diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
@@ -144,6 +144,19 @@
   return filteredDims;
 }
 
+// Creates a map to collapse the last dimension of the Depthwise convolution op
+// due to a shape mismatch
+static void createDepthwiseConvCollapseMap(
+    int64_t outputRank, SmallVector<ReassociationExprs, 4> &reassociationMap,
+    OpBuilder &rewriter) {
+  reassociationMap.resize(outputRank);
+  for (int i = 0; i < outputRank; i++) {
+    reassociationMap[i].push_back(rewriter.getAffineDimExpr(i));
+  }
+  reassociationMap[outputRank - 1].push_back(
+      rewriter.getAffineDimExpr(outputRank));
+}
+
 namespace {
 
 class ConvConverter : public OpConversionPattern<tosa::Conv2DOp> {
@@ -331,6 +344,7 @@
     ShapedType weightTy = weight.getType().cast<ShapedType>();
     ShapedType biasTy = bias.getType().cast<ShapedType>();
     ShapedType resultTy = op->getResult(0).getType().cast<ShapedType>();
+    int64_t resultRank = resultTy.getRank();
 
     Type inputETy = inputTy.getElementType();
     Type resultETy = resultTy.getElementType();
@@ -410,10 +424,10 @@
     // Broadcast the initial value to the output tensor before convolving.
     SmallVector<AffineMap, 4> indexingMaps;
     indexingMaps.push_back(AffineMap::get(
-        /*dimCount=*/resultTy.getRank(), /*symbolCount=*/0,
+        /*dimCount=*/resultRank, /*symbolCount=*/0,
         {rewriter.getAffineDimExpr(3)}, rewriter.getContext()));
-    indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultTy.getRank()));
-    indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultTy.getRank()));
+    indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
+    indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
 
     Attribute resultZeroAttr = rewriter.getZeroAttr(resultETy);
     Value initTensor = rewriter.create<linalg::InitTensorOp>(
@@ -432,14 +446,18 @@
                            loc, linalgConvTy, ValueRange{input, weight},
                            ValueRange{zeroTensor}, strideAttr, dilationAttr)
                        .getResult(0);
-      Value convReshape = rewriter.create<tosa::ReshapeOp>(
-          loc, resultTy, conv, rewriter.getI64ArrayAttr(resultTy.getShape()));
+
+      SmallVector<ReassociationExprs, 4> reassociationMap;
+      createDepthwiseConvCollapseMap(resultRank, reassociationMap, rewriter);
+      Value convReshape = rewriter.create<tensor::CollapseShapeOp>(
+          loc, resultTy, conv, reassociationMap);
+
       Value result =
           rewriter
               .create<linalg::GenericOp>(
                   loc, resultTy, ValueRange({bias, convReshape}),
                   biasInitTensor, indexingMaps,
-                  getNParallelLoopsAttrs(resultTy.getRank()),
+                  getNParallelLoopsAttrs(resultRank),
                   [&](OpBuilder &nestedBuilder, Location nestedLoc,
                       ValueRange args) {
                     Value added = nestedBuilder.create<arith::AddFOp>(
@@ -457,14 +475,16 @@
                   loc, linalgConvTy, ValueRange{input, weight, iZpVal, kZpVal},
                   ValueRange{zeroTensor}, strideAttr, dilationAttr)
               .getResult(0);
-      Value convReshape = rewriter.create<tosa::ReshapeOp>(
-          loc, resultTy, conv, rewriter.getI64ArrayAttr(resultTy.getShape()));
+      SmallVector<ReassociationExprs, 4> reassociationMap;
+      createDepthwiseConvCollapseMap(resultRank, reassociationMap, rewriter);
+      Value convReshape = rewriter.create<tensor::CollapseShapeOp>(
+          loc, resultTy, conv, reassociationMap);
       Value result =
           rewriter
               .create<linalg::GenericOp>(
                   loc, resultTy, ValueRange({bias, convReshape}),
                   biasInitTensor, indexingMaps,
-                  getNParallelLoopsAttrs(resultTy.getRank()),
+                  getNParallelLoopsAttrs(resultRank),
                   [&](OpBuilder &nestedBuilder, Location nestedLoc,
                       ValueRange args) {
                     Value added = nestedBuilder.create<arith::AddIOp>(
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
@@ -477,7 +477,7 @@
   // CHECK: [[FILL:%.+]] = linalg.fill ins([[CST0]]{{.*}}outs([[INIT]]
   // CHECK: [[OUT:%.+]] = linalg.init_tensor [1, 5, 5, 33]
   // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv_2d_nhwc_hwcm {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>) outs([[FILL]] : tensor<1x5x5x3x11xf32>)
-  // CHECK: [[COLLAPSED:%.+]] = "tosa.reshape"([[DEPTH]]) {new_shape = [1, 5, 5, 33]}
+  // CHECK: [[COLLAPSED:%.+]] = tensor.collapse_shape [[DEPTH]] {{\[}}[0], [1], [2], [3, 4]]
   // CHECK: [[BIAS:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2, [[COLLAPSED]] : tensor<33xf32>, tensor<1x5x5x33xf32>) outs([[OUT]] : tensor<1x5x5x33xf32>) {
   // CHECK: ^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
   // CHECK:   [[ADD:%.+]] = arith.addf %arg3, %arg4 : f32
@@ -501,7 +501,7 @@
   // CHECK: %[[FILL:.+]] = linalg.fill
   // CHECK: %[[OUT:.+]] = linalg.init_tensor [%[[BATCH]], 5, 5, 33]
   // CHECK: %[[DEPTH:.+]] = linalg.depthwise_conv_2d_nhwc_hwcm {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<?x7x5x3xf32>, tensor<3x1x3x11xf32>) outs(%[[FILL]] : tensor<?x5x5x3x11xf32>)
-  // CHECK: %[[COLLAPSED:.+]] = "tosa.reshape"(%[[DEPTH]]) {new_shape = [-1, 5, 5, 33]}
+  // CHECK: %[[COLLAPSED:.+]] = tensor.collapse_shape %[[DEPTH]] {{\[}}[0], [1], [2], [3, 4]]
   // CHECK: %[[BIAS:.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2, %[[COLLAPSED]] : tensor<33xf32>, tensor<?x5x5x33xf32>) outs(%[[OUT]] : tensor<?x5x5x33xf32>) {
   // CHECK: ^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
   // CHECK:   %[[ADD:.+]] = arith.addf %arg3, %arg4 : f32
@@ -523,7 +523,7 @@
   // CHECK: [[FILL:%.+]] = linalg.fill ins([[CST0]]{{.*}}outs([[INIT]]
   // CHECK: [[OUT:%.+]] = linalg.init_tensor [1, 5, 5, 33]
   // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv_2d_nhwc_hwcm {dilations = dense<1> : tensor<2xi64>, strides = dense<2> : tensor<2xi64>} ins(%arg0, %arg1 : tensor<1x11x9x3xf32>, tensor<3x1x3x11xf32>) outs([[FILL]] : tensor<1x5x5x3x11xf32>)
-  // CHECK: [[COLLAPSED:%.+]] = "tosa.reshape"([[DEPTH]]) {new_shape = [1, 5, 5, 33]}
+  // CHECK: [[COLLAPSED:%.+]] = tensor.collapse_shape [[DEPTH]] {{\[}}[0], [1], [2], [3, 4]]
   // CHECK: [[BIAS:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2, [[COLLAPSED]] : tensor<33xf32>, tensor<1x5x5x33xf32>) outs([[OUT]] : tensor<1x5x5x33xf32>) {
   // CHECK: ^bb0(%arg3: f32, %arg4: f32, %arg5: f32):  
   // CHECK:   [[ADD:%.+]] = arith.addf %arg3, %arg4 : f32
@@ -551,7 +551,7 @@
   // CHECK: [[C128:%.+]] = arith.constant -128
   // CHECK: [[C42:%.+]] = arith.constant 42
   // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv_2d_nhwc_hwcm_q {dilations = dense<1> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins([[PAD]], %arg1, [[C128]], [[C42]] : tensor<1x14x14x4xi8>, tensor<3x3x4x128xi8>, i32, i32) outs([[FILL]] : tensor<1x12x12x4x128xi32>)
-  // CHECK: [[COLLAPSED:%.+]] = "tosa.reshape"([[DEPTH]]) {new_shape = [1, 12, 12, 512]}
+  // CHECK: [[COLLAPSED:%.+]] = tensor.collapse_shape [[DEPTH]] {{\[}}[0], [1], [2], [3, 4]]
   // CHECK: [[BIAS:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2, [[COLLAPSED]] : tensor<512xi32>, tensor<1x12x12x512xi32>) outs([[OUT]] : tensor<1x12x12x512xi32>) {
   // CHECK: ^bb0(%arg3: i32, %arg4: i32, %arg5: i32):  
   // CHECK:   [[ADD:%.+]] = arith.addi %arg3, %arg4 : i32
@@ -575,7 +575,7 @@
   // CHECK: [[C128:%.+]] = arith.constant -128
   // CHECK: [[C42:%.+]] = arith.constant 42
   // CHECK: [[DEPTH:%.+]] = linalg.depthwise_conv_2d_nhwc_hwcm_q {dilations = dense<2> : tensor<2xi64>, strides = dense<1> : tensor<2xi64>} ins(%arg0, %arg1, [[C128]], [[C42]] : tensor<1x14x14x4xi8>, tensor<3x3x4x128xi8>, i32, i32) outs([[FILL]] : tensor<1x10x10x4x128xi32>)
-  // CHECK: [[COLLAPSED:%.+]] = "tosa.reshape"([[DEPTH]]) {new_shape = [1, 10, 10, 512]}
+  // CHECK: [[COLLAPSED:%.+]] = tensor.collapse_shape [[DEPTH]] {{\[}}[0], [1], [2], [3, 4]]
   // CHECK: [[BIAS:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2, [[COLLAPSED]] : tensor<512xi32>, tensor<1x10x10x512xi32>) outs([[OUT]] : tensor<1x10x10x512xi32>) {
   // CHECK: ^bb0(%arg3: i32, %arg4: i32, %arg5: i32):  
   // CHECK:   [[ADD:%.+]] = arith.addi %arg3, %arg4 : i32
@@ -596,7 +596,7 @@
   // CHECK: tensor.yield %cst : f32
   // CHECK:  } : tensor<2x?x?x3xf32> to tensor<2x?x?x3xf32>
   // CHECK: %[[CONV:.+]] = linalg.depthwise_conv_2d_nhwc_hwcm {dilations = dense<[2, 1]> : tensor<2xi64>, strides = dense<[1, 2]> : tensor<2xi64>} ins(%[[PADDED]], %arg1 : tensor<2x?x?x3xf32>, tensor<3x6x3x5xf32>) outs(%22 : tensor<2x?x?x3x5xf32>) -> tensor<2x?x?x3x5xf32>
-  // CHECK: %[[RESHAPED:.+]] = "tosa.reshape"(%[[CONV]]) {new_shape = [2, -1, -1, 15]} : (tensor<2x?x?x3x5xf32>) -> tensor<2x?x?x15xf32>
+  // CHECK: %[[COLLAPSED:.+]] = tensor.collapse_shape %[[CONV]] {{\[}}[0], [1], [2], [3, 4]]
   %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {pad = [1, 2, 3, 4], dilation = [2, 1], stride = [1, 2]} : (tensor<2x?x?x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>) -> tensor<2x?x?x15xf32>
   return
 }