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#include "caffe2/opt/nql/graphmatcher.h"
#include <gtest/gtest.h>
using namespace nom::nql;
using namespace nom::repr;
/// \brief Create tensor-nodes in \param graph with names specified in \param
/// names and \return a name->NodeRef map.
std::unordered_map<std::string, NNGraph::NodeRef> genTensors(
NNGraph& graph,
std::vector<std::string> names) {
std::unordered_map<std::string, NNGraph::NodeRef> result;
for (auto& name : names) {
result[name] = graph.createNode(std::make_unique<Tensor>(name));
}
return result;
}
TEST(Basic, MatchSingleNode) {
NNGraph graph;
auto reluInput = graph.createNode(std::make_unique<Tensor>("reluInput"));
auto relu = graph.createNode(std::make_unique<Relu>());
auto reluOutput = graph.createNode(std::make_unique<Tensor>("reluOutput"));
graph.createEdge(reluInput, relu);
graph.createEdge(relu, reluOutput);
GraphMatcher gm;
gm.initFromString(R"NQL(
def my_nn {
%x = Relu(%y)
})NQL");
EXPECT_TRUE(gm.findSubgraph(graph));
GraphMatcher gmMismatch;
gmMismatch.initFromString(R"NQL(
def my_nn {
%x = Foo(%y)
})NQL");
EXPECT_FALSE(gmMismatch.findSubgraph(graph));
}
TEST(Basic, SyntaxError) {
NNGraph graph;
auto reluInput = graph.createNode(std::make_unique<Tensor>("reluInput"));
auto relu = graph.createNode(std::make_unique<Relu>());
auto reluOutput = graph.createNode(std::make_unique<Tensor>("reluOutput"));
graph.createEdge(reluInput, relu);
graph.createEdge(relu, reluOutput);
GraphMatcher gm;
gm.initFromString(R"NQL(
def my_nn {
%x =
})NQL");
EXPECT_FALSE(gm.findSubgraph(graph));
}
TEST(Basic, Diamond) {
NNGraph graph;
/*
The graph we're building looks like this:
a b
\ /
Concat
/ \
c d
| |
Relu1 Relu2
| |
e f
\ /
Sum
|
x
*/
auto tensors = genTensors(graph, {"a", "b", "c", "d", "e", "f", "x"});
auto relu1 = graph.createNode(std::make_unique<Relu>());
auto relu2 = graph.createNode(std::make_unique<Relu>());
auto concat = graph.createNode(std::make_unique<Concat>());
auto sum = graph.createNode(std::make_unique<Sum>());
graph.createEdge(tensors["a"], concat);
graph.createEdge(tensors["b"], concat);
graph.createEdge(concat, tensors["c"]);
graph.createEdge(concat, tensors["d"]);
graph.createEdge(tensors["c"], relu1);
graph.createEdge(relu1, tensors["e"]);
graph.createEdge(tensors["d"], relu2);
graph.createEdge(relu2, tensors["f"]);
graph.createEdge(tensors["e"], sum);
graph.createEdge(tensors["f"], sum);
graph.createEdge(sum, tensors["x"]);
GraphMatcher gm1;
gm1.initFromString(R"NQL(
def my_nn {
%c, %d = Concat(%a, %b)
%e = Relu(%c)
%f = Relu(%d)
%x = Sum(%e, %f)
})NQL");
EXPECT_TRUE(gm1.findSubgraph(graph));
// Check that syntax with inlining works too.
GraphMatcher gm2;
gm2.initFromString(R"NQL(
def my_nn {
%c, %d = Concat(%a, %b)
%x = Sum(Relu(%c), Relu(%d))
})NQL");
EXPECT_TRUE(gm2.findSubgraph(graph));
// Check that we understand that the Relu nodes should use output from the
// same Concat node.
GraphMatcher gm3;
gm3.initFromString(R"NQL(
def my_nn {
%c = Concat(%a)
%d = Concat(%b)
%x = Sum(Relu(%c), Relu(%d))
})NQL");
EXPECT_FALSE(gm3.findSubgraph(graph));
}
TEST(Basic, BadDiamond) {
NNGraph graph;
/*
The graph we're building looks like this:
a b
| |
Concat1 Concat2
| |
c d
| |
Relu1 Relu2
| |
e f
\ /
Sum
|
x
*/
auto tensors = genTensors(graph, {"a", "b", "c", "d", "e", "f", "x"});
auto relu1 = graph.createNode(std::make_unique<Relu>());
auto relu2 = graph.createNode(std::make_unique<Relu>());
auto concat1 = graph.createNode(std::make_unique<Concat>());
auto concat2 = graph.createNode(std::make_unique<Concat>());
auto sum = graph.createNode(std::make_unique<Sum>());
graph.createEdge(tensors["a"], concat1);
graph.createEdge(tensors["b"], concat2);
graph.createEdge(concat1, tensors["c"]);
graph.createEdge(concat2, tensors["d"]);
graph.createEdge(tensors["c"], relu1);
graph.createEdge(relu1, tensors["e"]);
graph.createEdge(tensors["d"], relu2);
graph.createEdge(relu2, tensors["f"]);
graph.createEdge(tensors["e"], sum);
graph.createEdge(tensors["f"], sum);
graph.createEdge(sum, tensors["x"]);
// Check that we don't match this graph when looking for a diamond shape.
GraphMatcher gm;
gm.initFromString(R"NQL(
def my_nn {
%c, %d = Concat(%a, %b)
%x = Sum(Relu(%c), Relu(%d))
})NQL");
EXPECT_FALSE(gm.findSubgraph(graph));
EXPECT_EQ(gm.getMatchMap().size(), 0);
GraphMatcher gm2;
gm2.initFromString(R"NQL(
def my_nn {
%c = Concat(%a)
%d = Concat(%b)
%x = Sum(Relu(%c), Relu(%d))
})NQL");
EXPECT_TRUE(gm2.findSubgraph(graph));
auto matchMap = gm2.getMatchMap();
EXPECT_EQ(matchMap["%a"], tensors["a"]);
EXPECT_EQ(matchMap["%b"], tensors["b"]);
EXPECT_EQ(matchMap["%c"], tensors["c"]);
EXPECT_EQ(matchMap["%d"], tensors["d"]);
EXPECT_EQ(matchMap["%x"], tensors["x"]);
EXPECT_EQ(matchMap["Sum"], sum);
EXPECT_TRUE(
(matchMap["Concat"] == concat1) || (matchMap["Concat"] == concat2));
EXPECT_TRUE((matchMap["Relu"] == relu1) || (matchMap["Relu"] == relu2));
}
TEST(Basic, StarInputs) {
NNGraph graph;
/*
The graph we're building looks like this:
a b c d
| | | |
Relu Flatten FC Sum
| | | |
e f g h
\ | | /
\ | | /
\ | | /
Concat
|
x
*/
auto tensors =
genTensors(graph, {"a", "b", "c", "d", "e", "f", "g", "h", "x"});
auto concat = graph.createNode(std::make_unique<Concat>());
auto relu = graph.createNode(std::make_unique<Relu>());
auto flat = graph.createNode(std::make_unique<Flatten>());
auto fc = graph.createNode(std::make_unique<FC>());
auto sum = graph.createNode(std::make_unique<Sum>());
graph.createEdge(tensors["a"], relu);
graph.createEdge(relu, tensors["e"]);
graph.createEdge(tensors["b"], flat);
graph.createEdge(flat, tensors["f"]);
graph.createEdge(tensors["c"], fc);
graph.createEdge(fc, tensors["g"]);
graph.createEdge(tensors["d"], sum);
graph.createEdge(sum, tensors["h"]);
graph.createEdge(tensors["e"], concat);
graph.createEdge(tensors["f"], concat);
graph.createEdge(tensors["g"], concat);
graph.createEdge(tensors["h"], concat);
graph.createEdge(concat, tensors["x"]);
GraphMatcher gm1;
gm1.initFromString(R"NQL(
def my_nn {
%e = Relu(%a)
%f = Flatten(%b)
%g = FC(%c)
%h = Sum(%d)
%x = Concat(%e, %f, %g, %h)
})NQL");
EXPECT_TRUE(gm1.findSubgraph(graph));
EXPECT_EQ(gm1.getMatchMap()["Concat"], concat);
EXPECT_EQ(gm1.getMatchMap()["Relu"], relu);
EXPECT_EQ(gm1.getMatchMap()["Flatten"], flat);
EXPECT_EQ(gm1.getMatchMap()["FC"], fc);
EXPECT_EQ(gm1.getMatchMap()["Sum"], sum);
EXPECT_EQ(gm1.getMatchMap()["%e"], tensors["e"]);
EXPECT_EQ(gm1.getMatchMap()["%f"], tensors["f"]);
EXPECT_EQ(gm1.getMatchMap()["%g"], tensors["g"]);
EXPECT_EQ(gm1.getMatchMap()["%h"], tensors["h"]);
GraphMatcher gm2;
gm2.initFromString(R"NQL(
def my_nn {
%x = Concat(*)
})NQL");
EXPECT_TRUE(gm2.findSubgraph(graph));
EXPECT_EQ(gm2.getMatchMap()["Concat"], concat);
GraphMatcher gm3;
gm3.initFromString(R"NQL(
def my_nn {
%e = Relu(%a)
%x = Concat(%e, *)
})NQL");
EXPECT_TRUE(gm3.findSubgraph(graph));
EXPECT_EQ(gm3.getMatchMap()["Concat"], concat);
EXPECT_EQ(gm3.getMatchMap()["Relu"], relu);
EXPECT_EQ(gm3.getMatchMap()["%e"], tensors["e"]);
GraphMatcher gm4;
gm4.initFromString(R"NQL(
def my_nn {
%x = Concat(Sum(%a), *)
})NQL");
EXPECT_FALSE(gm4.findSubgraph(graph));
GraphMatcher gm5;
gm5.initFromString(R"NQL(
def my_nn {
%x = Concat(*, Sum(%d))
})NQL");
// '*' greedily matches all inputs, and then we fail to match an extra Sum
// input.
EXPECT_FALSE(gm5.findSubgraph(graph));
}
TEST(Basic, StarOutputs) {
NNGraph graph;
/*
The graph we're building looks like this:
a b c
\ | /
Concat
/ | \
d e f
*/
auto tensors = genTensors(graph, {"a", "b", "c", "d", "e", "f"});
auto concat = graph.createNode(std::make_unique<Concat>());
graph.createEdge(tensors["a"], concat);
graph.createEdge(tensors["b"], concat);
graph.createEdge(tensors["c"], concat);
graph.createEdge(concat, tensors["d"]);
graph.createEdge(concat, tensors["e"]);
graph.createEdge(concat, tensors["f"]);
GraphMatcher gm1;
gm1.initFromString(R"NQL(
def my_nn {
%a, %b, %c = Concat(%d, %e, %f)
})NQL");
EXPECT_TRUE(gm1.findSubgraph(graph));
EXPECT_EQ(gm1.getMatchMap()["Concat"], concat);
GraphMatcher gm2;
gm2.initFromString(R"NQL(
def my_nn {
* = Concat(*)
})NQL");
EXPECT_TRUE(gm2.findSubgraph(graph));
EXPECT_EQ(gm2.getMatchMap()["Concat"], concat);
GraphMatcher gm3;
gm3.initFromString(R"NQL(
def my_nn {
%a, * = Concat(*)
})NQL");
EXPECT_TRUE(gm3.findSubgraph(graph));
EXPECT_EQ(gm3.getMatchMap()["Concat"], concat);
GraphMatcher gm4;
gm4.initFromString(R"NQL(
def my_nn {
%a, %b, * = Concat(%d, %e, *)
})NQL");
EXPECT_TRUE(gm4.findSubgraph(graph));
EXPECT_EQ(gm4.getMatchMap()["Concat"], concat);
GraphMatcher gm5;
gm5.initFromString(R"NQL(
def my_nn {
%a = Concat(%d, %e, *)
})NQL");
// We ignore mismatches in outputs
EXPECT_TRUE(gm5.findSubgraph(graph));
EXPECT_EQ(gm5.getMatchMap()["Concat"], concat);
GraphMatcher gm6;
gm6.initFromString(R"NQL(
def my_nn {
%a, %b, %c, %x = Concat(%d, %e, %f)
})NQL");
// We ignore mismatches in outputs
EXPECT_TRUE(gm6.findSubgraph(graph));
EXPECT_EQ(gm6.getMatchMap()["Concat"], concat);
GraphMatcher gm7;
gm7.initFromString(R"NQL(
def my_nn {
%a, %b, %c = Concat(%d, %e)
})NQL");
// We don't ignore mismatches in inputs
EXPECT_FALSE(gm7.findSubgraph(graph));
}
TEST(Caffe2ToNQL, Basic) {
NNGraph graph;
/*
The graph we're building looks like this:
a
|
Concat
|
b
|
Relu
|
c
*/
auto tensors = genTensors(graph, {"a", "b", "c"});
auto relu = graph.createNode(std::make_unique<Relu>());
auto concat = graph.createNode(std::make_unique<Concat>());
graph.createEdge(tensors["a"], concat);
graph.createEdge(concat, tensors["b"]);
graph.createEdge(tensors["b"], relu);
graph.createEdge(relu, tensors["c"]);
EXPECT_EQ(convertToNQLString(graph), R"NQL(def nn {
%b = Concat(%a)
%c = Relu(%b)
}
)NQL");
}
TEST(Caffe2ToNQL, TensorsNameDeduplication) {
NNGraph graph;
/*
The graph we're building looks like this:
a
|
Concat
|
b
|
Relu
|
c
*/
std::unordered_map<std::string, NNGraph::NodeRef> tensors;
// Manually create tensors with the same names. NQL will have to disambiguate
// the names by adding a suffix.
tensors["a"] = graph.createNode(std::make_unique<Tensor>("tensor"));
tensors["b"] = graph.createNode(std::make_unique<Tensor>("tensor"));
tensors["c"] = graph.createNode(std::make_unique<Tensor>("tensor"));
auto relu = graph.createNode(std::make_unique<Relu>());
auto concat = graph.createNode(std::make_unique<Concat>());
graph.createEdge(tensors["a"], concat);
graph.createEdge(concat, tensors["b"]);
graph.createEdge(tensors["b"], relu);
graph.createEdge(relu, tensors["c"]);
EXPECT_EQ(convertToNQLString(graph), R"NQL(def nn {
%tensor_0 = Concat(%tensor)
%tensor_1 = Relu(%tensor_0)
}
)NQL");
}
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