[Mlir-commits] [mlir] Revert "[mlir][Vector] Support 0-d vectors natively in TransferOpRedu… (PR #114451)
llvmlistbot at llvm.org
llvmlistbot at llvm.org
Thu Oct 31 11:53:28 PDT 2024
llvmbot wrote:
<!--LLVM PR SUMMARY COMMENT-->
@llvm/pr-subscribers-mlir-vector
Author: Bangtian Liu (bangtianliu)
<details>
<summary>Changes</summary>
…ceRank (#<!-- -->112907)"
This reverts commit 1004865f1ca41a9581da8747f34b29862d3ebc3d.
---
Full diff: https://github.com/llvm/llvm-project/pull/114451.diff
4 Files Affected:
- (modified) mlir/lib/Dialect/Vector/Transforms/LowerVectorTransfer.cpp (+21)
- (modified) mlir/test/Conversion/VectorToSCF/vector-to-scf.mlir (+2-2)
- (modified) mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir (+10-10)
- (modified) mlir/test/Dialect/Vector/vector-transfer-to-vector-load-store.mlir (+2-2)
``````````diff
diff --git a/mlir/lib/Dialect/Vector/Transforms/LowerVectorTransfer.cpp b/mlir/lib/Dialect/Vector/Transforms/LowerVectorTransfer.cpp
index f9428a4ce286409..344cfc0cbffb93b 100644
--- a/mlir/lib/Dialect/Vector/Transforms/LowerVectorTransfer.cpp
+++ b/mlir/lib/Dialect/Vector/Transforms/LowerVectorTransfer.cpp
@@ -358,10 +358,31 @@ struct TransferOpReduceRank
op, "map is not a minor identity with broadcasting");
}
+ // TODO: support zero-dimension vectors natively. See:
+ // https://llvm.discourse.group/t/should-we-have-0-d-vectors/3097.
+ // In the meantime, lower these to a scalar load when they pop up.
+ if (reducedShapeRank == 0) {
+ Value newRead;
+ if (isa<TensorType>(op.getShapedType())) {
+ newRead = rewriter.create<tensor::ExtractOp>(
+ op.getLoc(), op.getSource(), op.getIndices());
+ } else {
+ newRead = rewriter.create<memref::LoadOp>(
+ op.getLoc(), originalVecType.getElementType(), op.getSource(),
+ op.getIndices());
+ }
+ return rewriter
+ .create<vector::BroadcastOp>(op.getLoc(), originalVecType, newRead)
+ .getVector();
+ }
+
SmallVector<int64_t> newShape(
originalVecType.getShape().take_back(reducedShapeRank));
SmallVector<bool> newScalableDims(
originalVecType.getScalableDims().take_back(reducedShapeRank));
+ // Vector rank cannot be zero. Handled by TransferReadToVectorLoadLowering.
+ if (newShape.empty())
+ return rewriter.notifyMatchFailure(op, "rank-reduced vector is 0-d");
VectorType newReadType = VectorType::get(
newShape, originalVecType.getElementType(), newScalableDims);
diff --git a/mlir/test/Conversion/VectorToSCF/vector-to-scf.mlir b/mlir/test/Conversion/VectorToSCF/vector-to-scf.mlir
index 5a6da3a06387a5e..c55a0c558bc2f19 100644
--- a/mlir/test/Conversion/VectorToSCF/vector-to-scf.mlir
+++ b/mlir/test/Conversion/VectorToSCF/vector-to-scf.mlir
@@ -503,8 +503,8 @@ func.func @transfer_read_within_async_execute(%A : memref<2x2xf32>) -> !async.to
// CHECK-LABEL: transfer_read_with_tensor
func.func @transfer_read_with_tensor(%arg: tensor<f32>) -> vector<1xf32> {
- // CHECK: %[[EXTRACTED:.*]] = vector.transfer_read %{{.*}}[], %{{.*}} : tensor<f32>, vector<f32>
- // CHECK-NEXT: %[[RESULT:.*]] = vector.broadcast %[[EXTRACTED]] : vector<f32> to vector<1xf32>
+ // CHECK: %[[EXTRACTED:.*]] = tensor.extract %{{.*}}[] : tensor<f32>
+ // CHECK-NEXT: %[[RESULT:.*]] = vector.broadcast %[[EXTRACTED]] : f32 to vector<1xf32>
// CHECK-NEXT: return %[[RESULT]] : vector<1xf32>
%f0 = arith.constant 0.0 : f32
%0 = vector.transfer_read %arg[], %f0 {permutation_map = affine_map<()->(0)>} :
diff --git a/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir b/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir
index e611a8e22ee23f3..3560ab2312a2e90 100644
--- a/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir
+++ b/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir
@@ -136,7 +136,9 @@ func.func @vectorize_nd_tensor_extract_transfer_read_basic(
// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]][%[[IDX1]], %[[IDX2]], %[[C0:.*]]], %[[CST_0]] {in_bounds = [true, true, true]} : tensor<3x3x3xf32>, vector<1x1x3xf32>
// CHECK: vector.transfer_write %[[READ]], %[[ARG1]][%[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true]} : vector<1x1x3xf32>, tensor<1x1x3xf32>
-// Same as example above, but reading into a column tensor.
+// Same as example above, but reading into a column tensor. Note that after the
+// vectorizatoin, the `TransferOpReduceRank` will replace
+// `vector.transfer_read` with `tensor.extract -> scalar`.
// TODO: Currently this fails to vectorise when the indices are non-constant.
@@ -160,10 +162,9 @@ func.func @vectorize_nd_tensor_extract_transfer_read_basic_column(
// CHECK-LABEL: func.func @vectorize_nd_tensor_extract_transfer_read_basic_column(
// CHECK-SAME: %[[INPUT:.*]]: tensor<3x3x3xf32>,
// CHECK-SAME: %[[OUTPUT:.*]]: tensor<3x1x1xf32>)
-// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[CST_0:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %[[READ:.*]] = vector.transfer_read %[[INPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]], %[[CST_0]] : tensor<3x3x3xf32>, vector<f32>
-// CHECK: %[[BCAST:.*]] = vector.broadcast %[[READ]] : vector<f32> to vector<3x1x1xf32>
+// CHECK: %[[C0:.*]] = arith.constant 0 : index
+// CHECK: %[[EXTRACT:.*]] = tensor.extract %[[INPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]] : tensor<3x3x3xf32>
+// CHECK: %[[BCAST:.*]] = vector.broadcast %[[EXTRACT]] : f32 to vector<3x1x1xf32>
// CHECK: %[[RES:.*]] = vector.transfer_write %[[BCAST]], %[[OUTPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true]} : vector<3x1x1xf32>, tensor<3x1x1xf32>
// CHECK: return %[[RES]] : tensor<3x1x1xf32>
@@ -540,9 +541,8 @@ func.func @vectorize_nd_tensor_extract_with_tensor_extract(%input_1: tensor<1x20
// CHECK-SAME: %[[INPUT_2:.*]]: tensor<257x24xf32>,
// CHECK: %[[EXTRACTED_0_IDX_0:.*]] = arith.constant 0 : index
// CHECK: %[[EXTRACTED_0_IDX_1:.*]] = vector.extractelement %{{.*}}[%{{.*}} : i32] : vector<4xindex>
-// First `vector.transfer_read` from the generic Op - loop invariant scalar load.
-// CHECK: vector.transfer_read %[[INPUT_1]][%[[EXTRACTED_0_IDX_0]], %[[EXTRACTED_0_IDX_1]]]
-// CHECK-SAME: tensor<1x20xi32>, vector<i32>
+// First `tensor.extract` from the generic Op - loop invariant scalar load.
+// CHECK: tensor.extract %[[INPUT_1]][%[[EXTRACTED_0_IDX_0]], %[[EXTRACTED_0_IDX_1]]] : tensor<1x20xi32>
// The following `tensor.extract` from the generic Op s a contiguous load (all Ops used
// for address calculation also satisfy the required conditions).
// CHECK: vector.transfer_read %[[INPUT_2]][%{{.*}}, %{{.*}}, %{{.*}} {in_bounds = [true, true]} : tensor<257x24xf32>, vector<1x4xf32>
@@ -745,8 +745,8 @@ func.func @vectorize_0d_tensor_extract(%arg0: tensor<f32>, %arg2: tensor<1x1x3xf
// CHECK-LABEL: func.func @vectorize_0d_tensor_extract(
// CHECK-SAME: %[[ARG_0:.*]]: tensor<f32>
-// CHECK: %[[EXTRACT:.*]] = vector.transfer_read %[[ARG_0]][], %{{.+}} : tensor<f32>
-// CHECK: vector.broadcast %[[EXTRACT]] : vector<f32> to vector<1x1x3xf32>
+// CHECK: %[[EXTRACT:.*]] = tensor.extract %[[ARG_0]][] : tensor<f32>
+// CHECK: vector.broadcast %[[EXTRACT]] : f32 to vector<1x1x3xf32>
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
diff --git a/mlir/test/Dialect/Vector/vector-transfer-to-vector-load-store.mlir b/mlir/test/Dialect/Vector/vector-transfer-to-vector-load-store.mlir
index f90111b4c88618e..4d8e4a8296fb5ae 100644
--- a/mlir/test/Dialect/Vector/vector-transfer-to-vector-load-store.mlir
+++ b/mlir/test/Dialect/Vector/vector-transfer-to-vector-load-store.mlir
@@ -26,8 +26,8 @@ func.func @vector_transfer_ops_0d_memref(%mem: memref<f32>, %vec: vector<1x1x1xf
func.func @vector_transfer_ops_0d_tensor(%src: tensor<f32>) -> vector<1xf32> {
%f0 = arith.constant 0.0 : f32
-// CHECK: %[[S:.*]] = vector.transfer_read %[[SRC]][]
-// CHECK: %[[V:.*]] = vector.broadcast %[[S]] : vector<f32> to vector<1xf32>
+// CHECK-NEXT: %[[S:.*]] = tensor.extract %[[SRC]][] : tensor<f32>
+// CHECK-NEXT: %[[V:.*]] = vector.broadcast %[[S]] : f32 to vector<1xf32>
%res = vector.transfer_read %src[], %f0 {in_bounds = [true], permutation_map = affine_map<()->(0)>} :
tensor<f32>, vector<1xf32>
``````````
</details>
https://github.com/llvm/llvm-project/pull/114451
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