ayo
7c5d3c9157
Add native K/M/RR inference engines, HIP kernels, replay and validation harnesses, and Windows DLSS setup and runtime support. Include GPU profiling tools, build and packaging scripts, platform integration patches, and implementation documentation.
47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
"""Regression for divergent finite loops in the public CPU PTX reference."""
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import pathlib
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import sys
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import tempfile
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import unittest
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import numpy as np
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ROOT = pathlib.Path(__file__).resolve().parents[2]
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sys.path.insert(0, str(ROOT / 'kernels/tools'))
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import ptxsim
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class ControlFlow(unittest.TestCase):
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def test_divergent_backward_loop_reconverges(self):
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source = '''.visible .entry pattern(.param .align 8 .b8 pattern_param_0[8])
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.maxntid 32, 1, 1
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{
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.reg .b32 %r<4>;
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.reg .pred %p<1>;
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mov.u32 %r0, %tid.x;
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and.b32 %r0, %r0, 3;
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add.u32 %r0, %r0, 1;
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mov.u32 %r1, 0;
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$loop:
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add.u32 %r1, %r1, %r0;
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sub.u32 %r0, %r0, 1;
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setp.ne.u32 %p0, %r0, 0;
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@%p0 bra $loop;
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add.u32 %r2, %r1, 7;
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ret;
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}
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'''
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with tempfile.TemporaryDirectory() as directory:
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path = pathlib.Path(directory) / 'pattern.ptx'
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path.write_text(source)
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statements, types, shared = ptxsim.parse_kernel(str(path), 'pattern')
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block = ptxsim.Block(statements, types, shared, None, 'pattern_param_0', (32, 1, 1), (1, 1, 1), (0, 0, 0))
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block.run(limit=1000)
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n = np.arange(32) % 4 + 1
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np.testing.assert_array_equal(block.regs['%r1'], n * (n + 1) // 2)
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np.testing.assert_array_equal(block.regs['%r2'], n * (n + 1) // 2 + 7)
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if __name__ == '__main__':
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unittest.main()
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