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Diffstat (limited to 'src/core/CL/cl_kernels/common/mat_mul_quantized.cl')
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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..7f81ac4549 --- /dev/null +++ b/src/core/CL/cl_kernels/common/mat_mul_quantized.cl @@ -0,0 +1,833 @@ +/* + * 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 "activation_float_helpers.h" +#include "helpers.h" +#include "tile_helpers.h" + +#ifdef BIAS +// This function performs in-place bias addition for integer datatype when bias is enabled. +// Note The tile's dimensions used for the LHS and RHS matrices (M0, N0) must be passed at compile time using -DN0, -DM0 (e.g. -DN0=8, -DM0=4). +inline void perform_bias_addition(uchar *bias_ptr, uint bias_offset_first_element_in_bytes, TILE(int, M0, N0, acc), uint x) +{ + TILE(int, 1, N0, bias_tile); + + // below expands to use bias_ptr and bias_offset_first_element_in_bytes + T_LOAD(int, 1, N0, BUFFER, bias, x, 0, 1, 0, bias_tile); + + // c = c + bias[broadcasted] + T_ELTWISE_BROADCAST_ADD_X(int, M0, N0, acc, bias_tile, acc); +} +#endif // defined(BIAS) + +#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 fused activation function used should be passed with -DACTIVATION_TYPE, -DA_VAL and -DB_VAL are used for min and max output with the relu and bounded relu operations. + * @note The value of 0 in quantized format is equivalent to the quantization offset of the output data. This should be passed with -DZERO_POINT + * @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[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr + * @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes) + * @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes) + * @param[in] bias_w (Optional) The size of the width dimension of the bias tensor + * @param[in] bias_h (Optional) The size of the height dimension of the bias tensor + * @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor + * @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor + * @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), +#ifdef BIAS + TENSOR3D_T(bias, BUFFER), +#endif // defined(BIAS) + 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; + +#ifdef BIAS + perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, acc, x); +#endif // defined(BIAS) + + // 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); + + T_ACTIVATION_QUANTIZED(DATA_TYPE, M0, N0, ACTIVATION_TYPE, ZERO_POINT, A_VAL, B_VAL, accq, 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_NT_T) +/** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS non-transposed, RHS 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 fused activation function used should be passed with -DACTIVATION_TYPE, -DA_VAL and -DB_VAL are used for min and max output bounded activation functions. + * @note The value of 0 in quantized format is equivalent to the quantization offset of the output data. This should be passed with -DZERO_POINT + * @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_T) + * @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, 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[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr + * @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes) + * @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes) + * @param[in] bias_w (Optional) The size of the width dimension of the bias tensor + * @param[in] bias_h (Optional) The size of the height dimension of the bias tensor + * @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor + * @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor + * @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_t( + TENSOR3D_T(lhs, BUFFER), + TENSOR3D_T(rhs, BUFFER), +#ifdef BIAS + TENSOR3D_T(bias, BUFFER), +#endif // defined(BIAS) + 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 * rhs_stride_y + 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, M0, a_sum); + a_sum[0].v = 0; + + TILE(int, 1, N0, b_sum); + b_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 lhs/rhs tensors + T_LOAD(DATA_TYPE, M0, K0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); + T_LOAD(DATA_TYPE, N0, K0, 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, N0, + { + LOOP_UNROLLING(int, j, 0, 1, K0, + { + b_sum[0].s[i] += (int)b[i].s[j]; + }) + }) + + lhs_offset_first_element_in_bytes += K0 * sizeof(DATA_TYPE); + rhs_offset_first_element_in_bytes += K0 * sizeof(DATA_TYPE); + } + +#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 lhs/rhs tensors + T_LOAD(DATA_TYPE, M0, 1, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); + T_LOAD(DATA_TYPE, N0, 1, 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, N0, + { + LOOP_UNROLLING(int, j, 0, 1, 1, + { + b_sum[0].s[i] += (int)b[i].s[j]; + }) + }) + + lhs_offset_first_element_in_bytes += 1 * sizeof(DATA_TYPE); + rhs_offset_first_element_in_bytes += 1 * sizeof(DATA_TYPE); + } +#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; + +#ifdef BIAS + perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, acc, x); +#endif // defined(BIAS) + + // 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); + + T_ACTIVATION_QUANTIZED(DATA_TYPE, M0, N0, ACTIVATION_TYPE, ZERO_POINT, A_VAL, B_VAL, accq, 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_T) + +#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 fused activation function used should be passed with -DACTIVATION_TYPE, -DA_VAL and -DB_VAL are used for min and max output with the relu and bounded relu operations. + * @note The value of 0 in quantized format is equivalent to the quantization offset of the output data. This should be passed with -DZERO_POINT + * @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[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr + * @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes) + * @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes) + * @param[in] bias_w (Optional) The size of the width dimension of the bias tensor + * @param[in] bias_h (Optional) The size of the height dimension of the bias tensor + * @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor + * @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor + * @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), +#ifdef BIAS + TENSOR3D_T(bias, BUFFER), +#endif // defined(BIAS) + 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; + +#ifdef BIAS + perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, acc, x); +#endif // defined(BIAS) + + // 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); + + T_ACTIVATION_QUANTIZED(DATA_TYPE, M0, N0, ACTIVATION_TYPE, ZERO_POINT, A_VAL, B_VAL, accq, 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) + +#if defined(MAT_MUL_NATIVE_QUANTIZED_T_T) +/** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS transposed, RHS 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 fused activation function used should be passed with -DACTIVATION_TYPE, -DA_VAL and -DB_VAL are used for min and max output with the relu and bounded relu operations. + * @note The value of 0 in quantized format is equivalent to the quantization offset of the output data. This should be passed with -DZERO_POINT + * @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_T) + * @note Only the following configurations of M0, N0 and K0 are currently supported: + * - M0 = 1, 2, 3, 4, 8, 16 + * - 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[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr + * @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes) + * @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes) + * @param[in] bias_w (Optional) The size of the width dimension of the bias tensor + * @param[in] bias_h (Optional) The size of the height dimension of the bias tensor + * @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor + * @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor + * @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_t( + TENSOR3D_T(lhs, BUFFER), + TENSOR3D_T(rhs, BUFFER), +#ifdef BIAS + TENSOR3D_T(bias, BUFFER), +#endif // defined(BIAS) + 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 * rhs_stride_y + 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, M0, a_sum); + a_sum[0].v = 0; + + TILE(int, 1, N0, b_sum); + b_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 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); + + // Load tile from the rhs tensor + T_LOAD(DATA_TYPE, N0, K0, 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, N0, + { + LOOP_UNROLLING(int, j, 0, 1, K0, + { + b_sum[0].s[i] += (int)b[i].s[j]; + }) + }) + + lhs_offset_first_element_in_bytes += K0 * lhs_stride_y; + rhs_offset_first_element_in_bytes += K0 * sizeof(DATA_TYPE); + } + +#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 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); + + // Load tile from the rhs tensor + T_LOAD(DATA_TYPE, N0, 1, 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, N0, + { + LOOP_UNROLLING(int, j, 0, 1, 1, + { + b_sum[0].s[i] += (int)b[i].s[j]; + }) + }) + + lhs_offset_first_element_in_bytes += 1 * lhs_stride_y; + rhs_offset_first_element_in_bytes += 1 * sizeof(DATA_TYPE); + } +#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; + +#ifdef BIAS + perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, acc, x); +#endif // defined(BIAS) + + // 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); + + T_ACTIVATION_QUANTIZED(DATA_TYPE, M0, N0, ACTIVATION_TYPE, ZERO_POINT, A_VAL, B_VAL, accq, 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_T) |