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[Core] Split LoRA layers (#24574)

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Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>
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Jee Jee Li 2025-09-10 22:47:51 +08:00 committed by GitHub
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10
tests/lora/test_layers.py
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@ -12,20 +12,20 @@ import torch
import torch.nn.functional as F
from vllm.config import LoRAConfig
from vllm.lora.fully_sharded_layers import (
ColumnParallelLinearWithShardedLoRA,
MergedColumnParallelLinearWithShardedLoRA,
MergedQKVParallelLinearWithShardedLoRA, QKVParallelLinearWithShardedLoRA,
RowParallelLinearWithShardedLoRA)
# yapf conflicts with isort for this block
# yapf: disable
from vllm.lora.layers import (BaseLayerWithLoRA, ColumnParallelLinearWithLoRA,
ColumnParallelLinearWithShardedLoRA,
LogitsProcessorWithLoRA, LoRAMapping,
MergedColumnParallelLinearWithLoRA,
MergedColumnParallelLinearWithShardedLoRA,
MergedQKVParallelLinearWithLoRA,
MergedQKVParallelLinearWithShardedLoRA,
QKVParallelLinearWithLoRA,
QKVParallelLinearWithShardedLoRA,
ReplicatedLinearWithLoRA,
RowParallelLinearWithLoRA,
RowParallelLinearWithShardedLoRA,
VocabParallelEmbeddingWithLoRA)
# yapf: enable
from vllm.lora.models import LoRALayerWeights, PackedLoRALayerWeights

355
vllm/lora/fully_sharded_layers.py
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@ -1,355 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# pylint: disable=unused-argument
from typing import TYPE_CHECKING, Optional, Union, cast
import torch
import torch.nn as nn
from transformers import PretrainedConfig
from vllm.config import LoRAConfig
from vllm.distributed.communication_op import (
tensor_model_parallel_all_gather, tensor_model_parallel_all_reduce)
from vllm.distributed.parallel_state import get_tensor_model_parallel_rank
from vllm.lora.layers import (ColumnParallelLinearWithLoRA,
MergedColumnParallelLinearWithLoRA,
MergedQKVParallelLinearWithLoRA,
QKVParallelLinearWithLoRA,
RowParallelLinearWithLoRA)
from vllm.platforms import current_platform
if TYPE_CHECKING:
pass
def _fully_sharded_can_replace(can_replace):
"""
decorator which adds the condition of fully sharded loras
intended to wrap can_replace_layer()
"""
def dec(*args, **kwargs):
return (can_replace(*args, **kwargs)
and kwargs["lora_config"].fully_sharded_loras)
return dec
def _mcp_apply(x, bias, layer: ColumnParallelLinearWithLoRA):
"""
For `ColumnParallelLinearWithLoRA` or classes that inherit from
`ColumnParallelLinearWithLoRA`, they share the same `apply` logic.
"""
assert (layer.n_slices == len(layer.lora_a_stacked) == 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)
x = x.view(-1, x.shape[-1])
output, out_orig_shape = output.view(-1, output.shape[-1]), output.shape
# Since communication is needed, the buffer is directly initialized as a
# tensor rather than a tuple of tensor.
buffers = torch.zeros(
(layer.n_slices, x.shape[0], layer.lora_a_stacked[0].shape[2]),
dtype=torch.float32,
device=x.device,
)
shrunk_buffers: Optional[torch.Tensor] = layer.punica_wrapper.add_shrink(
buffers, x, layer.lora_a_stacked, 1.0)
if not current_platform.can_update_inplace():
buffers = shrunk_buffers
buffers = tensor_model_parallel_all_gather(buffers)
lora_output: Optional[torch.Tensor] = layer.punica_wrapper.add_expand(
output,
buffers,
layer.lora_b_stacked,
layer.lora_bias_stacked,
layer.output_slices,
offset_start=0,
add_input=True)
if not current_platform.can_update_inplace():
output = lora_output
output = output.view(*out_orig_shape)
# now have column partitioned and packed output
return output
# these layers are based on the tensor parallelism strategy given in
# Y. Sheng et al., S-LoRA: Serving Thousands of Concurrent LoRA Adapters. 2023,
# https://arxiv.org/abs/2311.03285.
class ColumnParallelLinearWithShardedLoRA(ColumnParallelLinearWithLoRA):
"""
Differs from ColumnParallelLinearWithLoRA by slicing LoRA A also.
Based on S-LoRA, slicing happens along the rank dim.
"""
# For all LoRA layers where the `base_layer` is `ColumnParallelLinear`,
# their `lora_a` and `lora_b` have different sharding patterns. After
# completing the `lora_a` GEMM , a gather operation is performed.
# Therefore, the sharding of `lora_a` only needs to correspond with the
# gather operation.
def slice_lora_a(self, lora_a: torch.Tensor) -> torch.Tensor:
tp_rank = get_tensor_model_parallel_rank()
shard_size = self.lora_a_stacked[0].shape[2]
start_idx = tp_rank * shard_size
lora_a = lora_a[:, start_idx:start_idx + shard_size]
return lora_a
def apply(self,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
return _mcp_apply(x, bias, self)
@classmethod
@_fully_sharded_can_replace
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
) -> bool:
# specifying kwargs so they can be easily accessed in decorator
return super().can_replace_layer(
source_layer=source_layer,
lora_config=lora_config,
packed_modules_list=packed_modules_list,
model_config=model_config,
decorate=False,
)
class MergedColumnParallelLinearWithShardedLoRA(
MergedColumnParallelLinearWithLoRA):
"""
Differs from MergedColumnParallelLinearWithLoRA by slicing the
LoRA A's also.
Based on S-LoRA, slicing happens along the rank dim.
"""
def slice_lora_a(
self, lora_a: list[Union[torch.Tensor, None]]
) -> list[Union[torch.Tensor, None]]:
#NOTE: lora_a contains 2 subloras, and each sublora could be None.
output_shard_size = self.lora_a_stacked[0].shape[2]
output_start_idx = self.tp_rank * output_shard_size
lora_a = [
lora_a[0][:, output_start_idx:output_start_idx +
output_shard_size] if lora_a[0] is not None else None,
lora_a[1][:, output_start_idx:output_start_idx +
output_shard_size] if lora_a[1] is not None else None,
]
return lora_a
def apply(self,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
return _mcp_apply(x, bias, self)
@classmethod
@_fully_sharded_can_replace
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
) -> bool:
# specifying kwargs so they can be easily accessed in decorator
return super().can_replace_layer(
source_layer=source_layer,
lora_config=lora_config,
packed_modules_list=packed_modules_list,
model_config=model_config,
decorate=False,
)
class QKVParallelLinearWithShardedLoRA(QKVParallelLinearWithLoRA):
"""
Differs from QKVParallelLinearWithLoRA by slicing the
LoRA A's also.
Based on S-LoRA, slicing happens along the rank dim.
"""
def slice_lora_a(self, lora_a: torch.Tensor) -> torch.Tensor:
tp_rank = get_tensor_model_parallel_rank()
shard_size = self.lora_a_stacked[0].shape[2]
start_idx = tp_rank * shard_size
lora_a = lora_a[:, start_idx:start_idx + shard_size]
return lora_a
def apply(self,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
return _mcp_apply(x, bias, self)
@classmethod
@_fully_sharded_can_replace
def can_replace_layer(cls, source_layer: nn.Module,
lora_config: LoRAConfig, packed_modules_list: list,
model_config: Optional[PretrainedConfig]) -> bool:
# specifying kwargs so they can be easily accessed in decorator
return super().can_replace_layer(
source_layer=source_layer,
lora_config=lora_config,
packed_modules_list=packed_modules_list,
model_config=model_config,
decorate=False,
)
class MergedQKVParallelLinearWithShardedLoRA(MergedQKVParallelLinearWithLoRA):
"""
Differs from MergedQKVParallelLinearWithLoRA by slicing the
LoRA A's also.
Based on S-LoRA, slicing happens along the rank dim.
"""
def slice_lora_a(
self, lora_a: list[Union[torch.Tensor, None]]
) -> list[Union[torch.Tensor, None]]:
# NOTE: lora_a contains 3 subloras, and each sublora could be None.
shard_size = [self.lora_a_stacked[i].shape[2] for i in range(3)]
start_idx = [self.tp_rank * shard_size[i] for i in range(3)]
lora_a = [
lora_a[0][:, start_idx[0]:start_idx[0] +
shard_size[0]] if lora_a[0] is not None else None,
lora_a[1][:, start_idx[1]:start_idx[1] +
shard_size[1]] if lora_a[1] is not None else None,
lora_a[2][:, start_idx[2]:start_idx[2] +
shard_size[2]] if lora_a[2] is not None else None,
]
return lora_a
def apply(self,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
return _mcp_apply(x, bias, self)
@classmethod
@_fully_sharded_can_replace
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
) -> bool:
# specifying kwargs so they can be easily accessed in decorator
return super().can_replace_layer(
source_layer=source_layer,
lora_config=lora_config,
packed_modules_list=packed_modules_list,
model_config=model_config,
decorate=False,
)
class RowParallelLinearWithShardedLoRA(RowParallelLinearWithLoRA):
"""
Differs from RowParallelLinearWithLoRA by slicing the
LoRA B's also.
Based on S-LoRA, slicing happens along the output dim.
This yields a combined partial sum from the row parallel base
layer and column partitioned output from the LoRA.
"""
def slice_lora_b(self, lora_b: torch.Tensor) -> torch.Tensor:
shard_size = self.lora_b_stacked[0].shape[2]
start_idx = self.tp_rank * shard_size
end_idx = (self.tp_rank + 1) * shard_size
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
self.lora_bias_stacked = cast(tuple[torch.Tensor, ...],
self.lora_bias_stacked)
shard_size = self.lora_bias_stacked[0].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,
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
output = self.base_layer.quant_method.apply(self.base_layer, x)
x = x.view(-1, x.shape[-1])
output, out_orig_shape = output.view(-1,
output.shape[-1]), output.shape
buffer = torch.zeros(
(self.n_slices, x.shape[0], self.lora_a_stacked[0].shape[2]),
dtype=torch.float32,
device=x.device,
)
shrunk_buffer: Optional[torch.Tensor] = self.punica_wrapper.add_shrink(
buffer, x, self.lora_a_stacked, 1.0)
if not current_platform.can_update_inplace():
buffer = shrunk_buffer
buffer = tensor_model_parallel_all_reduce(buffer)
# following S-LoRA, allows the fusing of all_gather and all_reduce
# by adding the column partitioned lora output to a slice of output
# tensor, which is a partial sum due to row parallel. All that
# remains is a standard all_reduce. User should be aware though that
# the output is not the same as a normal row_parallel, it should be
# reduced before being used
# NOTE offset are based on the rank.
shard_size = self.lora_b_stacked[0].shape[2]
offset_start = self.tp_rank * shard_size
lora_output: Optional[torch.Tensor] = self.punica_wrapper.add_expand(
output,
buffer,
self.lora_b_stacked,
self.lora_bias_stacked,
self.output_slices,
offset_start=offset_start,
add_input=True,
)
if not current_platform.can_update_inplace():
output = lora_output
output = output.view(*out_orig_shape)
return output
@classmethod
@_fully_sharded_can_replace
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
) -> bool:
# specifying kwargs so they can be easily accessed in decorator
return super().can_replace_layer(
source_layer=source_layer,
lora_config=lora_config,
packed_modules_list=packed_modules_list,
model_config=model_config,
decorate=False,
)

1192
vllm/lora/layers.py
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34
vllm/lora/layers/__init__.py Normal file
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@ -0,0 +1,34 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from vllm.lora.layers.base import BaseLayerWithLoRA
from vllm.lora.layers.column_parallel_linear import (
ColumnParallelLinearWithLoRA, ColumnParallelLinearWithShardedLoRA,
MergedColumnParallelLinearWithLoRA,
MergedColumnParallelLinearWithShardedLoRA, MergedQKVParallelLinearWithLoRA,
MergedQKVParallelLinearWithShardedLoRA, QKVParallelLinearWithLoRA,
QKVParallelLinearWithShardedLoRA)
from vllm.lora.layers.logits_processor import LogitsProcessorWithLoRA
from vllm.lora.layers.replicated_linear import ReplicatedLinearWithLoRA
from vllm.lora.layers.row_parallel_linear import (
RowParallelLinearWithLoRA, RowParallelLinearWithShardedLoRA)
from vllm.lora.layers.utils import LoRAMapping
from vllm.lora.layers.vocal_parallel_embedding import (
VocabParallelEmbeddingWithLoRA)
__all__ = [
"BaseLayerWithLoRA",
"VocabParallelEmbeddingWithLoRA",
"LogitsProcessorWithLoRA",
"ColumnParallelLinearWithLoRA",
"ColumnParallelLinearWithShardedLoRA",
"MergedColumnParallelLinearWithLoRA",
"MergedColumnParallelLinearWithShardedLoRA",
"MergedQKVParallelLinearWithLoRA",
"MergedQKVParallelLinearWithShardedLoRA",
"QKVParallelLinearWithLoRA",
"QKVParallelLinearWithShardedLoRA",
"RowParallelLinearWithLoRA",
"RowParallelLinearWithShardedLoRA",
"ReplicatedLinearWithLoRA",
"LoRAMapping",
]

69
vllm/lora/layers/base.py Normal file
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@ -0,0 +1,69 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import TYPE_CHECKING, Optional, Union
import torch
import torch.nn as nn
from transformers import PretrainedConfig
from vllm.config import LoRAConfig
if TYPE_CHECKING:
from vllm.lora.punica_wrapper import PunicaWrapperBase
class BaseLayerWithLoRA(nn.Module):
def slice_lora_a(
self, lora_a: Union[torch.Tensor, list[Union[torch.Tensor, None]]]
) -> Union[torch.Tensor, list[Union[torch.Tensor, None]]]:
"""Slice lora a if splitting for tensor parallelism."""
...
def slice_lora_b(
self, lora_b: Union[torch.Tensor, list[Union[torch.Tensor, None]]]
) -> Union[torch.Tensor, list[Union[torch.Tensor, None]]]:
"""Slice lora b if splitting with tensor parallelism."""
...
def create_lora_weights(
self,
max_loras: int,
lora_config: LoRAConfig,
model_config: Optional[PretrainedConfig] = None,
) -> None:
"""Initializes lora matrices."""
...
def reset_lora(self, index: int):
"""Resets the lora weights at index back to 0."""
...
def set_lora(
self,
index: int,
lora_a: torch.Tensor,
lora_b: torch.Tensor,
embeddings_tensor: Optional[torch.Tensor],
bias: Optional[torch.Tensor] = None,
):
"""Overwrites lora tensors at index."""
...
def set_mapping(
self,
punica_wrapper,
):
self.punica_wrapper: PunicaWrapperBase = punica_wrapper
@classmethod
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
) -> bool:
"""Returns True if the layer can be replaced by this LoRA layer."""
raise NotImplementedError

184
vllm/lora/layers/base_linear.py Normal file
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@ -0,0 +1,184 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Optional, cast
import torch
from transformers import PretrainedConfig
from vllm.config import LoRAConfig
from vllm.distributed.utils import divide
# yapf: disable
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
LinearBase, ReplicatedLinear,
RowParallelLinear)
from vllm.platforms import current_platform
from .base import BaseLayerWithLoRA
from .utils import _get_lora_device
class BaseLinearLayerWithLoRA(BaseLayerWithLoRA):
def __init__(self, base_layer: LinearBase):
super().__init__()
self.base_layer = base_layer
self.input_size = self.base_layer.input_size
self.device = _get_lora_device(self.base_layer)
self.lora_bias_stacked: Optional[tuple[torch.Tensor, ...]] = None
self.output_slices: tuple[int, ...]
self.tp_size: int
self.output_size: int
self.n_slices: int
def create_lora_weights(
self,
max_loras: int,
lora_config: LoRAConfig,
model_config: Optional[PretrainedConfig] = None,
) -> None:
self.lora_config = lora_config
#
if isinstance(self.base_layer, ReplicatedLinear):
lora_a_out_size = lora_config.max_lora_rank
lora_b_out_size = self.output_size
elif isinstance(self.base_layer, ColumnParallelLinear):
lora_a_out_size = (lora_config.max_lora_rank if
not lora_config.fully_sharded_loras else divide(
lora_config.max_lora_rank, self.tp_size))
lora_b_out_size = self.output_size
elif isinstance(self.base_layer, RowParallelLinear):
lora_a_out_size = lora_config.max_lora_rank
lora_b_out_size = (self.output_size if
not lora_config.fully_sharded_loras else divide(
self.output_size, self.tp_size))
else:
raise NotImplementedError
self.lora_a_stacked = tuple(
torch.zeros(
max_loras,
1,
lora_a_out_size,
self.input_size,
dtype=lora_config.lora_dtype,
device=self.device,
) for _ in range(self.n_slices))
self.lora_b_stacked = tuple(
torch.zeros(
max_loras,
1,
lora_b_out_size,
lora_config.max_lora_rank,
dtype=lora_config.lora_dtype,
device=self.device,
) 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
# store weights in a tuple of size 1. These two layers will
# override this function.
assert (len(self.lora_a_stacked) == len(self.lora_b_stacked) ==
self.n_slices == 1)
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)
self.lora_a_stacked[0][index,
0, :lora_a.shape[1], :lora_a.shape[0]].copy_(
lora_a.T, non_blocking=True)
self.lora_b_stacked[0][index,
0, :lora_b.shape[1], :lora_b.shape[0]].copy_(
lora_b.T, 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.T, non_blocking=True)
def apply(self,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
output = self.base_layer.quant_method.apply(self.base_layer, x, bias)
# In transformers backend, x and output have extra batch dimension like
# (1, seq_len, hidden_dim), while punica expects (seq_len, hidden_dim),
# therefore we need to flatten the batch dimensions.
if x.ndim == 3 and output.ndim == 3:
output = output.flatten(0, 1)
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)
if not current_platform.can_update_inplace():
output = lora_output
return output
@property
def weight(self) -> torch.Tensor:
# unquantizedLinear
if hasattr(self.base_layer, "weight"):
return self.base_layer.weight
# Compressed Tensor
elif hasattr(self.base_layer, "weight_packed"):
return self.base_layer.weight_packed
# GPTQ/AWQ
elif hasattr(self.base_layer, "qweight"):
return self.base_layer.qweight
# marlin
elif hasattr(self.base_layer, "B"):
return self.base_layer.B
# HQQ marlin
elif hasattr(self.base_layer, "W_q"):
return self.base_layer.W_q
else:
raise ValueError(f"Unsupported base layer: {self.base_layer}")
@property
def bias(self) -> Optional[torch.Tensor]:
if hasattr(self.base_layer, "bias"):
return self.base_layer.bias
else:
return None

622
vllm/lora/layers/column_parallel_linear.py Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Optional, Union, cast
import torch
import torch.nn as nn
from transformers import PretrainedConfig
from vllm.config import LoRAConfig
from vllm.distributed import (get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
tensor_model_parallel_all_gather)
from vllm.distributed.utils import divide
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
MergedColumnParallelLinear,
QKVParallelLinear)
from vllm.platforms import current_platform
from .base_linear import BaseLinearLayerWithLoRA
from .utils import _fully_sharded_can_replace, _not_fully_sharded_can_replace
def _mcp_apply(x, bias, layer: "ColumnParallelLinearWithLoRA"):
"""
For `ColumnParallelLinearWithLoRA` or classes that inherit from
`ColumnParallelLinearWithLoRA`, they share the same `apply` logic.
"""
assert (layer.n_slices == len(layer.lora_a_stacked) == 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)
x = x.view(-1, x.shape[-1])
output, out_orig_shape = output.view(-1, output.shape[-1]), output.shape
# Since communication is needed, the buffer is directly initialized as a
# tensor rather than a tuple of tensor.
buffers = torch.zeros(
(layer.n_slices, x.shape[0], layer.lora_a_stacked[0].shape[2]),
dtype=torch.float32,
device=x.device,
)
shrunk_buffers: Optional[torch.Tensor] = layer.punica_wrapper.add_shrink(
buffers, x, layer.lora_a_stacked, 1.0)
if not current_platform.can_update_inplace():
buffers = shrunk_buffers
buffers = tensor_model_parallel_all_gather(buffers)
lora_output: Optional[torch.Tensor] = layer.punica_wrapper.add_expand(
output,
buffers,
layer.lora_b_stacked,
layer.lora_bias_stacked,
layer.output_slices,
offset_start=0,
add_input=True)
if not current_platform.can_update_inplace():
output = lora_output
output = output.view(*out_orig_shape)
# now have column partitioned and packed output
return output
class ColumnParallelLinearWithLoRA(BaseLinearLayerWithLoRA):
"""
LoRA on top of ColumnParallelLinear layer.
LoRA B is sliced for tensor parallelism.
There are two types for the `base_layer`:
1. ColumnParallelLinear, e.g.`dense_h_to_4h` in `FalconForCausalLM`.
2. MergedColumnParallelLinear, e.g.`gate_up_proj` in `Phi3ForCausalLM`.
"""
def __init__(self, base_layer: ColumnParallelLinear) -> None:
super().__init__(base_layer)
# The base_layer type is ColumnParallelLinear or
# MergedColumnParallelLinear, their weight sharding logic is
# inconsistent when TP is greater than 1.
self.is_merged_col_linear = type(
base_layer) is MergedColumnParallelLinear
self.tp_size = get_tensor_model_parallel_world_size()
self.output_size = self.base_layer.output_size_per_partition
# There is only one LoRA layer
self.n_slices = 1
def slice_lora_a(self, lora_a: torch.Tensor) -> torch.Tensor:
return lora_a
def slice_lora_b(self, lora_b: torch.Tensor) -> torch.Tensor:
# Applicable to cases where the base_layer is
# MergedColumnParallelLinear.
if self.is_merged_col_linear:
tp_rank = get_tensor_model_parallel_rank()
shard_size = self.output_size // 2
offset = lora_b.shape[-1] // 2
left_weight = lora_b[:, tp_rank * shard_size:(tp_rank + 1) *
shard_size]
right_weight = lora_b[:, offset + tp_rank * shard_size:offset +
(tp_rank + 1) * shard_size]
lora_b = torch.cat([left_weight, right_weight], dim=1)
# Applicable to cases where the base_layer is
# ColumnParallelLinear.
else:
tensor_model_parallel_rank = get_tensor_model_parallel_rank()
shard_size = self.output_size
start_idx = tensor_model_parallel_rank * shard_size
end_idx = (tensor_model_parallel_rank + 1) * shard_size
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
tensor_model_parallel_rank = get_tensor_model_parallel_rank()
shard_size = self.output_size
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 forward(
self, input_: torch.Tensor
) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[torch.Tensor]]]:
"""Forward of ColumnParallelLinear
Args:
input_: Tensor whose last dimension is `input_size`.
Returns:
- output
- bias
"""
bias = (self.base_layer.bias
if not self.base_layer.skip_bias_add else None)
# Matrix multiply.
output_parallel = self.apply(input_, bias)
if self.base_layer.gather_output:
# All-gather across the partitions.
output = tensor_model_parallel_all_gather(output_parallel)
else:
output = output_parallel
if not self.base_layer.return_bias:
return output
output_bias = (self.base_layer.bias
if self.base_layer.skip_bias_add else None)
return output, output_bias
@classmethod
@_not_fully_sharded_can_replace
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
) -> bool:
return type(source_layer) is ColumnParallelLinear or (
type(source_layer) is MergedColumnParallelLinear
and len(packed_modules_list) == 1)
class MergedColumnParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
"""ColumnParallelLinear layer that is composed of 2 sublayers (slices)
packed together (e.g. gate_proj + up_proj -> gate_up_proj).
This means we have 2 LoRAs, each applied to one half of the layer.
Both slices must have the same size.
"""
def __init__(
self, base_layer: Union[MergedColumnParallelLinear,
QKVParallelLinear]) -> None:
super().__init__(base_layer)
# There are two LoRA layers
self.tp_size = get_tensor_model_parallel_world_size()
self.tp_rank = get_tensor_model_parallel_rank()
# the output_sizes in MergedColumnParallelLinear is not sharded by tp
# we need to divide it by the tp_size to get correct slices size
output_sizes = self.base_layer.output_sizes
self.output_slices = tuple(
divide(output_size, self.tp_size) for output_size in output_sizes)
self.n_slices = len(self.output_slices)
self.output_ids = (self.tp_rank, ) * self.n_slices
def create_lora_weights(
self,
max_loras: int,
lora_config: LoRAConfig,
model_config: Optional[PretrainedConfig] = None,
) -> None:
"""
The main reason for overriding this function is to enhance code
maintainability.
"""
self.lora_config = lora_config
lora_a_output_size_per_partition = (
lora_config.max_lora_rank if not lora_config.fully_sharded_loras
else divide(lora_config.max_lora_rank, self.tp_size))
self.lora_a_stacked = tuple(
torch.zeros(
max_loras,
1,
lora_a_output_size_per_partition,
self.input_size,
dtype=lora_config.lora_dtype,
device=self.device,
) for _ in range(self.n_slices))
self.lora_b_stacked = tuple(
torch.zeros(
max_loras,
1,
output_size,
lora_config.max_lora_rank,
dtype=lora_config.lora_dtype,
device=self.device,
) 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]]
) -> list[Union[torch.Tensor, None]]:
return lora_a
def slice_lora_b(
self, lora_b: list[Union[torch.Tensor, None]]
) -> list[Union[torch.Tensor, None]]:
sliced_lora_b = [None] * self.n_slices
for i, (shard_id, shard_size) in enumerate(
zip(self.output_ids, self.output_slices)):
if (lora_b_i := lora_b[i]) is not None:
sliced_lora_b[i] = lora_b_i[:,
shard_size * shard_id:shard_size *
(shard_id + 1)]
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:
self.lora_a_stacked[i][
index, 0, :lora_a_i.shape[1], :lora_a_i.shape[0]].copy_(
lora_a_i.T, non_blocking=True)
if (lora_b_i := lora_b[i]) is not None:
self.lora_b_stacked[i][
index, 0, :lora_b_i.shape[1], :lora_b_i.shape[0]].copy_(
lora_b_i.T, 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.T,
non_blocking=True)
@classmethod
@_not_fully_sharded_can_replace
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
) -> bool:
return (type(source_layer) is MergedColumnParallelLinear
and len(packed_modules_list) == 2)
class QKVParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
"""
ColumnParallelLinear layer that is specifically designed for
qkv_proj. Certain models, such as chatglm3 and baichuan-7b,
only contains a single LoRA within their qkv_proj layer.
During inference with Tensor Parallel, the weights of lora_b
must be accurately partitioned according to the respective ranks.
Q slice may have different shape than K and V slices (which both have
the same shape).
"""
def __init__(self, base_layer: QKVParallelLinear) -> None:
super().__init__(base_layer)
self.q_proj_total_size = (self.base_layer.total_num_heads *
self.base_layer.head_size)
self.q_proj_shard_size = (self.base_layer.num_heads *
self.base_layer.head_size)
self.kv_proj_shard_size = (self.base_layer.num_kv_heads *
self.base_layer.head_size)
self.kv_proj_total_size = (self.base_layer.total_num_kv_heads *
self.base_layer.head_size)
# There is only one LoRA layer
self.n_slices = 1
def slice_lora_b(self, lora_b: torch.Tensor) -> torch.Tensor:
tp_rank = get_tensor_model_parallel_rank()
self.q_shard_id = tp_rank
self.kv_shard_id = tp_rank // self.base_layer.num_kv_head_replicas
lora_b_q = lora_b[:, 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
lora_b_k = lora_b[:, 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
lora_b_v = lora_b[:, v_offset +
self.kv_proj_shard_size * self.kv_shard_id:v_offset +
self.kv_proj_shard_size * (self.kv_shard_id + 1)]
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
@classmethod
@_not_fully_sharded_can_replace
def can_replace_layer(cls, source_layer: nn.Module,
lora_config: LoRAConfig, packed_modules_list: list,
model_config: Optional[PretrainedConfig]) -> bool:
return type(source_layer) is QKVParallelLinear and len(
packed_modules_list) == 1
class MergedQKVParallelLinearWithLoRA(MergedColumnParallelLinearWithLoRA):
"""MergedColumnParallelLinear layer that is composed of 3 sublayers (slices)
packed together in qkv proj fashion
(q_proj + k_proj + v_proj -> qkv_proj).
This means we have 3 LoRAs, each applied to one slice of the layer.
Q slice may have different shape than K and V slices (which both have
the same shape).
"""
def __init__(self, base_layer: QKVParallelLinear) -> None:
super().__init__(base_layer)
# There are three LoRA layer.
self.n_slices = len(self.base_layer.output_sizes)
self.tp_size = get_tensor_model_parallel_world_size()
self.tp_rank = get_tensor_model_parallel_rank()
self.q_proj_shard_size = (self.base_layer.num_heads *
self.base_layer.head_size)
self.kv_proj_shard_size = (self.base_layer.num_kv_heads *
self.base_layer.head_size)
self.q_shard_id = self.tp_rank
self.kv_shard_id = self.tp_rank // self.base_layer.num_kv_head_replicas
self.output_slices = (
self.q_proj_shard_size,
self.kv_proj_shard_size,
self.kv_proj_shard_size,
)
self.output_ids = (
self.q_shard_id,
self.kv_shard_id,
self.kv_shard_id,
)
def create_lora_weights(
self,
max_loras: int,
lora_config: LoRAConfig,
model_config: Optional[PretrainedConfig] = None,
) -> None:
"""
The main reason for overloading this function is to handle inconsistent
weight dimensions in qkv lora.
"""
super().create_lora_weights(max_loras, lora_config, model_config)
@classmethod
@_not_fully_sharded_can_replace
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
) -> bool:
return (type(source_layer) is QKVParallelLinear
and len(packed_modules_list) == 3)
# These following layers are based on the tensor parallelism strategy given in
# Y. Sheng et al., S-LoRA: Serving Thousands of Concurrent LoRA Adapters. 2023,
# https://arxiv.org/abs/2311.03285.
class ColumnParallelLinearWithShardedLoRA(ColumnParallelLinearWithLoRA):
"""
Differs from ColumnParallelLinearWithLoRA by slicing LoRA A also.
Based on S-LoRA, slicing happens along the rank dim.
"""
# For all LoRA layers where the `base_layer` is `ColumnParallelLinear`,
# their `lora_a` and `lora_b` have different sharding patterns. After
# completing the `lora_a` GEMM , a gather operation is performed.
# Therefore, the sharding of `lora_a` only needs to correspond with the
# gather operation.
def slice_lora_a(self, lora_a: torch.Tensor) -> torch.Tensor:
tp_rank = get_tensor_model_parallel_rank()
shard_size = self.lora_a_stacked[0].shape[2]
start_idx = tp_rank * shard_size
lora_a = lora_a[:, start_idx:start_idx + shard_size]
return lora_a
def apply(self,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
return _mcp_apply(x, bias, self)
@classmethod
@_fully_sharded_can_replace
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
) -> bool:
# specifying kwargs so they can be easily accessed in decorator
return super().can_replace_layer(
source_layer=source_layer,
lora_config=lora_config,
packed_modules_list=packed_modules_list,
model_config=model_config,
decorate=False,
)
class MergedColumnParallelLinearWithShardedLoRA(
MergedColumnParallelLinearWithLoRA):
"""
Differs from MergedColumnParallelLinearWithLoRA by slicing the
LoRA A's also.
Based on S-LoRA, slicing happens along the rank dim.
"""
def slice_lora_a(
self, lora_a: list[Union[torch.Tensor, None]]
) -> list[Union[torch.Tensor, None]]:
#NOTE: lora_a contains 2 subloras, and each sublora could be None.
output_shard_size = self.lora_a_stacked[0].shape[2]
output_start_idx = self.tp_rank * output_shard_size
lora_a = [
lora_a[0][:, output_start_idx:output_start_idx +
output_shard_size] if lora_a[0] is not None else None,
lora_a[1][:, output_start_idx:output_start_idx +
output_shard_size] if lora_a[1] is not None else None,
]
return lora_a
def apply(self,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
return _mcp_apply(x, bias, self)
@classmethod
@_fully_sharded_can_replace
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
) -> bool:
# specifying kwargs so they can be easily accessed in decorator
return super().can_replace_layer(
source_layer=source_layer,
lora_config=lora_config,
packed_modules_list=packed_modules_list,
model_config=model_config,
decorate=False,
)
class QKVParallelLinearWithShardedLoRA(QKVParallelLinearWithLoRA):
"""
Differs from QKVParallelLinearWithLoRA by slicing the
LoRA A's also.
Based on S-LoRA, slicing happens along the rank dim.
"""
def slice_lora_a(self, lora_a: torch.Tensor) -> torch.Tensor:
tp_rank = get_tensor_model_parallel_rank()
shard_size = self.lora_a_stacked[0].shape[2]
start_idx = tp_rank * shard_size
lora_a = lora_a[:, start_idx:start_idx + shard_size]
return lora_a
def apply(self,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
return _mcp_apply(x, bias, self)
@classmethod
@_fully_sharded_can_replace
def can_replace_layer(cls, source_layer: nn.Module,
lora_config: LoRAConfig, packed_modules_list: list,
model_config: Optional[PretrainedConfig]) -> bool:
# specifying kwargs so they can be easily accessed in decorator
return super().can_replace_layer(
source_layer=source_layer,
lora_config=lora_config,
packed_modules_list=packed_modules_list,
model_config=model_config,
decorate=False,
)
class MergedQKVParallelLinearWithShardedLoRA(MergedQKVParallelLinearWithLoRA):
"""
Differs from MergedQKVParallelLinearWithLoRA by slicing the
LoRA A's also.
Based on S-LoRA, slicing happens along the rank dim.
"""
def slice_lora_a(
self, lora_a: list[Union[torch.Tensor, None]]
) -> list[Union[torch.Tensor, None]]:
# NOTE: lora_a contains 3 subloras, and each sublora could be None.
shard_size = [self.lora_a_stacked[i].shape[2] for i in range(3)]
start_idx = [self.tp_rank * shard_size[i] for i in range(3)]
lora_a = [
lora_a[0][:, start_idx[0]:start_idx[0] +
shard_size[0]] if lora_a[0] is not None else None,
lora_a[1][:, start_idx[1]:start_idx[1] +
shard_size[1]] if lora_a[1] is not None else None,
lora_a[2][:, start_idx[2]:start_idx[2] +
shard_size[2]] if lora_a[2] is not None else None,
]
return lora_a
def apply(self,
x: torch.Tensor,
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
return _mcp_apply(x, bias, self)
@classmethod
@_fully_sharded_can_replace
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
) -> bool:
# specifying kwargs so they can be easily accessed in decorator
return super().can_replace_layer(
source_layer=source_layer,
lora_config=lora_config,
packed_modules_list=packed_modules_list,
model_config=model_config,
decorate=False,
)

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vllm/lora/layers/logits_processor.py Normal file
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@ -0,0 +1,247 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import math
from typing import Optional
import torch
import torch.nn as nn
from transformers import PretrainedConfig
from vllm.config import LoRAConfig
from vllm.distributed import (get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.vocab_parallel_embedding import (
VocabParallelEmbedding)
from vllm.platforms import current_platform
from .base import BaseLayerWithLoRA
class LogitsProcessorWithLoRA(BaseLayerWithLoRA):
"""
LoRA wrapper for LogitsProcessor, with extra logic to handle the
application of the LoRA adapter and added LoRA vocabulary.
Args:
base_layer: LogitsProcessor layer
hidden_size: hidden size of the model
dtype: data type of the model
device: device of the model
sharded_to_full_mapping: index mapping from sharded vocab to full vocab
received from base_layer.get_sharded_to_full_mapping(). If None,
no reindexing will be done.
"""
def __init__(self, base_layer: LogitsProcessor, hidden_size: int,
dtype: torch.dtype, device: torch.device,
sharded_to_full_mapping: Optional[list[int]]) -> None:
super().__init__()
self.base_layer = base_layer
self.hidden_size = hidden_size
self.dtype = dtype
self.device = device
self.tp_size = get_tensor_model_parallel_world_size()
self.tp_rank = get_tensor_model_parallel_rank()
self.sharded_to_full_mapping = sharded_to_full_mapping
@property
def logits_as_input(self):
return self.base_layer.logits_as_input
@property
def vocab_size(self):
return self.base_layer.vocab_size
@property
def scale(self):
return self.base_layer.scale
@property
def soft_cap(self):
return self.base_layer.soft_cap
@property
def use_all_gather(self):
return self.base_layer.use_all_gather
@property
def org_vocab_size(self):
return self.base_layer.org_vocab_size
@property
def include_gpu_probs_tensor(self):
return self.base_layer.include_gpu_probs_tensor
@property
def should_modify_greedy_probs_inplace(self):
return self.base_layer.should_modify_greedy_probs_inplace
def create_lora_weights(
self,
max_loras: int,
lora_config: LoRAConfig,
model_config: Optional[PretrainedConfig] = None,
) -> None:
# TODO: Verify if this condition can be further relaxed
if 32000 < self.base_layer.vocab_size > 257024:
raise ValueError("When using LoRA, vocab size must be "
"32000 >= vocab_size <= 257024")
self.lora_a_stacked = torch.zeros(
(
max_loras,
1,
lora_config.max_lora_rank,
self.hidden_size,
),
dtype=lora_config.lora_dtype,
device=self.device,
)
self.lora_b_stacked = torch.zeros(
(
max_loras,
1,
# Pad for kernel compatibility
math.ceil(self.base_layer.vocab_size /
lora_config.lora_vocab_padding_size) *
lora_config.lora_vocab_padding_size,
lora_config.max_lora_rank,
),
dtype=lora_config.lora_dtype,
device=self.device,
)
self.embeddings_tensors = torch.full(
(max_loras, lora_config.lora_extra_vocab_size, self.hidden_size),
fill_value=float("-inf"),
dtype=self.dtype,
device=self.device,
)
if self.sharded_to_full_mapping is not None:
self.sharded_to_full_mapping_gpu = torch.tensor(
self.sharded_to_full_mapping,
device=self.device,
dtype=torch.long)
else:
self.sharded_to_full_mapping_gpu = None
def reset_lora(self, index: int):
self.lora_a_stacked[index] = 0
self.lora_b_stacked[index] = 0
self.embeddings_tensors[index] = float("-inf")
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)
self.lora_a_stacked[index,
0, :lora_a.shape[1], :lora_a.shape[0]].copy_(
lora_a.T, non_blocking=True)
self.lora_b_stacked[index,
0, :lora_b.shape[1], :lora_b.shape[0]].copy_(
lora_b.T, non_blocking=True)
if embeddings_tensor is not None:
self.embeddings_tensors[
index,
:embeddings_tensor.shape[0],
:embeddings_tensor.shape[1],
] = embeddings_tensor
def _get_logits(
self,
hidden_states: torch.Tensor,
lm_head: VocabParallelEmbedding,
embedding_bias: Optional[torch.Tensor] = None,
) -> Optional[torch.Tensor]:
# Get the logits for the next tokens.
logits = lm_head.quant_method.apply(lm_head, hidden_states)
if embedding_bias is not None:
logits += embedding_bias
# Gather logits for TP
logits = self.base_layer._gather_logits(logits)
if logits is None:
return None
if self.sharded_to_full_mapping_gpu is not None:
# Reindex full logits tensor to ensure 1:1 mapping between
# index and token_id
# Example for:
# org_vocab_size = 4
# added_vocab_size = 2
# pad_to_size = 8
# tp_size = 2
# indices: [0, 1, 2, 3, 4, 5, 6, 7]
# token_id: [0, 1, 4, -1, 2, 3, 5, -1]
# Therefore, the mapping is expected to be:
# [0, 1, 4, 6, 2, 3, 5, 7] so that when we reindex,
# we get:
# indices: [0, 1, 2, 3, 4, 5, 6, 7]
# token_id: [0, 1, 2, 3, 4, 5, -1, -1]
logits = logits[:, self.sharded_to_full_mapping_gpu]
lora_logits = torch.empty(
self.embeddings_tensors.shape[0] + 1,
self.embeddings_tensors.shape[1],
hidden_states.shape[0],
dtype=self.embeddings_tensors.dtype,
device=self.embeddings_tensors.device,
)
torch.matmul(self.embeddings_tensors,
hidden_states.T,
out=lora_logits[:-1])
neg_inf, pos_inf = current_platform.get_infinity_values(
lora_logits.dtype)
lora_logits[-1] = neg_inf
lora_logits = lora_logits.mT
indices_padded = self.punica_wrapper.sampler_indices_padded
if current_platform.is_tpu() or current_platform.is_xpu():
indices_padded = indices_padded[:logits.size(0)]
lora_logits = (lora_logits.reshape(
lora_logits.shape[0] * lora_logits.shape[1],
lora_logits.shape[2],
).index_select(0, indices_padded).nan_to_num_(nan=neg_inf,
posinf=pos_inf,
neginf=neg_inf))
logits[:,
self.base_layer.org_vocab_size:self.base_layer.org_vocab_size +
lora_logits.shape[1]] = lora_logits
lora_output: Optional[
torch.Tensor] = self.punica_wrapper.add_lora_logits(
logits, hidden_states, self.lora_a_stacked,
self.lora_b_stacked, 1.0)
if not current_platform.can_update_inplace():
logits = lora_output
# Remove paddings in vocab (if any).
logits = logits[:, :self.base_layer.vocab_size]
return logits
def forward(self, *args, **kwargs):
return type(self.base_layer).forward(self, *args, **kwargs)
@classmethod
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
) -> bool:
# Special handling for the LogitsProcessor.
return False

8
vllm/lora/layers/qkv_x_parallel_linear.py Normal file
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@ -0,0 +1,8 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from .base import BaseLayerWithLoRA
#TODO: Implement this
class QKVCrossParallelLinearWithLoRA(BaseLayerWithLoRA):
pass

61
vllm/lora/layers/replicated_linear.py Normal file
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@ -0,0 +1,61 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Optional, Union
import torch
import torch.nn as nn
from transformers import PretrainedConfig
from vllm.config import LoRAConfig
from vllm.model_executor.layers.linear import ReplicatedLinear
from .base_linear import BaseLinearLayerWithLoRA
class ReplicatedLinearWithLoRA(BaseLinearLayerWithLoRA):
def __init__(self, base_layer: ReplicatedLinear) -> None:
super().__init__(base_layer, )
# To ensure interface compatibility, set to 1 always.
self.tp_size = 1
self.output_size = self.base_layer.output_size
self.n_slices = 1
def forward(
self, input_: torch.Tensor
) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[torch.Tensor]]]:
"""Forward of ReplicatedLinearWithLoRA
Args:
input_: Tensor whose last dimension is `input_size`.
Returns:
- output
- bias
"""
bias = (self.base_layer.bias
if not self.base_layer.skip_bias_add else None)
# Matrix multiply.
output = self.apply(input_, bias)
output_bias = (self.base_layer.bias
if self.base_layer.skip_bias_add else None)
if not self.base_layer.return_bias:
return output
return output, output_bias
# ReplicatedLinear should always be replaced, regardless of the fully
# sharded LoRAs setting, because it is, by definition, copied per GPU.
@classmethod
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
) -> bool:
return type(source_layer) is ReplicatedLinear

201
vllm/lora/layers/row_parallel_linear.py Normal file
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@ -0,0 +1,201 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Optional, Union, cast
import torch
import torch.nn as nn
from transformers import PretrainedConfig
from vllm.config import LoRAConfig
from vllm.distributed import (get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
split_tensor_along_last_dim,
tensor_model_parallel_all_reduce)
# yapf: disable
from vllm.model_executor.layers.linear import RowParallelLinear
from vllm.platforms import current_platform
from .base_linear import BaseLinearLayerWithLoRA
from .utils import _fully_sharded_can_replace, _not_fully_sharded_can_replace
class RowParallelLinearWithLoRA(BaseLinearLayerWithLoRA):
def __init__(self, base_layer: RowParallelLinear) -> None:
super().__init__(base_layer)
self.tp_size = get_tensor_model_parallel_world_size()
# reset input_size
self.input_size = self.base_layer.input_size_per_partition
self.output_size = self.base_layer.output_size
self.tp_rank = get_tensor_model_parallel_rank()
# There is only one LoRA layer.
self.n_slices = 1
def slice_lora_a(self, lora_a: torch.Tensor) -> torch.Tensor:
shard_size = self.input_size
start_idx = self.tp_rank * shard_size
end_idx = (self.tp_rank + 1) * shard_size
lora_a = lora_a[start_idx:end_idx, :]
return lora_a
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 forward(
self, input_: torch.Tensor
) -> Union[torch.Tensor, tuple[torch.Tensor, Optional[torch.Tensor]]]:
"""Forward of RowParallelLinear
Args:
input_: tensor whose last dimension is `input_size`. If
`input_is_parallel` is set, then the last dimension
is `input_size // tp_size`.
Returns:
- output
- bias
"""
# set up backprop all-reduce.
if self.base_layer.input_is_parallel:
input_parallel = input_
else:
# TODO: simplify code below
splitted_input = split_tensor_along_last_dim(
input_, num_partitions=self.base_layer.tp_size)
input_parallel = splitted_input[self.tp_rank].contiguous()
# Matrix multiply.
output_parallel = self.apply(input_parallel)
if self.base_layer.reduce_results and self.base_layer.tp_size > 1:
output_ = tensor_model_parallel_all_reduce(output_parallel)
else:
output_ = output_parallel
if not self.base_layer.skip_bias_add:
output = (output_ + self.base_layer.bias
if self.base_layer.bias is not None else output_)
output_bias = None
else:
output = output_
output_bias = self.base_layer.bias
if not self.base_layer.return_bias:
return output
return output, output_bias
@classmethod
@_not_fully_sharded_can_replace
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
) -> bool:
return type(source_layer) is RowParallelLinear
# The following layer is based on the tensor parallelism strategy given in
# Y. Sheng et al., S-LoRA: Serving Thousands of Concurrent LoRA Adapters. 2023,
# https://arxiv.org/abs/2311.03285.
class RowParallelLinearWithShardedLoRA(RowParallelLinearWithLoRA):
"""
Differs from RowParallelLinearWithLoRA by slicing the
LoRA B's also.
Based on S-LoRA, slicing happens along the output dim.
This yields a combined partial sum from the row parallel base
layer and column partitioned output from the LoRA.
"""
def slice_lora_b(self, lora_b: torch.Tensor) -> torch.Tensor:
shard_size = self.lora_b_stacked[0].shape[2]
start_idx = self.tp_rank * shard_size
end_idx = (self.tp_rank + 1) * shard_size
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
self.lora_bias_stacked = cast(tuple[torch.Tensor, ...],
self.lora_bias_stacked)
shard_size = self.lora_bias_stacked[0].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,
bias: Optional[torch.Tensor] = None) -> torch.Tensor:
output = self.base_layer.quant_method.apply(self.base_layer, x)
x = x.view(-1, x.shape[-1])
output, out_orig_shape = output.view(-1,
output.shape[-1]), output.shape
buffer = torch.zeros(
(self.n_slices, x.shape[0], self.lora_a_stacked[0].shape[2]),
dtype=torch.float32,
device=x.device,
)
shrunk_buffer: Optional[torch.Tensor] = self.punica_wrapper.add_shrink(
buffer, x, self.lora_a_stacked, 1.0)
if not current_platform.can_update_inplace():
buffer = shrunk_buffer
buffer = tensor_model_parallel_all_reduce(buffer)
# following S-LoRA, allows the fusing of all_gather and all_reduce
# by adding the column partitioned lora output to a slice of output
# tensor, which is a partial sum due to row parallel. All that
# remains is a standard all_reduce. User should be aware though that
# the output is not the same as a normal row_parallel, it should be
# reduced before being used
# NOTE offset are based on the rank.
shard_size = self.lora_b_stacked[0].shape[2]
offset_start = self.tp_rank * shard_size
lora_output: Optional[torch.Tensor] = self.punica_wrapper.add_expand(
output,
buffer,
self.lora_b_stacked,
self.lora_bias_stacked,
self.output_slices,
offset_start=offset_start,
add_input=True,
)
if not current_platform.can_update_inplace():
output = lora_output
output = output.view(*out_orig_shape)
return output
@classmethod
@_fully_sharded_can_replace
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
) -> bool:
# specifying kwargs so they can be easily accessed in decorator
return super().can_replace_layer(
source_layer=source_layer,
lora_config=lora_config,
packed_modules_list=packed_modules_list,
model_config=model_config,
decorate=False,
)

60
vllm/lora/layers/utils.py Normal file
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@ -0,0 +1,60 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from dataclasses import dataclass
import torch
import torch.nn as nn
from vllm.adapter_commons.layers import AdapterMapping
@dataclass
class LoRAMapping(AdapterMapping):
is_prefill: bool = False
def _get_lora_device(base_layer: nn.Module) -> torch.device:
# code borrowed from https://github.com/fmmoret/vllm/blob/fm-support-lora-on-quantized-models/vllm/lora/layers.py#L34
"""Returns the device for where to place the LoRA tensors."""
# unquantizedLinear
if hasattr(base_layer, "weight"):
return base_layer.weight.device
# Compressed Tensor
elif hasattr(base_layer, "weight_packed"):
return base_layer.weight_packed.device
# GPTQ/AWQ
elif hasattr(base_layer, "qweight"):
return base_layer.qweight.device
# HQQ marlin
elif hasattr(base_layer, "W_q"):
return base_layer.W_q.device
else:
raise ValueError(f"Unsupported base layer: {base_layer}")
def _not_fully_sharded_can_replace(can_replace):
"""
decorator which adds the condition of not using fully sharded loras
intended to wrap can_replace_layer()
"""
def dec(*args, **kwargs):
decorate = kwargs.pop("decorate") if "decorate" in kwargs else True
condition = (not kwargs["lora_config"].fully_sharded_loras
if decorate else True)
return can_replace(*args, **kwargs) and condition
return dec
def _fully_sharded_can_replace(can_replace):
"""
decorator which adds the condition of fully sharded loras
intended to wrap can_replace_layer()
"""
def dec(*args, **kwargs):
return (can_replace(*args, **kwargs)
and kwargs["lora_config"].fully_sharded_loras)
return dec

172
vllm/lora/layers/vocal_parallel_embedding.py Normal file
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@ -0,0 +1,172 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PretrainedConfig
from vllm.config import LoRAConfig
from vllm.model_executor.layers.vocab_parallel_embedding import (
VocabParallelEmbedding)
from vllm.platforms import current_platform
from .base import BaseLayerWithLoRA
class VocabParallelEmbeddingWithLoRA(BaseLayerWithLoRA):
def __init__(self, base_layer: VocabParallelEmbedding) -> None:
super().__init__()
self.base_layer = base_layer
self.embeddings_slice: Optional[tuple[int, int]]
self.embeddings_weights: Optional[torch.Tensor]
def create_lora_weights(
self,
max_loras: int,
lora_config: LoRAConfig,
model_config: Optional[PretrainedConfig] = None) -> None:
if self.base_layer.num_added_embeddings_per_partition > 0:
# We can start adding lora weights
self.embeddings_weights = self.base_layer.weight.data[
self.base_layer.num_org_embeddings_per_partition:self.
base_layer.num_org_embeddings_per_partition +
self.base_layer.num_added_embeddings_per_partition]
self.embeddings_slice = (
self.base_layer.shard_indices.added_vocab_start_index -
self.base_layer.org_vocab_size,
self.base_layer.shard_indices.added_vocab_end_index -
self.base_layer.org_vocab_size)
self.base_layer.weight.data[
self.base_layer.num_org_embeddings_per_partition:].fill_(0)
else:
self.embeddings_slice = None
self.embeddings_weights = None
self.embeddings_tensors = torch.zeros(
(
max_loras,
lora_config.lora_extra_vocab_size,
self.base_layer.embedding_dim,
),
dtype=self.base_layer.weight.dtype,
device=self.base_layer.weight.device,
)
self.lora_a_stacked = torch.zeros(
(
max_loras,
self.base_layer.org_vocab_size +
lora_config.lora_extra_vocab_size,
lora_config.max_lora_rank,
),
dtype=lora_config.lora_dtype,
device=self.base_layer.weight.device,
)
self.lora_b_stacked = torch.zeros(
(
max_loras,
1,
self.base_layer.embedding_dim,
lora_config.max_lora_rank,
),
dtype=lora_config.lora_dtype,
device=self.base_layer.weight.device,
)
self.lora_a_stacked_2d = self.lora_a_stacked.view(
self.lora_a_stacked.shape[0] * self.lora_a_stacked.shape[1],
self.lora_a_stacked.shape[2],
)
def reset_lora(self, index: int):
self.lora_a_stacked[index] = 0
self.lora_b_stacked[index] = 0
self.embeddings_tensors[index] = 0
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)
self.lora_a_stacked[index, :lora_a.shape[0], :lora_a.shape[1]].copy_(
lora_a, non_blocking=True)
self.lora_b_stacked[index,
0, :lora_b.shape[1], :lora_b.shape[0]].copy_(
lora_b.T, non_blocking=True)
if embeddings_tensor is not None:
self.embeddings_tensors[
index,
:embeddings_tensor.shape[0],
:embeddings_tensor.shape[1],
].copy_(embeddings_tensor, non_blocking=True)
if self.embeddings_slice is not None:
# TODO(yard1): Optimize this copy, we don't need to copy
# everything, just the modified part
embeddings = self.embeddings_tensors.view(
self.embeddings_tensors.shape[0] *
self.embeddings_tensors.shape[1],
self.embeddings_tensors.shape[2],
)[self.embeddings_slice[0]:self.embeddings_slice[1]]
assert self.embeddings_weights is not None
self.embeddings_weights[:embeddings.shape[0]].copy_(embeddings)
def forward(self, x: torch.Tensor) -> torch.Tensor:
added_tokens_mask = torch.where(x > self.base_layer.org_vocab_size - 1,
1, 0)
# NB: Don't use torch.narrow here. torch.narrow triggers some
# Dynamic Shape specialization in torch.compile
num_tokens = x.shape[0]
indices_1 = self.punica_wrapper._embeddings_indices[1][:num_tokens]
indices_0 = self.punica_wrapper._embeddings_indices[0][:num_tokens]
full_lora_a_embeddings = F.embedding(
x + indices_1,
self.lora_a_stacked_2d,
)
full_output = self.base_layer.forward(x +
(indices_0 * added_tokens_mask))
full_output_org = full_output
if full_output.ndim == 3:
full_output = full_output.view(
full_output.shape[0] * full_output.shape[1], -1)
if full_lora_a_embeddings.ndim == 3:
full_lora_a_embeddings = full_lora_a_embeddings.view(
full_lora_a_embeddings.shape[0] *
full_lora_a_embeddings.shape[1],
-1,
)
lora_output: Optional[
torch.Tensor] = self.punica_wrapper.add_lora_embedding(
full_output,
full_lora_a_embeddings,
self.lora_b_stacked,
add_input=True)
if not current_platform.can_update_inplace():
full_output = lora_output
return full_output.view_as(full_output_org)
@classmethod
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
) -> bool:
return type(source_layer) is VocabParallelEmbedding
@property
def weight(self):
return self.base_layer.weight

10
vllm/lora/utils.py
View File

@ -13,21 +13,21 @@ from transformers import PretrainedConfig
from vllm.config import LoRAConfig
from vllm.logger import init_logger
from vllm.lora.fully_sharded_layers import (
ColumnParallelLinearWithShardedLoRA,
MergedColumnParallelLinearWithShardedLoRA,
MergedQKVParallelLinearWithShardedLoRA, QKVParallelLinearWithShardedLoRA,
RowParallelLinearWithShardedLoRA)
# being imported for _all_lora_classes below
# yapf conflicts with isort for this block
# yapf: disable
from vllm.lora.layers import (BaseLayerWithLoRA, ColumnParallelLinearWithLoRA,
ColumnParallelLinearWithShardedLoRA,
LogitsProcessorWithLoRA,
MergedColumnParallelLinearWithLoRA,
MergedColumnParallelLinearWithShardedLoRA,
MergedQKVParallelLinearWithLoRA,
MergedQKVParallelLinearWithShardedLoRA,
QKVParallelLinearWithLoRA,
QKVParallelLinearWithShardedLoRA,
ReplicatedLinearWithLoRA,
RowParallelLinearWithLoRA,
RowParallelLinearWithShardedLoRA,
VocabParallelEmbeddingWithLoRA)
from vllm.model_executor.layers.linear import LinearBase