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0a5e788ab2
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/75808 Just as it is often difficult to write a single kernel that can handle both CPU and CUDA, so can it be difficult to do the same for NestedTensor. ghstack-source-id: 154171542 (Note: this ignores all push blocking failures!) Test Plan: CI? Reviewed By: bdhirsh Differential Revision: D35603836 fbshipit-source-id: fb0ebb19d34531ed96ce176aca325f8e2b5f90e6 (cherry picked from commit 0bcd753f93c04256c1b745f84a74ecccf0dceef5)
296 lines
11 KiB
Python
296 lines
11 KiB
Python
# Owner(s): ["module: nestedtensor"]
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import torch
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import torch.nn
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import unittest
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from torch.testing._internal.common_device_type import (
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dtypes,
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dtypesIfCUDA,
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instantiate_device_type_tests,
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skipMeta,
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)
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from torch.testing._internal.common_utils import TestCase, IS_FBCODE
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from torch import nested_tensor
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# Tests are ported from pytorch/nestedtensor.
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# This makes porting as_nested_tensor easier in the future.
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def _iter_constructors():
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# yield as_nested_tensor
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yield nested_tensor
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class TestNestedTensor(TestCase):
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@torch.inference_mode()
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def _test_unbind_case(self, a, b):
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nt = nested_tensor([a, b])
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a1, b1 = nt.unbind()
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self.assertTrue(a is not a1)
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self.assertTrue(b is not b1)
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nt = nested_tensor([a, b], dtype=a.dtype)
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a1, b1 = nt.unbind(0)
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self.assertEqual(a, a1)
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self.assertEqual(b, b1)
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a = torch.randn((2, 3)).add_(1)
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nt = nested_tensor([a])
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self.assertEqual(a, nt.unbind(0)[0])
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@torch.inference_mode()
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def test_unbind_0(self):
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self._test_unbind_case(
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torch.tensor([1, 2]), torch.tensor([7, 8]),
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)
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@torch.inference_mode()
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def test_unbind_1(self):
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self._test_unbind_case(
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torch.tensor([1]), torch.tensor([7]),
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)
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# @torch.inference_mode()
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# def test_unbind_2(self):
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# self._test_unbind_case(
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# torch.tensor(1), torch.tensor(7),
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# )
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@torch.inference_mode()
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def test_unbind_3(self):
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self._test_unbind_case(
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torch.tensor([1.0]), torch.tensor([]),
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)
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@torch.inference_mode()
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def test_unbind_4(self):
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self._test_unbind_case(
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torch.tensor([]), torch.tensor([]),
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)
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@torch.inference_mode()
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def test_unbind_dim(self):
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def _test_fn(unbind_fn):
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a = torch.rand(3, 2)
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b = torch.rand(2, 3)
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nt = nested_tensor([a, b])
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self.assertRaises(RuntimeError, lambda: unbind_fn(nt, 1))
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# Both of these tests are necessary, because we're using
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# torch_function.
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_test_fn(lambda x, dim: x.unbind(dim))
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# TODO: Re-enable this once using torch_dispatch
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# _test_fn(lambda x, dim: torch.unbind(x, dim))
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@torch.inference_mode()
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def test_nested_tensor(self):
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self.assertRaises(TypeError, lambda: nested_tensor([3.0]))
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self.assertRaises(TypeError, lambda: nested_tensor(torch.tensor([3.0])))
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self.assertRaises(TypeError, lambda: nested_tensor(4.0))
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@torch.inference_mode()
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def test_nested_tensor_matching_dim(self):
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self.assertRaisesRegex(
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RuntimeError,
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"Found dimension 1 for Tensor at index 1 and dimension 0 for Tensor at index 0.",
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lambda: nested_tensor([torch.tensor(1.0), torch.tensor([])]),
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)
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self.assertRaisesRegex(
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RuntimeError,
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"Found dimension 1 for Tensor at index 2 and dimension 0 for Tensor at index 1.",
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lambda: nested_tensor(
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[torch.tensor(1.0), torch.tensor(2.0), torch.tensor([])]
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),
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)
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@torch.inference_mode()
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def test_default_nested_tensor(self):
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self.assertRaises(TypeError, lambda: nested_tensor())
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default_nested_tensor = nested_tensor([])
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default_tensor = torch.tensor([])
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# self.assertEqual(default_nested_tensor.nested_dim(), 1)
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# self.assertEqual(default_nested_tensor.nested_size(), ())
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self.assertEqual(default_nested_tensor.dim(), default_tensor.dim())
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self.assertEqual(default_nested_tensor.layout, default_tensor.layout)
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self.assertEqual(default_nested_tensor.device, default_tensor.device)
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self.assertEqual(default_nested_tensor.dtype, default_tensor.dtype)
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self.assertEqual(
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default_nested_tensor.requires_grad, default_tensor.requires_grad
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)
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self.assertIsNone(default_tensor.grad)
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# TODO: Re-enable once we have a performance driven
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# use case and implementation.
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# self.assertEqual(default_nested_tensor.is_pinned(),
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# default_tensor.is_pinned())
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@torch.inference_mode()
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def test_dim(self):
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for constructor in _iter_constructors():
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a1 = constructor([])
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self.assertEqual(a1.dim(), 1)
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a1 = constructor([torch.tensor(3.0)])
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self.assertEqual(a1.dim(), 1)
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a1 = constructor([torch.tensor([1, 2, 3, 4])])
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self.assertEqual(a1.dim(), 2)
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@unittest.skipIf(IS_FBCODE, "numel is not virtual in fbcode.")
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@torch.inference_mode()
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def test_numel(self):
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for constructor in _iter_constructors():
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a1 = constructor([])
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self.assertRaisesRegex(
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RuntimeError, "numel is disabled", lambda: a1.numel(),
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)
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@torch.inference_mode()
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def test_size(self):
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for constructor in _iter_constructors():
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a1 = constructor([])
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self.assertRaisesRegex(
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RuntimeError,
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"Tensors of type NestedTensorImpl do not have sizes"
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if IS_FBCODE
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else "NestedTensorImpl doesn't support sizes",
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lambda: a1.size(),
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)
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@unittest.skipIf(IS_FBCODE, "stride is not virtual in fbcode.")
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@torch.inference_mode()
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def test_stride(self):
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for constructor in _iter_constructors():
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a1 = constructor([])
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self.assertRaisesRegex(
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RuntimeError,
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"NestedTensorImpl doesn't support strides",
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lambda: a1.stride(),
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)
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@unittest.skipIf(IS_FBCODE, "is_contiguous is not virtual in fbcode.")
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@torch.inference_mode()
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def test_is_contiguous(self):
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for constructor in _iter_constructors():
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a1 = constructor([])
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self.assertRaisesRegex(
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RuntimeError, "is_contiguous is disabled", lambda: a1.is_contiguous()
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)
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@torch.inference_mode()
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def test_repr_string(self):
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a = nested_tensor([])
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expected = "nested_tensor([" "\n\n])"
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self.assertEqual(str(a), expected)
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self.assertEqual(repr(a), expected)
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a = nested_tensor([torch.tensor(1.0)])
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expected = "nested_tensor([" "\n tensor(1.)" "\n])"
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self.assertEqual(str(a), expected)
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self.assertEqual(repr(a), expected)
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a = nested_tensor([torch.tensor([[1, 2]]), torch.tensor([[4, 5]])])
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expected = (
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"nested_tensor([" "\n tensor([[1, 2]])" "," "\n tensor([[4, 5]])" "\n])"
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)
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self.assertEqual(str(a), expected)
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self.assertEqual(repr(a), expected)
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@torch.inference_mode()
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def test_activations(self):
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for func in (torch.nn.functional.relu, torch.nn.functional.relu_, torch.nn.functional.gelu, torch._C._nn.gelu_):
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t = torch.tensor([-1, 0, 1], dtype=torch.float)
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nt = nested_tensor([t])
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nested_result = func(nt)
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self.assertTrue(nested_result.is_nested)
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self.assertEqual(func(t), nested_result.unbind()[0])
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def test_to_padded_tensor_on_empty_tensor(self):
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nt = torch.nested_tensor([])
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empty = nt.to_padded_tensor(4)
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self.assertEqual(empty, torch.tensor([]))
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class TestNestedTensorDeviceType(TestCase):
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@dtypes(torch.float)
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@skipMeta
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def test_to_then_from_padded_tensor_no_transform0213(self, device, dtype):
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t = torch.randn(4, 4, 4, device=device, dtype=dtype)
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ts = list(torch.unbind(t))
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ts[0] = ts[0][:-1]
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nt = torch.nested_tensor(ts, device=device, dtype=dtype)
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padded = nt.to_padded_tensor(0)
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nt_to = torch._nested_from_padded_and_nested_example(padded, nt)
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for (t1, t2) in zip(nt.unbind(), nt_to.unbind()):
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self.assertEqual(t1, t2)
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self.assertEqual(nt.device, nt_to.device)
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@dtypes(torch.float)
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@dtypesIfCUDA(torch.float, torch.half)
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@skipMeta
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@torch.inference_mode()
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def test_layer_norm(self, device, dtype):
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def _test(size):
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t0 = torch.randn(2, size, device=device, dtype=dtype, requires_grad=False)
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t1 = torch.randn(2, size, device=device, dtype=dtype, requires_grad=False)
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ts = [t0, t1, t0, t1]
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nt = torch.nested_tensor(ts, device=device, dtype=dtype)
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layer_norm = torch.nn.LayerNorm(size, device=device, dtype=dtype)
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nt_result = nt._nested_tensor_layer_norm(
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layer_norm.weight, layer_norm.bias, 1e-5
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)
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for (nt_subresult, t) in zip(nt_result.unbind(), ts):
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t_result = layer_norm(t.reshape(1, -1, size).squeeze(0))
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self.assertEqual(nt_subresult, t_result)
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for size in (1024, 1023, 513, 512, 256, 128, 2, 4, 32):
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_test(size)
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@skipMeta
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@torch.inference_mode()
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def test_embedding(self, device):
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inputs = [
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torch.randint(100, (L,), device=device, dtype=torch.int64)
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for L in torch.randint(5, 50, (8,))
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]
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x = torch.nested_tensor(inputs, device=device, dtype=torch.int64)
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emb = torch.nn.Embedding(100, 8, device=device)
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y = emb(x)
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ys = y.unbind()
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for i, inp in enumerate(inputs):
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self.assertEqual(emb(inp), ys[i])
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def test_to_padded_tensor_simple(self, device):
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t = torch.randn(4, 4, 4, device=device)
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ts = list(torch.unbind(t))
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ts[0] = ts[0][:-1]
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nt = torch.nested_tensor(ts, device=device)
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for padding_value in (0, 1):
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padded = nt.to_padded_tensor(padding_value)
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correct_output = t.clone()
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if padding_value == 0:
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correct_output[0][-1] = torch.zeros_like(correct_output[0][-1])
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else:
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correct_output[0][-1] = torch.ones_like(correct_output[0][-1])
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self.assertEqual(padded, correct_output)
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self.assertEqual(padded.device, torch.device(device))
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def test_to_padded_tensor_unrelated_shapes(self, device):
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ts = [
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torch.randn(1, 2, 3, device=device),
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torch.randn(2, 3, 4, device=device),
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torch.randn(4, 5, 6, device=device),
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]
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nt = torch.nested_tensor(ts, device=device)
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pad = 42
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correct_output = torch.cat(
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[torch.nn.ConstantPad3d((0, 6 - x.shape[2], 0, 5 - x.shape[1], 0, 4 - x.shape[0]), pad)(x.unsqueeze(0)) for x in ts])
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padded = nt.to_padded_tensor(pad)
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self.assertEqual(padded, correct_output)
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@skipMeta
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def test_device_checks(self, device):
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nt = torch.nested_tensor([], device=device)
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is_cuda = 'cuda' in str(device)
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self.assertEqual(nt.is_cuda, is_cuda)
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instantiate_device_type_tests(TestNestedTensorDeviceType, globals())
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