Add quantized CL MatMul kernels for Lhs NT/T, Rhs NT

Implement OpenCL kernels for batched Matrix Multiplication for the quantized data types QASYMM8 and QASYMM8_SIGNED.

Quantized MatMul is supported with the following MatMul attributes:
* adj_x = false, adj_y = false
* adj_x = true, adj_y = false

We consider native format kernels only. In other words, no reshaping of the operand matrices is done.

Resolves: COMPMID-5921, COMPMID-5922

Change-Id: I99e0f68054a2bd635c60ec2641acc2e7ff398473
Signed-off-by: Omar Al Khatib <omar.alkhatib@arm.com>
Signed-off-by: Gunes Bayir <gunes.bayir@arm.com>
Signed-off-by: Jakub Sujak <jakub.sujak@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9435
Reviewed-by: SiCong Li <sicong.li@arm.com>
Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>

2 files changed, 486 insertions, 0 deletions

diff --git a/src/core/CL/cl_kernels/common/mat_mul_quantized.cl b/src/core/CL/cl_kernels/common/mat_mul_quantized.cl
new file mode 100644
index 0000000000..c250b4b988
--- /dev/null
+++ b/src/core/CL/cl_kernels/common/mat_mul_quantized.cl
@@ -0,0 +1,387 @@
+/*
+ * Copyright (c) 2023 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.h"
+#include "tile_helpers.h"
+
+#if defined(MAT_MUL_NATIVE_QUANTIZED_NT_NT)
+/** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS non-transposed, RHS non-transposed - buffer only
+ *
+ * @note the "batch" here expresses the number of matrix multiplications to run in parallel. However, it
+ *       should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension
+ * @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=uchar)
+ * @note The block's dimensions used for the LHS and RHS matrices (M0, N0 and K0) must be passed at compile time using -DN0, -DM0 and -DK0 (e.g. -DN0=8, -DM0=4, -DK0=4).
+ * @note The number of leftover outputs rows/columns must be passed using -DPARTIAL_STORE_N0 and -DPARTIAL_STORE_M0 (e.g. -DPARTIAL_STORE_N0=2, -DPARTIAL_STORE_M0=3)
+ * @note The dimension K must be passed at compile time using -DK (e.g. -DK=6)
+ * @note The kernel name in uppercase must be passed at compile time (e.g. -DMAT_MUL_NATIVE_QUANTIZED_NT_NT)
+ * @note Only the following configurations of M0, N0 and K0 are currently supported:
+ *  - M0 > 0
+ *  - N0 = 1, 2, 3, 4, 8, 16
+ *  - K0 = 1, 2, 3, 4, 8, 16
+ * @note Values > 8 for M0 are not expected to be efficient
+ *
+ * @param[in]  lhs_ptr                           Pointer to the lhs matrix. Supported data types: QASYMM8_SIGNED/QASYMM8
+ * @param[in]  lhs_stride_y                      Stride of the lhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in]  lhs_stride_z                      Stride of the lhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in]  lhs_w                             The width of the lhs tensor
+ * @param[in]  lhs_h                             The height of the lhs tensor
+ * @param[in]  lhs_n                             Number of the matrices (buffers) in the batch
+ * @param[in]  lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
+ * @param[in]  rhs_ptr                           Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
+ * @param[in]  rhs_stride_y                      Stride of the rhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in]  rhs_stride_z                      Stride of the rhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in]  rhs_w                             The width of the rhs tensor
+ * @param[in]  rhs_h                             The height of the rhs tensor
+ * @param[in]  rhs_n                             Number of the matrices (buffers) in the batch
+ * @param[in]  rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
+ * @param[out] dst_ptr                           Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
+ * @param[in]  dst_stride_y                      Stride of the dst matrix in Y (2nd) dimension (in bytes)
+ * @param[in]  dst_stride_z                      Stride of the dst tensor in Z (3rd) dimension (in bytes)
+ * @param[in]  dst_w                             The width of the dst tensor
+ * @param[in]  dst_h                             The height of the dst tensor
+ * @param[in]  dst_n                             Number of the matrices (buffers) in the batch
+ * @param[in]  dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
+ */
+__kernel void mat_mul_native_quantized_nt_nt(
+    TENSOR3D_T(lhs, BUFFER),
+    TENSOR3D_T(rhs, BUFFER),
+    TENSOR3D_T(dst, BUFFER))
+{
+    const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0);
+    const uint y = GET_SPATIAL_IDX(1, M0, PARTIAL_STORE_M0);
+    const uint z = GET_SPATIAL_IDX(2, 1, 0);
+
+    // Compute LHS/RHS/DST matrix address
+    lhs_offset_first_element_in_bytes += y * lhs_stride_y + z * lhs_stride_z;
+    rhs_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + z * rhs_stride_z;
+    dst_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + y * dst_stride_y + z * dst_stride_z;
+
+    // Initialize the accumulators
+    TILE(int, M0, N0, acc);
+    LOOP_UNROLLING(int, i, 0, 1, M0,
+    {
+        acc[i].v = K * ((int)LHS_OFFSET) * ((int)RHS_OFFSET);
+    })
+
+    TILE(int, 1, N0, b_sum);
+    b_sum[0].v = 0;
+
+    TILE(int, 1, M0, a_sum);
+    a_sum[0].v = 0;
+
+    int k;
+    for(k = 0; k <= K - K0; k += K0)
+    {
+        TILE(DATA_TYPE, M0, K0, a);
+        TILE(DATA_TYPE, N0, K0, b);
+
+        LOOP_UNROLLING(int, i, 0, 1, M0,
+        {
+            a[i].v = 0;
+        })
+
+        LOOP_UNROLLING(int, i, 0, 1, N0,
+        {
+            b[i].v = 0;
+        })
+
+        // Load tile from the lhs tensor
+        T_LOAD(DATA_TYPE, M0, K0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a);
+
+        // Load tile from the rhs tensor in a transposed fashion
+        // in order to use T_MMUL_NT_T macro because only this macro
+        // can utilize dot product instruction for Int8/UInt8 by
+        // directly multiplying the rows of Lhs and Rhs tensors.
+        T_LOAD_TRANSPOSED(DATA_TYPE, K0, N0, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b);
+
+        T_MMUL(DATA_TYPE, DATA_TYPE, int, M0, N0, K0, NT, T, a, b, acc);
+
+        LOOP_UNROLLING(int, i, 0, 1, M0,
+        {
+            LOOP_UNROLLING(int, j, 0, 1, K0,
+            {
+                a_sum[0].s[i] += (int)a[i].s[j];
+            })
+        })
+
+        LOOP_UNROLLING(int, i, 0, 1, K0,
+        {
+            LOOP_UNROLLING(int, j, 0, 1, N0,
+            {
+                b_sum[0].s[j] += (int)b[j].s[i];
+            })
+        })
+
+        lhs_offset_first_element_in_bytes += K0 * sizeof(DATA_TYPE);
+        rhs_offset_first_element_in_bytes += K0 * rhs_stride_y;
+    }
+
+#if((K % K0) != 0)
+    /* Leftover Loop */
+    for(; k < K; ++k)
+    {
+        TILE(DATA_TYPE, M0, 1, a);
+        TILE(DATA_TYPE, N0, 1, b);
+
+        LOOP_UNROLLING(int, i, 0, 1, M0,
+        {
+            a[i].v = 0;
+        })
+
+        LOOP_UNROLLING(int, i, 0, 1, N0,
+        {
+            b[i].v = 0;
+        })
+
+        // Load tile from the lhs tensor
+        T_LOAD(DATA_TYPE, M0, 1, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a);
+
+        // Load tile from the rhs tensor in a transposed fashion.
+        // See the main loop for more explanation
+        T_LOAD_TRANSPOSED(DATA_TYPE, 1, N0, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b);
+
+        T_MMUL(DATA_TYPE, DATA_TYPE, int, M0, N0, 1, NT, T, a, b, acc);
+
+        LOOP_UNROLLING(int, i, 0, 1, M0,
+        {
+            LOOP_UNROLLING(int, j, 0, 1, 1,
+            {
+                a_sum[0].s[i] += (int)a[i].s[j];
+            })
+        })
+
+        LOOP_UNROLLING(int, i, 0, 1, 1,
+        {
+            LOOP_UNROLLING(int, j, 0, 1, N0,
+            {
+                b_sum[0].s[j] += (int)b[j].s[i];
+            })
+        })
+
+        lhs_offset_first_element_in_bytes += 1 * sizeof(DATA_TYPE);
+        rhs_offset_first_element_in_bytes += 1 * rhs_stride_y;
+    }
+#endif // ((K % K0) != 0)
+
+    LOOP_UNROLLING(int, i, 0, 1, M0,
+    {
+        LOOP_UNROLLING(int, j, 0, 1, N0,
+        {
+            acc[i].s[j] += ((int)RHS_OFFSET) * a_sum[0].s[i] + ((int)(LHS_OFFSET)) * b_sum[0].s[j];
+        })
+    })
+
+    const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0;
+    const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0;
+
+    // Quantize the tile
+    TILE(DATA_TYPE, M0, N0, accq);
+    T_QUANTIZE8_ASYMMETRIC(int, DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, acc, accq);
+
+    TILE(int, M0, 1, indirect_buffer);
+    LOOP_UNROLLING(int, _i, 0, 1, M0,
+    {
+        indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond));
+    });
+
+    T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, accq, indirect_buffer);
+}
+#endif // defined(MAT_MUL_NATIVE_QUANTIZED_NT_NT)
+
+#if defined(MAT_MUL_NATIVE_QUANTIZED_T_NT)
+/** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS transposed, RHS non-transposed
+ *
+ * @note the "batch" here expresses the number of matrix multiplications to run in parallel. However, it
+ *       should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension
+ * @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=uchar)
+ * @note The block's dimensions used for the LHS and RHS matrices (M0, N0 and K0) must be passed at compile time using -DN0, -DM0 and -DK0 (e.g. -DN0=8, -DM0=4, -DK0=4).
+ * @note The number of leftover outputs rows/columns must be passed using -DPARTIAL_STORE_N0 and -DPARTIAL_STORE_M0 (e.g. -DPARTIAL_STORE_N0=2, -DPARTIAL_STORE_M0=3)
+ * @note The dimension K must be passed at compile time using -DK (e.g. -DK=6)
+ * @note The kernel name in uppercase must be passed at compile time (e.g. -DMAT_MUL_NATIVE_QUANTIZED_T_NT)
+ * @note Only the following configurations of M0, N0 and K0 are currently supported:
+ *  - M0 > 0
+ *  - N0 = 1, 2, 3, 4, 8, 16
+ *  - K0 = 1, 2, 3, 4, 8, 16
+ * @note Values > 8 for M0, N0 and K0 are not expected to be efficient
+ *
+ * @param[in]  lhs_ptr                           Pointer to the lhs matrix. Supported data types: QASYMM8/QASYMM8_SIGNED
+ * @param[in]  lhs_stride_y                      Stride of the lhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in]  lhs_stride_z                      Stride of the lhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in]  lhs_w                             The width of the lhs tensor
+ * @param[in]  lhs_h                             The height of the lhs tensor
+ * @param[in]  lhs_n                             Number of the matrices (buffers) in the batch
+ * @param[in]  lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
+ * @param[in]  rhs_ptr                           Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
+ * @param[in]  rhs_stride_y                      Stride of the rhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in]  rhs_stride_z                      Stride of the rhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in]  rhs_w                             The width of the rhs tensor
+ * @param[in]  rhs_h                             The height of the rhs tensor
+ * @param[in]  rhs_n                             Number of the matrices (buffers) in the batch
+ * @param[in]  rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
+ * @param[out] dst_ptr                           Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
+ * @param[in]  dst_stride_y                      Stride of the dst matrix in Y (2nd) dimension (in bytes)
+ * @param[in]  dst_stride_z                      Stride of the dst tensor in Z (3rd) dimension (in bytes)
+ * @param[in]  dst_w                             The width of the dst tensor
+ * @param[in]  dst_h                             The height of the dst tensor
+ * @param[in]  dst_n                             Number of the matrices (buffers) in the batch
+ * @param[in]  dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
+ */
+__kernel void mat_mul_native_quantized_t_nt(
+    TENSOR3D_T(lhs, BUFFER),
+    TENSOR3D_T(rhs, BUFFER),
+    TENSOR3D_T(dst, BUFFER))
+{
+    const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0);
+    const uint y = GET_SPATIAL_IDX(1, M0, PARTIAL_STORE_M0);
+    const uint z = GET_SPATIAL_IDX(2, 1, 0);
+
+    // Compute LHS/RHS/DST matrix address
+    lhs_offset_first_element_in_bytes += y * sizeof(DATA_TYPE) + z * lhs_stride_z;
+    rhs_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + z * rhs_stride_z;
+    dst_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + y * dst_stride_y + z * dst_stride_z;
+
+    // Initialize the accumulators
+    TILE(int, M0, N0, acc);
+    LOOP_UNROLLING(int, i, 0, 1, M0,
+    {
+        acc[i].v = K * ((int)LHS_OFFSET) * ((int)RHS_OFFSET);
+    })
+
+    TILE(int, 1, N0, b_sum);
+    b_sum[0].v = 0;
+
+    TILE(int, 1, M0, a_sum);
+    a_sum[0].v = 0;
+
+    int k;
+    for(k = 0; k <= K - K0; k += K0)
+    {
+        TILE(DATA_TYPE, M0, K0, a);
+        TILE(DATA_TYPE, N0, K0, b);
+
+        LOOP_UNROLLING(int, i, 0, 1, M0,
+        {
+            a[i].v = 0;
+        })
+
+        LOOP_UNROLLING(int, i, 0, 1, N0,
+        {
+            b[i].v = 0;
+        })
+
+        // Load tile from the lhs/rhs tensors in a transposed fashion
+        // see mat_mul_native_quantized_nt_nt main loop for more explanation
+        T_LOAD_TRANSPOSED(DATA_TYPE, K0, M0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a);
+        T_LOAD_TRANSPOSED(DATA_TYPE, K0, N0, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b);
+
+        T_MMUL(DATA_TYPE, DATA_TYPE, int, M0, N0, K0, NT, T, a, b, acc);
+
+        LOOP_UNROLLING(int, i, 0, 1, K0,
+        {
+            LOOP_UNROLLING(int, j, 0, 1, M0,
+            {
+                a_sum[0].s[j] += (int)a[j].s[i];
+            })
+        })
+
+        LOOP_UNROLLING(int, i, 0, 1, K0,
+        {
+            LOOP_UNROLLING(int, j, 0, 1, N0,
+            {
+                b_sum[0].s[j] += (int)b[j].s[i];
+            })
+        })
+
+        lhs_offset_first_element_in_bytes += K0 * lhs_stride_y;
+        rhs_offset_first_element_in_bytes += K0 * rhs_stride_y;
+    }
+
+#if((K % K0) != 0)
+    /* Leftover Loop */
+    for(; k < K; ++k)
+    {
+        TILE(DATA_TYPE, M0, 1, a);
+        TILE(DATA_TYPE, N0, 1, b);
+
+        LOOP_UNROLLING(int, i, 0, 1, M0,
+        {
+            a[i].v = 0;
+        })
+
+        LOOP_UNROLLING(int, i, 0, 1, N0,
+        {
+            b[i].v = 0;
+        })
+
+        // Load tile from the lhs/rhs tensors in a transposed fashion
+        // see mat_mul_native_quantized_nt_nt main loop for more explanation
+        T_LOAD_TRANSPOSED(DATA_TYPE, 1, M0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a);
+        T_LOAD_TRANSPOSED(DATA_TYPE, 1, N0, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b);
+
+        T_MMUL(DATA_TYPE, DATA_TYPE, int, M0, N0, 1, NT, T, a, b, acc);
+
+        LOOP_UNROLLING(int, i, 0, 1, 1,
+        {
+            LOOP_UNROLLING(int, j, 0, 1, M0,
+            {
+                a_sum[0].s[j] += (int)a[j].s[i];
+            })
+        })
+
+        LOOP_UNROLLING(int, i, 0, 1, 1,
+        {
+            LOOP_UNROLLING(int, j, 0, 1, N0,
+            {
+                b_sum[0].s[j] += (int)b[j].s[i];
+            })
+        })
+
+        lhs_offset_first_element_in_bytes += 1 * lhs_stride_y;
+        rhs_offset_first_element_in_bytes += 1 * rhs_stride_y;
+    }
+#endif // ((K % K0) != 0)
+
+    LOOP_UNROLLING(int, i, 0, 1, M0,
+    {
+        LOOP_UNROLLING(int, j, 0, 1, N0,
+        {
+            acc[i].s[j] += ((int)(RHS_OFFSET)) * a_sum[0].s[i] + ((int)(LHS_OFFSET)) * b_sum[0].s[j];
+        })
+    })
+
+    const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0;
+    const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0;
+
+    // Quantize the tile
+    TILE(DATA_TYPE, M0, N0, accq);
+    T_QUANTIZE8_ASYMMETRIC(int, DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, acc, accq);
+
+    TILE(int, M0, 1, indirect_buffer);
+    LOOP_UNROLLING(int, _i, 0, 1, M0,
+    {
+        indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond));
+    });
+
+    T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, accq, indirect_buffer);
+}
+#endif // defined(MAT_MUL_NATIVE_QUANTIZED_T_NT)
diff --git a/src/core/CL/cl_kernels/tile_helpers.h b/src/core/CL/cl_kernels/tile_helpers.h
index 872f4c0b57..c9b5370dea 100644
--- a/src/core/CL/cl_kernels/tile_helpers.h
+++ b/src/core/CL/cl_kernels/tile_helpers.h
@@ -536,6 +536,100 @@
         })                                                                                                             \
     })
 
+/** Store a VECTOR variable (e.g. int4, int8, char2 etc.) to a specified column in the TILE object
+ *
+ * @param[in]      VECTOR Vector variable to store
+ * @param[in, out] TILE   Tile variable to store to
+ * @param[in]      WIDTH  Width of the vector variable, also height of the tile (e.g. 2 if char2)
+ * @param[in]      COLUMN Column index of the tile
+ */
+#define COPY_VECTOR_TO_TILE_COLUMN(VECTOR, TILE, WIDTH, COLUMN) COPY_VECTOR_TO_TILE_COLUMN_STR(VECTOR, TILE, WIDTH, COLUMN)
+#define COPY_VECTOR_TO_TILE_COLUMN_STR(VECTOR, TILE, WIDTH, COLUMN) COPY_##WIDTH##_VECTOR_TO_TILE_COLUMN(VECTOR, TILE, COLUMN)
+#define COPY_1_VECTOR_TO_TILE_COLUMN(VECTOR, TILE, COLUMN) \
+    ({                                                      \
+        TILE[0].s[COLUMN] = VECTOR;                         \
+    })
+
+#define COPY_2_VECTOR_TO_TILE_COLUMN(VECTOR, TILE, COLUMN) \
+    ({                                                      \
+        TILE[0].s[COLUMN] = VECTOR.s0;                      \
+        TILE[1].s[COLUMN] = VECTOR.s1;                      \
+    })
+
+#define COPY_3_VECTOR_TO_TILE_COLUMN(VECTOR, TILE, COLUMN) \
+    ({                                                      \
+        TILE[0].s[COLUMN] = VECTOR.s0;                      \
+        TILE[1].s[COLUMN] = VECTOR.s1;                      \
+        TILE[2].s[COLUMN] = VECTOR.s2;                      \
+    })
+
+#define COPY_4_VECTOR_TO_TILE_COLUMN(VECTOR, TILE, COLUMN) \
+    ({                                                      \
+        TILE[0].s[COLUMN] = VECTOR.s0;                      \
+        TILE[1].s[COLUMN] = VECTOR.s1;                      \
+        TILE[2].s[COLUMN] = VECTOR.s2;                      \
+        TILE[3].s[COLUMN] = VECTOR.s3;                      \
+    })
+
+#define COPY_8_VECTOR_TO_TILE_COLUMN(VECTOR, TILE, COLUMN) \
+    ({                                                      \
+        TILE[0].s[COLUMN] = VECTOR.s0;                      \
+        TILE[1].s[COLUMN] = VECTOR.s1;                      \
+        TILE[2].s[COLUMN] = VECTOR.s2;                      \
+        TILE[3].s[COLUMN] = VECTOR.s3;                      \
+        TILE[4].s[COLUMN] = VECTOR.s4;                      \
+        TILE[5].s[COLUMN] = VECTOR.s5;                      \
+        TILE[6].s[COLUMN] = VECTOR.s6;                      \
+        TILE[7].s[COLUMN] = VECTOR.s7;                      \
+    })
+
+#define COPY_16_VECTOR_TO_TILE_COLUMN(VECTOR, TILE, COLUMN) \
+    ({                                                      \
+        TILE[0].s[COLUMN] = VECTOR.s0;                      \
+        TILE[1].s[COLUMN] = VECTOR.s1;                      \
+        TILE[2].s[COLUMN] = VECTOR.s2;                      \
+        TILE[3].s[COLUMN] = VECTOR.s3;                      \
+        TILE[4].s[COLUMN] = VECTOR.s4;                      \
+        TILE[5].s[COLUMN] = VECTOR.s5;                      \
+        TILE[6].s[COLUMN] = VECTOR.s6;                      \
+        TILE[7].s[COLUMN] = VECTOR.s7;                      \
+        TILE[8].s[COLUMN] = VECTOR.s8;                      \
+        TILE[9].s[COLUMN] = VECTOR.s9;                      \
+        TILE[10].s[COLUMN] = VECTOR.sA;                     \
+        TILE[11].s[COLUMN] = VECTOR.sB;                     \
+        TILE[12].s[COLUMN] = VECTOR.sC;                     \
+        TILE[13].s[COLUMN] = VECTOR.sD;                     \
+        TILE[14].s[COLUMN] = VECTOR.sE;                     \
+        TILE[15].s[COLUMN] = VECTOR.sF;                     \
+    })
+
+/** Load SRC_HEIGHT x SRC_WIDTH elements from global memory (tensor), and store them in a SRC_WIDTH x SRC_HEIGHT tile
+ *
+ * @param[in]  DATA_TYPE     Data type
+ * @param[in]  SRC_HEIGHT    Number of source rows, or number of columns of the output tile
+ * @param[in]  SRC_WIDTH     Number of source columns, or number of tile rows
+ * @param[in]  TENSOR_TYPE   Type of cl_type used to store the tensor in global memory (BUFFER=cl_buffer, IMAGE=cl_image).
+ *                           In case of cl_image, only WIDTH multiples of 4 are supported (4, 8, 16)
+ * @param[in]  TENSOR        Tensor basename
+ * @param[in]  X             Starting X position
+ * @param[in]  Y             Starting Y position
+ * @param[in]  YI_MULTIPLIER Parameter used to multiply the internal row increment (_i).
+ *                           In common cases should be 1 but it becomes useful when we want to load rows which are multiple of STRIDE_Y.
+ *                           (e.g. loading the weights of convolution layer).
+ *                           In this case the address calculation is performed as: (Y + _i * Y_MULTIPLIER) * STRIDE_Y
+ * @param[in]  STRIDE_Y      Stride Y (in bytes) used to load each row.
+ * @param[out] dst           Output tile
+ */
+#define T_LOAD_TRANSPOSED(DATA_TYPE, SRC_HEIGHT, SRC_WIDTH, TENSOR_TYPE, TENSOR, X, Y, YI_MULTIPLIER, STRIDE_Y, dst)     \
+    ({                                                                                                                   \
+        LOOP_UNROLLING(int, _i, 0, 1, SRC_HEIGHT,                                                                        \
+        {                                                                                                                \
+            VEC_DATA_TYPE(DATA_TYPE, SRC_WIDTH)                                                                          \
+                tmp = V_LOAD(DATA_TYPE, SRC_WIDTH, TENSOR_TYPE, TENSOR, X, ((Y) + _i * (int)(YI_MULTIPLIER)), STRIDE_Y); \
+            COPY_VECTOR_TO_TILE_COLUMN(tmp, dst, SRC_WIDTH, _i);                                                         \
+        })                                                                                                               \
+    })
+
 /** Load a tile from global memory (tensor) using an indirect Y index tile
  *
  * @param[in]  DATA_TYPE   Data type
@@ -1259,6 +1353,11 @@
  * @param[in]      lhs           LHS tile
  * @param[in]      rhs           RHS tile
  * @param[in, out] dst           DST tile
+ *
+ * @note For Int8/UInt8 multiplications, we only have T_MMUL_NT_T because we need
+ *       the multiply the rows of Lhs and Rhs tensors to utilize dot product extension.
+ *       Addition of other versions requires dealing with on the fly transposition of
+ *       these tile elements and therefore is not favored.
  */
 #define T_MMUL(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, LHS_LAYOUT, RHS_LAYOUT, lhs, rhs, dst) T_MMUL_##LHS_LAYOUT##_##RHS_LAYOUT(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst)
 #define T_MMUL_NT_T(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_T_##LHS_DATA_TYPE##_##RHS_DATA_TYPE##_##DST_DATA_TYPE(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst)