57 lines
2.0 KiB
Python
57 lines
2.0 KiB
Python
import torch
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import torch.distributed as dist
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import torch.multiprocessing as mp
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from transformers import AutoTokenizer, LlamaForCausalLM
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from torch.nn.parallel import DistributedDataParallel as DDP
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from evalplus.data import get_human_eval_plus, write_jsonl
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import os
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from tqdm import tqdm # import tqdm
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def setup(rank, world_size):
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os.environ['MASTER_ADDR'] = 'localhost'
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os.environ['MASTER_PORT'] = '12355'
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dist.init_process_group("gloo", rank=rank, world_size=world_size)
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def cleanup():
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dist.destroy_process_group()
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def generate_one_completion(ddp_model, tokenizer, prompt: str):
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tokenizer.pad_token = tokenizer.eos_token
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inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=4096)
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# Generate
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generate_ids = ddp_model.module.generate(inputs.input_ids.to("cuda"), max_new_tokens=384, do_sample=True, top_p=0.75, top_k=40, temperature=0.1, pad_token_id=tokenizer.eos_token_id)
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completion = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
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completion = completion.replace(prompt, "").split("\n\n\n")[0]
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print("-------------------")
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print(completion)
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return completion
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def run(rank, world_size):
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setup(rank, world_size)
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model_path = "Nondzu/Mistral-7B-codealpaca-lora"
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model = LlamaForCausalLM.from_pretrained(model_path,load_in_8bit=True)
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ddp_model = DDP(model, device_ids=[rank])
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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problems = get_human_eval_plus()
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num_samples_per_task = 1
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samples = [
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dict(task_id=task_id, completion=generate_one_completion(ddp_model, tokenizer, problems[task_id]["prompt"]))
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for task_id in tqdm(problems) # add tqdm here
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for _ in range(num_samples_per_task)
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]
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write_jsonl(f"samples-Nondzu-Mistral-7B-codealpaca-lora-rank{rank}.jsonl", samples)
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cleanup()
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def main():
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world_size = 1
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mp.spawn(run, args=(world_size,), nprocs=world_size, join=True)
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if __name__=="__main__":
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main()
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