a8c866c32b
* add ppl benchmark * add license * add readme * add dataset argument * add dataset usage * fixed low bit args * correct result * fix terminal display * fix ppl update * enable fp16 fp32 bf16 * format the desc * fix model_kwargs * add more readme
58 lines
No EOL
2.1 KiB
Python
58 lines
No EOL
2.1 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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import torch
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from bigdl.llm.ggml.quantize import ggml_tensor_qtype
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from ppl import BigDLPPL
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from datasets import load_dataset
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import argparse
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def parse_kwargs(kwstr):
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kvpair = [item.split('=') for item in kwstr.split(',') if item != ""]
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return {k:v for k, v in kvpair}
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("--model_path", required=True, type=str)
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parser.add_argument("--precisions", required=False, type=str, default=None, nargs='+')
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parser.add_argument("--model_kwargs", required=False, type=str, default="")
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parser.add_argument("--torch_dtype", type=str, default=None)
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parser.add_argument("--device", type=str, default=None)
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parser.add_argument("--dataset", type=str, default='path=wikitext,name=wikitext-2-raw-v1')
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return parser.parse_args()
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def main():
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args = parse_args()
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text = load_dataset(**parse_kwargs(args.dataset), split="test")["text"]
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additional_model_kwargs = parse_kwargs(args.model_kwargs)
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summary = {}
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for precision in args.precisions:
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model_kwargs = additional_model_kwargs
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if precision in ggml_tensor_qtype.keys():
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model_kwargs['load_in_low_bit'] = precision
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else:
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model_kwargs['torch_dtype'] = getattr(torch, precision)
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print(model_kwargs)
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ppl_evaluator = BigDLPPL(model_path=args.model_path, device=args.device, **model_kwargs)
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ppl = ppl_evaluator.perplexity_hf(text)
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summary[precision] = ppl
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print(summary)
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main() |