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import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
from fla.ops.generalized_delta_rule.iplr.chunk import chunk_iplr_delta_rule
from fla.ops.generalized_delta_rule.iplr.fused_recurrent import fused_recurrent_iplr_delta_rule
from fla.utils import assert_close, device
def chunk_iplr_delta_rule_ref(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
a: torch.Tensor,
b: torch.Tensor,
initial_state: torch.Tensor = None,
output_final_state: bool = True,
scale: float = None,
chunk_size: int = 64,
):
BT = chunk_size
if scale is None:
scale = 1 / (q.shape[-1] ** 0.5)
q, k, v, a, b = map(lambda x: x.transpose(1, 2), (q, k, v, a, b))
T = q.shape[-2]
pad_len = (BT - (T % BT)) % BT
if pad_len > 0:
q = F.pad(q, (0, 0, 0, pad_len))
k = F.pad(k, (0, 0, 0, pad_len))
v = F.pad(v, (0, 0, 0, pad_len))
a = F.pad(a, (0, 0, 0, pad_len))
b = F.pad(b, (0, 0, 0, pad_len))
q, k, v, a, b = map(lambda x: x.to(torch.float32), [q, k, v, a, b])
B, H, L, DK = q.shape
DV = v.shape[-1]
q = q * scale
S = k.new_zeros(B, H, DK, DV)
if initial_state is not None:
S += initial_state
# note that diagonal is masked.
mask = torch.triu(torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=q.device), diagonal=0)
q, k, v, a, b = map(lambda x: rearrange(x, 'b h (n c) d -> b h n c d', c=chunk_size), [q, k, v, a, b])
v2 = (a @ k.transpose(-1, -2)).masked_fill_(mask, 0) @ v
attn = (a @ b.transpose(-1, -2)).masked_fill(mask, 0)
for i in range(1, chunk_size):
attn[..., i, :i] = attn[..., i, :i] + (attn[..., i, :, None].clone() * attn[..., :, :i].clone()).sum(-2)
attn = attn + torch.eye(chunk_size, dtype=torch.float, device=q.device)
u = attn @ v2
w = attn @ a
o = torch.zeros_like(v)
mask = torch.triu(torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=q.device), diagonal=1)
for i in range(0, L // chunk_size):
current_chunk_size = min(chunk_size, L - i * chunk_size) # to handle the last chunk with possibly padding
q_i = q[:, :, i, :current_chunk_size]
k_i = k[:, :, i, :current_chunk_size]
v_i = v[:, :, i, :current_chunk_size]
u_i = u[:, :, i, :current_chunk_size]
w_i = w[:, :, i, :current_chunk_size]
b_i = b[:, :, i, :current_chunk_size]
o_1 = (q_i @ k_i.transpose(-1, -2)).masked_fill_(mask, 0) @ v_i
v2_i = u_i + w_i @ S
o_2 = (q_i @ b_i.transpose(-1, -2)).masked_fill_(mask, 0) @ v2_i
o_3 = q_i @ S
o[:, :, i, :current_chunk_size] = o_1 + o_2 + o_3
S = S + k_i.transpose(-1, -2) @ v_i + b_i.transpose(-1, -2) @ v2_i
S = None if output_final_state is False else S
o = rearrange(o, 'b h n c d -> b h (n c) d')
o = o[:, :, :T]
o = o.transpose(1, 2)
return o, S
def recurrence_iplr_delta_rule_ref(
q,
k,
v,
a,
b,
initial_state: torch.Tensor | None = None,
output_final_state: bool = True,
scale: float | None = None,
):
orig_dtype = q.dtype
if scale is None:
scale = 1 / (q.shape[-1] ** 0.5)
q, k, v, a, b = map(lambda x: x.transpose(1, 2).to(torch.float32), [q, k, v, a, b])
q = q * scale
B, H, L, DK = q.shape
DV = v.shape[-1]
o = torch.zeros_like(v)
S = torch.zeros(B, H, DK, DV).to(v)
if initial_state is not None:
S += initial_state
for i in range(q.shape[-2]):
_k = k[:, :, i]
_q = q[:, :, i]
_v = v[:, :, i]
_a = a[:, :, i]
_b = b[:, :, i]
_kv = _k[..., None] * _v[..., None, :] + (S.clone() * _a[..., None]).sum(-2, keepdim=True) * _b[..., None]
S = S + _kv
o[:, :, i] = torch.einsum('bhd,bhdm->bhm', _q, S)
S = None if output_final_state is False else S
o = o.transpose(1, 2)
return o.to(orig_dtype), S
@pytest.mark.parametrize(
('B', 'T', 'H', 'D', 'scale', 'dtype'),
[
pytest.param(*test, id="B{}-T{}-H{}-D{}-scale{}-{}".format(*test))
for test in [
(1, 63, 1, 64, 1, torch.float),
(2, 1024, 4, 60, 1, torch.float),
(2, 1024, 8, 100, 1, torch.float),
(2, 1024, 8, 128, 0.1, torch.float),
(4, 2048, 8, 64, 0.1, torch.float),
]
],
)
def test_fused_recurrent(
B: int,
T: int,
H: int,
D: int,
scale: float,
dtype: torch.dtype,
):
q = torch.randn(B, T, H, D, dtype=dtype)
k = torch.randn(B, T, H, D, dtype=dtype)
v = torch.randn(B, T, H, D, dtype=dtype)
a = torch.rand(B, T, H, D, dtype=dtype)
a = F.normalize(a, p=2, dim=-1)
b = -a
h0 = torch.zeros(B, H, D, D, dtype=torch.float32)
q, k, v, a, b, h0 = map(lambda x: x.to(device).requires_grad_(True), (q, k, v, a, b, h0))
ref, ref_ht = recurrence_iplr_delta_rule_ref(
q=q.clone(),
k=k.clone(),
v=v.clone(),
a=a.clone(),
b=b.clone(),
scale=scale,
initial_state=h0.clone(),
output_final_state=True,
)
dht = torch.rand_like(h0)
do = torch.rand_like(ref)
((dht * ref_ht).sum() + (do * ref).sum()).backward()
dq, dk, dv, da, db, dh0 = map(lambda x: x.grad, (q, k, v, a, b, h0))
q.grad, k.grad, v.grad, a.grad, b.grad, h0.grad = None, None, None, None, None, None
tri, tri_ht = fused_recurrent_iplr_delta_rule(
q=q.clone(),
k=k.clone(),
v=v.clone(),
a=a.clone(),
b=b.clone(),
scale=scale,
initial_state=h0.clone(),
output_final_state=True,
)
((dht * tri_ht).sum() + (do * tri).sum()).backward()
assert_close('o', ref, tri, 0.003)
assert_close('ht', ref_ht, tri_ht, 0.003)
assert_close('dq', dq, q.grad, 0.003)
assert_close('dk', dk, k.grad, 0.003)
assert_close('dv', dv, v.grad, 0.003)
assert_close('da', da, a.grad, 0.003)
assert_close('db', db, b.grad, 0.003)
assert_close('dh0', dh0, h0.grad, 0.003)
@pytest.mark.parametrize(
('B', 'T', 'H', 'D', 'scale', 'dtype'),
[
pytest.param(*test, id="B{}-T{}-H{}-D{}-scale{}-{}".format(*test))
for test in [
(1, 63, 1, 64, 1, torch.float16),
(2, 500, 3, 60, 1, torch.float16),
(2, 1000, 3, 64, 0.1, torch.float16),
(2, 1024, 4, 100, 1, torch.float16),
(3, 1024, 4, 128, 0.1, torch.float16),
(4, 2048, 8, 64, 0.1, torch.float16),
]
],
)
def test_chunk(
B: int,
T: int,
H: int,
D: int,
scale: float,
dtype: torch.dtype,
):
q = torch.randn(B, T, H, D, dtype=dtype)
k = torch.randn(B, T, H, D, dtype=dtype)
v = torch.randn(B, T, H, D, dtype=dtype)
a = torch.rand(B, T, H, D, dtype=dtype)
a = F.normalize(a, p=2, dim=-1)
b = -a
h0 = torch.zeros(B, H, D, D, dtype=torch.float32)
q, k, v, a, b, h0 = map(lambda x: x.to(device).requires_grad_(), (q, k, v, a, b, h0))
ref, ref_ht = recurrence_iplr_delta_rule_ref(
q=q.clone(),
k=k.clone(),
v=v.clone(),
a=a.clone(),
b=b.clone(),
scale=scale,
initial_state=h0.clone(),
output_final_state=True,
)
tri, tri_ht = chunk_iplr_delta_rule(
q=q.clone(),
k=k.clone(),
v=v.clone(),
a=a.clone(),
b=b.clone(),
scale=scale,
initial_state=h0.clone(),
output_final_state=True,
)
assert_close('o', ref, tri, 0.007)
assert_close('ht', ref_ht, tri_ht, 0.008)
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