Optimize CL softmax

* The new softmax implementation consists of only a single kernel.
  - There are 2 versions of softmax, one for the x dimension
    and one for any other dimensions.
  - Softmax kernel handles both native and quantized data type.

Resolves: COMPMID-6447
Signed-off-by: Viet-Hoa Do <viet-hoa.do@arm.com>
Change-Id: I4a9ae5bc63f78aebeaa85ee48a0d102c9c245eda
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10489
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: SiCong Li <sicong.li@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>

2 files changed, 290 insertions, 986 deletions

diff --git a/src/core/CL/cl_kernels/common/softmax_layer.cl b/src/core/CL/cl_kernels/common/softmax_layer.cl
index 4d2d89dd73..58c458982d 100644
--- a/src/core/CL/cl_kernels/common/softmax_layer.cl
+++ b/src/core/CL/cl_kernels/common/softmax_layer.cl
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2021 Arm Limited.
+ * Copyright (c) 2017-2021, 2023 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -21,511 +21,344 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
+
 #include "helpers.h"
 
-#if defined(DATA_TYPE) && defined(MIN_VALUE) && defined(VECTOR_SIZE) && defined(VECTOR_SIZE_LEFTOVER)
+#define MIN_VALUE_float -FLT_MAX
+#define MIN_VALUE_half  -HALF_MAX
+#define MIN_VALUE_char  CHAR_MIN
+#define MIN_VALUE_uchar 0
+
+#define MIN_VALUE_TYPE_STR(data_type) MIN_VALUE_##data_type
+#define MIN_VALUE_TYPE(data_type) MIN_VALUE_TYPE_STR(data_type)
+#define MIN_VALUE MIN_VALUE_TYPE(DATA_TYPE)
+
+#ifdef SOFTMAX_X
 
-/** Divides all the values of the input tensor by the sum calculated from softmax_layer_shift_exp_sum kernel.
+/** 3-pass softmax in the x dimension.
  *
- * @note Datatype must be given as a preprocessor argument using -DDATA_TYPE, e.g. -DDATA_TYPE=float
- * @note The zero value for the given data type must be given as a preprocessor argument using -DMIN_VALUE, e.g. -DMIN_VALUE=0
- * @note Vector size should be given as a preprocessor argument using -DVECTOR_SIZE=size. e.g. -DVECTOR_SIZE=16
- * @note Leftover vector size has to be passed at compile time using -DVECTOR_SIZE_LEFTOVER. e.g. -DVECTOR_SIZE_LEFTOVER=3. It is defined as the remainder between the input's first dimension and VECTOR_SIZE
- * @note In case of log softmax, -DLOG_SOFTMAX must be passed.
+ * List of preprocessors:
+ *   - DATA_TYPE: the input/output data type.
+ *   - TMP_DATA_TYPE: the data type used for computing and temporary tensor storage.
+ *     If DATA_TYPE is quantized, TMP_DATA_TYPE is floating-point, otherwise TMP_DATA_TYPE is the same as DATA_TYPE.
+ *   - IS_LOG (optional): indicating whether this is log softmax.
+ *   - LENGTH: the number of elements in softmax axis in the input/output tensors.
+ *   - BETA: the beta coefficient.
+ *   - IS_QUANTIZED (optional): indicating whether the input/output data type is quantized data.
+ *   - VEC_SIZE: the size of the vector.
  *
- * @param[in]  src_ptr                           Pointer to the source tensor slice. Supported data types: 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]  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_y * number of elements along Y 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[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
+ * Additional preprocessors in case IS_QUANTIZED is present:
+ *   - SRC_SCALE and SRC_OFFSET: the quantization information of the source tensor.
+ *   - DST_SCALE and DST_OFFSET: the quantization information of the destination tensor.
+ *
+ * @param[in] src_ptr                  Pointer to the source tensor.
+ * @param[in] src_stride_0             Stride in bytes of the source tensor in the dimension corresponding to global ID 0.
+ * @param[in] src_stride_1             Stride in bytes of the source tensor in the dimension corresponding to global ID 1.
+ * @param[in] src_stride_2             Stride in bytes of the source tensor in the dimension corresponding to global ID 2.
+ * @param[in] src_offset_first_element Offset of the first element in the source tensor.
+ * @param[in] dst_ptr                  Pointer to the destination tensor.
+ * @param[in] dst_stride_0             Stride in bytes of the destination tensor in the dimension corresponding to global ID 0.
+ * @param[in] dst_stride_1             Stride in bytes of the destination tensor in the dimension corresponding to global ID 1.
+ * @param[in] dst_stride_2             Stride in bytes of the destination tensor in the dimension corresponding to global ID 2.
+ * @param[in] dst_offset_first_element Offset of the first element in the destination tensor.
+ * @param[in] tmp_ptr                  Pointer to the temporary tensor.
+ * @param[in] tmp_stride_0             Stride in bytes of the temporary tensor in the dimension corresponding to global ID 0.
+ * @param[in] tmp_stride_1             Stride in bytes of the temporary tensor in the dimension corresponding to global ID 1.
+ * @param[in] tmp_stride_2             Stride in bytes of the temporary tensor in the dimension corresponding to global ID 2.
+ * @param[in] tmp_offset_first_element Offset of the first element in the temporary tensor.
  */
-__kernel void softmax_layer_norm(
-    TENSOR3D_DECLARATION(src),
-    TENSOR3D_DECLARATION(sum),
-    TENSOR3D_DECLARATION(dst))
+__kernel void softmax_x(
+    __global uchar *src_ptr,
+    uint src_stride_0,
+    uint src_stride_1,
+    uint src_stride_2,
+    uint src_offset_first_element,
+
+    __global uchar *dst_ptr,
+    uint dst_stride_0,
+    uint dst_stride_1,
+    uint dst_stride_2,
+    uint dst_offset_first_element
+
+#ifdef IS_QUANTIZED
+    ,
+    __global uchar *tmp_ptr,
+    uint tmp_stride_0,
+    uint tmp_stride_1,
+    uint tmp_stride_2,
+    uint tmp_offset_first_element
+#endif // IS_QUANTIZED
+)
 {
-    const int x_offs = max((int)(get_global_id(0) * VECTOR_SIZE - (VECTOR_SIZE - VECTOR_SIZE_LEFTOVER) % VECTOR_SIZE), 0) * sizeof(DATA_TYPE);
+    const int dim_0 = get_global_id(0);
+    const int dim_1 = get_global_id(1);
+    const int dim_2 = get_global_id(2);
 
-    __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x_offs + get_global_id(1) * src_stride_y + get_global_id(2) * src_stride_z;
-    __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_offs + get_global_id(1) * dst_stride_y + get_global_id(2) * dst_stride_z;
+    src_ptr += src_offset_first_element + dim_2 * src_stride_2 + dim_1 * src_stride_1 + dim_0 * src_stride_0;
+    dst_ptr += dst_offset_first_element + dim_2 * dst_stride_2 + dim_1 * dst_stride_1 + dim_0 * dst_stride_0;
 
-    Image sum = CONVERT_TENSOR3D_TO_IMAGE_STRUCT_NO_STEP(sum);
+#ifdef IS_QUANTIZED
+    tmp_ptr += tmp_offset_first_element + dim_2 * tmp_stride_2 + dim_1 * tmp_stride_1 + dim_0 * tmp_stride_0;
+#else // IS_QUANTIZED
+    __global uchar *tmp_ptr = dst_ptr;
+#endif // IS_QUANTIZED
 
-    // Load max value of 1D logits vector (row)
-    DATA_TYPE sum_val = *((__global DATA_TYPE *)offset(&sum, 0, get_global_id(1)));
-    VEC_DATA_TYPE(DATA_TYPE, VECTOR_SIZE)
-    data0 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)src_addr);
+    // Calculate max value.
+    DATA_TYPE max_value = MIN_VALUE;
+    int i = 0;
 
-#if defined(LOG_SOFTMAX)
-    sum_val = log(sum_val);
-    data0 -= sum_val;
-#else  // defined(LOG_SOFTMAX)
-    data0 /= sum_val;
-#endif // defined(LOG_SOFTMAX)
+    for (i = 0; i < LENGTH - VEC_SIZE; i += VEC_SIZE)
+    {
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_ptr + i * sizeof(DATA_TYPE)));
 
-    STORE_VECTOR_SELECT(data, DATA_TYPE, dst_addr, VECTOR_SIZE, VECTOR_SIZE_LEFTOVER, VECTOR_SIZE_LEFTOVER != 0 && get_global_id(0) == 0)
-}
+        max_value = max(max_value, MAX_REDUCE(data, VEC_SIZE));
+    }
 
-#if defined(SRC_WIDTH) && defined(LOG_VECTOR_SIZE) && defined(MINVAL)
+    for (; i < LENGTH; ++i)
+    {
+        DATA_TYPE data = *(__global DATA_TYPE *)(src_ptr + i * sizeof(DATA_TYPE));
 
-/* Number of workitems in dimension 0. */
-#if !defined(GRID_SIZE)
-#define GRID_SIZE 1
-#endif /* !defined(GRID_SIZE) */
+        max_value = max(max_value, data);
+    }
 
-#define VEC_TYPE VEC_DATA_TYPE(DATA_TYPE, VECTOR_SIZE)
-#define SELECT_TYPE SELECT_VEC_DATA_TYPE(DATA_TYPE, VECTOR_SIZE)
+    // Regularize the data.
+    TMP_DATA_TYPE sum_value = 0;
 
-/** 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, e.g. -DDATA_TYPE=float
- * @note The zero value for the given data type must be given as a preprocessor argument using -DMIN_VALUE, e.g. -DMIN_VALUE=0
- * @note Vector size should be given as a preprocessor argument using -DVECTOR_SIZE=size. e.g. -DVECTOR_SIZE=16
- * @note Leftover vector size has to be passed at compile time using -DVECTOR_SIZE_LEFTOVER. e.g. -DVECTOR_SIZE_LEFTOVER=3. It is defined as the remainder between the input's first dimension and VECTOR_SIZE
- * @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).
- * @note In case of log softmax, -DLOG_SOFTMAX must be passed.
- * @note Based on the data type, the minimum possible value must be passed using -DMINVAL. For float it should be defined as -FLT_MAX, while for half it should be -HALF_MAX
- *
- * @param[in]  src_ptr                            Pointer to the source tensor slice. Supported data types: 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
- */
-__kernel void softmax_layer_max_shift_exp_sum_serial(
-    TENSOR3D_DECLARATION(src),
-    TENSOR3D_DECLARATION(maxo),
-    TENSOR3D_DECLARATION(dst),
-    TENSOR3D_DECLARATION(sum))
-{
-    __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + get_global_id(1) * src_stride_y + get_global_id(2) * src_stride_z;
-    __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + get_global_id(1) * dst_stride_y + get_global_id(2) * dst_stride_z;
+#ifdef IS_QUANTIZED
+    TMP_DATA_TYPE max_value_f = (CONVERT(max_value, TMP_DATA_TYPE) - SRC_OFFSET) * SRC_SCALE;
+    TMP_DATA_TYPE regularize_offset = -SRC_OFFSET * SRC_SCALE * (TMP_DATA_TYPE)BETA - max_value_f * (TMP_DATA_TYPE)BETA;
+# define REGULARIZE(x) ((x) * SRC_SCALE * (TMP_DATA_TYPE)BETA + regularize_offset)
+#else // IS_QUANTIZED
+# define REGULARIZE(x) (((x) - max_value) * (TMP_DATA_TYPE)BETA)
+#endif // IS_QUANTIZED
 
-    Image maxo = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(maxo);
-    Image sum  = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(sum);
+    for (i = 0; i < LENGTH - VEC_SIZE; i += VEC_SIZE)
+    {
+        VEC_DATA_TYPE(TMP_DATA_TYPE, VEC_SIZE) data = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_ptr + i * sizeof(DATA_TYPE))), VEC_DATA_TYPE(TMP_DATA_TYPE, VEC_SIZE));
 
-#ifdef BETA
-    // Initialize beta
-    VEC_TYPE beta = (VEC_TYPE)BETA;
-#endif /* BETA */
+        data = REGULARIZE(data);
 
-    // Initialize local maximum
-    VEC_TYPE max_val_vec = (VEC_TYPE)(MINVAL);
+#ifdef IS_LOG
+        sum_value += SUM_REDUCE(exp(data), VEC_SIZE);
+#else // IS_LOG
+        data = exp(data);
+        sum_value += SUM_REDUCE(data, VEC_SIZE);
+#endif // IS_LOG
 
-#ifdef NON_MULTIPLE_OF_VECTOR_SIZE
-    VEC_TYPE data    = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)src_addr);
-    SELECT_TYPE widx = (SELECT_TYPE)VECTOR_SIZE_LEFTOVER > VEC_OFFS(SELECT_DATA_TYPE(DATA_TYPE), VECTOR_SIZE);
-    max_val_vec      = max(max_val_vec, select((VEC_TYPE)(MINVAL), data, widx));
-#endif /* NON_MULTIPLE_OF_VECTOR_SIZE */
+        VSTORE(VEC_SIZE)(data, 0, (__global TMP_DATA_TYPE *)(tmp_ptr + i * sizeof(TMP_DATA_TYPE)));
+    }
 
-    for(uint i = VECTOR_SIZE_LEFTOVER; i < SRC_WIDTH; i += VECTOR_SIZE)
+    for (; i < LENGTH; ++i)
     {
-        VEC_TYPE data = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr + i * sizeof(DATA_TYPE)));
-        max_val_vec   = max(data, max_val_vec);
+        TMP_DATA_TYPE data = CONVERT(*(__global DATA_TYPE *)(src_ptr + i * sizeof(DATA_TYPE)), TMP_DATA_TYPE);
+
+        data = REGULARIZE(data);
+
+#ifdef IS_LOG
+        sum_value += exp(data);
+#else // IS_LOG
+        data = exp(data);
+        sum_value += data;
+#endif // IS_LOG
+
+        *(__global TMP_DATA_TYPE *)(tmp_ptr + i * sizeof(TMP_DATA_TYPE)) = data;
     }
 
-    // Perform max reduction
-    DATA_TYPE max_val                 = MAX_REDUCE(max_val_vec, VECTOR_SIZE);
-    *((__global DATA_TYPE *)maxo.ptr) = max_val;
-
-    /* Second section */
-
-    // Set sum vector
-    VEC_TYPE sum1D = 0;
-
-#ifdef NON_MULTIPLE_OF_VECTOR_SIZE
-    data -= max_val;
-#ifdef BETA
-    data *= beta;
-#endif /* BETA */
-#ifdef LOG_SOFTMAX
-    VSTORE_PARTIAL(VECTOR_SIZE, VECTOR_SIZE_LEFTOVER)
-    (data, 0, (__global DATA_TYPE *)dst_addr);
-    data = exp(data);
-    data = select(0, data, widx);
-#else  /* LOG_SOFTMAX */
-    data = exp(data);
-    data = select(0, data, widx);
-    VSTORE_PARTIAL(VECTOR_SIZE, VECTOR_SIZE_LEFTOVER)
-    (data, 0, (__global DATA_TYPE *)dst_addr);
-#endif /* LOG_SOFTMAX */
-    sum1D += data;
-#endif /* NON_MULTIPLE_OF_VECTOR_SIZE */
-
-    // Shift values, exp and sum
-    for(uint i = VECTOR_SIZE_LEFTOVER; i < SRC_WIDTH; i += VECTOR_SIZE)
+#undef REGULARIZE
+
+    // Normalize the data.
+#ifdef IS_QUANTIZED
+# if IS_LOG
+    TMP_DATA_TYPE norm_offset = -log(sum_value) + DST_OFFSET;
+#  define NORMALIZE(SIZE, x) CONVERT_SAT_ROUND((x) / DST_SCALE + norm_offset, VEC_DATA_TYPE(DATA_TYPE, SIZE), rte)
+# else // IS_LOG
+    TMP_DATA_TYPE norm_div = sum_value * DST_SCALE;
+#  define NORMALIZE(SIZE, x) CONVERT_SAT(add_sat(CONVERT_SAT_ROUND((x) / norm_div, VEC_DATA_TYPE(int, SIZE), rte), DST_OFFSET), VEC_DATA_TYPE(DATA_TYPE, SIZE))
+#  endif // IS_LOG
+#else // IS_QUANTIZED
+# if IS_LOG
+#  define NORMALIZE(SIZE, x) ((x) - log(sum_value))
+# else // IS_LOG
+#  define NORMALIZE(SIZE, x) ((x) / sum_value)
+# endif // IS_LOG
+#endif // IS_QUANTIZED
+
+    for (i = 0; i < LENGTH - VEC_SIZE; i += VEC_SIZE)
     {
-        VEC_TYPE data = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr + i * sizeof(DATA_TYPE)));
-        data -= max_val;
-#ifdef BETA
-        data *= beta;
-#endif /* BETA */
-#ifdef LOG_SOFTMAX
-        VSTORE(VECTOR_SIZE)
-        (data, 0, (__global DATA_TYPE *)(dst_addr + i * sizeof(DATA_TYPE)));
-        data = exp(data);
-#else  /* LOG_SOFTMAX */
-        data = exp(data);
-        VSTORE(VECTOR_SIZE)
-        (data, 0, (__global DATA_TYPE *)(dst_addr + i * sizeof(DATA_TYPE)));
-#endif /* LOG_SOFTMAX */
-        sum1D += data;
+        VEC_DATA_TYPE(TMP_DATA_TYPE, VEC_SIZE) data = VLOAD(VEC_SIZE)(0, (__global TMP_DATA_TYPE *)(tmp_ptr + i * sizeof(TMP_DATA_TYPE)));
+
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) result = NORMALIZE(VEC_SIZE, data);
+
+        VSTORE(VEC_SIZE)(result, 0, (__global DATA_TYPE *)(dst_ptr + i * sizeof(DATA_TYPE)));
     }
 
-    // Perform sum reduction
-    *((__global DATA_TYPE *)sum.ptr) = SUM_REDUCE(sum1D, VECTOR_SIZE);
+    for (; i < LENGTH; ++i)
+    {
+        TMP_DATA_TYPE data = *(__global TMP_DATA_TYPE *)(tmp_ptr + i * sizeof(TMP_DATA_TYPE));
+
+        DATA_TYPE result = NORMALIZE(1, data);
+
+        *(__global DATA_TYPE *)(dst_ptr + i * sizeof(DATA_TYPE)) = result;
+    }
+
+#undef NORMALIZE
 }
 
-/** 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.
+#endif // SOFTMAX_X
+
+#ifdef SOFTMAX_NON_X
+
+/** 3-pass softmax in any dimension higher than the x dimension.
+ *
+ * List of preprocessors:
+ *   - DATA_TYPE: the input/output data type.
+ *   - TMP_DATA_TYPE: the data type used for computing and temporary tensor storage.
+ *     If DATA_TYPE is quantized, TMP_DATA_TYPE is floating-point, otherwise TMP_DATA_TYPE is the same as DATA_TYPE.
+ *   - IS_LOG (optional): indicating whether this is log softmax.
+ *   - LENGTH: the number of elements in softmax axis in the input/output tensors.
+ *   - BETA: the beta coefficient.
+ *   - IS_QUANTIZED (optional): indicating whether the input/output data type is quantized data.
+ *   - VEC_SIZE: the size of the vector.
+ *   - VEC_SIZE_LEFTOVER: the size of the leftover part.
  *
- * @note Datatype must be given as a preprocessor argument using -DDATA_TYPE, e.g. -DDATA_TYPE=float
- * @note The zero value for the given data type must be given as a preprocessor argument using -DMIN_VALUE, e.g. -DMIN_VALUE=0
- * @note Vector size should be given as a preprocessor argument using -DVECTOR_SIZE=size. e.g. -DVECTOR_SIZE=16
- * @note Leftover vector size has to be passed at compile time using -DVECTOR_SIZE_LEFTOVER. e.g. -DVECTOR_SIZE_LEFTOVER=3. It is defined as the remainder between the input's first dimension and VECTOR_SIZE
- * @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).
- * @note In case of log softmax, -DLOG_SOFTMAX must be passed.
- * @note Based on the data type, the minimum possible value must be passed using -DMINVAL. For float it should be defined as -FLT_MAX, while for half it should be -HALF_MAX
+ * Additional preprocessors in case IS_QUANTIZED is present:
+ *   - SRC_SCALE and SRC_OFFSET: the quantization information of the source tensor.
+ *   - DST_SCALE and DST_OFFSET: the quantization information of the destination tensor.
  *
- * @param[in]  src_ptr                            Pointer to the source tensor slice. Supported data types: 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] src_ptr                  Pointer to the source tensor.
+ * @param[in] src_stride_0             Stride in bytes of the source tensor in the dimension corresponding to global ID 0.
+ * @param[in] src_stride_1             Stride in bytes of the source tensor in the dimension corresponding to global ID 1.
+ * @param[in] src_stride_2             Stride in bytes of the source tensor in the dimension corresponding to global ID 2.
+ * @param[in] src_offset_first_element Offset of the first element in the source tensor.
+ * @param[in] dst_ptr                  Pointer to the destination tensor.
+ * @param[in] dst_stride_0             Stride in bytes of the destination tensor in the dimension corresponding to global ID 0.
+ * @param[in] dst_stride_1             Stride in bytes of the destination tensor in the dimension corresponding to global ID 1.
+ * @param[in] dst_stride_2             Stride in bytes of the destination tensor in the dimension corresponding to global ID 2.
+ * @param[in] dst_offset_first_element Offset of the first element in the destination tensor.
+ * @param[in] tmp_ptr                  Pointer to the temporary tensor.
+ * @param[in] tmp_stride_0             Stride in bytes of the temporary tensor in the dimension corresponding to global ID 0.
+ * @param[in] tmp_stride_1             Stride in bytes of the temporary tensor in the dimension corresponding to global ID 1.
+ * @param[in] tmp_stride_2             Stride in bytes of the temporary tensor in the dimension corresponding to global ID 2.
+ * @param[in] tmp_offset_first_element Offset of the first element in the temporary tensor.
  */
-__kernel void softmax_layer_max_shift_exp_sum_parallel(
-    TENSOR3D_DECLARATION(src),
-    TENSOR3D_DECLARATION(maxo),
-    TENSOR3D_DECLARATION(dst),
-    TENSOR3D_DECLARATION(sum))
+__kernel void softmax_non_x(
+    __global uchar *src_ptr,
+    uint src_stride_0,
+    uint src_stride_1,
+    uint src_stride_2,
+    uint src_offset_first_element,
+
+    __global uchar *dst_ptr,
+    uint dst_stride_0,
+    uint dst_stride_1,
+    uint dst_stride_2,
+    uint dst_offset_first_element,
+
+    __global uchar *tmp_ptr,
+    uint tmp_stride_0,
+    uint tmp_stride_1,
+    uint tmp_stride_2,
+    uint tmp_offset_first_element,
+
+    uint src_stride_axis,
+    uint dst_stride_axis
+)
 {
-    const uint lid    = get_local_id(0);
-    const uint x_offs = (VECTOR_SIZE_LEFTOVER + lid * VECTOR_SIZE) * sizeof(DATA_TYPE);
+    const int dim_0 = max((int)get_global_id(0) * VEC_SIZE - (VEC_SIZE - VEC_SIZE_LEFTOVER) % VEC_SIZE, 0);
+    const int dim_1 = get_global_id(1);
+    const int dim_2 = get_global_id(2);
 
-    __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x_offs + get_global_id(1) * src_stride_y + get_global_id(2) * src_stride_z;
-    __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_offs + get_global_id(1) * dst_stride_y + get_global_id(2) * dst_stride_z;
+    src_ptr += src_offset_first_element + dim_2 * src_stride_2 + dim_1 * src_stride_1 + dim_0 * src_stride_0;
+    dst_ptr += dst_offset_first_element + dim_2 * dst_stride_2 + dim_1 * dst_stride_1 + dim_0 * dst_stride_0;
+    tmp_ptr += tmp_offset_first_element + dim_2 * tmp_stride_2 + dim_1 * tmp_stride_1 + dim_0 * tmp_stride_0;
 
-    Image maxo = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(maxo);
-    Image sum  = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(sum);
+    // Calculate max value and store the input data to the temporary tensor in suitable format.
+    VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) max_value = MIN_VALUE;
+    int i = 0;
 
-#ifdef BETA
-    // Initialize beta
-    VEC_TYPE beta = (VEC_TYPE)BETA;
-#endif /* BETA */
-
-    // Define one temporary vector per work-item.
-    __local VEC_TYPE tmp_local[GRID_SIZE];
-    __local DATA_TYPE max_local;
-
-    VEC_TYPE max_val_vec = (VEC_TYPE)(MINVAL);
-
-    // Number of iterations per work-item.
-    const uint width = (SRC_WIDTH / GRID_SIZE) >> LOG_VECTOR_SIZE;
-    // Calculate max of row
-    uint i = 0;
-    for(; i < width; ++i)
-    {
-        VEC_TYPE data_max = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE)));
-        max_val_vec       = max(data_max, max_val_vec);
-    }
-#ifdef NON_MULTIPLE_OF_GRID_SIZE
-    // How many work-items needed to complete the computation.
-    int boundary_workitems = (SRC_WIDTH % (GRID_SIZE * VECTOR_SIZE)) / VECTOR_SIZE;
-    if(lid < boundary_workitems)
+    for (i = 0; i < LENGTH; ++i)
     {
-        VEC_TYPE data_max = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE)));
-        max_val_vec       = max(data_max, max_val_vec);
-    }
-#ifdef NON_MULTIPLE_OF_VECTOR_SIZE
-    SELECT_TYPE widx;
-    if(lid == 0)
-    {
-        // Handle non multiple of 4
-        VEC_TYPE data_max = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr - VECTOR_SIZE_LEFTOVER * sizeof(DATA_TYPE)));
-        widx              = (SELECT_TYPE)VECTOR_SIZE_LEFTOVER > VEC_OFFS(SELECT_DATA_TYPE(DATA_TYPE), VECTOR_SIZE);
-        max_val_vec       = max(max_val_vec, select((VEC_TYPE)(MINVAL), data_max, widx));
-    }
-#endif /* NON_MULTIPLE_OF_VECTOR_SIZE */
-#endif /* NON_MULTIPLE_OF_GRID_SIZE */
-    tmp_local[lid] = max_val_vec;
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) data = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_ptr + i * src_stride_axis));
 
-    barrier(CLK_LOCAL_MEM_FENCE);
+        max_value = max(max_value, data);
 
-    if(GRID_SIZE >= 256)
-    {
-        if(lid < 128)
-        {
-            tmp_local[lid] = max(tmp_local[lid + 128], tmp_local[lid]);
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
+        VSTORE(VEC_SIZE)(data, 0, (__global DATA_TYPE *)(tmp_ptr + i * VEC_SIZE * sizeof(DATA_TYPE)));
     }
-    if(GRID_SIZE >= 128)
-    {
-        if(lid < 64)
-        {
-            tmp_local[lid] = max(tmp_local[lid + 64], tmp_local[lid]);
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 64)
-    {
-        if(lid < 32)
-        {
-            tmp_local[lid] = max(tmp_local[lid + 32], tmp_local[lid]);
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 32)
-    {
-        if(lid < 16)
-        {
-            tmp_local[lid] = max(tmp_local[lid + 16], tmp_local[lid]);
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 16)
-    {
-        if(lid < 8)
-        {
-            tmp_local[lid] = max(tmp_local[lid + 8], tmp_local[lid]);
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 8)
-    {
-        if(lid < 4)
-        {
-            tmp_local[lid] = max(tmp_local[lid + 4], tmp_local[lid]);
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 4)
-    {
-        if(lid < 2)
-        {
-            tmp_local[lid] = max(tmp_local[lid + 2], tmp_local[lid]);
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(lid == 0)
-    {
-        max_val_vec = max(tmp_local[lid + 1], tmp_local[lid]);
-        max_local   = MAX_REDUCE(max_val_vec, VECTOR_SIZE);
-    }
-    barrier(CLK_LOCAL_MEM_FENCE);
 
-    /* Second section */
+    // Regularize the data.
+    VEC_DATA_TYPE(TMP_DATA_TYPE, VEC_SIZE) sum_value = 0;
 
-    // Set sum vector
-    VEC_TYPE  sum1D   = 0;
-    DATA_TYPE max_val = max_local;
+#ifdef IS_QUANTIZED
+    VEC_DATA_TYPE(TMP_DATA_TYPE, VEC_SIZE) max_value_f = (CONVERT(max_value, VEC_DATA_TYPE(TMP_DATA_TYPE, VEC_SIZE)) - SRC_OFFSET) * SRC_SCALE;
+    VEC_DATA_TYPE(TMP_DATA_TYPE, VEC_SIZE) regularize_offset = -SRC_OFFSET * SRC_SCALE * (TMP_DATA_TYPE)BETA - max_value_f * (TMP_DATA_TYPE)BETA;
+# define REGULARIZE(x) ((x) * SRC_SCALE * (TMP_DATA_TYPE)BETA + regularize_offset)
+#else // IS_QUANTIZED
+# define REGULARIZE(x) (((x) - max_value) * (TMP_DATA_TYPE)BETA)
+#endif // IS_QUANTIZED
 
-    // Shift values, exp and sum
-    for(i = 0; i < width; ++i)
-    {
-        VEC_TYPE data = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE)));
-        data -= max_val;
-#ifdef BETA
-        data *= beta;
-#endif /* BETA */
-#ifdef LOG_SOFTMAX
-        VSTORE(VECTOR_SIZE)
-        (data, 0, (__global DATA_TYPE *)(dst_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE)));
-        data = exp(data);
-#else  /* LOG_SOFTMAX */
-        data = exp(data);
-        VSTORE(VECTOR_SIZE)
-        (data, 0, (__global DATA_TYPE *)(dst_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE)));
-#endif /* LOG_SOFTMAX */
-        sum1D += data;
-    }
-#ifdef NON_MULTIPLE_OF_GRID_SIZE
-    boundary_workitems = (SRC_WIDTH % (GRID_SIZE * VECTOR_SIZE)) / VECTOR_SIZE;
-    if(lid < boundary_workitems)
+    for (i = LENGTH - 1; i >= 0; --i)
     {
-        VEC_TYPE data = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(__global DATA_TYPE *)(src_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE)));
-        data -= max_val;
-#ifdef BETA
-        data *= beta;
-#endif /* BETA */
-#ifdef LOG_SOFTMAX
-        VSTORE(VECTOR_SIZE)
-        (data, 0, (__global DATA_TYPE *)(dst_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE)));
-        data = exp(data);
-#else  /* LOG_SOFTMAX */
+        // In case of processing quantized data, i.e. DATA_TYPE is smaller than TMP_DATA_TYPE:
+        //
+        // In the first pass (finding max), the quantized data is copied from the input tensor to the temporary tensor.
+        // Dequantization is not needed to find the max value and since dequantization widens the data, we defer it
+        // to the second pass pass to reduce memory bandwidth of the first pass.
+        //
+        // This pass reads the quantized data from the temporary tensor and writes the dequantized data
+        // back to the temporary tensor, hence we need to loop in reverse to avoid overwriting unprocessed data.
+
+        VEC_DATA_TYPE(TMP_DATA_TYPE, VEC_SIZE) data = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(tmp_ptr + i * VEC_SIZE * sizeof(DATA_TYPE))), VEC_DATA_TYPE(TMP_DATA_TYPE, VEC_SIZE));
+
+        data = REGULARIZE(data);
+
+#ifdef IS_LOG
+        sum_value += exp(data);
+#else // IS_LOG
         data = exp(data);
-        VSTORE(VECTOR_SIZE)
-        (data, 0, (__global DATA_TYPE *)(dst_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE)));
-#endif /* LOG_SOFTMAX */
-        sum1D += data;
+        sum_value += data;
+#endif // IS_LOG
+
+        VSTORE(VEC_SIZE)(data, 0, (__global TMP_DATA_TYPE *)(tmp_ptr + i * VEC_SIZE * sizeof(TMP_DATA_TYPE)));
     }
-#ifdef NON_MULTIPLE_OF_VECTOR_SIZE
-    if(lid == 0)
+
+#undef REGULARIZE
+
+    // Normalize the data.
+#ifdef IS_QUANTIZED
+# if IS_LOG
+    VEC_DATA_TYPE(TMP_DATA_TYPE, VEC_SIZE) norm_offset = -log(sum_value) + DST_OFFSET;
+#  define NORMALIZE(x) CONVERT_SAT_ROUND((x) / DST_SCALE + norm_offset, VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE), rte)
+# else // IS_LOG
+    VEC_DATA_TYPE(TMP_DATA_TYPE, VEC_SIZE) norm_div = sum_value * DST_SCALE;
+#  define NORMALIZE(x) CONVERT_SAT(add_sat(CONVERT_SAT_ROUND((x) / norm_div, VEC_DATA_TYPE(int, VEC_SIZE), rte), DST_OFFSET), VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE))
+#  endif // IS_LOG
+#else // IS_QUANTIZED
+# if IS_LOG
+#  define NORMALIZE(x) ((x) - log(sum_value))
+# else // IS_LOG
+#  define NORMALIZE(x) ((x) / sum_value)
+# endif // IS_LOG
+#endif // IS_QUANTIZED
+
+    for (i = 0; i < LENGTH; ++i)
     {
-        // Handle non multiple of vector size ((GRID_SIZE * i * 4) + 4, 0); move 4 float positions ahead, *4 is due to the stride
-        VEC_TYPE data = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr - VECTOR_SIZE_LEFTOVER * sizeof(DATA_TYPE)));
-        data -= max_val;
-#ifdef BETA
-        data *= beta;
-#endif /* BETA */
-#ifdef LOG_SOFTMAX
-        VSTORE_PARTIAL(VECTOR_SIZE, VECTOR_SIZE_LEFTOVER)
-        (data, 0, (__global DATA_TYPE *)(dst_addr - VECTOR_SIZE_LEFTOVER * sizeof(DATA_TYPE)));
-        data = exp(data);
-        data = select(0, data, widx);
-#else  /* LOG_SOFTMAX */
-        data = exp(data);
-        data = select(0, data, widx);
-        VSTORE_PARTIAL(VECTOR_SIZE, VECTOR_SIZE_LEFTOVER)
-        (data, 0, (__global DATA_TYPE *)(dst_addr - VECTOR_SIZE_LEFTOVER * sizeof(DATA_TYPE)));
-#endif /* LOG_SOFTMAX */
-        sum1D += data;
-    }
-#endif /* NON_MULTIPLE_OF_VECTOR_SIZE */
-#endif /* NON_MULTIPLE_OF_GRID_SIZE */
-    tmp_local[lid] = sum1D;
+        VEC_DATA_TYPE(TMP_DATA_TYPE, VEC_SIZE) data = VLOAD(VEC_SIZE)(0, (__global TMP_DATA_TYPE *)(tmp_ptr + i * VEC_SIZE * sizeof(TMP_DATA_TYPE)));
 
-    barrier(CLK_LOCAL_MEM_FENCE);
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) result0 = NORMALIZE(data);
 
-    if(GRID_SIZE >= 256)
-    {
-        if(lid < 128)
-        {
-            tmp_local[lid] += tmp_local[lid + 128];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 128)
-    {
-        if(lid < 64)
-        {
-            tmp_local[lid] += tmp_local[lid + 64];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 64)
-    {
-        if(lid < 32)
-        {
-            tmp_local[lid] += tmp_local[lid + 32];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 32)
-    {
-        if(lid < 16)
-        {
-            tmp_local[lid] += tmp_local[lid + 16];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 16)
-    {
-        if(lid < 8)
-        {
-            tmp_local[lid] += tmp_local[lid + 8];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 8)
-    {
-        if(lid < 4)
-        {
-            tmp_local[lid] += tmp_local[lid + 4];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 4)
-    {
-        if(lid < 2)
-        {
-            tmp_local[lid] += tmp_local[lid + 2];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(lid == 0)
-    {
-        sum1D = (tmp_local[lid + 1] + tmp_local[lid]);
-        // Perform sum reduction
-        *((__global DATA_TYPE *)sum.ptr) = SUM_REDUCE(sum1D, VECTOR_SIZE);
+        STORE_VECTOR_SELECT(result, DATA_TYPE, dst_ptr + i * dst_stride_axis, VEC_SIZE, VEC_SIZE_LEFTOVER, VEC_SIZE_LEFTOVER != 0 && get_global_id(0) == 0)
     }
+
+#undef NORMALIZE
 }
 
-#endif // defined(SRC_WIDTH) && defined(LOG_VECTOR_SIZE) && defined(MINVAL)
-#endif // defined(DATA_TYPE) && defined(MIN_VALUE) && defined(VECTOR_SIZE) && defined(VECTOR_SIZE_LEFTOVER)
\ No newline at end of file
+#endif // SOFTMAX_NON_X
+
+#undef MIN_VALUE
+#undef MIN_VALUE_TYPE
+#undef MIN_VALUE_TYPE_STR
+
+#undef MIN_VALUE_float
+#undef MIN_VALUE_half
+#undef MIN_VALUE_char
+#undef MIN_VALUE_uchar
diff --git a/src/core/CL/cl_kernels/common/softmax_layer_quantized.cl b/src/core/CL/cl_kernels/common/softmax_layer_quantized.cl
deleted file mode 100644
index 192c5f97a1..0000000000
--- a/src/core/CL/cl_kernels/common/softmax_layer_quantized.cl
+++ /dev/null
@@ -1,529 +0,0 @@
-/*
- * Copyright (c) 2017-2021 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#include "helpers_asymm.h"
-
-#if defined(DATA_TYPE) && defined(MIN_VALUE) && defined(VECTOR_SIZE) && defined(VECTOR_SIZE_LEFTOVER) && defined(DIFF_MIN)
-
-#define VEC_BASE VEC_DATA_TYPE(DATA_TYPE, VECTOR_SIZE)
-#define VEC_INT VEC_DATA_TYPE(int, VECTOR_SIZE)
-#define VEC_FLOAT VEC_DATA_TYPE(float, VECTOR_SIZE)
-
-/** Divides all the values of the input tensor by the sum calculated from softmax_layer_shift_exp_sum kernel.
- *
- * @note Datatype must be given as a preprocessor argument using -DDATA_TYPE, e.g. -DDATA_TYPE=uchar
- * @note The zero value for the given data type must be given as a preprocessor argument using -DMIN_VALUE, e.g. -DMIN_VALUE=-128
- * @note Vector size should be given as a preprocessor argument using -DVECTOR_SIZE=size. e.g. -DVECTOR_SIZE=16
- * @note Leftover vector size has to be passed at compile time using -DVECTOR_SIZE_LEFTOVER. e.g. -DVECTOR_SIZE_LEFTOVER=3. It is defined as the remainder between the input's first dimension and VECTOR_SIZE
- * @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 Additional quantization data must be passed at compile time using -DSCALED_DIFF_INT_BITS and -DEXP_ACCUMULATION_INT_BITS.
- * @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.
- * @note In case the input's data type is QASYMM8_SIGNED, -DQASYMM8_SIGNED must be passed.
- *
- * @param[in]  src_ptr                           Pointer to the source tensor slice. Supported data types: S32
- * @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]  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_y * number of elements along Y 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[out] dst_ptr                           Pointer to the destination tensor slice. Supported data types: QASYMM8/QASYMM8_SIGNED
- * @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
- */
-__kernel void softmax_layer_norm_quantized(
-    TENSOR3D_DECLARATION(src),
-    TENSOR3D_DECLARATION(sum),
-    TENSOR3D_DECLARATION(dst))
-{
-    const int x_offs = max((int)(get_global_id(0) * VECTOR_SIZE - (VECTOR_SIZE - VECTOR_SIZE_LEFTOVER) % VECTOR_SIZE), 0);
-
-    __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x_offs * sizeof(int) + get_global_id(1) * src_stride_y + get_global_id(2) * src_stride_z;
-    __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_offs * sizeof(DATA_TYPE) + get_global_id(1) * dst_stride_y + get_global_id(2) * dst_stride_z;
-
-    Image sum = CONVERT_TENSOR3D_TO_IMAGE_STRUCT_NO_STEP(sum);
-
-#ifdef BETA
-    // Initialize beta
-    VEC_FLOAT beta       = (VEC_FLOAT)BETA;
-    VEC_FLOAT scale_beta = -BETA * SCALE;
-#else  /* BETA */
-    VEC_FLOAT scale_beta = -SCALE;
-#endif /* BETA */
-
-    // Load max value of 1D logits vector (row)
-    float sum_val         = *((__global float *)offset(&sum, 0, get_global_id(1)));
-    float sum_val_inverse = 256.f / sum_val;
-
-    VEC_INT   data_diff   = VLOAD(VECTOR_SIZE)(0, (__global int *)src_addr);
-    VEC_FLOAT data_diff_f = CONVERT(data_diff, VEC_FLOAT);
-
-    data_diff_f *= scale_beta;
-    data_diff_f = exp(data_diff_f);
-    data_diff_f *= sum_val_inverse;
-
-#ifdef QASYMM8_SIGNED
-    data_diff_f -= 128.f;
-#endif /* QASYMM8_SIGNED */
-    VEC_INT  data  = CONVERT(data_diff_f, VEC_INT);
-    VEC_BASE data0 = CONVERT_SAT(data, VEC_DATA_TYPE(DATA_TYPE, VECTOR_SIZE));
-    STORE_VECTOR_SELECT(data, DATA_TYPE, dst_addr, VECTOR_SIZE, VECTOR_SIZE_LEFTOVER, VECTOR_SIZE_LEFTOVER != 0 && get_global_id(0) == 0);
-}
-
-#if defined(SRC_WIDTH) && defined(LOG_VECTOR_SIZE)
-
-/* Number of workitems in dimension 0. */
-#if !defined(GRID_SIZE)
-#define GRID_SIZE 1
-#endif /* !defined(GRID_SIZE) */
-
-#define VEC_UINT VEC_DATA_TYPE(uint, VECTOR_SIZE)
-
-VEC_INT mult_by_quantized_multiplier(VEC_INT 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, VECTOR_SIZE);
-    }
-#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 Datatype must be given as a preprocessor argument using -DDATA_TYPE, e.g. -DDATA_TYPE=uchar
- * @note The zero value for the given data type must be given as a preprocessor argument using -DMIN_VALUE, e.g. -DMIN_VALUE=-128
- * @note Vector size should be given as a preprocessor argument using -DVECTOR_SIZE=size. e.g. -DVECTOR_SIZE=16
- * @note Leftover vector size has to be passed at compile time using -DVECTOR_SIZE_LEFTOVER. e.g. -DVECTOR_SIZE_LEFTOVER=3. It is defined as the remainder between the input's first dimension and VECTOR_SIZE
- * @note In case the input is not multiple of VECTOR_SIZE -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 Additional quantization data must be passed at compile time using -DSCALED_DIFF_INT_BITS and -DEXP_ACCUMULATION_INT_BITS.
- * @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.
- * @note In case the input's data type is QASYMM8_SIGNED, -DQASYMM8_SIGNED must be passed.
- *
- * @param[in]  src_ptr                           Pointer to the source tensor slice. Supported data types: QASYMM8/QASYMM8_SIGNED
- * @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]  max_ptr                           Pointer to the max values tensor slice. Supported data types: same as @p src_ptr
- * @param[in]  max_stride_x                      Stride of the max values tensor in X dimension (in bytes)
- * @param[in]  max_step_x                        max_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in]  max_stride_y                      Stride of the max values tensor in Y dimension (in bytes)
- * @param[in]  max_step_y                        max_stride_y * number of elements along Y processed per workitem(in bytes)
- * @param[in]  max_stride_z                      Stride of the max values tensor in Z dimension (in bytes)
- * @param[in]  max_step_z                        max_stride_z * number of elements along Z processed per workitem(in bytes)
- * @param[in]  max_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: S32
- * @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 dst_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
- */
-__kernel void softmax_layer_max_shift_exp_sum_quantized_serial(
-    TENSOR3D_DECLARATION(src),
-    TENSOR3D_DECLARATION(maxo),
-    TENSOR3D_DECLARATION(dst),
-    TENSOR3D_DECLARATION(sum))
-{
-    __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + get_global_id(1) * src_stride_y + get_global_id(2) * src_stride_z;
-    __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + get_global_id(1) * dst_stride_y + get_global_id(2) * dst_stride_z;
-
-    Image maxo = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(maxo);
-    Image sum  = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(sum);
-
-    VEC_BASE max_val_vec = (VEC_BASE)(MIN_VALUE);
-
-#ifdef BETA
-    // Initialize beta
-    VEC_FLOAT beta       = (VEC_FLOAT)BETA;
-    VEC_FLOAT scale_beta = -BETA * SCALE;
-#else  /* BETA */
-    VEC_FLOAT scale_beta = -SCALE;
-#endif /* BETA */
-
-    // Calculate max of row
-#ifdef NON_MULTIPLE_OF_VECTOR_SIZE
-    VEC_BASE vec_min_val = (VEC_BASE)(MIN_VALUE);
-    VEC_BASE data        = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)src_addr);
-    VEC_INT widx         = (VEC_INT)VECTOR_SIZE_LEFTOVER > VEC_OFFS(int, VECTOR_SIZE);
-    max_val_vec          = max(max_val_vec, select(vec_min_val, data, CONVERT(widx, VEC_BASE)));
-#endif /* NON_MULTIPLE_OF_VECTOR_SIZE */
-
-    for(uint i = VECTOR_SIZE_LEFTOVER; i < SRC_WIDTH; i += VECTOR_SIZE)
-    {
-        VEC_BASE data = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr + i * sizeof(DATA_TYPE)));
-        max_val_vec   = max(data, max_val_vec);
-    }
-
-    // Perform max reduction
-    DATA_TYPE max_local               = MAX_REDUCE(max_val_vec, VECTOR_SIZE);
-    *((__global DATA_TYPE *)maxo.ptr) = max_local;
-
-    // Second part
-
-    // Load max value of 1D logits vector (row)
-    int       max_val = convert_int(max_local);
-    VEC_FLOAT sum1D_f = 0.f;
-    // Start with the leftover items
-#ifdef NON_MULTIPLE_OF_VECTOR_SIZE
-    VEC_INT   data_fp   = CONVERT(data, VEC_INT);
-    VEC_INT   data_diff = max_val - data_fp;
-    VEC_FLOAT data_fp_f = CONVERT(data_diff, VEC_FLOAT);
-    data_fp_f *= scale_beta;
-    data_fp_f = exp(data_fp_f);
-    data_fp_f = select(0, data_fp_f, widx);
-    VSTORE_PARTIAL(VECTOR_SIZE, VECTOR_SIZE_LEFTOVER)
-    (data_diff, 0, (__global int *)dst_addr);
-    sum1D_f += data_fp_f;
-#endif /* NON_MULTIPLE_OF_VECTOR_SIZE */
-    // Do the rest and compute exp and sum
-    for(uint i = VECTOR_SIZE_LEFTOVER; i < SRC_WIDTH; i += VECTOR_SIZE)
-    {
-        VEC_BASE data       = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr + i * sizeof(DATA_TYPE)));
-        VEC_INT   data_fp   = CONVERT(data, VEC_INT);
-        VEC_INT   data_diff = max_val - data_fp;
-        VEC_FLOAT data_fp_f = CONVERT(data_diff, VEC_FLOAT);
-        data_fp_f *= scale_beta;
-        data_fp_f = exp(data_fp_f);
-        sum1D_f += data_fp_f;
-        VSTORE(VECTOR_SIZE)
-        (data_diff, 0, (__global int *)(dst_addr + i * sizeof(int)));
-    }
-    // Perform sum reduction
-    *((__global float *)sum.ptr) = SUM_REDUCE(sum1D_f, VECTOR_SIZE);
-}
-
-/** 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, e.g. -DDATA_TYPE=uchar
- * @note The zero value for the given data type must be given as a preprocessor argument using -DMIN_VALUE, e.g. -DMIN_VALUE=-128
- * @note Vector size should be given as a preprocessor argument using -DVECTOR_SIZE=size. e.g. -DVECTOR_SIZE=16
- * @note Leftover vector size has to be passed at compile time using -DVECTOR_SIZE_LEFTOVER. e.g. -DVECTOR_SIZE_LEFTOVER=3. It is defined as the remainder between the input's first dimension and VECTOR_SIZE
- * @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 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 Additional quantization data must be passed at compile time using -DSCALED_DIFF_INT_BITS and -DEXP_ACCUMULATION_INT_BITS.
- * @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.
- * @note In case the input's data type is QASYMM8_SIGNED, -DQASYMM8_SIGNED must be passed.
- *
- * @param[in]  src_ptr                            Pointer to the source tensor slice. Supported data types: 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
- */
-__kernel void softmax_layer_max_shift_exp_sum_quantized_parallel(
-    TENSOR3D_DECLARATION(src),
-    TENSOR3D_DECLARATION(maxo),
-    TENSOR3D_DECLARATION(dst),
-    TENSOR3D_DECLARATION(sum))
-{
-    const uint lid    = get_local_id(0);
-    const uint x_offs = (VECTOR_SIZE_LEFTOVER + lid * VECTOR_SIZE);
-
-    __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x_offs * sizeof(DATA_TYPE) + get_global_id(1) * src_stride_y + get_global_id(2) * src_stride_z;
-    __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_offs * sizeof(int) + get_global_id(1) * dst_stride_y + get_global_id(2) * dst_stride_z;
-
-    Image maxo = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(maxo);
-    Image sum  = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(sum);
-
-    // Define one temporary vector per work-item.
-    __local VEC_INT tmp_local[GRID_SIZE];
-    __local DATA_TYPE max_local;
-
-    VEC_BASE vec_min_val = (VEC_BASE)(MIN_VALUE);
-    VEC_BASE max_val_vec = vec_min_val;
-
-    // Number of iterations per work-item.
-    const uint width = (SRC_WIDTH / GRID_SIZE) >> LOG_VECTOR_SIZE;
-    // Calculate max of row
-    uint i = 0;
-    for(; i < width; ++i)
-    {
-        VEC_BASE data_max = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE)));
-        max_val_vec       = max(data_max, max_val_vec);
-    }
-#ifdef NON_MULTIPLE_OF_GRID_SIZE
-    // How many work-items needed to complete the computation.
-    int boundary_workitems = (SRC_WIDTH % (GRID_SIZE * VECTOR_SIZE)) / VECTOR_SIZE;
-    if(lid < boundary_workitems)
-    {
-        VEC_BASE data_max = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE)));
-        max_val_vec       = max(data_max, max_val_vec);
-    }
-#ifdef NON_MULTIPLE_OF_VECTOR_SIZE
-    VEC_INT widx;
-    if(lid == 0)
-    {
-        // Handle non multiple of 4
-        VEC_BASE data_max = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr - VECTOR_SIZE_LEFTOVER * sizeof(DATA_TYPE)));
-        widx              = (VEC_INT)VECTOR_SIZE_LEFTOVER > VEC_OFFS(int, VECTOR_SIZE);
-        max_val_vec       = max(max_val_vec, select(vec_min_val, data_max, CONVERT(widx, VEC_BASE)));
-    }
-#endif /* NON_MULTIPLE_OF_VECTOR_SIZE */
-#endif /* NON_MULTIPLE_OF_GRID_SIZE */
-    tmp_local[lid] = CONVERT(max_val_vec, VEC_INT);
-
-    barrier(CLK_LOCAL_MEM_FENCE);
-
-    if(GRID_SIZE >= 256)
-    {
-        if(lid < 128)
-        {
-            tmp_local[lid] = max(tmp_local[lid + 128], tmp_local[lid]);
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 128)
-    {
-        if(lid < 64)
-        {
-            tmp_local[lid] = max(tmp_local[lid + 64], tmp_local[lid]);
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 64)
-    {
-        if(lid < 32)
-        {
-            tmp_local[lid] = max(tmp_local[lid + 32], tmp_local[lid]);
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 32)
-    {
-        if(lid < 16)
-        {
-            tmp_local[lid] = max(tmp_local[lid + 16], tmp_local[lid]);
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 16)
-    {
-        if(lid < 8)
-        {
-            tmp_local[lid] = max(tmp_local[lid + 8], tmp_local[lid]);
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 8)
-    {
-        if(lid < 4)
-        {
-            tmp_local[lid] = max(tmp_local[lid + 4], tmp_local[lid]);
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 4)
-    {
-        if(lid < 2)
-        {
-            tmp_local[lid] = max(tmp_local[lid + 2], tmp_local[lid]);
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(lid == 0)
-    {
-        max_val_vec = max(CONVERT((tmp_local[lid + 1]), VEC_BASE), CONVERT((tmp_local[lid]), VEC_BASE));
-        max_local   = MAX_REDUCE(max_val_vec, VECTOR_SIZE);
-    }
-    barrier(CLK_LOCAL_MEM_FENCE);
-
-    /* Second section */
-
-    // Set sum vector
-    VEC_INT sum1D   = 0;
-    int     max_val = convert_int(max_local);
-
-    // Shift values, exp and sum
-    for(i = 0; i < width; ++i)
-    {
-        VEC_BASE data          = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE)));
-        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(data_diff);
-        data_fp                = ASYMM_EXP_ON_NEGATIVE_VALUES(data_diff_mult, SCALED_DIFF_INT_BITS, VECTOR_SIZE);
-        data_fp                = ASYMM_RESCALE(data_fp, 0, EXP_ACCUMULATION_INT_BITS, VECTOR_SIZE);
-        VSTORE(VECTOR_SIZE)
-        (data_diff, 0, (__global int *)(dst_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(int)));
-        sum1D = sum1D + select(0, data_fp, data_diff >= (VEC_INT)(DIFF_MIN));
-    }
-#ifdef NON_MULTIPLE_OF_GRID_SIZE
-    boundary_workitems = (SRC_WIDTH % (GRID_SIZE * VECTOR_SIZE)) / VECTOR_SIZE;
-    if(lid < boundary_workitems)
-    {
-        VEC_BASE data          = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE)));
-        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(data_diff);
-        data_fp                = ASYMM_EXP_ON_NEGATIVE_VALUES(data_diff_mult, SCALED_DIFF_INT_BITS, VECTOR_SIZE);
-        data_fp                = ASYMM_RESCALE(data_fp, 0, EXP_ACCUMULATION_INT_BITS, VECTOR_SIZE);
-        VSTORE(VECTOR_SIZE)
-        (data_diff, 0, (__global int *)(dst_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(int)));
-        sum1D = sum1D + select(0, data_fp, data_diff >= (VEC_INT)(DIFF_MIN));
-    }
-#ifdef NON_MULTIPLE_OF_VECTOR_SIZE
-    if(lid == 0)
-    {
-        // Handle non multiple of vector size ((GRID_SIZE * i * 4) + 4, 0); move 4 float positions ahead, *4 is due to the stride
-        VEC_BASE data          = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr - VECTOR_SIZE_LEFTOVER * sizeof(DATA_TYPE)));
-        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(data_diff);
-        data_fp                = ASYMM_EXP_ON_NEGATIVE_VALUES(data_diff_mult, SCALED_DIFF_INT_BITS, VECTOR_SIZE);
-        data_fp                = ASYMM_RESCALE(data_fp, 0, EXP_ACCUMULATION_INT_BITS, VECTOR_SIZE);
-        VSTORE_PARTIAL(VECTOR_SIZE, VECTOR_SIZE_LEFTOVER)
-        (data_diff, 0, (__global int *)(dst_addr - VECTOR_SIZE_LEFTOVER * sizeof(int)));
-        data_fp = select(MIN_VALUE, data_fp, data_diff >= (VEC_INT)(DIFF_MIN));
-        data_fp = select(0, data_fp, widx);
-        sum1D   = sum1D + data_fp;
-    }
-#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] += tmp_local[lid + 128];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 128)
-    {
-        if(lid < 64)
-        {
-            tmp_local[lid] += tmp_local[lid + 64];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 64)
-    {
-        if(lid < 32)
-        {
-            tmp_local[lid] += tmp_local[lid + 32];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 32)
-    {
-        if(lid < 16)
-        {
-            tmp_local[lid] += tmp_local[lid + 16];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 16)
-    {
-        if(lid < 8)
-        {
-            tmp_local[lid] += tmp_local[lid + 8];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 8)
-    {
-        if(lid < 4)
-        {
-            tmp_local[lid] += tmp_local[lid + 4];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(GRID_SIZE >= 4)
-    {
-        if(lid < 2)
-        {
-            tmp_local[lid] += tmp_local[lid + 2];
-        }
-        barrier(CLK_LOCAL_MEM_FENCE);
-    }
-    if(lid == 0)
-    {
-        sum1D = (tmp_local[lid + 1] + tmp_local[lid]);
-        // Perform sum reduction
-        *((__global int *)sum.ptr) = SUM_REDUCE(sum1D, VECTOR_SIZE);
-    }
-}
-#endif // #if defined(SRC_WIDTH) && defined(LOG_VECTOR_SIZE)
-#endif /* defined(DATA_TYPE) && defined(DIFF_MIN) && defined(VECTOR_SIZE) && defined(VECTOR_SIZE_LEFTOVER) && defined(MIN_VALUE) */