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未验证 提交 65dbc7cc 编写于 作者: Y Yihua Xu 提交者： GitHub
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Merge branch 'develop' into develop_4f71a6ee_conv3d_mkldnn_opt

上级 ac803fed 8cda28f3
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Showing with 3133 addition and 544 deletion
cmake/external/ngraph.cmake
浏览文件 @ 65dbc7cc
......@@ -32,6 +32,8 @@ IF(NOT ${WITH_NGRAPH})
return()
ENDIF()
INCLUDE(GNUInstallDirs)
INCLUDE(ExternalProject)
SET(NGRAPH_PROJECT "extern_ngraph")
......@@ -40,10 +42,14 @@ SET(NGRAPH_GIT_TAG "f9fd9d4cc318dc59dd4b68448e7fbb5f67a28bd0")
SET(NGRAPH_SOURCES_DIR ${THIRD_PARTY_PATH}/ngraph)
SET(NGRAPH_INSTALL_DIR ${THIRD_PARTY_PATH}/install/ngraph)
SET(NGRAPH_INC_DIR ${NGRAPH_INSTALL_DIR}/include)
SET(NGRAPH_LIB_DIR ${NGRAPH_INSTALL_DIR}/${CMAKE_INSTALL_LIBDIR})
SET(NGRAPH_SHARED_LIB_NAME libngraph.so.${NGRAPH_VERSION})
SET(NGRAPH_CPU_LIB_NAME libcpu_backend.so)
SET(NGRAPH_TBB_LIB_NAME libtbb.so.2)
SET(NGRAPH_GIT_REPO "https://github.com/NervanaSystems/ngraph.git")
SET(NGRAPH_SHARED_LIB ${NGRAPH_LIB_DIR}/${NGRAPH_SHARED_LIB_NAME})
SET(NGRAPH_CPU_LIB ${NGRAPH_LIB_DIR}/${NGRAPH_CPU_LIB_NAME})
SET(NGRAPH_TBB_LIB ${NGRAPH_LIB_DIR}/${NGRAPH_TBB_LIB_NAME})
ExternalProject_Add(
${NGRAPH_PROJECT}
......@@ -63,18 +69,6 @@ ExternalProject_Add(
CMAKE_ARGS -DMKLDNN_LIB_DIR=${MKLDNN_INSTALL_DIR}/lib
)
if(UNIX AND NOT APPLE)
include(GNUInstallDirs)
SET(NGRAPH_LIB_DIR ${NGRAPH_INSTALL_DIR}/${CMAKE_INSTALL_LIBDIR})
else()
SET(NGRAPH_LIB_DIR ${NGRAPH_INSTALL_DIR}/lib)
endif()
MESSAGE(STATUS "nGraph lib will be installed at: ${NGRAPH_LIB_DIR}")
SET(NGRAPH_SHARED_LIB ${NGRAPH_LIB_DIR}/${NGRAPH_SHARED_LIB_NAME})
SET(NGRAPH_CPU_LIB ${NGRAPH_LIB_DIR}/${NGRAPH_CPU_LIB_NAME})
SET(NGRAPH_TBB_LIB ${NGRAPH_LIB_DIR}/${NGRAPH_TBB_LIB_NAME})
# Workaround for nGraph expecting mklml to be in mkldnn install directory.
ExternalProject_Add_Step(
${NGRAPH_PROJECT}
......
cmake/inference_lib.cmake
浏览文件 @ 65dbc7cc
......@@ -129,6 +129,15 @@ if (WITH_MKLDNN)
)
endif ()
if (WITH_NGRAPH)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/ngraph")
copy(ngraph_lib
SRCS ${NGRAPH_INC_DIR} ${NGRAPH_LIB_DIR}
DSTS ${dst_dir} ${dst_dir}
DEPS ngraph
)
endif ()
if (NOT WIN32)
if (NOT MOBILE_INFERENCE AND NOT RPI)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/snappy")
......
cmake/operators.cmake
浏览文件 @ 65dbc7cc
......@@ -166,6 +166,8 @@ function(op_library TARGET)
# Append first implemented MKLDNN activation operator
if (${MKLDNN_FILE} STREQUAL "activation_mkldnn_op")
file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(relu, MKLDNN);\n")
elseif(${MKLDNN_FILE} STREQUAL "conv_mkldnn_op")
file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL_WITH_CUSTOM_TYPE(conv2d, MKLDNN, FP32);\n")
else()
file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(${TARGET}, MKLDNN);\n")
endif()
......
paddle/fluid/API.spec
浏览文件 @ 65dbc7cc
......@@ -182,7 +182,7 @@ paddle.fluid.layers.clip ArgSpec(args=['x', 'min', 'max', 'name'], varargs=None,
paddle.fluid.layers.clip_by_norm ArgSpec(args=['x', 'max_norm', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.mean ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.mul ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None))
paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', 'ignore_index', 'name'], varargs=None, keywords=None, defaults=(-100, None))
paddle.fluid.layers.maxout ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.space_to_depth ArgSpec(args=['x', 'blocksize', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.affine_grid ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,))
......@@ -194,6 +194,8 @@ paddle.fluid.layers.grid_sampler ArgSpec(args=['x', 'grid', 'name'], varargs=Non
paddle.fluid.layers.log_loss ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None))
paddle.fluid.layers.add_position_encoding ArgSpec(args=['input', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.bilinear_tensor_product ArgSpec(args=['x', 'y', 'size', 'act', 'name', 'param_attr', 'bias_attr'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.layers.merge_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.get_tensor_from_selected_rows ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.lstm ArgSpec(args=['input', 'init_h', 'init_c', 'max_len', 'hidden_size', 'num_layers', 'dropout_prob', 'is_bidirec', 'is_test', 'name', 'default_initializer', 'seed'], varargs=None, keywords=None, defaults=(0.0, False, False, None, None, -1))
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
......@@ -299,6 +301,7 @@ paddle.fluid.layers.generate_proposals ArgSpec(args=['scores', 'bbox_deltas', 'i
paddle.fluid.layers.iou_similarity ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.box_coder ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name'], varargs=None, keywords=None, defaults=('encode_center_size', True, None))
paddle.fluid.layers.polygon_box_transform ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.yolov3_loss ArgSpec(args=['x', 'gtbox', 'gtlabel', 'anchors', 'class_num', 'ignore_thresh', 'loss_weight_xy', 'loss_weight_wh', 'loss_weight_conf_target', 'loss_weight_conf_notarget', 'loss_weight_class', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None))
paddle.fluid.layers.accuracy ArgSpec(args=['input', 'label', 'k', 'correct', 'total'], varargs=None, keywords=None, defaults=(1, None, None))
paddle.fluid.layers.auc ArgSpec(args=['input', 'label', 'curve', 'num_thresholds', 'topk', 'slide_steps'], varargs=None, keywords=None, defaults=('ROC', 4095, 1, 1))
paddle.fluid.layers.exponential_decay ArgSpec(args=['learning_rate', 'decay_steps', 'decay_rate', 'staircase'], varargs=None, keywords=None, defaults=(False,))
......@@ -419,3 +422,17 @@ paddle.fluid.Scope.drop_kids drop_kids(self: paddle.fluid.core.Scope) -> None
paddle.fluid.Scope.find_var find_var(self: paddle.fluid.core.Scope, arg0: unicode) -> paddle.fluid.core.Variable
paddle.fluid.Scope.new_scope new_scope(self: paddle.fluid.core.Scope) -> paddle.fluid.core.Scope
paddle.fluid.Scope.var var(self: paddle.fluid.core.Scope, arg0: unicode) -> paddle.fluid.core.Variable
paddle.reader.map_readers ArgSpec(args=['func'], varargs='readers', keywords=None, defaults=None)
paddle.reader.buffered ArgSpec(args=['reader', 'size'], varargs=None, keywords=None, defaults=None)
paddle.reader.compose ArgSpec(args=[], varargs='readers', keywords='kwargs', defaults=None)
paddle.reader.chain ArgSpec(args=[], varargs='readers', keywords=None, defaults=None)
paddle.reader.shuffle ArgSpec(args=['reader', 'buf_size'], varargs=None, keywords=None, defaults=None)
paddle.reader.firstn ArgSpec(args=['reader', 'n'], varargs=None, keywords=None, defaults=None)
paddle.reader.xmap_readers ArgSpec(args=['mapper', 'reader', 'process_num', 'buffer_size', 'order'], varargs=None, keywords=None, defaults=(False,))
paddle.reader.PipeReader.__init__ ArgSpec(args=['self', 'command', 'bufsize', 'file_type'], varargs=None, keywords=None, defaults=(8192, 'plain'))
paddle.reader.PipeReader.get_line ArgSpec(args=['self', 'cut_lines', 'line_break'], varargs=None, keywords=None, defaults=(True, '\n'))
paddle.reader.multiprocess_reader ArgSpec(args=['readers', 'use_pipe', 'queue_size'], varargs=None, keywords=None, defaults=(True, 1000))
paddle.reader.Fake.__init__ ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.reader.creator.np_array ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
paddle.reader.creator.text_file ArgSpec(args=['path'], varargs=None, keywords=None, defaults=None)
paddle.reader.creator.recordio ArgSpec(args=['paths', 'buf_size'], varargs=None, keywords=None, defaults=(100,))
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 65dbc7cc
......@@ -118,8 +118,9 @@ cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto)
cc_library(shape_inference SRCS shape_inference.cc DEPS ddim attribute device_context)
cc_library(transfer_scope_cache SRCS transfer_scope_cache.cc DEPS scope framework_proto device_context)
cc_library(op_kernel_type SRCS op_kernel_type.cc DEPS device_context place)
cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope glog
shape_inference data_transform lod_tensor profiler transfer_scope_cache)
shape_inference data_transform lod_tensor profiler transfer_scope_cache op_kernel_type)
cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry device_context)
......@@ -127,8 +128,9 @@ cc_library(version SRCS version.cc)
cc_test(version_test SRCS version_test.cc DEPS version)
cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS shape_inference op_info operator glog version)
cc_library(ngraph_bridge SRCS ngraph_bridge.cc DEPS operator framework_proto)
if(NOT WIN32)
cc_library(ngraph_bridge SRCS ngraph_bridge.cc DEPS operator framework_proto ngraph)
cc_library(ngraph_operator SRCS ngraph_operator.cc DEPS ngraph_bridge operator op_info device_context tensor scope glog
shape_inference data_transform lod_tensor profiler)
endif(NOT WIN32)
......@@ -190,7 +192,7 @@ cc_test(var_type_inference_test SRCS var_type_inference_test.cc DEPS op_registry
cc_library(selected_rows SRCS selected_rows.cc DEPS tensor)
cc_test(selected_rows_test SRCS selected_rows_test.cc DEPS selected_rows)
cc_test(op_kernel_type_test SRCS op_kernel_type_test.cc DEPS place device_context framework_proto)
cc_test(op_kernel_type_test SRCS op_kernel_type_test.cc DEPS place device_context framework_proto op_kernel_type)
cc_test(cow_ptr_tests SRCS details/cow_ptr_test.cc)
cc_test(tuple_test SRCS tuple_test.cc )
......
paddle/fluid/framework/details/all_reduce_op_handle.cc
浏览文件 @ 65dbc7cc
......@@ -48,7 +48,14 @@ AllReduceOpHandle::AllReduceOpHandle(ir::Node *node,
void AllReduceOpHandle::RunImpl() {
platform::RecordEvent record_event(Name(), dev_ctxes_.cbegin()->second);
// FIXME(typhoonzero): If scope0(global scope) have NCCL_ID_VAR,
// this is a distributed or inter-process call, find a better way.
#ifdef PADDLE_WITH_CUDA
if (NoDummyInputSize() == 1 &&
local_scopes_[0]->FindLocalVar(NCCL_ID_VARNAME) == nullptr) {
#else
if (NoDummyInputSize() == 1) {
#endif
return; // No need to all reduce when GPU count = 1;
} else {
// Wait input done
......
paddle/fluid/framework/details/build_strategy.cc
浏览文件 @ 65dbc7cc
......@@ -62,6 +62,8 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
auto multi_devices_pass = AppendPass("multi_devices_pass");
multi_devices_pass->SetNotOwned<const BuildStrategy>("strategy",
&strategy_);
multi_devices_pass->Set<int>("num_trainers",
new int(strategy_.num_trainers_));
// Add a graph print pass to record a graph with device info.
if (!strategy_.debug_graphviz_path_.empty()) {
......
paddle/fluid/framework/details/multi_devices_graph_pass.cc
浏览文件 @ 65dbc7cc
......@@ -133,6 +133,7 @@ static const char kPlaces[] = "places";
static const char kParams[] = "params";
static const char kLocalScopes[] = "local_scopes";
static const char kStrategy[] = "strategy";
static const char kNumTrainers[] = "num_trainers";
void MultiDevSSAGraphBuilder::Init() const {
all_vars_.clear();
......@@ -299,6 +300,8 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
auto nodes = graph->ReleaseNodes();
ir::Graph &result = *graph;
int num_trainers = Get<int>(kNumTrainers);
for (auto &node : nodes) {
if (node->IsVar() && node->Var()) {
all_vars_.emplace(node->Name(), node->Var());
......@@ -383,7 +386,7 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
CreateComputationalOps(&result, node, places_.size());
}
if (!is_forwarding && places_.size() > 1) {
if (!is_forwarding && (places_.size() > 1 || num_trainers > 1)) {
// Currently, we assume that once gradient is generated, it can be
// broadcast, and each gradient is only broadcast once.
if (static_cast<bool>(boost::get<int>(node->Op()->GetAttr(
......@@ -895,4 +898,5 @@ REGISTER_PASS(multi_devices_pass,
.RequirePassAttr(paddle::framework::details::kPlaces)
.RequirePassAttr(paddle::framework::details::kParams)
.RequirePassAttr(paddle::framework::details::kLocalScopes)
.RequirePassAttr(paddle::framework::details::kStrategy);
.RequirePassAttr(paddle::framework::details::kStrategy)
.RequirePassAttr(paddle::framework::details::kNumTrainers);
paddle/fluid/framework/details/op_registry.h
浏览文件 @ 65dbc7cc
......@@ -32,9 +32,7 @@ enum OpInfoFillType {
kOpProtoAndCheckerMaker = 1,
kGradOpDescMaker = 2,
kVarTypeInference = 3,
kShapeInference = 4,
kEstimateFlops = 5,
kUnknown = -1
kShapeInference = 4
};
template <typename T>
......@@ -50,10 +48,8 @@ struct OpInfoFillTypeID {
? kVarTypeInference
: (std::is_base_of<InferShapeBase, T>::value
? kShapeInference
: (std::is_base_of<EstimateFlopsBase,
T>::value
? kEstimateFlops
: kUnknown)))));
: static_cast<OpInfoFillType>(
-1)))));
}
};
......@@ -143,16 +139,6 @@ struct OpInfoFiller<T, kShapeInference> {
}
};
template <typename T>
struct OpInfoFiller<T, kEstimateFlops> {
void operator()(const char* op_tpe, OpInfo* info) const {
info->estimate_flops_ = [](InferShapeContext* ctx) {
T estimate_flops;
return estimate_flops(ctx);
};
}
};
} // namespace details
} // namespace framework
......
paddle/fluid/framework/ir/graph.h
浏览文件 @ 65dbc7cc
......@@ -177,14 +177,13 @@ class Graph {
return nullptr;
}
const ProgramDesc &program() const { return program_; }
std::map<std::string, std::vector<ir::Node *>> InitFromProgram(
const ProgramDesc &program);
void ResolveHazard(
const std::map<std::string, std::vector<ir::Node *>> &var_nodes);
private:
std::map<std::string, std::vector<ir::Node *>> InitFromProgram(
const ProgramDesc &program);
// This method takes ownership of `node`.
ir::Node *AddNode(ir::Node *node) {
PADDLE_ENFORCE(node_set_.find(node) == node_set_.end());
......
paddle/fluid/framework/ir/is_test_pass.cc
浏览文件 @ 65dbc7cc
......@@ -38,7 +38,7 @@ std::unique_ptr<ir::Graph> IsTestPass::ApplyImpl(
for (const Node* n : graph->Nodes()) {
if (n->IsOp()) {
auto* op = n->Op();
if (op->HasAttr("is_test")) {
if (n->RuntimeHasAttr("is_test")) {
op->SetAttr("is_test", true);
} else if (std::find(begin(op_list), end(op_list), op->Type()) !=
end(op_list)) {
......
paddle/fluid/framework/ir/is_test_pass_tester.cc
浏览文件 @ 65dbc7cc
......@@ -104,9 +104,9 @@ TEST(IsTestPass, basic) {
auto* op = node->Op();
auto op_name = boost::get<std::string>(op->GetAttr("name"));
if (op_name == "conv3") {
ASSERT_FALSE(op->HasAttr("is_test"));
ASSERT_FALSE(node->RuntimeHasAttr("is_test"));
} else {
ASSERT_TRUE(op->HasAttr("is_test"));
ASSERT_TRUE(node->RuntimeHasAttr("is_test"));
EXPECT_TRUE(boost::get<bool>(op->GetAttr("is_test")));
}
}
......
paddle/fluid/framework/ir/mkldnn_placement_pass.cc
浏览文件 @ 65dbc7cc
......@@ -22,7 +22,7 @@ std::unique_ptr<ir::Graph> MKLDNNPlacementPass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
VLOG(3) << "Aplies MKL-DNN placement strategy.";
for (const Node* n : graph->Nodes()) {
if (n->IsOp() && n->Op()->HasAttr("use_mkldnn")) {
if (n->IsOp() && n->RuntimeHasAttr("use_mkldnn")) {
n->Op()->SetAttr("use_mkldnn", true);
}
}
......
paddle/fluid/framework/ir/node.cc
浏览文件 @ 65dbc7cc
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/ir/node.h"
#include "paddle/fluid/framework/op_info.h"
namespace paddle {
namespace framework {
......@@ -24,10 +25,33 @@ constexpr char Node::kControlDepVarName[];
const char Node::kControlDepVarName[] = "__control_var";
#endif
std::unique_ptr<Node> CreateNodeForTest(const std::string& name,
std::unique_ptr<Node> CreateNodeForTest(const std::string &name,
Node::Type type) {
return std::unique_ptr<Node>(new Node(name, type));
}
bool Node::RuntimeHasAttr(const std::string &name) const {
if (Op()->HasAttr(name)) {
return true;
} else {
auto &op_info = OpInfoMap::Instance();
auto op_type = Op()->Type();
if (op_info.Has(op_type)) {
auto op_info_ptr = op_info.Get(op_type);
if (op_info_ptr.HasOpProtoAndChecker()) {
const proto::OpProto &proto = op_info_ptr.Proto();
for (int i = 0; i != proto.attrs_size(); ++i) {
const proto::OpProto::Attr &attr = proto.attrs(i);
if (attr.name() == name) {
return true;
}
}
}
}
}
return false;
}
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/node.h
浏览文件 @ 65dbc7cc
......@@ -108,6 +108,18 @@ class Node {
Name().find(ir::Node::kControlDepVarName) != std::string::npos;
}
// RuntimeHasAttr is different with HasAttr now.
// 1. For Op()->HasAttr(), it judges whether a stored program_desc_ has attr,
// thus, if stored program_desc_ are old which don't have an attr, a new
// library which adds the attr already will fail on this function.
// Details:
// https://github.com/PaddlePaddle/Paddle/pull/14608#issuecomment-442309087
// 2. For Op()->RuntimeHasAttr, it judges the attr in runtime to avoid above
// problem.
// TODO(luotao): Maybe we should enhance HasAttr later, instead of adding
// RuntimeHasAttr.
bool RuntimeHasAttr(const std::string& name) const;
std::vector<Node*> inputs;
std::vector<Node*> outputs;
......
paddle/fluid/framework/ngraph_bridge.cc
浏览文件 @ 65dbc7cc
......@@ -15,23 +15,105 @@ limitations under the License. */
#ifdef PADDLE_WITH_NGRAPH
#include <algorithm>
#include <functional>
#include <vector>
#include "paddle/fluid/framework/ngraph_bridge.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/enforce.h"
#include "ngraph/ngraph.hpp"
namespace paddle {
namespace framework {
static std::shared_ptr<ngraph::Node> GetNode(
const std::shared_ptr<OperatorBase>& op, const std::string prm,
const VariableNameMap& var_map,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto& var_names = var_map.at(prm);
PADDLE_ENFORCE_EQ(var_names.size(), 1,
"op %s prm %s expects one associated var", op->Type(), prm);
if (ngb_node_map->find(var_names[0]) != ngb_node_map->end()) {
return (*ngb_node_map)[var_names[0]];
} else {
return nullptr;
}
}
static std::shared_ptr<ngraph::Node> GetInputNode(
const std::shared_ptr<OperatorBase>& op, const std::string prm,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
return GetNode(op, prm, op->Inputs(), ngb_node_map);
}
static std::shared_ptr<ngraph::Node> GetOutputNode(
const std::shared_ptr<OperatorBase>& op, const std::string prm,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
return GetNode(op, prm, op->Outputs(), ngb_node_map);
}
static void SetOutputNode(
const std::shared_ptr<OperatorBase>& op, const std::string prm,
std::shared_ptr<ngraph::Node> node,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto& var_names = op->Outputs().at(prm);
if (var_names.size() == 1) {
(*ngb_node_map)[var_names[0]] = node;
} else if (var_names.size() == 0) {
(*ngb_node_map)[""] = node;
} else {
PADDLE_THROW("prm %s has more than 1 var_names.", prm);
}
}
static bool HasOutput(const std::shared_ptr<OperatorBase>& op,
const std::string prm) {
auto& outputs = op->Outputs();
if (outputs.find(prm) == outputs.end()) return false;
return outputs.at(prm).size() > 0;
}
template <typename T>
static void BuildBinaryNode(
const std::shared_ptr<OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto x = GetInputNode(op, "X", ngb_node_map);
auto y = GetInputNode(op, "Y", ngb_node_map);
auto out = std::make_shared<T>(x, y);
SetOutputNode(op, "Out", out, ngb_node_map);
}
template <typename T>
static void BuildUnaryNode(
const std::shared_ptr<OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto input = GetInputNode(op, "X", ngb_node_map);
auto out = std::make_shared<T>(input);
SetOutputNode(op, "Out", out, ngb_node_map);
}
std::map<std::string,
std::function<void(const std::shared_ptr<OperatorBase>&,
std::shared_ptr<std::unordered_map<
std::string, std::shared_ptr<ngraph::Node>>>)>>
NgraphBridge::NG_NODE_MAP = {};
NgraphBridge::NG_NODE_MAP = {{"relu", BuildUnaryNode<ngraph::op::Relu>},
{"tanh", BuildUnaryNode<ngraph::op::Tanh>}};
void NgraphBridge::build_graph(const std::shared_ptr<OperatorBase>& op) {
void NgraphBridge::BuildNgNode(const std::shared_ptr<OperatorBase>& op) {
auto& op_type = op->Type();
NG_NODE_MAP[op_type](op, ngb_node_map);
NG_NODE_MAP[op_type](op, ngb_node_map_);
}
} // namespace framework
......
paddle/fluid/framework/ngraph_bridge.h
浏览文件 @ 65dbc7cc
......@@ -20,16 +20,14 @@ limitations under the License. */
#include <map>
#include <string>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/enforce.h"
#include "ngraph/ngraph.hpp"
#include "ngraph/node.hpp"
namespace paddle {
namespace framework {
class OperatorBase;
class NgraphBridge {
public:
static std::map<
......@@ -43,14 +41,14 @@ class NgraphBridge {
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
var_node_map)
: ngb_node_map(var_node_map) {}
: ngb_node_map_(var_node_map) {}
void build_graph(const std::shared_ptr<OperatorBase>& op);
void BuildNgNode(const std::shared_ptr<OperatorBase>& op);
private:
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map;
ngb_node_map_;
};
} // namespace framework
......
paddle/fluid/framework/ngraph_operator.cc
浏览文件 @ 65dbc7cc
......@@ -19,14 +19,29 @@ limitations under the License. */
#include <map>
#include "paddle/fluid/framework/feed_fetch_type.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/ngraph_bridge.h"
#include "paddle/fluid/framework/ngraph_operator.h"
#include "paddle/fluid/framework/shape_inference.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/framework/var_desc.h"
#include "paddle/fluid/framework/var_type.h"
#include "ngraph/ngraph.hpp"
namespace paddle {
namespace framework {
static ngraph::Shape Ddim2Shape(const DDim& dims) {
ngraph::Shape sp;
for (int i = 0; i < dims.size(); ++i) {
int k = dims[i];
k = k == 0 ? 1 : k;
sp.push_back(k);
}
return sp;
}
static std::map<proto::VarType::Type, ngraph::element::Type> pd2ng_type_map = {
{proto::VarType::FP32, ngraph::element::f32},
{proto::VarType::FP64, ngraph::element::f64},
......@@ -42,6 +57,7 @@ typedef enum { /* nGraph support state on ops */
PARTIAL_TEST /* Support partial list of ops for test */
} op_state;
// perform graph build through bridge and execute computation
class NgraphOperator {
public:
explicit NgraphOperator(const Scope& scope, const platform::Place& place,
......@@ -59,13 +75,23 @@ class NgraphOperator {
persistables_(persist),
fetches_(fetches),
post_op_inputs_(post_op_inputs),
ng_op_state_(ng_op_state) {}
ng_op_state_(ng_op_state) {
var_in_node_map_ = std::make_shared<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>();
var_node_map_ = std::make_shared<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>();
BuildNgIO();
GetNgFunction();
}
void Run(const Scope& scope, const platform::Place& place) const;
private:
static std::unordered_map<std::string, std::shared_ptr<ngraph::Function>>
func_cache;
func_cache_;
const Scope& scope_;
const platform::Place& place_;
std::vector<std::shared_ptr<OperatorBase>> fused_ops_;
......@@ -74,6 +100,35 @@ class NgraphOperator {
std::unordered_set<std::string> fetches_;
std::unordered_set<std::string> post_op_inputs_;
op_state ng_op_state_;
// ngraph backend eg. CPU
static std::shared_ptr<ngraph::runtime::Backend> backend_;
// ngraph function to call and execute
std::shared_ptr<ngraph::Function> ngraph_function_;
// var_name of inputs
std::vector<std::string> var_in_;
// var_name of outputs from fetch in order
std::vector<std::string> var_out_;
// map input vars to nodes
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
var_in_node_map_;
// map each var name with a ngraph node
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
var_node_map_;
// cache key to check if function is cached
std::shared_ptr<std::string> GetCacheKey();
// get ngraph input and define ngraph input parameters
void GetNgInputShape(std::shared_ptr<OperatorBase> op);
// Call ngraph bridge to map ops
void BuildNgNodes();
// get the ngraph input and output var list
void BuildNgIO();
// build ngraph function call
void BuildNgFunction();
// Check cache for ngraph function or otherwise build the function
void GetNgFunction();
};
std::vector<std::vector<std::vector<std::unique_ptr<OperatorBase>>::iterator>>
......@@ -86,7 +141,7 @@ FusedOperator::FusedOpIntervals(
}
size_t size = ops->size();
size_t left = 0;
while (left < size && ops.at(left)->Type() != kFeedOpType) {
while (left < size && ops->at(left)->Type() != kFeedOpType) {
++left;
}
if (left == size) {
......@@ -116,7 +171,7 @@ FusedOperator::FusedOpIntervals(
size_t start = pivot, end = start;
while (pivot < right &&
(paddle::framework::NgraphBridge::NG_NODE_MAP.find(
ops.at(pivot)->Type()) !=
ops->at(pivot)->Type()) !=
paddle::framework::NgraphBridge::NG_NODE_MAP.end())) {
++pivot;
++end;
......@@ -136,7 +191,9 @@ FusedOperator::FusedOperator(
std::vector<std::unique_ptr<OperatorBase>>::iterator end,
const std::string& type, const VariableNameMap& inputs,
const VariableNameMap& outputs, const AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs), pdesc(prog), block(block_id) {
: OperatorBase(type, inputs, outputs, attrs),
pdesc_(prog),
block_(block_id) {
for (std::vector<std::unique_ptr<OperatorBase>>::iterator it = start;
it != end; ++it) {
fused_ops_.push_back(std::move(*it));
......@@ -152,7 +209,7 @@ FusedOperator::FusedOperator(
}
if ((*(start - 1))->Type() == kFeedOpType && (*end)->Type() == kFetchOpType) {
is_complete = true;
is_full_ = true;
}
Process();
......@@ -205,7 +262,7 @@ void FusedOperator::RunImpl(const Scope& scope,
}
}
if (is_full) {
if (is_full_) {
ng_op_state = ng_op_state == PARTIAL_TEST ? FULL_TEST : FULL_TRAIN;
}
......@@ -215,6 +272,280 @@ void FusedOperator::RunImpl(const Scope& scope,
ngraph_op.Run(scope, place);
}
std::unordered_map<std::string, std::shared_ptr<ngraph::Function>>
NgraphOperator::func_cache_ = {};
std::shared_ptr<ngraph::runtime::Backend> NgraphOperator::backend_ =
ngraph::runtime::Backend::create("CPU");
void NgraphOperator::GetNgInputShape(std::shared_ptr<OperatorBase> op) {
op->RuntimeInferShape(scope_, place_);
for (auto& var_name_item : op->Inputs()) {
for (auto& var_name : var_name_item.second) {
auto* var = scope_.FindVar(var_name);
if (var && var->IsType<LoDTensor>()) {
auto* tensor_pd = GetLoDTensorOrSelectedRowsValueFromVar(*var);
auto sp = Ddim2Shape(tensor_pd->dims());
if (std::find(var_in_.begin(), var_in_.end(), var_name) !=
var_in_.end()) {
if (var_node_map_->find(var_name) == var_node_map_->end()) {
auto ng_type = var_type_map_.at(var_name);
auto prm =
std::make_shared<ngraph::op::Parameter>(ng_type, sp, true);
(*var_node_map_)[var_name] = prm;
(*var_in_node_map_)[var_name] = prm;
}
}
}
}
}
}
void NgraphOperator::BuildNgNodes() {
for (auto& var_name : var_out_) {
if (var_node_map_->find(var_name) == var_node_map_->end()) {
auto* var = scope_.FindVar(var_name);
if (var && var->IsType<LoDTensor>()) {
auto* tensor_pd = GetLoDTensorOrSelectedRowsValueFromVar(*var);
auto& ddim = tensor_pd->dims();
auto ng_shape = Ddim2Shape(ddim);
auto ng_type = var_type_map_.at(var_name);
auto prm =
std::make_shared<ngraph::op::Parameter>(ng_type, ng_shape, true);
(*var_node_map_)[var_name] = prm;
}
}
}
paddle::framework::NgraphBridge ngb(var_node_map_);
for (auto& op : fused_ops_) {
ngb.BuildNgNode(op);
}
}
void NgraphOperator::BuildNgIO() {
std::unordered_set<std::string> inputs;
std::unordered_set<std::string> outputs;
for (auto& op : fused_ops_) {
for (auto& var_name_item : op->Inputs()) {
for (auto& var_name : var_name_item.second) {
inputs.insert(var_name);
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()) {
// fill var_in here to keep lhs and rhs order
var_in_.push_back(var_name);
}
}
}
if (op->Type() != "fill_constant") {
GetNgInputShape(op);
}
for (auto& var_name_item : op->Outputs()) {
PADDLE_ENFORCE_LE(var_name_item.second.size(), 1,
"op %s has more than 1 output - Not handling yet",
op->Type());
for (auto& var_name : var_name_item.second) {
outputs.insert(var_name);
}
}
}
// var_out.clear();
for (auto& op : fused_ops_) {
for (auto& var_name_item : op->Outputs()) {
PADDLE_ENFORCE_LE(var_name_item.second.size(), 1,
"op %s has more than 1 output - Not handling yet",
op->Type());
for (auto& var_name : var_name_item.second) {
switch (ng_op_state_) {
case PARTIAL_TEST:
if (post_op_inputs_.find(var_name) != post_op_inputs_.end() ||
fetches_.find(var_name) != fetches_.end()) {
var_out_.push_back(var_name);
}
break;
case FULL_TEST:
if (fetches_.find(var_name) != fetches_.end()) {
var_out_.push_back(var_name);
}
break;
case PARTIAL_TRAIN:
if (fetches_.find(var_name) != fetches_.end() ||
post_op_inputs_.find(var_name) != post_op_inputs_.end() ||
persistables_.find(var_name) != persistables_.end()) {
var_out_.push_back(var_name);
}
break;
case FULL_TRAIN:
if (fetches_.find(var_name) != fetches_.end() ||
persistables_.find(var_name) != persistables_.end()) {
var_out_.push_back(var_name);
}
break;
default:
var_out_.push_back(var_name);
}
}
}
}
}
void NgraphOperator::BuildNgFunction() {
BuildNgNodes();
ngraph_function_ = nullptr;
ngraph::NodeVector func_outputs;
ngraph::op::ParameterVector func_inputs;
for (auto& vo : var_out_) {
func_outputs.push_back(var_node_map_->at(vo));
}
for (auto& vi : var_in_) {
std::shared_ptr<ngraph::op::Parameter> prm =
std::dynamic_pointer_cast<ngraph::op::Parameter>(
var_in_node_map_->at(vi));
func_inputs.push_back(prm);
}
ngraph_function_ =
std::make_shared<ngraph::Function>(func_outputs, func_inputs);
}
std::shared_ptr<std::string> NgraphOperator::GetCacheKey() {
auto cache_key = std::make_shared<std::string>("");
*cache_key += std::to_string(fused_ops_.size());
for (auto& op : fused_ops_) {
*cache_key += op->Type();
}
for (auto& var_name : var_in_) {
auto shape = var_node_map_->at(var_name)->get_shape();
*cache_key += var_name;
*cache_key += var_type_map_.at(var_name).c_type_string();
for (size_t i = 0; i < shape.size(); ++i) {
*cache_key += std::to_string(shape.at(i));
}
}
for (auto& var_name : var_out_) {
auto* var = scope_.FindVar(var_name);
if (var && var->IsType<LoDTensor>()) {
auto* tensor_pd = GetLoDTensorOrSelectedRowsValueFromVar(*var);
auto& ddim = tensor_pd->dims();
for (int i = 0; i < ddim.size(); ++i) {
*cache_key += std::to_string(ddim[i]);
}
}
}
return cache_key;
}
void NgraphOperator::GetNgFunction() {
bool cache_on = true;
if (cache_on) {
std::string cache_key_val = *GetCacheKey();
if (func_cache_.find(cache_key_val) != func_cache_.end()) {
ngraph_function_ = func_cache_.at(cache_key_val);
} else {
BuildNgFunction();
func_cache_[cache_key_val] = ngraph_function_;
}
} else {
BuildNgFunction();
}
}
void NgraphOperator::Run(const Scope& scope,
const platform::Place& place) const {
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> t_in;
std::vector<std::shared_ptr<ngraph::runtime::Tensor>> t_out;
for (size_t i = 0; i < var_in_.size(); ++i) {
auto vi = var_in_.at(i);
auto sp = var_node_map_->at(vi)->get_shape();
std::shared_ptr<ngraph::runtime::Tensor> ti;
auto* var = scope.FindVar(vi);
if (var && var->IsType<LoDTensor>()) {
auto* tensor_pd = GetLoDTensorOrSelectedRowsValueFromVar(*var);
PADDLE_ENFORCE(sp == Ddim2Shape(tensor_pd->dims()),
"Ensure ngraph tensor layout align with paddle tensor");
if (tensor_pd->type().hash_code() ==
typeid(float).hash_code()) { // NOLINT
const float* arr = tensor_pd->data<float>();
ti = backend_->create_tensor(ngraph::element::f32, sp,
const_cast<float*>(arr));
} else if (tensor_pd->type().hash_code() ==
typeid(int).hash_code()) { // NOLINT
const int* arr = tensor_pd->data<int>();
ti = backend_->create_tensor(ngraph::element::i32, sp,
const_cast<int*>(arr));
} else if (tensor_pd->type().hash_code() == typeid(int64_t).hash_code()) {
const int64_t* arr = tensor_pd->data<int64_t>();
ti = backend_->create_tensor(ngraph::element::i64, sp,
const_cast<int64_t*>(arr));
} else if (tensor_pd->type().hash_code() ==
typeid(double).hash_code()) { // NOLINT
const double* arr = tensor_pd->data<double>();
ti = backend_->create_tensor(ngraph::element::f64, sp,
const_cast<double*>(arr));
} else if (tensor_pd->type().hash_code() ==
typeid(bool).hash_code()) { // NOLINT
const bool* arr = tensor_pd->data<bool>();
ti = backend_->create_tensor(ngraph::element::boolean, sp,
const_cast<bool*>(arr));
} else {
PADDLE_THROW("Data type not handling for var %s", vi);
}
} else {
PADDLE_THROW("Cannot find var or tensor with var name %s", vi);
}
bool is_test = (ng_op_state_ == PARTIAL_TEST || ng_op_state_ == FULL_TEST)
? true
: false;
bool is_persistable =
(persistables_.find(vi) != persistables_.end()) ? true : false;
if (is_test && is_persistable) {
ti->set_stale(false);
}
t_in.push_back(ti);
}
for (size_t i = 0; i < var_out_.size(); ++i) {
auto var_name = var_out_[i];
auto* var = scope.FindVar(var_name);
std::shared_ptr<ngraph::runtime::Tensor> to;
if (var && var->IsType<LoDTensor>()) {
auto* tensor_pd = GetMutableLoDTensorOrSelectedRowsValueFromVar(var);
auto dd = tensor_pd->dims();
ngraph::Shape sp = Ddim2Shape(dd);
auto ng_type = var_type_map_.at(var_name);
if (ng_type == ngraph::element::f32) {
auto pd_arr = tensor_pd->mutable_data<float>(place);
to = backend_->create_tensor(ngraph::element::f32, sp, pd_arr);
} else if (ng_type == ngraph::element::i64) {
auto pd_arr = tensor_pd->mutable_data<int64_t>(place);
to = backend_->create_tensor(ngraph::element::i64, sp, pd_arr);
} else if (ng_type == ngraph::element::f64) {
auto pd_arr = tensor_pd->mutable_data<double>(place);
to = backend_->create_tensor(ngraph::element::f64, sp, pd_arr);
} else if (ng_type == ngraph::element::boolean) {
auto pd_arr = tensor_pd->mutable_data<bool>(place);
to = backend_->create_tensor(ngraph::element::boolean, sp, pd_arr);
} else {
PADDLE_THROW("Data type not handled in for var %s", var_name);
}
t_out.push_back(to);
} else {
PADDLE_THROW("Cannot find var or tensor with var name %s", var_name);
}
}
backend_->call(ngraph_function_, t_out, t_in);
} // NgraphOperator::RunImpl
} // namespace framework
} // namespace paddle
#endif
paddle/fluid/framework/ngraph_operator.h
浏览文件 @ 65dbc7cc
......@@ -17,24 +17,19 @@ limitations under the License. */
#ifdef PADDLE_WITH_NGRAPH
#include <algorithm>
#include <atomic>
#include <string>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/framework/attribute.h"
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/ngraph_bridge.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/op_kernel_type.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/platform/variant.h"
#include "ngraph/ngraph.hpp"
#include "ngraph/type/element_type.hpp"
namespace paddle {
namespace framework {
......
paddle/fluid/framework/op_info.h
浏览文件 @ 65dbc7cc
......@@ -31,12 +31,6 @@ class InferShapeBase {
virtual void operator()(InferShapeContext*) const = 0;
};
class EstimateFlopsBase {
public:
virtual ~EstimateFlopsBase() = default;
virtual size_t operator()(InferShapeContext*) const = 0;
};
struct OpInfo {
OpCreator creator_;
GradOpMakerFN grad_op_maker_;
......@@ -44,7 +38,6 @@ struct OpInfo {
OpAttrChecker* checker_{nullptr};
InferVarTypeFN infer_var_type_;
InferShapeFN infer_shape_;
EstimateFlopsFN estimate_flops_;
bool HasOpProtoAndChecker() const {
return proto_ != nullptr && checker_ != nullptr;
......
paddle/fluid/framework/op_kernel_type.cc 0 → 100644
浏览文件 @ 65dbc7cc
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/op_kernel_type.h"
namespace paddle {
namespace framework {
size_t OpKernelType::Hash::operator()(const OpKernelType& key) const {
int cur_loc = 0;
int place = key.place_.which();
cur_loc += OpKernelType::kPlaceBits;
int data_type = static_cast<int>(key.data_type_) << cur_loc;
cur_loc += OpKernelType::kPrimaryDTypeBits;
int data_layout = static_cast<int>(key.data_layout_) << cur_loc;
cur_loc += OpKernelType::kLayoutBits;
int library_type = static_cast<int>(key.library_type_) << cur_loc;
cur_loc += OpKernelType::kLibBits;
int customized_value = key.customized_type_value_;
PADDLE_ENFORCE(customized_value < (1 << OpKernelType::kCustomizeBits));
customized_value = customized_value << cur_loc;
cur_loc += OpKernelType::kCustomizeBits;
PADDLE_ENFORCE(cur_loc < 64);
std::hash<int> hasher;
return hasher(place + data_type + data_layout + library_type +
customized_value);
}
bool OpKernelType::operator==(const OpKernelType& o) const {
return platform::places_are_same_class(place_, o.place_) &&
data_type_ == o.data_type_ && data_layout_ == o.data_layout_ &&
library_type_ == o.library_type_ &&
customized_type_value_ == o.customized_type_value_;
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/op_kernel_type.h
浏览文件 @ 65dbc7cc
......@@ -24,54 +24,55 @@ limitations under the License. */
namespace paddle {
namespace framework {
struct OpKernelType {
struct Hash {
size_t operator()(const OpKernelType& key) const {
int place = key.place_.which();
int data_type = static_cast<int>(key.data_type_) << LEFT_SHIFT;
int data_layout = static_cast<int>(key.data_layout_) << (LEFT_SHIFT * 2);
int library_type = static_cast<int>(key.library_type_)
<< (LEFT_SHIFT * 3);
std::hash<int> hasher;
return hasher(place + data_type + data_layout + library_type);
}
};
class OpKernelType {
public:
constexpr static int kDefaultCustomizedTypeValue = 0;
// place, data_type, library_type kinds less than 2^8
constexpr static int LEFT_SHIFT = 8;
proto::VarType::Type data_type_;
DataLayout data_layout_;
platform::Place place_;
LibraryType library_type_;
// In total should be smaller than 64.
constexpr static int kPlaceBits = 4;
constexpr static int kPrimaryDTypeBits = 8;
constexpr static int kLayoutBits = 4;
constexpr static int kLibBits = 4;
constexpr static int kCustomizeBits = 4;
OpKernelType(proto::VarType::Type data_type, platform::Place place,
DataLayout data_layout = DataLayout::kAnyLayout,
LibraryType library_type = LibraryType::kPlain)
LibraryType library_type = LibraryType::kPlain,
int customized_type_value = kDefaultCustomizedTypeValue)
: data_type_(data_type),
data_layout_(data_layout),
place_(place),
library_type_(library_type) {}
library_type_(library_type),
customized_type_value_(customized_type_value) {}
OpKernelType(proto::VarType::Type data_type,
const platform::DeviceContext& dev_ctx,
DataLayout data_layout = DataLayout::kAnyLayout,
LibraryType library_type = LibraryType::kPlain)
LibraryType library_type = LibraryType::kPlain,
int customized_type_value = kDefaultCustomizedTypeValue)
: data_type_(data_type),
data_layout_(data_layout),
place_(dev_ctx.GetPlace()),
library_type_(library_type) {}
library_type_(library_type),
customized_type_value_(customized_type_value) {}
virtual ~OpKernelType() {}
struct Hash {
size_t operator()(const OpKernelType& key) const;
};
size_t hash_key() const { return Hash()(*this); }
bool operator==(const OpKernelType& o) const {
return platform::places_are_same_class(place_, o.place_) &&
data_type_ == o.data_type_ && data_layout_ == o.data_layout_ &&
library_type_ == o.library_type_;
}
bool operator==(const OpKernelType& o) const;
bool operator!=(const OpKernelType& o) const { return !(*this == o); }
proto::VarType::Type data_type_;
DataLayout data_layout_;
platform::Place place_;
LibraryType library_type_;
int customized_type_value_;
};
inline std::ostream& operator<<(std::ostream& os,
......
paddle/fluid/framework/op_registry.h
浏览文件 @ 65dbc7cc
......@@ -35,6 +35,7 @@ limitations under the License. */
namespace paddle {
namespace framework {
class Registrar {
public:
// In our design, various kinds of classes, e.g., operators and kernels,
......@@ -78,7 +79,7 @@ struct OpKernelRegistrarFunctor;
template <typename PlaceType, typename T, typename Func>
inline void RegisterKernelClass(const char* op_type, const char* library_type,
Func func) {
int customized_type_value, Func func) {
std::string library(library_type);
std::string data_layout = "ANYLAYOUT";
if (library == "MKLDNN") {
......@@ -86,7 +87,7 @@ inline void RegisterKernelClass(const char* op_type, const char* library_type,
}
OpKernelType key(ToDataType(std::type_index(typeid(T))), PlaceType(),
StringToDataLayout(data_layout),
StringToLibraryType(library_type));
StringToLibraryType(library_type), customized_type_value);
OperatorWithKernel::AllOpKernels()[op_type][key] = func;
}
......@@ -95,22 +96,26 @@ struct OpKernelRegistrarFunctor<PlaceType, false, I, KernelTypes...> {
using KERNEL_TYPE =
typename std::tuple_element<I, std::tuple<KernelTypes...>>::type;
void operator()(const char* op_type, const char* library_type) const {
void operator()(const char* op_type, const char* library_type,
int customized_type_value) const {
using T = typename KERNEL_TYPE::ELEMENT_TYPE;
RegisterKernelClass<PlaceType, T>(
op_type, library_type, [](const framework::ExecutionContext& ctx) {
op_type, library_type, customized_type_value,
[](const framework::ExecutionContext& ctx) {
KERNEL_TYPE().Compute(ctx);
});
constexpr auto size = std::tuple_size<std::tuple<KernelTypes...>>::value;
OpKernelRegistrarFunctor<PlaceType, I + 1 == size, I + 1, KernelTypes...>
func;
func(op_type, library_type);
func(op_type, library_type, customized_type_value);
}
};
template <typename PlaceType, size_t I, typename... KernelType>
struct OpKernelRegistrarFunctor<PlaceType, true, I, KernelType...> {
void operator()(const char* op_type, const char* library_type) const {}
void operator()(const char* op_type, const char* library_type,
int customized_type_value) const {}
};
// User can register many kernel in one place. The data type could be
......@@ -118,9 +123,10 @@ struct OpKernelRegistrarFunctor<PlaceType, true, I, KernelType...> {
template <typename PlaceType, typename... KernelType>
class OpKernelRegistrar : public Registrar {
public:
explicit OpKernelRegistrar(const char* op_type, const char* library_type) {
explicit OpKernelRegistrar(const char* op_type, const char* library_type,
int customized_type_value) {
OpKernelRegistrarFunctor<PlaceType, false, 0, KernelType...> func;
func(op_type, library_type);
func(op_type, library_type, customized_type_value);
}
};
......@@ -130,17 +136,19 @@ struct OpKernelRegistrarFunctorEx;
template <typename PlaceType, typename... DataTypeAndKernelType>
class OpKernelRegistrarEx : public Registrar {
public:
explicit OpKernelRegistrarEx(const char* op_type, const char* library_type) {
explicit OpKernelRegistrarEx(const char* op_type, const char* library_type,
int customized_type_value) {
OpKernelRegistrarFunctorEx<PlaceType, false, 0, DataTypeAndKernelType...>
func;
func(op_type, library_type);
func(op_type, library_type, customized_type_value);
}
};
template <typename PlaceType, size_t I, typename... DataTypeAndKernelType>
struct OpKernelRegistrarFunctorEx<PlaceType, true, I,
DataTypeAndKernelType...> {
void operator()(const char* op_type, const char* library_type) const {}
void operator()(const char* op_type, const char* library_type,
int customized_type_value) const {}
};
template <typename PlaceType, size_t I, typename... DataTypeAndKernelType>
......@@ -153,18 +161,21 @@ struct OpKernelRegistrarFunctorEx<PlaceType, false, I,
typename std::tuple_element<I,
std::tuple<DataTypeAndKernelType...>>::type;
void operator()(const char* op_type, const char* library_type) const {
RegisterKernelClass<PlaceType, T>(op_type, library_type, Functor());
void operator()(const char* op_type, const char* library_type,
int customized_type_value) const {
RegisterKernelClass<PlaceType, T>(op_type, library_type,
customized_type_value, Functor());
constexpr auto size =
std::tuple_size<std::tuple<DataTypeAndKernelType...>>::value;
OpKernelRegistrarFunctorEx<PlaceType, I + 2 >= size, I + 2,
DataTypeAndKernelType...>
func;
func(op_type, library_type);
func(op_type, library_type, customized_type_value);
}
};
// clang-format off
/**
* check if MACRO is used in GLOBAL NAMESPACE.
*/
......@@ -199,42 +210,64 @@ struct OpKernelRegistrarFunctorEx<PlaceType, false, I,
/**
* Macro to register OperatorKernel.
*/
#define REGISTER_OP_KERNEL(op_type, library_type, place_class, ...) \
STATIC_ASSERT_GLOBAL_NAMESPACE( \
__reg_op_kernel_##op_type##_##library_type##__, \
"REGISTER_OP_KERNEL must be called in global namespace"); \
static ::paddle::framework::OpKernelRegistrar<place_class, __VA_ARGS__> \
__op_kernel_registrar_##op_type##_##library_type##__(#op_type, \
#library_type); \
int TouchOpKernelRegistrar_##op_type##_##library_type() { \
__op_kernel_registrar_##op_type##_##library_type##__.Touch(); \
return 0; \
#define REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE(op_type, library_type, \
place_class, customized_name, \
customized_type_value, ...) \
STATIC_ASSERT_GLOBAL_NAMESPACE( \
__reg_op_kernel_##op_type##_##library_type##_##customized_name##__, \
"REGISTER_OP_KERNEL must be called in " \
"global namespace"); \
static ::paddle::framework::OpKernelRegistrar<place_class, \
__VA_ARGS__> \
__op_kernel_registrar_##op_type##_##library_type##_##customized_name##__(\
#op_type, #library_type, customized_type_value); \
int TouchOpKernelRegistrar_##op_type##_##library_type##_##customized_name() {\
__op_kernel_registrar_##op_type##_##library_type##_##customized_name##__ \
.Touch(); \
return 0; \
}
#define REGISTER_OP_KERNEL(op_type, library_type, place_class, ...) \
REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE( \
op_type, library_type, place_class, DEFAULT_TYPE, \
::paddle::framework::OpKernelType::kDefaultCustomizedTypeValue, \
__VA_ARGS__)
#define REGISTER_OP_CUDA_KERNEL(op_type, ...) \
REGISTER_OP_KERNEL(op_type, CUDA, ::paddle::platform::CUDAPlace, __VA_ARGS__)
#define REGISTER_OP_CPU_KERNEL(op_type, ...) \
REGISTER_OP_KERNEL(op_type, CPU, ::paddle::platform::CPUPlace, __VA_ARGS__)
#define REGISTER_OP_KERNEL_EX(op_type, library_type, place_class, ...) \
STATIC_ASSERT_GLOBAL_NAMESPACE( \
__reg_op_kernel_##op_type##_##library_type##__, \
"REGISTER_OP_KERNEL_EX must be called in global namespace"); \
static ::paddle::framework::OpKernelRegistrarEx<place_class, __VA_ARGS__> \
__op_kernel_registrar_##op_type##_##library_type##__(#op_type, \
#library_type); \
int TouchOpKernelRegistrar_##op_type##_##library_type() { \
__op_kernel_registrar_##op_type##_##library_type##__.Touch(); \
return 0; \
#define REGISTER_OP_KERNEL_EX(op_type, library_type, place_class, \
customized_name, \
customized_type_value, \
...) \
STATIC_ASSERT_GLOBAL_NAMESPACE( \
__reg_op_kernel_##op_type##_##library_type##_##customized_name##__, \
"REGISTER_OP_KERNEL_EX must be called in " \
"global namespace"); \
static ::paddle::framework::OpKernelRegistrarEx<place_class, \
__VA_ARGS__> \
__op_kernel_registrar_##op_type##_##library_type##_##customized_name##__(\
#op_type, #library_type, customized_type_value); \
int TouchOpKernelRegistrar_##op_type##_##library_type##_##customized_name() {\
__op_kernel_registrar_##op_type##_##library_type##_##customized_name##__ \
.Touch(); \
return 0; \
}
#define REGISTER_OP_CUDA_KERNEL_FUNCTOR(op_type, ...) \
REGISTER_OP_KERNEL_EX(op_type, CUDA, ::paddle::platform::CUDAPlace, \
__VA_ARGS__)
REGISTER_OP_KERNEL_EX( \
op_type, CUDA, ::paddle::platform::CUDAPlace, DEFAULT_TYPE, \
::paddle::framework::OpKernelType::kDefaultCustomizedTypeValue, \
__VA_ARGS__)
#define REGISTER_OP_CPU_KERNEL_FUNCTOR(op_type, ...) \
REGISTER_OP_KERNEL_EX(op_type, CPU, ::paddle::platform::CPUPlace, __VA_ARGS__)
#define REGISTER_OP_CPU_KERNEL_FUNCTOR(op_type, ...) \
REGISTER_OP_KERNEL_EX( \
op_type, CPU, ::paddle::platform::CPUPlace, DEFAULT_TYPE, \
::paddle::framework::OpKernelType::kDefaultCustomizedTypeValue, \
__VA_ARGS__)
/**
* Macro to mark what Operator and Kernel
......@@ -248,13 +281,19 @@ struct OpKernelRegistrarFunctorEx<PlaceType, false, I,
extern int TouchOpRegistrar_##op_type(); \
UNUSED static int use_op_itself_##op_type##_ = TouchOpRegistrar_##op_type()
#define USE_OP_DEVICE_KERNEL(op_type, LIBRARY_TYPE) \
STATIC_ASSERT_GLOBAL_NAMESPACE( \
__use_op_kernel_##op_type##_##LIBRARY_TYPE##__, \
"USE_OP_DEVICE_KERNEL must be in global namespace"); \
extern int TouchOpKernelRegistrar_##op_type##_##LIBRARY_TYPE(); \
UNUSED static int use_op_kernel_##op_type##_##LIBRARY_TYPE##_ = \
TouchOpKernelRegistrar_##op_type##_##LIBRARY_TYPE()
#define USE_OP_DEVICE_KERNEL_WITH_CUSTOM_TYPE(op_type, \
LIBRARY_TYPE, \
customized_name) \
STATIC_ASSERT_GLOBAL_NAMESPACE( \
__use_op_kernel_##op_type##_##LIBRARY_TYPE##_##customized_name##__, \
"USE_OP_DEVICE_KERNEL must be in global namespace"); \
extern int \
TouchOpKernelRegistrar_##op_type##_##LIBRARY_TYPE##_##customized_name(); \
UNUSED static int use_op_kernel_##op_type##_##LIBRARY_TYPE##_##DEFAULT_TYPE##_ = /* NOLINT */ \
TouchOpKernelRegistrar_##op_type##_##LIBRARY_TYPE##_##customized_name()
#define USE_OP_DEVICE_KERNEL(op_type, LIBRARY_TYPE) \
USE_OP_DEVICE_KERNEL_WITH_CUSTOM_TYPE(op_type, LIBRARY_TYPE, DEFAULT_TYPE)
// TODO(fengjiayi): The following macros
// seems ugly, do we have better method?
......@@ -280,6 +319,7 @@ struct OpKernelRegistrarFunctorEx<PlaceType, false, I,
#define USE_OP(op_type) \
USE_OP_ITSELF(op_type); \
USE_OP_KERNEL(op_type)
// clang-format off
} // namespace framework
} // namespace paddle
paddle/fluid/framework/operator.cc
浏览文件 @ 65dbc7cc
......@@ -695,6 +695,12 @@ static void CheckTensorNANOrInf(const std::string& name,
"Tensor %s contains NAN", name);
}
void OperatorWithKernel::RuntimeInferShape(const Scope& scope,
const platform::Place& place) const {
RuntimeInferShapeContext infer_shape_ctx(*this, scope);
this->InferShape(&infer_shape_ctx);
}
void OperatorWithKernel::RunImpl(const Scope& scope,
const platform::Place& place) const {
RuntimeInferShapeContext infer_shape_ctx(*this, scope);
......
paddle/fluid/framework/operator.h
浏览文件 @ 65dbc7cc
......@@ -128,6 +128,8 @@ class OperatorBase {
virtual std::vector<std::string> OutputVars(bool has_intermediate) const;
void SetIsCalledByExecutor(bool x) { run_by_executor_ = x; }
virtual void RuntimeInferShape(const Scope& scope,
const platform::Place& place) const {}
protected:
std::string type_;
......@@ -348,6 +350,9 @@ class OperatorWithKernel : public OperatorBase {
OpInfoMap::Instance().Get(Type()).infer_shape_(ctx);
}
void RuntimeInferShape(const Scope& scope,
const platform::Place& place) const override;
protected:
virtual OpKernelType GetExpectedKernelType(const ExecutionContext& ctx) const;
virtual OpKernelType GetKernelTypeForVar(
......
paddle/fluid/framework/operator_test.cc
浏览文件 @ 65dbc7cc
......@@ -50,6 +50,8 @@ class OpWithoutKernelCheckerMaker : public OpProtoAndCheckerMaker {
AddInput("input", "input of test op");
AddOutput("output", "output of test op");
AddAttr<float>("scale", "scale of cosine op");
AddAttr<int>("kernel_sub_type", "kernels with different implementations.")
.SetDefault(0);
AddComment("This is test op");
}
};
......@@ -95,6 +97,8 @@ TEST(OperatorBase, all) {
namespace paddle {
namespace framework {
static int special_type_value = 1;
class OpKernelTestProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
public:
void Make() {
......@@ -103,11 +107,14 @@ class OpKernelTestProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
AddAttr<float>("scale", "scale of cosine op")
.SetDefault(1.0)
.GreaterThan(0.0);
AddAttr<int>("kernel_sub_type", "kernels with different implementations.")
.SetDefault(0);
AddComment("This is test op");
}
};
static int cpu_kernel_run_num = 0;
static int cpu_kernel2_run_num = 0;
class OpWithKernelTest : public OperatorWithKernel {
public:
......@@ -117,7 +124,10 @@ class OpWithKernelTest : public OperatorWithKernel {
void InferShape(framework::InferShapeContext* ctx) const override {}
OpKernelType GetExpectedKernelType(
const ExecutionContext& ctx) const override {
return OpKernelType(proto::VarType::FP32, ctx.GetPlace());
int sub_type = ctx.Attr<int>("kernel_sub_type");
return OpKernelType(proto::VarType::FP32, ctx.GetPlace(),
framework::DataLayout::kAnyLayout,
framework::LibraryType::kPlain, sub_type);
}
};
......@@ -132,6 +142,17 @@ class CPUKernelTest : public OpKernel<float> {
}
};
template <typename T1, typename T2>
class CPUKernel2Test : public OpKernel<float> {
public:
void Compute(const ExecutionContext& ctx) const {
std::cout << ctx.op().DebugString() << std::endl;
cpu_kernel2_run_num++;
ASSERT_EQ(ctx.op().Input("x"), "IN1");
ASSERT_EQ(ctx.op().Output("y"), "OUT1");
}
};
class OpKernelTestMultiInputsProtoAndCheckerMaker
: public OpProtoAndCheckerMaker {
public:
......@@ -142,6 +163,8 @@ class OpKernelTestMultiInputsProtoAndCheckerMaker
AddAttr<float>("scale", "scale of cosine op")
.SetDefault(1.0)
.GreaterThan(0.0);
AddAttr<int>("kernel_sub_type", "kernels with different implementations.")
.SetDefault(0);
AddComment("This is test op");
}
};
......@@ -189,9 +212,15 @@ class CPUKernalMultiInputsTest : public OpKernel<float> {
REGISTER_OP_WITHOUT_GRADIENT(
op_with_kernel, paddle::framework::OpWithKernelTest,
paddle::framework::OpKernelTestProtoAndCheckerMaker);
REGISTER_OP_CPU_KERNEL(op_with_kernel,
paddle::framework::CPUKernelTest<float, float>);
REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE(
op_with_kernel, CPU, paddle::platform::CPUPlace, MY_SPECIAL_NAME,
paddle::framework::special_type_value,
paddle::framework::CPUKernel2Test<float, float>);
// test with single input
TEST(OpKernel, all) {
paddle::framework::InitDevices(true);
......@@ -211,7 +240,19 @@ TEST(OpKernel, all) {
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
ASSERT_EQ(paddle::framework::cpu_kernel_run_num, 0);
op->Run(scope, cpu_place);
// kerne_sub_type = 0, hence cpu_kernel is called, cpu_kernel2 is not called.
ASSERT_EQ(paddle::framework::cpu_kernel_run_num, 1);
ASSERT_EQ(paddle::framework::cpu_kernel2_run_num, 0);
attr = op_desc.mutable_attrs()->Add();
attr->set_name("kernel_sub_type");
attr->set_type(paddle::framework::proto::AttrType::INT);
attr->set_i(1);
auto op2 = paddle::framework::OpRegistry::CreateOp(op_desc);
op2->Run(scope, cpu_place);
// kerne_sub_type = 1, hence cpu_kernel2 is called, cpu_kernel is not called.
ASSERT_EQ(paddle::framework::cpu_kernel_run_num, 1);
ASSERT_EQ(paddle::framework::cpu_kernel2_run_num, 1);
}
REGISTER_OP_WITHOUT_GRADIENT(
......
paddle/fluid/framework/selected_rows.h
浏览文件 @ 65dbc7cc
......@@ -32,8 +32,7 @@ namespace framework {
class SelectedRows {
/*
* @brief We can use the SelectedRows structure to reproduce a sparse table.
* A sparse table is a key-value structure that the key is an `int64_t`
* number,
* A sparse table is a key-value structure that the key is an `int64_t`,
* and the value is a Tensor which the first dimension is 0.
* You can use the following interface to operate the sparse table, and you
* can find
......
paddle/fluid/framework/type_defs.h
浏览文件 @ 65dbc7cc
......@@ -54,7 +54,5 @@ using InferVarTypeFN =
using InferShapeFN = std::function<void(InferShapeContext*)>;
using EstimateFlopsFN = std::function<void(InferShapeContext*)>;
} // namespace framework
} // namespace paddle
paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc
浏览文件 @ 65dbc7cc
......@@ -178,11 +178,12 @@ void TensorRtSubgraphPass::CreateTensorRTOp(framework::ir::Node *node,
output_mapping.push_back(output_name_map[name]);
}
*block_desc.Proto()->mutable_vars() =
const_cast<framework::ProgramDesc *>(&graph->program())
->Proto()
->blocks(0)
.vars();
auto *vars = block_desc.Proto()->mutable_vars();
for (framework::ir::Node *node : graph->Nodes()) {
if (node->IsVar() && node->Var()) {
*vars->Add() = *node->Var()->Proto();
}
}
PADDLE_ENFORCE(!block_desc.Proto()->vars().empty(),
"the block has no var-desc");
PADDLE_ENFORCE(!output_mapping.empty());
......
paddle/fluid/inference/api/demo_ci/CMakeLists.txt
浏览文件 @ 65dbc7cc
......@@ -79,6 +79,16 @@ link_directories("${PADDLE_LIB}/third_party/install/gflags/lib")
link_directories("${PADDLE_LIB}/third_party/install/xxhash/lib")
link_directories("${PADDLE_LIB}/paddle/lib")
if (NOT WIN32)
set(NGRAPH_PATH "${PADDLE_LIB}/third_party/install/ngraph")
if(EXISTS ${NGRAPH_PATH})
include(GNUInstallDirs)
include_directories("${NGRAPH_PATH}/include")
link_directories("${NGRAPH_PATH}/${CMAKE_INSTALL_LIBDIR}")
set(NGRAPH_LIB ${NGRAPH_PATH}/${CMAKE_INSTALL_LIBDIR}/libngraph${CMAKE_SHARED_LIBRARY_SUFFIX})
endif()
endif()
add_executable(${DEMO_NAME} ${DEMO_NAME}.cc)
if(WITH_MKL)
......@@ -106,7 +116,7 @@ endif()
if (NOT WIN32)
set(EXTERNAL_LIB "-lrt -ldl -lpthread")
set(DEPS ${DEPS}
${MATH_LIB} ${MKLDNN_LIB}
${MATH_LIB} ${MKLDNN_LIB} ${NGRAPH_LIB}
glog gflags protobuf snappystream snappy z xxhash
${EXTERNAL_LIB})
else()
......
paddle/fluid/memory/allocation/legacy_allocator.cc
浏览文件 @ 65dbc7cc
......@@ -14,11 +14,13 @@
#include "paddle/fluid/memory/allocation/legacy_allocator.h"
#include <string>
#include <vector>
#include "glog/logging.h"
#include "paddle/fluid/memory/detail/buddy_allocator.h"
#include "paddle/fluid/memory/detail/system_allocator.h"
#include "paddle/fluid/platform/gpu_info.h"
#include "paddle/fluid/string/printf.h"
#include "paddle/fluid/string/split.h"
DEFINE_bool(init_allocated_mem, false,
"It is a mistake that the values of the memory allocated by "
......@@ -86,7 +88,7 @@ struct NaiveAllocator {
template <>
void *Alloc<platform::CPUPlace>(const platform::CPUPlace &place, size_t size) {
VLOG(1) << "Allocate " << size << " bytes on " << platform::Place(place);
VLOG(10) << "Allocate " << size << " bytes on " << platform::Place(place);
void *p = GetCPUBuddyAllocator()->Alloc(size);
if (FLAGS_init_allocated_mem) {
memset(p, 0xEF, size);
......@@ -97,7 +99,7 @@ void *Alloc<platform::CPUPlace>(const platform::CPUPlace &place, size_t size) {
template <>
void Free<platform::CPUPlace>(const platform::CPUPlace &place, void *p) {
VLOG(1) << "Free pointer=" << p << " on " << platform::Place(place);
VLOG(10) << "Free pointer=" << p << " on " << platform::Place(place);
GetCPUBuddyAllocator()->Free(p);
}
......@@ -110,19 +112,21 @@ size_t Used<platform::CPUPlace>(const platform::CPUPlace &place) {
BuddyAllocator *GetGPUBuddyAllocator(int gpu_id) {
static std::once_flag init_flag;
static detail::BuddyAllocator **a_arr = nullptr;
static std::vector<int> devices;
std::call_once(init_flag, [gpu_id]() {
int gpu_num = platform::GetCUDADeviceCount();
PADDLE_ENFORCE(gpu_id < gpu_num, "gpu_id:%d should < gpu_num:%d", gpu_id,
gpu_num);
devices = platform::GetSelectedDevices();
int gpu_num = devices.size();
a_arr = new BuddyAllocator *[gpu_num];
for (int i = 0; i < gpu_num; i++) {
for (size_t i = 0; i < devices.size(); ++i) {
int dev_id = devices[i];
a_arr[i] = nullptr;
platform::SetDeviceId(i);
a_arr[i] = new BuddyAllocator(
std::unique_ptr<detail::SystemAllocator>(new detail::GPUAllocator(i)),
platform::GpuMinChunkSize(), platform::GpuMaxChunkSize());
platform::SetDeviceId(dev_id);
a_arr[i] = new BuddyAllocator(std::unique_ptr<detail::SystemAllocator>(
new detail::GPUAllocator(dev_id)),
platform::GpuMinChunkSize(),
platform::GpuMaxChunkSize());
VLOG(10) << "\n\nNOTE: each GPU device use "
<< FLAGS_fraction_of_gpu_memory_to_use * 100
......@@ -134,7 +138,9 @@ BuddyAllocator *GetGPUBuddyAllocator(int gpu_id) {
});
platform::SetDeviceId(gpu_id);
return a_arr[gpu_id];
auto pos = std::distance(devices.begin(),
std::find(devices.begin(), devices.end(), gpu_id));
return a_arr[pos];
}
#endif
......
paddle/fluid/operators/activation_op.cc
浏览文件 @ 65dbc7cc
......@@ -76,8 +76,8 @@ framework::OpKernelType GetKernelType(const framework::ExecutionContext& ctx,
}
#endif
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::Tensor>(name)->type()),
ctx.GetPlace(), layout, library);
framework::GetDataTypeOfVar(ctx.InputVar(name)), ctx.GetPlace(), layout,
library);
}
class ActivationOp : public framework::OperatorWithKernel {
......
paddle/fluid/operators/activation_op.h
浏览文件 @ 65dbc7cc
......@@ -41,6 +41,12 @@ static std::unordered_set<std::string> InplaceOpSet = {
"floor", "reciprocal", "relu6", "soft_relu", "hard_sigmoid",
};
/* The following operator can be used to process SelectedRows, because the
* output of those operator for zero is zero too.
*/
static std::unordered_set<std::string> CanBeUsedBySelectedRows = {
"abs", "abs_grad", "square", "square_grad", "sqrt", "sqrt_grad"};
static bool IsInplace(std::string op) { return InplaceOpSet.count(op); }
template <typename DeviceContext, typename Functor>
......@@ -50,16 +56,38 @@ class ActivationKernel
using T = typename Functor::ELEMENT_TYPE;
void Compute(const framework::ExecutionContext& context) const override {
auto& X = detail::Ref(context.Input<framework::Tensor>("X"),
"Cannot get input tensor X, variable name = %s",
context.op().Input("X"));
auto& Out = detail::Ref(context.Output<framework::Tensor>("Out"),
"Cannot get output tensor Out, variable name = %s",
context.op().Output("Out"));
Out.mutable_data<T>(context.GetPlace());
auto x_var = context.InputVar("X");
auto out_var = context.OutputVar("Out");
PADDLE_ENFORCE(x_var != nullptr,
"Cannot get input Variable X, variable name = %s",
context.op().Input("X"));
PADDLE_ENFORCE(out_var != nullptr,
"Cannot get output Variable Out, variable name = %s",
context.op().Output("Out"));
framework::Tensor X, *Out;
if (CanBeUsedBySelectedRows.count(context.op().Type())) {
X = detail::Ref(
paddle::framework::GetLoDTensorOrSelectedRowsValueFromVar(*x_var),
"Cannot get input Tensor X, variable name = %s",
context.op().Input("X"));
Out = paddle::framework::GetMutableLoDTensorOrSelectedRowsValueFromVar(
out_var);
} else {
X = detail::Ref(context.Input<framework::Tensor>("X"),
"Cannot get input Tensor X, variable name = %s",
context.op().Input("X"));
Out = context.Output<framework::Tensor>("Out");
}
PADDLE_ENFORCE(Out != nullptr,
"Cannot get output tensor Out, variable name = %s",
context.op().Output("Out"));
Out->mutable_data<T>(context.GetPlace());
auto x = framework::EigenVector<T>::Flatten(X);
auto out = framework::EigenVector<T>::Flatten(Out);
auto out = framework::EigenVector<T>::Flatten(*Out);
auto* place =
context.template device_context<DeviceContext>().eigen_device();
Functor functor;
......@@ -78,14 +106,54 @@ class ActivationGradKernel
public:
using T = typename Functor::ELEMENT_TYPE;
void Compute(const framework::ExecutionContext& context) const override {
auto* Out = context.Input<framework::Tensor>("Out");
auto* dOut =
context.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* dX = context.Output<framework::Tensor>(framework::GradVarName("X"));
auto out_var = context.InputVar("Out");
auto out_grad_var = context.InputVar(framework::GradVarName("Out"));
auto x_grad_var = context.OutputVar(framework::GradVarName("X"));
PADDLE_ENFORCE(out_var != nullptr,
"Cannot get input Variable Out, variable name = %s",
context.op().Input("Out"));
PADDLE_ENFORCE(out_grad_var != nullptr,
"Cannot get input Variable %s, variable name = %s",
framework::GradVarName("Out"),
context.op().Input(framework::GradVarName("Out")));
PADDLE_ENFORCE(x_grad_var != nullptr,
"Cannot get output Variable %s, variable name = %s",
framework::GradVarName("X"),
context.op().Output(framework::GradVarName("X")));
framework::Tensor Out, dOut, *dX;
if (CanBeUsedBySelectedRows.count(context.op().Type())) {
Out = detail::Ref(
paddle::framework::GetLoDTensorOrSelectedRowsValueFromVar(*out_var),
"Cannot get input Tensor Out, variable name = %s",
context.op().Input("Out"));
dOut =
detail::Ref(paddle::framework::GetLoDTensorOrSelectedRowsValueFromVar(
*out_grad_var),
"Cannot get input Tensor %s, variable name = %s",
framework::GradVarName("Out"),
context.op().Input(framework::GradVarName("Out")));
dX = paddle::framework::GetMutableLoDTensorOrSelectedRowsValueFromVar(
x_grad_var);
} else {
Out = detail::Ref(context.Input<framework::Tensor>("Out"),
"Cannot get input Tensor Out, variable name = %s",
context.op().Input("Out"));
dOut = detail::Ref(
context.Input<framework::Tensor>(framework::GradVarName("Out")),
"Cannot get input Tensor %s, variable name = %s",
framework::GradVarName("Out"),
context.op().Input(framework::GradVarName("Out")));
dX = context.Output<framework::Tensor>(framework::GradVarName("X"));
}
PADDLE_ENFORCE(dX != nullptr,
"Cannot get output tensor %s, variable name = %s",
framework::GradVarName("X"),
context.op().Output(framework::GradVarName("X")));
dX->mutable_data<T>(context.GetPlace());
auto dout = framework::EigenVector<T>::Flatten(*dOut);
auto out = framework::EigenVector<T>::Flatten(*Out);
auto dout = framework::EigenVector<T>::Flatten(dOut);
auto out = framework::EigenVector<T>::Flatten(Out);
auto dx = framework::EigenVector<T>::Flatten(*dX);
auto* place =
context.template device_context<DeviceContext>().eigen_device();
......@@ -96,8 +164,19 @@ class ActivationGradKernel
}
bool inplace = functor.Inplace();
if (!inplace) {
auto* X = context.Input<framework::Tensor>("X");
auto x = framework::EigenVector<T>::Flatten(*X);
auto x_var = context.InputVar("X");
PADDLE_ENFORCE(x_var != nullptr,
"Cannot get input tensor X, variable name = %s",
context.op().Input("X"));
framework::Tensor X;
if (CanBeUsedBySelectedRows.count(context.op().Type())) {
X = detail::Ref(
paddle::framework::GetLoDTensorOrSelectedRowsValueFromVar(*x_var));
} else {
X = detail::Ref(context.Input<framework::Tensor>("X"));
}
auto x = framework::EigenVector<T>::Flatten(X);
functor(*place, x, out, dout, dx);
} else {
VLOG(10) << " Inplace activation ";
......
paddle/fluid/operators/conv_mkldnn_op.cc
浏览文件 @ 65dbc7cc
......@@ -576,14 +576,22 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
namespace ops = paddle::operators;
REGISTER_OP_KERNEL(conv2d, MKLDNN, ::paddle::platform::CPUPlace,
ops::ConvMKLDNNOpKernel<float>);
REGISTER_OP_KERNEL(conv2d_grad, MKLDNN, ::paddle::platform::CPUPlace,
ops::ConvMKLDNNGradOpKernel<float>);
REGISTER_OP_KERNEL(conv3d, MKLDNN, ::paddle::platform::CPUPlace,
ops::ConvMKLDNNOpKernel<float>);
REGISTER_OP_KERNEL(conv3d_grad, MKLDNN, ::paddle::platform::CPUPlace,
ops::ConvMKLDNNGradOpKernel<float>);
REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE(conv2d, MKLDNN,
::paddle::platform::CPUPlace, FP32,
ops::kConvMKLDNNFP32,
ops::ConvMKLDNNOpKernel<float>);
REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE(conv2d_grad, MKLDNN,
::paddle::platform::CPUPlace, FP32,
ops::kConvMKLDNNFP32,
ops::ConvMKLDNNGradOpKernel<float>);
REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE(conv3d, MKLDNN,
::paddle::platform::CPUPlace, FP32,
ops::kConvMKLDNNFP32,
ops::ConvMKLDNNOpKernel<float>);
REGISTER_OP_KERNEL_WITH_CUSTOM_TYPE(conv3d_grad, MKLDNN,
::paddle::platform::CPUPlace, FP32,
ops::kConvMKLDNNFP32,
ops::ConvMKLDNNGradOpKernel<float>);
paddle/fluid/operators/conv_op.cc
浏览文件 @ 65dbc7cc
......@@ -74,6 +74,8 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
framework::OpKernelType ConvOp::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
int customized_type_value =
framework::OpKernelType::kDefaultCustomizedTypeValue;
framework::LibraryType library{framework::LibraryType::kPlain};
// TODO(pzelazko-intel): enable MKLDNN layout when it's ready
std::string data_format = ctx.Attr<std::string>("data_format");
......@@ -89,6 +91,7 @@ framework::OpKernelType ConvOp::GetExpectedKernelType(
platform::CanMKLDNNBeUsed(ctx)) {
library = framework::LibraryType::kMKLDNN;
layout = framework::DataLayout::kMKLDNN;
customized_type_value = kConvMKLDNNFP32;
}
#endif
......@@ -105,7 +108,7 @@ framework::OpKernelType ConvOp::GetExpectedKernelType(
}
return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout,
library);
library, customized_type_value);
}
void Conv2DOpMaker::Make() {
......@@ -358,6 +361,8 @@ void ConvOpGrad::InferShape(framework::InferShapeContext* ctx) const {
framework::OpKernelType ConvOpGrad::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
int customized_type_value =
framework::OpKernelType::kDefaultCustomizedTypeValue;
framework::LibraryType library_{framework::LibraryType::kPlain};
// TODO(pzelazko-intel): enable MKLDNN layout when it's ready
std::string data_format = ctx.Attr<std::string>("data_format");
......@@ -373,12 +378,13 @@ framework::OpKernelType ConvOpGrad::GetExpectedKernelType(
platform::CanMKLDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kMKLDNN;
layout_ = framework::DataLayout::kMKLDNN;
customized_type_value = kConvMKLDNNFP32;
}
#endif
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),
layout_, library_);
layout_, library_, customized_type_value);
}
} // namespace operators
......
paddle/fluid/operators/conv_op.h
浏览文件 @ 65dbc7cc
......@@ -27,6 +27,8 @@ namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
constexpr int kConvMKLDNNFP32 = 1;
constexpr int kConvMKLDNNINT8 = 2;
// Base convolution operator definations for other conv
// like operators to reuse the implementation.
......
paddle/fluid/operators/cudnn_lstm_op.cu.cc
浏览文件 @ 65dbc7cc
......@@ -177,11 +177,19 @@ struct CudnnRNNCache {
seed_));
CUDNN_ENFORCE(platform::dynload::cudnnCreateRNNDescriptor(&rnn_desc_));
#if CUDNN_VERSION >= 6000
CUDNN_ENFORCE(platform::dynload::cudnnSetRNNDescriptor_v6(
handle, rnn_desc_, hidden_size_, num_layers_, dropout_desc_,
CUDNN_LINEAR_INPUT,
is_bidirec_ ? CUDNN_BIDIRECTIONAL : CUDNN_UNIDIRECTIONAL, CUDNN_LSTM,
CUDNN_RNN_ALGO_STANDARD, CUDNN_DATA_FLOAT));
#else
CUDNN_ENFORCE(platform::dynload::cudnnSetRNNDescriptor(
rnn_desc_, hidden_size_, num_layers_, dropout_desc_, CUDNN_LINEAR_INPUT,
is_bidirec_ ? CUDNN_BIDIRECTIONAL : CUDNN_UNIDIRECTIONAL, CUDNN_LSTM,
CUDNN_DATA_FLOAT));
#endif
CUDNN_ENFORCE(platform::dynload::cudnnCreateFilterDescriptor(&w_desc_));
CUDNN_ENFORCE(platform::dynload::cudnnCreateFilterDescriptor(&dw_desc_));
......
paddle/fluid/operators/elementwise/elementwise_mul_op.h
浏览文件 @ 65dbc7cc
......@@ -60,15 +60,37 @@ template <typename DeviceContext, typename T>
class ElementwiseMulKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<framework::LoDTensor>("X");
auto x_var = ctx.InputVar("X");
PADDLE_ENFORCE(x_var != nullptr,
"Cannot get input Variable X, variable name = %s",
ctx.op().Input("X"));
auto* y = ctx.Input<framework::LoDTensor>("Y");
auto* z = ctx.Output<framework::LoDTensor>("Out");
framework::Tensor x, *z;
if (x_var->IsType<framework::SelectedRows>()) {
PADDLE_ENFORCE(y->dims().size() == 1 && y->dims()[0] == 1,
"For elementwise_op, if X is Sparse, Y must be scalar.");
auto& x_sele = x_var->Get<framework::SelectedRows>();
auto out_sele = ctx.Output<framework::SelectedRows>("Out");
x = x_sele.value();
out_sele->set_rows(x_sele.rows());
out_sele->set_height(x_sele.height());
out_sele->mutable_value()->Resize(x_sele.value().dims());
out_sele->mutable_value()->mutable_data(ctx.GetPlace(), x.type());
z = ctx.Output<framework::SelectedRows>("Out")->mutable_value();
} else if (x_var->IsType<framework::LoDTensor>()) {
x = x_var->Get<framework::LoDTensor>();
z = ctx.Output<framework::LoDTensor>("Out");
} else {
PADDLE_THROW("X's type[%s] is not supported by elementwise_op.",
x_var->Type().name());
}
z->mutable_data<T>(ctx.GetPlace());
if (x->numel() == y->numel()) {
elementwise_mul<DeviceContext, T>(ctx, x, y, z);
if (x.numel() == y->numel()) {
elementwise_mul<DeviceContext, T>(ctx, &x, y, z);
} else {
default_elementwise_mul<DeviceContext, T>(ctx, x, y, z);
default_elementwise_mul<DeviceContext, T>(ctx, &x, y, z);
}
}
};
......
paddle/fluid/operators/elementwise/elementwise_op.h
浏览文件 @ 65dbc7cc
......@@ -40,21 +40,28 @@ class ElementwiseOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of elementwise op should not be null.");
PADDLE_ENFORCE(
ctx->GetInputsVarType("X").front() ==
framework::proto::VarType::LOD_TENSOR,
"The input var's type should be LoDTensor, but the received is %s",
ctx->Inputs("X").front(), ctx->GetInputsVarType("X").front());
PADDLE_ENFORCE(
ctx->GetInputsVarType("Y").front() ==
framework::proto::VarType::LOD_TENSOR,
"The input var's type should be LoDTensor, but the received is %s",
ctx->Inputs("Y").front(), ctx->GetInputsVarType("Y").front());
auto x_dim = ctx->GetInputDim("X");
auto y_dim = ctx->GetInputDim("Y");
PADDLE_ENFORCE_GE(x_dim.size(), y_dim.size(),
"Rank of first input must >= rank of second input.");
"The input var's type should be LoDTensor, but the received is %s [%s]",
ctx->GetInputsVarType("Y").front(), ctx->Inputs("Y").front());
if (ctx->GetInputsVarType("X").front() ==
framework::proto::VarType::LOD_TENSOR) {
auto x_dim = ctx->GetInputDim("X");
auto y_dim = ctx->GetInputDim("Y");
PADDLE_ENFORCE_GE(x_dim.size(), y_dim.size(),
"Rank of first input must >= rank of second input.");
} else if (ctx->GetInputsVarType("X").front() ==
framework::proto::VarType::SELECTED_ROWS) {
PADDLE_ENFORCE((ctx->GetInputDim("Y").size() == 1u) &&
(ctx->GetInputDim("Y")[0] == 1),
"For elementwise_op, if X is Sparse, "
"Y must be scalar.");
} else {
PADDLE_THROW("X's type[%s] is not supported by elementwise_op.",
ctx->GetInputsVarType("X").front());
}
ctx->ShareDim("X", /*->*/ "Out");
ctx->ShareLoD("X", /*->*/ "Out");
......
paddle/fluid/operators/get_tensor_from_selected_rows_op.cc 0 → 100644
浏览文件 @ 65dbc7cc
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/tensor_util.h"
namespace paddle {
namespace operators {
class GetTensorFromSelectedRowsOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"GetTensorFromSelectedRowsOp must has input X.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"GetTensorFromSelectedRowsOp must has output Out.");
PADDLE_ENFORCE(
ctx->GetInputsVarType("X").front() ==
framework::proto::VarType::SELECTED_ROWS,
"The input X's type should be SelectedRows, but the received is %s",
ctx->Inputs("X").front(), ctx->GetInputsVarType("X").front());
PADDLE_ENFORCE(
ctx->GetOutputsVarType("Out").front() ==
framework::proto::VarType::LOD_TENSOR,
"The output Out's type should be LoDTensor, but the received is %s",
ctx->Outputs("Out").front(), ctx->GetOutputsVarType("Out").front());
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::GetDataTypeOfVar(ctx.InputVar("X")), ctx.device_context());
}
};
class GetTensorFromSelectedRowsKernel {
public:
void operator()(const framework::ExecutionContext &ctx) const {
auto *x = ctx.Input<framework::SelectedRows>("X");
auto *out = ctx.Output<framework::LoDTensor>("Out");
out->Resize(x->value().dims());
out->mutable_data(ctx.GetPlace(), x->value().type());
framework::TensorCopy(x->value(), ctx.GetPlace(), ctx.device_context(),
out);
}
};
class GetTensorFromSelectedRowsOpProtoMaker
: public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "The input type is SelectedRows.");
AddOutput("Out", "The output type is LoDTensor.");
AddComment(
R"DOC(
GetTensorFromSelectedRows Operator
GetTensorFromSelectedRows is used to get the tensor from SelectedRows.
)DOC");
}
};
class GetTensorFromSelectedRowsOpVarTypeInference
: public framework::VarTypeInference {
public:
void operator()(const framework::OpDesc &op_desc,
framework::BlockDesc *block) const final {
auto out_var_name = op_desc.Output("Out").front();
auto in_var_name = op_desc.Input("X").front();
auto out_var = block->FindRecursiveOrCreateVar(out_var_name);
auto in_var = block->FindRecursiveOrCreateVar(in_var_name);
out_var.SetType(framework::proto::VarType::LOD_TENSOR);
out_var.SetDataType(in_var.GetDataType());
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(get_tensor_from_selected_rows,
ops::GetTensorFromSelectedRowsOp,
ops::GetTensorFromSelectedRowsOpProtoMaker,
ops::GetTensorFromSelectedRowsOpVarTypeInference);
REGISTER_OP_CPU_KERNEL_FUNCTOR(get_tensor_from_selected_rows, float,
ops::GetTensorFromSelectedRowsKernel, double,
ops::GetTensorFromSelectedRowsKernel, int,
ops::GetTensorFromSelectedRowsKernel, int64_t,
ops::GetTensorFromSelectedRowsKernel);
#ifdef PADDLE_WITH_CUDA
REGISTER_OP_CUDA_KERNEL_FUNCTOR(get_tensor_from_selected_rows, float,
ops::GetTensorFromSelectedRowsKernel, double,
ops::GetTensorFromSelectedRowsKernel, int,
ops::GetTensorFromSelectedRowsKernel, int64_t,
ops::GetTensorFromSelectedRowsKernel);
#endif
paddle/fluid/operators/hierarchical_sigmoid_op.cc
浏览文件 @ 65dbc7cc
......@@ -158,6 +158,7 @@ class HierarchicalSigmoidGradOp : public framework::OperatorWithKernel {
ctx->SetOutputDim(framework::GradVarName("W"), ctx->GetInputDim("W"));
}
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
ctx->ShareLoD("X", /*->*/ framework::GradVarName("X"));
}
protected:
......
paddle/fluid/operators/hierarchical_sigmoid_op.h
浏览文件 @ 65dbc7cc
......@@ -185,7 +185,6 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
ctx.Output<framework::SelectedRows>(framework::GradVarName("W"));
w_grad->set_rows(real_rows);
// Build a map of id -> row_index to speed up finding the index of one id
w_grad->SyncIndex();
w_grad->set_height(w.dims()[0]);
auto* w_grad_value = w_grad->mutable_value();
framework::DDim temp_dim(w.dims());
......
paddle/fluid/operators/math/matrix_bit_code.cc
浏览文件 @ 65dbc7cc
......@@ -89,6 +89,8 @@ template <typename T>
void MatrixBitCodeFunctor<T>::Mul(framework::Tensor* tmat,
const framework::Tensor& weight,
const framework::Tensor& input) {
auto blas =
GetBlas<platform::CPUDeviceContext, T>(platform::CPUDeviceContext());
size_t num_samples = tmat->dims()[0];
size_t tmat_width = tmat->dims()[1];
size_t input_width = input.dims()[1];
......@@ -99,13 +101,12 @@ void MatrixBitCodeFunctor<T>::Mul(framework::Tensor* tmat,
for (size_t i = 0; i < num_samples; ++i) {
auto code = code_table_->get_code(i);
int code_length = code->get_length();
const T* input_row = input_value + input_width * i;
for (int j = 0; j < code_length; ++j) {
size_t index = code->calc_index(j);
const T* weight_row = weight_value + weight_width * index;
T sum = static_cast<T>(0.0);
for (size_t k = 0; k < input_width; ++k) {
sum += weight_value[weight_width * index + k] *
input_value[input_width * i + k];
}
sum = blas.DOT(input_width, weight_row, input_row);
tmat_value[i * tmat_width + j] += sum;
}
}
......@@ -115,6 +116,8 @@ template <typename T>
void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::Tensor& tmat,
framework::Tensor* weight,
const framework::Tensor& input) {
auto blas =
GetBlas<platform::CPUDeviceContext, T>(platform::CPUDeviceContext());
size_t num_samples = tmat.dims()[0];
size_t input_width = input.dims()[1];
size_t tmat_width = tmat.dims()[1];
......@@ -122,16 +125,25 @@ void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::Tensor& tmat,
auto tmat_value = tmat.data<T>();
auto weight_value = weight->data<T>();
auto input_value = input.data<T>();
std::unordered_map<int, std::vector<std::pair<T, const T*>>> ops;
for (size_t i = 0; i < num_samples; ++i) {
auto code = code_table_->get_code(i);
int code_length = code->get_length();
const T* input_value_row = input_value + input_width * i;
const T* tmat_row = tmat_value + i * tmat_width;
for (int j = 0; j < code_length; ++j) {
size_t index = code->calc_index(j);
for (size_t k = 0; k < input_width; ++k) {
weight_value[weight_width * index + k] +=
tmat_value[i * tmat_width + j] * input_value[input_width * i + k];
}
ops[code->calc_index(j)].emplace_back(tmat_row[j], input_value_row);
}
}
for (auto& op : ops) {
auto& op_in_row = op.second;
for (auto& pair : op_in_row) {
auto& scale = pair.first;
auto* input_row = pair.second;
T* weight_row = weight_value + op.first * weight_width;
blas.AXPY(input_width, scale, input_row, weight_row);
}
}
}
......@@ -140,6 +152,8 @@ template <typename T>
void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::Tensor& tmat,
framework::SelectedRows* weight,
const framework::Tensor& input) {
auto blas =
GetBlas<platform::CPUDeviceContext, T>(platform::CPUDeviceContext());
size_t num_samples = tmat.dims()[0];
size_t input_width = input.dims()[1];
size_t tmat_width = tmat.dims()[1];
......@@ -147,17 +161,28 @@ void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::Tensor& tmat,
auto tmat_value = tmat.data<T>();
auto weight_value = weight->mutable_value()->data<T>();
auto input_value = input.data<T>();
std::unordered_map<int, std::vector<std::pair<T, const T*>>> ops;
ops.reserve(weight->rows().size());
for (size_t i = 0; i < num_samples; ++i) {
auto code = code_table_->get_code(i);
int code_length = code->get_length();
const T* input_value_row = input_value + input_width * i;
const T* tmat_row = tmat_value + i * tmat_width;
for (int j = 0; j < code_length; ++j) {
size_t index = code->calc_index(j);
for (size_t k = 0; k < input_width; ++k) {
int64_t row_index = weight->GetIndexFromId(static_cast<int64_t>(index));
weight_value[row_index * weight_width + k] +=
tmat_value[i * tmat_width + j] * input_value[input_width * i + k];
}
ops[code->calc_index(j)].emplace_back(tmat_row[j], input_value_row);
}
}
for (auto& row : weight->rows()) {
auto& op_in_row = ops[row];
for (auto& pair : op_in_row) {
auto& scale = pair.first;
auto* input_row = pair.second;
blas.AXPY(input_width, scale, input_row, weight_value);
}
weight_value += weight_width;
}
}
......
paddle/fluid/operators/math/matrix_bit_code.h
浏览文件 @ 65dbc7cc
......@@ -13,10 +13,14 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <unordered_map>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/platform/device_context.h"
#if defined(_WIN32)
......
paddle/fluid/operators/merge_selected_rows_op.cc 0 → 100644
浏览文件 @ 65dbc7cc
/* 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/merge_selected_rows_op.h"
namespace paddle {
namespace operators {
class MergeSelectedRowsOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of MergeSelectedRowsOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of MergeSelectedRowsOp should not be null.");
ctx->ShareDim("X", /*->*/ "Out");
}
};
class MergeSelectedRowsOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"The input type is SelectedRows, and the selected rows may be "
"duplicated.");
AddOutput("Out",
"The output type is SelectedRows, and the selected rows are not "
"duplicated.");
AddComment(
R"DOC(
MergeSelectedRows Operator.
MergeSelectedRows is used to merge the duplicated rows of the input.
)DOC");
}
};
class MergeSelectedRowsOpInferVarType
: public framework::PassInDtypeAndVarTypeToOutput {
protected:
std::unordered_map<std::string, std::string> GetInputOutputWithSameType()
const override {
return std::unordered_map<std::string, std::string>{{"X", /*->*/ "Out"}};
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OPERATOR(merge_selected_rows, ops::MergeSelectedRowsOp,
ops::MergeSelectedRowsOpMaker,
ops::MergeSelectedRowsOpInferVarType);
REGISTER_OP_CPU_KERNEL(
merge_selected_rows,
ops::MergeSelectedRowsKernel<plat::CPUDeviceContext, float>,
ops::MergeSelectedRowsKernel<plat::CPUDeviceContext, double>);
paddle/fluid/operators/merge_selected_rows_op.cu.cc 0 → 100644
浏览文件 @ 65dbc7cc
/* 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/merge_selected_rows_op.h"
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
merge_selected_rows,
ops::MergeSelectedRowsKernel<plat::CUDADeviceContext, float>,
ops::MergeSelectedRowsKernel<plat::CUDADeviceContext, double>);
paddle/fluid/operators/merge_selected_rows_op.h 0 → 100644
浏览文件 @ 65dbc7cc
/* 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/framework/op_registry.h"
#include "paddle/fluid/operators/math/selected_rows_functor.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class MergeSelectedRowsKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* x = context.Input<framework::SelectedRows>("X");
auto* out = context.Output<framework::SelectedRows>("Out");
math::scatter::MergeAdd<DeviceContext, T> merge_func;
merge_func(context.template device_context<DeviceContext>(), *x, out);
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/sequence_ops/sequence_mask_op.h
浏览文件 @ 65dbc7cc
......@@ -36,12 +36,10 @@ class SequenceMaskOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must exist");
PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) must exist");
auto maxlen = ctx->Attrs().Get<int>("maxlen");
if (maxlen > 0) { // We can only infershape when maxlen > 0
auto dim = framework::vectorize2int(ctx->GetInputDim("X"));
dim.push_back(maxlen);
ctx->SetOutputDim("Y", framework::make_ddim(dim));
}
int maxlen = ctx->Attrs().Get<int>("maxlen");
auto dim = framework::vectorize2int(ctx->GetInputDim("X"));
dim.push_back(maxlen > 0 ? maxlen : -1);
ctx->SetOutputDim("Y", framework::make_ddim(dim));
}
};
......
paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.cc
浏览文件 @ 65dbc7cc
......@@ -18,6 +18,7 @@ namespace paddle {
namespace operators {
using framework::Tensor;
const int kIgnoreIndex = -100;
class SigmoidCrossEntropyWithLogitsOp : public framework::OperatorWithKernel {
public:
......@@ -100,6 +101,11 @@ class SigmoidCrossEntropyWithLogitsOpMaker
AddOutput("Out",
"(Tensor, default Tensor<float>), a 2-D tensor with shape N x D "
" of elementwise logistic losses.");
AddAttr<int>("ignore_index",
"(int, default kIgnoreIndex), Specifies a target value that "
"is ignored and"
"does not contribute to the input gradient.")
.SetDefault(kIgnoreIndex);
AddComment(R"DOC(
SigmoidCrossEntropyWithLogits Operator.
......
paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.h
浏览文件 @ 65dbc7cc
......@@ -15,33 +15,72 @@ limitations under the License. */
#pragma once
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/hostdevice.h"
#include "paddle/legacy/utils/Logging.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename T>
struct SigmoidCrossEntropyWithLogitsForward {
HOSTDEVICE SigmoidCrossEntropyWithLogitsForward(const int &ignore_index)
: ignore_index(ignore_index) {}
HOSTDEVICE T operator()(const T &x, const T &label) const {
if (static_cast<int>(label) == ignore_index) {
return static_cast<T>(0.);
}
T term1 = (x > 0) ? x : 0;
T term2 = x * label;
T term3 = std::log(static_cast<T>(1) + std::exp(-(std::abs(x))));
return term1 - term2 + term3;
}
int ignore_index;
};
template <typename T>
struct SigmoidCrossEntropyWithLogitsBackward {
HOSTDEVICE SigmoidCrossEntropyWithLogitsBackward(const int &ignore_index)
: ignore_index(ignore_index) {}
HOSTDEVICE T operator()(const T &x, const T &label) const {
if (static_cast<int>(label) == ignore_index) {
return static_cast<T>(0.);
}
T simoid_x = static_cast<T>(1) / (static_cast<T>(1) + std::exp(-x));
return simoid_x - label;
}
int ignore_index;
};
// Out = max(X, 0) - X * Labels + log(1 + exp(-abs(X)))
template <typename DeviceContext, typename T>
class SigmoidCrossEntropyWithLogitsKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
const framework::Tensor *X = context.Input<framework::Tensor>("X");
const framework::Tensor *Labels = context.Input<framework::Tensor>("Label");
framework::Tensor *Out = context.Output<framework::Tensor>("Out");
const Tensor *X = context.Input<Tensor>("X");
const Tensor *Labels = context.Input<Tensor>("Label");
Tensor *Out = context.Output<Tensor>("Out");
Out->mutable_data<T>(context.GetPlace());
int ignore_index = context.Attr<int>("ignore_index");
auto x = framework::EigenVector<T>::Flatten(*X);
auto labels = framework::EigenVector<T>::Flatten(*Labels);
auto out = framework::EigenVector<T>::Flatten(*Out);
auto x = EigenVector<T>::Flatten(*X);
auto labels = EigenVector<T>::Flatten(*Labels);
auto out = EigenVector<T>::Flatten(*Out);
auto &place = *context.device_context<DeviceContext>().eigen_device();
// term1 = max(x, 0)
auto term1 = x.cwiseMax(static_cast<T>(0));
// term2 = x * labels
auto term2 = x * labels;
// term3 = log(1 + exp(-abs(x)))
auto term3 = (static_cast<T>(1) + (-(x.abs())).exp()).log();
out.device(place) = term1 - term2 + term3;
out.device(place) = x.binaryExpr(
labels, SigmoidCrossEntropyWithLogitsForward<T>(ignore_index));
}
};
......@@ -50,23 +89,23 @@ template <typename DeviceContext, typename T>
class SigmoidCrossEntropyWithLogitsGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
const framework::Tensor *X = context.Input<framework::Tensor>("X");
const framework::Tensor *Labels = context.Input<framework::Tensor>("Label");
const framework::Tensor *dOut =
context.Input<framework::Tensor>(framework::GradVarName("Out"));
framework::Tensor *dX =
context.Output<framework::Tensor>(framework::GradVarName("X"));
const Tensor *X = context.Input<Tensor>("X");
const Tensor *Labels = context.Input<Tensor>("Label");
const Tensor *dOut = context.Input<Tensor>(framework::GradVarName("Out"));
Tensor *dX = context.Output<Tensor>(framework::GradVarName("X"));
dX->mutable_data<T>(context.GetPlace());
auto x = framework::EigenVector<T>::Flatten(*X);
auto labels = framework::EigenVector<T>::Flatten(*Labels);
auto dout = framework::EigenVector<T>::Flatten(*dOut);
auto dx = framework::EigenVector<T>::Flatten(*dX);
auto ignore_index = context.Attr<int>("ignore_index");
auto x = EigenVector<T>::Flatten(*X);
auto labels = EigenVector<T>::Flatten(*Labels);
auto dout = EigenVector<T>::Flatten(*dOut);
auto dx = EigenVector<T>::Flatten(*dX);
auto &place =
*context.template device_context<DeviceContext>().eigen_device();
auto sigmoid_x = static_cast<T>(1) / (static_cast<T>(1) + (-x).exp());
dx.device(place) = dout * (sigmoid_x - labels);
auto diff = x.binaryExpr(labels, SigmoidCrossEntropyWithLogitsBackward<T>(
static_cast<int>(ignore_index)));
dx.device(place) = dout * diff;
}
};
......
paddle/fluid/operators/yolov3_loss_op.cc 0 → 100644
浏览文件 @ 65dbc7cc
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/yolov3_loss_op.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
using framework::Tensor;
class Yolov3LossOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of Yolov3LossOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("GTBox"),
"Input(GTBox) of Yolov3LossOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("GTLabel"),
"Input(GTLabel) of Yolov3LossOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Loss"),
"Output(Loss) of Yolov3LossOp should not be null.");
auto dim_x = ctx->GetInputDim("X");
auto dim_gtbox = ctx->GetInputDim("GTBox");
auto dim_gtlabel = ctx->GetInputDim("GTLabel");
auto anchors = ctx->Attrs().Get<std::vector<int>>("anchors");
auto class_num = ctx->Attrs().Get<int>("class_num");
PADDLE_ENFORCE_EQ(dim_x.size(), 4, "Input(X) should be a 4-D tensor.");
PADDLE_ENFORCE_EQ(dim_x[2], dim_x[3],
"Input(X) dim[3] and dim[4] should be euqal.");
PADDLE_ENFORCE_EQ(dim_x[1], anchors.size() / 2 * (5 + class_num),
"Input(X) dim[1] should be equal to (anchor_number * (5 "
"+ class_num)).");
PADDLE_ENFORCE_EQ(dim_gtbox.size(), 3,
"Input(GTBox) should be a 3-D tensor");
PADDLE_ENFORCE_EQ(dim_gtbox[2], 4, "Input(GTBox) dim[2] should be 5");
PADDLE_ENFORCE_EQ(dim_gtlabel.size(), 2,
"Input(GTBox) should be a 2-D tensor");
PADDLE_ENFORCE_EQ(dim_gtlabel[0], dim_gtbox[0],
"Input(GTBox) and Input(GTLabel) dim[0] should be same");
PADDLE_ENFORCE_EQ(dim_gtlabel[1], dim_gtbox[1],
"Input(GTBox) and Input(GTLabel) dim[1] should be same");
PADDLE_ENFORCE_GT(anchors.size(), 0,
"Attr(anchors) length should be greater then 0.");
PADDLE_ENFORCE_EQ(anchors.size() % 2, 0,
"Attr(anchors) length should be even integer.");
PADDLE_ENFORCE_GT(class_num, 0,
"Attr(class_num) should be an integer greater then 0.");
std::vector<int64_t> dim_out({1});
ctx->SetOutputDim("Loss", framework::make_ddim(dim_out));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("X")->type()),
platform::CPUPlace());
}
};
class Yolov3LossOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"The input tensor of YOLO v3 loss operator, "
"This is a 4-D tensor with shape of [N, C, H, W]."
"H and W should be same, and the second dimention(C) stores"
"box locations, confidence score and classification one-hot"
"key of each anchor box");
AddInput("GTBox",
"The input tensor of ground truth boxes, "
"This is a 3-D tensor with shape of [N, max_box_num, 5], "
"max_box_num is the max number of boxes in each image, "
"In the third dimention, stores x, y, w, h coordinates, "
"x, y is the center cordinate of boxes and w, h is the "
"width and height and x, y, w, h should be divided by "
"input image height to scale to [0, 1].");
AddInput("GTLabel",
"The input tensor of ground truth label, "
"This is a 2-D tensor with shape of [N, max_box_num], "
"and each element shoudl be an integer to indicate the "
"box class id.");
AddOutput("Loss",
"The output yolov3 loss tensor, "
"This is a 1-D tensor with shape of [1]");
AddAttr<int>("class_num", "The number of classes to predict.");
AddAttr<std::vector<int>>("anchors",
"The anchor width and height, "
"it will be parsed pair by pair.");
AddAttr<float>("ignore_thresh",
"The ignore threshold to ignore confidence loss.");
AddAttr<float>("loss_weight_xy", "The weight of x, y location loss.")
.SetDefault(1.0);
AddAttr<float>("loss_weight_wh", "The weight of w, h location loss.")
.SetDefault(1.0);
AddAttr<float>(
"loss_weight_conf_target",
"The weight of confidence score loss in locations with target object.")
.SetDefault(1.0);
AddAttr<float>("loss_weight_conf_notarget",
"The weight of confidence score loss in locations without "
"target object.")
.SetDefault(1.0);
AddAttr<float>("loss_weight_class", "The weight of classification loss.")
.SetDefault(1.0);
AddComment(R"DOC(
This operator generate yolov3 loss by given predict result and ground
truth boxes.
The output of previous network is in shape [N, C, H, W], while H and W
should be the same, specify the grid size, each grid point predict given
number boxes, this given number is specified by anchors, it should be
half anchors length, which following will be represented as S. In the
second dimention(the channel dimention), C should be S * (class_num + 5),
class_num is the box categoriy number of source dataset(such as coco),
so in the second dimention, stores 4 box location coordinates x, y, w, h
and confidence score of the box and class one-hot key of each anchor box.
While the 4 location coordinates if $$tx, ty, tw, th$$, the box predictions
correspnd to:
$$
b_x = \sigma(t_x) + c_x
b_y = \sigma(t_y) + c_y
b_w = p_w e^{t_w}
b_h = p_h e^{t_h}
$$
While $$c_x, c_y$$ is the left top corner of current grid and $$p_w, p_h$$
is specified by anchors.
As for confidence score, it is the logistic regression value of IoU between
anchor boxes and ground truth boxes, the score of the anchor box which has
the max IoU should be 1, and if the anchor box has IoU bigger then ignore
thresh, the confidence score loss of this anchor box will be ignored.
Therefore, the yolov3 loss consist of three major parts, box location loss,
confidence score loss, and classification loss. The MSE loss is used for
box location, and binary cross entropy loss is used for confidence score
loss and classification loss.
Final loss will be represented as follow.
$$
loss = \loss_weight_{xy} * loss_{xy} + \loss_weight_{wh} * loss_{wh}
+ \loss_weight_{conf_target} * loss_{conf_target}
+ \loss_weight_{conf_notarget} * loss_{conf_notarget}
+ \loss_weight_{class} * loss_{class}
$$
)DOC");
}
};
class Yolov3LossOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Loss")),
"Input(Loss@GRAD) should not be null");
auto dim_x = ctx->GetInputDim("X");
if (ctx->HasOutput(framework::GradVarName("X"))) {
ctx->SetOutputDim(framework::GradVarName("X"), dim_x);
}
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("X")->type()),
platform::CPUPlace());
}
};
class Yolov3LossGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* op = new framework::OpDesc();
op->SetType("yolov3_loss_grad");
op->SetInput("X", Input("X"));
op->SetInput("GTBox", Input("GTBox"));
op->SetInput("GTLabel", Input("GTLabel"));
op->SetInput(framework::GradVarName("Loss"), OutputGrad("Loss"));
op->SetAttrMap(Attrs());
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetOutput(framework::GradVarName("GTBox"), {});
op->SetOutput(framework::GradVarName("GTLabel"), {});
return std::unique_ptr<framework::OpDesc>(op);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(yolov3_loss, ops::Yolov3LossOp, ops::Yolov3LossOpMaker,
ops::Yolov3LossGradMaker);
REGISTER_OPERATOR(yolov3_loss_grad, ops::Yolov3LossOpGrad);
REGISTER_OP_CPU_KERNEL(yolov3_loss, ops::Yolov3LossKernel<float>,
ops::Yolov3LossKernel<double>);
REGISTER_OP_CPU_KERNEL(yolov3_loss_grad, ops::Yolov3LossGradKernel<float>,
ops::Yolov3LossGradKernel<double>);
paddle/fluid/operators/yolov3_loss_op.h 0 → 100644
浏览文件 @ 65dbc7cc
此差异已折叠。
paddle/fluid/platform/device_tracer.cc
浏览文件 @ 65dbc7cc
......@@ -143,7 +143,7 @@ void CUPTIAPI bufferCompleted(CUcontext ctx, uint32_t streamId, uint8_t *buffer,
case CUPTI_ACTIVITY_KIND_CONCURRENT_KERNEL: {
auto *kernel =
reinterpret_cast<const CUpti_ActivityKernel3 *>(record);
tracer->AddKernelRecords(kernel->start, kernel->end,
tracer->AddKernelRecords(kernel->name, kernel->start, kernel->end,
kernel->deviceId, kernel->streamId,
kernel->correlationId);
break;
......@@ -224,8 +224,9 @@ class DeviceTracerImpl : public DeviceTracer {
stream_id, correlation_id, bytes});
}
void AddKernelRecords(uint64_t start, uint64_t end, int64_t device_id,
int64_t stream_id, uint32_t correlation_id) {
void AddKernelRecords(std::string name, uint64_t start, uint64_t end,
int64_t device_id, int64_t stream_id,
uint32_t correlation_id) {
// 0 means timestamp information could not be collected for the kernel.
if (start == 0 || end == 0) {
VLOG(3) << correlation_id << " cannot be traced";
......@@ -233,7 +234,7 @@ class DeviceTracerImpl : public DeviceTracer {
}
std::lock_guard<std::mutex> l(trace_mu_);
kernel_records_.push_back(
KernelRecord{start, end, device_id, stream_id, correlation_id});
KernelRecord{name, start, end, device_id, stream_id, correlation_id});
}
bool IsEnabled() {
......@@ -276,13 +277,13 @@ class DeviceTracerImpl : public DeviceTracer {
profile_pb.set_start_ns(start_ns_);
profile_pb.set_end_ns(end_ns_);
for (const KernelRecord &r : kernel_records_) {
if (correlations_.find(r.correlation_id) == correlations_.end()) {
fprintf(stderr, "cannot relate a kernel activity\n");
continue;
}
auto *event = profile_pb.add_events();
event->set_type(proto::Event::GPUKernel);
event->set_name(correlations_.at(r.correlation_id));
if (correlations_.find(r.correlation_id) != correlations_.end()) {
event->set_name(correlations_.at(r.correlation_id));
} else {
event->set_name(r.name);
}
event->set_start_ns(r.start_ns);
event->set_end_ns(r.end_ns);
event->set_sub_device_id(r.stream_id);
......
paddle/fluid/platform/device_tracer.h
浏览文件 @ 65dbc7cc
......@@ -39,6 +39,7 @@ inline uint64_t PosixInNsec() {
class DeviceTracer {
public:
struct KernelRecord {
std::string name;
uint64_t start_ns;
uint64_t end_ns;
int64_t device_id;
......@@ -84,8 +85,9 @@ class DeviceTracer {
// Add a cuda kernel stats. `correlation_id` will be mapped to annotation
// added before for human readability.
virtual void AddKernelRecords(uint64_t start, uint64_t end, int64_t device_id,
int64_t stream_id, uint32_t correlation_id) = 0;
virtual void AddKernelRecords(std::string name, uint64_t start, uint64_t end,
int64_t device_id, int64_t stream_id,
uint32_t correlation_id) = 0;
// Generate a proto after done (Disabled).
virtual proto::Profile GenProfile(const std::string& profile_path) = 0;
......
paddle/fluid/platform/dynload/cudnn.h
浏览文件 @ 65dbc7cc
......@@ -125,8 +125,7 @@ extern void EnforceCUDNNLoaded(const char* fn_name);
__macro(cudnnRNNBackwardWeights); \
__macro(cudnnRNNForwardInference); \
__macro(cudnnDestroyDropoutDescriptor); \
__macro(cudnnDestroyRNNDescriptor); \
__macro(cudnnSetRNNDescriptor_v6);
__macro(cudnnDestroyRNNDescriptor);
CUDNN_DNN_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
......@@ -165,6 +164,12 @@ CUDNN_DNN_ROUTINE_EACH_AFTER_R4(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
CUDNN_DNN_ROUTINE_EACH_R5(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
#endif
// APIs in R6
#if CUDNN_VERSION >= 6000
#define CUDNN_DNN_ROUTINE_EACH_R6(__macro) __macro(cudnnSetRNNDescriptor_v6);
CUDNN_DNN_ROUTINE_EACH_R6(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
#endif
#if CUDNN_VERSION >= 7001
#define CUDNN_DNN_ROUTINE_EACH_R7(__macro) \
__macro(cudnnSetConvolutionGroupCount); \
......
paddle/fluid/platform/gpu_info.cc
浏览文件 @ 65dbc7cc
......@@ -18,6 +18,7 @@ limitations under the License. */
#include "gflags/gflags.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/string/split.h"
#ifndef _WIN32
constexpr static float fraction_of_gpu_memory_to_use = 0.92f;
......@@ -45,6 +46,15 @@ DEFINE_bool(
"input and output must be half precision) and recurrent neural networks "
"(RNNs).");
DEFINE_string(selected_gpus, "",
"A list of device ids separated by comma, like: 0,1,2,3. "
"This option is useful when doing multi process training and "
"each process have only one device (GPU). If you want to use "
"all visible devices, set this to empty string. NOTE: the "
"reason of doing this is that we want to use P2P communication"
"between GPU devices, use CUDA_VISIBLE_DEVICES can only use"
"share-memory only.");
namespace paddle {
namespace platform {
......@@ -121,6 +131,24 @@ int GetCurrentDeviceId() {
return device_id;
}
//! Get a list of device ids from environment variable or use all.
std::vector<int> GetSelectedDevices() {
// use user specified GPUs in single-node multi-process mode.
std::vector<int> devices;
if (!FLAGS_selected_gpus.empty()) {
auto devices_str = paddle::string::Split(FLAGS_selected_gpus, ',');
for (auto id : devices_str) {
devices.push_back(atoi(id.c_str()));
}
} else {
int count = GetCUDADeviceCount();
for (int i = 0; i < count; ++i) {
devices.push_back(i);
}
}
return devices;
}
void SetDeviceId(int id) {
// TODO(qijun): find a better way to cache the cuda device count
PADDLE_ENFORCE_LT(id, GetCUDADeviceCount(), "id must less than GPU count");
......
paddle/fluid/platform/gpu_info.h
浏览文件 @ 65dbc7cc
......@@ -19,6 +19,7 @@ limitations under the License. */
#include <cuda_runtime.h>
#include <stddef.h>
#include <string>
#include <vector>
namespace paddle {
namespace platform {
......@@ -47,6 +48,9 @@ int GetCUDAMaxThreadsPerMultiProcessor(int i);
//! Get the current GPU device id in system.
int GetCurrentDeviceId();
//! Get a list of device ids from environment variable or use all.
std::vector<int> GetSelectedDevices();
//! Set the GPU device id for next execution.
void SetDeviceId(int device_id);
......
paddle/fluid/platform/init.cc
浏览文件 @ 65dbc7cc
......@@ -19,6 +19,7 @@ limitations under the License. */
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/cpu_helper.h"
#include "paddle/fluid/platform/cpu_info.h"
#include "paddle/fluid/string/split.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/cuda_device_guard.h"
#endif
......@@ -82,10 +83,8 @@ void InitDevices(bool init_p2p) {
std::vector<int> devices;
#ifdef PADDLE_WITH_CUDA
try {
int count = platform::GetCUDADeviceCount();
for (int i = 0; i < count; ++i) {
devices.push_back(i);
}
// use user specified GPUs in single-node multi-process mode.
devices = platform::GetSelectedDevices();
} catch (const std::exception &exp) {
LOG(WARNING) << "Compiled with WITH_GPU, but no GPU found in runtime.";
}
......@@ -95,20 +94,15 @@ void InitDevices(bool init_p2p) {
void InitDevices(bool init_p2p, const std::vector<int> devices) {
std::vector<platform::Place> places;
int count = 0;
#ifdef PADDLE_WITH_CUDA
try {
count = platform::GetCUDADeviceCount();
} catch (const std::exception &exp) {
LOG(WARNING) << "Compiled with WITH_GPU, but no GPU found in runtime.";
}
#endif
for (size_t i = 0; i < devices.size(); ++i) {
if (devices[i] >= count || devices[i] < 0) {
// In multi process multi gpu mode, we may have gpuid = 7
// but count = 1.
if (devices[i] < 0) {
LOG(WARNING) << "Invalid devices id.";
continue;
}
places.emplace_back(platform::CUDAPlace(devices[i]));
}
if (init_p2p) {
......
paddle/fluid/platform/nccl_helper.h
浏览文件 @ 65dbc7cc
......@@ -97,7 +97,7 @@ struct NCCLContextMap {
order_.size(), contexts_.size(),
"NCCL Context Map does not support contain two or more same device");
if (places.size() <= 1) {
if (places.size() <= 1 && num_trainers == 1) {
return;
}
std::unique_ptr<ncclComm_t[]> comms(new ncclComm_t[order_.size()]);
......@@ -111,12 +111,19 @@ struct NCCLContextMap {
{
int nranks = num_trainers * order_.size();
NCCLGroupGuard gurad;
for (auto &gpu_id : order_) {
int rank = trainer_id * order_.size() + gpu_id;
VLOG(3) << "init nccl rank: " << rank << " nranks: " << nranks;
for (size_t i = 0; i < order_.size(); ++i) {
int gpu_id = order_[i];
int rank;
if (order_.size() > 1) {
rank = trainer_id * order_.size() + i;
} else {
rank = trainer_id;
}
VLOG(30) << "init nccl rank: " << rank << " nranks: " << nranks
<< "gpu id: " << gpu_id;
PADDLE_ENFORCE(cudaSetDevice(gpu_id));
PADDLE_ENFORCE(platform::dynload::ncclCommInitRank(
comms.get() + gpu_id, nranks, *nccl_id, rank));
comms.get() + i, nranks, *nccl_id, rank));
}
}
}
......
paddle/fluid/string/CMakeLists.txt
浏览文件 @ 65dbc7cc
......@@ -3,3 +3,4 @@ cc_library(pretty_log SRCS pretty_log.cc)
cc_test(stringpiece_test SRCS piece_test.cc DEPS stringpiece glog gflags)
cc_test(stringprintf_test SRCS printf_test.cc DEPS glog gflags)
cc_test(to_string_test SRCS to_string_test.cc)
cc_test(split_test SRCS split_test.cc)
paddle/fluid/string/split.h 0 → 100644
浏览文件 @ 65dbc7cc
/* 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 <sstream>
#include <string>
#include <vector>
namespace paddle {
namespace string {
static inline std::vector<std::string> Split(std::string const& original,
char separator) {
std::vector<std::string> results;
std::string token;
std::istringstream is(original);
while (std::getline(is, token, separator)) {
if (!token.empty()) {
results.push_back(token);
}
}
return results;
}
} // namespace string
} // namespace paddle
paddle/fluid/string/split_test.cc 0 → 100644
浏览文件 @ 65dbc7cc
// 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/string/split.h"
#include <string>
#include "gtest/gtest.h"
TEST(StringSplit, StringSplit) {
std::string to_split = "0,1,2,3,4,5";
int i = 0;
for (auto s : paddle::string::Split(to_split, ',')) {
EXPECT_EQ(atoi(s.c_str()), i);
i++;
}
}
paddle/scripts/paddle_build.sh
浏览文件 @ 65dbc7cc
......@@ -437,11 +437,13 @@ EOF
export http_proxy=
export https_proxy=
# TODO: jiabin need to refine this part when these tests fixed on mac
ctest --output-on-failure -j $1
ctest --output-on-failure -j $2
# make install should also be test when unittest
make install -j 8
if [ "$1" == "cp27-cp27m" ]; then
pip install --user ${INSTALL_PREFIX:-/paddle/build}/opt/paddle/share/wheels/*.whl
set -e
python -c "import paddle.fluid"
elif [ "$1" == "cp35-cp35m" ]; then
pip3.5 install --user ${INSTALL_PREFIX:-/paddle/build}/opt/paddle/share/wheels/*.whl
elif [ "$1" == "cp36-cp36m" ]; then
......@@ -449,7 +451,7 @@ EOF
elif [ "$1" == "cp37-cp37m" ]; then
pip3.7 install --user ${INSTALL_PREFIX:-/paddle/build}/opt/paddle/share/wheels/*.whl
fi
if [[ ${WITH_FLUID_ONLY:-OFF} == "OFF" ]] ; then
paddle version
fi
......@@ -472,12 +474,15 @@ function assert_api_not_changed() {
virtualenv .env
source .env/bin/activate
pip install ${PADDLE_ROOT}/build/python/dist/*whl
python ${PADDLE_ROOT}/tools/print_signatures.py paddle.fluid > new.spec
python ${PADDLE_ROOT}/tools/print_signatures.py paddle.fluid,paddle.reader > new.spec
if [ "$1" == "cp35-cp35m" ] || [ "$1" == "cp36-cp36m" ] || [ "$1" == "cp37-cp37m" ]; then
# Use sed to make python2 and python3 sepc keeps the same
sed -i 's/arg0: str/arg0: unicode/g' new.spec
sed -i "s/\(.*Transpiler.*\).__init__ ArgSpec(args=\['self'].*/\1.__init__ /g" new.spec
fi
# ComposeNotAligned has significant difference between py2 and py3
sed -i '/.*ComposeNotAligned.*/d' new.spec
python ${PADDLE_ROOT}/tools/diff_api.py ${PADDLE_ROOT}/paddle/fluid/API.spec new.spec
deactivate
}
......@@ -487,7 +492,19 @@ function assert_api_spec_approvals() {
BRANCH="develop"
fi
API_FILES=("paddle/fluid/API.spec" "paddle/fluid/framework/operator.h")
API_FILES=("paddle/fluid/API.spec"
"paddle/fluid/framework/operator.h"
"paddle/fluid/framework/tensor.h"
"paddle/fluid/framework/lod_tensor.h"
"paddle/fluid/framework/selected_rows.h"
"paddle/fluid/framework/op_desc.h"
"paddle/fluid/framework/block_desc.h"
"paddle/fluid/framework/var_desc.h"
"paddle/fluid/framework/scope.h"
"paddle/fluid/framework/ir/node.h"
"paddle/fluid/framework/ir/graph.h"
"paddle/fluid/framework/framework.proto"
"paddle/fluid/operators/distributed/send_recv.proto.in")
for API_FILE in ${API_FILES[*]}; do
API_CHANGE=`git diff --name-only upstream/$BRANCH | grep "${API_FILE}" || true`
echo "checking ${API_FILE} change, PR: ${GIT_PR_ID}, changes: ${API_CHANGE}"
......@@ -901,7 +918,7 @@ function main() {
maccheck)
cmake_gen ${PYTHON_ABI:-""}
build_mac
run_mac_test ${PROC_RUN:-1}
run_mac_test ${PYTHON_ABI:-""} ${PROC_RUN:-1}
;;
macbuild)
cmake_gen ${PYTHON_ABI:-""}
......
python/paddle/fluid/__init__.py
浏览文件 @ 65dbc7cc
......@@ -147,7 +147,7 @@ def __bootstrap__():
read_env_flags += [
'fraction_of_gpu_memory_to_use', 'cudnn_deterministic',
'enable_cublas_tensor_op_math', 'conv_workspace_size_limit',
'cudnn_exhaustive_search'
'cudnn_exhaustive_search', 'selected_gpus'
]
core.init_gflags([sys.argv[0]] +
["--tryfromenv=" + ",".join(read_env_flags)])
......
python/paddle/fluid/clip.py
浏览文件 @ 65dbc7cc
......@@ -134,12 +134,12 @@ class GradientClipByValue(BaseGradientClipAttr):
Examples:
.. code-block:: python
w_param_attrs = ParamAttr(name=None,
initializer=UniformInitializer(low=-1.0, high=1.0, seed=0),
w_param_attrs = fluid.ParamAttr(name=None,
initializer=fluid.initializer.UniformInitializer(low=-1.0, high=1.0, seed=0),
learning_rate=1.0,
regularizer=L1Decay(1.0),
regularizer=fluid.regularizer.L1Decay(1.0),
trainable=True,
clip=GradientClipByValue(-1.0, 1.0))
clip=fluid.clip.GradientClipByValue(-1.0, 1.0))
y_predict = fluid.layers.fc(input=x, size=1, param_attr=w_param_attrs)
"""
......@@ -185,12 +185,12 @@ class GradientClipByNorm(BaseGradientClipAttr):
Examples:
.. code-block:: python
w_param_attrs = ParamAttr(name=None,
initializer=UniformInitializer(low=-1.0, high=1.0, seed=0),
w_param_attrs = flui.ParamAttr(name=None,
initializer=fluid.initializer.UniformInitializer(low=-1.0, high=1.0, seed=0),
learning_rate=1.0,
regularizer=L1Decay(1.0),
regularizer=fluid.regularizer.L1Decay(1.0),
trainable=True,
clip=GradientClipByNorm(clip_norm=2.0))
clip=fluid.clip.GradientClipByNorm(clip_norm=2.0))
y_predict = fluid.layers.fc(input=x, size=1, param_attr=w_param_attrs)
"""
......@@ -271,7 +271,12 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
"All parameters' 'clip_norm' of a same group should be the same"
)
square = grad * grad
merge_grad = grad
if grad.type == core.VarDesc.VarType.SELECTED_ROWS:
merge_grad = layers.merge_selected_rows(grad)
merge_grad = layers.get_tensor_from_selected_rows(merge_grad)
square = layers.square(merge_grad)
local_norm_var = layers.reduce_sum(input=square)
context[self.group_name].append(local_norm_var)
......@@ -292,6 +297,7 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
new_grad = layers.elementwise_mul(
x=grad, y=self.context[group_scale_name])
return param, new_grad
......
python/paddle/fluid/executor.py
浏览文件 @ 65dbc7cc
......@@ -20,7 +20,7 @@ import six
from .framework import Program, default_main_program, Variable
from . import core
__all__ = ['Executor', 'global_scope', 'scope_guard', '_switch_scope']
__all__ = ['Executor', 'global_scope', 'scope_guard']
g_scope = core.Scope()
......@@ -407,16 +407,17 @@ class Executor(object):
Examples:
>>> data = layers.data(name='X', shape=[1], dtype='float32')
>>> hidden = layers.fc(input=data, size=10)
>>> layers.assign(hidden, out)
>>> loss = layers.mean(out)
>>> data = fluid.layers.data(name='X', shape=[1], dtype='float32')
>>> out = fluid.layers.create_tensor(dtype='float32')
>>> hidden = fluid.layers.fc(input=data, size=10)
>>> fluid.layers.assign(hidden,out)
>>> loss = fluid.layers.mean(out)
>>> adam = fluid.optimizer.Adam()
>>> adam.minimize(loss)
>>> adam.minimize(loss)
>>> cpu = core.CPUPlace()
>>> exe = Executor(cpu)
>>> exe.run(default_startup_program())
>>> exe = fluid.Executor(cpu)
>>> exe.run(fluid.default_startup_program())
>>> x = numpy.random.random(size=(10, 1)).astype('float32')
>>> outs = exe.run(
......
python/paddle/fluid/framework.py
浏览文件 @ 65dbc7cc
......@@ -89,12 +89,13 @@ def name_scope(prefix=None):
Examples:
.. code-block:: python
with name_scope("encoder"):
...
with name_scope("decoder"):
...
with name_scope("attention"):
...
with name_scope("attention"):
...
"""
# TODO(panyx0718): Only [0-9a-z].
assert prefix, "namescope prefix cannot be empty."
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 65dbc7cc
......@@ -20,6 +20,7 @@ from __future__ import print_function
from .layer_function_generator import generate_layer_fn
from .layer_function_generator import autodoc, templatedoc
from ..layer_helper import LayerHelper
from ..framework import Variable
from . import tensor
from . import nn
from . import ops
......@@ -46,6 +47,7 @@ __all__ = [
'iou_similarity',
'box_coder',
'polygon_box_transform',
'yolov3_loss',
]
......@@ -401,6 +403,113 @@ def polygon_box_transform(input, name=None):
return output
@templatedoc(op_type="yolov3_loss")
def yolov3_loss(x,
gtbox,
gtlabel,
anchors,
class_num,
ignore_thresh,
loss_weight_xy=None,
loss_weight_wh=None,
loss_weight_conf_target=None,
loss_weight_conf_notarget=None,
loss_weight_class=None,
name=None):
"""
${comment}
Args:
x (Variable): ${x_comment}
gtbox (Variable): groud truth boxes, should be in shape of [N, B, 4],
in the third dimenstion, x, y, w, h should be stored
and x, y, w, h should be relative value of input image.
N is the batch number and B is the max box number in
an image.
gtlabel (Variable): class id of ground truth boxes, shoud be ins shape
of [N, B].
anchors (list|tuple): ${anchors_comment}
class_num (int): ${class_num_comment}
ignore_thresh (float): ${ignore_thresh_comment}
loss_weight_xy (float|None): ${loss_weight_xy_comment}
loss_weight_wh (float|None): ${loss_weight_wh_comment}
loss_weight_conf_target (float|None): ${loss_weight_conf_target_comment}
loss_weight_conf_notarget (float|None): ${loss_weight_conf_notarget_comment}
loss_weight_class (float|None): ${loss_weight_class_comment}
name (string): the name of yolov3 loss
Returns:
Variable: A 1-D tensor with shape [1], the value of yolov3 loss
Raises:
TypeError: Input x of yolov3_loss must be Variable
TypeError: Input gtbox of yolov3_loss must be Variable"
TypeError: Input gtlabel of yolov3_loss must be Variable"
TypeError: Attr anchors of yolov3_loss must be list or tuple
TypeError: Attr class_num of yolov3_loss must be an integer
TypeError: Attr ignore_thresh of yolov3_loss must be a float number
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')
gtbox = fluid.layers.data(name='gtbox', shape=[6, 5], dtype='float32')
gtlabel = fluid.layers.data(name='gtlabel', shape=[6, 1], dtype='int32')
anchors = [10, 13, 16, 30, 33, 23]
loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, class_num=80
anchors=anchors, ignore_thresh=0.5)
"""
helper = LayerHelper('yolov3_loss', **locals())
if not isinstance(x, Variable):
raise TypeError("Input x of yolov3_loss must be Variable")
if not isinstance(gtbox, Variable):
raise TypeError("Input gtbox of yolov3_loss must be Variable")
if not isinstance(gtlabel, Variable):
raise TypeError("Input gtlabel of yolov3_loss must be Variable")
if not isinstance(anchors, list) and not isinstance(anchors, tuple):
raise TypeError("Attr anchors of yolov3_loss must be list or tuple")
if not isinstance(class_num, int):
raise TypeError("Attr class_num of yolov3_loss must be an integer")
if not isinstance(ignore_thresh, float):
raise TypeError(
"Attr ignore_thresh of yolov3_loss must be a float number")
if name is None:
loss = helper.create_variable_for_type_inference(dtype=x.dtype)
else:
loss = helper.create_variable(
name=name, dtype=x.dtype, persistable=False)
attrs = {
"anchors": anchors,
"class_num": class_num,
"ignore_thresh": ignore_thresh,
}
if loss_weight_xy is not None and isinstance(loss_weight_xy, float):
self.attrs['loss_weight_xy'] = loss_weight_xy
if loss_weight_wh is not None and isinstance(loss_weight_wh, float):
self.attrs['loss_weight_wh'] = loss_weight_wh
if loss_weight_conf_target is not None and isinstance(
loss_weight_conf_target, float):
self.attrs['loss_weight_conf_target'] = loss_weight_conf_target
if loss_weight_conf_notarget is not None and isinstance(
loss_weight_conf_notarget, float):
self.attrs['loss_weight_conf_notarget'] = loss_weight_conf_notarget
if loss_weight_class is not None and isinstance(loss_weight_class, float):
self.attrs['loss_weight_class'] = loss_weight_class
helper.append_op(
type='yolov3_loss',
inputs={"X": x,
"GTBox": gtbox,
"GTLabel": gtlabel},
outputs={'Loss': loss},
attrs=attrs)
return loss
@templatedoc()
def detection_map(detect_res,
label,
......
python/paddle/fluid/layers/io.py
浏览文件 @ 65dbc7cc
......@@ -943,7 +943,18 @@ def __create_unshared_decorated_reader__(op_type, reader, attrs, name=None):
def shuffle(reader, buffer_size):
"""
Shuffle the reader.
Creates a data reader whose data output is shuffled.
Output from the iterator that created by original reader will be
buffered into shuffle buffer, and then shuffled. The size of shuffle buffer
is determined by argument buf_size.
Args:
param reader: the original reader whose output will be shuffled.
type reader: callable
param buf_size: shuffle buffer size.
type buf_size: int
return: the new reader whose output is shuffled.
rtype: callable
"""
return __create_unshared_decorated_reader__(
'create_shuffle_reader', reader, {'buffer_size': int(buffer_size)})
......
python/paddle/fluid/layers/layer_function_generator.py
浏览文件 @ 65dbc7cc
......@@ -20,7 +20,7 @@ import string
from six.moves import cStringIO
from ..proto import framework_pb2
from ..framework import OpProtoHolder, Variable
from ..framework import OpProtoHolder, Variable, core, convert_np_dtype_to_dtype_
from ..layer_helper import LayerHelper
__all__ = [
......@@ -178,6 +178,15 @@ def generate_layer_fn(op_type):
"operator {0} must input same dtype. {1} vs {2}".format(
op_type, dtype, each.dtype))
if dtype is None:
arg_dtype = kwargs.get("dtype")
if arg_dtype:
if not isinstance(arg_dtype, core.VarDesc.VarType):
dtype = convert_np_dtype_to_dtype_(arg_dtype)
else:
dtype = arg_dtype
else:
dtype = core.VarDesc.VarType.FP32
return dtype
def func(*args, **kwargs):
......
python/paddle/fluid/layers/learning_rate_scheduler.py
浏览文件 @ 65dbc7cc
......@@ -308,13 +308,9 @@ def piecewise_decay(boundaries, values):
def append_LARS(params_grads, learning_rate, weight_decay):
"""Applies LARS (LAYER-WISE ADAPTIVE RATE SCALING) to learning rate for
each layer.
```python
learning_rate *= local_gw_ratio * sqrt(sumsq(param))
/ (sqrt(sumsq(gradient))+ weight_decay * sqrt(sumsq(param)))
```
"""
Applies LARS (LAYER-WISE ADAPTIVE RATE SCALING) to learning rate for
each layer.
Args:
learning_rate: A learning rate Variable. This
......@@ -323,6 +319,11 @@ def append_LARS(params_grads, learning_rate, weight_decay):
Returns:
The decayed learning rate
Examples:
.. code-block:: python
learning_rate *= local_gw_ratio * sqrt(sumsq(param))
/ (sqrt(sumsq(gradient))+ weight_decay * sqrt(sumsq(param)))
"""
def _balanced_weight(param_norm, grad_norm):
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 65dbc7cc
......@@ -169,9 +169,13 @@ __all__ = [
'log_loss',
'add_position_encoding',
'bilinear_tensor_product',
'merge_selected_rows',
'get_tensor_from_selected_rows',
'lstm',
]
kIgnoreIndex = -100
def fc(input,
size,
......@@ -926,7 +930,7 @@ def dynamic_gru(input,
emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim])
hidden_dim = 512
x = fluid.layers.fc(input=emb, size=hidden_dim * 3)
hidden = fluid.layers.dynamic_gru(input=x, dim=hidden_dim)
hidden = fluid.layers.dynamic_gru(input=x, size=hidden_dim)
"""
helper = LayerHelper('gru', **locals())
......@@ -1267,7 +1271,7 @@ def dropout(x,
return out
def cross_entropy(input, label, soft_label=False, ignore_index=-100):
def cross_entropy(input, label, soft_label=False, ignore_index=kIgnoreIndex):
"""
**Cross Entropy Layer**
......@@ -1314,7 +1318,7 @@ def cross_entropy(input, label, soft_label=False, ignore_index=-100):
labels. Default: `False`.
ignore_index (int): Specifies a target value that is ignored and does
not contribute to the input gradient. Only valid
if soft_label is set to False. Default: -100
if soft_label is set to False. Default: kIgnoreIndex
Returns:
A 2-D tensor with shape [N x 1], the cross entropy loss.
......@@ -3584,6 +3588,7 @@ def beam_search_decode(ids, scores, beam_size, end_id, name=None):
Examples:
.. code-block:: python
# Suppose `ids` and `scores` are LodTensorArray variables reserving
# the selected ids and scores of all steps
finished_ids, finished_scores = layers.beam_search_decode(
......@@ -5081,7 +5086,7 @@ def im2sequence(input,
output.lod = [[4, 4]]
Examples:
Examples:
.. code-block:: python
......@@ -5185,7 +5190,7 @@ def multiplex(inputs, index):
def softmax_with_cross_entropy(logits,
label,
soft_label=False,
ignore_index=-100,
ignore_index=kIgnoreIndex,
numeric_stable_mode=False,
return_softmax=False):
"""
......@@ -5243,7 +5248,7 @@ def softmax_with_cross_entropy(logits,
labels as soft labels. By default, `soft_label` is set to False.
ignore_index (int): Specifies a target value that is ignored and does
not contribute to the input gradient. Only valid
if soft_label is set to False. Default: -100
if soft_label is set to False. Default: kIgnoreIndex
numeric_stable_mode (bool): A flag to indicate whether to use a more
numerically stable algorithm. Only valid
when soft_label is False and GPU is used.
......@@ -5868,24 +5873,23 @@ def pad_constant_like(x, y, pad_value=0., name=None):
[[38, 39, 40]],
[[41, 42, 43]]]]
Y.shape = (1, 3, 1, 3)
And
pad_value = -1,
And
pad_value = -1,
Return:
Out = [[[[35, 36, 37],
[-1, -1, -1]],
[[38, 39, 40],
[-1, -1, -1]],
[[41, 42, 43],
[-1, -1, -1]]],
[[[-1, -1, -1],
[-1, -1, -1]],
[[-1, -1, -1],
[-1, -1, -1]],
[[-1, -1, -1],
[-1, -1, -1]]]]
Out.shape = (2, 3, 2, 3)
Return:
Out = [[[[35, 36, 37],
[-1, -1, -1]],
[[38, 39, 40],
[-1, -1, -1]],
[[41, 42, 43],
[-1, -1, -1]]],
[[[-1, -1, -1],
[-1, -1, -1]],
[[-1, -1, -1],
[-1, -1, -1]],
[[-1, -1, -1],
[-1, -1, -1]]]]
Out.shape = (2, 3, 2, 3)
Args:
x (Variable): The input tensor variable.
......@@ -6124,6 +6128,7 @@ def image_resize(input,
Supporting resample methods:
'BILINEAR' : Bilinear interpolation
'NEAREST' : Nearest neighbor interpolation
Args:
......@@ -6779,7 +6784,7 @@ def crop(x, shape=None, offsets=None, name=None):
# or
z = fluid.layers.data(name="z", shape=[3, 5], dtype="float32")
crop = fluid.layers.crop(z, shape=[2, 3])
crop = fluid.layers.crop(z, shape=[-1, 2, 3])
"""
helper = LayerHelper('crop', **locals())
......@@ -7060,39 +7065,40 @@ def pad2d(input,
than height-1. And the width dimension has the same condition.
Example:
.. code-block:: text
Given that X is a channel of image from input:
Given that X is a channel of image from input:
X = [[1, 2, 3],
[4, 5, 6]]
X = [[1, 2, 3],
[4, 5, 6]]
Case 0:
Case 0:
paddings = [0, 1, 2, 3],
mode = 'constant'
pad_value = 0
paddings = [0, 1, 2, 3],
mode = 'constant'
pad_value = 0
Out = [[0, 0, 1, 2, 3, 0, 0, 0]
[0, 0, 4, 5, 6, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0]]
Out = [[0, 0, 1, 2, 3, 0, 0, 0]
[0, 0, 4, 5, 6, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0]]
Case 1:
Case 1:
paddings = [0, 1, 2, 1],
mode = 'reflect'
paddings = [0, 1, 2, 1],
mode = 'reflect'
Out = [[3, 2, 1, 2, 3, 2]
[6, 5, 4, 5, 6, 5]
[3, 2, 1, 2, 3, 2]]
Out = [[3, 2, 1, 2, 3, 2]
[6, 5, 4, 5, 6, 5]
[3, 2, 1, 2, 3, 2]]
Case 2:
Case 2:
paddings = [0, 1, 2, 1],
mode = 'edge'
paddings = [0, 1, 2, 1],
mode = 'edge'
Out = [[1, 1, 1, 2, 3, 3]
[4, 4, 4, 5, 6, 6]
[4, 4, 4, 5, 6, 6]]
Out = [[1, 1, 1, 2, 3, 3]
[4, 4, 4, 5, 6, 6]
[4, 4, 4, 5, 6, 6]]
Args:
......@@ -7330,13 +7336,13 @@ def prelu(x, mode, param_attr=None, name=None):
Args:
x (Variable): The input tensor.
param_attr(ParamAttr|None): The parameter attribute for the learnable
weight (alpha).
weight (alpha).
mode (string): The mode for weight sharing. It supports all, channel
and element. all: all elements share same weight
channel:elements in a channel share same weight
element:each element has a weight
and element. all: all elements share same weight
channel:elements in a channel share same weight
element:each element has a weight
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
will be named automatically.
Returns:
Variable: The output tensor with the same shape as input.
......@@ -8378,6 +8384,29 @@ def mean(x, name=None):
return out
@templatedoc()
def merge_selected_rows(x, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
helper = LayerHelper("merge_selected_rows", **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type="merge_selected_rows",
inputs={"X": x},
attrs={},
outputs={"Out": out})
return out
@templatedoc()
def mul(x, y, x_num_col_dims=1, y_num_col_dims=1, name=None):
"""
......@@ -8415,13 +8444,17 @@ def mul(x, y, x_num_col_dims=1, y_num_col_dims=1, name=None):
@templatedoc()
def sigmoid_cross_entropy_with_logits(x, label, name=None):
def sigmoid_cross_entropy_with_logits(x,
label,
ignore_index=kIgnoreIndex,
name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
label(${label_type}): ${label_comment}
ignore_index(&{ignore_index}): ${ignore_index_comment}
name(basestring|None): Name of the output.
Returns:
......@@ -8440,7 +8473,7 @@ def sigmoid_cross_entropy_with_logits(x, label, name=None):
type="sigmoid_cross_entropy_with_logits",
inputs={"X": x,
"Label": label},
attrs={},
attrs={"ignore_index": ignore_index},
outputs={"Out": out})
return out
......@@ -9026,3 +9059,26 @@ def bilinear_tensor_product(x,
# add activation
return helper.append_activation(out)
@templatedoc()
def get_tensor_from_selected_rows(x, name=None):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
name(basestring|None): Name of the output.
Returns:
out(${out_type}): ${out_comment}
"""
helper = LayerHelper('get_tensor_from_selected_rows', **locals())
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='get_tensor_from_selected_rows',
inputs={'X': x},
outputs={'Out': out},
attrs={})
return out
python/paddle/fluid/layers/tensor.py
浏览文件 @ 65dbc7cc
......@@ -622,7 +622,7 @@ def reverse(x, axis):
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='reverse',
inputs={'Input': x},
inputs={'X': x},
outputs={'Out': [out]},
attrs={'axis': axis})
return out
......
python/paddle/fluid/metrics.py
浏览文件 @ 65dbc7cc
......@@ -222,13 +222,13 @@ class Precision(MetricBase):
Examples:
.. code-block:: python
metric = fluid.metrics.Precision()
for pass in range(PASSES):
metric.reset()
for data in train_reader():
loss, preds, labels = exe.run(fetch_list=[cost, preds, labels])
metric.update(preds=preds, labels=labels)
numpy_precision = metric.eval()
metric = fluid.metrics.Precision()
for pass in range(PASSES):
metric.reset()
for data in train_reader():
loss, preds, labels = exe.run(fetch_list=[cost, preds, labels])
metric.update(preds=preds, labels=labels)
numpy_precision = metric.eval()
"""
def __init__(self, name=None):
......@@ -267,13 +267,13 @@ class Recall(MetricBase):
Examples:
.. code-block:: python
metric = fluid.metrics.Recall()
for pass in range(PASSES):
metric.reset()
for data in train_reader():
loss, preds, labels = exe.run(fetch_list=[cost, preds, labels])
metric.update(preds=preds, labels=labels)
numpy_recall = metric.eval()
metric = fluid.metrics.Recall()
for pass in range(PASSES):
metric.reset()
for data in train_reader():
loss, preds, labels = exe.run(fetch_list=[cost, preds, labels])
metric.update(preds=preds, labels=labels)
numpy_recall = metric.eval()
"""
def __init__(self, name=None):
......@@ -449,8 +449,9 @@ class EditDistance(MetricBase):
distance_evaluator.update(distances, seq_num)
distance, instance_error = distance_evaluator.eval()
In the above example:
In the above example:
'distance' is the average of the edit distance in a pass.
'instance_error' is the instance error rate in a pass.
"""
......
python/paddle/fluid/parallel_executor.py
浏览文件 @ 65dbc7cc
......@@ -95,7 +95,14 @@ class ParallelExecutor(object):
self._places = []
self._act_places = []
if use_cuda:
for i in six.moves.range(core.get_cuda_device_count()):
gpus = []
gpus_env = os.getenv("FLAGS_selected_gpus")
if gpus_env:
gpus = [int(s) for s in gpus_env.split(",")]
else:
for i in six.moves.range(core.get_cuda_device_count()):
gpus.append(i)
for i in gpus:
p = core.Place()
self._act_places.append(core.CUDAPlace(i))
p.set_place(self._act_places[-1])
......
python/paddle/fluid/param_attr.py
浏览文件 @ 65dbc7cc
......@@ -50,8 +50,9 @@ class ParamAttr(object):
w_param_attrs = fluid.ParamAttr(name="fc_weight",
learning_rate=0.5,
regularizer=fluid.L2Decay(1.0),
regularizer=fluid.regularizer.L2Decay(1.0),
trainable=True)
x = fluid.layers.data(name='X', shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=10, param_attr=w_param_attrs)
"""
......
python/paddle/fluid/tests/test_detection.py
浏览文件 @ 65dbc7cc
......@@ -388,5 +388,18 @@ class TestGenerateProposals(unittest.TestCase):
print(rpn_rois.shape)
class TestYoloDetection(unittest.TestCase):
def test_yolov3_loss(self):
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[30, 7, 7], dtype='float32')
gtbox = layers.data(name='gtbox', shape=[10, 4], dtype='float32')
gtlabel = layers.data(name='gtlabel', shape=[10], dtype='int32')
loss = layers.yolov3_loss(x, gtbox, gtlabel, [10, 13, 30, 13], 10,
0.5)
self.assertIsNotNone(loss)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/test_gradient_clip.py 已删除 100644 → 0
浏览文件 @ ac803fed
# 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 paddle
import paddle.fluid as fluid
BATCH_SIZE = 128
CLIP = 1
prog = fluid.framework.Program()
with fluid.program_guard(main_program=prog):
image = fluid.layers.data(name='x', shape=[784], dtype='float32')
hidden1 = fluid.layers.fc(input=image, size=128, act='relu')
hidden2 = fluid.layers.fc(input=hidden1, size=64, act='relu')
predict = fluid.layers.fc(input=hidden2, size=10, act='softmax')
label = fluid.layers.data(name='y', shape=[1], dtype='int64')
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
prog_clip = prog.clone()
avg_cost_clip = prog_clip.block(0).var(avg_cost.name)
p_g = fluid.backward.append_backward(loss=avg_cost)
p_g_clip = fluid.backward.append_backward(loss=avg_cost_clip)
with fluid.program_guard(main_program=prog_clip):
fluid.clip.set_gradient_clip(
fluid.clip.GradientClipByGlobalNorm(clip_norm=CLIP))
p_g_clip = fluid.clip.append_gradient_clip_ops(p_g_clip)
grad_list = [elem[1] for elem in p_g]
grad_clip_list = [elem[1] for elem in p_g_clip]
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=8192),
batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(feed_list=[image, label], place=place)
exe.run(fluid.default_startup_program())
count = 0
for data in train_reader():
count += 1
if count > 5:
break
out = exe.run(prog, feed=feeder.feed(data), fetch_list=grad_list)
out_clip = exe.run(prog_clip,
feed=feeder.feed(data),
fetch_list=grad_clip_list)
global_norm = 0
for v in out[1:]:
global_norm += np.sum(np.power(v, 2))
global_norm = np.sqrt(global_norm)
global_norm_clip = 0
for v in out_clip[1:]:
global_norm_clip += np.sum(np.power(v, 2))
global_norm_clip = np.sqrt(global_norm_clip)
if not np.isclose(
a=global_norm_clip, b=np.minimum(global_norm, CLIP), rtol=5e-3):
exit(1)
exit(0)
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ 65dbc7cc
......@@ -43,13 +43,14 @@ if(APPLE)
list(REMOVE_ITEM TEST_OPS test_desc_clone)
list(REMOVE_ITEM TEST_OPS test_program_code)
endif(NOT WITH_DISTRIBUTE)
message(WARNING "These tests has been disabled in OSX before being fixed: \n test_fuse_elewise_add_act_pass \n test_detection_map_op \n test_dist_se_resnext")
message(WARNING "These tests has been disabled in OSX before being fixed: \n test_gradient_clip \n test_fuse_elewise_add_act_pass \n test_detection_map_op \n test_dist_se_resnext")
# this op is not support on mac
list(REMOVE_ITEM TEST_OPS test_fusion_seqexpand_concat_fc_op)
# TODO: add the unitest back when it fixed
list(REMOVE_ITEM TEST_OPS test_detection_map_op)
list(REMOVE_ITEM TEST_OPS test_dist_se_resnext)
list(REMOVE_ITEM TEST_OPS test_fuse_elewise_add_act_pass)
list(REMOVE_ITEM TEST_OPS test_gradient_clip)
endif()
if(NOT WITH_MKLML)
# this op is not support on openblas
......@@ -95,13 +96,12 @@ if(WITH_DISTRIBUTE)
if(NOT APPLE)
set_tests_properties(test_dist_mnist PROPERTIES TIMEOUT 200)
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)
# FIXME(typhoonzero): add these tests back
# 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_transformer MODULES test_dist_transformer)
# set_tests_properties(test_dist_transformer PROPERTIES TIMEOUT 1000)
# TODO(typhoonzero): make dist test parallel when fix port management issue
set_tests_properties(test_dist_mnist test_dist_word2vec test_dist_ctr test_dist_simnet_bow test_dist_save_load test_dist_text_classification test_dist_mnist_batch_merge PROPERTIES RUN_SERIAL TRUE)
set_tests_properties(test_dist_ctr test_dist_mnist test_dist_mnist_batch_merge test_dist_save_load test_dist_se_resnext test_dist_simnet_bow test_dist_text_classification test_dist_train test_dist_word2vec PROPERTIES RUN_SERIAL TRUE)
endif(NOT APPLE)
py_test_modules(test_dist_transpiler MODULES test_dist_transpiler)
endif()
......
python/paddle/fluid/tests/unittests/test_dist_base.py
浏览文件 @ 65dbc7cc
......@@ -291,8 +291,8 @@ class TestDistBase(unittest.TestCase):
if check_error_log:
err_log.close()
sys.stderr.write('local_stdout: %s\n' % pickle.loads(local_out))
sys.stderr.write('local_stderr: %s\n' % local_err)
sys.stderr.write('local_stdout: %s\n' % pickle.loads(local_out))
return pickle.loads(local_out)
......
python/paddle/fluid/tests/unittests/test_get_tensor_from_selected_rows_op.py 0 → 100644
浏览文件 @ 65dbc7cc
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
import paddle.fluid.core as core
import numpy as np
from paddle.fluid.op import Operator
class TestGetTensorFromSelectedRows(unittest.TestCase):
def get_places(self):
places = [core.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(core.CUDAPlace(0))
return places
def check_with_place(self, place):
scope = core.Scope()
x_rows = [0, 5, 5, 4, 20]
height = 20
row_numel = 2
np_array = np.ones((len(x_rows), row_numel)).astype("float32")
np_array[1, :] = 2.0
np_array[2, :] = 3.0
np_array[3, :] = 4.0
# initialize input variable X
x = scope.var('X').get_selected_rows()
x.set_rows(x_rows)
x.set_height(height)
x_tensor = x.get_tensor()
x_tensor.set(np_array, place)
# initialize input variable Out
out = scope.var("Out").get_tensor()
op = Operator("get_tensor_from_selected_rows", X="X", Out="Out")
op.run(scope, place)
out_array = np.array(out)
self.assertEqual((5, 2), out_array.shape)
assert (out_array == np_array).all()
def test_check_output(self):
for place in self.get_places():
self.check_with_place(place)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_gradient_clip.py 0 → 100644
浏览文件 @ 65dbc7cc
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
import numpy as np
import paddle
import paddle.fluid.core as core
import paddle.fluid as fluid
BATCH_SIZE = 128
CLIP = 1
def bow_net(data,
label,
dict_dim,
emb_dim=128,
hid_dim=128,
hid_dim2=96,
class_dim=2):
"""
BOW net
This model is from https://github.com/PaddlePaddle/models:
fluid/PaddleNLP/text_classification/nets.py
"""
emb = fluid.layers.embedding(
input=data, is_sparse=True, size=[dict_dim, emb_dim])
bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
bow_tanh = fluid.layers.tanh(bow)
fc_1 = fluid.layers.fc(input=bow_tanh, size=hid_dim, act="tanh")
fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(x=cost)
return avg_cost
class TestGradientClip(unittest.TestCase):
def setUp(self):
self.word_dict = paddle.dataset.imdb.word_dict()
self.BATCH_SIZE = 2
self.train_data = paddle.batch(
paddle.dataset.imdb.train(self.word_dict),
batch_size=self.BATCH_SIZE)
def get_places(self):
places = [core.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(core.CUDAPlace(0))
return places
def check_operators(self, place):
prog = fluid.framework.Program()
startup_program = fluid.framework.Program()
with fluid.program_guard(
main_program=prog, startup_program=startup_program):
image = fluid.layers.data(name='x', shape=[784], dtype='float32')
label = fluid.layers.data(name='y', shape=[1], dtype='int64')
hidden1 = fluid.layers.fc(input=image, size=128, act='relu')
hidden2 = fluid.layers.fc(input=hidden1, size=64, act='relu')
predict = fluid.layers.fc(input=hidden2, size=10, act='softmax')
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
prog_clip = prog.clone()
avg_cost_clip = prog_clip.block(0).var(avg_cost.name)
p_g = fluid.backward.append_backward(loss=avg_cost)
p_g_clip = fluid.backward.append_backward(loss=avg_cost_clip)
with fluid.program_guard(main_program=prog_clip):
fluid.clip.set_gradient_clip(
fluid.clip.GradientClipByGlobalNorm(clip_norm=CLIP))
p_g_clip = fluid.clip.append_gradient_clip_ops(p_g_clip)
grad_list = [elem[1] for elem in p_g]
grad_clip_list = [elem[1] for elem in p_g_clip]
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=8192),
batch_size=BATCH_SIZE)
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(feed_list=[image, label], place=place)
exe.run(startup_program)
count = 0
for data in train_reader():
count += 1
if count > 5:
break
out = exe.run(prog, feed=feeder.feed(data), fetch_list=grad_list)
out_clip = exe.run(prog_clip,
feed=feeder.feed(data),
fetch_list=grad_clip_list)
global_norm = 0
for v in out[1:]:
global_norm += np.sum(np.power(v, 2))
global_norm = np.sqrt(global_norm)
global_norm_clip = 0
for v in out_clip[1:]:
global_norm_clip += np.sum(np.power(v, 2))
global_norm_clip = np.sqrt(global_norm_clip)
assert np.isclose(
a=global_norm_clip, b=np.minimum(global_norm, CLIP), rtol=5e-3)
def check_sparse_gradient_clip(self, place):
prog = fluid.framework.Program()
startup_program = fluid.framework.Program()
with fluid.program_guard(
main_program=prog, startup_program=startup_program):
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
cost = bow_net(data, label, len(self.word_dict))
fluid.clip.set_gradient_clip(
clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=5.0))
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.01)
sgd_optimizer.minimize(cost)
exe = fluid.Executor(place)
feeder = fluid.DataFeeder(feed_list=[data, label], place=place)
exe.run(startup_program)
data = next(self.train_data())
val = exe.run(prog, feed=feeder.feed(data), fetch_list=[cost])[0]
self.assertEqual((1, ), val.shape)
print(val)
self.assertFalse(np.isnan(val))
def test_operators(self):
self.check_operators(core.CPUPlace())
def test_sparse_gradient_clip(self):
for place in self.get_places():
self.check_sparse_gradient_clip(place)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 65dbc7cc
......@@ -170,9 +170,10 @@ class TestBook(unittest.TestCase):
with program_guard(program):
dat = layers.data(name='data', shape=[10], dtype='float32')
lbl = layers.data(name='label', shape=[10], dtype='float32')
ignore_index = -1
self.assertIsNotNone(
layers.sigmoid_cross_entropy_with_logits(
x=dat, label=lbl))
x=dat, label=lbl, ignore_index=ignore_index))
print(str(program))
def test_hsigmoid(self):
......
python/paddle/fluid/tests/unittests/test_merge_selectedrows_op.py 0 → 100644
浏览文件 @ 65dbc7cc
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
import paddle.fluid.core as core
import numpy as np
from paddle.fluid.op import Operator
class TestMergeSelectedRows(unittest.TestCase):
def get_places(self):
places = [core.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(core.CUDAPlace(0))
return places
def check_with_place(self, place):
scope = core.Scope()
x_rows = [0, 5, 5, 4, 20]
out_rows = [0, 4, 5, 20]
height = 20
row_numel = 2
np_array = np.ones((len(x_rows), row_numel)).astype("float32")
np_array[1, :] = 2.0
np_array[2, :] = 3.0
np_array[3, :] = 4.0
# initialize input variable X
x = scope.var('X').get_selected_rows()
x.set_rows(x_rows)
x.set_height(height)
x_tensor = x.get_tensor()
x_tensor.set(np_array, place)
# initialize input variable Out
out = scope.var("Out").get_selected_rows()
op = Operator("merge_selected_rows", X="X", Out="Out")
op.run(scope, place)
self.assertEqual(out.rows(), out_rows)
self.assertEqual(out.height(), height)
out_array = np.array(out.get_tensor())
self.assertEqual((4, 2), out_array.shape)
assert (out_array[0, :] == 1.0).all()
assert (out_array[1, :] == 4.0).all()
assert (out_array[2, :] == 5.0).all()
assert (out_array[3, :] == 1.0).all()
def test_check_output(self):
for place in self.get_places():
self.check_with_place(place)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_sigmoid_cross_entropy_with_logits_op.py
浏览文件 @ 65dbc7cc
......@@ -56,6 +56,40 @@ class TestSigmoidCrossEntropyWithLogitsOp2(OpTest):
"""Test sigmoid_cross_entropy_with_logit_op with probabalistic label
"""
def setUp(self):
self.op_type = "sigmoid_cross_entropy_with_logits"
batch_size = 64
num_classes = 20
ignore_index = -1
self.inputs = {
'X': logit(
np.random.uniform(0, 1, (batch_size, num_classes))
.astype("float32")),
'Label': np.random.randint(-1, 2, (batch_size, num_classes))
.astype("float32")
}
self.attrs = {'ignore_index': ignore_index, }
# Fw Pass is implemented as elementwise sigmoid followed by
# elementwise logistic loss
# Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
sigmoid_X = expit(self.inputs['X'])
term1 = self.inputs['Label'] * np.log(sigmoid_X)
term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
out = -term1 - term2
out[np.where(self.inputs['Label'] == ignore_index)] = 0
self.outputs = {'Out': out}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestSigmoidCrossEntropyWithLogitsOp3(OpTest):
"""Test sigmoid_cross_entropy_with_logit_op with probabalistic label
"""
def setUp(self):
self.op_type = "sigmoid_cross_entropy_with_logits"
batch_size = 64
......
python/paddle/fluid/tests/unittests/test_yolov3_loss_op.py 0 → 100644
浏览文件 @ 65dbc7cc
# 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 division
import unittest
import numpy as np
from op_test import OpTest
from paddle.fluid import core
def sigmoid(x):
return 1.0 / (1.0 + np.exp(-1.0 * x))
def mse(x, y, num):
return ((y - x)**2).sum() / num
def bce(x, y, mask):
x = x.reshape((-1))
y = y.reshape((-1))
mask = mask.reshape((-1))
error_sum = 0.0
count = 0
for i in range(x.shape[0]):
if mask[i] > 0:
error_sum += y[i] * np.log(x[i]) + (1 - y[i]) * np.log(1 - x[i])
count += 1
return error_sum / (-1.0 * count)
def box_iou(box1, box2):
b1_x1 = box1[0] - box1[2] / 2
b1_x2 = box1[0] + box1[2] / 2
b1_y1 = box1[1] - box1[3] / 2
b1_y2 = box1[1] + box1[3] / 2
b2_x1 = box2[0] - box2[2] / 2
b2_x2 = box2[0] + box2[2] / 2
b2_y1 = box2[1] - box2[3] / 2
b2_y2 = box2[1] + box2[3] / 2
b1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
b2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
inter_rect_x1 = max(b1_x1, b2_x1)
inter_rect_y1 = max(b1_y1, b2_y1)
inter_rect_x2 = min(b1_x2, b2_x2)
inter_rect_y2 = min(b1_y2, b2_y2)
inter_area = max(inter_rect_x2 - inter_rect_x1, 0) * max(
inter_rect_y2 - inter_rect_y1, 0)
return inter_area / (b1_area + b2_area + inter_area)
def build_target(gtboxs, gtlabel, attrs, grid_size):
n, b, _ = gtboxs.shape
ignore_thresh = attrs["ignore_thresh"]
anchors = attrs["anchors"]
class_num = attrs["class_num"]
an_num = len(anchors) // 2
obj_mask = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
noobj_mask = np.ones((n, an_num, grid_size, grid_size)).astype('float32')
tx = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
ty = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
tw = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
th = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
tconf = np.zeros((n, an_num, grid_size, grid_size)).astype('float32')
tcls = np.zeros(
(n, an_num, grid_size, grid_size, class_num)).astype('float32')
for i in range(n):
for j in range(b):
if gtboxs[i, j, :].sum() == 0:
continue
gt_label = gtlabel[i, j]
gx = gtboxs[i, j, 0] * grid_size
gy = gtboxs[i, j, 1] * grid_size
gw = gtboxs[i, j, 2] * grid_size
gh = gtboxs[i, j, 3] * grid_size
gi = int(gx)
gj = int(gy)
gtbox = [0, 0, gw, gh]
max_iou = 0
for k in range(an_num):
anchor_box = [0, 0, anchors[2 * k], anchors[2 * k + 1]]
iou = box_iou(gtbox, anchor_box)
if iou > max_iou:
max_iou = iou
best_an_index = k
if iou > ignore_thresh:
noobj_mask[i, best_an_index, gj, gi] = 0
obj_mask[i, best_an_index, gj, gi] = 1
noobj_mask[i, best_an_index, gj, gi] = 0
tx[i, best_an_index, gj, gi] = gx - gi
ty[i, best_an_index, gj, gi] = gy - gj
tw[i, best_an_index, gj, gi] = np.log(gw / anchors[2 *
best_an_index])
th[i, best_an_index, gj, gi] = np.log(
gh / anchors[2 * best_an_index + 1])
tconf[i, best_an_index, gj, gi] = 1
tcls[i, best_an_index, gj, gi, gt_label] = 1
return (tx, ty, tw, th, tconf, tcls, obj_mask, noobj_mask)
def YoloV3Loss(x, gtbox, gtlabel, attrs):
n, c, h, w = x.shape
an_num = len(attrs['anchors']) // 2
class_num = attrs["class_num"]
x = x.reshape((n, an_num, 5 + class_num, h, w)).transpose((0, 1, 3, 4, 2))
pred_x = sigmoid(x[:, :, :, :, 0])
pred_y = sigmoid(x[:, :, :, :, 1])
pred_w = x[:, :, :, :, 2]
pred_h = x[:, :, :, :, 3]
pred_conf = sigmoid(x[:, :, :, :, 4])
pred_cls = sigmoid(x[:, :, :, :, 5:])
tx, ty, tw, th, tconf, tcls, obj_mask, noobj_mask = build_target(
gtbox, gtlabel, attrs, x.shape[2])
obj_mask_expand = np.tile(
np.expand_dims(obj_mask, 4), (1, 1, 1, 1, int(attrs['class_num'])))
loss_x = mse(pred_x * obj_mask, tx * obj_mask, obj_mask.sum())
loss_y = mse(pred_y * obj_mask, ty * obj_mask, obj_mask.sum())
loss_w = mse(pred_w * obj_mask, tw * obj_mask, obj_mask.sum())
loss_h = mse(pred_h * obj_mask, th * obj_mask, obj_mask.sum())
loss_conf_target = bce(pred_conf * obj_mask, tconf * obj_mask, obj_mask)
loss_conf_notarget = bce(pred_conf * noobj_mask, tconf * noobj_mask,
noobj_mask)
loss_class = bce(pred_cls * obj_mask_expand, tcls * obj_mask_expand,
obj_mask_expand)
return attrs['loss_weight_xy'] * (loss_x + loss_y) \
+ attrs['loss_weight_wh'] * (loss_w + loss_h) \
+ attrs['loss_weight_conf_target'] * loss_conf_target \
+ attrs['loss_weight_conf_notarget'] * loss_conf_notarget \
+ attrs['loss_weight_class'] * loss_class
class TestYolov3LossOp(OpTest):
def setUp(self):
self.loss_weight_xy = 1.0
self.loss_weight_wh = 1.0
self.loss_weight_conf_target = 1.0
self.loss_weight_conf_notarget = 1.0
self.loss_weight_class = 1.0
self.initTestCase()
self.op_type = 'yolov3_loss'
x = np.random.random(size=self.x_shape).astype('float32')
gtbox = np.random.random(size=self.gtbox_shape).astype('float32')
gtlabel = np.random.randint(0, self.class_num,
self.gtbox_shape[:2]).astype('int32')
self.attrs = {
"anchors": self.anchors,
"class_num": self.class_num,
"ignore_thresh": self.ignore_thresh,
"loss_weight_xy": self.loss_weight_xy,
"loss_weight_wh": self.loss_weight_wh,
"loss_weight_conf_target": self.loss_weight_conf_target,
"loss_weight_conf_notarget": self.loss_weight_conf_notarget,
"loss_weight_class": self.loss_weight_class,
}
self.inputs = {'X': x, 'GTBox': gtbox, 'GTLabel': gtlabel}
self.outputs = {
'Loss': np.array(
[YoloV3Loss(x, gtbox, gtlabel, self.attrs)]).astype('float32')
}
def test_check_output(self):
place = core.CPUPlace()
self.check_output_with_place(place, atol=1e-3)
def test_check_grad_ignore_gtbox(self):
place = core.CPUPlace()
self.check_grad_with_place(
place, ['X'],
'Loss',
no_grad_set=set(["GTBox", "GTLabel"]),
max_relative_error=0.06)
def initTestCase(self):
self.anchors = [10, 13, 12, 12]
self.class_num = 10
self.ignore_thresh = 0.5
self.x_shape = (5, len(self.anchors) // 2 * (5 + self.class_num), 7, 7)
self.gtbox_shape = (5, 10, 4)
self.loss_weight_xy = 2.5
self.loss_weight_wh = 0.8
self.loss_weight_conf_target = 1.5
self.loss_weight_conf_notarget = 0.5
self.loss_weight_class = 1.2
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/transpiler/distribute_transpiler.py
浏览文件 @ 65dbc7cc
......@@ -125,13 +125,14 @@ def slice_variable(var_list, slice_count, min_block_size):
class DistributeTranspilerConfig(object):
"""
slice_var_up (bool): Do Tensor slice for pservers, default is True.
split_method (PSDispatcher): RoundRobin or HashName can be used
try to choose the best method to balance loads for pservers.
min_block_size (int): Minimum splitted element number in block.
According:https://github.com/PaddlePaddle/Paddle/issues/8638#issuecomment-369912156
We can use bandwidth effiently when data size is larger than 2MB.If you
want to change it, please be sure you see the slice_variable function.
Args:
slice_var_up (bool): Do Tensor slice for pservers, default is True.
split_method (PSDispatcher): RoundRobin or HashName can be used
try to choose the best method to balance loads for pservers.
min_block_size (int): Minimum splitted element number in block.
According:https://github.com/PaddlePaddle/Paddle/issues/8638#issuecomment-369912156
We can use bandwidth effiently when data size is larger than 2MB.If you
want to change it, please be sure you see the slice_variable function.
"""
slice_var_up = True
......@@ -163,35 +164,35 @@ class DistributeTranspiler(object):
Examples:
.. code-block:: python
# for pserver mode
pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
trainer_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
current_endpoint = "192.168.0.1:6174"
trainer_id = 0
trainers = 4
role = os.getenv("PADDLE_TRAINING_ROLE")
t = fluid.DistributeTranspiler()
t.transpile(
trainer_id, pservers=pserver_endpoints, trainers=trainers)
if role == "PSERVER":
pserver_program = t.get_pserver_program(current_endpoint)
pserver_startup_program = t.get_startup_program(current_endpoint,
# for pserver mode
pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
trainer_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
current_endpoint = "192.168.0.1:6174"
trainer_id = 0
trainers = 4
role = os.getenv("PADDLE_TRAINING_ROLE")
t = fluid.DistributeTranspiler()
t.transpile(
trainer_id, pservers=pserver_endpoints, trainers=trainers)
if role == "PSERVER":
pserver_program = t.get_pserver_program(current_endpoint)
pserver_startup_program = t.get_startup_program(current_endpoint,
pserver_program)
elif role == "TRAINER":
trainer_program = t.get_trainer_program()
# for nccl2 mode
config = fluid.DistributeTranspilerConfig()
config.mode = "nccl2"
t = fluid.DistributeTranspiler(config=config)
t.transpile(trainer_id, workers=workers, current_endpoint=curr_ep)
exe = fluid.ParallelExecutor(
use_cuda,
loss_name=loss_var.name,
num_trainers=len(trainers.split(",)),
trainer_id=trainer_id
)
elif role == "TRAINER":
trainer_program = t.get_trainer_program()
# for nccl2 mode
config = fluid.DistributeTranspilerConfig()
config.mode = "nccl2"
t = fluid.DistributeTranspiler(config=config)
t.transpile(trainer_id, workers=workers, current_endpoint=curr_ep)
exe = fluid.ParallelExecutor(
use_cuda,
loss_name=loss_var.name,
num_trainers=len(trainers.split(",)),
trainer_id=trainer_id
)
"""
def __init__(self, config=None):
......
python/setup.py.in
浏览文件 @ 65dbc7cc
......@@ -165,9 +165,9 @@ if '${WITH_MKL}' == 'ON':
shutil.copy('${MKLML_LIB}', libs_path)
shutil.copy('${MKLML_IOMP_LIB}', libs_path)
package_data['paddle.libs']+=['libmklml_intel' + ext_name,'libiomp5' + ext_name]
if '${CMAKE_BUILD_TYPE}' == 'Release':
# only change rpath in Release mode.
if '${WITH_MKLDNN}' == 'ON':
if '${WITH_MKLDNN}' == 'ON':
if '${CMAKE_BUILD_TYPE}' == 'Release':
# only change rpath in Release mode.
# TODO(typhoonzero): use install_name_tool to patch mkl libs once
# we can support mkl on mac.
#
......@@ -177,14 +177,19 @@ if '${CMAKE_BUILD_TYPE}' == 'Release':
command = "patchelf --set-rpath '$ORIGIN/' ${MKLDNN_SHARED_LIB}"
if os.system(command) != 0:
raise Exception("patch libmkldnn.so failed, command: %s" % command)
package_data['paddle.libs']+=['libmkldnn.so.0']
shutil.copy('${MKLDNN_SHARED_LIB}', libs_path)
package_data['paddle.libs']+=['libmkldnn.so.0']
shutil.copy('${MKLDNN_SHARED_LIB}', libs_path)
if '${WITH_NGRAPH}' == 'ON':
# only change rpath in Release mode,
# since in Debug mode, nGraph lib may be too large to be changed?
if '${CMAKE_BUILD_TYPE}' == 'Release':
# only change rpath in Release mode.
command = "patchelf --set-rpath '$ORIGIN/' ${NGRAPH_SHARED_LIB}"
if os.system(command) != 0:
raise Exception("patch ${NGRAPH_SHARED_LIB_NAME} failed, command: %s" % command)
if os.name != 'nt':
if "@APPLE@" == "1":
command = "install_name_tool -id \"@loader_path/\" ${NGRAPH_SHARED_LIB}"
else:
command = "patchelf --set-rpath '$ORIGIN/' ${NGRAPH_SHARED_LIB}"
if os.system(command) != 0:
raise Exception("patch ${NGRAPH_SHARED_LIB_NAME} failed, command: %s" % command)
shutil.copy('${NGRAPH_SHARED_LIB}', libs_path)
shutil.copy('${NGRAPH_CPU_LIB}', libs_path)
shutil.copy('${NGRAPH_TBB_LIB}', libs_path)
......
tools/print_signatures.py
浏览文件 @ 65dbc7cc
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