| # Owner(s): ["module: nestedtensor"] |
| |
| import torch |
| import torch.nn |
| import unittest |
| from torch.testing._internal.common_device_type import ( |
| dtypes, |
| dtypesIfCUDA, |
| instantiate_device_type_tests, |
| skipMeta, |
| ) |
| from torch.testing._internal.common_utils import TestCase, IS_FBCODE |
| from torch import nested_tensor |
| |
| # Tests are ported from pytorch/nestedtensor. |
| # This makes porting as_nested_tensor easier in the future. |
| def _iter_constructors(): |
| # yield as_nested_tensor |
| yield nested_tensor |
| |
| |
| class TestNestedTensor(TestCase): |
| @torch.inference_mode() |
| def _test_unbind_case(self, a, b): |
| nt = nested_tensor([a, b]) |
| a1, b1 = nt.unbind() |
| self.assertTrue(a is not a1) |
| self.assertTrue(b is not b1) |
| |
| nt = nested_tensor([a, b], dtype=a.dtype) |
| a1, b1 = nt.unbind(0) |
| self.assertEqual(a, a1) |
| self.assertEqual(b, b1) |
| |
| a = torch.randn((2, 3)).add_(1) |
| nt = nested_tensor([a]) |
| self.assertEqual(a, nt.unbind(0)[0]) |
| |
| @torch.inference_mode() |
| def test_unbind_0(self): |
| self._test_unbind_case( |
| torch.tensor([1, 2]), torch.tensor([7, 8]), |
| ) |
| |
| @torch.inference_mode() |
| def test_unbind_1(self): |
| self._test_unbind_case( |
| torch.tensor([1]), torch.tensor([7]), |
| ) |
| |
| # @torch.inference_mode() |
| # def test_unbind_2(self): |
| # self._test_unbind_case( |
| # torch.tensor(1), torch.tensor(7), |
| # ) |
| |
| @torch.inference_mode() |
| def test_unbind_3(self): |
| self._test_unbind_case( |
| torch.tensor([1.0]), torch.tensor([]), |
| ) |
| |
| @torch.inference_mode() |
| def test_unbind_4(self): |
| self._test_unbind_case( |
| torch.tensor([]), torch.tensor([]), |
| ) |
| |
| @torch.inference_mode() |
| def test_unbind_dim(self): |
| def _test_fn(unbind_fn): |
| a = torch.rand(3, 2) |
| b = torch.rand(2, 3) |
| nt = nested_tensor([a, b]) |
| self.assertRaises(RuntimeError, lambda: unbind_fn(nt, 1)) |
| |
| # Both of these tests are necessary, because we're using |
| # torch_function. |
| _test_fn(lambda x, dim: x.unbind(dim)) |
| # TODO: Re-enable this once using torch_dispatch |
| # _test_fn(lambda x, dim: torch.unbind(x, dim)) |
| |
| @torch.inference_mode() |
| def test_nested_tensor(self): |
| self.assertRaises(TypeError, lambda: nested_tensor([3.0])) |
| self.assertRaises(TypeError, lambda: nested_tensor(torch.tensor([3.0]))) |
| self.assertRaises(TypeError, lambda: nested_tensor(4.0)) |
| |
| @torch.inference_mode() |
| def test_nested_tensor_matching_dim(self): |
| self.assertRaisesRegex( |
| RuntimeError, |
| "Found dimension 1 for Tensor at index 1 and dimension 0 for Tensor at index 0.", |
| lambda: nested_tensor([torch.tensor(1.0), torch.tensor([])]), |
| ) |
| self.assertRaisesRegex( |
| RuntimeError, |
| "Found dimension 1 for Tensor at index 2 and dimension 0 for Tensor at index 1.", |
| lambda: nested_tensor( |
| [torch.tensor(1.0), torch.tensor(2.0), torch.tensor([])] |
| ), |
| ) |
| |
| @torch.inference_mode() |
| def test_default_nested_tensor(self): |
| self.assertRaises(TypeError, lambda: nested_tensor()) |
| default_nested_tensor = nested_tensor([]) |
| default_tensor = torch.tensor([]) |
| # self.assertEqual(default_nested_tensor.nested_dim(), 1) |
| # self.assertEqual(default_nested_tensor.nested_size(), ()) |
| self.assertEqual(default_nested_tensor.dim(), default_tensor.dim()) |
| self.assertEqual(default_nested_tensor.layout, default_tensor.layout) |
| self.assertEqual(default_nested_tensor.device, default_tensor.device) |
| self.assertEqual(default_nested_tensor.dtype, default_tensor.dtype) |
| self.assertEqual( |
| default_nested_tensor.requires_grad, default_tensor.requires_grad |
| ) |
| self.assertIsNone(default_tensor.grad) |
| # TODO: Re-enable once we have a performance driven |
| # use case and implementation. |
| # self.assertEqual(default_nested_tensor.is_pinned(), |
| # default_tensor.is_pinned()) |
| |
| @torch.inference_mode() |
| def test_dim(self): |
| for constructor in _iter_constructors(): |
| a1 = constructor([]) |
| self.assertEqual(a1.dim(), 1) |
| a1 = constructor([torch.tensor(3.0)]) |
| self.assertEqual(a1.dim(), 1) |
| a1 = constructor([torch.tensor([1, 2, 3, 4])]) |
| self.assertEqual(a1.dim(), 2) |
| |
| @unittest.skipIf(IS_FBCODE, "numel is not virtual in fbcode.") |
| @torch.inference_mode() |
| def test_numel(self): |
| for constructor in _iter_constructors(): |
| a1 = constructor([]) |
| self.assertRaisesRegex( |
| RuntimeError, "numel is disabled", lambda: a1.numel(), |
| ) |
| |
| @torch.inference_mode() |
| def test_size(self): |
| for constructor in _iter_constructors(): |
| a1 = constructor([]) |
| self.assertRaisesRegex( |
| RuntimeError, |
| "Tensors of type NestedTensorImpl do not have sizes" |
| if IS_FBCODE |
| else "NestedTensorImpl doesn't support sizes", |
| lambda: a1.size(), |
| ) |
| |
| @unittest.skipIf(IS_FBCODE, "stride is not virtual in fbcode.") |
| @torch.inference_mode() |
| def test_stride(self): |
| for constructor in _iter_constructors(): |
| a1 = constructor([]) |
| self.assertRaisesRegex( |
| RuntimeError, |
| "NestedTensorImpl doesn't support strides", |
| lambda: a1.stride(), |
| ) |
| |
| @unittest.skipIf(IS_FBCODE, "is_contiguous is not virtual in fbcode.") |
| @torch.inference_mode() |
| def test_is_contiguous(self): |
| for constructor in _iter_constructors(): |
| a1 = constructor([]) |
| self.assertRaisesRegex( |
| RuntimeError, "is_contiguous is disabled", lambda: a1.is_contiguous() |
| ) |
| |
| @torch.inference_mode() |
| def test_repr_string(self): |
| a = nested_tensor([]) |
| expected = "nested_tensor([" "\n\n])" |
| self.assertEqual(str(a), expected) |
| self.assertEqual(repr(a), expected) |
| |
| a = nested_tensor([torch.tensor(1.0)]) |
| expected = "nested_tensor([" "\n tensor(1.)" "\n])" |
| self.assertEqual(str(a), expected) |
| self.assertEqual(repr(a), expected) |
| |
| a = nested_tensor([torch.tensor([[1, 2]]), torch.tensor([[4, 5]])]) |
| expected = ( |
| "nested_tensor([" "\n tensor([[1, 2]])" "," "\n tensor([[4, 5]])" "\n])" |
| ) |
| self.assertEqual(str(a), expected) |
| self.assertEqual(repr(a), expected) |
| |
| @torch.inference_mode() |
| def test_activations(self): |
| for func in (torch.nn.functional.relu, torch.nn.functional.relu_, torch.nn.functional.gelu, torch._C._nn.gelu_): |
| t = torch.tensor([-1, 0, 1], dtype=torch.float) |
| nt = nested_tensor([t]) |
| nested_result = func(nt) |
| self.assertTrue(nested_result.is_nested) |
| self.assertEqual(func(t), nested_result.unbind()[0]) |
| |
| def test_to_padded_tensor_on_empty_tensor(self): |
| nt = torch.nested_tensor([]) |
| empty = nt.to_padded_tensor(4) |
| self.assertEqual(empty, torch.tensor([])) |
| |
| class TestNestedTensorDeviceType(TestCase): |
| @dtypes(torch.float) |
| @skipMeta |
| def test_to_then_from_padded_tensor_no_transform0213(self, device, dtype): |
| t = torch.randn(4, 4, 4, device=device, dtype=dtype) |
| ts = list(torch.unbind(t)) |
| ts[0] = ts[0][:-1] |
| nt = torch.nested_tensor(ts, device=device, dtype=dtype) |
| padded = nt.to_padded_tensor(0) |
| |
| nt_to = torch._nested_from_padded_and_nested_example(padded, nt) |
| |
| for (t1, t2) in zip(nt.unbind(), nt_to.unbind()): |
| self.assertEqual(t1, t2) |
| self.assertEqual(nt.device, nt_to.device) |
| |
| @dtypes(torch.float) |
| @dtypesIfCUDA(torch.float, torch.half) |
| @skipMeta |
| @torch.inference_mode() |
| def test_layer_norm(self, device, dtype): |
| def _test(size): |
| t0 = torch.randn(2, size, device=device, dtype=dtype, requires_grad=False) |
| t1 = torch.randn(2, size, device=device, dtype=dtype, requires_grad=False) |
| ts = [t0, t1, t0, t1] |
| nt = torch.nested_tensor(ts, device=device, dtype=dtype) |
| layer_norm = torch.nn.LayerNorm(size, device=device, dtype=dtype) |
| nt_result = nt._nested_tensor_layer_norm( |
| layer_norm.weight, layer_norm.bias, 1e-5 |
| ) |
| for (nt_subresult, t) in zip(nt_result.unbind(), ts): |
| t_result = layer_norm(t.reshape(1, -1, size).squeeze(0)) |
| self.assertEqual(nt_subresult, t_result) |
| |
| for size in (1024, 1023, 513, 512, 256, 128, 2, 4, 32): |
| _test(size) |
| |
| @skipMeta |
| @torch.inference_mode() |
| def test_embedding(self, device): |
| inputs = [ |
| torch.randint(100, (L,), device=device, dtype=torch.int64) |
| for L in torch.randint(5, 50, (8,)) |
| ] |
| x = torch.nested_tensor(inputs, device=device, dtype=torch.int64) |
| emb = torch.nn.Embedding(100, 8, device=device) |
| y = emb(x) |
| ys = y.unbind() |
| for i, inp in enumerate(inputs): |
| self.assertEqual(emb(inp), ys[i]) |
| |
| def test_to_padded_tensor_simple(self, device): |
| t = torch.randn(4, 4, 4, device=device) |
| ts = list(torch.unbind(t)) |
| ts[0] = ts[0][:-1] |
| nt = torch.nested_tensor(ts, device=device) |
| for padding_value in (0, 1): |
| padded = nt.to_padded_tensor(padding_value) |
| |
| correct_output = t.clone() |
| if padding_value == 0: |
| correct_output[0][-1] = torch.zeros_like(correct_output[0][-1]) |
| else: |
| correct_output[0][-1] = torch.ones_like(correct_output[0][-1]) |
| |
| self.assertEqual(padded, correct_output) |
| self.assertEqual(padded.device, torch.device(device)) |
| |
| def test_to_padded_tensor_unrelated_shapes(self, device): |
| ts = [ |
| torch.randn(1, 2, 3, device=device), |
| torch.randn(2, 3, 4, device=device), |
| torch.randn(4, 5, 6, device=device), |
| ] |
| nt = torch.nested_tensor(ts, device=device) |
| pad = 42 |
| correct_output = torch.cat( |
| [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]) |
| padded = nt.to_padded_tensor(pad) |
| self.assertEqual(padded, correct_output) |
| |
| @skipMeta |
| def test_device_checks(self, device): |
| nt = torch.nested_tensor([], device=device) |
| is_cuda = 'cuda' in str(device) |
| self.assertEqual(nt.is_cuda, is_cuda) |
| |
| instantiate_device_type_tests(TestNestedTensorDeviceType, globals()) |
