[misc] update license year & fix llama pro (#6814)

* fix llamapro script

* change year

scripts/llama_pro.py

@@ -1,4 +1,4 @@
-# Copyright 2024 Tencent Inc. and the LlamaFactory team.
+# Copyright 2025 Tencent Inc. and the LlamaFactory team.
 #
 # This code is inspired by the Tencent's LLaMA-Pro library.
 # https://github.com/TencentARC/LLaMA-Pro/blob/main/scripts/block_expansion.py
@@ -18,20 +18,15 @@
 import json
 import os
 from collections import OrderedDict
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Dict
 
 import fire
 import torch
+from huggingface_hub import split_torch_state_dict_into_shards
 from safetensors.torch import save_file
 from tqdm import tqdm
 from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, PreTrainedModel
-from transformers.modeling_utils import (
-    SAFE_WEIGHTS_INDEX_NAME,
-    SAFE_WEIGHTS_NAME,
-    WEIGHTS_INDEX_NAME,
-    WEIGHTS_NAME,
-    shard_checkpoint,
-)
+from transformers.modeling_utils import SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, WEIGHTS_INDEX_NAME, WEIGHTS_NAME
 
 
 if TYPE_CHECKING:
@@ -46,41 +41,36 @@ def block_expansion(
     model_name_or_path: str,
     output_dir: str,
     num_expand: int,
-    shard_size: str = "2GB",
+    shard_size: str = "5GB",
     save_safetensors: bool = True,
 ):
     r"""
-    Performs block expansion for LLaMA, Mistral, Qwen1.5 or Yi models.
+    Performs block expansion for LLaMA, Mistral, Qwen2 or Yi models.
     Usage: python llama_pro.py --model_name_or_path meta-llama/Llama-2-7b-hf --output_dir llama2_pro --num_expand 8
     """
-    config: "PretrainedConfig" = AutoConfig.from_pretrained(model_name_or_path)
+    config: "PretrainedConfig" = AutoConfig.from_pretrained(model_name_or_path, trust_remote_code=True)
     num_layers = getattr(config, "num_hidden_layers")
-    setattr(config, "num_hidden_layers", num_layers + num_expand)
-    config.save_pretrained(output_dir)
-
-    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
-    tokenizer.save_pretrained(output_dir)
-
-    config = AutoConfig.from_pretrained(model_name_or_path)  # load the original one
-    if save_safetensors:
-        setattr(config, "tie_word_embeddings", False)  # safetensors does not allow shared weights
-
-    model = AutoModelForCausalLM.from_pretrained(
-        model_name_or_path,
-        config=config,
-        torch_dtype="auto",
-        trust_remote_code=True,
-        low_cpu_mem_usage=True,
-    )
-    assert isinstance(model, PreTrainedModel)  # type hint
-    state_dict = model.state_dict()
-
     if num_layers % num_expand != 0:
         raise ValueError(f"`num_layers` {num_layers} should be divisible by `num_expand` {num_expand}.")
 
+    setattr(config, "num_hidden_layers", num_layers + num_expand)
+    config.save_pretrained(output_dir)
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
+    tokenizer.save_pretrained(output_dir)
+
+    print(f"Expanding model of {num_layers} layers to {num_layers + num_expand} layers.")
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name_or_path, torch_dtype="auto", device_map="cpu", trust_remote_code=True, low_cpu_mem_usage=True
+    )
+    assert isinstance(model, PreTrainedModel)  # type hint
+    if save_safetensors and getattr(model.config, "tie_word_embeddings", False):
+        del model.lm_head  # safetensors does not allow shared weights
+
     split = num_layers // num_expand
     layer_cnt = 0
-    output_state_dict = OrderedDict()
+    state_dict = model.state_dict()
+    output_state_dict: Dict[str, "torch.Tensor"] = OrderedDict()
     for i in range(num_layers):
         for key, value in state_dict.items():
             if f".{i:d}." in key:
@@ -104,17 +94,24 @@ def block_expansion(
             output_state_dict[key] = value
 
     weights_name = SAFE_WEIGHTS_NAME if save_safetensors else WEIGHTS_NAME
-    shards, index = shard_checkpoint(output_state_dict, max_shard_size=shard_size, weights_name=weights_name)
-
-    for shard_file, shard in tqdm(shards.items(), desc="Save weights"):
+    filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
+    state_dict_split = split_torch_state_dict_into_shards(
+        output_state_dict, filename_pattern=filename_pattern, max_shard_size=shard_size
+    )
+    for shard_file, tensors in tqdm(state_dict_split.filename_to_tensors.items(), desc="Save weights"):
+        shard = {tensor: output_state_dict[tensor].contiguous() for tensor in tensors}
         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:
+    if not state_dict_split.is_sharded:
         print(f"Model weights saved in {os.path.join(output_dir, weights_name)}.")
     else:
+        index = {
+            "metadata": state_dict_split.metadata,
+            "weight_map": state_dict_split.tensor_to_filename,
+        }
         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)