mirror of
https://github.com/pytorch/pytorch.git
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cc7a28d727
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/49712 Test Plan: Imported from OSS Reviewed By: zou3519 Differential Revision: D25673712 Pulled By: izdeby fbshipit-source-id: 4420d5d129026195097d914e410b75b144bea795
813 lines
41 KiB
Python
813 lines
41 KiB
Python
import torch
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import unittest
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from torch.testing._internal.common_utils import TestCase, run_tests, TEST_WITH_ROCM, TEST_WITH_SLOW
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from torch.testing._internal.common_device_type import \
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(instantiate_device_type_tests, dtypes, skipCUDAIfRocm, ops)
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from torch._six import inf, nan
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from torch.testing._internal.common_methods_invocations import foreach_unary_op_db
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# Includes some values such that N * N won't be a multiple of 4,
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# which should ensure we test the vectorized and non-vectorized
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# kernel code paths.
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N_values = [20, 23] if not TEST_WITH_SLOW else [23, 30, 300]
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class TestForeach(TestCase):
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bin_ops = [
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(torch._foreach_add, torch._foreach_add_, torch.add),
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(torch._foreach_sub, torch._foreach_sub_, torch.sub),
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(torch._foreach_mul, torch._foreach_mul_, torch.mul),
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(torch._foreach_div, torch._foreach_div_, torch.div),
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]
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def _get_test_data(self, device, dtype, N):
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if dtype in [torch.bfloat16, torch.bool, torch.float16]:
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tensors = [torch.randn(N, N, device=device).to(dtype) for _ in range(N)]
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elif dtype in torch.testing.get_all_int_dtypes():
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# Constrains the range between 1 and 10 for less stress on int8 tensors.
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tensors = [torch.randint(1, 10, (N, N), device=device, dtype=dtype) for _ in range(N)]
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else:
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tensors = [torch.randn(N, N, device=device, dtype=dtype) for _ in range(N)]
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return tensors
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def _test_bin_op_list(self, device, dtype, foreach_op, foreach_op_, torch_op):
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for N in N_values:
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tensors1 = self._get_test_data(device, dtype, N)
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tensors2 = self._get_test_data(device, dtype, N)
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# Mimics cuda kernel dtype flow. With fp16/bf16 input, runs in fp32 and casts output back to fp16/bf16.
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control_dtype = torch.float32 if (self.device_type == 'cuda' and
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(dtype is torch.float16 or dtype is torch.bfloat16)) else dtype
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expected = [torch_op(tensors1[i].to(dtype=control_dtype),
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tensors2[i].to(dtype=control_dtype)).to(dtype=dtype) for i in range(N)]
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res = foreach_op(tensors1, tensors2)
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foreach_op_(tensors1, tensors2)
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self.assertEqual(res, tensors1)
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if (dtype is torch.float16 or dtype is torch.bfloat16) and TEST_WITH_ROCM:
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self.assertEqual(tensors1, expected, atol=1.e-3, rtol=self.dtype_precisions[dtype][0])
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else:
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self.assertEqual(tensors1, expected)
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def _test_pointwise_op(self, device, dtype, foreach_op, foreach_op_, torch_op):
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for N in N_values:
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# Constrains the range a bit for int8 tensors.
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values = [2 + (i % 5) for i in range(N)]
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for vals in [values[0], values]:
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tensors = self._get_test_data(device, dtype, N)
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tensors1 = self._get_test_data(device, dtype, N)
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tensors2 = self._get_test_data(device, dtype, N)
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# Mimics cuda kernel dtype flow. With fp16/bf16 input, runs in fp32 and casts output back to fp16/bf16.
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control_dtype = torch.float32 if (self.device_type == 'cuda' and
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(dtype is torch.float16 or dtype is torch.bfloat16)) else dtype
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if not isinstance(vals, list):
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expected = [torch_op(tensors[i].to(dtype=control_dtype),
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tensors1[i].to(dtype=control_dtype),
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tensors2[i].to(dtype=control_dtype),
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value=values[0]).to(dtype=dtype) for i in range(N)]
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else:
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expected = [torch_op(tensors[i].to(dtype=control_dtype),
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tensors1[i].to(dtype=control_dtype),
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tensors2[i].to(dtype=control_dtype),
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value=values[i]).to(dtype=dtype) for i in range(N)]
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res = foreach_op(tensors, tensors1, tensors2, vals)
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foreach_op_(tensors, tensors1, tensors2, vals)
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self.assertEqual(res, tensors)
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if (dtype is torch.float16 or dtype is torch.bfloat16) and TEST_WITH_ROCM:
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self.assertEqual(tensors, expected, atol=3.e-3, rtol=self.dtype_precisions[dtype][0])
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else:
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self.assertEqual(tensors, expected)
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# test error cases
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for op in [torch._foreach_addcmul, torch._foreach_addcmul_, torch._foreach_addcdiv, torch._foreach_addcdiv_]:
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tensors = self._get_test_data(device, dtype, N)
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tensors1 = self._get_test_data(device, dtype, N)
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tensors2 = self._get_test_data(device, dtype, N)
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with self.assertRaisesRegex(RuntimeError, "Tensor list must have same number of elements as scalar list."):
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op(tensors, tensors1, tensors2, [2 for _ in range(N + 1)])
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with self.assertRaisesRegex(RuntimeError, "Tensor list must have same number of elements as scalar list."):
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op(tensors, tensors1, tensors2, [2 for _ in range(N - 1)])
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msg = "Tensor lists must have the same number of tensors, got {} and {}".format(N + 1, N)
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tensors = self._get_test_data(device, dtype, N + 1)
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with self.assertRaisesRegex(RuntimeError, msg):
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op(tensors, tensors1, tensors2, [2 for _ in range(N)])
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tensors1 = self._get_test_data(device, dtype, N + 1)
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with self.assertRaisesRegex(RuntimeError, msg):
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op(tensors, tensors1, tensors2, [2 for _ in range(N)])
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def _test_bin_op_list_alpha(self, device, dtype, foreach_op, foreach_op_, torch_op):
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for N in N_values:
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tensors1 = self._get_test_data(device, dtype, N)
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tensors2 = self._get_test_data(device, dtype, N)
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alpha = 2
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# Mimics cuda kernel dtype flow. With fp16/bf16 input, runs in fp32 and casts output back to fp16/bf16.
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control_dtype = torch.float32 if (self.device_type == 'cuda' and
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(dtype is torch.float16 or dtype is torch.bfloat16)) else dtype
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expected = [torch_op(tensors1[i].to(dtype=control_dtype),
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torch.mul(tensors2[i].to(dtype=control_dtype),
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alpha)).to(dtype=dtype) for i in range(N)]
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res = foreach_op(tensors1, tensors2, alpha=alpha)
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foreach_op_(tensors1, tensors2, alpha=alpha)
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self.assertEqual(res, tensors1)
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if dtype == torch.bool:
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expected = [e.to(torch.bool) for e in expected]
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if (dtype is torch.float16 or dtype is torch.bfloat16) and TEST_WITH_ROCM:
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self.assertEqual(tensors1, expected, atol=1.e-3, rtol=self.dtype_precisions[dtype][0])
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else:
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self.assertEqual(tensors1, expected)
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@ops(foreach_unary_op_db)
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def test_unary(self, device, dtype, op):
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for N in N_values:
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tensors = op.sample_inputs(device, dtype, N)
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expected = [op.ref(t) for t in tensors]
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method = op.get_method()
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inplace = op.get_inplace()
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actual = method(tensors)
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self.assertEqual(expected, actual)
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if op.safe_casts_outputs and dtype in torch.testing.integral_types_and(torch.bool):
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with self.assertRaisesRegex(RuntimeError, "can't be cast to the desired output type"):
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inplace(tensors)
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elif dtype in [torch.complex64, torch.complex128] and inplace == torch._foreach_abs_:
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# Special case for abs
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with self.assertRaisesRegex(RuntimeError, r"In-place abs is not supported for complex tensors."):
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inplace(tensors)
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else:
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inplace(tensors)
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self.assertEqual(tensors, actual)
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#
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# Pointwise ops
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#
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@dtypes(*torch.testing.get_all_dtypes(include_bfloat16=False, include_bool=False, include_complex=False))
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def test_addcmul(self, device, dtype):
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if self.device_type == 'cpu':
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if dtype == torch.half:
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with self.assertRaisesRegex(RuntimeError, r"\"addcmul_cpu_out\" not implemented for \'Half\'"):
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self._test_pointwise_op(device, dtype, torch._foreach_addcmul,
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torch._foreach_addcmul_, torch.addcmul)
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return
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self._test_pointwise_op(device, dtype, torch._foreach_addcmul, torch._foreach_addcmul_, torch.addcmul)
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@dtypes(*torch.testing.get_all_dtypes(include_bfloat16=False, include_bool=False, include_complex=False))
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def test_addcdiv(self, device, dtype):
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if dtype in [torch.int8, torch.int16, torch.int32, torch.int64, torch.uint8]:
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with self.assertRaisesRegex(RuntimeError,
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"Integer division with addcdiv is no longer supported, and in a future"):
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self._test_pointwise_op(device, dtype, torch._foreach_addcdiv, torch._foreach_addcdiv_, torch.addcdiv)
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return
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if self.device_type == 'cpu':
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if dtype == torch.half:
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with self.assertRaisesRegex(RuntimeError, r"\"addcdiv_cpu_out\" not implemented for \'Half\'"):
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self._test_pointwise_op(device, dtype, torch._foreach_addcdiv,
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torch._foreach_addcdiv_, torch.addcdiv)
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return
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self._test_pointwise_op(device, dtype, torch._foreach_addcdiv, torch._foreach_addcdiv_, torch.addcdiv)
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@dtypes(*torch.testing.get_all_dtypes(include_bfloat16=False, include_bool=False, include_complex=False))
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def test_min_max(self, device, dtype):
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for N in N_values:
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tensors1 = self._get_test_data(device, dtype, N)
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tensors2 = self._get_test_data(device, dtype, N)
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# Mimics cuda kernel dtype flow. With fp16/bf16 input, runs in fp32 and casts output back to fp16/bf16.
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control_dtype = torch.float32 if (self.device_type == 'cuda' and
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(dtype is torch.float16 or dtype is torch.bfloat16)) else dtype
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expected_max = [torch.max(tensors1[i].to(dtype=control_dtype),
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tensors2[i].to(dtype=control_dtype)).to(dtype=dtype) for i in range(N)]
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expected_min = [torch.min(tensors1[i].to(dtype=control_dtype),
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tensors2[i].to(dtype=control_dtype)).to(dtype=dtype) for i in range(N)]
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res_max = torch._foreach_maximum(tensors1, tensors2)
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self.assertEqual(res_max, expected_max)
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res_min = torch._foreach_minimum(tensors1, tensors2)
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self.assertEqual(res_min, expected_min)
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@dtypes(*(torch.testing.get_all_fp_dtypes(include_half=True, include_bfloat16=False)))
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def test_max_min_float_inf_nan(self, device, dtype):
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a = [
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torch.tensor([float('inf')], device=device, dtype=dtype),
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torch.tensor([-float('inf')], device=device, dtype=dtype),
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torch.tensor([float('nan')], device=device, dtype=dtype),
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torch.tensor([float('nan')], device=device, dtype=dtype)
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]
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b = [
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torch.tensor([-float('inf')], device=device, dtype=dtype),
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torch.tensor([float('inf')], device=device, dtype=dtype),
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torch.tensor([float('inf')], device=device, dtype=dtype),
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torch.tensor([float('nan')], device=device, dtype=dtype)
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]
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expected = [torch.max(a1, b1) for a1, b1 in zip(a, b)]
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res = torch._foreach_maximum(a, b)
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self.assertEqual(expected, res)
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expected = [torch.min(a1, b1) for a1, b1 in zip(a, b)]
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res = torch._foreach_minimum(a, b)
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self.assertEqual(expected, res)
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@dtypes(*(torch.testing.get_all_fp_dtypes(include_half=True, include_bfloat16=False)))
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def test_max_min_inf_nan(self, device, dtype):
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a = [
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torch.tensor([inf], device=device, dtype=dtype),
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torch.tensor([-inf], device=device, dtype=dtype),
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torch.tensor([nan], device=device, dtype=dtype),
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torch.tensor([nan], device=device, dtype=dtype)
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]
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b = [
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torch.tensor([-inf], device=device, dtype=dtype),
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torch.tensor([inf], device=device, dtype=dtype),
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torch.tensor([inf], device=device, dtype=dtype),
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torch.tensor([nan], device=device, dtype=dtype)
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]
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expected_max = [torch.max(a1, b1) for a1, b1 in zip(a, b)]
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res_max = torch._foreach_maximum(a, b)
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self.assertEqual(expected_max, res_max)
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expected_min = [torch.min(a1, b1) for a1, b1 in zip(a, b)]
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res_min = torch._foreach_minimum(a, b)
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self.assertEqual(expected_min, res_min)
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#
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# Ops with scalar
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#
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@skipCUDAIfRocm
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@dtypes(*torch.testing.get_all_dtypes())
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def test_tensorlist_int_scalar_op(self, device, dtype):
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for N in N_values:
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for foreach_bin_op, foreach_bin_op_, torch_bin_op in self.bin_ops:
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tensors = self._get_test_data(device, dtype, N)
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scalar = 3
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if dtype == torch.bool:
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if foreach_bin_op == torch._foreach_sub:
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with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator,"):
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res = foreach_bin_op(tensors, scalar)
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with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator,"):
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expected = [torch_bin_op(t, scalar) for t in tensors]
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# Test In-place
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with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator,"):
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foreach_bin_op_(tensors, scalar)
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with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator,"):
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[t.sub_(scalar) for t in tensors]
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continue
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res = foreach_bin_op(tensors, scalar)
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expected = [torch_bin_op(t, scalar) for t in tensors]
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self.assertEqual(res, expected)
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# Test In-place
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with self.assertRaisesRegex(RuntimeError, "can't be cast to the desired output type"):
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foreach_bin_op_(tensors, scalar)
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with self.assertRaisesRegex(RuntimeError, "can't be cast to the desired output type"):
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[t.div_(scalar) for t in tensors]
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with self.assertRaisesRegex(RuntimeError, "can't be cast to the desired output type"):
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[t.mul_(scalar) for t in tensors]
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with self.assertRaisesRegex(RuntimeError, "can't be cast to the desired output type"):
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[t.add_(scalar) for t in tensors]
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with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator, with a bool"):
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[t.sub_(scalar) for t in tensors]
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continue
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expected = [torch_bin_op(t, scalar) for t in tensors]
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res = foreach_bin_op(tensors, scalar)
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# In case of In-place division with integers, we can't change the dtype
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if foreach_bin_op_ == torch._foreach_div_ and dtype in torch.testing.integral_types():
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with self.assertRaisesRegex(RuntimeError, "can't be cast to the desired output type"):
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[t.div_(scalar) for t in tensors]
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with self.assertRaisesRegex(RuntimeError, "can't be cast to the desired output type"):
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torch._foreach_div_(tensors, scalar)
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continue
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self.assertEqual(res, expected)
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# In case of In-place op, we can't change the dtype
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foreach_bin_op_(tensors, scalar)
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self.assertEqual(tensors, expected)
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# TODO[Fix scalar list]:
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# We need to update codegen to correctly handle function overloads with float[] and int[].
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# As optimizers work with float tensors, the result will always be torch.float32 for now.
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# Current schema is using 'float[]' as scalar list type.
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@skipCUDAIfRocm
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@dtypes(*torch.testing.get_all_dtypes())
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def test_tensorlist_int_scalarlist_op(self, device, dtype):
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for N in N_values:
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for foreach_bin_op, foreach_bin_op_, torch_bin_op in self.bin_ops:
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tensors = self._get_test_data(device, dtype, N)
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scalars = [1 for _ in range(N)]
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expected = [torch_bin_op(t, s) for t, s in zip(tensors, scalars)]
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# we dont support bool and complex types on CUDA for now
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if dtype in torch.testing.get_all_complex_dtypes() and self.device_type == 'cuda':
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with self.assertRaisesRegex(RuntimeError, "not implemented for"):
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foreach_bin_op_(tensors, scalars)
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with self.assertRaisesRegex(RuntimeError, "not implemented for"):
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foreach_bin_op(tensors, scalars)
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return
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res = foreach_bin_op(tensors, scalars)
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if dtype == torch.bool:
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self.assertEqual(res, [torch_bin_op(t, s) for t, s in zip(tensors, scalars)])
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with self.assertRaisesRegex(RuntimeError, "can't be cast to the desired output type"):
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foreach_bin_op_(tensors, scalars)
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return
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# test out of place
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self.assertEqual(res, expected)
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# test in-place
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if dtype in torch.testing.floating_types() and self.device_type == 'cpu':
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foreach_bin_op_(tensors, scalars)
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return
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else:
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if foreach_bin_op_ == torch._foreach_div_ and dtype in torch.testing.integral_types():
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with self.assertRaisesRegex(RuntimeError, "can't be cast to the desired output type"):
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foreach_bin_op_(tensors, scalars)
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else:
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foreach_bin_op_(tensors, scalars)
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self.assertEqual(res, tensors)
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@skipCUDAIfRocm
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@dtypes(*torch.testing.get_all_dtypes())
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def test_tensorlist_float_scalar_op(self, device, dtype):
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for N in N_values:
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for foreach_bin_op, foreach_bin_op_, torch_bin_op in self.bin_ops:
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tensors = self._get_test_data(device, dtype, N)
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scalar = 3.3
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# Mimics cuda kernel dtype flow. With fp16/bf16 input, runs in fp32 and casts output back to fp16/bf16.
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control_dtype = torch.float32 if (self.device_type == 'cuda' and
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(dtype is torch.float16 or dtype is torch.bfloat16)) else dtype
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expected = [torch_bin_op(t.to(dtype=control_dtype),
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scalar) for t in tensors]
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if (dtype is torch.float16 or dtype is torch.bfloat16):
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expected = [e.to(dtype=dtype) for e in expected]
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if dtype == torch.bool:
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if foreach_bin_op == torch._foreach_sub:
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with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator, with two bool"):
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foreach_bin_op_(tensors, scalar)
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with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator, with two bool"):
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foreach_bin_op(tensors, scalar)
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return
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res = foreach_bin_op(tensors, scalar)
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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_tensorlist_float_scalarlist_op(self, device, dtype):
|
|
for N in N_values:
|
|
for foreach_bin_op, foreach_bin_op_, torch_bin_op in self.bin_ops:
|
|
tensors = self._get_test_data(device, dtype, N)
|
|
scalars = [1.1 for _ in range(N)]
|
|
|
|
# Bool case
|
|
if dtype == torch.bool:
|
|
if foreach_bin_op == torch._foreach_sub:
|
|
with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator, with a bool tensor"):
|
|
expected = [torch_bin_op(t, s) for t, s in zip(tensors, scalars)]
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator, with a bool tensor"):
|
|
res = foreach_bin_op(tensors, scalars)
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator, with a bool tensor"):
|
|
[t.sub_(scalar) for t, scalar in zip(tensors, scalars)]
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator, with a bool tensor"):
|
|
foreach_bin_op_(tensors, scalars)
|
|
continue
|
|
|
|
res = foreach_bin_op(tensors, scalars)
|
|
expected = [torch_bin_op(t, s) for t, s in zip(tensors, scalars)]
|
|
self.assertEqual(res, expected)
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "result type Float can't be cast to the desired output type"):
|
|
foreach_bin_op_(tensors, scalars)
|
|
continue
|
|
|
|
# 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 in torch.testing.integral_types() and self.device_type == 'cuda':
|
|
self.assertEqual(res, expected)
|
|
with self.assertRaisesRegex(RuntimeError, "result type Float can't be cast to the desired output type"):
|
|
foreach_bin_op_(tensors, scalars)
|
|
continue
|
|
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)
|
|
continue
|
|
|
|
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_tensorlist_complex_scalar_op(self, device, dtype):
|
|
for N in N_values:
|
|
for foreach_bin_op, foreach_bin_op_, torch_bin_op in self.bin_ops:
|
|
tensors = self._get_test_data(device, dtype, N)
|
|
scalar = 3 + 5j
|
|
|
|
# Bool case
|
|
if dtype == torch.bool:
|
|
if foreach_bin_op == torch._foreach_sub:
|
|
with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator,"):
|
|
foreach_bin_op_(tensors, scalar)
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator,"):
|
|
foreach_bin_op(tensors, scalar)
|
|
continue
|
|
|
|
res = foreach_bin_op(tensors, scalar)
|
|
expected = [torch_bin_op(t, scalar) for t in tensors]
|
|
self.assertEqual(res, expected)
|
|
|
|
if dtype in torch.testing.get_all_fp_dtypes() and self.device_type == 'cuda':
|
|
with self.assertRaisesRegex(RuntimeError, "can't be cast to the desired output type"):
|
|
foreach_bin_op_(tensors, scalar)
|
|
continue
|
|
|
|
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)
|
|
|
|
@skipCUDAIfRocm
|
|
@dtypes(*torch.testing.get_all_dtypes())
|
|
def test_tensorlist_complex_scalarlist_op(self, device, dtype):
|
|
for N in N_values:
|
|
for foreach_bin_op, foreach_bin_op_, torch_bin_op in self.bin_ops:
|
|
tensors = self._get_test_data(device, dtype, N)
|
|
scalars = [3 + 5j for _ in range(N)]
|
|
|
|
# Bool case
|
|
if dtype == torch.bool:
|
|
if foreach_bin_op == torch._foreach_sub:
|
|
with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator"):
|
|
foreach_bin_op_(tensors, scalars)
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator"):
|
|
foreach_bin_op(tensors, scalars)
|
|
continue
|
|
|
|
expected = [torch_bin_op(t, s) for t, s in zip(tensors, scalars)]
|
|
res = foreach_bin_op(tensors, scalars)
|
|
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, scalars)
|
|
else:
|
|
foreach_bin_op_(tensors, scalars)
|
|
self.assertEqual(res, tensors)
|
|
|
|
@skipCUDAIfRocm
|
|
@dtypes(*torch.testing.get_all_dtypes())
|
|
def test_tensorlist_bool_scalar_op(self, device, dtype):
|
|
for N in N_values:
|
|
for foreach_bin_op, foreach_bin_op_, torch_bin_op in self.bin_ops:
|
|
tensors = self._get_test_data(device, dtype, N)
|
|
scalar = True
|
|
|
|
if foreach_bin_op == torch._foreach_sub:
|
|
with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator"):
|
|
expected = [torch_bin_op(t, scalar) for t in tensors]
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator"):
|
|
foreach_bin_op(tensors, scalar)
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator"):
|
|
foreach_bin_op_(tensors, scalar)
|
|
continue
|
|
|
|
expected = [torch_bin_op(t, scalar) for t in tensors]
|
|
res = foreach_bin_op(tensors, scalar)
|
|
self.assertEqual(expected, res)
|
|
|
|
if dtype in torch.testing.integral_types_and(torch.bool) and foreach_bin_op == torch._foreach_div:
|
|
with self.assertRaisesRegex(RuntimeError, "can't be cast to the desired output"):
|
|
foreach_bin_op_(tensors, scalar)
|
|
else:
|
|
foreach_bin_op_(tensors, scalar)
|
|
self.assertEqual(tensors, res)
|
|
|
|
@skipCUDAIfRocm
|
|
@dtypes(*torch.testing.get_all_dtypes())
|
|
def test_tensorlist_bool_scalarlist_op(self, device, dtype):
|
|
for N in N_values:
|
|
for foreach_bin_op, foreach_bin_op_, torch_bin_op in self.bin_ops:
|
|
tensors = self._get_test_data(device, dtype, N)
|
|
scalars = [True for _ in range(N)]
|
|
|
|
# we dont support complex types on CUDA for now
|
|
if (dtype in torch.testing.get_all_complex_dtypes()) and self.device_type == 'cuda':
|
|
# There are a two types of different errors that will be thrown.
|
|
# - Not implemented
|
|
# - Subtraction with a bool tensor
|
|
with self.assertRaises(RuntimeError):
|
|
foreach_bin_op_(tensors, scalars)
|
|
|
|
with self.assertRaises(RuntimeError):
|
|
foreach_bin_op(tensors, scalars)
|
|
continue
|
|
|
|
if foreach_bin_op == torch._foreach_sub:
|
|
with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator"):
|
|
expected = [torch_bin_op(t, s) for t, s in zip(tensors, scalars)]
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator"):
|
|
foreach_bin_op(tensors, scalars)
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator"):
|
|
foreach_bin_op_(tensors, scalars)
|
|
continue
|
|
|
|
expected = [torch_bin_op(t, s) for t, s in zip(tensors, scalars)]
|
|
res = foreach_bin_op(tensors, scalars)
|
|
self.assertEqual(expected, res)
|
|
|
|
if dtype in torch.testing.integral_types_and(torch.bool) and foreach_bin_op == torch._foreach_div:
|
|
with self.assertRaisesRegex(RuntimeError, "can't be cast to the desired output"):
|
|
foreach_bin_op_(tensors, scalars)
|
|
else:
|
|
foreach_bin_op_(tensors, scalars)
|
|
self.assertEqual(tensors, res)
|
|
|
|
@dtypes(*torch.testing.get_all_dtypes())
|
|
def test_tensorlist_mixed_scalarlist_op(self, device, dtype):
|
|
for N in N_values:
|
|
for foreach_bin_op, foreach_bin_op_, torch_bin_op in self.bin_ops:
|
|
tensors = self._get_test_data(device, dtype, N)
|
|
scalars = [1, 1.1, 3 + 5j] + [True for _ in range(N - 3)]
|
|
|
|
if foreach_bin_op == torch._foreach_sub:
|
|
with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator"):
|
|
expected = [torch_bin_op(t, s) for t, s in zip(tensors, scalars)]
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "Subtraction, the `-` operator"):
|
|
foreach_bin_op(tensors, scalars)
|
|
|
|
# There are a two types of different errors that will be thrown.
|
|
# - Sub with bool is not allowed.
|
|
# - Result type can't be cast to the desired output type
|
|
with self.assertRaises(RuntimeError):
|
|
foreach_bin_op_(tensors, scalars)
|
|
continue
|
|
|
|
expected = [torch_bin_op(t, s) for t, s in zip(tensors, scalars)]
|
|
res = foreach_bin_op(tensors, scalars)
|
|
self.assertEqual(expected, res)
|
|
|
|
if dtype in torch.testing.get_all_complex_dtypes():
|
|
foreach_bin_op_(tensors, scalars)
|
|
self.assertEqual(expected, tensors)
|
|
else:
|
|
with self.assertRaisesRegex(RuntimeError, "can't be cast to the desired output type"):
|
|
foreach_bin_op_(tensors, scalars)
|
|
|
|
@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 self.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)])
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@unittest.skipIf(not torch.cuda.is_available(), "CUDA not found")
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@dtypes(*torch.testing.get_all_dtypes())
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def test_add_list_slow_path(self, device, dtype):
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# different strides
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tensor1 = torch.zeros(10, 10, device=device, dtype=dtype)
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tensor2 = torch.ones(10, 10, device=device, dtype=dtype)
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res = torch._foreach_add([tensor1], [tensor2.t()])
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torch._foreach_add_([tensor1], [tensor2])
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self.assertEqual(res, [tensor1])
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|
|
|
# non contiguous
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|
tensor1 = torch.randn(5, 2, 1, 3, device=device)[:, 0]
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tensor2 = torch.randn(5, 2, 1, 3, device=device)[:, 0]
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self.assertFalse(tensor1.is_contiguous())
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|
self.assertFalse(tensor2.is_contiguous())
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|
res = torch._foreach_add([tensor1], [tensor2])
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|
torch._foreach_add_([tensor1], [tensor2])
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|
self.assertEqual(res, [tensor1])
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|
|
|
instantiate_device_type_tests(TestForeach, globals())
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|
|
|
if __name__ == '__main__':
|
|
run_tests()
|