# Torch import torch import torch.cuda import torch.jit import torch.jit._logging import torch.jit.frontend import torch.jit.quantized # Testing utils from torch.testing._internal.common_dtype import floating_and_complex_types_and from torch.testing._internal.common_utils import TestCase, \ freeze_rng_state, TemporaryFileName, enable_profiling_mode_for_profiling_tests, is_iterable_of_tensors from torch.testing._internal.common_utils import enable_profiling_mode # noqa: F401 # Standard library from itertools import chain from typing import List, Union from torch._C import TensorType import io def check_output_types(self, func, ref_outputs, args, kwargs): graph = getattr(func, 'last_graph', None) types = [o.type() for o in graph.outputs()] self.assertTrue(len(types) == 1) t = types[0] torch._C._jit_assert_is_instance(ref_outputs, t) # Test names in this set are only checked for a single derivative nn_functional_single_grad = frozenset('test_nn_' + name for name in [ 'pdist', 'multilabel_margin_loss', 'max_unpool3d', 'multi_margin_loss', 'binary_cross_entropy', 'binary_cross_entropy_size_average', 'ctc_loss', 'grid_sample', ]) def check_against_reference(self, func, reference_func, output_func, args, kwargs=None, allow_unused=True, check_types=True, no_grad=False, no_gradgrad=False): """Verifies a function performs identically to some reference implementation. Commonly, this is used to verify that a JIT implementation (output_func) matches the behavior of the eager implementation (reference_func). """ kwargs = kwargs if kwargs else {} def allSum(vs): if isinstance(vs, torch.Tensor): vs = (vs,) return sum((i + 1) * v.sum() for i, v in enumerate(vs) if v is not None and v.dtype in floating_and_complex_types_and(torch.half, torch.bfloat16)) def clone_tensor(t, preserve_requires_grad): require_grad = preserve_requires_grad and t.requires_grad return t.detach().clone().requires_grad_(require_grad) def clone_inputs(preserve_requires_grad: bool): inputs: List[Union[torch.Tensor, List[torch.Tensor]]] = [] for arg in args: if isinstance(arg, torch.Tensor): inputs.append(clone_tensor(arg, preserve_requires_grad)) elif is_iterable_of_tensors(arg): inputs.append([clone_tensor(t, preserve_requires_grad) for t in arg]) else: inputs.append(arg) return inputs # Returns tensors in args that requires_grad, including tensors in TensorList args def get_recording_tensors(args): recording_tensors: List[torch.Tensor] = [] for arg in args: if isinstance(arg, torch.Tensor) and arg.requires_grad: recording_tensors.append(arg) elif is_iterable_of_tensors(arg): recording_tensors.extend(filter(lambda t: t.requires_grad, arg)) return recording_tensors # test no gradients case nograd_inputs = clone_inputs(preserve_requires_grad=False) outputs = self.runAndSaveRNG(reference_func, nograd_inputs, kwargs) with enable_profiling_mode_for_profiling_tests(): outputs_test = self.runAndSaveRNG(func, nograd_inputs, kwargs) self.assertEqual(outputs, outputs_test) if check_types: check_output_types(self, func, outputs_test, nograd_inputs, kwargs) if no_grad: # skip grad tests return with enable_profiling_mode_for_profiling_tests(): # test single grad case recording_inputs = clone_inputs(preserve_requires_grad=True) recording_tensors = get_recording_tensors(recording_inputs) outputs = output_func(self.runAndSaveRNG(reference_func, recording_inputs, kwargs)) grads = torch.autograd.grad(allSum(outputs), recording_tensors, allow_unused=allow_unused) outputs_test = output_func(self.runAndSaveRNG(func, recording_inputs, kwargs)) grads_test = torch.autograd.grad(allSum(outputs_test), recording_tensors, allow_unused=allow_unused) self.assertEqual(outputs, outputs_test) self.assertEqual(grads, grads_test) # test the grad grad case if self._testMethodName in nn_functional_single_grad or no_gradgrad: return outputs = output_func(self.runAndSaveRNG(reference_func, recording_inputs, kwargs)) l1 = allSum(outputs) grads = torch.autograd.grad(l1, recording_tensors, create_graph=True, allow_unused=allow_unused) l2 = (allSum(grads) * l1) grads2 = torch.autograd.grad(l2, recording_tensors, allow_unused=allow_unused) recording_inputs = clone_inputs(preserve_requires_grad=True) recording_tensors = get_recording_tensors(recording_inputs) outputs_test = output_func(self.runAndSaveRNG(func, recording_inputs, kwargs)) l1_test = allSum(outputs_test) grads_test = torch.autograd.grad( l1_test, recording_tensors, create_graph=True, allow_unused=allow_unused) l2_test = (allSum(grads_test) * l1_test) grads2_test = torch.autograd.grad(l2_test, recording_tensors, allow_unused=allow_unused) self.assertEqual(outputs, outputs_test) self.assertEqual(grads, grads_test) for g2, g2_test in zip(grads2, grads2_test): if g2 is None and g2_test is None: continue self.assertEqual(g2, g2_test, atol=5e-4, rtol=1e-4) class JitCommonTestCase(TestCase): def createFunctionFromGraph(self, trace): graph = trace if isinstance(trace, torch._C.Graph) else trace.graph() return torch._C._create_function_from_graph("forward", graph) def assertExportImport(self, trace, inputs): m = self.createFunctionFromGraph(trace) self.assertExportImportModule(m, inputs) def assertExportImportModule(self, m, inputs): m_import = self.getExportImportCopy(m) a = self.runAndSaveRNG(m, inputs) b = self.runAndSaveRNG(m_import, inputs) self.assertEqual(a, b, "Results of original model and " "exported/imported version of model differed") def runAndSaveRNG(self, func, inputs, kwargs=None): kwargs = kwargs if kwargs else {} with freeze_rng_state(): results = func(*inputs, **kwargs) return results def getExportImportCopy(self, m, also_test_file=True, map_location=None): buffer = io.BytesIO() torch.jit.save(m, buffer) buffer.seek(0) imported = torch.jit.load(buffer, map_location=map_location) if not also_test_file: return imported with TemporaryFileName() as fname: torch.jit.save(imported, fname) return torch.jit.load(fname, map_location=map_location) def autoDiffErrorMessage(self, should_autodiff_node, nodes_not_in_diff_graph, fusion_nodes_not_found, non_fusible_nodes_being_fused, fusion_nodes_found, nodes_in_diff_graph): err_msg = "\nFailure in testing nodes' autodifferentiation. " if should_autodiff_node: err_msg += "One or more nodes were expected to be autodiffed, " \ "but were not found in specified fusible/nonfusible " \ "DifferentiableGraph groups. \nSpecifically:" # The node is intended to appear in a differentiable graph but doesn't diff_nodes_missing = [] # The node is intended to appear in a differentiable graph # outside of a fusion group but instead is in a fusion group diff_nodes_in_fusion = [] # The node is intended to appear in a fusion group but doesn't fusion_nodes_missing = [] # The node is intended to appear in a fusion group but instead # is just in an outer differentiable graph fusion_nodes_in_diff = [] for node in nodes_not_in_diff_graph: if node in non_fusible_nodes_being_fused: diff_nodes_in_fusion.append(node) else: diff_nodes_missing.append(node) for node in fusion_nodes_not_found: if node in nodes_in_diff_graph: fusion_nodes_in_diff.append(node) else: fusion_nodes_missing.append(node) if len(diff_nodes_missing) > 0: err_msg += f"\n {diff_nodes_missing} were not in one of the " \ "DifferentiableGraphs when they were expected to be. " \ "Did you intend for these nodes to be autodiffed? " \ "If not, remove them from the list of nonfusible nodes." if len(diff_nodes_in_fusion) > 0: err_msg += f"\n {diff_nodes_in_fusion} were found in one of the FusionGroups " \ "when they were expected to be just in a DifferentiableGraph. If it was " \ "intended for these nodes to be in FusionGroups, reclassify these nodes as " \ "fusible nodes. If these nodes were not intended to be fused, your " \ "autodifferentiation logic might be wrong." if len(fusion_nodes_missing) > 0: err_msg += f"\n {fusion_nodes_missing} were not in one of the FusionGroups " \ "of the DifferentiableGraphs when they were expected to be. " \ "They were also not found in an outer DifferentiableGraph. Did you " \ "intend for these nodes to be autodifferentiated? If not, you should " \ "remove these nodes from the test's fusible nodes. Otherwise your " \ "autodifferentiation logic might be wrong." if len(fusion_nodes_in_diff) > 0: err_msg += f"\n {fusion_nodes_in_diff} were not in one of the FusionGroups " \ "of the DifferentiableGraphs when they were expected to be, " \ "instead they were found just in an outer DifferentiableGraph. " \ "Did you intend for these nodes to be fused? If not, you should " \ "move these nodes into the test's nonfusible nodes. Otherwise your " \ "autodifferentiation logic might be wrong." else: err_msg += "One or more nodes were not expected to be autodiffed " \ "but were found in a DifferentiableGraph or in a FusionGroup " \ "of a DifferentiableGraph. Did you intend for these nodes to be " \ "autodiffed? If so, change this test to expect autodifferentiation. " \ "\nSpecifically:" if len(fusion_nodes_found) > 0: err_msg += f"\n {fusion_nodes_found} were not expected to be in " \ "one of the DifferentiableGraphs, but appeared in a FusionGroup " \ "of a DifferentiableGraph. " if len(nodes_in_diff_graph) > 0: err_msg += f"\n {nodes_in_diff_graph} were not expected to " \ "be in one of the DifferentiableGraphs but were." return err_msg def assertAutodiffNode(self, graph, should_autodiff_node, nonfusible_nodes, fusible_nodes): diff_nodes = graph.findAllNodes('prim::DifferentiableGraph') diff_subgraphs = [node.g('Subgraph') for node in diff_nodes] # Note: currently no tests have fusible_nodes fusion_nodes = list(chain.from_iterable([g.findAllNodes('prim::FusionGroup') for g in diff_subgraphs])) fusion_subgraphs = [node.g('Subgraph') for node in fusion_nodes] # For any non-fusible node, it must show up in one of the DifferentiableGraphs. nodes_in_diff_graph = [] nodes_not_in_diff_graph = [] non_fusible_nodes_being_fused = [] for node in nonfusible_nodes: if any(g.findNode(node) is not None for g in diff_subgraphs): nodes_in_diff_graph.append(node) else: nodes_not_in_diff_graph.append(node) if any(g.findNode(node) is not None for g in fusion_subgraphs): non_fusible_nodes_being_fused.append(node) found_all_nonfusible_nodes = len(nodes_in_diff_graph) == len(nonfusible_nodes) # For any fusible node, it must show up in one of the FusionGroups in one of the DifferentiableGraphs. fusion_nodes_found = [] fusion_nodes_not_found = [] for node in fusible_nodes: if any(g.findNode(node) is not None for g in fusion_subgraphs): fusion_nodes_found.append(node) else: fusion_nodes_not_found.append(node) found_all_fusible_nodes = len(fusion_nodes_found) == len(fusible_nodes) if should_autodiff_node is not None: err_msg = self.autoDiffErrorMessage(should_autodiff_node, nodes_not_in_diff_graph, fusion_nodes_not_found, non_fusible_nodes_being_fused, fusion_nodes_found, nodes_in_diff_graph) self.assertEqual(should_autodiff_node, found_all_nonfusible_nodes and found_all_fusible_nodes, err_msg) def checkShapeAnalysis(self, out_sizes: Union[List[int], List[List[int]]], traced_graph, assert_propagation, constant_prop=True): # repropagte input shapes provided by tracing, prev_symbolic_shapes_test_enabled = torch._C._jit_symbolic_shapes_test_mode_enabled() for enable_test_mode in [True, False]: # here we are testing allowing/disallowing substituting in complete shapes as constants, # disallowing constants helps stress test partial eval and substitution pipeline torch._C._jit_set_symbolic_shapes_test_mode(enable_test_mode) torch._C._jit_erase_non_input_shape_information(traced_graph) if constant_prop: torch._C._jit_pass_constant_propagation(traced_graph) torch._C._jit_pass_propagate_shapes_on_graph(traced_graph) # Add sizes to default tensor type to avoid checking something out of scope # and difficulties with tracer leaving in other parts of tensor type output = next(traced_graph.outputs()).type() def test_type(type, actual_size): sizes = type.symbolic_sizes() out_type = TensorType.get().with_sizes(sizes) actual_type = TensorType.get().with_sizes(actual_size) # always check actual shape is a subtype of the output self.assertTrue(actual_type.isSubtypeOf(out_type)) # and then if assertion flag is provided, check shape analysis # is successful if assert_propagation: self.assertEqual(out_type.sizes(), actual_size) if output.isSubtypeOf(torch._C.TensorType.get()): test_type(output, out_sizes) else: tuple_elements = output.elements() for i in range(len(tuple_elements)): test_type(tuple_elements[i], out_sizes[i]) torch._C._jit_set_symbolic_shapes_test_mode(prev_symbolic_shapes_test_enabled)