torch.CudaIntTensor torch.CudaShortTensor torch.CudaLongTensor and torch.CudaHalfTensor when supported as indicated by cutorch.hasHalf; these are half-precision (16-bit) floats. Note: these are currently limited
x = Torch.tensor([[1, 2, 3], [4, 5, 6]]) Get the shape of a tensor x.shape There are many functions to create tensors, like a = Torch.rand(3) b = Torch.zeros(2, 3) Each tensor has four properties dtype - the data type - :uint8, :int8, :int16, :int32, :int64,...
parse.add_argument('--init_method', type=str) parse.add_argument('--rank', type=int) parse.add_argument('--ws', type=int) args = parse.parse_args() if args.init_method == 'TCP': dist.init_process_group('nccl', init_method='tcp://127.0.0.1:28765', rank=args.rank, world_siz...
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int refcount: 引用计数,有点像智能指针的计数char flag:还不清楚, 上图是THTensor结构体的访问函数,这个语法有点奇怪,也不知道该怎么查。 这里函数名字是:THTensor_(storage),难道这是个预定义? 从语法上理解,THTensor_只能是个宏定义,果然: 宏定义内部还有宏TH_CONCAT_4,不过这个从名字上看,就是连接4个...
=int(ctx.model_yaml_config["handler"]["max_length"]) self.max_new_tokens =int(ctx.model_yaml_config["handler"]["max_new_tokens"]) model_name = ctx.model_yaml_config["handler"]["model_name"] model_path = ctx.model_yaml_config["handler"]["model_path"] seed =int(ctx.model_yaml...
device (int, optional): if specified, all parameters will be copied to that device Returns: Module: self """ device = torch.device("npu") if torch_npu.npu.is_available(): with torch.no_grad(): self.cast_weight(device) return self._apply(lambda t: t.npu(device)) de...
上述代码prepare_fx(float_model, qconfig_dict)没有指定is_reference参数,那么convert后的pytorch模型就是实打实的量化模型,所有的算子的精度都是INT8然后运行在CPU上,Pytorch支持以下的INT8后端: x86 CPUs with AVX2 support or higher (without AVX2 some operations have inefficient implementations), via fbgemm ...
parser.add_argument('--canvas_size', default=1280, type=int, help='image size for inference') parser.add_argument('--mag_ratio', default=1.5, type=float, help='image magnification ratio') parser.add_argument('--poly', default=False ,action='store_true', help='enable polygon type') ...
result=i.exp()ctx.save_for_backward(result)# 保存所需内容,以备backward时使用,所需的结果会被保存在saved_tensors元组中;此处仅能保存tensor类型变量,若其余类型变量(Int等),可直接赋予ctx作为成员变量,也可以达到保存效果returnresult @staticmethod