File size: 8,199 Bytes
64f6172 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 | import argparse
import copy
import hashlib
import importlib.metadata
import json
import random
import shutil
import time
from pathlib import Path
import modelopt.torch.quantization as mtq
import pyarrow.parquet as parquet
import torch
from huggingface_hub import HfApi, hf_hub_download, save_torch_state_dict
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
from packed_int4 import PackedLinear, pack_weight
parser = argparse.ArgumentParser()
parser.add_argument('--method', choices=['awq', 'gptq'], required=True)
parser.add_argument('--source', required=True)
parser.add_argument('--output', required=True)
parser.add_argument('--samples', type=int, default=128)
args = parser.parse_args()
source, output = Path(args.source), Path(args.output)
output.mkdir(parents=True, exist_ok=False)
torch.set_num_threads(4)
torch.manual_seed(20261020)
tokenizer = AutoTokenizer.from_pretrained(source, trust_remote_code=True, local_files_only=True)
tokenizer.chat_template = (Path(__file__).parent / 'chat_template.jinja').read_text()
dataset = 'Salesforce/wikitext'
revision = HfApi(token=False).dataset_info(dataset).sha
data_path = hf_hub_download(dataset, 'wikitext-2-raw-v1/train-00000-of-00001.parquet',
repo_type='dataset', revision=revision)
text = '\n\n'.join(parquet.read_table(data_path, columns=['text'])['text'].to_pylist())
ids = tokenizer(text, return_tensors='pt', add_special_tokens=False).input_ids
rng = random.Random(20261020)
starts = [rng.randrange(ids.shape[1] - 512) for _ in range(args.samples)]
batches = [ids[:, start:start + 512].clone() for start in starts]
calibration = {'dataset': dataset, 'revision': revision, 'split': 'train', 'samples': args.samples,
'sequence_length': 512, 'seed': 20261020, 'token_offsets': starts,
'token_ids_sha256': hashlib.sha256(torch.cat(batches).numpy().tobytes()).hexdigest()}
(output / 'calibration.json').write_text(json.dumps(calibration, indent=2) + '\n')
config = AutoConfig.from_pretrained(source, trust_remote_code=True, local_files_only=True)
config.backend = 'torch'
config.prefill_chunk_size = 512
model = AutoModelForCausalLM.from_pretrained(
source, config=config, trust_remote_code=True, local_files_only=True,
dtype=torch.bfloat16, device_map={'': 0}).eval().requires_grad_(False)
model.generation_config.eos_token_id = [248046, 248044]
model.generation_config.pad_token_id = 248044
modules = {name: module for name, module in model.named_modules()
if isinstance(module, torch.nn.Linear) and name != 'lm_head'
and min(module.weight.shape) >= 128 and '.admission_' not in name}
assert len(modules) == 256
recipe = copy.deepcopy(mtq.INT4_AWQ_CFG)
for name, module in model.named_modules():
if isinstance(module, torch.nn.Linear) and name not in modules:
recipe['quant_cfg'].append({'quantizer_name': name + '.*', 'enable': False})
if args.method == 'gptq':
recipe['algorithm'] = {'method': 'gptq', 'block_size': 128, 'perc_damp': 0.01}
def forward_loop(current):
for index, batch in enumerate(batches):
current(input_ids=batch.to('cuda'), use_cache=False, logits_to_keep=1)
if index % 32 == 0:
print(json.dumps({'stage': 'calibration', 'batch': index, 'samples': args.samples}), flush=True)
started = time.perf_counter()
print(json.dumps({'stage': 'quantizing', 'method': args.method, 'modules': len(modules)}), flush=True)
with torch.inference_mode():
mtq.quantize(model, recipe, forward_loop=forward_loop)
manifest = {'method': args.method, 'bits': 4, 'group_size': 128,
'activation_dtype': 'bfloat16', 'scale_dtype': 'float32',
'zero_point': 8, 'awq_input_scaling': args.method == 'awq', 'modules': {}}
state = {}
maximum_weight_error = 0.0
maximum_kernel_relative_error = 0.0
for index, (name, module) in enumerate(modules.items()):
weight = module.weight_quantizer(module.weight).to(torch.bfloat16)
rows, columns = weight.shape
scale = (module.weight_quantizer.amax.float().reshape(rows, columns // 128) / 7).clamp_min(1e-12)
codes = (weight.float().reshape(rows, columns // 128, 128) / scale[..., None]).round().clamp(-8, 7).reshape(rows, columns).to(torch.int32) + 8
input_scale = getattr(module.input_quantizer, '_pre_quant_scale', None)
layer = PackedLinear(columns, rows, args.method == 'awq', input_scale is not None, module.bias is not None).to('cuda')
layer.qweight = pack_weight(codes, args.method == 'awq')
layer.scales = scale.t().contiguous()
layer.qzeros.fill_(-2004318072 if args.method == 'awq' else 2004318071)
if layer.g_idx is not None:
layer.g_idx = torch.arange(columns, device='cuda', dtype=torch.int32) // 128
if input_scale is not None:
layer.input_scale = input_scale.to(torch.bfloat16).reshape(-1).contiguous()
if module.bias is not None:
layer.bias = module.bias.detach()
dense = layer.dequantize()
error = (dense.float() - weight.float()).abs().max().item()
maximum_weight_error = max(maximum_weight_error, error)
inputs = torch.randn((3, columns), device='cuda', dtype=torch.bfloat16)
expected_inputs = inputs if layer.input_scale is None else inputs * layer.input_scale
expected = torch.nn.functional.linear(expected_inputs, dense, layer.bias)
actual = layer(inputs)
relative = (expected.float() - actual.float()).abs().max().item() / max(expected.float().abs().max().item(), 1e-12)
assert relative < 0.025, (name, relative)
maximum_kernel_relative_error = max(maximum_kernel_relative_error, relative)
manifest['modules'][name] = {'shape': [rows, columns], 'input_scale': input_scale is not None, 'bias': module.bias is not None}
state.update({name + '.' + field: value.detach().cpu().contiguous() for field, value in layer.state_dict().items()})
if index % 32 == 0:
print(json.dumps({'stage': 'packing', 'module': index, 'weight_error': error, 'kernel_relative_error': relative}), flush=True)
for name, tensor in model.state_dict().items():
if name == 'lm_head.weight' or 'quantizer' in name or name.removesuffix('.weight') in modules:
continue
state[name] = tensor.detach().cpu().contiguous()
save_torch_state_dict(state, output, max_shard_size='3GB', safe_serialization=True)
model.config.quantization_config = {
'quant_method': args.method, 'bits': 4, 'group_size': 128,
**({'version': 'gemm', 'zero_point': True} if args.method == 'awq'
else {'checkpoint_format': 'gptq', 'desc_act': False, 'sym': True, 'damp_percent': 0.01}),
'edgerock_runtime': 'packed_int4.load_int4',
}
model.config.save_pretrained(output)
model.generation_config.save_pretrained(output)
tokenizer.save_pretrained(output)
for name in ('configuration_edgerock.py', 'modeling_edgerock.py', 'LICENSE', 'LICENSE-CODE', 'NOTICE'):
shutil.copy2(source / name, output / name)
for name in ('packed_int4.py', 'quantize.py', 'validate.py', 'chat_template.jinja'):
shutil.copy2(Path(__file__).parent / name, output / name)
(output / 'edgerock_quantization.json').write_text(json.dumps(manifest, indent=2) + '\n')
versions = {name: importlib.metadata.version(name) for name in
('torch', 'transformers', 'nvidia-modelopt', 'triton', 'accelerate', 'safetensors', 'huggingface-hub')}
conversion = {'method': args.method, 'recipe': recipe, 'quantized_modules': len(modules),
'maximum_weight_repacking_error': maximum_weight_error,
'maximum_kernel_relative_error': maximum_kernel_relative_error,
'weight_bytes': sum(path.stat().st_size for path in output.glob('model*.safetensors')),
'seconds': time.perf_counter() - started, 'versions': versions,
'hardware': torch.cuda.get_device_name(0)}
(output / 'conversion.json').write_text(json.dumps(conversion, indent=2) + '\n')
print(json.dumps({'stage': 'conversion_complete', 'weight_bytes': conversion['weight_bytes'],
'seconds': conversion['seconds']}), flush=True)
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