torch/_dynamo/backends/common.py

import functools
import logging

import torch
from torch._dynamo import eval_frame
from torch._dynamo.utils import counters
from torch._functorch.aot_autograd import aot_module_simplified
from torch._subclasses import FakeTensor
from torch.utils._python_dispatch import _disable_current_modes

log = logging.getLogger(__name__)


def aot_autograd(**kwargs):
    def compiler_fn(gm: torch.fx.GraphModule, example_inputs):
        import functorch.compile

        # Hack to get around circular import problems with aot_eager_decomp_partition
        if callable(kwargs.get("decompositions")):
            kwargs["decompositions"] = kwargs["decompositions"]()

        # TODO: stop monkeypatching here (without even cleaning up, UGH!)
        functorch.compile.config.use_functionalize = True
        functorch.compile.config.use_fake_tensor = True

        counters["aot_autograd"]["total"] += 1
        use_fallback = False

        if use_fallback:
            log.debug("Unable to use AOT Autograd because graph has mutation")
            counters["aot_autograd"]["not_ok"] += 1
            return gm

        # OK attempt to compile

        def _wrapped_bw_compiler(*args, **kwargs):
            # stop TorchDynamo from trying to compile our generated backwards pass
            return eval_frame.disable(eval_frame.disable(bw_compiler)(*args, **kwargs))

        bw_compiler = kwargs.get("bw_compiler") or kwargs["fw_compiler"]
        kwargs["bw_compiler"] = _wrapped_bw_compiler

        from torch._inductor.debug import enable_aot_logging

        try:
            # NB: NOT cloned!
            with enable_aot_logging():
                cg = aot_module_simplified(gm, example_inputs, **kwargs)
                counters["aot_autograd"]["ok"] += 1
                return eval_frame.disable(cg)
        except Exception:
            counters["aot_autograd"]["not_ok"] += 1
            raise

    return compiler_fn


def mem_efficient_fusion_kwargs(use_decomps):
    from functorch.compile import (
        default_decompositions,
        min_cut_rematerialization_partition,
        ts_compile,
    )

    kwargs = {
        # these are taken from memory_efficient_fusion()
        "fw_compiler": ts_compile,
        "bw_compiler": ts_compile,
        "partition_fn": min_cut_rematerialization_partition,
    }

    if use_decomps:
        kwargs["decompositions"] = default_decompositions

    return kwargs


def fake_tensor_unsupported(fn):
    """
    Decorator for backends that need real inputs.  We swap out fake
    tensors for zero tensors.
    """

    def defake(x):
        if not isinstance(x, FakeTensor):
            return x
        if x._has_symbolic_sizes_strides:
            size = [s.node.shape_env.size_hint(s.node.expr) for s in x.size()]
            stride = [s.node.shape_env.size_hint(s.node.expr) for s in x.stride()]
        else:
            size = x.size()
            stride = x.stride()
        y = torch.empty_strided(
            size,
            stride,
            dtype=x.dtype,
            device=x.device,
            requires_grad=x.requires_grad,
        )
        y.zero_()
        return y

    @functools.wraps(fn)
    def wrapper(model, inputs, **kwargs):
        with _disable_current_modes():
            inputs = list(map(defake, inputs))
            return fn(model, inputs, **kwargs)

    return wrapper


def device_from_inputs(example_inputs) -> torch.device:
    for x in example_inputs:
        if hasattr(x, "device"):
            return x.device


def dtype_from_inputs(example_inputs) -> torch.dtype:
    for x in example_inputs:
        if hasattr(x, "dtype"):
            return x.dtype