Remove LoRA bias support (vllm-project#25807)

Signed-off-by: Ashwin Phadke <ashwinphadke12@rediffmail.com>
Signed-off-by: Ashwin Phadke <23502062+ashwin-phadke@users.noreply.github.com>
Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>
Co-authored-by: Jee Jee Li <pandaleefree@gmail.com>
Signed-off-by: 0xrushi <6279035+0xrushi@users.noreply.github.com>

Author: 2 people

tests/entrypoints/openai/test_lora_adapters.py

@@ -23,11 +23,6 @@
         {"r": 1024},
         "is greater than max_lora_rank",
     ),
-    (
-        "test_bias",
-        {"bias": "all"},
-        "Adapter bias cannot be used without bias_enabled",
-    ),
     ("test_dora", {"use_dora": True}, "does not yet support DoRA"),
     (
         "test_modules_to_save",

tests/lora/test_peft_helper.py

@@ -16,11 +16,6 @@
         {"r": 1024},
         "is greater than max_lora_rank",
     ),
-    (
-        "test_bias",
-        {"bias": "all"},
-        "Adapter bias cannot be used without bias_enabled",
-    ),
     ("test_dora", {"use_dora": True}, "does not yet support DoRA"),
     (
         "test_modules_to_save",

tests/lora/test_utils.py

@@ -21,7 +21,6 @@ class LoRANameParserTestConfig(NamedTuple):
     name: str
     module_name: str
     is_lora_a: bool
-    is_bias: bool
     weights_mapper: Optional[WeightsMapper] = None
 
 
@@ -37,44 +36,37 @@ def test_parse_fine_tuned_lora_name_valid():
             "base_model.model.model.embed_tokens.lora_embedding_A",
             "model.embed_tokens",
             True,
-            False,
         ),
         LoRANameParserTestConfig(
             "base_model.model.model.embed_tokens.lora_embedding_B",
             "model.embed_tokens",
             False,
-            False,
         ),
         LoRANameParserTestConfig(
             "base_model.model.model.layers.9.mlp.down_proj.lora_A.weight",
             "model.layers.9.mlp.down_proj",
             True,
-            False,
         ),
         LoRANameParserTestConfig(
             "base_model.model.model.layers.9.mlp.down_proj.lora_B.weight",
             "model.layers.9.mlp.down_proj",
             False,
-            False,
         ),
         LoRANameParserTestConfig(
             "language_model.layers.9.mlp.down_proj.lora_A.weight",
             "language_model.layers.9.mlp.down_proj",
             True,
-            False,
         ),
         LoRANameParserTestConfig(
             "language_model.layers.9.mlp.down_proj.lora_B.weight",
             "language_model.layers.9.mlp.down_proj",
             False,
-            False,
         ),
         # Test with WeightsMapper
         LoRANameParserTestConfig(
             "base_model.model.model.layers.9.mlp.down_proj.lora_A.weight",
             "language_model.model.layers.9.mlp.down_proj",
             True,
-            False,
             weights_mapper=WeightsMapper(
                 orig_to_new_prefix={"model.": "language_model.model."}
             ),
@@ -83,7 +75,6 @@ def test_parse_fine_tuned_lora_name_valid():
             "base_model.model.model.layers.9.mlp.down_proj.lora_B.weight",
             "language_model.model.layers.9.mlp.down_proj",
             False,
-            False,
             weights_mapper=WeightsMapper(
                 orig_to_new_prefix={"model.": "language_model.model."}
             ),
@@ -92,7 +83,6 @@ def test_parse_fine_tuned_lora_name_valid():
             "model.layers.9.mlp.down_proj.lora_A.weight",
             "language_model.model.layers.9.mlp.down_proj",
             True,
-            False,
             weights_mapper=WeightsMapper(
                 orig_to_new_prefix={"model.": "language_model.model."}
             ),
@@ -101,14 +91,13 @@ def test_parse_fine_tuned_lora_name_valid():
             "model.layers.9.mlp.down_proj.lora_B.weight",
             "language_model.model.layers.9.mlp.down_proj",
             False,
-            False,
             weights_mapper=WeightsMapper(
                 orig_to_new_prefix={"model.": "language_model.model."}
             ),
         ),
     ]
-    for name, module_name, is_lora_a, is_bias, weights_mapper in fixture:
-        assert (module_name, is_lora_a, is_bias) == parse_fine_tuned_lora_name(
+    for name, module_name, is_lora_a, weights_mapper in fixture:
+        assert (module_name, is_lora_a) == parse_fine_tuned_lora_name(
             name, weights_mapper
         )
 

vllm/config/lora.py

@@ -70,12 +70,6 @@ class LoRAConfig:
     per prompt. When run in offline mode, the lora IDs for n modalities
     will be automatically assigned to 1-n with the names of the modalities
     in alphabetic order."""
-    bias_enabled: bool = Field(
-        default=False,
-        deprecated="`bias_enabled` is deprecated and will be removed in v0.12.0.",
-    )
-    """[DEPRECATED] Enable bias for LoRA adapters. This option will be
-    removed in v0.12.0."""
 
     def compute_hash(self) -> str:
         """
@@ -96,7 +90,7 @@ def compute_hash(self) -> str:
         factors.append(self.lora_dtype)
         factors.append(self.lora_extra_vocab_size)
         factors.append(self.lora_vocab_padding_size)
-        factors.append(self.bias_enabled)
+
         hash_str = hashlib.md5(str(factors).encode(), usedforsecurity=False).hexdigest()
         return hash_str
 

vllm/engine/arg_utils.py

@@ -439,7 +439,6 @@ class EngineArgs:
     video_pruning_rate: float = MultiModalConfig.video_pruning_rate
     # LoRA fields
     enable_lora: bool = False
-    enable_lora_bias: bool = LoRAConfig.bias_enabled
     max_loras: int = LoRAConfig.max_loras
     max_lora_rank: int = LoRAConfig.max_lora_rank
     default_mm_loras: Optional[dict[str, str]] = LoRAConfig.default_mm_loras
@@ -916,7 +915,6 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
             action=argparse.BooleanOptionalAction,
             help="If True, enable handling of LoRA adapters.",
         )
-        lora_group.add_argument("--enable-lora-bias", **lora_kwargs["bias_enabled"])
         lora_group.add_argument("--max-loras", **lora_kwargs["max_loras"])
         lora_group.add_argument("--max-lora-rank", **lora_kwargs["max_lora_rank"])
         lora_group.add_argument(
@@ -1515,7 +1513,6 @@ def create_engine_config(
 
         lora_config = (
             LoRAConfig(
-                bias_enabled=self.enable_lora_bias,
                 max_lora_rank=self.max_lora_rank,
                 max_loras=self.max_loras,
                 default_mm_loras=self.default_mm_loras,

vllm/lora/layers/base.py

@@ -45,7 +45,6 @@ def set_lora(
         lora_a: torch.Tensor,
         lora_b: torch.Tensor,
         embeddings_tensor: Optional[torch.Tensor],
-        bias: Optional[torch.Tensor] = None,
     ):
         """Overwrites lora tensors at index."""
         ...

vllm/lora/layers/base_linear.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from typing import Optional, cast
+from typing import Optional
 
 import torch
 from transformers import PretrainedConfig
@@ -29,7 +29,6 @@ def __init__(self, base_layer: LinearBase):
         self.tp_size = self.base_layer.tp_size
         self.tp_rank = self.base_layer.tp_rank
         self.device = _get_lora_device(self.base_layer)
-        self.lora_bias_stacked: Optional[tuple[torch.Tensor, ...]] = None
         self.output_slices: tuple[int, ...]
         self.output_size: int
         self.n_slices: int
@@ -86,38 +85,19 @@ def create_lora_weights(
             )
             for _ in range(self.n_slices)
         )
-        if lora_config.bias_enabled:
-            lora_bias_out_size = lora_b_out_size
-            self.lora_bias_stacked = tuple(
-                torch.zeros(
-                    max_loras,
-                    1,
-                    lora_bias_out_size,
-                    dtype=lora_config.lora_dtype,
-                    device=self.device,
-                )
-                for _ in range(self.n_slices)
-            )
         self.output_slices = (self.lora_b_stacked[0].shape[2],)
 
     def reset_lora(self, index: int):
         for s_index in range(self.n_slices):
             self.lora_a_stacked[s_index][index] = 0
             self.lora_b_stacked[s_index][index] = 0
-            if self.lora_config.bias_enabled:
-                # Make mypy happy
-                self.lora_bias_stacked = cast(
-                    tuple[torch.Tensor, ...], self.lora_bias_stacked
-                )
-                self.lora_bias_stacked[s_index][index] = 0
 
     def set_lora(
         self,
         index: int,
         lora_a: torch.Tensor,
         lora_b: torch.Tensor,
         embeddings_tensor: Optional[torch.Tensor],
-        lora_bias: Optional[torch.Tensor] = None,
     ):
         # Except for QKVParallelLinearWithLoRA and
         # MergedColumnParallelLinearWithLoRA, all other linear LoRA layers
@@ -131,23 +111,13 @@ def set_lora(
         if self.tp_size > 1:
             lora_a = self.slice_lora_a(lora_a)
             lora_b = self.slice_lora_b(lora_b)
-            if lora_bias is not None:
-                lora_bias = self.slice_bias(lora_bias)
 
         self.lora_a_stacked[0][index, 0, : lora_a.shape[0], : lora_a.shape[1]].copy_(
             lora_a, non_blocking=True
         )
         self.lora_b_stacked[0][index, 0, : lora_b.shape[0], : lora_b.shape[1]].copy_(
             lora_b, non_blocking=True
         )
-        if lora_bias is not None:
-            self.lora_bias_stacked = cast(
-                tuple[torch.Tensor, ...], self.lora_bias_stacked
-            )
-            assert len(self.lora_bias_stacked)
-            self.lora_bias_stacked[0][index, 0, : lora_bias.shape[0]].copy_(
-                lora_bias, non_blocking=True
-            )
 
     def apply(
         self, x: torch.Tensor, bias: Optional[torch.Tensor] = None
@@ -162,13 +132,7 @@ def apply(
             x = x.flatten(0, 1)
 
         lora_output: Optional[torch.Tensor] = self.punica_wrapper.add_lora_linear(
-            output,
-            x,
-            self.lora_a_stacked,
-            self.lora_b_stacked,
-            self.lora_bias_stacked,
-            1.0,
-            self.output_slices,
+            output, x, self.lora_a_stacked, self.lora_b_stacked, 1.0, self.output_slices
         )
         if not current_platform.can_update_inplace():
             output = lora_output

vllm/lora/layers/column_parallel_linear.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from typing import Optional, Union, cast
+from typing import Optional, Union
 
 import torch
 import torch.nn as nn
@@ -32,8 +32,6 @@ def _mcp_apply(x, bias, layer: "ColumnParallelLinearWithLoRA"):
         == len(layer.lora_b_stacked)
         == len(layer.output_slices)
     )
-    if layer.lora_bias_stacked is not None:
-        assert layer.n_slices == len(layer.lora_bias_stacked)
 
     output = layer.base_layer.quant_method.apply(layer.base_layer, x, bias)
 
@@ -61,7 +59,6 @@ def _mcp_apply(x, bias, layer: "ColumnParallelLinearWithLoRA"):
         output,
         buffers,
         layer.lora_b_stacked,
-        layer.lora_bias_stacked,
         layer.output_slices,
         offset_start=0,
         add_input=True,
@@ -122,16 +119,6 @@ def slice_lora_b(self, lora_b: torch.Tensor) -> torch.Tensor:
             lora_b = lora_b[start_idx:end_idx, :]
         return lora_b
 
-    def slice_bias(self, bias: torch.Tensor) -> torch.Tensor:
-        # TODO: Fix the slicing logic of bias.
-        if bias is None:
-            return bias
-        shard_size = self.output_size
-        start_idx = self.tp_rank * shard_size
-        end_idx = (self.tp_rank + 1) * shard_size
-        bias = bias[start_idx:end_idx]
-        return bias
-
     def forward(
         self, input_: torch.Tensor
     ) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[torch.Tensor]]]:
@@ -238,17 +225,6 @@ def create_lora_weights(
             )
             for output_size in self.output_slices
         )
-        if lora_config.bias_enabled:
-            self.lora_bias_stacked = tuple(
-                torch.zeros(
-                    max_loras,
-                    1,
-                    output_size,
-                    dtype=lora_config.lora_dtype,
-                    device=self.device,
-                )
-                for output_size in self.output_slices
-            )
 
     def slice_lora_a(
         self, lora_a: list[Union[torch.Tensor, None]]
@@ -268,31 +244,18 @@ def slice_lora_b(
                 ]
         return sliced_lora_b
 
-    def slice_bias(
-        self, bias: list[Union[torch.Tensor, None]]
-    ) -> list[Union[torch.Tensor, None]]:
-        for i, (shard_id, shard_size) in enumerate(
-            zip(self.output_ids, self.output_slices)
-        ):
-            if (bias_i := bias[i]) is not None:
-                bias[i] = bias_i[shard_size * shard_id : shard_size * (shard_id + 1)]
-        return bias
-
     def set_lora(
         self,
         index: int,
         lora_a: torch.Tensor,
         lora_b: torch.Tensor,
         embeddings_tensor: Optional[torch.Tensor],
-        lora_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 lora_bias is not None:
-                lora_bias = self.slice_bias(lora_bias)
 
         for i in range(self.n_slices):
             if (lora_a_i := lora_a[i]) is not None:
@@ -304,16 +267,6 @@ def set_lora(
                     index, 0, : lora_b_i.shape[0], : lora_b_i.shape[1]
                 ].copy_(lora_b_i, non_blocking=True)
 
-        if lora_bias is not None:
-            self.lora_bias_stacked = cast(
-                tuple[torch.Tensor, ...], self.lora_bias_stacked
-            )
-            for i in range(self.n_slices):
-                if (lora_bias_i := lora_bias[i]) is not None:
-                    self.lora_bias_stacked[i][index, 0, : lora_bias_i.shape[0]].copy_(
-                        lora_bias_i, non_blocking=True
-                    )
-
     @classmethod
     @_not_fully_sharded_can_replace
     def can_replace_layer(
@@ -380,24 +333,6 @@ def slice_lora_b(self, lora_b: torch.Tensor) -> torch.Tensor:
         lora_b = torch.cat([lora_b_q, lora_b_k, lora_b_v], dim=0)
         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
-
     @classmethod
     @_not_fully_sharded_can_replace
     def can_replace_layer(