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ba3b05fdf3
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`. Pull Request resolved: https://github.com/pytorch/pytorch/pull/127122 Approved by: https://github.com/kit1980
128 lines
3.1 KiB
Python
128 lines
3.1 KiB
Python
# mypy: disable-error-code="possibly-undefined"
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# flake8: noqa
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from typing_extensions import assert_type
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import torch
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from torch.testing._internal.common_utils import TEST_NUMPY
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if TEST_NUMPY:
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import numpy as np
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# From the docs, there are quite a few ways to create a tensor:
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# https://pytorch.org/docs/stable/tensors.html
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# torch.tensor()
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torch.tensor([[0.1, 1.2], [2.2, 3.1], [4.9, 5.2]])
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torch.tensor([0, 1])
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torch.tensor(
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[[0.11111, 0.222222, 0.3333333]], dtype=torch.float64, device=torch.device("cuda:0")
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)
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torch.tensor(3.14159)
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# torch.sparse_coo_tensor
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i = torch.tensor([[0, 1, 1], [2, 0, 2]])
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v = torch.tensor([3, 4, 5], dtype=torch.float32)
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torch.sparse_coo_tensor(i, v, [2, 4])
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torch.sparse_coo_tensor(i, v)
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torch.sparse_coo_tensor(
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i, v, [2, 4], dtype=torch.float64, device=torch.device("cuda:0")
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)
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torch.sparse_coo_tensor(torch.empty([1, 0]), [], [1])
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torch.sparse_coo_tensor(torch.empty([1, 0]), torch.empty([0, 2]), [1, 2])
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# torch.as_tensor
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a = [1, 2, 3]
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torch.as_tensor(a)
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torch.as_tensor(a, device=torch.device("cuda"))
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# torch.as_strided
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x = torch.randn(3, 3)
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torch.as_strided(x, (2, 2), (1, 2))
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torch.as_strided(x, (2, 2), (1, 2), 1)
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# torch.from_numpy
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if TEST_NUMPY:
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torch.from_numpy(np.array([1, 2, 3]))
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# torch.zeros/zeros_like
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torch.zeros(2, 3)
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torch.zeros((2, 3))
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torch.zeros([2, 3])
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torch.zeros(5)
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torch.zeros_like(torch.empty(2, 3))
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# torch.ones/ones_like
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torch.ones(2, 3)
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torch.ones((2, 3))
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torch.ones([2, 3])
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torch.ones(5)
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torch.ones_like(torch.empty(2, 3))
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# torch.arange
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torch.arange(5)
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torch.arange(1, 4)
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torch.arange(1, 2.5, 0.5)
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# torch.range
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torch.range(1, 4)
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torch.range(1, 4, 0.5)
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# torch.linspace
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torch.linspace(3, 10, steps=5)
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torch.linspace(-10, 10, steps=5)
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torch.linspace(start=-10, end=10, steps=5)
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torch.linspace(start=-10, end=10, steps=1)
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# torch.logspace
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torch.logspace(start=-10, end=10, steps=5)
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torch.logspace(start=0.1, end=1.0, steps=5)
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torch.logspace(start=0.1, end=1.0, steps=1)
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torch.logspace(start=2, end=2, steps=1, base=2)
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# torch.eye
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torch.eye(3)
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# torch.empty/empty_like/empty_strided
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torch.empty(2, 3)
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torch.empty((2, 3))
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torch.empty([2, 3])
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torch.empty_like(torch.empty(2, 3), dtype=torch.int64)
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torch.empty_strided((2, 3), (1, 2))
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# torch.full/full_like
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torch.full((2, 3), 3.141592)
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torch.full_like(torch.full((2, 3), 3.141592), 2.71828)
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# torch.quantize_per_tensor
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torch.quantize_per_tensor(torch.tensor([-1.0, 0.0, 1.0, 2.0]), 0.1, 10, torch.quint8)
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# torch.quantize_per_channel
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x = torch.tensor([[-1.0, 0.0], [1.0, 2.0]])
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quant = torch.quantize_per_channel(
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x, torch.tensor([0.1, 0.01]), torch.tensor([10, 0]), 0, torch.quint8
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)
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# torch.dequantize
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torch.dequantize(x)
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# torch.complex
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real = torch.tensor([1, 2], dtype=torch.float32)
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imag = torch.tensor([3, 4], dtype=torch.float32)
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torch.complex(real, imag)
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# torch.polar
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abs = torch.tensor([1, 2], dtype=torch.float64)
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pi = torch.acos(torch.zeros(1)).item() * 2
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angle = torch.tensor([pi / 2, 5 * pi / 4], dtype=torch.float64)
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torch.polar(abs, angle)
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# torch.heaviside
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inp = torch.tensor([-1.5, 0, 2.0])
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values = torch.tensor([0.5])
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torch.heaviside(inp, values)
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# Parameter
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p = torch.nn.Parameter(torch.empty(1))
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assert_type(p, torch.nn.Parameter)
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