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author | Gunes Bayir <gunes.bayir@arm.com> | 2023-09-20 10:09:43 +0100 |
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committer | Gunes Bayir <gunes.bayir@arm.com> | 2023-09-29 11:07:32 +0000 |
commit | a396da19ee6e5c36ae07c11e4f16a6787e9bc143 (patch) | |
tree | d181f4185ce241667950dce52be87177af611262 /src/core/CL | |
parent | 6e56bf3b58719772111236d3b0030fbb5e8d2e16 (diff) | |
download | ComputeLibrary-a396da19ee6e5c36ae07c11e4f16a6787e9bc143.tar.gz |
Implement Quantized Matmul T/T and T/Nt kernels using MMUL extension
Resolves: COMPMID-6476, COMPMID-6477
Change-Id: Ied37c269d5a108ff72f70e3ad932cf372bda5562
Signed-off-by: Gunes Bayir <gunes.bayir@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10346
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Jakub Sujak <jakub.sujak@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'src/core/CL')
-rw-r--r-- | src/core/CL/cl_kernels/common/mat_mul_quantized_mmul.cl | 287 |
1 files changed, 285 insertions, 2 deletions
diff --git a/src/core/CL/cl_kernels/common/mat_mul_quantized_mmul.cl b/src/core/CL/cl_kernels/common/mat_mul_quantized_mmul.cl index 4ab81d13cc..fdfb75d39c 100644 --- a/src/core/CL/cl_kernels/common/mat_mul_quantized_mmul.cl +++ b/src/core/CL/cl_kernels/common/mat_mul_quantized_mmul.cl @@ -514,7 +514,9 @@ __kernel void mat_mul_native_quantized_mmul_nt_t( /** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS transposed, RHS non-transposed * * Supported block configurations: - * TODO: Report supported M0, N0, K0 + * - M0 = 1, 2, 3, 4, 8, 16 + * - N0 = 1, 2, 3, 4, 8, 16 + * - K0 = 4 * * Similar to mat_mul_native_quantized_mmul_nt_nt() */ @@ -526,6 +528,149 @@ __kernel void mat_mul_native_quantized_mmul_t_nt( #endif // defined(BIAS) TENSOR3D_T(dst, BUFFER)) { + const uint x0 = get_global_id(0); // [0, (N / N0) * MMUL_M0) + // The upper limit is a simplified version of (N / N0) / MMUL_N0) * MMUL_BLOCK_SIZE) + const uint y0 = get_global_id(1); // [0, (M / M0) / MMUL_M0) + const uint z = get_global_id(2); // Batch + + // Get section coordinates + const uint section_x = (x0 / MMUL_BLOCK_SIZE); + const uint section_y = y0; + + // Get thread coordinates within an mmul block + const uint thread_id = (x0 % MMUL_BLOCK_SIZE); + const uint thread_x = thread_id % MMUL_N0; + const uint thread_y = (thread_id / MMUL_N0); + + // Calculate dst coordinates + const uint dst_x_unclamped = thread_x * N0 + section_x * N0 * MMUL_N0; + const uint dst_y_unclamped = thread_y * M0 + section_y * M0 * MMUL_M0; + const uint dst_x = min(dst_x_unclamped, (uint)(N - N0)); + const uint dst_y = min(dst_y_unclamped, (uint)(M - M0)); + + // Starting LHS coordinates + const uint lhs_x = dst_y; + const uint lhs_y = K0 * thread_x; + + // Starting RHS coordinates + const uint rhs_x = dst_x; + const uint rhs_y = K0 * thread_y; + + // Compute LHS/RHS/DST matrix address + lhs_offset_first_element_in_bytes += lhs_x * sizeof(DATA_TYPE) + lhs_y * lhs_stride_y + z * lhs_stride_z; + rhs_offset_first_element_in_bytes += rhs_x * sizeof(DATA_TYPE) + rhs_y * rhs_stride_y + z * rhs_stride_z; + dst_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + dst_y * dst_stride_y + z * dst_stride_z; + + // Initialize the accumulators + TILE(int, M0, N0, c); + LOOP_UNROLLING(int, i, 0, 1, M0, + { + c[i].v = K * ((int)LHS_OFFSET) * ((int)RHS_OFFSET); + }) + + // Calculate row and column sums + TILE(int, 1, N0, b_sum); + b_sum[0].v = 0; + + TILE(int, 1, M0, a_sum); + a_sum[0].v = 0; + + VEC_DATA_TYPE(DATA_TYPE, K0) + vec_1 = (VEC_DATA_TYPE(DATA_TYPE, K0))(1, 1, 1, 1); + + for(int k = 0; k < lhs_h; k += MMUL_K0) + { + TILE(DATA_TYPE, K0, M0, a); + TILE(DATA_TYPE, K0, N0, b); + + // Load tile from the lhs/rhs tensors + T_LOAD(DATA_TYPE, K0, M0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); + T_LOAD(DATA_TYPE, K0, N0, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b); + + LOOP_UNROLLING(int, m0, 0, 1, M0, + { + VEC_DATA_TYPE(DATA_TYPE, K0) + vec_a = (VEC_DATA_TYPE(DATA_TYPE, K0))(a[0].s[m0], a[1].s[m0], a[2].s[m0], a[3].s[m0]); + + LOOP_UNROLLING(int, n0, 0, 1, N0, + { + VEC_DATA_TYPE(DATA_TYPE, K0) + vec_b = (VEC_DATA_TYPE(DATA_TYPE, K0))(b[0].s[n0], b[1].s[n0], b[2].s[n0], b[3].s[n0]); + + c[m0].s[n0] = arm_matrix_multiply(vec_a, vec_b, c[m0].s[n0]); + }) + +#if RHS_OFFSET != 0 + // Row Sum of A: Calculate the sum of rows by multiplying A with + // a matrix of 1's from Right + a_sum[0].s[m0] = arm_matrix_multiply(vec_a, vec_1, a_sum[0].s[m0]); +#endif // RHS_OFFSET != 0 + }) + +#if LHS_OFFSET != 0 + // Column Sum of B: Calculate the sum of columns by multiplying B + // with a matrix of 1's from Left + LOOP_UNROLLING(int, n0, 0, 1, N0, + { + VEC_DATA_TYPE(DATA_TYPE, K0) + vec_b = (VEC_DATA_TYPE(DATA_TYPE, K0))(b[0].s[n0], b[1].s[n0], b[2].s[n0], b[3].s[n0]); + + b_sum[0].s[n0] = arm_matrix_multiply(vec_1, vec_b, b_sum[0].s[n0]); + }) +#endif // LHS_OFFSET != 0 + + lhs_offset_first_element_in_bytes += MMUL_K0 * lhs_stride_y; + rhs_offset_first_element_in_bytes += MMUL_K0 * rhs_stride_y; + } + + // Do not write if the coordinates are out of bound + // But, read has to happen as arm_matrix_multiply() expects certain number of calls + if(dst_x_unclamped >= N || dst_y_unclamped >= M) + { + return; + } + +#if RHS_OFFSET != 0 || LHS_OFFSET != 0 + LOOP_UNROLLING(int, i, 0, 1, M0, + { + const int A = ((int)RHS_OFFSET) * a_sum[0].s[i]; + LOOP_UNROLLING(int, j, 0, 1, N0, + { + c[i].s[j] -= A + ((int)(LHS_OFFSET)) * b_sum[0].s[j]; + }) + }) +#endif // RHS_OFFSET != 0 || LHS_OFFSET != 0 + +#ifdef BIAS + perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, c, dst_x); +#endif // defined(BIAS) + + // Quantize the tile + TILE(DATA_TYPE, M0, N0, cq); + T_QUANTIZE8_ASYMMETRIC(int, DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, c, cq); + + 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) + (cq[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) + (cq[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y)); + } + }) + } } #endif // defined(MAT_MUL_NATIVE_QUANTIZED_MMUL_T_NT) @@ -533,7 +678,9 @@ __kernel void mat_mul_native_quantized_mmul_t_nt( /** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS transposed, RHS transposed * * Supported block configurations: - * TODO: Report supported M0, N0, K0 + * - M0 = 1, 2, 3, 4, 8, 16 + * - N0 = 1, 2, 3, 4, 8, 16 + * - K0 = 4 * * Similar to mat_mul_native_quantized_mmul_nt_nt() */ @@ -545,5 +692,141 @@ __kernel void mat_mul_native_quantized_mmul_t_t( #endif // defined(BIAS) TENSOR3D_T(dst, BUFFER)) { + const uint x0 = get_global_id(0); // [0, (N / N0) * MMUL_M0) + // The upper limit is a simplified version of (N / N0) / MMUL_N0) * MMUL_BLOCK_SIZE) + const uint y0 = get_global_id(1); // [0, (M / M0) / MMUL_M0) + const uint z = get_global_id(2); // Batch + + // Get section coordinates + const uint section_x = (x0 / MMUL_BLOCK_SIZE); + const uint section_y = y0; + + // Get thread coordinates within an mmul block + const uint thread_id = (x0 % MMUL_BLOCK_SIZE); + const uint thread_x = thread_id % MMUL_N0; + const uint thread_y = (thread_id / MMUL_N0); + + // Calculate dst coordinates + const uint dst_x_unclamped = thread_x * N0 + section_x * N0 * MMUL_N0; + const uint dst_y_unclamped = thread_y * M0 + section_y * M0 * MMUL_M0; + const uint dst_x = min(dst_x_unclamped, (uint)(N - N0)); + const uint dst_y = min(dst_y_unclamped, (uint)(M - M0)); + + // Starting LHS coordinates + const uint lhs_x = dst_y; + const uint lhs_y = K0 * thread_x; + + // Starting RHS coordinates + const uint rhs_x = K0 * thread_y; + const uint rhs_y = dst_x; + + // Compute LHS/RHS/DST matrix address + lhs_offset_first_element_in_bytes += lhs_x * sizeof(DATA_TYPE) + lhs_y * lhs_stride_y + z * lhs_stride_z; + rhs_offset_first_element_in_bytes += rhs_x * sizeof(DATA_TYPE) + rhs_y * rhs_stride_y + z * rhs_stride_z; + dst_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + dst_y * dst_stride_y + z * dst_stride_z; + + // Initialize the accumulators + TILE(int, M0, N0, c); + LOOP_UNROLLING(int, i, 0, 1, M0, + { + c[i].v = K * ((int)LHS_OFFSET) * ((int)RHS_OFFSET); + }) + + // Calculate row and column sums + TILE(int, 1, N0, b_sum); + b_sum[0].v = 0; + + TILE(int, 1, M0, a_sum); + a_sum[0].v = 0; + + VEC_DATA_TYPE(DATA_TYPE, K0) + vec_1 = (VEC_DATA_TYPE(DATA_TYPE, K0))(1, 1, 1, 1); + + for(int k = 0; k < lhs_h; k += MMUL_K0) + { + TILE(DATA_TYPE, K0, M0, a); + TILE(DATA_TYPE, N0, K0, b); + + // Load tile from the lhs/rhs tensors + T_LOAD(DATA_TYPE, K0, M0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); + T_LOAD(DATA_TYPE, N0, K0, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b); + + LOOP_UNROLLING(int, m0, 0, 1, M0, + { + VEC_DATA_TYPE(DATA_TYPE, K0) + vec_a = (VEC_DATA_TYPE(DATA_TYPE, K0))(a[0].s[m0], a[1].s[m0], a[2].s[m0], a[3].s[m0]); + + LOOP_UNROLLING(int, n0, 0, 1, N0, + { + c[m0].s[n0] = arm_matrix_multiply(vec_a, b[n0].v, c[m0].s[n0]); + }) +#if RHS_OFFSET != 0 + // Row Sum of A: Calculate the sum of rows by multiplying A with + // a matrix of 1's from Right + a_sum[0].s[m0] = arm_matrix_multiply(vec_a, vec_1, a_sum[0].s[m0]); +#endif // RHS_OFFSET != 0 + }) + +#if LHS_OFFSET != 0 + // Column Sum of B: Calculate the sum of columns by multiplying B + // with a matrix of 1's from Left + LOOP_UNROLLING(int, n0, 0, 1, N0, + { + b_sum[0].s[n0] = arm_matrix_multiply(vec_1, b[n0].v, b_sum[0].s[n0]); + }) +#endif // LHS_OFFSET != 0 + + lhs_offset_first_element_in_bytes += MMUL_K0 * lhs_stride_y; + rhs_offset_first_element_in_bytes += MMUL_K0 * sizeof(DATA_TYPE); + } + + // Do not write if the coordinates are out of bound + // But, read has to happen as arm_matrix_multiply() expects certain number of calls + if(dst_x_unclamped >= N || dst_y_unclamped >= M) + { + return; + } + +#if RHS_OFFSET != 0 || LHS_OFFSET != 0 + LOOP_UNROLLING(int, i, 0, 1, M0, + { + const int A = ((int)RHS_OFFSET) * a_sum[0].s[i]; + LOOP_UNROLLING(int, j, 0, 1, N0, + { + c[i].s[j] -= A + ((int)(LHS_OFFSET)) * b_sum[0].s[j]; + }) + }) +#endif // RHS_OFFSET != 0 || LHS_OFFSET != 0 + +#ifdef BIAS + perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, c, dst_x); +#endif // defined(BIAS) + + // Quantize the tile + TILE(DATA_TYPE, M0, N0, cq); + T_QUANTIZE8_ASYMMETRIC(int, DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, c, cq); + + 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) + (cq[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) + (cq[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y)); + } + }) + } } #endif // defined(MAT_MUL_NATIVE_QUANTIZED_MMUL_T_T) |