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# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import math
import equinox as eqx
import jax
import jax.numpy as jnp
from jaxtyping import Array, Float, PRNGKeyArray
def bounded_gain_logit(
value: float,
*,
max_gain: float,
init_fraction: float | None,
) -> float:
maximum = float(max_gain)
initial = float(value)
if not (math.isfinite(maximum) and maximum > 0.0):
raise ValueError(f"bounded update maximum must be positive, got {maximum}")
if init_fraction is None:
if not (math.isfinite(initial) and 0.0 < initial < maximum):
raise ValueError(
f"bounded update initial value must be in (0, {maximum}), got {initial}"
)
fraction = initial / maximum
else:
fraction = float(init_fraction)
if not (math.isfinite(fraction) and 0.0 < fraction < 1.0):
raise ValueError(
f"bounded update initial fraction must be in (0, 1), got {fraction}"
)
return math.log(fraction) - math.log1p(-fraction)
class BiasFreeLinear(eqx.Module):
weight: Float[Array, "in out"]
_use_id: str = eqx.field(static=True, default="")
def __init__(
self,
in_features: int,
out_features: int,
*,
key: PRNGKeyArray,
scale: float | None = None,
):
std = scale if scale is not None else in_features ** (-0.5)
self.weight = jax.random.normal(key, (in_features, out_features)) * std
self._use_id = ""
def __call__(
self,
x: Float[Array, "... in"],
*,
pathway: str | None = None,
kfac_structural_mask=None,
kfac_repeat_ndim: int = 0,
kfac_context_primal_reused_over_walkers: bool = False,
) -> Float[Array, "... out"]:
from hamiltonzero.model.tree import _tagged_dense_no_bias
if pathway is None:
pathway = "even"
return _tagged_dense_no_bias(
self.weight,
x,
tag_id=self._use_id,
pathway=pathway,
kfac_structural_mask=kfac_structural_mask,
kfac_scan_shared=False,
kfac_repeat_ndim=kfac_repeat_ndim,
kfac_context_primal_reused_over_walkers=kfac_context_primal_reused_over_walkers,
)
class Linear(eqx.Module):
weight: Float[Array, "in out"]
bias: Float[Array, "out"]
_use_id: str = eqx.field(static=True, default="")
def __init__(self, in_features: int, out_features: int, *, key: PRNGKeyArray):
std = in_features ** (-0.5)
self.weight = jax.random.normal(key, (in_features, out_features)) * std
self.bias = jnp.zeros((out_features,))
self._use_id = ""
def __call__(
self,
x: Float[Array, "... in"],
*,
pathway: str | None = None,
kfac_structural_mask=None,
kfac_repeat_ndim: int = 0,
kfac_context_primal_reused_over_walkers: bool = False,
) -> Float[Array, "... out"]:
from hamiltonzero.model.tree import _tagged_dense
if pathway is None:
pathway = "even"
return _tagged_dense(
self.weight,
self.bias,
x,
tag_id=self._use_id,
pathway=pathway,
kfac_structural_mask=kfac_structural_mask,
kfac_scan_shared=False,
kfac_repeat_ndim=kfac_repeat_ndim,
kfac_context_primal_reused_over_walkers=kfac_context_primal_reused_over_walkers,
)
class HypernetMatrix(eqx.Module):
U: Float[Array, "R d_out"]
V: Float[Array, "d_in R"]
W_h: Float[Array, "d_e R"]
_use_id_U: str = eqx.field(static=True, default="")
_use_id_V: str = eqx.field(static=True, default="")
_use_id_W_h: str = eqx.field(static=True, default="")
def __init__(
self, d_in: int, d_out: int, d_e: int, rank: int, *, key: PRNGKeyArray
):
k_u, k_v, k_h = jax.random.split(key, 3)
self.U = jax.random.normal(k_u, (rank, d_out)) * rank ** (-0.5)
self.V = jax.random.normal(k_v, (d_in, rank)) * d_in ** (-0.5)
self.W_h = jax.random.normal(k_h, (d_e, rank)) * d_e ** (-0.5)
self._use_id_U = ""
self._use_id_V = ""
self._use_id_W_h = ""
def apply(
self,
e: Float[Array, "d_e"],
z: Float[Array, "d_in"],
*,
e_pathway: str | None = None,
kfac_structural_mask=None,
kfac_scan_shared: bool = False,
kfac_repeat_ndim: int = 0,
kfac_context_primal_reused_over_walkers: bool = False,
kfac_all_primals_reused_over_walkers: bool = False,
) -> Float[Array, "d_out"]:
from hamiltonzero.model.tree import _tagged_dense_no_bias
eff_e_pathway = e_pathway if e_pathway is not None else "even"
h = _tagged_dense_no_bias(
self.W_h,
e,
tag_id=self._use_id_W_h,
pathway=eff_e_pathway,
kfac_structural_mask=kfac_structural_mask,
kfac_scan_shared=kfac_scan_shared,
kfac_repeat_ndim=kfac_repeat_ndim,
kfac_context_primal_reused_over_walkers=kfac_context_primal_reused_over_walkers
or kfac_all_primals_reused_over_walkers,
)
Vz = _tagged_dense_no_bias(
self.V,
z,
tag_id=self._use_id_V,
pathway="odd",
kfac_structural_mask=kfac_structural_mask,
kfac_scan_shared=kfac_scan_shared,
kfac_repeat_ndim=kfac_repeat_ndim,
kfac_context_primal_reused_over_walkers=kfac_all_primals_reused_over_walkers,
)
m = h * Vz
return _tagged_dense_no_bias(
self.U,
m,
tag_id=self._use_id_U,
pathway="odd",
kfac_structural_mask=kfac_structural_mask,
kfac_scan_shared=kfac_scan_shared,
kfac_repeat_ndim=kfac_repeat_ndim,
kfac_context_primal_reused_over_walkers=kfac_all_primals_reused_over_walkers,
)
class _RMS(eqx.Module):
weight: Float[Array, "d"]
eps: float = eqx.field(static=True, default=1e-05)
_use_id: str = eqx.field(static=True, default="")
def __init__(self, d: int):
self.weight = jnp.ones((d,))
self._use_id = ""
def __call__(
self,
x: Float[Array, "... d"],
*,
pathway: str | None = None,
kfac_structural_mask=None,
kfac_repeat_ndim: int = 0,
kfac_context_primal_reused_over_walkers: bool = False,
) -> Float[Array, "... d"]:
from hamiltonzero.model.tree import _tagged_rms_eqx_style
if pathway is None:
pathway = "even"
return _tagged_rms_eqx_style(
self.weight,
x,
self.eps,
tag_id=self._use_id,
pathway=pathway,
kfac_structural_mask=kfac_structural_mask,
kfac_scan_shared=False,
kfac_repeat_ndim=kfac_repeat_ndim,
kfac_context_primal_reused_over_walkers=kfac_context_primal_reused_over_walkers,
)
class MLP(eqx.Module):
in_proj: Linear
block_norms: list
block_l1s: list
block_l2s: list
out_norm: _RMS
out_proj: Linear
inner_gain: float = eqx.field(static=True)
def __init__(
self,
d_in: int,
d_hidden: int,
d_out: int,
*,
key: PRNGKeyArray,
n_blocks: int = 2,
inner_gain: float = 1.0,
):
keys = jax.random.split(key, 2 + 2 * n_blocks)
self.in_proj = Linear(d_in, d_hidden, key=keys[0])
self.block_norms = [_RMS(d_hidden) for _ in range(n_blocks)]
self.block_l1s = [
Linear(d_hidden, d_hidden, key=keys[1 + 2 * i]) for i in range(n_blocks)
]
self.block_l2s = [
Linear(d_hidden, d_hidden, key=keys[2 + 2 * i]) for i in range(n_blocks)
]
self.out_norm = _RMS(d_hidden)
self.out_proj = Linear(d_hidden, d_out, key=keys[-1])
self.inner_gain = float(inner_gain)
def _act(self, x):
return x * jax.nn.sigmoid(x)
def __call__(
self,
x: Float[Array, "... d_in"],
*,
pathway: str | None = None,
kfac_structural_mask=None,
kfac_repeat_ndim: int = 0,
kfac_context_primal_reused_over_walkers: bool = False,
) -> Float[Array, "... d_out"]:
kfac_kwargs = dict(
kfac_structural_mask=kfac_structural_mask,
kfac_repeat_ndim=kfac_repeat_ndim,
kfac_context_primal_reused_over_walkers=kfac_context_primal_reused_over_walkers,
)
x = self.in_proj(x, pathway=pathway, **kfac_kwargs)
for nrm, l1, l2 in zip(self.block_norms, self.block_l1s, self.block_l2s):
normed = nrm(x, pathway=pathway, **kfac_kwargs)
x = x + self.inner_gain * l2(
self._act(l1(normed, pathway=pathway, **kfac_kwargs)),
pathway=pathway,
**kfac_kwargs,
)
out_normed = self.out_norm(x, pathway=pathway, **kfac_kwargs)
return self.out_proj(out_normed, pathway=pathway, **kfac_kwargs)
class UnnormalizedMLP(eqx.Module):
in_proj: Linear
block_l1s: list
block_l2s: list
out_proj: Linear
inner_gain: float = eqx.field(static=True)
def __init__(
self,
d_in: int,
d_hidden: int,
d_out: int,
*,
key: PRNGKeyArray,
n_blocks: int = 1,
inner_gain: float = 1.0,
):
keys = jax.random.split(key, 2 + 2 * n_blocks)
self.in_proj = Linear(d_in, d_hidden, key=keys[0])
self.block_l1s = [
Linear(d_hidden, d_hidden, key=keys[1 + 2 * i]) for i in range(n_blocks)
]
self.block_l2s = [
Linear(d_hidden, d_hidden, key=keys[2 + 2 * i]) for i in range(n_blocks)
]
self.out_proj = Linear(d_hidden, d_out, key=keys[-1])
self.inner_gain = float(inner_gain)
def _act(self, x):
return x * jax.nn.sigmoid(x)
def __call__(
self,
x: Float[Array, "... d_in"],
*,
pathway: str | None = None,
kfac_structural_mask=None,
kfac_repeat_ndim: int = 0,
kfac_context_primal_reused_over_walkers: bool = False,
) -> Float[Array, "... d_out"]:
kfac_kwargs = dict(
kfac_structural_mask=kfac_structural_mask,
kfac_repeat_ndim=kfac_repeat_ndim,
kfac_context_primal_reused_over_walkers=kfac_context_primal_reused_over_walkers,
)
x = self.in_proj(x, pathway=pathway, **kfac_kwargs)
for l1, l2 in zip(self.block_l1s, self.block_l2s):
x = x + self.inner_gain * l2(
self._act(l1(x, pathway=pathway, **kfac_kwargs)),
pathway=pathway,
**kfac_kwargs,
)
return self.out_proj(x, pathway=pathway, **kfac_kwargs)
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