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# Source code for torch.jit._freeze

    """Freezing

    This is not intended to be imported directly; please use the exposed
    functionalities in `torch.jit`.
    """

    from typing import List, Optional

    import torch
    from torch.jit._script import RecursiveScriptModule, ScriptModule

    def freeze(
        mod, preserved_attrs: Optional[List[str]] = None, optimize_numerics: bool = True
    ):
        r"""
        Freezing a :class:`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.

        Freezing applies generic optimization that will speed up your model regardless of machine.
        To further optimize using server-specific settings, run `optimize_for_inference` after
        freezing.

        Args:
            mod (:class:`ScriptModule`): a module to be frozen
            preserved_attrs (Optional[List[str]]): a list of attributes to preserve in addition to the forward method.
                Attributes modified in preserved methods will also be preserved.
            optimize_numerics (bool): If ``True``, a set of optimization passes will be run that does not strictly
                preserve numerics. Full details of optimization can be found at `torch.jit.run_frozen_optimizations`.

        Returns:
            Frozen :class:`ScriptModule`.

        Example (Freezing a simple module with a Parameter):

        .. testcode::
            import torch
            class MyModule(torch.nn.Module):
                def __init__(self, N, M):
                    super().__init__()
                    self.weight = torch.nn.Parameter(torch.rand(N, M))
                    self.linear = torch.nn.Linear(N, M)

                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)

        .. testcode::
            import torch
            class MyModule2(torch.nn.Module):
                def __init__(self):
                    super().__init__()
                    self.modified_tensor = torch.tensor(10.)
                    self.version = 1

                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:
            Freezing submodule attributes is also supported:
            frozen_module = torch.jit.freeze(scripted_module, preserved_attrs=["submodule.version"])

        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.

        Note:
            Because freezing makes weights constants and removes module hierarchy, `to` and other
            nn.Module methods to manipulate device or dtype no longer work. As a workaround,
            You can remap devices by specifying `map_location` in `torch.jit.load`, however
            device-specific logic may have been baked into the model.
        """
        if not isinstance(mod, ScriptModule):
            raise RuntimeError(
                "Freezing expects a ScriptModule as input. "
                "Please use torch.jit.script or torch.jit.trace to script your 'nn.Module'."
            )

        if mod.training:
            raise RuntimeError(
                "Freezing is currently only implemented for modules in eval mode. "
                "Please call .eval() on your module before freezing."
            )

        preserved_attrs = preserved_attrs if preserved_attrs is not None else []

        out = RecursiveScriptModule(torch._C._freeze_module(mod._c, preserved_attrs))
        RecursiveScriptModule._finalize_scriptmodule(out)

        preserved_methods = [x for x in preserved_attrs if mod._c._has_method(x)]
        run_frozen_optimizations(out, optimize_numerics, preserved_methods)

        return out

    def run_frozen_optimizations(
        mod, optimize_numerics: bool = True, preserved_methods: Optional[List[str]] = None
    ):
        r"""
        Runs a series of optimizations looking for patterns that occur in frozen graphs.
        The current set of optimizations includes:
            - Dropout Removal
            - Pretranspose Linear Layers
            - Concat Linear Layers with same input Tensor
            - Conv -> Batchnorm folding
            - Conv -> Add/Sub folding
            - Conv -> Mul/Div folding

        Args:
            mod (:class:`ScriptModule`): a frozen module to be optimized

            optimize_numerics (bool): If ``True``, a set of optimization passes will be run that does not strictly
            preserve numerics. These optimizations preserve default rtol and atol of `torch.testing.assert_close`
            when applied on a single transformation, however in a module where many transformations are applied
            the rtol or atol may no longer fall within the default `assert_close` tolerance. Conv -> Batchnorm folding,
            Conv-Add/Sub, and Conv -> Mul/Div folding all may alter numerics.

        Returns:
            None

        Note:
            In rare occassions, this can result in slower execution.

        Example (Freezing a module with Conv->Batchnorm)
        .. code-block:: python
            import torch
            in_channels, out_channels = 3, 32
            conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=True)
            bn = torch.nn.BatchNorm2d(out_channels, eps=.001)
            mod = torch.nn.Sequential(conv, bn)
            # set optimize to False here, by default freezing runs run_frozen_optimizations
            frozen_mod = torch.jit.freeze(torch.jit.script(mod.eval()), optimize=False)
            # inspect frozen mod
            assert "batch_norm" in str(frozen_mod.graph)
            torch.jit.run_frozen_optimizations(frozen_mod)
            assert "batch_norm" not in str(frozen_mod.graph)

        """
        if mod._c._has_method("forward"):
            torch._C._jit_pass_optimize_frozen_graph(mod.graph, optimize_numerics)

        if preserved_methods is None:
            preserved_methods = []

        for method in preserved_methods:
            torch._C._jit_pass_optimize_frozen_graph(
                mod.__getattr__(method).graph, optimize_numerics
            )

    def optimize_for_inference(
        mod: ScriptModule, other_methods: Optional[List[str]] = None
    ) -> ScriptModule:
        """
        Performs a set of optimization passes to optimize a model for the
        purposes of inference. If the model is not already frozen, optimize_for_inference
        will invoke `torch.jit.freeze` automatically.

        In addition to generic optimizations that should speed up your model regardless
        of environment, prepare for inference will also bake in build specific settings
        such as the presence of CUDNN or MKLDNN, and may in the future make transformations
        which speed things up on one machine but slow things down on another. Accordingly,
        serialization is not implemented following invoking `optimize_for_inference` and
        is not guaranteed.

        This is still in prototype, and may have the potential to slow down your model.
        Primary use cases that have been targeted so far have been vision models on cpu
        and gpu to a lesser extent.

        Example (optimizing a module with Conv->Batchnorm)::

            import torch
            in_channels, out_channels = 3, 32
            conv = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=True)
            bn = torch.nn.BatchNorm2d(out_channels, eps=.001)
            mod = torch.nn.Sequential(conv, bn)
            frozen_mod = torch.jit.optimize_for_inference(torch.jit.script(mod.eval()))
            assert "batch_norm" not in str(frozen_mod.graph)
            # if built with MKLDNN, convolution will be run with MKLDNN weights
            assert "MKLDNN" in frozen_mod.graph
        """
        if not isinstance(mod, ScriptModule):
            raise RuntimeError(
                "optimize_for_inference expects a ScriptModule as input. "
                "Please use torch.jit.script or torch.jit.trace to script your 'nn.Module'."
            )

        if other_methods is None:
            other_methods = []

        if hasattr(mod, "training"):
            mod = freeze(mod.eval(), preserved_attrs=other_methods)

        torch._C._jit_pass_optimize_for_inference(mod._c, other_methods)

        return mod

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