Address the comments by amyeroberts

src/transformers/integrations/eetq.py

@@ -38,9 +38,10 @@ def _replace_with_eetq_linear(
 
     Returns the converted model and a boolean that indicates if the conversion has been successfull or not.
     """
-    for name, module in model.named_children():
-        if current_key_name is None:
-            current_key_name = []
+    if current_key_name is None:
+        current_key_name = []
+
+    for name, module in model.named_children():    
         current_key_name.append(name)
 
         if (isinstance(module, nn.Linear)) and name not in modules_to_not_convert:
@@ -105,6 +106,7 @@ def replace_with_eetq_linear(
 
     if quantization_config.modules_to_not_convert is not None:
         modules_to_not_convert.extend(quantization_config.modules_to_not_convert)
+    modules_to_not_convert = list(set(modules_to_not_convert))
     model, has_been_replaced = _replace_with_eetq_linear(
         model, modules_to_not_convert, current_key_name, quantization_config, pre_quantized=pre_quantized
     )

src/transformers/quantizers/quantizer_eetq.py

@@ -81,6 +81,13 @@ def validate_environment(self, *args, **kwargs):
     def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
         if torch_dtype is None:
             torch_dtype = torch.float16
+            logger.info(
+                "Overriding torch_dtype=%s with `torch_dtype=torch.float16` due to "
+                "requirements of `eetq` to enable model loading in 8-bit. "
+                "Pass your own torch_dtype to specify the dtype of the remaining non-linear layers or pass"
+                " torch_dtype=torch.float16 to remove this warning.",
+                torch_dtype,
+            )
         elif torch_dtype != torch.float16:
             logger.info("We suggest you to set `torch_dtype=torch.float16` for better efficiency with EETQ.")
         return torch_dtype

tests/quantization/eetq_integration/test_eetq.py

@@ -140,11 +140,6 @@ def test_quantized_model(self):
         output = self.quantized_model.generate(**input_ids, max_new_tokens=self.max_new_tokens)
         self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
 
-    def test_raise_if_non_quantized(self):
-        model_id = "facebook/opt-125m"
-        quantization_config = EetqConfig()
-        _ = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", quantization_config=quantization_config)
-
     def test_save_pretrained(self):
         """
         Simple test that checks if the quantized model is working properly after being saved and loaded