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提交 66406619 编写于 作者: N nhzlx
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Showing with 577 addition and 179 deletion
cmake/external/anakin.cmake
浏览文件 @ 66406619
......@@ -8,6 +8,7 @@ set(ANAKIN_INCLUDE "${ANAKIN_INSTALL_DIR}" CACHE STRING "root of Anakin header f
set(ANAKIN_LIBRARY "${ANAKIN_INSTALL_DIR}" CACHE STRING "path of Anakin library")
set(ANAKIN_COMPILE_EXTRA_FLAGS
-Wno-error=unused-but-set-variable -Wno-unused-but-set-variable
-Wno-error=unused-variable -Wno-unused-variable
-Wno-error=format-extra-args -Wno-format-extra-args
-Wno-error=comment -Wno-comment
......@@ -19,7 +20,7 @@ set(ANAKIN_COMPILE_EXTRA_FLAGS
-Wno-reorder
-Wno-error=cpp)
set(ANAKIN_LIBRARY_URL "https://github.com/pangge/Anakin/releases/download/3.0/anakin_release_simple.tar.gz")
set(ANAKIN_LIBRARY_URL "https://github.com/pangge/Anakin/releases/download/Version0.1.0/anakin.tar.gz")
# A helper function used in Anakin, currently, to use it, one need to recursively include
# nearly all the header files.
......@@ -41,9 +42,9 @@ if (NOT EXISTS "${ANAKIN_INSTALL_DIR}")
message(STATUS "Download Anakin library from ${ANAKIN_LIBRARY_URL}")
execute_process(COMMAND bash -c "mkdir -p ${ANAKIN_INSTALL_DIR}")
execute_process(COMMAND bash -c "rm -rf ${ANAKIN_INSTALL_DIR}/*")
execute_process(COMMAND bash -c "cd ${ANAKIN_INSTALL_DIR}; wget -q ${ANAKIN_LIBRARY_URL}")
execute_process(COMMAND bash -c "cd ${ANAKIN_INSTALL_DIR}; wget --no-check-certificate -q ${ANAKIN_LIBRARY_URL}")
execute_process(COMMAND bash -c "mkdir -p ${ANAKIN_INSTALL_DIR}")
execute_process(COMMAND bash -c "cd ${ANAKIN_INSTALL_DIR}; tar xzf anakin_release_simple.tar.gz")
execute_process(COMMAND bash -c "cd ${ANAKIN_INSTALL_DIR}; tar xzf anakin.tar.gz")
endif()
if (WITH_ANAKIN)
......
paddle/fluid/inference/analysis/argument.h
浏览文件 @ 66406619
......@@ -23,6 +23,7 @@
#pragma once
#include <string>
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/inference/analysis/data_flow_graph.h"
......
paddle/fluid/inference/analysis/data_flow_graph.h
浏览文件 @ 66406619
......@@ -176,7 +176,7 @@ struct GraphTraits<DataFlowGraph> {
// sub-graph is the inputs nodes and output nodes that doesn't inside the
// sub-graph.
std::pair<std::vector<Node *>, std::vector<Node *>>
ExtractInputAndOutputOfSubGraph(std::vector<Node *> &graph);
ExtractInputAndOutputOfSubGraph(std::vector<Node *> &graph); // NOLINT
} // namespace analysis
} // namespace inference
......
paddle/fluid/inference/analysis/model_store_pass.cc
浏览文件 @ 66406619
......@@ -12,11 +12,13 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/inference/analysis/model_store_pass.h"
#include <stdio.h>
#include <stdlib.h>
#include <string>
#include "paddle/fluid/inference/analysis/analyzer.h"
#include "paddle/fluid/inference/analysis/argument.h"
#include "paddle/fluid/inference/analysis/model_store_pass.h"
namespace paddle {
namespace inference {
......
paddle/fluid/inference/analysis/model_store_pass.h
浏览文件 @ 66406619
......@@ -17,6 +17,8 @@
* model in the disk, and that model can be reloaded for prediction.
*/
#pragma once
#include <string>
#include "paddle/fluid/inference/analysis/pass.h"
namespace paddle {
......
paddle/fluid/inference/api/CMakeLists.txt
浏览文件 @ 66406619
......@@ -19,6 +19,7 @@ endif(APPLE)
set(inference_deps paddle_inference_api paddle_fluid_api)
if(WITH_GPU AND TENSORRT_FOUND)
set(inference_deps ${inference_deps} paddle_inference_tensorrt_subgraph_engine)
endif()
......@@ -63,6 +64,8 @@ endif()
if (WITH_ANAKIN) # only needed in CI
# Due to Anakin do not have official library releases and the versions of protobuf and cuda do not match Paddle's,
# so anakin library will not be merged to our official inference library. To use anakin prediction API, one need to
# compile the libinference_anakin_api.a and compile with anakin.so.
fetch_include_recursively(${ANAKIN_INCLUDE})
# compile the libinference_anakin_api.a and anakin.so.
nv_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc)
nv_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc)
......@@ -73,7 +76,7 @@ if (WITH_ANAKIN) # only needed in CI
if (WITH_TESTING)
cc_test(inference_anakin_test SRCS api_anakin_engine_tester.cc
ARGS --model=${ANAKIN_INSTALL_DIR}/mobilenet_v2.anakin.bin
DEPS inference_anakin_api)
DEPS inference_anakin_api_shared)
target_compile_options(inference_anakin_test BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
endif(WITH_TESTING)
endif()
paddle/fluid/inference/api/api_anakin_engine.cc
浏览文件 @ 66406619
......@@ -18,26 +18,36 @@
namespace paddle {
PaddleInferenceAnakinPredictor::PaddleInferenceAnakinPredictor(
template <typename Target>
PaddleInferenceAnakinPredictor<Target>::PaddleInferenceAnakinPredictor(
const AnakinConfig &config) {
CHECK(Init(config));
}
bool PaddleInferenceAnakinPredictor::Init(const AnakinConfig &config) {
template <typename Target>
bool PaddleInferenceAnakinPredictor<Target>::Init(const AnakinConfig &config) {
if (!(graph_.load(config.model_file))) {
LOG(FATAL) << "fail to load graph from " << config.model_file;
return false;
}
graph_.ResetBatchSize("input_0", config.max_batch_size);
auto inputs = graph_.get_ins();
for (auto &input_str : inputs) {
graph_.ResetBatchSize(input_str, config.max_batch_size);
}
// optimization for graph
if (!(graph_.Optimize())) {
return false;
}
// construct executer
executor_.init(graph_);
if (executor_p_ == nullptr) {
executor_p_ = new anakin::Net<Target, anakin::saber::AK_FLOAT,
anakin::Precision::FP32>(graph_, true);
}
return true;
}
bool PaddleInferenceAnakinPredictor::Run(
template <typename Target>
bool PaddleInferenceAnakinPredictor<Target>::Run(
const std::vector<PaddleTensor> &inputs,
std::vector<PaddleTensor> *output_data, int batch_size) {
for (const auto &input : inputs) {
......@@ -46,7 +56,29 @@ bool PaddleInferenceAnakinPredictor::Run(
<< "'s type is not float";
return false;
}
auto d_tensor_in_p = executor_.get_in(input.name);
auto d_tensor_in_p = executor_p_->get_in(input.name);
auto net_shape = d_tensor_in_p->valid_shape();
if (net_shape.size() != input.shape.size()) {
LOG(ERROR) << " input " << input.name
<< "'s shape size should be equal to that of net";
return false;
}
int sum = 1;
for_each(input.shape.begin(), input.shape.end(), [&](int n) { sum *= n; });
if (sum > net_shape.count()) {
graph_.Reshape(input.name, input.shape);
delete executor_p_;
executor_p_ = new anakin::Net<Target, anakin::saber::AK_FLOAT,
anakin::Precision::FP32>(graph_, true);
d_tensor_in_p = executor_p_->get_in(input.name);
}
anakin::saber::Shape tmp_shape;
for (auto s : input.shape) {
tmp_shape.push_back(s);
}
d_tensor_in_p->reshape(tmp_shape);
float *d_data_p = d_tensor_in_p->mutable_data();
if (cudaMemcpy(d_data_p, static_cast<float *>(input.data.data()),
d_tensor_in_p->valid_size() * sizeof(float),
......@@ -56,16 +88,17 @@ bool PaddleInferenceAnakinPredictor::Run(
}
cudaStreamSynchronize(NULL);
}
executor_.prediction();
cudaDeviceSynchronize();
executor_p_->prediction();
cudaDeviceSynchronize();
if (output_data->empty()) {
LOG(ERROR) << "At least one output should be set with tensors' names.";
return false;
}
for (auto &output : *output_data) {
auto *tensor = executor_.get_out(output.name);
output.shape = tensor->shape();
auto *tensor = executor_p_->get_out(output.name);
output.shape = tensor->valid_shape();
if (output.data.length() < tensor->valid_size() * sizeof(float)) {
output.data.Resize(tensor->valid_size() * sizeof(float));
}
......@@ -81,19 +114,23 @@ bool PaddleInferenceAnakinPredictor::Run(
return true;
}
anakin::Net<anakin::NV, anakin::saber::AK_FLOAT, anakin::Precision::FP32>
&PaddleInferenceAnakinPredictor::get_executer() {
return executor_;
template <typename Target>
anakin::Net<Target, anakin::saber::AK_FLOAT, anakin::Precision::FP32>
&PaddleInferenceAnakinPredictor<Target>::get_executer() {
return *executor_p_;
}
// the cloned new Predictor of anakin share the same net weights from original
// Predictor
std::unique_ptr<PaddlePredictor> PaddleInferenceAnakinPredictor::Clone() {
template <typename Target>
std::unique_ptr<PaddlePredictor>
PaddleInferenceAnakinPredictor<Target>::Clone() {
VLOG(3) << "Anakin Predictor::clone";
std::unique_ptr<PaddlePredictor> cls(new PaddleInferenceAnakinPredictor());
std::unique_ptr<PaddlePredictor> cls(
new PaddleInferenceAnakinPredictor<Target>());
// construct executer from other graph
auto anakin_predictor_p =
dynamic_cast<PaddleInferenceAnakinPredictor *>(cls.get());
dynamic_cast<PaddleInferenceAnakinPredictor<Target> *>(cls.get());
if (!anakin_predictor_p) {
LOG(ERROR) << "fail to call Init";
return nullptr;
......@@ -103,14 +140,28 @@ std::unique_ptr<PaddlePredictor> PaddleInferenceAnakinPredictor::Clone() {
return std::move(cls);
}
template class PaddleInferenceAnakinPredictor<anakin::NV>;
template class PaddleInferenceAnakinPredictor<anakin::X86>;
// A factory to help create difference predictor.
template <>
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
AnakinConfig, PaddleEngineKind::kAnakin>(const AnakinConfig &config) {
VLOG(3) << "Anakin Predictor create.";
std::unique_ptr<PaddlePredictor> x(
new PaddleInferenceAnakinPredictor(config));
return x;
}
if (config.target_type == AnakinConfig::NVGPU) {
VLOG(3) << "Anakin Predictor create on [ NVIDIA GPU ].";
std::unique_ptr<PaddlePredictor> x(
new PaddleInferenceAnakinPredictor<anakin::NV>(config));
return x;
} else if (config.target_type == AnakinConfig::X86) {
VLOG(3) << "Anakin Predictor create on [ Intel X86 ].";
std::unique_ptr<PaddlePredictor> x(
new PaddleInferenceAnakinPredictor<anakin::X86>(config));
return x;
} else {
VLOG(3) << "Anakin Predictor create on unknown platform.";
return nullptr;
}
};
} // namespace paddle
paddle/fluid/inference/api/api_anakin_engine.h
浏览文件 @ 66406619
......@@ -20,14 +20,16 @@ limitations under the License. */
#pragma once
#include <vector>
#include "paddle/fluid/inference/api/paddle_inference_api.h"
// from anakin
#include "framework/core/net/net.h"
#include "framework/graph/graph.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "saber/core/shape.h"
#include "saber/saber_types.h"
namespace paddle {
template <typename Target>
class PaddleInferenceAnakinPredictor : public PaddlePredictor {
public:
PaddleInferenceAnakinPredictor() {}
......@@ -42,19 +44,21 @@ class PaddleInferenceAnakinPredictor : public PaddlePredictor {
std::unique_ptr<PaddlePredictor> Clone() override;
anakin::Net<anakin::NV, anakin::saber::AK_FLOAT, anakin::Precision::FP32>&
anakin::Net<Target, anakin::saber::AK_FLOAT, anakin::Precision::FP32>&
get_executer();
~PaddleInferenceAnakinPredictor() override{};
~PaddleInferenceAnakinPredictor() override {
delete executor_p_;
executor_p_ = nullptr;
};
private:
bool Init(const AnakinConfig& config);
anakin::graph::Graph<anakin::NV, anakin::saber::AK_FLOAT,
anakin::Precision::FP32>
anakin::graph::Graph<Target, anakin::saber::AK_FLOAT, anakin::Precision::FP32>
graph_;
anakin::Net<anakin::NV, anakin::saber::AK_FLOAT, anakin::Precision::FP32>
executor_;
anakin::Net<Target, anakin::saber::AK_FLOAT, anakin::Precision::FP32>*
executor_p_{nullptr};
AnakinConfig config_;
};
......
paddle/fluid/inference/api/api_anakin_engine_tester.cc
浏览文件 @ 66406619
......@@ -12,18 +12,20 @@ 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 <gflags/gflags.h>
#include <glog/logging.h>
#include <gtest/gtest.h>
#include "gflags/gflags.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
DEFINE_string(model, "", "Directory of the inference model.");
DEFINE_string(model, "", "Directory of the inference model(mobile_v2).");
namespace paddle {
AnakinConfig GetConfig() {
AnakinConfig config;
// using AnakinConfig::X86 if you need to use cpu to do inference
config.target_type = AnakinConfig::NVGPU;
config.model_file = FLAGS_model;
config.device = 0;
config.max_batch_size = 1;
......@@ -36,7 +38,6 @@ TEST(inference, anakin) {
CreatePaddlePredictor<AnakinConfig, PaddleEngineKind::kAnakin>(config);
float data[1 * 3 * 224 * 224] = {1.0f};
PaddleTensor tensor;
tensor.name = "input_0";
tensor.shape = std::vector<int>({1, 3, 224, 224});
......@@ -44,22 +45,20 @@ TEST(inference, anakin) {
tensor.dtype = PaddleDType::FLOAT32;
// For simplicity, we set all the slots with the same data.
std::vector<PaddleTensor> paddle_tensor_feeds;
paddle_tensor_feeds.emplace_back(std::move(tensor));
std::vector<PaddleTensor> paddle_tensor_feeds(1, tensor);
PaddleTensor tensor_out;
tensor_out.name = "prob_out";
tensor_out.shape = std::vector<int>({1000, 1});
tensor_out.shape = std::vector<int>({});
tensor_out.data = PaddleBuf();
tensor_out.dtype = PaddleDType::FLOAT32;
std::vector<PaddleTensor> outputs;
outputs.emplace_back(std::move(tensor_out));
std::vector<PaddleTensor> outputs(1, tensor_out);
ASSERT_TRUE(predictor->Run(paddle_tensor_feeds, &outputs));
float* data_o = static_cast<float*>(outputs[0].data.data());
for (size_t j = 0; j < 1000; ++j) {
for (size_t j = 0; j < outputs[0].data.length(); ++j) {
LOG(INFO) << "output[" << j << "]: " << data_o[j];
}
}
......
paddle/fluid/inference/api/demo_ci/vis_demo.cc
浏览文件 @ 66406619
......@@ -20,8 +20,8 @@ limitations under the License. */
#include <glog/logging.h> // use glog instead of PADDLE_ENFORCE to avoid importing other paddle header files.
#include <fstream>
#include <iostream>
#include "paddle/fluid/inference/demo_ci/utils.h"
#include "paddle/fluid/platform/enforce.h"
#include "utils.h"
#ifdef PADDLE_WITH_CUDA
DECLARE_double(fraction_of_gpu_memory_to_use);
......
paddle/fluid/inference/api/paddle_inference_api.h
浏览文件 @ 66406619
......@@ -44,7 +44,7 @@ class PaddleBuf {
PaddleBuf(void* data, size_t length)
: data_(data), length_(length), memory_owned_{false} {}
// Own memory.
PaddleBuf(size_t length)
explicit PaddleBuf(size_t length)
: data_(new char[length]), length_(length), memory_owned_(true) {}
// Resize to `length` bytes.
void Resize(size_t length);
......@@ -126,9 +126,11 @@ struct NativeConfig : public PaddlePredictor::Config {
// Configurations for Anakin engine.
struct AnakinConfig : public PaddlePredictor::Config {
enum TargetType { NVGPU = 0, X86 };
int device;
std::string model_file;
int max_batch_size{-1};
TargetType target_type;
};
struct TensorRTConfig : public NativeConfig {
......
paddle/fluid/inference/tensorrt/convert/fc_op.cc
浏览文件 @ 66406619
......@@ -38,7 +38,7 @@ void Reorder2(nvinfer1::DimsHW shape, const T* idata, nvinfer1::DimsHW istrides,
}
// indata c * k
// Reorder the data layout from CK to KC.
void ReorderCKtoKC(TensorRTEngine::Weight& iweights,
void ReorderCKtoKC(TensorRTEngine::Weight& iweights, // NOLINT
TensorRTEngine::Weight* oweights) {
int c = iweights.dims[0];
int k = iweights.dims[1];
......
paddle/fluid/operators/conv_mkldnn_op.cc
浏览文件 @ 66406619
......@@ -55,7 +55,7 @@ class ConvMKLDNNHandler : public platform::MKLDNNHandler {
std::shared_ptr<mkldnn::memory> AcquireSrcMemoryFromWeightsPrimitive(
const std::shared_ptr<mkldnn::memory> user_memory_p,
std::vector<mkldnn::primitive>& pipeline) {
std::vector<mkldnn::primitive>& pipeline) { // NOLINT
auto src_pd = conv_bwd_weights_pd_->src_primitive_desc();
auto user_pd = user_memory_p->get_primitive_desc();
return this->AcquireMemory(src_pd, user_pd, user_memory_p,
......@@ -64,7 +64,7 @@ class ConvMKLDNNHandler : public platform::MKLDNNHandler {
std::shared_ptr<mkldnn::memory> AcquireDiffDstMemoryFromWeightsPrimitive(
const std::shared_ptr<mkldnn::memory> user_memory_p,
std::vector<mkldnn::primitive>& pipeline) {
std::vector<mkldnn::primitive>& pipeline) { // NOLINT
auto diff_dst_pd = conv_bwd_weights_pd_->diff_dst_primitive_desc();
auto user_pd = user_memory_p->get_primitive_desc();
return this->AcquireMemory(diff_dst_pd, user_pd, user_memory_p,
......@@ -80,7 +80,7 @@ class ConvMKLDNNHandler : public platform::MKLDNNHandler {
std::shared_ptr<mkldnn::memory> AcquireDiffDstMemoryFromDataPrimitive(
const std::shared_ptr<mkldnn::memory> user_memory_p,
std::vector<mkldnn::primitive>& pipeline) {
std::vector<mkldnn::primitive>& pipeline) { // NOLINT
auto diff_dst_pd = conv_bwd_data_pd_->diff_dst_primitive_desc();
auto user_pd = user_memory_p->get_primitive_desc();
return this->AcquireMemory(diff_dst_pd, user_pd, user_memory_p,
......@@ -89,7 +89,7 @@ class ConvMKLDNNHandler : public platform::MKLDNNHandler {
std::shared_ptr<mkldnn::memory> AcquireWeightsMemoryFromDataPrimitive(
const std::shared_ptr<mkldnn::memory> user_weights_memory_p,
std::vector<mkldnn::primitive>& pipeline) {
std::vector<mkldnn::primitive>& pipeline) { // NOLINT
auto weights_pd = conv_bwd_data_pd_->weights_primitive_desc();
auto user_pd = user_weights_memory_p->get_primitive_desc();
return this->AcquireMemory(weights_pd, user_pd, user_weights_memory_p,
......@@ -109,7 +109,7 @@ class ConvMKLDNNHandler : public platform::MKLDNNHandler {
std::shared_ptr<mkldnn::memory> AcquireSrcMemoryFromPrimitive(
const std::shared_ptr<mkldnn::memory> user_memory_p,
std::vector<mkldnn::primitive>& pipeline) {
std::vector<mkldnn::primitive>& pipeline) { // NOLINT
auto src_pd = conv_pd_->src_primitive_desc();
auto user_pd = user_memory_p->get_primitive_desc();
return this->AcquireMemory(src_pd, user_pd, user_memory_p, "@src_mem_p",
......@@ -118,7 +118,7 @@ class ConvMKLDNNHandler : public platform::MKLDNNHandler {
std::shared_ptr<mkldnn::memory> AcquireWeightsMemoryFromPrimitive(
const std::shared_ptr<mkldnn::memory> user_weights_memory_p,
std::vector<mkldnn::primitive>& pipeline) {
std::vector<mkldnn::primitive>& pipeline) { // NOLINT
auto user_weights_pd = user_weights_memory_p->get_primitive_desc();
auto weights_pd = conv_pd_->weights_primitive_desc();
return this->AcquireMemory(weights_pd, user_weights_pd,
......@@ -197,12 +197,12 @@ class ConvMKLDNNHandler : public platform::MKLDNNHandler {
// Generate keys for storing/retriving primitives for this operator
// TODO(jczaja): Make hashing function more optimial
static std::string GetHash(memory::dims& input_dims,
memory::dims& weights_dims,
std::vector<int>& strides,
std::vector<int>& paddings,
std::vector<int>& dilations, int groups,
const std::string& suffix) {
static std::string GetHash(memory::dims& input_dims, // NOLINT
memory::dims& weights_dims, // NOLINT
std::vector<int>& strides, // NOLINT
std::vector<int>& paddings, // NOLINT
std::vector<int>& dilations, // NOLINT
int groups, const std::string& suffix) {
return dims2str(input_dims) + dims2str(weights_dims) + dims2str(strides) +
dims2str(paddings) + dims2str(dilations) + std::to_string(groups) +
suffix;
......
python/paddle/fluid/parallel_executor.py
浏览文件 @ 66406619
......@@ -121,7 +121,7 @@ class ParallelExecutor(object):
else:
cpu_num = int(
os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
exec_strategy.num_threads = cpu_num
exec_strategy.num_threads = cpu_num * 2
if build_strategy is None:
build_strategy = BuildStrategy()
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ 66406619
......@@ -49,6 +49,7 @@ list(REMOVE_ITEM TEST_OPS test_dist_train)
list(REMOVE_ITEM TEST_OPS test_parallel_executor_crf)
list(REMOVE_ITEM TEST_OPS test_parallel_executor_fetch_feed)
list(REMOVE_ITEM TEST_OPS test_dist_se_resnext)
list(REMOVE_ITEM TEST_OPS test_dist_transformer)
foreach(TEST_OP ${TEST_OPS})
py_test_modules(${TEST_OP} MODULES ${TEST_OP})
endforeach(TEST_OP)
......@@ -61,4 +62,5 @@ if(WITH_DISTRIBUTE)
endif()
py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SERIAL)
py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL)
py_test_modules(test_dist_transformer MODULES test_dist_transformer SERIAL)
py_test_modules(test_dist_se_resnext MODULES test_dist_se_resnext SERIAL)
python/paddle/fluid/tests/unittests/dist_transformer.py 0 → 100644
浏览文件 @ 66406619
# 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 numpy as np
import argparse
import time
import math
import paddle
import paddle.fluid as fluid
from paddle.fluid import core
import os
import sys
import transformer_model
import paddle.dataset.wmt16 as wmt16
# Fix seed for test
fluid.default_startup_program().random_seed = 1
fluid.default_main_program().random_seed = 1
WMT16_RECORDIO_FILE = "/tmp/wmt16.recordio"
class ModelHyperParams(object):
# Dictionary size for source and target language. This model directly uses
# paddle.dataset.wmt16 in which <bos>, <eos> and <unk> token has
# alreay been added, but the <pad> token is not added. Transformer requires
# sequences in a mini-batch are padded to have the same length. A <pad> token is
# added into the original dictionary in paddle.dateset.wmt16.
# size of source word dictionary.
src_vocab_size = 10000
# index for <pad> token in source language.
src_pad_idx = src_vocab_size
# size of target word dictionay
trg_vocab_size = 10000
# index for <pad> token in target language.
trg_pad_idx = trg_vocab_size
# position value corresponding to the <pad> token.
pos_pad_idx = 0
# max length of sequences. It should plus 1 to include position
# padding token for position encoding.
max_length = 50
# the dimension for word embeddings, which is also the last dimension of
# the input and output of multi-head attention, position-wise feed-forward
# networks, encoder and decoder.
d_model = 512
# size of the hidden layer in position-wise feed-forward networks.
d_inner_hid = 1024
# the dimension that keys are projected to for dot-product attention.
d_key = 64
# the dimension that values are projected to for dot-product attention.
d_value = 64
# number of head used in multi-head attention.
n_head = 8
# number of sub-layers to be stacked in the encoder and decoder.
n_layer = 6
# dropout rate used by all dropout layers.
dropout = 0.1
def prepare_batch_input(insts, src_pad_idx, trg_pad_idx, n_head):
"""
Pad the instances to the max sequence length in batch, and generate the
corresponding position data and attention bias. Then, convert the numpy
data to tensors and return a dict mapping names to tensors.
"""
def __pad_batch_data(insts,
pad_idx,
is_target=False,
return_pos=True,
return_attn_bias=True,
return_max_len=True):
"""
Pad the instances to the max sequence length in batch, and generate the
corresponding position data and attention bias.
"""
return_list = []
max_len = max(len(inst) for inst in insts)
inst_data = np.array(
[inst + [pad_idx] * (max_len - len(inst)) for inst in insts])
return_list += [inst_data.astype("int64").reshape([-1, 1])]
if return_pos:
inst_pos = np.array([[
pos_i + 1 if w_i != pad_idx else 0
for pos_i, w_i in enumerate(inst)
] for inst in inst_data])
return_list += [inst_pos.astype("int64").reshape([-1, 1])]
if return_attn_bias:
if is_target:
# This is used to avoid attention on paddings and subsequent
# words.
slf_attn_bias_data = np.ones((inst_data.shape[0], max_len,
max_len))
slf_attn_bias_data = np.triu(slf_attn_bias_data, 1).reshape(
[-1, 1, max_len, max_len])
slf_attn_bias_data = np.tile(slf_attn_bias_data,
[1, n_head, 1, 1]) * [-1e9]
else:
# This is used to avoid attention on paddings.
slf_attn_bias_data = np.array([[0] * len(inst) + [-1e9] *
(max_len - len(inst))
for inst in insts])
slf_attn_bias_data = np.tile(
slf_attn_bias_data.reshape([-1, 1, 1, max_len]),
[1, n_head, max_len, 1])
return_list += [slf_attn_bias_data.astype("float32")]
if return_max_len:
return_list += [max_len]
return return_list if len(return_list) > 1 else return_list[0]
src_word, src_pos, src_slf_attn_bias, src_max_len = __pad_batch_data(
[inst[0] for inst in insts], src_pad_idx, is_target=False)
trg_word, trg_pos, trg_slf_attn_bias, trg_max_len = __pad_batch_data(
[inst[1] for inst in insts], trg_pad_idx, is_target=True)
trg_src_attn_bias = np.tile(src_slf_attn_bias[:, :, ::src_max_len, :],
[1, 1, trg_max_len, 1]).astype("float32")
lbl_word = __pad_batch_data([inst[2] for inst in insts], trg_pad_idx, False,
False, False, False)
lbl_weight = (lbl_word != trg_pad_idx).astype("float32").reshape([-1, 1])
return [
src_word, src_pos, trg_word, trg_pos, src_slf_attn_bias,
trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight
]
def transformer(use_feed):
assert not use_feed, "transfomer doesn't support feed yet"
return transformer_model.transformer(
ModelHyperParams.src_vocab_size + 1,
ModelHyperParams.trg_vocab_size + 1, ModelHyperParams.max_length + 1,
ModelHyperParams.n_layer, ModelHyperParams.n_head,
ModelHyperParams.d_key, ModelHyperParams.d_value,
ModelHyperParams.d_model, ModelHyperParams.d_inner_hid,
ModelHyperParams.dropout, ModelHyperParams.src_pad_idx,
ModelHyperParams.trg_pad_idx, ModelHyperParams.pos_pad_idx)
def get_model():
avg_cost = transformer(use_feed=False)
optimizer = fluid.optimizer.Adam()
optimizer.minimize(avg_cost)
return avg_cost
def get_transpiler(trainer_id, main_program, pserver_endpoints, trainers):
t = fluid.DistributeTranspiler()
t.transpile(
trainer_id=trainer_id,
program=main_program,
pservers=pserver_endpoints,
trainers=trainers)
return t
class DistTransformer2x2(object):
def run_pserver(self, pserver_endpoints, trainers, current_endpoint,
trainer_id):
get_model()
t = get_transpiler(trainer_id,
fluid.default_main_program(), pserver_endpoints,
trainers)
pserver_prog = t.get_pserver_program(current_endpoint)
startup_prog = t.get_startup_program(current_endpoint, pserver_prog)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
exe.run(pserver_prog)
def _wait_ps_ready(self, pid):
retry_times = 20
while True:
assert retry_times >= 0, "wait ps ready failed"
time.sleep(3)
print("waiting ps ready: ", pid)
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:
retry_times -= 1
def run_trainer(self, place, endpoints, trainer_id, trainers, is_dist=True):
avg_cost = get_model()
if is_dist:
t = get_transpiler(trainer_id,
fluid.default_main_program(), endpoints,
trainers)
trainer_prog = t.get_trainer_program()
else:
trainer_prog = fluid.default_main_program()
startup_exe = fluid.Executor(place)
startup_exe.run(fluid.default_startup_program())
strategy = fluid.ExecutionStrategy()
strategy.num_threads = 1
strategy.allow_op_delay = False
exe = fluid.ParallelExecutor(
True, loss_name=avg_cost.name, exec_strategy=strategy)
first_loss, = exe.run(fetch_list=[avg_cost.name])
print(first_loss)
for i in xrange(5):
_ = exe.run(fetch_list=[avg_cost.name])
last_loss, = exe.run(fetch_list=[avg_cost.name])
print(last_loss)
def main(role="pserver",
endpoints="127.0.0.1:9123",
trainer_id=0,
current_endpoint="127.0.0.1:9123",
trainers=1,
is_dist=True):
reader = paddle.batch(
wmt16.train(ModelHyperParams.src_vocab_size,
ModelHyperParams.trg_vocab_size),
batch_size=transformer_model.batch_size)
with fluid.recordio_writer.create_recordio_writer(
WMT16_RECORDIO_FILE) as writer:
for batch in reader():
for tensor in prepare_batch_input(
batch, ModelHyperParams.src_pad_idx,
ModelHyperParams.trg_pad_idx, ModelHyperParams.n_head):
t = fluid.LoDTensor()
t.set(tensor, fluid.CPUPlace())
writer.append_tensor(t)
writer.complete_append_tensor()
model = DistTransformer2x2()
if role == "pserver":
model.run_pserver(endpoints, trainers, current_endpoint, trainer_id)
else:
p = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
) else fluid.CPUPlace()
model.run_trainer(p, endpoints, trainer_id, trainers, is_dist)
if __name__ == "__main__":
if len(sys.argv) != 7:
print(
"Usage: python dist_transformer.py [pserver/trainer] [endpoints] [trainer_id] [current_endpoint] [trainers] [is_dist]"
)
role = sys.argv[1]
endpoints = sys.argv[2]
trainer_id = int(sys.argv[3])
current_endpoint = sys.argv[4]
trainers = int(sys.argv[5])
is_dist = True if sys.argv[6] == "TRUE" else False
main(
role=role,
endpoints=endpoints,
trainer_id=trainer_id,
current_endpoint=current_endpoint,
trainers=trainers,
is_dist=is_dist)
python/paddle/fluid/tests/unittests/test_dist_base.py 0 → 100644
浏览文件 @ 66406619
# 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 time
import unittest
import os
import sys
import signal
import subprocess
class TestDistBase(unittest.TestCase):
def setUp(self):
self._trainers = 2
self._pservers = 2
self._ps_endpoints = "127.0.0.1:9123,127.0.0.1:9124"
self._python_interp = "python"
def start_pserver(self, model_file):
ps0_ep, ps1_ep = self._ps_endpoints.split(",")
ps0_cmd = "%s %s pserver %s 0 %s %d TRUE" % \
(self._python_interp, model_file, self._ps_endpoints, ps0_ep,
self._trainers)
ps1_cmd = "%s %s pserver %s 0 %s %d TRUE" % \
(self._python_interp, model_file, self._ps_endpoints, ps1_ep,
self._trainers)
ps0_proc = subprocess.Popen(
ps0_cmd.split(" "), stdout=subprocess.PIPE, stderr=subprocess.PIPE)
ps1_proc = subprocess.Popen(
ps1_cmd.split(" "), stdout=subprocess.PIPE, stderr=subprocess.PIPE)
return ps0_proc, ps1_proc
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 check_with_place(self, model_file, delta=1e-3):
# *ATTENTION* THIS TEST NEEDS AT LEAST 2GPUS TO RUN
required_envs = {
"PATH": os.getenv("PATH"),
"PYTHONPATH": os.getenv("PYTHONPATH"),
"LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH"),
"FLAGS_fraction_of_gpu_memory_to_use": "0.15"
}
# Run local to get a base line
env_local = {"CUDA_VISIBLE_DEVICES": "0"}
env_local.update(required_envs)
local_cmd = "%s %s trainer %s 0 %s %d FLASE" % \
(self._python_interp, model_file,
"127.0.0.1:1234", "127.0.0.1:1234", 1)
local_proc = subprocess.Popen(
local_cmd.split(" "),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
env=env_local)
local_proc.wait()
out, err = local_proc.communicate()
local_ret = out
sys.stderr.write('local_loss: %s\n' % local_ret)
sys.stderr.write('local_stderr: %s\n' % err)
# Run dist train to compare with local results
ps0, ps1 = self.start_pserver(model_file)
self._wait_ps_ready(ps0.pid)
self._wait_ps_ready(ps1.pid)
ps0_ep, ps1_ep = self._ps_endpoints.split(",")
tr0_cmd = "%s %s trainer %s 0 %s %d TRUE" % \
(self._python_interp, model_file, self._ps_endpoints, ps0_ep,
self._trainers)
tr1_cmd = "%s %s trainer %s 1 %s %d TRUE" % \
(self._python_interp, model_file, self._ps_endpoints, ps1_ep,
self._trainers)
env0 = {"CUDA_VISIBLE_DEVICES": "0"}
env1 = {"CUDA_VISIBLE_DEVICES": "1"}
env0.update(required_envs)
env1.update(required_envs)
FNULL = open(os.devnull, 'w')
tr0_proc = subprocess.Popen(
tr0_cmd.split(" "),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
env=env0)
tr1_proc = subprocess.Popen(
tr1_cmd.split(" "),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
env=env1)
tr0_proc.wait()
tr1_proc.wait()
out, err = tr0_proc.communicate()
sys.stderr.write('dist_stderr: %s\n' % err)
loss_data0 = out
sys.stderr.write('dist_loss: %s\n' % loss_data0)
lines = loss_data0.split("\n")
dist_first_loss = eval(lines[0].replace(" ", ","))[0]
dist_last_loss = eval(lines[1].replace(" ", ","))[0]
local_lines = local_ret.split("\n")
local_first_loss = eval(local_lines[0])[0]
local_last_loss = eval(local_lines[1])[0]
self.assertAlmostEqual(local_first_loss, dist_first_loss, delta=delta)
self.assertAlmostEqual(local_last_loss, dist_last_loss, delta=delta)
# check tr0_out
# FIXME: ensure the server process is killed
# replace with ps0.terminate()
os.kill(ps0.pid, signal.SIGKILL)
os.kill(ps1.pid, signal.SIGKILL)
FNULL.close()
python/paddle/fluid/tests/unittests/test_dist_se_resnext.py
浏览文件 @ 66406619
......@@ -11,127 +11,14 @@
# 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 numpy as np
import argparse
import time
import math
import unittest
import os
import sys
import signal
import subprocess
class TestDistSeResneXt2x2(unittest.TestCase):
def setUp(self):
self._trainers = 2
self._pservers = 2
self._ps_endpoints = "127.0.0.1:9123,127.0.0.1:9124"
self._python_interp = "python"
def start_pserver(self):
ps0_ep, ps1_ep = self._ps_endpoints.split(",")
ps0_cmd = "%s dist_se_resnext.py pserver %s 0 %s %d TRUE" % \
(self._python_interp, self._ps_endpoints, ps0_ep, self._trainers)
ps1_cmd = "%s dist_se_resnext.py pserver %s 0 %s %d TRUE" % \
(self._python_interp, self._ps_endpoints, ps1_ep, self._trainers)
ps0_proc = subprocess.Popen(
ps0_cmd.split(" "), stdout=subprocess.PIPE, stderr=subprocess.PIPE)
ps1_proc = subprocess.Popen(
ps1_cmd.split(" "), stdout=subprocess.PIPE, stderr=subprocess.PIPE)
return ps0_proc, ps1_proc
def _wait_ps_ready(self, pid):
retry_times = 20
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:
retry_times -= 1
def test_with_place(self):
# *ATTENTION* THIS TEST NEEDS AT LEAST 2GPUS TO RUN
required_envs = {
"PATH": os.getenv("PATH"),
"PYTHONPATH": os.getenv("PYTHONPATH"),
"LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH"),
"FLAGS_fraction_of_gpu_memory_to_use": "0.15"
}
# Run local to get a base line
env_local = {"CUDA_VISIBLE_DEVICES": "0"}
env_local.update(required_envs)
local_cmd = "%s dist_se_resnext.py trainer %s 0 %s %d FLASE" % \
(self._python_interp, "127.0.0.1:1234", "127.0.0.1:1234", 1)
local_proc = subprocess.Popen(
local_cmd.split(" "),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
env=env_local)
local_proc.wait()
out, err = local_proc.communicate()
local_ret = out
sys.stderr.write('local_loss: %s\n' % local_ret)
sys.stderr.write('local_stderr: %s\n' % err)
# Run dist train to compare with local results
ps0, ps1 = self.start_pserver()
self._wait_ps_ready(ps0.pid)
self._wait_ps_ready(ps1.pid)
ps0_ep, ps1_ep = self._ps_endpoints.split(",")
tr0_cmd = "%s dist_se_resnext.py trainer %s 0 %s %d TRUE" % \
(self._python_interp, self._ps_endpoints, ps0_ep, self._trainers)
tr1_cmd = "%s dist_se_resnext.py trainer %s 1 %s %d TRUE" % \
(self._python_interp, self._ps_endpoints, ps1_ep, self._trainers)
env0 = {"CUDA_VISIBLE_DEVICES": "0"}
env1 = {"CUDA_VISIBLE_DEVICES": "1"}
env0.update(required_envs)
env1.update(required_envs)
FNULL = open(os.devnull, 'w')
tr0_proc = subprocess.Popen(
tr0_cmd.split(" "),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
env=env0)
tr1_proc = subprocess.Popen(
tr1_cmd.split(" "),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
env=env1)
tr0_proc.wait()
tr1_proc.wait()
out, err = tr0_proc.communicate()
sys.stderr.write('dist_stderr: %s\n' % err)
loss_data0 = out
sys.stderr.write('dist_loss: %s\n' % loss_data0)
lines = loss_data0.split("\n")
dist_first_loss = eval(lines[0].replace(" ", ","))[0]
dist_last_loss = eval(lines[1].replace(" ", ","))[0]
local_lines = local_ret.split("\n")
local_first_loss = eval(local_lines[0])[0]
local_last_loss = eval(local_lines[1])[0]
from test_dist_base import TestDistBase
self.assertAlmostEqual(local_first_loss, dist_first_loss)
self.assertAlmostEqual(local_last_loss, dist_last_loss)
# check tr0_out
# FIXME: ensure the server process is killed
# replace with ps0.terminate()
os.kill(ps0.pid, signal.SIGKILL)
os.kill(ps1.pid, signal.SIGKILL)
FNULL.close()
class TestDistSeResneXt2x2(TestDistBase):
def test_se_resnext(self):
# TODO(paddle-dev): Is the delta too large?
self.check_with_place("dist_se_resnext.py", delta=0.2)
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/test_dist_transformer.py 0 → 100644
浏览文件 @ 66406619
# 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
from test_dist_base import TestDistBase
class TestDistTransformer2x2(TestDistBase):
def test_transformer(self):
# TODO(paddle-dev): check if the delta is OK.
# Usually start around ~8000 and converge to ~5000
self.check_with_place("dist_transformer.py", delta=400)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_parallel_executor_transformer.py
浏览文件 @ 66406619
......@@ -21,7 +21,7 @@ import paddle
import paddle.dataset.wmt16 as wmt16
import os
WMT16_RECORDIO_FILE = "./wmt16_test_pe.recordio"
WMT16_RECORDIO_FILE = "/tmp/wmt16.recordio"
class ModelHyperParams(object):
......
python/paddle/fluid/tests/unittests/transformer_model.py
浏览文件 @ 66406619
......@@ -403,7 +403,7 @@ def transformer(
trg_pad_idx,
pos_pad_idx, ):
file_obj = fluid.layers.open_recordio_file(
filename='./wmt16.recordio',
filename='/tmp/wmt16.recordio',
shapes=[
[batch_size * max_length, 1],
[batch_size * max_length, 1],
......
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