Anton Pogrebnjak
102 lines
2.7 KiB
Python
102 lines
2.7 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) 2026 by David Boetius
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# Licensed under the MIT License.
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"""Generate eval tests for the same operations that expose grad bugs.
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These eval tests verify that forward evaluation is correct for
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broadcasting mul/add and 1D dot — the same operations whose VJP rules
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are buggy.
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Run from the repository root:
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python tests/milestone1/base/unit/eval/generate_bugfix_eval_tests.py
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"""
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import json
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from pathlib import Path
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import numpy as np
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from minijax import core
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from minijax.compute_graph import make_graph
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from minijax.eval import Array
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from minijax.serialize import dump
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SCRIPT_DIR = Path(__file__).parent
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REPO_ROOT = SCRIPT_DIR.parents[4]
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DEFAULT_TOLERANCE = 1e-4
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def create_test(name, fn, inputs_data, *, tolerance=DEFAULT_TOLERANCE):
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test_dir = SCRIPT_DIR / name
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resources_dir = test_dir / "resources"
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resources_dir.mkdir(parents=True, exist_ok=True)
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arrays = [Array(d) for d in inputs_data]
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graph = make_graph(fn)(*arrays)
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network_file = f"{name}_network.mininn"
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dump(graph, resources_dir / network_file)
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input_files = []
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for i, d in enumerate(inputs_data):
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fname = f"input_{i}.bin"
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np.asarray(d, dtype=np.float64).tofile(resources_dir / fname)
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input_files.append(f"resources/{fname}")
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raw_out = fn(*arrays)
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if not isinstance(raw_out, (list, tuple)):
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raw_out = [raw_out]
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expected_files = []
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for i, out in enumerate(raw_out):
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fname = f"expected_output_{i}.bin"
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out.array.astype(np.float64).tofile(test_dir / fname)
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expected_files.append(fname)
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config = {
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"command": "eval",
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"network": f"resources/{network_file}",
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"inputs": input_files,
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"expected_outputs": expected_files,
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}
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if tolerance != DEFAULT_TOLERANCE:
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config["tolerance"] = tolerance
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(test_dir / "test.json").write_text(json.dumps(config, indent=4) + "\n")
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print(f" created {test_dir.relative_to(REPO_ROOT)}")
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def main():
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rng = np.random.default_rng(100)
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# mul with broadcasting: x (3, 4), y (1, 4)
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x_data = rng.standard_normal((3, 4))
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y_data = rng.standard_normal((1, 4))
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create_test(
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"mul_broadcast",
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lambda x, y: core.mul(x, y),
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[x_data, y_data],
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)
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# add with broadcasting: x (3, 4), y (1, 4)
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create_test(
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"add_broadcast",
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lambda x, y: core.add(x, y),
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[x_data, y_data],
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)
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# dot with 1D input: x (8,), y (8, 5)
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x_dot = rng.standard_normal((8,))
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y_dot = rng.standard_normal((8, 5))
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create_test(
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"dot_1d",
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lambda x, y: core.dot(x, y),
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[x_dot, y_dot],
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)
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print("Done.")
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if __name__ == "__main__":
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main()
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