[Mlir-commits] [mlir] [mlir][linalg] Implement Conv2D using Winograd Conv2D algorithm (PR #96181)

[Oleksandr Alex Zinenko]

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+//===- WinogradConv2D.cpp - Winograd Conv2D implementation ----------------===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+//
+// Implement Winograd Conv2D algorithm. The implementation is based on the
+// paper: Fast Algorithms for Convolutional Neural Networks
+// (https://arxiv.org/abs/1509.09308)
+//
+//===----------------------------------------------------------------------===//
+
+#include "mlir/Dialect/Linalg/IR/Linalg.h"
+#include "mlir/Dialect/Tensor/IR/Tensor.h"
+#include "mlir/Dialect/Tosa/Utils/ConversionUtils.h"
+#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
+#include "llvm/Support/MathExtras.h"
+
+namespace mlir {
+namespace linalg {
+
+namespace {
+
+using TransformMapKeyTy = std::pair<int, int>;
+
+// We use F(m, r) to define the size of minimal filtering algorithms.
+// m is the output dimension and r is the filter dimension. We can get
+// the input dimension, alpha, from the formula, alpha = m + r - 1.
+//
+// For example, when m = 2 and r = 3, we know its input size is 4.
+// The Conv2D will operate on 4x4 input data with 3x3 filter and get
+// 2x2 output result.
+constexpr TransformMapKeyTy F_2_3{2, 3};
+constexpr TransformMapKeyTy F_4_3{4, 3};
+constexpr TransformMapKeyTy F_2_5{2, 5};
+
+Value collapse2DData(RewriterBase &rewriter, Location loc, Value data) {
+  auto type = cast<ShapedType>(data.getType());
+  auto elementType = type.getElementType();
+  auto shape = type.getShape();
+  auto collapseType = RankedTensorType::get(
+      {shape[0] * shape[1] * shape[2] * shape[3], shape[4], shape[5]},
+      elementType);
+  SmallVector<ReassociationIndices> reassociation = {{0, 1, 2, 3}, {4}, {5}};
+  return rewriter.create<tensor::CollapseShapeOp>(loc, collapseType, data,
+                                                  reassociation);
+}
+
+// This function generates linalg.batch_matmul to multiply input with filter.
+// linalg.batch_matmul only supports 3-dimension data sets. We can treat
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ftynse wrote:

Same below. 

https://github.com/llvm/llvm-project/pull/96181