[Misc] Remove Dynin-Omni and dots.tts support (#7655)
Signed-off-by: wangyu <410167048@qq.com> Signed-off-by: Yueqian Lin <linyueqian@outlook.com> Co-authored-by: Yueqian Lin <linyueqian@outlook.com>
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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@@ -1,137 +0,0 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Regression tests for the dots.tts talker's stop-signal pairing and
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per-request state lifecycle.
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Pure-CPU tests on a bare talker (``__new__`` + the handful of attributes
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the methods under test read) — no weights, no GPU, no engine. Mirrors
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tests/model_executor/models/voxcpm2/test_talker_state_eviction.py.
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"""
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from __future__ import annotations
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import functools
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from types import SimpleNamespace
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import pytest
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pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
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torch = pytest.importorskip("torch")
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_VOCAB_SIZE = 151672
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@functools.lru_cache(maxsize=1)
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def _dots_tts_talker_mod():
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"""Defer talker import (pulls vLLM model_executor) until first use."""
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from vllm_omni.model_executor.models.dots_tts.dots_tts_talker import (
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DotsTTSForConditionalGeneration,
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_RequestState,
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)
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return DotsTTSForConditionalGeneration, _RequestState
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def _make_bare_talker():
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DotsTTSForConditionalGeneration, _ = _dots_tts_talker_mod()
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talker = DotsTTSForConditionalGeneration.__new__(DotsTTSForConditionalGeneration)
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talker.config = SimpleNamespace(vocab_size=_VOCAB_SIZE)
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talker._active_states = {}
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talker._pending_requests = []
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talker._results_queue = []
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talker._audio_queue = []
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talker._deferred_cleanup_ids = set()
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talker._beta_trace = False
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return talker
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def _add_state(talker, req_id: str, *, is_stopping: bool = False):
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_, _RequestState = _dots_tts_talker_mod()
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state = _RequestState(request_id=req_id)
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state.prefill_completed = True
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state.is_stopping = is_stopping
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talker._active_states[req_id] = state
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return state
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def _hidden(bsz: int) -> torch.Tensor:
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return torch.zeros(bsz, 1536, dtype=torch.float32)
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class TestComputeLogitsPairing:
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"""compute_logits pairs _results_queue entries with batch rows
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positionally, so every request must push exactly one entry per step —
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including prefills, which push a (req_id, None) placeholder."""
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def test_mixed_batch_prefill_placeholder_keeps_rows_aligned(self) -> None:
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talker = _make_bare_talker()
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_add_state(talker, "prefill-req")
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decode_state = _add_state(talker, "decode-req", is_stopping=True)
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decode_state.precomputed_stop_logits = torch.tensor([[0.1, 0.9]])
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# Row 0 is the prefill (None placeholder), row 1 the stopping decode.
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talker._results_queue = [
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("prefill-req", None),
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("decode-req", torch.tensor([[0.1, 0.9]])),
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]
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logits = talker.compute_logits(_hidden(2))
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assert logits.shape == (2, _VOCAB_SIZE)
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# Prefill row: forced continue.
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assert logits[0, 0].item() == 1.0
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assert logits[0, 1].item() == float("-inf")
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# Stopping decode row: stop slot wins.
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assert logits[1, 1].item() == 1.0
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assert logits[1, 0].item() == 0.0
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# Everything else stays masked.
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assert logits[0, 2].item() == float("-inf")
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assert logits[1, 2].item() == float("-inf")
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# Queue drained.
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assert talker._results_queue == []
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def test_below_threshold_stop_logits_force_continue(self) -> None:
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"""Raw (continue, stop) softmax must not reach the sampler: a
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prob_stop in (0.5, 0.8] would flip greedy argmax even though
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is_stopping stayed False (the 0.8-threshold contract)."""
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talker = _make_bare_talker()
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_add_state(talker, "req", is_stopping=False)
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talker._results_queue = [("req", torch.tensor([[0.3, 0.7]]))]
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logits = talker.compute_logits(_hidden(1))
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assert logits[0, 0].item() == 1.0
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assert logits[0, 1].item() == float("-inf")
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def test_empty_queue_defaults_to_all_continue(self) -> None:
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talker = _make_bare_talker()
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logits = talker.compute_logits(_hidden(3))
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assert torch.all(logits[:, 0] == 1.0)
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assert torch.all(logits[:, 1] == float("-inf"))
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class TestStateEviction:
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"""on_requests_finished fires BEFORE forward(), so eviction must be
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deferred to _flush_deferred_cleanup at the end of the step — the
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finishing request's audio still drains through the current forward."""
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def test_finished_request_survives_until_flush(self) -> None:
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talker = _make_bare_talker()
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_add_state(talker, "req-a")
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_add_state(talker, "req-b")
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talker.on_requests_finished(["req-a"])
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assert "req-a" in talker._active_states # not evicted yet
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talker._flush_deferred_cleanup()
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assert "req-a" not in talker._active_states
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assert "req-b" in talker._active_states
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assert talker._deferred_cleanup_ids == set()
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def test_unknown_request_id_is_ignored(self) -> None:
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talker = _make_bare_talker()
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talker.on_requests_finished(["never-seen"])
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talker._flush_deferred_cleanup()
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assert talker._active_states == {}
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@@ -1,40 +0,0 @@
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from __future__ import annotations
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import pytest
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from transformers import PreTrainedModel
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from vllm_omni.model_executor.models.dynin_omni.dynin_omni_token2audio import (
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_ensure_transformers_tied_weights_compat,
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)
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pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
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def test_transformers_tied_weights_compat(monkeypatch):
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"""Compat shim must not permanently mutate ``PreTrainedModel``.
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``monkeypatch.delattr(..., raising=False)`` is a no-op undo when the attr
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was absent. ``_ensure_transformers_tied_weights_compat`` then assigns a
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read-only ``property`` on the class that later PreTrainedModel subclasses
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(e.g. qwen3 TTS mask-cache) can trip over with
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``AttributeError: property ... has no setter``.
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"""
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monkeypatch.delattr(PreTrainedModel, "all_tied_weights_keys", raising=False)
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_ensure_transformers_tied_weights_compat()
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installed = PreTrainedModel.__dict__.get("all_tied_weights_keys")
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assert installed is not None
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# Re-own the installed descriptor through monkeypatch so teardown always
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# removes/restores it, even when the attr did not exist before this test.
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delattr(PreTrainedModel, "all_tied_weights_keys")
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monkeypatch.setattr(PreTrainedModel, "all_tied_weights_keys", installed, raising=False)
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model = PreTrainedModel.__new__(PreTrainedModel)
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model._tied_weights_keys = ["decoder.weight"]
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model._dynamic_tied_weights_keys = {"lm_head.weight": "decoder.weight"}
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assert model.all_tied_weights_keys == {
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"decoder.weight": None,
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"lm_head.weight": "decoder.weight",
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}
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