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 to copying/conversion, and several indexing and shaping operations (e.g. narrow, sel...
Something that explains the optional output dtype cannot be set to an Int, Long, or Boolean. Let me know what you think. igagankapoor commented Jul 19, 2024 There's probably a special case for the zero numel case that doesn't appropriately account for bool/int. Probably not hard to ...
登录提示 该操作需登录 Gitee 帐号,请先登录后再操作。 立即登录 没有帐号,去注册 编辑仓库简介 简介内容 https://github.com/llvm/torch-mlir 主页 取消 保存更改 1 https://gitee.com/EwenWan/torch-mlir.git git@gitee.com:EwenWan/torch-mlir.git EwenWan torch-mlir torch-mlir main北京...
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...
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 ...
""" super().initialize(ctx) model_dir = ctx.system_properties.get("model_dir") self.max_length = 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"]["...
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') ...