mirror of
https://github.com/wassname/alpaca_convert.git
synced 2026-07-05 18:45:31 +08:00
wassname
101 lines
3.5 KiB
Python
101 lines
3.5 KiB
Python
"""
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From https://raw.githubusercontent.com/tloen/alpaca-lora/main/export_hf_checkpoint.py
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"""
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import os
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from pathlib import Path
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import argparse
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import torch
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import transformers
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from peft import PeftModel
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from transformers import LlamaForCausalLM, LlamaTokenizer # noqa: F402
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import autograd_4bit
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from autograd_4bit import load_llama_model_4bit_low_ram, Autograd4bitQuantLinear
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def main(BASE_MODEL, LORA_MODEL, int4_checkpoint_path, output_path=None):
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if output_path is None:
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output_path = 'models/' + LORA_MODEL.split('/')[-1] + '-delorified'
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# load 4bit, from https://github.com/johnsmith0031/alpaca_lora_4bit/blob/fb7665726e5b69dcac6020707bbece7b0d39b865/text-generation-webui/custom_monkey_patch.py#L4
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model, tokenizer = load_llama_model_4bit_low_ram(config_path=BASE_MODEL, model_path=int4_checkpoint_path, groupsize=-1, is_v1_model=True)
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lora_model = PeftModel.from_pretrained(model, LORA_MODEL, device_map={'': "cpu"}, torch_dtype=torch.float16)
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print('{} Lora Applied.'.format(lora_path))
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print('Apply auto switch and half')
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for n, m in lora_model.named_modules():
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if isinstance(m, Autograd4bitQuantLinear) or isinstance(m, Linear4bitLt):
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if m.is_v1_model:
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m.zeros = m.zeros.half()
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m.scales = m.scales.half()
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m.bias = m.bias.half()
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autograd_4bit.use_new = True
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autograd_4bit.auto_switch = True
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# tokenizer = LlamaTokenizer.from_pretrained(BASE_MODEL)
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# base_model = LlamaForCausalLM.from_pretrained(
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# BASE_MODEL,
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# load_in_8bit=False,
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# torch_dtype=torch.float16,
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# device_map={"": "cpu"},
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# )
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# # TODO or load 4 bit?
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# first_weight = base_model.model.layers[0].self_attn.q_proj.weight
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# first_weight_old = first_weight.clone()
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# lora_model = PeftModel.from_pretrained(
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# base_model,
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# LORA_MODEL,
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# device_map={"": "cpu"},
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# torch_dtype=torch.float16,
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# )
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lora_weight = lora_model.base_model.model.model.layers[
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0
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].self_attn.q_proj.weight
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assert torch.allclose(first_weight_old, first_weight)
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# merge weights - new merging method from peft
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lora_model = lora_model.merge_and_unload()
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lora_model.train(False)
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# did we do anything?
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assert not torch.allclose(first_weight_old, first_weight)
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lora_model_sd = lora_model.state_dict()
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deloreanized_sd = {
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k.replace("base_model.model.", ""): v
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for k, v in lora_model_sd.items()
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if "lora" not in k
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}
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LlamaForCausalLM.save_pretrained(
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base_model, output_path, state_dict=deloreanized_sd, max_shard_size="400MB"
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)
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print(f'output {output_path}')
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LlamaTokenizer.save_pretrained(tokenizer, output_path)
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# FIXME also save tokenizer
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from alpaca_convert.test import test_conversation
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o = test_conversation(lora_model.float(), tokenizer)
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print(o)
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prompts_path = Path(output_path) / 'test_prompts.txt'
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print(prompts_path)
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prompts_path.open('w').write(o)
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if __name__=="__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('model', type=str)
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parser.add_argument('int4_checkpoint_path', type=str)
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parser.add_argument('-l', '--lora', type=str, default='main', help='Lora repo or path e.g. `tloen/alpaca-lora-7b`')
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parser.add_argument('-o', '--output', type=Path, default=None)
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"e.g. ./hf_ckpt. default will be lora name"
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args = parser.parse_args()
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print(args)
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main(args.model, args.lora, args.int4_checkpoint_path, args.output)
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