Automerge: [mlir][tosa] Fold PadOp to tensor operations (#132700)

Author: GeorgeARM

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

@@ -107,6 +107,7 @@ def Tosa_AvgPool2dOp : Tosa_InferShapedTypeOp<"avg_pool2d"> {
     LogicalResult verifyOutputZeroPoint(int64_t zp);
   }];
 
+  let hasCanonicalizer = 1;
   let hasVerifier = 1;
 }
 
@@ -153,6 +154,8 @@ def Tosa_Conv2DOp : Tosa_ConvOp<"conv2d"> {
   }];
 
   let builders = [Tosa_ConvOpQuantInfoBuilder];
+
+  let hasCanonicalizer = 1;
   let hasVerifier = 1;
 }
 
@@ -244,6 +247,8 @@ def Tosa_DepthwiseConv2DOp : Tosa_ConvOp<"depthwise_conv2d"> {
   }];
 
   let builders = [Tosa_ConvOpQuantInfoBuilder];
+
+  let hasCanonicalizer = 1;
   let hasVerifier = 1;
 }
 

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

@@ -39,6 +39,280 @@ using namespace mlir::tosa;
 // Operator Canonicalizers.
 //===----------------------------------------------------------------------===//
 
+//===----------------------------------------------------------------------===//
+// Tensor Data Engine Operators.
+//===----------------------------------------------------------------------===//
+
+// Check that the zero point of the tensor and padding operations are aligned.
+bool checkMatchingPadConstAndZp(Value padConst, Value zp) {
+  // Check that padConst is a constant value and a scalar tensor
+  DenseElementsAttr padConstAttr;
+  if (!matchPattern(padConst, m_Constant(&padConstAttr)) ||
+      (padConstAttr.size() != 1)) {
+    return false;
+  }
+
+  // Check that floating point pad is zero
+  if (auto padConstFpAttr = mlir::dyn_cast<DenseFPElementsAttr>(padConstAttr)) {
+    float padConstVal = (*padConstFpAttr.begin()).convertToFloat();
+    return padConstVal == 0.0f;
+  }
+
+  // Check that the zp and padConst align for the integer (quantized) case
+  if (auto padConstIntAttr =
+          mlir::dyn_cast<DenseIntElementsAttr>(padConstAttr)) {
+    DenseIntElementsAttr zpAttr;
+    // Check that zp is a constant value and a scalar tensor
+    if (!matchPattern(zp, m_Constant(&zpAttr)) || (padConstAttr.size() != 1)) {
+      return false;
+    }
+
+    // Check equality
+    int64_t zpVal = (*zpAttr.begin()).getSExtValue();
+    int64_t padConstVal = (*padConstIntAttr.begin()).getSExtValue();
+    return zpVal == padConstVal;
+  }
+
+  // Bail-out on unsupported type
+  return false;
+}
+
+namespace {
+template <typename OpTy>
+struct PoolPadFoldAdaptor;
+
+template <>
+struct PoolPadFoldAdaptor<tosa::AvgPool2dOp> {
+  using OpTy = tosa::AvgPool2dOp;
+  static bool checkKernelCompliance(OpTy op, const ArrayRef<int64_t> newPad) {
+    const llvm::ArrayRef<int64_t> kernel = op.getKernel();
+    if (newPad[2] >= kernel[1] || newPad[3] >= kernel[1] ||
+        newPad[0] >= kernel[0] || newPad[1] >= kernel[0])
+      return false;
+    return true;
+  }
+  static bool checkPadConstCompliance(OpTy op, Value padConst) {
+    return checkMatchingPadConstAndZp(padConst, op.getInputZp());
+  }
+  static void replaceOpWithNewPad(PatternRewriter &rewriter, OpTy op,
+                                  Value padInput, ArrayRef<int64_t> newPad) {
+    rewriter.replaceOpWithNewOp<tosa::AvgPool2dOp>(
+        op, op.getType(), padInput, op.getInputZp(), op.getOutputZp(),
+        op.getKernel(), op.getStride(), rewriter.getDenseI64ArrayAttr(newPad),
+        op.getAccType());
+  }
+};
+
+template <>
+struct PoolPadFoldAdaptor<tosa::MaxPool2dOp> {
+  using OpTy = tosa::MaxPool2dOp;
+  static bool checkKernelCompliance(OpTy op, const ArrayRef<int64_t> newPad) {
+    const llvm::ArrayRef<int64_t> kernel = op.getKernel();
+    if (newPad[2] >= kernel[1] || newPad[3] >= kernel[1] ||
+        newPad[0] >= kernel[0] || newPad[1] >= kernel[0])
+      return false;
+    return true;
+  }
+  static bool checkPadConstCompliance(OpTy, Value padConst) {
+    // Check that padConst is a constant value and a scalar tensor
+    DenseElementsAttr padConstAttr;
+    if (!matchPattern(padConst, m_Constant(&padConstAttr)) ||
+        padConstAttr.size() != 1) {
+      return false;
+    }
+
+    // Pad needs to be in the minimum value to be able to merge
+    if (auto padConstFpAttr =
+            mlir::dyn_cast<DenseFPElementsAttr>(padConstAttr)) {
+      const APFloat padConstVal = *padConstFpAttr.begin();
+      const APFloat lowestVal =
+          APFloat::getLargest(padConstVal.getSemantics(), true);
+      return padConstVal == lowestVal;
+    } else if (auto padConstIntAttr =
+                   mlir::dyn_cast<DenseIntElementsAttr>(padConstAttr)) {
+      const APInt padConstVal = *padConstIntAttr.begin();
+      const unsigned int bitWidth = padConstVal.getBitWidth();
+      const APInt lowestVal =
+          padConstIntAttr.getElementType().isUnsignedInteger()
+              ? APInt::getZero(bitWidth)
+              : APInt::getSignedMinValue(bitWidth);
+      return padConstVal == lowestVal;
+    }
+
+    // Bail-out on unsupported type
+    return false;
+  }
+  static void replaceOpWithNewPad(PatternRewriter &rewriter, OpTy op,
+                                  Value padInput, ArrayRef<int64_t> newPad) {
+    rewriter.replaceOpWithNewOp<tosa::MaxPool2dOp>(
+        op, op.getType(), padInput, op.getKernel(), op.getStride(),
+        rewriter.getDenseI64ArrayAttr(newPad), op.getNanMode());
+  }
+};
+
+template <typename OpTy>
+struct ConvPadFoldAdaptor {
+  static bool checkKernelCompliance(OpTy, const ArrayRef<int64_t>) {
+    return true;
+  }
+  static bool checkPadConstCompliance(OpTy op, Value padConst) {
+    return checkMatchingPadConstAndZp(padConst, op.getInputZp());
+  }
+  static void replaceOpWithNewPad(PatternRewriter &rewriter, OpTy op,
+                                  Value padInput, ArrayRef<int64_t> newPad) {
+    rewriter.replaceOpWithNewOp<OpTy>(
+        op, op.getResult().getType(), padInput, op.getWeight(), op.getBias(),
+        op.getInputZp(), op.getWeightZp(), newPad, op.getStrideAttr(),
+        op.getDilationAttr(), op.getAccType(), op.getLocalBound());
+  }
+};
+
+// Pattern attempts to fold a `tosa.pad` operator to a following tensor
+// operation like `tosa.conv2d` by merging the padding associated with the
+// pad operator directly to the implicit padding of the tensor operation.
+// This helps eliminate the explicit padding operator if unused.
+template <typename OpTy, typename AdaptorTy>
+struct FoldPadToTensorOp : public OpRewritePattern<OpTy> {
+  using OpRewritePattern<OpTy>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(OpTy tensorOp,
+                                PatternRewriter &rewriter) const override {
+    // Check producer is a tosa::PadOp
+    auto padOp = tensorOp.getInput().template getDefiningOp<tosa::PadOp>();
+    if (!padOp)
+      return rewriter.notifyMatchFailure(tensorOp,
+                                         "Producer must be a tosa::PadOp.");
+
+    // Validate that tensor operation has sane padding
+    const std::vector<int64_t> &tensorOpPad = tensorOp.getPad().vec();
+    if (tensorOpPad.size() != 4) // pad_top, pad_bottom, pad_left, pad_right
+      return rewriter.notifyMatchFailure(
+          tensorOp, "Tensor operation padding shall have 4 elements.");
+
+    // Validate tosa::PadOp padding
+    DenseIntElementsAttr padOpPadding;
+    if (!matchPattern(padOp.getPadding(), m_Constant(&padOpPadding))) {
+      return rewriter.notifyMatchFailure(
+          tensorOp,
+          "The `padding` input specified on the tosa::PadOp must be constant.");
+    }
+    // N_before, N_after, H_before, H_after, W_before, W_after, C_before,
+    // C_after
+    if (padOpPadding.size() != 8)
+      return rewriter.notifyMatchFailure(tensorOp,
+                                         "Pad padding should have 8 elements.");
+    int64_t padNBefore = (*(padOpPadding.begin() + 0)).getLimitedValue();
+    int64_t padNAfter = (*(padOpPadding.begin() + 1)).getLimitedValue();
+    int64_t padHBefore = (*(padOpPadding.begin() + 2)).getLimitedValue();
+    int64_t padHAfter = (*(padOpPadding.begin() + 3)).getLimitedValue();
+    int64_t padWBefore = (*(padOpPadding.begin() + 4)).getLimitedValue();
+    int64_t padWAfter = (*(padOpPadding.begin() + 5)).getLimitedValue();
+    int64_t padCBefore = (*(padOpPadding.begin() + 6)).getLimitedValue();
+    int64_t padCAfter = (*(padOpPadding.begin() + 7)).getLimitedValue();
+
+    if (padNBefore != 0 || padNAfter != 0 || padCBefore != 0 || padCAfter != 0)
+      return rewriter.notifyMatchFailure(
+          tensorOp, "Folding padding in N or C dimensions is not supported.");
+
+    // Fold padding from Pad into the tensor operation
+    // 4 elements - pad_top, pad_bottom, pad_left, pad_right
+    SmallVector<int64_t> foldedPad(tensorOpPad.size());
+    foldedPad[0] = padHBefore + tensorOpPad[0];
+    foldedPad[1] = padHAfter + tensorOpPad[1];
+    foldedPad[2] = padWBefore + tensorOpPad[2];
+    foldedPad[3] = padWAfter + tensorOpPad[3];
+
+    // Check kernel related restrictions
+    if (!AdaptorTy::checkKernelCompliance(tensorOp, foldedPad)) {
+      return rewriter.notifyMatchFailure(
+          tensorOp, "Padding size not aligned with kernel restrictions.");
+    }
+
+    // Check padding constant restrictions
+    if (!AdaptorTy::checkPadConstCompliance(tensorOp, padOp.getPadConst())) {
+      return rewriter.notifyMatchFailure(
+          tensorOp,
+          "Padding constant is not aligned with operator zero-point.");
+    }
+
+    // Check that padding doesn't grow more than 8K level (8192) for now
+    if (llvm::any_of(foldedPad, [](int64_t padVal) { return padVal > 8192; })) {
+      return rewriter.notifyMatchFailure(
+          tensorOp, "Padding size more than the 8K level limit.");
+    }
+
+    // Create operator
+    AdaptorTy::replaceOpWithNewPad(rewriter, tensorOp, padOp.getInput1(),
+                                   foldedPad);
+
+    return success();
+  }
+};
+} // namespace
+
+void AvgPool2dOp::getCanonicalizationPatterns(RewritePatternSet &results,
+                                              MLIRContext *context) {
+  results.add<FoldPadToTensorOp<tosa::AvgPool2dOp,
+                                PoolPadFoldAdaptor<tosa::AvgPool2dOp>>>(
+      context);
+}
+
+void Conv2DOp::getCanonicalizationPatterns(RewritePatternSet &results,
+                                           MLIRContext *context) {
+  results.add<
+      FoldPadToTensorOp<tosa::Conv2DOp, ConvPadFoldAdaptor<tosa::Conv2DOp>>>(
+      context);
+}
+
+void DepthwiseConv2DOp::getCanonicalizationPatterns(RewritePatternSet &results,
+                                                    MLIRContext *context) {
+  results.add<FoldPadToTensorOp<tosa::DepthwiseConv2DOp,
+                                ConvPadFoldAdaptor<tosa::DepthwiseConv2DOp>>>(
+      context);
+}
+
+struct MaxPool2dIsNoOp : public OpRewritePattern<tosa::MaxPool2dOp> {
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(tosa::MaxPool2dOp op,
+                                PatternRewriter &rewriter) const override {
+    Value input = op.getInput();
+    Value output = op.getOutput();
+    ShapedType inputType = llvm::cast<ShapedType>(input.getType());
+    ShapedType outputType = llvm::cast<ShapedType>(output.getType());
+
+    if (!inputType.hasStaticShape() || !outputType.hasStaticShape()) {
+      return failure();
+    }
+
+    // If the output and input shapes are 1x1, then this is a no op.
+    ArrayRef<int64_t> outputShape = outputType.getShape();
+    if (outputShape[1] != 1 || outputShape[2] != 1) {
+      return failure();
+    }
+
+    ArrayRef<int64_t> inputShape = inputType.getShape();
+    if (inputShape[1] != 1 || inputShape[2] != 1) {
+      return failure();
+    }
+
+    rewriter.replaceOp(op, input);
+    return success();
+  }
+};
+
+void MaxPool2dOp::getCanonicalizationPatterns(RewritePatternSet &results,
+                                              MLIRContext *context) {
+  results.add<MaxPool2dIsNoOp,
+              FoldPadToTensorOp<tosa::MaxPool2dOp,
+                                PoolPadFoldAdaptor<tosa::MaxPool2dOp>>>(
+      context);
+}
+
+//===----------------------------------------------------------------------===//
+// Data Layout / Memory Reinterpretation.
+//===----------------------------------------------------------------------===//
+
 struct ConcatOptimization : public OpRewritePattern<tosa::ConcatOp> {
   using OpRewritePattern<tosa::ConcatOp>::OpRewritePattern;
 
@@ -175,41 +449,6 @@ void TransposeOp::getCanonicalizationPatterns(RewritePatternSet &results,
   results.add<ConsolidateTransposeOptimization, TransposeIsReshape>(context);
 }
 
-struct MaxPool2dIsNoOp : public OpRewritePattern<tosa::MaxPool2dOp> {
-  using OpRewritePattern::OpRewritePattern;
-
-  LogicalResult matchAndRewrite(tosa::MaxPool2dOp op,
-                                PatternRewriter &rewriter) const override {
-    Value input = op.getInput();
-    Value output = op.getOutput();
-    ShapedType inputType = llvm::cast<ShapedType>(input.getType());
-    ShapedType outputType = llvm::cast<ShapedType>(output.getType());
-
-    if (!inputType.hasStaticShape() || !outputType.hasStaticShape()) {
-      return failure();
-    }
-
-    // If the output and input shapes are 1x1, then this is a no op.
-    ArrayRef<int64_t> outputShape = outputType.getShape();
-    if (outputShape[1] != 1 || outputShape[2] != 1) {
-      return failure();
-    }
-
-    ArrayRef<int64_t> inputShape = inputType.getShape();
-    if (inputShape[1] != 1 || inputShape[2] != 1) {
-      return failure();
-    }
-
-    rewriter.replaceOp(op, input);
-    return success();
-  }
-};
-
-void MaxPool2dOp::getCanonicalizationPatterns(RewritePatternSet &results,
-                                              MLIRContext *context) {
-  results.add<MaxPool2dIsNoOp>(context);
-}
-
 struct ClampIsNoOp : public OpRewritePattern<tosa::ClampOp> {
   using OpRewritePattern::OpRewritePattern;