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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 dataclasses import dataclass
from pathlib import Path
from typing import Any, Protocol
from hamiltonzero.config import EnergyConfig, EvalMCMCConfig, ModelConfig
@dataclass(frozen=True, slots=True)
class CanonicalContext:
context: Any
old_inverse: Any
@dataclass(frozen=True, slots=True)
class BeamCandidates:
permutations: Any
log_probabilities: Any
@dataclass(frozen=True, slots=True)
class LargeNCompilation:
wavefunction: Any
permutation: Any
log_probability: Any
@dataclass(frozen=True, slots=True)
class MCMCPopulation:
q: Any
sigma: Any
beta: Any
class EvalBackend(Protocol):
def load_system(self, path: Path, energy: EnergyConfig) -> Any: ...
def load_model(
self,
checkpoint: Path,
config: ModelConfig,
key: Any,
context: Any,
*,
contextualizer_attention: str | None,
) -> Any: ...
def canonicalize_context(self, context: Any) -> CanonicalContext: ...
def embedded_route(self, model: Any) -> Any | None: ...
def route_context(
self,
context: Any,
permutation: Any,
*,
compact_custom_lap: bool,
) -> Any: ...
def release_context(self, context: Any) -> None: ...
def virtual_context(self, context: Any, permutations: Any) -> Any: ...
def beam_candidates(
self,
model: Any,
context: Any,
*,
beam_width: int,
top_k: int,
temperature: float,
) -> BeamCandidates: ...
def compile_single(self, model: Any, routed_context: Any) -> Any: ...
def compile_embedded(self, model: Any) -> Any: ...
def compile_candidates(
self,
model: Any,
canonical_context: Any,
permutations: Any,
) -> Any: ...
def select_candidate(self, wavefunctions: Any, winner: int) -> Any: ...
def compile_large_n(
self,
model: Any,
canonical_context: Any,
*,
sequence_shards: int,
pair_tile_size: int,
temperature: float,
) -> LargeNCompilation: ...
def prepare_singular(
self,
model: Any,
context: Any,
state: Any,
) -> tuple[Any, Any, Any]: ...
def initialize_mcmc(
self,
key: Any,
model: Any,
context: Any,
config: EvalMCMCConfig,
) -> Any: ...
def step_mcmc(
self,
state: Any,
model: Any,
context: Any,
*,
replica_steps: int,
walker_chunk_size: int,
) -> Any: ...
def adapt_mcmc(self, state: Any, config: EvalMCMCConfig) -> Any: ...
def route_mcmc(self, state: Any, permutation: Any) -> Any: ...
def mcmc_population(self, state: Any) -> MCMCPopulation: ...
def replace_mcmc_population(
self,
state: Any,
population: MCMCPopulation,
) -> Any: ...
def cold_walkers(self, state: Any) -> Any: ...
def custom_lap_energy(
self,
model: Any,
context: Any,
q: Any,
config: EnergyConfig,
) -> tuple[Any, Any, Any, Any]: ...
def block_until_ready(self, value: Any) -> None: ...
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