Revert "Extend mxfp loading dtypes"

auto_round/inference/backend.py

@@ -107,11 +107,6 @@ class BackendInfo:
     "act_dynamic",
 ]
 
-MX_TENSOR_DATA_TYPES = [
-    "mx_fp",
-    "mx_fp_rceil",
-]
-
 
 def feature_multiply_checker(in_feature, out_feature, config, in_feature_multiplier, out_feature_multiplier=None):
     if out_feature_multiplier is None:
@@ -235,13 +230,13 @@ def fp8_static_scheme_checker(
     packing_format=LLM_COMPRESSOR_FORMAT,
     sym=[True],
     compute_dtype=["float32", "float16", "bfloat16"],
-    data_type=MX_TENSOR_DATA_TYPES,
+    data_type=["mx_fp", "max_fp_rceil"],
     group_size=[32],
     bits=[8],
     act_bits=[8],
     act_group_size=[32],
     act_sym=[True],
-    act_data_type=MX_TENSOR_DATA_TYPES,
+    act_data_type=["mx_fp_rceil"],
     act_dynamic=[True],
     priority=0,
     checkers=[feature_multiply_checker_32],
@@ -255,13 +250,13 @@ def fp8_static_scheme_checker(
     packing_format=LLM_COMPRESSOR_FORMAT,
     sym=[True],
     compute_dtype=["float32", "float16", "bfloat16"],
-    data_type=MX_TENSOR_DATA_TYPES,
+    data_type=["mx_fp"],
     group_size=[32],
     bits=[4],
     act_bits=[4],
     act_group_size=[32],
     act_sym=[True],
-    act_data_type=MX_TENSOR_DATA_TYPES,
+    act_data_type=["mx_fp_rceil"],
     act_dynamic=[True],
     priority=0,
     checkers=[feature_multiply_checker_32],

auto_round/testing_utils.py

@@ -268,11 +268,3 @@ def decorator(test_func: Callable) -> Callable:
         return unittest.skipUnless(require_package_version(package, version_spec, on_fail="skip"), reason)(test_func)
 
     return decorator
-
-
-def has_module(model: torch.nn.Module, target_module_type: torch.nn.Module) -> bool:
-    """Check if the model contains a specific module type."""
-    for _, module in model.named_modules():
-        if isinstance(module, target_module_type):
-            return True
-    return False

test/test_cuda/test_mxfp_and_nvfp_quant.py

@@ -10,7 +10,6 @@
 from auto_round.experimental import qmodules as ar_qmodules
 from auto_round.export.export_to_autoround import AutoRoundFormat
 from auto_round.export.export_to_autoround import qlinear_fp as ar_qlinear_fp
-from auto_round.testing_utils import has_module
 
 testing_schemes = [AutoRoundFormat.MXFP8.value, AutoRoundFormat.MXFP4.value, AutoRoundFormat.NVFP4.value]
 QMODULE_MAPPING = {
@@ -20,6 +19,14 @@
 }
 
 
+def has_module(model: torch.nn.Module, target_module_type: torch.nn.Module) -> bool:
+    """Check if the model contains a specific module type."""
+    for _, module in model.named_modules():
+        if isinstance(module, target_module_type):
+            return True
+    return False
+
+
 @pytest.mark.parametrize("scheme", testing_schemes)
 @torch.inference_mode()
 def test_e2e_quant_and_infer(scheme):