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import os
import pytest
import torch
import torch.nn.functional as F
from fla.ops.hgrn import chunk_hgrn, fused_recurrent_hgrn
from fla.ops.hgrn.naive import naive_recurrent_hgrn
from fla.utils import assert_close, device
@pytest.mark.parametrize(
('B', 'T', 'D', 'dtype'),
[
pytest.param(*test, id="B{}-T{}-D{}-{}".format(*test))
for test in [
(1, 63, 500, torch.float),
(2, 1024, 500, torch.float),
(2, 1024, 512, torch.float),
(2, 1024, 1000, torch.float),
(4, 2048, 2048, torch.float),
]
],
)
def test_fused_recurrent(
B: int,
T: int,
D: int,
dtype: torch.dtype,
):
torch.manual_seed(42)
os.environ['TRITON_F32_DEFAULT'] = 'ieee'
x = torch.randn((B, T, D), dtype=dtype, device=device)
g = torch.randn((B, T, D), dtype=dtype, device=device)
h0 = torch.randn_like(x[:, 0])
x, g = (1 - g.sigmoid()) * x, F.logsigmoid(g)
x, g, h0 = (i.detach().clone().to(dtype).requires_grad_() for i in (x, g, h0))
do = torch.randn_like(x)
dht = torch.randn_like(h0)
ref, ref_ht = naive_recurrent_hgrn(x, g, h0, output_final_state=True)
((ref * do).sum() + (ref_ht * dht).sum()).backward()
ref_dx, x.grad = x.grad.clone(), None
ref_dg, g.grad = g.grad.clone(), None
ref_dh0, h0.grad = h0.grad.clone(), None
tri, tri_ht = fused_recurrent_hgrn(x, g, h0, output_final_state=True)
((tri * do).sum() + (tri_ht * dht).sum()).backward()
tri_dx, x.grad = x.grad.clone(), None
tri_dg, g.grad = g.grad.clone(), None
tri_dh0, h0.grad = h0.grad.clone(), None
assert_close('o', ref, tri, 0.005)
assert_close('ht', ref_ht, tri_ht, 0.005)
assert_close('dx', ref_dx, tri_dx, 0.005)
assert_close('dg', ref_dg, tri_dg, 0.005)
assert_close('dh0', ref_dh0, tri_dh0, 0.005)
@pytest.mark.parametrize(
('D', 'cu_seqlens', 'dtype'),
[
pytest.param(*test, id="D{}-cu_seqlens{}-{}".format(*test))
for test in [
(500, [0, 15], torch.float),
(512, [0, 256, 500, 1000], torch.float),
(1000, [0, 15, 100, 300, 1200, 2000], torch.float),
(2048, [0, 200, 512, 1200, 2048], torch.float16),
]
],
)
def test_fused_recurrent_varlen(
D: int,
cu_seqlens: list[int],
dtype: torch.dtype,
):
torch.manual_seed(42)
os.environ['TRITON_F32_DEFAULT'] = 'ieee'
N = len(cu_seqlens) - 1
T = cu_seqlens[-1]
cu_seqlens = torch.tensor(cu_seqlens, dtype=torch.int32, device=device)
x = torch.randn((1, T, D), dtype=dtype, device=device)
g = torch.randn((1, T, D), dtype=dtype, device=device)
h0 = torch.randn(N, D, dtype=dtype, device=device)
x, g = (1 - g.sigmoid()) * x, F.logsigmoid(g)
x, g, h0 = (i.detach().clone().to(dtype).requires_grad_() for i in (x, g, h0))
do = torch.randn_like(x)
dht = torch.randn_like(h0)
refs, ref_hts = [], []
for i in range(N):
ref, ref_ht = naive_recurrent_hgrn(
x[:, cu_seqlens[i]:cu_seqlens[i+1]],
g[:, cu_seqlens[i]:cu_seqlens[i+1]],
h0[i:i+1],
output_final_state=True,
)
refs.append(ref)
ref_hts.append(ref_ht)
ref = torch.cat(refs, 1)
ref_ht = torch.cat(ref_hts, 0)
((ref * do).sum() + (ref_ht * dht).sum()).backward()
ref_dx, x.grad = x.grad.clone(), None
ref_dg, g.grad = g.grad.clone(), None
ref_dh0, h0.grad = h0.grad.clone(), None
tri, tri_ht = fused_recurrent_hgrn(x, g, h0, output_final_state=True, cu_seqlens=cu_seqlens)
((tri * do).sum() + (tri_ht * dht).sum()).backward()
tri_dx, x.grad = x.grad.clone(), None
tri_dg, g.grad = g.grad.clone(), None
tri_dh0, h0.grad = h0.grad.clone(), None
assert_close('o', ref, tri, 0.005)
assert_close('ht', ref_ht, tri_ht, 0.005)
assert_close('dx', ref_dx, tri_dx, 0.005)
assert_close('dg', ref_dg, tri_dg, 0.005)
assert_close('dh0', ref_dh0, tri_dh0, 0.005)
@pytest.mark.parametrize(
('B', 'T', 'D', 'dtype'),
[
pytest.param(*test, id="B{}-T{}-D{}-{}".format(*test))
for test in [
(1, 63, 500, torch.float16),
(2, 500, 1000, torch.float16),
(2, 1000, 1024, torch.float16),
(4, 2048, 2048, torch.float16),
]
],
)
def test_chunk(
B: int,
T: int,
D: int,
dtype: torch.dtype,
):
torch.manual_seed(42)
os.environ['TRITON_F32_DEFAULT'] = 'ieee'
x = torch.randn((B, T, D), dtype=dtype, device=device)
g = torch.randn((B, T, D), dtype=dtype, device=device)
x, g = (1 - g.sigmoid()) * x, F.logsigmoid(g)
x, g = (i.detach().clone().to(dtype).requires_grad_() for i in (x, g))
do = torch.randn_like(x)
h0 = torch.randn_like(x[:, 0])
ref, _ = fused_recurrent_hgrn(x, g, h0, output_final_state=True)
ref.backward(do)
ref_dx, x.grad = x.grad.clone(), None
ref_dg, g.grad = g.grad.clone(), None
tri, _ = chunk_hgrn(x, g, h0, output_final_state=True)
tri.backward(do)
tri_dx, x.grad = x.grad.clone(), None
tri_dg, g.grad = g.grad.clone(), None
assert_close('o', ref, tri, 0.005)
assert_close('dx', ref_dx, tri_dx, 0.005)
assert_close('dg', ref_dg, tri_dg, 0.005)
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