linitra24andJee Jee Li
c81eb3e69c
Signed-off-by: linitra24 <Joy25810@foxmail.com> Signed-off-by: linitra24 <renshuang.zhou@daocloud.io> Co-authored-by: Jee Jee Li <pandaleefree@gmail.com>
98 lines
3.2 KiB
Python
98 lines
3.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
|
|
|
|
import torch
|
|
from transformers import PretrainedConfig
|
|
|
|
from vllm.config.lora import LoRAConfig
|
|
from vllm.platforms import current_platform
|
|
|
|
from .replicated_linear import ReplicatedLinearWithLoRA
|
|
|
|
|
|
class ClassificationHeadWithLoRA(ReplicatedLinearWithLoRA):
|
|
def create_lora_weights(
|
|
self,
|
|
max_loras: int,
|
|
lora_config: LoRAConfig,
|
|
model_config: PretrainedConfig | None = None,
|
|
) -> None:
|
|
# Preserve ordinary LoRA A/B support for classification heads.
|
|
super().create_lora_weights(max_loras, lora_config, model_config)
|
|
self.full_weight_stacked = torch.zeros(
|
|
max_loras,
|
|
1,
|
|
self.output_size,
|
|
self.input_size,
|
|
dtype=lora_config.lora_dtype,
|
|
device=self.device,
|
|
)
|
|
self.full_bias_stacked = torch.zeros(
|
|
max_loras,
|
|
self.output_size,
|
|
dtype=self.base_layer.params_dtype,
|
|
device=self.device,
|
|
)
|
|
self.full_module_enabled = torch.zeros(
|
|
max_loras, dtype=torch.bool, device=self.device
|
|
)
|
|
|
|
def reset_module_to_save(self, index: int) -> None:
|
|
self.full_weight_stacked[index].zero_()
|
|
self.full_bias_stacked[index].zero_()
|
|
self.full_module_enabled[index] = False
|
|
|
|
def set_module_to_save(
|
|
self,
|
|
index: int,
|
|
weight: torch.Tensor,
|
|
bias: torch.Tensor | None,
|
|
) -> None:
|
|
self.reset_module_to_save(index)
|
|
self.full_weight_stacked[index, 0].copy_(weight, non_blocking=True)
|
|
if bias is not None:
|
|
self.full_bias_stacked[index].copy_(bias, non_blocking=True)
|
|
self.full_module_enabled[index] = True
|
|
|
|
def forward(self, input_: torch.Tensor) -> torch.Tensor:
|
|
# TODO base_result maybe don't need compute truly.
|
|
base_result = super().forward(input_)
|
|
output = base_result[0] if isinstance(base_result, tuple) else base_result
|
|
|
|
full_output = self.punica_wrapper.apply_lora_full_linear(
|
|
output,
|
|
input_.to(self.full_weight_stacked.dtype),
|
|
self.full_weight_stacked,
|
|
self.full_bias_stacked,
|
|
self.full_module_enabled,
|
|
)
|
|
|
|
if full_output is not None:
|
|
output = full_output
|
|
return output
|
|
|
|
def _apply_lora_to_output(
|
|
self, x: torch.Tensor, output: torch.Tensor
|
|
) -> torch.Tensor:
|
|
original_shape = output.shape if output.ndim == 3 else None
|
|
if x.ndim == 3 and output.ndim == 3:
|
|
output = output.flatten(0, 1)
|
|
x = x.flatten(0, 1)
|
|
|
|
# Classification outputs are request-level, so use prompt-based LoRA routing.
|
|
lora_output: torch.Tensor | None = self.punica_wrapper.add_lora_logits(
|
|
output,
|
|
x.to(self.lora_a_stacked[0].dtype),
|
|
self.lora_a_stacked[0],
|
|
self.lora_b_stacked[0],
|
|
1.0,
|
|
)
|
|
if not current_platform.can_update_inplace():
|
|
output = lora_output
|
|
|
|
if original_shape is not None:
|
|
output = output.reshape(original_shape)
|
|
|
|
return output
|