ff040c8f01
* enabling xetla only supports qtype=SYM_INT4 or FP8E5 * LISA Finetuning Example on gpu * update readme * add licence * Explain parameters of lisa & Move backend codes to src dir * fix style * fix style * update readme * support chatglm * fix style * fix style * update readme * fix
81 lines
3.2 KiB
Python
81 lines
3.2 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from transformers import TrainerCallback
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import numpy as np
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from ipex_llm.utils.common import invalidInputError
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# source: https://github.com/OptimalScale/LMFlow/blob/main/src/lmflow/pipeline/finetuner.py
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class DynamicLayerActivationCallback(TrainerCallback):
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def __init__(self, n_layers, interval_steps, model):
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super().__init__()
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self.n_layers = n_layers
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self.interval_steps = interval_steps
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self.model = model
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# Determine the way to access layers based on the model type
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class_to_layers_map = {
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'LlamaForCausalLM': 'model.model.layers',
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'Qwen2ForCausalLM': 'model.model.layers',
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'MistralForCausalLM': 'model.model.layers',
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'MixtralForCausalLM': 'model.model.layers',
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'GemmaForCausalLM': 'model.model.layers',
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'GPT2LMHeadModel': 'model.transformer.h',
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'ChatGLMModel': 'model.transformer.encoder.layers',
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}
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model_class_name = self.model.__class__.__name__
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if model_class_name in class_to_layers_map:
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self.layers_attribute = class_to_layers_map[model_class_name]
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else:
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# self.layers_attribute = training_args.lisa_layers_attribute
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invalidInputError(False, f"Model {model_class_name} not supported.")
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# Dynamically execute to get the number of layers
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self.total_layers = len(eval('self.' + self.layers_attribute))
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self.active_layers_indices = []
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def freeze_all_layers(self):
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layers = eval('self.' + self.layers_attribute) # Dynamically execute to get layers
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for layer in layers:
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for param in layer.parameters():
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param.requires_grad = False
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def on_step_begin(self, args, state, control, **kwargs):
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# Check if it's time to switch active layers, including at step 0
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if state.global_step % self.interval_steps == 0:
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self.switch_active_layers()
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def switch_active_layers(self):
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# First, disable gradients for all layers
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self.freeze_all_layers()
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# Randomly select n_layers to activate
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layers = eval('self.' + self.layers_attribute) # Re-fetch layer references
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self.active_layers_indices = np.random.choice(
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range(self.total_layers),
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self.n_layers,
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replace=False
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)
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print(
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f"Activating layers at indices: {self.active_layers_indices} for the next steps.",
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flush=True
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)
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# Enable gradients only for the selected layers
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for idx in self.active_layers_indices:
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for param in layers[idx].parameters():
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param.requires_grad = True
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