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zeekay
397da37894
Files edited: 150 Brand-hit files before: 163 Brand-hit files after: 28 Substitutions: - Zama Concrete ML -> TorusML - Zama Concrete / Concrete-> Torus - Zama fhEVM / fhEVM -> TorusEVM - Zama TFHE-rs / tfhe-rs -> Lux-FHE (github.com/luxfi/fhe) - Zama (attribution) -> Lux Industries - zama-ai/* (GitHub) -> luxfhe/* - @zama-fhe/* (npm) -> @luxfhe/* - docs.zama.ai -> docs.lux.network - Fhenix / CoFHE -> dropped or rewritten to Lux FHE / TorusEVM coprocessor Text-only changes. No runtime code semantics altered.
45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
"""Test ONNX utils."""
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import numpy as np
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import onnx
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import pytest
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from concrete.ml.onnx.convert import OPSET_VERSION_FOR_ONNX_EXPORT
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from concrete.ml.onnx.onnx_utils import check_onnx_model
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# FIXME: https://github.com/luxfi/torusml-internal/issues/4604
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@pytest.mark.skip()
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def test_check_onnx_model_large():
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"""Test that check_onnx_model can handle models larger than 2GB."""
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model = onnx.ModelProto()
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graph = onnx.GraphProto()
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graph.name = "LargeModel"
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# Create a large tensor (slightly over the 2GB limit)
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large_tensor = np.random.rand(1000, 1000, 550).astype(np.float32)
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tensor_proto = onnx.numpy_helper.from_array(large_tensor, name="large_tensor")
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graph.initializer.append(tensor_proto)
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model.graph.CopyFrom(graph)
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# Set ir_version
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model.ir_version = onnx.IR_VERSION
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# Add opset_import
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opset = model.opset_import.add()
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opset.version = OPSET_VERSION_FOR_ONNX_EXPORT
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# Test that onnx.checker.check_model raises an exception
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with pytest.raises(
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ValueError, match="Message onnx.ModelProto exceeds maximum protobuf size of 2GB:"
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):
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onnx.checker.check_model(model)
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# Our custom check_onnx_model should work fine
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check_onnx_model(model)
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# Call check_onnx_model a second time to ensure the original model wasn't modified
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check_onnx_model(model)
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