[Mlir-commits] [mlir] [mlir][linalg] Use inferConvolutionDims for generic convolution downscaling (PR #180586)

[Abhishek Varma]

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@@ -212,39 +234,19 @@ func.func @softmax(%arg0: tensor<2x16x32xf32>, %dst: tensor<2x16x32xf32>) -> ten
 
 // CHECK-LABEL:      func.func @softmax(
 // CHECK-SAME:           %[[ARG0:[a-zA-Z0-9_]+]]: tensor<2x16x32xf32>, %[[DST:[a-zA-Z0-9_]+]]: tensor<2x16x32xf32>) -> tensor<2x16x32xf32> {
-// CHECK-DAG:        %[[D1:.+]] = tensor.empty() : tensor<2x16xf32>
-// CHECK-DAG:        %[[CST:.+]] = arith.constant 0xFFC00000 : f32
-// CHECK:        %[[D2:.+]] = linalg.fill ins(%[[CST]] : f32) outs(%[[D1]] : tensor<2x16xf32>) -> tensor<2x16xf32>
-// CHECK:        %[[D3:.+]] = linalg.generic {indexing_maps = [#[[$MAP]], #[[$MAP1]]], iterator_types = ["parallel",
-// CHECK-SAME:     "parallel", "reduction"]} ins(%[[ARG0]] : tensor<2x16x32xf32>) outs(%[[D2]] : tensor<2x16xf32>) {
-// CHECK:        ^bb0(%[[IN:.+]]: f32, %[[OUT:.+]]: f32):
-// CHECK:          %[[D8:.+]] = arith.maxnumf %[[IN]], %[[OUT]] : f32
-// CHECK:          linalg.yield %[[D8]] : f32
-// CHECK:        } -> tensor<2x16xf32>
-// CHECK:        %[[D4:.+]] = linalg.generic {indexing_maps = [#[[$MAP]], #[[$MAP1]], #[[$MAP]]], iterator_types =
-// CHECK-SAME:     ["parallel", "parallel", "parallel"]} ins(%[[ARG0]], %[[D3]] : tensor<2x16x32xf32>, tensor<2x16xf32>)
-// CHECK-SAME:     outs(%[[DST]] : tensor<2x16x32xf32>) {
-// CHECK:        ^bb0(%[[IN:.+]]: f32, %[[IN_1:.+]]: f32, %[[OUT:.+]]: f32):
-// CHECK:          %[[D8]] = arith.subf %[[IN]], %[[IN_1]] : f32
-// CHECK:          %[[D9:.+]] = math.exp %[[D8]] : f32
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Abhishek-Varma wrote:

Yes - it was due to specialize pass. But removed now as https://github.com/llvm/llvm-project/pull/180586#discussion_r2786005449 is implemented.

https://github.com/llvm/llvm-project/pull/180586