File size: 1,614 Bytes
37351b6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | from reproduction.prepare_ruler_data import (
LLAMA3_ASSISTANT_SUFFIX,
LLAMA3_USER_PREFIX,
make_exact_llama3_chat_prompt,
normalize_row,
prefix_pad_text,
)
def test_normalize_ruler_row_matches_official_harness_schema():
row = {"index": 3, "input": "prompt", "outputs": ["answer"], "length": 32701}
assert normalize_row(row) == {
"index": 3,
"input": "prompt",
"outputs": ["answer"],
"length": 32701,
"length_w_model_temp": 32701,
"answer_prefix": "",
}
def test_prefix_padding_is_inserted_before_complete_original_prompt():
padded = prefix_pad_text("Question: preserved?", pad_tokens=3)
assert padded == " filler filler filler\nQuestion: preserved?"
assert padded.endswith("Question: preserved?")
class _WhitespaceTokenizer:
def __call__(self, text, add_special_tokens=False):
assert add_special_tokens is False
return {"input_ids": text.split()}
def test_exact_llama3_chat_prompt_preserves_task_and_assistant_generation_suffix():
raw = "Document: the answer-token is preserved. Question: what is it?"
target = 40
prompt = make_exact_llama3_chat_prompt(
raw, tokenizer=_WhitespaceTokenizer(), target_tokens=target
)
token_ids = _WhitespaceTokenizer()(prompt, add_special_tokens=False)["input_ids"]
truncated = token_ids[-target:]
assert len(token_ids) == target
assert truncated == token_ids
assert prompt.startswith(LLAMA3_USER_PREFIX)
assert prompt.endswith(LLAMA3_ASSISTANT_SUFFIX)
assert raw in prompt
assert "answer-token" in prompt
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