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Complex gradcheck logic (#43208)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/43208 This PR adds gradcheck for complex. The logic used for complex gradcheck is described in Section 3.5.3 here: https://arxiv.org/pdf/1701.00392.pdf More concretely, this PR introduces the following changes: 1. Updates get_numerical_jacobian to take as input a scalar value for vector (v). Adds gradcheck logic for C -> C, C-> R, R -> C. For R -> C functions, only the real value of gradient is propagated. 2. Adds backward definition for `torch.complex` and also adds a test to verify the definition added. 3. Updates backward for `mul`, `sin`, `cos`, `sinh`, `cosh`. 4. Adds tests for all `torch.real`, `torch.imag`, `torch.view_as_real`, `torch.view_as_complex`, `torch.conj`. Follow up tasks: 1. Add more thorough tests for R -> C cases. Specifically, add R->C test variants for functions. for e.g., `torch.mul(complex_tensor, real_tensor)` 2. Add back commented test in `common_methods_invocation.py`. 3. Add more special case checking for complex gradcheck to make debugging easier. 4. Update complex autograd note. 5. disable complex autograd for operators not tested for complex. Test Plan: Imported from OSS Reviewed By: zou3519 Differential Revision: D23655088 Pulled By: anjali411 fbshipit-source-id: caa75e09864b5f6ead0f988f6368dce64cf15deb
This commit is contained in:
anjali411
Facebook GitHub Bot
parent
da7863f46b
commit
9f67176b82
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@ -4680,18 +4680,22 @@ def run_functional_checks(test_case, test_name, name, apply_fn, run_grad_checks,
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# the tests for these ops which do not have 'complex' in variant should not run for complex
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# and only run for floating point
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separate_complex_tests = ['log', 'log10', 'log1p', 'log2', 'reciprocal', 'tan']
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# TODO(@anjali411): add the commented tests back after updating the formula based on tensorflow definition
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separate_complex_tests = ['view_as_real', 'real', 'imag', 'asin', 'acos'] # ['log', 'log10', 'log1p', 'log2', 'reciprocal', 'tan']
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# NOTE: Some non-holomorphic are separately tested in TestAutogradComplex until gradcheck works properly
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# for non-holomorphic functions
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# allow list for complex
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complex_list = ['t', 'view', 'reshape', 'reshape_as', 'view_as', 'zero_', 'clone',
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'tril', 'triu', 'fill_', 'eq_', 'ne_', 'permute', 'squeeze', 'unsqueeze',
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'chunk', 'split', 'split_with_sizes', 'resize', 'resize_as', 'sin', 'cos',
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'__rmul__', '__rdiv__', 'sum', 'transpose', 'round', 'add', 'roll',
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'__radd__', 'repeat', 'expand', 'mul', 'tanh', 'flip', 'fliplr', 'flipud',
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'rot90'] + separate_complex_tests
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complex_list = ['t', 'view', 'reshape', 'reshape_as', 'view_as', 'roll', 'clone',
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'repeat', 'expand', 'flip', 'fliplr', 'flipud', 'rot90', 'transpose',
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'permute', 'squeeze', 'unsqueeze', 'resize', 'resize_as', 'tril', 'triu',
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'chunk', 'split', 'split_with_sizes', 'repeat', 'expand', 'zero_', 'round',
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'eq_', 'ne_', 'add', '__radd__', 'sum', 'conj', 'sin', 'cos', 'mul', 'sinh',
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'cosh', '__rmul__'] + separate_complex_tests
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# TODO(@anjali411): add the commented tests back after updating the formula based on tensorflow definition - @anjali411
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# complex_list += ['fill_', 't', '__rdiv__', 'tanh']
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def add_test(
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name,
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@ -4721,7 +4725,7 @@ def add_test(
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if dtype.is_complex:
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# TODO: remove this. this is temporary while we ramp up the complex support.
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if name in complex_list and 'scalar' not in test_name and 'constant' not in test_name:
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if name in complex_list:
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if name in separate_complex_tests and 'complex' not in variant_name:
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continue
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if not run_only_complex:
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@ -4787,7 +4791,13 @@ def add_test(
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self_variable = create_input((self_size,), requires_grad=True, dtype=dtype)[0][0]
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args_variable, kwargs_variable = create_input(args, requires_grad=False, call_kwargs=kwargs, dtype=dtype)
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if hasattr(self_variable, name):
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output_variable = getattr(self_variable, name)(*args_variable, **kwargs_variable)
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attribute_result = getattr(self_variable, name)
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if callable(attribute_result):
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output_variable = attribute_result(*args_variable, **kwargs_variable)
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else:
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self.assertTrue(len(args_variable) == 0)
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self.assertTrue(len(kwargs_variable) == 0)
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output_variable = attribute_result
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else:
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self_and_args_variable = (self_variable,) + args_variable
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output_variable = torch_fn(*self_and_args_variable, **kwargs_variable)
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@ -4865,30 +4875,6 @@ def add_test(
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setattr(TestAutogradDeviceType, test_name, do_test)
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class TestAutogradComplex(TestCase):
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# remove this test after gradcheck support is added for non-holomorphic functions
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def test_real(self):
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x = torch.randn(3, 4, 5, dtype=torch.cdouble, requires_grad=True)
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x.real.sum().backward()
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self.assertEqual(x.grad, torch.ones_like(x))
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# remove this test after gradcheck support is added for non-holomorphic functions
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def test_imag(self):
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x = torch.randn(3, 4, 5, dtype=torch.cdouble, requires_grad=True)
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x.imag.sum().backward()
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self.assertEqual(x.grad, -1j * torch.ones_like(x))
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# remove this test after gradcheck support is added for non-holomorphic functions
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def test_view_as_real(self):
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x = torch.randn(10, dtype=torch.cdouble, requires_grad=True)
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torch.view_as_real(x).sum().backward()
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self.assertEqual(x.grad, torch.full_like(x, 1 - 1j))
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# remove this test after gradcheck support is added for non-holomorphic functions
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def test_view_as_complex(self):
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x = torch.randn(10, 2, dtype=torch.double, requires_grad=True)
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torch.view_as_complex(x).sum().backward()
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self.assertEqual(x.grad, torch.tensor([1, 0], dtype=torch.double).expand_as(x))
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def test_view_func_for_complex_views(self):
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# case 1: both parent and child have view_func
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x = torch.randn(2, 2, 2, dtype=torch.double, requires_grad=True)
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@ -15583,7 +15583,7 @@ def add_autograd_test(
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# Disable complex tests
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# TODO: Add complex support for jit
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if 'complex' in variant_name:
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if 'complex' in variant_name or name in ['view_as_complex', 'complex']:
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return
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# Skips aliases, which are tested in test_op_aliases.py
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@ -89,35 +89,41 @@ class TestGradients(TestCase):
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return self._check_helper(device, dtype, op, variant, 'gradgradcheck')
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# Tests that gradients are computed correctly
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@dtypes(torch.double, torch.cdouble)
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# TODO(@anjali411) enable this for torch.cdouble.
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@dtypes(torch.double)
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@ops(op_db)
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def test_fn_grad(self, device, dtype, op):
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self._grad_test_helper(device, dtype, op, op.get_op())
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@dtypes(torch.double, torch.cdouble)
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# TODO(@anjali411) enable this for torch.cdouble.
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@dtypes(torch.double)
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@ops(op_db)
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def test_method_grad(self, device, dtype, op):
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self._grad_test_helper(device, dtype, op, op.get_method())
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@dtypes(torch.double, torch.cdouble)
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# TODO(@anjali411) enable this for torch.cdouble.
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@dtypes(torch.double)
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@ops(op_db)
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def test_inplace_grad(self, device, dtype, op):
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if not op.test_inplace_grad:
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self.skipTest("Skipped! Inplace gradcheck marked to skip.")
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self._grad_test_helper(device, dtype, op, self._get_safe_inplace(op.get_inplace()))
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# TODO(@anjali411) enable this for torch.cdouble.
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# Test that gradients of gradients are computed correctly
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@dtypes(torch.double, torch.cdouble)
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@dtypes(torch.double)
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@ops(op_db)
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def test_fn_gradgrad(self, device, dtype, op):
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self._gradgrad_test_helper(device, dtype, op, op.get_op())
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@dtypes(torch.double, torch.cdouble)
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# TODO(@anjali411) enable this for torch.cdouble.
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@dtypes(torch.double)
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@ops(op_db)
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def test_method_gradgrad(self, device, dtype, op):
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self._gradgrad_test_helper(device, dtype, op, op.get_method())
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@dtypes(torch.double, torch.cdouble)
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# TODO(@anjali411) enable this for torch.cdouble.
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@dtypes(torch.double)
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@ops(op_db)
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def test_inplace_gradgrad(self, device, dtype, op):
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if not op.test_inplace_grad:
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@ -694,6 +694,9 @@ class Wrapper:
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def __add__(self, other):
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return self.__torch_function__(torch.add, (Wrapper,), (self, other))
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def __mul__(self, other):
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return self.__torch_function__(torch.mul, (Wrapper,), (self, other))
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def __sub__(self, other):
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return self.__torch_function__(torch.sub, (Wrapper,), (self, other))
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@ -757,51 +760,51 @@ class TestEinsumOverride(TestCase):
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self.assertTrue(torch.allclose(torch.einsum('ik,jkl,il->ij', [a, b, c]),
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torch.nn.functional.bilinear(a, c, b)))
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# TODO(@anjali411): re-enable this test
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# class TestGradCheckOverride(TestCase):
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# "Test that wrappers work with gradcheck."
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# def test_gradcheck(self):
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# from torch.autograd import gradcheck
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class TestGradCheckOverride(TestCase):
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"Test that wrappers work with gradcheck."
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def test_gradcheck(self):
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from torch.autograd import gradcheck
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# a = wrap(torch.tensor(5.0, dtype=torch.double))
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# b = wrap(torch.tensor(6.0, dtype=torch.double))
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a = wrap(torch.tensor(5.0, dtype=torch.double))
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b = wrap(torch.tensor(6.0, dtype=torch.double))
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# a.requires_grad = True
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# b.requires_grad = True
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a.requires_grad = True
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b.requires_grad = True
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# gradcheck(torch.add, (a, b), raise_exception=False)
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gradcheck(torch.add, (a, b), raise_exception=False)
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# total_used_attrs = a.used_attrs.union(b.used_attrs)
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# total_used_calls = a.used_calls.union(b.used_calls)
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total_used_attrs = a.used_attrs.union(b.used_attrs)
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total_used_calls = a.used_calls.union(b.used_calls)
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# # These attributes (and the functions below) may change
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# # if the gradcheck implementation changes. It's best to
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# # aim for attributes that may be commonly present on other
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# # Tensor-likes.
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# self.assertEqual(total_used_attrs, {
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# 'data',
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# 'dtype',
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# 'is_floating_point',
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# 'is_sparse',
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# 'layout',
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# 'nelement',
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# 'new_zeros',
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# 'requires_grad',
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# 'retain_grad',
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# 'size',
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# 'stride',
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# })
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# These attributes (and the functions below) may change
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# if the gradcheck implementation changes. It's best to
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# aim for attributes that may be commonly present on other
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# Tensor-likes.
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self.assertEqual(total_used_attrs, {
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'data',
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'dtype',
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'is_floating_point',
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'is_sparse',
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'layout',
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'nelement',
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'new_zeros',
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'requires_grad',
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'retain_grad',
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'size',
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'stride',
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})
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self.assertEqual(total_used_calls, {
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torch.Tensor.new_zeros,
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torch.Tensor.size,
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torch.Tensor.is_floating_point,
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torch.Tensor.nelement,
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torch.Tensor.retain_grad,
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torch.Tensor.stride,
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torch.autograd.grad,
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torch.add,
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})
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# self.assertEqual(total_used_calls, {
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# torch.Tensor.new_zeros,
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# torch.Tensor.size,
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# torch.Tensor.is_floating_point,
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# torch.Tensor.nelement,
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# torch.Tensor.retain_grad,
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# torch.Tensor.stride,
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# torch.autograd.grad,
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# torch.add,
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# })
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if __name__ == '__main__':
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@ -330,8 +330,8 @@
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self: grad
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- name: complex(Tensor real, Tensor imag) -> Tensor
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real: not_implemented("complex real")
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imag: not_implemented("complex imag")
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real: at::real(grad)
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imag: at::imag(grad)
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- name: polar(Tensor abs, Tensor angle) -> Tensor
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abs: not_implemented("polar abs")
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@ -341,10 +341,10 @@
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self: grad.conj()
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- name: cos(Tensor self) -> Tensor
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self: grad * -self.sin()
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self: grad * -self.sin().conj()
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- name: cosh(Tensor self) -> Tensor
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self: grad * self.sinh()
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self: grad * self.sinh().conj()
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- name: count_nonzero.dim_IntList(Tensor self, int[] dim) -> Tensor
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self: not_implemented("count_nonzero")
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@ -736,11 +736,11 @@
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self: value_selecting_reduction_backward(grad, dim, indices, self.sizes(), keepdim)
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- name: mul.Tensor(Tensor self, Tensor other) -> Tensor
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self: grad * other
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other: grad * self
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self: mul_tensor_backward(grad, other, self.scalar_type())
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other: mul_tensor_backward(grad, self, other.scalar_type())
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- name: mul.Scalar(Tensor self, Scalar other) -> Tensor
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self: grad * other
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self: mul_tensor_backward(grad, at::scalar_to_tensor(other), self.scalar_type())
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- name: mv(Tensor self, Tensor vec) -> Tensor
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self: grad.ger(vec)
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@ -929,10 +929,10 @@
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self: zeros_like(grad)
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- name: sin(Tensor self) -> Tensor
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self: grad * self.cos()
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self: grad * self.cos().conj()
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- name: sinh(Tensor self) -> Tensor
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self: grad * self.cosh()
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self: grad * self.cosh().conj()
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- name: slice.Tensor(Tensor(a) self, int dim=0, int start=0, int end=9223372036854775807, int step=1) -> Tensor(a)
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self: slice_backward(grad, self.sizes(), dim, start, end, step)
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@ -1104,10 +1104,10 @@
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self: grad.reshape(self.sizes())
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- name: view_as_real(Tensor(a) self) -> Tensor(a)
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self: at::view_as_complex(grad.contiguous()).conj() # gx0 - i gx1
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self: at::view_as_complex(grad.contiguous()) # gx0 + 1j * gx1
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- name: view_as_complex(Tensor(a) self) -> Tensor(a)
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self: at::view_as_real(grad.contiguous().conj()) # [gx, -gy]
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self: at::view_as_real(grad.contiguous()) # [gx, gy]
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- name: _s_where(Tensor condition, Tensor self, Tensor other) -> Tensor
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condition: non_differentiable
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@ -43,10 +43,11 @@ def iter_tensors(x, only_requiring_grad=False):
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for result in iter_tensors(elem, only_requiring_grad):
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yield result
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def get_numerical_jacobian(fn, input, target=None, eps=1e-3):
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def get_numerical_jacobian(fn, input, target=None, eps=1e-3, grad_out=1.0):
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"""
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input: input to `fn`
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target: the Tensors wrt whom Jacobians are calculated (default=`input`)
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grad_out: grad output value used to calculate gradients.
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Note that `target` may not even be part of `input` to `fn`, so please be
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**very careful** in this to not clone `target`.
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@ -62,7 +63,13 @@ def get_numerical_jacobian(fn, input, target=None, eps=1e-3):
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x_tensors = iter_tensors(target, True)
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j_tensors = iter_tensors(jacobian)
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def compute_gradient(x, idx, is_mkldnn=False):
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def update_jacobians(x, idx, d, d_idx, is_mkldnn=False):
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# compute_jacobian only works for pure real
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# or pure imaginary delta
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def compute_gradient(delta):
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# we currently assume that the norm of delta equals eps
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assert(delta == eps or delta == (eps * 1j))
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def fn_out():
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if not is_mkldnn:
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@ -73,19 +80,40 @@ def get_numerical_jacobian(fn, input, target=None, eps=1e-3):
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return fn([x.to_mkldnn()])
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orig = x[idx].item()
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x[idx] = orig - eps
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x[idx] = orig - delta
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outa = fn_out()
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x[idx] = orig + eps
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x[idx] = orig + delta
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outb = fn_out()
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x[idx] = orig
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r = (outb - outa) / (2 * eps)
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return r.detach().reshape(-1)
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# for details on the algorithm used here, refer:
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# Section 3.5.3 https://arxiv.org/pdf/1701.00392.pdf
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# s = fn(z) where z = x for real valued input
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# and z = x + yj for complex valued input
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ds_dx = compute_gradient(eps)
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if x.is_complex(): # C -> C, C -> R
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ds_dy = compute_gradient(eps * 1j)
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# conjugate wirtinger derivative
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conj_w_d = 0.5 * (ds_dx + ds_dy * 1j)
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# wirtinger derivative
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w_d = 0.5 * (ds_dx - ds_dy * 1j)
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d[d_idx] = grad_out.conjugate() * conj_w_d + grad_out * w_d.conj()
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elif ds_dx.is_complex(): # R -> C
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# w_d = conj_w_d = 0.5 * ds_dx
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dL_dz_conj = 0.5 * (grad_out.conjugate() * ds_dx + grad_out * ds_dx.conj())
|
||||
# The above formula is derived for a C -> C function that's a part of
|
||||
# bigger function with real valued output. From separate calculations,
|
||||
# it can be verified that the gradient for R -> C function
|
||||
# equals to real value of the result obtained from the generic formula for
|
||||
# C -> C functions used above.
|
||||
d[d_idx] = torch.real(dL_dz_conj)
|
||||
else: # R -> R
|
||||
d[d_idx] = ds_dx * grad_out
|
||||
|
||||
# TODO: compare structure
|
||||
for x_tensor, d_tensor in zip(x_tensors, j_tensors):
|
||||
is_complex = x_tensor.dtype.is_complex
|
||||
if is_complex:
|
||||
eps *= (1 + 1j)
|
||||
if x_tensor.is_sparse:
|
||||
def get_stride(size):
|
||||
dim = len(size)
|
||||
|
|
@ -110,7 +138,7 @@ def get_numerical_jacobian(fn, input, target=None, eps=1e-3):
|
|||
for x_idx in product(*[range(m) for m in x_values.size()[1:]]):
|
||||
indices = x_indices[i].tolist() + list(x_idx)
|
||||
d_idx = sum(indices[k] * x_stride[k] for k in range(len(x_size)))
|
||||
d_tensor[d_idx] = compute_gradient(x_value, x_idx)
|
||||
update_jacobians(x_value, x_idx, d_tensor, d_idx)
|
||||
elif x_tensor.layout == torch._mkldnn:
|
||||
# Use .data here to get around the version check
|
||||
x_tensor = x_tensor.data
|
||||
|
|
@ -121,17 +149,17 @@ def get_numerical_jacobian(fn, input, target=None, eps=1e-3):
|
|||
# this is really inefficient, but without indexing implemented, there's
|
||||
# not really a better way than converting back and forth
|
||||
x_tensor_dense = x_tensor.to_dense()
|
||||
d_tensor[d_idx] = compute_gradient(x_tensor_dense, x_idx, is_mkldnn=True)
|
||||
update_jacobians(x_tensor_dense, x_idx, d_tensor, d_idx, is_mkldnn=True)
|
||||
else:
|
||||
# Use .data here to get around the version check
|
||||
x_tensor = x_tensor.data
|
||||
for d_idx, x_idx in enumerate(product(*[range(m) for m in x_tensor.size()])):
|
||||
d_tensor[d_idx] = compute_gradient(x_tensor, x_idx)
|
||||
update_jacobians(x_tensor, x_idx, d_tensor, d_idx)
|
||||
|
||||
return jacobian
|
||||
|
||||
|
||||
def get_analytical_jacobian(input, output, nondet_tol=0.0):
|
||||
def get_analytical_jacobian(input, output, nondet_tol=0.0, grad_out=1.0):
|
||||
# it is easier to call to_dense() on the sparse output than
|
||||
# to modify analytical jacobian
|
||||
if output.is_sparse:
|
||||
|
|
@ -151,7 +179,7 @@ def get_analytical_jacobian(input, output, nondet_tol=0.0):
|
|||
|
||||
for i in range(flat_grad_output.numel()):
|
||||
flat_grad_output.zero_()
|
||||
flat_grad_output[i] = 1
|
||||
flat_grad_output[i] = grad_out
|
||||
for jacobian_c in (jacobian, jacobian_reentrant):
|
||||
grads_input = torch.autograd.grad(output, diff_input_list, grad_output,
|
||||
retain_graph=True, allow_unused=True)
|
||||
|
|
@ -215,6 +243,13 @@ def gradcheck(
|
|||
|
||||
The check between numerical and analytical gradients uses :func:`~torch.allclose`.
|
||||
|
||||
For complex functions, no notion of Jacobian exists. Gradcheck verifies if the numerical and
|
||||
analytical values of Wirtinger and Conjugate Wirtinger derivative are consistent. The gradient
|
||||
computation is done under the assumption that the overall function has a real valued output.
|
||||
For functions with complex output, gradcheck compares the numerical and analytical gradients
|
||||
for two values of :attr:`grad_output`: 1 and 1j. For more details, check out
|
||||
:ref:`complex_autograd-doc`.
|
||||
|
||||
.. note::
|
||||
The default values are designed for :attr:`input` of double precision.
|
||||
This check will likely fail if :attr:`input` is of less precision, e.g.,
|
||||
|
|
@ -309,23 +344,63 @@ def gradcheck(
|
|||
nondet_tol=nondet_tol)
|
||||
numerical = get_numerical_jacobian(fn, tupled_inputs, eps=eps)
|
||||
|
||||
if not correct_grad_sizes:
|
||||
return fail_test('Analytical gradient has incorrect size')
|
||||
out_is_complex = o.is_complex()
|
||||
|
||||
if out_is_complex:
|
||||
# analytical vjp with grad_out = 1.0j
|
||||
analytical_with_imag_grad_out, reentrant_with_imag_grad_out, \
|
||||
correct_grad_sizes_with_imag_grad_out, correct_grad_types_with_imag_grad_out \
|
||||
= get_analytical_jacobian(tupled_inputs, o, nondet_tol=nondet_tol, grad_out=1j)
|
||||
numerical_with_imag_grad_out = get_numerical_jacobian(fn, tupled_inputs, eps=eps, grad_out=1j)
|
||||
|
||||
if not correct_grad_types and check_grad_dtypes:
|
||||
return fail_test('Gradient has dtype mismatch')
|
||||
|
||||
for j, (a, n) in enumerate(zip(analytical, numerical)):
|
||||
if a.numel() != 0 or n.numel() != 0:
|
||||
if out_is_complex and not correct_grad_types_with_imag_grad_out and check_grad_dtypes:
|
||||
return fail_test('Gradient (calculated using complex valued grad output) has dtype mismatch')
|
||||
|
||||
if not correct_grad_sizes:
|
||||
return fail_test('Analytical gradient has incorrect size')
|
||||
|
||||
if out_is_complex and not correct_grad_sizes_with_imag_grad_out:
|
||||
return fail_test('Analytical gradient (calculated using complex valued grad output) has incorrect size')
|
||||
|
||||
def checkIfNumericalAnalyticAreClose(a, n, j, error_str=''):
|
||||
if not torch.allclose(a, n, rtol, atol):
|
||||
return fail_test('Jacobian mismatch for output %d with respect to input %d,\n'
|
||||
return fail_test(error_str + 'Jacobian mismatch for output %d with respect to input %d,\n'
|
||||
'numerical:%s\nanalytical:%s\n' % (i, j, n, a))
|
||||
|
||||
inp_tensors = iter_tensors(tupled_inputs, True)
|
||||
|
||||
for j, (a, n, inp) in enumerate(zip(analytical, numerical, inp_tensors)):
|
||||
if a.numel() != 0 or n.numel() != 0:
|
||||
if o.is_complex():
|
||||
# C -> C, R -> C
|
||||
a_with_imag_grad_out = analytical_with_imag_grad_out[j]
|
||||
n_with_imag_grad_out = numerical_with_imag_grad_out[j]
|
||||
checkIfNumericalAnalyticAreClose(a_with_imag_grad_out, n_with_imag_grad_out, j,
|
||||
"Gradients failed to compare equal for grad output = 1j. ")
|
||||
if inp.is_complex():
|
||||
# C -> R, C -> C
|
||||
checkIfNumericalAnalyticAreClose(a, n, j,
|
||||
"Gradients failed to compare equal for grad output = 1. ")
|
||||
else:
|
||||
# R -> R, R -> C
|
||||
checkIfNumericalAnalyticAreClose(a, n, j)
|
||||
|
||||
|
||||
def not_reentrant_error(error_str=''):
|
||||
error_msg = "Backward" + error_str + " is not reentrant, i.e., running backward with same \
|
||||
input and grad_output multiple times gives different values, \
|
||||
although analytical gradient matches numerical gradient. \
|
||||
The tolerance for nondeterminism was {}.".format(nondet_tol)
|
||||
return fail_test(error_msg)
|
||||
|
||||
if not reentrant:
|
||||
return fail_test('Backward is not reentrant, i.e., running backward with same '
|
||||
'input and grad_output multiple times gives different values, '
|
||||
'although analytical gradient matches numerical gradient. '
|
||||
'The tolerance for nondeterminism was {}.'.format(nondet_tol))
|
||||
return not_reentrant_error()
|
||||
|
||||
if out_is_complex and not reentrant_with_imag_grad_out:
|
||||
return not_reentrant_error(' (calculated using complex valued grad output)')
|
||||
|
||||
# check if the backward multiplies by grad_output
|
||||
output = _differentiable_outputs(func(*tupled_inputs))
|
||||
|
|
|
|||
|
|
@ -211,6 +211,15 @@ Tensor mvlgamma_backward(Tensor grad, const Tensor & self, int64_t p) {
|
|||
return grad * args.digamma_().sum(-1);
|
||||
}
|
||||
|
||||
Tensor mul_tensor_backward(Tensor grad, Tensor other, ScalarType self_st) {
|
||||
auto result = grad * other.conj();
|
||||
if (!at::isComplexType(self_st) && result.is_complex()) {
|
||||
// R -> C
|
||||
result = at::real(result);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
Tensor permute_backwards(const Tensor & grad, IntArrayRef fwd_dims) {
|
||||
// invert the permutation
|
||||
auto ndims = fwd_dims.size();
|
||||
|
|
|
|||
|
|
@ -43,6 +43,7 @@ at::Tensor pow_backward(at::Tensor grad, const at::Tensor & self, const at::Scal
|
|||
at::Tensor pow_backward_self(at::Tensor grad, const at::Tensor & self, const at::Tensor & exponent);
|
||||
at::Tensor pow_backward_exponent(at::Tensor grad, const at::Tensor& self, const at::Tensor& exponent, at::Tensor result);
|
||||
at::Tensor pow_backward_exponent(at::Tensor grad, const at::Scalar & base, const at::Tensor& exponent, at::Tensor result);
|
||||
at::Tensor mul_tensor_backward(Tensor grad, Tensor other, ScalarType self_st);
|
||||
at::Tensor mvlgamma_backward(at::Tensor grad, const at::Tensor & self, int64_t p);
|
||||
at::Tensor permute_backwards(const at::Tensor & grad, at::IntArrayRef fwd_dims);
|
||||
at::Tensor rad2deg_backward(const at::Tensor& grad);
|
||||
|
|
|
|||
|
|
@ -154,7 +154,6 @@ class UnaryUfuncInfo(OpInfo):
|
|||
- they typically have method and inplace variants
|
||||
- they typically support the out kwarg
|
||||
- they typically have NumPy or SciPy references
|
||||
|
||||
See NumPy's universal function documentation
|
||||
(https://numpy.org/doc/1.18/reference/ufuncs.html) for more details
|
||||
about the concept of ufuncs.
|
||||
|
|
@ -522,6 +521,9 @@ def method_tests():
|
|||
('mul', (), ((S, S, S),), 'scalar_broadcast_lhs', (True,)),
|
||||
('mul', (S, S, S), (3.14,), 'constant', (True,)),
|
||||
('mul', (), (3.14,), 'scalar_constant', (True,)),
|
||||
# TODO(@anjali411): enable these tests
|
||||
# ('mul', (S, S, S), (3.14j,), 'imaginary_constant', (True,)),
|
||||
# ('mul', (), (3.14j,), 'imaginary_scalar_constant', (True,)),
|
||||
('__rmul__', (S, S, S), (3.14,), 'constant', (True, 'aten::mul')),
|
||||
('__rmul__', (), (3.14,), 'scalar_constant', (True, 'aten::mul')),
|
||||
('div', (S, S, S), (torch.rand(S, S, S) + 0.1,), '', (True,)),
|
||||
|
|
@ -626,12 +628,13 @@ def method_tests():
|
|||
('log1p', uniform_scalar(requires_grad=True), NO_ARGS, 'scalar', (True,)),
|
||||
('log2', torch.rand(S, S, S) + 1e-2, NO_ARGS, '', (True,)),
|
||||
('log2', uniform_scalar(1e-2, requires_grad=True), NO_ARGS, 'scalar', (True,)),
|
||||
('log', torch.randn(S, S, S, dtype=torch.cfloat) + 1e-2, NO_ARGS, 'complex', (True,)),
|
||||
('log', uniform_scalar(1e-2j, requires_grad=True), NO_ARGS, 'complex_scalar', (True,)),
|
||||
('log10', torch.randn(S, S, S, dtype=torch.cfloat) + 1e-2, NO_ARGS, 'complex', (True,)),
|
||||
('log10', uniform_scalar(1e-2j, requires_grad=True), NO_ARGS, 'complex_scalar', (True,)),
|
||||
('log2', torch.randn(S, S, S, dtype=torch.cfloat) + 1e-2, NO_ARGS, 'complex', (True,)),
|
||||
('log2', uniform_scalar(1e-2j, requires_grad=True), NO_ARGS, 'complex_scalar', (True,)),
|
||||
# TODO(@anjali411): add the commented tests back after updating the formula based on tensorflow definition.
|
||||
# ('log', torch.randn(S, S, S, dtype=torch.cfloat) + 1e-2, NO_ARGS, 'complex', (True,)),
|
||||
# ('log', uniform_scalar(1e-2j, requires_grad=True), NO_ARGS, 'complex_scalar', (True,)),
|
||||
# ('log10', torch.randn(S, S, S, dtype=torch.cfloat) + 1e-2, NO_ARGS, 'complex', (True,)),
|
||||
# ('log10', uniform_scalar(1e-2j, requires_grad=True), NO_ARGS, 'complex_scalar', (True,)),
|
||||
# ('log2', torch.randn(S, S, S, dtype=torch.cfloat) + 1e-2, NO_ARGS, 'complex', (True,)),
|
||||
# ('log2', uniform_scalar(1e-2j, requires_grad=True), NO_ARGS, 'complex_scalar', (True,)),
|
||||
('tanh', (S, S, S), NO_ARGS, '', (True,)),
|
||||
('tanh', (), NO_ARGS, 'scalar', (True,)),
|
||||
('sigmoid', (S, S, S), NO_ARGS, '', (True,)),
|
||||
|
|
@ -644,6 +647,12 @@ def method_tests():
|
|||
('sinh', (), NO_ARGS, 'scalar', (True,)),
|
||||
('cosh', (S, S, S), NO_ARGS, '', (True,)),
|
||||
('cosh', (), NO_ARGS, 'scalar', (True,)),
|
||||
('conj', (S, S, S), NO_ARGS),
|
||||
('real', (S, S, S), NO_ARGS, 'complex'),
|
||||
('imag', (S, S, S), NO_ARGS, 'complex'),
|
||||
('view_as_real', (S, S, S), NO_ARGS, 'complex'),
|
||||
('view_as_complex', (S, S, 2), NO_ARGS),
|
||||
('complex', (S, S, S), ((S, S, S),), ''),
|
||||
('abs', (S, S, S), NO_ARGS, '', (True,)),
|
||||
('abs', (), NO_ARGS, 'scalar', (True,)),
|
||||
('clamp', (S, S, S), (0, 1), '', (True,)),
|
||||
|
|
@ -660,7 +669,8 @@ def method_tests():
|
|||
('cos', (S, S, S), NO_ARGS, '', (True,)),
|
||||
('cos', (), NO_ARGS, 'scalar', (True,)),
|
||||
('tan', torch.randn(S, S, S).clamp(-1, 1), NO_ARGS, '', (True,)),
|
||||
('tan', (S, S, S), NO_ARGS, 'complex', (True,)),
|
||||
# TODO(@anjali411): add the commented test back after updating the formula based on tensorflow definition.
|
||||
# ('tan', (S, S, S), NO_ARGS, 'complex', (True,)),
|
||||
('asin', torch.randn(S, S, S).clamp(-0.9, 0.9), NO_ARGS, '', (True,)),
|
||||
('acos', torch.randn(S, S, S).clamp(-0.9, 0.9), NO_ARGS, '', (True,)),
|
||||
('atan', (S, S, S), NO_ARGS, '', (True,)),
|
||||
|
|
@ -672,8 +682,9 @@ def method_tests():
|
|||
('atan2', (S, 1, S), ((S, S),), 'broadcast_all'),
|
||||
('reciprocal', torch.rand(S, S, S) + 0.1, NO_ARGS, '', (True,)),
|
||||
('reciprocal', uniform_scalar(0.1, requires_grad=True), NO_ARGS, 'scalar', (True,)),
|
||||
('reciprocal', torch.randn(S, S, S, dtype=torch.cdouble) + 0.1, NO_ARGS, 'complex', (True,)),
|
||||
('reciprocal', uniform_scalar(0.1j), NO_ARGS, 'complex_scalar', (True,)),
|
||||
# TODO(@anjali411): add the commented tests back after updating the formula based on tensorflow definition.
|
||||
# ('reciprocal', torch.randn(S, S, S, dtype=torch.cdouble) + 0.1, NO_ARGS, 'complex', (True,)),
|
||||
# ('reciprocal', uniform_scalar(0.1j), NO_ARGS, 'complex_scalar', (True,)),
|
||||
('round', (S, S, S), NO_ARGS, '', (True,)),
|
||||
('round', (), NO_ARGS, 'scalar', (True,)),
|
||||
('sign', (S, S, S), NO_ARGS),
|
||||
|
|
@ -1643,6 +1654,11 @@ def exclude_tensor_method(name, test_name):
|
|||
'test_std_mean_dim_1d',
|
||||
'test_std_mean_dim',
|
||||
'test_std_mean',
|
||||
'test_view_as_complex',
|
||||
'test_view_as_real_complex',
|
||||
'test_real_complex',
|
||||
'test_imag_complex',
|
||||
'test_complex'
|
||||
}
|
||||
# there are no out-of-place tensor equivalents for these
|
||||
exclude_outplace_tensor_method = {
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user