* Copyright (c) 2025 Huawei Technologies Co., Ltd.
* This program is free software, you can redistribute it and/or modify it under the terms and conditions of
* CANN Open Software License Agreement Version 2.0 (the "License").
* Please refer to the License for details. You may not use this file except in compliance with the License.
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,
* INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
* See LICENSE in the root of the software repository for the full text of the License.
*/
#include "transpose_kernel.h"
#include <memory>
#include <vector>
#include "framework/common/debug/log.h"
#include "formats/format_transfers/format_transfer_transpose.h"
#include "formats/formats.h"
#include "formats/utils/formats_trans_utils.h"
#include "framework/common/op/ge_op_utils.h"
#include "framework/common/framework_types_internal.h"
#include "framework/common/util.h"
#include "framework/common/debug/ge_log.h"
#include "framework/common/ge_inner_error_codes.h"
#include "host_kernels/kernel_utils.h"
#include "graph/utils/type_utils.h"
#include "host_kernels/kernel_factory.h"
namespace ge {
namespace {
const size_t kTransposeInputX = 0;
const size_t kTransposeInputPerm = 1;
const size_t kTransposeInputSize = 2;
const size_t kTransposeOutputY = 0;
const size_t kTransposeOutputSize = 1;
}
Status TransposeKernel::ValidateInput(const OpDescPtr &op_desc_ptr, const std::vector<ConstGeTensorPtr> &input) const {
if (op_desc_ptr == nullptr) {
GELOGW("Input opDescPtr is nullptr.");
return PARAM_INVALID;
}
if (op_desc_ptr->GetInputsSize() != kTransposeInputSize || op_desc_ptr->GetOutputsSize() != kTransposeOutputSize) {
GELOGW("The input_size(%zu) and output_size(%zu) of op are invalid, op name: %s.", op_desc_ptr->GetInputsSize(),
op_desc_ptr->GetOutputsSize(), op_desc_ptr->GetName().c_str());
return PARAM_INVALID;
}
if (input.size() != kTransposeInputSize) {
GELOGW("The size of input tensor vector is invalid, input size is %zu, op name: %s.", input.size(),
op_desc_ptr->GetName().c_str());
return PARAM_INVALID;
}
ConstGeTensorPtr tensor_x_ptr = input[kTransposeInputX];
ConstGeTensorPtr tensor_perm_ptr = input[kTransposeInputPerm];
if (tensor_x_ptr == nullptr || tensor_perm_ptr == nullptr) {
GELOGW("Input tensor of op is nullptr, node name: %s.", op_desc_ptr->GetName().c_str());
return PARAM_INVALID;
}
return SUCCESS;
}
Status TransposeKernel::Compute(const OpDescPtr op_desc_ptr, const std::vector<ConstGeTensorPtr> &input,
std::vector<GeTensorPtr> &v_output) {
GELOGD("TransposeKernel in.");
Status status = ValidateInput(op_desc_ptr, input);
if (status != SUCCESS) {
GELOGW("TransposeKernel input is invalid, failed to fold node.");
return NOT_CHANGED;
}
ConstGeTensorPtr const_weight_ptr = input[kTransposeInputX];
GeTensorDesc op_desc = op_desc_ptr->GetOutputDesc(kTransposeOutputY);
GeTensorDesc op_desc_in = op_desc_ptr->GetInputDesc(kTransposeInputX);
auto src_format = op_desc_in.GetFormat();
auto src_shape = op_desc_in.GetShape().GetDims();
auto src_data_type = op_desc_in.GetDataType();
auto data_shape = op_desc.GetShape().GetDims();
auto data_format = op_desc.GetFormat();
auto data_type = op_desc.GetDataType();
GELOGD(
"current node %s, format %s, input shape %s, data type %s, weight format %s, shape %s, data type %s. "
"output format %s, shape %s, data type %s",
op_desc_ptr->GetName().c_str(), TypeUtils::FormatToSerialString(src_format).c_str(),
formats::ShapeToString(src_shape).c_str(), TypeUtils::DataTypeToSerialString(src_data_type).c_str(),
TypeUtils::FormatToSerialString(const_weight_ptr->GetTensorDesc().GetFormat()).c_str(),
formats::ShapeToString(const_weight_ptr->GetTensorDesc().GetShape()).c_str(),
TypeUtils::DataTypeToSerialString(const_weight_ptr->GetTensorDesc().GetDataType()).c_str(),
TypeUtils::FormatToSerialString(data_format).c_str(), formats::ShapeToString(data_shape).c_str(),
TypeUtils::DataTypeToSerialString(data_type).c_str());
ConstGeTensorPtr tensor_perm_ptr = input[kTransposeInputPerm];
DataType data_dtype = tensor_perm_ptr->GetTensorDesc().GetDataType();
auto input_perm_shape = tensor_perm_ptr->GetTensorDesc().GetShape();
auto output_size = input_perm_shape.GetShapeSize();
uint32_t data_size = GetSizeByDataType(data_dtype);
if (static_cast<size_t>(output_size * data_size) != tensor_perm_ptr->GetData().size()) {
GELOGW("TransposeKernel input perm shape size and data size do not match.");
return NOT_CHANGED;
}
std::vector<int64_t> perm_list;
auto input_perm = tensor_perm_ptr->GetData().data();
if (data_dtype == DT_INT32) {
int32_t *input_perm_data = const_cast<int32_t *>(reinterpret_cast<const int32_t *>(input_perm));
for (int64_t i = 0; i < output_size; i++) {
perm_list.push_back(static_cast<int64_t>(input_perm_data[i]));
}
} else if (data_dtype == DT_INT64) {
int64_t *input_perm_data = const_cast<int64_t *>(reinterpret_cast<const int64_t *>(input_perm));
for (int64_t i = 0; i < output_size; i++) {
perm_list.push_back(input_perm_data[i]);
}
} else {
GELOGW("TransposeKernel input perm data type is invalid, data type is %s.",
TypeUtils::DataTypeToSerialString(data_dtype).c_str());
return NOT_CHANGED;
}
GELOGD("Transpose from %s to %s, shape %s to %s, perm_list %s, data type %s",
TypeUtils::FormatToSerialString(src_format).c_str(), TypeUtils::FormatToSerialString(data_format).c_str(),
formats::ShapeToString(src_shape).c_str(), formats::ShapeToString(data_shape).c_str(),
formats::ShapeToString(perm_list).c_str(), TypeUtils::DataTypeToSerialString(src_data_type).c_str());
if ((data_shape.empty()) || (src_data_type != data_type)) {
GELOGW("Transpose is not supported. Invalid shape (src: %s, dst: %s) or inconsistent datatype (src: %s, dst: %s)",
formats::ShapeToString(src_shape).c_str(), formats::ShapeToString(data_shape).c_str(),
TypeUtils::DataTypeToSerialString(src_data_type).c_str(),
TypeUtils::DataTypeToSerialString(data_type).c_str());
return NOT_CHANGED;
}
if (!KernelUtils::CheckSizeForTransOp(const_weight_ptr, op_desc_ptr)) {
GELOGW("CheckSize failed, input size is not equal to weight size");
return NOT_CHANGED;
}
const uint8_t *src_data = const_weight_ptr->GetData().data();
formats::TransResult trans_result;
auto ret = formats::TransposeWithShapeCheck(src_data, src_shape, data_shape, src_data_type, perm_list, trans_result);
if (ret != SUCCESS) {
GELOGW("Failed to Transpose from %s to %s, shape %s to %s, perm_list %s, data type %s",
TypeUtils::FormatToSerialString(src_format).c_str(), TypeUtils::FormatToSerialString(data_format).c_str(),
formats::ShapeToString(src_shape).c_str(), formats::ShapeToString(data_shape).c_str(),
formats::ShapeToString(perm_list).c_str(), TypeUtils::DataTypeToSerialString(src_data_type).c_str());
return NOT_CHANGED;
}
GeTensorPtr output_ptr = MakeShared<GeTensor>(op_desc_ptr->GetOutputDesc(kTransposeOutputY));
GE_CHECK_NOTNULL(output_ptr);
if (output_ptr->SetData(trans_result.data.get(), trans_result.length) != GRAPH_SUCCESS) {
GELOGW("Compute: SetData failed");
}
v_output.push_back(output_ptr);
GELOGI("TransposeKernel success.");
return SUCCESS;
}
REGISTER_COMPUTE_NODE_KERNEL(TRANSPOSE, TransposeKernel);
}
