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.
30 lines
1.1 KiB
Python
30 lines
1.1 KiB
Python
"""L3: vendored FLA Triton fwd vs naive chunked."""
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import pytest
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import torch
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import torch.nn.functional as F
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from kda.ops.reference.chunkwise import naive_chunk_kda
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from kda.ops.triton.chunk_fwd import chunk_kda_fwd
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pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
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def test_triton_fwd_matches_naive():
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B, T, H, HV, K, V = 2, 64, 4, 8, 32, 32
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torch.manual_seed(3)
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device = "cuda"
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q = F.normalize(torch.randn(B, T, H, K, device=device), dim=-1)
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k = F.normalize(torch.randn(B, T, H, K, device=device), dim=-1)
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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_ref, S_ref = naive_chunk_kda(
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q, k, v, g, beta, output_final_state=True, chunk_size=64
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)
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o_tr, S_tr = chunk_kda_fwd(
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q, k, v, g, beta, output_final_state=True, chunk_size=64
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)
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torch.testing.assert_close(o_tr.float(), o_ref.float(), rtol=2e-3, atol=2e-3)
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torch.testing.assert_close(S_tr.float(), S_ref.float(), rtol=2e-3, atol=2e-3)
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