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ffa8327 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | """Hub-layout adapters around Helion's synchronized PyTorch references."""
from __future__ import annotations
import torch
from ._helion_reference import (
chunked_linear_attn_reference,
naive_recurrent_reference,
rel_error,
recurrent_step_reference,
)
def relative_error(a: torch.Tensor | None, b: torch.Tensor | None) -> float:
return rel_error(a, b)
def make_inputs(
device: torch.device,
*,
b: int = 1,
t: int = 64,
h: int = 2,
d: int = 32,
dv: int = 32,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
tensors = [
torch.randn(b, t, h, dim, device=device, dtype=torch.float32)
for dim in (d, d, dv)
]
return tuple(x.to(torch.bfloat16) for x in tensors)
def _head_first(x: torch.Tensor) -> torch.Tensor:
return x.transpose(1, 2).contiguous()
def recurrent_reference(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
*,
g: torch.Tensor | None = None,
beta: torch.Tensor | None = None,
scale: float,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Run Helion's recurrent reference on time-first Hub inputs."""
qh, kh, vh = (_head_first(x) for x in (q, k, v))
gh = (
_head_first(g)
if g is not None
else torch.zeros(
q.size(0),
q.size(2),
q.size(1),
device=q.device,
dtype=torch.float32,
)
)
bh = _head_first(beta) if beta is not None else None
output = naive_recurrent_reference(
qh,
kh,
vh,
gh.float(),
beta=bh,
q_scale=scale,
)
state = torch.zeros(
q.size(0),
q.size(2),
q.size(3),
v.size(3),
device=q.device,
dtype=torch.float32,
)
for index in range(q.size(1)):
decay = gh[:, :, index : index + 1].float().exp()
beta_value = bh[:, :, index : index + 1].float() if bh is not None else None
_, state = recurrent_step_reference(
qh[:, :, index : index + 1].float() * scale,
kh[:, :, index : index + 1].float(),
vh[:, :, index : index + 1].float(),
state,
alpha=decay,
beta_val=beta_value,
)
return _head_first(output), state
def chunked_reference(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
*,
g: torch.Tensor | None = None,
beta: torch.Tensor | None = None,
scale: float,
chunk_size: int = 64,
) -> torch.Tensor:
"""Run Helion's differentiable chunked reference on Hub-layout inputs."""
qh, kh, vh = (_head_first(x) for x in (q, k, v))
gh = (
_head_first(g)
if g is not None
else torch.zeros(
q.size(0),
q.size(2),
q.size(1),
device=q.device,
dtype=torch.float32,
)
)
bh = _head_first(beta) if beta is not None else None
output = chunked_linear_attn_reference(
qh * scale,
kh,
vh,
gh,
beta=bh,
C=chunk_size,
)
return _head_first(output)
def assert_close(actual: torch.Tensor, expected: torch.Tensor) -> None:
torch.testing.assert_close(actual.float(), expected.float(), atol=6e-2, rtol=3e-2)
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