[Mlir-commits] [mlir] ff94061 - [mlir][sparse] remove reshape dot test (#70359)
llvmlistbot at llvm.org
llvmlistbot at llvm.org
Thu Oct 26 11:14:48 PDT 2023
Author: Aart Bik
Date: 2023-10-26T11:14:44-07:00
New Revision: ff94061a9f15c121fa1068caed680c6916a215e0
URL: https://github.com/llvm/llvm-project/commit/ff94061a9f15c121fa1068caed680c6916a215e0
DIFF: https://github.com/llvm/llvm-project/commit/ff94061a9f15c121fa1068caed680c6916a215e0.diff
LOG: [mlir][sparse] remove reshape dot test (#70359)
This no longer tests a required feature.
Added:
Modified:
Removed:
mlir/test/Dialect/SparseTensor/sparse_reshape_dot.mlir
################################################################################
diff --git a/mlir/test/Dialect/SparseTensor/sparse_reshape_dot.mlir b/mlir/test/Dialect/SparseTensor/sparse_reshape_dot.mlir
deleted file mode 100644
index c562d6845e84ffe..000000000000000
--- a/mlir/test/Dialect/SparseTensor/sparse_reshape_dot.mlir
+++ /dev/null
@@ -1,89 +0,0 @@
-//
-// TODO: this test case is temporarily disabled as we are improving zero-cost sparse tensor reshaping.
-// XFAIL: *
-//
-// RUN: mlir-opt %s --linalg-generalize-named-ops --sparsification --cse --canonicalize | FileCheck %s
-
-#COO_2D = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton), posWidth = 32, crdWidth = 32 }>
-#COO_3D = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : compressed(nonunique), d1 : singleton(nonunique), d2 : singleton), posWidth = 32, crdWidth = 32 }>
-
-
-// CHECK-LABEL: func.func @sparse_reshape_fused(
-// CHECK-SAME: %[[VAL_0:.*]]: tensor<5x6xf32>,
-// CHECK-SAME: %[[VAL_1:.*]]: tensor<6x2x3xf32, #sparse_tensor.encoding<{{{.*}}}>>) -> tensor<?x?x?xf32> {
-// CHECK-DAG: %[[VAL_2:.*]] = arith.constant false
-// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 5 : index
-// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 3 : index
-// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
-// CHECK-DAG: %[[VAL_7:.*]] = tensor.empty() : tensor<5x6xf32>
-// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index}
-// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index}
-// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index}
-// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 2 : index}
-// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_1]]
-// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_7]] : memref<5x6xf32>
-// CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] {
-// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_5]]] : memref<?xi32>
-// CHECK: %[[VAL_16:.*]] = arith.extui %[[VAL_15]] : i32 to i64
-// CHECK: %[[VAL_17:.*]] = arith.index_cast %[[VAL_16]] : i64 to index
-// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_6]]] : memref<?xi32>
-// CHECK: %[[VAL_19:.*]] = arith.extui %[[VAL_18]] : i32 to i64
-// CHECK: %[[VAL_20:.*]] = arith.index_cast %[[VAL_19]] : i64 to index
-// CHECK: %[[VAL_21:.*]] = scf.while (%[[VAL_22:.*]] = %[[VAL_17]]) : (index) -> index {
-// CHECK: %[[VAL_23:.*]] = arith.cmpi ult, %[[VAL_22]], %[[VAL_20]] : index
-// CHECK: scf.condition(%[[VAL_23]]) %[[VAL_22]] : index
-// CHECK: } do {
-// CHECK: ^bb0(%[[VAL_24:.*]]: index):
-// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_24]]] : memref<?xi32, strided<[?], offset: ?>>
-// CHECK: %[[VAL_26:.*]] = arith.extui %[[VAL_25]] : i32 to i64
-// CHECK: %[[VAL_27:.*]] = arith.index_cast %[[VAL_26]] : i64 to index
-// CHECK: %[[VAL_28:.*]] = scf.while (%[[VAL_29:.*]] = %[[VAL_24]]) : (index) -> index {
-// CHECK: %[[VAL_30:.*]] = arith.cmpi ult, %[[VAL_29]], %[[VAL_20]] : index
-// CHECK: %[[VAL_31:.*]] = scf.if %[[VAL_30]] -> (i1) {
-// CHECK: %[[VAL_32:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_29]]] : memref<?xi32, strided<[?], offset: ?>>
-// CHECK: %[[VAL_33:.*]] = arith.extui %[[VAL_32]] : i32 to i64
-// CHECK: %[[VAL_34:.*]] = arith.index_cast %[[VAL_33]] : i64 to index
-// CHECK: %[[VAL_35:.*]] = arith.cmpi eq, %[[VAL_34]], %[[VAL_27]] : index
-// CHECK: scf.yield %[[VAL_35]] : i1
-// CHECK: } else {
-// CHECK: scf.yield %[[VAL_2]] : i1
-// CHECK: }
-// CHECK: scf.condition(%[[VAL_36:.*]]) %[[VAL_29]] : index
-// CHECK: } do {
-// CHECK: ^bb0(%[[VAL_37:.*]]: index):
-// CHECK: %[[VAL_38:.*]] = arith.addi %[[VAL_37]], %[[VAL_6]] : index
-// CHECK: scf.yield %[[VAL_38]] : index
-// CHECK: }
-// CHECK: %[[VAL_39:.*]] = tensor.extract %[[VAL_0]]{{\[}}%[[VAL_14]], %[[VAL_27]]] : tensor<5x6xf32>
-// CHECK: scf.for %[[VAL_40:.*]] = %[[VAL_24]] to %[[VAL_41:.*]] step %[[VAL_6]] {
-// CHECK: %[[VAL_42:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_40]]] : memref<?xi32, strided<[?], offset: ?>>
-// CHECK: %[[VAL_43:.*]] = arith.extui %[[VAL_42]] : i32 to i64
-// CHECK: %[[VAL_44:.*]] = arith.index_cast %[[VAL_43]] : i64 to index
-// CHECK: %[[VAL_45:.*]] = arith.muli %[[VAL_44]], %[[VAL_4]] : index
-// CHECK: %[[VAL_46:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_40]]] : memref<?xi32, strided<[?], offset: ?>>
-// CHECK: %[[VAL_47:.*]] = arith.extui %[[VAL_46]] : i32 to i64
-// CHECK: %[[VAL_48:.*]] = arith.index_cast %[[VAL_47]] : i64 to index
-// CHECK: %[[VAL_49:.*]] = arith.addi %[[VAL_45]], %[[VAL_48]] : index
-// CHECK: %[[VAL_50:.*]] = tensor.extract %[[VAL_7]]{{\[}}%[[VAL_14]], %[[VAL_49]]] : tensor<5x6xf32>
-// CHECK: %[[VAL_51:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_40]]] : memref<?xf32>
-// CHECK: %[[VAL_52:.*]] = arith.mulf %[[VAL_39]], %[[VAL_51]] : f32
-// CHECK: %[[VAL_53:.*]] = arith.addf %[[VAL_50]], %[[VAL_52]] : f32
-// CHECK: memref.store %[[VAL_53]], %[[VAL_13]]{{\[}}%[[VAL_14]], %[[VAL_49]]] : memref<5x6xf32>
-// CHECK: } {"Emitted from" = "linalg.generic"}
-// CHECK: scf.yield %[[VAL_54:.*]] : index
-// CHECK: } attributes {"Emitted from" = "linalg.generic"}
-// CHECK: } {"Emitted from" = "linalg.generic"}
-// CHECK: %[[VAL_55:.*]] = bufferization.to_tensor %[[VAL_13]] : memref<5x6xf32>
-// CHECK: %[[VAL_56:.*]] = tensor.expand_shape %[[VAL_55]] {{\[\[}}0], [1, 2]] : tensor<5x6xf32> into tensor<5x2x3xf32>
-// CHECK: %[[VAL_57:.*]] = tensor.cast %[[VAL_56]] : tensor<5x2x3xf32> to tensor<?x?x?xf32>
-// CHECK: return %[[VAL_57]] : tensor<?x?x?xf32>
-// CHECK: }
-func.func @sparse_reshape_fused(%arg0: tensor<5x6xf32>, %arg1: tensor<6x2x3xf32, #COO_3D>) -> tensor<?x?x?xf32> {
- %collapsed = tensor.collapse_shape %arg1 [[0], [1, 2]] : tensor<6x2x3xf32, #COO_3D> into tensor<6x6xf32, #COO_2D>
- %0 = tensor.empty() : tensor<5x6xf32>
- %2 = linalg.matmul ins(%arg0, %collapsed : tensor<5x6xf32>, tensor<6x6xf32, #COO_2D>) outs(%0 : tensor<5x6xf32>) -> tensor<5x6xf32>
- %expanded = tensor.expand_shape %2 [[0], [1, 2]] : tensor<5x6xf32> into tensor<5x2x3xf32>
- %ret1 = tensor.cast %expanded : tensor<5x2x3xf32> to tensor<?x?x?xf32>
- return %ret1 : tensor<?x?x?xf32>
-}
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