COMPMID-2847: Fuse output stage in GEMMLowpMatrixMultiplyReshapedOnlyRHS
Change-Id: Icd60eb368a34295434e8c141885b4666973a92a1
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/2732
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index 5b59094..5d67240 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2016-2019 ARM Limited.
+ * Copyright (c) 2016-2020 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -341,6 +341,7 @@
{ "gemmlowp_mm_native", "gemmlowp.cl" },
{ "gemmlowp_mm_reshaped_lhs_nt_rhs_t", "gemmlowp.cl" },
{ "gemmlowp_mm_reshaped_only_rhs_t", "gemmlowp.cl" },
+ { "gemmlowp_mm_reshaped_only_rhs_t_fused_output_stage_fixedpoint", "gemmlowp.cl" },
{ "gemmlowp_offset_contribution", "gemmlowp.cl" },
{ "gemmlowp_offset_contribution_quantize_down", "gemmlowp.cl" },
{ "gemmlowp_offset_contribution_quantize_down_fixedpoint", "gemmlowp.cl" },
diff --git a/src/core/CL/cl_kernels/gemm_helpers.h b/src/core/CL/cl_kernels/gemm_helpers.h
index 66e83c3..79a9f09 100644
--- a/src/core/CL/cl_kernels/gemm_helpers.h
+++ b/src/core/CL/cl_kernels/gemm_helpers.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2019 ARM Limited.
+ * Copyright (c) 2019-2020 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -140,6 +140,118 @@
#define LOAD_BLOCK(M0, N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y, Z) LOAD_BLOCK_STR(M0, N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y, Z)
/** @} */ // end of group LOAD_BLOCK
+/** Loads the elements from 0 to n-1 in the given variables (BASENAME0 to BASENAMEn-1).
+ * @name LOAD_ELEMENT_n
+ *
+ * @param[in] N0 The number of rows to load
+ * @param[in] DATA_TYPE The data type of variables
+ * @param[in] BASENAME The basename of the destination variables for the loaded rows
+ * @param[in] PTR The base pointer
+ * @param[in] OFFSET The offset within a row
+ * @param[in] STRIDE_Y The stride value in y-axis direction
+ * @{
+ */
+#define LOAD_ELEMENT_1(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ VEC_DATA_TYPE(DATA_TYPE, N0) \
+ BASENAME##0 = *((__global DATA_TYPE *)(PTR + OFFSET + 0 * STRIDE_Y));
+
+#define LOAD_ELEMENT_2(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ LOAD_ELEMENT_1(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ VEC_DATA_TYPE(DATA_TYPE, N0) \
+ BASENAME##1 = *((__global DATA_TYPE *)(PTR + OFFSET + 1 * STRIDE_Y));
+
+#define LOAD_ELEMENT_3(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ LOAD_ELEMENT_2(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ VEC_DATA_TYPE(DATA_TYPE, N0) \
+ BASENAME##2 = *((__global DATA_TYPE *)(PTR + OFFSET + 2 * STRIDE_Y));
+
+#define LOAD_ELEMENT_4(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ LOAD_ELEMENT_3(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ VEC_DATA_TYPE(DATA_TYPE, N0) \
+ BASENAME##3 = *((__global DATA_TYPE *)(PTR + OFFSET + 3 * STRIDE_Y));
+
+#define LOAD_ELEMENT_5(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ LOAD_ELEMENT_4(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ VEC_DATA_TYPE(DATA_TYPE, N0) \
+ BASENAME##4 = *((__global DATA_TYPE *)(PTR + OFFSET + 4 * STRIDE_Y));
+
+#define LOAD_ELEMENT_6(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ LOAD_ELEMENT_5(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ VEC_DATA_TYPE(DATA_TYPE, N0) \
+ BASENAME##5 = *((__global DATA_TYPE *)(PTR + OFFSET + 5 * STRIDE_Y));
+
+#define LOAD_ELEMENT_7(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ LOAD_ELEMENT_6(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ VEC_DATA_TYPE(DATA_TYPE, N0) \
+ BASENAME##6 = *((__global DATA_TYPE *)(PTR + OFFSET + 6 * STRIDE_Y));
+
+#define LOAD_ELEMENT_8(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ LOAD_ELEMENT_7(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ VEC_DATA_TYPE(DATA_TYPE, N0) \
+ BASENAME##7 = *((__global DATA_TYPE *)(PTR + OFFSET + 7 * STRIDE_Y));
+
+#define LOAD_ELEMENT_9(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ LOAD_ELEMENT_8(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ VEC_DATA_TYPE(DATA_TYPE, N0) \
+ BASENAME##8 = *((__global DATA_TYPE *)(PTR + OFFSET + 8 * STRIDE_Y));
+
+#define LOAD_ELEMENT_10(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ LOAD_ELEMENT_9(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ VEC_DATA_TYPE(DATA_TYPE, N0) \
+ BASENAME##9 = *((__global DATA_TYPE *)(PTR + OFFSET + 9 * STRIDE_Y));
+
+#define LOAD_ELEMENT_11(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ LOAD_ELEMENT_10(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ VEC_DATA_TYPE(DATA_TYPE, N0) \
+ BASENAME##A = *((__global DATA_TYPE *)(PTR + OFFSET + 10 * STRIDE_Y));
+
+#define LOAD_ELEMENT_12(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ LOAD_ELEMENT_11(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ VEC_DATA_TYPE(DATA_TYPE, N0) \
+ BASENAME##B = *((__global DATA_TYPE *)(PTR + OFFSET + 11 * STRIDE_Y));
+
+#define LOAD_ELEMENT_13(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ LOAD_ELEMENT_12(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ VEC_DATA_TYPE(DATA_TYPE, N0) \
+ BASENAME##C = *((__global DATA_TYPE *)(PTR + OFFSET + 12 * STRIDE_Y));
+
+#define LOAD_ELEMENT_14(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ LOAD_ELEMENT_13(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ VEC_DATA_TYPE(DATA_TYPE, N0) \
+ BASENAME##D = *((__global DATA_TYPE *)(PTR + OFFSET + 13 * STRIDE_Y));
+
+#define LOAD_ELEMENT_15(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ LOAD_ELEMENT_14(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ VEC_DATA_TYPE(DATA_TYPE, N0) \
+ BASENAME##E = *((__global DATA_TYPE *)(PTR + OFFSET + 14 * STRIDE_Y));
+
+#define LOAD_ELEMENT_16(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ LOAD_ELEMENT_15(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) \
+ VEC_DATA_TYPE(DATA_TYPE, N0) \
+ BASENAME##F = *((__global DATA_TYPE *)(PTR + OFFSET + 15 * STRIDE_Y));
+
+/** @}*/ // end of group LOAD_ELEMENT_n
+
+/** Load Scalar as Vector (consecutive elements).
+ * @name LOAD_SCALAR_AS_VECTOR
+ *
+ * Supported cases are M0=1,2,3,...,16 and N0=1,2,3,4,8,16
+ * The data to load is expected to have consecutive names for each row.
+ * E.g., for M0=3, and BASENAME=c, the expected data is c0, c1 and c2.
+ *
+ * @param[in] M0 The number of consecutive rows
+ * @param[in] N0 The number of consecutive columns
+ * @param[in] DATA_TYPE The data type of the target
+ * @param[in] BASENAME The basename of the result variables
+ * @param[in] PTR The base pointer for the data
+ * @param[in] OFFSET The offset within a row
+ * @param[in] STRIDE_Y The stride in y-axis direction
+ * @{
+ */
+#define LOAD_SCALAR_AS_VECTOR_STR(M0, N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) LOAD_ELEMENT_##M0(N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y)
+#define LOAD_SCALAR_AS_VECTOR(M0, N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y) LOAD_SCALAR_AS_VECTOR_STR(M0, N0, DATA_TYPE, BASENAME, PTR, OFFSET, STRIDE_Y)
+/** @} */ // end of group LOAD_SCALAR_AS_VECTOR
+
/** Basic macros to calculate Z offset values from Z0 to Zn-1
* @name CALCULATE_Z_OFFSET_n
*
diff --git a/src/core/CL/cl_kernels/gemmlowp.cl b/src/core/CL/cl_kernels/gemmlowp.cl
index 8140b8f..a2a47d7 100644
--- a/src/core/CL/cl_kernels/gemmlowp.cl
+++ b/src/core/CL/cl_kernels/gemmlowp.cl
@@ -814,6 +814,301 @@
#undef RHS_OFFSET_X
#undef RHS_STEP_X
}
+
+#if defined(RESULT_OFFSET) && defined(RESULT_SHIFT) && defined(RESULT_MULTIPLIER)
+/** This OpenCL kernel computes the matrix multiplication between 2 matrices with fused output stage using fixed-point arithmetic.
+ * The LHS matrix is NOT reshaped
+ * The RHS matrix is reshaped with @ref CLGEMMReshapeRHSMatrixKernel and the block K0xN0 is transposed
+ *
+ * @note The input data type must be passed at compile time using -DDATA_TYPE (i.e. -DDATA_TYPE=uchar)
+ * @note The accumulator data type must be passed at compile time using -DACC_DATA_TYPE (i.e. -DACC_DATA_TYPE=uint)
+ * @note The number of columns of LHS matrix must be passed at compile time using -DK (i.e. -DK=64)
+ * @note The block's dimensions used for reshaping the RHS matrix (N0 and K0) must be passed at compile time using -DN0 and -DK0 (i.e. -DN0=8, -DK0=4).
+ * @note The number of M0 rows to process must be passed at compile time using -DM0 (i.e. -DM0=2)
+ * @note The number of K0xN0 horizontal blocks stored on the same output row of the reshaped RHS matrix must be passed at compile time using -DH0 (i.e. -DH0=2)
+ * @note If the K0xN0 blocks in the reshaped RHS matrix have been interleaved, the option -DRHS_INTERLEAVE must passed at compile time.
+ * @note Only the following configurations of M0, N0 and K0 are currently supported:
+ * - M0 = 1, 2, 3, 4, 5, 6, 7, 8
+ * - N0 = 2, 3, 4, 8, 16
+ * - K0 = 2, 3, 4, 8, 16
+ * - H0 >= 1
+ *
+ * @note In case the input or output have to be reinterpreted as a 3D tensor, the following information must be passed at compile time:
+ * -# REINTERPRET_INPUT_AS_3D: To reinterpret the input as 3D
+ * -# REINTERPRET_OUTPUT_AS_3D: To reinterpret the output as 3D
+ * -# HEIGHT_GEMM3D: The height of the output in case it has to be reinterpreted as a 3D tensor.
+ * -# DEPTH_GEMM3D: The depth of the output in case it has to be reinterpreted as a 3D tensor
+ * (HEIGHT_GEMM3D * DEPTH_GEMM3D) = columns LHS matrix
+ *
+ * @note The offset, scalar scale factor and number of bits to shift right of output tensor must be passed at compile time using -DRESULT_OFFSET, -RESULT_MULTIPLIER and -DRESULT_SHIFT
+ * @note In case the addition of int32 biases is required, -DADD_BIAS should be passed at compile time
+ * @note The output datatype should be passed at compile time using -DOUTPUT_DATA_TYPE
+ * @note In case the clamping of the result is required, the min and max bounds can be passed at compile time using -DMIN_BOUND and -DMAX_BOUND.
+ * These values can be used to implement "rectified linear unit" activation functions
+ * @note In case of per-channel quantization of matrix B, -DPER_CHANNEL_QUANTIZATION must be passed at compile time.
+ *
+ * @param[in] lhs_ptr Pointer to the LHS reshaped matrix. Supported data type: QASYMM8/QASYMM8_SIGNED
+ * @param[in] lhs_stride_x Stride of the LHS reshaped matrix in X dimension (in bytes)
+ * @param[in] lhs_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] lhs_stride_y Stride of the LHS reshaped matrix in Y dimension (in bytes)
+ * @param[in] lhs_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the LHS reshaped matrix
+ * @param[in] rhs_ptr Pointer to the RHS reshaped matrix. Supported data type: same as @p lhs_ptr
+ * @param[in] rhs_stride_x Stride of the RHS reshaped matrix in X dimension (in bytes)
+ * @param[in] rhs_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] rhs_stride_y Stride of the RHS reshaped matrix in Y dimension (in bytes)
+ * @param[in] rhs_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the RHS reshaped matrix
+ * @param[out] dst_ptr Pointer to the destination matrix Supported data type: same as @p lhs_ptr
+ * @param[in] dst_stride_x Stride of the destination matrix in X dimension (in bytes)
+ * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y Stride of the destination matrix in Y dimension (in bytes)
+ * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination matrix
+ * @param[in] lhs_stride_z Stride of the LHS reshaped matrix in Z dimension (in bytes)
+ * @param[in] rhs_stride_z Stride of the RHS reshaped matrix in Z dimension (in bytes)
+ * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in] lhs_cross_plane_pad (Optional) Bottom paddings for LHS matrix in unit of elements (only if defined REINTERPRET_INPUT_AS_3D)
+ * @param[in] dst_cross_plane_pad (Optional) Bottom paddings for the output matrix in unit of elements (only if defined REINTERPRET_OUTPUT_AS_3D)
+ * @param[in] sum_col_ptr (Optional) Pointer to the source tensor. Supported data type: S32
+ * @param[in] sum_col_stride_x (Optional) Stride of the source tensor in X dimension (in bytes)
+ * @param[in] sum_col_step_x (Optional) sum_col_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] sum_col_stride_y (Optional) Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] sum_col_step_y (Optional) sum_col_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] sum_col_offset_first_element_in_bytes (Optional) The offset of the first element in the source tensor
+ * @param[in] sum_row_ptr (Optional) Pointer to the source tensor. Supported data type: S32
+ * @param[in] sum_row_stride_x (Optional) Stride of the source tensor in X dimension (in bytes)
+ * @param[in] sum_row_step_x (Optional) sum_row_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] sum_row_stride_y (Optional) Stride of the source tensor in Y dimension (in bytes)
+ * @param[in] sum_row_step_y (Optional) sum_row_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] sum_row_offset_first_element_in_bytes (Optional) The offset of the first element in the source tensor
+ * @param[in] biases_ptr (Optional) Pointer to the biases tensor. Supported data type: S32
+ * @param[in] biases_stride_x (Optional) Stride of the biases tensor in X dimension (in bytes)
+ * @param[in] biases_step_x (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] biases_offset_first_element_in_bytes (Optional) The offset of the first element in the biases tensor
+ * @param[in] result_multipliers_ptr (Optional) Pointer to the output multipliers vector for per-channel quantization. Supported data types: S32
+ * @param[in] result_multipliers_stride_x (Optional) Stride of the output multipliers vector in X dimension (in bytes)
+ * @param[in] result_multipliers_step_x (Optional) output_multipliers_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] result_multipliers_offset_first_element_in_bytes (Optional) The offset of the first element in the output multipliers vector
+ * @param[in] result_shifts_ptr (Optional) Pointer to the output shifts vector for per-channel quantization. Supported data types: S32
+ * @param[in] result_shifts_stride_x (Optional) Stride of the output shifts vector in X dimension (in bytes)
+ * @param[in] result_shifts_step_x (Optional) output_shifts_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] result_shifts_offset_first_element_in_bytes (Optional) The offset of the first element in the output shifts vector
+ */
+__kernel void gemmlowp_mm_reshaped_only_rhs_t_fused_output_stage_fixedpoint(IMAGE_DECLARATION(lhs),
+ IMAGE_DECLARATION(rhs),
+ IMAGE_DECLARATION(dst),
+ uint lhs_stride_z,
+ uint rhs_stride_z,
+ uint dst_stride_z
+#if defined(REINTERPRET_INPUT_AS_3D)
+ ,
+ uint lhs_cross_plane_pad
+#endif // REINTERPRET_INPUT_AS_3D
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+ ,
+ uint dst_cross_plane_pad
+#endif // REINTERPRET_OUTPUT_AS_3D
+#if defined(A_OFFSET)
+ ,
+ IMAGE_DECLARATION(sum_col)
+#endif // defined(A_OFFSET)
+#if defined(B_OFFSET)
+ ,
+ IMAGE_DECLARATION(sum_row)
+#endif // defined(B_OFFSET)
+#if defined(ADD_BIAS)
+ ,
+ VECTOR_DECLARATION(biases)
+#endif // defined(ADD_BIAS)
+#if defined(PER_CHANNEL_QUANTIZATION)
+ ,
+ VECTOR_DECLARATION(result_multipliers),
+ VECTOR_DECLARATION(result_shifts)
+#endif // defined(PER_CHANNEL_QUANTIZATION)
+ )
+{
+ // Block size
+#define RHS_BLOCK_SIZE ((K0) * (N0))
+
+ // RHS offset and step X
+#if defined(RHS_INTERLEAVE)
+#define RHS_OFFSET_X (K0)
+#define RHS_STEP_X ((K0) * (H0))
+#define RHS_STEP_LOOP (1)
+#else // defined(RHS_INTERLEAVE)
+#define RHS_OFFSET_X (RHS_BLOCK_SIZE)
+#define RHS_STEP_X (K0)
+#define RHS_STEP_LOOP (H0)
+#endif // defined(RHS_INTERLEAVE)
+
+ uint x = get_global_id(0);
+ uint y = get_global_id(1);
+ uint z = get_global_id(2);
+
+#if defined(DUMMY_WORK_ITEMS)
+ if((x * N0 >= N) || (y * M0 >= M))
+ {
+ return;
+ }
+#endif // defined(DUMMY_WORK_ITEMS)
+
+ // Compute LHS matrix address
+ uint lhs_offset = lhs_offset_first_element_in_bytes + y * M0 * (uint)lhs_stride_y;
+
+ // Compute RHS matrix address
+ uint rhs_offset = rhs_offset_first_element_in_bytes + (x % H0) * (uint)RHS_OFFSET_X + (x / (uint)H0) * rhs_stride_y;
+
+#if defined(MATRIX_B_DEPTH)
+ // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3
+ rhs_offset += (z % MATRIX_B_DEPTH) * rhs_stride_z;
+#else // defined(MATRIX_B_DEPTH)
+ rhs_offset += z * rhs_stride_z;
+#endif // defined(MATRIX_B_DEPTH)
+
+ REPEAT_VAR_INIT_TO_CONST(8, uint, zlhs, 0); //uint zout0=0,zout1=0,zout2=0,... zout7=0;
+ REPEAT_VAR_INIT_TO_CONST(16, uint, zrhs, 0);
+
+#if defined(REINTERPRET_INPUT_AS_3D)
+ // The plane (zlhs) is calculated dividing M (y * M0) by HEIGHT_GEMM3D
+ CALCULATE_Z_OFFSET(M0, uint, zlhs, y, HEIGHT_GEMM3D, DEPTH_GEMM3D, lhs_cross_plane_pad, lhs_stride_y);
+
+ // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+ // multiply lhs_stride_z by DEPTH_GEMM3D
+ lhs_offset += z * lhs_stride_z * DEPTH_GEMM3D;
+
+#else // defined(REINTERPRET_INPUT_AS_3D)
+
+ // Add offset for batched GEMM
+ lhs_offset += z * lhs_stride_z;
+
+#endif // defined(REINTERPRET_INPUT_AS_3D)
+
+ // Initialize the accumulators
+ REPEAT_VAR_INIT_TO_CONST(M0, VEC_DATA_TYPE(ACC_DATA_TYPE, N0), c, 0); //VEC_DATA_TYPE(ACC_DATA_TYPE, N0) c0=0,c1=0,c2=0,... c(N0-1)=0;
+
+ for(int i = 0; i < K; i += K0)
+ {
+ // Load values from LHS matrix
+ LOAD_BLOCK(M0, K0, DATA_TYPE, a, lhs_ptr, lhs_offset, lhs_stride_y, zlhs);
+
+ // Load values from RHS matrix
+ LOAD_BLOCK(N0, K0, DATA_TYPE, b, rhs_ptr, rhs_offset, RHS_STEP_X, zrhs);
+
+ // Partial matrix multiplication M0,N0,K0
+ ARM_MM_K0XN0XM0(M0, N0, K0, a, b, c);
+
+ lhs_offset += K0;
+ rhs_offset += N0 * RHS_STEP_X * RHS_STEP_LOOP;
+ }
+
+ // Result of MM is of type DATA_TYPE
+ __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + (x * (uint)N0) * sizeof(DATA_TYPE) + (y * (uint)M0 * dst_stride_y);
+
+ REPEAT_VAR_INIT_TO_CONST(8, uint, zout, 0); //uint zout0=0,zout1=0,zout2=0,... zout7=0;
+
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+ // The plane (zout) is calculated dividing M (y * M0) by HEIGHT_GEMM3D
+ CALCULATE_Z_OFFSET(M0, uint, zout, y, HEIGHT_GEMM3D, DEPTH_GEMM3D, dst_cross_plane_pad, dst_stride_y);
+
+ // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+ // multiply dst_stride_z by DEPTH_GEMM3D
+ dst_addr += z * dst_stride_z * DEPTH_GEMM3D;
+
+#else // defined(REINTERPRET_OUTPUT_AS_3D)
+
+ // Add offset for batched GEMM
+ dst_addr += z * dst_stride_z;
+
+#endif // defined(REINTERPRET_OUTPUT_AS_3D)
+
+ // Convert result of matrix multiplication to S32
+ REPEAT_VAR_INIT_CONVERT_SAT(M0, VEC_DATA_TYPE(int, N0), c, c_int);
+
+ int batch_id = z;
+#if defined(DEPTH_GEMM3D)
+ batch_id /= (int)DEPTH_GEMM3D;
+#endif // defined(DEPTH_GEMM3D)
+
+ // Offset contribution: c += (A_OFFSET * sum_col) + (B_OFFSET * sum_row) + K_OFFSET;
+ REPEAT_VAR_INIT_TO_CONST(M0, VEC_DATA_TYPE(int, N0), offset_s32_, K_OFFSET);
+
+#if defined(A_OFFSET)
+ // Compute the offset contribution due to A_OFFSET
+ __global uchar *sum_col_addr = sum_col_ptr + sum_col_offset_first_element_in_bytes + (x * (uint)N0) * sizeof(int);
+
+#if defined(SUM_COL_HAS_BATCHES)
+ sum_col_addr += z * sum_col_stride_y;
+#endif // defined(SUM_COL_HAS_BATCHES)
+ VEC_DATA_TYPE(int, N0)
+ a_offset_s32 = VLOAD(N0)(0, (__global int *)sum_col_addr);
+ a_offset_s32 *= (VEC_DATA_TYPE(int, N0))A_OFFSET;
+
+ REPEAT_ADD_VECTOR_TO_VAR(M0, offset_s32_, a_offset_s32);
+#endif // defined(A_OFFSET)
+
+#if defined(B_OFFSET)
+ // Compute the offset contribution due to B_OFFSET
+ __global uchar *sum_row_addr = sum_row_ptr + sum_row_offset_first_element_in_bytes + (y * (uint)M0) * sizeof(int) + z * sum_row_stride_y;
+
+#if defined(HEIGHT_GEMM3D) && defined(DEPTH_GEMM3D)
+ sum_row_addr += (batch_id % (int)DEPTH_GEMM3D) * (int)HEIGHT_GEMM3D * sizeof(int);
+#endif // defined(HEIGHT_GEMM3D) && defined(DEPTH_GEMM3D)
+ LOAD_SCALAR_AS_VECTOR(M0, N0, int, b_offset_s32_, sum_row_addr, 0, sum_row_stride_x);
+
+ REPEAT_MLA_VAR_WITH_CONST_VEC(M0, offset_s32_, b_offset_s32_, (VEC_DATA_TYPE(int, N0))B_OFFSET);
+#endif // defined(B_OFFSET)
+
+#if defined(ADD_BIAS)
+ // Add bias
+ __global uchar *bias_addr = biases_ptr + biases_offset_first_element_in_bytes + (x * (uint)N0) * sizeof(int);
+
+ VEC_DATA_TYPE(int, N0)
+ bias_values = VLOAD(N0)(0, (__global int *)bias_addr);
+ REPEAT_ADD_VECTOR_TO_VAR(M0, offset_s32_, bias_values);
+#endif // defined(ADD_BIAS)
+
+ REPEAT_ADD_TWO_VARS(M0, c_int, offset_s32_);
+
+ // Multiply by result_mult_int and shift
+#if defined(PER_CHANNEL_QUANTIZATION)
+ __global uchar *result_multipliers_addr = result_multipliers_ptr + result_multipliers_offset_first_element_in_bytes + (x * (uint)N0) * sizeof(int);
+ __global uchar *result_shifts_addr = result_shifts_ptr + result_shifts_offset_first_element_in_bytes + (x * (uint)N0) * sizeof(int);
+
+ VEC_DATA_TYPE(int, N0)
+ res_mul = VLOAD(N0)(0, (__global int *)result_multipliers_addr);
+ VEC_DATA_TYPE(int, N0)
+ res_shift = VLOAD(N0)(0, (__global int *)result_shifts_addr);
+
+ REPEAT_ASYMM_MULT_BY_QUANT_MULTIPLIER_PER_CHANNEL(M0, N0, c_int, res_mul, res_shift);
+#else // defined(PER_CHANNEL_QUANTIZATION)
+
+#if RESULT_SHIFT < 0
+ REPEAT_ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(M0, N0, c_int, RESULT_MULTIPLIER, RESULT_SHIFT);
+#else // RESULT_SHIFT >= 0
+ REPEAT_ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(M0, N0, c_int, RESULT_MULTIPLIER, RESULT_SHIFT);
+#endif // RESULT_SHIFT < 0
+
+#endif // defined(PER_CHANNEL_QUANTIZATION)
+
+ // Add the offset terms to GEMM's result
+ REPEAT_ADD_CONST_TO_VAR(M0, VEC_DATA_TYPE(int, N0), c_int, RESULT_OFFSET);
+
+#if defined(MIN_BOUND)
+ REPEAT_MAX_CONST_VAR(M0, VEC_DATA_TYPE(int, N0), c_int, MIN_BOUND);
+#endif // defined(MIN_BOUND)
+#if defined(MAX_BOUND)
+ REPEAT_MIN_CONST_VAR(M0, VEC_DATA_TYPE(int, N0), c_int, MAX_BOUND);
+#endif // defined(MAX_BOUND)
+
+ // Convert and store output block (does convert saturate)
+ CONVERT_STORE_BLOCK(M0, N0, DATA_TYPE, c_int, dst_addr, dst_stride_y, zout);
+
+#undef RHS_BLOCK_SIZE
+#undef RHS_OFFSET_X
+#undef RHS_STEP_X
+}
+#endif // defined(RESULT_OFFSET) && defined(RESULT_SHIFT) && defined(RESULT_MULTIPLIER)
#endif // defined(M0) && defined(N0) && defined(K0) && defined(H0) && defined(DATA_TYPE) && defined(K)
#if defined(M0) && defined(N0) && defined(K0) && defined(K)
@@ -840,7 +1135,7 @@
* -# DEPTH_GEMM3D: The depth of the output in case it has to be reinterpreted as a 3D tensor
* (HEIGHT_GEMM3D * DEPTH_GEMM3D) = columns LHS matrix
*
- * @param[in] lhs_ptr Pointer to the LHS reshaped matrix. Supported data type: F16/F32
+ * @param[in] lhs_ptr Pointer to the LHS reshaped matrix. Supported data type: QASYMM8
* @param[in] lhs_stride_x Stride of the LHS reshaped matrix in X dimension (in bytes)
* @param[in] lhs_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] lhs_stride_y Stride of the LHS reshaped matrix in Y dimension (in bytes)
@@ -852,7 +1147,7 @@
* @param[in] rhs_stride_y Stride of the RHS reshaped matrix in Y dimension (in bytes)
* @param[in] rhs_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
* @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the RHS reshaped matrix
- * @param[out] dst_ptr Pointer to the destination matrix Supported data type: same as @p lhs_ptr
+ * @param[out] dst_ptr Pointer to the destination matrix Supported data type: S32
* @param[in] dst_stride_x Stride of the destination matrix in X dimension (in bytes)
* @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
* @param[in] dst_stride_y Stride of the destination matrix in Y dimension (in bytes)
@@ -1389,7 +1684,7 @@
vstore4(in_s32, 0, (__global int *)mm_result_addr);
}
-#if defined(RESULT_OFFSET) && defined(RESULT_MULTIPLIER) && defined(RESULT_SHIFT)
+#if defined(RESULT_OFFSET) && defined(RESULT_MULTIPLIER) && defined(RESULT_SHIFT) && defined(OUTPUT_DATA_TYPE)
/* OpenCL kernel used to add the offset contribution after @ref CLGEMMLowpMatrixMultiplyKernel and it quantizes down to uint8.
*
* This kernel takes a final int32 accumulator value (the output of @CLGEMMLowpMatrixMultiplyKernel), adds to it the offset contribution of matrix A and matrix B and quantizes to uint8 through the output stage.
@@ -1742,7 +2037,7 @@
// Store the result
vstore4(res, 0, (__global OUTPUT_DATA_TYPE *)dst_addr);
}
-#endif // defined(RESULT_OFFSET) && defined(RESULT_MULTIPLIER) && defined(RESULT_SHIFT)
+#endif // defined(RESULT_OFFSET) && defined(RESULT_MULTIPLIER) && defined(RESULT_SHIFT) && defined(OUTPUT_DATA_TYPE)
#endif // defined(K_OFFSET)
diff --git a/src/core/CL/cl_kernels/repeat.h b/src/core/CL/cl_kernels/repeat.h
index 691f7ae..4e761db 100644
--- a/src/core/CL/cl_kernels/repeat.h
+++ b/src/core/CL/cl_kernels/repeat.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2019 ARM Limited.
+ * Copyright (c) 2019-2020 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -24,6 +24,8 @@
#ifndef ARM_COMPUTE_REPEAT_H
#define ARM_COMPUTE_REPEAT_H
+#include "helpers.h"
+
/** Macros that help in loop unrolling */
//Repeat macros with 3 param, excluding the implicit ID param
#define REPEAT_3_1(P_X, P_A, P_B, P_C) P_X##_DEF(0, P_A, P_B, P_C)
@@ -76,8 +78,106 @@
#define REPEAT_DEF_3_N(P_NUM, P_OP, P_A, P_B, P_C) REPEAT_3_##P_NUM(P_OP, P_A, P_B, P_C) //One level of indirection to ensure order of expansion does not affect preprocessing P_NUM
#define REPEAT_3_N(P_NUM, P_OP, P_A, P_B, P_C) REPEAT_DEF_3_N(P_NUM, P_OP, P_A, P_B, P_C)
-//Macro for initializing N variables. generates N statements that defines VAR##N = RHS_ACCESSOR_DEF(...)
+// Repeat macros with 4 param, excluding the implicit ID param
+#define REPEAT_4_1(P_X, P_A, P_B, P_C, P_D) P_X##_DEF(0, P_A, P_B, P_C, P_D)
+#define REPEAT_4_2(P_X, P_A, P_B, P_C, P_D) \
+ P_X##_DEF(1, P_A, P_B, P_C, P_D); \
+ REPEAT_4_1(P_X, P_A, P_B, P_C, P_D)
+#define REPEAT_4_3(P_X, P_A, P_B, P_C, P_D) \
+ P_X##_DEF(2, P_A, P_B, P_C, P_D); \
+ REPEAT_4_2(P_X, P_A, P_B, P_C, P_D)
+#define REPEAT_4_4(P_X, P_A, P_B, P_C, P_D) \
+ P_X##_DEF(3, P_A, P_B, P_C, P_D); \
+ REPEAT_4_3(P_X, P_A, P_B, P_C, P_D)
+#define REPEAT_4_5(P_X, P_A, P_B, P_C, P_D) \
+ P_X##_DEF(4, P_A, P_B, P_C, P_D); \
+ REPEAT_4_4(P_X, P_A, P_B, P_C, P_D)
+#define REPEAT_4_6(P_X, P_A, P_B, P_C, P_D) \
+ P_X##_DEF(5, P_A, P_B, P_C, P_D); \
+ REPEAT_4_5(P_X, P_A, P_B, P_C, P_D)
+#define REPEAT_4_7(P_X, P_A, P_B, P_C, P_D) \
+ P_X##_DEF(6, P_A, P_B, P_C, P_D); \
+ REPEAT_4_6(P_X, P_A, P_B, P_C, P_D)
+#define REPEAT_4_8(P_X, P_A, P_B, P_C, P_D) \
+ P_X##_DEF(7, P_A, P_B, P_C, P_D); \
+ REPEAT_4_7(P_X, P_A, P_B, P_C, P_D)
+#define REPEAT_4_9(P_X, P_A, P_B, P_C, P_D) \
+ P_X##_DEF(8, P_A, P_B, P_C, P_D); \
+ REPEAT_4_8(P_X, P_A, P_B, P_C, P_D)
+#define REPEAT_4_10(P_X, P_A, P_B, P_C, P_D) \
+ P_X##_DEF(9, P_A, P_B, P_C, P_D); \
+ REPEAT_4_9(P_X, P_A, P_B, P_C, P_D)
+#define REPEAT_4_11(P_X, P_A, P_B, P_C, P_D) \
+ P_X##_DEF(A, P_A, P_B, P_C, P_D); \
+ REPEAT_4_10(P_X, P_A, P_B, P_C, P_D)
+#define REPEAT_4_12(P_X, P_A, P_B, P_C, P_D) \
+ P_X##_DEF(B, P_A, P_B, P_C, P_D); \
+ REPEAT_4_11(P_X, P_A, P_B, P_C, P_D)
+#define REPEAT_4_13(P_X, P_A, P_B, P_C, P_D) \
+ P_X##_DEF(C, P_A, P_B, P_C, P_D); \
+ REPEAT_4_12(P_X, P_A, P_B, P_C, P_D)
+#define REPEAT_4_14(P_X, P_A, P_B, P_C, P_D) \
+ P_X##_DEF(D, P_A, P_B, P_C, P_D); \
+ REPEAT_4_13(P_X, P_A, P_B, P_C, P_D)
+#define REPEAT_4_15(P_X, P_A, P_B, P_C, P_D) \
+ P_X##_DEF(E, P_A, P_B, P_C, P_D); \
+ REPEAT_4_14(P_X, P_A, P_B, P_C, P_D)
+#define REPEAT_4_16(P_X, P_A, P_B, P_C, P_D) \
+ P_X##_DEF(F, P_A, P_B, P_C, P_D); \
+ REPEAT_4_15(P_X, P_A, P_B, P_C, P_D)
+
+#define REPEAT_DEF_4_N(P_NUM, P_OP, P_A, P_B, P_C, P_D) REPEAT_4_##P_NUM(P_OP, P_A, P_B, P_C, P_D) //One level of indirection to ensure order of expansion does not affect preprocessing P_NUM
+#define REPEAT_4_N(P_NUM, P_OP, P_A, P_B, P_C, P_D) REPEAT_DEF_4_N(P_NUM, P_OP, P_A, P_B, P_C, P_D)
+
+// Macro for initializing N variables. Generates N statements that defines VAR##N = RHS_ACCESSOR_DEF(...)
#define VAR_INIT_TO_CONST_DEF(ID, TYPE, VAR, VAL) TYPE VAR##ID = VAL
#define REPEAT_VAR_INIT_TO_CONST(N, TYPE, VAR, VAL) REPEAT_3_N(N, VAR_INIT_TO_CONST, TYPE, VAR, VAL)
+// Macro for initializing N variables by converting the data type. Generates N statements that defines VAR##N = RHS_ACCESSOR_DEF(...)
+#define VAR_INIT_CONVERT_SAT_DEF(ID, TYPE_OUT, VAR_IN, VAR_OUT) TYPE_OUT VAR_OUT##ID = CONVERT_SAT(VAR_IN##ID, TYPE_OUT)
+#define REPEAT_VAR_INIT_CONVERT_SAT(N, TYPE_OUT, VAR_IN, VAR_OUT) REPEAT_3_N(N, VAR_INIT_CONVERT_SAT, TYPE_OUT, VAR_IN, VAR_OUT)
+
+// Macro for adding a constant to N variables. Generates N statements that defines VAR##N =RHS_ACCESSOR_DEF(...)
+#define ADD_CONST_TO_VAR_DEF(ID, TYPE, VAR, VAL) VAR##ID += (TYPE)VAL
+#define REPEAT_ADD_CONST_TO_VAR(N, TYPE, VAR, VAL) REPEAT_3_N(N, ADD_CONST_TO_VAR, TYPE, VAR, VAL)
+
+// Macro for multiplying N variables (VAR_B) by a constant (VAL) and adding to other N variables (VAR_A). Generates N statements that defines VAR_A##N =RHS_ACCESSOR_DEF(...)
+#define MLA_VAR_WITH_CONST_VEC_DEF(ID, VAR_A, VAR_B, VAL) VAR_A##ID += VAR_B##ID * VAL
+#define REPEAT_MLA_VAR_WITH_CONST_VEC(N, VAR_A, VAR_B, VAL) REPEAT_3_N(N, MLA_VAR_WITH_CONST_VEC, VAR_A, VAR_B, VAL)
+
+// Macro for adding a vector to N-variables. Generates N statements that defines VAR##N =RHS_ACCESSOR_DEF(...)
+#define ADD_VECTOR_TO_VAR_DEF(ID, TYPE, VAR, VEC) VAR##ID += VEC
+#define REPEAT_ADD_VECTOR_TO_VAR(N, VAR, VEC) REPEAT_3_N(N, ADD_VECTOR_TO_VAR, "", VAR, VEC)
+
+// Macro for adding a two N-variables. Generates N statements that defines VAR##N =RHS_ACCESSOR_DEF(...)
+#define ADD_TWO_VARS_DEF(ID, TYPE, VAR_A, VAR_B) VAR_A##ID += VAR_B##ID
+#define REPEAT_ADD_TWO_VARS(N, VAR_A, VAR_B) REPEAT_3_N(N, ADD_TWO_VARS, "", VAR_A, VAR_B)
+
+// Macro for performing Max between a constant and N variables. Generates N statements that defines VAR##N =RHS_ACCESSOR_DEF(...)
+#define MAX_CONST_VAR_DEF(ID, TYPE, VAR, VAL) VAR##ID = max(VAR##ID, (TYPE)VAL)
+#define REPEAT_MAX_CONST_VAR(N, TYPE, VAR, VAL) REPEAT_3_N(N, MAX_CONST_VAR, TYPE, VAR, VAL)
+
+// Macro for performing Min between a constant and N variables. Generates N statements that defines VAR##N =RHS_ACCESSOR_DEF(...)
+#define MIN_CONST_VAR_DEF(ID, TYPE, VAR, VAL) VAR##ID = min(VAR##ID, (TYPE)VAL)
+#define REPEAT_MIN_CONST_VAR(N, TYPE, VAR, VAL) REPEAT_3_N(N, MIN_CONST_VAR, TYPE, VAR, VAL)
+
+// Macro for performing ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE to N variables. Generates N statements that defines VAR##N =RHS_ACCESSOR_DEF(...)
+#define ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE_DEF(ID, SIZE, VAR, RES_MUL, RES_SHIFT) VAR##ID = ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(VAR##ID, RES_MUL, RES_SHIFT, SIZE)
+#define REPEAT_ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(N, SIZE, VAR, RES_MUL, RES_SHIFT) REPEAT_4_N(N, ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE, SIZE, VAR, RES_MUL, RES_SHIFT)
+
+// Macro for performing ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE to N variables. Generates N statements that defines VAR##N =RHS_ACCESSOR_DEF(...)
+#define ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE_DEF(ID, SIZE, VAR, RES_MUL, RES_SHIFT) VAR##ID = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(VAR##ID, RES_MUL, RES_SHIFT, SIZE)
+#define REPEAT_ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(N, SIZE, VAR, RES_MUL, RES_SHIFT) REPEAT_4_N(N, ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE, SIZE, VAR, RES_MUL, RES_SHIFT)
+
+// Macro for performing per-channel ASYMM_MULT_BY_QUANT_MULTIPLIER to N variables.
+#define ASYMM_MULT_BY_QUANT_MULTIPLIER_PER_CHANNEL_DEF(ID, SIZE, VAR, RES_MUL, RES_SHIFT) \
+ ({ \
+ VEC_DATA_TYPE(int, N0) \
+ VAR##ID_shift_lt0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(VAR##ID, RES_MUL, RES_SHIFT, N0); \
+ VEC_DATA_TYPE(int, N0) \
+ VAR##ID_shift_gt0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(VAR##ID, RES_MUL, RES_SHIFT, N0); \
+ VAR##ID = select(VAR##ID_shift_lt0, VAR##ID_shift_gt0, RES_SHIFT >= 0); \
+ })
+#define REPEAT_ASYMM_MULT_BY_QUANT_MULTIPLIER_PER_CHANNEL(N, SIZE, VAR, RES_MUL, RES_SHIFT) REPEAT_4_N(N, ASYMM_MULT_BY_QUANT_MULTIPLIER_PER_CHANNEL, SIZE, VAR, RES_MUL, RES_SHIFT)
+
#endif // ARM_COMPUTE_REPEAT_H
diff --git a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp
index 3fa2fad..c4ed691 100644
--- a/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2019 ARM Limited.
+ * Copyright (c) 2019-2020 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -50,21 +50,27 @@
{
using ElementsProcessed = Steps;
-Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
- const GEMMReshapeInfo &gemm_info)
+Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, const GEMMKernelInfo &gemm_info,
+ const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias,
+ const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4");
ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3");
+
+ const GEMMRHSMatrixInfo rhs_info = gemm_info.rhs_info;
+ const GEMMLHSMatrixInfo lhs_info = gemm_info.lhs_info;
+ const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage;
+
ARM_COMPUTE_RETURN_ERROR_ON_MSG((((rhs_info.k0 & (rhs_info.k0 - 1)) && rhs_info.k0 != 3) || (rhs_info.k0 > 16)), "Only 2,3,4,8,16 are supported for k0");
ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.m0 < 1 || lhs_info.m0 > 8);
ARM_COMPUTE_RETURN_ERROR_ON_MSG((((rhs_info.n0 & (rhs_info.n0 - 1)) && rhs_info.n0 != 3) || rhs_info.n0 > 16), "Only 2,3,4,8,16 are supported for n0");
- const int m = gemm_info.m();
- const int n = gemm_info.n();
- const int k = gemm_info.k();
+ const int m = gemm_info.m;
+ const int n = gemm_info.n;
+ const int k = gemm_info.k;
TensorShape tensor_shape1{ input1->tensor_shape() };
tensor_shape1.set(0, n);
@@ -74,7 +80,7 @@
const TensorInfo tensor_info_reshaped1 = input1->clone()->set_tensor_shape(compute_rhs_reshaped_shape(tensor_info1, rhs_info));
ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != static_cast<unsigned int>(k));
- if(gemm_info.reinterpret_input_as_3d())
+ if(gemm_info.reinterpret_input_as_3d)
{
ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) * input0->dimension(2) != static_cast<unsigned int>(m));
}
@@ -84,23 +90,118 @@
}
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1);
+ const TensorShape expected_output_shape = compute_mm_shape(*input0, *input1, gemm_info);
if(output->total_size() != 0)
{
- const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info));
+ const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(expected_output_shape);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
+ if(output_stage.type == GEMMLowpOutputStageType::NONE)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
+ }
}
+ if(bias != nullptr)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32);
+ ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(expected_output_shape[0] != bias->dimension(0));
+ }
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN) || (output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FLOAT),
+ "Only GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT is supported");
+
+ // Checks performed if the output stage needs to be fused
+ if(output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
+ {
+ // If a_offset == 0, vector_sum_col can be a nullptr
+ if(gemm_info.a_offset != 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_col, 1, DataType::S32);
+ ARM_COMPUTE_RETURN_ERROR_ON(vector_sum_col->dimension(0) != expected_output_shape[0]);
+ }
+
+ // If b_offset == 0, vector_sum_row can be a nullptr
+ if(gemm_info.b_offset != 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(vector_sum_row, 1, DataType::S32);
+
+ // Check if mm result is a 3D reinterpretation
+ const bool reinterpret_as_3d = expected_output_shape.num_dimensions() > 1 && expected_output_shape.y() != vector_sum_row->tensor_shape().x();
+
+ // Validate input
+ ARM_COMPUTE_RETURN_ERROR_ON(reinterpret_as_3d && vector_sum_row->dimension(0) != (expected_output_shape[1] * expected_output_shape[2]));
+ ARM_COMPUTE_RETURN_ERROR_ON(!reinterpret_as_3d && vector_sum_row->dimension(0) != expected_output_shape[1]);
+
+ if(expected_output_shape.num_dimensions() > 1)
+ {
+ const unsigned int output_batch_idx = reinterpret_as_3d ? 3 : 2;
+
+ TensorShape vector_sum_row_shape = vector_sum_row->tensor_shape();
+ vector_sum_row_shape.collapse_from(1);
+ TensorShape collapsed_output_shape(expected_output_shape);
+ collapsed_output_shape.collapse_from(output_batch_idx);
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_row_shape[1] != collapsed_output_shape[output_batch_idx],
+ "vector_sum_row must have the same number of batches of output tensor");
+
+ if(gemm_info.a_offset != 0)
+ {
+ TensorShape vector_sum_col_shape = vector_sum_col->tensor_shape();
+ vector_sum_col_shape.collapse_from(1);
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(vector_sum_col_shape[1] != 1 && vector_sum_col_shape[1] != vector_sum_row_shape[1],
+ "vector_sum_col tensor must have the same number of batches of vector_sum_row_shape or the number of batches must be set to 1");
+ }
+ }
+ }
+
+ PixelValue min_val{};
+ PixelValue max_val{};
+ if(output->total_size() != 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(output_stage.output_data_type != output->data_type());
+ std::tie(min_val, max_val) = get_min_max(output->data_type());
+ ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_max_bound > max_val.get<int32_t>());
+ ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_min_bound < min_val.get<int32_t>() || output_stage.gemmlowp_min_bound > output_stage.gemmlowp_max_bound);
+ }
+ else
+ {
+ std::tie(min_val, max_val) = get_min_max(output_stage.output_data_type);
+ ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_max_bound > max_val.get<int32_t>());
+ ARM_COMPUTE_RETURN_ERROR_ON(output_stage.gemmlowp_min_bound < min_val.get<int32_t>() || output_stage.gemmlowp_min_bound > output_stage.gemmlowp_max_bound);
+ }
+
+ if(output_multipliers != nullptr && output_shifts != nullptr)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_multipliers, 1, DataType::S32);
+ ARM_COMPUTE_RETURN_ERROR_ON(output_multipliers->num_dimensions() > 1);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output_shifts, 1, DataType::S32);
+ ARM_COMPUTE_RETURN_ERROR_ON(output_shifts->num_dimensions() > 1);
+ if(output_stage.is_quantized_per_channel)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(expected_output_shape[0] != output_shifts->dimension(0));
+ ARM_COMPUTE_RETURN_ERROR_ON(expected_output_shape[0] != output_multipliers->dimension(0));
+ }
+ }
+ }
return Status{};
}
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
- const GEMMReshapeInfo &gemm_info, ElementsProcessed &num_elements_processed)
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output, const GEMMKernelInfo &gemm_info,
+ ITensorInfo *vector_sum_col, ITensorInfo *vector_sum_row, ITensorInfo *bias,
+ ITensorInfo *output_multipliers, ITensorInfo *output_shifts, ElementsProcessed &num_elements_processed)
{
+ const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage;
+
unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];
- bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
- bool reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d() != 0);
+ bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d;
+ bool reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d != 0);
Window win{};
Window win_out{};
@@ -114,7 +215,15 @@
}
// Output tensor auto initialization if not yet initialized
- auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info)).set_data_type(DataType::S32));
+ const TensorShape expected_output_shape = compute_mm_shape(*input0, *input1, gemm_info);
+ if(output_stage.type != GEMMLowpOutputStageType::NONE)
+ {
+ auto_init_if_empty(*output, input0->clone()->set_tensor_shape(expected_output_shape).set_data_type(output_stage.output_data_type));
+ }
+ else
+ {
+ auto_init_if_empty(*output, input0->clone()->set_tensor_shape(expected_output_shape).set_data_type(DataType::S32));
+ }
TensorInfo tmp_info(*output);
@@ -128,19 +237,19 @@
}
// Configure kernel window
- num_elems_processed_per_iteration_x = rhs_info.n0;
- num_elems_processed_per_iteration_y = lhs_info.m0;
+ num_elems_processed_per_iteration_x = gemm_info.rhs_info.n0;
+ num_elems_processed_per_iteration_y = gemm_info.lhs_info.m0;
// Note: bottom paddings are calculated manually as the output can be reinterpreted as 3D tensor
// The only way to set properly the paddings, it is to set those explicitly through the AccessWindowStatic
- const int m = reinterpret_output_as_3d ? gemm_info.m() : input0->dimension(1);
+ const int m = reinterpret_output_as_3d ? gemm_info.m : input0->dimension(1);
const int bottom_pad = (num_elems_processed_per_iteration_y - (m % num_elems_processed_per_iteration_y)) % num_elems_processed_per_iteration_y;
win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
AccessWindowStatic input0_access(input0, 0, 0,
- ceil_to_multiple(input0->dimension(0), lhs_info.k0),
+ ceil_to_multiple(input0->dimension(0), gemm_info.lhs_info.k0),
input0->dimension(1) + bottom_pad);
AccessWindowStatic input1_access(input1, 0, 0,
input1->dimension(0),
@@ -152,6 +261,30 @@
window_changed = update_window_and_padding(win, input0_access, input1_access) || // window used by the execute_window_loop
update_window_and_padding(win_out, output_access); // window used to update the padding requirements of output tensor
+ if(output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
+ {
+ if(gemm_info.a_offset != 0)
+ {
+ AccessWindowHorizontal vector_sum_col_access(vector_sum_col, 0, num_elems_processed_per_iteration_x);
+ window_changed = window_changed || update_window_and_padding(win_out, vector_sum_col_access);
+ }
+ // No access window needed for vector_sum_row
+ ARM_COMPUTE_UNUSED(vector_sum_row);
+
+ if(bias != nullptr)
+ {
+ AccessWindowHorizontal bias_access(bias, 0, num_elems_processed_per_iteration_x);
+ window_changed = window_changed || update_window_and_padding(win_out, bias_access);
+ }
+
+ if(output_multipliers != nullptr && output_multipliers->dimension(0) > 1)
+ {
+ AccessWindowHorizontal output_multipliers_access(output_multipliers, 0, num_elems_processed_per_iteration_x);
+ AccessWindowHorizontal output_shifts_access(output_shifts, 0, num_elems_processed_per_iteration_x);
+ window_changed = window_changed || update_window_and_padding(win_out, output_multipliers_access, output_shifts_access);
+ }
+ }
+
output_access.set_valid_region(win_out, ValidRegion(Coordinates(), output->tensor_shape()));
// Collapse along the Z direction
@@ -166,23 +299,56 @@
} // namespace
CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel()
- : _input0(nullptr), _input1(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_input_as_3d(false), _reinterpret_output_as_3d(false), _use_dummy_work_items(false)
+ : _input0(nullptr),
+ _input1(nullptr),
+ _output(nullptr),
+ _vector_sum_col(nullptr),
+ _vector_sum_row(nullptr),
+ _bias(nullptr),
+ _output_multipliers(nullptr),
+ _output_shifts(nullptr),
+ _slide_matrix_b(true),
+ _reinterpret_input_as_3d(false),
+ _reinterpret_output_as_3d(false),
+ _use_dummy_work_items(false),
+ _is_quantized_per_channel(false),
+ _fuse_output_stage(false)
{
}
-void CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
- const GEMMReshapeInfo &gemm_info)
+void CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, const GEMMKernelInfo &gemm_info,
+ const ICLTensor *vector_sum_col, const ICLTensor *vector_sum_row, const ICLTensor *bias,
+ const ICLTensor *output_multipliers, const ICLTensor *output_shifts)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(),
+ input1->info(),
+ output->info(),
+ gemm_info,
+ vector_sum_col != nullptr ? vector_sum_col->info() : nullptr,
+ vector_sum_row != nullptr ? vector_sum_row->info() : nullptr,
+ bias != nullptr ? bias->info() : nullptr,
+ output_multipliers != nullptr ? output_multipliers->info() : nullptr,
+ output_shifts != nullptr ? output_shifts->info() : nullptr));
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), lhs_info, rhs_info, gemm_info));
+ const GEMMRHSMatrixInfo rhs_info = gemm_info.rhs_info;
+ const GEMMLHSMatrixInfo lhs_info = gemm_info.lhs_info;
+ const GEMMLowpOutputStageInfo output_stage = gemm_info.output_stage;
+ const int32_t a_offset = gemm_info.a_offset;
+ const int32_t b_offset = gemm_info.b_offset;
_input0 = input0;
_input1 = input1;
_output = output;
- _reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
- _reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d() != 0);
+ _vector_sum_col = vector_sum_col;
+ _vector_sum_row = vector_sum_row;
+ _bias = bias;
+ _output_multipliers = output_multipliers;
+ _output_shifts = output_shifts;
+ _reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d;
+ _reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d != 0);
_use_dummy_work_items = preferred_dummy_work_items_support(CLKernelLibrary::get().get_device());
+ _is_quantized_per_channel = output_stage.is_quantized_per_channel;
// In case both input and output have to be reinterpreted as 3D tensors,
// force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
@@ -199,7 +365,16 @@
ElementsProcessed num_elements_processed{};
// Configure kernel window
- auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), lhs_info, rhs_info, gemm_info, num_elements_processed);
+ auto win_config = validate_and_configure_window(input0->info(),
+ input1->info(),
+ output->info(),
+ gemm_info,
+ vector_sum_col != nullptr ? vector_sum_col->info() : nullptr,
+ vector_sum_row != nullptr ? vector_sum_row->info() : nullptr,
+ bias != nullptr ? bias->info() : nullptr,
+ output_multipliers != nullptr ? output_multipliers->info() : nullptr,
+ output_shifts != nullptr ? output_shifts->info() : nullptr,
+ num_elements_processed);
ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
ICLKernel::configure_internal(win_config.second);
@@ -213,8 +388,8 @@
build_opts.add_option_if(rhs_info.interleave, "-DRHS_INTERLEAVE");
build_opts.add_option_if(_use_dummy_work_items, "-DDUMMY_WORK_ITEMS");
build_opts.add_option("-DM=" + support::cpp11::to_string(input0->info()->dimension(1)));
- build_opts.add_option("-DN=" + support::cpp11::to_string(gemm_info.n()));
- build_opts.add_option("-DK=" + support::cpp11::to_string(gemm_info.k()));
+ build_opts.add_option("-DN=" + support::cpp11::to_string(gemm_info.n));
+ build_opts.add_option("-DK=" + support::cpp11::to_string(gemm_info.k));
build_opts.add_option("-DM0=" + support::cpp11::to_string(lhs_info.m0));
build_opts.add_option("-DN0=" + support::cpp11::to_string(rhs_info.n0));
build_opts.add_option("-DK0=" + support::cpp11::to_string(rhs_info.k0));
@@ -225,6 +400,35 @@
std::string kernel_name("gemmlowp_mm_reshaped_only_rhs_");
kernel_name += rhs_info.transpose ? "t" : "nt";
+ if(output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
+ {
+ kernel_name += "_fused_output_stage_fixedpoint";
+ _fuse_output_stage = true;
+ // If a_offset == 0, vector_sum_col can be a nullptr
+ if(a_offset != 0)
+ {
+ build_opts.add_option("-DA_OFFSET=" + support::cpp11::to_string(a_offset));
+ build_opts.add_option_if(vector_sum_col->info()->tensor_shape().num_dimensions() > 1, "-DSUM_COL_HAS_BATCHES");
+ }
+ // If b_offset == 0, vector_sum_row can be a nullptr
+ build_opts.add_option_if(b_offset != 0, "-DB_OFFSET=" + support::cpp11::to_string(b_offset));
+ build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(a_offset * b_offset * input0->info()->dimension(0)));
+ build_opts.add_option_if(bias != nullptr, "-DADD_BIAS");
+ build_opts.add_option("-DRESULT_OFFSET=" + support::cpp11::to_string(output_stage.gemmlowp_offset));
+ build_opts.add_option("-DRESULT_MULTIPLIER=" + support::cpp11::to_string(output_stage.gemmlowp_multipliers[0]));
+ build_opts.add_option("-DRESULT_SHIFT=" + support::cpp11::to_string(output_stage.gemmlowp_shifts[0]));
+ build_opts.add_option_if(_is_quantized_per_channel, "-DPER_CHANNEL_QUANTIZATION");
+
+ const int min = output_stage.gemmlowp_min_bound;
+ const int max = output_stage.gemmlowp_max_bound;
+
+ PixelValue min_val{};
+ PixelValue max_val{};
+ std::tie(min_val, max_val) = get_min_max(output->info()->data_type());
+ build_opts.add_option_if((min != min_val.get<int32_t>()) && (min != max), "-DMIN_BOUND=" + support::cpp11::to_string(min));
+ build_opts.add_option_if((max != max_val.get<int32_t>()) && (min != max), "-DMAX_BOUND=" + support::cpp11::to_string(max));
+ }
+
// Create kernel
_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
@@ -239,7 +443,7 @@
_config_id += "_";
_config_id += support::cpp11::to_string(output->info()->dimension(0));
_config_id += "_";
- _config_id += support::cpp11::to_string(gemm_info.k());
+ _config_id += support::cpp11::to_string(gemm_info.k);
_config_id += "_";
_config_id += support::cpp11::to_string(output->info()->dimension(2));
_config_id += "_";
@@ -254,17 +458,21 @@
_config_id += support::cpp11::to_string(rhs_info.interleave);
}
-Status CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info,
- const GEMMRHSMatrixInfo &rhs_info, const GEMMReshapeInfo &gemm_info)
+Status CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, const GEMMKernelInfo &gemm_info,
+ const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias,
+ const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts)
{
ElementsProcessed num_elements_processed{};
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, lhs_info, rhs_info, gemm_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, gemm_info, vector_sum_col, vector_sum_row, bias, output_multipliers, output_shifts));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(),
input1->clone().get(),
output->clone().get(),
- lhs_info,
- rhs_info,
gemm_info,
+ vector_sum_col != nullptr ? vector_sum_col->clone().get() : nullptr,
+ vector_sum_row != nullptr ? vector_sum_row->clone().get() : nullptr,
+ bias != nullptr ? bias->clone().get() : nullptr,
+ output_multipliers != nullptr ? output_multipliers->clone().get() : nullptr,
+ output_shifts != nullptr ? output_shifts->clone().get() : nullptr,
num_elements_processed)
.first);
@@ -304,6 +512,21 @@
_kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
}
+ // Set window for vector_sum_col
+ Window win_vector_sum_col = slice;
+ win_vector_sum_col.set(Window::DimY, Window::Dimension(0, 0, 0));
+ win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));
+
+ // Set window for vector_sum_row
+ Window win_vector_sum_row = slice;
+ win_vector_sum_row.set(Window::DimX, Window::Dimension(0, 0, 0));
+ win_vector_sum_row.set(Window::DimY, Window::Dimension(0, 0, 0));
+ win_vector_sum_col.set(Window::DimZ, Window::Dimension(0, 0, 0));
+
+ Window biases_slice = slice;
+ biases_slice.set(Window::DimY, Window::Dimension(0, 1, 1));
+ biases_slice.set(Window::DimZ, Window::Dimension(0, 1, 1));
+
do
{
Window slice_b = slice;
@@ -321,6 +544,26 @@
_kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input0->info()->strides_in_bytes()[2]));
_kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input1->info()->strides_in_bytes()[2]));
_kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[2]));
+ if(_reinterpret_input_as_3d)
+ {
+ // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor
+ idx++;
+ }
+
+ if(_reinterpret_output_as_3d)
+ {
+ // Pass bottom paddings to the kernel if the output has to be reinterpreted as 3D tensor
+ idx++;
+ }
+
+ if(_fuse_output_stage)
+ {
+ add_2D_tensor_argument_if((_vector_sum_col != nullptr), idx, _vector_sum_col, win_vector_sum_col);
+ add_2D_tensor_argument_if((_vector_sum_row != nullptr), idx, _vector_sum_row, win_vector_sum_row);
+ add_1D_tensor_argument_if((_bias != nullptr), idx, _bias, biases_slice);
+ add_1D_tensor_argument_if(_is_quantized_per_channel, idx, _output_multipliers, biases_slice);
+ add_1D_tensor_argument_if(_is_quantized_per_channel, idx, _output_shifts, biases_slice);
+ }
enqueue(queue, *this, slice, lws_hint(), _use_dummy_work_items);
}
while(window.slide_window_slice_3D(slice));