mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
f4ac8e0052
This lets users that are sure they won't use hooks avoid overhead related to dynamo guards on (assumedly) empty hook dicts on all nn modules. Only enable this flag if you are sure you won't change hook-behavior after compiling. It is ok to register a hook and then compile, if you promise never to remove/alter the hook. It is also ok to not register a hook and compile, if you never register a hook later. Note- this is not the best we can do, and hopefully in the future we can avoid the need for this option following some of these paths - make guards fast enough to not be an issue when guarding on hook dicts - make a mode where dynamo actually skips tracing __call__ so hooks are consistently ignored by compiled programs - use nnmodule versioning so hook changes can be guarded without explicit hook dict guards Pull Request resolved: https://github.com/pytorch/pytorch/pull/97830 Approved by: https://github.com/jansel
1363 lines
40 KiB
Python
1363 lines
40 KiB
Python
# Owner(s): ["module: dynamo"]
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import types
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from copy import deepcopy
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from typing import Tuple
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from unittest.mock import patch
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import torch
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import torch._dynamo.test_case
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import torch._dynamo.testing
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from torch._dynamo.eval_frame import unsupported
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from torch._dynamo.mutation_guard import GenerationTracker
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from torch._dynamo.testing import same
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from torch.nn import functional as F
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from torch.nn.modules.lazy import LazyModuleMixin
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from torch.nn.parameter import Parameter, UninitializedParameter
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try:
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from . import test_functions
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except ImportError:
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import test_functions
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class BasicModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.linear1 = torch.nn.Linear(10, 10)
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self.scale = torch.randn(1, 10)
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def forward(self, x):
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return F.relu(self.linear1(x)) * self.scale
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class FnMember(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.linear1 = torch.nn.Linear(10, 10)
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self.activation = F.relu
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def forward(self, x):
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x = self.linear1(x)
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if self.activation:
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x = self.activation(x)
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return x
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class FnMemberCmp(torch.nn.Module):
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def __init__(self, activation):
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super().__init__()
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self.linear1 = torch.nn.Linear(10, 10)
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self.activation = activation
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def forward(self, x):
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x = self.linear1(x)
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if self.activation is not None:
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x = self.activation(x)
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if self.activation is None:
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x = torch.sigmoid(x)
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return x
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class SubmoduleExample(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.layer1 = BasicModule()
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self.layer2 = BasicModule()
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self.scale = torch.randn(1, 10)
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def forward(self, x):
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x = self.layer1(x)
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x = self.layer2(x)
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return x * self.scale
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class IsTrainingCheck(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.linear1 = torch.nn.Linear(10, 10)
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self.linear2 = torch.nn.Linear(10, 10)
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self.train(True)
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def forward(self, x):
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if self.training:
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mod = self.linear1
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else:
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mod = self.linear2
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return F.relu(mod(x))
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class IsEvalCheck(IsTrainingCheck):
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def __init__(self):
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super().__init__()
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self.train(False)
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class ModuleMethodCall(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.layer1 = BasicModule()
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self.layer2 = BasicModule()
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self.scale = torch.randn(1, 10)
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def call_and_scale(self, mod, x):
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x = mod(x)
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return x * self.scale
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def forward(self, x):
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x1 = self.call_and_scale(self.layer1, x)
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x2 = self.call_and_scale(self.layer2, x)
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return x1 + x2
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class UnsupportedMethodCall(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.layer1 = BasicModule()
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self.scale = torch.randn(1, 10)
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def call_and_scale(self, mod, x):
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x = mod(x)
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x = x * self.scale
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return unsupported(x, x)
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def forward(self, x):
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x1 = self.call_and_scale(self.layer1, x)
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return x + x1
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class UnsupportedModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.layer1 = BasicModule()
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self.scale = torch.randn(1, 10)
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def forward(self, x):
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x = self.layer1(x) * self.scale
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return unsupported(x, x)
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class UnsupportedModuleCall(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.mod = UnsupportedModule()
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def forward(self, x):
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return 1 + self.mod(x * 1.5)
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class ModuleWithStaticForward(torch.nn.Module):
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@staticmethod
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def forward(x):
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return x * torch.sigmoid(x)
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class ModuleCallModuleWithStaticForward(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.mod = ModuleWithStaticForward()
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def forward(self, x):
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return self.mod(x)
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class ModuleStaticMethodCall(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.layer1 = BasicModule()
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self.layer2 = BasicModule()
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self.scale = torch.randn(1, 10)
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@staticmethod
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def call_and_scale(scale, mod, x):
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x = mod(x)
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return x * scale
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def forward(self, x):
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x1 = self.call_and_scale(self.scale, self.layer1, x)
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x2 = self.call_and_scale(self.scale, self.layer2, x)
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return x1 + x2
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class ModuleClassMethodCall(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.layer1 = BasicModule()
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self.layer2 = BasicModule()
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self.scale = torch.randn(1, 10)
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@classmethod
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def call_and_scale(cls, scale, mod, x):
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x = mod(x)
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return x * scale
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def forward(self, x):
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x1 = self.call_and_scale(self.scale, self.layer1, x)
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x2 = self.call_and_scale(self.scale, self.layer2, x)
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return x1 + x2
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class ModuleProperty(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.scale = torch.randn(1, 10)
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@property
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def scale_alias(self):
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return self.scale
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def forward(self, x):
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return x * self.scale_alias
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class ConstLoop(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.linear1 = torch.nn.Linear(10, 10)
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self.count = 3
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def forward(self, x):
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for i in range(self.count):
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x = torch.sigmoid(self.linear1(x))
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return x
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class ViaModuleCall(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.linear1 = torch.nn.Linear(10, 10)
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def forward(self, x):
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return test_functions.constant3(torch.sigmoid(self.linear1(x)), x)
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class IsNoneLayer(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.layer1 = torch.nn.Linear(10, 10)
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self.layer2 = None
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self.train(True)
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def forward(self, x):
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if self.layer1 is not None:
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x = self.layer1(x)
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if self.layer2 is not None:
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x = self.layer2(x)
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return x
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class LayerList(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.layers = [
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torch.nn.Linear(10, 10),
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torch.nn.ReLU(),
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torch.nn.Linear(10, 10),
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]
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def forward(self, x):
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for layer in self.layers:
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x = layer(x)
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return x
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class ModuleList(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.layers = torch.nn.ModuleList(
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[
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torch.nn.Linear(10, 10),
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torch.nn.ReLU(),
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torch.nn.Linear(10, 10),
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torch.nn.ReLU(),
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]
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)
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def forward(self, x):
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for i in range(len(self.layers)):
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x = self.layers[i](x)
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for layer in self.layers:
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x = layer(x)
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for layer, val in zip(self.layers, (x, x, x, x)):
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x = layer(x) + val
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for layer, val in zip(self.layers, (1, 2, 3, 4)):
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x = layer(x) + val
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for idx, layer in enumerate(self.layers):
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x = layer(x) * idx
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for idx, layer in enumerate(self.layers[::-1]):
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x = layer(x) * idx
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return x
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class ModuleDict(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.layers = torch.nn.ModuleDict(
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{
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"0": torch.nn.Linear(10, 10),
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}
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)
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def forward(self, x):
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# TODO(future PR): handle more logic
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x = self.layers["0"](x)
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return x
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class TensorList(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.layers = (
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torch.randn((1, 10)),
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torch.randn((10, 1)),
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torch.randn((1, 10)),
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torch.randn((10, 1)),
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)
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def forward(self, x):
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for layer in self.layers:
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x = x * layer
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return x
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class Children(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.l1 = torch.nn.Linear(10, 10)
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self.l2 = torch.nn.ReLU()
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self.l3 = torch.nn.Linear(10, 10)
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self.l4 = torch.nn.ReLU()
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def forward(self, x):
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for block in self.children():
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x = block(x)
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return x
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class NamedChildren(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.l1 = torch.nn.Linear(10, 10)
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self.l2 = torch.nn.ReLU()
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self.l3 = torch.nn.Linear(10, 10)
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self.l4 = torch.nn.ReLU()
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def forward(self, x):
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for _, block in self.named_children():
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x = block(x)
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return x
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class IntArg(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.layer1 = torch.nn.Linear(10, 10)
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def forward(self, x, offset=1):
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x = F.relu(self.layer1(x)) + offset
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return x
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class Seq(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.layers = torch.nn.Sequential(
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torch.nn.Linear(10, 10),
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torch.nn.ReLU(),
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torch.nn.Linear(10, 10),
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torch.nn.ReLU(),
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)
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def forward(self, x):
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return self.layers(x)
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class Cfg:
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def __init__(self):
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self.val = 0.5
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self.count = 3
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class CfgModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.cfg = Cfg()
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self.layer = torch.nn.Linear(10, 10)
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def forward(self, x):
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for i in range(self.cfg.count):
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x = self.layer(x + self.cfg.val)
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return x
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class StringMember(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.linear1 = torch.nn.Linear(10, 10)
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self.mode = "some_string"
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def forward(self, x):
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if self.mode == "some_string":
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return F.relu(self.linear1(x))
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class _Block(torch.nn.Module):
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def forward(self, x):
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return 1.5 * torch.cat(x, 1)
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class _DenseBlock(torch.nn.ModuleDict):
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_version = 2
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def __init__(
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self,
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num_layers: int = 3,
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) -> None:
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super().__init__()
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for i in range(num_layers):
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self.add_module("denselayer%d" % (i + 1), _Block())
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def forward(self, init_features):
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features = [init_features]
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for name, layer in self.items():
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new_features = layer(features)
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features.append(new_features)
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return torch.cat(features, 1)
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class DenseNetBlocks(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.layers = _DenseBlock()
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def forward(self, x):
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return self.layers(x)
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class MaterializedModule(torch.nn.Module):
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"""Once the below lazy module is initialized with its first input,
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it is transformed into this module."""
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param: Parameter
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def __init__(self):
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super().__init__()
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self.register_parameter("param", None)
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def forward(self, x):
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return x
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class LazyModule(LazyModuleMixin, MaterializedModule):
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param: UninitializedParameter
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cls_to_become = MaterializedModule
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def __init__(self):
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super().__init__()
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self.param = UninitializedParameter()
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def initialize_parameters(self, x):
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self.param.materialize(x.shape)
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def requires_grad1(module: torch.nn.Module, recurse: bool = False) -> bool:
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requires_grad = any([p.requires_grad for p in module.parameters(recurse)])
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return requires_grad
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def requires_grad2(module: torch.nn.Module, recurse: bool = False) -> bool:
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requires_grad = any(p.requires_grad for p in module.parameters(recurse))
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return requires_grad
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class ParametersModule1(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.linear1 = torch.nn.Linear(10, 10)
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self.scale = torch.nn.Parameter(torch.randn(1, 10))
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def forward(self, x):
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if not requires_grad1(self):
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return F.relu(self.linear1(x)) * self.scale
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else:
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return x + 1
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class ParametersModule2(ParametersModule1):
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def forward(self, x):
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if not requires_grad2(self):
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return F.relu(self.linear1(x)) * self.scale
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else:
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return x + 1
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class ParametersModule3(ParametersModule1):
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def forward(self, x):
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ones = torch.ones(10, dtype=next(self.parameters()).dtype)
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return F.relu(self.linear1(x)) * self.scale + ones
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class SuperModule(BasicModule):
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def forward(self, x):
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x = super().forward(x)
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return x + 10.0
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class SuperModule2(BasicModule):
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def forward(self, x):
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return BasicModule.forward(self, x)
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class ComplicatedSuperParent(torch.nn.Module):
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@classmethod
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def custom_add(cls, x):
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x = x + x
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return x
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class SuperChildCallsClassMethod(ComplicatedSuperParent):
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@classmethod
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def child_func(cls, x):
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x = super().custom_add(x)
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return x
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def forward(self, x):
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x = self.child_func(x)
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return x
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class HasAttrModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.scale = torch.nn.Parameter(torch.randn(1, 10))
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def forward(self, x):
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x = F.relu(x)
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if hasattr(self, "scale"):
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x *= self.scale
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if hasattr(self, "scale2"):
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x *= self.scale2
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return x
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class EnumValues(torch.nn.ModuleDict):
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def __init__(
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self,
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num_layers: int = 3,
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) -> None:
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super().__init__()
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for i in range(num_layers):
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self.add_module("denselayer%d" % (i + 1), _Block())
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def forward(self, init_features):
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features = [init_features]
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for idx, layer in enumerate(self.values()):
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new_features = layer(features)
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features.append(new_features)
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return torch.cat(features, 1)
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class AccessByKeys(torch.nn.ModuleDict):
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def __init__(
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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):
|
|
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 ModuleNameString(torch.nn.Module):
|
|
def __init__(self):
|
|
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):
|
|
return x * 2.0
|
|
|
|
|
|
class ModuleAttributePrecedence(ModuleAttributePrecedenceBase):
|
|
def __init__(self):
|
|
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):
|
|
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):
|
|
super().__init__()
|
|
for i in range(2):
|
|
self.add_module("l@yer-%d" % (i + 1), BasicModule())
|
|
|
|
def forward(self, x):
|
|
for _, layer in self.items():
|
|
x = layer(x)
|
|
return x
|
|
|
|
|
|
class ModulePatch1(torch.nn.Module):
|
|
pass
|
|
|
|
|
|
class ModulePatch2(torch.nn.Module):
|
|
def forward(self, x):
|
|
return x - 1
|
|
|
|
|
|
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
|
|
|
|
|
|
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_moduledict = make_test(ModuleDict())
|
|
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_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())
|
|
|
|
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))
|
|
self.assertEqual(cnt.frame_count, 3)
|
|
|
|
@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
|
|
|
|
class TensorProxy(torch.Tensor):
|
|
@classmethod
|
|
def __torch_function__(cls, func, types, args=(), kwargs=None):
|
|
return super().__torch_function__(func, types, args, kwargs)
|
|
|
|
torch._dynamo.config.traceable_tensor_subclasses.add(TensorProxy)
|
|
|
|
try:
|
|
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))
|
|
|
|
finally:
|
|
torch._dynamo.config.traceable_tensor_subclasses.remove(TensorProxy)
|
|
|
|
def test_torch_function_with_closure(self):
|
|
def run():
|
|
counter = 0
|
|
|
|
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
|
|
|
|
class TensorProxy(torch.Tensor):
|
|
@classmethod
|
|
def __torch_function__(cls, func, types, args=(), kwargs=None):
|
|
nonlocal counter
|
|
# for now, only support reads from closure cells
|
|
# TODO(future PR): support writes as well
|
|
counter + 1
|
|
return super().__torch_function__(func, types, args, kwargs)
|
|
|
|
torch._dynamo.config.traceable_tensor_subclasses.add(TensorProxy)
|
|
|
|
try:
|
|
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))
|
|
finally:
|
|
torch._dynamo.config.traceable_tensor_subclasses.remove(TensorProxy)
|
|
|
|
run()
|
|
|
|
@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))
|
|
|
|
def test_lazy_module(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)
|
|
|
|
opt_test_static_module = torch._dynamo.optimize(cnt)(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
|
|
|
|
opt_test_torch_static = torch._dynamo.optimize(cnt)(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.")
|
|
|
|
def test_call_fn_with_non_const_inputs_safe(self):
|
|
class ModuleSpecialFwd(torch.nn.Module):
|
|
def __init__(self):
|
|
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)))
|
|
|
|
|
|
class MockModule(torch.nn.Module):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.relu = torch.nn.ReLU()
|
|
self.linear = torch.nn.Linear(10, 10)
|
|
self.register_buffer("buf0", torch.randn(10, 10))
|
|
|
|
def forward(self, x):
|
|
return self.relu(self.linear(x) + self.buf0)
|
|
|
|
|
|
class OptimizedModuleTest(torch._dynamo.test_case.TestCase):
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def test_nn_module(self):
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mod = MockModule()
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cnt = torch._dynamo.testing.CompileCounter()
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opt_mod = torch._dynamo.optimize(cnt)(mod)
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self.assertIsInstance(opt_mod, torch._dynamo.OptimizedModule)
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|
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x = torch.randn(10, 10)
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self.assertTrue(torch._dynamo.testing.same(mod(x), opt_mod(x)))
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self.assertEqual(cnt.frame_count, 1)
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|
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def test_to(self):
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mod = MockModule()
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cnt = torch._dynamo.testing.CompileCounter()
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opt_mod = torch._dynamo.optimize(cnt)(mod)
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x = torch.randn(10, 10)
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self.assertTrue(torch._dynamo.testing.same(mod(x), opt_mod(x)))
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self.assertEqual(cnt.frame_count, 1)
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|
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# Ensure that there is no recompilation
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opt_mod(x)
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self.assertEqual(cnt.frame_count, 1)
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|
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opt_mod = opt_mod.to(device="cpu").to(dtype=torch.float64)
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self.assertIsInstance(opt_mod, torch._dynamo.OptimizedModule)
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x = torch.randn(10, 10).to(dtype=torch.float64)
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opt_mod(x)
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# Ensure that there is a recompilation
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self.assertEqual(cnt.frame_count, 2)
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|
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# Ensure that there is no recompilation
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opt_mod(x)
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self.assertEqual(cnt.frame_count, 2)
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|
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torch._dynamo.reset()
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opt_mod(x)
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self.assertEqual(cnt.frame_count, 3)
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|
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def test_attr(self):
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class MockModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.linear = torch.nn.Linear(10, 10)
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self.register_buffer("buf0", torch.randn(10, 10))
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|
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def forward(self, x):
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return self.r(torch.sin(x)) + self.buf0
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|
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mod = MockModule()
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opt_mod = torch._dynamo.optimize("eager")(mod)
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|
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# Check parameteres and buffers
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for p1, p2 in zip(mod.parameters(), opt_mod.parameters()):
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self.assertTrue(id(p1) == id(p2))
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|
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def test_recursion(self):
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mod = MockModule()
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cnt = torch._dynamo.testing.CompileCounter()
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opt_mod = torch._dynamo.optimize(cnt)(mod)
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|
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for _ in range(5):
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opt_mod = torch._dynamo.optimize(cnt)(opt_mod)
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opt_mod(torch.randn(10, 10))
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self.assertEqual(cnt.frame_count, 1)
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|
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def test_composition(self):
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class InnerModule(torch.nn.Module):
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def __init__(self):
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|
super().__init__()
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self.relu = torch.nn.ReLU()
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|
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def forward(self, x):
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return self.relu(torch.sin(x))
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|
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opt_inner_mod = InnerModule()
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|
|
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class OuterModule(torch.nn.Module):
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|
def __init__(self):
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|
super().__init__()
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|
self.mod = opt_inner_mod
|
|
|
|
def forward(self, x):
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|
return self.mod(torch.cos(x))
|
|
|
|
outer_mod = OuterModule()
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|
cnt = torch._dynamo.testing.CompileCounter()
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|
opt_outer_mod = torch._dynamo.optimize(cnt)(outer_mod)
|
|
|
|
x = torch.randn(4)
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self.assertIsInstance(opt_outer_mod, torch._dynamo.OptimizedModule)
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self.assertTrue(torch._dynamo.testing.same(outer_mod(x), opt_outer_mod(x)))
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|
self.assertEqual(cnt.frame_count, 1)
|
|
|
|
def test_composition_with_opt_mod(self):
|
|
class InnerModule(torch.nn.Module):
|
|
def __init__(self):
|
|
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):
|
|
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),
|
|
)
|
|
)
|
|
|
|
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, becuase 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__?
|
|
"""
|
|
|
|
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.keys" 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.assertTrue("check_obj_id(m._forward_hooks" in failure_reason)
|
|
|
|
@patch.object(torch._dynamo.config, "skip_nnmodule_hook_guards", True)
|
|
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)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
from torch._dynamo.test_case import run_tests
|
|
|
|
run_tests()
|