mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-09-20 16:25:59 +00:00
97 lines
2.9 KiB
Python
97 lines
2.9 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# This software may be used and distributed according to the terms of the GNU General Public License version 3.
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import json
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import os
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import sys
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import time
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from pathlib import Path
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from typing import Tuple
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import fire
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import torch
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from fairscale.nn.model_parallel.initialize import initialize_model_parallel
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from llama import LLaMA, ModelArgs, Tokenizer, Transformer
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os.environ['RANK'] = '0'
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os.environ['WORLD_SIZE'] = '1'
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os.environ['MP'] = '1'
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os.environ['MASTER_ADDR'] = '127.0.0.1'
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os.environ['MASTER_PORT'] = '2223'
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def setup_model_parallel() -> Tuple[int, int]:
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local_rank = int(os.environ.get("LOCAL_RANK", -1))
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world_size = int(os.environ.get("WORLD_SIZE", -1))
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torch.distributed.init_process_group("gloo")
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initialize_model_parallel(world_size)
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torch.cuda.set_device(local_rank)
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# seed must be the same in all processes
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torch.manual_seed(1)
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return local_rank, world_size
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def load(
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ckpt_dir: str,
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tokenizer_path: str,
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local_rank: int,
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world_size: int,
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max_seq_len: int,
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max_batch_size: int,
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) -> LLaMA:
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start_time = time.time()
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checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
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assert world_size == len(
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checkpoints
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), f"Loading a checkpoint for MP={len(checkpoints)} but world size is {world_size}"
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ckpt_path = checkpoints[local_rank]
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print("Loading")
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checkpoint = torch.load(ckpt_path, map_location="cpu")
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with open(Path(ckpt_dir) / "params.json", "r") as f:
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params = json.loads(f.read())
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model_args: ModelArgs = ModelArgs(
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max_seq_len=max_seq_len, max_batch_size=max_batch_size, **params
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)
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tokenizer = Tokenizer(model_path=tokenizer_path)
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model_args.vocab_size = tokenizer.n_words
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torch.set_default_tensor_type(torch.cuda.HalfTensor)
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model = Transformer(model_args)
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torch.set_default_tensor_type(torch.FloatTensor)
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model.load_state_dict(checkpoint, strict=False)
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generator = LLaMA(model, tokenizer)
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print(f"Loaded in {time.time() - start_time:.2f} seconds")
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return generator
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class LLaMAModel:
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def __init__(self):
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pass
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@classmethod
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def from_pretrained(self, path, max_seq_len=512, max_batch_size=32):
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tokenizer_path = path / "tokenizer.model"
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path = os.path.abspath(path)
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tokenizer_path = os.path.abspath(tokenizer_path)
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local_rank, world_size = setup_model_parallel()
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if local_rank > 0:
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sys.stdout = open(os.devnull, "w")
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generator = load(
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path, tokenizer_path, local_rank, world_size, max_seq_len, max_batch_size
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)
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result = self()
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result.pipeline = generator
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return result
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def generate(self, prompt, token_count=512, temperature=0.8, top_p=0.95):
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results = self.pipeline.generate(
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[prompt], max_gen_len=token_count, temperature=temperature, top_p=top_p
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)
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return results[0]
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