# 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, onlyCPU ) from torch.testing._internal.common_dtype import floating_types_and_half from torch.testing._internal.common_utils import TestCase, IS_FBCODE, run_tests, freeze_rng_state, parametrize, gradcheck # Tests are ported from pytorch/nestedtensor. # This makes porting as_nested_tensor easier in the future. def _iter_constructors(): # yield as_nested_tensor yield torch.nested.nested_tensor # Helper function to generate a pair of random nested tensors # one is contiguous, the other is not, but they appear to have same entries # an output nested tensor consists of # * `len(ragged_sizes)` matrices # * matrices[i].shape == (20, ragged_sizes[i]) def random_nt_noncontiguous_pair(ragged_sizes, device="cpu", dtype=torch.float16): xs = [] for size in ragged_sizes: xs.append(torch.randn((size, 20), device=device, dtype=dtype)) # contiguous nested tensor ys = [] for x in xs: ys.append(x.transpose(-1, -2)) nt_contiguous = torch.nested.nested_tensor(ys) # noncontiguous nested tensor n = len(ragged_sizes) nt_noncontiguous = torch.nested.nested_tensor(xs).transpose(-1, -2) return nt_contiguous, nt_noncontiguous # Helper functions to pad a noncontiguous nested tensor # can be replaced once to_padded_tensor supports noncontiguous memory def noncontiguous_to_padded_tensor(input, shape=None): tensors = input.unbind() ntensors = len(tensors) assert ntensors > 0 if shape is None: shape = [] for size in tensors[0].shape: shape.append(size) for i in range(1, ntensors): new_shape = tensors[i].shape for j in range(len(shape)): shape[j] = max(shape[j], new_shape[j]) shape = [ntensors] + shape result = tensors[0].new_zeros(shape) for itensor in range(ntensors): tensor = tensors[itensor] view = result[itensor] for idim in range(tensor.dim()): view = view.narrow(idim, 0, tensor.size(idim)) view.copy_(tensor) return result class TestNestedTensor(TestCase): @torch.inference_mode() def _test_unbind_case(self, a, b): nt = torch.nested.nested_tensor([a, b]) a1, b1 = nt.unbind() self.assertTrue(a is not a1) self.assertTrue(b is not b1) nt = torch.nested.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 = torch.nested.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_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 = torch.nested.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: torch.nested.nested_tensor([3.0])) self.assertRaises(TypeError, lambda: torch.nested.nested_tensor(torch.tensor([3.0]))) self.assertRaises(TypeError, lambda: torch.nested.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: torch.nested.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: torch.nested.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: torch.nested.nested_tensor()) default_nested_tensor = torch.nested.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(), ) def test_size_dim(self): a = torch.nested.nested_tensor([]) self.assertEqual(a.size(0), 0) a = torch.nested.nested_tensor([torch.tensor(1)]) self.assertEqual(a.size(0), 1) a = torch.nested.nested_tensor([torch.tensor(1), torch.tensor(2)]) self.assertEqual(a.size(0), 2) a = torch.nested.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.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) @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): # Test empty case nt_empty = torch.nested.nested_tensor([]) assert nt_empty.is_contiguous() self.assertEqual(nt_empty, nt_empty.contiguous()) nt_contiguous, nt_noncontiguous = random_nt_noncontiguous_pair((2, 3, 6, 7)) # Test contiguous case assert nt_contiguous.is_contiguous() self.assertEqual(nt_contiguous, nt_contiguous.contiguous()) # Test non_contiguous case assert not nt_noncontiguous.is_contiguous() self.assertEqual(nt_contiguous, nt_noncontiguous.contiguous()) @torch.inference_mode() def test_repr_string(self): a = torch.nested.nested_tensor([]) expected = "nested_tensor([" "\n\n])" self.assertEqual(str(a), expected) self.assertEqual(repr(a), expected) a = torch.nested.nested_tensor([torch.tensor(1.0)]) expected = "nested_tensor([" "\n tensor(1.)" "\n])" self.assertEqual(str(a), expected) self.assertEqual(repr(a), expected) a = torch.nested.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 = torch.nested.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.nested_tensor([]) empty = torch.nested.to_padded_tensor(nt, 4) self.assertEqual(empty, torch.tensor([])) def test_nested_namespace(self): nt = torch.nested.nested_tensor([torch.randn(2, 3), torch.randn(4, 5)]) result = nt.to_padded_tensor(4) nested_namespace_result = torch.nested.to_padded_tensor(nt, 4) self.assertEqual(result, nested_namespace_result) class TestNestedTensorDeviceType(TestCase): # Helper function to generate a random nested tensor 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.nested_tensor(ts1, device=device, dtype=dtype) # Helper function to generate a pair of random nested tensors # the 2 nested tensors have same shapes 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.nested_tensor(ts1, device=device, dtype=dtype), torch.nested.nested_tensor(ts2, device=device, dtype=dtype)) @dtypes(*floating_types_and_half()) def test_detach(self, device, dtype): a = torch.randn(2, 4, device=device, dtype=dtype, requires_grad=False) b = torch.randn(5, 4, device=device, dtype=dtype, requires_grad=False) x = torch.nested.nested_tensor([a, b], requires_grad=True) x_detach = x.detach() z = x_detach * 4 self.assertFalse(x_detach.requires_grad) self.assertFalse(z.requires_grad) a = torch.randn(2, 4, device=device, dtype=dtype, requires_grad=True) b = torch.randn(5, 4, device=device, dtype=dtype, requires_grad=True) x = torch.nested.as_nested_tensor([a, b]) y = x * 2 y = y.detach() self.assertFalse(y.requires_grad) self.assertIsNone(y.grad_fn) z = x + y torch.nested.to_padded_tensor(z, 0).sum().backward() # This is an incorrect gradient, but we assume that's what the user # wanted. detach() is an advanced option. self.assertEqual(a.grad, torch.ones(2, 4, device=device, dtype=dtype)) self.assertEqual(b.grad, torch.ones(5, 4, device=device, dtype=dtype)) @dtypes(torch.float, torch.float16, torch.double) def test_unbind_noncontiguous(self, device, dtype): nt_contiguous, nt_noncontiguous = random_nt_noncontiguous_pair((2, 3, 6, 7), device, dtype) ub_contiguous = nt_contiguous.unbind() ub_noncontiguous = nt_noncontiguous.unbind() self.assertEqual(len(ub_contiguous), len(ub_noncontiguous)) n = len(ub_contiguous) for i in range(n): self.assertEqual(ub_contiguous[i], ub_noncontiguous[i]) @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.nested_tensor(ts, device=device, dtype=dtype) padded = torch.nested.to_padded_tensor(nt, 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.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.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.nested_tensor(ts, device=device, dtype=dtype) for padding_value in (0, 1): padded = torch.nested.to_padded_tensor(nt, 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.nested_tensor(ts, device=device, dtype=dtype) for padding_value in (0, 1): padded = torch.nested.to_padded_tensor(nt, 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.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 = torch.nested.to_padded_tensor(nt, 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.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 = torch.nested.to_padded_tensor(nt, 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.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 = torch.nested.to_padded_tensor(nt, pad) self.assertEqual(padded, correct_output) # TODO: test noncontiguous to_padded_tensor # For now this tests the functionality of noncontiguous_to_padded_tensor # and the error message of to_padded_tensor # since to_padded_tensor does not support noncontiguous buffer yet @dtypes(torch.float, torch.float16, torch.double) @torch.inference_mode() def test_to_padded_tensor_noncontiguous(self, device, dtype): nt_contiguous, nt_noncontiguous = random_nt_noncontiguous_pair((2, 3, 6, 7), device, dtype) # test noncontiguous_to_padded_tensor functionality self.assertEqual( torch.nested.to_padded_tensor(nt_contiguous, 0.0), noncontiguous_to_padded_tensor(nt_noncontiguous)) # test to_padded_tensor error message self.assertRaisesRegex( RuntimeError, r"for now to_padded_tensor only supports contiguous nested tensor", lambda: torch.nested.to_padded_tensor(nt_noncontiguous, 0.0) ) @skipMeta def test_device_checks(self, device): nt = torch.nested.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.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.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) @dtypes(torch.float, torch.float16, torch.double) @torch.inference_mode() def test_nested_tensor_indexing_noncontiguous(self, device, dtype): nt_contiguous, nt_noncontiguous = random_nt_noncontiguous_pair((2, 3, 6, 7), device, dtype) self.assertEqual(nt_contiguous.size(0), nt_noncontiguous.size(0)) n = nt_contiguous.size(0) for i in range(n): self.assertEqual(nt_contiguous[i], nt_noncontiguous[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.nested_tensor([t1 + t2 for (t1, t2) in zip(nt1.unbind(), nt2.unbind())]) out = nt1 + nt2 self.assertEqual(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.nested_tensor([t1 * t2 for (t1, t2) in zip(nt1.unbind(), nt2.unbind())]) out = nt1 * nt2 self.assertEqual(ref, out) # nested tensor * scalar number = 10.0 scalar = torch.tensor(number).to(dtype).to(device) ref = torch.nested.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.assertEqual(out_number0, ref) self.assertEqual(out_number1, ref) self.assertEqual(out_scalar0, ref) self.assertEqual(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.nested_tensor([t1 + t2 for (t1, t2) in zip(nt1.unbind(), nt2.unbind())]) nt1 += nt2 self.assertEqual(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.nested_tensor([t1 * t2 for (t1, t2) in zip(nt1.unbind(), nt2.unbind())]) nt1 *= nt2 self.assertEqual(ref, nt1) # nested tensor * scalar number = 10.0 scalar = torch.tensor(number).to(dtype).to(device) ref = torch.nested.nested_tensor([t * number for t in nt1.unbind()]) out_number = nt1.clone() out_number *= number out_scalar = nt1.clone() out_scalar *= scalar self.assertEqual(out_number, ref) self.assertEqual(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) ) @onlyCPU @skipMeta @dtypes(torch.float) def test_nested_tensor_sum_dim(self, device, dtype): params = ((2, (1, 1)), ((4), (4, 4)), (10, (3, 5, 7))) def test_sum(device, dtype, ntensors, max_sizes, dim, keepdim=True): nt = self.random_nt(device, dtype, ntensors, max_sizes) nt2 = nt.clone() ub2 = nt2.unbind() nt.requires_grad_(True) [t.requires_grad_(True) for t in ub2] nt_sum = nt.sum(dim=dim, keepdim=keepdim) ub2_sum = [t.sum(-1, keepdim=keepdim) for t in ub2] self.assertEqual(nt_sum, torch.nested.nested_tensor(ub2_sum)) # test backward # generate gradient tensor that has the same size as the output size = nt_sum._nested_tensor_size() gt2 = [] for i in range(ntensors): gt2.append(torch.randn(size[i].tolist(), device=device, dtype=dtype)) gt = torch.nested.nested_tensor(gt2).clone() nt_sum.backward(gt) for t2, g2 in zip(ub2_sum, gt2): t2.backward(g2) self.assertEqual(nt.grad, torch.nested.nested_tensor([t.grad for t in ub2])) return for ntensors, max_sizes in params: test_sum(device, dtype, ntensors, max_sizes, len(max_sizes)) # Test error inputs with self.assertRaisesRegex(RuntimeError, "NestedTensor can only be reduced across the last"): torch.nested.nested_tensor([torch.tensor([3, 4, 5]), torch.tensor([1, 2])]).sum(0, keepdim=True) with self.assertRaisesRegex(RuntimeError, "NestedTensor only allows reduction of a single"): torch.nested.nested_tensor([torch.tensor([[3, 4, 5]]), torch.tensor([[1, 2]])]).sum([0, 1], keepdim=True) with self.assertRaisesRegex(RuntimeError, "NestedTensor always requires keepdim=True for now."): torch.nested.nested_tensor([torch.tensor([3, 4, 5]), torch.tensor([1, 2])]).sum(-1) @dtypes(torch.float, torch.float16) @skipMeta 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.assertEqual(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 = "Nested tensor clone supports Preserve and Contiguous memory formats, called clone with memory format: ChannelsLast" 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) def test_dropout(self, device, dtype): # edge case: empty nested tensor nt0 = torch.nested.nested_tensor([]) y = torch.nn.functional.dropout(nt0, 0.5) self.assertEqual(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.assertEqual(nt, y0) self.assertEqual(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.assertEqual(nt0, y0) self.assertEqual(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.assertEqual(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.assertEqual(y0, y1) @dtypes(torch.float, torch.double) def test_dropout_noncontiguous(self, device, dtype): ntensors = 4 nt0 = self.random_nt(device, dtype, ntensors, (4, 4)) nt1 = nt0.transpose(-1, -2) p = 0.3 with freeze_rng_state(): dropouter = torch.nn.Dropout(p) y0 = dropouter(nt0) with freeze_rng_state(): y1 = torch.nn.functional.dropout(nt1, p).transpose(-1, -2) self.assertEqual(y0, y1) # cannot test torch.float16 because: RuntimeError: "softmax_kernel_impl" not implemented for 'Half' @dtypes(torch.float, torch.double) 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.assertRaisesRegex( RuntimeError, "Cannot apply softmax across nested dimension 0", lambda: torch.nn.functional.softmax(nt, 0) ) self.assertRaisesRegex( RuntimeError, "Cannot apply softmax across nested dimension 0", 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.assertEqual(y0, y1) pt = torch.nested.to_padded_tensor(nt, 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(torch.nested.to_padded_tensor(y0, 0.0), expect) # edge case: empty nested tensor nt0 = torch.nested.nested_tensor([]) y = torch.nn.functional.softmax(nt0, 1) self.assertEqual(nt0, y) # edge case: nesting scalars nt1 = torch.nested.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.double) @torch.inference_mode() def test_softmax_noncontiguous(self, device, dtype): nt_contiguous, nt_noncontiguous = random_nt_noncontiguous_pair((2, 3, 6, 7), device, dtype) self.assertEqual( torch.nn.functional.softmax(nt_contiguous, -1), torch.nn.functional.softmax(nt_noncontiguous, -1)) # cannot test torch.float16 because: RuntimeError: "addmm_impl_cpu_" not implemented for 'Half' @dtypes(torch.float, torch.double) def test_bmm(self, device, dtype): # error case: one is nested but the other is not nt = torch.nested.nested_tensor([torch.randn(2), torch.randn(3)], device=device, dtype=dtype) t = torch.randn(4, device=device, dtype=dtype) self.assertRaisesRegex( RuntimeError, "Expected both to be nested, but got a nested self and non-nested other", lambda: nt.bmm(t) ) self.assertRaisesRegex( RuntimeError, "Expected both to be nested, but got a non-nested self and nested other", lambda: t.bmm(nt) ) # error case: not 3D tensors nt0 = torch.nested.nested_tensor([], device=device, dtype=dtype) nt1 = torch.nested.nested_tensor([torch.randn(2), torch.randn(3)], device=device, dtype=dtype) nt2 = torch.nested.nested_tensor([torch.randn((2, 4)), torch.randn((3, 4))], device=device, dtype=dtype) 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.nested_tensor([torch.randn((2, 4)), torch.randn((3, 4))], device=device, dtype=dtype) nt1 = torch.nested.nested_tensor([torch.randn((4, 6)), torch.randn((4, 5)), torch.randn((4, 7))], device=device, dtype=dtype) 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.nested_tensor([torch.randn((2, 4)), torch.randn((3, 4))], device=device, dtype=dtype) 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.nested_tensor([torch.randn((2, 4)), torch.randn((3, 7))], device=device, dtype=dtype) nt1 = torch.nested.nested_tensor([torch.randn((4, 6)), torch.randn((7, 5))], device=device, dtype=dtype) actual = torch.nested.to_padded_tensor(nt0.bmm(nt1), 0.0) expect = torch.nested.to_padded_tensor(nt0, 0.0).bmm(torch.nested.to_padded_tensor(nt1, 0.0)) self.assertEqual(actual, expect) # cannot test torch.float16 because: RuntimeError: "addmm_impl_cpu_" not implemented for 'Half' @dtypes(torch.float, torch.double) def test_bmm_noncontiguous(self, device, dtype): nt0_contiguous, nt0_noncontiguous = random_nt_noncontiguous_pair((2, 3), device, dtype) nt1_contiguous, nt1_noncontiguous = random_nt_noncontiguous_pair((6, 7), device, dtype) self.assertEqual( nt0_contiguous.transpose(-1, -2).bmm(nt1_contiguous), nt0_noncontiguous.transpose(-1, -2).bmm(nt1_noncontiguous)) # cannot test torch.float16 because: RuntimeError: "bmm" not implemented for 'Half' @dtypes(torch.float, torch.double) def test_matmul(self, device, dtype): # error case: one is nested but the other is not nt = torch.nested.nested_tensor([torch.randn(2), torch.randn(3)], device=device, dtype=dtype) t = torch.randn(4, device=device, dtype=dtype) self.assertRaisesRegex( RuntimeError, "Expected both to be nested, but got a nested self and non-nested other", lambda: torch.matmul(nt, t) ) self.assertRaisesRegex( RuntimeError, "Expected both to be nested, but got a non-nested self and nested other", lambda: torch.matmul(t, nt) ) # error case: not 3+D tensors nt0 = torch.nested.nested_tensor([], device=device, dtype=dtype) nt1 = torch.nested.nested_tensor([torch.randn(2), torch.randn(3)], device=device, dtype=dtype) nt2 = torch.nested.nested_tensor([torch.randn((2, 4)), torch.randn((3, 4))], device=device, dtype=dtype) self.assertRaisesRegex( RuntimeError, r"matmul: For nested tensors, only inputs with >= 3 dims are currently supported. 1st input has rank: [0-9]+", lambda: torch.matmul(nt0, nt0) ) self.assertRaisesRegex( RuntimeError, r"matmul: For nested tensors, only inputs with >= 3 dims are currently supported. 1st input has rank: [0-9]+", lambda: torch.matmul(nt0, nt1) ) self.assertRaisesRegex( RuntimeError, r"matmul: For nested tensors, only inputs with >= 3 dims are currently supported. 1st input has rank: [0-9]+", lambda: torch.matmul(nt0, nt2) ) self.assertRaisesRegex( RuntimeError, r"matmul: For nested tensors, only inputs with >= 3 dims are currently supported. 1st input has rank: [0-9]+", lambda: torch.matmul(nt1, nt0) ) self.assertRaisesRegex( RuntimeError, r"matmul: For nested tensors, only inputs with >= 3 dims are currently supported. 1st input has rank: [0-9]+", lambda: torch.matmul(nt1, nt1) ) self.assertRaisesRegex( RuntimeError, r"matmul: For nested tensors, only inputs with >= 3 dims are currently supported. 1st input has rank: [0-9]+", lambda: torch.matmul(nt1, nt2) ) self.assertRaisesRegex( RuntimeError, r"matmul: For nested tensors, only inputs with >= 3 dims are currently supported. 2nd input has rank: [0-9]+", lambda: torch.matmul(nt2, nt0) ) self.assertRaisesRegex( RuntimeError, r"matmul: For nested tensors, only inputs with >= 3 dims are currently supported. 2nd input has rank: [0-9]+", lambda: torch.matmul(nt2, nt1) ) # error case: incompatible batch size nt0 = torch.nested.nested_tensor([torch.randn((2, 4)), torch.randn((3, 4))], device=device, dtype=dtype) nt1 = torch.nested.nested_tensor([torch.randn((4, 6)), torch.randn((4, 5)), torch.randn((4, 7))], device=device, dtype=dtype) self.assertRaisesRegex( RuntimeError, r"matmul: Expected size for the 1st dimension of 2nd input tensor to be: [0-9]+ but got: [0-9]+.", lambda: torch.matmul(nt0, nt1) ) self.assertRaisesRegex( RuntimeError, r"matmul: Expected size for the 1st dimension of 2nd input tensor to be: [0-9]+ but got: [0-9]+.", lambda: torch.matmul(nt1, nt0) ) # error case: incompatible (wrong) batch sizes that shouldn't even broadcast? nt0 = torch.nested.nested_tensor([torch.randn((2, 2, 4)), torch.randn((2, 3, 4))], device=device, dtype=dtype) nt1 = torch.nested.nested_tensor([torch.randn((3, 4, 6)), torch.randn((3, 4, 5))], device=device, dtype=dtype) self.assertRaisesRegex( RuntimeError, "matmul(): For nested tensors, batch dimensions must have the same sizes,", lambda: torch.matmul(nt0, nt1) ) # error case: incompatible batch sizes that should technically broadcast nt0 = torch.nested.nested_tensor([torch.randn((2, 2, 4)), torch.randn((1, 3, 4))], device=device, dtype=dtype) nt1 = torch.nested.nested_tensor([torch.randn((1, 4, 6)), torch.randn((3, 4, 5))], device=device, dtype=dtype) self.assertRaisesRegex( RuntimeError, "matmul(): For nested tensors, batch dimensions must have the same sizes,", lambda: torch.matmul(nt0, nt1) ) # error case: underlying matrices cannot be multiplied nt0 = torch.nested.nested_tensor([torch.randn((2, 4)), torch.randn((3, 4))], device=device, dtype=dtype) self.assertRaisesRegex( RuntimeError, "matmul(): Nested tensors cannot be matrix multiplied", lambda: torch.matmul(nt0, nt0) ) # normal nested tensor: 3D nt0 = torch.nested.nested_tensor([torch.randn((2, 4)), torch.randn((3, 7))], device=device, dtype=dtype) nt1 = torch.nested.nested_tensor([torch.randn((4, 6)), torch.randn((7, 5))], device=device, dtype=dtype) actual = torch.nested.to_padded_tensor(torch.matmul(nt0, nt1), 0.0) expect = torch.matmul(torch.nested.to_padded_tensor(nt0, 0.0), torch.nested.to_padded_tensor(nt1, 0.0)) self.assertEqual(actual, expect) # normal nested tensor: 4D (with testing for batch_size=1) nt0 = torch.nested.nested_tensor([torch.randn((1, 2, 4)), torch.randn((8, 3, 7))], device=device, dtype=dtype) nt1 = torch.nested.nested_tensor([torch.randn((1, 4, 6)), torch.randn((8, 7, 5))], device=device, dtype=dtype) actual = torch.nested.to_padded_tensor(torch.matmul(nt0, nt1), 0.0) expect = torch.matmul(torch.nested.to_padded_tensor(nt0, 0.0), torch.nested.to_padded_tensor(nt1, 0.0)) self.assertEqual(actual, expect) # normal nested tensor: 5D nt0 = torch.nested.nested_tensor([torch.randn((8, 9, 2, 4)), torch.randn((8, 9, 3, 7))], device=device, dtype=dtype) nt1 = torch.nested.nested_tensor([torch.randn((8, 9, 4, 6)), torch.randn((8, 9, 7, 5))], device=device, dtype=dtype) actual = torch.nested.to_padded_tensor(torch.matmul(nt0, nt1), 0.0) expect = torch.matmul(torch.nested.to_padded_tensor(nt0, 0.0), torch.nested.to_padded_tensor(nt1, 0.0)) self.assertEqual(actual, expect) # cannot test torch.float16 because: RuntimeError: "bmm" not implemented for 'Half' @dtypes(torch.float, torch.double) def test_matmul_noncontiguous(self, device, dtype): nt0_contiguous, nt0_noncontiguous = random_nt_noncontiguous_pair((2, 3), device, dtype) nt1_contiguous, nt1_noncontiguous = random_nt_noncontiguous_pair((6, 7), device, dtype) self.assertEqual( torch.matmul(nt0_contiguous.transpose(-1, -2), nt1_contiguous), torch.matmul(nt0_noncontiguous.transpose(-1, -2), nt1_noncontiguous)) @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.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.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"Expected all tensors in nested tensor to have the same trailing dimension, instead last dimension equals:" nt2 = torch.nested.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) # TODO: test noncontiguous linear # For now this tests the error message of linear # since linear does not support noncontiguous buffer yet @dtypes(torch.float, torch.double) def test_linear_noncontiguous(self, device, dtype): nt_contiguous, nt_noncontiguous = random_nt_noncontiguous_pair((2, 3, 6, 7), device, dtype) weight = torch.randn((8, 5), device=device, dtype=dtype) self.assertRaisesRegex( RuntimeError, r"for now linear only supports contiguous nested tensor", lambda: torch.nn.functional.linear(nt_noncontiguous, weight) ) @dtypes(torch.float, torch.float16, torch.double) def test_transpose(self, device, dtype): nt = self.random_nt(device, dtype, 4, (4, 4)) # error case: transpose nested dimension self.assertRaisesRegex( RuntimeError, "Nested tensor dimension 0 cannot be transposed", lambda: nt.transpose(0, 1) ) self.assertRaisesRegex( RuntimeError, "Nested tensor dimension 0 cannot be transposed", lambda: nt.transpose(1, -3) ) # error case: dimension out of range self.assertRaises(IndexError, lambda: nt.transpose(1, 3)) self.assertRaises(IndexError, lambda: nt.transpose(-4, -1)) # normal case ntT = nt.transpose(-1, -2) ptT_from_ntT = noncontiguous_to_padded_tensor(ntT) pt = torch.nested.to_padded_tensor(nt, 0.0) ptT = pt.transpose(-1, -2) self.assertEqual(ptT, ptT_from_ntT) @dtypes(torch.float, torch.float16, torch.double) def test_transpose_inference_mode_interaction(self, device, dtype): nt = self.random_nt(device, dtype, 4, (4, 4)) # Construct in default mode and transpose while in inference mode with torch.inference_mode(): ntT = nt.transpose(-1, -2) ptT_from_ntT = noncontiguous_to_padded_tensor(ntT) pt = torch.nested.to_padded_tensor(nt, 0.0) ptT = pt.transpose(-1, -2) self.assertEqual(ptT, ptT_from_ntT) # Construct and transpose while in inference mode with torch.inference_mode(): nt = self.random_nt(device, dtype, 4, (4, 4)) ntT = nt.transpose(-1, -2) ptT_from_ntT = noncontiguous_to_padded_tensor(ntT) pt = torch.nested.to_padded_tensor(nt, 0.0) ptT = pt.transpose(-1, -2) self.assertEqual(ptT, ptT_from_ntT) @dtypes(torch.float, torch.float16, torch.double) def test_view(self, device, dtype): nt = self.random_nt(device, dtype, 4, (4, 4)) # error case: empty shape self.assertRaisesRegex( RuntimeError, r"shape '\[\]' is invalid for a nested tensor", lambda: nt.view(()) ) # error case: empty nested tensor nt_empty = torch.nested.nested_tensor([]) self.assertRaisesRegex( RuntimeError, "empty nested tensor cannot be reshaped", lambda: nt_empty.view(-1) ) # error case: -1 for batch size self.assertRaisesRegex( RuntimeError, r"view: For now nested view cannot change or infer the implicit batch dimension", lambda: nt.view(-1, 2, 3) ) self.assertRaisesRegex( RuntimeError, r"shape '\[.*\]' is invalid for input of size [0-9]+", lambda: nt.view(4, 2, 3) ) # normal case x0 = torch.randn((2, 20), device=device, dtype=dtype) x1 = torch.randn((3, 20), device=device, dtype=dtype) nt = torch.nested.nested_tensor([x0, x1]) pt = torch.nested.to_padded_tensor(nt, 0.0) # error case, trying to reshape batch dim to a legit shape self.assertRaisesRegex( RuntimeError, r"For now nested view cannot change or infer the implicit batch dimension", lambda: nt.transpose(-1, -2).view(40, -1) ) # inherit only the ragged dimension # (2, 20) -> (2, 5, 4) # (3, 20) -> (3, 5, 4) nt1 = nt.view(2, -1, 5, 4) # (2, 3, 20) -> (2, 3, 5, 4) -> (2, 4, 5, 4) pt1 = pt.view(2, -1, 5, 4) self.assertEqual(noncontiguous_to_padded_tensor(nt1), pt1) # more than one -1 (even for "old" dims), should fail # this attempts to do # (2, (2, 3), 5, 4) -> (2, (2, 3), 5, 2, 2) # but we ban "inherit old behavior" for >1 dimension self.assertRaisesRegex( RuntimeError, r"only one dimension can be inferred", lambda: nt1.view(2, -1, -1, 2, 2) ) @dtypes(torch.float, torch.float16, torch.double) def test_view_inference_mode_interaction(self, device, dtype): # Construct in default mode and view while in inference mode nt = torch.nested.nested_tensor([torch.randn((2, 20)), torch.randn((3, 20))], device=device, dtype=dtype) with torch.inference_mode(): ntT = nt.view(2, -1, 4, 5) ptT_from_ntT = noncontiguous_to_padded_tensor(ntT) pt = torch.nested.to_padded_tensor(nt, 0.0) ptT = pt.view(2, -1, 4, 5) self.assertEqual(ptT, ptT_from_ntT) # Construct and view while in inference mode with torch.inference_mode(): nt = torch.nested.nested_tensor([torch.randn((2, 20)), torch.randn((3, 20))], device=device, dtype=dtype) ntT = nt.view(2, -1, 4, 5) ptT_from_ntT = noncontiguous_to_padded_tensor(ntT) pt = torch.nested.to_padded_tensor(nt, 0.0) ptT = pt.view(2, -1, 4, 5) self.assertEqual(ptT, ptT_from_ntT) @dtypes(torch.float, torch.float16, torch.double) def test_reshape(self, device, dtype): nt = self.random_nt(device, dtype, 4, (4, 4)) # error case: empty shape self.assertRaisesRegex( RuntimeError, r"shape '\[\]' is invalid for a nested tensor", lambda: nt.reshape(()) ) # error case: empty nested tensor nt_empty = torch.nested.nested_tensor([]) self.assertRaisesRegex( RuntimeError, "empty nested tensor cannot be reshaped", lambda: nt_empty.reshape(-1) ) # error case: -1 for batch size self.assertRaisesRegex( RuntimeError, r"reshape: For now nested reshape cannot change or infer the implicit batch dimension", lambda: nt.reshape(-1, 2, 3) ) self.assertRaisesRegex( RuntimeError, r"shape '\[.*\]' is invalid for input of size [0-9]+", lambda: nt.reshape(4, 2, 3) ) # normal case x0 = torch.randn((2, 20), device=device, dtype=dtype) x1 = torch.randn((3, 20), device=device, dtype=dtype) nt = torch.nested.nested_tensor([x0, x1]) # (2, (2, 3), 20) pt = torch.nested.to_padded_tensor(nt, 0.0) # error case, trying to reshape batch dim to a legit shape self.assertRaisesRegex( RuntimeError, r"reshape: For now nested reshape cannot change or infer the implicit batch dimension", lambda: nt.transpose(-1, -2).reshape(40, -1) ) # inherit only the ragged dimension # (2, 20) -> (2, 5, 4) # (3, 20) -> (3, 5, 4) nt1 = nt.reshape(2, -1, 5, 4) # (2, 3, 20) -> (2, 3, 5, 4) -> (2, 4, 5, 4) pt1 = pt.reshape(2, -1, 5, 4) self.assertEqual(noncontiguous_to_padded_tensor(nt1), pt1) # more than one -1 (even for "old" dims), should fail # this attempts to do # (2, (2, 3), 5, 4) -> (2, (2, 3), 5, 2, 2) # but we ban "inherit old behavior" for >1 dimension self.assertRaisesRegex( RuntimeError, r"only one dimension can be inferred", lambda: nt1.reshape(2, -1, -1, 2, 2) ) @parametrize("input_dim", [3, 4]) def test_scaled_dot_product_attention(self, device, input_dim): def rand_tensor(*shape): return torch.randn(shape, device=device) E = 10 if input_dim == 3: # Shape: (N, L, E); ragged L query = torch.nested.nested_tensor([rand_tensor(2, E), rand_tensor(3, E), rand_tensor(4, E)]) # Shape: (N, S, E); ragged S key = torch.nested.nested_tensor([rand_tensor(3, E), rand_tensor(4, E), rand_tensor(5, E)]) value = torch.nested.nested_tensor([rand_tensor(3, E), rand_tensor(4, E), rand_tensor(5, E)]) elif input_dim == 4: # Shape: (N, N', L, E); ragged N' and L query = torch.nested.nested_tensor([rand_tensor(2, 2, E), rand_tensor(3, 3, E), rand_tensor(4, 4, E)]) # Shape: (N, N', S, E); ragged N' and S key = torch.nested.nested_tensor([rand_tensor(2, 3, E), rand_tensor(3, 4, E), rand_tensor(4, 5, E)]) value = torch.nested.nested_tensor([rand_tensor(2, 3, E), rand_tensor(3, 4, E), rand_tensor(4, 5, E)]) else: self.fail(f"Invalid input_dim {input_dim} encountered in SDP test") def rand_mask(size): return torch.randint(0, 2, size=size, dtype=torch.bool, device=device) # Shape: (N, L, S); ragged L and S matching above attn_mask = torch.nested.nested_tensor([rand_mask((2, 3)), rand_mask((3, 4)), rand_mask((4, 5))]) dropout_p = 0.0 # no dropout for reproducibility need_attn_weights: bool = True # Success case: no attn_mask set and is_causal=False. actual = torch.ops.aten._scaled_dot_product_attention( query, key, value, attn_mask=None, dropout_p=dropout_p, need_attn_weights=need_attn_weights) expected_outputs = [] expected_attn_weights = [] for q, k, v in zip(query.unbind(), key.unbind(), value.unbind()): (output, attn_weights) = torch.ops.aten._scaled_dot_product_attention( q.unsqueeze(0), k.unsqueeze(0), v.unsqueeze(0), attn_mask=None, dropout_p=dropout_p, need_attn_weights=need_attn_weights) expected_outputs.append(output.squeeze(0)) expected_attn_weights.append(attn_weights.squeeze(0)) expected_output_nested = torch.nested.nested_tensor(expected_outputs) expected_attn_weight_nested = torch.nested.nested_tensor(expected_attn_weights) self.assertEqual(actual[0], expected_output_nested) self.assertEqual(actual[1], expected_attn_weight_nested) # Error case: explicit attn_mask set. with self.assertRaisesRegex(RuntimeError, "not supported when an explicit attn_mask is set"): torch.ops.aten._scaled_dot_product_attention( query, key, value, attn_mask=attn_mask, dropout_p=dropout_p, need_attn_weights=need_attn_weights) # Error case: is_causal=True. with self.assertRaisesRegex(RuntimeError, "not supported when is_causal=True"): torch.ops.aten._scaled_dot_product_attention( query, key, value, dropout_p=dropout_p, need_attn_weights=need_attn_weights, is_causal=True) class TestNestedTensorAutograd(TestCase): # Note [Gradcheck args check_batched_grad=False] the common_utils testing version of gradcheck # includes the default parameters used for testing ops with gradcheck. However nested tensor # does not support the stack op therefore we turn it off for these tests def _create_leaf_nested_tensor_from_list(self, requires_grad=False): return torch.nested.nested_tensor([torch.randn(1, 2), torch.randn(7, 8)], requires_grad=requires_grad) def _create_nested_tensor_from_list(self, requires_grad=False): return torch.nested.as_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_as_nested_tensor_propagates_gradients(self): a = torch.arange(3, dtype=torch.float) b = torch.arange(5, dtype=torch.float) nt = torch.nested.as_nested_tensor([a, b]) # tensors with requires_grad=False are leaves self.assertTrue(nt.is_leaf) self.assertTrue(not nt.requires_grad) a = torch.arange(3, dtype=torch.float, requires_grad=True) b = torch.arange(5, dtype=torch.float, requires_grad=True) nt2 = torch.nested.as_nested_tensor([a, b]) fake_grad = torch.nested.nested_tensor([torch.ones_like(a), torch.zeros_like(b)]) nt2.backward(fake_grad) self.assertEqual(a.grad, fake_grad[0]) self.assertEqual(b.grad, fake_grad[1]) def test_nested_tensor_generates_leaf(self): a = torch.arange(3, dtype=torch.float, requires_grad=True) b = torch.arange(5, dtype=torch.float, requires_grad=True) nt = torch.nested.nested_tensor([a, b], requires_grad=False) self.assertTrue(nt.is_leaf) self.assertTrue(not nt.requires_grad) nt2 = torch.nested.nested_tensor([a, b], requires_grad=True) self.assertTrue(nt2.is_leaf) self.assertTrue(nt2.requires_grad) fake_grad = torch.nested.nested_tensor([torch.ones_like(a), torch.zeros_like(b)]) nt2.backward(fake_grad) self.assertEqual(nt2.grad, fake_grad) self.assertEqual(a.grad, None) self.assertEqual(b.grad, None) 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.assertEqual(nt_1.grad, grad_output) # Test Factory Functions def test_nested_tensor_to_padded_tensor(self): for padding_val in [0, 1]: nt = self._create_leaf_nested_tensor_from_list(True) out = torch.nested.to_padded_tensor(nt, padding_val) grad_output = torch.ones(out.shape) out.backward(grad_output) self.assertEqual(nt.grad, torch.nested.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 torch.nested.to_padded_tensor(nt, 0) assert gradcheck(grad_test_func, inputs=data, check_batched_grad=False) 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 torch.nested.to_padded_tensor(nt, 0) data = (padded_tensor, nested_size) assert gradcheck(grad_test_func, inputs=data, check_batched_grad=False) 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 torch.nested.to_padded_tensor(nt, 0) data = (padded_tensor, nested_size) assert gradcheck(grad_test_func, inputs=data, check_batched_grad=False) 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.as_nested_tensor([a, b, c]) # This implictily tests to_padded_tensor grads return torch.nested.to_padded_tensor(c, 0) data = (a, b, c) assert gradcheck(grad_test_func, inputs=data, check_batched_grad=False) def test_dropout_backward(self): nt = torch.nested.nested_tensor([torch.randn((2, 5)), torch.randn((3, 4))], requires_grad=True) p = 0.2 y = torch.nn.functional.dropout(nt, p) y.backward(nt.clone().detach()) self.assertEqual(nt.grad, y) def test_nested_tensor_bmm_gradcheck(self): a = torch.randn(2, 6, requires_grad=True, dtype=torch.float64) b = torch.randn(3, 6, requires_grad=True, dtype=torch.float64) c = torch.randn(6, 4, requires_grad=True, dtype=torch.float64) d = torch.randn(6, 5, requires_grad=True, dtype=torch.float64) def grad_test_func(a, b, c, d): nt0 = torch.nested.as_nested_tensor([a, b]) nt1 = torch.nested.as_nested_tensor([c, d]) result = nt0.bmm(nt1) return torch.nested.to_padded_tensor(result, 0.0) data = (a, b, c, d) assert torch.autograd.gradcheck(grad_test_func, inputs=data) def test_nested_tensor_bmm_backward(self): nt0 = torch.nested.nested_tensor([torch.randn((2, 6)), torch.randn((3, 6))], requires_grad=True) nt1 = torch.nested.nested_tensor([torch.randn((6, 4)), torch.randn((6, 5))], requires_grad=True) with torch.no_grad(): pt0 = torch.nested.to_padded_tensor(nt0, 0.0).requires_grad_(True) pt1 = torch.nested.to_padded_tensor(nt1, 0.0).requires_grad_(True) ynt = nt0.bmm(nt1) ypt = pt0.bmm(pt1) ynt.backward(ynt.clone()) ypt.backward(ypt.clone()) self.assertEqual(torch.nested.to_padded_tensor(nt0.grad, 0.0), pt0.grad) self.assertEqual(torch.nested.to_padded_tensor(nt1.grad, 0.0), pt1.grad) def test_nested_tensor_matmul_gradcheck(self): a = torch.randn(2, 6, requires_grad=True, dtype=torch.float64) b = torch.randn(3, 6, requires_grad=True, dtype=torch.float64) c = torch.randn(6, 4, requires_grad=True, dtype=torch.float64) d = torch.randn(6, 5, requires_grad=True, dtype=torch.float64) def grad_test_func(a, b, c, d): nt0 = torch.nested.as_nested_tensor([a, b]) nt1 = torch.nested.as_nested_tensor([c, d]) result = torch.matmul(nt0, nt1) return torch.nested.to_padded_tensor(result, 0.0) data = (a, b, c, d) assert torch.autograd.gradcheck(grad_test_func, inputs=data) def test_nested_tensor_matmul_backward(self): nt0 = torch.nested.nested_tensor([torch.randn((7, 2, 6)), torch.randn((7, 3, 6))], requires_grad=True) nt1 = torch.nested.nested_tensor([torch.randn((7, 6, 4)), torch.randn((7, 6, 5))], requires_grad=True) with torch.no_grad(): pt0 = torch.nested.to_padded_tensor(nt0, 0.0).requires_grad_(True) pt1 = torch.nested.to_padded_tensor(nt1, 0.0).requires_grad_(True) ynt = torch.matmul(nt0, nt1) ypt = torch.matmul(pt0, pt1) ynt.backward(ynt.clone()) ypt.backward(ypt.clone()) self.assertEqual(torch.nested.to_padded_tensor(nt0.grad, 0.0), pt0.grad) self.assertEqual(torch.nested.to_padded_tensor(nt1.grad, 0.0), pt1.grad) def test_nested_tensor_transpose_gradcheck(self): a = torch.randn(2, 5, requires_grad=True) b = torch.randn(3, 4, requires_grad=True) def grad_test_func(a, b): nt = torch.nested.as_nested_tensor([a, b]) result = nt.transpose(-2, -1).transpose(-2, -1) return torch.nested.to_padded_tensor(result, 0.0) data = (a, b) assert torch.autograd.gradcheck(grad_test_func, inputs=data, eps=1e-3) def test_nested_tensor_transpose_backward(self): nt = torch.nested.nested_tensor([torch.randn((2, 5)), torch.randn((3, 4))], requires_grad=True) with torch.no_grad(): pt = torch.nested.to_padded_tensor(nt, 0.0).requires_grad_(True) ynt = nt.transpose(-2, -1) ypt = pt.transpose(-2, -1) ynt.backward(ynt.clone()) ypt.backward(ypt.clone()) self.assertEqual(torch.nested.to_padded_tensor(nt.grad, 0.0), pt.grad) def test_nested_tensor_reshape_gradcheck(self): a = torch.randn(2, 6, requires_grad=True) b = torch.randn(3, 6, requires_grad=True) def grad_test_func(a, b): nt = torch.nested.as_nested_tensor([a, b]) result = nt.reshape(2, -1, 2, 3) return torch.nested.to_padded_tensor(result, 0.0) data = (a, b) assert torch.autograd.gradcheck(grad_test_func, inputs=data, eps=1e-3) def test_nested_tensor_reshape_backward(self): nt = torch.nested.nested_tensor([torch.randn((2, 6)), torch.randn((3, 6))], requires_grad=True) with torch.no_grad(): pt = torch.nested.to_padded_tensor(nt, 0.0).requires_grad_(True) ynt = nt.reshape(2, -1, 2, 3) ypt = pt.reshape(2, -1, 2, 3) ynt.backward(ynt.clone()) ypt.backward(ypt.clone()) self.assertEqual(torch.nested.to_padded_tensor(nt.grad, 0.0), pt.grad) 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.as_nested_tensor([a, b, c]) # This implicitly tests to_padded_tensor grads d = torch.functional.F.linear(nt, weight, bias) return torch.nested.to_padded_tensor(d, 0) data = (a, b, c, weight, bias) assert gradcheck(grad_test_func, inputs=data, check_batched_grad=False) # Test linear with no bias added data = (a, b, c, weight) assert gradcheck(grad_test_func, inputs=data, check_batched_grad=False) def test_nested_tensor_softmax(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) def grad_test_func(a, b, c, dim): nt = torch.nested.as_nested_tensor([a, b, c]) # This implicitly tests to_padded_tensor grads d = torch.functional.F.softmax(nt, dim=dim) return torch.nested.to_padded_tensor(d, 0) # softmax over last dim data = (a, b, c, -1) assert gradcheck(grad_test_func, inputs=data, check_batched_grad=False) 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.as_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 def test_values_grad_with_broadcast(self): a = torch.randn(1, 2, 4, requires_grad=True, dtype=torch.float64) b = torch.randn(2, 2, 4, requires_grad=True, dtype=torch.float64) c = torch.randn(3, 2, 4, requires_grad=True, dtype=torch.float64) def grad_test_func(a, b, c): nt = torch.nested.as_nested_tensor([a, b, c]) buffer = nt.values() return buffer.sum() data = (a, b, c) assert gradcheck(grad_test_func, inputs=data, check_batched_grad=False) def test_to_buffer_series_ops_grad_with_broadcast(self): a = torch.randn(1, 1, 2, requires_grad=True, dtype=torch.float64) b = torch.randn(1, 1, 2, requires_grad=True, dtype=torch.float64) c = torch.randn(1, 1, 2, requires_grad=True, dtype=torch.float64) def grad_test_func(a, b, c): nt = torch.nested.as_nested_tensor([a, b, c]) buffer = nt.values() buffer = buffer * 2 return buffer.exp() data = (a, b, c) assert gradcheck(grad_test_func, inputs=data, check_batched_grad=False) def test_unbind_flow_through(self): a = torch.randn(1, 2, 4, requires_grad=True, dtype=torch.float64) b = torch.randn(2, 2, 4, requires_grad=True, dtype=torch.float64) c = torch.randn(3, 2, 4, requires_grad=True, dtype=torch.float64) def grad_test_func(a, b, c): nt = torch.nested.as_nested_tensor([a, b, c]) ntT = nt.transpose(-1, -2) unbound = ntT.unbind() d = unbound[0] d = torch.pow(d, 2) return d data = (a, b, c) assert gradcheck(grad_test_func, inputs=data, check_batched_grad=False) def test_indexing_backward(self): x0 = torch.randn((2, 5)) x1 = torch.randn((3, 4)) nt = torch.nested.nested_tensor([x0, x1], requires_grad=True) self.assertEqual(nt[0], x0) self.assertEqual(nt[-1], x1) grad_x0 = torch.randn((2, 5)) nt[0].backward(grad_x0) expected_grad = torch.nested.nested_tensor([grad_x0, torch.zeros((3, 4))]) self.assertEqual(nt.grad, expected_grad) instantiate_device_type_tests(TestNestedTensorDeviceType, globals()) if __name__ == '__main__': run_tests()