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56 lines
2.1 KiB
Python
56 lines
2.1 KiB
Python
from mmengine.config import read_base
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with read_base():
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# LLM compression datasets
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from opencompass.configs.datasets.llm_compression.llm_compression import llm_compression_datasets
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# Model configs
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from opencompass.configs.models.qwen.hf_qwen1_5_7b import models as qwen1_5_7b
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from opencompass.configs.models.qwen.hf_qwen1_5_14b import models as qwen1_5_14b
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from opencompass.configs.models.hf_llama.hf_llama2_7b import models as llama2_7b
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from opencompass.configs.models.hf_llama.hf_llama2_13b import models as llama2_13b
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from opencompass.partitioners import NaivePartitioner
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from opencompass.runners import LocalRunner
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from opencompass.summarizers import LLMCompressionSummarizer
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from opencompass.tasks import OpenICLEvalTask, OpenICLInferTask
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# -------------Inference Stage ----------------------------------------
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datasets = [*llm_compression_datasets]
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workdir = 'outputs/llm_compression'
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models = [
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*qwen1_5_7b,
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*qwen1_5_14b,
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*llama2_7b,
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*llama2_13b,
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]
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# Set custom batch_size and num_gpus for faster loss calculation
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# Smaller batch_size should give more precise results, at the cost of worse performance
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model_cfg = dict(batch_size=8, run_cfg=dict(num_gpus=4, num_procs=1))
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for mdl in models:
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mdl.update(model_cfg)
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infer = dict(
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# The OpenCompass implementation of BPC currently only supports NaivePartitioner, as the sliding window approach requires the dataset to be loaded sequentially. Using other partitioner types may produce incorrect results.
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partitioner=dict(type=NaivePartitioner),
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runner=dict(
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type=LocalRunner,
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task=dict(type=OpenICLInferTask),
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max_num_workers=256, # Maximum concurrent evaluation task count
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),
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)
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# -------------Evaluation Stage ----------------------------------------
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eval = dict(partitioner=dict(type=NaivePartitioner),
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runner=dict(
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type=LocalRunner,
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task=dict(type=OpenICLEvalTask),
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max_num_workers=256,
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))
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# -------------Summarization Stage ----------------------------------------
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summarizer = dict(type=LLMCompressionSummarizer)
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