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parser.add_argument('dataset', type=str, help='Quantisation dataset')
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parser.add_argument('--num_samples', type=int, default=128, help='Number of dataset samples')
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parser.add_argument('--trust_remote_code', action="store_true", help='Trust remote code')
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parser.add_argument('--cache_examples', type=int, default=1, help='Cache examples on GPU')
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parser.add_argument('--use_fast', action="store_true", help='Use fast tokenizer')
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parser.add_argument('--use_triton', action="store_true", help='Use Triton for quantization')
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parser.add_argument('--bits', type=int, nargs='+', default=[4], help='Quantize bit(s)')
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parser.add_argument('--group_size', type=int, nargs='+', default=[128], help='Quantize group size(s)')
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parser.add_argument('--damp', type=float, nargs='+', default=[0.01], help='Quantize damp_percent(s)')
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parser.add_argument('--desc_act', type=int, nargs='+', default=[0], help='Quantize desc_act(s) - 1 = True, 0 = False')
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parser.add_argument('--dtype', type=str, choices=['float16', 'float32', 'bfloat16'], default='float16', help='Unquantised model dtype')
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parser.add_argument('--seqlen', type=int, default=2048, help='Model sequence length')
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parser.add_argument('--batch_size', type=int, default=1, help='Quantize batch size for processing dataset samples')
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parser.add_argument('--stop_file', type=str, help='Filename to look for to stop inference, specific to this instance')
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parser.add_argument('--make_folders', action="store_true", help='Make folders for each quantization using params in folder name')
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args = parser.parse_args()
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quantizer = QuantAutoGPTQ(args.pretrained_model_dir,
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args.output_dir_base,
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args.dataset,
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num_samples=args.num_samples,
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trust_remote_code=args.trust_remote_code,
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cache_examples=args.cache_examples,
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use_fast=args.use_fast,
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use_triton=args.use_triton,
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bits=args.bits,
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group_size=args.group_size,
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desc_act=args.desc_act,
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damp=args.damp,
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dtype=args.dtype,
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seqlen=args.seqlen,
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batch_size=args.batch_size,
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stop_file=args.stop_file,
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make_folder=args.make_folders)
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quantizer.run_quantization()
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# <FILESEP>
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import argparse
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import copy
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import json
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import os
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import sys
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import uuid
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from collections import OrderedDict
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from os.path import abspath, dirname
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from types import SimpleNamespace
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import torch
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import torch.nn as nn
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from tqdm import tqdm
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from logger import logger, setup_logger
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from model import CCF, HYM, F
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from utils import KHotCrossEntropyLoss, checkpoint, eval_classification, get_data, init_random, plot, smooth_one_hot, set_seed
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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def get_model_and_buffer(args, sample_q):
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if args.pxycontrast > 0 or args.pxcontrast > 0:
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f = HYM(args)
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else:
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model_cls = F if args.uncond else CCF
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f = model_cls(args)
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if not args.uncond:
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assert args.buffer_size % args.n_classes == 0, "Buffer size must be divisible by args.n_classes"
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if args.load_path is None:
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replay_buffer = init_random(args.buffer_size)
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else:
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print(f"loading model from {args.load_path}")
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ckpt_dict = torch.load(args.load_path)
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f.load_state_dict(ckpt_dict["model_state_dict"])
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replay_buffer = ckpt_dict["replay_buffer"]
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f = f.to(device)
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return f, replay_buffer
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def get_sample_q(args):
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def sample_p_0(replay_buffer, bs, y=None):
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if len(replay_buffer) == 0:
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return init_random(bs), []
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buffer_size = len(replay_buffer) if y is None else len(replay_buffer) // args.n_classes
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inds = torch.randint(0, buffer_size, (bs,))
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# if cond, convert inds to class conditional inds
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if y is not None:
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inds = y.cpu() * buffer_size + inds
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assert not args.uncond, "Can't drawn conditional samples without giving me y"
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buffer_samples = replay_buffer[inds]
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random_samples = init_random(bs)
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choose_random = (torch.rand(bs) < args.reinit_freq).float()[:, None, None, None]
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samples = choose_random * random_samples + (1 - choose_random) * buffer_samples
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return samples.to(device), inds
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def sample_q(f, replay_buffer, y=None, n_steps=args.n_steps, contrast=False):
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"""this func takes in replay_buffer now so we have the option to sample from
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scratch (i.e. replay_buffer==[]). See test_wrn_ebm.py for example.
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"""
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f.eval()
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# get batch size
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