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PaddleDetection
提交 6a4e9230 编写于 作者: T Tao Luo
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Merge branch 'develop' into mkldnn_test

上级 b8196843 078223b3
隐藏空白更改
内联 并排
Showing with 689 addition and 170 deletion
benchmark/fluid/run.sh 100644 → 100755
浏览文件 @ 6a4e9230
文件模式从 100644 更改为 100755
paddle/fluid/framework/op_desc.h
浏览文件 @ 6a4e9230
......@@ -100,16 +100,6 @@ class OpDesc {
std::vector<std::string> InputNames() const { return MapKeys(inputs_); }
std::vector<std::string> OutputNames() const { return MapKeys(outputs_); }
void SetInputMap(const VariableNameMap &input) {
this->inputs_ = input;
this->need_update_ = true;
}
void SetOutputMap(const VariableNameMap &output) {
this->outputs_ = output;
this->need_update_ = true;
}
const VariableNameMap &Inputs() const { return inputs_; }
const VariableNameMap &Outputs() const { return outputs_; }
......
paddle/fluid/inference/analysis/analyzer_tester.cc
浏览文件 @ 6a4e9230
......@@ -51,9 +51,7 @@ void TestWord2vecPrediction(const std::string& model_path) {
config.model_dir = model_path;
config.use_gpu = false;
config.device = 0;
auto predictor =
::paddle::CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(
config);
auto predictor = ::paddle::CreatePaddlePredictor<NativeConfig>(config);
// One single batch
......
paddle/fluid/inference/api/analysis_predictor_tester.cc
浏览文件 @ 6a4e9230
......@@ -27,9 +27,7 @@ TEST(AnalysisPredictor, ZeroCopy) {
config.model_dir = FLAGS_dirname + "/word2vec.inference.model";
config.use_feed_fetch_ops = false;
auto predictor =
CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
config);
auto predictor = CreatePaddlePredictor<AnalysisConfig>(config);
auto w0 = predictor->GetInputTensor("firstw");
auto w1 = predictor->GetInputTensor("secondw");
......
paddle/fluid/inference/api/api_tensorrt_subgraph_engine_tester.cc
浏览文件 @ 6a4e9230
......@@ -41,11 +41,8 @@ void CompareTensorRTWithFluid(bool enable_tensorrt) {
config1.device = 0;
config1.max_batch_size = 10;
auto predictor0 =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config0);
auto predictor1 =
CreatePaddlePredictor<MixedRTConfig,
PaddleEngineKind::kAutoMixedTensorRT>(config1);
auto predictor0 = CreatePaddlePredictor<NativeConfig>(config0);
auto predictor1 = CreatePaddlePredictor<MixedRTConfig>(config1);
for (int batch_id = 0; batch_id < 1; batch_id++) {
//# 2. Prepare input.
......
paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc
浏览文件 @ 6a4e9230
......@@ -308,18 +308,14 @@ TEST(Analyzer_rnn1, ZeroCopy) {
PaddlePlace place;
int output_size{0};
auto predictor =
CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
config);
auto predictor = CreatePaddlePredictor<AnalysisConfig>(config);
config.use_feed_fetch_ops = true;
auto native_predictor =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
config.use_feed_fetch_ops = true; // the analysis predictor needs feed/fetch.
auto analysis_predictor =
CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
config);
auto analysis_predictor = CreatePaddlePredictor<AnalysisConfig>(config);
#define NEW_TENSOR(name__) \
auto name__##_tensor = predictor->GetInputTensor(#name__);
......
paddle/fluid/inference/tests/api/tester_helper.h
浏览文件 @ 6a4e9230
......@@ -79,8 +79,7 @@ void CompareResult(const std::vector<PaddleTensor> &outputs,
std::unique_ptr<PaddlePredictor> CreateTestPredictor(
const AnalysisConfig &config, bool use_analysis = true) {
if (use_analysis) {
return CreatePaddlePredictor<contrib::AnalysisConfig,
PaddleEngineKind::kAnalysis>(config);
return CreatePaddlePredictor<contrib::AnalysisConfig>(config);
} else {
return CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(
config);
......
paddle/fluid/inference/tests/api/trt_models_tester.cc
浏览文件 @ 6a4e9230
......@@ -51,11 +51,8 @@ void CompareTensorRTWithFluid(int batch_size, std::string model_dirname) {
config1.model_dir = model_dirname;
config1.max_batch_size = batch_size;
auto predictor0 =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config0);
auto predictor1 =
CreatePaddlePredictor<MixedRTConfig,
PaddleEngineKind::kAutoMixedTensorRT>(config1);
auto predictor0 = CreatePaddlePredictor<NativeConfig>(config0);
auto predictor1 = CreatePaddlePredictor<MixedRTConfig>(config1);
// Prepare inputs
int height = 224;
int width = 224;
......
paddle/fluid/operators/distributed/CMakeLists.txt
浏览文件 @ 6a4e9230
......@@ -20,7 +20,7 @@ if(WITH_GRPC)
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)
cc_test(varhandle_test SRCS varhandle_test.cc)
cc_test(varhandle_test SRCS varhandle_test.cc DEPS profiler)
return()
endif()
......
paddle/fluid/operators/distributed/grpc_client.cc
浏览文件 @ 6a4e9230
......@@ -73,10 +73,11 @@ VarHandlePtr GRPCClient::AsyncSendVar(const std::string& ep,
const framework::Scope* p_scope = &scope;
const auto ch = GetChannel(ep_val);
SendProcessor* s = new SendProcessor(ch);
VarHandlePtr h(new VarHandle(ep, "Send", var_name_val, p_ctx, p_scope));
const std::string method = "SendRPC";
VarHandlePtr h(new VarHandle(ep, method, var_name_val, p_ctx, p_scope));
s->Prepare(h, time_out);
framework::AsyncIO([var_name_val, p_scope, p_ctx, s, this] {
framework::AsyncIO([var_name_val, p_scope, p_ctx, s, method, h, this] {
auto* var = p_scope->FindVar(var_name_val);
::grpc::ByteBuffer req;
......@@ -87,10 +88,16 @@ VarHandlePtr GRPCClient::AsyncSendVar(const std::string& ep,
// stub context
s->response_call_back_ = nullptr;
platform::RecordEvent record_event(method, p_ctx);
auto call = s->stub_g_.PrepareUnaryCall(
s->context_.get(), "/sendrecv.SendRecvService/SendVariable", req, &cq_);
call->StartCall();
call->Finish(&s->reply_, &s->status_, reinterpret_cast<void*>(s));
if (UNLIKELY(platform::IsProfileEnabled())) {
h->Wait();
}
});
req_count_++;
......@@ -122,10 +129,11 @@ VarHandlePtr GRPCClient::AsyncGetVar(const std::string& ep,
const framework::Scope* p_scope = &scope;
const auto ch = GetChannel(ep_val);
GetProcessor* s = new GetProcessor(ch);
VarHandlePtr h(new VarHandle(ep, "Get", var_name_val, p_ctx, p_scope));
const std::string method = "GetRPC";
VarHandlePtr h(new VarHandle(ep, method, var_name_val, p_ctx, p_scope));
s->Prepare(h, time_out);
framework::AsyncIO([var_name_val, s, this] {
framework::AsyncIO([var_name_val, s, method, p_ctx, h, this] {
// prepare input
sendrecv::VariableMessage req;
req.set_varname(var_name_val);
......@@ -137,10 +145,16 @@ VarHandlePtr GRPCClient::AsyncGetVar(const std::string& ep,
// stub context
s->response_call_back_ = ProcGetResponse;
platform::RecordEvent record_event(method, p_ctx);
auto call = s->stub_g_.PrepareUnaryCall(
s->context_.get(), "/sendrecv.SendRecvService/GetVariable", buf, &cq_);
call->StartCall();
call->Finish(&s->reply_, &s->status_, reinterpret_cast<void*>(s));
if (UNLIKELY(platform::IsProfileEnabled())) {
h->Wait();
}
});
req_count_++;
......@@ -161,12 +175,14 @@ VarHandlePtr GRPCClient::AsyncPrefetchVar(const std::string& ep,
const framework::Scope* p_scope = &scope;
const auto ch = GetChannel(ep_val);
GetProcessor* s = new GetProcessor(ch);
VarHandlePtr h(
new VarHandle(ep, "Prefetch", out_var_name_val, p_ctx, p_scope));
const std::string method = "PrefetchRPC";
VarHandlePtr h(new VarHandle(ep, method, out_var_name_val, p_ctx, p_scope));
s->Prepare(h, time_out);
framework::AsyncIO([in_var_name_val, out_var_name_val, ep_val, p_scope, p_ctx,
s, this] {
s, method, h, this] {
auto* var = p_scope->FindVar(in_var_name_val);
::grpc::ByteBuffer req;
......@@ -177,11 +193,17 @@ VarHandlePtr GRPCClient::AsyncPrefetchVar(const std::string& ep,
// stub context
s->response_call_back_ = ProcGetResponse;
platform::RecordEvent record_event(method, p_ctx);
auto call = s->stub_g_.PrepareUnaryCall(
s->context_.get(), "/sendrecv.SendRecvService/PrefetchVariable", req,
&cq_);
call->StartCall();
call->Finish(&s->reply_, &s->status_, static_cast<void*>(s));
if (UNLIKELY(platform::IsProfileEnabled())) {
h->Wait();
}
});
req_count_++;
......@@ -193,15 +215,24 @@ VarHandlePtr GRPCClient::AsyncSendBatchBarrier(const std::string& ep,
const auto ch = GetChannel(ep);
BatchBarrierProcessor* s = new BatchBarrierProcessor(ch);
VarHandlePtr h(new VarHandle(ep, "BatchBarrier", BATCH_BARRIER_MESSAGE,
nullptr, nullptr));
const std::string method = "BatchBarrierRPC";
VarHandlePtr h(
new VarHandle(ep, method, BATCH_BARRIER_MESSAGE, nullptr, nullptr));
s->Prepare(h, time_out);
sendrecv::VariableMessage req;
req.set_varname(BATCH_BARRIER_MESSAGE);
platform::RecordEvent record_event(method, nullptr);
auto rpc = s->stub_->AsyncSendVariable(s->context_.get(), req, &cq_);
rpc->Finish(&s->reply_, &s->status_, reinterpret_cast<void*>(s));
req_count_++;
if (UNLIKELY(platform::IsProfileEnabled())) {
h->Wait();
}
return h;
}
......@@ -209,15 +240,24 @@ VarHandlePtr GRPCClient::AsyncSendFetchBarrier(const std::string& ep,
int64_t time_out) {
const auto ch = GetChannel(ep);
FetchBarrierProcessor* s = new FetchBarrierProcessor(ch);
VarHandlePtr h(new VarHandle(ep, "FetchBarrier", FETCH_BARRIER_MESSAGE,
nullptr, nullptr));
const std::string method = "FetchBarrierRPC";
VarHandlePtr h(
new VarHandle(ep, method, FETCH_BARRIER_MESSAGE, nullptr, nullptr));
s->Prepare(h, time_out);
sendrecv::VariableMessage req;
req.set_varname(FETCH_BARRIER_MESSAGE);
platform::RecordEvent record_event(method, nullptr);
auto rpc = s->stub_->AsyncGetVariable(s->context_.get(), req, &cq_);
rpc->Finish(&s->reply_, &s->status_, reinterpret_cast<void*>(s));
req_count_++;
if (UNLIKELY(platform::IsProfileEnabled())) {
h->Wait();
}
return h;
}
......@@ -226,15 +266,23 @@ VarHandlePtr GRPCClient::AsyncSendComplete(const std::string& ep,
const auto ch = GetChannel(ep);
BatchBarrierProcessor* s = new BatchBarrierProcessor(ch);
VarHandlePtr h(
new VarHandle(ep, "SendComplete", COMPLETE_MESSAGE, nullptr, nullptr));
const std::string method = "SendCompleteRPC";
VarHandlePtr h(new VarHandle(ep, method, COMPLETE_MESSAGE, nullptr, nullptr));
s->Prepare(h, time_out);
sendrecv::VariableMessage req;
req.set_varname(COMPLETE_MESSAGE);
platform::RecordEvent record_event(method, nullptr);
auto rpc = s->stub_->AsyncSendVariable(s->context_.get(), req, &cq_);
rpc->Finish(&s->reply_, &s->status_, reinterpret_cast<void*>(s));
req_count_++;
if (UNLIKELY(platform::IsProfileEnabled())) {
h->Wait();
}
return h;
}
......@@ -244,17 +292,27 @@ VarHandlePtr GRPCClient::AsyncCheckpointNotify(const std::string& ep,
const auto ch = GetChannel(ep);
CheckpointNotifyProcessor* s = new CheckpointNotifyProcessor(ch);
VarHandlePtr h(new VarHandle(ep, "CheckPointNotify", CHECKPOINT_SAVE_MESSAGE,
nullptr, nullptr));
const std::string method = "CheckPointNotifyRPC";
VarHandlePtr h(
new VarHandle(ep, method, CHECKPOINT_SAVE_MESSAGE, nullptr, nullptr));
s->Prepare(h, time_out);
sendrecv::VariableMessage req;
req.set_varname(CHECKPOINT_SAVE_MESSAGE);
req.set_out_varname(dir);
platform::RecordEvent record_event(method, nullptr);
auto rpc = s->stub_->AsyncCheckpointNotify(s->context_.get(), req, &cq_);
rpc->Finish(&s->reply_, &s->status_, reinterpret_cast<void*>(s));
req_count_++;
if (UNLIKELY(platform::IsProfileEnabled())) {
h->Wait();
}
return h;
}
......@@ -273,6 +331,7 @@ void GRPCClient::Proceed() {
BaseProcessor* c = static_cast<BaseProcessor*>(tag);
GPR_ASSERT(ok);
PADDLE_ENFORCE(c);
if (c->status_.ok()) {
VLOG(3) << c->GetVarHandlePtr()->String() << " process";
c->Process();
......
paddle/fluid/operators/distributed/grpc_serde.cc
浏览文件 @ 6a4e9230
......@@ -36,6 +36,7 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
const platform::DeviceContext& ctx,
::grpc::ByteBuffer* msg,
const std::string& out_name) {
platform::RecordEvent record_event("serial", &ctx);
// Default DestroyCallback does nothing, When using GPU
// the CPU buffer need to be freed.
DestroyCallback destroy_callback = [](void* backing) {};
......@@ -147,6 +148,7 @@ void DeserializeFromByteBuffer(const ::grpc::ByteBuffer& msg,
const platform::DeviceContext& ctx,
const framework::Scope* scope,
framework::Variable** var) {
platform::RecordEvent record_event("deserial", &ctx);
operators::distributed::GRPCVariableResponse resp(scope, &ctx);
PADDLE_ENFORCE(resp.Parse(msg) == 0, "parse bytebuffer to tensor error!");
*var = resp.GetVar();
......
paddle/fluid/operators/listen_and_serv_op.cc
浏览文件 @ 6a4e9230
......@@ -66,7 +66,7 @@ static void ParallelExecuteBlocks(
<< "pointer: " << prepared[run_block].get();
executor->RunPreparedContext(prepared[run_block].get(), scope);
} catch (const std::exception &e) {
LOG(ERROR) << "run sub program error " << e.what();
LOG(FATAL) << "run sub program:" << idx << " error " << e.what();
}
}));
}
......
paddle/fluid/operators/momentum_op.cc
浏览文件 @ 6a4e9230
......@@ -24,7 +24,7 @@ class MomentumOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext *ctx) const override {
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Param"),
"Input(param) of Momentum should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Grad"),
......@@ -45,12 +45,15 @@ class MomentumOp : public framework::OperatorWithKernel {
"Output(VelocityOut) of Momentum should not be null.");
auto param_dim = ctx->GetInputDim("Param");
PADDLE_ENFORCE_EQ(
param_dim, ctx->GetInputDim("Grad"),
"Param and Grad input of MomentumOp should have the same dimension.");
PADDLE_ENFORCE_EQ(
param_dim, ctx->GetInputDim("Velocity"),
"Param and Velocity of MomentumOp should have the same dimension.");
if (ctx->GetInputsVarType("Grad")[0] ==
framework::proto::VarType::LOD_TENSOR) {
PADDLE_ENFORCE_EQ(
param_dim, ctx->GetInputDim("Grad"),
"Param and Grad input of MomentumOp should have the same dimension.");
PADDLE_ENFORCE_EQ(
param_dim, ctx->GetInputDim("Velocity"),
"Param and Velocity of MomentumOp should have the same dimension.");
}
PADDLE_ENFORCE_EQ(framework::product(ctx->GetInputDim("LearningRate")), 1,
"Learning_rate should be a scalar");
......@@ -58,13 +61,34 @@ class MomentumOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("VelocityOut", param_dim);
}
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
auto input_data_type =
framework::ToDataType(ctx.Input<Tensor>("Param")->type());
const framework::ExecutionContext& ctx) const override {
auto input_data_type = framework::GetDataTypeOfVar(ctx.InputVar("Param"));
return framework::OpKernelType(input_data_type, ctx.GetPlace());
}
};
class MomentumOpInferVarType : public framework::VarTypeInference {
public:
void operator()(const framework::OpDesc& op_desc,
framework::BlockDesc* block) const override {
auto input_var = op_desc.Input("Param")[0];
for (auto& out_var : op_desc.Output("ParamOut")) {
if (block->FindRecursiveOrCreateVar(input_var).GetType() ==
framework::proto::VarType::SELECTED_ROWS) {
block->FindRecursiveOrCreateVar(out_var).SetType(
framework::proto::VarType::SELECTED_ROWS);
} else if (block->FindRecursiveOrCreateVar(input_var).GetType() ==
framework::proto::VarType::LOD_TENSOR) {
block->FindRecursiveOrCreateVar(out_var).SetType(
framework::proto::VarType::LOD_TENSOR);
} else {
PADDLE_THROW(
"Only support LodTensor and SelectedRows, Unexpected Input Type.");
}
}
}
};
class MomentumOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
......@@ -115,6 +139,9 @@ $$
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(momentum, ops::MomentumOp, ops::MomentumOpMaker);
REGISTER_OP_CPU_KERNEL(momentum, ops::MomentumOpKernel<float>,
ops::MomentumOpKernel<double>);
REGISTER_OPERATOR(momentum, ops::MomentumOp, ops::MomentumOpMaker,
paddle::framework::EmptyGradOpMaker,
ops::MomentumOpInferVarType);
REGISTER_OP_CPU_KERNEL(
momentum, ops::MomentumOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::MomentumOpKernel<paddle::platform::CPUDeviceContext, double>);
paddle/fluid/operators/momentum_op.cu
浏览文件 @ 6a4e9230
......@@ -15,76 +15,7 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/momentum_op.h"
namespace paddle {
namespace operators {
template <typename T>
__global__ void MomentumKernel(const T* p, const T* g, const T* v,
const T* learning_rate, const T mu,
const int64_t num, bool use_nesterov, T* p_out,
T* v_out) {
T lr = learning_rate[0];
if (use_nesterov) {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < num;
i += blockDim.x * gridDim.x) {
T g_val = g[i];
T v_new = v[i] * mu + g_val;
v_out[i] = v_new;
p_out[i] = p[i] - (g_val + v_new * mu) * lr;
}
} else {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < num;
i += blockDim.x * gridDim.x) {
T v_new = v[i] * mu + g[i];
v_out[i] = v_new;
p_out[i] = p[i] - lr * v_new;
}
}
}
template <typename T>
class MomentumOpCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const auto* param_var = ctx.InputVar("Param");
PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s",
ctx.Inputs("Param").front(), param_var->Type().name());
const auto* grad_var = ctx.InputVar("Grad");
PADDLE_ENFORCE(grad_var->IsType<framework::LoDTensor>(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s",
ctx.Inputs("Grad").front(), grad_var->Type().name());
auto param_out = ctx.Output<framework::Tensor>("ParamOut");
auto velocity_out = ctx.Output<framework::Tensor>("VelocityOut");
auto param = ctx.Input<framework::Tensor>("Param");
auto velocity = ctx.Input<framework::Tensor>("Velocity");
auto grad = ctx.Input<framework::Tensor>("Grad");
auto learning_rate = ctx.Input<framework::Tensor>("LearningRate");
T* p_out = param_out->mutable_data<T>(ctx.GetPlace());
T* v_out = velocity_out->mutable_data<T>(ctx.GetPlace());
T mu = static_cast<T>(ctx.Attr<float>("mu"));
bool use_nesterov = ctx.Attr<bool>("use_nesterov");
auto* p = param->data<T>();
auto* v = velocity->data<T>();
auto* g = grad->data<T>();
auto* lr = learning_rate->data<T>();
int block = 512;
int grid = (param->numel() + block - 1) / block;
MomentumKernel<T><<<grid, block, 0, ctx.cuda_device_context().stream()>>>(
p, g, v, lr, mu, param->numel(), use_nesterov, p_out, v_out);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(momentum, ops::MomentumOpCUDAKernel<float>,
ops::MomentumOpCUDAKernel<double>);
REGISTER_OP_CUDA_KERNEL(
momentum, ops::MomentumOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::MomentumOpKernel<paddle::platform::CUDADeviceContext, double>);
paddle/fluid/operators/momentum_op.h
浏览文件 @ 6a4e9230
......@@ -13,35 +13,48 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <string>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/algorithm.h"
#include "paddle/fluid/operators/math/selected_rows_functor.h"
#include "paddle/fluid/platform/for_range.h"
namespace paddle {
namespace operators {
template <typename T>
class MomentumOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const auto* param_var = ctx.InputVar("Param");
PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
"The Var(%s)'s type should be LoDTensor, "
"but the received is %s",
ctx.Inputs("Param").front(), param_var->Type().name());
auto param_out = ctx.Output<framework::Tensor>("ParamOut");
auto velocity_out = ctx.Output<framework::Tensor>("VelocityOut");
auto param = ctx.Input<framework::Tensor>("Param");
auto velocity = ctx.Input<framework::Tensor>("Velocity");
auto grad = ctx.Input<framework::Tensor>("Grad");
auto learning_rate = ctx.Input<framework::Tensor>("LearningRate");
using framework::Tensor;
using framework::SelectedRows;
struct NoNesterov;
struct UseNesterov;
param_out->mutable_data<T>(ctx.GetPlace());
velocity_out->mutable_data<T>(ctx.GetPlace());
template <typename T>
class CPUDenseMomentumFunctor {
private:
const Tensor* param;
const Tensor* grad;
const Tensor* velocity;
const Tensor* learning_rate;
const T mu;
const T use_nesterov;
Tensor* param_out;
Tensor* velocity_out;
T mu = static_cast<T>(ctx.Attr<float>("mu"));
bool use_nesterov = ctx.Attr<bool>("use_nesterov");
public:
CPUDenseMomentumFunctor(const Tensor* param, const Tensor* grad,
const Tensor* velocity, const Tensor* learning_rate,
const T mu, const bool use_nesterov,
Tensor* param_out, Tensor* velocity_out)
: param(param),
grad(grad),
velocity(velocity),
learning_rate(learning_rate),
mu(mu),
use_nesterov(use_nesterov),
param_out(param_out),
velocity_out(velocity_out) {}
inline void operator()() {
auto p_out = framework::EigenVector<T>::Flatten(*param_out);
auto v_out = framework::EigenVector<T>::Flatten(*velocity_out);
......@@ -59,5 +72,283 @@ class MomentumOpKernel : public framework::OpKernel<T> {
}
};
template <typename T, typename UpdateMethod>
class DenseMomentumFunctor;
// NOTE(dzh) for performance.
// avoid if/else in inside kernel, implement GPU UseNesterov/NoNesterov as two
// functor.
template <typename T>
class DenseMomentumFunctor<T, UseNesterov> {
private:
const T* p_;
const T* g_;
const T* v_;
const T* lr_;
const T mu_;
const int64_t num_;
T* p_out_;
T* v_out_;
public:
DenseMomentumFunctor(const T* p, const T* g, const T* v,
const T* learning_rate, const T mu, const int64_t num,
T* p_out, T* v_out)
: p_(p),
g_(g),
v_(v),
lr_(learning_rate),
mu_(mu),
num_(num),
p_out_(p_out),
v_out_(v_out) {}
inline HOSTDEVICE void operator()(size_t i) const {
// put memory access in register
const T p = p_[i];
const T g = g_[i];
const T lr = lr_[0];
const T v = v_[i];
T v_out = v * mu_ + g;
T p_out = p - (g + v_out * mu_) * lr;
// write reigster to memory
v_out_[i] = v_out;
p_out_[i] = p_out;
}
};
template <typename T>
class DenseMomentumFunctor<T, NoNesterov> {
private:
const T* p_;
const T* g_;
const T* v_;
const T* lr_;
const T mu_;
const int64_t num_;
T* p_out_;
T* v_out_;
public:
DenseMomentumFunctor(const T* p, const T* g, const T* v,
const T* learning_rate, const T mu, const int64_t num,
T* p_out, T* v_out)
: p_(p),
g_(g),
v_(v),
lr_(learning_rate),
mu_(mu),
num_(num),
p_out_(p_out),
v_out_(v_out) {}
inline HOSTDEVICE void operator()(size_t i) const {
// put memory access in register
const T p = p_[i];
const T g = g_[i];
const T lr = lr_[0];
const T v = v_[i];
T v_out = v * mu_ + g;
T p_out = p - lr * v_out;
// write reigster to memory
v_out_[i] = v_out;
p_out_[i] = p_out;
}
};
template <typename T, typename UpdateMethod>
class SparseMomentumFunctor;
template <typename T>
class SparseMomentumFunctor<T, UseNesterov> {
private:
const T* p_;
const T* g_;
const T* v_;
const T* lr_;
const T mu_;
const int64_t* rows_;
const int64_t row_numel_;
const int64_t row_height_;
T* p_out_;
T* v_out_;
public:
SparseMomentumFunctor(const T* p, const T* g, const T* v, const T* lr,
const T mu, const int64_t* rows, int64_t row_numel,
int64_t row_height, T* p_out, T* v_out)
: p_(p),
g_(g),
v_(v),
lr_(lr),
mu_(mu),
rows_(rows),
row_numel_(row_numel),
row_height_(row_height),
p_out_(p_out),
v_out_(v_out) {}
inline HOSTDEVICE void operator()(size_t i) {
auto row_idx =
math::BinarySearch<int64_t>(rows_, row_height_, i / row_numel_);
T g = row_idx >= 0 ? g_[row_idx * row_numel_ + i % row_numel_] : 0;
// put memory access in register
const T p = p_[i];
const T lr = lr_[0];
const T v = v_[i];
T v_out = v * mu_ + g;
T p_out = p - (g + v_out * mu_) * lr;
// write reigster to memory
v_out_[i] = v_out;
p_out_[i] = p_out;
}
};
template <typename T>
class SparseMomentumFunctor<T, NoNesterov> {
private:
const T* p_;
const T* g_;
const T* v_;
const T* lr_;
const T mu_;
const int64_t* rows_;
const int64_t row_numel_;
const int64_t row_height_;
T* p_out_;
T* v_out_;
public:
SparseMomentumFunctor(const T* p, const T* g, const T* v, const T* lr,
const T mu, const int64_t* rows, int64_t row_numel,
int64_t row_height, T* p_out, T* v_out)
: p_(p),
g_(g),
v_(v),
lr_(lr),
mu_(mu),
rows_(rows),
row_numel_(row_numel),
row_height_(row_height),
p_out_(p_out),
v_out_(v_out) {}
inline HOSTDEVICE void operator()(size_t i) {
auto row_idx =
math::BinarySearch<int64_t>(rows_, row_height_, i / row_numel_);
T g = row_idx >= 0 ? g_[row_idx * row_numel_ + i % row_numel_] : 0;
// put memory access in register
const T p = p_[i];
const T lr = lr_[0];
const T v = v_[i];
T v_out = v * mu_ + g;
T p_out = p - v_out * lr;
// write reigster to memory
v_out_[i] = v_out;
p_out_[i] = p_out;
}
};
template <typename DeviceContext, typename T>
class MomentumOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
T mu = static_cast<T>(ctx.Attr<float>("mu"));
bool use_nesterov = ctx.Attr<bool>("use_nesterov");
auto learning_rate = ctx.Input<framework::Tensor>("LearningRate");
auto param = ctx.Input<framework::Tensor>("Param");
auto param_out = ctx.Output<framework::Tensor>("ParamOut");
auto* velocity = ctx.Input<framework::Tensor>("Velocity");
auto velocity_out = ctx.Output<framework::Tensor>("VelocityOut");
param_out->mutable_data<T>(ctx.GetPlace());
velocity_out->mutable_data<T>(ctx.GetPlace());
auto* grad_var = ctx.InputVar("Grad");
if (grad_var->IsType<framework::LoDTensor>()) {
auto grad = ctx.Input<framework::Tensor>("Grad");
if (platform::is_cpu_place(ctx.GetPlace())) {
CPUDenseMomentumFunctor<T> functor(param, grad, velocity, learning_rate,
mu, use_nesterov, param_out,
velocity_out);
functor();
} else if (platform::is_gpu_place(ctx.GetPlace())) {
platform::ForRange<DeviceContext> for_range(
static_cast<const DeviceContext&>(ctx.device_context()),
param->numel());
if (use_nesterov) {
DenseMomentumFunctor<T, UseNesterov> functor(
param->data<T>(), grad->data<T>(), velocity->data<T>(),
learning_rate->data<T>(), mu, param->numel(),
param_out->mutable_data<T>(ctx.GetPlace()),
velocity_out->mutable_data<T>(ctx.GetPlace()));
for_range(functor);
} else {
DenseMomentumFunctor<T, NoNesterov> functor(
param->data<T>(), grad->data<T>(), velocity->data<T>(),
learning_rate->data<T>(), mu, param->numel(),
param_out->mutable_data<T>(ctx.GetPlace()),
velocity_out->mutable_data<T>(ctx.GetPlace()));
for_range(functor);
}
}
} else if (grad_var->IsType<framework::SelectedRows>()) {
// sparse update embedding with selectedrows
auto grad = ctx.Input<framework::SelectedRows>("Grad");
// sparse update maybe empty.
if (grad->rows().size() == 0) {
VLOG(3) << "Grad SelectedRows contains no data!";
return;
}
auto* merged_grad = const_cast<framework::Scope&>(ctx.scope())
.Var()
->GetMutable<framework::SelectedRows>();
math::scatter::MergeAdd<DeviceContext, T> merge_func;
merge_func(ctx.template device_context<DeviceContext>(), *grad,
merged_grad);
const int64_t* rows = nullptr;
#ifdef PADDLE_WITH_CUDA
if (platform::is_gpu_place(ctx.GetPlace())) {
rows = merged_grad->rows().CUDAData(ctx.GetPlace());
} else {
#endif
rows = merged_grad->rows().data();
#ifdef PADDLE_WITH_CUDA
}
#endif
int64_t row_numel =
merged_grad->value().numel() / merged_grad->rows().size();
platform::ForRange<DeviceContext> for_range(
static_cast<const DeviceContext&>(ctx.device_context()),
param->numel());
if (use_nesterov) {
SparseMomentumFunctor<T, UseNesterov> functor(
param->data<T>(), merged_grad->value().data<T>(),
velocity->data<T>(), learning_rate->data<T>(), mu, rows, row_numel,
static_cast<int64_t>(merged_grad->rows().size()),
param_out->mutable_data<T>(ctx.GetPlace()),
velocity_out->mutable_data<T>(ctx.GetPlace()));
for_range(functor);
} else {
SparseMomentumFunctor<T, NoNesterov> functor(
param->data<T>(), merged_grad->value().data<T>(),
velocity->data<T>(), learning_rate->data<T>(), mu, rows, row_numel,
static_cast<int64_t>(merged_grad->rows().size()),
param_out->mutable_data<T>(ctx.GetPlace()),
velocity_out->mutable_data<T>(ctx.GetPlace()));
for_range(functor);
}
} else {
PADDLE_THROW(
string::Sprintf("MomentumOp only supports LoDTensor or SelectedRows "
"gradient, but the received Variable Type is %s",
grad_var->Type().name()));
}
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/sum_op.h
浏览文件 @ 6a4e9230
......@@ -43,17 +43,31 @@ class SumKernel : public framework::OpKernel<T> {
out->mutable_data<T>(context.GetPlace());
}
auto result = EigenVector<T>::Flatten(*out);
auto &place =
*context.template device_context<DeviceContext>().eigen_device();
int start = in_place ? 1 : 0;
if (!in_place) {
math::SetConstant<DeviceContext, T> constant_functor;
constant_functor(context.template device_context<DeviceContext>(), out,
0.0);
if ((in_num >= 2) && in_vars[0]->IsType<framework::LoDTensor>() &&
in_vars[1]->IsType<framework::LoDTensor>()) {
auto &in_0 = in_vars[0]->Get<framework::LoDTensor>();
auto &in_1 = in_vars[1]->Get<framework::LoDTensor>();
if (in_0.numel() && in_1.numel()) {
auto in_0_e = EigenVector<T>::Flatten(in_0);
auto in_1_e = EigenVector<T>::Flatten(in_1);
result.device(place) = in_0_e + in_1_e;
start = 2;
}
}
if (start != 2) {
math::SetConstant<DeviceContext, T> constant_functor;
constant_functor(context.template device_context<DeviceContext>(),
out, 0.0);
}
}
math::SelectedRowsAddToTensor<DeviceContext, T> functor;
auto &place =
*context.template device_context<DeviceContext>().eigen_device();
// If in_place, just skip the first tensor
for (size_t i = in_place ? 1 : 0; i < in_num; i++) {
for (size_t i = start; i < in_num; i++) {
if (in_vars[i]->IsType<framework::LoDTensor>()) {
auto &in_t = in_vars[i]->Get<framework::LoDTensor>();
if (in_t.numel() == 0) {
......
paddle/fluid/platform/profiler.h
浏览文件 @ 6a4e9230
......@@ -71,6 +71,7 @@ void PopEvent(const std::string& name, const DeviceContext* dev_ctx);
#if !defined(_WIN32)
struct RecordEvent {
// dev_ctx can be set to nullptr if device is cpu.
RecordEvent(const std::string& name, const DeviceContext* dev_ctx);
~RecordEvent();
......
paddle/scripts/paddle_build.sh
浏览文件 @ 6a4e9230
......@@ -390,7 +390,9 @@ function run_mac_test() {
Running unit tests ...
========================================
EOF
#remove proxy here to fix dist error on mac
export http_proxy=
export https_proxy=
# TODO: jiabin need to refine this part when these tests fixed on mac
ctest --output-on-failure -j $1
# make install should also be test when unittest
......
python/paddle/fluid/framework.py
浏览文件 @ 6a4e9230
......@@ -1522,13 +1522,17 @@ class Program(object):
>>> with program.lr_schedule_guard():
>>> lr = lr * decay
"""
tmp_role = self._current_role
tmp_var = self._op_role_var
OpRole = core.op_proto_and_checker_maker.OpRole
self._current_role = OpRole.LRSched
# TODO(typhoonzero): how to set target learning rate var
self._op_role_var = []
yield
self._op_role_var = []
self._current_role = OpRole.Forward
self._op_role_var = tmp_var
self._current_role = tmp_role
def __str__(self):
"""
......
python/paddle/fluid/optimizer.py
浏览文件 @ 6a4e9230
......@@ -15,7 +15,7 @@
from __future__ import print_function
import re
from collections import defaultdict
from paddle.fluid.framework import Program, Variable, name_scope
from paddle.fluid.framework import Program, Variable, name_scope, default_main_program
from . import framework
from . import layers
from .backward import append_backward
......@@ -111,7 +111,8 @@ class Optimizer(object):
if param_lr == 1.0:
return self._global_learning_rate()
else:
return self._global_learning_rate() * param_lr
with default_main_program()._lr_schedule_guard():
return self._global_learning_rate() * param_lr
def _create_accumulators(self, block, parameters):
"""Create all accumulators needed by the parameters
......
python/paddle/fluid/tests/unittests/test_dist_simnet_bow.py
浏览文件 @ 6a4e9230
......@@ -91,6 +91,8 @@ class TestDistSimnetBow2x2SparseAsync(TestDistBase):
need_envs=need_envs)
# FIXME(tangwei): Learningrate variable is not created on pserver.
"""
class TestDistSimnetBow2x2LookupTableSync(TestDistBase):
def _setup_config(self):
self._sync_mode = True
......@@ -105,7 +107,7 @@ class TestDistSimnetBow2x2LookupTableSync(TestDistBase):
self.check_with_place(
"dist_simnet_bow.py",
delta=1e-5,
check_error_log=False,
check_error_log=True,
need_envs=need_envs)
......@@ -143,7 +145,7 @@ class TestDistSimnetBow2x2LookupTableNotContainLRSync(TestDistBase):
delta=1e-5,
check_error_log=False,
need_envs=need_envs)
"""
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_momentum_op.py
浏览文件 @ 6a4e9230
......@@ -16,6 +16,8 @@ from __future__ import print_function
import unittest
import numpy as np
import paddle.fluid.core as core
from paddle.fluid.op import Operator
from op_test import OpTest
......@@ -88,5 +90,97 @@ class TestMomentumOp2(OpTest):
self.check_output()
class TestSparseMomentumOp(unittest.TestCase):
def setUp(self):
self.use_nesterov = False
def check_with_place(self, place):
self.init_kernel()
scope = core.Scope()
# create and initialize Grad Variable
height = 10
rows = [0, 4, 7]
row_numel = 12
mu = 1.0
use_nesterov = self.use_nesterov
# create and initialize Param Variable
param = scope.var('Param').get_tensor()
param_array = np.full((height, row_numel), 5.0).astype("float32")
param.set(param_array, place)
param_out = scope.var("ParamOut").get_tensor()
param_out_array = np.full((height, row_numel), 0.0).astype("float32")
param_out.set(param_out_array, place)
grad_selected_rows = scope.var('Grad').get_selected_rows()
grad_selected_rows.set_height(height)
grad_selected_rows.set_rows(rows)
grad_np_array = np.ones((len(rows), row_numel)).astype("float32")
grad_np_array[0, 0] = 2.0
grad_np_array[2, 8] = 4.0
grad_tensor = grad_selected_rows.get_tensor()
grad_tensor.set(grad_np_array, place)
velocity = scope.var('Velocity').get_tensor()
velocity_np_array = np.ones((height, row_numel)).astype("float32")
velocity.set(velocity_np_array, place)
velocity_out = scope.var('VelocityOut').get_tensor()
velocity_out_np_array = np.full((height, row_numel),
0.0).astype("float32")
velocity_out.set(velocity_out_np_array, place)
# create and initialize LeraningRate Variable
lr = scope.var('LearningRate').get_tensor()
lr_array = np.full((1), 2.0).astype("float32")
lr.set(lr_array, place)
# create and run operator
op = Operator(
"momentum",
Param='Param',
Grad='Grad',
Velocity='Velocity',
ParamOut='ParamOut',
VelocityOut='VelocityOut',
LearningRate='LearningRate',
mu=mu,
use_nesterov=use_nesterov)
op.run(scope, place)
# get and compare result
param_out_np_array = np.array(param_out)
velocity_out_np_array = np.array(velocity_out)
# TODO(dzh): add a more suitable general numpy interface
# for sparse update.
_grad_np_array = np.full((height, row_numel), 0.0).astype("float32")
for i in range(len(rows)):
_grad_np_array[rows[i]] = grad_np_array[i]
_velocity_out = mu * velocity_np_array + _grad_np_array
_param = param_array
if use_nesterov:
_param_out = _param - (_grad_np_array + _velocity_out * mu
) * lr_array
else:
_param_out = _param - lr_array * _velocity_out
self.assertTrue((_velocity_out == velocity_out_np_array).all())
self.assertTrue((_param_out == param_out_np_array).all())
def init_kernel(self):
pass
def test_sparse_momentum(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 TestSparseMomentumOp2(TestSparseMomentumOp):
def init_kernel(self):
self.use_nesterov = True
if __name__ == "__main__":
unittest.main()
python/paddle/utils/__init__.py
浏览文件 @ 6a4e9230
......@@ -12,4 +12,5 @@
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = ['dump_config']
from plot import Ploter
__all__ = ['dump_config', 'Ploter']
python/paddle/utils/plot.py 0 → 100644
浏览文件 @ 6a4e9230
# 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.
import os
class PlotData(object):
def __init__(self):
self.step = []
self.value = []
def append(self, step, value):
self.step.append(step)
self.value.append(value)
def reset(self):
self.step = []
self.value = []
class Ploter(object):
"""
Plot input data in a 2D graph
Args:
title: assign the title of input data.
step: x_axis of the data.
value: y_axis of the data.
"""
def __init__(self, *args):
self.__args__ = args
self.__plot_data__ = {}
for title in args:
self.__plot_data__[title] = PlotData()
# demo in notebooks will use Ploter to plot figure, but when we convert
# the ipydb to py file for testing, the import of matplotlib will make the
# script crash. So we can use `export DISABLE_PLOT=True` to disable import
# these libs
self.__disable_plot__ = os.environ.get("DISABLE_PLOT")
if not self.__plot_is_disabled__():
import matplotlib.pyplot as plt
from IPython import display
self.plt = plt
self.display = display
def __plot_is_disabled__(self):
return self.__disable_plot__ == "True"
def append(self, title, step, value):
"""
Feed data
Args:
title: assign the group data to this subtitle.
step: the x_axis of data.
value: the y_axis of data.
Examples:
.. code-block:: python
plot_curve = Ploter("Curve 1","Curve 2")
plot_curve.append(title="Curve 1",step=1,value=1)
"""
assert isinstance(title, basestring)
assert self.__plot_data__.has_key(title)
data = self.__plot_data__[title]
assert isinstance(data, PlotData)
data.append(step, value)
def plot(self, path=None):
"""
Plot data in a 2D graph
Args:
path: store the figure to this file path. Defaul None.
Examples:
.. code-block:: python
plot_curve = Ploter()
plot_cure.plot()
"""
if self.__plot_is_disabled__():
return
titles = []
for title in self.__args__:
data = self.__plot_data__[title]
assert isinstance(data, PlotData)
if len(data.step) > 0:
titles.append(title)
self.plt.plot(data.step, data.value)
self.plt.legend(titles, loc='upper left')
if path is None:
self.display.clear_output(wait=True)
self.display.display(self.plt.gcf())
else:
self.plt.savefig(path)
self.plt.gcf().clear()
def reset(self):
for key in self.__plot_data__:
data = self.__plot_data__[key]
assert isinstance(data, PlotData)
data.reset()
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