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[mypy] Forward pass function type hints in lora (#11740)

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Signed-off-by: lucast2021 <lucast2021@headroyce.org>
Co-authored-by: lucast2021 <lucast2021@headroyce.org>
This commit is contained in:
Lucas Tucker 2025-01-06 01:59:36 -06:00 committed by GitHub
parent 022c5c6944
commit 9c749713f6
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GPG Key ID: B5690EEEBB952194
3 changed files with 14 additions and 5 deletions
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12
vllm/lora/layers.py
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@ -405,7 +405,9 @@ class ReplicatedLinearWithLoRA(BaseLinearLayerWithLoRA):
self.output_size = self.base_layer.output_size self.output_size = self.base_layer.output_size
self.n_slices = 1 self.n_slices = 1
def forward(self, input_): def forward(
self, input_: torch.Tensor
) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]:
"""Forward of ReplicatedLinearWithLoRA """Forward of ReplicatedLinearWithLoRA
Args: Args:
@ -496,7 +498,9 @@ class ColumnParallelLinearWithLoRA(BaseLinearLayerWithLoRA):
bias = bias[start_idx:end_idx] bias = bias[start_idx:end_idx]
return bias return bias
def forward(self, input_): def forward(
self, input_: torch.Tensor
) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]:
"""Forward of ColumnParallelLinear """Forward of ColumnParallelLinear
Args: Args:
@ -833,7 +837,9 @@ class RowParallelLinearWithLoRA(BaseLinearLayerWithLoRA):
def slice_bias(self, bias: torch.Tensor) -> torch.Tensor: def slice_bias(self, bias: torch.Tensor) -> torch.Tensor:
return bias return bias
def forward(self, input_): def forward(
self, input_: torch.Tensor
) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]:
"""Forward of RowParallelLinear """Forward of RowParallelLinear
Args: Args:

3
vllm/lora/models.py
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@ -4,7 +4,7 @@ import math
import os import os
import re import re
from dataclasses import dataclass, field from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Sequence, Type from typing import Any, Callable, Dict, List, Optional, Sequence, Type, Union
import safetensors.torch import safetensors.torch
import torch import torch
@ -219,6 +219,7 @@ class LoRAModel(AdapterModel):
config["vllm_max_position_embeddings"] = max_position_embeddings config["vllm_max_position_embeddings"] = max_position_embeddings
peft_helper = PEFTHelper.from_dict(config) peft_helper = PEFTHelper.from_dict(config)
unexpected_modules: List[Union[list[str], str]]
if os.path.isfile(lora_tensor_path): if os.path.isfile(lora_tensor_path):
tensors: Dict[str, torch.Tensor] = {} tensors: Dict[str, torch.Tensor] = {}
# Find unexpected modules. # Find unexpected modules.

4
vllm/model_executor/layers/linear.py
View File

@ -238,7 +238,9 @@ class ReplicatedLinear(LinearBase):
assert param.size() == loaded_weight.size() assert param.size() == loaded_weight.size()
param.data.copy_(loaded_weight) param.data.copy_(loaded_weight)
def forward(self, x: torch.Tensor) -> torch.Tensor: def forward(
self, x: torch.Tensor
) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]:
bias = self.bias if not self.skip_bias_add else None bias = self.bias if not self.skip_bias_add else None
assert self.quant_method is not None assert self.quant_method is not None
output = self.quant_method.apply(self, x, bias) output = self.quant_method.apply(self, x, bias)