Delete patch before release (#3)

- Delete before release (7662a4150a62440e5cd64bc7c6cf1c556e00e573)


Co-authored-by: Pedro Cuenca <pcuenq@users.noreply.huggingface.co>

patch.diff

@@ -1,291 +0,0 @@
-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
-         )
- 
-