[hgemm] Interchanged hgemm_noTrans_1x8 and hgemm_noTrans_4x4 calls

Moving 1x8 kernel call after 4x4 kernel call.
Added couple of testcases.

Signed-off-by: Debadri Samaddar <s.debadri@samsung.com>

diff --git a/nntrainer/tensor/hgemm/hgemm.cpp b/nntrainer/tensor/hgemm/hgemm.cpp
index a41a5ba6dc12367a521cdc92e61bd15061ff9590..bea15a1d9358671158d901f6949a3a5dd01d52e2 100644 (file)
--- a/nntrainer/tensor/hgemm/hgemm.cpp
+++ b/nntrainer/tensor/hgemm/hgemm.cpp
@@ -58,10 +58,10 @@ void hgemm_noTrans(const __fp16 *A, const __fp16 *B, __fp16 *C, unsigned int M,
       hgemm_noTrans_8x8(M, N, K, A, K, B, N, C, N, alpha, beta);
     } else if (M % 4 == 0 && N % 8 == 0 && K % 4 == 0) {
       hgemm_noTrans_4x8(M, N, K, A, K, B, N, C, N, alpha, beta);
-    } else if (N % 8 == 0) {
-      hgemm_noTrans_1x8(M, N, K, A, K, B, N, C, N, alpha, beta);
     } else if (M % 4 == 0 && N % 4 == 0 && K % 4 == 0) {
       hgemm_noTrans_4x4(M, N, K, A, K, B, N, C, N, alpha, beta);
+    } else if (N % 8 == 0) {
+      hgemm_noTrans_1x8(M, N, K, A, K, B, N, C, N, alpha, beta);
     } else if (N % 4 == 0) {
       hgemm_noTrans_1x4(M, N, K, A, K, B, N, C, N, alpha, beta);
     }

diff --git a/test/unittest/unittest_nntrainer_tensor_neon_fp16.cpp b/test/unittest/unittest_nntrainer_tensor_neon_fp16.cpp
index 7b3cdae9fa232c197fa6651e4d319075f7ba2bce..d62be87aaf3e7352b9b11a5a9258dc1f8c3ba466 100644 (file)
--- a/test/unittest/unittest_nntrainer_tensor_neon_fp16.cpp
+++ b/test/unittest/unittest_nntrainer_tensor_neon_fp16.cpp
@@ -765,6 +765,128 @@ TEST(nntrainer_Tensor, dot_gemm_50_768_20000) {
   EXPECT_IN_RANGE((float)cosSimNeon, 0.99, 1);
 }
 
+TEST(nntrainer_Tensor, dot_gemm_512_520_1032) {
+  /// @note GEMM : A X B = C
+  int batch = 1;
+  int channel = 1;
+  int height = 512;
+  int width = 520;
+
+  int height_b = 520;
+  int width_b = 1032;
+
+  bool transA = false;
+  bool transB = false;
+
+  nntrainer::TensorDim::TensorType t_type_nchw_fp16 = {
+    nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16};
+
+  nntrainer::TensorDim::TensorType t_type_nchw_fp32 = {
+    nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP32};
+
+  nntrainer::Tensor A(batch, channel, height, width, t_type_nchw_fp16);
+  nntrainer::Tensor B(batch, channel, height_b, width_b, t_type_nchw_fp16);
+
+  nntrainer::Tensor A_fp32(batch, channel, height, width, t_type_nchw_fp32);
+  nntrainer::Tensor B_fp32(batch, channel, height_b, width_b, t_type_nchw_fp32);
+
+  const float alpha = 1e-1;
+  const int MOD = 10;
+
+  GEN_TEST_INPUT(A, ((i * (batch * height * channel) + j * (batch * height) +
+                      k * (width) + l + 1) %
+                     MOD) *
+                      alpha);
+  GEN_TEST_INPUT_B(B, ((i * (batch * height_b * channel) +
+                        j * (batch * height_b) + k * (width_b) + l + 1) %
+                       MOD) *
+                        alpha);
+  GEN_TEST_INPUT(A_fp32, ((i * (batch * height * channel) +
+                           j * (batch * height) + k * (width) + l + 1) %
+                          MOD) *
+                           alpha);
+  GEN_TEST_INPUT_B(B_fp32, ((i * (batch * height_b * channel) +
+                             j * (batch * height_b) + k * (width_b) + l + 1) %
+                            MOD) *
+                             alpha);
+
+  nntrainer::Tensor C = A.dot(B, transA, transB);
+
+  nntrainer::Tensor C_fp32 = A_fp32.dot(B_fp32, transA, transB);
+
+  float mseErrorNeon =
+    mse<__fp16>(C.getData<__fp16>(), C_fp32.getData<float>(), C.size());
+
+  double cosSimNeon = cosine_similarity<__fp16>(
+    C.getData<__fp16>(), C_fp32.getData<float>(), C.size());
+
+  const float epsilon = 1e-3 * width;
+
+  EXPECT_IN_RANGE(mseErrorNeon, 0, epsilon);
+  EXPECT_IN_RANGE((float)cosSimNeon, 0.99, 1);
+}
+
+TEST(nntrainer_Tensor, dot_gemm_1001_1024_20000) {
+  /// @note GEMM : A X B = C
+  int batch = 1;
+  int channel = 1;
+  int height = 1001;
+  int width = 1024;
+
+  int height_b = 1024;
+  int width_b = 20000;
+
+  bool transA = false;
+  bool transB = false;
+
+  nntrainer::TensorDim::TensorType t_type_nchw_fp16 = {
+    nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP16};
+
+  nntrainer::TensorDim::TensorType t_type_nchw_fp32 = {
+    nntrainer::Tformat::NCHW, nntrainer::Tdatatype::FP32};
+
+  nntrainer::Tensor A(batch, channel, height, width, t_type_nchw_fp16);
+  nntrainer::Tensor B(batch, channel, height_b, width_b, t_type_nchw_fp16);
+
+  nntrainer::Tensor A_fp32(batch, channel, height, width, t_type_nchw_fp32);
+  nntrainer::Tensor B_fp32(batch, channel, height_b, width_b, t_type_nchw_fp32);
+
+  const float alpha = 1e-1;
+  const int MOD = 10;
+
+  GEN_TEST_INPUT(A, ((i * (batch * height * channel) + j * (batch * height) +
+                      k * (width) + l + 1) %
+                     MOD) *
+                      alpha);
+  GEN_TEST_INPUT_B(B, ((i * (batch * height_b * channel) +
+                        j * (batch * height_b) + k * (width_b) + l + 1) %
+                       MOD) *
+                        alpha);
+  GEN_TEST_INPUT(A_fp32, ((i * (batch * height * channel) +
+                           j * (batch * height) + k * (width) + l + 1) %
+                          MOD) *
+                           alpha);
+  GEN_TEST_INPUT_B(B_fp32, ((i * (batch * height_b * channel) +
+                             j * (batch * height_b) + k * (width_b) + l + 1) %
+                            MOD) *
+                             alpha);
+
+  nntrainer::Tensor C = A.dot(B, transA, transB);
+
+  nntrainer::Tensor C_fp32 = A_fp32.dot(B_fp32, transA, transB);
+
+  float mseErrorNeon =
+    mse<__fp16>(C.getData<__fp16>(), C_fp32.getData<float>(), C.size());
+
+  double cosSimNeon = cosine_similarity<__fp16>(
+    C.getData<__fp16>(), C_fp32.getData<float>(), C.size());
+
+  const float epsilon = 1e-3 * width;
+
+  EXPECT_IN_RANGE(mseErrorNeon, 0, epsilon);
+  EXPECT_IN_RANGE((float)cosSimNeon, 0.99, 1);
+}
+
 TEST(nntrainer_Tensor, dot_gemm_50_768_516) {
   /// @note GEMM : A X B = C
   int batch = 1;