torch.jit.freeze

torch.jit.freeze(mod, preserved_attrs=None, optimize=True)

Freezing a ScriptModule will clone it and attempt to inline the cloned module’s submodules, parameters, and attributes as constants in the TorchScript IR Graph. By default, forward will be preserved, as well as attributes & methods specified in preserved_attrs. Additionally, any attribute that is modified within a preserved method will be preserved.

Freezing currently only accepts ScriptModules that are in eval mode.

Parameters

• mod (ScriptModule) – a module to be frozen

• preserved_attrs (Optional[List[str]]) – a list of attributes to preserve in addition to the forward method.

• modified in preserved methods will also be preserved. (Attributes) –

• optimize (bool) – If True, a set of optimization passes will be run to prepare the graph for inference,

• addition to the graph cleanup that already occurs. The details of the optimizations can be found in (in) –

• torch.jit.optimize_frozen_module. –

Returns

Frozen ScriptModule.

Example (Freezing a simple module with a Parameter):

    def forward(self, input):
        output = self.weight.mm(input)
        output = self.linear(output)
        return output

scripted_module = torch.jit.script(MyModule(2, 3).eval())
frozen_module = torch.jit.freeze(scripted_module)
# parameters have been removed and inlined into the Graph as constants
assert len(list(frozen_module.named_parameters())) == 0
# See the compiled graph as Python code
print(frozen_module.code)

Example (Freezing a module with preserved attributes)

    def forward(self, input):
        self.modified_tensor += 1
        return input + self.modified_tensor

scripted_module = torch.jit.script(MyModule2().eval())
frozen_module = torch.jit.freeze(scripted_module, preserved_attrs=["version"])
# we've manually preserved `version`, so it still exists on the frozen module and can be modified
assert frozen_module.version == 1
frozen_module.version = 2
# `modified_tensor` is detected as being mutated in the forward, so freezing preserves
# it to retain model semantics
assert frozen_module(torch.tensor(1)) == torch.tensor(12)
# now that we've run it once, the next result will be incremented by one
assert frozen_module(torch.tensor(1)) == torch.tensor(13)

Note

If you’re not sure why an attribute is not being inlined as a constant, you can run dump_alias_db on frozen_module.forward.graph to see if freezing has detected the attribute is being modified.