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/*
* Copyright (c) 2022-2023 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"
#if defined(GEMM_MM_RESHAPED_ONLY_RHS_NT_MMUL)
/** This OpenCL kernel computes the matrix multiplication between 2 matrices using the MMUL extension:
*
* The LHS matrix is NOT reshaped
* The RHS is reshaped with @ref ClGemmMatrixMultiplyReshapedOnlyRhsKernel and the block K0xN0 is NOT transposed
*
* @note The block's dimensions used for reshaping the RHS matrix (N0 and K0) must be passed at compile time using -DN0 and -DK0 (e.g. -DN0=8, -DK0=4).
* @note The number of M0 rows to process must be passed at compile time using -DM0 (e.g. -DM0=2)
* @note The number of output columns processed by the the cooperative mmul extension must be passed at compile time using -DMMUL_N0 (e.g., -DMMUL_N0=2)
* @note The number of output rows processed by the the cooperative mmul extension must be passed at compile time using -DMMUL_M0 (e.g., -DMMUL_M0=2)
* @note The number of lhs columns (or rhs rows) processed by the the cooperative mmul extension must be passed at compile time using -DMMUL_K0 (e.g., -DMMUL_K0=2)
* @note Only the following configurations of M0, N0 and K0 are currently supported:
* - M0 > 0
* - N0 = 1, 2, 3, 4, 8, 16
* - K0 = 1
*
* @note If the activation type were passed at compile time through -DACTIVATION_TYPE (e.g. -DACTIVATION_TYPE=RELU), A, B variables, required by some activation functions, should be passed at compile time as well using -DA_VAL= and -DB_VAL= respectively.
* The activation function is performed after the bias addition
*
* @param[in] lhs_ptr Pointer to the LHS tensor. Supported data types: F16/F32
* @param[in] lhs_stride_y Stride of the LHS tensor in Y dimension (in bytes)
* @param[in] lhs_stride_z Stride of the LHS tensor in Z dimension (in bytes)
* @param[in] lhs_w The size of the width dimension of the LHS tensor
* @param[in] lhs_h The size of the height dimension of the LHS tensor
* @param[in] lhs_n The size of the depth dimension of the LHS tensor
* @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the LHS tensor
* @param[in] rhs_ptr Pointer to the RHS reshaped tensor. Supported data type: same as @p lhs_ptr
* @param[in] rhs_stride_y Stride of the RHS tensor in Y dimension (in bytes)
* @param[in] rhs_stride_z Stride of the RHS tensor in Z dimension (in bytes)
* @param[in] rhs_w The size of the width dimension of the RHS tensor
* @param[in] rhs_h The size of the height dimension of the RHS tensor
* @param[in] rhs_n The size of the depth dimension of the RHS tensor
* @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the RHS tensor
* @param[in] bia_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr
* @param[in] bia_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes)
* @param[in] bia_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes)
* @param[in] bia_w (Optional) The size of the width dimension of the bias tensor
* @param[in] bia_h (Optional) The size of the height dimension of the bias tensor
* @param[in] bia_n (Optional) The size of the depth dimension of the bias tensor
* @param[in] bia_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
* @param[out] dst_ptr Pointer to the destination tensor. Supported data type: same as @p lhs_ptr
* @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
* @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
* @param[in] dst_w The size of the width dimension of the destination tensor
* @param[in] dst_h The size of the height dimension of the destination tensor
* @param[in] dst_n The size of the depth dimension of the destination tensor
* @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
* @param[in] M Number of rows in LHS matrix not reshaped
* @param[in] N Number of columns in RHS matrix not reshaped
* @param[in] K Number of columns in LHS matrix and rows in RHS matrix not reshaped
*/
__kernel void gemm_mm_reshaped_only_rhs_nt_mmul(
TENSOR3D_T(lhs, BUFFER),
TENSOR3D_T(rhs, BUFFER),
#if defined(BETA)
TENSOR3D_T(bia, BUFFER),
#endif // defined(BETA)
TENSOR3D_T(dst, BUFFER),
const int M,
const int N,
const int K)
{
#define MMUL_BLOCK_SIZE (MMUL_N0 * MMUL_K0)
uint x0 = get_global_id(0); // (N / N0) * MMUL_K0
uint y0 = get_global_id(1); // (M / M0) / MMUL_M0
uint z = get_global_id(2); // Batch
// Get block ID and thread ID within the block
uint block_id = (x0 / MMUL_BLOCK_SIZE);
uint thread_id = (x0 % MMUL_BLOCK_SIZE);
// Coordinate within a block
uint block_x = thread_id % MMUL_N0;
uint block_y = (thread_id / MMUL_M0);
// Starting destination coordinates
uint dst_x = min(block_x * N0 + block_id * MMUL_N0 * N0, (uint)(N - 1));
uint dst_y = min(block_y * M0 + y0 * M0 * MMUL_M0, (uint)(M - M0));
// Note: We need to clamp dst_x and dst_y because we always need to execute a complete MMUL block! Only after the matrix multiplication
// part can we exit the kernel if it is out-of-bound. Remember, we have a cooperative matrix multiplication. Therefore, we need a full block to get the correct results
// Starting LHS coordinates
uint lhs_x = block_x;
uint lhs_y = dst_y;
// Starting RHS coordinates
uint rhs_x = block_y * N0 * MMUL_N0 + block_x * N0;
uint rhs_y = block_id;
// Compute LHS/RHS/DST matrix address
#ifdef REINTERPRET_INPUT_AS_3D
lhs_offset_first_element_in_bytes += lhs_x * sizeof(DATA_TYPE) + (lhs_y + z * M) * lhs_stride_y;
#else // REINTERPRET_INPUT_AS_3D
lhs_offset_first_element_in_bytes += lhs_x * sizeof(DATA_TYPE) + lhs_y * lhs_stride_y + z * lhs_stride_z;
#endif // REINTERPRET_INPUT_AS_3D
#ifdef BATCHED_RHS
rhs_offset_first_element_in_bytes += rhs_x * sizeof(DATA_TYPE) + rhs_y * rhs_stride_y + z * rhs_stride_z;
#else // BATCHED_RHS
rhs_offset_first_element_in_bytes += rhs_x * sizeof(DATA_TYPE) + rhs_y * rhs_stride_y;
#endif // BATCHED_RHS
#ifdef REINTERPRET_OUTPUT_AS_3D
dst_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + (dst_y + z * M) * dst_stride_y;
#else // REINTERPRET_OUTPUT_AS_3D
dst_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + dst_y * dst_stride_y + z * dst_stride_z;
#endif // REINTERPRET_OUTPUT_AS_3D
// Note: If RHS derives from the weights of convolution 2d layer, RHS will always be 2D and rhs_stride_z will always be equal to 0 for
// not sliding the tensor
// Initialize the accumulators
// MMUL extension accumulate the result in F32 for both F32 and F16
TILE(float, M0, N0, c_f32);
#if !defined(HALF_PRECISION)
#define c c_f32
#endif // !defined(HALF_PRECISION)
LOOP_UNROLLING(int, i, 0, 1, M0,
{
c_f32[i].v = 0;
})
for(int k = 0; k <= K - MMUL_K0; k += MMUL_K0)
{
TILE(DATA_TYPE, M0, 1, a);
TILE(DATA_TYPE, 1, N0, b);
// Load tile from the lhs/rhs tensors
T_LOAD(DATA_TYPE, M0, 1, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a);
T_LOAD(DATA_TYPE, 1, N0, BUFFER, rhs, 0, 0, 1, 0, b);
LOOP_UNROLLING(int, m0, 0, 1, M0,
{
LOOP_UNROLLING(int, n0, 0, 1, N0,
{
c_f32[m0].s[n0] = arm_matrix_multiply(a[m0].s[0], b[0].s[n0], c_f32[m0].s[n0]);
})
})
lhs_offset_first_element_in_bytes += MMUL_K0 * sizeof(DATA_TYPE);
rhs_offset_first_element_in_bytes += MMUL_K0 * MMUL_N0 * N0 * sizeof(DATA_TYPE);
}
if(block_x * N0 + block_id * MMUL_N0 * N0 >= N)
{
return;
}
if(block_y * M0 + y0 * M0 * MMUL_M0 >= M)
{
return;
}
#if defined(HALF_PRECISION)
TILE(DATA_TYPE, M0, N0, c);
// Conversion required for the half precision
LOOP_UNROLLING(int, m0, 0, 1, M0,
{
LOOP_UNROLLING(int, n0, 0, 1, N0,
{
c[m0].s[n0] = c_f32[m0].s[n0];
})
})
#endif // defined(HALF_PRECISION)
// Multiply by the weight of matrix-matrix product and store the result
#if defined(ALPHA)
T_SCALE_CONSTANT(DATA_TYPE, M0, N0, c, (DATA_TYPE)ALPHA, c);
#endif // defined(ALPHA)
// Add beta*bias
#if defined(BETA)
#if defined(BROADCAST_BIAS)
bia_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE);
TILE(DATA_TYPE, 1, N0, bias0);
if(dst_x + N0 <= N || N0_LEFTOVER == 0)
{
bias0[0].v = VLOAD(N0)(0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes));
}
else
{
VLOAD_PARTIAL(N0, N0_LEFTOVER)
(bias0[0].v, 0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes));
}
#ifndef UNIT_BETA
T_SCALE_CONSTANT(DATA_TYPE, 1, N0, bias0, (DATA_TYPE)BETA, bias0);
#endif // UNIT_BIAS
// c = c + bias[broadcasted]
T_ELTWISE_BROADCAST_X(V_ADD, DATA_TYPE, M0, N0, c, bias0, c);
#else // defined(BROADCAST_BIAS)
TILE(DATA_TYPE, M0, N0, bias0);
bia_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + dst_y * bia_stride_y + z * bia_stride_z;
if(dst_x + N0 <= N || N0_LEFTOVER == 0)
{
LOOP_UNROLLING(int, m0, 0, 1, M0,
{
if(dst_y + m0 < M || M0_LEFTOVER == 0)
{
bias0[m0].v = VLOAD(N0)(0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes + m0 * bia_stride_y));
}
})
}
else
{
LOOP_UNROLLING(int, m0, 0, 1, M0,
{
if(dst_y + m0 < M || M0_LEFTOVER == 0)
{
VLOAD_PARTIAL(N0, N0_LEFTOVER)
(bias0[m0].v, 0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes + m0 * bia_stride_y));
}
})
}
#ifndef UNIT_BETA
T_SCALE_CONSTANT(DATA_TYPE, M0, N0, bias0, (DATA_TYPE)BETA, bias0);
#endif // UNIT_BIAS
// c = c + bias
T_ADD(DATA_TYPE, M0, N0, c, bias0, c);
// c = c + bias
#endif // defined(BROADCAST_BIAS)
#endif // defined(BETA)
T_ACTIVATION(DATA_TYPE, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, c, c);
// Store
if(dst_x + N0 <= N || N0_LEFTOVER == 0)
{
LOOP_UNROLLING(int, m0, 0, 1, M0,
{
if(dst_y + m0 < M || M0_LEFTOVER == 0)
{
VSTORE(N0)
(c[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y));
}
})
}
else
{
LOOP_UNROLLING(int, m0, 0, 1, M0,
{
if(dst_y + m0 < M || M0_LEFTOVER == 0)
{
VSTORE_PARTIAL(N0, N0_LEFTOVER)
(c[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y));
}
})
}
#undef RHS_BLOCK_SIZE
#undef RHS_OFFSET_X
#undef RHS_STEP_X
}
#endif // defined(GEMM_MM_RESHAPED_ONLY_RHS_MMUL)
#if defined(GEMM_MM_RESHAPED_ONLY_RHS_NT_MMUL_TEXTURE)
/** This OpenCL kernel computes the matrix multiplication between 2 matrices using the MMUL extension and the OpenCL image for RHS:
*
* The LHS matrix is NOT reshaped
* The RHS is reshaped with @ref ClGemmMatrixMultiplyReshapedOnlyRhsKernel and the block K0xN0 is NOT transposed
*
* @note The block's dimensions used for reshaping the RHS matrix (N0 and K0) must be passed at compile time using -DN0 and -DK0 (e.g. -DN0=8, -DK0=4).
* @note The number of M0 rows to process must be passed at compile time using -DM0 (e.g. -DM0=2)
* @note The number of output columns processed by the the cooperative mmul extension must be passed at compile time using -DMMUL_N0 (e.g., -DMMUL_N0=2)
* @note The number of output rows processed by the the cooperative mmul extension must be passed at compile time using -DMMUL_M0 (e.g., -DMMUL_M0=2)
* @note The number of lhs columns (or rhs rows) processed by the the cooperative mmul extension must be passed at compile time using -DMMUL_K0 (e.g., -DMMUL_K0=2)
* @note Only the following configurations of M0, N0 and K0 are currently supported:
* - M0 > 0
* - N0 = 1, 2, 3, 4, 8, 16
* - K0 = 1
*
* @note If the activation type were passed at compile time through -DACTIVATION_TYPE (e.g. -DACTIVATION_TYPE=RELU), A, B variables, required by some activation functions, should be passed at compile time as well using -DA_VAL= and -DB_VAL= respectively.
* The activation function is performed after the bias addition
*
* @param[in] lhs_ptr Pointer to the LHS tensor. Supported data types: F16/F32
* @param[in] lhs_stride_y Stride of the LHS tensor in Y dimension (in bytes)
* @param[in] lhs_stride_z Stride of the LHS tensor in Z dimension (in bytes)
* @param[in] lhs_w The size of the width dimension of the LHS tensor
* @param[in] lhs_h The size of the height dimension of the LHS tensor
* @param[in] lhs_n The size of the depth dimension of the LHS tensor
* @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the LHS tensor
* @param[in] rhs_ptr Pointer to the RHS reshaped tensor. Supported data type: same as @p lhs_ptr
* @param[in] rhs_stride_y Stride of the RHS tensor in Y dimension (in bytes)
* @param[in] rhs_stride_z Stride of the RHS tensor in Z dimension (in bytes)
* @param[in] rhs_w The size of the width dimension of the RHS tensor
* @param[in] rhs_h The size of the height dimension of the RHS tensor
* @param[in] rhs_n The size of the depth dimension of the RHS tensor
* @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the RHS tensor
* @param[in] bia_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr
* @param[in] bia_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes)
* @param[in] bia_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes)
* @param[in] bia_w (Optional) The size of the width dimension of the bias tensor
* @param[in] bia_h (Optional) The size of the height dimension of the bias tensor
* @param[in] bia_n (Optional) The size of the depth dimension of the bias tensor
* @param[in] bia_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
* @param[out] dst_ptr Pointer to the destination tensor. Supported data type: same as @p lhs_ptr
* @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes)
* @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
* @param[in] dst_w The size of the width dimension of the destination tensor
* @param[in] dst_h The size of the height dimension of the destination tensor
* @param[in] dst_n The size of the depth dimension of the destination tensor
* @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
* @param[in] M Number of rows in LHS matrix not reshaped
* @param[in] N Number of columns in RHS matrix not reshaped
* @param[in] K Number of columns in LHS matrix and rows in RHS matrix not reshaped
*/
__kernel void gemm_mm_reshaped_only_rhs_nt_mmul_texture(
TENSOR3D_T(lhs, BUFFER),
TENSOR3D_T(rhs, IMAGE),
#if defined(BETA)
TENSOR3D_T(bia, BUFFER),
#endif // defined(BETA)
TENSOR3D_T(dst, BUFFER),
const int M,
const int N,
const int K)
{
#define MMUL_BLOCK_SIZE (MMUL_N0 * MMUL_K0)
uint x0 = get_global_id(0); // (N / N0) * MMUL_K0
uint y0 = get_global_id(1); // (M / M0) / MMUL_M0
uint z = get_global_id(2); // Batch
// Get block ID and thread ID within the block
uint block_id = (x0 / MMUL_BLOCK_SIZE);
uint thread_id = (x0 % MMUL_BLOCK_SIZE);
// Coordinate within a block
uint block_x = thread_id % MMUL_N0;
uint block_y = (thread_id / MMUL_M0);
// Starting destination coordinates
uint dst_x = min(block_x * N0 + block_id * MMUL_N0 * N0, (uint)(N - 1));
uint dst_y = min(block_y * M0 + y0 * M0 * MMUL_M0, (uint)(M - M0));
// Note: We need to clamp dst_x and dst_y because we always need to execute a complete MMUL block! Only after the matrix multiplication
// part can we exit the kernel if it is out-of-bound. Remember, we have a cooperative matrix multiplication. Therefore, we need a full block to get the correct results
// Starting LHS coordinates
uint lhs_x = block_x;
uint lhs_y = dst_y;
// Starting RHS coordinates
uint rhs_x = block_y * N0 * MMUL_N0 + block_x * N0;
#ifdef BATCHED_RHS
uint rhs_y = block_id + z * rhs_h;
#else // BATCHED_RHS
uint rhs_y = block_id;
#endif // BATCHED_RHS
// Compute LHS/RHS/DST matrix address
#ifdef REINTERPRET_INPUT_AS_3D
lhs_offset_first_element_in_bytes += lhs_x * sizeof(DATA_TYPE) + (lhs_y + z * M) * lhs_stride_y;
#else // REINTERPRET_INPUT_AS_3D
lhs_offset_first_element_in_bytes += lhs_x * sizeof(DATA_TYPE) + lhs_y * lhs_stride_y + z * lhs_stride_z;
#endif // REINTERPRET_INPUT_AS_3D
#ifdef REINTERPRET_OUTPUT_AS_3D
dst_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + (dst_y + z * M) * dst_stride_y;
#else // REINTERPRET_OUTPUT_AS_3D
dst_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + dst_y * dst_stride_y + z * dst_stride_z;
#endif // REINTERPRET_OUTPUT_AS_3D
// Initialize the accumulators
// MMUL extension accumulate the result in F32 for both F32 and F16
TILE(float, M0, N0, c_f32);
#if !defined(HALF_PRECISION)
#define c c_f32
#endif // !defined(HALF_PRECISION)
LOOP_UNROLLING(int, i, 0, 1, M0,
{
c_f32[i].v = 0;
})
for(int k = 0; k <= K - MMUL_K0; k += MMUL_K0)
{
TILE(DATA_TYPE, M0, 1, a);
TILE(DATA_TYPE, 1, N0, b);
// Load tile from the lhs/rhs tensors
T_LOAD(DATA_TYPE, M0, 1, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a);
T_LOAD(DATA_TYPE, 1, N0, IMAGE, rhs, rhs_x, rhs_y, 1, rhs_stride_y, b);
LOOP_UNROLLING(int, m0, 0, 1, M0,
{
LOOP_UNROLLING(int, n0, 0, 1, N0,
{
c_f32[m0].s[n0] = arm_matrix_multiply(a[m0].s[0], b[0].s[n0], c_f32[m0].s[n0]);
})
})
lhs_offset_first_element_in_bytes += MMUL_K0 * sizeof(DATA_TYPE);
rhs_x += MMUL_K0 * MMUL_N0 * N0;
}
if(block_x * N0 + block_id * MMUL_N0 * N0 >= N)
{
return;
}
if(block_y * M0 + y0 * M0 * MMUL_M0 >= M)
{
return;
}
#if defined(HALF_PRECISION)
TILE(DATA_TYPE, M0, N0, c);
// Conversion required for the half precision
LOOP_UNROLLING(int, m0, 0, 1, M0,
{
LOOP_UNROLLING(int, n0, 0, 1, N0,
{
c[m0].s[n0] = c_f32[m0].s[n0];
})
})
#endif // defined(HALF_PRECISION)
// Multiply by the weight of matrix-matrix product and store the result
#if defined(ALPHA)
T_SCALE_CONSTANT(DATA_TYPE, M0, N0, c, (DATA_TYPE)ALPHA, c);
#endif // defined(ALPHA)
// Add beta*bias
#if defined(BETA)
#if defined(BROADCAST_BIAS)
bia_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE);
TILE(DATA_TYPE, 1, N0, bias0);
if(dst_x + N0 <= N || N0_LEFTOVER == 0)
{
bias0[0].v = VLOAD(N0)(0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes));
}
else
{
VLOAD_PARTIAL(N0, N0_LEFTOVER)
(bias0[0].v, 0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes));
}
#ifndef UNIT_BETA
T_SCALE_CONSTANT(DATA_TYPE, 1, N0, bias0, (DATA_TYPE)BETA, bias0);
#endif // UNIT_BIAS
// c = c + bias[broadcasted]
T_ELTWISE_BROADCAST_X(V_ADD, DATA_TYPE, M0, N0, c, bias0, c);
#else // defined(BROADCAST_BIAS)
TILE(DATA_TYPE, M0, N0, bias0);
bia_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + dst_y * bia_stride_y + z * bia_stride_z;
if(dst_x + N0 <= N || N0_LEFTOVER == 0)
{
LOOP_UNROLLING(int, m0, 0, 1, M0,
{
if(dst_y + m0 < M || M0_LEFTOVER == 0)
{
bias0[m0].v = VLOAD(N0)(0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes + m0 * bia_stride_y));
}
})
}
else
{
LOOP_UNROLLING(int, m0, 0, 1, M0,
{
if(dst_y + m0 < M || M0_LEFTOVER == 0)
{
VLOAD_PARTIAL(N0, N0_LEFTOVER)
(bias0[m0].v, 0, (DATA_TYPE *)(bia_ptr + bia_offset_first_element_in_bytes + m0 * bia_stride_y));
}
})
}
#ifndef UNIT_BETA
T_SCALE_CONSTANT(DATA_TYPE, M0, N0, bias0, (DATA_TYPE)BETA, bias0);
#endif // UNIT_BIAS
// c = c + bias
T_ADD(DATA_TYPE, M0, N0, c, bias0, c);
// c = c + bias
#endif // defined(BROADCAST_BIAS)
#endif // defined(BETA)
T_ACTIVATION(DATA_TYPE, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, c, c);
// Store
if(dst_x + N0 <= N || N0_LEFTOVER == 0)
{
LOOP_UNROLLING(int, m0, 0, 1, M0,
{
if(dst_y + m0 < M || M0_LEFTOVER == 0)
{
VSTORE(N0)
(c[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y));
}
})
}
else
{
LOOP_UNROLLING(int, m0, 0, 1, M0,
{
if(dst_y + m0 < M || M0_LEFTOVER == 0)
{
VSTORE_PARTIAL(N0, N0_LEFTOVER)
(c[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y));
}
})
}
#undef RHS_BLOCK_SIZE
#undef RHS_OFFSET_X
#undef RHS_STEP_X
}
#endif // defined(GEMM_MM_RESHAPED_ONLY_RHS_MMUL_TEXTURE)
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