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5ccb4fd | 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 | # Copyright (c) 2026 Simulacra Research Inc.
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from typing import Any
from .model import (
SpinAnsatz,
_normalize_leaf_carriers,
)
from .tree import (
_balanced_subtree_mask,
_quadrilinear_merge,
_tagged_dense,
_tagged_dense_no_bias,
_tagged_rms_eqx_style,
_tree_active_clock_depth,
_tree_depth_count_features,
_tree_sphere,
edge_merge_masked,
)
normalize_leaf_carriers = _normalize_leaf_carriers
quadrilinear_merge = _quadrilinear_merge
tagged_dense_no_bias = _tagged_dense_no_bias
tree_sphere = _tree_sphere
balanced_subtree_mask = _balanced_subtree_mask
tree_active_clock_depth = _tree_active_clock_depth
tree_depth_count_features = _tree_depth_count_features
tagged_dense = _tagged_dense
tagged_rms_eqx_style = _tagged_rms_eqx_style
def _attention_name(value: str) -> str:
if value == "tuned":
return "mhsea_tuned"
if value == "einsum":
return "einsum"
raise ValueError("attention must be 'tuned' or 'einsum'")
def build_model(config: Any, key, *, n_max: int) -> SpinAnsatz:
attention = _attention_name(str(config.attention))
model = SpinAnsatz(
d_e=int(config.d_e),
d_o=int(config.d_o),
d_c=int(config.d_c),
d_r=int(config.d_r),
n_heads=int(config.n_heads),
n_layers=int(config.n_layers),
rank=int(config.rank),
n_edge=int(config.edge_channels),
d_e_attn=int(config.attention_qk_dim),
d_c_attn=int(config.attention_v_dim),
trunk_edge_node_ctx_dim=int(config.trunk_edge_node_context_dim),
trunk_edge_hidden_dim=int(config.trunk_edge_hidden_dim),
trunk_attn_bias_hidden_dim=int(config.trunk_attention_bias_hidden_dim),
trunk_ffn_hidden_dim=int(config.trunk_ffn_hidden_dim),
trunk_two_hop_hidden_dim=int(config.trunk_two_hop_hidden_dim),
tree_edge_node_ctx_dim=int(config.tree_edge_node_context_dim),
attn_impl=attention,
global_d_g=int(config.global_dim),
d_m_merge=int(config.merge_dim),
merge_chain_hypernet_rank=int(config.merge_hypernet_rank),
feat_d_bond=int(config.featurizer_bond_dim),
feat_n_heads=int(config.featurizer_heads),
feat_head_dim=int(config.featurizer_head_dim),
feat_n_global_q=int(config.featurizer_global_queries),
feat_edge_hidden_dim=int(config.featurizer_edge_hidden_dim),
feat_zeeman_hidden_dim=int(config.featurizer_zeeman_hidden_dim),
feat_global_hidden_dim=int(config.featurizer_global_hidden_dim),
feat_combine_hidden_dim=int(config.featurizer_combine_hidden_dim),
feat_token_initial_scale=float(config.featurizer_token_initial_scale),
feat_d_edge=int(config.edge_channels),
polar_group_norm_tau=float(config.polar_group_norm_tau),
polar_group_norm_bond_hidden=int(config.polar_bond_hidden_dim),
polar_group_norm_n_bond_groups=int(config.polar_bond_groups),
polar_group_norm_d_bond_group=int(config.polar_bond_group_dim),
polar_group_norm_n_zeeman_groups=int(config.polar_zeeman_groups),
polar_group_norm_d_zeeman_group=int(config.polar_zeeman_group_dim),
route_pointer_max_n=max(int(config.router_max_n), int(n_max)),
route_pointer_d_model=int(config.router_model_dim),
route_pointer_n_heads=int(config.router_heads),
route_pointer_attn_dim=int(config.router_attention_dim),
route_pointer_score_dim=int(config.router_score_dim),
route_pointer_candidate_hidden=int(config.router_candidate_dim),
route_pointer_summary_hidden=int(config.router_summary_dim),
route_pointer_ffn_hidden=int(config.router_ffn_dim),
route_pointer_score_init_scale=float(config.router_score_initial_scale),
route_pointer_rope_base=float(config.router_rope_base),
route_pointer_rope_scaling=float(config.router_rope_scaling),
route_tree_prefix_layers=int(config.router_tree_prefix_layers),
route_tree_prefix_candidate_layers=int(config.router_tree_candidate_layers),
route_tree_prefix_merge_hidden=int(config.router_tree_merge_dim),
route_tree_prefix_post_prefix_suffix_layers=int(config.router_tree_post_layers),
route_contextualizer_layers=int(config.router_context_layers),
route_contextualizer_n_heads=int(config.router_context_heads),
route_contextualizer_attn_dim=int(config.router_context_attention_dim),
route_contextualizer_edge_node_ctx_dim=int(config.router_context_edge_node_dim),
level_edge_attn_n_heads=int(config.level_edge_heads),
level_edge_attn_edge_mlp_hidden=int(config.level_edge_mlp_dim),
level_edge_attn_edge_mlp_n_blocks=int(config.level_edge_mlp_blocks),
level_edge_attn_ffn_d_hidden=int(config.level_edge_ffn_dim),
level_edge_attn_rope_base=float(config.level_edge_rope_base),
level_edge_attn_rope_scaling=float(config.level_edge_rope_scaling),
root_readout_edge_rank=int(config.root_readout_edge_rank),
ngpt_alpha_initial=float(config.ngpt_alpha_initial),
ngpt_alpha_initial_fraction=float(config.ngpt_alpha_initial_fraction),
ngpt_alpha_maximum=float(config.ngpt_alpha_maximum),
global_ladder_tap_dim=int(config.global_ladder_tap_dim),
level_edge_attn_bias_mlp_hidden=int(config.level_edge_bias_mlp_dim),
level_edge_attn_bias_mlp_n_blocks=int(config.level_edge_bias_mlp_blocks),
merge_c_mlp_hidden=int(config.merge_context_mlp_dim),
readout_leaf_context_layers=int(config.readout_context_layers),
readout_leaf_context_n_heads=int(config.readout_context_heads),
readout_leaf_context_attn_dim=int(config.readout_context_attention_dim),
readout_leaf_context_edge_node_ctx_dim=int(
config.readout_context_edge_node_dim
),
readout_leaf_context_summary_hidden=int(config.readout_context_summary_dim),
readout_leaf_context_mlp_hidden=int(config.readout_context_mlp_dim),
readout_leaf_context_bias_hidden=int(config.readout_context_bias_dim),
readout_leaf_context_edge_ffn_hidden=int(config.readout_context_edge_ffn_dim),
readout_leaf_context_rope_base=float(config.readout_context_rope_base),
readout_leaf_context_rope_scaling=float(config.readout_context_rope_scaling),
two_hop_channels=int(config.two_hop_channels),
tree_edge_fwl_channels=int(config.tree_fwl_channels),
key=key,
)
return model
__all__ = [
"SpinAnsatz",
"balanced_subtree_mask",
"build_model",
"edge_merge_masked",
"normalize_leaf_carriers",
"quadrilinear_merge",
"tagged_dense",
"tagged_dense_no_bias",
"tagged_rms_eqx_style",
"tree_active_clock_depth",
"tree_depth_count_features",
"tree_sphere",
]
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