[mlir][linalg] Add transform operator for Winograd Conv2D algorithm

mlir/include/mlir/Dialect/Linalg/IR/LinalgOps.td

@@ -154,4 +154,121 @@ def Linalg_SoftmaxOp : Linalg_Op<"softmax",
   let hasVerifier = 1;
 }
 
+def Linalg_WinogradFilterTransformOp :
+    Linalg_Op<"winograd_filter_transform", [AllElementTypesMatch<["filter", "output"]>]> {
+  let summary = "Winograd filter transform operator";
+  let description = [{
+    Winograd Conv2D algorithm will convert linalg Conv2D operator into batched
+    matrix multiply. Before the matrix multiply, it will convert filter and
+    input into a format suitable for batched matrix multiply. After the matrix
+    multiply, it will convert output to the final result tensor.
+
+    The algorithm F(m x m, r x r) is
+
+    Y = A^T x [(G x g x G^T) @ (B^T x d x B)] x A
+
+    The size of output Y is m x m. The size of filter g is r x r. The size of
+    input d is (m + r - 1) x (m + r - 1). A^T, A, G^T, G, B^T, and B are
+    transformation matrices.
+
+    This operator is defined to represent the high level concept of filter
+    transformation (G x g x G^T) in the Winograd Conv2D algorithm.
+  }];
+
+  let arguments = (ins TensorRankOf<[AnyType], [4]>:$filter,
+                       TensorRankOf<[AnyType], [4]>:$output,
+                       I64Attr:$m,
+                       I64Attr:$r
+  );
+
+  let results = (outs TensorRankOf<[AnyType], [4]>:$result);
+  let assemblyFormat = [{
+    attr-dict
+    `m` `(` $m `)`
+    `r` `(` $r `)`
+    `ins` `(` $filter `:` type($filter) `)`
+    `outs` `(` $output `:` type($output) `)`
+    `->` type($result)
+  }];
+  let hasVerifier = 1;
+}
+
+def Linalg_WinogradInputTransformOp :
+    Linalg_Op<"winograd_input_transform", [AllElementTypesMatch<["input", "output"]>]> {
+  let summary = "Winograd input transform operator";
+  let description = [{
+    Winograd Conv2D algorithm will convert linalg Conv2D operator into batched
+    matrix multiply. Before the matrix multiply, it will convert filter and
+    input into a format suitable for batched matrix multiply. After the matrix
+    multiply, it will convert output to the final result tensor.
+
+    The algorithm F(m x m, r x r) is
+
+    Y = A^T x [(G x g x G^T) @ (B^T x d x B)] x A
+
+    The size of output Y is m x m. The size of filter g is r x r. The size of
+    input d is (m + r - 1) x (m + r - 1). A^T, A, G^T, G, B^T, and B are
+    transformation matrices.
+
+    This operator is defined to represent the high level concept of input
+    transformation (B^T x d x B) in the Winograd Conv2D algorithm.
+  }];
+
+  let arguments = (ins TensorRankOf<[AnyType], [4]>:$input,
+                       TensorRankOf<[AnyType], [6]>:$output,
+                       I64Attr:$m,
+                       I64Attr:$r
+  );
+
+  let results = (outs TensorRankOf<[AnyType], [6]>:$result);
+  let assemblyFormat = [{
+    attr-dict
+    `m` `(` $m `)`
+    `r` `(` $r `)`
+    `ins` `(` $input `:` type($input) `)`
+    `outs` `(` $output `:` type($output) `)`
+    `->` type($result)
+  }];
+  let hasVerifier = 1;
+}
+
+def Linalg_WinogradOutputTransformOp :
+    Linalg_Op<"winograd_output_transform", [AllElementTypesMatch<["value", "output"]>]> {
+  let summary = "Winograd output transform operator";
+  let description = [{
+    Winograd Conv2D algorithm will convert linalg Conv2D operator into batched
+    matrix multiply. Before the matrix multiply, it will convert filter and
+    input into a format suitable for batched matrix multiply. After the matrix
+    multiply, it will convert output to the final result tensor.
+
+    The algorithm F(m x m, r x r) is
+
+    Y = A^T x [(G x g x G^T) @ (B^T x d x B)] x A
+
+    The size of output Y is m x m. The size of filter g is r x r. The size of
+    input d is (m + r - 1) x (m + r - 1). A^T, A, G^T, G, B^T, and B are
+    transformation matrices.
+
+    This operator is defined to represent the high level concept of output
+    transformation (A^T x y x A) in the Winograd Conv2D algorithm.
+  }];
+
+  let arguments = (ins TensorRankOf<[AnyType], [6]>:$value,
+                       TensorRankOf<[AnyType], [4]>:$output,
+                       I64Attr:$m,
+                       I64Attr:$r
+  );
+
+  let results = (outs TensorRankOf<[AnyType], [4]>:$result);
+  let assemblyFormat = [{
+    attr-dict
+    `m` `(` $m `)`
+    `r` `(` $r `)`
+    `ins` `(` $value `:` type($value) `)`
+    `outs` `(` $output `:` type($output) `)`
+    `->` type($result)
+  }];
+  let hasVerifier = 1;
+}
+
 #endif // LINALG_OPS

mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td

@@ -2587,4 +2587,55 @@ def MapCopyToThreadsOp :
   }];
 }
 
+//===----------------------------------------------------------------------===//
+// Winograd Conv2D
+//===----------------------------------------------------------------------===//
+
+def WinogradConv2DOp : Op<Transform_Dialect,
+    "structured.winograd_conv2d",
+    [FunctionalStyleTransformOpTrait, MemoryEffectsOpInterface,
+     TransformOpInterface, TransformEachOpTrait,
+     ReportTrackingListenerFailuresOpTrait]> {
+  let description = [{
+    Winograd Conv2D algorithm will convert linalg Conv2D operation into batched
+    matrix multiply. Before the matrix multiply, it will convert filter and
+    input into a format suitable for batched matrix multiply. After the matrix
+    multiply, it will convert output to the final result tensor.
+
+    The algorithm F(m x m, r x r) is
+
+    Y = A^T x [(G x g x G^T) @ (B^T x d x B)] x A
+
+    The size of output Y is m x m. The size of filter g is r x r. The size of
+    input d is (m + r - 1) x (m + r - 1). A^T, A, G^T, G, B^T, and B are
+    transformation matrices.
+
+    #### Return modes:
+
+    This operation produces a silenceable failure if `target` is unsupported.
+    Otherwise, the operation succeeds and returns a handle of the sequence that
+    replaces the original convolution.
+  }];
+
+  let arguments = (ins TransformHandleTypeInterface:$target,
+                       I64Attr:$m,
+                       I64Attr:$r);
+  let results = (outs TransformHandleTypeInterface:$transformed);
+
+  let assemblyFormat =
+    "$target attr-dict `:` functional-type($target, results)";
+
+  let builders = [
+    OpBuilder<(ins "Value":$target)>
+  ];
+
+  let extraClassDeclaration = [{
+    ::mlir::DiagnosedSilenceableFailure applyToOne(
+        ::mlir::transform::TransformRewriter &rewriter,
+        ::mlir::linalg::LinalgOp target,
+        ::mlir::transform::ApplyToEachResultList &results,
+        ::mlir::transform::TransformState &state);
+  }];
+}
+
 #endif // LINALG_TRANSFORM_OPS

mlir/include/mlir/Dialect/Linalg/Transforms/Transforms.h

@@ -1312,6 +1312,13 @@ FailureOr<Operation *> transposeBatchMatmul(RewriterBase &rewriter,
                                             linalg::BatchMatmulOp op,
                                             bool transposeLHS = true);
 
+/// Convert linalg.conv_2d_nhwc_fhwc to Winograd Conv2D algorithm
+/// F(m x m, r x r). m is the dimension size of output and r is the dimension
+/// size of filter.
+FailureOr<Operation *> winogradConv2D(RewriterBase &rewriter,
+                                      linalg::Conv2DNhwcFhwcOp op, int64_t m,
+                                      int64_t r);
+
 //===----------------------------------------------------------------------===//
 // Rewrite patterns wrapping transformations.
 // TODO: every single such pattern should be a close to noop wrapper around a
@@ -1692,6 +1699,10 @@ void populateTransposeMatmulPatterns(RewritePatternSet &patterns,
 void populateBlockPackMatmulPatterns(RewritePatternSet &patterns,
                                      const ControlBlockPackMatmulFn &controlFn);
 
+/// Patterns to apply Winograd Conv2D algorithm F(m x m, r x r).
+void populateWinogradConv2DPatterns(RewritePatternSet &patterns, int64_t m,
+                                    int64_t r);
+
 } // namespace linalg
 } // namespace mlir
 

mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp

@@ -2734,6 +2734,113 @@ FailureOr<SmallVector<Value>> SoftmaxOp::decomposeOperation(OpBuilder &b) {
   return SmallVector<Value>{result};
 }
 
+//===----------------------------------------------------------------------===//
+// WinogradFilterTransformOp
+//===----------------------------------------------------------------------===//
+
+LogicalResult WinogradFilterTransformOp::verify() {
+  auto filterType = cast<ShapedType>(getFilter().getType());
+  ArrayRef<int64_t> filterShape = filterType.getShape();
+  int64_t filterH = filterShape[1];
+  int64_t filterW = filterShape[2];
+  int64_t r = getR();
+
+  if (filterH != r && filterH != 1)
+    return failure();
+  if (filterW != r && filterW != 1)
+    return failure();
+  if (filterH == 1 && filterW == 1)
+    return failure();
+
+  return success();
+}
+
+//===----------------------------------------------------------------------===//
+// WinogradInputTransformOp
+//===----------------------------------------------------------------------===//
+
+LogicalResult WinogradInputTransformOp::verify() {
+  auto inputType = cast<ShapedType>(getInput().getType());
+  ArrayRef<int64_t> inputShape = inputType.getShape();
+  int64_t inputH = inputShape[1];
+  int64_t inputW = inputShape[2];
+  auto outputType = cast<ShapedType>(getOutput().getType());
+  ArrayRef<int64_t> outputShape = outputType.getShape();
+  int64_t outputH = outputShape[0];
+  int64_t outputW = outputShape[1];
+  int64_t outputTileH = outputShape[2];
+  int64_t outputTileW = outputShape[3];
+  int m = getM();
+  int r = getR();
+  bool leftTransform = inputH != 1;
+  bool rightTransform = inputW != 1;
+
+  if (!leftTransform && !rightTransform)
+    return failure();
+
+  if (leftTransform) {
+    int64_t tileH = (inputH - (r - 1)) / m;
+    if (inputH != tileH * m + (r - 1))
+      return failure();
+    if (tileH != outputTileH)
+      return failure();
+    if (outputH != m + r - 1)
+      return failure();
+  }
+
+  if (rightTransform) {
+    int64_t tileW = (inputW - (r - 1)) / m;
+    if (inputW != tileW * m + (r - 1))
+      return failure();
+    if (tileW != outputTileW)
+      return failure();
+    if (outputW != m + r - 1)
+      return failure();
+  }
+
+  return success();
+}
+
+//===----------------------------------------------------------------------===//
+// WinogradOutputTransformOp
+//===----------------------------------------------------------------------===//
+
+LogicalResult WinogradOutputTransformOp::verify() {
+  auto valueType = cast<ShapedType>(getValue().getType());
+  ArrayRef<int64_t> valueShape = valueType.getShape();
+  int64_t valueH = valueShape[0];
+  int64_t valueW = valueShape[1];
+  int64_t valueTileH = valueShape[2];
+  int64_t valueTileW = valueShape[3];
+  auto outputType = cast<ShapedType>(getOutput().getType());
+  ArrayRef<int64_t> outputShape = outputType.getShape();
+  int64_t outputH = outputShape[1];
+  int64_t outputW = outputShape[2];
+  int m = getM();
+  int r = getR();
+  bool leftTransform = valueH != 1;
+  bool rightTransform = valueW != 1;
+
+  if (!leftTransform && !rightTransform)
+    return failure();
+
+  if (leftTransform) {
+    if (valueH != m + r - 1)
+      return failure();
+    if (outputH != m * valueTileH)
+      return failure();
+  }
+
+  if (rightTransform) {
+    if (valueW != m + r - 1)
+      return failure();
+    if (outputW != m * valueTileW)
+      return failure();
+  }
+
+  return success();
+}
+
 //===----------------------------------------------------------------------===//
 // LinalgDialect
 //===----------------------------------------------------------------------===//

mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp

@@ -3480,6 +3480,35 @@ DiagnosedSilenceableFailure transform::MapCopyToThreadsOp::applyToOne(
   return DiagnosedSilenceableFailure::success();
 }
 
+//===----------------------------------------------------------------------===//
+// WinogradConv2DOp
+//===----------------------------------------------------------------------===//
+
+DiagnosedSilenceableFailure transform::WinogradConv2DOp::applyToOne(
+    transform::TransformRewriter &rewriter, linalg::LinalgOp target,
+    transform::ApplyToEachResultList &results,
+    transform::TransformState &state) {
+  rewriter.setInsertionPoint(target);
+  FailureOr<Operation *> maybeTransformed = failure();
+  bool supported = TypeSwitch<Operation *, bool>(target)
+                       .Case([&](linalg::Conv2DNhwcFhwcOp op) {
+                         maybeTransformed =
+                             winogradConv2D(rewriter, op, getM(), getR());
+                         return true;
+                       })
+                       .Default([&](Operation *op) {
+                         op->emitError("not supported");
+                         return false;
+                       });
+
+  if (supported && failed(maybeTransformed)) {
+    return emitSilenceableError() << "apply Winograd Conv2D failed";
+  }
+
+  results.push_back(*maybeTransformed);
+  return DiagnosedSilenceableFailure::success();
+}
+
 #include "mlir/Dialect/Linalg/TransformOps/LinalgTransformOpsEnums.cpp.inc"
 
 #define GET_OP_CLASSES

mlir/lib/Dialect/Linalg/Transforms/CMakeLists.txt

@@ -38,6 +38,7 @@ add_mlir_dialect_library(MLIRLinalgTransforms
   Transforms.cpp
   TransposeConv2D.cpp
   Vectorization.cpp
+  WinogradConv2D.cpp
 
   ADDITIONAL_HEADER_DIRS
   ${MLIR_MAIN_INCLUDE_DIR}/mlir/Dialect/Linalg