Implement aten._local_scalar_dense | feat(torchlib)

onnxscript/function_libs/torch_lib/ops/core.py

@@ -39,6 +39,14 @@
 _MATH_PI = math.pi
 
 
+@torch_op("aten::_local_scalar_dense")
+def aten__local_scalar_dense(self: TTensor) -> TTensor:
+    """_local_scalar_dense(Tensor self) -> Scalar"""
+
+    # Return the first element in tensor as a scalar.
+    return op.Gather(op.Reshape(self, [-1]), 0)
+
+
 @torch_op("aten::abs")
 def aten_abs(self: TRealOrUInt8) -> TRealOrUInt8:
     """abs(Tensor self) -> Tensor"""

onnxscript/tests/function_libs/torch_lib/extra_opinfo.py

@@ -17,6 +17,33 @@
 from torch.testing._internal.opinfo import core as opinfo_core
 
 
+def sample_inputs__local_scalar_dense(op_info, device, dtype, requires_grad, **kwargs):
+    del op_info
+
+    shapes = (
+        (),
+        (1,),
+        (3,),
+        (1, 1),
+        (1, 2),
+        (2, 1),
+        (1, 1, 1),
+        (2, 2, 2),
+    )
+
+    for shape in shapes:
+        t = torch_testing.make_tensor(
+            shape,
+            low=0,
+            high=1,
+            device=device,
+            dtype=dtype,
+            requires_grad=requires_grad,
+            **kwargs,
+        )
+        yield opinfo_core.SampleInput(t)
+
+
 def sample_inputs_conv3d(op_info, device, dtype, requires_grad, **kwargs):
     del op_info
     make_arg = functools.partial(
@@ -527,6 +554,13 @@ def sample_inputs_bernoulli_p_deterministic(op_info, device, dtype, requires_gra
 
 
 OP_DB: List[opinfo_core.OpInfo] = [
+    opinfo_core.OpInfo(
+        "aten._local_scalar_dense",
+        op=torch.ops.aten._local_scalar_dense,  # pylint: disable=protected-access
+        aten_name="_local_scalar_dense",
+        dtypes=common_dtype.all_types(),
+        sample_inputs_func=sample_inputs__local_scalar_dense,
+    ),
     opinfo_core.OpInfo(
         "col2im",
         op=torch.ops.aten.col2im,

onnxscript/tests/function_libs/torch_lib/ops_test_data.py

@@ -408,6 +408,10 @@ def _where_input_wrangler(
 # Ops to be tested for numerical consistency between onnx and pytorch
 # Find the names of the OpInfos in torch/testing/_internal/common_methods_invocations.py
 TESTED_TORCHLIB_OPS: tuple[TorchLibOpInfo, ...] = (
+    TorchLibOpInfo(
+        "aten._local_scalar_dense",
+        core_ops.aten__local_scalar_dense,
+    ),
     TorchLibOpInfo("all_dim", core_ops.aten_all_dim).xfail(
         matcher=lambda sample: not (len(sample.kwargs) > 0),
         reason="this Aten overload only support one tensor as input and {dim,keepdim} as kwargs by design",