class XpuCommunicator(DeviceCommunicatorBase):
def __init__(
self,
cpu_group: ProcessGroup,
device: torch.device | None = None,
device_group: ProcessGroup | None = None,
unique_name: str = "",
use_all2all: bool = False,
):
super().__init__(
cpu_group, device, device_group, unique_name, use_all2all=use_all2all
)
self.ca_comm: None = None
if self.use_all2all:
if self.all2all_backend in ("naive", "allgather_reducescatter"):
from .all2all import AgRsAll2AllManager
self.all2all_manager = AgRsAll2AllManager(self.cpu_group)
logger.info("Using AgRs manager on XPU device.")
else: # type: ignore[has-type]
logger.warning(
"`%s` all2all manager is not supported on XPU. "
"Falling back to AgRs manager for XPU, "
"which is the Default backend",
self.all2all_backend, # type: ignore[has-type]
)
from .all2all import AgRsAll2AllManager
self.all2all_manager = AgRsAll2AllManager(self.cpu_group)
logger.info("Using AgRs manager on XPU device.")
def _fixed_rank_sum(self, input_: torch.Tensor) -> torch.Tensor:
flat_input = input_.reshape(-1)
gathered = torch.empty(
(self.world_size, flat_input.numel()),
dtype=input_.dtype,
device=input_.device,
)
dist.all_gather_single(gathered.view(-1), flat_input, group=self.device_group)
output = gathered[0].clone()
for rank in range(1, self.world_size):
output.add_(gathered[rank])
return output.view(input_.shape)
def all_reduce(self, input_: torch.Tensor) -> torch.Tensor:
if envs.VLLM_BATCH_INVARIANT:
# XCCL changes its reduction order with message size. Gather first
# and accumulate in rank order, independent of the token batch.
return self._fixed_rank_sum(input_)
output = input_.clone()
dist.all_reduce(output, group=self.device_group)
return output
def reduce_scatter(self, input_: torch.Tensor, dim: int = -1):
world_size = self.world_size
if dim < 0:
# Convert negative dim to positive.
dim += input_.dim()
# Note: This will produce an incorrect answer if we don't make
# the input_tensor contiguous. Possible bug in reduce_scatter_tensor?
input_tensor = input_.movedim(0, dim).contiguous()
assert input_tensor.shape[0] % world_size == 0
chunk_size = input_tensor.shape[0] // world_size
output_shape = (chunk_size,) + input_tensor.shape[1:]
output = torch.empty(
output_shape, dtype=input_tensor.dtype, device=input_tensor.device
)
if envs.VLLM_BATCH_INVARIANT:
reduced = self._fixed_rank_sum(input_tensor)
start = self.rank_in_group * chunk_size
output.copy_(reduced.narrow(0, start, chunk_size))
else:
dist.reduce_scatter_tensor(output, input_tensor, group=self.device_group)
# Reshape before returning
return output.movedim(0, dim).contiguous()
def reduce_scatterv(
self, input_: torch.Tensor, dim: int = -1, sizes: list[int] | None = None
):
world_size = self.world_size
if dim < 0:
# Convert negative dim to positive.
dim += input_.dim()
# Note: This will produce an incorrect answer if we don't make
# the input_tensor contiguous. Possible bug in reduce_scatter_tensor?
input_tensor = input_.movedim(0, dim).contiguous()
if sizes is not None:
assert len(sizes) == world_size
assert input_tensor.shape[0] == sum(sizes)
chunk_size = sizes[self.rank_in_group]
else:
assert input_tensor.shape[0] % world_size == 0
chunk_size = input_tensor.shape[0] // world_size
output_shape = (chunk_size,) + input_tensor.shape[1:]
output = torch.empty(
output_shape, dtype=input_tensor.dtype, device=input_tensor.device
)
if envs.VLLM_BATCH_INVARIANT:
reduced = self._fixed_rank_sum(input_tensor)
start = (
self.rank_in_group * chunk_size
if sizes is None
else sum(sizes[: self.rank_in_group])
)
output.copy_(reduced.narrow(0, start, chunk_size))
elif sizes is None or sizes.count(sizes[0]) == len(sizes):
dist.reduce_scatter_tensor(output, input_tensor, group=self.device_group)
elif torch.xpu.device_count() < self.world_size:
# uneven reduce_scatter is not supported when the
# process cannot see every device in the group (
# e.g. external-LBDP).
max_size = max(sizes)
padded = input_tensor.new_zeros(
(world_size * max_size,) + input_tensor.shape[1:]
)
for rank, split in enumerate(input_tensor.split(sizes, dim=0)):
padded[rank * max_size : rank * max_size + sizes[rank]] = split
padded_output = input_tensor.new_empty((max_size,) + input_tensor.shape[1:])
dist.reduce_scatter_tensor(padded_output, padded, group=self.device_group)
output = padded_output[:chunk_size]
else:
input_splits = list(input_tensor.split(sizes, dim=0))
dist.reduce_scatter(output, input_splits, group=self.device_group)
# Reshape before returning
return output.movedim(0, dim).contiguous()
def all_gatherv(
self,
input_: torch.Tensor | list[torch.Tensor],
dim: int = 0,
sizes: list[int] | None = None,
):
if dim != 0:
raise NotImplementedError("only dim 0 all-gatherv is supported")
world_size = self.world_size
# 'sizes' is not needed if all inputs in the same group have the same
# shape
if sizes is not None and all(s == sizes[0] for s in sizes):
sizes = None
def _all_gather_single(input_: torch.Tensor, sizes: list[int] | None = None):
input_size = input_.size()
if sizes is not None:
assert len(sizes) == world_size
assert input_.shape[dim] == sizes[self.rank_in_group], (
f"{input_.shape[dim]} != {sizes[self.rank_in_group]}"
)
output_size = (sum(sizes),) + input_size[1:]
else:
output_size = (input_size[0] * world_size,) + input_size[1:]
# Allocate output tensor.
output_tensor = torch.empty(
output_size, dtype=input_.dtype, device=input_.device
)
if sizes is not None:
all_gather_list = []
for size in sizes:
all_gather_list.append(
torch.empty(
(size,) + input_.shape[1:],
dtype=input_.dtype,
device=input_.device,
)
)
dist.all_gather(all_gather_list, input_, group=self.device_group)
output_tensor = torch.cat(all_gather_list, dim=0)
else:
dist.all_gather([output_tensor], input_, group=self.device_group)
return output_tensor
if isinstance(input_, torch.Tensor):
return _all_gather_single(input_, sizes)
output_list = []
for inp in input_:
output_list.append(_all_gather_single(inp, sizes=sizes))
return output_list
def gather(
self, input_: torch.Tensor, dst: int = 0, dim: int = -1
) -> torch.Tensor | None:
assert -input_.dim() <= dim < input_.dim(), (
f"Invalid dim ({dim}) for input tensor with shape {input_.size()}"
)
if dim < 0:
# Convert negative dim to positive.
dim += input_.dim()
# For xpu path, gather doesn't work properly together with ray
# cluster so we use all_gather instead for now.
input_size = input_.size()
# Allocate output tensor.
output_tensor = torch.empty(
(self.world_size,) + input_size, dtype=input_.dtype, device=input_.device
)
# All-gather.
dist.all_gather_single(output_tensor, input_, group=self.device_group)
if self.rank_in_group == dst:
# Reshape
output_tensor = output_tensor.movedim(0, dim)
output_tensor = output_tensor.reshape(
input_size[:dim]
+ (self.world_size * input_size[dim],)
+ input_size[dim + 1 :]
)
else:
output_tensor = None
return output_tensor
def broadcast(self, input_: torch.Tensor, src: int = 0) -> None:
dist.broadcast(input_, src=src, group=self.device_group)
def dispatch_router_logits(
self,
hidden_states: torch.Tensor,
router_logits: torch.Tensor,
is_sequence_parallel: bool = False,
extra_tensors: list[torch.Tensor] | None = None,
) -> (
tuple[torch.Tensor, torch.Tensor]
| tuple[torch.Tensor, torch.Tensor, list[torch.Tensor]]
):
"""Dispatch the hidden states and router logits to the appropriate device.
This is a no-op in the base class.
"""
assert self.all2all_manager is not None
return self.all2all_manager.dispatch_router_logits(
hidden_states,
router_logits,
is_sequence_parallel,
extra_tensors,
)
def dispatch(
self,
hidden_states: torch.Tensor,
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
is_sequence_parallel: bool = False,
extra_tensors: list[torch.Tensor] | None = None,
) -> (
tuple[torch.Tensor, torch.Tensor, torch.Tensor]
| tuple[torch.Tensor, torch.Tensor, torch.Tensor, list[torch.Tensor]]
):
"""Dispatch the hidden states and topk weights/ids to the appropriate device.
This is a no-op in the base class.
"""
assert self.all2all_manager is not None
return self.all2all_manager.dispatch(
hidden_states,
topk_weights,
topk_ids,
is_sequence_parallel,
extra_tensors=extra_tensors,
)
def combine(
self, hidden_states: torch.Tensor, is_sequence_parallel: bool = False
) -> torch.Tensor:
"""Combine the hidden states and router logits from the appropriate device.
This is a no-op in the base class.
"""
assert self.all2all_manager is not None
return self.all2all_manager.combine(
hidden_states,
is_sequence_parallel,
)