115 lines
4.9 KiB
Python
115 lines
4.9 KiB
Python
# coding=utf-8
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# Converts the InternLM2 model in the same format as LLaMA2.
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# Usage: python llamafy_internlm2.py --input_dir input --output_dir output
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# Warning: We have found that the converted model cannot infer correctly. It will be fixed later.
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import json
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import os
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from collections import OrderedDict
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from typing import Any, Dict, Optional
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import fire
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import torch
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from safetensors.torch import save_file
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from tqdm import tqdm
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from transformers.modeling_utils import (
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SAFE_WEIGHTS_INDEX_NAME,
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SAFE_WEIGHTS_NAME,
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WEIGHTS_INDEX_NAME,
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WEIGHTS_NAME,
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shard_checkpoint,
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)
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CONFIG_NAME = "config.json"
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def save_weight(input_dir: str, output_dir: str, shard_size: str, save_safetensors: bool):
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with open(os.path.join(input_dir, CONFIG_NAME), "r", encoding="utf-8") as f:
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internlm2_config_dict: Dict[str, Any] = json.load(f)
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internlm2_state_dict: Dict[str, torch.Tensor] = OrderedDict()
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for filepath in tqdm(os.listdir(input_dir), desc="Load weights"):
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if os.path.isfile(os.path.join(input_dir, filepath)) and filepath.endswith(".bin"):
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shard_weight = torch.load(os.path.join(input_dir, filepath), map_location="cpu")
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internlm2_state_dict.update(shard_weight)
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llama2_state_dict: Dict[str, torch.Tensor] = OrderedDict()
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for key, value in tqdm(internlm2_state_dict.items(), desc="Convert format"):
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if "output" in key:
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llama2_state_dict[key.replace("output", "lm_head")] = value
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elif "tok_embeddings" in key:
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llama2_state_dict[key.replace("tok_embeddings", "embed_tokens")] = value
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elif "wqkv" in key:
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num_q_heads = internlm2_config_dict["num_attention_heads"]
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num_kv_heads = internlm2_config_dict["num_key_value_heads"]
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q_size = value.size(0) // (num_q_heads + 2 * num_kv_heads) * num_q_heads
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kv_size = value.size(0) // (num_q_heads + 2 * num_kv_heads) * num_kv_heads
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llama2_state_dict[key.replace("attention.wqkv", "self_attn.q_proj")] = value[:q_size, ...]
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llama2_state_dict[key.replace("attention.wqkv", "self_attn.k_proj")] = value[
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q_size : q_size + kv_size, ...
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]
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llama2_state_dict[key.replace("attention.wqkv", "self_attn.v_proj")] = value[q_size + kv_size :, ...]
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elif "wo" in key:
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llama2_state_dict[key.replace("attention.wo", "self_attn.o_proj")] = value
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elif "attention_norm" in key:
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llama2_state_dict[key.replace("attention_norm", "input_layernorm")] = value
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elif "ffn_norm" in key:
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llama2_state_dict[key.replace("ffn_norm", "post_attention_layernorm")] = value
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elif "w1" in key:
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llama2_state_dict[key.replace("feed_forward.w1", "mlp.gate_proj")] = value
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elif "w2" in key:
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llama2_state_dict[key.replace("feed_forward.w2", "mlp.down_proj")] = value
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elif "w3" in key:
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llama2_state_dict[key.replace("feed_forward.w3", "mlp.up_proj")] = value
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else:
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llama2_state_dict[key] = value
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weights_name = SAFE_WEIGHTS_NAME if save_safetensors else WEIGHTS_NAME
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shards, index = shard_checkpoint(llama2_state_dict, max_shard_size=shard_size, weights_name=weights_name)
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for shard_file, shard in tqdm(shards.items(), desc="Save weights"):
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if save_safetensors:
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save_file(shard, os.path.join(output_dir, shard_file), metadata={"format": "pt"})
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else:
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torch.save(shard, os.path.join(output_dir, shard_file))
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if index is None:
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print("Model weights saved in {}".format(os.path.join(output_dir, WEIGHTS_NAME)))
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else:
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index_name = SAFE_WEIGHTS_INDEX_NAME if save_safetensors else WEIGHTS_INDEX_NAME
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with open(os.path.join(output_dir, index_name), "w", encoding="utf-8") as f:
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json.dump(index, f, indent=2, sort_keys=True)
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print("Model weights saved in {}".format(output_dir))
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def save_config(input_dir: str, output_dir: str):
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with open(os.path.join(input_dir, CONFIG_NAME), "r", encoding="utf-8") as f:
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llama2_config_dict: Dict[str, Any] = json.load(f)
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llama2_config_dict["architectures"] = ["LlamaForCausalLM"]
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llama2_config_dict.pop("auto_map", None)
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llama2_config_dict.pop("bias", None)
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llama2_config_dict.pop("rope_scaling", None)
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llama2_config_dict["model_type"] = "llama"
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with open(os.path.join(output_dir, CONFIG_NAME), "w", encoding="utf-8") as f:
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json.dump(llama2_config_dict, f, indent=2)
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print("Model config saved in {}".format(os.path.join(output_dir, CONFIG_NAME)))
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def llamafy_internlm2(
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input_dir: str, output_dir: str, shard_size: Optional[str] = "2GB", save_safetensors: Optional[bool] = False
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):
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try:
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os.makedirs(output_dir, exist_ok=False)
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except Exception as e:
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raise print("Output dir already exists", e)
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save_weight(input_dir, output_dir, shard_size, save_safetensors)
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save_config(input_dir, output_dir)
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if __name__ == "__main__":
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fire.Fire(llamafy_internlm2)
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