experiments/STT-tensorflow
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Automated rollback of changelist 219508843

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PiperOrigin-RevId: 239515718
This commit is contained in:
A. Unique TensorFlower 2019-03-20 18:10:54 -07:00 committed by TensorFlower Gardener
parent 41b7433161
commit 9e0b9b9ff2
4 changed files with 23 additions and 13 deletions
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4
tensorflow/core/common_runtime/function_test.cc
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@ -1622,7 +1622,9 @@ TEST_F(FunctionLibraryRuntimeTest, Gradient_AddSum) {
GraphDef actual;
g->ToGraphDef(&actual);
TF_EXPECT_GRAPH_EQ(expected, actual);
// The optimizer is non-deterministic, so we only check that the number of
// nodes is not greater than expected.
EXPECT_LE(actual.node_size(), expected.node_size());
}
}

2
tensorflow/core/graph/edgeset.cc
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@ -37,7 +37,7 @@ std::pair<EdgeSet::const_iterator, bool> EdgeSet::insert(value_type value) {
}
}
// array is full. convert to set.
s = new std::set<const Edge*>;
s = new gtl::FlatSet<const Edge*>;
s->insert(reinterpret_cast<const Edge**>(std::begin(ptrs_)),
reinterpret_cast<const Edge**>(std::end(ptrs_)));
ptrs_[0] = this;

20
tensorflow/core/graph/edgeset.h
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@ -17,17 +17,18 @@ limitations under the License.
#define TENSORFLOW_GRAPH_EDGESET_H_
#include <stddef.h>
#include <set>
#include "tensorflow/core/lib/gtl/flatset.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/macros.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/platform/logging.h"
namespace tensorflow {
class Edge;
// An unordered set of edges. Uses very little memory for small sets.
// Unlike std::set, EdgeSet does NOT allow mutations during iteration.
// Unlike gtl::FlatSet, EdgeSet does NOT allow mutations during
// iteration.
class EdgeSet {
public:
EdgeSet();
@ -54,12 +55,15 @@ class EdgeSet {
private:
// Up to kInline elements are stored directly in ptrs_ (nullptr means none).
// If ptrs_[0] == this then ptrs_[1] points to a set<const Edge*>.
static const int kInline = 4; // Must be >= 2.
// kInline must be >= 2, and is chosen such that ptrs_ fills a 64 byte
// cacheline.
static constexpr int kInline = 64 / sizeof(const void*);
const void* ptrs_[kInline];
std::set<const Edge*>* get_set() const {
gtl::FlatSet<const Edge*>* get_set() const {
if (ptrs_[0] == this) {
return static_cast<std::set<const Edge*>*>(const_cast<void*>(ptrs_[1]));
return static_cast<gtl::FlatSet<const Edge*>*>(
const_cast<void*>(ptrs_[1]));
} else {
return nullptr;
}
@ -99,7 +103,7 @@ class EdgeSet::const_iterator {
friend class EdgeSet;
void const* const* array_iter_ = nullptr;
typename std::set<const Edge*>::const_iterator tree_iter_;
typename gtl::FlatSet<const Edge*>::const_iterator tree_iter_;
#ifdef NDEBUG
inline void Init(const EdgeSet* e) {}

10
tensorflow/core/graph/optimizer_cse_test.cc
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@ -337,9 +337,13 @@ TEST_F(OptimizerCSETest, Constant_Dedup) {
EXPECT_EQ(OriginalGraph(),
"n/_0(Const);n/_1(Const);n/_2(Const);n/_3(Const);"
"n/_4(Const);n/_5(Const);n/_6(Const);n/_7(Const)|");
// In theory, there are 2^4 possible correct output of CSE. In this
// test, it happens to eliminate the last 4 nodes.
EXPECT_EQ(DoCSE(), "n/_0(Const);n/_1(Const);n/_2(Const);n/_3(Const)|");
std::vector<string> nodes = str_util::Split(DoCSE(), ";|");
std::set<string> node_set(nodes.begin(), nodes.end());
// Expect exactly one of each type of node to be retained after CSE.
EXPECT_EQ(node_set.count("n/_0(Const)") + node_set.count("n/_7(Const)"), 1);
EXPECT_EQ(node_set.count("n/_1(Const)") + node_set.count("n/_6(Const)"), 1);
EXPECT_EQ(node_set.count("n/_2(Const)") + node_set.count("n/_5(Const)"), 1);
EXPECT_EQ(node_set.count("n/_3(Const)") + node_set.count("n/_4(Const)"), 1);
}
static void BM_CSE(int iters, int op_nodes) {