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[mlir][tensor] Fix bug in tensor.extract(tensor.from_elements)
folder (#75109)
The folder for `tensor.extract` is not operating correctly when it is consuming the result of a `tensor.from_elements` operation. The existing unit test named `@extract_from_tensor.from_elements_3d` in `mlir/test/Dialect/Tensor/canonicalize.mlir` seems an attempt to stress this code. However, this unit tests creates a `tensor.from_elements` op exclusively from constants, which gets folded away into a single constant tensor. Therefore, the buggy code was never executed in unit tests. I have added a new unit test named `@extract_from_tensor.from_elements_variable_3d` that makes sure the `tensor.from_elements` op is not folded away by having its input operands come directly from function arguments. The original folder code would have made this test fail. This bug was notably affecting the lowering of the `tosa.pad` op in the `tosa-to-tensor` pass, where the generated code is likely to contain a `tensor.from_elements` + `tensor.extract` op sequence.
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@ -1116,9 +1116,8 @@ OpFoldResult ExtractOp::fold(FoldAdaptor adaptor) {
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int flatIndex = 0;
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int stride = 1;
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for (int i = rank - 1; i >= 0; --i) {
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if (i < rank - 1)
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stride *= tensorType.getDimSize(i);
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flatIndex += indices[i] * stride;
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stride *= tensorType.getDimSize(i);
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}
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// Prevent out of bounds accesses. This can happen in invalid code that
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// will never execute.
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@ -242,6 +242,50 @@ func.func @extract_from_tensor.from_elements_3d()
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// -----
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// CHECK-LABEL: func @extract_from_tensor.from_elements_variable_3d
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// CHECK-SAME: %[[ARG_0:[a-zA-Z0-9_]+]]: f32
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// CHECK-SAME: %[[ARG_1:[a-zA-Z0-9_]+]]: f32
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// CHECK-SAME: %[[ARG_2:[a-zA-Z0-9_]+]]: f32
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// CHECK-SAME: %[[ARG_3:[a-zA-Z0-9_]+]]: f32
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// CHECK-SAME: %[[ARG_4:[a-zA-Z0-9_]+]]: f32
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// CHECK-SAME: %[[ARG_5:[a-zA-Z0-9_]+]]: f32
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// CHECK-SAME: %[[ARG_6:[a-zA-Z0-9_]+]]: f32
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// CHECK-SAME: %[[ARG_7:[a-zA-Z0-9_]+]]: f32
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// CHECK-SAME: %[[ARG_8:[a-zA-Z0-9_]+]]: f32
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// CHECK-SAME: %[[ARG_9:[a-zA-Z0-9_]+]]: f32
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// CHECK-SAME: %[[ARG_10:[a-zA-Z0-9_]+]]: f32
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// CHECK-SAME: %[[ARG_11:[a-zA-Z0-9_]+]]: f32
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func.func @extract_from_tensor.from_elements_variable_3d(
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%f0: f32, %f1: f32, %f2: f32, %f3: f32, %f4: f32, %f5: f32,
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%f6: f32, %f7: f32, %f8: f32, %f9: f32, %f10: f32, %f11: f32)
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-> (f32, f32, f32, f32, f32, f32, f32, f32, f32, f32, f32, f32) {
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%tensor = tensor.from_elements %f0,%f1,%f2,%f3,%f4,%f5,%f6,%f7,%f8,%f9,%f10,%f11
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: tensor<3x2x2xf32>
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%c2 = arith.constant 2 : index
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%r0 = tensor.extract %tensor[%c0, %c0, %c0] : tensor<3x2x2xf32>
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%r1 = tensor.extract %tensor[%c0, %c0, %c1] : tensor<3x2x2xf32>
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%r2 = tensor.extract %tensor[%c0, %c1, %c0] : tensor<3x2x2xf32>
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%r3 = tensor.extract %tensor[%c0, %c1, %c1] : tensor<3x2x2xf32>
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%r4 = tensor.extract %tensor[%c1, %c0, %c0] : tensor<3x2x2xf32>
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%r5 = tensor.extract %tensor[%c1, %c0, %c1] : tensor<3x2x2xf32>
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%r6 = tensor.extract %tensor[%c1, %c1, %c0] : tensor<3x2x2xf32>
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%r7 = tensor.extract %tensor[%c1, %c1, %c1] : tensor<3x2x2xf32>
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%r8 = tensor.extract %tensor[%c2, %c0, %c0] : tensor<3x2x2xf32>
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%r9 = tensor.extract %tensor[%c2, %c0, %c1] : tensor<3x2x2xf32>
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%r10 = tensor.extract %tensor[%c2, %c1, %c0] : tensor<3x2x2xf32>
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%r11 = tensor.extract %tensor[%c2, %c1, %c1] : tensor<3x2x2xf32>
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return %r0,%r1,%r2,%r3,%r4,%r5,%r6,%r7,%r8,%r9,%r10,%r11
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: f32,f32,f32,f32,f32,f32,f32,f32,f32,f32,f32,f32
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}
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// CHECK: return %[[ARG_0]], %[[ARG_1]], %[[ARG_2]], %[[ARG_3]], %[[ARG_4]], %[[ARG_5]],
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// CHECK-SAME: %[[ARG_6]], %[[ARG_7]], %[[ARG_8]], %[[ARG_9]], %[[ARG_10]], %[[ARG_11]]
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// -----
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// CHECK-LABEL: func.func @extract_from_elements_complex_i() -> tensor<3xcomplex<i32>> {
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// CHECK-NEXT: %cst = arith.constant dense<[(1,2), (3,2), (1,2)]> : tensor<3xcomplex<i32>>
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// CHECK-NEXT: return %cst : tensor<3xcomplex<i32>>
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