[TORCH] Add support for aten.fliplr & aten.flipud ops via torch decomposition (#4197)

This PR takes care of issue:
#4192
**Feature**: Adds e2e support of two related ops `aten.fliplr` &
`aten.flipud` via decomposition to `aten.flip` op which is already
supported in torch-mlir.

---------

Signed-off-by: Zahid Wakeel <[email protected]>

Author: zahidwx

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

@@ -7226,6 +7226,52 @@ def Torch_AtenFlipOp : Torch_Op<"aten.flip", [
   }];
 }
 
+def Torch_AtenFliplrOp : Torch_Op<"aten.fliplr", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::fliplr : (Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self
+  );
+  let results = (outs
+    AnyTorchOptionalTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenFliplrOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 1, 1);
+    }
+    void AtenFliplrOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 1, 1);
+    }
+  }];
+}
+
+def Torch_AtenFlipudOp : Torch_Op<"aten.flipud", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::flipud : (Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self
+  );
+  let results = (outs
+    AnyTorchOptionalTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenFlipudOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 1, 1);
+    }
+    void AtenFlipudOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 1, 1);
+    }
+  }];
+}
+
 def Torch_AtenNativeBatchNormOp : Torch_Op<"aten.native_batch_norm", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -10281,6 +10281,34 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "  func.func @\"__torch_mlir_shape_fn.aten.flip\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>) -> !torch.list<int> {\n"
 "    return %arg0 : !torch.list<int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.fliplr\"(%arg0: !torch.list<int>) -> !torch.list<int> {\n"
+"    %none = torch.constant.none\n"
+"    %str = torch.constant.str \"AssertionError: \"\n"
+"    %int2 = torch.constant.int 2\n"
+"    %0 = torch.aten.len.t %arg0 : !torch.list<int> -> !torch.int\n"
+"    %1 = torch.aten.ge.int %0, %int2 : !torch.int, !torch.int -> !torch.bool\n"
+"    torch.prim.If %1 -> () {\n"
+"      torch.prim.If.yield\n"
+"    } else {\n"
+"      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
+"      torch.prim.If.yield\n"
+"    }\n"
+"    return %arg0 : !torch.list<int>\n"
+"  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.flipud\"(%arg0: !torch.list<int>) -> !torch.list<int> {\n"
+"    %none = torch.constant.none\n"
+"    %str = torch.constant.str \"AssertionError: \"\n"
+"    %int1 = torch.constant.int 1\n"
+"    %0 = torch.aten.len.t %arg0 : !torch.list<int> -> !torch.int\n"
+"    %1 = torch.aten.ge.int %0, %int1 : !torch.int, !torch.int -> !torch.bool\n"
+"    torch.prim.If %1 -> () {\n"
+"      torch.prim.If.yield\n"
+"    } else {\n"
+"      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
+"      torch.prim.If.yield\n"
+"    }\n"
+"    return %arg0 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.convolution_backward\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>, %arg2: !torch.list<int>, %arg3: !torch.optional<list<int>>, %arg4: !torch.list<int>, %arg5: !torch.list<int>, %arg6: !torch.list<int>, %arg7: !torch.bool, %arg8: !torch.list<int>, %arg9: !torch.int, %arg10: !torch.list<bool>) -> !torch.tuple<list<int>, list<int>, list<int>> {\n"
 "    %0 = call @__torch__.torch.jit._shape_functions.conv_backwards(%arg0, %arg1, %arg2, %arg3) : (!torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.optional<list<int>>) -> !torch.tuple<list<int>, list<int>, list<int>>\n"
 "    return %0 : !torch.tuple<list<int>, list<int>, list<int>>\n"
@@ -12590,6 +12618,14 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
 "    return %0#1 : !torch.int\n"
 "  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.fliplr\"(%arg0: !torch.tuple<int, int>) -> !torch.int {\n"
+"    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"    return %0#1 : !torch.int\n"
+"  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.flipud\"(%arg0: !torch.tuple<int, int>) -> !torch.int {\n"
+"    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"    return %0#1 : !torch.int\n"
+"  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.sign\"(%arg0: !torch.tuple<int, int>) -> !torch.int {\n"
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
 "    return %0#1 : !torch.int\n"

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -1372,6 +1372,84 @@ class DecomposeAtenDeg2radOp : public OpRewritePattern<AtenDeg2radOp> {
 };
 } // namespace
 
+namespace {
+class DecomposeAtenFliplrOp : public OpRewritePattern<AtenFliplrOp> {
+public:
+  using OpRewritePattern::OpRewritePattern;
+  LogicalResult matchAndRewrite(AtenFliplrOp op,
+                                PatternRewriter &rewriter) const override {
+    auto inputTy = dyn_cast<ValueTensorType>(op.getSelf().getType());
+    auto maybeSizes = inputTy.getOptionalSizes();
+    if (!maybeSizes) {
+      return rewriter.notifyMatchFailure(
+          op, "Expected input tensor to have known rank.");
+    }
+    auto inShape = maybeSizes.value();
+    auto inRank = inShape.size();
+
+    if (inRank < 2) {
+      return rewriter.notifyMatchFailure(op,
+                                         "Fliplr expects input rank >= 2D.");
+    }
+
+    Location loc = op.getLoc();
+    Value constI = rewriter.create<Torch::ConstantIntOp>(
+        loc, rewriter.getI64IntegerAttr(1));
+
+    SmallVector<Value> dims;
+    dims.push_back(constI);
+
+    Value flipDimList = rewriter.create<Torch::PrimListConstructOp>(
+        loc,
+        rewriter.getType<Torch::ListType>(rewriter.getType<Torch::IntType>()),
+        dims);
+    Value flip = rewriter.create<AtenFlipOp>(loc, op.getType(), op.getSelf(),
+                                             flipDimList);
+    rewriter.replaceOp(op, flip);
+    return success();
+  }
+};
+} // namespace
+
+namespace {
+class DecomposeAtenFlipudOp : public OpRewritePattern<AtenFlipudOp> {
+public:
+  using OpRewritePattern::OpRewritePattern;
+  LogicalResult matchAndRewrite(AtenFlipudOp op,
+                                PatternRewriter &rewriter) const override {
+    auto inputTy = dyn_cast<ValueTensorType>(op.getSelf().getType());
+    auto maybeSizes = inputTy.getOptionalSizes();
+    if (!maybeSizes) {
+      return rewriter.notifyMatchFailure(
+          op, "Expected input tensor to have known rank.");
+    }
+    auto inShape = maybeSizes.value();
+    auto inRank = inShape.size();
+
+    if (inRank < 1) {
+      return rewriter.notifyMatchFailure(op,
+                                         "Flipud expects input rank >= 1D.");
+    }
+
+    Location loc = op.getLoc();
+    Value constI = rewriter.create<Torch::ConstantIntOp>(
+        loc, rewriter.getI64IntegerAttr(0));
+
+    SmallVector<Value> dims;
+    dims.push_back(constI);
+
+    Value flipDimList = rewriter.create<Torch::PrimListConstructOp>(
+        loc,
+        rewriter.getType<Torch::ListType>(rewriter.getType<Torch::IntType>()),
+        dims);
+    Value flip = rewriter.create<AtenFlipOp>(loc, op.getType(), op.getSelf(),
+                                             flipDimList);
+    rewriter.replaceOp(op, flip);
+    return success();
+  }
+};
+} // namespace
+
 namespace {
 class DecomposeAtenSizeOp : public OpRewritePattern<AtenSizeOp> {
 public:
@@ -12048,6 +12126,8 @@ class DecomposeComplexOpsPass
     addPatternIfTargetOpIsIllegal<DecomposeAtenRreluWithNoiseBackwardOp>(
         patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenCeluOp>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAtenFliplrOp>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAtenFlipudOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenAtleast1dOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenAtleast2dOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenEinsumOp>(patterns);

lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp

@@ -579,6 +579,8 @@ static void markDecomposedOpsAsIllegal(MLIRContext *context,
   target.addIllegalOp<AtenFminOp>();
   target.addIllegalOp<AtenFmaxOp>();
   target.addIllegalOp<AtenSpecialExpm1Op>();
+  target.addIllegalOp<AtenFliplrOp>();
+  target.addIllegalOp<AtenFlipudOp>();
 
   for (auto &opName : backendLegalOpsSet) {
     target.addLegalOp(

projects/pt1/e2e_testing/xfail_sets.py

@@ -1910,6 +1910,10 @@
     "FlipModuleStaticShape_basic",
     "FlipModule_basic",
     "FlipNegativeIndexModule_basic",
+    "FliplrOddRankModule_basic",
+    "FliplrEvenRankModule_basic",
+    "FlipudOddRankModule_basic",
+    "FlipudEvenRankModule_basic",
     "Rot90BasicModule_basic",
     "Rot90DynamicDimsModule_basic",
     "Rot90MultipleRotationsModule_basic",
@@ -2697,6 +2701,10 @@
     "ElementwiseFminModule_basic",
     "ElementwiseFmaxModule_basic",
     "Exp2StaticModule_basic",
+    "FliplrOddRankModule_basic",
+    "FliplrEvenRankModule_basic",
+    "FlipudOddRankModule_basic",
+    "FlipudEvenRankModule_basic",
     "FloatPowerTensorTensorStaticModule_basic",
     "MultinomialModule2D_basic",
     "MultinomialModule2D_F32",
@@ -4400,6 +4408,10 @@
     "FlipModuleStaticShape_basic",
     "FlipModule_basic",
     "FlipNegativeIndexModule_basic",
+    "FliplrOddRankModule_basic",
+    "FliplrEvenRankModule_basic",
+    "FlipudOddRankModule_basic",
+    "FlipudEvenRankModule_basic",
     "FloatImplicitModule_basic",
     "FullLikeModuleDefaultDtype_basic",
     "FullLikeModuleFalsePinMemory_basic",

projects/pt1/python/torch_mlir/jit_ir_importer/build_tools/abstract_interp_lib_gen.py

@@ -1975,6 +1975,14 @@ def aten〇_convolution〇deprecated〡shape(input: List[int], weight: List[int]
 def aten〇flip〡shape(self: List[int], dims: List[int]) -> List[int]:
     return self
 
+def aten〇fliplr〡shape(self: List[int]) -> List[int]:
+    assert len(self) >= 2
+    return self
+
+def aten〇flipud〡shape(self: List[int]) -> List[int]:
+    assert len(self) >= 1
+    return self
+
 def aten〇convolution_backward〡shape(grad_output: List[int], input: List[int], weight: List[int], bias_sizes: Optional[List[int]], stride: List[int], padding: List[int], dilation: List[int], transposed: bool, output_padding: List[int], groups: int, output_mask: List[bool]) -> Tuple[List[int], List[int], List[int]]:
     return upstream_shape_functions.conv_backwards(grad_output, input, weight, bias_sizes)
 
@@ -3293,6 +3301,16 @@ def aten〇flip〡dtype(self_rank_dtype: Tuple[int, int], dims: List[int]) -> in
     self_rank, self_dtype = self_rank_dtype
     return self_dtype
 
+@check_dtype_function([Invocation(TensorOfShape(2, 3, dtype=torch.float32, device="cpu"))])
+def aten〇fliplr〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
+    self_rank, self_dtype = self_rank_dtype
+    return self_dtype
+
+@check_dtype_function([Invocation(TensorOfShape(2, 3, dtype=torch.float32, device="cpu"))])
+def aten〇flipud〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
+    self_rank, self_dtype = self_rank_dtype
+    return self_dtype
+
 @check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1))
 def aten〇sign〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
     self_rank, self_dtype = self_rank_dtype

projects/pt1/python/torch_mlir/jit_ir_importer/build_tools/torch_ods_gen.py

@@ -622,6 +622,8 @@ def emit_with_mutating_variants(key, **kwargs):
         "aten::convolution_backward : (Tensor, Tensor, Tensor, int[]?, int[], int[], int[], bool, int[], int, bool[]) -> (Tensor, Tensor, Tensor)"
     )
     emit("aten::flip : (Tensor, int[]) -> (Tensor)")
+    emit("aten::fliplr : (Tensor) -> (Tensor)")
+    emit("aten::flipud : (Tensor) -> (Tensor)")
     emit(
         "aten::native_batch_norm : (Tensor, Tensor?, Tensor?, Tensor?, Tensor?, bool, float, float) -> (Tensor, Tensor, Tensor)"
     )

projects/pt1/python/torch_mlir_e2e_test/test_suite/basic.py

@@ -10,6 +10,7 @@
 from torch_mlir_e2e_test.registry import register_test_case
 from torch_mlir_e2e_test.annotations import annotate_args, export
 
+
 # ==============================================================================
 
 
@@ -4267,6 +4268,98 @@ def FlipNegativeIndexModule_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+class FliplrOddRankModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([-1, -1, -1], torch.float32, True),
+        ]
+    )
+    def forward(self, a):
+        return torch.ops.aten.fliplr(a)
+
+
+@register_test_case(module_factory=lambda: FliplrOddRankModule())
+def FliplrOddRankModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 5, 2))
+
+
+# ==============================================================================
+
+
+class FliplrEvenRankModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([-1, -1, -1, -1], torch.float32, True),
+        ]
+    )
+    def forward(self, a):
+        return torch.ops.aten.fliplr(a)
+
+
+@register_test_case(module_factory=lambda: FliplrEvenRankModule())
+def FliplrEvenRankModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 5, 2, 4))
+
+
+# ==============================================================================
+
+
+class FlipudOddRankModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([-1, -1, -1], torch.float32, True),
+        ]
+    )
+    def forward(self, a):
+        return torch.ops.aten.flipud(a)
+
+
+@register_test_case(module_factory=lambda: FlipudOddRankModule())
+def FlipudOddRankModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 5, 2))
+
+
+# ==============================================================================
+
+
+class FlipudEvenRankModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([-1, -1, -1, -1], torch.float32, True),
+        ]
+    )
+    def forward(self, a):
+        return torch.ops.aten.flipud(a)
+
+
+@register_test_case(module_factory=lambda: FlipudEvenRankModule())
+def FlipudEvenRankModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 5, 2, 4))
+
+
+# ==============================================================================
+
+
 class DetachModule(torch.nn.Module):
     def __init__(self):
         super().__init__()