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import pytest
import torch
import torch.nn.functional as F
from einops import rearrange, repeat
from fla.ops.forgetting_attn.parallel import parallel_forgetting_attn
from fla.utils import assert_close, check_shared_mem, device, is_intel_alchemist
def naive_forgetting_attn(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
g: torch.Tensor,
scale: float | None = None,
):
_, T, HQ, D = q.shape
H = k.shape[2]
G = HQ // H
if scale is None:
scale = D ** -0.5
gc = g.float().cumsum(1)
mask = torch.tril(torch.ones((T, T), dtype=torch.bool, device=device))
ref = torch.einsum("bqhd,bkhd->bhqk", q.float() * scale, repeat(k, "b t h d -> b t (h g) d", g=G).float())
ref = ref + rearrange(gc, "b t h -> b h t 1") - rearrange(gc, "b t h -> b h 1 t")
ref = ref.masked_fill(~mask.unsqueeze(0).unsqueeze(0), -float('inf'))
ref = torch.einsum("bhqk,bkhd->bqhd", F.softmax(ref, dim=-1), repeat(v, "b t h d -> b t (h g) d", g=G).float())
return ref
@pytest.mark.parametrize(
('B', 'T', 'H', 'HQ', 'D', 'scale'),
[
pytest.param(*test, id="B{}-T{}-H{}-HQ{}-D{}-scale{}".format(*test))
for test in [
(1, 63, 1, 1, 64, 1.0),
(3, 111, 2, 2, 100, 1.0),
(3, 1024, 2, 8, 60, 0.1),
(3, 1024, 2, 8, 128, 0.1),
(4, 2048, 2, 8, 64, 0.1),
]
],
)
def test_parallel(
B: int,
T: int,
H: int,
HQ: int,
D: int,
scale: float,
):
torch.manual_seed(42)
dtype = torch.float16
if not check_shared_mem('hopper') and D > 128:
# maybe we can enable this test on Triton 3.3.0
pytest.skip("Skipping test because global shared memory is not available")
q = torch.randn((B, T, HQ, D), dtype=dtype, device=device).requires_grad_(True)
k = torch.randn((B, T, H, D), dtype=dtype, device=device).requires_grad_(True)
v = torch.randn((B, T, H, D), dtype=dtype, device=device).requires_grad_(True)
g = torch.randn((B, T, HQ), dtype=dtype, device=device).uniform_(-0.1, -0.01).requires_grad_(True)
do = torch.randn((B, T, HQ, D), dtype=dtype, device=device)
ref = naive_forgetting_attn(q, k, v, g, scale)
ref.backward(do)
ref_dq, q.grad = q.grad.clone(), None
ref_dk, k.grad = k.grad.clone(), None
ref_dv, v.grad = v.grad.clone(), None
ref_dg, g.grad = g.grad.clone(), None
tri = parallel_forgetting_attn(q=q, k=k, v=v, g=g, scale=scale)
tri.backward(do)
tri_dq, q.grad = q.grad.clone(), None
tri_dk, k.grad = k.grad.clone(), None
tri_dv, v.grad = v.grad.clone(), None
tri_dg, g.grad = g.grad.clone(), None
assert_close(" o", ref, tri, 0.005)
assert_close("dq", ref_dq, tri_dq, 0.005)
assert_close("dk", ref_dk, tri_dk, 0.005)
assert_close("dv", ref_dv, tri_dv, 0.005)
assert_close("dg", ref_dg, tri_dg, 0.005)
@pytest.mark.parametrize(
('H', 'HQ', 'D', 'cu_seqlens'),
[
pytest.param(*test, id="H{}-HQ{}-D{}-cu_seqlens{}".format(*test))
for test in [
(2, 2, 64, [0, 15]),
(2, 8, 64, [0, 256, 500, 1000]),
(2, 2, 100, [0, 15, 100, 300, 1200, 2000]),
]
],
)
@pytest.mark.skipif(
is_intel_alchemist,
reason="Intel Triton Failure",
)
def test_parallel_varlen(
H: int,
HQ: int,
D: int,
cu_seqlens: list[int],
):
torch.manual_seed(42)
T = cu_seqlens[-1]
cu_seqlens = torch.tensor(cu_seqlens, dtype=torch.int32, device=device)
dtype = torch.float16
# seq-first required for inputs with variable lengths
q = torch.randn((1, T, HQ, D), dtype=dtype, device=device).requires_grad_()
k = torch.randn((1, T, H, D), dtype=dtype, device=device).requires_grad_()
v = torch.randn((1, T, H, D), dtype=dtype, device=device).requires_grad_()
g = torch.rand((1, T, HQ), dtype=dtype, device=device).uniform_(-0.1, -0.01).requires_grad_(True)
do = torch.randn((1, T, HQ, D), dtype=dtype, device=device)
ref = q.new_empty(1, T, HQ, D)
for bos, eos in zip(cu_seqlens[:-1], cu_seqlens[1:], strict=False):
ref[:, bos:eos] = naive_forgetting_attn(
q=q[:, bos:eos],
k=k[:, bos:eos],
v=v[:, bos:eos],
g=g[:, bos:eos],
)
ref.backward(do)
ref_dq, q.grad = q.grad.clone(), None
ref_dk, k.grad = k.grad.clone(), None
ref_dv, v.grad = v.grad.clone(), None
ref_dg, g.grad = g.grad.clone(), None
tri = parallel_forgetting_attn(
q=q,
k=k,
v=v,
g=g,
cu_seqlens=cu_seqlens,
)
tri.backward(do)
tri_dq, q.grad = q.grad.clone(), None
tri_dk, k.grad = k.grad.clone(), None
tri_dv, v.grad = v.grad.clone(), None
tri_dg, g.grad = g.grad.clone(), None
assert_close(" o", ref, tri, 0.004)
assert_close(" dq", ref_dq.squeeze(), tri_dq.squeeze(), 0.005)
assert_close(" dk", ref_dk.squeeze(), tri_dk.squeeze(), 0.005)
assert_close(" dv", ref_dv.squeeze(), tri_dv.squeeze(), 0.005)
assert_close(" dg", ref_dg.squeeze(), tri_dg.squeeze(), 0.005)
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