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!33036 DVPP method adapt and lite whl package bugfix 

Merge pull request !33036 from chenping/runtime
This commit is contained in:
i-robot 2022-04-19 08:42:48 +00:00 committed by Gitee
parent aec1dc77fb 13a744ec3f
commit 644cc19eb8
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GPG Key ID: 173E9B9CA92EEF8F
7 changed files with 86 additions and 8 deletions
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1
include/api/types.h
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@ -140,6 +140,7 @@ class MS_API MSTensor {
MSTensor();
explicit MSTensor(const std::shared_ptr<Impl> &impl);
// if malloc data, user need to free after constructing MSTensor, else memory leak.
inline MSTensor(const std::string &name, DataType type, const std::vector<int64_t> &shape, const void *data,
size_t data_len);
explicit MSTensor(std::nullptr_t);

10
mindspore/lite/src/CMakeLists.txt
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@ -480,14 +480,18 @@ if(MSLITE_ENABLE_RUNTIME_CONVERT)
quantizer_mid fusion_mid proto_mid graph_pass_mid preprocess_mid
cpu_kernel_mid ccsrc_src_mid converter_src_mid anf_exporter_mid
config_parser_mid mslite_converter_plugin mindspore_core coder_mid
ccsrc_debug_common_mid_ mindir_proto_mid _mindspore_transform_express_ir_obj
mindir_serializer_mid mindspore::protobuf ${SECUREC_LIBRARY})
target_link_libraries(mindspore-lite_static
quantizer_mid fusion_mid proto_mid graph_pass_mid preprocess_mid
cpu_kernel_mid ccsrc_src_mid converter_src_mid anf_exporter_mid
config_parser_mid mslite_converter_plugin mindspore_core coder_mid
ccsrc_debug_common_mid_ mindir_proto_mid _mindspore_transform_express_ir_obj
mindir_serializer_mid mindspore::protobuf ${SECUREC_LIBRARY})
if(NOT ENABLE_CLOUD_AND_LITE)
target_link_libraries(mindspore-lite
ccsrc_debug_common_mid_ mindir_proto_mid _mindspore_transform_express_ir_obj)
target_link_libraries(mindspore-lite_static
ccsrc_debug_common_mid_ mindir_proto_mid _mindspore_transform_express_ir_obj)
endif()
if(MSLITE_ENABLE_ACL)
target_link_libraries(mindspore-lite lite_acl_mid
mindspore_shared_lib)
@ -552,4 +556,4 @@ endif()
if(ENABLE_CLOUD_AND_LITE)
set_target_properties(mindspore-lite PROPERTIES INSTALL_RPATH $ORIGIN)
endif()
endif()

22
mindspore/lite/src/common/tensor_util.cc
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@ -315,6 +315,20 @@ int CheckTensorsInvalid(const std::vector<Tensor *> &tensors) {
return RET_OK;
}
std::string ShapeToString(const std::vector<int> &shape) {
std::string result = "[";
int max_size = 40;
result.reserve(max_size);
for (size_t i = 0; i < shape.size(); ++i) {
result += std::to_string(shape[i]);
if (i + 1 < shape.size()) {
result += ", ";
}
}
result += "]";
return result;
}
int CheckGraphInputShapes(const std::vector<Tensor *> &inputs,
const std::unordered_map<Tensor *, std::vector<int>> &input_shape_map) {
for (const auto input : inputs) {
@ -324,9 +338,17 @@ int CheckGraphInputShapes(const std::vector<Tensor *> &inputs,
return RET_ERROR;
}
if (!input_shape_map.at(input).empty() && input_shape_map.at(input) != input->shape()) {
#ifndef ENABLE_LITE_ACL
MS_LOG(ERROR) << "graph input:" << input->tensor_name()
<< " shape has been illegally modified, please modify the input shape with method Resize().";
return RET_ERROR;
#else
MS_LOG(WARNING) << "Please check graph input " << input->tensor_name()
<< " shape:" << ShapeToString(input->shape())
<< " has been modified by DVPP method to shape:" << ShapeToString(input_shape_map.at(input))
<< "."
<< "If not, the modification is illegal, please modify the input shape with method Resize().";
#endif
}
}
return RET_OK;

7
mindspore/lite/src/cxx_api/model/model_impl.cc
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@ -302,8 +302,15 @@ Status ModelImpl::Predict(const std::vector<MSTensor> &inputs, std::vector<MSTen
if (user_input.MutableData() != input->data()) {
if (input->Size() != user_input.DataSize()) {
ResetTensorData(old_data, input_tensors);
#ifndef ENABLE_LITE_ACL
MS_LOG(ERROR) << "Tensor " << user_input.Name() << " has wrong data size.";
return kLiteInputTensorError;
#else
MS_LOG(WARNING) << "Please check tensor " << user_input.Name()
<< " has been modified data size by DVPP method.";
std::vector<int> truncate_shape = {static_cast<int>(user_input.DataSize())};
input->set_shape(truncate_shape);
#endif
}
input->set_data(user_input.MutableData());
}

19
mindspore/lite/src/ms_tensor.cc
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@ -104,7 +104,24 @@ tensor::MSTensor *tensor::MSTensor::CreateTensorByDeepCopy(const std::string &na
tensor->set_data(const_cast<void *>(new_data));
}
tensor->set_shape(shape);
size_t shape_size = 1;
if (shape.empty()) {
shape_size = 0;
} else {
for (size_t i = 0; i < shape.size(); ++i) {
if (shape[i] < 0) {
delete tensor;
return nullptr;
}
shape_size *= static_cast<size_t>(shape[i]);
}
}
if (data_len != shape_size * data_type_size) {
std::vector<int> truncate_shape = {static_cast<int>(data_len)};
tensor->set_shape(truncate_shape);
} else {
tensor->set_shape(shape);
}
tensor->set_tensor_name(name);
tensor->set_data_type(type);
return tensor;

26
mindspore/lite/src/runtime/kernel/ascend/src/model_process.cc
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@ -15,6 +15,7 @@
*/
#include "src/runtime/kernel/ascend/src/model_process.h"
#include <sys/time.h>
#include <utility>
#include <algorithm>
#include <map>
@ -426,9 +427,9 @@ STATUS ModelProcess::CheckTensorByTensorInfo(const std::vector<mindspore::MSTens
if (!IsDynamicShape()) {
for (size_t i = 0; i < tensor_info.size(); ++i) {
if (tensor[i].Shape() != tensor_info[i].dims) {
MS_LOG(ERROR) << "Note: input " << i << " shape not match, required " << ShapeToString(tensor_info[i].dims)
<< ", given " << ShapeToString(tensor[i].Shape());
return lite::RET_ERROR;
MS_LOG(WARNING) << "Note: input " << i << " shape not match, required " << ShapeToString(tensor_info[i].dims)
<< ", given " << ShapeToString(tensor[i].Shape()) << "."
<< "Please check input shape has been modified by DVPP method.";
}
if (tensor[i].DataType() != TransToDataType(tensor_info[i].data_type)) {
MS_LOG(ERROR) << "Note: input " << i << " data type not match, required "
@ -588,7 +589,24 @@ STATUS ModelProcess::PredictFromHost(const std::vector<mindspore::MSTensor> &inp
DestroyInputsDataset();
return ret; // forward status error
}
aclError acl_ret = aclmdlExecute(model_id_, inputs_, outputs_);
aclError acl_ret;
auto env = std::getenv("GLOG_v");
if (env != nullptr && env[0] == '1') {
struct timeval start_time;
struct timeval end_time;
(void)gettimeofday(&start_time, nullptr);
acl_ret = aclmdlExecute(model_id_, inputs_, outputs_);
(void)gettimeofday(&end_time, nullptr);
constexpr uint64_t kUSecondInSecond = 1000000;
uint64_t cost =
(kUSecondInSecond * static_cast<uint64_t>(end_time.tv_sec) + static_cast<uint64_t>(end_time.tv_usec)) -
(kUSecondInSecond * static_cast<uint64_t>(start_time.tv_sec) + static_cast<uint64_t>(start_time.tv_usec));
MS_LOG(INFO) << "Model execute in " << cost << " us";
} else {
acl_ret = aclmdlExecute(model_id_, inputs_, outputs_);
}
DestroyInputsDataset();
if (acl_ret != ACL_ERROR_NONE) {
MS_LOG(ERROR) << "Execute Model Failed, ret = " << acl_ret;

9
mindspore/lite/tools/converter/converter.cc
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@ -119,6 +119,15 @@ schema::MetaGraphT *Converter::Convert(const std::unique_ptr<converter::Flags> &
MS_LOG(ERROR) << "Parser/Import model return nullptr";
return nullptr;
}
MS_CHECK_TRUE_MSG(funcgraph_transform_ != nullptr, nullptr, "funcgraph_transform init failed.");
// funcgraph_transform
graph = funcgraph_transform_->Transform(graph, flag.get());
MS_CHECK_TRUE_MSG(graph != nullptr, nullptr, "Transform anf graph return nullptr.");
// export protobuf
auto status = MindIRSerialize(flag, graph);
if (status != RET_OK) {
MS_LOG(WARNING) << "Export to mindir proto return nullptr.";
}
return TransferFuncGraph(flag, graph);
}