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Running on Zero
Running on Zero
File size: 4,867 Bytes
2ed6676 aa831fd 2ed6676 aa831fd 2ed6676 aa831fd 2ed6676 aa831fd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 | import contextlib
import contextvars
import numpy as np
import pickle
import sys
import types
import weakref
import pytest
import torch
import compilation
def test_package_transfer_drops_live_cuda_runner():
packaged = compilation.PackagedFP16("/runtime/model.pt2")
# A native runner cannot be pickled and must never escape its GPU worker.
packaged.loaded = lambda value: value
restored = pickle.loads(pickle.dumps(packaged))
assert restored.package_path == "/runtime/model.pt2"
assert restored.loaded is None
def test_package_loads_once_across_zerogpu_requests(monkeypatch):
loads = []
def load(path):
loads.append(path)
return lambda value: value + 1
monkeypatch.setattr(torch._inductor, "aoti_load_package", load)
# The real helper reads Linux process maps; it cannot import on Windows.
cleanup = types.SimpleNamespace(_register_aoti_cleanup=contextlib.nullcontext)
monkeypatch.setitem(sys.modules, "spaces.zero.torch.aoti", cleanup)
packaged = compilation.PackagedFP16("/runtime/model.pt2")
# ZeroGPU workers run every request in a fresh copy of the worker context.
results = [contextvars.copy_context().run(packaged, 1) for _ in range(3)]
assert results == [2, 2, 2]
assert loads == ["/runtime/model.pt2"]
def test_disabled_compilation_preserves_working_model(monkeypatch):
monkeypatch.setenv("VRMBG_COMPILE", "0")
model = torch.nn.Linear(2, 1)
expected = model(torch.ones(1, 2)).detach().clone()
assert compilation.enable_compiled_model(model) is False
torch.testing.assert_close(model(torch.ones(1, 2)), expected)
def test_compile_failure_preserves_eager_model(monkeypatch, caplog):
monkeypatch.setenv("VRMBG_COMPILE", "1")
monkeypatch.setenv("SPACES_ZERO_GPU", "true")
monkeypatch.setattr(compilation.spaces, "GPU", lambda **kw: lambda fn: fn)
def fail(model):
raise RuntimeError("Compiler unavailable")
monkeypatch.setattr(compilation, "compile_fp16", fail)
model = torch.nn.Linear(2, 1)
model.register_buffer("integer_indices", torch.arange(4))
expected = model(torch.ones(1, 2)).detach().clone()
assert compilation.enable_compiled_model(model) is False
torch.testing.assert_close(model(torch.ones(1, 2)), expected)
assert "using eager FP16" in caplog.text
def test_zerogpu_startup_enables_compiled_forward(monkeypatch):
monkeypatch.delenv("VRMBG_COMPILE", raising=False)
monkeypatch.setenv("SPACES_ZERO_GPU", "true")
monkeypatch.setattr(compilation.spaces, "GPU", lambda **kw: lambda fn: fn)
model = torch.nn.Linear(2, 1)
model.register_buffer("integer_indices", torch.arange(4))
def compiled(value):
return value.sum(dim=1, keepdim=True)
monkeypatch.setattr(compilation, "compile_fp16", lambda model: compiled)
old_parameter = weakref.ref(model.weight)
assert compilation.enable_compiled_model(model) is True
torch.testing.assert_close(model(torch.ones(1, 2)), torch.tensor([[2.0]]))
assert all(tensor.numel() == 0 for tensor in model.state_dict().values())
assert old_parameter() is None
def test_apply_failure_is_not_reported_as_working_eager_fallback(monkeypatch):
monkeypatch.setenv("VRMBG_COMPILE", "1")
monkeypatch.setattr(compilation.spaces, "GPU", lambda **kw: lambda fn: fn)
monkeypatch.setattr(compilation, "compile_fp16", lambda model: object())
def fail_apply(candidate, model):
model.weight = None
raise RuntimeError("Apply failed after mutation")
monkeypatch.setattr(compilation, "apply_compiled_model", fail_apply)
with pytest.raises(RuntimeError, match="Apply failed"):
compilation.enable_compiled_model(torch.nn.Linear(2, 1))
def test_local_compilation_errors_require_a_fresh_process(monkeypatch):
monkeypatch.setenv("VRMBG_COMPILE", "1")
monkeypatch.delenv("SPACES_ZERO_GPU", raising=False)
monkeypatch.setattr(compilation.spaces, "GPU", lambda **kw: lambda fn: fn)
def fail(model):
raise RuntimeError("CUDA failure")
monkeypatch.setattr(compilation, "compile_fp16", fail)
with pytest.raises(RuntimeError, match="CUDA failure"):
compilation.enable_compiled_model(torch.nn.Linear(2, 1))
@pytest.mark.parametrize("bad", [np.nan, np.inf, 0.7])
def test_quality_check_rejects_invalid_or_changed_mattes(bad):
reference = np.full((3, 8, 8), 0.5, dtype=np.float32)
with pytest.raises(RuntimeError):
compilation.check_mattes(reference, np.full_like(reference, bad))
def test_quality_check_rejects_shape_changes():
with pytest.raises(RuntimeError):
compilation.check_mattes(np.zeros((3, 8, 8)), np.zeros((1, 8, 8)))
def test_quality_check_accepts_small_fp16_rounding():
reference = np.full((3, 8, 8), 0.5, dtype=np.float32)
compilation.check_mattes(reference, reference + 0.0001)
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