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test_ir.cpp
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test_ir.cpp
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#include <gtest/gtest.h>
#include <test/cpp/jit/test_utils.h>
#include <torch/csrc/jit/ir/irparser.h>
namespace torch {
namespace jit {
TEST(IRTest, Attributes) {
Graph g;
auto one = attr::alpha;
auto two = attr::device;
auto three = attr::end;
auto four = attr::perm;
Node* n = g.create(Symbol::fromQualString("foo::bar"));
Node& attr = *n;
attr.f_(one, 3.4)->i_(two, 5)->s_(three, "what");
ASSERT_EQ(attr.f(one), 3.4);
ASSERT_EQ(attr.s(three), "what");
ASSERT_EQ(attr.i(two), 5);
attr.s_(one, "no");
ASSERT_EQ(attr.s(one), "no");
ASSERT_TRUE(attr.hasAttribute(three));
ASSERT_TRUE(!attr.hasAttribute(four));
attr.ss_(two, {"hi", "now"});
ASSERT_EQ(attr.ss(two).at(1), "now");
Node* n2 = g.create(Symbol::fromQualString("foo::baz"));
Node& attr2 = *n2;
attr2.copyAttributes(attr);
ASSERT_EQ(attr2.s(one), "no");
attr2.f_(one, 5);
ASSERT_EQ(attr.s(one), "no");
ASSERT_EQ(attr2.f(one), 5);
}
TEST(IRTest, Blocks) {
auto g = std::make_shared<Graph>();
const auto graph_string = R"IR(
graph(%a : Tensor,
%b : Tensor,
%c : Tensor):
%2 : int = prim::Constant[value=1]()
%3 : Tensor = aten::add(%a, %b, %2)
%5 : Tensor = prim::If(%c)
block0():
%6 : int = prim::Constant[value=1]()
%7 : Tensor = aten::add(%3, %3, %6)
-> (%7)
block1():
%8 : int = prim::Constant[value=1]()
%9 : Tensor = aten::add(%b, %3, %8)
%10 : int = prim::Constant[value=1]()
%11 : Tensor = aten::add(%9, %3, %10)
-> (%11)
%12 : int = prim::Constant[value=1]()
%13 : Tensor = aten::add(%5, %3, %12)
return (%13))IR";
torch::jit::parseIR(graph_string, g.get());
g->lint();
testing::FileCheck()
.check("add")
->check("prim::If")
->check("block0")
->check("aten::add")
->check("block1")
->check_count("aten::add", 3)
->run(*g);
// Removes block0 of the conditional
for (auto* node : g->block()->nodes()) {
if (node->kind() == prim::If) {
node->eraseBlock(0);
break;
}
}
testing::FileCheck()
.check("add")
->check("prim::If")
->check("block0")
->check_not("block")
->run(*g);
g->lint();
// test recursive copy of blocks works
auto g2 = g->copy();
testing::FileCheck()
.check("add")
->check("prim::If")
->check("block0")
->check_not("block")
->run(*g2);
}
TEST(IRTest, CommonAncestor) {
std::string input_str = R"(
graph(%x : Tensor,
%a.1 : bool,
%b.1 : bool,
%c.1 : bool):
%4 : int = prim::If(%a.1)
block0():
%5 : int = prim::If(%b.1)
block0():
%6 : int = prim::Constant[value=2]()
-> (%6)
block1():
%7 : int = prim::Constant[value=3]()
-> (%7)
-> (%5)
block1():
%8 : int = prim::If(%c.1)
block0():
%9 : int = prim::Constant[value=4]()
-> (%9)
block1():
%10 : int = prim::Constant[value=5]()
-> (%10)
-> (%8)
return (%4)
)";
torch::jit::Graph g;
std::unordered_map<std::string, torch::jit::Value*> name_to_value;
torch::jit::parseIR(input_str, &g, name_to_value);
std::vector<std::string> value_names{"6", "7", "9", "10"};
std::unordered_set<std::string> value_names_set(
value_names.begin(), value_names.end());
/* clang-format off */
// NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays)
int ref_blocks_from_graph[4][4] = {
/* (6, 6), (6, 7), (6, 9), (6, 10) */
{ 2, 1, 0, 0 },
/* (7, 6), (7, 7), (7, 9), (7, 10) */
{ 1, 2, 0, 0 },
/* (9, 6), (9, 7), (9, 9), (9, 10) */
{ 0, 0, 2, 1, },
/* (10, 6),(10, 7),(10, 9),(10, 10) */
{ 0, 0, 1, 2 }
};
/* clang-format on */
for (size_t i = 0; i < value_names.size(); ++i) {
Value* i_val = name_to_value[value_names[i]];
for (size_t j = 0; j < value_names.size(); ++j) {
Value* j_val = name_to_value[value_names[j]];
Block* common_ancestor =
i_val->node()->findCommonAncestorBlockWith(j_val->node());
int blocks_from_graph_block =
common_ancestor->param_node()->blocksFromGraphBlock();
ASSERT_EQ(blocks_from_graph_block, ref_blocks_from_graph[i][j]);
}
}
}
TEST(IRTest, OperatorMap) {
OperatorMap<int> op_map;
const char* literal1 =
"aten::dropout(Tensor input, float p, bool train) -> Tensor";
const char* literal2 =
"aten::bernoulli(Tensor self, *, Generator? generator) -> Tensor";
const char* literal3 =
"aten::bernoulli(Tensor self, float p, *, Generator? generator) -> Tensor";
const char* literal4 =
"aten::normal(Tensor mean, Tensor std, *, Generator? generator) -> Tensor";
const char* literal5 =
"aten::normal(float mean, Tensor std, *, Generator? generator) -> Tensor";
const char* literal6 =
"aten::normal(Tensor mean, float std, *, Generator? generator) -> Tensor";
std::shared_ptr<Operator> op1 = getOperatorForLiteral(literal1);
std::shared_ptr<Operator> op2 = getOperatorForLiteral(literal2);
std::shared_ptr<Operator> op3 = getOperatorForLiteral(literal3);
std::shared_ptr<Operator> op4 = getOperatorForLiteral(literal4);
std::shared_ptr<Operator> op5 = getOperatorForLiteral(literal5);
std::shared_ptr<Operator> op6 = getOperatorForLiteral(literal6);
op_map.insert(op1, 1);
op_map.insert({{op2, 2}, {op3, 3}});
op_map.insert({{op4, 4}, {op5, 5}});
op_map.insert(op6, 6);
ASSERT_TRUE(op_map.contains(*op1));
ASSERT_TRUE(op_map.contains(*op2));
ASSERT_TRUE(op_map.contains(*op3));
ASSERT_TRUE(op_map.contains(*op4));
ASSERT_TRUE(op_map.contains(*op5));
ASSERT_TRUE(op_map.contains(*op6));
op_map.erase(op6);
op_map.erase(op3);
op_map.erase(op1);
ASSERT_FALSE(op_map.contains(*op1));
ASSERT_FALSE(op_map.contains(*op3));
ASSERT_FALSE(op_map.contains(*op6));
op_map.insert(op1, 1);
ASSERT_TRUE(op_map.contains(*op1));
std::optional<int> o1 = op_map.find(*op1);
ASSERT_TRUE(o1.has_value());
std::optional<int> o2 = op_map.find(*op2);
ASSERT_TRUE(o2.has_value());
std::optional<int> o3 = op_map.find(*op3);
ASSERT_FALSE(o3.has_value());
std::optional<int> o4 = op_map.find(*op4);
ASSERT_TRUE(o4.has_value());
std::optional<int> o5 = op_map.find(*op5);
ASSERT_TRUE(o5.has_value());
std::optional<int> o6 = op_map.find(*op6);
ASSERT_FALSE(o6.has_value());
}
} // namespace jit
} // namespace torch