# Owner(s): ["module: dynamo"] import collections import contextlib import copy import itertools import os import tempfile import traceback import types import unittest from copy import deepcopy from functools import partial from typing import Dict, NamedTuple, Tuple from unittest.mock import patch import torch import torch._dynamo.test_case import torch._dynamo.testing import torch.nn.functional as F from torch._dynamo.debug_utils import same_two_models from torch._dynamo.eval_frame import unsupported from torch._dynamo.mutation_guard import GenerationTracker from torch._dynamo.testing import expectedFailureDynamic, same from torch._dynamo.variables.torch_function import TensorWithTFOverrideVariable from torch.nn.modules.lazy import LazyModuleMixin from torch.nn.parameter import Parameter, UninitializedParameter try: from . import test_functions except ImportError: import test_functions _variable = 0 _variable1 = 0 def update_global(): global _variable, _variable1 _variable += 1 _variable1 += 1 class BasicModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.linear1 = torch.nn.Linear(10, 10) self.scale = torch.randn(1, 10) def forward(self, x): return F.relu(self.linear1(x)) * self.scale class FnMember(torch.nn.Module): def __init__(self) -> None: super().__init__() self.linear1 = torch.nn.Linear(10, 10) self.activation = F.relu def forward(self, x): x = self.linear1(x) if self.activation: x = self.activation(x) return x class FnMemberCmp(torch.nn.Module): def __init__(self, activation): super().__init__() self.linear1 = torch.nn.Linear(10, 10) self.activation = activation def forward(self, x): x = self.linear1(x) if self.activation is not None: x = self.activation(x) if self.activation is None: x = torch.sigmoid(x) return x class SubmoduleExample(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layer1 = BasicModule() self.layer2 = BasicModule() self.scale = torch.randn(1, 10) def forward(self, x): x = self.layer1(x) x = self.layer2(x) return x * self.scale class IsTrainingCheck(torch.nn.Module): def __init__(self) -> None: super().__init__() self.linear1 = torch.nn.Linear(10, 10) self.linear2 = torch.nn.Linear(10, 10) self.train(True) def forward(self, x): if self.training: mod = self.linear1 else: mod = self.linear2 return F.relu(mod(x)) class IsEvalCheck(IsTrainingCheck): def __init__(self) -> None: super().__init__() self.train(False) class ModuleMethodCall(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layer1 = BasicModule() self.layer2 = BasicModule() self.scale = torch.randn(1, 10) def call_and_scale(self, mod, x): x = mod(x) return x * self.scale def forward(self, x): x1 = self.call_and_scale(self.layer1, x) x2 = self.call_and_scale(self.layer2, x) return x1 + x2 class UnsupportedMethodCall(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layer1 = BasicModule() self.scale = torch.randn(1, 10) def call_and_scale(self, mod, x): x = mod(x) x = x * self.scale return unsupported(x, x) def forward(self, x): x1 = self.call_and_scale(self.layer1, x) return x + x1 class UnsupportedModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layer1 = BasicModule() self.scale = torch.randn(1, 10) def forward(self, x): x = self.layer1(x) * self.scale return unsupported(x, x) class UnsupportedModuleCall(torch.nn.Module): def __init__(self) -> None: super().__init__() self.mod = UnsupportedModule() def forward(self, x): return 1 + self.mod(x * 1.5) class ModuleWithStaticForward(torch.nn.Module): @staticmethod def forward(x): return x * torch.sigmoid(x) class ModuleCallModuleWithStaticForward(torch.nn.Module): def __init__(self) -> None: super().__init__() self.mod = ModuleWithStaticForward() def forward(self, x): return self.mod(x) class ModuleStaticMethodCall(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layer1 = BasicModule() self.layer2 = BasicModule() self.scale = torch.randn(1, 10) @staticmethod def call_and_scale(scale, mod, x): x = mod(x) return x * scale def forward(self, x): x1 = self.call_and_scale(self.scale, self.layer1, x) x2 = self.call_and_scale(self.scale, self.layer2, x) return x1 + x2 class ModuleClassMethodCall(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layer1 = BasicModule() self.layer2 = BasicModule() self.scale = torch.randn(1, 10) @classmethod def call_and_scale(cls, scale, mod, x): x = mod(x) return x * scale def forward(self, x): x1 = self.call_and_scale(self.scale, self.layer1, x) x2 = self.call_and_scale(self.scale, self.layer2, x) return x1 + x2 class ModuleProperty(torch.nn.Module): def __init__(self) -> None: super().__init__() self.scale = torch.randn(1, 10) @property def scale_alias(self): return self.scale def forward(self, x): return x * self.scale_alias class NestedModuleList(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layers = torch.nn.ModuleList([]) for _ in range(3): self.layers.append( torch.nn.ModuleList( [ torch.nn.Linear(10, 10), torch.nn.ReLU(), ] ) ) def forward(self, x): for layer, act in self.layers: x = act(layer(x)) return x class ConstLoop(torch.nn.Module): def __init__(self) -> None: super().__init__() self.linear1 = torch.nn.Linear(10, 10) self.count = 3 def forward(self, x): for i in range(self.count): x = torch.sigmoid(self.linear1(x)) return x class ViaModuleCall(torch.nn.Module): def __init__(self) -> None: super().__init__() self.linear1 = torch.nn.Linear(10, 10) def forward(self, x): return test_functions.constant3(torch.sigmoid(self.linear1(x)), x) class IsNoneLayer(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layer1 = torch.nn.Linear(10, 10) self.layer2 = None self.train(True) def forward(self, x): if self.layer1 is not None: x = self.layer1(x) if self.layer2 is not None: x = self.layer2(x) return x class LayerList(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layers = [ torch.nn.Linear(10, 10), torch.nn.ReLU(), torch.nn.Linear(10, 10), ] def forward(self, x): for layer in self.layers: x = layer(x) return x class ModuleList(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layers = torch.nn.ModuleList( [ torch.nn.Linear(10, 10), torch.nn.ReLU(), torch.nn.Linear(10, 10), torch.nn.ReLU(), ] ) def forward(self, x): for i in range(len(self.layers)): x = self.layers[i](x) for layer in self.layers: x = layer(x) for layer, val in zip(self.layers, (x, x, x, x)): x = layer(x) + val for layer, val in zip(self.layers, (1, 2, 3, 4)): x = layer(x) + val for idx, layer in enumerate(self.layers): x = layer(x) * idx for idx, layer in enumerate(self.layers[::-1]): x = layer(x) * idx return x class CustomGetItemModuleList(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layers = torch.nn.ModuleList( [ torch.nn.Linear(10, 10), torch.nn.ReLU(), torch.nn.Linear(10, 10), torch.nn.ReLU(), ] ) def __getitem__(self, idx: int): return self.layers[idx] def __len__(self) -> int: return len(self.layers) def forward(self, x): for i in range(len(self)): x = self[i](x) return x class ModuleDict(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layers = torch.nn.ModuleDict( { "0": torch.nn.Linear(10, 10), } ) def forward(self, x): # TODO(future PR): handle more logic x = self.layers["0"](x) return x class ParameterDict(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layers = torch.nn.ParameterDict( { "0": torch.nn.Parameter(torch.randn(10, 10)), } ) def forward(self, x): x = self.layers["0"].mm(x) return x class CustomGetItemParameterDict(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layers = torch.nn.ParameterDict( { "0": torch.nn.Parameter(torch.randn(10, 10)), } ) def __getitem__(self, key: str) -> torch.nn.Module: return self.layers[key] def forward(self, x): x = self["0"].mm(x) return x class CustomGetItemModuleDict(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layers = torch.nn.ModuleDict( { "0": torch.nn.Linear(10, 10), } ) def __getitem__(self, key: str) -> torch.nn.Module: return self.layers[key] def forward(self, x): x = self["0"](x) return x class TensorList(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layers = ( torch.randn((1, 10)), torch.randn((10, 1)), torch.randn((1, 10)), torch.randn((10, 1)), ) def forward(self, x): for layer in self.layers: x = x * layer return x class Children(torch.nn.Module): def __init__(self) -> None: super().__init__() self.l1 = torch.nn.Linear(10, 10) self.l2 = torch.nn.ReLU() self.l3 = torch.nn.Linear(10, 10) self.l4 = torch.nn.ReLU() def forward(self, x): for block in self.children(): x = block(x) return x class NamedChildren(torch.nn.Module): def __init__(self) -> None: super().__init__() self.l1 = torch.nn.Linear(10, 10) self.l2 = torch.nn.ReLU() self.l3 = torch.nn.Linear(10, 10) self.l4 = torch.nn.ReLU() def forward(self, x): for _, block in self.named_children(): x = block(x) return x class IntArg(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layer1 = torch.nn.Linear(10, 10) def forward(self, x, offset=1): x = F.relu(self.layer1(x)) + offset return x class Seq(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layers = torch.nn.Sequential( torch.nn.Linear(10, 10), torch.nn.ReLU(), torch.nn.Linear(10, 10), torch.nn.ReLU(), ) def forward(self, x): return self.layers(x) class Cfg: def __init__(self) -> None: self.val = 0.5 self.count = 3 class CfgModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.cfg = Cfg() self.layer = torch.nn.Linear(10, 10) def forward(self, x): for i in range(self.cfg.count): x = self.layer(x + self.cfg.val) return x class StringMember(torch.nn.Module): def __init__(self) -> None: super().__init__() self.linear1 = torch.nn.Linear(10, 10) self.mode = "some_string" def forward(self, x): if self.mode == "some_string": return F.relu(self.linear1(x)) class _Block(torch.nn.Module): def forward(self, x): return 1.5 * torch.cat(x, 1) class _DenseBlock(torch.nn.ModuleDict): _version = 2 def __init__( self, num_layers: int = 3, ) -> None: super().__init__() for i in range(num_layers): self.add_module("denselayer%d" % (i + 1), _Block()) def forward(self, init_features): features = [init_features] for layer in self.values(): new_features = layer(features) features.append(new_features) return torch.cat(features, 1) class DenseNetBlocks(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layers = _DenseBlock() def forward(self, x): return self.layers(x) class MaterializedModule(torch.nn.Module): """Once the below lazy module is initialized with its first input, it is transformed into this module.""" param: Parameter def __init__(self) -> None: super().__init__() self.register_parameter("param", None) def forward(self, x): return x class LazyModule(LazyModuleMixin, MaterializedModule): param: UninitializedParameter cls_to_become = MaterializedModule def __init__(self) -> None: super().__init__() self.param = UninitializedParameter() def initialize_parameters(self, x): # force graph break to ensure this was not inlined torch._dynamo.graph_break() self.param.materialize(x.shape) class LazyMLP(torch.nn.Module): def __init__(self) -> None: super().__init__() self.fc1 = torch.nn.LazyLinear(10) self.relu1 = torch.nn.ReLU() self.fc2 = torch.nn.LazyLinear(1) self.relu2 = torch.nn.ReLU() def forward(self, input): x = self.relu1(self.fc1(input)) y = self.relu2(self.fc2(x)) return y class MyInput(NamedTuple): x: Dict[str, Dict[str, torch.Tensor]] y: torch.Tensor class LazyLayerWithNamedTupleInput(LazyModuleMixin, torch.nn.Module): def __init__(self) -> None: super().__init__() def initialize_parameters(self, input): with torch.no_grad(): self._param = torch.nn.Parameter( torch.empty(input.x["a"][0].shape).fill_(0.5) ) def forward(self, input): input = input.x["a"] x = 0 for i in range(len(input)): x = x + input[i] return x class LazyModuleWithNamedTupleInput(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layer = LazyLayerWithNamedTupleInput() def forward(self, input): return self.layer(input) class LazyLayerWithListInput(LazyModuleMixin, torch.nn.Module): def __init__(self) -> None: super().__init__() def initialize_parameters(self, input): with torch.no_grad(): self._param = torch.nn.Parameter(torch.empty(input[0].shape).fill_(0.5)) def forward(self, input): x = 0 for i in range(len(input)): x = x + input[i] return x class LazyModuleWithListInput(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layer = LazyLayerWithListInput() def forward(self, input): return self.layer(input[:-1]) class LazyModuleWithLazySubmodule(LazyModuleMixin, torch.nn.Module): def __init__(self) -> None: super().__init__() def initialize_parameters(self, input): with torch.no_grad(): self.layer = LazyLayerWithListInput() def forward(self, x): return self.layer(x) class LazyLayerWithInputs(LazyModuleMixin, torch.nn.Module): def __init__(self) -> None: super().__init__() def initialize_parameters(self, x, y): with torch.no_grad(): self._param_x = torch.nn.Parameter(torch.empty(x[0].shape).fill_(0.5)) self._param_y = torch.nn.Parameter(torch.empty(y[0].shape).fill_(0.5)) def forward(self, x, y): res_x = 0 for i in range(len(x)): res_x = res_x + x[i] res_y = 0 for i in range(len(y)): res_y = res_y + y[i] return res_x + res_y class LazyModuleKwArgs(LazyModuleMixin, torch.nn.Module): def __init__(self) -> None: super().__init__() def initialize_parameters(self, *args, **kwargs): with torch.no_grad(): self.layer = LazyLayerWithInputs() def forward(self, x, y): return self.layer(x, y=y) class LazyParentModule(LazyModuleMixin, torch.nn.Module): def __init__(self) -> None: super().__init__() def impl(self, x): return x.cos() + self._val class LazyChildModuleNoClsToBecome(LazyParentModule): def __init__(self) -> None: super().__init__() def forward(self, x): return super().impl(x.sin()) def initialize_parameters(self, input): self._val = torch.nn.Parameter(torch.ones(2, 2)) def requires_grad1(module: torch.nn.Module, recurse: bool = False) -> bool: requires_grad = any(p.requires_grad for p in module.parameters(recurse)) return requires_grad def requires_grad2(module: torch.nn.Module, recurse: bool = False) -> bool: requires_grad = any(p.requires_grad for p in module.parameters(recurse)) return requires_grad class ParametersModule1(torch.nn.Module): def __init__(self) -> None: super().__init__() self.linear1 = torch.nn.Linear(10, 10) self.scale = torch.nn.Parameter(torch.randn(1, 10)) def forward(self, x): if not requires_grad1(self): return F.relu(self.linear1(x)) * self.scale else: return x + 1 class ParametersModule2(ParametersModule1): def forward(self, x): if not requires_grad2(self): return F.relu(self.linear1(x)) * self.scale else: return x + 1 class ParametersModule3(ParametersModule1): def forward(self, x): ones = torch.ones(10, dtype=next(self.parameters()).dtype) return F.relu(self.linear1(x)) * self.scale + ones class ParametersModule4(ParametersModule1): def forward(self, x): ones = torch.ones(10, dtype=next(self.parameters(recurse=False)).dtype) return F.relu(self.linear1(x)) * self.scale + ones class ParametersModule5(torch.nn.Module): def __init__(self) -> None: super().__init__() self.linear1 = torch.nn.Linear(10, 10) self.scale = torch.nn.Parameter(torch.randn(10, 10)) self.scale_dup = self.scale def forward(self, x): counter = 0 for param in self.parameters(): counter += 1 return x * self.scale * counter class SuperModule(BasicModule): def forward(self, x): x = super().forward(x) return x + 10.0 class SuperModule2(BasicModule): def forward(self, x): return BasicModule.forward(self, x) class ComplicatedSuperParent(torch.nn.Module): @classmethod def custom_add(cls, x): x = x + x return x class SuperChildCallsClassMethod(ComplicatedSuperParent): @classmethod def child_func(cls, x): x = super().custom_add(x) return x def forward(self, x): x = self.child_func(x) return x class HasAttrModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.scale = torch.nn.Parameter(torch.randn(1, 10)) def forward(self, x): x = F.relu(x) if hasattr(self, "scale"): x *= self.scale if hasattr(self, "scale2"): x *= self.scale2 return x class EnumValues(torch.nn.ModuleDict): def __init__( self, num_layers: int = 3, ) -> None: super().__init__() for i in range(num_layers): self.add_module("denselayer%d" % (i + 1), _Block()) def forward(self, init_features): features = [init_features] for idx, layer in enumerate(self.values()): new_features = layer(features) features.append(new_features) return torch.cat(features, 1) class AccessByKeys(torch.nn.ModuleDict): def __init__( self, num_layers: int = 3, ) -> None: super().__init__() for i in range(num_layers): self.add_module("denselayer%d" % (i + 1), _Block()) def forward(self, init_features): features = [init_features] for k in self.keys(): new_features = self[k](features) features.append(new_features) return torch.cat(features, 1) class CallForwardDirectly(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layer1 = BasicModule() self.layer2 = torch.nn.Linear(10, 10) def forward(self, x): x = self.layer1.forward(x) x = self.layer2.forward(x) return x class ConvCallForwardDirectly(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layer = torch.nn.Conv2d(3, 64, 3, 1, 1, bias=False) def forward(self, x): return self.layer.forward(x) class ConvTransposeCallForwardDirectly(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layer = torch.nn.ConvTranspose2d(4, 4, 4) def forward(self, x): return self.layer.forward(x) class ConvCallSuperForwardDirectly(torch.nn.Conv1d): def __init__(self, in_channels, out_channels, kernel_size, **kwargs): super().__init__( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, **kwargs, ) def forward(self, inputs, mask=None): outputs = super().forward(inputs) return outputs class ConvTransposeCallSuperForwardDirectly(torch.nn.ConvTranspose2d): def __init__(self, in_channels, out_channels, kernel_size, **kwargs): super().__init__( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, **kwargs, ) def forward(self, x): if x.numel() > 0: return super().forward(x) output_shape = [ ((i - 1) * d - 2 * p + (di * (k - 1) + 1) + op) for i, p, di, k, d, op in zip( x.shape[-2:], self.padding, self.dilation, self.kernel_size, self.stride, self.output_padding, ) ] output_shape = [x.shape[0], self.bias.shape[0]] + output_shape return _NewEmptyTensorOp.apply(x, output_shape) # noqa: F821 class ModuleNameString(torch.nn.Module): def __init__(self) -> None: super().__init__() self.linear1 = torch.nn.Linear(10, 10) def forward(self, x): if self.__class__.__name__ == "ABC": return 10 if self.linear1.__class__.__name__ == "Linear": return F.relu(self.linear1(x) + 10) return 11 class SelfMutatingModule(torch.nn.Module): def __init__(self, layer): super().__init__() self.layer = layer self.counter = 0 def forward(self, x): result = self.layer(x) + self.counter self.counter += 1 return F.relu(result) class ModuleAttributePrecedenceBase(torch.nn.Module): def linear(self, x, flag=None): if flag: return x * 2.0 return x * 3.0 class ModuleAttributePrecedence(ModuleAttributePrecedenceBase): def __init__(self) -> None: super().__init__() self.activation = torch.nn.ReLU() self.linear = torch.nn.Linear(10, 10) self.initializer = torch.ones([10, 10]) self.scale = 0.5 def activation(self, x): return x * 1.2 def initializer(self): return torch.zeros([10, 10]) def scale(self): return 2.0 def forward(self, x): # object attribute takes precedence unless it's a nn.Module return self.activation(self.linear(self.initializer + x)) * self.scale class ModuleForwardHasGraphBreak(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layer1 = BasicModule() self.layer2 = BasicModule() self.layer3 = torch.nn.Sequential(BasicModule(), BasicModule()) self.layer4 = torch.nn.ModuleList( [ torch.nn.Linear(10, 10), torch.nn.ReLU(), torch.nn.Linear(10, 10), torch.nn.ReLU(), ] ) self.layer5 = torch.nn.ModuleDict( { "0": torch.nn.Linear(10, 10), } ) self.scale = torch.randn(1, 10) def forward(self, x): """ This is used to test if the results of functions like `named_parameters` can be reconstructed correctly after graph break. https://github.com/pytorch/torchdynamo/issues/1931 """ x = self.layer1(x) params1 = dict(self.named_parameters()) params2 = list(self.parameters()) buffers1 = dict(self.named_buffers()) buffers2 = list(self.buffers()) modules1 = dict(self.named_modules()) modules2 = list(self.modules()) torch._dynamo.graph_break() y = modules2 y = modules1 y = buffers2 y = buffers1 y = params2 y = params1 x = ( self.layer2(x) + y["layer3.1.linear1.weight"] + y["layer4.2.weight"] + y["layer5.0.weight"] ) return x * self.scale class ModuleGuardNameIsValid(torch.nn.ModuleDict): # Guard names should be valid python identifier as we use eval() to get # corresponding guard value. Some guard names come from source(module path) # where special symbols are valid. But they are not valid python identifier, # we should identify these pattern and rewrite them with getattr. def __init__(self) -> None: super().__init__() for i in range(2): self.add_module("l@yer-%d" % (i + 1), BasicModule()) def forward(self, x): for layer in self.values(): x = layer(x) return x class SequentialWithDuplicatedModule(torch.nn.Module): # Sequential module(self.layer) contains three duplicated ReLU module. def __init__(self) -> None: super().__init__() self.relu = torch.nn.ReLU() self.layer = torch.nn.Sequential( torch.nn.Linear(10, 20), self.relu, torch.nn.Linear(20, 20), self.relu, torch.nn.Linear(20, 10), self.relu, ) def forward(self, x): return self.layer(x) class SequentialWithDuplicatedModule2(torch.nn.Module): def __init__(self) -> None: super().__init__() self.relu = torch.nn.ReLU() self.layer = torch.nn.Sequential( collections.OrderedDict( [ ("linear1", torch.nn.Linear(10, 20)), ("relu1", self.relu), ("linear2", torch.nn.Linear(20, 20)), ("relu2", self.relu), ("linear3", torch.nn.Linear(20, 10)), ("relu3", self.relu), ] ) ) def forward(self, x): return self.layer(x) class ModuleComparison(torch.nn.Module): def __init__(self) -> None: super().__init__() self.layer0 = torch.nn.Linear(10, 10) self.layer1 = torch.nn.Linear(10, 10) self.layer2 = torch.nn.Linear(10, 10) @property def encoder_layers(self): return [self.layer0, self.layer1, self.layer2] def forward(self, x): for layer in self.encoder_layers: output = layer(x) if layer is None or layer == self.layer0: output = F.relu6(output) else: output = F.relu(output) return output class ModulePatch1(torch.nn.Module): pass class ModulePatch2(torch.nn.Module): def forward(self, x): return x - 1 class UnspecNonInlinableModule(torch.nn.Module): torchdynamo_force_dynamic = True # forced to be a UnspecializedNNModule def forward(self, x): if x.sum() > 0: return x + 1 else: return x - 1 class UnspecNonInlinableToplevelModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.m = UnspecNonInlinableModule() def forward(self, x): return self.m(x) def make_test(fn, expected_ops=None): def test_fn(self): return torch._dynamo.testing.standard_test( self, fn=fn, nargs=1, expected_ops=expected_ops ) fn.eval() return test_fn @contextlib.contextmanager def temporary_tensor_subclass(torch_function=None): class TensorProxy(torch.Tensor): @classmethod def __torch_function__(cls, func, types, args=(), kwargs=None): if torch_function is not None: torch_function() return super().__torch_function__(func, types, args, kwargs) torch._dynamo.config.traceable_tensor_subclasses.add(TensorProxy) try: yield TensorProxy finally: torch._dynamo.config.traceable_tensor_subclasses.remove(TensorProxy) class NNModuleTests(torch._dynamo.test_case.TestCase): test_seq = make_test(Seq()) test_basicmodule1 = make_test(BasicModule()) test_basicmodule2 = make_test(BasicModule()) test_submodules1 = make_test(SubmoduleExample()) test_submodules2 = make_test(SubmoduleExample()) test_modulemethod1 = make_test(ModuleMethodCall()) test_modulemethod2 = make_test(ModuleMethodCall()) test_module_call_module_with_static_forward = make_test( ModuleCallModuleWithStaticForward() ) test_module_static_method = make_test(ModuleStaticMethodCall()) test_fnmember = make_test(FnMember()) test_fnmembercmp1 = make_test(FnMemberCmp(F.relu)) test_fnmembercmp2 = make_test(FnMemberCmp(None)) test_constloop = make_test(ConstLoop()) test_istraining1 = make_test(IsTrainingCheck()) test_istraining2 = make_test(IsTrainingCheck()) test_iseval1 = make_test(IsEvalCheck()) test_iseval2 = make_test(IsEvalCheck()) test_viamodulecall = make_test(ViaModuleCall()) test_isnonelayer = make_test(IsNoneLayer()) test_layerlist = make_test(LayerList()) test_tensorlist = make_test(TensorList()) test_intarg = make_test(IntArg()) test_cfgmod = make_test(CfgModule()) test_stringmember = make_test(StringMember()) test_modulelist = make_test(ModuleList()) test_modulelist_nested = make_test(NestedModuleList()) test_modulelist_custom = make_test(CustomGetItemModuleList()) test_moduledict = make_test(ModuleDict()) test_moduledict_custom = make_test(CustomGetItemModuleDict()) test_parameterdict = make_test(ParameterDict()) test_parameterdict_custom = make_test(CustomGetItemParameterDict()) test_super1 = make_test(SuperModule()) test_super2 = make_test(SuperModule2()) test_super_class_method = make_test(SuperChildCallsClassMethod()) test_children = make_test(Children()) test_named_children = make_test(NamedChildren()) test_densenet = make_test(DenseNetBlocks()) test_parameters1 = make_test(ParametersModule1()) test_parameters2 = make_test(ParametersModule2()) test_parameters3 = make_test(ParametersModule3(), expected_ops=5) test_parameters4 = make_test(ParametersModule4()) test_parameters5 = make_test(ParametersModule5()) test_hasattr = make_test(HasAttrModule()) test_enumvalues = make_test(EnumValues()) test_access_by_keys = make_test(AccessByKeys()) test_module_class_method = make_test(ModuleClassMethodCall()) test_module_property = make_test(ModuleProperty()) test_forward_directly = make_test(CallForwardDirectly()) test_module_name_string = make_test(ModuleNameString()) test_module_attribute_precedence = make_test(ModuleAttributePrecedence()) test_module_guard_name_is_valid = make_test(ModuleGuardNameIsValid()) test_sequential_with_duplicated_module = make_test(SequentialWithDuplicatedModule()) test_sequential_with_duplicated_module2 = make_test( SequentialWithDuplicatedModule2() ) test_module_comparison = make_test(ModuleComparison()) def test_module_forward_has_graph_break(self): m = ModuleForwardHasGraphBreak() x = torch.rand([10, 10]) ref = m(x) opt_m = torch._dynamo.optimize("eager")(m) res = opt_m(x) self.assertTrue(torch.allclose(ref, res)) def test_unsupportedmethod(self): m = UnsupportedMethodCall() i = torch.randn(10) cnt = torch._dynamo.testing.CompileCounter() opt_m = torch._dynamo.optimize(cnt)(m) r = opt_m(i) self.assertTrue(torch._dynamo.testing.same(r, m(i))) self.assertEqual(cnt.op_count, 5) def test_unsupportedmodule(self): m = UnsupportedModuleCall() i = torch.randn(10) cnt = torch._dynamo.testing.CompileCounter() opt_m = torch._dynamo.optimize(cnt)(m) r = opt_m(i) self.assertTrue(torch._dynamo.testing.same(r, m(i))) self.assertEqual(cnt.op_count, 6) def test_self_mutating1(self): m1 = torch.nn.Linear(10, 10) m2 = SelfMutatingModule(m1) m3 = SelfMutatingModule(m1) m4 = SelfMutatingModule(m1) i = torch.randn(10) out2 = [m2(i), m2(i), m2(i)] cnt = torch._dynamo.testing.CompileCounter() opt_m3 = torch._dynamo.optimize_assert(cnt)(m3) opt_m4 = torch._dynamo.optimize_assert(cnt)(m4) out3 = [opt_m3(i), opt_m3(i), opt_m3(i)] out4 = [opt_m4(i), opt_m4(i), opt_m4(i)] self.assertTrue(torch._dynamo.testing.same(out2, out3)) self.assertTrue(torch._dynamo.testing.same(out2, out4)) if torch._dynamo.config.assume_static_by_default: self.assertExpectedInline(cnt.frame_count, """2""") else: self.assertExpectedInline(cnt.frame_count, """1""") @patch.object(torch._dynamo.config, "raise_on_ctx_manager_usage", False) def test_generation_tag(self): cnt = torch._dynamo.testing.CompileCounter() # guarantee that we have installed # the generation tagging function with torch._dynamo.optimize_assert(cnt): pass m1 = torch.nn.Linear(10, 10) prev_generation = GenerationTracker.get_generation_value(m1) cur_generation = prev_generation + 1 with torch._dynamo.optimize_assert(cnt): m2 = torch.nn.Linear(10, 10) self.assertEqual(GenerationTracker.get_generation_value(m1), prev_generation) self.assertEqual(GenerationTracker.get_generation_value(m2), cur_generation) # check that newly constructed instances # also have the same generation (even if copied from an old instance) m3 = deepcopy(m1) self.assertEqual(GenerationTracker.get_generation_value(m3), cur_generation) def test_simple_torch_function(self): def foo(x): # function call, twice to test wrapping x = F.sigmoid(x) x = F.sigmoid(x) # method call, twice to test wrapping x = x.sigmoid() x = x.sigmoid() return x with temporary_tensor_subclass() as TensorProxy: x = torch.randn(1).as_subclass(TensorProxy) cnt = torch._dynamo.testing.CompileCounter() out1 = foo(x) opt_foo = torch._dynamo.optimize(cnt, nopython=True)(foo) out2 = opt_foo(x) self.assertEqual(cnt.op_count, 4) self.assertTrue(torch._dynamo.testing.same(out1, out2)) def test_torch_function_with_closure(self): def run(): def foo(x): # function call, twice to test wrapping x = F.sigmoid(x) x = F.sigmoid(x) # method call, twice to test wrapping x = x.sigmoid() x = x.sigmoid() return x counter = 0 def function(): nonlocal counter # for now, only support reads from closure cells # TODO(future PR): support writes as well counter + 1 with temporary_tensor_subclass(function) as TensorProxy: x = torch.randn(1).as_subclass(TensorProxy) x = torch.randn(1) cnt = torch._dynamo.testing.CompileCounter() out1 = foo(x) opt_foo = torch._dynamo.optimize(cnt, nopython=True)(foo) out2 = opt_foo(x) self.assertEqual(cnt.op_count, 4) self.assertTrue(torch._dynamo.testing.same(out1, out2)) run() def test_torch_mangled_class_name(self): original = TensorWithTFOverrideVariable.global_mangled_class_name results = [] def instrumented(self, tx): result = original(self, tx) results.append(result) return result TensorWithTFOverrideVariable.global_mangled_class_name = instrumented def one_break(x): x = F.sigmoid(x) print() # force break x = x.sigmoid() return x try: with temporary_tensor_subclass() as TensorProxy: x = torch.randn(1).as_subclass(TensorProxy) x1 = one_break(x) cnt = torch._dynamo.testing.CompileCounter() opt_one_break = torch._dynamo.optimize(cnt)(one_break) x2 = opt_one_break(x) self.assertTrue(torch._dynamo.testing.same(x1, x2)) self.assertEqual(cnt.frame_count, 2) self.assertEqual(cnt.op_count, 2) compile_ids = set() for r in results: # A mangled classname looks like __subclass_TensorProxy_94524181138240_c0 # where the last segment contains the compile_id. prefix = "__subclass_TensorProxy_" before, sep, after = r.partition(prefix) self.assertEqual(before, "") self.assertEqual(sep, prefix) class_type_id, compile_id = after.split("_") self.assertTrue(class_type_id.isnumeric()) self.assertTrue(compile_id.startswith("c")) cid = compile_id[1:] self.assertTrue(cid.isnumeric()) compile_ids.add(cid) self.assertEqual(len(compile_ids), 3) finally: TensorWithTFOverrideVariable.global_mangled_class_name = original @patch.object(torch._dynamo.config, "raise_on_ctx_manager_usage", False) def test_nn_moduledict_contains(self): class M(torch.nn.Module): def __init__(self, module_dict): super().__init__() self.module_dict = module_dict def forward(self, x): if "foo" in self.module_dict: x = torch.mul(x, 1.0) x = torch.add(x, 1.0) return x module_dict = torch.nn.ModuleDict({"foo": torch.nn.Conv2d(1, 1, 1)}) m = M(module_dict) data = torch.randn(1) out1 = m(data) cnt = torch._dynamo.testing.CompileCounter() opt_m = torch._dynamo.optimize(cnt, nopython=True)(m) out2 = opt_m(data) self.assertEqual(cnt.op_count, 2) self.assertTrue(torch._dynamo.testing.same(out1, out2)) module_dict = torch.nn.ModuleDict({"bar": torch.nn.Conv2d(1, 1, 1)}) m = M(module_dict) data = torch.randn(1) out1 = m(data) cnt = torch._dynamo.testing.CompileCounter() torch._dynamo.reset() opt_m = torch._dynamo.optimize(cnt, nopython=True)(m) out2 = opt_m(data) self.assertEqual(cnt.op_count, 1) self.assertTrue(torch._dynamo.testing.same(out1, out2)) module_dict = torch.nn.ModuleDict({"cat": torch.nn.Conv2d(1, 1, 1)}) pre = m(data) cnt.clear() with torch._dynamo.optimize(cnt, nopython=False): opt_pre = m(data) m = M(module_dict) data = torch.randn(1) out1 = m(data) out_post = m(data) self.assertEqual(cnt.frame_count, 1) self.assertEqual(cnt.op_count, 1) self.assertTrue(torch._dynamo.testing.same(pre, opt_pre)) self.assertTrue(torch._dynamo.testing.same(out1, out_post)) # RuntimeError: SymIntArrayRef expected to contain only concrete integers @expectedFailureDynamic def test_lazy_module1(self): input_shape = (16, 3, 6, 7, 8) cnt = torch._dynamo.testing.CompileCounter() module = LazyModule() def test_static_module(): input = torch.ones(*input_shape) module(input) # test no graph break opt_test_static_module = torch._dynamo.optimize(cnt, nopython=True)( test_static_module ) opt_test_static_module() self.assertTrue( isinstance(module, MaterializedModule), "Module should be transformed to an instance of MaterializedModule.", ) self.assertEqual(module.param.shape, input_shape) # test when mapped to UnspecializedNNModule module = LazyModule() def test_unspecialized(): nonlocal module module = LazyModule() input = torch.ones(*input_shape) module(input) opt_test_unspecialized = torch._dynamo.optimize(cnt)(test_unspecialized) opt_test_unspecialized() self.assertTrue( isinstance(module, MaterializedModule), "Module should be transformed to an instance of MaterializedModule.", ) self.assertEqual(module.param.shape, input_shape) # test with a static module in torch.* module = torch.nn.modules.LazyBatchNorm3d( affine=False, track_running_stats=False ) cnt = torch._dynamo.testing.CompileCounter() torch._dynamo.reset() def test_torch_static(): input = torch.ones(*input_shape) return module(input) # fully materialized # test no graph break opt_test_torch_static = torch._dynamo.optimize(cnt, nopython=True)( test_torch_static ) opt_test_torch_static() out = opt_test_torch_static() self.assertTrue(same(out, module(torch.ones(*input_shape)))) self.assertTrue( isinstance(module, torch.nn.modules.batchnorm.BatchNorm3d), "Module should be transformed to an instance of BatchNorm3d.", ) self.assertEqual(cnt.frame_count, 1, "No guards should have triggered.") # RuntimeError: SymIntArrayRef expected to contain only concrete integers @expectedFailureDynamic def test_lazy_module2(self): # Test FX graph 'call_module' works well if argument is lazy module m = LazyMLP() x = torch.rand([10, 10]) opt_m = torch._dynamo.optimize("eager", nopython=True)(m) # We should run compile mode firstly, otherwise the module # would be initialized when running eager mode. res = opt_m(x) ref = m(x) self.assertTrue(torch.allclose(ref, res)) # RuntimeError: SymIntArrayRef expected to contain only concrete integers @expectedFailureDynamic @unittest.skipIf(not torch.cuda.is_available(), "requires cuda") def test_lazy_module3(self): m = LazyMLP() x = torch.rand([10, 10]) cnt = torch._dynamo.testing.CompileCounter() opt_m = torch._dynamo.optimize(cnt, nopython=True)(m) # first iteration res = opt_m(x) ref = m(x) self.assertTrue(torch.allclose(ref, res)) # move to cuda and second iteration m = m.to("cuda") x = x.to("cuda") res = opt_m(x) ref = m(x) self.assertTrue(torch.allclose(ref, res)) self.assertEqual(cnt.frame_count, 2) # RuntimeError: SymIntArrayRef expected to contain only concrete integers @expectedFailureDynamic def test_lazy_module4(self): m = LazyMLP() x = torch.rand([10, 10]) cnt = torch._dynamo.testing.CompileCounter() opt_m = torch._dynamo.optimize(cnt, nopython=True)(m) # first iteration res = opt_m(x) ref = m(x) self.assertTrue(torch.allclose(ref, res)) # input shape changed and second iteration x = torch.rand([20, 20]) try: opt_m(x) except RuntimeError: self.assertIn("must have same reduction dim", traceback.format_exc()) # RuntimeError: SymIntArrayRef expected to contain only concrete integers @expectedFailureDynamic def test_lazy_module5(self): # Test lazy module works well with list/tuple input m = LazyModuleWithListInput() x = [torch.rand([5, 5])] * 3 + [None] opt_m = torch._dynamo.optimize("eager", nopython=True)(m) res = opt_m(x) ref = m(x) self.assertTrue(torch.allclose(ref, res)) # RuntimeError: SymIntArrayRef expected to contain only concrete integers @expectedFailureDynamic def test_lazy_module6(self): # Test new lazy submodule in lazy module's initialize_parameters m = LazyModuleWithLazySubmodule() x = [torch.rand([5, 5])] * 3 opt_m = torch._dynamo.optimize("eager", nopython=True)(m) res = opt_m(x) ref = m(x) self.assertTrue(torch.allclose(ref, res)) # RuntimeError: SymIntArrayRef expected to contain only concrete integers @expectedFailureDynamic def test_lazy_module7(self): # Test lazy module works well with namedtuple/dict input m = LazyModuleWithNamedTupleInput() x = MyInput( x={"a": [torch.rand([5, 5])] * 3, "b": torch.rand([5, 5])}, y=torch.rand([5, 5]), ) opt_m = torch.compile(backend="eager", fullgraph=True)(m) res = opt_m(x) ref = m(x) self.assertTrue(torch.allclose(ref, res)) def test_lazy_module_no_cls_to_become(self): # make sure super() works in the case where cls_to_become is None m = LazyChildModuleNoClsToBecome() x = torch.rand(2, 2) opt_m = torch._dynamo.optimize("eager", nopython=True)(m) res = opt_m(x) ref = m(x) self.assertTrue(torch.allclose(ref, res)) def test_lazy_module_kwargs(self): m = LazyModuleKwArgs() x = [torch.rand([5, 5])] * 3 y = [torch.rand([5, 5])] * 2 opt_m = torch.compile(backend="eager", fullgraph=True)(m) exp_res = m(x, y) self.assertTrue(torch.allclose(exp_res, opt_m(x, y))) def test_call_fn_with_non_const_inputs_safe(self): class ModuleSpecialFwd(torch.nn.Module): def __init__(self) -> None: super().__init__() self.conv = torch.nn.Conv2d( in_channels=3, out_channels=20, kernel_size=(5, 5) ) def _conv_forward(self, x): return self.conv._conv_forward(x, self.conv.weight, self.conv.bias) def forward(self, x): return self._conv_forward(x) mod = ModuleSpecialFwd() rx = torch.randn([3, 10, 10]) real = mod(rx) graph, _ = torch._dynamo.export(mod)(rx) self.assertTrue(torch._dynamo.testing.same(real, graph(rx))) def test_conv_call_forward_directly(self): m = ConvCallForwardDirectly() x = torch.rand([4, 3, 9, 9]) ref = m(x) opt_m = torch.compile(backend="eager", fullgraph=True)(m) res = opt_m(x) self.assertTrue(torch.allclose(ref, res)) def test_conv_transpose_call_forward_directly(self): m = ConvTransposeCallForwardDirectly() x = torch.rand([4, 4, 4, 4]) ref = m(x) opt_m = torch.compile(backend="eager", fullgraph=True)(m) res = opt_m(x) self.assertTrue(torch.allclose(ref, res)) def test_conv_call_super_forward_directly(self): x = torch.randn(4, 4) m = ConvCallSuperForwardDirectly(4, 4, 4) ref = m(x) opt_m = torch.compile(backend="eager", fullgraph=True)(m) res = opt_m(x) self.assertTrue(torch.allclose(ref, res)) def test_conv_transpose_call_super_forward_directly(self): x = torch.randn(4, 4, 4) m = ConvTransposeCallSuperForwardDirectly(4, 4, 4) ref = m(x) opt_m = torch.compile(backend="eager", fullgraph=True)(m) res = opt_m(x) self.assertTrue(torch.allclose(ref, res)) class MockModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.relu = torch.nn.ReLU() self.linear = torch.nn.Linear(10, 10) self.buf0 = torch.nn.Buffer(torch.randn(10, 10)) def forward(self, x): return self.relu(self.linear(x) + self.buf0) class OptimizedModuleTest(torch._dynamo.test_case.TestCase): def test_nn_module(self): mod = MockModule() cnt = torch._dynamo.testing.CompileCounter() opt_mod = torch._dynamo.optimize(cnt)(mod) self.assertIsInstance(opt_mod, torch._dynamo.OptimizedModule) x = torch.randn(10, 10) self.assertTrue(torch._dynamo.testing.same(mod(x), opt_mod(x))) self.assertEqual(cnt.frame_count, 1) @torch._dynamo.config.patch(guard_nn_modules=True) def test_attr_precedence(self): class Mod(torch.nn.Module): def __init__(self) -> None: super().__init__() self.a = 3 def forward(self, x, c=4): return x * c def linear(self, x): return x def b(self, x): raise RuntimeError("Should not be called") class MyMod(Mod): def __init__(self) -> None: super().__init__() self.linear = torch.nn.Linear(11, 11) self.a = 2 self.b = 2 self.scale = 1 def scale(self, x): # Should not be called because it is shadowed by the instance # attribute raise RuntimeError("Should not be called") def forward(self, x, c=None): return self.linear(x) * self.a * self.b * self.scale mod = MyMod() x = torch.ones(3, 3) ref = mod(x) cnts = torch._dynamo.testing.CompileCounter() opt_mod = torch.compile(mod, backend=cnts) opt_mod(torch.ones(3, 3)) res = opt_mod(torch.ones(3, 3)) self.assertEqual(cnts.frame_count, 1) self.assertEqual(ref, res) def test_to(self): mod = MockModule() cnt = torch._dynamo.testing.CompileCounter() opt_mod = torch._dynamo.optimize(cnt)(mod) x = torch.randn(10, 10) self.assertTrue(torch._dynamo.testing.same(mod(x), opt_mod(x))) self.assertEqual(cnt.frame_count, 1) # Ensure that there is no recompilation opt_mod(x) self.assertEqual(cnt.frame_count, 1) opt_mod = opt_mod.to(device="cpu").to(dtype=torch.float64) self.assertIsInstance(opt_mod, torch._dynamo.OptimizedModule) x = torch.randn(10, 10).to(dtype=torch.float64) opt_mod(x) # Ensure that there is a recompilation self.assertEqual(cnt.frame_count, 2) # Ensure that there is no recompilation opt_mod(x) self.assertEqual(cnt.frame_count, 2) torch._dynamo.reset() opt_mod(x) self.assertEqual(cnt.frame_count, 3) @torch._dynamo.config.patch(guard_nn_modules=True) def test_param_order(self): class MyModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.param1 = torch.nn.Parameter(torch.ones([1])) self.param2 = torch.nn.Parameter(torch.ones([2])) def forward(self, x): return x mod = MyModule() coeffs = [2, 3] def fn(x): for idx, p in enumerate(mod.parameters()): x += p.sum() * coeffs[idx] for idx, p in enumerate(mod.named_parameters()): x += p[1].sum() * coeffs[idx] return x ref = fn(torch.ones(1)) cnts = torch._dynamo.testing.CompileCounter() opt_fn = torch._dynamo.optimize(cnts)(fn) res = opt_fn(torch.ones(1)) self.assertEqual(ref, res) self.assertEqual(cnts.frame_count, 1) mod._parameters["param1"] = mod._parameters.pop("param1") ref = fn(torch.ones(1)) res = opt_fn(torch.ones(1)) self.assertEqual(ref, res) self.assertEqual(cnts.frame_count, 2) @torch._dynamo.config.patch(guard_nn_modules=True) def test_buffer_order(self): class MyModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.b1 = torch.nn.Buffer(torch.ones([1])) self.b2 = torch.nn.Buffer(torch.ones([2])) def forward(self, x): return x mod = MyModule() coeffs = [2, 3] def fn(x): for idx, p in enumerate(mod.buffers()): x += p.sum() * coeffs[idx] for idx, p in enumerate(mod.named_buffers()): x += p[1].sum() * coeffs[idx] return x ref = fn(torch.ones(1)) cnts = torch._dynamo.testing.CompileCounter() opt_fn = torch._dynamo.optimize(cnts)(fn) res = opt_fn(torch.ones(1)) self.assertEqual(ref, res) self.assertEqual(cnts.frame_count, 1) mod._buffers["b1"] = mod._buffers.pop("b1") ref = fn(torch.ones(1)) res = opt_fn(torch.ones(1)) self.assertEqual(ref, res) self.assertEqual(cnts.frame_count, 2) @torch._dynamo.config.patch(guard_nn_modules=True) def test_module_order(self): class MyModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.linear1 = torch.nn.Linear(3, 3) self.linear2 = torch.nn.Linear(10, 10) def forward(self, x): return x mod = MyModule() coeffs = [2, 3, 4] coeffs_for_mod = {mod: 10, mod.linear1: 20, mod.linear2: 30} # Check order of _modules def fn(x): for idx, p in enumerate(mod.modules()): # Something silly to force depedency on the order x += coeffs_for_mod[p] * coeffs[idx] for idx, p in enumerate(mod.named_modules()): x += coeffs_for_mod[p[1]] * coeffs[idx] for idx, p in enumerate(mod.children()): x += coeffs_for_mod[p] * coeffs[idx] for idx, p in enumerate(mod.named_children()): x += coeffs_for_mod[p[1]] * coeffs[idx] return x ref = fn(torch.ones(1)) cnts = torch._dynamo.testing.CompileCounter() opt_fn = torch._dynamo.optimize(cnts)(fn) res = opt_fn(torch.ones(1)) self.assertEqual(ref, res) self.assertEqual(cnts.frame_count, 1) mod._modules["linear1"] = mod._modules.pop("linear1") ref = fn(torch.ones(1)) res = opt_fn(torch.ones(1)) self.assertEqual(ref, res) self.assertEqual(cnts.frame_count, 2) def test_attr(self): class MockModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.linear = torch.nn.Linear(10, 10) self.buf0 = torch.nn.Buffer(torch.randn(10, 10)) def forward(self, x): return self.r(torch.sin(x)) + self.buf0 mod = MockModule() opt_mod = torch._dynamo.optimize("eager")(mod) # Check parameters and buffers for p1, p2 in zip(mod.parameters(), opt_mod.parameters()): self.assertTrue(id(p1) == id(p2)) for b1, b2 in zip(mod.buffers(), opt_mod.buffers()): self.assertTrue(id(b1) == id(b2)) def get_parameter_dtype(mod: torch.nn.Module): parameters_and_buffers = itertools.chain(mod.parameters(), mod.buffers()) return next(parameters_and_buffers).dtype opt_mod = torch._dynamo.optimize("eager")(get_parameter_dtype) out_dtype = opt_mod(mod) self.assertEqual(out_dtype, torch.float32) def test_dir(self): class MockModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.linear = torch.nn.Linear(10, 10) self.buf0 = torch.nn.Buffer(torch.nn.Buffer(torch.randn(10, 10))) self.register_parameter( name="param0", param=torch.nn.Parameter(torch.randn(10, 10)) ) def forward(self, x): return self.r(torch.sin(x)) + self.buf0 mod = MockModule() mod_keys = dir(mod) opt_mod = torch._dynamo.optimize("eager")(mod) opt_mod_keys = dir(opt_mod) # Check user-defined attributes, parameters and buffers self.assertIn("linear", opt_mod_keys) self.assertIn("buf0", opt_mod_keys) self.assertIn("param0", opt_mod_keys) # Check all attributes, parameters and buffers self.assertTrue(len(set(mod_keys).difference(opt_mod_keys)) == 0) def test_no_recompile_on_nn_guarded_modules(self): size = (10, 10) cache_size_limit = 1 num_submodules = 4 cnts = torch._dynamo.testing.CompileCounterWithBackend("eager") class SubModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.linear = torch.nn.Linear(*size) def forward(self, x): a = torch.sin(torch.cos(x)) return self.linear(a) class MockModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.mods = [SubModule() for _ in range(num_submodules)] self.mods = [torch.compile(mod, backend=cnts) for mod in self.mods] def forward(self, x): for mod in self.mods: x = mod(x) return x mod = MockModule() # Each submod is compiled separately and has a different nn module # guard. Ensure that recompilation logic is handle correctly. with unittest.mock.patch( "torch._dynamo.config.error_on_recompile", True ), unittest.mock.patch( "torch._dynamo.config.cache_size_limit", cache_size_limit, ): x = torch.randn(*size, requires_grad=True) mod(x) if torch._dynamo.config.inline_inbuilt_nn_modules: self.assertEqual(cnts.frame_count, 1) else: self.assertEqual(cnts.frame_count, num_submodules) @patch.object(torch._dynamo.config, "accumulated_cache_size_limit", 2) @patch.object(torch._dynamo.config, "inline_inbuilt_nn_modules", False) def test_recompile_limit_on_freed_module(self): class Mod(torch.nn.Module): def __init__(self) -> None: super().__init__() self.lin = torch.nn.Linear(5, 5) def forward(self, x): return self.lin(x) def fn(x, mod): return mod(x) cnts = torch._dynamo.testing.CompileCounterWithBackend("eager") opt_mod = torch.compile(fn, backend=cnts) for i in range(8): mod = Mod() opt_mod(torch.randn(5, 5), mod) # fn compiles twice self.assertEqual(cnts.frame_count, 2) @patch.object(torch._dynamo.config, "inline_inbuilt_nn_modules", True) def test_inline_inbuilt_nn_modules(self): size = (10, 10) cache_size_limit = 1 num_submodules = 4 cnts = torch._dynamo.testing.CompileCounterWithBackend("eager") class SubModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.linear = torch.nn.Linear(*size) def forward(self, x): a = torch.sin(torch.cos(x)) return self.linear(a) class MockModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.mods = [SubModule() for _ in range(num_submodules)] self.mods = [torch.compile(mod, backend=cnts) for mod in self.mods] def forward(self, x): for mod in self.mods: x = mod(x) return x mod = MockModule() # Each submod is compiled separately and has a different nn module # guard. Ensure that recompilation logic is handle correctly. with unittest.mock.patch( "torch._dynamo.config.error_on_recompile", True ), unittest.mock.patch( "torch._dynamo.config.cache_size_limit", cache_size_limit, ): x = torch.randn(*size, requires_grad=True) mod(x) self.assertEqual(cnts.frame_count, 1) def test_cache_size_limit_on_guarded_nn_modules(self): cache_size_limit = 2 num_submodules = 4 cnts = torch._dynamo.testing.CompileCounterWithBackend("eager") class SubModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.relu = torch.nn.ReLU() def forward(self, x): a = torch.sin(torch.cos(x)) return self.relu(a) class MockModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.mods = [SubModule() for _ in range(num_submodules)] self.mods = [torch.compile(mod, backend=cnts) for mod in self.mods] def forward(self, x): for mod in self.mods: x = mod(x) return x mod = MockModule() # For the third iteration, we would reach the cache size limit, and # therefore the total number of expected frame count is 2 * # num_submodules. with unittest.mock.patch( "torch._dynamo.config.cache_size_limit", cache_size_limit, ): for size in [ (4,), (4, 4), (4, 4, 4), ]: x = torch.randn(size) mod(x) if torch._dynamo.config.inline_inbuilt_nn_modules: self.assertEqual(cnts.frame_count, 2) else: self.assertEqual(cnts.frame_count, 2 * num_submodules) def test_recursion(self): mod = MockModule() cnt = torch._dynamo.testing.CompileCounter() opt_mod = torch._dynamo.optimize(cnt)(mod) for _ in range(5): opt_mod = torch._dynamo.optimize(cnt)(opt_mod) opt_mod(torch.randn(10, 10)) self.assertEqual(cnt.frame_count, 1) def test_composition(self): class InnerModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.relu = torch.nn.ReLU() def forward(self, x): return self.relu(torch.sin(x)) opt_inner_mod = InnerModule() class OuterModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.mod = opt_inner_mod def forward(self, x): return self.mod(torch.cos(x)) outer_mod = OuterModule() cnt = torch._dynamo.testing.CompileCounter() opt_outer_mod = torch._dynamo.optimize(cnt)(outer_mod) x = torch.randn(4) self.assertIsInstance(opt_outer_mod, torch._dynamo.OptimizedModule) self.assertTrue(torch._dynamo.testing.same(outer_mod(x), opt_outer_mod(x))) self.assertEqual(cnt.frame_count, 1) def test_composition_with_opt_mod(self): class InnerModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.relu = torch.nn.ReLU() def forward(self, x): return self.relu(torch.sin(x)) inner_mod = InnerModule() cnt = torch._dynamo.testing.CompileCounter() opt_inner_mod = torch._dynamo.optimize(cnt)(inner_mod) class OuterModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.mod = opt_inner_mod def forward(self, x): return self.mod(torch.cos(x)) outer_mod = OuterModule() opt_outer_mod = torch._dynamo.optimize(cnt)(outer_mod) x = torch.randn(4) self.assertIsInstance(opt_outer_mod, torch._dynamo.OptimizedModule) self.assertTrue(torch._dynamo.testing.same(outer_mod(x), opt_outer_mod(x))) # There will be a graph break for the inner mod being OptimizedModule self.assertEqual(cnt.frame_count, 2) def test_module_patch(self): mod = ModulePatch1() mod.forward = types.MethodType(ModulePatch2.forward, mod) def fn(x): return mod(x) self.assertTrue( torch.allclose( torch._dynamo.optimize("eager", nopython=True)(fn)(torch.ones(10)), torch.zeros(1), ) ) @patch.object(torch._dynamo.config, "skip_nnmodule_hook_guards", False) def test_hooks_outer(self): class TestModule(torch.nn.Module): def forward(self, x: torch.Tensor) -> torch.Tensor: return 2 * x + 1 m = TestModule() def forward_hook( module: torch.nn.Module, inputs: Tuple[torch.Tensor], output: torch.Tensor ) -> torch.Tensor: return 2 * output + 1 handle = m.register_forward_hook(forward_hook) inp = torch.tensor(1.0, requires_grad=True) failure_reason = None def guard_fail_fn(failure): nonlocal failure_reason failure_reason = failure[0] compiled_m = torch._dynamo.optimize( guard_fail_fn=guard_fail_fn, backend="eager" )(m) self.assertEqual(compiled_m(inp), m(inp)) self.assertEqual(compiled_m(inp).item(), 7) self.assertTrue(failure_reason is None) # what if we remove our hook? we should recompile? handle.remove() self.assertEqual(compiled_m(inp), m(inp)) self.assertEqual(compiled_m(inp).item(), 3) # self.assertTrue(failure_reason == "hook") """ Summary: - removing a hook doesn't fail a guard, because we weren't compiling the hook (at least into the same graph) as forward in the first place! We do correctly omit calling the removed hook, but since this hook is a post forward hook, the 'RETURN' from forward is breaking the graph. Why is 'forward' the entrypoint to an InstructionTranslator, after I changed the eval_frame entrypoint to Module.__call__? """ @patch.object(torch._dynamo.config, "skip_nnmodule_hook_guards", False) def test_hooks_inner(self): class TestModule(torch.nn.Module): def forward(self, x: torch.Tensor) -> torch.Tensor: return 2 * x + 1 m = TestModule() def forward_hook( module: torch.nn.Module, inputs: Tuple[torch.Tensor], output: torch.Tensor ) -> torch.Tensor: return 2 * output + 1 handle = m.register_forward_hook(forward_hook) def outer_func(tensor): x = tensor * 2 + 1 y = m(x) return y inp = torch.tensor(1.0, requires_grad=True) failure_reason = None def guard_fail_fn(failure): nonlocal failure_reason failure_reason = failure[0] cc = torch._dynamo.testing.CompileCounterWithBackend("aot_eager") compiled_func = torch._dynamo.optimize( guard_fail_fn=guard_fail_fn, backend=cc, )(outer_func) self.assertEqual(compiled_func(inp), outer_func(inp)) self.assertEqual(compiled_func(inp).item(), 15) # We are compiling 1 big graph for all 3 functions including the hook. self.assertEqual(cc.frame_count, 1) self.assertEqual(cc.op_count, 6) # If we remove the hook, we should recompile handle.remove() self.assertEqual(compiled_func(inp), outer_func(inp)) self.assertEqual(compiled_func(inp).item(), 7) self.assertTrue("forward_hooks" in failure_reason) self.assertEqual(cc.frame_count, 1 + 1) self.assertEqual(cc.op_count, 6 + 4) # what if instead of removing, we alter our hook? torch._dynamo.reset() m = TestModule() handle = m.register_forward_hook(forward_hook) failure_reason = None self.assertEqual(compiled_func(inp), outer_func(inp)) self.assertEqual(compiled_func(inp).item(), 15) def new_forward_hook( module: torch.nn.Module, inputs: Tuple[torch.Tensor], output: torch.Tensor ) -> torch.Tensor: return 2 * output + 2 m._forward_hooks[handle.id] = new_forward_hook self.assertEqual(compiled_func(inp), outer_func(inp)) self.assertEqual(compiled_func(inp).item(), 16) self.assertRegex(failure_reason, r"___check_obj_id\(L\['m'\]._forward_hooks") @patch.object(torch._dynamo.config, "guard_nn_modules", False) @patch.object(torch._dynamo.config, "skip_nnmodule_hook_guards", True) @patch.object(torch._dynamo.config, "inline_inbuilt_nn_modules", False) def test_hooks_skip_guards(self): class TestModule(torch.nn.Module): def forward(self, x: torch.Tensor) -> torch.Tensor: return 2 * x + 1 m = TestModule() def forward_hook( module: torch.nn.Module, inputs: Tuple[torch.Tensor], output: torch.Tensor ) -> torch.Tensor: return 2 * output + 1 handle = m.register_forward_hook(forward_hook) def outer_func(tensor): x = tensor * 2 + 1 y = m(x) return y inp = torch.tensor(1.0, requires_grad=True) failure_reason = None def guard_fail_fn(failure): nonlocal failure_reason failure_reason = failure[0] cc = torch._dynamo.testing.CompileCounterWithBackend("aot_eager") compiled_func = torch._dynamo.optimize( guard_fail_fn=guard_fail_fn, backend=cc, )(outer_func) m = TestModule() handle = m.register_forward_hook(forward_hook) failure_reason = None self.assertEqual(compiled_func(inp), outer_func(inp)) self.assertEqual(compiled_func(inp).item(), 15) self.assertEqual(cc.frame_count, 1) self.assertEqual(cc.op_count, 6) # if we remove the hook, dynamo shouldn't notice handle.remove() self.assertNotEqual(compiled_func(inp), outer_func(inp)) self.assertEqual(compiled_func(inp).item(), 15) self.assertEqual(cc.frame_count, 1) def _forward_hook_test_helper(self, model): forward_handles = {} compiled_activations = {} eager_activations = {} activations = None def save_activations(name, mod, inp, out): activations[name] = inp for name, module in model.named_modules(): forward_handles[name] = module.register_forward_hook( partial(save_activations, name) ) compiled_model = torch.compile(model, backend="aot_eager") activations = compiled_activations for i in range(2): # second iteration is key, hooks would have fired during aot trace # on first iter compiled_activations.clear() x = torch.randn((20, 10)) pred = compiled_model(x) loss = pred.sum() loss.backward() activations = eager_activations for i in range(2): # second iteration is key, hooks would have fired during aot trace # on first iter eager_activations.clear() x = torch.randn((20, 10)) pred = model(x) loss = pred.sum() loss.backward() print(f"Recorded Layers: {compiled_activations.keys()}\n\n") print(f"Expected Layers: {eager_activations.keys()}") self.assertTrue(compiled_activations.keys() == eager_activations.keys()) self.assertTrue(activations.keys() == forward_handles.keys()) def test_hooks_allowed_modules(self): # this test shouldn't care whether hook guards are enabled or not class ToyModel(torch.nn.Module): def __init__(self) -> None: super().__init__() self.net = torch.nn.Sequential( *[torch.nn.Linear(10, 10000), torch.nn.ReLU()] + [torch.nn.Linear(10000, 5), torch.nn.ReLU()] ) def forward(self, x): return self.net(x) model = ToyModel() self._forward_hook_test_helper(model) def test_hooks_allowed_modules_compiles(self): class ToyModel(torch.nn.Module): def __init__(self) -> None: super().__init__() self.net = torch.nn.Sequential( *[torch.nn.Linear(10, 10000), torch.nn.ReLU()] + [torch.nn.Linear(10000, 5), torch.nn.ReLU()] ) def forward(self, x): return self.net(x) model = ToyModel() activations = [] def save_activations(mod, inp, out): activations.append(inp) for name, module in model.named_modules(): module.register_forward_hook(save_activations) cnt = torch._dynamo.testing.CompileCounter() model = torch._dynamo.optimize(cnt, nopython=True)(model) for i in range(2): # second iteration is key, hooks would have fired during aot trace # on first iter activations.clear() x = torch.randn((20, 10)) pred = model(x) loss = pred.sum() loss.backward() self.assertEqual(len(activations), 6) self.assertEqual(cnt.frame_count, 1) def test_hooks_allowed_modules_compiles_self_contained(self): class ToyModel(torch.nn.Module): def __init__(self) -> None: super().__init__() self.net = torch.nn.Sequential( *[torch.nn.Linear(10, 10000), torch.nn.ReLU()] + [torch.nn.Linear(10000, 5), torch.nn.ReLU()] ) def forward(self, x): return self.net(x) * self.net(x) model = ToyModel() forward_handles = {} def output_modifying_hook(mod, inp, out): return 2 * out + 1 for name, module in model.named_modules(): forward_handles[name] = module.register_forward_hook(output_modifying_hook) cnt = torch._dynamo.testing.CompileCounter() x = torch.randn((20, 10)) pred_eager = model(x) loss_eager = pred_eager.sum() eager_loss_bwd = loss_eager.backward() model = torch._dynamo.optimize(cnt, nopython=True)(model) pred = model(x) loss = pred.sum() loss_bwd = loss.backward() self.assertEqual(eager_loss_bwd, loss_bwd) self.assertEqual(cnt.frame_count, 2) # Ndim change, recompile pred = model(torch.randn([10, 10, 10])) self.assertEqual(cnt.frame_count, 4) # Stable pred = model(torch.randn([10, 10, 10])) self.assertEqual(cnt.frame_count, 4) def test_dunder_call_explicitly(self): # hooks should be triggered if explicit calling `__call__` class ToyModel(torch.nn.Module): def __init__(self) -> None: super().__init__() self.linear = torch.nn.Linear(10, 10000) def forward(self, x): return self.linear.__call__(x) model = ToyModel() self._forward_hook_test_helper(model) def test_backward_hooks(self): # this test shouldn't care whether hook guards are enabled or not class CustomLinear(torch.nn.Module): # not an 'allowed module', so should not graph-break def __init__(self, a, b): super().__init__() self.weight = torch.nn.Parameter(torch.randn(a, b)) def forward(self, x): return torch.mm(x, self.weight) class ToyModel(torch.nn.Module): def __init__(self) -> None: super().__init__() self.net = torch.nn.Sequential( *[CustomLinear(10, 10)] + [CustomLinear(10, 10000)] + [CustomLinear(10000, 5)] ) def forward(self, x): return self.net(x) model = ToyModel() backward_hook_handles = {} pre_backward_hook_handles = {} grad_sizes = {} def backward_hook(name, mod, grad_inp, grad_out): grad_sizes[name] = ( (gi.shape for gi in grad_inp), (go.shape for go in grad_out), ) return None pre_grad_sizes = {} def backward_pre_hook(name, mod, grad_out): pre_grad_sizes[name] = (go.shape for go in grad_out) return None for name, module in model.named_modules(): backward_hook_handles[name] = module.register_full_backward_hook( partial(backward_hook, name) ) pre_backward_hook_handles[name] = module.register_full_backward_pre_hook( partial(backward_pre_hook, name) ) model = torch.compile(model, backend="aot_eager") for i in range(2): # second iteration is key, hooks would have fired during aot trace # on first iter x = torch.randn((20, 10)) pred = model(x) loss = pred.sum() loss.backward() self.assertTrue(grad_sizes.keys() == backward_hook_handles.keys()) self.assertTrue(pre_grad_sizes.keys() == pre_backward_hook_handles.keys()) def test_udo_instance_method_as_hook(self): class CustomClass: def __init__(self, module): self.module = module self.handle = self.module.register_forward_pre_hook( self.func1, prepend=True, with_kwargs=True ) def func1(self, module, args, kwargs): return (args[0] + 1,), kwargs def __call__(self, x): return self.module(x) class ToyModel(torch.nn.Module): def __init__(self) -> None: super().__init__() def forward(self, x): return x * x model = ToyModel() x = torch.zeros((3, 4)) obj = CustomClass(model) out = torch.compile(obj, fullgraph=True)(x) self.assertEqual(out, (x + 1) * (x + 1)) def test_module_dict_iter_name(self): class MyModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.activations = torch.nn.ModuleDict( [["lrelu", torch.nn.LeakyReLU()], ["prelu", torch.nn.PReLU()]] ) def forward(self, x): for activation_name in self.activations: x = self.activations[activation_name](x) return x cnt = torch._dynamo.testing.CompileCounter() # Eager eager_res = MyModule()(torch.ones(10, 10)) # Compile optim_res = torch._dynamo.optimize(cnt)(MyModule())(torch.ones(10, 10)) self.assertEqual(eager_res, optim_res) self.assertEqual(cnt.frame_count, 1) def test_module_dict_iter_keys(self): class MyModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.activations = torch.nn.ModuleDict( [["lrelu", torch.nn.LeakyReLU()], ["prelu", torch.nn.PReLU()]] ) def forward(self, x): for activation_name in self.activations.keys(): x = self.activations[activation_name](x) return x cnt = torch._dynamo.testing.CompileCounter() # Eager eager_res = MyModule()(torch.ones(10, 10)) # Compile optim_res = torch._dynamo.optimize(cnt)(MyModule())(torch.ones(10, 10)) self.assertEqual(eager_res, optim_res) self.assertEqual(cnt.frame_count, 1) def test_module_setattr(self): models = torch.nn.Sequential(torch.nn.Linear(3, 3)) models[0].abc = False def run(): models[0].abc = True x = torch.randn(1, 3) return models(x) run = torch.compile(run, fullgraph=True) run() self.assertTrue(models[0].abc) def test_assign_does_not_exist(self): class MyModule(torch.nn.Module): def forward(self, x): self.text_encoding = x + 1 return self.text_encoding mod = MyModule() out = torch.compile(mod, fullgraph=True)(torch.randn(10)) assert mod.text_encoding is out def test_module_dict_iter_values(self): class MyModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.activations = torch.nn.ModuleDict( [["lrelu", torch.nn.LeakyReLU()], ["prelu", torch.nn.PReLU()]] ) def forward(self, x): for activation in self.activations.values(): x = activation(x) return x cnt = torch._dynamo.testing.CompileCounter() # Eager eager_res = MyModule()(torch.ones(10, 10)) # Compile optim_res = torch._dynamo.optimize(cnt)(MyModule())(torch.ones(10, 10)) self.assertEqual(eager_res, optim_res) self.assertEqual(cnt.frame_count, 1) def test_unspecialized_seq(self): models = torch.nn.Sequential(torch.nn.Linear(3, 3)) def fn(x): models[0].training = False return models(x) opt_fn = torch._dynamo.optimize("eager")(fn) x = torch.randn(1, 3) ref = fn(x) res = opt_fn(x) self.assertEqual(ref, res) def test_no_op_assignment(self): class Mod(torch.nn.Module): def __init__(self) -> None: super().__init__() self.buffer = torch.rand([4]) def forward(self, x): # should be a no-op, but causes dynamo to lose the static input x = x + 1 self.buffer = self.buffer.to(x) return self.buffer + x compiles_without_buffers = 0 def debug_compile(gm, *args, **kwargs): nonlocal compiles_without_buffers compiles_without_buffers += len(list(gm.buffers())) == 0 return gm @torch.compile(backend=debug_compile) def foo(mod, x): return mod(x) mod = Mod() foo(mod, torch.rand([4])) if torch._dynamo.config.inline_inbuilt_nn_modules: self.assertEqual(compiles_without_buffers, 1) else: self.assertEqual(compiles_without_buffers, 0) foo(mod, torch.rand([4], dtype=torch.half)) if torch._dynamo.config.inline_inbuilt_nn_modules: self.assertEqual(compiles_without_buffers, 2) else: self.assertEqual(compiles_without_buffers, 1) class Mod2(Mod): def __setattr__(self, name, value): return super().__setattr__(name, value) foo(Mod2(), torch.rand([4])) # causes two compilations, bc unimplemented custom setattr self.assertTrue(compiles_without_buffers >= 2) def test_unspec_non_inlinable_module(self): mod = UnspecNonInlinableModule() opt_fn = torch._dynamo.optimize("eager")(mod) x = torch.randn(100) actual = opt_fn(x) expected = mod(x) self.assertEqual(actual, expected) @torch._dynamo.config.patch("inline_inbuilt_nn_modules", True) def test_mark_static_previously_seen_tensor(self): # This test verifies that dynamo will mark # the buffers/params of a module as static # even if this param was previously seen # (ex. as a different input) num_compiles = 0 def debug_compiler(gm, _): nonlocal num_compiles num_compiles += 1 input_nodes = [ n for n in gm.graph.nodes if n.op == "placeholder" and n.name == "l_b_" ] self.assertGreater(len(input_nodes), 0) for input_node in input_nodes: self.assertEqual( input_node.meta["tensor_dict"]["_dynamo_static_input_type"], "unguarded", ) return gm class TestModule(torch.nn.Module): def __init__(self, buf) -> None: super().__init__() # Changing this one to nn.Buffer fails because `nn.Buffer` does a .detach() # so the value in self.tx.output.side_effects will no longer evaluate to True self.register_buffer("buf", buf) def forward(self, x): return self.buf * x @torch._dynamo.optimize(backend=debug_compiler) def fn(x, b, mod): z = b + 1 return z * mod(x) buf = torch.ones(2, 2) inp = torch.ones(2) mod = TestModule(buf) fn(inp, buf, mod) self.assertEqual(num_compiles, 1) @torch._dynamo.config.patch("inline_inbuilt_nn_modules", True) def test_mark_static_nn_module_tensor(self): # This test verifies that dynamo will mark # the nn module tensor attributes as static num_compiles = 0 def debug_compiler(gm, _): nonlocal num_compiles num_compiles += 1 input_nodes = [ n for n in gm.graph.nodes if n.op == "placeholder" and n.name == "l_mod_buf" ] self.assertGreater(len(input_nodes), 0) for input_node in input_nodes: self.assertEqual( input_node.meta["tensor_dict"]["_dynamo_static_input_type"], "unguarded", ) return gm class TestModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.buf = torch.ones(2, 2) def forward(self, x): return self.buf * x mod = TestModule() @torch._dynamo.optimize(backend=debug_compiler) def fn(x): return x * mod(x) inp = torch.ones(2) fn(inp) self.assertEqual(num_compiles, 1) @torch._dynamo.config.patch("inline_inbuilt_nn_modules", True) @torch._inductor.config.patch("freezing", True) @torch.no_grad() def test_mark_static_with_freezing(self): # This test verifies that dynamo will # add buffers/params as attributes of the # graph w/ guards if freezing is enabled num_compiles = 0 def debug_compiler(gm, _): nonlocal num_compiles num_compiles += 1 input_nodes = [ n for n in gm.graph.nodes if n.op == "placeholder" and n.name == "l_b_" ] self.assertEqual(len(input_nodes), 0) self.assertEqual(len(list(gm.buffers())), 1) return gm class TestModule(torch.nn.Module): def __init__(self, buf) -> None: super().__init__() self.buf = torch.nn.Buffer(buf) def forward(self, x): return self.buf * x @torch._dynamo.optimize(backend=debug_compiler) def fn(x, mod): return mod(x) buf = torch.ones(2, 2) inp = torch.ones(2) mod = TestModule(buf) fn(inp, mod) self.assertEqual(num_compiles, 1) mod.buf = torch.rand_like(buf) fn(inp, mod) self.assertEqual(num_compiles, 2) @patch.object(torch._dynamo.config, "guard_nn_modules", True) def test_guard_on_torch_nn_modules(self): # https://github.com/pytorch/pytorch/issues/110048 class MockModule(torch.nn.Module): def __init__(self) -> None: super().__init__() self.linear = torch.nn.Linear(10, 10) self.multiplier = 10 def forward(self, x): return self.linear(x) * self.multiplier mod = MockModule() cnt = torch._dynamo.testing.CompileCounter() @torch.compile(backend=cnt) def generate(x, c): return mod(x) + c for _ in range(0, 10): generate(torch.randn(10, 10), 0) generate(torch.randn(10, 10), 1) self.assertEqual(cnt.frame_count, 2) # Ensure that modification in user module causes recompile mod.multiplier = 11 generate(torch.randn(10, 10), 0) self.assertEqual(cnt.frame_count, 3) def test_setattr_on_compiled_module(self): # https://github.com/pytorch/pytorch/issues/114844 class ReplayMutation(torch.nn.Module): def __init__(self, inp_size, out_size, inner_size): super().__init__() self.Linear1 = torch.nn.Linear(inp_size, inner_size) self.Linear2 = torch.nn.Linear(inner_size, out_size) self.x = None def forward(self, inp): res = self.Linear1(inp) self.x = res return self.Linear2(res) N, D_in, H, D_out, inner = 2, 2, 2, 2, 4 model = ReplayMutation(D_in, H, inner) model2 = copy.deepcopy(model) input = torch.ones(N, D_in) # Keep some intermediate value in model.x model.x = torch.tensor([[100, 100, 100, 100], [200, 200, 200, 200]]) model(input) compiled_model = torch.compile(model2, backend="eager") compiled_model.x = torch.tensor([[100, 100, 100, 100], [200, 200, 200, 200]]) compiled_model(input) self.assertEqual(model.x, compiled_model.x) def test_globals_change_in_other_file(self): @torch.compile(backend="eager", fullgraph=True) def fn(x): update_global() a = test_functions.update_global(x) # Ensure that the updated global values are read return x * a * (_variable + _variable1 + test_functions._variable) res = fn(torch.ones(10)) self.assertEqual(_variable, 1) self.assertEqual(_variable1, 1) # Ensure that the reconstructed bytecode updates the global value in the # other file. self.assertEqual(test_functions._variable, 1) self.assertEqual(res, 3 * torch.ones(10)) @unittest.skipIf( "inductor" not in torch._dynamo.list_backends(), "inductor backend is not available", ) def test_save_and_load_inductor(self): mod = MockModule() opt_mod = torch.compile(mod, backend="inductor") inp = torch.randn(10, 10) opt_mod(inp) with tempfile.TemporaryDirectory() as tmpdirname: torch.save(opt_mod, os.path.join(tmpdirname, "model.pt")) loaded_model = torch.load(os.path.join(tmpdirname, "model.pt")) loaded_model(inp) self.assertTrue(same_two_models(loaded_model, mod, [inp])) self.assertTrue(same_two_models(loaded_model, opt_mod, [inp])) torch._dynamo.reset() # force recompiles torch._inductor.metrics.generated_kernel_count = 0 loaded_model(inp) self.assertGreater(torch._inductor.metrics.generated_kernel_count, 0) def test_save_and_load_all_backends(self): mod = MockModule() inp = torch.randn(10, 10) for backend in torch._dynamo.list_backends(): try: opt_mod = torch.compile(mod, backend=backend) with tempfile.TemporaryDirectory() as tmpdirname: torch.save(opt_mod, os.path.join(tmpdirname, "model.pt")) loaded_model = torch.load(os.path.join(tmpdirname, "model.pt")) torch._dynamo.reset() # force recompiles torch._inductor.metrics.generated_kernel_count = 0 opt_mod(inp) opt_success = torch._inductor.metrics.generated_kernel_count == 0 torch._dynamo.reset() # force recompiles torch._inductor.metrics.generated_kernel_count = 0 loaded_model(inp) loaded_success = torch._inductor.metrics.generated_kernel_count == 0 self.assertEqual(opt_success, loaded_success) except torch._dynamo.exc.BackendCompilerFailed: pass def test_monkeypatching_forward(self): class FakeModule(torch.nn.Module): def forward(self, x): return torch.sin(x) class MyModule(torch.nn.Module): def __init__(self, x): super().__init__() def forward(self, x): return torch.cos(x) def helper(): torch._dynamo.reset() mod = MyModule(3) def fn(x): return mod(x) cnt = torch._dynamo.testing.CompileCounter() opt_fn = torch._dynamo.optimize(cnt)(fn) x = torch.randn(10) opt_fn(x) opt_fn(x) self.assertEqual(cnt.frame_count, 1) # Monkeypatch forward mod.forward = types.MethodType(FakeModule.forward, mod) ref = fn(x) res = opt_fn(x) self.assertEqual(ref, res) self.assertEqual(cnt.frame_count, 2) helper() with torch._dynamo.config.patch(inline_inbuilt_nn_modules=True): helper() def test_user_defined_nn_module_dynamic(self): class Conv2d(torch.nn.Conv2d): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def forward(self, x): x = torch.nn.functional.conv2d( x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups, ) return x cnts = torch._dynamo.testing.CompileCounter() mod1 = Conv2d(64, 64, kernel_size=(2, 2), stride=(1, 1)) mod2 = Conv2d(64, 64, kernel_size=(2, 2), stride=(2, 2)) mod3 = Conv2d(64, 64, kernel_size=(2, 2), stride=(3, 3)) opt_mod1 = torch.compile(mod1, backend=cnts, fullgraph=True) opt_mod2 = torch.compile(mod2, backend=cnts, fullgraph=True) opt_mod3 = torch.compile(mod3, backend=cnts, fullgraph=True) x = torch.randn(1, 64, 64, 64) opt_mod1(x) opt_mod2(x) opt_mod3(x) # Must be 3 compilations. If not marked static there would be 2, because strides would be converted to symints. self.assertEqual(cnts.frame_count, 3) if __name__ == "__main__": from torch._dynamo.test_case import run_tests run_tests()