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* [Feature] Support import configs/models/summarizers from whl * Update LCBench configs * Update * Update * Update * Update * update * Update * Update * Update * Update * Update
51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
from opencompass.openicl.icl_prompt_template import PromptTemplate
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from opencompass.openicl.icl_retriever import ZeroRetriever
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from opencompass.openicl.icl_inferencer import SWCELossInferencer
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from opencompass.openicl.icl_evaluator import BPCEvaluator
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from opencompass.datasets import LLMCompressionDataset
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# The three corpora for llm_compression used in the original paper
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# See configs/datasets/llm_compression/README.md for more details
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subset_mapping = {
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'arxiv_math': ['arxiv_math'],
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'commoncraw': ['cc'],
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'python': ['python'],
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}
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# Build LLM Compression datasets
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llm_compression_datasets = []
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for _name in subset_mapping.keys():
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llm_cmp_infer_cfg = dict(
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prompt_template=dict(
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type=PromptTemplate,
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template='{content}',
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),
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# No in-context example, using ZeroRetriever
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retriever=dict(type=ZeroRetriever),
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# Calculates cross entropy loss for each batch based on a sliding context window
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# Setting block_size=1900 and stride=512 according to the original paper
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inferencer=dict(type=SWCELossInferencer, block_size=1900, stride=512),
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)
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# Calculates Bits per Character (BPC) based on the CE loss from the inference stage
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llm_cmp_eval_cfg = dict(evaluator=dict(type=BPCEvaluator))
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llm_compression_datasets.append(
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dict(
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abbr=f'llm_compression-{_name}',
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type=LLMCompressionDataset,
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path='./data/llm-compression',
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name=_name,
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samples=None, # Set small samples for testing
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reader_cfg=dict(
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input_columns=['content'],
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output_column=None,
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),
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infer_cfg=llm_cmp_infer_cfg,
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eval_cfg=llm_cmp_eval_cfg,
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))
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del _name
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