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https://github.com/NVIDIA/TensorRT-LLM.git
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deaae40bd7
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
129 lines
4.7 KiB
Python
129 lines
4.7 KiB
Python
#! /usr/bin/env python3
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# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import datetime
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import logging
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import tempfile
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from pathlib import Path
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import torch
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import yaml
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from utils.convert import cpu_map_location
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from utils.nemo import unpack_nemo_ckpt
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from tensorrt_llm._utils import to_json_file, torch_to_numpy
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log_format = "%(asctime)s %(name)s [%(levelname)s] %(message)s"
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logging.basicConfig(format=log_format)
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LOGGER = logging.getLogger(__name__)
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def get_lora_keys(layer_id):
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in_key = f'model.language_model.encoder.layers.{layer_id}.self_attention.adapter_layer.lora_kqv_adapter.linear_in.weight'
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out_key = f'model.language_model.encoder.layers.{layer_id}.self_attention.adapter_layer.lora_kqv_adapter.linear_out.weight'
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return in_key, out_key
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def save_val(val, dir, key, tp_num=None):
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suffix = "bin" if tp_num is None else f"{tp_num}.bin"
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val.tofile(dir / f"model.{key}.{suffix}")
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def lora_convert(out_dir, lora_config, lora_weights, customization_id):
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saved_dir = Path(out_dir)
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saved_dir.mkdir(parents=True, exist_ok=True)
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num_layers = int(lora_config["num_layers"])
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config = {"lora_config": {"lora_kqv_adapter": {}}}
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for layer_id in range(num_layers):
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in_key, out_key = get_lora_keys(layer_id)
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config["lora_config"]["lora_kqv_adapter"]["0"] = {
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"key": f"{customization_id}",
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"low_rank": f"{lora_weights[in_key].shape[0]}",
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}
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linear_in_weight = lora_weights[in_key]
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linear_out_weight = lora_weights[out_key]
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save_val(torch_to_numpy(linear_in_weight.transpose(1, 0).contiguous()),
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saved_dir,
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in_key.replace("lora_kqv_adapter", f"lora_kqv_adapter.{0}"))
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save_val(torch_to_numpy(linear_out_weight.transpose(1, 0).contiguous()),
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saved_dir,
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out_key.replace("lora_kqv_adapter", f"lora_kqv_adapter.{0}"))
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to_json_file(config, saved_dir / "lora_weights.json")
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def main(args):
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start_time = datetime.datetime.now()
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with tempfile.TemporaryDirectory() as prompt_out_dir:
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prompt_out_dir = Path(prompt_out_dir)
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unpack_nemo_ckpt(args.in_file, prompt_out_dir)
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LOGGER.info("Spent %s (h:m:s) to unpack NeMo prompt archive",
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datetime.datetime.now() - start_time)
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model_weights_ckpt = "model_weights.ckpt"
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with open(prompt_out_dir / "model_config.yaml") as f:
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prompt_config = yaml.full_load(f)
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LOGGER.debug(prompt_config)
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start_time = datetime.datetime.now()
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weight_path = prompt_out_dir / model_weights_ckpt
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prompt_weights = torch.load(
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weight_path,
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map_location=cpu_map_location,
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)
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lora_convert(args.out_dir, prompt_config, prompt_weights,
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args.customization_id)
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LOGGER.info("Spent %s (h:m:s) to convert the prompt model",
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datetime.datetime.now() - start_time)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'--out-dir',
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'-o',
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type=Path,
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help='path to output embedding table file in the .npy format',
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required=True)
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parser.add_argument('--in-file',
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'-i',
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type=Path,
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help='path to input prompt-tuning checkpoint file',
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required=True)
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parser.add_argument("--storage-type",
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"-t",
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type=str,
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default="fp32",
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choices=["fp32", "fp16", "bf16"])
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parser.add_argument("--verbose",
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action="store_true",
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help="Provide verbose messages")
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parser.add_argument("--customization-id", type=str, default="lora")
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args = parser.parse_args()
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LOGGER.setLevel(logging.DEBUG if args.verbose else logging.INFO)
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print("\n=============== Argument ===============")
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for key in vars(args):
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print(f"{key}: {vars(args)[key]}")
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print("========================================")
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main(args)
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