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提交 6223770c 编写于 作者: H HexToString
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update pybind

上级 470a7db9
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Showing with 16 addition and 294 deletion
core/general-client/src/general_model.cpp 100755 → 100644
浏览文件 @ 6223770c
...@@ -145,59 +145,6 @@ int PredictorClient::create_predictor() { ...@@ -145,59 +145,6 @@ int PredictorClient::create_predictor() {
return 0; return 0;
} }
/*Determine whether the memory structure can be copied directly
if the memory offset stored in rows == the actual memory offset
if means the structure of memory is not changed by numpy(newaxis,numpy) or
numpy(1:numpy)
so you can directly copy the memory.
*/
template <typename T>
bool isCopyLegal(py::array_t<T> *feed_array) {
const ssize_t *shape = feed_array->shape();
ssize_t dims = feed_array->ndim();
ssize_t item_size = feed_array->itemsize();
ssize_t *middle = new ssize_t[dims];
// Calculates the memory offset stored in rows
int64_t memory_offset = 0;
for (int16_t i = dims - 1; i >= 0; --i) {
middle[i] = i == 0 ? (ssize_t)(shape[i] / 3) : (ssize_t)(shape[i] / 2);
int64_t one_dim_offset = middle[i];
for (int16_t j = i + 1; j < dims; ++j) {
one_dim_offset = one_dim_offset * shape[j];
}
memory_offset += item_size * one_dim_offset;
}
// Calculate the actual memory offset
int64_t feed_offset = 0;
switch (dims) {
case 6: {
feed_offset = feed_array->offset_at(
middle[0], middle[1], middle[2], middle[3], middle[4], middle[5]);
break;
}
case 5: {
feed_offset = feed_array->offset_at(
middle[0], middle[1], middle[2], middle[3], middle[4]);
break;
}
case 4: {
feed_offset =
feed_array->offset_at(middle[0], middle[1], middle[2], middle[3]);
break;
}
case 3: {
feed_offset = feed_array->offset_at(middle[0], middle[1], middle[2]);
break;
}
case 2: {
feed_offset = feed_array->offset_at(middle[0], middle[1]);
break;
}
}
delete[] middle;
return memory_offset == feed_offset;
}
int PredictorClient::numpy_predict( int PredictorClient::numpy_predict(
const std::vector<std::vector<py::array_t<float>>> &float_feed_batch, const std::vector<std::vector<py::array_t<float>>> &float_feed_batch,
const std::vector<std::string> &float_feed_name, const std::vector<std::string> &float_feed_name,
...@@ -271,7 +218,7 @@ int PredictorClient::numpy_predict( ...@@ -271,7 +218,7 @@ int PredictorClient::numpy_predict(
return -1; return -1;
} }
int nbytes = float_feed[vec_idx].nbytes(); int nbytes = float_feed[vec_idx].nbytes();
void *rawdata_ptr = (void*)(float_feed[vec_idx].data(0)); void *rawdata_ptr = (void *)(float_feed[vec_idx].data(0));
int total_number = float_feed[vec_idx].size(); int total_number = float_feed[vec_idx].size();
Tensor *tensor = tensor_vec[idx]; Tensor *tensor = tensor_vec[idx];
...@@ -284,120 +231,9 @@ int PredictorClient::numpy_predict( ...@@ -284,120 +231,9 @@ int PredictorClient::numpy_predict(
tensor->add_lod(float_lod_slot_batch[vec_idx][j]); tensor->add_lod(float_lod_slot_batch[vec_idx][j]);
} }
tensor->set_elem_type(P_FLOAT32); tensor->set_elem_type(P_FLOAT32);
if (isCopyLegal(&float_feed[vec_idx])) {
tensor->mutable_float_data()->Resize(total_number, 0);
memcpy(
tensor->mutable_float_data()->mutable_data(), rawdata_ptr, nbytes);
vec_idx++;
continue;
}
tensor->mutable_float_data()->Reserve(total_number);
const int float_shape_size = float_shape[vec_idx].size();
switch (float_shape_size) {
case 6: {
auto float_array = float_feed[vec_idx].unchecked<6>();
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) {
for (ssize_t m = 0; m < float_array.shape(4); ++m) {
for (ssize_t n = 0; n < float_array.shape(5); ++n) {
tensor->add_float_data(float_array(i, j, k, l, m, n));
}
}
}
}
}
}
break;
}
case 5: {
auto float_array = float_feed[vec_idx].unchecked<5>();
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) {
for (ssize_t m = 0; m < float_array.shape(4); ++m) {
tensor->add_float_data(float_array(i, j, k, l, m));
}
}
}
}
}
break;
}
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;
}
}
/*
// this is for debug.
std::cout << std::endl;
std::cout << "origin " <<std::endl;
std::cout << "tensor->float_data_size() = " << tensor->float_data_size()
<< std::endl;
std::cout << "&tensor->first = " <<
tensor->mutable_float_data()->mutable_data() << std::endl;
std::cout << "tensor->first = " <<
*tensor->mutable_float_data()->mutable_data() << std::endl;
std::cout << "&tensor->last = " <<
(tensor->mutable_float_data()->mutable_data()+total_number-1) <<
std::endl;
std::cout << "tensor->last = " <<
*(tensor->mutable_float_data()->mutable_data()+total_number-1) <<
std::endl;
std::cout << "&tensor->middle = " <<
(tensor->mutable_float_data()->mutable_data()+int(total_number/7)) <<
std::endl;
std::cout << "tensor->middle = " <<
*(tensor->mutable_float_data()->mutable_data()+int(total_number/7)) <<
std::endl;
for(int my =0; my <total_number/1000; my++){
std::cout << my << " : " <<
*(tensor->mutable_float_data()->mutable_data()+my) << " ";
}
std::cout << std::endl;
std::cout << std::endl;
*/
tensor->mutable_float_data()->Resize(total_number, 0);
memcpy(tensor->mutable_float_data()->mutable_data(), rawdata_ptr, nbytes);
vec_idx++; vec_idx++;
} }
...@@ -423,129 +259,14 @@ int PredictorClient::numpy_predict( ...@@ -423,129 +259,14 @@ int PredictorClient::numpy_predict(
tensor->add_lod(int_lod_slot_batch[vec_idx][j]); tensor->add_lod(int_lod_slot_batch[vec_idx][j]);
} }
tensor->set_elem_type(_type[idx]); tensor->set_elem_type(_type[idx]);
if (isCopyLegal(&int_feed[vec_idx])) {
if (_type[idx] == P_INT64) {
tensor->mutable_int64_data()->Resize(total_number, 0);
memcpy(tensor->mutable_int64_data()->mutable_data(),
rawdata_ptr,
nbytes);
vec_idx++;
} else {
tensor->mutable_int_data()->Resize(total_number, 0);
memcpy(
tensor->mutable_int_data()->mutable_data(), rawdata_ptr, nbytes);
vec_idx++;
}
continue;
}
if (_type[idx] == P_INT64) { if (_type[idx] == P_INT64) {
VLOG(2) << "prepare int feed " << name << " shape size " tensor->mutable_int64_data()->Resize(total_number, 0);
<< int_shape[vec_idx].size(); memcpy(
tensor->mutable_int64_data()->Reserve(total_number); tensor->mutable_int64_data()->mutable_data(), rawdata_ptr, nbytes);
} else { } else {
VLOG(2) << "prepare int32 feed " << name << " shape size " tensor->mutable_int_data()->Resize(total_number, 0);
<< int_shape[vec_idx].size(); memcpy(tensor->mutable_int_data()->mutable_data(), rawdata_ptr, nbytes);
tensor->mutable_int_data()->Reserve(total_number);
}
const int int_shape_size = int_shape[vec_idx].size();
switch (int_shape_size) {
case 6: {
auto int_array = int_feed[vec_idx].unchecked<6>();
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) {
for (ssize_t m = 0; k < int_array.shape(4); ++m) {
for (ssize_t n = 0; k < int_array.shape(5); ++n) {
if (_type[idx] == P_INT64) {
tensor->add_int64_data(int_array(i, j, k, l, m, n));
} else {
tensor->add_int_data(int_array(i, j, k, l, m, n));
}
}
}
}
}
}
}
break;
}
case 5: {
auto int_array = int_feed[vec_idx].unchecked<5>();
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) {
for (ssize_t m = 0; k < int_array.shape(4); ++m) {
if (_type[idx] == P_INT64) {
tensor->add_int64_data(int_array(i, j, k, l, m));
} else {
tensor->add_int_data(int_array(i, j, k, l, m));
}
}
}
}
}
}
break;
}
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++; vec_idx++;
} }
......
python/paddle_serving_client/client.py
浏览文件 @ 6223770c
...@@ -370,10 +370,10 @@ class Client(object): ...@@ -370,10 +370,10 @@ class Client(object):
int_lod_slot_batch.append([]) int_lod_slot_batch.append([])
if isinstance(feed_i[key], np.ndarray): if isinstance(feed_i[key], np.ndarray):
int_slot.append(feed_i[key]) int_slot.append(np.ascontiguousarray(feed_i[key]))
self.has_numpy_input = True self.has_numpy_input = True
else: else:
int_slot.append(feed_i[key]) int_slot.append(np.ascontiguousarray(feed_i[key]))
self.all_numpy_input = False self.all_numpy_input = False
elif self.feed_types_[key] in float_type: elif self.feed_types_[key] in float_type:
...@@ -395,10 +395,10 @@ class Client(object): ...@@ -395,10 +395,10 @@ class Client(object):
float_lod_slot_batch.append([]) float_lod_slot_batch.append([])
if isinstance(feed_i[key], np.ndarray): if isinstance(feed_i[key], np.ndarray):
float_slot.append(feed_i[key]) float_slot.append(np.ascontiguousarray(feed_i[key]))
self.has_numpy_input = True self.has_numpy_input = True
else: else:
float_slot.append(feed_i[key]) float_slot.append(np.ascontiguousarray(feed_i[key]))
self.all_numpy_input = False self.all_numpy_input = False
#if input is string, feed is not numpy. #if input is string, feed is not numpy.
elif self.feed_types_[key] in string_type: elif self.feed_types_[key] in string_type:
...@@ -410,7 +410,7 @@ class Client(object): ...@@ -410,7 +410,7 @@ class Client(object):
key)]) key)])
else: else:
string_lod_slot_batch.append([]) string_lod_slot_batch.append([])
string_slot.append(feed_i[key]) string_slot.append(np.ascontiguousarray(feed_i[key]))
self.has_numpy_input = True self.has_numpy_input = True
int_slot_batch.append(int_slot) int_slot_batch.append(int_slot)
int_lod_slot_batch.append(int_lod_slot) int_lod_slot_batch.append(int_lod_slot)
...@@ -628,6 +628,7 @@ class MultiLangClient(object): ...@@ -628,6 +628,7 @@ class MultiLangClient(object):
raise Exception("error tensor value type.") raise Exception("error tensor value type.")
else: else:
raise Exception("var must be list or ndarray.") raise Exception("var must be list or ndarray.")
data = np.ascontiguousarray(data)
tensor.data = data.tobytes() tensor.data = data.tobytes()
tensor.shape.extend(list(var.shape)) tensor.shape.extend(list(var.shape))
if "{}.lod".format(name) in feed.keys(): if "{}.lod".format(name) in feed.keys():
...@@ -702,7 +703,7 @@ class MultiLangClient(object): ...@@ -702,7 +703,7 @@ class MultiLangClient(object):
if batch is False: if batch is False:
for key in feed: for key in feed:
if ".lod" not in key: if ".lod" not in key:
feed[key] = feed[key][np.newaxis, :] feed[key] = np.expand_dims(feed_i[key], 0).repeat(1, axis=0)
if not asyn: if not asyn:
try: try:
self.profile_.record('py_prepro_0') self.profile_.record('py_prepro_0')
......
python/paddle_serving_server/rpc_service.py
浏览文件 @ 6223770c
...@@ -126,7 +126,7 @@ class MultiLangServerServiceServicer(multi_lang_general_model_service_pb2_grpc. ...@@ -126,7 +126,7 @@ class MultiLangServerServiceServicer(multi_lang_general_model_service_pb2_grpc.
else: else:
raise Exception("error type.") raise Exception("error type.")
data.shape = list(feed_inst.tensor_array[idx].shape) data.shape = list(feed_inst.tensor_array[idx].shape)
feed_dict[name] = data feed_dict[name] = np.ascontiguousarray(data)
if len(var.lod) > 0: if len(var.lod) > 0:
feed_dict["{}.lod".format(name)] = var.lod feed_dict["{}.lod".format(name)] = var.lod
feed_batch.append(feed_dict) feed_batch.append(feed_dict)
......
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