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60df24a | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 | """Hyena inference engine for Evo2 (StripedHyena2).
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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