forked from pytorch/pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
inliner.cpp
99 lines (89 loc) · 3.22 KB
/
inliner.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
#include <torch/csrc/jit/passes/inliner.h>
#include <ATen/core/interned_strings.h>
#include <torch/csrc/jit/api/function_impl.h>
#include <torch/csrc/jit/api/module.h>
#include <torch/csrc/jit/frontend/error_report.h>
#include <torch/csrc/jit/jit_log.h>
namespace torch {
namespace jit {
namespace prim {
using namespace ::c10::prim;
}
GraphFunction* tryToGraphFunction(Node* n) {
if (n->kind() == prim::CallFunction) {
AT_ASSERT(n->input(0)->node()->kind() == prim::Constant);
auto function_constant = n->input(0)->node();
auto fun_type = function_constant->output()->type()->expect<FunctionType>();
return tryToGraphFunction(*fun_type->function());
}
if (n->kind() == prim::CallMethod) {
const std::string& name = n->s(attr::name);
if (auto class_type = n->input(0)->type()->cast<ClassType>()) {
Function& function = class_type->getMethod(name);
return tryToGraphFunction(function);
}
}
return nullptr;
}
static void inlineCalls(Block* block) {
for (auto it = block->nodes().begin(), end = block->nodes().end();
it != end;) {
Node* cur = *it++;
switch (cur->kind()) {
case prim::CallFunction: {
if (auto graphFunction = tryToGraphFunction(cur)) {
auto function_constant = cur->input(0)->node();
auto fun_type =
function_constant->output()->type()->expect<FunctionType>();
cur->removeInput(0);
GRAPH_UPDATE(
"Inlining function '",
fun_type->function()->name(),
"' to ",
*cur);
std::shared_ptr<Graph> g = nullptr;
// inline optimized graph for debugging/testing purposes.
// we only insert fallback functions in JIT optimized graphs for
// execution, not on the Graph that is used for serialization
bool fallback =
function_constant->hasAttribute(Symbol::attr("fallback"));
if (fallback && graphFunction->get_executor().isOptimized()) {
auto exec_plans =
graphFunction->get_executor().getDebugState().execution_plans;
if (!exec_plans.empty()) {
g = exec_plans.begin()->second.graph;
// optimized_graph() calls Inline, so we only need to explicitly
// invoke inlining on the jit optimized graph with recursive
// fallback function calls
Inline(*g.get());
}
}
if (g == nullptr) {
g = graphFunction->optimized_graph();
}
GRAPH_UPDATE("Function body: ", g);
inlineCallTo(cur, graphFunction, g.get());
}
} break;
case prim::CallMethod: {
if (auto graphFunction = tryToGraphFunction(cur)) {
GRAPH_UPDATE("Inlining method '", cur->s(attr::name), "' to ", *cur);
GRAPH_UPDATE("Function body: ", graphFunction->optimized_graph());
inlineCallTo(cur, graphFunction);
}
} break;
default: {
for (auto b : cur->blocks()) {
inlineCalls(b);
}
} break;
}
}
}
void Inline(Graph& graph) {
GRAPH_DUMP("Before Inlining: ", &graph);
inlineCalls(graph.block());
GRAPH_DUMP("After Inlining: ", &graph);
}
} // namespace jit
} // namespace torch