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提交 5f1070ea 编写于 作者: Y yangfei
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load memory for CLImage in GPU_CL mode

上级 8a088d13
隐藏空白更改
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Showing with 186 addition and 52 deletion
src/framework/executor.cpp
浏览文件 @ 5f1070ea
......@@ -60,13 +60,13 @@ char *Get_binary_data(std::string filename) {
#pragma mark - executor
template<typename Dtype, Precision P>
template <typename Dtype, Precision P>
Executor<Dtype, P>::Executor(const framework::Program<Dtype> p, int batch_size,
bool use_optimize, bool loddable)
: program_(p),
batch_size_(batch_size),
use_optimize_(use_optimize),
loddable_(loddable) {
: program_(p),
batch_size_(batch_size),
use_optimize_(use_optimize),
loddable_(loddable) {
if (use_optimize_) {
to_predict_program_ = program_.optimizeProgram;
} else {
......@@ -77,7 +77,7 @@ Executor<Dtype, P>::Executor(const framework::Program<Dtype> p, int batch_size,
PADDLE_MOBILE_ENFORCE(to_predict_program_ != nullptr,
"to_predict_program_ == NULL!");
const std::vector<std::shared_ptr<framework::BlockDesc>> blocks =
to_predict_program_->Blocks();
to_predict_program_->Blocks();
#ifdef PADDLE_EXECUTOR_MULTITHREAD
depManager.resize(blocks.size());
#endif
......@@ -89,8 +89,8 @@ Executor<Dtype, P>::Executor(const framework::Program<Dtype> p, int batch_size,
std::shared_ptr<framework::OpDesc> op = ops[j];
DLOG << "create op: " << j << " " << op->Type();
auto op_base = framework::OpRegistry<Dtype>::CreateOp(
op->Type(), op->GetInputs(), op->GetOutputs(), op->GetAttrMap(),
program_.scope);
op->Type(), op->GetInputs(), op->GetOutputs(), op->GetAttrMap(),
program_.scope);
// use pre_infershape to pre resize , but if u use an lod mode tensor u
// need to resize in runtime
if (!loddable_) {
......@@ -109,7 +109,7 @@ Executor<Dtype, P>::Executor(const framework::Program<Dtype> p, int batch_size,
InitMemory();
}
std::shared_ptr<framework::BlockDesc> to_predict_block =
to_predict_program_->Block(0);
to_predict_program_->Block(0);
auto &ops = ops_of_block_[*to_predict_block.get()];
int i = 0;
for (const auto &op : ops) {
......@@ -118,7 +118,7 @@ Executor<Dtype, P>::Executor(const framework::Program<Dtype> p, int batch_size,
}
}
template<typename Dtype, Precision P>
template <typename Dtype, Precision P>
void Executor<Dtype, P>::LoadMemory(const framework::VarDesc var_desc,
framework::LoDTensor *tensor, char **data) {
// 1. version
......@@ -226,7 +226,7 @@ void Executor<Dtype, P>::LoadMemory(const framework::VarDesc var_desc,
}
}
template<typename Dtype, Precision P>
template <typename Dtype, Precision P>
void Executor<Dtype, P>::InitMemory() {
for (const auto &block : to_predict_program_->Blocks()) {
for (const auto &var_desc : block->Vars()) {
......@@ -238,7 +238,7 @@ void Executor<Dtype, P>::InitMemory() {
}
char *origin_data =
Get_binary_data(program_.model_path + "/" + var_desc->Name());
Get_binary_data(program_.model_path + "/" + var_desc->Name());
char *data = origin_data;
LoadMemory(*var_desc, tensor, &data);
......@@ -251,21 +251,21 @@ void Executor<Dtype, P>::InitMemory() {
is_mute_match = varInputMemory(var_desc, var, tensor);
PADDLE_MOBILE_ENFORCE(
is_mute_match,
"got unhandled var_desc->Tensor_desc().DataType(): %d",
var_desc->Tensor_desc().DataType());
is_mute_match,
"got unhandled var_desc->Tensor_desc().DataType(): %d",
var_desc->Tensor_desc().DataType());
}
}
}
}
}
template<typename Dtype, Precision P>
template <typename Dtype, Precision P>
void Executor<Dtype, P>::InitCombineMemory() {
char *origin_data;
if (program_.combined_params_buf && program_.combined_params_len) {
LOG(kLOG_INFO) << "use outter memory";
origin_data = (char *) program_.combined_params_buf;
origin_data = (char *)program_.combined_params_buf;
} else {
LOG(kLOG_INFO) << " begin init combine memory";
origin_data = Get_binary_data(program_.para_path);
......@@ -289,9 +289,9 @@ void Executor<Dtype, P>::InitCombineMemory() {
is_mute_match = varInputMemory(var_desc, var, tensor);
PADDLE_MOBILE_ENFORCE(
is_mute_match,
"got unhandled var_desc->Tensor_desc().DataType(): %d",
var_desc->Tensor_desc().DataType());
is_mute_match,
"got unhandled var_desc->Tensor_desc().DataType(): %d",
var_desc->Tensor_desc().DataType());
}
}
}
......@@ -300,10 +300,10 @@ void Executor<Dtype, P>::InitCombineMemory() {
LOG(kLOG_INFO) << " end init combine memory ";
}
template<typename Dtype, Precision P>
template <typename Dtype, Precision P>
bool Executor<Dtype, P>::varInputMemory(
const std::shared_ptr<framework::VarDesc> &var_desc, Variable *var,
framework::LoDTensor *tensor) const {
const std::shared_ptr<framework::VarDesc> &var_desc, Variable *var,
framework::LoDTensor *tensor) const {
bool is_mute_match = false;
switch (var_desc->Tensor_desc().DataType()) {
case framework::VARTYPE_TYPE_FP16: {
......@@ -338,24 +338,22 @@ bool Executor<Dtype, P>::varInputMemory(
break;
}
default: {
break;
}
default: { break; }
}
return is_mute_match;
}
template<typename Dtype, Precision P>
template <typename Dtype, Precision P>
std::shared_ptr<framework::Tensor> Executor<Dtype, P>::Predict(
const framework::Tensor &t) {
const framework::Tensor &t) {
framework::Variable *g_feed_value = program_.scope->Var("feed");
framework::Tensor *feed_tensor =
g_feed_value->GetMutable<framework::LoDTensor>();
g_feed_value->GetMutable<framework::LoDTensor>();
feed_tensor->Resize(t.dims());
feed_tensor->ShareDataWith(t);
std::shared_ptr<framework::BlockDesc> to_predict_block =
to_predict_program_->Block(0);
to_predict_program_->Block(0);
auto &ops = ops_of_block_[*to_predict_block.get()];
#ifdef PADDLE_MOBILE_PROFILE
......@@ -435,8 +433,8 @@ std::shared_ptr<framework::Tensor> Executor<Dtype, P>::Predict(
std::vector<std::string> out_keys = (*last_op)->GetOutKeys();
PADDLE_MOBILE_ENFORCE(out_keys.size() > 0, "the last op contains no output");
framework::LoDTensor *output_tensor =
framework::GetVarValue<framework::LoDTensor>(out_keys[0], output_map,
*(program_.scope));
framework::GetVarValue<framework::LoDTensor>(out_keys[0], output_map,
*(program_.scope));
#ifdef PADDLE_MOBILE_PROFILE
#ifdef PADDLE_EXECUTOR_MULTITHREAD
// TODO(haipeng): expose profile info as an interface, user can get them to
......@@ -488,18 +486,18 @@ std::shared_ptr<framework::Tensor> Executor<Dtype, P>::Predict(
return std::make_shared<framework::Tensor>(framework::Tensor(*output_tensor));
}
template<typename Dtype, Precision P>
template <typename Dtype, Precision P>
std::shared_ptr<framework::LoDTensor> Executor<Dtype, P>::PredictLod(
const framework::LoDTensor &t) {
const framework::LoDTensor &t) {
framework::Variable *g_feed_value = program_.scope->Var("feed");
framework::LoDTensor *feed_tensor =
g_feed_value->GetMutable<framework::LoDTensor>();
g_feed_value->GetMutable<framework::LoDTensor>();
feed_tensor->Resize(t.dims());
feed_tensor->ShareDataWith(t);
feed_tensor->set_lod(t.lod());
std::shared_ptr<framework::BlockDesc> to_predict_block =
to_predict_program_->Block(0);
to_predict_program_->Block(0);
auto &ops = ops_of_block_[*to_predict_block.get()];
......@@ -584,8 +582,8 @@ std::shared_ptr<framework::LoDTensor> Executor<Dtype, P>::PredictLod(
std::vector<std::string> out_keys = (*last_op)->GetOutKeys();
PADDLE_MOBILE_ENFORCE(out_keys.size() > 0, "the last op contains no output");
framework::LoDTensor *output_tensor =
framework::GetVarValue<framework::LoDTensor>(out_keys[0], output_map,
*(program_.scope));
framework::GetVarValue<framework::LoDTensor>(out_keys[0], output_map,
*(program_.scope));
#ifdef PADDLE_MOBILE_PROFILE
#ifdef PADDLE_EXECUTOR_MULTITHREAD
// TODO(haipeng): expose profile info as an interface, user can get them to
......@@ -635,22 +633,22 @@ std::shared_ptr<framework::LoDTensor> Executor<Dtype, P>::PredictLod(
printf("====================[---------]======================\n");
#endif
return std::make_shared<framework::LoDTensor>(
framework::LoDTensor(*output_tensor));
framework::LoDTensor(*output_tensor));
}
template<typename Dtype, Precision P>
template <typename Dtype, Precision P>
std::shared_ptr<framework::Tensor> Executor<Dtype, P>::Predict(
const framework::Tensor &t, int block_id) {
const framework::Tensor &t, int block_id) {
return Predict(t);
}
template<typename Dtype, Precision P>
template <typename Dtype, Precision P>
std::vector<typename Executor<Dtype, P>::Ptype> Executor<Dtype, P>::Predict(
const std::vector<Ptype> &input, const std::vector<int64_t> &dims) {
const std::vector<Ptype> &input, const std::vector<int64_t> &dims) {
framework::Tensor tensor(input, framework::make_ddim(dims));
std::shared_ptr<framework::Tensor> output_tensor = Predict(tensor, 0);
Executor<Dtype, P>::Ptype *output_ptr =
output_tensor->data<typename Executor<Dtype, P>::Ptype>();
output_tensor->data<typename Executor<Dtype, P>::Ptype>();
std::vector<typename Executor<Dtype, P>::Ptype> result_vector;
for (int j = 0; j < output_tensor->numel(); ++j) {
result_vector.push_back(output_ptr[j]);
......@@ -730,17 +728,153 @@ void Executor<Dtype, P>::Predict_To(int end) {
};
#endif
template
class Executor<CPU, Precision::FP32>;
#ifdef PADDLE_MOBILE_FPGA
template <typename Dtype, Precision P>
void Executor<Dtype, P>::InjectVariable(const framework::Tensor &t,
string var_name) {
framework::Variable *g_feed_value = program_.scope->Var(var_name);
framework::Tensor *feed_tensor =
g_feed_value->GetMutable<framework::LoDTensor>();
feed_tensor->Resize(t.dims());
feed_tensor->ShareDataWith(t);
};
template <typename Dtype, Precision P>
void Executor<Dtype, P>::FeedData(const framework::Tensor &t) {
InjectVariable(t, "feed");
};
template <typename Dtype, Precision P>
std::shared_ptr<framework::Tensor> Executor<Dtype, P>::FetchResult(int id) {
std::shared_ptr<framework::BlockDesc> to_predict_block =
to_predict_program_->Block(0);
auto &ops = ops_of_block_[*to_predict_block.get()];
PADDLE_MOBILE_ENFORCE(id < ops.size(), "Index out of range");
auto last_op = id < 0 ? ops[ops.size() - 1] : ops[id];
auto output_map = last_op->Outputs();
std::vector<std::string> out_keys = last_op->GetOutKeys();
PADDLE_MOBILE_ENFORCE(!out_keys.empty(), "the last op contains no output");
auto *output_tensor = framework::GetVarValue<framework::LoDTensor>(
out_keys[0], output_map, *(program_.scope));
return std::make_shared<framework::Tensor>(framework::Tensor(*output_tensor));
};
template <typename Dtype, Precision P>
void Executor<Dtype, P>::Predict_From_To(int start, int end) {
std::shared_ptr<framework::BlockDesc> to_predict_block =
to_predict_program_->Block(0);
auto &ops = ops_of_block_[*to_predict_block.get()];
end = end < 0 ? (int)ops.size() : end;
PADDLE_MOBILE_ENFORCE(start >= 0 && start < end && end <= ops.size(),
"start or end parameter is wrong");
#ifdef PADDLE_MOBILE_PROFILE
std::vector<ProfInfo> profile(ops.size());
#endif
for (int i = start; i < end; i++) {
#ifdef PADDLE_MOBILE_PROFILE
struct timespec ts;
clock_gettime(CLOCK_MONOTONIC, &ts);
profile[i].runBegin = (uint64_t)ts.tv_sec * 1e9 + ts.tv_nsec;
#endif
DLOG << "Running op: " << i << " " << ops[i]->Type();
ops[i]->Run();
#ifdef PADDLE_MOBILE_PROFILE
clock_gettime(CLOCK_MONOTONIC, &ts);
profile[i].runEnd = (uint64_t)ts.tv_sec * 1e9 + ts.tv_nsec;
#endif
}
};
template <typename Dtype, Precision P>
void Executor<Dtype, P>::Predict_From(int start) {
Predict_From_To(start);
};
template <typename Dtype, Precision P>
void Executor<Dtype, P>::Predict_To(int end) {
Predict_From_To(0, end);
};
#endif
#ifdef PADDLE_MOBILE_CL
template
class Executor<FPGA, Precision::FP32>;
template <>
void Executor<GPU_CL, Precision::FP32>::InitMemory() {
for (const auto &block : to_predict_program_->Blocks()) {
for (const auto &var_desc : block->Vars()) {
auto var = program_.scope->Var(var_desc->Name());
if (var_desc->Persistable()) {
auto cl_image = var->template GetMutable<framework::CLImage>();
if (var_desc->Name() == "feed" || var_desc->Name() == "fetch") {
continue;
}
template
class Executor<GPU_CL, Precision::FP32>;
char *origin_data =
Get_binary_data(program_.model_path + "/" + var_desc->Name());
cl_context context = program_.scope->GetCLScpoe()->Context();
template
class Executor<GPU_MALI, Precision::FP32>;
float *tensorInput = (float *)origin_data;
framework::DDim ddim = cl_image->dims();
cl_image->Init(context, tensorInput, ddim);
delete origin_data;
}
}
}
}
template <>
void Executor<GPU_CL, Precision::FP32>::InitCombineMemory() {
char *origin_data;
if (program_.combined_params_buf && program_.combined_params_len) {
LOG(kLOG_INFO) << "use outter memory";
origin_data = (char *)program_.combined_params_buf;
} else {
LOG(kLOG_INFO) << " begin init combine memory";
origin_data = Get_binary_data(program_.para_path);
}
PADDLE_MOBILE_ENFORCE(origin_data != nullptr, "origin_data==nullptr!!!");
float *data = (float *)origin_data;
for (const auto &block : to_predict_program_->Blocks()) {
for (const auto &var_desc : block->Vars()) {
auto var = program_.scope->Var(var_desc->Name());
if (var_desc->Persistable()) {
auto cl_image = var->template GetMutable<framework::CLImage>();
if (var_desc->Name() == "feed" || var_desc->Name() == "fetch") {
continue;
}
cl_context context = program_.scope->GetCLScpoe()->Context();
framework::DDim ddim = cl_image->dims();
int numel = 1;
for (int i = 0; i < ddim.size(); i++) {
numel = numel * ddim[i];
}
float *tensorInput = data;
data += numel;
cl_image->Init(context, tensorInput, ddim);
}
}
}
delete origin_data;
LOG(kLOG_INFO) << " end init combine memory ";
}
#endif
template class Executor<CPU, Precision::FP32>;
template class Executor<FPGA, Precision::FP32>;
template class Executor<GPU_CL, Precision::FP32>;
template class Executor<GPU_MALI, Precision::FP32>;
} // namespace framework
} // namespace paddle_mobile
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