[Mlir-commits] [mlir] [mlir] Add apply_patterns.linalg.pad_vectorization TD Op (PR #112504)

[Javed Absar]

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@@ -0,0 +1,272 @@
+// RUN: mlir-opt %s -transform-interpreter -split-input-file | FileCheck %s
+
+///----------------------------------------------------------------------------------------
+/// [Pattern: PadOpVectorizationWithTransferReadPattern]
+///----------------------------------------------------------------------------------------
+// CHECK-LABEL: func @pad_and_transfer_read
+//  CHECK-SAME:     %[[ARG0:.*]]: tensor<5x6xf32>
+//   CHECK-NOT:   tensor.pad
+//   CHECK-DAG:   %[[C0:.*]] = arith.constant 0 : index
+//   CHECK-DAG:   %[[C5:.*]] = arith.constant 5.0
+//       CHECK:   %[[RESULT:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], %[[C5]] : tensor<5x6xf32>, vector<7x9xf32>
+//       CHECK:   return %[[RESULT]]
+func.func @pad_and_transfer_read(%arg0: tensor<5x6xf32>) -> vector<7x9xf32> {
+  %c0 = arith.constant 0 : index
+  %c5 = arith.constant 5.0 : f32
+  %c6 = arith.constant 6.0 : f32
+  %0 = tensor.pad %arg0 low[0, 0] high[5, 7] {
+    ^bb0(%arg1: index, %arg2: index):
+      tensor.yield %c5 : f32
+  } : tensor<5x6xf32> to tensor<10x13xf32>
+  %1 = vector.transfer_read %0[%c0, %c0], %c6
+      : tensor<10x13xf32>, vector<7x9xf32>
+  return %1 : vector<7x9xf32>
+}
+
+module attributes {transform.with_named_sequence} {
+  transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
+    %func_op = transform.structured.match ops{["func.func"]} in %arg1 : (!transform.any_op) -> !transform.op<"func.func">
+
+    transform.apply_patterns to %func_op {
+      transform.apply_patterns.linalg.pad_vectorization
+    } : !transform.op<"func.func">
+    transform.yield
+  }
+}
+
+// -----
+
+///----------------------------------------------------------------------------------------
+/// [Pattern: PadOpVectorizationWithTransferReadPattern
+///----------------------------------------------------------------------------------------
+func.func private @make_vector() -> vector<7x9xf32>
+
+// CHECK-LABEL: func @pad_and_transfer_write_static
+//  CHECK-SAME:     %[[ARG0:.*]]: tensor<5x6xf32>
+//   CHECK-NOT:   tensor.pad
+//       CHECK:   %[[C0:.*]] = arith.constant 0 : index
+//       CHECK:   %[[VEC0:.*]] = call @make_vector() : () -> vector<7x9xf32>
+//       CHECK:   %[[RESULT:.*]] = vector.transfer_write %[[VEC0]], %[[ARG0]][%[[C0]], %[[C0]]] : vector<7x9xf32>, tensor<5x6xf32>
+//       CHECK:   return %[[RESULT]]
+func.func @pad_and_transfer_write_static(
+    %arg0: tensor<5x6xf32>) -> tensor<5x6xf32> {
+  %c0 = arith.constant 0 : index
+  %c5 = arith.constant 5.0 : f32
+  %0 = tensor.pad %arg0 low[0, 0] high[5, 7] {
+    ^bb0(%arg2: index, %arg3: index):
+      tensor.yield %c5 : f32
+  } : tensor<5x6xf32> to tensor<10x13xf32>
+  %1 = call @make_vector() : () -> vector<7x9xf32>
+  %2 = vector.transfer_write %1, %0[%c0, %c0]
+      : vector<7x9xf32>, tensor<10x13xf32>
+  %3 = tensor.extract_slice %2[0, 0] [5, 6] [1, 1] : tensor<10x13xf32> to tensor<5x6xf32>
+  return %3 : tensor<5x6xf32>
+}
+
+module attributes {transform.with_named_sequence} {
+  transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
+    %func_op = transform.structured.match ops{["func.func"]} in %arg1 : (!transform.any_op) -> !transform.op<"func.func">
+
+    transform.apply_patterns to %func_op {
+      transform.apply_patterns.linalg.pad_vectorization
+    } : !transform.op<"func.func">
+    transform.yield
+  }
+}
+
+// -----
+
+func.func private @make_vector() -> vector<7x9xf32>
+
+// CHECK-LABEL: func @pad_and_transfer_write_dynamic_static
+//  CHECK-SAME:     %[[ARG0:.*]]: tensor<?x?xf32>, %[[SIZE:.*]]: index, %[[PADDING:.*]]: index
+//   CHECK-NOT:   tensor.pad
+//       CHECK:   %[[C0:.*]] = arith.constant 0 : index
+//       CHECK:   %[[SUB:.*]] = tensor.extract_slice %[[ARG0]][0, 0] [%[[SIZE]], 6] [1, 1] : tensor<?x?xf32> to tensor<?x6xf32>
+//       CHECK:   %[[VEC0:.*]] = call @make_vector() : () -> vector<7x9xf32>
+//       CHECK:   %[[RESULT:.*]] = vector.transfer_write %[[VEC0]], %[[SUB]][%[[C0]], %[[C0]]] : vector<7x9xf32>, tensor<?x6xf32>
+//       CHECK:   return %[[RESULT]]
+func.func @pad_and_transfer_write_dynamic_static(
----------------
javedabsar1 wrote:

what is 'dynamic_static'  I guess you mean one part is dynamic and another is static ? 

https://github.com/llvm/llvm-project/pull/112504