diff --git a/tests/function_libs/torch_lib/ops_test_data.py b/tests/function_libs/torch_lib/ops_test_data.py index 7a475c9ad..9c7184387 100644 --- a/tests/function_libs/torch_lib/ops_test_data.py +++ b/tests/function_libs/torch_lib/ops_test_data.py @@ -1497,33 +1497,6 @@ def _where_input_wrangler( ), TorchLibOpInfo("stack", core_ops.aten_stack), TorchLibOpInfo("stack", core_ops.aten_stack_complex, complex=True), - TorchLibOpInfo( - "std_mean", - core_ops.aten_std_mean, - ).xfail( - # kwargs is 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_mean_dim", - core_ops.aten_std_mean_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_mean_correction", - core_ops.aten_std_mean_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("sub", core_ops.aten_sub), TorchLibOpInfo("sub", core_ops.aten_sub_complex, complex=True), # TorchLibOpInfo("sym_size", core_ops.aten_sym_size), # no test case in OPS_DB @@ -2191,33 +2164,6 @@ 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, @@ -2238,60 +2184,6 @@ def _where_input_wrangler( ), # Custom from extra_opinfo TorchLibOpInfo("transpose", core_ops.aten_transpose), TorchLibOpInfo("transpose", core_ops.aten_transpose_complex, complex=True), - TorchLibOpInfo( - "var_mean", - core_ops.aten_var_mean, - ).xfail( - # kwargs is 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( - "var_mean_dim", - core_ops.aten_var_mean_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( - "var_mean_correction", - core_ops.aten_var_mean_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( - "var", - core_ops.aten_var, - ).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( - "var_dim", - core_ops.aten_var_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( - "var_correction", - core_ops.aten_var_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("zeros_like", core_ops.aten_zeros_like), TorchLibOpInfo("torchvision.ops.nms", vision_ops.torchvision_nms), ) @@ -2364,10 +2256,6 @@ 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_mean", ("std_mean_dim", "std_mean_correction")) -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", ("var_dim", "var_correction")) ops_test_common.duplicate_opinfo(OPS_DB, "view_as_complex", ("view_as_complex_copy",)) ops_test_common.duplicate_opinfo(OPS_DB, "view_as_real", ("view_as_real_copy",)) @@ -2510,7 +2398,6 @@ def _where_input_wrangler( "transpose", "trunc", "uniform", - "var", "where", )