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# Copyright (c) 2024, EleutherAI
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import torch
import yaml
import shutil
from tqdm import auto as tqdm_lib
VOCAB_SIZE = 50432
IGNORED_MODEL_STATE_KEYS = [
"optimizer",
"random_rng_state",
"np_rng_state",
"torch_rng_state",
"cuda_rng_state",
"rng_tracker_states",
]
def modify_config(input_config_path, output_config_path, output_dir):
with open(input_config_path) as f:
loaded_config = yaml.full_load(f)
# replace model/pipeline parallel
loaded_config["model_parallel_size"] = 1
loaded_config["pipe_parallel_size"] = 1
# replace load / save directories:
loaded_config["load"] = output_dir
loaded_config["save"] = output_dir
# replace some other paths
loaded_config["vocab_file"] = os.path.join(output_dir, "20B_tokenizer.json")
loaded_config["log_dir"] = "./logs"
# we need to make sure the resulting vocab size is correct
# do this by modifying the 'make_vocab_size_divisible_by' argument to be
# orig * (orig_mp / mp_out)
loaded_config["make_vocab_size_divisible_by"] = VOCAB_SIZE
# remove zero optimizer
loaded_config["zero_optimization"]["stage"] = 0
with open(output_config_path, "w") as f:
yaml.dump(loaded_config, f)
def modify_model_states(input_model_state_path, output_model_state_path):
model_state = torch.load(input_model_state_path)
for key in IGNORED_MODEL_STATE_KEYS:
del model_state[key]
model_state["mp_world_size"] = 1
model_state["dp_world_size"] = 1 # could make this configurable?
model_state["args"]["model_parallel_size"] = 1
model_state["args"]["make_vocab_size_divisible_by"] = VOCAB_SIZE
torch.save(model_state, output_model_state_path)
def merge_model_weights(input_checkpoint_path, output_checkpoint_path):
pbar = tqdm_lib.tqdm(total=47)
# Load transformer layers
for layer_i in range(44):
pbar.set_description(f"Merging layer {layer_i}")
filename_tp1 = f"layer_{layer_i + 2:02d}-model_00-model_states.pt"
filename_tp2 = f"layer_{layer_i + 2:02d}-model_01-model_states.pt"
loaded_tp1 = torch.load(os.path.join(input_checkpoint_path, filename_tp1))
loaded_tp2 = torch.load(os.path.join(input_checkpoint_path, filename_tp2))
# noinspection PyDictCreation
merged = {}
# RowParallelLinear
merged["mlp.dense_4h_to_h.weight"] = torch.cat(
[
loaded_tp1["mlp.dense_4h_to_h.weight"],
loaded_tp2["mlp.dense_4h_to_h.weight"],
],
dim=1,
)
merged["attention.dense.weight"] = torch.cat(
[
loaded_tp1["attention.dense.weight"],
loaded_tp2["attention.dense.weight"],
],
dim=1,
)
merged["mlp.dense_4h_to_h.bias"] = (
loaded_tp1["mlp.dense_4h_to_h.bias"] + loaded_tp2["mlp.dense_4h_to_h.bias"]
)
merged["attention.dense.bias"] = (
loaded_tp1["attention.dense.bias"] + loaded_tp2["attention.dense.bias"]
)
# Layer Norms
merged["input_layernorm.weight"] = (
loaded_tp1["input_layernorm.weight"] + loaded_tp2["input_layernorm.weight"]
) / 2
merged["input_layernorm.bias"] = (
loaded_tp1["input_layernorm.bias"] + loaded_tp2["input_layernorm.bias"]
) / 2
merged["post_attention_layernorm.weight"] = (
loaded_tp1["post_attention_layernorm.weight"]
+ loaded_tp2["post_attention_layernorm.weight"]
) / 2
merged["post_attention_layernorm.bias"] = (
loaded_tp1["post_attention_layernorm.bias"]
+ loaded_tp2["post_attention_layernorm.bias"]
) / 2
# ColumnParallelLinear
merged["mlp.dense_h_to_4h.weight"] = torch.cat(
[
loaded_tp1["mlp.dense_h_to_4h.weight"],
loaded_tp2["mlp.dense_h_to_4h.weight"],
],
dim=0,
)
merged["mlp.dense_h_to_4h.bias"] = torch.cat(
[
loaded_tp1["mlp.dense_h_to_4h.bias"],
loaded_tp2["mlp.dense_h_to_4h.bias"],
],
dim=0,
)
merged["attention.query_key_value.weight"] = torch.cat(
[
loaded_tp1["attention.query_key_value.weight"],
loaded_tp2["attention.query_key_value.weight"],
],
dim=0,
)
merged["attention.query_key_value.bias"] = torch.cat(
[
loaded_tp1["attention.query_key_value.bias"],
loaded_tp2["attention.query_key_value.bias"],
],
dim=0,
)
# Just take one
merged["attention.rotary_emb.inv_freq"] = loaded_tp1[
"attention.rotary_emb.inv_freq"
]
torch.save(merged, os.path.join(output_checkpoint_path, filename_tp1))
del loaded_tp1
del loaded_tp2
pbar.update(1)
# Load input embedding
pbar.set_description(f"Merging input embedding")
loaded_tp1 = torch.load(
os.path.join(input_checkpoint_path, "layer_00-model_00-model_states.pt")
)
loaded_tp2 = torch.load(
os.path.join(input_checkpoint_path, "layer_00-model_01-model_states.pt")
)
merged = {
"word_embeddings.weight": torch.cat(
[
loaded_tp1["word_embeddings.weight"],
loaded_tp2["word_embeddings.weight"],
],
dim=0,
)
}
torch.save(
merged,
os.path.join(output_checkpoint_path, "layer_00-model_00-model_states.pt"),
)
del loaded_tp1
del loaded_tp2
pbar.update(1)
# Load final layer norm
pbar.set_description(f"Merging final layer norm")
loaded_tp1 = torch.load(
os.path.join(input_checkpoint_path, "layer_47-model_00-model_states.pt")
)
loaded_tp2 = torch.load(
os.path.join(input_checkpoint_path, "layer_47-model_01-model_states.pt")
)
merged = {
"norm.weight": (loaded_tp1["norm.weight"] + loaded_tp2["norm.weight"]) / 2,
"norm.bias": (loaded_tp1["norm.bias"] + loaded_tp2["norm.bias"]) / 2,
}
torch.save(
merged,
os.path.join(output_checkpoint_path, "layer_47-model_00-model_states.pt"),
)
del loaded_tp1
del loaded_tp2
pbar.update(1)
# Load output embedding
pbar.set_description(f"Merging output embedding")
loaded_tp1 = torch.load(
os.path.join(input_checkpoint_path, "layer_48-model_00-model_states.pt")
)
loaded_tp2 = torch.load(
os.path.join(input_checkpoint_path, "layer_48-model_01-model_states.pt")
)
merged = {
"final_linear.weight": torch.cat(
[
loaded_tp1["final_linear.weight"],
loaded_tp2["final_linear.weight"],
],
dim=0,
),
}
torch.save(
merged,
os.path.join(output_checkpoint_path, "layer_48-model_00-model_states.pt"),
)
del loaded_tp1
del loaded_tp2
pbar.update(1)
pbar.set_description("Done.")
def merge(input_dir, output_dir):
input_checkpoint_path = os.path.join(input_dir, "global_step150000")
output_checkpoint_path = os.path.join(output_dir, "global_step150000")
os.makedirs(output_checkpoint_path, exist_ok=True)
os.makedirs(os.path.join(output_dir, "configs"), exist_ok=True)
for i in range(8):
modify_model_states(
input_model_state_path=os.path.join(
input_checkpoint_path, f"mp_rank_{i:02d}_model_states.pt"
),
output_model_state_path=os.path.join(
output_checkpoint_path, f"mp_rank_{i:02d}_model_states.pt"
),
)
modify_config(
input_config_path=os.path.join(input_dir, "configs", "20B.yml"),
output_config_path=os.path.join(output_dir, "configs", "20B.yml"),
output_dir=output_dir,
)
merge_model_weights(
input_checkpoint_path=input_checkpoint_path,
output_checkpoint_path=output_checkpoint_path,
)
shutil.copyfile(
os.path.join(input_dir, "20B_tokenizer.json"),
os.path.join(output_dir, "20B_tokenizer.json"),
)
with open(os.path.join(output_dir, "latest"), "w") as f:
f.write("global_step150000")
def main():
parser = argparse.ArgumentParser(description="Merge 20B checkpoint.")
parser.add_argument(
"--input_dir",
type=str,
help='Checkpoint dir, which should contain (e.g. a folder named "global_step150000")',
)
parser.add_argument(
"--output_dir", type=str, help="Output dir, to save the 1-GPU weights configs"
)
args = parser.parse_args()
merge(args.input_dir, args.output_dir)
if __name__ == "__main__":
main()
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