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https://github.com/pytorch/pytorch.git
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8b02d1207b
Summary: Fixes https://github.com/pytorch/pytorch/issues/53906 Pull Request resolved: https://github.com/pytorch/pytorch/pull/54954 Reviewed By: glaringlee Differential Revision: D27474863 Pulled By: heitorschueroff fbshipit-source-id: cf8c1cac6fd1cceacd6be73a2eb49d28a5cfc20a
277 lines
13 KiB
Python
277 lines
13 KiB
Python
# Torch
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import torch
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import torch.cuda
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import torch.jit
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import torch.jit._logging
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import torch.jit.frontend
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import torch.jit.quantized
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# Testing utils
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from torch.testing import floating_and_complex_types_and
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from torch.testing._internal.common_utils import TestCase, \
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freeze_rng_state, TemporaryFileName, enable_profiling_mode_for_profiling_tests, is_iterable_of_tensors
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from torch.testing._internal.common_utils import enable_profiling_mode # noqa: F401
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# Standard library
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from itertools import chain
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from typing import List, Union
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import io
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def check_output_types(self, func, ref_outputs, args, kwargs):
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graph = getattr(func, 'last_graph', None)
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types = [o.type() for o in graph.outputs()]
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self.assertTrue(len(types) == 1)
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t = types[0]
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torch._C._jit_assert_is_instance(ref_outputs, t)
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# Test names in this set are only checked for a single derivative
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nn_functional_single_grad = frozenset('test_nn_' + name for name in [
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'pdist',
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'multilabel_margin_loss',
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'max_unpool3d',
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'multi_margin_loss',
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'binary_cross_entropy',
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'binary_cross_entropy_size_average',
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'ctc_loss',
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'grid_sample',
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])
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def check_against_reference(self, func, reference_func, output_func, args, kwargs=None,
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allow_unused=True, check_types=True, no_grad=False):
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kwargs = kwargs if kwargs else {}
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def allSum(vs):
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if isinstance(vs, torch.Tensor):
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vs = (vs,)
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return sum((i + 1) * v.sum()
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for i, v in enumerate(vs)
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if v is not None and v.dtype in floating_and_complex_types_and(torch.half, torch.bfloat16))
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def clone_tensor(t, preserve_requires_grad):
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require_grad = preserve_requires_grad and t.requires_grad
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return t.detach().clone().requires_grad_(require_grad)
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def clone_inputs(preserve_requires_grad: bool):
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inputs: List[Union[torch.Tensor, List[torch.Tensor]]] = []
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for arg in args:
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if isinstance(arg, torch.Tensor):
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inputs.append(clone_tensor(arg, preserve_requires_grad))
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elif is_iterable_of_tensors(arg):
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inputs.append([clone_tensor(t, preserve_requires_grad) for t in arg])
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else:
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inputs.append(arg)
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return inputs
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# Returns tensors in args that requires_grad, including tensors in TensorList args
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def get_recording_tensors(args):
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recording_tensors: List[torch.Tensor] = []
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for arg in args:
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if isinstance(arg, torch.Tensor) and arg.requires_grad:
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recording_tensors.append(arg)
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elif is_iterable_of_tensors(arg):
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recording_tensors.extend(filter(lambda t: t.requires_grad, arg))
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return recording_tensors
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# test no gradients case
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nograd_inputs = clone_inputs(preserve_requires_grad=False)
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outputs = self.runAndSaveRNG(reference_func, nograd_inputs, kwargs)
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with enable_profiling_mode_for_profiling_tests():
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outputs_test = self.runAndSaveRNG(func, nograd_inputs, kwargs)
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self.assertEqual(outputs, outputs_test)
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if check_types:
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check_output_types(self, func, outputs_test, nograd_inputs, kwargs)
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if no_grad:
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# skip grad tests
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return
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with enable_profiling_mode_for_profiling_tests():
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# test single grad case
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recording_inputs = clone_inputs(preserve_requires_grad=True)
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recording_tensors = get_recording_tensors(recording_inputs)
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outputs = output_func(self.runAndSaveRNG(reference_func, recording_inputs, kwargs))
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grads = torch.autograd.grad(allSum(outputs), recording_tensors,
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allow_unused=allow_unused)
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outputs_test = output_func(self.runAndSaveRNG(func, recording_inputs, kwargs))
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grads_test = torch.autograd.grad(allSum(outputs_test), recording_tensors,
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allow_unused=allow_unused)
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self.assertEqual(outputs, outputs_test)
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self.assertEqual(grads, grads_test)
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# test the grad grad case
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if self._testMethodName in nn_functional_single_grad:
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return
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outputs = output_func(self.runAndSaveRNG(reference_func, recording_inputs, kwargs))
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l1 = allSum(outputs)
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grads = torch.autograd.grad(l1, recording_tensors, create_graph=True,
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allow_unused=allow_unused)
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l2 = (allSum(grads) * l1)
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grads2 = torch.autograd.grad(l2, recording_tensors, allow_unused=allow_unused)
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recording_inputs = clone_inputs(preserve_requires_grad=True)
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recording_tensors = get_recording_tensors(recording_inputs)
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outputs_test = output_func(self.runAndSaveRNG(func, recording_inputs, kwargs))
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l1_test = allSum(outputs_test)
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grads_test = torch.autograd.grad(
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l1_test, recording_tensors, create_graph=True, allow_unused=allow_unused)
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l2_test = (allSum(grads_test) * l1_test)
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grads2_test = torch.autograd.grad(l2_test, recording_tensors, allow_unused=allow_unused)
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self.assertEqual(outputs, outputs_test)
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self.assertEqual(grads, grads_test)
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for g2, g2_test in zip(grads2, grads2_test):
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if g2 is None and g2_test is None:
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continue
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self.assertTrue(torch.allclose(g2, g2_test, atol=5e-4, rtol=1e-4))
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class JitCommonTestCase(TestCase):
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def createFunctionFromGraph(self, trace):
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graph = trace if isinstance(trace, torch._C.Graph) else trace.graph()
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return torch._C._create_function_from_graph("forward", graph)
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def assertExportImport(self, trace, inputs):
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m = self.createFunctionFromGraph(trace)
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self.assertExportImportModule(m, inputs)
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def assertExportImportModule(self, m, inputs):
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m_import = self.getExportImportCopy(m)
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a = self.runAndSaveRNG(m, inputs)
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b = self.runAndSaveRNG(m_import, inputs)
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self.assertEqual(a, b, "Results of original model and "
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"exported/imported version of model differed")
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def runAndSaveRNG(self, func, inputs, kwargs=None):
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kwargs = kwargs if kwargs else {}
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with freeze_rng_state():
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results = func(*inputs, **kwargs)
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return results
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def getExportImportCopy(self, m, also_test_file=True, map_location=None):
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buffer = io.BytesIO()
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torch.jit.save(m, buffer)
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buffer.seek(0)
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imported = torch.jit.load(buffer, map_location=map_location)
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if not also_test_file:
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return imported
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with TemporaryFileName() as fname:
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torch.jit.save(imported, fname)
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return torch.jit.load(fname, map_location=map_location)
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def autoDiffErrorMessage(self, should_autodiff_node, nodes_not_in_diff_graph,
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fusion_nodes_not_found, non_fusible_nodes_being_fused,
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fusion_nodes_found, nodes_in_diff_graph):
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err_msg = "\nFailure in testing nodes' autodifferentiation. "
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if should_autodiff_node:
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err_msg += "One or more nodes were expected to be autodiffed, " \
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"but were not found in specified fusible/nonfusible " \
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"DifferentiableGraph groups. \nSpecifically:"
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# The node is intended to appear in a differentiable graph but doesn't
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diff_nodes_missing = []
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# The node is intended to appear in a differentiable graph
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# outside of a fusion group but instead is in a fusion group
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diff_nodes_in_fusion = []
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# The node is intended to appear in a fusion group but doesn't
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fusion_nodes_missing = []
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# The node is intended to appear in a fusion group but instead
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# is just in an outer differentiable graph
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fusion_nodes_in_diff = []
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for node in nodes_not_in_diff_graph:
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if node in non_fusible_nodes_being_fused:
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diff_nodes_in_fusion.append(node)
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else:
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diff_nodes_missing.append(node)
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for node in fusion_nodes_not_found:
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if node in nodes_in_diff_graph:
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fusion_nodes_in_diff.append(node)
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else:
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fusion_nodes_missing.append(node)
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if len(diff_nodes_missing) > 0:
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err_msg += f"\n {diff_nodes_missing} were not in one of the " \
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"DifferentiableGraphs when they were expected to be. " \
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"Did you intend for these nodes to be autodiffed? " \
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"If not, remove them from the list of nonfusible nodes."
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if len(diff_nodes_in_fusion) > 0:
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err_msg += f"\n {diff_nodes_in_fusion} were found in one of the FusionGroups " \
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"when they were expected to be just in a DifferentiableGraph. If it was " \
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"intended for these nodes to be in FusionGroups, reclassify these nodes as " \
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"fusible nodes. If these nodes were not intended to be fused, your " \
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"autodifferentiation logic might be wrong."
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if len(fusion_nodes_missing) > 0:
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err_msg += f"\n {fusion_nodes_missing} were not in one of the FusionGroups " \
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"of the DifferentiableGraphs when they were expected to be. " \
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"They were also not found in an outer DifferentiableGraph. Did you " \
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"intend for these nodes to be autodifferentiated? If not, you should " \
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"remove these nodes from the test's fusible nodes. Otherwise your " \
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"autodifferentiation logic might be wrong."
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if len(fusion_nodes_in_diff) > 0:
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err_msg += f"\n {fusion_nodes_in_diff} were not in one of the FusionGroups " \
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"of the DifferentiableGraphs when they were expected to be, " \
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"instead they were found just in an outer DifferentiableGraph. " \
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"Did you intend for these nodes to be fused? If not, you should " \
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"move these nodes into the test's nonfusible nodes. Otherwise your " \
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"autodifferentiation logic might be wrong."
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else:
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err_msg += "One or more nodes were not expected to be autodiffed " \
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"but were found in a DifferentiableGraph or in a FusionGroup " \
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"of a DifferentiableGraph. Did you intend for these nodes to be " \
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"autodiffed? If so, change this test to expect autodifferentiation. " \
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"\nSpecifically:"
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if len(fusion_nodes_found) > 0:
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err_msg += f"\n {fusion_nodes_found} were not expected to be in " \
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"one of the DifferentiableGraphs, but appeared in a FusionGroup " \
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"of a DifferentiableGraph. "
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if len(nodes_in_diff_graph) > 0:
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err_msg += f"\n {nodes_in_diff_graph} were not expected to " \
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"be in one of the DifferentiableGraphs but were."
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return err_msg
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def assertAutodiffNode(self, graph, should_autodiff_node, nonfusible_nodes, fusible_nodes):
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diff_nodes = graph.findAllNodes('prim::DifferentiableGraph')
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diff_subgraphs = [node.g('Subgraph') for node in diff_nodes]
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# Note: currently no tests have fusible_nodes
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fusion_nodes = list(chain.from_iterable([g.findAllNodes('prim::FusionGroup') for g in diff_subgraphs]))
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fusion_subgraphs = [node.g('Subgraph') for node in fusion_nodes]
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# For any non-fusible node, it must show up in one of the DifferentiableGraphs.
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nodes_in_diff_graph = []
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nodes_not_in_diff_graph = []
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non_fusible_nodes_being_fused = []
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for node in nonfusible_nodes:
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if any(g.findNode(node) is not None for g in diff_subgraphs):
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nodes_in_diff_graph.append(node)
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else:
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nodes_not_in_diff_graph.append(node)
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if any(g.findNode(node) is not None for g in fusion_subgraphs):
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non_fusible_nodes_being_fused.append(node)
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found_all_nonfusible_nodes = len(nodes_in_diff_graph) == len(nonfusible_nodes)
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# For any fusible node, it must show up in one of the FusionGroups in one of the DifferentiableGraphs.
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fusion_nodes_found = []
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fusion_nodes_not_found = []
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for node in fusible_nodes:
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if any(g.findNode(node) is not None for g in fusion_subgraphs):
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fusion_nodes_found.append(node)
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else:
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fusion_nodes_not_found.append(node)
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found_all_fusible_nodes = len(fusion_nodes_found) == len(fusible_nodes)
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err_msg = self.autoDiffErrorMessage(should_autodiff_node,
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nodes_not_in_diff_graph,
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fusion_nodes_not_found,
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non_fusible_nodes_being_fused,
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fusion_nodes_found,
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nodes_in_diff_graph)
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self.assertEqual(should_autodiff_node,
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found_all_nonfusible_nodes and found_all_fusible_nodes, err_msg)
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