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import pytest
import torch
import torch.nn.functional as F
from fla.ops.gated_delta_product import chunk_gated_delta_product
from fla.ops.gated_delta_product.chunk_ref import chunk_gated_delta_product_ref
from fla.ops.gated_delta_product.naive import naive_recurrent_gated_delta_product
from fla.utils import assert_close, device
@pytest.mark.parametrize(
('B', 'T', 'H', 'D', 'scale', 'num_householder', 'use_qk_l2norm_in_kernel', 'dtype'),
[
pytest.param(
*test,
id="B{}-T{}-H{}-D{}-scale{}-num_householder{}-l2norm{}-{}".format(*test),
)
for test in [
(1, 63, 1, 64, 0.1, 1, False, torch.float16),
(2, 200, 3, 60, 0.1, 1, False, torch.float16),
(2, 1000, 4, 64, 0.1, 2, False, torch.float16),
(2, 1024, 4, 64, 1, 2, True, torch.float16),
(2, 1024, 6, 100, 1, 2, False, torch.float16),
(4, 1500, 8, 128, 0.1, 3, False, torch.float16),
(2, 2048, 8, 128, 1, 3, False, torch.float16),
(2, 2048, 8, 128, 1, 3, True, torch.float16),
]
],
)
def test_chunk(
B: int,
T: int,
H: int,
D: int,
scale: float,
num_householder: int,
use_qk_l2norm_in_kernel: bool,
dtype: torch.dtype,
):
torch.manual_seed(42)
q = torch.randn(B, T, H, D, dtype=dtype)
k = torch.randn(B, T * num_householder, H, D, dtype=dtype)
v = torch.randn(B, T * num_householder, H, D, dtype=dtype)
beta = torch.rand(B, T * num_householder, H, dtype=dtype).sigmoid()
h0 = torch.zeros(B, H, D, D, dtype=torch.float32)
q, k, v, beta, h0 = map(lambda x: x.to(device).requires_grad_(True), (q, k, v, beta, h0))
tri, tri_ht = chunk_gated_delta_product(
q=F.normalize(q.clone(), p=2, dim=-1) if not use_qk_l2norm_in_kernel else q.clone(),
k=F.normalize(k.clone(), p=2, dim=-1) if not use_qk_l2norm_in_kernel else k.clone(),
v=v.clone(),
g=None,
beta=beta.clone(),
num_householder=num_householder,
scale=scale,
output_final_state=True,
initial_state=h0.clone(),
use_qk_l2norm_in_kernel=use_qk_l2norm_in_kernel,
)
do = torch.randn_like(q)
dht = torch.randn_like(h0)
((tri * do).sum() + (tri_ht * dht).sum()).backward(retain_graph=True)
tri_dq, tri_dk, tri_dv, tri_dbeta, tri_dh0 = q.grad, k.grad, v.grad, beta.grad, h0.grad
q.grad = k.grad = v.grad = beta.grad = h0.grad = None
ref, ref_ht = chunk_gated_delta_product_ref(
q=F.normalize(q.clone(), p=2, dim=-1),
k=F.normalize(k.clone(), p=2, dim=-1),
v=v.clone(),
g=None,
beta=beta.clone(),
num_householder=num_householder,
scale=scale,
initial_state=h0.clone(),
output_final_state=True,
)
((ref * do).sum() + (ref_ht * dht).sum()).backward(retain_graph=True)
ref_dq, ref_dk, ref_dv, ref_dbeta, ref_dh0 = q.grad, k.grad, v.grad, beta.grad, h0.grad
assert_close('o', ref, tri, 0.005)
assert_close('ht', ref_ht, tri_ht, 0.005)
assert_close('dq', ref_dq, tri_dq, 0.008)
assert_close('dk', ref_dk, tri_dk, 0.008)
assert_close('dv', ref_dv, tri_dv, 0.008)
assert_close('db', ref_dbeta, tri_dbeta, 0.02)
assert_close('dh0', ref_dh0, tri_dh0, 0.008)
@pytest.mark.parametrize(
('H', 'D', 'num_householder', 'cu_seqlens', 'dtype'),
[
(2, 64, 3, [0, 63 ], torch.float16),
(2, 100, 2, [0, 63, 100, 500, 1000], torch.float16),
(2, 128, 2, [0, 100, 300, 800, 1500, 2000], torch.float16),
(2, 256, 3, [0, 100, 123, 300, 500, 800, 1000, 1500, 2048], torch.float16),
],
)
def test_chunk_varlen(
H: int,
D: int,
num_householder: int,
cu_seqlens: list[int],
dtype: torch.dtype,
):
torch.manual_seed(42)
T = cu_seqlens[-1]
N = len(cu_seqlens) - 1
cu_seqlens = torch.LongTensor(cu_seqlens).to(device)
scale = 1.0
q = torch.nn.functional.normalize(torch.randn((1, T, H, D), dtype=dtype), dim=-1, p=2)
k = torch.nn.functional.normalize(torch.randn(1, T*num_householder, H, D, dtype=dtype), dim=-1, p=2)
v = torch.randn((1, T*num_householder, H, D), dtype=dtype)
beta = torch.rand(1, T*num_householder, H, dtype=dtype).sigmoid()
h0 = torch.randn((N, H, D, D), dtype=dtype)
q, k, v, beta, h0 = map(lambda x: x.to(device).requires_grad_(), (q, k, v, beta, h0))
do = torch.randn_like(q)
dht = torch.rand_like(h0)
tri, tri_ht = chunk_gated_delta_product(
q=q.clone(),
k=k.clone(),
v=v.clone(),
beta=beta.clone(),
g=None,
scale=scale,
output_final_state=True,
num_householder=num_householder,
initial_state=h0.clone(),
cu_seqlens=cu_seqlens,
)
((tri * do).sum() + (tri_ht * dht).sum()).backward(retain_graph=True)
tri_dq, tri_dk, tri_dv, tri_dbeta, tri_dh0 = q.grad, k.grad, v.grad, beta.grad, h0.grad
q.grad = k.grad = v.grad = beta.grad = h0.grad = None
ref, ref_ht = chunk_gated_delta_product_ref(
q=q.clone(),
k=k.clone(),
v=v.clone(),
beta=beta.clone(),
g=None,
scale=scale,
output_final_state=True,
num_householder=num_householder,
initial_state=h0.clone(),
cu_seqlens=cu_seqlens,
)
((ref * do).sum() + (ref_ht * dht).sum()).backward(retain_graph=True)
ref_dq, ref_dk, ref_dv, ref_dbeta, ref_dh0 = q.grad, k.grad, v.grad, beta.grad, h0.grad
assert_close('o', ref, tri, 0.005)
assert_close('ht', ref_ht, tri_ht, 0.005)
assert_close('dq', ref_dq, tri_dq, 0.007)
assert_close('dk', ref_dk, tri_dk, 0.008)
assert_close('dv', ref_dv, tri_dv, 0.007)
assert_close('db', ref_dbeta, tri_dbeta, 0.015)
assert_close('dh0', ref_dh0, tri_dh0, 0.007)
q.grad = k.grad = v.grad = beta.grad = h0.grad = None
torch_ref = torch.zeros_like(ref)
torch_ref_ht = torch.zeros_like(ref_ht)
for i in range(len(cu_seqlens) - 1):
start, end = cu_seqlens[i], cu_seqlens[i+1]
q_i = q[:, start:end, :, :]
k_i = k[:, start*num_householder:end*num_householder, :, :]
v_i = v[:, start*num_householder:end*num_householder, :, :]
beta_i = beta[:, start*num_householder:end*num_householder, :]
o3_i, h3_i = naive_recurrent_gated_delta_product(
q_i, k_i, v_i, None, beta_i, scale=scale, cu_seqlens=None, output_final_state=True, num_householder=num_householder,
)
torch_ref[:, start:end, :, :] = o3_i
torch_ref_ht[i, :, :, :] = h3_i.squeeze(0)
((torch_ref * do).sum() + (torch_ref_ht * dht).sum()).backward(retain_graph=True)
assert_close('o', ref, tri, 0.005)
assert_close('ht', ref_ht, tri_ht, 0.005)
assert_close('dq', ref_dq, tri_dq, 0.007)
assert_close('dk', ref_dk, tri_dk, 0.008)
assert_close('dv', ref_dv, tri_dv, 0.007)
assert_close('db', ref_dbeta, tri_dbeta, 0.015)
assert_close('dh0', ref_dh0, tri_dh0, 0.007)
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