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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 import safe_open |
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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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from transformers.utils import check_min_version |
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try: |
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check_min_version("4.34.0") |
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except Exception: |
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raise ValueError("Please upgrade `transformers` to 4.34.0") |
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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) -> str: |
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qwen_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(".safetensors"): |
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with safe_open(os.path.join(input_dir, filepath), framework="pt", device="cpu") as f: |
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for key in f.keys(): |
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qwen_state_dict[key] = f.get_tensor(key) |
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llama2_state_dict: Dict[str, torch.Tensor] = OrderedDict() |
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torch_dtype = None |
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for key, value in tqdm(qwen_state_dict.items(), desc="Convert format"): |
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if torch_dtype is None: |
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torch_dtype = value.dtype |
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if "wte" in key: |
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llama2_state_dict["model.embed_tokens.weight"] = value |
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elif "ln_f" in key: |
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llama2_state_dict["model.norm.weight"] = value |
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else: |
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key = key.replace("transformer.h", "model.layers") |
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if "attn.c_attn" in key: |
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proj_size = value.size(0) // 3 |
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llama2_state_dict[key.replace("attn.c_attn", "self_attn.q_proj")] = value[:proj_size, ...] |
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llama2_state_dict[key.replace("attn.c_attn", "self_attn.k_proj")] = value[ |
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proj_size : 2 * proj_size, ... |
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] |
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llama2_state_dict[key.replace("attn.c_attn", "self_attn.v_proj")] = value[2 * proj_size :, ...] |
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elif "attn.c_proj" in key: |
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llama2_state_dict[key.replace("attn.c_proj", "self_attn.o_proj")] = value |
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llama2_state_dict[key.replace("attn.c_proj.weight", "self_attn.o_proj.bias")] = torch.zeros_like( |
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value[:, 0] |
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).squeeze() |
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elif "ln_1" in key: |
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llama2_state_dict[key.replace("ln_1", "input_layernorm")] = value |
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elif "ln_2" in key: |
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llama2_state_dict[key.replace("ln_2", "post_attention_layernorm")] = value |
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elif "mlp.w1" in key: |
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llama2_state_dict[key.replace("mlp.w1", "mlp.up_proj")] = value |
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elif "mlp.w2" in key: |
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llama2_state_dict[key.replace("mlp.w2", "mlp.gate_proj")] = value |
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elif "mlp.c_proj" in key: |
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llama2_state_dict[key.replace("mlp.c_proj", "mlp.down_proj")] = value |
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elif "lm_head" in key: |
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llama2_state_dict[key] = value |
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else: |
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raise KeyError("Unable to process key {}".format(key)) |
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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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return str(torch_dtype).replace("torch.", "") |
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def save_config(input_dir: str, output_dir: str, torch_dtype: str): |
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with open(os.path.join(input_dir, CONFIG_NAME), "r", encoding="utf-8") as f: |
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qwen_config_dict: Dict[str, Any] = json.load(f) |
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llama2_config_dict: Dict[str, Any] = OrderedDict() |
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llama2_config_dict["architectures"] = ["LlamaForCausalLM"] |
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llama2_config_dict["hidden_act"] = "silu" |
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llama2_config_dict["hidden_size"] = qwen_config_dict["hidden_size"] |
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llama2_config_dict["initializer_range"] = qwen_config_dict["initializer_range"] |
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llama2_config_dict["intermediate_size"] = qwen_config_dict["intermediate_size"] // 2 |
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llama2_config_dict["max_position_embeddings"] = qwen_config_dict["max_position_embeddings"] |
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llama2_config_dict["model_type"] = "llama" |
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llama2_config_dict["num_attention_heads"] = qwen_config_dict["num_attention_heads"] |
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llama2_config_dict["num_hidden_layers"] = qwen_config_dict["num_hidden_layers"] |
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llama2_config_dict["num_key_value_heads"] = qwen_config_dict["hidden_size"] // qwen_config_dict["kv_channels"] |
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llama2_config_dict["pretraining_tp"] = 1 |
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llama2_config_dict["rms_norm_eps"] = qwen_config_dict["layer_norm_epsilon"] |
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llama2_config_dict["rope_scaling"] = None |
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llama2_config_dict["tie_word_embeddings"] = qwen_config_dict["tie_word_embeddings"] |
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llama2_config_dict["torch_dtype"] = torch_dtype |
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llama2_config_dict["transformers_version"] = "4.34.0" |
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llama2_config_dict["use_cache"] = True |
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llama2_config_dict["vocab_size"] = qwen_config_dict["vocab_size"] |
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llama2_config_dict["attention_bias"] = True |
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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_qwen( |
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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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torch_dtype = save_weight(input_dir, output_dir, shard_size, save_safetensors) |
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save_config(input_dir, output_dir, torch_dtype) |
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if __name__ == "__main__": |
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fire.Fire(llamafy_qwen) |
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