parent
44ea3902b8
commit
da0a18c5e3
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@ -58,7 +58,6 @@ class MS_API Model {
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extern MS_API const char* kDeviceTypeAscendCL;
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extern MS_API const char* kDeviceTypeAscendMS;
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} // namespace api
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} // namespace mindspore
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#endif // MINDSPORE_INCLUDE_API_MODEL_H
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@ -307,7 +307,7 @@ Status ModelProcess::CheckAndInitInput(const std::map<std::string, Buffer> &inpu
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const auto &input = iter->second;
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const void *data = input.Data();
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void *input_buffer;
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void *input_buffer = nullptr;
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if (!is_run_on_device_) {
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ret = aclrtMemcpy(info.device_data, info.buffer_size, data, input.DataSize(), ACL_MEMCPY_HOST_TO_DEVICE);
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if (ret != ACL_ERROR_NONE) {
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@ -25,7 +25,6 @@
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namespace mindspore {
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namespace api {
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namespace {
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uint64_t kSharedMemorySize = 100ull << 20; // 100 MB
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}
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@ -63,7 +62,6 @@ Status MultiProcess::MainProcess(ProcessFuncCall parent_process, ProcessFuncCall
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}
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shmat_data_addr_ = shmat_addr_ + sizeof(MessageFlag) * 2;
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shmat_data_max_size_ = memory_size_ - (shmat_data_addr_ - shmat_addr_);
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MS_LOG_INFO << "Shm addr " << (uint64_t)shmat_addr_;
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if (pid == 0) {
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ChildProcess(child_process);
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@ -22,7 +22,6 @@
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namespace mindspore {
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namespace api {
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struct MessageFlag {
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uint64_t heartbeat = 0;
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uint64_t stop = false;
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@ -61,7 +60,6 @@ class MultiProcess {
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Status ParentProcess(ProcessFuncCall parent_process);
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void ChildProcess(ProcessFuncCall child_process);
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};
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} // namespace api
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} // namespace mindspore
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@ -21,7 +21,6 @@
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namespace mindspore {
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namespace api {
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Status SharedMemory::Create(uint64_t memory_size) {
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auto access_mode = S_IRUSR | S_IWUSR | S_IROTH | S_IWOTH | S_IRGRP | S_IWGRP;
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shm_id_ = shmget(IPC_PRIVATE, memory_size, IPC_CREAT | IPC_EXCL | access_mode);
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@ -64,6 +63,5 @@ void SharedMemory::Destroy() {
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MS_LOG_ERROR << errMsg;
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}
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}
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} // namespace api
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} // namespace mindspore
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@ -21,7 +21,6 @@
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namespace mindspore {
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namespace api {
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class SharedMemory {
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public:
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Status Create(uint64_t memory_size);
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@ -34,7 +33,6 @@ class SharedMemory {
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int shm_id_ = -1;
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uint8_t *shmat_addr_ = nullptr;
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};
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} // namespace api
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} // namespace mindspore
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@ -41,7 +41,6 @@ using std::vector;
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namespace py = pybind11;
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namespace mindspore {
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namespace api {
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MsModel::MsModel(uint32_t device_id) : device_id_(device_id) {}
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MsModel::~MsModel() = default;
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@ -320,7 +319,7 @@ void MsModel::RegAllOp() {
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}
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py::module c_expression = py::module::import("mindspore._c_expression");
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size_t ops_info_long = c_expression.attr("OpInfoLoaderPy")().attr("get_all_ops_info")().cast<size_t>();
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auto all_ops_info = reinterpret_cast<std::vector<kernel::OpInfo *> *>(ops_info_long);
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auto all_ops_info = reinterpret_cast<std::vector<kernel::OpInfo *> *>(static_cast<uintptr_t>(ops_info_long));
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for (auto op_info : *all_ops_info) {
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kernel::OpLib::RegOpInfo(std::shared_ptr<kernel::OpInfo>(op_info));
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}
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@ -414,6 +413,5 @@ Status MsModel::GetOutputsInfo(std::vector<Tensor> *tensor_list) const {
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}
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return SUCCESS;
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}
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} // namespace api
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} // namespace mindspore
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@ -79,7 +79,6 @@ class MsModel : public ModelImpl {
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};
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API_REG_MODEL(AscendMS, MsModel);
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} // namespace api
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_SESSION_SESSION_BASIC_H
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@ -17,8 +17,8 @@
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#include "nnacl/fp16/batchnorm_fp16.h"
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#include <math.h>
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void BatchNormFp16(const float16_t *input, const void *mean, const void *variance,
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BatchNormParameter *param, int task_id, float16_t *output) {
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void BatchNormFp16(const float16_t *input, const void *mean, const void *variance, BatchNormParameter *param,
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int task_id, float16_t *output) {
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int units_per_thread = UP_DIV(param->unit_, param->op_parameter_.thread_num_);
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int completed_units = task_id * units_per_thread;
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int cur_unit = MSMIN(units_per_thread, param->unit_ - completed_units);
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@ -47,9 +47,9 @@ void FusedBatchNormFp16(const void *input, const void *scale, const void *offset
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float16_t variance_sqrt = sqrt(((const float16_t *)variance)[c] + param->epsilon_);
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if (variance_sqrt != 0) {
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float16_t norm_val =
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(((const float16_t *)input)[cur_offset + c] - ((const float16_t *)mean)[c]) / variance_sqrt;
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(((const float16_t *)input)[cur_offset + c] - ((const float16_t *)mean)[c]) / variance_sqrt;
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((float16_t *)output)[cur_offset + c] =
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norm_val * ((const float16_t *)scale)[c] + ((const float16_t *)offset)[c];
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norm_val * ((const float16_t *)scale)[c] + ((const float16_t *)offset)[c];
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}
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}
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cur_offset += param->channel_;
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