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nemo_ckpt_convert.py
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nemo_ckpt_convert.py
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#! /usr/bin/env python3
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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 datetime
import logging
import multiprocessing
import sys
import tempfile
from collections import defaultdict
from pathlib import Path
import numpy as np
import torch
from tqdm import tqdm
from utils.convert import (cpu_map_location, gpu_map_location,
split_and_save_weight)
from utils.nemo import (UnpackedNemoCheckpointDir, copy_tokenizer_files,
extract_layers_with_prefix,
get_eos_bos_ids_from_tokenizer_config,
nemo_config_to_ini_config, unpack_nemo_ckpt,
update_tokenizer_paths)
from tensorrt_llm._utils import str_dtype_to_torch, torch_to_numpy
LOGGER = logging.getLogger(__name__)
def rename_key(old_key: str, pp_rank: int, num_layers: int, pp_size: int):
new_key = old_key
if "layers." in old_key:
split_key = old_key.split(".")
split_key[1] = str(int(split_key[1]) + pp_rank * num_layers // pp_size)
new_key = ".".join(split_key)
if "self_attention" in new_key:
new_key = new_key.replace("self_attention", "attention")
return new_key
@torch.no_grad()
def convert_checkpoint(unpacked_checkpoints_dir: UnpackedNemoCheckpointDir,
args):
nemo_model_config = unpacked_checkpoints_dir.model_config
checkpoints_paths = unpacked_checkpoints_dir.get_checkpoints_paths(
nemo_model_config.get("tensor_model_parallel_size", 1),
nemo_model_config.get("pipeline_model_parallel_size", 1),
)
# if checkpoints files could be found - start preparing output dir
out_dir = create_out_dir(args)
map_location_fn = gpu_map_location if args.load_checkpoints_on_gpu else cpu_map_location
storage_type = str_dtype_to_torch(args.storage_type)
# load position_embedding from rank 0
model_00 = torch.load(checkpoints_paths[0][0], map_location=map_location_fn)
model_00 = model_00.get("state_dict", model_00)
has_position_embedding = "model.language_model.embedding.position_embeddings.weight" in model_00
has_lm_head = "model.language_model.output_layer.weight" in model_00
num_layers = nemo_model_config["num_layers"]
training_tp_size = nemo_model_config.get("tensor_model_parallel_size", 1)
training_pp_size = nemo_model_config.get("pipeline_model_parallel_size", 1)
inference_tp_size = args.tensor_parallelism
export_config = {
"apply_layernorm_1p":
nemo_model_config.get('normalization', '') == "layernorm1p",
"tp_size":
training_tp_size,
"split_gated_activation":
"swiglu" in nemo_model_config.get('activation', "gelu"),
"num_attention_heads":
nemo_model_config["num_attention_heads"],
"use_attention_nemo_shape":
True,
"transpose_weights":
True,
}
# merge_factor: how many TP training nodes are merged into an inference TP node
# split_factor: in how many parts a TP training node is split
gcd = np.gcd(training_tp_size, inference_tp_size)
merge_factor = training_tp_size // gcd
split_factor = inference_tp_size // gcd
model_level_weights = defaultdict(list)
def handle_model_level_weights(model, tp_idx: int, pp_idx: int):
if tp_idx == 0 and pp_idx == 0:
if has_position_embedding:
val = model[
"model.language_model.embedding.position_embeddings.weight"]
# not weight, do not need to transpose
val = torch_to_numpy(val.to(storage_type).cpu())
val.tofile(out_dir / "model.wpe.bin")
model_level_weights["model.wpe.bin"].append(val)
if pp_idx == 0:
val = model.get(
"state_dict",
model)["model.language_model.embedding.word_embeddings.weight"]
val = torch_to_numpy(val.to(storage_type).cpu())
model_level_weights["model.wte.bin"].append(val)
if has_lm_head and pp_idx == training_pp_size - 1:
val = model.get("state_dict",
model)["model.language_model.output_layer.weight"]
val = torch_to_numpy(val.to(storage_type).cpu())
model_level_weights["model.lm_head.weight.bin"].append(val)
for tp_rank in range(training_tp_size // merge_factor):
for pp_rank in range(training_pp_size):
models = []
for k in range(merge_factor):
rank_weights = checkpoints_paths[tp_rank * merge_factor +
k][pp_rank]
model = torch.load(rank_weights, map_location=map_location_fn)
handle_model_level_weights(model, tp_rank * merge_factor + k,
pp_rank)
layers = extract_layers_with_prefix(
model, "model.language_model.encoder.")
models.append(layers)
starmap_args = []
for key in models[0].keys():
starmap_args.append((
tp_rank,
out_dir,
split_factor,
rename_key(key, pp_rank, num_layers, training_pp_size),
[model[key] for model in models],
storage_type,
None,
export_config,
))
starmap_args = tqdm(starmap_args, desc="saving weights")
if args.processes > 1:
with multiprocessing.Pool(args.processes) as pool:
pool.starmap(split_and_save_weight, starmap_args)
else:
# simpler for debug situations
for starmap_arg in starmap_args:
split_and_save_weight(*starmap_arg)
for key, values in model_level_weights.items():
model_level_weights[key] = np.concatenate(values, axis=0)
model_level_weights[key].tofile(out_dir / key)
vocab_size = model_level_weights["model.wte.bin"].shape[0]
tokenizer_config = update_tokenizer_paths(
nemo_model_config["tokenizer"],
unpacked_checkpoints_dir.get_all_tokenizer_file_paths())
copy_tokenizer_files(tokenizer_config, out_dir)
ini_config = nemo_config_to_ini_config(
nemo_model_config,
*get_eos_bos_ids_from_tokenizer_config(tokenizer_config), vocab_size,
args.storage_type)
config_path = out_dir / "config.ini"
with config_path.open("w") as config_file:
ini_config.write(config_file)
def create_out_dir(args):
out_dir = Path(args.out_dir) / f"{args.tensor_parallelism}-gpu/"
if not out_dir.exists():
out_dir.mkdir(parents=True)
return out_dir
def main():
torch.multiprocessing.set_start_method("spawn")
torch.multiprocessing.set_sharing_strategy("file_system")
parser = argparse.ArgumentParser(
formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('--out-dir',
'-o',
type=Path,
help='path to output directory',
required=True)
parser.add_argument('--in-file',
'-i',
type=Path,
help='path to input checkpoint file',
required=True)
parser.add_argument('--tensor-parallelism',
'-tp',
type=int,
help='Requested tensor parallelism for inference',
default=1)
parser.add_argument(
"--processes",
"-p",
type=int,
help=
"How many processes to spawn for conversion (default: 4). Set it to a lower value to reduce RAM usage.",
default=4)
parser.add_argument("--storage-type",
"-t",
type=str,
default="float32",
choices=["float32", "float16", "bfloat16"])
parser.add_argument("--load-checkpoints-on-gpu",
action="store_true",
help="Whether to load model weights to GPU")
parser.add_argument("--verbose",
action="store_true",
help="Provide verbose messages")
args = parser.parse_args()
log_format = "%(asctime)s %(name)s [%(levelname)s] %(message)s"
logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO,
format=log_format)
print("\n=============== Argument ===============")
for key in vars(args):
print(f"{key}: {vars(args)[key]}")
print("========================================")
if not args.in_file.exists():
LOGGER.error("%s does not exists", args.in_file)
sys.exit(1)
with tempfile.TemporaryDirectory() as temp_dir:
temp_dir = Path(temp_dir)
# unpack if needed
if args.in_file.is_dir():
nemo_dir = args.in_file
else:
start_time = datetime.datetime.now()
checkpoint_dir_path = temp_dir / "unpacked"
nemo_dir = unpack_nemo_ckpt(args.in_file, checkpoint_dir_path)
LOGGER.info("Spent %s (h:m:s) to unpack NeMo archive",
datetime.datetime.now() - start_time)
unpacked_checkpoint_dir = UnpackedNemoCheckpointDir(
nemo_dir, load_checkpoints_to_cpu=not args.load_checkpoints_on_gpu)
start_time = datetime.datetime.now()
convert_checkpoint(unpacked_checkpoint_dir, args)
LOGGER.info("Spent %s (h:m:s) to convert the model",
datetime.datetime.now() - start_time)
if __name__ == "__main__":
main()