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fix(vllm_strategy): llm.generate params 'prompt_token_ids' become part of 'prompts' #189

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fix(vllm_strategy): llm.generate params 'prompt_token_ids' become part of 'prompts' #189

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18 changes: 15 additions & 3 deletions roll/distributed/strategy/vllm_strategy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,10 +12,13 @@
import torch.distributed as dist
from torch.nn.utils.rnn import pad_sequence
from transformers import set_seed
import vllm
from vllm import RequestOutput, SamplingParams
from vllm.lora.request import LoRARequest
from vllm.utils import random_uuid

from packaging.version import Version

from mcore_adapter.models.converter.convert_utils import RecvBucketManager
from roll.distributed.executor.worker import Worker
from roll.distributed.scheduler.protocol import DataProto
Expand Down Expand Up @@ -138,9 +141,18 @@ def generate(self, batch: DataProto, generation_config) -> torch.Tensor:
if "multi_modal_data" in batch.non_tensor_batch:
vllm_input_args["prompts"] = batch.non_tensor_batch["multi_modal_data"]
else:
vllm_input_args["prompt_token_ids"] = gather_unpadded_input_ids(
input_ids=input_ids, attention_mask=attention_mask
)
# fix llm generate params for vllm 0.10.2
if Version("0.10.2") >= Version(vllm.__version__):
from vllm.inputs.data import TokensPrompt
prompt_token_ids_list = gather_unpadded_input_ids(
input_ids=input_ids, attention_mask=attention_mask
)
prompts = [TokensPrompt(prompt_token_ids=p_ids) for p_ids in prompt_token_ids_list]
vllm_input_args["prompts"] = prompts
else:
vllm_input_args["prompt_token_ids"] = gather_unpadded_input_ids(
input_ids=input_ids, attention_mask=attention_mask
)

lora_requests = None
if self.is_lora:
Expand Down