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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,9 +0,0 @@ | ||
| from __future__ import annotations | ||
| import os | ||
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| from hypothesis import HealthCheck, settings | ||
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| settings.register_profile('CI', settings(suppress_health_check=[HealthCheck.too_slow]), deadline=None) | ||
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| if 'CI' in os.environ: | ||
| settings.load_profile('CI') | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,78 @@ | ||
| from __future__ import annotations | ||
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| import typing as t | ||
| from openllm_core._typing_compat import TypedDict | ||
| from datasets import load_dataset | ||
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| if t.TYPE_CHECKING: | ||
| from transformers import PreTrainedTokenizerBase | ||
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| FIXED_OUTPUT_LENGTH = 128 | ||
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| class DatasetEntry(TypedDict): | ||
| human: str | ||
| gpt: str | ||
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| class SampledRequest(TypedDict): | ||
| prompt: str | ||
| prompt_length: int | ||
| output_length: int | ||
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| def prepare_sharegpt_request( | ||
| num_requests: int, tokenizer: PreTrainedTokenizerBase, max_output_length: int | None = None | ||
| ) -> list[SampledRequest]: | ||
| def transform(examples) -> DatasetEntry: | ||
| human, gpt = [], [] | ||
| for example in examples['conversations']: | ||
| human.append(example[0]['value']) | ||
| gpt.append(example[1]['value']) | ||
| return {'human': human, 'gpt': gpt} | ||
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| def process(examples, tokenizer, max_output_length: t.Optional[int]): | ||
| # Tokenize the 'human' and 'gpt' values in batches | ||
| prompt_token_ids = tokenizer(examples['human']).input_ids | ||
| completion_token_ids = tokenizer(examples['gpt']).input_ids | ||
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| # Create the transformed entries | ||
| return { | ||
| 'prompt': examples['human'], | ||
| 'prompt_length': [len(ids) for ids in prompt_token_ids], | ||
| 'output_length': [ | ||
| len(ids) if max_output_length is None else FIXED_OUTPUT_LENGTH for ids in completion_token_ids | ||
| ], | ||
| } | ||
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| def filter_length(examples) -> list[bool]: | ||
| result = [] | ||
| for prompt_length, output_length in zip(examples['prompt_length'], examples['output_length']): | ||
| if prompt_length < 4 or output_length < 4: | ||
| result.append(False) | ||
| elif prompt_length > 1024 or prompt_length + output_length > 2048: | ||
| result.append(False) | ||
| else: | ||
| result.append(True) | ||
| return result | ||
|
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||
| return ( | ||
| ( | ||
| dataset := load_dataset( | ||
| 'anon8231489123/ShareGPT_Vicuna_unfiltered', | ||
| data_files='ShareGPT_V3_unfiltered_cleaned_split.json', | ||
| split='train', | ||
| ) | ||
| ) | ||
| .filter(lambda example: len(example['conversations']) >= 2, num_proc=8) | ||
| .map(transform, remove_columns=dataset.column_names, batched=True) | ||
| .map( | ||
| process, | ||
| fn_kwargs={'tokenizer': tokenizer, 'max_output_length': max_output_length}, | ||
| remove_columns=['human', 'gpt'], | ||
| batched=True, | ||
| ) | ||
| .filter(filter_length, batched=True) | ||
| .shuffle(seed=42) | ||
| .to_list()[:num_requests] | ||
| ) |
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