Add decomposition for aten.roll (#1170)

* Add decomposition for aten.roll

* add e2e unittest

* refine type of torch.roll

* fix aten::cat output type

e2e_testing/torchscript/xfail_sets.py

@@ -127,6 +127,7 @@ MHLO_PASS_SET = {
     "ReshapeAliasCollapseModule_basic",
     "ReshapeAliasExpandModule_basic",
     "ReshapeExpandModule_basic",
+    "RollModule_basic",
     "TestMultipleTensorReturn_basic",
     "AdaptiveAvgPool2dUnitOutputSizeStaticModule_basic",
     "BaddbmmStaticModule_basic",
@@ -447,6 +448,7 @@ LTC_XFAIL_SET = {
     "QuantizedMLP_basic",
     "RandLikeDtypeModule_basic",
     "RandLikeModule_basic",
+    "RollModule_basic",
     "ScalarImplicitFloatModule_basic",
     "ScalarImplicitIntModule_basic",
     "SliceEndSleStartModule_basic",

include/torch-mlir/Dialect/Torch/IR/GeneratedTorchOps.td

@@ -3478,6 +3478,31 @@ def Torch_Aten_ConvolutionDeprecatedOp : Torch_Op<"aten._convolution.deprecated"
   }];
 }
 
+def Torch_AtenRollOp : Torch_Op<"aten.roll", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::roll : (Tensor, int[], int[]) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self,
+    AnyTorchListOfTorchIntType:$shifts,
+    AnyTorchListOfTorchIntType:$dims
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenRollOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 3, 1);
+    }
+    void AtenRollOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 3, 1);
+    }
+  }];
+}
+
 def Torch_AtenFlipOp : Torch_Op<"aten.flip", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Conversion/TorchToMhlo/Basic.cpp

@@ -979,7 +979,7 @@ LogicalResult ConvertAtenOp<AtenCatOp>::matchAndRewrite(
 
   size_t posDim = toPositiveDim(dim, outType.getRank());
   rewriter.replaceOpWithNewOp<mhlo::ConcatenateOp>(
-      op, ValueRange(builtinTensors), posDim);
+      op, outType, ValueRange(builtinTensors), posDim);
   return success();
 }
 } // namespace

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -709,6 +709,77 @@ public:
 };
 } // namespace
 
+// Decompose aten.roll into aten.slice and aten.cat ops.
+// https://pytorch.org/docs/stable/generated/torch.roll.html
+namespace {
+class DecomposeAtenRollOp : public OpRewritePattern<AtenRollOp> {
+public:
+  using OpRewritePattern::OpRewritePattern;
+  LogicalResult matchAndRewrite(AtenRollOp op,
+                                PatternRewriter &rewriter) const override {
+    SmallVector<Value> shifts;
+    if (!getListConstructElements(op.shifts(), shifts))
+      return rewriter.notifyMatchFailure(
+          op, "unimplemented: shifts not list of Scalar");
+    SmallVector<Value> dims;
+    if (!getListConstructElements(op.dims(), dims))
+      return rewriter.notifyMatchFailure(
+          op, "unimplemented: dims not list of Scalar");
+
+    if (shifts.size() != dims.size())
+      return op.emitError("list sizes of shifts and dims are not the same");
+
+    auto loc = op.getLoc();
+    Value constNone = rewriter.create<ConstantNoneOp>(loc);
+    Value constZero = rewriter.create<Torch::ConstantIntOp>(
+        loc, rewriter.getI64IntegerAttr(0));
+    Value constOne = rewriter.create<Torch::ConstantIntOp>(
+        loc, rewriter.getI64IntegerAttr(1));
+    auto self = op.self();
+    auto selfTy = self.getType().cast<BaseTensorType>();
+    // roll(input, shift, dim) = cat({
+    //   slice(input, dim, -shift, none),
+    //   slice(input, dim, 0, -shift)}, dim)
+    auto imitateRoll = [&](Value input, Value shift, Value dim,
+                           int64_t cstDim) {
+      Value negShift = rewriter.create<AtenNegIntOp>(loc, shift);
+      ArrayRef<int64_t> inputShape = selfTy.getSizes();
+      SmallVector<int64_t> sizes;
+      sizes.append(inputShape.begin(), inputShape.end());
+      sizes[cstDim] = ShapedType::kDynamicSize;
+      Type sliceTy = selfTy.getWithSizesAndDtype(llvm::makeArrayRef(sizes),
+                                                 selfTy.getDtype());
+      Value slice0 = rewriter.create<AtenSliceTensorOp>(
+          loc, sliceTy, input, dim, negShift, constNone, constOne);
+      Value slice1 = rewriter.create<AtenSliceTensorOp>(
+          loc, sliceTy, input, dim, constZero, negShift, constOne);
+
+      Type listType = Torch::ListType::get(sliceTy);
+      Value slices = rewriter.create<PrimListConstructOp>(
+          loc, listType, llvm::ArrayRef<Value>{slice0, slice1});
+      return rewriter.create<AtenCatOp>(loc, self.getType(), slices, dim);
+    };
+    int rank = getTensorRank(self);
+    if (rank < 0)
+      return rewriter.notifyMatchFailure(op, "Unimplemented: unranked tensor");
+    Value output = self;
+    auto nShifts = shifts.size();
+    for (size_t k = 0; k < nShifts; ++k) {
+      auto dim = dims[k];
+      int64_t cstDim = -1;
+      if (!matchPattern(dim, m_TorchConstantInt(&cstDim)))
+        return rewriter.notifyMatchFailure(
+            op, "unimplemented: dim must be constant");
+
+      cstDim = toPositiveDim(cstDim, rank);
+      output = imitateRoll(output, shifts[k], dim, cstDim);
+    }
+    rewriter.replaceOp(op, output);
+    return success();
+  }
+};
+} // namespace
+
 // Decompose aten.repeat into aten.expand and aten.view ops.
 //
 // Ref: https://pytorch.org/docs/stable/generated/torch.Tensor.repeat.html
@@ -2555,6 +2626,8 @@ public:
     patterns.add<DecomposeConstantTensorAllocLikeOp<AtenZerosLikeOp, 0>>(
         context);
     target.addIllegalOp<AtenZerosLikeOp>();
+    patterns.add<DecomposeAtenRollOp>(context);
+    target.addIllegalOp<AtenRollOp>();
     patterns.add<DecomposeAtenRepeatOp>(context);
     target.addIllegalOp<AtenRepeatOp>();
     patterns.add<DecomposeAtenExpandOp>(context);

lib/Dialect/Torch/Transforms/RefineTypes.cpp

@@ -658,7 +658,8 @@ void TypeAnalysis::visitOperation(Operation *op,
           AtenZero_Op, AtenIndexTensorOp, ValsemVariantAtenIndexPutImplOp,
           AtenIndexPutOp, ValsemVariantAtenCopyOp, AtenZeroOp,
           AtenIndexPutHackedTwinOp, AtenMaskedFillScalarOp, AtenFlipOp,
-          PrimAbsScalarOp, AtenNumpyTOp, AtenTriuOp, AtenMaskedFillTensorOp>(
+          PrimAbsScalarOp, AtenNumpyTOp, AtenTriuOp, AtenMaskedFillTensorOp,
+          AtenRollOp>(
           op)) {
     return incorporateKnowledge(op->getResult(0), operands[0]->getValue());
   }

lib/Dialect/Torch/Transforms/ShapeLibrary.cpp

@@ -4213,6 +4213,10 @@ module {
     }
     return %7 : !torch.list<int>
   }
+  func.func @__torch_mlir_shape_fn.aten.roll(%arg0: !torch.list<int>, %arg1: !torch.list<int>, %arg2: !torch.list<int>) -> !torch.list<int> {
+    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>
+    return %0 : !torch.list<int>
+  }
   func.func @__torch__.torch.jit._shape_functions.expand(%arg0: !torch.list<int>, %arg1: !torch.list<int>) -> !torch.list<int> {
     %int-1 = torch.constant.int -1
     %true = torch.constant.bool true

python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/shape_lib_gen.py

@@ -635,6 +635,9 @@ def aten〇repeat(self: List[int], repeats: List[int]) -> List[int]:
         out.append(self[i] * repeats[i + leading_rank])
     return out
 
+def aten〇roll(self: List[int], shifts: List[int], dims: List[int] = ()) -> List[int]:
+    return upstream_shape_functions.unary(self)
+
 def aten〇expand(self: List[int], size: List[int], implicit: bool = False) -> List[int]:
     return upstream_shape_functions.expand(self, size)
 

python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/torch_ods_gen.py

@@ -338,6 +338,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
     emit("aten::convolution_overrideable : (Tensor, Tensor, Tensor?, int[], int[], int[], bool, int[], int) -> (Tensor)")
     emit("aten::_convolution : (Tensor, Tensor, Tensor?, int[], int[], int[], bool, int[], int, bool, bool, bool, bool) -> (Tensor)")
     emit("aten::_convolution.deprecated : (Tensor, Tensor, Tensor?, int[], int[], int[], bool, int[], int, bool, bool, bool) -> (Tensor)")
+    emit("aten::roll : (Tensor, int[], int[]) -> (Tensor)"),
     emit("aten::flip : (Tensor, int[]) -> (Tensor)")
     emit(
         "aten::native_batch_norm : (Tensor, Tensor?, Tensor?, Tensor?, Tensor?, bool, float, float) -> (Tensor, Tensor, Tensor)"

python/torch_mlir_e2e_test/test_suite/basic.py

@@ -1047,6 +1047,27 @@ def BroadcastToModule_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+class RollModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([3, -1, 2], torch.float32, True),
+    ])
+    def forward(self, x):
+        return x.roll([2, -1], [0, 2])
+
+
+@register_test_case(module_factory=lambda: RollModule())
+def RollModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 1, 2))
+
+# ==============================================================================
+
+
 class RepeatModule(torch.nn.Module):
 
     def __init__(self):
@@ -1065,7 +1086,6 @@ class RepeatModule(torch.nn.Module):
 def RepeatModule_basic(module, tu: TestUtils):
     module.forward(tu.rand(3, 1, 2))
 
-
 # ==============================================================================
 
 

test/Dialect/Torch/decompose-complex-ops.mlir

@@ -1336,6 +1336,7 @@ func.func @torch.aten.std.dim(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vten
 }
 
 // -----
+
 // CHECK-LABEL:  func.func @torch.aten.flatten.using_ints(
 // CHECK-SAME:                                            %[[ARG0:.*]]: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?],f32> {
 // CHECK:         %[[INT0:.*]] = torch.constant.int 0
@@ -1350,3 +1351,34 @@ func.func @torch.aten.flatten.using_ints(%arg0: !torch.vtensor<[?,?,?,?],f32>) -
   %1 = torch.aten.flatten.using_ints %arg0, %int0, %int3: !torch.vtensor<[?,?,?,?],f32>, !torch.int, !torch.int -> !torch.vtensor<[?],f32>
   return %1 : !torch.vtensor<[?],f32>
 }
+
+// -----
+
+// CHECK-LABEL:  func.func @torch.aten.roll(
+// CHECK-SAME:         %[[ARG0:.*]]: !torch.vtensor<[?,?],f32>, %[[ARG1:.*]]: !torch.int, %[[ARG2:.*]]: !torch.int) -> !torch.vtensor<[?,?],f32> {
+// CHECK:         %[[T0:.*]] = torch.prim.ListConstruct %[[ARG1]], %[[ARG2]] : (!torch.int, !torch.int) -> !torch.list<int>
+// CHECK:         %[[INT1:.*]] = torch.constant.int 1
+// CHECK:         %[[INT:.*]]-2 = torch.constant.int -2
+// CHECK:         %[[T1:.*]] = torch.prim.ListConstruct %[[INT1]], %[[INT]]-2 : (!torch.int, !torch.int) -> !torch.list<int>
+// CHECK:         %[[NONE:.*]] = torch.constant.none
+// CHECK:         %[[INT0:.*]] = torch.constant.int 0
+// CHECK:         %[[INT1_0:.*]] = torch.constant.int 1
+// CHECK:         %[[T2:.*]] = torch.aten.neg.int %[[ARG1]] : !torch.int -> !torch.int
+// CHECK:         %[[T3:.*]] = torch.aten.slice.Tensor %[[ARG0]], %[[INT1]], %[[T2]], %[[NONE]], %[[INT1]]_0 : !torch.vtensor<[?,?],f32>, !torch.int, !torch.int, !torch.none, !torch.int -> !torch.vtensor<[?,?],f32>
+// CHECK:         %[[T4:.*]] = torch.aten.slice.Tensor %[[ARG0]], %[[INT1]], %[[INT0]], %[[T2]], %[[INT1]]_0 : !torch.vtensor<[?,?],f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[?,?],f32>
+// CHECK:         %[[T5:.*]] = torch.prim.ListConstruct %[[T3]], %[[T4]] : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32>) -> !torch.list<vtensor<[?,?],f32>>
+// CHECK:         %[[T6:.*]] = torch.aten.cat %[[T5]], %[[INT1]] : !torch.list<vtensor<[?,?],f32>>, !torch.int -> !torch.vtensor<[?,?],f32>
+// CHECK:         %[[T7:.*]] = torch.aten.neg.int %[[ARG2]] : !torch.int -> !torch.int
+// CHECK:         %[[T8:.*]] = torch.aten.slice.Tensor %[[T6]], %[[INT]]-2, %[[T7]], %[[NONE]], %[[INT]]1_0 : !torch.vtensor<[?,?],f32>, !torch.int, !torch.int, !torch.none, !torch.int -> !torch.vtensor<[?,?],f32>
+// CHECK:         %[[T9:.*]] = torch.aten.slice.Tensor %[[T6]], %[[INT]]-2, %[[INT]]0, %[[T7]], %[[INT]]1_0 : !torch.vtensor<[?,?],f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[?,?],f32>
+// CHECK:         %[[T10:.*]] = torch.prim.ListConstruct %[[T8]], %[[T9]] : (!torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32>) -> !torch.list<vtensor<[?,?],f32>>
+// CHECK:         %[[T11:.*]] = torch.aten.cat %[[T10]], %[[INT]]-2 : !torch.list<vtensor<[?,?],f32>>, !torch.int -> !torch.vtensor<[?,?],f32>
+// CHECK:         return %[[T11]] : !torch.vtensor<[?,?],f32>
+func.func @torch.aten.roll(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.int, %arg2: !torch.int) -> !torch.vtensor<[?,?],f32> {
+  %0 = torch.prim.ListConstruct %arg1, %arg2: (!torch.int, !torch.int) -> !torch.list<int>
+  %int1 = torch.constant.int 1
+  %int-2 = torch.constant.int -2
+  %1 = torch.prim.ListConstruct %int1, %int-2: (!torch.int, !torch.int) -> !torch.list<int>
+  %2 = torch.aten.roll %arg0, %0, %1 : !torch.vtensor<[?,?],f32>, !torch.list<int>, !torch.list<int> -> !torch.vtensor<[?,?],f32>
+  return %2 : !torch.vtensor<[?,?],f32>
+}