Improve the test coverage in small_blas_test
1. Add explicit tests for MatrixMatrixMultiplyNaive and
MatrixTransposeMatrixMultiplyNaive
2. Add tests that exercise a variety of matrix sizes for
MatrixVectorMultiply and MatrixTransposeVectorMultiply.
Change-Id: I0b25ec346b719f19b2067848f9d4bb64c9848750
diff --git a/internal/ceres/small_blas_test.cc b/internal/ceres/small_blas_test.cc
index ddb11ab..2914244 100644
--- a/internal/ceres/small_blas_test.cc
+++ b/internal/ceres/small_blas_test.cc
@@ -202,100 +202,276 @@
}
}
-TEST(BLAS, MatrixVectorMultiply) {
+// TODO(sameeragarwal): Dedup and reduce the amount of duplication of
+// test code in this file.
+
+TEST(BLAS, MatrixMatrixMultiplyNaive) {
+ const int kRowA = 3;
+ const int kColA = 5;
+ Matrix A(kRowA, kColA);
+ A.setOnes();
+
+ const int kRowB = 5;
+ const int kColB = 7;
+ Matrix B(kRowB, kColB);
+ B.setOnes();
+
+ for (int row_stride_c = kRowA; row_stride_c < 3 * kRowA; ++row_stride_c) {
+ for (int col_stride_c = kColB; col_stride_c < 3 * kColB; ++col_stride_c) {
+ Matrix C(row_stride_c, col_stride_c);
+ C.setOnes();
+
+ Matrix C_plus = C;
+ Matrix C_minus = C;
+ Matrix C_assign = C;
+
+ Matrix C_plus_ref = C;
+ Matrix C_minus_ref = C;
+ Matrix C_assign_ref = C;
+ for (int start_row_c = 0; start_row_c + kRowA < row_stride_c; ++start_row_c) {
+ for (int start_col_c = 0; start_col_c + kColB < col_stride_c; ++start_col_c) {
+ C_plus_ref.block(start_row_c, start_col_c, kRowA, kColB) +=
+ A * B;
+
+ MatrixMatrixMultiplyNaive<kRowA, kColA, kRowB, kColB, 1>(
+ A.data(), kRowA, kColA,
+ B.data(), kRowB, kColB,
+ C_plus.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
+
+ EXPECT_NEAR((C_plus_ref - C_plus).norm(), 0.0, kTolerance)
+ << "C += A * B \n"
+ << "row_stride_c : " << row_stride_c << "\n"
+ << "col_stride_c : " << col_stride_c << "\n"
+ << "start_row_c : " << start_row_c << "\n"
+ << "start_col_c : " << start_col_c << "\n"
+ << "Cref : \n" << C_plus_ref << "\n"
+ << "C: \n" << C_plus;
+
+
+ C_minus_ref.block(start_row_c, start_col_c, kRowA, kColB) -=
+ A * B;
+
+ MatrixMatrixMultiplyNaive<kRowA, kColA, kRowB, kColB, -1>(
+ A.data(), kRowA, kColA,
+ B.data(), kRowB, kColB,
+ C_minus.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
+
+ EXPECT_NEAR((C_minus_ref - C_minus).norm(), 0.0, kTolerance)
+ << "C -= A * B \n"
+ << "row_stride_c : " << row_stride_c << "\n"
+ << "col_stride_c : " << col_stride_c << "\n"
+ << "start_row_c : " << start_row_c << "\n"
+ << "start_col_c : " << start_col_c << "\n"
+ << "Cref : \n" << C_minus_ref << "\n"
+ << "C: \n" << C_minus;
+
+ C_assign_ref.block(start_row_c, start_col_c, kRowA, kColB) =
+ A * B;
+
+ MatrixMatrixMultiplyNaive<kRowA, kColA, kRowB, kColB, 0>(
+ A.data(), kRowA, kColA,
+ B.data(), kRowB, kColB,
+ C_assign.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
+
+ EXPECT_NEAR((C_assign_ref - C_assign).norm(), 0.0, kTolerance)
+ << "C = A * B \n"
+ << "row_stride_c : " << row_stride_c << "\n"
+ << "col_stride_c : " << col_stride_c << "\n"
+ << "start_row_c : " << start_row_c << "\n"
+ << "start_col_c : " << start_col_c << "\n"
+ << "Cref : \n" << C_assign_ref << "\n"
+ << "C: \n" << C_assign;
+ }
+ }
+ }
+ }
+}
+
+TEST(BLAS, MatrixTransposeMatrixMultiplyNaive) {
const int kRowA = 5;
const int kColA = 3;
Matrix A(kRowA, kColA);
A.setOnes();
- Vector b(kColA);
- b.setOnes();
+ const int kRowB = 5;
+ const int kColB = 7;
+ Matrix B(kRowB, kColB);
+ B.setOnes();
- Vector c(kRowA);
- c.setOnes();
+ for (int row_stride_c = kColA; row_stride_c < 3 * kColA; ++row_stride_c) {
+ for (int col_stride_c = kColB; col_stride_c < 3 * kColB; ++col_stride_c) {
+ Matrix C(row_stride_c, col_stride_c);
+ C.setOnes();
- Vector c_plus = c;
- Vector c_minus = c;
- Vector c_assign = c;
+ Matrix C_plus = C;
+ Matrix C_minus = C;
+ Matrix C_assign = C;
- Vector c_plus_ref = c;
- Vector c_minus_ref = c;
- Vector c_assign_ref = c;
+ Matrix C_plus_ref = C;
+ Matrix C_minus_ref = C;
+ Matrix C_assign_ref = C;
+ for (int start_row_c = 0; start_row_c + kColA < row_stride_c; ++start_row_c) {
+ for (int start_col_c = 0; start_col_c + kColB < col_stride_c; ++start_col_c) {
+ C_plus_ref.block(start_row_c, start_col_c, kColA, kColB) +=
+ A.transpose() * B;
- c_plus_ref += A * b;
- MatrixVectorMultiply<kRowA, kColA, 1>(A.data(), kRowA, kColA,
- b.data(),
- c_plus.data());
- EXPECT_NEAR((c_plus_ref - c_plus).norm(), 0.0, kTolerance)
- << "c += A * b \n"
- << "c_ref : \n" << c_plus_ref << "\n"
- << "c: \n" << c_plus;
+ MatrixTransposeMatrixMultiplyNaive<kRowA, kColA, kRowB, kColB, 1>(
+ A.data(), kRowA, kColA,
+ B.data(), kRowB, kColB,
+ C_plus.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
- c_minus_ref -= A * b;
- MatrixVectorMultiply<kRowA, kColA, -1>(A.data(), kRowA, kColA,
- b.data(),
- c_minus.data());
- EXPECT_NEAR((c_minus_ref - c_minus).norm(), 0.0, kTolerance)
- << "c += A * b \n"
- << "c_ref : \n" << c_minus_ref << "\n"
- << "c: \n" << c_minus;
+ EXPECT_NEAR((C_plus_ref - C_plus).norm(), 0.0, kTolerance)
+ << "C += A' * B \n"
+ << "row_stride_c : " << row_stride_c << "\n"
+ << "col_stride_c : " << col_stride_c << "\n"
+ << "start_row_c : " << start_row_c << "\n"
+ << "start_col_c : " << start_col_c << "\n"
+ << "Cref : \n" << C_plus_ref << "\n"
+ << "C: \n" << C_plus;
- c_assign_ref = A * b;
- MatrixVectorMultiply<kRowA, kColA, 0>(A.data(), kRowA, kColA,
- b.data(),
- c_assign.data());
- EXPECT_NEAR((c_assign_ref - c_assign).norm(), 0.0, kTolerance)
- << "c += A * b \n"
- << "c_ref : \n" << c_assign_ref << "\n"
- << "c: \n" << c_assign;
+ C_minus_ref.block(start_row_c, start_col_c, kColA, kColB) -=
+ A.transpose() * B;
+
+ MatrixTransposeMatrixMultiplyNaive<kRowA, kColA, kRowB, kColB, -1>(
+ A.data(), kRowA, kColA,
+ B.data(), kRowB, kColB,
+ C_minus.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
+
+ EXPECT_NEAR((C_minus_ref - C_minus).norm(), 0.0, kTolerance)
+ << "C -= A' * B \n"
+ << "row_stride_c : " << row_stride_c << "\n"
+ << "col_stride_c : " << col_stride_c << "\n"
+ << "start_row_c : " << start_row_c << "\n"
+ << "start_col_c : " << start_col_c << "\n"
+ << "Cref : \n" << C_minus_ref << "\n"
+ << "C: \n" << C_minus;
+
+ C_assign_ref.block(start_row_c, start_col_c, kColA, kColB) =
+ A.transpose() * B;
+
+ MatrixTransposeMatrixMultiplyNaive<kRowA, kColA, kRowB, kColB, 0>(
+ A.data(), kRowA, kColA,
+ B.data(), kRowB, kColB,
+ C_assign.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
+
+ EXPECT_NEAR((C_assign_ref - C_assign).norm(), 0.0, kTolerance)
+ << "C = A' * B \n"
+ << "row_stride_c : " << row_stride_c << "\n"
+ << "col_stride_c : " << col_stride_c << "\n"
+ << "start_row_c : " << start_row_c << "\n"
+ << "start_col_c : " << start_col_c << "\n"
+ << "Cref : \n" << C_assign_ref << "\n"
+ << "C: \n" << C_assign;
+ }
+ }
+ }
+ }
+}
+
+TEST(BLAS, MatrixVectorMultiply) {
+ for (int num_rows_a = 1; num_rows_a < 10; ++num_rows_a) {
+ for (int num_cols_a = 1; num_cols_a < 10; ++num_cols_a) {
+ Matrix A(num_rows_a, num_cols_a);
+ A.setOnes();
+
+ Vector b(num_cols_a);
+ b.setOnes();
+
+ Vector c(num_rows_a);
+ c.setOnes();
+
+ Vector c_plus = c;
+ Vector c_minus = c;
+ Vector c_assign = c;
+
+ Vector c_plus_ref = c;
+ Vector c_minus_ref = c;
+ Vector c_assign_ref = c;
+
+ c_plus_ref += A * b;
+ MatrixVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
+ A.data(), num_rows_a, num_cols_a,
+ b.data(),
+ c_plus.data());
+ EXPECT_NEAR((c_plus_ref - c_plus).norm(), 0.0, kTolerance)
+ << "c += A * b \n"
+ << "c_ref : \n" << c_plus_ref << "\n"
+ << "c: \n" << c_plus;
+
+ c_minus_ref -= A * b;
+ MatrixVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, -1>(
+ A.data(), num_rows_a, num_cols_a,
+ b.data(),
+ c_minus.data());
+ EXPECT_NEAR((c_minus_ref - c_minus).norm(), 0.0, kTolerance)
+ << "c += A * b \n"
+ << "c_ref : \n" << c_minus_ref << "\n"
+ << "c: \n" << c_minus;
+
+ c_assign_ref = A * b;
+ MatrixVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 0>(
+ A.data(), num_rows_a, num_cols_a,
+ b.data(),
+ c_assign.data());
+ EXPECT_NEAR((c_assign_ref - c_assign).norm(), 0.0, kTolerance)
+ << "c += A * b \n"
+ << "c_ref : \n" << c_assign_ref << "\n"
+ << "c: \n" << c_assign;
+ }
+ }
}
TEST(BLAS, MatrixTransposeVectorMultiply) {
- const int kRowA = 5;
- const int kColA = 3;
- Matrix A(kRowA, kColA);
- A.setRandom();
+ for (int num_rows_a = 1; num_rows_a < 10; ++num_rows_a) {
+ for (int num_cols_a = 1; num_cols_a < 10; ++num_cols_a) {
+ Matrix A(num_rows_a, num_cols_a);
+ A.setRandom();
- Vector b(kRowA);
- b.setRandom();
+ Vector b(num_rows_a);
+ b.setRandom();
- Vector c(kColA);
- c.setOnes();
+ Vector c(num_cols_a);
+ c.setOnes();
- Vector c_plus = c;
- Vector c_minus = c;
- Vector c_assign = c;
+ Vector c_plus = c;
+ Vector c_minus = c;
+ Vector c_assign = c;
- Vector c_plus_ref = c;
- Vector c_minus_ref = c;
- Vector c_assign_ref = c;
+ Vector c_plus_ref = c;
+ Vector c_minus_ref = c;
+ Vector c_assign_ref = c;
- c_plus_ref += A.transpose() * b;
- MatrixTransposeVectorMultiply<kRowA, kColA, 1>(A.data(), kRowA, kColA,
- b.data(),
- c_plus.data());
- EXPECT_NEAR((c_plus_ref - c_plus).norm(), 0.0, kTolerance)
- << "c += A' * b \n"
- << "c_ref : \n" << c_plus_ref << "\n"
- << "c: \n" << c_plus;
+ c_plus_ref += A.transpose() * b;
+ MatrixTransposeVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
+ A.data(), num_rows_a, num_cols_a,
+ b.data(),
+ c_plus.data());
+ EXPECT_NEAR((c_plus_ref - c_plus).norm(), 0.0, kTolerance)
+ << "c += A' * b \n"
+ << "c_ref : \n" << c_plus_ref << "\n"
+ << "c: \n" << c_plus;
- c_minus_ref -= A.transpose() * b;
- MatrixTransposeVectorMultiply<kRowA, kColA, -1>(A.data(), kRowA, kColA,
- b.data(),
- c_minus.data());
- EXPECT_NEAR((c_minus_ref - c_minus).norm(), 0.0, kTolerance)
- << "c += A' * b \n"
- << "c_ref : \n" << c_minus_ref << "\n"
- << "c: \n" << c_minus;
+ c_minus_ref -= A.transpose() * b;
+ MatrixTransposeVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, -1>(
+ A.data(), num_rows_a, num_cols_a,
+ b.data(),
+ c_minus.data());
+ EXPECT_NEAR((c_minus_ref - c_minus).norm(), 0.0, kTolerance)
+ << "c += A' * b \n"
+ << "c_ref : \n" << c_minus_ref << "\n"
+ << "c: \n" << c_minus;
- c_assign_ref = A.transpose() * b;
- MatrixTransposeVectorMultiply<kRowA, kColA, 0>(A.data(), kRowA, kColA,
- b.data(),
- c_assign.data());
- EXPECT_NEAR((c_assign_ref - c_assign).norm(), 0.0, kTolerance)
- << "c += A' * b \n"
- << "c_ref : \n" << c_assign_ref << "\n"
- << "c: \n" << c_assign;
+ c_assign_ref = A.transpose() * b;
+ MatrixTransposeVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 0>(
+ A.data(), num_rows_a, num_cols_a,
+ b.data(),
+ c_assign.data());
+ EXPECT_NEAR((c_assign_ref - c_assign).norm(), 0.0, kTolerance)
+ << "c += A' * b \n"
+ << "c_ref : \n" << c_assign_ref << "\n"
+ << "c: \n" << c_assign;
+ }
+ }
}
} // namespace internal
