torch/_prims/executor.py

from typing import Callable

import torch

from torch.fx import GraphModule
from torch.fx.experimental.proxy_tensor import make_fx
from torch._prims.utils import getnvFuserDtype
from torch._prims.context import TorchRefsMode
import torch.overrides
from torch.utils._pytree import tree_map

if torch.cuda.is_available():
    from torch._C._nvfuser import Fusion, FusionDefinition  # type: ignore[import]


def execute(gm: GraphModule, *args, executor: str = "aten", **kwargs):
    """
    Prototype ATen executor.

    Just executes the context's graph.
    """

    if executor == "aten":
        return gm.forward(*args, **kwargs)
    elif executor == "nvfuser":
        if not torch.cuda.is_available():
            raise RuntimeError(
                "Attempting to use nvFuser trace executor but CUDA is not available!"
            )

        # PROTOTYPE nvfuser executor
        # Everything in the graph must support nvfuser

        fusion = Fusion()
        with FusionDefinition(fusion) as fd:

            class FusionInterpreter(torch.fx.Interpreter):
                def call_function(self, target, args, kwargs):
                    target = target.impl_nvfuser
                    args = (fd,) + args
                    return target(*args, **kwargs)

            def to_nv(arg):
                if isinstance(arg, torch.Tensor):
                    x = fd.define_tensor(
                        arg.size(), arg.stride(), getnvFuserDtype(arg.dtype)
                    )
                    fd.add_input(x)
                    return x
                else:
                    return arg

            # Transforms graph to call nvfuser lowerings
            nv_args = tree_map(to_nv, args)
            nv_kwargs = tree_map(to_nv, kwargs)

            out = FusionInterpreter(gm).run(*nv_args, **nv_kwargs)
            flat_out, unflatten_spec = torch.utils._pytree.tree_flatten(out)
            for o in flat_out:
                fd.add_output(o)

            return torch.utils._pytree.tree_unflatten(
                fusion.execute(
                    tuple(arg for arg in args if isinstance(arg, torch.Tensor))
                ),
                unflatten_spec,
            )

    msg = "Received unexpected value for 'executor': {0}. Allowed values are: aten, nvfuser.".format(
        executor
    )
    raise ValueError(msg)


def make_traced(fn: Callable):
    """
    Returns a function that, when called, will
    trace its torch operations to prims and then
    execute those prims on the requested trace executor
    (possibly lowering them to that trace executor first).

    Only supports the torch operations defined in _torch_to_reference_map
    in context.py and operations with positional args. All args must
    be tensors.
    In the near future all these restrictions will be lifted.

    Example usage:

    def foo(a, b):
      return torch.add(a, b)

    traced_foo = make_traced(foo)

    a = torch.randn((1, 2, 3, 4, 5), device='cuda')
    b = torch.randn((1, 2, 3, 4, 5), device='cuda')
    result = traced_foo(a, b, executor='nvfuser')

    Executor may be either 'aten' or 'nvfuser'.
    """

    def _traced(*args, executor="aten"):
        # TODO: caching
        with TorchRefsMode.push():
            gm = make_fx(fn)(*args)
        return execute(gm, *args, executor=executor)

    return _traced