[Torch] support aten.column_stack (#3867)

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

@@ -14460,6 +14460,29 @@ def Torch_AtenHstackOp : Torch_Op<"aten.hstack", [
   }];
 }
 
+def Torch_AtenColumnStackOp : Torch_Op<"aten.column_stack", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::column_stack : (Tensor[]) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchListOfTensorType:$tensors
+  );
+  let results = (outs
+    AnyTorchOptionalTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenColumnStackOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 1, 1);
+    }
+    void AtenColumnStackOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 1, 1);
+    }
+  }];
+}
+
 def Torch_AtenAppendTOp : Torch_Op<"aten.append.t", [
     AllowsTypeRefinement
   ]> {

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -10633,6 +10633,37 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    }\n"
 "    return %5 : !torch.list<int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.column_stack\"(%arg0: !torch.list<list<int>>) -> !torch.list<int> {\n"
+"    %true = torch.constant.bool true\n"
+"    %int0 = torch.constant.int 0\n"
+"    %int1 = torch.constant.int 1\n"
+"    %0 = torch.prim.ListConstruct  : () -> !torch.list<list<int>>\n"
+"    %1 = torch.aten.len.t %arg0 : !torch.list<list<int>> -> !torch.int\n"
+"    torch.prim.Loop %1, %true, init() {\n"
+"    ^bb0(%arg1: !torch.int):\n"
+"      %3 = torch.aten.__getitem__.t %arg0, %arg1 : !torch.list<list<int>>, !torch.int -> !torch.list<int>\n"
+"      %4 = torch.aten.len.t %3 : !torch.list<int> -> !torch.int\n"
+"      %5 = torch.aten.eq.int %4, %int0 : !torch.int, !torch.int -> !torch.bool\n"
+"      %6 = torch.prim.If %5 -> (!torch.list<int>) {\n"
+"        %8 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>\n"
+"        torch.prim.If.yield %8 : !torch.list<int>\n"
+"      } else {\n"
+"        %8 = torch.aten.len.t %3 : !torch.list<int> -> !torch.int\n"
+"        %9 = torch.aten.eq.int %8, %int1 : !torch.int, !torch.int -> !torch.bool\n"
+"        torch.prim.If %9 -> () {\n"
+"          %10 = torch.aten.append.t %3, %int1 : !torch.list<int>, !torch.int -> !torch.list<int>\n"
+"          torch.prim.If.yield\n"
+"        } else {\n"
+"          torch.prim.If.yield\n"
+"        }\n"
+"        torch.prim.If.yield %3 : !torch.list<int>\n"
+"      }\n"
+"      %7 = torch.aten.append.t %0, %6 : !torch.list<list<int>>, !torch.list<int> -> !torch.list<list<int>>\n"
+"      torch.prim.Loop.condition %true, iter()\n"
+"    } : (!torch.int, !torch.bool) -> ()\n"
+"    %2 = call @__torch__.torch.jit._shape_functions.cat(%0, %int1) : (!torch.list<list<int>>, !torch.int) -> !torch.list<int>\n"
+"    return %2 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.fft_fft\"(%arg0: !torch.list<int>, %arg1: !torch.optional<int>, %arg2: !torch.int, %arg3: !torch.optional<str>) -> !torch.list<int> {\n"
 "    return %arg0 : !torch.list<int>\n"
 "  }\n"
@@ -15284,6 +15315,33 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %5 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.promote_dtypes(%0, %1) : (!torch.list<optional<int>>, !torch.list<int>) -> !torch.int\n"
 "    return %5 : !torch.int\n"
 "  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.column_stack\"(%arg0: !torch.list<tuple<int, int>>) -> !torch.int {\n"
+"    %true = torch.constant.bool true\n"
+"    %none = torch.constant.none\n"
+"    %str = torch.constant.str \"AssertionError: \"\n"
+"    %int0 = torch.constant.int 0\n"
+"    %0 = torch.prim.ListConstruct  : () -> !torch.list<optional<int>>\n"
+"    %1 = torch.prim.ListConstruct  : () -> !torch.list<int>\n"
+"    %2 = torch.aten.len.t %arg0 : !torch.list<tuple<int, int>> -> !torch.int\n"
+"    %3 = torch.aten.ne.int %2, %int0 : !torch.int, !torch.int -> !torch.bool\n"
+"    torch.prim.If %3 -> () {\n"
+"      torch.prim.If.yield\n"
+"    } else {\n"
+"      torch.prim.RaiseException %str, %none : !torch.str, !torch.none\n"
+"      torch.prim.If.yield\n"
+"    }\n"
+"    %4 = torch.aten.len.t %arg0 : !torch.list<tuple<int, int>> -> !torch.int\n"
+"    torch.prim.Loop %4, %true, init() {\n"
+"    ^bb0(%arg1: !torch.int):\n"
+"      %6 = torch.aten.__getitem__.t %arg0, %arg1 : !torch.list<tuple<int, int>>, !torch.int -> !torch.tuple<int, int>\n"
+"      %7:2 = torch.prim.TupleUnpack %6 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"      %8 = torch.aten.append.t %0, %7#0 : !torch.list<optional<int>>, !torch.int -> !torch.list<optional<int>>\n"
+"      %9 = torch.aten.append.t %1, %7#1 : !torch.list<int>, !torch.int -> !torch.list<int>\n"
+"      torch.prim.Loop.condition %true, iter()\n"
+"    } : (!torch.int, !torch.bool) -> ()\n"
+"    %5 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.promote_dtypes(%0, %1) : (!torch.list<optional<int>>, !torch.list<int>) -> !torch.int\n"
+"    return %5 : !torch.int\n"
+"  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.einsum\"(%arg0: !torch.str, %arg1: !torch.list<tuple<int, int>>, %arg2: !torch.optional<list<int>>) -> !torch.int {\n"
 "    %true = torch.constant.bool true\n"
 "    %none = torch.constant.none\n"

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -3831,6 +3831,68 @@ class DecomposeAtenHstackOp : public OpRewritePattern<AtenHstackOp> {
 };
 } // namespace
 
+// Decompose `aten.column_stack` into `aten.reshape` and `aten.cat`.
+// https://github.com/pytorch/pytorch/blob/207564bab1c4fe42750931765734ee604032fb69/torch/_refs/__init__.py#L2822
+namespace {
+class DecomposeAtenColumnStackOp : public OpRewritePattern<AtenColumnStackOp> {
+public:
+  using OpRewritePattern::OpRewritePattern;
+  LogicalResult matchAndRewrite(AtenColumnStackOp op,
+                                PatternRewriter &rewriter) const override {
+    Location loc = op.getLoc();
+
+    SmallVector<Value> tensors;
+    if (!getListConstructElements(op.getTensors(), tensors))
+      return rewriter.notifyMatchFailure(
+          op, "unimplemented: the tensor list is not from list construct");
+
+    for (auto tensor : tensors) {
+      auto tTy = dyn_cast<BaseTensorType>(tensor.getType());
+      if (!tTy || !tTy.hasSizes())
+        return rewriter.notifyMatchFailure(
+            op, "unimplemented: one tensor does not have known sizes");
+    }
+
+    SmallVector<Value> tensors2d;
+    for (auto tensor : tensors) {
+      auto tTy = dyn_cast<BaseTensorType>(tensor.getType());
+      SmallVector<int64_t> tSizes(tTy.getSizes());
+      if (tSizes.size() <= 1) {
+        if (tSizes.size() == 0) {
+          tSizes.push_back(1);
+        }
+        tSizes.push_back(1);
+        auto newTy = tTy.getWithSizesAndDtype(tSizes, tTy.getDtype());
+        SmallVector<Value> newShapeList;
+        for (auto tSize : tSizes) {
+          newShapeList.push_back(rewriter.create<ConstantIntOp>(
+              loc, rewriter.getI64IntegerAttr(tSize)));
+        }
+        auto newShape = rewriter.create<PrimListConstructOp>(
+            loc, Torch::ListType::get(rewriter.getType<IntType>()),
+            newShapeList);
+        Value tensor2d =
+            rewriter.create<AtenReshapeOp>(loc, newTy, tensor, newShape);
+        tensors2d.push_back(tensor2d);
+      } else {
+        tensors2d.push_back(tensor);
+      }
+    }
+
+    auto elemType = cast<BaseTensorType>(tensors2d[0].getType())
+                        .getWithSizesAndDtype(std::nullopt, nullptr);
+    Value newTensors = rewriter.create<PrimListConstructOp>(
+        loc, Torch::ListType::get(elemType), tensors2d);
+
+    rewriter.replaceOpWithNewOp<AtenCatOp>(
+        op, op.getType(), newTensors,
+        rewriter.create<ConstantIntOp>(loc, rewriter.getI64IntegerAttr(1)));
+
+    return success();
+  }
+};
+} // namespace
+
 // Decompose aten.roll into aten.slice and aten.cat ops.
 // https://pytorch.org/docs/stable/generated/torch.roll.html
 namespace {
@@ -10077,6 +10139,7 @@ class DecomposeComplexOpsPass
         DecomposeConstantTensorAllocLikeOp<AtenZerosLikeOp, 0>>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenStackOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenHstackOp>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAtenColumnStackOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenRollOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenRepeatOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenRepeatInterleaveSelfIntOp>(

lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp

@@ -381,6 +381,7 @@ static void markDecomposedOpsAsIllegal(MLIRContext *context,
   target.addIllegalOp<AtenZerosLikeOp>();
   target.addIllegalOp<AtenStackOp>();
   target.addIllegalOp<AtenHstackOp>();
+  target.addIllegalOp<AtenColumnStackOp>();
   target.addIllegalOp<AtenRollOp>();
   target.addIllegalOp<AtenRepeatOp>();
   target.addIllegalOp<AtenRepeatInterleaveSelfIntOp>();

projects/pt1/e2e_testing/xfail_sets.py

@@ -2494,6 +2494,9 @@
     "CollapsePartialDynamicModule_basic",
     "CollapseRank1DynamicModule_basic",
     "CollapseStaticModule_basic",
+    "ColumnStackBasicIntModule_basic",
+    "ColumnStack1dModule_basic",
+    "ColumnStack0dModule_basic",
     "ConstantBoolParameterModule_basic",
     "ContainsIntList_False",
     "ContainsIntList_True",

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

@@ -2175,6 +2175,20 @@ def aten〇hstack〡shape(tensors: List[List[int]]) -> List[int]:
 
     return upstream_shape_functions.cat(tensors_atleast1d, dim=1)
 
+@check_shape_function([
+    Invocation([LongTensorOfShape(2, 4, 3), LongTensorOfShape(2, 5, 3)]), # Basic case.
+])
+def aten〇column_stack〡shape(tensors: List[List[int]]) -> List[int]:
+    tensors2d:  List[List[int]] = []
+    for tensor in tensors:
+        if len(tensor) == 0:
+            tensor = [1, 1]
+        elif len(tensor) == 1:
+            tensor.append(1)
+        tensors2d.append(tensor)
+
+    return upstream_shape_functions.cat(tensors2d, dim=1)
+
 def aten〇fft_fft〡shape(self: List[int], n: Optional[int] = None, dim: int = -1, norm: Optional[str] = None) -> List[int]:
     return self
 
@@ -5414,6 +5428,23 @@ def aten〇hstack〡dtype(tensors_rank_dtype: List[Tuple[int, int]]) -> int:
 
     return promote_dtypes(ranks, dtypes)
 
+@check_dtype_function(
+    [Invocation([NonZeroDTensorWithDtype(torch.bool), NonZeroDTensorWithDtype(torch.int32), NonZeroDTensorWithDtype(torch.int64)]),
+     Invocation([NonZeroDTensorWithDtype(torch.float32), NonZeroDTensorWithDtype(torch.int32)]),
+     Invocation([NonZeroDTensorWithDtype(torch.float16), NonZeroDTensorWithDtype(torch.float64)]),
+     Invocation([NonZeroDTensorWithDtype(torch.float32), NonZeroDTensorWithDtype(torch.int32),
+                 NonZeroDTensorWithDtype(torch.complex64)])])
+def aten〇column_stack〡dtype(tensors_rank_dtype: List[Tuple[int, int]]) -> int:
+    ranks: List[Optional[int]] = []
+    dtypes: List[int] = []
+    assert len(tensors_rank_dtype) != 0
+    for tensor_rank_dtype in tensors_rank_dtype:
+        tensor_rank, tensor_dtype = tensor_rank_dtype
+        ranks.append(tensor_rank)
+        dtypes.append(tensor_dtype)
+
+    return promote_dtypes(ranks, dtypes)
+
 @check_dtype_function(
     [Invocation("i,j->ij", [TensorOfShape(1, dtype=torch.float32),
                             TensorOfShape(1, dtype=torch.int32)]),])

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

@@ -1036,6 +1036,7 @@ def emit_with_mutating_variants(key, **kwargs):
     )
     emit("aten::stack : (Tensor[], int) -> (Tensor)")
     emit("aten::hstack : (Tensor[]) -> (Tensor)")
+    emit("aten::column_stack : (Tensor[]) -> (Tensor)")
     emit("aten::append.t : (t[], t) -> (t[])")
     emit("aten::add.t : (t[], t[]) -> (t[])", has_canonicalizer=True)
     emit("aten::eq.int_list : (int[], int[]) -> (bool)", has_folder=True)

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

@@ -1409,6 +1409,83 @@ def HstackBasicComplexModule_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+class ColumnStackBasicIntModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([2, 3, 4], torch.bool, True),
+            ([2, 3, 4], torch.int32, True),
+            ([2, 3, 4], torch.int64, True),
+        ]
+    )
+    def forward(self, x, y, z):
+        return torch.ops.aten.column_stack([x, y, z])
+
+
+@register_test_case(module_factory=lambda: ColumnStackBasicIntModule())
+def ColumnStackBasicIntModule_basic(module, tu: TestUtils):
+    module.forward(
+        tu.randint(2, 3, 4, low=0, high=2).bool(),
+        tu.randint(2, 3, 4, low=0, high=100).int(),
+        tu.randint(2, 3, 4, low=0, high=100).long(),
+    )
+
+
+# ==============================================================================
+
+
+class ColumnStack1dModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([4], torch.float32, True),
+            ([4], torch.float32, True),
+        ]
+    )
+    def forward(self, x, y):
+        return torch.ops.aten.column_stack([x, y])
+
+
+@register_test_case(module_factory=lambda: ColumnStack1dModule())
+def ColumnStack1dModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(4), tu.rand(4))
+
+
+# ==============================================================================
+
+
+class ColumnStack0dModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args(
+        [
+            None,
+            ([], torch.float32, True),
+            ([], torch.float32, True),
+        ]
+    )
+    def forward(self, x, y):
+        return torch.ops.aten.column_stack([x, y])
+
+
+@register_test_case(module_factory=lambda: ColumnStack0dModule())
+def ColumnStack0dModule_basic(module, tu: TestUtils):
+    module.forward(torch.tensor(4.0), torch.tensor(1.0))
+
+
+# ==============================================================================
+
+
 class GatherModule(torch.nn.Module):
     def __init__(self):
         super().__init__()