-
Notifications
You must be signed in to change notification settings - Fork 21.4k
/
quantize.py
940 lines (832 loc) · 41.6 KB
/
quantize.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
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
import torch
from torch.fx import (
GraphModule,
Proxy,
map_arg
)
from torch.fx.graph import (
Graph,
Node,
)
from torch.quantization import (
propagate_qconfig_,
convert,
)
from ..quantization_mappings import (
get_default_qat_module_mappings,
)
from ..quantize import _remove_qconfig
from .pattern_utils import (
is_match,
get_default_quant_patterns,
)
from .standalone_module import (
mark_observed_standalone_module,
is_observed_standalone_module,
)
from .quantization_patterns import *
from .utils import (
_parent_name,
quantize_node,
activation_is_statically_quantized,
)
from collections import OrderedDict
import warnings
import copy
import re
from typing import Optional
# ------------------------
# Helper Functions
# ------------------------
# Returns a function that can get a new attribute name for module with given prefix
# for example,
# >> get_new_observer_name = get_new_attr_name_with_prefix('_observer')
# >> new_name = get_new_observer_name(module)
# new_name will be an unused attribute name on module, e.g. `_observer_1`
def get_new_attr_name_with_prefix(prefix):
def get_new_attr_name(module):
def get_attr_name(i):
return prefix + str(i)
i = 0
attr_name = get_attr_name(i)
while hasattr(module, attr_name):
i += 1
attr_name = get_attr_name(i)
return attr_name
return get_new_attr_name
def collect_producer_nodes(node):
r''' Starting from a target node, trace back until we hit inpu or
getattr node. This is used to extract the chain of operators
starting from getattr to the target node, for example
def forward(self, x):
observed = self.observer(self.weight)
return F.linear(x, observed)
collect_producer_nodes(observed) will either return a list of nodes that produces
the observed node or None if we can't extract a self contained graph without
free variables(inputs of the forward function).
'''
nodes = [node]
frontier = [node]
while frontier:
node = frontier.pop()
all_args = list(node.args) + list(node.kwargs.values())
for arg in all_args:
if not isinstance(arg, Node):
continue
if arg.op == 'placeholder':
# hit input, can't fold in this case
return None
nodes.append(arg)
if not (arg.op == 'call_function' and arg.target == getattr):
frontier.append(arg)
return nodes
def graph_module_from_producer_nodes(root, producer_nodes):
r''' Construct a graph module from extracted producer nodes
from `collect_producer_nodes` function
Args:
root: the root module for the original graph
producer_nodes: a list of nodes we use to construct the graph
Return:
A graph module constructed from the producer nodes
'''
assert len(producer_nodes) > 0, 'list of producer nodes can not be empty'
# since we traced back from node to getattrr
producer_nodes.reverse()
graph = Graph()
env = {}
def load_arg(a):
return map_arg(a, lambda node: env[node])
for producer_node in producer_nodes:
env[producer_node] = graph.node_copy(producer_node, load_arg)
graph.output(load_arg(producer_nodes[-1]))
graph_module = GraphModule(root, graph)
return graph_module
def assert_and_get_unique_device(module):
"""
Returns the unique device for a module, or None if no device is found.
Throws an error if multiple devices are detected.
"""
devices = {p.device for p in module.parameters()} | \
{p.device for p in module.buffers()}
assert len(devices) <= 1, (
"prepare only works with cpu or single-device CUDA modules, "
"but got devices {}".format(devices)
)
device = next(iter(devices)) if len(devices) > 0 else None
return device
def is_activation_post_process(module):
return (isinstance(module, torch.quantization.ObserverBase) or
isinstance(module, torch.quantization.FakeQuantize))
def is_submodule_of_fake_quant(name, module, named_modules):
parent_name, _ = _parent_name(name)
return is_activation_post_process(named_modules[parent_name])
def get_flattened_qconfig_dict(qconfig_dict):
""" flatten the global, object_type and module_name qconfig
to the same qconfig_dict so that it can be used by
propagate_qconfig_ function.
"module_name_regex" is ignored for now since it's not supported
in propagate_qconfig_, but it can be fixed later.
For example:
Input: {
"": qconfig,
"object_type": [
(torch.add, qconfig)
],
"module_name": [
("conv", qconfig)
]
}
Output: {
"": qconfig,
torch.add: qconfig,
"conv": qconfig
}
"""
flattened = dict()
if '' in qconfig_dict:
flattened[''] = qconfig_dict['']
def flatten_key(key):
if key in qconfig_dict:
for obj, qconfig in qconfig_dict[key]:
flattened[obj] = qconfig
flatten_key('object_type')
flatten_key('module_name')
return flattened
def convert_dict_to_ordered_dict(qconfig_dict):
""" Convert dict in qconfig_dict to ordered dict
"""
# convert a qconfig list for a type to OrderedDict
def _convert_to_ordered_dict(key, qconfig_dict):
qconfig_dict[key] = OrderedDict(qconfig_dict.get(key, []))
_convert_to_ordered_dict('object_type', qconfig_dict)
_convert_to_ordered_dict('module_name_regex', qconfig_dict)
_convert_to_ordered_dict('module_name', qconfig_dict)
# A dictionary for querying the weight index for a given op
WEIGHT_INDEX_DICT = {
torch.nn.functional.conv2d : [1],
torch.nn.functional.linear : [1],
}
# weight prepacking ops
WEIGHT_PREPACK_OPS = {
torch._ops.ops.quantized.linear_prepack,
torch._ops.ops.quantized.linear_prepack_fp16,
torch._ops.ops.quantized.conv2d_prepack,
}
class Quantizer:
def __init__(self):
# mapping from matched node to activation_post_process
# must be filled before convert
self.activation_post_process_map = None
# mapping from node name to qconfig that should be used for that node
# filled out for a model during _generate_qconfig_map
self.qconfig_map = None
# mapping from fully qualified module name to module instance
# for example,
# {
# '': Model(...),
# 'linear': Linear(...),
# 'linear.weight_fake_quant': PerChannelMinMaxObserver(...),
# }
self.modules = None
# mapping from a tuple of nodes in reverse order to uninitialized
# QuantizeHandler subclass. For example,
# {
# # match a single node
# (<class 'torch.nn.modules.conv.Conv3d'>:
# <class 'torch.quantization.fx.quantize.ConvRelu'>),
# # match multiple nodes in reverse order
# ((<function relu at 0x7f766a7360d0>, <built-in function add>):
# <class 'torch.quantization.fx.quantize.Add'>),
# }
self.patterns = None
def _qat_swap_modules(self, root, additional_qat_module_mapping):
all_mappings = dict(get_default_qat_module_mappings(), **additional_qat_module_mapping)
convert(root, mapping=all_mappings, inplace=True, remove_qconfig=False)
def _generate_qconfig_map(self,
root,
input_graph,
qconfig_dict):
global_qconfig = qconfig_dict.get('', None)
def get_module_type_qconfig(
module_type, fallback_qconfig=global_qconfig):
return qconfig_dict['object_type'].get(module_type, fallback_qconfig)
def get_function_qconfig(
function, fallback_qconfig=global_qconfig):
return qconfig_dict['object_type'].get(function, fallback_qconfig)
def get_module_name_regex_qconfig(
module_name, fallback_qconfig=global_qconfig):
for regex_pattern, qconfig in qconfig_dict['module_name_regex'].items():
if re.match(regex_pattern, module_name):
# first match wins
return qconfig
return fallback_qconfig
def get_module_name_qconfig(
module_name, fallback_qconfig=global_qconfig):
if module_name == '':
# module name qconfig not found
return fallback_qconfig
if module_name in qconfig_dict['module_name']:
return qconfig_dict['module_name'][module_name]
else:
parent, _ = _parent_name(module_name)
return get_module_name_qconfig(parent, fallback_qconfig)
# get qconfig for module_name,
# fallback to module_name_regex_qconfig, module_type_qconfig, global_qconfig
# if necessary
def get_qconfig(module_name):
module_type_qconfig = \
get_module_type_qconfig(type(self.modules[module_name]))
module_name_regex_qconfig = \
get_module_name_regex_qconfig(module_name, module_type_qconfig)
module_name_qconfig = \
get_module_name_qconfig(module_name, module_name_regex_qconfig)
return module_name_qconfig
self.qconfig_map = dict()
for node in input_graph.nodes:
if node.op == 'get_attr':
module_name, _ = _parent_name(node.target)
self.qconfig_map[node.name] = get_qconfig(module_name)
elif node.op == 'call_function':
# precedence: [TODO] module_name_qconfig (need scope support from fx)
# > function_qconfig > global_qconfig
function_qconfig = get_function_qconfig(node.target)
self.qconfig_map[node.name] = function_qconfig
elif node.op == 'call_method':
self_obj = node.args[0]
# qconfig for call_method should be the same as the `self` object for the call
if self_obj.name in self.qconfig_map:
qconfig = self.qconfig_map[self_obj.name]
else:
# need scope info for each node to support this
warnings.warn("Scope info is not yet supported, taking default qconfig for value {}".format(node.name))
qconfig = get_qconfig('')
self.qconfig_map[node.name] = qconfig
elif node.op == 'call_module':
module_qconfig = get_qconfig(node.target)
# regex is not supported eager mode propagate_qconfig_, we'll need to
# set the qconfig explicitly here in case regex
# is used
self.modules[node.target].qconfig = module_qconfig
self.qconfig_map[node.name] = module_qconfig
def _prepare(self, model, qconfig_dict, inplace, prepare_custom_config_dict, is_standalone_module):
""" standalone_module means it a submodule that is not inlined in parent module,
and will be quantized separately as one unit.
When we are preparing a standalone module:
input of the module is observed in parent module, output of the module
is observed in the standalone module.
Returns:
model(GraphModule): prepared standalone module with following attributes:
_standalone_module_observed_input_idxs(List[Int]): a list of indexs for the graph inputs that
needs to be observed in parent module
_output_is_observed(Bool): a boolean variable indicate whether the output of the
custom module is observed or not
"""
if prepare_custom_config_dict is None:
prepare_custom_config_dict = {}
if not inplace:
model = copy.deepcopy(model)
additional_quant_patterns = prepare_custom_config_dict.get("additional_quant_pattern", {})
self.patterns = dict(get_default_quant_patterns, **additional_quant_patterns)
flattened_qconfig_dict = get_flattened_qconfig_dict(qconfig_dict)
# TODO: support regex as well
propagate_qconfig_(model, flattened_qconfig_dict)
if model.training:
additional_qat_module_mapping = prepare_custom_config_dict.get("additioanl_qat_module_mapping", {})
self._qat_swap_modules(model, additional_qat_module_mapping)
self.modules = dict(model.named_modules())
convert_dict_to_ordered_dict(qconfig_dict)
# map from node name to qconfig, used in _find_matches
self._generate_qconfig_map(model, model.graph, qconfig_dict)
# match the patterns that will get quantized
standalone_module_names = prepare_custom_config_dict.get("standalone_module_name", None)
custom_module_class_mapping = prepare_custom_config_dict.get("float_to_observed_custom_module_class", None)
matches = self._find_matches(
model.graph, self.modules, self.patterns, standalone_module_names, custom_module_class_mapping)
# find _inputs_ to matched nodes that are not quantized, these
# have to be quantized, which requires measuring stats,
# initialize an DefaultQuant object for each
quants = self._find_quants(model.graph, matches)
self.activation_post_process_map = dict()
env = {}
observed_graph = Graph()
observed_node_names_set = set()
def load_arg(a):
return map_arg(a, lambda node: env[node.name])
# indexes for the inputs that needs to be observed
standalone_module_observed_input_idxs = []
graph_inputs = []
for node in model.graph.nodes:
if node.op == 'placeholder':
graph_inputs.append(node.name)
get_new_observer_name = get_new_attr_name_with_prefix('activation_post_process_')
result_node : Optional[Node] = None
for node in model.graph.nodes:
if node.op == 'output':
observed_graph.output(load_arg(node.args[0]))
result_node = node
continue
if node.name in observed_node_names_set:
continue
prefix = node.name + '_activation_post_process_'
root_node, _, obj, qconfig = matches.get(node.name, (None, None, None, None))
if root_node is None:
env[node.name] = observed_graph.node_copy(node, load_arg)
elif root_node is node:
env[node.name] = observed_graph.node_copy(node, load_arg)
if qconfig is None:
continue
def insert_observer(node, observer, device):
get_new_observer_name = get_new_attr_name_with_prefix(prefix)
observer_name = get_new_observer_name(model)
setattr(model, observer_name, observer)
self.activation_post_process_map[node.name] = observer
env[node.name] = observed_graph.create_node('call_module', observer_name, (load_arg(node),), {})
observed_node_names_set.add(node.name)
if device:
getattr(model, observer_name).to(device)
if isinstance(obj, CustomModuleQuantizeHandler):
custom_module = self.modules[node.target]
observed_custom_module_class = \
custom_module_class_mapping[type(custom_module)]
observed_custom_module = \
observed_custom_module_class.from_float(custom_module)
parent_name, name = _parent_name(node.target)
setattr(self.modules[parent_name], name, observed_custom_module)
# index for input of custom module that needs to be observed in parent
standalone_module_input_idxs = None
if isinstance(obj, StandaloneModuleQuantizeHandler):
# observe standalone module
standalone_module = self.modules[node.target]
prepare = torch.quantization.quantize_fx._prepare_standalone_module_fx
observed_standalone_module = prepare(standalone_module, {'': qconfig})
observed_standalone_module.qconfig = qconfig
standalone_module_input_idxs = observed_standalone_module._standalone_module_observed_input_idxs
observed_standalone_module = mark_observed_standalone_module(observed_standalone_module)
parent_name, name = _parent_name(node.target)
setattr(self.modules[parent_name], name, observed_standalone_module)
self.modules[node.target] = observed_standalone_module
# don't need to insert observer for output if activation does not
# need to be statically quantized
if not activation_is_statically_quantized(qconfig):
continue
# inserting observers for output of observed module, or mark the output
# as observed
if isinstance(obj, CopyNode):
assert node.op in [
'call_module',
'call_function',
'call_method'], \
'CopyNode of type ' + node.op + ' is not handled'
def is_observed(input_arg):
if isinstance(input_arg, Node):
return input_arg.name in observed_node_names_set
elif isinstance(input_arg, list):
return all(map(is_observed, input_arg))
# propagate observed property from input
if is_observed(node.args[0]):
observed_node_names_set.add(node.name)
elif (isinstance(obj, Add) or isinstance(obj, Mul)) and not obj.all_nodes:
if node.args[0].name in observed_node_names_set:
observed_node_names_set.add(node.name)
elif isinstance(obj, StandaloneModuleQuantizeHandler):
assert node.op == 'call_module'
output_is_observed = self.modules[node.target]._output_is_observed
if output_is_observed:
observed_node_names_set.add(node.name)
elif qconfig is not None and obj.all_nodes:
# observer for outputs
new_observer = qconfig.activation()
# respect device affinity when adding observers
device = assert_and_get_unique_device(model)
insert_observer(node, new_observer, device)
# insert observer for input of standalone module
if standalone_module_input_idxs is not None:
for idx in standalone_module_input_idxs:
if node.args[idx].name not in observed_node_names_set:
new_observer = qconfig.activation()
device = assert_and_get_unique_device(model)
insert_observer(node.args[idx], new_observer, device)
else:
env[node.name] = observed_graph.node_copy(node, load_arg)
if node.name not in observed_node_names_set and node.name in quants:
if is_standalone_module and node.name in graph_inputs:
# we'll insert observer for input of standalone module
# in parent graph
standalone_module_observed_input_idxs.append(graph_inputs.index(node.name))
continue
get_new_observer_name = get_new_attr_name_with_prefix(prefix)
observer_name = get_new_observer_name(model)
_, qconfig, is_weight = quants[node.name]
if qconfig is not None:
# TODO: use insert_observer
new_observer = \
qconfig.weight() if is_weight else qconfig.activation()
# respect device affinity when adding observers
device = assert_and_get_unique_device(model)
if device:
new_observer.to(device)
self.activation_post_process_map[node.name] = new_observer
setattr(model, observer_name, self.activation_post_process_map[node.name])
env[node.name] = observed_graph.create_node('call_module', observer_name, (load_arg(node),), {})
observed_node_names_set.add(node.name)
model = GraphModule(model, observed_graph)
self.save_state(model)
if is_standalone_module:
assert result_node is not None
assert isinstance(result_node.args[0], Node), \
'standalone module returning dict is not yet supported'
# indicator for whether output is observed or not.
# This used for correctly quantize standalone modules
output_is_observed = result_node.args[0].name in observed_node_names_set
model._standalone_module_observed_input_idxs = standalone_module_observed_input_idxs
model._output_is_observed = output_is_observed
return model
def save_state(self, observed):
observed._activation_post_process_map = self.activation_post_process_map
observed._patterns = self.patterns
observed._qconfig_map = self.qconfig_map
def restore_state(self, observed):
err_msg = 'please make sure the model is produced by prepare'
assert hasattr(observed, '_activation_post_process_map'), 'did not found ' + \
'_activation_post_process attribute ' + err_msg
assert hasattr(observed, '_patterns'), 'did not found ' + \
'_patterns attribute ' + err_msg
assert hasattr(observed, '_qconfig_map'), 'did not found ' + \
'_qconfig_map attribute ' + err_msg
self.activation_post_process_map = observed._activation_post_process_map
self.patterns = observed._patterns
self.qconfig_map = observed._qconfig_map
def prepare(self, model, qconfig_dict, inplace=False, prepare_custom_config_dict=None, is_standalone_module=False):
return self._prepare(model, qconfig_dict, inplace, prepare_custom_config_dict, is_standalone_module)
def _run_weight_observers(self, observed):
r''' Extract the subgraph that produces the weight for dynamic quant
or weight only quant node and run the subgraph to observe the weight.
Note that the observers of dynamic quant or weight only quant ops are run during
the convert step.
'''
for node in observed.graph.nodes:
if node.op == 'call_function' and node.target in WEIGHT_INDEX_DICT:
for i, node_arg in enumerate(node.args):
if i in WEIGHT_INDEX_DICT[node.target]:
# node_arg is weight
weight_observer_nodes = collect_producer_nodes(node_arg)
if weight_observer_nodes is not None:
weight_observer_module = graph_module_from_producer_nodes(
observed, weight_observer_nodes)
# run the weight observer
weight_observer_module()
return
def _convert(self, model, inplace=False, debug=False, convert_custom_config_dict=None, is_standalone_module=False):
""" standalone_module means it a submodule that is not inlined in parent module,
and will be quantized separately as one unit.
For standalone module: the inputs will be quantized by parent module,
checks `_standalone_module_observed_input_idxs` of
input observed model and will treat these inputs as quantized
also will not dequantize the final output.
Returns a quantized standalone module which accepts quantized input(if needed)
and produces quantized output (if needed).
"""
if convert_custom_config_dict is None:
convert_custom_config_dict = {}
self.restore_state(model)
if not inplace:
model = copy.deepcopy(model)
# always run weight observers in the top level forward method
# for dynamic quant ops or weight only quant ops
self._run_weight_observers(model)
# move to cpu since we only have quantized cpu kernels
model.eval().cpu()
self.modules = dict(model.named_modules())
custom_module_class_mapping = convert_custom_config_dict.get("observed_to_quantized_custom_module_class", None)
matches = self._find_matches(
model.graph, self.modules, self.patterns,
custom_module_class_mapping=custom_module_class_mapping)
quants = self._find_quants(model.graph, matches)
self.quantized_graph = Graph()
env = {}
quant_env = {}
graph_inputs = []
for node in model.graph.nodes:
if node.op == 'placeholder':
graph_inputs.append(node.name)
def load_non_quantized(n):
if n.name not in env:
assert n.name in quant_env, \
'trying to load float node but did not find node:' + n.name + \
' in quantized or non quantized environment, env: ' + str(env) + \
' quant_env:' + str(quant_env)
env[n.name] = Proxy(quant_env[n.name]).dequantize().node
return env[n.name]
def load_quantized(n):
if n.name not in quant_env:
assert n.name in env, \
'trying to load quantized node but did not find node:' + n.name + \
' in float environment:' + str(env)
assert n.name in quants, 'did not find quant object for node:' + n.name
quant = quants[n.name][0]
quant_env[n.name] = quant.convert(self, env[n.name])
return quant_env[n.name]
def load_x(n):
assert n.name in env or n.name in quant_env, \
'node ' + n.name + ' does not exist in either environment'
if n.name in quant_env:
return quant_env[n.name]
else:
return env[n.name]
def load_arg(quantized):
"""
Input: quantized, which can be None, list, boolean or tuple
- if quantized is a list or tuple, then arg should be a list and the args with corresponding
indexes will be quantized
- if quantized is a boolean, then all args will be quantized/not quantized
- if quantized is None, then we'll load the node as long as it exists
Output: fn which takes arg_or_args, and loads them from the corresponding
environment depending on the value of quantized.
"""
assert quantized is None or isinstance(quantized, (tuple, list, bool)), type(quantized)
def load_arg_impl(arg_or_args):
if quantized is None:
return map_arg(arg_or_args, load_x)
if isinstance(quantized, bool):
return map_arg(arg_or_args, load_quantized if quantized else load_non_quantized)
elif isinstance(quantized, (tuple, list)):
assert isinstance(arg_or_args, (tuple, list)), arg_or_args
loaded_args = []
# for now, we only support quantizing positional arguments
for i, a in enumerate(arg_or_args):
if i in quantized:
loaded_args.append(map_arg(a, load_quantized))
else:
loaded_args.append(map_arg(a, load_non_quantized))
return type(arg_or_args)(loaded_args)
return load_arg_impl
def is_quantized(node):
if isinstance(node, Node):
assert node.name in env or node.name in quant_env, 'Expecting node to be in the environment'
# there might be nodes appearing in both environemnts, but quant_env will take
# precedence
if node.name in quant_env:
return True
elif node.name in env:
return False
elif isinstance(node, list):
quantized = map(is_quantized, node)
if all(quantized):
return True
elif not any(quantized):
return False
else:
raise Exception("partially quantized inputs in list not handled yet")
for node in model.graph.nodes:
if node.op == 'output':
if is_standalone_module:
# result are kept quantized in the quantized standalone module
graph_output = map_arg(node.args[0], load_x)
else:
graph_output = map_arg(node.args[0], load_non_quantized)
self.quantized_graph.output(graph_output)
continue
root_node, matched, obj, qconfig = matches.get(node.name, (None, None, None, None))
if root_node is node:
if qconfig is None:
result = self.quantized_graph.node_copy(node, load_non_quantized)
quantized = False
else:
result = obj.convert(self, node, load_arg, debug=debug, convert_custom_config_dict=convert_custom_config_dict)
if node.op == 'call_module' and is_observed_standalone_module(self.modules[node.target]):
quantized = self.modules[node.target]._output_is_observed
else:
quantized = True
# Need to get correct quantized/non-quantized state for the output of CopyNode
if isinstance(obj, CopyNode):
assert node.op in [
'call_module',
'call_function',
'call_method'], \
'CopyNode of type ' + node.op + ' is not handled'
quantized = is_quantized(node.args[0])
if not activation_is_statically_quantized(qconfig):
quantized = False
if quantized:
quant_env[node.name] = result
else:
env[node.name] = result
continue
elif root_node is not None:
continue
# handle activation post process calls
if node.op == 'call_module':
if is_activation_post_process(self.modules[node.target]):
observer_module = self.modules[node.target]
prev_node = node.args[0]
if observer_module.dtype == torch.float16:
# activations are not quantized for
# fp16 dynamic quantization
# copy the activaiton_post_process node here
# since we may need it when we insert prepack
# op for weight of linear, this will be removed
# later in a separate pass
env[node.name] = self.quantized_graph.node_copy(node, load_non_quantized)
continue
if prev_node.name in quant_env:
# if previous node is already quantized, we'll just remove the activation_post_process
quant_env[node.name] = quant_env[prev_node.name]
continue
# replace activation post process with quantization ops
root_module = self.modules['']
quant_env[node.name] = quantize_node(
root_module, self.quantized_graph,
load_non_quantized(node.args[0]), observer_module)
continue
if is_standalone_module and node.op == 'placeholder' and \
graph_inputs.index(node.name) in model._standalone_module_observed_input_idxs:
# the node is quantized in parent module
quant_env[node.name] = self.quantized_graph.node_copy(node, load_non_quantized)
else:
# dequantize inputs for the node that are not quantized
env[node.name] = self.quantized_graph.node_copy(node, load_non_quantized)
# remove activation post process
act_post_process_removed_graph = Graph()
env = {}
def load_arg(a):
return map_arg(a, lambda node: env[node.name])
for node in self.quantized_graph.nodes:
if node.op == 'output':
act_post_process_removed_graph.output(map_arg(node.args[0], load_arg))
continue
if node.op == 'call_module' and \
is_activation_post_process(self.modules[node.target]):
# remove activation post process node
env[node.name] = env[node.args[0].name]
else:
env[node.name] = act_post_process_removed_graph.node_copy(node, load_arg)
module_dict = dict(model.named_modules())
to_be_removed = []
for name, module in model.named_modules():
if is_activation_post_process(module) and not is_submodule_of_fake_quant(name, module, module_dict):
to_be_removed.append(name)
for n in to_be_removed:
delattr(model, n)
_remove_qconfig(model)
model = GraphModule(model, act_post_process_removed_graph)
return model
# Trace back from the weight node util we hit getattr, reconstruct the graph module
# with the traced nodes and run the graph module to pack the weight. then replace
# the original chain of ops with the packed weight.
def _fold_weight(self, quantized):
packed_weights = dict()
# map from folded node name to the prepacked weight name
folded_nodes = dict()
# get packed weights
for node in quantized.graph.nodes:
if node.op == 'call_function' and node.target in WEIGHT_PREPACK_OPS:
nodes_to_fold = collect_producer_nodes(node)
if nodes_to_fold is not None:
for node_to_fold in nodes_to_fold:
folded_nodes[node_to_fold.name] = node
prepacking_module = graph_module_from_producer_nodes(
quantized, nodes_to_fold)
packed_weight = prepacking_module()
packed_weights[node.name] = packed_weight
# remove folded nodes and replace the prepacking node with getattr
folded_graph = Graph()
env = {}
def load_arg(a):
return map_arg(a, lambda node: env[node.name])
get_new_packed_weight_name = get_new_attr_name_with_prefix('_fx_pass_packed_weight_')
quantized_root = quantized
quantized_graph = quantized.graph
for node in quantized_graph.nodes:
prepack_node = folded_nodes.get(node.name, None)
if prepack_node is node:
packed_weight = packed_weights[node.name]
# add a prepacked attribute to root
packed_weight_name = get_new_packed_weight_name(quantized_root)
setattr(quantized_root, packed_weight_name, packed_weight)
# replace prepack node with a getattr node
env[node.name] = folded_graph.create_node(
'get_attr', packed_weight_name, (), {})
elif prepack_node is not None:
# remove the foled node
continue
else:
# copy other nodes
env[node.name] = folded_graph.node_copy(node, load_arg)
quantized = GraphModule(quantized_root, folded_graph)
return quantized
def convert(self, model, inplace=False, debug=False, convert_custom_config_dict=None, is_standalone_module=False):
quantized = self._convert(model, inplace, debug, convert_custom_config_dict, is_standalone_module)
if not debug:
quantized = self._fold_weight(quantized)
return quantized
def _find_matches(
self, graph, modules, patterns,
standalone_module_names=None, custom_module_class_mapping=None):
"""
Matches the nodes in the input graph to quantization patterns, and
outputs the information needed to quantize them in future steps.
Inputs:
- graph: an fx.Graph object
- modules: a mapping of fully qualified module name to instance,
for example, {'foo': ModuleFoo, ...}
- patterns: a mapping from a tuple of nodes in reverse order to
uninitialized QuantizeHandler subclass.
Outputs a map of
node_name ->
(node, matched_values, QuantizeHandler instance, qconfig)
For example, {
'relu_1': (relu_1, [relu_1], <CopyNode instance>, QConfig(...)),
...
}
"""
if custom_module_class_mapping is None:
custom_module_class_mapping = {}
match_map = {}
all_matched = set()
def record_match(pattern, node, matched):
if isinstance(pattern, tuple):
s, *args = pattern
record_match(s, node, matched)
if pattern[0] is not getattr:
for subpattern, arg in zip(args, node.args):
record_match(subpattern, arg, matched)
else:
matched.append(node)
for node in reversed(graph.nodes):
if node.name not in match_map and node.name not in all_matched:
for pattern, value in patterns.items():
if is_match(modules, node, pattern):
matched = []
record_match(pattern, node, matched)
for n in matched:
match_map[n.name] = (node, matched, value(self, node), self.qconfig_map[n.name])
all_matched.add(n.name)
# break after finding the first match
break
# add custom module instances to the match result
for node in graph.nodes:
if node.op == 'call_module' and \
type(self.modules[node.target]) in custom_module_class_mapping:
custom_module_qconfig = self.qconfig_map[node.name]
match_map[node.name] = (
node, [node], CustomModuleQuantizeHandler(self, node), custom_module_qconfig)
def is_standalone_module(module_path):
if standalone_module_names is None:
return False
return module_path in standalone_module_names
# add standalone modules to the match
for node in graph.nodes:
if node.op == 'call_module' and \
(is_standalone_module(node.target) or
is_observed_standalone_module(self.modules[node.target])):
# add node to matched nodes
custom_module_qconfig = self.qconfig_map[node.name]
match_map[node.name] = (
node, [node], StandaloneModuleQuantizeHandler(self, node), custom_module_qconfig)
return match_map
def _find_quants(self, graph, matches):
"""
Takes the nodes in the input graph and pending matches, and finds and
returns the input and output nodes which need to be quantized.
Inputs:
- graph: an fx.Graph object
- matches: output of self._find_matches function
Outputs a map of
node_name -> (QuantizeHandler instance (always DefaultQuant), qconfig)
"""
quants = {}
def visit(node, qconfig):
def visit_arg(arg):
# note: we have to measure quantization information
# even for nodes where we might not use it because it is already
# quantized. This is because each match has the option to
# say NotImplemented (if for instance, it is an __add__ and the data type is not appropriate)
is_weight = False
if isinstance(node, Node) and node.op == 'call_function' and node.target in WEIGHT_INDEX_DICT:
for i, node_arg in enumerate(node.args):
if arg is node_arg and i in WEIGHT_INDEX_DICT[node.target]:
is_weight = True
if qconfig is not None and \
(activation_is_statically_quantized(qconfig) or is_weight):
# overwrite previous quant config
quants[arg.name] = (DefaultQuant(self, arg), qconfig, is_weight)
return visit_arg
for node in graph.nodes:
if node.name in matches:
root_node, matched, obj, qconfig = matches[node.name]
# don't attach observer/fake_quant for CopyNode
if isinstance(obj, CopyNode):
qconfig = None
if root_node is node:
# matched[-1] is the first op in the sequence and
# matched[0] is the last op in the sequence
# inputs
map_arg(matched[-1].args, visit(matched[-1], qconfig))
map_arg(matched[-1].kwargs, visit(matched[-1], qconfig))
# output
if isinstance(obj, StandaloneModuleQuantizeHandler):
# we don't insert observer for output of custom
# module
continue
map_arg(matched[0], visit(None, qconfig))
return quants