mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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139 lines
4.9 KiB
Python
139 lines
4.9 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 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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"""
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Adapted from examples/quantization/hf_ptq.py
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"""
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import argparse
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import random
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import numpy as np
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import torch
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from datasets import load_dataset
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from torch.utils.data import DataLoader
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from tensorrt_llm._utils import str_dtype_to_torch
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from tensorrt_llm.logger import logger
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from tensorrt_llm.models.quantized.ammo import quantize_and_export
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def get_calib_dataloader(data="cnn_dailymail",
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tokenizer=None,
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batch_size=1,
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calib_size=512,
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block_size=512):
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print("Loading calibration dataset")
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if data == "pileval":
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dataset = load_dataset(
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"json",
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data_files="https://the-eye.eu/public/AI/pile/val.jsonl.zst",
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split="train")
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dataset = dataset["text"][:calib_size]
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elif data == "cnn_dailymail":
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dataset = load_dataset("cnn_dailymail", name="3.0.0", split="train")
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dataset = dataset["article"][:calib_size]
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else:
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raise NotImplementedError
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batch_encoded = tokenizer.batch_encode_plus(dataset,
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return_tensors="pt",
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padding=True,
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max_length=block_size)
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batch_encoded = batch_encoded["input_ids"]
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batch_encoded = batch_encoded.cuda()
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calib_dataloader = DataLoader(batch_encoded,
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batch_size=batch_size,
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shuffle=False)
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return calib_dataloader
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def get_tokenizer(ckpt_path, **kwargs):
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logger.info(f"Loading tokenizer from {ckpt_path}")
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tokenizer = AutoTokenizer.from_pretrained(ckpt_path,
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padding_side="left",
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**kwargs)
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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return tokenizer
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def get_model(ckpt_path, dtype="float16"):
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logger.info(f"Loading model from {ckpt_path}")
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torch_dtype = str_dtype_to_torch(dtype)
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model = AutoModelForCausalLM.from_pretrained(
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ckpt_path,
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device_map="auto",
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trust_remote_code=True,
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torch_dtype=torch_dtype,
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)
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model.eval()
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model = model.to(memory_format=torch.channels_last)
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return model
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def get_args():
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--model_dir",
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type=str,
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required=True,
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help="Directory of a HF model checkpoint")
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parser.add_argument("--dtype", help="Model data type.", default="float16")
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parser.add_argument("--qformat",
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type=str,
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choices=['fp8', 'int8_sq'],
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default='fp8',
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help='Quantization format.')
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parser.add_argument("--calib_size",
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type=int,
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default=512,
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help="Number of samples for calibration.")
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parser.add_argument("--export_path", default="exported_model")
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parser.add_argument("--tp_size",
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type=int,
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default=1,
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help='Tensor parallel size.')
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parser.add_argument('--seed', type=int, default=None, help='Random seed')
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args = parser.parse_args()
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return args
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def main():
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if not torch.cuda.is_available():
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raise EnvironmentError("GPU is required for inference.")
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args = get_args()
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if args.seed is not None:
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random.seed(args.seed)
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np.random.seed(args.seed)
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tokenizer = get_tokenizer(args.model_dir)
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model = get_model(args.model_dir, args.dtype)
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calib_dataloader = get_calib_dataloader(tokenizer=tokenizer,
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calib_size=args.calib_size)
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model = quantize_and_export(model,
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qformat=args.qformat,
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calib_dataloader=calib_dataloader,
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export_path=args.export_path,
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tensor_parallel_size=args.tp_size)
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if __name__ == "__main__":
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main()
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