experiments/STT-tensorflow
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[SparseToDense] Fix benchmark reporting for the SparseToDense op.

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Previously, we were using a concatenated benchmark arg for the size and rank, which appears to have led to name collisions when the benchmark reporter makes the benchmark name human readable. This change switches the benchmark to using an explicit `ArgPair` for those arguments, which reports them separately.

In addition, this change switches the benchmark to use a DT_INT64 indices tensor, which matches the common case for tf.SparseTensor usage in the TensorFlow API, and avoids a cast in the SparseToDenseOp implementation.

PiperOrigin-RevId: 295985981
Change-Id: I94df39e2ef64b387f4c158ef0c8b33153c3cca4c
This commit is contained in:
Derek Murray 2020-02-19 09:25:57 -08:00 committed by TensorFlower Gardener
parent 508c5311cc
commit 0085ae0f60
1 changed files with 19 additions and 25 deletions

44
tensorflow/core/kernels/sparse_to_dense_op_test.cc
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@ -198,13 +198,7 @@ TEST_F(SparseToDenseTest, ThreeD_MultValues) {
} // namespace
static int BM_Arg(int ndim, int n) { return (ndim * 1000000) + n; }
static int NDIM_from_arg(int bm_arg) { return bm_arg / 1000000; }
static int N_from_arg(int bm_arg) { return bm_arg % 1000000; }
static void BM_SparseToDense(int iters, const int bm_arg) {
const int NDIM = NDIM_from_arg(bm_arg);
const int N = N_from_arg(bm_arg);
static void BM_SparseToDense(int iters, int NDIM, int N) {
// TODO(zhifengc): Switch to use kernel_benchmark_testlib.h
tensorflow::testing::StopTiming();
@ -217,7 +211,7 @@ static void BM_SparseToDense(int iters, const int bm_arg) {
// Create a dense tensor with dims [1, ..., 1, N]
Tensor output_shape(DT_INT32, TensorShape({NDIM}));
Tensor sparse_indices(DT_INT32, TensorShape({N, NDIM}));
Tensor sparse_indices(DT_INT64, TensorShape({N, NDIM}));
Tensor sparse_values(DT_FLOAT, TensorShape({N}));
Tensor default_value(DT_FLOAT, TensorShape({}));
auto output_shape_t = output_shape.vec<int32>();
@ -225,7 +219,7 @@ static void BM_SparseToDense(int iters, const int bm_arg) {
output_shape_t(d) = (d == IndexDim) ? N : 3;
}
auto sparse_indices_t = sparse_indices.matrix<int32>();
auto sparse_indices_t = sparse_indices.matrix<int64>();
for (int n = 0; n < N; ++n) {
for (int d = 0; d < NDIM; ++d)
sparse_indices_t(n, d) = (d == IndexDim) ? n : 0;
@ -274,21 +268,21 @@ static void BM_SparseToDense(int iters, const int bm_arg) {
}
BENCHMARK(BM_SparseToDense)
->Arg(BM_Arg(1, 10))
->Arg(BM_Arg(1, 100))
->Arg(BM_Arg(1, 1000))
->Arg(BM_Arg(1, 10000))
->Arg(BM_Arg(2, 10))
->Arg(BM_Arg(2, 100))
->Arg(BM_Arg(2, 1000))
->Arg(BM_Arg(2, 10000))
->Arg(BM_Arg(3, 10))
->Arg(BM_Arg(3, 100))
->Arg(BM_Arg(3, 1000))
->Arg(BM_Arg(3, 10000))
->Arg(BM_Arg(5, 10))
->Arg(BM_Arg(5, 100))
->Arg(BM_Arg(5, 1000))
->Arg(BM_Arg(5, 10000));
->ArgPair(1, 10)
->ArgPair(1, 100)
->ArgPair(1, 1000)
->ArgPair(1, 10000)
->ArgPair(2, 10)
->ArgPair(2, 100)
->ArgPair(2, 1000)
->ArgPair(2, 10000)
->ArgPair(3, 10)
->ArgPair(3, 100)
->ArgPair(3, 1000)
->ArgPair(3, 10000)
->ArgPair(5, 10)
->ArgPair(5, 100)
->ArgPair(5, 1000)
->ArgPair(5, 10000);
} // namespace tensorflow