[mlir][Linalg] Refine how broadcast dims are treated

This PR fixes how broadcast dims (identified as "zero" results in
permutation maps) corresponding to a reduction iterator are vectorised
in the case of generic Ops. Here's an example:

```mlir
  #map = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
  #map1 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, 0)>

  func.func @generic_with_reduction_and_broadcast(%arg0: tensor<1x12x197x197xf32>) -> (tensor<1x12x197x1xf32>) {
    %0 = tensor.empty() : tensor<1x12x197x1xf32>

    %1 = linalg.generic {indexing_maps = [#map, #map1],
                        iterator_types = ["parallel", "parallel", "parallel", "reduction"]}
      ins(%arg0 : tensor<1x12x197x197xf32>)
      outs(%0 : tensor<1x12x197x1xf32>) {

    ^bb0(%in: f32, %out: f32):
      %818 = arith.addf %in, %out : f32
      linalg.yield %818 : f32
    } -> tensor<1x12x197x1xf32>
    return %1 : tensor<1x12x197x1xf32>
  }
```

This is a perfectly valid Generic Op, but currently triggers two issues
in the vectoriser. The root cause is this map:

```mlir
  #map1 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, 0)>
```

This map triggers an assert in `reindexIndexingMap` -  this hook
incorrectly assumes that every result in the input map is a `dim`
expression and that there are no constants. That's not the case in this
example. `reindexIndexingMap` is extended to allow maps like the one
above. For now, only constant "zero" results are allowed. This can be
extended in the future once a good motivating example is available.

Separately, the permutation map highlighted above "breaks" mask
calculation (ATM masks are always computed, even in the presence of
static shapes). When applying the following permutation:
```mlir
  (d0, d1, d2, d3) -> (d0, d1, d2, 0)
```

to these canonical shapes (corresponding to the example above):
```
  (1, 12, 197, 197)
```
we end up with the following error:
```bash
error: vector types must have positive constant sizes but got 1, 12, 197, 0
```

The error makes sense and indicates that we should update the
permutation map above to:
```
  (d0, d1, d2, d3) -> (d0, d1, d2)
```

This would correctly give the following vector type:
```
  vector<1x12x197xi1>
```

Fixes #97247

Author: banach-space

mlir/include/mlir/IR/AffineMap.h

@@ -354,6 +354,10 @@ class AffineMap {
   /// returns the resulting values. `this` must be symbol-less.
   SmallVector<int64_t, 4> compose(ArrayRef<int64_t> values) const;
 
+  size_t numOfZeroResults() const;
+
+  AffineMap dropZeros();
+
   /// Returns true if the AffineMap represents a subset (i.e. a projection) of a
   /// symbol-less permutation map. `allowZeroInResults` allows projected
   /// permutation maps with constant zero result expressions.

mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp

@@ -476,7 +476,7 @@ static AffineMap reindexIndexingMap(AffineMap map) {
   assert(map.isProjectedPermutation(/*allowZeroInResults=*/true) &&
          "expected projected permutation");
   auto res = compressUnusedDims(map);
-  assert(res.getNumDims() == res.getNumResults() &&
+  assert(res.getNumDims() == (res.getNumResults() - res.numOfZeroResults()) &&
          "expected reindexed map with same number of dims and results");
   return res;
 }
@@ -639,7 +639,21 @@ static Value buildVectorWrite(RewriterBase &rewriter, Value value,
         loc, value, outputOperand->get(), ValueRange{});
   }
 
-  write = state.maskOperation(rewriter, write, linalgOp, opOperandMap);
+  // The operand map may contain "zero" results, e.g.:
+  //    (d0, d1, d2, d3) -> (d0, d1, d2, 0)
+  // When applied to canonical vector shapes like these:
+  //    (1, 16, 16, 4)
+  // we would get:
+  //    (1, 16, 16, 0)
+  // Instead, we should extract the following map:
+  //    (d0, d1, d2, d3) -> (d0, d1, d2)
+  // This way, the corresponding vector/mask type will be:
+  //    vector<1x16x16xty>
+  // rather than:
+  //    vector<1x16x16x0xty>
+  auto opOperantMapWithoutZeros = opOperandMap.dropZeros();
+  write =
+      state.maskOperation(rewriter, write, linalgOp, opOperantMapWithoutZeros);
 
   // If masked, set in-bounds to true. Masking guarantees that the access will
   // be in-bounds.

mlir/lib/IR/AffineMap.cpp

@@ -553,6 +553,18 @@ AffineMap AffineMap::dropResults(const llvm::SmallBitVector &positions) const {
   return AffineMap::get(getNumDims(), getNumSymbols(), exprs, getContext());
 }
 
+AffineMap AffineMap::dropZeros() {
+  auto exprs = llvm::to_vector<4>(getResults());
+  SmallVector<AffineExpr, 8> newExprs;
+
+  for (auto expr : getResults()) {
+    auto constExpr = dyn_cast<AffineConstantExpr>(expr);
+    if (!constExpr)
+      newExprs.push_back(expr);
+  }
+  return AffineMap::get(getNumDims(), getNumSymbols(), newExprs, getContext());
+}
+
 AffineMap AffineMap::compose(AffineMap map) const {
   assert(getNumDims() == map.getNumResults() && "Number of results mismatch");
   // Prepare `map` by concatenating the symbols and rewriting its exprs.
@@ -592,6 +604,17 @@ SmallVector<int64_t, 4> AffineMap::compose(ArrayRef<int64_t> values) const {
   return res;
 }
 
+size_t AffineMap::numOfZeroResults() const {
+  size_t res = 0;
+  for (auto expr : getResults()) {
+    auto constExpr = dyn_cast<AffineConstantExpr>(expr);
+    if (constExpr && constExpr.getValue() == 0)
+      res++;
+  }
+
+  return res;
+}
+
 bool AffineMap::isProjectedPermutation(bool allowZeroInResults) const {
   if (getNumSymbols() > 0)
     return false;

mlir/test/Dialect/Linalg/vectorization-with-patterns.mlir

@@ -1964,3 +1964,43 @@ module attributes {transform.with_named_sequence} {
 //       CHECK:     %[[VAL_8:.*]] = vector.transpose %[[VAL_7]], [1, 0] : vector<1x4xf32> to vector<4x1xf32>
 //       CHECK:     vector.transfer_write %[[VAL_8]], %{{.*}} {in_bounds = [true, true]} : vector<4x1xf32>, tensor<4x1xf32>
 //       CHECK:     vector.transfer_write %[[VAL_7]], %{{.*}} {in_bounds = [true, true]} : vector<1x4xf32>, tensor<1x4xf32>
+
+// -----
+
+// Extracted from: https://github.com/llvm/llvm-project/issues/97247
+
+#map = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+#map1 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, 0)>
+
+func.func @generic_with_reduction_and_broadcast(%arg0: tensor<1x12x197x197xf32>) -> (tensor<1x12x197x1xf32>) {
+  %0 = tensor.empty() : tensor<1x12x197x1xf32>
+  %1 = linalg.generic {indexing_maps = [#map, #map1], iterator_types = ["parallel", "parallel", "parallel", "reduction"]} ins(%arg0 : tensor<1x12x197x197xf32>) outs(%0 : tensor<1x12x197x1xf32>) {
+  ^bb0(%in: f32, %out: f32):
+    %818 = arith.addf %in, %out : f32
+    linalg.yield %818 : f32
+  } -> tensor<1x12x197x1xf32>
+  return %1 : tensor<1x12x197x1xf32>
+}
+module attributes {transform.with_named_sequence} {
+  transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
+    %0 = transform.structured.match ops{["linalg.generic"]} in %arg0 : (!transform.any_op) -> !transform.any_op
+    %1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+    %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
+    transform.yield
+  }
+}
+
+// CHECK: #[[$ATTR_32:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>
+
+// CHECK-LABEL:   func.func @generic_with_reduction_and_broadcast(
+// CHECK-SAME:                                                    %[[VAL_0:.*]]: tensor<1x12x197x197xf32>) -> tensor<1x12x197x1xf32> {
+// CHECK:           %[[VAL_1:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK:           %[[VAL_2:.*]] = arith.constant 0 : index
+// CHECK:           %[[VAL_3:.*]] = tensor.empty() : tensor<1x12x197x1xf32>
+// CHECK:           %[[VAL_4:.*]] = vector.transfer_read %[[VAL_0]]{{\[}}%[[VAL_2]], %[[VAL_2]], %[[VAL_2]], %[[VAL_2]]], %[[VAL_1]] {in_bounds = [true, true, true, true]} : tensor<1x12x197x197xf32>, vector<1x12x197x197xf32>
+// CHECK:           %[[VAL_5:.*]] = vector.transfer_read %[[VAL_3]]{{\[}}%[[VAL_2]], %[[VAL_2]], %[[VAL_2]], %[[VAL_2]]], %[[VAL_1]] {in_bounds = [true, true, true], permutation_map = #[[$ATTR_32]]} : tensor<1x12x197x1xf32>, vector<1x12x197xf32>
+// CHECK:           %[[VAL_6:.*]] = vector.multi_reduction <add>, %[[VAL_4]], %[[VAL_5]] [3] : vector<1x12x197x197xf32> to vector<1x12x197xf32>
+// CHECK:           %[[VAL_7:.*]] = vector.broadcast %[[VAL_6]] : vector<1x12x197xf32> to vector<1x1x12x197xf32>
+// CHECK:           %[[VAL_8:.*]] = vector.transpose %[[VAL_7]], [1, 2, 3, 0] : vector<1x1x12x197xf32> to vector<1x12x197x1xf32>
+// CHECK:           %[[VAL_9:.*]] = vector.transfer_write %[[VAL_8]], %[[VAL_3]]{{\[}}%[[VAL_2]], %[[VAL_2]], %[[VAL_2]], %[[VAL_2]]] {in_bounds = [true, true, true, true]} : vector<1x12x197x1xf32>, tensor<1x12x197x1xf32>
+// CHECK:           return %[[VAL_9]] : tensor<1x12x197x1xf32>

mlir/test/Dialect/Linalg/vectorization.mlir

@@ -147,6 +147,51 @@ module attributes {transform.with_named_sequence} {
 
 // -----
 
+#map = affine_map<(d0, d1) -> (d0, d1)>
+#map1 = affine_map<(d0, d1) -> (d0, 0)>
+
+func.func @dynamic_generic_with_reduction_and_broadcast(%arg0: tensor<?x?xf32>, %init: tensor<?x?xf32>) -> (tensor<?x?xf32>) {
+  %0 = linalg.generic { indexing_maps = [#map, #map1],
+                        iterator_types = ["parallel", "reduction"]}
+    ins(%arg0 : tensor<?x?xf32>)
+    outs(%init : tensor<?x?xf32>) {
+  ^bb0(%in: f32, %out: f32):
+    %1 = arith.addf %in, %out : f32
+    linalg.yield %1 : f32
+  } -> tensor<?x?xf32>
+  return %0 : tensor<?x?xf32>
+}
+// CHECK: #[[$MAP:.+]] = affine_map<(d0, d1) -> (d0)>
+
+// CHECK-LABEL:   func.func @dynamic_generic_with_reduction_and_broadcast(
+// CHECK-SAME:      %[[VAL_0:.*]]: tensor<?x?xf32>,
+// CHECK-SAME:      %[[VAL_1:.*]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
+// CHECK:           %[[VAL_2:.*]] = arith.constant 0 : index
+// CHECK:           %[[VAL_3:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?xf32>
+// CHECK:           %[[VAL_4:.*]] = arith.constant 1 : index
+// CHECK:           %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor<?x?xf32>
+// CHECK:           %[[VAL_6:.*]] = arith.constant 0 : index
+// CHECK:           %[[VAL_7:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK:           %[[VAL_8:.*]] = vector.create_mask %[[VAL_3]], %[[VAL_5]] : vector<4x4xi1>
+// CHECK:           %[[VAL_9:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read %[[VAL_0]]{{\[}}%[[VAL_6]], %[[VAL_6]]], %[[VAL_7]] {in_bounds = [true, true]} : tensor<?x?xf32>, vector<4x4xf32> } : vector<4x4xi1> -> vector<4x4xf32>
+// CHECK:           %[[VAL_10:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK:           %[[VAL_11:.*]] = vector.create_mask %[[VAL_3]] : vector<4xi1>
+// CHECK:           %[[VAL_12:.*]] = vector.mask %[[VAL_11]] { vector.transfer_read %[[VAL_1]]{{\[}}%[[VAL_6]], %[[VAL_6]]], %[[VAL_10]] {in_bounds = [true], permutation_map = #[[$MAP]]} : tensor<?x?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
+// CHECK:           %[[VAL_13:.*]] = vector.mask %[[VAL_8]] { vector.multi_reduction <add>, %[[VAL_9]], %[[VAL_12]] [1] : vector<4x4xf32> to vector<4xf32> } : vector<4x4xi1> -> vector<4xf32>
+// CHECK:           %[[VAL_14:.*]] = arith.constant 0 : index
+// CHECK:           %[[VAL_15:.*]] = vector.mask %[[VAL_11]] { vector.transfer_write %[[VAL_13]], %[[VAL_1]]{{\[}}%[[VAL_14]], %[[VAL_14]]] {in_bounds = [true], permutation_map = #[[$MAP]]} : vector<4xf32>, tensor<?x?xf32> } : vector<4xi1> -> tensor<?x?xf32>
+// CHECK:           return %[[VAL_15]] : tensor<?x?xf32>
+
+module attributes {transform.with_named_sequence} {
+  transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
+    %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    transform.structured.vectorize %0 vector_sizes [4, 4] : !transform.any_op
+    transform.yield
+  }
+}
+
+// -----
+
 func.func @vectorize_dynamic_2d_transpose(%arg0: tensor<?x?xf32>,
                                           %arg1: tensor<?x?xf32>,
                                           %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {