# Owner(s): ["module: unknown"] from functools import partial import torch from torch.testing._internal.common_device_type import ( instantiate_device_type_tests, OpDTypes, ops, ) from torch.testing._internal.common_methods_invocations import op_db from torch.testing._internal.common_utils import ( run_tests, TestCase, TestGradients, unMarkDynamoStrictTest, ) from torch.testing._internal.custom_op_db import custom_op_db from torch.testing._internal.hop_db import hop_db # gradcheck requires double precision _gradcheck_ops = partial( ops, dtypes=OpDTypes.supported, allowed_dtypes=[torch.double, torch.cdouble] ) @unMarkDynamoStrictTest class TestBwdGradients(TestGradients): # Tests that gradients are computed correctly @_gradcheck_ops(op_db + hop_db + custom_op_db) def test_fn_grad(self, device, dtype, op): # This is verified by test_dtypes in test_ops.py if dtype not in op.supported_backward_dtypes(torch.device(device).type): self.skipTest("Skipped! Dtype is not in supported backward dtypes!") else: self._grad_test_helper(device, dtype, op, op.get_op()) # Method grad (and gradgrad, see below) tests are disabled since they're # costly and redundant with function grad (and gradgad) tests # @_gradcheck_ops(op_db) # def test_method_grad(self, device, dtype, op): # self._skip_helper(op, device, dtype) # self._grad_test_helper(device, dtype, op, op.get_method()) @_gradcheck_ops(op_db + custom_op_db) def test_inplace_grad(self, device, dtype, op): self._skip_helper(op, device, dtype) if not op.inplace_variant: self.skipTest("Op has no inplace variant!") # Verifies an operation doesn't support inplace autograd if it claims not to if not op.supports_inplace_autograd: inplace = self._get_safe_inplace(op.get_inplace()) for sample in op.sample_inputs(device, dtype, requires_grad=True): if sample.broadcasts_input: continue with self.assertRaises(Exception): result = inplace(sample) result.sum().backward() else: self._grad_test_helper( device, dtype, op, self._get_safe_inplace(op.get_inplace()) ) # Test that gradients of gradients are computed correctly @_gradcheck_ops(op_db + hop_db + custom_op_db) def test_fn_gradgrad(self, device, dtype, op): self._skip_helper(op, device, dtype) if not op.supports_gradgrad: self.skipTest( "Op claims it doesn't support gradgrad. This is not verified." ) else: self._check_helper(device, dtype, op, op.get_op(), "bwgrad_bwgrad") # Test that gradients of gradients are properly raising @_gradcheck_ops(op_db + custom_op_db) def test_fn_fail_gradgrad(self, device, dtype, op): self._skip_helper(op, device, dtype) if op.supports_gradgrad: self.skipTest("Skipped! Operation does support gradgrad") err_msg = r"derivative for .* is not implemented" with self.assertRaisesRegex(RuntimeError, err_msg): self._check_helper(device, dtype, op, op.get_op(), "bwgrad_bwgrad") # Method gradgrad (and grad, see above) tests are disabled since they're # costly and redundant with function gradgrad (and grad) tests # @_gradcheck_ops(op_db) # def test_method_gradgrad(self, device, dtype, op): # self._skip_helper(op, device, dtype) # self._gradgrad_test_helper(device, dtype, op, op.get_method()) @_gradcheck_ops(op_db) def test_inplace_gradgrad(self, device, dtype, op): self._skip_helper(op, device, dtype) if not op.inplace_variant or not op.supports_inplace_autograd: self.skipTest("Skipped! Operation does not support inplace autograd.") self._check_helper( device, dtype, op, self._get_safe_inplace(op.get_inplace()), "bwgrad_bwgrad" ) instantiate_device_type_tests(TestBwdGradients, globals()) if __name__ == "__main__": TestCase._default_dtype_check_enabled = True run_tests()