FIX: Error with OLoRA init when using bnb

src/peft/tuners/lora/layer.py

@@ -25,7 +25,7 @@
 from transformers.pytorch_utils import Conv1D
 
 from peft.tuners.tuners_utils import BaseTunerLayer, check_adapters_to_merge
-from peft.utils.integrations import dequantize_module_weight, gather_params_ctx
+from peft.utils.integrations import dequantize_module_weight, gather_params_ctx, get_bnb_param_type
 from peft.utils.other import transpose
 
 from .config import LoraConfig
@@ -167,11 +167,16 @@ def reset_lora_parameters(self, adapter_name, init_lora_weights):
             nn.init.normal_(self.lora_embedding_B[adapter_name])
 
     def olora_init(self, adapter_name):
-        dtype = self.get_base_layer().weight.dtype
-        if dtype in [torch.int8, torch.uint8]:
-            weight_tensor = dequantize_module_weight(self.get_base_layer())
+        base_layer = self.get_base_layer()
+        orig_weight = base_layer.weight
+        bnb_param_type = get_bnb_param_type(orig_weight)
+        dtype = orig_weight.dtype
+
+        if bnb_param_type:
+            # check without importing bitsandbytes and robust to bnb_4bit_quant_storage=float*
+            weight_tensor = dequantize_module_weight(base_layer)
         elif dtype in [torch.float32, torch.float16, torch.bfloat16]:
-            weight_tensor = self.get_base_layer().weight
+            weight_tensor = orig_weight
         else:
             raise TypeError(f"Unsupported data type for the base layer. Got {dtype}.")
 
@@ -186,8 +191,25 @@ def olora_init(self, adapter_name):
         self.lora_B[adapter_name].weight.data = Qr.contiguous()
 
         weight_tensor.data -= scale_factor * self.lora_B[adapter_name].weight @ self.lora_A[adapter_name].weight
-        weight_tensor = weight_tensor.to(dtype)
-        self.get_base_layer().weight.data = weight_tensor
+        if bnb_param_type == "4bit":
+            weight_tensor = orig_weight.__class__(
+                weight_tensor,
+                quant_type=orig_weight.quant_type,
+                quant_storage=orig_weight.quant_storage,
+                compress_statistics=orig_weight.compress_statistics,
+                module=orig_weight.module,
+            ).to(orig_weight.device)
+            base_layer.weight = weight_tensor
+        elif bnb_param_type == "8bit":
+            weight_tensor = orig_weight.__class__(
+                weight_tensor,
+                requires_grad=orig_weight.requires_grad,
+                has_fp16_weights=orig_weight.has_fp16_weights,
+            ).to(orig_weight.device)
+            base_layer.weight = weight_tensor
+        else:
+            weight_tensor = weight_tensor.to(dtype)
+            base_layer.weight.data = weight_tensor
 
     def pissa_init(self, adapter_name, init_lora_weights):
         weight = self.get_base_layer().weight

src/peft/utils/integrations.py

@@ -12,7 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 from contextlib import contextmanager
+from typing import Literal
 
 import packaging.version
 import torch
@@ -104,3 +107,12 @@ def dequantize_bnb_weight(weight: torch.nn.Parameter, state=None):
     if is_cpu:
         dequantized = dequantized.to(device)
     return dequantized
+
+
+def get_bnb_param_type(param: torch.nn.Parameter) -> Literal[False, "4bit", "8bit"]:
+    """Returns '4bit' or '8bit' if bitsandbytes parameter, else False"""
+    if param.__class__.__name__ == "Params4bit":
+        return "4bit"
+    if param.__class__.__name__ == "Int8Params":
+        return "8bit"
+    return False

tests/test_gpu_examples.py

@@ -1786,6 +1786,41 @@ def test_bloomz_olora_8bit(self, device, tmp_path):
         # Same test as test_bloomz_olora_4bit but with 8 bits.
         self.get_errors(bits=8, device=device, tmp_path=tmp_path)
 
+    @pytest.mark.parametrize("bits", [4, 8])
+    def test_olora_with_quantized_model(self, bits):
+        import bitsandbytes as bnb
+
+        # issue 1999
+        model_id = "hf-internal-testing/tiny-random-OPTForCausalLM"
+        if bits == 4:
+            bnb_config = BitsAndBytesConfig(
+                load_in_4bit=True,
+                bnb_4bit_quant_type="nf4",
+                bnb_4bit_compute_dtype=torch.float16,
+                bnb_4bit_quant_storage=torch.float16,
+                bnb_4bit_use_double_quant=True,
+            )
+        elif bits == 8:
+            bnb_config = BitsAndBytesConfig(load_in_8bit=True)
+        else:
+            raise ValueError("bits must be 4 or 8")
+
+        model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config)
+        model = prepare_model_for_kbit_training(model)
+        config = LoraConfig(init_lora_weights="olora")
+        model = get_peft_model(model, config)
+
+        # check that the correct type is used for the weights
+        base_layer = model.base_model.model.model.decoder.layers[0].self_attn.v_proj.base_layer.weight
+        if bits == 4:
+            assert isinstance(base_layer, bnb.nn.modules.Params4bit)
+        else:
+            assert isinstance(base_layer, bnb.nn.modules.Int8Params)
+
+        inputs = torch.arange(10).unsqueeze(0).to(model.device)
+        logits = model(inputs).logits  # does not raise
+        assert torch.isfinite(logits).all()
+
 
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires a GPU")
 class TestLoftQ: