🐛 Describe the bug torch.compile fails on pack and unpack functions Minimal repro minimalrepo.py.zip Versions Python: 3.10.14 Torch nightly : 2.4.0.dev20240526 Error logs (ao) (base) james@instance-20240521-0
🐛 Describe the bug import torch import fbgemm_gpu import torch.nn.functional as F torch._dynamo.config.capture_dynamic_output_shape_ops = True @torch.compile(mode="default", fullgraph=False) def forward( x: torch.Tensor, x_offsets: torch...
1. binding.cpp文件的92行修改成如下: int probs_size = THCudaTensor_size(state, probs, 2); 2. binding.cpp文件的105行修改成如下: void* gpu_workspace; THCudaMalloc(state, &gpu_workspace, gpu_size_bytes); 解决。 10. 接下重新执行:python setup.py install 安装完毕。
在尝试将NumPy数组转换为张量(tensor)时遇到“unsupported object type int”错误,通常意味着NumPy数组中的数据类型不是张量库(如PyTorch或TensorFlow)所期望的。基于你提供的提示,我将逐步解释如何解决这个问题。 1. 确认NumPy数组的数据类型 首先,我们需要检查NumPy数组的数据类型。这可以通过numpy.ndarray.dtype属性来...
TensorFlow saved_model: export failure: can’t convert cuda:0 device type tensor to numpy. 对于此类问题,作者在issue中的统一回答是:新版本已解决了该问题,请使用新版本。 然而,直接使用新版本毕竟不方便,因为在工程中很可能已经做了很多别的修改,使用新版本会直接覆盖这些修改。因此,解决思路是用新版本的修...
File "../lib/python3.7/site-packages/torch/tensor.py", line 492, in __array__ return self.numpy() TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first. Traceback (most recent call last): File "train.py", line 511...
B: Tensor | None = None # magic to support tensor shape modifications and splitting class LoraTorchTensor: _lora_A: Tensor # (n_rank, row_size) _lora_B: Tensor # (col_size, n_rank) _rank: int def __init__(self, A: Tensor, B: Tensor): ...
运行export.py,尝试将pytorch训练pt模型转换成Tensorflow支持tflite模型,然而遇到报错: TensorFlow saved_model: export failure: can...’t convert cuda:0 device type tensor to numpy...x.numel() for x in torch_m_.parameters()) # number params m_.i, m_.f, m_.type, m_.np = i, f,...
return_tensors="pt", ) onnx_export( pipeline.text_encoder, # casting to torch.int32 until the CLIP fix is released: https://github.com/huggingface/transformers/pull/18515/files model_args=(text_input.input_ids.to(torch.int32)),
1, firstly convert pytorch model to onnx import torch torch.onnx.export(mymodel,(input_tensor,),'./data/model.onnx') 2, convert the onnx model to openvino import openvino as ov core = ov.Core() ov_model = core.read_model('data/model.onnx') 0 Kudos Copy link...