Add atleast3d op

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

@@ -10979,6 +10979,29 @@ def Torch_AtenAtleast2dOp : Torch_Op<"aten.atleast_2d", [
   }];
 }
 
+def Torch_AtenAtleast3dOp : Torch_Op<"aten.atleast_3d", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::atleast_3d : (Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self
+  );
+  let results = (outs
+    AnyTorchOptionalTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenAtleast3dOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 1, 1);
+    }
+    void AtenAtleast3dOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 1, 1);
+    }
+  }];
+}
+
 def Torch_AtenEinsumOp : Torch_Op<"aten.einsum", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -10966,6 +10966,38 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    }\n"
 "    return %2 : !torch.list<int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.atleast_3d\"(%arg0: !torch.list<int>) -> !torch.list<int> {\n"
+"    %int0 = torch.constant.int 0\n"
+"    %int1 = torch.constant.int 1\n"
+"    %int2 = torch.constant.int 2\n"
+"    %0 = torch.aten.len.t %arg0 : !torch.list<int> -> !torch.int\n"
+"    %1 = torch.aten.eq.int %0, %int0 : !torch.int, !torch.int -> !torch.bool\n"
+"    %2 = torch.prim.If %1 -> (!torch.list<int>) {\n"
+"      %3 = torch.prim.ListConstruct %int1, %int1, %int1 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>\n"
+"      torch.prim.If.yield %3 : !torch.list<int>\n"
+"    } else {\n"
+"      %3 = torch.aten.len.t %arg0 : !torch.list<int> -> !torch.int\n"
+"      %4 = torch.aten.eq.int %3, %int1 : !torch.int, !torch.int -> !torch.bool\n"
+"      %5 = torch.prim.If %4 -> (!torch.list<int>) {\n"
+"        %6 = torch.aten.__getitem__.t %arg0, %int0 : !torch.list<int>, !torch.int -> !torch.int\n"
+"        %7 = torch.prim.ListConstruct %int1, %6, %int1 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>\n"
+"        torch.prim.If.yield %7 : !torch.list<int>\n"
+"      } else {\n"
+"        %6 = torch.aten.len.t %arg0 : !torch.list<int> -> !torch.int\n"
+"        %7 = torch.aten.eq.int %6, %int2 : !torch.int, !torch.int -> !torch.bool\n"
+"        %8 = torch.prim.If %7 -> (!torch.list<int>) {\n"
+"          %9:2 = torch.prim.ListUnpack %arg0 : !torch.list<int> -> !torch.int, !torch.int\n"
+"          %10 = torch.prim.ListConstruct %9#0, %9#1, %int1 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>\n"
+"          torch.prim.If.yield %10 : !torch.list<int>\n"
+"        } else {\n"
+"          torch.prim.If.yield %arg0 : !torch.list<int>\n"
+"        }\n"
+"        torch.prim.If.yield %8 : !torch.list<int>\n"
+"      }\n"
+"      torch.prim.If.yield %5 : !torch.list<int>\n"
+"    }\n"
+"    return %2 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.stack\"(%arg0: !torch.list<list<int>>, %arg1: !torch.int) -> !torch.list<int> {\n"
 "    %0 = call @__torch__.torch.jit._shape_functions.stack(%arg0, %arg1) : (!torch.list<list<int>>, !torch.int) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
@@ -15858,6 +15890,10 @@ 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.atleast_3d\"(%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.hstack\"(%arg0: !torch.list<tuple<int, int>>) -> !torch.int {\n"
 "    %true = torch.constant.bool true\n"
 "    %none = torch.constant.none\n"

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -1941,6 +1941,41 @@ class DecomposeAtenAtleast2dOp : public OpRewritePattern<AtenAtleast2dOp> {
 };
 } // namespace
 
+class DecomposeAtenAtleast3dOp : public OpRewritePattern<AtenAtleast3dOp> {
+public:
+  using OpRewritePattern::OpRewritePattern;
+  LogicalResult matchAndRewrite(AtenAtleast3dOp op,
+                                PatternRewriter &rewriter) const override {
+    Location loc = op.getLoc();
+    Value input = op.getSelf();
+    Type opType = op.getType();
+
+    auto inputType = cast<BaseTensorType>(input.getType());
+    SmallVector<int64_t> inputShape(inputType.getSizes());
+
+    if (inputShape.size() >= 3) {
+      rewriter.replaceOp(op, input);
+      return success();
+    }
+
+    auto atleast2dResShape =
+        inputShape.empty()
+            ? SmallVector<int64_t>{1, 1}
+            : (inputShape.size() == 1 ? SmallVector<int64_t>{1, inputShape[0]}
+                                      : inputShape);
+    auto atleast2dResType = rewriter.getType<ValueTensorType>(
+        atleast2dResShape, inputType.getOptionalDtype());
+    auto atleast2dRes =
+        rewriter.create<AtenAtleast2dOp>(loc, atleast2dResType, input);
+
+    Value zero = rewriter.create<Torch::ConstantIntOp>(
+        loc, rewriter.getI64IntegerAttr(0));
+    rewriter.replaceOpWithNewOp<AtenUnsqueezeOp>(op, opType, atleast2dRes,
+                                                 zero);
+    return success();
+  }
+};
+
 namespace {
 // Decompose AtenEinsumOp to AtenMatmulOp, and supports possible reduce
 // operation and permute operation. Currently, this pass doesn't support
@@ -11722,6 +11757,7 @@ class DecomposeComplexOpsPass
     addPatternIfTargetOpIsIllegal<DecomposeAtenCeluOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenAtleast1dOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenAtleast2dOp>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAtenAtleast3dOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenEinsumOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAten_TrilinearOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenTraceOp>(patterns);

lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp

@@ -400,6 +400,7 @@ static void markDecomposedOpsAsIllegal(MLIRContext *context,
   target.addIllegalOp<AtenTanhBackwardOp>();
   target.addIllegalOp<AtenAtleast1dOp>();
   target.addIllegalOp<AtenAtleast2dOp>();
+  target.addIllegalOp<AtenAtleast3dOp>();
   target.addIllegalOp<AtenEinsumOp>();
   target.addIllegalOp<Aten_TrilinearOp>();
   target.addIllegalOp<AtenTraceOp>();

projects/pt1/e2e_testing/xfail_sets.py

@@ -1004,6 +1004,11 @@
     "Atleast2dModule0dInput_basic",
     "Atleast2dModule1dInput_basic",
     "Atleast2dModule2dInput_basic",
+    "Atleast2dModule3dInput_basic",
+    "Atleast3dModule0dInput_basic",
+    "Atleast3dModule1dInput_basic",
+    "Atleast3dModule2dInput_basic",
+    "Atleast3dModule3dInput_basic",
     "AtenLinear1D_basic",
     "AtenLinear2D_basic",
     "AtenLinear3DBias_basic",
@@ -2006,6 +2011,11 @@
     "Atleast2dModule0dInput_basic",
     "Atleast2dModule1dInput_basic",
     "Atleast2dModule2dInput_basic",
+    "Atleast2dModule3dInput_basic",
+    "Atleast3dModule0dInput_basic",
+    "Atleast3dModule1dInput_basic",
+    "Atleast3dModule2dInput_basic",
+    "Atleast3dModule3dInput_basic",
     "AtenLinear2D_basic",
     "AtenLinear3DBias_basic",
     "ElementwiseAddScalar_NumToTensorFloat_Module_basic",

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

@@ -2318,6 +2318,18 @@ def aten〇atleast_2d〡shape(self: List[int]) -> List[int]:
     else:
         return self
 
+def aten〇atleast_3d〡shape(self: List[int]) -> List[int]:
+    if len(self)==0:
+        return [1, 1, 1]
+    elif len(self)==1:
+        x=self[0]
+        return [1, 1, x]
+    elif len(self)==2:
+        x, y = self
+        return [1, x, y]
+    else: 
+        return self
+
 def aten〇stack〡shape(tensors: List[List[int]], dim: int = 0) -> List[int]:
     return upstream_shape_functions.stack(tensors, dim)
 
@@ -5676,6 +5688,11 @@ def aten〇atleast_2d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atleast_3d〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
+    self_rank, self_dtype = self_rank_dtype
+    return self_dtype
+
 @check_dtype_function(
     [Invocation([NonZeroDTensorWithDtype(torch.bool), NonZeroDTensorWithDtype(torch.int32), NonZeroDTensorWithDtype(torch.int64)]),
      Invocation([NonZeroDTensorWithDtype(torch.float32), NonZeroDTensorWithDtype(torch.int32)]),

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

@@ -844,6 +844,7 @@ def emit_with_mutating_variants(key, **kwargs):
     emit("aten::one_hot : (Tensor, int) -> (Tensor)")
     emit("aten::atleast_1d : (Tensor) -> (Tensor)")
     emit("aten::atleast_2d : (Tensor) -> (Tensor)")
+    emit("aten::atleast_3d : (Tensor) -> (Tensor)")
     emit("aten::einsum : (str, Tensor[], int[]?) -> (Tensor)")
     emit("aten::trace : (Tensor) -> (Tensor)")
     emit("aten::bucketize.Tensor : (Tensor, Tensor, bool, bool) -> (Tensor)")

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

@@ -1551,6 +1551,102 @@ def Atleast1dModule1dInput_basic(module, tu: TestUtils):
     module.forward(tu.rand(4))
 
 
+class Atleast2dModule0dInput(torch.nn.Module):
+    @export
+    @annotate_args([None, [(), torch.float32, True]])
+    def forward(self, x):
+        return torch.ops.aten.atleast_2d(x)
+
+
+@register_test_case(module_factory=lambda: Atleast2dModule0dInput())
+def Atleast2dModule0dInput_basic(module, tu: TestUtils):
+    module.forward(tu.rand())
+
+
+class Atleast2dModule1dInput(torch.nn.Module):
+    @export
+    @annotate_args([None, [(10,), torch.float32, True]])
+    def forward(self, x):
+        return torch.ops.aten.atleast_2d(x)
+
+
+@register_test_case(module_factory=lambda: Atleast2dModule1dInput())
+def Atleast2dModule1dInput_basic(module, tu: TestUtils):
+    module.forward(tu.rand(10))
+
+
+class Atleast2dModule2dInput(torch.nn.Module):
+    @export
+    @annotate_args([None, [(3, 4), torch.float32, True]])
+    def forward(self, x):
+        return torch.ops.aten.atleast_2d(x)
+
+
+@register_test_case(module_factory=lambda: Atleast2dModule2dInput())
+def Atleast2dModule2dInput_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 4))
+
+
+class Atleast2dModule3dInput(torch.nn.Module):
+    @export
+    @annotate_args([None, [(2, 3, 4), torch.float32, True]])
+    def forward(self, x):
+        return torch.ops.aten.atleast_2d(x)
+
+
+@register_test_case(module_factory=lambda: Atleast2dModule3dInput())
+def Atleast2dModule3dInput_basic(module, tu: TestUtils):
+    result = module.forward(tu.rand(2, 3, 4))
+
+
+class Atleast3dModule0dInput(torch.nn.Module):
+    @export
+    @annotate_args([None, [(), torch.float32, True]])
+    def forward(self, x):
+        return torch.ops.aten.atleast_3d(x)
+
+
+@register_test_case(module_factory=lambda: Atleast3dModule0dInput())
+def Atleast3dModule0dInput_basic(module, tu: TestUtils):
+    result = module.forward(tu.rand())
+
+
+class Atleast3dModule1dInput(torch.nn.Module):
+    @export
+    @annotate_args([None, [(10,), torch.float32, True]])
+    def forward(self, x):
+        return torch.ops.aten.atleast_3d(x)
+
+
+@register_test_case(module_factory=lambda: Atleast3dModule1dInput())
+def Atleast3dModule1dInput_basic(module, tu: TestUtils):
+    result = module.forward(tu.rand(10))
+
+
+class Atleast3dModule2dInput(torch.nn.Module):
+    @export
+    @annotate_args([None, [(4, 5), torch.float32, True]])
+    def forward(self, x):
+        return torch.ops.aten.atleast_3d(x)
+
+
+@register_test_case(module_factory=lambda: Atleast3dModule2dInput())
+def Atleast3dModule2dInput_basic(module, tu: TestUtils):
+    result = module.forward(tu.rand(4, 5))
+
+
+class Atleast3dModule3dInput(torch.nn.Module):
+    @export
+    @annotate_args([None, [(2, 3, 4), torch.float32, True]])
+    def forward(self, x):
+        return torch.ops.aten.atleast_3d(x)
+
+
+@register_test_case(module_factory=lambda: Atleast3dModule3dInput())
+def Atleast3dModule3dInput_basic(module, tu: TestUtils):
+    result = module.forward(tu.rand(2, 3, 4))
+
+
 # ==============================================================================