FEAT / Bitsandbytes: Add dequantize API for bitsandbytes quantized models (#30806)

* add  method

* change method name

* more comments

* Apply suggestions from code review

Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* fixup

* add docstrings and fix comment

* warn users on the de-quantized dtype

* Update src/transformers/quantizers/base.py

Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>

* Update src/transformers/integrations/bitsandbytes.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* final suggestion - use private method

---------

Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

tests/quantization/bnb/test_4bit.py

@@ -239,6 +239,23 @@ class Bnb4BitTest(Base4bitTest):
 
         self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
 
+    def test_generate_quality_dequantize(self):
+        r"""
+        Test that loading the model and unquantize it produce correct results
+        """
+        bnb_config = BitsAndBytesConfig(load_in_4bit=True)
+
+        model_4bit = AutoModelForCausalLM.from_pretrained(
+            self.model_name, quantization_config=bnb_config, device_map="auto"
+        )
+
+        model_4bit.dequantize()
+
+        encoded_input = self.tokenizer(self.input_text, return_tensors="pt")
+        output_sequences = model_4bit.generate(input_ids=encoded_input["input_ids"].to(0), max_new_tokens=10)
+
+        self.assertIn(self.tokenizer.decode(output_sequences[0], skip_special_tokens=True), self.EXPECTED_OUTPUTS)
+
     def test_device_and_dtype_assignment(self):
         r"""
         Test whether trying to cast (or assigning a device to) a model after converting it in 8-bit will throw an error.