[Mlir-commits] [mlir] [mlir][vector] Determine vector sizes from the result shape in the ca… (PR #88249)
Prashant Kumar
llvmlistbot at llvm.org
Tue Apr 16 02:33:28 PDT 2024
https://github.com/pashu123 updated https://github.com/llvm/llvm-project/pull/88249
>From 2932ae238da75ae966a44756a2390b4c40e7d61a Mon Sep 17 00:00:00 2001
From: Prashant Kumar <pk5561 at gmail.com>
Date: Wed, 10 Apr 2024 05:57:12 -0400
Subject: [PATCH] [mlir][vector] Determine vector sizes from the result shape
in the case of tensor pack
When the vector sizes are not passed as inputs to the vector transform operation,
the vector sizes are queried from the static result shape in the case of tensor.pack op.
---
.../Linalg/Transforms/Vectorization.cpp | 47 ++++++++++++----
.../Linalg/vectorization-unsupported.mlir | 17 ++++++
mlir/test/Dialect/Linalg/vectorization.mlir | 55 +++++++++++++++++++
3 files changed, 109 insertions(+), 10 deletions(-)
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
index 25785653a71675..ca09b7d994c516 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
@@ -1412,10 +1412,11 @@ static SmallVector<int64_t> getTiledPackShape(tensor::PackOp packOp,
/// Create a TransferReadOp from `source` with static shape `readShape`. If the
/// vector type for the read is not the same as the type of `source`, then a
-/// mask is created on the read.
+/// mask is created on the read. If `doMasking` parameter is set to false we
+/// update the `inBounds` attribute instead of masking.
static Value createReadOrMaskedRead(OpBuilder &builder, Location loc,
Value source, ArrayRef<int64_t> readShape,
- Value padValue) {
+ Value padValue, bool doMasking = true) {
assert(llvm::none_of(readShape,
[](int64_t s) { return s == ShapedType::kDynamic; }));
auto sourceShape = dyn_cast<ShapedType>(source.getType()).getShape();
@@ -1424,14 +1425,21 @@ static Value createReadOrMaskedRead(OpBuilder &builder, Location loc,
auto vectorType = VectorType::get(readShape, padValue.getType());
int64_t readRank = readShape.size();
auto zero = builder.create<arith::ConstantIndexOp>(loc, 0);
+ SmallVector<bool> inBoundsVal(readRank, true);
+ if (!doMasking) {
+ // Update the inBounds attribute.
+ for (unsigned i = 0; i < readRank; i++)
+ inBoundsVal[i] = sourceShape[i] == readShape[i];
+ }
auto transferReadOp = builder.create<vector::TransferReadOp>(
loc,
/*vectorType=*/vectorType,
/*source=*/source,
/*indices=*/SmallVector<Value>(readRank, zero),
/*padding=*/padValue,
- /*inBounds=*/SmallVector<bool>(readRank, true));
- if (llvm::equal(readShape, sourceShape)) {
+ /*inBounds=*/inBoundsVal);
+
+ if (llvm::equal(readShape, sourceShape) || !doMasking) {
return transferReadOp;
}
SmallVector<OpFoldResult> mixedSourceDims =
@@ -1525,6 +1533,17 @@ vectorizeAsTensorPackOp(RewriterBase &rewriter, tensor::PackOp packOp,
(void)status; // prevent unused variable warning on non-assert builds.
assert(succeeded(status) && "failed to reify result shapes");
+ ArrayRef<int64_t> resultTensorShape = packOp.getDestType().getShape();
+ bool doMasking = true;
+
+ // If the input vector sizes are not provided, then the vector sizes are
+ // determined by the result tensor shape. In case the vector sizes aren't
+ // provided, we update the inBounds attribute instead of masking.
+ if (inputVectorSizes.empty()) {
+ inputVectorSizes = resultTensorShape.take_front(packOp.getSourceRank());
+ doMasking = false;
+ }
+
// Create masked TransferReadOp.
SmallVector<int64_t> inputShape(inputVectorSizes);
auto innerTiles = packOp.getStaticInnerTiles();
@@ -1536,7 +1555,7 @@ vectorizeAsTensorPackOp(RewriterBase &rewriter, tensor::PackOp packOp,
for (auto [idx, size] : enumerate(innerTiles))
inputShape[innerDimsPos[idx]] *= size;
auto maskedRead = createReadOrMaskedRead(rewriter, loc, packOp.getSource(),
- inputShape, padValue);
+ inputShape, padValue, doMasking);
// Create ShapeCastOp.
SmallVector<int64_t> destShape(inputVectorSizes);
@@ -1763,7 +1782,7 @@ vectorizeDynamicLinalgOpPrecondition(linalg::LinalgOp op,
/// Returns success if `inputVectorSizes` is a valid masking configuraion for
/// given `shape`, i.e., it meets:
/// 1. The numbers of elements in both array are equal.
-/// 2. `inputVectorSizes` does nos have dynamic dimensions.
+/// 2. `inputVectorSizes` does not have dynamic dimensions.
/// 3. All the values in `inputVectorSizes` are greater than or equal to
/// static sizes in `shape`.
static LogicalResult
@@ -1881,18 +1900,26 @@ static LogicalResult vectorizeLinalgOpPrecondition(
return success();
}
-/// TODO: Use a matcher to check for a constant padding value.
static LogicalResult
vectorizePackOpPrecondition(tensor::PackOp packOp,
ArrayRef<int64_t> inputVectorSizes) {
auto padValue = packOp.getPaddingValue();
- if (padValue && !padValue.getDefiningOp<arith::ConstantOp>()) {
+ Attribute cstAttr;
+ if (padValue && !matchPattern(padValue, m_Constant(&cstAttr))) {
LDBG("pad value is not constant: " << packOp << "\n");
return failure();
}
-
ArrayRef<int64_t> resultTensorShape = packOp.getDestType().getShape();
- if (failed(isValidMaskedInputVector(
+ ArrayRef<int64_t> inputTensorShape = packOp.getSourceType().getShape();
+ bool satisfyEmptyCond = true;
+ if (inputVectorSizes.empty()) {
+ if (ShapedType::isDynamicShape(resultTensorShape) ||
+ ShapedType::isDynamicShape(inputTensorShape))
+ satisfyEmptyCond = false;
+ }
+
+ if (!satisfyEmptyCond &&
+ failed(isValidMaskedInputVector(
resultTensorShape.take_front(packOp.getSourceRank()),
inputVectorSizes)))
return failure();
diff --git a/mlir/test/Dialect/Linalg/vectorization-unsupported.mlir b/mlir/test/Dialect/Linalg/vectorization-unsupported.mlir
index 9127eac5da9510..5d3c07c8e23c1e 100644
--- a/mlir/test/Dialect/Linalg/vectorization-unsupported.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization-unsupported.mlir
@@ -109,3 +109,20 @@ module attributes {transform.with_named_sequence} {
transform.yield
}
}
+
+ // -----
+
+func.func @test_pack_no_vectorize_dynamic_shape(%arg0: tensor<?xf32>, %arg1: tensor<4x16xf32>) -> tensor<4x16xf32> {
+ %pad = arith.constant 0.000000e+00 : f32
+ // expected-error @+1 {{Attempted to vectorize, but failed}}
+ %pack = tensor.pack %arg0 padding_value(%pad : f32) inner_dims_pos = [0] inner_tiles = [16] into %arg1 : tensor<?xf32> -> tensor<4x16xf32>
+ return %pack : tensor<4x16xf32>
+}
+
+module attributes {transform.with_named_sequence} {
+ transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
+ %0 = transform.structured.match ops{["tensor.pack"]} in %arg0 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 : !transform.any_op
+ transform.yield
+ }
+}
diff --git a/mlir/test/Dialect/Linalg/vectorization.mlir b/mlir/test/Dialect/Linalg/vectorization.mlir
index 2d01d57304013c..3e6476be9941ca 100644
--- a/mlir/test/Dialect/Linalg/vectorization.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization.mlir
@@ -812,3 +812,58 @@ func.func @test_vectorize_unpack_no_masks(%source: tensor<8x8x32x16xf32>, %dest:
transform.yield
}
}
+
+ // -----
+
+// CHECK-LABEL: test_vectorize_pack_no_vector_sizes
+func.func @test_vectorize_pack_no_vector_sizes(%arg0: tensor<64x4xf32>, %arg1: tensor<2x4x16x2xf32>) -> tensor<2x4x16x2xf32> {
+ %pack = tensor.pack %arg0 outer_dims_perm = [1, 0] inner_dims_pos = [0, 1] inner_tiles = [16, 2] into %arg1 : tensor<64x4xf32> -> tensor<2x4x16x2xf32>
+ return %pack : tensor<2x4x16x2xf32>
+}
+// CHECK-DAG: %[[cst:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index
+// CHECK: %[[read:.*]] = vector.transfer_read %{{.*}}[%[[c0]], %[[c0]]], %[[cst]]
+// CHECK-SAME: {in_bounds = [true, true]} : tensor<64x4xf32>, vector<64x4xf32>
+// CHECK: %[[shape_cast:.*]] = vector.shape_cast %[[read]] : vector<64x4xf32> to vector<4x16x2x2xf32>
+// CHECK: %[[transpose:.*]] = vector.transpose %[[shape_cast]], [2, 0, 1, 3] : vector<4x16x2x2xf32> to vector<2x4x16x2xf32>
+// CHECK-DAG: %[[c0_1:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[empty:.*]] = tensor.empty() : tensor<2x4x16x2xf32>
+// CHECK: %[[write:.*]] = vector.transfer_write %[[transpose]], %[[empty]][%[[c0_1]], %[[c0_1]], %[[c0_1]], %[[c0_1]]]
+// CHECK-SAME: {in_bounds = [true, true, true, true]} : vector<2x4x16x2xf32>, tensor<2x4x16x2xf32>
+// CHECK: return %[[write]] : tensor<2x4x16x2xf32>
+
+module attributes {transform.with_named_sequence} {
+ transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
+ %0 = transform.structured.match ops{["tensor.pack"]} in %arg0 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 : !transform.any_op
+ transform.yield
+ }
+}
+
+ // -----
+
+// CHECK-LABEL: test_vectorize_padded_pack_no_vector_sizes
+func.func @test_vectorize_padded_pack_no_vector_sizes(%arg0: tensor<32x7x15xf32>, %arg1: tensor<32x4x1x16x2xf32>) -> tensor<32x4x1x16x2xf32> {
+ %pad = arith.constant 0.000000e+00 : f32
+ %pack = tensor.pack %arg0 padding_value(%pad : f32) inner_dims_pos = [2, 1] inner_tiles = [16, 2] into %arg1 : tensor<32x7x15xf32> -> tensor<32x4x1x16x2xf32>
+ return %pack : tensor<32x4x1x16x2xf32>
+}
+// CHECK-DAG: %[[cst:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index
+// CHECK: %[[transfer_read:.*]] = vector.transfer_read %{{.*}}[%[[c0]], %[[c0]], %[[c0]]], %[[cst]]
+// CHECK-SAME: {in_bounds = [true, false, false]} : tensor<32x7x15xf32>, vector<32x8x16xf32>
+// CHECK: %[[shape_cast:.*]] = vector.shape_cast %[[transfer_read]] : vector<32x8x16xf32> to vector<32x4x2x1x16xf32>
+// CHECK: %[[transpose:.*]] = vector.transpose %[[shape_cast]], [0, 1, 3, 4, 2] : vector<32x4x2x1x16xf32> to vector<32x4x1x16x2xf32>
+// CHECK-DAG: %[[c0_1:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[empty:.*]] = tensor.empty() : tensor<32x4x1x16x2xf32>
+// CHECK: %[[write:.*]] = vector.transfer_write %[[transpose]], %[[empty]][%[[c0_1]], %[[c0_1]], %[[c0_1]], %[[c0_1]], %[[c0_1]]]
+// CHECK-SAME: {in_bounds = [true, true, true, true, true]} : vector<32x4x1x16x2xf32>, tensor<32x4x1x16x2xf32>
+// CHECK: return %[[write]] : tensor<32x4x1x16x2xf32>
+
+module attributes {transform.with_named_sequence} {
+ transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
+ %0 = transform.structured.match ops{["tensor.pack"]} in %arg0 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 : !transform.any_op
+ transform.yield
+ }
+}
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