Add op (std, std.dim, std.correction) | feat(torchlib)

onnxscript/function_libs/torch_lib/ops/core.py

@@ -7821,10 +7821,44 @@
     return op.ConcatFromSequence(tensors, axis=dim, new_axis=1)
 
 
-def aten_std(self: TensorType, unbiased: bool = True) -> TensorType:
+@torch_op("aten::std", trace_only=True)
+def aten_std(self: TReal, unbiased: bool = True) -> TReal:
     """std(Tensor self, bool unbiased=True) -> Tensor"""
+    var = _aten_var_onnx(self, correction=float(unbiased), keepdim=False)
+    return op.Sqrt(var)
 
-    raise NotImplementedError()
+
+@torch_op("aten::std.dim", trace_only=True)
+def aten_std_dim(
+    self: TReal,
+    dim: Sequence[int],
+    unbiased: Optional[bool] = True,
+    keepdim: Optional[bool] = False,
+) -> TReal:
+    """std.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> Tensor"""
+
+    var = _aten_var_dim_onnx(self, dims=dim, correction=float(unbiased), keepdim=keepdim)
+    return op.Sqrt(var)
+
+
+@torch_op("aten::var.correction", trace_only=True)
+def aten_std_correction(
+    self: TReal,
+    # FIXME(justinchuby): Make dim Optional[Sequence[int]]
+    dim: Optional[int] = None,
+    correction: Optional[float] = None,
+    keepdim: bool = False,
+) -> TReal:
+    """std.correction(Tensor self, int[1]? dim=None, *, Scalar? correction=None, bool keepdim=False) -> Tensor"""
+
+    if correction is None:
+        correction = 1.0
+
+    if dim is None:
+        var = _aten_var_onnx(self, correction=correction, keepdim=keepdim)
+    else:
+        var = _aten_var_dim_onnx(self, dims=dim, correction=correction, keepdim=keepdim)
+    return op.Sqrt(var)
 
 
 def aten_std_mean(self: TensorType, unbiased: bool = True) -> tuple[TensorType, TensorType]:

tests/function_libs/torch_lib/ops_test_data.py

@@ -2151,6 +2151,33 @@ def _where_input_wrangler(
         dtypes=(torch.float16,),
         reason="RuntimeError: MKL FFT doesn't support tensors of type: Half",
     ),
+    TorchLibOpInfo(
+        "std",
+        core_ops.aten_std,
+    ).xfail(
+        # kwargs must be empty
+        matcher=lambda sample: len(sample.kwargs) > 0,
+        reason="this Aten overload only support input[0]=tensor and input[1]=bool as input without any kwargs",
+    ),
+    TorchLibOpInfo(
+        "std_dim",
+        core_ops.aten_std_dim,
+    ).xfail(
+        # kwargs["dim"] must exist, kwargs["correction"] must not exist
+        matcher=lambda sample: not (
+            sample.kwargs.get("dim", None) is not None
+            and sample.kwargs.get("correction", None) is None
+        ),
+        reason="this Aten overload only support with 'dim' argument and without 'correction' argument",
+    ),
+    TorchLibOpInfo(
+        "std_correction",
+        core_ops.aten_std_correction,
+    ).skip(
+        # Don't accept input[1]=bool and 'correction' must be in kwargs
+        matcher=lambda sample: len(sample.args) > 0 or "correction" not in sample.kwargs,
+        reason="this Aten overload only support when correction attribute exists",
+    ),
     TorchLibOpInfo(
         "sum",
         core_ops.aten_sum_dim_IntList,
@@ -2300,6 +2327,8 @@ def _where_input_wrangler(
 ops_test_common.duplicate_opinfo(OPS_DB, "ops.aten._softmax", ("ops.aten._softmax_half",))
 ops_test_common.duplicate_opinfo(OPS_DB, "round", ("round_decimals",))
 ops_test_common.duplicate_opinfo(OPS_DB, "squeeze", ("squeeze_dim",))
+ops_test_common.duplicate_opinfo(OPS_DB, "std", ("std_dim", "std_correction"))
+ops_test_common.duplicate_opinfo(OPS_DB, "var_mean", ("var_mean_dim", "var_mean_correction"))
 ops_test_common.duplicate_opinfo(OPS_DB, "var_mean", ("var_mean_dim", "var_mean_correction"))
 ops_test_common.duplicate_opinfo(OPS_DB, "var", ("var_dim", "var_correction"))
 ops_test_common.duplicate_opinfo(OPS_DB, "view_as_complex", ("view_as_complex_copy",))