# Copyright 2023-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 import builtins from typing import Optional, Union import torch import torch.nn as nn from .config import XLoraConfig Number = Union[builtins.int, builtins.float, builtins.bool] class TemperatureScaledSoftmax(nn.Module): def __init__(self, temperature=1.0): super().__init__() self.temperature = temperature self.softmax = nn.Softmax(dim=-1) def forward(self, logits): # Scale logits by the temperature scaled_logits = logits / self.temperature # Apply softmax to the scaled logits return self.softmax(scaled_logits) class XLoraClassifier(nn.Module): """ A classifier to select LoRA layers for XLora. """ def __init__( self, model: nn.Module, # PeftModel config: XLoraConfig, n_classes: int, n_layers: int, device: torch.device, ): """ Construct an X-LoRA classifier from a model, config and some metadata. Note that n_layers is the number of LoRA adapter layers, not the number of model layers. """ super().__init__() self.n_classes = n_classes self.n_layers = n_layers self.config = config self.log_scalings = [] self.softmax = TemperatureScaledSoftmax(temperature=self.config.softmax_temperature) self.override_scaling_pass_value: Number = config.scaling_pass_value self.scalings_logging = False self.dtype = next(model.parameters()).dtype add_dropout = config.xlora_dropout_p > 0.0 layers = [] if self.config.xlora_depth == 1: if config.layerwise_scalings: # bias=False if we have just one layer last = nn.Linear(config.hidden_size, n_classes * n_layers, bias=True).to(device).to(self.dtype) else: last = nn.Linear(config.hidden_size, n_classes, bias=True).to(device).to(self.dtype) else: if self.config.xlora_depth <= 0: raise ValueError("X-LoRA depth must be strictly positive.") layers.append(nn.Linear(config.hidden_size, config.xlora_size, bias=True).to(device).to(self.dtype)) layers.append(nn.ReLU()) if add_dropout: layers.append(nn.Dropout(p=config.xlora_dropout_p)) for _ in range(config.xlora_depth - 2): layers.append(nn.Linear(config.xlora_size, config.xlora_size, bias=True).to(device).to(self.dtype)) layers.append(nn.ReLU()) if add_dropout: layers.append(nn.Dropout(p=config.xlora_dropout_p)) if config.layerwise_scalings: last = nn.Linear(config.xlora_size, n_classes * n_layers, bias=True).to(device).to(self.dtype) else: last = nn.Linear(config.xlora_size, n_classes, bias=True).to(device).to(self.dtype) self.layers = nn.Sequential(*layers, last) def make_dummy_scalings( self, input_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, *args, **kwargs, ) -> torch.Tensor: """ Make some dummy scalings for the scalings pass (the one to get the logits for the X-LoRA classifier). These are of shape (batch_size, seq_len, n_layers, n_classes) and filled with the override scalings pass value. Note that n_layers is the number of LoRA adapter layers, not the number of model layers. """ if input_ids is not None: batch_size = input_ids.shape[0] device = input_ids.device seq_len = input_ids.shape[1] else: batch_size = inputs_embeds.shape[0] device = inputs_embeds.device seq_len = inputs_embeds.shape[1] return torch.full( # type: ignore (batch_size, seq_len, self.n_layers, self.n_classes), self.override_scaling_pass_value, ).to(device=device, dtype=self.dtype) def forward( self, result, input_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, *args, **kwargs, ) -> torch.Tensor: """ Using the hidden states of the model, predict `n_classes` LoRA alpha values. Returns the scalings. """ if input_ids is not None: batch_size = input_ids.shape[0] seq_len = input_ids.shape[1] else: batch_size = inputs_embeds.shape[0] seq_len = inputs_embeds.shape[1] hidden_states = result.hidden_states # type: ignore hidden_state = hidden_states[-1] # Get the last hidden state ### Classifier run # hidden_state=[batch_size, seq_len, hidden_size] logits = self.layers.forward(hidden_state) ### Repeat to make layerwise scalings ### If layerwise_scalings=False, then the classifier only outputs logits which are not layer-wise. ### So, we expand them to the correct shape. if not self.config.layerwise_scalings: logits = logits.unsqueeze(2) logits = logits.expand(-1, -1, self.n_layers, -1) ### Classifier run scalings = logits.reshape(batch_size, seq_len, self.n_layers, self.n_classes) # scalings = [batch_size, seq_len, n_layers, n_classes] if self.config.enable_softmax: scalings = self.softmax(scalings) if self.scalings_logging: self.log_scalings.append(scalings) return scalings def _get_bucketed_scalings(self) -> dict[int, tuple[list[int], list[torch.Tensor]]]: """ Returns bucketed scalings, bucketed by seq_len. Each value consists of the positions (the first) and the associated tensors. The positions are paired with the associated tensors and give the position in the scaling log. Each scaling is a tensor of shape (batch_size, seq_len, n_layers, n_classes)). """ seqlens_map: dict[int, tuple[list[int], list[torch.Tensor]]] = {} for i, scaling in enumerate(self.log_scalings): seq_len = scaling.shape[1] if seq_len not in seqlens_map: seqlens_map[seq_len] = ([i], [scaling]) else: seqlens_map[seq_len][0].append(i) seqlens_map[seq_len][1].append(scaling) return seqlens_map def _set_override_scaling_pass_value(self, value: Union[Number, None]): if value is None: self.override_scaling_pass_value = 1 / self.n_classes else: self.override_scaling_pass_value = value self.config.scaling_pass_value = self.override_scaling_pass_value