[Mlir-commits] [mlir] [mlir][linalg][elementwise] Fold broadcast into new elementwise (PR #167626)

[Javed Absar]

================
@@ -28,16 +28,212 @@ func.func @unary_transpose(%A : tensor<16x8x32xf32>, %B: tensor<8x16x32xf32>) ->
 // CHECK-SAME:              ins(%[[A]], %[[B]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[C]] : tensor<?x?xf32>) -> tensor<?x?xf32>
 // CHECK-NEXT:  return %[[RES]] : tensor<?x?xf32>
 //
-func.func @binary_transposed(%A : tensor<?x?xf32>, %B: tensor<?x?xf32>, %C: tensor<?x?xf32>) ->  tensor<?x?xf32> {
+func.func @binary_transposed(%A: tensor<?x?xf32>, %B: tensor<?x?xf32>, %C: tensor<?x?xf32>) -> tensor<?x?xf32> {
   %c0 = arith.constant 0 : index
   %c1 = arith.constant 1 : index
   %dim0 = tensor.dim %A, %c0 : tensor<?x?xf32>
   %dim1 = tensor.dim %A, %c1 : tensor<?x?xf32>
 
   %empty = tensor.empty(%dim1, %dim0) : tensor<?x?xf32>
-  %transposed_B = linalg.transpose ins(%B : tensor<?x?xf32>) outs(%empty :  tensor<?x?xf32>) permutation = [1, 0]
+  %transposed_B = linalg.transpose ins(%B : tensor<?x?xf32>) outs(%empty : tensor<?x?xf32>) permutation = [1, 0]
   %result = linalg.elementwise kind=#linalg.elementwise_kind<add>
-                          ins(%A, %transposed_B : tensor<?x?xf32>,  tensor<?x?xf32>)
-                          outs(%C: tensor<?x?xf32>) -> tensor<?x?xf32>
+                          ins(%A, %transposed_B : tensor<?x?xf32>, tensor<?x?xf32>)
+                          outs(%C : tensor<?x?xf32>) -> tensor<?x?xf32>
   return %result : tensor<?x?xf32>
 }
+
+// -----
+
+// CHECK-DAG: #[[IDENTITY:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
+// CHECK-DAG: #[[BROADCASTED:.+]] = affine_map<(d0, d1, d2) -> (d0, d2)>
+//
+// CHECK:  func.func @unary_broadcasted(%[[A:.+]]: tensor<8x32xf32>, %[[B:.+]]: tensor<8x16x32xf32>) -> tensor<8x16x32xf32> {
+// CHECK-NEXT:  %[[RES:.+]] = linalg.elementwise kind=#linalg.elementwise_kind<exp>
+// CHECK-SAME:       indexing_maps = [#[[BROADCASTED]], #[[IDENTITY]]]
+// CHECK-SAME:       ins(%[[A]] : tensor<8x32xf32>) outs(%[[B]] : tensor<8x16x32xf32>) -> tensor<8x16x32xf32>
+// CHECK-NEXT:    return %[[RES]] : tensor<8x16x32xf32>
+//
+func.func @unary_broadcasted(%A: tensor<8x32xf32>, %B: tensor<8x16x32xf32>) -> tensor<8x16x32xf32> {
+  %empty = tensor.empty() : tensor<8x16x32xf32>
+  %broadcasted_A = linalg.broadcast ins(%A : tensor<8x32xf32>) outs(%empty : tensor<8x16x32xf32>) dimensions = [1]
+  %result = linalg.elementwise kind=#linalg.elementwise_kind<exp>
+                          ins(%broadcasted_A : tensor<8x16x32xf32>) outs(%B : tensor<8x16x32xf32>) -> tensor<8x16x32xf32>
+  return %result : tensor<8x16x32xf32>
+}
+
+// -----
+
+// CHECK-DAG: #[[IDENTITY:.+]] = affine_map<(d0, d1) -> (d0, d1)>
+// CHECK-DAG: #[[BROADCASTED:.+]] = affine_map<(d0, d1) -> (d0)>
+//
+// CHECK:  func.func @binary_broadcasted(%[[A:.+]]: tensor<?x?xf32>, %[[B:.+]]: tensor<?xf32>, %[[C:.+]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
+// CHECK-NEXT:  %[[RES:.+]] = linalg.elementwise kind=#linalg.elementwise_kind<add>
+// CHECK-SAME:              indexing_maps = [#[[IDENTITY]], #[[BROADCASTED]], #[[IDENTITY]]]
+// CHECK-SAME:              ins(%[[A]], %[[B]] : tensor<?x?xf32>, tensor<?xf32>) outs(%[[C]] : tensor<?x?xf32>) -> tensor<?x?xf32>
+// CHECK-NEXT:  return %[[RES]] : tensor<?x?xf32>
+//
+func.func @binary_broadcasted(%A: tensor<?x?xf32>, %B: tensor<?xf32>, %C: tensor<?x?xf32>) -> tensor<?x?xf32> {
+  %c0 = arith.constant 0 : index
+  %c1 = arith.constant 1 : index
+  %dim0 = tensor.dim %A, %c0 : tensor<?x?xf32>
+  %dim1 = tensor.dim %A, %c1 : tensor<?x?xf32>
+
+  %empty = tensor.empty(%dim1, %dim0) : tensor<?x?xf32>
+  %broadcasted_B = linalg.broadcast ins(%B : tensor<?xf32>) outs(%empty : tensor<?x?xf32>) dimensions = [1]
+  %result = linalg.elementwise kind=#linalg.elementwise_kind<add>
+                          ins(%A, %broadcasted_B : tensor<?x?xf32>, tensor<?x?xf32>)
+                          outs(%C : tensor<?x?xf32>) -> tensor<?x?xf32>
+  return %result : tensor<?x?xf32>
+}
+
+// -----
+
+// CHECK-DAG: #[[IDENTITY:.+]] = affine_map<(d0, d1) -> (d0, d1)>
+// CHECK-DAG: #[[COMPOSED_MAP:.+]] = affine_map<(d0, d1) -> (d0)>
+//
+// CHECK:  func.func @fold_broadcast_after_transpose_fold(%[[A:.+]]: tensor<16xf32>, %[[B:.+]]: tensor<16x32xf32>) -> tensor<16x32xf32> {
+// CHECK-NEXT:  %[[RES:.+]] = linalg.elementwise kind=#linalg.elementwise_kind<exp>
+// CHECK-SAME:              indexing_maps = [#[[COMPOSED_MAP]], #[[IDENTITY]]]
+// CHECK-SAME:              ins(%[[A]] : tensor<16xf32>) outs(%[[B]] : tensor<16x32xf32>) -> tensor<16x32xf32>
+// CHECK-NEXT:  return %[[RES]] : tensor<16x32xf32>
+//
+func.func @fold_broadcast_after_transpose_fold(%A: tensor<16xf32>, %B: tensor<16x32xf32>) -> tensor<16x32xf32> {
+  %empty_b = tensor.empty() : tensor<32x16xf32>
+  %broadcasted_A = linalg.broadcast ins(%A : tensor<16xf32>) outs(%empty_b : tensor<32x16xf32>) dimensions = [0]
+
+  %empty_t = tensor.empty() : tensor<16x32xf32>
+  %transposed_A = linalg.transpose ins(%broadcasted_A : tensor<32x16xf32>) outs(%empty_t : tensor<16x32xf32>) permutation = [1, 0]
+
+  %result = linalg.elementwise kind=#linalg.elementwise_kind<exp>
+                          ins(%transposed_A : tensor<16x32xf32>) outs(%B : tensor<16x32xf32>) -> tensor<16x32xf32>
+  return %result : tensor<16x32xf32>
+}
+
+// -----
+
+// CHECK-DAG: #[[IDENTITY:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
+// CHECK-DAG: #[[COMPOSED_MAP:.+]] = affine_map<(d0, d1, d2) -> (d2, d1)>
+//
+// CHECK:  func.func @fold_transpose_after_broadcast_fold(%[[A:.+]]: tensor<32x16xf32>, %[[B:.+]]: tensor<8x16x32xf32>) -> tensor<8x16x32xf32> {
+// CHECK-NEXT:  %[[RES:.+]] = linalg.elementwise kind=#linalg.elementwise_kind<exp>
+// CHECK-SAME:              indexing_maps = [#[[COMPOSED_MAP]], #[[IDENTITY]]]
+// CHECK-SAME:              ins(%[[A]] : tensor<32x16xf32>) outs(%[[B]] : tensor<8x16x32xf32>) -> tensor<8x16x32xf32>
+// CHECK-NEXT:  return %[[RES]] : tensor<8x16x32xf32>
+//
+func.func @fold_transpose_after_broadcast_fold(%A: tensor<32x16xf32>, %B: tensor<8x16x32xf32>) -> tensor<8x16x32xf32> {
+  %empty_t = tensor.empty() : tensor<16x32xf32>
+  %transposed_A = linalg.transpose ins(%A : tensor<32x16xf32>) outs(%empty_t : tensor<16x32xf32>) permutation = [1, 0]
+
+  %empty_b = tensor.empty() : tensor<8x16x32xf32>
+  %broadcasted_A = linalg.broadcast ins(%transposed_A : tensor<16x32xf32>) outs(%empty_b : tensor<8x16x32xf32>) dimensions = [0]
+
+  %result = linalg.elementwise kind=#linalg.elementwise_kind<exp>
+                          ins(%broadcasted_A : tensor<8x16x32xf32>) outs(%B : tensor<8x16x32xf32>) -> tensor<8x16x32xf32>
+  return %result : tensor<8x16x32xf32>
+}
+
+// -----
+
+// CHECK-DAG: #[[IDENTITY:.+]] = affine_map<(d0, d1) -> (d0, d1)>
+// CHECK-DAG: #[[COMPOSED_MAP:.+]] = affine_map<(d0, d1) -> (d0)>
+//
+// CHECK:  func.func @fold_broadcast_after_transpose_fold_binary(%[[A:.+]]: tensor<?xf32>, %[[B:.+]]: tensor<?x?xf32>, %[[C:.+]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
+// CHECK-NEXT:  %[[RES:.+]] = linalg.elementwise kind=#linalg.elementwise_kind<add>
+// CHECK-SAME:              indexing_maps = [#[[COMPOSED_MAP]], #[[IDENTITY]], #[[IDENTITY]]]
+// CHECK-SAME:              ins(%[[A]], %[[B]] : tensor<?xf32>, tensor<?x?xf32>) outs(%[[C]] : tensor<?x?xf32>) -> tensor<?x?xf32>
+// CHECK-NEXT:  return %[[RES]] : tensor<?x?xf32>
+//
----------------
javedabsar1 wrote:

The composition is not very obvious because the fold behind the scene first folds transpose and then the broadcast. IF the input IR shown  is after the transpose then it would shorten the example and make the composition more obvious.

https://github.com/llvm/llvm-project/pull/167626