pytorch/torch/distributed/tensor/_dispatch.py

# Copyright (c) Meta Platforms, Inc. and affiliates
import contextlib
import functools
import logging
import operator
import warnings
from collections.abc import Sequence
from typing import cast, Optional

import torch
import torch.distributed as dist
import torch.distributed.tensor._api as dtensor
import torch.distributed.tensor._random as random
from torch.distributed.device_mesh import DeviceMesh
from torch.distributed.tensor._dtensor_spec import DTensorSpec, TensorMeta
from torch.distributed.tensor._op_schema import OpInfo, OpSchema, OutputSpecType
from torch.distributed.tensor._random import is_rng_supported_mesh
from torch.distributed.tensor._redistribute import redistribute_local_tensor
from torch.distributed.tensor._sharding_prop import ShardingPropagator
from torch.distributed.tensor._tp_conv import (
    convolution_backward_handler,
    convolution_handler,
)
from torch.distributed.tensor._utils import try_find_mesh_from_args
from torch.distributed.tensor.placement_types import Partial, Placement, Replicate
from torch.utils._python_dispatch import return_and_correct_aliasing


try:
    from torch.utils import _cxx_pytree as pytree
except ImportError:
    from torch.utils import _pytree as pytree  # type: ignore[no-redef]

aten = torch.ops.aten
logger = logging.getLogger(__name__)


def is_same_size_handler(
    op_call: torch._ops.OpOverload,
    args: tuple[object, ...],
    kwargs: dict[str, object],
) -> bool:
    lhs = cast(torch.Tensor, args[0])
    rhs = cast(torch.Tensor, args[1])
    return lhs.shape == rhs.shape


def found_inf_reduce_handler(
    op_call: torch._ops.OpOverload,
    args: tuple[object, ...],
    kwargs: dict[str, object],
) -> None:
    op_info = dtensor.DTensor._op_dispatcher.unwrap_to_op_info(op_call, args, kwargs)
    local_tensor_args = pytree.tree_unflatten(
        cast(list[object], op_info.local_args),
        op_info.args_tree_spec,  # type: ignore[arg-type]
    )
    local_tensor_args = cast(tuple[object, ...], local_tensor_args)
    op_call(*local_tensor_args, **op_info.local_kwargs)

    grad_dtensor = cast(list[dtensor.DTensor], args[0])[0]
    grad_placements = grad_dtensor.placements
    mesh = grad_dtensor.device_mesh

    found_inf_placements: list[Placement] = []
    for placement in grad_placements:
        if isinstance(placement, Replicate):
            found_inf_placements.append(placement)
        else:
            found_inf_placements.append(Partial("max"))

    target_tensor = cast(torch.Tensor, args[1])
    spec = DTensorSpec(
        mesh=mesh,
        placements=tuple(found_inf_placements),
        tensor_meta=TensorMeta(
            shape=target_tensor.size(),
            stride=target_tensor.stride(),
            dtype=target_tensor.dtype,
        ),
    )
    found_inf_dtensor = dtensor.DTensor(
        local_tensor=target_tensor, spec=spec, requires_grad=False
    )
    found_inf = found_inf_dtensor.full_tensor()
    target_tensor.copy_(found_inf)


class OpDispatcher:
    """
    Op dispatching class instance to handle args/kwargs pre-processing (un-wrapping), sharding
    propagation, redistribute local args, local compute, and post-processing (re-wrapping). It
    also handles any op specific logic if necessary.

    NOTE: Given the runtime overhead of Tensor subclass (__torch_dispatch__), the OpDispatcher
    is designed to minimize the CPU overhead by using the tricks of proper unflattening, faster
    pytree if needed, and leveraging various caching mechanisms implemented in the sharding
    propagation and redistribute modules. The CPU overhead is critical to eager mode performance,
    one need to carefully measure the CPU overhead when making significant changes to the
    OpDispatcher and ShardingPropagator.
    """

    def __init__(self) -> None:
        self.sharding_propagator = ShardingPropagator()
        self._random_ops = {
            aten.native_dropout.default,
            aten.normal_.default,
            aten.rand_like.default,
            aten.randn_like.default,
            aten.randint_like.default,
            aten.randint_like.low_dtype,
            aten.randint_like.low_dtype_out,
            aten.uniform_.default,
            aten.bernoulli.default,
            aten.bernoulli_.float,
        }
        self._custom_op_handlers = {
            aten.is_same_size.default: is_same_size_handler,
            aten.convolution.default: convolution_handler,
            aten.convolution_backward.default: convolution_backward_handler,
            aten._amp_foreach_non_finite_check_and_unscale_.default: found_inf_reduce_handler,
        }

        # This flag is used internally to control whether we treat the torch.Tensor(non-DTensor)
        # as implicitly replicated or we throw error to user.
        # NOTE: It is EXTREMELY UNSAFE to turn this flag on by default so we intentionally leave
        # it as False by default.
        self._allow_implicit_replication = False

    def dispatch(
        self,
        op_call: torch._ops.OpOverload,
        args: tuple[object, ...],
        kwargs: dict[str, object],
    ) -> object:
        """
        Main dispatching logic.  Follows precedence order:
        (1) custom_op_handler
        (2) registered sharding strategy, then rule
        (3) composite implicit autograd decomposition
        """
        if op_call in self._custom_op_handlers:
            return self._custom_op_handlers[op_call](op_call, args, kwargs)  # type: ignore[operator]

        # extract local tensor and sharding infos to a OpInfo
        op_info = self.unwrap_to_op_info(op_call, args, kwargs)
        logger.debug("Dispatching op_call: %s", op_info.schema)

        try:
            self.sharding_propagator.propagate(op_info)
        except NotImplementedError:
            if torch._C._dispatch_has_kernel_for_dispatch_key(
                op_call.name(), torch._C.DispatchKey.CompositeImplicitAutograd
            ):
                # When running under inference mode, CompositeImplicitAutograd ops show up in __torch_dispatch__,
                # so we manually decompose them, here
                out = op_call.decompose(*args, **kwargs)
                assert out is not NotImplemented
                return out
            else:
                raise
        except Exception as e:
            raise RuntimeError(
                f"Sharding propagation failed for {op_info.schema}"
            ) from e

        output_sharding = op_info.output_sharding
        logger.debug("output_sharding for %s: %s", op_call, output_sharding)
        assert output_sharding is not None, "output sharding should not be None"

        mesh = op_info.compute_mesh
        participating = mesh.get_coordinate() is not None
        if participating:
            # computation that happens in the current rank of the mesh, normal case
            if output_sharding.needs_redistribute:
                # If sharding propagation decision needs redistribute, perform redistribute
                # on args first, which could potentially modify args (i.e. allgather certain arg)
                assert output_sharding.redistribute_schema is not None
                self.redistribute_local_args(
                    op_info,
                    output_sharding.redistribute_schema,
                    output_sharding.use_val_from_redistribute_schema,
                )

            local_tensor_args = (
                pytree.tree_unflatten(
                    cast(list[object], op_info.local_args), op_info.args_tree_spec
                )
                if op_info.args_tree_spec
                else op_info.local_args
            )

            # run local op computation with potentially modified args/kwargs
            local_tensor_args = cast(tuple[object, ...], local_tensor_args)
            if op_call in self._random_ops:
                if not random._rng_tracker and is_rng_supported_mesh(mesh):
                    # Default to `OffsetBasedRNGTracker` if the parallelism API
                    # did not already construct one
                    random._rng_tracker = random.OffsetBasedRNGTracker(mesh)

                first_arg, first_local_arg = (
                    cast(dtensor.DTensor, args[0]),
                    cast(torch.Tensor, local_tensor_args[0]),
                )

                # If the user provided a generator, we hook it up to our RNG manager, but we also pop it from kwargs
                # so the op_call does not directly use it (we want op_call to fall back to the 'default' which is
                # our RNG manager)
                maybe_user_generator = op_info.local_kwargs.pop("generator", None)
                assert maybe_user_generator is None or isinstance(
                    maybe_user_generator, torch.Generator
                )
                # maybe_user_generator = None
                rng_context = (
                    random._rng_tracker._distribute_region(
                        first_arg._spec, generator=maybe_user_generator
                    )
                    if random._rng_tracker and not first_local_arg.is_meta
                    else contextlib.nullcontext()
                )
                # For DTensor random operator, run it within a RNGTracker context to
                # ensure the random number generator is properly distributed.
                with rng_context:
                    local_results = op_call(*local_tensor_args, **op_info.local_kwargs)
            else:
                # normal case, run local sharded op computation
                local_results = op_call(*local_tensor_args, **op_info.local_kwargs)

        else:
            # For a non-participating device (happens on rank that does not belong to
            # the device mesh), we do:
            #   1. if the return type is scalar, set the local result to None.
            #   2. if the return type is Tensor or List[Tensor], return empty
            #   tensor(s) with correct dtype.
            spec = output_sharding.output_spec
            ret_list = op_info.schema.op._schema.returns

            if spec is None:
                # For a scalar return type, the non-participating device has None
                # as its local result
                local_results = None
            else:

                def default_tensor(spec: DTensorSpec) -> torch.Tensor:
                    if spec.tensor_meta is not None:
                        shape = spec.tensor_meta.shape
                        dtype = spec.tensor_meta.dtype
                        if len(shape) == 0:
                            # scalar tensor
                            return torch.zeros((), dtype=dtype)
                        else:
                            # non-scalar tensor
                            return torch.tensor([], dtype=dtype)
                    else:
                        raise RuntimeError(f"{spec} has no tensor metadata.")

                if isinstance(spec, DTensorSpec):
                    # return a Tensor value
                    local_results = default_tensor(spec)
                elif isinstance(spec, Sequence):
                    # return a List[Tensor] value
                    local_results = [
                        default_tensor(s) if s is not None else None for s in spec
                    ]
                    assert isinstance(local_results, list)
                    if None in local_results:
                        ret_type = str(ret_list[0].type)
                        raise NotImplementedError(
                            f"return type {ret_type} in DTensor op is not supported"
                        )

        if output_sharding.output_spec is None:
            if op_call == aten.equal.default:
                # For equal operator, The local results from all devices should be all-gathered
                # and a reduce op (AND) will be performed on the list of results to ensure SPMD
                # execution. We can extend this for more ops if necessary.
                obj_list = [None for _ in range(dist.get_world_size())]
                dist.all_gather_object(obj_list, local_results)  # type: ignore[possibly-undefined]
                obj_list = list(filter(lambda x: x is not None, obj_list))
                # perform reduce on the collection with AND op
                local_results = functools.reduce(operator.and_, obj_list, True)

        if op_info.schema.is_inplace_op():
            # inplace op should return self instead of re-wrapping
            if output_sharding.output_spec is not None:
                # NOTE: aten.squeeze_.dim is an inplace op but it also may change
                # the inplace argument's tensor meta. Here we choose to special case
                # this op because as far as I know this is the only inplace op that
                # has such as behavior. We can extend this special case if necessary.
                if op_call == aten.squeeze_.dim:
                    output_spec = output_sharding.output_spec
                    assert isinstance(output_spec, DTensorSpec)
                    assert isinstance(args[0], dtensor.DTensor)
                    args[0]._spec = output_spec
                    # use return_and_correct_aliasing to match the outer and the inner
                    # aliasing. See https://github.com/pytorch/pytorch/pull/158954
                    return return_and_correct_aliasing(op_call, args, kwargs, args[0])
                else:
                    return args[0]
            else:
                return None
        elif op_info.schema.is_out_variant_op():
            # out variant could possibly have multiple out args (i.e. lu_unpack.out)
            output_specs = (
                (output_sharding.output_spec,)
                if not isinstance(output_sharding.output_spec, tuple)
                else output_sharding.output_spec
            )
            out_dts = []
            spec_idx = 0
            for argument in op_call._schema.arguments:
                if argument.is_out:
                    out_dt = cast(dtensor.DTensor, kwargs[argument.name])
                    out_dt._spec = cast(DTensorSpec, output_specs[spec_idx])
                    out_dts.append(out_dt)
                    spec_idx += 1

            assert len(out_dts) >= 1, "out variant should have at least one out arg"
            return tuple(out_dts) if len(out_dts) > 1 else out_dts[0]
        else:
            ret = self.wrap(local_results, output_sharding.output_spec)  # type: ignore[possibly-undefined]
            if participating and op_info.schema.is_view_op():
                return return_and_correct_aliasing(op_call, args, kwargs, ret)
            else:
                return ret

    @staticmethod
    def redistribute_local_args(
        op_info: OpInfo,
        suggested_input_schema: OpSchema,
        use_val_from_redistribute_schema: bool,
    ) -> None:
        # NOTE: it's very rare that we need to reshard kwargs so we intentionally skip it
        if op_info.args_tree_spec is not None:
            flatten_args_schema_to_reshard = tuple(
                pytree.tree_leaves(suggested_input_schema.args_schema)
            )
        else:
            flatten_args_schema_to_reshard = suggested_input_schema.args_schema

        new_local_args: list[object] = []
        for i, arg_spec in enumerate(op_info.flat_args_schema):
            reshard_arg_spec = flatten_args_schema_to_reshard[i]
            if isinstance(arg_spec, DTensorSpec):
                local_tensor = cast(torch.Tensor, op_info.local_args[i])
                if arg_spec != reshard_arg_spec:
                    resharded_local_tensor = redistribute_local_tensor(
                        local_tensor, arg_spec, reshard_arg_spec
                    )
                    new_local_args.append(resharded_local_tensor)
                else:
                    new_local_args.append(local_tensor)
            else:
                if use_val_from_redistribute_schema:
                    # args can be updated for view related ops, we refer to the
                    # update in redistribute_schema.
                    new_local_args.append(reshard_arg_spec)
                else:
                    new_local_args.append(arg_spec)

        op_info.local_args = tuple(new_local_args)

    def unwrap_to_op_info(
        self,
        op_call: torch._ops.OpOverload,
        args: tuple[object, ...],
        kwargs: dict[str, object],
    ) -> OpInfo:
        # get runtime schema info to determine whether to use pytree to flatten inputs
        runtime_schema_info = self.sharding_propagator.op_to_schema_info.get(
            op_call, None
        )

        if runtime_schema_info is not None and runtime_schema_info.needs_pytree:
            # flatten args/kwargs when op says necessary
            tree_args, args_spec = pytree.tree_flatten(args)
            args_list: Sequence[object] = tree_args
        else:
            args_list, args_spec = args, None

        args_schema: list[object] = []
        kwargs_schema: dict[str, object] = {}
        local_args: list[object] = []
        local_kwargs: dict[str, object] = {}
        compute_mesh: Optional[DeviceMesh] = None

        for arg in args_list:
            if isinstance(arg, dtensor.DTensor):
                local_args.append(arg._local_tensor)
                args_schema.append(arg._spec)
                if compute_mesh is None:
                    # record the first compute device mesh from args
                    compute_mesh = arg.device_mesh
            elif isinstance(arg, torch.Tensor):
                compute_mesh = compute_mesh or try_find_mesh_from_args(
                    op_call, args_list
                )
                args_schema.append(
                    self._try_replicate_spec_for_scalar_tensor(
                        op_call, arg, compute_mesh
                    )
                )
                local_args.append(arg)
            else:
                # non DTensor/Tensor args (i.e. int/float/bool), just add to args_schema/local_args
                args_schema.append(arg)
                local_args.append(arg)

        for k, v in kwargs.items():
            if isinstance(v, dtensor.DTensor):
                local_kwargs[k] = v._local_tensor
                kwargs_schema[k] = v._spec
            elif isinstance(v, torch.Tensor):
                compute_mesh = compute_mesh or try_find_mesh_from_args(
                    op_call, args_list
                )
                kwargs_schema[k] = self._try_replicate_spec_for_scalar_tensor(
                    op_call, v, compute_mesh
                )
                local_kwargs[k] = v
            else:
                # non DTensor/Tensor args (i.e. int/float/bool), just add to args_schema/local_args
                kwargs_schema[k] = v
                local_kwargs[k] = v

        assert compute_mesh is not None, (
            f"found no DeviceMesh from dtensor args for {op_call}!"
        )
        op_info = OpInfo(
            compute_mesh,
            OpSchema(
                op_call,
                (
                    pytree.tree_unflatten(args_schema, args_spec)
                    if args_spec
                    else tuple(args_schema)
                ),
                kwargs_schema,
                schema_info=runtime_schema_info,
            ),
            args_schema,
            tuple(local_args),
            local_kwargs,
            args_spec,
        )
        return op_info

    @staticmethod
    def wrap(res: object, spec: OutputSpecType) -> object:
        if isinstance(res, torch.Tensor):
            if spec is not None:
                assert isinstance(spec, DTensorSpec), (
                    f"output spec does not match with output! Expected DTensorSpec, got {spec}."
                )
                return dtensor.DTensor(res, spec, requires_grad=res.requires_grad)
            else:
                # if output does not have a DTensorSpec due to specific ops, it must be a scalar tensor
                assert res.ndim == 0, "output tensor should be scalar!"
                return res
        elif isinstance(res, (list, tuple)):
            assert spec is not None and isinstance(spec, (list, tuple)), (
                f"output spec does not match with output! Expected list/tuple, got {spec}."
            )
            res_list = []
            for e, s in zip(res, spec):
                res_list.append(OpDispatcher.wrap(e, s))

            return tuple(res_list) if isinstance(res, tuple) else res_list
        else:
            # if the res contains only non tensor values (i.e. int/float/none), we simply return it
            # without rewrapping to DTensor.
            return res

    def _try_replicate_spec_for_scalar_tensor(
        self,
        op_call: torch._ops.OpOverload,
        tensor_arg: torch.Tensor,
        compute_mesh: DeviceMesh,
    ) -> DTensorSpec:
        # util function to produce a replicate spec for a scalar tensor arg/kwarg
        if tensor_arg.numel() == 1 and tensor_arg.ndim == 1:
            warnings.warn(
                "Found a non-scalar tensor with numel=1 and ndim!=0, "
                "we are implicitly creating a replicated DTensor for it. "
                "However, please consider changing it to a scalar tensor "
                "or explicitly create a DTensor under distributed environment."
            )

        if tensor_arg.numel() == 1 or self._allow_implicit_replication:
            # scalar tensor can be safely treated as replicated
            replication_spec = DTensorSpec(
                compute_mesh,
                (Replicate(),) * compute_mesh.ndim,
                tensor_meta=TensorMeta(
                    shape=tensor_arg.shape,
                    stride=tensor_arg.stride(),
                    dtype=tensor_arg.dtype,
                ),
            )
        else:
            raise RuntimeError(
                f"{op_call}: got mixed torch.Tensor and DTensor, need to convert all"
                " torch.Tensor to DTensor before calling distributed operators!"
            )
        return replication_spec