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提交 4f42504e 编写于 作者: S shippingwang
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into add_sqrt_doc

上级 98d9552f 6429d2a8
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Showing with 4666 addition and 675 deletion
CMakeLists.txt
浏览文件 @ 4f42504e
......@@ -24,6 +24,8 @@ message(STATUS "CXX compiler: ${CMAKE_CXX_COMPILER}, version: "
"${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION}")
message(STATUS "C compiler: ${CMAKE_C_COMPILER}, version: "
"${CMAKE_C_COMPILER_ID} ${CMAKE_C_COMPILER_VERSION}")
message(STATUS "AR tools: ${CMAKE_AR}")
if(WIN32)
set(CMAKE_SUPPRESS_REGENERATION ON)
set(CMAKE_STATIC_LIBRARY_PREFIX lib)
......
cmake/operators.cmake
浏览文件 @ 4f42504e
......@@ -110,7 +110,7 @@ function(op_library TARGET)
# Define operators that don't need pybind here.
foreach(manual_pybind_op "compare_op" "logical_op" "nccl_op"
"tensor_array_read_write_op" "tensorrt_engine_op" "conv_fusion_op"
"fusion_transpose_flatten_concat_op" "fusion_conv_inception_op")
"fusion_transpose_flatten_concat_op" "fusion_conv_inception_op" "sync_batch_norm_op")
if ("${TARGET}" STREQUAL "${manual_pybind_op}")
set(pybind_flag 1)
endif()
......
paddle/fluid/API.spec
浏览文件 @ 4f42504e
......@@ -68,7 +68,7 @@ paddle.fluid.initializer.MSRAInitializer.__init__ (ArgSpec(args=['self', 'unifor
paddle.fluid.initializer.force_init_on_cpu (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '6d0f3e22c90d9d500d36ff57daf056ee'))
paddle.fluid.initializer.init_on_cpu (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'a6d7011ca3d8c0d454dac3a56eae0c29'))
paddle.fluid.initializer.NumpyArrayInitializer.__init__ (ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.layers.fc (ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', 'act', 'is_test', 'name'], varargs=None, keywords=None, defaults=(1, None, None, None, False, None)), ('document', '1929058262994f212620599c63aea6bd'))
paddle.fluid.layers.fc (ArgSpec(args=['input', 'size', 'num_flatten_dims', 'param_attr', 'bias_attr', 'act', 'is_test', 'name'], varargs=None, keywords=None, defaults=(1, None, None, None, False, None)), ('document', '424e898365195e3ccbc2e7dc8b63605e'))
paddle.fluid.layers.embedding (ArgSpec(args=['input', 'size', 'is_sparse', 'is_distributed', 'padding_idx', 'param_attr', 'dtype'], varargs=None, keywords=None, defaults=(False, False, None, None, 'float32')), ('document', '89c2c55a0b0656b106064048e068e77a'))
paddle.fluid.layers.dynamic_lstm (ArgSpec(args=['input', 'size', 'h_0', 'c_0', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'float32', None)), ('document', 'dfbb624f85015df29e994ca6999e8ff6'))
paddle.fluid.layers.dynamic_lstmp (ArgSpec(args=['input', 'size', 'proj_size', 'param_attr', 'bias_attr', 'use_peepholes', 'is_reverse', 'gate_activation', 'cell_activation', 'candidate_activation', 'proj_activation', 'dtype', 'name', 'h_0', 'c_0', 'cell_clip', 'proj_clip'], varargs=None, keywords=None, defaults=(None, None, True, False, 'sigmoid', 'tanh', 'tanh', 'tanh', 'float32', None, None, None, None, None)), ('document', 'b4b608b986eb9617aa0525e1be21d32d'))
......@@ -91,7 +91,7 @@ paddle.fluid.layers.pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'po
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)), ('document', '043de7333b79ee0ac55053c14ed81625'))
paddle.fluid.layers.adaptive_pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None)), ('document', '859b887174d06f361658f69cb7c06d95'))
paddle.fluid.layers.adaptive_pool3d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None)), ('document', '120f4323a3d7ed9c0916f15a59f0e497'))
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', 'use_global_stats'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, None, None, None, False, False, False)), ('document', 'c527b71b8a4c60dca8df8a745c2b598d'))
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', 'use_global_stats'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, None, None, None, False, False, False)), ('document', '320c6973b02ea179fa89fecc80796464'))
paddle.fluid.layers.data_norm (ArgSpec(args=['input', 'act', 'epsilon', 'param_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var'], varargs=None, keywords=None, defaults=(None, 1e-05, None, 'NCHW', False, None, None, None, False)), ('document', 'e45e09e65a2658e07cad987222f0d9ab'))
paddle.fluid.layers.beam_search_decode (ArgSpec(args=['ids', 'scores', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b0b8d53821716cd50c42e09b593f3feb'))
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)), ('document', '03993955ab1e6d3044c44e6f17fc85e9'))
......@@ -330,7 +330,8 @@ paddle.fluid.layers.generate_mask_labels (ArgSpec(args=['im_info', 'gt_classes',
paddle.fluid.layers.iou_similarity (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '587845f60c5d97ffdf2dfd21da52eca1'))
paddle.fluid.layers.box_coder (ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name', 'axis'], varargs=None, keywords=None, defaults=('encode_center_size', True, None, 0)), ('document', '032d0f4b7d8f6235ee5d91e473344f0e'))
paddle.fluid.layers.polygon_box_transform (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '0e5ac2507723a0b5adec473f9556799b'))
paddle.fluid.layers.yolov3_loss (ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '991e934c3e09abf0edec7c9c978b4691'))
paddle.fluid.layers.yolov3_loss (ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'gtscore', 'use_label_smooth', 'name'], varargs=None, keywords=None, defaults=(None, True, None)), ('document', '57fa96922e42db8f064c3fb77f2255e8'))
paddle.fluid.layers.yolo_box (ArgSpec(args=['x', 'img_size', 'anchors', 'class_num', 'conf_thresh', 'downsample_ratio', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5566169a5ab993d177792c023c7fb340'))
paddle.fluid.layers.box_clip (ArgSpec(args=['input', 'im_info', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '397e9e02b451d99c56e20f268fa03f2e'))
paddle.fluid.layers.multiclass_nms (ArgSpec(args=['bboxes', 'scores', 'score_threshold', 'nms_top_k', 'keep_top_k', 'nms_threshold', 'normalized', 'nms_eta', 'background_label', 'name'], varargs=None, keywords=None, defaults=(0.3, True, 1.0, 0, None)), ('document', 'ca7d1107b6c5d2d6d8221039a220fde0'))
paddle.fluid.layers.distribute_fpn_proposals (ArgSpec(args=['fpn_rois', 'min_level', 'max_level', 'refer_level', 'refer_scale', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '7bb011ec26bace2bc23235aa4a17647d'))
......@@ -367,7 +368,7 @@ paddle.fluid.contrib.BeamSearchDecoder.read_array (ArgSpec(args=['self', 'init',
paddle.fluid.contrib.BeamSearchDecoder.update_array (ArgSpec(args=['self', 'array', 'value'], varargs=None, keywords=None, defaults=None), ('document', '5754e9b3212b7c09497151516a0de5a7'))
paddle.fluid.contrib.memory_usage (ArgSpec(args=['program', 'batch_size'], varargs=None, keywords=None, defaults=None), ('document', '8fcb2f93bb743693baa8d4860a5ccc47'))
paddle.fluid.contrib.op_freq_statistic (ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None), ('document', '4d43687113c4bf5b29d15aee2f4e4afa'))
paddle.fluid.contrib.QuantizeTranspiler.__init__ (ArgSpec(args=['self', 'weight_bits', 'activation_bits', 'activation_quantize_type', 'weight_quantize_type', 'window_size'], varargs=None, keywords=None, defaults=(8, 8, 'abs_max', 'abs_max', 10000)), ('document', '14b39f1fcd5667ff556b1aad94357d1d'))
paddle.fluid.contrib.QuantizeTranspiler.__init__ (ArgSpec(args=['self', 'weight_bits', 'activation_bits', 'activation_quantize_type', 'weight_quantize_type', 'window_size', 'moving_rate'], varargs=None, keywords=None, defaults=(8, 8, 'abs_max', 'abs_max', 10000, 0.9)), ('document', '14b39f1fcd5667ff556b1aad94357d1d'))
paddle.fluid.contrib.QuantizeTranspiler.convert_to_int8 (ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.QuantizeTranspiler.freeze_program (ArgSpec(args=['self', 'program', 'place', 'fuse_bn', 'scope'], varargs=None, keywords=None, defaults=(False, None)), ('document', '909675a1ab055c69b436a7893fcae4fd'))
paddle.fluid.contrib.QuantizeTranspiler.training_transpile (ArgSpec(args=['self', 'program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None)), ('document', '6dd9909f10b283ba2892a99058a72884'))
......@@ -392,9 +393,9 @@ paddle.fluid.contrib.MagnitudePruner.__init__ (ArgSpec(args=['self', 'threshold'
paddle.fluid.contrib.MagnitudePruner.prune (ArgSpec(args=['self', 'param', 'threshold'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.RatioPruner.__init__ (ArgSpec(args=['self', 'ratios'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e7a81a325b296a9ca502ee5adb4fc85d'))
paddle.fluid.contrib.RatioPruner.prune (ArgSpec(args=['self', 'param', 'ratio'], varargs=None, keywords=None, defaults=(None,)), ('document', '358cbf2978c91028fb96a195a9884645'))
paddle.fluid.contrib.load_persistables_for_increment (ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var', 'lookup_table_var_path'], varargs=None, keywords=None, defaults=None), ('document', '11fbf7e8dd2289805de291b453a33ee7'))
paddle.fluid.contrib.load_persistables_for_inference (ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var_name'], varargs=None, keywords=None, defaults=None), ('document', '5b5577bb3d24070da819674255d16196'))
paddle.fluid.contrib.convert_dist_to_sparse_program (ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None), ('document', '4efbd93876832d4d35497cdbc7a1e6d8'))
paddle.fluid.contrib.load_persistables_for_increment (ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var', 'lookup_table_var_path'], varargs=None, keywords=None, defaults=None), ('document', '2ab36d4f7a564f5f65e455807ad06c67'))
paddle.fluid.contrib.load_persistables_for_inference (ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var_name'], varargs=None, keywords=None, defaults=None), ('document', '59066bac9db0ac6ce414d05780b7333f'))
paddle.fluid.contrib.convert_dist_to_sparse_program (ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None), ('document', '74c39c595dc70d6be2f16d8e462d282b'))
paddle.fluid.contrib.HDFSClient.__init__ (ArgSpec(args=['self', 'hadoop_home', 'configs'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.HDFSClient.delete (ArgSpec(args=['self', 'hdfs_path'], varargs=None, keywords=None, defaults=None), ('document', 'c3721aa2d4d9ef5a857dd47b2681c03e'))
paddle.fluid.contrib.HDFSClient.download (ArgSpec(args=['self', 'hdfs_path', 'local_path', 'overwrite', 'unzip'], varargs=None, keywords=None, defaults=(False, False)), ('document', 'ca55bde92184d3fd0f9f5c963b25e634'))
......
paddle/fluid/framework/details/build_strategy.cc
浏览文件 @ 4f42504e
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <glog/logging.h>
#include <memory>
#include <utility>
#include "paddle/fluid/framework/details/memory_optimize_helper.h"
#include "paddle/fluid/framework/details/multi_devices_graph_pass.h"
......@@ -49,6 +50,11 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
AppendPass("sequential_execution_pass");
}
// Add op fusion.
if (strategy.sync_batch_norm_) {
AppendPass("sync_batch_norm_pass");
}
// Add op fusion.
if (strategy.fuse_relu_depthwise_conv_) {
AppendPass("fuse_relu_depthwise_conv_pass");
......@@ -227,6 +233,7 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
} // namespace framework
} // namespace paddle
USE_PASS(sync_batch_norm_pass);
USE_PASS(fuse_relu_depthwise_conv_pass);
USE_PASS(fuse_elewise_add_act_pass);
USE_PASS(graph_viz_pass);
......
paddle/fluid/framework/details/build_strategy.h
浏览文件 @ 4f42504e
......@@ -77,6 +77,8 @@ struct BuildStrategy {
bool fuse_relu_depthwise_conv_{false};
bool sync_batch_norm_{false};
bool memory_optimize_{true};
// TODO(dzhwinter):
// make enable_inplace, memory_optimize_
......
paddle/fluid/framework/details/memory_optimize_helper.cc
浏览文件 @ 4f42504e
......@@ -337,7 +337,6 @@ bool NodeCanReused(const VarDesc& node) {
auto type = node.GetType();
// only these types holds bulk of gpu memory
if (!(type == proto::VarType::LOD_TENSOR ||
type == proto::VarType::SELECTED_ROWS ||
type == proto::VarType::LOD_TENSOR_ARRAY)) {
return false;
}
......
paddle/fluid/framework/grad_op_desc_maker.h
浏览文件 @ 4f42504e
......@@ -14,7 +14,9 @@ limitations under the License. */
#pragma once
#include <algorithm>
#include <memory>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include "paddle/fluid/framework/op_desc.h"
......@@ -55,11 +57,11 @@ class GradOpDescMakerBase {
std::back_inserter(ret_val),
[this](const std::string& fwd_var_name) -> std::string {
auto g_name = GradVarName(fwd_var_name);
if (no_grad_set_.count(g_name)) {
return kEmptyVarName;
} else {
if (no_grad_set_.empty() || !no_grad_set_.count(g_name)) {
(*this->grad_to_var_)[g_name] = fwd_var_name;
return g_name;
} else {
return kEmptyVarName;
}
});
if (!drop_empty_grad) {
......
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ 4f42504e
......@@ -46,6 +46,8 @@ cc_library(fuse_pass_base SRCS fuse_pass_base.cc DEPS pass)
pass_library(graph_to_program_pass base)
pass_library(graph_viz_pass base)
pass_library(lock_free_optimize_pass base)
pass_library(cpu_quantize_pass inference)
pass_library(cpu_quantize_squash_pass inference)
pass_library(fc_fuse_pass inference)
pass_library(attention_lstm_fuse_pass inference)
pass_library(infer_clean_graph_pass inference)
......@@ -66,6 +68,7 @@ pass_library(conv_elementwise_add_fuse_pass inference)
pass_library(conv_affine_channel_fuse_pass inference)
pass_library(transpose_flatten_concat_fuse_pass inference)
pass_library(identity_scale_op_clean_pass base)
pass_library(sync_batch_norm_pass base)
# There may be many transpose-flatten structures in a model, and the output of
# these structures will be used as inputs to the concat Op. This pattern will
......@@ -100,6 +103,11 @@ cc_test(test_graph_pattern_detector SRCS graph_pattern_detector_tester.cc DEPS g
cc_test(test_fc_fuse_pass SRCS fc_fuse_pass_tester.cc DEPS fc_fuse_pass framework_proto)
cc_test(test_seqpool_concat_fuse_pass SRCS seqpool_concat_fuse_pass_tester.cc DEPS seqpool_concat_fuse_pass framework_proto)
cc_test(test_is_test_pass SRCS is_test_pass_tester.cc DEPS is_test_pass)
cc_test(test_cpu_quantize_pass SRCS cpu_quantize_pass_tester.cc DEPS cpu_quantize_pass naive_executor)
cc_test(test_cpu_quantize_squash_pass SRCS cpu_quantize_squash_pass_tester.cc DEPS cpu_quantize_squash_pass naive_executor)
if(NOT WIN32)
cc_test(test_sync_batch_norm_pass SRCS sync_batch_norm_pass_tester.cc DEPS sync_batch_norm_pass)
endif()
if (WITH_MKLDNN)
cc_test(test_depthwise_conv_mkldnn_pass SRCS mkldnn/depthwise_conv_mkldnn_pass_tester.cc DEPS depthwise_conv_mkldnn_pass)
cc_test(test_conv_bias_mkldnn_fuse_pass SRCS mkldnn/conv_bias_mkldnn_fuse_pass_tester.cc DEPS conv_bias_mkldnn_fuse_pass naive_executor)
......
paddle/fluid/framework/ir/cpu_quantize_pass.cc 0 → 100644
浏览文件 @ 4f42504e
// Copyright (c) 2019 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/cpu_quantize_pass.h"
#include <utility>
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/string/pretty_log.h"
namespace paddle {
namespace framework {
namespace ir {
namespace {
void UnlinkNodes(ir::Node* a, ir::Node* b) {
a->outputs.erase(std::remove(a->outputs.begin(), a->outputs.end(), b),
a->outputs.end());
b->inputs.erase(std::remove(b->inputs.begin(), b->inputs.end(), a),
b->inputs.end());
}
} // namespace
enum { U8_MAX = 255, S8_MAX = 127 };
using EigenVectorArrayMap = Eigen::Map<Eigen::Array<double, Eigen::Dynamic, 1>>;
using string::PrettyLogDetail;
void CPUQuantizePass::QuantizeInput(Graph* g, Node* op, Node* input,
std::string input_name, double scale_to_one,
bool is_unsigned,
std::string scale_attr_name) const {
unsigned max = is_unsigned ? U8_MAX : S8_MAX;
float scale = scale_to_one * max;
// Create quantize output variable
VarDesc quantize_out_desc(patterns::PDNodeName("quantize", "out"));
auto* quantize_out_node = g->CreateVarNode(&quantize_out_desc);
// create a quantize op node
OpDesc q_desc;
q_desc.SetType("quantize");
q_desc.SetInput("Input", std::vector<std::string>({input->Name()}));
q_desc.SetOutput("Output",
std::vector<std::string>({quantize_out_node->Name()}));
q_desc.SetAttr("Scale", scale);
q_desc.SetAttr("is_negative_input", !is_unsigned);
auto quantize_op = g->CreateOpNode(&q_desc); // OpDesc will be copied.
// update op's input
op->Op()->SetInput(input_name,
std::vector<std::string>({quantize_out_node->Name()}));
// link quantize op
UnlinkNodes(input, op);
IR_NODE_LINK_TO(input, quantize_op);
IR_NODE_LINK_TO(quantize_op, quantize_out_node);
IR_NODE_LINK_TO(quantize_out_node, op);
if (!scale_attr_name.empty()) op->Op()->SetAttr(scale_attr_name, scale);
}
void CPUQuantizePass::DequantizeOutput(Graph* g, Node* op, Node* output,
std::string output_name,
double scale_to_one, bool is_unsigned,
std::string scale_attr_name) const {
unsigned max = is_unsigned ? U8_MAX : S8_MAX;
float scale = scale_to_one * max;
// Create dequantize input variable
VarDesc dequantize_in_desc(patterns::PDNodeName("dequantize", "in"));
auto* dequantize_in_node = g->CreateVarNode(&dequantize_in_desc);
// create a dequantize op node for output.
OpDesc deq_desc;
deq_desc.SetType("dequantize");
deq_desc.SetInput("Input",
std::vector<std::string>({dequantize_in_node->Name()}));
deq_desc.SetOutput("Output", std::vector<std::string>({output->Name()}));
deq_desc.SetAttr("Scale", scale);
auto dequantize_op = g->CreateOpNode(&deq_desc); // OpDesc will be copied.
// update op's output
op->Op()->SetOutput(output_name,
std::vector<std::string>({dequantize_in_node->Name()}));
// link dequantize op
UnlinkNodes(op, output);
IR_NODE_LINK_TO(op, dequantize_in_node);
IR_NODE_LINK_TO(dequantize_in_node, dequantize_op);
IR_NODE_LINK_TO(dequantize_op, output);
if (!scale_attr_name.empty()) op->Op()->SetAttr(scale_attr_name, scale);
}
void CPUQuantizePass::QuantizeConv(Graph* graph,
bool with_residual_data) const {
GraphPatternDetector gpd;
auto pattern = gpd.mutable_pattern();
patterns::ConvResidual conv_pattern{pattern, name_scope_};
conv_pattern(with_residual_data);
int quantize_conv_count = 0;
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
VLOG(4) << "Quantize conv2d op";
GET_IR_NODE_FROM_SUBGRAPH(conv_op, conv_op, conv_pattern);
auto* conv_op_desc = conv_op->Op();
// skip if should not be quantized
if (!conv_op_desc->HasAttr("use_quantizer") ||
!boost::get<bool>(conv_op_desc->GetAttr("use_quantizer")))
return;
GET_IR_NODE_FROM_SUBGRAPH(conv_filter, conv_filter, conv_pattern);
GET_IR_NODE_FROM_SUBGRAPH(conv_input, conv_input, conv_pattern);
GET_IR_NODE_FROM_SUBGRAPH(conv_output, conv_output, conv_pattern);
// get scales calculated after warmup, they scale variables to MAX=1.0
auto scales = Get<VarQuantScale>("quant_var_scales");
auto input_scale = scales[conv_input->Name()].second.data<double>()[0];
bool is_input_unsigned = scales[conv_input->Name()].first;
QuantizeInput(g, conv_op, conv_input, "Input", input_scale,
is_input_unsigned, "Scale_in");
auto filter_scale_tensor = scales[conv_filter->Name()].second;
EigenVectorArrayMap eigen_tensor{filter_scale_tensor.data<double>(),
filter_scale_tensor.numel(), 1};
eigen_tensor *= static_cast<double>(S8_MAX);
std::vector<float> filter_scale{
filter_scale_tensor.data<double>(),
filter_scale_tensor.data<double>() + filter_scale_tensor.numel()};
conv_op->Op()->SetAttr("Scale_weights", filter_scale);
if (with_residual_data) {
GET_IR_NODE_FROM_SUBGRAPH(conv_residual_data, conv_residual_data,
conv_pattern);
auto residual_scale =
scales[conv_residual_data->Name()].second.data<double>()[0];
bool is_residual_unsigned = scales[conv_residual_data->Name()].first;
QuantizeInput(g, conv_op, conv_residual_data, "ResidualData",
residual_scale, is_residual_unsigned, "Scale_in_eltwise");
}
auto output_scale = scales[conv_output->Name()].second.data<double>()[0];
bool is_output_unsigned = scales[conv_output->Name()].first;
DequantizeOutput(g, conv_op, conv_output, "Output", output_scale,
is_output_unsigned, "Scale_out");
++quantize_conv_count;
};
gpd(graph, handler);
AddStatis(quantize_conv_count);
std::stringstream msg_ss;
msg_ss << "--- quantized " << quantize_conv_count << " conv2d ops";
if (with_residual_data) msg_ss << " with residual connection";
PrettyLogDetail(msg_ss.str().c_str());
}
void CPUQuantizePass::QuantizePool(Graph* graph) const {
GraphPatternDetector gpd;
auto pattern = gpd.mutable_pattern();
patterns::Pool pool_pattern{pattern, name_scope_};
pool_pattern();
int quantize_pool_count = 0;
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
VLOG(4) << "Quantize pool2d op";
GET_IR_NODE_FROM_SUBGRAPH(pool_op, pool_op, pool_pattern);
auto* pool_op_desc = pool_op->Op();
// skip if should not be quantized
if (!pool_op_desc->HasAttr("use_quantizer") ||
!boost::get<bool>(pool_op_desc->GetAttr("use_quantizer")))
return;
GET_IR_NODE_FROM_SUBGRAPH(pool_input, pool_input, pool_pattern);
GET_IR_NODE_FROM_SUBGRAPH(pool_output, pool_output, pool_pattern);
// get scales calculated after warmup, they scale variables to MAX=1.0
auto scales = Get<VarQuantScale>("quant_var_scales");
auto input_scale = scales[pool_input->Name()].second.data<double>()[0];
bool is_input_unsigned = scales[pool_input->Name()].first;
QuantizeInput(g, pool_op, pool_input, "X", input_scale, is_input_unsigned);
auto output_scale = scales[pool_output->Name()].second.data<double>()[0];
bool is_output_unsigned = scales[pool_output->Name()].first;
DequantizeOutput(g, pool_op, pool_output, "Out", output_scale,
is_output_unsigned);
++quantize_pool_count;
};
gpd(graph, handler);
AddStatis(quantize_pool_count);
PrettyLogDetail("--- quantized %d pool2d ops", quantize_pool_count);
}
std::unique_ptr<ir::Graph> CPUQuantizePass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
VLOG(3) << "Quantizing the graph.";
PADDLE_ENFORCE(graph.get());
FusePassBase::Init(name_scope_, graph.get());
PADDLE_ENFORCE(param_scope());
QuantizeConv(graph.get(), true /* with_residual_data */);
QuantizeConv(graph.get());
QuantizePool(graph.get());
return graph;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(cpu_quantize_pass, paddle::framework::ir::CPUQuantizePass)
.RequirePassAttr("quant_var_scales");
paddle/fluid/framework/ir/cpu_quantize_pass.h 0 → 100644
浏览文件 @ 4f42504e
// Copyright (c) 2019 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 <memory>
#include <string>
#include <unordered_map>
#include <utility>
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
namespace paddle {
namespace framework {
namespace ir {
/*
* Map variable name to tensor of scaling factors scaling it to MAX=1.0.
* bool denotes whether quantization of the variable should be done to unsigned
* type.
*/
using VarQuantScale =
std::unordered_map<std::string, std::pair<bool, LoDTensor>>;
/*
* Quantize all supported operators.
*/
class CPUQuantizePass : public FusePassBase {
public:
virtual ~CPUQuantizePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
void QuantizeConv(Graph* graph, bool with_residual_data = false) const;
void QuantizePool(Graph* graph) const;
void QuantizeInput(Graph* g, Node* op, Node* input, std::string input_name,
double scale_to_one, bool is_unsigned,
std::string scale_attr_name = "") const;
void DequantizeOutput(Graph* g, Node* op, Node* output,
std::string output_name, double scale_to_one,
bool is_unsigned,
std::string scale_attr_name = "") const;
const std::string name_scope_{"quantize"};
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/cpu_quantize_pass_tester.cc 0 → 100644
浏览文件 @ 4f42504e
// Copyright (c) 2019 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/cpu_quantize_pass.h"
#include <gtest/gtest.h>
#include "paddle/fluid/framework/naive_executor.h"
#include "paddle/fluid/platform/place.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,
bool use_quantizer = false) {
auto* op = prog->MutableBlock(0)->AppendOp();
op->SetType(type);
op->SetAttr("use_mkldnn", use_mkldnn);
op->SetAttr("name", name);
if (type == "conv2d") {
op->SetInput("Input", {inputs[0]});
op->SetInput("Filter", {inputs[1]});
if (inputs.size() > 2)
op->SetInput("Bias", {inputs[2]});
else
op->SetInput("Bias", {});
if (inputs.size() > 3) {
op->SetInput("ResidualData", {inputs[3]});
op->SetAttr("fuse_residual_connection", true);
} else {
op->SetInput("ResidualData", {});
op->SetAttr("fuse_residual_connection", false);
}
op->SetOutput("Output", {outputs[0]});
op->SetAttr("use_quantizer", use_quantizer);
op->SetAttr("Scale_in", 1.0f);
op->SetAttr("Scale_out", 1.0f);
op->SetAttr("Scale_weights", std::vector<float>{1.0f});
} else if (type == "pool2d") {
op->SetInput("X", {inputs[0]});
op->SetOutput("Out", {outputs[0]});
op->SetAttr("use_quantizer", use_quantizer);
} else if (type == "dropout") {
op->SetInput("X", {inputs[0]});
op->SetOutput("Out", {outputs[0]});
} else if (type == "fc") {
op->SetInput("Input", {inputs[0]});
if (inputs.size() > 1) op->SetInput("W", {inputs[1]});
if (inputs.size() > 2) op->SetInput("Bias", {inputs[2]});
op->SetOutput("Out", {outputs[0]});
}
}
static const std::initializer_list<std::string> variable_names{
"a", "w1", "c", "d", "w2", "e", "f", "g",
"h", "w3", "b1", "i", "j", "w4", "b2"};
// (a,w1)->Conv1->c and c->Pool1->d
//
// (d,w2)->Conv2->e and e->Pool2->f
//
// d->Dropout1->g and g->Fc1->h and (h,w3,b1,i)->Conv3->j
//
// (d,w4, b2)->Conv4->i
ProgramDesc BuildProgramDesc(bool use_mkldnn, bool use_quantizer) {
ProgramDesc prog;
for (auto& v : variable_names) {
auto* var = prog.MutableBlock(0)->Var(v);
if (v.find("w") == 0 || v.find("b") == 0) {
var->SetPersistable(true);
}
}
SetOp(&prog, "conv2d", "Conv1", {"a", "w1"}, {"c"}, use_mkldnn,
use_quantizer);
SetOp(&prog, "pool2d", "Pool1", {"c"}, {"d"}, use_mkldnn, use_quantizer);
SetOp(&prog, "conv2d", "Conv2", {"d", "w2"}, {"e"}, use_mkldnn,
use_quantizer);
SetOp(&prog, "pool2d", "Pool2", {"e"}, {"f"}, use_mkldnn, use_quantizer);
SetOp(&prog, "dropout", "Dropout1", {"d"}, {"g"}, use_mkldnn);
SetOp(&prog, "fc", "Fc1", {"g"}, {"h"}, use_mkldnn);
SetOp(&prog, "conv2d", "Conv3", {"h", "w3", "b1", "i"}, {"j"}, use_mkldnn,
use_quantizer);
SetOp(&prog, "conv2d", "Conv4", {"c", "w4", "b2"}, {"i"}, use_mkldnn,
use_quantizer);
return prog;
}
void InitTensorHolder(Scope* scope, const paddle::platform::Place& place,
const char* var_name) {
auto x = scope->Var(var_name);
auto tensor = x->GetMutable<LoDTensor>();
tensor->mutable_data(place, proto::VarType::FP32,
::paddle::memory::Allocator::kDefault, 1);
}
void MainTest(const ProgramDesc& prog, int conv_count, int pool_count,
int quant_count, int dequant_count, int added_nodes_count,
float scale) {
std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
// Init scope, as it is used in pass
auto place = paddle::platform::CPUPlace();
NaiveExecutor exe{place};
Scope scope;
exe.CreateVariables(prog, 0, true, &scope);
auto* scales = new VarQuantScale();
for (auto& v : variable_names) {
InitTensorHolder(&scope, place, v.c_str());
LoDTensor tensor;
tensor.Resize({1});
auto* ptr = tensor.mutable_data<double>(place);
ptr[0] = 2.0;
(*scales)[v] = std::make_pair(false, std::move(tensor));
}
graph->Set(kParamScopeAttr, new framework::Scope*(&scope));
auto pass = PassRegistry::Instance().Get("cpu_quantize_pass");
pass->Set("quant_var_scales", scales);
int original_nodes_num = graph->Nodes().size();
graph = pass->Apply(std::move(graph));
int current_nodes_num = graph->Nodes().size();
int quantize_nodes_count = 0;
int dequantize_nodes_count = 0;
int conv2d_nodes_count = 0;
int pool2d_nodes_count = 0;
for (auto* node : graph->Nodes()) {
if (node->IsOp()) {
auto* op = node->Op();
if (op->Type() == "conv2d") {
conv2d_nodes_count++;
auto op_name = boost::get<std::string>(op->GetAttr("name"));
EXPECT_EQ(boost::get<float>(op->GetAttr("Scale_in")), scale)
<< "Scale_in for node '" + op_name + "'.";
EXPECT_EQ(boost::get<float>(op->GetAttr("Scale_out")), scale)
<< "Scale_out for node '" + op_name + "'.";
EXPECT_EQ(
boost::get<std::vector<float>>(op->GetAttr("Scale_weights"))[0],
scale)
<< "Scale_weights for node '" + op_name + "'.";
} else if (op->Type() == "pool2d") {
pool2d_nodes_count++;
} else if (op->Type() == "quantize") {
quantize_nodes_count++;
} else if (op->Type() == "dequantize") {
dequantize_nodes_count++;
}
}
}
EXPECT_EQ(conv2d_nodes_count, conv_count);
EXPECT_EQ(pool2d_nodes_count, pool_count);
EXPECT_EQ(quantize_nodes_count, quant_count);
EXPECT_EQ(dequantize_nodes_count, dequant_count);
EXPECT_EQ(original_nodes_num + added_nodes_count, current_nodes_num);
}
TEST(CpuQuantizePass, quantize) {
bool use_mkldnn = true;
bool use_quantizer = true;
// (a->QUANT1->IN1,w1)->Conv1->OUT1->DEQUANT1->c and
// c->QUANT2->IN2->Pool1->OUT2->DEQUANT2->d
//
// (d->QUANT3->IN3,w2)->Conv2->OUT3->DEQUANT3->e and
// e->QUANT4->IN4->Pool2->OUT4->DEQUANT4->f
//
// d->Dropout1->g and g->Fc1->h and
// (h->QUANT5->IN5,w3,b1,i->QUANT6->IN6)->Conv3->OUT5->DEQUANT5->j
//
// (d->QUANT7->IN7,w4, b2)->Conv4->DEQUANT6->OUT6->i
// Insert nodes: 7 Quant + 7 IN + 6 OUT + 6 DEQUANT
int added_nodes = 7 + 7 + 6 + 6;
MainTest(BuildProgramDesc(use_mkldnn, use_quantizer), 4, 2, 7, 6, added_nodes,
2.0f * 127);
}
TEST(CpuQuantizePass, do_not_quantize) {
bool use_mkldnn = true;
bool use_quantizer = false;
int added_nodes = 0;
MainTest(BuildProgramDesc(use_mkldnn, use_quantizer), 4, 2, 0, 0, added_nodes,
1.0f);
}
} // namespace ir
} // namespace framework
} // namespace paddle
USE_PASS(cpu_quantize_pass);
paddle/fluid/framework/ir/cpu_quantize_squash_pass.cc 0 → 100644
浏览文件 @ 4f42504e
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file eint8_outcept 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 eint8_outpress or
// implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/ir/cpu_quantize_squash_pass.h"
#include <string>
#include <vector>
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/string/pretty_log.h"
namespace paddle {
namespace framework {
namespace ir {
using string::PrettyLogDetail;
void CPUQuantizeSquashPass::FindNodesToKeep(
Graph* graph,
std::unordered_map<const Node*, int>* nodes_keep_counter) const {
GraphPatternDetector gpd;
patterns::DequantAny deq_any_pattern{gpd.mutable_pattern(), "deqant_any"};
deq_any_pattern();
int found_count = 0;
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
GET_IR_NODE_FROM_SUBGRAPH(dequant_out, dequant_out, deq_any_pattern);
if (nodes_keep_counter->find(dequant_out) == nodes_keep_counter->end())
(*nodes_keep_counter)[dequant_out] = 1;
else
(*nodes_keep_counter)[dequant_out] += 1;
found_count++;
};
gpd(graph, handler);
AddStatis(found_count);
}
void CPUQuantizeSquashPass::Squash(
Graph* graph,
std::unordered_map<const Node*, int>* nodes_keep_counter) const {
GraphPatternDetector gpd;
patterns::DequantQuantAny squash_pattern{gpd.mutable_pattern(), "squash"};
squash_pattern();
int found_squash_count = 0;
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
VLOG(4) << "squash requantize-quantize ops pair";
GET_IR_NODE_FROM_SUBGRAPH(dequant_in, dequant_in, squash_pattern);
GET_IR_NODE_FROM_SUBGRAPH(dequant_op, dequant_op, squash_pattern);
GET_IR_NODE_FROM_SUBGRAPH(dequant_out, dequant_out, squash_pattern);
GET_IR_NODE_FROM_SUBGRAPH(quant_op, quant_op, squash_pattern);
GET_IR_NODE_FROM_SUBGRAPH(quant_out, quant_out, squash_pattern);
GET_IR_NODE_FROM_SUBGRAPH(next_op, next_op, squash_pattern);
auto* next_op_desc = next_op->Op();
float dequant_scale = boost::get<float>(dequant_op->Op()->GetAttr("Scale"));
float quant_scale = boost::get<float>(quant_op->Op()->GetAttr("Scale"));
PADDLE_ENFORCE(nodes_keep_counter->find(dequant_out) !=
nodes_keep_counter->end());
// check if dequantize op should be kept or removed, decrease the counter
bool keep_dequant = (*nodes_keep_counter)[dequant_out]-- > 1;
if (dequant_scale == quant_scale) {
// squash dequantize-quantize to nothing
auto quant_out_var_name = quant_out->Name();
auto next_op_inputs = next_op_desc->InputNames();
for (const auto& name : next_op_inputs) {
auto var_name = next_op_desc->Input(name)[0];
if (var_name.compare(quant_out_var_name) == 0) {
next_op_desc->SetInput(
name, std::vector<std::string>({dequant_in->Name()}));
break;
}
}
if (keep_dequant)
GraphSafeRemoveNodes(graph, {quant_op, quant_out});
else
GraphSafeRemoveNodes(graph,
{dequant_op, quant_op, dequant_out, quant_out});
IR_NODE_LINK_TO(dequant_in, next_op);
found_squash_count++;
} else {
// squash dequantize-quantize to requantize op
OpDesc desc;
desc.SetType("requantize");
desc.SetInput("Input", std::vector<std::string>({dequant_in->Name()}));
desc.SetOutput("Output", std::vector<std::string>({quant_out->Name()}));
desc.SetAttr("Scale_in", dequant_scale);
desc.SetAttr("Scale_out", quant_scale);
auto requant_op = g->CreateOpNode(&desc);
if (keep_dequant)
GraphSafeRemoveNodes(graph, {quant_op});
else
GraphSafeRemoveNodes(graph, {dequant_op, quant_op, dequant_out});
IR_NODE_LINK_TO(dequant_in, requant_op);
IR_NODE_LINK_TO(requant_op, quant_out);
found_squash_count++;
}
};
gpd(graph, handler);
AddStatis(found_squash_count);
PrettyLogDetail("--- squashed %d dequantize-quantize pairs",
found_squash_count);
}
std::unique_ptr<ir::Graph> CPUQuantizeSquashPass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
PADDLE_ENFORCE(graph.get());
FusePassBase::Init("cpu_quantize_squash_pass", graph.get());
std::unordered_map<const Node*, int> nodes_keep_counter;
FindNodesToKeep(graph.get(), &nodes_keep_counter);
Squash(graph.get(), &nodes_keep_counter);
return graph;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(cpu_quantize_squash_pass,
paddle::framework::ir::CPUQuantizeSquashPass);
paddle/fluid/framework/ir/cpu_quantize_squash_pass.h 0 → 100644
浏览文件 @ 4f42504e
// Copyright (c) 2019 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 <memory>
#include <string>
#include <unordered_map>
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
#include "paddle/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace ir {
/*
* Squash dequantize->quantize pair pattern into requantize op
*/
class CPUQuantizeSquashPass : public FusePassBase {
public:
virtual ~CPUQuantizeSquashPass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
/*
* For each dequantize's output find the number of operators it is an input to
*/
void FindNodesToKeep(
Graph* graph,
std::unordered_map<const Node*, int>* nodes_keep_counter) const;
/*
* Squash dequantize-quantize ops pairs into requantize or nothing
*/
void Squash(Graph* graph,
std::unordered_map<const Node*, int>* nodes_keep_counter) const;
const std::string name_scope_{"squash"};
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/cpu_quantize_squash_pass_tester.cc 0 → 100644
浏览文件 @ 4f42504e
// Copyright (c) 2019 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/cpu_quantize_squash_pass.h"
#include <gtest/gtest.h>
#include "paddle/fluid/framework/naive_executor.h"
#include "paddle/fluid/platform/place.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,
float scale = 0) {
auto* op = prog->MutableBlock(0)->AppendOp();
op->SetType(type);
op->SetAttr("use_mkldnn", use_mkldnn);
op->SetAttr("name", name);
if (type == "conv2d") {
op->SetInput("Input", {inputs[0]});
if (inputs.size() > 1) op->SetInput("Filter", {inputs[1]});
if (inputs.size() > 2) op->SetInput("Bias", {inputs[2]});
op->SetOutput("Output", {outputs[0]});
} else if (type == "quantize") {
op->SetInput("Input", {inputs[0]});
op->SetOutput("Output", {outputs[0]});
op->SetAttr("Scale", scale);
} else if (type == "dequantize") {
op->SetInput("Input", {inputs[0]});
op->SetOutput("Output", {outputs[0]});
op->SetAttr("Scale", scale);
}
}
// (a,w1,b1)->Conv1->d
// d->Dequant->e
// e->Quant->f
// (f,w2,b2)->Conv2->i
ProgramDesc BuildProgramDesc(bool use_mkldnn, float scale1, float scale2) {
ProgramDesc prog;
for (auto& v : std::initializer_list<std::string>(
{"a", "w1", "b1", "d", "e", "f", "w2", "b2", "i"})) {
auto* var = prog.MutableBlock(0)->Var(v);
if (v.find("w") == 0 || v.find("b") == 0) {
var->SetPersistable(true);
}
}
SetOp(&prog, "conv2d", "Conv1", {"a", "w1", "b1"}, {"d"}, use_mkldnn);
SetOp(&prog, "dequantize", "Dequant", {"d"}, {"e"}, use_mkldnn, scale1);
SetOp(&prog, "quantize", "Quant", {"e"}, {"f"}, use_mkldnn, scale2);
SetOp(&prog, "conv2d", "Conv2", {"f", "w2", "b2"}, {"i"}, use_mkldnn);
return prog;
}
static const std::initializer_list<std::string> variable_names{
"a", "b", "c", "d", "e", "f", "g", "h"};
// a->Conv1->b
// b->Dequant->c
//
// c->Quant1->d and d->Conv2->e
//
// c->Conv3->f
//
// c->Quant2->g and g->Conv4->h
//
ProgramDesc BuildProgramDesc2(bool use_mkldnn, float scale1, float scale2,
float scale3) {
ProgramDesc prog;
for (auto& v : variable_names) {
prog.MutableBlock(0)->Var(v);
}
SetOp(&prog, "conv2d", "Conv1", {"a"}, {"b"}, use_mkldnn);
SetOp(&prog, "dequantize", "Dequant", {"b"}, {"c"}, use_mkldnn, scale1);
SetOp(&prog, "quantize", "Quant1", {"c"}, {"d"}, use_mkldnn, scale2);
SetOp(&prog, "conv2d", "Conv2", {"d"}, {"e"}, use_mkldnn);
SetOp(&prog, "conv2d", "Conv3", {"c"}, {"f"}, use_mkldnn);
SetOp(&prog, "quantize", "Quant2", {"c"}, {"g"}, use_mkldnn, scale3);
SetOp(&prog, "conv2d", "Conv4", {"g"}, {"h"}, use_mkldnn);
return prog;
}
void InitTensorHolder(Scope* scope, const paddle::platform::Place& place,
const char* var_name) {
auto x = scope->Var(var_name);
auto tensor = x->GetMutable<LoDTensor>();
tensor->mutable_data(place, proto::VarType::FP32,
::paddle::memory::Allocator::kDefault, 1);
}
void MainTest(const ProgramDesc& prog, int removed_nodes_num) {
std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
// Init scope, as it is used in pass
auto place = paddle::platform::CPUPlace();
NaiveExecutor exe{place};
Scope scope;
exe.CreateVariables(prog, 0, true, &scope);
for (auto& v : variable_names) {
InitTensorHolder(&scope, place, v.c_str());
}
graph->Set(kParamScopeAttr, new framework::Scope*(&scope));
auto pass = PassRegistry::Instance().Get("cpu_quantize_squash_pass");
int original_nodes_num = graph->Nodes().size();
graph = pass->Apply(std::move(graph));
int current_nodes_num = graph->Nodes().size();
EXPECT_EQ(original_nodes_num - removed_nodes_num, current_nodes_num);
}
TEST(CpuQuantizeSquashPass, equal_scales) {
auto scale = 1.2345f;
auto use_mkldnn = true;
// Remove 4 nodes: Dequant, Quant, e, f
auto remove_nodes = 4;
MainTest(BuildProgramDesc(use_mkldnn, scale, scale), remove_nodes);
use_mkldnn = !use_mkldnn;
MainTest(BuildProgramDesc(use_mkldnn, scale, scale), remove_nodes);
}
TEST(CpuQuantizeSquashPass, inequal_scales) {
auto scale1 = 1.2345f;
auto scale2 = 21.0f;
auto use_mkldnn = true;
// Remove 3 nodes: Dequant, Quant, e
// Insert 1 node: requantize
auto remove_nodes = 2;
MainTest(BuildProgramDesc(use_mkldnn, scale1, scale2), remove_nodes);
use_mkldnn = !use_mkldnn;
MainTest(BuildProgramDesc(use_mkldnn, scale1, scale2), remove_nodes);
}
TEST(CpuQuantizeSquashPass, branch_to_equal_inequal_and_fp32) {
// Delete both quantize ops,
// bypass dequantize in both branches,
// insert requantize on one branch
auto scale = 1.2345f;
auto scale2 = 21.0f;
auto use_mkldnn = true;
// Remove 3 nodes: Quant1, Quant2, g
// Insert 1 node: requantize
auto remove_nodes = 2;
MainTest(BuildProgramDesc2(use_mkldnn, scale, scale, scale2), remove_nodes);
use_mkldnn = !use_mkldnn;
MainTest(BuildProgramDesc2(use_mkldnn, scale, scale, scale2), remove_nodes);
}
} // namespace ir
} // namespace framework
} // namespace paddle
USE_PASS(cpu_quantize_squash_pass);
paddle/fluid/framework/ir/graph_pattern_detector.cc
浏览文件 @ 4f42504e
......@@ -90,7 +90,8 @@ void GraphPatternDetector::operator()(Graph *graph,
ValidateByNodeRole(&subgraphs);
if (subgraphs.empty()) return;
PrettyLogEndl(Style::detail(), "--- detect %d subgraphs", subgraphs.size());
PrettyLogEndl(Style::detail(), "--- detected %d subgraphs",
subgraphs.size());
int id = 0;
for (auto &g : subgraphs) {
VLOG(3) << "optimizing #" << id++ << " subgraph";
......@@ -1074,9 +1075,53 @@ PDNode *patterns::Conv::operator()() {
->AsOutput()
->assert_is_op_output("conv2d", "Output");
conv_op->LinksFrom({input_var, filter_var});
conv_op->LinksTo({output_var});
conv_op->LinksFrom({input_var, filter_var}).LinksTo({output_var});
return output_var;
}
PDNode *patterns::ConvResidual::operator()(bool with_residual_data) {
auto conv_op = pattern->NewNode(conv_op_repr())->assert_is_op("conv2d");
if (!with_residual_data)
conv_op->assert_op_attr("fuse_residual_connection", false);
auto input_var = pattern->NewNode(conv_input_repr())
->AsInput()
->assert_is_op_input("conv2d", "Input");
auto filter_var = pattern->NewNode(conv_filter_repr())
->AsInput()
->assert_is_op_input("conv2d", "Filter");
auto output_var = pattern->NewNode(conv_output_repr())
->AsOutput()
->assert_is_op_output("conv2d", "Output");
std::vector<PDNode *> links_from{input_var, filter_var};
if (with_residual_data) {
auto res_conn_var = pattern->NewNode(conv_residual_data_repr())
->AsInput()
->assert_is_op_input("conv2d", "ResidualData");
links_from.push_back(res_conn_var);
}
conv_op->LinksFrom(links_from).LinksTo({output_var});
return output_var;
}
PDNode *patterns::Pool::operator()() {
auto pool_op = pattern->NewNode(pool_op_repr())->assert_is_op("pool2d");
auto input_var = pattern->NewNode(pool_input_repr())
->AsInput()
->assert_is_op_input("pool2d", "X");
auto output_var = pattern->NewNode(pool_output_repr())
->AsOutput()
->assert_is_op_output("pool2d", "Out");
pool_op->LinksFrom({input_var}).LinksTo({output_var});
return output_var;
}
......@@ -1301,6 +1346,51 @@ PDNode *patterns::ConvAffineChannel::operator()(
return ac_out_var;
}
PDNode *patterns::DequantQuantAny::operator()() {
auto *dequant_in = pattern->NewNode(dequant_in_repr())
->AsInput()
->assert_is_op_input("dequantize", "Input");
auto *dequant_op =
pattern->NewNode(dequant_op_repr())->assert_is_op("dequantize");
auto *dequant_out = pattern->NewNode(dequant_out_repr())
->AsOutput()
->assert_is_op_output("dequantize", "Output");
auto *quant_op = pattern->NewNode(quant_op_repr())
->assert_is_op("quantize")
->AsIntermediate();
auto *quant_out = pattern->NewNode(quant_out_repr())
->AsOutput()
->assert_is_op_output("quantize");
auto *next_op = pattern->NewNode(next_op_repr())->assert_is_op();
dequant_op->LinksFrom({dequant_in}).LinksTo({dequant_out});
quant_op->LinksFrom({dequant_out}).LinksTo({quant_out});
next_op->LinksFrom({quant_out});
return quant_out;
}
PDNode *patterns::DequantAny::operator()() {
auto *dequant_op =
pattern->NewNode(dequant_op_repr())->assert_is_op("dequantize");
auto *dequant_out = pattern->NewNode(dequant_out_repr())
->AsOutput()
->assert_is_op_output("dequantize", "Output");
auto *next_op = pattern->NewNode(next_op_repr())->assert_is_op();
dequant_op->LinksTo({dequant_out});
next_op->LinksFrom({dequant_out});
return dequant_out;
}
// a -> transpose_op(1) -> transpose_out_a -> flatten_op(1) -> flatten_out_a
// b -> transpose_op(2) -> transpose_out_b -> flatten_op(2) -> flatten_out_b
// ...
......
paddle/fluid/framework/ir/graph_pattern_detector.h
浏览文件 @ 4f42504e
......@@ -18,8 +18,11 @@
#include <gtest/gtest_prod.h>
#endif
#include <memory>
#include <numeric>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/ir/graph.h"
......@@ -656,6 +659,35 @@ struct Conv : public PatternBase {
PATTERN_DECL_NODE(conv_output);
};
// Convolution op with residual data
struct ConvResidual : public PatternBase {
ConvResidual(PDPattern* pattern, const std::string& name_scope)
: PatternBase(pattern, name_scope, "conv_residual") {}
PDNode* operator()(bool with_residual_data);
PATTERN_DECL_NODE(conv_op);
PATTERN_DECL_NODE(conv_input);
PATTERN_DECL_NODE(conv_filter);
PATTERN_DECL_NODE(conv_residual_data);
PATTERN_DECL_NODE(conv_output);
};
// Pool op
// Forward pass for pooling.
// pool_input is the input.
// pool_output is a result of the operator.
struct Pool : public PatternBase {
Pool(PDPattern* pattern, const std::string& name_scope)
: PatternBase(pattern, name_scope, "pooling") {}
PDNode* operator()();
PATTERN_DECL_NODE(pool_op);
PATTERN_DECL_NODE(pool_input);
PATTERN_DECL_NODE(pool_output);
};
// ElementwiseAdd used in residual connections.
// y_var is used and convolution output.
// The operator is removed, when residual
......@@ -766,6 +798,34 @@ struct ConvAffineChannel : public PatternBase {
PATTERN_DECL_NODE(ac_out); // Out
};
// Dequantize + Quantize + anyOP
// This pattern is used for squashing the dequantize-quantize pairs.
struct DequantQuantAny : public PatternBase {
DequantQuantAny(PDPattern* pattern, const std::string& name_scope)
: PatternBase(pattern, name_scope, "dequant_quant_any") {}
PDNode* operator()();
PATTERN_DECL_NODE(dequant_in);
PATTERN_DECL_NODE(dequant_op);
PATTERN_DECL_NODE(dequant_out);
PATTERN_DECL_NODE(quant_op);
PATTERN_DECL_NODE(quant_out);
PATTERN_DECL_NODE(next_op);
};
// Dequantize + anyOP
// This quantize is used for getting number of ops the Dequantize's
// output is an input to.
struct DequantAny : public PatternBase {
DequantAny(PDPattern* pattern, const std::string& name_scope)
: PatternBase(pattern, name_scope, "dequant_any") {}
PDNode* operator()();
PATTERN_DECL_NODE(dequant_op);
PATTERN_DECL_NODE(dequant_out);
PATTERN_DECL_NODE(next_op);
};
struct TransposeFlattenConcat : public PatternBase {
TransposeFlattenConcat(PDPattern* pattern, const std::string& name_scope)
: PatternBase(pattern, name_scope, "transpose_flatten_concat") {}
......
paddle/fluid/framework/ir/sync_batch_norm_pass.cc 0 → 100644
浏览文件 @ 4f42504e
/* Copyright (c) 2019 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/sync_batch_norm_pass.h"
#include <memory>
#include <string>
#include <utility>
namespace paddle {
namespace framework {
namespace ir {
std::unique_ptr<ir::Graph> SyncBatchNormPass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
VLOG(3) << "Use synchronous batch norm";
for (const Node* n : graph->Nodes()) {
if (n->IsOp()) {
auto* op = n->Op();
if (op->Type() == "batch_norm") {
op->SetType("sync_batch_norm");
}
if (op->Type() == "batch_norm_grad") {
op->SetType("sync_batch_norm_grad");
}
}
}
return graph;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(sync_batch_norm_pass, paddle::framework::ir::SyncBatchNormPass);
paddle/fluid/framework/ir/sync_batch_norm_pass.h 0 → 100644
浏览文件 @ 4f42504e
/* Copyright (c) 2019 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 <memory>
#include "paddle/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace ir {
class SyncBatchNormPass : public Pass {
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/sync_batch_norm_pass_tester.cc 0 → 100644
浏览文件 @ 4f42504e
// Copyright (c) 2019 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/sync_batch_norm_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) {
auto* op = prog->MutableBlock(0)->AppendOp();
op->SetType(type);
op->SetAttr("name", name);
op->SetInput("X", inputs);
op->SetOutput("Out", outputs);
}
// (a, conv_w)->conv2d->b
// (b, bn_scale, bn_bias, mean, var)->batch_norm
// ->(c, mean, var, save_mean, save_inv_var)
ProgramDesc BuildProgramDesc() {
ProgramDesc prog;
for (auto& v : std::vector<std::string>({"a", "conv_w", "b", "bn_scale",
"bn_bias", "mean", "var", "c",
"save_mean", "save_inv_var"})) {
auto* var = prog.MutableBlock(0)->Var(v);
if (v == "conv_w" || v == "bn_scale" || v == "bn_bias" || v == "mean" ||
v == "var") {
var->SetPersistable(true);
}
}
SetOp(&prog, "conv2d", "conv", std::vector<std::string>({"a", "conv_w"}),
std::vector<std::string>({"b"}));
SetOp(&prog, "batch_norm", "bn",
std::vector<std::string>({"b", "bn_scale", "bn_bias", "mean", "var"}),
std::vector<std::string>(
{"c", "mean", "var", "save_mean", "save_inv_var"}));
return prog;
}
TEST(IsTestPass, basic) {
auto prog = BuildProgramDesc();
std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
auto pass = PassRegistry::Instance().Get("sync_batch_norm_pass");
graph = pass->Apply(std::move(graph));
for (auto* node : graph->Nodes()) {
if (node->IsOp()) {
auto* op = node->Op();
auto op_name = boost::get<std::string>(op->GetAttr("name"));
if (op_name == "bn") {
ASSERT_EQ(op->Type(), "sync_batch_norm");
}
}
}
}
} // namespace ir
} // namespace framework
} // namespace paddle
USE_PASS(sync_batch_norm_pass);
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ 4f42504e
......@@ -14,8 +14,10 @@ limitations under the License. */
#include "paddle/fluid/framework/parallel_executor.h"
#include <algorithm>
#include <memory>
#include <string>
#include <tuple>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/ir/graph_helper.h"
......@@ -251,6 +253,20 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
member_->nccl_ctxs_.reset(new platform::NCCLContextMap(
member_->places_, nccl_id, build_strategy.num_trainers_,
build_strategy.trainer_id_));
std::unique_ptr<platform::NCCLContextMap> dev_nccl_ctxs;
dev_nccl_ctxs.reset(new platform::NCCLContextMap(member_->places_));
// Initialize device context's nccl comm
// Note, more than one ParallelExecutor with same place, the nccl comm will
// be rewrite and there will be some problem.
for (size_t dev_id = 0; dev_id < member_->places_.size(); ++dev_id) {
auto &nccl_ctx = dev_nccl_ctxs->at(dev_id);
platform::DeviceContextPool &pool =
platform::DeviceContextPool::Instance();
auto *dev_ctx = static_cast<platform::CUDADeviceContext *>(
pool.Get(member_->places_[dev_id]));
dev_ctx->set_nccl_comm(nccl_ctx.comm());
}
#else
PADDLE_THROW("Not compiled with CUDA");
#endif
......
paddle/fluid/imperative/CMakeLists.txt
浏览文件 @ 4f42504e
......@@ -2,4 +2,5 @@ if(WITH_PYTHON)
cc_library(layer SRCS layer.cc DEPS proto_desc operator device_context blas pybind)
cc_library(tracer SRCS tracer.cc DEPS proto_desc device_context pybind)
cc_library(engine SRCS engine.cc)
cc_library(imperative_profiler SRCS profiler.cc)
endif()
paddle/fluid/imperative/layer.cc
浏览文件 @ 4f42504e
......@@ -214,10 +214,8 @@ framework::LoDTensor& VarBase::GradValue() {
}
std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
if (grad_op_descs_.empty() && backward_id_ <= 0) {
VLOG(3) << "op with no grad: " << Type();
return {};
}
PADDLE_ENFORCE(!grad_op_descs_.empty() || backward_id_ > 0,
"%s has no backward implementation", Type());
VLOG(3) << "apply op grad: " << Type();
std::vector<framework::VariableValueMap> tmp_grad_outputs;
......@@ -239,7 +237,7 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
VLOG(3) << "apply grad op " << grad_op_desc->Type();
// Allocate tmp grad output variable
for (auto it : grad_output_variable_map) {
for (const auto& it : grad_output_variable_map) {
auto& outputs = tmp_grad_outputs[k][it.first];
outputs.reserve(it.second.size());
for (size_t i = 0; i < it.second.size(); ++i) {
......@@ -273,9 +271,9 @@ std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
// Add tmp grad outputs to original grad vars
for (size_t k = 0; k < grad_output_vars_.size(); ++k) {
for (auto it : grad_output_vars_[k]) {
for (const auto& it : grad_output_vars_[k]) {
auto& outputs = tmp_grad_outputs[k][it.first];
auto& origin_outputs = it.second;
const auto& origin_outputs = it.second;
PADDLE_ENFORCE_EQ(outputs.size(), origin_outputs.size());
for (size_t i = 0; i < outputs.size(); ++i) {
......
paddle/fluid/imperative/layer.h
浏览文件 @ 4f42504e
......@@ -294,17 +294,23 @@ class PYBIND11_HIDDEN OpBase {
void InvokeBackwardHooks();
void TrackPreOp(const VarBase* inp_var, const std::string& inp_name) {
if (inp_var->PreOp() && !inp_var->IsStopGradient()) {
VLOG(3) << "add pre op " << inp_var->PreOp()->Type() << " in slot "
<< inp_name;
pre_ops_[inp_name].push_back(inp_var->PreOp());
pre_ops_out_idx_[inp_name].push_back(inp_var->PreOpOutIdx());
} else {
VLOG(3) << "no pre op in slot " << inp_name
<< " input var stop_gradient: " << inp_var->IsStopGradient();
pre_ops_[inp_name].push_back(nullptr);
// pre_ops_out_idx_[inp_name].push_back(-1);
void TrackPreOp(const std::string& inp_name,
const std::vector<VarBase*>& inputs) {
auto& pre_ops_list = pre_ops_[inp_name];
pre_ops_list.reserve(inputs.size());
auto& pre_ops_out_idx_list = pre_ops_out_idx_[inp_name];
for (VarBase* inp_var : inputs) {
if (inp_var->PreOp() && !inp_var->IsStopGradient()) {
VLOG(3) << "add pre op " << inp_var->PreOp()->Type() << " in slot "
<< inp_name;
pre_ops_list.emplace_back(inp_var->PreOp());
pre_ops_out_idx_list.push_back(inp_var->PreOpOutIdx());
} else {
VLOG(3) << "no pre op in slot " << inp_name
<< " input var stop_gradient: " << inp_var->IsStopGradient();
pre_ops_list.emplace_back(nullptr);
// pre_ops_out_idx_list.push_back(-1);
}
}
}
......
paddle/fluid/imperative/profiler.cc 0 → 100644
浏览文件 @ 4f42504e
// 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/imperative/profiler.h"
#ifdef WITH_GPERFTOOLS
#include "gperftools/profiler.h"
#endif
#include <gflags/gflags.h>
#include <glog/logging.h>
#include <mutex> // NOLINT
#include <thread> // NOLINT
DEFINE_string(
tracer_profile_fname, "xxgperf",
"Profiler filename for imperative tracer, which generated by gperftools."
"Only valid when compiled `WITH_PROFILER=ON`. Empty if disable.");
namespace paddle {
namespace imperative {
static std::once_flag gTracerProfileOnce;
#ifdef WITH_GPERFTOOLS
static bool gTracerProfilerStarted = false;
#endif
void StartProfile() {
if (!FLAGS_tracer_profile_fname.empty()) {
std::call_once(gTracerProfileOnce, [] {
#ifdef WITH_GPERFTOOLS
ProfilerStart(FLAGS_tracer_profile_fname.c_str());
gTracerProfilerStarted = true;
#else
LOG(WARNING) << "Paddle is not compiled with gperftools. "
"FLAGS_tracer_profile_fname will be ignored";
#endif
});
}
}
void StopProfile() {
#ifdef WITH_GPERFTOOLS
ProfilerFlush();
#else
LOG(WARNING) << "Paddle is not compiled with gperftools. "
"FLAGS_tracer_profile_fname will be ignored";
#endif
}
} // namespace imperative
} // namespace paddle
paddle/fluid/imperative/profiler.h 0 → 100644
浏览文件 @ 4f42504e
// 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
namespace paddle {
namespace imperative {
extern void StartProfile();
extern void StopProfile();
} // namespace imperative
} // namespace paddle
paddle/fluid/imperative/tracer.cc
浏览文件 @ 4f42504e
......@@ -23,34 +23,21 @@
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/enforce.h"
#ifdef WITH_GPERFTOOLS
#include "gperftools/profiler.h"
#endif
DEFINE_string(
tracer_profile_fname, "",
"Profiler filename for imperative tracer, which generated by gperftools."
"Only valid when compiled `WITH_PROFILER=ON`. Empty if disable.");
namespace paddle {
namespace imperative {
static std::once_flag gTracerProfileOnce;
#ifdef WITH_GPERFTOOLS
static bool gTracerProfilerStarted = false;
#endif
void CreateGradOp(const framework::OpDesc& op_desc,
const std::unordered_set<std::string>& no_grad_set,
const std::vector<framework::BlockDesc*>& grad_sub_block,
std::vector<framework::OpDesc*>* grad_op_descs,
std::unordered_map<std::string, std::string>* grad_to_var) {
PADDLE_ENFORCE(grad_op_descs->empty());
std::vector<std::unique_ptr<framework::OpDesc>> descs =
framework::OpInfoMap::Instance()
.Get(op_desc.Type())
.GradOpMaker()(op_desc, no_grad_set, grad_to_var, grad_sub_block);
const framework::OpInfo& op_info =
framework::OpInfoMap::Instance().Get(op_desc.Type());
if (!op_info.grad_op_maker_) return;
std::vector<std::unique_ptr<framework::OpDesc>> descs =
op_info.GradOpMaker()(op_desc, no_grad_set, grad_to_var, grad_sub_block);
for (auto& desc : descs) {
grad_op_descs->emplace_back(desc.release());
}
......@@ -145,31 +132,13 @@ framework::VariableNameMap CreateOutputVarNameMap(
return result;
}
Tracer::Tracer(framework::BlockDesc* root_block) : root_block_(root_block) {
if (!FLAGS_tracer_profile_fname.empty()) {
std::call_once(gTracerProfileOnce, [] {
#ifdef WITH_GPERFTOOLS
ProfilerStart(FLAGS_tracer_profile_fname.c_str());
gTracerProfilerStarted = true;
#else
LOG(WARNING) << "Paddle is not compiled with gperftools. "
"FLAGS_tracer_profile_fname will be ignored";
#endif
});
}
}
Tracer::Tracer(framework::BlockDesc* root_block) : root_block_(root_block) {}
std::set<std::string> Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
const VarBasePtrMap& outputs,
framework::AttributeMap attrs_map,
const platform::Place expected_place,
const bool stop_gradient) {
#ifdef WITH_GPERFTOOLS
if (gTracerProfilerStarted) {
ProfilerFlush();
}
#endif
framework::VariableValueMap invars_map;
framework::VariableValueMap outvars_map;
......@@ -184,7 +153,6 @@ std::set<std::string> Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
inp->Name());
invars.emplace_back(inp->var_);
op->TrackPreOp(inp, it.first);
if (!stop_gradient) {
current_vars_map[inp->Name()] = inp;
}
......@@ -192,6 +160,7 @@ std::set<std::string> Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
<< " inited: " << inp->var_->IsInitialized()
<< " stop_grad: " << inp->IsStopGradient();
}
op->TrackPreOp(it.first, it.second);
}
op->output_vars_ = outputs;
......@@ -319,9 +288,7 @@ std::vector<VarBase*> Tracer::PyTrace(OpBase* op,
std::vector<framework::Variable*> ret_vars =
PyLayer::Apply(op->forward_id_, inputs);
for (VarBase* inp : inputs) {
op->TrackPreOp(inp, PyLayer::kFwdInp);
}
op->TrackPreOp(PyLayer::kFwdInp, inputs);
std::vector<VarBase*>& outputs = op->output_vars_[PyLayer::kFwdOut];
outputs.reserve(ret_vars.size());
......
paddle/fluid/inference/CMakeLists.txt
浏览文件 @ 4f42504e
......@@ -91,5 +91,5 @@ if(WITH_TESTING)
add_subdirectory(tests/book)
if(WITH_INFERENCE_API_TEST)
add_subdirectory(tests/api)
endif()
endif()
endif()
paddle/fluid/inference/analysis/argument.h
浏览文件 @ 4f42504e
......@@ -27,6 +27,7 @@
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/ir/graph.h"
......@@ -38,7 +39,10 @@
namespace paddle {
namespace inference {
namespace analysis {
using framework::ir::Graph;
using VarQuantScale =
std::unordered_map<std::string, std::pair<bool, framework::LoDTensor>>;
/*
* The argument definition of both Pass and PassManagers.
......@@ -127,6 +131,8 @@ struct Argument {
// Pass a set of op types to enable its mkldnn kernel
DECL_ARGUMENT_FIELD(mkldnn_enabled_op_types, MKLDNNEnabledOpTypes,
std::unordered_set<std::string>);
// Scales for variables to be quantized
DECL_ARGUMENT_FIELD(quant_var_scales, QuantVarScales, VarQuantScale);
// Passed from config.
DECL_ARGUMENT_FIELD(use_gpu, UseGPU, bool);
......
paddle/fluid/inference/analysis/ir_pass_manager.cc
浏览文件 @ 4f42504e
......@@ -14,6 +14,7 @@
#include "paddle/fluid/inference/analysis/ir_pass_manager.h"
#include <string>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph.h"
......@@ -55,14 +56,14 @@ void IRPassManager::CreatePasses(Argument *argument,
".dot";
pass->Set("graph_viz_path", new std::string(std::move(dot_file_path)));
pass_num++;
}
if (pass_name == "mkldnn_placement_pass") {
} else if (pass_name == "mkldnn_placement_pass") {
pass->Set("mkldnn_enabled_op_types",
new std::unordered_set<std::string>(
argument->mkldnn_enabled_op_types()));
}
if (pass_name == "tensorrt_subgraph_pass") {
} else if (pass_name == "cpu_quantize_pass") {
pass->Set("quant_var_scales",
new VarQuantScale(argument->quant_var_scales()));
} else if (pass_name == "tensorrt_subgraph_pass") {
pass->Set("workspace_size", new int(argument->tensorrt_workspace_size()));
pass->Set("max_batch_size", new int(argument->tensorrt_max_batch_size()));
pass->Set("min_subgraph_size",
......
paddle/fluid/inference/api/analysis_config.cc
浏览文件 @ 4f42504e
......@@ -219,7 +219,14 @@ void AnalysisConfig::Update() {
}
if (enable_memory_optim_) {
pass_builder()->AppendAnalysisPass("memory_optimize_pass");
auto analysis_passes = pass_builder()->AnalysisPasses();
auto memory_opti_pass_name = "memory_optimize_pass";
bool already_exists =
std::find(analysis_passes.begin(), analysis_passes.end(),
memory_opti_pass_name) != analysis_passes.end();
if (!already_exists) {
pass_builder()->AppendAnalysisPass(memory_opti_pass_name);
}
}
if (ir_debug_) {
......
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ 4f42504e
......@@ -44,10 +44,10 @@ if (WITH_DISTRIBUTE)
SET(OP_PREFETCH_DEPS ${OP_PREFETCH_DEPS} parameter_prefetch)
endif()
register_operators(EXCLUDES py_func_op warpctc_op conv_fusion_op DEPS ${OP_HEADER_DEPS} ${OP_PREFETCH_DEPS})
register_operators(EXCLUDES py_func_op warpctc_op conv_fusion_op sync_batch_norm_op DEPS ${OP_HEADER_DEPS} ${OP_PREFETCH_DEPS})
# warpctc_op needs cudnn 7 above
if (WITH_GPU)
# warpctc_op needs cudnn 7 above
if (${CUDNN_MAJOR_VERSION} VERSION_LESS 7)
op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale SRCS warpctc_op.cc warpctc_op.cu.cc)
else()
......@@ -58,6 +58,10 @@ if (WITH_GPU)
op_library(conv_fusion_op)
file(APPEND ${pybind_file} "USE_CUDA_ONLY_OP(conv2d_fusion);\n")
endif()
if (NOT WIN32)
op_library(sync_batch_norm_op)
file(APPEND ${pybind_file} "USE_CUDA_ONLY_OP(sync_batch_norm);\n")
endif()
else()
op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale)
endif()
......
paddle/fluid/operators/batch_norm_op.cu
浏览文件 @ 4f42504e
......@@ -33,26 +33,6 @@ using CudnnDataType = platform::CudnnDataType<T>;
template <typename T>
using BatchNormParamType = typename CudnnDataType<T>::BatchNormParamType;
void ExtractNCWHD(const framework::DDim &dims, const DataLayout &data_layout,
int *N, int *C, int *H, int *W, int *D) {
*N = dims[0];
if (dims.size() == 2) {
*C = dims[1];
*H = 1;
*W = 1;
*D = 1;
} else {
*C = data_layout == DataLayout::kNCHW ? dims[1] : dims[dims.size() - 1];
*H = data_layout == DataLayout::kNCHW ? dims[2] : dims[1];
*W = dims.size() > 3
? (data_layout == DataLayout::kNCHW ? dims[3] : dims[2])
: 1;
*D = dims.size() > 4
? (data_layout == DataLayout::kNCHW ? dims[4] : dims[3])
: 1;
}
}
template <typename T>
class BatchNormKernel<platform::CUDADeviceContext, T>
: public framework::OpKernel<T> {
......@@ -196,22 +176,6 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
}
};
template <typename T, framework::DataLayout layout>
static __global__ void KeBNBackwardData(const T *dy,
const BatchNormParamType<T> *scale,
const BatchNormParamType<T> *variance,
const double epsilon, const int C,
const int HxW, const int num, T *dx) {
int gid = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
for (int i = gid; i < num; i += stride) {
const int c = layout == framework::DataLayout::kNCHW ? i / HxW % C : i % C;
BatchNormParamType<T> inv_var = 1.0 / sqrt(variance[c] + epsilon);
dx[i] = static_cast<T>(static_cast<BatchNormParamType<T>>(dy[i]) *
scale[c] * inv_var);
}
}
template <typename T, int BlockDim, framework::DataLayout layout>
static __global__ void KeBNBackwardScaleBias(
const T *dy, const T *x, const BatchNormParamType<T> *mean,
......@@ -248,6 +212,22 @@ static __global__ void KeBNBackwardScaleBias(
}
}
template <typename T, framework::DataLayout layout>
static __global__ void KeBNBackwardData(const T *dy,
const BatchNormParamType<T> *scale,
const BatchNormParamType<T> *variance,
const double epsilon, const int C,
const int HxW, const int num, T *dx) {
int gid = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
for (int i = gid; i < num; i += stride) {
const int c = layout == framework::DataLayout::kNCHW ? i / HxW % C : i % C;
BatchNormParamType<T> inv_var = 1.0 / sqrt(variance[c] + epsilon);
dx[i] = static_cast<T>(static_cast<BatchNormParamType<T>>(dy[i]) *
scale[c] * inv_var);
}
}
template <typename T>
class BatchNormGradKernel<platform::CUDADeviceContext, T>
: public framework::OpKernel<T> {
......@@ -383,7 +363,7 @@ class BatchNormGradKernel<platform::CUDADeviceContext, T>
KeBNBackwardScaleBias<T, block, framework::DataLayout::kNCHW><<<
grid2, block, 0, dev_ctx.stream()>>>(
d_y->data<T>(), x->data<T>(), running_mean_data, running_var_data,
epsilon, C, H * W, num, d_scale->data<BatchNormParamType<T>>(),
epsilon, N, C, H * W * D, d_scale->data<BatchNormParamType<T>>(),
d_bias->data<BatchNormParamType<T>>());
}
} else {
......@@ -394,10 +374,10 @@ class BatchNormGradKernel<platform::CUDADeviceContext, T>
running_var_data, epsilon, C, H * W, num, d_x->data<T>());
}
if (d_scale && d_bias) {
KeBNBackwardScaleBias<T, block, framework::DataLayout::kNCHW><<<
KeBNBackwardScaleBias<T, block, framework::DataLayout::kNHWC><<<
grid2, block, 0, dev_ctx.stream()>>>(
d_y->data<T>(), x->data<T>(), running_mean_data, running_var_data,
epsilon, C, H * W, num, d_scale->data<BatchNormParamType<T>>(),
epsilon, N, C, H * W * D, d_scale->data<BatchNormParamType<T>>(),
d_bias->data<BatchNormParamType<T>>());
}
}
......
paddle/fluid/operators/batch_norm_op.h
浏览文件 @ 4f42504e
......@@ -13,6 +13,9 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <memory>
#include <string>
#include <unordered_map>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
......@@ -35,17 +38,84 @@ template <typename T>
using ConstEigenVectorArrayMap =
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, 1>>;
class BatchNormOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override;
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override;
};
class BatchNormGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override;
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override;
};
class BatchNormOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override;
};
class BatchNormGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override;
virtual std::string GradOpType() const {
return this->ForwardOpType() + "_grad";
}
};
class BatchNormOpInferVarType
: public framework::PassInDtypeAndVarTypeToOutput {
protected:
std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
const override {
return std::unordered_map<std::string, std::string>{{"X", /*->*/ "Y"}};
}
};
template <typename DeviceContext, typename T>
class BatchNormKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override;
void Compute(const framework::ExecutionContext &ctx) const override;
};
template <typename DeviceContext, typename T>
class BatchNormGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override;
void Compute(const framework::ExecutionContext &ctx) const override;
};
inline void ExtractNCWHD(const framework::DDim &dims,
const DataLayout &data_layout, int *N, int *C, int *H,
int *W, int *D) {
*N = dims[0];
if (dims.size() == 2) {
*C = dims[1];
*H = 1;
*W = 1;
*D = 1;
} else {
*C = data_layout == DataLayout::kNCHW ? dims[1] : dims[dims.size() - 1];
*H = data_layout == DataLayout::kNCHW ? dims[2] : dims[1];
*W = dims.size() > 3
? (data_layout == DataLayout::kNCHW ? dims[3] : dims[2])
: 1;
*D = dims.size() > 4
? (data_layout == DataLayout::kNCHW ? dims[4] : dims[3])
: 1;
}
}
} // namespace operators
} // namespace paddle
paddle/fluid/operators/conv_op.cc
浏览文件 @ 4f42504e
......@@ -14,6 +14,7 @@ limitations under the License. */
#include "paddle/fluid/operators/conv_op.h"
#include <memory>
#include <string>
#include <vector>
......@@ -194,6 +195,12 @@ void Conv2DOpMaker::Make() {
AddAttr<bool>("use_mkldnn",
"(bool, default false) Only used in mkldnn kernel")
.SetDefault(false);
AddAttr<bool>("use_quantizer",
"(bool, default false) "
"Set to true for operators that should be quantized and use "
"int8 kernel. "
"Only used on CPU.")
.SetDefault(false);
AddAttr<bool>("fuse_relu", "(bool, default false) Only used in mkldnn kernel")
.SetDefault(false);
AddAttr<bool>("fuse_residual_connection",
......
paddle/fluid/operators/cross_entropy_op.cc
浏览文件 @ 4f42504e
......@@ -248,10 +248,14 @@ class CrossEntropyOp2 : public CrossEntropyOpBase {
PADDLE_ENFORCE(ctx->HasOutput("XShape"),
"Output(XShape) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("MatchX"),
"Output(MatchX) should be not null.");
auto x_dims = ctx->GetInputDim("X");
auto x_dims_vec = framework::vectorize(x_dims);
x_dims_vec.push_back(0);
ctx->SetOutputDim("XShape", framework::make_ddim(x_dims_vec));
x_dims[x_dims.size() - 1] = 1;
ctx->SetOutputDim("MatchX", x_dims);
ctx->ShareLoD("X", /*->*/ "XShape");
}
......@@ -264,6 +268,10 @@ class CrossEntropyOp2 : public CrossEntropyOpBase {
class CrossEntropyGradientOp2 : public CrossEntropyGradientOpBase {
public:
using CrossEntropyGradientOpBase::CrossEntropyGradientOpBase;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("MatchX"), "Input(MatchX) must exist");
CrossEntropyGradientOpBase::InferShape(ctx);
}
protected:
virtual framework::DDim GetXDim(framework::InferShapeContext* ctx) const {
......@@ -295,6 +303,8 @@ class CrossEntropyOpMaker2 : public framework::OpProtoAndCheckerMaker {
"with 'X' except that the last dimension size is 1. It "
"represents the cross entropy loss.");
AddOutput("XShape", "Temporaily variable to save shape and LoD of X.");
AddOutput("MatchX",
"X value that matches label, used for gradient computation.");
AddAttr<int>("ignore_index",
"(int, default -100), Specifies a target value that is"
"ignored and does not contribute to the input gradient."
......@@ -327,7 +337,7 @@ class CrossEntropyGradOpDescMaker2 : public framework::SingleGradOpDescMaker {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("cross_entropy_grad2");
op->SetInput("Label", Input("Label"));
op->SetInput("Y", Output("Y"));
op->SetInput("MatchX", Output("MatchX"));
op->SetInput("XShape", Output("XShape"));
op->SetInput(framework::GradVarName("Y"), OutputGrad("Y"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
......
paddle/fluid/operators/cross_entropy_op.h
浏览文件 @ 4f42504e
......@@ -138,15 +138,48 @@ class CrossEntropyGradientOpKernel : public framework::OpKernel<T> {
}
};
template <typename T>
struct HardLabelCrossEntropyForwardFunctor {
HardLabelCrossEntropyForwardFunctor(const T* x, T* y, T* match_x,
const int64_t* label,
int64_t ignore_index,
int64_t feature_size)
: x_(x),
y_(y),
match_x_(match_x),
label_(label),
ignore_index_(ignore_index),
feature_size_(feature_size) {}
HOSTDEVICE void operator()(int64_t idx) const {
auto label = label_[idx];
if (label != ignore_index_) {
auto match_x = x_[idx * feature_size_ + label];
y_[idx] = -math::TolerableValue<T>()(real_log(match_x));
match_x_[idx] = match_x;
} else {
y_[idx] = 0;
match_x_[idx] = 0; // any value is ok
}
}
const T* x_;
T* y_;
T* match_x_;
const int64_t* label_;
int64_t ignore_index_;
int64_t feature_size_;
};
template <typename T>
struct HardLabelCrossEntropyBackwardFunctor {
HardLabelCrossEntropyBackwardFunctor(T* dx, const T* y, const T* dy,
HardLabelCrossEntropyBackwardFunctor(T* dx, const T* dy, const T* match_x,
const int64_t* label,
int64_t ignore_index,
int64_t feature_size)
: dx_(dx),
y_(y),
dy_(dy),
match_x_(match_x),
label_(label),
ignore_index_(ignore_index),
feature_size_(feature_size) {}
......@@ -156,15 +189,15 @@ struct HardLabelCrossEntropyBackwardFunctor {
auto col_idx = idx % feature_size_;
auto label = label_[row_idx];
if (label == col_idx && label != ignore_index_) {
dx_[idx] = -dy_[row_idx] * real_exp(y_[row_idx]);
dx_[idx] = -dy_[row_idx] / match_x_[row_idx];
} else {
dx_[idx] = 0;
}
}
T* dx_;
const T* y_;
const T* dy_;
const T* match_x_;
const int64_t* label_;
int64_t ignore_index_;
int64_t feature_size_;
......@@ -174,20 +207,26 @@ template <typename DeviceContext, typename T>
class CrossEntropyOpKernel2 : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x_original = ctx.Input<Tensor>("X");
int rank = x_original->dims().size();
auto x = framework::ReshapeToMatrix(*x_original, rank - 1);
auto label =
framework::ReshapeToMatrix(*ctx.Input<Tensor>("Label"), rank - 1);
auto* x = ctx.Input<Tensor>("X");
auto* label = ctx.Input<Tensor>("Label");
auto* y = ctx.Output<Tensor>("Y");
y->mutable_data<T>(ctx.GetPlace());
auto* match_x = ctx.Output<Tensor>("MatchX");
auto& x_dims = x->dims();
auto feature_size = x_dims[x_dims.size() - 1];
auto batch_size = framework::product(x->dims()) / feature_size;
auto* p_x = x->data<T>();
auto* p_label = label->data<int64_t>();
auto* p_y = y->mutable_data<T>(ctx.GetPlace());
auto* p_match_x = match_x->mutable_data<T>(ctx.GetPlace());
auto ignore_index = ctx.Attr<int>("ignore_index");
math::CrossEntropyFunctor<DeviceContext, T>()(
ctx.template device_context<DeviceContext>(), y, &x, &label, false,
ignore_index);
platform::ForRange<DeviceContext> for_range(
ctx.template device_context<DeviceContext>(), batch_size);
for_range(HardLabelCrossEntropyForwardFunctor<T>(
p_x, p_y, p_match_x, p_label, ignore_index, feature_size));
}
};
......@@ -196,13 +235,13 @@ class CrossEntropyGradientOpKernel2 : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* y = ctx.Input<Tensor>("Y");
auto* dy = ctx.Input<Tensor>(framework::GradVarName("Y"));
auto* match_x = ctx.Input<Tensor>("MatchX");
auto* label = ctx.Input<Tensor>("Label");
auto* p_dx = dx->mutable_data<T>(ctx.GetPlace());
auto* p_y = y->data<T>();
auto* p_dy = dy->data<T>();
auto* p_match_x = match_x->data<T>();
auto* p_label = label->data<int64_t>();
int64_t ignore_index = ctx.Attr<int>("ignore_index");
......@@ -214,7 +253,7 @@ class CrossEntropyGradientOpKernel2 : public framework::OpKernel<T> {
ctx.template device_context<DeviceContext>(),
batch_size * feature_size);
for_range(HardLabelCrossEntropyBackwardFunctor<T>(
p_dx, p_y, p_dy, p_label, ignore_index, feature_size));
p_dx, p_dy, p_match_x, p_label, ignore_index, feature_size));
}
};
......
paddle/fluid/operators/detection/CMakeLists.txt
浏览文件 @ 4f42504e
......@@ -33,6 +33,7 @@ detection_library(rpn_target_assign_op SRCS rpn_target_assign_op.cc)
detection_library(generate_proposal_labels_op SRCS generate_proposal_labels_op.cc)
detection_library(box_clip_op SRCS box_clip_op.cc box_clip_op.cu)
detection_library(yolov3_loss_op SRCS yolov3_loss_op.cc)
detection_library(yolo_box_op SRCS yolo_box_op.cc yolo_box_op.cu)
detection_library(box_decoder_and_assign_op SRCS box_decoder_and_assign_op.cc box_decoder_and_assign_op.cu)
if(WITH_GPU)
......
paddle/fluid/operators/detection/box_coder_op.cc
浏览文件 @ 4f42504e
......@@ -60,14 +60,15 @@ class BoxCoderOp : public framework::OperatorWithKernel {
} else if (code_type == BoxCodeType::kDecodeCenterSize) {
PADDLE_ENFORCE_EQ(target_box_dims.size(), 3,
"The rank of Input TargetBox must be 3");
if (axis == 0) {
PADDLE_ENFORCE_EQ(target_box_dims[1], prior_box_dims[0]);
} else if (axis == 1) {
PADDLE_ENFORCE_EQ(target_box_dims[0], prior_box_dims[0]);
} else {
PADDLE_THROW("axis must be 0 or 1.");
PADDLE_ENFORCE(axis == 0 || axis == 1, "axis must be 0 or 1");
if (ctx->IsRuntime()) {
if (axis == 0) {
PADDLE_ENFORCE_EQ(target_box_dims[1], prior_box_dims[0]);
} else if (axis == 1) {
PADDLE_ENFORCE_EQ(target_box_dims[0], prior_box_dims[0]);
}
PADDLE_ENFORCE_EQ(target_box_dims[2], prior_box_dims[1]);
}
PADDLE_ENFORCE_EQ(target_box_dims[2], prior_box_dims[1]);
ctx->ShareDim("TargetBox", /*->*/ "OutputBox");
}
......
paddle/fluid/operators/detection/yolo_box_op.cc 0 → 100644
浏览文件 @ 4f42504e
/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
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/detection/yolo_box_op.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
using framework::Tensor;
class YoloBoxOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of YoloBoxOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("ImgSize"),
"Input(ImgSize) of YoloBoxOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Boxes"),
"Output(Boxes) of YoloBoxOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Scores"),
"Output(Scores) of YoloBoxOp should not be null.");
auto dim_x = ctx->GetInputDim("X");
auto dim_imgsize = ctx->GetInputDim("ImgSize");
auto anchors = ctx->Attrs().Get<std::vector<int>>("anchors");
int anchor_num = anchors.size() / 2;
auto class_num = ctx->Attrs().Get<int>("class_num");
PADDLE_ENFORCE_EQ(dim_x.size(), 4, "Input(X) should be a 4-D tensor.");
PADDLE_ENFORCE_EQ(
dim_x[1], anchor_num * (5 + class_num),
"Input(X) dim[1] should be equal to (anchor_mask_number * (5 "
"+ class_num)).");
PADDLE_ENFORCE_EQ(dim_imgsize.size(), 2,
"Input(ImgSize) should be a 2-D tensor.");
PADDLE_ENFORCE_EQ(
dim_imgsize[0], dim_x[0],
"Input(ImgSize) dim[0] and Input(X) dim[0] should be same.");
PADDLE_ENFORCE_EQ(dim_imgsize[1], 2, "Input(ImgSize) dim[1] should be 2.");
PADDLE_ENFORCE_GT(anchors.size(), 0,
"Attr(anchors) length should be greater than 0.");
PADDLE_ENFORCE_EQ(anchors.size() % 2, 0,
"Attr(anchors) length should be even integer.");
PADDLE_ENFORCE_GT(class_num, 0,
"Attr(class_num) should be an integer greater than 0.");
int box_num = dim_x[2] * dim_x[3] * anchor_num;
std::vector<int64_t> dim_boxes({dim_x[0], box_num, 4});
ctx->SetOutputDim("Boxes", framework::make_ddim(dim_boxes));
std::vector<int64_t> dim_scores({dim_x[0], box_num, class_num});
ctx->SetOutputDim("Scores", framework::make_ddim(dim_scores));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
ctx.GetPlace());
}
};
class YoloBoxOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"The input tensor of YoloBox operator is a 4-D tensor with "
"shape of [N, C, H, W]. The second dimension(C) stores "
"box locations, confidence score and classification one-hot "
"keys of each anchor box. Generally, X should be the output "
"of YOLOv3 network.");
AddInput("ImgSize",
"The image size tensor of YoloBox operator, "
"This is a 2-D tensor with shape of [N, 2]. This tensor holds "
"height and width of each input image used for resizing output "
"box in input image scale.");
AddOutput("Boxes",
"The output tensor of detection boxes of YoloBox operator, "
"This is a 3-D tensor with shape of [N, M, 4], N is the "
"batch num, M is output box number, and the 3rd dimension "
"stores [xmin, ymin, xmax, ymax] coordinates of boxes.");
AddOutput("Scores",
"The output tensor of detection boxes scores of YoloBox "
"operator, This is a 3-D tensor with shape of "
"[N, M, :attr:`class_num`], N is the batch num, M is "
"output box number.");
AddAttr<int>("class_num", "The number of classes to predict.");
AddAttr<std::vector<int>>("anchors",
"The anchor width and height, "
"it will be parsed pair by pair.")
.SetDefault(std::vector<int>{});
AddAttr<int>("downsample_ratio",
"The downsample ratio from network input to YoloBox operator "
"input, so 32, 16, 8 should be set for the first, second, "
"and thrid YoloBox operators.")
.SetDefault(32);
AddAttr<float>("conf_thresh",
"The confidence scores threshold of detection boxes. "
"Boxes with confidence scores under threshold should "
"be ignored.")
.SetDefault(0.01);
AddComment(R"DOC(
This operator generates YOLO detection boxes from output of YOLOv3 network.
The output of previous network is in shape [N, C, H, W], while H and W
should be the same, H and W specify the grid size, each grid point predict
given number boxes, this given number, which following will be represented as S,
is specified by the number of anchors. In the second dimension(the channel
dimension), C should be equal to S * (5 + class_num), class_num is the object
category number of source dataset(such as 80 in coco dataset), so the
second(channel) dimension, apart from 4 box location coordinates x, y, w, h,
also includes confidence score of the box and class one-hot key of each anchor
box.
Assume the 4 location coordinates are :math:`t_x, t_y, t_w, t_h`, the box
predictions should be as follows:
$$
b_x = \\sigma(t_x) + c_x
$$
$$
b_y = \\sigma(t_y) + c_y
$$
$$
b_w = p_w e^{t_w}
$$
$$
b_h = p_h e^{t_h}
$$
in the equation above, :math:`c_x, c_y` is the left top corner of current grid
and :math:`p_w, p_h` is specified by anchors.
The logistic regression value of the 5th channel of each anchor prediction boxes
represents the confidence score of each prediction box, and the logistic
regression value of the last :attr:`class_num` channels of each anchor prediction
boxes represents the classifcation scores. Boxes with confidence scores less than
:attr:`conf_thresh` should be ignored, and box final scores is the product of
confidence scores and classification scores.
$$
score_{pred} = score_{conf} * score_{class}
$$
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(yolo_box, ops::YoloBoxOp, ops::YoloBoxOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(yolo_box, ops::YoloBoxKernel<float>,
ops::YoloBoxKernel<double>);
paddle/fluid/operators/detection/yolo_box_op.cu 0 → 100644
浏览文件 @ 4f42504e
/* Copyright (c) 2019 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/detection/yolo_box_op.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T>
__global__ void KeYoloBoxFw(const T* input, const int* imgsize, T* boxes,
T* scores, const float conf_thresh,
const int* anchors, const int n, const int h,
const int w, const int an_num, const int class_num,
const int box_num, int input_size) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
T box[4];
for (; tid < n * box_num; tid += stride) {
int grid_num = h * w;
int i = tid / box_num;
int j = (tid % box_num) / grid_num;
int k = (tid % grid_num) / w;
int l = tid % w;
int an_stride = (5 + class_num) * grid_num;
int img_height = imgsize[2 * i];
int img_width = imgsize[2 * i + 1];
int obj_idx =
GetEntryIndex(i, j, k * w + l, an_num, an_stride, grid_num, 4);
T conf = sigmoid<T>(input[obj_idx]);
if (conf < conf_thresh) {
continue;
}
int box_idx =
GetEntryIndex(i, j, k * w + l, an_num, an_stride, grid_num, 0);
GetYoloBox<T>(box, input, anchors, l, k, j, h, input_size, box_idx,
grid_num, img_height, img_width);
box_idx = (i * box_num + j * grid_num + k * w + l) * 4;
CalcDetectionBox<T>(boxes, box, box_idx, img_height, img_width);
int label_idx =
GetEntryIndex(i, j, k * w + l, an_num, an_stride, grid_num, 5);
int score_idx = (i * box_num + j * grid_num + k * w + l) * class_num;
CalcLabelScore<T>(scores, input, label_idx, score_idx, class_num, conf,
grid_num);
}
}
template <typename T>
class YoloBoxOpCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<Tensor>("X");
auto* img_size = ctx.Input<Tensor>("ImgSize");
auto* boxes = ctx.Output<Tensor>("Boxes");
auto* scores = ctx.Output<Tensor>("Scores");
auto anchors = ctx.Attr<std::vector<int>>("anchors");
int class_num = ctx.Attr<int>("class_num");
float conf_thresh = ctx.Attr<float>("conf_thresh");
int downsample_ratio = ctx.Attr<int>("downsample_ratio");
const int n = input->dims()[0];
const int h = input->dims()[2];
const int w = input->dims()[3];
const int box_num = boxes->dims()[1];
const int an_num = anchors.size() / 2;
int input_size = downsample_ratio * h;
auto& dev_ctx = ctx.cuda_device_context();
auto& allocator =
platform::DeviceTemporaryAllocator::Instance().Get(dev_ctx);
int bytes = sizeof(int) * anchors.size();
auto anchors_ptr = allocator.Allocate(sizeof(int) * anchors.size());
int* anchors_data = reinterpret_cast<int*>(anchors_ptr->ptr());
const auto gplace = boost::get<platform::CUDAPlace>(ctx.GetPlace());
const auto cplace = platform::CPUPlace();
memory::Copy(gplace, anchors_data, cplace, anchors.data(), bytes,
dev_ctx.stream());
const T* input_data = input->data<T>();
const int* imgsize_data = img_size->data<int>();
T* boxes_data = boxes->mutable_data<T>({n, box_num, 4}, ctx.GetPlace());
T* scores_data =
scores->mutable_data<T>({n, box_num, class_num}, ctx.GetPlace());
math::SetConstant<platform::CUDADeviceContext, T> set_zero;
set_zero(dev_ctx, boxes, static_cast<T>(0));
set_zero(dev_ctx, scores, static_cast<T>(0));
int grid_dim = (n * box_num + 512 - 1) / 512;
grid_dim = grid_dim > 8 ? 8 : grid_dim;
KeYoloBoxFw<T><<<grid_dim, 512, 0, ctx.cuda_device_context().stream()>>>(
input_data, imgsize_data, boxes_data, scores_data, conf_thresh,
anchors_data, n, h, w, an_num, class_num, box_num, input_size);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(yolo_box, ops::YoloBoxOpCUDAKernel<float>,
ops::YoloBoxOpCUDAKernel<double>);
paddle/fluid/operators/detection/yolo_box_op.h 0 → 100644
浏览文件 @ 4f42504e
/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
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 <algorithm>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/hostdevice.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T>
HOSTDEVICE inline T sigmoid(T x) {
return 1.0 / (1.0 + std::exp(-x));
}
template <typename T>
HOSTDEVICE inline void GetYoloBox(T* box, const T* x, const int* anchors, int i,
int j, int an_idx, int grid_size,
int input_size, int index, int stride,
int img_height, int img_width) {
box[0] = (i + sigmoid<T>(x[index])) * img_width / grid_size;
box[1] = (j + sigmoid<T>(x[index + stride])) * img_height / grid_size;
box[2] = std::exp(x[index + 2 * stride]) * anchors[2 * an_idx] * img_width /
input_size;
box[3] = std::exp(x[index + 3 * stride]) * anchors[2 * an_idx + 1] *
img_height / input_size;
}
HOSTDEVICE inline int GetEntryIndex(int batch, int an_idx, int hw_idx,
int an_num, int an_stride, int stride,
int entry) {
return (batch * an_num + an_idx) * an_stride + entry * stride + hw_idx;
}
template <typename T>
HOSTDEVICE inline void CalcDetectionBox(T* boxes, T* box, const int box_idx,
const int img_height,
const int img_width) {
boxes[box_idx] = box[0] - box[2] / 2;
boxes[box_idx + 1] = box[1] - box[3] / 2;
boxes[box_idx + 2] = box[0] + box[2] / 2;
boxes[box_idx + 3] = box[1] + box[3] / 2;
boxes[box_idx] = boxes[box_idx] > 0 ? boxes[box_idx] : static_cast<T>(0);
boxes[box_idx + 1] =
boxes[box_idx + 1] > 0 ? boxes[box_idx + 1] : static_cast<T>(0);
boxes[box_idx + 2] = boxes[box_idx + 2] < img_width - 1
? boxes[box_idx + 2]
: static_cast<T>(img_width - 1);
boxes[box_idx + 3] = boxes[box_idx + 3] < img_height - 1
? boxes[box_idx + 3]
: static_cast<T>(img_height - 1);
}
template <typename T>
HOSTDEVICE inline void CalcLabelScore(T* scores, const T* input,
const int label_idx, const int score_idx,
const int class_num, const T conf,
const int stride) {
for (int i = 0; i < class_num; i++) {
scores[score_idx + i] = conf * sigmoid<T>(input[label_idx + i * stride]);
}
}
template <typename T>
class YoloBoxKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<Tensor>("X");
auto* imgsize = ctx.Input<Tensor>("ImgSize");
auto* boxes = ctx.Output<Tensor>("Boxes");
auto* scores = ctx.Output<Tensor>("Scores");
auto anchors = ctx.Attr<std::vector<int>>("anchors");
int class_num = ctx.Attr<int>("class_num");
float conf_thresh = ctx.Attr<float>("conf_thresh");
int downsample_ratio = ctx.Attr<int>("downsample_ratio");
const int n = input->dims()[0];
const int h = input->dims()[2];
const int w = input->dims()[3];
const int box_num = boxes->dims()[1];
const int an_num = anchors.size() / 2;
int input_size = downsample_ratio * h;
const int stride = h * w;
const int an_stride = (class_num + 5) * stride;
Tensor anchors_;
auto anchors_data =
anchors_.mutable_data<int>({an_num * 2}, ctx.GetPlace());
std::copy(anchors.begin(), anchors.end(), anchors_data);
const T* input_data = input->data<T>();
const int* imgsize_data = imgsize->data<int>();
T* boxes_data = boxes->mutable_data<T>({n, box_num, 4}, ctx.GetPlace());
memset(boxes_data, 0, boxes->numel() * sizeof(T));
T* scores_data =
scores->mutable_data<T>({n, box_num, class_num}, ctx.GetPlace());
memset(scores_data, 0, scores->numel() * sizeof(T));
T box[4];
for (int i = 0; i < n; i++) {
int img_height = imgsize_data[2 * i];
int img_width = imgsize_data[2 * i + 1];
for (int j = 0; j < an_num; j++) {
for (int k = 0; k < h; k++) {
for (int l = 0; l < w; l++) {
int obj_idx =
GetEntryIndex(i, j, k * w + l, an_num, an_stride, stride, 4);
T conf = sigmoid<T>(input_data[obj_idx]);
if (conf < conf_thresh) {
continue;
}
int box_idx =
GetEntryIndex(i, j, k * w + l, an_num, an_stride, stride, 0);
GetYoloBox<T>(box, input_data, anchors_data, l, k, j, h, input_size,
box_idx, stride, img_height, img_width);
box_idx = (i * box_num + j * stride + k * w + l) * 4;
CalcDetectionBox<T>(boxes_data, box, box_idx, img_height,
img_width);
int label_idx =
GetEntryIndex(i, j, k * w + l, an_num, an_stride, stride, 5);
int score_idx = (i * box_num + j * stride + k * w + l) * class_num;
CalcLabelScore<T>(scores_data, input_data, label_idx, score_idx,
class_num, conf, stride);
}
}
}
}
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detection/yolov3_loss_op.cc
浏览文件 @ 4f42504e
......@@ -10,6 +10,7 @@
limitations under the License. */
#include "paddle/fluid/operators/detection/yolov3_loss_op.h"
#include <memory>
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
......@@ -72,6 +73,18 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_GT(class_num, 0,
"Attr(class_num) should be an integer greater then 0.");
if (ctx->HasInput("GTScore")) {
auto dim_gtscore = ctx->GetInputDim("GTScore");
PADDLE_ENFORCE_EQ(dim_gtscore.size(), 2,
"Input(GTScore) should be a 2-D tensor");
PADDLE_ENFORCE_EQ(
dim_gtscore[0], dim_gtbox[0],
"Input(GTBox) and Input(GTScore) dim[0] should be same");
PADDLE_ENFORCE_EQ(
dim_gtscore[1], dim_gtbox[1],
"Input(GTBox) and Input(GTScore) dim[1] should be same");
}
std::vector<int64_t> dim_out({dim_x[0]});
ctx->SetOutputDim("Loss", framework::make_ddim(dim_out));
......@@ -112,6 +125,12 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
"This is a 2-D tensor with shape of [N, max_box_num], "
"and each element should be an integer to indicate the "
"box class id.");
AddInput("GTScore",
"The score of GTLabel, This is a 2-D tensor in same shape "
"GTLabel, and score values should in range (0, 1). This "
"input is for GTLabel score can be not 1.0 in image mixup "
"augmentation.")
.AsDispensable();
AddOutput("Loss",
"The output yolov3 loss tensor, "
"This is a 1-D tensor with shape of [N]");
......@@ -143,6 +162,9 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<float>("ignore_thresh",
"The ignore threshold to ignore confidence loss.")
.SetDefault(0.7);
AddAttr<bool>("use_label_smooth",
"Whether to use label smooth. Default True.")
.SetDefault(true);
AddComment(R"DOC(
This operator generates yolov3 loss based on given predict result and ground
truth boxes.
......@@ -204,6 +226,15 @@ class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
loss = (loss_{xy} + loss_{wh}) * weight_{box}
+ loss_{conf} + loss_{class}
$$
While :attr:`use_label_smooth` is set to be :attr:`True`, the classification
target will be smoothed when calculating classification loss, target of
positive samples will be smoothed to :math:`1.0 - 1.0 / class\_num` and target of
negetive samples will be smoothed to :math:`1.0 / class\_num`.
While :attr:`GTScore` is given, which means the mixup score of ground truth
boxes, all losses incured by a ground truth box will be multiplied by its
mixup score.
)DOC");
}
};
......@@ -240,6 +271,7 @@ class Yolov3LossGradMaker : public framework::SingleGradOpDescMaker {
op->SetInput("X", Input("X"));
op->SetInput("GTBox", Input("GTBox"));
op->SetInput("GTLabel", Input("GTLabel"));
op->SetInput("GTScore", Input("GTScore"));
op->SetInput(framework::GradVarName("Loss"), OutputGrad("Loss"));
op->SetInput("ObjectnessMask", Output("ObjectnessMask"));
op->SetInput("GTMatchMask", Output("GTMatchMask"));
......@@ -249,6 +281,7 @@ class Yolov3LossGradMaker : public framework::SingleGradOpDescMaker {
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetOutput(framework::GradVarName("GTBox"), {});
op->SetOutput(framework::GradVarName("GTLabel"), {});
op->SetOutput(framework::GradVarName("GTScore"), {});
return std::unique_ptr<framework::OpDesc>(op);
}
};
......
paddle/fluid/operators/detection/yolov3_loss_op.h
浏览文件 @ 4f42504e
......@@ -37,8 +37,8 @@ static T SigmoidCrossEntropy(T x, T label) {
}
template <typename T>
static T L2Loss(T x, T y) {
return 0.5 * (y - x) * (y - x);
static T L1Loss(T x, T y) {
return std::abs(y - x);
}
template <typename T>
......@@ -47,8 +47,8 @@ static T SigmoidCrossEntropyGrad(T x, T label) {
}
template <typename T>
static T L2LossGrad(T x, T y) {
return x - y;
static T L1LossGrad(T x, T y) {
return x > y ? 1.0 : -1.0;
}
static int GetMaskIndex(std::vector<int> mask, int val) {
......@@ -121,47 +121,49 @@ template <typename T>
static void CalcBoxLocationLoss(T* loss, const T* input, Box<T> gt,
std::vector<int> anchors, int an_idx,
int box_idx, int gi, int gj, int grid_size,
int input_size, int stride) {
int input_size, int stride, T score) {
T tx = gt.x * grid_size - gi;
T ty = gt.y * grid_size - gj;
T tw = std::log(gt.w * input_size / anchors[2 * an_idx]);
T th = std::log(gt.h * input_size / anchors[2 * an_idx + 1]);
T scale = (2.0 - gt.w * gt.h);
T scale = (2.0 - gt.w * gt.h) * score;
loss[0] += SigmoidCrossEntropy<T>(input[box_idx], tx) * scale;
loss[0] += SigmoidCrossEntropy<T>(input[box_idx + stride], ty) * scale;
loss[0] += L2Loss<T>(input[box_idx + 2 * stride], tw) * scale;
loss[0] += L2Loss<T>(input[box_idx + 3 * stride], th) * scale;
loss[0] += L1Loss<T>(input[box_idx + 2 * stride], tw) * scale;
loss[0] += L1Loss<T>(input[box_idx + 3 * stride], th) * scale;
}
template <typename T>
static void CalcBoxLocationLossGrad(T* input_grad, const T loss, const T* input,
Box<T> gt, std::vector<int> anchors,
int an_idx, int box_idx, int gi, int gj,
int grid_size, int input_size, int stride) {
int grid_size, int input_size, int stride,
T score) {
T tx = gt.x * grid_size - gi;
T ty = gt.y * grid_size - gj;
T tw = std::log(gt.w * input_size / anchors[2 * an_idx]);
T th = std::log(gt.h * input_size / anchors[2 * an_idx + 1]);
T scale = (2.0 - gt.w * gt.h);
T scale = (2.0 - gt.w * gt.h) * score;
input_grad[box_idx] =
SigmoidCrossEntropyGrad<T>(input[box_idx], tx) * scale * loss;
input_grad[box_idx + stride] =
SigmoidCrossEntropyGrad<T>(input[box_idx + stride], ty) * scale * loss;
input_grad[box_idx + 2 * stride] =
L2LossGrad<T>(input[box_idx + 2 * stride], tw) * scale * loss;
L1LossGrad<T>(input[box_idx + 2 * stride], tw) * scale * loss;
input_grad[box_idx + 3 * stride] =
L2LossGrad<T>(input[box_idx + 3 * stride], th) * scale * loss;
L1LossGrad<T>(input[box_idx + 3 * stride], th) * scale * loss;
}
template <typename T>
static inline void CalcLabelLoss(T* loss, const T* input, const int index,
const int label, const int class_num,
const int stride) {
const int stride, const T pos, const T neg,
T score) {
for (int i = 0; i < class_num; i++) {
T pred = input[index + i * stride];
loss[0] += SigmoidCrossEntropy<T>(pred, (i == label) ? 1.0 : 0.0);
loss[0] += SigmoidCrossEntropy<T>(pred, (i == label) ? pos : neg) * score;
}
}
......@@ -169,11 +171,13 @@ template <typename T>
static inline void CalcLabelLossGrad(T* input_grad, const T loss,
const T* input, const int index,
const int label, const int class_num,
const int stride) {
const int stride, const T pos, const T neg,
T score) {
for (int i = 0; i < class_num; i++) {
T pred = input[index + i * stride];
input_grad[index + i * stride] =
SigmoidCrossEntropyGrad<T>(pred, (i == label) ? 1.0 : 0.0) * loss;
SigmoidCrossEntropyGrad<T>(pred, (i == label) ? pos : neg) * score *
loss;
}
}
......@@ -188,8 +192,8 @@ static inline void CalcObjnessLoss(T* loss, const T* input, const T* objness,
for (int l = 0; l < w; l++) {
T obj = objness[k * w + l];
if (obj > 1e-5) {
// positive sample: obj = 1
loss[i] += SigmoidCrossEntropy<T>(input[k * w + l], 1.0);
// positive sample: obj = mixup score
loss[i] += SigmoidCrossEntropy<T>(input[k * w + l], 1.0) * obj;
} else if (obj > -0.5) {
// negetive sample: obj = 0
loss[i] += SigmoidCrossEntropy<T>(input[k * w + l], 0.0);
......@@ -215,7 +219,8 @@ static inline void CalcObjnessLossGrad(T* input_grad, const T* loss,
T obj = objness[k * w + l];
if (obj > 1e-5) {
input_grad[k * w + l] =
SigmoidCrossEntropyGrad<T>(input[k * w + l], 1.0) * loss[i];
SigmoidCrossEntropyGrad<T>(input[k * w + l], 1.0) * obj *
loss[i];
} else if (obj > -0.5) {
input_grad[k * w + l] =
SigmoidCrossEntropyGrad<T>(input[k * w + l], 0.0) * loss[i];
......@@ -252,6 +257,7 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
auto* input = ctx.Input<Tensor>("X");
auto* gt_box = ctx.Input<Tensor>("GTBox");
auto* gt_label = ctx.Input<Tensor>("GTLabel");
auto* gt_score = ctx.Input<Tensor>("GTScore");
auto* loss = ctx.Output<Tensor>("Loss");
auto* objness_mask = ctx.Output<Tensor>("ObjectnessMask");
auto* gt_match_mask = ctx.Output<Tensor>("GTMatchMask");
......@@ -260,6 +266,7 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
int class_num = ctx.Attr<int>("class_num");
float ignore_thresh = ctx.Attr<float>("ignore_thresh");
int downsample_ratio = ctx.Attr<int>("downsample_ratio");
bool use_label_smooth = ctx.Attr<bool>("use_label_smooth");
const int n = input->dims()[0];
const int h = input->dims()[2];
......@@ -272,6 +279,13 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
const int stride = h * w;
const int an_stride = (class_num + 5) * stride;
T label_pos = 1.0;
T label_neg = 0.0;
if (use_label_smooth) {
label_pos = 1.0 - 1.0 / static_cast<T>(class_num);
label_neg = 1.0 / static_cast<T>(class_num);
}
const T* input_data = input->data<T>();
const T* gt_box_data = gt_box->data<T>();
const int* gt_label_data = gt_label->data<int>();
......@@ -283,6 +297,19 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
int* gt_match_mask_data =
gt_match_mask->mutable_data<int>({n, b}, ctx.GetPlace());
const T* gt_score_data;
if (!gt_score) {
Tensor gtscore;
gtscore.mutable_data<T>({n, b}, ctx.GetPlace());
math::SetConstant<platform::CPUDeviceContext, T>()(
ctx.template device_context<platform::CPUDeviceContext>(), &gtscore,
static_cast<T>(1.0));
gt_score = &gtscore;
gt_score_data = gtscore.data<T>();
} else {
gt_score_data = gt_score->data<T>();
}
// calc valid gt box mask, avoid calc duplicately in following code
Tensor gt_valid_mask;
bool* gt_valid_mask_data =
......@@ -355,19 +382,20 @@ class Yolov3LossKernel : public framework::OpKernel<T> {
int mask_idx = GetMaskIndex(anchor_mask, best_n);
gt_match_mask_data[i * b + t] = mask_idx;
if (mask_idx >= 0) {
T score = gt_score_data[i * b + t];
int box_idx = GetEntryIndex(i, mask_idx, gj * w + gi, mask_num,
an_stride, stride, 0);
CalcBoxLocationLoss<T>(loss_data + i, input_data, gt, anchors, best_n,
box_idx, gi, gj, h, input_size, stride);
box_idx, gi, gj, h, input_size, stride, score);
int obj_idx = (i * mask_num + mask_idx) * stride + gj * w + gi;
obj_mask_data[obj_idx] = 1.0;
obj_mask_data[obj_idx] = score;
int label = gt_label_data[i * b + t];
int label_idx = GetEntryIndex(i, mask_idx, gj * w + gi, mask_num,
an_stride, stride, 5);
CalcLabelLoss<T>(loss_data + i, input_data, label_idx, label,
class_num, stride);
class_num, stride, label_pos, label_neg, score);
}
}
}
......@@ -384,6 +412,7 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
auto* input = ctx.Input<Tensor>("X");
auto* gt_box = ctx.Input<Tensor>("GTBox");
auto* gt_label = ctx.Input<Tensor>("GTLabel");
auto* gt_score = ctx.Input<Tensor>("GTScore");
auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* loss_grad = ctx.Input<Tensor>(framework::GradVarName("Loss"));
auto* objness_mask = ctx.Input<Tensor>("ObjectnessMask");
......@@ -392,6 +421,7 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
auto anchor_mask = ctx.Attr<std::vector<int>>("anchor_mask");
int class_num = ctx.Attr<int>("class_num");
int downsample_ratio = ctx.Attr<int>("downsample_ratio");
bool use_label_smooth = ctx.Attr<bool>("use_label_smooth");
const int n = input_grad->dims()[0];
const int c = input_grad->dims()[1];
......@@ -404,6 +434,13 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
const int stride = h * w;
const int an_stride = (class_num + 5) * stride;
T label_pos = 1.0;
T label_neg = 0.0;
if (use_label_smooth) {
label_pos = 1.0 - 1.0 / static_cast<T>(class_num);
label_neg = 1.0 / static_cast<T>(class_num);
}
const T* input_data = input->data<T>();
const T* gt_box_data = gt_box->data<T>();
const int* gt_label_data = gt_label->data<int>();
......@@ -414,25 +451,41 @@ class Yolov3LossGradKernel : public framework::OpKernel<T> {
input_grad->mutable_data<T>({n, c, h, w}, ctx.GetPlace());
memset(input_grad_data, 0, input_grad->numel() * sizeof(T));
const T* gt_score_data;
if (!gt_score) {
Tensor gtscore;
gtscore.mutable_data<T>({n, b}, ctx.GetPlace());
math::SetConstant<platform::CPUDeviceContext, T>()(
ctx.template device_context<platform::CPUDeviceContext>(), &gtscore,
static_cast<T>(1.0));
gt_score = &gtscore;
gt_score_data = gtscore.data<T>();
} else {
gt_score_data = gt_score->data<T>();
}
for (int i = 0; i < n; i++) {
for (int t = 0; t < b; t++) {
int mask_idx = gt_match_mask_data[i * b + t];
if (mask_idx >= 0) {
T score = gt_score_data[i * b + t];
Box<T> gt = GetGtBox(gt_box_data, i, b, t);
int gi = static_cast<int>(gt.x * w);
int gj = static_cast<int>(gt.y * h);
int box_idx = GetEntryIndex(i, mask_idx, gj * w + gi, mask_num,
an_stride, stride, 0);
CalcBoxLocationLossGrad<T>(
input_grad_data, loss_grad_data[i], input_data, gt, anchors,
anchor_mask[mask_idx], box_idx, gi, gj, h, input_size, stride);
CalcBoxLocationLossGrad<T>(input_grad_data, loss_grad_data[i],
input_data, gt, anchors,
anchor_mask[mask_idx], box_idx, gi, gj, h,
input_size, stride, score);
int label = gt_label_data[i * b + t];
int label_idx = GetEntryIndex(i, mask_idx, gj * w + gi, mask_num,
an_stride, stride, 5);
CalcLabelLossGrad<T>(input_grad_data, loss_grad_data[i], input_data,
label_idx, label, class_num, stride);
label_idx, label, class_num, stride, label_pos,
label_neg, score);
}
}
}
......
paddle/fluid/operators/fake_quantize_op.cc
浏览文件 @ 4f42504e
......@@ -81,6 +81,30 @@ struct FindRangeAbsMaxFunctor<platform::CPUDeviceContext, T> {
template struct FindRangeAbsMaxFunctor<platform::CPUDeviceContext, float>;
template <typename T>
struct FindMovingAverageAbsMaxFunctor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& ctx,
const framework::Tensor& in_accum,
const framework::Tensor& in_state, const T* cur_scale,
const float rate, framework::Tensor* out_state,
framework::Tensor* out_accum, framework::Tensor* out_scale) {
T accum = in_accum.data<T>()[0];
T state = in_state.data<T>()[0];
T scale = cur_scale[0];
state = rate * state + 1;
accum = rate * accum + scale;
scale = accum / state;
out_state->mutable_data<T>(ctx.GetPlace())[0] = state;
out_accum->mutable_data<T>(ctx.GetPlace())[0] = accum;
out_scale->mutable_data<T>(ctx.GetPlace())[0] = scale;
}
};
template struct FindMovingAverageAbsMaxFunctor<platform::CPUDeviceContext,
float>;
class FakeQuantizeAbsMaxOp : public framework::OperatorWithKernel {
public:
FakeQuantizeAbsMaxOp(const std::string& type,
......@@ -255,6 +279,78 @@ $$Out = round(X/scale * range)$$
}
};
class FakeQuantizeMovingAverageAbsMaxOp : public framework::OperatorWithKernel {
public:
FakeQuantizeMovingAverageAbsMaxOp(const std::string& type,
const framework::VariableNameMap& inputs,
const framework::VariableNameMap& outputs,
const framework::AttributeMap& attrs)
: OperatorWithKernel(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(
ctx->HasInput("X"),
"Input(X) of FakeQuantizeMovingAverageAbsMaxOp should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("Out"),
"Output(Out) of FakeQuantizeMovingAverageAbsMaxOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("OutScale"),
"Output(OutScale) of FakeQuantizeMovingAverageAbsMaxOp "
"should not be null");
if (ctx->HasOutput("OutState")) {
ctx->SetOutputDim("OutState", {1});
}
if (ctx->HasOutput("OutAccum")) {
ctx->SetOutputDim("OutAccum", {1});
}
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->SetOutputDim("OutScale", {1});
ctx->ShareLoD("X", /*->*/ "Out");
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(ctx.Input<framework::LoDTensor>("X")->type(),
ctx.device_context());
}
};
class FakeQuantizeMovingAverageAbsMaxOpMaker
: public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(Tensor) Input is float data type.");
AddInput("InScale", "Last scale.");
AddInput("InAccum", "Last accum.").AsDispensable();
AddInput("InState", "Last state.").AsDispensable();
AddOutput("Out", "(Tensor) Output of quantized low level tensor.");
AddOutput("OutScale", " Current scale");
AddOutput("OutState", "(Tensor) state buffer.").AsDispensable();
AddOutput("OutAccum", "(Tensor) accum buffer.").AsDispensable();
AddAttr<float>("moving_rate", "(float, default 0.9) moving rate.")
.SetDefault(0.9);
AddAttr<int>("bit_length", "(int, default 8), quantization bit number.")
.SetDefault(8)
.AddCustomChecker([](const int& bit_length) {
PADDLE_ENFORCE(bit_length >= 1 && bit_length <= 16,
"'bit_length' should be between 1 and 16.");
});
AddAttr<bool>("is_test",
"(bool, default false) Set to true for inference only, false "
"for training. Some layers may run faster when this is true.")
.SetDefault(false);
AddComment(R"DOC(
FakeQuantize operator is used in static quantization.
$$scale = (0.9*max(abs(x))+accum)/(0.9*state+1)$$
$$range = 2^{bit_length - 1} - 1$$
$$Out = round(X/scale * range)$$
)DOC");
}
};
} // namespace operators
} // namespace paddle
......@@ -273,6 +369,12 @@ REGISTER_OPERATOR(fake_quantize_range_abs_max, ops::FakeQuantizeRangeAbsMaxOp,
REGISTER_OP_CPU_KERNEL(fake_quantize_range_abs_max,
ops::FakeQuantizeRangeAbsMaxKernel<CPU, float>);
REGISTER_OPERATOR(fake_quantize_moving_average_abs_max,
ops::FakeQuantizeMovingAverageAbsMaxOp,
ops::FakeQuantizeMovingAverageAbsMaxOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(fake_quantize_moving_average_abs_max,
ops::FakeQuantizeMovingAverageAbsMaxKernel<CPU, float>);
REGISTER_OPERATOR(fake_channel_wise_quantize_abs_max,
ops::FakeChannelWiseQuantizeAbsMaxOp,
ops::FakeChannelWiseQuantizeAbsMaxOpMaker,
......
paddle/fluid/operators/fake_quantize_op.cu
浏览文件 @ 4f42504e
......@@ -147,6 +147,41 @@ struct FindRangeAbsMaxFunctor<platform::CUDADeviceContext, T> {
template struct FindRangeAbsMaxFunctor<platform::CUDADeviceContext, float>;
template <typename T>
struct FindMovingAverageAbsMaxFunctor<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& ctx,
const framework::Tensor& in_accum,
const framework::Tensor& in_state, const T* cur_scale,
const float rate, framework::Tensor* out_state,
framework::Tensor* out_accum, framework::Tensor* out_scale) {
const auto gpu_place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
T accum;
memory::Copy(platform::CPUPlace(), &accum, gpu_place, in_accum.data<T>(),
sizeof(T), 0);
T state;
memory::Copy(platform::CPUPlace(), &state, gpu_place, in_state.data<T>(),
sizeof(T), 0);
T scale;
memory::Copy(platform::CPUPlace(), &scale, gpu_place, cur_scale, sizeof(T),
0);
state = rate * state + 1;
accum = rate * accum + scale;
scale = accum / state;
memory::Copy(gpu_place, out_accum->mutable_data<T>(gpu_place),
platform::CPUPlace(), &accum, sizeof(T), 0);
memory::Copy(gpu_place, out_state->mutable_data<T>(gpu_place),
platform::CPUPlace(), &state, sizeof(T), 0);
memory::Copy(gpu_place, out_scale->mutable_data<T>(gpu_place),
platform::CPUPlace(), &scale, sizeof(T), 0);
}
};
template struct FindMovingAverageAbsMaxFunctor<platform::CUDADeviceContext,
float>;
template <typename T>
struct ClipAndFakeQuantFunctor<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& ctx,
......@@ -178,3 +213,6 @@ REGISTER_OP_CUDA_KERNEL(fake_channel_wise_quantize_abs_max,
ops::FakeChannelWiseQuantizeAbsMaxKernel<CUDA, float>);
REGISTER_OP_CUDA_KERNEL(fake_quantize_range_abs_max,
ops::FakeQuantizeRangeAbsMaxKernel<CUDA, float>);
REGISTER_OP_CUDA_KERNEL(
fake_quantize_moving_average_abs_max,
ops::FakeQuantizeMovingAverageAbsMaxKernel<CUDA, float>);
paddle/fluid/operators/fake_quantize_op.h
浏览文件 @ 4f42504e
......@@ -42,12 +42,20 @@ struct FindRangeAbsMaxFunctor {
framework::Tensor* scales_arr, framework::Tensor* out_scale);
};
template <typename DeviceContext, typename T>
struct FindMovingAverageAbsMaxFunctor {
void operator()(const DeviceContext& ctx, const framework::Tensor& in_accum,
const framework::Tensor& in_state,
const framework::Tensor& cur_scale,
framework::Tensor* out_state, framework::Tensor* out_accum,
framework::Tensor* out_scale);
};
template <typename DeviceContext, typename T>
class FakeQuantizeAbsMaxKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in = context.Input<framework::Tensor>("X");
auto* out = context.Output<framework::Tensor>("Out");
auto* out_scale = context.Output<framework::Tensor>("OutScale");
T* out_s = out_scale->mutable_data<T>(context.GetPlace());
......@@ -138,5 +146,54 @@ class FakeQuantizeRangeAbsMaxKernel : public framework::OpKernel<T> {
}
};
template <typename DeviceContext, typename T>
class FakeQuantizeMovingAverageAbsMaxKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in = context.Input<framework::Tensor>("X");
auto* in_scale = context.Input<framework::Tensor>("InScale");
auto* out = context.Output<framework::Tensor>("Out");
out->mutable_data<T>(context.GetPlace());
bool is_test = context.Attr<bool>("is_test");
int bit_length = context.Attr<int>("bit_length");
int bin_cnt = std::pow(2, bit_length - 1) - 1;
auto& dev_ctx = context.template device_context<DeviceContext>();
// testing
if (is_test) {
ClipAndFakeQuantFunctor<DeviceContext, T>()(dev_ctx, *in, *in_scale,
bin_cnt, out);
return;
}
// training
auto* in_accum = context.Input<framework::Tensor>("InAccum");
auto* in_state = context.Input<framework::Tensor>("InState");
auto& allocator =
platform::DeviceTemporaryAllocator::Instance().Get(dev_ctx);
auto cur_scale = allocator.Allocate(1 * sizeof(T));
T* cur_scale_data = static_cast<T*>(cur_scale->ptr());
FindAbsMaxFunctor<DeviceContext, T>()(dev_ctx, in->data<T>(), in->numel(),
cur_scale_data);
auto* out_state = context.Output<framework::Tensor>("OutState");
auto* out_accum = context.Output<framework::Tensor>("OutAccum");
auto* out_scale = context.Output<framework::Tensor>("OutScale");
out_state->mutable_data<T>(context.GetPlace());
out_accum->mutable_data<T>(context.GetPlace());
out_scale->mutable_data<T>(context.GetPlace());
float moving_rate = context.Attr<float>("moving_rate");
FindMovingAverageAbsMaxFunctor<DeviceContext, T>()(
dev_ctx, *in_accum, *in_state, cur_scale_data, moving_rate, out_state,
out_accum, out_scale);
ClipAndFakeQuantFunctor<DeviceContext, T>()(dev_ctx, *in, *out_scale,
bin_cnt, out);
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/fc_op.cc
浏览文件 @ 4f42504e
......@@ -55,17 +55,8 @@ void FCOp::InferShape(framework::InferShapeContext* ctx) const {
"The input tensor Input's rank of FCOp should be larger than "
"in_num_col_dims.");
auto in_mat_dims = framework::flatten_to_2d(in_dims, in_num_col_dims);
PADDLE_ENFORCE_EQ(
in_mat_dims[1], w_dims[0],
"Fully Connected input and weigth size do not match. %s, %s");
std::vector<int64_t> output_dims;
output_dims.reserve(static_cast<size_t>(in_num_col_dims + 1));
for (int i = 0; i < in_num_col_dims; ++i) {
output_dims.push_back(in_dims[i]);
}
output_dims.push_back(w_dims[1]);
FCOutputSize(in_dims, w_dims, output_dims, in_num_col_dims);
ctx->SetOutputDim("Out", framework::make_ddim(output_dims));
ctx->ShareLoD("Input", "Out");
......@@ -128,6 +119,9 @@ void FCOpMaker::Make() {
AddAttr<bool>("use_mkldnn",
"(bool, default false) Only used in mkldnn kernel")
.SetDefault(false);
AddAttr<bool>(framework::kAllKernelsMustComputeRuntimeShape,
"Skip calling InferShape() function in the runtime.")
.SetDefault(true);
AddComment(R"DOC(
Fully Connected Operator.
......@@ -142,13 +136,20 @@ class FCOpKernel : public framework::OpKernel<T> {
void Compute(const paddle::framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()),
"It must use CPUPlace.");
auto input = ctx.Input<Tensor>("Input");
auto input = ctx.Input<framework::LoDTensor>("Input");
auto w = ctx.Input<Tensor>("W");
auto bias = ctx.Input<Tensor>("Bias");
auto output = ctx.Output<Tensor>("Out");
auto output = ctx.Output<framework::LoDTensor>("Out");
int in_num_col_dims = ctx.Attr<int>("in_num_col_dims");
auto w_dims = w->dims();
std::vector<int64_t> output_dims;
FCOutputSize(input->dims(), w_dims, output_dims, in_num_col_dims);
output->Resize(framework::make_ddim(output_dims));
output->set_lod(input->lod());
auto out_dims = output->dims();
int M = framework::product(out_dims) / out_dims[out_dims.size() - 1];
int M = framework::product(out_dims) / w_dims[1];
const T* input_data = input->data<T>();
const T* w_data = w->data<T>();
......
paddle/fluid/operators/fc_op.h
浏览文件 @ 4f42504e
......@@ -48,5 +48,21 @@ class FCOpMaker : public framework::OpProtoAndCheckerMaker {
void Make() override;
};
inline void FCOutputSize(const framework::DDim& in_dims,
const framework::DDim& w_dims,
std::vector<int64_t>& out_dims, // NOLINT
int in_num_col_dims) {
auto in_mat_dims = framework::flatten_to_2d(in_dims, in_num_col_dims);
PADDLE_ENFORCE_EQ(
in_mat_dims[1], w_dims[0],
"Fully Connected input and weigth size do not match. %s, %s");
out_dims.reserve(static_cast<size_t>(in_num_col_dims + 1));
for (int i = 0; i < in_num_col_dims; ++i) {
out_dims.push_back(in_dims[i]);
}
out_dims.push_back(w_dims[1]);
}
} // namespace operators
} // namespace paddle
paddle/fluid/operators/fused/fused_embedding_seq_pool_op.cc
浏览文件 @ 4f42504e
......@@ -88,7 +88,8 @@ class FusedEmbeddingSeqPoolOpMaker : public framework::OpProtoAndCheckerMaker {
"(boolean, default false) "
"Sparse update.")
.SetDefault(false);
AddAttr<bool>(framework::kAllKernelsMustComputeRuntimeShape, "")
AddAttr<bool>(framework::kAllKernelsMustComputeRuntimeShape,
"Skip calling InferShape() function in the runtime.")
.SetDefault(true);
AddComment(R"DOC(
FusedEmbeddingSeqPool Operator.
......
paddle/fluid/operators/hash_op.cc
浏览文件 @ 4f42504e
......@@ -54,7 +54,8 @@ $$Out = scale * X$$
)DOC");
AddAttr<int>("num_hash", "").SetDefault(1);
AddAttr<int>("mod_by", "").SetDefault(100000);
AddAttr<bool>(framework::kAllKernelsMustComputeRuntimeShape, "")
AddAttr<bool>(framework::kAllKernelsMustComputeRuntimeShape,
"Skip calling InferShape() function in the runtime.")
.SetDefault(true);
}
};
......
paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc
浏览文件 @ 4f42504e
......@@ -592,6 +592,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
platform::SetDstMemoryHandler<uint8_t>(ctx, output, handler,
&dst_memory_p);
} else {
need_s8_to_u8 = fuse_relu;
platform::SetDstMemoryHandler<int8_t>(ctx, output, handler,
&dst_memory_p);
}
......
paddle/fluid/operators/mkldnn/fc_mkldnn_op.cc
浏览文件 @ 4f42504e
......@@ -123,7 +123,7 @@ class FCMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
const auto& mkldnn_engine = dev_ctx.GetEngine();
auto input = ctx.Input<Tensor>("Input");
auto input = ctx.Input<framework::LoDTensor>("Input");
auto w = ctx.Input<Tensor>("W");
auto bias = ctx.Input<Tensor>("Bias");
......@@ -151,7 +151,13 @@ class FCMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
const T* input_data = input->data<T>();
const T* w_data = w->data<T>();
auto output = ctx.Output<Tensor>("Out");
auto output = ctx.Output<framework::LoDTensor>("Out");
int in_num_col_dims = ctx.Attr<int>("in_num_col_dims");
std::vector<int64_t> output_dims;
FCOutputSize(input->dims(), w->dims(), output_dims, in_num_col_dims);
output->Resize(framework::make_ddim(output_dims));
output->set_lod(input->lod());
T* output_data = output->mutable_data<T>(ctx.GetPlace());
auto dst_memory = mem.dst(output_data);
......@@ -204,19 +210,21 @@ class FCMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
Tensor* input_grad = ctx.Output<Tensor>(framework::GradVarName("Input"));
Tensor* w_grad = ctx.Output<Tensor>(framework::GradVarName("W"));
const Tensor* input = ctx.Input<Tensor>("Input");
const T* input_data = input->data<T>();
const Tensor* w = ctx.Input<Tensor>("W");
const T* w_data = w->data<T>();
if (input_grad) {
input_grad->Resize(input->dims());
input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
}
if (w_grad) {
w_grad->Resize(w->dims());
w_grad_data = w_grad->mutable_data<T>(ctx.GetPlace());
}
const Tensor* input = ctx.Input<Tensor>("Input");
const T* input_data = input->data<T>();
const Tensor* w = ctx.Input<Tensor>("W");
const T* w_data = w->data<T>();
const Tensor* out_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
const T* out_grad_data = out_grad->data<T>();
......
paddle/fluid/operators/mkldnn/transpose_mkldnn_op.cc
浏览文件 @ 4f42504e
......@@ -73,6 +73,29 @@ class TransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
}
};
template <typename T>
class TransposeINT8MKLDNNOpKernel : public paddle::framework::OpKernel<T> {
public:
void Compute(const paddle::framework::ExecutionContext& ctx) const override {
std::vector<int> axis = ctx.Attr<std::vector<int>>("axis");
std::vector<int> axis_int8 = {0, 2, 3, 1};
if (axis.size() != 1) {
PADDLE_ENFORCE_EQ(axis.size(), axis_int8.size());
for (size_t i = 0; i < axis.size(); i++) {
PADDLE_ENFORCE_EQ(axis[i], axis_int8[i],
"Current INT8 MKLDNN Transpose kernel only surpport "
"axis with [0, 2, 3, 1] due to MKL-DNN kernel "
"implementation.");
}
}
auto* input = ctx.Input<Tensor>("X");
auto* output = ctx.Output<Tensor>("Out");
output->ShareDataWith(*input);
output->set_layout(DataLayout::kMKLDNN);
output->set_format(input->format());
}
};
template <typename T>
class TransposeMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
public:
......@@ -140,7 +163,10 @@ class TransposeMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
namespace ops = paddle::operators;
REGISTER_OP_KERNEL(transpose2, MKLDNN, ::paddle::platform::CPUPlace,
ops::TransposeMKLDNNOpKernel<float>);
ops::TransposeMKLDNNOpKernel<float>,
ops::TransposeINT8MKLDNNOpKernel<uint8_t>,
ops::TransposeINT8MKLDNNOpKernel<int8_t>);
REGISTER_OP_KERNEL(transpose, MKLDNN, ::paddle::platform::CPUPlace,
ops::TransposeMKLDNNOpKernel<float>);
......
paddle/fluid/operators/optimizers/adam_op.h
浏览文件 @ 4f42504e
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <math.h> // for sqrt in CPU and CUDA
#include <Eigen/Dense>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/threadpool.h"
......@@ -311,17 +312,17 @@ struct SparseAdamFunctor<T, CPUAdam> {
T beta1_pow = *beta1_pow_;
T beta2_pow = *beta2_pow_;
lr *= sqrt(1 - beta2_pow) / (1 - beta1_pow);
size_t row_count = numel / row_numel_;
int64_t row_count = static_cast<int64_t>(numel / row_numel_);
for (size_t i = 0U, j = 0U; i != row_count; ++i) {
for (int64_t i = 0, j = 0; i != row_count; ++i) {
if (i == *(rows_ + j)) {
for (size_t k = 0U; k != row_numel_; ++k) {
for (int64_t k = 0; k != row_numel_; ++k) {
T g = grad_[j * row_numel_ + k];
adam_update(i * row_numel_ + k, g);
}
++j;
} else {
for (size_t k = 0U; k != row_numel_; ++k) {
for (int64_t k = 0; k != row_numel_; ++k) {
T mom1 = moment1_[i * row_numel_ + k];
T mom2 = moment2_[i * row_numel_ + k];
T p = param_[i * row_numel_ + k];
......@@ -427,43 +428,23 @@ class AdamOpKernel : public framework::OpKernel<T> {
}
}
framework::SelectedRows cpu_grad_merge;
framework::SelectedRows tmp_grad_merge;
const framework::SelectedRows* grad_merge_ptr;
if (is_strict_sorted) {
grad_merge_ptr = &grad;
} else {
// merge duplicated rows if any.
// The rows of grad_merge have been sorted inside MergeAdd functor
framework::SelectedRows* grad_merge_var;
scatter::MergeAdd<DeviceContext, T> merge_func;
if (platform::is_cpu_place(ctx.GetPlace())) {
grad_merge_var = &cpu_grad_merge;
} else {
// FIXME(qiao): GPU also need to fix this
grad_merge_var = const_cast<framework::Scope&>(ctx.scope())
.Var()
->GetMutable<framework::SelectedRows>();
}
merge_func(ctx.template device_context<DeviceContext>(), grad,
grad_merge_var, true);
grad_merge_ptr = grad_merge_var;
&tmp_grad_merge, true);
grad_merge_ptr = &tmp_grad_merge;
}
auto& grad_merge = *grad_merge_ptr;
auto& grad_tensor = grad_merge.value();
const T* grad_data = grad_tensor.template data<T>();
const int64_t* rows = nullptr;
// When compiled without CUDA, the CUDAData() interface should not be
// provided.
#if defined(PADDLE_WITH_CUDA)
if (platform::is_gpu_place(ctx.GetPlace())) {
rows = grad_merge.rows().CUDAData(ctx.GetPlace());
} else {
#endif
rows = grad_merge.rows().data();
#if defined(PADDLE_WITH_CUDA)
}
#endif
const int64_t* rows = grad_merge.rows().Data(ctx.GetPlace());
auto row_numel = grad_tensor.numel() / grad_merge.rows().size();
if (platform::is_cpu_place(ctx.GetPlace())) {
......@@ -488,7 +469,7 @@ class AdamOpKernel : public framework::OpKernel<T> {
}
}
#ifndef _WIN32
else if (FLAGS_inner_op_parallelism > 1 &&
else if (FLAGS_inner_op_parallelism > 1 && // NOLINT
min_row_size_to_use_multithread > 0 &&
param.dims()[0] > min_row_size_to_use_multithread) {
VLOG(3) << "use multi thread, inner_op_parallelism="
......@@ -516,11 +497,11 @@ class AdamOpKernel : public framework::OpKernel<T> {
for (int i = 0; i < FLAGS_inner_op_parallelism; ++i) {
int64_t start = i * line_in_each_thread;
int64_t end = (i + 1) * line_in_each_thread;
if (start >= param_row_count) {
if (start >= static_cast<int64_t>(param_row_count)) {
break;
}
if (end > param_row_count) {
end = param_row_count;
if (end > static_cast<int64_t>(param_row_count)) {
end = static_cast<int64_t>(param_row_count);
}
fs.push_back(
framework::Async([&functor, &row_id_to_grad_row_offset,
......@@ -545,8 +526,8 @@ class AdamOpKernel : public framework::OpKernel<T> {
}
for (size_t i = 0; i < fs.size(); ++i) fs[i].wait();
}
#endif // !_WIN32
else {
#endif // !_WIN32
else { // NOLINT
functor(param.numel());
}
} else if (platform::is_gpu_place(ctx.GetPlace())) {
......
paddle/fluid/operators/optimizers/momentum_op.h
浏览文件 @ 4f42504e
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <memory>
#include <string>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
......@@ -69,6 +70,7 @@ class MomentumOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("ParamOut", param_dim);
ctx->SetOutputDim("VelocityOut", param_dim);
}
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
auto input_data_type = framework::GetDataTypeOfVar(ctx.InputVar("Param"));
......@@ -351,23 +353,14 @@ class MomentumOpKernel : public framework::OpKernel<T> {
VLOG(3) << "Grad SelectedRows contains no data!";
return;
}
auto* merged_grad = const_cast<framework::Scope&>(ctx.scope())
.Var()
->GetMutable<framework::SelectedRows>();
framework::SelectedRows tmp_merged_grad;
framework::SelectedRows* merged_grad = &tmp_merged_grad;
math::scatter::MergeAdd<DeviceContext, T> merge_func;
merge_func(ctx.template device_context<DeviceContext>(), *grad,
merged_grad);
const int64_t* rows = nullptr;
#ifdef PADDLE_WITH_CUDA
if (platform::is_gpu_place(ctx.GetPlace())) {
rows = merged_grad->rows().CUDAData(ctx.GetPlace());
} else {
#endif
rows = merged_grad->rows().data();
#ifdef PADDLE_WITH_CUDA
}
#endif
const int64_t* rows = merged_grad->rows().Data(ctx.GetPlace());
int64_t row_numel =
merged_grad->value().numel() / merged_grad->rows().size();
platform::ForRange<DeviceContext> for_range(
......
paddle/fluid/operators/optimizers/rmsprop_op.h
浏览文件 @ 4f42504e
......@@ -216,24 +216,14 @@ class RmspropOpKernel : public framework::OpKernel<T> {
}
} else if (grad_var->IsType<framework::SelectedRows>()) {
auto &grad = grad_var->Get<framework::SelectedRows>();
auto *merged_grad = const_cast<framework::Scope &>(ctx.scope())
.Var()
->GetMutable<framework::SelectedRows>();
framework::SelectedRows tmp_merged_grad;
framework::SelectedRows *merged_grad = &tmp_merged_grad;
math::scatter::MergeAdd<DeviceContext, T> merge_func;
merge_func(dev_ctx, grad, merged_grad);
platform::ForRange<DeviceContext> for_range(dev_ctx, limit);
const int64_t *rows;
#ifdef PADDLE_WITH_CUDA
if (platform::is_gpu_place(ctx.GetPlace())) {
rows = merged_grad->rows().CUDAData(ctx.GetPlace());
} else {
#endif
rows = merged_grad->rows().data();
#ifdef PADDLE_WITH_CUDA
}
#endif
const int64_t *rows = merged_grad->rows().Data(ctx.GetPlace());
auto &merged_tensor = merged_grad->value();
int64_t row_count = merged_grad->rows().size();
int64_t row_numel = merged_tensor.numel() / row_count;
......
paddle/fluid/operators/pool_op.cc
浏览文件 @ 4f42504e
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/pool_op.h"
#include <unordered_map>
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/cudnn_helper.h"
#endif
......@@ -212,6 +213,12 @@ void Pool2dOpMaker::Make() {
AddAttr<bool>("use_mkldnn",
"(bool, default false) Only used in mkldnn kernel")
.SetDefault(false);
AddAttr<bool>("use_quantizer",
"(bool, default false) "
"Set to true for operators that should be quantized and use "
"int8 kernel. "
"Only used on CPU.")
.SetDefault(false);
AddAttr<std::string>(
"data_format",
"(string, default NCHW) Only used in "
......
paddle/fluid/operators/reshape_op.cc
浏览文件 @ 4f42504e
......@@ -219,14 +219,6 @@ class ReshapeKernel {
std::vector<int>(shape_data, shape_data + shape_tensor->numel());
out_dims = ReshapeOp::ValidateShape(shape, in->dims());
}
if (!in->lod().empty()) {
PADDLE_ENFORCE_EQ(
out_dims[0], in->dims()[0],
"Reshape operator cannot reshape an input sequence batch "
"into an output sequence batch that has a different "
"number of time steps. Please consider using "
"sequence_reshape op.");
}
out->mutable_data(ctx.GetPlace(), in->type());
framework::TensorCopy(
......
paddle/fluid/operators/sequence_ops/sequence_enumerate_op.cc
浏览文件 @ 4f42504e
......@@ -59,7 +59,8 @@ class SequenceEnumerateOpMaker : public framework::OpProtoAndCheckerMaker {
});
AddAttr<int>("pad_value", "(int) The enumerate sequence padding value.")
.SetDefault(0);
AddAttr<bool>(framework::kAllKernelsMustComputeRuntimeShape, "")
AddAttr<bool>(framework::kAllKernelsMustComputeRuntimeShape,
"Skip calling InferShape() function in the runtime.")
.SetDefault(true);
AddComment(R"DOC(
Sequence Enumerate Operator.
......
paddle/fluid/operators/slice_op.cu
浏览文件 @ 4f42504e
......@@ -12,18 +12,138 @@ 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 <thrust/device_vector.h>
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/slice_op.h"
#include "paddle/fluid/platform/cuda_device_function.h"
#include "paddle/fluid/platform/cuda_primitives.h"
#include "paddle/fluid/platform/float16.h"
namespace paddle {
namespace operators {
using platform::PADDLE_CUDA_NUM_THREADS;
template <size_t D>
__global__ void Padding(const paddle::platform::float16* d_out,
const int* out_dims, const int* in_dims,
const int* offsets, int64_t n,
paddle::platform::float16* d_in) {
int64_t out_idx = threadIdx.x + blockDim.x * blockIdx.x;
if (out_idx < n) {
int coords[D] = {0};
for (int i = D - 1; i >= 0; --i) {
coords[i] = out_idx % out_dims[i];
out_idx /= out_dims[i];
coords[i] += offsets[i];
}
int64_t in_idx = 0;
for (int i = 0; i < D - 1; ++i) {
in_idx += coords[i] * in_dims[i + 1];
}
in_idx += coords[D - 1];
d_in[in_idx] = d_out[out_idx];
}
}
template <>
class SliceGradKernel<paddle::platform::CUDADeviceContext,
paddle::platform::float16>
: public framework::OpKernel<paddle::platform::float16> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* d_in = ctx.Output<framework::Tensor>(framework::GradVarName("Input"));
d_in->mutable_data<paddle::platform::float16>(ctx.GetPlace());
auto out_dims = d_out->dims();
auto in_dims = d_in->dims();
int rank = out_dims.size();
std::vector<int> offsets(rank, 0);
auto axes = ctx.Attr<std::vector<int>>("axes");
auto starts = ctx.Attr<std::vector<int>>("starts");
for (size_t i = 0; i < starts.size(); ++i) {
if (starts[i] < 0) {
starts[i] += in_dims[axes[i]];
}
offsets[axes[i]] = std::max(starts[i], 0);
}
math::SetConstant<paddle::platform::CUDADeviceContext,
paddle::platform::float16>
set_zero;
auto& dev_ctx =
ctx.template device_context<paddle::platform::CUDADeviceContext>();
set_zero(dev_ctx, d_in, static_cast<paddle::platform::float16>(0));
int64_t numel = d_out->numel();
dim3 blocks((numel - 1) / PADDLE_CUDA_NUM_THREADS + 1, 1, 1);
dim3 threads(PADDLE_CUDA_NUM_THREADS, 1, 1);
auto stream = ctx.cuda_device_context().stream();
auto out_shape = framework::vectorize2int(out_dims);
thrust::device_vector<int> out_dims_vec(out_shape.begin(), out_shape.end());
auto in_shape = framework::vectorize2int(in_dims);
thrust::device_vector<int> in_dims_vec(in_shape.begin(), in_shape.end());
thrust::device_vector<int> offsets_vec(offsets.begin(), offsets.end());
const int* out_dims_ptr = thrust::raw_pointer_cast(out_dims_vec.data());
const int* in_dims_ptr = thrust::raw_pointer_cast(in_dims_vec.data());
const int* offsets_ptr = thrust::raw_pointer_cast(offsets_vec.data());
switch (rank) {
case 1:
Padding<1><<<blocks, threads, 0, stream>>>(
d_out->data<paddle::platform::float16>(), out_dims_ptr, in_dims_ptr,
offsets_ptr, numel, d_in->data<paddle::platform::float16>());
break;
case 2:
Padding<2><<<blocks, threads, 0, stream>>>(
d_out->data<paddle::platform::float16>(), out_dims_ptr, in_dims_ptr,
offsets_ptr, numel, d_in->data<paddle::platform::float16>());
break;
case 3:
Padding<3><<<blocks, threads, 0, stream>>>(
d_out->data<paddle::platform::float16>(), out_dims_ptr, in_dims_ptr,
offsets_ptr, numel, d_in->data<paddle::platform::float16>());
break;
case 4:
Padding<4><<<blocks, threads, 0, stream>>>(
d_out->data<paddle::platform::float16>(), out_dims_ptr, in_dims_ptr,
offsets_ptr, numel, d_in->data<paddle::platform::float16>());
break;
case 5:
Padding<5><<<blocks, threads, 0, stream>>>(
d_out->data<paddle::platform::float16>(), out_dims_ptr, in_dims_ptr,
offsets_ptr, numel, d_in->data<paddle::platform::float16>());
break;
case 6:
Padding<6><<<blocks, threads, 0, stream>>>(
d_out->data<paddle::platform::float16>(), out_dims_ptr, in_dims_ptr,
offsets_ptr, numel, d_in->data<paddle::platform::float16>());
break;
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
slice, ops::SliceKernel<paddle::platform::CUDADeviceContext, float>,
ops::SliceKernel<paddle::platform::CUDADeviceContext, double>,
ops::SliceKernel<paddle::platform::CUDADeviceContext, int>,
ops::SliceKernel<paddle::platform::CUDADeviceContext, int64_t>);
ops::SliceKernel<paddle::platform::CUDADeviceContext, int64_t>,
ops::SliceKernel<paddle::platform::CUDADeviceContext, plat::float16>);
REGISTER_OP_CUDA_KERNEL(
slice_grad,
ops::SliceGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::SliceGradKernel<paddle::platform::CUDADeviceContext, double>,
ops::SliceGradKernel<paddle::platform::CUDADeviceContext, int>,
ops::SliceGradKernel<paddle::platform::CUDADeviceContext, int64_t>);
ops::SliceGradKernel<paddle::platform::CUDADeviceContext, int64_t>,
ops::SliceGradKernel<paddle::platform::CUDADeviceContext, plat::float16>);
paddle/fluid/operators/softmax_with_cross_entropy_op.cu
浏览文件 @ 4f42504e
......@@ -439,7 +439,8 @@ class SoftmaxWithCrossEntropyGradCUDAKernel : public framework::OpKernel<T> {
context.Input<Tensor>(framework::GradVarName("Loss"))->data<T>();
Tensor* logit_grad =
context.Output<Tensor>(framework::GradVarName("Logits"));
logit_grad->ShareDataWith(*context.Input<Tensor>("Softmax"));
framework::TensorCopy(*context.Input<Tensor>("Softmax"), context.GetPlace(),
context.device_context(), logit_grad);
T* logit_grad_data = logit_grad->data<T>();
const int batch_size = logit_grad->dims()[0];
......
paddle/fluid/operators/squeeze_op.cc
浏览文件 @ 4f42504e
......@@ -94,6 +94,7 @@ class SqueezeOpInferShape : public framework::InferShapeBase {
}
};
// TODO(paddle-dev): Should use OpKernel.
class SqueezeOp : public framework::OperatorBase {
public:
using OperatorBase::OperatorBase;
......
paddle/fluid/operators/sync_batch_norm_op.cc 0 → 100644
浏览文件 @ 4f42504e
/* Copyright (c) 2019 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/batch_norm_op.h"
namespace ops = paddle::operators;
REGISTER_OPERATOR(sync_batch_norm, ops::BatchNormOp, ops::BatchNormOpMaker,
ops::BatchNormOpInferVarType, ops::BatchNormGradMaker);
REGISTER_OPERATOR(sync_batch_norm_grad, ops::BatchNormGradOp);
paddle/fluid/operators/sync_batch_norm_op.cu 0 → 100644
浏览文件 @ 4f42504e
/* Copyright (c) 2019 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 <algorithm>
#include <cfloat>
#include <string>
#include <vector>
#include "cub/cub.cuh"
#include "paddle/fluid/framework/data_layout.h"
#include "paddle/fluid/operators/batch_norm_op.h"
#include "paddle/fluid/platform/cudnn_helper.h"
#include "paddle/fluid/platform/float16.h"
#include "paddle/fluid/platform/nccl_helper.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using DataLayout = framework::DataLayout;
template <typename T>
using CudnnDataType = platform::CudnnDataType<T>;
template <typename T, int BlockDim, framework::DataLayout layout>
__global__ void KeLocalStats(const T *x, int N, int M, int C, T *mean_var) {
typedef cub::BlockReduce<T, BlockDim> BlockReduce;
__shared__ typename BlockReduce::TempStorage temp_storage;
for (int k = blockIdx.x; k < C; k += gridDim.x) {
T x_sum = 0;
T x2_sum = 0;
for (int i = threadIdx.x; i < N * M; i += BlockDim) {
int id = layout == framework::DataLayout::kNCHW
? (i / M) * C * M + k * M + i % M
: i * C + k;
T x_in = x[id];
x_sum += x_in;
x2_sum += x_in * x_in;
}
__syncthreads();
T out = BlockReduce(temp_storage).Reduce(x_sum, cub::Sum());
__syncthreads();
if (threadIdx.x == 0) {
mean_var[k] = out / (N * M);
}
out = BlockReduce(temp_storage).Reduce(x2_sum, cub::Sum());
__syncthreads();
if (threadIdx.x == 0) {
mean_var[k + C] = out / (N * M);
}
}
if (blockIdx.x == 0 && threadIdx.x == 0) {
mean_var[2 * C] = static_cast<T>(1.0);
}
}
template <typename T>
__global__ void KeSyncAndMovingStats(T *means, T *variances, T *num_dev,
const int C, const T momentum,
const double epsilon, T *sv_mean_data,
T *sv_inv_var_data, T *moving_means,
T *moving_variances) {
// sync stats across multi-devices
int gid = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
for (int i = gid; i < C; i += stride) {
T mean = means[i] / (*num_dev);
T var = variances[i] / (*num_dev);
var = var - mean * mean;
// sync stats
sv_mean_data[i] = mean;
sv_inv_var_data[i] = 1.0 / sqrt(var + epsilon);
variances[i] = var;
// moving stats
moving_means[i] = moving_means[i] * momentum + mean * (1. - momentum);
moving_variances[i] =
moving_variances[i] * momentum + var * (1. - momentum);
}
}
template <typename T, framework::DataLayout layout>
static __global__ void KeNormAffine(const T *x, const T *scale, const T *bias,
const T *mean, const T *variance,
const double epsilon, const int C,
const int M, const int num, T *y) {
int gid = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
for (int i = gid; i < num; i += stride) {
const int c = layout == framework::DataLayout::kNCHW ? (i / M) % C : i % C;
y[i] = (x[i] - mean[c]) / sqrt(variance[c] + epsilon) * scale[c] + bias[c];
}
}
template <typename DeviceContext, typename T>
class SyncBatchNormKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
double epsilon = static_cast<double>(ctx.Attr<float>("epsilon"));
const float momentum = ctx.Attr<float>("momentum");
const bool is_test = ctx.Attr<bool>("is_test");
const std::string layout_str = ctx.Attr<std::string>("data_layout");
const DataLayout layout = framework::StringToDataLayout(layout_str);
const bool use_global_stats = ctx.Attr<bool>("use_global_stats");
PADDLE_ENFORCE(
!use_global_stats,
"sync_batch_norm doesn't support to set use_global_stats True. ",
"Please use batch_norm in this case.");
const auto *x = ctx.Input<Tensor>("X");
const auto &x_dims = x->dims();
PADDLE_ENFORCE(x_dims.size() >= 2 && x_dims.size() <= 5,
"The Input dim size should be between 2 and 5");
int N, C, H, W, D;
ExtractNCWHD(x_dims, layout, &N, &C, &H, &W, &D);
int x_numel = x->numel();
const T *x_d = x->data<T>();
const T *s_d = ctx.Input<Tensor>("Scale")->data<T>();
const T *b_d = ctx.Input<Tensor>("Bias")->data<T>();
auto *y = ctx.Output<Tensor>("Y");
T *y_d = y->mutable_data<T>(ctx.GetPlace());
const T *mean_data = nullptr;
const T *var_data = nullptr;
auto &dev_ctx = ctx.cuda_device_context();
auto stream = dev_ctx.stream();
auto *comm = dev_ctx.nccl_comm();
const int block = 512;
int max_threads = dev_ctx.GetMaxPhysicalThreadCount();
paddle::memory::AllocationPtr alloc_ptr{nullptr};
if (is_test) {
const auto *est_mean = ctx.Input<Tensor>("Mean");
const auto *est_var = ctx.Input<Tensor>("Variance");
mean_data = est_mean->data<T>();
var_data = est_var->data<T>();
} else {
auto &allocator =
platform::DeviceTemporaryAllocator::Instance().Get(dev_ctx);
// x, x^2, 1, here 1 is used to calc device num
// device num also can be got from platform::DeviceContextPool
const int bytes = (C * 2 + 1) * sizeof(T);
alloc_ptr = allocator.Allocate(bytes);
T *stats = reinterpret_cast<T *>(alloc_ptr->ptr());
const int threads = 256;
int grid = std::min(C, (max_threads + threads - 1) / threads);
if (layout == framework::DataLayout::kNCHW) {
KeLocalStats<
T, threads,
framework::DataLayout::kNCHW><<<grid, threads, 0, stream>>>(
x_d, N, H * W * D, C, stats);
} else {
KeLocalStats<
T, threads,
framework::DataLayout::kNHWC><<<grid, threads, 0, stream>>>(
x_d, N, H * W * D, C, stats);
}
Tensor c_g_st;
T *c_g_st_d = c_g_st.mutable_data<T>({2 * C + 1}, platform::CPUPlace());
auto gplace = boost::get<platform::CUDAPlace>(ctx.GetPlace());
memory::Copy(platform::CPUPlace(), c_g_st_d, gplace, stats, bytes, 0);
int dtype = platform::ToNCCLDataType(x->type());
// In-place operation
PADDLE_ENFORCE(platform::dynload::ncclAllReduce(
stats, stats, 2 * C + 1, static_cast<ncclDataType_t>(dtype), ncclSum,
comm, stream));
// moving mean/variance
auto *mean_out = ctx.Output<Tensor>("MeanOut");
auto *variance_out = ctx.Output<Tensor>("VarianceOut");
T *est_mean_data = mean_out->mutable_data<T>(ctx.GetPlace());
T *est_var_data = variance_out->mutable_data<T>(ctx.GetPlace());
auto *saved_mean = ctx.Output<Tensor>("SavedMean");
auto *saved_inv_variance = ctx.Output<Tensor>("SavedVariance");
T *sv_mean_data = saved_mean->mutable_data<T>(ctx.GetPlace());
T *sv_inv_var_data = saved_inv_variance->mutable_data<T>(ctx.GetPlace());
// Note, Input('Mean')/Input('Variance') share variable with
// Output('MeanOut')/Output('VarianceOut')
KeSyncAndMovingStats<T><<<(C + block - 1) / block, block, 0, stream>>>(
stats, stats + C, stats + 2 * C, C, momentum, epsilon, sv_mean_data,
sv_inv_var_data, est_mean_data, est_var_data);
mean_data = sv_mean_data;
var_data = stats + C;
}
int grid2 = (std::min(x_numel, max_threads) + block - 1) / block;
if (layout == framework::DataLayout::kNCHW) {
KeNormAffine<T,
framework::DataLayout::kNCHW><<<grid2, block, 0, stream>>>(
x_d, s_d, b_d, mean_data, var_data, epsilon, C, H * W * D, x_numel,
y_d);
} else {
KeNormAffine<T,
framework::DataLayout::kNHWC><<<grid2, block, 0, stream>>>(
x_d, s_d, b_d, mean_data, var_data, epsilon, C, H * W * D, x_numel,
y_d);
}
}
};
template <typename T, const int BlockDim, framework::DataLayout layout>
__global__ void KeBackwardLocalStats(const T *dy, const T *x, const T *means,
int N, int M, int C, T *sum_dy_prod) {
typedef cub::BlockReduce<double, BlockDim> BlockReduce;
__shared__ typename BlockReduce::TempStorage temp_storage;
for (int k = blockIdx.x; k < C; k += gridDim.x) {
T sum1 = 0;
T sum2 = 0;
T mean = means[k];
for (int i = threadIdx.x; i < N * M; i += blockDim.x) {
int id = layout == framework::DataLayout::kNCHW
? (i / M) * C * M + k * M + i % M
: i * C + k;
T g = dy[id];
sum1 += g;
sum2 += g * (x[id] - mean);
}
__syncthreads();
T out = BlockReduce(temp_storage).Reduce(sum1, cub::Sum());
__syncthreads();
if (threadIdx.x == 0) {
sum_dy_prod[k] = out;
}
out = BlockReduce(temp_storage).Reduce(sum2, cub::Sum());
__syncthreads();
if (threadIdx.x == 0) {
sum_dy_prod[k + C] = out;
}
}
if (blockIdx.x == 0 && threadIdx.x == 0) {
sum_dy_prod[2 * C] = static_cast<T>(1.0);
}
}
template <typename T, int BlockDim, framework::DataLayout layout>
static __global__ void KeBNBackwardScaleBias(const T *dy, const T *x,
const T *mean,
const T *inv_variance,
const double epsilon, const int N,
const int C, const int HxW,
T *dscale, T *dbias) {
const int outer_size = C;
const int inner_size = N * HxW;
typedef cub::BlockReduce<double, BlockDim> BlockReduce;
__shared__ typename BlockReduce::TempStorage temp_storage;
for (int i = blockIdx.x; i < outer_size; i += gridDim.x) {
T ds_sum = static_cast<T>(0);
T db_sum = static_cast<T>(0);
T inv_var_i = inv_variance[i];
T mean_i = mean[i];
for (int j = threadIdx.x; j < inner_size; j += blockDim.x) {
const int id = layout == framework::DataLayout::kNCHW
? ((j / HxW) * C + i) * HxW + (j % HxW)
: j * outer_size + i;
ds_sum += dy[id] * (x[id] - mean_i);
db_sum += dy[id];
}
__syncthreads();
double os = BlockReduce(temp_storage)
.Reduce(static_cast<double>(ds_sum), cub::Sum());
__syncthreads();
double ob = BlockReduce(temp_storage)
.Reduce(static_cast<double>(db_sum), cub::Sum());
__syncthreads();
if (threadIdx.x == 0) {
dscale[i] = static_cast<T>(os * inv_var_i);
dbias[i] = static_cast<T>(ob);
}
__syncthreads();
}
}
template <typename T, framework::DataLayout layout>
static __global__ void KeBNBackwardData(const T *dy, const T *x, const T *beta,
const T *mean, const T *inv_variance,
const T *g_sum_dy,
const T *g_sum_dy_prod,
const T *num_dev, const double epsilon,
const int C, const int HxW,
const int num, T *dx) {
int gid = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
T scale = static_cast<T>(C) / num;
T dev_num = num_dev[0];
for (int i = gid; i < num; i += stride) {
const int c = layout == framework::DataLayout::kNCHW ? i / HxW % C : i % C;
T inv_var = inv_variance[c];
T s_d = beta[c];
T gvar = -1.0 * (g_sum_dy_prod[c] / dev_num) * s_d * inv_var *
(inv_var * inv_var);
T gmean = -1.0 * (g_sum_dy[c] / dev_num) * s_d * inv_var;
dx[i] =
dy[i] * s_d * inv_var + gmean * scale + gvar * scale * (x[i] - mean[c]);
}
}
// Deriving the Gradient for the Backward Pass of Batch Normalization
// https://kevinzakka.github.io/2016/09/14/batch_normalization/
template <typename DeviceContext, typename T>
class SyncBatchNormGradKernel : 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.");
double epsilon = static_cast<double>(ctx.Attr<float>("epsilon"));
const std::string layout_str = ctx.Attr<std::string>("data_layout");
const DataLayout layout = framework::StringToDataLayout(layout_str);
const auto *x = ctx.Input<Tensor>("X");
const auto *d_y = ctx.Input<Tensor>(framework::GradVarName("Y"));
const auto *scale = ctx.Input<Tensor>("Scale");
const auto &x_dims = x->dims();
PADDLE_ENFORCE(x_dims.size() >= 2 && x_dims.size() <= 5,
"The Input dim size should be between 2 and 5");
int N, C, H, W, D;
ExtractNCWHD(x_dims, layout, &N, &C, &H, &W, &D);
// init output
auto *d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
auto *d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
auto *d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
d_x->mutable_data<T>(ctx.GetPlace());
if (d_scale && d_bias) {
d_scale->mutable_data<T>(ctx.GetPlace());
d_bias->mutable_data<T>(ctx.GetPlace());
}
PADDLE_ENFORCE_EQ(scale->dims().size(), 1UL);
PADDLE_ENFORCE_EQ(scale->dims()[0], C);
std::vector<int> dims;
std::vector<int> strides;
if (layout == DataLayout::kNCHW) {
dims = {N, C, H, W, D};
strides = {C * H * W * D, H * W * D, W * D, D, 1};
} else {
dims = {N, C, H, W, D};
strides = {H * W * C * D, 1, W * D * C, D * C, C};
}
const T *x_d = x->data<T>();
const T *dy_d = d_y->data<T>();
auto &dev_ctx = ctx.cuda_device_context();
auto stream = dev_ctx.stream();
auto *comm = dev_ctx.nccl_comm();
const T *saved_mean = ctx.Input<Tensor>("SavedMean")->data<T>();
const T *saved_inv_var = ctx.Input<Tensor>("SavedVariance")->data<T>();
auto &allocator =
platform::DeviceTemporaryAllocator::Instance().Get(dev_ctx);
const int bytes = (C * 2 + 1) * sizeof(T);
auto alloc_ptr = allocator.Allocate(bytes);
T *stats = reinterpret_cast<T *>(alloc_ptr->ptr());
const int threads = 256;
int max_threads = dev_ctx.GetMaxPhysicalThreadCount();
int grid = std::min(C, (max_threads + threads - 1) / threads);
int x_numel = x->numel();
int fsize = H * W * D;
if (layout == framework::DataLayout::kNCHW) {
KeBackwardLocalStats<
T, threads,
framework::DataLayout::kNCHW><<<grid, threads, 0, stream>>>(
dy_d, x_d, saved_mean, N, fsize, C, stats);
} else {
KeBackwardLocalStats<
T, threads,
framework::DataLayout::kNHWC><<<grid, threads, 0, stream>>>(
dy_d, x_d, saved_mean, N, fsize, C, stats);
}
int dtype = platform::ToNCCLDataType(x->type());
// In-place operation
PADDLE_ENFORCE(platform::dynload::ncclAllReduce(
stats, stats, 2 * C + 1, static_cast<ncclDataType_t>(dtype), ncclSum,
comm, stream));
const int block = 512;
int grid2 = (std::min(x_numel, max_threads) + block - 1) / block;
if (layout == framework::DataLayout::kNCHW) {
if (d_scale && d_bias) {
KeBNBackwardScaleBias<
T, threads,
framework::DataLayout::kNCHW><<<grid, threads, 0, stream>>>(
dy_d, x_d, saved_mean, saved_inv_var, epsilon, N, C, fsize,
d_scale->data<T>(), d_bias->data<T>());
}
if (d_x) {
KeBNBackwardData<
T, framework::DataLayout::kNCHW><<<grid2, block, 0, stream>>>(
dy_d, x_d, scale->data<T>(), saved_mean, saved_inv_var, stats,
stats + C, stats + 2 * C, epsilon, C, fsize, x->numel(),
d_x->data<T>());
}
} else {
if (d_scale && d_bias) {
KeBNBackwardScaleBias<
T, threads,
framework::DataLayout::kNHWC><<<grid, threads, 0, stream>>>(
dy_d, x_d, saved_mean, saved_inv_var, epsilon, N, C, fsize,
d_scale->data<T>(), d_bias->data<T>());
}
if (d_x) {
KeBNBackwardData<
T, framework::DataLayout::kNHWC><<<grid2, block, 0, stream>>>(
dy_d, x_d, scale->data<T>(), saved_mean, saved_inv_var, stats,
stats + C, stats + 2 * C, epsilon, C, fsize, x->numel(),
d_x->data<T>());
}
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
sync_batch_norm, ops::SyncBatchNormKernel<plat::CUDADeviceContext, float>,
ops::SyncBatchNormKernel<plat::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
sync_batch_norm_grad,
ops::SyncBatchNormGradKernel<plat::CUDADeviceContext, float>,
ops::SyncBatchNormGradKernel<plat::CUDADeviceContext, double>);
paddle/fluid/platform/device_context.cc
浏览文件 @ 4f42504e
......@@ -57,7 +57,6 @@ DeviceContextPool::DeviceContextPool(
for (auto& p : places) {
set.insert(p);
}
for (auto& p : set) {
if (platform::is_cpu_place(p)) {
#ifdef PADDLE_WITH_MKLDNN
......@@ -317,6 +316,9 @@ CUDADeviceContext::~CUDADeviceContext() {
eigen_stream_.reset();
eigen_device_.reset();
PADDLE_ENFORCE(cudaStreamDestroy(stream_));
#if !defined(_WIN32)
PADDLE_ENFORCE(dynload::ncclCommDestroy(nccl_comm_));
#endif
}
Place CUDADeviceContext::GetPlace() const { return place_; }
......
paddle/fluid/platform/device_context.h
浏览文件 @ 4f42504e
......@@ -265,6 +265,14 @@ class CUDADeviceContext : public DeviceContext {
/*! \brief Return cuda stream in the device context. */
cudaStream_t stream() const;
#if !defined(_WIN32)
/*! \brief Return nccl communicators. */
ncclComm_t nccl_comm() const { return nccl_comm_; }
/*! \brief Set nccl communicators. */
void set_nccl_comm(ncclComm_t comm) { nccl_comm_ = comm; }
#endif
template <typename Callback>
void RecordEvent(cudaEvent_t ev, Callback callback) {
callback();
......@@ -289,6 +297,15 @@ class CUDADeviceContext : public DeviceContext {
std::unique_ptr<CublasHandleHolder> cublas_handle_;
std::unique_ptr<CublasHandleHolder> cublas_tensor_core_handle_;
#if !defined(_WIN32)
// NCCL communicator (single process version) for NCCL collective operations.
// NCCL collective operations provides fast collectives over multiple GPUs
// both within and across nodes.
// But, this collectives is used for collectives over multiple GPUs within
// nodes.
ncclComm_t nccl_comm_{nullptr};
#endif
int compute_capability_;
int runtime_version_;
int driver_version_;
......
paddle/fluid/platform/init.cc
浏览文件 @ 4f42504e
......@@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include <string.h> // for strdup
#include <algorithm>
#include <memory>
#include <set>
#include <stdexcept>
#include <string>
......@@ -140,6 +142,7 @@ void InitDevices(bool init_p2p, const std::vector<int> devices) {
places.emplace_back(platform::CPUPlace());
platform::DeviceContextPool::Init(places);
platform::DeviceTemporaryAllocator::Init();
#ifndef PADDLE_WITH_MKLDNN
platform::SetNumThreads(FLAGS_paddle_num_threads);
#endif
......
paddle/fluid/platform/nccl_helper.h
浏览文件 @ 4f42504e
......@@ -16,9 +16,11 @@
#pragma once
#include <stdio.h>
#include <memory>
#include <string>
#include <thread> // NOLINT
#include <typeindex>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/platform/dynload/nccl.h"
......@@ -78,6 +80,8 @@ struct NCCLContext {
cudaStream_t stream() const { return ctx_->stream(); }
ncclComm_t comm() const { return comm_; }
int device_id() const {
return boost::get<platform::CUDAPlace>(ctx_->GetPlace()).device;
}
......
paddle/fluid/pybind/CMakeLists.txt
浏览文件 @ 4f42504e
set(PYBIND_DEPS pybind python proto_desc memory executor async_executor prune
feed_fetch_method pass_builder parallel_executor profiler layer scope_pool
tracer analysis_predictor)
tracer analysis_predictor imperative_profiler)
if(WITH_PYTHON)
list(APPEND PYBIND_DEPS py_func_op)
......
paddle/fluid/pybind/imperative.cc
浏览文件 @ 4f42504e
......@@ -42,6 +42,7 @@ void BindTracer(pybind11::module* m) {
framework::AttributeMap attrs_map,
const platform::CPUPlace expected_place,
const bool stop_gradient = false) {
pybind11::gil_scoped_release release;
return self.Trace(op, inputs, outputs, attrs_map, expected_place,
stop_gradient);
})
......@@ -52,6 +53,7 @@ void BindTracer(pybind11::module* m) {
framework::AttributeMap attrs_map,
const platform::CUDAPlace expected_place,
const bool stop_gradient = false) {
pybind11::gil_scoped_release release;
return self.Trace(op, inputs, outputs, attrs_map, expected_place,
stop_gradient);
})
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ 4f42504e
......@@ -36,6 +36,7 @@ limitations under the License. */
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/version.h"
#include "paddle/fluid/imperative/layer.h"
#include "paddle/fluid/imperative/profiler.h"
#include "paddle/fluid/memory/allocation/allocator_strategy.h"
#include "paddle/fluid/memory/allocation/legacy_allocator.h"
#include "paddle/fluid/operators/activation_op.h"
......@@ -156,6 +157,11 @@ PYBIND11_MODULE(core, m) {
m.def("print_mem_usage",
[]() { return memory::allocation::GPUMemMonitor.PrintMemUsage(); });
m.def("start_imperative_gperf_profiler",
[]() { imperative::StartProfile(); });
m.def("stop_imperative_gperf_profiler", []() { imperative::StopProfile(); });
py::class_<imperative::VarBase>(m, "VarBase", R"DOC()DOC")
.def(
py::init<const std::string &, paddle::framework::proto::VarType::Type,
......@@ -1230,6 +1236,21 @@ All parameter, weight, gradient are variables in Paddle.
it will save GPU memory and may make the execution faster.
This options is only available in GPU devices.
Default False)DOC")
.def_property(
"sync_batch_norm",
[](const BuildStrategy &self) { return self.sync_batch_norm_; },
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE(!self.IsFinalized(), "BuildStrategy is finlaized.");
self.sync_batch_norm_ = b;
},
R"DOC(The type is BOOL, sync_batch_norm indicates whether to use
synchronous batch normalization which synchronizes the mean
and variance through multi-devices in training phase.
Current implementation doesn't support FP16 training and CPU.
And only synchronous on one machine, not all machines.
Default False)DOC")
.def_property(
"memory_optimize",
[](const BuildStrategy &self) { return self.memory_optimize_; },
......
python/paddle/fluid/__init__.py
浏览文件 @ 4f42504e
......@@ -132,7 +132,8 @@ def __bootstrap__():
'allocator_strategy', 'reader_queue_speed_test_mode',
'print_sub_graph_dir', 'pe_profile_fname', 'warpctc_dir',
'inner_op_parallelism', 'enable_parallel_graph',
'multiple_of_cupti_buffer_size', 'enable_subgraph_optimize'
'multiple_of_cupti_buffer_size', 'enable_subgraph_optimize',
'tracer_profile_fname'
]
if 'Darwin' not in sysstr:
read_env_flags.append('use_pinned_memory')
......
python/paddle/fluid/compiler.py
浏览文件 @ 4f42504e
......@@ -223,6 +223,9 @@ class CompiledProgram(object):
tps), "num_trainers == len(end_points)"
self._build_strategy.trainers_endpoints = tps
if self._build_strategy.sync_batch_norm:
self._build_strategy.enable_sequential_execution = True
self._persistable_vars = []
for node in self._graph.nodes():
if node.is_var() and node.var() is not None and node.var().persistable() and \
......
python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py
浏览文件 @ 4f42504e
......@@ -164,6 +164,9 @@ class TestQuantizationTransformPass(unittest.TestCase):
def test_linear_fc_quant_range_abs_max(self):
self.linear_fc_quant('range_abs_max', for_ci=True)
def test_linear_fc_quant_moving_average_abs_max(self):
self.linear_fc_quant('moving_average_abs_max', for_ci=True)
def residual_block_quant(self, quant_type, for_ci=False):
main = fluid.Program()
startup = fluid.Program()
......@@ -201,6 +204,9 @@ class TestQuantizationTransformPass(unittest.TestCase):
def test_residual_block_range_abs_max(self):
self.residual_block_quant('range_abs_max', for_ci=True)
def test_residual_block_moving_average_abs_max(self):
self.residual_block_quant('moving_average_abs_max', for_ci=True)
class TestQuantizationFreezePass(unittest.TestCase):
def freeze_graph(self, use_cuda, seed, quant_type, for_ci=False):
......@@ -380,11 +386,18 @@ class TestQuantizationFreezePass(unittest.TestCase):
with fluid.unique_name.guard():
self.freeze_graph(
True, seed=1, quant_type='range_abs_max', for_ci=True)
self.freeze_graph(
True,
seed=1,
quant_type='moving_average_abs_max',
for_ci=True)
def test_freeze_graph_cpu_static(self):
with fluid.unique_name.guard():
self.freeze_graph(
False, seed=2, quant_type='range_abs_max', for_ci=True)
self.freeze_graph(
False, seed=2, quant_type='moving_average_abs_max', for_ci=True)
if __name__ == '__main__':
......
python/paddle/fluid/framework.py
浏览文件 @ 4f42504e
......@@ -430,6 +430,11 @@ class Variable(object):
Returns:
str: The debug string.
"""
if _in_imperative_mode():
# TODO(panyx0718): add more imperative debug info.
return 'name %s, dtype: %s shape: %s' % (self.name, self.dtype,
self.shape)
assert isinstance(throw_on_error, bool) and isinstance(with_details,
bool)
protostr = self.desc.serialize_to_string()
......
python/paddle/fluid/imperative/__init__.py
浏览文件 @ 4f42504e
......@@ -26,8 +26,12 @@ from .nn import *
from . import tracer
from .tracer import *
from . import profiler
from .profiler import *
__all__ = []
__all__ += layers.__all__
__all__ += base.__all__
__all__ += nn.__all__
__all__ += tracer.__all__
__all__ += profiler.__all__
python/paddle/fluid/imperative/profiler.py 0 → 100644
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# 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
from .. import core
__all__ = [
'start_gperf_profiler',
'stop_gperf_profiler',
]
def start_gperf_profiler():
core.start_imperative_gperf_profiler()
def stop_gperf_profiler():
core.stop_imperative_gperf_profiler()
python/paddle/fluid/layers/nn.py
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此差异已折叠。
python/paddle/fluid/tests/unittests/test_yolo_box_op.py 0 → 100644
浏览文件 @ 4f42504e
此差异已折叠。
tools/manylinux1/build_scripts/build.sh
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......@@ -153,3 +153,9 @@ done
# Restore LD_LIBRARY_PATH
LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}"
# According to ar issues: https://lists.gnu.org/archive/html/bug-binutils/2016-05/msg00211.html
# we should install new version ar with 64-bit supported here
wget https://ftp.gnu.org/gnu/binutils/binutils-2.27.tar.gz
tar xzf binutils-2.27.tar.gz && cd binutils-2.27
./configure --prefix=/opt/rh/devtoolset-2/root/usr/ --enable-64-bit-archive && make -j `nproc` && make install
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