add llamafy_internlm2

src/llmtuner/eval/evaluator.py

@@ -64,7 +64,8 @@ class Evaluator:
                 name=subject,
                 cache_dir=self.model_args.cache_dir,
                 download_mode=self.eval_args.download_mode,
-                token=self.model_args.hf_hub_token
+                token=self.model_args.hf_hub_token,
+                trust_remote_code=True
             )
             pbar.set_postfix_str(categorys[subject]["name"])
             inputs, outputs, labels = [], [], []

tests/llamafy_internlm2.py

@@ -0,0 +1,121 @@
+# coding=utf-8
+# Converts the InternLM2 model in the same format as LLaMA2.
+# Usage: python llamafy_internlm2.py --input_dir input --output_dir output --shard_size 10GB
+
+import os
+import fire
+import json
+import torch
+from tqdm import tqdm
+from collections import OrderedDict
+from safetensors.torch import save_file
+from transformers.modeling_utils import (
+    shard_checkpoint,
+    SAFE_WEIGHTS_NAME,
+    SAFE_WEIGHTS_INDEX_NAME,
+    WEIGHTS_NAME,
+    WEIGHTS_INDEX_NAME
+)
+from typing import Any, Dict, Optional
+
+
+CONFIG_NAME = "config.json"
+
+
+def save_weight(
+    input_dir: str,
+    output_dir: str,
+    shard_size: str,
+    save_safetensors: bool
+):
+    with open(os.path.join(input_dir, CONFIG_NAME), "r", encoding="utf-8") as f:
+        internlm2_config_dict: Dict[str, Any] = json.load(f)
+
+    internlm2_state_dict: Dict[str, torch.Tensor] = OrderedDict()
+    for filepath in os.listdir(input_dir):
+        if os.path.isfile(os.path.join(input_dir, filepath)) and filepath.endswith(".bin"):
+            shard_weight = torch.load(os.path.join(input_dir, filepath), map_location="cpu")
+            internlm2_state_dict.update(shard_weight)
+
+    llama2_state_dict: Dict[str, torch.Tensor] = OrderedDict()
+    for key, value in tqdm(internlm2_state_dict.items(), desc="Convert format"):
+        if "output" in key:
+            llama2_state_dict["lm_head"] = value
+        elif "tok_embeddings" in key:
+            llama2_state_dict["embed_tokens"] = value
+        elif "attention_norm" in key:
+            llama2_state_dict[key.replace("attention_norm", "input_layernorm")] = value
+        elif "wqkv" in key:
+            proj_size = value.size(0) // 3
+            num_q_heads = internlm2_config_dict["num_attention_heads"]
+            num_kv_heads = internlm2_config_dict["num_key_value_heads"]
+            q_size = proj_size // (num_q_heads + num_kv_heads) * num_q_heads
+            kv_size = proj_size // (num_q_heads + num_kv_heads) * num_kv_heads
+            llama2_state_dict[key.replace("attention.wqkv", "self_attn.q_proj")] = value[:q_size, ...]
+            llama2_state_dict[key.replace("attention.wqkv", "self_attn.k_proj")] = value[q_size:q_size+kv_size, ...]
+            llama2_state_dict[key.replace("attention.wqkv", "self_attn.v_proj")] = value[q_size+kv_size:, ...]
+        elif "wo" in key:
+            llama2_state_dict[key.replace("attention.wo", "self_attn.o_proj")] = value
+        elif "ffn_norm" in key:
+            llama2_state_dict[key.replace("ffn_norm", "post_attention_layernorm")] = value
+        elif "w1" in key:
+            llama2_state_dict[key.replace("feed_forward.w1", "mlp.gate_proj")] = value
+        elif "w2" in key:
+            llama2_state_dict[key.replace("feed_forward.w2", "mlp.down_proj")] = value
+        elif "w3" in key:
+            llama2_state_dict[key.replace("feed_forward.w3", "mlp.up_proj")] = value
+        else:
+            raise KeyError("Unable to process key {}".format(key))
+
+    weights_name = SAFE_WEIGHTS_NAME if save_safetensors else WEIGHTS_NAME
+    shards, index = shard_checkpoint(llama2_state_dict, max_shard_size=shard_size, weights_name=weights_name)
+
+    for shard_file, shard in tqdm(shards.items(), desc="Save weights"):
+        if save_safetensors:
+            save_file(shard, os.path.join(output_dir, shard_file), metadata={"format": "pt"})
+        else:
+            torch.save(shard, os.path.join(output_dir, shard_file))
+
+    if index is None:
+        print("Model weights saved in {}".format(os.path.join(output_dir, WEIGHTS_NAME)))
+    else:
+        index_name = SAFE_WEIGHTS_INDEX_NAME if save_safetensors else WEIGHTS_INDEX_NAME
+        with open(os.path.join(output_dir, index_name), "w", encoding="utf-8") as f:
+            json.dump(index, f, indent=2, sort_keys=True)
+        print("Model weights saved in {}".format(output_dir))
+
+
+def save_config(
+    input_dir: str,
+    output_dir: str
+):
+    with open(os.path.join(input_dir, CONFIG_NAME), "r", encoding="utf-8") as f:
+        llama2_config_dict: Dict[str, Any] = json.load(f)
+
+    llama2_config_dict["architectures"] = ["LlamaForCausalLM"]
+    llama2_config_dict.pop("auto_map", None)
+    llama2_config_dict.pop("bias", None)
+    llama2_config_dict["model_type"] = "llama"
+
+    with open(os.path.join(output_dir, CONFIG_NAME), "w", encoding="utf-8") as f:
+        json.dump(llama2_config_dict, f, indent=2)
+    print("Model config saved in {}".format(os.path.join(output_dir, CONFIG_NAME)))
+
+
+def llamafy_internlm2(
+    input_dir: str,
+    output_dir: str,
+    shard_size: str,
+    save_safetensors: Optional[bool] = False
+):
+    try:
+        os.makedirs(output_dir, exist_ok=False)
+    except Exception as e:
+        raise print("Output dir already exists", e)
+
+    save_weight(input_dir, output_dir, shard_size, save_safetensors)
+    save_config(input_dir, output_dir)
+
+
+if __name__ == "__main__":
+    fire.Fire(llamafy_internlm2)