[torch-mlir][sparse] recognize sparse tensor conversion

lib/Conversion/TorchToLinalg/DataMovement.cpp

@@ -20,6 +20,7 @@
 #include "mlir/Dialect/ControlFlow/IR/ControlFlowOps.h"
 #include "mlir/Dialect/Linalg/IR/Linalg.h"
 #include "mlir/Dialect/Math/IR/Math.h"
+#include "mlir/Dialect/SparseTensor/IR/SparseTensor.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/IR/Matchers.h"
 #include "torch-mlir/Conversion/TorchToLinalg/Utils.h"
@@ -2423,6 +2424,42 @@ class ConvertAtenDiagEmbedOp : public OpConversionPattern<AtenDiagEmbedOp> {
 };
 } // namespace
 
+namespace {
+class ConvertSparseOperatorOp : public OpConversionPattern<OperatorOp> {
+public:
+  using OpConversionPattern::OpConversionPattern;
+
+  static bool isSparsePrimitive(StringRef prim) {
+    return llvm::find(legalizedNames, prim) != legalizedNames.end();
+  }
+
+  // Rewriting method.
+  LogicalResult
+  matchAndRewrite(OperatorOp op, OpAdaptor adaptor,
+                  ConversionPatternRewriter &rewriter) const override {
+    if (!isSparsePrimitive(op.getNameAttr()))
+      return failure();
+    // Conversion is completed specified by information in the sparse tensor
+    // type. Thus, we can rewrite all legalizedNames to the same construct.
+    RankedTensorType resultType = getTypeConverter()
+                                      ->convertType(op->getResult(0).getType())
+                                      .cast<RankedTensorType>();
+    rewriter.replaceOpWithNewOp<sparse_tensor::ConvertOp>(
+        op, resultType, adaptor.getOperands()[0]);
+    return success();
+  }
+
+private:
+  // The operators that legalize to sparse tensor conversions.
+  static SmallVector<StringRef> legalizedNames;
+};
+// Static initializer.
+SmallVector<StringRef> ConvertSparseOperatorOp::legalizedNames = {
+    "torch.aten._to_sparse", "torch.aten._to_csr", "torch.aten._to_csc",
+    "torch.aten._to_bsr",    "torch.aten._to_bsc",
+};
+} // namespace
+
 void mlir::torch::torch_to_linalg::populateDataMovementPatternsAndLegality(
     TypeConverter &typeConverter, RewritePatternSet &patterns,
     ConversionTarget &target) {
@@ -2469,4 +2506,9 @@ void mlir::torch::torch_to_linalg::populateDataMovementPatternsAndLegality(
   patterns.add<ConvertAtenDiagonalOp>(typeConverter, context);
   target.addIllegalOp<AtenDiagEmbedOp>();
   patterns.add<ConvertAtenDiagEmbedOp>(typeConverter, context);
+  // Rewrite all special sparse conversions hidden as operators.
+  target.addDynamicallyLegalOp<OperatorOp>([&](Torch::OperatorOp op) {
+    return !ConvertSparseOperatorOp::isSparsePrimitive(op.getNameAttr());
+  });
+  patterns.add<ConvertSparseOperatorOp>(typeConverter, context);
 }

lib/Conversion/TorchToLinalg/TorchToLinalg.cpp

@@ -17,6 +17,7 @@
 #include "mlir/Dialect/Func/IR/FuncOps.h"
 #include "mlir/Dialect/Linalg/IR/Linalg.h"
 #include "mlir/Dialect/Math/IR/Math.h"
+#include "mlir/Dialect/SparseTensor/IR/SparseTensor.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "torch-mlir/Dialect/Torch/IR/TorchDialect.h"
 #include "torch-mlir/Dialect/Torch/IR/TorchOps.h"
@@ -53,10 +54,10 @@ class ConvertTorchToLinalg
   void runOnOperation() override {
     MLIRContext *context = &getContext();
     ConversionTarget target(*context);
-    target.addLegalDialect<linalg::LinalgDialect, func::FuncDialect,
-                           cf::ControlFlowDialect, math::MathDialect,
-                           tensor::TensorDialect, arith::ArithDialect,
-                           complex::ComplexDialect>();
+    target.addLegalDialect<
+        linalg::LinalgDialect, func::FuncDialect, cf::ControlFlowDialect,
+        math::MathDialect, sparse_tensor::SparseTensorDialect,
+        tensor::TensorDialect, arith::ArithDialect, complex::ComplexDialect>();
     target.addLegalOp<TorchConversion::GetNextSeedOp>();
 
     TypeConverter typeConverter;

test/Conversion/TorchToLinalg/sparse.mlir

@@ -34,3 +34,35 @@ func.func @SpMM(%arg0: !torch.vtensor<[8,16],f32,#CSR>,
        !torch.vtensor<[16,8],f32> -> !torch.vtensor<[8,8],f32>
   return %0 : !torch.vtensor<[8,8],f32>
 }
+
+// -----
+
+#sparse = #sparse_tensor.encoding<{
+  map = (d0, d1, d2, d3, d4) ->
+    (d0 : compressed(nonunique),
+     d1 : singleton(nonunique, soa),
+     d2 : singleton(nonunique, soa),
+     d3 : singleton(nonunique, soa),
+     d4 : singleton(soa)
+    ),
+    posWidth = 64,
+    crdWidth = 64
+}>
+
+// CHECK: #[[$ST:.*]] = #sparse_tensor.encoding<{ map = (d0, d1, d2, d3, d4) -> (d0 : compressed(nonunique), d1 : singleton(nonunique, soa), d2 : singleton(nonunique, soa), d3 : singleton(nonunique, soa), d4 : singleton(soa)), posWidth = 64, crdWidth = 64 }>
+// CHECK-LABEL: func.func @activate(
+// CHECK-SAME:  %[[A:.*]]: !torch.vtensor<[128,64,30,30,6],f32>)
+// CHECK:       %[[D:.*]] = torch_c.to_builtin_tensor %arg0 : !torch.vtensor<[128,64,30,30,6],f32> -> tensor<128x64x30x30x6xf32>
+// CHECK:       %[[C:.*]] = sparse_tensor.convert %0 : tensor<128x64x30x30x6xf32> to tensor<128x64x30x30x6xf32, #[[$ST]]>
+// CHECK:       %[[R:.*]] = torch_c.from_builtin_tensor %[[C]] : tensor<128x64x30x30x6xf32, #[[$ST]]>
+// CHECK:       return %[[R]] : !torch.vtensor<[128,64,30,30,6],f32,#[[$ST]]>
+func.func @activate(%arg0: !torch.vtensor<[128,64,30,30,6],f32>)
+                        -> !torch.vtensor<[128,64,30,30,6],f32,#sparse> {
+  %none_0 = torch.constant.none
+  %none_1 = torch.constant.none
+  %none_2 = torch.constant.none
+  %result = torch.operator "torch.aten._to_sparse"(%arg0, %none_0, %none_1, %none_2)
+    : (!torch.vtensor<[128,64,30,30,6],f32>, !torch.none, !torch.none, !torch.none)
+    -> !torch.vtensor<[128,64,30,30,6],f32,#sparse>
+  return %result : !torch.vtensor<[128,64,30,30,6],f32,#sparse>
+}