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[Feature] support custom all-reduce (#2758)

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* [Feature] support custom all-reduce

* add vllm adapted
This commit is contained in:
zhink
2025-07-09 16:00:27 +08:00
committed by GitHub
parent be21ef5047
commit b89180f1cd
16 changed files with 1194 additions and 2 deletions
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19
fastdeploy/distributed/communication_op.py
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@@ -16,13 +16,28 @@
import paddle
import paddle.distributed as dist
from paddle.distributed import fleet
from fastdeploy.distributed.parallel_state import get_tensor_model_parallel_world_size
_TP_AR = None
def use_custom_allreduce(custom_all_reduce_max_bytes: int = 8192 * 1024):
hcg = fleet.get_hybrid_communicate_group()
model_parallel_group = hcg.get_model_parallel_group()
global _TP_AR
if get_tensor_model_parallel_world_size() > 1 and paddle.is_compiled_with_cuda():
from fastdeploy.distributed.custom_all_reduce import CustomAllreduce
_TP_AR = CustomAllreduce(model_parallel_group, custom_all_reduce_max_bytes)
try:
@paddle.jit.marker.unified
def tensor_model_parallel_all_reduce(input_: paddle.Tensor) -> paddle.Tensor:
"""All-reduce the input tensor across model parallel group."""
if paddle.in_dynamic_mode():
hcg = dist.fleet.get_hybrid_communicate_group()
global _TP_AR
if _TP_AR is not None and _TP_AR.should_custom_ar(input_) :
_TP_AR.all_reduce(input_, input_)
elif paddle.in_dynamic_mode():
hcg = fleet.get_hybrid_communicate_group()
mp_group = hcg.get_model_parallel_group()
dist.all_reduce(input_, group=mp_group)
else: