File size: 31,037 Bytes
be8b183
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
from torch.nn import Linear, Embedding
from torch.nn.parameter import Parameter
import torch.nn.functional as F

import os
import bz2
import torch
import base64
import ctypes
import sys
from transformers.utils import logging

from typing import List
from functools import partial

logger = logging.get_logger(__name__)

try:
    from cpm_kernels.kernels.base import LazyKernelCModule, KernelFunction, round_up


    class Kernel:
        def __init__(self, code: bytes, function_names: List[str]):
            self.code = code
            self._function_names = function_names
            self._cmodule = LazyKernelCModule(self.code)

            for name in self._function_names:
                setattr(self, name, KernelFunction(self._cmodule, name))


    quantization_code = "$QlpoOTFBWSZTWU9yuJUAQHN//////////f/n/8/n///n//bt4dTidcVx8X3V9FV/92/v4B7/AD5FBQFAAAChSgKpFCFAFVSigUAAAEKhSgUUqgFBKigqVREQAABQBQIANDTTIGI00BkZBkNGE0A0BkBkGQGRkaNAaAGQNBoGgDIAAYIGTI0DQAQAaGmmQMRpoDIyDIaMJoBoDIDIMgMjI0aA0AMgaDQNAGQAAwQMmRoGgAgA0NNMgYjTQGRkGQ0YTQDQGQGQZAZGRo0BoAZA0GgaAMgABggZMjQNABABoaaZAxGmgMjIMhowmgGgMgMgyAyMjRoDQAyBoNA0AZAADBAyZGgaAAmqU1NEgJqnptU/Sn4jRR6J6epk2pqb1Q/SgAPUGgyNNGjQ2SBpoAZAAGg0NB6mgDIAAAAA2oaApSREBNAARhGiYEaEwU8pvImlP0k2aam1GaGqbFNM1MHpTwmkepmyU9R6nqPKekHqNNPUxNGhp6n6p6QaZ6o9TG1GMqcoV9ly6nRanHlq6zPNbnGZNi6HSug+2nPiZ13XcnFYZW+45W11CumhzYhchOJ2GLLV1OBjBjGf4TptOddTSOcVxhqYZMYwZXZZY00zI1paX5X9J+b+f4e+x43RXSxXPOdquiGpduatGyXneN696M9t4HU2eR5XX/kPhP261NTx3JO1Ow7LyuDmeo9a7d351T1ZxnvnrvYnrXv/hXxPCeuYx2XsNmO003eg9J3Z6U7b23meJ4ri01OdzTk9BNO96brz+qT5nuvvH3ds/G+m/JcG/F2XYuhXlvO+jP7U3XgrzPN/lr8Sf1n6j4j7jZs+s/T0tNaNNYzTs12rxjwztHlnire3Nzc3N1wuBwOBwXBvZfoHpD7rFmR99V5vj3aXza3xdBbXMalubTg/jIv5dfAi54Pdc75j4z412n3Npj3Ld/ENm7a3b/Cod6h/ret1/5vn/C+l+gdslMvgPSLJ8d8q+U66fevYn/tW1chleEtNTGlcHCbLRlq0tHzF5tsbbZZfHjjLgZu42XCuC3NrdjTasZGNzgxPIrGqp7r3p7L2p5XjnpPSmTd5XtzqnB6U87zzg1Ol0zd0zsLszxR6lkxp35u6/teL0L0W922cR7Lu1lpL9CsHirzuM2T+BgsyViT6LHcm0/Vr6U/7LGGyJeqTEjt0PHWhF5mCT7R9mtlDwriYv0Tyr/OxYt6qp5r0mPVT0608TqnqMZaarU2nFwrTzzlrs1ed7z1ux60wyr4ydCaTi3enW8x68x0zU7tXSlcmPSW1mGpWJMg4zmPC2lK96tp0OE80y4MfEvnZj8zGluR6b22ki1Ou9V2nCd9xovcPvcYMZYy0lvN60ScZ45vN6yeCeeXFb1lVjnnCar5fwXwE2bzJ4HI1XVPXfXZMm44GUsMpYsmLB65TuVdm0cl0b+i/wGNN66XjeV7zuPpHcnK/juhhjdfId5jMdE5nN0dGmmm2zZs2cexD5n9p/dY352XsvXHaZNWWsmmS1atjR452nYudzvqv2HMRyvNNnlMcDl3R2+yx2uVrBubTW9icHDVtbNXlZm7jma1rM4VurZZd2y6nUau7ZXZ7bVU+mnoOVxZGMrVmvX60605JwmzGZhhhjTWtaaaMaaGTGmNMZasY0iX8VMUl8eepaIrzGSpemWOQyZORk2bNpjUybMmxqYmknCGCFynutfksaZpjTNMaaatM0xsxcGR0sociNqxNSmhhR1ZJPbsn8qyF0t2qH6iYBclclalbtTTcHTDsPaX6rlnElph2Jyumumtynv2Kk8GI7rsvXbIcJgHJOSaSXnnGaI3m87RtVXJOZ/YtgdTE6Wpha6ZlE8ayXkef1fh602r2WwvfMXtMdLlkfnLFdYYwYso+bWqm7yJqHXZGw2nrS5ZanSYnWlxBxMF1V940K2wdrI7R6OYf7DGGamMmTSbRhlS45xmVOumF1EyPCmHrrN8wwZOOrdNtLeMtzFzDlWnfTBxMk2NaXIZHBYxYLD4w8yju0ao65Vz1OIXoS9dLanwCe1PWrYuWMqf1if1z2k2yYfKJ741PDgno1ZQ8DRqvUny3mNoWTzGO6m1DkrJI8JiR5cSd+vZdGOO8nrMoc5+NDUFsMSXaZJeNlMmGLtJsovOsUp7I9S5VojKxF6bTVEelXqlfJobQr3LozSh2Jk7VcrVMfhXqszGWMzNqGhqZY0OadxkyyMssKugZR0KNFXBHlqwmJgTE/BNVMk6ItJXZMR0H47GpXv/DMOvNkmVuaV1PRfEdxuqc7Hcd+ZV/zTLaRxWk0nl9CdCeM6mn5rstHIBcpiuwmUZXeq81DacHI2rmrZ5SuE5mOZd6LQrZg9mx32TprA8BMo5jKN6yLTCi3WzQaZSuhzTtM1fUTGVpG8Tw+KXI0tjEpiWxtLYynOlktSbVlaI5kxP8TDH8kx50xoxi5KcA4pcja8KWLRlO/Ks6q06ergnvm1ca3Tq8Uw7LTUsmWyctXPWmpitl/uvGcWTGXGuAXDfhqazGmjkxcJW5hMMMMpYsXl2TZYtVOddG3XCarUt6Ptq9CZXSNzyuRzqRZOjsxdBbFVz6OA5HI43r1jityVlVpVkxmOsyaYWE1NTGq1sOVh36mHMcxtSvcy70edG0ZGR3I1Go1GRlV7mWWo1G0ZGRqlvH40l7o4m5xMWLLLYyNjnqc8556mdPqLJ31n/1nWOncxzG1tizrHs/Z+d2vP/B/l8wdJ6rHUn2nbbDq4p6htFtYzMMMTaZis1K5GKzGNmxhmUx2DDlZ/qNnIx41xnaMfCZWYaZWtNLTNW8ND4Fw1MyZOCdM428suKG1ehW8TesOydg7J+YYcD4cYR+8dFK6M4E3HM9ZfRNNL+Sn6rsl4DsrDl2HpPCnfxjGXtbZtYys1ttlyJ4T+BvexjGWRjMszK4Jpc77D3GyuVD7q0+G8m9G+2+rGm7cOR2y7FdtY2XUYx/oNlfRYxhMYyYZkyyg55enna9Kt/FFi6GMMwYwdwxWgxGMLKYmUyGExTKMZkMFhkymKuh0NOBNnBu+23LdwDoZYYzGGMxtORaTU1pjTGWTTGGtMrNWUsyyTTLLG1qy2ZjbK2DBllWqxMtBMaYZQmcE7zvvRcTkclUwdkxTaSdyySt/7fpL+T1v516Ji97fwr5JbLu305zMn5+GMTTZ9F+y7ExwmGVfG44yxn3dLv6l5i+Wth1jCrDq21nW9LqvvDzz3Vf3LLH/O/32TJ/erx3bXftO4eF+G956D952K/An4NfvOpjFjExjevP/UmE0fIoZXx6/w6lX/no3D0bLt+ixjieBM6ksRd0yB4Lt2SwYNE+gd1detlZWUnpiZfGfFaK+4PyCa/v18V8X75pe9fLXzp7l3VjF76vWZmHwGz1IZNWT7b8yddJ4q5kyrVdfru6atWc7bVYztL9Jf4GXvT+Y8m9/YsXP6H018a8D4XVOqvfzqeR+6yZOD8dPv0+U7/q5Pl+2dNb0MjzGVH5p6MNQ7cOWvw62U9aHE8DprDek+McLyvDz+te+9Zhq5+YTruufMcWMabqysTmZVWjKPfnK0wyVcrsuhjZRdLkHNvD72b9abriOSGIxiLixMOoalNPXzy+wT/tf+U6HHONfsz+xe8ufHBdQWWGWLA9if0rsnmrxK5LvRZQeWsTCsrmOYy8VteVfuRfcVTtDLItLIsMYxZLdU/DbtSemxF6Z6Zo5WBXE4tFdCyVMMXMTEMZXVlS6Xec2T4e0tHsRcEuWshcJ2YsNF5rUx1E8ifCq6Z+ZP7qdCeu/aTwFd53l16/o0NOw6O3dLavP4Hbi4RdmuDk6DoYaninC0+o4uZjbJ7Rxeu0/FbuFg+q7DVS6fQe0rZ6NDGUNNU6DEqOaLTicKnYZMnBWruljQxoaS3dZhocDge0bSTyOvdAbG5hxe2xji7E/L55xX13wWNDi6HCekcFxfCPGxY0MXC+s7afWaMdDyjyr+o8Rudm/NabOZvdl274zH4f5XK9z6On1Pe/K5TdPAslg77BjuO6Y3eO7GqvOPG/stknp1leyvLL0Z7bl9I4noMvLkzytLhWYzrOZzLXCORe028rORzOg4N/L0HlMOQ3Pgmnbb6KczlabORpu980q37TBqRu0/p3PO6234Bl03Ynuz+9W7gnsEcmvYaYY3aMYY0wx3pYd+ujsXauWdaY5Xkbtl23fPzFHiDB/QMo0yFjBllYxTQYYyxkrwn7JufwJ/PfgJ+C83X69ni6zvXcnyXabv0ncbLwsceS+RNlyN2mnneJtX0ngYO0+e+0+UnA+Wch3ji8hj5an4h+i6XBySU4n+R0roVcbw5yvHrmr4Yw8Y7x6c+9POPYHI5HI5HI5HI5HGXGww4nE4nrVyOR8XeqPEO7PLOiukYa3Novk5hV4cdtYZLI93e+uxff2jRo0aNGjRo0aNG1bVtW1dy3m83m8+tQ5ZzHw3nObwOu8La9Rc1dtkdS8A3eTk823tnktXWlxN6Oixe06zrN70Isd9jiOgZFq9yfkPqP/SLhN2Myl8jDM43bl1nbcb4cO57jlh8Jow6pzXZdL4dyODTuuhu77FyO27DdwdRxmvO+O+3N2+BdqyTwLHVczDVY4UPE4O66/ZO2cx1LFzVdSXtF7G4HMbrauOHRw6c8FdZ5m9fHZHYZXfTlZquyynSyTTKke6vcffSD9pzPA/G7n7jxPmuhc1DHMynPMrGL6AdewYmwu5ko+UUyTwrMv27rPH1v1nGqd87+p6N6LU8k3NEng53xXyHS97+44OSg/sy/hn+Se6yfYNjW0/uTgP+PvWYzLMmjhcLB/gGpri6H83/84eUXWT6T9Hsv7785z/7z4icpW+zfXypuR7rx/gMdZb1/wC678pcs8/2a3mDitGHxl9mfPlll5MafWWqxk/eYuTDgcNMzDGWLWvsuglNxs53GtN6uWpktlW1tZZYcuinMMWmnNnJydze3b2Y1McBxrBkXw799izLMZZYyy0TkbsGM4p03S2uVu5s/XXUdSdec6smVxZYYGpVmT8A+8ajuEyV5FatkvVru2x6uxGXXbH4A+jvgP4GMYy3iPLXzq/6z65+E005ey+cwMZD3fZcqc6xpjTFjQ0P3U+e++cPYmTIwj0nrK5NPTfl3WvpfLtXDcb2HQMudYOxFXQBor4L4T6vrOauFctYXJQ++NUWmJe5bmx1jDiZS1dTqWxo4GR8jm3fttpmPHppk9PEyv4/y8/sO07XacOmcqc0x2Vi9BvNJvN5oW8x4mOsydpidRxMYJPx06m1bqPzq9KtK8sxXNXFodD/+MYYaJTLwOhc9brCsV18oOR1i4tXChyTkq4lf4y1Ke+9axjDHqs1mfBbMXuP4Hzi+X7t8vzv7bHerrUPgPCxhjre4fXdfLNtNM+Jd+Zdh8xd8wP87uNPoPgv4W7/5P2BuxfsMabNnMnza+54Pdi5U671GPZY8CehX8Voeoo7FHpkeEc6715FwHZrIrUrHaviPUbPZHND+IhczrP6FcYvhOZ0Di/ETt0OI+YwNWR9r7tpf6WDeZKZDB1+z2IthOl1mPyb5FluvEx9h9d0NnM0Y1XPFkWIsk1WotJ0PBMmkvjvQTd0e71tfeV+8r8lQ/tpzpsmxJ+InrI/dj2UajUajVTUajatRqNRtGo1Go1Go4wjeMpZFMVV9CHbofPraLsJ3JpWV2XOoanCuFky4y3PPNxucK2uKC1Lbdb1eo+m5XomN6HfeZsabHLHRX/K+offtNGGmHWctcVcG44MdSqsOLY9VzX+Zxfxn2HPdWTpzWvkrtJ8M5zorrKcquRytJ5N5DZmcaW02l76nWO+BqPXm1A2Ry/0q71dH/mqrqeFjkYxjEXtsX8qubTk67rGycyqsdm4tZx5D6D5hhi0waaWmiaMP81Yjii5qxPlPuU/GfTL1Y5E6Jyfiq63qTa39A4J0sOGDgO9WF9bOXl0XfPRbsY2bPNKPy1YrFYrFYmRhhlTIyMjJWJYZHXuCXI8OoXsvfljGLFicNifpp2XunoPiG1wtx3p1Tah+/DD66OnVtVXP9rKbVxOnL0tR/rHtqB5UDErUVcl11D4qqvjpOcxX7armUNJB3LpW6bxVvD08e8h3odKKvyCFZBdSh2FVcST9xV3n3T8t1j7Kr9qgrqXg+13Pt5U7JCvFXVIV1YG5lRhkVYZJYYDDD4KOIMoHCp26WS8GB7uBh2zIdgq/PKyInjV2STShuoapUdCpX1yTwqq/z1VvET7Kh5nVPkO8YyxjLt2MaaMmWTLQvx3qnzltnXW0p2jxgbEtSny/Osv8Y9pLMXYoHVPAhkVdWVeODhR6q9/Sxe2liwwZWMVvFXfRkeIDxAePUPIrdJ4ey6yquzH+PD/bUOWAu05qVHtFd8rrKHSoeNIOUqrYr3FXyToqfYJgwmJdKpXXOwYYegNNGMzfZPp/t3t/DVs4zjNTN61rRqaWaa4NYbRjTa0tWwy2Y2tGN8ZO8ofNKq4j9SL7I+cSm4/6ovLV5HNXLI0jJidwrtk6ynCaP6Z++GjRlWS3tLeW129Mi9evxU9mtz6s5J3Z7M2ngTgnKvmpomxpaLCzPfmx0JWE+m3NLDDGOX47RctdYYNK5jakdqLkRlI39n590T5zctGSwwZZDJj6kW8XSi6ot2MmWWJ0DUT3nuvebBudScjZ79g8cWJ8av0k+/bE5WKd5MdbFpbDVMxu1DVMmtNZGJvq1mtRbn6M+g/kP0FwDwr7quZs7xosNGpbscyxhhd9TyJyFwbLcxlTasg75vW7TsV5K7ji44XPMMrdoj+Y3rT0Hie62nlYV/pwczzOmdLqLhYkzGMzCZWGMQzGMSsZYY6Di1t4nlJ+Em63mJxrVLxPbYxNEdgc1dU2iOKyoYYWjNrEeHTYybVk0atSa7ehuwsWMWTqn1TrnS6hYsi71d1+s+k+ic70e20fzE/VaTdxT9ZtU4GIXdeNx3X77guYYfpHeTQjaMX6brOu4OY4K7Y2d9mbHarI5ox3p4GpJ2Vd/Tst60f7j999pppjR+Q/Qf8J/VaORs3cji7FfFuN61+ui9s8hix1OCh5KGVV23BPXvZfz3CLyHpix+exi8z/KnCnosY2eunor+cxyPO/xJ0vKey9OvE9VjqaYu0x3Z3jd6o2b1T12D+F8l232lwaaacD5LE8LBxu7WTlbWraWpew8Xexjel3E+wWD4APITdNqR8F3R3T0lunCQ4GaE9R37DxeCYfcHi4xci5ovKfxVs55y2hf+65E/Xdp6jR5nrebTmi5incpkyOjs50JvrZwstbbW6kfuuQw+2mykf/EXNFzxfKTrxew929TR6bWnGL//F3JFOFCQT3K4lQ"

    kernels = Kernel(
        bz2.decompress(base64.b64decode(quantization_code)),
        [
            "int4WeightCompression",
            "int4WeightExtractionFloat",
            "int4WeightExtractionHalf",
            "int8WeightExtractionFloat",
            "int8WeightExtractionHalf",
        ],
    )
except Exception as exception:
    kernels = None
    logger.warning("Failed to load cpm_kernels:", exception)


class W8A16Linear(torch.autograd.Function):
    @staticmethod
    def forward(ctx, inp: torch.Tensor, quant_w: torch.Tensor, scale_w: torch.Tensor, weight_bit_width):
        ctx.inp_shape = inp.size()
        ctx.weight_bit_width = weight_bit_width
        out_features = quant_w.size(0)
        inp = inp.contiguous().view(-1, inp.size(-1))
        weight = extract_weight_to_half(quant_w, scale_w, weight_bit_width)
        ctx.weight_shape = weight.size()
        output = inp.mm(weight.t())
        ctx.save_for_backward(inp, quant_w, scale_w)
        return output.view(*(ctx.inp_shape[:-1] + (out_features,)))

    @staticmethod
    def backward(ctx, grad_output: torch.Tensor):
        inp, quant_w, scale_w = ctx.saved_tensors
        weight = extract_weight_to_half(quant_w, scale_w, ctx.weight_bit_width)
        grad_output = grad_output.contiguous().view(-1, weight.size(0))
        grad_input = grad_output.mm(weight)
        grad_weight = grad_output.t().mm(inp)
        return grad_input.view(ctx.inp_shape), grad_weight.view(ctx.weight_shape), None, None


class W8A16LinearCPU(torch.autograd.Function):
    @staticmethod
    def forward(ctx, inp: torch.Tensor, quant_w: torch.Tensor, scale_w: torch.Tensor, weight_bit_width,
                quantization_cache=None):
        ctx.inp_shape = inp.size()
        ctx.weight_bit_width = weight_bit_width
        out_features = quant_w.size(0)
        inp = inp.contiguous().view(-1, inp.size(-1))
        weight = extract_weight_to_float(quant_w, scale_w, weight_bit_width, quantization_cache=quantization_cache)
        ctx.weight_shape = weight.size()
        output = inp.mm(weight.t())
        ctx.save_for_backward(inp, quant_w, scale_w)
        return output.view(*(ctx.inp_shape[:-1] + (out_features,)))

    @staticmethod
    def backward(ctx, grad_output: torch.Tensor):
        inp, quant_w, scale_w = ctx.saved_tensors
        weight = extract_weight_to_float(quant_w, scale_w, ctx.weight_bit_width)
        grad_output = grad_output.contiguous().view(-1, weight.size(0))
        grad_input = grad_output.mm(weight)
        grad_weight = grad_output.t().mm(inp)
        return grad_input.view(ctx.inp_shape), grad_weight.view(ctx.weight_shape), None, None


default_cpu_kernel_code_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "quantization_kernels.c")
default_cpu_kernel_code = "QlpoOTFBWSZTWXLbSoQAAgzbgERwQXxmTwAAr/ff3kABt0Q2oRVT0hpo9RtEAAAAyBEiSQ9EGjQGQAAAwANGhowjJoNGmgMEUplMTNSMJ5TQaDJpsoMyRMj8P4mZzFSVVwqSXG8GG7MlVwiToYEQwVD7noBxMhNfkeZYtYFtbgOBUSIGtIQjhNHCEnPJsadhb3yBmRIOD3TeAtNLSaU5GgvKUBWSNuuOIHmVt0YhW6rsmDMDUjeUJGJ64R1Jm5lrh0Aa0tKjhFwPdWcGogxLDSXPWQUWTM8Sd3Qz1HMYNxx3HMeiNqNo4jeRDEfZ3gUSHIcU/heomq0vEzL1Msz5KKGxH8FrNOYw3KaxdqaEmNHYMxJFgQbR0DyRknL2L4kwUSxKRdhjRpEtUqilVfggFL1klaMS3PPRDfNqbBOPWO7m4JTVGhS9QTBDDJaEbLbrUQNB+IpJSKQbG5SZZ5gkwJEhJ3aYKJipZ/i7kinChIOW2lQg"
default_cpu_parallel_kernel_code_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
                                                     "quantization_kernels_parallel.c")
default_cpu_parallel_kernel_code = "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"

cpu_kernels = None


class CPUKernel:
    def __init__(self, kernel_file="", source_code=default_cpu_kernel_code_path, compile_parallel_kernel=None,
                 parallel_num=None):
        self.load = False
        self.int8WeightExtractionFloat = None
        self.int4WeightExtractionFloat = None
        self.int4WeightCompression = None
        self.SetNumThreads = lambda x: x

        try:
            if not os.path.exists(default_cpu_kernel_code_path):
                with open(default_cpu_kernel_code_path, "w", encoding="utf-8") as file:
                    code = default_cpu_kernel_code
                    cpu_quantization_code = bz2.decompress(base64.b64decode(code)).decode()
                    file.write(cpu_quantization_code)

            if not os.path.exists(default_cpu_parallel_kernel_code_path):
                with open(default_cpu_parallel_kernel_code_path, "w", encoding="utf-8") as file:
                    code = default_cpu_parallel_kernel_code
                    cpu_quantization_code = bz2.decompress(base64.b64decode(code)).decode()
                    file.write(cpu_quantization_code)

        except Exception as ex:
            print("Error when generating default cpu kernel code(can be ignored when using custom kernels).")

        if compile_parallel_kernel is None:
            compile_parallel_kernel = bool(int(os.cpu_count()) >= 4)

        if compile_parallel_kernel and source_code == default_cpu_kernel_code_path:
            source_code = default_cpu_parallel_kernel_code_path

        kernels = None

        if (not kernel_file) or (not os.path.exists(kernel_file)):
            print("No compiled kernel found.")
            try:
                if os.path.exists(source_code):
                    print("Compiling kernels :", source_code)
                    kernel_file = source_code[:-2] + ".so"

                    if compile_parallel_kernel:
                        if sys.platform != 'darwin':
                            compile_command = "gcc -O3 -fPIC -pthread -fopenmp -std=c99 {} -shared -o {}".format(
                                source_code, kernel_file)
                        else:
                            compile_command = "clang -O3 -fPIC -pthread -Xclang -fopenmp -lomp -std=c99 {} -shared -o {}".format(
                                source_code, kernel_file)
                        print("Compiling", compile_command)
                        exit_state = os.system(compile_command)
                        if not exit_state:
                            try:
                                kernels = ctypes.cdll.LoadLibrary(kernel_file)
                                print("Load kernel :", kernel_file)
                            except:
                                kernels = None
                                print("Load parallel cpu kernel failed, using default cpu kernel code:")
                                import traceback
                                exception = traceback.format_exc()
                                print(exception)
                        else:
                            print("Compile default cpu kernel failed, using default cpu kernel code.")

                        if kernels is None:  # adjust config, use default cpu kernel
                            compile_parallel_kernel = False
                            source_code = default_cpu_kernel_code_path
                            kernel_file = source_code[:-2] + ".so"

                    if kernels is None:
                        compile_command = "gcc -O3 -fPIC -std=c99 {} -shared -o {}".format(source_code, kernel_file)
                        print("Compiling", compile_command)
                        exit_state = os.system(compile_command)
                        if not exit_state:
                            try:
                                kernels = ctypes.cdll.LoadLibrary(kernel_file)
                                print("Load kernel :", kernel_file)
                            except:
                                kernels = None
                                print("Load default cpu kernel failed:")
                                import traceback
                                exception = traceback.format_exc()
                                print(exception)
                        else:
                            print("Compile default cpu kernel failed.")
                else:
                    print("Kernel source code not found.")
                    return
            except:
                print("Failed to build cpu kernel:")
                import traceback
                exception = traceback.format_exc()
                print(exception)
                return
        else:
            try:
                kernels = ctypes.cdll.LoadLibrary(kernel_file)
                print("Load kernel :", kernel_file)
            except:
                kernels = None
                print("Load custom cpu kernel failed:")
                import traceback
                exception = traceback.format_exc()
                print(exception)

        if kernels is not None:
            self.int8WeightExtractionFloat = kernels.extract_int8_weight_to_float
            self.int4WeightExtractionFloat = kernels.extract_int4_weight_to_float
            self.int4WeightCompression = kernels.compress_int4_weight
            if compile_parallel_kernel:
                try:
                    self.SetNumThreads = kernels.set_num_threads
                except:
                    print("No set_num_threads() found in kernel.")
            self.load = True
        else:
            print("Failed to load kernel.")
            return

        if compile_parallel_kernel:
            if parallel_num is None:
                parallel_num = max(os.cpu_count() // 2, 1)
            print("Setting CPU quantization kernel threads to", parallel_num)
            if parallel_num < 4:
                print("Parallel kernel is not recommended when parallel num < 4.")
            self.SetNumThreads(parallel_num)

        self.parallel_num = parallel_num


def compress_int4_weight(weight: torch.Tensor):  # (n, m)
    """compress weight on cpu or cuda to int4"""
    if weight.device == torch.device("cpu"):
        assert isinstance(cpu_kernels, CPUKernel)
        n, m = weight.size(0), weight.size(1)
        assert m % 2 == 0
        m = m // 2
        out = torch.empty(n, m, dtype=torch.int8, device="cpu")
        cpu_kernels.int4WeightCompression(
            ctypes.c_void_p(weight.data_ptr()),
            ctypes.c_void_p(out.data_ptr()),
            ctypes.c_int32(n),
            ctypes.c_int32(m)
        )
        return out
    else:
        with torch.cuda.device(weight.device):
            n, m = weight.size(0), weight.size(1)
            assert m % 2 == 0
            m = m // 2
            out = torch.empty(n, m, dtype=torch.int8, device="cuda")
            stream = torch.cuda.current_stream()

            gridDim = (n, 1, 1)
            blockDim = (min(round_up(m, 32), 1024), 1, 1)

            kernels.int4WeightCompression(
                gridDim,
                blockDim,
                0,
                stream,
                [ctypes.c_void_p(weight.data_ptr()), ctypes.c_void_p(out.data_ptr()), ctypes.c_int32(n),
                 ctypes.c_int32(m)],
            )
            return out


def extract_weight_to_half(weight: torch.Tensor, scale_list: torch.Tensor, source_bit_width: int):
    if source_bit_width == 8:
        func = kernels.int8WeightExtractionHalf
    elif source_bit_width == 4:
        func = kernels.int4WeightExtractionHalf
    else:
        assert False, "Unsupported bit-width"

    with torch.cuda.device(weight.device):
        n, m = weight.size(0), weight.size(1)
        out = torch.empty(n, m * (8 // source_bit_width), dtype=torch.half, device="cuda")
        stream = torch.cuda.current_stream()

        gridDim = (n, 1, 1)
        blockDim = (min(round_up(m, 32), 1024), 1, 1)

        func(
            gridDim,
            blockDim,
            0,
            stream,
            [
                ctypes.c_void_p(weight.data_ptr()),
                ctypes.c_void_p(scale_list.data_ptr()),
                ctypes.c_void_p(out.data_ptr()),
                ctypes.c_int32(n),
                ctypes.c_int32(m),
            ],
        )
        return out


def extract_weight_to_float(weight: torch.Tensor, scale_list: torch.Tensor, source_bit_width: int,
                            quantization_cache=None):
    """extract weight on cpu to float32"""
    if source_bit_width == 8:
        func = cpu_kernels.int8WeightExtractionFloat
    elif source_bit_width == 4:
        func = cpu_kernels.int4WeightExtractionFloat
    else:
        assert False, "Unsupported bit-width"

    n, m = weight.size(0), weight.size(1)

    if quantization_cache is not None:
        out = quantization_cache
        func(
            ctypes.c_void_p(weight.data_ptr()),
            ctypes.c_void_p(scale_list.data_ptr()),
            ctypes.c_void_p(out.data_ptr()),
            ctypes.c_int32(n),
            ctypes.c_int32(m)
        )
        return out.tensor
    else:
        out = torch.empty(n, m * (8 // source_bit_width), dtype=torch.float, device="cpu")
        func(
            ctypes.c_void_p(weight.data_ptr()),
            ctypes.c_void_p(scale_list.data_ptr()),
            ctypes.c_void_p(out.data_ptr()),
            ctypes.c_int32(n),
            ctypes.c_int32(m)
        )
        return out


class CacheTensor():
    def __init__(self, *args, **kwargs):
        self.tensor = torch.empty(*args, **kwargs)

    def to(self, *args, **kwargs):
        self.tensor = self.tensor.to(*args, **kwargs)

    def data_ptr(self):
        return self.tensor.data_ptr()


class QuantizedLinear(Linear):
    def __init__(self, weight_bit_width: int, weight_tensor=None, bias_tensor=None, quantized_weight=None,
                 quantized_weight_scale=None, quantization_cache=None, empty_init=False, *args, **kwargs):
        super(QuantizedLinear, self).__init__(*args, **kwargs)
        self.weight_bit_width = weight_bit_width
        self.quantization_cache = quantization_cache

        if (quantized_weight is not None) and (quantized_weight_scale is not None):
            del self.weight
            self.weight = Parameter(quantized_weight.to(kwargs["device"]), requires_grad=False)
            self.weight_scale = Parameter(quantized_weight_scale.to(kwargs["device"]), requires_grad=False)
        else:
            shape = self.weight.shape
            del self.weight

            if weight_tensor is None or empty_init:
                self.weight = torch.empty(
                    shape[0], shape[1] * weight_bit_width // 8, dtype=torch.int8, device=kwargs["device"]
                )
                self.weight_scale = torch.empty(shape[0], dtype=kwargs["dtype"], device=kwargs["device"])
            else:
                self.weight_scale = (weight_tensor.abs().max(dim=-1).values / ((2 ** (weight_bit_width - 1)) - 1)).to(
                    kwargs["dtype"])
                self.weight = torch.round(weight_tensor / self.weight_scale[:, None]).to(torch.int8)
                if weight_bit_width == 4:
                    self.weight = compress_int4_weight(self.weight)

            self.weight = Parameter(self.weight.to(kwargs["device"]), requires_grad=False)
            self.weight_scale = Parameter(self.weight_scale.to(kwargs["device"]), requires_grad=False)

        if bias_tensor is not None:
            self.bias = Parameter(bias_tensor.to(kwargs["device"]), requires_grad=False)
        else:
            self.bias = None

    def reset_parameters(self):
        """To accelerate initialization"""
        pass

    def forward(self, input):
        if self.weight.device == torch.device("cpu"):
            output = W8A16LinearCPU.apply(input, self.weight, self.weight_scale, self.weight_bit_width,
                                          self.quantization_cache)
        else:
            output = W8A16Linear.apply(input, self.weight, self.weight_scale, self.weight_bit_width)
        if self.bias is not None:
            output = output + self.bias
        return output

    def _apply(self, fn):
        self_obj = super()._apply(fn)
        if self.quantization_cache is not None:
            self.quantization_cache.to(self_obj.weight.device)
            self.quantization_cache.to(self_obj.weight_scale.dtype)
        return self_obj


class QuantizedEmbedding(Embedding):  # TODO: backward, check empty_init
    def __init__(self, weight_bit_width: int, weight_tensor=None, quantized_weight=None, quantized_weight_scale=None,
                 empty_init=False, *args, **kwargs):
        super(QuantizedEmbedding, self).__init__(*args, **kwargs)
        self.weight_bit_width = weight_bit_width

        if (quantized_weight is not None) and (quantized_weight_scale is not None):
            del self.weight
            self.weight = Parameter(quantized_weight.to(kwargs["device"]), requires_grad=False)
            self.weight_scale = Parameter(quantized_weight_scale.to(kwargs["device"]), requires_grad=False)
        else:
            shape = self.weight.shape
            del self.weight

            if weight_tensor is None or empty_init:
                self.weight = torch.empty(
                    shape[0], shape[1] * weight_bit_width // 8, dtype=torch.int8, device=kwargs["device"]
                )
                self.weight_scale = torch.empty(shape[0], dtype=kwargs["dtype"], device=kwargs["device"])
            else:
                self.weight_scale = (weight_tensor.abs().max(dim=-1).values / ((2 ** (weight_bit_width - 1)) - 1)).to(
                    kwargs["dtype"])
                self.weight = torch.round(weight_tensor / self.weight_scale[:, None]).to(torch.int8)
                if weight_bit_width == 4:
                    self.weight = compress_int4_weight(self.weight)

            self.weight = Parameter(self.weight.to(kwargs["device"]), requires_grad=False)
            self.weight_scale = Parameter(self.weight_scale.to(kwargs["device"]), requires_grad=False)

    def forward(self, input):
        if self.weight.device == torch.device("cpu"):
            original_weight = extract_weight_to_float(weight=self.weight, scale_list=self.weight_scale,
                                                      source_bit_width=self.weight_bit_width)
        else:
            original_weight = extract_weight_to_half(weight=self.weight, scale_list=self.weight_scale,
                                                     source_bit_width=self.weight_bit_width)
        output = F.embedding(
            input, original_weight, self.padding_idx, self.max_norm,
            self.norm_type, self.scale_grad_by_freq, self.sparse
        )
        return output


def load_cpu_kernel(**kwargs):
    global cpu_kernels
    cpu_kernels = CPUKernel(**kwargs)


def quantize(model, weight_bit_width, use_quantization_cache=False, empty_init=False, **kwargs):
    """Replace fp16 linear with quantized linear"""

    query_key_value_quantization_cache = None
    dense_quantization_cache = None
    dense_h_to_4h_quantization_cache = None
    dense_4h_to_h_quantization_cache = None

    load_cpu_kernel(**kwargs)
    if not cpu_kernels.load:
        if kernels is None:  # CUDA kernels failed
            print("Cannot load cpu or cuda kernel, quantization failed:")
            assert kernels is not None
        print("Cannot load cpu kernel, don't use quantized model on cpu.")

    current_device = model.device

    if model.device == torch.device("cpu"):
        dtype = torch.float32
    else:
        dtype = torch.half

    QuantizedLinearWithPara = partial(
        QuantizedLinear,
        weight_bit_width=weight_bit_width,
        bias=True,
        dtype=dtype,
        empty_init=empty_init
    )

    if use_quantization_cache:
        print("Using quantization cache")
        layer = model.layers[0]
        weight = layer.attention.query_key_value.weight
        n, m = weight.size(0), weight.size(1)
        query_key_value_quantization_cache = CacheTensor(n, m, dtype=dtype, device=current_device, requires_grad=False)
        weight = layer.attention.dense.weight
        n, m = weight.size(0), weight.size(1)
        dense_quantization_cache = CacheTensor(n, m, dtype=dtype, device=current_device, requires_grad=False)
        weight = layer.mlp.dense_h_to_4h.weight
        n, m = weight.size(0), weight.size(1)
        dense_h_to_4h_quantization_cache = CacheTensor(n, m, dtype=dtype, device=current_device, requires_grad=False)
        weight = layer.mlp.dense_4h_to_h.weight
        n, m = weight.size(0), weight.size(1)
        dense_4h_to_h_quantization_cache = CacheTensor(n, m, dtype=dtype, device=current_device, requires_grad=False)

    print("Applying quantization to glm layers")

    for layer in model.layers:
        layer.attention.query_key_value = QuantizedLinearWithPara(
            weight_tensor=layer.attention.query_key_value.weight.to(current_device),
            bias_tensor=layer.attention.query_key_value.bias,
            in_features=layer.attention.query_key_value.in_features,
            out_features=layer.attention.query_key_value.out_features,
            device=layer.attention.query_key_value.weight.device,
            quantization_cache=query_key_value_quantization_cache
        )
        layer.attention.dense = QuantizedLinearWithPara(
            weight_tensor=layer.attention.dense.weight.to(current_device),
            bias_tensor=layer.attention.dense.bias,
            in_features=layer.attention.dense.in_features,
            out_features=layer.attention.dense.out_features,
            device=layer.attention.dense.weight.device,
            quantization_cache=dense_quantization_cache
        )
        layer.mlp.dense_h_to_4h = QuantizedLinearWithPara(
            weight_tensor=layer.mlp.dense_h_to_4h.weight.to(current_device),
            bias_tensor=layer.mlp.dense_h_to_4h.bias,
            in_features=layer.mlp.dense_h_to_4h.in_features,
            out_features=layer.mlp.dense_h_to_4h.out_features,
            device=layer.mlp.dense_h_to_4h.weight.device,
            quantization_cache=dense_h_to_4h_quantization_cache
        )
        layer.mlp.dense_4h_to_h = QuantizedLinearWithPara(
            weight_tensor=layer.mlp.dense_4h_to_h.weight.to(current_device),
            bias_tensor=layer.mlp.dense_4h_to_h.bias,
            in_features=layer.mlp.dense_4h_to_h.in_features,
            out_features=layer.mlp.dense_4h_to_h.out_features,
            device=layer.mlp.dense_4h_to_h.weight.device,
            quantization_cache=dense_4h_to_h_quantization_cache
        )
    return model