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# 馃毃馃毃馃毃 Experimental parallelism support for Diffusers 馃毃馃毃馃毃
# Experimental changes are subject to change and APIs may break without warning.
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Literal, Optional, Tuple, Union
import torch
import torch.distributed as dist
from ..utils import get_logger
if TYPE_CHECKING:
pass
logger = get_logger(__name__) # pylint: disable=invalid-name
# TODO(aryan): add support for the following:
# - Unified Attention
# - More dispatcher attention backends
# - CFG/Data Parallel
# - Tensor Parallel
@dataclass
class ContextParallelConfig:
"""
Configuration for context parallelism.
Args:
ring_degree (`int`, *optional*, defaults to `1`):
Number of devices to use for Ring Attention. Sequence is split across devices. Each device computes
attention between its local Q and KV chunks passed sequentially around ring. Lower memory (only holds 1/N
of KV at a time), overlaps compute with communication, but requires N iterations to see all tokens. Best
for long sequences with limited memory/bandwidth. Number of devices to use for ring attention within a
context parallel region. Must be a divisor of the total number of devices in the context parallel mesh.
ulysses_degree (`int`, *optional*, defaults to `1`):
Number of devices to use for Ulysses Attention. Sequence split is across devices. Each device computes
local QKV, then all-gathers all KV chunks to compute full attention in one pass. Higher memory (stores all
KV), requires high-bandwidth all-to-all communication, but lower latency. Best for moderate sequences with
good interconnect bandwidth.
convert_to_fp32 (`bool`, *optional*, defaults to `True`):
Whether to convert output and LSE to float32 for ring attention numerical stability.
rotate_method (`str`, *optional*, defaults to `"allgather"`):
Method to use for rotating key/value states across devices in ring attention. Currently, only `"allgather"`
is supported.
"""
ring_degree: Optional[int] = None
ulysses_degree: Optional[int] = None
convert_to_fp32: bool = True
# TODO: support alltoall
rotate_method: Literal["allgather", "alltoall"] = "allgather"
# Whether to enable ulysses anything attention to support
# any sequence lengths and any head numbers.
ulysses_anything: bool = False
_rank: int = None
_world_size: int = None
_device: torch.device = None
_mesh: torch.distributed.device_mesh.DeviceMesh = None
_flattened_mesh: torch.distributed.device_mesh.DeviceMesh = None
_ring_mesh: torch.distributed.device_mesh.DeviceMesh = None
_ulysses_mesh: torch.distributed.device_mesh.DeviceMesh = None
_ring_local_rank: int = None
_ulysses_local_rank: int = None
def __post_init__(self):
if self.ring_degree is None:
self.ring_degree = 1
if self.ulysses_degree is None:
self.ulysses_degree = 1
if self.ring_degree == 1 and self.ulysses_degree == 1:
raise ValueError(
"Either ring_degree or ulysses_degree must be greater than 1 in order to use context parallel inference"
)
if self.ring_degree < 1 or self.ulysses_degree < 1:
raise ValueError("`ring_degree` and `ulysses_degree` must be greater than or equal to 1.")
if self.rotate_method != "allgather":
raise NotImplementedError(
f"Only rotate_method='allgather' is supported for now, but got {self.rotate_method}."
)
if self.ulysses_anything:
if self.ulysses_degree == 1:
raise ValueError("ulysses_degree must be greater than 1 for ulysses_anything to be enabled.")
if self.ring_degree > 1:
raise ValueError("ulysses_anything cannot be enabled when ring_degree > 1.")
@property
def mesh_shape(self) -> Tuple[int, int]:
return (self.ring_degree, self.ulysses_degree)
@property
def mesh_dim_names(self) -> Tuple[str, str]:
"""Dimension names for the device mesh."""
return ("ring", "ulysses")
def setup(self, rank: int, world_size: int, device: torch.device, mesh: torch.distributed.device_mesh.DeviceMesh):
self._rank = rank
self._world_size = world_size
self._device = device
self._mesh = mesh
if self.ulysses_degree * self.ring_degree > world_size:
raise ValueError(
f"The product of `ring_degree` ({self.ring_degree}) and `ulysses_degree` ({self.ulysses_degree}) must not exceed the world size ({world_size})."
)
self._flattened_mesh = self._mesh._flatten()
self._ring_mesh = self._mesh["ring"]
self._ulysses_mesh = self._mesh["ulysses"]
self._ring_local_rank = self._ring_mesh.get_local_rank()
self._ulysses_local_rank = self._ulysses_mesh.get_local_rank()
@dataclass
class ParallelConfig:
"""
Configuration for applying different parallelisms.
Args:
context_parallel_config (`ContextParallelConfig`, *optional*):
Configuration for context parallelism.
"""
context_parallel_config: Optional[ContextParallelConfig] = None
_rank: int = None
_world_size: int = None
_device: torch.device = None
_mesh: torch.distributed.device_mesh.DeviceMesh = None
def setup(
self,
rank: int,
world_size: int,
device: torch.device,
*,
mesh: Optional[torch.distributed.device_mesh.DeviceMesh] = None,
):
self._rank = rank
self._world_size = world_size
self._device = device
self._mesh = mesh
if self.context_parallel_config is not None:
self.context_parallel_config.setup(rank, world_size, device, mesh)
@dataclass(frozen=True)
class ContextParallelInput:
"""
Configuration for splitting an input tensor across context parallel region.
Args:
split_dim (`int`):
The dimension along which to split the tensor.
expected_dims (`int`, *optional*):
The expected number of dimensions of the tensor. If provided, a check will be performed to ensure that the
tensor has the expected number of dimensions before splitting.
split_output (`bool`, *optional*, defaults to `False`):
Whether to split the output tensor of the layer along the given `split_dim` instead of the input tensor.
This is useful for layers whose outputs should be split after it does some preprocessing on the inputs (ex:
RoPE).
"""
split_dim: int
expected_dims: Optional[int] = None
split_output: bool = False
def __repr__(self):
return f"ContextParallelInput(split_dim={self.split_dim}, expected_dims={self.expected_dims}, split_output={self.split_output})"
@dataclass(frozen=True)
class ContextParallelOutput:
"""
Configuration for gathering an output tensor across context parallel region.
Args:
gather_dim (`int`):
The dimension along which to gather the tensor.
expected_dims (`int`, *optional*):
The expected number of dimensions of the tensor. If provided, a check will be performed to ensure that the
tensor has the expected number of dimensions before gathering.
"""
gather_dim: int
expected_dims: Optional[int] = None
def __repr__(self):
return f"ContextParallelOutput(gather_dim={self.gather_dim}, expected_dims={self.expected_dims})"
# A dictionary where keys denote the input to be split across context parallel region, and the
# value denotes the sharding configuration.
# If the key is a string, it denotes the name of the parameter in the forward function.
# If the key is an integer, split_output must be set to True, and it denotes the index of the output
# to be split across context parallel region.
ContextParallelInputType = Dict[
Union[str, int], Union[ContextParallelInput, List[ContextParallelInput], Tuple[ContextParallelInput, ...]]
]
# A dictionary where keys denote the output to be gathered across context parallel region, and the
# value denotes the gathering configuration.
ContextParallelOutputType = Union[
ContextParallelOutput, List[ContextParallelOutput], Tuple[ContextParallelOutput, ...]
]
# A dictionary where keys denote the module id, and the value denotes how the inputs/outputs of
# the module should be split/gathered across context parallel region.
ContextParallelModelPlan = Dict[str, Union[ContextParallelInputType, ContextParallelOutputType]]
# Example of a ContextParallelModelPlan (QwenImageTransformer2DModel):
#
# Each model should define a _cp_plan attribute that contains information on how to shard/gather
# tensors at different stages of the forward:
#
# ```python
# _cp_plan = {
# "": {
# "hidden_states": ContextParallelInput(split_dim=1, expected_dims=3, split_output=False),
# "encoder_hidden_states": ContextParallelInput(split_dim=1, expected_dims=3, split_output=False),
# "encoder_hidden_states_mask": ContextParallelInput(split_dim=1, expected_dims=2, split_output=False),
# },
# "pos_embed": {
# 0: ContextParallelInput(split_dim=0, expected_dims=2, split_output=True),
# 1: ContextParallelInput(split_dim=0, expected_dims=2, split_output=True),
# },
# "proj_out": ContextParallelOutput(gather_dim=1, expected_dims=3),
# }
# ```
#
# The dictionary is a set of module names mapped to their respective CP plan. The inputs/outputs of layers will be
# split/gathered according to this at the respective module level. Here, the following happens:
# - "":
# we specify that we want to split the various inputs across the sequence dim in the pre-forward hook (i.e. before
# the actual forward logic of the QwenImageTransformer2DModel is run, we will splitthe inputs)
# - "pos_embed":
# we specify that we want to split the outputs of the RoPE layer. Since there are two outputs (imag & text freqs),
# we can individually specify how they should be split
# - "proj_out":
# before returning to the user, we gather the entire sequence on each rank in the post-forward hook (after the linear
# layer forward has run).
#
# ContextParallelInput:
# specifies how to split the input tensor in the pre-forward or post-forward hook of the layer it is attached to
#
# ContextParallelOutput:
# specifies how to gather the input tensor in the post-forward hook in the layer it is attached to
# Below are utility functions for distributed communication in context parallelism.
def gather_size_by_comm(size: int, group: dist.ProcessGroup) -> List[int]:
r"""Gather the local size from all ranks.
size: int, local size return: List[int], list of size from all ranks
"""
# NOTE(Serving/CP Safety):
# Do NOT cache this collective result.
#
# In "Ulysses Anything" mode, `size` (e.g. per-rank local seq_len / S_LOCAL)
# may legitimately differ across ranks. If we cache based on the *local* `size`,
# different ranks can have different cache hit/miss patterns across time.
#
# That can lead to a catastrophic distributed hang:
# - some ranks hit cache and *skip* dist.all_gather()
# - other ranks miss cache and *enter* dist.all_gather()
# This mismatched collective participation will stall the process group and
# eventually trigger NCCL watchdog timeouts (often surfacing later as ALLTOALL
# timeouts in Ulysses attention).
world_size = dist.get_world_size(group=group)
# HACK: Use Gloo backend for all_gather to avoid H2D and D2H overhead
comm_backends = str(dist.get_backend(group=group))
# NOTE: e.g., dist.init_process_group(backend="cpu:gloo,cuda:nccl")
gather_device = "cpu" if "cpu" in comm_backends else torch.accelerator.current_accelerator()
gathered_sizes = [torch.empty((1,), device=gather_device, dtype=torch.int64) for _ in range(world_size)]
dist.all_gather(
gathered_sizes,
torch.tensor([size], device=gather_device, dtype=torch.int64),
group=group,
)
gathered_sizes = [s[0].item() for s in gathered_sizes]
# NOTE: DON'T use tolist here due to graph break - Explanation:
# Backend compiler `inductor` failed with aten._local_scalar_dense.default
return gathered_sizes