diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
@@ -34,17 +34,6 @@
 } // namespace tosa
 } // namespace mlir
 
-//===----------------------------------------------------------------------===//
-// Utility Functions
-//===----------------------------------------------------------------------===//
-namespace mlir {
-namespace tosa {
-/// Appends the canonicalization patterns for all the TOSA ops to the `patterns`
-void populateTosaOpsCanonicalizationPatterns(MLIRContext *ctx,
-                                             RewritePatternSet &patterns);
-} // namespace tosa
-} // namespace mlir
-
 #define GET_ATTRDEF_CLASSES
 #include "mlir/Dialect/Tosa/IR/TosaAttributes.h.inc"
 
diff --git a/mlir/lib/Dialect/Tosa/CMakeLists.txt b/mlir/lib/Dialect/Tosa/CMakeLists.txt
--- a/mlir/lib/Dialect/Tosa/CMakeLists.txt
+++ b/mlir/lib/Dialect/Tosa/CMakeLists.txt
@@ -1,7 +1,8 @@
 add_mlir_dialect_library(MLIRTosaDialect
+  IR/TosaOps.cpp
+  IR/TosaCanonicalizations.cpp
   Utils/ConversionUtils.cpp
   Utils/QuantUtils.cpp
-  IR/TosaOps.cpp
 
   ADDITIONAL_HEADER_DIRS
   ${MLIR_MAIN_INCLUDE_DIR}/mlir/Dialect/Tosa
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp b/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
new file mode 100644
--- /dev/null
+++ b/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
@@ -0,0 +1,544 @@
+//===- TosaCanonicalizations.cpp - Canonicalization patterns and folders ---===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+//
+// \file
+// TOSA canonicalization patterns and folders.
+//
+//===----------------------------------------------------------------------===//
+
+#include "mlir/Dialect/Tosa/IR/TosaOps.h"
+#include "mlir/Dialect/Quant/QuantOps.h"
+#include "mlir/Dialect/Tensor/IR/Tensor.h"
+#include "mlir/Dialect/Tosa/Utils/QuantUtils.h"
+#include "mlir/Dialect/Tosa/Utils/ShapeUtils.h"
+#include "mlir/IR/BuiltinTypes.h"
+#include "mlir/IR/DialectImplementation.h"
+#include "mlir/IR/Matchers.h"
+#include "mlir/IR/PatternMatch.h"
+#include "mlir/Transforms/FoldUtils.h"
+#include "mlir/Transforms/InliningUtils.h"
+#include "mlir/Transforms/RegionUtils.h"
+#include "llvm/ADT/DenseMap.h"
+#include "llvm/ADT/TypeSwitch.h"
+
+using namespace mlir;
+using namespace mlir::tosa;
+
+//===----------------------------------------------------------------------===//
+// Operator Canonicalizers.
+//===----------------------------------------------------------------------===//
+
+struct ConcatOptimization : public OpRewritePattern<tosa::ConcatOp> {
+  using OpRewritePattern<tosa::ConcatOp>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(tosa::ConcatOp op,
+                                PatternRewriter &rewriter) const override {
+    if (op.input1().size() != 1)
+      return failure();
+    if (op.input1().front().getType() != op.getType()) {
+      rewriter
+          .replaceOpWithNewOp<tensor::CastOp>(op, op.getType(),
+                                              op.input1().front())
+          .getResult();
+      return success();
+    }
+
+    rewriter.replaceOp(op, op.input1().front());
+    return success();
+  }
+};
+
+void ConcatOp::getCanonicalizationPatterns(RewritePatternSet &results,
+                                           MLIRContext *context) {
+  results.add<ConcatOptimization>(context);
+}
+
+struct ReshapeReshapeOptimization : public OpRewritePattern<tosa::ReshapeOp> {
+  using OpRewritePattern<tosa::ReshapeOp>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(tosa::ReshapeOp op,
+                                PatternRewriter &rewriter) const override {
+    Value input = op.input1();
+    Operation *definingOp = input.getDefiningOp();
+    if (!definingOp)
+      return failure();
+
+    if (tosa::ReshapeOp reshapeOp = dyn_cast<tosa::ReshapeOp>(definingOp)) {
+      rewriter.replaceOpWithNewOp<tosa::ReshapeOp>(
+          op, op.getType(), reshapeOp.input1(), op.new_shape());
+      return success();
+    }
+
+    return failure();
+  }
+};
+
+struct ReshapeConstOptimization : public OpRewritePattern<tosa::ReshapeOp> {
+  using OpRewritePattern<tosa::ReshapeOp>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(tosa::ReshapeOp op,
+                                PatternRewriter &rewriter) const override {
+    Value input = op.input1();
+    ArrayAttr newShape = op.new_shape();
+
+    // Check if input is constant
+    DenseElementsAttr inputAttr;
+    if (!matchPattern(input, m_Constant(&inputAttr)))
+      return failure();
+
+    // Check if has >1 consumer and is not splat
+    if (!input.hasOneUse() && !inputAttr.isSplat())
+      return failure();
+
+    // Grab the new shape
+    SmallVector<int64_t> newShapeValues = llvm::to_vector<6>(
+        llvm::map_range(newShape.getValue(), [](const Attribute &val) {
+          return val.cast<IntegerAttr>().getValue().getSExtValue();
+        }));
+
+    // Build new const op with correct output shape
+    ShapedType inputShape = input.getType().cast<ShapedType>();
+    DenseElementsAttr outputAttr =
+        inputAttr.reshape(inputShape.clone(newShapeValues));
+    rewriter.replaceOpWithNewOp<tosa::ConstOp>(op, outputAttr.getType(),
+                                               outputAttr);
+    return success();
+  }
+};
+
+void ReshapeOp::getCanonicalizationPatterns(RewritePatternSet &results,
+                                            MLIRContext *context) {
+  results.add<ReshapeReshapeOptimization>(context);
+  results.add<ReshapeConstOptimization>(context);
+}
+
+LogicalResult SelectOp::canonicalize(SelectOp op, PatternRewriter &rewriter) {
+  auto notOp = op.pred().getDefiningOp<tosa::LogicalNotOp>();
+  if (!notOp)
+    return failure();
+  rewriter.updateRootInPlace(op, [&]() {
+    op.getOperation()->setOperands(
+        {notOp.input1(), op.on_false(), op.on_true()});
+  });
+  return success();
+}
+
+struct NoOpOptimization : public OpRewritePattern<tosa::TransposeOp> {
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(tosa::TransposeOp op,
+                                PatternRewriter &rewriter) const override {
+    auto perm = op.perms();
+
+    DenseIntElementsAttr permAttr;
+    if (!matchPattern(perm, m_Constant(&permAttr))) {
+      return failure();
+    }
+
+    SmallVector<int64_t> permValues = llvm::to_vector<6>(
+        llvm::map_range(permAttr.getValues<APInt>(),
+                        [](const APInt &val) { return val.getSExtValue(); }));
+
+    for (int i = 0, s = permValues.size(); i < s; i++) {
+      if (i != permValues[i]) {
+        return failure();
+      }
+    }
+
+    rewriter.replaceOp(op, op.input1());
+    return success();
+  }
+};
+
+void TransposeOp::getCanonicalizationPatterns(RewritePatternSet &results,
+                                              MLIRContext *context) {
+  results.add<NoOpOptimization>(context);
+}
+
+struct AddZeroOptimization : public OpRewritePattern<tosa::AddOp> {
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(tosa::AddOp op,
+                                PatternRewriter &rewriter) const override {
+    auto input1 = op.input1();
+    auto input2 = op.input2();
+
+    DenseElementsAttr input1Attr;
+    if (matchPattern(input1, m_Constant(&input1Attr)) && input1Attr.isSplat() &&
+        input2.getType() == op.getType()) {
+      if (input1Attr.getType().getElementType().isa<IntegerType>() &&
+          input1Attr.getSplatValue<APInt>().isZero()) {
+        rewriter.replaceOp(op, op.input2());
+        return success();
+      }
+    }
+
+    DenseElementsAttr input2Attr;
+    if (matchPattern(input2, m_Constant(&input2Attr)) && input2Attr.isSplat() &&
+        input1.getType() == op.getType()) {
+      if (input2Attr.getType().getElementType().isa<IntegerType>() &&
+          input2Attr.getSplatValue<APInt>().isZero()) {
+        rewriter.replaceOp(op, op.input1());
+        return success();
+      }
+    }
+
+    return failure();
+  }
+};
+
+void AddOp::getCanonicalizationPatterns(RewritePatternSet &results,
+                                        MLIRContext *context) {
+  results.add<AddZeroOptimization>(context);
+}
+
+struct MulOneOptimization : public OpRewritePattern<tosa::MulOp> {
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(tosa::MulOp op,
+                                PatternRewriter &rewriter) const override {
+    auto input1 = op.input1();
+    auto input2 = op.input2();
+
+    DenseElementsAttr input1Attr;
+    if (matchPattern(input1, m_Constant(&input1Attr)) && input1Attr.isSplat() &&
+        input2.getType() == op.getType()) {
+      if (input1Attr.getType().getElementType().isa<FloatType>() &&
+          input1Attr.getSplatValue<APFloat>().isExactlyValue(1)) {
+        rewriter.replaceOp(op, op.input2());
+        return success();
+      }
+
+      if (input1Attr.getType().getElementType().isa<IntegerType>() &&
+          matchPattern(input1, m_One())) {
+        rewriter.replaceOp(op, op.input2());
+        return success();
+      }
+    }
+
+    DenseElementsAttr input2Attr;
+    if (matchPattern(input2, m_Constant(&input2Attr)) && input2Attr.isSplat() &&
+        input1.getType() == op.getType()) {
+      if (input2Attr.getType().getElementType().isa<FloatType>() &&
+          input2Attr.getSplatValue<APFloat>().isExactlyValue(1)) {
+        rewriter.replaceOp(op, op.input1());
+        return success();
+      }
+
+      if (input2Attr.getType().getElementType().isa<IntegerType>() &&
+          matchPattern(input2, m_One())) {
+        rewriter.replaceOp(op, op.input1());
+        return success();
+      }
+    }
+
+    return failure();
+  }
+};
+
+void MulOp::getCanonicalizationPatterns(RewritePatternSet &results,
+                                        MLIRContext *context) {
+  results.add<MulOneOptimization>(context);
+}
+
+struct MaterializePadValue : public OpRewritePattern<tosa::PadOp> {
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(tosa::PadOp op,
+                                PatternRewriter &rewriter) const override {
+    if (op.pad_const())
+      return failure();
+
+    auto input = op.input1();
+    auto padding = op.padding();
+
+    ShapedType inputTy = input.getType().cast<ShapedType>();
+    Type elementTy = inputTy.getElementType();
+
+    Attribute constantAttr;
+    if (elementTy.isa<FloatType>()) {
+      constantAttr = rewriter.getFloatAttr(elementTy, 0.0);
+    } else if (elementTy.isa<IntegerType>() && !op.quantization_info()) {
+      constantAttr = rewriter.getIntegerAttr(elementTy, 0);
+    } else if (elementTy.isa<IntegerType>() && op.quantization_info()) {
+      auto value = op.quantization_info()->getInputZp();
+      constantAttr = rewriter.getIntegerAttr(elementTy, value);
+    }
+
+    if (!constantAttr) {
+      return rewriter.notifyMatchFailure(
+          op,
+          "tosa.pad to linalg lowering encountered an unknown element type");
+    }
+
+    auto denseAttr = DenseElementsAttr::get(
+        RankedTensorType::get({}, elementTy), constantAttr);
+    auto constantVal = rewriter.create<tosa::ConstOp>(
+        op.getLoc(), denseAttr.getType(), denseAttr);
+
+    rewriter.replaceOpWithNewOp<tosa::PadOp>(
+        op, op.getType(), ValueRange{input, padding, constantVal},
+        op->getAttrs());
+    return success();
+  }
+};
+
+void PadOp::getCanonicalizationPatterns(RewritePatternSet &results,
+                                        MLIRContext *context) {
+  results.add<MaterializePadValue>(context);
+}
+
+struct MaxPool2dIsNoOp : public OpRewritePattern<tosa::MaxPool2dOp> {
+  using OpRewritePattern::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(tosa::MaxPool2dOp op,
+                                PatternRewriter &rewriter) const override {
+    Value input = op.input();
+    Value output = op.output();
+    ShapedType inputType = input.getType().cast<ShapedType>();
+    ShapedType outputType = output.getType().cast<ShapedType>();
+
+    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;
+
+  LogicalResult matchAndRewrite(tosa::ClampOp op,
+                                PatternRewriter &rewriter) const override {
+    Value input = op.input();
+    auto inputType = op.input().getType().template dyn_cast<RankedTensorType>();
+    auto inputElementType = inputType.getElementType();
+
+    if (!inputType.hasStaticShape()) {
+      return failure();
+    }
+
+    if (inputElementType.isF32()) {
+      auto minClamp = op.min_fp();
+      auto maxClamp = op.max_fp();
+      bool isMin = (minClamp.isLargest() || minClamp.isInfinity()) &&
+                   minClamp.isNegative();
+      bool isMax = (maxClamp.isLargest() || maxClamp.isInfinity()) &&
+                   !maxClamp.isNegative();
+
+      if (isMin && isMax) {
+        rewriter.replaceOp(op, input);
+        return success();
+      }
+      return failure();
+    }
+
+    if (inputElementType.isUnsignedInteger()) {
+      int64_t minClamp = op.min_int();
+      int64_t maxClamp = op.max_int();
+
+      int64_t intMin =
+          APInt::getMinValue(inputElementType.getIntOrFloatBitWidth())
+              .getZExtValue();
+      int64_t intMax =
+          APInt::getMaxValue(inputElementType.getIntOrFloatBitWidth())
+              .getZExtValue();
+
+      if (minClamp <= intMin && maxClamp >= intMax) {
+        rewriter.replaceOp(op, input);
+        return success();
+      }
+      return failure();
+    }
+
+    if (inputElementType.isa<IntegerType>()) {
+      int64_t minClamp = op.min_int();
+      int64_t maxClamp = op.max_int();
+
+      int64_t intMin =
+          APInt::getSignedMinValue(inputElementType.getIntOrFloatBitWidth())
+              .getSExtValue();
+      int64_t intMax =
+          APInt::getSignedMaxValue(inputElementType.getIntOrFloatBitWidth())
+              .getSExtValue();
+
+      if (minClamp <= intMin && maxClamp >= intMax) {
+        rewriter.replaceOp(op, input);
+        return success();
+      }
+      return failure();
+    }
+
+    return failure();
+  }
+};
+
+struct ClampClampOptimization : public OpRewritePattern<tosa::ClampOp> {
+  using OpRewritePattern<tosa::ClampOp>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(tosa::ClampOp op,
+                                PatternRewriter &rewriter) const override {
+    Value input = op.input();
+
+    Operation *definingOp = input.getDefiningOp();
+    if (!definingOp)
+      return failure();
+
+    if (tosa::ClampOp clampOp = dyn_cast<tosa::ClampOp>(definingOp)) {
+      auto minFp = std::max(op.min_fp(), clampOp.min_fp()).convertToFloat();
+      auto maxFp = std::min(op.max_fp(), clampOp.max_fp()).convertToFloat();
+
+      auto minInt = std::max(op.min_int(), clampOp.min_int());
+      auto maxInt = std::min(op.max_int(), clampOp.max_int());
+
+      rewriter.replaceOpWithNewOp<tosa::ClampOp>(
+          op, op.getType(), clampOp.input(), rewriter.getI64IntegerAttr(minInt),
+          rewriter.getI64IntegerAttr(maxInt), rewriter.getF32FloatAttr(minFp),
+          rewriter.getF32FloatAttr(maxFp));
+      return success();
+    }
+
+    return failure();
+  }
+};
+
+void ClampOp::getCanonicalizationPatterns(RewritePatternSet &results,
+                                          MLIRContext *context) {
+  results.add<ClampIsNoOp>(context);
+  results.add<ClampClampOptimization>(context);
+}
+
+//===----------------------------------------------------------------------===//
+// Operator Folders.
+//===----------------------------------------------------------------------===//
+
+OpFoldResult CastOp::fold(ArrayRef<Attribute> operands) {
+  if (input().getType() == getType())
+    return input();
+  return {};
+}
+
+OpFoldResult ConstOp::fold(ArrayRef<Attribute> operands) {
+  assert(operands.empty() && "constant has no operands");
+  return valueAttr();
+}
+
+#define REDUCE_FOLDER(OP)                                                      \
+  OpFoldResult OP::fold(ArrayRef<Attribute> operands) {                        \
+    ShapedType inputTy = input().getType().cast<ShapedType>();                 \
+    if (!inputTy.hasRank())                                                    \
+      return {};                                                               \
+    if (inputTy.getDimSize(axis()) == 1)                                       \
+      return input();                                                          \
+    return {};                                                                 \
+  }
+
+REDUCE_FOLDER(ReduceAllOp)
+REDUCE_FOLDER(ReduceAnyOp)
+REDUCE_FOLDER(ReduceMaxOp)
+REDUCE_FOLDER(ReduceMinOp)
+REDUCE_FOLDER(ReduceProdOp)
+REDUCE_FOLDER(ReduceSumOp)
+#undef REDUCE_FOLDER
+
+OpFoldResult ReshapeOp::fold(ArrayRef<Attribute> operands) {
+  auto inputTy = input1().getType().dyn_cast<RankedTensorType>();
+  auto outputTy = getType().dyn_cast<RankedTensorType>();
+
+  if (!inputTy || !outputTy || inputTy != outputTy)
+    return {};
+  return input1();
+}
+
+OpFoldResult PadOp::fold(ArrayRef<Attribute> operands) {
+  // If the pad is all zeros we can fold this operation away.
+  if (operands[1]) {
+    auto densePad = operands[1].cast<DenseElementsAttr>();
+    if (densePad.isSplat() && densePad.getSplatValue<APInt>().isZero()) {
+      return input1();
+    }
+  }
+
+  return {};
+}
+
+OpFoldResult SliceOp::fold(ArrayRef<Attribute> operands) {
+  auto inputTy = input().getType().dyn_cast<RankedTensorType>();
+  auto outputTy = getType().dyn_cast<RankedTensorType>();
+
+  if (!inputTy || !outputTy || inputTy != outputTy)
+    return {};
+  if (inputTy.hasStaticShape())
+    return input();
+
+  return {};
+}
+
+OpFoldResult tosa::SelectOp::fold(ArrayRef<Attribute> operands) {
+  if (on_true() == on_false())
+    return on_true();
+
+  auto predicate = operands[0].dyn_cast_or_null<DenseIntElementsAttr>();
+  if (!predicate)
+    return {};
+
+  if (!predicate.isSplat())
+    return {};
+  return predicate.getSplatValue<APInt>().getBoolValue() ? on_true()
+                                                         : on_false();
+}
+
+OpFoldResult TileOp::fold(ArrayRef<Attribute> operands) {
+  bool allOnes = true;
+  for (Attribute val : multiples().getValue()) {
+    allOnes = allOnes && val.cast<IntegerAttr>().getValue().getSExtValue() == 1;
+  }
+
+  if (allOnes && input1().getType() == getType())
+    return input1();
+  return {};
+}
+
+OpFoldResult TransposeOp::fold(ArrayRef<Attribute> operands) {
+  if (!operands[1])
+    return {};
+
+  // Transposing splat values just means reshaping.
+  if (auto input = operands[0].dyn_cast_or_null<DenseElementsAttr>()) {
+    if (input.isSplat())
+      return input.reshape(getType().cast<ShapedType>());
+  }
+
+  auto perms = llvm::to_vector<6>(llvm::map_range(
+      operands[1].cast<DenseIntElementsAttr>().getValues<APInt>(),
+      [](const APInt &val) { return val.getSExtValue(); }));
+
+  if (llvm::equal(llvm::seq<int64_t>(0, perms.size()), perms) &&
+      input1().getType() == getType())
+    return input1();
+  return {};
+}
+
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -21,9 +21,7 @@
 #include "mlir/IR/DialectImplementation.h"
 #include "mlir/IR/Matchers.h"
 #include "mlir/IR/PatternMatch.h"
-#include "mlir/Transforms/FoldUtils.h"
 #include "mlir/Transforms/InliningUtils.h"
-#include "mlir/Transforms/RegionUtils.h"
 #include "llvm/ADT/DenseMap.h"
 #include "llvm/ADT/TypeSwitch.h"
 
@@ -96,533 +94,6 @@
   return nullptr;
 }
 
-//===----------------------------------------------------------------------===//
-// Operator Canonicalizers.
-//===----------------------------------------------------------------------===//
-
-template <typename... Args>
-void addOpsCanonicalizations(MLIRContext *ctx, RewritePatternSet &patterns) {
-  (void)std::initializer_list<int>{
-      0, (Args::getCanonicalizationPatterns(patterns, ctx), 0)...};
-}
-
-void mlir::tosa::populateTosaOpsCanonicalizationPatterns(
-    MLIRContext *ctx, RewritePatternSet &patterns) {
-  addOpsCanonicalizations<
-#define GET_OP_LIST
-#include "mlir/Dialect/Tosa/IR/TosaOps.cpp.inc"
-      >(ctx, patterns);
-}
-
-struct ConcatOptimization : public OpRewritePattern<tosa::ConcatOp> {
-  using OpRewritePattern<tosa::ConcatOp>::OpRewritePattern;
-
-  LogicalResult matchAndRewrite(tosa::ConcatOp op,
-                                PatternRewriter &rewriter) const override {
-    if (op.input1().size() != 1)
-      return failure();
-    if (op.input1().front().getType() != op.getType()) {
-      rewriter
-          .replaceOpWithNewOp<tensor::CastOp>(op, op.getType(),
-                                              op.input1().front())
-          .getResult();
-      return success();
-    }
-
-    rewriter.replaceOp(op, op.input1().front());
-    return success();
-  }
-};
-
-void ConcatOp::getCanonicalizationPatterns(RewritePatternSet &results,
-                                           MLIRContext *context) {
-  results.add<ConcatOptimization>(context);
-}
-
-struct ReshapeReshapeOptimization : public OpRewritePattern<tosa::ReshapeOp> {
-  using OpRewritePattern<tosa::ReshapeOp>::OpRewritePattern;
-
-  LogicalResult matchAndRewrite(tosa::ReshapeOp op,
-                                PatternRewriter &rewriter) const override {
-    Value input = op.input1();
-    Operation *definingOp = input.getDefiningOp();
-    if (!definingOp)
-      return failure();
-
-    if (tosa::ReshapeOp reshapeOp = dyn_cast<tosa::ReshapeOp>(definingOp)) {
-      rewriter.replaceOpWithNewOp<tosa::ReshapeOp>(
-          op, op.getType(), reshapeOp.input1(), op.new_shape());
-      return success();
-    }
-
-    return failure();
-  }
-};
-
-struct ReshapeConstOptimization : public OpRewritePattern<tosa::ReshapeOp> {
-  using OpRewritePattern<tosa::ReshapeOp>::OpRewritePattern;
-
-  LogicalResult matchAndRewrite(tosa::ReshapeOp op,
-                                PatternRewriter &rewriter) const override {
-    Value input = op.input1();
-    ArrayAttr newShape = op.new_shape();
-
-    // Check if input is constant
-    DenseElementsAttr inputAttr;
-    if (!matchPattern(input, m_Constant(&inputAttr)))
-      return failure();
-
-    // Check if has >1 consumer and is not splat
-    if (!input.hasOneUse() && !inputAttr.isSplat())
-      return failure();
-
-    // Grab the new shape
-    SmallVector<int64_t> newShapeValues = llvm::to_vector<6>(
-        llvm::map_range(newShape.getValue(), [](const Attribute &val) {
-          return val.cast<IntegerAttr>().getValue().getSExtValue();
-        }));
-
-    // Build new const op with correct output shape
-    ShapedType inputShape = input.getType().cast<ShapedType>();
-    DenseElementsAttr outputAttr =
-        inputAttr.reshape(inputShape.clone(newShapeValues));
-    rewriter.replaceOpWithNewOp<tosa::ConstOp>(op, outputAttr.getType(),
-                                               outputAttr);
-    return success();
-  }
-};
-
-void ReshapeOp::getCanonicalizationPatterns(RewritePatternSet &results,
-                                            MLIRContext *context) {
-  results.add<ReshapeReshapeOptimization>(context);
-  results.add<ReshapeConstOptimization>(context);
-}
-
-LogicalResult SelectOp::canonicalize(SelectOp op, PatternRewriter &rewriter) {
-  auto notOp = op.pred().getDefiningOp<tosa::LogicalNotOp>();
-  if (!notOp)
-    return failure();
-  rewriter.updateRootInPlace(op, [&]() {
-    op.getOperation()->setOperands(
-        {notOp.input1(), op.on_false(), op.on_true()});
-  });
-  return success();
-}
-
-struct NoOpOptimization : public OpRewritePattern<tosa::TransposeOp> {
-  using OpRewritePattern::OpRewritePattern;
-
-  LogicalResult matchAndRewrite(tosa::TransposeOp op,
-                                PatternRewriter &rewriter) const override {
-    auto perm = op.perms();
-
-    DenseIntElementsAttr permAttr;
-    if (!matchPattern(perm, m_Constant(&permAttr))) {
-      return failure();
-    }
-
-    SmallVector<int64_t> permValues = llvm::to_vector<6>(
-        llvm::map_range(permAttr.getValues<APInt>(),
-                        [](const APInt &val) { return val.getSExtValue(); }));
-
-    for (int i = 0, s = permValues.size(); i < s; i++) {
-      if (i != permValues[i]) {
-        return failure();
-      }
-    }
-
-    rewriter.replaceOp(op, op.input1());
-    return success();
-  }
-};
-
-void TransposeOp::getCanonicalizationPatterns(RewritePatternSet &results,
-                                              MLIRContext *context) {
-  results.add<NoOpOptimization>(context);
-}
-
-struct AddZeroOptimization : public OpRewritePattern<tosa::AddOp> {
-  using OpRewritePattern::OpRewritePattern;
-
-  LogicalResult matchAndRewrite(tosa::AddOp op,
-                                PatternRewriter &rewriter) const override {
-    auto input1 = op.input1();
-    auto input2 = op.input2();
-
-    DenseElementsAttr input1Attr;
-    if (matchPattern(input1, m_Constant(&input1Attr)) && input1Attr.isSplat() &&
-        input2.getType() == op.getType()) {
-      if (input1Attr.getType().getElementType().isa<IntegerType>() &&
-          input1Attr.getSplatValue<APInt>().isZero()) {
-        rewriter.replaceOp(op, op.input2());
-        return success();
-      }
-    }
-
-    DenseElementsAttr input2Attr;
-    if (matchPattern(input2, m_Constant(&input2Attr)) && input2Attr.isSplat() &&
-        input1.getType() == op.getType()) {
-      if (input2Attr.getType().getElementType().isa<IntegerType>() &&
-          input2Attr.getSplatValue<APInt>().isZero()) {
-        rewriter.replaceOp(op, op.input1());
-        return success();
-      }
-    }
-
-    return failure();
-  }
-};
-
-void AddOp::getCanonicalizationPatterns(RewritePatternSet &results,
-                                        MLIRContext *context) {
-  results.add<AddZeroOptimization>(context);
-}
-
-struct MulOneOptimization : public OpRewritePattern<tosa::MulOp> {
-  using OpRewritePattern::OpRewritePattern;
-
-  LogicalResult matchAndRewrite(tosa::MulOp op,
-                                PatternRewriter &rewriter) const override {
-    auto input1 = op.input1();
-    auto input2 = op.input2();
-
-    DenseElementsAttr input1Attr;
-    if (matchPattern(input1, m_Constant(&input1Attr)) && input1Attr.isSplat() &&
-        input2.getType() == op.getType()) {
-      if (input1Attr.getType().getElementType().isa<FloatType>() &&
-          input1Attr.getSplatValue<APFloat>().isExactlyValue(1)) {
-        rewriter.replaceOp(op, op.input2());
-        return success();
-      }
-
-      if (input1Attr.getType().getElementType().isa<IntegerType>() &&
-          matchPattern(input1, m_One())) {
-        rewriter.replaceOp(op, op.input2());
-        return success();
-      }
-    }
-
-    DenseElementsAttr input2Attr;
-    if (matchPattern(input2, m_Constant(&input2Attr)) && input2Attr.isSplat() &&
-        input1.getType() == op.getType()) {
-      if (input2Attr.getType().getElementType().isa<FloatType>() &&
-          input2Attr.getSplatValue<APFloat>().isExactlyValue(1)) {
-        rewriter.replaceOp(op, op.input1());
-        return success();
-      }
-
-      if (input2Attr.getType().getElementType().isa<IntegerType>() &&
-          matchPattern(input2, m_One())) {
-        rewriter.replaceOp(op, op.input1());
-        return success();
-      }
-    }
-
-    return failure();
-  }
-};
-
-void MulOp::getCanonicalizationPatterns(RewritePatternSet &results,
-                                        MLIRContext *context) {
-  results.add<MulOneOptimization>(context);
-}
-
-struct MaterializePadValue : public OpRewritePattern<tosa::PadOp> {
-  using OpRewritePattern::OpRewritePattern;
-
-  LogicalResult matchAndRewrite(tosa::PadOp op,
-                                PatternRewriter &rewriter) const override {
-    if (op.pad_const())
-      return failure();
-
-    auto input = op.input1();
-    auto padding = op.padding();
-
-    ShapedType inputTy = input.getType().cast<ShapedType>();
-    Type elementTy = inputTy.getElementType();
-
-    Attribute constantAttr;
-    if (elementTy.isa<FloatType>()) {
-      constantAttr = rewriter.getFloatAttr(elementTy, 0.0);
-    } else if (elementTy.isa<IntegerType>() && !op.quantization_info()) {
-      constantAttr = rewriter.getIntegerAttr(elementTy, 0);
-    } else if (elementTy.isa<IntegerType>() && op.quantization_info()) {
-      auto value = op.quantization_info()->getInputZp();
-      constantAttr = rewriter.getIntegerAttr(elementTy, value);
-    }
-
-    if (!constantAttr) {
-      return rewriter.notifyMatchFailure(
-          op,
-          "tosa.pad to linalg lowering encountered an unknown element type");
-    }
-
-    auto denseAttr = DenseElementsAttr::get(
-        RankedTensorType::get({}, elementTy), constantAttr);
-    auto constantVal = rewriter.create<tosa::ConstOp>(
-        op.getLoc(), denseAttr.getType(), denseAttr);
-
-    rewriter.replaceOpWithNewOp<tosa::PadOp>(
-        op, op.getType(), ValueRange{input, padding, constantVal},
-        op->getAttrs());
-    return success();
-  }
-};
-
-void PadOp::getCanonicalizationPatterns(RewritePatternSet &results,
-                                        MLIRContext *context) {
-  results.add<MaterializePadValue>(context);
-}
-
-struct MaxPool2dIsNoOp : public OpRewritePattern<tosa::MaxPool2dOp> {
-  using OpRewritePattern::OpRewritePattern;
-
-  LogicalResult matchAndRewrite(tosa::MaxPool2dOp op,
-                                PatternRewriter &rewriter) const override {
-    Value input = op.input();
-    Value output = op.output();
-    ShapedType inputType = input.getType().cast<ShapedType>();
-    ShapedType outputType = output.getType().cast<ShapedType>();
-
-    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;
-
-  LogicalResult matchAndRewrite(tosa::ClampOp op,
-                                PatternRewriter &rewriter) const override {
-    Value input = op.input();
-    auto inputType = op.input().getType().template dyn_cast<RankedTensorType>();
-    auto inputElementType = inputType.getElementType();
-
-    if (!inputType.hasStaticShape()) {
-      return failure();
-    }
-
-    if (inputElementType.isF32()) {
-      auto minClamp = op.min_fp();
-      auto maxClamp = op.max_fp();
-      bool isMin = (minClamp.isLargest() || minClamp.isInfinity()) &&
-                   minClamp.isNegative();
-      bool isMax = (maxClamp.isLargest() || maxClamp.isInfinity()) &&
-                   !maxClamp.isNegative();
-
-      if (isMin && isMax) {
-        rewriter.replaceOp(op, input);
-        return success();
-      }
-      return failure();
-    }
-
-    if (inputElementType.isUnsignedInteger()) {
-      int64_t minClamp = op.min_int();
-      int64_t maxClamp = op.max_int();
-
-      int64_t intMin =
-          APInt::getMinValue(inputElementType.getIntOrFloatBitWidth())
-              .getZExtValue();
-      int64_t intMax =
-          APInt::getMaxValue(inputElementType.getIntOrFloatBitWidth())
-              .getZExtValue();
-
-      if (minClamp <= intMin && maxClamp >= intMax) {
-        rewriter.replaceOp(op, input);
-        return success();
-      }
-      return failure();
-    }
-
-    if (inputElementType.isa<IntegerType>()) {
-      int64_t minClamp = op.min_int();
-      int64_t maxClamp = op.max_int();
-
-      int64_t intMin =
-          APInt::getSignedMinValue(inputElementType.getIntOrFloatBitWidth())
-              .getSExtValue();
-      int64_t intMax =
-          APInt::getSignedMaxValue(inputElementType.getIntOrFloatBitWidth())
-              .getSExtValue();
-
-      if (minClamp <= intMin && maxClamp >= intMax) {
-        rewriter.replaceOp(op, input);
-        return success();
-      }
-      return failure();
-    }
-
-    return failure();
-  }
-};
-
-struct ClampClampOptimization : public OpRewritePattern<tosa::ClampOp> {
-  using OpRewritePattern<tosa::ClampOp>::OpRewritePattern;
-
-  LogicalResult matchAndRewrite(tosa::ClampOp op,
-                                PatternRewriter &rewriter) const override {
-    Value input = op.input();
-
-    Operation *definingOp = input.getDefiningOp();
-    if (!definingOp)
-      return failure();
-
-    if (tosa::ClampOp clampOp = dyn_cast<tosa::ClampOp>(definingOp)) {
-      auto minFp = std::max(op.min_fp(), clampOp.min_fp()).convertToFloat();
-      auto maxFp = std::min(op.max_fp(), clampOp.max_fp()).convertToFloat();
-
-      auto minInt = std::max(op.min_int(), clampOp.min_int());
-      auto maxInt = std::min(op.max_int(), clampOp.max_int());
-
-      rewriter.replaceOpWithNewOp<tosa::ClampOp>(
-          op, op.getType(), clampOp.input(), rewriter.getI64IntegerAttr(minInt),
-          rewriter.getI64IntegerAttr(maxInt), rewriter.getF32FloatAttr(minFp),
-          rewriter.getF32FloatAttr(maxFp));
-      return success();
-    }
-
-    return failure();
-  }
-};
-
-void ClampOp::getCanonicalizationPatterns(RewritePatternSet &results,
-                                          MLIRContext *context) {
-  results.add<ClampIsNoOp>(context);
-  results.add<ClampClampOptimization>(context);
-}
-
-//===----------------------------------------------------------------------===//
-// Operator Folders.
-//===----------------------------------------------------------------------===//
-
-OpFoldResult CastOp::fold(ArrayRef<Attribute> operands) {
-  if (input().getType() == getType())
-    return input();
-  return {};
-}
-
-OpFoldResult ConstOp::fold(ArrayRef<Attribute> operands) {
-  assert(operands.empty() && "constant has no operands");
-  return valueAttr();
-}
-
-#define REDUCE_FOLDER(OP)                                                      \
-  OpFoldResult OP::fold(ArrayRef<Attribute> operands) {                        \
-    ShapedType inputTy = input().getType().cast<ShapedType>();                 \
-    if (!inputTy.hasRank())                                                    \
-      return {};                                                               \
-    if (inputTy.getDimSize(axis()) == 1)                                       \
-      return input();                                                          \
-    return {};                                                                 \
-  }
-
-REDUCE_FOLDER(ReduceAllOp)
-REDUCE_FOLDER(ReduceAnyOp)
-REDUCE_FOLDER(ReduceMaxOp)
-REDUCE_FOLDER(ReduceMinOp)
-REDUCE_FOLDER(ReduceProdOp)
-REDUCE_FOLDER(ReduceSumOp)
-#undef REDUCE_FOLDER
-
-OpFoldResult ReshapeOp::fold(ArrayRef<Attribute> operands) {
-  auto inputTy = input1().getType().dyn_cast<RankedTensorType>();
-  auto outputTy = getType().dyn_cast<RankedTensorType>();
-
-  if (!inputTy || !outputTy || inputTy != outputTy)
-    return {};
-  return input1();
-}
-
-OpFoldResult PadOp::fold(ArrayRef<Attribute> operands) {
-  // If the pad is all zeros we can fold this operation away.
-  if (operands[1]) {
-    auto densePad = operands[1].cast<DenseElementsAttr>();
-    if (densePad.isSplat() && densePad.getSplatValue<APInt>().isZero()) {
-      return input1();
-    }
-  }
-
-  return {};
-}
-
-OpFoldResult SliceOp::fold(ArrayRef<Attribute> operands) {
-  auto inputTy = input().getType().dyn_cast<RankedTensorType>();
-  auto outputTy = getType().dyn_cast<RankedTensorType>();
-
-  if (!inputTy || !outputTy || inputTy != outputTy)
-    return {};
-  if (inputTy.hasStaticShape())
-    return input();
-
-  return {};
-}
-
-OpFoldResult tosa::SelectOp::fold(ArrayRef<Attribute> operands) {
-  if (on_true() == on_false())
-    return on_true();
-
-  auto predicate = operands[0].dyn_cast_or_null<DenseIntElementsAttr>();
-  if (!predicate)
-    return {};
-
-  if (!predicate.isSplat())
-    return {};
-  return predicate.getSplatValue<APInt>().getBoolValue() ? on_true()
-                                                         : on_false();
-}
-
-OpFoldResult TileOp::fold(ArrayRef<Attribute> operands) {
-  bool allOnes = true;
-  for (Attribute val : multiples().getValue()) {
-    allOnes = allOnes && val.cast<IntegerAttr>().getValue().getSExtValue() == 1;
-  }
-
-  if (allOnes && input1().getType() == getType())
-    return input1();
-  return {};
-}
-
-OpFoldResult TransposeOp::fold(ArrayRef<Attribute> operands) {
-  if (!operands[1])
-    return {};
-
-  // Transposing splat values just means reshaping.
-  if (auto input = operands[0].dyn_cast_or_null<DenseElementsAttr>()) {
-    if (input.isSplat())
-      return input.reshape(getType().cast<ShapedType>());
-  }
-
-  auto perms = llvm::to_vector<6>(llvm::map_range(
-      operands[1].cast<DenseIntElementsAttr>().getValues<APInt>(),
-      [](const APInt &val) { return val.getSExtValue(); }));
-
-  if (llvm::equal(llvm::seq<int64_t>(0, perms.size()), perms) &&
-      input1().getType() == getType())
-    return input1();
-  return {};
-}
-
 //===----------------------------------------------------------------------===//
 // TOSA Operator Verifiers.
 //===----------------------------------------------------------------------===//
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaLayerwiseConstantFoldPass.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaLayerwiseConstantFoldPass.cpp
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaLayerwiseConstantFoldPass.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaLayerwiseConstantFoldPass.cpp
@@ -21,6 +21,20 @@
 
 namespace {
 
+template <typename... Args>
+void addOpsCanonicalizations(MLIRContext *ctx, RewritePatternSet &patterns) {
+  (void)std::initializer_list<int>{
+      0, (Args::getCanonicalizationPatterns(patterns, ctx), 0)...};
+}
+
+void populateTosaOpsCanonicalizationPatterns(MLIRContext *ctx,
+                                             RewritePatternSet &patterns) {
+  addOpsCanonicalizations<
+#define GET_OP_LIST
+#include "mlir/Dialect/Tosa/IR/TosaOps.cpp.inc"
+      >(ctx, patterns);
+}
+
 struct TosaLayerwiseConstantFoldPass
     : public TosaLayerwiseConstantFoldPassBase<TosaLayerwiseConstantFoldPass> {
   void runOnOperation() override {
@@ -29,7 +43,7 @@
     auto func = getOperation();
 
     mlir::tosa::populateTosaFoldConstantTransposePatterns(ctx, patterns);
-    mlir::tosa::populateTosaOpsCanonicalizationPatterns(ctx, patterns);
+    populateTosaOpsCanonicalizationPatterns(ctx, patterns);
 
     if (applyPatternsAndFoldGreedily(func, std::move(patterns)).failed())
       signalPassFailure();