blob: 39caa9e7c4ec82a38c87fb0f6a47228dea06c9fd [file] [log] [blame]
# Owner(s): ["module: unknown"]
from functools import partial
import torch
from torch.testing._internal.common_utils import TestGradients, run_tests
from torch.testing._internal.common_methods_invocations import op_db
from torch.testing._internal.control_flow_opinfo_db import control_flow_opinfo_db
from torch.testing._internal.custom_op_db import custom_op_db
from torch.testing._internal.common_device_type import \
(instantiate_device_type_tests, ops, OpDTypes)
# TODO: fixme https://github.com/pytorch/pytorch/issues/68972
torch.set_default_dtype(torch.float32)
# gradcheck requires double precision
_gradcheck_ops = partial(ops, dtypes=OpDTypes.supported,
allowed_dtypes=[torch.double, torch.cdouble])
class TestBwdGradients(TestGradients):
# Tests that gradients are computed correctly
@_gradcheck_ops(op_db + control_flow_opinfo_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 + control_flow_opinfo_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__':
run_tests()