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# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# 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 trl import SFTTrainer
class LayerSkipSFTTrainer(SFTTrainer):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.early_exit_layer = 0 # initialize with 0
self.always_last_layer = True
self.early_exit_loss_scale = 1.0
def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):
self.early_exit_layer = (
self.early_exit_layer % (model.config.num_hidden_layers - 1)
) + 1 # rotates between [1, num_hidden_layers-1]
bs, seqlen = inputs.input_ids.shape
labels = inputs.pop("labels")
outputs = model(**inputs, output_hidden_states=True)
hidden_state = outputs["hidden_states"][self.early_exit_layer].to(model.dtype)
if self.early_exit_layer != model.config.num_hidden_layers:
hidden_state = model.model.norm(hidden_state)
logits = model.lm_head(hidden_state)
loss_early = model.loss_function(logits=logits, labels=labels, vocab_size=model.vocab_size)
if self.always_last_layer:
loss_last = model.loss_function(logits=outputs["logits"], labels=labels, vocab_size=model.vocab_size)
loss = self.early_exit_loss_scale * loss_early.to(loss_last.device) + 1.0 * loss_last
# normalize loss scales
loss = loss / (1.0 + self.early_exit_loss_scale)
else:
loss = loss_early
return loss
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