File size: 1,540 Bytes
7298fd0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | from __future__ import annotations
import pytest
torch = pytest.importorskip("torch")
pytest.importorskip("triton")
@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
def test_decode_matches_sdpa_full_kv():
import torch.nn.functional as F
import hydra
torch.manual_seed(0)
q = torch.randn(1, 32, 1, 128, device="cuda", dtype=torch.bfloat16)
k = torch.randn(1, 8, 256, 128, device="cuda", dtype=torch.bfloat16)
v = torch.randn(1, 8, 256, 128, device="cuda", dtype=torch.bfloat16)
out = hydra.hydra(q, k, v)
k_rep = k.repeat_interleave(4, dim=1)
v_rep = v.repeat_interleave(4, dim=1)
ref = F.scaled_dot_product_attention(q, k_rep, v_rep, is_causal=False)
torch.testing.assert_close(out, ref, atol=3e-2, rtol=3e-2)
@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
def test_decode_matches_sdpa_sliding_window():
import torch.nn.functional as F
import hydra
torch.manual_seed(1)
q = torch.randn(1, 32, 1, 128, device="cuda", dtype=torch.bfloat16)
k = torch.randn(1, 8, 257, 128, device="cuda", dtype=torch.bfloat16)
v = torch.randn(1, 8, 257, 128, device="cuda", dtype=torch.bfloat16)
window = 96
out = hydra.hydra(q, k, v, sliding_window=window)
k_rep = k[:, :, -window:, :].repeat_interleave(4, dim=1)
v_rep = v[:, :, -window:, :].repeat_interleave(4, dim=1)
ref = F.scaled_dot_product_attention(q, k_rep, v_rep, is_causal=False)
torch.testing.assert_close(out, ref, atol=3e-2, rtol=3e-2)
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