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046e88a291
Rewrite Python built-in class `super()` calls. Only non-semantic changes should be applied. - #94587 - #94588 - #94592 Also, methods with only a `super()` call are removed: ```diff class MyModule(nn.Module): - def __init__(self): - super().__init__() - def forward(self, ...): ... ``` Some cases that change the semantics should be kept unchanged. E.g.:f152a79be9/caffe2/python/net_printer.py (L184-L190)f152a79be9/test/test_jit_fuser_te.py (L2628-L2635)Pull Request resolved: https://github.com/pytorch/pytorch/pull/94592 Approved by: https://github.com/ezyang, https://github.com/seemethere
35 lines
1002 B
Python
35 lines
1002 B
Python
import torch
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# https://pytorch.org/docs/stable/torch.html#random-sampling
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class SamplingOpsModule(torch.nn.Module):
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def forward(self):
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a = torch.empty(3, 3).uniform_(0.0, 1.0)
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size = (1, 4)
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weights = torch.tensor([0, 10, 3, 0], dtype=torch.float)
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return len(
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# torch.seed(),
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# torch.manual_seed(0),
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torch.bernoulli(a),
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# torch.initial_seed(),
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torch.multinomial(weights, 2),
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torch.normal(2.0, 3.0, size),
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torch.poisson(a),
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torch.rand(2, 3),
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torch.rand_like(a),
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torch.randint(10, size),
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torch.randint_like(a, 4),
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torch.rand(4),
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torch.randn_like(a),
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torch.randperm(4),
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a.bernoulli_(),
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a.cauchy_(),
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a.exponential_(),
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a.geometric_(0.5),
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a.log_normal_(),
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a.normal_(),
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a.random_(),
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a.uniform_(),
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)
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