forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
fixup_trace_scope_blocks.cpp
553 lines (508 loc) · 20.2 KB
/
fixup_trace_scope_blocks.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
#include <torch/csrc/jit/passes/fixup_trace_scope_blocks.h>
#include <c10/util/irange.h>
#include <torch/csrc/jit/frontend/schema_matching.h>
#include <torch/csrc/jit/passes/canonicalize.h>
#include <torch/csrc/jit/passes/dead_code_elimination.h>
#include <torch/csrc/jit/passes/inliner.h>
#include <torch/csrc/jit/passes/lower_tuples.h>
#include <algorithm>
namespace torch::jit {
namespace {
bool isEligibleNode(Node* n) {
return n->kind() == prim::TracedModuleForward ||
n->kind() == prim::TracedFork;
}
// This pass does several things:
// 1) It looks at TracedModuleForward nodes and resolves the type of `self`
// for that (to-be) method call. It adds an input of that type to the
// block, and adds the TracedAttr value corresponding to that `self`
// value as a Node input. This ensures `self` is an explicit Use on
// the node, a property we take advantage of downstream. Example:
// 2) Convert all references to prim::TracedAttr values to prim::GetAttr
// calls in the tightest scope possible. Concretely, for each use of
// a prim::TracedAttr value, we compare the scope of that attribute
// to the scope of the Use. We emit GetAttr nodes for all atoms
// that are not shared between the two. For example, if an
// attribute `f.param` is referenced in scope `f`, we emit a
// GetAttr[name="param"](%self) node in the `f` block, where
// `self` is the previously-added `self` argument to the block.
// 3) Destroy all the prim::TracedAttr nodes, as they should have
// no more uses.
//
// A quick example:
//
//
// Input graph:
//
// graph(%self : ClassType<Module>,
// %x : Float(3, 4)):
// %1 : bool = prim::TracedAttr[scope="__module.training"]()
// %2 : ClassType<Module> = prim::TracedAttr[scope="__module.f"]()
// %3 : Float(4, 4) = prim::TracedAttr[scope="__module.f.param"]()
// %4 : bool = prim::TracedAttr[scope="__module.f.training"]()
// = prim::TracedModuleForward[scope="__module.f"](),
// block0():
// %6 : Float(3, 4) = aten::mm(%x, %3),
// -> ()
// return (%6)
//
// The diff after step (1)
//
// - = prim::TracedModuleForward[scope="__module.f"](),
// - block0():
// + = prim::TracedModuleForward[scope="__module.f"](%2),
// + block0(%self : ClassType<Module>):
//
// The diff after step (2)
//
// graph(%self.1 : ClassType<Module>,
// %x : Float(3, 4)):
// + %9 : ClassType<Module> = prim::GetAttr[name="f"](%self.1)
// %1 : bool = prim::TracedAttr[scope="__module.training"]()
// <....>
// %4 : bool = prim::TracedAttr[scope="__module.f.training"]()
// - = prim::TracedModuleForward[scope="__module.f"](%2),
// + = prim::TracedModuleForward[scope="__module.f"](%9),
// block0(%self : ClassType<Module>):
// - %6 : Float(3, 4) = aten::mm(%x, %3),
// + %8 : Tensor = prim::GetAttr[name="param"](%self)
// + %6 : Float(3, 4) = aten::mm(%x, %8),
// -> ()
// return (%6)
//
// The diff after step (3)
//
// - %1 : bool = prim::TracedAttr[scope="__module.training"]()
// - %2 : ClassType<Module> = prim::TracedAttr[scope="__module.f"]()
// - %3 : Float(4, 4) = prim::TracedAttr[scope="__module.f.param"]()
// - %4 : bool = prim::TracedAttr[scope="__module.f.training"]()
struct ConvertTracedAttrReferences {
void run(const std::shared_ptr<Graph>& graph) {
// Build a table mapping--for each TracedAttr node--the
// qualified name of the attribute to the Value* output
// of the Node.
buildAttrMap(graph);
// Step 1
addSelfArgToTracedForwardNodes(graph->block());
// Step 2
convertAttrReferencesToLocalGetAttrs(
graph->block(), "__module", graph->inputs()[0]);
// Step 3
destroyTracedAttrNodes(graph);
}
private:
void buildAttrMap(const std::shared_ptr<Graph>& graph) {
for (Node* n : graph->nodes()) {
if (n->kind() == prim::TracedAttr) {
attr_qualname_to_value[n->s(attr::scope)] = n->output();
}
}
}
void addSelfArgToTracedForwardNodes(Block* b) {
for (Node* n : b->nodes()) {
if (n->kind() == prim::TracedModuleForward) {
n->addInput(attr_qualname_to_value.at(n->s(attr::scope)));
n->blocks()[0]->addInput("self")->setType(
attr_qualname_to_value.at(n->s(attr::scope))->type());
addSelfArgToTracedForwardNodes(n->blocks()[0]);
}
if (n->kind() == prim::TracedFork) {
addSelfArgToTracedForwardNodes(n->blocks()[0]);
}
}
}
// This is a recursive function that descends down all blocks in the Graph
// (NB: not just TracedModuleForward blocks). Each descension has a
// corresponding `prefix`, i.e. the qualified name of the scope this
// Block represents (or the scope in which this block resides for
// non-TracedModuleForward nodes). We use this prefix to make decisions
// about whether to emit a GetAttr node for an attribute reference, or
// to defer that emission to the caller (in the case where an attribute
// reference does not reside in the `prefix` scope).
std::vector<Value*> convertAttrReferencesToLocalGetAttrs(
Block* b,
const c10::QualifiedName& prefix,
Value* self) {
// Store away Value*'s which are references to TracedAttr's which are
// not in the `prefix` scope. We pass this back to the caller, who
// should add these Values as explicit inputs as well as inductively
// make the same decision on those Values.
std::vector<Value*> unresolved_tracedattrs;
// To ensure we don't emit redundant GetAttr Nodes in a given scope,
// we maintain this map of original TracedAttr Value* to the Value*
// corresponding to the GetAttr for that attribute.
// We don't rely on CSE here because we currently can't reason about
// the correctness of CSE over GetAttr Nodes (i think)
std::unordered_map<Value*, Value*> local_remaps;
for (Node* n : b->nodes()) {
// The only difference between these two branches is for
// TracedModuleForward we advance the scope, but for other
// Nodes with Blocks we don't
if (n->kind() == prim::TracedModuleForward) {
auto sub_unresolved = convertAttrReferencesToLocalGetAttrs(
n->blocks()[0], n->s(attr::scope), n->blocks()[0]->inputs()[0]);
for (Value* v : sub_unresolved) {
n->addInput(v);
}
} else if (!n->blocks().empty()) {
for (Block* sub_block : n->blocks()) {
auto sub_unresolved =
convertAttrReferencesToLocalGetAttrs(sub_block, prefix, self);
for (Value* v : sub_unresolved) {
n->addInput(v);
}
}
}
for (size_t inp_idx = 0; inp_idx < n->inputs().size(); ++inp_idx) {
Value* inp = n->input(inp_idx);
// Short circuit: if we've already emitted a new Value for this
// attribute, just use that.
if (local_remaps.count(inp)) {
n->replaceInput(inp_idx, local_remaps[inp]);
continue;
}
WithInsertPoint guard(b->param_node()->next());
replaceTracedAttrInputOnNode(
n, inp_idx, prefix, self, local_remaps, unresolved_tracedattrs);
} // for (Value *inp : n->inputs())
} // for (Node *n : b->nodes())
return unresolved_tracedattrs;
}
void replaceTracedAttrInputOnNode(
Node* n,
size_t inp_idx,
const c10::QualifiedName& prefix,
Value* self,
std::unordered_map<Value*, Value*>& local_remaps,
std::vector<Value*>& unresolved_tracedattrs) {
auto inp = n->inputs()[inp_idx];
auto inp_node = inp->node();
auto prefix_atoms = prefix.atoms();
if (inp_node->kind() == prim::TracedAttr) {
auto attr_qualname = c10::QualifiedName(inp_node->s(attr::scope));
if (prefix.isPrefixOf(attr_qualname)) {
// Prefix case: the attribute resides in this scope or a
// sub-scope. Continually emit GetAttr nodes until we've reached
// the proper attribute.
auto attr_atoms = attr_qualname.atoms();
Value* replaced_value = self;
for (const auto i : c10::irange(attr_atoms.size())) {
if (i < prefix_atoms.size()) {
TORCH_INTERNAL_ASSERT(attr_atoms[i] == prefix_atoms[i]);
} else {
replaced_value = n->owningBlock()->owningGraph()->insertGetAttr(
replaced_value, attr_atoms[i]);
} // if (i < prefix_atoms.size())
} // for(const auto i : c10::irange(attr_atoms.size()))
n->replaceInput(inp_idx, replaced_value);
local_remaps[inp] = replaced_value;
} else {
// Non-prefix case: this is a use of an attribute somewhere
// higher in the Module hierarchy. Add a captured input to
// the block for this attribute and add to the vector of
// Value*'s for the caller to handle.
Value* remapped = n->owningBlock()->addInput()->copyMetadata(inp);
n->replaceInput(inp_idx, remapped);
unresolved_tracedattrs.push_back(inp);
local_remaps[inp] = remapped;
} // if (prefix.isPrefixOf(attr_qualname))
} // if (inp_node->kind() == prim::TracedAttr)
}
// The previous pass should have deleted all uses of TracedAttr
// nodes. Let's explicitly delete them here.
void destroyTracedAttrNodes(const std::shared_ptr<Graph>& graph) {
for (auto& kv : attr_qualname_to_value) {
kv.second->node()->destroy();
}
}
// For each prim::TracedAttr, record the `scope` value mapped
// to the Value* in the graph for that attribute.
std::unordered_map<std::string, Value*> attr_qualname_to_value;
};
// Iterate through all the nodes in program order and--for each use--
// if the Value referenced is not in a scope that dominates the node,
// add block and Node outputs to lift it into a scope in which
// it dominates the Use.
struct MakeDefsDominateUses {
MakeDefsDominateUses() = default;
void run(Block* b) {
processNode(b->param_node(), b);
for (Node* n : b->nodes()) {
processNode(n, b);
}
processNode(b->return_node(), b);
}
private:
void processNode(Node* n, Block* b) {
for (size_t i = 0; i < n->inputs().size(); ++i) {
Value* inp = n->inputs()[i];
// Already lifted to this level by a previously processed Use, switch to
// remapped value
Value* inp_remapped = inp;
if (remap.count(inp_remapped)) {
n->replaceInput(i, remap[inp_remapped]);
inp_remapped = remap[inp_remapped];
}
// This conditional isn't strictly necessary, but saves a lot of
// computation in the common case that we're using a local value.
if (inp_remapped->node()->owningBlock() != b) {
// Find the common ancestor block between this node and the node that
// produced this input. For this input Use to be valid, the Value's
// def must be present in this common ancestor node.
Block* common_ancestor =
n->findCommonAncestorBlockWith(inp_remapped->node());
Value* v_itr = inp_remapped;
Block* b_itr = inp_remapped->node()->owningBlock();
// Starting from the initial def for this input, iterate to
// wider and wider blocks, adding Block outputs and Node outputs
// along the way. Then, log the lifted values in the remap table
// so we can make subsequent Uses refer to the lifted value, if
// the domination condition is met.
while (b_itr != common_ancestor) {
b_itr->registerOutput(v_itr);
Value* remapped =
b_itr->owningNode()->addOutput()->setType(v_itr->type());
v_itr = remapped;
b_itr = b_itr->owningNode()->owningBlock();
}
// From now on, references to `inp` will be replaced with
// references to `v_itr`, the lifted Value
remap[inp] = v_itr;
n->replaceInput(i, remap[inp]);
}
}
if (isEligibleNode(n)) {
run(n->blocks()[0]);
}
}
// This holds the mapping between a Value* we would see in a Use
// and the lifted value, if present. We use this to ensure that
// Uses refer to a Value* that is in a dominating scope.
using RemappingTable = std::unordered_map<Value*, Value*>;
RemappingTable remap;
};
// For all blocks except graph->block(), convert multiple block
// returns to a TupleConstruct. This is required for turning the
// blocks into Methods. (and in the case that self is nullptr,
// it is required to properly inline the blocks).
void convertReturnsToTuples(Block* b) {
for (Node* n : b->nodes()) {
if (n->kind() == prim::TracedFork) {
convertReturnsToTuples(n->blocks()[0]);
} else if (n->kind() == prim::TracedModuleForward) {
TORCH_INTERNAL_ASSERT(n->blocks().size() == 1);
convertReturnsToTuples(n->blocks()[0]);
Graph* g = b->owningGraph();
Block* sub_block = n->blocks()[0];
if (sub_block->outputs().size() > 1) {
{
// Make block returns go through a Tuple
WithInsertPoint guard(sub_block->return_node());
Node* return_tup =
g->insertNode(g->createTuple(sub_block->outputs()));
while (!sub_block->outputs().empty()) {
sub_block->eraseOutput(0);
}
sub_block->registerOutput(return_tup->output());
}
// Make node outputs a single tuple;
std::vector<TypePtr> types;
for (size_t i = 0; i < n->outputs().size(); ++i) {
types.push_back(n->output(i)->type());
}
Value* tup_output = n->addOutput()->setType(TupleType::create(types));
Node* tup_unpack = g->createTupleUnpack(tup_output)->insertAfter(n);
for (size_t i = 0; i < tup_unpack->outputs().size(); ++i) {
auto rev_idx = tup_unpack->outputs().size() - i - 1;
n->output(rev_idx)->replaceAllUsesWith(tup_unpack->output(rev_idx));
n->eraseOutput(rev_idx);
}
} else if (sub_block->outputs().empty()) {
WithInsertPoint guard(sub_block->return_node());
sub_block->registerOutput(g->insertNode(g->createNone())->output());
n->addOutput()->setType(NoneType::get());
}
}
}
}
// Lambda lift Values (i.e. add Graph inputs for the purpose of
// referencing values that dominate the block) and convert
// the block to a Graph. blocks()[0] on each TracedModuleForward then
// appears as a Graph attribute attr::Subgraph
void lambdaLiftBlocksAndConvertToGraph(Block* b) {
for (Node* n : b->nodes()) {
if (isEligibleNode(n)) {
lambdaLiftBlocksAndConvertToGraph(n->blocks()[0]);
auto graph = std::make_shared<Graph>();
std::unordered_map<Value*, Value*> remaps;
graph->block()->cloneFrom(n->blocks()[0], [&](Value* v) {
if (!remaps.count(v)) {
remaps[v] = graph->addInput()->copyMetadata(v);
n->addInput(v);
}
return remaps[v];
});
LintGraph(graph);
n->g_(attr::Subgraph, graph);
n->eraseBlock(0);
}
}
}
// Find a unique name to add this method as
// We try {method_name}, {method_name}1, {method_name}2, ...
std::string mangleMethodName(
const std::string& method_name,
const ClassTypePtr& mod_type) {
for (size_t method_idx = 0;; method_idx++) {
auto mangled = method_name;
if (method_idx != 0) {
mangled += std::to_string(method_idx);
}
bool found = false;
for (Function* fn : mod_type->methods()) {
if (fn->name() == mangled) {
found = true;
break;
}
}
if (!found) {
return mangled;
}
}
TORCH_INTERNAL_ASSERT(false);
}
// Register the attr::Subgraph Graph values as Functions in the
// class compilation unit and register that Function as a method
// on the corresponding Module in the Module hierarchy. Note that we
// unique the methods by naming them forward, forward1, forward2...
void createMethodCalls(const std::shared_ptr<Graph>& g) {
for (auto node_itr = g->nodes().begin(); node_itr != g->nodes().end();) {
Node* n = *node_itr++;
if (n->kind() == prim::TracedFork) {
createMethodCalls(n->g(attr::Subgraph));
} else if (n->kind() == prim::TracedModuleForward) {
WithInsertPoint ip(n);
ClassTypePtr callee_mod_type = n->input(0)->type()->expect<ClassType>();
createMethodCalls(n->g(attr::Subgraph));
auto mangled_method_name = mangleMethodName("forward", callee_mod_type);
auto qualname = c10::QualifiedName(
callee_mod_type->name().value(), mangled_method_name);
Function* f = callee_mod_type->compilation_unit()->create_function(
qualname, n->g(attr::Subgraph));
callee_mod_type->addMethod(f);
std::vector<NamedValue> nvs;
for (Value* i : n->inputs()) {
nvs.emplace_back(i->node()->sourceRange(), i);
}
auto schema = matchSchema(f->getSchema(), n->sourceRange(), *g, nvs, {});
Value* retval = g->insertMethodCall(f->qualname().name(), schema);
n->output()->replaceAllUsesWith(retval);
n->destroy();
}
}
}
void inlineScopeBlocks(Block* b) {
for (auto n_itr = b->nodes().begin(); n_itr != b->nodes().end();) {
Node* n = *n_itr++;
for (Block* sub_b : n->blocks()) {
inlineScopeBlocks(sub_b);
}
if (n->kind() == prim::TracedModuleForward) {
// Convert the block to a graph so we can inline it
auto graph = std::make_shared<Graph>();
std::unordered_map<Value*, Value*> remaps;
graph->block()->cloneFrom(n->blocks()[0], [&](Value* v) {
remaps[v] = graph->block()->addInput()->copyMetadata(v);
n->addInput(v);
return remaps[v];
});
WithInsertPoint insert_point(n);
AT_ASSERT(n->inputs().size() == graph->inputs().size());
auto new_outputs = insertGraph(*n->owningGraph(), *graph, n->inputs());
const auto& old_outputs = n->outputs();
AT_ASSERT(new_outputs.size() == old_outputs.size());
for (const auto i : c10::irange(old_outputs.size())) {
old_outputs[i]->replaceAllUsesWith(new_outputs[i]);
}
n->destroy();
}
}
}
void convertTracedForksToRealForks(const std::shared_ptr<Graph>& g) {
for (auto itr = g->nodes().begin(); itr != g->nodes().end();) {
Node* n = *itr++;
if (n->kind() == prim::TracedFork) {
WithInsertPoint guard(n);
Node* new_fork_node =
g->insertNode(g->create(prim::fork, n->outputs().size()))
->copyAttributes(*n);
for (Value* i : n->inputs()) {
new_fork_node->addInput(i);
}
for (size_t i = 0; i < new_fork_node->outputs().size(); ++i) {
new_fork_node->outputs()[i]->copyMetadata(n->outputs()[i]);
n->outputs()[i]->replaceAllUsesWith(new_fork_node->outputs()[i]);
}
n->destroy();
}
}
}
// Run a few clean-up passes to make the graph a bit cleaner.
void runCleanupPasses(const std::shared_ptr<Graph>& g) {
for (Node* n : g->nodes()) {
if (n->kind() == prim::TracedFork) {
auto subgraph = n->g(attr::Subgraph);
if (getInlineEverythingMode()) {
Inline(*subgraph);
}
convertTracedForksToRealForks(subgraph);
LowerSimpleTuples(subgraph);
EliminateDeadCode(subgraph);
LintGraph(subgraph);
}
}
if (getInlineEverythingMode()) {
Inline(*g);
}
convertTracedForksToRealForks(g);
LowerSimpleTuples(g);
EliminateDeadCode(g);
LintGraph(g);
}
void runCleanupPasses(Module* m) {
auto methods = m->get_methods();
for (auto module : m->children()) {
runCleanupPasses(&module);
}
for (auto& method : methods) {
runCleanupPasses(method.graph());
}
}
} // namespace
void FixupTraceScopeBlocks(std::shared_ptr<Graph>& graph, Module* self) {
if (self) {
ConvertTracedAttrReferences().run(graph);
} else {
for (Node* n : graph->nodes()) {
TORCH_INTERNAL_ASSERT(n->kind() != prim::TracedAttr);
}
}
MakeDefsDominateUses().run(graph->block());
convertReturnsToTuples(graph->block());
if (!self) {
// We have no Module, so we're just going to inline everything.
// This should give us a totally flat graph.
inlineScopeBlocks(graph->block());
// For TracedFork nodes
lambdaLiftBlocksAndConvertToGraph(graph->block());
runCleanupPasses(graph);
} else {
lambdaLiftBlocksAndConvertToGraph(graph->block());
createMethodCalls(graph);
runCleanupPasses(self);
// `graph` isn't referenced in `self` yet, so we need to run
// this separately
runCleanupPasses(graph);
}
}
} // namespace torch::jit