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-rw-r--r--src/core/CL/cl_kernels/common/mat_mul_quantized.cl387
-rw-r--r--src/core/CL/cl_kernels/tile_helpers.h99
-rw-r--r--src/gpu/cl/ClKernelLibrary.cpp6
-rw-r--r--src/gpu/cl/kernels/ClMatMulLowpNativeKernel.cpp224
-rw-r--r--src/gpu/cl/kernels/ClMatMulLowpNativeKernel.h69
5 files changed, 785 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)
diff --git a/src/gpu/cl/ClKernelLibrary.cpp b/src/gpu/cl/ClKernelLibrary.cpp
index 44b086f2fc..e657687887 100644
--- a/src/gpu/cl/ClKernelLibrary.cpp
+++ b/src/gpu/cl/ClKernelLibrary.cpp
@@ -323,6 +323,8 @@ const std::map<std::string, std::string> ClKernelLibrary::_kernel_program_map =
{ "mat_mul_native_nt_t", "common/mat_mul.cl" },
{ "mat_mul_native_t_nt", "common/mat_mul.cl" },
{ "mat_mul_native_t_t", "common/mat_mul.cl" },
+ { "mat_mul_native_quantized_nt_nt", "common/mat_mul_quantized.cl" },
+ { "mat_mul_native_quantized_t_nt", "common/mat_mul_quantized.cl" },
{ "max_unpooling_layer_2", "common/unpooling_layer.cl" },
{ "mean_stddev_normalization", "common/mean_stddev_normalization.cl" },
{ "memset", "common/memset.cl" },
@@ -794,6 +796,10 @@ const std::map<std::string, std::string> ClKernelLibrary::_program_source_map =
"common/mat_mul.cl",
#include "./cl_kernels/common/mat_mul.clembed"
},
+ {
+ "common/mat_mul_quantized.cl",
+#include "./cl_kernels/common/mat_mul_quantized.clembed"
+ },
#ifdef ENABLE_NCHW_KERNELS
{
"nchw/batch_to_space.cl",
diff --git a/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.cpp b/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.cpp
new file mode 100644
index 0000000000..d5ecdf7dd2
--- /dev/null
+++ b/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.cpp
@@ -0,0 +1,224 @@
+/*
+ * 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 "src/gpu/cl/kernels/ClMatMulLowpNativeKernel.h"
+
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/ITensorPack.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+
+#include "src/common/utils/Log.h"
+#include "src/core/helpers/AutoConfiguration.h"
+#include "src/core/helpers/WindowHelpers.h"
+#include "src/gpu/cl/ClCompileContext.h"
+
+#include "support/Cast.h"
+#include "support/StringSupport.h"
+
+namespace arm_compute
+{
+namespace opencl
+{
+namespace kernels
+{
+namespace
+{
+Status validate_matmul_kernel_info(const MatMulKernelInfo &matmul_kernel_info)
+{
+ const bool adj_lhs = matmul_kernel_info.adj_lhs;
+ const bool adj_rhs = matmul_kernel_info.adj_rhs;
+ const int m0 = matmul_kernel_info.m0;
+ const int n0 = matmul_kernel_info.n0;
+ const int k0 = matmul_kernel_info.k0;
+
+ // Validate M0
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(m0 < 1, "Only positive integers are supported for M0");
+
+ if(adj_lhs)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(((m0 & (m0 - 1)) && (m0 != 3)) || (m0 > 16), "Only 1,2,3,4,8,16 are supported for M0 for Lhs transposed");
+ }
+
+ // Validate N0
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(n0 < 1, "Only positive integers are supported for N0");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(((n0 & (n0 - 1)) && (n0 != 3)) || (n0 > 16), "Only 1,2,3,4,8,16 are supported for N0");
+
+ // Validate K0
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(k0 < 1, "Only positive integers are supported for K0");
+ if(!adj_lhs || adj_rhs)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(((k0 & (k0 - 1)) && (k0 != 3)) || (k0 > 16), "Only 1,2,3,4,8,16 are supported for K0");
+ }
+
+ return Status{};
+}
+
+Status validate_input_shapes(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const MatMulKernelInfo &matmul_kernel_info)
+{
+ const size_t lhs_k = matmul_kernel_info.adj_lhs ? lhs_shape.y() : lhs_shape.x();
+ const size_t rhs_k = matmul_kernel_info.adj_rhs ? rhs_shape.x() : rhs_shape.y();
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs_k != rhs_k, "K dimension in Lhs and Rhs matrices must match.");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs_shape.total_size() == 0, "Lhs tensor can't be empty");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(rhs_shape.total_size() == 0, "Rhs tensor can't be empty");
+
+ constexpr size_t batch_dim_start = 2;
+ for(size_t i = batch_dim_start; i < Coordinates::num_max_dimensions; ++i)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs_shape[i] != rhs_shape[i], "Batch dimension broadcasting is not supported");
+ }
+
+ return Status{};
+}
+}
+ClMatMulLowpNativeKernel::ClMatMulLowpNativeKernel()
+{
+ _type = CLKernelType::GEMM;
+}
+Status ClMatMulLowpNativeKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *output, const MatMulKernelInfo &matmul_kernel_info)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lhs, rhs, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs);
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_matmul_kernel_info(matmul_kernel_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_input_shapes(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info));
+
+ if(output->total_size() != 0)
+ {
+ const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info));
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, output);
+ }
+
+ return Status{};
+}
+void ClMatMulLowpNativeKernel::configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *output, const MatMulKernelInfo &matmul_kernel_info)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, output, &compile_context, &matmul_kernel_info);
+ ARM_COMPUTE_LOG_PARAMS(lhs, rhs, output, matmul_kernel_info);
+ ARM_COMPUTE_ERROR_THROW_ON(validate(lhs, rhs, output, matmul_kernel_info));
+
+ // output tensor auto initialization if not yet initialized
+ auto_init_if_empty(*output, lhs->clone()->set_tensor_shape(misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info)));
+
+ const int m = output->dimension(1);
+ const int n = output->dimension(0);
+ const int k = matmul_kernel_info.adj_lhs ? lhs->tensor_shape().y() : lhs->tensor_shape().x();
+ const bool adj_lhs = matmul_kernel_info.adj_lhs;
+
+ int m0 = adj_lhs ? adjust_vec_size(matmul_kernel_info.m0, m) : std::min(matmul_kernel_info.m0, m);
+ int n0 = adjust_vec_size(matmul_kernel_info.n0, n);
+
+ // Configure kernel window
+ Window win = calculate_max_window(*output, Steps(n0, m0));
+ win = win.collapse(win, Window::DimZ);
+ IClKernel::configure_internal(win);
+
+ // Calculate partial (store instead of load) M0 and partial N0 for the partial blocks at the end of a row/column if any. This is to avoid padding.
+ const unsigned int partial_store_m0 = m % m0;
+ const unsigned int partial_store_n0 = n % n0;
+
+ CLBuildOptions build_opts;
+ build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(lhs->data_type()));
+ build_opts.add_option("-DM0=" + support::cpp11::to_string(m0));
+ build_opts.add_option("-DN0=" + support::cpp11::to_string(n0));
+ build_opts.add_option("-DK0=" + support::cpp11::to_string(matmul_kernel_info.k0));
+ build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
+ build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
+ build_opts.add_option("-DK=" + support::cpp11::to_string(k));
+
+ const UniformQuantizationInfo lqinfo = lhs->quantization_info().uniform();
+ const UniformQuantizationInfo rqinfo = rhs->quantization_info().uniform();
+ const UniformQuantizationInfo dqinfo = output->quantization_info().uniform();
+
+ float multiplier = lqinfo.scale * rqinfo.scale / dqinfo.scale;
+ int output_multiplier = 0;
+ int output_shift = 0;
+ arm_compute::quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift);
+
+ build_opts.add_option("-DDST_MULTIPLIER=" + support::cpp11::to_string(output_multiplier));
+ build_opts.add_option("-DDST_SHIFT=" + support::cpp11::to_string(output_shift));
+
+ build_opts.add_option("-DLHS_OFFSET=" + support::cpp11::to_string(-lqinfo.offset)); // Note this is passed as negative to maintain similarity with CLDirectConv2D
+ build_opts.add_option("-DRHS_OFFSET=" + support::cpp11::to_string(-rqinfo.offset)); // Note this is passed as negative to maintain similarity with CLDirectConv2D
+ build_opts.add_option("-DDST_OFFSET=" + support::cpp11::to_string(dqinfo.offset)); // Passed as positive (unlike the above two)
+
+ std::string kernel_name("mat_mul_native_quantized");
+ kernel_name += matmul_kernel_info.adj_lhs ? "_t" : "_nt";
+ kernel_name += matmul_kernel_info.adj_rhs ? "_t" : "_nt";
+
+ // A macro guard to compile ONLY the kernel of interest
+ build_opts.add_option("-D" + upper_string(kernel_name));
+
+ // Create kernel
+ _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
+
+ // Set config_id for enabling LWS tuning
+ const size_t number_of_batches = output->tensor_shape().total_size() / (m * n);
+
+ _config_id = kernel_name;
+ _config_id += "_";
+ _config_id += lower_string(string_from_data_type(lhs->data_type()));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(m);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(n);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(k);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(number_of_batches);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(m0);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(n0);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(matmul_kernel_info.k0);
+}
+
+void ClMatMulLowpNativeKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
+{
+ ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+ ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
+
+ const ICLTensor *lhs = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
+ const ICLTensor *rhs = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
+ ICLTensor *output = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
+ ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, output);
+ ARM_COMPUTE_LOG_PARAMS(lhs, rhs, output);
+
+ unsigned int idx = 0;
+ Window window_collapsed = window.collapse(ICLKernel::window(), Window::DimZ);
+
+ add_3d_tensor_nhw_argument(idx, lhs);
+ add_3d_tensor_nhw_argument(idx, rhs);
+ add_3d_tensor_nhw_argument(idx, output);
+
+ enqueue(queue, *this, window_collapsed, lws_hint());
+}
+
+} // namespace kernels
+} // namespace opencl
+} // namespace arm_compute
diff --git a/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.h b/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.h
new file mode 100644
index 0000000000..13a33fbd62
--- /dev/null
+++ b/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.h
@@ -0,0 +1,69 @@
+/*
+ * 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.
+ */
+#ifndef ACL_SRC_GPU_CL_KERNELS_CLMATMULLOWPNATIVEKERNEL
+#define ACL_SRC_GPU_CL_KERNELS_CLMATMULLOWPNATIVEKERNEL
+
+#include "src/core/common/Macros.h"
+#include "src/gpu/cl/ClCompileContext.h"
+#include "src/gpu/cl/IClKernel.h"
+
+namespace arm_compute
+{
+// Forward declerations
+struct MatMulKernelInfo;
+namespace opencl
+{
+namespace kernels
+{
+class ClMatMulLowpNativeKernel : public IClKernel
+{
+public:
+ ClMatMulLowpNativeKernel();
+ ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(ClMatMulLowpNativeKernel);
+ /** Initialise the kernel's input and output.
+ *
+ * @param[in] compile_context The compile context to be used.
+ * @param[in] lhs Input tensor for the LHS matrix. Data type supported: QASYMM8_SIGNED/QASYMM8.
+ * Dimensions above 2 are collapsed onto dimension 2 and represent the batch.
+ * @param[in] rhs Input tensor for the RHS matrix. Data type supported: same as @p lhs.
+ * Dimensions above 2 are collapsed onto dimension 2 and represent the batch.
+ * @param[out] output Output tensor info. Data type supported: same as @p lhs
+ * @param[in] matmul_info Attributes for Batch MatMul Kernel
+ */
+ void configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *output, const MatMulKernelInfo &matmul_info);
+ /** Static function to check if given info will lead to a valid configuration
+ *
+ * Similar to @ref ClMatMulLowpNativeKernel::configure()
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *output, const MatMulKernelInfo &matmul_info);
+
+ // Inherited methods overridden:
+ void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override;
+};
+} // namespace kernels
+} // namespace opencl
+} // namespace arm_compute
+#endif /* ACL_SRC_GPU_CL_KERNELS_CLMATMULLOWPNATIVEKERNEL */
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-13 17:13:46 +0000