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patch.diff

@@ -0,0 +1,291 @@
+diff --git a/src/transformers/models/llama/convert_llama_weights_to_hf.py b/src/transformers/models/llama/convert_llama_weights_to_hf.py
+index a0fbe4680..8b0ce2b13 100644
+--- a/src/transformers/models/llama/convert_llama_weights_to_hf.py
++++ b/src/transformers/models/llama/convert_llama_weights_to_hf.py
+@@ -17,10 +17,10 @@ import json
+ import os
+ import shutil
+ import warnings
+-
++from typing import List
+ import torch
+ 
+-from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer, PreTrainedTokenizerFast
++from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer, PreTrainedTokenizerFast, GenerationConfig
+ from transformers.convert_slow_tokenizer import TikTokenConverter
+ 
+ 
+@@ -85,8 +85,12 @@ NUM_SHARDS = {
+     "65B": 8,
+     "70B": 8,
+     "70Bf": 8,
++    "405B": 8,
++    "405B-MP16": 16,
+ }
+ 
++CONTEXT_LENGTH_FOR_VERSION = {"3.1": 131072, "3": 8192, "2": 4096, "1": 2048}
++
+ 
+ def compute_intermediate_size(n, ffn_dim_multiplier=1, multiple_of=256):
+     return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) + multiple_of - 1) // multiple_of)
+@@ -107,9 +111,10 @@ def write_model(
+     input_base_path,
+     model_size=None,
+     safe_serialization=True,
+-    llama_version=1,
++    llama_version="1",
+     vocab_size=None,
+     num_shards=None,
++    instruct=False,
+ ):
+     os.makedirs(model_path, exist_ok=True)
+     tmp_model_path = os.path.join(model_path, "tmp")
+@@ -125,18 +130,11 @@ def write_model(
+     dims_per_head = dim // n_heads
+     base = params.get("rope_theta", 10000.0)
+     inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
+-    if base > 10000.0 and llama_version != 3:
++    if base > 10000.0 and float(llama_version) < 3:
+         max_position_embeddings = 16384
+     else:
+-        # Depending on the Llama version, the default max_position_embeddings has different values.
+-        if llama_version == 1:
+-            max_position_embeddings = 2048
+-        elif llama_version == 2:
+-            max_position_embeddings = 4096
+-        elif llama_version == 3:
+-            max_position_embeddings = 8192
+-
+-    vocab_size = vocab_size if vocab_size is not None else 32000
++        max_position_embeddings = CONTEXT_LENGTH_FOR_VERSION[llama_version]
++
+     if params.get("n_kv_heads", None) is not None:
+         num_key_value_heads = params["n_kv_heads"]  # for GQA / MQA
+         num_key_value_heads_per_shard = num_key_value_heads // num_shards
+@@ -144,8 +142,7 @@ def write_model(
+     else:  # compatibility with other checkpoints
+         num_key_value_heads = n_heads
+         num_key_value_heads_per_shard = n_heads_per_shard
+-        key_value_dim = dims_per_head * num_key_value_heads
+-    print(num_shards, num_key_value_heads, num_key_value_heads_per_shard, key_value_dim)
++        key_value_dim = dim
+ 
+     # permute for sliced rotary
+     def permute(w, n_heads, dim1=dim, dim2=dim):
+@@ -159,11 +156,9 @@ def write_model(
+         loaded = torch.load(os.path.join(input_base_path, "consolidated.00.pth"), map_location="cpu")
+     else:
+         # Sharded
+-        loaded = [
+-            torch.load(os.path.join(input_base_path, file), map_location="cpu")
+-            for file in os.listdir(input_base_path)
+-            if file.endswith(".pth")
+-        ]
++        checkpoint_list = sorted([file for file in os.listdir(input_base_path) if file.endswith(".pth")])
++        print("Loading in order:", checkpoint_list)
++        loaded = [torch.load(os.path.join(input_base_path, file), map_location="cpu") for file in checkpoint_list]
+     param_count = 0
+     index_dict = {"weight_map": {}}
+     for layer_i in range(n_layers):
+@@ -263,7 +258,7 @@ def write_model(
+             "lm_head.weight": loaded["output.weight"],
+         }
+     else:
+-        concat_dim = 0 if llama_version == 3 else 1
++        concat_dim = 0 if llama_version in ['3', '3.1'] else 1
+         state_dict = {
+             "model.norm.weight": loaded[0]["norm.weight"],
+             "model.embed_tokens.weight": torch.cat(
+@@ -282,6 +277,18 @@ def write_model(
+     write_json(index_dict, os.path.join(tmp_model_path, "pytorch_model.bin.index.json"))
+     ffn_dim_multiplier = params["ffn_dim_multiplier"] if "ffn_dim_multiplier" in params else 1
+     multiple_of = params["multiple_of"] if "multiple_of" in params else 256
++
++    if llama_version in ['3', '3.1']:
++        bos_token_id = 128000
++
++        if instruct:
++            eos_token_id = [128001, 128008, 128009]
++        else:
++            eos_token_id = 128001
++    else:
++        bos_token_id = 1
++        eos_token_id = 2
++
+     config = LlamaConfig(
+         hidden_size=dim,
+         intermediate_size=compute_intermediate_size(dim, ffn_dim_multiplier, multiple_of),
+@@ -292,11 +299,21 @@ def write_model(
+         vocab_size=vocab_size,
+         rope_theta=base,
+         max_position_embeddings=max_position_embeddings,
+-        bos_token_id=128000 if llama_version == 3 else 1,
+-        eos_token_id=128001 if llama_version == 3 else 2,
++        bos_token_id=bos_token_id,
++        eos_token_id=eos_token_id,
+     )
+     config.save_pretrained(tmp_model_path)
+ 
++    if instruct:
++        generation_config = GenerationConfig(
++            do_sample=True,
++            temperature=0.6,
++            top_p=0.9,
++            bos_token_id=bos_token_id,
++            eos_token_id=eos_token_id,
++        )
++        generation_config.save_pretrained(tmp_model_path)
++
+     # Make space so we can load the model properly now.
+     del state_dict
+     del loaded
+@@ -313,7 +330,7 @@ def write_model(
+ 
+ 
+ class Llama3Converter(TikTokenConverter):
+-    def __init__(self, vocab_file, num_reserved_special_tokens=256, **kwargs):
++    def __init__(self, vocab_file, special_tokens=None, instruct=False, model_max_length=None, **kwargs):
+         super().__init__(vocab_file, **kwargs)
+         tokenizer = self.converted()
+         chat_template = (
+@@ -327,34 +344,27 @@ class Llama3Converter(TikTokenConverter):
+             "{% endfor %}"
+             "{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}"
+         )
+-        num_reserved_special_tokens = 256
+-        special_tokens = [
+-            "<|begin_of_text|>",
+-            "<|end_of_text|>",
+-            "<|reserved_special_token_0|>",
+-            "<|reserved_special_token_1|>",
+-            "<|reserved_special_token_2|>",
+-            "<|reserved_special_token_3|>",
+-            "<|start_header_id|>",
+-            "<|end_header_id|>",
+-            "<|reserved_special_token_4|>",
+-            "<|eot_id|>",  # end of turn
+-        ] + [f"<|reserved_special_token_{i}|>" for i in range(5, num_reserved_special_tokens - 5)]
+         tokenizer.add_special_tokens(special_tokens)
+ 
+         self.tokenizer = PreTrainedTokenizerFast(
+             tokenizer_object=tokenizer,
+             bos_token="<|begin_of_text|>",
+-            eos_token="<|end_of_text|>",
+-            chat_template=chat_template,
++            eos_token="<|end_of_text|>" if not instruct else "<|eot_id|>",
++            chat_template=chat_template if instruct else None,
+             model_input_names=["input_ids", "attention_mask"],
++            model_max_length=model_max_length,
+         )
+ 
+ 
+-def write_tokenizer(tokenizer_path, input_tokenizer_path, llama_version=2):
++def write_tokenizer(tokenizer_path, input_tokenizer_path, llama_version="2", special_tokens=None, instruct=False):
+     tokenizer_class = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
+-    if llama_version == 3:
+-        tokenizer = Llama3Converter(input_tokenizer_path).tokenizer
++    if llama_version in ["3", "3.1"]:
++        tokenizer = Llama3Converter(
++            input_tokenizer_path,
++            special_tokens,
++            instruct,
++            model_max_length=CONTEXT_LENGTH_FOR_VERSION[llama_version]
++        ).tokenizer
+     else:
+         tokenizer = tokenizer_class(input_tokenizer_path)
+     print(f"Saving a {tokenizer_class.__name__} to {tokenizer_path}.")
+@@ -362,6 +372,37 @@ def write_tokenizer(tokenizer_path, input_tokenizer_path, llama_version=2):
+     return tokenizer
+ 
+ 
++DEFAULT_LLAMA_SPECIAL_TOKENS = {
++    "3": [
++        "<|begin_of_text|>",
++        "<|end_of_text|>",
++        "<|reserved_special_token_0|>",
++        "<|reserved_special_token_1|>",
++        "<|reserved_special_token_2|>",
++        "<|reserved_special_token_3|>",
++        "<|start_header_id|>",
++        "<|end_header_id|>",
++        "<|reserved_special_token_4|>",
++        "<|eot_id|>",  # end of turn
++    ]
++    + [f"<|reserved_special_token_{i}|>" for i in range(5, 256 - 5)],
++    "3.1": [
++        "<|begin_of_text|>",
++        "<|end_of_text|>",
++        "<|reserved_special_token_0|>",
++        "<|reserved_special_token_1|>",
++        "<|finetune_right_pad_id|>",
++        "<|reserved_special_token_2|>",
++        "<|start_header_id|>",
++        "<|end_header_id|>",
++        "<|eom_id|>",  # end of message
++        "<|eot_id|>",  # end of turn
++        "<|python_tag|>",
++    ]
++    + [f"<|reserved_special_token_{i}|>" for i in range(3, 256 - 8)],
++}
++
++
+ def main():
+     parser = argparse.ArgumentParser()
+     parser.add_argument(
+@@ -383,9 +424,9 @@ def main():
+     # Different Llama versions used different default values for max_position_embeddings, hence the need to be able to specify which version is being used.
+     parser.add_argument(
+         "--llama_version",
+-        choices=[1, 2, 3],
+-        default=1,
+-        type=int,
++        choices=["1", "2", "3", "3.1"],
++        default="1",
++        type=str,
+         help="Version of the Llama model to convert. Currently supports Llama1 and Llama2. Controls the context size",
+     )
+     parser.add_argument(
+@@ -394,11 +435,34 @@ def main():
+         type=int,
+         help="The number of individual shards used for the model. Does not have to be the same as the number of consolidated_xx.pth",
+     )
++    parser.add_argument(
++        "--special_tokens",
++        default=None,
++        type=List[str],
++        help="The list of special tokens that should be added to the model.",
++    )
++    parser.add_argument(
++        "--instruct",
++        default=False,
++        type=bool,
++        help="Whether the model is an instruct model or not. Will affect special tokens for llama 3.1.",
++    )
+     args = parser.parse_args()
+     if args.model_size is None and args.num_shards is None:
+         raise ValueError("You have to set at least `num_shards` if you are not giving the `model_size`")
++    if args.special_tokens is None:
++        args.special_tokens = DEFAULT_LLAMA_SPECIAL_TOKENS[str(args.llama_version)]
++
+     spm_path = os.path.join(args.input_dir, "tokenizer.model")
+-    vocab_size = len(write_tokenizer(args.output_dir, spm_path, llama_version=args.llama_version))
++    vocab_size = len(
++        write_tokenizer(
++            args.output_dir,
++            spm_path,
++            llama_version=args.llama_version,
++            special_tokens=args.special_tokens,
++            instruct=args.instruct
++        )
++    )
+     if args.model_size != "tokenizer_only":
+         write_model(
+             model_path=args.output_dir,
+@@ -408,6 +472,7 @@ def main():
+             llama_version=args.llama_version,
+             vocab_size=vocab_size,
+             num_shards=args.num_shards,
++            instruct=args.instruct
+         )
+ 
+