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# Copyright 2024-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from typing import Any, Callable, Optional
import torch
import torch.nn as nn
from torch import Tensor
from peft.tuners import lora
from .config import XLoraConfig
class XLoraLayer:
"""
A XLoraLayer wraps any LoraLayer and performs the XLora operation on the LoRA adaptors specified. Its primary API
is the forward method, which uses the scalings to execute the XLora algorithm.
"""
def __init__(
self,
model: nn.Module, # XLoraModel
target: lora.LoraLayer,
target_forward: Callable[..., Any],
layer_number: int,
config: XLoraConfig,
) -> None:
self.model = model
self.target_forward = target_forward
self.target = target
self.layer_number = layer_number
self.config = config
"""
Apply the scalings for the adapter.
"""
@staticmethod
def apply_scalings_to_x(x: torch.Tensor, scalings_layer: torch.Tensor, adapter: int) -> torch.Tensor:
# scalings_layer = [batch_size, seq_len, n_classes]
scalings = scalings_layer[:, :, adapter].unsqueeze(-1)
# scalings_layer = [batch_size, seq_len, 1]
return x * scalings
"""
Get the scalings for this layer, potentially applying topk and topk+softmax. This is called before
`apply_scalings_to_x`
"""
def get_maybe_topk_scalings(self, scalings) -> torch.Tensor:
# xlora_scalings = [batch_size, seq_len, n_classes]
xlora_scalings: Tensor = scalings[:, :, self.layer_number, :] # type: ignore
if self.config.top_k_lora is not None:
_, topk_indices = torch.topk(xlora_scalings, k=self.config.top_k_lora, dim=-1)
# Mask the topk to True, the rest to False
mask = torch.zeros_like(xlora_scalings, dtype=torch.bool)
mask.scatter_(-1, topk_indices, True)
xlora_scalings = xlora_scalings * mask.to(xlora_scalings.dtype)
if self.config.enable_softmax_topk:
nonzero_mask = xlora_scalings != 0
softmax_res_nonzero = torch.softmax(xlora_scalings[nonzero_mask], dim=-1)
xlora_scalings[nonzero_mask] = softmax_res_nonzero
return xlora_scalings
class XLoraLinearLayer(XLoraLayer):
def __init__(
self,
model: nn.Module,
target: lora.Linear,
target_forward: Callable[..., Any],
layer_number: int,
config: XLoraConfig,
) -> None:
super().__init__(model, target, target_forward, layer_number, config)
def forward(self, x: Tensor, *args: Any, scalings: Optional[Tensor] = None, **kwargs: Any) -> Tensor:
"""
This method is designed to be a drop-in-replacement for the LoRA layers' .forward method. To use it, a bound
method must be created (bound to an instance of the XLoraLayer class).
"""
previous_dtype = x.dtype
if scalings is not None:
xlora_scalings = self.get_maybe_topk_scalings(scalings)
result = self.target.base_layer(x, *args, **kwargs)
# Ignore if disabled. We want to make sure this is always run.
if not self.target.merged:
for adapter_n, active_adapter in enumerate(self.target.active_adapters):
# TODO: implement X-LoRA with Lora+Dora layers
if self.target.use_dora[active_adapter]:
raise ValueError("X-LoRA currently does not support LoRA layers with DoRA")
if active_adapter not in self.target.lora_A.keys():
continue
lora_A = self.target.lora_A[active_adapter]
lora_B = self.target.lora_B[active_adapter]
dropout = self.target.lora_dropout[active_adapter]
scaling = self.target.scaling[active_adapter]
x = x.to(lora_A.weight.dtype) # type: ignore
if scalings is not None:
x_mod = self.apply_scalings_to_x(x, xlora_scalings, adapter_n)
scaling_weight = self.config.global_scaling_weight
else:
x_mod = x
scaling_weight = 1
result += lora_B(lora_A(dropout(x_mod))) * scaling * scaling_weight
result = result.to(previous_dtype)
return result
class XLoraEmbeddingLayer(XLoraLayer):
def __init__(
self,
model: nn.Module,
target: lora.Embedding,
target_forward: Callable[..., Any],
layer_number: int,
config: XLoraConfig,
) -> None:
super().__init__(model, target, target_forward, layer_number, config)
def forward(self, x: Tensor, *args: Any, scalings: Optional[Tensor] = None, **kwargs: Any) -> Tensor:
"""
This method is designed to be a drop-in-replacement for the LoRA layers' .forward method. To use it, a bound
method must be created (bound to an instance of the XLoraLayer class).
"""
if scalings is not None:
xlora_scalings = self.get_maybe_topk_scalings(scalings)
result = self.target.base_layer(x, *args, **kwargs)
# Ignore if disabled. We want to make sure this is always run.
if not self.target.merged:
for adapter_n, active_adapter in enumerate(self.target.active_adapters):
# TODO: implement X-LoRA with Lora+Dora layers
if self.target.use_dora.get(active_adapter, False):
raise ValueError("X-LoRA currently does not support LoRA layers with DoRA")
if active_adapter not in self.target.lora_embedding_A:
continue
embedding_A = self.target.lora_embedding_A[active_adapter].T
embedding_B = self.target.lora_embedding_B[active_adapter].T
scaling = self.target.scaling[active_adapter]
after_A = self.target._embed(x, embedding_A) # type: ignore
if scalings is not None:
after_A_mod = self.apply_scalings_to_x(after_A, xlora_scalings, adapter_n)
scaling_weight = self.config.global_scaling_weight
else:
after_A_mod = after_A
scaling_weight = 1
result += (after_A_mod @ embedding_B) * scaling * scaling_weight
return result
class XLoraConv2dLayer(XLoraLayer):
def __init__(
self,
model: nn.Module,
target: lora.Conv2d,
target_forward: Callable[..., Any],
layer_number: int,
config: XLoraConfig,
) -> None:
super().__init__(model, target, target_forward, layer_number, config)
def forward(self, x: Tensor, *args: Any, scalings: Optional[Tensor] = None, **kwargs: Any) -> Tensor:
"""
This method is designed to be a drop-in-replacement for the LoRA layers' .forward method. To use it, a bound
method must be created (bound to an instance of the XLoraLayer class).
"""
previous_dtype = x.dtype
if scalings is not None:
xlora_scalings = self.get_maybe_topk_scalings(scalings)
result = self.target.base_layer(x, *args, **kwargs)
# Ignore if disabled. We want to make sure this is always run.
if not self.target.merged:
for adapter_n, active_adapter in enumerate(self.target.active_adapters):
# TODO: implement X-LoRA with Lora+Dora layers
if self.target.use_dora[active_adapter]:
raise ValueError("X-LoRA currently does not support LoRA layers with DoRA")
if active_adapter not in self.target.lora_A.keys():
continue
lora_A = self.target.lora_A[active_adapter]
lora_B = self.target.lora_B[active_adapter]
dropout = self.target.lora_dropout[active_adapter]
scaling = self.target.scaling[active_adapter]
x = x.to(lora_A.weight.dtype) # type: ignore
if scalings is not None:
x_mod = self.apply_scalings_to_x(x, xlora_scalings, adapter_n)
scaling_weight = self.config.global_scaling_weight
else:
x_mod = x
scaling_weight = 1
result += lora_B(lora_A(dropout(x_mod))) * scaling * scaling_weight
result = result.to(previous_dtype)
return result
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