set_default_tensor_type(torch.cuda.FloatTensor) hidden = torch.zeros((num_layers, self.b, self.h), dtype=typ) output, _ = rnn(packed_seq, hidden) self.assertEqual(output.data.type(), HALF) output.data.float().su
3) print(b.dtype, b.device) print(torch.get_default_dtype()) # torch.float32 cuda:0 # torch.float32 torch.set_default_tensor_type(torch.FloatTensor) c = torch.
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. ...
torch.set_default_dtype(d)[source] Sets the default floating point dtype tod. This type will be used as default floating point type for type inference intorch.tensor(). The default floating point dtype is initiallytorch.float32. Parameters d(torch.dtype) – the floating point dtype to make ...
)) # 包含小数 #output: #Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, # [1., 2., 3., 4.]) Torch把元组或列表转为Tensor类型时,dtype默认是float32(不显示dtype),与 torch.FloatTensor()返回结果是一样的;而paddle 如果全为整数默认dtype为int64,如有一个小数则d...
float_model.eval()# 因为是PTQ,所以就推理模式就够了 qconfig=get_default_qconfig("fbgemm")# 指定量化细节配置 qconfig_dict={"":qconfig}# 指定量化选项 defcalibrate(model,data_loader):# 校准功能函数 model.eval()withtorch.no_grad():forimage,targetindata_loader:model(image)prepared_model=prepare...
🐛 Describe the bug Hi, Investigating why a model implementation using SDPA vs no SDPA was not yielding the exact same output using fp16 with the math backend, I pinned it down to a different behavior of torch.softmax(inp, dtype=torch.flo...
dtype) # Prints "torch.int64", currently 64-bit integer type x = x.type(torch.FloatTensor) print(x.dtype) # Prints "torch.float32", now 32-bit float print(x.float()) # Still "torch.float32" print(x.type(torch.DoubleTensor)) # Prints "tensor([0., 1., 2., 3.], dtype=torch...
作为上下文管理器使用时,混合精度计算 enable 区域得到的 FP16 数值精度的变量在 enable 区域外需要显式的转成 FP32: # Creates some tensors in default dtype (here assumed to be float32) a_float32 = torch.rand((8, 8), device="cuda") b_float32 = torch.rand((8, 8), device="cuda") c...
[rank1]: ValueError: Must flatten tensors with uniform dtype but got torch.bfloat16 and torch.float32 I am running a code modified from this script https://github.com/huggingface/trl/blob/main/examples/scripts/dpo.py And I am runing with QLoRA. And source code for BnB config is modifie...