diff --git a/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp b/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp
--- a/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Transforms.cpp
@@ -643,6 +643,7 @@
 
 void linalg::populateDecomposeConvolutionPatterns(RewritePatternSet &patterns,
                                                   PatternBenefit benefit) {
+  // TODO: Separately need to add a downscale for pooling.
   patterns.add<DownscaleSizeOneWindowed2DConvolution<linalg::Conv2DNhwcHwcfOp,
                                                      Conv1DNwcWcfOp>,
                DownscaleSizeOneWindowed2DConvolution<linalg::Conv2DNchwFchwOp,
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
--- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
@@ -1449,6 +1449,12 @@
     maybeKind = getCombinerOpKind(reduceOp);
     if (!maybeKind || *maybeKind != vector::CombiningKind::ADD)
       return;
+
+    // TODO: this is very-specific to conv, and would fail with pooling.
+    // If (region has 2 ops (reduction + yield) and rhs is not used) then it is
+    // the body of a pooling ?
+    // TODO: Rob: there may be a cast i8 -> i32 before the reduction operation.
+
     // Check for single `mul` predecessor. The `mul` operands must be block
     // arguments or extension of block arguments.
     Operation *mulOp = nullptr;
@@ -1504,6 +1510,7 @@
                   ((wSize - 1) * strideW + 1) + ((kwSize - 1) * dilationW + 1) -
                       1,
                   cSize};
+      // TODO: irrelevant for pooling
       rhsShape = {kwSize, cSize, fSize};
       resShape = {nSize, wSize, fSize};
       break;
@@ -1518,6 +1525,7 @@
                   // Perform the proper inclusive -> exclusive -> inclusive.
                   ((wSize - 1) * strideW + 1) + ((kwSize - 1) * dilationW + 1) -
                       1};
+      // TODO: irrelevant for pooling
       rhsShape = {fSize, cSize, kwSize};
       resShape = {nSize, fSize, wSize};
       break;
@@ -1532,6 +1540,8 @@
     int64_t wSizeStep = strideW == 1 ? wSize : 1;
 
     Type lhsEltType = lhsShapedType.getElementType();
+
+    // TODO: irrelevant for pooling
     Type rhsEltType = rhsShapedType.getElementType();
     Type resEltType = resShapedType.getElementType();
     auto lhsType = VectorType::get(lhsShape, lhsEltType);
@@ -1542,6 +1552,8 @@
     Value lhs = builder.create<vector::TransferReadOp>(
         loc, lhsType, lhsShaped, ValueRange{zero, zero, zero});
     // Read rhs slice of size {kw, c, f} @ [0, 0, 0].
+
+    // TODO: if pooling do not load
     Value rhs = builder.create<vector::TransferReadOp>(
         loc, rhsType, rhsShaped, ValueRange{zero, zero, zero});
     // Read res slice of size {n, w, f} @ [0, 0, 0].
@@ -1562,6 +1574,8 @@
       lhs = builder.create<vector::TransposeOp>(loc, lhs, permLhs);
       // fcw -> wcf
       static constexpr std::array<int64_t, 3> permRhs = {2, 1, 0};
+
+      // TODO: irrelevant for pooling
       rhs = builder.create<vector::TransposeOp>(loc, rhs, permRhs);
       // nfw -> nwf
       static constexpr std::array<int64_t, 3> permRes = {0, 2, 1};
@@ -1606,6 +1620,7 @@
     // Compute contraction: O{n, w, f} += I{n, sw * w + dw * kw, c} * F{c, f}
     for (int64_t kw = 0; kw < kwSize; ++kw) {
       for (int64_t w = 0; w < wSize; w += wSizeStep) {
+        // TODO: pool1DSliceAsReduction (maybe contraction)
         resVals[w] = conv1dSliceAsContraction(
             builder, loc, lhsVals[linearIndex(kw, w)], rhsVals[kw], resVals[w]);
       }
diff --git a/mlir/test/Dialect/Linalg/transform-op-decompose.mlir b/mlir/test/Dialect/Linalg/transform-op-decompose.mlir
--- a/mlir/test/Dialect/Linalg/transform-op-decompose.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-decompose.mlir
@@ -56,6 +56,9 @@
   return %0: tensor<1x1x56x96xf32>
 }
 
+// TODO: add an extra test(s) here for decompose pooling.
+// DO NOT create C++ copde to test.
+
 transform.sequence failures(propagate) {
 ^bb1(%arg1: !pdl.operation):
   %0 = transform.structured.match interface{LinalgOp} in %arg1
diff --git a/mlir/test/Dialect/Linalg/vectorize-convolution.mlir b/mlir/test/Dialect/Linalg/vectorize-convolution.mlir
--- a/mlir/test/Dialect/Linalg/vectorize-convolution.mlir
+++ b/mlir/test/Dialect/Linalg/vectorize-convolution.mlir
@@ -571,3 +571,7 @@
 //      CHECK:   %[[CONT:.*]] = vector.contract
 //        {{.*}} %[[V_INPUT_R]], %[[V_FILTER_1]], %[[V_OUTPUT_R]] : vector<1x2x3xf16>, vector<3x2xf16> into vector<1x2x2xf32>
 //      CHECK:   vector.transfer_write %[[CONT]], %[[OUTPUT]][%[[C0]], %[[C0]], %[[C0]]]
+
+
+
+// TODO: add an extra test(s) here for vectorize pooling.