[mlir][sparse] remove sparse encoding propagation pass. (#93593)

Author: Peiming Liu

mlir/include/mlir/Dialect/SparseTensor/Transforms/Passes.h

@@ -65,12 +65,6 @@ void populateSparseAssembler(RewritePatternSet &patterns, bool directOut);
 std::unique_ptr<Pass> createSparseAssembler();
 std::unique_ptr<Pass> createSparseAssembler(bool directOut);
 
-//===----------------------------------------------------------------------===//
-// The SparseEncodingPropagation pass.
-//===----------------------------------------------------------------------===//
-
-std::unique_ptr<Pass> createSparseEncodingPropagationPass();
-
 //===----------------------------------------------------------------------===//
 // The SparseReinterpretMap pass.
 //===----------------------------------------------------------------------===//

mlir/include/mlir/Dialect/SparseTensor/Transforms/Passes.td

@@ -40,42 +40,6 @@ def SparseAssembler : Pass<"sparse-assembler", "ModuleOp"> {
   ];
 }
 
-def SparseEncodingPropagation : Pass<"sparse-encoding-propagation", "func::FuncOp"> {
-  let summary = "Propagate sparse tensor encodings";
-  let description = [{
-    A pass that propagates sparse tensor encodings.
-
-    Background: To avoid introducing repetitive operations, sparse tensors
-    in MLIR try to reuse tensor operations whenever available. However, most
-    tensor operations are canonicalized/transformed without the knowledge
-    of sparsity. The pass tries to propagate missing sparse encodings.
-
-    For example:
-    ```mlir
-    %s = tensor.extract_slice %input[0, 0,] [2, 1] [1, 1]
-       : tensor<2x3xf32, #sparse> to tensor<2x1xf32, #sparse>
-
-    // After rank reducing (by tensor dialect transformation)
-    %t = tensor.extract_slice %input[0, 0,] [2, 1] [1, 1]
-       : tensor<2x3xf32, #sparse> to tensor<2xf32>
-    %s = tensor.expand_shape [[0, 1]] %t
-       : tensor<2xf32> to tensor<2x1xf32, #sparse>
-
-    // After sparsity propagation
-    %t = tensor.extract_slice %input[0, 0,] [2, 1] [1, 1]
-       : tensor<2x3xf32, #sparse> to tensor<2xf32, #sparse1>
-    %s = tensor.expand_shape [[0, 1]] %t
-       : tensor<2xf32, #sparse1> to tensor<2x1xf32, #sparse>
-    ```
-  }];
-
-  let constructor = "mlir::createSparseEncodingPropagationPass()";
-  let dependentDialects = [
-    "sparse_tensor::SparseTensorDialect",
-    "tensor::TensorDialect",
-  ];
-}
-
 def SparseReinterpretMap : Pass<"sparse-reinterpret-map", "ModuleOp"> {
   let summary = "Reinterprets sparse tensor type mappings";
   let description = [{

mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorPasses.cpp

@@ -23,7 +23,6 @@
 
 namespace mlir {
 #define GEN_PASS_DEF_SPARSEASSEMBLER
-#define GEN_PASS_DEF_SPARSEENCODINGPROPAGATION
 #define GEN_PASS_DEF_SPARSEREINTERPRETMAP
 #define GEN_PASS_DEF_PRESPARSIFICATIONREWRITE
 #define GEN_PASS_DEF_SPARSIFICATIONPASS
@@ -61,14 +60,6 @@ struct SparseAssembler : public impl::SparseAssemblerBase<SparseAssembler> {
   }
 };
 
-struct SparseEncodingPropagation
-    : public impl::SparseEncodingPropagationBase<SparseEncodingPropagation> {
-  SparseEncodingPropagation() = default;
-  SparseEncodingPropagation(const SparseEncodingPropagation &pass) = default;
-
-  void runOnOperation() override {}
-};
-
 struct SparseReinterpretMap
     : public impl::SparseReinterpretMapBase<SparseReinterpretMap> {
   SparseReinterpretMap() = default;
@@ -407,10 +398,6 @@ std::unique_ptr<Pass> mlir::createSparseAssembler() {
   return std::make_unique<SparseAssembler>();
 }
 
-std::unique_ptr<Pass> mlir::createSparseEncodingPropagationPass() {
-  return std::make_unique<SparseEncodingPropagation>();
-}
-
 std::unique_ptr<Pass> mlir::createSparseReinterpretMapPass() {
   return std::make_unique<SparseReinterpretMap>();
 }