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Three operator families are exercised by Evo2 blocks:
* parallel_fir(gate=False) -- outer FIR used by all hyena blocks before any
channel split (input projection convolution).
* parallel_fir(gate=True) -- inner FIR cascade used by hcm/hcs blocks
(x1 * v gated, then convolved by `h`,
then multiplied by x2 postgate).
* parallel_iir -- modal-form IIR (long convolution via FFT)
used by hcl blocks; poles + residues parameterize
a stable, long-range linear filter.
Sequential step paths (step_fir / step_iir) are used during generation.
Layout conventions match vortex exactly so checkpoints are bit-identical.
"""
from __future__ import annotations
import torch
import torch.nn.functional as F
IIR_PREFILL_MODES = ["recurrence", "modal-fft"]
def adjust_filter_shape_for_broadcast(u, h):
h = h.squeeze()
if len(u.shape) > len(h.shape):
h = h.unsqueeze(0)
if len(u.shape) > 3:
h = h.unsqueeze(1)
return h
def fftconv_func(u, k, D, dropout_mask, gelu=True, k_rev=None, bidirectional=False, **kwargs):
"""FFT convolution for long FIR filters (length >= 128 path)."""
seqlen = u.shape[-1]
fft_size = 2 * seqlen
k_f = torch.fft.rfft(k, n=fft_size) / fft_size
k_f = adjust_filter_shape_for_broadcast(u, k_f)
k = k.squeeze()
if bidirectional:
u_f = torch.fft.rfft(u.to(dtype=k.dtype), n=fft_size)
k, k2 = k.split(k.shape[1] // 2, dim=1)
k2_f = torch.fft.rfft(k2, n=fft_size) / fft_size
y1 = u_f * k_f
y2 = u_f.conj() * k2_f.conj()
y = torch.fft.irfft(y1 + y2, n=fft_size, norm="forward")[..., :seqlen]
else:
if k_rev is not None:
k_rev_f = torch.fft.rfft(k_rev, n=fft_size) / fft_size
k_f = k_f + k_rev_f.conj()
u_f = torch.fft.rfft(u.to(dtype=k.dtype), n=fft_size)
y = torch.fft.irfft(u_f * k_f, n=fft_size, norm="forward")[..., :seqlen]
out = y + u * D.unsqueeze(-1)
return out.to(dtype=u.dtype)
def _column_split(x, num_heads, head_size):
"""Compatibility helper for column_split_hyena=True (not used by Evo2)."""
x = x.reshape(x.shape[0], num_heads, 3 * head_size, x.shape[2])
x2 = x[:, :, :head_size].reshape(x.shape[0], -1, x.shape[-1])
x1 = x[:, :, head_size : 2 * head_size].reshape(x.shape[0], -1, x.shape[-1])
v = x[:, :, 2 * head_size :].reshape(x.shape[0], -1, x.shape[-1])
return x2, x1, v
class HyenaInferenceEngine:
def __init__(
self,
layer_idx: int | None = None,
iir_prefill_style: str = "modal-fft",
hyena_flip_x1x2: bool = False,
) -> None:
assert iir_prefill_style in IIR_PREFILL_MODES, iir_prefill_style
self.iir_prefill_style = iir_prefill_style
self.layer_idx = layer_idx
self.low_mem_mode = False
self.hyena_flip_x1x2 = hyena_flip_x1x2
# ---------------------------------------------------------------- FIR
def parallel_fir(
self,
fir_fn,
u,
weight,
bias,
L,
dims,
groups=None,
gated_bias=False,
column_split_hyena=False,
dim_last=True,
fir_length=3,
gate=False,
inference_params=None,
padding_mask=None,
):
L = u.shape[1] if dim_last else u.shape[2]
if gate:
hidden_size, num_attention_heads, hidden_size_per_attention_head, _, _ = dims
if column_split_hyena:
x2, x1, v = _column_split(u, num_attention_heads, hidden_size_per_attention_head)
else:
x2, x1, v = u.split([hidden_size, hidden_size, hidden_size], dim=1)
if self.hyena_flip_x1x2:
x1, x2 = x2, x1
u = x1 * v
if fir_length >= 128:
with torch.autocast("cuda"):
z = fftconv_func(
u.to(torch.float32),
weight[:, :, :L].to(torch.float32),
bias,
None,
gelu=False,
bidirectional=False,
groups=groups,
)
z = z.to(u.dtype)
else:
if dim_last:
u = u.permute(0, 2, 1) # B, D, L
z = fir_fn(
u.to(torch.float32),
weight.to(torch.float32),
bias=None,
stride=1,
padding=fir_length - 1,
groups=u.shape[1],
)[..., :L]
z = z.to(u.dtype)
if bias is not None:
if gated_bias:
z = z + bias[None, :, None] * u
else:
z = z + bias[None, :, None]
if isinstance(padding_mask, torch.Tensor):
z = z * padding_mask[:, None]
if gate:
z = x2 * z
if inference_params is not None:
fir_state = u[..., -fir_length + 1 :]
else:
fir_state = None
return z, fir_state
# ---------------------------------------------------------------- IIR
def parallel_iir(
self,
z_pre,
h,
D,
L,
poles,
residues,
t,
dims,
layer_idx,
inference_params=None,
prefill_style: str = "fft",
fftconv_fn=None,
padding_mask=None,
use_flashfft: bool = False,
column_split_hyena: bool = False,
long_fir_threshold: int | None = None,
):
fft_size = 2 * L
hidden_size, num_attention_heads, hidden_size_per_attention_head, _, _ = dims
if column_split_hyena:
x2, x1, v = _column_split(z_pre, num_attention_heads, hidden_size_per_attention_head)
else:
x2, x1, v = z_pre.split([hidden_size, hidden_size, hidden_size], dim=1)
if self.hyena_flip_x1x2:
x1, x2 = x2, x1
x1v = x1 * v
X_s = None
if inference_params is not None and prefill_style == "recurrence":
y = self.prefill_via_direct_recurrence(
inference_params=inference_params, x1v=x1v, L=L,
poles=poles, residues=residues,
)
else:
if use_flashfft and (L % 2) == 0:
y = fftconv_fn(
x1v.to(dtype=torch.bfloat16).contiguous(),
h.to(dtype=torch.float32),
)
elif long_fir_threshold is None:
H = torch.fft.rfft(h.to(dtype=torch.float32), n=fft_size) / fft_size
X_s = torch.fft.fft(x1v.to(dtype=torch.float32), n=fft_size)
X = X_s[..., : H.shape[-1]]
if len(z_pre.shape) > 3:
H = H.unsqueeze(1)
y = torch.fft.irfft(X * H, n=fft_size, norm="forward")[..., :L]
else:
assert h.shape[0] == 1, "batch size must be 1 for long_fir_threshold"
h = h[0][:, None]
h = h[..., :long_fir_threshold]
y = F.conv1d(
x1v, h.to(dtype=x1v.dtype),
stride=1, groups=x1v.shape[1],
padding=h.shape[-1] - 1,
)[..., :L]
y = y.to(dtype=x1v.dtype)
y = (y + x1v * D.unsqueeze(-1)) * x2
if inference_params is not None and prefill_style == "fft":
self.prefill_via_modal_fft(
inference_params=inference_params, x1v=x1v, X_s=X_s, L=L,
t=t, poles=poles, dims=dims, layer_idx=layer_idx,
use_flashfft=use_flashfft, fftconv_fn=fftconv_fn,
)
return y.permute(0, 2, 1)
# --------------------------------------------------------- step (decode)
def step_fir(self, u, fir_state, weight, bias=None, gated_bias=False, flip_filter=False):
"""Single-step FIR. fir_state holds the last (filter_len - 1) inputs."""
weight = weight.squeeze()
cache_size = fir_state.shape[-1]
filter_length = weight.shape[-1]
if flip_filter:
weight = weight.flip(-1)
weight = weight[..., -cache_size - 1 :].unsqueeze(0)
else:
weight = weight[..., : cache_size + 1].unsqueeze(0)
input_dtype = u.dtype
weight = weight.to(torch.float32)
u = u.to(torch.float32)
fir_state = fir_state.to(torch.float32)
bias = bias.to(torch.float32) if bias is not None else None
h0, h = weight[..., -1], weight[..., :-1]
y = h0 * u + torch.sum(fir_state * h, dim=-1)
if bias is not None:
if gated_bias:
y = y + bias * u
else:
y = y + bias
if cache_size < filter_length - 1:
fir_state = torch.cat([fir_state, u[..., None]], dim=-1)
else:
fir_state = torch.roll(fir_state, -1, dims=2)
fir_state[..., -1] = u
return y.to(input_dtype), fir_state
def step_iir(self, x2, x1, v, D, residues, poles, iir_state, iir_groups=1):
x1v = x1 * v
# `poles` arg contains log_poles (real, in modal form for evo2)
poles = torch.exp(poles)
poles = poles[..., 0][None]
residues = residues[None]
iir_state = poles * iir_state + x1v[..., None]
res_state = torch.sum(residues * iir_state, dim=-1)
if iir_groups > 1:
raise NotImplementedError
y = x2 * (res_state + D * x1v)
return y, iir_state
def prefill_via_direct_recurrence(self, inference_params, x1v, L, residues, poles, *args, **kwargs):
state_dim = poles.shape[1]
x1v_ = x1v[..., None, None]
x1v_ = x1v_.repeat(1, 1, 1, state_dim, 2)
x1v_[..., 1] = 0
state = 0 * x1v_[:, :, 0]
output = 0 * x1v_[:, :, :, 0, 0]
poles = poles[:, :, 0][None]
residues = residues[:, :, 0][None].repeat(x1v_.shape[0], 1, 1, 1)
for i in range(L):
state[..., 0] = poles[..., 0] * state[..., 0] - poles[..., 1] * state[..., 1] + x1v_[:, :, i, :, 0]
state[..., 1] = poles[..., 0] * state[..., 1] + poles[..., 1] * state[..., 0] + x1v_[:, :, i, :, 1]
output[:, :, i] = torch.sum(residues * state, dim=-2)[..., 0]
inference_params.state_dict[self.layer_idx] = state.to(dtype=torch.float32)
return output
def prefill_via_modal_fft(
self, inference_params, x1v, L, poles, t, dims, layer_idx,
X_s=None, use_flashfft=False, fftconv_fn=None,
state_dtype=torch.float32, *args, **kwargs,
):
"""Compute IIR state via a single FFT.
Evo2 uses *real* `log_poles` (not the complex view-as-real layout
that Evo1 uses), so the impulse-response IFFT is mathematically real;
the imaginary component is FFT round-off. We take ``.real`` explicitly
instead of relying on torch's lossy complex->real cast, which avoids
the "Casting complex values to real discards the imaginary part"
UserWarning at every decoded token.
"""
hidden_size, _, _, state_size, hyena_filter_groups = dims
assert X_s is not None
bs = x1v.shape[0]
fft_size = 2 * L
state_s = (poles.to(torch.float32) * t).exp()
state_S = torch.fft.fft(state_s, n=fft_size).repeat(bs, 1, 1, 1)
if hyena_filter_groups > 1:
state_S = state_S.repeat_interleave(hidden_size // hyena_filter_groups, 1)
state = torch.fft.ifft(X_s[..., None, :] * state_S, n=fft_size)
inference_params.state_dict[layer_idx] = state[..., L - 1].real.to(dtype=state_dtype)
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