-
Notifications
You must be signed in to change notification settings - Fork 78
Expand file tree
/
Copy pathquant_analysis.py
More file actions
484 lines (369 loc) · 14.3 KB
/
Copy pathquant_analysis.py
File metadata and controls
484 lines (369 loc) · 14.3 KB
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
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
import argparse
import functools
import gc
import os
import sys
import torch
from loguru import logger
from tqdm import tqdm
from transformers import AutoConfig, AutoModelForCausalLM
sys.path.append(os.path.join(os.path.dirname(__file__), '../'))
import matplotlib.pyplot as plt
import torch.nn as nn
from llmc.compression.quantization import FakeQuantLinear, Quantizer
from llmc.compression.quantization.module_utils import (
_LLMC_LINEAR_TYPES_, _TRANSFORMERS_LINEAR_TYPES_, RotateLinear)
from llmc.data import BaseDataset, BaseTokenizer
from llmc.models import *
from llmc.utils import check_config, mkdirs, seed_all
from llmc.utils.registry_factory import ALGO_REGISTRY, MODEL_REGISTRY
def calculate_kurtosis_channel(signal):
"""Calculates the kurtosis of a given signal.
Args:
signal (torch.Tensor): Input signal, shape (4096, 1024).
Returns:
float: The average kurtosis value of the rows.
"""
signal = signal.float()
mean = torch.mean(signal, dim=1, keepdim=True)
std = torch.std(signal, dim=1, keepdim=True)
std[std == 0] = 1e-8 # Avoid division by zero
standardized_signal = (signal - mean) / std
kurtosis = torch.mean(
standardized_signal**4, dim=1
) # Calculate kurtosis for each row
average_kurtosis = torch.mean(kurtosis)
return average_kurtosis.item()
def calculate_kurtosis(signal):
"""Calculates the kurtosis of a given signal.
Args:
signal (torch.Tensor): Input signal, shape (N, *).
Returns:
float: The kurtosis value.
"""
signal = signal.float()
signal = signal.view(1, -1)
mean = torch.mean(signal)
std = torch.std(signal)
if std == 0:
return float('inf')
standardized_signal = (signal - mean) / (std + 1e-8)
kurtosis = torch.mean(standardized_signal**4) # - 3
return kurtosis.item()
def draw(save_path, save_name, X, Y1, Y2):
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(X, Y1)
ax.plot(X, Y2)
plt.xlabel('channel')
plt.ylabel('value')
plt.title(save_name)
fig.savefig(f'{save_path}/{save_name}.jpg')
plt.close(fig)
plt.cla()
def analysis_block_cosine(res, t_res, args):
cosine_sim = nn.CosineSimilarity()
for name in res:
oups = res[name]
t_oups = t_res[name]
layer_cosine_dict = {}
for j in range(oups.shape[0]):
cos = cosine_sim(oups[j].float().view(1, -1), t_oups[j].float().view(1, -1))
if name not in layer_cosine_dict:
layer_cosine_dict[name] = []
layer_cosine_dict[name].append(cos.item())
for name in layer_cosine_dict:
cos_values = layer_cosine_dict[name]
min_cos = min(cos_values)
avg_cos = sum(cos_values) / len(cos_values)
logger.info(name)
logger.info(f'min_cos : {min_cos}')
logger.info(f'avg_cos : {avg_cos}')
def avg_k_a(a, k):
result = (a[:, None] * k[None, :]).sum(dim=0)
total_sum = result.sum()
print(result.shape)
average = total_sum / result.numel()
return average
def analysis_block_outlier(res, t_res, org_w, trans_w, arg):
if args.prof_gra in ['per_channel', 'per_group']:
kurt_func = calculate_kurtosis_channel
else:
kurt_func = calculate_kurtosis
for name in res:
logger.info(name)
weight = org_w[name]
t_weight = trans_w[name]
if args.prof_gra == 'per_group':
weight = wquanter.reshape_tensor(weight)
t_weight = wquanter.reshape_tensor(t_weight)
k_w = kurt_func(weight)
k_t_w = kurt_func(t_weight)
logger.info(f'The kurtosis of org weight is :{k_w}')
logger.info(f'The kurtosis of trans weight is :{k_t_w}')
tensor = res[name].mean(dim=0)
tensor = tensor.float()
t_tensor = t_res[name].mean(dim=0)
t_tensor = t_tensor.float()
k_a = kurt_func(tensor)
k_t_a = kurt_func(t_tensor)
logger.info(f'The kurtosis of org act is :{k_a}')
logger.info(f'The kurtosis of trans act is :{k_t_a}')
if args.draw:
save_outlier_path = os.path.join(args.save_path, 'outlier')
save_t_outlier_path = os.path.join(args.save_path, 't_outlier')
t_min_val = t_tensor.amin(dim=0).detach().cpu().numpy()
t_max_val = t_tensor.amax(dim=0).detach().cpu().numpy()
min_val = tensor.amin(dim=0).detach().cpu().numpy()
max_val = tensor.amax(dim=0).detach().cpu().numpy()
if not os.path.exists(args.save_path):
mkdirs(save_outlier_path)
mkdirs(save_t_outlier_path)
draw(
save_path=save_outlier_path,
save_name=name,
X=range(tensor.shape[-1]),
Y1=min_val,
Y2=max_val,
)
draw(
save_path=save_t_outlier_path,
save_name=name,
X=range(t_tensor.shape[-1]),
Y1=t_min_val,
Y2=t_max_val,
)
def register_hook(block, idx, args):
hooks = []
for name, m in block.named_modules():
if not args.cosine:
if isinstance(m, tuple(_LLMC_LINEAR_TYPES_ + _TRANSFORMERS_LINEAR_TYPES_)):
hooks.append(
m.register_forward_hook(
functools.partial(
stat_input_hook,
w=m.weight.data,
name=name,
idx=idx,
args=args,
)
)
)
else:
if isinstance(m, tuple(_LLMC_LINEAR_TYPES_ + _TRANSFORMERS_LINEAR_TYPES_)):
hooks.append(
m.register_forward_hook(
functools.partial(
stat_output_hook, name=name, idx=idx, args=args
)
)
)
return hooks
def stat_input_hook(m, x, y, w, name, idx, args):
if isinstance(x, tuple):
x = x[0]
layer_name = f'block_{idx}.{name}'
if args.online_rotate and t:
if 'down_proj' in layer_name:
x = down_rotater.rotate(x)
elif 'o_proj' in layer_name:
x = o_rotater.rotate(x)
if t:
t_res[layer_name] = x
trans_w[layer_name] = w
else:
res[layer_name] = x
org_w[layer_name] = w
def stat_output_hook(m, x, y, name, idx, args):
if isinstance(y, tuple):
y = y[0]
layer_name = f'block_{idx}.{name}'
if t:
t_res[layer_name] = y
else:
res[layer_name] = y
def block_forward(block, input_data, input_kwargs):
output = []
for i in range(len(input_data)):
input_data[i] = input_data[i].to(
device=next(block.parameters()).device,
dtype=next(block.parameters()).dtype,
)
if (
'attention_mask' in input_kwargs[i]
and input_kwargs[i]['attention_mask'] is not None
):
input_kwargs[i]['attention_mask'] = input_kwargs[i]['attention_mask'].cuda()
with torch.no_grad():
out = block(input_data[i], **input_kwargs[i])[0]
output.append(out)
return output
class analysis_quanter(Quantizer):
def __init__(self, bit, symmetric, granularity, **kwargs):
super().__init__(bit, symmetric, granularity, **kwargs)
def fake_quant_weight_dynamic(self, module, args={}):
weight = module.weight
if 'int_indices' in args:
if self.granularity == 'per_group':
assert len(args['int_indices']) % self.group_size == 0
q_weight = weight[:, args['int_indices']]
fp_weight = weight[:, args['fp_indices']]
elif 'dim' in args and 'ic' in args['dim']:
q_weight = weight.T
else:
q_weight = weight
if 'current_bit' in args:
org_bit = self.bit
self.bit = args['current_bit']
org_w_shape = q_weight.shape
org_w_dtype = q_weight.dtype
q_weight, scales, zeros, max_int, min_int = self.get_tensor_qparams(
q_weight, args
)
q_weight = self.quant_dequant(q_weight, scales, zeros, max_int, min_int)
q_weight = self.restore_tensor(q_weight, org_w_shape).to(org_w_dtype)
if 'current_bit' in args:
self.bit = org_bit
if 'int_indices' in args:
mix_weight = torch.zeros_like(weight)
mix_weight[:, args['int_indices']] = q_weight
mix_weight[:, args['fp_indices']] = fp_weight
return mix_weight
elif 'dim' in args and 'ic' in args['dim']:
q_weight = q_weight.T
return q_weight
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--dataset_name', type=str)
parser.add_argument('--data_path', type=str)
parser.add_argument('--n_samples', type=int, default=128)
parser.add_argument('--bs', type=int, default=-1)
parser.add_argument('--seq_len', type=int, default=512)
parser.add_argument('--seed', type=int, default=42)
parser.add_argument('--preproc', type=str, default='general')
parser.add_argument('--save_path', type=str, default='./save')
parser.add_argument('--draw', action='store_true')
parser.add_argument('--cosine', action='store_true')
parser.add_argument('--model_type', type=str, required=True)
parser.add_argument('--model_path', type=str, required=True)
parser.add_argument('--t_model_path', type=str)
parser.add_argument('--torch_dtype', type=str, default='auto')
parser.add_argument('--tokenizer_mode', type=str, default='slow')
parser.add_argument('--w_only', action='store_true')
parser.add_argument('--wbit', type=int, default=6)
parser.add_argument('--wsym', action='store_true')
parser.add_argument('--wgra', type=str, default='per_channel')
parser.add_argument('--group_size', type=int, default=-1)
parser.add_argument('--abit', type=int, default=6)
parser.add_argument('--asym', action='store_true')
parser.add_argument('--agra', type=str, default='per_token')
parser.add_argument('--log_dir', type=str, default='log.txt')
parser.add_argument('--prof_gra', type=str, default='per_tensor')
parser.add_argument('--config_path', type=str)
parser.add_argument('--online_rotate', action='store_true')
args = parser.parse_args()
seed_all(args.seed)
logger.remove()
logger.add(args.log_dir, level='INFO', mode='w')
logger.info(f'args : {args}')
calib_cfg = {
'name': args.dataset_name,
'download': False,
'path': args.data_path,
'n_samples': args.n_samples,
'bs': args.bs,
'seq_len': args.seq_len,
'preproc': args.preproc,
'seed': args.seed,
}
model_config = {
'type': args.model_type,
'path': args.model_path,
'torch_dtype': args.torch_dtype,
}
model = MODEL_REGISTRY[args.model_type](args.model_path, args.torch_dtype)
t_model = MODEL_REGISTRY[args.model_type](args.t_model_path, args.torch_dtype)
if args.online_rotate:
# import gc
import yaml
from easydict import EasyDict
with open(args.config_path, 'r') as file:
config = yaml.safe_load(file)
config = EasyDict(config)
tokenizer = BaseTokenizer(args.model_path, args.tokenizer_mode)
dataset = BaseDataset(tokenizer.get_tokenizer(), config.calib)
calib_data = dataset.get_calib_dataset()
t_model.collect_first_block_input(calib_data)
del calib_data
gc.collect()
torch.cuda.empty_cache()
blockwise_opt = ALGO_REGISTRY[config.quant.method](
t_model, config.quant, t_model.get_first_block_input(), None, config
)
blockwise_opt.run_block_loop()
t_model = blockwise_opt.model
for n, m in t_model.model.named_modules():
if isinstance(m, RotateLinear):
logger.info(m)
if 'down_proj' in n:
down_rotater = m.rotater
else:
o_rotater = m.rotater
logger.info(t_model)
logger.info(model)
tokenizer = BaseTokenizer(args.model_path, args.tokenizer_mode)
dataset = BaseDataset(tokenizer.get_tokenizer(), calib_cfg)
calib_data = dataset.get_calib_dataset()
model.collect_first_block_input(calib_data)
t_model.collect_first_block_input(calib_data)
fp_inps = model.get_first_block_input()
t_fp_inps = t_model.get_first_block_input()
res = {}
t_res = {}
org_w = {}
trans_w = {}
wquanter = analysis_quanter(
bit=args.wbit,
symmetric=args.wsym,
granularity=args.wgra,
group_size=args.group_size,
)
if not args.w_only:
aquanter = Quantizer(bit=args.abit, symmetric=args.asym, granularity=args.agra)
def a_qdq(act, module=None):
return aquanter.fake_quant_act_dynamic(act)
if args.cosine:
params_dict = {}
params_dict['w_qdq'] = wquanter.fake_quant_weight_dynamic
params_dict['a_qdq'] = None if args.w_only else a_qdq
t_model.replace_module_all(FakeQuantLinear, params_dict)
with torch.no_grad():
for i in tqdm(range(len(model.blocks))):
block = model.blocks[i]
t_block = t_model.blocks[i]
block.cuda()
t_block.cuda()
t_hooks = register_hook(t_block, i, args)
t = True
t_fp_inps['data'] = block_forward(
t_block, t_fp_inps['data'], t_fp_inps['kwargs']
)
hooks = register_hook(block, i, args)
t = False
fp_inps['data'] = block_forward(block, fp_inps['data'], fp_inps['kwargs'])
block.cpu()
t_block.cpu()
for h in hooks:
h.remove()
for t_h in t_hooks:
t_h.remove()
if args.cosine:
analysis_block_cosine(res, t_res, args)
else:
analysis_block_outlier(res, t_res, org_w, trans_w, args)
res.clear()
t_res.clear()
org_w.clear()
trans_w.clear()
gc.collect()
torch.cuda.empty_cache()