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提交 dbfdaac6 编写于 作者: H HexToString
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Optimize pybind efficiency

上级 1ba49a3f
隐藏空白更改
内联 并排
Showing with 34 addition and 117 deletion
core/general-client/src/general_model.cpp 100755 → 100644
浏览文件 @ dbfdaac6
......@@ -90,9 +90,10 @@ int PredictorClient::init(const std::vector<std::string> &conf_file) {
if (conf_file.size() > 1) {
model_config.Clear();
if (configure::read_proto_conf(conf_file[conf_file.size()-1].c_str(), &model_config) != 0) {
if (configure::read_proto_conf(conf_file[conf_file.size() - 1].c_str(),
&model_config) != 0) {
LOG(ERROR) << "Failed to load general model config"
<< ", file path: " << conf_file[conf_file.size()-1];
<< ", file path: " << conf_file[conf_file.size() - 1];
return -1;
}
}
......@@ -154,16 +155,17 @@ int PredictorClient::numpy_predict(
const std::vector<std::string> &int_feed_name,
const std::vector<std::vector<int>> &int_shape,
const std::vector<std::vector<int>> &int_lod_slot_batch,
const std::vector<std::vector<std::string>>& string_feed_batch,
const std::vector<std::string>& string_feed_name,
const std::vector<std::vector<int>>& string_shape,
const std::vector<std::vector<int>>& string_lod_slot_batch,
const std::vector<std::vector<std::string>> &string_feed_batch,
const std::vector<std::string> &string_feed_name,
const std::vector<std::vector<int>> &string_shape,
const std::vector<std::vector<int>> &string_lod_slot_batch,
const std::vector<std::string> &fetch_name,
PredictorRes &predict_res_batch,
const int &pid,
const uint64_t log_id) {
int batch_size = std::max(float_feed_batch.size(), int_feed_batch.size());
batch_size = batch_size > string_feed_batch.size() ? batch_size : string_feed_batch.size();
batch_size = batch_size > string_feed_batch.size() ? batch_size
: string_feed_batch.size();
VLOG(2) << "batch size: " << batch_size;
predict_res_batch.clear();
Timer timeline;
......@@ -207,7 +209,8 @@ int PredictorClient::numpy_predict(
tensor_vec.push_back(inst->add_tensor_array());
}
VLOG(2) << "batch [" << bi << "] " << "prepared";
VLOG(2) << "batch [" << bi << "] "
<< "prepared";
vec_idx = 0;
for (auto &name : float_feed_name) {
......@@ -216,6 +219,11 @@ int PredictorClient::numpy_predict(
LOG(ERROR) << "idx > tensor_vec.size()";
return -1;
}
int nbytes = float_feed[vec_idx].nbytes();
// int ndims = float_feed[vec_idx].ndim();
void *rawdata_ptr = reinterpret_cast<void *>(float_feed[vec_idx].data(0));
int total_number = float_feed[vec_idx].size();
// float* end_ptr = (rawdata_ptr + total_number);
Tensor *tensor = tensor_vec[idx];
VLOG(2) << "prepare float feed " << name << " shape size "
<< float_shape[vec_idx].size();
......@@ -226,52 +234,11 @@ int PredictorClient::numpy_predict(
tensor->add_lod(float_lod_slot_batch[vec_idx][j]);
}
tensor->set_elem_type(P_FLOAT32);
const int float_shape_size = float_shape[vec_idx].size();
switch (float_shape_size) {
case 4: {
auto float_array = float_feed[vec_idx].unchecked<4>();
for (ssize_t i = 0; i < float_array.shape(0); i++) {
for (ssize_t j = 0; j < float_array.shape(1); j++) {
for (ssize_t k = 0; k < float_array.shape(2); k++) {
for (ssize_t l = 0; l < float_array.shape(3); l++) {
tensor->add_float_data(float_array(i, j, k, l));
}
}
}
}
break;
}
case 3: {
auto float_array = float_feed[vec_idx].unchecked<3>();
for (ssize_t i = 0; i < float_array.shape(0); i++) {
for (ssize_t j = 0; j < float_array.shape(1); j++) {
for (ssize_t k = 0; k < float_array.shape(2); k++) {
tensor->add_float_data(float_array(i, j, k));
}
}
}
break;
}
case 2: {
auto float_array = float_feed[vec_idx].unchecked<2>();
for (ssize_t i = 0; i < float_array.shape(0); i++) {
for (ssize_t j = 0; j < float_array.shape(1); j++) {
tensor->add_float_data(float_array(i, j));
}
}
break;
}
case 1: {
auto float_array = float_feed[vec_idx].unchecked<1>();
for (ssize_t i = 0; i < float_array.shape(0); i++) {
tensor->add_float_data(float_array(i));
}
break;
}
}
tensor->mutable_float_data()->Resize(total_number, 0);
memcpy(tensor->mutable_float_data()->mutable_data(), rawdata_ptr, nbytes);
vec_idx++;
}
VLOG(2) << "batch [" << bi << "] "
<< "float feed value prepared";
......@@ -283,6 +250,9 @@ int PredictorClient::numpy_predict(
return -1;
}
Tensor *tensor = tensor_vec[idx];
int nbytes = int_feed[vec_idx].nbytes();
void *rawdata_ptr = reinterpret_cast<void *>(int_feed[vec_idx].data(0));
int total_number = int_feed[vec_idx].size();
for (uint32_t j = 0; j < int_shape[vec_idx].size(); ++j) {
tensor->add_shape(int_shape[vec_idx][j]);
......@@ -295,71 +265,17 @@ int PredictorClient::numpy_predict(
if (_type[idx] == P_INT64) {
VLOG(2) << "prepare int feed " << name << " shape size "
<< int_shape[vec_idx].size();
tensor->mutable_int64_data()->Resize(total_number, 0);
memcpy(
tensor->mutable_int64_data()->mutable_data(), rawdata_ptr, nbytes);
vec_idx++;
} else {
VLOG(2) << "prepare int32 feed " << name << " shape size "
<< int_shape[vec_idx].size();
tensor->mutable_int_data()->Resize(total_number, 0);
memcpy(tensor->mutable_int_data()->mutable_data(), rawdata_ptr, nbytes);
vec_idx++;
}
const int int_shape_size = int_shape[vec_idx].size();
switch (int_shape_size) {
case 4: {
auto int_array = int_feed[vec_idx].unchecked<4>();
for (ssize_t i = 0; i < int_array.shape(0); i++) {
for (ssize_t j = 0; j < int_array.shape(1); j++) {
for (ssize_t k = 0; k < int_array.shape(2); k++) {
for (ssize_t l = 0; k < int_array.shape(3); l++) {
if (_type[idx] == P_INT64) {
tensor->add_int64_data(int_array(i, j, k, l));
} else {
tensor->add_int_data(int_array(i, j, k, l));
}
}
}
}
}
break;
}
case 3: {
auto int_array = int_feed[vec_idx].unchecked<3>();
for (ssize_t i = 0; i < int_array.shape(0); i++) {
for (ssize_t j = 0; j < int_array.shape(1); j++) {
for (ssize_t k = 0; k < int_array.shape(2); k++) {
if (_type[idx] == P_INT64) {
tensor->add_int64_data(int_array(i, j, k));
} else {
tensor->add_int_data(int_array(i, j, k));
}
}
}
}
break;
}
case 2: {
auto int_array = int_feed[vec_idx].unchecked<2>();
for (ssize_t i = 0; i < int_array.shape(0); i++) {
for (ssize_t j = 0; j < int_array.shape(1); j++) {
if (_type[idx] == P_INT64) {
tensor->add_int64_data(int_array(i, j));
} else {
tensor->add_int_data(int_array(i, j));
}
}
}
break;
}
case 1: {
auto int_array = int_feed[vec_idx].unchecked<1>();
for (ssize_t i = 0; i < int_array.shape(0); i++) {
if (_type[idx] == P_INT64) {
tensor->add_int64_data(int_array(i));
} else {
tensor->add_int_data(int_array(i));
}
}
break;
}
}
vec_idx++;
}
VLOG(2) << "batch [" << bi << "] "
......@@ -383,10 +299,11 @@ int PredictorClient::numpy_predict(
tensor->set_elem_type(P_STRING);
const int string_shape_size = string_shape[vec_idx].size();
//string_shape[vec_idx] = [1];cause numpy has no datatype of string.
//we pass string via vector<vector<string> >.
// string_shape[vec_idx] = [1];cause numpy has no datatype of string.
// we pass string via vector<vector<string> >.
if (string_shape_size != 1) {
LOG(ERROR) << "string_shape_size should be 1-D, but received is : " << string_shape_size;
LOG(ERROR) << "string_shape_size should be 1-D, but received is : "
<< string_shape_size;
return -1;
}
switch (string_shape_size) {
......@@ -397,7 +314,7 @@ int PredictorClient::numpy_predict(
}
vec_idx++;
}
VLOG(2) << "batch [" << bi << "] "
<< "string feed value prepared";
}
......
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