# 需要导入模块: import torch [as 别名]# 或者: from torch importbfloat16[as 别名]defmove_to_cpu(sample):def_move_to_cpu(tensor):# PyTorch has poor support for half tensors (float16) on CPU.# Move any such tensors to float32.iftensor.dtypein{torch.bfloat16, torch.float16}: tensor...
下面是一个使用 NF4 量化加载 4 位模型的示例,使用计算数据类型 bfloat16 进行双量化以加快训练速度:from transformers import BitsAndBytesConfignf4_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16...
bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16 ) Run Code Online (Sandbox Code Playgroud) 当您使用 from_pretrained() Transformers 方法加载模型时: ...
🚀 The feature, motivation and pitch I'd like to usetorch.fft.rfftfunction with bfloat16 tensor, but the operator doesn't support bfloat16 complex type. Repro code below: importtorchx=torch.zeros(3,4,dtype=torch.bfloat16)torch.fft.rfft(x) Is there any plan to support this in the ...
Thus I wonder if I misunderstood something. I have another project running to train LLaMA using bfloat16 (essentially usingrun_clm.pyfrom the official repo with--torch_dtype=bfloat16 --bf16command line flag), so if it turns out that I should not usetorch_dtypefor training then it means ...
dtype is torch.float32: return t.half() return t def apply_bfloat16(t): if t.dtype is torch.float32: return t.to(dtype=torch.bfloat16) return t if self.args.fp16: sample = utils.apply_to_sample(apply_half, sample) if self.args.bf16: sample = utils.apply_to_sample(apply_b...
torch_dtype参数 在使用torch_dtype参数时,可以传入的值包括以下几种常见的数据类型: 1. torch.float32或者torch.float,32位浮点型数据类型。 2. torch.float64或者torch.double,64位浮点型数据类型。 3. torch.float16或者torch.half,16位半精度浮点型数据类型。 4. torch.int8,8位有符号整型数据类型。 5. ...
默认情况下其他的模块(例如torch.nn.LayerNorm)会被转化为torch.float16,但是其实你也可以使用上文中提及的torch_dtype强行改成3232位。 importtorchfromtransformersimportAutoModelForCausalLMmodel_8bit = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", load_in_8bit=True, torch_dtype=torch.float...
在加载模型时，可能也会在from_pretrained（）函数中设置bnb_4bit_compute_dtype=torch.float16。
b.dtype: torch.float32 tensor([[ 1, 2, 3, 4, 5], [10, 20, 30, 40, 50]], dtype=torch.int32) b.dtype: torch.int32 type: <class 'torch.Tensor'> size: torch.Size([2, 5]) shape: torch.Size([2, 5]) dim: 2 num_element: 10 ...