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import os
import re
from pathlib import Path
from typing import Optional
from collections import OrderedDict
import torch
from tqdm.auto import tqdm
from transformers import LlamaForCausalLM
scale2emb = {
'7B': 4096,
'13B': 5120,
'30B': 6656,
'34B': 8192,
'65B': 8192,
'70B': 8192,
}
key_to_dim = {
"w1": 0,
"w2": -1,
"w3": 0,
"wo": -1,
"wq": 0,
"wk": 0,
"wv": 0,
"output": 0,
"tok_embeddings": -1,
"ffn_norm": None,
"attention_norm": None,
"norm": None,
"rope": None,
}
def init_merged_ckpt(pth_00, num_pth=8, emb_dim=8192):
merged_ckpt = OrderedDict()
for parameter_name, parameter in pth_00.items():
short_name = parameter_name.split(".")[-2]
if key_to_dim[short_name] is None:
merged_ckpt[parameter_name] = parameter
del parameter
elif key_to_dim[short_name] == 0:
size = parameter.shape[0]
merged_param_shape = [ parameter.shape[0] * num_pth, parameter.shape[1] ]
merged_ckpt[parameter_name] = torch.zeros(merged_param_shape)
merged_ckpt[parameter_name][0 : size, :] = parameter
del parameter
elif key_to_dim[short_name] == -1:
size = parameter.shape[-1]
merged_param_shape = [ parameter.shape[0], parameter.shape[1] * num_pth]
merged_ckpt[parameter_name] = torch.zeros(merged_param_shape)
merged_ckpt[parameter_name][:, 0 : size] = parameter
del parameter
return merged_ckpt
def merge_meta_llama(size: int, root_dir: Path):
paths = sorted(path for path in root_dir.iterdir()
if re.match(r"^consolidated\.[0-9]+\.pth$", path.name))
if len(paths) == 1: # no sharded checkpoints, return everything
return torch.load(paths[0], map_location=torch.device("cpu"))
num_pth = len(paths)
for i, ckpt_path in enumerate(tqdm(paths, desc="Merging llama")):
llama_config = torch.load(ckpt_path, map_location=torch.device('cpu'))
if i == 0:
merged_ckpt = init_merged_ckpt(llama_config, num_pth=num_pth,
emb_dim=scale2emb[f"{size}B"])
else:
for parameter_name, parameter in llama_config.items():
short_name = parameter_name.split(".")[-2]
if key_to_dim[short_name] == 0:
size = parameter.shape[0]
merged_param_shape = [ parameter.shape[0] * num_pth, parameter.shape[1] ]
merged_ckpt[parameter_name][size * i : size * (i + 1), :] = parameter
del parameter
if key_to_dim[short_name] == -1:
size = parameter.shape[-1]
merged_param_shape = [ parameter.shape[0], parameter.shape[1] * num_pth]
merged_ckpt[parameter_name][:, size * i : size * (i + 1)] = parameter
del parameter
del llama_config
return merged_ckpt
def merge_hf_llama(size: int, version: int, cache_dir: Optional[Path] = None,
model_path: Optional[str] = None):
if model_path is None and version == 1:
model_path = f"decapoda-research/llama-{size}b-hf"
elif model_path is None and version == 2:
model_path = f"meta-llama/Llama-2-{size}b-hf"
weights = LlamaForCausalLM.from_pretrained(model_path, cache_dir=cache_dir).state_dict()
weights["tok_embeddings.weight"] = weights.pop("model.embed_tokens.weight")
weights["norm.weight"] = weights.pop("model.norm.weight")
weights["output.weight"] = weights.pop("lm_head.weight")
for key in list(weights.keys()):
if rmatch := re.match(r"^model\.(layers\.[0-9]+\.)(.+)(\.weight)$", key):
new_key = {
"self_attn.q_proj": "attention.wq",
"self_attn.k_proj": "attention.wk",
"self_attn.v_proj": "attention.wv",
"self_attn.o_proj": "attention.wo",
"mlp.gate_proj": "feed_forward.w1",
"mlp.down_proj": "feed_forward.w2",
"mlp.up_proj": "feed_forward.w3",
"input_layernorm": "attention_norm",
"post_attention_layernorm": "ffn_norm"
}[rmatch.group(2)]
weights[rmatch.group(1) + new_key + rmatch.group(3)] = weights.pop(key)
return weights
def merge_llama(size: int, version: int, root_dir: Optional[Path] = None,
model_path: Optional[str] = None):
if root_dir is not None and (root_dir/"consolidated.00.pth").exists():
return merge_meta_llama(size, root_dir), "meta"
print(f"Weights at {root_dir} do not look like a meta checkpoint, assuming "
"huggingface cache_dir instead")
return merge_hf_llama(size, version, root_dir, model_path), "hf"