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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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| # Copyright (c) Microsoft Corporation. | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
|
|
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| # DeepSpeed Team | ||
|
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| from .optimized_linear import LoRAOptimizedLinear, OptimizedLinear | ||
|
|
||
| import torch | ||
|
|
||
| try: | ||
| import transformers | ||
| except ImportError: | ||
| transformers = None | ||
|
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||
|
|
||
| def init_lora(model): | ||
| model.requires_grad_(False) | ||
| for m in model.modules(): | ||
| if isinstance(m, LoRAOptimizedLinear): | ||
| m.init_lora() | ||
|
|
||
|
|
||
| class Init(object): | ||
| """ | ||
| Init context wrapper similar in style to zero.Init. Allows for injecting OptimizedLinear during model | ||
| construction which will shard base weights and reduce overall memory usage during model init. Primarily | ||
| useful when initializing a model via transformers.AutoModelForCausalLM. | ||
| Example usage: | ||
| lora_config = deepspeed.linear.LoRAConfig(..) | ||
| quant_config = deepspeed.linear.QuantizationConfig(..) | ||
| with deepspeed.linear.Init(lora_config=lora_config, quant_config=quant_config): | ||
| model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3.1-405B") | ||
| """ | ||
|
|
||
| def __init__(self, lora_config=None, quant_config=None): | ||
| self._orig_nn_linear = torch.nn.Linear | ||
| self._orig_causallm_pretrained = None | ||
| if transformers != None: | ||
| self._orig_causallm_pretrained = transformers.AutoModelForCausalLM.from_pretrained | ||
| self._orig_causallm_config = transformers.AutoModelForCausalLM.from_config | ||
| self.lora_config = lora_config | ||
| self.quant_config = quant_config | ||
| self._post_init_complete = False | ||
|
|
||
| def __enter__(self): | ||
|
|
||
| class OptLinearWrapper: | ||
| _orig_nn_linear = self._orig_nn_linear | ||
| _lora_config = self.lora_config | ||
| _quant_config = self.quant_config | ||
|
|
||
| def __new__(self, *args, **kwargs): | ||
| self._lora_config.delay_lora_init = True | ||
| kwargs['lora_config'] = self._lora_config | ||
| kwargs['quantization_config'] = self._quant_config | ||
| kwargs['linear_cls'] = self._orig_nn_linear | ||
| return OptimizedLinear(*args, **kwargs) | ||
|
|
||
| def _model_init(model): | ||
| if self.lora_config != None: | ||
| init_lora(model) | ||
| self._post_init_complete = True | ||
| return model | ||
|
|
||
| # ensures non-lora params are frozen and lora weights are initialized | ||
| def from_pretrained(*args, **kwargs): | ||
| model = self._orig_causallm_pretrained(*args, **kwargs) | ||
| return _model_init(model) | ||
|
|
||
| def from_config(*args, **kwargs): | ||
| model = self._orig_causallm_config(*args, **kwargs) | ||
| return _model_init(model) | ||
|
|
||
| torch.nn.Linear = OptLinearWrapper | ||
| if transformers != None: | ||
| transformers.AutoModelForCausalLM.from_pretrained = from_pretrained | ||
| transformers.AutoModelForCausalLM.from_config = from_config | ||
|
|
||
| def __exit__(self, *args, **kwargs): | ||
| torch.nn.Linear = self._orig_nn_linear | ||
| if not self._post_init_complete: | ||
| print('WARNING: For some reason LoRA modules are not initialized, this is usually done automatically ' | ||
| 'if using transformers via (AutoModelForCausalLM from_pretrained/from_config). ' | ||
| 'You must call `init_lora` on each module in order to use DeepSpeed LoRA, otherwise ' | ||
| 'you will error out during runtime.') | ||
| else: | ||
| transformers.AutoModelForCausalLM.from_pretrained = self._orig_causallm_pretrained | ||
| transformers.AutoModelForCausalLM.from_config = self._orig_causallm_config |
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