[mlir][tensor] Make getMixedPadImpl return static values when possible. #85016
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If low and high are constants (i.e., not attributes), users still prefer attributes. Otherwise, there could be failures in type inference.
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@llvm/pr-subscribers-mlir-linalg @llvm/pr-subscribers-mlir Author: Han-Chung Wang (hanhanW) ChangesIf low and high are constants (i.e., not attributes), users still prefer attributes. Otherwise, there could be failures in type inference. A failure is introduced by 60e562d, see the drop_known_unit_constant_low_high test for more details. Full diff: https://github.com/llvm/llvm-project/pull/85016.diff 4 Files Affected:
diff --git a/mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td b/mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td
index 670202fe4372e6..cf7f3e89079c1c 100644
--- a/mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td
+++ b/mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td
@@ -1364,7 +1364,7 @@ def Tensor_PadOp : Tensor_Op<"pad", [
unsigned count = staticAttrs.size();
for (unsigned idx = 0; idx < count; ++idx) {
if (ShapedType::isDynamic(staticAttrs[idx]))
- res.push_back(values[numDynamic++]);
+ res.push_back(getAsOpFoldResult(values[numDynamic++]));
else
res.push_back(builder.getI64IntegerAttr(staticAttrs[idx]));
}
diff --git a/mlir/test/Conversion/TensorToLinalg/tensor-ops-to-linalg.mlir b/mlir/test/Conversion/TensorToLinalg/tensor-ops-to-linalg.mlir
index 238c0c51312a6b..a0a676edceb745 100644
--- a/mlir/test/Conversion/TensorToLinalg/tensor-ops-to-linalg.mlir
+++ b/mlir/test/Conversion/TensorToLinalg/tensor-ops-to-linalg.mlir
@@ -22,7 +22,6 @@ func.func @generalize_pad_tensor_static_shape(%arg0: tensor<1x28x28x1xf32>) -> t
// CHECK-LABEL: func @generalize_pad_tensor_dynamic_shape(
// CHECK-SAME: %[[IN:.*]]: tensor<4x?x2x?xf32>,
// CHECK-SAME: %[[OFFSET:.*]]: index) -> tensor<4x?x?x?xf32> {
-// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
// CHECK: %[[DIM1:.*]] = tensor.dim %[[IN]], %[[C1]] : tensor<4x?x2x?xf32>
@@ -33,7 +32,7 @@ func.func @generalize_pad_tensor_static_shape(%arg0: tensor<1x28x28x1xf32>) -> t
// CHECK: %[[OUT_DIM3:.*]] = arith.addi %[[DIM3]], %[[OFFSET]] : index
// CHECK: %[[INIT:.*]] = tensor.empty(%[[DIM1]], %[[OUT_DIM2]], %[[OUT_DIM3]]) : tensor<4x?x?x?xf32>
// CHECK: %[[FILL:.*]] = linalg.fill ins(%[[CST]] : f32) outs(%[[INIT]] : tensor<4x?x?x?xf32>) -> tensor<4x?x?x?xf32>
-// CHECK: %[[PADDED:.*]] = tensor.insert_slice %[[IN]] into %[[FILL]]{{\[}}%[[C0]], %[[C0]], %[[OFFSET]], %[[C0]]] [4, %[[DIM1]], 2, %[[DIM3]]] [1, 1, 1, 1] : tensor<4x?x2x?xf32> into tensor<4x?x?x?xf32>
+// CHECK: %[[PADDED:.*]] = tensor.insert_slice %[[IN]] into %[[FILL]][0, 0, %[[OFFSET]], 0] [4, %[[DIM1]], 2, %[[DIM3]]] [1, 1, 1, 1] : tensor<4x?x2x?xf32> into tensor<4x?x?x?xf32>
// CHECK: return %[[PADDED]] : tensor<4x?x?x?xf32>
// CHECK: }
func.func @generalize_pad_tensor_dynamic_shape(%arg0: tensor<4x?x2x?xf32>, %arg1: index) -> tensor<4x?x?x?xf32> {
diff --git a/mlir/test/Dialect/Linalg/drop-unit-extent-dims.mlir b/mlir/test/Dialect/Linalg/drop-unit-extent-dims.mlir
index f2c490b832076f..c140b6abcc37a2 100644
--- a/mlir/test/Dialect/Linalg/drop-unit-extent-dims.mlir
+++ b/mlir/test/Dialect/Linalg/drop-unit-extent-dims.mlir
@@ -1033,3 +1033,23 @@ func.func @do_not_drop_non_constant_padding(%arg0: tensor<1x1x3x1x1xf32>, %pad:
// CHECK-SLICES-LABEL: func @do_not_drop_non_constant_padding
// CHECK-SLICES: tensor.pad %{{.*}} low[0, 1, 0, %c0, 0] high[0, 0, 0, %c0, 2]
// CHECK-SLICES: } : tensor<1x1x3x1x1xf32> to tensor<1x2x3x1x3xf32>
+
+// -----
+
+func.func @drop_known_unit_constant_low_high(%arg0: tensor<1x383x128xf32>) -> tensor<1x384x128xf32> {
+ %c0 = arith.constant 0 : index
+ %c1 = arith.constant 1 : index
+ %cst = arith.constant 0.000000e+00 : f32
+ %padded = tensor.pad %arg0 low[%c0, %c1, %c0] high[%c0, %c0, %c0] {
+ ^bb0(%arg1: index, %arg2: index, %arg3: index):
+ tensor.yield %cst : f32
+ } : tensor<1x383x128xf32> to tensor<1x384x128xf32>
+ return %padded : tensor<1x384x128xf32>
+}
+// CHECK-LABEL: func @drop_known_unit_constant_low_high
+// CHECK: %[[COLLAPSE:.+]] = tensor.collapse_shape
+// CHECK-SAME: {{\[}}[0, 1], [2]] : tensor<1x383x128xf32> into tensor<383x128xf32>
+// CHECK: %[[PADDED:.+]] = tensor.pad %[[COLLAPSE]] low[1, 0] high[0, 0]
+// CHECK: } : tensor<383x128xf32> to tensor<384x128xf32>
+// CHECK: tensor.expand_shape %[[PADDED]]
+// CHECK-SAME: {{\[}}[0, 1], [2]] : tensor<384x128xf32> into tensor<1x384x128xf32>
diff --git a/mlir/test/Dialect/Linalg/generalize-pad-tensor.mlir b/mlir/test/Dialect/Linalg/generalize-pad-tensor.mlir
index ac0eb48fb37940..2beab31b613d54 100644
--- a/mlir/test/Dialect/Linalg/generalize-pad-tensor.mlir
+++ b/mlir/test/Dialect/Linalg/generalize-pad-tensor.mlir
@@ -19,7 +19,6 @@ func.func @generalize_pad_tensor_static_shape(%arg0: tensor<1x28x28x1xf32>) -> t
// CHECK-LABEL: func @generalize_pad_tensor_dynamic_shape(
// CHECK-SAME: %[[IN:.*]]: tensor<4x?x2x?xf32>,
// CHECK-SAME: %[[OFFSET:.*]]: index) -> tensor<4x?x?x?xf32> {
-// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
@@ -32,7 +31,7 @@ func.func @generalize_pad_tensor_static_shape(%arg0: tensor<1x28x28x1xf32>) -> t
// CHECK: %[[FILL:.*]] = linalg.fill ins(%[[CST]] : f32) outs(%[[INIT]] : tensor<4x?x?x?xf32>) -> tensor<4x?x?x?xf32>
// CHECK: %[[DIM1_1:.*]] = tensor.dim %[[IN]], %[[C1]] : tensor<4x?x2x?xf32>
// CHECK: %[[DIM3_1:.*]] = tensor.dim %[[IN]], %[[C3]] : tensor<4x?x2x?xf32>
-// CHECK: %[[PADDED:.*]] = tensor.insert_slice %[[IN]] into %[[FILL]]{{\[}}%[[C0]], %[[C0]], %[[OFFSET]], %[[C0]]] [4, %[[DIM1_1]], 2, %[[DIM3_1]]] [1, 1, 1, 1] : tensor<4x?x2x?xf32> into tensor<4x?x?x?xf32>
+// CHECK: %[[PADDED:.*]] = tensor.insert_slice %[[IN]] into %[[FILL]][0, 0, %[[OFFSET]], 0] [4, %[[DIM1_1]], 2, %[[DIM3_1]]] [1, 1, 1, 1] : tensor<4x?x2x?xf32> into tensor<4x?x?x?xf32>
// CHECK: return %[[PADDED]] : tensor<4x?x?x?xf32>
// CHECK: }
func.func @generalize_pad_tensor_dynamic_shape(%arg0: tensor<4x?x2x?xf32>, %arg1: index) -> tensor<4x?x?x?xf32> {
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hanhanW
added a commit
to iree-org/iree
that referenced
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Mar 13, 2024
matthias-springer
approved these changes
Mar 13, 2024
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If low and high are constants (i.e., not attributes), users still prefer attributes. Otherwise, there could be failures in type inference. A failure is introduced by 60e562d, see the drop_known_unit_constant_low_high test for more details.