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2f044d4ee5
Summary: Some of our machines have only 1 device. Pull Request resolved: https://github.com/pytorch/pytorch/pull/44068 Reviewed By: wanchaol Differential Revision: D23485730 Pulled By: izdeby fbshipit-source-id: df6bc0aba18feefc50c56a8f376103352fa2a2ea
218 lines
10 KiB
Python
218 lines
10 KiB
Python
import torch
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import unittest
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from torch.testing._internal.common_utils import TestCase, run_tests
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from torch.testing._internal.common_device_type import instantiate_device_type_tests, dtypes
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class TestForeach(TestCase):
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@dtypes(*torch.testing.get_all_dtypes())
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def test_int_scalar(self, device, dtype):
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tensors = [torch.zeros(10, 10, device=device, dtype=dtype) for _ in range(10)]
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int_scalar = 1
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# bool tensor + 1 will result in int64 tensor
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if dtype == torch.bool:
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expected = [torch.ones(10, 10, device=device, dtype=torch.int64) for _ in range(10)]
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else:
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expected = [torch.ones(10, 10, device=device, dtype=dtype) for _ in range(10)]
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res = torch._foreach_add(tensors, int_scalar)
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self.assertEqual(res, expected)
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if dtype in [torch.bool]:
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with self.assertRaisesRegex(RuntimeError,
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"result type Long can't be cast to the desired output type Bool"):
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torch._foreach_add_(tensors, int_scalar)
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else:
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torch._foreach_add_(tensors, int_scalar)
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self.assertEqual(res, tensors)
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@dtypes(*torch.testing.get_all_dtypes())
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def test_float_scalar(self, device, dtype):
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tensors = [torch.zeros(10, 10, device=device, dtype=dtype) for _ in range(10)]
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float_scalar = 1.
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# float scalar + integral tensor will result in float tensor
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if dtype in [torch.uint8, torch.int8, torch.int16,
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torch.int32, torch.int64, torch.bool]:
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expected = [torch.ones(10, 10, device=device, dtype=torch.float32) for _ in range(10)]
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else:
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expected = [torch.ones(10, 10, device=device, dtype=dtype) for _ in range(10)]
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res = torch._foreach_add(tensors, float_scalar)
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self.assertEqual(res, expected)
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if dtype in [torch.uint8, torch.int8, torch.int16,
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torch.int32, torch.int64, torch.bool]:
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self.assertRaises(RuntimeError, lambda: torch._foreach_add_(tensors, float_scalar))
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else:
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torch._foreach_add_(tensors, float_scalar)
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self.assertEqual(res, tensors)
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@dtypes(*torch.testing.get_all_dtypes())
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def test_complex_scalar(self, device, dtype):
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tensors = [torch.zeros(10, 10, device=device, dtype=dtype) for _ in range(10)]
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complex_scalar = 3 + 5j
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# bool tensor + 1 will result in int64 tensor
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expected = [torch.add(complex_scalar, torch.zeros(10, 10, device=device, dtype=dtype)) for _ in range(10)]
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if dtype in [torch.float16, torch.float32, torch.float64, torch.bfloat16] and device == 'cuda:0':
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# value cannot be converted to dtype without overflow:
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self.assertRaises(RuntimeError, lambda: torch._foreach_add_(tensors, complex_scalar))
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self.assertRaises(RuntimeError, lambda: torch._foreach_add(tensors, complex_scalar))
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return
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res = torch._foreach_add(tensors, complex_scalar)
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self.assertEqual(res, expected)
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if dtype not in [torch.complex64, torch.complex128]:
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self.assertRaises(RuntimeError, lambda: torch._foreach_add_(tensors, complex_scalar))
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else:
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torch._foreach_add_(tensors, complex_scalar)
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self.assertEqual(res, tensors)
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@dtypes(*torch.testing.get_all_dtypes())
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def test_bool_scalar(self, device, dtype):
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tensors = [torch.zeros(10, 10, device=device, dtype=dtype) for _ in range(10)]
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bool_scalar = True
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expected = [torch.ones(10, 10, device=device, dtype=dtype) for _ in range(10)]
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res = torch._foreach_add(tensors, bool_scalar)
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self.assertEqual(res, expected)
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torch._foreach_add_(tensors, bool_scalar)
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self.assertEqual(res, tensors)
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@dtypes(*torch.testing.get_all_dtypes())
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def test_add_scalar_with_different_size_tensors(self, device, dtype):
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if dtype == torch.bool:
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return
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tensors = [torch.zeros(10 + n, 10 + n, device=device, dtype=dtype) for n in range(10)]
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expected = [torch.ones(10 + n, 10 + n, device=device, dtype=dtype) for n in range(10)]
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torch._foreach_add_(tensors, 1)
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self.assertEqual(expected, tensors)
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@dtypes(*torch.testing.get_all_dtypes())
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def test_add_scalar_with_empty_list_and_empty_tensor(self, device, dtype):
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# TODO: enable empty list case
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for tensors in [[torch.randn([0])]]:
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res = torch._foreach_add(tensors, 1)
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self.assertEqual(res, tensors)
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torch._foreach_add_(tensors, 1)
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self.assertEqual(res, tensors)
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@dtypes(*torch.testing.get_all_dtypes())
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def test_add_scalar_with_overlapping_tensors(self, device, dtype):
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tensors = [torch.ones(1, 1, device=device, dtype=dtype).expand(2, 1, 3)]
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expected = [torch.tensor([[[2, 2, 2]], [[2, 2, 2]]], dtype=dtype, device=device)]
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# bool tensor + 1 will result in int64 tensor
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if dtype == torch.bool:
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expected[0] = expected[0].to(torch.int64).add(1)
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res = torch._foreach_add(tensors, 1)
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self.assertEqual(res, expected)
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def test_add_scalar_with_different_tensor_dtypes(self, device):
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tensors = [torch.tensor([1.1], dtype=torch.float, device=device),
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torch.tensor([1], dtype=torch.long, device=device)]
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self.assertRaises(RuntimeError, lambda: torch._foreach_add(tensors, 1))
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def test_add_list_error_cases(self, device):
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tensors1 = []
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tensors2 = []
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# Empty lists
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with self.assertRaises(RuntimeError):
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torch._foreach_add(tensors1, tensors2)
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with self.assertRaises(RuntimeError):
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torch._foreach_add_(tensors1, tensors2)
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# One empty list
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tensors1.append(torch.tensor([1], device=device))
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with self.assertRaisesRegex(RuntimeError, "Tensor list must have at least one tensor."):
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torch._foreach_add(tensors1, tensors2)
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with self.assertRaisesRegex(RuntimeError, "Tensor list must have at least one tensor."):
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torch._foreach_add_(tensors1, tensors2)
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# Lists have different amount of tensors
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tensors2.append(torch.tensor([1], device=device))
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tensors2.append(torch.tensor([1], device=device))
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with self.assertRaisesRegex(RuntimeError, "Tensor lists must have the same number of tensors, got 1 and 2"):
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torch._foreach_add(tensors1, tensors2)
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with self.assertRaisesRegex(RuntimeError, "Tensor lists must have the same number of tensors, got 1 and 2"):
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torch._foreach_add_(tensors1, tensors2)
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# Different dtypes
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tensors1 = [torch.zeros(10, 10, device=device, dtype=torch.float) for _ in range(10)]
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tensors2 = [torch.ones(10, 10, device=device, dtype=torch.int) for _ in range(10)]
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with self.assertRaisesRegex(RuntimeError, "All tensors in the tensor list must have the same dtype."):
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torch._foreach_add(tensors1, tensors2)
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with self.assertRaisesRegex(RuntimeError, "All tensors in the tensor list must have the same dtype."):
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torch._foreach_add_(tensors1, tensors2)
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# different devices
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if torch.cuda.is_available() and torch.cuda.device_count() > 1:
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tensor1 = torch.zeros(10, 10, device="cuda:0")
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tensor2 = torch.ones(10, 10, device="cuda:1")
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with self.assertRaisesRegex(RuntimeError, "Expected all tensors to be on the same device"):
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torch._foreach_add([tensor1], [tensor2])
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with self.assertRaisesRegex(RuntimeError, "Expected all tensors to be on the same device"):
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torch._foreach_add_([tensor1], [tensor2])
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# Coresponding tensors with different sizes
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tensors1 = [torch.zeros(10, 10, device=device) for _ in range(10)]
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tensors2 = [torch.ones(11, 11, device=device) for _ in range(10)]
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with self.assertRaisesRegex(RuntimeError, "Corresponding tensors in lists must have the same size"):
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torch._foreach_add(tensors1, tensors2)
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with self.assertRaisesRegex(RuntimeError, r", got \[10, 10\] and \[11, 11\]"):
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torch._foreach_add_(tensors1, tensors2)
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@dtypes(*torch.testing.get_all_dtypes())
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def test_add_list_same_size(self, device, dtype):
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tensors1 = [torch.zeros(10, 10, device=device, dtype=dtype) for _ in range(10)]
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tensors2 = [torch.ones(10, 10, device=device, dtype=dtype) for _ in range(10)]
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res = torch._foreach_add(tensors1, tensors2)
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torch._foreach_add_(tensors1, tensors2)
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self.assertEqual(res, tensors1)
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self.assertEqual(res[0], torch.ones(10, 10, device=device, dtype=dtype))
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@dtypes(*torch.testing.get_all_dtypes())
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def test_add_list_different_sizes(self, device, dtype):
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tensors1 = [torch.zeros(10 + n, 10 + n, device=device, dtype=dtype) for n in range(10)]
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tensors2 = [torch.ones(10 + n, 10 + n, device=device, dtype=dtype) for n in range(10)]
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res = torch._foreach_add(tensors1, tensors2)
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torch._foreach_add_(tensors1, tensors2)
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self.assertEqual(res, tensors1)
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self.assertEqual(res, [torch.ones(10 + n, 10 + n, device=device, dtype=dtype) for n in range(10)])
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@unittest.skipIf(not torch.cuda.is_available(), "CUDA not found")
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@dtypes(*torch.testing.get_all_dtypes())
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def test_add_list_slow_path(self, device, dtype):
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# different strides
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tensor1 = torch.zeros(10, 10, device=device, dtype=dtype)
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tensor2 = torch.ones(10, 10, device=device, dtype=dtype)
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res = torch._foreach_add([tensor1], [tensor2.t()])
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torch._foreach_add_([tensor1], [tensor2])
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self.assertEqual(res, [tensor1])
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# non contiguous
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tensor1 = torch.randn(5, 2, 1, 3, device=device)[:, 0]
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tensor2 = torch.randn(5, 2, 1, 3, device=device)[:, 0]
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self.assertFalse(tensor1.is_contiguous())
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self.assertFalse(tensor2.is_contiguous())
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res = torch._foreach_add([tensor1], [tensor2])
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torch._foreach_add_([tensor1], [tensor2])
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self.assertEqual(res, [tensor1])
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instantiate_device_type_tests(TestForeach, globals())
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if __name__ == '__main__':
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run_tests()
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