Fix module name extraction logic in quant_api.py

test/quantization/test_quant_api.py

@@ -1122,6 +1122,56 @@ def reset_memory():
             assert param.is_cuda
         self.assertLess(memory_streaming, memory_baseline)
 
+    @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
+    def test_fqn_config_quantized_nested_module(self):
+        class NestedModule(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.linear = torch.nn.Linear(16, 16)
+
+        class TopLevelModule(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.nested = NestedModule()
+                self.linear1 = torch.nn.Linear(16, 16)
+
+        m = TopLevelModule()
+        quant_config = FqnToConfig(
+            {
+                "nested.linear": Int8WeightOnlyConfig(),
+                "linear1": Int8WeightOnlyConfig(),
+            }
+        )
+        quantize_(m, quant_config, filter_fn=None)
+
+        assert isinstance(m.nested.linear.weight, AffineQuantizedTensor)
+        assert isinstance(m.linear1.weight, AffineQuantizedTensor)
+
+    @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
+    def test_fqn_config_quantized_nested_module_param(self):
+        class NestedModule(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.linear = torch.nn.Linear(16, 16)
+
+        class TopLevelModule(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.nested = NestedModule()
+                self.linear1 = torch.nn.Linear(16, 16)
+
+        m = TopLevelModule()
+        quant_config = FqnToConfig(
+            {
+                "nested.linear.weight": Int8WeightOnlyConfig(),
+                "linear1.weight": Int8WeightOnlyConfig(),
+            }
+        )
+        quantize_(m, quant_config, filter_fn=None)
+
+        assert isinstance(m.nested.linear.weight, AffineQuantizedTensor)
+        assert isinstance(m.linear1.weight, AffineQuantizedTensor)
+
 
 if __name__ == "__main__":
     unittest.main()

torchao/quantization/quant_api.py

@@ -484,11 +484,8 @@ def quantize_(
                 or _module_param_matches_fqn_config(module, module_fqn, config)
                 or ("_default" in config.fqn_to_config and _is_linear(module))
             ):
-                module_name = (
-                    module_fqn.rsplit(".", 1) if "." in module_fqn else module_fqn
-                )
                 # this replaces inplace, so no need to reassign
-                _fqn_to_config_handler(module, module_name, config)
+                _fqn_to_config_handler(module, module_fqn, config)
                 if device is not None:
                     module.to(device=device)
         return