wehub-resource-sync
599 lines
20 KiB
Python
599 lines
20 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Replay tests for engine parsers (holdback, skip-tool-parsing, adapters).
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Replays dynamically built token sequences at different chunk sizes and
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holdback depths to verify chunk-size invariance and terminal-token hygiene.
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Parser discovery is automatic: any ``ParserEngine`` subclass registered in
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``registered_adapters`` that also has a builder in ``trace_builder._BUILDERS``
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is picked up with zero manual wiring.
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"""
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from __future__ import annotations
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import dataclasses
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from typing import NamedTuple
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import pytest
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from tests.parser.engine.replay_harness import (
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DUMMY_TOOLS,
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MockTokenizer,
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Sample,
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_test_request,
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assert_no_terminal_leakage,
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assert_parse_output,
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collect_output,
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make_mock_tokenizer,
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parse_non_streaming,
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replay_streaming,
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replay_with_text_holdback,
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)
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from tests.parser.engine.trace_builder import _BUILDERS, build_samples
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from vllm.parser.engine import registered_adapters as _adapters_mod
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from vllm.parser.engine.parser_engine import ParserEngine
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from vllm.parser.engine.parser_engine_config import ParserState
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# ── Parser discovery ─────────────────────────────────────────────────
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class _ParserInfo(NamedTuple):
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parser_cls: type[ParserEngine]
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name: str
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samples: tuple
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terminals: list[str]
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tool_end: str
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think_end: str
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tool_start: str
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def _discover_parsers() -> list[_ParserInfo]:
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"""Discover engine parsers from registered_adapters that have test builders.
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Returns one ``_ParserInfo`` per parser, sorted by config name.
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Raises ``RuntimeError`` if any registered parser lacks a builder.
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"""
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bare_tok = MockTokenizer(vocab={}, tokens=[])
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found: list[_ParserInfo] = []
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missing_builders: list[str] = []
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for obj in vars(_adapters_mod).values():
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if not (
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isinstance(obj, type)
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and issubclass(obj, ParserEngine)
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and obj is not ParserEngine
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):
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continue
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cfg = obj(bare_tok, None).parser_engine_config
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if cfg.name not in _BUILDERS:
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missing_builders.append(f"{obj.__name__} (config.name={cfg.name!r})")
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continue
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tool_end = cfg.token_id_terminals.get("TOOL_END")
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if not tool_end:
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raise RuntimeError(
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f"{obj.__name__} config missing 'TOOL_END' in token_id_terminals"
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)
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all_vals = set(cfg.terminals.values()) | set(cfg.token_id_terminals.values())
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found.append(
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_ParserInfo(
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parser_cls=obj,
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name=cfg.name,
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samples=build_samples(cfg.name),
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terminals=sorted(v for v in all_vals if len(v) > 1),
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tool_end=tool_end,
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think_end=cfg.terminals.get("THINK_END", ""),
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tool_start=(
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cfg.terminals["TOOL_SECTION_START"]
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if (ParserState.CONTENT, "TOOL_SECTION_START") in cfg.transitions
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else cfg.terminals.get("TOOL_START", "")
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),
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)
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)
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if missing_builders:
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raise RuntimeError(
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f"Engine parsers in registered_adapters have no test builder "
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f"in trace_builder._BUILDERS: {', '.join(missing_builders)}. "
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f"Add a builder to _BUILDERS for each new parser."
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)
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found.sort(key=lambda p: p.name)
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return found
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_PARSERS = _discover_parsers()
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def _make_parser(parser_cls: type[ParserEngine], tokenizer, sample: Sample, **extra):
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kwargs = dict(extra)
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if sample.chat_template_kwargs:
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kwargs["chat_template_kwargs"] = sample.chat_template_kwargs
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return parser_cls(tokenizer, sample.tools, **kwargs)
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_ENGINE_PARSERS: dict[str, type[ParserEngine]] = {
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f"{p.name}_engine": p.parser_cls for p in _PARSERS
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}
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# ── Parametrize sample lists ─────────────────────────────────────────
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HOLDBACK_CONFIGS = [6, 12, 24]
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_REPLAY_SAMPLES = [(p.parser_cls, s, p.terminals) for p in _PARSERS for s in p.samples]
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@pytest.mark.parametrize("holdback", HOLDBACK_CONFIGS, ids=lambda h: f"holdback{h}")
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@pytest.mark.parametrize("chunk_size", [3, 5, 10], ids=lambda c: f"chunk{c}")
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@pytest.mark.parametrize(
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"parser_cls,sample,terminals",
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_REPLAY_SAMPLES,
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ids=lambda v: v.id if hasattr(v, "id") else "",
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)
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class TestReplayWithHoldback:
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"""Replay all parsers with simulated detokenizer holdback."""
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def test_replay(self, parser_cls, sample, terminals, chunk_size, holdback):
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tokenizer = make_mock_tokenizer(sample)
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parser = _make_parser(parser_cls, tokenizer, sample)
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deltas = replay_streaming(
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parser,
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sample.tokens,
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chunk_size=chunk_size,
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holdback_chars=holdback,
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prompt_token_ids=sample.prompt_token_ids,
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)
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output = collect_output(deltas)
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assert_parse_output(output, sample)
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assert_no_terminal_leakage(
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output,
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terminals,
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context=f"chunk_size={chunk_size}, holdback={holdback}",
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)
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TEXT_HOLDBACK_DELAYS = [1, 2, 3]
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@pytest.mark.parametrize("delay", TEXT_HOLDBACK_DELAYS, ids=lambda d: f"delay{d}")
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@pytest.mark.parametrize(
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"parser_cls,sample,terminals",
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_REPLAY_SAMPLES,
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ids=lambda v: v.id if hasattr(v, "id") else "",
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)
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class TestTextHoldback:
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"""Replay with production-like text/token-ID misalignment.
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In production the detokenizer sends token IDs immediately but holds
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back text by N tokens. This exercises the TokenIDScanner deferred
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terminal path that aligned-holdback tests do not cover.
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"""
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def test_replay(self, parser_cls, sample, terminals, delay):
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tokenizer = make_mock_tokenizer(sample)
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parser = _make_parser(parser_cls, tokenizer, sample)
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deltas = replay_with_text_holdback(
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parser,
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sample.tokens,
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text_delay=delay,
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prompt_token_ids=sample.prompt_token_ids,
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)
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output = collect_output(deltas)
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assert_parse_output(output, sample)
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assert_no_terminal_leakage(
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output,
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terminals,
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context=f"text_delay={delay}",
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)
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@pytest.mark.parametrize(
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"chunk_size", [1, 2, 3, 5, 10, 19, 20, None], ids=lambda c: f"chunk{c}"
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)
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@pytest.mark.parametrize(
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"parser_cls,sample,terminals",
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_REPLAY_SAMPLES,
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ids=lambda v: v.id if hasattr(v, "id") else "",
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)
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class TestReplay:
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"""Replay all parsers at varied chunk sizes without holdback."""
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def test_replay(self, parser_cls, sample, terminals, chunk_size):
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tokenizer = make_mock_tokenizer(sample)
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parser = _make_parser(parser_cls, tokenizer, sample)
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deltas = replay_streaming(
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parser,
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sample.tokens,
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chunk_size=chunk_size,
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prompt_token_ids=sample.prompt_token_ids,
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)
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output = collect_output(deltas)
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assert_parse_output(output, sample)
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assert_no_terminal_leakage(output, terminals)
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_DEFERRAL_SAMPLES = [
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(p.parser_cls, s, p.tool_end)
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for p in _PARSERS
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for s in p.samples
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if s.expected_tool_calls
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]
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@pytest.mark.parametrize(
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"parser_cls,sample,tool_end_text",
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_DEFERRAL_SAMPLES,
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ids=lambda v: v.id if hasattr(v, "id") else getattr(v, "__name__", ""),
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)
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class TestDeferralFinish:
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"""Test that parse_delta(finished=True) resolves deferred scanner state.
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Simulates a production failure where delta_text is missing the
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tool-call-end text but delta_token_ids has the token, causing the
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scanner to defer it. Without finish(), the deferred state is lost
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and tool call arguments are empty.
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"""
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def test_misaligned_last_delta_with_finish(self, parser_cls, sample, tool_end_text):
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tokenizer = make_mock_tokenizer(sample)
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parser = _make_parser(parser_cls, tokenizer, sample)
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request = _test_request()
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all_ids = [tid for tid, _ in sample.tokens]
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all_texts = [text for _, text in sample.tokens]
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tool_end_id = sample.vocab.get(tool_end_text)
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split_idx = None
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for i in range(len(all_ids) - 1, -1, -1):
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if all_ids[i] == tool_end_id:
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split_idx = i
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break
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if split_idx is None:
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pytest.skip(f"no {tool_end_text} token found")
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first_ids = all_ids[:split_idx]
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first_text = "".join(all_texts[:split_idx])
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last_ids = all_ids[split_idx:]
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last_text_missing = "".join(all_texts[split_idx:]).replace(tool_end_text, "")
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result1 = parser.parse_delta(
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first_text,
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first_ids,
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request,
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prompt_token_ids=[],
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finished=False,
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)
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result2 = parser.parse_delta(
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last_text_missing, last_ids, request, finished=True
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)
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output = collect_output([result1, result2])
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tool_calls_only = dataclasses.replace(
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sample, expected_reasoning=None, expected_content=None
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)
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assert_parse_output(output, tool_calls_only)
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@pytest.mark.parametrize(
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"parser_cls,sample",
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[(p.parser_cls, p.samples[0]) for p in _PARSERS],
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ids=[p.name for p in _PARSERS],
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)
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class TestParserEngineAdjustRequest:
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"""Verify ParserEngine and its adapters set skip_special_tokens=False."""
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def test_adjust_request_disables_skip_special_tokens(self, parser_cls, sample):
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tokenizer = make_mock_tokenizer(sample)
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parser = parser_cls(tokenizer, sample.tools)
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request = _test_request()
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assert request.skip_special_tokens is True
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adjusted = parser.adjust_request(request)
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assert adjusted.skip_special_tokens is False
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_TOOL_CALL_SAMPLES = [
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(p.parser_cls, s, p.think_end, p.tool_start)
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for p in _PARSERS
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for s in p.samples
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if s.expected_tool_calls and s.expected_reasoning
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]
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def _tool_suppression_expectations(
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sample, think_end: str, tool_start: str, *, include_tool_block: bool
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) -> tuple[str, str]:
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"""Expected (reasoning, content) when tool calls are not extracted.
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With ``include_tool_block=True`` (skip_tool_parsing / reasoning
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adapter first pass), tool terminal text is preserved as content so
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a second-pass parser can see it.
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With ``include_tool_block=False`` (_suppress_tool_calls /
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tool_choice='none'), the state machine consumes tool blocks and
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only non-tool content survives.
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"""
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full_text = "".join(text for _, text in sample.tokens)
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reasoning = sample.expected_reasoning
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idx = full_text.find(reasoning)
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if idx < 0:
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return (full_text, "")
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after_reasoning = full_text[idx + len(reasoning) :]
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if think_end:
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pos = after_reasoning.find(think_end)
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if pos >= 0:
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if include_tool_block:
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return (reasoning, after_reasoning[pos + len(think_end) :])
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after_reasoning = after_reasoning[pos + len(think_end) :]
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if tool_start:
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pos = after_reasoning.find(tool_start)
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if pos >= 0:
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if include_tool_block:
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return (reasoning, after_reasoning[pos:])
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return (reasoning, after_reasoning[:pos])
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if include_tool_block:
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return (full_text, "")
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return (reasoning, after_reasoning)
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@pytest.mark.parametrize("chunk_size", [1, 5, None], ids=lambda c: f"chunk{c}")
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@pytest.mark.parametrize(
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"mode",
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["skip_tool_parsing", "suppress_tool_calls"],
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ids=["skip_tool_parsing", "suppress_tool_calls"],
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)
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@pytest.mark.parametrize(
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"parser_cls,sample,think_end,tool_start",
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_TOOL_CALL_SAMPLES,
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ids=lambda v: v.id if hasattr(v, "id") else getattr(v, "__name__", ""),
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)
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class TestToolCallFilteringReplay:
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"""Replay with tool calls not extracted, in both filtering modes.
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``skip_tool_parsing`` (reasoning adapter first pass): tool terminal
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text is preserved as content for a second-pass tool parser.
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``suppress_tool_calls`` (tool_choice='none'): tool call blocks are
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consumed by the state machine and do not leak into content.
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"""
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def test_replay(self, parser_cls, sample, think_end, tool_start, mode, chunk_size):
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tokenizer = make_mock_tokenizer(sample)
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kwargs = {}
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if sample.chat_template_kwargs:
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kwargs["chat_template_kwargs"] = sample.chat_template_kwargs
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parser = parser_cls(tokenizer, **kwargs)
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request = _test_request()
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request.tools = DUMMY_TOOLS
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if mode == "skip_tool_parsing":
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parser.skip_tool_parsing = True
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else:
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request.tool_choice = "none"
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all_ids = [tid for tid, _ in sample.tokens]
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all_texts = [text for _, text in sample.tokens]
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if chunk_size is None:
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chunk_size = len(all_ids)
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results = []
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chunks = list(range(0, len(all_ids), chunk_size))
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for i, start in enumerate(chunks):
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end = min(start + chunk_size, len(all_ids))
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is_last = i == len(chunks) - 1
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result = parser.parse_delta(
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"".join(all_texts[start:end]),
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all_ids[start:end],
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request,
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prompt_token_ids=(sample.prompt_token_ids or [])
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if start == 0
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else None,
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finished=is_last,
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)
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results.append(result)
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output = collect_output(results)
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include_block = mode == "skip_tool_parsing"
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expected_reasoning, expected_content = _tool_suppression_expectations(
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sample, think_end, tool_start, include_tool_block=include_block
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)
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assert output.reasoning == expected_reasoning, (
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f"Reasoning mismatch (mode={mode}):\n"
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f" expected: {expected_reasoning!r}\n"
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f" actual: {output.reasoning!r}"
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)
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assert output.tool_calls == [], (
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f"Expected no tool calls (mode={mode}) but got {output.tool_calls}"
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)
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assert output.content == expected_content, (
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f"Content mismatch (mode={mode}):\n"
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f" expected: {expected_content!r}\n"
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f" actual: {output.content!r}"
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)
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@pytest.mark.parametrize(
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"parser_cls,sample,think_end,tool_start",
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_TOOL_CALL_SAMPLES,
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ids=lambda v: v.id if hasattr(v, "id") else getattr(v, "__name__", ""),
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)
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class TestToolCallFilteringNonStreaming:
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"""Non-streaming parse() with tool_choice='none' must suppress tool
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calls and not leak special tokens into content."""
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def test_parse(self, parser_cls, sample, think_end, tool_start):
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tokenizer = make_mock_tokenizer(sample)
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kwargs = {}
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if sample.chat_template_kwargs:
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kwargs["chat_template_kwargs"] = sample.chat_template_kwargs
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parser = parser_cls(tokenizer, **kwargs)
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request = _test_request()
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request.tools = DUMMY_TOOLS
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request.tool_choice = "none"
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output = parse_non_streaming(parser, sample, request)
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expected_reasoning, expected_content = _tool_suppression_expectations(
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sample, think_end, tool_start, include_tool_block=False
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)
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assert output.reasoning == expected_reasoning, (
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f"Reasoning mismatch:\n"
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f" expected: {expected_reasoning!r}\n"
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f" actual: {output.reasoning!r}"
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)
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assert output.tool_calls == [], (
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f"Expected no tool calls but got {output.tool_calls}"
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)
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assert output.content == expected_content, (
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f"Content mismatch:\n"
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f" expected: {expected_content!r}\n"
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f" actual: {output.content!r}"
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)
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_WS_TOOL_SAMPLES = [(t[0], t[1]) for t in _TOOL_CALL_SAMPLES if "whitespace" in t[1].id]
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@pytest.mark.parametrize(
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"parser_cls,sample",
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_WS_TOOL_SAMPLES,
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ids=lambda v: v.id if hasattr(v, "id") else getattr(v, "__name__", ""),
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)
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class TestToolChoiceNoneStreamingParity:
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"""Streaming and non-streaming must return the same content
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when tool_choice='none' suppresses tool calls."""
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def test_content_matches(self, parser_cls, sample):
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tokenizer = make_mock_tokenizer(sample)
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kwargs = {}
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if sample.chat_template_kwargs:
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kwargs["chat_template_kwargs"] = sample.chat_template_kwargs
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request = _test_request()
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request.tools = DUMMY_TOOLS
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request.tool_choice = "none"
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ns_output = parse_non_streaming(
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parser_cls(tokenizer, **kwargs),
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sample,
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request,
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)
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s_parser = parser_cls(tokenizer, **kwargs)
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results = []
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for i, (tid, text) in enumerate(sample.tokens):
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is_last = i == len(sample.tokens) - 1
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results.append(
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s_parser.parse_delta(
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text,
|
|
[tid],
|
|
request,
|
|
prompt_token_ids=(sample.prompt_token_ids or [])
|
|
if i == 0
|
|
else None,
|
|
finished=is_last,
|
|
)
|
|
)
|
|
s_output = collect_output(results)
|
|
|
|
assert ns_output.content == s_output.content, (
|
|
f"Streaming/non-streaming content mismatch:\n"
|
|
f" streaming: {s_output.content!r}\n"
|
|
f" non-streaming: {ns_output.content!r}"
|
|
)
|
|
|
|
|
|
_DROP_TOKENS = {"<bos>": 99990, "<eos>": 99991}
|
|
|
|
|
|
def _inject_drop_tokens(sample):
|
|
"""Insert <bos> at stream start and <eos> between the first two tokens."""
|
|
new_vocab = {**sample.vocab, **_DROP_TOKENS}
|
|
tokens = list(sample.tokens)
|
|
tokens.insert(0, (99990, "<bos>"))
|
|
if len(tokens) >= 3:
|
|
tokens.insert(2, (99991, "<eos>"))
|
|
else:
|
|
tokens.append((99991, "<eos>"))
|
|
return dataclasses.replace(sample, vocab=new_vocab, tokens=tokens)
|
|
|
|
|
|
class TestDropTokenReplay:
|
|
"""Verify unconfigured special tokens are silently dropped across
|
|
all parsers and chunk sizes."""
|
|
|
|
@pytest.mark.parametrize(
|
|
"parser_info",
|
|
_PARSERS,
|
|
ids=[p.name for p in _PARSERS],
|
|
)
|
|
@pytest.mark.parametrize("chunk_size", [1, 3, None])
|
|
def test_drop_tokens_removed_from_output(self, parser_info, chunk_size):
|
|
for sample in parser_info.samples:
|
|
injected = _inject_drop_tokens(sample)
|
|
tokenizer = make_mock_tokenizer(injected)
|
|
parser = _make_parser(parser_info.parser_cls, tokenizer, sample)
|
|
|
|
results = replay_streaming(
|
|
parser,
|
|
injected.tokens,
|
|
chunk_size=chunk_size,
|
|
tools=sample.tools,
|
|
prompt_token_ids=sample.prompt_token_ids,
|
|
)
|
|
output = collect_output(results)
|
|
|
|
assert_no_terminal_leakage(
|
|
output,
|
|
list(_DROP_TOKENS.keys()),
|
|
context=f"parser={parser_info.name}, chunk={chunk_size}",
|
|
)
|
|
assert_parse_output(output, sample)
|
|
|
|
|
|
class TestDropTokenNonStreaming:
|
|
"""Non-streaming parse() must also strip unconfigured special tokens."""
|
|
|
|
@pytest.mark.parametrize(
|
|
"parser_info",
|
|
_PARSERS,
|
|
ids=[p.name for p in _PARSERS],
|
|
)
|
|
def test_drop_tokens_removed_from_output(self, parser_info):
|
|
for sample in parser_info.samples:
|
|
injected = _inject_drop_tokens(sample)
|
|
tokenizer = make_mock_tokenizer(injected)
|
|
parser = _make_parser(parser_info.parser_cls, tokenizer, sample)
|
|
|
|
request = _test_request(tools=sample.tools)
|
|
output = parse_non_streaming(parser, injected, request)
|
|
|
|
assert_no_terminal_leakage(
|
|
output,
|
|
list(_DROP_TOKENS.keys()),
|
|
context=f"parser={parser_info.name}",
|
|
)
|
|
|
|
|
|
class TestAdapterReferences:
|
|
"""Verify make_adapters sets reasoning/tool parser class refs on parser engine
|
|
parser classes so the serving layer finds them and calls adjust_request."""
|
|
|
|
@pytest.mark.parametrize(
|
|
"parser_name",
|
|
list(_ENGINE_PARSERS.keys()),
|
|
)
|
|
def test_adapter_cls_refs_set(self, parser_name):
|
|
parser_cls = _ENGINE_PARSERS[parser_name]
|
|
assert parser_cls.reasoning_parser_cls is not None, (
|
|
f"{parser_name}: reasoning_parser_cls is None"
|
|
)
|
|
assert parser_cls.tool_parser_cls is not None, (
|
|
f"{parser_name}: tool_parser_cls is None"
|
|
)
|