fix(auto): warn when unavailable accelerators fall back to CPU (#3742)

fix(auto): warn when unavailable accelerators fall back to CPU

funasr/auto/auto_model.py

@@ -527,7 +527,8 @@ class AutoModel:
         Args:
             model (str): Model name (hub alias or full ID) or local path.
             device (str): Device for inference. "cuda:0", "cpu", "mps", "npu:0".
-                Falls back to CPU if specified device is unavailable.
+                Logs a warning and falls back to CPU if unavailable (default: "cuda").
+                Explicit CPU use or ngpu=0 does not emit a fallback warning.
             vad_model (str, optional): VAD model for long audio segmentation.
                 Enables processing of any-length audio.
             vad_kwargs (dict, optional): VAD config, e.g. {"device": "cpu"}.
@@ -673,6 +674,14 @@ class AutoModel:
             or (device.startswith("npu") and not is_npu_available())
             or kwargs.get("ngpu", 1) == 0
         ):
+            if kwargs.get("ngpu", 1) != 0:
+                logging.warning(
+                    "AutoModel: requested device '%s' is unavailable for model '%s'; "
+                    "falling back to CPU. Check your PyTorch build and accelerator "
+                    "availability, or set device='cpu' to use CPU explicitly.",
+                    device,
+                    kwargs["model"],
+                )
             device = "cpu"
             kwargs["batch_size"] = 1
         kwargs["device"] = device

tests/test_submodel_device.py

@@ -5,10 +5,12 @@ models retain CPU weights; these tests do not claim CUDA/MPS hardware execution.
 """
 
 import copy
+import logging
 import unittest
 from unittest.mock import patch
 
 import numpy as np
+import pytest
 import torch
 
 from funasr.auto.auto_model import AutoModel
@@ -153,5 +155,127 @@ class TestSubmodelDevice(unittest.TestCase):
             self.assertEqual(configs[role]["device"], "cuda:0")
 
 
+@pytest.fixture
+def fallback_model(monkeypatch):
+    from funasr.auto.auto_model import _current_blas_threads, _limit_blas_threads
+
+    original_threads = torch.get_num_threads()
+    original_blas = _current_blas_threads()
+    for role in ("asr", "vad", "punc", "spk"):
+        monkeypatch.setitem(tables.model_classes, f"device-test-{role}", RecordingModel)
+
+    def reject_download(**kwargs):
+        raise AssertionError("device tests must not access model hubs")
+
+    monkeypatch.setattr("funasr.auto.auto_model.download_model", reject_download)
+    with patch("funasr.auto.auto_model.ClusterBackend", create=True):
+        def build(**kwargs):
+            return AutoModel(
+                model="device-test-asr",
+                model_conf={},
+                ncpu=1,
+                disable_update=True,
+                disable_pbar=True,
+                **kwargs,
+            )
+
+        yield build
+    if original_blas is not None:
+        _limit_blas_threads(original_blas)
+    torch.set_num_threads(original_threads)
+
+
+_ACCELERATOR_PROBES = [
+    ("cuda:0", "torch.cuda.is_available"),
+    ("xpu:0", "torch.xpu.is_available"),
+    ("mps", "torch.backends.mps.is_available"),
+    ("npu:0", "funasr.auto.auto_model.is_npu_available"),
+]
+
+
+@pytest.mark.parametrize("device,probe", _ACCELERATOR_PROBES)
+def test_unavailable_accelerator_warns_and_preserves_cpu_fallback(
+    fallback_model, caplog, device, probe
+):
+    with patch(probe, return_value=False), caplog.at_level(logging.WARNING):
+        model = fallback_model(device=device, batch_size=8)
+    assert model.kwargs["device"] == model.model.placement == "cpu"
+    assert model.kwargs["batch_size"] == 1
+    assert len(caplog.records) == 1
+    assert caplog.records[0].levelno == logging.WARNING
+    message = caplog.records[0].getMessage()
+    assert device in message
+    assert "device-test-asr" in message
+    assert "falling back to CPU" in message
+    assert "PyTorch" in message
+
+
+@pytest.mark.parametrize("device,probe", _ACCELERATOR_PROBES)
+def test_available_accelerator_does_not_warn(fallback_model, caplog, device, probe):
+    with patch(probe, return_value=True), caplog.at_level(logging.WARNING):
+        model = fallback_model(device=device, batch_size=8)
+    assert model.kwargs["device"] == model.model.placement == device
+    assert model.kwargs["batch_size"] == 8
+    assert not caplog.records
+
+
+@pytest.mark.parametrize("device,probe", _ACCELERATOR_PROBES)
+@pytest.mark.parametrize("available", [False, True])
+def test_ngpu_zero_is_intentional_cpu_use(
+    fallback_model, caplog, device, probe, available
+):
+    with patch(probe, return_value=available), caplog.at_level(logging.WARNING):
+        model = fallback_model(device=device, ngpu=0, batch_size=8)
+    assert model.kwargs["device"] == model.model.placement == "cpu"
+    assert model.kwargs["batch_size"] == 1
+    assert not caplog.records
+
+
+def test_explicit_cpu_does_not_warn(fallback_model, caplog):
+    with caplog.at_level(logging.WARNING):
+        model = fallback_model(device="cpu", batch_size=8)
+    assert model.kwargs["device"] == model.model.placement == "cpu"
+    assert model.kwargs["batch_size"] == 8
+    assert not caplog.records
+
+
+def test_default_cuda_fallback_warns_once_with_inherited_submodels(fallback_model, caplog):
+    configs = {
+        f"{role}_{kind}": f"device-test-{role}" if kind == "model" else {"model_conf": {}}
+        for role in ("vad", "punc", "spk")
+        for kind in ("model", "kwargs")
+    }
+    original = copy.deepcopy(configs)
+    with patch("torch.cuda.is_available", return_value=False), caplog.at_level(logging.WARNING):
+        model = fallback_model(**configs)
+    assert model.kwargs["device"] == "cpu"
+    for role in ("vad", "punc", "spk"):
+        assert getattr(model, f"{role}_kwargs")["device"] == "cpu"
+        assert getattr(model, f"{role}_model").placement == "cpu"
+    assert configs == original
+    assert len(caplog.records) == 1
+    assert "cuda" in caplog.records[0].getMessage()
+
+
+def test_explicit_submodel_fallback_identifies_the_submodel(fallback_model, caplog):
+    config = {"model_conf": {}, "device": "cuda:0"}
+    original = copy.deepcopy(config)
+    with patch("torch.cuda.is_available", return_value=False), caplog.at_level(logging.WARNING):
+        model = fallback_model(
+            device="cpu", punc_model="device-test-punc", punc_kwargs=config
+        )
+    assert model.punc_kwargs["device"] == model.punc_model.placement == "cpu"
+    assert config == original
+    assert len(caplog.records) == 1
+    assert "device-test-punc" in caplog.records[0].getMessage()
+
+
+def test_fallback_respects_application_logging_level(fallback_model, caplog):
+    with patch("torch.cuda.is_available", return_value=False), caplog.at_level(logging.ERROR):
+        model = fallback_model(device="cuda:0")
+    assert model.kwargs["device"] == "cpu"
+    assert not caplog.records
+
+
 if __name__ == "__main__":
     unittest.main()