安谋科技、此芯科技与瑞莎计算机联合打造了面向AI PC、边缘、机器人等不同场景的“星睿O6”开发套件,该套件异构集成了Arm®v9 CPU核心、Arm Immortalis™ GPU以及安谋科技“周易”NPU RVM 人像分割简介 稳定视频抠像(RVM)是一款功能强大的实时视频人像抠图技术,其由字节跳动项目组开发。不同于现有
make[2]: *** [CMakeFiles/torch_npu.dir/build.make:524: CMakeFiles/torch_npu.dir/torch_npu/csrc/aten/ops/StreamAndEventKernelNpu.cpp.o] Error 1 make[2]: *** Waitingforunfinishedjobs... /home/dsr/myprojects/operators/pytorch-v2.1.0-6.0.0/torch_npu/csrc/aten/ops/CopyFromAndResizeKe...
<!DOCTYPE html> 概述 本章节包含常用亲和库接口,提供模型中常用的组合类接口,无需自行完成接口或导入第三方库。 表1 torch_npu contrib APIAPI 名称 原生函数/参考链接 说明 (beta)torch_npu.contrib.npu_fused_attention_with_layernorm - bert自注意力与前层规范的融
(beta)torch_npu.npu_get_float_status (beta)torch_npu.npu_giou (beta)torch_npu.npu_grid_assign_positive (beta)torch_npu.npu_gru (beta)torch_npu.npu_ifmr (beta)torch_npu.npu_indexing (beta)torch_npu.npu_iou (beta)torch_npu.npu_layer_norm_eval (beta)torch_npu.npu_linear (beta)torch...
ops.npu.npu_sort_v2(self, dim, descending) return out class _NPULayerNormEvalOP(object): @staticmethod def forward(input_, normalized_shape, weight=None, bias=None, eps=1e-05): if torch.onnx.is_in_onnx_export(): return torch.layer_norm(input_, normalized_shape, ...
We are releasing torchtune v0.5.0 with lots of exciting new features! This includes Kaggle integration, a QAT + LoRA training recipe, improved integrations with Hugging Face and vLLM, Gemma2 models, a recipe enabling finetuning for LayerSkip via early exit, and support for NPU devices. ...
eleuther_eval.py full_finetune_distributed.py full_finetune_single_device.py generate.py lora_dpo_distributed.py lora_dpo_single_device.py lora_finetune_distributed.py lora_finetune_single_device.py quantization.md quantize.py tests torchtune ...
return g.op("npu::NPURmsNorm", self, gamma, epsilon_f=epsilon, outputs=2) class NPUAddRmsNormOP(torch.autograd.Function): @staticmethod def forward(ctx, *args, **kwargs): return torch.ops.npu.npu_add_rms_norm(*args, **kwargs) @staticmethod def symbolic(g, ...
def layernorm_forward(self, input: torch.Tensor) -> torch.Tensor: if self.training or (not input.is_npu): return torch.nn.functional.layer_norm( input, self.normalized_shape, self.weight, self.bias, self.eps) else: return torch_npu.npu_layer_norm_eval(input, self.normalized_sha...
fromtorch_npu.utils._error_codeimportErrCode,ops_error __all__=["SyncBatchNorm"] classSyncBatchNorm(Function): @staticmethod defforward(self,input_tensor,weight,bias,running_mean,running_var,eps,momentum,process_group,world_size): input_tensor=input_tensor.contiguous() ...