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https://github.com/NVIDIA/TensorRT-LLM.git
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c89653021e
* Update TensorRT-LLM --------- Co-authored-by: Eddie-Wang1120 <81598289+Eddie-Wang1120@users.noreply.github.com> Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
141 lines
5.3 KiB
Python
141 lines
5.3 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/llama/quantize.py
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"""
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import random
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from typing import Optional
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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 ..._utils import str_dtype_to_torch
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from ...logger import logger
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from ...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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cache_dir=None):
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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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cache_dir=cache_dir)
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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",
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name="3.0.0",
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split="train",
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cache_dir=cache_dir)
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dataset = dataset["article"][:calib_size]
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else:
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raise NotImplementedError
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dataset_input_ids = tokenizer(dataset,
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return_tensors="pt",
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padding=True,
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truncation=True,
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max_length=block_size).input_ids.cuda()
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calib_dataloader = DataLoader(dataset_input_ids,
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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", cache_dir=None):
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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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cache_dir=cache_dir,
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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 quantize_llama_and_export(hf_model_dir,
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export_path,
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qformat: str = 'fp8',
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dtype: Optional[str] = 'float16',
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calib_size: Optional[int] = 512,
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hf_cache_dir: Optional[str] = None,
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seed: Optional[int] = None,
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quantize_lm_head=False):
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'''
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Quantize a llama model from HF model dir and save it as export_path.
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Parameters:
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hf_model_dir: huggingface model directory
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export_path: a path to save the quantized weights and scales tensors
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qformat: quantization format, currently 'int4_awq' and 'fp8' are supported
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dtype: the datatype to run the HF/pytorch model forward during quantization
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calib_size: Number of samples for calibration.
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seed: the seed to be used in the random and np.random package during quantization
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Return: None, raises exception if the quantization failed due to any reason.
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'''
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assert qformat in ['int4_awq', 'fp8'
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], "More quantization format supported in future release"
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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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if seed is not None:
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random.seed(seed)
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np.random.seed(seed)
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tokenizer = get_tokenizer(hf_model_dir, cache_dir=hf_cache_dir)
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model = get_model(hf_model_dir, dtype, cache_dir=hf_cache_dir)
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calib_dataloader = get_calib_dataloader(tokenizer=tokenizer,
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calib_size=calib_size,
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cache_dir=hf_cache_dir)
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quant_cfg_dict = {}
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if quantize_lm_head:
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quant_cfg_dict.update({
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"*lm_head*": {
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"enable": True
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},
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})
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model = quantize_and_export(model,
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qformat=qformat,
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calib_dataloader=calib_dataloader,
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export_path=export_path,
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quant_cfg_dict=quant_cfg_dict)
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