例如,如果你只是想检查两个张量是否相等,你可以使用 PyTorch 提供的 torch.equal 函数: python import torch tensor1 = torch.tensor([1, 2, 3]) tensor2 = torch.tensor([1, 2, 3]) if torch.equal(tensor1, tensor2): print("The tensors are equal.") else: print("The tensors are not equal...
在学习torch应用进行采样时,创建了一个小测试用例,意外的发现运行异常 RuntimeError: Expected all tensors to be on the same device. Expected NPU tensor, please check whether the input tensor device is correct. [ERROR] 2024-10-14-19:05:41 (PID:18047, Device:0, RankID:-1) ERR01002 OPS inva...
so I guess they index different tensor dimensions. But the loop variable x0 is incremented by 1 each time, so tmp0 from second iteration is equal to tmp9 from previous iteration and it looks strange... jansel added ciflow/inductor module: aotinductor labels Dec 19, 2024 pytorch-bot bot...
🐛 Bug The function torch.pow doesn't seem to check if the input tensors are on the same device. To Reproduce Steps to reproduce the behavior: a = torch.tensor(2.0, device=torch.device('cuda:0')) b = torch.tensor(1.0) torch.pow(a,b) Expec...
def xml_shape(shape: np.ndarray, element: Element): for d in shape: dim = SubElement(element, 'dim') if d < 0: #raise Error('The value "{}" for shape is less 0. May be the input shape of the topology is ' # 'wrong.'.format(d)) pass if int(d) != d: raise ...
There are two reasons to do so. First, because of the possible disadvantages of such an approach, which we’ve mentioned at the beginning, and second because it’s a huge topic which should be described as a separate post. Please let us know if you are interested in checking such ...
isclose( actual, expected, rtol=rtol, atol=atol, equal_nan=equal_nan ) if self.aggregate_matches(matches): return There a few minor concerns: While atol and rtol apply to number to number conversions as well, the new parameter would only apply to tensors. I don't think this is ...
28 + "quantization": "compressed-tensors" 29 + }), 30 + ("meta-llama/Meta-Llama-3-8B", {}), 31 + ] 32 + 33 + # TODO: enable in pytorch 2.5 34 + if False and is_quant_method_supported("aqlm"): # noqa: SIM223 35 + TEST_MODELS.append(("ISTA-DASLab/Llama-2...
TL;DR: Thunder's fusion pass needs to change to consider the memory usage of the operations and the intermediate tensors. It should avoid fusing operations that increase peak memory usage. Use memory_peak_efficient_func as the target fun...
if not grad_output.is_contiguous(): grad_output = grad_output.contiguous() # tvm requires all input tensors to be contiguous grad_output = GraphMM._prepare_tensors(grad_output) # http://cs231n.github.io/optimization-2/ # https://pytorch.org/docs/master/notes/extending.html # grad_t1...