mirror of
https://github.com/opencv/opencv.git
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Abhishek Gola
bdf348c13a
Added MLAS third party module and integrated into GeMM path #28934 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
532 lines
14 KiB
C++
532 lines
14 KiB
C++
/*++
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Copyright (c) Microsoft Corporation. All rights reserved.
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Licensed under the MIT License.
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Module Name:
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sgemmc.cpp
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Abstract:
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This module implements the kernels for the single precision matrix/matrix
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multiply operation (SGEMM).
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--*/
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#include "mlasi.h"
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template<bool ZeroMode, bool ProcessTwoRows>
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size_t
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MlasSgemmKernel(
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const float* A,
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const float* B,
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float* C,
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size_t CountK,
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size_t CountN,
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size_t lda,
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size_t ldc,
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float alpha
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)
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/*++
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Routine Description:
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This routine is an inner kernel to compute matrix multiplication for a
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set of rows.
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Arguments:
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A - Supplies the address of matrix A.
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B - Supplies the address of matrix B. The matrix data has been packed using
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MlasSgemmCopyPackB or MlasSgemmTransposePackB.
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C - Supplies the address of matrix C.
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CountK - Supplies the number of columns from matrix A and the number of rows
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from matrix B to iterate over.
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CountN - Supplies the number of columns from matrix B and matrix C to
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iterate over.
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lda - Supplies the first dimension of matrix A.
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ldc - Supplies the first dimension of matrix C.
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alpha - Supplies the scalar multiplier (see SGEMM definition).
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Return Value:
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Returns the number of rows handled.
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--*/
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{
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float32x4_t Row0Block0;
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float32x4_t Row0Block1;
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float32x4_t Row0Block2;
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float32x4_t Row0Block3;
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float32x4_t Row1Block0;
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float32x4_t Row1Block1;
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float32x4_t Row1Block2;
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float32x4_t Row1Block3;
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#if defined(_WIN32)
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if (!ProcessTwoRows) {
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UNREFERENCED_PARAMETER(lda);
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UNREFERENCED_PARAMETER(ldc);
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}
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#endif
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do {
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float32x4_t BElements0;
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float32x4_t BElements1;
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float32x4_t BElements2;
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float32x4_t BElements3;
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float32x2_t Row0AElements;
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float32x2_t Row1AElements;
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//
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// Clear the block accumulators.
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//
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Row0Block0 = vdupq_n_f32(0.0f);
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Row0Block1 = vdupq_n_f32(0.0f);
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Row0Block2 = vdupq_n_f32(0.0f);
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Row0Block3 = vdupq_n_f32(0.0f);
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if (ProcessTwoRows) {
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Row1Block0 = vdupq_n_f32(0.0f);
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Row1Block1 = vdupq_n_f32(0.0f);
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Row1Block2 = vdupq_n_f32(0.0f);
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Row1Block3 = vdupq_n_f32(0.0f);
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}
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//
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// Compute the 16x1 or 16x2 output block.
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//
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const float* a = A;
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size_t k = CountK;
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while (k >= 2) {
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Row0AElements = vld1_f32(a);
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if (ProcessTwoRows) {
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Row1AElements = vld1_f32(a + lda);
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}
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BElements0 = vld1q_f32(B + 0);
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BElements1 = vld1q_f32(B + 4);
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BElements2 = vld1q_f32(B + 8);
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BElements3 = vld1q_f32(B + 12);
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Row0Block0 = vmlaq_lane_f32(Row0Block0, BElements0, Row0AElements, 0);
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Row0Block1 = vmlaq_lane_f32(Row0Block1, BElements1, Row0AElements, 0);
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Row0Block2 = vmlaq_lane_f32(Row0Block2, BElements2, Row0AElements, 0);
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Row0Block3 = vmlaq_lane_f32(Row0Block3, BElements3, Row0AElements, 0);
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if (ProcessTwoRows) {
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Row1Block0 = vmlaq_lane_f32(Row1Block0, BElements0, Row1AElements, 0);
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Row1Block1 = vmlaq_lane_f32(Row1Block1, BElements1, Row1AElements, 0);
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Row1Block2 = vmlaq_lane_f32(Row1Block2, BElements2, Row1AElements, 0);
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Row1Block3 = vmlaq_lane_f32(Row1Block3, BElements3, Row1AElements, 0);
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}
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BElements0 = vld1q_f32(B + 16);
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BElements1 = vld1q_f32(B + 20);
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BElements2 = vld1q_f32(B + 24);
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BElements3 = vld1q_f32(B + 28);
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Row0Block0 = vmlaq_lane_f32(Row0Block0, BElements0, Row0AElements, 1);
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Row0Block1 = vmlaq_lane_f32(Row0Block1, BElements1, Row0AElements, 1);
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Row0Block2 = vmlaq_lane_f32(Row0Block2, BElements2, Row0AElements, 1);
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Row0Block3 = vmlaq_lane_f32(Row0Block3, BElements3, Row0AElements, 1);
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if (ProcessTwoRows) {
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Row1Block0 = vmlaq_lane_f32(Row1Block0, BElements0, Row1AElements, 1);
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Row1Block1 = vmlaq_lane_f32(Row1Block1, BElements1, Row1AElements, 1);
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Row1Block2 = vmlaq_lane_f32(Row1Block2, BElements2, Row1AElements, 1);
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Row1Block3 = vmlaq_lane_f32(Row1Block3, BElements3, Row1AElements, 1);
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}
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a += 2;
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B += 32;
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k -= 2;
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}
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if (k > 0) {
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Row0AElements = vld1_dup_f32(a);
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if (ProcessTwoRows) {
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Row1AElements = vld1_dup_f32(a + lda);
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}
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BElements0 = vld1q_f32(B + 0);
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BElements1 = vld1q_f32(B + 4);
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BElements2 = vld1q_f32(B + 8);
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BElements3 = vld1q_f32(B + 12);
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Row0Block0 = vmlaq_lane_f32(Row0Block0, BElements0, Row0AElements, 0);
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Row0Block1 = vmlaq_lane_f32(Row0Block1, BElements1, Row0AElements, 0);
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Row0Block2 = vmlaq_lane_f32(Row0Block2, BElements2, Row0AElements, 0);
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Row0Block3 = vmlaq_lane_f32(Row0Block3, BElements3, Row0AElements, 0);
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if (ProcessTwoRows) {
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Row1Block0 = vmlaq_lane_f32(Row1Block0, BElements0, Row1AElements, 0);
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Row1Block1 = vmlaq_lane_f32(Row1Block1, BElements1, Row1AElements, 0);
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Row1Block2 = vmlaq_lane_f32(Row1Block2, BElements2, Row1AElements, 0);
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Row1Block3 = vmlaq_lane_f32(Row1Block3, BElements3, Row1AElements, 0);
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}
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B += 16;
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}
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//
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// Multiply by the alpha value.
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//
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Row0Block0 = vmulq_n_f32(Row0Block0, alpha);
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Row0Block1 = vmulq_n_f32(Row0Block1, alpha);
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Row0Block2 = vmulq_n_f32(Row0Block2, alpha);
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Row0Block3 = vmulq_n_f32(Row0Block3, alpha);
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if (ProcessTwoRows) {
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Row1Block0 = vmulq_n_f32(Row1Block0, alpha);
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Row1Block1 = vmulq_n_f32(Row1Block1, alpha);
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Row1Block2 = vmulq_n_f32(Row1Block2, alpha);
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Row1Block3 = vmulq_n_f32(Row1Block3, alpha);
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}
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if (CountN >= 16) {
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//
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// Store the entire output block.
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//
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if (!ZeroMode) {
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Row0Block0 = vaddq_f32(Row0Block0, vld1q_f32(C));
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Row0Block1 = vaddq_f32(Row0Block1, vld1q_f32(C + 4));
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Row0Block2 = vaddq_f32(Row0Block2, vld1q_f32(C + 8));
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Row0Block3 = vaddq_f32(Row0Block3, vld1q_f32(C + 12));
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}
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vst1q_f32(C, Row0Block0);
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vst1q_f32(C + 4, Row0Block1);
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vst1q_f32(C + 8, Row0Block2);
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vst1q_f32(C + 12, Row0Block3);
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if (ProcessTwoRows) {
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if (!ZeroMode) {
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Row1Block0 = vaddq_f32(Row1Block0, vld1q_f32(C + ldc));
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Row1Block1 = vaddq_f32(Row1Block1, vld1q_f32(C + ldc + 4));
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Row1Block2 = vaddq_f32(Row1Block2, vld1q_f32(C + ldc + 8));
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Row1Block3 = vaddq_f32(Row1Block3, vld1q_f32(C + ldc + 12));
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}
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vst1q_f32(C + ldc, Row1Block0);
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vst1q_f32(C + ldc + 4, Row1Block1);
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vst1q_f32(C + ldc + 8, Row1Block2);
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vst1q_f32(C + ldc + 12, Row1Block3);
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}
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} else {
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//
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// Store the partial output block.
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//
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if ((CountN & 8) != 0) {
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if (!ZeroMode) {
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Row0Block0 = vaddq_f32(Row0Block0, vld1q_f32(C));
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Row0Block1 = vaddq_f32(Row0Block1, vld1q_f32(C + 4));
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}
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vst1q_f32(C, Row0Block0);
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vst1q_f32(C + 4, Row0Block1);
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Row0Block0 = Row0Block2;
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Row0Block1 = Row0Block3;
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if (ProcessTwoRows) {
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if (!ZeroMode) {
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Row1Block0 = vaddq_f32(Row1Block0, vld1q_f32(C + ldc));
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Row1Block1 = vaddq_f32(Row1Block1, vld1q_f32(C + ldc + 4));
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}
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vst1q_f32(C + ldc, Row1Block0);
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vst1q_f32(C + ldc + 4, Row1Block1);
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Row1Block0 = Row1Block2;
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Row1Block1 = Row1Block3;
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}
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C += 8;
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}
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if ((CountN & 4) != 0) {
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if (!ZeroMode) {
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Row0Block0 = vaddq_f32(Row0Block0, vld1q_f32(C));
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}
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vst1q_f32(C, Row0Block0);
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Row0Block0 = Row0Block1;
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if (ProcessTwoRows) {
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if (!ZeroMode) {
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Row1Block0 = vaddq_f32(Row1Block0, vld1q_f32(C + ldc));
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}
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vst1q_f32(C + ldc, Row1Block0);
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Row1Block0 = Row1Block1;
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}
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C += 4;
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}
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float32x2_t Row0Block0High;
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float32x2_t Row0Block0Low;
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float32x2_t Row1Block0High;
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float32x2_t Row1Block0Low;
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Row0Block0High = vget_high_f32(Row0Block0);
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Row0Block0Low = vget_low_f32(Row0Block0);
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if (ProcessTwoRows) {
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Row1Block0High = vget_high_f32(Row1Block0);
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Row1Block0Low = vget_low_f32(Row1Block0);
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}
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if ((CountN & 2) != 0) {
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if (!ZeroMode) {
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Row0Block0Low = vadd_f32(Row0Block0Low, vld1_f32(C));
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}
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vst1_f32(C, Row0Block0Low);
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Row0Block0Low = Row0Block0High;
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if (ProcessTwoRows) {
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if (!ZeroMode) {
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Row1Block0Low = vadd_f32(Row1Block0Low, vld1_f32(C + ldc));
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}
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vst1_f32(C + ldc, Row1Block0Low);
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Row1Block0Low = Row1Block0High;
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}
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C += 2;
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}
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if ((CountN & 1) != 0) {
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if (!ZeroMode) {
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Row0Block0Low = vadd_f32(Row0Block0Low, vld1_dup_f32(C));
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}
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vst1_lane_f32(C, Row0Block0Low, 0);
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if (ProcessTwoRows) {
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if (!ZeroMode) {
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Row1Block0Low = vadd_f32(Row1Block0Low, vld1_dup_f32(C + ldc));
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}
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vst1_lane_f32(C + ldc, Row1Block0Low, 0);
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}
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}
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break;
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}
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C += 16;
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CountN -= 16;
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} while (CountN > 0);
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return ProcessTwoRows ? 2 : 1;
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}
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template<bool ZeroMode>
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size_t
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MlasSgemmKernel(
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const float* A,
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const float* B,
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float* C,
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size_t CountK,
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size_t CountM,
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size_t CountN,
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size_t lda,
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size_t ldc,
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float alpha
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)
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/*++
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Routine Description:
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This routine is an inner kernel to compute matrix multiplication for a
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set of rows.
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Arguments:
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A - Supplies the address of matrix A.
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B - Supplies the address of matrix B. The matrix data has been packed using
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MlasSgemmCopyPackB or MlasSgemmTransposePackB.
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C - Supplies the address of matrix C.
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CountK - Supplies the number of columns from matrix A and the number of rows
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from matrix B to iterate over.
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CountM - Supplies the maximum number of rows that can be processed for
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matrix A and matrix C. The actual number of rows handled for this
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invocation depends on the kernel implementation.
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CountN - Supplies the number of columns from matrix B and matrix C to
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iterate over.
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lda - Supplies the first dimension of matrix A.
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ldc - Supplies the first dimension of matrix C.
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alpha - Supplies the scalar multiplier (see SGEMM definition).
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Return Value:
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Returns the number of rows handled.
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--*/
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{
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size_t RowsHandled;
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if (CountM >= 2) {
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RowsHandled = MlasSgemmKernel<ZeroMode, true>(A, B, C, CountK, CountN, lda, ldc, alpha);
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} else {
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RowsHandled = MlasSgemmKernel<ZeroMode, false>(A, B, C, CountK, CountN, lda, ldc, alpha);
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}
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return RowsHandled;
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}
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size_t
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MLASCALL
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MlasSgemmKernelZero(
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const float* A,
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const float* B,
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float* C,
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size_t CountK,
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size_t CountM,
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size_t CountN,
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size_t lda,
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size_t ldc,
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float alpha
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)
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/*++
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Routine Description:
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This routine is an inner kernel to compute matrix multiplication for a
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set of rows.
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Arguments:
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A - Supplies the address of matrix A.
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B - Supplies the address of matrix B. The matrix data has been packed using
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MlasSgemmCopyPackB or MlasSgemmTransposePackB.
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C - Supplies the address of matrix C.
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CountK - Supplies the number of columns from matrix A and the number of rows
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from matrix B to iterate over.
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CountM - Supplies the maximum number of rows that can be processed for
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matrix A and matrix C. The actual number of rows handled for this
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invocation depends on the kernel implementation.
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CountN - Supplies the number of columns from matrix B and matrix C to
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iterate over.
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lda - Supplies the first dimension of matrix A.
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ldc - Supplies the first dimension of matrix C.
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alpha - Supplies the scalar multiplier (see SGEMM definition).
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Return Value:
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Returns the number of rows handled.
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--*/
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{
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return MlasSgemmKernel<true>(A, B, C, CountK, CountM, CountN, lda, ldc, alpha);
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}
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size_t
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MLASCALL
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MlasSgemmKernelAdd(
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const float* A,
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const float* B,
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float* C,
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size_t CountK,
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size_t CountM,
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size_t CountN,
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size_t lda,
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size_t ldc,
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float alpha
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)
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/*++
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Routine Description:
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This routine is an inner kernel to compute matrix multiplication for a
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set of rows.
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Arguments:
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A - Supplies the address of matrix A.
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B - Supplies the address of matrix B. The matrix data has been packed using
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MlasSgemmCopyPackB or MlasSgemmTransposePackB.
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C - Supplies the address of matrix C.
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CountK - Supplies the number of columns from matrix A and the number of rows
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from matrix B to iterate over.
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CountM - Supplies the maximum number of rows that can be processed for
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matrix A and matrix C. The actual number of rows handled for this
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invocation depends on the kernel implementation.
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CountN - Supplies the number of columns from matrix B and matrix C to
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iterate over.
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lda - Supplies the first dimension of matrix A.
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ldc - Supplies the first dimension of matrix C.
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alpha - Supplies the scalar multiplier (see SGEMM definition).
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Return Value:
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Returns the number of rows handled.
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--*/
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{
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return MlasSgemmKernel<false>(A, B, C, CountK, CountM, CountN, lda, ldc, alpha);
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}
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