dela
584f7e9e73
Standalone tree split from LLMRL/projects/kda. Includes Triton dt_bias backward fix, train_k3 --preset 0.5b, SFT, Docker runtime, and tests.
31 lines
1007 B
Python
31 lines
1007 B
Python
"""L6: fused_recurrent decode matches naive recurrent."""
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import pytest
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import torch
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from kda.ops.recurrent.fused import fused_recurrent_kda
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from kda.ops.reference.recurrent import naive_kda
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pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
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def test_step_matches_naive():
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B, T, H, HV, K, V = 2, 32, 2, 4, 8, 8
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torch.manual_seed(20)
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device = "cuda"
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q = torch.randn(B, T, H, K, device=device)
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k = torch.randn(B, T, H, K, device=device)
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v = torch.randn(B, T, HV, V, device=device)
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g = -torch.rand(B, T, HV, K, device=device) * 2
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beta = torch.rand(B, T, HV, device=device)
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o_naive, _ = naive_kda(
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q.double(), k.double(), v.double(), g.double(),
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beta.double(), output_final_state=False,
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)
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o_step, _ = fused_recurrent_kda(q, k, v, g, beta, output_final_state=False)
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torch.testing.assert_close(o_step.float(), o_naive.float(), rtol=2e-3, atol=2e-3)
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if __name__ == "__main__":
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test_step_matches_naive()
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