if (tail_dim * type_size[promoteType] % 32 != 0) {
add1.update_output_desc_##outputName(outputName##outputIndex##_desc);
inputs.push_back(placeholder##intputIndex);
char *endptr;
pData[i] = value + (i % 3) * 0.4f; // 让数据更有意义
return 2;
return 4;
if (inputs->Size() > 512 \|\| inputs->Size() < 33) {
catMaxInputSize = 512;
FILE *fp;
add1.set_attr_##attrName(attrValue);
auto randomUniform = GetRandomUniformNoReplaceMent(selfContiguous, randomParams, uniqueExecutor.get());
return SqueezeWithAxes(x_shape, squeeze_dims, y_shape);
tiling.set_endTensorIdx(endTensorIdx);
tiling.set_endTensorOffset(endTensorOffset);
if (!IsAxesRangeValid(squeeze_dims, static_cast<int64_t>(x_shape->GetDimNum()), axes)) {
AscendC::And(..., mask, len / SHIFT_LEFT_32, { 1, 1, 1, 8,
std::vector<float> outHostData(8, 0);
std::vector<uint8_t> maskOutHostData(16, 0);
std::vector<uint8_t> outHostData(16, 0);
0
catMaxInputs = 512;
std::vector<int8_t> outHostData(8, 0);
std::vector<float> outHostData(16, 0);
std::vector<float> outHostData(8, 1);
std::vector<double> outHostData(8, 0);
本目录仅包含Squeezev2算子对应的aclnn接口；如您想要贡献该算子的AscendC实现，请参考贡献流程。
