forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
interpreter.cpp
1265 lines (1146 loc) · 41.3 KB
/
interpreter.cpp
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
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
#include <torch/csrc/jit/interpreter.h>
#include <ATen/Parallel.h>
#include <ATen/core/ivalue.h>
#include <c10/core/thread_pool.h>
#include <c10/util/Exception.h>
#include <torch/csrc/autograd/edge.h>
#include <torch/csrc/autograd/grad_mode.h>
#include <torch/csrc/autograd/variable.h>
#include <torch/csrc/jit/constants.h>
#include <torch/csrc/jit/exception_message.h>
#include <torch/csrc/jit/graph_executor.h>
#include <torch/csrc/jit/instruction.h>
#include <torch/csrc/jit/ir.h>
#include <torch/csrc/jit/jit_log.h>
#include <torch/csrc/jit/operator.h>
#include <torch/csrc/jit/passes/bailout_graph.h>
#include <torch/csrc/jit/script/compilation_unit.h>
#include <torch/csrc/jit/script/jit_exception.h>
#include <exception>
#include <iostream>
#include <memory>
#include <mutex>
#include <ostream>
#include <stdexcept>
#include <typeinfo>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
namespace torch {
namespace jit {
// Before we translate to intepreter instructions, we do
// some preprocessing of the graph to turn it into a form that is closer
// to what the instructions will look like.
// In particular we:
// * Computes whether a input to a node is the last use, so we can issue MOVE
// rather than LOAD instructions.
// * Drop nodes are inserted for any node that is unused to create a dummy use
// that will cause the interpreter to free the node.
// A drop node just pops its input off the stack to ensure the interpreter
// releases references to nodes that are never used. Drop nodes are also
// inserted when the last use of a node is in some conditionally run control
// flow (e.g. one side of an If) and the interpreter must free the node only
// after the control flow has reconverged
// Outputs are:
// * graph - the post processed copy of g
// * move_flags[n] - a list of booleans, one for each input,
// indicating whether this is the last use of the value. The interpreter
// should generate a move rather than a copy in this case.
TensorTypePtr tensorTypeInCurrentExecutionContext(const at::Tensor& t) {
if (!t.defined()) {
return TensorType::get()->withUndefined();
}
auto r = TensorType::create(t);
if (!at::GradMode::is_enabled()) {
return r->withRequiresGrad(false);
}
return r;
}
namespace {
// insert Drop nodes to kill references for anything unused:
// this can happen in a few places, e.g. when a node returns
// many values but only one is used
// a, b = foo()
// return a
void dropUnused(Block* b) {
auto createDropIfUnused = [&](ArrayRef<Value*> values) -> Node* {
std::vector<Value*> to_drop;
for (auto v : values) {
if (v->uses().size() == 0 && v->node()->kind() != prim::Constant)
to_drop.push_back(v);
}
if (to_drop.size() == 0)
return nullptr;
return b->owningGraph()->create(prim::Drop, to_drop, 0);
};
if (auto d = createDropIfUnused(b->inputs())) {
b->prependNode(d);
}
for (auto n : b->nodes()) {
if (auto d = createDropIfUnused(n->outputs())) {
d->insertAfter(n);
}
for (auto b : n->blocks())
dropUnused(b);
}
}
// ensure every value has a final use in the same block where it is defined.
// This already true for most nodes. The exceptions are:
// 1. A value that is unused.
// 2. A value whose last use is nested in some control flow.
// For (1) we simply add a prim::Drop node that uses the value right after
// it is defined. For (2), we insert a prim::Drop right after the control
// flow node where the last use occurs
void insertLastUses(Graph& g) {
// struct to share common data structures
struct InsertLastUses {
Graph& graph;
// have we seen this value, yet, if not, it is the last use of the value
std::unordered_set<Value*> seen;
// A map from an If or Loop node to the optional Drop block that
// occurs directly after it to release any tensors that go out of scope
// when the If/Loop exits. These are created and inserted on demand.
std::unordered_map<Node*, Node*> drop_for_node;
InsertLastUses(Graph& g) : graph(g) {
scanBlock(graph.block());
}
void scanBlock(Block* b) {
scanNode(b->return_node());
for (auto n : b->nodes().reverse()) {
scanNode(n);
}
}
void scanNode(Node* n) {
for (auto b : n->blocks()) {
scanBlock(b);
}
// scan backwards so if a value is used twice in the list then it is a
// move
for (size_t i = n->inputs().size(); i > 0; --i) {
scanUse(n, i - 1);
}
}
void scanUse(Node* n, size_t i) {
auto v = n->inputs()[i];
auto inserted = seen.insert(v).second;
if (!inserted) {
return;
}
// the last use of v may be in a nested block of an If or Loop statement
// find the node 'same_depth_node' at the same depth as the definition of
// v, and consider that node to be the last use of v. This ensures we do
// not delete nodes in nested scopes that may be executed multiple times
// and that nodes used on one side of an if
// but not the other get deleted regardless of the branch
// e.g.
// a = 4
// while <...>:
// y = a + a
// drop(a)
// In other words, we find the first program point for v that
// _reverse_ dominates the definition of v, and add a drop point there.
Node* same_depth_node = findOwnerInBlock(n, v->node()->owningBlock());
AT_ASSERT(
same_depth_node); // failure means v is not in scope for n, use lint!
// In the case where v and n are in the same block,
// we have a legit final use already.
if (same_depth_node == n) {
return;
}
// in the case where the use is nested in a block
// add a Drop node after that block which will drop 'v'.
addToDropIfNotExists(
findOrCreateDropInstructionForNode(same_depth_node), v);
}
// finds the node in block 'block' that contains in 'n'
// or nullptr if no such node exists, e.g.:
// n0: a = 4
// n1: if <cond>:
// n2: b = a + a
// findOwnerInBlock(n2, n0.block()) == n1
Node* findOwnerInBlock(Node* n, Block* block) {
while (n != nullptr && block != n->owningBlock()) {
n = n->owningBlock()->owningNode();
}
return n;
}
Node* findOrCreateDropInstructionForNode(Node* n) {
auto it = drop_for_node.find(n);
if (it == drop_for_node.end()) {
auto drop_node = graph.create(prim::Drop, 0);
drop_node->insertAfter(n);
it = drop_for_node.emplace(n, drop_node).first;
}
return it->second;
}
void addToDropIfNotExists(Node* drop, Value* v) {
if (v->node()->kind() == prim::Constant) {
return;
}
for (auto i : drop->inputs()) {
// we already accounted for this use
if (i == v)
return;
}
drop->addInput(v);
}
};
InsertLastUses ilu(g);
}
} // namespace
std::ostream& operator<<(std::ostream& out, Instruction inst);
/*
This is an optimization that reduces the number of store/load/move nodes needed
by recognizing that parts of the graph are simple trees like a*x + b*y. When
this happens it is possible to work directly off of the stack by emitting the
tree in a depth-first left-to-right manner:
load a
load x
mul # stack now is a*x
load b
load y
mul # stack now is a*x, b*y
add
can_emit_inline_[node] == true means that this node participates as a non-root
member of one of these trees. The code emitter will not emit this node when
it is encountered in the node. Instead the node is emitted in a depth first
traversal from where it is used in a tree.
To participate in a tree a node must have a single use (otherwise it is not
tree-like) and output a single value (for simplicity.) If our IR was functional,
these would be the only constraints. However, many nodes have side effects, so
we must ensure that emitting the nodes in depth first order from the tree's root
_does not reorder the emission of the nodes_. To ensure this, we work backward
from the root of a potential tree, visiting its inputs in reverse depth first
order, while scanning the node list backward (with the block_point node). When
these traversal line up we know it is safe to emit the tree in this way. We
ignore constant nodes, which do not have side effects.
*/
struct CanEmitInline {
CanEmitInline(const std::shared_ptr<Graph>& graph) {
scanBlock(graph->block());
}
bool canInline(Value* v) {
return v->node()->kind() != prim::Param &&
// without this a BailOut may float downstream past some later
// BailOut
// and receive a higher jf_index. Then a GUARD instruction
// we generated for the floated BailOut will get popped up from the
// instruction stack
// by the later BailOut in createBailoutBlock and its jf_index
// will become invalid.
v->node()->kind() != prim::BailOut && v->uses().size() == 1 &&
v->node()->outputs().size() == 1;
}
Node* previousNonConstant(Node* n) {
do {
n = n->prev();
} while (n->kind() == prim::Constant);
return n;
}
Node* scanValue(Node* block_point, Value* v) {
// this node is a candidate for inline, if our reverse scan of the
// node list lines up with the use of v, we know it will be emitted in
// tree order, and we can inlining. Scan continutes for further nodes.
if (v->node() == block_point && canInline(v)) {
// since we inlined this node, we may be able to recursively inline
// its inputs, so we continue scanning it
block_point = scanNode(v->node());
can_emit_inline_[v->node()] = true;
}
// if it does not line up, we can't inline 'v', and will just generate
// a load/move for it. However, other inputs may still appear in tree
// order so we continue the scan of the inputs.
return block_point;
}
Node* scanNode(Node* n) {
// don't bother to scan nodes we have already determined to be inline
if (can_emit_inline_.count(n)) {
return nullptr;
}
for (auto b : n->blocks()) {
scanBlock(b);
}
Node* block_point = previousNonConstant(n);
for (auto it = n->inputs().rbegin(), end = n->inputs().rend(); it != end;
++it) {
block_point = scanValue(block_point, *it);
}
return block_point;
}
void scanBlock(Block* b) {
scanNode(b->return_node());
for (auto node : b->nodes().reverse()) {
scanNode(node);
}
}
std::unordered_map<Node*, bool> can_emit_inline_;
};
// pre-processing that happens once per graph
struct PreprocessGraph {
PreprocessGraph(Graph& g) : graph(g.copy()) {
dropUnused(graph->block());
// fill in move_flags by scanning blocks;
insertLastUses(*graph);
can_emit_inline = std::move(CanEmitInline(graph).can_emit_inline_);
}
// Outputs of the preprocessing:
std::shared_ptr<Graph> graph;
std::unordered_map<Node*, bool> can_emit_inline;
};
// for keeping track of the current node
struct WithCurrentNode {
WithCurrentNode(Node** loc, Node* new_value) : loc_(loc), old_value_(*loc_) {
*loc = new_value;
}
~WithCurrentNode() {
*loc_ = old_value_;
}
private:
Node** loc_;
Node* old_value_;
};
// BailoutBlocks are used to temporarily store
// instructions (typically, argument LOADs and TAIL_CALL)
// generated for prim::BailOut nodes
// before they are merged back into
// CodeImpl._instructions_ by insertBailoutBlocks
struct BailoutBlock {
size_t jf_instruction_index; // this node gets patched to jump here on failure
std::vector<Instruction> instructions; // ends in a TAIL_CALL
};
struct CodeImpl {
friend struct InterpreterState;
std::vector<Instruction> instructions_;
// same length as instructions.
// what node in the graph cause this
// instruction to be emitted?
std::vector<Node*> instructions_source_;
std::vector<IValue> constant_table_;
std::vector<Operation> operator_table_;
std::vector<Function*> function_table_;
std::vector<TypePtr> type_table_;
int register_size_ = 0;
size_t n_outputs;
size_t n_inputs;
TypePtr return_type_;
// We MUST hold onto graph here because some Operators stored in the
// instruction lists have dependencies on meta-data stored in the graph
// that would be dead otherwise.
// It is also very useful for debugging interpreter problems to
// keep this around.
std::shared_ptr<Graph> graph_;
c10::optional<std::vector<GraphExecutor*>> grad_executors_;
PreprocessGraph preprocess_;
// map from unique of nodes to register in register table
std::unordered_map<Value*, int> value_to_reg_;
// running count of uses as we emit. When we reach use_count_[v] =
// v.uses().size() we know it is the final use and we can move rather than
// load.
std::unordered_map<Value*, size_t> use_count_;
Node* current_node_; // used in creation of code to keep track
// of node being emitted
Node* last_inserted_op_ = nullptr;
// out-of-line jumps for bailouts that are patched in at the end
std::vector<BailoutBlock> bailout_blocks_;
std::vector<std::unique_ptr<Function>> bailout_functions_;
size_t remaining_bailout_depth_;
CodeImpl(const std::shared_ptr<Graph>& graph, size_t remaining_bailout_depth)
: preprocess_(*graph),
current_node_(preprocess_.graph->return_node()),
remaining_bailout_depth_(remaining_bailout_depth) {
graph_ = preprocess_.graph;
n_outputs = graph_->outputs().size();
if (n_outputs == 1) {
return_type_ = graph->outputs().at(0)->type();
} else {
return_type_ = TupleType::create(
fmap(graph->outputs(), [](const Value* v) { return v->type(); }));
}
n_inputs = graph_->inputs().size();
// std::cout << *graph_ << "\n";
emitCodeForBlock(graph_->block());
insertInstruction(RET);
// we deferred the emission of bailout blocks so they appear at the end
// emit them now and patch up the jumps
insertBailoutBlocks();
}
const std::vector<c10::IValue>& constant_table() const {
return constant_table_;
}
void request_bailout(size_t index) {
auto count = index;
for (size_t instr_index = 0; instr_index < instructions_.size();
instr_index++) {
if (instructions_[instr_index].op == GUARD || instructions_[instr_index].op == FAIL_GUARD) {
if (count-- == 0) {
// patching GUARD to FAIL_GUARD
instructions_[instr_index] =
Instruction(FAIL_GUARD, instructions_[instr_index].X, 0);
GRAPH_DEBUG(
"Added a bailout request for ",
index,
" at instruction ",
instr_index);
break;
}
}
}
}
const std::vector<Instruction>& instructions() const {
return instructions_;
}
const std::vector<Node*>& instructions_source() const {
return instructions_source_;
}
void insertInstruction(OpCode op, int64_t X = 0, uint64_t N = 0) {
instructions_.emplace_back(op, X, N);
instructions_source_.emplace_back(current_node_);
// check that we didn't accidentally emit nodes out of topological order
if (op == OP) {
if (last_inserted_op_ != nullptr && current_node_ != last_inserted_op_ &&
current_node_->owningBlock() == last_inserted_op_->owningBlock()) {
TORCH_INTERNAL_ASSERT(
current_node_->isAfter(last_inserted_op_),
*current_node_,
" is not after ",
*last_inserted_op_);
}
last_inserted_op_ = current_node_;
}
}
void truncateInstructions(size_t size) {
while(instructions_.size() > size) {
instructions_.pop_back();
instructions_source_.pop_back();
}
}
void createBailoutBlock(size_t jf_index) {
bailout_blocks_.emplace_back(BailoutBlock{jf_index});
auto& bailout_instructions = bailout_blocks_.back().instructions;
bailout_instructions.insert(
bailout_instructions.end(),
instructions_.begin() + jf_index + 1,
instructions_.end());
truncateInstructions(jf_index + 1);
}
int allocRegs(at::ArrayRef<Value*> vs) {
int result = register_size_ + 1;
for (Value* v : vs) {
AT_ASSERT(value_to_reg_.count(v) == 0);
value_to_reg_[v] = ++register_size_;
}
return result;
}
int registerFor(Value* v) {
return value_to_reg_.at(v);
}
void emitUse(Value* input, bool drop) {
// drop - if true, we are not actually going to use this thing
// and we can short circuit doing many instructions here
// by either clearing the register (DROPR) or just popping the stack
// (DROP)
if (preprocess_.can_emit_inline[input->node()]) {
emitNode(input->node());
if (drop) {
insertInstruction(DROP);
}
} else {
int reg = registerFor(input);
bool moved = input->uses().size() == ++use_count_[input];
OpCode op;
if (input->node()->kind() == prim::Constant) {
op = LOADC;
} else if (drop) {
op = DROPR;
} else if (moved) {
op = MOVE;
} else {
op = LOAD;
}
insertInstruction(op, reg);
}
}
void emitLoadInputs(at::ArrayRef<Value*> inputs) {
for (Value* input : inputs) {
emitUse(input, false);
}
}
void emitOperator(Node* node) {
emitLoadInputs(node->inputs());
insertInstruction(OP, operator_table_.size());
operator_table_.emplace_back(getOperation(node));
}
void emitWait(Node* node) {
emitLoadInputs(node->inputs());
insertInstruction(WAIT);
}
void emitDrop(at::ArrayRef<Value*> to_drop) {
for (Value* input : to_drop) {
emitUse(input, true);
}
}
void emitStoreOutputs(Node* node) {
size_t N = node->outputs().size();
if (N == 0)
return;
int regs = allocRegs(node->outputs());
if (N == 1) {
insertInstruction(STORE, regs);
} else {
insertInstruction(STOREN, regs, node->outputs().size());
}
}
int insertConstant(IValue value) {
int result = constant_table_.size();
constant_table_.emplace_back(std::move(value));
return result;
}
void emitConstant(Node* node) {
if (node->output()->type()->kind() == FunctionType::Kind) {
return;
}
// constants are just put in the constant table
value_to_reg_[node->output()] =
insertConstant(toIValue(node->output()).value());
}
void emitIf(Node* node) {
emitLoadInputs(node->inputs());
size_t start_if = instructions_.size();
insertInstruction(JF, 0); // dummy offset to be filled in
emitCodeForBlock(node->blocks().at(0));
insertInstruction(JMP, 0); // dummy offset
size_t start_else = instructions_.size();
instructions_[start_if].X = start_else - start_if;
emitCodeForBlock(node->blocks().at(1));
instructions_[start_else - 1].X = instructions_.size() - (start_else - 1);
}
void emitLoop(Node* loop) {
insertInstruction(LOADC, insertConstant(0));
emitLoadInputs(loop->inputs());
size_t start = instructions_.size();
insertInstruction(LOOP, 0, loop->inputs().size()); // dummy offset
emitCodeForBlock(loop->blocks().at(0));
insertInstruction(JMP, start - instructions_.size());
instructions_[start].X = instructions_.size() - start;
}
void emitCall(
Function* func,
at::ArrayRef<Value*> inputs) {
emitLoadInputs(inputs);
insertInstruction(CALL, function_table_.size());
function_table_.emplace_back(std::move(func));
}
void emitNodeAtBlockLevel(Node* node) {
WithCurrentNode guard(¤t_node_, node);
switch (node->kind()) {
case prim::Constant:
emitConstant(node);
break;
case prim::Return:
emitLoadInputs(node->inputs());
break;
default:
if (!preprocess_.can_emit_inline[node]) {
emitNode(node);
emitStoreOutputs(node);
}
break;
}
}
size_t emitGuard(Node* node) {
// unoptimized graph is at index 0
// guarded input is at index 1
// the rest of args follow
emitLoadInputs(node->inputs().slice(1, 1));
insertInstruction(GUARD, type_table_.size());
type_table_.emplace_back(node->outputs().at(0)->type());
insertInstruction(JF, 0 /* to be patched */);
return instructions_.size() - 1;
}
void emitBailOut(Node* node) {
auto jf_index = emitGuard(node);
auto unoptimized_graph = node->inputs().at(0)->node()->g(attr::Subgraph);
// note, guaded input is already loaded onto the stack
// for GUARD instruction
emitLoadInputs(node->inputs().slice(2));
insertInstruction(TAIL_CALL, function_table_.size());
TORCH_INTERNAL_ASSERT(node->kind() == prim::BailOut);
auto bailout_index = node->i(attr::index);
TORCH_INTERNAL_ASSERT(bailout_index >= 0);
auto build_bailout_graph = [bailout_index,
unoptimized_graph](Function &func) {
BuildBailOutGraphFrom(bailout_index, unoptimized_graph, func.graph());
};
auto empty_graph = std::make_shared<Graph>();
auto func = torch::make_unique<Function>(
"bailout", empty_graph, build_bailout_graph);
function_table_.emplace_back(func.get());
bailout_functions_.emplace_back(std::move(func));
createBailoutBlock(jf_index);
}
void emitGetAttr(Node* node) {
emitLoadInputs(node->inputs());
const auto type = node->input()->type()->expect<ClassType>();
const auto& field = node->s(attr::name);
const auto slot = type->getAttributeSlot(field);
insertInstruction(GET_ATTR, slot);
}
void emitSetAttr(Node* node) {
emitLoadInputs(node->inputs());
const auto type = node->inputs().at(0)->type()->expect<ClassType>();
const auto& field = node->s(attr::name);
const auto slot = type->getAttributeSlot(field);
insertInstruction(SET_ATTR, slot);
}
void insertBailoutBlocks() {
for(const BailoutBlock& block : bailout_blocks_) {
TORCH_INTERNAL_ASSERT(instructions_[block.jf_instruction_index].op == JF)
instructions_[block.jf_instruction_index].X =
instructions_.size() - block.jf_instruction_index;
instructions_.insert(
instructions_.end(),
block.instructions.begin(),
block.instructions.end());
instructions_source_.insert(
instructions_source_.end(),
block.instructions.size(),
instructions_source_[block.jf_instruction_index]);
}
}
void emitInterfaceCall(
std::string method_name_str,
c10::ArrayRef<Value*> inputs) {
emitLoadInputs(inputs);
auto method_name = insertConstant(std::move(method_name_str));
insertInstruction(INTERFACE_CALL, method_name, inputs.size());
}
void emitNode(Node* node) {
WithCurrentNode guard(¤t_node_, node);
switch (node->kind()) {
default:
emitOperator(node);
break;
case prim::Drop:
emitDrop(node->inputs());
break;
case prim::Constant:
emitConstant(node);
break;
case prim::If:
emitIf(node);
break;
case prim::Loop:
emitLoop(node);
break;
case aten::wait:
emitWait(node);
break;
case prim::Param:
break;
case prim::CallFunction:
emitCall(
node->inputs().at(0)->type()->expect<FunctionType>()->function(),
node->inputs().slice(1));
break;
case prim::CallMethod:
if (auto class_type = node->inputs().at(0)->type()->cast<ClassType>()) {
emitCall(class_type->getMethod(node->s(attr::name)), node->inputs());
} else {
emitInterfaceCall(node->s(attr::name), node->inputs());
}
break;
case prim::BailOut:
emitBailOut(node);
break;
case prim::GetAttr:
emitGetAttr(node);
break;
case prim::SetAttr:
emitSetAttr(node);
break;
}
}
void emitCodeForBlock(Block* block) {
emitNodeAtBlockLevel(block->param_node());
for (auto node : block->nodes()) {
emitNodeAtBlockLevel(node);
}
emitNodeAtBlockLevel(block->return_node());
}
const std::vector<GraphExecutor*>& grad_executors() {
if (!grad_executors_) {
grad_executors_.emplace();
for (Operation& op : operator_table_) {
if (auto executor = detail::getGradExecutor(op)) {
grad_executors_->push_back(executor);
}
}
}
return *grad_executors_;
}
void dump(std::ostream& out, size_t i) const {
out << i << " " << instructions_[i];
if (instructions_[i].op == OP || instructions_[i].op == CALL) {
out << " # " << *instructions_source_[i];
} else {
out << "\n";
}
}
void dump(std::ostream& out) const {
out << *graph_ << "\n";
for (size_t i = 0; i < instructions_.size(); ++i) {
dump(out, i);
}
}
};
// InterpreterState state that and used to compute a Code
struct InterpreterStateImpl : c10::intrusive_ptr_target {
InterpreterStateImpl(const Code& code) {
enterFrame(code, 0);
}
private:
// if we need to suspend, where do we reset the stack?
// answer: to where it was when we were called, not
// including any inputs to this function
int64_t stack_start_ = -1;
c10::intrusive_ptr<Future> future_;
// this holds all the tensors for this interpreter run
// we don't bother minimizing the size of this vector, since the extra
// memory used by the pointers in this will be small
// instead we are very aggresive about releasing tensors when they become dead
// to make sure memory management happens efficiently.
// We optimize for the case where derivatives are run with retain_graph=False
// in the case where it is true, then the interpreter and this array get
// copied if this every becomes a bottleneck then we _should_ consider
// minimizing the total number or register
std::vector<IValue> registers;
// A Frame captures function's state
// (e.g. `pc` and `base_pointer`)
// Each Frame corresponds to a call to a `Frame::function`
// which has not yet returned
// The arguments for `Frame::function`
// are located at [base_pointer + arg_number]
struct Frame {
std::shared_ptr<CodeImpl> function;
// program counter corresponds to the index
// of the currently executed instruction
size_t pc;
// marks the start index of the frame
// base_pointer is used by TAIL_CALL
// to replace the current frame
// with a frame of a bailout graph
size_t base_pointer;
};
// saved-by-value stuff that can exist on the stack inside runInterpreter
struct ActiveFrame {
size_t pc;
Instruction* instructions;
IValue* constants;
Operation* operators;
Function** functions;
TypePtr* types;
ActiveFrame(const Frame& frame)
: pc(frame.pc),
instructions(frame.function->instructions_.data()),
constants(frame.function->constant_table_.data()),
operators(frame.function->operator_table_.data()),
functions(frame.function->function_table_.data()),
types(frame.function->type_table_.data()) {}
};
std::vector<Frame> frames;
c10::intrusive_ptr<InterpreterStateImpl> intrusive_from_this() {
c10::raw::intrusive_ptr::incref(this);
return c10::intrusive_ptr<InterpreterStateImpl>::reclaim(this);
}
void enterFrame(const Code& code, size_t base_pointer) {
frames.emplace_back(Frame{code.pImpl, 0, base_pointer});
registers.resize(registers.size() + code.pImpl->register_size_);
// frames.back().function->dump(std::cout);
}
void leaveFrame() {
registers.resize(registers.size() - frames.back().function->register_size_);
frames.pop_back();
}
// relative to the end of the register list so that when we call
// functions we are referring to the registers of the currenly executing
// function.
IValue& reg(size_t reg) {
return *(registers.end() - reg);
}
void dump(std::ostream& out, const Stack& stack) const {
out << "Stack:\n";
for (const auto& val : stack) {
out << val;
out << "\n";
}
}
bool runImpl(Stack& stack) {
// if we have never run before, then we might have to return the
// stack when we suspend, record where it starts so we return the right
// stack
if (stack_start_ == -1) {
TORCH_INTERNAL_ASSERT(stack.size() >= frames.back().function->n_inputs);
stack_start_ = stack.size() - frames.back().function->n_inputs;
} else {
// during restarts, all of the stack is always our own, so we leave
// nothing
stack_start_ = 0;
}
ActiveFrame af(frames.back());
try {
while (true) {
// std::cout << "RUNNING ";
// frames.back().function->dump(std::cout, af.pc);
Instruction inst = af.instructions[af.pc];
switch (inst.op) {
case OP:
af.operators[inst.X](stack);
++af.pc;
break;
case OPN:
AT_ERROR("OPN is currently supported in mobile mode only.");
break;
case LOAD:
stack.emplace_back(reg(inst.X));
++af.pc;
break;
case MOVE:
stack.emplace_back(std::move(reg(inst.X)));
++af.pc;
break;
case STORE:
reg(inst.X) = pop(stack);
++af.pc;
break;
case STOREN:
for (size_t i = inst.N; i > 0; --i) {
reg(inst.X + i - 1) = pop(stack);
}
++af.pc;
break;
case DROP:
pop(stack);
++af.pc;
break;
case DROPR:
reg(inst.X) = IValue();
++af.pc;
break;
case LOADC:
stack.emplace_back(af.constants[inst.X]);
++af.pc;
break;
case GET_ATTR: {
auto userObj = pop(stack).toObject();
auto value = userObj->getSlot(inst.X);
push(stack, std::move(value));
++af.pc;
} break;
case SET_ATTR: {
auto v = pop(stack);
auto userObj = pop(stack).toObject();
userObj->setSlot(inst.X, std::move(v));
++af.pc;
} break;
case JF:
af.pc += (pop(stack).toBool()) ? 1 : inst.X;
break;
case JMP:
af.pc += inst.X;
break;
case LOOP: {
// stack: iteration_count, max_iter, cond, loop_carried_deps...
auto frame = stack.end() - (inst.N + 1);
int64_t trip_count = frame[0].toInt();
int64_t max_trip_count = frame[1].toInt();
bool cond = frame[2].toBool();
if (trip_count < max_trip_count && cond) {
frame[2] = trip_count;
frame[0] = trip_count + 1;
++af.pc;
} else {
size_t n_loop_carried = inst.N - 2;
for (size_t i = 0; i < n_loop_carried; ++i) {
frame[i] = std::move(frame[i + 3]);
}
drop(stack, 3); // iteration_count, max_iter, cond
af.pc += inst.X;
}
} break;
case CALL: {
const Code& code =
// consider passing
// `frames.back().function->remaining_bailout_depth_` into
// `get_executor().getPlanFor()` to propagate caller's depth
// restrictions onto children while this strategy has a
// potential to reduce the number of compilations for too
// dynamic callers we might miss opportunities where a caller is
// dynamic but a callee gets stable arguments
af.functions[inst.X]
->get_executor()
.getPlanFor(stack, GraphExecutor::getDefaultNumBailOuts())
.code;
frames.back().pc = af.pc + 1;
enterFrame(code, stack.size() - code.num_inputs());
af = ActiveFrame(frames.back());
} break;
case INTERFACE_CALL: {
// note the hash table lookup to find the function
// this can be more optimized if necessary, caching parts
// of the hashing computation or storing the offset when
// the object is turned into an interface
// consider passing
// `frames.back().function->remaining_bailout_depth_` into
// `get_executor().getPlanFor()` to propagate caller's depth
// restrictions onto children while this strategy has a potential to
// reduce the number of compilations for too dynamic callers we
// might miss opportunities where a caller is dynamic but a callee
// gets stable arguments
auto function = peek(stack, 0, inst.N)
.toObject()
->type()
->getMethod(af.constants[inst.X].toStringRef());
const Code& code =
function->get_executor()
.getPlanFor(stack, GraphExecutor::getDefaultNumBailOuts())
.code;
frames.back().pc = af.pc + 1;