pytorch/torch/_export/wrappers.py

# mypy: allow-untyped-defs
from contextlib import contextmanager

import torch
import torch._custom_ops
from torch._C import DispatchKey
from torch._higher_order_ops.flat_apply import (
    _ConstantFunction,
    flat_apply,
    to_graphable,
)
from torch._higher_order_ops.strict_mode import strict_mode
from torch._higher_order_ops.utils import autograd_not_implemented
from torch._ops import HigherOrderOperator
from torch._subclasses.fake_tensor import FakeTensorMode
from torch.fx.experimental.proxy_tensor import (
    get_proxy_slot,
    PreDispatchTorchFunctionMode,
    ProxyTorchDispatchMode,
    track_tensor_tree,
)
from torch.utils import _pytree as pytree
from torch.utils._python_dispatch import is_traceable_wrapper_subclass_type


class ExportTracepoint(HigherOrderOperator):
    def __init__(self):
        super().__init__("_export_tracepoint")

    def __call__(self, *args, **kwargs):
        return super().__call__(*args, **kwargs)


_export_tracepoint = ExportTracepoint()


@_export_tracepoint.py_impl(ProxyTorchDispatchMode)
def export_tracepoint_dispatch_mode(mode, *args, **kwargs):
    p_args, p_kwargs = pytree.tree_map(mode.tracer.unwrap_proxy, (args, kwargs))
    proxy = mode.tracer.create_proxy(
        "call_function", _export_tracepoint, p_args, p_kwargs
    )
    return track_tensor_tree(args, proxy, constant=None, tracer=mode.tracer)


@_export_tracepoint.py_impl(FakeTensorMode)
def export_tracepoint_fake_tensor_mode(mode, *args, **kwargs):
    with mode:
        return args


@_export_tracepoint.py_functionalize_impl
def export_tracepoint_functional(ctx, *args, **kwargs):
    unwrapped_args = ctx.unwrap_tensors(args)
    unwrapped_kwargs = ctx.unwrap_tensors(kwargs)

    with ctx.redispatch_to_next():
        _export_tracepoint(*unwrapped_args, **unwrapped_kwargs)
        return args


_export_tracepoint.py_impl(DispatchKey.Autograd)(
    autograd_not_implemented(_export_tracepoint, deferred_error=True)
)


@_export_tracepoint.py_impl(DispatchKey.CPU)
def export_tracepoint_cpu(*args, **kwargs):
    return args


def _wrap_submodule(mod, path, module_call_specs):
    assert isinstance(mod, torch.nn.Module)
    assert path != ""
    submodule = torch.fx.graph_module._get_attr(mod, path)

    def update_module_call_signatures(path, in_spec, out_spec):
        if path in module_call_specs:
            assert module_call_specs[path]["in_spec"] == in_spec
            assert module_call_specs[path]["out_spec"] == out_spec
        module_call_specs[path] = {"in_spec": in_spec, "out_spec": out_spec}

    def check_flattened(flat_args):
        for a in flat_args:
            if not (isinstance(a, (torch.Tensor, str, int, float, bool)) or a is None):
                raise AssertionError(
                    f"Only Tensors or scalars are supported as pytree flattened inputs, got: {a}"
                )

    def pre_hook(module, args, kwargs):
        flat_args, in_spec = pytree.tree_flatten((args, kwargs))
        check_flattened(flat_args)
        flat_args = _export_tracepoint(*flat_args, kind="module_call_inputs", path=path)
        args, kwargs = pytree.tree_unflatten(flat_args, in_spec)
        return args, kwargs

    def post_hook(module, args, kwargs, res):
        _, in_spec = pytree.tree_flatten((args, kwargs))
        flat_res, out_spec = pytree.tree_flatten(res)
        check_flattened(flat_res)
        flat_res = _export_tracepoint(*flat_res, kind="module_call_outputs", path=path)
        update_module_call_signatures(path, in_spec, out_spec)
        return pytree.tree_unflatten(flat_res, out_spec)

    pre_handle = submodule.register_forward_pre_hook(pre_hook, with_kwargs=True)
    post_handle = submodule.register_forward_hook(post_hook, with_kwargs=True)
    return pre_handle, post_handle


@contextmanager
def _wrap_submodules(f, preserve_signature, module_call_signatures):
    handles = []

    try:
        for path in preserve_signature:
            handles.extend(_wrap_submodule(f, path, module_call_signatures))
        yield
    finally:
        for handle in handles:
            handle.remove()


def _mark_strict_experimental(cls):
    def call(self, *args):
        return strict_mode(self, args)

    cls.__call__ = call
    return cls


def _register_subclass_spec_proxy_in_tracer(tracer, name, spec):
    """
    This is a wrapper utility method on top of tracer to cache the
    already registered subclass spec attribute. This is useful because
    Subclass.__init__ will be same for each subclass. By default, fx will
    create multiple attributes/proxies for given attribute.
    """
    fx_name = name + "0"
    if hasattr(tracer.root, fx_name):
        assert getattr(tracer.root, fx_name) == spec
        return tracer.create_proxy("get_attr", fx_name, (), {})

    qualname = tracer.get_fresh_qualname(name)
    setattr(tracer.root, qualname, spec)
    return tracer.create_proxy("get_attr", qualname, (), {})


def mark_subclass_constructor_exportable_experimental(constructor_subclass):
    """
    Experimental decorator that makes subclass to be traceable in export
    with pre-dispatch IR. To make your subclass traceble in export, you need to:
        1. Implement __init__ method for your subclass (Look at DTensor implementation)
        2. Decorate your __init__ method with _mark_constructor_exportable_experimental
        3. Put torch._dynamo_disable decorator to prevent dynamo from peeking into its' impl

    Example:

    class FooTensor(torch.Tensor):
        @staticmethod
        def __new__(cls, elem, *, requires_grad=False):
            # ...
            return torch.Tensor._make_subclass(cls, elem, requires_grad=requires_grad)

        @torch._dynamo_disable
        @mark_subclass_constructor_exportable_experimental
        def __init__(self, elem, ...):
            # ...
    """

    def _is_init(fn):
        return callable(fn) and fn.__name__ == "__init__"

    if not _is_init(constructor_subclass):
        raise RuntimeError(
            f"torch._export.wrappers.mark_constructor_exportable_experimental can only be applied on subclass tensor.__init__"
            f"But, you are adding it on {constructor_subclass.__name__} which is not supported. "
            f"If __init__ doesn't exist on your subclass, please add it. Look at DTensor.__init__ implementation for example"
        )

    def wrapper(*args, **kwargs):
        if not is_traceable_wrapper_subclass_type(type(args[0])):
            assert constructor_subclass.__qualname__.endswith("__init__")
            obj_name = constructor_subclass.__qualname__[: -len("__init__")]
            raise RuntimeError(
                f"Applying mark_constructor_exportable_experimental on {obj_name} is not valid as it is not a traceable "
                f"tensor subclass. Please look at DTensor.__init__ implementation as an example of proper usage of this API."
            )
        constructor_subclass(*args, **kwargs)
        if not torch._C._is_torch_function_mode_enabled():
            return
        torch_function_mode_stack = torch.overrides._get_current_function_mode_stack()

        pre_dispatch_tf_modes = [
            mode
            for mode in torch_function_mode_stack
            if isinstance(mode, PreDispatchTorchFunctionMode)
        ]
        assert (
            len(pre_dispatch_tf_modes) <= 1
        ), f"Expected only one PreDispatchTorchFunctionMode, found {len(pre_dispatch_tf_modes)}"

        if len(pre_dispatch_tf_modes) == 0:
            return

        mode = pre_dispatch_tf_modes[0]

        tracer = mode.tracer
        subclass = args[0]

        flat_args, in_spec = to_graphable((tuple(args[1:]), kwargs))

        constructor_spec_name = "_".join(
            constructor_subclass.__qualname__.lower().split(".")
        )
        qualname = tracer.get_fresh_qualname(constructor_spec_name)  # type: ignore[union-attr]
        setattr(tracer.root, qualname, in_spec)  # type: ignore[union-attr]
        spec_proxy = tracer.create_proxy("get_attr", qualname, (), {})
        flat_proxy_args = pytree.tree_map_only(
            torch.Tensor, lambda x: get_proxy_slot(x, tracer).proxy, flat_args
        )

        _, func_spec = torch.utils._pytree.tree_flatten(
            _ConstantFunction(type(subclass))
        )

        # We actually don't want to create a new spec for each instance
        # In fx graph, it will look like dtensor_const_func_spec
        # We can't directly shove DTensor.__init__ into fx as it is not
        # allowed type.
        fxable_constructor_call_spec_name = (
            type(subclass).__name__.lower() + "_const_func_spec"
        )

        # We should try to reuse the constructor call spec as it is guaranteed to be same
        # for each subclass type. This is different from proxy-ing the init arguments which
        # can't be reused because for example, DTensor can receive different DeviceMesh etc
        # as it's arguments
        func_spec_proxy = _register_subclass_spec_proxy_in_tracer(
            tracer, fxable_constructor_call_spec_name, func_spec
        )

        inner_proxy = tracer.create_proxy(
            "call_function",
            flat_apply,
            (func_spec_proxy, spec_proxy, *flat_proxy_args),
            {},
        )
        track_tensor_tree(subclass, inner_proxy, constant=None, tracer=tracer)
        return

    return wrapper