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PaddleDetection

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提交 bc1b7b96 编写于 作者: F fengjiayi
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into dev_exception_holder

上级 3e4083ed 91fb0156
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Showing with 2938 addition and 779 deletion
AUTHORS.md
浏览文件 @ bc1b7b96
......@@ -46,6 +46,7 @@
| tianbingsz | Tian-Bing Xu |
| tpatejko | Tomasz Patejko |
| typhoonzero | Yi Wu |
| velconia | Qi-Yang Min |
| wanghaoshuang | Hao-Shuang Wang |
| wangyang59 | Yang Wang |
| wangzhen-nlp | Zhen Wang |
......
benchmark/fluid/fluid_benchmark.py
浏览文件 @ bc1b7b96
......@@ -85,8 +85,7 @@ def dist_transpile(trainer_id, args):
trainer_id,
pservers=pserver_endpoints,
trainers=trainers,
sync_mode=not args.async_mode,
slice_var_up=not args.no_split_var)
sync_mode=not args.async_mode)
if training_role == "PSERVER":
pserver_program = t.get_pserver_program(current_endpoint)
pserver_startup_program = t.get_startup_program(current_endpoint,
......
cmake/external/grpc.cmake
浏览文件 @ bc1b7b96
......@@ -50,7 +50,7 @@ ExternalProject_Add(
UPDATE_COMMAND ""
CONFIGURE_COMMAND ""
BUILD_IN_SOURCE 1
PATCH_COMMAND git apply ${PADDLE_SOURCE_DIR}/patches/grpc/fix_too_early_destory.patch
PATCH_COMMAND cp ${PADDLE_SOURCE_DIR}/patches/grpc/grpc_library.h ${GRPC_SOURCES_DIR}/src/extern_grpc/include/grpcpp/impl/codegen/grpc_library.h && cp ${PADDLE_SOURCE_DIR}/patches/grpc/completion_queue.h ${GRPC_SOURCES_DIR}/src/extern_grpc/include/grpcpp/impl/codegen/completion_queue.h
# NOTE(yuyang18):
# Disable -Werror, otherwise the compile will fail in MacOS.
# It seems that we cannot configure that by make command.
......
cmake/generic.cmake
浏览文件 @ bc1b7b96
......@@ -263,7 +263,7 @@ function(cc_test TARGET_NAME)
COMMAND ${TARGET_NAME} ${cc_test_ARGS}
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
if (${cc_test_SERIAL})
set_property(TEST ${TARGET_NAME} PROPERTY SERIAL 1)
set_property(TEST ${TARGET_NAME} PROPERTY RUN_SERIAL 1)
set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_init_allocated_mem=true)
endif()
endif()
......@@ -328,7 +328,7 @@ function(nv_test TARGET_NAME)
add_dependencies(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main lod_tensor memory gtest gflags glog)
add_test(${TARGET_NAME} ${TARGET_NAME})
if (nv_test_SERIAL)
set_property(TEST ${TARGET_NAME} PROPERTY SERIAL 1)
set_property(TEST ${TARGET_NAME} PROPERTY RUN_SERIAL 1)
set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_init_allocated_mem=true)
endif()
endif()
......
cmake/inference_lib.cmake
浏览文件 @ bc1b7b96
......@@ -148,18 +148,11 @@ if (WITH_ANAKIN AND WITH_GPU)
list(APPEND inference_deps anakin_inference_lib)
endif()
copy(inference_api_lib DEPS paddle_inference_api paddle_inference_api_shared
SRCS ${src_dir}/${module}/paddle_inference_api.h
${src_dir}/${module}/demo_ci
${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/libpaddle_inference_api*
DSTS ${dst_dir}/inference ${dst_dir}/inference ${dst_dir}/inference
)
list(APPEND inference_deps inference_api_lib)
set(module "inference")
copy(inference_lib DEPS ${inference_deps}
SRCS ${src_dir}/${module}/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/inference/libpaddle_fluid.*
DSTS ${dst_dir}/${module} ${dst_dir}/${module}
${src_dir}/${module}/api/paddle_inference_api.h ${src_dir}/${module}/api/demo_ci
DSTS ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module}
)
set(module "platform")
......
doc/fluid/design/ir/draft.md
浏览文件 @ bc1b7b96
......@@ -64,6 +64,41 @@ can also contain other things that describe some properties of
the `Graph` or `Graph` nodes. `Attribute` can be passed
across `Pass`. However, it should be used with care.
```cpp
class Graph {
public:
explicit Graph(const ProgramDesc &program);
bool Has(const std::string &attr_name) const;
template <typename AttrType>
AttrType &Get(const std::string &attr_name) const;
template <typename AttrType>
void Set(const std::string &attr_name, AttrType *attr);
const std::unordered_set<ir::Node *> &Nodes() const;
// Create a normal variable with non-null VarDesc.
ir::Node *CreateVarNode(VarDesc *var_desc);
// Create a normal runnable operator with OpDesc.
ir::Node *CreateOpNode(OpDesc *op_desc);
// Create a control dependency var that connects 2 operations. The
// var doesn't hold any data. Other than that, it's no different from
// other var, considering dependency analysis.
ir::Node *CreateControlDepVar();
// A more free style way of creating a graph node. Mostly use for test
// or "copy" from another node. Avoid using it if possible.
ir::Node *CreateEmptyNode(const std::string &name, ir::Node::Type type);
// Clear all node information of the graph and return the ownership of the
// nodes.
std::vector<std::unique_ptr<ir::Node>> ReleaseNodes();
};
```
#### Pass
`Pass` represents a transformation of `Graph`. Its input
......@@ -71,6 +106,54 @@ is a `Graph` and its output is also a `Graph`. For example,
a `Pass` can simply print out the `Graph`. A `Pass`
can also fuse some `Graph`'s `Node`s.
```cpp
class Pass {
public:
std::unique_ptr<Graph> Apply(std::unique_ptr<Graph> graph) const {
// Some correctness check.
auto new_graph = ApplyImpl(std::move(graph));
// Some correctness check.
return new_graph;
}
// Get a reference to the attributed previously set.
template <typename AttrType>
AttrType &Get(const std::string &attr_name) const;
// Set a pointer to the attribute. Pass takes ownership of the attribute.
template <typename AttrType>
void Set(const std::string &attr_name, AttrType *attr) ;
// Set a pointer to the attribute. Pass doesn't take ownership. Caller
// should delete the attribute.
template <typename AttrType>
void SetNotOwned(const std::string &attr_name, AttrType *attr);
protected:
virtual std::unique_ptr<Graph> ApplyImpl(std::unique_ptr<Graph> graph) const = 0;
};
// In my_pass.cc
class MyPass : public Pass {
protected:
std::unique_ptr<Graph> ApplyImpl(std::unique_ptr<Graph> graph) const override {
// do something.
return graph;
}
}
REGISTER_PASS(my_pass, MyPass)
.RequirePassAttr("places")
.RequireGraphAttr("dep_vars");
// To use the pass.
auto my_pass = ir::PassRegistry::Instance().Get("my_pass");
graph = my_pass->Apply(std::move(graph));
// Note: to force link my_pass.cc, in the code:
USE_PASS(my_pass);
```
#### Optimize
`Optimize` contains a series of `Pass` with defined order.
......@@ -86,4 +169,17 @@ maintaining the original modeling logic.
* Graph is transformed from raw model logic to a
form that is efficient to execute.
Program->ProgramToGraph->Graph->Pass1->Graph->Pass2->Graph->Pass3->Graph->Executor
```
// Program->ProgramToGraph->Graph->Pass1->Graph->Pass2->Graph->Pass3->Graph->Executor
auto graph = Graph(program);
graph = PassRegistry::Instance().Get("op_fuse_pass").Apply(std::move(grah));
// For more complex Pass, Optimize Process can provide Pass attributes.
auto mem_opt_pass = PassRegistry::Instance().Get("memory_optimization_pass");
mem_opt_pass.SetNotOwned<int>("optimize_level", 1);
mem_opt_pass->Apply(std::move(graph));
graph = PassRegistry::Instance().Get("multi_device_pass").Apply(std::move(grah));
graph = PassRegistry::Instance().Get("multi_device_check_pass").Apply(std::move(grah));
Executor exe;
exe.Run(graph);
```
paddle/fluid/API.spec
浏览文件 @ bc1b7b96
......@@ -170,6 +170,7 @@ paddle.fluid.layers.mean_iou ArgSpec(args=['input', 'label', 'num_classes'], var
paddle.fluid.layers.relu ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.log ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.crop ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.rank_loss ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,))
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_recordio_file ArgSpec(args=['filename', 'shapes', 'lod_levels', 'dtypes', 'pass_num', 'for_parallel'], varargs=None, keywords=None, defaults=(1, 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))
......@@ -201,7 +202,6 @@ paddle.fluid.layers.zeros ArgSpec(args=['shape', 'dtype', 'force_cpu'], varargs=
paddle.fluid.layers.reverse ArgSpec(args=['x', 'axis'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.While.__init__ ArgSpec(args=['self', 'cond', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.While.block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.While.complete ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.Switch.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.Switch.case ArgSpec(args=['self', 'condition'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.Switch.default ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
......@@ -225,17 +225,14 @@ paddle.fluid.layers.DynamicRNN.static_input ArgSpec(args=['self', 'x'], varargs=
paddle.fluid.layers.DynamicRNN.step_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.DynamicRNN.update_memory ArgSpec(args=['self', 'ex_mem', 'new_mem'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.StaticRNN.__init__ ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.StaticRNN.complete_op ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.StaticRNN.memory ArgSpec(args=['self', 'init', 'shape', 'batch_ref', 'init_value', 'init_batch_dim_idx', 'ref_batch_dim_idx'], varargs=None, keywords=None, defaults=(None, None, None, 0.0, 0, 1))
paddle.fluid.layers.StaticRNN.output ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None)
paddle.fluid.layers.StaticRNN.parent_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.StaticRNN.step ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.StaticRNN.step_input ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.StaticRNN.step_output ArgSpec(args=['self', 'o'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.StaticRNN.update_memory ArgSpec(args=['self', 'mem', 'var'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.reorder_lod_tensor_by_rank ArgSpec(args=['x', 'rank_table'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.ParallelDo.__init__ ArgSpec(args=['self', 'places', 'use_nccl', 'name'], varargs=None, keywords=None, defaults=(False, None))
paddle.fluid.layers.ParallelDo.complete_op ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.ParallelDo.do ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.ParallelDo.get_parameters ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.ParallelDo.parent_block ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
......
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ bc1b7b96
......@@ -8,9 +8,9 @@ cc_test(ddim_test SRCS ddim_test.cc DEPS ddim)
nv_test(dim_test SRCS dim_test.cu DEPS ddim)
cc_library(data_type SRCS data_type.cc DEPS framework_proto ddim device_context)
if(WITH_GPU)
nv_library(tensor SRCS tensor.cc tensor_util.cu DEPS place memory data_type)
nv_library(tensor SRCS tensor.cc tensor_util.cu DEPS place memory data_type device_context)
else()
cc_library(tensor SRCS tensor.cc tensor_util.cc DEPS place memory data_type)
cc_library(tensor SRCS tensor.cc tensor_util.cc DEPS place memory data_type device_context)
endif()
cc_test(tensor_test SRCS tensor_test.cc DEPS tensor)
......@@ -99,7 +99,7 @@ else()
endif()
cc_library(parallel_executor SRCS parallel_executor.cc DEPS ssa_graph_builder_factory threaded_ssa_graph_executor scope_buffered_ssa_graph_executor graph)
cc_library(parallel_executor SRCS parallel_executor.cc DEPS threaded_ssa_graph_executor scope_buffered_ssa_graph_executor graph graph_viz_pass multi_devices_graph_builder ssa_graph_printer ssa_graph_checker)
cc_library(prune SRCS prune.cc DEPS framework_proto)
cc_test(prune_test SRCS prune_test.cc DEPS op_info prune recurrent_op device_context)
......@@ -110,7 +110,7 @@ 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(cow_ptr_tests SRCS details/cow_ptr_test.cc)
# cc_test(channel_test SRCS channel_test.cc)
cc_test(tuple_test SRCS tuple_test.cc )
......
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @ bc1b7b96
......@@ -31,9 +31,6 @@ cc_library(fuse_vars_op_handle SRCS fuse_vars_op_handle.cc DEPS op_handle_base s
cc_library(multi_devices_graph_builder SRCS multi_devices_graph_builder.cc DEPS ssa_graph_builder computation_op_handle
scale_loss_grad_op_handle rpc_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle data_balance_op_handle)
cc_library(ssa_graph_builder_factory SRCS ssa_graph_builder_factory.cc DEPS multi_devices_graph_builder ssa_graph_printer ssa_graph_checker)
cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS graph framework_proto)
cc_library(threaded_ssa_graph_executor SRCS threaded_ssa_graph_executor.cc DEPS fetch_op_handle ssa_graph_executor scope
simple_threadpool device_context)
......
paddle/fluid/framework/details/multi_devices_graph_builder.cc
浏览文件 @ bc1b7b96
......@@ -34,30 +34,22 @@ namespace paddle {
namespace framework {
namespace details {
static const char kLossVarName[] = "loss_var_name";
static const char kPlaces[] = "places";
static const char kParams[] = "params";
static const char kLocalScopes[] = "local_scopes";
static const char kStrategy[] = "strategy";
void MultiDevSSAGraphBuilder::Init() const {
loss_var_name_ = Get<const std::string>(kLossVarName);
places_ = Get<const std::vector<platform::Place>>(kPlaces);
local_scopes_ = Get<const std::vector<Scope *>>(kLocalScopes);
strategy_ = Get<const BuildStrategy>(kStrategy);
#ifdef PADDLE_WITH_CUDA
MultiDevSSAGraphBuilder::MultiDevSSAGraphBuilder(
const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::unordered_set<std::string> &params,
const std::vector<Scope *> &local_scopes,
platform::NCCLContextMap *nccl_ctxs, const BuildStrategy &strategy)
: loss_var_name_(loss_var_name),
places_(places),
local_scopes_(local_scopes),
nccl_ctxs_(nccl_ctxs),
strategy_(strategy) {
#else
MultiDevSSAGraphBuilder::MultiDevSSAGraphBuilder(
const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::unordered_set<std::string> &params,
const std::vector<Scope *> &local_scopes, const BuildStrategy &strategy)
: loss_var_name_(loss_var_name),
places_(places),
local_scopes_(local_scopes),
strategy_(strategy) {
nccl_ctxs_ = &Get<platform::NCCLContextMap>("nccl_ctxs");
#endif
for (auto &p : params) {
for (auto &p : Get<const std::unordered_set<std::string>>(kParams)) {
grad_names_.insert(GradVarName(p));
}
balance_vars_.resize(places_.size(), 0);
......@@ -72,7 +64,7 @@ void MultiDevSSAGraphBuilder::CreateOpHandleIOs(ir::Graph *result,
ir::Node *node,
size_t place_id) const {
auto p = places_[place_id];
auto *op_handle = result->Get<GraphOps>("ops").back().get();
auto *op_handle = result->Get<GraphOps>(kGraphOps).back().get();
op_handle->SetDeviceContext(p,
platform::DeviceContextPool::Instance().Get(p));
......@@ -239,8 +231,9 @@ std::vector<ir::Node *> SortOpsAndDelayOptimizeOp(const ir::Graph &graph) {
return sorted_ret;
}
std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::Apply(
std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
Init();
// Give the topology sort order and rebuild the graph structure.
std::vector<ir::Node *> sorted_ops = SortOpsAndDelayOptimizeOp(*graph);
auto nodes = graph->ReleaseNodes();
......@@ -254,9 +247,10 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::Apply(
std::unordered_set<std::string> og_has_been_broadcast;
// We cannot invoke resize. It is a bug of GCC 4.8
result.Set("vars", new GraphVars(places_.size()));
result.Set("dep_vars", new GraphDepVars);
result.Set("ops", new GraphOps);
result.Set(kGraphVars, new GraphVars(places_.size()));
result.Set(kGraphDepVars, new GraphDepVars);
result.Set(kGraphOps, new GraphOps);
result.Set(kShardedVarDevice, new ShardedVarDevice);
// find send/recv vars so that we can place the distributed training
// related op in the place 0
......@@ -289,11 +283,12 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::Apply(
// the block.
is_forwarding = false;
} else {
int op_dev_id = GetOpDeviceID(node);
int op_dev_id = GetOpDeviceID(result, node);
if (op_dev_id != -1) { // This op only runs on one specific device.
CreateComputationalOp(&result, node, op_dev_id);
for (ir::Node *n : node->outputs) {
var_name_on_devices_.emplace(n->Name(), op_dev_id);
graph->Get<ShardedVarDevice>(kShardedVarDevice)
.emplace(n->Name(), op_dev_id);
}
} else {
// This op runs on all devices, and its output may have parameter's
......@@ -330,7 +325,8 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::Apply(
case BuildStrategy::ReduceStrategy::kReduce:
cur_device_id = GetAppropriateDeviceID({g_name});
CreateReduceOp(&result, g_name, cur_device_id);
var_name_on_devices_.emplace(g_name, cur_device_id);
graph->Get<ShardedVarDevice>(kShardedVarDevice)
.emplace(g_name, cur_device_id);
bcast_var_name_set[cur_device_id].emplace(p_name);
break;
case BuildStrategy::ReduceStrategy::kAllReduce:
......@@ -416,16 +412,16 @@ void MultiDevSSAGraphBuilder::CreateBroadcastOp(ir::Graph *result,
result->CreateEmptyNode("broadcast", ir::Node::Type::kOperation),
local_scopes_, places_);
#endif
result->Get<GraphOps>("ops").emplace_back(op_handle);
result->Get<GraphOps>(kGraphOps).emplace_back(op_handle);
auto *in =
result->Get<GraphVars>("vars").at(src_dev_id).at(p_name).back().get();
result->Get<GraphVars>(kGraphVars).at(src_dev_id).at(p_name).back().get();
op_handle->AddInput(in);
for (size_t i = 0; i < places_.size(); ++i) {
auto &p = places_[i];
SetCommunicationContext(op_handle, p);
auto &vars = result->Get<GraphVars>("vars").at(i).at(p_name);
auto &vars = result->Get<GraphVars>(kGraphVars).at(i).at(p_name);
auto *out_var = new VarHandle(
result->CreateEmptyNode(p_name, ir::Node::Type::kVariable), vars.size(),
i, p_name, p);
......@@ -437,7 +433,7 @@ void MultiDevSSAGraphBuilder::CreateBroadcastOp(ir::Graph *result,
void MultiDevSSAGraphBuilder::CreateComputationalOp(ir::Graph *result,
ir::Node *node,
int dev_id) const {
result->Get<GraphOps>("ops").emplace_back(
result->Get<GraphOps>(kGraphOps).emplace_back(
new ComputationOpHandle(result->CreateOpNode(node->Op()),
local_scopes_[dev_id], places_[dev_id]));
CreateOpHandleIOs(result, node, dev_id);
......@@ -446,20 +442,20 @@ void MultiDevSSAGraphBuilder::CreateComputationalOp(ir::Graph *result,
void MultiDevSSAGraphBuilder::InsertAllReduceOp(ir::Graph *result,
const std::string &og) const {
#ifdef PADDLE_WITH_CUDA
result->Get<GraphOps>("ops").emplace_back(new AllReduceOpHandle(
result->Get<GraphOps>(kGraphOps).emplace_back(new AllReduceOpHandle(
result->CreateEmptyNode("allreduce", ir::Node::Type::kOperation),
local_scopes_, places_, nccl_ctxs_));
#else
result->Get<GraphOps>("ops").emplace_back(new AllReduceOpHandle(
result->Get<GraphOps>(kGraphOps).emplace_back(new AllReduceOpHandle(
result->CreateEmptyNode("allreduce", ir::Node::Type::kOperation),
local_scopes_, places_));
#endif
auto *op_handle = result->Get<GraphOps>("ops").back().get();
auto *op_handle = result->Get<GraphOps>(kGraphOps).back().get();
for (size_t i = 0; i < places_.size(); ++i) {
auto &p = places_[i];
SetCommunicationContext(op_handle, p);
auto &vars = result->Get<GraphVars>("vars")[i][og];
auto &vars = result->Get<GraphVars>(kGraphVars)[i][og];
PADDLE_ENFORCE(!vars.empty());
auto &prev_grad = vars.back();
op_handle->AddInput(prev_grad.get());
......@@ -475,20 +471,20 @@ void MultiDevSSAGraphBuilder::InsertAllReduceOp(ir::Graph *result,
void MultiDevSSAGraphBuilder::InsertDataBalanceOp(
ir::Graph *result, const std::vector<std::string> &datas) const {
#ifdef PADDLE_WITH_CUDA
result->Get<GraphOps>("ops").emplace_back(new DataBalanceOpHandle(
result->Get<GraphOps>(kGraphOps).emplace_back(new DataBalanceOpHandle(
result->CreateEmptyNode("data_balance", ir::Node::Type::kOperation),
local_scopes_, places_, nccl_ctxs_));
#else
result->Get<GraphOps>("ops").emplace_back(new DataBalanceOpHandle(
result->Get<GraphOps>(kGraphOps).emplace_back(new DataBalanceOpHandle(
result->CreateEmptyNode("data_balance", ir::Node::Type::kOperation),
local_scopes_, places_));
#endif
auto *op_handle = result->Get<GraphOps>("ops").back().get();
auto *op_handle = result->Get<GraphOps>(kGraphOps).back().get();
for (size_t i = 0; i < places_.size(); ++i) {
auto &p = places_[i];
SetCommunicationContext(op_handle, p);
for (const std::string &d_name : datas) {
auto &vars = result->Get<GraphVars>("vars")[i][d_name];
auto &vars = result->Get<GraphVars>(kGraphVars)[i][d_name];
PADDLE_ENFORCE(!vars.empty());
op_handle->AddInput(vars.back().get());
auto var = new VarHandle(
......@@ -512,7 +508,8 @@ bool MultiDevSSAGraphBuilder::IsParameterGradientOnce(
return is_pg_once;
}
int MultiDevSSAGraphBuilder::GetOpDeviceID(ir::Node *node) const {
int MultiDevSSAGraphBuilder::GetOpDeviceID(const ir::Graph &graph,
ir::Node *node) const {
if (strategy_.reduce_ != BuildStrategy::ReduceStrategy::kReduce) {
return -1;
}
......@@ -525,15 +522,17 @@ int MultiDevSSAGraphBuilder::GetOpDeviceID(ir::Node *node) const {
node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleVarAttrName()));
PADDLE_ENFORCE_EQ(param_grad.size(), 2U);
int dev_id = GetVarDeviceID(param_grad[1]);
int dev_id = GetVarDeviceID(graph, param_grad[1]);
PADDLE_ENFORCE_NE(dev_id, -1, "dev_id should not be -1.[%s, %s, %s]",
node->Op()->Type(), param_grad[0], param_grad[1]);
return dev_id;
}
int MultiDevSSAGraphBuilder::GetVarDeviceID(const std::string &varname) const {
auto got = var_name_on_devices_.find(varname);
return got == var_name_on_devices_.end() ? -1 : got->second;
int MultiDevSSAGraphBuilder::GetVarDeviceID(const ir::Graph &graph,
const std::string &varname) const {
auto &sharded_var_device = graph.Get<ShardedVarDevice>(kShardedVarDevice);
auto got = sharded_var_device.find(varname);
return got == sharded_var_device.end() ? -1 : got->second;
}
void MultiDevSSAGraphBuilder::CreateScaleLossGradOp(ir::Graph *result) const {
......@@ -551,7 +550,7 @@ void MultiDevSSAGraphBuilder::CreateScaleLossGradOp(ir::Graph *result) const {
result->CreateEmptyNode("scale_loss_grad", ir::Node::Type::kOperation),
local_scopes_.size(), local_scopes_[i], places_[i],
communication_dev_ctx);
result->Get<GraphOps>("ops").emplace_back(op_handle);
result->Get<GraphOps>(kGraphOps).emplace_back(op_handle);
// FIXME: Currently ScaleLossGradOp only use device_count as scale
// factor. So it does not depend on any other operators.
......@@ -572,7 +571,7 @@ void MultiDevSSAGraphBuilder::CreateComputationalOps(ir::Graph *result,
for (size_t scope_idx = 0; scope_idx < num_places; ++scope_idx) {
auto p = places_[scope_idx];
auto s = local_scopes_[scope_idx];
result->Get<GraphOps>("ops").emplace_back(
result->Get<GraphOps>(kGraphOps).emplace_back(
new ComputationOpHandle(result->CreateOpNode(node->Op()), s, p));
CreateOpHandleIOs(result, node, scope_idx);
}
......@@ -582,25 +581,25 @@ VarHandle *MultiDevSSAGraphBuilder::CreateReduceOp(ir::Graph *result,
const std::string &og,
int dst_dev_id) const {
#ifdef PADDLE_WITH_CUDA
result->Get<GraphOps>("ops").emplace_back(new ReduceOpHandle(
result->Get<GraphOps>(kGraphOps).emplace_back(new ReduceOpHandle(
result->CreateEmptyNode("reduce", ir::Node::Type::kOperation),
local_scopes_, places_, nccl_ctxs_));
#else
result->Get<GraphOps>("ops").emplace_back(new ReduceOpHandle(
result->Get<GraphOps>(kGraphOps).emplace_back(new ReduceOpHandle(
result->CreateEmptyNode("reduce", ir::Node::Type::kOperation),
local_scopes_, places_));
#endif
auto *op_handle = result->Get<GraphOps>("ops").back().get();
auto *op_handle = result->Get<GraphOps>(kGraphOps).back().get();
for (size_t i = 0; i < places_.size(); ++i) {
auto &p = places_[i];
SetCommunicationContext(op_handle, p);
auto &vars = result->Get<GraphVars>("vars")[i][og];
auto &vars = result->Get<GraphVars>(kGraphVars)[i][og];
PADDLE_ENFORCE(!vars.empty());
auto &prev_grad = vars.back();
op_handle->AddInput(prev_grad.get());
}
auto &vars = result->Get<GraphVars>("vars")[dst_dev_id][og];
auto &vars = result->Get<GraphVars>(kGraphVars)[dst_dev_id][og];
auto var =
new VarHandle(result->CreateEmptyNode(og, ir::Node::Type::kVariable),
vars.size(), dst_dev_id, og, places_[dst_dev_id]);
......@@ -613,11 +612,11 @@ VarHandle *MultiDevSSAGraphBuilder::CreateReduceOp(ir::Graph *result,
// on it.
void MultiDevSSAGraphBuilder::ConnectOp(ir::Graph *result, OpHandleBase *op,
const std::string &prev_op_name) const {
for (auto &prev_op : result->Get<GraphOps>("ops")) {
for (auto &prev_op : result->Get<GraphOps>(kGraphOps)) {
if (prev_op->Name() == prev_op_name) {
auto *dep_var = new DummyVarHandle(result->CreateControlDepVar());
prev_op->AddOutput(dep_var);
result->Get<GraphDepVars>("dep_vars").emplace(dep_var);
result->Get<GraphDepVars>(kGraphDepVars).emplace(dep_var);
op->AddInput(dep_var);
}
}
......@@ -638,20 +637,23 @@ void MultiDevSSAGraphBuilder::CreateDistTrainOp(ir::Graph *result,
if (node->Op()->Type() == "split_byref" ||
node->Op()->Type() == "split_selected_rows") {
// TODO(paddle-dev): getting the first var is not safe.
op_dev_id = GetVarDeviceID(input_var_names[0]);
op_dev_id = GetVarDeviceID(*result, input_var_names[0]);
if (strategy_.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce) {
op_dev_id = GetAppropriateDeviceID(input_var_names);
for (auto &varname : input_var_names) {
var_name_on_devices_.emplace(varname, op_dev_id);
result->Get<ShardedVarDevice>(kShardedVarDevice)
.emplace(varname, op_dev_id);
}
}
for (auto &varname : output_var_names) {
var_name_on_devices_.emplace(varname, op_dev_id);
result->Get<ShardedVarDevice>(kShardedVarDevice)
.emplace(varname, op_dev_id);
}
} else if (node->Op()->Type() == "concat") {
op_dev_id = GetVarDeviceID(input_var_names[0]);
op_dev_id = GetVarDeviceID(*result, input_var_names[0]);
for (auto &varname : output_var_names) {
var_name_on_devices_.emplace(varname, op_dev_id);
result->Get<ShardedVarDevice>(kShardedVarDevice)
.emplace(varname, op_dev_id);
}
} else {
PADDLE_ENFORCE(
......@@ -665,7 +667,7 @@ void MultiDevSSAGraphBuilder::CreateDistTrainOp(ir::Graph *result,
CreateComputationalOp(result, node, op_dev_id);
if (node->Op()->Type() == "concat") {
ConnectOp(result, result->Get<GraphOps>("ops").back().get(),
ConnectOp(result, result->Get<GraphOps>(kGraphOps).back().get(),
"fetch_barrier");
}
}
......@@ -676,7 +678,7 @@ void MultiDevSSAGraphBuilder::CreateRPCOp(ir::Graph *result,
int op_dev_id = -1;
if (node->Op()->Type() == "send") {
// TODO(paddle-dev): getting the first var is not safe.
op_dev_id = GetVarDeviceID(node->inputs[0]->Name());
op_dev_id = GetVarDeviceID(*result, node->inputs[0]->Name());
PADDLE_ENFORCE(!ir::IsControlDepVar(*node->inputs[0]),
"This hack no longer holds, please fix.");
// the variable name which contains .block means it was splited by
......@@ -691,7 +693,8 @@ void MultiDevSSAGraphBuilder::CreateRPCOp(ir::Graph *result,
}
op_dev_id = GetAppropriateDeviceID(input_var_names);
for (auto &varname : input_var_names) {
var_name_on_devices_.emplace(varname, op_dev_id);
result->Get<ShardedVarDevice>(kShardedVarDevice)
.emplace(varname, op_dev_id);
}
}
} else if (node->Op()->Type() == "recv") {
......@@ -701,7 +704,8 @@ void MultiDevSSAGraphBuilder::CreateRPCOp(ir::Graph *result,
}
op_dev_id = GetAppropriateDeviceID(output_var_names);
for (auto &varname : output_var_names) {
var_name_on_devices_.emplace(varname, op_dev_id);
result->Get<ShardedVarDevice>(kShardedVarDevice)
.emplace(varname, op_dev_id);
}
} else {
// send_barrier and fetch_barrier op can be scheduled on device 0
......@@ -711,17 +715,18 @@ void MultiDevSSAGraphBuilder::CreateRPCOp(ir::Graph *result,
PADDLE_ENFORCE(op_dev_id != -1, "can not find the right place for rpc op: %s",
node->Op()->Type());
result->Get<GraphOps>("ops").emplace_back(new RPCOpHandle(
result->Get<GraphOps>(kGraphOps).emplace_back(new RPCOpHandle(
result->CreateOpNode(node->Op()), *node->Op(), local_scopes_[op_dev_id],
node->Op()->Type(), places_[op_dev_id]));
// TODO(panyx0718): This might not be needed anymore.
if (node->Op()->Type() == "send_barrier") {
ConnectOp(result, result->Get<GraphOps>("ops").back().get(), "send");
ConnectOp(result, result->Get<GraphOps>(kGraphOps).back().get(), "send");
} else if (node->Op()->Type() == "recv") {
ConnectOp(result, result->Get<GraphOps>("ops").back().get(),
ConnectOp(result, result->Get<GraphOps>(kGraphOps).back().get(),
"send_barrier");
} else if (node->Op()->Type() == "fetch_barrier") {
ConnectOp(result, result->Get<GraphOps>("ops").back().get(), "recv");
ConnectOp(result, result->Get<GraphOps>(kGraphOps).back().get(), "recv");
} else if (node->Op()->Type() == "send") {
// do nothing
} else {
......@@ -743,3 +748,11 @@ bool MultiDevSSAGraphBuilder::IsScaleLossOp(ir::Node *node) const {
} // namespace details
} // namespace framework
} // namespace paddle
REGISTER_PASS(multi_device_pass,
paddle::framework::details::MultiDevSSAGraphBuilder)
.RequirePassAttr(paddle::framework::details::kLossVarName)
.RequirePassAttr(paddle::framework::details::kPlaces)
.RequirePassAttr(paddle::framework::details::kParams)
.RequirePassAttr(paddle::framework::details::kLocalScopes)
.RequirePassAttr(paddle::framework::details::kStrategy);
paddle/fluid/framework/details/multi_devices_graph_builder.h
浏览文件 @ bc1b7b96
......@@ -31,39 +31,27 @@ class Scope;
namespace details {
class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
public:
#ifdef PADDLE_WITH_CUDA
MultiDevSSAGraphBuilder(const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::unordered_set<std::string> &params,
const std::vector<Scope *> &local_scopes,
platform::NCCLContextMap *nccl_ctxs,
const BuildStrategy &strategy);
#else
MultiDevSSAGraphBuilder(const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::unordered_set<std::string> &params,
const std::vector<Scope *> &local_scopes,
const BuildStrategy &strategy);
#endif
std::unique_ptr<ir::Graph> Apply(
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
int GetVarDeviceID(const std::string &varname) const override;
private:
void CreateOpHandleIOs(ir::Graph *result, ir::Node *node,
size_t device_id) const;
void Init() const;
private:
std::string loss_var_name_;
const std::vector<platform::Place> &places_;
const std::vector<Scope *> &local_scopes_;
std::unordered_set<std::string> grad_names_;
mutable std::string loss_var_name_;
mutable std::vector<platform::Place> places_;
mutable std::vector<Scope *> local_scopes_;
mutable std::unordered_set<std::string> grad_names_;
#ifdef PADDLE_WITH_CUDA
platform::NCCLContextMap *nccl_ctxs_;
mutable platform::NCCLContextMap *nccl_ctxs_;
#endif
int GetVarDeviceID(const ir::Graph &graph, const std::string &varname) const;
bool IsScaleLossOp(ir::Node *node) const;
void CreateRPCOp(ir::Graph *result, ir::Node *node) const;
......@@ -97,7 +85,7 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
const std::string &og,
std::unordered_set<std::string> *og_has_been_broadcast) const;
int GetOpDeviceID(ir::Node *node) const;
int GetOpDeviceID(const ir::Graph &graph, ir::Node *node) const;
void InsertAllReduceOp(ir::Graph *result, const std::string &og) const;
......@@ -113,9 +101,8 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
const std::vector<std::string> &var_names) const;
private:
BuildStrategy strategy_;
mutable BuildStrategy strategy_;
mutable std::unordered_map<std::string, VarDesc *> all_vars_;
mutable std::unordered_map<std::string, int> var_name_on_devices_;
mutable std::vector<int64_t> balance_vars_;
void SetCommunicationContext(OpHandleBase *op_handle,
......
paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h
浏览文件 @ bc1b7b96
......@@ -40,6 +40,11 @@ class ScopeBufferedSSAGraphExecutor : public SSAGraphExecutor {
ExecutionStrategy strategy, std::vector<Scope*> local_scopes,
std::vector<VariableInfo> var_infos, std::vector<platform::Place> places,
std::unique_ptr<SSAGraphExecutor>&& underlying_executor);
const ir::Graph& Graph() const override {
return underlying_executor_->Graph();
}
FeedFetchList Run(const std::vector<std::string>& fetch_tensors) override;
private:
......
paddle/fluid/framework/details/ssa_graph_builder.cc
浏览文件 @ bc1b7b96
......@@ -18,7 +18,7 @@ namespace paddle {
namespace framework {
namespace details {
void SSAGraphBuilder::PolishGraphToSupportDataHazards(ir::Graph *graph) {
for (auto &var_map : graph->Get<GraphVars>("vars")) {
for (auto &var_map : graph->Get<GraphVars>(kGraphVars)) {
for (auto &name_pair : var_map) {
if (name_pair.second.size() <= 1) {
continue;
......@@ -50,7 +50,7 @@ void SSAGraphBuilder::PolishGraphToSupportDataHazards(ir::Graph *graph) {
auto *dep_var = new DummyVarHandle(graph->CreateControlDepVar());
read_op->AddOutput(dep_var);
write_op->AddInput(dep_var);
graph->Get<GraphDepVars>("dep_vars").emplace(dep_var);
graph->Get<GraphDepVars>(kGraphDepVars).emplace(dep_var);
}
}
}
......@@ -60,7 +60,7 @@ void SSAGraphBuilder::PolishGraphToSupportDataHazards(ir::Graph *graph) {
VarHandle *SSAGraphBuilder::CreateOrGetLatestVarHandle(
ir::Graph *graph, ir::Node *node, const platform::Place &place,
size_t place_offset) {
auto &var_holders = graph->Get<GraphVars>("vars")[place_offset];
auto &var_holders = graph->Get<GraphVars>(kGraphVars)[place_offset];
auto &var_holder = var_holders[node->Name()];
VarHandle *var = nullptr;
if (var_holder.empty()) {
......@@ -83,7 +83,8 @@ void SSAGraphBuilder::CreateOpOutput(ir::Graph *graph, OpHandleBase *op_handle,
ir::Node *new_node,
const platform::Place &place,
size_t place_offset) {
auto &vars = graph->Get<GraphVars>("vars")[place_offset][new_node->Name()];
auto &vars =
graph->Get<GraphVars>(kGraphVars)[place_offset][new_node->Name()];
size_t version = vars.size();
auto var =
new VarHandle(new_node, version, place_offset, new_node->Name(), place);
......@@ -92,12 +93,12 @@ void SSAGraphBuilder::CreateOpOutput(ir::Graph *graph, OpHandleBase *op_handle,
}
void SSAGraphBuilder::AddOutputToLeafOps(ir::Graph *graph) {
for (auto &op : graph->Get<GraphOps>("ops")) {
for (auto &op : graph->Get<GraphOps>(kGraphOps)) {
if (!op->Outputs().empty()) {
continue;
}
auto *dummy_leaf = new DummyVarHandle(graph->CreateControlDepVar());
graph->Get<GraphDepVars>("dep_vars").emplace(dummy_leaf);
graph->Get<GraphDepVars>(kGraphDepVars).emplace(dummy_leaf);
op->AddOutput(dummy_leaf);
}
}
......
paddle/fluid/framework/details/ssa_graph_builder.h
浏览文件 @ bc1b7b96
......@@ -39,21 +39,25 @@ namespace details {
typedef std::vector<
std::unordered_map<std::string, std::vector<std::unique_ptr<VarHandle>>>>
GraphVars;
const char kGraphVars[] = "vars";
// aux variables to represent dependency. Useful to resolve data hazard.
typedef std::unordered_set<std::unique_ptr<VarHandleBase>> GraphDepVars;
const char kGraphDepVars[] = "dep_vars";
// all operators. NOTE that even we use a vector here, the operators is
// unordered.
typedef std::vector<std::unique_ptr<OpHandleBase>> GraphOps;
const char kGraphOps[] = "ops";
typedef std::unordered_map<std::string, int> ShardedVarDevice;
const char kShardedVarDevice[] = "sharded_var_device";
class SSAGraphBuilder : public ir::Pass {
public:
SSAGraphBuilder() {}
virtual ~SSAGraphBuilder() {}
virtual int GetVarDeviceID(const std::string &var_name) const = 0;
DISABLE_COPY_AND_ASSIGN(SSAGraphBuilder);
protected:
......
paddle/fluid/framework/details/ssa_graph_builder_factory.h 已删除 100644 → 0
浏览文件 @ 3e4083ed
// 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 <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/details/build_strategy.h"
#include "paddle/fluid/framework/details/ssa_graph_builder.h"
#include "paddle/fluid/platform/place.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/nccl_helper.h"
#endif
namespace paddle {
namespace framework {
class Scope;
namespace details {
class SSAGraphBuilderFactory {
public:
SSAGraphBuilderFactory(const std::vector<platform::Place>& places,
const std::string& loss_var_name,
const std::unordered_set<std::string>& param_names,
const std::vector<Scope*>& local_scopes,
const BuildStrategy& strategy)
: places_(places),
loss_var_name_(loss_var_name),
param_names_(param_names),
local_scopes_(local_scopes),
strategy_(strategy) {
#ifdef PADDLE_WITH_CUDA
nccl_ctxs_ = nullptr;
#endif
}
#ifdef PADDLE_WITH_CUDA
void SetNCCLContextMap(platform::NCCLContextMap* nccl_ctxs) {
nccl_ctxs_ = nccl_ctxs;
}
#endif
std::unique_ptr<SSAGraphBuilder> Create();
private:
std::vector<platform::Place> places_;
std::string loss_var_name_;
std::unordered_set<std::string> param_names_;
std::vector<Scope*> local_scopes_;
BuildStrategy strategy_;
#ifdef PADDLE_WITH_CUDA
platform::NCCLContextMap* nccl_ctxs_;
#endif
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/ssa_graph_checker.cc
浏览文件 @ bc1b7b96
......@@ -33,7 +33,7 @@ bool SSAGraghBuilderWithChecker::IsValidGraph(const ir::Graph *graph) const {
}
};
for (auto &var_map : graph->Get<GraphVars>("vars")) {
for (auto &var_map : graph->Get<GraphVars>(kGraphVars)) {
for (auto &name_pair : var_map) {
for (auto &version_pair : name_pair.second) {
insert_pending_var(version_pair.get());
......@@ -41,11 +41,11 @@ bool SSAGraghBuilderWithChecker::IsValidGraph(const ir::Graph *graph) const {
}
}
for (auto &var : graph->Get<GraphDepVars>("dep_vars")) {
for (auto &var : graph->Get<GraphDepVars>(kGraphDepVars)) {
insert_pending_var(var.get());
}
for (auto &op : graph->Get<GraphOps>("ops")) {
for (auto &op : graph->Get<GraphOps>(kGraphOps)) {
if (op->Inputs().empty()) {
ready_ops.insert(op.get());
} else {
......@@ -85,3 +85,10 @@ bool SSAGraghBuilderWithChecker::IsValidGraph(const ir::Graph *graph) const {
} // namespace details
} // namespace framework
} // namespace paddle
REGISTER_PASS(multi_device_check_pass,
paddle::framework::details::SSAGraghBuilderWithChecker)
.RequireGraphAttr(paddle::framework::details::kGraphVars)
.RequireGraphAttr(paddle::framework::details::kGraphDepVars)
.RequireGraphAttr(paddle::framework::details::kGraphOps)
.RequireGraphAttr(paddle::framework::details::kShardedVarDevice);
paddle/fluid/framework/details/ssa_graph_checker.h
浏览文件 @ bc1b7b96
......@@ -23,26 +23,14 @@ namespace framework {
namespace details {
class SSAGraghBuilderWithChecker : public SSAGraphBuilder {
public:
explicit SSAGraghBuilderWithChecker(
std::unique_ptr<SSAGraphBuilder>&& builder)
: builder_(std::move(builder)) {}
std::unique_ptr<ir::Graph> Apply(
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override {
auto new_graph = builder_->Apply(std::move(graph));
PADDLE_ENFORCE(IsValidGraph(new_graph.get()));
return new_graph;
}
int GetVarDeviceID(const std::string& var_name) const override {
return builder_->GetVarDeviceID(var_name);
PADDLE_ENFORCE(IsValidGraph(graph.get()));
return graph;
}
bool IsValidGraph(const ir::Graph* graph) const;
private:
std::unique_ptr<SSAGraphBuilder> builder_;
};
} // namespace details
......
paddle/fluid/framework/details/ssa_graph_executor.h
浏览文件 @ bc1b7b96
......@@ -32,7 +32,9 @@ class SSAGraphExecutor {
virtual ~SSAGraphExecutor();
virtual FeedFetchList Run(const std::vector<std::string> &fetch_tensors) = 0;
virtual const ir::Graph& Graph() const = 0;
virtual FeedFetchList Run(const std::vector<std::string>& fetch_tensors) = 0;
};
} // namespace details
} // namespace framework
......
paddle/fluid/framework/details/ssa_graph_printer.cc
浏览文件 @ bc1b7b96
......@@ -22,7 +22,7 @@ namespace details {
template <typename Callback>
static inline void IterAllVar(const ir::Graph &graph, Callback callback) {
for (auto &each : graph.Get<GraphVars>("vars")) {
for (auto &each : graph.Get<GraphVars>(kGraphVars)) {
for (auto &pair1 : each) {
for (auto &pair2 : pair1.second) {
callback(*pair2);
......@@ -30,7 +30,7 @@ static inline void IterAllVar(const ir::Graph &graph, Callback callback) {
}
}
for (auto &var : graph.Get<GraphDepVars>("dep_vars")) {
for (auto &var : graph.Get<GraphDepVars>(kGraphDepVars)) {
callback(*var);
}
}
......@@ -61,7 +61,7 @@ void GraphvizSSAGraphPrinter::Print(const ir::Graph &graph,
});
size_t op_id = 0;
for (auto &op : graph.Get<GraphOps>("ops")) {
for (auto &op : graph.Get<GraphOps>(kGraphOps)) {
std::string op_name = "op_" + std::to_string(op_id++);
sout << op_name << " [label=\"" << op->Name() << "\", shape=rect]"
<< std::endl;
......@@ -81,3 +81,6 @@ void GraphvizSSAGraphPrinter::Print(const ir::Graph &graph,
} // namespace details
} // namespace framework
} // namespace paddle
REGISTER_PASS(multi_device_print_pass,
paddle::framework::details::SSAGraghBuilderWithPrinter);
paddle/fluid/framework/details/ssa_graph_printer.h
浏览文件 @ bc1b7b96
......@@ -14,7 +14,9 @@
#pragma once
#include <fstream>
#include <iosfwd>
#include <ostream>
#include <string>
#include "paddle/fluid/framework/details/ssa_graph_builder.h"
......@@ -34,38 +36,15 @@ class GraphvizSSAGraphPrinter : public SSAGraphPrinter {
};
class SSAGraghBuilderWithPrinter : public SSAGraphBuilder {
public:
SSAGraghBuilderWithPrinter(std::ostream& sout,
std::unique_ptr<SSAGraphPrinter>&& printer,
std::unique_ptr<SSAGraphBuilder>&& builder)
: printer_(std::move(printer)),
builder_(std::move(builder)),
stream_ref_(sout) {}
SSAGraghBuilderWithPrinter(std::unique_ptr<std::ostream>&& sout,
std::unique_ptr<SSAGraphPrinter>&& printer,
std::unique_ptr<SSAGraphBuilder>&& builder)
: printer_(std::move(printer)),
builder_(std::move(builder)),
stream_ptr_(std::move(sout)),
stream_ref_(*stream_ptr_) {}
std::unique_ptr<ir::Graph> Apply(
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override {
auto new_graph = builder_->Apply(std::move(graph));
printer_->Print(*new_graph, stream_ref_);
return new_graph;
std::unique_ptr<std::ostream> fout(
new std::ofstream(Get<const std::string>("debug_graphviz_path")));
PADDLE_ENFORCE(fout->good());
Get<GraphvizSSAGraphPrinter>("graph_printer").Print(*graph, *fout);
return graph;
}
int GetVarDeviceID(const std::string& var_name) const override {
return builder_->GetVarDeviceID(var_name);
}
private:
std::unique_ptr<SSAGraphPrinter> printer_;
std::unique_ptr<SSAGraphBuilder> builder_;
std::unique_ptr<std::ostream> stream_ptr_;
std::ostream& stream_ref_;
};
} // namespace details
......
paddle/fluid/framework/details/threaded_ssa_graph_executor.cc
浏览文件 @ bc1b7b96
......@@ -45,18 +45,18 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
std::unordered_set<OpHandleBase *> delayed_ops;
// Transform SSAGraph to pending_ops & pending_vars
for (auto &var_map : graph_->Get<details::GraphVars>("vars")) {
for (auto &var_map : graph_->Get<details::GraphVars>(details::kGraphVars)) {
for (auto &name_pair : var_map) {
for (auto &version_pair : name_pair.second) {
InsertPendingVar(&pending_vars, &ready_vars, version_pair.get());
}
}
}
for (auto &var : graph_->Get<details::GraphDepVars>("dep_vars")) {
for (auto &var : graph_->Get<details::GraphDepVars>(details::kGraphDepVars)) {
InsertPendingVar(&pending_vars, &ready_vars, var.get());
}
for (auto &op : graph_->Get<details::GraphOps>("ops")) {
for (auto &op : graph_->Get<details::GraphOps>(details::kGraphOps)) {
if (op->Inputs().empty()) { // Special case, Op has no input.
ready_ops.insert(op.get());
} else {
......@@ -150,7 +150,7 @@ void ThreadedSSAGraphExecutor::InsertFetchOps(
std::unordered_map<std::string, std::vector<VarHandleBase *>> fetched_vars;
for (auto &fetch_var_name : fetch_tensors) {
for (auto &var_map : graph_->Get<details::GraphVars>("vars")) {
for (auto &var_map : graph_->Get<details::GraphVars>(details::kGraphVars)) {
auto it = var_map.find(fetch_var_name);
if (it != var_map.end()) {
fetched_vars[fetch_var_name].push_back(it->second.rbegin()->get());
......
paddle/fluid/framework/details/threaded_ssa_graph_executor.h
浏览文件 @ bc1b7b96
......@@ -43,6 +43,7 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
const std::vector<platform::Place> &places,
std::unique_ptr<ir::Graph> &&graph);
const ir::Graph &Graph() const override { return *graph_; }
// Run a SSAGraph by a thread pool
// Use topological sort algorithm
FeedFetchList Run(const std::vector<std::string> &fetch_tensors) override;
......
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ bc1b7b96
cc_library(node SRCS node.cc DEPS proto_desc)
cc_library(graph SRCS graph.cc DEPS node)
cc_library(graph_helper SRCS graph_helper.cc DEPS graph)
cc_library(pass SRCS pass.cc DEPS graph node)
cc_test(graph_test SRCS graph_test.cc DEPS graph op_registry)
cc_test(graph_helper_test SRCS graph_helper_test.cc DEPS graph_helper op_registry)
cc_library(pass SRCS pass.cc DEPS graph node graph_helper)
cc_library(graph_viz_pass SRCS graph_viz_pass.cc DEPS graph pass graph_helper)
cc_test(pass_test SRCS pass_test.cc DEPS graph pass graph_helper)
cc_test(graph_test SRCS graph_test.cc DEPS graph graph_helper op_registry)
cc_test(graph_helper_test SRCS graph_helper_test.cc DEPS graph graph_helper op_registry)
paddle/fluid/framework/ir/graph.cc
浏览文件 @ bc1b7b96
......@@ -24,6 +24,68 @@ namespace paddle {
namespace framework {
namespace ir {
std::vector<std::string> FindDistTrainSendVars(
const std::vector<ir::Node *> &nodes) {
std::vector<std::string> send_vars;
// since parameters are all in block 0,
// it's enough to only scan send ops in block 0
for (auto &node : nodes) {
auto op_vars = node->Op()->InputArgumentNames();
send_vars.reserve(send_vars.size() +
std::distance(op_vars.begin(), op_vars.end()));
send_vars.insert(send_vars.end(), op_vars.begin(), op_vars.end());
}
return send_vars;
}
std::vector<std::string> FindDistTrainRecvVars(
const std::vector<ir::Node *> &nodes) {
std::vector<std::string> recv_vars;
for (auto &node : nodes) {
auto op_vars = node->Op()->OutputArgumentNames();
recv_vars.reserve(recv_vars.size() +
std::distance(op_vars.begin(), op_vars.end()));
recv_vars.insert(recv_vars.end(), op_vars.begin(), op_vars.end());
}
return recv_vars;
}
bool IsDistTrainOp(ir::Node *node, const std::vector<std::string> &send_vars,
const std::vector<std::string> &recv_vars) {
if (send_vars.size() == 0 || recv_vars.size() == 0) {
return false;
}
/**
* Check any of opvars contains `.block` and in sendvars
*/
auto checker = [](const std::vector<std::string> &opvars,
const std::vector<std::string> &rpc_vars) -> bool {
for (auto &var : opvars) {
// a variable name with the suffix `.block` means it's a splited
// variable by (DistributeTranspiler)
// [python/paddle/fluid/transpiler/distribute_transpiler.py]
if (var.find(".block") != std::string::npos &&
std::find(rpc_vars.begin(), rpc_vars.end(), var) != rpc_vars.end()) {
return true;
}
}
return false;
};
std::vector<std::string> input_var_names;
std::vector<std::string> output_var_names;
for (ir::Node *input : node->inputs) {
input_var_names.push_back(input->Name());
}
for (ir::Node *output : node->outputs) {
output_var_names.push_back(output->Name());
}
return checker(output_var_names, send_vars) ||
checker(input_var_names, recv_vars);
}
Graph::Graph(const ProgramDesc &program) : program_(program) {
VLOG(3) << "block in program:" << program_.Size();
std::unordered_map<std::string, VarDesc *> all_vars;
......@@ -61,6 +123,64 @@ Graph::Graph(const ProgramDesc &program) : program_(program) {
var->inputs.push_back(node);
}
}
std::vector<ir::Node *> send_ops;
ir::Node *send_bar = nullptr;
std::vector<ir::Node *> recv_ops;
ir::Node *fetch_bar = nullptr;
for (ir::Node *node : Nodes()) {
if (node->Name() == "send") {
send_ops.push_back(node);
} else if (node->Name() == "send_barrier") {
PADDLE_ENFORCE(!send_bar, "only has one send barrier");
send_bar = node;
} else if (node->Name() == "recv") {
recv_ops.push_back(node);
} else if (node->Name() == "fetch_barrier") {
PADDLE_ENFORCE(!fetch_bar, "only has one fetch barrier");
fetch_bar = node;
}
}
if (send_bar) {
for (ir::Node *send : send_ops) {
ir::Node *dep_var = CreateControlDepVar();
send->outputs.push_back(dep_var);
dep_var->inputs.push_back(send);
send_bar->inputs.push_back(dep_var);
dep_var->outputs.push_back(send_bar);
}
for (ir::Node *recv : recv_ops) {
ir::Node *dep_var = CreateControlDepVar();
recv->inputs.push_back(dep_var);
dep_var->outputs.push_back(recv);
send_bar->outputs.push_back(dep_var);
dep_var->inputs.push_back(send_bar);
}
}
if (fetch_bar) {
for (ir::Node *recv : recv_ops) {
ir::Node *dep_var = CreateControlDepVar();
recv->outputs.push_back(dep_var);
dep_var->inputs.push_back(recv);
fetch_bar->inputs.push_back(dep_var);
dep_var->outputs.push_back(fetch_bar);
}
}
std::vector<std::string> send_vars = FindDistTrainSendVars(send_ops);
std::vector<std::string> recv_vars = FindDistTrainRecvVars(recv_ops);
for (ir::Node *node : Nodes()) {
if (IsDistTrainOp(node, send_vars, recv_vars)) {
if (fetch_bar && node->Name() == "concat") {
ir::Node *dep_var = CreateControlDepVar();
fetch_bar->outputs.push_back(dep_var);
dep_var->inputs.push_back(fetch_bar);
node->inputs.push_back(dep_var);
dep_var->outputs.push_back(node);
}
}
}
/**
* We only handle write after read(WAR), since it should not have a write
* after write in program. If there are write after write operators, we need
......
paddle/fluid/framework/ir/graph.h
浏览文件 @ bc1b7b96
......@@ -40,14 +40,21 @@ class Graph {
attr_dels_.clear();
}
bool Has(const std::string &attr_name) const {
return attrs_.find(attr_name) != attrs_.end();
}
template <typename AttrType>
AttrType &Get(const std::string &attr_name) const {
PADDLE_ENFORCE(Has(attr_name), "%s attr not registered for graph.",
attr_name);
return *boost::any_cast<AttrType *>(attrs_.at(attr_name));
}
template <typename AttrType>
void Set(const std::string &attr_name, AttrType *attr) {
PADDLE_ENFORCE(attrs_.count(attr_name) == 0);
PADDLE_ENFORCE(attrs_.count(attr_name) == 0, "%s already set in the graph",
attr_name);
attrs_[attr_name] = attr;
attr_dels_[attr_name] = [attr, attr_name]() {
VLOG(3) << "deleting " << attr_name;
......
paddle/fluid/framework/ir/graph_viz_pass.cc 0 → 100644
浏览文件 @ bc1b7b96
/* 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 <algorithm>
#include <unordered_set>
#include "paddle/fluid/framework/ir/graph_viz_pass.h"
namespace paddle {
namespace framework {
namespace ir {
static const char kGraphVizPath[] = "graph_viz_path";
std::unique_ptr<ir::Graph> GraphVizPass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
const std::string graph_viz_path = Get<std::string>(kGraphVizPath);
std::unique_ptr<std::ostream> fout(new std::ofstream(graph_viz_path));
PADDLE_ENFORCE(fout->good());
std::ostream& sout = *fout;
size_t var_id = 0;
std::unordered_map<const ir::Node*, size_t> vars;
sout << "digraph G {\n";
for (const ir::Node* n : graph->Nodes()) {
if (n->NodeType() != ir::Node::Type::kVariable) continue;
size_t cur_var_id = var_id++;
vars[n] = cur_var_id;
sout << "var_" << cur_var_id << " [label=\"" << n->Name() << "\"]"
<< std::endl;
}
size_t op_id = 0;
for (const ir::Node* n : graph->Nodes()) {
if (n->NodeType() != ir::Node::Type::kOperation) continue;
std::string op_name = "op_" + std::to_string(op_id++);
sout << op_name << " [label=\"" << n->Name() << "\", shape=rect]"
<< std::endl;
for (auto in : n->inputs) {
std::string var_name = "var_" + std::to_string(vars[in]);
sout << var_name << " -> " << op_name << std::endl;
}
for (auto out : n->outputs) {
std::string var_name = "var_" + std::to_string(vars[out]);
sout << op_name << " -> " << var_name << std::endl;
}
}
sout << "}\n";
return graph;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(graph_viz_pass, paddle::framework::ir::GraphVizPass)
.RequirePassAttr(paddle::framework::ir::kGraphVizPath);
paddle/fluid/framework/ir/graph_viz_pass.h 0 → 100644
浏览文件 @ bc1b7b96
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <fstream>
#include <map>
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace ir {
class GraphVizPass : public Pass {
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/pass.cc
浏览文件 @ bc1b7b96
......@@ -13,7 +13,34 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
namespace paddle {
namespace framework {} // namespace framework
namespace framework {
namespace ir {
std::unique_ptr<Graph> Pass::Apply(std::unique_ptr<Graph> graph) const {
PADDLE_ENFORCE(!applied_, "Pass can only Apply() once.");
PADDLE_ENFORCE(graph.get(), "graph passed to Pass::Apply() cannot be empty.");
for (const std::string& attr : required_pass_attrs_) {
PADDLE_ENFORCE(attrs_.find(attr) != attrs_.end(),
"Required pass atrribute %s not set.", attr);
}
for (const std::string& attr : required_graph_attrs_) {
PADDLE_ENFORCE(graph->Has(attr), "Required graph atrribute %s not set.",
attr);
}
auto applied_graph = ApplyImpl(std::move(graph));
// TODO(panyx0718): Add more verifications.
PADDLE_ENFORCE(!HasCircle(*applied_graph),
"Illegal Pass. Generated graph shouldn't has cycle.");
applied_ = true;
return applied_graph;
}
PassRegistry& PassRegistry::Instance() {
static PassRegistry g_pass_info_map;
return g_pass_info_map;
}
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/pass.h
浏览文件 @ bc1b7b96
......@@ -14,21 +14,187 @@ limitations under the License. */
#pragma once
#include <functional>
#include <map>
#include <string>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/node.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/platform/variant.h"
namespace paddle {
namespace framework {
namespace ir {
template <typename PassType>
struct PassRegistrar;
class Pass {
public:
Pass() = default;
virtual ~Pass() {}
virtual ~Pass() {
for (auto &attr : attrs_) {
if (attr_dels_.find(attr.first) != attr_dels_.end()) {
attr_dels_[attr.first]();
}
}
attrs_.clear();
attr_dels_.clear();
}
std::unique_ptr<Graph> Apply(std::unique_ptr<Graph> graph) const;
// Get a reference to the attributed previously set.
template <typename AttrType>
AttrType &Get(const std::string &attr_name) const {
PADDLE_ENFORCE(attrs_.find(attr_name) != attrs_.end(),
"%s attr not registered for pass.", attr_name);
return *boost::any_cast<AttrType *>(attrs_.at(attr_name));
}
// Set a pointer to the attribute. Pass takes ownership of the attribute.
template <typename AttrType>
void Set(const std::string &attr_name, AttrType *attr) {
PADDLE_ENFORCE(attrs_.count(attr_name) == 0, "%s already set in the pass",
attr_name);
attrs_[attr_name] = attr;
attr_dels_[attr_name] = [attr, attr_name]() {
VLOG(3) << "deleting " << attr_name;
delete attr;
};
}
// Set a pointer to the attribute. Pass doesn't take ownership. Caller
// should delete the attribute.
template <typename AttrType>
void SetNotOwned(const std::string &attr_name, AttrType *attr) {
PADDLE_ENFORCE(attrs_.count(attr_name) == 0);
attrs_[attr_name] = attr;
}
protected:
virtual std::unique_ptr<Graph> ApplyImpl(
std::unique_ptr<Graph> graph) const = 0;
private:
template <typename PassType>
friend struct PassRegistrar;
void RegisterRequiredPassAttrs(const std::unordered_set<std::string> &attrs) {
required_pass_attrs_.insert(attrs.begin(), attrs.end());
}
void RegisterRequiredGraphAttrs(
const std::unordered_set<std::string> &attrs) {
required_graph_attrs_.insert(attrs.begin(), attrs.end());
}
mutable bool applied_{false};
std::unordered_set<std::string> required_pass_attrs_;
std::unordered_set<std::string> required_graph_attrs_;
std::map<std::string, boost::any> attrs_;
std::map<std::string, std::function<void(void)>> attr_dels_;
};
using PassCreator = std::function<std::unique_ptr<Pass>()>;
class Registrar {
public:
// In our design, various kinds of passes,
// have their corresponding registry and registrar. The action of
// registration is in the constructor of a global registrar variable, which
// are not used in the code that calls package framework, and would
// be removed from the generated binary file by the linker. To avoid such
// removal, we add Touch to all registrar classes and make USE_PASS macros to
// call this method. So, as long as the callee code calls USE_PASS, the global
// registrar variable won't be removed by the linker.
void Touch() {}
};
virtual std::unique_ptr<Graph> Apply(std::unique_ptr<Graph> graph) const = 0;
class PassRegistry {
public:
static PassRegistry &Instance();
bool Has(const std::string &pass_type) const {
return map_.find(pass_type) != map_.end();
}
void Insert(const std::string &pass_type, const PassCreator &pass_creator) {
PADDLE_ENFORCE(!Has(pass_type), "Pass %s has been registered", pass_type);
map_.insert({pass_type, pass_creator});
}
std::unique_ptr<Pass> Get(const std::string &pass_type) const {
PADDLE_ENFORCE(Has(pass_type), "Pass %s has not been registered",
pass_type);
return map_.at(pass_type)();
}
private:
PassRegistry() = default;
std::unordered_map<std::string, PassCreator> map_;
DISABLE_COPY_AND_ASSIGN(PassRegistry);
};
template <typename PassType>
struct PassRegistrar : public Registrar {
explicit PassRegistrar(const char *pass_type) {
PADDLE_ENFORCE(!PassRegistry::Instance().Has(pass_type),
"'%s' is registered more than once.", pass_type);
PassRegistry::Instance().Insert(
pass_type, [this]() -> std::unique_ptr<Pass> {
std::unique_ptr<Pass> pass(new PassType());
pass->RegisterRequiredPassAttrs(this->required_pass_attrs_);
pass->RegisterRequiredGraphAttrs(this->required_graph_attrs_);
return pass;
});
}
PassRegistrar<PassType> &RequirePassAttr(const std::string &attr) {
required_pass_attrs_.insert(attr);
return *this;
}
PassRegistrar<PassType> &RequireGraphAttr(const std::string &attr) {
required_graph_attrs_.insert(attr);
return *this;
}
private:
std::unordered_set<std::string> required_pass_attrs_;
std::unordered_set<std::string> required_graph_attrs_;
};
#define STATIC_ASSERT_PASS_GLOBAL_NAMESPACE(uniq_name, msg) \
struct __test_global_namespace_##uniq_name##__ {}; \
static_assert(std::is_same<::__test_global_namespace_##uniq_name##__, \
__test_global_namespace_##uniq_name##__>::value, \
msg)
// Register a new pass that can be applied on the IR.
#define REGISTER_PASS(pass_type, pass_class) \
STATIC_ASSERT_PASS_GLOBAL_NAMESPACE( \
__reg_pass__##pass_type, \
"REGISTER_PASS must be called in global namespace"); \
static ::paddle::framework::ir::PassRegistrar<pass_class> \
__pass_registrar_##pass_type##__(#pass_type); \
int TouchPassRegistrar_##pass_type() { \
__pass_registrar_##pass_type##__.Touch(); \
return 0; \
} \
static ::paddle::framework::ir::PassRegistrar<pass_class> \
&__pass_tmp_registrar_##pass_type##__ __attribute__((unused)) = \
__pass_registrar_##pass_type##__
#define USE_PASS(pass_type) \
STATIC_ASSERT_PASS_GLOBAL_NAMESPACE( \
__use_pass_itself_##pass_type, \
"USE_PASS must be called in global namespace"); \
extern int TouchPassRegistrar_##pass_type(); \
static int use_pass_itself_##pass_type##_ __attribute__((unused)) = \
TouchPassRegistrar_##pass_type()
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/pass_test.cc 0 → 100644
浏览文件 @ bc1b7b96
/* 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/ir/pass.h"
#include <string>
#include "gtest/gtest.h"
#include "paddle/fluid/framework/ir/graph.h"
namespace paddle {
namespace framework {
namespace ir {
void BuildCircleGraph(Graph* g) {
ir::Node* o1 = g->CreateEmptyNode("op1", Node::Type::kOperation);
ir::Node* o2 = g->CreateEmptyNode("op2", Node::Type::kOperation);
ir::Node* v1 = g->CreateEmptyNode("var1", Node::Type::kVariable);
ir::Node* v2 = g->CreateEmptyNode("var2", Node::Type::kVariable);
o1->outputs.push_back(v1);
o2->inputs.push_back(v1);
v1->inputs.push_back(o1);
v1->outputs.push_back(o2);
o2->outputs.push_back(v2);
o1->inputs.push_back(v2);
v2->inputs.push_back(o2);
v2->outputs.push_back(o1);
}
class TestPass : public Pass {
protected:
std::unique_ptr<Graph> ApplyImpl(std::unique_ptr<Graph> graph) const {
graph->Set<int>("copy_test_pass_attr", new int);
graph->Set<int>("copy_test_graph_attr", new int);
int test_pass_attr = this->Get<int>("test_pass_attr");
graph->Get<int>("copy_test_pass_attr") = test_pass_attr + 1;
int test_graph_attr = graph->Get<int>("test_graph_attr");
graph->Get<int>("copy_test_graph_attr") = test_graph_attr + 1;
return graph;
}
};
TEST(PassTest, TestPassAttrCheck) {
ProgramDesc prog;
auto pass = PassRegistry::Instance().Get("test_pass");
std::unique_ptr<Graph> graph(new Graph(prog));
std::string exception;
try {
graph = pass->Apply(std::move(graph));
} catch (paddle::platform::EnforceNotMet e) {
exception = std::string(e.what());
}
ASSERT_TRUE(exception.find("test_pass_attr not set") != exception.npos);
int val = 1;
graph.reset(new Graph(prog));
pass->SetNotOwned<int>("test_pass_attr", &val);
try {
graph = pass->Apply(std::move(graph));
} catch (paddle::platform::EnforceNotMet e) {
exception = std::string(e.what());
}
ASSERT_TRUE(exception.find("test_graph_attr not set") != exception.npos);
graph.reset(new Graph(prog));
graph->Set<int>("test_graph_attr", new int);
graph->Get<int>("test_graph_attr") = 1;
graph = pass->Apply(std::move(graph));
ASSERT_EQ(graph->Get<int>("copy_test_pass_attr"), 2);
ASSERT_EQ(graph->Get<int>("copy_test_graph_attr"), 2);
try {
graph = pass->Apply(std::move(graph));
} catch (paddle::platform::EnforceNotMet e) {
exception = std::string(e.what());
}
ASSERT_TRUE(exception.find("Pass can only Apply() once") != exception.npos);
pass = PassRegistry::Instance().Get("test_pass");
pass->SetNotOwned<int>("test_pass_attr", &val);
graph.reset(new Graph(prog));
BuildCircleGraph(graph.get());
graph->Set<int>("test_graph_attr", new int);
graph->Get<int>("test_graph_attr") = 2;
try {
auto tmp = pass->Apply(std::move(graph));
} catch (paddle::platform::EnforceNotMet e) {
exception = std::string(e.what());
}
ASSERT_TRUE(exception.find("shouldn't has cycle") != exception.npos);
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(test_pass, paddle::framework::ir::TestPass)
.RequirePassAttr("test_pass_attr")
.RequireGraphAttr("test_graph_attr");
paddle/fluid/framework/operator.cc
浏览文件 @ bc1b7b96
......@@ -679,6 +679,8 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
if (var == nullptr) continue;
if (var->IsType<framework::LoDTensor>()) {
CheckTensorNANOrInf(vname, var->Get<framework::LoDTensor>());
} else if (var->IsType<framework::SelectedRows>()) {
CheckTensorNANOrInf(vname, var->Get<framework::SelectedRows>().value());
}
}
}
......
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ bc1b7b96
......@@ -19,19 +19,80 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_viz_pass.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/nccl_helper.h"
#endif
#include "paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h"
#include "paddle/fluid/framework/details/ssa_graph_builder_factory.h"
#include "paddle/fluid/framework/details/ssa_graph_checker.h"
#include "paddle/fluid/framework/details/ssa_graph_printer.h"
#include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
namespace framework {
std::unique_ptr<ir::Graph> ApplyParallelExecutorPass(
const ProgramDesc &main_program, const std::vector<platform::Place> &places,
const std::string &loss_var_name,
const std::unordered_set<std::string> &param_names,
const std::vector<Scope *> &local_scopes, const bool use_cuda,
#ifdef PADDLE_WITH_CUDA
const BuildStrategy &strategy, platform::NCCLContextMap *nccl_ctxs) {
#else
const BuildStrategy &strategy) {
#endif
// Convert the program to graph.
std::unique_ptr<ir::Graph> graph(new ir::Graph(main_program));
// Apply a graph viz pass to record a graph.
if (!strategy.debug_graphviz_path_.empty()) {
auto viz_pass = ir::PassRegistry::Instance().Get("graph_viz_pass");
const std::string graph_path = string::Sprintf(
"%s%s", strategy.debug_graphviz_path_.c_str(), "_original_graph");
viz_pass->Set<std::string>("graph_viz_path", new std::string(graph_path));
graph = viz_pass->Apply(std::move(graph));
}
// Convert graph to run on multi-devices.
auto multi_device_pass =
ir::PassRegistry::Instance().Get("multi_device_pass");
multi_device_pass->SetNotOwned<const std::vector<platform::Place>>("places",
&places);
multi_device_pass->SetNotOwned<const std::string>("loss_var_name",
&loss_var_name);
multi_device_pass->SetNotOwned<const std::unordered_set<std::string>>(
"params", &param_names);
multi_device_pass->SetNotOwned<const std::vector<Scope *>>("local_scopes",
&local_scopes);
multi_device_pass->SetNotOwned<const BuildStrategy>("strategy", &strategy);
#ifdef PADDLE_WITH_CUDA
platform::NCCLContextMap *nctx = use_cuda ? nccl_ctxs : nullptr;
multi_device_pass->SetNotOwned<platform::NCCLContextMap>("nccl_ctxs", nctx);
#endif
graph = multi_device_pass->Apply(std::move(graph));
// Apply a graph print pass to record a graph with device info.
if (!strategy.debug_graphviz_path_.empty()) {
auto multi_device_print_pass =
ir::PassRegistry::Instance().Get("multi_device_print_pass");
multi_device_print_pass->SetNotOwned<const std::string>(
"debug_graphviz_path", &strategy.debug_graphviz_path_);
multi_device_print_pass->Set<details::GraphvizSSAGraphPrinter>(
"graph_printer", new details::GraphvizSSAGraphPrinter);
graph = multi_device_print_pass->Apply(std::move(graph));
}
// Verify that the graph is correct for multi-device executor.
auto multi_device_check_pass =
ir::PassRegistry::Instance().Get("multi_device_check_pass");
graph = multi_device_check_pass->Apply(std::move(graph));
return graph;
}
class ParallelExecutorPrivate {
public:
explicit ParallelExecutorPrivate(const std::vector<platform::Place> &places)
......@@ -119,21 +180,19 @@ ParallelExecutor::ParallelExecutor(
var_infos.back().persistable_ = var->Persistable();
}
// Step 3. Convert main_program to SSA form and dependency graph. Also, insert
// ncclOp
details::SSAGraphBuilderFactory builder_factory(
member_->places_, loss_var_name, params, member_->local_scopes_,
build_strategy);
if (member_->use_cuda_) {
// Step 3. Convert main_program to SSA form and dependency graph. Also, insert
// ncclOp
#ifdef PADDLE_WITH_CUDA
builder_factory.SetNCCLContextMap(member_->nccl_ctxs_.get());
std::unique_ptr<ir::Graph> graph = ApplyParallelExecutorPass(
main_program, member_->places_, loss_var_name, params,
member_->local_scopes_, member_->use_cuda_, build_strategy,
member_->nccl_ctxs_.get());
#else
PADDLE_THROW("Not compiled with CUDA.");
std::unique_ptr<ir::Graph> graph = ApplyParallelExecutorPass(
main_program, member_->places_, loss_var_name, params,
member_->local_scopes_, member_->use_cuda_, build_strategy);
#endif
}
builder_ = builder_factory.Create();
std::unique_ptr<ir::Graph> graph(new ir::Graph(main_program));
graph = builder_->Apply(std::move(graph));
member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
exec_strategy, member_->local_scopes_, places, std::move(graph)));
member_->executor_.reset(new details::ScopeBufferedSSAGraphExecutor(
......@@ -146,11 +205,18 @@ void ParallelExecutor::BCastParamsToDevices(
// the initializing bcast, all vars would be bcast from device(0),
// otherwise
// bcast from the specified device.
bool initializing = builder_.get() == nullptr ? true : false;
bool initializing = member_->executor_ ? false : true;
for (auto &var : vars) {
int var_dev_id =
builder_.get() == nullptr ? -1 : builder_->GetVarDeviceID(var);
int var_dev_id = -1;
if (member_->executor_) {
auto &sharded_var_device =
member_->executor_->Graph().Get<details::ShardedVarDevice>(
details::kShardedVarDevice);
if (sharded_var_device.find(var) != sharded_var_device.end()) {
var_dev_id = sharded_var_device.at(var);
}
}
if (!initializing && var_dev_id == -1) continue;
framework::Variable *main_var = nullptr;
......@@ -286,3 +352,8 @@ ParallelExecutor::~ParallelExecutor() {
} // namespace framework
} // namespace paddle
USE_PASS(graph_viz_pass);
USE_PASS(multi_device_pass);
USE_PASS(multi_device_check_pass);
USE_PASS(multi_device_print_pass);
paddle/fluid/framework/parallel_executor.h
浏览文件 @ bc1b7b96
......@@ -70,7 +70,6 @@ class ParallelExecutor {
private:
ParallelExecutorPrivate *member_;
std::unique_ptr<details::SSAGraphBuilder> builder_;
};
} // namespace framework
......
paddle/fluid/inference/CMakeLists.txt
浏览文件 @ bc1b7b96
......@@ -14,8 +14,15 @@ cc_library(paddle_fluid_api
get_property(fluid_modules GLOBAL PROPERTY FLUID_MODULES)
# paddle_fluid_origin exclude inference api interface
cc_library(paddle_fluid_origin DEPS ${fluid_modules} paddle_fluid_api)
if(NOT APPLE)
add_subdirectory(api)
endif()
# Create static library
cc_library(paddle_fluid DEPS ${fluid_modules} paddle_fluid_api)
cc_library(paddle_fluid DEPS ${fluid_modules} paddle_fluid_api paddle_inference_api)
if(NOT APPLE)
# TODO(liuyiqu: Temporarily disable the link flag because it is not support on Mac.
set(LINK_FLAGS "-Wl,--retain-symbols-file ${CMAKE_CURRENT_SOURCE_DIR}/paddle_fluid.sym")
......@@ -24,7 +31,7 @@ endif()
# Create shared library
cc_library(paddle_fluid_shared SHARED
SRCS io.cc
SRCS io.cc ${CMAKE_CURRENT_SOURCE_DIR}/api/api.cc ${CMAKE_CURRENT_SOURCE_DIR}/api/api_impl.cc
DEPS ${fluid_modules} paddle_fluid_api)
set_target_properties(paddle_fluid_shared PROPERTIES OUTPUT_NAME paddle_fluid)
......@@ -32,12 +39,21 @@ if(NOT APPLE)
# TODO(liuyiqun): Temporarily disable the link flag because it is not support on Mac.
set(LINK_FLAGS "-Wl,--version-script ${CMAKE_CURRENT_SOURCE_DIR}/paddle_fluid.map")
set_target_properties(paddle_fluid_shared PROPERTIES LINK_FLAGS "${LINK_FLAGS}")
# check symbol hidden
FILE(WRITE ${CMAKE_CURRENT_BINARY_DIR}/check_symbol.cmake
"execute_process(COMMAND bash -c \"${CMAKE_CURRENT_SOURCE_DIR}/check_symbol.sh"
" ${CMAKE_CURRENT_BINARY_DIR}/libpaddle_fluid.so\" RESULT_VARIABLE symbol_res)\n"
"if(NOT \"\${symbol_res}\" STREQUAL \"0\")\n"
" message(FATAL_ERROR \"Check symbol failed.\")\n"
"endif()\n")
add_custom_command(
OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/.check_symbol"
COMMAND ${CMAKE_COMMAND} -P "${CMAKE_CURRENT_BINARY_DIR}/check_symbol.cmake"
DEPENDS paddle_fluid_shared)
add_custom_target(check_symbol ALL DEPENDS "${CMAKE_CURRENT_BINARY_DIR}/.check_symbol")
endif()
if(WITH_TESTING)
# both tests/book and analysis depends the models that generated by python/paddle/fluid/tests/book
add_subdirectory(tests/book)
endif()
if(NOT APPLE)
add_subdirectory(api)
endif()
paddle/fluid/inference/analysis/data_flow_graph.h
浏览文件 @ bc1b7b96
......@@ -36,6 +36,8 @@ namespace analysis {
/*
* DataFlowGraph - A container of Value and Function Nodes.
*
* This is the base graph for any other type of graphs, such as SSA or CFG.
*/
struct DataFlowGraph {
NodeMap nodes;
......
paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.cc
浏览文件 @ bc1b7b96
......@@ -40,6 +40,8 @@ void FluidToDataFlowGraphPass::Run(DataFlowGraph *graph) {
PADDLE_ENFORCE(graph);
PADDLE_ENFORCE(desc_);
// insert vars
// The `var2id` keeps a map from a variable's name to its Node-id, the Node-id
// will keep updating to its latest alias during the graph-building.
std::unordered_map<std::string, size_t> var2id;
auto &main_block = desc_->blocks(framework::kRootBlockIndex);
for (int i = 0; i < main_block.vars_size(); i++) {
......@@ -51,6 +53,15 @@ void FluidToDataFlowGraphPass::Run(DataFlowGraph *graph) {
var2id[var.name()] = v->id();
}
// The variables in a SSA can only write once, so if a variable is written
// multiple times(quite common in our ProgramDesc design), multiple alias
// Nodes of this variable will be created, and each will just write once.
// An set that keep all the names of the variables(the original, not alias)
// that have been written(as outputs). Once an Op's output variable hit the
// set, it should create a new alias and update the global alias for this
// variable. And that make a Data Flow Graph a SSA.
std::unordered_set<Node *> unique_written_vars;
for (int i = 0; i < main_block.ops_size(); i++) {
const auto &op = main_block.ops(i);
auto *o = graph->nodes.Create(Node::Type::kFunction);
......@@ -62,33 +73,33 @@ void FluidToDataFlowGraphPass::Run(DataFlowGraph *graph) {
o->SetPbMsg(op.SerializeAsString());
// set inputs and outputs
std::unordered_set<Node *> inlinks;
for (int j = 0; j < op.inputs_size(); j++) {
auto &in_var = op.inputs(j);
for (int k = 0; k < in_var.arguments_size(); k++) {
auto *in = graph->nodes.GetMutable(var2id.at(in_var.arguments(k)));
in->outlinks.push_back(o);
o->inlinks.push_back(in);
inlinks.insert(in);
}
}
for (int j = 0; j < op.outputs_size(); j++) {
auto &out_var = op.outputs(j);
for (int k = 0; k < out_var.arguments_size(); k++) {
auto *out = graph->nodes.GetMutable(var2id[out_var.arguments(k)]);
if (inlinks.count(out)) {
if (unique_written_vars.count(out)) {
// Loop found, for example, a = op(a), use SSA, change to a1 = op(a).
auto *out_alias = graph->nodes.Create(Node::Type::kValue);
out_alias->SetName(out->name());
out_alias->SetPbDesc(out->pb_desc());
out_alias->SetPbMsg(out->pb_msg());
var2id[out_alias->name()] = out_alias->id(); // update a -> a0
var2id[out_alias->name()] =
out_alias->id(); // update variable's alias Node
LOG(INFO) << "loop found in graph, create SSA alias node ["
<< out_alias->repr() << "] for [" << out->repr() << "]";
out = out_alias;
}
out->inlinks.push_back(o);
o->outlinks.push_back(out);
unique_written_vars.insert(out);
}
}
}
......
paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h
浏览文件 @ bc1b7b96
......@@ -30,7 +30,7 @@ namespace inference {
namespace analysis {
/*
* Transform a FluidDesc to a data flow graph.
* Transform a FluidDesc to a SSA.
*/
class FluidToDataFlowGraphPass final : public DataFlowGraphPass {
public:
......
paddle/fluid/inference/api/CMakeLists.txt
浏览文件 @ bc1b7b96
......@@ -42,35 +42,8 @@ function(inference_api_test TARGET_NAME)
endif(WITH_TESTING)
endfunction(inference_api_test)
cc_library(paddle_inference_api
SRCS api.cc api_impl.cc
DEPS ${FLUID_CORE_MODULES} ${GLOB_OP_LIB})
if(NOT APPLE)
set(LINK_FLAGS "-Wl,--retain-symbols-file ${CMAKE_CURRENT_SOURCE_DIR}/api.sym")
set_target_properties(paddle_inference_api PROPERTIES LINK_FLAGS "${LINK_FLAGS}")
endif()
# Here the shared library doesn't depend on other fluid libraries, or double free will occur.
cc_library(paddle_inference_api_shared SHARED
SRCS api.cc api_impl.cc)
add_dependencies(paddle_inference_api_shared ${FLUID_CORE_MODULES} ${GLOB_OP_LIB})
set_target_properties(paddle_inference_api_shared PROPERTIES OUTPUT_NAME paddle_inference_api)
cc_library(paddle_inference_api SRCS api.cc api_impl.cc DEPS lod_tensor)
if(NOT APPLE)
set(LINK_FLAGS "-Wl,--version-script ${CMAKE_CURRENT_SOURCE_DIR}/api.map")
set_target_properties(paddle_inference_api_shared PROPERTIES LINK_FLAGS "${LINK_FLAGS}")
FILE(WRITE ${CMAKE_CURRENT_BINARY_DIR}/check_symbol.cmake
"execute_process(COMMAND bash -c \"${CMAKE_CURRENT_SOURCE_DIR}/check_symbol.sh"
" ${CMAKE_CURRENT_BINARY_DIR}/libpaddle_inference_api.so\" RESULT_VARIABLE symbol_res)\n"
"if(NOT \"\${symbol_res}\" STREQUAL \"0\")\n"
" message(FATAL_ERROR \"Check symbol failed.\")\n"
"endif()\n")
add_custom_command(
OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/.check_symbol"
COMMAND ${CMAKE_COMMAND} -P "${CMAKE_CURRENT_BINARY_DIR}/check_symbol.cmake"
DEPENDS paddle_inference_api_shared)
add_custom_target(check_symbol ALL DEPENDS "${CMAKE_CURRENT_BINARY_DIR}/.check_symbol")
endif()
cc_test(test_paddle_inference_api
SRCS api_tester.cc
......
paddle/fluid/inference/api/demo_ci/CMakeLists.txt
浏览文件 @ bc1b7b96
......@@ -55,11 +55,9 @@ endif()
# Note: libpaddle_inference_api.so/a must put before libpaddle_fluid.so/a
if(WITH_STATIC_LIB)
set(DEPS
${PADDLE_LIB}/paddle/fluid/inference/libpaddle_inference_api.a
${PADDLE_LIB}/paddle/fluid/inference/libpaddle_fluid.a)
else()
set(DEPS
${PADDLE_LIB}/paddle/fluid/inference/libpaddle_inference_api.so
${PADDLE_LIB}/paddle/fluid/inference/libpaddle_fluid.so)
endif()
set(EXTERNAL_LIB "-lrt -ldl -lpthread")
......
paddle/fluid/inference/api/check_symbol.sh → paddle/fluid/inference/check_symbol.sh
浏览文件 @ bc1b7b96
......@@ -3,8 +3,8 @@
lib=$1
if [ $# -ne 1 ]; then echo "No input library"; exit -1 ; fi
num_paddle_syms=$(nm -D --defined-only ${lib} | grep paddle | wc -l)
num_google_syms=$(nm -D --defined-only ${lib} | grep google | wc -l)
num_paddle_syms=$(nm -D ${lib} | grep paddle | wc -l)
num_google_syms=$(nm -D ${lib} | grep google | grep -v paddle | grep T | wc -l)
if [ $num_paddle_syms -le 0 ]; then echo "Have no paddle symbols"; exit -1 ; fi
if [ $num_google_syms -ge 1 ]; then echo "Have some google symbols"; exit -1 ; fi
......
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
浏览文件 @ bc1b7b96
# Add TRT tests
nv_library(tensorrt_converter
SRCS mul_op.cc conv2d_op.cc fc_op.cc
DEPS tensorrt_engine mul_op)
SRCS mul_op.cc conv2d_op.cc fc_op.cc pool2d_op.cc
DEPS tensorrt_engine operator scope framework_proto op_registry)
nv_test(test_op_converter SRCS test_op_converter.cc DEPS
${FLUID_CORE_MODULES} tensorrt_engine tensorrt_converter)
......@@ -13,3 +13,6 @@ nv_test(test_trt_fc_op SRCS test_fc_op.cc fc_op.cc
DEPS ${FLUID_CORE_MODULES} tensorrt_engine mul_op SERIAL)
nv_test(test_trt_activation_op SRCS test_activation_op.cc activation_op.cc
DEPS ${FLUID_CORE_MODULES} tensorrt_engine activation_op SERIAL)
nv_test(test_trt_pool2d_op SRCS test_pool2d_op.cc pool2d_op.cc
DEPS ${FLUID_CORE_MODULES} tensorrt_engine pool_op SERIAL)
paddle/fluid/inference/tensorrt/convert/mul_op.cc
浏览文件 @ bc1b7b96
......@@ -49,5 +49,4 @@ class MulOpConverter : public OpConverter {
} // namespace inference
} // namespace paddle
USE_OP(mul);
REGISTER_TRT_OP_CONVERTER(mul, MulOpConverter);
paddle/fluid/inference/tensorrt/convert/pool2d_op.cc 0 → 100644
浏览文件 @ bc1b7b96
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
namespace paddle {
namespace inference {
namespace tensorrt {
/*
* Pool2dOp, IPoolingLayer in TRT. This Layer doesn't has weights.
*/
class Pool2dOpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope, bool test_mode) override {
VLOG(4)
<< "convert a fluid pool2d op to tensorrt pool2d layer without bias";
framework::OpDesc op_desc(op, nullptr);
// Declare inputs
PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
PADDLE_ENFORCE_EQ(op_desc.Output("Out").size(), 1);
auto* input1 = engine_->GetITensor(op_desc.Input("X")[0]);
std::string pool_type =
boost::get<std::string>(op_desc.GetAttr("pooling_type"));
std::vector<int> ksize =
boost::get<std::vector<int>>(op_desc.GetAttr("ksize"));
std::vector<int> strides =
boost::get<std::vector<int>>(op_desc.GetAttr("strides"));
std::vector<int> paddings =
boost::get<std::vector<int>>(op_desc.GetAttr("paddings"));
const nvinfer1::DimsHW nv_ksize(ksize[0], ksize[1]);
const nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
const nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
PADDLE_ENFORCE_EQ(input1->getDimensions().nbDims, 3UL);
nvinfer1::PoolingType nv_pool_type = nvinfer1::PoolingType::kMAX;
if (pool_type == "max") {
nv_pool_type = nvinfer1::PoolingType::kMAX;
} else if (pool_type == "avg") {
nv_pool_type = nvinfer1::PoolingType::kAVERAGE;
} else {
PADDLE_THROW("TensorRT unsupported pooling type!");
}
auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Pooling,
*const_cast<nvinfer1::ITensor*>(input1),
nv_pool_type, nv_ksize);
PADDLE_ENFORCE_NOT_NULL(layer, "pool layer could not be created.");
layer->setStride(nv_strides);
layer->setPadding(nv_paddings);
auto output_name = op_desc.Output("Out")[0];
engine_->SetITensor(output_name, layer->getOutput(0));
if (test_mode) {
engine_->DeclareOutput(output_name);
}
}
};
} // namespace tensorrt
} // namespace inference
} // namespace paddle
USE_OP(pool2d);
REGISTER_TRT_OP_CONVERTER(pool2d, Pool2dOpConverter);
paddle/fluid/inference/tensorrt/convert/test_activation_op.cc
浏览文件 @ bc1b7b96
......@@ -37,7 +37,7 @@ TEST(ReluOpConverter, main) {
validator.SetOp(*desc.Proto());
LOG(INFO) << "execute";
validator.Execute(1);
validator.Execute(5);
}
} // namespace tensorrt
......
paddle/fluid/inference/tensorrt/convert/test_fc_op.cc
浏览文件 @ bc1b7b96
......@@ -24,9 +24,8 @@ TEST(fc_op, test) {
std::unordered_set<std::string> parameters({"mul-Y"});
framework::Scope scope;
TRTConvertValidation validator(10, parameters, scope, 1000);
validator.DeclInputVar("mul-X", nvinfer1::Dims4(1, 10, 1, 1));
validator.DeclInputVar("mul-X", nvinfer1::Dims3(10, 1, 1));
validator.DeclParamVar("mul-Y", nvinfer1::Dims2(10, 2));
// validator.DeclParamVar("mul-Y", nvinfer1::Dims2(8, 2));
validator.DeclOutputVar("mul-Out", nvinfer1::Dims2(1, 2));
// Prepare Op description
......@@ -38,7 +37,7 @@ TEST(fc_op, test) {
validator.SetOp(*desc.Proto());
validator.Execute(1);
validator.Execute(10);
}
} // namespace tensorrt
......
paddle/fluid/inference/tensorrt/convert/test_mul_op.cc
浏览文件 @ bc1b7b96
......@@ -23,7 +23,7 @@ namespace tensorrt {
TEST(MulOpConverter, main) {
framework::Scope scope;
std::unordered_set<std::string> parameters;
TRTConvertValidation validator(10, parameters, scope, 1000);
TRTConvertValidation validator(10, parameters, scope, 1000, false);
validator.DeclInputVar("mul-X", nvinfer1::Dims2(10, 6));
validator.DeclInputVar("mul-Y", nvinfer1::Dims2(6, 10));
validator.DeclOutputVar("mul-Out", nvinfer1::Dims2(10, 10));
......@@ -39,7 +39,7 @@ TEST(MulOpConverter, main) {
validator.SetOp(*desc.Proto());
LOG(INFO) << "execute";
validator.Execute(1);
validator.Execute(2);
}
} // namespace tensorrt
......
paddle/fluid/inference/tensorrt/convert/test_pool2d_op.cc 0 → 100644
浏览文件 @ bc1b7b96
/* 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 <gtest/gtest.h>
#include <fstream>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
namespace paddle {
namespace inference {
namespace tensorrt {
TEST(Pool2dOpConverter, main) {
framework::Scope scope;
std::unordered_set<std::string> parameters;
TRTConvertValidation validator(5, parameters, scope, 1 << 15);
// The ITensor's Dims should not contain the batch size.
// So, the ITensor's Dims of input and output should be C * H * W.
validator.DeclInputVar("pool2d-X", nvinfer1::Dims3(3, 4, 4));
validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 2, 2));
// Prepare Op description
framework::OpDesc desc;
desc.SetType("pool2d");
desc.SetInput("X", {"pool2d-X"});
desc.SetOutput("Out", {"pool2d-Out"});
std::vector<int> ksize({2, 2});
std::vector<int> strides({2, 2});
std::vector<int> paddings({0, 0});
std::string pooling_t = "max";
desc.SetAttr("pooling_type", pooling_t);
desc.SetAttr("ksize", ksize);
desc.SetAttr("strides", strides);
desc.SetAttr("paddings", paddings);
LOG(INFO) << "set OP";
validator.SetOp(*desc.Proto());
LOG(INFO) << "execute";
validator.Execute(3);
}
} // namespace tensorrt
} // namespace inference
} // namespace paddle
USE_OP(pool2d);
paddle/fluid/inference/tensorrt/convert/ut_helper.h
浏览文件 @ bc1b7b96
......@@ -63,13 +63,16 @@ class TRTConvertValidation {
public:
TRTConvertValidation() = delete;
TRTConvertValidation(int batch_size,
TRTConvertValidation(int max_batch_size,
const std::unordered_set<std::string>& parameters,
framework::Scope& scope, // NOLINT
int workspace_size = 1 << 10)
: parameters_(parameters), scope_(scope) {
int workspace_size = 1 << 10, bool if_add_batch = true)
: parameters_(parameters),
scope_(scope),
if_add_batch_(if_add_batch),
max_batch_size_(max_batch_size) {
// create engine.
engine_.reset(new TensorRTEngine(batch_size, workspace_size, &stream_));
engine_.reset(new TensorRTEngine(max_batch_size, workspace_size, &stream_));
engine_->InitNetwork();
PADDLE_ENFORCE_EQ(cudaStreamCreate(&stream_), 0);
......@@ -84,7 +87,7 @@ class TRTConvertValidation {
// Declare a parameter varaible in the scope.
void DeclParamVar(const std::string& name, const nvinfer1::Dims& dims) {
DeclVar(name, dims);
DeclVar(name, dims, true);
}
void DeclOutputVar(const std::string& name, const nvinfer1::Dims& dims) {
......@@ -92,12 +95,18 @@ class TRTConvertValidation {
}
// Declare a variable in a fluid Scope.
void DeclVar(const std::string& name, const nvinfer1::Dims& dims) {
void DeclVar(const std::string& name, const nvinfer1::Dims& dims,
bool is_param = false) {
platform::CPUPlace place;
platform::CPUDeviceContext ctx(place);
// Init Fluid tensor.
std::vector<int> dim_vec(dims.d, dims.d + dims.nbDims);
// There is no batchsize in ITensor's shape, but We should add it to
// tensor's shape of fluid. If the variable is not parameter and the
// if_add_batch_ flag is true, add the max batchsize to dim_vec.
if (is_param != true && if_add_batch_ == true)
dim_vec.insert(dim_vec.begin(), max_batch_size_);
auto* x = scope_.Var(name);
auto* x_tensor = x->GetMutable<framework::LoDTensor>();
x_tensor->Resize(framework::make_ddim(dim_vec));
......@@ -131,6 +140,7 @@ class TRTConvertValidation {
void Execute(int batch_size) {
// Execute Fluid Op
PADDLE_ENFORCE_LE(batch_size, max_batch_size_);
platform::CPUPlace place;
platform::CPUDeviceContext ctx(place);
op_->Run(scope_, place);
......@@ -149,9 +159,15 @@ class TRTConvertValidation {
auto* var = scope_.FindVar(output);
auto tensor = var->GetMutable<framework::LoDTensor>();
framework::TensorToVector(*tensor, ctx, &fluid_out);
size_t fluid_out_size = fluid_out.size();
if (if_add_batch_ == true) {
fluid_out_size =
batch_size * (framework::product(tensor->dims()) / max_batch_size_);
}
// Compare two output
ASSERT_FALSE(fluid_out.empty());
for (size_t i = 0; i < fluid_out.size(); i++) {
for (size_t i = 0; i < fluid_out_size; i++) {
// Loose the threshold for CI in different machine model.
EXPECT_LT(std::abs(fluid_out[i] - trt_out[i]), 2e-5);
}
......@@ -167,6 +183,12 @@ class TRTConvertValidation {
std::unique_ptr<framework::OpDesc> op_desc_;
const std::unordered_set<std::string>& parameters_;
framework::Scope& scope_;
// The ITensor of trt does not cotain the batch size,
// bug, in most cases, we need to set batch size for
// fluid's tensor shape. This variable indicates
// whether to add batch size to tensor shape of fluid.
bool if_add_batch_;
int max_batch_size_;
};
} // namespace tensorrt
......
paddle/fluid/inference/tensorrt/test_engine.cc
浏览文件 @ bc1b7b96
......@@ -113,7 +113,7 @@ TEST_F(TensorRTEngineTest, add_layer_multi_dim) {
ASSERT_EQ(y_cpu[1], 14.5);
}
TEST_F(TensorRTEngineTest, test_conv2d_temp) {
TEST_F(TensorRTEngineTest, test_conv2d) {
// Weight in CPU memory.
float raw_weight[9] = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
float raw_bias[1] = {0};
......@@ -146,6 +146,37 @@ TEST_F(TensorRTEngineTest, test_conv2d_temp) {
ASSERT_EQ(y_cpu[1], 6.0);
}
TEST_F(TensorRTEngineTest, test_pool2d) {
// Weight in CPU memory.
auto* x = engine_->DeclareInput("x", nvinfer1::DataType::kFLOAT,
nvinfer1::Dims3{1, 2, 2});
nvinfer1::PoolingType pool_t = nvinfer1::PoolingType::kAVERAGE;
auto* pool_layer =
TRT_ENGINE_ADD_LAYER(engine_, Pooling, *const_cast<nvinfer1::ITensor*>(x),
pool_t, nvinfer1::DimsHW{2, 2});
PADDLE_ENFORCE(pool_layer != nullptr);
pool_layer->setStride(nvinfer1::DimsHW{1, 1});
pool_layer->setPadding(nvinfer1::DimsHW{0, 0});
engine_->DeclareOutput(pool_layer, 0, "y");
engine_->FreezeNetwork();
ASSERT_EQ(engine_->engine()->getNbBindings(), 2);
float x_v[8] = {1.0, 2.0, 5.0, 0.0, 2.0, 3.0, 5.0, 10.0};
engine_->SetInputFromCPU("x", reinterpret_cast<void*>(&x_v),
8 * sizeof(float));
engine_->Execute(2);
LOG(INFO) << "to get output";
float* y_cpu = new float[2];
engine_->GetOutputInCPU("y", &y_cpu[0], 2 * sizeof(float));
ASSERT_EQ(y_cpu[0], 2.0);
ASSERT_EQ(y_cpu[1], 5.0);
}
} // namespace tensorrt
} // namespace inference
} // namespace paddle
paddle/fluid/inference/tests/book/CMakeLists.txt
浏览文件 @ bc1b7b96
......@@ -17,7 +17,7 @@ function(inference_test TARGET_NAME)
string(REGEX REPLACE "^_$" "" arg "${arg}")
cc_test(test_inference_${TARGET_NAME}${arg}
SRCS test_inference_${TARGET_NAME}.cc
DEPS paddle_fluid
DEPS paddle_fluid_origin
ARGS --dirname=${PYTHON_TESTS_DIR}/book/${TARGET_NAME}${arg}.inference.model)
set_tests_properties(test_inference_${TARGET_NAME}${arg}
PROPERTIES DEPENDS test_${TARGET_NAME})
......@@ -43,6 +43,6 @@ inference_test(word2vec)
# TODO(TJ): clean me up
cc_test(test_inference_nlp
SRCS test_inference_nlp.cc
DEPS paddle_fluid
DEPS paddle_fluid_origin
ARGS
--model_path=${PADDLE_BINARY_DIR}/python/paddle/fluid/tests/book/recognize_digits_mlp.inference.model)
paddle/fluid/inference/tests/book/test_inference_nlp.cc
浏览文件 @ bc1b7b96
......@@ -20,9 +20,6 @@ limitations under the License. */
#include "gtest/gtest.h"
#include "paddle/fluid/inference/tests/test_helper.h"
#include "paddle/fluid/platform/cpu_helper.h"
#ifdef PADDLE_WITH_MKLML
#include <omp.h>
#endif
DEFINE_string(model_path, "", "Directory of the inference model.");
DEFINE_string(data_file, "", "File of input index data.");
......@@ -30,6 +27,7 @@ DEFINE_int32(repeat, 100, "Running the inference program repeat times");
DEFINE_bool(prepare_vars, true, "Prepare variables before executor");
DEFINE_int32(num_threads, 1, "Number of threads should be used");
DECLARE_bool(use_mkldnn);
DECLARE_int32(paddle_num_threads);
inline double GetCurrentMs() {
struct timeval time;
......@@ -160,12 +158,7 @@ TEST(inference, nlp) {
std::unique_ptr<paddle::framework::Scope> scope(
new paddle::framework::Scope());
#ifdef PADDLE_WITH_MKLML
// only use 1 thread number per std::thread
omp_set_dynamic(0);
omp_set_num_threads(1);
paddle::platform::SetNumThreads(1);
#endif
paddle::platform::SetNumThreads(FLAGS_paddle_num_threads);
double start_ms = 0, stop_ms = 0;
if (FLAGS_num_threads > 1) {
......
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ bc1b7b96
......@@ -270,6 +270,9 @@ op_library(cos_sim_op DEPS cos_sim_functor)
op_library(parallel_do_op DEPS executor)
op_library(unsqueeze_op DEPS reshape_op)
op_library(squeeze_op DEPS reshape_op)
op_library(extract_rows_op DEPS memory)
op_library(flatten_op DEPS reshape_op)
if (WITH_GPU)
op_library(conv_op DEPS vol2col depthwise_conv im2col)
......
paddle/fluid/operators/distributed/CMakeLists.txt
浏览文件 @ bc1b7b96
......@@ -17,9 +17,9 @@ if(WITH_GRPC)
set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
set_source_files_properties(grpc_serde_test.cc rpc_server_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
cc_test(grpc_serde_test SRCS grpc_serde_test.cc
DEPS grpc++_unsecure grpc_unsecure gpr cares zlib protobuf sendrecvop_grpc scope profiler math_function SERIAL)
cc_test(rpc_server_test SRCS rpc_server_test.cc
DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf executor proto_desc lookup_table_op SERIAL)
DEPS grpc++_unsecure grpc_unsecure gpr cares zlib protobuf sendrecvop_grpc scope profiler math_function SERIAL)
cc_test(rpc_server_test SRCS rpc_server_test.cc
DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf executor proto_desc lookup_sparse_table_op SERIAL)
return()
endif()
......
paddle/fluid/operators/distributed/grpc_client.cc
浏览文件 @ bc1b7b96
......@@ -49,6 +49,7 @@ void GRPCClient::SendComplete() {
}
GRPCClient::~GRPCClient() {
stopped_ = true;
Wait();
cq_.Shutdown();
{
......@@ -275,7 +276,7 @@ void GRPCClient::Proceed() {
void* tag = nullptr;
bool ok = false;
while (cq_.Next(&tag, &ok)) {
while (!stopped_ && cq_.Next(&tag, &ok)) {
BaseProcessor* c = static_cast<BaseProcessor*>(tag);
GPR_ASSERT(ok);
PADDLE_ENFORCE(c);
......
paddle/fluid/operators/distributed/grpc_client.h
浏览文件 @ bc1b7b96
......@@ -174,7 +174,7 @@ class CheckpointNotifyProcessor : public BaseProcessor {
class GRPCClient : public RPCClient {
public:
GRPCClient() : ok_(true), completed_(false) {}
GRPCClient() : ok_(true), completed_(false), stopped_(false) {}
virtual ~GRPCClient();
bool AsyncSendVar(const std::string& ep, const platform::DeviceContext& ctx,
......@@ -237,6 +237,8 @@ class GRPCClient : public RPCClient {
// mutex for sending complete message only once
std::mutex completed_mutex_;
bool completed_;
volatile bool stopped_;
};
} // namespace distributed
......
paddle/fluid/operators/distributed/rpc_server_test.cc
浏览文件 @ bc1b7b96
......@@ -30,7 +30,7 @@ namespace framework = paddle::framework;
namespace platform = paddle::platform;
namespace distributed = paddle::operators::distributed;
USE_OP(lookup_table);
USE_NO_KERNEL_OP(lookup_sparse_table);
std::unique_ptr<distributed::RPCServer> g_rpc_service;
std::unique_ptr<distributed::RequestHandler> g_req_handler;
......@@ -42,13 +42,13 @@ framework::BlockDesc* AppendPrefetchBlcok(framework::ProgramDesc* program) {
framework::VariableNameMap input({{"W", {"w"}}, {"Ids", {"ids"}}});
framework::VariableNameMap output({{"Output", {"out"}}});
auto op = block->AppendOp();
op->SetType("lookup_table");
op->SetType("lookup_sparse_table");
op->SetInput("W", {"w"});
op->SetInput("Ids", {"ids"});
op->SetOutput("Out", {"out"});
auto& out = *root_block->Var("out");
out.SetType(framework::proto::VarType::SELECTED_ROWS);
out.SetType(framework::proto::VarType::LOD_TENSOR);
out.SetShape({10, 10});
return block;
......@@ -59,20 +59,19 @@ void CreateVarsOnScope(framework::Scope* scope, platform::CPUPlace* place) {
w_var->GetMutable<framework::SelectedRows>();
auto out_var = scope->Var("out");
out_var->GetMutable<framework::SelectedRows>();
out_var->GetMutable<framework::LoDTensor>();
auto ids_var = scope->Var("ids");
ids_var->GetMutable<framework::SelectedRows>();
ids_var->GetMutable<framework::LoDTensor>();
}
void InitTensorsOnClient(framework::Scope* scope, platform::CPUPlace* place,
int64_t rows_numel) {
CreateVarsOnScope(scope, place);
auto ids_var = scope->Var("ids")->GetMutable<framework::SelectedRows>();
auto rows = ids_var->mutable_rows();
for (int64_t i = 0; i < rows_numel; ++i) rows->push_back(i * 2);
ids_var->mutable_value()->Resize({rows_numel, 1});
ids_var->mutable_value()->mutable_data<float>(*place);
auto ids_var = scope->Var("ids")->GetMutable<framework::LoDTensor>();
int64_t* ids_ptr =
ids_var->mutable_data<int64_t>(framework::DDim({rows_numel, 1}), *place);
for (int64_t i = 0; i < rows_numel; ++i) ids_ptr[i] = i * 2;
}
void InitTensorsOnServer(framework::Scope* scope, platform::CPUPlace* place,
......@@ -148,11 +147,11 @@ TEST(PREFETCH, CPU) {
client->AsyncPrefetchVar(ep, ctx, scope, in_var_name, out_var_name);
client->Wait();
auto var = scope.Var(out_var_name);
auto value = var->GetMutable<framework::SelectedRows>()->value();
auto ptr = value.mutable_data<float>(place);
auto value = var->GetMutable<framework::LoDTensor>();
auto ptr = value->mutable_data<float>(place);
for (int64_t i = 0; i < rows_numel; ++i) {
EXPECT_EQ(ptr[0 + i * value.dims()[1]], static_cast<float>(i * 2));
EXPECT_EQ(ptr[0 + i * value->dims()[1]], static_cast<float>(i * 2));
}
}
......
paddle/fluid/operators/extract_rows_op.cc 0 → 100644
浏览文件 @ bc1b7b96
/* 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 <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
class ExtractRowsOpInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of ExtractRowsOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of ExtractRowsOp should not be null.");
PADDLE_ENFORCE_EQ(ctx->GetInputsVarType("X")[0],
framework::proto::VarType::SELECTED_ROWS,
"The type of input(X) must be SelectedRows.");
auto in_dims = ctx->GetInputDim("X");
ctx->SetOutputDim(
"Out", framework::make_ddim(std::vector<int64_t>{in_dims[0], 1}));
}
};
class ExtractRowsOp : public framework::OperatorBase {
public:
ExtractRowsOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto &in = scope.FindVar(Input("X"))->Get<framework::SelectedRows>();
auto out = scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>();
auto in_rows = in.rows();
auto out_dim = framework::make_ddim(
std::vector<int64_t>{static_cast<int64_t>(in_rows.size()), 1});
auto dst_ptr = out->mutable_data<int64_t>(out_dim, in.place());
if (paddle::platform::is_gpu_place(in.place())) {
#ifdef PADDLE_WITH_CUDA
platform::DeviceContextPool &pool =
platform::DeviceContextPool::Instance();
auto *dev_ctx = pool.Get(in.place());
auto src_ptr = in_rows.Data(in.place());
auto stream =
reinterpret_cast<const platform::CUDADeviceContext &>(*dev_ctx)
.stream();
memory::Copy(boost::get<platform::CUDAPlace>(out->place()), dst_ptr,
boost::get<platform::CUDAPlace>(in.place()), src_ptr,
in_rows.size() * sizeof(int64_t), stream);
#else
PADDLE_THROW("Not compiled with CUDA.");
#endif
} else {
memory::Copy(platform::CPUPlace(), dst_ptr, platform::CPUPlace(),
in_rows.data(), in_rows.size() * sizeof(int64_t));
}
}
};
class ExtractRowsOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(SelectedRows). The input tensor of extract_rows operator,"
" and its type is SelectedRows.");
AddOutput("Out", "(Tensor). The the rows of input(X).");
AddComment(R"DOC(
ExtractRows Operator.
The function of extract_rows_op is extracting the rows from the input(X)
whose type is SelectedRows.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(extract_rows, ops::ExtractRowsOp, ops::ExtractRowsOpMaker,
ops::ExtractRowsOpInferShape);
paddle/fluid/operators/flatten_op.cc 0 → 100644
浏览文件 @ bc1b7b96
/* 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 <vector>
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
class FlattenOpInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input (X) of Flatten op should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output (Output) of Flatten op should not be null.");
const auto &axis = ctx->Attrs().Get<int>("axis");
const auto &in_dims = ctx->GetInputDim("X");
PADDLE_ENFORCE(axis >= 0, "The axis should be greater than or equal to 0.");
PADDLE_ENFORCE(
axis <= in_dims.size(),
"The axis should be less than or equal to input tensor's rank.");
const auto &out_dims = GetOutputShape(axis, in_dims);
ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
if (in_dims[0] == out_dims[0]) {
// Only pass LoD when the first dimension of output and Input(X)
// are the same.
ctx->ShareLoD("X", "Out");
}
}
static std::vector<int32_t> GetOutputShape(const int axis,
const framework::DDim &in_dims) {
int64_t outer = 1, inner = 1;
for (int i = 0; i < in_dims.size(); ++i) {
if (i < axis) {
outer *= in_dims[i];
} else {
inner *= in_dims[i];
}
}
std::vector<int32_t> out_shape(2);
out_shape[0] = outer;
out_shape[1] = inner;
return out_shape;
}
};
class FlattenOp : public framework::OperatorBase {
public:
using OperatorBase::OperatorBase;
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto &axis = Attr<int>("axis");
auto in_dims =
scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
const auto &out_dims = FlattenOpInferShape::GetOutputShape(axis, in_dims);
framework::AttributeMap attrs;
attrs["shape"] = out_dims;
attrs["inplace"] = false;
// Invoke Reshape Op
auto reshape_op = framework::OpRegistry::CreateOp(
"reshape", {{"X", {Input("X")}}, {"Shape", {}}},
{{"Out", {Output("Out")}}}, attrs);
reshape_op->Run(scope, place);
}
};
class FlattenOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(Tensor) A tensor of rank >= axis.");
AddOutput("Out",
"A 2D tensor is reshaped input tensor. The input dimensions"
"up to axis are flattened to the outer dimension of the output"
"and the remaining input dimensions are flattened into the inner"
"dimension of the output.");
AddAttr<int>("axis",
"(int)"
"Indicate up to which input dimensions (exclusive) should be"
"flattened to the outer dimension of the output. The value"
"for axis must be in the range [0, R], where R is the rank of"
"the input tensor. When axis = 0, the shape of the output"
"tensor is (1, (d_0 X d_1 ... d_n), where the shape of the"
"input tensor is (d_0, d_1, ... d_n).")
.SetDefault(1);
AddComment(R"DOC(
Flatten Operator
Flattens the input tensor into a 2D matrix.
Examples:
Case 1:
Given
X.shape = (3, 100, 100, 4)
and
axis = 2
We get:
Out.shape = (3 * 100, 4 * 100)
Case 2:
Given
X.shape = (3, 100, 100, 4)
and
axis = 0
We get:
Out.shape = (1, 3 * 100 * 100 * 4)
)DOC");
}
};
class FlattenGradInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
context->SetOutputDim(framework::GradVarName("X"),
context->GetInputDim("X"));
context->ShareLoD("X", framework::GradVarName("X"));
}
};
class FlattenGradOp : public framework::OperatorBase {
public:
using OperatorBase::OperatorBase;
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto dx_name = Output(framework::GradVarName("X"));
auto dout_name = Input(framework::GradVarName("Out"));
auto in_dims =
scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
framework::AttributeMap attrs;
attrs["shape"] = framework::vectorize2int(in_dims);
attrs["inplace"] = false;
auto reshape_op = framework::OpRegistry::CreateOp(
"reshape", {{"X", {dout_name}}, {"Shape", {}}}, {{"Out", {dx_name}}},
attrs);
reshape_op->Run(scope, place);
}
};
} // namespace operators
} // namespace paddle
USE_OP(reshape);
namespace ops = paddle::operators;
REGISTER_OPERATOR(flatten, ops::FlattenOp, ops::FlattenOpMaker,
ops::FlattenOpInferShape,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(flatten_grad, ops::FlattenGradOp, ops::FlattenGradInferShape);
paddle/fluid/operators/lookup_table_op.cc
浏览文件 @ bc1b7b96
......@@ -33,19 +33,15 @@ class LookupTableOp : public framework::OperatorWithKernel {
auto table_dims = ctx->GetInputDim("W");
auto ids_dims = ctx->GetInputDim("Ids");
auto ids_var_type = ctx->GetInputsVarType("Ids").front();
// The type of Ids(Input) is SelectedRows or LoDTensor, when Ids's type
// is LoDTensor, this tensor contains the ids to be looked up in W
// and it must be a column vector with rank = 2 while the 2nd dimension
// size must be 1, when Ids's type is SelectedRows, the rows of Ids
// contains the ids to be looked up in W;
if (ids_var_type == framework::proto::VarType::LOD_TENSOR) {
PADDLE_ENFORCE_EQ(ids_dims.size(), 2);
PADDLE_ENFORCE_EQ(ids_dims[1], 1);
}
PADDLE_ENFORCE_EQ(ids_dims.size(), 2);
PADDLE_ENFORCE_EQ(ids_dims[1], 1);
ctx->SetOutputDim("Out", {ids_dims[0], table_dims[1]});
ctx->ShareLoD("Ids", /*->*/ "Out");
if (ctx->GetOutputsVarType("Out")[0] ==
framework::proto::VarType::LOD_TENSOR) {
ctx->ShareLoD("Ids", /*->*/ "Out");
}
}
protected:
......@@ -62,17 +58,12 @@ class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("W",
"(Tensor) The input represents embedding tensors, "
"which is a learnable parameter.");
AddInput(
"Ids",
"(Tensor or SelectedRows) Ids's type can be Tensor or "
"SelectedRows, when Ids's type is Tensor, this tensor contains "
"the ids to be looked up in W and it must be a column vector with "
"rank = 2 while the 2nd dimension size must be 1; when Ids's type is "
"SelectedRows, the rows of Ids contains the ids to be looked up "
"in W.");
AddOutput("Out",
"(Tensor or SelectedRows) The lookup results, which have the "
"same type as W.");
AddInput("Ids",
"An input with type int32 or int64 "
"contains the ids to be looked up in W. "
"Ids must be a column vector with rank = 2. "
"The 2nd dimension size must be 1.");
AddOutput("Out", "The lookup results, which have the same type as W.");
AddAttr<bool>("is_sparse",
"(boolean, default false) "
"Sparse update.")
......@@ -90,15 +81,10 @@ class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
Lookup Table Operator.
This operator is used to perform lookups on the parameter W,
then concatenated into a dense or sparse tensor.
The type of Ids(Input) is SelectedRows, Tensor or LoDTensor, when Ids's
type is SelectedRows, the rows of Ids contains the ids to be looked up in W;
when Ids's type is Tensor, this tensor contains the ids to be looked up in W
and it must be a column vector with rank = 2 while the 2nd dimension size must be 1,
at this time, Ids can carry the LoD (Level of Details) information, or not, and
the output only shares the LoD information with input Ids.
then concatenated into a dense tensor.
The input Ids can carry the LoD (Level of Details) information,
or not. And the output only shares the LoD information with input Ids.
)DOC");
}
......
paddle/fluid/operators/lookup_table_op.cu
浏览文件 @ bc1b7b96
......@@ -23,7 +23,7 @@ namespace operators {
template <typename T, int BlockDimX, int BlockDimY, int GridDimX,
bool PaddingFlag>
__global__ void LookupTable(T* output, const T* table, const int64_t* ids,
__global__ void LookupTable(T *output, const T *table, const int64_t *ids,
const int64_t N, const int64_t K, const int64_t D,
const int64_t padding_idx) {
int idx = threadIdx.x;
......@@ -33,8 +33,8 @@ __global__ void LookupTable(T* output, const T* table, const int64_t* ids,
int64_t id = ids[idy];
PADDLE_ASSERT(id >= 0);
PADDLE_ASSERT(id < N);
T* out = output + idy * D;
const T* tab = table + id * D;
T *out = output + idy * D;
const T *tab = table + id * D;
for (int i = idx; i < D; i += BlockDimX) {
if (PaddingFlag) {
if (id == padding_idx)
......@@ -50,7 +50,7 @@ __global__ void LookupTable(T* output, const T* table, const int64_t* ids,
}
template <typename T, int BlockDimX, int BlockDimY, int GridDimX>
__global__ void LookupTableGrad(T* table, const T* output, const int64_t* ids,
__global__ void LookupTableGrad(T *table, const T *output, const int64_t *ids,
const int64_t N, const int64_t K,
const int64_t D) {
int idx = threadIdx.x;
......@@ -60,8 +60,8 @@ __global__ void LookupTableGrad(T* table, const T* output, const int64_t* ids,
int id = ids[idy];
PADDLE_ASSERT(id >= 0);
PADDLE_ASSERT(id < N);
const T* out = output + idy * D;
T* tab = table + id * D;
const T *out = output + idy * D;
T *tab = table + id * D;
for (int i = idx; i < D; i += BlockDimX) {
paddle::platform::CudaAtomicAdd(&tab[i], out[i]);
}
......@@ -72,36 +72,19 @@ __global__ void LookupTableGrad(T* table, const T* output, const int64_t* ids,
template <typename T>
class LookupTableCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* table_t = context.Input<LoDTensor>("W");
void Compute(const framework::ExecutionContext &context) const override {
auto *table_t = context.Input<LoDTensor>("W");
auto *ids_t = context.Input<LoDTensor>("Ids");
auto *output_t = context.Output<LoDTensor>("Out");
int64_t padding_idx = context.Attr<int64_t>("padding_idx");
auto* ids_var = context.InputVar("Ids");
Tensor* output_t = context.Output<Tensor>("Out");
int64_t* ids;
int64_t K;
// The type of Ids(Input) is SelectedRows or LoDTensor, when Ids's type
// is LoDTensor, this tensor contains the ids to be looked up in W;
// when Ids's type is SelectedRows, the rows of Ids contains the
// ids to be looked up in W.
if (ids_var->IsType<framework::LoDTensor>()) {
auto* ids_t = context.Input<LoDTensor>("Ids");
ids = const_cast<int64_t*>(ids_t->data<int64_t>());
K = ids_t->numel();
} else if (ids_var->IsType<framework::SelectedRows>()) {
auto* ids_t = context.Input<framework::SelectedRows>("Ids");
ids = const_cast<int64_t*>(ids_t->rows().CUDAData(context.GetPlace()));
K = ids_t->rows().size();
output_t->Resize({K, table_t->dims()[1]});
} else {
PADDLE_THROW("Unsupported Variable Type of Ids");
}
size_t N = table_t->dims()[0];
size_t D = table_t->dims()[1];
auto* table = table_t->data<T>();
auto* output = output_t->mutable_data<T>(context.GetPlace());
size_t K = ids_t->numel();
auto *ids = ids_t->data<int64_t>();
auto *table = table_t->data<T>();
auto *output = output_t->mutable_data<T>(context.GetPlace());
dim3 threads(128, 8);
dim3 grids(8, 1);
......@@ -122,19 +105,19 @@ class LookupTableCUDAKernel : public framework::OpKernel<T> {
template <typename T>
class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto& dev_ctx =
void Compute(const framework::ExecutionContext &context) const override {
auto &dev_ctx =
context.template device_context<platform::CUDADeviceContext>();
bool is_sparse = context.Attr<bool>("is_sparse");
// Since paddings are not trainable and fixed in forward, the gradient of
// paddings makes no sense and we don't deal with it in backward.
if (is_sparse) {
auto* ids = context.Input<LoDTensor>("Ids");
auto* table = context.Input<LoDTensor>("W");
auto* d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto* d_table = context.Output<SelectedRows>(framework::GradVarName("W"));
auto *ids = context.Input<LoDTensor>("Ids");
auto *table = context.Input<LoDTensor>("W");
auto *d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto *d_table = context.Output<SelectedRows>(framework::GradVarName("W"));
auto* ids_data = ids->data<int64_t>();
auto *ids_data = ids->data<int64_t>();
auto ids_dim = ids->dims();
auto stream = dev_ctx.stream();
......@@ -150,12 +133,12 @@ class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
d_table->set_rows(new_rows);
auto* d_table_value = d_table->mutable_value();
auto *d_table_value = d_table->mutable_value();
d_table_value->Resize({ids_dim[0], table->dims()[1]});
d_table_value->mutable_data<T>(context.GetPlace());
auto* d_table_data = d_table_value->data<T>();
auto* d_output_data = d_output->data<T>();
auto *d_table_data = d_table_value->data<T>();
auto *d_output_data = d_output->data<T>();
PADDLE_ENFORCE_EQ(d_table_value->dims(), d_output->dims());
memory::Copy(gpu_place, d_table_data, gpu_place, d_output_data,
d_output->numel() * sizeof(T), stream);
......@@ -168,9 +151,9 @@ class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
int N = d_table_t->dims()[0];
int D = d_table_t->dims()[1];
int K = ids_t->numel();
const int64_t* ids = ids_t->data<int64_t>();
const T* d_output = d_output_t->data<T>();
T* d_table = d_table_t->mutable_data<T>(context.GetPlace());
const int64_t *ids = ids_t->data<int64_t>();
const T *d_output = d_output_t->data<T>();
T *d_table = d_table_t->mutable_data<T>(context.GetPlace());
auto t = framework::EigenVector<T>::Flatten(*d_table_t);
t.device(*dev_ctx.eigen_device()) = t.constant(static_cast<T>(0));
......
paddle/fluid/operators/lookup_table_op.h
浏览文件 @ bc1b7b96
......@@ -36,43 +36,13 @@ template <typename T>
class LookupTableKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto *ids_t = context.Input<LoDTensor>("Ids"); // int tensor
auto *output_t = context.Output<LoDTensor>("Out"); // float tensor
auto *table_var = context.InputVar("W");
auto *ids_var = context.InputVar("Ids");
Tensor *output_t = context.Output<Tensor>("Out");
int64_t padding_idx = context.Attr<int64_t>("padding_idx");
DDim table_dim;
if (table_var->IsType<LoDTensor>()) {
table_dim = context.Input<LoDTensor>("W")->dims();
} else if (table_var->IsType<SelectedRows>()) {
auto *table_t = context.Input<SelectedRows>("W");
table_dim = table_t->value().dims();
} else {
PADDLE_THROW(
"The parameter W of a LookupTable "
"must be either LoDTensor or SelectedRows");
}
int64_t *ids;
int64_t ids_numel;
// The type of Ids(Input) is SelectedRows or LoDTensor, when Ids's type
// is LoDTensor, this tensor contains the ids to be looked up in W;
// when Ids's type is SelectedRows, the rows of Ids contains the
// ids to be looked up in W.
if (ids_var->IsType<LoDTensor>()) {
auto *ids_t = context.Input<LoDTensor>("Ids");
ids = const_cast<int64_t *>(ids_t->data<int64_t>());
ids_numel = ids_t->numel();
} else if (ids_var->IsType<SelectedRows>()) {
auto *ids_t = context.Input<SelectedRows>("Ids");
ids = const_cast<int64_t *>(ids_t->rows().data());
ids_numel = ids_t->rows().size();
output_t->Resize({ids_numel, table_dim[1]});
} else {
PADDLE_THROW("Unsupported Variable Type of Ids");
}
int64_t padding_idx = context.Attr<int64_t>("padding_idx");
int64_t *ids = const_cast<int64_t *>(ids_t->data<int64_t>());
int64_t ids_numel = ids_t->numel();
if (table_var->IsType<LoDTensor>()) {
auto *table_t = context.Input<LoDTensor>("W");
......
paddle/fluid/operators/math/im2col.cc
浏览文件 @ bc1b7b96
......@@ -14,6 +14,7 @@ limitations under the License. */
#include "paddle/fluid/operators/math/im2col.h"
#include <vector>
#include "paddle/fluid/operators/math/im2col_cfo_cpu.h"
namespace paddle {
namespace operators {
......@@ -35,61 +36,18 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
PADDLE_ENFORCE(im.dims().size() == 3);
PADDLE_ENFORCE(col->dims().size() == 5);
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int output_height = col->dims()[3];
int output_width = col->dims()[4];
int channels_col = im_channels * filter_height * filter_width;
const T* im_data = im.data<T>();
T* col_data = col->data<T>();
// TODO(TJ): change me to template
// further optimaze:
// 1. padding != 1
// 2. could also support stride_h != 1
if (stride[0] == 1 && stride[1] == 1 && dilation[0] == 1 &&
dilation[1] == 1 && padding[0] == 0 && padding[1] == 0) {
int col_matrix_width = output_width * output_height;
size_t copy_size = sizeof(T) * output_width;
for (int oh = 0; oh < output_height; ++oh) {
const T* im_data_start = im_data + oh * im_width;
T* dst_data = col_data + oh * output_width;
for (int ic = 0; ic < im_channels; ++ic) {
const T* src_data = im_data_start + ic * im_height * im_width;
for (int kh = 0; kh < filter_height; ++kh) {
for (int kw = 0; kw < filter_width; ++kw) {
std::memcpy(dst_data, src_data + kw, copy_size);
dst_data = dst_data + col_matrix_width;
}
src_data = src_data + im_width;
}
}
}
return;
}
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / (filter_width * filter_height);
for (int h = 0; h < output_height; ++h) {
int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < output_width; ++w) {
int im_col_idx = w * stride[1] - padding[1] + w_offset * dilation[1];
int col_idx = (c * output_height + h) * output_width + w;
int im_idx = (im_row_idx + c_im * im_height) * im_width + im_col_idx;
col_data[col_idx] = (im_row_idx < 0 || im_row_idx >= im_height ||
im_col_idx < 0 || im_col_idx >= im_width)
? static_cast<T>(0)
: im_data[im_idx];
}
dilation[1] == 1) {
if (padding[0] == 0 && padding[1] == 0) {
im2col_sh1sw1dh1dw1ph0pw0<T>(im, col);
return;
} else if (padding[0] == 1 && padding[1] == 1) {
im2col_sh1sw1dh1dw1ph1pw1<T>(im, col);
return;
}
// TODO(TJ): complete padding >=2
}
im2col_common<T>(im, dilation, stride, padding, col);
}
};
......
paddle/fluid/operators/math/im2col_cfo_cpu.h 0 → 100644
浏览文件 @ bc1b7b96
/* 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 <vector>
#include "paddle/fluid/framework/tensor.h"
namespace paddle {
namespace operators {
namespace math {
/**
* The most common im2col algorithm.
* Support dilation, stride and padding.
*/
template <typename T>
inline void im2col_common(const framework::Tensor& im,
const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding,
framework::Tensor* col) {
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int output_height = col->dims()[3];
int output_width = col->dims()[4];
int channels_col = im_channels * filter_height * filter_width;
const T* im_data = im.data<T>();
T* col_data = col->data<T>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / (filter_width * filter_height);
for (int h = 0; h < output_height; ++h) {
int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < output_width; ++w) {
int im_col_idx = w * stride[1] - padding[1] + w_offset * dilation[1];
int col_idx = (c * output_height + h) * output_width + w;
int im_idx = (im_row_idx + c_im * im_height) * im_width + im_col_idx;
col_data[col_idx] = (im_row_idx < 0 || im_row_idx >= im_height ||
im_col_idx < 0 || im_col_idx >= im_width)
? static_cast<T>(0)
: im_data[im_idx];
}
}
}
}
/**
* im2col algorithm with strides == 1, dilations == 1, paddings == 0
*/
template <typename T>
inline void im2col_sh1sw1dh1dw1ph0pw0(const framework::Tensor& im,
framework::Tensor* col) {
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int output_height = col->dims()[3];
int output_width = col->dims()[4];
const T* im_data = im.data<T>();
T* col_data = col->data<T>();
int col_matrix_width = output_width * output_height;
int im_size = im_height * im_width;
size_t copy_size = sizeof(T) * output_width;
const T* im_data_oh = im_data;
T* dst_data_oh = col_data;
for (int oh = 0; oh < output_height; ++oh) {
const T* src_data_ic = im_data_oh;
T* dst_data = dst_data_oh;
for (int ic = 0; ic < im_channels; ++ic) {
const T* src_data = src_data_ic;
for (int kh = 0; kh < filter_height; ++kh) {
for (int kw = 0; kw < filter_width; ++kw) {
std::memcpy(dst_data, src_data + kw, copy_size);
dst_data = dst_data + col_matrix_width;
}
src_data = src_data + im_width;
}
src_data_ic = src_data_ic + im_size;
}
im_data_oh = im_data_oh + im_width;
dst_data_oh = dst_data_oh + output_width;
}
}
/**
* im2col algorithm with strides == 1, dilations == 1, paddings == 1
* and filter_width == 1 have a special implementation
*/
template <typename T>
inline void im2col_sh1sw1dh1dw1ph1pw1(const framework::Tensor& im,
framework::Tensor* col) {
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int output_height = col->dims()[3];
int output_width = col->dims()[4];
constexpr int plh = 1;
constexpr int prh = 1;
constexpr int plw = 1;
constexpr int prw = 1;
const T* im_data = im.data<T>();
T* col_data = col->data<T>();
int im_size = im_height * im_width;
int col_matrix_width = output_width * output_height;
int col_block_fh = filter_width * col_matrix_width; // fw*oh*ow
int col_block_ic = filter_height * col_block_fh; // fh*fw*oh*ow
// fill height padding
{
size_t copy_size = sizeof(T) * output_width;
T* col_start_l = col_data;
T* col_start_r = col_data + (filter_height - 1) * col_block_fh +
col_matrix_width - output_width;
for (int ic = 0; ic < im_channels; ++ic) {
T* dst_data_l = col_start_l;
T* dst_data_r = col_start_r;
for (int kw = 0; kw < filter_width; ++kw) {
std::memset(dst_data_l, 0, copy_size);
std::memset(dst_data_r, 0, copy_size);
dst_data_l = dst_data_l + col_matrix_width;
dst_data_r = dst_data_r + col_matrix_width;
}
col_start_l = col_start_l + col_block_ic;
col_start_r = col_start_r + col_block_ic;
}
}
auto pad = static_cast<T>(0);
if (filter_width == 1) {
// fill width padding
T* dst_data_ic = col_data;
for (int ic = 0; ic < im_channels; ++ic) {
T* dst_data_kh = dst_data_ic;
for (int kh = 0; kh < filter_height; ++kh) {
T* dst_data = dst_data_kh;
for (int oh = 0; oh < output_height; ++oh) {
*dst_data = pad;
dst_data = dst_data + output_width - 1;
*dst_data = pad;
++dst_data;
}
dst_data_kh = dst_data_kh + col_block_fh;
}
dst_data_ic = dst_data_ic + col_block_ic;
}
// fill core
size_t copy_size = sizeof(T) * (output_width - plw - prw);
for (int oh = 0; oh < output_height; ++oh) {
const T* im_data_start =
im_data + (oh - plh > 0 ? oh - plh : 0) * im_width;
T* dst_data = col_data + oh * output_width;
for (int ic = 0; ic < im_channels; ++ic) {
const T* src_data = im_data_start + ic * im_size;
for (int kh = 0; kh < filter_height; ++kh) {
if ((oh < plh && kh < plh) || (oh > (output_height - prh - 1) &&
kh > (filter_height - prh - 1))) {
dst_data = dst_data + col_matrix_width;
continue;
}
std::memcpy(dst_data + plw, src_data, copy_size);
dst_data = dst_data + col_matrix_width;
src_data = src_data + im_width;
}
}
}
return;
}
// filter_width != 1
// fill width padding
T* dst_data_ic = col_data;
for (int ic = 0; ic < im_channels; ++ic) {
T* dst_data_kh = dst_data_ic;
for (int kh = 0; kh < filter_height; ++kh) {
for (T* dst_data :
{dst_data_kh, dst_data_kh + (filter_width - prw) * col_matrix_width +
output_width - 1}) {
// TODO(TJ): from plh, saving repeated assignment
for (int oh = 0; oh < output_height; ++oh) {
*dst_data = pad;
dst_data = dst_data + output_width;
}
}
dst_data_kh = dst_data_kh + col_block_fh;
}
dst_data_ic = dst_data_ic + col_block_ic;
}
// TODO(TJ): use array like: size_t copy_size[kw]={sizeof(T) *
// (output_width-1)}
// length of copy_size is equal kw.
for (int oh = 0; oh < output_height; ++oh) {
const T* im_data_start = im_data + (oh - plh > 0 ? oh - plh : 0) * im_width;
T* dst_data = col_data + oh * output_width;
for (int ic = 0; ic < im_channels; ++ic) {
const T* src_data = im_data_start + ic * im_size;
for (int kh = 0; kh < filter_height; ++kh) {
if ((oh < plh && kh < plh) || (oh > (output_height - prh - 1) &&
kh > (filter_height - prh - 1))) {
dst_data = dst_data + filter_width * col_matrix_width;
continue;
}
// TODO(TJ): reuse plw-kw outside this for
// try to unify
for (int kw = 0; kw < plw; ++kw) {
std::memcpy(dst_data + (plw - kw), src_data,
sizeof(T) * (output_width - (plw - kw)));
dst_data = dst_data + col_matrix_width;
}
for (int kw = plw; kw < filter_width - prw; ++kw) {
std::memcpy(dst_data, src_data + (kw - plw),
sizeof(T) * output_width);
dst_data = dst_data + col_matrix_width;
}
int i = 1;
for (int kw = filter_width - prw; kw < filter_width; ++kw, ++i) {
std::memcpy(dst_data, src_data + (kw - plw),
sizeof(T) * (output_width - i));
dst_data = dst_data + col_matrix_width;
}
src_data = src_data + im_width;
}
}
}
}
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/math/im2col_test.cc
浏览文件 @ bc1b7b96
......@@ -14,7 +14,9 @@ limitations under the License. */
#include "paddle/fluid/operators/math/im2col.h"
#include <gtest/gtest.h>
#include <sys/time.h>
#include <vector>
#include "paddle/fluid/operators/math/im2col_cfo_cpu.h"
template <typename DeviceContext, typename Place>
void testIm2col() {
......@@ -160,82 +162,111 @@ void testIm2col() {
delete context;
}
void testIm2colCPU(int ic, int ih, int iw, int fh, int fw, int ph, int pw) {
paddle::framework::Tensor input;
paddle::framework::Tensor output;
paddle::framework::Tensor ref_output;
std::vector<int> padding({ph, pw});
std::vector<int> stride({1, 1}); // stride_y, stride_x
std::vector<int> dilation({1, 1}); // dilation_y, dilation_x
int output_height = (ih - fh + padding[0] * 2) / stride[0] + 1;
int output_width = (iw - fw + padding[1] * 2) / stride[1] + 1;
float* input_ptr =
input.mutable_data<float>({ic, ih, iw}, paddle::platform::CPUPlace());
for (int i = 0; i < input.numel(); ++i) {
input_ptr[i] = static_cast<float>(i + 1);
}
paddle::platform::CPUPlace place;
paddle::platform::CPUDeviceContext context(place);
output.mutable_data<float>({ic, fh, fw, output_height, output_width}, place);
ref_output.mutable_data<float>({ic, fh, fw, output_height, output_width},
place);
paddle::operators::math::Im2ColFunctor<
paddle::operators::math::ColFormat::kCFO,
paddle::platform::CPUDeviceContext, float>
im2col;
im2col(context, input, dilation, stride, padding, &output);
auto ref_im2col = [&](
const paddle::framework::Tensor& im, const std::vector<int>& dilation,
const std::vector<int>& stride, const std::vector<int>& padding,
paddle::framework::Tensor* col) {
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int output_height = col->dims()[3];
int output_width = col->dims()[4];
int channels_col = im_channels * filter_height * filter_width;
const float* im_data = im.data<float>();
float* col_data = col->data<float>();
for (int c = 0; c < channels_col; ++c) {
int w_offset = c % filter_width;
int h_offset = (c / filter_width) % filter_height;
int c_im = c / (filter_width * filter_height);
for (int h = 0; h < output_height; ++h) {
int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < output_width; ++w) {
int im_col_idx = w * stride[1] - padding[1] + w_offset * dilation[1];
int col_idx = (c * output_height + h) * output_width + w;
int im_idx = (im_row_idx + c_im * im_height) * im_width + im_col_idx;
col_data[col_idx] = (im_row_idx < 0 || im_row_idx >= im_height ||
im_col_idx < 0 || im_col_idx >= im_width)
? 0.f
: im_data[im_idx];
}
}
}
};
ref_im2col(input, dilation, stride, padding, &ref_output);
float* out_cfo_ptr = output.data<float>();
float* out_ref_ptr = ref_output.data<float>();
for (int i = 0; i < output.numel(); ++i) {
EXPECT_EQ(out_cfo_ptr[i], out_ref_ptr[i]);
}
}
TEST(math, im2col) {
testIm2col<paddle::platform::CPUDeviceContext, paddle::platform::CPUPlace>();
testIm2colCPU(/*ic*/ 3, /*ih*/ 5, /*iw*/ 5, /*fh*/ 3, /*fw*/ 2, /*ph*/ 0,
/*pw*/ 0);
testIm2colCPU(/*ic*/ 2, /*ih*/ 5, /*iw*/ 4, /*fh*/ 3, /*fw*/ 3, /*ph*/ 1,
/*pw*/ 1);
#ifdef PADDLE_WITH_CUDA
testIm2col<paddle::platform::CUDADeviceContext,
paddle::platform::CUDAPlace>();
#endif
}
#define PREPARE_IM2COL_CPU \
paddle::platform::CPUPlace place; \
paddle::platform::CPUDeviceContext context(place); \
paddle::framework::Tensor input; \
paddle::framework::Tensor out; \
paddle::framework::Tensor ref; \
std::vector<int> padding({ph, pw}); \
std::vector<int> stride({1, 1}); \
std::vector<int> dilation({1, 1}); \
float* input_ptr = input.mutable_data<float>({ic, ih, iw}, place); \
for (int i = 0; i < input.numel(); ++i) { \
input_ptr[i] = static_cast<float>(i + 1); \
} \
int output_height = (ih - fh + padding[0] * 2) / stride[0] + 1; \
int output_width = (iw - fw + padding[1] * 2) / stride[1] + 1; \
out.mutable_data<float>({ic, fh, fw, output_height, output_width}, place); \
ref.mutable_data<float>({ic, fh, fw, output_height, output_width}, place); \
paddle::operators::math::Im2ColFunctor< \
paddle::operators::math::ColFormat::kCFO, \
paddle::platform::CPUDeviceContext, float> \
im2col
void testIm2colCPU(int ic, int ih, int iw, int fh, int fw, int ph, int pw) {
PREPARE_IM2COL_CPU;
im2col(context, input, dilation, stride, padding, &out);
paddle::operators::math::im2col_common<float>(input, dilation, stride,
padding, &ref);
float* ref_data = ref.data<float>();
float* out_data = out.data<float>();
for (int i = 0; i < out.numel(); ++i) {
EXPECT_EQ(out_data[i], ref_data[i]);
}
}
void benchIm2col(int ic, int ih, int iw, int fh, int fw, int ph, int pw) {
PREPARE_IM2COL_CPU;
constexpr int repeat = 100;
auto GetCurrentMs = []() -> double {
struct timeval time;
gettimeofday(&time, NULL);
return 1e+3 * time.tv_sec + 1e-3 * time.tv_usec;
};
auto t1 = GetCurrentMs();
for (int i = 0; i < repeat; ++i) {
im2col(context, input, dilation, stride, padding, &out);
}
auto t2 = GetCurrentMs();
for (int i = 0; i < repeat; ++i) {
paddle::operators::math::im2col_common<float>(input, dilation, stride,
padding, &ref);
}
auto t3 = GetCurrentMs();
LOG(INFO) << "before: " << (t3 - t2) / repeat
<< ",after: " << (t2 - t1) / repeat
<< ",boost: " << ((t3 - t2) / (t2 - t1) - 1) * 100 << "%";
}
TEST(math, im2col_cputest) {
// padding_h == padding_w
for (int p = 0; p < 4; ++p) {
// width == height
testIm2colCPU(/*ic*/ 2, /*ih*/ 5, /*iw*/ 5, /*fh*/ 4, /*fw*/ 4, /*ph*/ p,
/*pw*/ p);
testIm2colCPU(/*ic*/ 2, /*ih*/ 4, /*iw*/ 4, /*fh*/ 3, /*fw*/ 3, /*ph*/ p,
/*pw*/ p);
testIm2colCPU(/*ic*/ 2, /*ih*/ 4, /*iw*/ 4, /*fh*/ 2, /*fw*/ 2, /*ph*/ p,
/*pw*/ p);
// height != width
testIm2colCPU(/*ic*/ 2, /*ih*/ 5, /*iw*/ 4, /*fh*/ 2, /*fw*/ 3, /*ph*/ p,
/*pw*/ p);
testIm2colCPU(/*ic*/ 2, /*ih*/ 5, /*iw*/ 4, /*fh*/ 1, /*fw*/ 3, /*ph*/ p,
/*pw*/ p);
testIm2colCPU(/*ic*/ 2, /*ih*/ 4, /*iw*/ 5, /*fh*/ 3, /*fw*/ 1, /*ph*/ p,
/*pw*/ p);
// filter == 1
testIm2colCPU(/*ic*/ 3, /*ih*/ 4, /*iw*/ 4, /*fh*/ 1, /*fw*/ 1, /*ph*/ p,
/*pw*/ p);
testIm2colCPU(/*ic*/ 3, /*ih*/ 3, /*iw*/ 4, /*fh*/ 1, /*fw*/ 1, /*ph*/ p,
/*pw*/ p);
}
// padding_h != padding_w
testIm2colCPU(/*ic*/ 2, /*ih*/ 4, /*iw*/ 4, /*fh*/ 2, /*fw*/ 3, /*ph*/ 1,
/*pw*/ 2);
// benchmark
for (int p : {0, 1}) {
for (int k : {1, 3, 5}) {
LOG(INFO) << "padding == " << p << ", filter == " << k;
benchIm2col(/*ic*/ 3, /*ih*/ 224, /*iw*/ 224, /*fh*/ k, /*fw*/ k,
/*ph*/ p, /*pw*/ p);
}
}
}
paddle/fluid/operators/reshape_op.cc
浏览文件 @ bc1b7b96
......@@ -127,12 +127,6 @@ class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("Out", "(Tensor). The output tensor of reshape operator.");
AddAttr<std::vector<int>>(
"shape", "(std::vector<int>) Target shape of reshape operator.");
AddAttr<bool>("inplace",
"(default: false) Change the source tensor's shape without "
"memory copy. When Attr(inplace) is set true, the output "
"tensor shares memory with Input(X), otherwise, a new output "
"tensor is created, and its data are copied from Input(x).")
.SetDefault(false);
AddComment(R"DOC(
Reshape Operator.
......@@ -233,16 +227,9 @@ class ReshapeKernel {
"sequence_reshape op.");
}
bool inplace = ctx.Attr<bool>("inplace");
out->mutable_data(ctx.GetPlace(), in->type());
framework::TensorCopySync(*in, ctx.GetPlace(), out);
out->Resize(out_dims);
if (!inplace) {
out->mutable_data(ctx.GetPlace(), in->type());
framework::TensorCopySync(*in, ctx.GetPlace(), out);
out->Resize(out_dims);
} else {
out->ShareDataWith(*in);
out->Resize(out_dims);
}
}
};
......@@ -251,19 +238,11 @@ class ReshapeGradKernel {
void operator()(const framework::ExecutionContext &ctx) const {
auto *d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto *d_x = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto in_dims = d_x->dims();
d_x->mutable_data(ctx.GetPlace(), d_out->type());
bool inplace = ctx.Attr<bool>("inplace");
auto in_dims = d_x->dims();
if (!inplace) {
framework::TensorCopy(*d_out, ctx.GetPlace(), ctx.device_context(), d_x);
ctx.device_context().Wait();
d_x->Resize(in_dims);
} else {
d_x->ShareDataWith(*d_out);
d_x->Resize(in_dims);
}
framework::TensorCopySync(*d_out, ctx.GetPlace(), d_x);
d_x->Resize(in_dims);
}
};
......
paddle/fluid/operators/split_ids_op.h
浏览文件 @ bc1b7b96
......@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include <unordered_map>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/selected_rows_functor.h"
......@@ -67,10 +68,15 @@ class SplitIdsOpKernel : public framework::OpKernel<T> {
const auto &ids_rows = ids_selected_rows->rows();
auto outs = ctx.MultiOutput<framework::SelectedRows>("Out");
const size_t shard_num = outs.size();
for (auto &out : outs) {
out->mutable_rows()->clear();
}
// get rows for outputs
for (auto &id : ids_rows) {
size_t shard_id = static_cast<size_t>(id) % shard_num;
outs[shard_id]->mutable_rows()->push_back(id);
std::unordered_map<int64_t, size_t> id_to_index;
for (size_t i = 0; i < ids_rows.size(); ++i) {
id_to_index[ids_rows[i]] = i;
size_t shard_id = static_cast<size_t>(ids_rows[i]) % shard_num;
outs[shard_id]->mutable_rows()->push_back(ids_rows[i]);
}
int64_t row_width = ids_dims[1];
......@@ -80,7 +86,8 @@ class SplitIdsOpKernel : public framework::OpKernel<T> {
{static_cast<int64_t>(out->rows().size()), row_width});
T *output = out->mutable_value()->mutable_data<T>(ddim, place);
for (int64_t i = 0; i < ddim[0]; ++i) {
memcpy(output + i * row_width, ids + out->rows()[i] * row_width,
memcpy(output + i * row_width,
ids + id_to_index[out->rows()[i]] * row_width,
row_width * sizeof(T));
}
}
......
paddle/fluid/operators/tensorrt_engine_op.cc
浏览文件 @ bc1b7b96
......@@ -163,7 +163,4 @@ REGISTER_OP_CPU_KERNEL(
ops::TensorRTEngineKernel<paddle::platform::CPUDeviceContext, int>,
ops::TensorRTEngineKernel<paddle::platform::CPUDeviceContext, int64_t>);
// A trick to compile with the needed TensorRT op converter.
USE_TRT_CONVERTER(mul)
#endif // PADDLE_WITH_CUDA
paddle/fluid/platform/CMakeLists.txt
浏览文件 @ bc1b7b96
......@@ -60,3 +60,7 @@ cc_test(profiler_test SRCS profiler_test.cc DEPS profiler)
nv_test(float16_gpu_test SRCS float16_test.cu DEPS lod_tensor)
cc_test(float16_test SRCS float16_test.cc DEPS lod_tensor)
IF(WITH_GPU)
nv_test(cuda_helper_test SRCS cuda_helper_test.cu)
ENDIF()
paddle/fluid/platform/cpu_helper.cc
浏览文件 @ bc1b7b96
......@@ -16,6 +16,7 @@ limitations under the License. */
#include "paddle/fluid/platform/enforce.h"
#ifdef PADDLE_WITH_MKLML
#include <omp.h>
#include "paddle/fluid/platform/dynload/mklml.h"
#endif
......@@ -33,6 +34,7 @@ void SetNumThreads(int num_threads) {
#elif defined(PADDLE_WITH_MKLML)
int real_num_threads = num_threads > 1 ? num_threads : 1;
platform::dynload::MKL_Set_Num_Threads(real_num_threads);
omp_set_num_threads(num_threads);
#else
PADDLE_ENFORCE(false, "To be implemented.");
#endif
......
paddle/fluid/platform/cuda_device_function.h
浏览文件 @ bc1b7b96
......@@ -14,6 +14,10 @@ limitations under the License. */
#pragma once
#include <cuda.h>
// NOTE(): support float16 to half in header file.
#define PADDLE_CUDA_FP16
#include <cuda_fp16.h>
#include "paddle/fluid/platform/float16.h"
namespace paddle {
namespace platform {
......@@ -36,6 +40,18 @@ __forceinline__ __device__ T CudaShuffleDownSync(unsigned mask, T val,
#endif
}
// CUDA 9.0 have native compatible float16 shfl_down
#if CUDA_VERSION < 9000
template <>
__forceinline__ __device__ float16 CudaShuffleDownSync(unsigned mask,
float16 val, int delta,
int width) {
half tmp = static_cast<half>(val);
__shfl_down(tmp, static_cast<unsigned>(delta), width);
return float16(tmp);
}
#endif
template <typename T>
__forceinline__ __device__ T CudaShuffleSync(unsigned mask, T val, int src_line,
int width = 32) {
......@@ -46,6 +62,11 @@ __forceinline__ __device__ T CudaShuffleSync(unsigned mask, T val, int src_line,
#endif
}
template <typename T>
HOSTDEVICE T Infinity() {
return INFINITY;
}
template <typename T>
__device__ T reduceSum(T val, int tid, int len) {
// NOTE(zcd): The warp size should be taken from the
......
paddle/fluid/platform/cuda_helper_test.cu 0 → 100644
浏览文件 @ bc1b7b96
// 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 <gtest/gtest.h>
#include <bitset>
#include <iostream>
#include <random>
#define PADDLE_CUDA_FP16
#include "paddle/fluid/platform/cuda_device_function.h"
#include "paddle/fluid/platform/cuda_primitives.h"
#include "paddle/fluid/platform/float16.h"
using paddle::platform::PADDLE_CUDA_NUM_THREADS;
using paddle::platform::float16;
#define CUDA_ATOMIC_KERNEL(op, T) \
__global__ void op##Kernel(const T* data_a, T* data_b, size_t num) { \
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < num; \
i += blockDim.x * gridDim.x) { \
paddle::platform::CudaAtomic##op(&data_b[i], data_a[i]); \
} \
}
template <typename T>
struct AddFunctor {
T operator()(const T& a, const T& b) { return a + b; }
};
template <typename T>
struct SubFunctor {
T operator()(const T& a, const T& b) { return a - b; }
};
// NOTE(dzhwinter): the float16 add has small underflow/overflow
// so we use EXPECT_NEAR to check the result.
#define ARITHMETIC_KERNEL_LAUNCH(op, T) \
void Test##T##op(size_t num) { \
T *in1, *in2, *out; \
T *d_in1, *d_in2; \
size_t size = sizeof(T) * num; \
cudaMalloc(reinterpret_cast<void**>(&d_in1), size); \
cudaMalloc(reinterpret_cast<void**>(&d_in2), size); \
in1 = reinterpret_cast<T*>(malloc(size)); \
in2 = reinterpret_cast<T*>(malloc(size)); \
out = reinterpret_cast<T*>(malloc(size)); \
std::minstd_rand engine; \
std::uniform_real_distribution<double> dist(0.0, 1.0); \
for (size_t i = 0; i < num; ++i) { \
in1[i] = static_cast<T>(dist(engine)); \
in2[i] = static_cast<T>(dist(engine)); \
} \
cudaMemcpy(d_in1, in1, size, cudaMemcpyHostToDevice); \
cudaMemcpy(d_in2, in2, size, cudaMemcpyHostToDevice); \
op##Kernel<<<1, PADDLE_CUDA_NUM_THREADS>>>(d_in1, d_in2, num); \
cudaDeviceSynchronize(); \
cudaMemcpy(out, d_in2, size, cudaMemcpyDeviceToHost); \
cudaDeviceSynchronize(); \
for (size_t i = 0; i < num; ++i) { \
EXPECT_NEAR(static_cast<float>(out[i]), \
static_cast<float>(op##Functor<T>()(in1[i], in2[i])), \
0.001); \
} \
free(in1); \
free(in2); \
free(out); \
cudaFree(d_in1); \
cudaFree(d_in2); \
}
CUDA_ATOMIC_KERNEL(Add, float);
CUDA_ATOMIC_KERNEL(Add, double);
CUDA_ATOMIC_KERNEL(Add, float16);
ARITHMETIC_KERNEL_LAUNCH(Add, float);
ARITHMETIC_KERNEL_LAUNCH(Add, double);
ARITHMETIC_KERNEL_LAUNCH(Add, float16);
namespace paddle {
namespace platform {
USE_CUDA_ATOMIC(Sub, int);
};
};
CUDA_ATOMIC_KERNEL(Sub, int);
ARITHMETIC_KERNEL_LAUNCH(Sub, int);
// cuda primitives
TEST(CudaAtomic, Add) {
TestfloatAdd(static_cast<size_t>(10));
TestfloatAdd(static_cast<size_t>(1024 * 1024));
TestdoubleAdd(static_cast<size_t>(10));
TestdoubleAdd(static_cast<size_t>(1024 * 1024));
}
TEST(CudaAtomic, Sub) {
TestintSub(static_cast<size_t>(10));
TestintSub(static_cast<size_t>(1024 * 1024));
}
TEST(CudaAtomic, float16) {
using paddle::platform::float16;
Testfloat16Add(static_cast<size_t>(1));
Testfloat16Add(static_cast<size_t>(2));
Testfloat16Add(static_cast<size_t>(3));
Testfloat16Add(static_cast<size_t>(10));
Testfloat16Add(static_cast<size_t>(1024 * 1024));
}
paddle/fluid/platform/cuda_primitives.h
浏览文件 @ bc1b7b96
......@@ -14,12 +14,14 @@ limitations under the License. */
#pragma once
#include <cuda.h>
#include <stdio.h>
#include "paddle/fluid/platform/float16.h"
namespace paddle {
namespace platform {
#define CUDA_ATOMIC_WRAPPER(op, T) \
__device__ __forceinline__ T CudaAtomic##op(T* address, const T val)
__device__ __forceinline__ T CudaAtomic##op(T *address, const T val)
#define USE_CUDA_ATOMIC(op, T) \
CUDA_ATOMIC_WRAPPER(op, T) { return atomic##op(address, val); }
......@@ -42,17 +44,17 @@ CUDA_ATOMIC_WRAPPER(Add, int64_t) {
static_assert(sizeof(int64_t) == sizeof(long long int), // NOLINT
"long long should be int64");
return CudaAtomicAdd(
reinterpret_cast<unsigned long long int*>(address), // NOLINT
static_cast<unsigned long long int>(val)); // NOLINT
reinterpret_cast<unsigned long long int *>(address), // NOLINT
static_cast<unsigned long long int>(val)); // NOLINT
}
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 600
USE_CUDA_ATOMIC(Add, double);
#else
CUDA_ATOMIC_WRAPPER(Add, double) {
unsigned long long int* address_as_ull = // NOLINT
reinterpret_cast<unsigned long long int*>(address); // NOLINT
unsigned long long int old = *address_as_ull, assumed; // NOLINT
unsigned long long int *address_as_ull = // NOLINT
reinterpret_cast<unsigned long long int *>(address); // NOLINT
unsigned long long int old = *address_as_ull, assumed; // NOLINT
do {
assumed = old;
......@@ -64,6 +66,67 @@ CUDA_ATOMIC_WRAPPER(Add, double) {
return __longlong_as_double(old);
}
#endif
#ifdef PADDLE_CUDA_FP16
// NOTE(dzhwinter): cuda do not have atomicCAS for half.
// Just use the half address as a unsigned value address and
// do the atomicCAS. According to the value store at high 16 bits
// or low 16 bits, then do a different sum and CAS.
// Given most warp-threads will failed on the atomicCAS, so this
// implemented should be avoided in high concurrency. It's will be
// slower than the way convert value into 32bits and do a full atomicCAS.
// convert the value into float and do the add arithmetic.
// then store the result into a uint32.
inline __device__ uint32_t add_to_low_half(uint32_t val, float x) {
float16 low_half;
// the float16 in lower 16bits
low_half.x = static_cast<uint16_t>(val & 0xffffu);
low_half = static_cast<float16>(static_cast<float>(low_half) + x);
return (val & 0xffff0000u) | low_half.x;
}
inline __device__ uint32_t add_to_high_half(uint32_t val, float x) {
float16 high_half;
// the float16 in higher 16bits
high_half.x = static_cast<uint16_t>(val >> 16);
high_half = static_cast<float16>(static_cast<float>(high_half) + x);
return (val & 0xffffu) | (static_cast<uint32_t>(high_half.x) << 16);
}
CUDA_ATOMIC_WRAPPER(Add, float16) {
// concrete packed float16 value may exsits in lower or higher 16bits
// of the 32bits address.
uint32_t *address_as_ui =
reinterpret_cast<uint32_t *>(reinterpret_cast<char *>(address) -
(reinterpret_cast<size_t>(address) & 2));
float val_f = static_cast<float>(val);
uint32_t old = *address_as_ui;
uint32_t sum;
uint32_t newval;
uint32_t assumed;
if (((size_t)address & 2) == 0) {
// the float16 value stay at lower 16 bits of the address.
do {
assumed = old;
old = atomicCAS(address_as_ui, assumed, add_to_low_half(assumed, val_f));
} while (old != assumed);
float16 ret;
ret.x = old & 0xffffu;
return ret;
} else {
// the float16 value stay at higher 16 bits of the address.
do {
assumed = old;
old = atomicCAS(address_as_ui, assumed, add_to_high_half(assumed, val_f));
} while (old != assumed);
float16 ret;
ret.x = old >> 16;
return ret;
}
}
#endif
} // namespace platform
} // namespace paddle
paddle/fluid/platform/float16.h
浏览文件 @ bc1b7b96
......@@ -67,8 +67,11 @@ struct float16;
} // namespace platform
} // namespace paddle
// NOTE():
// Do not move the eigen.h header, otherwise the eigen_vector<bool> will failed.
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/platform/hostdevice.h"
#include "unsupported/Eigen/CXX11/Tensor"
namespace paddle {
namespace platform {
......@@ -898,6 +901,30 @@ struct is_pod<paddle::platform::float16> {
is_standard_layout<paddle::platform::float16>::value;
};
template <>
struct is_floating_point<paddle::platform::float16>
: std::integral_constant<
bool, std::is_same<paddle::platform::float16,
typename std::remove_cv<
paddle::platform::float16>::type>::value> {};
template <>
struct is_signed<paddle::platform::float16> {
static const bool value = true;
};
template <>
struct is_unsigned<paddle::platform::float16> {
static const bool value = false;
};
inline bool isnan(const paddle::platform::float16& a) {
return paddle::platform::isnan(a);
}
inline bool isinf(const paddle::platform::float16& a) {
return paddle::platform::isinf(a);
}
template <>
struct numeric_limits<paddle::platform::float16> {
static const bool is_specialized = true;
......
paddle/fluid/platform/float16_test.cc
浏览文件 @ bc1b7b96
......@@ -141,10 +141,36 @@ TEST(float16, lod_tensor_cpu) {
}
}
TEST(float16, floating) {
// compile time assert.
PADDLE_ASSERT(std::is_floating_point<float16>::value);
}
TEST(float16, print) {
float16 a = float16(1.0f);
std::cout << a << std::endl;
}
// CPU test
TEST(float16, isinf) {
float16 a;
a.x = 0x7c00;
float16 b = float16(INFINITY);
float16 c = static_cast<float16>(INFINITY);
EXPECT_EQ(std::isinf(a), true);
EXPECT_EQ(std::isinf(b), true);
EXPECT_EQ(std::isinf(c), true);
}
TEST(float16, isnan) {
float16 a;
a.x = 0x7fff;
float16 b = float16(NAN);
float16 c = static_cast<float16>(NAN);
EXPECT_EQ(std::isnan(a), true);
EXPECT_EQ(std::isnan(b), true);
EXPECT_EQ(std::isnan(c), true);
}
} // namespace platform
} // namespace paddle
paddle/fluid/platform/float16_test.cu
浏览文件 @ bc1b7b96
......@@ -11,11 +11,13 @@ limitations under the License. */
#include "paddle/fluid/platform/float16.h"
#include <glog/logging.h>
#include <gtest/gtest.h>
#include <bitset>
#include <iostream>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/legacy/utils/Logging.h"
#define ARITHMETIC_KERNEL(op_type, sign) \
__global__ void op_type(const half* in1, const half* in2, half* out) { \
......@@ -241,6 +243,72 @@ TEST(float16, lod_tensor_on_gpu) {
}
}
template <typename T>
struct Functor {
bool operator()(const T& val) {
return std::type_index(typeid(T)) ==
std::type_index(typeid(platform::float16));
}
};
TEST(float16, typeid) {
// the framework heavily used typeid hash
Functor<float16> functor;
float16 a = float16(.0f);
Functor<int> functor2;
int b(0);
// compile time assert
PADDLE_ASSERT(functor(a) == true);
PADDLE_ASSERT(functor2(b) == false);
}
// GPU test
TEST(float16, isinf) {
float16 a;
a.x = 0x7c00;
float16 b = float16(INFINITY);
// underflow to 0
float16 native_a(5e-40f);
// overflow to inf
float16 native_b(5e40f);
EXPECT_EQ(std::isinf(a), true);
EXPECT_EQ(std::isinf(b), true);
EXPECT_EQ(std::isinf(native_b), true);
EXPECT_EQ(native_a, float16(0));
}
TEST(float16, isnan) {
float16 a;
a.x = 0x7fff;
float16 b = float16(NAN);
float16 c = float16(5e40);
// inf * +-0 will get a nan
float16 d = c * float16(0);
EXPECT_EQ(std::isnan(a), true);
EXPECT_EQ(std::isnan(b), true);
EXPECT_EQ(std::isnan(d), true);
}
TEST(float16, cast) {
float16 a;
a.x = 0x0070;
auto b = a;
{
// change semantic, keep the same value
float16 c = reinterpret_cast<float16&>(reinterpret_cast<unsigned&>(b));
EXPECT_EQ(b, c);
}
{
// use uint32 low 16 bit store float16
uint32_t c = reinterpret_cast<uint32_t&>(b);
float16 d;
d.x = c;
EXPECT_EQ(b, d);
}
}
} // namespace platform
} // namespace paddle
#endif // PADDLE_CUDA_FP16
paddle/fluid/platform/init.cc
浏览文件 @ bc1b7b96
......@@ -23,6 +23,9 @@ limitations under the License. */
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/string/piece.h"
DEFINE_int32(paddle_num_threads, 1,
"Number of threads for each paddle instance.");
namespace paddle {
namespace framework {
......@@ -115,7 +118,7 @@ void InitDevices(bool init_p2p, const std::vector<int> devices) {
places.emplace_back(platform::CPUPlace());
platform::DeviceContextPool::Init(places);
#ifndef PADDLE_WITH_MKLDNN
platform::SetNumThreads(1);
platform::SetNumThreads(FLAGS_paddle_num_threads);
#endif
}
......
patches/grpc/completion_queue.h 0 → 100644
浏览文件 @ bc1b7b96
// 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.
/// A completion queue implements a concurrent producer-consumer queue, with
/// two main API-exposed methods: \a Next and \a AsyncNext. These
/// methods are the essential component of the gRPC C++ asynchronous API.
/// There is also a \a Shutdown method to indicate that a given completion queue
/// will no longer have regular events. This must be called before the
/// completion queue is destroyed.
/// All completion queue APIs are thread-safe and may be used concurrently with
/// any other completion queue API invocation; it is acceptable to have
/// multiple threads calling \a Next or \a AsyncNext on the same or different
/// completion queues, or to call these methods concurrently with a \a Shutdown
/// elsewhere.
/// \remark{All other API calls on completion queue should be completed before
/// a completion queue destructor is called.}
#ifndef GRPCPP_IMPL_CODEGEN_COMPLETION_QUEUE_H
#define GRPCPP_IMPL_CODEGEN_COMPLETION_QUEUE_H
#include <typeinfo>
#include <grpc/impl/codegen/atm.h>
#include <grpcpp/impl/codegen/completion_queue_tag.h>
#include <grpcpp/impl/codegen/core_codegen_interface.h>
#include <grpcpp/impl/codegen/grpc_library.h>
#include <grpcpp/impl/codegen/status.h>
#include <grpcpp/impl/codegen/time.h>
struct grpc_completion_queue;
namespace grpc {
template <class R>
class ClientReader;
template <class W>
class ClientWriter;
template <class W, class R>
class ClientReaderWriter;
template <class R>
class ServerReader;
template <class W>
class ServerWriter;
namespace internal {
template <class W, class R>
class ServerReaderWriterBody;
} // namespace internal
class Channel;
class ChannelInterface;
class ClientContext;
class CompletionQueue;
class Server;
class ServerBuilder;
class ServerContext;
class ServerInterface;
namespace internal {
class CompletionQueueTag;
class RpcMethod;
template <class ServiceType, class RequestType, class ResponseType>
class RpcMethodHandler;
template <class ServiceType, class RequestType, class ResponseType>
class ClientStreamingHandler;
template <class ServiceType, class RequestType, class ResponseType>
class ServerStreamingHandler;
template <class ServiceType, class RequestType, class ResponseType>
class BidiStreamingHandler;
class UnknownMethodHandler;
template <class Streamer, bool WriteNeeded>
class TemplatedBidiStreamingHandler;
template <class InputMessage, class OutputMessage>
class BlockingUnaryCallImpl;
} // namespace internal
extern CoreCodegenInterface* g_core_codegen_interface;
/// A thin wrapper around \ref grpc_completion_queue (see \ref
/// src/core/lib/surface/completion_queue.h).
/// See \ref doc/cpp/perf_notes.md for notes on best practices for high
/// performance servers.
class CompletionQueue : private GrpcLibraryCodegen {
public:
/// Default constructor. Implicitly creates a \a grpc_completion_queue
/// instance.
CompletionQueue()
: CompletionQueue(grpc_completion_queue_attributes{
GRPC_CQ_CURRENT_VERSION, GRPC_CQ_NEXT, GRPC_CQ_DEFAULT_POLLING}) {}
/// Wrap \a take, taking ownership of the instance.
///
/// \param take The completion queue instance to wrap. Ownership is taken.
explicit CompletionQueue(grpc_completion_queue* take);
/// Destructor. Destroys the owned wrapped completion queue / instance.
~CompletionQueue() {
if (typeid(*g_core_codegen_interface).hash_code() !=
typeid(CoreCodegenInterface).hash_code()) {
g_core_codegen_interface->grpc_completion_queue_destroy(cq_);
}
}
/// Tri-state return for AsyncNext: SHUTDOWN, GOT_EVENT, TIMEOUT.
enum NextStatus {
SHUTDOWN, ///< The completion queue has been shutdown and fully-drained
GOT_EVENT, ///< Got a new event; \a tag will be filled in with its
///< associated value; \a ok indicating its success.
TIMEOUT ///< deadline was reached.
};
/// Read from the queue, blocking until an event is available or the queue is
/// shutting down.
///
/// \param tag[out] Updated to point to the read event's tag.
/// \param ok[out] true if read a successful event, false otherwise.
///
/// Note that each tag sent to the completion queue (through RPC operations
/// or alarms) will be delivered out of the completion queue by a call to
/// Next (or a related method), regardless of whether the operation succeeded
/// or not. Success here means that this operation completed in the normal
/// valid manner.
///
/// Server-side RPC request: \a ok indicates that the RPC has indeed
/// been started. If it is false, the server has been Shutdown
/// before this particular call got matched to an incoming RPC.
///
/// Client-side StartCall/RPC invocation: \a ok indicates that the RPC is
/// going to go to the wire. If it is false, it not going to the wire. This
/// would happen if the channel is either permanently broken or
/// transiently broken but with the fail-fast option. (Note that async unary
/// RPCs don't post a CQ tag at this point, nor do client-streaming
/// or bidi-streaming RPCs that have the initial metadata corked option set.)
///
/// Client-side Write, Client-side WritesDone, Server-side Write,
/// Server-side Finish, Server-side SendInitialMetadata (which is
/// typically included in Write or Finish when not done explicitly):
/// \a ok means that the data/metadata/status/etc is going to go to the
/// wire. If it is false, it not going to the wire because the call
/// is already dead (i.e., canceled, deadline expired, other side
/// dropped the channel, etc).
///
/// Client-side Read, Server-side Read, Client-side
/// RecvInitialMetadata (which is typically included in Read if not
/// done explicitly): \a ok indicates whether there is a valid message
/// that got read. If not, you know that there are certainly no more
/// messages that can ever be read from this stream. For the client-side
/// operations, this only happens because the call is dead. For the
/// server-sider operation, though, this could happen because the client
/// has done a WritesDone already.
///
/// Client-side Finish: \a ok should always be true
///
/// Server-side AsyncNotifyWhenDone: \a ok should always be true
///
/// Alarm: \a ok is true if it expired, false if it was canceled
///
/// \return true if got an event, false if the queue is fully drained and
/// shut down.
bool Next(void** tag, bool* ok) {
return (AsyncNextInternal(tag,
ok,
g_core_codegen_interface->gpr_inf_future(
GPR_CLOCK_REALTIME)) != SHUTDOWN);
}
/// Read from the queue, blocking up to \a deadline (or the queue's shutdown).
/// Both \a tag and \a ok are updated upon success (if an event is available
/// within the \a deadline). A \a tag points to an arbitrary location usually
/// employed to uniquely identify an event.
///
/// \param tag[out] Upon sucess, updated to point to the event's tag.
/// \param ok[out] Upon sucess, true if a successful event, false otherwise
/// See documentation for CompletionQueue::Next for explanation of ok
/// \param deadline[in] How long to block in wait for an event.
///
/// \return The type of event read.
template <typename T>
NextStatus AsyncNext(void** tag, bool* ok, const T& deadline) {
TimePoint<T> deadline_tp(deadline);
return AsyncNextInternal(tag, ok, deadline_tp.raw_time());
}
/// EXPERIMENTAL
/// First executes \a F, then reads from the queue, blocking up to
/// \a deadline (or the queue's shutdown).
/// Both \a tag and \a ok are updated upon success (if an event is available
/// within the \a deadline). A \a tag points to an arbitrary location usually
/// employed to uniquely identify an event.
///
/// \param F[in] Function to execute before calling AsyncNext on this queue.
/// \param tag[out] Upon sucess, updated to point to the event's tag.
/// \param ok[out] Upon sucess, true if read a regular event, false otherwise.
/// \param deadline[in] How long to block in wait for an event.
///
/// \return The type of event read.
template <typename T, typename F>
NextStatus DoThenAsyncNext(F&& f, void** tag, bool* ok, const T& deadline) {
CompletionQueueTLSCache cache = CompletionQueueTLSCache(this);
f();
if (cache.Flush(tag, ok)) {
return GOT_EVENT;
} else {
return AsyncNext(tag, ok, deadline);
}
}
/// Request the shutdown of the queue.
///
/// \warning This method must be called at some point if this completion queue
/// is accessed with Next or AsyncNext. \a Next will not return false
/// until this method has been called and all pending tags have been drained.
/// (Likewise for \a AsyncNext returning \a NextStatus::SHUTDOWN .)
/// Only once either one of these methods does that (that is, once the queue
/// has been \em drained) can an instance of this class be destroyed.
/// Also note that applications must ensure that no work is enqueued on this
/// completion queue after this method is called.
void Shutdown();
/// Returns a \em raw pointer to the underlying \a grpc_completion_queue
/// instance.
///
/// \warning Remember that the returned instance is owned. No transfer of
/// owership is performed.
grpc_completion_queue* cq() { return cq_; }
protected:
/// Private constructor of CompletionQueue only visible to friend classes
CompletionQueue(const grpc_completion_queue_attributes& attributes) {
cq_ = g_core_codegen_interface->grpc_completion_queue_create(
g_core_codegen_interface->grpc_completion_queue_factory_lookup(
&attributes),
&attributes,
NULL);
InitialAvalanching(); // reserve this for the future shutdown
}
private:
// Friend synchronous wrappers so that they can access Pluck(), which is
// a semi-private API geared towards the synchronous implementation.
template <class R>
friend class ::grpc::ClientReader;
template <class W>
friend class ::grpc::ClientWriter;
template <class W, class R>
friend class ::grpc::ClientReaderWriter;
template <class R>
friend class ::grpc::ServerReader;
template <class W>
friend class ::grpc::ServerWriter;
template <class W, class R>
friend class ::grpc::internal::ServerReaderWriterBody;
template <class ServiceType, class RequestType, class ResponseType>
friend class ::grpc::internal::RpcMethodHandler;
template <class ServiceType, class RequestType, class ResponseType>
friend class ::grpc::internal::ClientStreamingHandler;
template <class ServiceType, class RequestType, class ResponseType>
friend class ::grpc::internal::ServerStreamingHandler;
template <class Streamer, bool WriteNeeded>
friend class ::grpc::internal::TemplatedBidiStreamingHandler;
friend class ::grpc::internal::UnknownMethodHandler;
friend class ::grpc::Server;
friend class ::grpc::ServerContext;
friend class ::grpc::ServerInterface;
template <class InputMessage, class OutputMessage>
friend class ::grpc::internal::BlockingUnaryCallImpl;
/// EXPERIMENTAL
/// Creates a Thread Local cache to store the first event
/// On this completion queue queued from this thread. Once
/// initialized, it must be flushed on the same thread.
class CompletionQueueTLSCache {
public:
CompletionQueueTLSCache(CompletionQueue* cq);
~CompletionQueueTLSCache();
bool Flush(void** tag, bool* ok);
private:
CompletionQueue* cq_;
bool flushed_;
};
NextStatus AsyncNextInternal(void** tag, bool* ok, gpr_timespec deadline);
/// Wraps \a grpc_completion_queue_pluck.
/// \warning Must not be mixed with calls to \a Next.
bool Pluck(internal::CompletionQueueTag* tag) {
auto deadline =
g_core_codegen_interface->gpr_inf_future(GPR_CLOCK_REALTIME);
auto ev = g_core_codegen_interface->grpc_completion_queue_pluck(
cq_, tag, deadline, nullptr);
bool ok = ev.success != 0;
void* ignored = tag;
GPR_CODEGEN_ASSERT(tag->FinalizeResult(&ignored, &ok));
GPR_CODEGEN_ASSERT(ignored == tag);
// Ignore mutations by FinalizeResult: Pluck returns the C API status
return ev.success != 0;
}
/// Performs a single polling pluck on \a tag.
/// \warning Must not be mixed with calls to \a Next.
///
/// TODO: sreek - This calls tag->FinalizeResult() even if the cq_ is already
/// shutdown. This is most likely a bug and if it is a bug, then change this
/// implementation to simple call the other TryPluck function with a zero
/// timeout. i.e:
/// TryPluck(tag, gpr_time_0(GPR_CLOCK_REALTIME))
void TryPluck(internal::CompletionQueueTag* tag) {
auto deadline = g_core_codegen_interface->gpr_time_0(GPR_CLOCK_REALTIME);
auto ev = g_core_codegen_interface->grpc_completion_queue_pluck(
cq_, tag, deadline, nullptr);
if (ev.type == GRPC_QUEUE_TIMEOUT) return;
bool ok = ev.success != 0;
void* ignored = tag;
// the tag must be swallowed if using TryPluck
GPR_CODEGEN_ASSERT(!tag->FinalizeResult(&ignored, &ok));
}
/// Performs a single polling pluck on \a tag. Calls tag->FinalizeResult if
/// the pluck() was successful and returned the tag.
///
/// This exects tag->FinalizeResult (if called) to return 'false' i.e expects
/// that the tag is internal not something that is returned to the user.
void TryPluck(internal::CompletionQueueTag* tag, gpr_timespec deadline) {
auto ev = g_core_codegen_interface->grpc_completion_queue_pluck(
cq_, tag, deadline, nullptr);
if (ev.type == GRPC_QUEUE_TIMEOUT || ev.type == GRPC_QUEUE_SHUTDOWN) {
return;
}
bool ok = ev.success != 0;
void* ignored = tag;
GPR_CODEGEN_ASSERT(!tag->FinalizeResult(&ignored, &ok));
}
/// Manage state of avalanching operations : completion queue tags that
/// trigger other completion queue operations. The underlying core completion
/// queue should not really shutdown until all avalanching operations have
/// been finalized. Note that we maintain the requirement that an avalanche
/// registration must take place before CQ shutdown (which must be maintained
/// elsehwere)
void InitialAvalanching() {
gpr_atm_rel_store(&avalanches_in_flight_, static_cast<gpr_atm>(1));
}
void RegisterAvalanching() {
gpr_atm_no_barrier_fetch_add(&avalanches_in_flight_,
static_cast<gpr_atm>(1));
}
void CompleteAvalanching();
grpc_completion_queue* cq_; // owned
gpr_atm avalanches_in_flight_;
};
/// A specific type of completion queue used by the processing of notifications
/// by servers. Instantiated by \a ServerBuilder.
class ServerCompletionQueue : public CompletionQueue {
public:
bool IsFrequentlyPolled() { return polling_type_ != GRPC_CQ_NON_LISTENING; }
private:
grpc_cq_polling_type polling_type_;
friend class ServerBuilder;
/// \param is_frequently_polled Informs the GRPC library about whether the
/// server completion queue would be actively polled (by calling Next() or
/// AsyncNext()). By default all server completion queues are assumed to be
/// frequently polled.
ServerCompletionQueue(grpc_cq_polling_type polling_type)
: CompletionQueue(grpc_completion_queue_attributes{
GRPC_CQ_CURRENT_VERSION, GRPC_CQ_NEXT, polling_type}),
polling_type_(polling_type) {}
};
} // namespace grpc
#endif // GRPCPP_IMPL_CODEGEN_COMPLETION_QUEUE_H
patches/grpc/fix_too_early_destory.patch 已删除 100644 → 0
浏览文件 @ 3e4083ed
diff --git a/include/grpcpp/impl/codegen/completion_queue.h b/include/grpcpp/impl/codegen/completion_queue.h
index 80c7c41982..3f7d8a7714 100644
--- a/include/grpcpp/impl/codegen/completion_queue.h
+++ b/include/grpcpp/impl/codegen/completion_queue.h
@@ -32,6 +32,8 @@
#ifndef GRPCPP_IMPL_CODEGEN_COMPLETION_QUEUE_H
#define GRPCPP_IMPL_CODEGEN_COMPLETION_QUEUE_H
+#include <typeinfo>
+
#include <grpc/impl/codegen/atm.h>
#include <grpcpp/impl/codegen/completion_queue_tag.h>
#include <grpcpp/impl/codegen/core_codegen_interface.h>
@@ -106,7 +108,9 @@ class CompletionQueue : private GrpcLibraryCodegen {
/// Destructor. Destroys the owned wrapped completion queue / instance.
~CompletionQueue() {
- g_core_codegen_interface->grpc_completion_queue_destroy(cq_);
+ if (typeid(*g_core_codegen_interface).hash_code() != typeid(CoreCodegenInterface).hash_code()) {
+ g_core_codegen_interface->grpc_completion_queue_destroy(cq_);
+ }
}
/// Tri-state return for AsyncNext: SHUTDOWN, GOT_EVENT, TIMEOUT.
diff --git a/include/grpcpp/impl/codegen/grpc_library.h b/include/grpcpp/impl/codegen/grpc_library.h
index 17c904d71a..a092b2204d 100644
--- a/include/grpcpp/impl/codegen/grpc_library.h
+++ b/include/grpcpp/impl/codegen/grpc_library.h
@@ -19,6 +19,8 @@
#ifndef GRPCPP_IMPL_CODEGEN_GRPC_LIBRARY_H
#define GRPCPP_IMPL_CODEGEN_GRPC_LIBRARY_H
+#include <typeinfo>
+
#include <grpcpp/impl/codegen/core_codegen_interface.h>
namespace grpc {
@@ -47,7 +49,8 @@ class GrpcLibraryCodegen {
}
}
virtual ~GrpcLibraryCodegen() {
- if (grpc_init_called_) {
+ if (grpc_init_called_ &&
+ typeid(*g_glip).hash_code() != typeid(GrpcLibraryInterface).hash_code()) {
GPR_CODEGEN_ASSERT(g_glip &&
"gRPC library not initialized. See "
"grpc::internal::GrpcLibraryInitializer.");
paddle/fluid/framework/details/ssa_graph_builder_factory.cc → patches/grpc/grpc_library.h
浏览文件 @ bc1b7b96
......@@ -12,39 +12,53 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/ssa_graph_builder_factory.h"
#include <fstream>
#include "paddle/fluid/framework/details/multi_devices_graph_builder.h"
#include "paddle/fluid/framework/details/ssa_graph_checker.h"
#include "paddle/fluid/framework/details/ssa_graph_printer.h"
namespace paddle {
namespace framework {
namespace details {
std::unique_ptr<SSAGraphBuilder> SSAGraphBuilderFactory::Create() {
std::unique_ptr<SSAGraphBuilder> res(
#ifdef PADDLE_WITH_CUDA
new MultiDevSSAGraphBuilder(places_, loss_var_name_, param_names_,
local_scopes_, nccl_ctxs_, strategy_)
#else
new MultiDevSSAGraphBuilder(places_, loss_var_name_, param_names_,
local_scopes_, strategy_)
#endif
); // NOLINT
if (!strategy_.debug_graphviz_path_.empty()) {
std::unique_ptr<std::ostream> fout(
new std::ofstream(strategy_.debug_graphviz_path_));
PADDLE_ENFORCE(fout->good());
std::unique_ptr<GraphvizSSAGraphPrinter> graphviz_printer(
new GraphvizSSAGraphPrinter());
res.reset(new SSAGraghBuilderWithPrinter(
std::move(fout), std::move(graphviz_printer), std::move(res)));
#ifndef GRPCPP_IMPL_CODEGEN_GRPC_LIBRARY_H
#define GRPCPP_IMPL_CODEGEN_GRPC_LIBRARY_H
#include <typeinfo>
#include <grpcpp/impl/codegen/core_codegen_interface.h>
namespace grpc {
class GrpcLibraryInterface {
public:
virtual ~GrpcLibraryInterface() = default;
virtual void init() = 0;
virtual void shutdown() = 0;
};
/// Initialized by \a grpc::GrpcLibraryInitializer from
/// <grpcpp/impl/grpc_library.h>
extern GrpcLibraryInterface* g_glip;
/// Classes that require gRPC to be initialized should inherit from this class.
class GrpcLibraryCodegen {
public:
GrpcLibraryCodegen(bool call_grpc_init = true) : grpc_init_called_(false) {
if (call_grpc_init) {
GPR_CODEGEN_ASSERT(g_glip &&
"gRPC library not initialized. See "
"grpc::internal::GrpcLibraryInitializer.");
g_glip->init();
grpc_init_called_ = true;
}
}
virtual ~GrpcLibraryCodegen() {
if (grpc_init_called_ &&
typeid(*g_glip).hash_code() !=
typeid(GrpcLibraryInterface).hash_code()) {
GPR_CODEGEN_ASSERT(g_glip &&
"gRPC library not initialized. See "
"grpc::internal::GrpcLibraryInitializer.");
g_glip->shutdown();
}
}
res.reset(new SSAGraghBuilderWithChecker(std::move(res)));
return res;
}
} // namespace details
} // namespace framework
} // namespace paddle
private:
bool grpc_init_called_;
};
} // namespace grpc
#endif // GRPCPP_IMPL_CODEGEN_GRPC_LIBRARY_H
python/paddle/fluid/__init__.py
浏览文件 @ bc1b7b96
......@@ -123,7 +123,7 @@ def __bootstrap__():
read_env_flags = [
'use_pinned_memory', 'check_nan_inf', 'benchmark', 'warpctc_dir',
'eager_delete_scope', 'use_mkldnn', 'initial_cpu_memory_in_mb',
'init_allocated_mem', 'free_idle_memory'
'init_allocated_mem', 'free_idle_memory', 'paddle_num_threads'
]
if core.is_compiled_with_dist():
read_env_flags.append('rpc_deadline')
......
python/paddle/fluid/layers/control_flow.py
浏览文件 @ bc1b7b96
......@@ -21,6 +21,7 @@ from ..layer_helper import LayerHelper, unique_name
from ..initializer import force_init_on_cpu
from ops import logical_and, logical_not, logical_or
import numpy
import warnings
__all__ = [
'While',
......@@ -280,6 +281,9 @@ class ParallelDo(object):
"""
def __init__(self, places, use_nccl=False, name=None):
warnings.warn(
"API ParallelDo is deprecated since 0.15.0. Please use ParallelExecutor instead.",
Warning)
self.helper = LayerHelper("parallel_do", name=name)
self.inputs = []
self.places = places
......@@ -338,7 +342,7 @@ class ParallelDo(object):
return [parent_block.var(name) for name in params]
def complete_op(self):
def _complete_op(self):
main_program = self.helper.main_program
current_block = main_program.current_block()
parent_block = self.parent_block()
......@@ -394,7 +398,7 @@ class BlockGuardWithCompletion(BlockGuard):
if exc_type is not None:
return False
self.rnn.status = StaticRNN.AFTER_RNN_BLOCK
self.rnn.complete_op()
self.rnn._complete_op()
return super(BlockGuardWithCompletion, self).__exit__(exc_type, exc_val,
exc_tb)
......@@ -470,7 +474,7 @@ class StaticRNN(object):
if shape is None or batch_ref is None:
raise ValueError(
"if init is None, memory at least need shape and batch_ref")
parent_block = self.parent_block()
parent_block = self._parent_block()
var_name = unique_name.generate("@".join(
[self.helper.name, "memory_boot"]))
boot_var = parent_block.create_var(
......@@ -527,7 +531,7 @@ class StaticRNN(object):
outputs={'Out': tmp_o},
attrs={'dtype': o.dtype})
out_var = self.parent_block().create_var(
out_var = self._parent_block().create_var(
name=tmp_o.name,
shape=[self.seq_len] + list(tmp_o.shape),
dtype=tmp_o.dtype)
......@@ -543,7 +547,7 @@ class StaticRNN(object):
raise TypeError("update memory should take variables")
self.memories[mem.name].mem = var
def parent_block(self):
def _parent_block(self):
prog = self.helper.main_program
parent_idx = prog.current_block().parent_idx
assert parent_idx >= 0
......@@ -560,10 +564,10 @@ class StaticRNN(object):
else:
return self.outputs
def complete_op(self):
def _complete_op(self):
main_program = self.helper.main_program
rnn_block = main_program.current_block()
parent_block = self.parent_block()
parent_block = self._parent_block()
local_inputs = set()
......@@ -643,7 +647,7 @@ class WhileGuard(BlockGuard):
if exc_type is not None:
return False
self.while_op.status = While.AFTER_WHILE_BLOCK
self.while_op.complete()
self.while_op._complete()
return super(WhileGuard, self).__exit__(exc_type, exc_val, exc_tb)
......@@ -690,7 +694,7 @@ class While(object):
def block(self):
return WhileGuard(self)
def complete(self):
def _complete(self):
main_program = self.helper.main_program
while_block = main_program.current_block()
parent_block = main_program.block(main_program.current_block()
......
python/paddle/fluid/layers/nn.py
浏览文件 @ bc1b7b96
......@@ -4473,15 +4473,14 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
"except one unknown dimension.")
helper = LayerHelper("reshape", **locals())
reshaped = helper.create_tmp_variable(dtype=x.dtype)
out = helper.create_tmp_variable(dtype=x.dtype)
helper.append_op(
type="reshape",
inputs=inputs,
attrs={"shape": shape,
"inplace": inplace},
outputs={"Out": reshaped})
attrs={"shape": shape},
outputs={"Out": out})
return helper.append_activation(reshaped)
return helper.append_activation(out)
def lod_reset(x, y=None, target_lod=None):
......
python/paddle/fluid/regularizer.py
浏览文件 @ bc1b7b96
......@@ -142,14 +142,20 @@ class L2DecayRegularizer(WeightDecayRegularizer):
dtype="float32", shape=param.shape, lod_level=param.lod_level)
if grad.type == core.VarDesc.VarType.SELECTED_ROWS:
idx = block.create_var(
dtype="int64",
shape=param.shape,
type=core.VarDesc.VarType.LOD_TENSOR)
decay = block.create_var(
dtype="float32",
shape=param.shape,
type=core.VarDesc.VarType.SELECTED_ROWS)
block.append_op(
type='extract_rows', inputs={'X': grad}, outputs={'Out': idx})
block.append_op(
type='lookup_table',
inputs={'W': param,
'Ids': grad},
'Ids': idx},
outputs={'Out': decay},
attrs={'is_sparse': True})
param = decay
......@@ -216,14 +222,20 @@ class L1DecayRegularizer(WeightDecayRegularizer):
dtype="float32", shape=param.shape, lod_level=param.lod_level)
if grad.type == core.VarDesc.VarType.SELECTED_ROWS:
idx = block.create_var(
dtype="int64",
shape=param.shape,
type=core.VarDesc.VarType.LOD_TENSOR)
decay = block.create_var(
dtype="float32",
shape=param.shape,
type=core.VarDesc.VarType.SELECTED_ROWS)
block.append_op(
type='extract_rows', inputs={'X': grad}, outputs={'Out': idx})
block.append_op(
type='lookup_table',
inputs={'W': param,
'Ids': grad},
'Ids': idx},
outputs={'Out': decay},
attrs={'is_sparse': True})
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ bc1b7b96
......@@ -40,7 +40,7 @@ function(py_test_modules TARGET_NAME)
${PYTHON_EXECUTABLE} ${PADDLE_SOURCE_DIR}/tools/test_runner.py ${py_test_modules_MODULES}
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
if (py_test_modules_SERIAL)
set_property(TEST ${TARGET_NAME} PROPERTY SERIAL 1)
set_property(TEST ${TARGET_NAME} PROPERTY RUN_SERIAL 1)
endif()
endif()
endfunction()
......
python/paddle/fluid/tests/unittests/dist_se_resnext.py
浏览文件 @ bc1b7b96
......@@ -278,7 +278,7 @@ class DistSeResneXt2x2:
def run_trainer(self, place, endpoints, trainer_id, trainers, is_dist=True):
test_program, avg_cost, train_reader, test_reader, batch_acc, predict = get_model(
batch_size=20)
batch_size=2)
if is_dist:
t = get_transpiler(trainer_id,
fluid.default_main_program(), endpoints,
......@@ -294,11 +294,7 @@ class DistSeResneXt2x2:
strategy.num_threads = 1
strategy.allow_op_delay = False
exe = fluid.ParallelExecutor(
True,
loss_name=avg_cost.name,
exec_strategy=strategy,
num_trainers=trainers,
trainer_id=trainer_id)
True, loss_name=avg_cost.name, exec_strategy=strategy)
feed_var_list = [
var for var in trainer_prog.global_block().vars.itervalues()
......
python/paddle/fluid/tests/unittests/test_dist_se_resnext.py
浏览文件 @ bc1b7b96
......@@ -19,6 +19,7 @@ import math
import unittest
import os
import sys
import signal
import subprocess
......@@ -56,7 +57,7 @@ class TestDistSeResneXt2x2(unittest.TestCase):
except os.error:
retry_times -= 1
def non_test_with_place(self):
def test_with_place(self):
# *ATTENTION* THIS TEST NEEDS AT LEAST 2GPUS TO RUN
required_envs = {
"PATH": os.getenv("PATH"),
......@@ -70,9 +71,15 @@ class TestDistSeResneXt2x2(unittest.TestCase):
local_cmd = "%s dist_se_resnext.py trainer %s 0 %s %d FLASE" % \
(self._python_interp, "127.0.0.1:1234", "127.0.0.1:1234", 1)
local_proc = subprocess.Popen(
local_cmd.split(" "), stdout=subprocess.PIPE, env=env_local)
local_cmd.split(" "),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
env=env_local)
local_proc.wait()
local_ret = local_proc.stdout.read()
out, err = local_proc.communicate()
local_ret = out
sys.stderr.write('local_loss: %s\n' % local_ret)
sys.stderr.write('local_stderr: %s\n' % err)
# Run dist train to compare with local results
ps0, ps1 = self.start_pserver()
......@@ -92,13 +99,22 @@ class TestDistSeResneXt2x2(unittest.TestCase):
FNULL = open(os.devnull, 'w')
tr0_proc = subprocess.Popen(
tr0_cmd.split(" "), stdout=subprocess.PIPE, stderr=FNULL, env=env0)
tr0_cmd.split(" "),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
env=env0)
tr1_proc = subprocess.Popen(
tr1_cmd.split(" "), stdout=subprocess.PIPE, stderr=FNULL, env=env1)
tr1_cmd.split(" "),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
env=env1)
tr0_proc.wait()
tr1_proc.wait()
loss_data0 = tr0_proc.stdout.read()
out, err = tr0_proc.communicate()
sys.stderr.write('dist_stderr: %s\n' % err)
loss_data0 = out
sys.stderr.write('dist_loss: %s\n' % loss_data0)
lines = loss_data0.split("\n")
dist_first_loss = eval(lines[0].replace(" ", ","))[0]
dist_last_loss = eval(lines[1].replace(" ", ","))[0]
......
python/paddle/fluid/tests/unittests/test_extract_rows_op.py 0 → 100644
浏览文件 @ bc1b7b96
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import paddle.fluid.core as core
from paddle.fluid.op import Operator
from op_test import OpTest
class TestExtractRows(OpTest):
def check_with_place(self, place):
scope = core.Scope()
# create and initialize Variable
feature_len = 12
rows = [0, 4, 4, 7]
np_array = np.ones((len(rows), feature_len)).astype("float32")
in_x = scope.var('X').get_selected_rows()
in_x.set_height(len(rows))
in_x.set_rows(rows)
in_x_tensor = in_x.get_tensor()
in_x_tensor.set(np_array, place)
# create Out Variable
out_tensor = scope.var('Out').get_tensor()
# create and run lookup_table operator
extract_rows_op = Operator("extract_rows", X='X', Out='Out')
extract_rows_op.run(scope, place)
# get result from Out
result_array = np.array(out_tensor)
result_array = [ele[0] for ele in result_array]
assert result_array == rows
def test_concat_rows(self):
places = [core.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(core.CUDAPlace(0))
for place in places:
self.check_with_place(place)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_flatten_op.py 0 → 100644
浏览文件 @ bc1b7b96
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
from op_test import OpTest
class TestFlattenOp(OpTest):
def setUp(self):
self.op_type = "flatten"
self.init_test_case()
self.inputs = {"X": np.random.random(self.in_shape).astype("float32")}
self.init_attrs()
self.outputs = {"Out": self.inputs["X"].reshape(self.new_shape)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out")
def init_test_case(self):
self.in_shape = (3, 2, 2, 5)
self.axis = 1
self.new_shape = (3, 20)
def init_attrs(self):
self.attrs = {"axis": self.axis}
class TestFlattenOp(TestFlattenOp):
def init_test_case(self):
self.in_shape = (3, 2, 2, 3)
self.axis = 0
self.new_shape = (1, 36)
class TestFlattenOpWithDefaultAxis(TestFlattenOp):
def init_test_case(self):
self.in_shape = (3, 2, 2, 3)
self.new_shape = (3, 12)
def init_attrs(self):
self.attrs = {}
class TestFlattenOpSixDims(TestFlattenOp):
def init_test_case(self):
self.in_shape = (3, 2, 3, 2, 4, 4)
self.axis = 4
self.new_shape = (36, 16)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_lookup_table_op.py
浏览文件 @ bc1b7b96
......@@ -49,53 +49,6 @@ class TestLookupTableOpWithPadding(TestLookupTableOp):
pass
class TestLookupTableIdsIsSelectedRows(OpTest):
def check_with_place(self, place):
scope = core.Scope()
# create and initialize Variable
height = 10
rows = [0, 4, 4, 7]
row_numel = 12
# create and initialize W Variable
W = scope.var('W').get_tensor()
W_array = np.full((height, row_numel), 1.0).astype("float32")
for i in range(height):
W_array[i] *= i
W.set(W_array, place)
# create and initialize Ids Variable
ids_selected_rows = scope.var('Ids').get_selected_rows()
ids_selected_rows.set_height(len(rows))
ids_selected_rows.set_rows(rows)
np_array = np.ones((len(rows), row_numel)).astype("float32")
ids_tensor = ids_selected_rows.get_tensor()
ids_tensor.set(np_array, place)
# create Out Variable
Out = scope.var('Out').get_selected_rows()
# create and run lookup_table operator
concat_rows_op = Operator("lookup_table", W='W', Ids='Ids', Out='Out')
concat_rows_op.run(scope, place)
# get result from Out
Out_tensor = Out.get_tensor()
result_array = np.array(Out_tensor)
# all(): return True if all elements of the iterable are true (or if the iterable is empty)
for idx, row in enumerate(rows):
assert (row == result_array[idx]).all()
def test_concat_rows(self):
places = [core.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(core.CUDAPlace(0))
for place in places:
self.check_with_place(place)
class TestLookupTableWIsSelectedRows(OpTest):
def check_with_place(self, place):
scope = core.Scope()
......
tools/codestyle/cpplint_pre_commit.hook
浏览文件 @ bc1b7b96
......@@ -4,7 +4,7 @@ TOTAL_ERRORS=0
# The trick to remove deleted files: https://stackoverflow.com/a/2413151
for file in $(git diff --cached --name-status | awk '$1 != "D" {print $2}'); do
if [[ $file =~ ^(paddle/legacy/api/.*|paddle/legacy/capi/.*|paddle/contrib/.*|paddle/legacy/cuda/.*|paddle/legacy/function/.*|paddle/legacy/gserver/.*|paddle/legacy/math/.*|paddle/legacy/optimizer/.*|paddle/legacy/parameter/.*|paddle/legacy/pserver/.*|paddle/legacy/trainer/.*|paddle/legacy/utils/.*|paddle/testing/TestUtil.*) ]]; then
if [[ $file =~ ^(paddle/legacy/api/.*|paddle/legacy/capi/.*|paddle/contrib/.*|paddle/legacy/cuda/.*|paddle/legacy/function/.*|paddle/legacy/gserver/.*|paddle/legacy/math/.*|paddle/legacy/optimizer/.*|paddle/legacy/parameter/.*|paddle/legacy/pserver/.*|paddle/legacy/trainer/.*|paddle/legacy/utils/.*|paddle/testing/TestUtil.*|patches/grpc/.*) ]]; then
continue;
else
cpplint --filter=-readability/fn_size $file;
......
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