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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
)
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