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ca43ecd5996b15178de88960d167ccc31458b607
pytorch/test/dynamo/test_modules.py
Mikayla Gawarecki ca43ecd599 Flip default on weights_only (#137602) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137602
Approved by: https://github.com/malfet, https://github.com/albanD
ghstack dependencies: #138936, #139221, #139433, #139541
2024-11-04 18:30:29 +00:00

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# 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""")
def test_nn_module_setattr(self):
class Mod(torch.nn.Module):
def __init__(self):
super().__init__()
self.var = 0
@torch.compile(backend="eager", dynamic=False)
def f(x, m):
return x + m.var
inp = torch.ones(3)
m = Mod()
self.assertEqual(f(inp, m), inp)
# In 3.13.0, setattr will not fire a __dict__'s watchers,
# so guards may not be invalidated.
m.var = 1
# should trigger a recompile
self.assertEqual(f(inp, m), inp + 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)))
# RuntimeError: SymIntArrayRef expected to contain only concrete integers
@expectedFailureDynamic
def test_lazy_module_speculation_log_divergence(self):
class ModWithOneLazyLinear(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
self.layer = torch.nn.LazyLinear(8)
def forward(self, x):
return self.layer(x)
# This allows us to restart tracing without clearing speculation log
def id_and_fail_inlining(x):
torch._dynamo.graph_break()
return x
cnt = torch._dynamo.testing.CompileCounter()
@torch.compile(backend=cnt)
def test(mod, x):
res = mod(x)
# Speculation log must not diverge in the 2nd round of tracing,
# after we've initialized the `LazyLinear` into a `Linear` in the
# 1st round.
res2 = id_and_fail_inlining(res)
return res
mod = ModWithOneLazyLinear()
x = torch.ones(10, 3)
# Make sure we don't get recompilation across multiple runs
actual_res = test(mod, x)
expect_res = mod(x)
self.assertTrue(torch.allclose(expect_res, actual_res))
actual_res = test(mod, x)
expect_res = mod(x)
self.assertTrue(torch.allclose(expect_res, actual_res))
self.assertEqual(cnt.frame_count, 1)
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"))
# weights_only=False as this is a legacy use case that loads a module
loaded_model = torch.load(
os.path.join(tmpdirname, "model.pt"), weights_only=False
)
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"))
# weights_only=False as this is a legacy use case that loads a module
loaded_model = torch.load(
os.path.join(tmpdirname, "model.pt"), weights_only=False
)
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)
@patch.object(torch._dynamo.config, "inline_inbuilt_nn_modules", True)
def test_overridden_call(self):
class OverRiddenCallModule(torch.nn.Module):
def __init__(self):
super().__init__()
def __call__(self, x):
# Overrides the __call__ method of torch.nn.Module
return 5 * self.forward(x)
def forward(self, x):
return x * 3
m = OverRiddenCallModule()
def fn(x):
return m(x)
x = torch.ones(4)
ref = fn(x)
opt_fn = torch.compile(fn, backend="eager", fullgraph=True)
res = opt_fn(x)
self.assertEqual(ref, res)
@patch.object(
torch._dynamo.config, "skip_tensor_guards_with_matching_dict_tags", False
)
def test_param_requires_grad(self):
def adjust_model(model):
to_freeze = model.num_iter % 2 == 0
if to_freeze:
for param in model.layer2.parameters():
param.requires_grad = False
else:
for param in model.layer2.parameters():
param.requires_grad = True
class MyModule(torch.nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super().__init__()
self.layer1 = torch.nn.Linear(hidden_size, hidden_size)
self.layer2 = torch.nn.Linear(hidden_size, hidden_size)
self.num_iter = 0
def forward(self, x):
x = self.layer2(x + self.layer1.bias)
self.num_iter += 1
return x
input_size = 1024
hidden_size = 1024
output_size = 1
num_samples = 2048
features = torch.randn(num_samples, input_size)
model = MyModule(input_size, hidden_size, output_size)
cnt = torch._dynamo.testing.CompileCounter()
opt_model = torch.compile(model, backend=cnt, fullgraph=True)
for _ in range(3):
model.zero_grad(True)
adjust_model(model)
res = opt_model(features)
res.sum().backward()
# Check that we have recompiled twice, which leads to 3 frames
self.assertEqual(cnt.frame_count, 3)
if __name__ == "__main__":
from torch._dynamo.test_case import run_tests
run_tests()
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