| # 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, run_tests, freeze_rng_state |
| 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.assertEqual(a1.numel(), 0) |
| a1 = constructor([torch.tensor(3.0), torch.tensor(4.0)]) |
| self.assertEqual(a1.numel(), 2) |
| a1 = constructor([torch.randn(2, 2, 2)]) |
| self.assertEqual(a1.numel(), 8) |
| a1 = constructor([torch.randn([1, 2, 3]), torch.randn(3, 2, 1)]) |
| self.assertEqual(a1.numel(), 12) |
| a1 = constructor([torch.randn([1, 1, 3]), torch.randn(3, 2, 4)]) |
| self.assertEqual(a1.numel(), 27) |
| a1 = constructor([torch.randn([5, 5, 5]), torch.randn(6, 6, 6)]) |
| self.assertEqual(a1.numel(), 341) |
| |
| # Interesting edge case |
| a1 = constructor([torch.randn([1, 2, 3]), torch.randn(1, 2, 0)]) |
| self.assertEqual(a1.numel(), 6) |
| |
| @torch.inference_mode() |
| def test_size(self): |
| for constructor in _iter_constructors(): |
| a1 = constructor([]) |
| self.assertRaisesRegex( |
| RuntimeError, |
| "Tensors of type NestedTensorImpl do not have sym 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]) |
| |
| @dtypes(torch.float, torch.float16) |
| def test_to_padded_tensor_simple(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) |
| 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)) |
| self.assertEqual(padded.dtype, dtype) |
| |
| @dtypes(torch.float, torch.float16) |
| def test_to_padded_tensor_output_size(self, device, dtype): |
| t = torch.randn(4, 4, 4, device=device, dtype=dtype) |
| output_size = (4, 6, 5) |
| ts = list(torch.unbind(t)) |
| ts[0] = ts[0][:-1] |
| nt = torch.nested_tensor(ts, device=device, dtype=dtype) |
| for padding_value in (0, 1): |
| padded = nt.to_padded_tensor(padding_value, output_size=output_size) |
| correct_output = torch.ones(output_size, device=device, dtype=dtype) * padding_value |
| correct_output[:4:, :4, :4] = t.clone() |
| if padding_value == 0: |
| correct_output[0][3] = torch.zeros_like(correct_output[0][3]) |
| else: |
| correct_output[0][3] = torch.ones_like(correct_output[0][3]) |
| |
| self.assertEqual(padded, correct_output) |
| self.assertEqual(padded.device, torch.device(device)) |
| self.assertEqual(padded.dtype, dtype) |
| |
| @dtypes(torch.float, torch.float16, torch.double) |
| def test_to_padded_tensor_dim2(self, device, dtype): |
| ts = [ |
| torch.randn(160, device=device, dtype=dtype), |
| torch.randn(1240, device=device, dtype=dtype), |
| torch.randn(2400, device=device, dtype=dtype), |
| ] |
| nt = torch.nested_tensor(ts, device=device, dtype=dtype) |
| pad = 42 |
| correct_output = [] |
| for t in ts: |
| next_output = torch.ones_like(ts[2]) * pad |
| correct_output.append(next_output) |
| next_output[:t.size(0)].copy_(t) |
| correct_output = torch.stack(correct_output) |
| padded = nt.to_padded_tensor(pad) |
| self.assertEqual(padded, correct_output) |
| |
| @dtypes(torch.float, torch.float16, torch.double) |
| def test_to_padded_tensor_dim3(self, device, dtype): |
| ts = [ |
| torch.randn(16, 21, device=device, dtype=dtype), |
| torch.randn(24, 32, device=device, dtype=dtype), |
| torch.randn(40, 53, device=device, dtype=dtype), |
| ] |
| nt = torch.nested_tensor(ts, device=device, dtype=dtype) |
| pad = 42 |
| correct_output = [] |
| for t in ts: |
| next_output = torch.ones_like(ts[2]) * pad |
| correct_output.append(next_output) |
| next_output[:t.size(0), :t.size(1)].copy_(t) |
| correct_output = torch.stack(correct_output) |
| padded = nt.to_padded_tensor(pad) |
| self.assertEqual(padded, correct_output) |
| |
| @dtypes(torch.float, torch.float16, torch.double) |
| def test_to_padded_tensor_dim4(self, device, dtype): |
| ts = [ |
| torch.randn(16, 21, 13, device=device, dtype=dtype), |
| torch.randn(24, 32, 14, device=device, dtype=dtype), |
| torch.randn(40, 53, 16, device=device, dtype=dtype), |
| ] |
| nt = torch.nested_tensor(ts, device=device, dtype=dtype) |
| pad = 42 |
| correct_output = [] |
| for t in ts: |
| next_output = torch.ones_like(ts[2]) * pad |
| correct_output.append(next_output) |
| next_output[:t.size(0), :t.size(1), :t.size(2)].copy_(t) |
| correct_output = torch.stack(correct_output) |
| 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) |
| |
| @dtypes(torch.float, torch.float16, torch.double) |
| def test_nested_tensor_indexing(self, device, dtype): |
| # edge case: empty nested tensor |
| nt0 = torch.nested_tensor([]) |
| self.assertRaises(IndexError, lambda: nt0[0]) |
| # normal case |
| x0 = torch.randn((2, 5), device=device, dtype=dtype) |
| x1 = torch.randn((3, 4), device=device, dtype=dtype) |
| nt = torch.nested_tensor([x0, x1]) |
| # single index: only support integer in the batch dimension |
| self.assertEqual(nt[0], x0) |
| self.assertEqual(nt[-1], x1) |
| self.assertRaises(IndexError, lambda: nt[2]) |
| self.assertRaises(IndexError, lambda: nt[-3]) |
| self.assertRaises(NotImplementedError, lambda: nt[:]) |
| self.assertRaises(NotImplementedError, lambda: nt[None]) |
| self.assertRaises(NotImplementedError, lambda: nt[...]) |
| # tuple of indices: only support integer in the batch dimension |
| # + all possible indexing in the original tensor dimensions |
| self.assertEqual(nt[0, 0, 0], x0[0, 0]) |
| self.assertEqual(nt[0, 1, :], x0[1, :]) |
| self.assertEqual(nt[1, ...], x1) |
| self.assertRaises(IndexError, lambda: nt[1, 4, 2]) |
| self.assertRaises(NotImplementedError, lambda: nt[:, 1, 1]) |
| # make sure indexing returns a view |
| nt[0].fill_(100.0) |
| answer = torch.tensor(100.0, device=device, dtype=dtype).expand((2, 5)) |
| self.assertEqual(nt[0], answer) |
| nt[1, 1, :].fill_(200.0) |
| answer = torch.tensor(200.0, device=device, dtype=dtype).expand(4) |
| self.assertEqual(nt[1, 1, :], answer) |
| |
| # Helper functions for testing elementwise ops |
| def random_nt(self, device, dtype, num_tensors, max_dims, min_dims=None): |
| if min_dims is None: |
| min_dims = tuple([0] * len(max_dims)) |
| ts1 = [] |
| for _ in range(num_tensors): |
| tensor_dims = tuple([torch.randint(low=min_dim, high=max_dim, size=(1,)).item() |
| for (min_dim, max_dim) in zip(min_dims, max_dims)]) |
| t1 = torch.randn(tensor_dims, device=device, dtype=dtype) |
| ts1.append(t1) |
| return torch.nested_tensor(ts1, device=device, dtype=dtype) |
| |
| # Helper functions for testing elementwise ops |
| def random_nt_pair(self, device, dtype, num_tensors, max_dims): |
| ts1 = [] |
| ts2 = [] |
| for _ in range(num_tensors): |
| tensor_dims = tuple([torch.randint(low=0, high=max_dim, size=(1,)).item() for max_dim in max_dims]) |
| t1 = torch.randn(tensor_dims, device=device, dtype=dtype) |
| t2 = torch.randn(tensor_dims, device=device, dtype=dtype) |
| ts1.append(t1) |
| ts2.append(t2) |
| return (torch.nested_tensor(ts1, device=device, dtype=dtype), |
| torch.nested_tensor(ts2, device=device, dtype=dtype)) |
| |
| def nt_equal(self, nt1, nt2): |
| self.assertEqual(nt1.dtype, nt2.dtype) |
| self.assertEqual(nt1.device, nt2.device) |
| ub1 = nt1.unbind() |
| ub2 = nt2.unbind() |
| self.assertEqual(len(ub1), len(ub2)) |
| n = len(ub1) |
| for i in range(n): |
| self.assertEqual(ub1[i], ub2[i]) |
| |
| @dtypes(torch.float, torch.float16) |
| @skipMeta |
| @torch.inference_mode() |
| def test_nested_tensor_add(self, device, dtype): |
| (nt1, nt2) = self.random_nt_pair(device, dtype, 4, (4, 4)) |
| ref = torch.nested_tensor([t1 + t2 for (t1, t2) in zip(nt1.unbind(), nt2.unbind())]) |
| out = nt1 + nt2 |
| self.nt_equal(ref, out) |
| |
| @dtypes(torch.float, torch.float16) |
| @skipMeta |
| @torch.inference_mode() |
| def test_nested_tensor_mul(self, device, dtype): |
| # nested tensor * nested tensor |
| (nt1, nt2) = self.random_nt_pair(device, dtype, 4, (4, 4)) |
| ref = torch.nested_tensor([t1 * t2 for (t1, t2) in zip(nt1.unbind(), nt2.unbind())]) |
| out = nt1 * nt2 |
| self.nt_equal(ref, out) |
| # nested tensor * scalar |
| number = 10.0 |
| scalar = torch.tensor(number).to(dtype).to(device) |
| ref = torch.nested_tensor([t * number for t in nt1.unbind()]) |
| out_number0 = nt1 * number |
| out_number1 = number * nt1 |
| out_scalar0 = nt1 * scalar |
| out_scalar1 = scalar * nt1 |
| self.nt_equal(out_number0, ref) |
| self.nt_equal(out_number1, ref) |
| self.nt_equal(out_scalar0, ref) |
| self.nt_equal(out_scalar1, ref) |
| # error case: numel == 1 but dim > 0 |
| vector = torch.tensor([number]).to(dtype).to(device) |
| self.assertRaisesRegex( |
| RuntimeError, |
| "Expected both self and other to be nested, but got a nested self and non-nested other", |
| lambda: nt1.mul(vector) |
| ) |
| self.assertRaisesRegex( |
| RuntimeError, |
| "Expected both self and other to be nested, but got a non-nested self and nested other", |
| lambda: vector.mul(nt1) |
| ) |
| |
| @dtypes(torch.float, torch.float16) |
| @skipMeta |
| @torch.inference_mode() |
| def test_nested_tensor_add_in_place(self, device, dtype): |
| (nt1, nt2) = self.random_nt_pair(device, dtype, 4, (4, 4)) |
| ref = torch.nested_tensor([t1 + t2 for (t1, t2) in zip(nt1.unbind(), nt2.unbind())]) |
| nt1 += nt2 |
| self.nt_equal(ref, nt1) |
| |
| @dtypes(torch.float, torch.float16) |
| @skipMeta |
| @torch.inference_mode() |
| def test_nested_tensor_mul_in_place(self, device, dtype): |
| # nested tensor * nested tensor |
| (nt1, nt2) = self.random_nt_pair(device, dtype, 4, (4, 4)) |
| ref = torch.nested_tensor([t1 * t2 for (t1, t2) in zip(nt1.unbind(), nt2.unbind())]) |
| nt1 *= nt2 |
| self.nt_equal(ref, nt1) |
| # nested tensor * scalar |
| number = 10.0 |
| scalar = torch.tensor(number).to(dtype).to(device) |
| ref = torch.nested_tensor([t * number for t in nt1.unbind()]) |
| out_number = nt1.clone() |
| out_number *= number |
| out_scalar = nt1.clone() |
| out_scalar *= scalar |
| self.nt_equal(out_number, ref) |
| self.nt_equal(out_scalar, ref) |
| self.assertRaisesRegex( |
| RuntimeError, |
| r"output with shape \[.*\] doesn't match the broadcast shape \[.*\]", |
| lambda: scalar.mul_(nt1) |
| ) |
| # error case: numel == 1 but dim > 0 |
| vector = torch.tensor([number]).to(dtype).to(device) |
| self.assertRaisesRegex( |
| RuntimeError, |
| "Expected both self and other to be nested, but got a nested self and non-nested other", |
| lambda: nt1.mul_(vector) |
| ) |
| self.assertRaisesRegex( |
| RuntimeError, |
| "Expected both self and other to be nested, but got a non-nested self and nested other", |
| lambda: vector.mul_(nt1) |
| ) |
| |
| @dtypes(torch.float, torch.float16) |
| @skipMeta |
| @torch.inference_mode() |
| def test_clone(self, device, dtype): |
| nt1 = self.random_nt(device, dtype, 4, (4, 4), (1, 1)) |
| nt2 = nt1.clone() |
| # Verify the values match |
| self.nt_equal(nt1, nt2) |
| # Verify modifying nt2 doesn't affect nt1 |
| nt2.mul_(nt1) |
| ub1 = nt1.unbind() |
| ub2 = nt2.unbind() |
| for i in range(len(ub1)): |
| self.assertNotEqual(ub1[i], ub2[i]) |
| |
| nt1.clone(memory_format=torch.preserve_format) |
| msg = "clone_nested only supports memory format Preserve, but got ChannelsLast instead." |
| with self.assertRaisesRegex(RuntimeError, msg): |
| nt1.clone(memory_format=torch.channels_last) |
| |
| # cannot test torch.float16 because: RuntimeError: "bernoulli_scalar_cpu_" not implemented for 'Half' |
| @dtypes(torch.float, torch.double) |
| @torch.inference_mode() |
| def test_dropout(self, device, dtype): |
| # edge case: empty nested tensor |
| nt0 = torch.nested_tensor([]) |
| y = torch.nn.functional.dropout(nt0, 0.5) |
| self.nt_equal(nt0, y) |
| # normal nested tensor |
| ntensors = 4 |
| nt = self.random_nt(device, dtype, ntensors, (4, 4)) |
| # edge case: invalid dropout |
| self.assertRaises(ValueError, lambda: torch.nn.Dropout(-0.1)) |
| self.assertRaises(ValueError, lambda: torch.nn.Dropout(1.1)) |
| self.assertRaises(ValueError, lambda: torch.nn.functional.dropout(nt, -0.1)) |
| self.assertRaises(ValueError, lambda: torch.nn.functional.dropout(nt, 1.1)) |
| # edge case: no dropout |
| dropouter = torch.nn.Dropout(0.0) |
| y0 = dropouter(nt) |
| y1 = torch.nn.functional.dropout(nt, 0.0) |
| self.nt_equal(nt, y0) |
| self.nt_equal(nt, y1) |
| # edge case: all dropout |
| dropouter = torch.nn.Dropout(1.0) |
| y0 = dropouter(nt) |
| y1 = torch.nn.functional.dropout(nt, 1.0) |
| nt0 = nt.clone() |
| for i in range(ntensors): |
| nt0[i].fill_(0.0) |
| self.nt_equal(nt0, y0) |
| self.nt_equal(nt0, y1) |
| # normal case: normal dropout |
| p = 0.2 |
| y = torch.nn.functional.dropout(nt, p) |
| expect = nt.clone() |
| for i in range(ntensors): |
| actual_tensor = y[i].view(-1) |
| expect_tensor = expect[i].view(-1) |
| for j in range(actual_tensor.shape[0]): |
| if actual_tensor[j].item() == 0.0: |
| expect_tensor[j] = 0.0 |
| else: |
| expect_tensor[j] /= 1.0 - p |
| self.nt_equal(y, expect) |
| with freeze_rng_state(): |
| dropouter = torch.nn.Dropout(p) |
| y0 = dropouter(nt) |
| with freeze_rng_state(): |
| y1 = torch.nn.functional.dropout(nt, p) |
| self.nt_equal(y0, y1) |
| # inplace |
| # in principle, since we have established the correctness of functional, we could simply compare inplace vs functional |
| # in practice, cuda functional has its own implementation to skip `bernoulli_` |
| # so cuda functional will differ from cuda inplace causing test failure |
| # in `test_dropout_cuda_float64 (__main__.TestNestedTensorDeviceTypeCUDA)` |
| # on `linux-xenial-cuda11.3-py3.7-gcc7 / test (default, 2, 4, linux.4xlarge.nvidia.gpu)` |
| expect = nt.clone() |
| torch.nn.functional.dropout(nt, p, inplace=True) |
| for i in range(ntensors): |
| actual_tensor = nt[i].view(-1) |
| expect_tensor = expect[i].view(-1) |
| for j in range(actual_tensor.shape[0]): |
| if actual_tensor[j].item() == 0.0: |
| expect_tensor[j] = 0.0 |
| else: |
| expect_tensor[j] /= 1.0 - p |
| self.nt_equal(nt, expect) |
| |
| # cannot test torch.float16 because: RuntimeError: "softmax_kernel_impl" not implemented for 'Half' |
| @dtypes(torch.float, torch.double) |
| @torch.inference_mode() |
| def test_softmax(self, device, dtype): |
| # normal nested tensor |
| ntensors = 4 |
| nt = self.random_nt(device, dtype, ntensors, (4, 4)) |
| # error case: softmax across nested dimension |
| self.assertRaises(RuntimeError, lambda: torch.nn.functional.softmax(nt, 0)) |
| self.assertRaises(RuntimeError, lambda: torch.nn.functional.softmax(nt, -3)) |
| # error case: dimension out of range |
| self.assertRaises(IndexError, lambda: torch.nn.functional.softmax(nt, 3)) |
| self.assertRaises(IndexError, lambda: torch.nn.functional.softmax(nt, -4)) |
| # normal case: should equal to padding -inf |
| softmaxer = torch.nn.Softmax(1) |
| y0 = softmaxer(nt) |
| y1 = torch.nn.functional.softmax(nt, 1) |
| self.nt_equal(y0, y1) |
| pt = nt.to_padded_tensor(float("-inf")) |
| # if an entire slice is padded, then softmax will return 0.0 / 0.0 = nan |
| # however, physically speaking that should be 0.0 |
| expect = torch.nn.functional.softmax(pt, 1).nan_to_num_(0.0) |
| self.assertEqual(y0.to_padded_tensor(0.0), expect) |
| # edge case: empty nested tensor |
| nt0 = torch.nested_tensor([]) |
| y = torch.nn.functional.softmax(nt0, 1) |
| self.nt_equal(nt0, y) |
| # edge case: nesting scalars |
| nt1 = torch.nested_tensor([torch.tensor(0.0), torch.tensor(1.0)]) |
| self.assertRaises(RuntimeError, lambda: torch.nn.functional.softmax(nt1, 0)) |
| self.assertRaises(IndexError, lambda: torch.nn.functional.softmax(nt1, 1)) |
| |
| @dtypes(torch.float, torch.float16, torch.double) |
| @torch.inference_mode() |
| def test_bmm(self, device, dtype): |
| # error case: not 3D tensors |
| nt0 = torch.nested_tensor([]) |
| nt1 = torch.nested_tensor([torch.randn(2), torch.randn(3)]) |
| nt2 = torch.nested_tensor([torch.randn((2, 4)), torch.randn((3, 4))]) |
| self.assertRaisesRegex(RuntimeError, "batch1 must be a 3D tensor", lambda: nt0.bmm(nt0)) |
| self.assertRaisesRegex(RuntimeError, "batch1 must be a 3D tensor", lambda: nt0.bmm(nt1)) |
| self.assertRaisesRegex(RuntimeError, "batch1 must be a 3D tensor", lambda: nt0.bmm(nt2)) |
| self.assertRaisesRegex(RuntimeError, "batch1 must be a 3D tensor", lambda: nt1.bmm(nt0)) |
| self.assertRaisesRegex(RuntimeError, "batch1 must be a 3D tensor", lambda: nt1.bmm(nt1)) |
| self.assertRaisesRegex(RuntimeError, "batch1 must be a 3D tensor", lambda: nt1.bmm(nt2)) |
| self.assertRaisesRegex(RuntimeError, "batch2 must be a 3D tensor", lambda: nt2.bmm(nt0)) |
| self.assertRaisesRegex(RuntimeError, "batch2 must be a 3D tensor", lambda: nt2.bmm(nt1)) |
| # error case: incompatible batch size |
| nt0 = torch.nested_tensor([torch.randn((2, 4)), torch.randn((3, 4))]) |
| nt1 = torch.nested_tensor([torch.randn((4, 6)), torch.randn((4, 5)), torch.randn((4, 7))]) |
| self.assertRaisesRegex( |
| RuntimeError, |
| "Expected size for the 1st dimension of batch2 tensor to be: 2 but got: 3.", |
| lambda: nt0.bmm(nt1) |
| ) |
| self.assertRaisesRegex( |
| RuntimeError, |
| "Expected size for the 1st dimension of batch2 tensor to be: 3 but got: 2.", |
| lambda: nt1.bmm(nt0) |
| ) |
| # error case: underlying matrices cannot be multiplied |
| nt0 = torch.nested_tensor([torch.randn((2, 4)), torch.randn((3, 4))]) |
| self.assertRaisesRegex( |
| RuntimeError, |
| r"0-th nested matrices in batch cannot be multiplied \(2x4 and 2x4\)", |
| lambda: nt0.bmm(nt0) |
| ) |
| # normal nested tensor |
| nt0 = torch.nested_tensor([torch.randn((2, 4)), torch.randn((3, 7))]) |
| nt1 = torch.nested_tensor([torch.randn((4, 6)), torch.randn((7, 5))]) |
| actual = nt0.bmm(nt1) |
| expect = nt0.to_padded_tensor(0.0).bmm(nt1.to_padded_tensor(0.0)) |
| self.assertEqual(actual.to_padded_tensor(0.0), expect) |
| |
| @dtypes(torch.float, torch.double) |
| def test_linear(self, device, dtype): |
| a = torch.randn(1, 2, device=device, dtype=dtype) |
| b = torch.randn(2, 2, device=device, dtype=dtype) |
| c = torch.randn(3, 2, device=device, dtype=dtype) |
| nt = torch.nested_tensor([a, b, c]) |
| |
| weight = torch.randn(2, 2, device=device, dtype=dtype) |
| bias = torch.randn(2, device=device, dtype=dtype) |
| # success case |
| torch.functional.F.linear(nt, weight, bias) |
| |
| # invalid nested tensor dimension |
| msg = r'Linear requires nested_tensor.dim == 3 and dense_matrix.dim == 2. Nested tensor dim: 2. Dense tensor dim: 2' |
| nt1 = torch.nested_tensor([torch.randn(1, device=device, dtype=dtype), |
| torch.randn(2, device=device, dtype=dtype)]) |
| with self.assertRaisesRegex(RuntimeError, msg): |
| torch.functional.F.linear(nt1, weight, bias) |
| |
| # invalid weight shape |
| msg = r'Linear requires nested_tensor.dim == 3 and dense_matrix.dim == 2. Nested tensor dim: 3. Dense tensor dim: 3' |
| weight1 = torch.randn(2, 2, 3, device=device, dtype=dtype) |
| with self.assertRaisesRegex(RuntimeError, msg): |
| torch.functional.F.linear(nt, weight1, bias) |
| |
| # inconsistent last dim of nested tensor |
| msg = r"all tensors in NestedTensor must have the same trailing dim" |
| nt2 = torch.nested_tensor([torch.randn(1, 2, device=device, dtype=dtype), |
| torch.randn(2, 3, device=device, dtype=dtype)]) |
| with self.assertRaisesRegex(RuntimeError, msg): |
| torch.functional.F.linear(nt2, weight, bias) |
| |
| # Mismatch of nested tensor last dim and weight dimension |
| weight2 = torch.randn(2, 4, device=device, dtype=dtype) |
| msg = r"Shape mismatch for NestedTensor Linear: Expected input's \(a nested tensor\) 'last_dim'" \ |
| r" to equal 'weight.size\(1\), but got: last_dim = 2, and weight.size\(1\) = 4" |
| with self.assertRaisesRegex(RuntimeError, msg): |
| torch.functional.F.linear(nt, weight2, bias) |
| |
| # Nested tensor input and nested weight |
| nt_weight = nt.clone() |
| msg = r"Linear does not support nested weight when input is a nested tensor." |
| with self.assertRaisesRegex(RuntimeError, msg): |
| torch.functional.F.linear(nt, nt_weight, bias) |
| class TestNestedTensorAutograd(TestCase): |
| def nt_equal(self, nt1, nt2): |
| self.assertEqual(nt1.dtype, nt2.dtype) |
| self.assertEqual(nt1.device, nt2.device) |
| ub1 = nt1.unbind() |
| ub2 = nt2.unbind() |
| self.assertEqual(len(ub1), len(ub2)) |
| n = len(ub1) |
| for i in range(n): |
| self.assertEqual(ub1[i], ub2[i]) |
| |
| def _create_nested_tensor_from_list(self, requires_grad=False): |
| return torch.nested_tensor([torch.randn(1, 2, requires_grad=requires_grad), |
| torch.randn(7, 8, requires_grad=requires_grad)]) |
| |
| def _create_nested_tensor_from_mask(self, requires_grad=False): |
| data = torch.randn(2, 3, 4, requires_grad=requires_grad) |
| mask = torch.ones_like(data[:, :, 0]).bool() |
| return torch._nested_tensor_from_mask(data, mask) |
| |
| def test_set_requires_grad_from_list(self): |
| nt = self._create_nested_tensor_from_list() |
| nt.requires_grad_() |
| assert nt.requires_grad |
| |
| def test_set_requires_grad_from_mask(self): |
| nt = self._create_nested_tensor_from_mask() |
| nt.requires_grad_() |
| assert nt.requires_grad |
| |
| def test_backward_for_add_op(self): |
| nt_1 = self._create_nested_tensor_from_mask() |
| nt_2 = self._create_nested_tensor_from_mask() |
| |
| nt_1.requires_grad_() |
| c = nt_1 + nt_2 |
| |
| assert nt_1.requires_grad |
| assert c.requires_grad |
| grad_output = self._create_nested_tensor_from_mask() |
| c.backward(grad_output) |
| |
| # Grad check doesn't work with nested yet. |
| # d/dnt_1 (nt + nt_1) = 1*grad_output |
| self.nt_equal(nt_1.grad, grad_output) |
| |
| # Test Factory Functions |
| def test_nested_tensor_to_padded_tensor(self): |
| for padding_val in [0, 1]: |
| nt = torch.nested_tensor([torch.randn(1, 2), torch.randn(7, 8)]) |
| nt.requires_grad_() |
| |
| out = nt.to_padded_tensor(padding_val) |
| grad_output = torch.ones(out.shape) |
| out.backward(grad_output) |
| |
| self.nt_equal(nt.grad, torch.nested_tensor([torch.ones(1, 2), torch.ones(7, 8)])) |
| |
| def test_nested_tensor_from_mask_and_to_padded(self): |
| N, L, D = 2, 4, 4 |
| mask = torch.ones(N, L) |
| for i in range(1, N): |
| end = torch.randint(1, L - 1, (1,)) |
| mask[i, end:] = 0 |
| |
| mask[0, :] = 1 |
| mask = mask.bool() |
| |
| data = torch.randn(N, L, D, requires_grad=True, dtype=torch.float64) |
| |
| def grad_test_func(inpt): |
| nt = torch._nested_tensor_from_mask(inpt, mask) |
| # This implicitly tests to_padded_tensor grads |
| return nt.to_padded_tensor(0) |
| assert torch.autograd.gradcheck(grad_test_func, inputs=data) |
| |
| def test_nested_tensor_from_padded(self): |
| nested_size = torch.tensor([[1, 2], [2, 2]]) |
| padded_tensor = torch.randn(2, 2, 2, dtype=torch.float64) |
| padded_tensor[0, 1, :] = 0 |
| padded_tensor.requires_grad_() |
| |
| def grad_test_func(tensor, nested_size): |
| nt = torch._nested_from_padded(tensor, nested_size, fuse_transform_0213=False) |
| # This implicitly tests to_padded_tensor grads |
| return nt.to_padded_tensor(0) |
| |
| data = (padded_tensor, nested_size) |
| assert torch.autograd.gradcheck(grad_test_func, inputs=data) |
| |
| def test_nested_tensor_from_padded_fused(self): |
| nested_size = torch.tensor([[1, 8], [2, 8]]) |
| padded_tensor = torch.randn(2, 2, 2, 4, dtype=torch.float64) |
| padded_tensor[0, 1, :] = 0 |
| padded_tensor.requires_grad_() |
| |
| def grad_test_func(tensor, nested_size): |
| nt = torch._nested_from_padded(tensor, nested_size, fuse_transform_0213=True) |
| # This implicitly tests to_padded_tensor grads |
| return nt.to_padded_tensor(0) |
| data = (padded_tensor, nested_size) |
| assert torch.autograd.gradcheck(grad_test_func, inputs=data) |
| |
| def test_nested_tensor_from_list(self): |
| |
| a = torch.randn(1, 2, requires_grad=True, dtype=torch.float64) |
| b = torch.randn(2, 2, requires_grad=True, dtype=torch.float64) |
| c = torch.randn(10, 2, requires_grad=True, dtype=torch.float64) |
| |
| def grad_test_func(a, b, c): |
| c = torch.nested_tensor([a, b, c]) |
| # This implictily tests to_padded_tensor grads |
| return c.to_padded_tensor(0) |
| data = (a, b, c) |
| assert torch.autograd.gradcheck(grad_test_func, inputs=data) |
| |
| def test_size_dim(self): |
| a = torch.nested_tensor([]) |
| self.assertEqual(a.size(0), 0) |
| |
| a = torch.nested_tensor([torch.tensor(1)]) |
| self.assertEqual(a.size(0), 1) |
| |
| a = torch.nested_tensor([torch.tensor(1), torch.tensor(2)]) |
| self.assertEqual(a.size(0), 2) |
| |
| a = torch.nested_tensor([torch.rand(1, 2), |
| torch.rand(1, 8)]) |
| self.assertEqual(a.size(0), 2) |
| self.assertEqual(a.size(1), 1) |
| self.assertRaisesRegex( |
| RuntimeError, "Given dimension 2 is irregular and does not have a size", lambda: a.size(2)) |
| |
| a = torch.nested_tensor([torch.rand(3, 4), |
| torch.rand(5, 4)]) |
| self.assertEqual(a.size(0), 2) |
| self.assertRaisesRegex( |
| RuntimeError, "Given dimension 1 is irregular and does not have a size", lambda: a.size(1)) |
| self.assertEqual(a.size(2), 4) |
| |
| def test_nested_tensor_linear(self): |
| |
| a = torch.randn(1, 2, requires_grad=True, dtype=torch.float64) |
| b = torch.randn(2, 2, requires_grad=True, dtype=torch.float64) |
| c = torch.randn(3, 2, requires_grad=True, dtype=torch.float64) |
| |
| weight = torch.randn(2, 2, requires_grad=True, dtype=torch.float64) |
| bias = torch.randn(2, requires_grad=True, dtype=torch.float64) |
| |
| def grad_test_func(a, b, c, weight, bias=None): |
| nt = torch.nested_tensor([a, b, c]) |
| # This implicitly tests to_padded_tensor grads |
| d = torch.functional.F.linear(nt, weight, bias) |
| return d.to_padded_tensor(0) |
| data = (a, b, c, weight, bias) |
| assert torch.autograd.gradcheck(grad_test_func, inputs=data) |
| |
| # Test linear with no bias added |
| data = (a, b, c, weight) |
| assert torch.autograd.gradcheck(grad_test_func, inputs=data) |
| |
| def test_nested_tensor_linear_backward(self): |
| a = torch.randn(1, 2, requires_grad=False) |
| b = torch.randn(2, 2, requires_grad=False) |
| c = torch.randn(3, 2, requires_grad=False) |
| |
| weight = torch.randn(2, 2, requires_grad=True) |
| bias = torch.randn(2, requires_grad=True) |
| nt = torch.nested_tensor([a, b, c]) |
| |
| out = torch.functional.F.linear(nt, weight, bias) |
| |
| out.backward(out.clone()) |
| |
| assert weight.grad is not None |
| assert bias.grad is not None |
| |
| assert a.grad is None |
| assert b.grad is None |
| assert c.grad is None |
| |
| |
| |
| instantiate_device_type_tests(TestNestedTensorDeviceType, globals()) |
| |
| if __name__ == '__main__': |
| run_tests() |
