chore: import upstream snapshot with attribution

2026-07-13 13:33:03 +08:00
commit 5b57521aa1
8226 changed files with 3425766 additions and 0 deletions
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+//
+//  ConvolutionWinogradImpl.cpp
+//  MNN
+//
+//  Created by MNN on 2022/01/20.
+//  Copyright © 2018 - 2022, Alibaba Group Holding Limited
+//
+
+#include "backend/cpu/compute/ConvolutionWinogradImpl.hpp"
+#include <math.h>
+#include "backend/cpu/compute/CommonOptFunction.h"
+#include "core/Concurrency.h"
+#include "backend/cpu/compute/ConvOpt.h"
+#include "core/Macro.h"
+#include "core/TensorUtils.hpp"
+#include "math/WingoradGenerater.hpp"
+#include <MNN/AutoTime.hpp>
+#include "core/MemoryFormater.h"
+
+
+//#define MNN_WINOGRAD_PRINT_REDUCE_RATE
+//#define MNN_WINO_TRANFORM_TEST_CLOSE
+namespace MNN {
+ConvolutionWinogradImpl::ConvolutionWinogradImpl(const Convolution2DCommon *convOp, Backend *b)
+    : MNN::CPUConvolution(convOp, b) {
+
+}
+
+ConvolutionWinogradImpl::~ConvolutionWinogradImpl() {
+
+}
+
+
+WinogradConfig ConvolutionWinogradImpl::bestWinogradUnit(const Convolution2DCommon *common, const Tensor *inputTensor,
+                                          const Tensor *outputTensor, int threadNumber, Backend* b, const PerfConfig& denseConfig) {
+    return WinogradConfig();
+}
+
+bool ConvolutionWinogradImpl::canUseWinograd(const Convolution2DCommon *common) {
+    if (common->kernelY() != common->kernelX() || common->kernelY() <= 1) {
+        return false;
+    }
+    if (common->dilateX() != 1 || common->dilateY() != 1) {
+        return false;
+    }
+    if (common->strideX() != 1 || common->strideY() != 1) {
+        return false;
+    }
+    return true;
+}
+
+} // namespace MNN