[ blas/neon ] Add NEON fp16 function for sdot

Enable neon sdot function for Android (ARM) fp16 computation.
Add unit test for fp16 sdot function in Android(ARM).

**Self evaluation:**
1. Build test:	 [X]Passed [ ]Failed [ ]Skipped
2. Run test:	 [X]Passed [ ]Failed [ ]Skipped

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

diff --git a/nntrainer/tensor/blas_interface.cpp b/nntrainer/tensor/blas_interface.cpp
index 8882a8e..d82b14f 100644 (file)
--- a/nntrainer/tensor/blas_interface.cpp
+++ b/nntrainer/tensor/blas_interface.cpp
@@ -108,10 +108,25 @@ static void sgemv_FP16(CBLAS_ORDER order, CBLAS_TRANSPOSE TransA,
 static _FP16 sdot_FP16(const unsigned int N, const _FP16 *X,
                        const unsigned int incX, const _FP16 *Y,
                        const unsigned int incY) {
+
+  if (incX < 0 or incY < 0)
+    throw std::invalid_argument("Error: negative inc not supported");
+
   _FP16 ret = 0;
+
+#ifdef USE__FP16
+  if (incX == 1 && incY == 1) {
+    ret = nntrainer::neon::sdot_neon_fp16(N, X, Y);
+  } else {
+    for (unsigned int i = 0; i < N; ++i) {
+      ret += X[i * incX] * Y[i * incY];
+    }
+  }
+#else
   for (unsigned int i = 0; i < N; ++i) {
     ret += X[i * incX] * Y[i * incY];
   }
+#endif
   return ret;
 }
 

diff --git a/nntrainer/tensor/blas_neon.cpp b/nntrainer/tensor/blas_neon.cpp
index 57d331c..0a9094e 100644 (file)
--- a/nntrainer/tensor/blas_neon.cpp
+++ b/nntrainer/tensor/blas_neon.cpp
@@ -519,7 +519,8 @@ void sgemv_transpose_neon_fp16(const __fp16 *A, const __fp16 *X, __fp16 *Y,
   }
 }
 
-void saxpy_neon_fp16(const unsigned int N, const float alpha, const __fp16 *X, __fp16 *Y) {
+void saxpy_neon_fp16(const unsigned int N, const float alpha, const __fp16 *X,
+                     __fp16 *Y) {
 
   const float16x8_t v_alphaX8 = vmovq_n_f16(alpha);
   const float16x4_t v_alphaX4 = vmov_n_f16(alpha);
@@ -527,7 +528,7 @@ void saxpy_neon_fp16(const unsigned int N, const float alpha, const __fp16 *X, _
   unsigned int idx = 0;
 
   // processing batch of 8
-  for(; (N - idx) >= 8 ; idx += 8){
+  for (; (N - idx) >= 8; idx += 8) {
     float16x8_t x = vld1q_f16(&X[idx]);
     float16x8_t y = vld1q_f16(&Y[idx]);
 
@@ -537,7 +538,7 @@ void saxpy_neon_fp16(const unsigned int N, const float alpha, const __fp16 *X, _
   }
 
   // processing remaining batch of 4
-  for(; (N - idx) >= 4 ; idx += 4){
+  for (; (N - idx) >= 4; idx += 4) {
     float16x4_t x = vld1_f16(&X[idx]);
     float16x4_t y = vld1_f16(&Y[idx]);
 
@@ -551,4 +552,52 @@ void saxpy_neon_fp16(const unsigned int N, const float alpha, const __fp16 *X, _
     Y[idx] = Y[idx] + alpha * X[idx];
 }
 
+__fp16 sdot_neon_fp16(const unsigned int N, const __fp16 *X, const __fp16 *Y) {
+
+  float16x8_t accX8 = vmovq_n_f16(0);
+  float16x4_t accX4 = vmov_n_f16(0);
+
+  unsigned int idx = 0;
+  __fp16 ret = 0;
+
+  // processing batch of 8
+  for (; (N - idx) >= 8; idx += 8) {
+    float16x8_t x = vld1q_f16(&X[idx]);
+    float16x8_t y = vld1q_f16(&Y[idx]);
+
+    // x*y + accX8 -> accX8
+    accX8 = vfmaq_f16(accX8, x, y);
+  }
+
+  // check at least one batch of 8 is processed
+  if (N - 8 >= 0) {
+    __fp16 result[8];
+    vst1q_f16(result, accX8);
+    for (unsigned int i = 0; i < 8; i++)
+      ret += result[i];
+  }
+
+  // processing remaining batch of 4
+  for (; (N - idx) >= 4; idx += 4) {
+    float16x4_t x = vld1_f16(&X[idx]);
+    float16x4_t y = vld1_f16(&Y[idx]);
+
+    // x*y + accX4 -> accX4
+    accX4 = vfma_f16(accX4, x, y);
+  }
+
+  // check at least one batch of 4 is processed
+  if (N % 8 >= 4) {
+    __fp16 result[4];
+    vst1_f16(result, accX4);
+    ret += result[0] + result[1] + result[2] + result[3];
+  }
+
+  // pocessing remaining values
+  for (; idx < N; idx++)
+    ret += X[idx] * Y[idx];
+
+  return ret;
+}
+
 } // namespace nntrainer::neon

diff --git a/nntrainer/tensor/blas_neon.h b/nntrainer/tensor/blas_neon.h
index c573409..649ea89 100644 (file)
--- a/nntrainer/tensor/blas_neon.h
+++ b/nntrainer/tensor/blas_neon.h
@@ -83,7 +83,16 @@ void sgemv_transpose_neon_fp16(const __fp16 *A, const __fp16 *X, __fp16 *Y,
  * @param[in] X __fp16 * for Vector X
  * @param[in] Y __fp16 * for Vector Y
  */
-void saxpy_neon_fp16(const unsigned int N, const float alpha, const __fp16 *X, __fp16 *Y);
+void saxpy_neon_fp16(const unsigned int N, const float alpha, const __fp16 *X,
+                     __fp16 *Y);
+
+/**
+ * @brief     sdot computation with neon: sum of all X * Y
+ * @param[in] N number of elements in Y
+ * @param[in] X __fp16 * for Vector X
+ * @param[in] Y __fp16 * for Vector Y
+ */
+__fp16 sdot_neon_fp16(const unsigned int N, const __fp16 *X, const __fp16 *Y);
 
 } // namespace nntrainer::neon
 

diff --git a/test/unittest/unittest_nntrainer_tensor_neon_fp16.cpp b/test/unittest/unittest_nntrainer_tensor_neon_fp16.cpp
index 5d4e71a..aa95447 100644 (file)
--- a/test/unittest/unittest_nntrainer_tensor_neon_fp16.cpp
+++ b/test/unittest/unittest_nntrainer_tensor_neon_fp16.cpp
@@ -65,11 +65,65 @@ TEST(nntrainer_Tensor, add_i) {
 
   double cosSimNeon = cosine_similarity<__fp16>(
     input.getData<__fp16>(), input_fp32.getData<float>(), input.size());
-  
+
   EXPECT_IN_RANGE(mseErrorNeon, 0, epsilon);
   EXPECT_IN_RANGE(cosSimNeon, 0.99, 1);
 }
 
+TEST(nntrainer_Tensor, dot) {
+
+  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};
+
+  // conditions for fp16 sdot call:
+  // this->(batch * channel * height) = arg->(width) = 1;
+
+  size_t width = 23;
+
+  __fp16 a_data[] = {0,  1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 11,
+                     12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+  nntrainer::Tensor input(
+    nntrainer::TensorDim(1, 1, 1, width, t_type_nchw_fp16), a_data);
+  __fp16 b_data[] = {0,  1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 11,
+                     12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+  nntrainer::Tensor input_2(
+    nntrainer::TensorDim(1, 1, width, 1, t_type_nchw_fp16), b_data);
+
+  float a_data_fp32[] = {0,  1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 11,
+                         12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+  nntrainer::Tensor input_fp32(
+    nntrainer::TensorDim(1, 1, 1, width, t_type_nchw_fp32), a_data_fp32);
+  float b_data_fp32[] = {0,  1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 11,
+                         12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
+  nntrainer::Tensor input_fp32_2(
+    nntrainer::TensorDim(1, 1, width, 1, t_type_nchw_fp32), b_data_fp32);
+
+  nntrainer::Tensor result_neon;
+  nntrainer::Tensor result_fp32;
+
+  // NEON fp16
+  result_neon = input.dot(input_2, false, false);
+
+  // fp32
+  result_fp32 = input_fp32.dot(input_fp32_2, false, false);
+
+  float mseErrorNeon =
+    mse<__fp16>(result_neon.getData<__fp16>(), result_fp32.getData<float>(),
+                result_neon.size());
+
+  double cosSimNeon =
+    cosine_similarity<__fp16>(result_neon.getData<__fp16>(),
+                              result_fp32.getData<float>(), result_neon.size());
+
+  const float epsilon = 1e-4;
+
+  EXPECT_IN_RANGE(mseErrorNeon, 0, epsilon);
+  EXPECT_IN_RANGE((float)cosSimNeon, 0.99, 1);
+}
+
 GTEST_API_ int main(int argc, char **argv) {
   int result = -1;