[Awq] Enable the possibility to skip quantization for some target modules (#27950)

* v1

* add docstring

* add tests

* add awq 0.1.8

* oops

* fix test

tests/quantization/autoawq/test_awq.py

@@ -88,6 +88,7 @@ class AwqConfigTest(unittest.TestCase):
 class AwqTest(unittest.TestCase):
     model_name = "TheBloke/Mistral-7B-v0.1-AWQ"
     dummy_transformers_model_name = "bigscience/bloom-560m"
+    model_with_no_k_proj_quantized = "hf-internal-testing/opt-125m-awq-no-k-proj"
 
     input_text = "Hello my name is"
 
@@ -223,6 +224,24 @@ class AwqTest(unittest.TestCase):
 
         self.assertEqual(self.tokenizer.decode(output[0], skip_special_tokens=True), self.EXPECTED_OUTPUT)
 
+    def test_quantized_model_no_k_proj_quantized(self):
+        """
+        Simple test that checks if the quantized model is working properly with multiple GPUs
+        """
+        dummy_input = torch.LongTensor([[0, 1, 0]]).to(torch_device)
+
+        quantized_model = AutoModelForCausalLM.from_pretrained(self.model_with_no_k_proj_quantized).to(torch_device)
+
+        self.assertTrue(isinstance(quantized_model.model.decoder.layers[0].self_attn.k_proj, torch.nn.Linear))
+        self.assertFalse(isinstance(quantized_model.model.decoder.layers[0].self_attn.v_proj, torch.nn.Linear))
+
+        EXPECTED_OUTPUT = torch.LongTensor([[0, 1, 0, 50118, 50118, 133, 248, 12, 134, 16, 10, 372, 2031]]).to(
+            torch_device
+        )
+
+        output = quantized_model.generate(dummy_input, max_new_tokens=10)
+        self.assertTrue((EXPECTED_OUTPUT == output).all())
+
 
 @slow
 @require_torch_gpu