Merge pull request #304 from Xilinx/bump_to_ad1083dc

[AutoBump] Merge with fixes of ad1083d (May 14) (45)

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

@@ -65,6 +65,12 @@ 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

@@ -40,6 +40,42 @@ 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

@@ -23,6 +23,7 @@
 
 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
@@ -60,6 +61,14 @@ 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;
@@ -398,6 +407,10 @@ 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>();
 }