[Mlir-commits] [mlir] Reapply "[mlir][linalg] Relax tensor.extract vectorization" (#102232) (PR #102321)
Andrzej Warzyński
llvmlistbot at llvm.org
Wed Aug 7 08:01:35 PDT 2024
https://github.com/banach-space created https://github.com/llvm/llvm-project/pull/102321
[This reverts commit 6662523d6b2ca0198141c94ee80ebbb41601df9f]
Simplifies the vectorization of tensor.extract so that:
* all cases that read into a genuinely multi-dim vector (*) are
considered a gather load,
* all other cases are considered as potential contiguous loads.
This change means that the following extraction from a "column" tensor
is correctly identified as a scalar load followed by a broadcast (rather
than a gather load).
```mlir
func.func @vectorize_scalar_broadcast_column_tensor(%in: tensor<1x1x4xi32>) -> tensor<1x1x4xi32> {
%c4 = arith.constant 4 : index
%c0 = arith.constant 0 : index
%cst = arith.constant dense<[...]> : tensor<15x1xi32>
%out = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
iterator_types = ["parallel", "parallel", "parallel"]}
outs(%in : tensor<1x1x4xi32>) {
^bb0(%out: i32):
%8 = linalg.index 0 : index
%idx_0 = linalg.index 0 : index
%extracted = tensor.extract %cst[%idx_0, %c0] : tensor<15x1xi32>
linalg.yield %extracted : i32
} -> tensor<1x1x4xi32>
return %out:tensor<1x1x4xi32>
}
```
Overview of the delta when compared to the original submission:
* removed an assert representing a conditon that is being relaxed
here,
* added a test (reading from a column tensor) based on a repro from
@hanhanW.
(*) `vector<1x4x1xf32>` is considered as 1D vector in this context.
>From 5362092292038fc7c33ca90406e3fac872729dbb Mon Sep 17 00:00:00 2001
From: Andrzej Warzynski <andrzej.warzynski at arm.com>
Date: Wed, 7 Aug 2024 10:46:56 +0100
Subject: [PATCH] Reapply "[mlir][linalg] Relax tensor.extract vectorization"
(#102232)
[This reverts commit 6662523d6b2ca0198141c94ee80ebbb41601df9f]
Simplifies the vectorization of tensor.extract so that:
* all cases that read into a genuinely multi-dim vector (*) are
considered a gather load,
* all other cases are considered as potential contiguous loads.
This change means that the following extraction from a "column" tensor
is correctly identified as a scalar load followed by a broadcast (rather
than a gather load).
```mlir
func.func @vectorize_scalar_broadcast_column_tensor(%in: tensor<1x1x4xi32>) -> tensor<1x1x4xi32> {
%c4 = arith.constant 4 : index
%c0 = arith.constant 0 : index
%cst = arith.constant dense<[...]> : tensor<15x1xi32>
%out = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
iterator_types = ["parallel", "parallel", "parallel"]}
outs(%in : tensor<1x1x4xi32>) {
^bb0(%out: i32):
%8 = linalg.index 0 : index
%idx_0 = linalg.index 0 : index
%extracted = tensor.extract %cst[%idx_0, %c0] : tensor<15x1xi32>
linalg.yield %extracted : i32
} -> tensor<1x1x4xi32>
return %out:tensor<1x1x4xi32>
}
```
Overview of the delta when compared to the original submission:
* removed an assert representing a conditon that is being relaxed
here,
* added a test (reading from a column tensor) based on a repro from
@hanhanW.
(*) `vector<1x4x1xf32>` is considered as 1D vector in this context.
---
.../Linalg/Transforms/Vectorization.cpp | 41 +++---
.../Linalg/vectorize-tensor-extract.mlir | 117 ++++++++++++++++--
2 files changed, 121 insertions(+), 37 deletions(-)
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
index 3d0d6abf702d7..63dcda78d0f2b 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
@@ -814,11 +814,9 @@ enum VectorMemoryAccessKind { ScalarBroadcast, Contiguous, Gather };
static bool isLoopInvariantIdx(LinalgOp &linalgOp, Value &val) {
auto targetShape = linalgOp.getStaticLoopRanges();
- assert(((llvm::count_if(targetShape,
- [](int64_t dimSize) { return dimSize > 1; }) == 1)) &&
+ assert(llvm::count_if(targetShape,
+ [](int64_t dimSize) { return dimSize > 1; }) == 1 &&
"n-D vectors are not yet supported");
- assert(targetShape.back() != 1 &&
- "1-D vectors with the trailing dim eqaual 1 are not yet supported");
// Blocks outside _this_ linalg.generic are effectively loop invariant.
// However, analysing block arguments for _this_ linalg.generic Op is a bit
@@ -879,8 +877,6 @@ static bool isContiguousLoadIdx(LinalgOp &linalgOp, Value &val,
assert(((llvm::count_if(targetShape,
[](int64_t dimSize) { return dimSize > 1; }) == 1)) &&
"n-D vectors are not yet supported");
- assert(targetShape.back() != 1 &&
- "1-D vectors with the trailing dim 1 are not yet supported");
// Blocks outside _this_ linalg.generic are effectively loop invariant.
// However, analysing block arguments for _this_ linalg.generic Op is a bit
@@ -946,27 +942,22 @@ getTensorExtractMemoryAccessPattern(tensor::ExtractOp extractOp,
if (linalgOp.hasDynamicShape())
return VectorMemoryAccessKind::Gather;
- // 1. Assume that it's a gather load when reading _into_:
- // * an n-D "vector", like `tensor<1x2x4xi32` or `tensor<2x1x4xi32>`, or
- // * a 1-D "vector" with the trailing dim equal 1, e.g. `tensor<1x4x1xi32`.
- // TODO: Relax these conditions.
- // FIXME: This condition assumes non-dynamic sizes.
- if ((llvm::count_if(targetShape,
- [](int64_t dimSize) { return dimSize > 1; }) != 1) ||
- targetShape.back() == 1)
- return VectorMemoryAccessKind::Gather;
+ // True for vectors that are effectively 1D, e.g. `vector<1x4x1xi32>`, false
+ // otherwise.
+ bool isOutput1DVector = (llvm::count_if(targetShape, [](int64_t dimSize) {
+ return dimSize > 1;
+ }) == 1);
- // 2. Assume that it's a gather load when reading _from_ a tensor for which
- // the trailing dimension is 1, e.g. `tensor<1x4x1xi32>`.
- // TODO: Relax this condition.
- if (inputShape.getShape().back() == 1)
+ // 1. Assume that it's a gather load when reading non-1D vector.
+ if (!isOutput1DVector)
return VectorMemoryAccessKind::Gather;
bool leadingIdxsLoopInvariant = true;
- // 3. Analyze the leading indices of `extractOp`.
+ // 2. Analyze the leading indices of `extractOp`.
// Look at the way each index is calculated and decide whether it is suitable
- // for a contiguous load, i.e. whether it's loop invariant.
+ // for a contiguous load, i.e. whether it's loop invariant. If not, it's a
+ // gather load.
auto indices = extractOp.getIndices();
auto leadIndices = indices.drop_back(1);
@@ -982,13 +973,13 @@ getTensorExtractMemoryAccessPattern(tensor::ExtractOp extractOp,
return VectorMemoryAccessKind::Gather;
}
- // 4. Analyze the trailing index for `extractOp`.
+ // 3. Analyze the trailing index for `extractOp`.
// At this point we know that the leading indices are loop invariant. This
// means that is potentially a scalar or a contiguous load. We can decide
// based on the trailing idx.
auto extractOpTrailingIdx = indices.back();
- // 4a. Scalar broadcast load
+ // 3a. Scalar broadcast load
// If the trailing index is loop invariant then this is a scalar load.
if (leadingIdxsLoopInvariant &&
isLoopInvariantIdx(linalgOp, extractOpTrailingIdx)) {
@@ -997,7 +988,7 @@ getTensorExtractMemoryAccessPattern(tensor::ExtractOp extractOp,
return VectorMemoryAccessKind::ScalarBroadcast;
}
- // 4b. Contiguous loads
+ // 3b. Contiguous loads
// The trailing `extractOp` index should increment with every loop iteration.
// This effectively means that it must be based on the trailing loop index.
// This is what the following bool captures.
@@ -1011,7 +1002,7 @@ getTensorExtractMemoryAccessPattern(tensor::ExtractOp extractOp,
return VectorMemoryAccessKind::Contiguous;
}
- // 5. Fallback case - gather load.
+ // 4. Fallback case - gather load.
LDBG("Found gather load: " << extractOp);
return VectorMemoryAccessKind::Gather;
}
diff --git a/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir b/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir
index 85e1c56dd45a0..bdaa20c3bf971 100644
--- a/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir
+++ b/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir
@@ -37,6 +37,7 @@ module attributes {transform.with_named_sequence} {
}
// -----
+
#map = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
func.func @vectorize_nd_tensor_extract_constant_idx(%arg0: tensor<3x3xf32>, %arg2: tensor<1x1x3xf32>) -> tensor<1x1x3xf32> {
%c0 = arith.constant 1 : index
@@ -74,20 +75,24 @@ module attributes {transform.with_named_sequence} {
// -----
-#map1 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
-func.func @vectorize_nd_tensor_extract_transfer_read_basic(%arg0: tensor<3x3x3xf32>, %arg2: tensor<1x1x3xf32>) -> tensor<1x1x3xf32> {
- %1 = linalg.generic {
- indexing_maps = [#map1],
+#map = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
+func.func @vectorize_nd_tensor_extract_transfer_read_basic(
+ %arg0: tensor<3x3x3xf32>,
+ %arg1: tensor<1x1x3xf32>) -> tensor<1x1x3xf32> {
+
+ %res = linalg.generic {
+ indexing_maps = [#map],
iterator_types = ["parallel", "parallel", "parallel"]
- } outs(%arg2 : tensor<1x1x3xf32>) {
- ^bb0(%arg4: f32):
- %2 = linalg.index 0 : index
- %3 = linalg.index 1 : index
- %4 = linalg.index 2 : index
- %5 = tensor.extract %arg0[%2, %3, %4] : tensor<3x3x3xf32>
- linalg.yield %5 : f32
+ } outs(%arg1 : tensor<1x1x3xf32>) {
+ ^bb0(%out: f32):
+ %1 = linalg.index 0 : index
+ %2 = linalg.index 1 : index
+ %3 = linalg.index 2 : index
+ %4 = tensor.extract %arg0[%1, %2, %3] : tensor<3x3x3xf32>
+ linalg.yield %4 : f32
} -> tensor<1x1x3xf32>
- return %1 : tensor<1x1x3xf32>
+
+ return %res : tensor<1x1x3xf32>
}
// CHECK-LABEL: func.func @vectorize_nd_tensor_extract_transfer_read_basic
@@ -104,6 +109,38 @@ func.func @vectorize_nd_tensor_extract_transfer_read_basic(%arg0: tensor<3x3x3xf
// 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. 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.
+
+func.func @vectorize_nd_tensor_extract_transfer_read_basic_column(
+ %input: tensor<3x3x3xf32>,
+ %output: tensor<3x1x1xf32>) -> tensor<3x1x1xf32> {
+
+ %c0 = arith.constant 0 : index
+ %res = linalg.generic {
+ indexing_maps = [#map],
+ iterator_types = ["parallel", "parallel", "parallel"]
+ } outs(%output : tensor<3x1x1xf32>) {
+ ^bb0(%out: f32):
+ %5 = tensor.extract %input[%c0, %c0, %c0] : tensor<3x3x3xf32>
+ linalg.yield %5 : f32
+ } -> tensor<3x1x1xf32>
+
+ return %res : tensor<3x1x1xf32>
+}
+
+// CHECK-LABEL: func.func @vectorize_nd_tensor_extract_transfer_read_basic_column(
+// CHECK-SAME: %[[INPUT:.*]]: tensor<3x3x3xf32>,
+// CHECK-SAME: %[[OUTPUT:.*]]: tensor<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>
+
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
@@ -595,3 +632,59 @@ module attributes {transform.with_named_sequence} {
transform.yield
}
}
+
+
+// -----
+
+func.func @vectorize_scalar_broadcast_column_tensor(%in: tensor<1x1x4xi32>) -> tensor<1x1x4xi32> {
+ %c4 = arith.constant 4 : index
+ %c0 = arith.constant 0 : index
+ %cst = arith.constant dense<[[0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14]]> : tensor<15x1xi32>
+
+ %out = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>], iterator_types = ["parallel", "parallel", "parallel"]} outs(%in : tensor<1x1x4xi32>) {
+ ^bb0(%out: i32):
+ %8 = linalg.index 0 : index
+ %idx_0 = linalg.index 0 : index
+ %extracted = tensor.extract %cst[%idx_0, %c0] : tensor<15x1xi32>
+ linalg.yield %extracted : i32
+ } -> tensor<1x1x4xi32>
+
+ return %out:tensor<1x1x4xi32>
+}
+
+// CHECK: #[[$ATTR_1:.+]] = affine_map<(d0, d1) -> (0, 0, 0)>
+// CHECK-LABEL: func.func @vectorize_scalar_broadcast_column_tensor(
+// CHECK-SAME: %[[VAL_0:.*]]: tensor<1x1x4xi32>) -> tensor<1x1x4xi32> {
+// CHECK: %[[VAL_1:.*]] = arith.constant 4 : index
+// CHECK: %[[VAL_2:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_3:.*]] = arith.constant dense<{{\[\[}}0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14]]> : tensor<15x1xi32>
+// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index
+// CHECK: %[[VAL_5:.*]] = arith.constant 1 : index
+// CHECK: %[[VAL_6:.*]] = arith.constant 4 : index
+// CHECK: %[[VAL_7:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_8:.*]] = arith.constant 0 : i32
+// CHECK: %[[VAL_9:.*]] = vector.transfer_read %[[VAL_0]]{{\[}}%[[VAL_7]], %[[VAL_7]], %[[VAL_7]]], %[[VAL_8]] : tensor<1x1x4xi32>, vector<1x1x4xi32>
+// CHECK: %[[VAL_10:.*]] = vector.step : vector<1xindex>
+// CHECK: %[[VAL_11:.*]] = vector.broadcast %[[VAL_10]] : vector<1xindex> to vector<4x1x1xindex>
+// CHECK: %[[VAL_12:.*]] = vector.transpose %[[VAL_11]], [2, 1, 0] : vector<4x1x1xindex> to vector<1x1x4xindex>
+// CHECK: %[[VAL_13:.*]] = vector.step : vector<1xindex>
+// CHECK: %[[VAL_14:.*]] = vector.broadcast %[[VAL_13]] : vector<1xindex> to vector<4x1x1xindex>
+// CHECK: %[[VAL_15:.*]] = vector.transpose %[[VAL_14]], [2, 1, 0] : vector<4x1x1xindex> to vector<1x1x4xindex>
+// CHECK: %[[VAL_16:.*]] = arith.constant dense<true> : vector<1x1x4xi1>
+// CHECK: %[[VAL_17:.*]] = arith.constant dense<0> : vector<1x1x4xi32>
+// CHECK: %[[VAL_18:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_19:.*]] = arith.constant 0 : i32
+// CHECK: %[[VAL_20:.*]] = vector.shape_cast %[[VAL_15]] : vector<1x1x4xindex> to vector<4xindex>
+// CHECK: %[[VAL_21:.*]] = vector.extractelement %[[VAL_20]]{{\[}}%[[VAL_19]] : i32] : vector<4xindex>
+// CHECK: %[[VAL_22:.*]] = arith.constant 0 : i32
+// CHECK: %[[VAL_23:.*]] = vector.transfer_read %[[VAL_3]]{{\[}}%[[VAL_21]], %[[VAL_2]]], %[[VAL_22]] {in_bounds = [true, true, true], permutation_map = #[[$ATTR_1]]} : tensor<15x1xi32>, vector<1x1x4xi32>
+// CHECK: %[[VAL_24:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_25:.*]] = vector.transfer_write %[[VAL_23]], %[[VAL_0]]{{\[}}%[[VAL_24]], %[[VAL_24]], %[[VAL_24]]] : vector<1x1x4xi32>, tensor<1x1x4xi32>
+
+module attributes {transform.with_named_sequence} {
+ transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [1, 1, 4]{ vectorize_nd_extract } : !transform.any_op
+ transform.yield
+ }
+}
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