[mlir][tosa] Add tosa.conv2d as fully_connected canonicalization
For a 1x1 weight and stride of 1, the input/weight can be reshaped and passed into a fully connected op then reshaped back
Reviewed By: rsuderman
Differential Revision: https://reviews.llvm.org/D114757
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
index 4de9005..554023d 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
@@ -118,6 +118,8 @@
let builders = [Tosa_ConvOpQuantInfoBuilder];
let verifier = [{ return verifyConvOp(*this); }];
+
+ let hasCanonicalizer = 1;
}
//===----------------------------------------------------------------------===//
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
index 2a9d5d4..1583033 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -423,6 +423,100 @@
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.
//===----------------------------------------------------------------------===//
diff --git a/mlir/test/Dialect/Tosa/canonicalize.mlir b/mlir/test/Dialect/Tosa/canonicalize.mlir
index 70f2665..39554d1 100644
--- a/mlir/test/Dialect/Tosa/canonicalize.mlir
+++ b/mlir/test/Dialect/Tosa/canonicalize.mlir
@@ -66,12 +66,48 @@
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>
}
@@ -82,7 +118,7 @@
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>
}
@@ -93,7 +129,7 @@
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>
}
@@ -104,7 +140,7 @@
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>
}
