mirror of
https://github.com/deepseek-ai/DeepSeek-Prover-V1.5
synced 2024-11-22 03:17:43 +00:00
53 lines
1.9 KiB
Python
53 lines
1.9 KiB
Python
import os
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import time
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import torch
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import torch.multiprocessing as mp
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from vllm import LLM, SamplingParams
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from prover.utils import AttrDict, MODEL_FORMAT
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class GeneratorProcess(mp.Process):
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def __init__(self, local_rank, node_rank, model_path, task_queue, request_statuses, lock, args):
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super().__init__()
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self.local_rank = local_rank
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self.node_rank = node_rank
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self.model_path = model_path
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self.task_queue = task_queue
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self.request_statuses = request_statuses
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self.lock = lock
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self.sampling_params = SamplingParams(
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temperature=args.temperature,
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max_tokens=args.max_tokens,
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top_p=args.top_p,
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n=1,
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)
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self.prompt_func = MODEL_FORMAT[args.mode]['prompt']
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self.output_func = MODEL_FORMAT[args.mode]['output']
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def run(self):
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seed = int(time.time()) % 1000 + (self.node_rank * 8 + self.local_rank) * 1000
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os.environ['LOCAL_RANK'] = str(self.local_rank)
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llm = LLM(model=self.model_path, max_num_batched_tokens=8192, seed=seed, trust_remote_code=True)
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while True:
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inputs = self.task_queue.get()
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if inputs is None: # Terminate when receiving None
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break
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model_inputs = [
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''.join([
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item.get('_extra_header', str()),
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self.prompt_func(item),
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item.get('_extra_prompt', str()),
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]) for _, _, item in inputs
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]
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model_outputs = llm.generate(
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model_inputs,
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self.sampling_params,
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use_tqdm=False,
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)
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outputs = [self.output_func(_output.outputs[0].text) for _output in model_outputs]
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with self.lock:
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for (_, request_id, _), output in zip(inputs, outputs):
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self.request_statuses[request_id] = output
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