diff --git a/mlir/include/mlir/Conversion/TosaToLinalg/TosaToLinalg.h b/mlir/include/mlir/Conversion/TosaToLinalg/TosaToLinalg.h
new file mode 100644
--- /dev/null
+++ b/mlir/include/mlir/Conversion/TosaToLinalg/TosaToLinalg.h
@@ -0,0 +1,36 @@
+//===-- TosaToLinalg.h - TOSA optimization pass declarations ----------*- C++
+//-*-===//
+//
+// 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
+//
+//===----------------------------------------------------------------------===//
+//
+// This file declares the passes for the TOSA Linalg Dialect in MLIR.
+//
+//===----------------------------------------------------------------------===//
+
+#ifndef MLIR_CONVERSION_TOSATOLINALG_TOSATOLINALG_H
+#define MLIR_CONVERSION_TOSATOLINALG_TOSATOLINALG_H
+
+#include "mlir/Pass/Pass.h"
+
+namespace mlir {
+namespace tosa {
+
+std::unique_ptr<Pass> createTosaToLinalgOnTensors();
+
+/// Populates passes to convert from TOSA to Linalg on buffers. At the end of
+/// the pass, the function will only contain linalg ops or standard ops if the
+/// pipeline succeeds.
+void addTosaToLinalgOnTensorsPasses(OpPassManager &pm);
+
+/// Populates conversion passes from TOSA dialect to Linalg dialect.
+void populateTosaToLinalgOnTensorsConversionPatterns(
+    MLIRContext *context, OwningRewritePatternList *patterns);
+
+} // namespace tosa
+} // namespace mlir
+
+#endif // MLIR_CONVERSION_TOSATOLINALG_TOSATOLINALG_H
diff --git a/mlir/include/mlir/Dialect/Tosa/Transforms/Passes.h b/mlir/include/mlir/Dialect/Tosa/Transforms/Passes.h
--- a/mlir/include/mlir/Dialect/Tosa/Transforms/Passes.h
+++ b/mlir/include/mlir/Dialect/Tosa/Transforms/Passes.h
@@ -20,6 +20,7 @@
 
 std::unique_ptr<Pass> createTosaMakeBroadcastablePass();
 std::unique_ptr<Pass> createTosaTestQuantUtilAPIPass();
+std::unique_ptr<Pass> createTosaToLinalgOnTensors();
 
 #define GEN_PASS_REGISTRATION
 #include "mlir/Dialect/Tosa/Transforms/Passes.h.inc"
diff --git a/mlir/include/mlir/Dialect/Tosa/Transforms/Passes.td b/mlir/include/mlir/Dialect/Tosa/Transforms/Passes.td
--- a/mlir/include/mlir/Dialect/Tosa/Transforms/Passes.td
+++ b/mlir/include/mlir/Dialect/Tosa/Transforms/Passes.td
@@ -6,7 +6,8 @@
 //
 //===----------------------------------------------------------------------===//
 //
-// This file declares the optimization passes for the TOSA Dialect in MLIR.
+// This file declares the optimization and lowerings passes for the TOSA Dialect
+// in MLIR.
 //
 //===----------------------------------------------------------------------===//
 
@@ -24,3 +25,14 @@
 
   let constructor = "createTosaMakeBroadcastablePass()";
 }
+
+def TosaToLinalgOnTensors : FunctionPass<"tosa-to-linalg-on-tensors"> {
+  let summary = "Lower TOSA to LinAlg on tensors";
+  let description = [{
+    Pass that converts TOSA operations to the equivalent operations using the
+    tensor operations in LinAlg.
+  }];
+
+  let constructor = "createTosaToLinalgOnTensors()";
+}
+
diff --git a/mlir/lib/Conversion/CMakeLists.txt b/mlir/lib/Conversion/CMakeLists.txt
--- a/mlir/lib/Conversion/CMakeLists.txt
+++ b/mlir/lib/Conversion/CMakeLists.txt
@@ -20,6 +20,7 @@
 add_subdirectory(SPIRVToLLVM)
 add_subdirectory(StandardToLLVM)
 add_subdirectory(StandardToSPIRV)
+add_subdirectory(TosaToLinalg)
 add_subdirectory(ArmSVEToLLVM)
 add_subdirectory(VectorToROCDL)
 add_subdirectory(VectorToLLVM)
diff --git a/mlir/lib/Conversion/TosaToLinalg/CMakeLists.txt b/mlir/lib/Conversion/TosaToLinalg/CMakeLists.txt
new file mode 100644
--- /dev/null
+++ b/mlir/lib/Conversion/TosaToLinalg/CMakeLists.txt
@@ -0,0 +1,20 @@
+add_mlir_conversion_library(MLIRTosaToLinalg
+  TosaToLinalg.cpp
+  TosaToLinalgPass.cpp
+
+  ADDITIONAL_HEADER_DIRS
+  ${MLIR_MAIN_INCLUDE_DIR}/mlir/Dialect/Tosa
+  ${MLIR_MAIN_INCLUDE_DIR}/mlir/IR
+
+  DEPENDS
+  MLIRConversionPassIncGen
+
+  LINK_LIBS PUBLIC
+  MLIRIR
+  MLIRLinalg
+  MLIRLinalgUtils
+  MLIRPass
+  MLIRTosa
+  MLIRTosaTransforms
+  MLIRSupport
+  )
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
new file mode 100644
--- /dev/null
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
@@ -0,0 +1,190 @@
+//===- TosaToLinalg.cpp - Lowering Tosa to Linalg Dialect -----------------===//
+//
+// 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
+//
+//===----------------------------------------------------------------------===//
+//
+// These rewriters lower from the Tosa to the Linalg dialect.
+//
+//===----------------------------------------------------------------------===//
+
+#include "mlir/Dialect/Linalg/IR/LinalgOps.h"
+#include "mlir/Dialect/StandardOps/IR/Ops.h"
+#include "mlir/Dialect/Tosa/IR/TosaOps.h"
+#include "mlir/Dialect/Tosa/Transforms/PassDetail.h"
+#include "mlir/Dialect/Tosa/Transforms/Passes.h"
+#include "mlir/Dialect/Tosa/Utils/QuantUtils.h"
+#include "mlir/IR/PatternMatch.h"
+#include "mlir/Transforms/DialectConversion.h"
+#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
+
+using namespace mlir;
+
+namespace {
+
+SmallVector<StringRef, 3> getNParallelLoopsAttrs(unsigned nParallelLoops) {
+  return SmallVector<StringRef, 3>(nParallelLoops,
+                                   getParallelIteratorTypeName());
+}
+
+Value getInitTensor(OpBuilder &b, Location loc, ShapedType type) {
+  return b.create<linalg::InitTensorOp>(loc, ArrayRef<Value>({}),
+                                        type.getShape(), type.getElementType());
+}
+
+} // namespace
+
+namespace mlir {
+namespace tosa {
+namespace {
+
+template <typename SrcOp>
+class PointwiseConverter : public OpConversionPattern<SrcOp> {
+public:
+  using OpConversionPattern<SrcOp>::OpConversionPattern;
+
+  virtual Operation *
+  createGenericOp(Location loc, int nloops, ArrayRef<AffineMap> indexingMaps,
+                  ArrayRef<Value> args, ArrayRef<Value> outputBuffers,
+                  ArrayRef<Type> opResultTypes, ArrayRef<Type> bodyArgTypes,
+                  ArrayRef<Type> bodyResultTypes,
+                  ConversionPatternRewriter &rewriter) const = 0;
+
+  LogicalResult
+  matchAndRewriteHelper(Operation *operation, ArrayRef<Value> args,
+                        ConversionPatternRewriter &rewriter) const {
+    auto loc = operation->getLoc();
+    auto results = operation->getResults();
+    auto t0 = args[0].getType().template dyn_cast<ShapedType>();
+    if (!t0)
+      return rewriter.notifyMatchFailure(operation,
+                                         "All results must be a shaped type");
+
+    // For now require no broadcasting. Consider making it support broadcasting
+    // operations.
+    auto uniqueTy = operation->getOperand(0).getType();
+    bool allInputTypesEqual =
+        llvm::all_of(operation->getOperandTypes(),
+                     [&](Type operandTy) { return operandTy == uniqueTy; });
+    if (!allInputTypesEqual)
+      return rewriter.notifyMatchFailure(
+          operation, "All operands must have the same type");
+    bool allResultTypesEqual =
+        llvm::all_of(operation->getResultTypes(),
+                     [&](Type resultTy) { return resultTy == uniqueTy; });
+    if (!allResultTypesEqual)
+      return rewriter.notifyMatchFailure(
+          operation, "All results must have the same type as the input");
+
+    unsigned nloops = t0.getRank();
+    auto checkValidShapedType = [&](Type t) {
+      auto st = t.dyn_cast<ShapedType>();
+      return !t || !st || !st.hasRank() ||
+             !st.getElementType().isSignlessIntOrFloat();
+    };
+
+    auto checkValidShapedValue = [&](Value v) {
+      return checkValidShapedType(v.getType());
+    };
+
+    if (llvm::any_of(args, checkValidShapedValue) ||
+        llvm::any_of(operation->getResultTypes(), checkValidShapedType))
+      return emitError(loc, "tosa to linalg conversion expects ranked args of "
+                            "signless int, float or complex element type with ")
+             << nloops << " parallel iterators: " << *(operation);
+
+    // Construct the indexing maps needed for linalg.generic ops.
+    SmallVector<Type, 4> bodyArgTypes, bodyResultTypes, opResultTypes;
+
+    for (Value in : args)
+      bodyArgTypes.emplace_back(getElementTypeOrSelf(in.getType()));
+
+    SmallVector<Value, 4> outputBuffers;
+    for (auto result : results) {
+      auto resultType = result.getType().template cast<ShapedType>();
+      if (!resultType.hasStaticShape())
+        return rewriter.notifyMatchFailure(
+            operation,
+            "tosa to linalg conversion expects statically shaped tensors");
+
+      outputBuffers.push_back(getInitTensor(rewriter, loc, resultType));
+      opResultTypes.push_back(result.getType());
+    }
+
+    bodyResultTypes = llvm::to_vector<4>(llvm::map_range(
+        outputBuffers, [](Value v) { return getElementTypeOrSelf(v); }));
+
+    // Supports only non-broadcasted operation. Shoudl consider update indexing
+    // map to be multidimensional.
+    AffineMap commonIndexingMap = rewriter.getMultiDimIdentityMap(nloops);
+    SmallVector<AffineMap, 2> indexingMaps(args.size() + bodyResultTypes.size(),
+                                           commonIndexingMap);
+
+    auto linalgOp =
+        createGenericOp(loc, nloops, indexingMaps, args, outputBuffers,
+                        opResultTypes, bodyArgTypes, bodyResultTypes, rewriter);
+    if (!linalgOp)
+      return failure();
+
+    rewriter.replaceOp(operation, linalgOp->getResults());
+    return success();
+  }
+
+  LogicalResult
+  matchAndRewrite(SrcOp op, ArrayRef<Value> args,
+                  ConversionPatternRewriter &rewriter) const final {
+    return matchAndRewriteHelper(op, args, rewriter);
+  }
+};
+
+template <typename SrcOp, typename ElementType, typename ScalarOp>
+class SimplePointwiseConverter : public PointwiseConverter<SrcOp> {
+public:
+  using PointwiseConverter<SrcOp>::PointwiseConverter;
+
+  Operation *
+  createGenericOp(Location loc, int nloops, ArrayRef<AffineMap> indexingMaps,
+                  ArrayRef<Value> args, ArrayRef<Value> outputBuffers,
+                  ArrayRef<Type> opResultTypes, ArrayRef<Type> bodyArgTypes,
+                  ArrayRef<Type> bodyResultTypes,
+                  ConversionPatternRewriter &rewriter) const override {
+    if (!bodyResultTypes.front().isa<ElementType>())
+      return nullptr;
+
+    return rewriter.create<linalg::GenericOp>(
+        loc, opResultTypes, ValueRange(args), outputBuffers, indexingMaps,
+        getNParallelLoopsAttrs(nloops),
+        [&](OpBuilder &nestedBuilder, Location nestedLoc,
+            ValueRange blockArgs) {
+          Value opResult = rewriter.create<ScalarOp>(
+              loc, bodyResultTypes, blockArgs.take_front(args.size()));
+          nestedBuilder.create<linalg::YieldOp>(loc, opResult);
+        });
+  }
+};
+
+} // namespace
+
+void populateTosaToLinalgOnTensorsConversionPatterns(
+    MLIRContext *context, OwningRewritePatternList *patterns) {
+  patterns->insert<
+      SimplePointwiseConverter<tosa::AddOp, FloatType, mlir::AddFOp>,
+      SimplePointwiseConverter<tosa::AddOp, IntegerType, mlir::AddIOp>,
+      SimplePointwiseConverter<tosa::SubOp, FloatType, mlir::SubFOp>,
+      SimplePointwiseConverter<tosa::SubOp, IntegerType, mlir::SubIOp>,
+      SimplePointwiseConverter<tosa::PowOp, FloatType, mlir::PowFOp>,
+      SimplePointwiseConverter<tosa::AbsOp, FloatType, mlir::AbsFOp>,
+      SimplePointwiseConverter<tosa::TanhOp, FloatType, mlir::TanhOp>,
+      SimplePointwiseConverter<tosa::BitwiseAndOp, IntegerType, mlir::AndOp>,
+      SimplePointwiseConverter<tosa::BitwiseOrOp, IntegerType, mlir::OrOp>,
+      SimplePointwiseConverter<tosa::BitwiseXorOp, IntegerType, mlir::XOrOp>,
+      SimplePointwiseConverter<tosa::LogicalLeftShiftOp, IntegerType,
+                               mlir::ShiftLeftOp>,
+      SimplePointwiseConverter<tosa::LogicalRightShiftOp, IntegerType,
+                               mlir::UnsignedShiftRightOp>>(context);
+}
+
+} // namespace tosa
+} // namespace mlir
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgPass.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgPass.cpp
new file mode 100644
--- /dev/null
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgPass.cpp
@@ -0,0 +1,58 @@
+//===- TosaToLinalgPass.cpp - Lowering Tosa to Linalg Dialect -------------===//
+//
+// 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
+//
+//===----------------------------------------------------------------------===//
+//
+// This transformation pass legalizes Tosa operations to the Linalg dialect.
+//
+//===----------------------------------------------------------------------===//
+
+#include "mlir/Conversion/TosaToLinalg/TosaToLinalg.h"
+#include "mlir/Dialect/Linalg/IR/LinalgOps.h"
+#include "mlir/Dialect/StandardOps/IR/Ops.h"
+#include "mlir/Dialect/Tosa/IR/TosaOps.h"
+#include "mlir/Dialect/Tosa/Transforms/PassDetail.h"
+#include "mlir/Dialect/Tosa/Transforms/Passes.h"
+#include "mlir/Dialect/Tosa/Utils/QuantUtils.h"
+#include "mlir/IR/PatternMatch.h"
+#include "mlir/Pass/PassManager.h"
+#include "mlir/Transforms/DialectConversion.h"
+#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
+
+using namespace mlir;
+
+namespace {
+struct TosaToLinalgOnTensors
+    : public TosaToLinalgOnTensorsBase<TosaToLinalgOnTensors> {
+public:
+  void getDependentDialects(DialectRegistry &registry) const override {
+    registry.insert<linalg::LinalgDialect, StandardOpsDialect>();
+  }
+
+  void runOnFunction() override {
+    OwningRewritePatternList patterns;
+    ConversionTarget target(getContext());
+    target.addLegalDialect<linalg::LinalgDialect, StandardOpsDialect>();
+    target.addIllegalDialect<tosa::TosaDialect>();
+    target.markUnknownOpDynamicallyLegal([](Operation *) { return true; });
+
+    auto func = getFunction();
+    mlir::tosa::populateTosaToLinalgOnTensorsConversionPatterns(
+        func.getContext(), &patterns);
+    if (failed(applyFullConversion(func, target, std::move(patterns))))
+      signalPassFailure();
+  }
+};
+} // namespace
+
+std::unique_ptr<Pass> mlir::tosa::createTosaToLinalgOnTensors() {
+  return std::make_unique<TosaToLinalgOnTensors>();
+}
+
+void mlir::tosa::addTosaToLinalgOnTensorsPasses(OpPassManager &pm) {
+  pm.addNestedPass<FuncOp>(createTosaMakeBroadcastablePass());
+  pm.addNestedPass<FuncOp>(createTosaToLinalgOnTensors());
+}
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
new file mode 100644
--- /dev/null
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
@@ -0,0 +1,257 @@
+// RUN: mlir-opt --split-input-file --tosa-to-linalg-on-tensors %s -verify-diagnostics -o -| FileCheck %s
+
+// CHECK-LABEL: @test_abs
+func @test_abs(%arg0: tensor<f32>) -> tensor<f32> {
+  // CHECK: [[INIT:%.+]] = linalg.init_tensor [] : tensor<f32>
+  // CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#map, #map], iterator_types = []} ins(%arg0 : tensor<f32>) outs([[INIT]] : tensor<f32>) {
+  // CHECK: ^bb0(%arg1: f32, %arg2: f32):
+  // CHECK:   [[ELEMENT:%.+]] = absf %arg1
+  // CHECK:   linalg.yield [[ELEMENT]] : f32
+  // CHECK: } -> tensor<f32>
+
+  %0 = "tosa.abs"(%arg0) : (tensor<f32>) -> tensor<f32>
+
+  // CHECK: return [[GENERIC]]
+  return %0 : tensor<f32>
+}
+
+// -----
+
+// CHECK-LABEL: @test_abs
+func @test_abs(%arg0: tensor<1xf32>) -> tensor<1xf32> {
+  // CHECK: [[GENERIC:%.+]] = linalg.generic
+  // CHECK: absf
+  %0 = "tosa.abs"(%arg0) : (tensor<1xf32>) -> tensor<1xf32>
+
+  // CHECK: return [[GENERIC]]
+  return %0 : tensor<1xf32>
+}
+
+
+// -----
+
+// CHECK-LABEL: @test_tanh
+func @test_tanh(%arg0: tensor<1xf32>) -> tensor<1xf32> {
+  // CHECK: [[GENERIC:%.+]] = linalg.generic
+  // CHECK: tanh
+  %0 = "tosa.tanh"(%arg0) : (tensor<1xf32>) -> tensor<1xf32>
+
+  // CHECK: return [[GENERIC]]
+  return %0 : tensor<1xf32>
+}
+
+// -----
+
+// CHECK-LABEL: @test_add
+func @test_add(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32> {
+  // CHECK: [[GENERIC:%.+]] = linalg.generic
+  // CHECK: add
+  %0 = "tosa.add"(%arg0, %arg1) : (tensor<f32>, tensor<f32>) -> tensor<f32>
+
+  // CHECK: return [[GENERIC]]
+  return %0 : tensor<f32>
+}
+
+// -----
+
+// CHECK-LABEL: @test_add
+func @test_add(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
+  // CHECK: [[GENERIC:%.+]] = linalg.generic
+  // CHECK: add
+  %0 = "tosa.add"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
+
+  // CHECK: return [[GENERIC]]
+  return %0 : tensor<1xf32>
+}
+
+// -----
+
+func @test_add(%arg0: tensor<1xf32>, %arg1: tensor<2xf32>) -> tensor<2xf32> {
+  // expected-error @+1 {{failed to legalize operation 'tosa.add'}}
+  %0 = "tosa.add"(%arg0, %arg1) : (tensor<1xf32>, tensor<2xf32>) -> tensor<2xf32>
+  return %0 : tensor<2xf32>
+}
+
+// -----
+
+// CHECK-LABEL: @test_add
+func @test_add(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) -> tensor<1xi32> {
+  // CHECK: [[GENERIC:%.+]] = linalg.generic
+  // CHECK: add
+   %0 = "tosa.add"(%arg0, %arg1) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
+
+  // CHECK: return [[GENERIC]]
+  return %0 : tensor<1xi32>
+}
+
+// -----
+
+// CHECK-LABEL: @test_sub
+func @test_sub(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32> {
+  // CHECK: [[GENERIC:%.+]] = linalg.generic
+  // CHECK: sub
+  %0 = "tosa.sub"(%arg0, %arg1) : (tensor<f32>, tensor<f32>) -> tensor<f32>
+
+  // CHECK: return [[GENERIC]]
+  return %0 : tensor<f32>
+}
+
+
+// -----
+
+// CHECK-LABEL: @test_sub
+func @test_sub(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
+  // CHECK: [[GENERIC:%.+]] = linalg.generic
+  // CHECK: sub
+  %0 = "tosa.sub"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
+
+  // CHECK: return [[GENERIC]]
+  return %0 : tensor<1xf32>
+}
+
+// -----
+
+// CHECK-LABEL: @test_sub
+func @test_sub(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) -> tensor<1xi32> {
+  // CHECK: [[GENERIC:%.+]] = linalg.generic
+  // CHECK: sub
+  %0 = "tosa.sub"(%arg0, %arg1) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
+
+  // CHECK: return [[GENERIC]]
+  return %0 : tensor<1xi32>
+}
+
+// -----
+
+// CHECK-LABEL: @test_pow
+func @test_pow(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
+  // CHECK: [[GENERIC:%.+]] = linalg.generic
+  // CHECK: pow
+  %0 = "tosa.pow"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
+
+  // CHECK: return [[GENERIC]]
+  return %0 : tensor<1xf32>
+}
+
+// -----
+
+// CHECK-LABEL: @test_and
+func @test_and(%arg0: tensor<i32>, %arg1: tensor<i32>) -> tensor<i32> {
+  // CHECK: [[GENERIC:%.+]] = linalg.generic
+  // CHECK: and
+  %0 = "tosa.bitwise_and"(%arg0, %arg1) : (tensor<i32>, tensor<i32>) -> tensor<i32>
+
+  // CHECK: return [[GENERIC]]
+  return %0 : tensor<i32>
+}
+
+// -----
+
+// CHECK-LABEL: @test_and
+func @test_and(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) -> tensor<1xi32> {
+  // CHECK: [[GENERIC:%.+]] = linalg.generic
+  // CHECK: and
+  %0 = "tosa.bitwise_and"(%arg0, %arg1) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
+
+  // CHECK: return [[GENERIC]]
+  return %0 : tensor<1xi32>
+}
+
+
+// -----
+
+// CHECK-LABEL: @test_or
+func @test_or(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) -> tensor<1xi32> {
+  // CHECK: [[GENERIC:%.+]] = linalg.generic
+  // CHECK: or
+  %0 = "tosa.bitwise_or"(%arg0, %arg1) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
+
+  // CHECK: return [[GENERIC]]
+  return %0 : tensor<1xi32>
+}
+
+// -----
+
+// CHECK-LABEL: @test_or
+func @test_or(%arg0: tensor<i32>, %arg1: tensor<i32>) -> tensor<i32> {
+  // CHECK: [[GENERIC:%.+]] = linalg.generic
+  // CHECK: or
+  %0 = "tosa.bitwise_or"(%arg0, %arg1) : (tensor<i32>, tensor<i32>) -> tensor<i32>
+
+  // CHECK: return [[GENERIC]]
+  return %0 : tensor<i32>
+}
+
+
+// -----
+
+// CHECK-LABEL: @test_xor
+func @test_xor(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) -> tensor<1xi32> {
+  // CHECK: [[GENERIC:%.+]] = linalg.generic
+  // CHECK: xor
+  %0 = "tosa.bitwise_xor"(%arg0, %arg1) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
+
+  // CHECK: return [[GENERIC]]
+  return %0 : tensor<1xi32>
+}
+
+// -----
+
+// CHECK-LABEL: @test_xor
+func @test_xor(%arg0: tensor<i32>, %arg1: tensor<i32>) -> tensor<i32> {
+  // CHECK: [[GENERIC:%.+]] = linalg.generic
+  // CHECK: xor
+  %0 = "tosa.bitwise_xor"(%arg0, %arg1) : (tensor<i32>, tensor<i32>) -> tensor<i32>
+
+  // CHECK: return [[GENERIC]]
+  return %0 : tensor<i32>
+}
+
+
+// -----
+
+// CHECK-LABEL: @test_logical_left_shift
+func @test_logical_left_shift(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) -> tensor<1xi32> {
+  // CHECK: [[GENERIC:%.+]] = linalg.generic
+  // CHECK: shift_left
+  %0 = "tosa.logical_left_shift"(%arg0, %arg1) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
+
+  // CHECK: return [[GENERIC]]
+  return %0 : tensor<1xi32>
+}
+
+// -----
+
+// CHECK-LABEL: @test_logical_left_shift
+func @test_logical_left_shift(%arg0: tensor<i32>, %arg1: tensor<i32>) -> tensor<i32> {
+  // CHECK: [[GENERIC:%.+]] = linalg.generic
+  // CHECK: shift_left
+  %0 = "tosa.logical_left_shift"(%arg0, %arg1) : (tensor<i32>, tensor<i32>) -> tensor<i32>
+
+  // CHECK: return [[GENERIC]]
+  return %0 : tensor<i32>
+}
+// -----
+
+// CHECK-LABEL: @test_logical_right_shift
+func @test_logical_right_shift(%arg0: tensor<1xi32>, %arg1: tensor<1xi32>) -> tensor<1xi32> {
+  // CHECK: [[GENERIC:%.+]] = linalg.generic
+  // CHECK: shift_right_unsigned
+  %0 = "tosa.logical_right_shift"(%arg0, %arg1) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
+
+  // CHECK: return [[GENERIC]]
+  return %0 : tensor<1xi32>
+}
+
+// -----
+
+// CHECK-LABEL: @test_logical_right_shift
+func @test_logical_right_shift(%arg0: tensor<i32>, %arg1: tensor<i32>) -> tensor<i32> {
+  // CHECK: [[GENERIC:%.+]] = linalg.generic
+  // CHECK: shift_right_unsigned
+  %0 = "tosa.logical_right_shift"(%arg0, %arg1) : (tensor<i32>, tensor<i32>) -> tensor<i32>
+
+  // CHECK: return [[GENERIC]]
+  return %0 : tensor<i32>
+}
+