| import torch |
| import unittest |
| from torch.testing._internal.common_utils import TestCase, run_tests, TEST_WITH_ROCM, TEST_WITH_SLOW |
| from torch.testing._internal.common_device_type import instantiate_device_type_tests, dtypes, skipCUDAIfRocm |
| from torch._six import inf, nan |
| |
| N_values = [20] if not TEST_WITH_SLOW else [30, 300] |
| |
| 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, |
| ] |
| |
| unary_ops = [ |
| # foreach_op, foreach_op_, torch_op, bf16, complex64/128 |
| (torch._foreach_sqrt, torch._foreach_sqrt_, torch.sqrt, True , True), |
| (torch._foreach_exp, torch._foreach_exp_, torch.exp, True, True), |
| (torch._foreach_acos, torch._foreach_acos_, torch.acos, False, True), |
| (torch._foreach_asin, torch._foreach_asin_, torch.asin, False, True), |
| (torch._foreach_atan, torch._foreach_atan_, torch.atan, False, True), |
| (torch._foreach_cos, torch._foreach_cos_, torch.cos, True, True), |
| (torch._foreach_cosh, torch._foreach_cosh_, torch.cosh, False, True), |
| (torch._foreach_log, torch._foreach_log_, torch.log, True, True), |
| (torch._foreach_log10, torch._foreach_log10_, torch.log10, True, True), |
| (torch._foreach_log2, torch._foreach_log2_, torch.log2, True, True), |
| (torch._foreach_neg, torch._foreach_neg_, torch.neg, True, True), |
| (torch._foreach_tan, torch._foreach_tan_, torch.tan, False, True), |
| (torch._foreach_tanh, torch._foreach_tanh_, torch.tanh, True, True), |
| (torch._foreach_sin, torch._foreach_sin_, torch.sin, False, True), |
| (torch._foreach_sinh, torch._foreach_sinh_, torch.sinh, False, True), |
| (torch._foreach_ceil, torch._foreach_ceil_, torch.ceil, False, False), |
| (torch._foreach_erf, torch._foreach_erf_, torch.erf, True, False), |
| (torch._foreach_erfc, torch._foreach_erfc_, torch.erfc, False, False), |
| (torch._foreach_expm1, torch._foreach_expm1_, torch.expm1, False, False), |
| (torch._foreach_floor, torch._foreach_floor_, torch.floor, False, False), |
| (torch._foreach_log1p, torch._foreach_log1p_, torch.log1p, True, False), |
| (torch._foreach_round, torch._foreach_round_, torch.round, False, False), |
| (torch._foreach_frac, torch._foreach_frac_, torch.frac, False, False), |
| (torch._foreach_reciprocal, torch._foreach_reciprocal_, torch.reciprocal, True, True), |
| (torch._foreach_sigmoid, torch._foreach_sigmoid_, torch.sigmoid, True, False), |
| (torch._foreach_trunc, torch._foreach_trunc_, torch.trunc, False, False), |
| |
| # See test_abs |
| # (torch._foreach_abs, torch._foreach_abs_, torch.abs, True, True), |
| ] |
| |
| 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 N_values: |
| tensors1 = self._get_test_data(device, dtype, N) |
| tensors2 = self._get_test_data(device, dtype, N) |
| |
| # Mimics cuda kernel dtype flow. With fp16/bf16 input, runs in fp32 and casts output back to fp16/bf16. |
| control_dtype = torch.float32 if (self.device_type == 'cuda' and |
| (dtype is torch.float16 or dtype is torch.bfloat16)) else dtype |
| expected = [torch_op(tensors1[i].to(dtype=control_dtype), |
| tensors2[i].to(dtype=control_dtype)).to(dtype=dtype) for i in range(N)] |
| res = foreach_op(tensors1, tensors2) |
| foreach_op_(tensors1, tensors2) |
| self.assertEqual(res, tensors1) |
| if (dtype is torch.float16 or dtype is torch.bfloat16) and TEST_WITH_ROCM: |
| self.assertEqual(tensors1, expected, atol=1.e-3, rtol=self.dtype_precisions[dtype][0]) |
| else: |
| self.assertEqual(tensors1, expected) |
| |
| def _test_pointwise_op(self, device, dtype, foreach_op, foreach_op_, torch_op): |
| for N in N_values: |
| values = [2 + i for i in range(N)] |
| for vals in [values[0], values]: |
| tensors = self._get_test_data(device, dtype, N) |
| tensors1 = self._get_test_data(device, dtype, N) |
| tensors2 = self._get_test_data(device, dtype, N) |
| |
| # Mimics cuda kernel dtype flow. With fp16/bf16 input, runs in fp32 and casts output back to fp16/bf16. |
| control_dtype = torch.float32 if (self.device_type == 'cuda' and |
| (dtype is torch.float16 or dtype is torch.bfloat16)) else dtype |
| |
| if not isinstance(vals, list): |
| expected = [torch_op(tensors[i].to(dtype=control_dtype), |
| tensors1[i].to(dtype=control_dtype), |
| tensors2[i].to(dtype=control_dtype), |
| value=values[0]).to(dtype=dtype) for i in range(N)] |
| else: |
| expected = [torch_op(tensors[i].to(dtype=control_dtype), |
| tensors1[i].to(dtype=control_dtype), |
| tensors2[i].to(dtype=control_dtype), |
| value=values[i]).to(dtype=dtype) for i in range(N)] |
| |
| res = foreach_op(tensors, tensors1, tensors2, vals) |
| foreach_op_(tensors, tensors1, tensors2, vals) |
| self.assertEqual(res, tensors) |
| |
| if (dtype is torch.float16 or dtype is torch.bfloat16) and TEST_WITH_ROCM: |
| self.assertEqual(tensors, expected, atol=1.e-3, rtol=self.dtype_precisions[dtype][0]) |
| else: |
| self.assertEqual(tensors, expected) |
| |
| # test error cases |
| for op in [torch._foreach_addcmul, torch._foreach_addcmul_, torch._foreach_addcdiv, torch._foreach_addcdiv_]: |
| tensors = self._get_test_data(device, dtype, N) |
| tensors1 = self._get_test_data(device, dtype, N) |
| tensors2 = self._get_test_data(device, dtype, N) |
| |
| with self.assertRaisesRegex(RuntimeError, "Tensor list must have same number of elements as scalar list."): |
| op(tensors, tensors1, tensors2, [2 for _ in range(N + 1)]) |
| |
| with self.assertRaisesRegex(RuntimeError, "Tensor list must have same number of elements as scalar list."): |
| op(tensors, tensors1, tensors2, [2 for _ in range(N - 1)]) |
| |
| tensors = self._get_test_data(device, dtype, N + 1) |
| with self.assertRaisesRegex(RuntimeError, "Tensor lists must have the same number of tensors, got 21 and 20"): |
| op(tensors, tensors1, tensors2, [2 for _ in range(N)]) |
| |
| tensors1 = self._get_test_data(device, dtype, N + 1) |
| with self.assertRaisesRegex(RuntimeError, "Tensor lists must have the same number of tensors, got 21 and 20"): |
| op(tensors, tensors1, tensors2, [2 for _ in range(N)]) |
| |
| def _test_bin_op_list_alpha(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) |
| alpha = 2 |
| |
| # Mimics cuda kernel dtype flow. With fp16/bf16 input, runs in fp32 and casts output back to fp16/bf16. |
| control_dtype = torch.float32 if (self.device_type == 'cuda' and |
| (dtype is torch.float16 or dtype is torch.bfloat16)) else dtype |
| expected = [torch_op(tensors1[i].to(dtype=control_dtype), |
| torch.mul(tensors2[i].to(dtype=control_dtype), |
| alpha)).to(dtype=dtype) 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] |
| if (dtype is torch.float16 or dtype is torch.bfloat16) and TEST_WITH_ROCM: |
| self.assertEqual(tensors1, expected, atol=1.e-3, rtol=self.dtype_precisions[dtype][0]) |
| else: |
| self.assertEqual(tensors1, expected) |
| |
| # |
| # Unary ops |
| # |
| @dtypes(*(torch.testing.floating_and_complex_types_and(torch.bfloat16, torch.half))) |
| def test_unary_ops(self, device, dtype): |
| for fe_op, fe_op_, torch_op, support_bfloat16, support_complex in self.unary_ops: |
| for N in N_values: |
| tensors1 = self._get_test_data(device, dtype, N) |
| # Mimics cuda kernel dtype flow. With fp16/bf16 input, runs in fp32 and casts output back to fp16/bf16. |
| control_dtype = torch.float32 if (self.device_type == 'cuda' and |
| (dtype is torch.float16 or dtype is torch.bfloat16)) else dtype |
| |
| if self.device_type == 'cpu' and dtype == torch.half and torch_op not in [torch.neg, torch.frac, torch.reciprocal]: |
| with self.assertRaisesRegex(RuntimeError, r"not implemented for \'Half\'"): |
| expected = [torch_op(tensors1[i]) for i in range(N)] |
| |
| with self.assertRaisesRegex(RuntimeError, r"not implemented for \'Half\'"): |
| res = fe_op(tensors1) |
| break |
| |
| if dtype == torch.bfloat16 and not support_bfloat16: |
| if self.device_type == 'cuda' or torch_op in [torch.sinh, torch.cosh]: |
| with self.assertRaisesRegex(RuntimeError, r"not implemented for \'BFloat16\'"): |
| expected = [torch_op(tensors1[i]) for i in range(N)] |
| |
| with self.assertRaisesRegex(RuntimeError, r"not implemented for \'BFloat16\'"): |
| res = fe_op(tensors1) |
| break |
| |
| if dtype in [torch.complex64, torch.complex128] and not support_complex: |
| if not (self.device_type == 'cpu' and torch_op in [torch.sigmoid]): |
| # not using assertRaisesRegex due to different error messages |
| with self.assertRaises(RuntimeError): |
| expected = [torch_op(tensors1[i]) for i in range(N)] |
| |
| with self.assertRaises(RuntimeError): |
| res = fe_op(tensors1) |
| break |
| |
| expected = [torch_op(tensors1[i].to(dtype=control_dtype)).to(dtype=dtype) for i in range(N)] |
| res = fe_op(tensors1) |
| if (dtype is torch.float16 or dtype is torch.bfloat16) and TEST_WITH_ROCM: |
| self.assertEqual(res, expected, atol=1.e-3, rtol=self.dtype_precisions[dtype][0]) |
| |
| fe_op_(tensors1) |
| self.assertEqual(res, tensors1) |
| else: |
| self.assertEqual(res, expected) |
| |
| fe_op_(tensors1) |
| self.assertEqual(res, tensors1) |
| |
| # Separate test for abs due to a lot of special cases |
| # Absolute value of a complex number a + bj is defined as sqrt(a^2 + b^2), i.e. a floating point |
| @dtypes(*(torch.testing.floating_and_complex_types_and(torch.bfloat16, torch.half))) |
| def test_abs(self, device, dtype): |
| for N in N_values: |
| tensors1 = self._get_test_data(device, dtype, N) |
| # Mimics cuda kernel dtype flow. With fp16/bf16 input, runs in fp32 and casts output back to fp16/bf16. |
| control_dtype = torch.float32 if (self.device_type == 'cuda' and |
| (dtype is torch.float16 or dtype is torch.bfloat16)) else dtype |
| |
| expected = [torch.abs(tensors1[i].to(dtype=control_dtype)).to(dtype=dtype) for i in range(N)] |
| res = torch._foreach_abs(tensors1) |
| if (dtype is torch.float16 or dtype is torch.bfloat16) and TEST_WITH_ROCM: |
| self.assertEqual(res, expected, atol=1.e-3, rtol=self.dtype_precisions[dtype][0]) |
| |
| torch._foreach_abs_(tensors1) |
| self.assertEqual(res, tensors1) |
| else: |
| expected = [torch.abs(tensors1[i]) for i in range(N)] |
| self.assertEqual(res, expected) |
| |
| if dtype in [torch.complex64, torch.complex128]: |
| with self.assertRaisesRegex(RuntimeError, r"In-place abs is not supported for complex tensors."): |
| torch._foreach_abs_(tensors1) |
| else: |
| torch._foreach_abs_(tensors1) |
| self.assertEqual(res, tensors1) |
| |
| # |
| # Pointwise ops |
| # |
| @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) |
| |
| @dtypes(*torch.testing.get_all_dtypes(include_bfloat16=False, include_bool=False, include_complex=False)) |
| def test_min_max(self, device, dtype): |
| for N in N_values: |
| tensors1 = self._get_test_data(device, dtype, N) |
| tensors2 = self._get_test_data(device, dtype, N) |
| |
| # Mimics cuda kernel dtype flow. With fp16/bf16 input, runs in fp32 and casts output back to fp16/bf16. |
| control_dtype = torch.float32 if (self.device_type == 'cuda' and |
| (dtype is torch.float16 or dtype is torch.bfloat16)) else dtype |
| |
| expected_max = [torch.max(tensors1[i].to(dtype=control_dtype), |
| tensors2[i].to(dtype=control_dtype)).to(dtype=dtype) for i in range(N)] |
| |
| expected_min = [torch.min(tensors1[i].to(dtype=control_dtype), |
| tensors2[i].to(dtype=control_dtype)).to(dtype=dtype) for i in range(N)] |
| |
| res_max = torch._foreach_maximum(tensors1, tensors2) |
| self.assertEqual(res_max, expected_max) |
| |
| res_min = torch._foreach_minimum(tensors1, tensors2) |
| self.assertEqual(res_min, expected_min) |
| |
| |
| @dtypes(*(torch.testing.get_all_fp_dtypes(include_half=True, include_bfloat16=False))) |
| def test_max_min_float_inf_nan(self, device, dtype): |
| a = [ |
| torch.tensor([float('inf')], device=device, dtype=dtype), |
| torch.tensor([-float('inf')], device=device, dtype=dtype), |
| torch.tensor([float('nan')], device=device, dtype=dtype), |
| torch.tensor([float('nan')], device=device, dtype=dtype) |
| ] |
| |
| b = [ |
| torch.tensor([-float('inf')], device=device, dtype=dtype), |
| torch.tensor([float('inf')], device=device, dtype=dtype), |
| torch.tensor([float('inf')], device=device, dtype=dtype), |
| torch.tensor([float('nan')], device=device, dtype=dtype) |
| ] |
| |
| expected = [torch.max(a1, b1) for a1, b1 in zip(a, b)] |
| res = torch._foreach_maximum(a, b) |
| self.assertEqual(expected, res) |
| |
| expected = [torch.min(a1, b1) for a1, b1 in zip(a, b)] |
| res = torch._foreach_minimum(a, b) |
| self.assertEqual(expected, res) |
| |
| @dtypes(*(torch.testing.get_all_fp_dtypes(include_half=True, include_bfloat16=False))) |
| def test_max_min_inf_nan(self, device, dtype): |
| a = [ |
| torch.tensor([inf], device=device, dtype=dtype), |
| torch.tensor([-inf], device=device, dtype=dtype), |
| torch.tensor([nan], device=device, dtype=dtype), |
| torch.tensor([nan], device=device, dtype=dtype) |
| ] |
| |
| b = [ |
| torch.tensor([-inf], device=device, dtype=dtype), |
| torch.tensor([inf], device=device, dtype=dtype), |
| torch.tensor([inf], device=device, dtype=dtype), |
| torch.tensor([nan], device=device, dtype=dtype) |
| ] |
| |
| expected_max = [torch.max(a1, b1) for a1, b1 in zip(a, b)] |
| res_max = torch._foreach_maximum(a, b) |
| self.assertEqual(expected_max, res_max) |
| |
| expected_min = [torch.min(a1, b1) for a1, b1 in zip(a, b)] |
| res_min = torch._foreach_minimum(a, b) |
| self.assertEqual(expected_min, res_min) |
| |
| # |
| # Ops with scalar |
| # |
| @skipCUDAIfRocm |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_int_scalar(self, device, dtype): |
| for N in N_values: |
| 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. |
| @skipCUDAIfRocm |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_int_scalarlist(self, device, dtype): |
| for N in N_values: |
| 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) |
| |
| @skipCUDAIfRocm |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_float_scalar(self, device, dtype): |
| for N in N_values: |
| 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 |
| |
| # Mimics cuda kernel dtype flow. With fp16/bf16 input, runs in fp32 and casts output back to fp16/bf16. |
| control_dtype = torch.float32 if (self.device_type == 'cuda' and |
| (dtype is torch.float16 or dtype is torch.bfloat16)) else dtype |
| expected = [torch_bin_op(t.to(dtype=control_dtype), |
| scalar) for t in tensors] |
| if (dtype is torch.float16 or dtype is torch.bfloat16): |
| expected = [e.to(dtype=dtype) for e in expected] |
| |
| 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) |
| if (dtype is torch.float16 or dtype is torch.bfloat16) and TEST_WITH_ROCM: |
| self.assertEqual(res, expected, atol=1.e-3, rtol=self.dtype_precisions[dtype][0]) |
| 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 output type"): |
| foreach_bin_op_(tensors, scalar) |
| return |
| |
| foreach_bin_op_(tensors, scalar) |
| if (dtype is torch.float16 or dtype is torch.bfloat16) and TEST_WITH_ROCM: |
| self.assertEqual(tensors, expected, atol=1.e-3, rtol=self.dtype_precisions[dtype][0]) |
| else: |
| self.assertEqual(tensors, expected) |
| |
| @skipCUDAIfRocm |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_float_scalarlist(self, device, dtype): |
| for N in N_values: |
| 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)] |
| |
| # If incoming dtype is float16 or bfloat16, runs in float32 and casts output back to dtype. |
| control_dtype = torch.float32 if (self.device_type == 'cuda' and |
| (dtype is torch.float16 or dtype is torch.bfloat16)) else dtype |
| expected = [torch_bin_op(t.to(dtype=control_dtype), |
| s) for t, s in zip(tensors, scalars)] |
| if (dtype is torch.float16 or dtype is torch.bfloat16): |
| expected = [e.to(dtype=dtype) for e in expected] |
| |
| # 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: |
| if (dtype is torch.float16 or dtype is torch.bfloat16) and TEST_WITH_ROCM: |
| self.assertEqual(res, expected, atol=1.e-3, rtol=self.dtype_precisions[dtype][0]) |
| 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) |
| if (dtype is torch.float16 or dtype is torch.bfloat16) and TEST_WITH_ROCM: |
| self.assertEqual(tensors, expected, atol=1.e-3, rtol=self.dtype_precisions[dtype][0]) |
| else: |
| self.assertEqual(tensors, expected) |
| |
| @skipCUDAIfRocm |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_complex_scalar(self, device, dtype): |
| for N in N_values: |
| 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 N_values: |
| 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) |
| |
| @skipCUDAIfRocm |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_bool_scalar(self, device, dtype): |
| for N in N_values: |
| 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) |
| |
| @skipCUDAIfRocm |
| @dtypes(*torch.testing.get_all_dtypes()) |
| def test_bool_scalarlist(self, device, dtype): |
| for N in N_values: |
| 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 [torch_bin_op(t, 1) for t in tensors]]) |
| |
| with self.assertRaisesRegex(RuntimeError, "result type Float can't be cast to the "): |
| foreach_bin_op_(tensors, scalars) |
| else: |
| self.assertEqual(res, [torch_bin_op(t, 1) for t in tensors]) |
| 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 [torch_bin_op(t, 1) for t in tensors]]) |
| else: |
| self.assertEqual(res, [torch_bin_op(t, 1) for t in tensors]) |
| |
| 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_bin_op_list_error_cases(self, device): |
| for bin_op, bin_op_ in zip(self.foreach_bin_ops, self.foreach_bin_ops_): |
| tensors1 = [] |
| tensors2 = [] |
| |
| # Empty lists |
| with self.assertRaisesRegex(RuntimeError, "There were no tensor arguments to this function"): |
| bin_op(tensors1, tensors2) |
| with self.assertRaisesRegex(RuntimeError, "There were no tensor arguments to this function"): |
| bin_op_(tensors1, tensors2) |
| |
| # One empty list |
| tensors1.append(torch.tensor([1], device=device)) |
| with self.assertRaisesRegex(RuntimeError, "Tensor list must have same number of elements as scalar list."): |
| bin_op(tensors1, tensors2) |
| with self.assertRaisesRegex(RuntimeError, "Tensor list must have same number of elements as scalar list."): |
| 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.assertRaisesRegex(RuntimeError, "Tensor lists must have the same number of tensors, got 1 and 2"): |
| bin_op(tensors1, tensors2) |
| with self.assertRaisesRegex(RuntimeError, "Tensor lists must have the same number of tensors, got 1 and 2"): |
| bin_op_(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."): |
| bin_op(tensors1, tensors2) |
| with self.assertRaisesRegex(RuntimeError, "All tensors in the tensor list must have the same dtype."): |
| bin_op_(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"): |
| bin_op([tensor1], [tensor2]) |
| with self.assertRaisesRegex(RuntimeError, "Expected all tensors to be on the same device"): |
| bin_op_([tensor1], [tensor2]) |
| |
| # Corresponding 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"): |
| bin_op(tensors1, tensors2) |
| with self.assertRaisesRegex(RuntimeError, r", got \[10, 10\] and \[11, 11\]"): |
| bin_op_(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 N_values: |
| 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) |
| |
| @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() |
