January 1, 2023 - Sorts the elements of the input tensor along its first dimension in ascending order by value · torch.msort(t) is equivalent to torch.sort(t, dim=0)[0]. See also torch.sort()

January 1, 2023 - torch.argsort(input, dim=-1, descending=False, *, stable=False) → Tensor# Returns the indices that sort a tensor along a given dimension in ascending order by value. This is the second value returned by torch.sort(). See its documentation ...

November 2, 2023 - import torch import torch.nn as nn import traceback def forward(x, device): x = torch.argmin(input=x, dim=0,keepdim=True) x = torch.argsort(input=x, descending=True,dim=1,stable=False) return x input_tensor = torch.rand([10, 9], dtype=torch.float32).to('cpu') cuda_tensor = input_tensor.clone().to('cuda') no_op_info = forward(input_tensor, 'cpu') print("build succeded") op_info = torch.compile(forward, mode='max-autotune',fullgraph=False,dynamic=True)(cuda_tensor, 'cuda') same_val = torch.allclose(no_op_info.to('cpu'), op_info.to('cpu'), rtol=1e-3, atol=1e-3, equal_nan=True) if same_val == False : print("BUGBUG DIFFERENTIAL") raise ValueError('diff value') else : print("no_error")

November 5, 2024 - import torch my_tensor = torch.tensor([7, 1, -5, 7, 9, -3, 0, -3]) torch.argsort(input=my_tensor) my_tensor.argsort() torch.argsort(input=my_tensor, dim=0, descending=False, stable=False) torch.argsort(input=my_tensor, dim=-1, descending=False, stable=False) torch.argsort(input=my_tensor, dim=0, descending=False, stable=True) torch.argsort(input=my_tensor, dim=-1, descending=False, stable=True) # tensor([2, 5, 7, 6, 1, 0, 3, 4]) torch.argsort(input=my_tensor, dim=0, descending=True, stable=False) torch.argsort(input=my_tensor, dim=-1, descending=True, stable=False) # tensor([4, 0, 3, 1, 6, 5,

January 19, 2022 - torch.cummin, torch.cummax, torch.sort and torch.argsort do not check the dim when the input is 0-d tensor ·

torch.argsort · Shortcuts · torch.argsort(input, dim=-1, descending=False) → LongTensor¶ · Returns the indices that sort a tensor along a given dimension in ascending order by value. This is the second value returned by torch.sort(). See its documentation for the exact semantics of this ...

May 18, 2020 - def stable_argsort(arr, dim=-1, descending=False): arr_np = arr.detach().cpu().numpy() if descending: indices = np.argsort(-arr_np, axis=dim, kind='stable') else: indices = np.argsort(arr_np, axis=dim, kind='stable') return torch.from_numpy(indices).long().to(arr.device)

February 17, 2020 - import torch from torch import nn import numpy as np class Demo(torch.nn.Module): def __init__(self): super().__init__() def forward(self, x): v, inds = x.sort(descending=True) # inds = x.argsort(descending=True) return inds if __name__ == "__main__": input_tensor = torch.range(20, 80) demo = Demo() out = demo(input_tensor) torch.onnx.export(demo, input_tensor, "debug.onnx", verbose=True, input_names=['data'], opset_version=11, do_constant_folding=True, dynamic_axes={'data':{0:'batch'}})

June 21, 2023 - Output of my CNN network is log probabilities. I want to get 10 best indices of the output and further use the indices to get loss. Hence, I have to use torch.argsort. But using torch.argsort breaks gradients. Can anyon…

January 1, 2023 - See torch.sort() On this page · Show Source · PyTorch Libraries · torchao · torchrec · torchft · TorchCodec · torchvision · ExecuTorch · PyTorch on XLA Devices · Access comprehensive developer documentation for PyTorch View Docs · Get in-depth tutorials for beginners and advanced ...

February 29, 2024 - import torch input_data = torch.tensor([3, 1, 4, 1, 5, 9, 2, 6, 5]) # CPU sorted_indices = torch.argsort(input_data) print(sorted_indices) # GPU input_data = input_data.cuda() sorted_indices = torch.argsort(input_data) print(sorted_indices)

August 11, 2019 - Documentation for torch.argsort includes an out argument#24158 · Copy link · Labels · enhancementNot as big of a feature, but technically not a bug. Should be easy to fixNot as big of a feature, but technically not a bug.

import numpy as np import torch import mindspore.ops as ops from mindspore import Tensor, Parameter from mindspore import dtype as mstype # MindSpore x = Tensor(np.array([[8, 2, 1], [5, 9, 3], [4, 6, 7]]), mstype.float16) sort = ops.Sort() output = sort(x) print(output) # Out: # (Tensor(shape=[3, 3], dtype=Float16, value= # [[ 1.0000e+00, 2.0000e+00, 8.0000e+00], # [ 3.0000e+00, 5.0000e+00, 9.0000e+00], # [ 4.0000e+00, 6.0000e+00, 7.0000e+00]]), Tensor(shape=[3, 3], dtype=Int32, value= # [[2, 1, 0], # [2, 0, 1], # [0, 1, 2]])) # Pytorch a = torch.tensor([[8, 2, 1], [5, 9, 3], [4, 6, 7]], dtype=torch.int8) torch.argsort(a, dim=1) # Out: # tensor([[2, 1, 0], # [2, 0, 1], # [0, 1, 2]])

January 6, 2019 - get_top = torch.argsort(similarity, dim=1) [20485, 30807, 27706, ..., 117, 6, 1], [29628, 16835, 23989, ..., 6773, 10377, 2], ..., [30120, 23853, 24914, ..., 118, 50, 47], [23528, 23663, 25283, ..., 78, 77, 48], [31738, 17062, 29731, ..., 1240, 79, 49]

January 1, 2023 - inline at::Tensor at::argsort(const at::Tensor &self, at::Dimname dim, bool descending = false)# On this page · Show Source · PyTorch Libraries · ExecuTorch · Helion · torchao · kineto · torchtitan · TorchRL · torchvision · torchaudio · tensordict ·