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
|
#include <torch/csrc/jit/codegen/cuda/evaluator_common.h>
#include <torch/csrc/jit/codegen/cuda/expr_evaluator.h>
#include <torch/csrc/jit/codegen/cuda/fusion.h>
#include <torch/csrc/jit/codegen/cuda/instrumentation.h>
#include <torch/csrc/jit/codegen/cuda/ir_all_nodes.h>
#include <torch/csrc/jit/codegen/cuda/ir_iostream.h>
#include <iostream>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
namespace {
bool equals(Val* value, const IntOrDouble& concrete_value) {
switch (value->getDataType().value()) {
case DataType::Int: {
if (!concrete_value.is_int()) {
return false;
}
auto val = value->getInt();
return val.has_value() && val.value() == concrete_value.as<int64_t>();
}
case DataType::Double: {
if (concrete_value.is_int()) {
return false;
}
auto val = value->getDouble();
return val.has_value() && val.value() == concrete_value.as<double>();
}
default:
TORCH_INTERNAL_ASSERT(false);
}
}
template <typename T>
c10::optional<IntOrDouble> toOptionalIntOrDouble(c10::optional<T> i) {
if (!i) {
return c10::nullopt;
}
return IntOrDouble(i.value());
}
} // namespace
void ExpressionEvaluator::bind(Val* value, const IntOrDouble& concrete_value) {
if (equals(value, concrete_value)) {
return;
}
TORCH_CHECK(!value->isConstScalar(), "Tried to bind to a constant value");
TORCH_CHECK(
value->definition() == nullptr,
"Tried to bind to a value that is computed in the fusion IR");
known_values_[value] = concrete_value;
}
c10::optional<IntOrDouble> ExpressionEvaluator::evaluate(Val* value) {
if (evaluator_precomputed_values_ != nullptr) {
return toOptionalIntOrDouble(
evaluator_precomputed_values_->getMaybeValueFor(value));
} else {
auto maybe_concrete_value = getValue(value);
if (!maybe_concrete_value.has_value()) {
if (value->definition() != nullptr) {
OptOutDispatch::handle(value->definition());
maybe_concrete_value = getValue(value);
}
}
return maybe_concrete_value;
}
return c10::nullopt;
}
void ExpressionEvaluator::print() const {
std::cout << "\nEvaluation context\n";
std::cout << "--------------------\n";
for (const auto& kv : known_values_) {
TORCH_INTERNAL_ASSERT(!kv.first->isConstScalar());
std::cout << kv.first << " = " << kv.second << " ; "
<< *kv.first->getValType() << "\n";
}
std::cout << "--------------------\n\n";
}
c10::optional<IntOrDouble> ExpressionEvaluator::getValue(Val* value) {
TORCH_INTERNAL_ASSERT(
value->isAnInt() || value->isADouble(),
"Expression Evaluation does not support values other than integers at this time.");
if (value->getValType().value() == ValType::Scalar) {
if (value->isAnInt() && value->as<Int>()->value().has_value()) {
return toOptionalIntOrDouble(value->as<Int>()->value());
}
if (value->isADouble() && value->as<Double>()->value().has_value()) {
return toOptionalIntOrDouble(value->as<Double>()->value());
}
}
const auto it = known_values_.find(value);
return it != known_values_.end() ? c10::optional<IntOrDouble>(it->second)
: c10::nullopt;
}
void ExpressionEvaluator::handle(UnaryOp* uop) {
const auto in = evaluate(uop->in());
if (in.has_value()) {
switch (uop->getUnaryOpType()) {
case UnaryOpType::Neg:
known_values_[uop->out()] = -*in;
break;
case UnaryOpType::Set:
known_values_[uop->out()] = *in;
break;
case UnaryOpType::Cast:
if (uop->out()->getDataType() == DataType::Int) {
known_values_[uop->out()] = in->cast<int64_t>();
} else if (uop->out()->getDataType() == DataType::Double) {
known_values_[uop->out()] = in->cast<double>();
} else {
TORCH_INTERNAL_ASSERT(false, "dtype not supported in evaluator");
}
break;
default:
TORCH_CHECK(
!"Unexpected operator type ",
uop->getUnaryOpType(),
" in ",
uop->toString());
}
}
}
void ExpressionEvaluator::handle(BinaryOp* bop) {
using namespace IntOrDouble_functions;
const auto lhs = evaluate(bop->lhs());
const auto rhs = evaluate(bop->rhs());
if (lhs.has_value() && rhs.has_value()) {
switch (bop->getBinaryOpType()) {
case BinaryOpType::Add:
known_values_[bop->out()] = *lhs + *rhs;
break;
case BinaryOpType::Sub:
known_values_[bop->out()] = *lhs - *rhs;
break;
case BinaryOpType::Mul:
known_values_[bop->out()] = *lhs * *rhs;
break;
case BinaryOpType::Div:
TORCH_CHECK(*rhs != 0);
known_values_[bop->out()] = *lhs / *rhs;
break;
case BinaryOpType::Mod:
TORCH_CHECK(*rhs != 0);
known_values_[bop->out()] = *lhs % *rhs;
break;
case BinaryOpType::CeilDiv:
TORCH_CHECK(*rhs != 0);
known_values_[bop->out()] = ceildiv(*lhs, *rhs);
break;
case BinaryOpType::And:
known_values_[bop->out()] = *lhs && *rhs;
break;
case BinaryOpType::Max:
known_values_[bop->out()] = max(*lhs, *rhs);
break;
case BinaryOpType::Min:
known_values_[bop->out()] = min(*lhs, *rhs);
break;
default:
TORCH_CHECK(!"Unexpected operator type");
}
}
}
} // namespace cuda
} // namespace fuser
} // namespace jit
} // namespace torch
|
