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import pytest
import torch
from fla.ops.linear_attn import chunk_linear_attn, fused_chunk_linear_attn, fused_recurrent_linear_attn
from fla.ops.linear_attn.naive import naive_recurrent_linear_attn
from fla.utils import assert_close, device
@pytest.mark.parametrize(
('B', 'T', 'H', 'D', 'scale', 'dtype'),
[
pytest.param(*test, id="B{}-T{}-H{}-D{}-scale{}-{}".format(*test))
for test in [
(1, 64, 1, 64, None, torch.float),
(2, 512, 4, 60, None, torch.float),
(3, 1024, 8, 128, 1., torch.float),
(3, 1024, 8, 128, 0.1, torch.float),
(3, 1024, 8, 128, None, torch.float),
(2, 2048, 8, 256, None, torch.float16),
(2, 2048, 4, 256, None, torch.float16),
]
],
)
def test_fused_recurrent(
B: int,
T: int,
H: int,
D: int,
scale: float | None,
dtype: torch.dtype,
):
torch.manual_seed(42)
q = torch.randn((B, T, H, D), dtype=dtype, device=device).requires_grad_()
k = torch.randn((B, T, H, D), dtype=dtype, device=device).requires_grad_()
v = torch.randn((B, T, H, D), dtype=dtype, device=device).requires_grad_()
h0 = torch.randn((B, H, D, D), dtype=torch.float, device=device).requires_grad_()
do = torch.randn_like(v)
dht = torch.randn_like(h0)
ref, ref_ht = naive_recurrent_linear_attn(q, k, v, scale=scale, initial_state=h0, output_final_state=True, normalize=False)
((ref * do).sum() + (ref_ht * dht).sum()).backward()
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_dh0, h0.grad = h0.grad.clone(), None
tri, tri_ht = fused_recurrent_linear_attn(q, k, v, scale=scale, initial_state=h0, output_final_state=True, normalize=False)
((tri * do).sum() + (tri_ht * dht).sum()).backward()
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_dh0, h0.grad = h0.grad.clone(), None
assert_close('o', ref, tri, 0.001)
assert_close('ht', ref_ht, tri_ht, 0.001)
assert_close('dq', ref_dq, tri_dq, 0.001)
assert_close('dk', ref_dk, tri_dk, 0.001)
assert_close('dv', ref_dv, tri_dv, 0.001)
assert_close('dh0', ref_dh0, tri_dh0, 0.001)
@pytest.mark.parametrize(
('B', 'T', 'H', 'D', 'dtype'),
[
pytest.param(*test, id="B{}-T{}-H{}-D{}-{}".format(*test))
for test in [
(1, 63, 1, 64, torch.float16),
(2, 500, 3, 60, torch.float16),
(2, 1000, 3, 128, torch.float16),
(3, 1000, 4, 64, torch.float16),
(2, 2048, 4, 256, torch.float16),
]
],
)
def test_chunk(
B: int,
T: int,
H: int,
D: int,
dtype: torch.dtype,
):
torch.manual_seed(42)
q = torch.randn((B, T, H, D), dtype=dtype, device=device).requires_grad_()
k = torch.randn((B, T, H, D), dtype=dtype, device=device).requires_grad_()
v = torch.randn((B, T, H, D), dtype=dtype, device=device).requires_grad_()
h0 = torch.randn((B, H, D, D), dtype=torch.float, device=device).requires_grad_()
do = torch.randn_like(v)
dht = torch.randn_like(h0)
ref, ref_ht = fused_recurrent_linear_attn(
q.to(torch.float32),
k.to(torch.float32),
v.to(torch.float32),
initial_state=h0,
output_final_state=True,
normalize=False,
)
((ref * do).sum() + (ref_ht * dht).sum()).backward()
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_dh0, h0.grad = h0.grad.clone(), None
tri, tri_ht = chunk_linear_attn(
q=q,
k=k,
v=v,
initial_state=h0,
output_final_state=True,
normalize=False,
)
((tri * do).sum() + (tri_ht * dht).sum()).backward()
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_dh0, h0.grad = h0.grad.clone(), None
assert_close('o', ref, tri, 0.001)
assert_close('ht', ref_ht, tri_ht, 0.001)
assert_close('dq', ref_dq, tri_dq, 0.001)
assert_close('dk', ref_dk, tri_dk, 0.001)
assert_close('dv', ref_dv, tri_dv, 0.001)
assert_close('dh0', ref_dh0, tri_dh0, 0.001)
@pytest.mark.parametrize(
('B', 'T', 'H', 'D', 'dtype'),
[
pytest.param(*test, id="B{}-T{}-H{}-D{}-{}".format(*test))
for test in [
(1, 63, 1, 64, torch.float16),
(2, 500, 3, 60, torch.float16),
(2, 1000, 3, 128, torch.float16),
(3, 1000, 4, 64, torch.float16),
(2, 2048, 4, 256, torch.float16),
]
],
)
def test_fused_chunk(
B: int,
T: int,
H: int,
D: int,
dtype: torch.dtype,
):
torch.manual_seed(42)
q = torch.randn((B, T, H, D), dtype=dtype, device=device).requires_grad_()
k = torch.randn((B, T, H, D), dtype=dtype, device=device).requires_grad_()
v = torch.randn((B, T, H, D), dtype=dtype, device=device).requires_grad_()
h0 = torch.randn((B, H, D, D), dtype=torch.float, device=device).requires_grad_()
do = torch.randn_like(v)
dht = torch.randn_like(h0)
ref, ref_ht = fused_recurrent_linear_attn(
q.to(torch.float32),
k.to(torch.float32),
v.to(torch.float32),
initial_state=h0,
output_final_state=True,
normalize=False,
)
((ref * do).sum() + (ref_ht * dht).sum()).backward()
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_dh0, h0.grad = h0.grad.clone(), None
tri, tri_ht = fused_chunk_linear_attn(
q=q,
k=k,
v=v,
initial_state=h0,
output_final_state=True,
normalize=False,
)
((tri * do).sum() + (tri_ht * dht).sum()).backward()
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_dh0, h0.grad = h0.grad.clone(), None
assert_close('o', ref, tri, 0.001)
assert_close('ht', ref_ht, tri_ht, 0.001)
assert_close('dq', ref_dq, tri_dq, 0.001)
assert_close('dk', ref_dk, tri_dk, 0.001)
assert_close('dv', ref_dv, tri_dv, 0.001)
assert_close('dh0', ref_dh0, tri_dh0, 0.001)
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