forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathdecomposition_registry.cpp
212 lines (184 loc) · 6.86 KB
/
decomposition_registry.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
#include <torch/csrc/jit/frontend/ir_emitter.h>
#include <torch/csrc/jit/jit_log.h>
#include <torch/csrc/jit/passes/constant_propagation.h>
#include <torch/csrc/jit/passes/peephole.h>
#include <torch/csrc/jit/runtime/decomposition_registry.h>
#include <torch/csrc/jit/runtime/decomposition_registry_util.h>
#include <torch/csrc/jit/runtime/operator.h>
#include <torch/csrc/jit/serialization/import_source.h>
#include <c10/util/Exception.h>
#include <torch/csrc/autograd/jit_decomp_interface.h>
#include <torch/csrc/jit/ir/ir.h>
#include <torch/csrc/jit/passes/inliner.h>
#include <torch/csrc/jit/runtime/graph_executor.h>
#include <memory>
#include <unordered_map>
namespace torch::jit {
namespace {
std::mutex lock;
// CompilationUnit that holds all these Functions and keeps them alive.
auto compilation_unit = std::make_shared<CompilationUnit>();
std::unordered_map<const FunctionSchema*, std::shared_ptr<Graph>>
schema_to_decomposition;
// Holds User-Registered Functions and keeps them alive
std::unordered_map<const FunctionSchema*, std::unique_ptr<Function>>
user_registered_funcs;
std::unordered_map<const FunctionSchema*, Function*> schema_to_function;
void loadModule(const CompilationUnit& module) {
const auto& mappings = GetDecompositionMapping().getAllKeysAndValues();
for (const auto& pair : mappings) {
const FunctionSchema* schema = &pair.first->schema();
const std::string& decomposition_function_name = pair.second;
Function& decomposition_function =
module.get_function(decomposition_function_name);
std::shared_ptr<Graph> graph =
toGraphFunction(decomposition_function).graph();
schema_to_function[schema] = &decomposition_function;
schema_to_decomposition[schema] = graph;
}
}
void loadDecompositionFunctions() {
std::lock_guard<std::mutex> guard(lock);
if (!schema_to_decomposition.empty()) {
return;
}
auto src = std::make_shared<Source>(GetSerializedDecompositions());
std::stringstream ss;
std::vector<at::IValue> constantTable;
auto resolver = std::make_shared<SourceImporterImpl>(
compilation_unit,
&constantTable,
[&](const std::string& name) -> std::shared_ptr<Source> { return src; },
1);
compilation_unit->define(
std::nullopt, GetSerializedDecompositions(), resolver, nullptr);
loadModule(*compilation_unit);
}
} // anonymous namespace
static void DecomposeOp(Node* n) {
auto schema = n->maybeSchema();
if (!schema) {
return;
}
auto decomposition = GetDecomposition(n->schema());
if (!decomposition) {
return;
}
WithInsertPoint guard(n);
auto outputs = insertGraph(*n->owningGraph(), **decomposition, n->inputs());
TORCH_INTERNAL_ASSERT(outputs.size() == n->outputs().size());
for (size_t i : c10::irange(outputs.size())) {
n->outputs().at(i)->replaceAllUsesWith(outputs[i]);
}
n->destroy();
}
static void RunDecompositions(Block* block) {
for (auto it = block->nodes().begin(); it != block->nodes().end();) {
Node* n = *it;
it++; // advance iterator bc the current node may be destroyed
for (Block* b : n->blocks()) {
RunDecompositions(b);
}
DecomposeOp(n);
}
}
void RunDecompositions(std::shared_ptr<Graph> g) {
RunDecompositions(g->block());
for ([[maybe_unused]] const auto _ : c10::irange(2)) {
PeepholeOptimize(g, /*disable_shape_peephole*/ true);
ConstantPropagation(g);
}
}
std::optional<std::shared_ptr<Graph>> GetDecomposition(
const FunctionSchema& schema) {
loadDecompositionFunctions();
GRAPH_DEBUG("Trying to find schema: ", schema);
auto cache_it = schema_to_decomposition.find(&schema);
if (cache_it != schema_to_decomposition.end()) {
return cache_it->second;
}
GRAPH_DEBUG("Could not find schema: ", schema);
return std::nullopt;
}
std::optional<GraphFunction*> GetDecompositionFunction(
const FunctionSchema& schema) {
loadDecompositionFunctions();
auto cache_it = schema_to_function.find(&schema);
GRAPH_DEBUG("Trying to find schema: ", schema);
if (cache_it == schema_to_function.end()) {
GRAPH_DEBUG("Could not find schema: ", schema);
return std::nullopt;
}
auto& func = toGraphFunction(*cache_it->second);
// Simple Executor:
// To allow decomposition to run on tensor subclasses such as batched tensors,
// we set decomposition execution to use the simple executor so that
// optimizations that do not compose with arbitrary subclasses (such as
// fusion) do not run
func._set_initial_executor_execution_mode(ExecutorExecutionMode::SIMPLE);
return &func;
}
// Decomposition registers a Graph so that we can initialize a GraphFunction
// that will run with Simple Executor
void RegisterDecomposition(
const FunctionSchema& schema,
std::shared_ptr<Graph> g) {
loadDecompositionFunctions();
std::lock_guard<std::mutex> guard(lock);
Inline(*g);
for (const auto i : c10::irange(2)) {
(void)i; // Suppress unused variable warning
PeepholeOptimize(g);
ConstantPropagationImmutableTypes(g);
}
auto new_func = std::make_unique<GraphFunction>(
schema.name(), g, nullptr, ExecutorExecutionMode::SIMPLE);
user_registered_funcs.emplace(&schema, std::move(new_func));
schema_to_function[&schema] = user_registered_funcs[&schema].get();
schema_to_decomposition[&schema] = g;
}
// see NOTE: [Jit Decomposition Interface]
struct JitDecomp final : torch::autograd::impl::JitDecompInterface {
bool has_jit_decomposition(const c10::FunctionSchema& schema) const override;
void run_jit_decomposition(
const c10::OperatorHandle& op,
torch::jit::Stack* stack) const override;
};
JitDecomp jitDecomp;
torch::autograd::impl::JitDecompRegisterer registerJitDecomp(&jitDecomp);
void JitDecomp::run_jit_decomposition(
const c10::OperatorHandle& op,
torch::jit::Stack* stack) const {
::torch::jit::run_jit_decomposition(op, stack);
}
bool JitDecomp::has_jit_decomposition(const FunctionSchema& schema) const {
return ::torch::jit::has_jit_decomposition(schema);
}
void run_jit_decomposition(
const c10::OperatorHandle& op,
torch::jit::Stack* stack) {
const auto& schema = op.schema();
// TODO: templatize based on op and keep static trace_exec
auto* trace_exec = torch::jit::GetDecompositionExecutor(schema);
trace_exec->run((*stack));
if (stack->back().isTuple()) {
at::IValue tup = std::move(stack->back());
stack->pop_back();
for (const auto& elem : tup.toTuple()->elements()) {
stack->push_back(elem);
}
}
}
bool has_jit_decomposition(const FunctionSchema& schema) {
return GetDecompositionFunction(schema).has_value();
}
Function* GetDecompositionExecutor(const FunctionSchema& schema) {
auto maybe_func = GetDecompositionFunction(schema);
TORCH_INTERNAL_ASSERT(maybe_func);
return *maybe_func;
}
Function* GetDecompositionExecutor(const char* schema_literal) {
auto& schema = getOperatorForLiteral(schema_literal)->schema();
return GetDecompositionExecutor(schema);
}
} // namespace torch::jit