File size: 6,033 Bytes
fcbe832 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 | # Original copyright by Jason Phang
# https://github.com/zphang
# Taken here
# https://github.com/huggingface/transformers/pull/21955/commits/8978f28e6c44b083c0b190d3931902c2904c940a#diff-110a445233a8b15a0875998eeaf75cb8607b38a5daa736291dd058766879bbdd
import argparse
import json
import os
import shutil
import torch
"""
Sample usage:
```
python merge_weights.py --input_dir D:\Downloads\LLaMA --model_size 13B
```
"""
INTERMEDIATE_SIZE_MAP = {
"7B": 11008,
"13B": 13824,
"30B": 17920,
"65B": 22016,
}
NUM_SHARDS = {
"7B": 1,
"13B": 2,
"30B": 4,
"65B": 8,
}
def read_json(path):
with open(path, "r") as f:
return json.loads(f.read())
def write_model(input_base_path, model_size):
assert model_size in INTERMEDIATE_SIZE_MAP
params = read_json(os.path.join(input_base_path, "params.json"))
num_shards = NUM_SHARDS[model_size]
n_layers = params["n_layers"]
n_heads = params["n_heads"]
n_heads_per_shard = n_heads // num_shards
dim = params["dim"]
dims_per_head = dim // n_heads
# Load weights
if model_size == "7B":
loaded = torch.load(os.path.join(input_base_path, "consolidated.00.pth"), map_location="cuda")
else:
loaded = [
torch.load(os.path.join(input_base_path, f"consolidated.{i:02d}.pth"), map_location="cuda")
for i in range(num_shards)
]
state_dict = {}
for layer_i in range(n_layers):
if model_size == "7B":
state_dict |= {
f"layers.{layer_i}.attention.wq.weight": loaded[
f"layers.{layer_i}.attention.wq.weight"
],
f"layers.{layer_i}.attention.wk.weight": loaded[
f"layers.{layer_i}.attention.wk.weight"
],
f"layers.{layer_i}.attention.wv.weight": loaded[
f"layers.{layer_i}.attention.wv.weight"
],
f"layers.{layer_i}.attention.wo.weight": loaded[
f"layers.{layer_i}.attention.wo.weight"
],
f"layers.{layer_i}.feed_forward.w1.weight": loaded[
f"layers.{layer_i}.feed_forward.w1.weight"
],
f"layers.{layer_i}.feed_forward.w2.weight": loaded[
f"layers.{layer_i}.feed_forward.w2.weight"
],
f"layers.{layer_i}.feed_forward.w3.weight": loaded[
f"layers.{layer_i}.feed_forward.w3.weight"
],
f"layers.{layer_i}.attention_norm.weight": loaded[
f"layers.{layer_i}.attention_norm.weight"
],
f"layers.{layer_i}.ffn_norm.weight": loaded[f"layers.{layer_i}.ffn_norm.weight"],
}
else:
state_dict |= {
f"layers.{layer_i}.attention_norm.weight": loaded[0][
f"layers.{layer_i}.attention_norm.weight"
],
f"layers.{layer_i}.ffn_norm.weight": loaded[0][f"layers.{layer_i}.ffn_norm.weight"],
}
state_dict[f"layers.{layer_i}.attention.wq.weight"] = torch.cat(
[
loaded[i][f"layers.{layer_i}.attention.wq.weight"].view(n_heads_per_shard, dims_per_head, dim)
for i in range(num_shards)
],
dim=0,
).reshape(dim, dim)
state_dict[f"layers.{layer_i}.attention.wk.weight"] = torch.cat(
[
loaded[i][f"layers.{layer_i}.attention.wk.weight"].view(n_heads_per_shard, dims_per_head, dim)
for i in range(num_shards)
],
dim=0,
).reshape(dim, dim)
state_dict[f"layers.{layer_i}.attention.wv.weight"] = torch.cat(
[
loaded[i][f"layers.{layer_i}.attention.wv.weight"].view(n_heads_per_shard, dims_per_head, dim)
for i in range(num_shards)
],
dim=0,
).reshape(dim, dim)
state_dict[f"layers.{layer_i}.attention.wo.weight"] = torch.cat(
[loaded[i][f"layers.{layer_i}.attention.wo.weight"] for i in range(num_shards)], dim=1
)
state_dict[f"layers.{layer_i}.feed_forward.w1.weight"] = torch.cat(
[loaded[i][f"layers.{layer_i}.feed_forward.w1.weight"] for i in range(num_shards)], dim=0
)
state_dict[f"layers.{layer_i}.feed_forward.w2.weight"] = torch.cat(
[loaded[i][f"layers.{layer_i}.feed_forward.w2.weight"] for i in range(num_shards)], dim=1
)
state_dict[f"layers.{layer_i}.feed_forward.w3.weight"] = torch.cat(
[loaded[i][f"layers.{layer_i}.feed_forward.w3.weight"] for i in range(num_shards)], dim=0
)
if model_size == "7B":
state_dict |= {
"tok_embeddings.weight": loaded["tok_embeddings.weight"],
"norm.weight": loaded["norm.weight"],
"output.weight": loaded["output.weight"],
}
else:
state_dict |= {
"norm.weight": loaded[0]["norm.weight"],
"tok_embeddings.weight": torch.cat(
[loaded[i]["tok_embeddings.weight"] for i in range(num_shards)], dim=1
),
"output.weight": torch.cat([loaded[i]["output.weight"] for i in range(num_shards)], dim=0),
}
torch.save(state_dict, 'merged.pth')
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--input_dir",
help="Location of LLaMA weights, which contains tokenizer.model and model folders",
)
parser.add_argument(
"--model_size",
choices=["7B", "13B", "30B", "65B"],
)
args = parser.parse_args()
write_model(
input_base_path=os.path.join(args.input_dir, args.model_size),
model_size=args.model_size,
)
if __name__ == "__main__":
main()
|