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Support torchrun vllm DP #3304

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27 changes: 27 additions & 0 deletions lm_eval/models/vllm_causallms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -172,6 +172,7 @@ def __init__(
"swap_space": int(swap_space),
"quantization": quantization,
"seed": int(seed),
"data_parallel_size": int(data_parallel_size),
"enable_lora": True if lora_local_path else False,
"max_lora_rank": int(max_lora_rank),
}
Expand All @@ -181,8 +182,18 @@ def __init__(
if isinstance(batch_size, str) and "auto" in batch_size
else int(batch_size)
)

self.dp_group = None
self.torch_dist = None
self.is_external_launcher_dp = self.model_args.get("distributed_executor_backend", None) == "external_launcher" and self.data_parallel_size > 1
if self.data_parallel_size <= 1:
self.model = LLM(**self.model_args)
elif self.is_external_launcher_dp:
self.model = LLM(**self.model_args)
from vllm.distributed.parallel_state import get_dp_group
import torch.distributed as dist
self.dp_group = get_dp_group()
self.torch_dist = dist
else:
eval_logger.warning(
"You might experience occasional issues with model weight downloading when data_parallel is in use. To ensure stable performance, run with data_parallel_size=1 until the weights are downloaded and cached."
Expand Down Expand Up @@ -417,6 +428,22 @@ def run_inference_one_model(
list(sp) for sp in distribute(self.data_parallel_size, sampling_params)
)
procs, resq = [], Queue()
if self.is_external_launcher_dp:
dp_rank = self.model.llm_engine.vllm_config.parallel_config.data_parallel_rank
local_requests = list(requests)[dp_rank]
local_sampling_params = list(sampling_params)[dp_rank]
local_results = self.model.generate(
[TokensPrompt(prompt_token_ids=request) for request in local_requests],
sampling_params=local_sampling_params,
use_tqdm=True if self.batch_size == "auto" else False,
)
# All gather results across data parallel group
assert self.dp_group is not None
assert self.torch_dist is not None
# Gather results from all DP ranks
gathered_results = [None] * self.dp_group.world_size
self.torch_dist.all_gather_object(gathered_results, local_results, group=self.dp_group.cpu_group)
return undistribute(gathered_results)
# We use Process as it is non-daemonic
try:
for rank, (sp, req) in enumerate(zip(requests, sampling_params)):
Expand Down