init

2026-08-02 12:30:42 +08:00 · 2023-04-10 16:15:52 +08:00
parent 5d3267d80d
commit 6e6c8f8e7a
22 changed files with 776 additions and 1982 deletions
@@ -0,0 +1,81 @@
+"""
+From https://raw.githubusercontent.com/tloen/alpaca-lora/main/export_hf_checkpoint.py
+"""
+import os
+from pathlib import Path
+import argparse
+import torch
+import transformers
+from peft import PeftModel
+from transformers import LlamaForCausalLM, LlamaTokenizer  # noqa: F402
+
+def main(BASE_MODEL, LORA_MODEL, output_path=None):
+    
+    if output_path is None:
+        output_path = 'models/' + LORA_MODEL.split('/')[-1] + '-delorified'
+
+    # BASE_MODEL = os.environ.get("BASE_MODEL", None)
+    # assert (
+    #     BASE_MODEL
+    # ), "Please specify a value for BASE_MODEL environment variable, e.g. `export BASE_MODEL=huggyllama/llama-7b`"  # noqa: E501
+
+
+    # LORA_MODEL = os.environ.get("BASE_MODEL", None)
+    # assert (
+    #     LORA_MODEL
+    # ), "Please specify a value for LORA_MODEL environment variable, e.g. `export BASE_MODEL=tloen/alpaca-lora-7b`"  # noqa: E501
+
+    tokenizer = LlamaTokenizer.from_pretrained(BASE_MODEL)
+
+    base_model = LlamaForCausalLM.from_pretrained(
+        BASE_MODEL,
+        load_in_8bit=False,
+        torch_dtype=torch.float16,
+        device_map={"": "cpu"},
+    )
+
+    first_weight = base_model.model.layers[0].self_attn.q_proj.weight
+    first_weight_old = first_weight.clone()
+
+    lora_model = PeftModel.from_pretrained(
+        base_model,
+        LORA_MODEL,
+        device_map={"": "cpu"},
+        torch_dtype=torch.float16,
+    )
+
+    lora_weight = lora_model.base_model.model.model.layers[
+        0
+    ].self_attn.q_proj.weight
+
+    assert torch.allclose(first_weight_old, first_weight)
+
+    # merge weights - new merging method from peft
+    lora_model = lora_model.merge_and_unload()
+
+    lora_model.train(False)
+
+    # did we do anything?
+    assert not torch.allclose(first_weight_old, first_weight)
+
+    lora_model_sd = lora_model.state_dict()
+    deloreanized_sd = {
+        k.replace("base_model.model.", ""): v
+        for k, v in lora_model_sd.items()
+        if "lora" not in k
+    }
+
+    LlamaForCausalLM.save_pretrained(
+        base_model, output_path, state_dict=deloreanized_sd, max_shard_size="400MB"
+    )
+    print(f'output {output_path}')
+
+if __name__=="__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('model', type=str)
+    parser.add_argument('-l', '--lora', type=str, default='main', help='Lora repo or path e.g. `tloen/alpaca-lora-7b`')
+    parser.add_argument('-o', '--output', type=Path, default=None)
+    "e.g. ./hf_ckpt. default will be lora name"
+    args = parser.parse_args()
+    main(args.model, args.lora, args.output)
+