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gpu_available() probed torch, then ONNX Runtime, then nvidia-smi, but never paddle. The OCR bundle ships paddlepaddle-gpu with no torch or ONNX, so on an OCR-only GPU host every probe missed the GPU: nvidia-smi saw it but returned False by design, and OCR silently fell back to Tesseract (CPU, lower quality) with no signal why. Add a paddle probe as the last resort in gpu_available(). It runs only after nvidia-smi confirms a GPU is physically present, and in an isolated subprocess, because importing paddlepaddle-gpu on a GPU-less host segfaults and would wedge the shared AI dispatcher. It returns True only when paddle reports both a CUDA build and a visible device, signalling the result through the exit code so paddle's own import chatter on stdout cannot corrupt the reading. CPU-only and torch/ONNX GPU hosts are unaffected: the probe never runs on the former (nvidia-smi finds nothing) and is never reached on the latter (the torch step already returns True first). Claude-Session: https://claude.ai/code/session_01NfaRxjek8ex5nawvx3mVMf
83 lines
3.3 KiB
Python
83 lines
3.3 KiB
Python
"""gpu_available() must recognize a GPU that only paddle can use.
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The OCR feature bundle ships paddlepaddle-gpu but no torch or ONNX Runtime. On an
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OCR-only GPU host the torch and ONNX probes both come up empty, so before this
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fix gpu_available() fell through to a plain nvidia-smi check that returned False
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by design, and OCR silently downgraded to Tesseract. gpu_available() now probes
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paddle too, in an isolated subprocess, but only after nvidia-smi confirms a GPU
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is physically present (importing paddlepaddle-gpu in-process segfaults on a
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GPU-less host and would wedge the shared AI dispatcher).
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"""
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import os
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import subprocess
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import sys
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
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import gpu # noqa: E402
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# --- The isolated paddle probe --------------------------------------------
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def test_paddle_probe_true_when_subprocess_reports_cuda(monkeypatch):
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def fake_run(cmd, **kwargs):
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return subprocess.CompletedProcess(cmd, returncode=0, stdout="", stderr="")
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monkeypatch.setattr(gpu.subprocess, "run", fake_run)
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assert gpu._try_paddle_cuda_subprocess() is True
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def test_paddle_probe_false_when_subprocess_reports_no_cuda(monkeypatch):
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def fake_run(cmd, **kwargs):
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return subprocess.CompletedProcess(cmd, returncode=1, stdout="", stderr="")
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monkeypatch.setattr(gpu.subprocess, "run", fake_run)
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assert gpu._try_paddle_cuda_subprocess() is False
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def test_paddle_probe_false_on_timeout(monkeypatch):
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def fake_run(cmd, **kwargs):
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raise subprocess.TimeoutExpired(cmd, 30)
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monkeypatch.setattr(gpu.subprocess, "run", fake_run)
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assert gpu._try_paddle_cuda_subprocess() is False
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# --- gpu_available() orchestration -----------------------------------------
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def _patch_probes(monkeypatch, torch, onnx, smi):
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"""Stub the three existing probes and reset the lru_cache for one call."""
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monkeypatch.delenv("SNAPOTTER_GPU", raising=False)
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monkeypatch.setattr(gpu, "_try_torch_cuda", lambda: torch)
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monkeypatch.setattr(gpu, "_try_onnx_cuda", lambda: onnx)
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monkeypatch.setattr(gpu, "_nvidia_smi_gpu_name", lambda: smi)
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gpu.gpu_available.cache_clear()
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def test_gpu_available_true_when_only_paddle_sees_gpu(monkeypatch):
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# OCR-only GPU box: torch and ONNX absent, GPU present, paddle can use it.
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_patch_probes(monkeypatch, torch=False, onnx=False, smi="NVIDIA GeForce RTX 4070")
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monkeypatch.setattr(gpu, "_try_paddle_cuda_subprocess", lambda: True)
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assert gpu.gpu_available() is True
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def test_gpu_available_false_when_paddle_cannot_use_gpu(monkeypatch):
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# GPU present but paddle is a CPU build or cannot init CUDA: stay conservative.
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_patch_probes(monkeypatch, torch=False, onnx=False, smi="NVIDIA GeForce RTX 4070")
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monkeypatch.setattr(gpu, "_try_paddle_cuda_subprocess", lambda: False)
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assert gpu.gpu_available() is False
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def test_gpu_available_never_probes_paddle_without_a_gpu(monkeypatch):
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# Safety invariant: on a GPU-less host a paddlepaddle-gpu import segfaults, so
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# the probe must never run when nvidia-smi finds no GPU.
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_patch_probes(monkeypatch, torch=False, onnx=False, smi=None)
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called = {"paddle": False}
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def spy():
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called["paddle"] = True
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return True
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monkeypatch.setattr(gpu, "_try_paddle_cuda_subprocess", spy)
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assert gpu.gpu_available() is False
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assert called["paddle"] is False
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