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ba48cf6535
See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501. Most changes are auto-generated by linter. You can review these PRs via: ```bash git diff --ignore-all-space --ignore-blank-lines HEAD~1 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/129757 Approved by: https://github.com/ezyang
110 lines
4.2 KiB
Python
110 lines
4.2 KiB
Python
# Owner(s): ["module: unknown"]
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from functools import partial
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import torch
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from torch.testing._internal.common_device_type import (
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instantiate_device_type_tests,
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OpDTypes,
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ops,
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)
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from torch.testing._internal.common_methods_invocations import op_db
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from torch.testing._internal.common_utils import (
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run_tests,
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TestCase,
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TestGradients,
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unMarkDynamoStrictTest,
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)
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from torch.testing._internal.custom_op_db import custom_op_db
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from torch.testing._internal.hop_db import hop_db
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# gradcheck requires double precision
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_gradcheck_ops = partial(
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ops, dtypes=OpDTypes.supported, allowed_dtypes=[torch.double, torch.cdouble]
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)
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@unMarkDynamoStrictTest
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class TestBwdGradients(TestGradients):
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# Tests that gradients are computed correctly
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@_gradcheck_ops(op_db + hop_db + custom_op_db)
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def test_fn_grad(self, device, dtype, op):
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# This is verified by test_dtypes in test_ops.py
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if dtype not in op.supported_backward_dtypes(torch.device(device).type):
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self.skipTest("Skipped! Dtype is not in supported backward dtypes!")
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else:
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self._grad_test_helper(device, dtype, op, op.get_op())
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# Method grad (and gradgrad, see below) tests are disabled since they're
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# costly and redundant with function grad (and gradgad) tests
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# @_gradcheck_ops(op_db)
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# def test_method_grad(self, device, dtype, op):
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# self._skip_helper(op, device, dtype)
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# self._grad_test_helper(device, dtype, op, op.get_method())
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@_gradcheck_ops(op_db + custom_op_db)
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def test_inplace_grad(self, device, dtype, op):
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self._skip_helper(op, device, dtype)
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if not op.inplace_variant:
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self.skipTest("Op has no inplace variant!")
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# Verifies an operation doesn't support inplace autograd if it claims not to
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if not op.supports_inplace_autograd:
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inplace = self._get_safe_inplace(op.get_inplace())
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for sample in op.sample_inputs(device, dtype, requires_grad=True):
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if sample.broadcasts_input:
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continue
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with self.assertRaises(Exception):
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result = inplace(sample)
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result.sum().backward()
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else:
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self._grad_test_helper(
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device, dtype, op, self._get_safe_inplace(op.get_inplace())
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)
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# Test that gradients of gradients are computed correctly
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@_gradcheck_ops(op_db + hop_db + custom_op_db)
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def test_fn_gradgrad(self, device, dtype, op):
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self._skip_helper(op, device, dtype)
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if not op.supports_gradgrad:
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self.skipTest(
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"Op claims it doesn't support gradgrad. This is not verified."
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)
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else:
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self._check_helper(device, dtype, op, op.get_op(), "bwgrad_bwgrad")
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# Test that gradients of gradients are properly raising
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@_gradcheck_ops(op_db + custom_op_db)
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def test_fn_fail_gradgrad(self, device, dtype, op):
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self._skip_helper(op, device, dtype)
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if op.supports_gradgrad:
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self.skipTest("Skipped! Operation does support gradgrad")
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err_msg = r"derivative for .* is not implemented"
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with self.assertRaisesRegex(RuntimeError, err_msg):
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self._check_helper(device, dtype, op, op.get_op(), "bwgrad_bwgrad")
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# Method gradgrad (and grad, see above) tests are disabled since they're
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# costly and redundant with function gradgrad (and grad) tests
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# @_gradcheck_ops(op_db)
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# def test_method_gradgrad(self, device, dtype, op):
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# self._skip_helper(op, device, dtype)
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# self._gradgrad_test_helper(device, dtype, op, op.get_method())
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@_gradcheck_ops(op_db)
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def test_inplace_gradgrad(self, device, dtype, op):
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self._skip_helper(op, device, dtype)
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if not op.inplace_variant or not op.supports_inplace_autograd:
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self.skipTest("Skipped! Operation does not support inplace autograd.")
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self._check_helper(
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device, dtype, op, self._get_safe_inplace(op.get_inplace()), "bwgrad_bwgrad"
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)
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instantiate_device_type_tests(TestBwdGradients, globals())
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if __name__ == "__main__":
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TestCase._default_dtype_check_enabled = True
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run_tests()
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