4987d951ce
apply_chat_template returns a BatchEncoding ({input_ids: [...]}) on transformers
>= 5 where 4.x returned a bare list[int]. build_masked_example treated the render
as a dict, so len/slicing were wrong and the prefix-differencing spuriously
raised "chat template is not additive" on every real model. Extract the id
sequence via a _render_ids helper; verified the assistant-only mask against
mistralai/Mistral-7B-Instruct-v0.2. The fake tokenizer returned a bare list and
missed this, so a BatchEncoding-returning variant now guards it.
98 lines
4 KiB
Python
98 lines
4 KiB
Python
"""Unit tests for the SFT render canonicalisation and assistant-only mask.
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These run anywhere: a fake additive tokenizer stands in for a real chat
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template, so no torch/transformers is needed."""
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from __future__ import annotations
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from typing import Any
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import pytest
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from tiararodney.sekft import sft
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class FakeTok:
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"""Additive chat template: each turn renders to ``<role> tokens... </e>``;
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the generation prompt appends ``<assistant>``."""
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def apply_chat_template(self, msgs: list[dict[str, str]], add_generation_prompt: bool = False,
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return_tensors: Any = None) -> list[str]:
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toks: list[str] = []
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for m in msgs:
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toks.append(f"<{m['role']}>")
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toks += m["content"].split()
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toks.append("</e>")
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if add_generation_prompt:
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toks.append("<assistant>")
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return toks
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class FakeTokBatchEncoding(FakeTok):
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"""Like FakeTok, but returns a dict as transformers >= 5's
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``apply_chat_template`` does (a BatchEncoding), to exercise the id-extraction."""
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def apply_chat_template(self, msgs: list[dict[str, str]], add_generation_prompt: bool = False,
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return_tensors: Any = None) -> dict[str, list[str]]:
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return {"input_ids": super().apply_chat_template(msgs, add_generation_prompt, return_tensors)}
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def test_normalize_folds_system_and_merges_consecutive() -> None:
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raw = [
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{"role": "system", "content": "orient"},
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{"role": "user", "content": "login"},
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{"role": "user", "content": "prompt"},
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{"role": "assistant", "content": "cat f"},
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{"role": "user", "content": "out"},
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{"role": "user", "content": "prompt"},
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{"role": "assistant", "content": "exit"},
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]
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norm = sft.normalize_for_template(raw)
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assert [m["role"] for m in norm] == ["user", "assistant", "user", "assistant"]
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assert norm[0]["content"] == "orient\nlogin\nprompt"
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def test_normalize_leaves_clean_alternation_untouched() -> None:
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raw = [{"role": "user", "content": "a"}, {"role": "assistant", "content": "b"}]
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assert sft.normalize_for_template(raw) == raw
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def test_mask_trains_assistant_turns_only() -> None:
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raw = [
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{"role": "system", "content": "orient"},
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{"role": "user", "content": "login"},
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{"role": "assistant", "content": "cat f"},
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{"role": "user", "content": "out"},
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{"role": "assistant", "content": "exit"},
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]
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ex = sft.build_masked_example(raw, FakeTok())
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trained = [t for t, lab in zip(ex["input_ids"], ex["labels"]) if lab != -100]
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masked = [t for t, lab in zip(ex["input_ids"], ex["labels"]) if lab == -100]
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assert set(trained) <= {"<assistant>", "cat", "f", "exit", "</e>"}
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assert "cat" in trained and "exit" in trained # both commands present
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assert {"orient", "login", "out"} <= set(masked) # environment masked
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def test_mask_handles_batchencoding_return() -> None:
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# transformers >= 5 returns a BatchEncoding ({input_ids: [...]}) rather than a
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# bare list[int]; the mask must come out identical. Regression for the 5.x bug
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# that made every real template look "not additive".
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raw = [
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{"role": "user", "content": "login"},
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{"role": "assistant", "content": "cat f"},
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{"role": "user", "content": "out"},
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{"role": "assistant", "content": "exit"},
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]
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assert (sft.build_masked_example(raw, FakeTokBatchEncoding())
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== sft.build_masked_example(raw, FakeTok()))
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def test_mask_raises_on_non_additive_template() -> None:
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class BadTok:
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def apply_chat_template(self, msgs: list[dict[str, str]], add_generation_prompt: bool = False,
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return_tensors: Any = None) -> list[int]:
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return list(range(len(msgs), 0, -1)) # reversed: prefixes do not nest
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with pytest.raises(ValueError):
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sft.build_masked_example(
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[{"role": "user", "content": "a"}, {"role": "assistant", "content": "b"}],
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BadTok())
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