59 lines
2.2 KiB
Python
59 lines
2.2 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 bigdl.llm.transformers import AutoModelForCausalLM
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import inspect
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from lm_eval.models.huggingface import AutoCausalLM
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from lm_eval import utils
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from functools import partial
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# wrap and force the Reorderer to be in a decrease order
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# This is a workaround to avoid frequent memory allocation which may cause OOM
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def force_decrease_order(Reorderer):
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def DecreaseReorderer(arr, fn):
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def _collate(x):
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len, tokens = fn(x)
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len = - abs(len)
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return len, tokens
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return Reorderer(arr, _collate)
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return DecreaseReorderer
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utils.Reorderer = force_decrease_order(utils.Reorderer)
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class BigDLLM(AutoCausalLM):
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AUTO_MODEL_CLASS = AutoModelForCausalLM
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AutoCausalLM_ARGS = inspect.getfullargspec(AutoCausalLM.__init__).args
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def __init__(self, *args, **kwargs):
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if 'device' in kwargs and 'xpu' in kwargs['device']:
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import intel_extension_for_pytorch
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self.bigdl_llm_kwargs = {}
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keys = list(kwargs.keys())
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for k in keys:
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if k not in self.AutoCausalLM_ARGS:
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self.bigdl_llm_kwargs[k] = kwargs.pop(k)
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self.bigdl_llm_kwargs['use_cache'] = kwargs.get('use_cache', True)
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self.bigdl_llm_kwargs['optimize_model'] = kwargs.get('optimize_model', True)
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AutoModelForCausalLM.from_pretrained = partial(AutoModelForCausalLM.from_pretrained, **self.bigdl_llm_kwargs)
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kwargs['trust_remote_code'] = kwargs.get('trust_remote_code', True)
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super().__init__(*args, **kwargs)
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@property
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def add_special_tokens(self) -> bool:
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return False
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