"""L3: vendored FLA Triton fwd vs naive chunked."""
import pytest
import torch
import torch.nn.functional as F

from kda.ops.reference.chunkwise import naive_chunk_kda
from kda.ops.triton.chunk_fwd import chunk_kda_fwd

pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")


def test_triton_fwd_matches_naive():
    B, T, H, HV, K, V = 2, 64, 4, 8, 32, 32
    torch.manual_seed(3)
    device = "cuda"
    q = F.normalize(torch.randn(B, T, H, K, device=device), dim=-1)
    k = F.normalize(torch.randn(B, T, H, K, device=device), dim=-1)
    v = torch.randn(B, T, HV, V, device=device)
    g = -torch.rand(B, T, HV, K, device=device) * 2
    beta = torch.rand(B, T, HV, device=device)

    o_ref, S_ref = naive_chunk_kda(
        q, k, v, g, beta, output_final_state=True, chunk_size=64
    )
    o_tr, S_tr = chunk_kda_fwd(
        q, k, v, g, beta, output_final_state=True, chunk_size=64
    )
    torch.testing.assert_close(o_tr.float(), o_ref.float(), rtol=2e-3, atol=2e-3)
    torch.testing.assert_close(S_tr.float(), S_ref.float(), rtol=2e-3, atol=2e-3)