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# 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.
'''
Script that prunes TRT-LLM checkpoints.
'''
import argparse
import json
import os
from pathlib import Path
from typing import Dict
import safetensors
import torch
from safetensors.torch import save_file
from tensorrt_llm.logger import logger
from tensorrt_llm.models import MODEL_MAP, PretrainedConfig
SUPPORTED_MODELS = list(MODEL_MAP.keys())
PRUNABLE_WEIGHTS = [
'attention.qkv.weight',
'attention.proj.weight',
'mlp.fc.weight',
'mlp.proj.weight',
'mlp.gate.weight',
]
def can_prune(key: str) -> bool:
for w in PRUNABLE_WEIGHTS:
if w in key:
return True
return False
def load_config(config_path: Path) -> Dict[str, any]:
if not config_path.exists():
return {}
with open(str(config_path), 'r') as f:
return json.load(f)
def prune_and_save(ckpt_dir: str, out_dir: str, prune_all: bool):
logger.info(f'Checkpoint Dir: {ckpt_dir}, Out Dir: {out_dir}')
model_config = PretrainedConfig.from_json_file(
os.path.join(ckpt_dir, 'config.json'))
architecture = model_config.architecture
if architecture not in MODEL_MAP:
raise RuntimeError(f'Unsupported model architecture: {architecture}')
if not os.path.exists(out_dir):
os.makedirs(out_dir)
for rank in range(model_config.mapping.world_size):
pruned_weights = {}
with safetensors.safe_open(os.path.join(ckpt_dir,
f'rank{rank}.safetensors'),
framework='pt',
device='cpu') as f:
for key in f.keys():
tensor = f.get_tensor(key)
if prune_all or can_prune(key):
pruned_weights[key] = torch.tensor([], dtype=tensor.dtype)
else:
pruned_weights[key] = tensor
save_file(pruned_weights,
os.path.join(out_dir, f'rank{rank}.safetensors'))
config_path = Path(ckpt_dir, 'config.json')
with open(str(Path(out_dir, 'config.json')), 'w') as f:
config = load_config(config_path)
config['is_pruned'] = True
json.dump(config, f)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint_dir', type=str, default=None)
parser.add_argument('--prune_all',
default=False,
action='store_true',
help='Remove all weights in the checkpoint')
parser.add_argument(
'--out_dir',
type=str,
default=None,
help=
'Path to write pruned checkpoint. Defaults to the same directory append with `.pruned`'
)
args = parser.parse_args()
if args.checkpoint_dir is None:
raise RuntimeError(
"No `--checkpoint_dir` supplied to checkpoint pruner.")
if args.out_dir is None:
ckpt_path = Path(args.checkpoint_dir)
ckpt_name = ckpt_path.name
args.out_dir = str(
Path(args.checkpoint_dir).with_name(ckpt_name + '.pruned'))
prune_and_save(os.path.abspath(args.checkpoint_dir),
os.path.abspath(args.out_dir), args.prune_all)
if __name__ == '__main__':
main()
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