40 lines
1.1 KiB
Python
40 lines
1.1 KiB
Python
import pytest
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import torch
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from sgl_kernel import sampling_scaling_penalties
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batch_sizes = [1, 2, 4, 8, 16, 32, 64, 65]
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vocab_sizes = [2048, 4096, 8192, 16384, 32768, 32767]
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dtypes = [torch.float32, torch.half, torch.bfloat16]
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@pytest.mark.parametrize("batch_size", batch_sizes)
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@pytest.mark.parametrize("vocab_size", vocab_sizes)
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@pytest.mark.parametrize("dtype", dtypes)
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def test_sampling_scaling_penalties(batch_size, vocab_size, dtype):
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device = torch.device("cuda")
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rtol = 1e-3
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atol = 1e-3
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logits = torch.randn(batch_size, vocab_size, device=device, dtype=dtype)
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scaling_penalties = (
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torch.rand(batch_size, vocab_size, device=device, dtype=dtype) + 0.5
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)
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ref_output = torch.where(
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logits > 0, logits / scaling_penalties, logits * scaling_penalties
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)
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kernel_output = sampling_scaling_penalties(logits, scaling_penalties)
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torch.testing.assert_close(
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kernel_output,
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ref_output,
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rtol=rtol,
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atol=atol,
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msg=f"Failed for batch_size={batch_size}, vocab_size={vocab_size}, dtype={dtype}",
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)
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if __name__ == "__main__":
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pytest.main([__file__])
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