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PaddleDetection
未验证 提交 74037cc1 编写于 作者: K Kaipeng Deng 提交者： GitHub
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Merge branch 'develop' into yolo_box

上级 0ff9a403 92b9ce34
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Showing with 4527 addition and 911 deletion
benchmark/fluid/fluid_benchmark.py
浏览文件 @ 74037cc1
......@@ -179,7 +179,6 @@ def train_parallel(train_args, test_args, args, train_prog, test_prog,
else:
build_strategy.reduce_strategy = fluid.BuildStrategy(
).ReduceStrategy.AllReduce
build_strategy.fuse_broadcast_op = args.fuse_broadcast_op
avg_loss = train_args[0]
......
cmake/operators.cmake
浏览文件 @ 74037cc1
......@@ -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 @ 74037cc1
......@@ -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'))
......@@ -293,6 +293,7 @@ paddle.fluid.layers.sigmoid (ArgSpec(args=['x', 'name'], varargs=None, keywords=
paddle.fluid.layers.logsigmoid (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '81ccb7acafd06c7728e11581f5d342e3'))
paddle.fluid.layers.exp (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e6b3e769413d96aab4176f96db25984b'))
paddle.fluid.layers.tanh (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e9d586a0b5bd05f67ee78048f9d503b6'))
paddle.fluid.layers.atan (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '3a46e0b5f9ce82348406478e610f14c9'))
paddle.fluid.layers.tanh_shrink (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '1e521554b9fdda9061ec6d306f0709b7'))
paddle.fluid.layers.softshrink (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '9eef31597bbafa2bd49691e072296e13'))
paddle.fluid.layers.sqrt (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '072a8541e0f632366bba10f67cb0db27'))
......@@ -300,6 +301,8 @@ paddle.fluid.layers.abs (ArgSpec(args=['x', 'name'], varargs=None, keywords=None
paddle.fluid.layers.ceil (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'c75d67dc5fe28f68e4cfffead4f698ad'))
paddle.fluid.layers.floor (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '647b16c5da5ef909649ae02abb434973'))
paddle.fluid.layers.cos (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '485f2686bcc2fe37a4bd893769c8a3e2'))
paddle.fluid.layers.acos (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '920a47734482276c069ba24c61c26b25'))
paddle.fluid.layers.asin (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'cf4ee2c9b9d7293556f8c5173dfb5d2c'))
paddle.fluid.layers.sin (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '01f1766aa76eff1df30147505b59f7c4'))
paddle.fluid.layers.round (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b47f5da13913d3e56bdb1e612a73f3f2'))
paddle.fluid.layers.reciprocal (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'cc6ac2f14f03c52aaa83a59bf83b8d26'))
......@@ -327,7 +330,7 @@ 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/framework/details/build_strategy.cc
浏览文件 @ 74037cc1
......@@ -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 @ 74037cc1
......@@ -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/inplace_op_pass.cc
浏览文件 @ 74037cc1
......@@ -16,6 +16,7 @@
#include <algorithm>
#include <deque>
#include <iterator>
#include <memory>
#include <stack>
#include <string>
#include <unordered_map>
......@@ -263,6 +264,10 @@ void InplacePass::WithdrawModify(const NodeSwapQueue& nodes,
void InplacePass::TryInplaceOpInputOutput(ir::Node* op,
ir::Graph* graph) const {
VLOG(4) << "Try to inplace op " << op->Name();
// FIXME(liuwei1031): Graph is not aware of the existence of BlockDescs and
// ProgramDescs.
// The operations related to BlockDesc or ProgramDesc should perform on Graph
// or Node directly!
PADDLE_ENFORCE(op->Op() != nullptr && op->Op()->Block() != nullptr,
"op_desc is nullptr");
// some pre-requirments need to meet if the op want to inplaced.
......
paddle/fluid/framework/details/memory_optimize_helper.cc
浏览文件 @ 74037cc1
......@@ -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/details/memory_optimize_pass.cc
浏览文件 @ 74037cc1
......@@ -24,6 +24,7 @@
#include <sstream>
#include <string>
#include <type_traits>
#include <unordered_set>
#include <vector>
#include "gflags/gflags.h"
#include "paddle/fluid/framework/data_type.h"
......@@ -191,6 +192,10 @@ void MemoryOptimizePass::SubGraphOptimize(OpDesc* op_desc) const {
// immediately to make the subblock variable reuse strategy take
// effect. Because it is a single op in graph. No need to
// update the ir nodes.
// FIXME(liuwei1031): Graph is not aware of the existence of
// BlockDescs and ProgramDescs.
// The operations related to BlockDesc or ProgramDesc should perform
// on Graph or Node directly!
sub_op_desc->Rename(var->Name(), cache->Name());
if (sub_op_desc->Block() != nullptr &&
sub_op_desc->Block()->HasVar(var->Name())) {
......
paddle/fluid/framework/executor.cc
浏览文件 @ 74037cc1
......@@ -34,11 +34,11 @@ limitations under the License. */
#ifdef PADDLE_WITH_NGRAPH
#include "paddle/fluid/operators/ngraph/ngraph_engine.h"
DEFINE_bool(use_ngraph, false, "Use NGRAPH to run");
#endif
DECLARE_bool(benchmark);
DEFINE_bool(use_mkldnn, false, "Use MKLDNN to run");
DEFINE_bool(use_ngraph, false, "Use NGRAPH to run");
namespace paddle {
namespace framework {
......@@ -194,9 +194,6 @@ void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
bool force_disable_gc) {
platform::RecordBlock b(block_id);
if (FLAGS_use_mkldnn) EnableMKLDNN(pdesc);
#ifdef PADDLE_WITH_NGRAPH
if (FLAGS_use_ngraph) operators::NgraphEngine::EnableNgraph(pdesc);
#endif
auto ctx = Prepare(pdesc, block_id, skip_ref_cnt_vars, force_disable_gc);
RunPreparedContext(ctx.get(), scope, create_local_scope, create_vars);
}
......@@ -372,6 +369,12 @@ std::unique_ptr<ExecutorPrepareContext> Executor::Prepare(
for (auto& op_desc : block.AllOps()) {
ctx->ops_.push_back(OpRegistry::CreateOp(*op_desc));
}
#ifdef PADDLE_WITH_NGRAPH
if (FLAGS_use_ngraph) {
paddle::operators::NgraphEngine::FuseNgraphOps(
ctx->prog_.Block(ctx->block_id_), &ctx->ops_);
}
#endif
return ctx;
}
......
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ 74037cc1
......@@ -46,6 +46,7 @@ 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_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 +67,7 @@ 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 +102,8 @@ 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_sync_batch_norm_pass SRCS sync_batch_norm_pass_tester.cc DEPS sync_batch_norm_pass)
cc_test(test_cpu_quantize_squash_pass SRCS cpu_quantize_squash_pass_tester.cc DEPS cpu_quantize_squash_pass naive_executor)
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_squash_pass.cc 0 → 100644
浏览文件 @ 74037cc1
// 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 @ 74037cc1
// 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 @ 74037cc1
// 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.cc
浏览文件 @ 74037cc1
......@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include <algorithm>
#include <unordered_set>
#include <unordered_map>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/op_proto_maker.h"
......@@ -152,6 +152,39 @@ void Graph::ResolveHazard(
}
}
std::shared_ptr<Graph> Graph::Clone() {
auto cloned_graph = std::make_shared<Graph>(this->program_);
cloned_graph->ReleaseNodes();
cloned_graph->num_node_created_ = 0;
std::unordered_map<ir::Node *, ir::Node *> origin_to_cloned;
for (auto *n : this->node_set_) {
ir::Node *cloned_node = nullptr;
if (n->IsCtrlVar()) {
cloned_node = cloned_graph->CreateControlDepVar();
} else if (!n->var_desc_ && !n->op_desc_) { // empty node
cloned_node = cloned_graph->CreateEmptyNode(n->Name(), n->NodeType());
} else if (n->IsVar()) {
cloned_node = cloned_graph->CreateVarNode(n->Var());
} else if (n->IsOp()) {
cloned_node = cloned_graph->CreateOpNode(n->Op());
}
if (cloned_node) {
origin_to_cloned[n] = cloned_node;
} else {
PADDLE_THROW("The cloned node's type is not supported!");
}
}
for (auto *n : this->node_set_) {
for (auto it = n->inputs.begin(); it != n->inputs.end(); it++) {
origin_to_cloned[n]->inputs.push_back(origin_to_cloned[*it]);
}
for (auto it = n->outputs.begin(); it != n->outputs.end(); it++) {
origin_to_cloned[n]->outputs.push_back(origin_to_cloned[*it]);
}
}
return cloned_graph;
}
bool IsControlDepVar(const ir::Node &var) {
return var.Name().find(ir::Node::kControlDepVarName) != std::string::npos;
}
......
paddle/fluid/framework/ir/graph.h
浏览文件 @ 74037cc1
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <map>
#include <memory>
#include <string>
#include <unordered_set>
#include <vector>
#include "paddle/fluid/framework/ir/node.h"
......@@ -199,7 +200,12 @@ class Graph {
// WARN: After a series of passes, the current graph can be quite
// different from OriginProgram. Caller shouldn't assume much from
// the returned OriginProgram.
const ProgramDesc &OriginProgram() const { return program_; }
const ProgramDesc &OriginProgram() const {
LOG(WARNING) << "WARN: After a series of passes, the current graph can be "
"quite different from OriginProgram. So, please avoid "
"using the `OriginProgram()` method!";
return program_;
}
// This method takes ownership of `node`.
ir::Node *AddNode(ir::Node *node) {
......@@ -212,6 +218,10 @@ class Graph {
void ResolveHazard(
const std::map<std::string, std::vector<ir::Node *>> &var_nodes);
// Create a new and duplicated graph.
// WARN: The method only clones the graph structure, not its attributes.
std::shared_ptr<Graph> Clone();
private:
std::map<std::string, std::vector<ir::Node *>> InitFromProgram(
const ProgramDesc &program);
......
paddle/fluid/framework/ir/graph_pattern_detector.cc
浏览文件 @ 74037cc1
......@@ -1301,6 +1301,51 @@ 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 @ 74037cc1
......@@ -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"
......@@ -766,6 +769,34 @@ 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/node.h
浏览文件 @ 74037cc1
......@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include <memory>
#include <string>
#include <typeindex>
#include <typeinfo>
......
paddle/fluid/framework/ir/sync_batch_norm_pass.cc 0 → 100644
浏览文件 @ 74037cc1
/* 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 @ 74037cc1
/* 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 @ 74037cc1
// 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/operator.cc
浏览文件 @ 74037cc1
......@@ -186,14 +186,14 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
VLOG(3) << place << " " << DebugStringEx(&scope);
} catch (platform::EnforceNotMet exception) {
if (Attrs().count("sub_block") != 0) {
throw;
throw std::move(exception);
}
auto& callstack = Attr<std::vector<std::string>>(
OpProtoAndCheckerMaker::OpCreationCallstackAttrName());
if (callstack.empty()) {
throw;
throw std::move(exception);
}
std::ostringstream sout;
sout << "Invoke operator " << Type() << " error.\n";
......@@ -204,7 +204,7 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
sout << "C++ Callstacks: \n";
sout << exception.err_str_;
exception.err_str_ = sout.str();
throw;
throw std::move(exception);
} catch (...) {
std::rethrow_exception(std::current_exception());
}
......@@ -926,8 +926,10 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
dev_ctx = pool.Get(expected_kernel_key.place_);
}
RuntimeInferShapeContext infer_shape_ctx(*this, exec_scope, ctx);
this->InferShape(&infer_shape_ctx);
if (!HasAttr(kAllKernelsMustComputeRuntimeShape)) {
RuntimeInferShapeContext infer_shape_ctx(*this, exec_scope, ctx);
this->InferShape(&infer_shape_ctx);
}
// TODO(panyx0718): ExecutionContext should only depend on RuntimeContext
// not Scope. Imperative mode only pass inputs and get outputs.
kernel_iter->second(
......
paddle/fluid/framework/operator.h
浏览文件 @ 74037cc1
......@@ -62,6 +62,15 @@ constexpr char kZeroVarSuffix[] = "@ZERO";
/// Variables with this suffix are the new Gradient.
constexpr char kNewGradSuffix[] = "@NEWGRAD@";
/// If an Op has this attribute, all its kernels should calculate output
/// variable's shape in the corresponding Compute() function. And
/// OperatorWithKernel::RunImpl() would skip call this Op's InferShape()
/// function in its runtime for speedup.
/// TODO(luotao): Note that this temporal attribute would be deleted after all
/// ops contain it.
constexpr char kAllKernelsMustComputeRuntimeShape[] =
"@ALL_KERNELS_MUST_COMPUTE_RUNTIME_SHAPE@";
// define some kernel priority
/* Define multiple kernel type fallback order*/
extern std::vector<std::tuple<platform::Place, LibraryType>> kKernelPriority;
......
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ 74037cc1
......@@ -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"
......@@ -181,13 +183,14 @@ std::vector<Scope *> &ParallelExecutor::GetLocalScopes() {
return member_->local_scopes_;
}
ParallelExecutor::ParallelExecutor(
const std::vector<platform::Place> &places,
const std::unordered_set<std::string> &bcast_vars,
const std::string &loss_var_name, Scope *scope,
const std::vector<Scope *> &local_scopes,
const ExecutionStrategy &exec_strategy, const BuildStrategy &build_strategy,
ir::Graph *graph)
ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
const std::vector<std::string> &bcast_vars,
const std::string &loss_var_name,
Scope *scope,
const std::vector<Scope *> &local_scopes,
const ExecutionStrategy &exec_strategy,
const BuildStrategy &build_strategy,
ir::Graph *graph)
: member_(new ParallelExecutorPrivate(places)) {
member_->global_scope_ = scope;
member_->use_cuda_ = exec_strategy.use_cuda_;
......@@ -250,13 +253,41 @@ ParallelExecutor::ParallelExecutor(
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
}
if (member_->local_scopes_.size() != 1 && local_scopes.empty()) {
BCastParamsToDevices(bcast_vars);
// broadcast parameters from the 0th device to others:
auto need_broadcast = [&]() -> bool {
if (build_strategy.num_trainers_ > 1) {
// 1. num_tariners would be grater than 1 for nccl distributed training.
return true;
} else if (member_->local_scopes_.size() != 1 && local_scopes.empty()) {
// 2. Only one trainer process, but ParallelExecutor hold multiple
// devices.
return true;
}
return false;
};
if (need_broadcast()) {
BCastParamsToDevices(bcast_vars, build_strategy.trainer_id_);
}
// Startup Program has been run. All local scopes has correct parameters.
// Step 2. Convert main_program to SSA form and dependency graph. Also, insert
......@@ -338,7 +369,7 @@ ParallelExecutor::ParallelExecutor(
}
void ParallelExecutor::BCastParamsToDevices(
const std::unordered_set<std::string> &vars) const {
const std::vector<std::string> &vars, int trainer_id) const {
// the initializing bcast, all vars would be bcast from device(0).
for (auto &var : vars) {
framework::Variable *main_var = member_->local_scopes_[0]->FindVar(var);
......@@ -362,7 +393,7 @@ void ParallelExecutor::BCastParamsToDevices(
auto place = member_->places_[i];
void *buffer;
if (i == 0) {
if (i == 0 && trainer_id == 0) {
buffer = const_cast<void *>(main_tensor.data<void>());
} else {
auto local_scope = member_->local_scopes_[i];
......
paddle/fluid/framework/parallel_executor.h
浏览文件 @ 74037cc1
......@@ -14,9 +14,11 @@ limitations under the License. */
#pragma once
#include <memory>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/build_strategy.h"
......@@ -45,7 +47,7 @@ class ParallelExecutor {
public:
explicit ParallelExecutor(const std::vector<platform::Place> &places,
const std::unordered_set<std::string> &bcast_vars,
const std::vector<std::string> &bcast_vars,
const std::string &loss_var_name, Scope *scope,
const std::vector<Scope *> &local_scopes,
const ExecutionStrategy &exec_strategy,
......@@ -70,7 +72,10 @@ class ParallelExecutor {
const std::string &fetched_var_name);
private:
void BCastParamsToDevices(const std::unordered_set<std::string> &vars) const;
// broadcast the parameters from the 0th device.
// trainer_id the trainer index in nccl distributed training.
void BCastParamsToDevices(const std::vector<std::string> &vars,
int trainer_id = 0) const;
bool EnableParallelGraphExecution(const ir::Graph &graph,
const ExecutionStrategy &exec_strategy,
const BuildStrategy &build_strategy) const;
......
paddle/fluid/memory/allocation/CMakeLists.txt
浏览文件 @ 74037cc1
......@@ -3,7 +3,7 @@ cc_library(cpu_allocator SRCS cpu_allocator.cc DEPS allocator)
cc_library(best_fit_allocator SRCS best_fit_allocator.cc DEPS allocator)
cc_library(locked_allocator SRCS locked_allocator.cc DEPS allocator)
cc_library(buffered_allocator SRCS buffered_allocator.cc DEPS allocator)
cc_library(legacy_allocator SRCS legacy_allocator.cc DEPS allocator buddy_allocator)
cc_library(legacy_allocator SRCS legacy_allocator.cc DEPS allocator buddy_allocator profiler)
cc_test(buffered_allocator_test SRCS buffered_allocator_test.cc DEPS best_fit_allocator locked_allocator buffered_allocator cpu_allocator)
if (WITH_GPU)
......
paddle/fluid/memory/allocation/legacy_allocator.cc
浏览文件 @ 74037cc1
......@@ -12,8 +12,6 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/memory/allocation/legacy_allocator.h"
#include <memory>
#include <string>
#include <utility>
......@@ -24,9 +22,11 @@
#endif
#include "glog/logging.h"
#include "paddle/fluid/memory/allocation/legacy_allocator.h"
#include "paddle/fluid/memory/detail/buddy_allocator.h"
#include "paddle/fluid/memory/detail/system_allocator.h"
#include "paddle/fluid/platform/gpu_info.h"
#include "paddle/fluid/platform/profiler.h"
#include "paddle/fluid/string/printf.h"
#include "paddle/fluid/string/split.h"
......@@ -329,18 +329,22 @@ size_t Usage::operator()(const platform::CUDAPinnedPlace &cuda_pinned) const {
} // namespace legacy
namespace allocation {
LegacyMemMonitor GPUMemMonitor;
Allocation *LegacyAllocator::AllocateImpl(size_t size, Allocator::Attr attr) {
void *ptr = boost::apply_visitor(legacy::AllocVisitor(size), place_);
return new Allocation(ptr, size, place_);
auto *tmp_alloc = new Allocation(ptr, size, place_);
platform::MemEvenRecorder::Instance().PushMemRecord(
static_cast<void *>(tmp_alloc), place_, size);
return tmp_alloc;
}
void LegacyAllocator::Free(Allocation *allocation) {
boost::apply_visitor(
legacy::FreeVisitor(allocation->ptr(), allocation->size()),
allocation->place());
platform::MemEvenRecorder::Instance().PopMemRecord(
static_cast<void *>(allocation), place_);
delete allocation;
}
......
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ 74037cc1
......@@ -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 +58,8 @@ if (WITH_GPU)
op_library(conv_fusion_op)
file(APPEND ${pybind_file} "USE_CUDA_ONLY_OP(conv2d_fusion);\n")
endif()
op_library(sync_batch_norm_op)
file(APPEND ${pybind_file} "USE_CUDA_ONLY_OP(sync_batch_norm);\n")
else()
op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale)
endif()
......
paddle/fluid/operators/activation_op.cc
浏览文件 @ 74037cc1
......@@ -13,7 +13,9 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/activation_op.h"
#include <memory>
#include <string>
#include <unordered_map>
#include "paddle/fluid/operators/mkldnn/mkldnn_activation_op.h"
#include "paddle/fluid/platform/port.h"
#ifdef PADDLE_WITH_CUDA
......@@ -269,6 +271,48 @@ $$out = \\frac{x}{1 + \|x\|}$$
)DOC";
class AcosOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "Input of acos operator");
AddOutput("Out", "Output of acos operator");
AddComment(R"DOC(
Arccosine Activation Operator.
$$out = \cos^{-1}(x)$$
)DOC");
}
};
class AsinOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "Input of asin operator");
AddOutput("Out", "Output of asin operator");
AddComment(R"DOC(
Arcsine Activation Operator.
$$out = \sin^{-1}(x)$$
)DOC");
}
};
class AtanOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "Input of atan operator");
AddOutput("Out", "Output of atan operator");
AddComment(R"DOC(
Arctanh Activation Operator.
$$out = \tanh^{-1}(x)$$
)DOC");
}
};
class LeakyReluOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
......@@ -543,7 +587,10 @@ namespace ops = paddle::operators;
__macro(SoftShrink, softshrink); \
__macro(Abs, abs); \
__macro(Cos, cos); \
__macro(Acos, acos); \
__macro(Sin, sin); \
__macro(Asin, asin); \
__macro(Atan, atan); \
__macro(Round, round); \
__macro(Log, log); \
__macro(Square, square); \
......
paddle/fluid/operators/activation_op.h
浏览文件 @ 74037cc1
......@@ -39,9 +39,8 @@ namespace operators {
Please refer to the layer_helper.py and get the details.
*/
static std::unordered_set<std::string> InplaceOpSet = {
"sigmoid", "exp", "relu", "tanh", "sqrt", "ceil",
"floor", "reciprocal", "relu6", "soft_relu", "hard_sigmoid",
};
"sigmoid", "exp", "relu", "tanh", "sqrt", "ceil",
"floor", "reciprocal", "relu6", "soft_relu", "hard_sigmoid"};
static bool IsInplace(const std::string& op) {
bool inplace = InplaceOpSet.count(op);
......@@ -553,6 +552,101 @@ struct SinFunctor : public BaseActivationFunctor<T> {
}
};
template <typename T>
struct Acos {
HOSTDEVICE T operator()(const T& val) const { return acos(val); }
};
template <>
struct Acos<platform::float16> {
HOSTDEVICE platform::float16 operator()(const platform::float16& val) const {
return platform::float16(acos(static_cast<float>(val)));
}
};
// Acos(x) = acos(x)
template <typename T>
struct AcosFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.unaryExpr(Acos<T>());
}
};
// acos'(x) = -1/sqrt(1-x^2)
template <typename T>
struct AcosGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) =
-dout * static_cast<T>(1) / (static_cast<T>(1) - x.square()).sqrt();
}
};
template <typename T>
struct Asin {
HOSTDEVICE T operator()(const T& val) const { return asin(val); }
};
template <>
struct Asin<platform::float16> {
HOSTDEVICE platform::float16 operator()(const platform::float16& val) const {
return platform::float16(asin(static_cast<float>(val)));
}
};
// Asin(x) = asin(x)
template <typename T>
struct AsinFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.unaryExpr(Asin<T>());
}
};
// asin'(x) = 1/sqrt(1-x^2)
template <typename T>
struct AsinGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) =
dout * static_cast<T>(1) / (static_cast<T>(1) - x.square()).sqrt();
}
};
template <typename T>
struct Atan {
HOSTDEVICE T operator()(const T& val) const { return atan(val); }
};
template <>
struct Atan<platform::float16> {
HOSTDEVICE platform::float16 operator()(const platform::float16& val) const {
return platform::float16(atan(static_cast<float>(val)));
}
};
// Atan(x) = atan(x)
template <typename T>
struct AtanFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) const {
out.device(d) = x.unaryExpr(Atan<T>());
}
};
// atan'(x) = 1 / (1 + x^2)
template <typename T>
struct AtanGradFunctor : public BaseActivationFunctor<T> {
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
dx.device(d) = dout * static_cast<T>(1) / (static_cast<T>(1) + x.square());
}
};
// round(x) = [x]
template <typename T>
struct RoundFunctor : public BaseActivationFunctor<T> {
......@@ -1001,13 +1095,16 @@ struct SwishGradFunctor : public BaseActivationFunctor<T> {
__macro(relu, ReluFunctor, ReluGradFunctor); \
__macro(gelu, GeluFunctor, GeluGradFunctor); \
__macro(tanh, TanhFunctor, TanhGradFunctor); \
__macro(atan, AtanFunctor, AtanGradFunctor); \
__macro(softshrink, SoftShrinkFunctor, SoftShrinkGradFunctor); \
__macro(sqrt, SqrtFunctor, SqrtGradFunctor); \
__macro(abs, AbsFunctor, AbsGradFunctor); \
__macro(ceil, CeilFunctor, ZeroGradFunctor); \
__macro(floor, FloorFunctor, ZeroGradFunctor); \
__macro(cos, CosFunctor, CosGradFunctor); \
__macro(acos, AcosFunctor, AcosGradFunctor); \
__macro(sin, SinFunctor, SinGradFunctor); \
__macro(asin, AsinFunctor, AsinGradFunctor); \
__macro(round, RoundFunctor, ZeroGradFunctor); \
__macro(reciprocal, ReciprocalFunctor, ReciprocalGradFunctor); \
__macro(log, LogFunctor, LogGradFunctor); \
......
paddle/fluid/operators/batch_norm_op.cu
浏览文件 @ 74037cc1
......@@ -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 @ 74037cc1
......@@ -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/cross_entropy_op.cc
浏览文件 @ 74037cc1
......@@ -13,18 +13,21 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/cross_entropy_op.h"
#include <memory>
#include <string>
#include <unordered_map>
namespace paddle {
namespace operators {
class CrossEntropyOp : public framework::OperatorWithKernel {
class CrossEntropyOpBase : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) should be not null.");
auto x_dims = ctx->GetInputDim("X");
......@@ -43,7 +46,8 @@ class CrossEntropyOp : public framework::OperatorWithKernel {
"Input(X) and Input(Label) shall have the same shape "
"except the last dimension.");
}
if (ctx->Attrs().Get<bool>("soft_label")) {
if (IsSoftLabel(ctx)) {
if (check) {
PADDLE_ENFORCE_EQ(x_dims[rank - 1], label_dims[rank - 1],
"If Attr(soft_label) == true, the last dimension of "
......@@ -69,21 +73,24 @@ class CrossEntropyOp : public framework::OperatorWithKernel {
return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
ctx.device_context());
}
virtual bool IsSoftLabel(framework::InferShapeContext* ctx) const {
return ctx->Attrs().Get<bool>("soft_label");
}
};
class CrossEntropyGradientOp : public framework::OperatorWithKernel {
class CrossEntropyGradientOpBase : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
void InferShape(framework::InferShapeContext* ctx) const {
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")),
"Input(Y@GRAD) shoudl be not null.");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
"Output(X@GRAD) should be not null.");
auto x_dims = ctx->GetInputDim("X");
auto x_dims = GetXDim(ctx);
auto label_dims = ctx->GetInputDim("Label");
auto dy_dims = ctx->GetInputDim(framework::GradVarName("Y"));
int rank = x_dims.size();
......@@ -108,9 +115,7 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel {
"The Input(X) and Input(Y@Grad) should have the same "
"shape except the last dimension.");
}
PADDLE_ENFORCE_EQ(dy_dims[rank - 1], 1,
"The last dimension of Input(Y@Grad) should be 1.");
if (ctx->Attrs().Get<bool>("soft_label")) {
if (IsSoftLabel(ctx)) {
if (check) {
PADDLE_ENFORCE_EQ(
x_dims[rank - 1], label_dims[rank - 1],
......@@ -123,7 +128,10 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel {
"Input(Label) should be 1.");
}
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
ctx->ShareLoD("X", framework::GradVarName("X"));
PADDLE_ENFORCE_EQ(dy_dims[rank - 1], 1,
"The last dimension of Input(Y@Grad) should be 1.");
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
ctx->ShareLoD(VarNameWithXLoD(), framework::GradVarName("X"));
}
protected:
......@@ -131,8 +139,28 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel {
// is determined by its input "X".
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
ctx.device_context());
return framework::OpKernelType(
ctx.Input<Tensor>(framework::GradVarName("Y"))->type(),
ctx.device_context());
}
virtual framework::DDim GetXDim(framework::InferShapeContext* ctx) const {
return ctx->GetInputDim("X");
}
virtual const char* VarNameWithXLoD() const { return "X"; }
virtual bool IsSoftLabel(framework::InferShapeContext* ctx) const {
return ctx->Attrs().Get<bool>("soft_label");
}
};
class CrossEntropyOpInferVarType
: public framework::PassInDtypeAndVarTypeToOutput {
protected:
std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
const override {
return std::unordered_map<std::string, std::string>{{"X", /*->*/ "Y"}};
}
};
......@@ -200,22 +228,132 @@ or not. But the output only shares the LoD information with input X.
}
};
class CrossEntropyOpInferVarType
: public framework::PassInDtypeAndVarTypeToOutput {
class CrossEntropyGradientOp : public CrossEntropyGradientOpBase {
public:
using CrossEntropyGradientOpBase::CrossEntropyGradientOpBase;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
CrossEntropyGradientOpBase::InferShape(ctx);
}
};
class CrossEntropyOp2 : public CrossEntropyOpBase {
public:
using CrossEntropyOpBase::CrossEntropyOpBase;
void InferShape(framework::InferShapeContext* ctx) const override {
CrossEntropyOpBase::InferShape(ctx);
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");
}
protected:
std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
const override {
return std::unordered_map<std::string, std::string>{{"X", /*->*/ "Y"}};
bool IsSoftLabel(framework::InferShapeContext* ctx) const override {
return false;
}
};
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 {
auto x_shape = ctx->GetInputDim("XShape");
return framework::DDim(x_shape.Get(), x_shape.size() - 1);
}
virtual const char* VarNameWithXLoD() const { return "XShape"; }
virtual bool IsSoftLabel(framework::InferShapeContext* ctx) const {
return false;
}
};
class CrossEntropyOpMaker2 : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(Tensor, default Tensor<float>), a tensor whose last dimension "
"size is equal to the number of classes. This input is a "
"probability computed by the previous operator, which is almost "
"always the result of a softmax operator.");
AddInput(
"Label",
"(Tensor), the tensor which represents the ground truth. It has the "
"same shape with 'X' except the last dimension. One hot Tensor.");
AddOutput("Y",
"(Tensor, default Tensor<float>), a tensor whose shape is same "
"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."
"Only valid if soft_label is set to False")
.SetDefault(-100);
AddComment(R"DOC(
Hard-label CrossEntropy Operator.
The input 'X' and 'Label' will first be logically flattened to 2-D matrixs.
The matrix's second dimension(row length) is as same as the original last
dimension, and the first dimension(column length) is the product of all other
original dimensions. Then the softmax computation will take palce on each raw
of flattened matrixs.
Only support hard label.
Both the input X and Label can carry the LoD (Level of Details) information,
or not. But the output only shares the LoD information with input X.
)DOC");
}
};
class CrossEntropyGradOpDescMaker2 : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("cross_entropy_grad2");
op->SetInput("Label", Input("Label"));
op->SetInput("MatchX", Output("MatchX"));
op->SetInput("XShape", Output("XShape"));
op->SetInput(framework::GradVarName("Y"), OutputGrad("Y"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
using CPUCtx = paddle::platform::CPUDeviceContext;
REGISTER_OPERATOR(cross_entropy, ops::CrossEntropyOp, ops::CrossEntropyOpMaker,
ops::CrossEntropyOpInferVarType,
REGISTER_OPERATOR(cross_entropy, ops::CrossEntropyOpBase,
ops::CrossEntropyOpMaker, ops::CrossEntropyOpInferVarType,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(cross_entropy_grad, ops::CrossEntropyGradientOp);
REGISTER_OP_CPU_KERNEL(cross_entropy, ops::CrossEntropyOpKernel<CPUCtx, float>,
......@@ -223,3 +361,14 @@ REGISTER_OP_CPU_KERNEL(cross_entropy, ops::CrossEntropyOpKernel<CPUCtx, float>,
REGISTER_OP_CPU_KERNEL(cross_entropy_grad,
ops::CrossEntropyGradientOpKernel<CPUCtx, float>,
ops::CrossEntropyGradientOpKernel<CPUCtx, double>);
REGISTER_OPERATOR(cross_entropy2, ops::CrossEntropyOp2,
ops::CrossEntropyOpMaker2, ops::CrossEntropyOpInferVarType,
ops::CrossEntropyGradOpDescMaker2);
REGISTER_OPERATOR(cross_entropy_grad2, ops::CrossEntropyGradientOp2);
REGISTER_OP_CPU_KERNEL(cross_entropy2,
ops::CrossEntropyOpKernel2<CPUCtx, float>,
ops::CrossEntropyOpKernel2<CPUCtx, double>);
REGISTER_OP_CPU_KERNEL(cross_entropy_grad2,
ops::CrossEntropyGradientOpKernel2<CPUCtx, float>,
ops::CrossEntropyGradientOpKernel2<CPUCtx, double>);
paddle/fluid/operators/cross_entropy_op.cu
浏览文件 @ 74037cc1
......@@ -27,3 +27,13 @@ REGISTER_OP_CUDA_KERNEL(
cross_entropy_grad, ops::CrossEntropyGradientOpKernel<CUDACtx, float>,
ops::CrossEntropyGradientOpKernel<CUDACtx, double>,
ops::CrossEntropyGradientOpKernel<CUDACtx, plat::float16>);
REGISTER_OP_CUDA_KERNEL(cross_entropy2,
ops::CrossEntropyOpKernel2<CUDACtx, float>,
ops::CrossEntropyOpKernel2<CUDACtx, double>,
ops::CrossEntropyOpKernel2<CUDACtx, plat::float16>);
REGISTER_OP_CUDA_KERNEL(
cross_entropy_grad2, ops::CrossEntropyGradientOpKernel2<CUDACtx, float>,
ops::CrossEntropyGradientOpKernel2<CUDACtx, double>,
ops::CrossEntropyGradientOpKernel2<CUDACtx, plat::float16>);
paddle/fluid/operators/cross_entropy_op.h
浏览文件 @ 74037cc1
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math.h"
#include "paddle/fluid/operators/math/cross_entropy.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/platform/for_range.h"
......@@ -137,5 +138,124 @@ 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* dy, const T* match_x,
const int64_t* label,
int64_t ignore_index,
int64_t feature_size)
: dx_(dx),
dy_(dy),
match_x_(match_x),
label_(label),
ignore_index_(ignore_index),
feature_size_(feature_size) {}
HOSTDEVICE void operator()(int64_t idx) const {
auto row_idx = idx / feature_size_;
auto col_idx = idx % feature_size_;
auto label = label_[row_idx];
if (label == col_idx && label != ignore_index_) {
dx_[idx] = -dy_[row_idx] / match_x_[row_idx];
} else {
dx_[idx] = 0;
}
}
T* dx_;
const T* dy_;
const T* match_x_;
const int64_t* label_;
int64_t ignore_index_;
int64_t feature_size_;
};
template <typename DeviceContext, typename T>
class CrossEntropyOpKernel2 : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<Tensor>("X");
auto* label = ctx.Input<Tensor>("Label");
auto* y = ctx.Output<Tensor>("Y");
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");
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));
}
};
template <typename DeviceContext, typename T>
class CrossEntropyGradientOpKernel2 : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
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_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");
int rank = dx->dims().size();
int64_t feature_size = dx->dims()[rank - 1];
int64_t batch_size = framework::product(dx->dims()) / feature_size;
platform::ForRange<DeviceContext> for_range(
ctx.template device_context<DeviceContext>(),
batch_size * feature_size);
for_range(HardLabelCrossEntropyBackwardFunctor<T>(
p_dx, p_dy, p_match_x, p_label, ignore_index, feature_size));
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detection/box_coder_op.h
浏览文件 @ 74037cc1
......@@ -20,7 +20,7 @@ namespace operators {
enum class BoxCodeType { kEncodeCenterSize = 0, kDecodeCenterSize = 1 };
inline BoxCodeType GetBoxCodeType(const std::string& type) {
inline BoxCodeType GetBoxCodeType(const std::string &type) {
if (type == "encode_center_size") {
return BoxCodeType::kEncodeCenterSize;
} else if (type == "decode_center_size") {
......@@ -32,24 +32,23 @@ inline BoxCodeType GetBoxCodeType(const std::string& type) {
template <typename DeviceContext, typename T>
class BoxCoderKernel : public framework::OpKernel<T> {
public:
void EncodeCenterSize(const framework::Tensor* target_box,
const framework::Tensor* prior_box,
const framework::Tensor* prior_box_var,
void EncodeCenterSize(const framework::Tensor *target_box,
const framework::Tensor *prior_box,
const framework::Tensor *prior_box_var,
const bool normalized,
const std::vector<float> variance, T* output) const {
const std::vector<float> variance, T *output) const {
int64_t row = target_box->dims()[0];
int64_t col = prior_box->dims()[0];
int64_t len = prior_box->dims()[1];
auto* target_box_data = target_box->data<T>();
auto* prior_box_data = prior_box->data<T>();
const T* prior_box_var_data = nullptr;
if (prior_box_var) prior_box_var_data = prior_box_var->data<T>();
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for collapse(2)
#endif
for (int64_t i = 0; i < row; ++i) {
for (int64_t j = 0; j < col; ++j) {
auto *target_box_data = target_box->data<T>();
auto *prior_box_data = prior_box->data<T>();
size_t offset = i * col * len + j * len;
T prior_box_width = prior_box_data[j * len + 2] -
prior_box_data[j * len] + (normalized == false);
T prior_box_height = prior_box_data[j * len + 3] -
......@@ -69,7 +68,6 @@ class BoxCoderKernel : public framework::OpKernel<T> {
target_box_data[i * len + 1] +
(normalized == false);
size_t offset = i * col * len + j * len;
output[offset] =
(target_box_center_x - prior_box_center_x) / prior_box_width;
output[offset + 1] =
......@@ -78,44 +76,61 @@ class BoxCoderKernel : public framework::OpKernel<T> {
std::log(std::fabs(target_box_width / prior_box_width));
output[offset + 3] =
std::log(std::fabs(target_box_height / prior_box_height));
if (prior_box_var) {
int prior_var_offset = j * len;
output[offset] /= prior_box_var_data[prior_var_offset];
output[offset + 1] /= prior_box_var_data[prior_var_offset + 1];
output[offset + 2] /= prior_box_var_data[prior_var_offset + 2];
output[offset + 3] /= prior_box_var_data[prior_var_offset + 3];
} else if (!(variance.empty())) {
}
}
if (prior_box_var) {
const T *prior_box_var_data = prior_box_var->data<T>();
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for collapse(3)
#endif
for (int64_t i = 0; i < row; ++i) {
for (int64_t j = 0; j < col; ++j) {
for (int k = 0; k < 4; ++k) {
size_t offset = i * col * len + j * len;
int prior_var_offset = j * len;
output[offset + k] /= prior_box_var_data[prior_var_offset + k];
}
}
}
} else if (!(variance.empty())) {
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for collapse(3)
#endif
for (int64_t i = 0; i < row; ++i) {
for (int64_t j = 0; j < col; ++j) {
for (int k = 0; k < 4; ++k) {
size_t offset = i * col * len + j * len;
output[offset + k] /= static_cast<T>(variance[k]);
}
}
}
}
}
template <int axis, int var_size>
void DecodeCenterSize(const framework::Tensor* target_box,
const framework::Tensor* prior_box,
const framework::Tensor* prior_box_var,
void DecodeCenterSize(const framework::Tensor *target_box,
const framework::Tensor *prior_box,
const framework::Tensor *prior_box_var,
const bool normalized, std::vector<float> variance,
T* output) const {
T *output) const {
int64_t row = target_box->dims()[0];
int64_t col = target_box->dims()[1];
int64_t len = target_box->dims()[2];
auto* target_box_data = target_box->data<T>();
auto* prior_box_data = prior_box->data<T>();
const T* prior_box_var_data = nullptr;
if (var_size == 2) prior_box_var_data = prior_box_var->data<T>();
int prior_box_offset = 0;
T var_data[4] = {1., 1., 1., 1.};
T* var_ptr = var_data;
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for collapse(2)
#endif
for (int64_t i = 0; i < row; ++i) {
for (int64_t j = 0; j < col; ++j) {
auto *target_box_data = target_box->data<T>();
auto *prior_box_data = prior_box->data<T>();
T var_data[4] = {1., 1., 1., 1.};
T *var_ptr = var_data;
size_t offset = i * col * len + j * len;
prior_box_offset = axis == 0 ? j * len : i * len;
int prior_box_offset = axis == 0 ? j * len : i * len;
T prior_box_width = prior_box_data[prior_box_offset + 2] -
prior_box_data[prior_box_offset] +
(normalized == false);
......@@ -131,10 +146,10 @@ class BoxCoderKernel : public framework::OpKernel<T> {
T target_box_width = 0, target_box_height = 0;
int prior_var_offset = axis == 0 ? j * len : i * len;
if (var_size == 2) {
std::memcpy(var_ptr, prior_box_var_data + prior_var_offset,
std::memcpy(var_ptr, prior_box_var->data<T>() + prior_var_offset,
4 * sizeof(T));
} else if (var_size == 1) {
var_ptr = reinterpret_cast<T*>(variance.data());
var_ptr = reinterpret_cast<T *>(variance.data());
}
T box_var_x = *var_ptr;
T box_var_y = *(var_ptr + 1);
......@@ -162,11 +177,11 @@ class BoxCoderKernel : public framework::OpKernel<T> {
}
}
void Compute(const framework::ExecutionContext& context) const override {
auto* prior_box = context.Input<framework::Tensor>("PriorBox");
auto* prior_box_var = context.Input<framework::Tensor>("PriorBoxVar");
auto* target_box = context.Input<framework::LoDTensor>("TargetBox");
auto* output_box = context.Output<framework::Tensor>("OutputBox");
void Compute(const framework::ExecutionContext &context) const override {
auto *prior_box = context.Input<framework::Tensor>("PriorBox");
auto *prior_box_var = context.Input<framework::Tensor>("PriorBoxVar");
auto *target_box = context.Input<framework::LoDTensor>("TargetBox");
auto *output_box = context.Output<framework::Tensor>("OutputBox");
std::vector<float> variance = context.Attr<std::vector<float>>("variance");
const int axis = context.Attr<int>("axis");
if (target_box->lod().size()) {
......@@ -194,7 +209,7 @@ class BoxCoderKernel : public framework::OpKernel<T> {
output_box->mutable_data<T>({row, col, len}, context.GetPlace());
T* output = output_box->data<T>();
T *output = output_box->data<T>();
if (code_type == BoxCodeType::kEncodeCenterSize) {
EncodeCenterSize(target_box, prior_box, prior_box_var, normalized,
variance, output);
......
paddle/fluid/operators/detection/yolov3_loss_op.cc
浏览文件 @ 74037cc1
......@@ -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 @ 74037cc1
......@@ -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/expand_op.cc
浏览文件 @ 74037cc1
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/expand_op.h"
#include <memory>
#include <vector>
namespace paddle {
......@@ -138,12 +139,28 @@ class ExpandGradOp : public framework::OperatorWithKernel {
}
};
class ExpandGradOpDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("expand_grad");
op->SetInput("X", Input("X"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(expand, ops::ExpandOp, ops::ExpandOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::ExpandGradOpDescMaker);
REGISTER_OPERATOR(expand_grad, ops::ExpandGradOp);
REGISTER_OP_CPU_KERNEL(
expand, ops::ExpandKernel<paddle::platform::CPUDeviceContext, float>,
......
paddle/fluid/operators/fake_dequantize_op.cc
浏览文件 @ 74037cc1
......@@ -14,6 +14,7 @@ limitations under the License. */
#include "paddle/fluid/operators/fake_dequantize_op.h"
#include <string>
#include <vector>
namespace paddle {
namespace operators {
......@@ -76,6 +77,63 @@ $$Out = \frac{scale*X}{ max_range }$$
}
};
class FakeChannelWiseDequantizeMaxAbsOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(
ctx->HasInput("X"),
"Input(X) of FakeChannelWiseDequantizeMaxAbsOp should not be null.");
PADDLE_ENFORCE(ctx->HasInputs("Scales"),
"Input(Scales) of FakeChannelWiseDequantizeMaxAbsOp "
"should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("Out"),
"Output(Out) of FakeChannelWiseDequantizeMaxAbsOp should not be null.");
ctx->ShareDim("X", /*->*/ "Out");
ctx->ShareLoD("X", /*->*/ "Out");
}
};
class FakeChannelWiseDequantizeMaxAbsOpMaker
: public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(Tensor) The input with float-32/64 type is the "
"low precision tensor.");
AddInput("Scales",
"(Tensors) The scales in quantization stage. "
"Now, `Scales` is a vector with at most two tensors. "
"If Scales has two elements, the second tensor should only have "
"one value.")
.AsDuplicable();
AddOutput("Out",
"(Tensor) The output is the dequantized high "
"precision tensor.");
AddAttr<std::vector<int>>(
"quant_bits",
"Quantization bit numbers in quantization stage. "
"The size of `quant_bits` should be equal to the size of `Scales`.")
.SetDefault({8});
AddComment(R"DOC(
FakeChannelWiseDequantizeMaxAbsOp operator.
This calculation is an opposite operation of FakeChannelWiseQuantizeMaxAbsOp:
$$Out_c = \frac{X_c\prod_{i=1}^{n}Scales_{ic}}{\prod_{i=1}^{n}(2^{quant\_bits_i-1}-1)}$$
In the above formula, the range value of $c$ can be represented as $0 \leq c \lt \ the\ channel\ number\ of\ X$.
Besides, the size of $quant\_bits$ should be equal to the size of $Scales$, and it is called $n$ in the formula.
Notes: In general, the per-channel quantization is only applied to weights and the activations use per-layer quantization.
)DOC");
}
};
} // namespace operators
} // namespace paddle
......@@ -88,3 +146,11 @@ REGISTER_OPERATOR(fake_dequantize_max_abs, ops::FakeDequantizeMaxAbsOp,
REGISTER_OP_CPU_KERNEL(fake_dequantize_max_abs,
ops::FakeDequantizeMaxAbsKernel<CPU, float>,
ops::FakeDequantizeMaxAbsKernel<CPU, double>);
REGISTER_OPERATOR(fake_channel_wise_dequantize_max_abs,
ops::FakeChannelWiseDequantizeMaxAbsOp,
ops::FakeChannelWiseDequantizeMaxAbsOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(fake_channel_wise_dequantize_max_abs,
ops::FakeChannelWiseDequantizeMaxAbsKernel<CPU, float>,
ops::FakeChannelWiseDequantizeMaxAbsKernel<CPU, double>);
paddle/fluid/operators/fake_dequantize_op.cu
浏览文件 @ 74037cc1
......@@ -55,3 +55,7 @@ using CUDA = paddle::platform::CUDADeviceContext;
REGISTER_OP_CUDA_KERNEL(fake_dequantize_max_abs,
ops::FakeDequantizeMaxAbsKernel<CUDA, float>,
ops::FakeDequantizeMaxAbsKernel<CUDA, double>);
REGISTER_OP_CUDA_KERNEL(
fake_channel_wise_dequantize_max_abs,
ops::FakeChannelWiseDequantizeMaxAbsKernel<CUDA, float>,
ops::FakeChannelWiseDequantizeMaxAbsKernel<CUDA, double>);
paddle/fluid/operators/fake_dequantize_op.h
浏览文件 @ 74037cc1
......@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
......@@ -45,5 +46,42 @@ class FakeDequantizeMaxAbsKernel : public framework::OpKernel<T> {
}
};
template <typename DeviceContext, typename T>
class FakeChannelWiseDequantizeMaxAbsKernel : public framework::OpKernel<T> {
public:
virtual void Compute(const framework::ExecutionContext& ctx) const {
auto* in = ctx.Input<framework::Tensor>("X");
auto scales = ctx.MultiInput<framework::Tensor>("Scales");
auto* out = ctx.Output<framework::Tensor>("Out");
PADDLE_ENFORCE_EQ(scales[0]->numel(), in->dims()[0],
"The number of first scale values must be the same with "
"first dimension value of Input(X).");
auto quant_bits = ctx.Attr<std::vector<int>>("quant_bits");
int max_range = std::pow(2, quant_bits[0] - 1) - 1;
auto& dev_ctx = ctx.template device_context<DeviceContext>();
out->mutable_data<T>(dev_ctx.GetPlace());
auto dequant = DequantizeFunctor<DeviceContext, T>();
for (int64_t i = 0; i < in->dims()[0]; i++) {
framework::Tensor one_channel_in = in->Slice(i, i + 1);
framework::Tensor one_channel_out = out->Slice(i, i + 1);
framework::Tensor one_channel_scale = scales[0]->Slice(i, i + 1);
dequant(dev_ctx, &one_channel_in, &one_channel_scale,
static_cast<T>(max_range), &one_channel_out);
}
if (scales.size() == 2) {
PADDLE_ENFORCE_EQ(
scales[1]->numel(), 1,
"The second scale tensor should only have one value at now.");
max_range = std::pow(2, quant_bits[1] - 1) - 1;
dequant(dev_ctx, out, scales[1], static_cast<T>(max_range), out);
}
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/fake_quantize_op.cc
浏览文件 @ 74037cc1
......@@ -134,6 +134,60 @@ $$Out = round(X/scale * range)$$
}
};
class FakeChannelWiseQuantizeAbsMaxOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of FakeChannelWiseQuantizeOp should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("Out"),
"Output(Out) of FakeChannelWiseQuantizeOp should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("OutScales"),
"Output(Scales) of FakeChannelWiseQuantizeOp should not be null.");
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->SetOutputDim("OutScales", {ctx->GetInputDim("X")[0]});
ctx->ShareLoD("X", /*->*/ "Out");
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(ctx.Input<framework::LoDTensor>("X")->type(),
ctx.GetPlace());
}
};
class FakeChannelWiseQuantizeAbsMaxOpMaker
: public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(Tensor) Input is float data type.");
AddOutput("Out",
"(Tensor) Output of quantized low level tensor, "
"but also saved as float data type.");
AddOutput("OutScales", "(Tensor) Current channel wise scale");
AddAttr<int>("bit_length", "(int, default 8)")
.SetDefault(8)
.AddCustomChecker([](const int& bit_length) {
PADDLE_ENFORCE(bit_length >= 1 && bit_length <= 16,
"'bit_length' should be between 1 and 16.");
});
AddComment(R"DOC(
The scale of FakeChannelWiseQuantize operator is a vector.
In detail, each channel of the input X has a scale value.
$$scale_c = max(abs(X_c))$$
$$range = 2^{bit\_length - 1} - 1$$
$$Out_c = round(\frac{X_c * range} {scale_c})$$
In above three formulas, the range value of c is as follow:
$$0 \leq c \lt \ the\ channel\ number\ of\ X$$
)DOC");
}
};
class FakeQuantizeRangeAbsMaxOp : public framework::OperatorWithKernel {
public:
FakeQuantizeRangeAbsMaxOp(const std::string& type,
......@@ -218,3 +272,10 @@ REGISTER_OPERATOR(fake_quantize_range_abs_max, ops::FakeQuantizeRangeAbsMaxOp,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(fake_quantize_range_abs_max,
ops::FakeQuantizeRangeAbsMaxKernel<CPU, float>);
REGISTER_OPERATOR(fake_channel_wise_quantize_abs_max,
ops::FakeChannelWiseQuantizeAbsMaxOp,
ops::FakeChannelWiseQuantizeAbsMaxOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(fake_channel_wise_quantize_abs_max,
ops::FakeChannelWiseQuantizeAbsMaxKernel<CPU, float>);
paddle/fluid/operators/fake_quantize_op.cu
浏览文件 @ 74037cc1
......@@ -174,5 +174,7 @@ namespace ops = paddle::operators;
using CUDA = paddle::platform::CUDADeviceContext;
REGISTER_OP_CUDA_KERNEL(fake_quantize_abs_max,
ops::FakeQuantizeAbsMaxKernel<CUDA, float>);
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>);
paddle/fluid/operators/fake_quantize_op.h
浏览文件 @ 74037cc1
......@@ -63,6 +63,39 @@ class FakeQuantizeAbsMaxKernel : public framework::OpKernel<T> {
}
};
template <typename DeviceContext, typename T>
class FakeChannelWiseQuantizeAbsMaxKernel : 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_scales = context.Output<framework::Tensor>("OutScales");
T* out_scales_data = out_scales->mutable_data<T>(context.GetPlace());
out->mutable_data<T>(context.GetPlace());
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>();
auto find_abs_max = FindAbsMaxFunctor<DeviceContext, T>();
for (int64_t i = 0; i < in->dims()[0]; i++) {
framework::Tensor one_channel = in->Slice(i, i + 1);
const T* one_channel_data = one_channel.data<T>();
find_abs_max(dev_ctx, one_channel_data, one_channel.numel(),
&out_scales_data[i]);
}
auto clip_quant = ClipAndFakeQuantFunctor<DeviceContext, T>();
for (int64_t i = 0; i < in->dims()[0]; i++) {
framework::Tensor one_channel_in = in->Slice(i, i + 1);
framework::Tensor one_channel_out = out->Slice(i, i + 1);
framework::Tensor one_channel_scale = out_scales->Slice(i, i + 1);
clip_quant(dev_ctx, one_channel_in, one_channel_scale, bin_cnt,
&one_channel_out);
}
}
};
template <typename DeviceContext, typename T>
class FakeQuantizeRangeAbsMaxKernel : public framework::OpKernel<T> {
public:
......
paddle/fluid/operators/fused/fused_embedding_seq_pool_op.cc
浏览文件 @ 74037cc1
......@@ -23,9 +23,6 @@ class FusedEmbeddingSeqPoolOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
if (ctx->IsRuntime()) {
return;
}
PADDLE_ENFORCE(ctx->HasInput("W"),
"Input W of FusedEmbeddingSeqPoolOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Ids"),
......@@ -91,6 +88,8 @@ class FusedEmbeddingSeqPoolOpMaker : public framework::OpProtoAndCheckerMaker {
"(boolean, default false) "
"Sparse update.")
.SetDefault(false);
AddAttr<bool>(framework::kAllKernelsMustComputeRuntimeShape, "")
.SetDefault(true);
AddComment(R"DOC(
FusedEmbeddingSeqPool Operator.
......
paddle/fluid/operators/fused/fused_embedding_seq_pool_op.h
浏览文件 @ 74037cc1
......@@ -121,6 +121,8 @@ class FusedEmbeddingSeqPoolGradKernel : public framework::OpKernel<T> {
auto *ids = context.Input<LoDTensor>("Ids");
auto *d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto *d_table = context.Output<SelectedRows>(framework::GradVarName("W"));
// runtime shape
d_table->set_height(table_dim[0]);
auto *ids_data = ids->data<int64_t>();
int64_t ids_num = ids->numel();
......
paddle/fluid/operators/hash_op.cc
浏览文件 @ 74037cc1
......@@ -26,9 +26,6 @@ class HashOp : public framework::OperatorWithKernel {
: OperatorWithKernel(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
if (ctx->IsRuntime()) {
return;
}
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of HashOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
......@@ -57,6 +54,8 @@ $$Out = scale * X$$
)DOC");
AddAttr<int>("num_hash", "").SetDefault(1);
AddAttr<int>("mod_by", "").SetDefault(100000);
AddAttr<bool>(framework::kAllKernelsMustComputeRuntimeShape, "")
.SetDefault(true);
}
};
......
paddle/fluid/operators/math.h 0 → 100644
浏览文件 @ 74037cc1
// 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 "paddle/fluid/platform/float16.h"
#include "paddle/fluid/platform/hostdevice.h"
#include "math.h" // NOLINT
namespace paddle {
namespace operators {
inline HOSTDEVICE platform::float16 real_exp(platform::float16 x) {
return static_cast<platform::float16>(::expf(static_cast<float>(x)));
}
inline HOSTDEVICE float real_exp(float x) { return ::expf(x); }
inline HOSTDEVICE double real_exp(double x) { return ::exp(x); }
inline HOSTDEVICE platform::float16 real_log(platform::float16 x) {
return static_cast<platform::float16>(::logf(static_cast<float>(x)));
}
inline HOSTDEVICE float real_log(float x) { return ::logf(x); }
inline HOSTDEVICE double real_log(double x) { return ::log(x); }
} // namespace operators
} // namespace paddle
paddle/fluid/operators/math/cross_entropy.cu
浏览文件 @ 74037cc1
......@@ -12,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/math.h"
#include "paddle/fluid/operators/math/cross_entropy.h"
#include "paddle/fluid/platform/cuda_device_function.h"
#include "paddle/fluid/platform/cuda_primitives.h"
......@@ -20,17 +21,6 @@ namespace paddle {
namespace operators {
namespace math {
namespace {
__device__ __forceinline__ float real_log(float x) { return logf(x); }
__device__ __forceinline__ double real_log(double x) { return log(x); }
__device__ __forceinline__ platform::float16 real_log(
const platform::float16& val) {
return static_cast<platform::float16>(logf(static_cast<float>(val)));
}
template <typename T>
__global__ void CrossEntropyKernel(T* Y, const T* X, const int64_t* label,
const int N, const int D,
......@@ -61,7 +51,6 @@ __global__ void SoftCrossEntropyKernel(T* Y, const T* X, const T* label,
Y[blockIdx.x] = -val;
}
}
} // namespace
template <typename T>
class CrossEntropyFunctor<platform::CUDADeviceContext, T> {
......
paddle/fluid/operators/ngraph/ngraph_engine.h
浏览文件 @ 74037cc1
......@@ -12,12 +12,18 @@ 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. */
#ifndef PADDLE_FLUID_OPERATORS_NGRAPH_NGRAPH_ENGINE_H_
#define PADDLE_FLUID_OPERATORS_NGRAPH_NGRAPH_ENGINE_H_
#include <memory>
#include <set>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/var_desc.h"
#include "ngraph/ngraph.hpp"
......@@ -33,29 +39,47 @@ enum class OpState { /* nGraph support state on ops */
UNKNOWN /* Output all for debug purpose */
};
// cache engine repetitives
struct EngineCache {
std::shared_ptr<ngraph::Function> ngraph_function;
std::set<std::string> persistables;
std::vector<std::string> var_in;
std::vector<std::string> var_out;
std::vector<size_t> var_in_updates;
bool is_test = true;
};
// perform graph build through bridge and execute computation
class NgraphEngine {
public:
explicit NgraphEngine(const framework::Scope& scope,
const platform::Place& place,
const std::string& serialized_graph,
const std::vector<int>& interval);
const framework::ExecutionContext& ctx);
void Run(const framework::Scope& scope, const platform::Place& place) const;
static void EnableNgraph(const framework::ProgramDesc& program);
static const framework::BlockDesc* p_bdesc;
static std::vector<std::string> feed_vars, fetch_vars;
static void FuseNgraphOps(
const framework::BlockDesc& prog,
std::vector<std::unique_ptr<framework::OperatorBase>>* ops);
private:
static std::unordered_map<std::string, std::shared_ptr<ngraph::Function>>
func_cache_;
static std::unordered_map<std::string, EngineCache> engine_cache;
static std::unordered_map<
std::string, std::vector<std::shared_ptr<ngraph::runtime::Tensor>>>
t_in_cache_;
static framework::Variable* pre_var_ptr;
const framework::Scope& scope_;
const platform::Place& place_;
std::vector<std::shared_ptr<framework::OperatorBase>> fused_ops_;
std::unordered_map<std::string, ngraph::element::Type> var_type_map_;
std::unordered_set<std::string> persistables_;
std::unordered_set<std::string> fetches_;
std::set<std::string> persistables_;
std::unordered_set<std::string> post_op_inputs_;
OpState ng_op_state_ = OpState::UNKNOWN;
OpState op_state_ = OpState::UNKNOWN;
bool is_test_{true};
std::string func_cache_key_;
// ngraph backend eg. CPU
......@@ -66,6 +90,8 @@ class NgraphEngine {
std::vector<std::string> var_in_;
// var_name of outputs from fetch in order
std::vector<std::string> var_out_;
// non-persitable var_in
std::vector<size_t> var_in_updates_;
// map input vars to nodes
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
......@@ -74,20 +100,23 @@ class NgraphEngine {
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
var_node_map_;
// prepare info for nraph engine
void Prepare(const framework::BlockDesc& block,
const std::vector<int>& interval);
// prepare info for ngraph engine need
void Prepare(const std::vector<int>& interval);
// get ngraph engine input and output list
void BuildNgIO(const std::vector<framework::OpDesc*>& op_descs,
const std::vector<int>& interval);
// get ngraph input and define ngraph input parameters
void GetNgInputShape(std::shared_ptr<framework::OperatorBase> op);
void GetNgInputShape();
// Call ngraph bridge to map ops
void BuildNgNodes();
// get the ngraph input and output var list
void BuildNgIO();
// run paddle RuntimeInferShape to get the tensor shape
void RunInferShape();
// build ngraph function call
void BuildNgFunction();
void BuildNgFunction(const std::vector<int>& interval);
// Check cache for ngraph function or otherwise build the function
void GetNgFunction();
void GetNgFunction(std::string engine_key, const std::vector<int>& interval);
};
} // namespace operators
} // namespace paddle
#endif // PADDLE_FLUID_OPERATORS_NGRAPH_NGRAPH_ENGINE_H_
paddle/fluid/operators/ngraph/ngraph_engine_op.cc
浏览文件 @ 74037cc1
......@@ -29,6 +29,7 @@ class NgraphEngineOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Xs", "A list of inputs.").AsDispensable();
AddOutput("Ys", "A list of outputs").AsDispensable();
AddAttr<std::string>("graph", "the graph.");
AddAttr<std::string>("engine_key", "the engine hash key.");
AddAttr<std::vector<int>>("interval", "op interval supported by ngraph");
AddComment("ngraph engine operator.");
}
......
paddle/fluid/operators/ngraph/ngraph_engine_op.h
浏览文件 @ 74037cc1
......@@ -46,10 +46,8 @@ class NgraphEngineKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
auto& scope = ctx.scope();
auto place = ctx.GetPlace();
std::string serialized_graph = ctx.Attr<std::string>("graph");
auto interval = ctx.Attr<std::vector<int>>("interval");
NgraphEngine ngraph_engine(scope, place, serialized_graph, interval);
NgraphEngine ngraph_engine(scope, place, ctx);
ngraph_engine.Run(scope, place);
}
};
......
paddle/fluid/operators/reshape_op.cc
浏览文件 @ 74037cc1
......@@ -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/selu_op.h
浏览文件 @ 74037cc1
......@@ -15,13 +15,12 @@ limitations under the License. */
#pragma once
#include <string>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math.h"
#include "paddle/fluid/platform/for_range.h"
namespace paddle {
namespace operators {
static HOSTDEVICE float real_exp(float x) { return expf(x); }
static HOSTDEVICE float real_exp(double x) { return exp(x); }
template <typename T>
struct SeluFunctor {
SeluFunctor(const T* x_data_ptr, float alpha, float scale, T* y_data_ptr)
......
paddle/fluid/operators/sequence_ops/sequence_enumerate_op.cc
浏览文件 @ 74037cc1
......@@ -22,9 +22,6 @@ class SequenceEnumerateOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
if (ctx->IsRuntime()) {
return;
}
PADDLE_ENFORCE(
ctx->HasInput("X"),
"Input(X) of SequecceEnumerate operator should not be null.");
......@@ -62,6 +59,8 @@ class SequenceEnumerateOpMaker : public framework::OpProtoAndCheckerMaker {
});
AddAttr<int>("pad_value", "(int) The enumerate sequence padding value.")
.SetDefault(0);
AddAttr<bool>(framework::kAllKernelsMustComputeRuntimeShape, "")
.SetDefault(true);
AddComment(R"DOC(
Sequence Enumerate Operator.
......
paddle/fluid/operators/sequence_ops/sequence_softmax_op.cu
浏览文件 @ 74037cc1
......@@ -14,6 +14,7 @@ limitations under the License. */
#include <algorithm>
#include <cub/cub.cuh> // NOLINT
#include "paddle/fluid/operators/math.h"
#include "paddle/fluid/operators/sequence_ops/sequence_softmax_op.h"
namespace paddle {
......@@ -21,9 +22,6 @@ namespace operators {
using LoDTensor = framework::LoDTensor;
__device__ __forceinline__ float real_exp(float x) { return expf(x); }
__device__ __forceinline__ double real_exp(double x) { return exp(x); }
template <typename T, int BlockDim>
using BlockReduce = cub::BlockReduce<T, BlockDim>;
......
paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.cu
浏览文件 @ 74037cc1
......@@ -12,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "cub/cub.cuh"
#include "paddle/fluid/operators/math.h"
#include "paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
#include "paddle/fluid/platform/hostdevice.h"
......@@ -21,11 +22,6 @@ namespace operators {
using Tensor = framework::Tensor;
static HOSTDEVICE float real_exp(float x) { return expf(x); }
static HOSTDEVICE float real_exp(double x) { return exp(x); }
static HOSTDEVICE float real_log(float x) { return logf(x); }
static HOSTDEVICE float real_log(double x) { return log(x); }
static constexpr int kNumCUDAThreads = 512;
static constexpr int kNumMaxinumNumBlocks = 4096;
......
paddle/fluid/operators/slice_op.cu
浏览文件 @ 74037cc1
......@@ -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/sync_batch_norm_op.cc 0 → 100644
浏览文件 @ 74037cc1
/* 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 @ 74037cc1
/* 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 @ 74037cc1
......@@ -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 +316,7 @@ CUDADeviceContext::~CUDADeviceContext() {
eigen_stream_.reset();
eigen_device_.reset();
PADDLE_ENFORCE(cudaStreamDestroy(stream_));
PADDLE_ENFORCE(dynload::ncclCommDestroy(nccl_comm_));
}
Place CUDADeviceContext::GetPlace() const { return place_; }
......
paddle/fluid/platform/event.h
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paddle/fluid/platform/init.cc
浏览文件 @ 74037cc1
......@@ -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
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paddle/fluid/platform/profiler.cc
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paddle/fluid/platform/profiler.h
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paddle/fluid/pybind/ir.cc
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此差异已折叠。
paddle/fluid/pybind/pybind.cc
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paddle/scripts/paddle_build.sh
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python/paddle/fluid/__init__.py
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python/paddle/fluid/compiler.py
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python/paddle/fluid/framework.py
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python/paddle/fluid/layers/nn.py
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python/paddle/fluid/layers/ops.py
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python/paddle/fluid/tests/unittests/test_gru_op.py
浏览文件 @ 74037cc1
......@@ -156,7 +156,7 @@ class TestGRUOp(OpTest):
}
def test_check_output(self):
self.check_output(atol=1e-8)
self.check_output(atol=1e-8, check_imperative=True)
def test_check_grad(self):
self.check_grad(['Input', 'H0', 'Weight', 'Bias'], ['Hidden'])
......
tools/diff_api.py
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tools/print_signatures.py
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tools/timeline.py
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