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from __future__ import annotations
import copy
from collections.abc import Sequence
from dataclasses import dataclass, replace
from typing import Any
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils.checkpoint import checkpoint
from transformers.cache_utils import Cache
from transformers.modeling_outputs import BaseModelOutputWithPast
from transformers.masking_utils import (
ALL_MASK_ATTENTION_FUNCTIONS,
bidirectional_mask_function,
)
from transformers.models.diffusion_gemma import (
DiffusionGemmaDecoderModel,
DiffusionGemmaEncoderTextModel,
DiffusionGemmaPreTrainedModel,
)
from transformers.models.diffusion_gemma.modeling_diffusion_gemma import (
DiffusionGemmaRMSNorm,
DiffusionGemmaTextRouter,
)
from .mps_ops import register_mps_backends
from .adapters import LoRAConfig, inject_lora
from .configuration_modilify_mk2 import ModilifyMk2Config, DENOISE_TEMPERATURE
from .generation_modilify_mk2 import ModilifyMk2GenerationConfig, ModilifyMk2GenerationMixin
from .latent_deliberation import LatentDeliberationState, LatentProcessorOutput, _sdpa_mask_value
from .latent_deliberation import LatentDeliberationTransformer
from .vocab_ops import (
chunked_vocab_statistics,
chunked_vocab_training_statistics,
)
register_mps_backends()
@dataclass
class ModilifyMk2DecoderOutput(BaseModelOutputWithPast):
token_embeddings: torch.FloatTensor | None = None
latent_residual_diagnostics: dict[str, torch.Tensor] | None = None
@dataclass
class ModilifyMk2ModelOutput(BaseModelOutputWithPast):
token_embeddings: torch.FloatTensor | None = None
encoder_last_hidden_state: torch.FloatTensor | None = None
latent_residual_diagnostics: dict[str, torch.Tensor] | None = None
@dataclass
class ModilifyMk2BlockDiffusionOutput:
logits: torch.FloatTensor | None
heavy_hidden_state: torch.FloatTensor
next_latent_state: LatentDeliberationState
gold_nll: torch.FloatTensor | None = None
loss: torch.FloatTensor | None = None
past_key_values: Cache | None = None
hidden_states: tuple[torch.FloatTensor, ...] | None = None
attentions: tuple[torch.FloatTensor, ...] | None = None
encoder_last_hidden_state: torch.FloatTensor | None = None
temporal_context: torch.FloatTensor | None = None
heavy_context_leaf: torch.FloatTensor | None = None
temporal_memory_slots: torch.FloatTensor | None = None
heavy_memory_leaf: torch.FloatTensor | None = None
working_state: torch.FloatTensor | None = None
latent_residual_diagnostics: dict[str, torch.Tensor] | None = None
temperature_gold_nll: torch.FloatTensor | None = None
proposal: torch.LongTensor | None = None
proposal_confidence: torch.FloatTensor | None = None
sampled_nll: torch.FloatTensor | None = None
token_entropy: torch.FloatTensor | None = None
greedy_proposal: torch.LongTensor | None = None
greedy_confidence: torch.FloatTensor | None = None
def to_tuple(self) -> tuple[Any, ...]:
return tuple(value for value in (
self.loss, self.logits, self.past_key_values, self.hidden_states,
self.attentions, self.encoder_last_hidden_state,
self.heavy_hidden_state, self.next_latent_state,
) if value is not None)
def __getitem__(self, key: str | int) -> Any:
return getattr(self, key) if isinstance(key, str) else self.to_tuple()[key]
class ModilifyMk2TextRouter(DiffusionGemmaTextRouter):
"""Official router parameters with fp32 softmax so top-k weights stay finite."""
def __init__(self, config: Any) -> None:
super().__init__(config)
self.norm = DiffusionGemmaRMSNorm(self.hidden_size, eps=self.eps, with_scale=False)
def forward(
self, hidden_states: torch.Tensor
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
hidden_states = self.norm(hidden_states)
hidden_states = hidden_states * self.scale * self.scalar_root_size
expert_scores = self.proj(hidden_states)
# Match the official DiffusionGemma router: bf16 softmax underflows to
# all-zero top-k rows, then 0/0 NaNs the MoE residual and every loss.
router_probabilities = F.softmax(expert_scores, dim=-1, dtype=torch.float32)
top_k_weights, top_k_index = torch.topk(
router_probabilities,
k=self.config.top_k_experts,
dim=-1,
)
top_k_weights = top_k_weights / top_k_weights.sum(dim=-1, keepdim=True).clamp_min(
torch.finfo(torch.float32).tiny
)
top_k_weights = top_k_weights * self.per_expert_scale[top_k_index]
return router_probabilities, top_k_weights, top_k_index
def _finite_decoder_attention_mask(
mask: torch.Tensor | None,
*,
dtype: torch.dtype,
) -> torch.Tensor | None:
"""Keep SDPA masks boolean and unmask fully-blocked query rows.
Transformers' SDPA mask is True=attend. A fully-False query row makes
softmax(-inf) in the backward. Converted additive 8k masks also double
decoder workspace on long prefixes, so stay in bool and only apply the
torch<2.5 empty-row workaround (pytorch/pytorch#110213).
"""
del dtype
if mask is None:
return None
if mask.dtype == torch.bool:
return mask | ~mask.any(dim=-1, keepdim=True)
return mask.clamp_min(_sdpa_mask_value(mask.dtype))
def install_modilify_mk2_trunk_semantics(module: nn.Module) -> None:
"""Swap official leaf modules on this instance. Never patch Transformers classes."""
for name, child in list(module.named_children()):
if type(child) is DiffusionGemmaTextRouter:
replacement = ModilifyMk2TextRouter(child.config)
replacement.load_state_dict(child.state_dict())
setattr(module, name, replacement)
else:
install_modilify_mk2_trunk_semantics(child)
class ModilifyMk2EncoderModel(DiffusionGemmaPreTrainedModel):
"""Checkpoint-compatible wrapper around the text encoder trunk."""
config_class = ModilifyMk2Config
def __init__(self, config: ModilifyMk2Config):
super().__init__(config)
self.language_model = DiffusionGemmaEncoderTextModel(config.text_config)
install_modilify_mk2_trunk_semantics(self)
self.post_init()
def get_input_embeddings(self) -> nn.Module:
return self.language_model.embed_tokens
def set_input_embeddings(self, value: nn.Module) -> None:
self.language_model.embed_tokens = value
def forward(self, **kwargs: Any) -> BaseModelOutputWithPast:
return self.language_model(**kwargs)
class ModilifyMk2DecoderModel(DiffusionGemmaDecoderModel):
"""Diffusion decoder accepting compact self-conditioning embeddings."""
config_class = ModilifyMk2Config
latent_residual_rms_ratio_cap = 0.5
def __init__(self, config: ModilifyMk2Config):
super().__init__(config)
self.gradient_checkpointing = False
install_modilify_mk2_trunk_semantics(self)
@staticmethod
def create_diffusion_decoder_attention_mask(
config: Any,
inputs_embeds: torch.Tensor,
past_key_values: Cache,
decoder_attention_mask: torch.Tensor | dict | None = None,
) -> dict[str, torch.Tensor | None]:
"""Official mask builder without the all-True sliding-window skip."""
if past_key_values is None:
raise ValueError(
"The diffusion mask requires `past_key_values` to construct the next attention mask correctly"
)
if (
decoder_attention_mask is None
or config._attn_implementation
not in ALL_MASK_ATTENTION_FUNCTIONS._global_mapping
):
return {"full_attention": None, "sliding_attention": None}
if isinstance(decoder_attention_mask, dict) and all(
mask.ndim == 4 for mask in decoder_attention_mask.values()
):
return {
name: _finite_decoder_attention_mask(
mask, dtype=inputs_embeds.dtype
)
for name, mask in decoder_attention_mask.items()
}
text_config = config.get_text_config() if hasattr(config, "get_text_config") else config
q_length = inputs_embeds.shape[1]
q_offset = past_key_values.get_seq_length()
if isinstance(q_offset, torch.Tensor):
q_offset = q_offset.to(inputs_embeds.device)
additional_kv_length = (
getattr(config, "canvas_length", 0) if past_key_values.is_compileable else 0
)
mask_mapping: dict[str, torch.Tensor | None] = {}
for layer_pattern in set(text_config.layer_types):
layer_idx = past_key_values.is_sliding.index(
layer_pattern == "sliding_attention"
)
kv_length, kv_offset = past_key_values.get_mask_sizes(q_length, layer_idx)
kv_length += additional_kv_length
if layer_pattern == "sliding_attention" and past_key_values.is_compileable:
sliding_layer = past_key_values.layers[layer_idx]
max_length = sliding_layer.get_max_length() + additional_kv_length
if kv_length >= max_length:
kv_length = max_length
mask_mapping[layer_pattern] = _finite_decoder_attention_mask(
ALL_MASK_ATTENTION_FUNCTIONS[config._attn_implementation](
batch_size=inputs_embeds.shape[0],
q_length=q_length,
kv_length=kv_length,
q_offset=q_offset,
kv_offset=kv_offset,
mask_function=bidirectional_mask_function,
attention_mask=decoder_attention_mask,
allow_is_causal_skip=False,
allow_is_bidirectional_skip=True,
local_size=getattr(text_config, "sliding_window", None),
dtype=inputs_embeds.dtype,
config=text_config,
use_vmap=False,
device=inputs_embeds.device,
),
dtype=inputs_embeds.dtype,
)
return mask_mapping
def merge_latent_context(
self,
token_embeddings: torch.Tensor,
latent_context: torch.Tensor | None,
*,
collect_diagnostics: bool = True,
) -> tuple[torch.Tensor, dict[str, torch.Tensor]]:
"""Map latent context through the frozen native self-conditioning bridge."""
if latent_context is None:
context = torch.zeros_like(token_embeddings)
else:
if latent_context.shape != token_embeddings.shape:
raise ValueError("Temporal context must match canvas hidden-state shape.")
context = latent_context.to(token_embeddings)
mapper = self.self_conditioning
normalized_context = mapper.pre_norm(context)
mapped_context = mapper.down_proj(
mapper.act_fn(mapper.gate_proj(normalized_context))
* mapper.up_proj(normalized_context)
)
mapped_fp32 = mapped_context.float()
# Use the energy directly in the cap denominator. Computing
# sqrt(E[x²]) and immediately squaring it again has an undefined
# backward at the identity-init point x=0 (0/0 in d(sqrt)/dx), which
# poisoned the detached temporal-context VJP on every denoise.
mapped_token_energy = mapped_fp32.square().mean(dim=-1, keepdim=True)
token_rms_per_token = (
token_embeddings.float().square().mean(dim=-1, keepdim=True).sqrt()
)
residual_cap = self.latent_residual_rms_ratio_cap * token_rms_per_token
soft_cap_scale = residual_cap / torch.sqrt(
mapped_token_energy + residual_cap.square() + 1.0e-12
)
mapped_context = (mapped_fp32 * soft_cap_scale).to(dtype=mapped_context.dtype)
combined = token_embeddings + mapped_context
diagnostics: dict[str, torch.Tensor] = {}
if collect_diagnostics:
token_rms = token_embeddings.detach().float().square().mean().sqrt()
mapped_rms = mapped_context.detach().float().square().mean().sqrt()
diagnostics = {
"token_embedding_rms": token_rms,
"latent_context_rms": context.detach().float().square().mean().sqrt(),
"mapped_sc_rms": mapped_rms,
"actual_residual_rms": mapped_rms,
"latent_to_embedding_rms_ratio": (
mapped_rms / token_rms.clamp_min(1.0e-12)
),
"merged_input_rms": combined.detach().float().square().mean().sqrt(),
}
return mapper.post_norm(combined), diagnostics
def _run_stack(
self,
inputs_embeds: torch.Tensor,
*,
past_key_values: Cache | None,
decoder_attention_mask: torch.Tensor | dict | None,
decoder_position_ids: torch.LongTensor | None,
**kwargs: Any,
) -> torch.Tensor:
if decoder_position_ids is None:
prefix = past_key_values.get_seq_length(layer_idx=0) if past_key_values is not None else 0
decoder_position_ids = torch.arange(
prefix, prefix + inputs_embeds.shape[1], device=inputs_embeds.device
).unsqueeze(0)
if not isinstance(mask_mapping := decoder_attention_mask, dict):
mask_mapping = self.create_diffusion_decoder_attention_mask(
config=self.text_config,
inputs_embeds=inputs_embeds,
past_key_values=past_key_values,
decoder_attention_mask=decoder_attention_mask,
)
working_bus = kwargs.pop("working_bus", None)
working_state = kwargs.pop("working_state", None)
working_canvas_head = kwargs.pop("working_canvas_head", None)
persistent_bus = kwargs.pop("persistent_bus", None) or kwargs.pop("memory_bus", None)
memory_slots = kwargs.pop("memory_slots", None)
slot_identity = kwargs.pop("slot_identity", None)
cache = past_key_values
if self.gradient_checkpointing and self.training and cache is not None:
cache = copy.copy(cache)
cache.layers = [copy.copy(layer) for layer in cache.layers]
hidden = inputs_embeds
positions = {
layer_type: self.rotary_emb(hidden, decoder_position_ids, layer_type)
for layer_type in self.unique_layer_types
}
working_kv = None
if working_bus is not None and working_state is not None:
working_kv = working_bus.prepare_kv(working_state)
memory_kv = None
if persistent_bus is not None and memory_slots is not None:
memory_kv = persistent_bus.prepare_kv(memory_slots, slot_identity)
working_reader = 0
reader_index = 0
for index in range(self.text_config.num_hidden_layers):
layer = self.layers[index]
layer_type = self.text_config.layer_types[index]
if self.gradient_checkpointing and self.training:
def layer_forward(
values: torch.Tensor,
selected: nn.Module = layer,
selected_type: str = layer_type,
) -> torch.Tensor:
return selected(
values,
position_embeddings=positions[selected_type],
attention_mask=mask_mapping[selected_type],
position_ids=decoder_position_ids,
past_key_values=cache,
**kwargs,
)
hidden = self._gradient_checkpointing_func(layer_forward, hidden)
else:
hidden = layer(
hidden,
position_embeddings=positions[layer_type],
attention_mask=mask_mapping[layer_type],
position_ids=decoder_position_ids,
past_key_values=cache,
**kwargs,
)
if layer_type == "full_attention":
if (
working_kv is not None
and working_reader < working_bus.num_readers
):
hidden = working_bus.read(
hidden, working_reader, *working_kv,
positions=(
decoder_position_ids if working_canvas_head is not None else None
),
)
working_reader += 1
if (
memory_kv is not None
and reader_index < persistent_bus.num_readers
):
hidden = persistent_bus.read(
hidden, reader_index, memory_kv[0], memory_kv[1]
)
reader_index += 1
return self.norm(hidden)
def forward(
self,
decoder_input_ids: torch.LongTensor,
past_key_values: Cache | None = None,
decoder_token_embeddings: torch.FloatTensor | None = None,
temporal_context_embeddings: torch.FloatTensor | None = None,
decoder_attention_mask: torch.Tensor | dict | None = None,
decoder_position_ids: torch.LongTensor | None = None,
collect_latent_diagnostics: bool = True,
memory_slots: torch.Tensor | None = None,
working_bus: Any | None = None,
working_state: torch.Tensor | None = None,
persistent_bus: Any | None = None,
slot_identity: torch.Tensor | None = None,
**kwargs: Any,
) -> ModilifyMk2DecoderOutput:
if "use_cache" in kwargs:
raise ValueError("The diffusion decoder always reads the supplied cache.")
if decoder_token_embeddings is None:
token_embeddings = self.embed_tokens(decoder_input_ids)
else:
token_embeddings = decoder_token_embeddings
expected = (*decoder_input_ids.shape, self.text_config.hidden_size)
if token_embeddings.shape != expected:
raise ValueError("Precomputed decoder embeddings have the wrong shape.")
context_embeddings = (
torch.zeros_like(token_embeddings)
if temporal_context_embeddings is None
else temporal_context_embeddings.to(token_embeddings)
)
inputs_embeds, diagnostics = self.merge_latent_context(
token_embeddings,
context_embeddings if temporal_context_embeddings is not None else None,
collect_diagnostics=collect_latent_diagnostics,
)
hidden = self._run_stack(
inputs_embeds,
past_key_values=past_key_values,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
memory_slots=memory_slots,
working_bus=working_bus,
working_state=working_state,
persistent_bus=persistent_bus,
slot_identity=slot_identity,
**kwargs,
)
return ModilifyMk2DecoderOutput(
last_hidden_state=hidden,
past_key_values=past_key_values,
token_embeddings=token_embeddings,
latent_residual_diagnostics=diagnostics,
)
class ModilifyMk2Model(DiffusionGemmaPreTrainedModel):
config_class = ModilifyMk2Config
_tied_weights_keys = {
"encoder.language_model.norm.weight": "decoder.norm.weight",
r"encoder.language_model.layers\.(?:[^.]+\.)*weight": r"decoder.layers\.(?:[^.]+\.)*weight",
r"encoder.language_model.layers\.(?:[^.]+\.)*scale": r"decoder.layers\.(?:[^.]+\.)*scale",
r"encoder.language_model.layers\.(?:[^.]+\.)*per_expert_scale": r"decoder.layers\.(?:[^.]+\.)*per_expert_scale",
r"encoder.language_model.layers\.(?:[^.]+\.)*gate_up_proj": r"decoder.layers\.(?:[^.]+\.)*gate_up_proj",
r"encoder.language_model.layers\.(?:[^.]+\.)*down_proj": r"decoder.layers\.(?:[^.]+\.)*down_proj",
"encoder.language_model.embed_tokens.weight": "decoder.embed_tokens.weight",
}
def __init__(self, config: ModilifyMk2Config):
super().__init__(config)
self.encoder = ModilifyMk2EncoderModel(config)
self.decoder = ModilifyMk2DecoderModel(config)
install_modilify_mk2_trunk_semantics(self)
self.post_init()
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
def get_input_embeddings(self):
return self.encoder.get_input_embeddings()
def set_input_embeddings(self, value):
self.encoder.set_input_embeddings(value)
self.decoder.embed_tokens = value
def forward(
self,
*,
input_ids: torch.LongTensor | None = None,
attention_mask: torch.Tensor | dict | None = None,
past_key_values: Cache | None = None,
position_ids: torch.LongTensor | None = None,
decoder_input_ids: torch.LongTensor,
decoder_token_embeddings: torch.FloatTensor | None = None,
temporal_context_embeddings: torch.FloatTensor | None = None,
decoder_attention_mask: torch.Tensor | dict | None = None,
decoder_position_ids: torch.LongTensor | None = None,
return_encoder_outputs: bool = True,
collect_latent_diagnostics: bool = True,
memory_slots: torch.Tensor | None = None,
working_bus: Any | None = None,
working_state: torch.Tensor | None = None,
persistent_bus: Any | None = None,
slot_identity: torch.Tensor | None = None,
**kwargs: Any,
) -> ModilifyMk2ModelOutput:
encoder_hidden = None
if input_ids is not None:
encoded = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
past_key_values=past_key_values,
position_ids=position_ids,
**kwargs,
)
past_key_values = encoded.past_key_values
if return_encoder_outputs:
encoder_hidden = encoded.last_hidden_state
elif past_key_values is None:
raise ValueError("Either `input_ids` or `past_key_values` is required.")
decoded = self.decoder(
decoder_input_ids=decoder_input_ids,
decoder_token_embeddings=decoder_token_embeddings,
past_key_values=past_key_values,
temporal_context_embeddings=temporal_context_embeddings,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
collect_latent_diagnostics=collect_latent_diagnostics,
memory_slots=memory_slots,
working_bus=working_bus,
working_state=working_state,
persistent_bus=persistent_bus,
slot_identity=slot_identity,
**kwargs,
)
return ModilifyMk2ModelOutput(
last_hidden_state=decoded.last_hidden_state,
past_key_values=past_key_values,
token_embeddings=decoded.token_embeddings,
encoder_last_hidden_state=encoder_hidden,
latent_residual_diagnostics=decoded.latent_residual_diagnostics,
)
class ModilifyMk2ForBlockDiffusion(DiffusionGemmaPreTrainedModel, ModilifyMk2GenerationMixin):
config_class = ModilifyMk2Config
_tied_weights_keys = {"lm_head.weight": "model.decoder.embed_tokens.weight"}
generation_config_class = ModilifyMk2GenerationConfig
_no_split_modules = ["DiffusionGemmaDecoderLayer", "LatentDeliberationTransformer"]
supports_gradient_checkpointing = True
@torch.no_grad()
def _init_weights(self, module: nn.Module) -> None:
super()._init_weights(module)
if isinstance(module, LatentDeliberationTransformer):
module.reset_identity_parameters()
module.working_memory_bus.freeze()
module.persistent_memory_bus.freeze()
def __init__(self, config: ModilifyMk2Config):
super().__init__(config)
self.model = ModilifyMk2Model(config)
layer_types = tuple(getattr(config.text_config, "layer_types", None) or ())
self.latent_deliberation = LatentDeliberationTransformer(
hidden_size=config.text_config.hidden_size,
latent_dim=config.latent_dim,
ffn_dim=config.latent_ffn_dim,
num_layers=config.latent_num_layers,
num_heads=config.latent_num_heads,
local_attention_window=config.latent_local_attention_window,
tape_probes=config.latent_tape_probes,
history_kv_rank=config.latent_history_kv_rank,
num_memory_readers=sum(layer_type == "full_attention" for layer_type in layer_types),
num_working_readers=(
sum(layer_type == "full_attention" for layer_type in layer_types)
if config.working_memory_bus else 0
),
num_persistent_readers=(
sum(layer_type == "full_attention" for layer_type in layer_types)
if config.persistent_memory_bus else 0
),
working_last_block_global=config.latent_working_last_block_global,
commit_sequence_dim=config.commit_sequence_dim,
max_canvas_length=config.canvas_length,
)
self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
self.final_logit_softcapping = config.text_config.final_logit_softcapping
self.post_init()
install_modilify_mk2_trunk_semantics(self)
if getattr(config, "lora_config", None):
payload = dict(config.lora_config)
payload["target_modules"] = tuple(payload["target_modules"])
inject_lora(self, LoRAConfig(**payload))
# The same adapters serve the encoder and decoder, just like the
# tied backbone. Tell from_pretrained/save_pretrained about them.
self._tied_weights_keys = dict(type(self)._tied_weights_keys)
self._tied_weights_keys.update({
name: name.replace("model.encoder.language_model.", "model.decoder.", 1)
for name, _ in self.named_parameters(remove_duplicate=False)
if name.startswith("model.encoder.language_model.") and ".lora_" in name
})
self.all_tied_weights_keys = self.get_expanded_tied_weights_keys(all_submodels=True)
self.tie_weights(recompute_mapping=False)
config.text_config._experts_implementation = "modilify_mk2_lora"
# These 26 small parameters were trained and saved in FP32. Preserve
# their dtype even when from_pretrained is called with BF16/FP16.
self._keep_in_fp32_modules_strict = {
name for name, _ in self.named_parameters()
if name.startswith("latent_deliberation.") and (
name.endswith((".dt_bias", ".a_log")) or
(name.endswith(".weight") and "norm" in name.rsplit(".", 2)[-2])
)
}
def _finalize_logits(self, hidden: torch.Tensor) -> torch.Tensor:
logits = self.lm_head(hidden)
return torch.tanh(logits / self.final_logit_softcapping) * self.final_logit_softcapping
def _compute_gold_nll(
self, logits: torch.Tensor, labels: torch.LongTensor | None
) -> torch.Tensor | None:
if labels is None:
return None
labels = labels.to(logits.device)
return F.cross_entropy(
logits.flatten(0, 1), labels.flatten(),
ignore_index=-100, reduction="none",
).view_as(labels)
def _prepare_latent_context(
self,
decoder_input_ids: torch.LongTensor,
*,
confidence: torch.Tensor | None,
entropy: torch.Tensor | None,
age: torch.Tensor | None,
latent_state: LatentDeliberationState | None,
canvas_head: torch.Tensor | None,
) -> tuple[torch.Tensor, LatentDeliberationState, torch.Tensor]:
batch, canvas = decoder_input_ids.shape
dtype = self.model.decoder.embed_tokens.weight.dtype
if latent_state is None:
latent_state = LatentDeliberationState.empty(
batch_size=batch, canvas_length=canvas,
latent_dim=self.config.latent_dim, memory_slots=self.config.latent_memory_slots,
device=decoder_input_ids.device, dtype=dtype, enable_gdn2=True,
)
confidence = latent_state.confidence if confidence is None else confidence.squeeze(-1).float()
entropy = latent_state.entropy if entropy is None else entropy.squeeze(-1).float()
if age is not None:
latent_state = replace(
latent_state,
age=age.to(device=decoder_input_ids.device, dtype=torch.int32),
)
token_embeddings = self.model.decoder.embed_tokens(decoder_input_ids)
use_outer_checkpoint = (
self.training
and torch.is_grad_enabled()
and self.latent_deliberation.activation_checkpointing
)
if use_outer_checkpoint:
def run_latent_processor(
embeddings: torch.Tensor,
memory_slots: torch.Tensor,
working_cells: torch.Tensor,
working_row: torch.Tensor,
state_confidence: torch.Tensor,
state_entropy: torch.Tensor,
state_age: torch.Tensor,
state_token_changed: torch.Tensor,
state_confidence_delta: torch.Tensor,
state_entropy_delta: torch.Tensor,
state_ponder_steps: torch.Tensor,
state_stagnation_steps: torch.Tensor,
) -> torch.Tensor:
checkpoint_state = LatentDeliberationState(
memory_slots=memory_slots,
confidence=state_confidence,
entropy=state_entropy,
age=state_age,
token_changed=state_token_changed,
confidence_delta=state_confidence_delta,
entropy_delta=state_entropy_delta,
ponder_steps=state_ponder_steps,
stagnation_steps=state_stagnation_steps,
gdn2=replace(
latent_state.gdn2, cells=working_cells,
row=working_row, persistent=memory_slots,
),
)
result = self.latent_deliberation(
token_embeddings=embeddings,
confidence=confidence,
entropy=entropy,
state=checkpoint_state,
canvas_head=canvas_head,
)
return result.context
latent_context = checkpoint(
run_latent_processor,
token_embeddings,
latent_state.memory_slots,
latent_state.gdn2.cells,
latent_state.gdn2.row,
latent_state.confidence,
latent_state.entropy,
latent_state.age,
latent_state.token_changed,
latent_state.confidence_delta,
latent_state.entropy_delta,
latent_state.ponder_steps,
latent_state.stagnation_steps,
use_reentrant=False,
preserve_rng_state=True,
)
next_state = LatentDeliberationState(
memory_slots=latent_state.memory_slots,
confidence=confidence.to(dtype=torch.float32),
entropy=entropy.to(dtype=torch.float32),
age=latent_state.age,
token_changed=latent_state.token_changed,
confidence_delta=latent_state.confidence_delta,
entropy_delta=latent_state.entropy_delta,
ponder_steps=latent_state.ponder_steps,
stagnation_steps=latent_state.stagnation_steps,
gdn2=latent_state.gdn2,
)
else:
processed: LatentProcessorOutput = self.latent_deliberation(
token_embeddings=token_embeddings,
confidence=confidence,
entropy=entropy,
state=latent_state,
canvas_head=canvas_head,
)
latent_context = processed.context
next_state = processed.state
return (
latent_context,
next_state,
token_embeddings,
)
def forward(
self,
*,
input_ids: torch.LongTensor | None = None,
attention_mask: torch.Tensor | dict | None = None,
past_key_values: Cache | None = None,
position_ids: torch.LongTensor | None = None,
decoder_input_ids: torch.LongTensor,
previous_confidence: torch.FloatTensor | None = None,
previous_entropy: torch.FloatTensor | None = None,
token_age: torch.Tensor | None = None,
latent_state: LatentDeliberationState | None = None,
canvas_head: torch.Tensor | None = None,
decoder_attention_mask: torch.Tensor | dict | None = None,
decoder_position_ids: torch.LongTensor | None = None,
labels: torch.LongTensor | None = None,
return_encoder_outputs: bool = True,
detach_temporal_context_for_heavy: bool = False,
compact_vocab: bool = False,
repetition_token_mask: torch.BoolTensor | None = None,
repetition_penalty: float = 1.0,
sampling_generators: Sequence[torch.Generator] | None = None,
collect_latent_diagnostics: bool = True,
**kwargs: Any,
) -> ModilifyMk2BlockDiffusionOutput:
(
latent_context,
next_state,
decoder_token_embeddings,
) = self._prepare_latent_context(
decoder_input_ids,
confidence=previous_confidence,
entropy=previous_entropy,
age=token_age,
latent_state=latent_state,
canvas_head=canvas_head,
)
heavy_context = latent_context
heavy_context_leaf = None
temporal_memory_slots = next_state.memory_slots
heavy_memory_slots = temporal_memory_slots
heavy_memory_leaf = None
working_bus = self.latent_deliberation.working_memory_bus
persistent_bus = self.latent_deliberation.persistent_memory_bus
working_readers_active = (
working_bus.num_readers > 0
and (not working_bus.training or working_bus.enabled)
)
memory_readers_active = (
persistent_bus.num_readers > 0
and (not persistent_bus.training or persistent_bus.enabled)
)
if detach_temporal_context_for_heavy and latent_context.requires_grad:
# Clone so the heavy decoder cannot alias the temporal graph storage.
# `detach()` alone shares storage; an in-place decoder write would
# corrupt the later latent backward and can produce non-finite grads.
heavy_context_leaf = latent_context.detach().clone().requires_grad_(True)
heavy_context = heavy_context_leaf
if (
detach_temporal_context_for_heavy
and memory_readers_active
and temporal_memory_slots.requires_grad
):
# Memory readers are part of the heavy decoder too. Keep their
# backward separate from the recurrent temporal graph for the same
# reason as the canvas context above; both gradients are replayed
# together at the TBPTT boundary.
heavy_memory_leaf = (
temporal_memory_slots.detach().clone().requires_grad_(True)
)
heavy_memory_slots = heavy_memory_leaf
# The production LoRA path freezes the tied embedding table. Reuse its
# lookup there; a trainable table needs two independent autograd paths
# because heavy and temporal backward execute at different times.
reusable_decoder_embeddings = (
None
if self.model.decoder.embed_tokens.weight.requires_grad
else decoder_token_embeddings
)
outputs = self.model(
input_ids=input_ids, attention_mask=attention_mask,
past_key_values=past_key_values, position_ids=position_ids,
decoder_input_ids=decoder_input_ids,
decoder_token_embeddings=reusable_decoder_embeddings,
temporal_context_embeddings=heavy_context,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
return_encoder_outputs=return_encoder_outputs,
collect_latent_diagnostics=collect_latent_diagnostics,
working_bus=working_bus if working_readers_active else None,
working_state=(
heavy_context,
# The Torch decoder keeps physical ring order and supplies
# logical positions through RoPE; its mask must stay physical.
next_state.gdn2.seen,
) if working_readers_active else None,
working_canvas_head=canvas_head,
persistent_bus=persistent_bus if memory_readers_active else None,
memory_slots=heavy_memory_slots if memory_readers_active else None,
slot_identity=(
next_state.gdn2.seen
if memory_readers_active else None
),
**kwargs,
)
latent_residual_diagnostics = outputs.latent_residual_diagnostics
proposal = proposal_confidence = token_entropy = None
greedy_proposal = greedy_confidence = None
temperature_gold_nll = None
sampled_nll = None
if compact_vocab:
logits = None
if labels is None:
(
proposal,
proposal_confidence,
token_entropy,
greedy_proposal,
greedy_confidence,
) = (
chunked_vocab_statistics(
outputs.last_hidden_state.detach(),
self.lm_head.weight.detach(),
softcap=self.final_logit_softcapping,
temperature=DENOISE_TEMPERATURE,
chunk_size=self.config.vocab_chunk_size,
repetition_token_mask=repetition_token_mask,
repetition_penalty=repetition_penalty,
sampling_generators=sampling_generators,
top_k=getattr(self.config, "commit_top_k", 40),
min_p=getattr(self.config, "commit_min_p", 0.05),
)
)
gold_nll = None
else:
(
gold_nll,
temperature_gold_nll,
proposal,
proposal_confidence,
token_entropy,
greedy_proposal,
greedy_confidence,
sampled_nll,
) = chunked_vocab_training_statistics(
outputs.last_hidden_state,
self.lm_head.weight,
labels,
return_sampled_nll=True,
softcap=self.final_logit_softcapping,
temperature=DENOISE_TEMPERATURE,
chunk_size=self.config.vocab_chunk_size,
top_k=getattr(self.config, "commit_top_k", 40),
min_p=getattr(self.config, "commit_min_p", 0.05),
)
else:
logits = self._finalize_logits(outputs.last_hidden_state)
gold_nll = self._compute_gold_nll(logits, labels)
loss = None
if gold_nll is not None and labels is not None:
valid = labels.to(gold_nll.device).ne(-100)
loss = gold_nll.masked_fill(~valid, 0.0).sum() / valid.sum().clamp_min(1)
return ModilifyMk2BlockDiffusionOutput(
logits=logits, loss=loss,
past_key_values=outputs.past_key_values,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
heavy_hidden_state=outputs.last_hidden_state,
next_latent_state=next_state,
gold_nll=gold_nll,
temporal_context=latent_context,
heavy_context_leaf=heavy_context_leaf,
temporal_memory_slots=temporal_memory_slots,
heavy_memory_leaf=heavy_memory_leaf,
working_state=latent_context,
latent_residual_diagnostics=latent_residual_diagnostics,
temperature_gold_nll=temperature_gold_nll,
proposal=proposal,
proposal_confidence=proposal_confidence,
sampled_nll=sampled_nll,
token_entropy=token_entropy,
greedy_proposal=greedy_proposal,
greedy_confidence=greedy_confidence,
)
__all__ = [
"ModilifyMk2BlockDiffusionOutput", "ModilifyMk2Config", "ModilifyMk2DecoderModel",
"ModilifyMk2EncoderModel", "ModilifyMk2ForBlockDiffusion", "ModilifyMk2Model",
"ModilifyMk2TextRouter", "install_modilify_mk2_trunk_semantics",
]
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