| import torch |
| import unittest |
| from torch.testing._internal.common_utils import TestCase, run_tests |
| from torch.testing._internal.common_device_type import instantiate_device_type_tests, dtypes, skipCUDAIfRocm |
| |
| class TestForeach(TestCase): |
| foreach_bin_ops = [ |
| torch._foreach_add, |
| torch._foreach_sub, |
| torch._foreach_mul, |
| torch._foreach_div, |
| ] |
| |
| foreach_bin_ops_ = [ |
| torch._foreach_add_, |
| torch._foreach_sub_, |
| torch._foreach_mul_, |
| torch._foreach_div_, |
| ] |
| |
| torch_bin_ops = [ |
| torch.add, |
| torch.sub, |
| torch.mul, |
| torch.div, |
| ] |
| |
| def _get_test_data(self, device, dtype, N): |
| if dtype in [torch.bfloat16, torch.bool, torch.float16]: |
| tensors = [torch.randn(N, N, device=device).to(dtype) for _ in range(N)] |
| elif dtype in torch.testing.get_all_int_dtypes(): |
| tensors = [torch.randint(1, 100, (N, N), device=device, dtype=dtype) for _ in range(N)] |
| else: |
| tensors = [torch.randn(N, N, device=device, dtype=dtype) for _ in range(N)] |
| |
| return tensors |
| |
| def _test_bin_op_list(self, device, dtype, foreach_op, foreach_op_, torch_op): |
| for N in [30, 300]: |
| tensors1 = self._get_test_data(device, dtype, N) |
| tensors2 = self._get_test_data(device, dtype, N) |
| |
| expected = [torch_op(tensors1[i], tensors2[i]) for i in range(N)] |
| res = foreach_op(tensors1, tensors2) |
| foreach_op_(tensors1, tensors2) |
| self.assertEqual(res, tensors1) |
| self.assertEqual(tensors1, res) |
| |
| def _test_unary_op(self, device, dtype, foreach_op, foreach_op_, torch_op): |
| for N in [30, 300]: |
| tensors1 = self._get_test_data(device, dtype, N) |
| expected = [torch_op(tensors1[i]) for i in range(N)] |
| res = foreach_op(tensors1) |
| foreach_op_(tensors1) |
| self.assertEqual(res, tensors1) |
| self.assertEqual(tensors1, expected) |
| |
| def _test_pointwise_op(self, device, dtype, foreach_op, foreach_op_, torch_op): |
| for N in [30, 300]: |
| tensors = self._get_test_data(device, dtype, N) |
| tensors1 = self._get_test_data(device, dtype, N) |
| tensors2 = self._get_test_data(device, dtype, N) |
| value = 2 |
| |
| expected = [torch_op(tensors[i], tensors1[i], tensors2[i], value=value) for i in range(N)] |
| |
| res = foreach_op(tensors, tensors1, tensors2, value) |
| foreach_op_(tensors, tensors1, tensors2, value) |
| self.assertEqual(res, tensors) |
| self.assertEqual(tensors, expected) |
| |
| def _test_bin_op_list_alpha(self, device, dtype, foreach_op, foreach_op_, torch_op, N=20): |
| tensors1 = self._get_test_data(device, dtype, N) |
| tensors2 = self._get_test_data(device, dtype, N) |
| alpha = 2 |
| |
| expected = [torch_op(tensors1[i], torch.mul(tensors2[i], alpha)) for i in range(N)] |
| res = foreach_op(tensors1, tensors2, alpha=alpha) |
| foreach_op_(tensors1, tensors2, alpha=alpha) |
| self.assertEqual(res, tensors1) |
| |
| if dtype == torch.bool: |
| expected = [e.to(torch.bool) for e in expected] |
| self.assertEqual(tensors1, expected) |
| |
| # |
| # Unary ops |
| # |
| @dtypes(*[torch.float, torch.double, torch.complex64, torch.complex128]) |
| def test_sqrt(self, device, dtype): |
| self._test_unary_op(device, dtype, torch._foreach_sqrt, torch._foreach_sqrt_, torch.sqrt) |
| |
| @dtypes(*[torch.float, torch.double, torch.complex64, torch.complex128]) |
| def test_exp(self, device, dtype): |
| self._test_unary_op(device, dtype, torch._foreach_exp, torch._foreach_exp_, torch.exp) |
| |
| # |
| # Pointwise ops |
| # |
| @skipCUDAIfRocm |
| @dtypes(*torch.testing.get_all_dtypes(include_bfloat16=False, include_bool=False, include_complex=False)) |
| def test_addcmul(self, device, dtype): |
| if self.device_type == 'cpu': |
| if dtype == torch.half: |
| with self.assertRaisesRegex(RuntimeError, r"\"addcmul_cpu_out\" not implemented for \'Half\'"): |
| self._test_pointwise_op(device, dtype, torch._foreach_addcmul, |
| torch._foreach_addcmul_, torch.addcmul) |
| return |
| |
| self._test_pointwise_op(device, dtype, torch._foreach_addcmul, torch._foreach_addcmul_, torch.addcmul) |
| |
| @dtypes(*torch.testing.get_all_dtypes(include_bfloat16=False, include_bool=False, include_complex=False)) |
| def test_addcdiv(self, device, dtype): |
| if dtype in [torch.int8, torch.int16, torch.int32, torch.int64, torch.uint8]: |
| with self.assertRaisesRegex(RuntimeError, |
| "Integer division with addcdiv is no longer supported, and in a future"): |
| self._test_pointwise_op(device, dtype, torch._foreach_addcdiv, torch._foreach_addcdiv_, torch.addcdiv) |
| return |
| |
| if self.device_type == 'cpu': |
| if dtype == torch.half: |
| with self.assertRaisesRegex(RuntimeError, r"\"addcdiv_cpu_out\" not implemented for \'Half\'"): |
| self._test_pointwise_op(device, dtype, torch._foreach_addcdiv, |
| torch._foreach_addcdiv_, torch.addcdiv) |
| return |
| self._test_pointwise_op(device, dtype, torch._foreach_addcdiv, torch._foreach_addcdiv_, torch.addcdiv) |
| |
| # |
| # Ops with scalar |
| # |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_int_scalar(self, device, dtype): |
| for N in [30, 300]: |
| for foreach_bin_op, foreach_bin_op_, torch_bin_op in zip(self.foreach_bin_ops, |
| self.foreach_bin_ops_, |
| self.torch_bin_ops): |
| tensors = self._get_test_data(device, dtype, N) |
| scalar = 3 |
| expected = [torch_bin_op(t, scalar) for t in tensors] |
| |
| res = foreach_bin_op(tensors, scalar) |
| |
| if dtype == torch.bool: |
| self.assertEqual(res, expected) |
| |
| with self.assertRaisesRegex(RuntimeError, "can't be cast to the desired output type"): |
| foreach_bin_op_(tensors, scalar) |
| return |
| |
| |
| if foreach_bin_op_ == torch._foreach_div_ and dtype in torch.testing.integral_types() and self.device_type == "cpu": |
| with self.assertRaisesRegex(RuntimeError, |
| "can't be cast to the desired output type"): |
| foreach_bin_op_(tensors, scalar) |
| return |
| |
| # TODO[type promotion]: Fix once type promotion is enabled. |
| if dtype in torch.testing.integral_types() and self.device_type == 'cuda': |
| self.assertEqual(res, [e.to(dtype) for e in expected]) |
| |
| foreach_bin_op_(tensors, scalar) |
| self.assertEqual(tensors, [e.to(dtype) for e in expected]) |
| else: |
| self.assertEqual(res, expected) |
| foreach_bin_op_(tensors, scalar) |
| self.assertEqual(tensors, expected) |
| |
| # TODO[Fix scalar list]: |
| # We need to update codegen to correctly handle function overloads with float[] and int[]. |
| # As optimizers work with float tensors, the result will always be torch.float32 for now. |
| # Current schema is using 'float[]' as scalar list type. |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_int_scalarlist(self, device, dtype): |
| for N in [30, 300]: |
| for foreach_bin_op, foreach_bin_op_, torch_bin_op in zip(self.foreach_bin_ops, |
| self.foreach_bin_ops_, |
| self.torch_bin_ops): |
| tensors = self._get_test_data(device, dtype, N) |
| scalars = [1 for _ in range(N)] |
| expected = [torch_bin_op(t, s) for t, s in zip(tensors, scalars)] |
| |
| # we dont support bool and complex types on CUDA for now |
| if (dtype in torch.testing.get_all_complex_dtypes() or dtype == torch.bool) and self.device_type == 'cuda': |
| with self.assertRaisesRegex(RuntimeError, "not implemented for"): |
| foreach_bin_op_(tensors, scalars) |
| |
| with self.assertRaisesRegex(RuntimeError, "not implemented for"): |
| foreach_bin_op(tensors, scalars) |
| return |
| |
| res = foreach_bin_op(tensors, scalars) |
| |
| if dtype == torch.bool: |
| self.assertEqual(res, [torch_bin_op(t.to(torch.float32), s) for t, s in zip(tensors, scalars)]) |
| |
| with self.assertRaisesRegex(RuntimeError, "result type Float can't be cast to the desired output type"): |
| foreach_bin_op_(tensors, scalars) |
| return |
| |
| if dtype in torch.testing.integral_types(): |
| if self.device_type == 'cpu': |
| self.assertEqual(res, [e.to(torch.float32) for e in expected]) |
| else: |
| # TODO[type promotion]: Fix once type promotion is enabled. |
| self.assertEqual(res, [e.to(dtype) for e in expected]) |
| else: |
| self.assertEqual(res, expected) |
| |
| if dtype in torch.testing.integral_types() and self.device_type == 'cpu': |
| with self.assertRaisesRegex(RuntimeError, "result type Float can't be cast to the desired output type"): |
| foreach_bin_op_(tensors, scalars) |
| return |
| else: |
| foreach_bin_op_(tensors, scalars) |
| self.assertEqual(res, tensors) |
| |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_float_scalar(self, device, dtype): |
| for N in [30, 300]: |
| for foreach_bin_op, foreach_bin_op_, torch_bin_op in zip(self.foreach_bin_ops, |
| self.foreach_bin_ops_, |
| self.torch_bin_ops): |
| tensors = self._get_test_data(device, dtype, N) |
| scalar = 3.3 |
| expected = [torch_bin_op(t, scalar) for t in tensors] |
| |
| if dtype == torch.bool: |
| if foreach_bin_op == torch._foreach_sub: |
| with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator, with two bool"): |
| foreach_bin_op_(tensors, scalar) |
| |
| with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator, with two bool"): |
| foreach_bin_op(tensors, scalar) |
| return |
| |
| res = foreach_bin_op(tensors, scalar) |
| self.assertEqual(res, expected) |
| |
| if dtype in torch.testing.integral_types(): |
| with self.assertRaisesRegex(RuntimeError, "result type Float can't be cast to the desired output type"): |
| foreach_bin_op_(tensors, scalar) |
| return |
| |
| foreach_bin_op_(tensors, scalar) |
| self.assertEqual(tensors, expected) |
| |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_float_scalarlist(self, device, dtype): |
| for N in [30, 300]: |
| for foreach_bin_op, foreach_bin_op_, torch_bin_op in zip(self.foreach_bin_ops, |
| self.foreach_bin_ops_, |
| self.torch_bin_ops): |
| tensors = self._get_test_data(device, dtype, N) |
| scalars = [1.1 for _ in range(N)] |
| expected = [torch_bin_op(t, s) for t, s in zip(tensors, scalars)] |
| |
| # we dont support bool and complex types on CUDA for now |
| if (dtype in torch.testing.get_all_complex_dtypes() or dtype == torch.bool) and self.device_type == 'cuda': |
| with self.assertRaisesRegex(RuntimeError, "not implemented for"): |
| foreach_bin_op_(tensors, scalars) |
| |
| with self.assertRaisesRegex(RuntimeError, "not implemented for"): |
| foreach_bin_op(tensors, scalars) |
| return |
| |
| res = foreach_bin_op(tensors, scalars) |
| |
| if dtype == torch.bool: |
| # see TODO[Fix scalar list] |
| self.assertEqual(res, [torch_bin_op(t.to(torch.float32), s) for t, s in zip(tensors, scalars)]) |
| |
| with self.assertRaisesRegex(RuntimeError, "result type Float can't be cast to the desired output type"): |
| foreach_bin_op_(tensors, scalars) |
| return |
| |
| if dtype in torch.testing.integral_types() and self.device_type == 'cuda': |
| # see TODO[Fix scalar list] |
| self.assertEqual(res, [e.to(dtype) for e in expected]) |
| |
| foreach_bin_op_(tensors, scalars) |
| self.assertEqual(tensors, res) |
| return |
| else: |
| self.assertEqual(res, expected) |
| |
| if dtype in torch.testing.integral_types() and self.device_type == "cpu": |
| with self.assertRaisesRegex(RuntimeError, "result type Float can't be cast to the desired output type"): |
| foreach_bin_op_(tensors, scalars) |
| return |
| |
| foreach_bin_op_(tensors, scalars) |
| self.assertEqual(tensors, expected) |
| |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_complex_scalar(self, device, dtype): |
| for N in [30, 300]: |
| for foreach_bin_op, foreach_bin_op_, torch_bin_op in zip(self.foreach_bin_ops, |
| self.foreach_bin_ops_, |
| self.torch_bin_ops): |
| tensors = self._get_test_data(device, dtype, N) |
| scalar = 3 + 5j |
| expected = [torch_bin_op(t, scalar) for t in tensors] |
| |
| if dtype == torch.bool: |
| if foreach_bin_op == torch._foreach_sub: |
| with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator, with two bool"): |
| foreach_bin_op_(tensors, scalar) |
| |
| with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator, with two bool"): |
| foreach_bin_op(tensors, scalar) |
| return |
| |
| if dtype in torch.testing.get_all_fp_dtypes(include_half=True, include_bfloat16=True) and \ |
| self.device_type == 'cuda': |
| with self.assertRaisesRegex(RuntimeError, "value cannot be converted to type"): |
| foreach_bin_op_(tensors, scalar) |
| |
| with self.assertRaisesRegex(RuntimeError, "value cannot be converted to type"): |
| foreach_bin_op(tensors, scalar) |
| return |
| |
| res = foreach_bin_op(tensors, scalar) |
| self.assertEqual(res, expected) |
| |
| if dtype not in [torch.complex64, torch.complex128]: |
| with self.assertRaisesRegex(RuntimeError, "can't be cast to the desired output type"): |
| foreach_bin_op_(tensors, scalar) |
| else: |
| foreach_bin_op_(tensors, scalar) |
| self.assertEqual(res, tensors) |
| |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_complex_scalarlist(self, device, dtype): |
| for N in [30, 300]: |
| for foreach_bin_op, foreach_bin_op_, torch_bin_op in zip(self.foreach_bin_ops, |
| self.foreach_bin_ops_, |
| self.torch_bin_ops): |
| tensors = self._get_test_data(device, dtype, N) |
| scalars = [3 + 5j for _ in range(N)] |
| expected = [torch_bin_op(t, s) for t, s in zip(tensors, scalars)] |
| |
| if dtype == torch.bool: |
| if foreach_bin_op == torch._foreach_sub: |
| with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator, with two bool"): |
| foreach_bin_op_(tensors, scalar) |
| |
| with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator, with two bool"): |
| foreach_bin_op(tensors, scalar) |
| return |
| |
| with self.assertRaisesRegex(TypeError, "argument 'scalars' must be tuple of floats"): |
| res = foreach_bin_op(tensors, scalars) |
| |
| with self.assertRaisesRegex(TypeError, "argument 'scalars' must be tuple of floats"): |
| foreach_bin_op_(tensors, scalars) |
| |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_bool_scalar(self, device, dtype): |
| for N in [30, 300]: |
| for foreach_bin_op, foreach_bin_op_, torch_bin_op in zip(self.foreach_bin_ops, |
| self.foreach_bin_ops_, |
| self.torch_bin_ops): |
| tensors = self._get_test_data(device, dtype, N) |
| scalar = True |
| |
| if dtype == torch.bool: |
| expected = [torch_bin_op(t, scalar) for t in tensors] |
| res = foreach_bin_op(tensors, scalar) |
| |
| foreach_bin_op_(tensors, scalar) |
| self.assertEqual(tensors, res) |
| return |
| |
| if foreach_bin_op == torch._foreach_sub and self.device_type == "cpu": |
| with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator"): |
| res = foreach_bin_op(tensors, scalar) |
| |
| with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator"): |
| foreach_bin_op_(tensors, scalar) |
| elif foreach_bin_op == torch._foreach_sub and self.device_type == 'cuda': |
| res = foreach_bin_op(tensors, scalar) |
| self.assertEqual(res, foreach_bin_op(tensors, 1)) |
| |
| foreach_bin_op_(tensors, scalar) |
| self.assertEqual(tensors, res) |
| else: |
| expected = [torch_bin_op(t, scalar) for t in tensors] |
| res = foreach_bin_op(tensors, scalar) |
| |
| # TODO[type promotion]: Fix once type promotion is enabled. |
| if dtype in torch.testing.integral_types() and self.device_type == 'cuda': |
| self.assertEqual(res, [e.to(dtype) for e in expected]) |
| else: |
| self.assertEqual(res, expected) |
| |
| if dtype in torch.testing.integral_types(): |
| if foreach_bin_op == torch._foreach_div and self.device_type == "cpu": |
| with self.assertRaisesRegex(RuntimeError, "result type Float can't be cast to the desired "): |
| foreach_bin_op_(tensors, scalar) |
| else: |
| foreach_bin_op_(tensors, scalar) |
| self.assertEqual(tensors, res) |
| else: |
| foreach_bin_op_(tensors, scalar) |
| self.assertEqual(tensors, expected) |
| |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_bool_scalarlist(self, device, dtype): |
| for N in [30, 300]: |
| for foreach_bin_op, foreach_bin_op_, torch_bin_op in zip(self.foreach_bin_ops, |
| self.foreach_bin_ops_, |
| self.torch_bin_ops): |
| tensors = self._get_test_data(device, dtype, N) |
| scalars = [True for _ in range(N)] |
| |
| if dtype == torch.bool: |
| if self.device_type == 'cuda': |
| with self.assertRaisesRegex(RuntimeError, "not implemented for"): |
| foreach_bin_op(tensors, scalars) |
| |
| with self.assertRaisesRegex(RuntimeError, "not implemented for"): |
| foreach_bin_op_(tensors, scalars) |
| return |
| else: |
| if foreach_bin_op == torch._foreach_sub: |
| with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator, with a bool tensor"): |
| foreach_bin_op_(tensors, scalars) |
| |
| with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator, with a bool tensor"): |
| foreach_bin_op(tensors, scalars) |
| else: |
| with self.assertRaisesRegex(RuntimeError, "result type Float can't be cast to the desired"): |
| foreach_bin_op_(tensors, scalars) |
| |
| res = foreach_bin_op(tensors, scalars) |
| for r in res: |
| self.assertTrue(r.dtype == torch.float32) |
| else: |
| # we dont support bool and complex types on CUDA for now |
| if (dtype in torch.testing.get_all_complex_dtypes()) and self.device_type == 'cuda': |
| with self.assertRaisesRegex(RuntimeError, "not implemented for"): |
| foreach_bin_op_(tensors, scalars) |
| |
| with self.assertRaisesRegex(RuntimeError, "not implemented for"): |
| foreach_bin_op(tensors, scalars) |
| return |
| |
| if foreach_bin_op == torch._foreach_sub: |
| if self.device_type == "cpu": |
| # see TODO[Fix scalar list] |
| res = foreach_bin_op(tensors, scalars) |
| if dtype in torch.testing.integral_types(): |
| self.assertEqual(res, [r.to(torch.float32) for r in foreach_bin_op(tensors, 1)]) |
| |
| with self.assertRaisesRegex(RuntimeError, "esult type Float can't be cast to the "): |
| foreach_bin_op_(tensors, scalars) |
| else: |
| self.assertEqual(res, foreach_bin_op(tensors, 1)) |
| foreach_bin_op_(tensors, scalars) |
| self.assertEqual(res, tensors) |
| else: |
| # see TODO[Fix scalar list] |
| res = foreach_bin_op(tensors, scalars) |
| if dtype in torch.testing.integral_types(): |
| self.assertEqual(res, [r.to(dtype) for r in foreach_bin_op(tensors, 1)]) |
| else: |
| self.assertEqual(res, foreach_bin_op(tensors, 1)) |
| |
| foreach_bin_op_(tensors, scalars) |
| self.assertEqual(res, tensors) |
| else: |
| if self.device_type == "cpu": |
| expected = [torch_bin_op(t, s) for t, s in zip(tensors, scalars)] |
| res = foreach_bin_op(tensors, scalars) |
| |
| # see TODO[Fix scalar list] |
| if dtype in torch.testing.integral_types(): |
| self.assertEqual(res, [e.to(torch.float32) for e in expected]) |
| else: |
| self.assertEqual(res, expected) |
| |
| if dtype in torch.testing.integral_types(): |
| with self.assertRaisesRegex(RuntimeError, "result type Float can't be cast to the desired "): |
| foreach_bin_op_(tensors, scalars) |
| else: |
| foreach_bin_op_(tensors, scalars) |
| self.assertEqual(tensors, expected) |
| else: |
| expected = [torch_bin_op(t, s) for t, s in zip(tensors, scalars)] |
| res = foreach_bin_op(tensors, scalars) |
| |
| if dtype in torch.testing.integral_types(): |
| self.assertEqual(res, [e.to(dtype) for e in expected]) |
| else: |
| self.assertEqual(res, expected) |
| |
| foreach_bin_op_(tensors, scalars) |
| self.assertEqual(res, tensors) |
| |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_add_with_different_size_tensors(self, device, dtype): |
| if dtype == torch.bool: |
| return |
| tensors = [torch.zeros(10 + n, 10 + n, device=device, dtype=dtype) for n in range(10)] |
| expected = [torch.ones(10 + n, 10 + n, device=device, dtype=dtype) for n in range(10)] |
| |
| torch._foreach_add_(tensors, 1) |
| self.assertEqual(expected, tensors) |
| |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_add_scalar_with_empty_list_and_empty_tensor(self, device, dtype): |
| # TODO: enable empty list case |
| for tensors in [[torch.randn([0])]]: |
| res = torch._foreach_add(tensors, 1) |
| self.assertEqual(res, tensors) |
| |
| torch._foreach_add_(tensors, 1) |
| self.assertEqual(res, tensors) |
| |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_add_scalar_with_overlapping_tensors(self, device, dtype): |
| tensors = [torch.ones(1, 1, device=device, dtype=dtype).expand(2, 1, 3)] |
| expected = [torch.tensor([[[2, 2, 2]], [[2, 2, 2]]], dtype=dtype, device=device)] |
| |
| # bool tensor + 1 will result in int64 tensor |
| if dtype == torch.bool: |
| expected[0] = expected[0].to(torch.int64).add(1) |
| |
| res = torch._foreach_add(tensors, 1) |
| self.assertEqual(res, expected) |
| |
| def test_bin_op_scalar_with_different_tensor_dtypes(self, device): |
| tensors = [torch.tensor([1.1], dtype=torch.float, device=device), |
| torch.tensor([1], dtype=torch.long, device=device)] |
| self.assertRaises(RuntimeError, lambda: torch._foreach_add(tensors, 1)) |
| |
| # |
| # Ops with list |
| # |
| def test_add_list_error_cases(self, device): |
| tensors1 = [] |
| tensors2 = [] |
| |
| # Empty lists |
| with self.assertRaises(RuntimeError): |
| torch._foreach_add(tensors1, tensors2) |
| with self.assertRaises(RuntimeError): |
| torch._foreach_add_(tensors1, tensors2) |
| |
| # One empty list |
| tensors1.append(torch.tensor([1], device=device)) |
| with self.assertRaisesRegex(RuntimeError, "Scalars list must have at least one value."): |
| torch._foreach_add(tensors1, tensors2) |
| with self.assertRaisesRegex(RuntimeError, "Scalars list must have at least one value."): |
| torch._foreach_add_(tensors1, tensors2) |
| |
| # Lists have different amount of tensors |
| tensors2.append(torch.tensor([1], device=device)) |
| tensors2.append(torch.tensor([1], device=device)) |
| with self.assertRaisesRegex(RuntimeError, "Tensor lists must have the same number of tensors, got 1 and 2"): |
| torch._foreach_add(tensors1, tensors2) |
| with self.assertRaisesRegex(RuntimeError, "Tensor lists must have the same number of tensors, got 1 and 2"): |
| torch._foreach_add_(tensors1, tensors2) |
| |
| # Different dtypes |
| tensors1 = [torch.zeros(10, 10, device=device, dtype=torch.float) for _ in range(10)] |
| tensors2 = [torch.ones(10, 10, device=device, dtype=torch.int) for _ in range(10)] |
| |
| with self.assertRaisesRegex(RuntimeError, "All tensors in the tensor list must have the same dtype."): |
| torch._foreach_add(tensors1, tensors2) |
| with self.assertRaisesRegex(RuntimeError, "All tensors in the tensor list must have the same dtype."): |
| torch._foreach_add_(tensors1, tensors2) |
| |
| # different devices |
| if torch.cuda.is_available() and torch.cuda.device_count() > 1: |
| tensor1 = torch.zeros(10, 10, device="cuda:0") |
| tensor2 = torch.ones(10, 10, device="cuda:1") |
| with self.assertRaisesRegex(RuntimeError, "Expected all tensors to be on the same device"): |
| torch._foreach_add([tensor1], [tensor2]) |
| with self.assertRaisesRegex(RuntimeError, "Expected all tensors to be on the same device"): |
| torch._foreach_add_([tensor1], [tensor2]) |
| |
| # Coresponding tensors with different sizes |
| tensors1 = [torch.zeros(10, 10, device=device) for _ in range(10)] |
| tensors2 = [torch.ones(11, 11, device=device) for _ in range(10)] |
| with self.assertRaisesRegex(RuntimeError, "Corresponding tensors in lists must have the same size"): |
| torch._foreach_add(tensors1, tensors2) |
| with self.assertRaisesRegex(RuntimeError, r", got \[10, 10\] and \[11, 11\]"): |
| torch._foreach_add_(tensors1, tensors2) |
| |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_add_list(self, device, dtype): |
| self._test_bin_op_list(device, dtype, torch._foreach_add, torch._foreach_add_, torch.add) |
| self._test_bin_op_list_alpha(device, dtype, torch._foreach_add, torch._foreach_add_, torch.add) |
| |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_sub_list(self, device, dtype): |
| if dtype == torch.bool: |
| with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator, with two bool"): |
| self._test_bin_op_list(device, dtype, torch._foreach_sub, torch._foreach_sub_, torch.sub) |
| |
| with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator, with a bool tensor"): |
| self._test_bin_op_list_alpha(device, dtype, torch._foreach_sub, torch._foreach_sub_, torch.sub) |
| else: |
| self._test_bin_op_list(device, dtype, torch._foreach_sub, torch._foreach_sub_, torch.sub) |
| self._test_bin_op_list_alpha(device, dtype, torch._foreach_sub, torch._foreach_sub_, torch.sub) |
| |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_mul_list(self, device, dtype): |
| self._test_bin_op_list(device, dtype, torch._foreach_mul, torch._foreach_mul_, torch.mul) |
| |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_div_list(self, device, dtype): |
| if dtype in torch.testing.integral_types_and(torch.bool): |
| if self.device_type == 'cpu': |
| with self.assertRaisesRegex(RuntimeError, "result type Float can't be cast to the desired output type"): |
| self._test_bin_op_list(device, dtype, torch._foreach_div, torch._foreach_div_, torch.div) |
| else: |
| self.skipTest("Skipped! See https://github.com/pytorch/pytorch/issues/44489") |
| return |
| |
| for N in [30, 300]: |
| tensors1 = self._get_test_data(device, dtype, N) |
| |
| if dtype in [torch.bfloat16, torch.bool, torch.float16]: |
| tensors2 = [torch.zeros(N, N, device=device, dtype=dtype).add(2) for _ in range(N)] |
| else: |
| tensors2 = self._get_test_data(device, dtype, N) |
| |
| expected = [torch.div(tensors1[i], tensors2[i]) for i in range(N)] |
| res = torch._foreach_div(tensors1, tensors2) |
| torch._foreach_div_(tensors1, tensors2) |
| self.assertEqual(res, tensors1) |
| self.assertEqual(tensors1, res) |
| |
| def test_bin_op_list_error_cases(self, device): |
| tensors1 = [] |
| tensors2 = [] |
| |
| for bin_op in self.foreach_bin_ops + self.foreach_bin_ops_: |
| # Empty lists |
| with self.assertRaises(RuntimeError): |
| bin_op(tensors1, tensors2) |
| |
| # One empty list |
| tensors1.append(torch.tensor([1], device=device)) |
| with self.assertRaises(RuntimeError): |
| bin_op(tensors1, tensors2) |
| |
| # Lists have different amount of tensors |
| tensors2.append(torch.tensor([1], device=device)) |
| tensors2.append(torch.tensor([1], device=device)) |
| with self.assertRaises(RuntimeError): |
| bin_op(tensors1, tensors2) |
| |
| # Different dtypes |
| tensors1 = [torch.zeros(2, 2, device=device, dtype=torch.float) for _ in range(2)] |
| tensors2 = [torch.ones(2, 2, device=device, dtype=torch.int) for _ in range(2)] |
| |
| with self.assertRaises(RuntimeError): |
| bin_op(tensors1, tensors2) |
| |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_add_list_different_sizes(self, device, dtype): |
| tensors1 = [torch.zeros(10 + n, 10 + n, device=device, dtype=dtype) for n in range(10)] |
| tensors2 = [torch.ones(10 + n, 10 + n, device=device, dtype=dtype) for n in range(10)] |
| |
| res = torch._foreach_add(tensors1, tensors2) |
| torch._foreach_add_(tensors1, tensors2) |
| self.assertEqual(res, tensors1) |
| self.assertEqual(res, [torch.ones(10 + n, 10 + n, device=device, dtype=dtype) for n in range(10)]) |
| |
| @unittest.skipIf(not torch.cuda.is_available(), "CUDA not found") |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_add_list_slow_path(self, device, dtype): |
| # different strides |
| tensor1 = torch.zeros(10, 10, device=device, dtype=dtype) |
| tensor2 = torch.ones(10, 10, device=device, dtype=dtype) |
| res = torch._foreach_add([tensor1], [tensor2.t()]) |
| torch._foreach_add_([tensor1], [tensor2]) |
| self.assertEqual(res, [tensor1]) |
| |
| # non contiguous |
| tensor1 = torch.randn(5, 2, 1, 3, device=device)[:, 0] |
| tensor2 = torch.randn(5, 2, 1, 3, device=device)[:, 0] |
| self.assertFalse(tensor1.is_contiguous()) |
| self.assertFalse(tensor2.is_contiguous()) |
| res = torch._foreach_add([tensor1], [tensor2]) |
| torch._foreach_add_([tensor1], [tensor2]) |
| self.assertEqual(res, [tensor1]) |
| |
| instantiate_device_type_tests(TestForeach, globals()) |
| |
| if __name__ == '__main__': |
| run_tests() |
