[`RWKV`] Final fix RWMV 4bit (#26134)

* Final fix RWMV 4bit

* fixup

* add a test

* add more clarifications

src/transformers/models/rwkv/modeling_rwkv.py

@@ -31,6 +31,7 @@ from ...utils import (
     add_code_sample_docstrings,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
+    is_bitsandbytes_available,
     is_ninja_available,
     is_torch_cuda_available,
     logging,
@@ -735,18 +736,35 @@ class RwkvModel(RwkvPreTrainedModel):
                             block.attention.output.weight.SCB.div_(2 ** int(block_id // self.config.rescale_every))
                             block.feed_forward.value.weight.SCB.div_(2 ** int(block_id // self.config.rescale_every))
                         elif hasattr(block.attention.output.weight, "quant_state"):
-                            block.attention.output.weight.quant_state[0].div_(
-                                2 ** int(block_id // self.config.rescale_every)
-                            )
-                            block.feed_forward.value.weight.quant_state[0].div_(
-                                2 ** int(block_id // self.config.rescale_every)
-                            )
+                            self._bnb_4bit_dequantize_and_rescale(block.attention.output, block_id)
+                            self._bnb_4bit_dequantize_and_rescale(block.feed_forward.value, block_id)
                         else:
                             block.attention.output.weight.div_(2 ** int(block_id // self.config.rescale_every))
                             block.feed_forward.value.weight.div_(2 ** int(block_id // self.config.rescale_every))
 
         self.layers_are_rescaled = not self.training
 
+    def _bnb_4bit_dequantize_and_rescale(self, target_layer, block_id):
+        r"""
+        Perform the dequantization and rescaling of the weights of a given layer. After that operation the layer will
+        be quantized again.
+        """
+        if not is_bitsandbytes_available():
+            raise ImportError("Please install bitsandbytes to use this method.")
+        import bitsandbytes as bnb
+
+        dequant_weights = bnb.functional.dequantize_4bit(target_layer.weight.data, target_layer.weight.quant_state)
+
+        dequant_weights.div_(2 ** int(block_id // self.config.rescale_every))
+
+        # re-quantize the model:
+        # we need to put it first on CPU then back to the device
+        # this will create an overhead :/
+        # We set requires_grad=False as we cannot compute gradients on top of 4bit parameters anyway and to avoid
+        # bugs with bnb
+        quant_weight = bnb.nn.Params4bit(dequant_weights.to("cpu"), requires_grad=False).to(dequant_weights.device)
+        setattr(target_layer, "weight", quant_weight)
+
 
 @add_start_docstrings(
     """

tests/quantization/bnb/test_4bit.py

@@ -172,6 +172,22 @@ class Bnb4BitTest(Base4bitTest):
                     # 4-bit parameters are packed in uint8 variables
                     self.assertTrue(module.weight.dtype == torch.uint8)
 
+    def test_rwkv_4bit(self):
+        r"""
+        A simple test to check if 4-bit RWKV inference works as expected.
+        """
+        model_id = "RWKV/rwkv-4-169m-pile"
+
+        quantization_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_use_double_quant=True)
+
+        model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=quantization_config)
+        tok = AutoTokenizer.from_pretrained(model_id)
+
+        text = "Hello my name is"
+        input_ids = tok.encode(text, return_tensors="pt").to(0)
+
+        _ = model.generate(input_ids, max_new_tokens=30)
+
     def test_generate_quality(self):
         r"""
         Test the generation quality of the quantized model and see that we are matching the expected output.