[LoRA] Adds support for bias in LoRA (#5733)

Signed-off-by: Umesh Deshpande <udeshpa@us.ibm.com>
Co-authored-by: Umesh Deshpande <udeshpa@us.ibm.com>

tests/lora/conftest.py

@@ -152,6 +152,11 @@ def sql_lora_files(sql_lora_huggingface_id):
     return snapshot_download(repo_id=sql_lora_huggingface_id)
 
 
+@pytest.fixture(scope="session")
+def lora_bias_files():
+    return snapshot_download(repo_id="followumesh/granite-3b-lora8-bias")
+
+
 @pytest.fixture(scope="session")
 def mixtral_lora_files():
     # Note: this module has incorrect adapter_config.json to test

tests/lora/test_lora_bias_e2e.py

@@ -0,0 +1,52 @@
+from typing import List
+
+import pytest
+
+import vllm
+from vllm.lora.request import LoRARequest
+
+MODEL_PATH = "ibm-granite/granite-3b-code-base"
+
+
+def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> List[str]:
+    prompts = [
+        "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE candidate (people_id VARCHAR, unsure_rate INTEGER); CREATE TABLE people (sex VARCHAR, people_id VARCHAR)\n\n question: which gender got the highest average uncertain ratio. [/user] [assistant]",  # noqa: E501
+        "[user] Write a SQL query to answer the question based on the table schema.\n\n context: CREATE TABLE table_28138035_4 (womens_doubles VARCHAR, mens_singles VARCHAR)\n\n question: Name the women's doubles for werner schlager [/user] [assistant]"  # noqa: E501
+    ]
+    sampling_params = vllm.SamplingParams(temperature=0,
+                                          max_tokens=256,
+                                          stop=["[/assistant]"])
+    outputs = llm.generate(
+        prompts,
+        sampling_params,
+        lora_request=LoRARequest(str(lora_id), lora_id, lora_path)
+        if lora_id else None)
+    generated_texts: List[str] = []
+    for output in outputs:
+        generated_text = output.outputs[0].text
+        generated_texts.append(generated_text)
+    return generated_texts
+
+
+@pytest.mark.parametrize("lora_bias", [True])
+@pytest.mark.parametrize("fully_sharded", [True, False])
+def test_lora_bias(lora_bias_files: str, lora_bias: bool, fully_sharded: bool):
+    llm = vllm.LLM(MODEL_PATH,
+                   enable_lora=True,
+                   max_num_seqs=16,
+                   max_lora_rank=8,
+                   max_loras=1,
+                   enable_lora_bias=lora_bias,
+                   tensor_parallel_size=1,
+                   fully_sharded_loras=fully_sharded)
+
+    print("lora adapter created")
+    output1 = do_sample(llm, lora_bias_files, lora_id=0)
+
+    print("lora")
+    output2 = do_sample(llm, lora_bias_files, lora_id=1)
+
+    if lora_bias:
+        assert output1 != output2
+    else:
+        assert output1 == output2

tests/lora/test_utils.py

@@ -12,36 +12,40 @@ from vllm.utils import LRUCache
 
 def test_parse_fine_tuned_lora_name_valid():
     fixture = {
-        ("base_model.model.lm_head.lora_A.weight", "lm_head", True),
-        ("base_model.model.lm_head.lora_B.weight", "lm_head", False),
+        ("base_model.model.lm_head.lora_A.weight", "lm_head", True, False),
+        ("base_model.model.lm_head.lora_B.weight", "lm_head", False, False),
         (
             "base_model.model.model.embed_tokens.lora_embedding_A",
             "model.embed_tokens",
             True,
+            False,
         ),
         (
             "base_model.model.model.embed_tokens.lora_embedding_B",
             "model.embed_tokens",
             False,
+            False,
         ),
         (
             "base_model.model.model.layers.9.mlp.down_proj.lora_A.weight",
             "model.layers.9.mlp.down_proj",
             True,
+            False,
         ),
         (
             "base_model.model.model.layers.9.mlp.down_proj.lora_B.weight",
             "model.layers.9.mlp.down_proj",
             False,
+            False,
         ),
     }
-    for name, module_name, is_lora_a in fixture:
-        assert (module_name, is_lora_a) == parse_fine_tuned_lora_name(name)
+    for name, module_name, is_lora_a, is_bias in fixture:
+        assert (module_name, is_lora_a,
+                is_bias) == parse_fine_tuned_lora_name(name)
 
 
 def test_parse_fine_tuned_lora_name_invalid():
     fixture = {
-        "weight",
         "base_model.weight",
         "base_model.model.weight",
     }

vllm/config.py

@@ -1687,6 +1687,7 @@ class LoRAConfig:
     # This is a constant.
     lora_vocab_padding_size: ClassVar[int] = 256
     long_lora_scaling_factors: Optional[Tuple[float]] = None
+    bias_enabled: bool = False
 
     def __post_init__(self):
         # Setting the maximum rank to 256 should be able to satisfy the vast

vllm/engine/arg_utils.py

@@ -143,6 +143,7 @@ class EngineArgs:
     limit_mm_per_prompt: Optional[Mapping[str, int]] = None
     mm_processor_kwargs: Optional[Dict[str, Any]] = None
     enable_lora: bool = False
+    enable_lora_bias: bool = False
     max_loras: int = 1
     max_lora_rank: int = 16
     enable_prompt_adapter: bool = False
@@ -584,6 +585,9 @@ class EngineArgs:
         parser.add_argument('--enable-lora',
                             action='store_true',
                             help='If True, enable handling of LoRA adapters.')
+        parser.add_argument('--enable-lora-bias',
+                            action='store_true',
+                            help='If True, enable bias for LoRA adapters.')
         parser.add_argument('--max-loras',
                             type=int,
                             default=EngineArgs.max_loras,
@@ -1148,6 +1152,7 @@ class EngineArgs:
                              and parallel_config.use_ray),
             policy=self.scheduling_policy)
         lora_config = LoRAConfig(
+            bias_enabled=self.enable_lora_bias,
             max_lora_rank=self.max_lora_rank,
             max_loras=self.max_loras,
             fully_sharded_loras=self.fully_sharded_loras,

vllm/lora/fully_sharded_layers.py

@@ -70,6 +70,14 @@ class ColumnParallelLinearWithShardedLoRA(ColumnParallelLinearWithLoRA):
                                        self.lora_b_stacked,
                                        add_input=True)
         # now have column partitioned output
+
+        if self.bias_stacked is not None:
+            self.bias_stacked = self.bias_stacked.view(
+                -1, self.bias_stacked.shape[-1])
+            self.bias_stacked = self.bias_stacked[
+                self.punica_wrapper.token_lora_indices]
+            output += self.bias_stacked
+
         output = output.view(*out_orig_shape)
         return output
 
@@ -121,6 +129,15 @@ def _mcp_apply(x, bias, layer: QKVParallelLinearWithLora):
     left_offset = 0
     for idx in range(n):
         shard_size = layer.lora_b_stacked[idx].shape[2]
+
+        if layer.bias_stacked is not None:
+            bias = layer.bias_stacked[idx]
+            if bias is not None:
+                bias = bias.view(-1, bias.shape[-1])
+                bias = bias[layer.punica_wrapper.token_lora_indices]
+                bias[layer.punica_wrapper.token_lora_indices == -1] = 0
+                output[:, left_offset:left_offset + shard_size] += bias
+
         layer.punica_wrapper.add_expand_slice(
             output,
             buffers[idx],
@@ -295,6 +312,15 @@ class RowParallelLinearWithShardedLoRA(RowParallelLinearWithLoRA):
         lora_b = lora_b[:, start_idx:end_idx]
         return lora_b
 
+    def slice_bias(self, bias: torch.Tensor) -> torch.Tensor:
+        if bias is None:
+            return bias
+        shard_size = self.bias_stacked.shape[2]
+        start_idx = self.tp_rank * shard_size
+        end_idx = (self.tp_rank + 1) * shard_size
+        bias = bias[start_idx:end_idx]
+        return bias
+
     def apply(self, x: torch.Tensor) -> torch.Tensor:
         output = self.base_layer.quant_method.apply(self.base_layer, x)
 
@@ -318,6 +344,13 @@ class RowParallelLinearWithShardedLoRA(RowParallelLinearWithLoRA):
         # reduced before being used
         shard_size = self.lora_b_stacked.shape[2]
         start_idx = self.tp_rank * shard_size
+
+        if self.bias_stacked is not None:
+            bias = self.bias_stacked.view(-1, self.bias_stacked.shape[-1])
+            bias = bias[self.punica_wrapper.token_lora_indices]
+            bias[self.punica_wrapper.token_lora_indices == -1] = 0
+            output += bias
+
         self.punica_wrapper.add_expand_slice(output, buffer,
                                              self.lora_b_stacked, start_idx,
                                              shard_size)

vllm/lora/layers.py

@@ -67,6 +67,63 @@ def _not_fully_sharded_can_replace(can_replace):
     return dec
 
 
+def apply_bias(
+    indices: torch.Tensor,
+    output: torch.Tensor,
+    bias_stacked: torch.Tensor,
+):
+    """Applies bias to output
+
+    Input shapes:
+        bias_stacked:    (num_loras, output_dim)
+        indices:         (batch_size)
+        output:          (batch_size, output_dim)
+    """
+    org_output = output
+    output = output.view(-1, output.shape[-1])
+    indices = indices.view(-1)
+
+    bias_stacked = bias_stacked.view(-1, bias_stacked.shape[-1])
+    bias_stacked = bias_stacked[indices]
+    bias_stacked[indices == -1] = 0
+    output += bias_stacked
+
+    return output.view_as(org_output)
+
+
+def apply_bias_packed_nslice(
+    indices: torch.Tensor,
+    output: torch.Tensor,
+    output_slices: Tuple[int, ...],
+    bias_stacked: Tuple[torch.Tensor, torch.Tensor, torch.Tensor],
+):
+    """Applies bias to output
+
+    Input shapes:
+        bias_stacked:      3 element tuple of (num_loras, output_dim)
+        indices:           (batch_size)
+        output:            (batch_size, q_slice_size + 2*kv_slice_size)
+        output_slices:     n-1 element tuple of (slice_size...),
+                           where n is number of slices
+    """
+    org_output = output
+    output = output.view(-1, output.shape[-1])
+    indices = indices.view(-1)
+
+    offset_left = 0
+    for slice_idx, slice in enumerate(output_slices):
+        bias = bias_stacked[slice_idx]
+        if bias is not None:
+            bias = bias.view(-1, bias.shape[-1])
+            bias = bias[indices]
+            bias[indices == -1] = 0
+            output[:, offset_left:offset_left + slice] += bias
+
+        offset_left += slice
+
+    return output.view_as(org_output)
+
+
 @dataclass
 class LoRAMapping(AdapterMapping):
     is_prefill: bool = False
@@ -105,6 +162,7 @@ class BaseLayerWithLoRA(nn.Module):
         lora_a: torch.Tensor,
         lora_b: torch.Tensor,
         embeddings_tensor: Optional[torch.Tensor],
+        bias: Optional[torch.Tensor] = None,
     ):
         """Overwrites lora tensors at index."""
         ...
@@ -203,6 +261,7 @@ class VocabParallelEmbeddingWithLoRA(BaseLayerWithLoRA):
         lora_a: torch.Tensor,
         lora_b: torch.Tensor,
         embeddings_tensor: Optional[torch.Tensor],
+        bias: Optional[torch.Tensor] = None,
     ):
         self.reset_lora(index)
         self.lora_a_stacked[index, :lora_a.shape[0], :lora_a.shape[1]].copy_(
@@ -299,10 +358,22 @@ class ReplicatedLinearWithLoRA(BaseLayerWithLoRA):
             dtype=lora_config.lora_dtype,
             device=self.device,
         )
+        if lora_config.bias_enabled:
+            self.bias_stacked = torch.zeros(
+                max_loras,
+                1,
+                self.output_size,
+                dtype=lora_config.lora_dtype,
+                device=self.device,
+            )
+        else:
+            self.bias_stacked = None
 
     def reset_lora(self, index: int):
         self.lora_a_stacked[index] = 0
         self.lora_b_stacked[index] = 0
+        if self.lora_config.bias_enabled:
+            self.bias_stacked[index] = 0
 
     def set_lora(
         self,
@@ -310,6 +381,7 @@ class ReplicatedLinearWithLoRA(BaseLayerWithLoRA):
         lora_a: torch.Tensor,
         lora_b: torch.Tensor,
         embeddings_tensor: Optional[torch.Tensor],
+        bias: Optional[torch.Tensor] = None,
     ):
         self.reset_lora(index)
 
@@ -319,10 +391,21 @@ class ReplicatedLinearWithLoRA(BaseLayerWithLoRA):
         self.lora_b_stacked[index,
                             0, :lora_b.shape[1], :lora_b.shape[0]].copy_(
                                 lora_b.T, non_blocking=True)
+        if bias is not None:
+            self.bias_stacked[index,
+                              0, :bias.shape[0]].copy_(bias.T,
+                                                       non_blocking=True)
 
     def apply(self, x: torch.Tensor,
               bias: Optional[torch.Tensor]) -> torch.Tensor:
         output = self.base_layer.quant_method.apply(self.base_layer, x, bias)
+        if self.bias_stacked is not None:
+            self.indices = self.punica_wrapper.token_lora_indices
+            output = apply_bias(
+                self.indices,
+                output,
+                self.bias_stacked,
+            )
         self.punica_wrapper.add_lora(output, x, self.lora_a_stacked,
                                      self.lora_b_stacked, 1.0)
         return output
@@ -401,11 +484,25 @@ class ColumnParallelLinearWithLoRA(BaseLayerWithLoRA):
             dtype=lora_config.lora_dtype,
             device=self.device,
         )
+
+        if lora_config.bias_enabled:
+            self.bias_stacked = torch.zeros(
+                max_loras,
+                1,
+                self.output_size,
+                dtype=lora_config.lora_dtype,
+                device=self.device,
+            )
+        else:
+            self.bias_stacked = None
+
         self.output_dim = self.lora_b_stacked.shape[2]
 
     def reset_lora(self, index: int):
         self.lora_a_stacked[index] = 0
         self.lora_b_stacked[index] = 0
+        if self.lora_config.bias_enabled:
+            self.bias_stacked[index] = 0
 
     def slice_lora_a(self, lora_a: torch.Tensor) -> torch.Tensor:
         return lora_a
@@ -418,18 +515,30 @@ class ColumnParallelLinearWithLoRA(BaseLayerWithLoRA):
         lora_b = lora_b[:, start_idx:end_idx]
         return lora_b
 
+    def slice_bias(self, bias: torch.Tensor) -> torch.Tensor:
+        if bias is None:
+            return bias
+        tensor_model_parallel_rank = get_tensor_model_parallel_rank()
+        shard_size = self.output_dim
+        start_idx = tensor_model_parallel_rank * shard_size
+        end_idx = (tensor_model_parallel_rank + 1) * shard_size
+        bias = bias[start_idx:end_idx]
+        return bias
+
     def set_lora(
         self,
         index: int,
         lora_a: torch.Tensor,
         lora_b: torch.Tensor,
         embeddings_tensor: Optional[torch.Tensor],
+        bias: Optional[torch.Tensor] = None,
     ):
         self.reset_lora(index)
 
         if self.tp_size > 1:
             lora_a = self.slice_lora_a(lora_a)
             lora_b = self.slice_lora_b(lora_b)
+            bias = self.slice_bias(bias)
 
         self.lora_a_stacked[index,
                             0, :lora_a.shape[1], :lora_a.shape[0]].copy_(
@@ -437,10 +546,21 @@ class ColumnParallelLinearWithLoRA(BaseLayerWithLoRA):
         self.lora_b_stacked[index,
                             0, :lora_b.shape[1], :lora_b.shape[0]].copy_(
                                 lora_b.T, non_blocking=True)
+        if bias is not None:
+            self.bias_stacked[index,
+                              0, :bias.shape[0]].copy_(bias.T,
+                                                       non_blocking=True)
 
     def apply(self, x: torch.Tensor,
               bias: Optional[torch.Tensor]) -> torch.Tensor:
         output = self.base_layer.quant_method.apply(self.base_layer, x, bias)
+        if self.bias_stacked is not None:
+            self.indices = self.punica_wrapper.token_lora_indices
+            output = apply_bias(
+                self.indices,
+                output,
+                self.bias_stacked,
+            )
         self.punica_wrapper.add_lora(output, x, self.lora_a_stacked,
                                      self.lora_b_stacked, 1.0)
         return output
@@ -534,6 +654,17 @@ class MergedColumnParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
                 dtype=lora_config.lora_dtype,
                 device=self.device,
             ) for _ in range(n_slices))
+        if lora_config.bias_enabled:
+            self.bias_stacked = tuple(
+                torch.zeros(
+                    max_loras,
+                    1,
+                    self.output_size // 2,
+                    dtype=lora_config.lora_dtype,
+                    device=self.device,
+                ) for _ in range(n_slices))
+        else:
+            self.bias_stacked = None
 
         self.output_dim = self.lora_b_stacked[0].shape[2]
 
@@ -542,6 +673,9 @@ class MergedColumnParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
         self.lora_a_stacked[1][index] = 0
         self.lora_b_stacked[0][index] = 0
         self.lora_b_stacked[1][index] = 0
+        if self.lora_config.bias_enabled:
+            self.bias_stacked[0][index] = 0
+            self.bias_stacked[1][index] = 0
 
     def slice_lora_a(
         self, lora_a: List[Union[torch.Tensor, None]]
@@ -562,18 +696,32 @@ class MergedColumnParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
         ]
         return lora_b
 
+    def slice_bias(
+        self, bias: List[Union[torch.Tensor,
+                               None]]) -> List[Union[torch.Tensor, None]]:
+        if bias[0] is None or bias[1] is None:
+            return bias
+        shard_size = self.output_dim
+        start_idx = self.tp_rank * shard_size
+        end_idx = (self.tp_rank + 1) * shard_size
+        bias = [bias[0][start_idx:end_idx], bias[1][start_idx:end_idx]]
+        return bias
+
     def set_lora(
         self,
         index: int,
         lora_a: torch.Tensor,
         lora_b: torch.Tensor,
         embeddings_tensor: Optional[torch.Tensor],
+        bias: Optional[torch.Tensor] = None,
     ):
         self.reset_lora(index)
 
         if self.tp_size > 1:
             lora_a = self.slice_lora_a(lora_a)
             lora_b = self.slice_lora_b(lora_b)
+            if bias is not None:
+                bias = self.slice_bias(bias)
 
         if lora_a[0] is not None:
             self.lora_a_stacked[0][
@@ -582,6 +730,10 @@ class MergedColumnParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
             self.lora_b_stacked[0][
                 index, 0, :lora_b[0].shape[1], :lora_b[0].shape[0]].copy_(
                     lora_b[0].T, non_blocking=True)
+        if bias is not None and bias[0] is not None:
+            self.bias_stacked[0][index,
+                                 0, :bias[0].shape[0]].copy_(bias[0].T,
+                                                             non_blocking=True)
         if lora_a[1] is not None:
             self.lora_a_stacked[1][
                 index, 0, :lora_a[1].shape[1], :lora_a[1].shape[0]].copy_(
@@ -589,10 +741,22 @@ class MergedColumnParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
             self.lora_b_stacked[1][
                 index, 0, :lora_b[1].shape[1], :lora_b[1].shape[0]].copy_(
                     lora_b[1].T, non_blocking=True)
+        if bias is not None and bias[1] is not None:
+            self.bias_stacked[1][index,
+                                 0, :bias[1].shape[0]].copy_(bias[1].T,
+                                                             non_blocking=True)
 
     def apply(self, x: torch.Tensor,
               bias: Optional[torch.Tensor]) -> torch.Tensor:
         output = self.base_layer.quant_method.apply(self.base_layer, x, bias)
+        if self.bias_stacked is not None:
+            self.indices = self.punica_wrapper.token_lora_indices
+            output = apply_bias_packed_nslice(
+                self.indices,
+                output,
+                (self.output_dim, self.output_dim),
+                self.bias_stacked,
+            )
         self.punica_wrapper.add_lora_packed_nslice(
             output, x, self.lora_a_stacked, self.lora_b_stacked, 1.0,
             (self.output_dim, self.output_dim))
@@ -654,17 +818,35 @@ class QKVParallelLinearWithLora(ColumnParallelLinearWithLoRA):
         lora_b = torch.cat([lora_b_q, lora_b_k, lora_b_v], dim=1)
         return lora_b
 
+    def slice_bias(self, bias: torch.Tensor) -> torch.Tensor:
+        bias_q = bias[self.q_proj_shard_size *
+                      self.q_shard_id:self.q_proj_shard_size *
+                      (self.q_shard_id + 1)]
+        k_offset = self.q_proj_total_size
+        bias_k = bias[k_offset +
+                      self.kv_proj_shard_size * self.kv_shard_id:k_offset +
+                      self.kv_proj_shard_size * (self.kv_shard_id + 1)]
+        v_offset = k_offset + self.kv_proj_total_size
+        bias_v = bias[v_offset +
+                      self.kv_proj_shard_size * self.kv_shard_id:v_offset +
+                      self.kv_proj_shard_size * (self.kv_shard_id + 1)]
+        bias = torch.cat([bias_q, bias_k, bias_v], dim=1)
+        return bias
+
     def set_lora(
         self,
         index: int,
         lora_a: torch.Tensor,
         lora_b: torch.Tensor,
         embeddings_tensor: Optional[torch.Tensor],
+        bias: Optional[torch.Tensor] = None,
     ):
         self.reset_lora(index)
         if self.tp_size > 1:
             lora_a = self.slice_lora_a(lora_a)
             lora_b = self.slice_lora_b(lora_b)
+            if bias is not None:
+                bias = self.slice_bias(bias)
 
         self.lora_a_stacked[index,
                             0, :lora_a.shape[1], :lora_a.shape[0]].copy_(
@@ -672,6 +854,10 @@ class QKVParallelLinearWithLora(ColumnParallelLinearWithLoRA):
         self.lora_b_stacked[index,
                             0, :lora_b.shape[1], :lora_b.shape[0]].copy_(
                                 lora_b.T, non_blocking=True)
+        if bias is not None:
+            self.bias_stacked[index,
+                              0, :bias.shape[0]].copy_(bias.T,
+                                                       non_blocking=True)
 
     @classmethod
     @_not_fully_sharded_can_replace
@@ -768,6 +954,32 @@ class MergedQKVParallelLinearWithLora(ColumnParallelLinearWithLoRA):
                 device=self.device,
             ),
         )
+        if lora_config.bias_enabled:
+            self.bias_stacked = (
+                torch.zeros(
+                    max_loras,
+                    1,
+                    self.q_proj_shard_size,
+                    dtype=lora_config.lora_dtype,
+                    device=self.device,
+                ),
+                torch.zeros(
+                    max_loras,
+                    1,
+                    self.kv_proj_shard_size,
+                    dtype=lora_config.lora_dtype,
+                    device=self.device,
+                ),
+                torch.zeros(
+                    max_loras,
+                    1,
+                    self.kv_proj_shard_size,
+                    dtype=lora_config.lora_dtype,
+                    device=self.device,
+                ),
+            )
+        else:
+            self.bias_stacked = None
 
         self.output_slices = (
             self.q_proj_shard_size,
@@ -787,6 +999,10 @@ class MergedQKVParallelLinearWithLora(ColumnParallelLinearWithLoRA):
         self.lora_b_stacked[1][index] = 0
         self.lora_a_stacked[2][index] = 0
         self.lora_b_stacked[2][index] = 0
+        if self.lora_config.bias_enabled:
+            self.bias_stacked[0][index] = 0
+            self.bias_stacked[1][index] = 0
+            self.bias_stacked[2][index] = 0
 
     def slice_lora_a(
         self, lora_a: List[Union[torch.Tensor, None]]
@@ -812,18 +1028,40 @@ class MergedQKVParallelLinearWithLora(ColumnParallelLinearWithLoRA):
         lora_b = [lora_b_q, lora_b_k, lora_b_v]
         return lora_b
 
+    def slice_bias(
+        self, bias: List[Union[torch.Tensor,
+                               None]]) -> List[Union[torch.Tensor, None]]:
+        bias_q, bias_k, bias_v = bias
+        if bias_q is not None:
+            bias_q = bias_q[self.q_proj_shard_size *
+                            self.q_shard_id:self.q_proj_shard_size *
+                            (self.q_shard_id + 1)]
+        if bias_k is not None:
+            bias_k = bias_k[self.kv_proj_shard_size *
+                            self.kv_shard_id:self.kv_proj_shard_size *
+                            (self.kv_shard_id + 1)]
+        if bias_v is not None:
+            bias_v = bias_v[self.kv_proj_shard_size *
+                            self.kv_shard_id:self.kv_proj_shard_size *
+                            (self.kv_shard_id + 1)]
+        bias = [bias_q, bias_k, bias_v]
+        return bias
+
     def set_lora(
         self,
         index: int,
         lora_a: torch.Tensor,
         lora_b: torch.Tensor,
         embeddings_tensor: Optional[torch.Tensor],
+        bias: Optional[torch.Tensor] = None,
     ):
         self.reset_lora(index)
 
         if self.tp_size > 1:
             lora_a = self.slice_lora_a(lora_a)
             lora_b = self.slice_lora_b(lora_b)
+            if bias is not None:
+                bias = self.slice_bias(bias)
 
         if lora_b[0] is not None:
             lora_b_q = lora_b[0]
@@ -854,9 +1092,28 @@ class MergedQKVParallelLinearWithLora(ColumnParallelLinearWithLoRA):
                 index, 0, :lora_a[2].shape[1], :lora_a[2].shape[0]].copy_(
                     lora_a[2].T, non_blocking=True)
 
+        if bias is not None:
+            if bias[0] is not None:
+                self.bias_stacked[0][index, 0, :bias[0].shape[0]].copy_(
+                    bias[0].T, non_blocking=True)
+            if bias[1] is not None:
+                self.bias_stacked[1][index, 0, :bias[1].shape[0]].copy_(
+                    bias[1].T, non_blocking=True)
+            if bias[2] is not None:
+                self.bias_stacked[2][index, 0, :bias[2].shape[0]].copy_(
+                    bias[2].T, non_blocking=True)
+
     def apply(self, x: torch.Tensor,
               bias: Optional[torch.Tensor]) -> torch.Tensor:
         output = self.base_layer.quant_method.apply(self.base_layer, x, bias)
+        if self.bias_stacked is not None:
+            self.indices = self.punica_wrapper.token_lora_indices
+            output = apply_bias_packed_nslice(
+                self.indices,
+                output,
+                self.output_slices,
+                self.bias_stacked,
+            )
         self.punica_wrapper.add_lora_packed_nslice(output, x,
                                                    self.lora_a_stacked,
                                                    self.lora_b_stacked, 1.0,
@@ -919,9 +1176,27 @@ class RowParallelLinearWithLoRA(BaseLayerWithLoRA):
             device=self.device,
         )
 
+        if lora_config.bias_enabled:
+            self.bias_stacked = torch.zeros(
+                (
+                    max_loras,
+                    1,
+                    self.output_size,
+                ),
+                dtype=lora_config.lora_dtype,
+                device=self.device,
+            )
+        else:
+            self.bias_stacked = None
+        # Lazily initialized
+        self.indices: torch.Tensor
+        self.indices_len: List[int]
+
     def reset_lora(self, index: int):
         self.lora_a_stacked[index] = 0
         self.lora_b_stacked[index] = 0
+        if self.lora_config.bias_enabled:
+            self.bias_stacked[index] = 0
 
     def slice_lora_a(self, lora_a: torch.Tensor) -> torch.Tensor:
         tensor_model_parallel_rank = get_tensor_model_parallel_rank()
@@ -934,18 +1209,24 @@ class RowParallelLinearWithLoRA(BaseLayerWithLoRA):
     def slice_lora_b(self, lora_b: torch.Tensor) -> torch.Tensor:
         return lora_b
 
+    def slice_bias(self, bias: torch.Tensor) -> torch.Tensor:
+        return bias
+
     def set_lora(
         self,
         index: int,
         lora_a: torch.Tensor,
         lora_b: torch.Tensor,
         embeddings_tensor: Optional[torch.Tensor],
+        bias: Optional[torch.Tensor] = None,
     ):
         self.reset_lora(index)
 
         if self.base_layer.tp_size > 1:
             lora_a = self.slice_lora_a(lora_a)
             lora_b = self.slice_lora_b(lora_b)
+            if bias is not None:
+                bias = self.slice_bias(bias)
 
         self.lora_a_stacked[index,
                             0, :lora_a.shape[1], :lora_a.shape[0]].copy_(
@@ -953,9 +1234,20 @@ class RowParallelLinearWithLoRA(BaseLayerWithLoRA):
         self.lora_b_stacked[index,
                             0, :lora_b.shape[1], :lora_b.shape[0]].copy_(
                                 lora_b.T, non_blocking=True)
+        if bias is not None:
+            self.bias_stacked[index,
+                              0, :bias.shape[0]].copy_(bias.T,
+                                                       non_blocking=True)
 
     def apply(self, x: torch.Tensor) -> torch.Tensor:
         output = self.base_layer.quant_method.apply(self.base_layer, x)
+        if self.bias_stacked is not None:
+            self.indices = self.punica_wrapper.token_lora_indices
+            output = apply_bias(
+                self.indices,
+                output,
+                self.bias_stacked,
+            )
         self.punica_wrapper.add_lora(output, x, self.lora_a_stacked,
                                      self.lora_b_stacked, 1.0)
         return output
@@ -1132,6 +1424,7 @@ class LogitsProcessorWithLoRA(BaseLayerWithLoRA):
         lora_a: torch.Tensor,
         lora_b: torch.Tensor,
         embeddings_tensor: Optional[torch.Tensor],
+        bias: Optional[torch.Tensor] = None,
     ):
         self.reset_lora(index)
         self.lora_a_stacked[index,
@@ -1199,7 +1492,7 @@ class LogitsProcessorWithLoRA(BaseLayerWithLoRA):
                                                       neginf=float("-inf")))
         logits[:,
                self.base_layer.org_vocab_size:self.base_layer.org_vocab_size +
-               lora_logits.shape[1], ] = lora_logits
+               lora_logits.shape[1]] = lora_logits
 
         # LogitsProcessorWithLoRA always using bgmv
         self.punica_wrapper.add_lora_logits(logits, hidden_states,
@@ -1276,6 +1569,7 @@ class LinearScalingRotaryEmbeddingWithLora(BaseLayerWithLoRA):
         lora_a: torch.Tensor,
         lora_b: torch.Tensor,
         embeddings_tensor: Optional[torch.Tensor],
+        bias: Optional[torch.Tensor] = None,
     ):
         ...
 

vllm/lora/lora.py

@@ -17,6 +17,7 @@ class LoRALayerWeights:
         lora_alpha: int,
         lora_a: torch.Tensor,
         lora_b: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
         embeddings_tensor: Optional[torch.Tensor] = None,
         scaling: Optional[float] = None,
     ) -> None:
@@ -25,6 +26,7 @@ class LoRALayerWeights:
         self.lora_alpha = lora_alpha
         self.lora_a = lora_a
         self.lora_b = lora_b
+        self.bias = bias
         self.embeddings_tensor = embeddings_tensor
 
         if scaling is None:
@@ -66,7 +68,8 @@ class LoRALayerWeights:
             rank: int,
             dtype: torch.dtype,
             device: torch.types.Device,
-            embeddings_tensor_dim: Optional[int] = None) -> "LoRALayerWeights":
+            embeddings_tensor_dim: Optional[int] = None,
+            bias_enabled: Optional[bool] = False) -> "LoRALayerWeights":
         pin_memory = str(device) == "cpu" and is_pin_memory_available()
         lora_a = torch.zeros([input_dim, rank],
                              dtype=dtype,
@@ -76,6 +79,14 @@ class LoRALayerWeights:
                              dtype=dtype,
                              device=device,
                              pin_memory=pin_memory)
+        if bias_enabled:
+            bias = torch.zeros([output_dim],
+                               dtype=dtype,
+                               device=device,
+                               pin_memory=pin_memory)
+        else:
+            bias = None
+
         embeddings_tensor = torch.rand(
             10,
             embeddings_tensor_dim,
@@ -88,6 +99,7 @@ class LoRALayerWeights:
             lora_alpha=1,
             lora_a=lora_a,
             lora_b=lora_b,
+            bias=bias,
             embeddings_tensor=embeddings_tensor,
         )
 
@@ -102,6 +114,7 @@ class PackedLoRALayerWeights(LoRALayerWeights):
         lora_alphas: List[Optional[int]],
         lora_a: List[Optional[torch.Tensor]],
         lora_b: List[Optional[torch.Tensor]],
+        bias: Optional[List[Optional[torch.Tensor]]] = None,
         scaling: Optional[List[float]] = None,
     ) -> None:
         super().__init__(
@@ -110,6 +123,7 @@ class PackedLoRALayerWeights(LoRALayerWeights):
             lora_alpha=0,
             lora_a=lora_a,
             lora_b=lora_b,
+            bias=bias,
             scaling=scaling,  # type: ignore
             embeddings_tensor=None,
         )
@@ -141,6 +155,7 @@ class PackedLoRALayerWeights(LoRALayerWeights):
             [lora.lora_alpha if lora is not None else None for lora in loras],
             [lora.lora_a if lora is not None else None for lora in loras],
             [lora.lora_b if lora is not None else None for lora in loras],
+            [lora.bias if lora is not None else None for lora in loras],
             scaling=[
                 1 if lora is not None else None  # type: ignore
                 for lora in loras

vllm/lora/models.py

@@ -4,7 +4,7 @@ import math
 import os
 import re
 from dataclasses import dataclass, field
-from typing import Any, Callable, Dict, List, Optional, Type
+from typing import Any, Callable, Dict, List, Optional, Sequence, Type
 
 import safetensors.torch
 import torch
@@ -119,7 +119,8 @@ class LoRAModel(AdapterModel):
         pin_memory = str(device) == "cpu" and is_pin_memory_available()
         loras: Dict[str, LoRALayerWeights] = {}
         for tensor_name, tensor in tensors.items():
-            module_name, is_lora_a = parse_fine_tuned_lora_name(tensor_name)
+            module_name, is_lora_a, is_bias = parse_fine_tuned_lora_name(
+                tensor_name)
             if module_name not in loras:
                 lora_embeddings_tensor = None
                 if embeddings:
@@ -136,8 +137,16 @@ class LoRAModel(AdapterModel):
                                 lora_embeddings_tensor.pin_memory())
                 loras[module_name] = LoRALayerWeights(module_name, rank,
                                                       lora_alpha, None, None,
+                                                      None,
                                                       lora_embeddings_tensor)
-            if is_lora_a:
+            if is_bias:
+                loras[module_name].bias = tensor.to(device=device,
+                                                    dtype=dtype).t()
+                bias = tensor.to(device=device, dtype=dtype).t()
+                if pin_memory:
+                    bias = bias.pin_memory()
+                loras[module_name].bias = bias
+            elif is_lora_a:
                 loras[module_name].lora_a = tensor.to(device=device,
                                                       dtype=dtype).t()
                 if pin_memory:
@@ -215,7 +224,7 @@ class LoRAModel(AdapterModel):
             with safetensors.safe_open(lora_tensor_path,
                                        framework="pt") as f:  # type: ignore
                 for lora_module in f.keys():  # noqa
-                    module_name, _ = parse_fine_tuned_lora_name(lora_module)
+                    module_name, _, _ = parse_fine_tuned_lora_name(lora_module)
                     part_name = module_name.split(".")[-1]
                     if part_name not in expected_lora_modules:
                         unexpected_modules.append(module_name)
@@ -386,8 +395,19 @@ class LoRAModelManager(AdapterModelManager):
             module_lora = lora_model.get_lora(module_name)
             if module_lora:
                 module_lora.optimize()
+                # Bias is not explicitly enabled with the flag enable_lora_bias.
+                bias = module_lora.bias
+                if ((torch.is_tensor(bias) or
+                     (isinstance(bias, Sequence) and any(b is not None
+                                                         for b in bias)))
+                        and not self.lora_config.bias_enabled):
+                    module_lora.bias = None
+                    raise ValueError(
+                        f"Adapter bias cannot be used for {module_name}"
+                        " without --enable-lora-bias.")
                 module.set_lora(index, module_lora.lora_a, module_lora.lora_b,
-                                module_lora.embeddings_tensor)
+                                module_lora.embeddings_tensor,
+                                module_lora.bias)
             else:
                 module.reset_lora(index)
         return True
@@ -509,6 +529,7 @@ class LoRAModelManager(AdapterModelManager):
         """Create zero-initialized LoRAModel for warmup."""
         model = LoRAModel(lora_id, rank, {}, scaling_factor)
         for module_name, module in self.model.named_modules():
+            bias_enabled = self.lora_config.bias_enabled
             if (not self._match_target_modules(module_name)
                     or not isinstance(module, BaseLayerWithLoRA)
                     or isinstance(module, LinearScalingRotaryEmbeddingWithLora)
@@ -536,7 +557,8 @@ class LoRAModelManager(AdapterModelManager):
                         rank,
                         module.lora_a_stacked.dtype,
                         "cpu",
-                        embeddings_tensor_dim=embeddings_tensor_dim)
+                        embeddings_tensor_dim=embeddings_tensor_dim,
+                        bias_enabled=bias_enabled)
                 else:
                     lora = LoRALayerWeights.create_dummy_lora_weights(
                         module_name,
@@ -545,6 +567,7 @@ class LoRAModelManager(AdapterModelManager):
                         rank,
                         module.lora_a_stacked.dtype,
                         "cpu",
+                        bias_enabled=bias_enabled,
                     )
                 lora.optimize()
             else:
@@ -559,6 +582,7 @@ class LoRAModelManager(AdapterModelManager):
                         rank,
                         module.lora_a_stacked[i].dtype,
                         "cpu",
+                        bias_enabled=bias_enabled,
                     )
                     lora.optimize()
                     subloras.append(lora)

vllm/lora/utils.py

@@ -91,7 +91,7 @@ def replace_submodule(model: nn.Module, module_name: str,
     return new_module
 
 
-def parse_fine_tuned_lora_name(name: str) -> Tuple[str, bool]:
+def parse_fine_tuned_lora_name(name: str) -> Tuple[str, bool, bool]:
     """Parse the name of lora weights.
 
     args:
@@ -101,15 +101,18 @@ def parse_fine_tuned_lora_name(name: str) -> Tuple[str, bool]:
         Tuple(module_name, is_lora_a):
             module_name: the name of the module, e.g. model.dense1,
             is_lora_a whether the tensor is lora_a or lora_b.
+            is_bias whether the tensor is lora bias.
     """
     parts = name.split(".")
+    if parts[-1] == "weight" and (parts[-2] == "lora_A"
+                                  or parts[-2] == "lora_B"):
+        return ".".join(parts[2:-2]), parts[-2] == "lora_A", False
 
-    if len(parts) >= 2 and parts[0] == "base_model" and parts[1] == "model":
-        if parts[-1] == "weight":
-            if parts[-2] == "lora_A" or parts[-2] == "lora_B":
-                return ".".join(parts[2:-2]), parts[-2] == "lora_A"
-        elif parts[-1] == "lora_embedding_A" or parts[-1] == "lora_embedding_B":
-            return ".".join(parts[2:-1]), parts[-1] == "lora_embedding_A"
+    if parts[-1] == "lora_embedding_A" or parts[-1] == "lora_embedding_B":
+        return ".".join(parts[2:-1]), parts[-1] == "lora_embedding_A", False
+
+    if parts[-1] == "bias":
+        return ".".join(parts[2:-2]), False, True
 
     raise ValueError(f"{name} is unsupported LoRA weight")