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7c5e40e | 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 | """STRATA decoder language model."""
from __future__ import annotations
from pathlib import Path
import torch
from torch import nn
from torch.nn import functional as F
from strata.modeling.config import StrataConfig
from strata.modeling.graph_object import GraphObject
from strata.modeling.modules import GraphMode, RMSNorm, StrataDecoderBlock
from strata.modeling.outputs import GraphObjectBlockOutput, PredicateBlockOutput, StrataCausalLMOutput, StrataModelOutput
class StrataModel(nn.Module):
"""Decoder backbone with local attention and predicate-memory blocks."""
def __init__(self, config: StrataConfig) -> None:
super().__init__()
self.config = config
self.token_embeddings = nn.Embedding(config.vocab_size, config.d_model)
self.position_embeddings = nn.Embedding(
config.max_position_embeddings, config.d_model
)
self.blocks = nn.ModuleList(
[
StrataDecoderBlock(
config,
has_predicate_block=(layer_index + 1) % config.predicate_block_every == 0,
)
for layer_index in range(config.num_layers)
]
)
# Only the deepest predicate block emits prediction heads (the ones the
# losses/eval consume) unless the config restores all-block heads.
predicate_indices = [
i for i in range(config.num_layers) if (i + 1) % config.predicate_block_every == 0
]
self._final_predicate_index = predicate_indices[-1] if predicate_indices else -1
self.final_norm = RMSNorm(config.d_model)
self.dropout = nn.Dropout(config.dropout)
self.apply(self._init_weights)
def forward(
self,
input_ids: torch.Tensor,
*,
attention_mask: torch.Tensor | None = None,
graph_attention_bias: torch.Tensor | None = None,
predicate_memory_bias: torch.Tensor | None = None,
mode: GraphMode = "causal_lm",
return_edge_logits: bool = False,
predicate_memory_intervention: str = "none",
predicate_memory_residual_scale: torch.Tensor | None = None,
graph_object: GraphObject | None = None,
graph_object_intervention: str = "none",
graph_object_residual_scale: torch.Tensor | float | int | None = None,
return_graph_object_logits: bool = False,
) -> StrataModelOutput:
if input_ids.ndim != 2:
raise ValueError(f"input_ids must have shape [batch, seq], got {tuple(input_ids.shape)}")
batch_size, seq_len = input_ids.shape
if seq_len > self.config.max_position_embeddings:
raise ValueError(
f"sequence length {seq_len} exceeds max_position_embeddings "
f"{self.config.max_position_embeddings}"
)
if mode not in {"causal_lm", "full_graph"}:
raise ValueError("mode must be 'causal_lm' or 'full_graph'")
if attention_mask is not None and attention_mask.shape != input_ids.shape:
raise ValueError(
f"attention_mask must match input_ids shape {tuple(input_ids.shape)}, "
f"got {tuple(attention_mask.shape)}"
)
if predicate_memory_bias is not None and predicate_memory_bias.shape != (batch_size, seq_len, seq_len):
raise ValueError(
f"predicate_memory_bias must have shape ({batch_size}, {seq_len}, {seq_len}), "
f"got {tuple(predicate_memory_bias.shape)}"
)
_validate_residual_scale(predicate_memory_residual_scale, batch_size, name="predicate_memory_residual_scale")
_validate_residual_scale(graph_object_residual_scale, batch_size, name="graph_object_residual_scale")
positions = torch.arange(seq_len, device=input_ids.device).unsqueeze(0)
positions = positions.expand(batch_size, seq_len)
hidden_states = self.token_embeddings(input_ids) + self.position_embeddings(positions)
hidden_states = self.dropout(hidden_states)
if attention_mask is not None:
hidden_states = hidden_states * attention_mask.to(hidden_states.dtype).unsqueeze(-1)
predicate_outputs: list[PredicateBlockOutput] = []
graph_object_outputs: list[GraphObjectBlockOutput] = []
replacement_gates: list[torch.Tensor] = []
for layer_index, block in enumerate(self.blocks):
emit_heads = self.config.emit_all_block_graph_heads or layer_index == self._final_predicate_index
hidden_states, predicate_output, replacement_gate, graph_object_output = block(
hidden_states,
attention_mask=attention_mask,
graph_attention_bias=graph_attention_bias,
predicate_memory_bias=predicate_memory_bias,
mode=mode,
return_edge_logits=return_edge_logits,
emit_heads=emit_heads,
predicate_memory_intervention=predicate_memory_intervention,
predicate_memory_residual_scale=predicate_memory_residual_scale,
graph_object=graph_object,
graph_object_intervention=graph_object_intervention,
graph_object_residual_scale=graph_object_residual_scale,
return_graph_object_logits=return_graph_object_logits and emit_heads,
)
if predicate_output is not None:
predicate_outputs.append(predicate_output)
if graph_object_output is not None:
graph_object_outputs.append(graph_object_output)
if replacement_gate is not None:
replacement_gates.append(replacement_gate.reshape(1))
hidden_states = self.final_norm(hidden_states)
if replacement_gates:
gates = torch.cat(replacement_gates)
else:
gates = torch.empty(0, device=input_ids.device)
return StrataModelOutput(
last_hidden_state=hidden_states,
predicate_outputs=tuple(predicate_outputs),
graph_object_outputs=tuple(graph_object_outputs),
attention_replacement_gates=gates,
)
def _init_weights(self, module: nn.Module) -> None:
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
class StrataForCausalLM(nn.Module):
"""STRATA decoder with tied causal language-modeling head."""
def __init__(self, config: StrataConfig) -> None:
super().__init__()
self.config = config
self.model = StrataModel(config)
self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
if config.tie_word_embeddings:
self.lm_head.weight = self.model.token_embeddings.weight
else:
nn.init.normal_(
self.lm_head.weight,
mean=0.0,
std=self.config.initializer_range,
)
def forward(
self,
input_ids: torch.Tensor,
*,
attention_mask: torch.Tensor | None = None,
labels: torch.Tensor | None = None,
graph_attention_bias: torch.Tensor | None = None,
predicate_memory_bias: torch.Tensor | None = None,
mode: GraphMode = "causal_lm",
return_edge_logits: bool = False,
predicate_memory_intervention: str = "none",
predicate_memory_residual_scale: torch.Tensor | None = None,
graph_object: GraphObject | None = None,
graph_object_intervention: str = "none",
graph_object_residual_scale: torch.Tensor | float | int | None = None,
return_graph_object_logits: bool = False,
) -> StrataCausalLMOutput:
model_output = self.model(
input_ids,
attention_mask=attention_mask,
graph_attention_bias=graph_attention_bias,
predicate_memory_bias=predicate_memory_bias,
mode=mode,
return_edge_logits=return_edge_logits,
predicate_memory_intervention=predicate_memory_intervention,
predicate_memory_residual_scale=predicate_memory_residual_scale,
graph_object=graph_object,
graph_object_intervention=graph_object_intervention,
graph_object_residual_scale=graph_object_residual_scale,
return_graph_object_logits=return_graph_object_logits,
)
logits = self.lm_head(model_output.last_hidden_state)
loss = None
if labels is not None:
if labels.shape != input_ids.shape:
raise ValueError(
f"labels must match input_ids shape {tuple(input_ids.shape)}, "
f"got {tuple(labels.shape)}"
)
shift_logits = logits[:, :-1, :].contiguous()
shift_labels = labels[:, 1:].contiguous()
loss = F.cross_entropy(
shift_logits.view(-1, self.config.vocab_size),
shift_labels.view(-1),
ignore_index=-100,
)
return StrataCausalLMOutput(
logits=logits,
loss=loss,
hidden_states=model_output.last_hidden_state,
predicate_outputs=model_output.predicate_outputs,
graph_object_outputs=model_output.graph_object_outputs,
attention_replacement_gates=model_output.attention_replacement_gates,
)
def save_pretrained(self, output_dir: str | Path, *, exist_ok: bool = False) -> None:
"""Save config and weights to a run-scoped artifact directory."""
destination = Path(output_dir)
if destination.exists() and any(destination.iterdir()) and not exist_ok:
raise FileExistsError(
f"refusing to overwrite non-empty model directory: {destination}"
)
destination.mkdir(parents=True, exist_ok=True)
self.config.to_json_file(destination / "config.json")
torch.save(self.state_dict(), destination / "model.pt")
@classmethod
def from_pretrained(
cls,
model_dir: str | Path,
*,
map_location: str | torch.device | None = None,
) -> "StrataForCausalLM":
"""Load a STRATA checkpoint saved by :meth:`save_pretrained`."""
source = Path(model_dir)
config = StrataConfig.from_json_file(source / "config.json")
model = cls(config)
state_dict = torch.load(
source / "model.pt",
map_location=map_location,
weights_only=True,
)
try:
model.load_state_dict(state_dict)
except RuntimeError:
if not config.use_graph_object_memory:
raise
current = model.state_dict()
compatible = {
key: value
for key, value in state_dict.items()
if key in current and current[key].shape == value.shape
}
unexpected = sorted(key for key in state_dict if key not in current)
missing = sorted(key for key in current if key not in compatible)
mismatched = sorted(
key
for key, value in state_dict.items()
if key in current and current[key].shape != value.shape
)
non_graph_missing = [key for key in missing if "graph_object" not in key]
non_graph_mismatched = [key for key in mismatched if "graph_object" not in key]
if unexpected or non_graph_missing or non_graph_mismatched:
raise
model.load_state_dict(compatible, strict=False)
return model
def _validate_residual_scale(scale: torch.Tensor | float | int | None, batch_size: int, *, name: str) -> None:
if scale is None or isinstance(scale, (float, int)):
return
scale_shape = tuple(scale.shape)
if scale_shape in {(), (batch_size,), (batch_size, 1), (batch_size, 1, 1)}:
return
raise ValueError(
f"{name} must be scalar or have shape ({batch_size},), "
f"({batch_size}, 1), or ({batch_size}, 1, 1); got {scale_shape}"
)
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