COMPMID-905 Optimize CLSoftmaxLayer for QASYMM8

Change-Id: I3512d67b8a72b17db1381842ca42780e39cc511c
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/120605
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>

1 files changed, 425 insertions, 99 deletions

diff --git a/src/core/CL/cl_kernels/softmax_layer_quantized.cl b/src/core/CL/cl_kernels/softmax_layer_quantized.cl
index 31f402f627..7521c8e1ee 100644
--- a/src/core/CL/cl_kernels/softmax_layer_quantized.cl
+++ b/src/core/CL/cl_kernels/softmax_layer_quantized.cl
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -21,76 +21,51 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#include "asymm_helper.h"
-#include "helpers.h"
+#include "helpers_asymm.h"
 
 #define MAX_OP(x, y, type, size) max((x), (y))
 #define ADD_OP(x, y, type, size) ((x) + (y))
 
-__constant uchar16 type_min = 0;
-__constant uint16 idx16     = (uint16)(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15);
+/* Number of workitems in dimension 0. */
+#if !defined(GRID_SIZE)
+#define GRID_SIZE 1
+#endif /* !defined(GRID_SIZE) */
 
-/** Identifies the maximum value across the 1st dimension.
- *
- * @note In case the input is not multiple of 16 -DNON_MULTIPLE_OF_16 must be passed.
- *
- * @param[in]  src_ptr                           Pointer to the source tensor slice. Supported data types: QASYMM8
- * @param[in]  src_stride_x                      Stride of the source tensor in X dimension (in bytes)
- * @param[in]  src_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  src_stride_y                      Stride of the source tensor in Y dimension (in bytes)
- * @param[in]  src_step_y                        src_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
- * @param[in]  src_step_z                        src_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  src_offset_first_element_in_bytes The offset of the first element in the source tensor
- * @param[out] dst_ptr                           Pointer to the destination tensor slice. Supported data types: same as @p src_ptr
- * @param[in]  dst_stride_x                      Stride of the destination tensor in X dimension (in bytes)
- * @param[in]  dst_step_x                        dst_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  dst_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
- * @param[in]  dst_step_y                        dst_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  dst_stride_z                      Stride of the destination tensor in Z dimension (in bytes)
- * @param[in]  dst_step_z                        dst_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
- * @param[in]  width                             Input image width
- */
-__kernel void softmax_layer_max_quantized(
-    TENSOR3D_DECLARATION(src),
-    TENSOR3D_DECLARATION(dst),
-    uint width)
-{
-    Image src = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(src);
-    Image dst = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst);
+#if VECTOR_SIZE == 2
+__constant uint2 idx__ = (uint2)(0, 1);
+#define asymm_mult(a, b) ASYMM_MULT(a, b, 2)
+#define asymm_exp_on_negative_values(a, k_integer_bits) ASYMM_EXP_ON_NEGATIVE_VALUES(a, k_integer_bits, 2)
+#define asymm_rescale(value, src_integer_bits, dst_integer_bits) ASYMM_RESCALE(value, src_integer_bits, dst_integer_bits, 2)
 
-    // Initialize local maximum
-    uchar16 max_val = 0;
+#elif VECTOR_SIZE == 4
+__constant uint4 idx__ = (uint4)(0, 1, 2, 3);
+#define asymm_mult(a, b) ASYMM_MULT(a, b, 4)
+#define asymm_exp_on_negative_values(a, k_integer_bits) ASYMM_EXP_ON_NEGATIVE_VALUES(a, k_integer_bits, 4)
+#define asymm_rescale(value, src_integer_bits, dst_integer_bits) ASYMM_RESCALE(value, src_integer_bits, dst_integer_bits, 4)
 
-    // Calculate max of row
-    const uint width4 = width >> 4;
-    for(uint i = 0; i < width4; i++)
-    {
-        uchar16 data = vload16(0, (__global uchar *)offset(&src, i << 4, 0));
-        max_val      = MAX_OP(data, max_val, uchar, 16);
-    }
+#elif VECTOR_SIZE == 8
+__constant uint8 idx__ = (uint8)(0, 1, 2, 3, 4, 5, 6, 7);
+#define asymm_mult(a, b) ASYMM_MULT(a, b, 8)
+#define asymm_exp_on_negative_values(a, k_integer_bits) ASYMM_EXP_ON_NEGATIVE_VALUES(a, k_integer_bits, 8)
+#define asymm_rescale(value, src_integer_bits, dst_integer_bits) ASYMM_RESCALE(value, src_integer_bits, dst_integer_bits, 8)
 
-#ifdef NON_MULTIPLE_OF_16
-    // Handle non multiple of 16
-    uchar16 data = vload16(0, (__global uchar *)offset(&src, width4 << 4, 0));
-    uchar16 widx = convert_uchar16(((uint16)(width4 << 4) + idx16) < width);
-    max_val      = MAX_OP(max_val, select(type_min, data, widx), uchar, 16);
-#endif /* NON_MULTIPLE_OF_16 */
+#else /* VECTOR_SIZE DEFAULT */
+#define VECTOR_SIZE 16
+#define LOG_VECTOR_SIZE 4
+__constant uint16 idx__ = (uint16)(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15);
+#define asymm_mult(a, b) ASYMM_MULT(a, b, 16)
+#define asymm_exp_on_negative_values(a, k_integer_bits) ASYMM_EXP_ON_NEGATIVE_VALUES(a, k_integer_bits, 16)
+#define asymm_rescale(value, src_integer_bits, dst_integer_bits) ASYMM_RESCALE(value, src_integer_bits, dst_integer_bits, 16)
 
-    // Perform max reduction
-    max_val.s01234567 = MAX_OP(max_val.s01234567, max_val.s89ABCDEF, uchar, 8);
-    max_val.s0123     = MAX_OP(max_val.s0123, max_val.s4567, uchar, 4);
-    max_val.s01       = MAX_OP(max_val.s01, max_val.s23, uchar, 2);
-    max_val.s0        = MAX_OP(max_val.s0, max_val.s1, uchar, 1);
+#endif /* VECTOR_SIZE END */
 
-    // Store result
-    *((__global uchar *)dst.ptr) = max_val.s0;
-}
+#define VEC_UCHAR VEC_DATA_TYPE(uchar, VECTOR_SIZE)
+#define VEC_UINT VEC_DATA_TYPE(uint, VECTOR_SIZE)
+#define VEC_INT VEC_DATA_TYPE(int, VECTOR_SIZE)
 
 #if defined(DIFF_MIN)
 
-int16 mult_by_quantized_multiplier(int16 data)
+VEC_INT mult_by_quantized_multiplier_serial(VEC_INT data)
 {
 #if defined(INPUT_BETA_MULTIPLIER) && defined(INPUT_BETA_LEFT_SHIFT)
     if(INPUT_BETA_MULTIPLIER > 1)
@@ -101,10 +76,21 @@ int16 mult_by_quantized_multiplier(int16 data)
     return data;
 }
 
+int4 mult_by_quantized_multiplier_parallel(int4 data)
+{
+#if defined(INPUT_BETA_MULTIPLIER) && defined(INPUT_BETA_LEFT_SHIFT)
+    if(INPUT_BETA_MULTIPLIER > 1)
+    {
+        return ASYMM_MULT(data * (1 << INPUT_BETA_LEFT_SHIFT), INPUT_BETA_MULTIPLIER, 4);
+    }
+#endif /* defined(INPUT_BETA_MULTIPLIER) && defined(INPUT_BETA_LEFT_SHIFT) */
+    return data;
+}
+
 /** Shifts the values of the input tensor by the max calculated in softmax_layer_max kernel,
  * then gets the exponent of each element as sums all elements across each row.
  *
- * @note In case the input is not multiple of 16 -DNON_MULTIPLE_OF_16 must be passed.
+ * @note In case the input is not multiple of 16 -DNON_MULTIPLE_OF_VECTOR_SIZE must be passed.
  * @note Quantized beta can be optionally passed at compile time using -DINPUT_BETA_MULTIPLIER and -DINPUT_BETA_LEFT_SHIFT (if undefined, assume beta equals 1.0)
  * @note -DDIFF_MIN must be passed at compile time. It is threshold difference between maximum value of input data and current processed value, it defines whether the value will be taken into account or not.
  *
@@ -142,62 +128,396 @@ int16 mult_by_quantized_multiplier(int16 data)
  * @param[in]  sum_offset_first_element_in_bytes The offset of the first element in the sum values tensor
  * @param[in]  width                             Input image width
  */
-__kernel void softmax_layer_shift_exp_sum_quantized(
+__kernel void softmax_layer_max_shift_exp_sum_quantized_serial(
     TENSOR3D_DECLARATION(src),
-    TENSOR3D_DECLARATION(max),
+    TENSOR3D_DECLARATION(maxo),
     TENSOR3D_DECLARATION(dst),
     TENSOR3D_DECLARATION(sum),
     uint width)
 {
-    Image src = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(src);
-    Image dst = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst);
-    Image max = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(max);
-    Image sum = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(sum);
+    Image src  = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(src);
+    Image dst  = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst);
+    Image maxo = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(maxo);
+    Image sum  = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(sum);
+
+    VEC_UCHAR max_val_vec = 0;
+
+    // Calculate max of row
+    const uint width4 = width >> LOG_VECTOR_SIZE;
+    for(uint i = 0; i < width4; i++)
+    {
+        VEC_UCHAR data = VLOAD(VECTOR_SIZE)(0, (__global uchar *)offset(&src, i << LOG_VECTOR_SIZE, 0));
+        max_val_vec    = MAX_OP(data, max_val_vec, uchar, 16);
+    }
+
+#ifdef NON_MULTIPLE_OF_VECTOR_SIZE
+    // Handle non multiple of 16
+    VEC_UCHAR uchar_min = (VEC_UCHAR)0;
+    VEC_UCHAR data      = VLOAD(VECTOR_SIZE)(0, (__global uchar *)offset(&src, width4 << LOG_VECTOR_SIZE, 0));
+    VEC_UCHAR widx      = CONVERT(((VEC_UINT)(width4 << LOG_VECTOR_SIZE) + idx__) < width, VEC_UCHAR);
+    max_val_vec         = MAX_OP(max_val_vec, select(uchar_min, data, widx), uchar, 16);
+#endif /* NON_MULTIPLE_OF_VECTOR_SIZE */
+
+    // Perform max reduction
+#if VECTOR_SIZE == 16
+    max_val_vec.s01234567 = MAX_OP(max_val_vec.s01234567, max_val_vec.s89ABCDEF, uchar, 8);
+#endif /* VECTOR SIZE 16 END */
+#if VECTOR_SIZE >= 8
+    max_val_vec.s0123 = MAX_OP(max_val_vec.s0123, max_val_vec.s4567, uchar, 4);
+#endif /* VECTOR SIZE 8 END */
+#if VECTOR_SIZE >= 4
+    max_val_vec.s01 = MAX_OP(max_val_vec.s01, max_val_vec.s23, uchar, 2);
+#endif /* VECTOR SIZE 4 END */
+    max_val_vec.s0 = MAX_OP(max_val_vec.s0, max_val_vec.s1, uchar, 1);
+
+    // Store result
+    *((__global uchar *)maxo.ptr) = max_val_vec.s0;
+
+    // Second part
 
     // Load max value of 1D logits vector (row)
-    int max_val = convert_int(*((__global uchar *)offset(&max, 0, 0)));
+    int max_val = convert_int(*((__global uchar *)offset(&maxo, 0, 0)));
 
     // Set sum vector, Q(EXP_ACCUMULATION_INT_BITS)
-    int16 sum1D = 0;
+    VEC_INT sum1D = 0;
 
     // Shift values, exp and sum
-    const uint width4 = width >> 4;
     for(uint i = 0; i < width4; i++)
     {
-        uchar16 data         = vload16(0, (__global uchar *)offset(&src, i << 4, 0));
-        int16 data_fp        = convert_int16(data);
-        int16 data_diff      = data_fp - max_val;
-        int16 data_diff_mult = mult_by_quantized_multiplier(data_diff);
-        data_fp              = asymm_exp_on_negative_values(data_diff_mult, SCALED_DIFF_INT_BITS);
-        data_fp              = asymm_rescale(data_fp, 0, EXP_ACCUMULATION_INT_BITS);
-        vstore16(data_diff, 0, (__global int *)offset(&dst, i << 4, 0));
-        sum1D = sum1D + select(0, data_fp, data_diff >= (int16)(DIFF_MIN));
+        VEC_UCHAR data         = VLOAD(VECTOR_SIZE)(0, (__global uchar *)offset(&src, i << LOG_VECTOR_SIZE, 0));
+        VEC_INT data_fp        = CONVERT(data, VEC_INT);
+        VEC_INT data_diff      = data_fp - max_val;
+        VEC_INT data_diff_mult = mult_by_quantized_multiplier_serial(data_diff);
+        data_fp                = asymm_exp_on_negative_values(data_diff_mult, SCALED_DIFF_INT_BITS);
+        data_fp                = asymm_rescale(data_fp, 0, EXP_ACCUMULATION_INT_BITS);
+        VSTORE(VECTOR_SIZE)
+        (data_diff, 0, (__global int *)offset(&dst, i << LOG_VECTOR_SIZE, 0));
+        sum1D = sum1D + select(0, data_fp, data_diff >= (VEC_INT)(DIFF_MIN));
     }
 
-#ifdef NON_MULTIPLE_OF_16
+#ifdef NON_MULTIPLE_OF_VECTOR_SIZE
     // Handle non multiple of 16
-    uchar16 data         = vload16(0, (__global uchar *)offset(&src, width4 << 4, 0));
-    int16 data_fp        = convert_int16(data);
-    int16 data_diff      = data_fp - max_val;
-    int16 data_diff_mult = mult_by_quantized_multiplier(data_diff);
-    data_fp              = asymm_exp_on_negative_values(data_diff_mult, SCALED_DIFF_INT_BITS);
-    data_fp              = asymm_rescale(data_fp, 0, EXP_ACCUMULATION_INT_BITS);
-    int16 widx           = convert_int16(((uint16)(width4 << 4) + idx16) < width);
-    vstore16(data_diff, 0, (__global int *)offset(&dst, width4 << 4, 0));
-    data_fp = select(0, data_fp, data_diff >= (int16)(DIFF_MIN));
-    sum1D   = sum1D + select(0, data_fp, widx);
-#endif /* NON_MULTIPLE_OF_16 */
-
-    // Perform min/max reduction
-    sum1D.s01234567 = ADD_OP(sum1D.s01234567, sum1D.s89ABCDEF, qs16, 8);
-    sum1D.s0123     = ADD_OP(sum1D.s0123, sum1D.s4567, qs16, 4);
-    sum1D.s01       = ADD_OP(sum1D.s01, sum1D.s23, qs16, 2);
-    sum1D.s0        = ADD_OP(sum1D.s0, sum1D.s1, qs16, 1);
+    data                   = VLOAD(VECTOR_SIZE)(0, (__global uchar *)offset(&src, width4 << LOG_VECTOR_SIZE, 0));
+    VEC_INT data_fp        = CONVERT(data, VEC_INT);
+    VEC_INT data_diff      = data_fp - max_val;
+    VEC_INT data_diff_mult = mult_by_quantized_multiplier_serial(data_diff);
+    data_fp                = asymm_exp_on_negative_values(data_diff_mult, SCALED_DIFF_INT_BITS);
+    data_fp                = asymm_rescale(data_fp, 0, EXP_ACCUMULATION_INT_BITS);
+    VEC_INT widx_          = CONVERT(((VEC_UINT)(width4 << LOG_VECTOR_SIZE) + idx__) < width, VEC_INT);
+    VSTORE(VECTOR_SIZE)
+    (data_diff, 0, (__global int *)offset(&dst, width4 << LOG_VECTOR_SIZE, 0));
+    data_fp = select(0, data_fp, data_diff >= (VEC_INT)(DIFF_MIN));
+    sum1D   = sum1D + select(0, data_fp, widx_);
+#endif /* NON_MULTIPLE_OF_VECTOR_SIZE */
+
+    // Perform sum reduction
+#if VECTOR_SIZE == 16
+    sum1D.s01234567 = ADD_OP(sum1D.s01234567, sum1D.s89ABCDEF, uchar, 8);
+#endif /* VECTOR SIZE 16 END */
+#if VECTOR_SIZE >= 8
+    sum1D.s0123 = ADD_OP(sum1D.s0123, sum1D.s4567, uchar, 4);
+#endif /* VECTOR SIZE 8 END */
+#if VECTOR_SIZE >= 4
+    sum1D.s01 = ADD_OP(sum1D.s01, sum1D.s23, uchar, 2);
+#endif /* VECTOR SIZE 4 END */
+    sum1D.s0 = ADD_OP(sum1D.s0, sum1D.s1, uchar, 1);
 
     // Calculate and store result
     *((__global int *)sum.ptr) = sum1D.s0;
 }
 
+/** Identifies the maximum value across the 1st dimension and shifts the values of the input tensor by this maximum value,
+ * then gets the exponent of each element as sums all elements across each row.
+ *
+ * @note Datatype must be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=short
+ * @note Fixed point position must be given as a preprocessor argument using -DFIXED_POINT_POSITION=pos. e.g. DFIXED_POINT_POSITION=4
+ * @note In case the input is not a multiple of VECTOR_SIZE (2,4,8,16) -DNON_MULTIPLE_OF_VECTOR_SIZE must be passed.
+ * @note Beta can be optionally passed at compile time using -DBETA (by default, it is 1.0).
+ *
+ * @param[in]  src_ptr                            Pointer to the source tensor slice. Supported data types: QS8/QS16/F16/F32
+ * @param[in]  src_stride_x                       Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  src_step_x                         src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  src_stride_y                       Stride of the source tensor in Y dimension (in bytes)
+ * @param[in]  src_step_y                         src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  src_stride_z                       Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  src_step_z                         src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  src_offset_first_element_in_bytes  The offset of the first element in the source tensor
+ * @param[in]  maxo_ptr                           Pointer to the max values tensor slice. Supported data types: same as @p src_ptr
+ * @param[in]  maxo_stride_x                      Stride of the max values tensor in X dimension (in bytes)
+ * @param[in]  maxo_step_x                        max_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  maxo_stride_y                      Stride of the max values tensor in Y dimension (in bytes)
+ * @param[in]  maxo_step_y                        max_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  maxo_stride_z                      Stride of the max values tensor in Z dimension (in bytes)
+ * @param[in]  maxo_step_z                        max_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  maxo_offset_first_element_in_bytes The offset of the first element in the max values tensor
+ * @param[out] dst_ptr                            Pointer to the destination tensor slice. Supported data types: same as @p src_ptr
+ * @param[in]  dst_stride_x                       Stride of the destination tensor in X dimension (in bytes)
+ * @param[in]  dst_step_x                         dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  dst_stride_y                       Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in]  dst_step_y                         dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  dst_stride_z                       Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in]  dst_step_z                         dst_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  dst_offset_first_element_in_bytes  The offset of the first element in the destination tensor
+ * @param[out] sum_ptr                            Pointer to the sum values tensor slice. Supported data types: same as @p src_ptr
+ * @param[in]  sum_stride_x                       Stride of the sum values tensor in X dimension (in bytes)
+ * @param[in]  sum_step_x                         sum_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  sum_stride_y                       Stride of the sum values tensor in Y dimension (in bytes)
+ * @param[in]  sum_step_y                         sum_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  sum_stride_z                       Stride of the sum values tensor in Z dimension (in bytes)
+ * @param[in]  sum_step_z                         sum_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  sum_offset_first_element_in_bytes  The offset of the first element in the sum values tensor
+ * @param[in]  width                              Input image width
+ */
+__kernel void softmax_layer_max_shift_exp_sum_quantized_parallel(
+    TENSOR3D_DECLARATION(src),
+    TENSOR3D_DECLARATION(maxo),
+    TENSOR3D_DECLARATION(dst),
+    TENSOR3D_DECLARATION(sum),
+    uint width)
+{
+    Image src  = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(src);
+    Image dst  = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst);
+    Image maxo = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(maxo);
+    Image sum  = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(sum);
+
+    const uint4 idx4 = (uint4)(0, 1, 2, 3);
+    const uint  lid  = get_local_id(0);
+
+    // Define one temporary vector per work-item.
+    __local int4 tmp_local[GRID_SIZE];
+    __local uchar max_local;
+
+    uchar4 uchar_min   = (uchar4)0;
+    uchar4 max_val_vec = (uchar4)uchar_min;
+
+    // Number of elements per work-item.
+    const uint row = width / GRID_SIZE;
+    // Number of iterations per work-item.
+    const uint width_ = row >> 2;
+    // Calculate max of row
+    uint i = 0;
+    for(; i < width_; i++)
+    {
+        uchar4 data_max = vload4(0, (__global uchar *)offset(&src, i * GRID_SIZE * 4, 0));
+        max_val_vec     = MAX_OP(data_max, max_val_vec, uchar, 4);
+    }
+#ifdef NON_MULTIPLE_OF_GRID_SIZE
+    // How many work-items needed to complete the computation.
+    //TODO: Optimize this calculation (avoid %).
+    int boundary_workitems = (width % (GRID_SIZE * 4)) / 4;
+    if(lid < boundary_workitems)
+    {
+        uchar4 data_max = vload4(0, (__global uchar *)offset(&src, i * GRID_SIZE * 4, 0));
+        max_val_vec     = MAX_OP(data_max, max_val_vec, uchar, 4);
+    }
+#ifdef NON_MULTIPLE_OF_VECTOR_SIZE
+    if(boundary_workitems == 0)
+    {
+        boundary_workitems = GRID_SIZE;
+        i--;
+    }
+    if(lid == (boundary_workitems - 1))
+    {
+        // Handle non multiple of 4
+        uchar4 data_max = vload4(0, (__global uchar *)offset(&src, (GRID_SIZE * i * 4) + 4, 0));
+        uchar4 widx     = convert_uchar4(((uint4)(GRID_SIZE * i * 4) + boundary_workitems * 4 + idx4) < width);
+        max_val_vec     = MAX_OP(max_val_vec, select(uchar_min, data_max, widx), uchar, 4);
+    }
+#endif /* NON_MULTIPLE_OF_VECTOR_SIZE */
+#endif /* NON_MULTIPLE_OF_GRID_SIZE */
+    tmp_local[lid] = convert_int4(max_val_vec);
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if(GRID_SIZE >= 256)
+    {
+        if(lid < 128)
+        {
+            tmp_local[lid] = MAX_OP(tmp_local[lid + 128], tmp_local[lid], int, 4);
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if(GRID_SIZE >= 128)
+    {
+        if(lid < 64)
+        {
+            tmp_local[lid] = MAX_OP(tmp_local[lid + 64], tmp_local[lid], int, 4);
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if(GRID_SIZE >= 64)
+    {
+        if(lid < 32)
+        {
+            tmp_local[lid] = MAX_OP(tmp_local[lid + 32], tmp_local[lid], int, 4);
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if(GRID_SIZE >= 32)
+    {
+        if(lid < 16)
+        {
+            tmp_local[lid] = MAX_OP(tmp_local[lid + 16], tmp_local[lid], int, 4);
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if(GRID_SIZE >= 16)
+    {
+        if(lid < 8)
+        {
+            tmp_local[lid] = MAX_OP(tmp_local[lid + 8], tmp_local[lid], int, 4);
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if(GRID_SIZE >= 8)
+    {
+        if(lid < 4)
+        {
+            tmp_local[lid] = MAX_OP(tmp_local[lid + 4], tmp_local[lid], int, 4);
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if(GRID_SIZE >= 4)
+    {
+        if(lid < 2)
+        {
+            tmp_local[lid] = MAX_OP(tmp_local[lid + 2], tmp_local[lid], int, 4);
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if(lid == 0)
+    {
+        max_val_vec     = MAX_OP(convert_uchar4(tmp_local[lid + 1]), convert_uchar4(tmp_local[lid]), uchar, 4);
+        max_val_vec.s01 = MAX_OP(max_val_vec.s01, max_val_vec.s23, uchar, 2);
+        max_val_vec.s0  = MAX_OP(max_val_vec.s0, max_val_vec.s1, uchar, 1);
+        max_local       = max_val_vec.s0;
+    }
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    /* Second section */
+
+    // Set sum vector
+    int4 sum1D   = 0;
+    int  max_val = convert_int(max_local);
+
+    // Shift values, exp and sum
+    for(i = 0; i < width_; i++)
+    {
+        uchar4 data         = vload4(0, (__global uchar *)offset(&src, i * GRID_SIZE * 4, 0));
+        int4 data_fp        = convert_int4(data);
+        int4 data_diff      = data_fp - max_val;
+        int4 data_diff_mult = mult_by_quantized_multiplier_parallel(data_diff);
+        data_fp             = ASYMM_EXP_ON_NEGATIVE_VALUES(data_diff_mult, SCALED_DIFF_INT_BITS, 4);
+        data_fp             = ASYMM_RESCALE(data_fp, 0, EXP_ACCUMULATION_INT_BITS, 4);
+        vstore4(data_diff, 0, (__global int *)offset(&dst, i * GRID_SIZE * 4, 0));
+        sum1D = sum1D + select(0, data_fp, data_diff >= (int4)(DIFF_MIN));
+    }
+#ifdef NON_MULTIPLE_OF_GRID_SIZE
+    //TODO: Optimize the calculation (avoid %).
+    boundary_workitems = (width % (GRID_SIZE * 4)) / 4;
+    if(lid < boundary_workitems)
+    {
+        uchar4 data         = vload4(0, (__global uchar *)offset(&src, i * GRID_SIZE * 4, 0));
+        int4 data_fp        = convert_int4(data);
+        int4 data_diff      = data_fp - max_val;
+        int4 data_diff_mult = mult_by_quantized_multiplier_parallel(data_diff);
+        data_fp             = ASYMM_EXP_ON_NEGATIVE_VALUES(data_diff_mult, SCALED_DIFF_INT_BITS, 4);
+        data_fp             = ASYMM_RESCALE(data_fp, 0, EXP_ACCUMULATION_INT_BITS, 4);
+        vstore4(data_diff, 0, (__global int *)offset(&dst, i * GRID_SIZE * 4, 0));
+        sum1D = sum1D + select(0, data_fp, data_diff >= (int4)(DIFF_MIN));
+    }
+#ifdef NON_MULTIPLE_OF_VECTOR_SIZE
+    if(boundary_workitems == 0)
+    {
+        boundary_workitems = GRID_SIZE;
+        i--;
+    }
+    if(lid == (boundary_workitems - 1))
+    {
+        // Handle non multiple of vector size ((GRID_SIZE * i * 4) + 4, 0); move 4 float positions ahead, *4 is due to the stride
+        uchar4 data         = vload4(0, (__global uchar *)offset(&src, i * GRID_SIZE * 4 + 4, 0));
+        int4 data_fp        = convert_int4(data);
+        int4 data_diff      = data_fp - max_val;
+        int4 data_diff_mult = mult_by_quantized_multiplier_parallel(data_diff);
+        data_fp             = ASYMM_EXP_ON_NEGATIVE_VALUES(data_diff_mult, SCALED_DIFF_INT_BITS, 4);
+        data_fp             = ASYMM_RESCALE(data_fp, 0, EXP_ACCUMULATION_INT_BITS, 4);
+        uchar4 widx         = convert_uchar4(((uint4)(GRID_SIZE * i * 4) + boundary_workitems * 4 + idx4) < width);
+        data                = select(uchar_min, data, widx);
+        vstore4(data_diff, 0, (__global int *)offset(&dst, i * GRID_SIZE * 4 + 4, 0));
+        sum1D = sum1D + select(0, data_fp, data_diff >= (int4)(DIFF_MIN));
+    }
+#endif /* NON_MULTIPLE_OF_VECTOR_SIZE */
+#endif /* NON_MULTIPLE_OF_GRID_SIZE */
+    tmp_local[lid] = sum1D;
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    if(GRID_SIZE >= 256)
+    {
+        if(lid < 128)
+        {
+            tmp_local[lid] = ADD_OP(tmp_local[lid + 128], tmp_local[lid], int, 4);
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if(GRID_SIZE >= 128)
+    {
+        if(lid < 64)
+        {
+            tmp_local[lid] = ADD_OP(tmp_local[lid + 64], tmp_local[lid], int, 4);
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if(GRID_SIZE >= 64)
+    {
+        if(lid < 32)
+        {
+            tmp_local[lid] = ADD_OP(tmp_local[lid + 32], tmp_local[lid], int, 4);
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if(GRID_SIZE >= 32)
+    {
+        if(lid < 16)
+        {
+            tmp_local[lid] = ADD_OP(tmp_local[lid + 16], tmp_local[lid], int, 4);
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if(GRID_SIZE >= 16)
+    {
+        if(lid < 8)
+        {
+            tmp_local[lid] = ADD_OP(tmp_local[lid + 8], tmp_local[lid], int, 4);
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if(GRID_SIZE >= 8)
+    {
+        if(lid < 4)
+        {
+            tmp_local[lid] = ADD_OP(tmp_local[lid + 4], tmp_local[lid], int, 4);
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if(GRID_SIZE >= 4)
+    {
+        if(lid < 2)
+        {
+            tmp_local[lid] = ADD_OP(tmp_local[lid + 2], tmp_local[lid], int, 4);
+        }
+        barrier(CLK_LOCAL_MEM_FENCE);
+    }
+    if(lid == 0)
+    {
+        sum1D = ADD_OP(tmp_local[lid + 1], tmp_local[lid], int, 4);
+        // Perform max reduction
+        sum1D.s01                  = ADD_OP(sum1D.s01, sum1D.s23, int, 2);
+        sum1D.s0                   = ADD_OP(sum1D.s0, sum1D.s1, int, 1);
+        *((__global int *)sum.ptr) = sum1D.s0;
+    }
+}
+
 /** Divides all the values of the input tensor by the sum calculated from softmax_layer_shift_exp_sum kernel.
  *
  * @note Fixed point position must be given as a preprocessor argument using -DFIXED_POINT_POSITION=pos. e.g. DFIXED_POINT_POSITION=4
@@ -247,15 +567,21 @@ __kernel void softmax_layer_norm_quantized(
     int   num_bits_over_unit      = EXP_ACCUMULATION_INT_BITS - headroom_plus_one;
     int   shifted_sum_minus_one_1 = convert_int((sum_val_u << headroom_plus_one) - (1u << 31));
     int16 shifted_sum_minus_one   = shifted_sum_minus_one_1;
-    int16 shifted_scale           = asymm_one_over_one_plus_x_for_x_in_0_1(shifted_sum_minus_one);
+    int16 shifted_scale           = ASYMM_ONE_OVER_ONE_PLUS_X_FOR_X_IN_0_1(shifted_sum_minus_one, 16);
 
     // It was already calculated in prev layer, should be stored into tmp output and reused
     int16 data_diff      = vload16(0, (__global int *)offset(&src, 0, 0));
-    int16 data_diff_mult = mult_by_quantized_multiplier(data_diff);
-    int16 data           = asymm_exp_on_negative_values(data_diff_mult, SCALED_DIFF_INT_BITS);
+    int16 data_diff_mult = data_diff;
+#if defined(INPUT_BETA_MULTIPLIER) && defined(INPUT_BETA_LEFT_SHIFT)
+    if(INPUT_BETA_MULTIPLIER > 1)
+    {
+        data_diff_mult = ASYMM_MULT(data_diff * (1 << INPUT_BETA_LEFT_SHIFT), INPUT_BETA_MULTIPLIER, 16);
+    }
+#endif /* defined(INPUT_BETA_MULTIPLIER) && defined(INPUT_BETA_LEFT_SHIFT) */
+    int16 data = ASYMM_EXP_ON_NEGATIVE_VALUES(data_diff_mult, SCALED_DIFF_INT_BITS, 16);
 
-    data = asymm_mult(shifted_scale, data);
-    data = asymm_rounding_divide_by_pow2(data, num_bits_over_unit + 31 - 8);
+    data = ASYMM_MULT(shifted_scale, data, 16);
+    data = ASYMM_ROUNDING_DIVIDE_BY_POW2(data, num_bits_over_unit + 31 - 8, 16);
     data = select(0, data, data_diff >= (int16)(DIFF_MIN));
     vstore16(convert_uchar16_sat(data), 0, (__global uchar *)offset(&dst, 0, 0));
 }