[Mlir-commits] [mlir] 34e34a0 - [mlir][linalg] Mark xfers as in-bounds when masking depthwise convs (#96771)
llvmlistbot at llvm.org
llvmlistbot at llvm.org
Thu Jun 27 07:47:03 PDT 2024
Author: Benjamin Maxwell
Date: 2024-06-27T15:47:00+01:00
New Revision: 34e34a03ac83b51e90f8788945f9668446e468f8
URL: https://github.com/llvm/llvm-project/commit/34e34a03ac83b51e90f8788945f9668446e468f8
DIFF: https://github.com/llvm/llvm-project/commit/34e34a03ac83b51e90f8788945f9668446e468f8.diff
LOG: [mlir][linalg] Mark xfers as in-bounds when masking depthwise convs (#96771)
If this is not set the fact that the dynamic channel is in-bounds cannot
be inferred automatically (like it can for static sizes), which
eventually leads to it being marked as out-of-bounds (which prevents
some rewrites).
Added:
Modified:
mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
mlir/test/Dialect/Linalg/vectorize-conv-masked-and-scalable.mlir
Removed:
################################################################################
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
index 511835a226e7a..3a75d2ac08157 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
@@ -3234,6 +3234,11 @@ struct Conv1DGenerator
auto maskType =
VectorType::get(maskShape, rewriter.getI1Type(), scalableDims);
+ SmallVector<bool> inBounds(maskShape.size(), true);
+ auto xferOp = cast<VectorTransferOpInterface>(opToMask);
+ xferOp->setAttr(xferOp.getInBoundsAttrName(),
+ rewriter.getBoolArrayAttr(inBounds));
+
SmallVector<OpFoldResult> mixedDims = vector::getMixedSizesXfer(
cast<LinalgOp>(op).hasPureTensorSemantics(), opToMask, rewriter);
diff --git a/mlir/test/Dialect/Linalg/vectorize-conv-masked-and-scalable.mlir b/mlir/test/Dialect/Linalg/vectorize-conv-masked-and-scalable.mlir
index 84b556d80887c..4964a8d2e0db8 100644
--- a/mlir/test/Dialect/Linalg/vectorize-conv-masked-and-scalable.mlir
+++ b/mlir/test/Dialect/Linalg/vectorize-conv-masked-and-scalable.mlir
@@ -34,18 +34,18 @@ module attributes {transform.with_named_sequence} {
// CHECK: %[[C8:.*]] = arith.constant 8 : index
// CHECK: %[[MASK_IN:.*]] = vector.create_mask %[[C1]], %[[C8]], %[[CH_DIM_IN]] : vector<1x8x4xi1>
/// Read the input tensor
-// CHECK: %[[VEC_IN:.*]] = vector.mask %[[MASK_IN]] { vector.transfer_read %[[INPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] : tensor<1x8x?xi8>, vector<1x8x4xi8> } : vector<1x8x4xi1> -> vector<1x8x4xi8>
+// CHECK: %[[VEC_IN:.*]] = vector.mask %[[MASK_IN]] { vector.transfer_read %[[INPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, true, true]} : tensor<1x8x?xi8>, vector<1x8x4xi8> } : vector<1x8x4xi1> -> vector<1x8x4xi8>
/// Create a mask for the filter tensor
// CHECK: %[[CH_DIM_FLT:.*]] = tensor.dim %[[FILTER]], %[[C1]] : tensor<1x?xi8>
// CHECK: %[[MASK_FLT:.*]] = vector.create_mask %[[C1]], %[[CH_DIM_FLT]] : vector<1x4xi1>
/// Read the filter tensor
-// CHECK: %[[VEC_FLT:.*]] = vector.mask %[[MASK_FLT]] { vector.transfer_read %[[FILTER]]{{\[}}%[[C0]], %[[C0]]], %[[PAD]] : tensor<1x?xi8>, vector<1x4xi8> } : vector<1x4xi1> -> vector<1x4xi8>
+// CHECK: %[[VEC_FLT:.*]] = vector.mask %[[MASK_FLT]] { vector.transfer_read %[[FILTER]]{{\[}}%[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, true]} : tensor<1x?xi8>, vector<1x4xi8> } : vector<1x4xi1> -> vector<1x4xi8>
/// Create a mask for the output tensor
// CHECK: %[[CH_DIM_OUT:.*]] = tensor.dim %[[OUTPUT]], %[[C2]] : tensor<1x8x?xi8>
// CHECK: %[[MASK_OUT:.*]] = vector.create_mask %[[C1]], %[[C8]], %[[CH_DIM_OUT]] : vector<1x8x4xi1>
-// CHECK: %[[VEC_OUT:.*]] = vector.mask %[[MASK_OUT]] { vector.transfer_read %[[OUTPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] : tensor<1x8x?xi8>, vector<1x8x4xi8> } : vector<1x8x4xi1> -> vector<1x8x4xi8>
+// CHECK: %[[VEC_OUT:.*]] = vector.mask %[[MASK_OUT]] { vector.transfer_read %[[OUTPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, true, true]} : tensor<1x8x?xi8>, vector<1x8x4xi8> } : vector<1x8x4xi1> -> vector<1x8x4xi8>
/// Convolution
// CHECK: %[[IN_1:.*]] = vector.extract_strided_slice %[[VEC_IN]] {offsets = [0, 0, 0], sizes = [1, 8, 4], strides = [1, 1, 1]} : vector<1x8x4xi8> to vector<1x8x4xi8>
@@ -55,7 +55,7 @@ module attributes {transform.with_named_sequence} {
// CHECK: %[[MULI:.*]] = arith.muli %[[IN_1]], %[[FLT_1_B]] : vector<1x8x4xi8>
// CHECK: %[[ADDI:.*]] = arith.addi %[[MULI]], %[[OUT_1]] : vector<1x8x4xi8>
// CHECK: %[[OUT_INS:.*]] = vector.insert_strided_slice %[[ADDI]], %[[VEC_OUT]] {offsets = [0, 0, 0], strides = [1, 1, 1]} : vector<1x8x4xi8> into vector<1x8x4xi8>
-// CHECK: %[[OUT:.*]] = vector.mask %[[MASK_OUT]] { vector.transfer_write %[[OUT_INS]], %[[OUTPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]] : vector<1x8x4xi8>, tensor<1x8x?xi8> } : vector<1x8x4xi1> -> tensor<1x8x?xi8>
+// CHECK: %[[OUT:.*]] = vector.mask %[[MASK_OUT]] { vector.transfer_write %[[OUT_INS]], %[[OUTPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true]} : vector<1x8x4xi8>, tensor<1x8x?xi8> } : vector<1x8x4xi1> -> tensor<1x8x?xi8>
// CHECK: return %[[OUT]] : tensor<1x8x?xi8>
// -----
@@ -95,19 +95,19 @@ module attributes {transform.with_named_sequence} {
// CHECK: %[[C8:.*]] = arith.constant 8 : index
// CHECK: %[[MASK_IN:.*]] = vector.create_mask %[[C1]], %[[C8]], %[[CH_DIM_IN]] : vector<1x8x[4]xi1>
/// Read the input tensor
-// CHECK: %[[VEC_IN:.*]] = vector.mask %[[MASK_IN]] { vector.transfer_read %[[INPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] : tensor<1x8x?xi8>, vector<1x8x[4]xi8> } : vector<1x8x[4]xi1> -> vector<1x8x[4]xi8>
+// CHECK: %[[VEC_IN:.*]] = vector.mask %[[MASK_IN]] { vector.transfer_read %[[INPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, true, true]} : tensor<1x8x?xi8>, vector<1x8x[4]xi8> } : vector<1x8x[4]xi1> -> vector<1x8x[4]xi8>
/// Create a mask for the filter tensor
// CHECK: %[[CH_DIM_FLT:.*]] = tensor.dim %[[FILTER]], %[[C1]] : tensor<1x?xi8>
// CHECK: %[[MASK_FLT:.*]] = vector.create_mask %[[C1]], %[[CH_DIM_FLT]] : vector<1x[4]xi1>
/// Read the filter tensor
-// CHECK: %[[VEC_FLT:.*]] = vector.mask %[[MASK_FLT]] { vector.transfer_read %[[FILTER]]{{\[}}%[[C0]], %[[C0]]], %[[PAD]] : tensor<1x?xi8>, vector<1x[4]xi8> } : vector<1x[4]xi1> -> vector<1x[4]xi8>
+// CHECK: %[[VEC_FLT:.*]] = vector.mask %[[MASK_FLT]] { vector.transfer_read %[[FILTER]]{{\[}}%[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, true]} : tensor<1x?xi8>, vector<1x[4]xi8> } : vector<1x[4]xi1> -> vector<1x[4]xi8>
/// Create a mask for the output tensor
// CHECK: %[[CH_DIM_OUT:.*]] = tensor.dim %[[OUTPUT]], %[[C2]] : tensor<1x8x?xi8>
// CHECK: %[[MASK_OUT:.*]] = vector.create_mask %[[C1]], %[[C8]], %[[CH_DIM_OUT]] : vector<1x8x[4]xi1>
/// Read the output tensor
-// CHECK: %[[VEC_OUT:.*]] = vector.mask %[[MASK_OUT]] { vector.transfer_read %[[OUTPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] : tensor<1x8x?xi8>, vector<1x8x[4]xi8> } : vector<1x8x[4]xi1> -> vector<1x8x[4]xi8>
+// CHECK: %[[VEC_OUT:.*]] = vector.mask %[[MASK_OUT]] { vector.transfer_read %[[OUTPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, true, true]} : tensor<1x8x?xi8>, vector<1x8x[4]xi8> } : vector<1x8x[4]xi1> -> vector<1x8x[4]xi8>
/// Convolution
// CHECK: %[[IN_1:.*]] = vector.extract_strided_slice %[[VEC_IN]] {offsets = [0, 0, 0], sizes = [1, 8, 4], strides = [1, 1, 1]} : vector<1x8x[4]xi8> to vector<1x8x[4]xi8>
@@ -117,7 +117,7 @@ module attributes {transform.with_named_sequence} {
// CHECK: %[[MULI:.*]] = arith.muli %[[IN_1]], %[[FLT_1_B]] : vector<1x8x[4]xi8>
// CHECK: %[[ADDI:.*]] = arith.addi %[[MULI]], %[[OUT_1]] : vector<1x8x[4]xi8>
// CHECK: %[[OUT_INS:.*]] = vector.insert_strided_slice %[[ADDI]], %[[VEC_OUT]] {offsets = [0, 0, 0], strides = [1, 1, 1]} : vector<1x8x[4]xi8> into vector<1x8x[4]xi8>
-// CHECK: %[[OUT:.*]] = vector.mask %[[MASK_OUT]] { vector.transfer_write %[[OUT_INS]], %[[OUTPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]] : vector<1x8x[4]xi8>, tensor<1x8x?xi8> } : vector<1x8x[4]xi1> -> tensor<1x8x?xi8>
+// CHECK: %[[OUT:.*]] = vector.mask %[[MASK_OUT]] { vector.transfer_write %[[OUT_INS]], %[[OUTPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true]} : vector<1x8x[4]xi8>, tensor<1x8x?xi8> } : vector<1x8x[4]xi1> -> tensor<1x8x?xi8>
// CHECK: return %[[OUT]] : tensor<1x8x?xi8>
// -----
@@ -157,19 +157,19 @@ module attributes {transform.with_named_sequence} {
// CHECK: %[[C5:.*]] = arith.constant 5 : index
// CHECK: %[[MASK_IN:.*]] = vector.create_mask %[[C3]], %[[C5]], %[[CH_DIM_IN]] : vector<3x4x[4]xi1>
/// Read the input tensor
-// CHECK: %[[VEC_IN:.*]] = vector.mask %[[MASK_IN]] { vector.transfer_read %[[INPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] : memref<3x5x?xf32>, vector<3x4x[4]xf32> } : vector<3x4x[4]xi1> -> vector<3x4x[4]xf32>
+// CHECK: %[[VEC_IN:.*]] = vector.mask %[[MASK_IN]] { vector.transfer_read %[[INPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, true, true]} : memref<3x5x?xf32>, vector<3x4x[4]xf32> } : vector<3x4x[4]xi1> -> vector<3x4x[4]xf32>
/// Create a mask for the filter tensor
// CHECK: %[[CH_DIM_FLT:.*]] = memref.dim %[[FILTER]], %[[C1]] : memref<2x?xf32>
// CHECK: %[[MASK_FLT:.*]] = vector.create_mask %[[C2]], %[[CH_DIM_FLT]] : vector<2x[4]xi1>
/// Read the filter tensor
-// CHECK: %[[VEC_FLT:.*]] = vector.mask %[[MASK_FLT]] { vector.transfer_read %[[FILTER]]{{\[}}%[[C0]], %[[C0]]], %[[PAD]] : memref<2x?xf32>, vector<2x[4]xf32> } : vector<2x[4]xi1> -> vector<2x[4]xf32>
+// CHECK: %[[VEC_FLT:.*]] = vector.mask %[[MASK_FLT]] { vector.transfer_read %[[FILTER]]{{\[}}%[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, true]} : memref<2x?xf32>, vector<2x[4]xf32> } : vector<2x[4]xi1> -> vector<2x[4]xf32>
/// Create a mask for the output tensor
// CHECK: %[[CH_DIM_OUT:.*]] = memref.dim %[[OUTPUT]], %[[C2]] : memref<3x2x?xf32>
// CHECK: %[[MASK_OUT:.*]] = vector.create_mask %[[C3]], %[[C2]], %[[CH_DIM_OUT]] : vector<3x2x[4]xi1>
/// Read the output tensor
-// CHECK: %[[VEC_OUT:.*]] = vector.mask %[[MASK_OUT]] { vector.transfer_read %[[OUTPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] : memref<3x2x?xf32>, vector<3x2x[4]xf32> } : vector<3x2x[4]xi1> -> vector<3x2x[4]xf32>
+// CHECK: %[[VEC_OUT:.*]] = vector.mask %[[MASK_OUT]] { vector.transfer_read %[[OUTPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, true, true]} : memref<3x2x?xf32>, vector<3x2x[4]xf32> } : vector<3x2x[4]xi1> -> vector<3x2x[4]xf32>
/// Convolution
// CHECK: %[[IN_1:.*]] = vector.extract_strided_slice %[[VEC_IN]] {offsets = [0, 0, 0], sizes = [3, 2, 4], strides = [1, 1, 1]} : vector<3x4x[4]xf32> to vector<3x2x[4]xf32>
@@ -182,4 +182,4 @@ module attributes {transform.with_named_sequence} {
// CHECK: %[[FLT_2_B:.*]] = vector.broadcast %[[FLT_2]] : vector<[4]xf32> to vector<3x2x[4]xf32>
// CHECK: %[[FMA_2:.*]] = vector.fma %[[IN_2]], %[[FLT_2_B]], %[[FMA_1]] : vector<3x2x[4]xf32>
// CHECK: %[[OUT_INS:.*]] = vector.insert_strided_slice %[[FMA_2]], %[[VEC_OUT]] {offsets = [0, 0, 0], strides = [1, 1, 1]} : vector<3x2x[4]xf32> into vector<3x2x[4]xf32>
-// CHECK: vector.mask %[[MASK_OUT]] { vector.transfer_write %[[OUT_INS]], %[[OUTPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]] : vector<3x2x[4]xf32>, memref<3x2x?xf32> } : vector<3x2x[4]xi1>
+// CHECK: vector.mask %[[MASK_OUT]] { vector.transfer_write %[[OUT_INS]], %[[OUTPUT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true]} : vector<3x2x[4]xf32>, memref<3x2x?xf32> } : vector<3x2x[4]xi1>
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