete != op_deps->size()) {
size_t num_comp = complete_q.Pop();
size_t num_comp = complete_q->Pop();
if (num_comp == -1UL) {
int remaining = 0;
while (true) {
......@@ -107,7 +107,7 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run(
break;
}
for (int i = 0; i < remaining; ++i) {
complete_q.Pop();
complete_q->Pop();
}
}
exception_.ReThrow();
......@@ -120,7 +120,8 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run(
}
void FastThreadedSSAGraphExecutor::RunOpAsync(
std::unordered_map<OpHandleBase *, std::atomic<int>> *op_deps,
OpHandleBase *op, BlockingQueue<size_t> *complete_q) {
OpHandleBase *op,
const std::shared_ptr<BlockingQueue<size_t>> &complete_q) {
++remaining_;
this->pool_.enqueue([=] {
OpHandleBase *op_to_run = op;
......@@ -144,7 +145,7 @@ void FastThreadedSSAGraphExecutor::RunOpAsync(
if (op_to_run == nullptr) {
op_to_run = pending_op;
} else {
this->RunOpAsync(op_deps, pending_op, complete_q);
RunOpAsync(op_deps, pending_op, complete_q);
}
}
}
......@@ -156,8 +157,7 @@ void FastThreadedSSAGraphExecutor::RunOpAsync(
}
void FastThreadedSSAGraphExecutor::PrepareAtomicOpDeps() {
atomic_op_deps_ = pool_.enqueue([&] {
std::unordered_map<OpHandleBase *, std::atomic<int>> *op_deps =
new std::unordered_map<OpHandleBase *, std::atomic<int>>;
auto *op_deps = new std::unordered_map<OpHandleBase *, std::atomic<int>>;
for (auto &pair : op_deps_) {
(*op_deps)[pair.first] = pair.second;
}
......
......@@ -50,7 +50,8 @@ class FastThreadedSSAGraphExecutor : public SSAGraphExecutor {
std::atomic<int> remaining_;
void RunOpAsync(std::unordered_map<OpHandleBase *, std::atomic<int>> *op_deps,
OpHandleBase *op, BlockingQueue<size_t> *complete_q);
OpHandleBase *op,
const std::shared_ptr<BlockingQueue<size_t>> &complete_q);
void PrepareAtomicOpDeps();
......
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/fused_broadcast_op_handle.h"
#include "gtest/gtest.h"
#include "paddle/fluid/framework/details/broadcast_op_handle_test.h"
namespace paddle {
namespace framework {
namespace details {
struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
std::vector<std::string> out_varnames_;
void InitFusedBroadcastOp(std::vector<size_t> input_scope_idxes) {
// initialize scope and var
for (size_t i = 0; i < place_list_.size(); ++i) {
local_scopes_.push_back(&(g_scope_.NewScope()));
Scope& local_scope = local_scopes_.back()->NewScope();
*local_scopes_.back()
->Var(details::kLocalExecScopeName)
->GetMutable<Scope*>() = &local_scope;
for (size_t j = 0; j < input_scope_idxes.size(); ++j) {
local_scope.Var("out_var" + j);
if (i == j) local_scope.Var("in_var" + j);
}
param_scopes_.emplace_back(&local_scope);
}
// create op handle node
std::unique_ptr<ir::Node> n =
ir::CreateNodeForTest("fused_broadcast", ir::Node::Type::kOperation);
if (use_gpu_) {
#ifdef PADDLE_WITH_CUDA
op_handle_.reset(new FusedBroadcastOpHandle(
n.get(), local_scopes_, place_list_, nccl_ctxs_.get()));
#else
PADDLE_THROW("CUDA is not supported.");
#endif
} else {
#ifdef PADDLE_WITH_CUDA
op_handle_.reset(new FusedBroadcastOpHandle(
n.get(), local_scopes_, place_list_, nccl_ctxs_.get()));
#else
op_handle_.reset(
new FusedBroadcastOpHandle(n.get(), local_scopes_, place_list_));
#endif
}
for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
// add input var handle
std::unique_ptr<ir::Node> in_node =
ir::CreateNodeForTest("in_node" + i, ir::Node::Type::kVariable);
VarHandle* in_var_handle =
new VarHandle(in_node.get(), 1, input_scope_idxes[i], "in_var" + i,
place_list_[input_scope_idxes[i]]);
vars_.emplace_back(in_var_handle);
op_handle_->AddInput(in_var_handle);
// add output var handle
for (size_t j = 0; j < place_list_.size(); ++j) {
std::unique_ptr<ir::Node> out_node =
ir::CreateNodeForTest("out_node" + i, ir::Node::Type::kVariable);
VarHandle* out_var_handle =
new VarHandle(out_node.get(), 2, j, "out_var" + i, place_list_[j]);
vars_.emplace_back(out_var_handle);
op_handle_->AddOutput(out_var_handle);
}
}
}
void TestFusedBroadcastLoDTensor(std::vector<size_t> input_scope_idxes) {
std::vector<std::vector<float>> send_vec;
f::LoD lod{{0, 10, 20}};
for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
const std::string varname("in_var" + i);
float val_scalar = static_cast<float>(i);
send_vec.push_back(
InitLoDTensor(varname, input_scope_idxes[i], lod, val_scalar));
}
op_handle_->Run(false);
WaitAll();
for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
const std::string& varname("out_var" + i);
for (size_t j = 0; j < place_list_.size(); ++j) {
LoDTensorEqual(varname, send_vec[i], lod, param_scopes_[j]);
}
}
}
void TestFusedBroadcastSelectedRows(std::vector<size_t> input_scope_idxes) {
std::vector<std::vector<float>> send_vector;
std::vector<int64_t> rows{0, 1, 2, 3, 3, 0, 14, 7, 3, 1,
2, 4, 6, 3, 1, 1, 1, 1, 3, 7};
int height = static_cast<int>(kDims[0] * 2);
for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
const std::string varname("in_var" + i);
float val_scalar = static_cast<float>(i);
send_vector.push_back(InitSelectedRows(varname, input_scope_idxes[i],
rows, height, val_scalar));
}
op_handle_->Run(false);
WaitAll();
for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
const std::string& varname("out_var" + i);
for (size_t j = 0; j < place_list_.size(); ++j) {
SelectedRowsEqual(varname, input_scope_idxes[i], send_vector[i], rows,
height);
}
}
}
};
TEST(FusedBroadcastTester, CPULodTensor) {
TestFusedBroadcastOpHandle test_op;
std::vector<size_t> input_scope_idxes = {0, 1};
test_op.InitCtxOnGpu(false);
test_op.InitFusedBroadcastOp(input_scope_idxes);
test_op.TestFusedBroadcastLoDTensor(input_scope_idxes);
}
TEST(FusedBroadcastTester, CPUSelectedRows) {
TestFusedBroadcastOpHandle test_op;
std::vector<size_t> input_scope_idxes = {0, 1};
test_op.InitCtxOnGpu(false);
test_op.InitFusedBroadcastOp(input_scope_idxes);
test_op.TestFusedBroadcastSelectedRows(input_scope_idxes);
}
#ifdef PADDLE_WITH_CUDA
TEST(FusedBroadcastTester, GPULodTensor) {
TestFusedBroadcastOpHandle test_op;
std::vector<size_t> input_scope_idxes = {0, 1};
test_op.InitCtxOnGpu(true);
test_op.InitFusedBroadcastOp(input_scope_idxes);
test_op.TestFusedBroadcastLoDTensor(input_scope_idxes);
}
TEST(FusedBroadcastTester, GPUSelectedRows) {
TestFusedBroadcastOpHandle test_op;
std::vector<size_t> input_scope_idxes = {0, 1};
test_op.InitCtxOnGpu(true);
test_op.InitFusedBroadcastOp(input_scope_idxes);
test_op.TestFusedBroadcastSelectedRows(input_scope_idxes);
}
#endif
} // namespace details
} // namespace framework
} // namespace paddle
......@@ -36,7 +36,7 @@ void GatherOpHandle::RunImpl() {
VarHandle *out_var_handle;
{
auto out_var_handles = DynamicCast<VarHandle>(outputs_);
auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
PADDLE_ENFORCE_EQ(out_var_handles.size(), 1,
"The number of output should be one.");
out_var_handle = out_var_handles.front();
......@@ -99,7 +99,7 @@ void GatherOpHandle::RunImpl() {
Tensor *out_tensor = out_value->mutable_value();
// copy
auto dev_ctx = dev_ctxes_[out_var_handle->place_];
auto dev_ctx = dev_ctxes_.at(out_var_handle->place_);
RunAndRecordEvent(out_var_handle->place_, [in_tensors, out_tensor, &dev_ctx,
t_out_p] {
int s = 0, e = 0;
......
......@@ -103,7 +103,7 @@ void OpHandleBase::WaitInputVarGenerated() {
void OpHandleBase::WaitInputVarGenerated(const platform::Place &place) {
for (auto *in : inputs_) {
if (NeedWait(in)) {
in->GeneratedOp()->RecordWaitEventOnCtx(dev_ctxes_[place]);
in->GeneratedOp()->RecordWaitEventOnCtx(dev_ctxes_.at(place));
}
}
}
......
......@@ -27,7 +27,7 @@ namespace framework {
namespace details {
void ReduceOpHandle::RunImpl() {
platform::RecordEvent record_event(Name(), dev_ctxes_.begin()->second);
platform::RecordEvent record_event(Name(), dev_ctxes_.cbegin()->second);
if (places_.size() == 1) return;
// the input and output may have dummy var.
......
......@@ -46,7 +46,8 @@ struct ReduceOpHandle : public OpHandleBase {
nccl_ctxs_(nccl_ctxs) {
if (nccl_ctxs_) {
for (auto &p_ctx : nccl_ctxs_->contexts_) {
dev_ctxes_[platform::CUDAPlace(p_ctx.first)] = p_ctx.second.ctx_.get();
this->SetDeviceContext(platform::CUDAPlace(p_ctx.first),
p_ctx.second.ctx_.get());
}
}
}
......
......@@ -38,7 +38,7 @@ void RPCOpHandle::RunImpl() {
continue;
}
if (in->GeneratedOp()) {
in->GeneratedOp()->RecordWaitEventOnCtx(dev_ctxes_[p]);
in->GeneratedOp()->RecordWaitEventOnCtx(dev_ctxes_.at(p));
}
}
auto &tmp_scope = local_scope_->FindVar(kLocalExecScopeName)->Get<Scope *>();
......
......@@ -27,7 +27,7 @@ ScaleLossGradOpHandle::ScaleLossGradOpHandle(ir::Node *node, size_t num_dev,
coeff_(static_cast<float>(1.0 / num_dev)),
scope_(scope),
place_(place) {
dev_ctxes_[place_] = dev_ctx;
this->SetDeviceContext(place_, dev_ctx);
}
ScaleLossGradOpHandle::~ScaleLossGradOpHandle() {}
......@@ -46,9 +46,9 @@ void ScaleLossGradOpHandle::RunImpl() {
} else {
#ifdef PADDLE_WITH_CUDA
this->RunAndRecordEvent([&] {
auto stream =
static_cast<platform::CUDADeviceContext *>(this->dev_ctxes_[place_])
->stream();
auto stream = static_cast<platform::CUDADeviceContext *>(
this->dev_ctxes_.at(place_))
->stream();
memory::Copy(boost::get<platform::CUDAPlace>(place_), tmp,
platform::CPUPlace(), &coeff_, sizeof(float), stream);
VLOG(10) << place_ << "RUN Scale loss grad op";
......
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/sequential_execution_pass.h"
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include "paddle/fluid/framework/op_proto_maker.h"
namespace paddle {
namespace framework {
namespace details {
static bool IsSameOpDesc(OpDesc *op1, OpDesc *op2) {
return op1->Type() == op2->Type() && op1->Inputs() == op2->Inputs() &&
op1->Outputs() == op2->Outputs();
}
std::unique_ptr<ir::Graph> SequentialExecutionPass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
// FIXME(zjl): Insert dependencies between some distributed ops may cause
// the multi_devices_graph_pass fails. So we skip these ops here.
// Indeed, maybe we should not insert dependencies between these ops
// casually, which may cause deadlock easily.
// We should add more skipped distributed ops when found errors in
// multi_devices_graph_pass
static std::unordered_set<std::string> skip_dist_ops{
"send", "recv", "send_barrier", "fetch_barrier"};
auto &ops = Get<const std::vector<OpDesc *>>(kAllOpDescs);
std::vector<ir::Node *> op_node_list;
op_node_list.reserve(ops.size());
std::unordered_map<ir::Node *, size_t> op_deps;
std::unordered_map<ir::Node *, std::unordered_set<ir::Node *>> pending_ops;
std::unordered_set<ir::Node *> ready_ops;
for (ir::Node *node : graph->Nodes()) {
if (!node->IsOp()) continue;
std::unordered_set<ir::Node *> preceding_ops;
for (auto *in : node->inputs) {
PADDLE_ENFORCE(in->IsVar(),
"Preceding Node of Op Nodes must be Var Node");
if (in->inputs.empty()) continue;
PADDLE_ENFORCE(in->inputs.size() == 1 && in->inputs[0]->IsOp(),
"Preceding Op Node of Var Node must be unique");
preceding_ops.insert(in->inputs[0]);
pending_ops[in->inputs[0]].insert(node);
}
op_deps[node] = preceding_ops.size();
if (preceding_ops.empty()) {
ready_ops.insert(node);
}
}
for (auto *op_desc : ops) {
ir::Node *found_node = nullptr;
for (auto *node : ready_ops) {
if (IsSameOpDesc(op_desc, node->Op())) {
PADDLE_ENFORCE(found_node == nullptr,
"Found multiple op_desc in graph: %s", op_desc->Type());
found_node = node;
}
}
PADDLE_ENFORCE_NOT_NULL(found_node, "Cannot find op_desc in graph: %s",
op_desc->Type());
for (auto *pending_op : pending_ops[found_node]) {
if (--op_deps.at(pending_op) == 0) {
ready_ops.insert(pending_op);
}
}
ready_ops.erase(found_node);
if (skip_dist_ops.count(op_desc->Type()) == 0) {
op_node_list.push_back(found_node);
}
}
for (size_t i = 1; i < op_node_list.size(); ++i) {
auto *dep_var = graph->CreateControlDepVar();
op_node_list[i]->inputs.push_back(dep_var);
op_node_list[i - 1]->outputs.push_back(dep_var);
dep_var->outputs.push_back(op_node_list[i]);
dep_var->inputs.push_back(op_node_list[i - 1]);
VLOG(10) << "Add dependencies between " << op_node_list[i - 1]->Name()
<< " and " << op_node_list[i]->Name();
}
return graph;
}
} // namespace details
} // namespace framework
} // namespace paddle
REGISTER_PASS(sequential_execution_pass,
paddle::framework::details::SequentialExecutionPass)
.RequirePassAttr(paddle::framework::details::kAllOpDescs);
// 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.
#pragma once
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace details {
constexpr char kAllOpDescs[] = "all_op_descs";
class SequentialExecutionPass : public ir::Pass {
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
};
} // namespace details
} // namespace framework
} // namespace paddle
......@@ -39,7 +39,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
new platform::RecordEvent("ThreadedSSAGraphExecutorPrepare", nullptr));
std::unordered_map<OpHandleBase *, size_t> pending_ops;
std::unordered_set<VarHandleBase *> pending_vars;
BlockingQueue<VarHandleBase *> ready_vars;
auto ready_vars = std::make_shared<BlockingQueue<VarHandleBase *>>();
std::unordered_set<OpHandleBase *> ready_ops;
// For ops (e.g. nccl_all_reduce) that need to coordinate multiple
// streams from multiple GPUs, it's faster to buffer them and schedule
......@@ -51,12 +51,12 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
for (auto &var_map : graph_->Get<details::GraphVars>(details::kGraphVars)) {
for (auto &name_pair : var_map) {
for (auto &version_pair : name_pair.second) {
InsertPendingVar(&pending_vars, &ready_vars, version_pair.get());
InsertPendingVar(&pending_vars, ready_vars.get(), version_pair.get());
}
}
}
for (auto &var : graph_->Get<details::GraphDepVars>(details::kGraphDepVars)) {
InsertPendingVar(&pending_vars, &ready_vars, var.get());
InsertPendingVar(&pending_vars, ready_vars.get(), var.get());
}
for (auto &op : graph_->Get<details::GraphOps>(details::kGraphOps)) {
......@@ -73,12 +73,12 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
FeedFetchList fetch_data(fetch_tensors.size());
InsertFetchOps(fetch_tensors, &fetch_ops, &fetch_dependencies, &pending_ops,
&pending_vars, &ready_vars, &fetch_data);
&pending_vars, ready_vars.get(), &fetch_data);
auto run_all_ops = [&](std::unordered_set<OpHandleBase *> &set) {
for (auto *op : set) {
running_ops_++;
RunOp(&ready_vars, op);
RunOp(ready_vars, op);
}
set.clear();
};
......@@ -87,7 +87,6 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
run_op_futures_.clear();
exception_holder_.Clear();
event.reset(nullptr);
// Step 3. Execution
while (!pending_vars.empty()) {
// 1. Run All Ready ops
......@@ -103,7 +102,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
// 2. Find ready variable
bool timeout;
auto cur_ready_vars = ready_vars.PopAll(1, &timeout);
auto cur_ready_vars = ready_vars->PopAll(1, &timeout);
if (timeout) {
if (exception_holder_.IsCaught()) {
......@@ -133,7 +132,6 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
}
}
PADDLE_ENFORCE(ready_ops.empty());
// Wait FetchOps.
ClearFetchOp(graph_.get(), &fetch_ops);
......@@ -206,7 +204,8 @@ void ThreadedSSAGraphExecutor::InsertPendingVar(
}
void ThreadedSSAGraphExecutor::RunOp(
BlockingQueue<VarHandleBase *> *ready_var_q, details::OpHandleBase *op) {
const std::shared_ptr<BlockingQueue<VarHandleBase *>> &ready_var_q,
details::OpHandleBase *op) {
auto op_run = [ready_var_q, op, this] {
try {
if (VLOG_IS_ON(10)) {
......
......@@ -51,7 +51,7 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
~ThreadedSSAGraphExecutor() {}
private:
void RunOp(BlockingQueue<VarHandleBase *> *ready_var_q,
void RunOp(const std::shared_ptr<BlockingQueue<VarHandleBase *>> &ready_var_q,
details::OpHandleBase *op);
private:
......
......@@ -41,6 +41,7 @@ pass_library(conv_bn_fuse_pass inference)
pass_library(seqconv_eltadd_relu_fuse_pass inference)
if(WITH_MKLDNN)
pass_library(mkldnn_placement_pass base)
pass_library(depthwise_conv_mkldnn_pass base)
pass_library(conv_bias_mkldnn_fuse_pass inference)
pass_library(conv_relu_mkldnn_fuse_pass inference)
pass_library(conv_elementwise_add_mkldnn_fuse_pass inference)
......@@ -59,6 +60,7 @@ cc_test(graph_to_program_pass_test SRCS graph_to_program_pass_test.cc DEPS graph
cc_test(test_graph_pattern_detector SRCS graph_pattern_detector_tester.cc DEPS graph_pattern_detector)
cc_test(test_fc_fuse_pass SRCS fc_fuse_pass_tester.cc DEPS fc_fuse_pass framework_proto)
if (WITH_MKLDNN)
cc_test(test_depthwise_conv_mkldnn_pass SRCS depthwise_conv_mkldnn_pass_tester.cc DEPS depthwise_conv_mkldnn_pass)
cc_test(test_conv_relu_mkldnn_fuse_pass SRCS conv_relu_mkldnn_fuse_pass_tester.cc DEPS conv_relu_mkldnn_fuse_pass)
cc_test(test_conv_elementwise_add_mkldnn_fuse_pass SRCS conv_elementwise_add_mkldnn_fuse_pass_tester.cc DEPS conv_elementwise_add_mkldnn_fuse_pass)
endif ()
......@@ -31,7 +31,8 @@ class ConvReLUFusePass : public FusePassBase {
virtual ~ConvReLUFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
};
} // namespace ir
......
......@@ -15,6 +15,7 @@
#include "paddle/fluid/framework/ir/conv_relu_mkldnn_fuse_pass.h"
#include <gtest/gtest.h>
#include "paddle/fluid/framework/op_proto_maker.h"
namespace paddle {
namespace framework {
......@@ -36,6 +37,8 @@ void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
op->SetInput("X", inputs);
}
op->SetOutput("Out", outputs);
op->SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
static_cast<int>(OpRole::kForward));
}
// a->OP0->b
......
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/ir/depthwise_conv_mkldnn_pass.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
namespace paddle {
namespace framework {
namespace ir {
#define GET_NODE(id, pattern) \
PADDLE_ENFORCE(subgraph.count(pattern.RetrieveNode(#id)), \
"pattern has no Node called %s", #id); \
auto* id = subgraph.at(pattern.RetrieveNode(#id)); \
PADDLE_ENFORCE_NOT_NULL(id, "subgraph has no node %s", #id);
std::unique_ptr<ir::Graph> DepthwiseConvMKLDNNPass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
PADDLE_ENFORCE(graph.get());
FusePassBase::Init("depthwise_conv_mkldnn_pass", graph.get());
GraphPatternDetector gpd;
auto* pattern = gpd.mutable_pattern();
pattern->NewNode("depthwise_conv")
->assert_is_op("depthwise_conv2d")
->assert_op_attr("use_mkldnn", true);
int found_depthwise_conv_mkldnn_count = 0;
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
VLOG(3) << "handle DepthwiseConvMKLDNN fuse";
GET_NODE(depthwise_conv, (*pattern));
depthwise_conv->Op()->SetType("conv2d");
found_depthwise_conv_mkldnn_count++;
};
gpd(graph.get(), handler);
AddStatis(found_depthwise_conv_mkldnn_count);
return graph;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(depthwise_conv_mkldnn_pass,
paddle::framework::ir::DepthwiseConvMKLDNNPass);
/* 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. */
#pragma once
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
namespace paddle {
namespace framework {
namespace ir {
class DepthwiseConvMKLDNNPass : public FusePassBase {
public:
virtual ~DepthwiseConvMKLDNNPass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
};
} // namespace ir
} // namespace framework
} // namespace paddle
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/ir/depthwise_conv_mkldnn_pass.h"
#include <gtest/gtest.h>
namespace paddle {
namespace framework {
namespace ir {
void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
const std::vector<std::string>& inputs,
const std::vector<std::string>& outputs, bool use_mkldnn = false) {
auto* op = prog->MutableBlock(0)->AppendOp();
op->SetType(type);
op->SetAttr("use_mkldnn", use_mkldnn);
op->SetAttr("name", name);
op->SetInput("Input", {inputs[0]});
op->SetInput("Filter", {inputs[1]});
op->SetInput("Bias", {inputs[2]});
op->SetOutput("Out", outputs);
}
// (a, weights, bias)->depthwise conv mkldnn->b
// (b, weights2, bias2)->depthwise conv no mkldnn->c
// (c, weights3, bias3)->conv mkldnn->d
// (d, weights3, bias3)->conv no mkldnn->e
ProgramDesc BuildProgramDesc() {
ProgramDesc prog;
for (auto& v : std::vector<std::string>(
{"a", "b", "c", "d", "e", "weights", "bias", "weights2", "bias2",
"weights3", "bias3", "weights4", "bias4"})) {
auto* var = prog.MutableBlock(0)->Var(v);
var->SetType(proto::VarType::SELECTED_ROWS);
if (v == "weights" || v == "bias" || v == "weights2" || v == "bias2" ||
v == "weights3" || v == "bias3" || v == "weights4" || v == "bias4") {
var->SetPersistable(true);
}
}
// depthwise conv with MKL-DNN
SetOp(&prog, "depthwise_conv2d", "conv1",
std::vector<std::string>({"a", "weights", "bias"}),
std::vector<std::string>({"b"}), true);
// depthwise conv without MKL-DNN
SetOp(&prog, "depthwise_conv2d", "conv2",
std::vector<std::string>({"b", "weights2", "bias2"}),
std::vector<std::string>({"c"}), false);
// conv with MKL-DNN
SetOp(&prog, "conv2d", "conv3",
std::vector<std::string>({"c", "weights3", "bias3"}),
std::vector<std::string>({"d"}), true);
// conv without MKL-dNN
SetOp(&prog, "conv2d", "conv4",
std::vector<std::string>({"d", "weights4", "bias4"}),
std::vector<std::string>({"e"}), false);
return prog;
}
TEST(DepthwiseConvMKLDNNPass, basic) {
auto prog = BuildProgramDesc();
std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
auto pass = PassRegistry::Instance().Get("depthwise_conv_mkldnn_pass");
struct counters {
int mkldnn_depthwise_conv_nodes;
int other_depthwise_conv_nodes;
int mkldnn_conv_nodes;
int other_conv_nodes;
};
counters before{1, 1, 1, 1};
graph = pass->Apply(std::move(graph));
// initialize counters before loop
counters after{0, 0, 0, 0};
for (auto* node : graph->Nodes()) {
if (node->IsOp()) {
auto* op = node->Op();
if (op->Type() == "conv2d") {
if (boost::get<bool>(op->GetAttr("use_mkldnn")))
after.mkldnn_conv_nodes++;
else
after.other_conv_nodes++;
} else if (op->Type() == "depthwise_conv2d") {
if (boost::get<bool>(op->GetAttr("use_mkldnn")))
after.mkldnn_depthwise_conv_nodes++;
else
after.other_depthwise_conv_nodes++;
}
}
}
EXPECT_EQ(after.other_depthwise_conv_nodes,
before.other_depthwise_conv_nodes);
EXPECT_EQ(after.other_conv_nodes, before.other_conv_nodes);
EXPECT_EQ(after.mkldnn_depthwise_conv_nodes,
before.mkldnn_depthwise_conv_nodes - 1);
EXPECT_EQ(after.mkldnn_conv_nodes, before.mkldnn_conv_nodes + 1);
}
} // namespace ir
} // namespace framework
} // namespace paddle
USE_PASS(depthwise_conv_mkldnn_pass);
......@@ -15,6 +15,7 @@
#include "paddle/fluid/framework/ir/fc_fuse_pass.h"
#include <gtest/gtest.h>
#include "paddle/fluid/framework/op_proto_maker.h"
namespace paddle {
namespace framework {
......@@ -32,6 +33,8 @@ void SetOp(ProgramDesc* prog, const std::string& type,
op->SetInput("X", inputs);
}
op->SetOutput("Out", outputs);
op->SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(),
static_cast<int>(OpRole::kForward));
}
// a->OP0->b
......
......@@ -23,8 +23,67 @@ limitations under the License. */
namespace paddle {
namespace framework {
namespace ir {
namespace {
void CheckProgram(const ProgramDesc &program) {
#define _INT(role) static_cast<int>(role)
std::map<int, bool> visit;
for (OpDesc *op : program.Block(0).AllOps()) {
// For backward compatibility, some program doesn't have role added.
if (!op->HasAttr(OpProtoAndCheckerMaker::OpRoleAttrName())) continue;
int role_id =
boost::get<int>(op->GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName()));
visit[role_id] = true;
switch (role_id) {
case _INT(OpRole::kForward):
if (visit.find(_INT(OpRole::kBackward)) != visit.end()) {
LOG(ERROR)
<< "Cannot add backward operator before forward operator %s."
<< op->Type();
}
break;
case _INT(OpRole::kBackward):
case _INT(OpRole::kBackward) | _INT(OpRole::kLoss):
PADDLE_ENFORCE(
visit.find(_INT(OpRole::kOptimize)) == visit.end(),
"Cannot add backward operator %s after optimize operator.",
op->Type());
break;
case _INT(OpRole::kForward) | _INT(OpRole::kLoss):
PADDLE_ENFORCE(visit.find(_INT(OpRole::kBackward) |
_INT(OpRole::kLoss)) == visit.end(),
"Cannot add backward|loss operator before "
"forward|loss operator %s.",
op->Type());
PADDLE_ENFORCE(
visit.find(_INT(OpRole::kOptimize)) == visit.end(),
"Cannot add forward|loss operator %s after optimize operator.",
op->Type());
break;
case _INT(OpRole::kOptimize):
case _INT(OpRole::kOptimize) | _INT(OpRole::kLRSched):
PADDLE_ENFORCE(visit.find(_INT(OpRole::kBackward)) != visit.end(),
"Optimize operators %s must follow backward operator.",
op->Type());
break;
case _INT(OpRole::kLRSched):
case _INT(OpRole::kDist):
case _INT(OpRole::kRPC):
case _INT(OpRole::kNotSpecified):
break;
default:
LOG(FATAL) << "Unknown operator role. Don't add new role because "
"you don't know what you are doing.";
}
}
#undef _INT
}
} // namespace
Graph::Graph(const ProgramDesc &program) : program_(program) {
CheckProgram(program_);
// Make the nodes id start from 0.
Node::ResetId();
auto var_nodes = InitFromProgram(program_);
......
......@@ -259,6 +259,15 @@ GraphPatternDetector::DetectPatterns() {
return result;
}
bool GraphItemCMP(const std::pair<PDNode *, Node *> &a,
const std::pair<PDNode *, Node *> &b) {
if (a.first != b.first) {
return a.first < b.first;
} else {
return a.second < b.second;
}
}
// TODO(Superjomn) enhance the function as it marks unique unique as duplicates
// see https://github.com/PaddlePaddle/Paddle/issues/13550
void GraphPatternDetector::UniquePatterns(
......@@ -267,12 +276,16 @@ void GraphPatternDetector::UniquePatterns(
std::vector<GraphPatternDetector::subgraph_t> result;
std::unordered_set<size_t> set;
std::hash<std::string> hasher;
for (auto &g : *subgraphs) {
size_t key = 0;
for (auto &item : g) {
key ^= std::hash<void *>{}(item.first);
key ^= std::hash<void *>{}(item.second);
// Sort the items in the sub-graph, and transform to a string key.
std::vector<std::pair<PDNode *, Node *>> sorted_keys(g.begin(), g.end());
std::sort(sorted_keys.begin(), sorted_keys.end(), GraphItemCMP);
std::stringstream ss;
for (auto &item : sorted_keys) {
ss << item.first << ":" << item.second;
}
auto key = hasher(ss.str());
if (!set.count(key)) {
result.emplace_back(g);
set.insert(key);
......
......@@ -418,7 +418,7 @@ void LoDTensor::MergeLoDTensor(
PADDLE_ENFORCE_EQ(new_lod.size(), lod.size());
for (size_t j = 0; j < lod.size(); ++j) {
auto &sub_lod = new_lod[j];
auto &offset = sub_lod.back();
size_t offset = sub_lod.back();
for (size_t k = 1; k < lod[j].size(); ++k) {
sub_lod.push_back(lod[j][k] + offset);
}
......
......@@ -19,81 +19,7 @@ limitations under the License. */
namespace paddle {
namespace framework {
// NOTE The vector<LoDTensor> can't be replaced with the class LoDTensorArray
// directly, because there are many vector<LoDTensor> used accross the project,
// and some of them are treated as LoDTensorArray.
#if !defined(PADDLE_ON_INFERENCE)
using LoDTensorArray = std::vector<LoDTensor>;
#else // !PADDLE_ON_INFERENCE
#pragma message "LoDTensorArray is replaced with the inference one."
/*
* A LoDTensorArray which will not deallocate buffer when resized, fix the data
* diff in inference, and more performance friendly in the concurrency
* scenerios.
*/
class LoDTensorArray {
public:
LoDTensorArray() = default;
using iterator = std::vector<LoDTensor>::iterator;
using const_iterator = std::vector<LoDTensor>::const_iterator;
const_iterator begin() const { return array_.begin(); }
const_iterator end() const { return array_.begin() + size_; }
iterator begin() { return array_.begin(); }
iterator end() { return array_.begin() + size_; }
void push_back(const LoDTensor& x) {
if (size_ < array_.size()) {
array_[size_++] = x;
} else {
array_.push_back(x);
++size_;
}
}
void resize(size_t size) {
if (array_.size() < size) {
array_.resize(size);
}
size_ = size;
}
void emplace_back() { array_.emplace_back(); }
void emplace_back(LoDTensor&& x) { array_.emplace_back(std::move(x)); }
LoDTensor& back() { return array_.back(); }
size_t space() const { return array_.size(); }
void reserve(size_t size) {
// Naive warning to tell user this array might be to large. The memory and
// buffer used by this TensorArray will not be deleted during the training
// and inference phase, so attention not to make it expand too long.
if (size > 800UL) {
LOG(WARNING) << "TensorArray has more than 800 items";
}
array_.reserve(size);
}
bool empty() const { return size_ == 0UL; }
void clear() { size_ = 0UL; }
LoDTensor& operator[](size_t id) { return array_[id]; }
const LoDTensor& operator[](size_t id) const { return array_[id]; }
LoDTensor& at(size_t id) { return array_.at(id); }
const LoDTensor& at(size_t id) const { return array_.at(id); }
size_t size() const { return size_; }
private:
size_t size_{0};
std::vector<LoDTensor> array_;
};
#endif // !PADDLE_ON_INFERENCE
} // namespace framework
} // namespace paddle
......@@ -354,18 +354,18 @@ void OperatorBase::GenerateTemporaryNames() {
}
}
static bool VarIsTensor(const Variable* var) {
return var->IsType<LoDTensor>() || var->IsType<SelectedRows>();
static bool VarIsTensor(const Variable& var) {
return var.IsType<LoDTensor>() || var.IsType<SelectedRows>();
}
const Tensor* GetTensorFromVar(Variable* var) {
if (var->IsType<LoDTensor>()) {
return var->GetMutable<LoDTensor>();
} else if (var->IsType<SelectedRows>()) {
return var->GetMutable<SelectedRows>()->mutable_value();
const Tensor* GetTensorFromVar(const Variable& var) {
if (var.IsType<LoDTensor>()) {
return static_cast<const Tensor*>(&(var.Get<LoDTensor>()));
} else if (var.IsType<SelectedRows>()) {
return &(var.Get<SelectedRows>().value());
} else {
PADDLE_THROW("Variable type_id %s, expect LoDTensor/SelectedRows.",
var->Type().name());
var.Type().name());
}
}
......@@ -415,8 +415,7 @@ bool ExecutionContext::HasOutput(const std::string& name) const {
template <>
const Tensor* ExecutionContext::Input<Tensor>(const std::string& name) const {
auto* var = InputVar(name);
return var == nullptr ? nullptr
: GetTensorFromVar(const_cast<Variable*>(var));
return var == nullptr ? nullptr : GetTensorFromVar(*var);
}
template <>
......@@ -428,7 +427,7 @@ const std::vector<const Tensor*> ExecutionContext::MultiInput<Tensor>(
std::transform(names.begin(), names.end(), std::back_inserter(res),
[&](const std::string& sub_name) {
auto var = scope_.FindVar(sub_name);
return var == nullptr ? nullptr : GetTensorFromVar(var);
return var == nullptr ? nullptr : GetTensorFromVar(*var);
});
return res;
}
......@@ -770,8 +769,10 @@ void OperatorWithKernel::TransferInplaceVarsBack(
for (auto& var_name : inplace_vars) {
VLOG(3) << "share inplace var " + var_name + " back to it's original scope";
auto* original_tensor = GetMutableTensorFromVar(scope.FindVar(var_name));
auto* transformed_tensor =
GetTensorFromVar(transfer_scope.FindVar(var_name));
auto* var = transfer_scope.FindVar(var_name);
PADDLE_ENFORCE(var != nullptr, "The var[%s] should not be nullptr",
var_name);
auto* transformed_tensor = GetTensorFromVar(*var);
original_tensor->ShareDataWith(*transformed_tensor);
}
}
......@@ -784,11 +785,11 @@ Scope* OperatorWithKernel::TryTransferData(
for (auto& var_name : var_name_item.second) {
auto* var = scope.FindVar(var_name);
// Only tensor can be tranfer to another device.
if (var == nullptr || !VarIsTensor(var)) {
if (var == nullptr || !VarIsTensor(*var)) {
continue;
}
auto* tensor_in = GetTensorFromVar(var);
auto* tensor_in = GetTensorFromVar(*var);
if (!tensor_in->IsInitialized()) {
continue;
}
......
......@@ -63,7 +63,7 @@ inline std::string GradVarName(const std::string& var_name) {
}
proto::VarType::Type GetDataTypeOfVar(const Variable* var);
const Tensor* GetTensorFromVar(Variable* var);
const Tensor* GetTensorFromVar(const Variable& var);
class OperatorBase;
class ExecutionContext;
......
......@@ -303,10 +303,8 @@ void ParallelExecutor::FeedAndSplitTensorIntoLocalScopes(
}
ParallelExecutor::~ParallelExecutor() {
const auto dev_ctxs =
platform::DeviceContextPool::Instance().GetAllDeviceContexts();
for (auto &dev_ctx : dev_ctxs) {
dev_ctx->Wait();
for (auto &p : member_->places_) {
platform::DeviceContextPool::Instance().Get(p)->Wait();
}
if (member_->own_local_scope_) {
......
......@@ -75,6 +75,19 @@ TEST(Tensor, MutableData) {
platform::CPUPlace());
EXPECT_EQ(p1, p2);
}
// Not sure if it's desired, but currently, Tensor type can be changed.
{
framework::Tensor src_tensor;
int8_t* p1 = src_tensor.mutable_data<int8_t>(framework::make_ddim({1}),
platform::CPUPlace());
EXPECT_NE(p1, nullptr);
*p1 = 1;
uint8_t* p2 = src_tensor.mutable_data<uint8_t>(framework::make_ddim({1}),
platform::CPUPlace());
EXPECT_NE(p2, nullptr);
EXPECT_EQ(static_cast<int>(p2[0]), 1);
}
#ifdef PADDLE_WITH_CUDA
{
......
......@@ -153,6 +153,12 @@ void TensorCopySync(const Tensor& src, const platform::Place& dst_place,
auto src_gpu_place = boost::get<platform::CUDAPlace>(src_place);
auto dst_gpu_place = boost::get<platform::CUDAPlace>(dst_place);
memory::Copy(dst_gpu_place, dst_ptr, src_gpu_place, src_ptr, size, nullptr);
} else if (platform::is_cuda_pinned_place(src_place) &&
platform::is_gpu_place(dst_place)) {
auto src_pinned_place = boost::get<platform::CUDAPinnedPlace>(src_place);
auto dst_gpu_place = boost::get<platform::CUDAPlace>(dst_place);
memory::Copy(dst_gpu_place, dst_ptr, src_pinned_place, src_ptr, size,
nullptr);
}
#endif
}
......
if(WITH_TESTING)
include(test.cmake) # some generic cmake funtion for inference
endif()
# analysis and tensorrt must be added before creating static library,
# otherwise, there would be undefined reference to them in static library.
add_subdirectory(analysis)
......
......@@ -20,22 +20,17 @@ cc_test(test_node SRCS node_tester.cc DEPS analysis)
cc_test(test_dot SRCS dot_tester.cc DEPS analysis)
cc_binary(inference_analyzer SRCS analyzer_main.cc DEPS analysis paddle_fluid)
function (inference_analysis_test TARGET)
if(WITH_TESTING)
set(options "")
set(oneValueArgs "")
set(multiValueArgs SRCS ARGS EXTRA_DEPS)
cmake_parse_arguments(analysis_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(mem_opt "")
if(WITH_GPU)
set(mem_opt "--fraction_of_gpu_memory_to_use=0.5")
endif()
cc_test(${TARGET}
SRCS "${analysis_test_SRCS}"
DEPS analysis pass ${GLOB_PASS_LIB} ${analysis_test_EXTRA_DEPS}
ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model ${mem_opt} ${analysis_test_ARGS})
set_tests_properties(${TARGET} PROPERTIES DEPENDS test_word2vec)
endif(WITH_TESTING)
function(inference_analysis_test TARGET)
if(WITH_TESTING)
set(options "")
set(oneValueArgs "")
set(multiValueArgs SRCS ARGS EXTRA_DEPS)
cmake_parse_arguments(analysis_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
inference_base_test(${TARGET}
SRCS ${analysis_test_SRCS}
DEPS analysis pass ${GLOB_PASS_LIB} ${analysis_test_EXTRA_DEPS}
ARGS --inference_model_dir=${WORD2VEC_MODEL_DIR} ${analysis_test_ARGS})
endif()
endfunction(inference_analysis_test)
inference_analysis_test(test_analyzer SRCS analyzer_tester.cc EXTRA_DEPS paddle_inference_api)
......
......@@ -79,6 +79,7 @@ class Analyzer : public OrderedRegistry<PassManager> {
"conv_bn_fuse_pass", //
"conv_eltwiseadd_bn_fuse_pass", //
#ifdef PADDLE_WITH_MKLDNN
"depthwise_conv_mkldnn_pass", //
"conv_bias_mkldnn_fuse_pass", //
"conv_relu_mkldnn_fuse_pass", //
"conv_elementwise_add_mkldnn_fuse_pass", //
......
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/inference/analysis/data_flow_graph.h"
#include "paddle/fluid/framework/op_proto_maker.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/inference/analysis/ut_helper.h"
......@@ -130,6 +131,8 @@ void SetOp(framework::ProgramDesc* prog, const std::string& type,
op->SetType(type);
op->SetInput("Xs", inputs);
op->SetOutput("Xs", outputs);
op->SetAttr(framework::OpProtoAndCheckerMaker::OpRoleAttrName(),
static_cast<int>(framework::OpRole::kForward));
}
TEST(DataFlowGraph, Build_IR_Graph) {
......
......@@ -17,39 +17,12 @@ if(APPLE)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-error=pessimizing-move")
endif(APPLE)
set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager naive_executor ${GLOB_PASS_LIB}
)
set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager naive_executor ${GLOB_PASS_LIB})
if(WITH_GPU AND TENSORRT_FOUND)
set(inference_deps ${inference_deps} paddle_inference_tensorrt_subgraph_engine analysis_predictor)
endif()
function(inference_api_test TARGET_NAME)
if (WITH_TESTING)
set(options "")
set(oneValueArgs SRC)
set(multiValueArgs ARGS)
cmake_parse_arguments(inference_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
if (WITH_GPU)
cc_test(${TARGET_NAME}
SRCS ${inference_test_SRC}
DEPS "${inference_deps}"
ARGS --dirname=${PYTHON_TESTS_DIR}/book/ --fraction_of_gpu_memory_to_use=0.15)
else()
cc_test(${TARGET_NAME}
SRCS ${inference_test_SRC}
DEPS "${inference_deps}"
ARGS --dirname=${PYTHON_TESTS_DIR}/book/)
endif()
if(inference_test_ARGS)
set_tests_properties(${TARGET_NAME}
PROPERTIES DEPENDS "${inference_test_ARGS}")
endif()
endif(WITH_TESTING)
endfunction(inference_api_test)
cc_library(reset_tensor_array SRCS details/reset_tensor_array.cc DEPS lod_tensor scope)
cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS reset_tensor_array lod_tensor scope)
cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis naive_executor zero_copy_tensor)
......@@ -59,8 +32,11 @@ cc_test(test_paddle_inference_api
SRCS api_tester.cc
DEPS paddle_inference_api)
inference_api_test(test_api_impl SRC api_impl_tester.cc
ARGS test_word2vec test_image_classification)
if(WITH_TESTING)
inference_base_test(test_api_impl SRCS api_impl_tester.cc DEPS ${inference_deps}
ARGS --word2vec_dirname=${WORD2VEC_MODEL_DIR} --book_dirname=${PYTHON_TESTS_DIR}/book)
set_tests_properties(test_api_impl PROPERTIES DEPENDS test_image_classification)
endif()
cc_test(test_analysis_predictor SRCS analysis_predictor_tester.cc DEPS analysis_predictor ${inference_deps} paddle_inference_api
ARGS --dirname=${PYTHON_TESTS_DIR}/book)
......@@ -68,8 +44,10 @@ if(WITH_GPU AND TENSORRT_FOUND)
cc_library(paddle_inference_tensorrt_subgraph_engine
SRCS api_tensorrt_subgraph_engine.cc
DEPS paddle_inference_api analysis tensorrt_engine paddle_inference_api paddle_fluid_api tensorrt_converter zero_copy_tensor_dummy)
inference_api_test(test_api_tensorrt_subgraph_engine SRC api_tensorrt_subgraph_engine_tester.cc ARGS test_word2vec)
if(WITH_TESTING)
inference_base_test(test_api_tensorrt_subgraph_engine SRCS api_tensorrt_subgraph_engine_tester.cc DEPS ${inference_deps}
ARGS --dirname=${WORD2VEC_MODEL_DIR})
endif()
endif()
if (WITH_ANAKIN AND WITH_MKL) # only needed in CI
......
......@@ -22,12 +22,14 @@ limitations under the License. */
#include "paddle/fluid/inference/tests/test_helper.h"
#ifdef __clang__
#define ACC_DIFF 4e-2
#define ACC_DIFF 4e-3
#else
#define ACC_DIFF 1e-2
#define ACC_DIFF 1e-3
#endif
DEFINE_string(dirname, "", "Directory of the inference model.");
DEFINE_string(word2vec_dirname, "",
"Directory of the word2vec inference model.");
DEFINE_string(book_dirname, "", "Directory of the book inference model.");
namespace paddle {
......@@ -49,7 +51,7 @@ PaddleTensor LodTensorToPaddleTensor(framework::LoDTensor* t) {
NativeConfig GetConfig() {
NativeConfig config;
config.model_dir = FLAGS_dirname + "/word2vec.inference.model";
config.model_dir = FLAGS_word2vec_dirname;
LOG(INFO) << "dirname " << config.model_dir;
config.fraction_of_gpu_memory = 0.15;
#ifdef PADDLE_WITH_CUDA
......@@ -116,7 +118,7 @@ void MainImageClassification(bool use_gpu) {
NativeConfig config = GetConfig();
config.use_gpu = use_gpu;
config.model_dir =
FLAGS_dirname + "/image_classification_resnet.inference.model";
FLAGS_book_dirname + "/image_classification_resnet.inference.model";
const bool is_combined = false;
std::vector<std::vector<int64_t>> feed_target_shapes =
......@@ -220,7 +222,7 @@ void MainThreadsImageClassification(bool use_gpu) {
NativeConfig config = GetConfig();
config.use_gpu = use_gpu;
config.model_dir =
FLAGS_dirname + "/image_classification_resnet.inference.model";
FLAGS_book_dirname + "/image_classification_resnet.inference.model";
auto main_predictor = CreatePaddlePredictor<NativeConfig>(config);
std::vector<framework::LoDTensor> jobs(num_jobs);
......
......@@ -29,13 +29,13 @@ void CompareTensorRTWithFluid(bool enable_tensorrt) {
//# 1. Create PaddlePredictor with a config.
NativeConfig config0;
config0.model_dir = FLAGS_dirname + "word2vec.inference.model";
config0.model_dir = FLAGS_dirname;
config0.use_gpu = true;
config0.fraction_of_gpu_memory = 0.3;
config0.device = 0;
MixedRTConfig config1;
config1.model_dir = FLAGS_dirname + "word2vec.inference.model";
config1.model_dir = FLAGS_dirname;
config1.use_gpu = true;
config1.fraction_of_gpu_memory = 0.3;
config1.device = 0;
......
......@@ -62,7 +62,7 @@ for WITH_STATIC_LIB in ON OFF; do
-DWITH_GPU=$TEST_GPU_CPU \
-DWITH_STATIC_LIB=$WITH_STATIC_LIB
make -j
word2vec_model=${PADDLE_ROOT}'/build/python/paddle/fluid/tests/book/word2vec.inference.model'
word2vec_model=$DATA_DIR'/word2vec/word2vec.inference.model'
if [ -d $word2vec_model ]; then
for use_gpu in $use_gpu_list; do
./simple_on_word2vec \
......
set(INFERENCE_URL "http://paddle-inference-dist.cdn.bcebos.com" CACHE STRING "inference download url")
set(INFERENCE_DEMO_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo" CACHE STRING
"A path setting inference demo download directories.")
function (inference_download install_dir url filename)
message(STATUS "Download inference test stuff from ${url}/${filename}")
execute_process(COMMAND bash -c "mkdir -p ${install_dir}")
execute_process(COMMAND bash -c "cd ${install_dir} && wget -q ${url}/${filename}")
message(STATUS "finish downloading ${filename}")
endfunction()
function (inference_download_and_uncompress install_dir url filename)
inference_download(${install_dir} ${url} ${filename})
execute_process(COMMAND bash -c "cd ${install_dir} && tar xzf ${filename}")
endfunction()
set(WORD2VEC_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/word2vec")
if (NOT EXISTS ${WORD2VEC_INSTALL_DIR})
inference_download_and_uncompress(${WORD2VEC_INSTALL_DIR} ${INFERENCE_URL} "word2vec.inference.model.tar.gz")
endif()
set(WORD2VEC_MODEL_DIR "${WORD2VEC_INSTALL_DIR}/word2vec.inference.model")
function (inference_base_test TARGET)
set(options "")
set(oneValueArgs "")
set(multiValueArgs SRCS ARGS DEPS)
cmake_parse_arguments(base_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
if(WITH_GPU)
set(mem_opt "--fraction_of_gpu_memory_to_use=0.5")
endif()
cc_test(${TARGET} SRCS ${base_test_SRCS} DEPS ${base_test_DEPS} ARGS ${mem_opt} ${base_test_ARGS})
endfunction()
set(INFERENCE_URL "http://paddle-inference-dist.cdn.bcebos.com")
set(INFERENCE_DEMO_INSTALL_DIR "${THIRD_PARTY_PATH}/inference_demo" CACHE STRING
"A path setting inference demo download directories.")
set(INFERENCE_EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis_predictor)
function (inference_download install_dir url filename)
message(STATUS "Download inference test stuff from ${url}/${filename}")
execute_process(COMMAND bash -c "mkdir -p ${install_dir}")
execute_process(COMMAND bash -c "cd ${install_dir} && wget -q ${url}/${filename}")
message(STATUS "finish downloading ${filename}")
endfunction()
function (inference_download_and_uncompress install_dir url filename)
inference_download(${install_dir} ${url} ${filename})
execute_process(COMMAND bash -c "cd ${install_dir} && tar xzf ${filename}")
endfunction()
function(download_model_and_data install_dir model_name data_name)
if (NOT EXISTS ${install_dir})
......
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/cudnn_helper.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using ScopedSpatialTransformerDescriptor =
platform::ScopedSpatialTransformerDescriptor;
template <typename T>
class CUDNNAffineGridOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
"It must use CUDAPlace.");
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto handle = dev_ctx.cudnn_handle();
auto* theta = ctx.Input<Tensor>("Theta");
auto* output = ctx.Output<Tensor>("Output");
const T* theta_data = theta->data<T>();
int n = theta->dims()[0];
auto size_attr = ctx.Attr<std::vector<int>>("output_shape");
Tensor h_sizes;
int* h_size_data;
if (size_attr.size() == 0) {
auto* output_shape = ctx.Input<Tensor>("OutputShape");
framework::TensorCopy(*output_shape, platform::CPUPlace(), &h_sizes);
h_size_data = h_sizes.data<int>();
} else {
h_size_data = h_sizes.mutable_data<int>({4}, platform::CPUPlace());
h_size_data[0] = n;
h_size_data[1] = size_attr[1];
h_size_data[2] = size_attr[2];
h_size_data[3] = size_attr[3];
}
T* output_data = output->mutable_data<T>(
{n, h_size_data[2], h_size_data[3], 2}, ctx.GetPlace());
ScopedSpatialTransformerDescriptor st_desc;
cudnnSpatialTransformerDescriptor_t cudnn_st_desc =
st_desc.descriptor<T>(4, h_size_data);
PADDLE_ENFORCE(platform::dynload::cudnnSpatialTfGridGeneratorForward(
handle, cudnn_st_desc, theta_data, output_data));
}
};
template <typename T>
class CUDNNAffineGridGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
"It must use CUDAPlace.");
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto handle = dev_ctx.cudnn_handle();
auto output_grad = ctx.Input<Tensor>(framework::GradVarName("Output"));
auto theta_grad = ctx.Output<Tensor>(framework::GradVarName("Theta"));
int n = output_grad->dims()[0];
auto size_attr = ctx.Attr<std::vector<int>>("output_shape");
Tensor h_sizes;
int* h_size_data;
if (size_attr.size() == 0) {
auto* output_shape = ctx.Input<Tensor>("OutputShape");
framework::TensorCopy(*output_shape, platform::CPUPlace(), &h_sizes);
h_size_data = h_sizes.data<int>();
} else {
h_size_data = h_sizes.mutable_data<int>({4}, platform::CPUPlace());
h_size_data[0] = n;
h_size_data[1] = size_attr[1];
h_size_data[2] = size_attr[2];
h_size_data[3] = size_attr[3];
}
ScopedSpatialTransformerDescriptor st_desc;
cudnnSpatialTransformerDescriptor_t cudnn_st_desc =
st_desc.descriptor<T>(4, h_size_data);
const T* output_grad_data = output_grad->data<T>();
T* theta_grad_data = theta_grad->mutable_data<T>(ctx.GetPlace());
PADDLE_ENFORCE(platform::dynload::cudnnSpatialTfGridGeneratorBackward(
handle, cudnn_st_desc, output_grad_data, theta_grad_data));
}
};
} // namespace operators
} // namespace paddle
namespace plat = paddle::platform;
REGISTER_OP_KERNEL(affine_grid, CUDNN, plat::CUDAPlace,
paddle::operators::CUDNNAffineGridOpKernel<float>,
paddle::operators::CUDNNAffineGridOpKernel<double>);
REGISTER_OP_KERNEL(affine_grid_grad, CUDNN, plat::CUDAPlace,
paddle::operators::CUDNNAffineGridGradOpKernel<float>,
paddle::operators::CUDNNAffineGridGradOpKernel<double>);
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/affine_grid_op.h"
#include <string>
#include "paddle/fluid/framework/op_registry.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/cudnn_helper.h"
#endif
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T>
struct Linspace<paddle::platform::CPUDeviceContext, T> {
framework::Tensor operator()(T start, T end, int count,
const framework::ExecutionContext& ctx) {
Tensor numbers;
T* number_data = numbers.mutable_data<T>({count}, platform::CPUPlace());
T slice = (end - start) / (T)(count - 1);
for (int i = 0; i < count; ++i) {
number_data[i] = start + (T)i * slice;
}
return numbers;
}
};
class AffineGridOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Theta"),
"Input(Theta) of AffineGridOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Output"),
"Output(Output) of AffineGridOp should not be null.");
auto theta_dims = ctx->GetInputDim("Theta");
PADDLE_ENFORCE(theta_dims.size() == 3,
"AffineGrid's Input(Theta) should be 3-D tensor.");
auto output_shape = ctx->Attrs().Get<std::vector<int>>("output_shape");
if (output_shape.size() == 0) {
PADDLE_ENFORCE(ctx->HasInput("OutputShape"),
"Input(OutputShape) of AffineGridOp should not be null if "
"attr(output_shape) is not configured.");
auto output_shape_dims = ctx->GetInputDim("OutputShape");
PADDLE_ENFORCE(output_shape_dims.size() == 1,
"AffineGrid's Input(OutputShape) should be 1-D tensor.");
} else {
PADDLE_ENFORCE(output_shape.size() == 4,
"The size of attr(output_shape) should be 4.");
}
PADDLE_ENFORCE(theta_dims[1] == 2, "Input(theta) dims[1] should be 2.");
PADDLE_ENFORCE(theta_dims[2] == 3, "Input(theta) dims[2] should be 3.");
// N * H * W * 2
ctx->SetOutputDim("Output",
framework::make_ddim({theta_dims[0], -1, -1, 2}));
ctx->ShareLoD("Theta", "Output");
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
framework::LibraryType library{framework::LibraryType::kPlain};
#ifdef PADDLE_WITH_CUDA
if (platform::CanCUDNNBeUsed(ctx)) {
library = framework::LibraryType::kCUDNN;
}
#endif
auto data_type = framework::ToDataType(ctx.Input<Tensor>("Theta")->type());
return framework::OpKernelType(data_type, ctx.GetPlace(),
framework::DataLayout::kAnyLayout, library);
}
};
class AffineGridOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput(
"Theta",
"(Tensor) A batch of affine transform parameters with shape [N, 2, 3]. "
"It is used to transform coordinate (x_0, y_0) to coordinate (x_1, "
"y_1).");
AddInput("OutputShape",
"(Tensor) The shape of target image with format [N, C, H, W].")
.AsDispensable();
AddOutput("Output", "(Tensor) Output Tensor with shape [N, H, W, 2].");
AddAttr<bool>(
"use_cudnn",
"(bool, default false) Only used in cudnn kernel, need install cudnn")
.SetDefault(true);
AddAttr<std::vector<int>>(
"output_shape",
"The target output image shape with format [N, C, H, W].")
.SetDefault(std::vector<int>());
AddComment(R"DOC(
It generates a grid of (x,y) coordinates using the parameters of the
affine transformation that correspond to a set of points where the input
feature map should be sampled to produce the transformed output feature map.
Given:
Theta = [[[x_11, x_12, x_13]
[x_14, x_15, x_16]]
[[x_21, x_22, x_23]
[x_24, x_25, x_26]]]
OutputShape = [2, 3, 5, 5]
Step 1:
Generate relative coordinates according to OutputShape.
The values of relative coordinates are in the interval between -1 and 1.
The shape of the relative coordinates is [2, H, W] as below:
C = [[[-1. -1. -1. -1. -1. ]
[-0.5 -0.5 -0.5 -0.5 -0.5]
[ 0. 0. 0. 0. 0. ]
[ 0.5 0.5 0.5 0.5 0.5]
[ 1. 1. 1. 1. 1. ]]
[[-1. -0.5 0. 0.5 1. ]
[-1. -0.5 0. 0.5 1. ]
[-1. -0.5 0. 0.5 1. ]
[-1. -0.5 0. 0.5 1. ]
[-1. -0.5 0. 0.5 1. ]]]
C[0] is the coordinates in height axis and C[1] is the coordinates in width axis.
Step2:
Tanspose and reshape C to shape [H * W, 2] and append ones to last dimension. The we get:
C_ = [[-1. -1. 1. ]
[-0.5 -1. 1. ]
[ 0. -1. 1. ]
[ 0.5 -1. 1. ]
[ 1. -1. 1. ]
[-1. -0.5 1. ]
[-0.5 -0.5 1. ]
[ 0. -0.5 1. ]
[ 0.5 -0.5 1. ]
[ 1. -0.5 1. ]
[-1. 0. 1. ]
[-0.5 0. 1. ]
[ 0. 0. 1. ]
[ 0.5 0. 1. ]
[ 1. 0. 1. ]
[-1. 0.5 1. ]
[-0.5 0.5 1. ]
[ 0. 0.5 1. ]
[ 0.5 0.5 1. ]
[ 1. 0.5 1. ]
[-1. 1. 1. ]
[-0.5 1. 1. ]
[ 0. 1. 1. ]
[ 0.5 1. 1. ]
[ 1. 1. 1. ]]
Step3:
Compute output by equation $$Output[i] = C_ * Theta[i]^T$$
)DOC");
}
};
class AffineGridOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
auto theta_dims = ctx->GetInputDim("Theta");
if (ctx->HasOutput(framework::GradVarName("Theta"))) {
ctx->SetOutputDim(framework::GradVarName("Theta"), theta_dims);
}
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
framework::LibraryType library_{framework::LibraryType::kPlain};
#ifdef PADDLE_WITH_CUDA
if (platform::CanCUDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kCUDNN;
}
#endif
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("Theta")->type()),
ctx.GetPlace(), framework::DataLayout::kAnyLayout, library_);
}
};
class AffineGridGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* op = new framework::OpDesc();
op->SetType("affine_grid_grad");
op->SetInput("Theta", Input("Theta"));
op->SetInput("OutputShape", Input("OutputShape"));
op->SetInput(framework::GradVarName("Output"), OutputGrad("Output"));
op->SetAttrMap(Attrs());
op->SetOutput(framework::GradVarName("Theta"), InputGrad("Theta"));
return std::unique_ptr<framework::OpDesc>(op);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(affine_grid, ops::AffineGridOp, ops::AffineGridOpMaker,
ops::AffineGridGradMaker);
REGISTER_OPERATOR(affine_grid_grad, ops::AffineGridOpGrad);
REGISTER_OP_CPU_KERNEL(
affine_grid,
ops::AffineGridOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::AffineGridOpKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
affine_grid_grad,
ops::AffineGridGradOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::AffineGridGradOpKernel<paddle::platform::CPUDeviceContext, double>);
/* 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. */
#pragma once
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
using Array1 = Eigen::DSizes<int64_t, 1>;
using Array2 = Eigen::DSizes<int64_t, 2>;
using Array3 = Eigen::DSizes<int64_t, 3>;
using Array4 = Eigen::DSizes<int64_t, 4>;
/**
*Return a tensor with evenly spaced numbers over a specified interval.
*/
template <typename DeviceContext, typename T>
struct Linspace {
framework::Tensor operator()(T start, T end, int count,
const framework::ExecutionContext& ctx);
};
template <typename DeviceContext, typename T>
class AffineGridOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
auto* theta = ctx.Input<Tensor>("Theta");
int n = theta->dims()[0];
auto size_attr = ctx.Attr<std::vector<int>>("output_shape");
int h = 0;
int w = 0;
if (size_attr.size() == 0) {
auto* output_shape = ctx.Input<Tensor>("OutputShape");
Tensor h_sizes;
framework::TensorCopy(*output_shape, platform::CPUPlace(), &h_sizes);
const int* h_size_data = h_sizes.data<int>();
h = h_size_data[2];
w = h_size_data[3];
} else {
h = size_attr[2];
w = size_attr[3];
}
auto* output = ctx.Output<Tensor>("Output");
output->mutable_data<T>({n, h, w, 2}, ctx.GetPlace());
math::SetConstant<DeviceContext, T>()(
ctx.template device_context<DeviceContext>(), output,
static_cast<T>(0));
Linspace<DeviceContext, T> linspace;
// Get indexes of height with shape [height, width, 1]
auto h_idx = linspace((T)-1, (T)1, h, ctx);
auto h_idx_t = EigenTensor<T, 1>::From(h_idx);
// Get indexes of width with shape [height, width, 1]
auto w_idx = linspace((T)-1, (T)1, w, ctx);
auto w_idx_t = EigenTensor<T, 1>::From(w_idx);
// Get constant ones tensor with shape [height, width, 1]
Tensor ones;
ones.mutable_data<T>({h, w, 1}, ctx.GetPlace());
auto ones_t = EigenTensor<T, 3>::From(ones).setConstant((T)1);
// Get grid tensor with shape [n, h, w, 3] by concatenating h_idx, w_idx and
// ones
Tensor grid;
grid.mutable_data<T>({n, h, w, 3}, ctx.GetPlace());
auto grid_t = EigenTensor<T, 4>::From(grid);
grid_t.device(place) = w_idx_t.reshape(Array2(1, w))
.broadcast(Array2(h, 1))
.reshape(Array3(h, w, 1))
.concatenate(h_idx_t.reshape(Array2(1, h))
.broadcast(Array2(w, 1))
.shuffle(Array2(1, 0))
.reshape(Array3(h, w, 1)),
2)
.eval()
.concatenate(ones_t, 2)
.reshape(Array4(1, h, w, 3))
.broadcast(Array4(n, 1, 1, 1));
// output = grid * theta.T
// TODO(wanghaoshuang): Refine batched matrix multiply
auto blas = math::GetBlas<DeviceContext, T>(ctx);
for (int i = 0; i < n; ++i) {
Tensor sliced_grid = grid.Slice(i, i + 1).Resize({h * w, 3});
Tensor sliced_theta = theta->Slice(i, i + 1).Resize({2, 3});
Tensor sliced_out = output->Slice(i, i + 1).Resize({h * w, 2});
blas.MatMul(sliced_grid, false, sliced_theta, true, T(1), &sliced_out,
T(0));
}
}
};
template <typename DeviceContext, typename T>
class AffineGridGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
auto output_grad = ctx.Input<Tensor>(framework::GradVarName("Output"));
auto theta_grad = ctx.Output<Tensor>(framework::GradVarName("Theta"));
int n = output_grad->dims()[0];
auto size_attr = ctx.Attr<std::vector<int>>("output_shape");
int h = 0;
int w = 0;
if (size_attr.size() == 0) {
auto* output_shape = ctx.Input<Tensor>("OutputShape");
Tensor h_sizes;
framework::TensorCopy(*output_shape, platform::CPUPlace(), &h_sizes);
const int* h_size_data = h_sizes.data<int>();
h = h_size_data[2];
w = h_size_data[3];
} else {
h = size_attr[2];
w = size_attr[3];
}
theta_grad->mutable_data<T>({n, 2, 3}, ctx.GetPlace());
math::SetConstant<DeviceContext, T>()(
ctx.template device_context<DeviceContext>(), theta_grad,
static_cast<T>(0));
Linspace<DeviceContext, T> linspace;
// Get indexes of height with shape [height, width, 1]
auto h_idx = linspace((T)-1, (T)1, h, ctx);
auto h_idx_t = EigenTensor<T, 1>::From(h_idx);
// Get indexes of width with shape [height, width, 1]
auto w_idx = linspace((T)-1, (T)1, w, ctx);
auto w_idx_t = EigenTensor<T, 1>::From(w_idx);
// Get constant ones tensor with shape [height, width, 1]
Tensor ones;
ones.mutable_data<T>({h, w, 1}, ctx.GetPlace());
auto ones_t = EigenTensor<T, 3>::From(ones).setConstant((T)1);
// Get grid tensor with shape [n, h, w, 3] by concatenating h_idx, w_idx and
// ones
Tensor grid;
grid.mutable_data<T>({n, h, w, 3}, ctx.GetPlace());
auto grid_t = EigenTensor<T, 4>::From(grid);
grid_t.device(place) = w_idx_t.reshape(Array2(1, w))
.broadcast(Array2(h, 1))
.reshape(Array3(h, w, 1))
.concatenate(h_idx_t.reshape(Array2(1, h))
.broadcast(Array2(w, 1))
.shuffle(Array2(1, 0))
.reshape(Array3(h, w, 1)),
2)
.eval()
.concatenate(ones_t, 2)
.reshape(Array4(1, h, w, 3))
.broadcast(Array4(n, 1, 1, 1));
// output = grid * theta.T
// TODO(wanghaoshuang): Refine batched matrix multiply
auto blas = math::GetBlas<DeviceContext, T>(ctx);
for (int i = 0; i < n; ++i) {
Tensor sliced_grid = grid.Slice(i, i + 1).Resize({h * w, 3});
Tensor sliced_out_grad = output_grad->Slice(i, i + 1).Resize({h * w, 2});
Tensor sliced_theta_grad = theta_grad->Slice(i, i + 1).Resize({2, 3});
blas.MatMul(sliced_out_grad, true, sliced_grid, false, T(1),
&sliced_theta_grad, T(0));
}
}
};
} // namespace operators
} // namespace paddle
......@@ -32,6 +32,11 @@ class DeleteVarOp : public framework::OperatorBase {
}
};
class DeleteVarOpShapeInference : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *ctx) const override {}
};
class DeleteVarOpInfoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
......@@ -48,4 +53,5 @@ It should not be configured by users directly.
REGISTER_OPERATOR(delete_var, paddle::operators::DeleteVarOp,
paddle::framework::EmptyGradOpMaker,
paddle::operators::DeleteVarOpInfoMaker);
paddle::operators::DeleteVarOpInfoMaker,
paddle::operators::DeleteVarOpShapeInference);
......@@ -102,7 +102,9 @@ REGISTER_OPERATOR(gather, ops::GatherOp, ops::GatherOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(gather_grad, ops::GatherGradOp);
REGISTER_OP_CPU_KERNEL(gather, ops::GatherOpKernel<float>,
ops::GatherOpKernel<int>, ops::GatherOpKernel<double>);
ops::GatherOpKernel<double>, ops::GatherOpKernel<int>,
ops::GatherOpKernel<int64_t>);
REGISTER_OP_CPU_KERNEL(gather_grad, ops::GatherGradientOpKernel<float>,
ops::GatherGradientOpKernel<double>,
ops::GatherGradientOpKernel<int>,
ops::GatherGradientOpKernel<double>);
ops::GatherGradientOpKernel<int64_t>);
......@@ -61,5 +61,11 @@ class GatherGradOpCUDAKernel : public framework::OpKernel<T> {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(gather, ops::GatherOpCUDAKernel<float>);
REGISTER_OP_CUDA_KERNEL(gather_grad, ops::GatherGradOpCUDAKernel<float>);
REGISTER_OP_CUDA_KERNEL(gather, ops::GatherOpCUDAKernel<float>,
ops::GatherOpCUDAKernel<double>,
ops::GatherOpCUDAKernel<int64_t>,
ops::GatherOpCUDAKernel<int>);
REGISTER_OP_CUDA_KERNEL(gather_grad, ops::GatherGradOpCUDAKernel<float>,
ops::GatherGradOpCUDAKernel<double>,
ops::GatherGradOpCUDAKernel<int64_t>,
ops::GatherGradOpCUDAKernel<int>);
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/cudnn_helper.h"
namespace paddle {
namespace operators {
using framework::Tensor;
using ScopedTensorDescriptor = platform::ScopedTensorDescriptor;
using DataLayout = platform::DataLayout;
using ScopedSpatialTransformerDescriptor =
platform::ScopedSpatialTransformerDescriptor;
template <typename T>
using CudnnDataType = platform::CudnnDataType<T>;
template <typename T>
class CUDNNGridSampleOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
"It must use CUDAPlace");
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto handle = dev_ctx.cudnn_handle();
auto* input = ctx.Input<Tensor>("X");
auto* grid = ctx.Input<Tensor>("Grid");
auto* output = ctx.Output<Tensor>("Output");
int n = input->dims()[0];
int c = input->dims()[1];
int h = input->dims()[2];
int w = input->dims()[3];
const int size[4] = {n, c, h, w};
const T* input_data = input->data<T>();
const T* grid_data = grid->data<T>();
T* output_data = output->mutable_data<T>({n, c, h, w}, ctx.GetPlace());
ScopedSpatialTransformerDescriptor st_desc;
cudnnSpatialTransformerDescriptor_t cudnn_st_desc =
st_desc.descriptor<T>(4, size);
ScopedTensorDescriptor input_desc;
ScopedTensorDescriptor output_desc;
cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
DataLayout::kNCHW, framework::vectorize2int(input->dims()));
cudnnTensorDescriptor_t cudnn_output_desc = output_desc.descriptor<T>(
DataLayout::kNCHW, framework::vectorize2int(output->dims()));
CUDNN_ENFORCE(platform::dynload::cudnnSpatialTfSamplerForward(
handle, cudnn_st_desc, CudnnDataType<T>::kOne(), cudnn_input_desc,
input_data, grid_data, CudnnDataType<T>::kZero(), cudnn_output_desc,
output_data));
}
};
template <typename T>
class CUDNNGridSampleGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
"It must use CUDAPlace");
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto handle = dev_ctx.cudnn_handle();
auto* input = ctx.Input<Tensor>("X");
auto* grid = ctx.Input<Tensor>("Grid");
auto* output_grad = ctx.Input<Tensor>(framework::GradVarName("Output"));
auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* grid_grad = ctx.Output<Tensor>(framework::GradVarName("Grid"));
auto output_grad_dims = output_grad->dims();
const int n = output_grad_dims[0];
const int c = output_grad_dims[1];
const int h = output_grad_dims[2];
const int w = output_grad_dims[3];
const int size[4] = {n, c, h, w};
ScopedSpatialTransformerDescriptor st_dest;
cudnnSpatialTransformerDescriptor_t cudnn_st_dest =
st_dest.descriptor<T>(4, size);
const T* input_data = input->data<T>();
const T* grid_data = grid->data<T>();
const T* output_grad_data = output_grad->data<T>();
T* input_grad_data =
input_grad->mutable_data<T>(output_grad_dims, ctx.GetPlace());
T* grid_grad_data =
grid_grad->mutable_data<T>({n, h, w, 2}, ctx.GetPlace());
ScopedTensorDescriptor input_desc;
ScopedTensorDescriptor input_grad_desc;
ScopedTensorDescriptor output_grad_desc;
cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
DataLayout::kNCHW, framework::vectorize2int(input->dims()));
cudnnTensorDescriptor_t cudnn_input_grad_desc =
input_grad_desc.descriptor<T>(
DataLayout::kNCHW, framework::vectorize2int(input_grad->dims()));
cudnnTensorDescriptor_t cudnn_output_grad_desc =
output_grad_desc.descriptor<T>(
DataLayout::kNCHW, framework::vectorize2int(output_grad->dims()));
CUDNN_ENFORCE(platform::dynload::cudnnSpatialTfSamplerBackward(
handle, cudnn_st_dest, CudnnDataType<T>::kOne(), cudnn_input_desc,
input_data, CudnnDataType<T>::kZero(), cudnn_input_grad_desc,
input_grad_data, CudnnDataType<T>::kOne(), cudnn_output_grad_desc,
output_grad_data, grid_data, CudnnDataType<T>::kZero(),
grid_grad_data));
}
};
} // namespace operators
} // namespace paddle
namespace plat = paddle::platform;
REGISTER_OP_KERNEL(grid_sampler, CUDNN, plat::CUDAPlace,
paddle::operators::CUDNNGridSampleOpKernel<float>,
paddle::operators::CUDNNGridSampleOpKernel<double>);
REGISTER_OP_KERNEL(grid_sampler_grad, CUDNN, plat::CUDAPlace,
paddle::operators::CUDNNGridSampleGradOpKernel<float>,
paddle::operators::CUDNNGridSampleGradOpKernel<double>);
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/grid_sampler_op.h"
#include "paddle/fluid/framework/op_registry.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/cudnn_helper.h"
#endif
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
class GridSampleOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of GridSampleOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Grid"),
"Input(Grid) of GridSampleOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Output"),
"Output(Output) of GridSampleOp should not be null.");
auto x_dims = ctx->GetInputDim("X");
auto grid_dims = ctx->GetInputDim("Grid");
PADDLE_ENFORCE(x_dims.size() == 4,
"Input(X) of GridSampleOp should be 4-D Tensor.");
PADDLE_ENFORCE(grid_dims.size() == 4,
"Input(Grid) of GridSampleOp should be 4-D Tensor.");
PADDLE_ENFORCE(grid_dims[3] == 2, "Input(Grid) dims[3] should be 2.");
PADDLE_ENFORCE_EQ(grid_dims[0], x_dims[0],
"Input(X) and Input(Grid) dims[0] should be equal.");
PADDLE_ENFORCE_EQ(
grid_dims[1], x_dims[2],
"Input(X) dims[2] and Input(Grid) dims[1] should be equal.");
PADDLE_ENFORCE_EQ(
grid_dims[2], x_dims[3],
"Input(X) dims[3] and Input(Grid) dims[2] should be equal.");
ctx->SetOutputDim("Output", x_dims);
ctx->ShareLoD("X", "Output");
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
framework::LibraryType library_{framework::LibraryType::kPlain};
#ifdef PADDLE_WITH_CUDA
if (platform::CanCUDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kCUDNN;
}
#endif
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
framework::DataLayout::kAnyLayout, library_);
}
};
class GridSampleOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(Tensor) The input data of GridSampleOp, "
"This is a 4-D tensor with shape of [N, C, H, W]");
AddInput(
"Grid",
"(Tensor) The input grid of GridSampleOp generated by AffineGridOp, "
"This is a 4-D tensor with shape of [N, H, W, 2] is the concatenation "
"of x and y coordinates with shape [N, H, W] in last dimention");
AddOutput("Output", "(Tensor) Output tensor with shape [N, C, H, W]");
AddAttr<bool>(
"use_cudnn",
"(bool, default true) Only used in cudnn kernel, need install cudnn")
.SetDefault(true);
AddComment(R"DOC(
This operation samples input X by using bilinear interpolation based on
flow field grid, which is usually gennerated by affine_grid. The grid of
shape [N, H, W, 2] is the concatenation of (grid_x, grid_y) coordinates
with shape [N, H, W] each, where grid_x is indexing the 4th dimension
(in width dimension) of input data x and grid_y is indexng the 3rd
dimention (in height dimension), finally results is the bilinear
interpolation value of 4 nearest corner points.
Step 1:
Get (x, y) grid coordinates and scale to [0, H-1/W-1].
grid_x = 0.5 * (grid[:, :, :, 0] + 1) * (W - 1)
grid_y = 0.5 * (grid[:, :, :, 1] + 1) * (H - 1)
Step 2:
Indices input data X with grid (x, y) in each [H, W] area, and bilinear
interpolate point value by 4 nearest points.
wn ------- y_n ------- en
| | |
| d_n |
| | |
x_w --d_w-- grid--d_e-- x_e
| | |
| d_s |
| | |
ws ------- y_s ------- wn
x_w = floor(x) // west side x coord
x_e = x_w + 1 // east side x coord
y_n = floor(y) // north side y coord
y_s = y_s + 1 // south side y coord
d_w = grid_x - x_w // distance to west side
d_e = x_e - grid_x // distance to east side
d_n = grid_y - y_n // distance to north side
d_s = y_s - grid_y // distance to south side
wn = X[:, :, y_n, x_w] // north-west point value
en = X[:, :, y_n, x_e] // north-east point value
ws = X[:, :, y_s, x_w] // south-east point value
es = X[:, :, y_s, x_w] // north-east point value
output = wn * d_e * d_s + en * d_w * d_s
+ ws * d_e * d_n + es * d_w * d_n
)DOC");
}
};
class GridSampleOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
auto input_dims = ctx->GetInputDim("X");
auto grid_dims = ctx->GetInputDim("Grid");
if (ctx->HasOutput(framework::GradVarName("X"))) {
ctx->SetOutputDim(framework::GradVarName("X"), input_dims);
}
if (ctx->HasOutput(framework::GradVarName("Grid"))) {
ctx->SetOutputDim(framework::GradVarName("Grid"), grid_dims);
}
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
framework::LibraryType library_{framework::LibraryType::kPlain};
#ifdef PADDLE_WITH_CUDA
if (platform::CanCUDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kCUDNN;
}
#endif
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
framework::DataLayout::kAnyLayout, library_);
}
};
class GridSampleGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* op = new framework::OpDesc();
op->SetType("grid_sampler_grad");
op->SetInput("X", Input("X"));
op->SetInput("Grid", Input("Grid"));
op->SetInput(framework::GradVarName("Output"), OutputGrad("Output"));
op->SetAttrMap(Attrs());
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetOutput(framework::GradVarName("Grid"), InputGrad("Grid"));
return std::unique_ptr<framework::OpDesc>(op);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(grid_sampler, ops::GridSampleOp, ops::GridSampleOpMaker,
ops::GridSampleGradMaker);
REGISTER_OPERATOR(grid_sampler_grad, ops::GridSampleOpGrad);
REGISTER_OP_CPU_KERNEL(
grid_sampler,
ops::GridSampleOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::GridSampleOpKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
grid_sampler_grad,
ops::GridSampleGradOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::GridSampleGradOpKernel<paddle::platform::CPUDeviceContext, double>);
/* 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. */
#pragma once
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/gather.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/platform/hostdevice.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
using Array3 = Eigen::DSizes<int64_t, 3>;
using Array4 = Eigen::DSizes<int64_t, 4>;
template <typename T>
static inline bool isInBound(T x, T y, T x_max, T y_max) {
if (x < 0 || x > x_max || y < 0 || y > y_max) {
return false;
}
return true;
}
template <typename T>
static void CalcGridLocations(const platform::CPUDeviceContext& ctx,
const Tensor& grid, Tensor* x_w, Tensor* x_e,
Tensor* y_n, Tensor* y_s, Tensor* d_w,
Tensor* d_e, Tensor* d_n, Tensor* d_s) {
auto& place = *ctx.eigen_device();
const int n = grid.dims()[0];
const int h = grid.dims()[1];
const int w = grid.dims()[2];
const T x_max = static_cast<T>(w - 1);
const T y_max = static_cast<T>(h - 1);
// split grid with shape (n, h, w, 2) into (x, y) by the 3rd Dim
Tensor grid_x, grid_y;
T* grid_x_data = grid_x.mutable_data<T>({n, h, w}, ctx.GetPlace());
T* grid_y_data = grid_y.mutable_data<T>({n, h, w}, ctx.GetPlace());
const T* grid_data = grid.data<T>();
for (int i = 0; i < n * h * w; i++) {
grid_x_data[i] = grid_data[2 * i];
grid_y_data[i] = grid_data[(2 * i) + 1];
}
Tensor ones;
ones.mutable_data<T>({n, h, w}, ctx.GetPlace());
auto ones_t = EigenTensor<T, 3>::From(ones).setConstant(1.0);
// scale grid to [0, h-1/w-1]
auto grid_x_t = EigenTensor<T, 3>::From(grid_x);
auto grid_y_t = EigenTensor<T, 3>::From(grid_y);
grid_x_t.device(place) = 0.5 * ((grid_x_t + ones_t) * x_max);
grid_y_t.device(place) = 0.5 * ((grid_y_t + ones_t) * y_max);
// calculate coords of 4 corner points
x_w->mutable_data<T>({n, h, w}, ctx.GetPlace());
x_e->mutable_data<T>({n, h, w}, ctx.GetPlace());
y_n->mutable_data<T>({n, h, w}, ctx.GetPlace());
y_s->mutable_data<T>({n, h, w}, ctx.GetPlace());
auto x_w_t = EigenTensor<T, 3>::From(*x_w);
auto x_e_t = EigenTensor<T, 3>::From(*x_e);
auto y_n_t = EigenTensor<T, 3>::From(*y_n);
auto y_s_t = EigenTensor<T, 3>::From(*y_s);
x_w_t.device(place) = grid_x_t.floor();
x_e_t.device(place) = x_w_t + ones_t;
y_n_t.device(place) = grid_y_t.floor();
y_s_t.device(place) = y_n_t + ones_t;
// calculate distances to 4 sides
d_w->mutable_data<T>({n, h, w}, ctx.GetPlace());
d_e->mutable_data<T>({n, h, w}, ctx.GetPlace());
d_n->mutable_data<T>({n, h, w}, ctx.GetPlace());
d_s->mutable_data<T>({n, h, w}, ctx.GetPlace());
auto d_w_t = EigenTensor<T, 3>::From(*d_w);
auto d_e_t = EigenTensor<T, 3>::From(*d_e);
auto d_n_t = EigenTensor<T, 3>::From(*d_n);
auto d_s_t = EigenTensor<T, 3>::From(*d_s);
d_w_t.device(place) = grid_x_t - x_w_t;
d_e_t.device(place) = x_e_t - grid_x_t;
d_n_t.device(place) = grid_y_t - y_n_t;
d_s_t.device(place) = y_s_t - grid_y_t;
}
template <typename T>
static void GetGridPointValue(const Tensor& input, Tensor* output,
const Tensor& x, const Tensor& y) {
const int n = input.dims()[0];
const int c = input.dims()[1];
const int h = input.dims()[2];
const int w = input.dims()[3];
auto x_t = EigenTensor<T, 3>::From(x);
auto y_t = EigenTensor<T, 3>::From(y);
auto output_t = EigenTensor<T, 4>::From(*output).setConstant((T)0);
auto input_t = EigenTensor<T, 4>::From(input);
for (int i = 0; i < n; i++) {
for (int k = 0; k < h; k++) {
for (int l = 0; l < w; l++) {
if (isInBound(x_t(i, k, l), y_t(i, k, l), (T)(w - 1), (T)(h - 1))) {
for (int j = 0; j < c; j++) {
output_t(i, j, k, l) =
input_t(i, j, static_cast<int>(round(y_t(i, k, l))),
static_cast<int>(round(x_t(i, k, l))));
}
}
}
}
}
}
template <typename T>
static void GatherOutputGradToInputGrad(const Tensor& output_grad,
Tensor* input_grad, const Tensor& x,
const Tensor& y, const Tensor& d1,
const Tensor& d2) {
const int n = output_grad.dims()[0];
const int c = output_grad.dims()[1];
const int h = output_grad.dims()[2];
const int w = output_grad.dims()[3];
auto x_t = EigenTensor<T, 3>::From(x);
auto y_t = EigenTensor<T, 3>::From(y);
auto d1_t = EigenTensor<T, 3>::From(d1);
auto d2_t = EigenTensor<T, 3>::From(d2);
auto input_grad_t = EigenTensor<T, 4>::From(*input_grad);
auto output_grad_t = EigenTensor<T, 4>::From(output_grad);
for (int i = 0; i < n; i++) {
for (int k = 0; k < h; k++) {
for (int l = 0; l < w; l++) {
if (isInBound(x_t(i, k, l), y_t(i, k, l), (T)(w - 1), (T)(h - 1))) {
for (int j = 0; j < c; j++) {
input_grad_t(i, j, static_cast<int>(round(y_t(i, k, l))),
static_cast<int>(round(x_t(i, k, l)))) +=
output_grad_t(i, j, k, l) * d1_t(i, k, l) * d2_t(i, k, l);
}
}
}
}
}
}
template <typename DeviceContext, typename T>
class GridSampleOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
auto* input = ctx.Input<Tensor>("X");
auto* grid = ctx.Input<Tensor>("Grid");
const int n = input->dims()[0];
const int c = input->dims()[1];
const int h = input->dims()[2];
const int w = input->dims()[3];
// calc locations and distances of 4 corner points
Tensor x_w, x_e, y_n, y_s;
Tensor d_w, d_e, d_n, d_s;
CalcGridLocations<T>(
ctx.template device_context<platform::CPUDeviceContext>(), *grid, &x_w,
&x_e, &y_n, &y_s, &d_w, &d_e, &d_n, &d_s);
auto* output = ctx.Output<Tensor>("Output");
output->mutable_data<T>({n, c, h, w}, ctx.GetPlace());
math::SetConstant<DeviceContext, T>()(
ctx.template device_context<DeviceContext>(), output,
static_cast<T>(0));
// calc 4 corner points value
Tensor v_wn, v_en, v_ws, v_es;
v_wn.mutable_data<T>({n, c, h, w}, ctx.GetPlace());
v_en.mutable_data<T>({n, c, h, w}, ctx.GetPlace());
v_ws.mutable_data<T>({n, c, h, w}, ctx.GetPlace());
v_es.mutable_data<T>({n, c, h, w}, ctx.GetPlace());
GetGridPointValue<T>(*input, &v_wn, x_w, y_n);
GetGridPointValue<T>(*input, &v_en, x_e, y_n);
GetGridPointValue<T>(*input, &v_ws, x_w, y_s);
GetGridPointValue<T>(*input, &v_es, x_e, y_s);
auto d_w_t = EigenTensor<T, 3>::From(d_w);
auto d_e_t = EigenTensor<T, 3>::From(d_e);
auto d_n_t = EigenTensor<T, 3>::From(d_n);
auto d_s_t = EigenTensor<T, 3>::From(d_s);
auto d_w_scaled_t =
d_w_t.reshape(Array4(n, 1, h, w)).broadcast(Array4(1, c, 1, 1));
auto d_e_scaled_t =
d_e_t.reshape(Array4(n, 1, h, w)).broadcast(Array4(1, c, 1, 1));
auto d_n_scaled_t =
d_n_t.reshape(Array4(n, 1, h, w)).broadcast(Array4(1, c, 1, 1));
auto d_s_scaled_t =
d_s_t.reshape(Array4(n, 1, h, w)).broadcast(Array4(1, c, 1, 1));
auto v_wn_t = EigenTensor<T, 4>::From(v_wn);
auto v_en_t = EigenTensor<T, 4>::From(v_en);
auto v_ws_t = EigenTensor<T, 4>::From(v_ws);
auto v_es_t = EigenTensor<T, 4>::From(v_es);
auto output_t = EigenTensor<T, 4>::From(*output);
// bilinear interpolaetion by 4 corner points
output_t.device(place) = v_wn_t * d_e_scaled_t * d_s_scaled_t +
v_en_t * d_w_scaled_t * d_s_scaled_t +
v_ws_t * d_e_scaled_t * d_n_scaled_t +
v_es_t * d_w_scaled_t * d_n_scaled_t;
}
};
template <typename DeviceContext, typename T>
class GridSampleGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<Tensor>("X");
auto* grid = ctx.Input<Tensor>("Grid");
auto* output_grad = ctx.Input<Tensor>(framework::GradVarName("Output"));
const int n = input->dims()[0];
const int c = input->dims()[1];
const int h = input->dims()[2];
const int w = input->dims()[3];
auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
input_grad->mutable_data<T>({n, c, h, w}, ctx.GetPlace());
math::SetConstant<DeviceContext, T>()(
ctx.template device_context<DeviceContext>(), input_grad,
static_cast<T>(0));
auto* grid_grad = ctx.Output<Tensor>(framework::GradVarName("Grid"));
grid_grad->mutable_data<T>({n, h, w, 2}, ctx.GetPlace());
math::SetConstant<DeviceContext, T>()(
ctx.template device_context<DeviceContext>(), grid_grad,
static_cast<T>(0));
Tensor x_w, x_e, y_n, y_s;
Tensor d_w, d_e, d_n, d_s;
CalcGridLocations<T>(
ctx.template device_context<platform::CPUDeviceContext>(), *grid, &x_w,
&x_e, &y_n, &y_s, &d_w, &d_e, &d_n, &d_s);
// gather output grad value to input grad by corner point coords and weight
GatherOutputGradToInputGrad<T>(*output_grad, input_grad, x_w, y_n, d_e,
d_s);
GatherOutputGradToInputGrad<T>(*output_grad, input_grad, x_w, y_s, d_e,
d_n);
GatherOutputGradToInputGrad<T>(*output_grad, input_grad, x_e, y_n, d_w,
d_s);
GatherOutputGradToInputGrad<T>(*output_grad, input_grad, x_e, y_s, d_w,
d_n);
// calc 4 corner points value
Tensor v_wn, v_en, v_ws, v_es;
v_wn.mutable_data<T>({n, c, h, w}, ctx.GetPlace());
v_en.mutable_data<T>({n, c, h, w}, ctx.GetPlace());
v_ws.mutable_data<T>({n, c, h, w}, ctx.GetPlace());
v_es.mutable_data<T>({n, c, h, w}, ctx.GetPlace());
GetGridPointValue<T>(*input, &v_wn, x_w, y_n);
GetGridPointValue<T>(*input, &v_en, x_e, y_n);
GetGridPointValue<T>(*input, &v_ws, x_w, y_s);
GetGridPointValue<T>(*input, &v_es, x_e, y_s);
auto v_wn_t = EigenTensor<T, 4>::From(v_wn);
auto v_en_t = EigenTensor<T, 4>::From(v_en);
auto v_ws_t = EigenTensor<T, 4>::From(v_ws);
auto v_es_t = EigenTensor<T, 4>::From(v_es);
auto d_w_t = EigenTensor<T, 3>::From(d_w);
auto d_e_t = EigenTensor<T, 3>::From(d_e);
auto d_n_t = EigenTensor<T, 3>::From(d_n);
auto d_s_t = EigenTensor<T, 3>::From(d_s);
auto output_grad_t = EigenTensor<T, 4>::From(*output_grad);
Tensor grid_grad_x, grid_grad_y;
grid_grad_x.mutable_data<T>({n, h, w}, ctx.GetPlace());
grid_grad_y.mutable_data<T>({n, h, w}, ctx.GetPlace());
auto grid_grad_x_t = EigenTensor<T, 3>::From(grid_grad_x).setConstant(0.0);
auto grid_grad_y_t = EigenTensor<T, 3>::From(grid_grad_y).setConstant(0.0);
for (int i = 0; i < n; i++) {
for (int j = 0; j < c; j++) {
for (int k = 0; k < h; k++) {
for (int l = 0; l < w; l++) {
grid_grad_x_t(i, k, l) +=
((v_en_t(i, j, k, l) - v_wn_t(i, j, k, l)) * d_s_t(i, k, l) +
(v_es_t(i, j, k, l) - v_ws_t(i, j, k, l)) * d_n_t(i, k, l)) *
output_grad_t(i, j, k, l);
grid_grad_y_t(i, k, l) +=
((v_ws_t(i, j, k, l) - v_wn_t(i, j, k, l)) * d_e_t(i, k, l) +
(v_es_t(i, j, k, l) - v_en_t(i, j, k, l)) * d_w_t(i, k, l)) *
output_grad_t(i, j, k, l);
}
}
}
}
const T x_max = static_cast<T>(w - 1);
const T y_max = static_cast<T>(h - 1);
grid_grad_x_t = grid_grad_x_t * (x_max / (T)2);
grid_grad_y_t = grid_grad_y_t * (y_max / (T)2);
// gather grid_grad [x, y] in 3rd Dim
T* grid_grad_data = grid_grad->data<T>();
T* grid_grad_x_data = grid_grad_x.data<T>();
T* grid_grad_y_data = grid_grad_y.data<T>();
for (int i = 0; i < n * h * w; i++) {
grid_grad_data[2 * i] = grid_grad_x_data[i];
grid_grad_data[2 * i + 1] = grid_grad_y_data[i];
}
}
};
} // namespace operators
} // namespace paddle
......@@ -76,6 +76,6 @@ endif()
cc_test(concat_test SRCS concat_test.cc DEPS concat_and_split)
cc_test(cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info)
cc_library(jit_kernel
SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_rnn.cc jit_kernel_crf_decode.cc
DEPS cpu_info cblas)
SRCS jit_kernel.cc jit_gen.cc jit_code.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_rnn.cc jit_kernel_crf_decode.cc
DEPS cpu_info cblas gflags enforce)
cc_test(jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel)
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/math/jit_code.h"
#include "paddle/fluid/operators/math/jit_kernel.h"
#include "paddle/fluid/platform/cpu_info.h"
namespace paddle {
namespace operators {
namespace math {
namespace jitkernel {
namespace gen {
using namespace platform::jit; // NOLINT
bool VMulJitCode::init(int d) {
// TODO(TJ): maybe one AVX is enough, AVX above would slow down freq
// try more with avx2 or avx512
if (MayIUse(avx) || MayIUse(avx2)) {
return d % AVX_FLOAT_BLOCK == 0;
} else {
return false;
}
}
void VMulJitCode::generate() {
// do not need push stack, and do not need save avx512reg if do not use avx512
int stride = sizeof(float) * AVX_FLOAT_BLOCK;
for (int i = 0; i < num_ / AVX_FLOAT_BLOCK; ++i) {
vmovups(ymm_src1, ptr[param1 + i * stride]);
vmovups(ymm_src2, ptr[param2 + i * stride]);
vmulps(ymm_dst, ymm_src1, ymm_src2);
vmovups(ptr[param3 + stride * i], ymm_dst);
}
ret();
}
} // namespace gen
} // namespace jitkernel
} // namespace math
} // namespace operators
} // namespace paddle
/* 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. */
#pragma once
#include "paddle/fluid/operators/math/jit_gen.h"
namespace paddle {
namespace operators {
namespace math {
namespace jitkernel {
namespace gen {
using reg64_t = const Xbyak::Reg64;
using reg32_t = const Xbyak::Reg32;
using xmm_t = const Xbyak::Xmm;
using ymm_t = const Xbyak::Ymm;
using zmm_t = const Xbyak::Zmm;
using Label = Xbyak::Label;
class VMulJitCode : public JitCode {
public:
DECLARE_JIT_CODE(VMulJitCode);
explicit VMulJitCode(int d, size_t code_size = 256 * 1024,
void* code_ptr = nullptr)
: JitCode(code_size, code_ptr), num_(d) {}
static bool init(int d);
void generate() override;
private:
int num_;
reg64_t param1{abi_param1};
reg64_t param2{abi_param2};
reg64_t param3{abi_param3};
xmm_t xmm_src1 = xmm_t(0);
ymm_t ymm_src1 = ymm_t(0);
zmm_t zmm_src1 = zmm_t(0);
xmm_t xmm_src2 = xmm_t(1);
ymm_t ymm_src2 = ymm_t(1);
zmm_t zmm_src2 = zmm_t(1);
xmm_t xmm_dst = xmm_t(2);
ymm_t ymm_dst = ymm_t(2);
zmm_t zmm_dst = zmm_t(2);
};
} // namespace gen
} // namespace jitkernel
} // namespace math
} // namespace operators
} // namespace paddle
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/math/jit_gen.h"
#include <fstream>
#include <iostream>
#include <sstream>
#include "paddle/fluid/platform/cpu_info.h"
DEFINE_bool(dump_jitcode, false, "Whether to dump the jitcode to file");
namespace paddle {
namespace operators {
namespace math {
namespace jitkernel {
namespace gen {
constexpr Xbyak::Operand::Code g_abi_regs[] = {
Xbyak::Operand::RBX, Xbyak::Operand::RBP, Xbyak::Operand::R12,
Xbyak::Operand::R13, Xbyak::Operand::R14, Xbyak::Operand::R15};
constexpr int num_g_abi_regs = sizeof(g_abi_regs) / sizeof(g_abi_regs[0]);
void JitCode::preCode() {
for (int i = 0; i < num_g_abi_regs; ++i) {
push(Xbyak::Reg64(g_abi_regs[i]));
}
if (platform::jit::MayIUse(platform::jit::avx512f)) {
mov(reg_EVEX_max_8b_offt, 2 * EVEX_max_8b_offt);
}
}
void JitCode::postCode() {
for (int i = 0; i < num_g_abi_regs; ++i) {
pop(Xbyak::Reg64(g_abi_regs[num_g_abi_regs - 1 - i]));
}
ret();
}
void JitCode::dumpCode(const Xbyak::uint8 *code) const {
if (code) {
static int counter = 0;
std::ostringstream filename;
filename << "paddle_jitcode_" << name() << "." << counter << ".bin";
counter++;
std::ofstream fout(filename.str(), std::ios::out);
if (fout.is_open()) {
fout.write(reinterpret_cast<const char *>(code), getSize());
fout.close();
}
}
}
Xbyak::Address JitCode::EVEX_compress_addr(Xbyak::Reg64 base, int offt,
bool bcast) {
int scale = 0;
if (EVEX_max_8b_offt <= offt && offt < 3 * EVEX_max_8b_offt) {
offt = offt - 2 * EVEX_max_8b_offt;
scale = 1;
} else if (3 * EVEX_max_8b_offt <= offt && offt < 5 * EVEX_max_8b_offt) {
offt = offt - 4 * EVEX_max_8b_offt;
scale = 2;
}
auto re = Xbyak::RegExp() + base + offt;
if (scale) {
re = re + reg_EVEX_max_8b_offt * scale;
}
if (bcast) {
return zword_b[re];
} else {
return zword[re];
}
}
} // namespace gen
} // namespace jitkernel
} // namespace math
} // namespace operators
} // namespace paddle
/* 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. */
#pragma once
#include <gflags/gflags.h>
#include <type_traits>
#include "paddle/fluid/platform/macros.h"
#define XBYAK_USE_MMAP_ALLOCATOR
#include "xbyak/xbyak.h"
#include "xbyak/xbyak_util.h"
DECLARE_bool(dump_jitcode);
namespace paddle {
namespace operators {
namespace math {
namespace jitkernel {
namespace gen {
#define DECLARE_JIT_CODE(codename) \
const char *name() const override { return #codename; }
// Application Binary Interface
constexpr Xbyak::Operand::Code abi_param1(Xbyak::Operand::RDI),
abi_param2(Xbyak::Operand::RSI), abi_param3(Xbyak::Operand::RDX),
abi_param4(Xbyak::Operand::RCX), abi_not_param1(Xbyak::Operand::RCX);
class JitCode : public Xbyak::CodeGenerator {
public:
explicit JitCode(size_t code_size = 256 * 1024, void *code_ptr = nullptr)
: Xbyak::CodeGenerator(code_size, code_ptr) {}
virtual ~JitCode() {}
virtual const char *name() const = 0;
virtual void generate() = 0;
template <typename FUNC>
const FUNC getCode() {
this->generate();
const Xbyak::uint8 *code = CodeGenerator::getCode();
if (FLAGS_dump_jitcode) {
this->dumpCode(code);
}
return reinterpret_cast<const FUNC>(code);
}
DISABLE_COPY_AND_ASSIGN(JitCode);
protected:
Xbyak::Reg64 param1{abi_param1};
const int EVEX_max_8b_offt = 0x200;
const Xbyak::Reg64 reg_EVEX_max_8b_offt = rbp;
void preCode();
void postCode();
void dumpCode(const Xbyak::uint8 *code) const;
void L(const char *label) { Xbyak::CodeGenerator::L(label); }
void L(const Xbyak::Label &label) { Xbyak::CodeGenerator::L(label); }
// Enhanced vector extension
Xbyak::Address EVEX_compress_addr(Xbyak::Reg64 base, int offt,
bool bcast = false);
};
} // namespace gen
} // namespace jitkernel
} // namespace math
} // namespace operators
} // namespace paddle
......@@ -39,6 +39,7 @@ class Kernel {
public:
Kernel() = default;
virtual ~Kernel() = default;
// TODO(TJ): below members should be deprecated.
int num_{0};
int end_{0};
int rest_{0};
......@@ -64,7 +65,7 @@ class KernelPool {
template <typename T>
class VMulKernel : public Kernel {
public:
virtual void Compute(const T *x, const T *y, T *z) const = 0;
void (*Compute)(const T *, const T *, T *, int);
};
template <typename T>
......
......@@ -14,7 +14,10 @@ limitations under the License. */
#include "paddle/fluid/operators/math/jit_kernel.h"
#include <string>
#include "paddle/fluid/operators/math/jit_code.h"
#include "paddle/fluid/operators/math/jit_kernel_macro.h"
#include "paddle/fluid/platform/enforce.h"
#ifdef PADDLE_WITH_MKLML
#include "paddle/fluid/platform/dynload/mklml.h"
#endif
......@@ -27,65 +30,76 @@ namespace paddle {
namespace operators {
namespace math {
namespace jitkernel {
namespace jit = platform::jit;
template <typename T>
void VMulRefer(const T* x, const T* y, T* z, int n) {
for (int i = 0; i < n; ++i) {
z[i] = x[i] * y[i];
}
}
#ifdef PADDLE_WITH_MKLML
template <typename T>
void VMulMKL(const T* x, const T* y, T* z, int n);
template <>
void VMulMKL<float>(const float* x, const float* y, float* z, int n) {
platform::dynload::vsMul(n, x, y, z);
}
template <>
void VMulMKL<double>(const double* x, const double* y, double* z, int n) {
platform::dynload::vdMul(n, x, y, z);
}
#endif
/* VMUL JitKernel */
template <typename T, platform::jit::cpu_isa_t isa, jit_block>
template <typename T>
class VMulKernelImpl : public VMulKernel<T> {
public:
explicit VMulKernelImpl(int d) : VMulKernel<T>() { this->num_ = d; }
void Compute(const T* x, const T* y, T* z) const override {
for (int i = 0; i < this->num_; ++i) {
z[i] = x[i] * y[i];
}
static inline std::string name(int d) {
PADDLE_THROW("DType should be either float or double");
}
};
static inline bool useJIT(int d) { return false; }
static inline bool useMKL(int d) { return false; }
explicit VMulKernelImpl(int d) : VMulKernel<T>() {
if (useJIT(d)) {
constexpr size_t sz = 256 * 1024; // TODO(TJ): should be related with d
jitcode_.reset(new gen::VMulJitCode(d, sz));
this->Compute =
jitcode_->getCode<void (*)(const T*, const T*, T*, int)>();
return;
}
#ifdef PADDLE_WITH_MKLML
#define MKL_FLOAT(isa, block) \
template <> \
void VMulKernelImpl<float, isa, block>::Compute( \
const float* x, const float* y, float* z) const { \
platform::dynload::vsMul(this->num_, x, y, z); \
if (useMKL(d)) {
this->Compute = VMulMKL<T>;
return;
}
#endif
this->Compute = VMulRefer<T>;
}
#define MKL_DOUBLE(isa, block) \
template <> \
void VMulKernelImpl<double, isa, block>::Compute( \
const double* x, const double* y, double* z) const { \
platform::dynload::vdMul(this->num_, x, y, z); \
}
private:
std::unique_ptr<gen::VMulJitCode> jitcode_{nullptr};
};
FOR_EACH_ISA(MKL_FLOAT, kGT16);
FOR_EACH_ISA_BLOCK(MKL_DOUBLE);
#endif
template <>
bool VMulKernelImpl<float>::useJIT(int d) {
return gen::VMulJitCode::init(d);
}
#define INTRI8_FLOAT(isa) \
template <> \
void VMulKernelImpl<float, isa, kEQ8>::Compute( \
const float* x, const float* y, float* z) const { \
__m256 tmpx, tmpy; \
tmpx = _mm256_loadu_ps(x); \
tmpy = _mm256_loadu_ps(y); \
tmpx = _mm256_mul_ps(tmpx, tmpy); \
_mm256_storeu_ps(z, tmpx); \
}
template <>
bool VMulKernelImpl<float>::useMKL(int d) {
return jit::MayIUse(jit::avx512f) && d > 512;
}
// avx > for > mkl
#ifdef __AVX__
INTRI8_FLOAT(jit::avx);
#endif
#ifdef __AVX2__
INTRI8_FLOAT(jit::avx2);
#endif
#ifdef __AVX512F__
INTRI8_FLOAT(jit::avx512f);
#endif
// TODO(TJ): eq16 test and complete avx512
#undef INTRI8_FLOAT
#undef MKL_FLOAT
#undef MKL_DOUBLE
template <>
bool VMulKernelImpl<double>::useMKL(int d) {
return true;
}
REGISTER_JITKERNEL(vmul, VMulKernel);
/* VADD JitKernel */
template <typename T, platform::jit::cpu_isa_t isa, jit_block>
......@@ -465,13 +479,12 @@ INTRI_COMMON_FLOAT(jit::avx512f, kGT16);
#undef INTRI16_FLOAT
#undef INTRI_COMMON_FLOAT
REGISTER_JITKERNEL(vmul, VMulKernel);
REGISTER_JITKERNEL(vadd, VAddKernel);
REGISTER_JITKERNEL(vscal, VScalKernel);
REGISTER_JITKERNEL(vaddb, VAddBiasKernel);
REGISTER_JITKERNEL(vrelu, VReluKernel);
REGISTER_JITKERNEL(vaddrelu, VAddReluKernel);
REGISTER_JITKERNEL(videntity, VIdentityKernel);
REGISTER_JITKERNEL_DEPRECATED(vadd, VAddKernel);
REGISTER_JITKERNEL_DEPRECATED(vscal, VScalKernel);
REGISTER_JITKERNEL_DEPRECATED(vaddb, VAddBiasKernel);
REGISTER_JITKERNEL_DEPRECATED(vrelu, VReluKernel);
REGISTER_JITKERNEL_DEPRECATED(vaddrelu, VAddReluKernel);
REGISTER_JITKERNEL_DEPRECATED(videntity, VIdentityKernel);
} // namespace jitkernel
} // namespace math
......
......@@ -288,7 +288,7 @@ INTRIAVX512_FLOAT(kGT16);
#undef INIT_ALPHA
#undef UPDATE_ALPHA
REGISTER_JITKERNEL(crf_decode, CRFDecodeKernel);
REGISTER_JITKERNEL_DEPRECATED(crf_decode, CRFDecodeKernel);
} // namespace jitkernel
} // namespace math
......
......@@ -250,7 +250,7 @@ INTRI16_FLOAT(jit::avx512f, detail::ExpAVX2);
#undef MKL_FLOAT
#undef MKL_DOUBLE
REGISTER_JITKERNEL(vexp, VExpKernel);
REGISTER_JITKERNEL_DEPRECATED(vexp, VExpKernel);
/* VSigmoid JitKernel */
template <typename T, jit::cpu_isa_t isa, jit_block>
......@@ -396,7 +396,7 @@ INTRI16_FLOAT(jit::avx512f, detail::ExpAVX2);
#undef INTRI_GT16_FLOAT
#undef INTRI_VSIGMOID
REGISTER_JITKERNEL(vsigmoid, VSigmoidKernel);
REGISTER_JITKERNEL_DEPRECATED(vsigmoid, VSigmoidKernel);
/* VTanh JitKernel */
template <typename T, jit::cpu_isa_t isa, jit_block>
......@@ -531,7 +531,7 @@ INTRI16_FLOAT(jit::avx512f, detail::ExpAVX2);
#undef INTRI_GT16_FLOAT
#undef INTRI_VTANH
REGISTER_JITKERNEL(vtanh, VTanhKernel);
REGISTER_JITKERNEL_DEPRECATED(vtanh, VTanhKernel);
#undef JITKERNEL_NEW_ACT_IMPL
......
......@@ -21,8 +21,71 @@ namespace operators {
namespace math {
namespace jitkernel {
namespace jit = platform::jit;
#define JITKERNEL_DEFINE_NAME(ker_key, ker_class) \
template <> \
std::string ker_class##Impl<float>::name(int d) { \
std::string key(#ker_key "f"); \
if (useJIT(d)) { \
/* only jit code need record d*/ \
return key + "jit" + std::to_string(d); \
} else if (useMKL(d)) { \
return key + "mkl"; \
} else { \
return key + "any"; \
} \
} \
template <> \
std::string ker_class##Impl<double>::name(int d) { \
std::string key(#ker_key "d"); \
/* jit code do not support double yet*/ \
if (useMKL(d)) { \
return key + "mkl"; \
} else { \
return key + "any"; \
} \
}
#define JITKERNEL_DECLARE(ker_class, ker_dtype) \
template <> \
std::shared_ptr<const ker_class<ker_dtype>> \
KernelPool::Get<ker_class<ker_dtype>, int>(int d)
#define JITKERNEL_FIND_KEY(ker_class, ker_dtype) \
std::string key = ker_class##Impl<ker_dtype>::name(d)
#define JITKERNEL_IMPL(ker_class, ker_dtype) \
p = std::dynamic_pointer_cast<ker_class<ker_dtype>>( \
std::make_shared<ker_class##Impl<ker_dtype>>(d))
#define REGISTER_JITKERNEL_WITH_DTYPE(ker_class, ker_dtype, marco_declare, \
macro_find_key, macro_impl) \
marco_declare(ker_class, ker_dtype) { \
macro_find_key(ker_class, ker_dtype); \
if (kers_.find(key) == kers_.end()) { \
std::shared_ptr<ker_class<ker_dtype>> p; \
macro_impl(ker_class, ker_dtype); \
kers_.insert({key, std::dynamic_pointer_cast<Kernel>(p)}); \
return p; \
} \
return std::dynamic_pointer_cast<const ker_class<ker_dtype>>( \
kers_.at(key)); \
}
#define REGISTER_JITKERNEL_ARGS(ker_key, ker_class, marco_define_name, \
marco_declare, macro_find_key, macro_impl) \
marco_define_name(ker_key, ker_class); \
REGISTER_JITKERNEL_WITH_DTYPE(ker_class, float, JITKERNEL_DECLARE, \
JITKERNEL_FIND_KEY, JITKERNEL_IMPL); \
REGISTER_JITKERNEL_WITH_DTYPE(ker_class, double, JITKERNEL_DECLARE, \
JITKERNEL_FIND_KEY, JITKERNEL_IMPL)
#define REGISTER_JITKERNEL(ker_key, ker_class) \
REGISTER_JITKERNEL_ARGS(ker_key, ker_class, JITKERNEL_DEFINE_NAME, \
JITKERNEL_DECLARE, JITKERNEL_FIND_KEY, \
JITKERNEL_IMPL)
namespace jit = platform::jit;
// TODO(TJ): below defines are deprecated, would be remove recently
#define SEARCH_BLOCK(macro_, ker, dtype, isa) \
if (d < AVX_FLOAT_BLOCK) { \
macro_(ker, dtype, isa, kLT8); \
......@@ -47,44 +110,42 @@ namespace jit = platform::jit;
SEARCH_BLOCK(macro_, ker, dtype, jit::isa_any); \
}
#define JITKERNEL_DECLARE(ker_class, ker_dtype) \
template <> \
std::shared_ptr<const ker_class<ker_dtype>> \
KernelPool::Get<ker_class<ker_dtype>, int>(int d)
#define JITKERNEL_KEY(ker_key, dtype_key) \
#ker_key #dtype_key + std::to_string(d)
#define JITKERNEL_NEW_IMPL(ker, dtype, isa, k) \
p = std::dynamic_pointer_cast<ker<dtype>>( \
#define JITKERNEL_NEW_IMPL_DEPRECATED(ker, dtype, isa, k) \
p = std::dynamic_pointer_cast<ker<dtype>>( \
std::make_shared<ker##Impl<dtype, isa, k>>(d))
#define JITKERNEL_WITH_DTYPE(ker_key, ker_class, ker_dtype, dtype_key, \
marco_declare, macro_key, macro_impl) \
marco_declare(ker_class, ker_dtype) { \
std::string key = macro_key(ker_key, dtype_key); \
if (kers_.find(key) == kers_.end()) { \
std::shared_ptr<ker_class<ker_dtype>> p; \
SEARCH_ISA_BLOCK(macro_impl, ker_class, ker_dtype); \
kers_.insert({key, std::dynamic_pointer_cast<Kernel>(p)}); \
return p; \
} \
return std::dynamic_pointer_cast<const ker_class<ker_dtype>>( \
kers_.at(key)); \
#define JITKERNEL_WITH_DTYPE_DEPRECATED(ker_key, ker_class, ker_dtype, \
dtype_key, marco_declare, macro_key, \
macro_impl) \
marco_declare(ker_class, ker_dtype) { \
std::string key = macro_key(ker_key, dtype_key); \
if (kers_.find(key) == kers_.end()) { \
std::shared_ptr<ker_class<ker_dtype>> p; \
SEARCH_ISA_BLOCK(macro_impl, ker_class, ker_dtype); \
kers_.insert({key, std::dynamic_pointer_cast<Kernel>(p)}); \
return p; \
} \
return std::dynamic_pointer_cast<const ker_class<ker_dtype>>( \
kers_.at(key)); \
}
#define REGISTER_JITKERNEL(ker_key, ker_class) \
JITKERNEL_WITH_DTYPE(ker_key, ker_class, float, f, JITKERNEL_DECLARE, \
JITKERNEL_KEY, JITKERNEL_NEW_IMPL); \
JITKERNEL_WITH_DTYPE(ker_key, ker_class, double, d, JITKERNEL_DECLARE, \
JITKERNEL_KEY, JITKERNEL_NEW_IMPL)
#define REGISTER_JITKERNEL_ARGS(ker_key, ker_class, marco_declare, macro_key, \
macro_impl) \
JITKERNEL_WITH_DTYPE(ker_key, ker_class, float, f, marco_declare, macro_key, \
macro_impl); \
JITKERNEL_WITH_DTYPE(ker_key, ker_class, double, d, marco_declare, \
macro_key, macro_impl)
#define REGISTER_JITKERNEL_DEPRECATED(ker_key, ker_class) \
JITKERNEL_WITH_DTYPE_DEPRECATED(ker_key, ker_class, float, f, \
JITKERNEL_DECLARE, JITKERNEL_KEY, \
JITKERNEL_NEW_IMPL_DEPRECATED); \
JITKERNEL_WITH_DTYPE_DEPRECATED(ker_key, ker_class, double, d, \
JITKERNEL_DECLARE, JITKERNEL_KEY, \
JITKERNEL_NEW_IMPL_DEPRECATED)
#define REGISTER_JITKERNEL_ARGS_DEPRECATED(ker_key, ker_class, marco_declare, \
macro_key, macro_impl) \
JITKERNEL_WITH_DTYPE_DEPRECATED(ker_key, ker_class, float, f, marco_declare, \
macro_key, macro_impl); \
JITKERNEL_WITH_DTYPE_DEPRECATED(ker_key, ker_class, double, d, \
marco_declare, macro_key, macro_impl)
#define FOR_EACH_ISA(macro_, block) \
macro_(jit::avx512f, block); \
......
......@@ -179,23 +179,23 @@ class LSTMKernelImpl : public LSTMKernel<T> {
/* C_t = C_t-1 * fgated + cand_gated * igated */
act_cand_d_->Compute(gates, gates);
vmul_d_->Compute(gates, gates + d_, gates + d_);
vmul_d_->Compute(ct_1, gates + d2_, gates + d2_);
vmul_d_->Compute(gates, gates + d_, gates + d_, d_);
vmul_d_->Compute(ct_1, gates + d2_, gates + d2_, d_);
vadd_d_->Compute(gates + d_, gates + d2_, ct);
/* H_t = act_cell(C_t) * ogated */
act_cell_d_->Compute(ct, gates + d2_);
vmul_d_->Compute(gates + d2_, gates + d3_, ht);
vmul_d_->Compute(gates + d2_, gates + d3_, ht, d_);
}
void ComputeC1H1(T* gates, T* ct, T* ht, const T* wp_data) const override {
/* C_t = igated * cgated*/
act_gate_d_->Compute(gates + d_, gates + d_);
act_cand_d_->Compute(gates, gates);
vmul_d_->Compute(gates, gates + d_, ct);
vmul_d_->Compute(gates, gates + d_, ct, d_);
/* H_t = act_cell(C_t) * ogated */
act_gate_d_->Compute(gates + d3_, gates + d3_);
act_cell_d_->Compute(ct, gates + d2_);
vmul_d_->Compute(gates + d2_, gates + d3_, ht);
vmul_d_->Compute(gates + d2_, gates + d3_, ht, d_);
}
private:
......@@ -289,36 +289,36 @@ class PeepholeKernelImpl : public LSTMKernel<T> {
void ComputeCtHt(T* gates, const T* ct_1, T* ct, T* ht, const T* wp_data,
T* checked) const override {
/* get fgated and igated*/
vmul_d_->Compute(wp_data, ct_1, checked);
vmul_d_->Compute(wp_data + d_, ct_1, checked + d_);
vmul_d_->Compute(wp_data, ct_1, checked, d_);
vmul_d_->Compute(wp_data + d_, ct_1, checked + d_, d_);
vadd_d2_->Compute(checked, gates + d_, gates + d_);
act_gate_d2_->Compute(gates + d_, gates + d_);
/* C_t = C_t-1 * fgated + cand_gated * igated*/
act_cand_d_->Compute(gates, gates);
vmul_d_->Compute(gates, gates + d_, gates + d_);
vmul_d_->Compute(ct_1, gates + d2_, gates + d2_);
vmul_d_->Compute(gates, gates + d_, gates + d_, d_);
vmul_d_->Compute(ct_1, gates + d2_, gates + d2_, d_);
vadd_d_->Compute(gates + d_, gates + d2_, ct);
/* get ogated*/
vmul_d_->Compute(wp_data + d2_, ct, gates + d_);
vmul_d_->Compute(wp_data + d2_, ct, gates + d_, d_);
vadd_d_->Compute(gates + d_, gates + d3_, gates + d3_);
act_gate_d_->Compute(gates + d3_, gates + d3_);
/* H_t = act_cell(C_t) * ogated */
act_cell_d_->Compute(ct, gates + d2_);
vmul_d_->Compute(gates + d2_, gates + d3_, ht);
vmul_d_->Compute(gates + d2_, gates + d3_, ht, d_);
}
void ComputeC1H1(T* gates, T* ct, T* ht, const T* wp_data) const override {
/* C_t = igated * cgated*/
act_gate_d_->Compute(gates + d_, gates + d_);
act_cand_d_->Compute(gates, gates);
vmul_d_->Compute(gates, gates + d_, ct);
vmul_d_->Compute(gates, gates + d_, ct, d_);
/* get outgated, put W_oc * C_t on igated */
vmul_d_->Compute(wp_data + d2_, ct, gates + d_);
vmul_d_->Compute(wp_data + d2_, ct, gates + d_, d_);
vadd_d_->Compute(gates + d_, gates + d3_, gates + d3_);
/* H_t = act_cell(C_t) * ogated */
act_gate_d_->Compute(gates + d3_, gates + d3_);
act_cell_d_->Compute(ct, gates + d2_);
vmul_d_->Compute(gates + d2_, gates + d3_, ht);
vmul_d_->Compute(gates + d2_, gates + d3_, ht, d_);
}
private:
......@@ -352,8 +352,8 @@ class PeepholeKernelImpl : public LSTMKernel<T> {
act_cell, d)); \
}
REGISTER_JITKERNEL_ARGS(lstm, LSTMKernel, JITKERNEL_DECLARE_LSTM,
JITKERNEL_KEY_LSTM, JITKERNEL_NEW_LSTM_IMPL);
REGISTER_JITKERNEL_ARGS_DEPRECATED(lstm, LSTMKernel, JITKERNEL_DECLARE_LSTM,
JITKERNEL_KEY_LSTM, JITKERNEL_NEW_LSTM_IMPL);
#undef INTRI8_FLOAT
#undef JITKERNEL_DECLARE_LSTM
......@@ -378,13 +378,13 @@ class GRUKernelImpl : public GRUKernel<T> {
void ComputeH1(T* gates, T* ht) const override {
act_gate_d_->Compute(gates, gates);
act_state_d_->Compute(gates + d2_, gates + d2_);
vmul_d_->Compute(gates, gates + d2_, ht);
vmul_d_->Compute(gates, gates + d2_, ht, d_);
}
void ComputeHtPart1(T* gates, const T* ht_1, T* ht) const override {
// W: {W_update, W_reset; W_state}
act_gate_d2_->Compute(gates, gates);
vmul_d_->Compute(ht_1, gates + d_, ht);
vmul_d_->Compute(ht_1, gates + d_, ht, d_);
}
void ComputeHtPart2(T* gates, const T* ht_1, T* ht) const override {
......@@ -472,8 +472,8 @@ INTRI8_FLOAT(jit::avx512f);
p = std::dynamic_pointer_cast<ker<dtype>>( \
std::make_shared<ker##Impl<dtype, isa, k>>(act_gate, act_state, d));
REGISTER_JITKERNEL_ARGS(gru, GRUKernel, JITKERNEL_DECLARE_GRU,
JITKERNEL_KEY_GRU, JITKERNEL_NEW_GRU_IMPL);
REGISTER_JITKERNEL_ARGS_DEPRECATED(gru, GRUKernel, JITKERNEL_DECLARE_GRU,
JITKERNEL_KEY_GRU, JITKERNEL_NEW_GRU_IMPL);
#undef INTRI8_FLOAT
#undef JITKERNEL_NEW_GRU_IMPL
......
......@@ -369,12 +369,12 @@ void lstm_ctht_better(
int d2 = d * 2;
vsigmoid_3d->Compute(gates + d, gates + d);
vtanh_d->Compute(gates, gates);
vmul_d->Compute(gates, gates + d, gates + d);
vmul_d->Compute(ct_1, gates + d2, gates + d2);
vmul_d->Compute(gates, gates + d, gates + d, d);
vmul_d->Compute(ct_1, gates + d2, gates + d2, d);
vadd_d->Compute(gates + d, gates + d2, ct);
/* H_t = act_cell(C_t) * ogated */
vtanh_d->Compute(ct, gates + d2);
vmul_d->Compute(gates + d2, gates + d * 3, ht);
vmul_d->Compute(gates + d2, gates + d * 3, ht, d);
}
TEST(JitKernel, lstm) {
......@@ -578,7 +578,7 @@ void vmul_mkl(const int n, const float* x, const float* y, float* z) {
TEST(JitKernel, vmul) {
namespace jit = paddle::operators::math::jitkernel;
for (int d : {7, 8, 15, 16, 30, 256, 512}) {
for (int d : {7, 8, 15, 16, 30, 256, 512, 1000, 1024}) {
std::vector<float> x(d), y(d);
std::vector<float> zref(d), ztgt(d);
RandomVec<float>(d, x.data());
......@@ -616,7 +616,7 @@ TEST(JitKernel, vmul) {
auto ttgts = GetCurrentUS();
for (int i = 0; i < repeat; ++i) {
ker->Compute(x_data, y_data, ztgt_data);
ker->Compute(x_data, y_data, ztgt_data, d);
}
auto ttgte = GetCurrentUS();
......@@ -800,8 +800,8 @@ TEST(JitKernel, pool) {
EXPECT_TRUE(std::dynamic_pointer_cast<const jit::Kernel>(pvmul_f) !=
std::dynamic_pointer_cast<const jit::Kernel>(pvmul_d));
const auto& pvmul_from_key = jit::KernelPool::Instance().Get("vmulf4");
const auto& pvmul_from_key = jit::KernelPool::Instance().Get("vmulfany");
EXPECT_EQ(pvmul_f, pvmul_from_key);
const auto& pvmul_from_key2 = jit::KernelPool::Instance().Get("vmulf5");
const auto& pvmul_from_key2 = jit::KernelPool::Instance().Get("vmulfjit");
EXPECT_TRUE(pvmul_from_key2 == nullptr);
}
......@@ -31,7 +31,7 @@ class Pool2dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
const framework::Tensor& input, const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_process,
framework::Tensor* output) {
bool exclusive, framework::Tensor* output) {
const int batch_size = input.dims()[0];
const int input_height = input.dims()[2];
const int input_width = input.dims()[3];
......@@ -68,7 +68,8 @@ class Pool2dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
pool_process.compute(input_data[h * input_width + w], &ele);
}
}
int pool_size = (hend - hstart) * (wend - wstart);
int pool_size = exclusive ? (hend - hstart) * (wend - wstart)
: ksize_height * ksize_width;
pool_process.finalize(static_cast<T>(pool_size), &ele);
output_data[ph * output_width + pw] = ele;
}
......@@ -93,7 +94,7 @@ class Pool2dGradFunctor<platform::CPUDeviceContext, PoolProcess, T> {
const framework::Tensor& output, const framework::Tensor& output_grad,
const std::vector<int>& ksize, const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_grad_process,
framework::Tensor* input_grad) {
bool exclusive, framework::Tensor* input_grad) {
const int batch_size = input.dims()[0];
const int input_height = input.dims()[2];
const int input_width = input.dims()[3];
......@@ -124,7 +125,8 @@ class Pool2dGradFunctor<platform::CPUDeviceContext, PoolProcess, T> {
int wstart = pw * stride_width - padding_width;
int wend = std::min(wstart + ksize_width, input_width);
wstart = std::max(wstart, 0);
int pool_size = (hend - hstart) * (wend - wstart);
int pool_size = exclusive ? (hend - hstart) * (wend - wstart)
: ksize_height * ksize_width;
float scale = 1.0 / pool_size;
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
......@@ -249,7 +251,7 @@ class Pool3dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
const framework::Tensor& input, const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_process,
framework::Tensor* output) {
bool exclusive, framework::Tensor* output) {
const int batch_size = input.dims()[0];
const int input_depth = input.dims()[2];
const int input_height = input.dims()[3];
......@@ -300,7 +302,9 @@ class Pool3dFunctor<platform::CPUDeviceContext, PoolProcess, T> {
}
}
int pool_size =
(dend - dstart) * (hend - hstart) * (wend - wstart);
exclusive
? (dend - dstart) * (hend - hstart) * (wend - wstart)
: ksize_depth * ksize_height * ksize_width;
pool_process.finalize(static_cast<T>(pool_size), &ele);
output_data[output_idx] = ele;
}
......@@ -326,7 +330,7 @@ class Pool3dGradFunctor<platform::CPUDeviceContext, PoolProcess, T> {
const framework::Tensor& output, const framework::Tensor& output_grad,
const std::vector<int>& ksize, const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_grad_process,
framework::Tensor* input_grad) {
bool exclusive, framework::Tensor* input_grad) {
const int batch_size = input.dims()[0];
const int input_depth = input.dims()[2];
const int input_height = input.dims()[3];
......@@ -369,7 +373,9 @@ class Pool3dGradFunctor<platform::CPUDeviceContext, PoolProcess, T> {
wstart = std::max(wstart, 0);
int pool_size =
(dend - dstart) * (hend - hstart) * (wend - wstart);
exclusive
? (dend - dstart) * (hend - hstart) * (wend - wstart)
: ksize_depth * ksize_height * ksize_width;
float scale = 1.0 / pool_size;
for (int d = dstart; d < dend; ++d) {
for (int h = hstart; h < hend; ++h) {
......
......@@ -29,7 +29,7 @@ __global__ void KernelPool2D(const int nthreads, const T* input_data,
const int ksize_width, const int stride_height,
const int stride_width, const int padding_height,
const int padding_width, PoolProcess pool_process,
T* output_data) {
bool exclusive, T* output_data) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) {
int pw = index % output_width;
......@@ -52,7 +52,8 @@ __global__ void KernelPool2D(const int nthreads, const T* input_data,
pool_process.compute(input_data[h * input_width + w], &ele);
}
}
int pool_size = (hend - hstart) * (wend - wstart);
int pool_size = exclusive ? (hend - hstart) * (wend - wstart)
: ksize_height * ksize_width;
pool_process.finalize(static_cast<T>(pool_size), &ele);
output_data[index] = ele;
}
......@@ -65,7 +66,7 @@ __global__ void KernelPool2DGrad(
const int input_width, const int output_height, const int output_width,
const int ksize_height, const int ksize_width, const int stride_height,
const int stride_width, const int padding_height, const int padding_width,
PoolProcess pool_process, T* input_grad) {
PoolProcess pool_process, bool exclusive, T* input_grad) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) {
int offsetW = index % input_width + padding_width;
......@@ -95,7 +96,8 @@ __global__ void KernelPool2DGrad(
int wend = min(wstart + ksize_width, input_width);
hstart = max(hstart, 0);
wstart = max(wstart, 0);
int pool_size = (hend - hstart) * (wend - wstart);
int pool_size = exclusive ? (hend - hstart) * (wend - wstart)
: ksize_height * ksize_width;
int output_sub_idx = ph * output_width + pw;
pool_process.compute(input, output_data[output_sub_idx],
output_grad[output_sub_idx],
......@@ -163,7 +165,7 @@ class Pool2dFunctor<platform::CUDADeviceContext, PoolProcess, T> {
const framework::Tensor& input, const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_process,
framework::Tensor* output) {
bool exclusive, framework::Tensor* output) {
const int batch_size = input.dims()[0];
const int input_channels = input.dims()[1];
const int input_height = input.dims()[2];
......@@ -189,7 +191,8 @@ class Pool2dFunctor<platform::CUDADeviceContext, PoolProcess, T> {
KernelPool2D<PoolProcess, T><<<grid, threads, 0, context.stream()>>>(
nthreads, input_data, input_channels, input_height, input_width,
output_height, output_width, ksize_height, ksize_width, stride_height,
stride_width, padding_height, padding_width, pool_process, output_data);
stride_width, padding_height, padding_width, pool_process, exclusive,
output_data);
}
};
......@@ -208,7 +211,7 @@ class Pool2dGradFunctor<platform::CUDADeviceContext, PoolProcess, T> {
const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_process,
framework::Tensor* input_grad) {
bool exclusive, framework::Tensor* input_grad) {
const int batch_size = input.dims()[0];
const int input_channels = input.dims()[1];
const int input_height = input.dims()[2];
......@@ -236,7 +239,7 @@ class Pool2dGradFunctor<platform::CUDADeviceContext, PoolProcess, T> {
nthreads, input_data, output_data, output_grad_data, input_channels,
input_height, input_width, output_height, output_width, ksize_height,
ksize_width, stride_height, stride_width, padding_height, padding_width,
pool_process, input_grad_data);
pool_process, exclusive, input_grad_data);
}
};
......@@ -313,16 +316,14 @@ template class Pool2dGradFunctor<platform::CUDADeviceContext,
double>;
template <typename PoolProcess, typename T>
__global__ void KernelPool3D(const int nthreads, const T* input_data,
const int channels, const int input_depth,
const int input_height, const int input_width,
const int output_depth, const int output_height,
const int output_width, const int ksize_depth,
const int ksize_height, const int ksize_width,
const int stride_depth, const int stride_height,
const int stride_width, const int padding_depth,
const int padding_height, const int padding_width,
PoolProcess pool_process, T* output_data) {
__global__ void KernelPool3D(
const int nthreads, const T* input_data, const int channels,
const int input_depth, const int input_height, const int input_width,
const int output_depth, const int output_height, const int output_width,
const int ksize_depth, const int ksize_height, const int ksize_width,
const int stride_depth, const int stride_height, const int stride_width,
const int padding_depth, const int padding_height, const int padding_width,
PoolProcess pool_process, bool exclusive, T* output_data) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) {
int pw = index % output_width;
......@@ -351,7 +352,9 @@ __global__ void KernelPool3D(const int nthreads, const T* input_data,
}
}
}
int pool_size = (dend - dstart) * (hend - hstart) * (wend - wstart);
int pool_size = exclusive
? (dend - dstart) * (hend - hstart) * (wend - wstart)
: ksize_depth * ksize_height * ksize_width;
pool_process.finalize(static_cast<T>(pool_size), &ele);
output_data[index] = ele;
}
......@@ -366,7 +369,7 @@ __global__ void KernelPool3DGrad(
const int ksize_height, const int ksize_width, const int stride_depth,
const int stride_height, const int stride_width, const int padding_depth,
const int padding_height, const int padding_width, PoolProcess pool_process,
T* input_grad) {
bool exclusive, T* input_grad) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) {
int offsetW = index % input_width + padding_width;
......@@ -409,7 +412,9 @@ __global__ void KernelPool3DGrad(
dstart = max(dstart, 0);
hstart = max(hstart, 0);
wstart = max(wstart, 0);
int pool_size = (dend - dstart) * (hend - hstart) * (wend - wstart);
int pool_size =
exclusive ? (dend - dstart) * (hend - hstart) * (wend - wstart)
: ksize_depth * ksize_height * ksize_width;
int output_sub_idx = (pd * output_height + ph) * output_width + pw;
pool_process.compute(input, output_data[output_sub_idx],
output_grad[output_sub_idx],
......@@ -484,7 +489,7 @@ class Pool3dFunctor<platform::CUDADeviceContext, PoolProcess, T> {
const framework::Tensor& input, const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_process,
framework::Tensor* output) {
bool exclusive, framework::Tensor* output) {
const int batch_size = input.dims()[0];
const int input_channels = input.dims()[1];
const int input_depth = input.dims()[2];
......@@ -517,7 +522,7 @@ class Pool3dFunctor<platform::CUDADeviceContext, PoolProcess, T> {
nthreads, input_data, input_channels, input_depth, input_height,
input_width, output_depth, output_height, output_width, ksize_depth,
ksize_height, ksize_width, stride_depth, stride_height, stride_width,
padding_depth, padding_height, padding_width, pool_process,
padding_depth, padding_height, padding_width, pool_process, exclusive,
output_data);
}
};
......@@ -537,7 +542,7 @@ class Pool3dGradFunctor<platform::CUDADeviceContext, PoolProcess, T> {
const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_process,
framework::Tensor* input_grad) {
bool exclusive, framework::Tensor* input_grad) {
const int batch_size = input.dims()[0];
const int input_channels = input.dims()[1];
const int input_depth = input.dims()[2];
......@@ -573,7 +578,7 @@ class Pool3dGradFunctor<platform::CUDADeviceContext, PoolProcess, T> {
input_depth, input_height, input_width, output_depth, output_height,
output_width, ksize_depth, ksize_height, ksize_width, stride_depth,
stride_height, stride_width, padding_depth, padding_height,
padding_width, pool_process, input_grad_data);
padding_width, pool_process, exclusive, input_grad_data);
}
};
......
......@@ -89,7 +89,7 @@ class Pool2dFunctor {
const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_compute,
framework::Tensor* output);
bool exclusive, framework::Tensor* output);
};
template <typename DeviceContext, typename PoolProcess, typename T>
......@@ -101,7 +101,7 @@ class Pool2dGradFunctor {
const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_compute,
framework::Tensor* input_grad);
bool exclusive, framework::Tensor* input_grad);
};
template <typename DeviceContext, class T>
......@@ -123,7 +123,7 @@ class Pool3dFunctor {
const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_compute,
framework::Tensor* output);
bool exclusive, framework::Tensor* output);
};
template <typename DeviceContext, typename PoolProcess, typename T>
......@@ -135,7 +135,7 @@ class Pool3dGradFunctor {
const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_compute,
framework::Tensor* input_grad);
bool exclusive, framework::Tensor* input_grad);
};
template <typename DeviceContext, class T>
......
......@@ -31,7 +31,7 @@ template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename T>
template <typename T, bool is_test>
class MaxSeqPoolFunctor {
public:
void operator()(const platform::CPUDeviceContext& context,
......@@ -70,7 +70,41 @@ class MaxSeqPoolFunctor {
}
}
};
// Instantisation of Max Sequence Pooling for test phase eg. no need to fill
// index buffer
template <typename T>
class MaxSeqPoolFunctor<T, true> {
public:
void operator()(const platform::CPUDeviceContext& context,
const framework::LoDTensor& input, framework::Tensor* output,
framework::Tensor* index) {
auto in_dims = input.dims();
auto out_dims = output->dims();
PADDLE_ENFORCE_GT(in_dims.size(), 1);
PADDLE_ENFORCE_GT(out_dims.size(), 1);
for (int64_t i = 1; i < in_dims.size(); ++i) {
PADDLE_ENFORCE_EQ(in_dims[i], out_dims[i]);
}
auto starts = input.lod()[0];
const T* in_data = input.data<T>();
T* out_data = output->data<T>();
int64_t num_seq = out_dims[0];
int64_t dim = output->numel() / num_seq;
for (int64_t i = 0; i < num_seq; ++i) {
std::memcpy(&out_data[i * dim], &in_data[starts[i] * dim],
dim * sizeof(T));
for (size_t j = starts[i] + 1; j < starts[i + 1]; ++j) {
for (int64_t k = 0; k < dim; ++k) {
if (in_data[j * dim + k] > out_data[i * dim + k]) {
out_data[i * dim + k] = in_data[j * dim + k];
}
}
}
}
}
};
template <typename T>
class MaxSeqPoolGradFunctor {
public:
......@@ -188,11 +222,16 @@ class SequencePoolFunctor<platform::CPUDeviceContext, T> {
/* max pool has index output */
void operator()(const platform::CPUDeviceContext& context,
const std::string pooltype, const framework::LoDTensor& input,
framework::Tensor* output,
framework::Tensor* output, bool is_test,
framework::Tensor* index = nullptr) {
if (pooltype == "MAX") {
math::MaxSeqPoolFunctor<T> max_pool;
max_pool(context, input, output, index);
if (is_test) {
math::MaxSeqPoolFunctor<T, true> max_pool;
max_pool(context, input, output, index);
} else {
math::MaxSeqPoolFunctor<T, false> max_pool;
max_pool(context, input, output, index);
}
return;
}
if (pooltype == "LAST") {
......@@ -200,6 +239,7 @@ class SequencePoolFunctor<platform::CPUDeviceContext, T> {
last_pool(context, input, output);
return;
}
if (pooltype == "FIRST") {
math::FirstSeqPoolFunctor<T> first_pool;
first_pool(context, input, output);
......
......@@ -133,7 +133,7 @@ class SequencePoolFunctor<platform::CUDADeviceContext, T> {
public:
void operator()(const platform::CUDADeviceContext& context,
const std::string pooltype, const framework::LoDTensor& input,
framework::Tensor* output,
framework::Tensor* output, bool is_test,
framework::Tensor* index = nullptr) {
auto& lod = input.lod()[0];
const size_t item_dim = output->numel() / output->dims()[0];
......
......@@ -28,7 +28,7 @@ class SequencePoolFunctor {
/* max pool has index output */
void operator()(const DeviceContext& context, const std::string pooltype,
const framework::LoDTensor& input, framework::Tensor* output,
framework::Tensor* index = nullptr);
bool is_test = false, framework::Tensor* index = nullptr);
};
template <typename DeviceContext, typename T>
......
......@@ -41,6 +41,7 @@ class PoolCUDNNOpKernel : public framework::OpKernel<T> {
T *output_data = output->mutable_data<T>(ctx.GetPlace());
std::string pooling_type = ctx.Attr<std::string>("pooling_type");
bool exclusive = ctx.Attr<bool>("exclusive");
std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
......@@ -72,7 +73,8 @@ class PoolCUDNNOpKernel : public framework::OpKernel<T> {
if (pooling_type == "max") {
pooling_mode = PoolingMode::kMaximum;
} else {
pooling_mode = PoolingMode::kAverage;
pooling_mode = exclusive ? PoolingMode::kAverageExclusive
: PoolingMode::kAverageInclusive;
}
cudnnPoolingDescriptor_t cudnn_pool_desc =
......@@ -101,6 +103,7 @@ class PoolCUDNNGradOpKernel : public framework::OpKernel<T> {
Tensor *input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
std::string pooling_type = ctx.Attr<std::string>("pooling_type");
bool exclusive = ctx.Attr<bool>("exclusive");
std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
......@@ -141,7 +144,8 @@ class PoolCUDNNGradOpKernel : public framework::OpKernel<T> {
pooling_mode = PoolingMode::kMaximum;
}
} else {
pooling_mode = PoolingMode::kAverage;
pooling_mode = exclusive ? PoolingMode::kAverageExclusive
: PoolingMode::kAverageInclusive;
}
cudnnPoolingDescriptor_t cudnn_pool_desc =
......
......@@ -180,6 +180,12 @@ void Pool2dOpMaker::Make() {
"operator."
"If global_pooling = true, paddings and ksize will be ignored.")
.SetDefault({0, 0});
AddAttr<bool>(
"exclusive",
"(bool, default True) When true, will exclude the zero-padding in the "
"averaging calculating, otherwise, include the zero-padding. Note, it "
"is only used when pooling_type is avg. The defalut is True.")
.SetDefault(true);
AddAttr<bool>(
"use_cudnn",
"(bool, default false) Only used in cudnn kernel, need install cudnn")
......@@ -236,6 +242,23 @@ Example:
W_{out} = \\frac{(W_{in} - ksize[1] + 2 * paddings[1] + strides[1] - 1)}{strides[1]} + 1
$$
For exclusive = true:
$$
hstart = i * strides[0] - paddings[0]
hend = hstart + ksize[0]
wstart = j * strides[1] - paddings[1]
wend = wstart + ksize[1]
Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{ksize[0] * ksize[1]}
$$
For exclusive = false:
$$
hstart = max(0, i * strides[0] - paddings[0])
hend = min(H, hstart + ksize[0])
wstart = max(0, j * strides[1] - paddings[1])
wend = min(W, wstart + ksize[1])
Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
$$
)DOC");
}
......@@ -283,6 +306,12 @@ void Pool3dOpMaker::Make() {
"If global_pooling = true, ksize and paddings will be ignored.")
.SetDefault({0, 0, 0}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<bool>(
"exclusive",
"(bool, default True) When true, will exclude the zero-padding in the "
"averaging calculating, otherwise, include the zero-padding. Note, it "
"is only used when pooling_type is avg. The defalut is True.")
.SetDefault(true);
AddAttr<bool>(
"use_cudnn",
......
......@@ -69,6 +69,7 @@ class PoolKernel : public framework::OpKernel<T> {
std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
bool exclusive = context.Attr<bool>("exclusive");
if (context.Attr<bool>("global_pooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
......@@ -84,7 +85,7 @@ class PoolKernel : public framework::OpKernel<T> {
pool2d_forward;
paddle::operators::math::MaxPool<T> pool_process;
pool2d_forward(dev_ctx, *in_x, ksize, strides, paddings, pool_process,
out);
true, out);
} else if (pooling_type == "avg") {
paddle::operators::math::Pool2dFunctor<
......@@ -92,7 +93,7 @@ class PoolKernel : public framework::OpKernel<T> {
pool2d_forward;
paddle::operators::math::AvgPool<T> pool_process;
pool2d_forward(dev_ctx, *in_x, ksize, strides, paddings, pool_process,
out);
exclusive, out);
}
} break;
case 3: {
......@@ -102,14 +103,14 @@ class PoolKernel : public framework::OpKernel<T> {
pool3d_forward;
paddle::operators::math::MaxPool<T> pool_process;
pool3d_forward(dev_ctx, *in_x, ksize, strides, paddings, pool_process,
out);
true, out);
} else if (pooling_type == "avg") {
paddle::operators::math::Pool3dFunctor<
DeviceContext, paddle::operators::math::AvgPool<T>, T>
pool3d_forward;
paddle::operators::math::AvgPool<T> pool_process;
pool3d_forward(dev_ctx, *in_x, ksize, strides, paddings, pool_process,
out);
exclusive, out);
}
} break;
default: { PADDLE_THROW("Pool op only supports 2D and 3D input."); }
......@@ -131,6 +132,7 @@ class PoolGradKernel : public framework::OpKernel<T> {
std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
bool exclusive = context.Attr<bool>("exclusive");
if (context.Attr<bool>("global_pooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
......@@ -157,7 +159,7 @@ class PoolGradKernel : public framework::OpKernel<T> {
pool2d_backward;
paddle::operators::math::AvgPoolGrad<T> pool_process;
pool2d_backward(dev_ctx, *in_x, *out, *out_grad, ksize, strides,
paddings, pool_process, in_x_grad);
paddings, pool_process, exclusive, in_x_grad);
}
} break;
case 3: {
......@@ -172,7 +174,7 @@ class PoolGradKernel : public framework::OpKernel<T> {
pool3d_backward;
paddle::operators::math::AvgPoolGrad<T> pool_process;
pool3d_backward(dev_ctx, *in_x, *out, *out_grad, ksize, strides,
paddings, pool_process, in_x_grad);
paddings, pool_process, exclusive, in_x_grad);
}
} break;
default: { PADDLE_THROW("Pool op only supports 2D and 3D input."); }
......
......@@ -47,6 +47,7 @@ class SequencePoolOpMaker : public framework::OpProtoAndCheckerMaker {
"(Tensor<int>) This tensor is used for the sequence max-pooling "
"to record the max indexes.")
.AsIntermediate();
AddAttr<bool>("is_test", "").SetDefault(false);
AddAttr<std::string>(
"pooltype",
"(string, default 'AVERAGE') the pooling pooltype of SequencePoolOp.")
......
......@@ -32,10 +32,6 @@ class SequencePoolKernel : public framework::OpKernel<T> {
auto* in = context.Input<LoDTensor>("X");
auto* out = context.Output<Tensor>("Out");
std::string pooltype = context.Attr<std::string>("pooltype");
Tensor* index = nullptr;
if (pooltype == "MAX") {
index = context.Output<Tensor>("MaxIndex");
}
auto dims = in->dims();
auto lod = in->lod();
......@@ -48,13 +44,22 @@ class SequencePoolKernel : public framework::OpKernel<T> {
dims[0] = lod[0].size() - 1;
out->Resize({dims});
out->mutable_data<T>(context.GetPlace());
if (pooltype == "MAX") {
Tensor* index = nullptr;
const bool is_test = context.Attr<bool>("is_test");
// Do not create index buffer for inference (is_test) mode
// TODO(jczaja): Skip index buffer creation for other devices eg. GPU
if (pooltype == "MAX" &&
(is_test == false ||
platform::is_cpu_place(context.GetPlace()) == false)) {
index = context.Output<Tensor>("MaxIndex");
index->Resize({dims});
index->mutable_data<int>(context.GetPlace());
}
math::SequencePoolFunctor<DeviceContext, T> pool;
pool(context.template device_context<DeviceContext>(), pooltype, *in, out,
index);
is_test, index);
}
};
......
......@@ -67,4 +67,5 @@ namespace ops = paddle::operators;
REGISTER_OPERATOR(sign, ops::SignOp, ops::SignOpMaker<float>,
ops::SignGradMaker);
REGISTER_OP_CPU_KERNEL(
sign, ops::SignKernel<paddle::platform::CPUDeviceContext, float>);
sign, ops::SignKernel<paddle::platform::CPUDeviceContext, float>,
ops::SignKernel<paddle::platform::CPUDeviceContext, double>);
......@@ -13,7 +13,11 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/sign_op.h"
#include "paddle/fluid/platform/float16.h"
REGISTER_OP_CUDA_KERNEL(
sign,
paddle::operators::SignKernel<paddle::platform::CUDADeviceContext, float>);
paddle::operators::SignKernel<paddle::platform::CUDADeviceContext, float>,
paddle::operators::SignKernel<paddle::platform::CUDADeviceContext, double>,
paddle::operators::SignKernel<paddle::platform::CUDADeviceContext,
paddle::platform::float16>);
......@@ -44,6 +44,12 @@ class SoftmaxWithCrossEntropyOpMaker
"(bool, default: false), A flag to indicate whether to interpretate "
"the given labels as soft labels.")
.SetDefault(false);
AddAttr<bool>(
"numeric_stable_mode",
"(bool, default: false), A flag to indicate whether to use more "
"numerically stable algorithm. This flag is only valid when "
"soft_label is false and GPU is used.")
.SetDefault(false);
AddAttr<int>(
"ignore_index",
"(int, default -100), Specifies a target value that is ignored and"
......
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <cub/cub.cuh>
#include "paddle/fluid/operators/math/cross_entropy.h"
#include "paddle/fluid/operators/softmax_with_cross_entropy_op.h"
#include "paddle/fluid/platform/for_range.h"
namespace paddle {
namespace operators {
......@@ -117,8 +118,8 @@ using BlockReduceTempStorage = typename BlockReduce<T, BlockDim>::TempStorage;
// Make sure that BlockDim <= feature_size
// This kernel is used to calculate the max element of each row
template <typename T, int BlockDim>
__global__ void RowReductionForMax(const T* logits_data, T* max_data,
int feature_size) {
static __global__ void RowReductionForMax(const T* logits_data, T* max_data,
int feature_size) {
__shared__ BlockReduceTempStorage<T, BlockDim> temp_storage;
auto beg_idx = feature_size * blockIdx.x + threadIdx.x;
......@@ -141,9 +142,10 @@ __global__ void RowReductionForMax(const T* logits_data, T* max_data,
}
// Make sure that BlockDim <= feature_size
template <typename T, int BlockDim>
__global__ void RowReductionForDiffMaxSum(const T* logits_data, T* max_data,
T* softmax, int feature_size) {
template <typename T, int BlockDim, bool CalculateLogSoftmax = false>
static __global__ void RowReductionForDiffMaxSum(const T* logits_data,
T* max_data, T* softmax,
int feature_size) {
__shared__ BlockReduceTempStorage<T, BlockDim> temp_storage;
auto beg_idx = feature_size * blockIdx.x + threadIdx.x;
......@@ -153,24 +155,34 @@ __global__ void RowReductionForDiffMaxSum(const T* logits_data, T* max_data,
softmax[beg_idx] = logits_data[beg_idx] - block_max;
T diff_max_sum = real_exp(softmax[beg_idx]);
beg_idx += BlockDim;
while (beg_idx < end_idx) {
softmax[beg_idx] = logits_data[beg_idx] - block_max;
diff_max_sum += real_exp(softmax[beg_idx]);
beg_idx += BlockDim;
auto idx = beg_idx + BlockDim;
while (idx < end_idx) {
softmax[idx] = logits_data[idx] - block_max;
diff_max_sum += real_exp(softmax[idx]);
idx += BlockDim;
}
diff_max_sum =
BlockReduce<T, BlockDim>(temp_storage).Reduce(diff_max_sum, cub::Sum());
if (threadIdx.x == 0) max_data[blockIdx.x] = real_log(diff_max_sum);
if (!CalculateLogSoftmax) return;
__syncthreads();
diff_max_sum = max_data[blockIdx.x];
softmax[beg_idx] -= diff_max_sum;
beg_idx += BlockDim;
while (beg_idx < end_idx) {
softmax[beg_idx] -= diff_max_sum;
beg_idx += BlockDim;
}
if (threadIdx.x == 0) max_data[blockIdx.x] = 0;
}
// Make sure that BlockDim <= feature_size
template <typename T, int BlockDim>
__global__ void RowReductionForSoftmaxAndCrossEntropy(const T* logits_data,
const T* labels_data,
T* loss_data, T* softmax,
int feature_size) {
static __global__ void RowReductionForSoftmaxAndCrossEntropy(
const T* logits_data, const T* labels_data, T* loss_data, T* softmax,
int feature_size) {
__shared__ BlockReduceTempStorage<T, BlockDim> temp_storage;
auto beg_idx = feature_size * blockIdx.x + threadIdx.x;
......@@ -194,11 +206,134 @@ __global__ void RowReductionForSoftmaxAndCrossEntropy(const T* logits_data,
}
template <typename T>
__global__ void SetSoftmaxToOneWhenFeatureSizeIsOne(T* out, int batch_size) {
struct HardLabelSoftmaxWithCrossEntropyFunctor {
public:
HardLabelSoftmaxWithCrossEntropyFunctor(const T* logits,
const int64_t* labels, T* loss,
T* log_softmax, int feature_size)
: logits_(logits),
labels_(labels),
loss_(loss),
log_softmax_(log_softmax),
feature_size_(feature_size) {}
__device__ void operator()(int idx) const {
auto row_idx = idx / feature_size_;
auto col_idx = idx % feature_size_;
if (col_idx != labels_[row_idx]) {
log_softmax_[idx] = real_exp(log_softmax_[idx]);
} else {
auto softmax = log_softmax_[idx];
log_softmax_[idx] = real_exp(softmax);
loss_[row_idx] = -softmax;
}
}
private:
const T* logits_;
const int64_t* labels_;
T* loss_;
T* log_softmax_;
int feature_size_;
};
template <typename T>
struct HardLabelSoftmaxWithCrossEntropyFunctorWithIgnoreIdx {
public:
HardLabelSoftmaxWithCrossEntropyFunctorWithIgnoreIdx(const T* logits,
const int64_t* labels,
T* loss, T* log_softmax,
int feature_size,
int ignore_idx)
: logits_(logits),
labels_(labels),
loss_(loss),
log_softmax_(log_softmax),
feature_size_(feature_size),
ignore_idx_(ignore_idx) {}
__device__ void operator()(int idx) const {
auto row_idx = idx / feature_size_;
auto col_idx = idx % feature_size_;
if (col_idx != labels_[row_idx] || col_idx == ignore_idx_) {
log_softmax_[idx] = real_exp(log_softmax_[idx]);
} else {
auto softmax = log_softmax_[idx];
log_softmax_[idx] = real_exp(softmax);
loss_[row_idx] = -softmax;
}
}
private:
const T* logits_;
const int64_t* labels_;
T* loss_;
T* log_softmax_;
int feature_size_;
int ignore_idx_;
};
template <typename T>
static __global__ void SetSoftmaxToOneWhenFeatureSizeIsOne(T* out,
int batch_size) {
auto idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < batch_size) out[idx] = static_cast<T>(1);
}
template <typename T>
static void HardLabelSoftmaxWithCrossEntropy(
const platform::CUDADeviceContext& ctx, const T* logits_data,
const int64_t* labels_data, T* loss_data, T* softmax_data, int batch_size,
int feature_size, int ignore_idx) {
constexpr int kMaxBlockDim = 512;
int block_dim = feature_size >= kMaxBlockDim
? kMaxBlockDim
: (1 << static_cast<int>(std::log2(feature_size)));
auto stream = ctx.stream();
#define CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(BlockDim) \
case BlockDim: { \
RowReductionForMax<T, BlockDim><<<batch_size, BlockDim, 0, stream>>>( \
logits_data, loss_data, feature_size); \
RowReductionForDiffMaxSum<T, BlockDim, \
true><<<batch_size, BlockDim, 0, stream>>>( \
logits_data, loss_data, softmax_data, feature_size); \
platform::ForRange<platform::CUDADeviceContext> for_range( \
ctx, batch_size* feature_size); \
if (ignore_idx >= 0 && ignore_idx < feature_size) { \
for_range(HardLabelSoftmaxWithCrossEntropyFunctorWithIgnoreIdx<T>( \
logits_data, labels_data, loss_data, softmax_data, feature_size, \
ignore_idx)); \
} else { \
for_range(HardLabelSoftmaxWithCrossEntropyFunctor<T>( \
logits_data, labels_data, loss_data, softmax_data, feature_size)); \
} \
} break
switch (block_dim) {
CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(512);
CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(256);
CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(128);
CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(64);
CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(32);
CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(16);
CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(8);
CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(4);
CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL(2);
case 1:
SetSoftmaxToOneWhenFeatureSizeIsOne<<<(batch_size + kMaxBlockDim - 1) /
kMaxBlockDim,
kMaxBlockDim, 0, stream>>>(
softmax_data, batch_size);
cudaMemsetAsync(loss_data, 0, batch_size * sizeof(T), stream);
break;
default:
PADDLE_THROW("BlockDim must be 2^n in softmax_with_cross_entropy_op");
break;
}
#undef CALL_HARD_LABEL_SOFTMAX_WITH_CROSS_ENTROPY_FUSED_KERNEL
}
template <typename T>
static void SoftmaxWithCrossEntropyFusedKernel(const T* logits_data,
const T* labels_data,
......@@ -237,7 +372,7 @@ static void SoftmaxWithCrossEntropyFusedKernel(const T* logits_data,
kMaxBlockDim,
kMaxBlockDim, 0, stream>>>(
softmax_data, batch_size);
cudaMemsetAsync(loss_data, 0, batch_size, stream);
cudaMemsetAsync(loss_data, 0, batch_size * sizeof(T), stream);
break;
default:
PADDLE_THROW("BlockDim must be 2^n in softmax_with_cross_entropy_op");
......@@ -272,11 +407,21 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
logits_data, labels_data, softmax_data, loss_data, batch_size,
feature_size, context.cuda_device_context().stream());
} else {
math::SoftmaxCUDNNFunctor<T>()(context.cuda_device_context(), logits,
softmax);
math::CrossEntropyFunctor<platform::CUDADeviceContext, T>()(
context.cuda_device_context(), loss, softmax, labels, false,
ignore_index);
if (!context.Attr<bool>("numeric_stable_mode")) {
math::SoftmaxCUDNNFunctor<T>()(context.cuda_device_context(), logits,
softmax);
math::CrossEntropyFunctor<platform::CUDADeviceContext, T>()(
context.cuda_device_context(), loss, softmax, labels, false,
ignore_index);
} else {
int batch_size = logits->dims()[0];
int feature_size = logits->dims()[1];
auto* logits_data = logits->data<T>();
auto* labels_data = labels->data<int64_t>();
HardLabelSoftmaxWithCrossEntropy<T>(
context.cuda_device_context(), logits_data, labels_data, loss_data,
softmax_data, batch_size, feature_size, ignore_index);
}
}
}
};
......
......@@ -56,12 +56,14 @@ class SppKernel : public framework::OpKernel<T> {
math::Pool2dFunctor<DeviceContext, math::MaxPool<T>, T> pool_forward;
math::MaxPool<T> max_process;
pool_forward(context.template device_context<DeviceContext>(), *in_x,
kernel_size, strides, paddings, max_process, &out_level);
kernel_size, strides, paddings, max_process, true,
&out_level);
} else if (pooling_type == "avg") {
math::Pool2dFunctor<DeviceContext, math::AvgPool<T>, T> pool_forward;
math::AvgPool<T> avg_process;
pool_forward(context.template device_context<DeviceContext>(), *in_x,
kernel_size, strides, paddings, avg_process, &out_level);
kernel_size, strides, paddings, avg_process, true,
&out_level);
}
// flatten pooling output shape
int output_flatten_w = in_x->dims()[1] * bins * bins;
......@@ -154,7 +156,7 @@ class SppGradKernel : public framework::OpKernel<T> {
math::AvgPoolGrad<T> avg_process;
pool_backward(context.template device_context<DeviceContext>(), *in_x,
*&out_level, *&outgrad_level, kernel_size, strides,
paddings, avg_process, in_x_grad);
paddings, avg_process, true, in_x_grad);
}
}
}
......
......@@ -67,6 +67,7 @@ class SumOp : public framework::OperatorWithKernel {
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
auto x_vars = ctx.MultiInputVar("X");
auto x_vars_name = ctx.Inputs("X");
framework::LibraryType library{framework::LibraryType::kPlain};
framework::DataLayout layout{framework::DataLayout::kAnyLayout};
......@@ -81,10 +82,11 @@ class SumOp : public framework::OperatorWithKernel {
if (x_vars[0]->IsType<framework::LoDTensor>()) {
int dtype = -1;
for (auto& x_var : x_vars) {
for (size_t idx = 0; idx < x_vars.size(); ++idx) {
PADDLE_ENFORCE(x_vars[idx] != nullptr,
"Input var[%s] should not be nullptr", x_vars_name[idx]);
// FIXME(zcd): The input x_var may be SelectedRows or LoDTensor.
auto tensor = framework::GetTensorFromVar(
const_cast<framework::Variable*>(x_var));
auto tensor = framework::GetTensorFromVar(*x_vars[idx]);
if (tensor->numel() == 0) {
continue;
}
......
......@@ -76,8 +76,9 @@ enum class DataLayout { // Not use
enum class PoolingMode {
kMaximum,
kAverage,
kMaximumDeterministic,
kAverageExclusive,
kAverageInclusive,
};
#if CUDNN_VERSION < 6000
......@@ -91,8 +92,10 @@ inline cudnnPoolingMode_t GetPoolingMode(const PoolingMode& mode) {
switch (mode) {
case PoolingMode::kMaximumDeterministic:
return CUDNN_POOLING_MAX;
case PoolingMode::kAverage:
case PoolingMode::kAverageExclusive:
return CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING;
case PoolingMode::kAverageInclusive:
return CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
case PoolingMode::kMaximum:
return CUDNN_POOLING_MAX;
default:
......@@ -105,8 +108,10 @@ inline cudnnPoolingMode_t GetPoolingMode(const PoolingMode& mode) {
switch (mode) {
case PoolingMode::kMaximumDeterministic:
return CUDNN_POOLING_MAX_DETERMINISTIC;
case PoolingMode::kAverage:
case PoolingMode::kAverageExclusive:
return CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING;
case PoolingMode::kAverageInclusive:
return CUDNN_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
case PoolingMode::kMaximum:
return CUDNN_POOLING_MAX;
default:
......@@ -341,6 +346,28 @@ class ScopedPoolingDescriptor {
DISABLE_COPY_AND_ASSIGN(ScopedPoolingDescriptor);
};
class ScopedSpatialTransformerDescriptor {
public:
ScopedSpatialTransformerDescriptor() {
PADDLE_ENFORCE(dynload::cudnnCreateSpatialTransformerDescriptor(&desc_));
}
~ScopedSpatialTransformerDescriptor() {
PADDLE_ENFORCE(dynload::cudnnDestroySpatialTransformerDescriptor(desc_));
}
template <typename T>
inline cudnnSpatialTransformerDescriptor_t descriptor(const int nbDims,
const int dimA[]) {
PADDLE_ENFORCE(dynload::cudnnSetSpatialTransformerNdDescriptor(
desc_, CUDNN_SAMPLER_BILINEAR, CudnnDataType<T>::type, nbDims, dimA));
return desc_;
}
private:
cudnnSpatialTransformerDescriptor_t desc_;
DISABLE_COPY_AND_ASSIGN(ScopedSpatialTransformerDescriptor);
};
inline bool CanCUDNNBeUsed(const framework::ExecutionContext& ctx) {
bool use_cudnn = ctx.Attr<bool>("use_cudnn");
use_cudnn &= paddle::platform::is_gpu_place(ctx.GetPlace());
......
......@@ -32,23 +32,25 @@ platform::DeviceContext* DeviceContextPool::Get(const platform::Place& place) {
"'Place' is not supported, Please re-compile with WITH_GPU "
"option");
}
return it->second.get();
return it->second.get().get();
}
const std::vector<const DeviceContext*>
DeviceContextPool::GetAllDeviceContexts() const {
std::vector<const DeviceContext*> all_device_ctx;
all_device_ctx.reserve(device_contexts_.size());
for (auto& dev_ctx : device_contexts_) {
all_device_ctx.emplace_back(dev_ctx.second.get());
}
return all_device_ctx;
template <typename DevCtx, typename PlaceType>
inline void EmplaceDeviceContext(
std::map<Place, std::shared_future<std::unique_ptr<DeviceContext>>>*
map_ptr,
platform::Place p) {
using PtrType = std::unique_ptr<DeviceContext>;
map_ptr->emplace(p, std::async(std::launch::deferred, [=] {
// lazy evaluation. i.e., only create device context at
// first `Get`
return PtrType(new DevCtx(boost::get<PlaceType>(p)));
}));
}
DeviceContextPool::DeviceContextPool(
const std::vector<platform::Place>& places) {
PADDLE_ENFORCE_GT(places.size(), 0);
using PtrType = std::unique_ptr<DeviceContext>;
std::set<Place> set;
for (auto& p : places) {
set.insert(p);
......@@ -57,16 +59,13 @@ DeviceContextPool::DeviceContextPool(
for (auto& p : set) {
if (platform::is_cpu_place(p)) {
#ifdef PADDLE_WITH_MKLDNN
device_contexts_.emplace(
p, PtrType(new MKLDNNDeviceContext(boost::get<CPUPlace>(p))));
EmplaceDeviceContext<MKLDNNDeviceContext, CPUPlace>(&device_contexts_, p);
#else
device_contexts_.emplace(
p, PtrType(new CPUDeviceContext(boost::get<CPUPlace>(p))));
EmplaceDeviceContext<CPUDeviceContext, CPUPlace>(&device_contexts_, p);
#endif
} else if (platform::is_gpu_place(p)) {
#ifdef PADDLE_WITH_CUDA
device_contexts_.emplace(
p, PtrType(new CUDADeviceContext(boost::get<CUDAPlace>(p))));
EmplaceDeviceContext<CUDADeviceContext, CUDAPlace>(&device_contexts_, p);
#else
PADDLE_THROW(
"'CUDAPlace' is not supported, Please re-compile with WITH_GPU "
......@@ -74,9 +73,8 @@ DeviceContextPool::DeviceContextPool(
#endif
} else if (platform::is_cuda_pinned_place(p)) {
#ifdef PADDLE_WITH_CUDA
device_contexts_.emplace(
p,
PtrType(new CUDAPinnedDeviceContext(boost::get<CUDAPinnedPlace>(p))));
EmplaceDeviceContext<CUDAPinnedDeviceContext, CUDAPinnedPlace>(
&device_contexts_, p);
#else
PADDLE_THROW(
"'CUDAPlace' is not supported, Please re-compile with WITH_GPU "
......
......@@ -10,6 +10,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <future> // NOLINT
#include <memory>
#include <mutex> // NOLINT
#include <string>
......@@ -223,9 +224,6 @@ class DeviceContextPool {
/*! \brief Return handle of single device context. */
platform::DeviceContext* Get(const platform::Place& place);
/*! \brief Return all the device contexts. */
const std::vector<const DeviceContext*> GetAllDeviceContexts() const;
template <typename Place>
const typename DefaultDeviceContextType<Place>::TYPE* GetByPlace(
const Place& place) {
......@@ -237,7 +235,8 @@ class DeviceContextPool {
private:
static DeviceContextPool* pool;
std::map<Place, std::unique_ptr<DeviceContext>> device_contexts_;
std::map<Place, std::shared_future<std::unique_ptr<DeviceContext>>>
device_contexts_;
DISABLE_COPY_AND_ASSIGN(DeviceContextPool);
};
......
......@@ -65,44 +65,51 @@ extern void EnforceCUDNNLoaded(const char* fn_name);
* include all needed cudnn functions in HPPL
* different cudnn version has different interfaces
**/
#define CUDNN_DNN_ROUTINE_EACH(__macro) \
__macro(cudnnSetTensor4dDescriptor); \
__macro(cudnnSetTensor4dDescriptorEx); \
__macro(cudnnSetTensorNdDescriptor); \
__macro(cudnnGetTensorNdDescriptor); \
__macro(cudnnGetConvolutionNdForwardOutputDim); \
__macro(cudnnGetConvolutionForwardAlgorithm); \
__macro(cudnnCreateTensorDescriptor); \
__macro(cudnnDestroyTensorDescriptor); \
__macro(cudnnCreateFilterDescriptor); \
__macro(cudnnSetFilter4dDescriptor); \
__macro(cudnnSetFilterNdDescriptor); \
__macro(cudnnGetFilterNdDescriptor); \
__macro(cudnnSetPooling2dDescriptor); \
__macro(cudnnSetPoolingNdDescriptor); \
__macro(cudnnGetPoolingNdDescriptor); \
__macro(cudnnDestroyFilterDescriptor); \
__macro(cudnnCreateConvolutionDescriptor); \
__macro(cudnnCreatePoolingDescriptor); \
__macro(cudnnDestroyPoolingDescriptor); \
__macro(cudnnSetConvolution2dDescriptor); \
__macro(cudnnDestroyConvolutionDescriptor); \
__macro(cudnnSetConvolutionNdDescriptor); \
__macro(cudnnGetConvolutionNdDescriptor); \
__macro(cudnnDeriveBNTensorDescriptor); \
__macro(cudnnCreate); \
__macro(cudnnDestroy); \
__macro(cudnnSetStream); \
__macro(cudnnActivationForward); \
__macro(cudnnConvolutionForward); \
__macro(cudnnConvolutionBackwardBias); \
__macro(cudnnGetConvolutionForwardWorkspaceSize); \
__macro(cudnnTransformTensor); \
__macro(cudnnPoolingForward); \
__macro(cudnnPoolingBackward); \
__macro(cudnnSoftmaxBackward); \
__macro(cudnnSoftmaxForward); \
__macro(cudnnGetVersion); \
#define CUDNN_DNN_ROUTINE_EACH(__macro) \
__macro(cudnnSetTensor4dDescriptor); \
__macro(cudnnSetTensor4dDescriptorEx); \
__macro(cudnnSetTensorNdDescriptor); \
__macro(cudnnGetTensorNdDescriptor); \
__macro(cudnnGetConvolutionNdForwardOutputDim); \
__macro(cudnnGetConvolutionForwardAlgorithm); \
__macro(cudnnCreateTensorDescriptor); \
__macro(cudnnDestroyTensorDescriptor); \
__macro(cudnnCreateFilterDescriptor); \
__macro(cudnnSetFilter4dDescriptor); \
__macro(cudnnSetFilterNdDescriptor); \
__macro(cudnnGetFilterNdDescriptor); \
__macro(cudnnSetPooling2dDescriptor); \
__macro(cudnnSetPoolingNdDescriptor); \
__macro(cudnnGetPoolingNdDescriptor); \
__macro(cudnnDestroyFilterDescriptor); \
__macro(cudnnCreateConvolutionDescriptor); \
__macro(cudnnCreatePoolingDescriptor); \
__macro(cudnnDestroyPoolingDescriptor); \
__macro(cudnnSetConvolution2dDescriptor); \
__macro(cudnnDestroyConvolutionDescriptor); \
__macro(cudnnSetConvolutionNdDescriptor); \
__macro(cudnnGetConvolutionNdDescriptor); \
__macro(cudnnDeriveBNTensorDescriptor); \
__macro(cudnnCreateSpatialTransformerDescriptor); \
__macro(cudnnSetSpatialTransformerNdDescriptor); \
__macro(cudnnDestroySpatialTransformerDescriptor); \
__macro(cudnnSpatialTfGridGeneratorForward); \
__macro(cudnnSpatialTfGridGeneratorBackward); \
__macro(cudnnSpatialTfSamplerForward); \
__macro(cudnnSpatialTfSamplerBackward); \
__macro(cudnnCreate); \
__macro(cudnnDestroy); \
__macro(cudnnSetStream); \
__macro(cudnnActivationForward); \
__macro(cudnnConvolutionForward); \
__macro(cudnnConvolutionBackwardBias); \
__macro(cudnnGetConvolutionForwardWorkspaceSize); \
__macro(cudnnTransformTensor); \
__macro(cudnnPoolingForward); \
__macro(cudnnPoolingBackward); \
__macro(cudnnSoftmaxBackward); \
__macro(cudnnSoftmaxForward); \
__macro(cudnnGetVersion); \
__macro(cudnnGetErrorString);
CUDNN_DNN_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
......
......@@ -821,6 +821,13 @@ All parameter, weight, gradient are variables in Paddle.
[](BuildStrategy &self, bool b) {
self.enable_data_balance_ = b;
}) // FIXME(chengudo): enable_data_balance seems not important
.def_property("enable_sequential_execution",
[](const BuildStrategy &self) {
return self.enable_sequential_execution_;
},
[](BuildStrategy &self, bool b) {
self.enable_sequential_execution_ = b;
})
.def_property(
"fuse_elewise_add_act_ops",
[](const BuildStrategy &self) {
......
......@@ -147,13 +147,11 @@ function cmake_gen() {
-DWITH_SWIG_PY=${WITH_SWIG_PY:-ON}
-DCUDNN_ROOT=/usr/
-DWITH_TESTING=${WITH_TESTING:-ON}
-DWITH_FAST_BUNDLE_TEST=ON
-DCMAKE_MODULE_PATH=/opt/rocm/hip/cmake
-DCMAKE_EXPORT_COMPILE_COMMANDS=ON
-DWITH_FLUID_ONLY=${WITH_FLUID_ONLY:-OFF}
-DCMAKE_EXPORT_COMPILE_COMMANDS=ON
-DWITH_CONTRIB=${WITH_CONTRIB:-ON}
-DWITH_INFERENCE=${WITH_INFERENCE:-ON}
-DWITH_INFERENCE_API_TEST=${WITH_INFERENCE_API_TEST:-ON}
-DINFERENCE_DEMO_INSTALL_DIR=${INFERENCE_DEMO_INSTALL_DIR}
-DWITH_ANAKIN=${WITH_ANAKIN:-OFF}
......@@ -181,12 +179,10 @@ EOF
-DWITH_PYTHON=${WITH_PYTHON:-ON} \
-DCUDNN_ROOT=/usr/ \
-DWITH_TESTING=${WITH_TESTING:-ON} \
-DWITH_FAST_BUNDLE_TEST=ON \
-DCMAKE_MODULE_PATH=/opt/rocm/hip/cmake \
-DWITH_FLUID_ONLY=${WITH_FLUID_ONLY:-OFF} \
-DCMAKE_EXPORT_COMPILE_COMMANDS=ON \
-DWITH_CONTRIB=${WITH_CONTRIB:-ON} \
-DWITH_INFERENCE=${WITH_INFERENCE:-ON} \
-DWITH_INFERENCE_API_TEST=${WITH_INFERENCE_API_TEST:-ON} \
-DINFERENCE_DEMO_INSTALL_DIR=${INFERENCE_DEMO_INSTALL_DIR} \
-DWITH_ANAKIN=${WITH_ANAKIN:-OFF} \
......@@ -653,7 +649,7 @@ function gen_capi_package() {
function gen_fluid_lib() {
mkdir -p ${PADDLE_ROOT}/build
cd ${PADDLE_ROOT}/build
if [[ ${WITH_C_API:-OFF} == "OFF" && ${WITH_INFERENCE:-ON} == "ON" ]] ; then
if [[ ${WITH_C_API:-OFF} == "OFF" ]] ; then
cat <<EOF
========================================
Generating fluid library for train and inference ...
......@@ -666,7 +662,7 @@ EOF
}
function tar_fluid_lib() {
if [[ ${WITH_C_API:-OFF} == "OFF" && ${WITH_INFERENCE:-ON} == "ON" ]] ; then
if [[ ${WITH_C_API:-OFF} == "OFF" ]] ; then
cat <<EOF
========================================
Taring fluid library for train and inference ...
......@@ -681,7 +677,7 @@ EOF
}
function test_fluid_lib() {
if [[ ${WITH_C_API:-OFF} == "OFF" && ${WITH_INFERENCE:-ON} == "ON" ]] ; then
if [[ ${WITH_C_API:-OFF} == "OFF" ]] ; then
cat <<EOF
========================================
Testing fluid library for inference ...
......
......@@ -884,12 +884,13 @@ def _load_slice_up_vars(executor, dirname, slice_vars_and_attrs):
load_prog = Program()
load_block = load_prog.global_block()
need_delete_vars = []
for var_tuple in slice_vars_and_attrs:
orig_var = var_tuple[0]
start = var_tuple[1]
slice_var = var_tuple[2]
end = start + reduce(lambda x, y: x * y, slice_var.shape)
end = start + slice_var.shape[0]
clone_orig_var = load_block.create_var(
name=orig_var.name,
......@@ -917,5 +918,8 @@ def _load_slice_up_vars(executor, dirname, slice_vars_and_attrs):
attrs={'axes': [0],
'starts': [start],
'ends': [end]})
need_delete_vars.append(clone_orig_var)
load_block.append_op(
type='delete_var',
inputs={'X': need_delete_vars}, )
executor.run(load_prog)
......@@ -1586,8 +1586,7 @@ class DynamicRNN(object):
self.lod_rank_table = None
self.max_seq_len = None
self.step_idx = None
self.zero_idx = fill_constant(
shape=[1], value=0, dtype='int64', force_cpu=True)
self.zero_idx = None
self.mem_dict = dict()
self.output_array = []
self.outputs = []
......@@ -1792,6 +1791,7 @@ class DynamicRNN(object):
"""
self._assert_in_rnn_block_('memory')
self._init_zero_idx_()
if init is not None:
if not isinstance(init, Variable):
raise TypeError(
......@@ -1905,6 +1905,22 @@ class DynamicRNN(object):
array_write(x=each, i=self.step_idx, array=outside_array)
self.output_array.append(outside_array)
def _init_zero_idx_(self):
if self.zero_idx is None:
parent_block = self._parent_block_()
self.zero_idx = parent_block.create_var(
name=unique_name.generate('zero_idx'), dtype='int64')
parent_block.append_op(
type='fill_constant',
inputs={},
outputs={'Out': [self.zero_idx]},
attrs={
'shape': [1],
'dtype': self.zero_idx.dtype,
'value': float(0),
'force_cpu': True
})
def _parent_block_(self):
prog = self.helper.main_program
parent_idx = prog.current_block().parent_idx
......
......@@ -154,9 +154,11 @@ __all__ = [
'mul',
'sigmoid_cross_entropy_with_logits',
'maxout',
'affine_grid',
'sequence_reverse',
'affine_channel',
'hash',
'grid_sampler',
'log_loss',
'add_position_encoding',
]
......@@ -710,8 +712,18 @@ def dynamic_gru(input,
The first part are weights of the update gate and reset gate with
shape :math:`(D \\times 2D)`, and the second part are weights for
candidate hidden state with shape :math:`(D \\times D)`.
bias_attr(ParamAttr): The parameter attribute for learnable the
hidden-hidden bias.
If it is set to None or one attribute of ParamAttr, dynamic_gru will
create ParamAttr as param_attr. If the Initializer of the param_attr
is not set, the parameter is initialized with Xavier. Default: None.
bias_attr (ParamAttr|bool|None): The parameter attribute for the bias
of GRU. Note that the bias with :math:`(1 \\times 3D)` concatenates
the bias in the update gate, reset gate and candidate calculations.
If it is set to False, no bias will be applied to the update gate,
reset gate and candidate calculations. If it is set to None or one
attribute of ParamAttr, dynamic_gru will create ParamAttr as
bias_attr. If the Initializer of the bias_attr is not set, the bias
is initialized zero. Default: None.
is_reverse(bool): Whether to compute reversed GRU, default
:attr:`False`.
gate_activation(str): The activation for update gate and reset gate.
......@@ -749,7 +761,7 @@ def dynamic_gru(input,
attr=helper.bias_attr, shape=[1, 3 * size], dtype=dtype, is_bias=True)
batch_size = input.shape[0]
inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
if h_0 != None:
if h_0:
assert h_0.shape == (
batch_size, size
), 'The shape of h0 should be(batch_size, %d)' % size
......@@ -810,10 +822,29 @@ def gru_unit(input,
Args:
input (Variable): The fc transformed input value of current step.
hidden (Variable): The hidden value of lstm unit from previous step.
hidden (Variable): The hidden value of gru unit from previous step.
size (integer): The input dimension value.
param_attr (ParamAttr): The weight parameters for gru unit. Default: None
bias_attr (ParamAttr): The bias parameters for gru unit. Default: None
param_attr(ParamAttr|None): The parameter attribute for the learnable
hidden-hidden weight matrix. Note:
- The shape of the weight matrix is :math:`(T \\times 3D)`, where
:math:`D` is the hidden size.
- All elements in the weight matrix can be divided into two parts.
The first part are weights of the update gate and reset gate with
shape :math:`(D \\times 2D)`, and the second part are weights for
candidate hidden state with shape :math:`(D \\times D)`.
If it is set to None or one attribute of ParamAttr, gru_unit will
create ParamAttr as param_attr. If the Initializer of the param_attr
is not set, the parameter is initialized with Xavier. Default: None.
bias_attr (ParamAttr|bool|None): The parameter attribute for the bias
of GRU. Note that the bias with :math:`(1 \\times 3D)` concatenates
the bias in the update gate, reset gate and candidate calculations.
If it is set to False, no bias will be applied to the update gate,
reset gate and candidate calculations. If it is set to None or one
attribute of ParamAttr, gru_unit will create ParamAttr as
bias_attr. If the Initializer of the bias_attr is not set, the bias
is initialized zero. Default: None.
activation (string): The activation type for cell (actNode).
Default: 'tanh'
gate_activation (string): The activation type for gates (actGate).
......@@ -1825,7 +1856,7 @@ def conv3d(input,
return helper.append_activation(pre_act)
def sequence_pool(input, pool_type):
def sequence_pool(input, pool_type, is_test=False):
"""
This function add the operator for sequence pooling.
It pools features of all time-steps of each instance, and is applied
......@@ -1862,6 +1893,7 @@ def sequence_pool(input, pool_type):
input(variable): The input variable which is a LoDTensor.
pool_type (string): The pooling type of sequence_pool.
It supports average, sum, sqrt and max.
is_test(bool, Default False): Used distinguish training from scoring mode.
Returns:
The sequence pooling variable which is a Tensor.
......@@ -1889,7 +1921,8 @@ def sequence_pool(input, pool_type):
inputs={"X": input},
outputs={"Out": pool_out,
"MaxIndex": max_index},
attrs={"pooltype": pool_type.upper()})
attrs={"pooltype": pool_type.upper(),
"is_test": is_test})
# when pool_type is max, variable max_index is initialized,
# so we stop the gradient explicitly here
......@@ -2069,7 +2102,8 @@ def pool2d(input,
global_pooling=False,
use_cudnn=True,
ceil_mode=False,
name=None):
name=None,
exclusive=True):
"""
${comment}
......@@ -2083,11 +2117,13 @@ def pool2d(input,
pool_type: ${pooling_type_comment}
pool_stride (int): stride of the pooling layer.
pool_padding (int): padding size.
global_pooling: ${global_pooling_comment}
use_cudnn: ${use_cudnn_comment}
ceil_mode: ${ceil_mode_comment}
global_pooling (bool): ${global_pooling_comment}
use_cudnn (bool): ${use_cudnn_comment}
ceil_mode (bool): ${ceil_mode_comment}
name (str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
exclusive (bool): Whether to exclude padding points in average pooling
mode, default is true
Returns:
Variable: The pooling result.
......@@ -2145,7 +2181,8 @@ def pool2d(input,
"paddings": pool_padding,
"use_cudnn": use_cudnn,
"ceil_mode": ceil_mode,
"use_mkldnn": False
"use_mkldnn": False,
"exclusive": exclusive,
})
return pool_out
......@@ -2159,7 +2196,8 @@ def pool3d(input,
global_pooling=False,
use_cudnn=True,
ceil_mode=False,
name=None):
name=None,
exclusive=True):
"""
This function adds the operator for pooling in 3-dimensions, using the
pooling configurations mentioned in input parameters.
......@@ -2175,6 +2213,8 @@ def pool3d(input,
ceil_mode (bool): ${ceil_mode_comment}
name (str): A name for this layer(optional). If set None, the layer
will be named automatically.
exclusive (bool): Whether to exclude padding points in average pooling
mode, default is true
Returns:
Variable: output of pool3d layer.
......@@ -2213,7 +2253,8 @@ def pool3d(input,
"paddings": pool_padding,
"use_cudnn": use_cudnn,
"ceil_mode": ceil_mode,
"use_mkldnn": False
"use_mkldnn": False,
"exclusive": exclusive,
})
return pool_out
......@@ -3018,7 +3059,8 @@ def sequence_pad(x, pad_value, maxlen=None, name=None):
x = fluid.layers.data(name='y', shape=[10, 5],
dtype='float32', lod_level=1)
pad_value = fluid.layers.assign(input=numpy.array([0]))
pad_value = fluid.layers.assign(
input=numpy.array([0], dtype=numpy.float32))
out = fluid.layers.sequence_pad(x=x, pad_value=pad_value)
"""
......@@ -4440,7 +4482,10 @@ def transpose(x, perm, name=None):
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[5, 10, 15], dtype='float32')
# use append_batch_size=False to avoid prepending extra
# batch size in shape
x = fluid.layers.data(name='x', shape=[5, 10, 15],
dtype='float32', append_batch_size=False)
x_transposed = layers.transpose(x, perm=[1, 0, 2])
"""
......@@ -4677,7 +4722,8 @@ def multiplex(inputs, index):
def softmax_with_cross_entropy(logits,
label,
soft_label=False,
ignore_index=-100):
ignore_index=-100,
numeric_stable_mode=False):
"""
**Softmax With Cross Entropy Operator.**
......@@ -4711,6 +4757,18 @@ def softmax_with_cross_entropy(logits,
\\left(\\text{logit}_i - \\log\\left(\\sum_{i=0}^{K}
\\exp(\\text{logit}_i)\\right)\\right), j = 1,...,K
3) If numeric_stable_mode is True, softmax is calculated first by:
.. math::
max_j = \\max_{i=0}^{K}{\\text{logit}_i}
log\\_max\\_sum_j = \\log\\sum_{i=0}^{K}\\exp(logit_i - max_j)
softmax_j = \\exp(logit_j - max_j - {log\\_max\\_sum}_j)
and then cross entropy loss is calculated by softmax and label.
Args:
logits (Variable): The unscaled log probabilities, which is a 2-D tensor
with shape [N x K]. N is the batch_size, and K is the class number.
......@@ -4722,6 +4780,13 @@ def softmax_with_cross_entropy(logits,
ignore_index (int): Specifies a target value that is ignored and does
not contribute to the input gradient. Only valid
if soft_label is set to False. Default: -100
numeric_stable_mode (bool): A flag to indicate whether to use a more
numerically stable algorithm. Only valid
when soft_label is False and GPU is used.
When soft_label is True or CPU is used,
the algorithm is always numerically stable.
Note that the speed may be slower when use
stable algorithm. Default: False
Returns:
Variable: The cross entropy loss is a 2-D tensor with shape [N x 1].
......@@ -4744,8 +4809,11 @@ def softmax_with_cross_entropy(logits,
'Label': label},
outputs={'Softmax': softmax,
'Loss': loss},
attrs={'soft_label': soft_label,
'ignore_index': ignore_index})
attrs={
'soft_label': soft_label,
'ignore_index': ignore_index,
'numeric_stable_mode': numeric_stable_mode
})
return loss
......@@ -6105,6 +6173,124 @@ def crop(x, shape=None, offsets=None, name=None):
return out
def affine_grid(theta, out_shape, name=None):
"""
It generates a grid of (x,y) coordinates using the parameters of
the affine transformation that correspond to a set of points where
the input feature map should be sampled to produce the transformed
output feature map.
.. code-block:: text
* Case 1:
Given:
theta = [[[x_11, x_12, x_13]
[x_14, x_15, x_16]]
[[x_21, x_22, x_23]
[x_24, x_25, x_26]]]
out_shape = [2, 3, 5, 5]
Step 1:
Generate normalized coordinates according to out_shape.
The values of the normalized coordinates are in the interval between -1 and 1.
The shape of the normalized coordinates is [2, H, W] as below:
C = [[[-1. -1. -1. -1. -1. ]
[-0.5 -0.5 -0.5 -0.5 -0.5]
[ 0. 0. 0. 0. 0. ]
[ 0.5 0.5 0.5 0.5 0.5]
[ 1. 1. 1. 1. 1. ]]
[[-1. -0.5 0. 0.5 1. ]
[-1. -0.5 0. 0.5 1. ]
[-1. -0.5 0. 0.5 1. ]
[-1. -0.5 0. 0.5 1. ]
[-1. -0.5 0. 0.5 1. ]]]
C[0] is the coordinates in height axis and C[1] is the coordinates in width axis.
Step2:
Tanspose and reshape C to shape [H * W, 2] and append ones to last dimension. The we get:
C_ = [[-1. -1. 1. ]
[-0.5 -1. 1. ]
[ 0. -1. 1. ]
[ 0.5 -1. 1. ]
[ 1. -1. 1. ]
[-1. -0.5 1. ]
[-0.5 -0.5 1. ]
[ 0. -0.5 1. ]
[ 0.5 -0.5 1. ]
[ 1. -0.5 1. ]
[-1. 0. 1. ]
[-0.5 0. 1. ]
[ 0. 0. 1. ]
[ 0.5 0. 1. ]
[ 1. 0. 1. ]
[-1. 0.5 1. ]
[-0.5 0.5 1. ]
[ 0. 0.5 1. ]
[ 0.5 0.5 1. ]
[ 1. 0.5 1. ]
[-1. 1. 1. ]
[-0.5 1. 1. ]
[ 0. 1. 1. ]
[ 0.5 1. 1. ]
[ 1. 1. 1. ]]
Step3:
Compute output by equation $$Output[i] = C_ * Theta[i]^T$$
Args:
theta (Variable): A batch of affine transform parameters with shape [N, 2, 3].
out_shape (Variable | list | tuple): The shape of target output with format [N, C, H, W].
out_shape can be a Variable or a list or tuple.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The output with shape [N, H, W, 2].
Raises:
ValueError: If the type of arguments is not supported.
Examples:
.. code-block:: python
theta = fluid.layers.data(name="x", shape=[2, 3], dtype="float32")
out_shape = fluid.layers.data(name="y", shape=[-1], dtype="float32")
data = fluid.layers.affine_grid(theta, out_shape)
# or
data = fluid.layers.affine_grid(theta, [5, 3, 28, 28])
"""
helper = LayerHelper('affine_grid')
if not (isinstance(out_shape, list) or isinstance(out_shape, tuple) or \
isinstance(out_shape, Variable)):
raise ValueError("The out_shape should be a list, tuple or Variable.")
if not isinstance(theta, Variable):
raise ValueError("The theta should be a Variable.")
out = helper.create_variable_for_type_inference(theta.dtype)
ipts = {'Theta': theta}
attrs = {}
if isinstance(out_shape, Variable):
ipts['OutputShape'] = out_shape
else:
attrs['output_shape'] = out_shape
helper.append_op(
type='affine_grid',
inputs=ipts,
outputs={'Output': out},
attrs=None if len(attrs) == 0 else attrs)
return out
def rank_loss(label, left, right, name=None):
"""
**Rank loss layer for RankNet**
......@@ -7319,10 +7505,10 @@ def clip(x, min, max, name=None):
helper = LayerHelper("clip", **locals())
if name is None:
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
name = unique_name.generate(".".join([helper.name, 'tmp']))
out = helper.create_variable(
type=x.type, name=name, dtype=x.dtype, persistable=False)
helper.append_op(
type="clip",
......@@ -7351,10 +7537,10 @@ def clip_by_norm(x, max_norm, name=None):
helper = LayerHelper("clip_by_norm", **locals())
if name is None:
out = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
out = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
name = unique_name.generate(".".join([helper.name, 'tmp']))
out = helper.create_variable(
type=x.type, name=name, dtype=x.dtype, persistable=False)
helper.append_op(
type="clip_by_norm",
......@@ -7558,19 +7744,59 @@ def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None):
def hash(input, hash_size, num_hash=1, name=None):
"""
hash the input
Args:
input (Variable): The input variable which is a one-hot word.
hash_size (int): The space size for hash algorithm.
Hash the input to an integer whose value is less than the given hash size.
The hash algorithm we used was xxHash - Extremely fast hash algorithm
(https://github.com/Cyan4973/xxHash/tree/v0.6.5)
A simple example as below:
.. code-block:: text
Given:
# shape [2, 2]
input.data = [
[[1], [2]],
[[3], [4]],
]
input.lod = [[0, 2]]
hash_size = 10000
num_hash = 4
Then:
Hash op will take all number in input's 2nd dimension as hash algorithm's
input for each time. Each input will be hashed for 4 times, and get an
array whose length is 4. Each value in the array ranges from 0 to 9999.
# shape [2, 4]
output.data = [
[[9662], [9217], [1129], [8487]],
[[8310], [1327], [1654], [4567]],
]
output.lod = [[0, 2]]
Args:
input (Variable): The input variable which is a one-hot word. The
dimensions of the input variable must be 2.
hash_size (int): The space size for hash algorithm. The output value
will keep in the range:math:`[0, hash_size - 1]`.
num_hash (int): The times of hash, default 1.
name (str, default None): The name of this layer.
Returns:
Variable: The hash result variable which is a LoDTensor.
Examples:
.. code-block:: python
word_dict = paddle.dataset.imdb.word_dict()
x = fluid.layers.data(shape[1], dtype='int32', lod_level=1)
out = fluid.layers.hash(input=x, len(word_dict))
Returns:
Variable: The hash result variable which is a LoDTensor.
Examples:
.. code-block:: python
word_dict = paddle.dataset.imdb.word_dict()
x = fluid.layers.data(shape[1], dtype='int32', lod_level=1)
out = fluid.layers.hash(input=x, num_hash=4, hash_size=1000)
"""
helper = LayerHelper('hash', **locals())
out = helper.create_variable_for_type_inference(
......@@ -7584,6 +7810,87 @@ def hash(input, hash_size, num_hash=1, name=None):
return out
@templatedoc()
def grid_sampler(x, grid, name=None):
"""
This operation samples input X by using bilinear interpolation based on
flow field grid, which is usually gennerated by affine_grid. The grid of
shape [N, H, W, 2] is the concatenation of (grid_x, grid_y) coordinates
with shape [N, H, W] each, where grid_x is indexing the 4th dimension
(in width dimension) of input data x and grid_y is indexng the 3rd
dimention (in height dimension), finally results is the bilinear
interpolation value of 4 nearest corner points.
Step 1:
Get (x, y) grid coordinates and scale to [0, H-1/W-1].
grid_x = 0.5 * (grid[:, :, :, 0] + 1) * (W - 1)
grid_y = 0.5 * (grid[:, :, :, 1] + 1) * (H - 1)
Step 2:
Indices input data X with grid (x, y) in each [H, W] area, and bilinear
interpolate point value by 4 nearest points.
wn ------- y_n ------- en
| | |
| d_n |
| | |
x_w --d_w-- grid--d_e-- x_e
| | |
| d_s |
| | |
ws ------- y_s ------- wn
x_w = floor(x) // west side x coord
x_e = x_w + 1 // east side x coord
y_n = floor(y) // north side y coord
y_s = y_s + 1 // south side y coord
d_w = grid_x - x_w // distance to west side
d_e = x_e - grid_x // distance to east side
d_n = grid_y - y_n // distance to north side
d_s = y_s - grid_y // distance to south side
wn = X[:, :, y_n, x_w] // north-west point value
en = X[:, :, y_n, x_e] // north-east point value
ws = X[:, :, y_s, x_w] // south-east point value
es = X[:, :, y_s, x_w] // north-east point value
output = wn * d_e * d_s + en * d_w * d_s
+ ws * d_e * d_n + es * d_w * d_n
Args:
x(Variable): Input data of shape [N, C, H, W].
grid(Variable): Input grid tensor of shape [N, H, W, 2].
name (str, default None): The name of this layer.
Returns:
out(Variable): Output of shape [N, C, H, W] data samples input X
using bilnear interpolation based on input grid.
Exmples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[3, 10, 32, 32], dtype='float32')
theta = fluid.layers.data(name='theta', shape=[3, 2, 3], dtype='float32')
grid = fluid.layers.affine_grid(input=theta, size=[3, 10, 32, 32]})
out = fluid.layers.grid_sampler(x=x, grid=grid)
"""
helper = LayerHelper("grid_sampler", **locals())
if not isinstance(x, Variable):
return ValueError("The x should be a Variable")
if not isinstance(grid, Variable):
return ValueError("The grid should be a Variable")
out = helper.create_variable_for_type_inference(x.dtype)
ipts = {'X': x, 'Grid': grid}
helper.append_op(type='grid_sampler', inputs=ipts, outputs={'Output': out})
return out
def log_loss(input, label, epsilon=1e-4, name=None):
"""
**Negative Log Loss Layer**
......
file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
# default test
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
endforeach()
if(NOT APPLE)
# default test
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
endforeach()
else()
foreach(src ${TEST_OPS})
if(${src} STREQUAL "test_image_classification_vgg")
message(WARNING "These tests has been disabled in OSX for random fail: \n" ${src})
elseif(${src} STREQUAL "test_image_classification_resnet")
message(WARNING "These tests has been disabled in OSX for random fail: \n" ${src})
elseif()
py_test(${src} SRCS ${src}.py)
endif()
endforeach()
endif()
......@@ -17,6 +17,10 @@ if(NOT WITH_DISTRIBUTE)
list(REMOVE_ITEM TEST_OPS test_listen_and_serv_op)
LIST(REMOVE_ITEM TEST_OPS test_dist_mnist)
LIST(REMOVE_ITEM TEST_OPS test_dist_word2vec)
LIST(REMOVE_ITEM TEST_OPS test_dist_ctr)
LIST(REMOVE_ITEM TEST_OPS test_dist_simnet_bow)
LIST(REMOVE_ITEM TEST_OPS test_dist_mnist_batch_merge)
LIST(REMOVE_ITEM TEST_OPS test_dist_text_classification)
endif(NOT WITH_DISTRIBUTE)
list(REMOVE_ITEM TEST_OPS test_seq_concat_op) # FIXME(helin): https://github.com/PaddlePaddle/Paddle/issues/8290
......@@ -55,6 +59,7 @@ function(py_test_modules TARGET_NAME)
if (py_test_modules_SERIAL)
set_property(TEST ${TARGET_NAME} PROPERTY RUN_SERIAL 1)
endif()
set_tests_properties(${TARGET_NAME} PROPERTIES TIMEOUT 600)
endif()
endfunction()
list(REMOVE_ITEM TEST_OPS test_warpctc_op)
......@@ -88,4 +93,6 @@ py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SE
py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL)
set_tests_properties(test_parallel_executor_fetch_feed PROPERTIES TIMEOUT 150)
py_test_modules(test_parallel_executor_transformer MODULES test_parallel_executor_transformer SERIAL)
py_test_modules(test_image_classification_resnet MODULES test_image_classification_resnet SERIAL)
if(NOT APPLE)
py_test_modules(test_image_classification_resnet MODULES test_image_classification_resnet SERIAL)
endif()
......@@ -90,8 +90,10 @@ class TestDistMnist2x2(TestDistRunnerBase):
inference_program = fluid.default_main_program().clone()
# Optimization
opt = fluid.optimizer.AdamOptimizer(
learning_rate=0.001, beta1=0.9, beta2=0.999)
# TODO(typhoonzero): fix distributed adam optimizer
# opt = fluid.optimizer.AdamOptimizer(
# learning_rate=0.001, beta1=0.9, beta2=0.999)
opt = fluid.optimizer.Momentum(learning_rate=0.001, momentum=0.9)
# Reader
train_reader = paddle.batch(
......
# 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.
from __future__ import print_function
import os
import sys
import signal
import subprocess
import argparse
import time
import math
import random
from multiprocessing import Process
from functools import reduce
import numpy as np
import unittest
import six
import paddle
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid import io
from test_dist_base import TestDistRunnerBase, runtime_main, RUN_STEP
from dist_simnet_bow import TestDistSimnetBow2x2, DATA_URL, DATA_MD5
class TestDistSaveLoad2x2(TestDistSimnetBow2x2):
def _load_persistable_vars(self, executor, dirname, program):
def _is_checkpoint_var(var):
"""
the checkpoint will not save or load all the variables.
var type is FEED_MINIBATCH/FETCH_LIST/RAW or var name ends with @GRAD are discarded.
: param var(Variable)
"""
if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
var.desc.type() == core.VarDesc.VarType.FETCH_LIST or \
var.desc.type() == core.VarDesc.VarType.RAW:
return False
# @GRAD are named for gradient variables, checkpoint will not save it.
if "@GRAD" in var.name:
return False
# .trainer_ are named for distribute train variables, checkpoint will not save it.
if ".trainer_" in var.name:
return False
# .block is named for distribute train variables, checkpoint will not save it.
if ".block" in var.name:
return False
if "tmp_" in var.name:
return False
return var.persistable
io.load_vars(
executor,
dirname=dirname,
main_program=program,
predicate=_is_checkpoint_var,
filename=None)
def run_pserver(self, args):
self.get_model(batch_size=2)
# NOTE: pserver should not call memory optimize
t = self.get_transpiler(args.trainer_id,
fluid.default_main_program(), args.endpoints,
args.trainers, args.sync_mode)
pserver_prog = t.get_pserver_program(args.current_endpoint)
startup_prog = t.get_startup_program(args.current_endpoint,
pserver_prog)
need_load = bool(int(os.getenv("LOAD", "0")))
model_dir = os.getenv("MODEL_DIR", "")
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
if need_load and model_dir:
self._load_persistable_vars(exe, model_dir, startup_prog)
exe.run(pserver_prog)
def run_trainer(self, args):
test_program, avg_cost, train_reader, test_reader, batch_acc, predict = \
self.get_model(batch_size=2)
if args.mem_opt:
fluid.memory_optimize(fluid.default_main_program(), skip_grads=True)
if args.is_dist:
t = self.get_transpiler(args.trainer_id,
fluid.default_main_program(),
args.endpoints, args.trainers,
args.sync_mode)
trainer_prog = t.get_trainer_program()
else:
trainer_prog = fluid.default_main_program()
if args.use_cuda:
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
startup_exe = fluid.Executor(place)
startup_exe.run(fluid.default_startup_program())
strategy = fluid.ExecutionStrategy()
strategy.num_threads = 1
strategy.allow_op_delay = False
build_stra = fluid.BuildStrategy()
if args.use_reduce:
build_stra.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
else:
build_stra.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.AllReduce
exe = fluid.ParallelExecutor(
args.use_cuda,
loss_name=avg_cost.name,
exec_strategy=strategy,
build_strategy=build_stra)
feed_var_list = [
var for var in trainer_prog.global_block().vars.values()
if var.is_data
]
feeder = fluid.DataFeeder(feed_var_list, place)
reader_generator = train_reader()
def get_data():
origin_batch = next(reader_generator)
if args.is_dist and args.use_reader_alloc:
new_batch = []
for offset, item in enumerate(origin_batch):
if offset % 2 == args.trainer_id:
new_batch.append(item)
return new_batch
else:
return origin_batch
need_save = bool(int(os.getenv("SAVE", "0")))
model_dir = os.getenv("MODEL_DIR", "")
if need_save:
for _ in six.moves.xrange(RUN_STEP):
loss, = exe.run(fetch_list=[avg_cost.name],
feed=feeder.feed(get_data()))
if need_save and model_dir:
io.save_persistables(startup_exe, model_dir, trainer_prog)
var = np.array(fluid.global_scope().find_var('__fc_b__').get_tensor())
print(np.ravel(var).tolist())
if __name__ == "__main__":
paddle.dataset.common.download(DATA_URL, 'simnet', DATA_MD5, "train")
runtime_main(TestDistSaveLoad2x2)
......@@ -40,7 +40,8 @@ class TestParallelExecutorBase(unittest.TestCase):
use_reduce=False,
fuse_elewise_add_act_ops=False,
optimizer=fluid.optimizer.Adam,
use_fast_executor=False):
use_fast_executor=False,
enable_sequential_execution=False):
def run_executor(exe, feed, fetch_list, program=None):
if isinstance(exe, fluid.ParallelExecutor):
res = exe.run(fetch_list=fetch_list, feed=feed)
......@@ -80,6 +81,7 @@ class TestParallelExecutorBase(unittest.TestCase):
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce \
if use_reduce else fluid.BuildStrategy.ReduceStrategy.AllReduce
build_strategy.fuse_elewise_add_act_ops = fuse_elewise_add_act_ops
build_strategy.enable_sequential_execution = enable_sequential_execution
if use_parallel_executor:
exe = fluid.ParallelExecutor(
......
# 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 unittest
import numpy as np
from op_test import OpTest
def AffineGrid(theta, size):
n = size[0]
w = size[3]
h = size[2]
h_idx = np.repeat(
np.linspace(-1, 1, h)[np.newaxis, :], w, axis=0).T[:, :, np.newaxis]
w_idx = np.repeat(
np.linspace(-1, 1, w)[np.newaxis, :], h, axis=0)[:, :, np.newaxis]
grid = np.concatenate(
[w_idx, h_idx, np.ones([h, w, 1])], axis=2) # h * w * 3
grid = np.repeat(grid[np.newaxis, :], size[0], axis=0) # n * h * w *3
ret = np.zeros([n, h * w, 2])
theta = theta.transpose([0, 2, 1])
for i in range(len(theta)):
ret[i] = np.dot(grid[i].reshape([h * w, 3]), theta[i])
# print ret.reshape([h * w, 2]).astype("float32")
return ret.reshape([n, h, w, 2]).astype("float32")
class TestAffineGridOp(OpTest):
def setUp(self):
self.initTestCase()
self.op_type = "affine_grid"
theta = np.random.randint(1, 3, self.theta_shape).astype("float32")
theta = np.ones(self.theta_shape).astype("float32")
self.inputs = {'Theta': theta}
self.attrs = {"use_cudnn": True}
if self.dynamic_shape:
self.inputs['OutputShape'] = self.output_shape
else:
self.attrs['output_shape'] = self.output_shape
self.outputs = {'Output': AffineGrid(theta, self.output_shape)}
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(
['Theta'],
'Output',
no_grad_set=['OutputShape'],
max_relative_error=0.006)
def initTestCase(self):
self.theta_shape = (3, 2, 3)
self.output_shape = np.array([3, 2, 5, 7]).astype("int32")
self.dynamic_shape = False
class TestAffineGridOpCase1(TestAffineGridOp):
def initTestCase(self):
self.theta_shape = (3, 2, 3)
self.output_shape = np.array([3, 2, 5, 7]).astype("int32")
self.dynamic_shape = True
if __name__ == '__main__':
unittest.main()
......@@ -22,6 +22,8 @@ import signal
import subprocess
import six
import argparse
import pickle
import numpy as np
import paddle.fluid as fluid
......@@ -128,10 +130,15 @@ class TestDistRunnerBase(object):
else:
return origin_batch
out_losses = []
for _ in six.moves.xrange(RUN_STEP):
loss, = exe.run(fetch_list=[avg_cost.name],
feed=feeder.feed(get_data()))
print(loss)
out_losses.append(loss[0])
if six.PY2:
print(pickle.dumps(out_losses))
else:
sys.stdout.buffer.write(pickle.dumps(out_losses))
def runtime_main(test_class):
......@@ -149,7 +156,7 @@ def runtime_main(test_class):
parser.add_argument('--use_cuda', action='store_true')
parser.add_argument('--use_reduce', action='store_true')
parser.add_argument(
'--use_reader_alloc', action='store_true', required=False, default=True)
'--use_reader_alloc', action='store_true', required=False)
parser.add_argument('--batch_size', required=False, type=int, default=2)
parser.add_argument(
'--batch_merge_repeat', required=False, type=int, default=1)
......@@ -188,7 +195,7 @@ class TestDistBase(unittest.TestCase):
self._pservers = 2
self._ps_endpoints = "127.0.0.1:%s,127.0.0.1:%s" % (
self._find_free_port(), self._find_free_port())
self._python_interp = "python"
self._python_interp = sys.executable
self._sync_mode = True
self._enforce_place = None
self._mem_opt = False
......@@ -237,21 +244,6 @@ class TestDistBase(unittest.TestCase):
return ps0_proc, ps1_proc, ps0_pipe, ps1_pipe
def _wait_ps_ready(self, pid):
retry_times = 50
while True:
assert retry_times >= 0, "wait ps ready failed"
time.sleep(3)
try:
# the listen_and_serv_op would touch a file which contains the listen port
# on the /tmp directory until it was ready to process all the RPC call.
os.stat("/tmp/paddle.%d.port" % pid)
return
except os.error as e:
sys.stderr.write('waiting for pserver: %s, left retry %d\n' %
(e, retry_times))
retry_times -= 1
def _run_local(self,
model,
envs,
......@@ -288,23 +280,20 @@ class TestDistBase(unittest.TestCase):
env=envs)
local_out, local_err = local_proc.communicate()
local_ret = cpt.to_text(local_out)
if check_error_log:
err_log.close()
sys.stderr.write('local_stdout: %s\n' % local_ret)
sys.stderr.write('local_stdout: %s\n' % pickle.loads(local_out))
sys.stderr.write('local_stderr: %s\n' % local_err)
local_losses = local_ret.split("\n")
return local_losses
return pickle.loads(local_out)
def _run_cluster(self, model, envs, check_error_log):
# Run dist train to compare with local results
ps0, ps1, ps0_pipe, ps1_pipe = self.start_pserver(model,
check_error_log, envs)
self._wait_ps_ready(ps0.pid)
self._wait_ps_ready(ps1.pid)
ps0_ep, ps1_ep = self._ps_endpoints.split(",")
tr_cmd = "%s %s --role trainer --endpoints %s --trainer_id %d --current_endpoint %s --trainers %d --is_dist"
......@@ -339,8 +328,8 @@ class TestDistBase(unittest.TestCase):
env0.update(envs)
env1.update(envs)
print("tr0_cmd:{}, env0: {}".format(tr0_cmd, env0))
print("tr1_cmd:{}, env1: {}".format(tr1_cmd, env1))
print("tr0_cmd:{}".format(tr0_cmd))
print("tr1_cmd:{}".format(tr1_cmd))
tr0_pipe = open("/tmp/tr0_err.log", "wb")
tr1_pipe = open("/tmp/tr1_err.log", "wb")
......@@ -356,9 +345,7 @@ class TestDistBase(unittest.TestCase):
env=env1)
tr0_out, tr0_err = tr0_proc.communicate()
tr0_loss_text = cpt.to_text(tr0_out)
tr1_out, tr1_err = tr1_proc.communicate()
tr1_loss_text = cpt.to_text(tr1_out)
# close trainer file
tr0_pipe.close()
......@@ -373,15 +360,13 @@ class TestDistBase(unittest.TestCase):
ps1.terminate()
# print log
sys.stderr.write('trainer 0 stdout:\n %s\n' % tr0_loss_text)
sys.stderr.write('trainer 0 stderr:\n %s\n' % tr0_err)
sys.stderr.write('trainer 1 stdout: %s\n' % tr1_loss_text)
sys.stderr.write('trainer 0 stdout: %s\n' % pickle.loads(tr0_out))
sys.stderr.write('trainer 0 stderr: %s\n' % tr0_err)
sys.stderr.write('trainer 1 stdout: %s\n' % pickle.loads(tr1_out))
sys.stderr.write('trainer 1 stderr: %s\n' % tr1_err)
tr0_losses = tr0_loss_text.split("\n")
tr1_losses = tr1_loss_text.split("\n")
return tr0_losses, tr1_losses
# return tr0_losses, tr1_losses
return pickle.loads(tr0_out), pickle.loads(tr1_out)
def check_with_place(self,
model_file,
......@@ -411,9 +396,9 @@ class TestDistBase(unittest.TestCase):
check_error_log)
for step_id in range(RUN_STEP):
local_loss = eval(local_losses[step_id])[0]
tr0_loss = eval(tr0_losses[step_id])[0]
tr1_loss = eval(tr1_losses[step_id])[0]
dist_loss = (tr0_loss + tr1_loss) / 2
print(str(local_loss) + ":" + str(dist_loss))
self.assertAlmostEqual(local_loss, dist_loss, delta=delta)
local_loss = local_losses[step_id]
tr0_loss = tr0_losses[step_id]
tr1_loss = tr1_losses[step_id]
dist_loss = (np.array([tr0_loss]) + np.array([tr1_loss])) / 2
print("=======", local_loss, ":", dist_loss[0], "=======")
self.assertAlmostEqual(local_loss, dist_loss[0], delta=delta)
# 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.
from __future__ import print_function
import os
import shutil
import unittest
import tempfile
import numpy as np
from test_dist_base import TestDistBase, RUN_STEP
class TestDistSaveLoadDense2x2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._enforce_place = "CPU"
def check_with_place(self,
model_file,
delta=1e-3,
check_error_log=False,
need_envs={}):
required_envs = {
"PATH": os.getenv("PATH", ""),
"PYTHONPATH": os.getenv("PYTHONPATH", ""),
"LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
"http_proxy": ""
}
required_envs.update(need_envs)
if check_error_log:
required_envs["GLOG_v"] = "7"
required_envs["GLOG_logtostderr"] = "1"
model_dir = tempfile.mkdtemp()
local_env = {}
local_env["SAVE"] = "1"
local_env["MODEL_DIR"] = model_dir
local_env.update(required_envs)
cluster_env = {}
cluster_env["LOAD"] = "1"
cluster_env["MODEL_DIR"] = model_dir
cluster_env.update(required_envs)
local_var = self._run_local(model_file, local_env, check_error_log)
tr0_var, tr1_var = self._run_cluster(model_file, cluster_env,
check_error_log)
shutil.rmtree(model_dir)
local_np = np.array(eval(local_var[0]))
train0_np = np.array(eval(tr0_var[0]))
train1_np = np.array(eval(tr1_var[0]))
self.assertAlmostEqual(local_np.all(), train0_np.all(), delta=delta)
self.assertAlmostEqual(local_np.all(), train1_np.all(), delta=delta)
self.assertAlmostEqual(train0_np.all(), train1_np.all(), delta=delta)
@unittest.skip(reason="CI fail")
def test_dist(self):
need_envs = {
"IS_DISTRIBUTED": '0',
"IS_SPARSE": '0',
'IS_SELF_CONTAINED_LR': '1'
}
self.check_with_place(
"dist_save_load.py",
delta=0,
check_error_log=False,
need_envs=need_envs)
if __name__ == "__main__":
unittest.main()
......@@ -23,16 +23,17 @@ class TestDistSeResneXt2x2(TestDistBase):
self._use_reader_alloc = False
def test_dist_train(self):
self.check_with_place("dist_se_resnext.py", delta=100)
self.check_with_place("dist_se_resnext.py", delta=1e-7)
class TestDistseResnXt2x2WithMemopt(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._mem_opt = True
self._use_reader_alloc = False
def test_dist_train(self):
self.check_with_place("dist_se_resnext.py", delta=100)
self.check_with_place("dist_se_resnext.py", delta=1e-7)
class TestDistSeResneXt2x2Async(TestDistBase):
......
......@@ -283,6 +283,25 @@ class TestDecayedAdagrad(TranspilerTest):
trainer, _ = self.get_trainer()
class TestFtrl(TranspilerTest):
def net_conf(self):
x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
y_predict = fluid.layers.fc(input=x,
size=1000,
act=None,
param_attr=fluid.ParamAttr(name='fc_w'),
bias_attr=fluid.ParamAttr(name='fc_b'))
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
opt = fluid.optimizer.Ftrl(learning_rate=0.1)
opt.minimize(avg_cost)
def transpiler_test_impl(self):
pserver, startup = self.get_pserver(self.pserver1_ep)
trainer, _ = self.get_trainer()
class TestLRDecayConditional(TranspilerTest):
def net_conf(self):
x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
......@@ -405,18 +424,43 @@ class TestL2DecayWithPiecewise(TranspilerTest):
["sum", "scale", "scale", "elementwise_add", "momentum"])
class TestEmptyPserverOptimizeBlocks(TranspilerTest):
def net_conf(self):
x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
# only one parameter
y_predict = fluid.layers.fc(input=x,
size=1000,
act=None,
param_attr=fluid.ParamAttr(name='fc_w'),
bias_attr=False)
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=1.0)
sgd_optimizer.minimize(avg_cost)
def transpiler_test_impl(self):
config = fluid.DistributeTranspilerConfig()
config.slice_var_up = False
pserver, startup = self.get_pserver(ep=self.pserver2_ep, config=config)
self.assertEqual(len(pserver.blocks), 2)
self.assertEqual(len(pserver.blocks[1].ops), 0)
class TestDistLookupTableBase(TranspilerTest):
def network_with_table(self, is_sparse, is_distributed):
self.table_size = 1000
self.emb_size = 64
self.lookup_table_name = 'shared_w'
def emb_pool(ids):
def emb_pool(ids, table_name, is_distributed):
emb = fluid.layers.embedding(
input=ids,
size=[self.table_size, self.emb_size],
dtype='float32',
param_attr=self.lookup_table_name, # share parameter
param_attr=table_name,
is_sparse=is_sparse,
is_distributed=is_distributed)
pool = fluid.layers.sequence_pool(input=emb, pool_type='average')
......@@ -426,9 +470,13 @@ class TestDistLookupTableBase(TranspilerTest):
name='title_ids', shape=[1], dtype='int64', lod_level=1)
brand_ids = fluid.layers.data(
name='brand_ids', shape=[1], dtype='int64', lod_level=1)
title_emb = emb_pool(title_ids)
brand_emb = emb_pool(brand_ids)
fc0 = fluid.layers.concat(input=[title_emb, brand_emb], axis=1)
profile_ids = fluid.layers.data(
name='brand_ids', shape=[1], dtype='int64', lod_level=1)
title_emb = emb_pool(title_ids, self.lookup_table_name, is_distributed)
brand_emb = emb_pool(brand_ids, self.lookup_table_name, is_distributed)
profile_emb = emb_pool(profile_ids, "profile_emb", False)
fc0 = fluid.layers.concat(
input=[title_emb, brand_emb, profile_emb], axis=1)
predict = fluid.layers.fc(input=fc0,
size=2,
act=None,
......@@ -449,7 +497,7 @@ class TestLocalLookupTable(TestDistLookupTableBase):
def transpiler_test_impl(self):
pserver1, startup1 = self.get_pserver(self.pserver1_ep)
self.assertEqual(len(pserver1.blocks), 3)
self.assertEqual(len(pserver1.blocks), 4)
# 0 listen_and_serv
# 1 optimize for fc_w or fc_b adam
self.assertEqual([op.type for op in pserver1.blocks[1].ops],
......@@ -459,16 +507,23 @@ class TestLocalLookupTable(TestDistLookupTableBase):
self.assertEqual([op.type for op in pserver1.blocks[2].ops],
["sum", "scale", "adam", "scale", "scale"])
# 3 optimize for table 2 adam
# NOTE: if param is not selected rows, the grad will scaled to grad / trainer_num
self.assertEqual([op.type for op in pserver1.blocks[3].ops],
["sum", "scale", "adam", "scale", "scale"])
trainer, _ = self.get_trainer()
self.assertEqual(len(trainer.blocks), 1)
ops = [
'lookup_table', 'sequence_pool', 'lookup_table', 'sequence_pool',
'concat', 'mul', 'elementwise_add', 'cross_entropy', 'mean',
'fill_constant', 'mean_grad', 'cross_entropy_grad',
'elementwise_add_grad', 'send', 'mul_grad', 'send', 'concat_grad',
'sequence_pool_grad', 'lookup_table_grad', 'sequence_pool_grad',
'lookup_table_grad', 'sum', 'split_selected_rows', 'send',
'send_barrier', 'recv', 'recv', 'recv', 'fetch_barrier', 'concat'
'lookup_table', 'sequence_pool', 'concat', 'mul', 'elementwise_add',
'cross_entropy', 'mean', 'fill_constant', 'mean_grad',
'cross_entropy_grad', 'elementwise_add_grad', 'send', 'mul_grad',
'send', 'concat_grad', 'sequence_pool_grad', 'lookup_table_grad',
'split_selected_rows', 'send', 'sequence_pool_grad',
'lookup_table_grad', 'sequence_pool_grad', 'lookup_table_grad',
'sum', 'split_selected_rows', 'send', 'send_barrier', 'recv',
'recv', 'recv', 'recv', 'fetch_barrier', 'concat', 'concat'
]
self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
......@@ -480,39 +535,45 @@ class TestDistLookupTable(TestDistLookupTableBase):
def transpiler_test_impl(self):
pserver1, startup1 = self.get_pserver(self.pserver1_ep)
self.assertEqual(len(pserver1.blocks), 5)
self.assertEqual(len(pserver1.blocks), 6)
# 0 listen_and_serv
# 1 optimize for fc_w or fc_b adam
self.assertEqual([op.type for op in pserver1.blocks[1].ops],
["sum", "scale", "adam", "scale", "scale"])
# 2 optimize for table sgd
# 4 prefetch -> lookup_sparse_table for data0
self.assertEqual([op.type for op in pserver1.blocks[2].ops],
["sum", "scale", "adam", "scale", "scale"])
# 2 optimize for table sgd
self.assertEqual([op.type for op in pserver1.blocks[3].ops],
["sum", "sgd"])
# 3 prefetch -> lookup_sparse_table for data0
self.assertEqual([op.type for op in pserver1.blocks[3].ops],
self.assertEqual([op.type for op in pserver1.blocks[4].ops],
["lookup_sparse_table"])
# 4 save table
self.assertEqual([op.type for op in pserver1.blocks[4].ops], ["save"])
# 5 save table
self.assertEqual([op.type for op in pserver1.blocks[5].ops], ["save"])
trainer, trainer_startup = self.get_trainer()
self.assertEqual(len(trainer.blocks), 1)
ops = [
'split_ids', 'prefetch', 'merge_ids', 'sequence_pool',
'sequence_pool', 'concat', 'mul', 'elementwise_add',
'cross_entropy', 'mean', 'fill_constant', 'mean_grad',
'cross_entropy_grad', 'elementwise_add_grad', 'send', 'mul_grad',
'send', 'concat_grad', 'sequence_pool_grad', 'lookup_table_grad',
'sequence_pool_grad', 'lookup_table_grad', 'sum', 'split_ids',
'send', 'send_barrier', 'recv', 'recv', 'fetch_barrier'
'sequence_pool', 'lookup_table', 'sequence_pool', 'concat', 'mul',
'elementwise_add', 'cross_entropy', 'mean', 'fill_constant',
'mean_grad', 'cross_entropy_grad', 'elementwise_add_grad', 'send',
'mul_grad', 'send', 'concat_grad', 'sequence_pool_grad',
'lookup_table_grad', 'split_selected_rows', 'send',
'sequence_pool_grad', 'lookup_table_grad', 'sequence_pool_grad',
'lookup_table_grad', 'sum', 'split_ids', 'send', 'send_barrier',
'recv', 'recv', 'recv', 'fetch_barrier', 'concat'
]
self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
startup_ops = [
'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant',
'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant',
'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant',
'fill_constant', 'fill_constant', 'uniform_random', 'recv', 'recv',
'fetch_barrier', 'fake_init'
'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant',
'fill_constant', 'fill_constant', 'uniform_random',
'uniform_random', 'recv', 'recv', 'recv', 'fetch_barrier', 'concat',
'fake_init'
]
self.assertEqual([op.type for op in trainer_startup.blocks[0].ops],
startup_ops)
......@@ -526,7 +587,7 @@ class TestAsyncLocalLookupTable(TestDistLookupTableBase):
config = fluid.DistributeTranspilerConfig()
pserver1, startup1 = self.get_pserver(self.pserver1_ep, config, False)
self.assertEqual(len(pserver1.blocks), 3)
self.assertEqual(len(pserver1.blocks), 4)
# 0 listen_and_serv
# 1 optimize for fc_w or fc_b adam
self.assertEqual([op.type for op in pserver1.blocks[1].ops],
......@@ -535,17 +596,23 @@ class TestAsyncLocalLookupTable(TestDistLookupTableBase):
# NOTE: if param is not selected rows, the grad will scaled to grad / trainer_num
self.assertEqual([op.type for op in pserver1.blocks[2].ops],
["adam", "scale", "scale"])
# 3 optimize for table adam
# NOTE: if param is not selected rows, the grad will scaled to grad / trainer_num
self.assertEqual([op.type for op in pserver1.blocks[3].ops],
["adam", "scale", "scale"])
trainer, _ = self.get_trainer(config)
self.assertEqual(len(trainer.blocks), 1)
ops = [
'lookup_table', 'sequence_pool', 'lookup_table', 'sequence_pool',
'concat', 'mul', 'elementwise_add', 'cross_entropy', 'mean',
'fill_constant', 'mean_grad', 'cross_entropy_grad',
'elementwise_add_grad', 'send', 'mul_grad', 'send', 'concat_grad',
'sequence_pool_grad', 'lookup_table_grad', 'sequence_pool_grad',
'lookup_table_grad', 'sum', 'split_selected_rows', 'send', 'recv',
'recv', 'recv', 'concat'
'lookup_table', 'sequence_pool', 'concat', 'mul', 'elementwise_add',
'cross_entropy', 'mean', 'fill_constant', 'mean_grad',
'cross_entropy_grad', 'elementwise_add_grad', 'send', 'mul_grad',
'send', 'concat_grad', 'sequence_pool_grad', 'lookup_table_grad',
'split_selected_rows', 'send', 'sequence_pool_grad',
'lookup_table_grad', 'sequence_pool_grad', 'lookup_table_grad',
'sum', 'split_selected_rows', 'send', 'recv', 'recv', 'recv',
'recv', 'concat', 'concat'
]
self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
......@@ -559,29 +626,34 @@ class TestAsyncDistLookupTable(TestDistLookupTableBase):
pserver1, startup1 = self.get_pserver(self.pserver1_ep, config, False)
self.assertEqual(len(pserver1.blocks), 5)
self.assertEqual(len(pserver1.blocks), 6)
# 0 listen_and_serv
# 1 optimize for fc_w or fc_b adam
self.assertEqual([op.type for op in pserver1.blocks[1].ops],
["adam", "scale", "scale"])
# 2 optimize for table sgd
self.assertEqual([op.type for op in pserver1.blocks[2].ops], ["sgd"])
# 3 prefetch -> lookup_sparse_table for data0
self.assertEqual([op.type for op in pserver1.blocks[3].ops],
# 2 optimize for table adam
self.assertEqual([op.type for op in pserver1.blocks[2].ops],
["adam", "scale", "scale"])
# 3 optimize for table sgd
self.assertEqual([op.type for op in pserver1.blocks[3].ops], ["sgd"])
# 4 prefetch -> lookup_sparse_table for data0
self.assertEqual([op.type for op in pserver1.blocks[4].ops],
["lookup_sparse_table"])
# 4 save table
self.assertEqual([op.type for op in pserver1.blocks[4].ops], ["save"])
# 5 save table
self.assertEqual([op.type for op in pserver1.blocks[5].ops], ["save"])
trainer, _ = self.get_trainer(config)
self.assertEqual(len(trainer.blocks), 1)
ops = [
'split_ids', 'prefetch', 'merge_ids', 'sequence_pool',
'sequence_pool', 'concat', 'mul', 'elementwise_add',
'cross_entropy', 'mean', 'fill_constant', 'mean_grad',
'cross_entropy_grad', 'elementwise_add_grad', 'send', 'mul_grad',
'send', 'concat_grad', 'sequence_pool_grad', 'lookup_table_grad',
'sequence_pool_grad', 'lookup_table_grad', 'sum', 'split_ids',
'send', 'recv', 'recv'
'sequence_pool', 'lookup_table', 'sequence_pool', 'concat', 'mul',
'elementwise_add', 'cross_entropy', 'mean', 'fill_constant',
'mean_grad', 'cross_entropy_grad', 'elementwise_add_grad', 'send',
'mul_grad', 'send', 'concat_grad', 'sequence_pool_grad',
'lookup_table_grad', 'split_selected_rows', 'send',
'sequence_pool_grad', 'lookup_table_grad', 'sequence_pool_grad',
'lookup_table_grad', 'sum', 'split_ids', 'send', 'recv', 'recv',
'recv', 'concat'
]
self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
......
# 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 unittest
import numpy as np
from op_test import OpTest
def AffineGrid(theta, size):
n = size[0]
h = size[2]
w = size[3]
h_idx = np.repeat(
np.linspace(-1, 1, h)[np.newaxis, :], w, axis=0).T[:, :, np.newaxis]
w_idx = np.repeat(
np.linspace(-1, 1, w)[np.newaxis, :], h, axis=0)[:, :, np.newaxis]
grid = np.concatenate(
[w_idx, h_idx, np.ones([h, w, 1])], axis=2) # h * w * 3
grid = np.repeat(grid[np.newaxis, :], size[0], axis=0) # n * h * w *3
ret = np.zeros([n, h * w, 2])
theta = theta.transpose([0, 2, 1])
for i in range(len(theta)):
ret[i] = np.dot(grid[i].reshape([h * w, 3]), theta[i])
return ret.reshape([n, h, w, 2]).astype("float32")
def getGridPointValue(data, x, y):
data_shape = data.shape
N = data_shape[0]
H = data_shape[2]
W = data_shape[3]
out = np.zeros(data_shape, dtype='float')
for i in range(N):
for j in range(H):
for k in range(W):
if y[i, j, k] < 0 or y[i, j, k] > H - 1 or x[i, j, k] < 0 or x[
i, j, k] > W - 1:
out[i, :, j, k] = 0
else:
out[i, :, j, k] = data[i, :, y[i, j, k], x[i, j, k]]
return out
def GridSampler(data, grid):
dims = data.shape
N = dims[0]
C = dims[1]
H = dims[2]
W = dims[3]
x = grid[:, :, :, 0]
y = grid[:, :, :, 1]
y_max = H - 1
x_max = W - 1
x = 0.5 * ((x.astype('float32') + 1.0) * x_max)
y = 0.5 * ((y.astype('float32') + 1.0) * y_max)
x0 = np.floor(x).astype('int32')
x1 = x0 + 1
y0 = np.floor(y).astype('int32')
y1 = y0 + 1
wa = np.tile(((x1 - x) * (y1 - y)).reshape((N, 1, H, W)), (1, C, 1, 1))
wb = np.tile(((x1 - x) * (y - y0)).reshape((N, 1, H, W)), (1, C, 1, 1))
wc = np.tile(((x - x0) * (y1 - y)).reshape((N, 1, H, W)), (1, C, 1, 1))
wd = np.tile(((x - x0) * (y - y0)).reshape((N, 1, H, W)), (1, C, 1, 1))
va = getGridPointValue(data, x0, y0)
vb = getGridPointValue(data, x0, y1)
vc = getGridPointValue(data, x1, y0)
vd = getGridPointValue(data, x1, y1)
out = (wa * va + wb * vb + wc * vc + wd * vd).astype('float32')
return out
class TestGridSamplerOp(OpTest):
def setUp(self):
self.initTestCase()
self.op_type = 'grid_sampler'
x = np.random.randint(0, 255, self.x_shape).astype('float32')
theta = np.zeros(self.theta_shape).astype('float32')
for i in range(self.theta_shape[0]):
for j in range(2):
for k in range(3):
theta[i, j, k] = np.random.rand(1)[0]
grid = AffineGrid(theta, self.x_shape)
self.inputs = {'X': x, 'Grid': grid}
self.attrs = {'use_cudnn': True}
self.outputs = {'Output': GridSampler(x, grid)}
def test_check_output(self):
self.check_output(atol=1e-3)
def test_check_grad_normal(self):
self.check_grad(['X', 'Grid'], 'Output', max_relative_error=0.61)
def initTestCase(self):
self.x_shape = (2, 5, 7, 3)
self.grid_shape = (2, 7, 3, 2)
self.theta_shape = (2, 2, 3)
if __name__ == "__main__":
unittest.main()
......@@ -865,6 +865,31 @@ class TestBook(unittest.TestCase):
self.assertIsNotNone(out)
print(str(program))
def test_grid_sampler(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[3, 5, 7], dtype='float32')
grid = layers.data(name='grid', shape=[5, 7, 2], dtype='float32')
out = layers.grid_sampler(x, grid)
self.assertIsNotNone(out)
print(str(program))
def test_affine_grid(self):
program = Program()
with program_guard(program):
data = layers.data(name='data', shape=[2, 3, 3], dtype="float32")
out, ids = layers.argsort(input=data, axis=1)
theta = layers.data(name="theta", shape=[2, 3], dtype="float32")
out_shape = layers.data(
name="out_shape", shape=[-1], dtype="float32")
data_0 = layers.affine_grid(theta, out_shape)
data_1 = layers.affine_grid(theta, [5, 3, 28, 28])
self.assertIsNotNone(data_0)
self.assertIsNotNone(data_1)
print(str(program))
if __name__ == '__main__':
unittest.main()
......@@ -55,6 +55,46 @@ def run_pserver(use_cuda, sync_mode, ip, port, trainers, trainer_id):
exe.run(pserver_prog)
def run_pserver_with_empty_block(use_cuda, sync_mode, ip, port, trainers,
trainer_id):
x = fluid.layers.data(name='x', shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None, bias_attr=False)
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
# loss function
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
# optimizer
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
ps1 = ip + ":" + str(int(port) + 1)
ps2 = ip + ":" + port
pserver_endpoints = ps1 + "," + ps2
config = fluid.DistributeTranspilerConfig()
config.slice_var_up = False
t = fluid.DistributeTranspiler(config=config)
t.transpile(
trainer_id,
pservers=pserver_endpoints,
trainers=trainers,
sync_mode=sync_mode)
pserver_prog = t.get_pserver_program(ps2)
# pserver2 have no parameter
assert (len(pserver_prog.blocks) == 2)
assert (len(pserver_prog.blocks[1].ops) == 0)
pserver_startup = t.get_startup_program(ps2, pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
class TestListenAndServOp(OpTest):
def setUp(self):
self.ps_timeout = 5
......@@ -63,9 +103,9 @@ class TestListenAndServOp(OpTest):
self.trainers = 1
self.trainer_id = 0
def _start_pserver(self, use_cuda, sync_mode):
def _start_pserver(self, use_cuda, sync_mode, pserver_func):
p = Process(
target=run_pserver,
target=pserver_func,
args=(use_cuda, sync_mode, self.ip, self.port, self.trainers,
self.trainer_id))
p.daemon = True
......@@ -92,7 +132,24 @@ class TestListenAndServOp(OpTest):
def test_handle_signal_in_serv_op(self):
# run pserver on CPU in sync mode
p1 = self._start_pserver(False, True)
p1 = self._start_pserver(False, True, run_pserver)
self._wait_ps_ready(p1.pid)
# raise SIGTERM to pserver
os.kill(p1.pid, signal.SIGINT)
p1.join()
# run pserver on CPU in async mode
p2 = self._start_pserver(False, False, run_pserver)
self._wait_ps_ready(p2.pid)
# raise SIGTERM to pserver
os.kill(p2.pid, signal.SIGTERM)
p2.join()
def test_list_and_serv_run_empty_optimize_block(self):
# run pserver on CPU in sync mode
p1 = self._start_pserver(False, True, run_pserver_with_empty_block)
self._wait_ps_ready(p1.pid)
# raise SIGTERM to pserver
......@@ -100,7 +157,7 @@ class TestListenAndServOp(OpTest):
p1.join()
# run pserver on CPU in async mode
p2 = self._start_pserver(False, False)
p2 = self._start_pserver(False, False, run_pserver_with_empty_block)
self._wait_ps_ready(p2.pid)
# raise SIGTERM to pserver
......
......@@ -232,6 +232,46 @@ class TestResnet(TestParallelExecutorBase):
for loss in zip(all_reduce_last_loss, reduce_last_loss):
self.assertAlmostEquals(loss[0], loss[1], delta=delta2)
if not use_cuda:
return
all_reduce_first_loss_seq, all_reduce_last_loss_seq = self.check_network_convergence(
model,
feed_dict={"image": img,
"label": label},
iter=iter,
batch_size=batch_size,
use_cuda=use_cuda,
use_reduce=False,
optimizer=optimizer,
enable_sequential_execution=True)
reduce_first_loss_seq, reduce_last_loss_seq = self.check_network_convergence(
model,
feed_dict={"image": img,
"label": label},
iter=iter,
batch_size=batch_size,
use_cuda=use_cuda,
use_reduce=True,
optimizer=optimizer,
enable_sequential_execution=True)
for loss in zip(all_reduce_first_loss, all_reduce_first_loss_seq):
self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
for loss in zip(all_reduce_last_loss, all_reduce_last_loss_seq):
self.assertAlmostEquals(loss[0], loss[1], delta=delta2)
for loss in zip(reduce_first_loss, reduce_first_loss_seq):
self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
for loss in zip(reduce_last_loss, reduce_last_loss_seq):
self.assertAlmostEquals(loss[0], loss[1], delta=delta2)
for loss in zip(all_reduce_first_loss_seq, reduce_first_loss_seq):
self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
for loss in zip(all_reduce_last_loss_seq, reduce_last_loss_seq):
self.assertAlmostEquals(loss[0], loss[1], delta=delta2)
def _check_resnet_convergence(self,
model,
use_cuda=True,
......
......@@ -173,6 +173,8 @@ class TestTransformer(TestParallelExecutorBase):
def test_main(self):
if core.is_compiled_with_cuda():
self.check_network_convergence(transformer, use_cuda=True)
self.check_network_convergence(
transformer, use_cuda=True, enable_sequential_execution=True)
self.check_network_convergence(transformer, use_cuda=False, iter=5)
......
......@@ -26,7 +26,8 @@ def max_pool2D_forward_naive(x,
strides,
paddings,
global_pool=0,
ceil_mode=False):
ceil_mode=False,
exclusive=True):
N, C, H, W = x.shape
if global_pool == 1:
ksize = [H, W]
......@@ -54,7 +55,8 @@ def avg_pool2D_forward_naive(x,
strides,
paddings,
global_pool=0,
ceil_mode=False):
ceil_mode=False,
exclusive=True):
N, C, H, W = x.shape
if global_pool == 1:
ksize = [H, W]
......@@ -73,8 +75,9 @@ def avg_pool2D_forward_naive(x,
c_end = np.min((j * strides[1] + ksize[1] - paddings[1], W))
x_masked = x[:, :, r_start:r_end, c_start:c_end]
out[:, :, i, j] = np.sum(x_masked, axis=(2, 3)) / (
(r_end - r_start) * (c_end - c_start))
field_size = ((r_end - r_start) * (c_end - c_start)) if exclusive \
else (ksize[0] * ksize[1])
out[:, :, i, j] = np.sum(x_masked, axis=(2, 3)) / field_size
return out
......@@ -89,12 +92,13 @@ class TestPool2d_Op(OpTest):
self.init_kernel_type()
self.init_pool_type()
self.init_ceil_mode()
self.init_exclusive()
if self.global_pool:
self.paddings = [0 for _ in range(len(self.paddings))]
input = np.random.random(self.shape).astype(self.dtype)
output = self.pool2D_forward_naive(input, self.ksize, self.strides,
self.paddings, self.global_pool,
self.ceil_mode).astype(self.dtype)
output = self.pool2D_forward_naive(
input, self.ksize, self.strides, self.paddings, self.global_pool,
self.ceil_mode, self.exclusive).astype(self.dtype)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(input)}
self.attrs = {
......@@ -106,7 +110,9 @@ class TestPool2d_Op(OpTest):
'use_cudnn': self.use_cudnn,
'use_mkldnn': self.use_mkldnn,
'ceil_mode': self.ceil_mode,
'data_format': 'AnyLayout' # TODO(dzhwinter) : should be fix latter
'data_format':
'AnyLayout', # TODO(dzhwinter) : should be fix latter
'exclusive': self.exclusive
}
self.outputs = {'Out': output}
......@@ -150,6 +156,9 @@ class TestPool2d_Op(OpTest):
def init_ceil_mode(self):
self.ceil_mode = False
def init_exclusive(self):
self.exclusive = True
class TestCase1(TestPool2d_Op):
def init_test_case(self):
......@@ -322,5 +331,15 @@ class TestCeilModeCase4(TestCase2):
self.ceil_mode = True
class TestAvgInclude(TestCase2):
def init_exclusive(self):
self.exclusive = False
class TestCUDNNAvgInclude(TestCUDNNCase3):
def init_exclusive(self):
self.exclusive = False
if __name__ == '__main__':
unittest.main()
......@@ -26,7 +26,8 @@ def max_pool3D_forward_naive(x,
strides,
paddings,
global_pool=0,
ceil_mode=False):
ceil_mode=False,
exclusive=True):
N, C, D, H, W = x.shape
if global_pool == 1:
ksize = [D, H, W]
......@@ -60,7 +61,8 @@ def avg_pool3D_forward_naive(x,
strides,
paddings,
global_pool=0,
ceil_mode=False):
ceil_mode=False,
exclusive=True):
N, C, D, H, W = x.shape
if global_pool == 1:
ksize = [D, H, W]
......@@ -85,8 +87,10 @@ def avg_pool3D_forward_naive(x,
w_end = np.min((j * strides[1] + ksize[1] - paddings[1], W))
x_masked = x[:, :, d_start:d_end, h_start:h_end, w_start:w_end]
out[:, :, k, i, j] = np.sum(x_masked, axis=(2, 3, 4)) / (
(d_end - d_start) * (h_end - h_start) * (w_end - w_start))
field_size = (d_end - d_start) * (h_end - h_start) * (w_end - w_start) \
if exclusive else ksize[0] * ksize[1] * ksize[2]
out[:, :, k, i, j] = np.sum(x_masked, axis=(2, 3,
4)) / field_size
return out
......@@ -100,13 +104,14 @@ class TestPool3d_Op(OpTest):
self.init_kernel_type()
self.init_pool_type()
self.init_ceil_mode()
self.init_exclusive()
if self.global_pool:
self.paddings = [0 for _ in range(len(self.paddings))]
input = np.random.random(self.shape).astype(self.dtype)
output = self.pool3D_forward_naive(input, self.ksize, self.strides,
self.paddings, self.global_pool,
self.ceil_mode).astype(self.dtype)
output = self.pool3D_forward_naive(
input, self.ksize, self.strides, self.paddings, self.global_pool,
self.ceil_mode, self.exclusive).astype(self.dtype)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(input)}
self.attrs = {
......@@ -117,7 +122,9 @@ class TestPool3d_Op(OpTest):
'global_pooling': self.global_pool,
'use_cudnn': self.use_cudnn,
'ceil_mode': self.ceil_mode,
'data_format': 'AnyLayout' # TODO(dzhwinter) : should be fix latter
'data_format':
'AnyLayout', # TODO(dzhwinter) : should be fix latter
'exclusive': self.exclusive
}
self.outputs = {'Out': output}
......@@ -161,6 +168,9 @@ class TestPool3d_Op(OpTest):
def init_ceil_mode(self):
self.ceil_mode = False
def init_exclusive(self):
self.exclusive = True
class TestCase1(TestPool3d_Op):
def init_test_case(self):
......@@ -333,5 +343,15 @@ class TestCeilModeCase4(TestCase2):
self.ceil_mode = True
class TestAvgInclude(TestCase2):
def init_exclusive(self):
self.exclusive = False
class TestCUDNNAvgInclude(TestCUDNNCase3):
def init_exclusive(self):
self.exclusive = False
if __name__ == '__main__':
unittest.main()
# 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.
from __future__ import print_function
import unittest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import numpy as np
from threading import Thread
def user_reader(inputs):
def _reader():
for d in inputs:
yield d
return _reader
def batch_feeder(batch_reader, pin_memory=False, img_dtype="float32"):
def _feeder():
for batch_data in batch_reader():
sample_batch = []
label_batch = []
for sample, label in batch_data:
sample_batch.append(sample)
label_batch.append([label])
tensor = core.LoDTensor()
label = core.LoDTensor()
place = core.CUDAPinnedPlace() if pin_memory else core.CPUPlace()
tensor.set(np.array(sample_batch, dtype=img_dtype), place)
label.set(np.array(label_batch, dtype="int64"), place)
yield [tensor, label]
return _feeder
class TestPyReader(unittest.TestCase):
def setUp(self):
self.capacity = 10
self.shapes = [(-1, 3, 2, 1), (-1, 1)]
self.lod_levels = [0, 0]
self.dtypes = ['float32', 'int64']
def test_pin_memory_pyreader(self):
with fluid.program_guard(fluid.Program(), fluid.Program()):
place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
) else fluid.CPUPlace()
executor = fluid.Executor(place)
data_file = fluid.layers.py_reader(
capacity=self.capacity,
dtypes=self.dtypes,
lod_levels=self.lod_levels,
shapes=self.shapes)
# feed_queue = data_file.queue
read_out_data = fluid.layers.read_file(data_file)
self.inputs = []
for _ in range(10):
sample = np.random.uniform(
low=0, high=1, size=[3, 2, 1]).astype("float32")
label = np.random.uniform(
low=0, high=10, size=[1]).astype("int64")
self.inputs.append((sample, label))
self.input_tensors = []
for d, l in batch_feeder(
paddle.batch(
user_reader(self.inputs), batch_size=2),
pin_memory=True
if fluid.core.is_compiled_with_cuda() else False)():
ta = fluid.LoDTensorArray()
ta.append(d)
ta.append(l)
self.input_tensors.append(ta)
self.batched_inputs = []
for batch in paddle.batch(user_reader(self.inputs), batch_size=2)():
feed_d = []
feed_l = []
for d, l in batch:
feed_d.append(d)
feed_l.append([l])
self.batched_inputs.append([feed_d, feed_l])
data_file.decorate_tensor_provider(
batch_feeder(
paddle.batch(
user_reader(self.inputs), batch_size=2),
pin_memory=True
if fluid.core.is_compiled_with_cuda() else False))
executor.run(fluid.default_startup_program())
self.outputs = []
data_file.start()
for _ in self.input_tensors:
self.outputs.append(
executor.run(fetch_list=list(read_out_data)))
data_file.reset()
self.validate()
def validate(self):
self.assertEqual(len(self.batched_inputs), len(self.outputs))
for in_data_list, out_data_list in zip(self.batched_inputs,
self.outputs):
self.assertEqual(len(in_data_list), len(out_data_list))
in_data_list_np = [
np.array(in_lod_tensor) for in_lod_tensor in in_data_list
]
for in_data, out_data in zip(in_data_list_np, out_data_list):
self.assertTrue((in_data == out_data).all())
if __name__ == '__main__':
unittest.main()
......@@ -184,6 +184,20 @@ class TestSeqMaxPool2D(TestSeqAvgPool2D):
out[i] = np.reshape(np.amax(sub_x, axis=0), (3, 11))
class TestSeqMaxPool2DInference(TestSeqMaxPool2D):
def compute(self, x, offset, out):
self.attrs = {'pooltype': "MAX", 'is_test': True}
for i in range(len(offset[0]) - 1):
sub_x = np.reshape(x[offset[0][i]:offset[0][i + 1], :],
(-1, 3 * 11))
out[i] = np.reshape(np.amax(sub_x, axis=0), (3, 11))
def test_check_grad(self):
"""Grad computation does not apply to Sequence MAX
Pool executed when is_test is true """
return
class TestSeqLastPool2D(TestSeqAvgPool2D):
def compute(self, x, offset, out):
self.attrs = {'pooltype': "LAST"}
......
......@@ -26,7 +26,11 @@ class TestSoftmaxWithCrossEntropyOp(OpTest):
Test softmax with cross entropy operator with discreate one-hot labels.
"""
def initParams(self):
self.numeric_stable_mode = False
def setUp(self):
self.initParams()
self.op_type = "softmax_with_cross_entropy"
batch_size = 41
class_num = 37
......@@ -46,6 +50,7 @@ class TestSoftmaxWithCrossEntropyOp(OpTest):
"Softmax": softmax.astype("float64"),
"Loss": cross_entropy.astype("float64")
}
self.attrs = {"numeric_stable_mode": self.numeric_stable_mode}
def test_check_output(self):
self.check_output()
......@@ -54,6 +59,11 @@ class TestSoftmaxWithCrossEntropyOp(OpTest):
self.check_grad(["Logits"], "Loss")
class TestSoftmaxWithCrossEntropyOpNoCudnn(TestSoftmaxWithCrossEntropyOp):
def initParams(self):
self.numeric_stable_mode = True
class TestSoftmaxWithCrossEntropyOp2(OpTest):
"""
Test softmax with cross entropy operator with soft labels.
......@@ -93,7 +103,11 @@ class TestSoftmaxWithCrossEntropyOp3(OpTest):
Test softmax with cross entropy operator with ignore_index.
"""
def initParams(self):
self.numeric_stable_mode = False
def setUp(self):
self.initParams()
self.op_type = "softmax_with_cross_entropy"
batch_size = 41
class_num = 37
......@@ -114,7 +128,10 @@ class TestSoftmaxWithCrossEntropyOp3(OpTest):
"Softmax": softmax.astype("float64"),
"Loss": cross_entropy.astype("float64")
}
self.attrs = {"ignore_index": ignore_index}
self.attrs = {
"ignore_index": ignore_index,
"numeric_stable_mode": self.numeric_stable_mode
}
def test_check_output(self):
self.check_output()
......@@ -123,5 +140,10 @@ class TestSoftmaxWithCrossEntropyOp3(OpTest):
self.check_grad(["Logits"], "Loss")
class TestSoftmaxWithCrossEntropyOp3NoCudnn(TestSoftmaxWithCrossEntropyOp3):
def initParams(self):
self.numeric_stable_mode = True
if __name__ == "__main__":
unittest.main()
......@@ -35,6 +35,7 @@ import sys
import numpy as np
import collections
import six
import logging
from .ps_dispatcher import RoundRobin, HashName, PSDispatcher
from .. import core, framework
......@@ -767,6 +768,15 @@ in a single call.")
prefetch_var_name_to_block_id.extend(
lookup_table_var_name_to_block_id)
if len(optimize_blocks) == 0:
logging.warn("pserver [" + str(endpoint) +
"] has no optimize block!!")
pre_block_idx = pserver_program.num_blocks - 1
empty_block = pserver_program._create_block(pre_block_idx)
optimize_blocks.append(empty_block)
# In some case, some parameter server will have no parameter to optimize
# So we give an empty optimize block to parameter server.
attrs = {
"optimize_blocks": optimize_blocks,
"endpoint": endpoint,
......@@ -910,11 +920,11 @@ to transpile() call.")
block_idx = int(block_name.split(block_suffix)[1])
orig_var = self.origin_program.global_block().vars[orig_var_name]
skip_numel = 0
skip_dim0 = 0
slice_vars = self.param_var_mapping[orig_var_name]
for slice_var in slice_vars[:block_idx]:
skip_numel += reduce(lambda x, y: x * y, slice_var.shape)
slice_vars_and_attrs.append([orig_var, skip_numel, param])
skip_dim0 += slice_var.shape[0]
slice_vars_and_attrs.append([orig_var, skip_dim0, param])
return slice_vars_and_attrs
......@@ -1065,7 +1075,12 @@ to transpile() call.")
continue_search_lookup_table_op = False
all_ops = program.global_block().ops
for op in all_ops:
if op.type == LOOKUP_TABLE_TYPE:
if op.type == LOOKUP_TABLE_TYPE and self.table_name == op.input(
"W")[0]:
if not op.attr('is_distributed'):
raise RuntimeError(
"lookup_table_op that lookup an distributed embedding table"
"should set is_distributed to true")
continue_search_lookup_table_op = True
lookup_table_op_index = lookup_table_op_index if lookup_table_op_index != -1 else list(
......@@ -1275,7 +1290,6 @@ to transpile() call.")
}
outputs = {"ParamOut": [param_var]}
# only support sgd now
import logging
logging.warn(
"distribute lookup table only support sgd optimizer, change it's optimizer to sgd instead of "
+ table_opt_op.type)
......@@ -1442,6 +1456,9 @@ to transpile() call.")
elif op_type == "decayed_adagrad":
if varkey == "Moment":
return param_shape
elif op_type == "ftrl":
if varkey in ["SquaredAccumulator", "LinearAccumulator"]:
return param_shape
elif op_type == "sgd":
pass
else:
......
......@@ -61,6 +61,9 @@ class InferenceTranspiler(object):
raise TypeError("scope should be as Scope type or None")
use_mkldnn = bool(os.getenv("FLAGS_use_mkldnn", False))
if use_mkldnn:
self._depthwise_conv_mkldnn(program)
self._fuse_batch_norm(program, place, scope)
if use_mkldnn:
self._fuse_conv_bias_mkldnn(program)
......@@ -70,6 +73,31 @@ class InferenceTranspiler(object):
program) # ResNet residual block merging
self._fuse_bn_relu_mkldnn(program)
def _depthwise_conv_mkldnn(self, program):
'''
Transpile the program by replacing depthwise_conv2d to conv2d for MKLDNN program.
The result is:
- before:
- any_other_op->depthwise_conv->any_other_op
- after:
- any_other_op->conv->any_other_op
:param program: program to transpile
:type program: Program
'''
self.block = program.block(0)
i = 0
while i < len(self.block.ops):
current_op = self.block.ops[i]
if current_op.type == 'depthwise_conv2d':
current_op.desc.set_type("conv2d")
i = i + 1
# TODO(luotao): use clone() method to flush the program.desc in force,
# since some large program.desc will not be flushed immediately.
# And a better solution will be considered later.
program = program.clone()
def _fuse_conv_eltwise_mkldnn(self, program):
'''
Transpile the program fusing elementwise_add into conv for MKLDNN
......
......@@ -27,7 +27,7 @@ def _get_version_detail(idx):
if re.match('@TAG_VERSION_REGEX@', '@PADDLE_VERSION@'):
version_details = '@PADDLE_VERSION@'.split('.')
if len(version_details) == 3:
if len(version_details) >= 3:
return version_details[idx]
return 0
......
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