[mlir][tosa] Add tosa.conv2d as fully_connected canonicalization

[not-jenni]

Summary

For a 1x1 weight and stride of 1, the input/weight can be reshaped and passed into a fully connected op then reshaped back

mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td

def Tosa_Conv2DOp : Tosa_Op<"conv2d", [
 
   let results = (outs
     Tosa_Tensor4D:$output
   );
 
   let builders = [Tosa_ConvOpQuantInfoBuilder];
 
   let verifier = [{ return verifyConvOp(*this); }];
+
+  let hasCanonicalizer = 1;
 }
 
 //===----------------------------------------------------------------------===//
 // Operator: conv3d
 //===----------------------------------------------------------------------===//
 def Tosa_Conv3DOp : Tosa_Op<"conv3d", [
     DeclareOpInterfaceMethods<InferShapedTypeOpInterface,
                               ["inferReturnTypeComponents"]>,

mlir/lib/Dialect/Tosa/IR/TosaOps.cpp

struct MaterializePadValue : public OpRewritePattern<tosa::PadOp> {
   }
 };
 
 void PadOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
                                         MLIRContext *context) {
   results.insert<MaterializePadValue>(context);
 }
 
+struct Conv2DFullyConnectedOptimization
+    : public OpRewritePattern<tosa::Conv2DOp> {
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(tosa::Conv2DOp op,
+                                PatternRewriter &rewriter) const override {
+    Value input = op.input();
+    Value weight = op.weight();
+    ShapedType inputType = input.getType().cast<ShapedType>();
+    ShapedType weightType = weight.getType().cast<ShapedType>();
+
+    if (!inputType.hasStaticShape() || !weightType.hasRank()) {
+      return failure();
+    }
+
+    // Stride must be 1 for this optimization.
+    for (Attribute stride : op.stride().getValue()) {
+      if (!stride.cast<IntegerAttr>().getValue().isOne()) {
+        return failure();
+      }
+    }
+
+    // Only works for a 1x1 kernel.
+    ArrayRef<int64_t> weightShape = weightType.getShape();
+    if (weightShape[1] != 1 || weightShape[2] != 1) {
+      return failure();
+    }
+
+    // Reshape input to [N,IH,IW,IC] -> [N * IH * IW, IC].
+    ArrayRef<int64_t> inputShape = inputType.getShape();
+    llvm::SmallVector<int64_t, 2> revisedInputShape{
+        inputShape[0] * inputShape[1] * inputShape[2], inputShape[3]};
+    auto revisedInputShapeType = RankedTensorType::get(
+        revisedInputShape,
+        input.getType().dyn_cast<RankedTensorType>().getElementType());
+    auto reshapedInput = rewriter
+                             .create<tosa::ReshapeOp>(
+                                 op.getLoc(), revisedInputShapeType, input,
+                                 rewriter.getI64ArrayAttr(revisedInputShape))
+                             .getResult();
+
+    // Reshape kernel to [OC,KH,KW,IC] -> [OC, IC].
+    llvm::SmallVector<int64_t, 2> revisedWeightShape{weightShape[0],
+                                                     weightShape[3]};
+    auto revisedWeightShapeType = RankedTensorType::get(
+        revisedWeightShape,
+        weight.getType().dyn_cast<RankedTensorType>().getElementType());
+    auto reshapedWeight = rewriter
+                              .create<tosa::ReshapeOp>(
+                                  op.getLoc(), revisedWeightShapeType, weight,
+                                  rewriter.getI64ArrayAttr(revisedWeightShape))
+                              .getResult();
+
+    // Perform a fully connected network over the reshaped input and weight.
+    llvm::SmallVector<int64_t, 2> fullyConnectedShape{
+        inputShape[0] * inputShape[1] * inputShape[2], inputShape[3]};
+    auto fullyConnectedShapeType = RankedTensorType::get(
+        fullyConnectedShape,
+        weight.getType().dyn_cast<RankedTensorType>().getElementType());
+
+    Value fullyConnectedValue;
+    if (op.quantization_info()) {
+      fullyConnectedValue =
+          rewriter
+              .create<tosa::FullyConnectedOp>(
+                  op.getLoc(), fullyConnectedShapeType, reshapedInput,
+                  reshapedWeight, op.bias(), op.quantization_info().getValue())
+              .getResult();
+    } else {
+      fullyConnectedValue = rewriter
+                                .create<tosa::FullyConnectedOp>(
+                                    op.getLoc(), fullyConnectedShapeType,
+                                    reshapedInput, reshapedWeight, op.bias())
+                                .getResult();
+    }
+
+    // Reshape output to [N, IH, IW, OC].
+    llvm::SmallVector<int64_t, 4> outputShape{inputShape[0], inputShape[1],
+                                              inputShape[2], weightShape[0]};
+    auto outputShapeType = RankedTensorType::get(
+        outputShape,
+        input.getType().dyn_cast<RankedTensorType>().getElementType());
+    rewriter.replaceOpWithNewOp<tosa::ReshapeOp>(
+        op, outputShapeType, fullyConnectedValue,
+        rewriter.getI64ArrayAttr(outputShape));
+    return success();
+  }
+};
+
+void Conv2DOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
+                                           MLIRContext *context) {
+  results.insert<Conv2DFullyConnectedOptimization>(context);
+}
+
 //===----------------------------------------------------------------------===//
 // Operator Folders.
 //===----------------------------------------------------------------------===//
 
 OpFoldResult CastOp::fold(ArrayRef<Attribute> operands) {
   if (input().getType() == getType())
     return input();
   return {};

mlir/test/Dialect/Tosa/canonicalize.mlir

 // CHECK-LABEL: @concat_fold_cast
 func @concat_fold_cast(%arg0: tensor<?x1xf32>) -> tensor<?x?xf32> {
   // CHECK: %[[VAR0:.*]] = tensor.cast %arg0
   // CHECK: return %[[VAR0]]
   %0 = "tosa.concat"(%arg0) {axis = 0 : i64}: (tensor<?x1xf32>) -> tensor<?x?xf32>
   return %0 : tensor<?x?xf32>
 }
 
+// -----
+
+// CHECK-LABEL: @conv2d_as_fully_connected
+func @conv2d_as_fully_connected(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor<3x1x1x2xf32>, %arg2: tensor<3xf32>) -> tensor<4x10x10x3xf32> {
+  // CHECK-NOT: "tosa.conv2d"
+  // CHECK: %[[VAR0:.*]] = "tosa.reshape"(%arg0) {new_shape = [400, 2]}
+  // CHECK: %[[VAR1:.*]] = "tosa.reshape"(%arg1) {new_shape = [3, 2]}
+  // CHECK: %[[VAR2:.*]] = "tosa.fully_connected"(%[[VAR0]], %[[VAR1]], %arg2)
+  // CHECK: %[[VAR3:.*]] = "tosa.reshape"(%[[VAR2]]) {new_shape = [4, 10, 10, 3]}
+  // CHECK: return %[[VAR3]]
+  %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {pad = [0, 0, 0, 0], stride = [1, 1], dilation = [1, 1]} : (tensor<4x10x10x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<4x10x10x3xf32>
+  return %0 : tensor<4x10x10x3xf32>
+}
+
+// -----
+
+// CHECK-LABEL: @conv2d_stride_2
+func @conv2d_stride_2(%arg0: tensor<4x10x10x2xf32>) -> tensor<4x10x10x3xf32> {
+  // CHECK: "tosa.conv2d"
+  %weight = "tosa.const"() {value = dense<[[[[1.0, 1.0]]], [[[1.0, 1.0]]], [[[1.0, 1.0]]]]> : tensor<3x1x1x2xf32>} : ()-> tensor<3x1x1x2xf32>
+  %bias = "tosa.const"() {value = dense<0.0> : tensor<3xf32>} : ()-> tensor<3xf32>
+  %0 = "tosa.conv2d"(%arg0, %weight, %bias) {pad = [0, 0, 0, 0], stride = [2, 2], dilation = [1, 1]} : (tensor<4x10x10x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<4x10x10x3xf32>
+  return %0 : tensor<4x10x10x3xf32>
+}
+
+// -----
+
+// CHECK-LABEL: @conv2d_weight_2x2
+func @conv2d_weight_2x2(%arg0: tensor<4x10x10x1xf32>) -> tensor<4x10x10x1xf32> {
+  // CHECK: "tosa.conv2d"
+  %weight = "tosa.const"() {value = dense<[[[[1.0], [1.0]], [[1.0], [1.0]]]]> : tensor<1x2x2x1xf32>} : ()-> tensor<1x2x2x1xf32>
+  %bias = "tosa.const"() {value = dense<0.0> : tensor<1xf32>} : ()-> tensor<1xf32>
+  %0 = "tosa.conv2d"(%arg0, %weight, %bias) {pad = [0, 0, 0, 0], stride = [1, 1], dilation = [1, 1]} : (tensor<4x10x10x1xf32>, tensor<1x2x2x1xf32>, tensor<1xf32>) -> tensor<4x10x10x1xf32>
+  return %0 : tensor<4x10x10x1xf32>
+}
+
 // ----
 
 // CHECK-LABEL: @pad_noop
 func @pad_noop(%arg0: tensor<?x?xf32>) -> tensor<?x?xf32> {
   // CHECK: return %arg0
-  %0 = "tosa.const"() { value = dense<0> : tensor<2x2xi32>} : () -> tensor<2x2xi32> 
+  %0 = "tosa.const"() { value = dense<0> : tensor<2x2xi32>} : () -> tensor<2x2xi32>
   %1 = "tosa.pad"(%arg0, %0) : (tensor<?x?xf32>, tensor<2x2xi32>) -> tensor<?x?xf32>
   return %1 : tensor<?x?xf32>
 }
 
 // ----
 
 // CHECK-LABEL: @pad_determine_val_i32
 func @pad_determine_val_i32(%arg0: tensor<?x?xi32>, %arg1 : tensor<2x2xi32>) -> tensor<?x?xi32> {
   // CHECK: %[[ZERO:.+]] = "tosa.const"() {value = dense<0> : tensor<i32>}
   // CHECK: "tosa.pad"(%arg0, %arg1, %[[ZERO]])
-  %0 = "tosa.const"() { value = dense<[[1, 0], [0, 1]]> : tensor<2x2xi32>} : () -> tensor<2x2xi32> 
+  %0 = "tosa.const"() { value = dense<[[1, 0], [0, 1]]> : tensor<2x2xi32>} : () -> tensor<2x2xi32>
   %1 = "tosa.pad"(%arg0, %arg1) : (tensor<?x?xi32>, tensor<2x2xi32>) -> tensor<?x?xi32>
   return %1 : tensor<?x?xi32>
 }
 
 // ----
 
 // CHECK-LABEL: @pad_determine_val_f32
 func @pad_determine_val_f32(%arg0: tensor<?x?xf32>, %arg1 : tensor<2x2xi32>) -> tensor<?x?xf32> {
   // CHECK: %[[ZERO:.+]] = "tosa.const"() {value = dense<0.000000e+00> : tensor<f32>}
   // CHECK: "tosa.pad"(%arg0, %arg1, %[[ZERO]])
-  %0 = "tosa.const"() { value = dense<[[1, 0], [0, 1]]> : tensor<2x2xi32>} : () -> tensor<2x2xi32> 
+  %0 = "tosa.const"() { value = dense<[[1, 0], [0, 1]]> : tensor<2x2xi32>} : () -> tensor<2x2xi32>
   %1 = "tosa.pad"(%arg0, %arg1) : (tensor<?x?xf32>, tensor<2x2xi32>) -> tensor<?x?xf32>
   return %1 : tensor<?x?xf32>
 }
 
 // ----
 
 // CHECK-LABEL: @pad_determine_val_quant
 func @pad_determine_val_quant(%arg0: tensor<?x?xi32>, %arg1 : tensor<2x2xi32>) -> tensor<?x?xi32> {
   // CHECK: %[[ZERO:.+]] = "tosa.const"() {value = dense<42> : tensor<i32>}
   // CHECK: "tosa.pad"(%arg0, %arg1, %[[ZERO]])
-  %0 = "tosa.const"() { value = dense<[[1, 0], [0, 1]]> : tensor<2x2xi32>} : () -> tensor<2x2xi32> 
+  %0 = "tosa.const"() { value = dense<[[1, 0], [0, 1]]> : tensor<2x2xi32>} : () -> tensor<2x2xi32>
   %1 = "tosa.pad"(%arg0, %arg1) { quantization_info = {input_zp = 42:i32} } : (tensor<?x?xi32>, tensor<2x2xi32>) -> tensor<?x?xi32>
   return %1 : tensor<?x?xi32>
 }
 
 // -----
 
 // CHECK-LABEL: @mul_one_different_shape
 func @mul_one_different_shape(%arg0: tensor<2x3xf32>) -> tensor<4x2x3xf32> {