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# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import equinox as eqx
import jax
import jax.numpy as jnp
from jaxtyping import Array, Float, Int, PRNGKeyArray
from .odd_ops import Linear, _RMS
from .tree import (
_inline_norm_forward,
_tagged_bounded_ngpt_gain,
_tagged_dense,
_tagged_dense_no_bias,
_tree_sphere,
)
def _pool_masked_softmax(scores, mask, axis):
neg = jnp.asarray(-1.0e30, dtype=scores.dtype)
scores = jnp.where(mask > 0, scores, neg)
return jax.nn.softmax(scores, axis=axis)
class GDescriptorPool(eqx.Module):
W_q: Float[Array, "d_g hq"]
K: Linear
V: Linear
ln_in: _RMS
n_heads: int = eqx.field(static=True)
d_k: int = eqx.field(static=True)
d_v: int = eqx.field(static=True)
tag: str = eqx.field(static=True, default="")
def __init__(
self,
d_g: int,
d_x: int,
*,
key: PRNGKeyArray,
n_heads: int = 4,
d_k: int = 64,
d_v: int = 64,
tag: str = "",
):
kq, kk, kv = jax.random.split(key, 3)
self.W_q = jax.random.normal(kq, (d_g, n_heads * d_k)) * (d_g**-0.5)
self.K = Linear(d_x, n_heads * d_k, key=kk)
self.V = Linear(d_x, n_heads * d_v, key=kv)
self.ln_in = _RMS(d_x)
self.n_heads = n_heads
self.d_k = d_k
self.d_v = d_v
self.tag = tag
@property
def d_out(self) -> int:
return self.n_heads * self.d_v
def __call__(
self,
g,
xs,
mask,
*,
kfac_structural_mask=None,
kfac_update_mask=None,
kfac_scan_shared: bool = False,
kfac_repeat_ndim: int = 1,
kfac_context_primal_reused_over_walkers: bool = False,
):
n = xs.shape[0]
xn = _inline_norm_forward(
self.ln_in,
xs,
pathway="even",
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
),
)
q_structural_mask = (
jnp.any(jnp.asarray(kfac_structural_mask).astype(bool))
if kfac_update_mask is None and kfac_structural_mask is not None
else kfac_update_mask
)
q = _tagged_dense_no_bias(
self.W_q,
g,
tag_id=f"{self.tag}.W_q",
pathway="even",
kfac_structural_mask=q_structural_mask,
kfac_scan_shared=kfac_scan_shared,
kfac_repeat_ndim=0,
kfac_context_primal_reused_over_walkers=(
kfac_context_primal_reused_over_walkers
),
).reshape(self.n_heads, self.d_k)
k = _tagged_dense(
self.K.weight,
self.K.bias,
xn,
tag_id=self.K._use_id,
pathway="even",
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
),
).reshape(n, self.n_heads, self.d_k)
v = _tagged_dense(
self.V.weight,
self.V.bias,
xn,
tag_id=self.V._use_id,
pathway="even",
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
),
).reshape(n, self.n_heads, self.d_v)
scores = jnp.einsum("hd,nhd->hn", q, k) / jnp.sqrt(
jnp.asarray(self.d_k, dtype=xs.dtype)
)
attn = _pool_masked_softmax(scores, mask[None, :], axis=-1)
out = jnp.einsum("hn,nhv->hv", attn, v)
return out.reshape(-1)
def _global_update_parameters(d_g: int, d_pool: int, tap_dim: int, key):
tap = int(tap_dim)
if tap < 1 or tap >= int(d_g):
raise ValueError("global tap dimension must be positive and smaller than d_g")
d_in = tap + int(d_pool)
d_hidden = 2 * int(d_g)
k1, k2, k3 = jax.random.split(key, 3)
return (
jax.random.normal(k1, (d_in, d_hidden)) * (d_in**-0.5),
jnp.zeros((d_hidden,)),
jax.random.normal(k2, (d_hidden, d_g)) * (d_hidden**-0.5),
jnp.zeros((d_g,)),
jnp.ones((d_in,)),
jax.random.normal(k3, (d_g, tap)) * (d_g**-0.5),
)
def _global_delta(
update,
g,
pool,
*,
kfac_structural_mask,
kfac_g_structural_mask,
kfac_scan_shared,
kfac_repeat_ndim,
kfac_context_primal_reused_over_walkers,
):
from hamiltonzero.model.tree import _tagged_rms_eqx_style
from hamiltonzero.model.fused_silu import fused_silu
g_mask = (
kfac_structural_mask
if kfac_g_structural_mask is None
else kfac_g_structural_mask
)
g_in = _tagged_dense_no_bias(
update.g_tap_w,
g,
tag_id=f"{update.tag}.gtap",
pathway="even",
kfac_structural_mask=g_mask,
kfac_scan_shared=(
kfac_scan_shared if kfac_g_structural_mask is None else False
),
kfac_repeat_ndim=(kfac_repeat_ndim if kfac_g_structural_mask is None else 0),
kfac_context_primal_reused_over_walkers=(
kfac_context_primal_reused_over_walkers
),
)
x = jnp.concatenate([g_in, pool.astype(g.dtype)])
x = _tagged_rms_eqx_style(
update.ln_s,
x,
tag_id=f"{update.tag}.ln",
pathway="even",
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
),
)
hidden = fused_silu(
_tagged_dense(
update.w1,
update.b1,
x,
tag_id=f"{update.tag}.ffn1",
pathway="even",
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
),
)
)
return _tagged_dense(
update.w2,
update.b2,
hidden,
tag_id=f"{update.tag}.ffn2",
pathway="even",
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
),
)
class ResidualGlobalUpdate(eqx.Module):
w1: Float[Array, "d_in d_hidden"]
b1: Float[Array, "d_hidden"]
w2: Float[Array, "d_hidden d_g"]
b2: Float[Array, "d_g"]
ln_s: Float[Array, "d_in"]
g_tap_w: Float[Array, "d_g tap_dim"]
residual_gain: float = eqx.field(static=True)
tag: str = eqx.field(static=True)
def __init__(
self,
d_g: int,
d_pool: int,
*,
key: PRNGKeyArray,
tap_dim: int,
residual_gain: float,
tag: str,
):
(
self.w1,
self.b1,
self.w2,
self.b2,
self.ln_s,
self.g_tap_w,
) = _global_update_parameters(d_g, d_pool, tap_dim, key)
self.residual_gain = float(residual_gain)
self.tag = tag
def __call__(
self,
g,
pool,
*,
kfac_structural_mask=None,
kfac_g_structural_mask=None,
kfac_scan_shared: bool = False,
kfac_repeat_ndim: int = 0,
kfac_context_primal_reused_over_walkers: bool = False,
):
delta = _global_delta(
self,
g,
pool,
kfac_structural_mask=kfac_structural_mask,
kfac_g_structural_mask=kfac_g_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
),
)
return g + self.residual_gain * delta
class BoundaryGlobalUpdate(eqx.Module):
w1: Float[Array, "d_in d_hidden"]
b1: Float[Array, "d_hidden"]
w2: Float[Array, "d_hidden d_g"]
b2: Float[Array, "d_g"]
ln_s: Float[Array, "d_in"]
g_tap_w: Float[Array, "d_g tap_dim"]
tag: str = eqx.field(static=True)
def __init__(
self, d_g: int, d_pool: int, *, key: PRNGKeyArray, tap_dim: int, tag: str
):
(
self.w1,
self.b1,
self.w2,
self.b2,
self.ln_s,
self.g_tap_w,
) = _global_update_parameters(d_g, d_pool, tap_dim, key)
self.tag = tag
def __call__(
self,
g,
pool,
*,
kfac_structural_mask=None,
kfac_g_structural_mask=None,
kfac_scan_shared: bool = False,
kfac_repeat_ndim: int = 0,
kfac_context_primal_reused_over_walkers: bool = False,
):
delta = _global_delta(
self,
g,
pool,
kfac_structural_mask=kfac_structural_mask,
kfac_g_structural_mask=kfac_g_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
),
)
return _tree_sphere(g + delta)
class TreeGlobalUpdate(eqx.Module):
w1: Float[Array, "d_in d_hidden"]
b1: Float[Array, "d_hidden"]
w2: Float[Array, "d_hidden d_g"]
b2: Float[Array, "d_g"]
ln_s: Float[Array, "d_in"]
alpha: Float[Array, "d_g"]
g_tap_w: Float[Array, "d_g tap_dim"]
alpha_max: float = eqx.field(static=True)
tag: str = eqx.field(static=True)
def __init__(
self,
d_g: int,
d_pool: int,
*,
key: PRNGKeyArray,
tap_dim: int,
alpha_init: float,
alpha_max: float,
tag: str,
):
(
self.w1,
self.b1,
self.w2,
self.b2,
self.ln_s,
self.g_tap_w,
) = _global_update_parameters(d_g, d_pool, tap_dim, key)
self.alpha = float(alpha_init) * jnp.ones((d_g,))
self.alpha_max = float(alpha_max)
self.tag = tag
def __call__(
self,
g,
pool,
update_mask=None,
*,
kfac_structural_mask=None,
kfac_g_structural_mask=None,
kfac_scan_shared: bool = False,
kfac_repeat_ndim: int = 0,
kfac_context_primal_reused_over_walkers: bool = False,
):
delta = _global_delta(
self,
g,
pool,
kfac_structural_mask=kfac_structural_mask,
kfac_g_structural_mask=kfac_g_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
),
)
skip = _tree_sphere(g)
proposal = _tree_sphere(delta)
direction = proposal - skip
gain = _tagged_bounded_ngpt_gain(
self.alpha,
direction,
max_gain=self.alpha_max,
tag_id=f"{self.tag}.alpha",
pathway="even",
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
),
)
updated = _tree_sphere(skip + gain * direction)
if update_mask is None:
return updated
active = jnp.asarray(update_mask).astype(bool)
while active.ndim < updated.ndim:
active = active[..., None]
return jnp.where(active, updated, g)
class EdgeRowColGlobalUpdate(eqx.Module):
row_pool: GDescriptorPool
col_pool: GDescriptorPool
set_pool: GDescriptorPool
update: BoundaryGlobalUpdate
def __init__(
self,
d_g: int,
d_edge: int,
*,
key: PRNGKeyArray,
n_heads: int = 4,
d_k: int = 64,
d_v: int = 64,
tag: str = "",
tap_dim: int,
):
kr, kc, ks, ku = jax.random.split(key, 4)
self.row_pool = GDescriptorPool(
d_g, d_edge, key=kr, n_heads=n_heads, d_k=d_k, d_v=d_v, tag=f"{tag}.row"
)
self.col_pool = GDescriptorPool(
d_g, d_edge, key=kc, n_heads=n_heads, d_k=d_k, d_v=d_v, tag=f"{tag}.col"
)
self.set_pool = GDescriptorPool(
d_g,
n_heads * d_v,
key=ks,
n_heads=n_heads,
d_k=d_k,
d_v=d_v,
tag=f"{tag}.set",
)
self.update = BoundaryGlobalUpdate(
d_g,
n_heads * d_v,
key=ku,
tag=f"{tag}.upd",
tap_dim=tap_dim,
)
def __call__(self, g, edge, mask):
system_active = jnp.any(mask.astype(bool))
rows = jax.vmap(
lambda ei, mi: self.row_pool(
g,
ei,
mask,
kfac_structural_mask=mi * mask,
kfac_update_mask=system_active,
kfac_scan_shared=False,
kfac_repeat_ndim=2,
)
)(edge, mask)
cols = jax.vmap(
lambda ej, mj: self.col_pool(
g,
ej,
mask,
kfac_structural_mask=mj * mask,
kfac_update_mask=system_active,
kfac_scan_shared=False,
kfac_repeat_ndim=2,
)
)(jnp.swapaxes(edge, 0, 1), mask)
descs = jnp.concatenate([rows, cols], axis=0)
dmask = jnp.concatenate([mask, mask], axis=0)
pooled = self.set_pool(
g,
descs,
dmask,
kfac_structural_mask=dmask,
kfac_update_mask=system_active,
kfac_scan_shared=False,
kfac_repeat_ndim=1,
)
return self.update(
g,
pooled,
kfac_structural_mask=system_active,
kfac_scan_shared=False,
)
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