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未验证 提交 86511f51 编写于 作者: L lujun 提交者： GitHub
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Merge pull request #13 from PaddlePaddle/develop 

merge to local
上级 5bb04ea4 ec11135d
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Showing with 4851 addition and 1274 deletion
paddle/fluid/API.spec
浏览文件 @ 86511f51
......@@ -56,7 +56,7 @@ paddle.fluid.io.save_persistables (ArgSpec(args=['executor', 'dirname', 'main_pr
paddle.fluid.io.load_vars (ArgSpec(args=['executor', 'dirname', 'main_program', 'vars', 'predicate', 'filename'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '0a5308f496632ab1ec3ba1f1377e6f95'))
paddle.fluid.io.load_params (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', '41779819cef32f2246e83aebc5a002e2'))
paddle.fluid.io.load_persistables (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', '28df5bfe26ca7a077f91156abb0fe6d2'))
paddle.fluid.io.save_inference_model (ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename', 'export_for_deployment'], varargs=None, keywords=None, defaults=(None, None, None, True)), ('document', '582d87b8df75a5a639a107db8ff86f9c'))
paddle.fluid.io.save_inference_model (ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename', 'export_for_deployment'], varargs=None, keywords=None, defaults=(None, None, None, True)), ('document', '70f4f53f13572436ac72d1c8b5efeb9d'))
paddle.fluid.io.load_inference_model (ArgSpec(args=['dirname', 'executor', 'model_filename', 'params_filename', 'pserver_endpoints'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '7a5255386075dac3c75b7058254fcdcb'))
paddle.fluid.initializer.ConstantInitializer.__init__ (ArgSpec(args=['self', 'value', 'force_cpu'], varargs=None, keywords=None, defaults=(0.0, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.initializer.UniformInitializer.__init__ (ArgSpec(args=['self', 'low', 'high', 'seed'], varargs=None, keywords=None, defaults=(-1.0, 1.0, 0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
......@@ -109,7 +109,7 @@ paddle.fluid.layers.reduce_prod (ArgSpec(args=['input', 'dim', 'keep_dim', 'name
paddle.fluid.layers.sequence_first_step (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '2b290d3d77882bfe9bb8d331cac8cdd3'))
paddle.fluid.layers.sequence_last_step (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', 'c16a892f44f7fe71bfa5afc32d3f34ce'))
paddle.fluid.layers.sequence_slice (ArgSpec(args=['input', 'offset', 'length', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'fdcea0e8b5bc7d8d4b1b072c521014e6'))
paddle.fluid.layers.dropout (ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name', 'dropout_implementation'], varargs=None, keywords=None, defaults=(False, None, None, 'downgrade_in_infer')), ('document', 'dc7042734c6d8b8ce97321f017f01d6f'))
paddle.fluid.layers.dropout (ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name', 'dropout_implementation'], varargs=None, keywords=None, defaults=(False, None, None, 'downgrade_in_infer')), ('document', 'f1dd22f7351f7f9853212958e0d8aa7a'))
paddle.fluid.layers.split (ArgSpec(args=['input', 'num_or_sections', 'dim', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '652625345c2acb900029c78cc75f8aa6'))
paddle.fluid.layers.ctc_greedy_decoder (ArgSpec(args=['input', 'blank', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ebbf2adbd79683dc93db03454dfa18c2'))
paddle.fluid.layers.edit_distance (ArgSpec(args=['input', 'label', 'normalized', 'ignored_tokens'], varargs=None, keywords=None, defaults=(True, None)), ('document', '97f0262f97602644c83142789d784571'))
......@@ -205,7 +205,7 @@ paddle.fluid.layers.maxout (ArgSpec(args=['x', 'groups', 'name'], varargs=None,
paddle.fluid.layers.space_to_depth (ArgSpec(args=['x', 'blocksize', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5f207ae10589ebe38a63575ef6ff8e1e'))
paddle.fluid.layers.affine_grid (ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '51def402b8910e163cbace9d0c0526ed'))
paddle.fluid.layers.sequence_reverse (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '77a6d80aa5551ca70324fc975c44507f'))
paddle.fluid.layers.affine_channel (ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None)), ('document', '2f46f1ff39a13ab00857e7b9f44b2fa7'))
paddle.fluid.layers.affine_channel (ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name', 'act'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None, None)), ('document', 'ab84fdc6dc60f3ad9aa397e6007e3bf9'))
paddle.fluid.layers.similarity_focus (ArgSpec(args=['input', 'axis', 'indexes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '70e3b5182a18b40b47ecabd7c8490a35'))
paddle.fluid.layers.hash (ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', '9bb77f8dc002dd2ce75d4769eaaf5007'))
paddle.fluid.layers.grid_sampler (ArgSpec(args=['x', 'grid', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd256cba1c41a5ed92ce3f31e24a2ca6d'))
......@@ -296,7 +296,7 @@ paddle.fluid.layers.tanh (ArgSpec(args=['x', 'name'], varargs=None, keywords=Non
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'))
paddle.fluid.layers.sqrt (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e9e27491c39ac74d0b1ffe506aec0ebb'))
paddle.fluid.layers.abs (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '64650ac42cf82e9920cb0b172b1d29fd'))
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/framework/details/CMakeLists.txt
浏览文件 @ 86511f51
......@@ -9,6 +9,7 @@ cc_library(rpc_op_handle SRCS rpc_op_handle.cc DEPS framework_proto scope place
cc_library(multi_devices_helper SRCS multi_devices_helper.cc DEPS graph graph_helper)
cc_library(multi_devices_graph_print_pass SRCS multi_devices_graph_print_pass.cc DEPS multi_devices_helper)
cc_library(multi_devices_graph_check_pass SRCS multi_devices_graph_check_pass.cc DEPS multi_devices_helper)
cc_library(alloc_continuous_space_for_grad_pass SRCS alloc_continuous_space_for_grad_pass.cc DEPS graph graph_helper)
cc_library(variable_visitor SRCS variable_visitor.cc DEPS lod_tensor selected_rows)
......@@ -22,6 +23,8 @@ endif()
if(WITH_GPU)
nv_library(all_reduce_op_handle SRCS all_reduce_op_handle.cc DEPS op_handle_base scope lod_tensor ddim memory
dynload_cuda variable_visitor)
nv_library(fused_all_reduce_op_handle SRCS fused_all_reduce_op_handle.cc DEPS op_handle_base scope lod_tensor ddim memory
dynload_cuda variable_visitor)
if(WITH_DISTRIBUTE)
nv_library(reduce_op_handle SRCS reduce_op_handle.cc DEPS op_handle_base variable_visitor scope
ddim dynload_cuda selected_rows_functor sendrecvop_rpc)
......@@ -35,6 +38,8 @@ if(WITH_GPU)
else()
cc_library(all_reduce_op_handle SRCS all_reduce_op_handle.cc DEPS op_handle_base scope lod_tensor ddim memory
variable_visitor)
cc_library(fused_all_reduce_op_handle SRCS fused_all_reduce_op_handle.cc DEPS op_handle_base scope lod_tensor ddim memory
variable_visitor)
if(WITH_DISTRIBUTE)
cc_library(reduce_op_handle SRCS reduce_op_handle.cc DEPS op_handle_base variable_visitor scope
ddim selected_rows_functor sendrecvop_rpc)
......@@ -46,9 +51,7 @@ else()
cc_library(fused_broadcast_op_handle SRCS fused_broadcast_op_handle.cc DEPS broadcast_op_handle)
endif()
cc_library(data_balance_op_handle SRCS data_balance_op_handle.cc DEPS op_handle_base scope lod_tensor)
cc_library(gather_op_handle SRCS gather_op_handle.cc DEPS op_handle_base scope ddim memory variable_visitor)
cc_library(fuse_vars_op_handle SRCS fuse_vars_op_handle.cc DEPS op_handle_base scope)
if(WITH_GPU)
cc_library(memory_optimize_helper SRCS memory_optimize_helper.cc DEPS graph graph_helper gpu_info)
......@@ -69,7 +72,9 @@ cc_library(sequential_execution_pass SRCS sequential_execution_pass.cc DEPS grap
cc_library(all_reduce_deps_pass SRCS all_reduce_deps_pass.cc DEPS graph graph_helper pass)
cc_library(multi_devices_graph_pass SRCS multi_devices_graph_pass.cc DEPS multi_devices_helper computation_op_handle
scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle fused_broadcast_op_handle)
scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle fused_broadcast_op_handle)
cc_library(fuse_all_reduce_op_pass SRCS fuse_all_reduce_op_pass.cc DEPS graph graph_helper fused_all_reduce_op_handle)
set(SSA_GRAPH_EXECUTOR_DEPS graph framework_proto sequential_execution_pass modify_op_lock_and_record_event_pass all_reduce_deps_pass reference_count_pass eager_deletion_pass memory_optimize_pass inplace_op_pass)
if (WITH_GPU)
......@@ -99,4 +104,4 @@ cc_library(build_strategy SRCS build_strategy.cc DEPS
multi_devices_graph_print_pass multi_devices_graph_check_pass
fuse_elewise_add_act_pass multi_batch_merge_pass
fuse_relu_depthwise_conv_pass
memory_optimize_pass lock_free_optimize_pass)
memory_optimize_pass lock_free_optimize_pass alloc_continuous_space_for_grad_pass fuse_all_reduce_op_pass)
paddle/fluid/framework/details/all_reduce_op_handle.cc
浏览文件 @ 86511f51
......@@ -11,9 +11,8 @@
// 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 "paddle/fluid/framework/details/all_reduce_op_handle.h"
#include <algorithm>
#include "paddle/fluid/framework/details/container_cast.h"
#include "paddle/fluid/framework/details/reduce_and_gather.h"
#include "paddle/fluid/framework/details/variable_visitor.h"
......@@ -56,6 +55,7 @@ void AllReduceOpHandle::RunImpl() {
platform::RecordEvent record_event(Name());
WaitInputVarGenerated();
auto in_var_handles = DynamicCast<VarHandle>(this->Inputs());
auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
PADDLE_ENFORCE_EQ(
......
paddle/fluid/framework/details/alloc_continuous_space_for_grad_pass.cc 0 → 100644
浏览文件 @ 86511f51
// 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 <string>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/build_strategy.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/op_registry.h"
DEFINE_uint32(fuse_parameter_memory_size, 0, // 0 KB
"fuse_parameter_memory_size is up limited memory size "
"of one group parameters' gradient which is the input "
"of communication calling(e.g NCCLAllReduce). "
"The default value is 0, it means that "
"not set group according to memory_size.");
DEFINE_int32(
fuse_parameter_groups_size, 3,
"fuse_parameter_groups_size is the size of one group parameters' gradient. "
"The default value is a experimental result. If the "
"fuse_parameter_groups_size is 1, it means that the groups size is "
"the number of parameters' gradient. If the fuse_parameter_groups_size is "
"-1, it means that there are only one group. The default value is 3, it is "
"an experimental value.");
namespace paddle {
namespace framework {
namespace details {
static const char kUnKnow[] = "@UNKNOW@";
static framework::proto::VarType::Type kDefaultDtype =
framework::proto::VarType::Type::VarType_Type_BOOL;
class AllocContinuousSpaceForGradPass : public ir::Pass {
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override {
ir::Graph &result = *graph;
auto &places = Get<const std::vector<platform::Place>>(kPlaces);
auto &local_scopes = Get<const std::vector<Scope *>>(kLocalScopes);
ResetAttribute<ParamsAndGrads>(kParamsAndGrads, &result);
ResetAttribute<GroupGradsAndParams>(kGroupGradsAndParams, &result);
// NOTE: The operator nodes should be in topology order.
std::vector<ir::Node *> topo_nodes = ir::TopologySortOperations(result);
auto &params_grads = result.Get<ParamsAndGrads>(kParamsAndGrads);
for (auto &node : topo_nodes) {
RecordParamsAndGrads(node, &params_grads);
}
if (params_grads.size() == 0) {
VLOG(10) << "Doesn't find gradients";
return std::move(graph);
}
std::unordered_map<std::string, ir::Node *> vars;
for (ir::Node *node : result.Nodes()) {
if (node->IsVar() && node->Var()) {
// Note: The graph may have the same name node. For example, parameter
// is the input of operator and it also is the output of optimizer;
vars.emplace(node->Var()->Name(), node);
}
}
auto &group_grads_params =
result.Get<GroupGradsAndParams>(kGroupGradsAndParams);
// Note: the order of params_grads may be changed by SetGroupGradsAndParams.
SetGroupGradsAndParams(vars, params_grads, &group_grads_params);
params_grads.clear();
for (auto &group_p_g : group_grads_params) {
params_grads.insert(params_grads.begin(), group_p_g.begin(),
group_p_g.end());
}
for (auto &p_g : params_grads) {
std::swap(p_g.first, p_g.second);
}
// Set Gradients as Persistable to prevent this var becoming reusable.
auto dtype = kDefaultDtype;
for (auto &p_g : params_grads) {
// Get gradient var
auto iter = vars.find(p_g.second);
PADDLE_ENFORCE(iter != vars.end(), "%s is not found.", p_g.second);
iter->second->Var()->SetPersistable(true);
PADDLE_ENFORCE(IsSupportedVarType(iter->second->Var()->GetType()));
// Get Dtype
auto ele_dtype = iter->second->Var()->GetDataType();
if (dtype == kDefaultDtype) {
dtype = ele_dtype;
PADDLE_ENFORCE_NE(ele_dtype, kDefaultDtype);
}
PADDLE_ENFORCE_EQ(ele_dtype, dtype);
}
// Create the fused variable name.
if (!result.Has(kFusedVars)) {
result.Set(kFusedVars, new FusedVars);
}
const std::string prefix(kFusedVarNamePrefix);
// The fused_var_name should be unique.
auto fused_var_name = prefix + "GRAD@" + params_grads[0].second;
auto &fused_var_set = result.Get<FusedVars>(kFusedVars);
PADDLE_ENFORCE_EQ(fused_var_set.count(fused_var_name), 0);
fused_var_set.insert(fused_var_name);
InitFusedVarsAndAllocSpaceForVars(places, local_scopes, vars,
fused_var_name, params_grads);
return std::move(graph);
}
template <typename AttrType>
void ResetAttribute(const std::string &attr_name, ir::Graph *graph) const {
if (graph->Has(attr_name)) {
VLOG(10) << attr_name << " is reset.";
graph->Erase(attr_name);
}
graph->Set(attr_name, new AttrType);
}
void SetGroupGradsAndParams(
const std::unordered_map<std::string, ir::Node *> &var_nodes,
const ParamsAndGrads &params_grads,
GroupGradsAndParams *group_grads_params) const {
SetGroupAccordingToLayers(var_nodes, params_grads, group_grads_params);
SetGroupAccordingToMemorySize(var_nodes, group_grads_params);
SetGroupAccordingToGroupSize(var_nodes, group_grads_params);
}
void SetGroupAccordingToLayers(
const std::unordered_map<std::string, ir::Node *> &var_nodes,
const ParamsAndGrads &params_grads,
GroupGradsAndParams *group_grads_params) const {
std::unordered_map<std::string, std::vector<int>> layer_params;
for (size_t i = 0; i < params_grads.size(); ++i) {
auto pos = params_grads[i].first.find_first_of(".");
if (pos == std::string::npos) {
layer_params[std::string(kUnKnow)].emplace_back(i);
} else {
layer_params[params_grads[i].first.substr(0, pos)].emplace_back(i);
}
}
group_grads_params->reserve(layer_params.size());
for (size_t i = 0; i < params_grads.size(); ++i) {
auto pos = params_grads[i].first.find_first_of(".");
std::string key = kUnKnow;
if (pos != std::string::npos) {
key = params_grads[i].first.substr(0, pos);
}
auto iter = layer_params.find(key);
if (iter == layer_params.end()) continue;
group_grads_params->emplace_back();
auto &local_group_grads_params = group_grads_params->back();
for (auto &idx : iter->second) {
local_group_grads_params.emplace_back(
std::make_pair(params_grads[idx].second, params_grads[idx].first));
}
layer_params.erase(iter);
}
VLOG(10) << "SetGroupAccordingToLayers: ";
for (size_t i = 0; i < group_grads_params->size(); ++i) {
VLOG(10) << "group " << i;
std::stringstream out;
for (auto &p_g : group_grads_params->at(i)) {
out << "(" << p_g.second << ", " << p_g.first << "), ";
}
VLOG(10) << out.str();
}
}
void SetGroupAccordingToMemorySize(
const std::unordered_map<std::string, ir::Node *> &var_nodes,
GroupGradsAndParams *group_grads_params) const {
if (FLAGS_fuse_parameter_memory_size == 0) {
return;
}
size_t group_memory_size =
static_cast<size_t>(FLAGS_fuse_parameter_memory_size);
GroupGradsAndParams local_group_grads_params;
size_t j = 0;
while (j < group_grads_params->size()) {
local_group_grads_params.emplace_back();
auto &group_p_g = local_group_grads_params.back();
size_t local_group_memory_size = 0;
while (j < group_grads_params->size()) {
std::for_each(
group_grads_params->at(j).begin(), group_grads_params->at(j).end(),
[&local_group_memory_size,
&var_nodes](const std::pair<std::string, std::string> &g_p) {
auto iter = var_nodes.find(g_p.second);
PADDLE_ENFORCE(iter != var_nodes.end(), "%s is not found.",
g_p.second);
auto shape = iter->second->Var()->GetShape();
size_t size =
framework::SizeOfType(iter->second->Var()->GetDataType());
std::for_each(shape.begin(), shape.end(),
[&size](const int64_t &n) { size *= n; });
local_group_memory_size += size;
});
group_p_g.insert(group_p_g.end(), group_grads_params->at(j).begin(),
group_grads_params->at(j).end());
++j;
if (local_group_memory_size >= group_memory_size) {
break;
}
}
}
std::swap(*group_grads_params, local_group_grads_params);
VLOG(10) << string::Sprintf(
"SetGroupAccordingToMemorySize(memory_size: %d):",
FLAGS_fuse_parameter_memory_size);
for (size_t i = 0; i < group_grads_params->size(); ++i) {
VLOG(10) << "group " << i;
std::stringstream out;
for (auto &g_p : group_grads_params->at(i)) {
auto iter = var_nodes.find(g_p.second);
PADDLE_ENFORCE(iter != var_nodes.end(), "%s is not found.", g_p.second);
auto shape = iter->second->Var()->GetShape();
size_t size = framework::SizeOfType(iter->second->Var()->GetDataType());
std::for_each(shape.begin(), shape.end(),
[&size](const int64_t &n) { size *= n; });
out << string::Sprintf("(%s(%d), %s)", g_p.second, size, g_p.first);
}
VLOG(10) << out.str();
}
}
void SetGroupAccordingToGroupSize(
const std::unordered_map<std::string, ir::Node *> &var_nodes,
GroupGradsAndParams *group_grads_params) const {
if (FLAGS_fuse_parameter_groups_size == 1) {
return;
}
size_t group_size = static_cast<size_t>(FLAGS_fuse_parameter_groups_size);
if (FLAGS_fuse_parameter_groups_size == -1) {
group_size = group_grads_params->size();
}
PADDLE_ENFORCE_GT(group_size, 1);
size_t groups = (group_grads_params->size() + group_size - 1) / group_size;
GroupGradsAndParams local_group_grads_params;
local_group_grads_params.reserve(groups);
size_t j = 0;
for (size_t i = 0; i < groups; ++i) {
local_group_grads_params.emplace_back();
auto &group_p_g = local_group_grads_params.back();
group_p_g.reserve(group_size);
while (j < group_grads_params->size()) {
group_p_g.insert(group_p_g.end(), group_grads_params->at(j).begin(),
group_grads_params->at(j).end());
++j;
if (j % group_size == 0) break;
}
}
std::swap(*group_grads_params, local_group_grads_params);
VLOG(10) << "SetGroupAccordingToGroupSize(group_size: " << group_size
<< "): ";
for (size_t i = 0; i < group_grads_params->size(); ++i) {
VLOG(10) << "group " << i;
std::stringstream out;
for (auto &p_g : group_grads_params->at(i)) {
out << "(" << p_g.second << ", " << p_g.first << "), ";
}
VLOG(10) << out.str();
}
}
private:
bool IsSupportedVarType(const proto::VarType::Type &type) const {
// Current only support LOD_TENSOR.
return type == proto::VarType::LOD_TENSOR;
}
void AppendAllocSpaceForVarsOp(const std::vector<std::string> &params_name,
const std::vector<std::string> &grads_name,
const std::string &fused_var_name,
BlockDesc *global_block) const {
auto op_desc = global_block->AppendOp();
op_desc->SetType("alloc_continuous_space");
op_desc->SetInput("Input", params_name);
op_desc->SetOutput("Output", grads_name);
op_desc->SetOutput("FusedOutput", {fused_var_name});
}
void RecordParamsAndGrads(ir::Node *node,
ParamsAndGrads *params_grads) const {
try {
bool is_bk_op =
static_cast<bool>(boost::get<int>(node->Op()->GetAttr(
OpProtoAndCheckerMaker::OpRoleAttrName())) &
static_cast<int>(OpRole::kBackward));
if (!is_bk_op) return;
// Currently, we assume that once gradient is generated, it can be
// broadcast, and each gradient is only broadcast once.
auto backward_vars =
boost::get<std::vector<std::string>>(node->Op()->GetNullableAttr(
OpProtoAndCheckerMaker::OpRoleVarAttrName()));
PADDLE_ENFORCE_EQ(backward_vars.size() % 2, static_cast<size_t>(0));
for (size_t i = 0; i < backward_vars.size(); i += 2) {
VLOG(10) << "Trainable parameter: " << backward_vars[i]
<< ", gradient: " << backward_vars[i + 1];
params_grads->emplace_back(std::make_pair(
backward_vars[i] /*param*/, backward_vars[i + 1] /*grad*/));
}
} catch (boost::bad_get e) {
}
}
void InitFusedVarsAndAllocSpaceForVars(
const std::vector<platform::Place> &places,
const std::vector<Scope *> &local_scopes,
const std::unordered_map<std::string, ir::Node *> &vars,
const std::string &fused_var_name,
const ParamsAndGrads &params_grads) const {
// Init Gradients and FusedVars
VLOG(10) << "Init FusedVars and Gradients.";
for (auto it = local_scopes.rbegin(); it != local_scopes.rend(); ++it) {
auto &scope = *it;
PADDLE_ENFORCE(scope->FindVar(fused_var_name) == nullptr,
"%s has existed in scope.", fused_var_name);
scope->Var(fused_var_name)->GetMutable<LoDTensor>();
for (auto &p_g : params_grads) {
auto iter = vars.find(p_g.second);
PADDLE_ENFORCE(iter != vars.end());
PADDLE_ENFORCE_NOT_NULL(iter->second->Var());
PADDLE_ENFORCE_EQ(iter->second->Var()->GetType(),
proto::VarType::LOD_TENSOR);
scope->Var(p_g.second)->GetMutable<LoDTensor>();
}
}
std::vector<std::string> grads_name;
std::vector<std::string> params_name;
grads_name.reserve(params_grads.size());
params_name.reserve(params_grads.size());
for (auto &p_g : params_grads) {
params_name.emplace_back(p_g.first);
grads_name.emplace_back(p_g.second);
}
framework::ProgramDesc program_desc;
AppendAllocSpaceForVarsOp(params_name, grads_name, fused_var_name,
program_desc.MutableBlock(0));
// Run Only Once Programs
for (size_t i = 0; i < local_scopes.size(); ++i) {
for (auto &op_desc : program_desc.Block(0).AllOps()) {
auto op = OpRegistry::CreateOp(*op_desc);
op->Run(*local_scopes[i], places[i]);
}
}
}
};
} // namespace details
} // namespace framework
} // namespace paddle
REGISTER_PASS(alloc_continuous_space_for_grad_pass,
paddle::framework::details::AllocContinuousSpaceForGradPass)
.RequirePassAttr(paddle::framework::details::kPlaces)
.RequirePassAttr(paddle::framework::details::kLocalScopes);
paddle/fluid/framework/details/broadcast_op_handle.h
浏览文件 @ 86511f51
......@@ -57,7 +57,7 @@ struct BroadcastOpHandle : public OpHandleBase {
std::string Name() const override;
bool IsMultiDeviceTransfer() override { return false; };
bool IsMultiDeviceTransfer() override { return true; };
protected:
void RunImpl() override;
......
paddle/fluid/framework/details/build_strategy.cc
浏览文件 @ 86511f51
......@@ -46,7 +46,16 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
public:
explicit ParallelExecutorPassBuilder(const BuildStrategy &strategy)
: ir::PassBuilder(), strategy_(strategy) {
// Add a graph viz pass to record a graph.
if (!strategy_.debug_graphviz_path_.empty()) {
auto viz_pass = AppendPass("graph_viz_pass");
const std::string graph_path = string::Sprintf(
"%s%s", strategy_.debug_graphviz_path_.c_str(), "_original_graph");
viz_pass->Set<std::string>("graph_viz_path", new std::string(graph_path));
}
if (strategy_.enable_sequential_execution_) {
VLOG(10) << "Add sequential_execution_pass";
AppendPass("sequential_execution_pass");
}
......@@ -57,6 +66,7 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
// Add op fusion.
if (strategy.fuse_relu_depthwise_conv_) {
VLOG(10) << "Add fuse_relu_depthwise_conv_pass";
AppendPass("fuse_relu_depthwise_conv_pass");
}
......@@ -68,27 +78,28 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
// Add automatically inplace.
if (strategy_.enable_inplace_) {
VLOG(10) << "Add inplace_pass";
AppendPass("inplace_pass");
}
// Add a graph viz pass to record a graph.
if (!strategy_.debug_graphviz_path_.empty()) {
auto viz_pass = AppendPass("graph_viz_pass");
const std::string graph_path = string::Sprintf(
"%s%s", strategy_.debug_graphviz_path_.c_str(), "_original_graph");
viz_pass->Set<std::string>("graph_viz_path", new std::string(graph_path));
if (strategy.fuse_elewise_add_act_ops_) {
VLOG(10) << "Add fuse_elewise_add_act_pass";
AppendPass("fuse_elewise_add_act_pass");
}
// for single card training, fuse_all_reduce_ops is unnecessary.
// alloc_continuous_space_for_grad_pass should be before of MultiDevPass.
if (strategy.fuse_all_reduce_ops_) {
VLOG(10) << "Add alloc_continuous_space_for_grad_pass";
AppendPass("alloc_continuous_space_for_grad_pass");
}
if (strategy.fuse_elewise_add_act_ops_) {
auto fuse_elewise_add_act_pass = AppendPass("fuse_elewise_add_act_pass");
// Add a graph viz pass to record a graph.
if (!strategy.debug_graphviz_path_.empty()) {
auto viz_pass = AppendPass("graph_viz_pass");
const std::string graph_path = string::Sprintf(
"%s%s", strategy.debug_graphviz_path_.c_str(), "_fused_graph");
viz_pass->Set<std::string>("graph_viz_path",
new std::string(graph_path));
}
viz_pass->Set<std::string>("graph_viz_path", new std::string(graph_path));
}
CollectiveContext *context = CollectiveContext::GetInstance();
......@@ -108,11 +119,19 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
// A side-effect of that, memory optimize cannot forsee the fetched vars
// , so fetchlist should be set persistable before call the Run interface.
if (strategy.memory_optimize_) {
auto memory_optimize_pass = AppendPass("memory_optimize_pass");
VLOG(10) << "Add memory_optimize_pass";
AppendPass("memory_optimize_pass");
}
AppendMultiDevPass(strategy);
if (strategy.fuse_all_reduce_ops_) {
// NOTE: fuse_all_reduce_ops will count the number of all_reduce operator
// first, if the number is zero, fuse_all_reduce_ops will do nothing.
VLOG(10) << "Add fuse_all_reduce_op_pass";
AppendPass("fuse_all_reduce_op_pass");
}
// Add a graph print pass to record a graph with device info.
if (!strategy_.debug_graphviz_path_.empty()) {
auto multi_devices_print_pass = AppendPass("multi_devices_print_pass");
......@@ -128,28 +147,34 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
// Verify that the graph is correct for multi-device executor.
AppendPass("multi_devices_check_pass");
if (VLOG_IS_ON(2)) {
AppendPass("all_reduce_deps_pass");
}
if (SeqOnlyAllReduceOps(strategy)) {
VLOG(10) << "Add all_reduce_deps_pass";
AppendPass("all_reduce_deps_pass");
}
if (strategy_.remove_unnecessary_lock_) {
VLOG(10) << "Add modify_op_lock_and_record_event_pass";
AppendPass("modify_op_lock_and_record_event_pass");
}
}
// Convert graph to run on multi-devices.
void AppendMultiDevPass(const BuildStrategy &strategy) {
ir::Pass *multi_devices_pass;
ir::Pass *multi_devices_pass = nullptr;
if (strategy_.is_distribution_) {
VLOG(3) << "multi device parameter server mode";
VLOG(10) << "Add dist_multi_devices_pass";
multi_devices_pass = AppendPass("dist_multi_devices_pass").get();
} else {
if (strategy.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce) {
VLOG(3) << "multi devices collective mode with allreduce";
VLOG(10) << "Add all_reduce_mode_multi_devices_pass";
multi_devices_pass =
AppendPass("allreduce_mode_multi_devices_pass").get();
AppendPass("all_reduce_mode_multi_devices_pass").get();
} else if (strategy.reduce_ == BuildStrategy::ReduceStrategy::kReduce) {
VLOG(3) << "multi deivces collective mode with reduce";
VLOG(10) << "Add reduce_mode_multi_devices_pass";
multi_devices_pass = AppendPass("reduce_mode_multi_devices_pass").get();
} else {
PADDLE_THROW("Unknown reduce strategy.");
......@@ -206,9 +231,26 @@ std::unique_ptr<ir::Graph> BuildStrategy::Apply(
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
platform::NCCLContextMap *nctx = use_cuda ? nccl_ctxs : nullptr;
pass->Erase("nccl_ctxs");
pass->SetNotOwned<platform::NCCLContextMap>("nccl_ctxs", nctx);
pass->Erase(kNCCLCtxs);
pass->SetNotOwned<platform::NCCLContextMap>(kNCCLCtxs, nctx);
#endif
} else if (pass->Type() == "fuse_all_reduce_op_pass") {
pass->Erase(kPlaces);
pass->SetNotOwned<const std::vector<platform::Place>>(kPlaces, &places);
pass->Erase(kLocalScopes);
pass->SetNotOwned<const std::vector<Scope *>>(kLocalScopes,
&local_scopes);
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
platform::NCCLContextMap *nctx = use_cuda ? nccl_ctxs : nullptr;
pass->Erase(kNCCLCtxs);
pass->SetNotOwned<platform::NCCLContextMap>(kNCCLCtxs, nctx);
#endif
} else if (pass->Type() == "alloc_continuous_space_for_grad_pass") {
pass->Erase(kPlaces);
pass->SetNotOwned<const std::vector<platform::Place>>(kPlaces, &places);
pass->Erase(kLocalScopes);
pass->SetNotOwned<const std::vector<Scope *>>(kLocalScopes,
&local_scopes);
} else if (pass->Type() == "sequential_execution_pass") {
LOG(INFO) << "set enable_sequential_execution:"
<< enable_sequential_execution_;
......@@ -239,7 +281,7 @@ USE_PASS(fuse_elewise_add_act_pass);
USE_PASS(graph_viz_pass);
USE_PASS(multi_batch_merge_pass);
USE_PASS(reduce_mode_multi_devices_pass);
USE_PASS(allreduce_mode_multi_devices_pass);
USE_PASS(all_reduce_mode_multi_devices_pass);
USE_PASS(dist_multi_devices_pass);
USE_PASS(multi_devices_check_pass);
USE_PASS(multi_devices_print_pass);
......@@ -249,4 +291,6 @@ USE_PASS(all_reduce_deps_pass);
USE_PASS(modify_op_lock_and_record_event_pass);
USE_PASS(inplace_pass);
USE_PASS(lock_free_optimize_pass);
USE_PASS(alloc_continuous_space_for_grad_pass);
USE_PASS(graph_to_program_pass);
USE_PASS(fuse_all_reduce_op_pass);
paddle/fluid/framework/details/build_strategy.h
浏览文件 @ 86511f51
......@@ -16,6 +16,7 @@
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/ir/pass_builder.h"
......@@ -75,6 +76,8 @@ struct BuildStrategy {
bool fuse_elewise_add_act_ops_{false};
bool fuse_all_reduce_ops_{false};
bool fuse_relu_depthwise_conv_{false};
bool sync_batch_norm_{false};
......
paddle/fluid/framework/details/data_balance_op_handle.cc 已删除 100644 → 0
浏览文件 @ 5bb04ea4
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/data_balance_op_handle.h"
#include <algorithm>
#include "paddle/fluid/framework/details/container_cast.h"
namespace paddle {
namespace framework {
namespace details {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
DataBalanceOpHandle::DataBalanceOpHandle(
ir::Node *node, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
const platform::NCCLContextMap *ctxs)
: OpHandleBase(node), local_scopes_(local_scopes), places_(places) {
if (ctxs) {
for (auto &p : places_) {
this->SetDeviceContext(p, ctxs->DevCtx(p));
}
}
}
#else
DataBalanceOpHandle::DataBalanceOpHandle(
ir::Node *node, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places)
: OpHandleBase(node), local_scopes_(local_scopes), places_(places) {}
#endif
std::string DataBalanceOpHandle::Name() const { return "data balance"; }
std::vector<std::array<int, 3>> DataBalanceOpHandle::GetBalancePlan(
const std::vector<int> &device_sizes) {
int device_num = device_sizes.size();
int total_size = 0;
int empty_num = 0;
std::vector<std::array<int, 2>> size_device_vec;
size_device_vec.reserve(device_num);
for (int i = 0; i < device_num; ++i) {
if (device_sizes[i] == 0) {
++empty_num;
}
total_size += device_sizes[i];
size_device_vec.push_back({{device_sizes[i], i}});
}
std::vector<std::array<int, 3>> res;
if (empty_num == 0) {
// No need to do data balance.
return res;
}
if (total_size < device_num) {
// No enough data.
PADDLE_THROW_EOF();
}
std::sort(size_device_vec.begin(), size_device_vec.end(),
[](const std::array<int, 2> &a, const std::array<int, 2> &b) {
return a[0] > b[0];
});
int expected_device_size = total_size / device_num;
int src_idx = 0;
for (int dst_idx = device_num - empty_num; dst_idx < device_num; ++dst_idx) {
if (size_device_vec[src_idx][0] <= expected_device_size) {
++src_idx;
PADDLE_ENFORCE_LT(
src_idx, device_num - empty_num,
"In current srategy an empty tensor should not be copy source.");
}
size_device_vec[src_idx][0] -= expected_device_size;
size_device_vec[dst_idx][0] += expected_device_size;
res.push_back({{size_device_vec[src_idx][1], size_device_vec[dst_idx][1],
expected_device_size}});
}
return res;
}
void DataBalanceOpHandle::RunImpl() {
PADDLE_ENFORCE_GT(places_.size(), 1UL,
"Data balance can only be enabled when the number of "
"places to run larger than 1.");
auto in_var_handles = DynamicCast<VarHandle>(this->Inputs());
auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
PADDLE_ENFORCE(in_var_handles.size() % places_.size() == 0);
PADDLE_ENFORCE_EQ(
in_var_handles.size(), out_var_handles.size(),
"The NoDummyInputSize and NoDummyOutputSize should be equal.");
int data_num = in_var_handles.size() / places_.size();
WaitInputVarGenerated();
std::vector<std::vector<LoDTensor *>> lod_tensors(data_num);
std::vector<int> device_sizes;
for (int i = 0; i < static_cast<int>(in_var_handles.size()); ++i) {
PADDLE_ENFORCE_EQ(in_var_handles[i]->name(), out_var_handles[i]->name(),
"The name of input and output should be equal.");
int place_idx = i / data_num;
int data_idx = i % data_num;
auto *local_scope =
local_scopes_[place_idx]->FindVar(kLocalExecScopeName)->Get<Scope *>();
auto *tensor_var = local_scope->FindVar(in_var_handles[i]->name());
PADDLE_ENFORCE(tensor_var->IsType<LoDTensor>());
auto *tensor = tensor_var->GetMutable<LoDTensor>();
lod_tensors[data_idx].push_back(tensor);
int ins_size =
tensor->lod().empty() ? tensor->dims()[0] : tensor->NumElements();
if (data_idx == 0) {
device_sizes.emplace_back(ins_size);
} else {
PADDLE_ENFORCE_EQ(
ins_size, device_sizes.at(place_idx),
"All data on the same device shall have the same batch size.");
}
}
const auto &balance_plan = GetBalancePlan(device_sizes);
for (const auto &trans : balance_plan) {
for (int data_idx = 0; data_idx < data_num; ++data_idx) {
LoDTensor *src_tensor = lod_tensors[data_idx][trans[0]];
LoDTensor *dst_tensor = lod_tensors[data_idx][trans[1]];
int trans_ins_size = trans[2];
LoD src_lod = src_tensor->lod();
int src_ins_size =
src_lod.empty() ? src_tensor->dims()[0] : src_tensor->NumElements();
int cut_point = src_ins_size - trans_ins_size;
if (!src_lod.empty()) {
for (auto &level : src_lod) {
cut_point = level[cut_point];
}
}
TensorCopySync(src_tensor->Slice(cut_point, src_tensor->dims()[0]),
dst_tensor->place(), dst_tensor);
src_tensor->ShareDataWith(src_tensor->Slice(0, cut_point));
if (!src_lod.empty()) {
dst_tensor->set_lod(SliceInLevel(
src_lod, 0, src_ins_size - trans_ins_size, src_ins_size));
src_tensor->set_lod(
SliceInLevel(src_lod, 0, 0, src_ins_size - trans_ins_size));
}
}
}
}
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/fetch_op_handle.cc
浏览文件 @ 86511f51
......@@ -82,6 +82,8 @@ void FetchOpHandle::WaitInputVarGenerated(const platform::Place &place) {
}
}
bool FetchOpHandle::IsMultiDeviceTransfer() { return true; }
std::string FetchOpHandle::Name() const { return "Fetch"; }
} // namespace details
......
paddle/fluid/framework/details/fetch_op_handle.h
浏览文件 @ 86511f51
......@@ -39,6 +39,8 @@ struct FetchOpHandle : public OpHandleBase {
std::string Name() const override;
bool IsMultiDeviceTransfer() override;
protected:
void RunImpl() override;
......
paddle/fluid/framework/details/fuse_all_reduce_op_pass.cc 0 → 100644
浏览文件 @ 86511f51
// 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 <string>
#include <vector>
#include "paddle/fluid/framework/details/all_reduce_op_handle.h"
#include "paddle/fluid/framework/details/container_cast.h"
#include "paddle/fluid/framework/details/fused_all_reduce_op_handle.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
namespace paddle {
namespace framework {
namespace details {
class FuseAllReduceOpPass : public ir::Pass {
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override {
ir::Graph &result = *graph;
auto &places = Get<const std::vector<platform::Place>>(kPlaces);
auto &local_scopes = Get<const std::vector<Scope *>>(kLocalScopes);
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
auto *nccl_ctxs = &Get<platform::NCCLContextMap>(kNCCLCtxs);
#endif
std::unordered_set<std::string> grads;
auto &params_grads = result.Get<ParamsAndGrads>(kParamsAndGrads);
size_t num_of_all_reduce = params_grads.size();
grads.reserve(num_of_all_reduce);
for (auto p_g : params_grads) {
grads.insert(p_g.second);
}
size_t num_place = places.size();
std::unordered_map<std::string, ir::Node *> all_reduce_ops;
all_reduce_ops.reserve(grads.size());
for (auto &node : result.Nodes()) {
if (node->IsOp()) {
PADDLE_ENFORCE(node->IsWrappedBy<OpHandleBase>());
auto *all_reduce_op_handle =
dynamic_cast<AllReduceOpHandle *>(&node->Wrapper<OpHandleBase>());
if (all_reduce_op_handle) {
auto inputs = DynamicCast<VarHandle>(all_reduce_op_handle->Inputs());
PADDLE_ENFORCE_EQ(inputs.size(), num_place);
// The inputs' name should be the same.
auto &grad_name = inputs[0]->name();
for (size_t i = 1; i < inputs.size(); ++i) {
PADDLE_ENFORCE_EQ(inputs[i]->name(), grad_name,
"The input name should be the same.");
}
PADDLE_ENFORCE_NE(grads.count(grad_name), static_cast<size_t>(0));
all_reduce_ops.emplace(grad_name, node);
}
}
}
VLOG(10) << "Find all_reduce_ops: " << all_reduce_ops.size();
if (all_reduce_ops.size() == 0) {
return std::move(graph);
}
PADDLE_ENFORCE_EQ(all_reduce_ops.size(), grads.size(),
"The number of all_reduce OpHandle is not equal to the "
"number of grads. Maybe some gradients are sparse type, "
"it is not supported currently.");
VLOG(10) << "Insert fused_all_reduce";
auto &group_grads_params =
graph->Get<GroupGradsAndParams>(kGroupGradsAndParams);
for (auto &group_g_p : group_grads_params) {
size_t group_size = group_g_p.size();
PADDLE_ENFORCE_GT(group_size, static_cast<size_t>(0));
std::vector<ir::Node *> group_all_reduce_ops;
group_all_reduce_ops.reserve(group_size);
for (auto &g_p : group_g_p) {
group_all_reduce_ops.emplace_back(all_reduce_ops.at(g_p.first));
}
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
InsertFusedAllReduce(places, local_scopes, group_size,
group_all_reduce_ops, nccl_ctxs, &result);
#else
InsertFusedAllReduce(places, local_scopes, group_size,
group_all_reduce_ops, &result);
#endif
}
return std::move(graph);
}
void InsertFusedAllReduce(const std::vector<platform::Place> &places,
const std::vector<Scope *> &local_scopes,
const size_t num_of_all_reduce,
const std::vector<ir::Node *> &all_reduce_ops,
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
const platform::NCCLContextMap *nccl_ctxs,
#endif
ir::Graph *result) const {
std::vector<VarHandleBase *> inputs;
std::vector<VarHandleBase *> outputs;
for (auto &op : all_reduce_ops) {
auto &op_handle = op->Wrapper<OpHandleBase>();
inputs.insert(inputs.end(), op_handle.Inputs().begin(),
op_handle.Inputs().end());
// Remove output
for_each(op_handle.Inputs().begin(), op_handle.Inputs().end(),
[&op_handle](VarHandleBase *var_handle) {
var_handle->RemoveOutput(&op_handle, op_handle.Node());
});
outputs.insert(outputs.end(), op_handle.Outputs().begin(),
op_handle.Outputs().end());
// Remove Input
for_each(
op_handle.Outputs().begin(), op_handle.Outputs().end(),
[](VarHandleBase *var_handle) { var_handle->ClearGeneratedOp(); });
result->RemoveNode(op_handle.Node());
}
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
CreateFusedAllReduceOp(inputs, outputs, num_of_all_reduce, places,
local_scopes, nccl_ctxs, result);
#else
CreateFusedAllReduceOp(inputs, outputs, num_of_all_reduce, places,
local_scopes, result);
#endif
}
private:
void CreateFusedAllReduceOp(const std::vector<VarHandleBase *> &inputs,
const std::vector<VarHandleBase *> &outputs,
const size_t num_of_all_reduce,
const std::vector<platform::Place> &places,
const std::vector<Scope *> &local_scopes,
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
const platform::NCCLContextMap *nccl_ctxs,
#endif
ir::Graph *result) const {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
auto *op_handle = new FusedAllReduceOpHandle(
result->CreateEmptyNode("fused_all_reduce", ir::Node::Type::kOperation),
local_scopes, places, num_of_all_reduce, nccl_ctxs);
#else
auto *op_handle = new FusedAllReduceOpHandle(
result->CreateEmptyNode("fused_all_reduce", ir::Node::Type::kOperation),
local_scopes, places, num_of_all_reduce);
#endif
for (auto in : inputs) {
op_handle->AddInput(in);
}
for (auto out : outputs) {
op_handle->AddOutput(out);
}
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
if (!nccl_ctxs) {
SetCommunicationContext(places, op_handle);
}
#else
SetCommunicationContext(places, op_handle);
#endif
}
void SetCommunicationContext(const std::vector<platform::Place> &places,
FusedAllReduceOpHandle *op_handle) const {
for (size_t i = 0; i < places.size(); ++i) {
op_handle->SetDeviceContext(
places[i], platform::DeviceContextPool::Instance().Get(places[i]));
}
}
};
} // namespace details
} // namespace framework
} // namespace paddle
REGISTER_PASS(fuse_all_reduce_op_pass,
paddle::framework::details::FuseAllReduceOpPass);
paddle/fluid/framework/details/fuse_vars_op_handle.cc 已删除 100644 → 0
浏览文件 @ 5bb04ea4
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/fuse_vars_op_handle.h"
namespace paddle {
namespace framework {
namespace details {
void FuseVarsOpHandle::RunImpl() {
WaitInputVarGenerated(place_);
auto in_var_handles = DynamicCast<VarHandle>(this->Inputs());
auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
PADDLE_ENFORCE_EQ(in_var_handles.size(), 0UL);
PADDLE_ENFORCE_EQ(out_var_handles.size() - 1, inputs_numel_.size(), "");
auto scope = local_scope_->FindVar(kLocalExecScopeName)->Get<Scope *>();
auto out_var_handle = out_var_handles[0];
auto out_var = scope->Var(out_var_handle->name());
auto out_tensor = out_var->GetMutable<LoDTensor>();
out_tensor->Resize({total_numel_}).mutable_data(this->place_, type_);
int64_t s = 0;
for (size_t i = 1; i < out_var_handles.size(); ++i) {
auto out_name = out_var_handles[i]->name();
auto out_t = scope->Var(out_name)->GetMutable<LoDTensor>();
auto numel = this->inputs_numel_.at(out_name);
out_t->ShareDataWith(out_tensor->Slice(s, s + numel));
s += numel;
}
this->RunAndRecordEvent([] {});
}
std::string FuseVarsOpHandle::Name() const { return "fuse vars"; }
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/fuse_vars_op_handle.h 已删除 100644 → 0
浏览文件 @ 5bb04ea4
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <map>
#include <string>
#include <vector>
#include "paddle/fluid/framework/details/container_cast.h"
#include "paddle/fluid/framework/details/op_handle_base.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/platform/device_context.h"
namespace paddle {
namespace framework {
namespace details {
struct FuseVarsOpHandle : public OpHandleBase {
public:
FuseVarsOpHandle(ir::Node *node, Scope *local_scope,
const platform::Place &place,
const std::unordered_map<std::string, int64_t> &inputs_numel,
const proto::VarType::Type var_type)
: OpHandleBase(node),
local_scope_(local_scope),
place_(place),
inputs_numel_(inputs_numel),
type_(var_type) {
total_numel_ = 0;
for (auto in_numel : inputs_numel) {
PADDLE_ENFORCE_GT(in_numel.second, 0);
total_numel_ += in_numel.second;
}
}
std::string Name() const override;
bool IsMultiDeviceTransfer() override { return false; };
protected:
void RunImpl() override;
private:
Scope *local_scope_;
const platform::Place place_;
const std::unordered_map<std::string, int64_t> inputs_numel_;
const proto::VarType::Type type_;
int64_t total_numel_;
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/fused_all_reduce_op_handle.cc 0 → 100644
浏览文件 @ 86511f51
// 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/details/fused_all_reduce_op_handle.h"
#include <algorithm>
#include <utility>
#include "paddle/fluid/framework/details/container_cast.h"
#include "paddle/fluid/framework/details/reduce_and_gather.h"
#include "paddle/fluid/framework/details/variable_visitor.h"
#include "paddle/fluid/platform/profiler.h"
DEFINE_bool(skip_fused_all_reduce_check, false, "");
namespace paddle {
namespace framework {
namespace details {
typedef std::vector<std::vector<std::pair<std::string, const LoDTensor *>>>
GradientAndLoDTensor;
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
FusedAllReduceOpHandle::FusedAllReduceOpHandle(
ir::Node *node, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places, const size_t num_of_all_reduce,
const platform::NCCLContextMap *ctxs)
: OpHandleBase(node),
local_scopes_(local_scopes),
places_(places),
num_of_all_reduce_(num_of_all_reduce),
nccl_ctxs_(ctxs) {
if (nccl_ctxs_) {
for (auto &p : places_) {
this->SetDeviceContext(p, nccl_ctxs_->DevCtx(p));
}
}
PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
}
#else
FusedAllReduceOpHandle::FusedAllReduceOpHandle(
ir::Node *node, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places, const size_t num_of_all_reduce)
: OpHandleBase(node),
local_scopes_(local_scopes),
places_(places),
num_of_all_reduce_(num_of_all_reduce) {
PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
}
#endif
void FusedAllReduceOpHandle::RunImpl() {
platform::RecordEvent record_event(Name());
VLOG(4) << this->DebugString();
WaitInputVarGenerated();
// The input: grad0(dev0), grad0(dev1), grad1(dev0), grad1(dev1)...
// The output: grad0(dev0), grad0(dev1), grad1(dev0), grad1(dev1)...
auto in_var_handles = DynamicCast<VarHandle>(this->Inputs());
auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
size_t place_num = places_.size();
PADDLE_ENFORCE_EQ(
in_var_handles.size(), place_num * num_of_all_reduce_,
"The NoDummyInputSize should be equal to the number of places.");
PADDLE_ENFORCE_EQ(
in_var_handles.size(), out_var_handles.size(),
"The NoDummyInputSize and NoDummyOutputSize should be equal.");
GradientAndLoDTensor grads_tensor;
grads_tensor.resize(place_num);
int64_t numel = -1;
auto dtype = static_cast<framework::proto::VarType::Type>(0);
for (size_t scope_idx = 0; scope_idx < local_scopes_.size(); ++scope_idx) {
auto &g_tensor = grads_tensor.at(scope_idx);
g_tensor.reserve(num_of_all_reduce_);
GetGradLoDTensor(scope_idx, in_var_handles, out_var_handles, &g_tensor);
int64_t element_num = 0;
framework::proto::VarType::Type ele_dtype =
static_cast<framework::proto::VarType::Type>(0);
GetDTypeAndNumel(g_tensor, &ele_dtype, &element_num);
if (numel == -1) {
numel = element_num;
}
if (dtype == static_cast<framework::proto::VarType::Type>(0)) {
dtype = ele_dtype;
PADDLE_ENFORCE_NE(ele_dtype,
static_cast<framework::proto::VarType::Type>(0));
}
PADDLE_ENFORCE_EQ(ele_dtype, dtype);
// Check whether the address space is contiguous.
std::sort(
g_tensor.begin(), g_tensor.end(),
[](const std::pair<std::string, const LoDTensor *> &grad1,
const std::pair<std::string, const LoDTensor *> &grad2) -> bool {
return grad1.second->data<void>() < grad2.second->data<void>();
});
for (size_t k = 1; k < g_tensor.size(); ++k) {
const void *cur_address = g_tensor.at(k - 1).second->data<void>();
int64_t len = g_tensor.at(k - 1).second->numel();
auto offset = len * framework::SizeOfType(dtype);
void *infer_next_address = reinterpret_cast<void *>(
reinterpret_cast<uintptr_t>(cur_address) + offset);
const void *next_address = g_tensor.at(k).second->data<void>();
VLOG(10) << string::Sprintf(
"Input[%d](%s) address: 0X%02x, Input[%d](%s) address: 0X%02x, Infer "
"input[%d] address: 0X%02x. The offset: %d",
k - 1, g_tensor.at(k - 1).first, cur_address, g_tensor.at(k).first, k,
next_address, k, infer_next_address, offset);
PADDLE_ENFORCE_EQ(infer_next_address, next_address,
"The address is not consistent.");
}
}
if (!FLAGS_skip_fused_all_reduce_check) {
for (size_t scope_idx = 0; scope_idx < place_num; ++scope_idx) {
for (size_t j = 1; j < num_of_all_reduce_; ++j) {
PADDLE_ENFORCE_EQ(grads_tensor.at(0).at(j).first,
grads_tensor.at(scope_idx).at(j).first);
}
}
}
std::vector<const void *> lod_tensor_data;
for (size_t scope_idx = 0; scope_idx < place_num; ++scope_idx) {
auto data = grads_tensor.at(scope_idx).at(0).second->data<void>();
lod_tensor_data.emplace_back(data);
}
if (platform::is_gpu_place(places_[0])) {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
PADDLE_ENFORCE(nccl_ctxs_, "nccl_ctxs should not be nullptr.");
int nccl_dtype = platform::ToNCCLDataType(dtype);
std::vector<std::function<void()>> all_reduce_calls;
for (size_t i = 0; i < local_scopes_.size(); ++i) {
auto &p = places_[i];
void *buffer = const_cast<void *>(lod_tensor_data.at(i));
int dev_id = boost::get<platform::CUDAPlace>(p).device;
auto &nccl_ctx = nccl_ctxs_->at(dev_id);
auto stream = nccl_ctx.stream();
auto comm = nccl_ctx.comm_;
all_reduce_calls.emplace_back([=] {
PADDLE_ENFORCE(platform::dynload::ncclAllReduce(
buffer, buffer, numel, static_cast<ncclDataType_t>(nccl_dtype),
ncclSum, comm, stream));
});
}
this->RunAndRecordEvent([&] {
if (all_reduce_calls.size() == 1UL) {
// Do not use NCCLGroup when manage NCCL by per thread per device
all_reduce_calls[0]();
} else {
platform::NCCLGroupGuard guard;
for (auto &call : all_reduce_calls) {
call();
}
}
});
#else
PADDLE_THROW("Not compiled with CUDA");
#endif
} else {
// Special handle CPU only Operator's gradient. Like CRF
auto grad_name = grads_tensor.at(0).at(0).first;
auto &trg = *this->local_scopes_[0]
->FindVar(kLocalExecScopeName)
->Get<Scope *>()
->FindVar(grad_name)
->GetMutable<framework::LoDTensor>();
// Reduce All data to trg in CPU
ReduceBufferData func(lod_tensor_data, trg.data<void>(), numel);
VisitDataType(trg.type(), func);
for (size_t i = 1; i < local_scopes_.size(); ++i) {
auto &scope =
*local_scopes_[i]->FindVar(kLocalExecScopeName)->Get<Scope *>();
auto &p = places_[i];
auto *var = scope.FindVar(grad_name);
auto *dev_ctx = dev_ctxes_.at(p);
size_t size = numel * SizeOfType(trg.type());
RunAndRecordEvent(p, [&trg, var, dev_ctx, p, size] {
auto dst_ptr = var->GetMutable<framework::LoDTensor>()->data<void>();
platform::CPUPlace cpu_place;
memory::Copy(cpu_place, dst_ptr, cpu_place, trg.data<void>(), size);
});
}
}
}
void FusedAllReduceOpHandle::GetGradLoDTensor(
const size_t &scope_idx, const std::vector<VarHandle *> &in_var_handles,
const std::vector<VarHandle *> &out_var_handles,
std::vector<std::pair<std::string, const LoDTensor *>> *grad_tensor) const {
auto *local_scope =
local_scopes_.at(scope_idx)->FindVar(kLocalExecScopeName)->Get<Scope *>();
size_t place_num = places_.size();
for (size_t j = 0; j < in_var_handles.size(); j += place_num) {
auto var_name = in_var_handles[j]->name();
PADDLE_ENFORCE_EQ(var_name, out_var_handles[j]->name());
auto &lod_tensor = local_scope->FindVar(var_name)->Get<LoDTensor>();
PADDLE_ENFORCE_EQ(lod_tensor.place(), places_.at(scope_idx));
grad_tensor->emplace_back(std::make_pair(var_name, &lod_tensor));
}
}
void FusedAllReduceOpHandle::GetDTypeAndNumel(
const std::vector<std::pair<std::string, const LoDTensor *>> &grad_tensor,
proto::VarType::Type *dtype, int64_t *numel) const {
*numel = 0;
for (size_t i = 0; i < grad_tensor.size(); ++i) {
// Get element number
int64_t len = grad_tensor.at(i).second->numel();
PADDLE_ENFORCE_GT(len, 0);
*numel += len;
// Get dtype
auto ele_type = grad_tensor.at(i).second->type();
if (i == 0) {
*dtype = ele_type;
}
PADDLE_ENFORCE_EQ(ele_type, *dtype);
}
}
std::string FusedAllReduceOpHandle::Name() const { return "fused_all_reduce"; }
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/data_balance_op_handle.h → paddle/fluid/framework/details/fused_all_reduce_op_handle.h
浏览文件 @ 86511f51
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
// 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.
......@@ -15,6 +15,7 @@
#pragma once
#include <string>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/op_handle_base.h"
#include "paddle/fluid/framework/lod_tensor.h"
......@@ -27,31 +28,47 @@ namespace paddle {
namespace framework {
namespace details {
struct DataBalanceOpHandle : public OpHandleBase {
public:
struct FusedAllReduceOpHandle : public OpHandleBase {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
DataBalanceOpHandle(ir::Node *node, const std::vector<Scope *> &local_scopes,
FusedAllReduceOpHandle(ir::Node *node,
const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
const size_t num_of_all_reduce,
const platform::NCCLContextMap *ctxs);
#else
DataBalanceOpHandle(ir::Node *node, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places);
FusedAllReduceOpHandle(ir::Node *node,
const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
const size_t num_of_all_reduce);
#endif
std::string Name() const override;
bool IsMultiDeviceTransfer() override { return false; };
// Delay and buffer nccl_all_reduce together can significantly increase
// performance. Disable this feature by returning false.
bool IsMultiDeviceTransfer() override { return true; };
protected:
void RunImpl() override;
private:
// std::vector<(src_dev_id, dst_dev_id, trans_size)>
std::vector<std::array<int, 3>> GetBalancePlan(
const std::vector<int> &batch_size_per_device);
std::vector<Scope *> local_scopes_;
std::vector<platform::Place> places_;
size_t num_of_all_reduce_;
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
const platform::NCCLContextMap *nccl_ctxs_;
#endif
// Check the dtype of the input
void GetDTypeAndNumel(
const std::vector<std::pair<std::string, const LoDTensor *>> &g_tensor,
proto::VarType::Type *dtype, int64_t *total_num) const;
const std::vector<Scope *> local_scopes_;
const std::vector<platform::Place> places_;
// Get gradient's name and LoDTensor
void GetGradLoDTensor(const size_t &scope_idx,
const std::vector<VarHandle *> &in_var_handles,
const std::vector<VarHandle *> &out_var_handles,
std::vector<std::pair<std::string, const LoDTensor *>>
*grad_tensor) const;
};
} // namespace details
......
paddle/fluid/framework/details/multi_devices_graph_pass.cc
浏览文件 @ 86511f51
......@@ -11,18 +11,19 @@
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/multi_devices_graph_pass.h"
#include <algorithm>
#include <fstream>
#include <memory>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/all_reduce_op_handle.h"
#include "paddle/fluid/framework/details/broadcast_op_handle.h"
#include "paddle/fluid/framework/details/computation_op_handle.h"
#include "paddle/fluid/framework/details/data_balance_op_handle.h"
#include "paddle/fluid/framework/details/fused_broadcast_op_handle.h"
#include "paddle/fluid/framework/details/multi_devices_graph_pass.h"
#include "paddle/fluid/framework/details/reduce_op_handle.h"
#include "paddle/fluid/framework/details/rpc_op_handle.h"
#include "paddle/fluid/framework/details/scale_loss_grad_op_handle.h"
......@@ -134,21 +135,26 @@ void AddOutputToLeafOps(ir::Graph *graph) {
}
} // namespace
void MultiDevSSAGraphBuilderBase::CheckGraph(const ir::Graph &graph) const {}
void MultiDevSSAGraphBuilderBase::Init() const {
all_vars_.clear();
loss_var_name_ = Get<const std::string>(kLossVarName);
VLOG(10) << "Init MultiDevSSAGraphBuilder, loss name: " << loss_var_name_;
places_ = Get<const std::vector<platform::Place>>(kPlaces);
local_scopes_ = Get<const std::vector<Scope *>>(kLocalScopes);
strategy_ = Get<const BuildStrategy>(kStrategy);
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
nccl_ctxs_ = &Get<platform::NCCLContextMap>("nccl_ctxs");
nccl_ctxs_ = &Get<platform::NCCLContextMap>(kNCCLCtxs);
#endif
PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
}
std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilderBase::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
Init();
CheckGraph(*graph);
std::vector<ir::Node *> sorted_ops = SortOperations(*graph);
auto nodes = graph->ReleaseNodes();
......@@ -166,7 +172,6 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilderBase::ApplyImpl(
result.Set(kGraphOps, new GraphOps);
bool is_forwarding = true;
bool insert_collection_ops = NeedCollectiveOps();
for (ir::Node *node : sorted_ops) {
if (DealWithSpecialOp(&result, node)) {
......@@ -185,8 +190,8 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilderBase::ApplyImpl(
CreateComputationalOps(&result, node, places_.size());
}
// Insert collection ops
if (!is_forwarding && insert_collection_ops) {
// Insert collective ops if nranks > 1
if (!is_forwarding && Get<size_t>(kNRanks) > 1) {
try {
bool is_bk_op =
static_cast<bool>(boost::get<int>(node->Op()->GetAttr(
......@@ -200,14 +205,14 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilderBase::ApplyImpl(
boost::get<std::vector<std::string>>(node->Op()->GetNullableAttr(
OpProtoAndCheckerMaker::OpRoleVarAttrName()));
PADDLE_ENFORCE_EQ(backward_vars.size() % 2, 0);
for (size_t i = 0; i < backward_vars.size(); i += 2) {
auto &p_name = backward_vars[i];
auto &g_name = backward_vars[i + 1];
VLOG(10) << "Bcast " << g_name << " for parameter " << p_name;
if (NeedCollectiveForGrad(g_name, sorted_ops)) {
InsertCollectiveOp(&result, p_name, g_name);
}
}
} catch (boost::bad_get e) {
}
}
......@@ -226,6 +231,7 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilderBase::ApplyImpl(
* Only variables should be the leaves of graph.
*/
AddOutputToLeafOps(&result);
result.Erase(kGraphOps);
return graph;
}
......@@ -258,6 +264,11 @@ void MultiDevSSAGraphBuilderBase::InsertScaleLossGradOp(
}
}
bool MultiDevSSAGraphBuilderBase::DealWithSpecialOp(ir::Graph *result,
ir::Node *node) const {
return false;
}
std::vector<ir::Node *> MultiDevSSAGraphBuilderBase::SortOperations(
const ir::Graph &graph) const {
return ir::TopologySortOperations(graph);
......@@ -271,8 +282,20 @@ bool MultiDevSSAGraphBuilderBase::UseGPU() const {
return use_gpu;
}
bool MultiDevSSAGraphBuilderBase::NeedCollectiveOps() const {
return Get<size_t>(kNRanks) > 1;
bool MultiDevSSAGraphBuilderBase::NeedCollectiveForGrad(
const std::string &grad_name, std::vector<ir::Node *> ops) const {
// if we have allreduce_op for current gradient variable in the graph,
// then we don't need to add allreduce_op_handle for this gradient
// NOTE: This is for the case that all gradients should add collective ops
for (auto *node : ops) {
if (node->Op()->Type() != "allreduce") continue;
for (auto in_name : node->Op()->InputArgumentNames()) {
if (in_name == grad_name) {
return false;
}
}
}
return true;
}
void MultiDevSSAGraphBuilderBase::CreateOpHandleIOs(ir::Graph *result,
......@@ -496,20 +519,17 @@ VarHandle *MultiDevSSAGraphBuilderBase::CreateReduceOp(ir::Graph *result,
}
bool MultiDevSSAGraphBuilderBase::IsScaleLossOp(ir::Node *node) const {
return boost::get<int>(
return !loss_var_name_.empty() && node->Op() &&
boost::get<int>(
node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName())) ==
(static_cast<int>(OpRole::kBackward) |
static_cast<int>(OpRole::kLoss)) &&
!loss_var_name_.empty(); // If loss_var is empty. This is test mode
static_cast<int>(OpRole::kLoss));
}
bool MultiDevSSAGraphBuilderBase::IsSparseGradient(
const std::string &og) const {
PADDLE_ENFORCE(all_vars_.count(og) != 0);
if (all_vars_.at(og)->GetType() == proto::VarType::SELECTED_ROWS) {
return true;
}
return false;
return all_vars_.at(og)->GetType() == proto::VarType::SELECTED_ROWS;
}
void AllReduceSSAGraphBuilder::InsertCollectiveOp(
......@@ -995,7 +1015,7 @@ static int MultiDevSSAGraphBuilderRegister(const std::string &builder_mode) {
REGISTER_MULTI_DEVICES_PASS(reduce_mode_multi_devices_pass,
paddle::framework::details::ReduceSSAGraphBuilder);
REGISTER_MULTI_DEVICES_PASS(
allreduce_mode_multi_devices_pass,
all_reduce_mode_multi_devices_pass,
paddle::framework::details::AllReduceSSAGraphBuilder);
REGISTER_MULTI_DEVICES_PASS(dist_multi_devices_pass,
paddle::framework::details::DistSSAGraphBuilder);
paddle/fluid/framework/details/multi_devices_graph_pass.h
浏览文件 @ 86511f51
......@@ -14,7 +14,10 @@
#pragma once
#include <memory>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
......@@ -31,12 +34,6 @@ namespace framework {
class Scope;
namespace details {
constexpr char kLossVarName[] = "loss_var_name";
constexpr char kPlaces[] = "places";
constexpr char kLocalScopes[] = "local_scopes";
constexpr char kStrategy[] = "strategy";
constexpr char kNRanks[] = "nranks";
class MultiDevSSAGraphBuilderBase : public ir::Pass {
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
......@@ -44,18 +41,21 @@ class MultiDevSSAGraphBuilderBase : public ir::Pass {
virtual void Init() const;
virtual void CheckGraph(const ir::Graph &graph) const;
virtual std::vector<ir::Node *> SortOperations(const ir::Graph &graph) const;
virtual void InsertCollectiveOp(ir::Graph *result, const std::string &p_name,
const std::string &g_name) const = 0;
virtual bool DealWithSpecialOp(ir::Graph *result, ir::Node *node) const = 0;
virtual bool DealWithSpecialOp(ir::Graph *result, ir::Node *node) const;
virtual void InsertPostprocessOps(ir::Graph *result) const = 0;
bool UseGPU() const;
bool NeedCollectiveOps() const;
bool NeedCollectiveForGrad(const std::string &grad_name,
std::vector<ir::Node *> ops) const;
bool IsScaleLossOp(ir::Node *node) const;
......@@ -109,10 +109,6 @@ class AllReduceSSAGraphBuilder : public MultiDevSSAGraphBuilderBase {
virtual void InsertCollectiveOp(ir::Graph *result, const std::string &p_name,
const std::string &g_name) const;
virtual bool DealWithSpecialOp(ir::Graph *result, ir::Node *node) const {
return false;
}
virtual void InsertPostprocessOps(ir::Graph *result) const {}
};
......
paddle/fluid/framework/details/multi_devices_helper.h
浏览文件 @ 86511f51
......@@ -16,6 +16,9 @@
#include <memory>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/op_handle_base.h"
......@@ -44,6 +47,26 @@ const char kGraphVars[] = "vars";
typedef std::unordered_set<VarHandleBase *> GraphDepVars;
const char kGraphDepVars[] = "dep_vars";
constexpr char kNCCLCtxs[] = "nccl_ctxs";
constexpr char kLossVarName[] = "loss_var_name";
constexpr char kPlaces[] = "places";
constexpr char kLocalScopes[] = "local_scopes";
constexpr char kStrategy[] = "strategy";
constexpr char kNRanks[] = "nranks";
typedef std::unordered_set<std::string> FusedVars;
constexpr char kFusedVars[] = "fused_vars";
typedef std::vector<std::pair<std::string, std::string>> ParamsAndGrads;
constexpr char kParamsAndGrads[] = "params_grads";
typedef std::vector<std::vector<std::pair<std::string, std::string>>>
GroupGradsAndParams;
constexpr char kGroupGradsAndParams[] = "group_grads_params";
constexpr char kFusedVarNamePrefix[] = "@FUSEDVAR@";
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/op_handle_base.cc
浏览文件 @ 86511f51
......@@ -13,6 +13,7 @@
// limitations under the License.
#include "paddle/fluid/framework/details/op_handle_base.h"
#include <map>
#include <unordered_set>
namespace paddle {
namespace framework {
......@@ -41,15 +42,42 @@ OpHandleBase::~OpHandleBase() {
void OpHandleBase::Run(bool use_cuda) {
#ifdef PADDLE_WITH_CUDA
if (events_.empty() && use_cuda) {
if (events_.empty() && use_cuda && dev_ctxes_.size() > 0) {
for (auto &p : dev_ctxes_) {
int dev_id = boost::get<platform::CUDAPlace>(p.first).device;
PADDLE_ENFORCE(cudaSetDevice(dev_id));
PADDLE_ENFORCE(
cudaEventCreateWithFlags(&events_[dev_id], cudaEventDisableTiming));
}
if (IsMultiDeviceTransfer() && dev_ctxes_.size() > 0) {
for (auto &out_var : outputs_) {
auto *out_var_handle = dynamic_cast<VarHandle *>(out_var);
if (out_var_handle) {
int dev_id =
boost::get<platform::CUDAPlace>(out_var_handle->place()).device;
out_var_handle->SetGenerateEvent(events_[dev_id]);
}
}
} else {
PADDLE_ENFORCE_EQ(dev_ctxes_.size(), 1UL,
"%s should have only one dev_ctx.", Name());
auto &place = dev_ctxes_.begin()->first;
int dev_id = boost::get<platform::CUDAPlace>(place).device;
for (auto &out_var : outputs_) {
auto *out_var_handle = dynamic_cast<VarHandle *>(out_var);
if (out_var_handle) {
PADDLE_ENFORCE(
platform::is_same_place(place, out_var_handle->place()),
"The place of input(%s) is not consistent with the "
"place of current op(%s).",
out_var_handle->Name(), Name());
out_var_handle->SetGenerateEvent(events_[dev_id]);
}
}
}
}
#else
PADDLE_ENFORCE(!use_cuda);
#endif
......@@ -93,17 +121,48 @@ void OpHandleBase::AddOutput(VarHandleBase *out) {
void OpHandleBase::WaitInputVarGenerated() {
for (auto in_var : inputs_) {
if (NeedWait(in_var)) {
for (auto &pair : dev_ctxes_) {
in_var->GeneratedOp()->RecordWaitEventOnCtx(pair.second);
// Dummy Variable is used to represent dependencies between operators, so
// there doesn't add event for it.
auto *in_var_handle = dynamic_cast<VarHandle *>(in_var);
if (in_var_handle) {
auto &place = in_var_handle->place();
if (platform::is_gpu_place(place)) {
#ifdef PADDLE_WITH_CUDA
auto stream =
static_cast<platform::CUDADeviceContext *>(dev_ctxes_.at(place))
->stream();
PADDLE_ENFORCE(
cudaStreamWaitEvent(stream, in_var_handle->GetEvent(), 0));
#else
PADDLE_THROW("Doesn't compile the GPU.");
#endif
}
// There are nothing to do when the place is CPUPlace.
}
}
}
}
void OpHandleBase::WaitInputVarGenerated(const platform::Place &place) {
for (auto *in : inputs_) {
if (NeedWait(in)) {
in->GeneratedOp()->RecordWaitEventOnCtx(dev_ctxes_.at(place));
for (auto in_var : inputs_) {
if (NeedWait(in_var)) {
// Dummy Variable is used to represent dependencies between operators, so
// there doesn't add event for it.
auto *in_var_handle = dynamic_cast<VarHandle *>(in_var);
if (in_var_handle) {
if (platform::is_gpu_place(in_var_handle->place())) {
#ifdef PADDLE_WITH_CUDA
auto stream = static_cast<platform::CUDADeviceContext *>(
dev_ctxes_.at(in_var_handle->place()))
->stream();
PADDLE_ENFORCE(
cudaStreamWaitEvent(stream, in_var_handle->GetEvent(), 0));
#else
PADDLE_THROW("Doesn't compile the GPU.");
#endif
}
// There are nothing to do when the place is CPUPlace.
}
}
}
}
......
paddle/fluid/framework/details/reduce_and_gather.h
浏览文件 @ 86511f51
......@@ -53,6 +53,31 @@ struct ReduceLoDTensor {
}
};
struct ReduceBufferData {
const std::vector<const void *> &src_data_;
void *dst_data_;
int64_t numel_;
ReduceBufferData(const std::vector<const void *> &src, void *dst,
int64_t numel)
: src_data_(src), dst_data_(dst), numel_(numel) {}
template <typename T>
void apply() const {
T *dst_data = reinterpret_cast<T *>(dst_data_);
for (size_t i = 0; i < src_data_.size(); ++i) {
auto srd_data = reinterpret_cast<const T *>(src_data_[i]);
VLOG(10) << "dst: " << dst_data_ << ", " << srd_data;
if (srd_data == dst_data_) {
continue;
}
std::transform(srd_data, srd_data + numel_, dst_data, dst_data,
[](T a, T b) -> T { return a + b; });
}
}
};
inline void GatherLocalSelectedRows(
const std::vector<const SelectedRows *> &src_selecte_rows_,
const std::vector<platform::Place> &in_places,
......
paddle/fluid/framework/details/threaded_ssa_graph_executor.cc
浏览文件 @ 86511f51
......@@ -14,7 +14,6 @@
#include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -27,62 +26,49 @@ ThreadedSSAGraphExecutor::ThreadedSSAGraphExecutor(
: graph_(graph),
pool_(strategy.num_threads_ >= 2 ? new ::ThreadPool(strategy.num_threads_)
: nullptr),
prepare_pool_(1),
local_scopes_(local_scopes),
places_(places),
fetch_ctxs_(places),
running_ops_(0),
strategy_(strategy) {}
strategy_(strategy) {
PrepareOpDeps();
CopyOpDeps();
}
FeedFetchList ThreadedSSAGraphExecutor::Run(
const std::vector<std::string> &fetch_tensors) {
std::unique_ptr<platform::RecordEvent> event(
new platform::RecordEvent("ThreadedSSAGraphExecutorPrepare"));
std::unordered_map<OpHandleBase *, size_t> pending_ops;
std::unordered_set<VarHandleBase *> pending_vars;
auto ready_vars = std::make_shared<BlockingQueue<VarHandleBase *>>();
std::unordered_set<OpHandleBase *> ready_ops;
std::unique_ptr<OpDependentData> op_deps = op_deps_futures_.get();
CopyOpDeps();
VLOG(10) << "ThreadedSSAGraphExecutor::Run";
std::shared_ptr<BlockingQueue<VarHandleBase *>> ready_vars(
new BlockingQueue<VarHandleBase *>);
auto &pending_ops = op_deps->pending_ops_;
auto &pending_vars = op_deps->pending_vars_;
auto &ready_ops = op_deps->ready_ops_;
// For ops (e.g. nccl_all_reduce) that need to coordinate multiple
// streams from multiple GPUs, it's faster to buffer them and schedule
// together since we currently cannot overlap computation and memcpy streams.
// Should revisit it if overlapping is available.
std::unordered_set<OpHandleBase *> delayed_ops;
// Transform SSAGraph to pending_ops & pending_vars
for (auto &var_map : graph_->Get<details::GraphVars>(details::kGraphVars)) {
for (auto &name_pair : var_map) {
for (auto &version_pair : name_pair.second) {
InsertPendingVar(&pending_vars, ready_vars.get(), version_pair);
}
}
}
for (auto &var : graph_->Get<details::GraphDepVars>(details::kGraphDepVars)) {
InsertPendingVar(&pending_vars, ready_vars.get(), var);
}
for (auto &op : ir::FilterByNodeWrapper<OpHandleBase>(*graph_)) {
if (op->Inputs().empty()) { // Special case, Op has no input.
ready_ops.insert(op);
} else {
InsertPendingOp(&pending_ops, op);
}
}
// Step 2. Insert FetchOps
std::vector<FetchOpHandle *> fetch_ops;
std::unordered_set<VarHandleBase *> fetch_dependencies;
FeedFetchList fetch_data(fetch_tensors.size());
InsertFetchOps(fetch_tensors, &fetch_ops, &fetch_dependencies, &pending_ops,
&pending_vars, ready_vars.get(), &fetch_data);
InsertFetchOps(fetch_tensors, &fetch_ops, &fetch_dependencies, &ready_ops,
&pending_ops, &pending_vars, &fetch_data);
auto run_all_ops = [&](std::unordered_set<OpHandleBase *> &set) {
for (auto *op : set) {
running_ops_++;
RunOp(ready_vars, op);
}
set.clear();
};
auto run_all_op = [&](OpHandleBase *op) { RunOp(ready_vars, op); };
// Clean run context
run_op_futures_.clear();
exception_holder_.Clear();
......@@ -91,19 +77,11 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
while (!pending_vars.empty()) {
// 1. Run All Ready ops
// Keep loop until all vars are ready.
//
// NOTE: DelayedOps have a lower priority. It will be scheduled after all
// ready_ops have been performed.
if (ready_ops.empty() && strategy_.allow_op_delay_ && running_ops_ == 0) {
run_all_ops(delayed_ops);
} else {
run_all_ops(ready_ops);
}
// 2. Find ready variable
bool timeout;
auto cur_ready_vars = ready_vars->PopAll(1, &timeout);
if (timeout) {
if (exception_holder_.IsCaught()) {
for (auto &run_op_future : run_op_futures_) {
......@@ -115,6 +93,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
continue;
}
}
// 3. Remove the dependency of ready_var.
// Find the ready_ops after the ready_var.
for (auto ready_var : cur_ready_vars) {
......@@ -123,11 +102,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
auto &deps = pending_ops[op];
--deps;
if (deps == 0) {
if (op->IsMultiDeviceTransfer() && strategy_.allow_op_delay_) {
delayed_ops.insert(op);
} else {
ready_ops.insert(op);
}
run_all_op(op);
}
}
}
......@@ -143,16 +118,17 @@ void ThreadedSSAGraphExecutor::InsertFetchOps(
const std::vector<std::string> &fetch_tensors,
std::vector<FetchOpHandle *> *fetch_ops,
std::unordered_set<VarHandleBase *> *fetch_dependencies,
std::unordered_set<OpHandleBase *> *ready_ops,
std::unordered_map<OpHandleBase *, size_t> *pending_ops,
std::unordered_set<VarHandleBase *> *pending_vars,
BlockingQueue<VarHandleBase *> *ready_vars, FeedFetchList *fetch_data) {
FeedFetchList *fetch_data) {
std::unordered_map<std::string, std::vector<VarHandleBase *>> fetched_vars;
std::unordered_set<VarHandleBase *> local_ready_vars;
for (auto &fetch_var_name : fetch_tensors) {
for (auto &var_map : graph_->Get<details::GraphVars>(details::kGraphVars)) {
auto it = var_map.find(fetch_var_name);
if (it != var_map.end()) {
fetched_vars[fetch_var_name].push_back(*it->second.rbegin());
fetched_vars[fetch_var_name].emplace_back(*it->second.rbegin());
}
}
}
......@@ -161,8 +137,9 @@ void ThreadedSSAGraphExecutor::InsertFetchOps(
auto &var_name = fetch_tensors[i];
auto fetched_var_it = fetched_vars.find(var_name);
PADDLE_ENFORCE(fetched_var_it != fetched_vars.end(),
"Cannot find fetched variable.(Perhaps the main_program "
"is not set to ParallelExecutor)");
"Cannot find fetched variable(%s).(Perhaps the main_program "
"is not set to ParallelExecutor)",
var_name);
auto &vars = fetched_var_it->second;
......@@ -184,9 +161,23 @@ void ThreadedSSAGraphExecutor::InsertFetchOps(
auto *fetch_dummy = new DummyVarHandle(fetch_var);
op->AddOutput(fetch_dummy);
fetch_dependencies->emplace(fetch_dummy);
this->InsertPendingVar(pending_vars, ready_vars, fetch_dummy);
this->InsertPendingOp(pending_ops, op);
this->InsertPendingVar(pending_vars, &local_ready_vars, fetch_dummy);
size_t wait_input_num = 0;
std::unordered_set<VarHandleBase *> input_set(vars.begin(), vars.end());
for (auto *var : input_set) {
if (pending_vars->count(var)) {
++wait_input_num;
}
}
if (wait_input_num) {
pending_ops->insert({op, wait_input_num});
} else {
ready_ops->insert(static_cast<OpHandleBase *>(op));
}
}
PADDLE_ENFORCE_EQ(local_ready_vars.size(), 0);
}
void ThreadedSSAGraphExecutor::InsertPendingOp(
......@@ -197,13 +188,65 @@ void ThreadedSSAGraphExecutor::InsertPendingOp(
void ThreadedSSAGraphExecutor::InsertPendingVar(
std::unordered_set<VarHandleBase *> *pending_vars,
BlockingQueue<VarHandleBase *> *ready_vars, VarHandleBase *var) const {
std::unordered_set<VarHandleBase *> *ready_vars, VarHandleBase *var) const {
pending_vars->insert(var);
if (var->GeneratedOp() == nullptr) {
ready_vars->Push(var);
ready_vars->insert(var);
}
}
void ThreadedSSAGraphExecutor::PrepareOpDeps() {
op_deps_.reset(new OpDependentData());
std::unordered_map<OpHandleBase *, size_t> &pending_ops =
op_deps_->pending_ops_;
std::unordered_set<VarHandleBase *> &pending_vars = op_deps_->pending_vars_;
std::unordered_set<OpHandleBase *> &ready_ops = op_deps_->ready_ops_;
std::unordered_set<VarHandleBase *> ready_vars;
// Transform SSAGraph to pending_ops & pending_vars
for (auto &var_map : graph_->Get<details::GraphVars>(details::kGraphVars)) {
for (auto &name_pair : var_map) {
for (auto &version_pair : name_pair.second) {
InsertPendingVar(&pending_vars, &ready_vars, version_pair);
}
}
}
for (auto &var : graph_->Get<details::GraphDepVars>(details::kGraphDepVars)) {
InsertPendingVar(&pending_vars, &ready_vars, var);
}
for (auto &op : ir::FilterByNodeWrapper<OpHandleBase>(*graph_)) {
if (op->Inputs().empty()) { // Special case, Op has no input.
ready_ops.insert(op);
} else {
InsertPendingOp(&pending_ops, op);
}
}
for (auto ready_var : ready_vars) {
pending_vars.erase(ready_var);
for (auto *op : ready_var->PendingOps()) {
auto &deps = pending_ops[op];
--deps;
if (deps == 0) {
ready_ops.insert(op);
}
}
}
}
void ThreadedSSAGraphExecutor::CopyOpDeps() {
op_deps_futures_ = prepare_pool_.enqueue([&] {
auto *op_deps = new OpDependentData();
op_deps->pending_ops_.insert(op_deps_->pending_ops_.begin(),
op_deps_->pending_ops_.end());
op_deps->pending_vars_.insert(op_deps_->pending_vars_.begin(),
op_deps_->pending_vars_.end());
op_deps->ready_ops_.insert(op_deps_->ready_ops_.begin(),
op_deps_->ready_ops_.end());
return std::unique_ptr<OpDependentData>(op_deps);
});
}
void ThreadedSSAGraphExecutor::RunOp(
const std::shared_ptr<BlockingQueue<VarHandleBase *>> &ready_var_q,
details::OpHandleBase *op) {
......@@ -216,7 +259,6 @@ void ThreadedSSAGraphExecutor::RunOp(
op->Run(strategy_.use_cuda_);
}
VLOG(10) << op << " " << op->Name() << " Done ";
running_ops_--;
ready_var_q->Extend(op->Outputs());
VLOG(10) << op << " " << op->Name() << " Signal posted";
} catch (...) {
......
paddle/fluid/framework/details/threaded_ssa_graph_executor.h
浏览文件 @ 86511f51
......@@ -15,18 +15,20 @@
#pragma once
#include <deque>
#include <functional>
#include <list>
#include <memory>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include <functional>
#include "ThreadPool.h" // ThreadPool in thrird party
#include "paddle/fluid/framework/blocking_queue.h"
#include "paddle/fluid/framework/details/exception_holder.h"
#include "paddle/fluid/framework/details/execution_strategy.h"
#include "paddle/fluid/framework/details/fetch_op_handle.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/details/ssa_graph_executor.h"
#include "paddle/fluid/framework/ir/graph.h"
......@@ -36,6 +38,12 @@ class Scope;
namespace details {
struct OpDependentData {
std::unordered_map<OpHandleBase *, size_t> pending_ops_;
std::unordered_set<VarHandleBase *> pending_vars_;
std::unordered_set<OpHandleBase *> ready_ops_;
};
class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
public:
ThreadedSSAGraphExecutor(const ExecutionStrategy &strategy,
......@@ -57,29 +65,35 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
private:
ir::Graph *graph_;
std::unique_ptr<::ThreadPool> pool_;
::ThreadPool prepare_pool_;
std::vector<Scope *> local_scopes_;
std::vector<platform::Place> places_;
platform::DeviceContextPool fetch_ctxs_;
ExceptionHolder exception_holder_;
std::atomic<int> running_ops_;
void InsertPendingOp(std::unordered_map<OpHandleBase *, size_t> *pending_ops,
OpHandleBase *op_instance) const;
void InsertPendingVar(std::unordered_set<VarHandleBase *> *pending_vars,
BlockingQueue<VarHandleBase *> *ready_vars,
std::unordered_set<VarHandleBase *> *ready_vars,
VarHandleBase *var) const;
void InsertFetchOps(const std::vector<std::string> &fetch_tensors,
std::vector<FetchOpHandle *> *fetch_ops,
std::unordered_set<VarHandleBase *> *fetch_dependencies,
std::unordered_set<OpHandleBase *> *ready_ops,
std::unordered_map<OpHandleBase *, size_t> *pending_ops,
std::unordered_set<VarHandleBase *> *pending_vars,
BlockingQueue<VarHandleBase *> *ready_vars,
FeedFetchList *fetch_data);
void PrepareOpDeps();
void CopyOpDeps();
private:
std::future<std::unique_ptr<OpDependentData>> op_deps_futures_;
ExecutionStrategy strategy_;
std::unique_ptr<OpDependentData> op_deps_;
// use std::list because clear(), push_back, and for_each are O(1)
std::list<std::future<void>> run_op_futures_;
};
......
paddle/fluid/framework/details/var_handle.h
浏览文件 @ 86511f51
......@@ -43,6 +43,7 @@ struct VarHandleBase {
virtual ~VarHandleBase();
virtual std::string DebugString() const = 0;
virtual const std::string& Name() const = 0;
void AddInput(OpHandleBase* in, ir::Node* node) {
node_->inputs.clear();
......@@ -95,8 +96,6 @@ struct VarHandleBase {
//
// NOTE: runtime variables have place.
struct VarHandle : public VarHandleBase {
explicit VarHandle(ir::Node* node) : VarHandleBase(node) {}
virtual ~VarHandle();
std::string DebugString() const override;
......@@ -109,6 +108,20 @@ struct VarHandle : public VarHandleBase {
name_(std::move(name)),
place_(std::move(place)) {}
#ifdef PADDLE_WITH_CUDA
bool HasEvent() { return has_event_; }
const cudaEvent_t& GetEvent() {
PADDLE_ENFORCE(HasEvent(), "The event is not set.");
return event_;
}
void SetGenerateEvent(const cudaEvent_t& event) {
has_event_ = true;
event_ = event;
}
#endif
// version field currently is not used, however, just store the version to
// debug easily.
private:
......@@ -116,6 +129,11 @@ struct VarHandle : public VarHandleBase {
size_t scope_idx_;
std::string name_;
platform::Place place_;
#ifdef PADDLE_WITH_CUDA
// Only when this event is triggered, var is generated.
cudaEvent_t event_;
bool has_event_{false};
#endif
public:
bool IsTheSameVar(const VarHandle& o) const {
......@@ -125,6 +143,7 @@ struct VarHandle : public VarHandleBase {
size_t version() const { return version_; }
size_t scope_idx() const { return scope_idx_; }
const std::string& Name() const override { return name_; }
const std::string& name() const { return name_; }
const platform::Place& place() const { return place_; }
};
......@@ -136,6 +155,10 @@ struct DummyVarHandle : public VarHandleBase {
virtual ~DummyVarHandle();
std::string DebugString() const override;
public:
const std::string& Name() const override { return name_; }
std::string name_{"DummyVar"};
};
} // namespace details
......
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ 86511f51
......@@ -70,6 +70,7 @@ 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)
pass_library(runtime_context_cache_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
......
paddle/fluid/framework/ir/cpu_quantize_pass.cc
浏览文件 @ 86511f51
......@@ -224,8 +224,8 @@ std::unique_ptr<ir::Graph> CPUQuantizePass::ApplyImpl(
PADDLE_ENFORCE(param_scope());
QuantizeConv(graph.get(), false /* with_residual_data */);
QuantizeConv(graph.get(), true /* with_residual_data */);
QuantizeConv(graph.get());
QuantizePool(graph.get());
return graph;
......
paddle/fluid/framework/ir/graph_pattern_detector.cc
浏览文件 @ 86511f51
......@@ -599,10 +599,19 @@ bool VarLinksToOp(Node *node, const std::string &op_type) {
bool IsNthInput(Node *var, Node *op, const std::string &argument, size_t nth) {
PADDLE_ENFORCE(var->IsVar());
PADDLE_ENFORCE(op->IsOp());
if (op->Op()->Input(argument).size() <= nth) return false;
if (!HasInput(op, argument) || op->Op()->Input(argument).size() <= nth)
return false;
return var->Name() == op->Op()->Input(argument)[nth];
}
bool HasInput(Node *op, const std::string &argument) {
PADDLE_ENFORCE(op->IsOp());
auto const &names = op->Op()->InputNames();
if (std::find(names.begin(), names.end(), argument) == names.end())
return false;
return true;
}
bool IsNthOutput(Node *var, Node *op, const std::string &argument, size_t nth) {
PADDLE_ENFORCE(var->IsVar());
PADDLE_ENFORCE(op->IsOp());
......@@ -1082,8 +1091,15 @@ PDNode *patterns::Conv::operator()() {
PDNode *patterns::ConvResidual::operator()(bool with_residual_data) {
auto conv_op = pattern->NewNode(conv_op_repr())->assert_is_op("conv2d");
if (!with_residual_data)
conv_op->assert_op_attr("fuse_residual_connection", false);
if (!with_residual_data) {
conv_op->assert_more([&](Node *x) {
auto node_names = x->Op()->InputNames();
if (!HasInput(x, "ResidualData") ||
x->Op()->Input("ResidualData").size() == 0)
return true;
return false;
});
}
auto input_var = pattern->NewNode(conv_input_repr())
->AsInput()
......
paddle/fluid/framework/ir/graph_pattern_detector.h
浏览文件 @ 86511f51
......@@ -305,6 +305,9 @@ bool VarLinksFromOp(Node* node, const std::string& op_type);
// Check whether a var node is a op node's nth input.
bool IsNthInput(Node* var, Node* op, const std::string& argument, size_t nth);
// Check whether the op node has input of given name.
bool HasInput(Node* op, const std::string& argument);
// Tell whether a var node is a op node's nth output.
bool IsNthOutput(Node* var, Node* op, const std::string& argument, size_t nth);
......
paddle/fluid/framework/ir/mkldnn/mkldnn_placement_pass.h
浏览文件 @ 86511f51
......@@ -14,12 +14,16 @@ limitations under the License. */
#pragma once
#include <memory>
#include "paddle/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace ir {
/*
* Specifies which operators should use MKLDNN.
*/
class MKLDNNPlacementPass : public Pass {
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
......
paddle/fluid/framework/ir/runtime_context_cache_pass.cc 0 → 100644
浏览文件 @ 86511f51
/* 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/runtime_context_cache_pass.h"
#include <memory>
#include "paddle/fluid/framework/operator.h"
namespace paddle {
namespace framework {
namespace ir {
std::unique_ptr<ir::Graph> RuntimeContextCachePass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
VLOG(3) << "Applies Runtime Context Cache strategy.";
for (const Node* n : graph->Nodes()) {
if (n->IsOp()) {
n->Op()->SetAttr(kEnableCacheRuntimeContext, true);
}
}
return graph;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(runtime_context_cache_pass,
paddle::framework::ir::RuntimeContextCachePass);
paddle/fluid/framework/ir/runtime_context_cache_pass.h 0 → 100644
浏览文件 @ 86511f51
/* 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 RuntimeContextCachePass : 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/operator.cc
浏览文件 @ 86511f51
......@@ -876,7 +876,22 @@ std::vector<KernelConfig>* OperatorWithKernel::GetKernelConfig(
void OperatorWithKernel::RunImpl(const Scope& scope,
const platform::Place& place) const {
if (!HasAttr(kEnableCacheRuntimeContext)) {
RuntimeContext ctx(Inputs(), Outputs(), scope);
RunImpl(scope, place, &ctx);
} else {
const Scope* cur_scope = &scope;
if (!runtime_ctx_ || pre_scope_ != cur_scope) {
runtime_ctx_.reset(new RuntimeContext(Inputs(), Outputs(), scope));
pre_scope_ = cur_scope;
}
RunImpl(scope, place, runtime_ctx_.get());
}
}
void OperatorWithKernel::RunImpl(const Scope& scope,
const platform::Place& place,
RuntimeContext* runtime_ctx) const {
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto* dev_ctx = pool.Get(place);
......@@ -891,7 +906,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
OpKernelMap& kernels = kernels_iter->second;
auto expected_kernel_key = this->GetExpectedKernelType(
ExecutionContext(*this, scope, *dev_ctx, ctx, nullptr));
ExecutionContext(*this, scope, *dev_ctx, *runtime_ctx, nullptr));
VLOG(3) << "expected_kernel_key:" << expected_kernel_key;
auto kernel_iter = kernels.find(expected_kernel_key);
......@@ -915,8 +930,8 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
// do data transformScope &transfer_scope;
std::vector<std::string> transfered_inplace_vars;
auto* transfer_scope =
PrepareData(scope, expected_kernel_key, &transfered_inplace_vars, &ctx);
auto* transfer_scope = PrepareData(scope, expected_kernel_key,
&transfered_inplace_vars, runtime_ctx);
// exec scope is the scope that kernel actually executed on.
const Scope& exec_scope =
......@@ -927,13 +942,13 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
}
if (!HasAttr(kAllKernelsMustComputeRuntimeShape)) {
RuntimeInferShapeContext infer_shape_ctx(*this, exec_scope, ctx);
RuntimeInferShapeContext infer_shape_ctx(*this, exec_scope, *runtime_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(
ExecutionContext(*this, exec_scope, *dev_ctx, ctx, kernel_configs));
kernel_iter->second(ExecutionContext(*this, exec_scope, *dev_ctx,
*runtime_ctx, kernel_configs));
if (!transfered_inplace_vars.empty()) {
// there is inplace variable has been transfered.
......
paddle/fluid/framework/operator.h
浏览文件 @ 86511f51
......@@ -62,6 +62,14 @@ constexpr char kZeroVarSuffix[] = "@ZERO";
/// Variables with this suffix are the new Gradient.
constexpr char kNewGradSuffix[] = "@NEWGRAD@";
/// RuntimeContext is used to relate input/output names of Operator with
/// the corresponding variables in name scope.
/// If an Op has attribute kEnableCacheRuntimeContext, it means that in a same
/// name scope, since the input/output names of this Op do not change in the
/// execution, RuntimeContext could be created only at the first iteration of
/// this Op's execution to save the elapsed time.
constexpr char kEnableCacheRuntimeContext[] = "@ENABLE_CACHE_RUNTIME_CONTEXT@";
/// 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()
......@@ -456,6 +464,8 @@ class OperatorWithKernel : public OperatorBase {
// same.
proto::VarType::Type IndicateDataType(const ExecutionContext& ctx) const;
void RunImpl(const Scope& scope, const platform::Place& place) const final;
void RunImpl(const Scope& scope, const platform::Place& place,
RuntimeContext* runtime_ctx) const;
/**
* Transfer data from scope to a transfered scope. If there is no data need to
......@@ -474,6 +484,8 @@ class OperatorWithKernel : public OperatorBase {
protected:
mutable OpKernelConfigsMap kernel_configs_map_;
mutable std::unique_ptr<RuntimeContext> runtime_ctx_;
mutable const Scope* pre_scope_ = nullptr;
};
extern bool OpSupportGPU(const std::string& op_type);
......
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ 86511f51
......@@ -254,18 +254,29 @@ ParallelExecutor::ParallelExecutor(const std::vector<platform::Place> &places,
member_->places_, nccl_id, build_strategy.num_trainers_,
build_strategy.trainer_id_));
// Initialize device context's nccl comm, will be used by normal
// Operators like sync_batch_norm, and collective ops.
// NOTE: more than one ParallelExecutor with same place, the nccl comm will
// be rewrite and there will be some problem.
// NOTE: NCCL group-calls and non-group-calls can not use the same
// NCCL communicator, so for ParallelGraph and Multi-Process mode, re-use
// same communicators.
std::unique_ptr<platform::NCCLContextMap> dev_nccl_ctxs;
if (nccl_id == nullptr) {
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]));
if (nccl_id != nullptr) {
auto &nccl_ctx = member_->nccl_ctxs_->at(member_->places_[dev_id]);
dev_ctx->set_nccl_comm(nccl_ctx.comm());
} else {
auto &nccl_ctx = dev_nccl_ctxs->at(member_->places_[dev_id]);
dev_ctx->set_nccl_comm(nccl_ctx.comm());
}
}
#else
PADDLE_THROW("Not compiled with CUDA");
......
paddle/fluid/framework/scope.cc
浏览文件 @ 86511f51
......@@ -34,7 +34,7 @@ DEFINE_double(
"Memory size threshold (GB) when the garbage collector clear tensors."
"Disabled when this value is less than 0");
DEFINE_bool(fast_eager_deletion_mode, false,
DEFINE_bool(fast_eager_deletion_mode, true,
"Fast eager deletion mode. If enabled, memory would release "
"immediately without waiting GPU kernel ends.");
......
paddle/fluid/inference/analysis/argument.h
浏览文件 @ 86511f51
......@@ -131,6 +131,15 @@ struct Argument {
// Pass a set of op types to enable its mkldnn kernel
DECL_ARGUMENT_FIELD(mkldnn_enabled_op_types, MKLDNNEnabledOpTypes,
std::unordered_set<std::string>);
// A set of op types to enable their quantized kernels
DECL_ARGUMENT_FIELD(quantize_enabled_op_types, QuantizeEnabledOpTypes,
std::unordered_set<std::string>);
// A set of op IDs to exclude from enabling their quantized kernels
DECL_ARGUMENT_FIELD(quantize_excluded_op_ids, QuantizeExcludedOpIds,
std::unordered_set<int>);
// Scales for variables to be quantized
DECL_ARGUMENT_FIELD(quant_var_scales, QuantVarScales, VarQuantScale);
......
paddle/fluid/inference/analysis/ir_pass_manager.cc
浏览文件 @ 86511f51
......@@ -15,6 +15,7 @@
#include "paddle/fluid/inference/analysis/ir_pass_manager.h"
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph.h"
......@@ -60,6 +61,13 @@ void IRPassManager::CreatePasses(Argument *argument,
pass->Set("mkldnn_enabled_op_types",
new std::unordered_set<std::string>(
argument->mkldnn_enabled_op_types()));
} else if (pass_name == "cpu_quantize_placement_pass") {
pass->Set("quantize_enabled_op_types",
new std::unordered_set<std::string>(
argument->quantize_enabled_op_types()));
pass->Set(
"quantize_excluded_op_ids",
new std::unordered_set<int>(argument->quantize_excluded_op_ids()));
} else if (pass_name == "cpu_quantize_pass") {
pass->Set("quant_var_scales",
new VarQuantScale(argument->quant_var_scales()));
......
paddle/fluid/inference/api/analysis_config.cc
浏览文件 @ 86511f51
......@@ -202,6 +202,7 @@ void AnalysisConfig::Update() {
// Append after the Affine_channel_conv_fuse pass.
pass_builder()->InsertPass(3, "tensorrt_subgraph_pass");
}
pass_builder()->DeletePass("runtime_context_cache_pass");
}
if (use_mkldnn_) {
......
paddle/fluid/inference/api/paddle_pass_builder.cc
浏览文件 @ 86511f51
......@@ -80,6 +80,7 @@ GpuPassStrategy::GpuPassStrategy() : PassStrategy({}) {
"conv_elementwise_add_act_fuse_pass", //
"conv_elementwise_add2_act_fuse_pass", //
"conv_elementwise_add_fuse_pass", //
"runtime_context_cache_pass", //
#endif
});
......@@ -90,6 +91,10 @@ GpuPassStrategy::GpuPassStrategy() : PassStrategy({}) {
use_gpu_ = true;
}
void GpuPassStrategy::EnableQuantizer() {
LOG(ERROR) << "GPU not support quantization yet";
}
void PaddlePassBuilder::AppendAnalysisPass(const std::string &pass) {
analysis_passes_.push_back(pass);
}
......@@ -115,6 +120,7 @@ CpuPassStrategy::CpuPassStrategy() : PassStrategy({}) {
"conv_eltwiseadd_bn_fuse_pass", //
"is_test_pass", //
"identity_scale_op_clean_pass", //
"runtime_context_cache_pass", //
});
use_gpu_ = false;
}
......
paddle/fluid/inference/api/paddle_pass_builder.h
浏览文件 @ 86511f51
......@@ -84,6 +84,10 @@ class PassStrategy : public PaddlePassBuilder {
*/
virtual void EnableMKLDNN() {}
/** Enable quantize optimization
*/
virtual void EnableQuantizer() {}
bool use_gpu() const { return use_gpu_; }
virtual ~PassStrategy() = default;
......@@ -124,6 +128,16 @@ class CpuPassStrategy : public PassStrategy {
use_mkldnn_ = false;
#endif
}
void EnableQuantizer() override {
if (!use_quantizer_) {
passes_.push_back("cpu_quantize_placement_pass");
}
use_quantizer_ = true;
}
protected:
bool use_quantizer_{false};
};
/** The GPU passes strategy, it is used in AnalysisPredictor with GPU mode.
......@@ -138,6 +152,7 @@ class GpuPassStrategy : public PassStrategy {
}
void EnableMKLDNN() override;
void EnableQuantizer() override;
virtual ~GpuPassStrategy() = default;
};
......
paddle/fluid/inference/utils/CMakeLists.txt
浏览文件 @ 86511f51
cc_library(benchmark SRCS benchmark.cc DEPS enforce)
cc_test(test_benchmark SRCS benchmark_tester.cc DEPS benchmark)
cc_binary(visualizer SRCS visualizer.cc DEPS analysis
paddle_pass_builder ir_pass_manager pass graph_viz_pass analysis_passes)
paddle/fluid/inference/utils/visualizer.cc 已删除 100644 → 0
浏览文件 @ 5bb04ea4
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/inference/utils/visualizer.h"
#include <gflags/gflags.h>
#include <glog/logging.h>
#include <fstream>
#include <memory>
#include "paddle/fluid/framework/ir/graph_viz_pass.h"
#include "paddle/fluid/inference/analysis/analyzer.h"
#include "paddle/fluid/inference/analysis/passes/ir_analysis_pass.h"
#include "paddle/fluid/platform/init.h"
DEFINE_string(model_dir, "", "model directory");
DEFINE_string(model_program_path, "", "model program path");
DEFINE_string(model_params_path, "", "model params path");
using paddle::inference::analysis::Argument;
namespace paddle {
namespace inference {
namespace utils {
void Visualizer::SetArgument(Argument *argument) { argument_ = argument; }
bool Visualizer::Run() {
paddle::framework::InitDevices(false);
paddle::inference::analysis::Analyzer().Run(argument_);
return true;
}
} // namespace utils
} // namespace inference
} // namespace paddle
// Generate a dot file describing the structure of graph.
// To use this tool, run command: ./visualizer [options...]
// Options:
// --model_dir: the directory of model
// --model_program_path: the path of program
// --model_params_path: the path of params
int main(int argc, char *argv[]) {
gflags::ParseCommandLineFlags(&argc, &argv, true);
google::InitGoogleLogging(argv[0]);
paddle::inference::analysis::Argument argument;
argument.SetUseGPU(false);
argument.SetUseTensorRT(false);
if (FLAGS_model_dir.empty()) {
if (FLAGS_model_program_path.empty() || FLAGS_model_params_path.empty()) {
LOG(ERROR) << "Please set model_dir"
" or model_program_path and model_params_path";
return -1;
} else {
argument.SetModelProgramPath(FLAGS_model_program_path);
argument.SetModelParamsPath(FLAGS_model_params_path);
}
} else {
argument.SetModelDir(FLAGS_model_dir);
}
// Only 1 pass, default filename is 0_ir_origin.dot
// For more details, looking for paddle::inference::analysis::IRPassManager
argument.SetIrAnalysisPasses({"infer_clean_graph_pass", "graph_viz_pass"});
std::unique_ptr<paddle::framework::Scope> scope{
new paddle::framework::Scope()};
argument.SetScopeNotOwned(
const_cast<paddle::framework::Scope *>(scope.get()));
paddle::inference::utils::Visualizer visualizer;
visualizer.SetArgument(&argument);
visualizer.Run();
return 0;
}
USE_PASS(infer_clean_graph_pass);
USE_PASS(graph_viz_pass);
USE_PASS(graph_to_program_pass);
paddle/fluid/inference/utils/visualizer.h 已删除 100644 → 0
浏览文件 @ 5bb04ea4
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <string>
#include "paddle/fluid/inference/analysis/argument.h"
namespace paddle {
namespace inference {
namespace utils {
using paddle::inference::analysis::Argument;
class Visualizer final {
public:
Visualizer() = default;
~Visualizer() = default;
Visualizer(const Visualizer &) = delete;
Visualizer &operator=(const Visualizer &) = delete;
void SetArgument(Argument *);
bool Run();
private:
Argument *argument_;
};
} // namespace utils
} // namespace inference
} // namespace paddle
paddle/fluid/operators/activation_op.cc
浏览文件 @ 86511f51
......@@ -76,12 +76,16 @@ framework::OpKernelType GetKernelType(const framework::ExecutionContext& ctx,
const std::string& name) {
framework::LibraryType library{framework::LibraryType::kPlain};
framework::DataLayout layout = framework::DataLayout::kAnyLayout;
#ifdef PADDLE_WITH_CUDA
auto it1 = oper.Attrs().find("use_cudnn");
if (it1 != oper.Attrs().end() && platform::CanCUDNNBeUsed(ctx)) {
library = framework::LibraryType::kCUDNN;
}
#endif
// FIXME(liuwei1031) temporarily disable the code to unblock users
// TODO(liuwei1031) figure out the reason behind
// https://github.com/PaddlePaddle/Paddle/issues/16096
// and re-enable this in the future
// #ifdef PADDLE_WITH_CUDA
// auto it1 = oper.Attrs().find("use_cudnn");
// if (it1 != oper.Attrs().end() && platform::CanCUDNNBeUsed(ctx)) {
// library = framework::LibraryType::kCUDNN;
// }
// #endif
#ifdef PADDLE_WITH_MKLDNN
auto it = oper.Attrs().find("use_mkldnn");
if (library == framework::LibraryType::kPlain && it != oper.Attrs().end() &&
......@@ -188,6 +192,9 @@ $$out = x - \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
UNUSED constexpr char SqrtDoc[] = R"DOC(
Sqrt Activation Operator.
Please make sure legal input, when input a negative value closed to zero,
you should add a small epsilon(1e-12) to avoid negative number caused by numerical errors.
$out = \sqrt{x}$
)DOC";
......
paddle/fluid/operators/affine_channel_op.cc
浏览文件 @ 86511f51
......@@ -67,6 +67,22 @@ class AffineChannelOp : public framework::OperatorWithKernel {
"Input(Bias) of AffineChannelOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of AffineChannelOp should not be null.");
auto x_dims = ctx->GetInputDim("X");
auto scale_dims = ctx->GetInputDim("Scale");
auto b_dims = ctx->GetInputDim("Bias");
const framework::DataLayout data_layout = framework::StringToDataLayout(
ctx->Attrs().Get<std::string>("data_layout"));
const int64_t C = (data_layout == framework::DataLayout::kNCHW
? x_dims[1]
: x_dims[x_dims.size() - 1]);
PADDLE_ENFORCE_EQ(scale_dims.size(), 1UL);
PADDLE_ENFORCE_EQ(scale_dims[0], C);
PADDLE_ENFORCE_EQ(b_dims.size(), 1UL);
PADDLE_ENFORCE_EQ(b_dims[0], C);
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->ShareLoD("X", "Out");
}
......@@ -97,6 +113,27 @@ class AffineChannelOpGrad : public framework::OperatorWithKernel {
}
};
class AffineChannelGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* op = new framework::OpDesc();
op->SetType("affine_channel_grad");
op->SetInput("X", Input("X"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetInput("Scale", Input("Scale"));
op->SetAttrMap(Attrs());
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetOutput(framework::GradVarName("Scale"), InputGrad("Scale"));
op->SetOutput(framework::GradVarName("Bias"), InputGrad("Bias"));
return std::unique_ptr<framework::OpDesc>(op);
}
};
template <typename T>
using EigenArrayMap =
Eigen::Map<Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
......@@ -244,8 +281,7 @@ namespace ops = paddle::operators;
using CPU = paddle::platform::CPUDeviceContext;
REGISTER_OPERATOR(affine_channel, ops::AffineChannelOp,
ops::AffineChannelOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::AffineChannelOpMaker, ops::AffineChannelGradMaker);
REGISTER_OPERATOR(affine_channel_grad, ops::AffineChannelOpGrad);
REGISTER_OP_CPU_KERNEL(affine_channel, ops::AffineChannelKernel<CPU, float>,
......
paddle/fluid/operators/concat_op.cc
浏览文件 @ 86511f51
......@@ -57,7 +57,7 @@ class ConcatOp : public framework::OperatorWithKernel {
"elements except the specify axis.");
} else {
// not check -1 with other in compile time
if (out_dims[j] != -1 && ins[i][j] != -1) {
if (out_dims[j] > 0 && ins[i][j] > 0) {
PADDLE_ENFORCE_EQ(out_dims[j], ins[i][j],
"Input tensors should have the same "
"elements except the specify axis.");
......
paddle/fluid/operators/conv_transpose_op.cc
浏览文件 @ 86511f51
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/conv_transpose_op.h"
#include <memory>
#include <string>
#include <vector>
......@@ -344,6 +345,28 @@ framework::OpKernelType ConvTransposeOpGrad::GetExpectedKernelType(
ctx.GetPlace(), layout_, library_);
}
class ConvTransposeGradOpDescMaker : 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(ForwardOp().Type() + "_grad");
op->SetInput("Input", Input("Input"));
op->SetInput("Filter", Input("Filter"));
op->SetOutput(framework::GradVarName("Input"), InputGrad("Input"));
op->SetOutput(framework::GradVarName("Filter"), InputGrad("Filter"));
if (ForwardOp().Inputs().count("Bias") > 0) {
op->SetInput("Bias", Input("Bias"));
op->SetOutput(framework::GradVarName("Bias"), InputGrad("Bias"));
}
op->SetInput(framework::GradVarName("Output"), OutputGrad("Output"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
......@@ -352,7 +375,7 @@ namespace ops = paddle::operators;
// conv2d_transpose
REGISTER_OPERATOR(conv2d_transpose, ops::ConvTransposeOp,
ops::Conv2DTransposeOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::ConvTransposeGradOpDescMaker);
REGISTER_OPERATOR(conv2d_transpose_grad, ops::ConvTransposeOpGrad);
REGISTER_OP_CPU_KERNEL(
......@@ -368,7 +391,7 @@ REGISTER_OP_CPU_KERNEL(
// conv3d_transpose
REGISTER_OPERATOR(conv3d_transpose, ops::ConvTransposeOp,
ops::Conv3DTransposeOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::ConvTransposeGradOpDescMaker);
REGISTER_OPERATOR(conv3d_transpose_grad, ops::ConvTransposeOpGrad);
REGISTER_OP_CPU_KERNEL(
......@@ -384,7 +407,7 @@ REGISTER_OP_CPU_KERNEL(
// depthwise conv2d_transpose
REGISTER_OPERATOR(depthwise_conv2d_transpose, ops::ConvTransposeOp,
ops::Conv2DTransposeOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::ConvTransposeGradOpDescMaker);
REGISTER_OPERATOR(depthwise_conv2d_transpose_grad, ops::ConvTransposeOpGrad);
REGISTER_OP_CPU_KERNEL(
......
paddle/fluid/operators/cos_sim_op.cc
浏览文件 @ 86511f51
......@@ -74,6 +74,9 @@ class CosSimOpMaker : public framework::OpProtoAndCheckerMaker {
"Norm of the second input, reduced along the 1st "
"dimension.")
.AsIntermediate();
AddAttr<bool>(framework::kAllKernelsMustComputeRuntimeShape,
"Skip calling InferShape() function in the runtime.")
.SetDefault(true);
AddComment(R"DOC(
**Cosine Similarity Operator**
......
paddle/fluid/operators/cos_sim_op.h
浏览文件 @ 86511f51
......@@ -28,17 +28,21 @@ class CosSimKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
// get Tensor
auto* in_x = context.Input<Tensor>("X");
auto* in_x = context.Input<framework::LoDTensor>("X");
auto* in_y = context.Input<Tensor>("Y");
auto* out_z = context.Output<Tensor>("Out");
auto* out_z = context.Output<framework::LoDTensor>("Out");
auto* out_x_norm = context.Output<Tensor>("XNorm");
auto* out_y_norm = context.Output<Tensor>("YNorm");
out_z->mutable_data<T>(context.GetPlace());
out_x_norm->mutable_data<T>(context.GetPlace());
out_y_norm->mutable_data<T>(context.GetPlace());
int rows_x = in_x->dims()[0];
int rows_y = in_y->dims()[0];
out_z->Resize({rows_x, 1});
out_x_norm->Resize({rows_x, 1});
out_y_norm->Resize({rows_y, 1});
out_z->mutable_data<T>(context.GetPlace());
out_x_norm->mutable_data<T>(context.GetPlace());
out_y_norm->mutable_data<T>(context.GetPlace());
out_z->set_lod(in_x->lod());
int cols = framework::product(in_x->dims()) / rows_x;
......@@ -81,6 +85,7 @@ class CosSimGradKernel : public framework::OpKernel<T> {
if (rows_x == rows_y) {
if (out_grad_x) {
out_grad_x->Resize(in_x->dims());
math::CosSimGradFunctor<T> functor(
in_x_norm->data<T>(), in_y_norm->data<T>(), in_x->data<T>(),
in_y->data<T>(), in_z->data<T>(), in_grad_z->data<T>(),
......@@ -91,6 +96,7 @@ class CosSimGradKernel : public framework::OpKernel<T> {
for_range(functor);
}
if (out_grad_y) {
out_grad_y->Resize(in_y->dims());
math::CosSimGradFunctor<T> functor(
in_y_norm->data<T>(), in_x_norm->data<T>(), in_y->data<T>(),
in_x->data<T>(), in_z->data<T>(), in_grad_z->data<T>(),
......@@ -102,6 +108,7 @@ class CosSimGradKernel : public framework::OpKernel<T> {
}
} else {
if (out_grad_x) {
out_grad_x->Resize(in_x->dims());
math::CosSimDxFunctor<T> functor(
in_x_norm->data<T>(), in_y_norm->data<T>(), in_x->data<T>(),
in_y->data<T>(), in_z->data<T>(), in_grad_z->data<T>(),
......@@ -112,6 +119,7 @@ class CosSimGradKernel : public framework::OpKernel<T> {
for_range(functor);
}
if (out_grad_y) {
out_grad_y->Resize(in_y->dims());
out_grad_y->mutable_data<T>(context.GetPlace());
math::SetConstant<DeviceContext, T> set_zero;
auto& dev_ctx = context.template device_context<DeviceContext>();
......
paddle/fluid/operators/distributed_ops/allreduce_op.cc 0 → 100644
浏览文件 @ 86511f51
/* Copyright (c) 2016 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 <future> // NOLINT
#include <ostream>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/nccl_helper.h"
#endif
namespace paddle {
namespace operators {
struct MutableDataFunctor {
MutableDataFunctor(void** data, framework::LoDTensor* tensor,
const platform::Place& place)
: data_(data), tensor_(tensor), place_(place) {}
template <typename T>
void apply() {
*data_ = tensor_->mutable_data<T>(place_);
}
void** data_;
framework::LoDTensor* tensor_;
platform::Place place_;
};
class AllReduceOp : public framework::OperatorBase {
using OperatorBase::OperatorBase;
void RunImpl(const framework::Scope& scope,
const platform::Place& place) const override {
PADDLE_ENFORCE(is_gpu_place(place),
"AllReduce op can run on gpu place only for now.");
#ifdef PADDLE_WITH_CUDA
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto* ctx = pool.Get(place);
auto in_names = Inputs("X");
auto out_names = Outputs("Out");
PADDLE_ENFORCE_EQ(in_names.size(), 1, "Only support one input");
PADDLE_ENFORCE_EQ(out_names.size(), 1, "Only support one output");
auto* in = scope.FindVar(in_names[0]);
auto* out = scope.FindVar(out_names[0]);
PADDLE_ENFORCE(in->IsType<framework::LoDTensor>() ||
out->IsType<framework::LoDTensor>(),
"Only support allreduce LoDTensors");
int dtype = -1;
auto in_tensor = in->Get<framework::LoDTensor>();
dtype = platform::ToNCCLDataType(in_tensor.type());
int64_t numel = in_tensor.numel();
auto* sendbuff = in_tensor.data<void>();
auto* out_tensor = out->GetMutable<framework::LoDTensor>();
out_tensor->Resize(in_tensor.dims());
void* recvbuff = nullptr;
framework::VisitDataType(in_tensor.type(),
MutableDataFunctor(&recvbuff, out_tensor, place));
auto cuda_ctx = static_cast<platform::CUDADeviceContext*>(ctx);
auto* comm = cuda_ctx->nccl_comm();
// FIXME(typhoonzero): should use nccl stream here.
auto stream = cuda_ctx->stream();
int reduce_type = Attr<int>("reduce_type");
ncclRedOp_t red_type = ncclSum;
switch (reduce_type) {
case 0:
red_type = ncclSum;
break;
case 1:
red_type = ncclProd;
break;
case 2:
red_type = ncclMax;
break;
case 3:
red_type = ncclMin;
break;
}
PADDLE_ENFORCE(platform::dynload::ncclAllReduce(
sendbuff, recvbuff, numel, static_cast<ncclDataType_t>(dtype), red_type,
comm, stream));
#endif
}
};
class AllReduceOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "(Tensor), tensor to be allreduced.");
AddOutput("Out", "(Tensor) the result of allreduced.");
AddAttr<int>("reduce_type", "(int) determin the reduce type.")
.SetDefault(0);
AddComment(R"DOC(
***AllReduce Operator***
Call NCCL AllReduce internally. Note that this op must be used when one
thread is managing one GPU device.
For speed reasons, reduce_type should be an integer:
0: sum
1: prod
2: max
3: min
If input and output are the same variable, in-place allreduce will be used.
)DOC");
}
};
class AllReduceOpShapeInference : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext* ctx) const override {}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(allreduce, ops::AllReduceOp,
paddle::framework::EmptyGradOpMaker, ops::AllReduceOpMaker,
ops::AllReduceOpShapeInference);
paddle/fluid/operators/dropout_op.cc
浏览文件 @ 86511f51
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/dropout_op.h"
#include <memory>
#include <string>
namespace paddle {
......@@ -70,7 +71,7 @@ class DropoutOpMaker : public framework::OpProtoAndCheckerMaker {
"1. downgrade_in_infer(default), downgrade the outcome at inference "
"time"
" train: out = input * mask"
" inference: out = input * dropout_prob"
" inference: out = input * (1.0 - dropout_prob)"
"2. upscale_in_train, upscale the outcome at training time, do nothing "
"in inference"
" train: out = input * mask / ( 1.0 - dropout_prob )"
......@@ -106,21 +107,31 @@ class DropoutOpGrad : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(ctx->Attrs().Get<bool>("is_test"), false,
"GradOp is only callable when is_test is false");
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
PADDLE_ENFORCE(ctx->HasInput("Mask"), "Mask must not be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) must not be null.");
auto x_dims = ctx->GetInputDim("X");
auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
PADDLE_ENFORCE_EQ(x_dims, out_dims,
"Dimensions of Input(X) and Out@Grad must be the same.");
auto mask_dims = ctx->GetInputDim("Mask");
PADDLE_ENFORCE_EQ(x_dims, mask_dims,
"Dimensions of Input(X) and Mask must be the same.");
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
ctx->ShareLoD("X", /*->*/ framework::GradVarName("X"));
ctx->SetOutputDim(framework::GradVarName("X"), out_dims);
ctx->ShareLoD(framework::GradVarName("Out"),
/*->*/ framework::GradVarName("X"));
}
};
class DropoutGradOpDescMaker : 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("dropout_grad");
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetInput("Mask", Output("Mask"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
......@@ -129,7 +140,7 @@ class DropoutOpGrad : public framework::OperatorWithKernel {
namespace ops = paddle::operators;
REGISTER_OPERATOR(dropout, ops::DropoutOp, ops::DropoutOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::DropoutGradOpDescMaker);
REGISTER_OPERATOR(dropout_grad, ops::DropoutOpGrad);
REGISTER_OP_CPU_KERNEL(
dropout, ops::CPUDropoutKernel<paddle::platform::CPUDeviceContext, float>,
......
paddle/fluid/operators/elementwise/elementwise_floordiv_op.cc 0 → 100644
浏览文件 @ 86511f51
/* 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/elementwise/elementwise_floordiv_op.h"
#include <string>
#include "paddle/fluid/operators/elementwise/elementwise_op.h"
namespace paddle {
namespace operators {
class ElementwiseFloorDivOpMaker : public ElementwiseOpMaker {
protected:
std::string GetName() const override { return "FloorDiv"; }
std::string GetEquation() const override { return "Out = X // Y"; }
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(elementwise_floordiv, ops::ElementwiseOp,
ops::ElementwiseFloorDivOpMaker);
REGISTER_OP_CPU_KERNEL(
elementwise_floordiv,
ops::ElementwiseFloorDivKernel<paddle::platform::CPUDeviceContext, int>,
ops::ElementwiseFloorDivKernel<paddle::platform::CPUDeviceContext,
int64_t>);
paddle/fluid/operators/elementwise/elementwise_floordiv_op.cu 0 → 100644
浏览文件 @ 86511f51
/* 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/elementwise/elementwise_floordiv_op.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
elementwise_floordiv,
ops::ElementwiseFloorDivKernel<plat::CUDADeviceContext, int>,
ops::ElementwiseFloorDivKernel<plat::CUDADeviceContext, int64_t>);
paddle/fluid/operators/elementwise/elementwise_floordiv_op.h 0 → 100644
浏览文件 @ 86511f51
/* 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/framework/eigen.h"
#include "paddle/fluid/operators/elementwise/elementwise_op.h"
#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
#include "paddle/fluid/operators/math/blas.h"
namespace paddle {
namespace operators {
template <typename T>
struct FloorDivFunctor {
inline HOSTDEVICE T operator()(T a, T b) const { return a / b; }
};
template <typename DeviceContext, typename T>
void elementwise_floor_div(const framework::ExecutionContext &ctx,
const framework::Tensor *x,
const framework::Tensor *y, framework::Tensor *z) {
int axis = ctx.Attr<int>("axis");
ElementwiseComputeEx<FloorDivFunctor<T>, DeviceContext, T>(
ctx, x, y, axis, FloorDivFunctor<T>(), z);
}
template <typename DeviceContext, typename T>
class ElementwiseFloorDivKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto *x = ctx.Input<framework::LoDTensor>("X");
auto *y = ctx.Input<framework::LoDTensor>("Y");
auto *z = ctx.Output<framework::LoDTensor>("Out");
z->mutable_data<T>(ctx.GetPlace());
// dtype of x and y is int64 or int32
elementwise_floor_div<DeviceContext, T>(ctx, x, y, z);
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/elementwise/elementwise_mod_op.cc 0 → 100644
浏览文件 @ 86511f51
/* 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/elementwise/elementwise_mod_op.h"
#include <string>
#include "paddle/fluid/operators/elementwise/elementwise_op.h"
namespace paddle {
namespace operators {
class ElementwiseModOpMaker : public ElementwiseOpMaker {
protected:
std::string GetName() const override { return "Mod"; }
std::string GetEquation() const override { return "Out = X % Y"; }
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(elementwise_mod, ops::ElementwiseOp,
ops::ElementwiseModOpMaker);
REGISTER_OP_CPU_KERNEL(
elementwise_mod,
ops::ElementwiseModKernel<paddle::platform::CPUDeviceContext, int>,
ops::ElementwiseModKernel<paddle::platform::CPUDeviceContext, int64_t>);
paddle/fluid/operators/elementwise/elementwise_mod_op.cu 0 → 100644
浏览文件 @ 86511f51
/* 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/elementwise/elementwise_mod_op.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
elementwise_mod, ops::ElementwiseModKernel<plat::CUDADeviceContext, int>,
ops::ElementwiseModKernel<plat::CUDADeviceContext, int64_t>);
paddle/fluid/operators/elementwise/elementwise_mod_op.h 0 → 100644
浏览文件 @ 86511f51
/* 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/framework/eigen.h"
#include "paddle/fluid/operators/elementwise/elementwise_op.h"
#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
#include "paddle/fluid/operators/math/blas.h"
namespace paddle {
namespace operators {
template <typename T>
struct ModFunctor {
inline HOSTDEVICE T operator()(T a, T b) const { return a % b; }
};
template <typename DeviceContext, typename T>
void elementwise_mod(const framework::ExecutionContext &ctx,
const framework::Tensor *x, const framework::Tensor *y,
framework::Tensor *z) {
int axis = ctx.Attr<int>("axis");
ElementwiseComputeEx<ModFunctor<T>, DeviceContext, T>(ctx, x, y, axis,
ModFunctor<T>(), z);
}
template <typename DeviceContext, typename T>
class ElementwiseModKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto *x = ctx.Input<framework::LoDTensor>("X");
auto *y = ctx.Input<framework::LoDTensor>("Y");
auto *z = ctx.Output<framework::LoDTensor>("Out");
z->mutable_data<T>(ctx.GetPlace());
// dtype of x and y is int64 or int32
elementwise_mod<DeviceContext, T>(ctx, x, y, z);
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/fake_dequantize_op.cc
浏览文件 @ 86511f51
......@@ -33,8 +33,51 @@ struct DequantizeFunctor<platform::CPUDeviceContext, T> {
}
};
template <typename T>
struct ChannelDequantizeFunctor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& dev_ctx,
const framework::Tensor* in, const framework::Tensor** scales,
const int scale_num, T max_range, framework::Tensor* out) {
if (scale_num == 1) {
const int channel = in->dims()[0];
const T* scale_factor = scales[0]->data<T>();
for (int i = 0; i < channel; i++) {
T s = scale_factor[i];
framework::Tensor one_channel_in = in->Slice(i, i + 1);
framework::Tensor one_channel_out = out->Slice(i, i + 1);
auto in_e = framework::EigenVector<T>::Flatten(one_channel_in);
auto out_e = framework::EigenVector<T>::Flatten(one_channel_out);
auto& dev = *dev_ctx.eigen_device();
out_e.device(dev) = (s / max_range) * in_e;
}
} else if (scale_num == 2) {
int batch_size = in->dims()[0];
int channel = in->dims()[1];
const T* scale_one = scales[0]->data<T>();
const T* scale_two = scales[1]->data<T>();
for (int i = 0; i < batch_size; i++) {
framework::Tensor one_batch_in = in->Slice(i, i + 1).Resize(
framework::slice_ddim(in->dims(), 1, in->dims().size()));
framework::Tensor one_batch_out = out->Slice(i, i + 1).Resize(
framework::slice_ddim(out->dims(), 1, out->dims().size()));
for (int j = 0; j < channel; j++) {
T s = scale_one[j];
framework::Tensor one_channel_in = one_batch_in.Slice(j, j + 1);
framework::Tensor one_channel_out = one_batch_out.Slice(j, j + 1);
auto in_e = framework::EigenVector<T>::Flatten(one_channel_in);
auto out_e = framework::EigenVector<T>::Flatten(one_channel_out);
auto& dev = *dev_ctx.eigen_device();
out_e.device(dev) = (s * scale_two[0] / max_range) * in_e;
}
}
}
}
};
template struct DequantizeFunctor<platform::CPUDeviceContext, float>;
template struct DequantizeFunctor<platform::CPUDeviceContext, double>;
template struct ChannelDequantizeFunctor<platform::CPUDeviceContext, float>;
template struct ChannelDequantizeFunctor<platform::CPUDeviceContext, double>;
class FakeDequantizeMaxAbsOp : public framework::OperatorWithKernel {
public:
......
paddle/fluid/operators/fake_dequantize_op.cu
浏览文件 @ 86511f51
......@@ -44,8 +44,66 @@ struct DequantizeFunctor<platform::CUDADeviceContext, T> {
}
};
template <typename T>
__global__ void DequantizeOneScale(const T* in, const T* scale, T max_range,
int num, int channel, T* out) {
int tid = threadIdx.x;
int channel_size = num / channel;
const T* in_c = in + blockIdx.x * channel_size;
T* out_c = out + blockIdx.x * channel_size;
for (int i = tid; i < channel_size; i += blockDim.x) {
out_c[i] = in_c[i] * scale[blockIdx.x] / max_range;
}
}
template <typename T>
__global__ void DequantizeTwoScale(const T* in, const T* scale_one,
const T* scale_two, T max_range, int num,
int batch_size, int channel, T* out) {
int tid = threadIdx.x;
int channel_size = num / (batch_size * channel);
int scale_index = blockIdx.x % channel;
const T* in_c = in + blockIdx.x * channel_size;
T* out_c = out + blockIdx.x * channel_size;
for (int i = tid; i < channel_size; i += blockDim.x) {
out_c[i] = in_c[i] * scale_one[scale_index] * scale_two[0] / max_range;
}
}
template <typename T>
struct ChannelDequantizeFunctor<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& dev_ctx,
const framework::Tensor* in, const framework::Tensor** scales,
const int scale_num, T max_range, framework::Tensor* out) {
const T* in_data = in->data<T>();
T* out_data = out->mutable_data<T>(dev_ctx.GetPlace());
if (scale_num == 1) {
int num = in->numel();
int channel = in->dims()[0];
const T* scale_factor = scales[0]->data<T>();
int block = 1024;
int grid = channel;
DequantizeOneScale<T><<<grid, block, 0, dev_ctx.stream()>>>(
in_data, scale_factor, max_range, num, channel, out_data);
} else if (scale_num == 2) {
int num = in->numel();
int batch_size = in->dims()[0];
int channel = in->dims()[1];
const T* scale_one = scales[0]->data<T>();
const T* scale_two = scales[1]->data<T>();
int block = 1024;
int grid = batch_size * channel;
DequantizeTwoScale<T><<<grid, block, 0, dev_ctx.stream()>>>(
in_data, scale_one, scale_two, max_range, num, batch_size, channel,
out_data);
}
}
};
template struct DequantizeFunctor<platform::CUDADeviceContext, float>;
template struct DequantizeFunctor<platform::CUDADeviceContext, double>;
template struct ChannelDequantizeFunctor<platform::CUDADeviceContext, float>;
template struct ChannelDequantizeFunctor<platform::CUDADeviceContext, double>;
} // namespace operators
} // namespace paddle
......
paddle/fluid/operators/fake_dequantize_op.h
浏览文件 @ 86511f51
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <vector>
#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
......@@ -28,6 +29,13 @@ struct DequantizeFunctor {
framework::Tensor* out);
};
template <typename DeviceContext, typename T>
struct ChannelDequantizeFunctor {
void operator()(const DeviceContext& dev_ctx, const framework::Tensor* in,
const framework::Tensor** scales, const int scale_num,
T max_range, framework::Tensor* out);
};
template <typename DeviceContext, typename T>
class FakeDequantizeMaxAbsKernel : public framework::OpKernel<T> {
public:
......@@ -54,32 +62,33 @@ class FakeChannelWiseDequantizeMaxAbsKernel : public framework::OpKernel<T> {
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;
int max_range = 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) {
int scale_num = scales.size();
if (scale_num == 1) {
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) when the `Scales` has only one "
"element.");
max_range *= (std::pow(2, quant_bits[0] - 1) - 1);
} else if (scale_num == 2) {
PADDLE_ENFORCE_EQ(
scales[0]->numel(), in->dims()[1],
"The number of first scale values must be the same with "
"second dimension value of Input(X) when the `Scales` has two "
"elements.");
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);
max_range *= (std::pow(2, quant_bits[0] - 1) - 1) *
(std::pow(2, quant_bits[1] - 1) - 1);
}
ChannelDequantizeFunctor<DeviceContext, T>()(
dev_ctx, in, scales.data(), scale_num, static_cast<T>(max_range), out);
}
};
......
paddle/fluid/operators/fake_quantize_op.cc
浏览文件 @ 86511f51
......@@ -37,6 +37,21 @@ struct FindAbsMaxFunctor<platform::CPUDeviceContext, T> {
template struct FindAbsMaxFunctor<platform::CPUDeviceContext, float>;
template <typename T>
struct FindChannelAbsMaxFunctor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& ctx, const T* in,
const int num, const int channel, T* out) {
const int channel_size = num / channel;
for (int i = 0; i < channel; i++) {
auto* start = in + i * channel_size;
auto* end = in + (i + 1) * channel_size;
out[i] = std::abs(*(std::max_element(start, end, Compare<T>())));
}
}
};
template struct FindChannelAbsMaxFunctor<platform::CPUDeviceContext, float>;
template <typename T>
struct ClipAndFakeQuantFunctor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& ctx,
......@@ -53,6 +68,36 @@ struct ClipAndFakeQuantFunctor<platform::CPUDeviceContext, T> {
template struct ClipAndFakeQuantFunctor<platform::CPUDeviceContext, float>;
template <typename T>
struct ChannelClipAndFakeQuantFunctor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& ctx,
const framework::Tensor& in, const framework::Tensor& scale,
const int bin_cnt, const int channel,
framework::Tensor* out) {
auto* scale_data = scale.data<T>();
auto* in_data = in.data<T>();
auto* out_data = out->mutable_data<T>(ctx.GetPlace());
const int channel_size = in.numel() / channel;
platform::Transform<platform::CPUDeviceContext> trans;
for (int i = 0; i < channel; i++) {
T s = scale_data[i];
auto* start = in_data + i * channel_size;
auto* end = in_data + (i + 1) * channel_size;
trans(ctx, start, end, out_data + i * channel_size,
ClipFunctor<T>(-s, s));
}
for (int i = 0; i < channel; i++) {
T s = scale_data[i];
framework::Tensor one_channel_out = out->Slice(i, i + 1);
auto out_e = framework::EigenVector<T>::Flatten(one_channel_out);
out_e.device(*ctx.eigen_device()) = (bin_cnt / s * out_e).round();
}
}
};
template struct ChannelClipAndFakeQuantFunctor<platform::CPUDeviceContext,
float>;
template <typename T>
struct FindRangeAbsMaxFunctor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& ctx,
......@@ -169,10 +214,10 @@ class FakeChannelWiseQuantizeAbsMaxOp : public framework::OperatorWithKernel {
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->HasOutput("OutScale"),
"Output(Scale) of FakeChannelWiseQuantizeOp should not be null.");
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->SetOutputDim("OutScales", {ctx->GetInputDim("X")[0]});
ctx->SetOutputDim("OutScale", {ctx->GetInputDim("X")[0]});
ctx->ShareLoD("X", /*->*/ "Out");
}
......@@ -192,7 +237,7 @@ class FakeChannelWiseQuantizeAbsMaxOpMaker
AddOutput("Out",
"(Tensor) Output of quantized low level tensor, "
"but also saved as float data type.");
AddOutput("OutScales", "(Tensor) Current channel wise scale");
AddOutput("OutScale", "(Tensor) Current channel wise scale");
AddAttr<int>("bit_length", "(int, default 8)")
.SetDefault(8)
.AddCustomChecker([](const int& bit_length) {
......
paddle/fluid/operators/fake_quantize_op.cu
浏览文件 @ 86511f51
......@@ -74,6 +74,45 @@ struct FindAbsMaxFunctor<platform::CUDADeviceContext, T> {
template struct FindAbsMaxFunctor<platform::CUDADeviceContext, float>;
template <typename T>
__global__ void FindChannelAbsMaxKernel(const T* in, const int n, const int c,
T* out) {
int tid = threadIdx.x;
int channel_size = n / c;
const T* in_c = in + blockIdx.x * channel_size;
extern __shared__ T shared_max_data[];
shared_max_data[tid] = T(0);
for (int i = tid; i < channel_size; i += blockDim.x) {
T tmp = fabs(in_c[i]);
if (tmp > shared_max_data[tid]) {
shared_max_data[tid] = tmp;
}
}
__syncthreads();
for (int i = blockDim.x / 2; i > 0; i >>= 1) {
if (tid < i && (shared_max_data[tid] < shared_max_data[tid + i])) {
shared_max_data[tid] = shared_max_data[tid + i];
}
__syncthreads();
}
if (tid == 0) {
out[blockIdx.x] = shared_max_data[0];
}
}
template <typename T>
struct FindChannelAbsMaxFunctor<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& ctx, const T* in,
const int num, const int channel, T* out) {
int block = 1024;
int grid = channel;
FindChannelAbsMaxKernel<T><<<grid, block, 1024 * sizeof(T), ctx.stream()>>>(
in, num, channel, out);
}
};
template struct FindChannelAbsMaxFunctor<platform::CUDADeviceContext, float>;
template <typename T>
__global__ void ClipAndQuantKernel(const T* in, const T* scale,
const int bin_cnt, const int n, T* out) {
......@@ -82,14 +121,76 @@ __global__ void ClipAndQuantKernel(const T* in, const T* scale,
T s = scale[0];
for (int i = bid; i < n; i += blockDim.x * gridDim.x) {
T x = in[bid];
T x = in[i];
T v = x > s ? s : x;
v = v < -s ? -s : v;
v = bin_cnt / s * v;
out[i] = round(v);
}
}
template <typename T>
struct ClipAndFakeQuantFunctor<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& ctx,
const framework::Tensor& in, const framework::Tensor& scale,
const int bin_cnt, framework::Tensor* out) {
int num = in.numel();
int block = 1024;
int grid = (block - 1 + num) / block;
const T* in_data = in.data<T>();
const T* scale_data = scale.data<T>();
T* out_data = out->mutable_data<T>(ctx.GetPlace());
ClipAndQuantKernel<T><<<grid, block, 0, ctx.stream()>>>(
in_data, scale_data, bin_cnt, num, out_data);
}
};
template struct ClipAndFakeQuantFunctor<platform::CUDADeviceContext, float>;
template <typename T>
__global__ void ChannelClipAndQuantKernel(const T* in, const T* scale,
const int bin_cnt, const int n,
const int c, T* out) {
int tid = threadIdx.x;
int channel_size = n / c;
const T* in_c = in + blockIdx.x * channel_size;
T* out_c = out + blockIdx.x * channel_size;
T s = scale[blockIdx.x];
for (int i = tid; i < channel_size; i += blockDim.x) {
T x = in_c[i];
T v = x > s ? s : x;
v = v < -s ? -s : v;
v = bin_cnt / s * v;
out[bid] = round(v);
out_c[i] = round(v);
}
}
template <typename T>
struct ChannelClipAndFakeQuantFunctor<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& ctx,
const framework::Tensor& in, const framework::Tensor& scale,
const int bin_cnt, const int channel,
framework::Tensor* out) {
int num = in.numel();
int block = 1024;
int grid = channel;
const T* in_data = in.data<T>();
const T* scale_data = scale.data<T>();
T* out_data = out->mutable_data<T>(ctx.GetPlace());
ChannelClipAndQuantKernel<T><<<grid, block, 0, ctx.stream()>>>(
in_data, scale_data, bin_cnt, num, channel, out_data);
}
};
template struct ChannelClipAndFakeQuantFunctor<platform::CUDADeviceContext,
float>;
template <typename T>
__global__ void FindRangeAbsMaxAndFillArray(const T* cur_scale,
const T* last_scale,
......@@ -182,26 +283,6 @@ struct FindMovingAverageAbsMaxFunctor<platform::CUDADeviceContext, T> {
template struct FindMovingAverageAbsMaxFunctor<platform::CUDADeviceContext,
float>;
template <typename T>
struct ClipAndFakeQuantFunctor<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& ctx,
const framework::Tensor& in, const framework::Tensor& scale,
const int bin_cnt, framework::Tensor* out) {
int num = in.numel();
int block = 1024;
int grid = (block - 1 + num) / block;
const T* in_data = in.data<T>();
const T* scale_data = scale.data<T>();
T* out_data = out->mutable_data<T>(ctx.GetPlace());
ClipAndQuantKernel<T><<<grid, block, 0, ctx.stream()>>>(
in_data, scale_data, bin_cnt, num, out_data);
}
};
template struct ClipAndFakeQuantFunctor<platform::CUDADeviceContext, float>;
} // namespace operators
} // namespace paddle
......
paddle/fluid/operators/fake_quantize_op.h
浏览文件 @ 86511f51
......@@ -42,6 +42,19 @@ struct FindRangeAbsMaxFunctor {
framework::Tensor* scales_arr, framework::Tensor* out_scale);
};
template <typename DeviceContext, typename T>
struct FindChannelAbsMaxFunctor {
void operator()(const DeviceContext& ctx, const T* in, const int num,
const int channel, T* out);
};
template <typename DeviceContext, typename T>
struct ChannelClipAndFakeQuantFunctor {
void operator()(const DeviceContext& ctx, const framework::Tensor& in,
const framework::Tensor& scale, const int bin_cnt,
const int channel, framework::Tensor* out);
};
template <typename DeviceContext, typename T>
struct FindMovingAverageAbsMaxFunctor {
void operator()(const DeviceContext& ctx, const framework::Tensor& in_accum,
......@@ -78,29 +91,18 @@ class FakeChannelWiseQuantizeAbsMaxKernel : public framework::OpKernel<T> {
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());
auto* out_scale = context.Output<framework::Tensor>("OutScale");
T* out_scale_data = out_scale->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);
}
FindChannelAbsMaxFunctor<DeviceContext, T>()(
dev_ctx, in->data<T>(), in->numel(), in->dims()[0], out_scale_data);
ChannelClipAndFakeQuantFunctor<DeviceContext, T>()(
dev_ctx, *in, *out_scale, bin_cnt, in->dims()[0], out);
}
};
......
paddle/fluid/operators/layer_norm_op.cc
浏览文件 @ 86511f51
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/layer_norm_op.h"
#include <memory>
namespace paddle {
namespace operators {
......@@ -133,7 +134,7 @@ class LayerNormGradOp : public framework::OperatorWithKernel {
}
if (ctx->HasOutput(framework::GradVarName("Bias"))) {
ctx->SetOutputDim(framework::GradVarName("Bias"),
ctx->GetInputDim("Bias"));
ctx->GetInputDim("Scale"));
}
}
......@@ -157,12 +158,39 @@ class LayerNormGradOp : public framework::OperatorWithKernel {
}
};
class LayerNormGradOpDescMaker : 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("layer_norm_grad");
op->SetInput("X", Input("X"));
op->SetInput("Mean", Output("Mean"));
op->SetInput("Variance", Output("Variance"));
if (ForwardOp().Inputs().count("Scale") > 0) {
op->SetInput("Scale", Input("Scale"));
op->SetOutput(framework::GradVarName("Scale"), InputGrad("Scale"));
}
if (ForwardOp().Inputs().count("Bias") > 0) {
op->SetOutput(framework::GradVarName("Bias"), InputGrad("Bias"));
}
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;
REGISTER_OPERATOR(layer_norm, ops::LayerNormOp, ops::LayerNormOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::LayerNormGradOpDescMaker);
REGISTER_OPERATOR(layer_norm_grad, ops::LayerNormGradOp);
REGISTER_OP_CPU_KERNEL(
layer_norm, ops::LayerNormKernel<paddle::platform::CPUDeviceContext, float>,
......
paddle/fluid/operators/layer_norm_op.h
浏览文件 @ 86511f51
......@@ -245,11 +245,9 @@ class LayerNormGradKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
const float epsilon = ctx.Attr<float>("epsilon");
auto x = *ctx.Input<Tensor>("X");
auto* y = ctx.Input<Tensor>("Y");
auto* mean = ctx.Input<Tensor>("Mean");
auto* var = ctx.Input<Tensor>("Variance");
auto* scale = ctx.Input<Tensor>("Scale");
auto* bias = ctx.Input<Tensor>("Bias");
auto d_y = *ctx.Input<Tensor>(framework::GradVarName("Y"));
const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
......@@ -275,10 +273,6 @@ class LayerNormGradKernel : public framework::OpKernel<T> {
x.Resize(matrix_shape);
temp.mutable_data<T>(matrix_shape, ctx.GetPlace());
if (!(bias && scale)) {
temp_norm.ShareDataWith(*y);
temp_norm.Resize(matrix_shape);
} else {
temp_norm.mutable_data<T>(matrix_shape, ctx.GetPlace());
// get x_norm
ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
......@@ -287,7 +281,6 @@ class LayerNormGradKernel : public framework::OpKernel<T> {
ctx, &temp_norm, var, /*axis*/ 0,
DivAndSqrtFunctor<T>(static_cast<T>(epsilon)), &temp_norm);
}
}
if (d_bias) {
d_bias->mutable_data<T>(ctx.GetPlace());
......
paddle/fluid/operators/load_combine_op.cc
浏览文件 @ 86511f51
......@@ -11,89 +11,27 @@ 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 <fstream>
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device_context.h"
#include <string>
#include <vector>
#include "paddle/fluid/operators/load_combine_op.h"
namespace paddle {
namespace operators {
class LoadCombineOp : public framework::OperatorBase {
class LoadCombineOp : public framework::OperatorWithKernel {
public:
LoadCombineOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto filename = Attr<std::string>("file_path");
auto load_as_fp16 = Attr<bool>("load_as_fp16");
auto model_from_memory = Attr<bool>("model_from_memory");
auto out_var_names = Outputs("Out");
PADDLE_ENFORCE_GT(
static_cast<int>(out_var_names.size()), 0,
"The number of output variables should be greater than 0.");
if (!model_from_memory) {
std::ifstream fin(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fin),
"Cannot open file %s for load_combine op", filename);
LoadParamsFromBuffer(scope, place, &fin, load_as_fp16, out_var_names);
} else {
PADDLE_ENFORCE(!filename.empty(), "Cannot load file from memory");
std::stringstream fin(filename, std::ios::in | std::ios::binary);
LoadParamsFromBuffer(scope, place, &fin, load_as_fp16, out_var_names);
}
}
void LoadParamsFromBuffer(
const framework::Scope &scope, const platform::Place &place,
std::istream *buffer, bool load_as_fp16,
const std::vector<std::string> &out_var_names) const {
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
for (size_t i = 0; i < out_var_names.size(); i++) {
auto *out_var = scope.FindVar(out_var_names[i]);
PADDLE_ENFORCE(out_var != nullptr, "Output variable %s cannot be found",
out_var_names[i]);
auto *tensor = out_var->GetMutable<framework::LoDTensor>();
// Error checking
PADDLE_ENFORCE(static_cast<bool>(*buffer), "Cannot read more");
// Get data from fin to tensor
DeserializeFromStream(*buffer, tensor, dev_ctx);
auto in_dtype = tensor->type();
auto out_dtype =
load_as_fp16 ? framework::proto::VarType::FP16 : in_dtype;
if (in_dtype != out_dtype) {
// convert to float16 tensor
auto in_kernel_type = framework::OpKernelType(in_dtype, place);
auto out_kernel_type = framework::OpKernelType(out_dtype, place);
framework::LoDTensor fp16_tensor;
// copy LoD info to the new tensor
fp16_tensor.set_lod(tensor->lod());
framework::TransDataType(in_kernel_type, out_kernel_type, *tensor,
&fp16_tensor);
// reset output tensor
out_var->Clear();
tensor = out_var->GetMutable<framework::LoDTensor>();
tensor->set_lod(fp16_tensor.lod());
tensor->ShareDataWith(fp16_tensor);
}
}
buffer->peek();
PADDLE_ENFORCE(buffer->eof(),
"You are not allowed to load partial data via "
"load_combine_op, use load_op instead.");
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
framework::OpKernelType kt = framework::OpKernelType(
framework::proto::VarType::FP32, ctx.GetPlace());
return kt;
}
};
......@@ -136,9 +74,18 @@ that were saved using the SaveCombine operator.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(load_combine, ops::LoadCombineOp,
ops::LoadCombineOpProtoMaker);
REGISTER_OP_CPU_KERNEL(
load_combine,
ops::LoadCombineOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::LoadCombineOpKernel<paddle::platform::CPUDeviceContext, double>,
ops::LoadCombineOpKernel<paddle::platform::CPUDeviceContext, int>,
ops::LoadCombineOpKernel<paddle::platform::CPUDeviceContext, int64_t>);
paddle/fluid/operators/load_combine_op.cu 0 → 100644
浏览文件 @ 86511f51
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/load_combine_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
load_combine,
ops::LoadCombineOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::LoadCombineOpKernel<paddle::platform::CUDADeviceContext, double>,
ops::LoadCombineOpKernel<paddle::platform::CUDADeviceContext, int>,
ops::LoadCombineOpKernel<paddle::platform::CUDADeviceContext, int8_t>,
ops::LoadCombineOpKernel<paddle::platform::CUDADeviceContext, int64_t>);
paddle/fluid/operators/load_combine_op.h 0 → 100644
浏览文件 @ 86511f51
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <fstream>
#include <string>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device_context.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class LoadCombineOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto place = ctx.GetPlace();
auto filename = ctx.Attr<std::string>("file_path");
auto load_as_fp16 = ctx.Attr<bool>("load_as_fp16");
auto model_from_memory = ctx.Attr<bool>("model_from_memory");
auto &out_var_names = ctx.Outputs("Out");
PADDLE_ENFORCE_GT(
static_cast<int>(out_var_names.size()), 0,
"The number of output variables should be greater than 0.");
if (!model_from_memory) {
std::ifstream fin(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fin),
"Cannot open file %s for load_combine op", filename);
LoadParamsFromBuffer(ctx, place, &fin, load_as_fp16, out_var_names);
} else {
PADDLE_ENFORCE(!filename.empty(), "Cannot load file from memory");
std::stringstream fin(filename, std::ios::in | std::ios::binary);
LoadParamsFromBuffer(ctx, place, &fin, load_as_fp16, out_var_names);
}
}
void LoadParamsFromBuffer(
const framework::ExecutionContext &context, const platform::Place &place,
std::istream *buffer, bool load_as_fp16,
const std::vector<std::string> &out_var_names) const {
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
auto out_vars = context.MultiOutputVar("Out");
for (size_t i = 0; i < out_var_names.size(); i++) {
PADDLE_ENFORCE(out_vars[i] != nullptr,
"Output variable %s cannot be found", out_var_names[i]);
auto *tensor = out_vars[i]->GetMutable<framework::LoDTensor>();
// Error checking
PADDLE_ENFORCE(static_cast<bool>(*buffer), "Cannot read more");
// Get data from fin to tensor
DeserializeFromStream(*buffer, tensor, dev_ctx);
auto in_dtype = tensor->type();
auto out_dtype =
load_as_fp16 ? framework::proto::VarType::FP16 : in_dtype;
if (in_dtype != out_dtype) {
// convert to float16 tensor
auto in_kernel_type = framework::OpKernelType(in_dtype, place);
auto out_kernel_type = framework::OpKernelType(out_dtype, place);
framework::LoDTensor fp16_tensor;
// copy LoD info to the new tensor
fp16_tensor.set_lod(tensor->lod());
framework::TransDataType(in_kernel_type, out_kernel_type, *tensor,
&fp16_tensor);
// reset output tensor
out_vars[i]->Clear();
tensor = out_vars[i]->GetMutable<framework::LoDTensor>();
tensor->set_lod(fp16_tensor.lod());
tensor->ShareDataWith(fp16_tensor);
}
}
buffer->peek();
PADDLE_ENFORCE(buffer->eof(),
"You are not allowed to load partial data via "
"load_combine_op, use load_op instead.");
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/load_op.cc
浏览文件 @ 86511f51
......@@ -11,89 +11,26 @@ 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 <fstream>
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/profiler.h"
#include <string>
#include "paddle/fluid/operators/load_op.h"
namespace paddle {
namespace operators {
class LoadOp : public framework::OperatorBase {
class LoadOp : public framework::OperatorWithKernel {
public:
LoadOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
// FIXME(yuyang18): We save variable to local file now, but we should change
// it to save an output stream.
auto filename = Attr<std::string>("file_path");
std::ifstream fin(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot open file %s for load op",
filename);
using framework::OperatorWithKernel::OperatorWithKernel;
auto out_var_name = Output("Out");
auto *out_var = scope.FindVar(out_var_name);
PADDLE_ENFORCE(out_var != nullptr,
"Output variable %s cannot be found in scope %p",
out_var_name, &scope);
void InferShape(framework::InferShapeContext *ctx) const override {}
if (out_var->IsType<framework::LoDTensor>()) {
LoadLodTensor(fin, place, out_var);
} else if (out_var->IsType<framework::SelectedRows>()) {
LoadSelectedRows(fin, place, out_var);
} else {
PADDLE_ENFORCE(
false,
"Load only support LoDTensor and SelectedRows, %s has wrong type",
out_var_name);
}
}
void LoadLodTensor(std::istream &fin, const platform::Place &place,
framework::Variable *var) const {
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
auto *tensor = var->GetMutable<framework::LoDTensor>();
DeserializeFromStream(fin, tensor, dev_ctx);
auto load_as_fp16 = Attr<bool>("load_as_fp16");
auto in_dtype = tensor->type();
auto out_dtype = load_as_fp16 ? framework::proto::VarType::FP16 : in_dtype;
if (in_dtype != out_dtype) {
// convert to float16 tensor
auto in_kernel_type = framework::OpKernelType(in_dtype, place);
auto out_kernel_type = framework::OpKernelType(out_dtype, place);
framework::LoDTensor fp16_tensor;
// copy LoD info to the new tensor
fp16_tensor.set_lod(tensor->lod());
framework::TransDataType(in_kernel_type, out_kernel_type, *tensor,
&fp16_tensor);
// reset output tensor
var->Clear();
tensor = var->GetMutable<framework::LoDTensor>();
tensor->set_lod(fp16_tensor.lod());
tensor->ShareDataWith(fp16_tensor);
}
}
void LoadSelectedRows(std::istream &fin, const platform::Place &place,
framework::Variable *var) const {
auto *selectedRows = var->GetMutable<framework::SelectedRows>();
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
framework::DeserializeFromStream(fin, selectedRows, dev_ctx);
selectedRows->SyncIndex();
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
framework::OpKernelType kt = framework::OpKernelType(
framework::proto::VarType::FP32, platform::CPUPlace());
return kt;
}
};
......@@ -116,8 +53,15 @@ class LoadOpProtoMaker : public framework::OpProtoAndCheckerMaker {
"file.");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(load, ops::LoadOp, ops::LoadOpProtoMaker);
REGISTER_OP_CPU_KERNEL(
load, ops::LoadOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::LoadOpKernel<paddle::platform::CPUDeviceContext, double>,
ops::LoadOpKernel<paddle::platform::CPUDeviceContext, int>,
ops::LoadOpKernel<paddle::platform::CPUDeviceContext, int64_t>);
paddle/fluid/operators/load_op.cu 0 → 100644
浏览文件 @ 86511f51
/* Copyright (c) 2016 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/load_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
load, ops::LoadOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::LoadOpKernel<paddle::platform::CUDADeviceContext, double>,
ops::LoadOpKernel<paddle::platform::CUDADeviceContext, int>,
ops::LoadOpKernel<paddle::platform::CUDADeviceContext, int8_t>,
ops::LoadOpKernel<paddle::platform::CUDADeviceContext, int64_t>);
paddle/fluid/operators/load_op.h 0 → 100644
浏览文件 @ 86511f51
/* Copyright (c) 2016 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 <fstream>
#include <string>
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class LoadOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto place = ctx.GetPlace();
// FIXME(yuyang18): We save variable to local file now, but we should change
// it to save an output stream.
auto filename = ctx.Attr<std::string>("file_path");
std::ifstream fin(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot open file %s for load op",
filename);
auto out_var_name = ctx.Outputs("Out").data();
auto *out_var = ctx.OutputVar("Out");
PADDLE_ENFORCE(out_var != nullptr, "Output variable %s cannot be found ",
out_var_name);
PADDLE_ENFORCE(out_var != nullptr, "Output variable cannot be found ");
if (out_var->IsType<framework::LoDTensor>()) {
LoadLodTensor(fin, place, out_var, ctx);
} else if (out_var->IsType<framework::SelectedRows>()) {
LoadSelectedRows(fin, place, out_var);
} else {
PADDLE_ENFORCE(
false,
"Load only support LoDTensor and SelectedRows, %s has wrong type",
out_var_name);
}
}
void LoadLodTensor(std::istream &fin, const platform::Place &place,
framework::Variable *var,
const framework::ExecutionContext &ctx) const {
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
auto *tensor = var->GetMutable<framework::LoDTensor>();
DeserializeFromStream(fin, tensor, dev_ctx);
auto load_as_fp16 = ctx.Attr<bool>("load_as_fp16");
auto in_dtype = tensor->type();
auto out_dtype = load_as_fp16 ? framework::proto::VarType::FP16 : in_dtype;
if (in_dtype != out_dtype) {
// convert to float16 tensor
auto in_kernel_type = framework::OpKernelType(in_dtype, place);
auto out_kernel_type = framework::OpKernelType(out_dtype, place);
framework::LoDTensor fp16_tensor;
// copy LoD info to the new tensor
fp16_tensor.set_lod(tensor->lod());
framework::TransDataType(in_kernel_type, out_kernel_type, *tensor,
&fp16_tensor);
// reset output tensor
var->Clear();
tensor = var->GetMutable<framework::LoDTensor>();
tensor->set_lod(fp16_tensor.lod());
tensor->ShareDataWith(fp16_tensor);
}
}
void LoadSelectedRows(std::istream &fin, const platform::Place &place,
framework::Variable *var) const {
auto *selectedRows = var->GetMutable<framework::SelectedRows>();
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
framework::DeserializeFromStream(fin, selectedRows, dev_ctx);
selectedRows->SyncIndex();
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/lod_reset_op.cc
浏览文件 @ 86511f51
......@@ -32,7 +32,10 @@ class LoDResetOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_GT(level0.size(), 1,
"If Input(Y) not provided, the target lod should be "
"specified by attribute `target_lod`.");
} else {
ctx->ShareLoD("Y", "Out");
}
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
}
......
paddle/fluid/operators/math/sequence_padding.cu
浏览文件 @ 86511f51
......@@ -78,12 +78,6 @@ class PaddingLoDTensorFunctor<platform::CUDADeviceContext, T> {
"The numel of 'pad_value' can only be 1 or be equal to the "
"'step_width'.");
if (!norm_by_times && seq_num == 1UL && pad_seq_len == max_seq_len) {
TensorCopy(seq_tensor, context.GetPlace(), context, pad_tensor);
pad_tensor->Resize(pad_tensor_dims);
return;
}
const int kBlockSize = 512;
/* At least use 32 threads to copy sequence_width elements,
......@@ -129,12 +123,13 @@ class UnpaddingLoDTensorFunctor<platform::CUDADeviceContext, T> {
CheckDims(seq_tensor_dims, pad_tensor_dims, seq_offsets, pad_seq_len,
step_width, layout);
/*
if (!norm_by_times && seq_num == 1UL && pad_seq_len == max_seq_len) {
TensorCopy(pad_tensor, context.GetPlace(), context, seq_tensor);
seq_tensor->Resize(seq_tensor_dims);
return;
}
*/
const int kBlockSize = 512;
......
paddle/fluid/operators/matmul_op.cc
浏览文件 @ 86511f51
......@@ -290,8 +290,10 @@ class MatMulOp : public framework::OperatorWithKernel {
context->Attrs().Get<bool>("transpose_Y"));
PADDLE_ENFORCE_EQ(mat_dim_x.width_, mat_dim_y.height_);
if (context->IsRuntime()) {
PADDLE_ENFORCE(mat_dim_x.batch_size_ == mat_dim_y.batch_size_ ||
mat_dim_x.batch_size_ == 0 || mat_dim_y.batch_size_ == 0);
}
std::vector<int64_t> dim_out;
if (mat_dim_x.batch_size_ != 0) {
dim_out = framework::vectorize(dim_x);
......
paddle/fluid/operators/mkldnn/concat_mkldnn_op.cc
浏览文件 @ 86511f51
......@@ -15,6 +15,7 @@ limitations under the License. */
#include <memory>
#include "paddle/fluid/operators/concat_op.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
#include "paddle/fluid/platform/mkldnn_reuse.h"
namespace paddle {
namespace operators {
......@@ -38,15 +39,20 @@ static void EnforceLayouts(const std::vector<const Tensor*> inputs) {
}
static memory::primitive_desc CreateMemPrimDesc(const Tensor& input,
const mkldnn::engine& engine) {
constexpr auto data_type = mkldnn::memory::f32;
const mkldnn::engine& engine,
const memory::data_type& dt) {
const auto dims = paddle::framework::vectorize2int(input.dims());
const auto format = input.format();
auto description = memory::desc(dims, data_type, format);
auto description = memory::desc(dims, dt, format);
auto mem_prim_desc = memory::primitive_desc(description, engine);
return mem_prim_desc;
}
static mkldnn::memory::format GetDstMemFormat(
const concat::primitive_desc& concat_pd) {
return (memory::format)concat_pd.dst_primitive_desc().desc().data.format;
}
static platform::CPUPlace GetCpuPlace(
const paddle::framework::ExecutionContext& ctx) {
auto place = ctx.GetPlace();
......@@ -61,14 +67,30 @@ static const mkldnn::engine& GetMKLDNNEngine(
return dev_ctx.GetEngine();
}
std::string CreateKey(const paddle::framework::ExecutionContext& ctx,
const std::vector<const Tensor*> multi_input,
const int64_t& concat_axis, const memory::data_type& dt) {
std::string key;
key.reserve(platform::MKLDNNHandler::MaxKeyLength);
for (size_t i = 0; i < multi_input.size(); i++) {
platform::MKLDNNHandler::AppendKeyDims(
&key, paddle::framework::vectorize2int(multi_input[i]->dims()));
}
platform::MKLDNNHandler::AppendKey(&key, std::to_string(concat_axis));
platform::MKLDNNHandler::AppendKey(&key, ctx.op().Output("Out"));
platform::MKLDNNHandler::AppendKey(&key, std::to_string(dt));
return key;
}
template <typename T>
class ConcatPrimitiveFactory {
public:
concat::primitive_desc CreateConcatPrimDescriptor(
const std::vector<const Tensor*> multi_input, Tensor* output,
int concat_axis, const mkldnn::engine& mkldnn_engine) {
CreateSourcesDescriptors(multi_input, mkldnn_engine);
auto dst_desc = CreateDstMemDescriptor(output);
int concat_axis, const mkldnn::engine& mkldnn_engine,
const memory::data_type& dt = memory::data_type::f32) {
CreateSourcesDescriptors(multi_input, mkldnn_engine, dt);
auto dst_desc = CreateDstMemDescriptor(output, dt);
return concat::primitive_desc(dst_desc, concat_axis, srcs_pd);
}
......@@ -79,23 +101,39 @@ class ConcatPrimitiveFactory {
return concat(concat_pd, inputs, dst_mem.get());
}
void SetSrcDataHandleByIndex(const std::vector<memory>& srcs, const size_t& i,
void* handler) {
srcs[i].set_data_handle(handler);
}
void SetDstDataHandle(const memory& dst_mem, void* handler) {
dst_mem.set_data_handle(handler);
}
std::vector<memory> GetSrcs() { return srcs; }
memory GetDst() { return dst_mem.get(); }
private:
memory::desc CreateDstMemDescriptor(Tensor* output) {
memory::desc CreateDstMemDescriptor(Tensor* output,
const memory::data_type& dt) {
auto dst_dims = paddle::framework::vectorize2int(output->dims());
return memory::desc(dst_dims, platform::MKLDNNGetDataType<T>(),
memory::format::any);
return memory::desc(dst_dims, dt, memory::format::any);
}
mkldnn::memory CreateDstMemory(const concat::primitive_desc& concat_pd,
Tensor* output, platform::CPUPlace place) {
Tensor* output,
const platform::CPUPlace& place) {
return memory(concat_pd.dst_primitive_desc(),
output->mutable_data<T>(place));
}
void CreateSourcesDescriptors(const std::vector<const Tensor*> multi_input,
const mkldnn::engine& mkldnn_engine) {
const mkldnn::engine& mkldnn_engine,
const memory::data_type& dt) {
for (size_t i = 0; i < multi_input.size(); i++) {
auto mem_prim_desc = CreateMemPrimDesc(*multi_input[i], mkldnn_engine);
auto mem_prim_desc =
CreateMemPrimDesc(*multi_input[i], mkldnn_engine, dt);
srcs_pd.push_back(mem_prim_desc);
srcs.push_back(
memory(mem_prim_desc, to_void_cast(multi_input[i]->data<T>())));
......@@ -120,21 +158,59 @@ template <typename T>
class ConcatMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
public:
void Compute(const paddle::framework::ExecutionContext& ctx) const override {
auto place = GetCpuPlace(ctx);
const auto& mkldnn_engine = GetMKLDNNEngine(ctx);
auto multi_input = ctx.MultiInput<Tensor>("X");
EnforceLayouts(multi_input);
Tensor* output = ctx.Output<Tensor>("Out");
int64_t concat_axis = static_cast<int64_t>(ctx.Attr<int>("axis"));
auto& dev_ctx =
ctx.template device_context<paddle::platform::MKLDNNDeviceContext>();
auto place = GetCpuPlace(ctx);
memory::data_type dt =
paddle::framework::ToMKLDNNDataType(multi_input[0]->type());
ConcatPrimitiveFactory<T> prim_creator;
auto concat_pd = prim_creator.CreateConcatPrimDescriptor(
multi_input, output, static_cast<int>(concat_axis), mkldnn_engine);
auto concat = prim_creator.CreateConcatPrimitive(concat_pd, output, place);
stream(stream::kind::eager).submit({concat}).wait();
std::string key = CreateKey(ctx, multi_input, concat_axis, dt);
const std::string key_prim = key + "@concat_p";
const std::string key_concat_pd = key + "@concat_pd";
const std::string key_srcs = key + "@concat_srcs";
const std::string key_dst = key + "@concat_dst";
std::shared_ptr<concat::primitive_desc> concat_pd;
std::shared_ptr<std::vector<memory>> srcs;
std::shared_ptr<memory> dst_mem;
auto concat_p = std::static_pointer_cast<concat>(dev_ctx.GetBlob(key_prim));
if (concat_p == nullptr) {
const auto& mkldnn_engine = dev_ctx.GetEngine();
concat_pd = std::make_shared<concat::primitive_desc>(
prim_creator.CreateConcatPrimDescriptor(multi_input, output,
static_cast<int>(concat_axis),
mkldnn_engine, dt));
concat_p = std::make_shared<concat>(
prim_creator.CreateConcatPrimitive(*concat_pd, output, place));
srcs = std::make_shared<std::vector<memory>>(prim_creator.GetSrcs());
dst_mem = std::make_shared<memory>(prim_creator.GetDst());
dev_ctx.SetBlob(key_prim, concat_p);
dev_ctx.SetBlob(key_concat_pd, concat_pd);
dev_ctx.SetBlob(key_srcs, srcs);
dev_ctx.SetBlob(key_dst, dst_mem);
} else {
srcs = std::static_pointer_cast<std::vector<memory>>(
dev_ctx.GetBlob(key_srcs));
dst_mem = std::static_pointer_cast<memory>(dev_ctx.GetBlob(key_dst));
concat_pd = std::static_pointer_cast<concat::primitive_desc>(
dev_ctx.GetBlob(key_concat_pd));
for (size_t i = 0; i < multi_input.size(); i++) {
prim_creator.SetSrcDataHandleByIndex(
*srcs, i, to_void_cast<T>(multi_input[i]->data<T>()));
}
prim_creator.SetDstDataHandle(*dst_mem, output->mutable_data<T>(place));
}
stream(stream::kind::eager).submit({*concat_p}).wait();
output->set_mkldnn_prim_desc(concat_pd.dst_primitive_desc());
output->set_mkldnn_prim_desc(concat_pd->dst_primitive_desc());
}
};
} // namespace operators
......@@ -143,4 +219,6 @@ class ConcatMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
namespace ops = paddle::operators;
REGISTER_OP_KERNEL(concat, MKLDNN, ::paddle::platform::CPUPlace,
ops::ConcatMKLDNNOpKernel<float>)
ops::ConcatMKLDNNOpKernel<float>,
ops::ConcatMKLDNNOpKernel<int8_t>,
ops::ConcatMKLDNNOpKernel<uint8_t>);
paddle/fluid/operators/ngraph/ngraph_engine.cc
浏览文件 @ 86511f51
......@@ -92,12 +92,10 @@ static std::vector<std::vector<int>> NgraphOpIntervals(
int size = ops->size();
int left = 0;
while (left < size && ops->at(left)->Type() != framework::kFeedOpType) {
while (left < size && ops->at(left)->Type() != framework::kFeedOpType &&
ops->at(left)->Type() != framework::kFetchOpType) {
++left;
}
if (left == size) {
return intervals;
}
while (left < size && ops->at(left)->Type() == framework::kFeedOpType) {
for (auto& var_name_item : ops->at(left)->Outputs()) {
......@@ -112,10 +110,6 @@ static std::vector<std::vector<int>> NgraphOpIntervals(
while (right < size && ops->at(right)->Type() != framework::kFetchOpType) {
++right;
}
if (right == size) {
return intervals;
}
if (left >= right) return intervals;
int index = right;
while (index < size && ops->at(index)->Type() == framework::kFetchOpType) {
......@@ -127,6 +121,10 @@ static std::vector<std::vector<int>> NgraphOpIntervals(
++index;
}
if (left == size || ops->at(left)->Type() == framework::kFetchOpType) {
left = 0;
}
// (left, right - 1) represents indices between feed and fetch
int pivot = left;
while (pivot < right) {
......@@ -234,6 +232,7 @@ NgraphEngine::NgraphEngine(const framework::Scope& scope,
}
void NgraphEngine::Prepare(const std::vector<int>& interval) {
bool has_fetch = false, is_full = false;
for (auto& var : p_bdesc->AllVars()) {
if (!(var->GetType() == framework::proto::VarType::SELECTED_ROWS ||
var->GetType() == framework::proto::VarType::LOD_TENSOR ||
......@@ -264,6 +263,9 @@ void NgraphEngine::Prepare(const std::vector<int>& interval) {
std::vector<paddle::framework::OpDesc*> ops_desc;
for (auto op_desc : p_bdesc->AllOps()) {
ops_desc.emplace_back(op_desc);
if (op_desc->Type() == framework::kFetchOpType) {
has_fetch = true;
}
}
for (auto op_desc : ops_desc) {
......@@ -276,11 +278,11 @@ void NgraphEngine::Prepare(const std::vector<int>& interval) {
if (interval[0] > 0 &&
ops_desc.at(interval[0] - 1)->Type() == framework::kFeedOpType &&
interval[1] < static_cast<int>(ops_desc.size()) &&
ops_desc.at(interval.at(1))->Type() == framework::kFetchOpType) {
this->op_state_ = OpState::FULL;
ops_desc.at(interval[1])->Type() == framework::kFetchOpType) {
is_full = true;
}
if (this->op_state_ == OpState::FULL) {
if (is_full) {
this->op_state_ = this->is_test_ ? OpState::FULL_TEST : OpState::FULL_TRAIN;
} else {
this->op_state_ =
......@@ -293,7 +295,8 @@ void NgraphEngine::Prepare(const std::vector<int>& interval) {
framework::OpRegistry::CreateOp(*(ops_desc[idx])));
++idx;
}
while (ops_desc.at(idx)->Type() != framework::kFetchOpType) {
while (idx < static_cast<int>(ops_desc.size()) &&
ops_desc.at(idx)->Type() != framework::kFetchOpType) {
auto op_desc = ops_desc.at(idx);
for (auto& var_name_item : op_desc->Inputs()) {
for (auto& var_name : var_name_item.second) {
......@@ -303,6 +306,10 @@ void NgraphEngine::Prepare(const std::vector<int>& interval) {
++idx;
}
if (!has_fetch) {
op_state_ = OpState::UNKNOWN;
}
BuildNgIO(ops_desc, interval);
}
......@@ -318,7 +325,8 @@ void NgraphEngine::BuildNgIO(const std::vector<framework::OpDesc*>& ops_desc,
const bool is_output = outputs.find(var_name) != outputs.end();
if (!is_output &&
std::find(var_in_.begin(), var_in_.end(), var_name) ==
var_in_.end()) {
var_in_.end() &&
scope_.FindVar(var_name)) {
// fill var_in here to keep lhs and rhs order
this->var_in_.emplace_back(var_name);
}
......@@ -378,6 +386,7 @@ void NgraphEngine::BuildNgIO(const std::vector<framework::OpDesc*>& ops_desc,
}
}
}
for (size_t i = 0; i < var_in_.size(); ++i) {
auto var_name = var_in_[i];
if (persistables_.find(var_name) == persistables_.end()) {
......
paddle/fluid/operators/ngraph/ngraph_engine.h
浏览文件 @ 86511f51
......@@ -12,8 +12,8 @@ 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_
#pragma once
#include <memory>
#include <set>
#include <string>
......@@ -35,7 +35,6 @@ enum class OpState { /* nGraph support state on ops */
PARTIAL_TRAIN, /* Support partial ops for train */
FULL_TEST, /* Support full list of ops for test */
PARTIAL_TEST, /* Support partial list of ops for test */
FULL, /* All ops supported from feed to fetch */
UNKNOWN /* Output all for debug purpose */
};
......@@ -119,4 +118,3 @@ class NgraphEngine {
} // namespace operators
} // namespace paddle
#endif // PADDLE_FLUID_OPERATORS_NGRAPH_NGRAPH_ENGINE_H_
paddle/fluid/operators/ngraph/ops/cross_entropy_op.h
浏览文件 @ 86511f51
......@@ -27,13 +27,9 @@ namespace paddle {
namespace operators {
namespace ngraphs {
void BuildCrossEntropyNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
auto label = paddle::platform::GetInputNode(op, "Label", ngb_node_map);
std::shared_ptr<ngraph::Node> GetCrossEntropy(
std::shared_ptr<ngraph::Node> x, std::shared_ptr<ngraph::Node> label,
const bool is_soft_label, int ignore_index) {
auto label_shape = label->get_shape();
auto x_shape = x->get_shape();
auto label_rank = label_shape.size();
......@@ -46,18 +42,16 @@ void BuildCrossEntropyNode(
label_2d = paddle::platform::NgReshaper(label, label_2d_shape);
}
if (x_rank > 2) {
x_2d_shape = paddle::platform::FlattenTo2d(x_shape, x_rank - 1);
x_2d = paddle::platform::NgReshaper(x, x_2d_shape);
x_2d_shape = platform::FlattenTo2d(x_shape, x_rank - 1);
x_2d = platform::NgReshaper(x, x_2d_shape);
}
auto batch_size = x_2d_shape.at(0);
auto op_attrs = paddle::framework::AttrReader(op->Attrs());
const bool is_soft_label = op_attrs.Get<bool>("soft_label");
std::shared_ptr<ngraph::Node> node_1_hot = label_2d;
if (!is_soft_label) {
auto label_1d = paddle::platform::NgReshaper(
label_2d, ngraph::Shape{label_2d_shape.at(0)});
auto label_1d =
platform::NgReshaper(label_2d, ngraph::Shape{label_2d_shape.at(0)});
node_1_hot = std::make_shared<ngraph::op::OneHot>(label_1d, x_2d_shape, 1);
}
if (x->get_element_type() != node_1_hot->get_element_type()) {
......@@ -76,11 +70,9 @@ void BuildCrossEntropyNode(
auto node_sum =
std::make_shared<ngraph::op::Sum>(node_mul, ngraph::AxisSet{1});
auto node_neg = std::make_shared<ngraph::op::Negative>(node_sum);
auto xe =
paddle::platform::NgReshaper(node_neg, ngraph::Shape{batch_size, 1});
auto xe = platform::NgReshaper(node_neg, ngraph::Shape{batch_size, 1});
if (!is_soft_label) {
auto ignore_index = op_attrs.Get<int>("ignore_index");
auto ignore_node = ngraph::op::Constant::create(
label->get_element_type(), label_2d_shape, {ignore_index});
auto not_equal_node =
......@@ -89,21 +81,13 @@ void BuildCrossEntropyNode(
xe->get_element_type());
xe = xe * mask;
}
paddle::platform::SetOutputNode(op, "Y", xe, ngb_node_map);
return xe;
}
void BuildCrossEntropyGradNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto op_attrs = paddle::framework::AttrReader(op->Attrs());
const bool is_soft_label = op_attrs.Get<bool>("soft_label");
auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
auto label = paddle::platform::GetInputNode(op, "Label", ngb_node_map);
auto dy = paddle::platform::GetInputNode(op, "Y@GRAD", ngb_node_map);
std::shared_ptr<ngraph::Node> GetCrossEntropyGrad(
std::shared_ptr<ngraph::Node> x, std::shared_ptr<ngraph::Node> label,
std::shared_ptr<ngraph::Node> dy, const bool is_soft_label,
int ignore_index) {
auto x_shape = x->get_shape();
auto rank = x_shape.size();
......@@ -111,9 +95,8 @@ void BuildCrossEntropyGradNode(
if (!is_soft_label) {
auto label_shape = label->get_shape();
label_shape.pop_back();
label = paddle::platform::NgReshaper(label, label_shape);
label = platform::NgReshaper(label, label_shape);
auto ignore_index = op_attrs.Get<int>("ignore_index");
auto ignore_node = ngraph::op::Constant::create(
label->get_element_type(), label_shape, {ignore_index});
auto not_equal_node =
......@@ -128,7 +111,7 @@ void BuildCrossEntropyGradNode(
auto dy_shape = dy->get_shape();
dy_shape.pop_back();
auto dy_reshape = paddle::platform::NgReshaper(dy, dy_shape);
auto dy_reshape = platform::NgReshaper(dy, dy_shape);
auto dy_bcast = std::make_shared<ngraph::op::Broadcast>(
dy_reshape, x_shape, ngraph::AxisSet{rank - 1});
if (x->get_element_type() != label->get_element_type()) {
......@@ -140,7 +123,35 @@ void BuildCrossEntropyGradNode(
if (!is_soft_label) {
xe_grad = xe_grad * mask;
}
return xe_grad;
}
void BuildCrossEntropyNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
auto label = paddle::platform::GetInputNode(op, "Label", ngb_node_map);
auto op_attrs = paddle::framework::AttrReader(op->Attrs());
const bool is_soft_label = op_attrs.Get<bool>("soft_label");
int ignore_index = op_attrs.Get<int>("ignore_index");
auto xe = GetCrossEntropy(x, label, is_soft_label, ignore_index);
paddle::platform::SetOutputNode(op, "Y", xe, ngb_node_map);
}
void BuildCrossEntropyGradNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto op_attrs = paddle::framework::AttrReader(op->Attrs());
const bool is_soft_label = op_attrs.Get<bool>("soft_label");
int ignore_index = op_attrs.Get<int>("ignore_index");
auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
auto label = paddle::platform::GetInputNode(op, "Label", ngb_node_map);
auto dy = paddle::platform::GetInputNode(op, "Y@GRAD", ngb_node_map);
auto xe_grad = GetCrossEntropyGrad(x, label, dy, is_soft_label, ignore_index);
paddle::platform::SetOutputNode(op, "X@GRAD", xe_grad, ngb_node_map);
}
} // namespace ngraphs
......
paddle/fluid/operators/ngraph/ops/softmax_op.h
浏览文件 @ 86511f51
......@@ -27,12 +27,7 @@ namespace paddle {
namespace operators {
namespace ngraphs {
void BuildSoftmaxNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
std::shared_ptr<ngraph::Node> GetSoftmax(std::shared_ptr<ngraph::Node> x) {
auto x_shape = x->get_shape();
int rank = x_shape.size();
auto x_2d_shape = paddle::platform::FlattenTo2d(x_shape, rank - 1);
......@@ -47,16 +42,11 @@ void BuildSoftmaxNode(
-64., x_shifted);
auto softmax =
std::make_shared<ngraph::op::Softmax>(x_clipped, ngraph::AxisSet{1});
paddle::platform::SetOutputNode(op, "Out", softmax, ngb_node_map);
return softmax;
}
void BuildSoftmaxGradNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto out = paddle::platform::GetInputNode(op, "Out", ngb_node_map);
auto dout = paddle::platform::GetInputNode(op, "Out@GRAD", ngb_node_map);
std::shared_ptr<ngraph::Node> GetSoftmaxGrad(
std::shared_ptr<ngraph::Node> out, std::shared_ptr<ngraph::Node> dout) {
auto out_shape = out->get_shape();
int rank = out_shape.size();
auto out_2d_shape = paddle::platform::FlattenTo2d(out_shape, rank - 1);
......@@ -70,6 +60,27 @@ void BuildSoftmaxGradNode(
auto node_bcast = std::make_shared<ngraph::op::Broadcast>(
node_sum, out_2d_shape, ngraph::AxisSet{1});
auto dx = (dout - node_bcast) * out;
return dx;
}
void BuildSoftmaxNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
auto softmax = GetSoftmax(x);
paddle::platform::SetOutputNode(op, "Out", softmax, ngb_node_map);
}
void BuildSoftmaxGradNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto out = paddle::platform::GetInputNode(op, "Out", ngb_node_map);
auto dout = paddle::platform::GetInputNode(op, "Out@GRAD", ngb_node_map);
auto dx = GetSoftmaxGrad(out, dout);
paddle::platform::SetOutputNode(op, "X@GRAD", dx, ngb_node_map);
}
} // namespace ngraphs
......
paddle/fluid/operators/ngraph/ops/softmax_with_cross_entropy_op.h 0 → 100644
浏览文件 @ 86511f51
/*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 <vector>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/operators/ngraph/ops/cross_entropy_op.h"
#include "paddle/fluid/operators/ngraph/ops/softmax_op.h"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
namespace operators {
namespace ngraphs {
void BuildSoftmaxWithCrossEntropyNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto logits = paddle::platform::GetInputNode(op, "Logits", ngb_node_map);
auto label = paddle::platform::GetInputNode(op, "Label", ngb_node_map);
auto softmax = paddle::operators::ngraphs::GetSoftmax(logits);
auto op_attrs = framework::AttrReader(op->Attrs());
const bool is_soft_label = op_attrs.Get<bool>("soft_label");
int ignore_index = op_attrs.Get<int>("ignore_index");
auto xe = paddle::operators::ngraphs::GetCrossEntropy(
softmax, label, is_soft_label, ignore_index);
paddle::platform::SetOutputNode(op, "Softmax", softmax, ngb_node_map);
paddle::platform::SetOutputNode(op, "Loss", xe, ngb_node_map);
}
void BuildSoftmaxWithCrossEntropyGradNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto op_attrs = framework::AttrReader(op->Attrs());
const bool is_soft_label = op_attrs.Get<bool>("soft_label");
auto label = paddle::platform::GetInputNode(op, "Label", ngb_node_map);
auto softmax = paddle::platform::GetInputNode(op, "Softmax", ngb_node_map);
auto loss_grad =
paddle::platform::GetInputNode(op, "Loss@GRAD", ngb_node_map);
auto softmax_shape = softmax->get_shape();
auto rank = softmax_shape.size();
if (!is_soft_label) {
auto label_shape = label->get_shape();
label_shape.pop_back();
label = platform::NgReshaper(label, label_shape);
label =
std::make_shared<ngraph::op::OneHot>(label, softmax_shape, rank - 1);
}
auto loss_grad_shape = loss_grad->get_shape();
loss_grad_shape.pop_back();
auto loss_grad_reshape = platform::NgReshaper(loss_grad, loss_grad_shape);
auto loss_grad_bcast = std::make_shared<ngraph::op::Broadcast>(
loss_grad_reshape, softmax_shape, ngraph::AxisSet{rank - 1});
if (softmax->get_element_type() != label->get_element_type()) {
label = std::make_shared<ngraph::op::Convert>(label,
softmax->get_element_type());
}
auto logits_grad = loss_grad_bcast * (softmax - label);
paddle::platform::SetOutputNode(op, "Logits@GRAD", logits_grad, ngb_node_map);
}
} // namespace ngraphs
} // namespace operators
} // namespace paddle
REGISTER_NG_OP(softmax_with_cross_entropy, BuildSoftmaxWithCrossEntropyNode);
REGISTER_NG_OP(softmax_with_cross_entropy_grad,
BuildSoftmaxWithCrossEntropyGradNode);
paddle/fluid/operators/save_combine_op.cc
浏览文件 @ 86511f51
......@@ -12,87 +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. */
#include <stdint.h>
#include <fstream>
#include <numeric>
#include <sstream>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/port.h"
#include <string>
#include "paddle/fluid/operators/save_combine_op.h"
namespace paddle {
namespace operators {
class SaveCombineOp : public framework::OperatorBase {
class SaveCombineOp : public framework::OperatorWithKernel {
public:
SaveCombineOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto filename = Attr<std::string>("file_path");
auto overwrite = Attr<bool>("overwrite");
auto save_as_fp16 = Attr<bool>("save_as_fp16");
bool is_present = FileExists(filename);
if (is_present && !overwrite) {
PADDLE_THROW("%s exists!, cannot save_combine to it when overwrite=false",
filename, overwrite);
}
MkDirRecursively(DirName(filename).c_str());
std::ofstream fout(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
filename);
auto inp_var_names = Inputs("X");
PADDLE_ENFORCE_GT(static_cast<int>(inp_var_names.size()), 0,
"The number of input variables should be greater than 0");
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
for (size_t i = 0; i < inp_var_names.size(); i++) {
auto *var = scope.FindVar(inp_var_names[i]);
using framework::OperatorWithKernel::OperatorWithKernel;
PADDLE_ENFORCE(var != nullptr,
"Cannot find variable %s for save_combine_op",
inp_var_names[i]);
PADDLE_ENFORCE(var->IsType<framework::LoDTensor>(),
"SaveCombineOp only supports LoDTensor, %s has wrong type",
inp_var_names[i]);
auto &tensor = var->Get<framework::LoDTensor>();
// Serialize tensors one by one
// Check types to see if a fp16 transformation is required
auto in_dtype = tensor.type();
auto out_dtype =
save_as_fp16 ? framework::proto::VarType::FP16 : in_dtype;
if (in_dtype != out_dtype) {
auto in_kernel_type = framework::OpKernelType(in_dtype, place);
auto out_kernel_type = framework::OpKernelType(out_dtype, place);
framework::LoDTensor out;
// copy LoD info to the new tensor
out.set_lod(tensor.lod());
framework::TransDataType(in_kernel_type, out_kernel_type, tensor, &out);
framework::SerializeToStream(fout, out, dev_ctx);
} else {
framework::SerializeToStream(fout, tensor, dev_ctx);
}
}
fout.close();
}
void InferShape(framework::InferShapeContext *ctx) const override {}
};
class SaveCombineOpProtoMaker : public framework::OpProtoAndCheckerMaker {
......@@ -134,3 +65,10 @@ namespace ops = paddle::operators;
REGISTER_OPERATOR(save_combine, ops::SaveCombineOp,
ops::SaveCombineOpProtoMaker);
REGISTER_OP_CPU_KERNEL(
save_combine,
ops::SaveCombineOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::SaveCombineOpKernel<paddle::platform::CPUDeviceContext, double>,
ops::SaveCombineOpKernel<paddle::platform::CPUDeviceContext, int>,
ops::SaveCombineOpKernel<paddle::platform::CPUDeviceContext, int64_t>);
paddle/fluid/operators/save_combine_op.cu 0 → 100644
浏览文件 @ 86511f51
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/save_combine_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
save_combine,
ops::SaveCombineOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::SaveCombineOpKernel<paddle::platform::CUDADeviceContext, double>,
ops::SaveCombineOpKernel<paddle::platform::CUDADeviceContext, int>,
ops::SaveCombineOpKernel<paddle::platform::CUDADeviceContext, int8_t>,
ops::SaveCombineOpKernel<paddle::platform::CUDADeviceContext, int64_t>);
paddle/fluid/operators/save_combine_op.h 0 → 100644
浏览文件 @ 86511f51
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <stdint.h>
#include <fstream>
#include <numeric>
#include <sstream>
#include <string>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/port.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class SaveCombineOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto place = ctx.GetPlace();
auto filename = ctx.Attr<std::string>("file_path");
auto overwrite = ctx.Attr<bool>("overwrite");
auto save_as_fp16 = ctx.Attr<bool>("save_as_fp16");
bool is_present = FileExists(filename);
if (is_present && !overwrite) {
PADDLE_THROW("%s exists!, cannot save_combine to it when overwrite=false",
filename, overwrite);
}
MkDirRecursively(DirName(filename).c_str());
std::ofstream fout(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
filename);
auto &inp_var_names = ctx.Inputs("X");
auto &inp_vars = ctx.MultiInputVar("X");
PADDLE_ENFORCE_GT(static_cast<int>(inp_var_names.size()), 0,
"The number of input variables should be greater than 0");
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
for (size_t i = 0; i < inp_var_names.size(); i++) {
PADDLE_ENFORCE(inp_vars[i] != nullptr,
"Cannot find variable %s for save_combine_op",
inp_var_names[i]);
PADDLE_ENFORCE(inp_vars[i]->IsType<framework::LoDTensor>(),
"SaveCombineOp only supports LoDTensor, %s has wrong type",
inp_var_names[i]);
auto &tensor = inp_vars[i]->Get<framework::LoDTensor>();
// Serialize tensors one by one
// Check types to see if a fp16 transformation is required
auto in_dtype = tensor.type();
auto out_dtype =
save_as_fp16 ? framework::proto::VarType::FP16 : in_dtype;
if (in_dtype != out_dtype) {
auto in_kernel_type = framework::OpKernelType(in_dtype, place);
auto out_kernel_type = framework::OpKernelType(out_dtype, place);
framework::LoDTensor out;
// copy LoD info to the new tensor
out.set_lod(tensor.lod());
framework::TransDataType(in_kernel_type, out_kernel_type, tensor, &out);
framework::SerializeToStream(fout, out, dev_ctx);
} else {
framework::SerializeToStream(fout, tensor, dev_ctx);
}
}
fout.close();
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/save_load_combine_op_test.cc
浏览文件 @ 86511f51
......@@ -19,8 +19,8 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/float16.h"
USE_NO_KERNEL_OP(save_combine);
USE_NO_KERNEL_OP(load_combine);
USE_CPU_ONLY_OP(save_combine);
USE_CPU_ONLY_OP(load_combine);
template <typename T, typename U>
T* CreateForSaveCombineOp(int x, int y, const std::vector<int>& lod_info,
......
paddle/fluid/operators/save_load_op_test.cc
浏览文件 @ 86511f51
......@@ -16,8 +16,8 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/float16.h"
USE_NO_KERNEL_OP(save);
USE_NO_KERNEL_OP(load);
USE_CPU_ONLY_OP(save);
USE_CPU_ONLY_OP(load);
TEST(SaveLoadOp, CPU) {
paddle::framework::Scope scope;
......
paddle/fluid/operators/save_op.cc
浏览文件 @ 86511f51
......@@ -15,118 +15,24 @@ limitations under the License. */
#include <stdint.h>
#include <fstream>
#include <numeric>
#include <string>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/variable.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/port.h"
#include "paddle/fluid/operators/save_op.h"
namespace paddle {
namespace operators {
// define LOOKUP_TABLE_PATH for checkpoint notify to save lookup table variables
// to directory specified.
constexpr char LOOKUP_TABLE_PATH[] = "kLookupTablePath";
class SaveOp : public framework::OperatorBase {
class SaveOp : public framework::OperatorWithKernel {
public:
SaveOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto iname = Input("X");
auto *var = scope.FindVar(iname);
PADDLE_ENFORCE(var != nullptr, "Cannot find variable %s for save_op",
iname);
if (var->IsType<framework::LoDTensor>()) {
SaveLodTensor(place, var);
} else if (var->IsType<framework::SelectedRows>()) {
SaveSelectedRows(scope, place, var);
} else {
PADDLE_ENFORCE(
false,
"SaveOp only support LoDTensor and SelectedRows, %s has wrong type",
iname);
}
}
void SaveLodTensor(const platform::Place &place,
framework::Variable *var) const {
auto filename = Attr<std::string>("file_path");
auto overwrite = Attr<bool>("overwrite");
if (FileExists(filename) && !overwrite) {
PADDLE_THROW("%s is existed, cannot save to it when overwrite=false",
filename, overwrite);
}
using framework::OperatorWithKernel::OperatorWithKernel;
MkDirRecursively(DirName(filename).c_str());
auto &tensor = var->Get<framework::LoDTensor>();
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
// FIXME(yuyang18): We save variable to local file now, but we should change
// it to save an output stream.
std::ofstream fout(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
filename);
auto save_as_fp16 = Attr<bool>("save_as_fp16");
auto in_dtype = tensor.type();
auto out_dtype = save_as_fp16 ? framework::proto::VarType::FP16 : in_dtype;
if (in_dtype != out_dtype) {
auto in_kernel_type = framework::OpKernelType(in_dtype, place);
auto out_kernel_type = framework::OpKernelType(out_dtype, place);
framework::LoDTensor out;
framework::TransDataType(in_kernel_type, out_kernel_type, tensor, &out);
// copy LoD info to the new tensor
out.set_lod(tensor.lod());
framework::SerializeToStream(fout, out, dev_ctx);
} else {
framework::SerializeToStream(fout, tensor, dev_ctx);
}
fout.close();
}
void InferShape(framework::InferShapeContext *ctx) const override {}
void SaveSelectedRows(const framework::Scope &scope,
const platform::Place &place,
framework::Variable *var) const {
auto *lt_var = scope.FindVar(LOOKUP_TABLE_PATH)->GetMutable<std::string>();
PADDLE_ENFORCE(
lt_var != nullptr,
"Can not find variable kLookupTablePath for SaveSelectedRows");
std::string filename = lt_var->data();
VLOG(4) << "SaveSelectedRows get File name: " << filename;
MkDirRecursively(DirName(filename).c_str());
auto &selectedRows = var->Get<framework::SelectedRows>();
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
// FIXME(yuyang18): We save variable to local file now, but we should change
// it to save an output stream.
std::ofstream fout(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
filename);
framework::SerializeToStream(fout, selectedRows, dev_ctx);
fout.close();
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(ctx.Input<framework::LoDTensor>("X")->type(),
ctx.GetPlace());
}
};
......@@ -154,14 +60,20 @@ This operator will serialize and write LoDTensor / SelectedRows variable to file
"The \"file_path\" where the variable will be saved.")
.AddCustomChecker(
[](const std::string &path) { return !path.empty(); });
AddOutput(LOOKUP_TABLE_PATH,
"(string)"
"for pserver: The \"kLookupTablePath\" where checkpoint notify "
"to save lookup table variables"
" to directory specified.")
.AsDispensable();
}
};
class SaveOpVarTypeInference : public framework::VarTypeInference {
public:
void operator()(framework::InferVarTypeContext *ctx) const override {
auto out_var_name = ctx->Output(LOOKUP_TABLE_PATH).front();
ctx->SetType(out_var_name, framework::proto::VarType::RAW);
auto var_type = framework::proto::VarType::RAW;
ctx->SetType(LOOKUP_TABLE_PATH, var_type);
}
};
......@@ -169,11 +81,18 @@ class SaveOpShapeInference : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *ctx) const override {}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(save, ops::SaveOp, paddle::framework::EmptyGradOpMaker,
ops::SaveOpProtoMaker, ops::SaveOpVarTypeInference,
ops::SaveOpShapeInference);
REGISTER_OPERATOR(save, ops::SaveOp, ops::SaveOpProtoMaker,
ops::SaveOpVarTypeInference, ops::SaveOpShapeInference);
REGISTER_OP_CPU_KERNEL(
save, ops::SaveOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::SaveOpKernel<paddle::platform::CPUDeviceContext, double>,
ops::SaveOpKernel<paddle::platform::CPUDeviceContext, int>,
ops::SaveOpKernel<paddle::platform::CPUDeviceContext, int8_t>,
ops::SaveOpKernel<paddle::platform::CPUDeviceContext, int64_t>);
paddle/fluid/operators/save_op.cu 0 → 100644
浏览文件 @ 86511f51
/* Copyright (c) 2016 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/save_op.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
save, ops::SaveOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::SaveOpKernel<paddle::platform::CUDADeviceContext, double>,
ops::SaveOpKernel<paddle::platform::CUDADeviceContext, int>,
ops::SaveOpKernel<paddle::platform::CUDADeviceContext, int8_t>,
ops::SaveOpKernel<paddle::platform::CUDADeviceContext, int64_t>,
ops::SaveOpKernel<paddle::platform::CUDADeviceContext,
paddle::platform::float16>);
paddle/fluid/operators/save_op.h 0 → 100644
浏览文件 @ 86511f51
/* Copyright (c) 2016 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 <stdint.h>
#include <fstream>
#include <numeric>
#include <string>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/data_type_transform.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/variable.h"
namespace paddle {
namespace operators {
// define LOOKUP_TABLE_PATH for checkpoint notify to save lookup table variables
// to directory specified.
constexpr char LOOKUP_TABLE_PATH[] = "kLookupTablePath";
template <typename DeviceContext, typename T>
class SaveOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
auto place = ctx.GetPlace();
auto *input_var = ctx.InputVar("X");
auto iname = ctx.Inputs("X").data();
PADDLE_ENFORCE(input_var != nullptr, "Cannot find variable %s for save_op",
iname);
if (input_var->IsType<framework::LoDTensor>()) {
SaveLodTensor(ctx, place, input_var);
} else if (input_var->IsType<framework::SelectedRows>()) {
SaveSelectedRows(ctx, place, input_var);
} else {
PADDLE_ENFORCE(
false,
"SaveOp only support LoDTensor and SelectedRows, %s has wrong type",
iname);
}
}
void SaveLodTensor(const framework::ExecutionContext &ctx,
const platform::Place &place,
const framework::Variable *var) const {
auto filename = ctx.Attr<std::string>("file_path");
auto overwrite = ctx.Attr<bool>("overwrite");
if (FileExists(filename) && !overwrite) {
PADDLE_THROW("%s is existed, cannot save to it when overwrite=false",
filename, overwrite);
}
MkDirRecursively(DirName(filename).c_str());
auto &tensor = var->Get<framework::LoDTensor>();
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
// FIXME(yuyang18): We save variable to local file now, but we should change
// it to save an output stream.
std::ofstream fout(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
filename);
auto save_as_fp16 = ctx.Attr<bool>("save_as_fp16");
auto in_dtype = tensor.type();
auto out_dtype = save_as_fp16 ? framework::proto::VarType::FP16 : in_dtype;
if (in_dtype != out_dtype) {
auto in_kernel_type = framework::OpKernelType(in_dtype, place);
auto out_kernel_type = framework::OpKernelType(out_dtype, place);
framework::LoDTensor out;
framework::TransDataType(in_kernel_type, out_kernel_type, tensor, &out);
// copy LoD info to the new tensor
out.set_lod(tensor.lod());
framework::SerializeToStream(fout, out, dev_ctx);
} else {
framework::SerializeToStream(fout, tensor, dev_ctx);
}
fout.close();
}
void SaveSelectedRows(const framework::ExecutionContext &ctx,
const platform::Place &place,
const framework::Variable *var) const {
framework::Variable *out_put_var = ctx.OutputVar(LOOKUP_TABLE_PATH);
PADDLE_ENFORCE(
out_put_var != nullptr,
"Can not find variable kLookupTablePath for SaveSelectedRows");
auto *lt_var = out_put_var->GetMutable<std::string>();
std::string filename = lt_var->data();
VLOG(4) << "SaveSelectedRows get File name: " << filename;
MkDirRecursively(DirName(filename).c_str());
auto &selectedRows = var->Get<framework::SelectedRows>();
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
// FIXME(yuyang18): We save variable to local file now, but we should change
// it to save an output stream.
std::ofstream fout(filename, std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fout), "Cannot open %s to write",
filename);
framework::SerializeToStream(fout, selectedRows, dev_ctx);
fout.close();
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/softmax_with_cross_entropy_op.cc
浏览文件 @ 86511f51
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/softmax_with_cross_entropy_op.h"
#include <memory>
namespace paddle {
namespace operators {
......@@ -187,7 +188,6 @@ class SoftmaxGradMaker : public framework::SingleGradOpDescMaker {
grad_op->SetType("softmax_with_cross_entropy_grad");
grad_op->SetInput("Label", Input("Label"));
grad_op->SetInput("Softmax", Output("Softmax"));
grad_op->SetInput("Loss", Output("Loss"));
grad_op->SetInput(framework::GradVarName("Softmax"), OutputGrad("Softmax"));
grad_op->SetInput(framework::GradVarName("Loss"), OutputGrad("Loss"));
grad_op->SetOutput(framework::GradVarName("Logits"), InputGrad("Logits"));
......
paddle/fluid/operators/squeeze_op.cc
浏览文件 @ 86511f51
......@@ -40,7 +40,7 @@ class SqueezeOpInferShape : public framework::InferShapeBase {
"tensor's rank.");
}
auto out_dims = GetOutputShape(axes, x_dims);
auto out_dims = GetOutputShape(axes, x_dims, false);
ctx->SetOutputDim("Out", out_dims);
if (x_dims[0] == out_dims[0]) {
// Only pass LoD when the first dimension of output and Input(X)
......@@ -50,7 +50,8 @@ class SqueezeOpInferShape : public framework::InferShapeBase {
}
static framework::DDim GetOutputShape(const std::vector<int> squeeze_dims,
const framework::DDim &in_dims) {
const framework::DDim &in_dims,
bool is_runtime) {
size_t num_squeeze_dims = squeeze_dims.size();
int cnt_squeezed_dims = 0;
bool should_squeeze[9] = {false};
......@@ -71,9 +72,12 @@ class SqueezeOpInferShape : public framework::InferShapeBase {
// Check current index, the upper limit has beed checked in line 36.
PADDLE_ENFORCE(current >= 0,
"Invalid axis, the negative axis is out of range.");
if (is_runtime) {
PADDLE_ENFORCE(in_dims[current] == 1,
"Invalid axis index, the axis that will be squeezed "
"should be equal to 1.");
}
if (!(should_squeeze[current])) {
++cnt_squeezed_dims;
......@@ -104,7 +108,7 @@ class SqueezeOp : public framework::OperatorBase {
const platform::Place &place) const override {
auto &axes = Attr<std::vector<int>>("axes");
auto x_dims = scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
auto out_dims = SqueezeOpInferShape::GetOutputShape(axes, x_dims);
auto out_dims = SqueezeOpInferShape::GetOutputShape(axes, x_dims, true);
framework::AttributeMap attrs;
attrs["shape"] = framework::vectorize2int(out_dims);
......@@ -224,7 +228,7 @@ class Squeeze2Op : public framework::OperatorBase {
const platform::Place &place) const override {
auto &axes = Attr<std::vector<int>>("axes");
auto x_dims = scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
auto out_dims = Squeeze2OpInferShape::GetOutputShape(axes, x_dims);
auto out_dims = Squeeze2OpInferShape::GetOutputShape(axes, x_dims, true);
framework::AttributeMap attrs;
attrs["shape"] = framework::vectorize2int(out_dims);
......
paddle/fluid/operators/top_k_op.cc
浏览文件 @ 86511f51
......@@ -34,8 +34,11 @@ class TopkOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_GE(k, 1, "k must >= 1");
PADDLE_ENFORCE_GE(input_dims.size(), 1, "input must have >= 1d shape");
if (ctx->IsRuntime()) {
PADDLE_ENFORCE_GE(input_dims[input_dims.size() - 1], k,
"input must have >= k columns");
}
framework::DDim dims = input_dims;
dims[dims.size() - 1] = k;
......
paddle/fluid/platform/device_context.h
浏览文件 @ 86511f51
......@@ -23,6 +23,9 @@ limitations under the License. */
#include "paddle/fluid/platform/cuda_helper.h"
#include "paddle/fluid/platform/dynload/cublas.h"
#include "paddle/fluid/platform/dynload/cudnn.h"
#if !defined(__APPLE__) && !defined(_WIN32)
#include "paddle/fluid/platform/dynload/nccl.h"
#endif
#include "paddle/fluid/platform/gpu_info.h"
#endif
......
paddle/fluid/platform/nccl_helper.h
浏览文件 @ 86511f51
......@@ -22,6 +22,7 @@
#include <typeindex>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/platform/dynload/nccl.h"
#include "paddle/fluid/platform/enforce.h"
......@@ -79,7 +80,6 @@ struct NCCLContext {
: ctx_(new CUDADeviceContext(CUDAPlace(dev_id))), comm_{nullptr} {}
cudaStream_t stream() const { return ctx_->stream(); }
ncclComm_t comm() const { return comm_; }
int device_id() const {
......@@ -105,9 +105,6 @@ struct NCCLContextMap {
order_.size(), contexts_.size(),
"NCCL Context Map does not support contain two or more same device");
if (places.size() <= 1 && num_trainers == 1) {
return;
}
std::unique_ptr<ncclComm_t[]> comms(new ncclComm_t[order_.size()]);
// if num_trainers == 1, should create a new nccl id for local comms.
if (num_trainers == 1 && nccl_id == nullptr) {
......@@ -127,8 +124,8 @@ struct NCCLContextMap {
} else {
rank = trainer_id;
}
VLOG(30) << "init nccl rank: " << rank << " nranks: " << nranks
<< "gpu id: " << gpu_id;
VLOG(3) << "init nccl rank: " << rank << " nranks: " << nranks
<< " gpu id: " << gpu_id;
PADDLE_ENFORCE(cudaSetDevice(gpu_id));
PADDLE_ENFORCE(platform::dynload::ncclCommInitRank(
comms.get() + i, nranks, *nccl_id, rank));
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ 86511f51
......@@ -1263,6 +1263,10 @@ All parameter, weight, gradient are variables in Paddle.
"enable_inplace",
[](const BuildStrategy &self) { return self.enable_inplace_; },
[](BuildStrategy &self, bool b) { self.enable_inplace_ = b; })
.def_property(
"fuse_all_reduce_ops",
[](const BuildStrategy &self) { return self.fuse_all_reduce_ops_; },
[](BuildStrategy &self, bool b) { self.fuse_all_reduce_ops_ = b; })
.def("_finalize_strategy_and_create_passes",
[](BuildStrategy &self) -> std::shared_ptr<ir::PassBuilder> {
return self.CreatePassesFromStrategy(true);
......
paddle/scripts/paddle_build.sh
浏览文件 @ 86511f51
......@@ -453,6 +453,7 @@ function assert_api_spec_approvals() {
echo "checking ${API_FILE} change, PR: ${GIT_PR_ID}, changes: ${API_CHANGE}"
if [ ${API_CHANGE} ] && [ "${GIT_PR_ID}" != "" ]; then
# NOTE: per_page=10000 should be ok for all cases, a PR review > 10000 is not human readable.
# approval_user_list: velconia 1979255,panyx0718 2887803,XiaoguangHu01 46782768,chengduoZH 30176695,Xreki 12538138,luotao1 6836917,sneaxiy 32832641,tensor-tang 21351065,jacquesqiao 3048612,typhoonzero 13348433,shanyi15 35982308.
if [ "$API_FILE" == "paddle/fluid/API.spec" ];then
APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \
python ${PADDLE_ROOT}/tools/check_pr_approval.py 2 2887803 35982308 46782768 30176695`
......@@ -462,14 +463,14 @@ function assert_api_spec_approvals() {
fi
else
APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \
python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 2887803`
python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 2887803 1979255 21351065 3048612 13348433 46782768 30176695 12538138 6836917 32832641`
fi
echo "current pr ${GIT_PR_ID} got approvals: ${APPROVALS}"
if [ "${APPROVALS}" == "FALSE" ]; then
if [ "$API_FILE" == "paddle/fluid/API.spec" ];then
echo "You must have one RD (panyx0718 or chengduoZH or XiaoguangHu01) and one PM (shanyi15) approval for the api change! ${API_FILE}"
else
echo "You must have panyx0718 approval for the api change! ${API_FILE}"
echo "You must have one RD (velconia,panyx0718,XiaoguangHu01,chengduoZH,Xreki,luotao1,sneaxiy,tensor-tang,jacquesqiao,typhoonzero) approval for the api change! ${API_FILE}"
fi
exit 1
fi
......@@ -479,10 +480,10 @@ function assert_api_spec_approvals() {
HAS_CONST_CAST=`git diff -U0 upstream/$BRANCH |grep -o -m 1 "const_cast" || true`
if [ ${HAS_CONST_CAST} ] && [ "${GIT_PR_ID}" != "" ]; then
APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \
python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 2887803`
python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 2887803 1979255 21351065 3048612 13348433 46782768 30176695 12538138 6836917 32832641`
echo "current pr ${GIT_PR_ID} got approvals: ${APPROVALS}"
if [ "${APPROVALS}" == "FALSE" ]; then
echo "You must have panyx0718 approval for the const_cast"
echo "You must have one RD (velconia,panyx0718,XiaoguangHu01,chengduoZH,Xreki,luotao1,sneaxiy,tensor-tang,jacquesqiao,typhoonzero) approval for the api change! ${API_FILE}"
exit 1
fi
fi
......
python/paddle/fluid/__init__.py
浏览文件 @ 86511f51
......@@ -132,7 +132,8 @@ def __bootstrap__():
'allocator_strategy', 'reader_queue_speed_test_mode',
'print_sub_graph_dir', 'pe_profile_fname', 'warpctc_dir',
'inner_op_parallelism', 'enable_parallel_graph',
'multiple_of_cupti_buffer_size', 'enable_subgraph_optimize',
'fuse_parameter_groups_size', 'multiple_of_cupti_buffer_size',
'enable_subgraph_optimize', 'fuse_parameter_memory_size',
'tracer_profile_fname'
]
if 'Darwin' not in sysstr:
......
python/paddle/fluid/contrib/slim/quantization/quantization_pass.py
浏览文件 @ 86511f51
......@@ -22,6 +22,7 @@ from ....framework import IrGraph
from ....framework import IrNode
from ....framework import Program
from ....initializer import Constant
from ....initializer import NumpyArrayInitializer
from .... import unique_name
__all__ = [
......@@ -54,14 +55,15 @@ class QuantizationTransformPass(object):
the bias is not quantized.
activation_bits (int): quantization bit number for activation.
activation_quantize_type (str): quantization type for activation,
now support 'abs_max', 'range_abs_max'. If use 'abs_max' mode,
the quantization scale will be calculated dynamically each step
in both training and testing period. If use 'range_abs_max',
a static quantization scale will be calculated during training
and used in inference.
now support 'abs_max', 'range_abs_max' and 'moving_average_abs_max'.
If use 'abs_max' mode, the quantization scale will be calculated
dynamically each step in both training and testing period. If use
'range_abs_max', a static quantization scale will be calculated
during training and used in inference.
weight_quantize_type (str): quantization type for weights,
support 'abs_max'. The 'range_abs_max' usually is not used for
weight, since weights are fixed once the model is well trained.
support 'abs_max' and 'channel_wise_abs_max'. The 'range_abs_max'
usually is not used for weight, since weights are fixed once the
model is well trained.
window_size (int): the window size for 'range_abs_max' quantization.
Examples:
......@@ -84,7 +86,11 @@ class QuantizationTransformPass(object):
self._weight_bits = weight_bits
self._activation_bits = activation_bits
quant_type = ['abs_max', 'range_abs_max', 'moving_average_abs_max']
quant_type = [
'abs_max', 'channel_wise_abs_max', 'range_abs_max',
'moving_average_abs_max'
]
assert activation_quantize_type != 'channel_wise_abs_max', "The activation quantization type does not support 'channel_wise_abs_max'."
if activation_quantize_type not in quant_type:
raise ValueError(
"Unknown activation_quantize_type : '%s'. It can only be ",
......@@ -93,7 +99,7 @@ class QuantizationTransformPass(object):
if weight_quantize_type not in quant_type:
raise ValueError(
"Unknown weight_quantize_type: '%s'. It can only be ",
"'abs_max' or 'range_abs_max' or 'moving_average_abs_max'.",
"'abs_max' or 'channel_wise_abs_max' or 'range_abs_max' or 'moving_average_abs_max'.",
str(weight_quantize_type))
self._activation_quantize_type = activation_quantize_type
......@@ -103,6 +109,7 @@ class QuantizationTransformPass(object):
self._need_initialized = collections.OrderedDict()
self._quantizable_ops = ['conv2d', 'depthwise_conv2d', 'mul']
self._conv_ops = ['conv2d', 'depthwise_conv2d']
self._quantizable_grad_ops = [
'%s_grad' % (op) for op in self._quantizable_ops
]
......@@ -135,6 +142,22 @@ class QuantizationTransformPass(object):
else self._activation_bits
quant_type = self._weight_quantize_type if var_node.name() \
in persistable_vars else self._activation_quantize_type
if quant_type == 'channel_wise_abs_max':
assert var_node.name(
) in persistable_vars, "'channel_wise_abs_max' can only be applied on weights."
if op.name() in self._conv_ops:
quant_var_node, scale_var_node = self._insert_channel_quant_op(
graph, var_node, quant_bits)
dequant_var_node = self._insert_channel_dequant_op(
graph, quant_var_node, [scale_var_node],
[quant_bits])
else:
quant_var_node, scale_var_node = self._insert_quant_op(
graph, var_node, quant_bits, 'abs_max')
dequant_var_node = self._insert_dequant_op(
graph, quant_var_node, scale_var_node,
quant_bits)
else:
quant_var_node, scale_var_node = self._insert_quant_op(
graph, var_node, quant_bits, quant_type)
dequant_var_node = self._insert_dequant_op(
......@@ -244,7 +267,7 @@ class QuantizationTransformPass(object):
scale_var_node = graph.create_var_node(
name=self._quantized_scale_name(var_node.name()),
var_type=var_node.type(),
shape=var_node.shape(),
shape=[1],
var_dtype=var_node.dtype())
quant_op_node = graph.create_op_node(
op_type='fake_quantize_abs_max',
......@@ -384,6 +407,36 @@ class QuantizationTransformPass(object):
return quant_var_node, scale_out_node
def _insert_channel_quant_op(self, graph, var_node, quant_bits):
"""
Insert fake_channel_wise_quantize_abs_max op in the graph.
"""
assert var_node.is_var(), '{} is not a var'.format(var_node.name())
quant_var_node = graph.create_var_node(
name=self._quantized_var_name(var_node.name()),
var_type=var_node.type(),
shape=var_node.shape(),
var_dtype=var_node.dtype())
scale_var_node = graph.create_var_node(
name=self._quantized_scale_name(var_node.name()),
var_type=var_node.type(),
shape=[var_node.shape()[0]],
var_dtype=var_node.dtype())
quant_op_node = graph.create_op_node(
op_type='fake_channel_wise_quantize_abs_max',
attrs={
'bit_length': quant_bits,
'op_role': core.op_proto_and_checker_maker.OpRole.Forward
},
inputs={'X': var_node},
outputs={'Out': quant_var_node,
'OutScale': scale_var_node})
graph.link_to(var_node, quant_op_node)
graph.link_to(quant_op_node, quant_var_node)
graph.link_to(quant_op_node, scale_var_node)
return quant_var_node, scale_var_node
def _insert_dequant_op(self, graph, var_node, scale_var_node, quant_bits):
"""
Insert fake_dequantize_op in the graph.
......@@ -410,6 +463,33 @@ class QuantizationTransformPass(object):
graph.link_to(dequant_op_node, dequant_var_node)
return dequant_var_node
def _insert_channel_dequant_op(self, graph, var_node, scale_var_nodes,
quant_bits):
"""
Insert fake_channel_wise_dequantize_max_abs in the graph.
"""
assert var_node.is_var(), '{} is not a var'.format(var_node.name())
dequant_var_node = graph.create_var_node(
name=self._dequantized_var_name(var_node.name()),
var_type=var_node.type(),
shape=var_node.shape(),
var_dtype=var_node.dtype())
dequant_op_node = graph.create_op_node(
op_type='fake_channel_wise_dequantize_max_abs',
attrs={
'quant_bits': quant_bits,
'op_role': core.op_proto_and_checker_maker.OpRole.Forward
},
inputs={'X': var_node,
'Scales': scale_var_nodes},
outputs={'Out': dequant_var_node})
graph.link_to(var_node, dequant_op_node)
for scale_n in scale_var_nodes:
graph.link_to(scale_n, dequant_op_node)
graph.link_to(dequant_op_node, dequant_var_node)
return dequant_var_node
def _quantized_var_name(self, var_name):
"""
Return quantized variable name for the input `var_name`.
......@@ -442,7 +522,7 @@ class QuantizationFreezePass(object):
place(fluid.CPUPlace|fluid.CUDAPlace): place is used to restore the weight tensors.
weight_bits (int): quantization bit number for weights.
activation_bits (int): quantization bit number for activation.
weight_quantize_type (str): quantization type for weights, support 'abs_max'.
weight_quantize_type (str): quantization type for weights, support 'abs_max' and 'channel_wise_abs_max'.
The 'range_abs_max' usually is not used for weight, since weights are fixed once the
model is well trained.
"""
......@@ -463,11 +543,15 @@ class QuantizationFreezePass(object):
self._activation_bits = activation_bits
self._weight_quantize_type = weight_quantize_type
self._quantizable_ops = ['conv2d', 'depthwise_conv2d', 'mul']
self._conv_ops = ['conv2d', 'depthwise_conv2d']
self._fake_quant_op_names = [
'fake_quantize_abs_max', 'fake_quantize_range_abs_max',
'fake_quantize_moving_average_abs_max'
'fake_quantize_moving_average_abs_max',
'fake_channel_wise_quantize_abs_max'
]
self._fake_dequant_op_names = [
'fake_dequantize_max_abs', 'fake_channel_wise_dequantize_max_abs'
]
self._fake_dequant_op_names = ['fake_dequantize_max_abs']
self._op_input_rename_map = collections.OrderedDict()
self._op_output_rename_map = collections.OrderedDict()
self._var_scale_map = collections.OrderedDict()
......@@ -489,20 +573,27 @@ class QuantizationFreezePass(object):
if self._weight_quantize_type == 'abs_max':
param = self._load_var(input_arg_name)
scale_v = np.max(np.abs(param))
elif self._weight_quantize_type == 'channel_wise_abs_max':
param = self._load_var(input_arg_name)
if len(param.shape) == 4: # conv2d or depthwise_conv2d
scale_v = []
for i in range(param.shape[0]):
scale_v.append(np.max(np.abs(param[i])))
else:
scale_v = np.max(np.abs(param))
else:
scale_v = self._load_var(
op_node.output('OutScale')[0])[0]
self._var_scale_map[input_arg_name] = scale_v
else:
scale_v = graph.var_node(op_node.output('OutScale')[0])
self._var_scale_map[input_arg_name] = scale_v
if input_arg_name in persistable_vars:
self._remove_fake_quant_and_dequant_op(graph, op_node)
# quantize weight and restore
param_v = self._load_var(input_arg_name)
quantized_param_v = self._quant(param_v, scale_v,
self._weight_bits)
self._restore_var(input_arg_name, quantized_param_v)
else:
scale_v = graph.var_node(op_node.output('OutScale')[0])
self._var_scale_map[input_arg_name] = scale_v
ops = graph.all_op_nodes()
for op_node in ops:
......@@ -514,6 +605,9 @@ class QuantizationFreezePass(object):
for op_node in ops:
op_name = op_node.name()
if op_name in self._quantizable_ops:
if self._weight_quantize_type == 'channel_wise_abs_max' and op_name in self._conv_ops:
self._insert_post_channel_dequant_op(graph, op_node)
else:
self._insert_post_dequant_op(graph, op_node)
for op_node in ops:
......@@ -538,9 +632,73 @@ class QuantizationFreezePass(object):
self._op_input_rename_map[k] = self._op_input_rename_map[v]
graph.safe_remove_nodes(op_node)
def _insert_post_channel_dequant_op(self, graph, op_node):
persistable_vars = [p.name() for p in graph.all_persistable_nodes()]
for var_node in op_node.inputs:
name = var_node.name()
if name in self._op_input_rename_map:
old_in = graph.var_node(name)
new_in = graph.var_node(self._op_input_rename_map[name])
new_in.clear_outputs()
graph.update_input_link(old_in, new_in, op_node)
original_var_name = self._original_var_name(name)
scale_v = self._var_scale_map[original_var_name]
if original_var_name in persistable_vars:
assert isinstance(
scale_v,
list), 'The scale of parameter %s is not a list.' % (
original_var_name)
channel_scale = np.array(scale_v)
else:
assert isinstance(scale_v, IrNode)
scale_var_node = self._var_scale_map[original_var_name]
if len(op_node.outputs) != 1:
raise ValueError("Only support one output, but op %s has"
" more than one output." % (op_node.name()))
output_var_node = op_node.outputs[0]
weight_scale_node = graph.create_persistable_node(
name=unique_name.generate('channel_scale'),
var_type=core.VarDesc.VarType.LOD_TENSOR,
shape=[channel_scale.shape[0]],
var_dtype=output_var_node.dtype())
init_program = Program()
weight_scale_var = init_program.global_block().create_var(
name=weight_scale_node.name(),
shape=weight_scale_node.shape(),
dtype=weight_scale_node.dtype(),
type=weight_scale_node.type(),
lod_level=weight_scale_node.var().lod_level(),
persistable=weight_scale_node.persistable())
initializer = NumpyArrayInitializer(value=channel_scale)
initializer(weight_scale_var, init_program.global_block())
exe = Executor(self._place)
exe.run(program=init_program, scope=self._scope)
dequant_var_node = graph.create_var_node(
name=self._dequantized_var_name(output_var_node.name()),
var_type=output_var_node.type(),
shape=output_var_node.shape(),
var_dtype=output_var_node.dtype())
dequant_op_node = graph.create_op_node(
op_type='fake_channel_wise_dequantize_max_abs',
attrs={
'quant_bits': [self._weight_bits, self._activation_bits],
'op_role': core.op_proto_and_checker_maker.OpRole.Forward
},
inputs={
'X': output_var_node,
'Scales': [weight_scale_node, scale_var_node]
},
outputs={'Out': dequant_var_node})
graph.link_to(output_var_node, dequant_op_node)
graph.link_to(scale_var_node, dequant_op_node)
graph.link_to(weight_scale_node, dequant_op_node)
graph.link_to(dequant_op_node, dequant_var_node)
self._op_output_rename_map[output_var_node.name()] = dequant_var_node
return dequant_var_node
def _insert_post_dequant_op(self, graph, op_node):
max_range = None
scale_var_node = None
persistable_vars = [p.name() for p in graph.all_persistable_nodes()]
for var_node in op_node.inputs:
name = var_node.name()
......@@ -637,6 +795,11 @@ class QuantizationFreezePass(object):
or isinstance(v, np.float64)
def _quant(self, x, scale, num_bits):
if isinstance(scale, list):
for i, s in enumerate(scale):
x[i] = np.round(x[i] / s * ((1 << (num_bits - 1)) - 1))
return x
else:
return np.round(x / scale * ((1 << (num_bits - 1)) - 1))
......@@ -731,9 +894,13 @@ class TransformForMobilePass(object):
def __init__(self):
self._fake_quant_op_names = [
'fake_quantize_abs_max', 'fake_quantize_range_abs_max'
'fake_quantize_abs_max', 'fake_quantize_range_abs_max',
'fake_quantize_moving_average_abs_max',
'fake_channel_wise_quantize_abs_max'
]
self._fake_dequant_op_names = [
'fake_dequantize_max_abs', 'fake_channel_wise_dequantize_max_abs'
]
self._fake_dequant_op_names = ['fake_dequantize_max_abs']
def apply(self, graph):
"""
......
python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py
浏览文件 @ 86511f51
......@@ -127,7 +127,7 @@ class TestQuantizationTransformPass(unittest.TestCase):
arg_name.endswith('.quantized.dequantized'))
self.assertTrue(arg_name in quantized_ops)
def linear_fc_quant(self, quant_type, for_ci=False):
def linear_fc_quant(self, activation_quant_type, for_ci=False):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
......@@ -140,14 +140,15 @@ class TestQuantizationTransformPass(unittest.TestCase):
transform_pass = QuantizationTransformPass(
scope=fluid.global_scope(),
place=place,
activation_quantize_type=quant_type)
activation_quantize_type=activation_quant_type)
transform_pass.apply(graph)
if not for_ci:
marked_nodes = set()
for op in graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
graph.draw('.', 'quantize_fc_' + quant_type, marked_nodes)
graph.draw('.', 'quantize_fc_' + activation_quant_type,
marked_nodes)
program = graph.to_program()
self.check_program(transform_pass, program)
val_graph = IrGraph(core.Graph(program.desc), for_test=False)
......@@ -156,7 +157,8 @@ class TestQuantizationTransformPass(unittest.TestCase):
for op in val_graph.all_op_nodes():
if op.name().find('quantize') > -1:
val_marked_nodes.add(op)
val_graph.draw('.', 'val_fc_' + quant_type, val_marked_nodes)
val_graph.draw('.', 'val_fc_' + activation_quant_type,
val_marked_nodes)
def test_linear_fc_quant_abs_max(self):
self.linear_fc_quant('abs_max', for_ci=True)
......@@ -167,7 +169,7 @@ class TestQuantizationTransformPass(unittest.TestCase):
def test_linear_fc_quant_moving_average_abs_max(self):
self.linear_fc_quant('moving_average_abs_max', for_ci=True)
def residual_block_quant(self, quant_type, for_ci=False):
def residual_block_quant(self, activation_quant_type, for_ci=False):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
......@@ -180,14 +182,15 @@ class TestQuantizationTransformPass(unittest.TestCase):
transform_pass = QuantizationTransformPass(
scope=fluid.global_scope(),
place=place,
activation_quantize_type=quant_type)
activation_quantize_type=activation_quant_type)
transform_pass.apply(graph)
if not for_ci:
marked_nodes = set()
for op in graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
graph.draw('.', 'quantize_residual_' + quant_type, marked_nodes)
graph.draw('.', 'quantize_residual_' + activation_quant_type,
marked_nodes)
program = graph.to_program()
self.check_program(transform_pass, program)
val_graph = IrGraph(core.Graph(program.desc), for_test=False)
......@@ -196,7 +199,8 @@ class TestQuantizationTransformPass(unittest.TestCase):
for op in val_graph.all_op_nodes():
if op.name().find('quantize') > -1:
val_marked_nodes.add(op)
val_graph.draw('.', 'val_residual_' + quant_type, val_marked_nodes)
val_graph.draw('.', 'val_residual_' + activation_quant_type,
val_marked_nodes)
def test_residual_block_abs_max(self):
self.residual_block_quant('abs_max', for_ci=True)
......@@ -209,7 +213,12 @@ class TestQuantizationTransformPass(unittest.TestCase):
class TestQuantizationFreezePass(unittest.TestCase):
def freeze_graph(self, use_cuda, seed, quant_type, for_ci=False):
def freeze_graph(self,
use_cuda,
seed,
activation_quant_type,
weight_quant_type='abs_max',
for_ci=False):
def build_program(main, startup, is_test):
main.random_seed = seed
startup.random_seed = seed
......@@ -243,7 +252,12 @@ class TestQuantizationFreezePass(unittest.TestCase):
with fluid.scope_guard(scope):
exe.run(startup)
transform_pass = QuantizationTransformPass(
scope=scope, place=place, activation_quantize_type=quant_type)
scope=scope,
place=place,
activation_quantize_type=activation_quant_type,
weight_quantize_type=weight_quant_type)
#transform_pass = QuantizationTransformPass(
# scope=scope, place=place, activation_quantize_type=activation_quant_type)
transform_pass.apply(main_graph)
transform_pass.apply(test_graph)
dev_name = '_gpu_' if use_cuda else '_cpu_'
......@@ -252,12 +266,14 @@ class TestQuantizationFreezePass(unittest.TestCase):
for op in main_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
main_graph.draw('.', 'main' + dev_name + quant_type, marked_nodes)
main_graph.draw('.', 'main' + dev_name + activation_quant_type + '_'
+ weight_quant_type, marked_nodes)
marked_nodes = set()
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test' + dev_name + quant_type, marked_nodes)
test_graph.draw('.', 'test' + dev_name + activation_quant_type + '_'
+ weight_quant_type, marked_nodes)
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = False
......@@ -282,8 +298,9 @@ class TestQuantizationFreezePass(unittest.TestCase):
feed=feeder.feed(data),
fetch_list=[loss])
if not for_ci:
print('{}: {}'.format('loss' + dev_name + quant_type,
loss_v))
print('{}: {}'.format('loss' + dev_name +
activation_quant_type + '_' +
weight_quant_type, loss_v))
test_data = next(test_reader())
with fluid.program_guard(quantized_test_program):
......@@ -296,14 +313,17 @@ class TestQuantizationFreezePass(unittest.TestCase):
fetch_list=[loss, w_var])
# Freeze graph for inference, but the weight of fc/conv is still float type.
freeze_pass = QuantizationFreezePass(scope=scope, place=place)
freeze_pass = QuantizationFreezePass(
scope=scope, place=place, weight_quantize_type=weight_quant_type)
#freeze_pass = QuantizationFreezePass(scope=scope, place=place)
freeze_pass.apply(test_graph)
if not for_ci:
marked_nodes = set()
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_freeze' + dev_name + quant_type,
test_graph.draw('.', 'test_freeze' + dev_name +
activation_quant_type + '_' + weight_quant_type,
marked_nodes)
server_program = test_graph.to_program()
......@@ -313,18 +333,20 @@ class TestQuantizationFreezePass(unittest.TestCase):
fetch_list=[loss])
self.assertAlmostEqual(test_loss1, test_loss2, delta=5e-3)
if not for_ci:
print('{}: {}'.format('test_loss1' + dev_name + quant_type,
test_loss1))
print('{}: {}'.format('test_loss2' + dev_name + quant_type,
test_loss2))
print(
'{}: {}'.format('test_loss1' + dev_name + activation_quant_type
+ '_' + weight_quant_type, test_loss1))
print(
'{}: {}'.format('test_loss2' + dev_name + activation_quant_type
+ '_' + weight_quant_type, test_loss2))
w_freeze = np.array(scope.find_var('conv2d_1.w_0').get_tensor())
# Maybe failed, this is due to the calculation precision
# self.assertAlmostEqual(np.sum(w_freeze), np.sum(w_quant))
if not for_ci:
print('{}: {}'.format('w_freeze' + dev_name + quant_type,
np.sum(w_freeze)))
print('{}: {}'.format('w_quant' + dev_name + quant_type,
np.sum(w_quant)))
print('{}: {}'.format('w_freeze' + dev_name + activation_quant_type
+ '_' + weight_quant_type, np.sum(w_freeze)))
print('{}: {}'.format('w_quant' + dev_name + activation_quant_type +
'_' + weight_quant_type, np.sum(w_quant)))
# Convert parameter to 8-bit.
convert_int8_pass = ConvertToInt8Pass(scope=scope, place=place)
......@@ -334,26 +356,28 @@ class TestQuantizationFreezePass(unittest.TestCase):
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_int8' + dev_name + quant_type,
marked_nodes)
test_graph.draw('.', 'test_int8' + dev_name + activation_quant_type
+ '_' + weight_quant_type, marked_nodes)
server_program_int8 = test_graph.to_program()
# Save the 8-bit parameter and model file.
with fluid.scope_guard(scope):
fluid.io.save_inference_model('server_int8' + dev_name + quant_type,
['image', 'label'], [loss], exe,
fluid.io.save_inference_model(
'server_int8' + dev_name + activation_quant_type + '_' +
weight_quant_type, ['image', 'label'], [loss], exe,
server_program_int8)
# Test whether the 8-bit parameter and model file can be loaded successfully.
[infer, feed, fetch] = fluid.io.load_inference_model(
'server_int8' + dev_name + quant_type, exe)
'server_int8' + dev_name + activation_quant_type + '_' +
weight_quant_type, exe)
# Check the loaded 8-bit weight.
w_8bit = np.array(scope.find_var('conv2d_1.w_0.int8').get_tensor())
self.assertEqual(w_8bit.dtype, np.int8)
self.assertEqual(np.sum(w_8bit), np.sum(w_freeze))
if not for_ci:
print('{}: {}'.format('w_8bit' + dev_name + quant_type,
np.sum(w_8bit)))
print('{}: {}'.format('w_freeze' + dev_name + quant_type,
np.sum(w_freeze)))
print('{}: {}'.format('w_8bit' + dev_name + activation_quant_type +
'_' + weight_quant_type, np.sum(w_8bit)))
print('{}: {}'.format('w_freeze' + dev_name + activation_quant_type
+ '_' + weight_quant_type, np.sum(w_freeze)))
mobile_pass = TransformForMobilePass()
mobile_pass.apply(test_graph)
......@@ -362,42 +386,103 @@ class TestQuantizationFreezePass(unittest.TestCase):
for op in test_graph.all_op_nodes():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_mobile' + dev_name + quant_type,
test_graph.draw('.', 'test_mobile' + dev_name +
activation_quant_type + '_' + weight_quant_type,
marked_nodes)
mobile_program = test_graph.to_program()
with fluid.scope_guard(scope):
fluid.io.save_inference_model('mobile_int8' + dev_name + quant_type,
['image', 'label'], [loss], exe,
fluid.io.save_inference_model(
'mobile_int8' + dev_name + activation_quant_type + '_' +
weight_quant_type, ['image', 'label'], [loss], exe,
mobile_program)
def test_freeze_graph_cuda_dynamic(self):
if fluid.core.is_compiled_with_cuda():
with fluid.unique_name.guard():
self.freeze_graph(
True, seed=1, quant_type='abs_max', for_ci=True)
True,
seed=1,
activation_quant_type='abs_max',
weight_quant_type='abs_max',
for_ci=True)
with fluid.unique_name.guard():
self.freeze_graph(
True,
seed=1,
activation_quant_type='abs_max',
weight_quant_type='channel_wise_abs_max',
for_ci=True)
def test_freeze_graph_cpu_dynamic(self):
with fluid.unique_name.guard():
self.freeze_graph(False, seed=2, quant_type='abs_max', for_ci=True)
self.freeze_graph(
False,
seed=2,
activation_quant_type='abs_max',
weight_quant_type='abs_max',
for_ci=True)
self.freeze_graph(
False,
seed=2,
activation_quant_type='abs_max',
weight_quant_type='channel_wise_abs_max',
for_ci=True)
def test_freeze_graph_cuda_static(self):
if fluid.core.is_compiled_with_cuda():
with fluid.unique_name.guard():
self.freeze_graph(
True, seed=1, quant_type='range_abs_max', for_ci=True)
True,
seed=1,
activation_quant_type='range_abs_max',
weight_quant_type='abs_max',
for_ci=True)
self.freeze_graph(
True,
seed=1,
activation_quant_type='moving_average_abs_max',
weight_quant_type='abs_max',
for_ci=True)
self.freeze_graph(
True,
seed=1,
activation_quant_type='range_abs_max',
weight_quant_type='channel_wise_abs_max',
for_ci=True)
self.freeze_graph(
True,
seed=1,
quant_type='moving_average_abs_max',
activation_quant_type='moving_average_abs_max',
weight_quant_type='channel_wise_abs_max',
for_ci=True)
def test_freeze_graph_cpu_static(self):
with fluid.unique_name.guard():
self.freeze_graph(
False, seed=2, quant_type='range_abs_max', for_ci=True)
False,
seed=2,
activation_quant_type='range_abs_max',
weight_quant_type='abs_max',
for_ci=True)
self.freeze_graph(
False,
seed=2,
activation_quant_type='moving_average_abs_max',
weight_quant_type='abs_max',
for_ci=True)
self.freeze_graph(
False,
seed=2,
activation_quant_type='range_abs_max',
weight_quant_type='channel_wise_abs_max',
for_ci=True)
self.freeze_graph(
False, seed=2, quant_type='moving_average_abs_max', for_ci=True)
False,
seed=2,
activation_quant_type='moving_average_abs_max',
weight_quant_type='channel_wise_abs_max',
for_ci=True)
if __name__ == '__main__':
......
python/paddle/fluid/framework.py
浏览文件 @ 86511f51
......@@ -644,10 +644,9 @@ class Operator(object):
outputs={"Out": [var1]})
"""
OP_WITHOUT_KERNEL_SET = {
'feed', 'fetch', 'save', 'load', 'recurrent', 'go',
'rnn_memory_helper_grad', 'conditional_block', 'while', 'send', 'recv',
'listen_and_serv', 'save_combine', 'load_combine', 'ncclInit', 'select',
'checkpoint_notify', 'gen_nccl_id'
'feed', 'fetch', 'recurrent', 'go', 'rnn_memory_helper_grad',
'conditional_block', 'while', 'send', 'recv', 'listen_and_serv',
'ncclInit', 'select', 'checkpoint_notify', 'gen_nccl_id'
}
def __init__(self,
......
python/paddle/fluid/imperative/__init__.py
浏览文件 @ 86511f51
......@@ -29,9 +29,13 @@ from .tracer import *
from . import profiler
from .profiler import *
from . import checkpoint
from .checkpoint import *
__all__ = []
__all__ += layers.__all__
__all__ += base.__all__
__all__ += nn.__all__
__all__ += tracer.__all__
__all__ += profiler.__all__
__all__ += checkpoint.__all__
python/paddle/fluid/imperative/checkpoint.py 0 → 100644
浏览文件 @ 86511f51
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import os
import collections
from .. import core
from ..framework import Variable, default_main_program
__all__ = ['save_persistables', 'load_persistables']
def save_persistables(vardict, dirname, filename=None):
"""
This function filters out all variables in layer.parameters from the
give `layer` and then trys to load these variables from the folder
`dirname` or the file `filename`.
Use the `dirname` to specify the folder where persistable variables were
saved. If variables were saved in separate files, set `filename` None;
if all variables were saved in a single file, use `filename` to specify
the file name.
Args:
vardict(dict of Parameters): The parameters will
be saved. If it is None, nothing
will be deal.
dirname(str): The directory path.
filename(str|None): The file which saved all variables. If variables were
saved in differnet files, set it to None.
Default: None
Returns:
Examples:
.. code-block:: python
ptb_model = PtbModel(
hidden_size=hidden_size,
vocab_size=vocab_size,
num_layers=num_layers,
num_steps=num_steps,
init_scale=init_scale)
x_data = np.arange(12).reshape(4, 3).astype('int64')
y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
x_data = x_data.reshape((-1, num_steps, 1))
y_data = y_data.reshape((-1, 1))
init_hidden_data = np.zeros(
(num_layers, batch_size, hidden_size), dtype='float32')
init_cell_data = np.zeros(
(num_layers, batch_size, hidden_size), dtype='float32')
x = to_variable(x_data)
y = to_variable(y_data)
init_hidden = to_variable(init_hidden_data)
init_cell = to_variable(init_cell_data)
dy_loss, last_hidden, last_cell = ptb_model(x, y, init_hidden,
init_cell)
param_path = "./my_paddle_model"
fluid.imperative.checkpoint.save_persistables(ptb_model.state_dict(), dirname=param_path,
layer=ptb_model)
"""
if isinstance(vardict, collections.OrderedDict):
_save_var_to_file(vardict, dirname, filename)
def load_persistables(vardict, dirname, filename=None):
"""
This function trys to load persistable variables from the folder
`dirname` or the file `filename`.
Use the `dirname` to specify the folder where persistable variables were
saved. If variables were saved in separate files, set `filename` None;
if all variables were saved in a single file, use `filename` to specify
the file name.
Args:
vardict(dict of Parameters): The parameters will be loaded.
dirname(str): The directory path.
filename(str|None): The file which saved all variables, this file path should be end with '.npz'. If variables were
saved in differnet files, set it to None.
Default: None
Returns:
dict: The parameter-dict resumed from file
Examples:
.. code-block:: python
my_layer = layer(fluid.imperative.Layer)
param_path = "./my_paddle_model"
param_dict = fluid.imperative.checkpoint.load_persistables(my_layer.parameters(), param_path)
param_1 = param_dict['PtbModel_0.w_1']
or:
my_layer = layer(fluid.imperative.Layer)
param_path = "./my_paddle_model"
filename = "model.file"
param_dict = fluid.imperative.checkpoint.load_persistables(my_layer.state_dict(), param_path,
filename=filename)
param_1 = param_dict['PtbModel_0.w_1']
"""
if isinstance(vardict, collections.OrderedDict):
return _load_var_from_file(vardict, dirname, filename)
return {}
def _save_var_to_file(stat_dict, file_dir, file_name):
save_block = default_main_program().global_block()
save_var_map = {}
for each_var in stat_dict.items():
save_var_map[each_var.name] = each_var
if file_name is None:
save_block.append_op(
type='save',
inputs={'X': [each_var]},
outputs={},
attrs={'file_path': os.path.join(file_dir, each_var.name)})
if file_name is not None:
save_var_list = []
for name in sorted(save_var_map.keys()):
save_var_list.append(save_var_map[name])
save_block.append_op(
type='save_combine',
inputs={'X': save_var_list},
outputs={},
attrs={'file_path': os.path.join(file_dir, file_name)})
def _load_var_from_file(stat_dict, file_dir, file_name):
load_block = default_main_program().global_block()
load_var_map = {}
for each_var in stat_dict.items():
assert isinstance(each_var, Variable)
if each_var.type == core.VarDesc.VarType.RAW:
continue
new_var = _clone_var_in_block_(load_block, each_var)
if file_name is None:
load_block.append_op(
type='load',
inputs={},
outputs={'Out': [new_var]},
attrs={'file_path': os.path.join(file_dir, each_var.name)})
load_var_map[new_var.name] = new_var
if file_name is not None:
load_var_list = []
for name in sorted(load_var_map.keys()):
load_var_list.append(load_var_map[name])
load_block.append_op(
type='load_combine',
inputs={},
outputs={"Out": load_var_list},
attrs={'file_path': os.path.join(file_dir, file_name)})
for res_var in load_var_list:
load_var_map[res_var.name] = res_var
return load_var_map
def _clone_var_in_block_(block, var):
assert isinstance(var, Variable)
return block.create_var(
name=var.name,
shape=var.shape,
dtype=var.dtype,
type=var.type,
lod_level=var.lod_level,
persistable=True)
python/paddle/fluid/imperative/layers.py
浏览文件 @ 86511f51
......@@ -212,6 +212,34 @@ class Layer(core.Layer):
else:
object.__delattr__(self, name)
def state_dict(self, destination=None, prefix='', include_sublayers=True):
if destination is None:
destination = collections.OrderedDict()
for name, data in self._parameters.items():
if data is not None:
destination[prefix + name] = data
if include_sublayers:
for layer_name, layer_item in self._sub_layers.items():
if layer_item is not None:
destination_temp = destination.copy()
destination_temp.update(
layer_item.state_dict(destination_temp, prefix +
layer_name + ".",
include_sublayers))
destination = destination_temp
return destination
def load_dict(self, stat_dict, include_sublayers=True):
for name, item in self.__dict__.get('_parameters', None).items():
if item.name in stat_dict:
self.__setattr__(name, stat_dict[item.name])
if include_sublayers:
for layer_name, layer_item in self._sub_layers.items():
if layer_item is not None:
layer_item.load_dict(stat_dict)
class PyLayer(core.PyLayer):
"""Layers composed of user-defined python codes."""
......
python/paddle/fluid/io.py
浏览文件 @ 86511f51
......@@ -896,7 +896,7 @@ def save_inference_model(dirname,
True is supported.
Returns:
None
target_var_name_list(list): The fetch variables' name list
Raises:
ValueError: If `feed_var_names` is not a list of basestring.
......@@ -949,11 +949,13 @@ def save_inference_model(dirname,
# TODO(Superjomn) add an IR pass to remove 1-scale op.
with program_guard(main_program):
uniq_target_vars = []
for var in target_vars:
for i, var in enumerate(target_vars):
if isinstance(var, Variable):
var1 = layers.scale(var, 1.)
uniq_target_vars.append(var1)
var = layers.scale(
var, 1., name="save_infer_model/scale_{}".format(i))
uniq_target_vars.append(var)
target_vars = uniq_target_vars
target_var_name_list = [var.name for var in target_vars]
# when a pserver and a trainer running on the same machine, mkdir may conflict
try:
......@@ -1010,6 +1012,7 @@ def save_inference_model(dirname,
params_filename = os.path.basename(params_filename)
save_persistables(executor, dirname, main_program, params_filename)
return target_var_name_list
def load_inference_model(dirname,
......
python/paddle/fluid/layers/__init__.py
浏览文件 @ 86511f51
......@@ -33,6 +33,7 @@ from .detection import *
from . import metric_op
from .metric_op import *
from .learning_rate_scheduler import *
from .collective import *
__all__ = []
__all__ += nn.__all__
......
python/paddle/fluid/layers/collective.py 0 → 100644
浏览文件 @ 86511f51
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
from ..layer_helper import LayerHelper, unique_name
def _allreduce(x, out=None, reduce_type="sum"):
helper = LayerHelper("allreduce", **locals())
# Convert string reduce type to op int type
red_typ_int = 0
if reduce_type == "sum":
red_typ_int = 0
elif reduce_type == "prod":
red_typ_int = 1
elif reduce_type == "max":
red_typ_int = 2
elif reduce_type == "min":
red_typ_int = 3
else:
raise TypeError("reduce type can only be [sum|prod|max|min]")
if out is None:
out = helper.create_variable(
name=unique_name.generate(".".join([x.name, 'tmp'])),
shape=x.shape,
dtype=x.dtype,
type=x.type,
persistable=x.persistable,
stop_gradient=True)
helper.append_op(
type='allreduce',
inputs={'X': [x]},
outputs={'Out': [out]},
attrs={"reduce_type": red_typ_int})
return out
python/paddle/fluid/layers/control_flow.py
浏览文件 @ 86511f51
......@@ -28,21 +28,9 @@ import six
from functools import reduce
__all__ = [
'While',
'Switch',
'increment',
'array_write',
'create_array',
'less_than',
'equal',
'array_read',
'array_length',
'IfElse',
'DynamicRNN',
'StaticRNN',
'reorder_lod_tensor_by_rank',
'Print',
'is_empty',
'While', 'Switch', 'increment', 'array_write', 'create_array', 'less_than',
'equal', 'array_read', 'array_length', 'IfElse', 'DynamicRNN', 'StaticRNN',
'reorder_lod_tensor_by_rank', 'Print', 'is_empty'
]
......
python/paddle/fluid/layers/math_op_patch.py
浏览文件 @ 86511f51
......@@ -174,6 +174,8 @@ def monkey_patch_variable():
("__rtruediv__", "elementwise_div", True),
("__pow__", "elementwise_pow", False),
("__rpow__", "elementwise_pow", True),
("__floordiv__", "elementwise_floordiv", False),
("__mod__", "elementwise_mod", False),
# for logical compare
("__eq__", "equal", False),
("__ne__", "not_equal", False),
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 86511f51
......@@ -1348,7 +1348,7 @@ def dropout(x,
1. downgrade_in_infer(default), downgrade the outcome at inference
- train: out = input * mask
- inference: out = input * dropout_prob
- inference: out = input * (1.0 - dropout_prob)
(mask is a tensor same shape with input, value is 0 or 1
ratio of 0 is dropout_prob)
......@@ -4901,6 +4901,9 @@ def matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None):
if len(y_shape) > 2 and len(x_shape) > 2:
for i, dim_x in enumerate(x_shape[:-2]):
# don't check neg shape
if dim_x < 0 or y_shape[i] < 0:
continue
if dim_x != y_shape[i]:
raise ValueError("Invalid inputs for matmul. x(%s), y(%s)" %
(x.shape, y.shape))
......@@ -9228,9 +9231,24 @@ def elementwise_pow(x, y, axis=-1, act=None, name=None):
return _elementwise_op(LayerHelper('elementwise_pow', **locals()))
def elementwise_mod(x, y, axis=-1, act=None, name=None):
return _elementwise_op(LayerHelper('elementwise_mod', **locals()))
def elementwise_floordiv(x, y, axis=-1, act=None, name=None):
return _elementwise_op(LayerHelper('elementwise_floordiv', **locals()))
for func in [
elementwise_add, elementwise_div, elementwise_sub, elementwise_mul,
elementwise_max, elementwise_min, elementwise_pow
elementwise_add,
elementwise_div,
elementwise_sub,
elementwise_mul,
elementwise_max,
elementwise_min,
elementwise_pow,
elementwise_mod,
elementwise_floordiv,
]:
op_proto = OpProtoHolder.instance().get_op_proto(func.__name__)
func.__doc__ = _generate_doc_string_(
......@@ -9706,7 +9724,12 @@ def sequence_reverse(x, name=None):
return out
def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None):
def affine_channel(x,
scale=None,
bias=None,
data_layout='NCHW',
name=None,
act=None):
"""
Applies a separate affine transformation to each channel of the input.
Useful for replacing spatial batch norm with its equivalent fixed
......@@ -9725,6 +9748,7 @@ def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None):
data_layout (string, default NCHW): NCHW or NHWC. If input is 2D
tensor, you can ignore data_layout.
name (str, default None): The name of this layer.
act (str, default None): Activation to be applied to the output of this layer.
Returns:
out (Variable): A tensor of the same shape and data layout with x.
......@@ -9744,7 +9768,7 @@ def affine_channel(x, scale=None, bias=None, data_layout='NCHW', name=None):
'Bias': bias},
attrs={"data_layout": data_layout},
outputs={"Out": out})
return out
return helper.append_activation(pre_activation)
def similarity_focus(input, axis, indexes, name=None):
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ 86511f51
......@@ -70,6 +70,7 @@ list(REMOVE_ITEM TEST_OPS test_dist_transpiler)
list(REMOVE_ITEM TEST_OPS test_parallel_executor_crf)
list(REMOVE_ITEM TEST_OPS test_parallel_executor_fetch_feed)
list(REMOVE_ITEM TEST_OPS test_dist_se_resnext)
list(REMOVE_ITEM TEST_OPS test_dist_se_resnext_nccl)
list(REMOVE_ITEM TEST_OPS test_dist_transformer)
list(REMOVE_ITEM TEST_OPS test_parallel_executor_transformer)
list(REMOVE_ITEM TEST_OPS test_image_classification_resnet)
......@@ -97,6 +98,8 @@ if(WITH_DISTRIBUTE)
set_tests_properties(test_dist_word2vec PROPERTIES TIMEOUT 200)
py_test_modules(test_dist_se_resnext MODULES test_dist_se_resnext)
set_tests_properties(test_dist_se_resnext PROPERTIES TIMEOUT 1000)
py_test_modules(test_dist_se_resnext_nccl MODULES test_dist_se_resnext_nccl)
set_tests_properties(test_dist_se_resnext_nccl PROPERTIES TIMEOUT 1000)
# FIXME(typhoonzero): add these tests back
# py_test_modules(test_dist_transformer MODULES test_dist_transformer)
# set_tests_properties(test_dist_transformer PROPERTIES TIMEOUT 1000)
......
python/paddle/fluid/tests/unittests/dist_allreduce_op.py 0 → 100644
浏览文件 @ 86511f51
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import numpy as np
import argparse
import time
import math
import paddle
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler
from paddle.fluid import core
import unittest
from multiprocessing import Process
import os
import signal
from functools import reduce
from test_dist_base import TestDistRunnerBase, runtime_main
DTYPE = "float32"
paddle.dataset.mnist.fetch()
# Fix seed for test
fluid.default_startup_program().random_seed = 1
fluid.default_main_program().random_seed = 1
def cnn_model(data):
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=data,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu",
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
value=0.01)))
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
pool_size=2,
pool_stride=2,
act="relu",
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
value=0.01)))
SIZE = 10
input_shape = conv_pool_2.shape
param_shape = [reduce(lambda a, b: a * b, input_shape[1:], 1)] + [SIZE]
scale = (2.0 / (param_shape[0]**2 * SIZE))**0.5
predict = fluid.layers.fc(
input=conv_pool_2,
size=SIZE,
act="softmax",
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01)))
return predict
class TestDistMnist2x2(TestDistRunnerBase):
def get_model(self, batch_size=2, single_device=False):
# Input data
images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE)
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
# Train program
predict = cnn_model(images)
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
# Evaluator
batch_size_tensor = fluid.layers.create_tensor(dtype='int64')
batch_acc = fluid.layers.accuracy(
input=predict, label=label, total=batch_size_tensor)
inference_program = fluid.default_main_program().clone()
# Reader
train_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=batch_size)
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=batch_size)
# Optimization
# TODO(typhoonzero): fix distributed adam optimizer
# opt = fluid.optimizer.AdamOptimizer(
# learning_rate=0.001, beta1=0.9, beta2=0.999)
opt = fluid.optimizer.Momentum(learning_rate=self.lr, momentum=0.9)
if single_device:
opt.minimize(avg_cost)
else:
# multi device or distributed multi device
params_grads = opt.backward(avg_cost)
data_parallel_param_grads = []
for p, g in params_grads:
# NOTE: scale will be done on loss scale in multi_devices_graph_pass using nranks.
grad_reduce = fluid.layers.collective._allreduce(g)
data_parallel_param_grads.append([p, grad_reduce])
opt.apply_gradients(data_parallel_param_grads)
return inference_program, avg_cost, train_reader, test_reader, batch_acc, predict
if __name__ == "__main__":
runtime_main(TestDistMnist2x2)
python/paddle/fluid/tests/unittests/mkldnn/test_concat_int8_mkldnn_op.py 0 → 100644
浏览文件 @ 86511f51
# 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.
from __future__ import print_function
import unittest
import numpy as np
from paddle.fluid.tests.unittests.op_test import OpTest
class TestConcatOp(OpTest):
def setUp(self):
self.op_type = "concat"
self.use_mkldnn = True
self._cpu_only = True
self.init_axis()
self.init_shape()
self.init_test_data()
self.inputs = {'X': [('x0', self.x0), ('x1', self.x1), ('x2', self.x2)]}
self.attrs = {'axis': self.axis, 'use_mkldnn': True}
self.output = np.concatenate(
(self.x0, self.x1, self.x2), axis=self.axis).astype('int')
self.outputs = {'Out': self.output}
def test_check_output(self):
self.check_output()
#--------------------test concat s8 in with axis 0--------------------
def init_test_data(self):
self.x0 = (np.random.randint(0, 100, self.x0_shape) - 50).astype('int8')
self.x1 = (np.random.randint(0, 80, self.x1_shape) - 30).astype('int8')
self.x2 = (np.random.randint(0, 110, self.x2_shape) - 80).astype('int8')
def init_axis(self):
self.axis = 0
def init_shape(self):
self.x0_shape = [2, 2, 1, 2]
self.x1_shape = [1, 2, 1, 2]
self.x2_shape = [3, 2, 1, 2]
#--------------------test concat u8 in with axis 0--------------------
class TestConcatOp2(TestConcatOp):
def init_test_data(self):
self.x0 = (np.random.randint(0, 100, self.x0_shape)).astype('uint8')
self.x1 = (np.random.randint(0, 50, self.x1_shape)).astype('uint8')
self.x2 = (np.random.randint(0, 80, self.x2_shape)).astype('uint8')
def init_axis(self):
self.axis = 0
def init_shape(self):
self.x0_shape = [2, 1, 5, 5]
self.x1_shape = [1, 1, 5, 5]
self.x2_shape = [3, 1, 5, 5]
def create_test_int8_class(parent):
#--------------------test concat s8/u8 in with axis 1--------------------
class TestAxis1Case(parent):
def init_axis(self):
self.axis = 1
def init_shape(self):
self.x0_shape = [1, 1, 5, 5]
self.x1_shape = [1, 2, 5, 5]
self.x2_shape = [1, 3, 5, 5]
#--------------------test concat s8/u8 in with axis 2--------------------
class TestAxis2Case(parent):
def init_axis(self):
self.axis = 2
def init_shape(self):
self.x0_shape = [2, 3, 4, 5]
self.x1_shape = [2, 3, 5, 5]
self.x2_shape = [2, 3, 6, 5]
#--------------------test concat s8/u8 in with axis 3--------------------
class TestAxis3Case(parent):
def init_axis(self):
self.axis = 3
def init_shape(self):
self.x0_shape = [2, 3, 5, 5]
self.x1_shape = [2, 3, 5, 6]
self.x2_shape = [2, 3, 5, 7]
cls_name_1 = "{0}_axis_{1}".format(parent.__name__, "1")
cls_name_2 = "{0}_axis_{1}".format(parent.__name__, "2")
cls_name_3 = "{0}_axis_{1}".format(parent.__name__, "3")
TestAxis1Case.__name__ = cls_name_1
TestAxis2Case.__name__ = cls_name_2
TestAxis3Case.__name__ = cls_name_3
globals()[cls_name_1] = TestAxis1Case
globals()[cls_name_2] = TestAxis2Case
globals()[cls_name_3] = TestAxis3Case
create_test_int8_class(TestConcatOp)
create_test_int8_class(TestConcatOp2)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/ngraph/test_softmax_with_cross_entropy_ngraph_op.py 0 → 100644
浏览文件 @ 86511f51
# 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.
from __future__ import print_function
import unittest
from paddle.fluid.tests.unittests.test_softmax_with_cross_entropy_op import TestSoftmaxWithCrossEntropyOp, TestSoftmaxWithCrossEntropyOp2, TestSoftmaxWithCrossEntropyOp3
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/parallel_executor_test_base.py
浏览文件 @ 86511f51
......@@ -43,6 +43,7 @@ class TestParallelExecutorBase(unittest.TestCase):
use_ir_memory_optimize=True,
enable_inplace=True,
fuse_elewise_add_act_ops=False,
fuse_all_reduce_ops=False,
fuse_relu_depthwise_conv=False,
optimizer=fluid.optimizer.Adam,
use_fast_executor=False,
......@@ -80,6 +81,7 @@ class TestParallelExecutorBase(unittest.TestCase):
build_strategy.fuse_elewise_add_act_ops = fuse_elewise_add_act_ops
build_strategy.fuse_relu_depthwise_conv = fuse_relu_depthwise_conv
build_strategy.memory_optimize = False if memory_opt else use_ir_memory_optimize
build_strategy.fuse_all_reduce_ops = fuse_all_reduce_ops
# python memory optimization is conflict with inplace pass.
# Use ir graph memory optimization after inplace pass is the correct way.
build_strategy.enable_inplace = False if memory_opt else enable_inplace
......
python/paddle/fluid/tests/unittests/test_dist_allreduce_op.py 0 → 100644
浏览文件 @ 86511f51
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
from test_dist_base import TestDistBase
class TestDistMnistNCCL2(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._use_reduce = False
self._use_reader_alloc = False
self._nccl2_mode = True
self._nccl2_reduce_layer = True
def test_dist_train(self):
import paddle.fluid as fluid
if fluid.core.is_compiled_with_cuda():
self.check_with_place("dist_allreduce_op.py", delta=1e-5)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_dist_base.py
浏览文件 @ 86511f51
......@@ -33,7 +33,10 @@ DEFAULT_BATCH_SIZE = 2
class TestDistRunnerBase(object):
def get_model(self, batch_size=DEFAULT_BATCH_SIZE, lr=0.1):
def get_model(self,
batch_size=DEFAULT_BATCH_SIZE,
lr=0.1,
single_device=False):
raise NotImplementedError(
"get_model should be implemented by child classes.")
......@@ -76,6 +79,10 @@ class TestDistRunnerBase(object):
def run_trainer(self, args):
self.lr = args.lr
if args.nccl2_reduce_layer_local_run:
test_program, avg_cost, train_reader, test_reader, batch_acc, predict = \
self.get_model(batch_size=args.batch_size, single_device=True)
else:
test_program, avg_cost, train_reader, test_reader, batch_acc, predict = \
self.get_model(batch_size=args.batch_size)
......@@ -87,7 +94,7 @@ class TestDistRunnerBase(object):
args.endpoints, args.trainers,
args.sync_mode, args.dc_asgd)
trainer_prog = t.get_trainer_program()
elif args.update_method == "nccl2":
elif args.update_method == "nccl2" or args.update_method == "nccl2_reduce_layer":
# transpile for nccl2
config = fluid.DistributeTranspilerConfig()
config.mode = "nccl2"
......@@ -103,16 +110,17 @@ class TestDistRunnerBase(object):
trainer_prog = fluid.default_main_program()
if args.use_cuda:
place = fluid.CUDAPlace(0)
device_id = int(os.getenv("FLAGS_selected_gpus", "0"))
place = fluid.CUDAPlace(device_id)
else:
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
strategy = fluid.ExecutionStrategy()
strategy.num_threads = 1
strategy.allow_op_delay = False
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.num_threads = 1
exec_strategy.allow_op_delay = False
build_stra = fluid.BuildStrategy()
# FIXME force disable enable_inplace and memory_optimize
......@@ -124,23 +132,25 @@ class TestDistRunnerBase(object):
else:
build_stra.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.AllReduce
pass_builder = None
if args.batch_merge_repeat > 1:
pass_builder = build_stra._finalize_strategy_and_create_passes()
mypass = pass_builder.insert_pass(
len(pass_builder.all_passes()) - 3, "multi_batch_merge_pass")
mypass.set("num_repeats", args.batch_merge_repeat)
if args.update_method == "nccl2":
if args.update_method == "nccl2" or args.update_method == "nccl2_reduce_layer":
build_stra.num_trainers = len(args.endpoints.split(","))
build_stra.trainer_id = args.trainer_id
else:
# case args.update_method == "nccl2_reduce_layer":
build_stra.num_trainers = 1
build_stra.trainer_id = 0
binary = compiler.CompiledProgram(trainer_prog).with_data_parallel(
loss_name=avg_cost.name,
build_strategy=build_stra,
exec_strategy=strategy)
exec_strategy=exec_strategy)
feed_var_list = [
var for var in trainer_prog.global_block().vars.values()
......@@ -182,7 +192,7 @@ def runtime_main(test_class):
'--update_method',
type=str,
default="local",
choices=["pserver", "nccl2", "local"])
choices=["pserver", "nccl2", "local", "nccl2_reduce_layer"])
parser.add_argument('--trainer_id', type=int, required=False, default=0)
parser.add_argument('--trainers', type=int, required=False, default=1)
parser.add_argument(
......@@ -198,6 +208,11 @@ def runtime_main(test_class):
parser.add_argument('--lr', required=False, type=float, default=0.001)
parser.add_argument(
'--batch_merge_repeat', required=False, type=int, default=1)
parser.add_argument(
'--nccl2_reduce_layer_local_run',
required=False,
type=bool,
default=False)
args = parser.parse_args()
......@@ -242,6 +257,12 @@ class TestDistBase(unittest.TestCase):
self._dc_asgd = False # must use with async mode
self._use_reader_alloc = True
self._nccl2_mode = False
self._mp_mode = False
# FIXME(typhoonzero): I added this stupid argument to enable
# testing allreduce layers, which users can call layers.allreduce
# to accumulate tensors at anywhere. Find a better way to do this
# test, reduce check this argument everywhere.
self._nccl2_reduce_layer = False
self._lr = 0.001
self._setup_config()
self._after_setup_config()
......@@ -307,10 +328,16 @@ class TestDistBase(unittest.TestCase):
cmd += " --batch_size %d" % batch_size
if batch_merge_repeat > 1:
cmd += " --batch_merge_repeat %d" % batch_merge_repeat
if self._nccl2_reduce_layer:
cmd += " --nccl2_reduce_layer_local_run 1"
if self.__use_cuda:
cmd += " --use_cuda"
env_local = {"CUDA_VISIBLE_DEVICES": "0"}
env_local = {
"CUDA_VISIBLE_DEVICES": "0",
"PADDLE_TRAINERS_NUM": "1",
"PADDLE_TRAINER_ID": "0"
}
else:
env_local = {'CPU_NUM': '1'}
......@@ -427,29 +454,30 @@ class TestDistBase(unittest.TestCase):
sys.stderr.write("ps1 stderr: %s\n" % fn.read())
# print log
if stat0 == 0:
sys.stderr.write('trainer 0 stdout: %s\n' % pickle.loads(tr0_out))
with open("/tmp/tr0_err.log", "r") as fn:
sys.stderr.write('trainer 0 stderr: %s\n' % fn.read())
if stat1 == 0:
sys.stderr.write('trainer 1 stdout: %s\n' % pickle.loads(tr1_out))
with open("/tmp/tr1_err.log", "r") as fn:
sys.stderr.write('trainer 1 stderr: %s\n' % fn.read())
return pickle.loads(tr0_out), pickle.loads(tr1_out)
def _run_cluster_nccl2(self, model, envs, check_error_log):
def _run_cluster_nccl2(self, model, envs, nccl2_reduce_layer,
check_error_log):
# NOTE: we reuse ps_endpoints as nccl2 worker endpoints
worker_endpoints = self._ps_endpoints.split(",")
w0_ep, w1_ep = worker_endpoints
if nccl2_reduce_layer:
update_method = "nccl2_reduce_layer"
else:
update_method = "nccl2"
tr_cmd = "%s %s --role trainer --endpoints %s --trainer_id %d --current_endpoint %s --update_method nccl2 --lr %f"
tr_cmd = "%s %s --role trainer --endpoints %s --trainer_id %d --current_endpoint %s --update_method %s --lr %f"
tr0_cmd = tr_cmd % \
(self._python_interp, model, self._ps_endpoints,
0, w0_ep, self._lr)
0, w0_ep, update_method, self._lr)
tr1_cmd = tr_cmd % \
(self._python_interp, model, self._ps_endpoints,
1, w1_ep, self._lr)
1, w1_ep, update_method, self._lr)
if self._mem_opt:
tr0_cmd += " --mem_opt"
......@@ -463,12 +491,25 @@ class TestDistBase(unittest.TestCase):
if self.__use_cuda:
tr0_cmd += " --use_cuda"
tr1_cmd += " --use_cuda"
env0 = {"CUDA_VISIBLE_DEVICES": "0"}
env1 = {"CUDA_VISIBLE_DEVICES": "1"}
env0 = {
"CUDA_VISIBLE_DEVICES": "0",
# for test nccl2 layer
"PADDLE_TRAINERS_NUM": "2",
"PADDLE_TRAINER_ID": "0"
}
env1 = {
"CUDA_VISIBLE_DEVICES": "1",
"PADDLE_TRAINERS_NUM": "2",
"PADDLE_TRAINER_ID": "1"
}
else:
env0 = {'CPU_NUM': '1'}
env1 = {'CPU_NUM': '1'}
if self._mp_mode:
env0 = {"FLAGS_selected_gpus": "0"}
env1 = {"FLAGS_selected_gpus": "1"}
env0.update(envs)
env1.update(envs)
......@@ -498,8 +539,6 @@ class TestDistBase(unittest.TestCase):
# print log
sys.stderr.write('trainer 0 stderr: %s\n' % tr0_err)
sys.stderr.write('trainer 1 stderr: %s\n' % tr1_err)
sys.stderr.write('trainer 0 stdout: %s\n' % tr0_out)
sys.stderr.write('trainer 1 stdout: %s\n' % tr1_out)
return pickle.loads(tr0_out), pickle.loads(tr1_out)
......@@ -530,8 +569,12 @@ class TestDistBase(unittest.TestCase):
= self._run_local(model_file, required_envs,
check_error_log)
if self._nccl2_mode:
if self._nccl2_reduce_layer:
tr0_losses, tr1_losses = self._run_cluster_nccl2(
model_file, required_envs, check_error_log)
model_file, required_envs, True, check_error_log)
else:
tr0_losses, tr1_losses = self._run_cluster_nccl2(
model_file, required_envs, False, check_error_log)
else:
tr0_losses, tr1_losses = self._run_cluster(
model_file, required_envs, check_error_log)
......
python/paddle/fluid/tests/unittests/test_dist_se_resnext_nccl.py 0 → 100644
浏览文件 @ 86511f51
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
from test_dist_base import TestDistBase
import os
def skip_ci(func):
on_ci = bool(int(os.environ.get("SKIP_UNSTABLE_CI", '0')))
def __func__(*args, **kwargs):
if on_ci:
return
return func(*args, **kwargs)
return __func__
class TestDistSeResneXtNCCL(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._use_reader_alloc = False
self._nccl2_mode = True
@skip_ci
def test_dist_train(self):
import paddle.fluid as fluid
if fluid.core.is_compiled_with_cuda():
self.check_with_place("dist_se_resnext.py", delta=1e-5)
class TestDistSeResneXtNCCLMP(TestDistBase):
def _setup_config(self):
self._sync_mode = True
self._use_reader_alloc = False
self._nccl2_mode = True
self._mp_mode = True
@skip_ci
def test_dist_train(self):
import paddle.fluid as fluid
if fluid.core.is_compiled_with_cuda():
self.check_with_place(
"dist_se_resnext.py",
delta=1e-5,
need_envs={"NCCL_P2P_DISABLE": "1"})
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_elementwise_floordiv_op.py 0 → 100644
浏览文件 @ 86511f51
# 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.
from __future__ import print_function
import unittest
import numpy as np
import paddle.fluid.core as core
from op_test import OpTest
import random
class TestElementwiseModOp(OpTest):
def init_kernel_type(self):
self.use_mkldnn = False
def setUp(self):
self.op_type = "elementwise_floordiv"
self.dtype = np.int32
self.axis = -1
self.init_dtype()
self.init_input_output()
self.init_kernel_type()
self.init_axis()
self.inputs = {
'X': OpTest.np_dtype_to_fluid_dtype(self.x),
'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
}
self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
self.outputs = {'Out': self.out}
def test_check_output(self):
self.check_output()
def init_input_output(self):
self.x = np.random.uniform(0, 10000, [10, 10]).astype(self.dtype)
self.y = np.random.uniform(0, 1000, [10, 10]).astype(self.dtype)
self.out = np.floor_divide(self.x, self.y)
def init_dtype(self):
pass
def init_axis(self):
pass
class TestElementwiseModOp_scalar(TestElementwiseModOp):
def init_input_output(self):
scale_x = random.randint(0, 100000000)
scale_y = random.randint(1, 100000000)
self.x = (np.random.rand(2, 3, 4) * scale_x).astype(self.dtype)
self.y = (np.random.rand(1) * scale_y + 1).astype(self.dtype)
self.out = np.floor_divide(self.x, self.y)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_elementwise_mod_op.py 0 → 100644
浏览文件 @ 86511f51
# 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.
from __future__ import print_function
import unittest
import numpy as np
import paddle.fluid.core as core
from op_test import OpTest
import random
class TestElementwiseModOp(OpTest):
def init_kernel_type(self):
self.use_mkldnn = False
def setUp(self):
self.op_type = "elementwise_mod"
self.dtype = np.int32
self.axis = -1
self.init_dtype()
self.init_input_output()
self.init_kernel_type()
self.init_axis()
self.inputs = {
'X': OpTest.np_dtype_to_fluid_dtype(self.x),
'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
}
self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
self.outputs = {'Out': self.out}
def test_check_output(self):
self.check_output()
def init_input_output(self):
self.x = np.random.uniform(0, 10000, [10, 10]).astype(self.dtype)
self.y = np.random.uniform(0, 1000, [10, 10]).astype(self.dtype)
self.out = np.mod(self.x, self.y)
def init_dtype(self):
pass
def init_axis(self):
pass
class TestElementwiseModOp_scalar(TestElementwiseModOp):
def init_input_output(self):
scale_x = random.randint(0, 100000000)
scale_y = random.randint(1, 100000000)
self.x = (np.random.rand(2, 3, 4) * scale_x).astype(self.dtype)
self.y = (np.random.rand(1) * scale_y + 1).astype(self.dtype)
self.out = np.mod(self.x, self.y)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_fake_dequantize_op.py
浏览文件 @ 86511f51
......@@ -31,15 +31,27 @@ def dequantize_max_abs(x, scale, max_range):
return y
def channel_wise_quantize_max_abs(x, quant_bit=8):
def channel_wise_quantize_max_abs(x, quant_bit=8, use_second_dim=False):
scales = []
if not use_second_dim:
for i in range(x.shape[0]):
scales.append(np.max(np.abs(x[i])).astype("float32"))
y = x.copy()
max_range = math.pow(2, quant_bit - 1) - 1
for i, scale in enumerate(scales):
y[i] = np.round(y[i] / scale * max_range)
y[i] = np.round(x[i] / scale * max_range)
else:
for i in range(x.shape[0]):
s = []
for j in range(x.shape[1]):
s.append(np.max(np.abs(x[i][j])).astype("float32"))
scales.append(s)
scales = np.amax(np.array(scales), axis=0)
y = x.copy()
max_range = math.pow(2, quant_bit - 1) - 1
for i in range(x.shape[0]):
for j, scale in enumerate(scales):
y[i][j] = np.round(x[i][j] / scale * max_range)
return y, scales
......@@ -47,10 +59,16 @@ def channel_wise_dequantize_max_abs(x,
scales,
quant_bits,
activation_scale=None):
if activation_scale is None:
y = x.copy()
for i in range(x.shape[0]):
y[i] = (scales[i] / (math.pow(2, quant_bits[0] - 1) - 1)) * x[i]
else:
y = x.copy()
for i in range(x.shape[0]):
y[i] = (scales[i] / (math.pow(2, quant_bits[0] - 1) - 1)) * y[i]
if activation_scale is not None:
for j in range(x.shape[1]):
y[i][j] = (scales[j] /
(math.pow(2, quant_bits[0] - 1) - 1)) * x[i][j]
y *= activation_scale / (math.pow(2, quant_bits[1] - 1) - 1)
return y
......@@ -65,7 +83,8 @@ class TestFakeChannelWiseDequantizeMaxAbsOpTwoScales(OpTest):
self.set_args()
self.op_type = "fake_channel_wise_dequantize_max_abs"
x = np.random.randn(4, 3, 64, 64).astype(self.data_type)
yq, scales = channel_wise_quantize_max_abs(x, self.quant_bits[0])
yq, scales = channel_wise_quantize_max_abs(
x, self.quant_bits[0], use_second_dim=True)
ydq = channel_wise_dequantize_max_abs(yq, scales, self.quant_bits,
self.activation_scale)
......
python/paddle/fluid/tests/unittests/test_fake_quantize_op.py
浏览文件 @ 86511f51
......@@ -53,7 +53,7 @@ class TestFakeChannelWiseQuantizeOp(OpTest):
self.outputs = {
'Out': outputs,
'OutScales': np.array(scales).astype("float32"),
'OutScale': np.array(scales).astype("float32"),
}
def test_check_output(self):
......
python/paddle/fluid/tests/unittests/test_fuse_all_reduce_pass.py 0 → 100644
浏览文件 @ 86511f51
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from parallel_executor_test_base import TestParallelExecutorBase
import paddle.fluid as fluid
import paddle.fluid.core as core
import numpy as np
import paddle
import paddle.dataset.mnist as mnist
import unittest
import os
def simple_fc_net(use_feed):
img = fluid.layers.data(name='image', shape=[784], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
hidden = img
for _ in range(4):
hidden = fluid.layers.fc(
hidden,
size=200,
act='relu',
bias_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=1.0)))
prediction = fluid.layers.fc(hidden, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.mean(loss)
return loss
def fc_with_batchnorm(use_feed):
img = fluid.layers.data(name='image', shape=[784], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
hidden = img
for _ in range(2):
hidden = fluid.layers.fc(
hidden,
size=200,
act='relu',
bias_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=1.0)))
hidden = fluid.layers.batch_norm(input=hidden)
prediction = fluid.layers.fc(hidden, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
loss = fluid.layers.mean(loss)
return loss
class TestMNIST(TestParallelExecutorBase):
@classmethod
def setUpClass(cls):
os.environ['CPU_NUM'] = str(4)
def _init_data(self, random=True):
np.random.seed(5)
if random:
img = np.random.random(size=[32, 784]).astype(np.float32)
else:
img = np.ones(shape=[32, 784], dtype='float32')
label = np.ones(shape=[32, 1], dtype='int64')
return img, label
def _compare_fuse_all_reduce_ops(self, model, use_cuda, random_data=True):
if use_cuda and not core.is_compiled_with_cuda():
return
img, label = self._init_data(random_data)
def _optimizer(learning_rate=1e-6):
optimizer = fluid.optimizer.SGD(
learning_rate=learning_rate,
regularization=fluid.regularizer.L2Decay(1e-6))
return optimizer
not_fuse_op_first_loss, not_fuse_op_last_loss = self.check_network_convergence(
model,
feed_dict={"image": img,
"label": label},
use_cuda=use_cuda,
fuse_all_reduce_ops=False,
memory_opt=False,
optimizer=_optimizer)
fuse_op_first_loss, fuse_op_last_loss = self.check_network_convergence(
model,
feed_dict={"image": img,
"label": label},
use_cuda=use_cuda,
fuse_all_reduce_ops=True,
memory_opt=False,
optimizer=_optimizer)
for loss in zip(not_fuse_op_first_loss, fuse_op_first_loss):
self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
for loss in zip(not_fuse_op_last_loss, fuse_op_last_loss):
self.assertAlmostEquals(loss[0], loss[1], delta=1e-6)
def test_simple_fc_with_fuse_op(self):
self._compare_fuse_all_reduce_ops(simple_fc_net, True)
self._compare_fuse_all_reduce_ops(simple_fc_net, False)
def test_batchnorm_fc_with_fuse_op(self):
self._compare_fuse_all_reduce_ops(fc_with_batchnorm, True)
self._compare_fuse_all_reduce_ops(fc_with_batchnorm, False)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_imperative_checkpoint.py 0 → 100644
浏览文件 @ 86511f51
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.optimizer import SGDOptimizer
from paddle.fluid.imperative.nn import Conv2D, Pool2D, FC
from paddle.fluid.imperative.base import to_variable
class SimpleImgConvPool(fluid.imperative.Layer):
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
pool_size,
pool_stride,
pool_padding=0,
pool_type='max',
global_pooling=False,
conv_stride=1,
conv_padding=0,
conv_dilation=1,
conv_groups=1,
act=None,
use_cudnn=False,
param_attr=None,
bias_attr=None):
super(SimpleImgConvPool, self).__init__(name_scope)
self._conv2d = Conv2D(
self.full_name(),
num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=conv_stride,
padding=conv_padding,
dilation=conv_dilation,
groups=conv_groups,
param_attr=None,
bias_attr=None,
use_cudnn=use_cudnn)
self._pool2d = Pool2D(
self.full_name(),
pool_size=pool_size,
pool_type=pool_type,
pool_stride=pool_stride,
pool_padding=pool_padding,
global_pooling=global_pooling,
use_cudnn=use_cudnn)
def forward(self, inputs):
x = self._conv2d(inputs)
x = self._pool2d(x)
return x
class MNIST(fluid.imperative.Layer):
def __init__(self, name_scope):
super(MNIST, self).__init__(name_scope)
self._simple_img_conv_pool_1 = SimpleImgConvPool(
self.full_name(), 1, 20, 5, 2, 2, act="relu")
self._simple_img_conv_pool_2 = SimpleImgConvPool(
self.full_name(), 20, 50, 5, 2, 2, act="relu")
pool_2_shape = 50 * 4 * 4
SIZE = 10
scale = (2.0 / (pool_2_shape**2 * SIZE))**0.5
self._fc = FC(self.full_name(),
10,
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.NormalInitializer(
loc=0.0, scale=scale)),
act="softmax")
def forward(self, inputs):
x = self._simple_img_conv_pool_1(inputs)
x = self._simple_img_conv_pool_2(x)
x = self._fc(x)
return x
class TestImperativeCheckpoint(unittest.TestCase):
def save_load_persistables(self):
seed = 90
epoch_num = 1
with fluid.imperative.guard():
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
mnist = MNIST("mnist")
sgd = SGDOptimizer(learning_rate=1e-3)
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=128, drop_last=True)
dy_param_init_value = {}
step = 0
for epoch in range(epoch_num):
for batch_id, data in enumerate(train_reader()):
dy_x_data = np.array(
[x[0].reshape(1, 28, 28)
for x in data]).astype('float32')
y_data = np.array(
[x[1] for x in data]).astype('int64').reshape(128, 1)
img = to_variable(dy_x_data)
label = to_variable(y_data)
label._stop_gradient = True
cost = mnist(img)
loss = fluid.layers.cross_entropy(cost, label)
avg_loss = fluid.layers.mean(loss)
dy_out = avg_loss._numpy()
avg_loss._backward()
sgd.minimize(avg_loss)
fluid.imperative.save_persistables(mnist, "save_dir")
mnist.clear_gradients()
for param in mnist.parameters():
dy_param_init_value[param.name] = param._numpy()
mnist.load_dict(
fluid.imperative.load_persistables(mnist, "save_dir"))
restore = mnist.parameters()
self.assertEqual(len(dy_param_init_value), len(restore))
for value in restore:
self.assertTrue(
np.allclose(value, dy_param_init_value[value.name]))
self.assertTrue(np.isfinite(value.all()))
self.assertFalse(np.isnan(value.any()))
step += 1
if step > 20:
break
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_parallel_executor_seresnext.py
浏览文件 @ 86511f51
......@@ -13,6 +13,9 @@
# limitations under the License.
from __future__ import print_function
import os
os.environ['FLAGS_fuse_parameter_memory_size'] = "131072"
os.environ['FLAGS_fuse_parameter_groups_size'] = "3"
import paddle.fluid as fluid
import paddle.fluid.layers.ops as ops
......@@ -22,7 +25,6 @@ import paddle.fluid.core as core
from parallel_executor_test_base import TestParallelExecutorBase
import unittest
import math
import os
import numpy as np
# FIXME(zcd): If the neural net has dropout_op, the output of ParallelExecutor
......@@ -312,17 +314,59 @@ class TestResnet(TestParallelExecutorBase):
self.assertAlmostEquals(
np.mean(parallel_last_loss), single_last_loss[0], delta=delta2)
def _compare_with_fused_all_reduce(self,
model,
use_cuda,
iter=20,
delta2=1e-5):
if use_cuda and not core.is_compiled_with_cuda():
return
global remove_bn
remove_bn = True
img, label = self._init_data(batch_size=batch_size)
all_reduce_first_loss, all_reduce_last_loss = self.check_network_convergence(
model,
feed_dict={"image": img,
"label": label},
iter=iter,
batch_size=batch_size,
use_cuda=use_cuda,
fuse_all_reduce_ops=False,
optimizer=optimizer)
reduce_first_loss, reduce_last_loss = self.check_network_convergence(
model,
feed_dict={"image": img,
"label": label},
iter=iter,
batch_size=batch_size,
use_cuda=use_cuda,
fuse_all_reduce_ops=True,
optimizer=optimizer)
for loss in zip(all_reduce_first_loss, reduce_first_loss):
self.assertAlmostEquals(loss[0], loss[1], delta=1e-5)
for loss in zip(all_reduce_last_loss, reduce_last_loss):
self.assertAlmostEquals(loss[0], loss[1], delta=delta2)
def test_seresnext_with_learning_rate_decay(self):
self._check_resnet_convergence(model=SE_ResNeXt50Small, use_cuda=True)
self._check_resnet_convergence(
model=SE_ResNeXt50Small, use_cuda=False, iter=2, delta2=1e-3)
def test_seresnext_with_new_strategy(self):
def test_seresnext_with_reduce(self):
self._compare_reduce_and_allreduce(
model=SE_ResNeXt50Small, use_cuda=True, delta2=1e-2)
self._compare_reduce_and_allreduce(
model=SE_ResNeXt50Small, use_cuda=False, iter=5)
def test_seresnext_with_fused_all_reduce(self):
self._compare_with_fused_all_reduce(
model=SE_ResNeXt50Small, use_cuda=True, delta2=1e-3)
self._compare_with_fused_all_reduce(
model=SE_ResNeXt50Small, use_cuda=False, iter=2, delta2=1e-3)
if __name__ == '__main__':
unittest.main()
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