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提交 5bfdefae 编写于 作者: T tangwei12
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Merge branch 'Pdv' into samplingIdOp

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doc/fluid/howto/optimization/timeline_cn.md
浏览文件 @ 5bfdefae
# 如何使用timeline工具做性能分析
1. 在训练的主循环外加上`with profiler.profiler(...)`。运行之后，代码会在`/tmp/profile`目录下生成一个profile的记录文件。
1. 在训练的主循环外加上`profiler.start_profiler(...)`和`profiler.stop_profiler(...)`。运行之后，代码会在`/tmp/profile`目录下生成一个profile的记录文件。
**提示：**
请不要在timeline记录信息时运行太多次迭代，因为timeline中的记录数量和迭代次数是成正比的。
```python
with profiler.profiler('All', 'total', '/tmp/profile') as prof:
for pass_id in range(pass_num):
for batch_id, data in enumerate(train_reader()):
exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[])
for pass_id in range(pass_num):
for batch_id, data in enumerate(train_reader()):
if pass_id == 0 and batch_id == 5:
profiler.start_profiler("All")
elif pass_id == 0 and batch_id == 10:
profiler.stop_profiler("total", "/tmp/profile")
exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[])
...
```
1. 运行`python paddle/tools/timeline.py`来处理`/tmp/profile`，这个程序默认会生成一个`/tmp/timeline`文件，你也可以用命令行参数来修改这个路径，请参考[timeline.py](https://github.com/PaddlePaddle/Paddle/blob/develop/tools/timeline.py)。
```python
python Paddle/tools/timeline.py --profile_path=/tmp/profile --timeline_path=timeline
```
1. 打开chrome浏览器，访问<chrome://tracing/>，用`load`按钮来加载生成的`timeline`文件。
......
doc/fluid/howto/optimization/timeline_en.md
浏览文件 @ 5bfdefae
# how to use timeline tool to do profile
1. Add `with profiler.profiler(...)` to the main training loop. After run, the code will generate a profile record file `/tmp/profile`. **Warning**: Please do not run too many batches when use profiler to record timeline information, for the profile record will grow with the batch number.
1. Add `profiler.start_profiler(...)`和`profiler.stop_profiler(...)` to the main training loop. After run, the code will generate a profile record file `/tmp/profile`. **Warning**: Please do not run too many batches when use profiler to record timeline information, for the profile record will grow with the batch number.
```python
with profiler.profiler('All', 'total', '/tmp/profile') as prof:
for pass_id in range(pass_num):
for batch_id, data in enumerate(train_reader()):
exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[],
use_program_cache=True)
for pass_id in range(pass_num):
for batch_id, data in enumerate(train_reader()):
if pass_id == 0 and batch_id == 5:
profiler.start_profiler("All")
elif pass_id == 0 and batch_id == 10:
profiler.stop_profiler("total", "/tmp/profile")
exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[])
...
```
......@@ -17,6 +19,10 @@
file `/tmp/timeline` by default. You can change the path by cmd parameter, please take a look at
[timeline.py](https://github.com/PaddlePaddle/Paddle/blob/develop/tools/timeline.py) for details.
```python
python Paddle/tools/timeline.py --profile_path=/tmp/profile --timeline_path=timeline
```
1. Open chrome and visit <chrome://tracing/>, use `load` button to load the generated `timeline` file.
![chrome tracing](./tracing.jpeg)
......
paddle/fluid/framework/details/all_reduce_op_handle.cc
浏览文件 @ 5bfdefae
......@@ -17,6 +17,7 @@
#include "paddle/fluid/framework/details/container_cast.h"
#include "paddle/fluid/framework/details/reduce_and_gather.h"
#include "paddle/fluid/framework/details/variable_visitor.h"
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
namespace framework {
......@@ -45,6 +46,7 @@ AllReduceOpHandle::AllReduceOpHandle(ir::Node *node,
#endif
void AllReduceOpHandle::RunImpl() {
platform::RecordEvent r("all_reduce", nullptr);
if (NoDummyInputSize() == 1) {
return; // No need to all reduce when GPU count = 1;
} else {
......
paddle/fluid/framework/details/reduce_op_handle.cc
浏览文件 @ 5bfdefae
......@@ -16,12 +16,14 @@
#include "paddle/fluid/framework/details/container_cast.h"
#include "paddle/fluid/framework/details/reduce_and_gather.h"
#include "paddle/fluid/framework/details/variable_visitor.h"
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
namespace framework {
namespace details {
void ReduceOpHandle::RunImpl() {
platform::RecordEvent r("reduce", nullptr);
if (places_.size() == 1) return;
// the input and output may have dummy var.
auto in_var_handles = DynamicCast<VarHandle>(inputs_);
......
paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.cc
浏览文件 @ 5bfdefae
......@@ -17,6 +17,7 @@
#include <string>
#include <vector>
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
namespace framework {
......@@ -62,6 +63,7 @@ FeedFetchList ScopeBufferedSSAGraphExecutor::Run(
eptr = std::current_exception();
}
platform::RecordEvent e("ScopeBufferedSSAGraphExecutorAfterRun", nullptr);
drop_scope_counter_ += 1;
if (!fetch_tensors.empty() ||
drop_scope_counter_ == strategy_.num_iteration_per_drop_scope_) {
......
paddle/fluid/framework/details/threaded_ssa_graph_executor.cc
浏览文件 @ 5bfdefae
......@@ -15,6 +15,7 @@
#include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
#include "paddle/fluid/framework/details/ssa_graph_builder.h"
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
namespace framework {
......@@ -34,6 +35,8 @@ ThreadedSSAGraphExecutor::ThreadedSSAGraphExecutor(
FeedFetchList ThreadedSSAGraphExecutor::Run(
const std::vector<std::string> &fetch_tensors) {
std::unique_ptr<platform::RecordEvent> event(
new platform::RecordEvent("ThreadedSSAGraphExecutorPrepare", nullptr));
std::unordered_map<OpHandleBase *, size_t> pending_ops;
std::unordered_set<VarHandleBase *> pending_vars;
BlockingQueue<VarHandleBase *> ready_vars;
......@@ -84,6 +87,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
// Clean run context
run_op_futures_.clear();
exception_holder_.Clear();
event.reset(nullptr);
// Step 3. Execution
while (!pending_vars.empty()) {
......
paddle/fluid/framework/operator.cc
浏览文件 @ 5bfdefae
......@@ -136,6 +136,8 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
platform::SetDeviceId(dev_id);
#endif
}
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
platform::RecordEvent record_event(Type(), pool.Get(place));
RunImpl(scope, place);
VLOG(10) << "+ " << DebugStringEx(&scope);
}
......@@ -639,9 +641,6 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto* dev_ctx = pool.Get(place);
// For profiling, don't move out of this function because that will result
// in the failure of multi-GPU profiling.
platform::RecordEvent record_event(Type(), dev_ctx);
// check if op[type] has kernel registered.
auto& all_op_kernels = AllOpKernels();
auto kernels_iter = all_op_kernels.find(type_);
......
paddle/fluid/inference/api/CMakeLists.txt
浏览文件 @ 5bfdefae
......@@ -74,9 +74,10 @@ if (WITH_ANAKIN) # only needed in CI
target_link_libraries(inference_anakin_api anakin anakin_saber_common)
target_link_libraries(inference_anakin_api_shared anakin anakin_saber_common)
if (WITH_TESTING)
cc_test(inference_anakin_test SRCS api_anakin_engine_tester.cc
ARGS --model=${ANAKIN_INSTALL_DIR}/mobilenet_v2.anakin.bin
DEPS inference_anakin_api_shared)
target_compile_options(inference_anakin_test BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
# this test is unstable, disable it first.
#cc_test(inference_anakin_test SRCS api_anakin_engine_tester.cc
#ARGS --model=${ANAKIN_INSTALL_DIR}/mobilenet_v2.anakin.bin
#DEPS inference_anakin_api_shared)
#target_compile_options(inference_anakin_test BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
endif(WITH_TESTING)
endif()
paddle/fluid/operators/feed_op.cc
浏览文件 @ 5bfdefae
......@@ -31,7 +31,6 @@ class FeedOp : public framework::OperatorBase {
const platform::Place &place) const override {
// get device context from pool
auto *dev_ctx = platform::DeviceContextPool::Instance().Get(place);
platform::RecordEvent record_event(Type(), dev_ctx);
auto feed_var_name = Input("X");
auto *feed_var = scope.FindVar(feed_var_name);
......
paddle/fluid/operators/fetch_barrier_op.cc
浏览文件 @ 5bfdefae
......@@ -36,12 +36,6 @@ class FetchBarrierOp : public framework::OperatorBase {
void RunImpl(const framework::Scope& scope,
const platform::Place& place) const override {
std::vector<std::string> eps = Attr<std::vector<std::string>>("endpoints");
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto& ctx = *pool.Get(place);
// For profiling
platform::RecordEvent record_event(Type(), &ctx);
distributed::RPCClient* rpc_client =
distributed::RPCClient::GetInstance<RPCCLIENT_T>();
......
paddle/fluid/operators/fetch_op.cc
浏览文件 @ 5bfdefae
......@@ -30,9 +30,6 @@ class FetchOp : public framework::OperatorBase {
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
platform::RecordEvent record_event(Type(), pool.Get(place));
auto fetch_var_name = Input("X");
auto *fetch_var = scope.FindVar(fetch_var_name);
PADDLE_ENFORCE(fetch_var != nullptr,
......
paddle/fluid/operators/load_op.cc
浏览文件 @ 5bfdefae
......@@ -31,9 +31,6 @@ class LoadOp : public framework::OperatorBase {
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto *dev_ctx = platform::DeviceContextPool::Instance().Get(place);
platform::RecordEvent record_event(Type(), dev_ctx);
// FIXME(yuyang18): We save variable to local file now, but we should change
// it to save an output stream.
auto filename = Attr<std::string>("file_path");
......
paddle/fluid/operators/lookup_table_op.cc
浏览文件 @ 5bfdefae
......@@ -32,11 +32,16 @@ class LookupTableOp : public framework::OperatorWithKernel {
auto table_dims = ctx->GetInputDim("W");
auto ids_dims = ctx->GetInputDim("Ids");
int ids_rank = ids_dims.size();
PADDLE_ENFORCE_EQ(ids_dims.size(), 2);
PADDLE_ENFORCE_EQ(ids_dims[1], 1);
PADDLE_ENFORCE_EQ(table_dims.size(), 2);
PADDLE_ENFORCE_EQ(ids_dims[ids_rank - 1], 1,
"The last dimension of the 'Ids' tensor must be 1.");
ctx->SetOutputDim("Out", {ids_dims[0], table_dims[1]});
auto output_dims =
framework::vectorize(framework::slice_ddim(ids_dims, 0, ids_rank - 1));
output_dims.push_back(table_dims[1]);
ctx->SetOutputDim("Out", framework::make_ddim(output_dims));
if (ctx->GetOutputsVarType("Out")[0] ==
framework::proto::VarType::LOD_TENSOR) {
......@@ -61,8 +66,7 @@ class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
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.");
"The last dimension size must be 1.");
AddOutput("Out", "The lookup results, which have the same type as W.");
AddAttr<bool>("is_sparse",
"(boolean, default false) "
......
paddle/fluid/operators/lookup_table_op.cu
浏览文件 @ 5bfdefae
......@@ -118,28 +118,31 @@ class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
auto *d_table = context.Output<SelectedRows>(framework::GradVarName("W"));
auto *ids_data = ids->data<int64_t>();
auto ids_dim = ids->dims();
int64_t ids_num = ids->numel();
auto stream = dev_ctx.stream();
// copy GPU memory to CPU pinned memory
framework::Vector<int64_t> new_rows;
new_rows.resize(ids_dim[0]);
new_rows.resize(ids_num);
auto gpu_place = boost::get<platform::CUDAPlace>(context.GetPlace());
// TODO(yuyang18): Strange code here.
memory::Copy(platform::CPUPlace(),
new_rows.CUDAMutableData(context.GetPlace()), gpu_place,
ids_data, ids_dim[0] * sizeof(int64_t), stream);
ids_data, ids_num * sizeof(int64_t), stream);
d_table->set_rows(new_rows);
auto *d_table_value = d_table->mutable_value();
d_table_value->Resize({ids_dim[0], table->dims()[1]});
d_table_value->Resize({ids_num, 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>();
PADDLE_ENFORCE_EQ(d_table_value->dims(), d_output->dims());
auto d_output_dims = d_output->dims();
PADDLE_ENFORCE_EQ(
d_table_value->dims(),
framework::flatten_to_2d(d_output_dims, d_output_dims.size() - 1));
memory::Copy(gpu_place, d_table_data, gpu_place, d_output_data,
d_output->numel() * sizeof(T), stream);
......
paddle/fluid/operators/lookup_table_op.h
浏览文件 @ 5bfdefae
......@@ -109,17 +109,17 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
auto *d_table = context.Output<SelectedRows>(framework::GradVarName("W"));
auto *ids_data = ids->data<int64_t>();
auto ids_dim = ids->dims();
int64_t ids_num = ids->numel();
framework::Vector<int64_t> new_rows;
new_rows.reserve(ids_dim[0]);
for (int64_t i = 0; i < ids_dim[0]; i++) {
new_rows.reserve(ids_num);
for (int64_t i = 0; i < ids_num; i++) {
new_rows.push_back(ids_data[i]);
}
d_table->set_rows(new_rows);
auto *d_table_value = d_table->mutable_value();
d_table_value->Resize({ids_dim[0], table_dim[1]});
d_table_value->Resize({ids_num, table_dim[1]});
d_table_value->mutable_data<T>(context.GetPlace());
d_table->set_height(table_dim[0]);
......@@ -127,7 +127,10 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
auto *d_output_data = d_output->data<T>();
auto *d_table_data = d_table_value->data<T>();
PADDLE_ENFORCE_EQ(d_table_value->dims(), d_output->dims());
auto d_output_dims = d_output->dims();
PADDLE_ENFORCE_EQ(
d_table_value->dims(),
framework::flatten_to_2d(d_output_dims, d_output_dims.size() - 1));
memcpy(d_table_data, d_output_data, sizeof(T) * d_output->numel());
} else {
auto *ids = context.Input<LoDTensor>("Ids");
......@@ -135,10 +138,9 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
auto *d_table = context.Output<LoDTensor>(framework::GradVarName("W"));
auto *ids_data = ids->data<int64_t>();
auto ids_dim = ids->dims();
int N = table_dim[0];
int D = d_output->dims()[1];
int D = table_dim[1];
auto *d_output_data = d_output->data<T>();
auto *d_table_data = d_table->mutable_data<T>(context.GetPlace());
......
paddle/fluid/operators/parallel_do_op.cc
浏览文件 @ 5bfdefae
......@@ -18,7 +18,6 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/threadpool.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
namespace operators {
......@@ -166,8 +165,6 @@ class ParallelDoOp : public framework::OperatorBase {
workers.emplace_back(
framework::Async([program, cur_scope, place, block, place_idx] {
// Give the thread an id to distinguish parallel block with same id.
platform::RecordThread rt(static_cast<int>(place_idx) + 1);
framework::Executor executor(place);
executor.Run(*program, cur_scope, block->ID(),
false /*create_local_scope*/);
......@@ -244,8 +241,6 @@ class ParallelDoGradOp : public framework::OperatorBase {
// execute
workers.emplace_back(
framework::Async([program, cur_scope, place, block, i] {
// Give the thread an id to distinguish parallel block with same id.
platform::RecordThread rt(static_cast<int>(i) + 1);
framework::Executor executor(place);
executor.Run(*program, cur_scope, block->ID(),
false /*create_local_scope*/);
......
paddle/fluid/operators/recv_op.cc
浏览文件 @ 5bfdefae
......@@ -40,8 +40,6 @@ class RecvOp : public framework::OperatorBase {
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto& ctx = *pool.Get(place);
// For profiling
platform::RecordEvent record_event(Type(), &ctx);
distributed::RPCClient* rpc_client =
distributed::RPCClient::GetInstance<RPCCLIENT_T>();
......
paddle/fluid/operators/send_barrier_op.cc
浏览文件 @ 5bfdefae
......@@ -39,11 +39,6 @@ class SendBarrierOp : public framework::OperatorBase {
std::vector<std::string> eps = Attr<std::vector<std::string>>("endpoints");
bool sync_mode = Attr<bool>("sync_mode");
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto& ctx = *pool.Get(place);
// For profiling
platform::RecordEvent record_event(Type(), &ctx);
distributed::RPCClient* rpc_client =
distributed::RPCClient::GetInstance<RPCCLIENT_T>();
......
paddle/fluid/operators/send_op.cc
浏览文件 @ 5bfdefae
......@@ -42,9 +42,6 @@ class SendOp : public framework::OperatorBase {
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto& ctx = *pool.Get(place);
// For profiling
platform::RecordEvent record_event(Type(), &ctx);
distributed::RPCClient* rpc_client =
distributed::RPCClient::GetInstance<RPCCLIENT_T>();
......
paddle/fluid/operators/softmax_cudnn_op.cu.cc
浏览文件 @ 5bfdefae
......@@ -30,8 +30,16 @@ class SoftmaxCUDNNKernel : public framework::OpKernel<T> {
// allocate memory on device.
Out->mutable_data<T>(context.GetPlace());
auto dims = X->dims();
auto flattened_dims = framework::flatten_to_2d(dims, dims.size() - 1);
framework::LoDTensor flattened_x;
framework::LoDTensor flattened_out;
flattened_x.ShareDataWith(*X).Resize(flattened_dims);
flattened_out.ShareDataWith(*Out).Resize(flattened_dims);
math::SoftmaxCUDNNFunctor<T>()(
context.template device_context<platform::CUDADeviceContext>(), X, Out);
context.template device_context<platform::CUDADeviceContext>(),
&flattened_x, &flattened_out);
}
};
......@@ -46,9 +54,18 @@ class SoftmaxGradCUDNNKernel : public framework::OpKernel<T> {
// allocate memory on device.
dX->mutable_data<T>(context.GetPlace());
auto dims = Out->dims();
auto flattened_dims = framework::flatten_to_2d(dims, dims.size() - 1);
framework::LoDTensor flattened_out;
framework::LoDTensor flattened_d_out;
framework::LoDTensor flattened_d_x;
flattened_out.ShareDataWith(*Out).Resize(flattened_dims);
flattened_d_out.ShareDataWith(*dOut).Resize(flattened_dims);
flattened_d_x.ShareDataWith(*dX).Resize(flattened_dims);
math::SoftmaxGradCUDNNFunctor<T>()(
context.template device_context<platform::CUDADeviceContext>(), Out,
dOut, dX);
context.template device_context<platform::CUDADeviceContext>(),
&flattened_out, &flattened_d_out, &flattened_d_x);
}
};
......
paddle/fluid/operators/softmax_mkldnn_op.cc
浏览文件 @ 5bfdefae
......@@ -26,9 +26,9 @@ using paddle::platform::MKLDNNMemDesc;
using mkldnn::memory; // Note: paddle has also "memory" namespace
using mkldnn::primitive;
using mkldnn::softmax_forward;
using mkldnn::softmax_backward;
using mkldnn::prop_kind;
using mkldnn::softmax_backward;
using mkldnn::softmax_forward;
using mkldnn::stream;
using platform::to_void_cast;
......@@ -113,17 +113,27 @@ class SoftmaxMKLDNNKernel : public paddle::framework::OpKernel<T> {
auto mkldnn_engine = dev_ctx.GetEngine();
const Tensor* input = ctx.Input<Tensor>("X");
Tensor* output = ctx.Output<Tensor>("Out");
PADDLE_ENFORCE(input->dims().size() == 2UL,
"The input of softmax op must be a 2D matrix.");
const T* input_data = input->data<T>();
// allocate memory for output
T* output_data = output->mutable_data<T>(ctx.GetPlace());
std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims());
std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
// MKL-DNN does support softmax over selected axis. Having 2D Tensor,
// we will make normalization after final eg. axis: 1
PADDLE_ENFORCE(((src_tz[0] == dst_tz[0]) && (src_tz[1] == dst_tz[1])),
"Softmax input and output dimensions should match");
PADDLE_ENFORCE_EQ(
input->dims(), output->dims(),
"The shape of softmax's input and output must be identical.");
// make sure 'output' holds memory, which will be shared by
// 'flattened_output' later.
output->mutable_data<T>(ctx.GetPlace());
// flatten input and output to 2-D matrixs
auto dims = input->dims(); // input and output share the same shape
auto flattened_dims = framework::flatten_to_2d(dims, dims.size() - 1);
framework::Tensor flattened_input;
framework::Tensor flattened_output;
flattened_input.ShareDataWith(*input).Resize(flattened_dims);
flattened_output.ShareDataWith(*output).Resize(flattened_dims);
const T* input_data = flattened_input.data<T>();
T* output_data = flattened_output.mutable_data<T>(ctx.GetPlace());
std::vector<int> src_tz = paddle::framework::vectorize2int(flattened_dims);
std::vector<int> dst_tz = src_tz;
// Same memory descriptor to be used for input and output
memory::dims softmax_tz = {src_tz[0], src_tz[1]};
// Generate keys for storing/retriving primitives for this operator
......@@ -174,23 +184,34 @@ class SoftmaxMKLDNNGradKernel : public paddle::framework::OpKernel<T> {
auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
auto mkldnn_engine = dev_ctx.GetEngine();
const Tensor* output = ctx.Input<Tensor>("Out");
const T* dst_data = output->data<T>();
auto* dout = ctx.template Input<Tensor>(framework::GradVarName("Out"));
const auto* diff_dst_ptr = dout->template data<T>();
auto* dx =
ctx.template Output<framework::Tensor>(framework::GradVarName("X"));
T* diff_src_ptr = dx->template mutable_data<T>(ctx.GetPlace());
std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
PADDLE_ENFORCE_EQ(
dout->dims(), dx->dims(),
"The shape of softmax_grad's input and output must be identical.");
// make sure 'dx' holds memory, which will be shared by 'flattened_dx'
// later.
dx->template mutable_data<T>(ctx.GetPlace());
auto dims = dout->dims(); // input and output share the same shape
auto flattened_dims = framework::flatten_to_2d(dims, dims.size() - 1);
framework::Tensor flattened_output;
framework::Tensor flattened_dout;
framework::Tensor flattened_dx;
flattened_output.ShareDataWith(*output).Resize(flattened_dims);
flattened_dout.ShareDataWith(*dout).Resize(flattened_dims);
flattened_dx.ShareDataWith(*dx).Resize(flattened_dims);
const T* dst_data = flattened_output.data<T>();
const T* diff_dst_ptr = flattened_dout.template data<T>();
T* diff_src_ptr = flattened_dx.template mutable_data<T>(ctx.GetPlace());
std::vector<int> dst_tz = paddle::framework::vectorize2int(flattened_dims);
std::vector<int> src_tz(dst_tz);
PADDLE_ENFORCE(output->dims().size() == 2UL,
"The input of softmax op must be a 2D matrix.");
// MKL-DNN does support softmax over selected axis. Having 2D Tensor,
// we will make normalization after final eg. axis: 1
PADDLE_ENFORCE(((src_tz[0] == dst_tz[0]) && (src_tz[1] == dst_tz[1])),
"Softmax input and output dimensions should match");
// Same memory descriptor to be used for input and output
memory::dims softmax_tz = {src_tz[0], src_tz[1]};
// Currently only supports NC data format
......
paddle/fluid/operators/softmax_op.cc
浏览文件 @ 5bfdefae
......@@ -37,10 +37,7 @@ class SoftmaxOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SoftmaxOp should not be null.");
auto x_dims = ctx->GetInputDim("X");
PADDLE_ENFORCE(x_dims.size() == 2UL,
"The input of softmax op must be a matrix.");
ctx->SetOutputDim("Out", x_dims);
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->ShareLoD("X", /*->*/ "Out");
}
......@@ -81,8 +78,8 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"The input tensor of softmax. "
"2-D with shape [batch_size, input_feature_dimensions].");
"The input tensor of softmax, "
"whose last dimension is the input_feature_dimensions.");
AddOutput("Out", "The normalized values with the same shape as X.")
.Reuse("X");
AddAttr<bool>(
......@@ -105,20 +102,23 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
Softmax Operator.
The input of the softmax operator is a 2-D tensor with shape N x K (N is the
batch_size, K is the dimension of input feature). The output tensor has the
same shape as the input tensor.
The input of the softmax operator is a tensor of any rank. The output tensor
has the same shape as the input.
For each row of the input tensor, the softmax operator squashes the
K-dimensional vector of arbitrary real values to a K-dimensional vector of real
values in the range [0, 1] that add up to 1.
The input tensor will first be logically flattened to a 2-D matrix. The matrix's
second dimension(row length) is as same as the last dimension of the input
tensor, and the first dimension(column length) is the product of all other
dimensions of the input tensor. For each row of the matrix, the softmax operator
squashes the K-dimensional(K is the width of the matrix, which is also the size
of the input tensor's last dimension) vector of arbitrary real values to a
K-dimensional vector of real values in the range [0, 1] that add up to 1.
It computes the exponential of the given dimension and the sum of exponential
values of all the other dimensions in the K-dimensional vector input.
Then the ratio of the exponential of the given dimension and the sum of
exponential values of all the other dimensions is the output of the softmax
operator.
For each row $i$ and each column $j$ in Input(X), we have:
For each row $i$ and each column $j$ in the matrix, we have:
$$Out[i, j] = \frac{\exp(X[i, j])}{\sum_j(exp(X[i, j])}$$
)DOC");
......
paddle/fluid/operators/softmax_op.h
浏览文件 @ 5bfdefae
......@@ -31,8 +31,16 @@ class SoftmaxKernel : public framework::OpKernel<T> {
// allocate memory on device.
Out->mutable_data<T>(context.GetPlace());
auto dims = X->dims();
auto flattened_dims = framework::flatten_to_2d(dims, dims.size() - 1);
framework::LoDTensor flattened_x;
framework::LoDTensor flattened_out;
flattened_x.ShareDataWith(*X).Resize(flattened_dims);
flattened_out.ShareDataWith(*Out).Resize(flattened_dims);
math::SoftmaxFunctor<DeviceContext, T>()(
context.template device_context<DeviceContext>(), X, Out);
context.template device_context<DeviceContext>(), &flattened_x,
&flattened_out);
}
};
......@@ -47,8 +55,18 @@ class SoftmaxGradKernel : public framework::OpKernel<T> {
// allocate memory on device.
dX->mutable_data<T>(context.GetPlace());
auto dims = Out->dims();
auto flattened_dims = framework::flatten_to_2d(dims, dims.size() - 1);
framework::LoDTensor flattened_out;
framework::LoDTensor flattened_d_out;
framework::LoDTensor flattened_d_x;
flattened_out.ShareDataWith(*Out).Resize(flattened_dims);
flattened_d_out.ShareDataWith(*dOut).Resize(flattened_dims);
flattened_d_x.ShareDataWith(*dX).Resize(flattened_dims);
math::SoftmaxGradFunctor<DeviceContext, T>()(
context.template device_context<DeviceContext>(), Out, dOut, dX);
context.template device_context<DeviceContext>(), &flattened_out,
&flattened_d_out, &flattened_d_x);
}
};
......
paddle/fluid/platform/device_tracer.cc
浏览文件 @ 5bfdefae
......@@ -30,9 +30,6 @@ limitations under the License. */
namespace paddle {
namespace platform {
namespace {
// Current thread's id. Note, we don't distinguish nested threads
// for now.
thread_local int cur_thread_id = 0;
// Tracking the nested block stacks of each thread.
thread_local std::deque<int> block_id_stack;
// Tracking the nested event stacks.
......@@ -413,12 +410,5 @@ void SetCurBlock(int block_id) { block_id_stack.push_back(block_id); }
void ClearCurBlock() { block_id_stack.pop_back(); }
int BlockDepth() { return block_id_stack.size(); }
void SetCurThread(int thread_id) { cur_thread_id = thread_id; }
void ClearCurThread() { cur_thread_id = 0; }
int CurThread() { return cur_thread_id; }
} // namespace platform
} // namespace paddle
paddle/fluid/platform/device_tracer.h
浏览文件 @ 5bfdefae
......@@ -99,9 +99,5 @@ std::string CurAnnotation();
void SetCurBlock(int block_id);
void ClearCurBlock();
int BlockDepth();
void SetCurThread(int thread_id);
void ClearCurThread();
int CurThread();
} // namespace platform
} // namespace paddle
paddle/fluid/platform/profiler.cc
浏览文件 @ 5bfdefae
......@@ -110,6 +110,8 @@ Event::Event(EventType type, std::string name, uint32_t thread_id,
has_cuda_ = dev_ctx ? platform::is_gpu_place(dev_ctx->GetPlace()) : false;
if (has_cuda_) {
auto* cuda_dev_ctx = static_cast<const CUDADeviceContext*>(dev_ctx);
PADDLE_ENFORCE(cudaSetDevice(
boost::get<platform::CUDAPlace>(cuda_dev_ctx->GetPlace()).device));
PADDLE_ENFORCE(cudaGetDevice(&device_));
PADDLE_ENFORCE(cudaEventCreate(&event_));
auto stream = cuda_dev_ctx->stream();
......@@ -176,6 +178,7 @@ void PopEvent(const std::string& name, const DeviceContext* dev_ctx) {
RecordEvent::RecordEvent(const std::string& name, const DeviceContext* dev_ctx)
: is_enabled_(false), start_ns_(PosixInNsec()) {
std::lock_guard<std::mutex> l(profiler_mu);
if (g_state == ProfilerState::kDisabled) return;
is_enabled_ = true;
dev_ctx_ = dev_ctx;
......@@ -186,11 +189,12 @@ RecordEvent::RecordEvent(const std::string& name, const DeviceContext* dev_ctx)
}
RecordEvent::~RecordEvent() {
std::lock_guard<std::mutex> l(profiler_mu);
if (g_state == ProfilerState::kDisabled || !is_enabled_) return;
DeviceTracer* tracer = GetDeviceTracer();
if (tracer) {
tracer->AddCPURecords(CurAnnotation(), start_ns_, PosixInNsec(),
BlockDepth(), CurThread());
BlockDepth(), g_thread_id);
}
ClearCurAnnotation();
PopEvent(name_, dev_ctx_);
......@@ -198,6 +202,7 @@ RecordEvent::~RecordEvent() {
RecordBlock::RecordBlock(int block_id)
: is_enabled_(false), start_ns_(PosixInNsec()) {
std::lock_guard<std::mutex> l(profiler_mu);
if (g_state == ProfilerState::kDisabled) return;
is_enabled_ = true;
SetCurBlock(block_id);
......@@ -205,27 +210,18 @@ RecordBlock::RecordBlock(int block_id)
}
RecordBlock::~RecordBlock() {
std::lock_guard<std::mutex> l(profiler_mu);
if (g_state == ProfilerState::kDisabled || !is_enabled_) return;
DeviceTracer* tracer = GetDeviceTracer();
if (tracer) {
// We try to put all blocks at the same nested depth in the
// same timeline lane. and distinguish the using thread_id.
tracer->AddCPURecords(name_, start_ns_, PosixInNsec(), BlockDepth(),
CurThread());
g_thread_id);
}
ClearCurBlock();
}
RecordThread::RecordThread(int thread_id) {
if (g_state == ProfilerState::kDisabled) return;
SetCurThread(thread_id);
}
RecordThread::~RecordThread() {
if (g_state == ProfilerState::kDisabled) return;
ClearCurThread();
}
void EnableProfiler(ProfilerState state) {
PADDLE_ENFORCE(state != ProfilerState::kDisabled,
"Can't enbale profling, since the input state is ",
......
paddle/fluid/platform/profiler.h
浏览文件 @ 5bfdefae
......@@ -95,11 +95,6 @@ struct RecordBlock {
uint64_t start_ns_;
};
struct RecordThread {
explicit RecordThread(int thread_id);
~RecordThread();
};
// Return the event list of all threads. Assumed the returned value calls
// event_lists, event_lists[i][j] represents the j-th Event of i-th thread.
std::vector<std::vector<Event>> GetAllEvents();
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 5bfdefae
......@@ -1313,13 +1313,16 @@ def sequence_softmax(input, param_attr=None, bias_attr=None, use_cudnn=True):
def softmax(input, param_attr=None, bias_attr=None, use_cudnn=True, name=None):
"""
The input of the softmax layer is a 2-D tensor with shape N x K (N is the
batch_size, K is the dimension of input feature). The output tensor has the
same shape as the input tensor.
The input of the softmax operator is a tensor of any rank. The output tensor
has the same shape as the input.
For each row of the input tensor, the softmax operator squashes the
K-dimensional vector of arbitrary real values to a K-dimensional vector of real
values in the range [0, 1] that add up to 1.
The input tensor will first be logically flattened to a 2-D matrix. The matrix's
second dimension(row length) is as same as the last dimension of the input
tensor, and the first dimension(column length) is the product of all other
dimensions of the input tensor. For each row of the matrix, the softmax operator
squashes the K-dimensional(K is the width of the matrix, which is also the size
of the input tensor's last dimension) vector of arbitrary real values to a
K-dimensional vector of real values in the range [0, 1] that add up to 1.
It computes the exponential of the given dimension and the sum of exponential
values of all the other dimensions in the K-dimensional vector input.
......@@ -1327,7 +1330,7 @@ def softmax(input, param_attr=None, bias_attr=None, use_cudnn=True, name=None):
exponential values of all the other dimensions is the output of the softmax
operator.
For each row :math:`i` and each column :math:`j` in Input(X), we have:
For each row :math:`i` and each column :math:`j` in the matrix, we have:
.. math::
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ 5bfdefae
......@@ -50,6 +50,8 @@ list(REMOVE_ITEM TEST_OPS test_parallel_executor_crf)
list(REMOVE_ITEM TEST_OPS test_parallel_executor_fetch_feed)
list(REMOVE_ITEM TEST_OPS test_dist_se_resnext)
list(REMOVE_ITEM TEST_OPS test_dist_transformer)
list(REMOVE_ITEM TEST_OPS test_parallel_executor_transformer)
list(REMOVE_ITEM TEST_OPS test_image_classification_resnet)
foreach(TEST_OP ${TEST_OPS})
py_test_modules(${TEST_OP} MODULES ${TEST_OP})
endforeach(TEST_OP)
......@@ -64,3 +66,5 @@ py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SE
py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL)
py_test_modules(test_dist_transformer MODULES test_dist_transformer SERIAL)
py_test_modules(test_dist_se_resnext MODULES test_dist_se_resnext SERIAL)
py_test_modules(test_parallel_executor_transformer MODULES test_parallel_executor_transformer SERIAL)
py_test_modules(test_image_classification_resnet MODULES test_image_classification_resnet SERIAL)
python/paddle/fluid/tests/unittests/test_lookup_table_op.py
浏览文件 @ 5bfdefae
......@@ -35,6 +35,22 @@ class TestLookupTableOp(OpTest):
self.check_grad(['W'], 'Out', no_grad_set=set('Ids'))
class TestLookupTableOpWithTensorIds(OpTest):
def setUp(self):
self.op_type = "lookup_table"
table = np.random.random((17, 31)).astype("float32")
ids = np.random.randint(
low=0, high=17, size=(2, 4, 5, 1)).astype("int64")
self.inputs = {'W': table, 'Ids': ids}
self.outputs = {'Out': table[ids.flatten()].reshape((2, 4, 5, 31))}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['W'], 'Out', no_grad_set=set('Ids'))
class TestLookupTableOpWithPadding(TestLookupTableOp):
def test_check_output(self):
ids = np.squeeze(self.inputs['Ids'])
......@@ -44,21 +60,34 @@ class TestLookupTableOpWithPadding(TestLookupTableOp):
self.check_output()
def test_check_grad(self):
# Since paddings are not trainable and fixed in forward, the gradient of
# Since paddings are not trainable and fixed in forward, the gradient of
# paddings makes no sense and we don't test the gradient here.
pass
class TestLookupTableWIsSelectedRows(OpTest):
def check_with_place(self, place):
scope = core.Scope()
class TestLookupTableOpWithTensorIdsAndPadding(TestLookupTableOpWithTensorIds):
def test_check_output(self):
ids = self.inputs['Ids']
flatten_idx = ids.flatten()
padding_idx = np.random.choice(flatten_idx, 1)[0]
self.outputs['Out'][np.squeeze(ids == padding_idx)] = np.zeros(31)
self.attrs = {'padding_idx': long(padding_idx)}
self.check_output()
def test_check_grad(self):
# Since paddings are not trainable and fixed in forward, the gradient of
# paddings makes no sense and we don't test the gradient here.
pass
# create and initialize Id Variable
class TestLookupTableWIsSelectedRows(OpTest):
def prepare_ids(self, scope, place):
ids_tensor = scope.var('Ids').get_tensor()
ids_array = np.array([[0], [4], [3], [5]]).astype("int64")
ids_tensor.set(ids_array, place)
return ids_array
# create and initialize W Variable
def prepare_w(self, scope, place):
rows = [0, 1, 2, 3, 4, 5, 6]
row_numel = 12
......@@ -71,8 +100,22 @@ class TestLookupTableWIsSelectedRows(OpTest):
w_tensor = w_selected_rows.get_tensor()
w_tensor.set(w_array, place)
# create Out Variable
out_tensor = scope.var('Out').get_tensor()
def create_out_tensor(self, scope, place):
return scope.var('Out').get_tensor()
def check_result(self, ids_array, result_array):
# all(): return True if all elements of the iterable are true (or if the iterable is empty)
for idx, row in enumerate(ids_array):
assert (row[0] == result_array[idx]).all()
def check_with_place(self, place):
scope = core.Scope()
ids_array = self.prepare_ids(scope, place)
self.prepare_w(scope, place)
out_tensor = self.create_out_tensor(scope, place)
# create and run lookup_table operator
lookup_table = Operator("lookup_table", W='W', Ids='Ids', Out='Out')
......@@ -80,9 +123,8 @@ class TestLookupTableWIsSelectedRows(OpTest):
# get result from Out
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(ids_array):
assert (row[0] == result_array[idx]).all()
self.check_result(ids_array, result_array)
def test_w_is_selected_rows(self):
places = [core.CPUPlace()]
......@@ -91,5 +133,19 @@ class TestLookupTableWIsSelectedRows(OpTest):
self.check_with_place(place)
class TestLookupTableWithTensorIdsWIsSelectedRows(
TestLookupTableWIsSelectedRows):
def prepare_ids(self, scope, place):
ids_tensor = scope.var('Ids').get_tensor()
ids_array = np.random.randint(
low=0, high=6, size=(2, 4, 3, 1)).astype("int64")
ids_tensor.set(ids_array, place)
return ids_array
def check_result(self, ids_array, result_array):
for idx, row in np.ndenumerate(ids_array):
assert (row == result_array[idx]).all()
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_softmax_op.py
浏览文件 @ 5bfdefae
......@@ -26,15 +26,22 @@ def stable_softmax(x):
class TestSoftmaxOp(OpTest):
def get_x_shape(self):
return [10, 10]
def setUp(self):
self.op_type = "softmax"
self.use_cudnn = False
self.use_mkldnn = False
self.dtype = np.float32
self.init_kernel_type()
self.shape = self.get_x_shape()
x = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
out = np.apply_along_axis(stable_softmax, 1,
x.reshape([-1, self.shape[-1]]))
out = out.reshape(self.shape)
x = np.random.uniform(0.1, 1, [10, 10]).astype(self.dtype)
out = np.apply_along_axis(stable_softmax, 1, x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
self.attrs = {
......@@ -63,6 +70,11 @@ class TestSoftmaxOp(OpTest):
self.check_grad(["X"], "Out", max_relative_error=0.01)
class TestSoftmaxOp2(TestSoftmaxOp):
def get_x_shape(self):
return [2, 3, 4, 5]
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestSoftmaxCUDNNOp(TestSoftmaxOp):
......@@ -70,6 +82,13 @@ class TestSoftmaxCUDNNOp(TestSoftmaxOp):
self.use_cudnn = True
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestSoftmaxCUDNNOp2(TestSoftmaxCUDNNOp):
def get_x_shape(self):
return [2, 3, 4, 5]
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestSoftmaxFP16Op(TestSoftmaxOp):
......@@ -83,6 +102,13 @@ class TestSoftmaxFP16Op(TestSoftmaxOp):
self.check_output_with_place(place, atol=1e-3)
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestSoftmaxFP16Op2(TestSoftmaxFP16Op):
def get_x_shape(self):
return [2, 3, 4, 5]
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestSoftmaxFP16CUDNNOp(TestSoftmaxOp):
......@@ -97,10 +123,22 @@ class TestSoftmaxFP16CUDNNOp(TestSoftmaxOp):
self.check_output_with_place(place, atol=1e-3)
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestSoftmaxFP16CUDNNOp2(TestSoftmaxFP16CUDNNOp):
def get_x_shape(self):
return [2, 3, 4, 5]
class TestSoftmaxMKLDNNOp(TestSoftmaxOp):
def init_kernel_type(self):
self.use_mkldnn = True
class TestSoftmaxMKLDNNOp2(TestSoftmaxMKLDNNOp):
def get_x_shape(self):
return [2, 3, 4, 5]
if __name__ == "__main__":
unittest.main()
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