forked from TimDettmers/bitsandbytes
-
Notifications
You must be signed in to change notification settings - Fork 6
/
_functions.py
577 lines (483 loc) · 21.8 KB
/
_functions.py
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
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
import operator
import warnings
from dataclasses import dataclass
from functools import reduce # Required in Python 3
from typing import Tuple, Optional, List
from warnings import warn
import torch
import bitsandbytes.functional as F
# math.prod not compatible with python < 3.8
def prod(iterable):
return reduce(operator.mul, iterable, 1)
tensor = torch.Tensor
# The inverse transformation for the colTuring and colAmpere format were contributed by Alex Borzunov:
# https://github.com/bigscience-workshop/petals/blob/main/src/petals/utils/linear8bitlt_patch.py
"""
This class pools outlier dimensions across layers.
This is particularly important for small models where outlier features
are less systematic and occur with low frequency.
"""
class GlobalOutlierPooler:
_instance = None
def __init__(self):
raise RuntimeError("Call get_instance() instead")
def initialize(self):
self.outliers = set()
self.model_dim = None
@classmethod
def get_instance(cls):
if cls._instance is None:
cls._instance = cls.__new__(cls)
cls._instance.initialize()
return cls._instance
def add_outliers(self, outlier_idx, feature_dim):
if self.model_dim is None:
self.model_dim = feature_dim
if feature_dim != self.model_dim:
return # we do not encode outliers for the 2nd FFN layer
self.outliers.update(outlier_idx.tolist())
def get_current_outlier_idx(self):
return torch.Tensor(list(self.outliers)).to(torch.int64)
def get_inverse_transform_indices(transform_tile: callable, tile_size: Tuple[int, int]):
"""
Compute a permutation of indices that invert the specified (tiled) matrix transformation
:param transform_tile: a function that applies forward transform to a tensor of shape [dim1, dim2]
:param tile_size: higher-level tile dimensions, i.e. (8, 32) for Turing and (32, 32) for Ampere
:note: we assume that tile_transform applies to a cpu-based int8 tensor of shape tile_size
:example: transform_tile function for the turing layout (bitsandbytes.functional as F)
:returns: indices
"""
d1, d2 = tile_size
assert 0 < d1 * d2 < 2**64
tile_indices = torch.arange(d1 * d2, dtype=torch.int64).view(d1, d2)
# encode each position in tile as a tuple of <= 8 unique bytes
permuted_tile_indices = torch.zeros_like(tile_indices)
for i in range(8):
# select i-th byte, apply transformation and trace where each index ended up
ith_dim_indices = torch.div(tile_indices, 256**i, rounding_mode="trunc") % 256
sample_tile_i = (ith_dim_indices - 128).to(torch.int8).contiguous()
assert torch.all(sample_tile_i.int() + 128 == ith_dim_indices), "int overflow"
permuted_tile_i = transform_tile(sample_tile_i)
ith_permuted_indices = permuted_tile_i.to(tile_indices.dtype) + 128
permuted_tile_indices += ith_permuted_indices * (256**i)
if d1 * d2 < 256**i:
break # if all indices fit in i bytes, stop early
return permuted_tile_indices
def undo_layout(permuted_tensor: torch.Tensor, tile_indices: torch.LongTensor) -> torch.Tensor:
"""
Undo a tiled permutation such as turing or ampere layout
:param permuted_tensor: torch tensor in a permuted layout
:param tile_indices: reverse transformation indices, from get_inverse_transform_indices
:return: contiguous row-major tensor
"""
(rows, cols), (tile_rows, tile_cols) = permuted_tensor.shape, tile_indices.shape
assert rows % tile_rows == cols % tile_cols == 0, "tensor must contain a whole number of tiles"
tensor = permuted_tensor.reshape(-1, tile_indices.numel()).t()
outputs = torch.empty_like(tensor) # note: not using .index_copy because it was slower on cuda
outputs[tile_indices.flatten()] = tensor
outputs = outputs.reshape(tile_rows, tile_cols, cols // tile_cols, rows // tile_rows)
outputs = outputs.permute(3, 0, 2, 1) # (rows // tile_rows, tile_rows), (cols // tile_cols, tile_cols)
return outputs.reshape(rows, cols).contiguous()
class MatMul8bit(torch.autograd.Function):
@staticmethod
def forward(ctx, A, B, out=None, quant_type="vector", precision=None):
if precision is None:
precision = [8, 8, 8]
if precision[0] != 8:
with torch.no_grad():
output = torch.matmul(A, B)
else:
if len(B.shape) == 2:
dim = 0
else:
dim = 1
qA, SA = F.vectorwise_quant(A, dim=-1, quant_type=quant_type)
qB, SB = F.vectorwise_quant(B, dim=dim, quant_type=quant_type)
iout = F.igemm(qA, qB)
output = F.vectorwise_mm_dequant(iout, SA, SB, A.dtype, quant_type)
if A.requires_grad or B.requires_grad:
ctx.save_for_backward(A, B)
ctx.quant_type = quant_type
ctx.precision = precision
return output
@staticmethod
def backward(ctx, grad_output):
A, B = ctx.saved_tensors
quant_type = ctx.quant_type
precision = ctx.precision
grad_A = grad_B = None
if B.requires_grad:
if len(A.shape) == 3:
dims = [0, 1]
# bsi -> ibs
permute_dim = [0, 2, 1]
else:
dims = [0]
# bs -> sb
permute_dim = [1, 0]
if precision[1] != 8:
with torch.no_grad():
grad_B = torch.matmul(A.permute(permute_dim), grad_output)
else:
if len(B.shape) == 2 and len(A.shape) == 3:
grad_output = grad_output.contiguous()
if not grad_output.is_contiguous():
grad_output.contiguous()
qgrad_output, S1 = F.vectorwise_quant(
grad_output.view(-1, grad_output.shape[2]),
dim=0,
quant_type=quant_type,
)
if not A.is_contiguous():
A = A.contiguous()
qA, S2 = F.vectorwise_quant(
A.view(-1, A.shape[2]), dim=0, quant_type=quant_type
)
igrad_B = F.igemm(qA.t(), qgrad_output)
grad_B = F.vectorwise_mm_dequant(
igrad_B, S2.t(), S1, grad_output.dtype, quant_type
)
else:
qgrad_output, S1 = F.vectorwise_quant(
grad_output, dim=dims, quant_type=quant_type
)
qA, S2 = F.vectorwise_quant(
A, dim=dims, quant_type=quant_type
)
igrad_B = F.igemm(qA.permute(permute_dim), qgrad_output)
grad_B = F.vectorwise_mm_dequant(
igrad_B,
S2.permute(permute_dim),
S1,
grad_output.dtype,
quant_type,
)
if A.requires_grad:
if len(grad_output.shape) == 3:
dims = [2]
else:
dims = [1]
if len(B.shape) == 3:
# bio -> boi
permute_dim = [0, 2, 1]
dim_B = dims
else:
# io -> oi
permute_dim = [1, 0]
dim_B = [1]
if precision[2] != 8:
with torch.no_grad():
grad_A = torch.matmul(grad_output, B.permute(permute_dim))
else:
qgrad_output, S1 = F.vectorwise_quant(
grad_output, dim=dims, quant_type=quant_type
)
qB, S3 = F.vectorwise_quant(B, dim=dim_B, quant_type=quant_type)
igrad_A = F.igemm(qgrad_output, qB.permute(permute_dim))
grad_A = F.vectorwise_mm_dequant(
igrad_A,
S1,
S3.permute(permute_dim),
grad_output.dtype,
quant_type,
)
return grad_A, grad_B, None, None, None
mm_cublas = MatMul8bit.apply
bmm_cublas = MatMul8bit.apply
matmul_cublas = MatMul8bit.apply
def supports_igemmlt(device: torch.device) -> bool:
"""check if this device supports the optimized int8 kernel"""
if torch.cuda.get_device_capability(device=device) < (7, 5) or torch.version.hip:
return False
device_name = torch.cuda.get_device_name(device=device)
nvidia16_models = ('GTX 1630', 'GTX 1650', 'GTX 1660') # https://en.wikipedia.org/wiki/GeForce_16_series
if any(model_name in device_name for model_name in nvidia16_models):
return False # these devices are technically cuda 7.5-capable, but they lack tensor cores
return True
def _get_tile_size(format):
assert format in (
"col_turing",
"col_ampere",
), f"please find this assert and manually enter tile size for {format}"
return (8, 32) if format == "col_turing" else (32, 32)
def get_tile_inds(format, device):
transform = lambda x: F.transform(x.to(device), from_order="row", to_order=format)[0].to(x.device)
with torch.no_grad():
return get_inverse_transform_indices(transform, _get_tile_size(format)).to(device)
@dataclass
class MatmulLtState:
_tile_indices: Optional[torch.Tensor] = None
force_no_igemmlt: bool = False
CB = None
CxB = None
SB = None
SCB = None
CxBt = None
SBt = None
CBt = None
subB = None
outlier_pool = None
has_accumulated_gradients = False
threshold = 0.0
idx = None
is_training = True
has_fp16_weights = True
memory_efficient_backward = False
use_pool = False
formatB = F.get_special_format_str()
def reset_grads(self):
self.CB = None
self.CxB = None
self.SB = None
self.SCB = None
self.CxBt = None
self.SBt = None
self.CBt = None
@property
def tile_indices(self):
if self._tile_indices is None:
self._tile_indices = get_tile_inds(self.formatB, self.CxB.device)
return self._tile_indices
class MatMul8bitLt(torch.autograd.Function):
# forward is the same, but we added the fallback for pre-turing GPUs
# backward is mostly the same, but adds one extra clause (see "elif state.CxB is not None")
@staticmethod
def forward(ctx, A, B, out=None, bias=None, state=MatmulLtState):
using_igemmlt = supports_igemmlt(A.device) and not state.force_no_igemmlt
# default of pytorch behavior if inputs are empty
ctx.is_empty = False
if prod(A.shape) == 0:
ctx.is_empty = True
ctx.A = A
ctx.B = B
ctx.bias = bias
if A.shape[-1] == B.shape[0]:
return torch.empty(A.shape[:-1] + B.shape[1:], dtype=A.dtype, device=A.device)
else:
return torch.empty(A.shape[:-1] + B.shape[:1], dtype=A.dtype, device=A.device)
# 1. Quantize A
# 2. Quantize B
# 3. Matmul
# 4. Mixed-precision decomposition matmul
# 5. Save state
formatB = state.formatB
input_shape = A.shape
if state.outlier_pool is None:
state.outlier_pool = GlobalOutlierPooler.get_instance()
# Cast A to fp16
if A.dtype != torch.float16:
warnings.warn(f"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization")
# 1. Quantize A
if len(A.shape) == 3:
A = A.reshape(-1, A.shape[-1])
CA, CAt, SCA, SCAt, coo_tensorA = F.double_quant(A.to(torch.float16), threshold=state.threshold)
if state.threshold > 0.0 and coo_tensorA is not None:
if state.has_fp16_weights:
idx = torch.unique(coo_tensorA.colidx).long()
CA[:, idx] = 0
CAt[:, idx] = 0
subA = A[:, idx]
state.subB = B[:, idx].t().contiguous()
state.idx = idx
else:
if state.CxB is None and using_igemmlt:
# B in in 8-bit row-major, we can transform it back to 16-bit to extract outlier dimensions
# we also need to convert it to the turing/ampere format
state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
else:
if not state.has_fp16_weights and state.CxB is None and using_igemmlt:
state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
subA = None
# 2. Quantize B
if state.has_fp16_weights:
has_grad = True if (getattr(B, "grad", None) is not None) else False
is_transposed = not B.is_contiguous() and B.shape[0] == B.stride(1)
if is_transposed:
B = B.contiguous()
if (state.is_training and not has_grad) or state.CxB is None:
state.reset_grads()
(
CB,
state.CBt,
state.SCB,
state.SCBt,
coo_tensorB,
) = F.double_quant(B.to(torch.float16))
if using_igemmlt:
state.CxB, state.SB = F.transform(CB, to_order=formatB)
else:
state.CB = CB
else:
has_grad = False
if coo_tensorA is not None and not state.has_fp16_weights:
# extract outliers
outlier_idx = torch.unique(coo_tensorA.colidx)
state.idx = outlier_idx
# state.outlier_pool.add_outliers(outlier_idx, A.shape[-1])
# if state.use_pool and state.outlier_pool.model_dim == A.shape[-1]:
# # do not use pool for 2nd FFN layer
# state.idx = state.outlier_pool.get_current_outlier_idx().to(A.device)
# else:
# state.idx = outlier_idx
if state.CxB is not None:
outliers = F.extract_outliers(state.CxB, state.SB, state.idx.int())
else:
outliers = state.CB[:, state.idx.long()].clone()
state.subB = (outliers * state.SCB.view(-1, 1) / 127.0).t().contiguous().to(A.dtype)
CA[:, state.idx.long()] = 0
CAt[:, state.idx.long()] = 0
subA = A[:, state.idx.long()]
shapeB = state.SB[0] if state.SB else B.shape
if len(input_shape) == 3:
output_shape = (input_shape[0], input_shape[1], shapeB[0])
else:
output_shape = (input_shape[0], shapeB[0])
# 3. Matmul
if using_igemmlt:
C32A, SA = F.transform(CA, "col32")
out32, Sout32 = F.igemmlt(C32A, state.CxB, SA, state.SB)
if bias is None or bias.dtype == torch.float16:
# we apply the fused bias here
output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=bias)
output = output.to(A.dtype)
else: # apply bias separately
output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=None)
output = output.to(A.dtype).add_(bias)
else:
A_wo_outliers = A.clone()
if state.idx is not None:
A_wo_outliers[:, state.idx.long()] = 0
output = torch.nn.functional.linear(A_wo_outliers, state.CB.to(A.dtype))
output = output.mul_(state.SCB.unsqueeze(0).mul(1.0 / 127.0))
if bias is not None:
output = output.add_(bias)
# 4. Mixed-precision decomposition matmul
if coo_tensorA is not None and subA is not None:
output += torch.matmul(subA, state.subB)
# 5. Save state
ctx.state = state
ctx.formatB = formatB
ctx.grad_shape = input_shape
ctx.dtype_A, ctx.dtype_B, ctx.dtype_bias = A.dtype, B.dtype, None if bias is None else bias.dtype
if any(ctx.needs_input_grad[:2]):
ctx.tensors = (CAt, subA, A)
ctx.tensor_states = (SCAt, state.idx)
else:
ctx.tensors = [None, None, A]
ctx.tensor_states = (None, None)
ctx.save_for_backward(None, None)
clone_func = torch.clone if len(output_shape) == 3 else lambda x: x
return clone_func(output.view(output_shape))
@staticmethod
def backward(ctx, grad_output):
if ctx.is_empty:
bias_grad = None if ctx.bias is None else torch.zeros_like(ctx.bias)
return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, bias_grad, None
req_gradA, req_gradB, _, req_gradBias, _ = ctx.needs_input_grad
CAt, subA, A = ctx.tensors
SCAt, idx = ctx.tensor_states
formatB = ctx.formatB
state = ctx.state
grad_A = grad_B = grad_bias = None
if req_gradBias:
# compute grad_bias first before changing grad_output dtype
grad_bias = grad_output.sum(0, dtype=ctx.dtype_bias)
# Cast grad_output to fp16
if len(grad_output.shape) == 3:
grad_output = grad_output.reshape(-1, grad_output.shape[-1]).contiguous()
Cgrad, Cgradt, SCgrad, SCgradt, coo_tensor = F.double_quant(grad_output.to(torch.float16))
if req_gradB:
CxAt, SAt = F.transform(CAt, formatB, transpose=True)
C32grad, Sgrad = F.transform(Cgradt, "col32", transpose=True)
gradB32, SgradB32 = F.igemmlt(C32grad, CxAt, Sgrad, SAt)
grad_B = F.mm_dequant(gradB32, SgradB32, SCgradt, SCAt)
if state.threshold > 0.0 and subA is not None:
grad_B[:, idx] += torch.matmul(grad_output.t(), subA)
if req_gradA:
if state.CBt is not None:
C32grad, Sgrad = F.transform(Cgrad, "col32")
if state.CxBt is None:
state.CxBt, state.SBt = F.transform(state.CBt, to_order=formatB, transpose=True)
gradA32, SgradA32 = F.igemmlt(C32grad, state.CxBt, Sgrad, state.SBt)
grad_A = F.mm_dequant(gradA32, SgradA32, SCgrad, state.SCBt).view(ctx.grad_shape).to(ctx.dtype_A)
elif state.CB is not None:
CB = state.CB.to(ctx.dtype_A, copy=True).mul_(state.SCB.unsqueeze(1).mul(1.0 / 127.0))
grad_A = torch.matmul(grad_output, CB).view(ctx.grad_shape).to(ctx.dtype_A)
elif state.CxB is not None:
CB = (
undo_layout(state.CxB, state.tile_indices)
.to(ctx.dtype_A)
.mul_(state.SCB.unsqueeze(1).mul(1.0 / 127.0))
)
grad_A = torch.matmul(grad_output, CB).view(ctx.grad_shape).to(ctx.dtype_A)
else:
raise Exception("State must contain either CBt or CB or CxB matrix for backward")
return grad_A, grad_B, None, grad_bias, None
class MatMul4Bit(torch.autograd.Function):
# forward is the same, but we added the fallback for pre-turing GPUs
# backward is mostly the same, but adds one extra clause (see "elif state.CxB is not None")
@staticmethod
def forward(ctx, A, B, out=None, bias=None, quant_state: F.QuantState = None):
# default of pytorch behavior if inputs are empty
ctx.is_empty = False
if prod(A.shape) == 0:
ctx.is_empty = True
ctx.A = A
ctx.B = B
ctx.bias = bias
B_shape = quant_state.shape
if A.shape[-1] == B_shape[0]:
return torch.empty(A.shape[:-1] + B_shape[1:], dtype=A.dtype, device=A.device)
else:
return torch.empty(A.shape[:-1] + B_shape[:1], dtype=A.dtype, device=A.device)
# 1. Dequantize
# 2. MatmulnN
output = torch.nn.functional.linear(A, F.dequantize_4bit(B, quant_state).to(A.dtype).t(), bias)
# 3. Save state
ctx.state = quant_state
ctx.dtype_A, ctx.dtype_B, ctx.dtype_bias = A.dtype, B.dtype, None if bias is None else bias.dtype
if any(ctx.needs_input_grad[:2]):
ctx.tensors = (A, B)
else:
ctx.tensors = (None, None)
return output
@staticmethod
def backward(ctx, grad_output):
if ctx.is_empty:
bias_grad = None if ctx.bias is None else torch.zeros_like(ctx.bias)
return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, bias_grad, None
req_gradA, _, _, req_gradBias, _= ctx.needs_input_grad
A, B = ctx.tensors
grad_A, grad_B, grad_bias = None, None, None
if req_gradBias:
# compute grad_bias first before changing grad_output dtype
grad_bias = grad_output.sum(0, dtype=ctx.dtype_bias)
# not supported by PyTorch. TODO: create work-around
#if req_gradB: grad_B = torch.matmul(grad_output.t(), A)
if req_gradA: grad_A = torch.matmul(grad_output, F.dequantize_4bit(B, ctx.state).to(grad_output.dtype).t())
return grad_A, grad_B, None, grad_bias, None
def matmul(
A: tensor,
B: tensor,
out: tensor = None,
state: MatmulLtState = None,
threshold=0.0,
bias=None
):
state = state or MatmulLtState()
if threshold > 0.0:
state.threshold = threshold
return MatMul8bitLt.apply(A, B, out, bias, state)
def matmul_4bit(A: tensor, B: tensor, quant_state: F.QuantState, out: tensor = None, bias=None):
assert quant_state is not None
if A.numel() == A.shape[-1] and A.requires_grad == False:
if A.shape[-1] % quant_state.blocksize != 0:
warn(f'Some matrices hidden dimension is not a multiple of {quant_state.blocksize} and efficient inference kernels are not supported for these (slow). Matrix input size found: {A.shape}')
return MatMul4Bit.apply(A, B, out, bias, quant_state)
else:
out = F.gemv_4bit(A, B.t(), out, state=quant_state)
if bias is not None:
out += bias
return out
else:
return MatMul4Bit.apply(A, B, out, bias, quant_state)