headroom/tests/test_kompress_request_nonblocking.py

"""The proxy request path must never block on a cold Kompress model download.

Counterpart to ``test_kompress_preload_deferral.py`` (which covers the startup
path). A first deep-path request used to resolve the 274MB ONNX artifact via an
inline ``hf_hub_download`` on the request thread, where it raced the proxy's
``HEADROOM_COMPRESSION_TIMEOUT_SECONDS`` budget (GH #946 / #1146): the fetch was
cancelled mid-transfer, nothing cached, and every request re-hung and failed
open. The request path now resolves the model cache-only and pulls it down once
in a background daemon thread instead.
"""

from __future__ import annotations

import threading

from headroom.transforms import kompress_compressor as kc
from headroom.transforms.content_router import ContentRouter, ContentRouterConfig
from headroom.transforms.kompress_compressor import KompressCompressor, KompressConfig


def test_compress_cache_only_passes_through_without_network(monkeypatch):
    """compress(allow_download=False) on a cold cache must not hit the network."""
    from huggingface_hub.errors import LocalEntryNotFoundError

    monkeypatch.setattr(kc, "_kompress_cache", {})
    monkeypatch.setattr(kc, "_selected_backend", lambda: "onnx")

    def fake_local_first(repo_id, filename, *, allow_network=True):
        assert allow_network is False, "request path must resolve the model cache-only"
        raise LocalEntryNotFoundError("not cached")

    monkeypatch.setattr(kc, "hf_hub_download_local_first", fake_local_first)

    text = " ".join(["token"] * 50)  # >= 10 words: not the short-content passthrough
    result = KompressCompressor(KompressConfig(min_input_words=10)).compress(
        text, allow_download=False
    )

    assert result.compressed == text
    assert result.compression_ratio == 1.0


def test_ensure_background_download_runs_one_thread_per_model(monkeypatch):
    """At most one download thread per model; retried after it dies; skipped once cached."""
    monkeypatch.setattr(kc, "_kompress_cache", {})
    monkeypatch.setattr(kc, "_download_threads", {})

    created: list[object] = []

    class FakeThread:
        def __init__(self, *, target, args, name, daemon):
            self.target, self.args, self.name, self.daemon = target, args, name, daemon
            self._alive = True
            created.append(self)

        def start(self):  # do not actually run — simulate a live download
            pass

        def is_alive(self):
            return self._alive

    monkeypatch.setattr(kc.threading, "Thread", FakeThread)

    kc.ensure_background_download("org/model", "cpu")
    kc.ensure_background_download("org/model", "cpu")  # thread alive -> no second start
    assert len(created) == 1
    assert created[0].daemon is True

    created[0]._alive = False  # simulate the download finishing/failing
    kc.ensure_background_download("org/model", "cpu")  # dead -> retry
    assert len(created) == 2

    kc._kompress_cache["org/model"] = ("model", "tokenizer", "onnx")
    kc.ensure_background_download("org/model", "cpu")  # cached -> no-op
    assert len(created) == 2


def _kompress_router() -> ContentRouter:
    return ContentRouter(
        ContentRouterConfig(
            enable_kompress=True,
            enable_code_aware=False,
            enable_smart_crusher=False,
        )
    )


def test_router_skips_deep_path_and_fetches_in_background_when_not_ready(monkeypatch):
    router = _kompress_router()
    calls = {"ensure": 0, "compress": 0}

    class NotReadyKompress:
        def is_ready(self) -> bool:
            return False

        def ensure_background_load(self) -> None:
            calls["ensure"] += 1

        def compress(self, *args, **kwargs):
            calls["compress"] += 1
            raise AssertionError("must not run the deep path before the model is cached")

    monkeypatch.setattr(router, "_get_kompress", lambda: NotReadyKompress())

    text = " ".join(["content"] * 40)
    out, tokens = router._try_ml_compressor(text, context="")

    assert out == text  # passthrough, unchanged
    assert calls["ensure"] == 1  # background fetch kicked off
    assert calls["compress"] == 0  # deep path skipped, no inline download


def test_router_compresses_cache_only_when_ready(monkeypatch):
    router = _kompress_router()
    seen: dict[str, object] = {}

    class ReadyResult:
        compressed = "kept words"
        compressed_tokens = 2

    class ReadyKompress:
        def is_ready(self) -> bool:
            return True

        def ensure_background_load(self) -> None:
            raise AssertionError("must not fetch when the model is already cached")

        def compress(
            self, content, *, context="", question=None, target_ratio=None, allow_download=True
        ):
            seen["allow_download"] = allow_download
            return ReadyResult()

    monkeypatch.setattr(router, "_get_kompress", lambda: ReadyKompress())

    text = " ".join(["content"] * 40)
    out, tokens = router._try_ml_compressor(text, context="")

    assert seen["allow_download"] is False  # request path stays cache-only even when ready
    assert out == "kept words"


def test_saturation_fail_open_does_not_hang_request(monkeypatch):
    """A saturated execution slot must fail open instead of blocking indefinitely."""

    class _FakeEncoding(dict):
        def __init__(self, word_count: int):
            self._ids = list(range(word_count))
            super().__init__()
            self["input_ids"] = [[1 for _ in range(word_count)]]
            self["attention_mask"] = [[1 for _ in range(word_count)]]

        def word_ids(self, batch_index: int = 0):
            return self._ids

    class _FakeModel:
        def get_scores(self, input_ids, attention_mask):
            return [[0.0 for _ in input_ids[0]]]

    class _FakeTokenizer:
        def __call__(self, chunk_words, **kwargs):
            return _FakeEncoding(len(chunk_words))

    execution_semaphore = threading.BoundedSemaphore(1)
    execution_semaphore.acquire()

    monkeypatch.setattr(kc, "_execution_semaphore", lambda *_a, **_k: execution_semaphore)
    monkeypatch.setattr(
        kc,
        "_load_kompress",
        lambda *args, **kwargs: (_FakeModel(), _FakeTokenizer(), "onnx"),
    )
    monkeypatch.setenv("HEADROOM_KOMPRESS_EXECUTION_TIMEOUT_MS", "1")

    before = kc.get_kompress_execution_stats()["execution_timeout_skips_total"]
    text = " ".join(["word"] * 40)
    result_holder: dict[str, object] = {}

    def _run() -> None:
        result_holder["result"] = KompressCompressor(KompressConfig(min_input_words=10)).compress(
            text, allow_download=False
        )

    worker = threading.Thread(target=_run)
    worker.start()
    worker.join(timeout=0.25)
    try:
        assert not worker.is_alive(), (
            "Kompress saturation path is blocking request progress; expected fail-open under pressure"
        )
        assert "result" in result_holder
    finally:
        try:
            execution_semaphore.release()
        except ValueError:
            pass
        worker.join(timeout=1.0)

    result = result_holder["result"]
    assert result.compressed == text
    assert result.compression_ratio == 1.0
    after = kc.get_kompress_execution_stats()["execution_timeout_skips_total"]
    assert after == before + 1


def test_capacity_available_still_compresses(monkeypatch):
    """When execution semaphore capacity is available, compression is still attempted."""

    class _FakeEncoding(dict):
        def __init__(self, word_count: int):
            self._ids = list(range(word_count))
            self["input_ids"] = [[1 for _ in range(word_count)]]
            self["attention_mask"] = [[1 for _ in range(word_count)]]

        def word_ids(self, batch_index: int = 0):
            return self._ids

    class _FakeModel:
        def get_scores(self, input_ids, attention_mask):
            return [[1.0 if idx % 2 == 0 else 0.0 for idx in range(len(input_ids[0]))]]

        def get_keep_mask(self, input_ids, attention_mask):
            return [[idx % 2 == 0 for idx in range(len(input_ids[0]))]]

    class _FakeTokenizer:
        def __call__(self, chunk_words, **kwargs):
            return _FakeEncoding(len(chunk_words))

    monkeypatch.setattr(
        kc, "_execution_semaphore", lambda *_args, **_kwargs: threading.BoundedSemaphore(1)
    )
    monkeypatch.setattr(
        kc,
        "_load_kompress",
        lambda *args, **kwargs: (_FakeModel(), _FakeTokenizer(), "onnx"),
    )

    result = KompressCompressor(KompressConfig(min_input_words=10)).compress(
        " ".join(["word"] * 20), allow_download=False
    )
    assert 0 < result.compression_ratio < 1.0
    assert result.compressed != " ".join(["word"] * 20)


def test_validation_probe_waits_for_execution_slot(monkeypatch):
    """Model-load validation must block for a slot instead of failing open."""

    class _FakeTensor:
        def to(self, _device):
            return self

    class _FakeEncoding(dict):
        def __init__(self):
            super().__init__()
            self["input_ids"] = _FakeTensor()
            self["attention_mask"] = _FakeTensor()

    class _FakeTokenizer:
        def __call__(self, *_args, **_kwargs):
            return _FakeEncoding()

    class _FakeScore:
        def detach(self):
            return self

        def cpu(self):
            return self

    class _FakeModel:
        def __init__(self):
            self.calls = 0

        def get_scores(self, input_ids, attention_mask):
            self.calls += 1
            return [_FakeScore()]

    semaphore = threading.BoundedSemaphore(1)
    semaphore.acquire()
    model = _FakeModel()

    monkeypatch.setattr(kc, "_execution_semaphore", lambda *_args, **_kwargs: semaphore)

    worker = threading.Thread(
        target=kc._validate_pytorch_device,
        args=(model, _FakeTokenizer(), "mps"),
    )
    worker.start()
    worker.join(timeout=0.05)
    assert worker.is_alive(), "validation should wait for an execution slot"

    semaphore.release()
    worker.join(timeout=1.0)

    assert not worker.is_alive()
    assert model.calls == 1