mirror of
https://github.com/zebrajr/pytorch.git
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bc1ce58451
Summary: Push rocm to slow path Pull Request resolved: https://github.com/pytorch/pytorch/pull/46216 Reviewed By: bwasti Differential Revision: D24263731 Pulled By: izdeby fbshipit-source-id: 98ede2478b8f075ceed44a9e4f2aa292f523b8e2
859 lines
44 KiB
Python
859 lines
44 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 instantiate_device_type_tests, dtypes, skipCUDAIfRocm
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from torch._six import inf, nan
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N_values = [20] if not TEST_WITH_SLOW else [30, 300]
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class TestForeach(TestCase):
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foreach_bin_ops = [
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torch._foreach_add,
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torch._foreach_sub,
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torch._foreach_mul,
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torch._foreach_div,
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]
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foreach_bin_ops_ = [
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torch._foreach_add_,
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torch._foreach_sub_,
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torch._foreach_mul_,
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torch._foreach_div_,
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]
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torch_bin_ops = [
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torch.add,
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torch.sub,
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torch.mul,
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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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tensors = [torch.randint(1, 100, (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_unary_op(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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# 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)).to(dtype=dtype) for i in range(N)]
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res = foreach_op(tensors1)
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foreach_op_(tensors1)
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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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values = [2 + i 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=1.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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tensors = self._get_test_data(device, dtype, N + 1)
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with self.assertRaisesRegex(RuntimeError, "Tensor lists must be of the same length, got 21 and 20"):
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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, "Tensor lists must be of the same length, got 21 and 20"):
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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 [30, 300]:
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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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#
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# Unary ops
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#
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@dtypes(*[torch.float, torch.double, torch.complex64, torch.complex128])
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def test_sqrt(self, device, dtype):
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self._test_unary_op(device, dtype, torch._foreach_sqrt, torch._foreach_sqrt_, torch.sqrt)
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@dtypes(*[torch.float, torch.double, torch.complex64, torch.complex128])
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def test_exp(self, device, dtype):
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self._test_unary_op(device, dtype, torch._foreach_exp, torch._foreach_exp_, torch.exp)
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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_int_scalar(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 zip(self.foreach_bin_ops,
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self.foreach_bin_ops_,
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self.torch_bin_ops):
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tensors = self._get_test_data(device, dtype, N)
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scalar = 3
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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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if dtype == torch.bool:
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self.assertEqual(res, expected)
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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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return
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if foreach_bin_op_ == torch._foreach_div_ and dtype in torch.testing.integral_types() and self.device_type == "cpu":
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with self.assertRaisesRegex(RuntimeError,
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"can't be cast to the desired output type"):
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foreach_bin_op_(tensors, scalar)
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return
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# TODO[type promotion]: Fix once type promotion is enabled.
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if dtype in torch.testing.integral_types() and self.device_type == 'cuda':
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self.assertEqual(res, [e.to(dtype) for e in expected])
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foreach_bin_op_(tensors, scalar)
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self.assertEqual(tensors, [e.to(dtype) for e in expected])
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else:
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self.assertEqual(res, expected)
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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_int_scalarlist(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 zip(self.foreach_bin_ops,
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self.foreach_bin_ops_,
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self.torch_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() or dtype == torch.bool) 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.to(torch.float32), s) for t, s in zip(tensors, scalars)])
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with self.assertRaisesRegex(RuntimeError, "result type Float 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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if dtype in torch.testing.integral_types():
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if self.device_type == 'cpu':
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self.assertEqual(res, [e.to(torch.float32) for e in expected])
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else:
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# TODO[type promotion]: Fix once type promotion is enabled.
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self.assertEqual(res, [e.to(dtype) for e in expected])
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else:
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self.assertEqual(res, expected)
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if dtype in torch.testing.integral_types() and self.device_type == 'cpu':
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with self.assertRaisesRegex(RuntimeError, "result type Float 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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else:
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foreach_bin_op_(tensors, scalars)
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self.assertEqual(res, tensors)
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@dtypes(*torch.testing.get_all_dtypes())
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def test_float_scalar(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 zip(self.foreach_bin_ops,
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self.foreach_bin_ops_,
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self.torch_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:
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self.assertEqual(res, expected, atol=1.e-3, rtol=self.dtype_precisions[dtype][0])
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else:
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self.assertEqual(res, expected)
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if dtype in torch.testing.integral_types():
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with self.assertRaisesRegex(RuntimeError, "result type Float can't be cast to the desired output type"):
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foreach_bin_op_(tensors, scalar)
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return
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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(tensors, expected, atol=1.e-3, rtol=self.dtype_precisions[dtype][0])
|
|
else:
|
|
self.assertEqual(tensors, expected)
|
|
|
|
@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)
|
|
|
|
@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_add_list_error_cases(self, device):
|
|
tensors1 = []
|
|
tensors2 = []
|
|
|
|
# Empty lists
|
|
with self.assertRaisesRegex(RuntimeError, "There were no tensor arguments to this function"):
|
|
torch._foreach_add(tensors1, tensors2)
|
|
with self.assertRaisesRegex(RuntimeError, "There were no tensor arguments to this function"):
|
|
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 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)
|
|
|
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def test_bin_op_list_error_cases(self, device):
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tensors1 = []
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tensors2 = []
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for bin_op in self.foreach_bin_ops + self.foreach_bin_ops_:
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# Empty lists
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with self.assertRaises(RuntimeError):
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bin_op(tensors1, tensors2)
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# One empty list
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tensors1.append(torch.tensor([1], device=device))
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with self.assertRaises(RuntimeError):
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bin_op(tensors1, tensors2)
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# Lists have different amount of tensors
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tensors2.append(torch.tensor([1], device=device))
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tensors2.append(torch.tensor([1], device=device))
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with self.assertRaises(RuntimeError):
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bin_op(tensors1, tensors2)
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# Different dtypes
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tensors1 = [torch.zeros(2, 2, device=device, dtype=torch.float) for _ in range(2)]
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tensors2 = [torch.ones(2, 2, device=device, dtype=torch.int) for _ in range(2)]
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|
|
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with self.assertRaises(RuntimeError):
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bin_op(tensors1, tensors2)
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|
|
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@dtypes(*torch.testing.get_all_dtypes())
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def test_add_list_different_sizes(self, device, dtype):
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tensors1 = [torch.zeros(10 + n, 10 + n, device=device, dtype=dtype) for n in range(10)]
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tensors2 = [torch.ones(10 + n, 10 + n, device=device, dtype=dtype) for n in range(10)]
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|
|
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res = torch._foreach_add(tensors1, tensors2)
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torch._foreach_add_(tensors1, tensors2)
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self.assertEqual(res, tensors1)
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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")
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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)
|
|
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]
|
|
self.assertFalse(tensor1.is_contiguous())
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|
self.assertFalse(tensor2.is_contiguous())
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|
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()
|