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[mlir][tosa] Add tosa.conv2d as fully_connected canonicalization
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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
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@not-jenni @rsuderman
not-jenni authored and rsuderman committed Nov 30, 2021
1 parent 2e114e3 commit 13bdb7a
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Showing 3 changed files with 136 additions and 4 deletions.
2 changes: 2 additions & 0 deletions mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
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Expand Up @@ -118,6 +118,8 @@ def Tosa_Conv2DOp : Tosa_Op<"conv2d", [
let builders = [Tosa_ConvOpQuantInfoBuilder];

let verifier = [{ return verifyConvOp(*this); }];

let hasCanonicalizer = 1;
}

//===----------------------------------------------------------------------===//
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94 changes: 94 additions & 0 deletions mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
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Expand Up @@ -423,6 +423,100 @@ void PadOp::getCanonicalizationPatterns(OwningRewritePatternList &results,
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.
//===----------------------------------------------------------------------===//
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44 changes: 40 additions & 4 deletions mlir/test/Dialect/Tosa/canonicalize.mlir
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Expand Up @@ -66,12 +66,48 @@ func @concat_fold_cast(%arg0: 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>
}
Expand All @@ -82,7 +118,7 @@ func @pad_noop(%arg0: tensor<?x?xf32>) -> tensor<?x?xf32> {
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>
}
Expand All @@ -93,7 +129,7 @@ func @pad_determine_val_i32(%arg0: tensor<?x?xi32>, %arg1 : tensor<2x2xi32>) ->
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>
}
Expand All @@ -104,7 +140,7 @@ func @pad_determine_val_f32(%arg0: tensor<?x?xf32>, %arg1 : tensor<2x2xi32>) ->
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>
}
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