COMPMID-2790: Add support for QASYMM8_SIGNED in CLGEMMLowpMatrixMultiplyCore

Change-Id: Ifdaeb53c512ba697f174649c026075010f54f628
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/2472
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Sang-Hoon Park <sang-hoon.park@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Giuseppe Rossini <giuseppe.rossini@arm.com>

5 files changed, 125 insertions, 85 deletions

diff --git a/src/core/CL/cl_kernels/gemmlowp.cl b/src/core/CL/cl_kernels/gemmlowp.cl
index 2a1c1561da..74ea96551d 100644
--- a/src/core/CL/cl_kernels/gemmlowp.cl
+++ b/src/core/CL/cl_kernels/gemmlowp.cl
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2019 ARM Limited.
+ * Copyright (c) 2017-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -999,6 +999,8 @@ __kernel void gemmlowp_mm_native(IMAGE_DECLARATION(lhs),
  *       https://github.com/google/gemmlowp/blob/master/doc/low-precision.md
  *
  * @attention The number of matrix A columns needs to be passed at compile time using -DCOLS_A
+ * @note The input data type must be passed at compile time using -DDATA_TYPE (i.e. -DDATA_TYPE=uchar)
+ * @note The data type for the accumulation must be passed at compile time using -DDATA_ACC_TYPE (i.e. -DDATA_ACC_TYPE=uint)
  *
  * @param[in]  src_ptr                           Pointer to the source tensor. Supported data type: QASYMM8
  * @param[in]  src_stride_x                      Stride of the source tensor in X dimension (in bytes)
@@ -1022,28 +1024,30 @@ __kernel void gemmlowp_matrix_a_reduction(TENSOR3D_DECLARATION(src),
     Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src);
     Image    dst = CONVERT_TO_IMAGE_STRUCT(dst);
 
-    uint4 sum_row_u32 = (uint4)0;
-    uint  sum_row     = 0;
+    VEC_DATA_TYPE(DATA_ACC_TYPE, 4)
+    sum_row_32            = (VEC_DATA_TYPE(DATA_ACC_TYPE, 4))0;
+    DATA_ACC_TYPE sum_row = 0;
 
-    __global const uchar *matrix_a = (__global const uchar *)(src.ptr + get_global_id(0) * src_stride_y + get_global_id(1) * src_stride_z);
+    __global const DATA_TYPE *matrix_a = (__global const DATA_TYPE *)(src.ptr + get_global_id(0) * src_stride_y + get_global_id(1) * src_stride_z);
 
     int i = 0;
 
     // This for loop performs 16 accumulations
     for(; i <= ((int)COLS_A - 16); i += 16)
     {
-        const uchar16 a0_u8 = vload16(0, matrix_a + i);
+        const VEC_DATA_TYPE(DATA_TYPE, 16) a0 = vload16(0, matrix_a + i);
 
-        sum_row_u32 += convert_uint4(a0_u8.s0123) + convert_uint4(a0_u8.s4567) + convert_uint4(a0_u8.s89AB) + convert_uint4(a0_u8.sCDEF);
+        sum_row_32 += CONVERT(a0.s0123, VEC_DATA_TYPE(DATA_ACC_TYPE, 4)) + CONVERT(a0.s4567, VEC_DATA_TYPE(DATA_ACC_TYPE, 4)) + CONVERT(a0.s89AB, VEC_DATA_TYPE(DATA_ACC_TYPE, 4)) + CONVERT(a0.sCDEF,
+                      VEC_DATA_TYPE(DATA_ACC_TYPE, 4));
     }
 
     // This for loop performs the leftover accumulations
     for(; i < COLS_A; ++i)
     {
-        sum_row += matrix_a[i];
+        sum_row += (DATA_ACC_TYPE)matrix_a[i];
     }
 
-    sum_row += sum_row_u32.s0 + sum_row_u32.s1 + sum_row_u32.s2 + sum_row_u32.s3;
+    sum_row += sum_row_32.s0 + sum_row_32.s1 + sum_row_32.s2 + sum_row_32.s3;
 
     *((__global int *)dst.ptr) = (int)sum_row;
 }
@@ -1055,6 +1059,8 @@ __kernel void gemmlowp_matrix_a_reduction(TENSOR3D_DECLARATION(src),
  *       https://github.com/google/gemmlowp/blob/master/doc/low-precision.md
  *
  * @attention The number of matrix A columns needs to be passed at compile time using -DCOLS_A
+ * @note The input data type must be passed at compile time using -DDATA_TYPE (i.e. -DDATA_TYPE=uchar)
+ * @note The data type for the accumulation must be passed at compile time using -DDATA_ACC_TYPE (i.e. -DDATA_ACC_TYPE=uint)
  *
  * @param[in]  src_ptr                           Pointer to the source tensor. Supported data type: QASYMM8
  * @param[in]  src_stride_x                      Stride of the source tensor in X dimension (in bytes)
@@ -1078,34 +1084,35 @@ __kernel void gemmlowp_matrix_a_reduction_dot8(TENSOR3D_DECLARATION(src),
     Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src);
     Image    dst = CONVERT_TO_IMAGE_STRUCT(dst);
 
-    uint sum_row = 0;
+    DATA_ACC_TYPE sum_row = 0;
 
-    __global const uchar *matrix_a = (__global const uchar *)(src.ptr + get_global_id(0) * src_stride_y + get_global_id(1) * src_stride_z);
+    __global const DATA_TYPE *matrix_a = (__global const DATA_TYPE *)(src.ptr + get_global_id(0) * src_stride_y + get_global_id(1) * src_stride_z);
 
     int i = 0;
 
     // This for loop performs 16 accumulations
     for(; i <= ((int)COLS_A - 32); i += 32)
     {
-        uchar16 a0_u8 = vload16(0, matrix_a + i);
+        VEC_DATA_TYPE(DATA_TYPE, 16)
+        a0 = vload16(0, matrix_a + i);
 
-        sum_row += arm_dot(a0_u8.s0123, (uchar4)(1));
-        sum_row += arm_dot(a0_u8.s4567, (uchar4)(1));
-        sum_row += arm_dot(a0_u8.s89AB, (uchar4)(1));
-        sum_row += arm_dot(a0_u8.sCDEF, (uchar4)(1));
+        sum_row += arm_dot(a0.s0123, (VEC_DATA_TYPE(DATA_TYPE, 4))(1));
+        sum_row += arm_dot(a0.s4567, (VEC_DATA_TYPE(DATA_TYPE, 4))(1));
+        sum_row += arm_dot(a0.s89AB, (VEC_DATA_TYPE(DATA_TYPE, 4))(1));
+        sum_row += arm_dot(a0.sCDEF, (VEC_DATA_TYPE(DATA_TYPE, 4))(1));
 
-        a0_u8 = vload16(1, matrix_a + i);
+        a0 = vload16(1, matrix_a + i);
 
-        sum_row += arm_dot(a0_u8.s0123, (uchar4)(1));
-        sum_row += arm_dot(a0_u8.s4567, (uchar4)(1));
-        sum_row += arm_dot(a0_u8.s89AB, (uchar4)(1));
-        sum_row += arm_dot(a0_u8.sCDEF, (uchar4)(1));
+        sum_row += arm_dot(a0.s0123, (VEC_DATA_TYPE(DATA_TYPE, 4))(1));
+        sum_row += arm_dot(a0.s4567, (VEC_DATA_TYPE(DATA_TYPE, 4))(1));
+        sum_row += arm_dot(a0.s89AB, (VEC_DATA_TYPE(DATA_TYPE, 4))(1));
+        sum_row += arm_dot(a0.sCDEF, (VEC_DATA_TYPE(DATA_TYPE, 4))(1));
     }
 
     // This for loop performs the leftover accumulations
     for(; i < COLS_A; ++i)
     {
-        sum_row += matrix_a[i];
+        sum_row += (DATA_ACC_TYPE)matrix_a[i];
     }
 
     *((__global int *)dst.ptr) = (int)sum_row;
@@ -1120,6 +1127,8 @@ __kernel void gemmlowp_matrix_a_reduction_dot8(TENSOR3D_DECLARATION(src),
  *       https://github.com/google/gemmlowp/blob/master/doc/low-precision.md
  *
  * @attention The number of matrix B columns and rows needs to be passed at compile time using -DCOLS_B and -DROWS_B
+ * @note The input data type must be passed at compile time using -DDATA_TYPE (i.e. -DDATA_TYPE=uchar)
+ * @note The data type for the accumulation must be passed at compile time using -DDATA_ACC_TYPE (i.e. -DDATA_ACC_TYPE=uint)
  *
  * @param[in]  src_ptr                           Pointer to the source tensor. Supported data type: QASYMM8
  * @param[in]  src_stride_x                      Stride of the source tensor in X dimension (in bytes)
@@ -1143,20 +1152,26 @@ __kernel void gemmlowp_matrix_b_reduction(TENSOR3D_DECLARATION(src),
     Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src);
     Image    dst = CONVERT_TO_IMAGE_STRUCT(dst);
 
-    uint16 sum_col_u32 = (uint16)0;
+    VEC_DATA_TYPE(DATA_ACC_TYPE, 16)
+    sum_col_32 = (VEC_DATA_TYPE(DATA_ACC_TYPE, 16))0;
 
-    __global const uchar *matrix_b = (__global const uchar *)(src.ptr + get_global_id(1) * src_stride_z);
+    __global const DATA_TYPE *matrix_b = (__global const DATA_TYPE *)(src.ptr + get_global_id(1) * src_stride_z);
 
     int i = 0;
     // This for loop performs 4 accumulations
     for(; i <= ((int)ROWS_B - 4); i += 4)
     {
-        const uchar16 b0_u8 = vload16(0, matrix_b + 0 * src_stride_y);
-        const uchar16 b1_u8 = vload16(0, matrix_b + 1 * src_stride_y);
-        const uchar16 b2_u8 = vload16(0, matrix_b + 2 * src_stride_y);
-        const uchar16 b3_u8 = vload16(0, matrix_b + 3 * src_stride_y);
-
-        sum_col_u32 += convert_uint16(b0_u8) + convert_uint16(b1_u8) + convert_uint16(b2_u8) + convert_uint16(b3_u8);
+        const VEC_DATA_TYPE(DATA_TYPE, 16)
+        b0 = vload16(0, matrix_b + 0 * src_stride_y);
+        const VEC_DATA_TYPE(DATA_TYPE, 16)
+        b1 = vload16(0, matrix_b + 1 * src_stride_y);
+        const VEC_DATA_TYPE(DATA_TYPE, 16)
+        b2 = vload16(0, matrix_b + 2 * src_stride_y);
+        const VEC_DATA_TYPE(DATA_TYPE, 16)
+        b3 = vload16(0, matrix_b + 3 * src_stride_y);
+
+        sum_col_32 += CONVERT(b0, VEC_DATA_TYPE(DATA_ACC_TYPE, 16)) + CONVERT(b1, VEC_DATA_TYPE(DATA_ACC_TYPE, 16)) + CONVERT(b2, VEC_DATA_TYPE(DATA_ACC_TYPE, 16)) + CONVERT(b3, VEC_DATA_TYPE(DATA_ACC_TYPE,
+                      16));
 
         matrix_b += 4 * src_stride_y;
     }
@@ -1164,14 +1179,15 @@ __kernel void gemmlowp_matrix_b_reduction(TENSOR3D_DECLARATION(src),
     // This for loop perfoms the leftover accumulations
     for(; i < (int)ROWS_B; ++i)
     {
-        const uchar16 b0_u8 = vload16(0, matrix_b);
+        const VEC_DATA_TYPE(DATA_TYPE, 16)
+        b0 = vload16(0, matrix_b);
 
-        sum_col_u32 += convert_uint16(b0_u8);
+        sum_col_32 += CONVERT(b0, VEC_DATA_TYPE(DATA_ACC_TYPE, 16));
 
         matrix_b += src_stride_y;
     }
 
-    vstore16(convert_int16(sum_col_u32), 0, (__global int *)dst.ptr);
+    vstore16(convert_int16(sum_col_32), 0, (__global int *)dst.ptr);
 }
 #endif // defined(COLS_B) && defined(ROWS_B)
 
@@ -1391,18 +1407,21 @@ __kernel void gemmlowp_offset_contribution(TENSOR3D_DECLARATION(mm_result)
  *                   (sum_row[i] * B_OFFSET) +
  *                   (K_OFFSET)
  *
- * This result is quantized down to uint8 using the output stage. The output stage computes the following operations:
+ * This result is quantized down to uint8/int8 using the output stage. The output stage computes the following operations:
  *
  *  -# Add offset terms to final result
  *  -# Multiply each entry of result by result_mult_int
  *  -# Add bias to final result (if -DADD_BIAS is passed at compile time)
  *  -# Shift the int32 accumulator by result_shift
  *  -# Clamp the value between the specified min and max bounds (if -DMIN_BOUND and/or -DMAX_BOUND are passed at compile time)
- *  -# Clamp the resulting int32 values to the [0..255] range and cast to QASYMM8.
+ *  -# Clamp the resulting int32 values:
+ *      - to the [0..255] range and cast to QASYMM8.
+ *      - to the [-128..127] range and cast to QASYMM8_SIGNED.
  *
  * @attention The offset, scalar scale factor and number of bits to shift right of output tensor must be passed at compile time using -DRESULT_OFFSET, -RESULT_MULT_INT and -DRESULT_SHIFT
  *
  * @note In case the addition of int32 biases is required, -DADD_BIAS should be passed at compile time
+ * @note The output datatype should be passed at compile time using -DOUTPUT_DATA_TYPE
  * @note In case the clamping of the result is required, the min and max bounds can be passed at compile time using -DMIN_BOUND and -DMAX_BOUND.
  *       These values can be used to implement "rectified linear unit" activation functions
  *
@@ -1430,7 +1449,7 @@ __kernel void gemmlowp_offset_contribution(TENSOR3D_DECLARATION(mm_result)
  * @param[in]  biases_stride_x                                  (Optional) Stride of the biases tensor in X dimension (in bytes)
  * @param[in]  biases_step_x                                    (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes)
  * @param[in]  biases_offset_first_element_in_bytes             (Optional) The offset of the first element in the biases tensor
- * @param[out] dst_ptr                                          Pointer to the destination tensor Supported data type: QASYMM8
+ * @param[out] dst_ptr                                          Pointer to the destination tensor Supported data type: QASYMM8/QASYMM8_SIGNED
  * @param[in]  dst_stride_x                                     Stride of the destination tensor in X dimension (in bytes)
  * @param[in]  dst_step_x                                       dst_gx_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)
@@ -1531,17 +1550,18 @@ __kernel void gemmlowp_offset_contribution_quantize_down(TENSOR3D_DECLARATION(mm
     in_s32 >>= RESULT_SHIFT;
 #endif // defined(PER_CHANNEL_QUANTIZATION)
 
-    uchar4 res = convert_uchar4_sat(in_s32);
+    VEC_DATA_TYPE(OUTPUT_DATA_TYPE, 4)
+    res = CONVERT_SAT(in_s32, VEC_DATA_TYPE(OUTPUT_DATA_TYPE, 4));
 
 #if defined(MIN_BOUND)
-    res = max(res, (uchar4)MIN_BOUND);
+    res = max(res, (VEC_DATA_TYPE(OUTPUT_DATA_TYPE, 4))MIN_BOUND);
 #endif // defined(MIN_BOUND)
 #if defined(MAX_BOUND)
-    res = min(res, (uchar4)MAX_BOUND);
+    res = min(res, (VEC_DATA_TYPE(OUTPUT_DATA_TYPE, 4))MAX_BOUND);
 #endif // defined(MAX_BOUND)
 
     // Store the result
-    vstore4(res, 0, dst_addr);
+    vstore4(res, 0, (__global OUTPUT_DATA_TYPE *)dst_addr);
 }
 
 /* OpenCL kernel used to add the offset contribution after matrix multiplication and it quantizes down to uint8.
@@ -1561,18 +1581,21 @@ __kernel void gemmlowp_offset_contribution_quantize_down(TENSOR3D_DECLARATION(mm
  *                   (sum_row[i] * B_OFFSET) +
  *                   (K_OFFSET)
  *
- * This result is quantized down to uint8 using the output stage. The output stage computes the following operations:
+ * This result is quantized down to uint8/int8 using the output stage. The output stage computes the following operations:
  *
  *  -# Compute fixed point multiplication between each entry of input by result_fixedpoint_multiplier
  *  -# Add bias to final result if bias tensor is not a nullptr
  *  -# Round to nearest division by a power-of-two using result_shift
  *  -# Add offset to each result
  *  -# Clamp the value between the specified min and max bounds
- *  -# Clamp the resulting int32 values to the [0..255] range and cast to QASYMM8.
+ *  -# Clamp the resulting int32 values:
+ *      - to the [0..255] range and cast to QASYMM8.
+ *      - to the [-128..127] range and cast to QASYMM8_SIGNED.
  *
  * @attention The offset, scalar scale factor and number of bits to shift right of output tensor must be passed at compile time using -DRESULT_OFFSET, -RESULT_MULT_INT and -DRESULT_SHIFT
  *
  * @note In case the addition of int32 biases is required, -DADD_BIAS should be passed at compile time
+ * @note The output datatype should be passed at compile time using -DOUTPUT_DATA_TYPE
  * @note In case the clamping of the result is required, the min and max bounds can be passed at compile time using -DMIN_BOUND and -DMAX_BOUND.
  *       These values can be used to implement "rectified linear unit" activation functions
  *
@@ -1706,17 +1729,18 @@ __kernel void gemmlowp_offset_contribution_quantize_down_fixedpoint(TENSOR3D_DEC
     // Add the offset terms to GEMM's result
     in_s32 += (int4)RESULT_OFFSET;
 
-    uchar4 res = convert_uchar4_sat(in_s32);
+    VEC_DATA_TYPE(OUTPUT_DATA_TYPE, 4)
+    res = CONVERT_SAT(in_s32, VEC_DATA_TYPE(OUTPUT_DATA_TYPE, 4));
 
 #if defined(MIN_BOUND)
-    res = max(res, (uchar4)MIN_BOUND);
+    res = max(res, (VEC_DATA_TYPE(OUTPUT_DATA_TYPE, 4))MIN_BOUND);
 #endif // defined(MIN_BOUND)
 #if defined(MAX_BOUND)
-    res = min(res, (uchar4)MAX_BOUND);
+    res = min(res, (VEC_DATA_TYPE(OUTPUT_DATA_TYPE, 4))MAX_BOUND);
 #endif // defined(MAX_BOUND)
 
     // Store the result
-    vstore4(res, 0, dst_addr);
+    vstore4(res, 0, (__global OUTPUT_DATA_TYPE *)dst_addr);
 }
 #endif // defined(RESULT_OFFSET) && defined(RESULT_MULTIPLIER) && defined(RESULT_SHIFT)
 
@@ -1814,9 +1838,9 @@ __kernel void gemmlowp_output_stage_quantize_down(TENSOR3D_DECLARATION(src),
 #endif // defined(RESULT_OFFSET) && defined(RESULT_MULT_INT) && defined(RESULT_SHIFT)
 
 #if defined(RESULT_OFFSET_AFTER_SHIFT) && defined(RESULT_FIXEDPOINT_MULTIPLIER) && defined(RESULT_SHIFT)
-/** This OpenCL kernel is used to quantize down the int32 accumulator values of GEMMLowp to QASYMM8
+/** This OpenCL kernel is used to quantize down the int32 accumulator values of GEMMLowp to QASYMM8/QASYMM8_SIGNED
  *
- * This kernel takes a final int32 accumulator value (the output of matrix multiplication), and processes it to obtain the final QASYMM8 value.
+ * This kernel takes a final int32 accumulator value (the output of matrix multiplication), and processes it to obtain the final QASYMM8/QASYMM8_SIGNED value.
  * The following computations will be performed by the kernel:
  *
  *  -# Compute fixed point multiplication between each entry of input by result_fixedpoint_multiplier
diff --git a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.cpp b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.cpp
index 3e887d8163..5b50c5c827 100644
--- a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019 ARM Limited.
+ * Copyright (c) 2019-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -54,6 +54,7 @@ Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1,
                           const GEMMReshapeInfo &gemm_info)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3");
diff --git a/src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.cpp b/src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.cpp
index 2ebd76e1bf..5550003f33 100644
--- a/src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2018-2019 ARM Limited.
+ * Copyright (c) 2018-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -24,6 +24,7 @@
 #include "arm_compute/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h"
 
 #include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
@@ -45,9 +46,6 @@ Status validate_arguments(const ITensorInfo *mm_result, const ITensorInfo *vecto
                           int32_t a_offset, int32_t b_offset, const GEMMLowpOutputStageInfo &output_stage, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(mm_result, 1, DataType::S32);
-    ARM_COMPUTE_RETURN_ERROR_ON(output_stage.type == GEMMLowpOutputStageType::NONE);
-    ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_max_bound > 255);
-    ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_min_bound < 0 || output_stage.gemmlowp_min_bound > output_stage.gemmlowp_max_bound);
 
     if(bias != nullptr)
     {
@@ -108,26 +106,42 @@ Status validate_arguments(const ITensorInfo *mm_result, const ITensorInfo *vecto
         }
     }
 
-    if(output->total_size() != 0)
+    ARM_COMPUTE_RETURN_ERROR_ON(output_stage.type == GEMMLowpOutputStageType::NONE);
+    // Checks performed when output is configured
+    if((output != nullptr) && (output->total_size() != 0))
     {
-        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8);
+        ARM_COMPUTE_RETURN_ERROR_ON(output_stage.output_data_type != output->data_type());
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mm_result, output);
+        PixelValue min_val{};
+        PixelValue max_val{};
+        std::tie(min_val, max_val) = get_min_max(output->data_type());
+        ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_max_bound > max_val.get<int32_t>());
+        ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_min_bound < min_val.get<int32_t>() || output_stage.gemmlowp_min_bound > output_stage.gemmlowp_max_bound);
+    }
+    else
+    {
+        // Output will be configured as depending on the chosen output data type in the output stage
+        PixelValue min_val{};
+        PixelValue max_val{};
+        std::tie(min_val, max_val) = get_min_max(output_stage.output_data_type);
+        ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_max_bound > max_val.get<int32_t>());
+        ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_min_bound < min_val.get<int32_t>() || output_stage.gemmlowp_min_bound > output_stage.gemmlowp_max_bound);
     }
 
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(output_stage.gemmlowp_multipliers.size() != output_stage.gemmlowp_shifts.size(),
-                                    "per channel quantization info is incorrect");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(output_stage.gemmlowp_multipliers.size() != output_stage.gemmlowp_shifts.size(), "per channel quantization info is incorrect");
 
     return Status{};
 }
 
 std::pair<Status, Window> validate_and_configure_window(ITensorInfo *mm_result, ITensorInfo *vector_sum_col, ITensorInfo *vector_sum_row, ITensorInfo *bias, ITensorInfo *output,
-                                                        int32_t a_offset, int32_t b_offset, ITensorInfo *output_multipliers, ITensorInfo *output_shifts)
+                                                        int32_t a_offset, int32_t b_offset, const GEMMLowpOutputStageInfo &output_stage, ITensorInfo *output_multipliers, ITensorInfo *output_shifts)
 {
     constexpr unsigned int num_elems_processed_per_iteration = 4;
     bool                   window_changed                    = false;
 
     // Auto initialize the output
-    auto_init_if_empty(*output, mm_result->clone()->set_data_type(DataType::QASYMM8));
+    auto_init_if_empty(*output, mm_result->clone()->set_data_type(output_stage.output_data_type));
 
     // Configure kernel window
     Window win = calculate_max_window(*mm_result, Steps(num_elems_processed_per_iteration));
@@ -229,20 +243,16 @@ void CLGEMMLowpOffsetContributionOutputStageKernel::configure(const ICLTensor *m
     build_opts.add_option("-DRESULT_MULTIPLIER=" + support::cpp11::to_string(output_stage.gemmlowp_multipliers[0]));
     build_opts.add_option("-DRESULT_SHIFT=" + support::cpp11::to_string(output_stage.gemmlowp_shifts[0]));
     build_opts.add_option_if(_is_quantized_per_channel, "-DPER_CHANNEL_QUANTIZATION");
-    build_opts.add_option_if((min != 0) && (min != max), "-DMIN_BOUND=" + support::cpp11::to_string(min));
-    build_opts.add_option_if((max != 255) && (min != max), "-DMAX_BOUND=" + support::cpp11::to_string(max));
+    build_opts.add_option("-DOUTPUT_DATA_TYPE=" + get_cl_type_from_data_type(output->info()->data_type()));
 
-    std::string kernel_name("gemmlowp_offset_contribution");
+    PixelValue min_val{};
+    PixelValue max_val{};
+    std::tie(min_val, max_val) = get_min_max(output->info()->data_type());
+    build_opts.add_option_if((min != min_val.get<int32_t>()) && (min != max), "-DMIN_BOUND=" + support::cpp11::to_string(min));
+    build_opts.add_option_if((max != max_val.get<int32_t>()) && (min != max), "-DMAX_BOUND=" + support::cpp11::to_string(max));
 
-    // Fuse output stage
-    if(output_stage.type != GEMMLowpOutputStageType::NONE)
-    {
-        kernel_name += "_" + string_from_gemmlowp_output_stage(output_stage.type);
-    }
-    else
-    {
-        ARM_COMPUTE_ERROR("GEMMLowpOutputStage can not be NONE!");
-    }
+    std::string kernel_name("gemmlowp_offset_contribution");
+    kernel_name += "_" + string_from_gemmlowp_output_stage(output_stage.type);
 
     // Create kernel
     _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
@@ -253,7 +263,7 @@ void CLGEMMLowpOffsetContributionOutputStageKernel::configure(const ICLTensor *m
                                                     vector_sum_row != nullptr ? vector_sum_row->info() : nullptr,
                                                     bias != nullptr ? bias->info() : nullptr,
                                                     output->info(),
-                                                    a_offset, b_offset,
+                                                    a_offset, b_offset, output_stage,
                                                     output_multipliers->info(), output_shifts->info()); // NOLINT
     ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
     ICLKernel::configure_internal(win_config.second);
@@ -277,7 +287,7 @@ Status CLGEMMLowpOffsetContributionOutputStageKernel::validate(const ITensorInfo
                                                               vector_sum_row != nullptr ? vector_sum_row->clone().get() : nullptr,
                                                               bias != nullptr ? bias->clone().get() : nullptr,
                                                               output->clone().get(),
-                                                              a_offset, b_offset,
+                                                              a_offset, b_offset, output_stage,
                                                               output_multipliers->clone().get(), output_shifts->clone().get())
                                 .first); // NOLINT
 
diff --git a/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp b/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp
index 3a59b43823..7900c83f3d 100644
--- a/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2019 ARM Limited.
+ * Copyright (c) 2017-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -46,7 +46,7 @@ namespace
 {
 Status validate_arguments_matrix_a_reduction(const ITensorInfo *input, const ITensorInfo *output)
 {
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
 
     return Status{};
@@ -70,7 +70,7 @@ std::pair<Status, Window> validate_and_configure_window_matrix_a_reduction(ITens
 
 Status validate_arguments_matrix_b_reduction(const ITensorInfo *input, const ITensorInfo *output)
 {
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
 
     return Status{};
@@ -112,6 +112,8 @@ void CLGEMMLowpMatrixAReductionKernel::configure(const ICLTensor *mtx_a, ICLTens
     // Set the arguments to pass at compile time
     CLBuildOptions build_opts;
     build_opts.add_option("-DCOLS_A=" + support::cpp11::to_string(mtx_a->info()->dimension(0)));
+    build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(mtx_a->info()->data_type()));
+    build_opts.add_option("-DDATA_ACC_TYPE=" + get_cl_dot8_acc_type_from_data_type(mtx_a->info()->data_type()));
 
     const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device());
 
@@ -178,6 +180,8 @@ void CLGEMMLowpMatrixBReductionKernel::configure(const ICLTensor *mtx_b, ICLTens
     CLBuildOptions build_opts;
     build_opts.add_option("-DCOLS_B=" + support::cpp11::to_string(mtx_b->info()->dimension(0)));
     build_opts.add_option("-DROWS_B=" + support::cpp11::to_string(mtx_b->info()->dimension(1)));
+    build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(mtx_b->info()->data_type()));
+    build_opts.add_option("-DDATA_ACC_TYPE=" + get_cl_dot8_acc_type_from_data_type(mtx_b->info()->data_type()));
 
     // Create kernel
     _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemmlowp_matrix_b_reduction", build_opts.options()));
diff --git a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
index 4c0a521de8..cdb78c291d 100644
--- a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
+++ b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2019 ARM Limited.
+ * Copyright (c) 2017-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -206,8 +206,10 @@ void CLGEMMLowpMatrixMultiplyCore::configure(const ICLTensor *a, const ICLTensor
         _gemm_output_stage_multipliers.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32));
         _gemm_output_stage_shifts.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32));
 
+        GEMMLowpOutputStageInfo gemmlowp_output_stage = gemm_info.gemmlowp_output_stage();
+        gemmlowp_output_stage.output_data_type        = _matrix_a->info()->data_type();
         _offset_contribution_output_stage_kernel.configure(&_mm_result_s32, _a_offset == 0 ? nullptr : &_vector_sum_col, _b_offset == 0 ? nullptr : &_vector_sum_row, c, output, a->info()->dimension(0),
-                                                           _a_offset, _b_offset, gemm_info.gemmlowp_output_stage(), &_gemm_output_stage_multipliers, &_gemm_output_stage_shifts);
+                                                           _a_offset, _b_offset, gemmlowp_output_stage, &_gemm_output_stage_multipliers, &_gemm_output_stage_shifts);
 
         _gemm_output_stage_multipliers.allocator()->allocate();
         _gemm_output_stage_shifts.allocator()->allocate();
@@ -271,13 +273,10 @@ void CLGEMMLowpMatrixMultiplyCore::configure(const ICLTensor *a, const ICLTensor
 
 Status CLGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, const GEMMInfo &gemm_info)
 {
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QASYMM8);
-    if(b->data_type() == DataType::QSYMM8_PER_CHANNEL)
-    {
-        //DataType::QSYMM8_PER_CHANNEL supported only for weights
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->data_type() != DataType::QASYMM8, "Matrix A is not quantized while Matrix B is");
-    }
-    else
+    ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
+    //DataType::QSYMM8_PER_CHANNEL supported only for weights
+    if(b->data_type() != DataType::QSYMM8_PER_CHANNEL)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(a, b);
     }
@@ -388,13 +387,15 @@ Status CLGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITenso
 
         const TensorInfo gemm_output_stage_multipliers_shifts_info(TensorInfo(TensorShape(num_filters), 1, DataType::S32));
 
+        GEMMLowpOutputStageInfo gemmlowp_output_stage = gemm_info.gemmlowp_output_stage();
+        gemmlowp_output_stage.output_data_type        = a->data_type();
         ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMLowpOffsetContributionOutputStageKernel::validate(&mm_result_s32_info,
                                                                                             a_offset == 0 ? nullptr : &info_vector_sum_col,
                                                                                             b_offset == 0 ? nullptr : &info_vector_sum_row,
                                                                                             c,
                                                                                             output,
                                                                                             a_offset, b_offset,
-                                                                                            gemm_info.gemmlowp_output_stage(),
+                                                                                            gemmlowp_output_stage,
                                                                                             &gemm_output_stage_multipliers_shifts_info,
                                                                                             &gemm_output_stage_multipliers_shifts_info));
     }