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import itertools
import math
from dataclasses import dataclass
import torch
from torch import nn
from torch.nn import functional as F
from transformers import PreTrainedModel
from transformers.utils import ModelOutput
from .configuration_omni import OmniConfig
class Attention(nn.Module):
def __init__(self, width, head_dim):
super().__init__()
self.head_dim = head_dim
self.q_proj = nn.Linear(width, width, bias=False)
self.k_proj = nn.Linear(width, width, bias=False)
self.v_proj = nn.Linear(width, width, bias=False)
self.o_proj = nn.Linear(width, width, bias=False)
def forward(self, x, causal=False):
b, s, d = x.shape
def heads(value):
return value.reshape(b, s, d // self.head_dim, self.head_dim).transpose(1, 2)
q, k, v = (heads(layer(x)) for layer in (self.q_proj, self.k_proj, self.v_proj))
y = F.scaled_dot_product_attention(q, k, v, is_causal=causal)
return self.o_proj(y.transpose(1, 2).reshape_as(x))
class Expert(nn.Module):
def __init__(self, width, intermediate):
super().__init__()
self.gate_proj = nn.Linear(width, intermediate, bias=False)
self.up_proj = nn.Linear(width, intermediate, bias=False)
self.down_proj = nn.Linear(intermediate, width, bias=False)
def forward(self, x):
return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
class SparseMLP(nn.Module):
def __init__(self, width, experts, expansion, selected):
super().__init__()
self.selected = selected
self.router = nn.Linear(width, experts, bias=False) if experts > 1 else None
self.experts = nn.ModuleList([Expert(width, width * expansion) for _ in range(experts)])
def forward(self, x):
if self.router is None:
return self.experts[0](x)
flat = x.reshape(-1, x.shape[-1])
scores, ids = self.router(flat).topk(self.selected, dim=-1)
scores = scores.softmax(-1)
result = torch.zeros_like(flat)
for expert_id, expert in enumerate(self.experts):
tokens, slots = torch.where(ids == expert_id)
if tokens.numel():
result = result.index_add(
0, tokens, expert(flat[tokens]) * scores[tokens, slots, None]
)
return result.reshape_as(x)
class Block(nn.Module):
def __init__(self, width, experts, expansion, selected, head_dim):
super().__init__()
self.attention_norm = nn.RMSNorm(width)
self.attention = Attention(width, head_dim)
self.ffn_norm = nn.RMSNorm(width)
self.mlp = SparseMLP(width, experts, expansion, selected)
def forward(self, x, causal=False):
x = x + self.attention(self.attention_norm(x), causal)
return x + self.mlp(self.ffn_norm(x))
class Chunker(nn.Module):
"""Learned boundaries, weighted pooling, and explicit source-to-chunk mapping.
Single-example sequences avoid treating padding as real observations. Processing
a complete observed prefix is bidirectional; next-token targets are not inputs.
"""
def __init__(self, width, context_dim):
super().__init__()
self.boundary = nn.Linear(width, 1)
self.condition = nn.Linear(context_dim, width, bias=False)
def forward(self, x, context):
gate = torch.sigmoid(self.boundary(x + self.condition(context)[:, None]))
starts = gate[0, :, 0] > 0.5
starts = starts.clone()
starts[0] = True
assignment = starts.long().cumsum(0) - 1
groups = int(assignment[-1]) + 1
weights = gate[0]
sums = x.new_zeros(groups, x.shape[-1]).index_add(0, assignment, x[0] * weights)
counts = x.new_zeros(groups, 1).index_add(0, assignment, weights)
pooled = (sums / counts.clamp_min(torch.finfo(x.dtype).eps))[None]
return pooled, assignment
class Stage(nn.Module):
def __init__(self, row, config, adaptive):
super().__init__()
width, depth, experts, expansion, selected = row
self.blocks = nn.ModuleList(
[Block(width, experts, expansion, selected, config.head_dim) for _ in range(depth)]
)
self.chunker = Chunker(width, config.context_dim) if adaptive else None
def forward(self, x, context, causal=False):
for block in self.blocks:
x = block(x, causal)
detail = x
if self.chunker is None:
return x, None, detail
pooled, assignment = self.chunker(x, context)
return pooled, assignment, detail
class Hierarchy(nn.Module):
def __init__(self, name, config, adaptive=False):
super().__init__()
rows = config.stages[name]
self.stages = nn.ModuleList([Stage(row, config, adaptive) for row in rows])
self.transitions = nn.ModuleList(
[nn.Linear(a[0], b[0], bias=False) for a, b in itertools.pairwise(rows)]
)
self.dechunk_projections = (
nn.ModuleList([nn.Linear(b[0], a[0], bias=False) for a, b in itertools.pairwise(rows)])
if adaptive
else nn.ModuleList()
)
def forward(self, x, context, causal=False):
details, assignments = [], []
for i, stage in enumerate(self.stages):
x, assignment, detail = stage(x, context, causal)
details.append(detail)
assignments.append(assignment)
if i < len(self.transitions):
x = self.transitions[i](x)
return x, (details, assignments)
def dechunk(self, x, trace):
details, assignments = trace
for i in reversed(range(len(self.stages))):
if assignments[i] is not None:
x = x[:, assignments[i]] + details[i]
if i:
x = self.dechunk_projections[i - 1](x)
return x
class Encoder(nn.Module):
def __init__(self, name, config):
super().__init__()
width = config.stages[name][0][0]
self.name = name
self.config = config
if name == "text_encoder":
self.embedding = nn.Embedding(config.vocab_size, width)
elif name == "image_encoder":
self.patch_embedding = nn.Conv2d(
3, width, config.patch_size, stride=config.patch_size, bias=False
)
elif name == "video_encoder":
self.tubelet_embedding = nn.Conv3d(
3,
width,
(2, config.patch_size, config.patch_size),
stride=(2, config.patch_size, config.patch_size),
bias=False,
)
elif name == "audio_encoder":
self.waveform_projection = nn.Linear(config.audio_frame_size, width, bias=False)
self.spectrum_projection = nn.Linear(
config.audio_frame_size // 2 + 1, width, bias=False
)
elif name == "eeg_encoder":
self.electrode_projection = nn.Linear(config.eeg_channels, width, bias=False)
self.graph_mix = nn.Linear(config.eeg_channels, config.eeg_channels, bias=False)
self.hierarchy = Hierarchy(name, config, adaptive=True)
def forward(self, value, context, adjacency=None):
rank = {
"text_encoder": 2,
"audio_encoder": 2,
"image_encoder": 4,
"video_encoder": 5,
"eeg_encoder": 3,
}[self.name]
if value.ndim != rank or value.shape[0] != 1 or 0 in value.shape:
raise ValueError(
f"{self.name} requires a nonempty rank-{rank} input with batch size one"
)
if self.name in ("image_encoder", "video_encoder") and (
value.shape[1] != 3 or any(n % self.config.patch_size for n in value.shape[-2:])
):
raise ValueError("RGB spatial dimensions must be divisible by patch_size")
if self.name == "video_encoder" and value.shape[2] % 2:
raise ValueError("Video frame count must be divisible by two")
if self.name == "eeg_encoder" and value.shape[1] != self.config.eeg_channels:
raise ValueError("EEG channel count must match config")
if self.name == "text_encoder":
x = self.embedding(value)
elif self.name == "image_encoder":
x = self.patch_embedding(value).flatten(2).transpose(1, 2)
elif self.name == "video_encoder":
x = self.tubelet_embedding(value).flatten(2).transpose(1, 2)
elif self.name == "audio_encoder":
frame = self.config.audio_frame_size
if value.shape[-1] % frame:
raise ValueError("Audio must contain complete frames")
frames = value.unfold(-1, frame, frame)
spectrum = torch.fft.rfft(frames.float()).abs().to(value.dtype)
x = self.waveform_projection(frames) + self.spectrum_projection(spectrum)
else:
channels = self.config.eeg_channels
if adjacency is None or adjacency.shape != (channels, channels):
raise ValueError("EEG requires an explicit electrode adjacency matrix")
if (
(adjacency < 0).any()
or not torch.isfinite(adjacency).all()
or (adjacency.sum(-1) <= 0).any()
):
raise ValueError("Invalid EEG adjacency")
graph = adjacency / adjacency.sum(-1, keepdim=True)
mixed = (graph @ value).transpose(1, 2)
x = self.electrode_projection(mixed + self.graph_mix(mixed))
position = torch.arange(x.shape[1], device=x.device)[:, None]
rate = torch.exp(-math.log(10000) * torch.arange(x.shape[2], device=x.device) / x.shape[2])
x = x + torch.sin(position * rate).to(x.dtype)[None]
high, trace = self.hierarchy(x, context)
# Fuse reconstructed detail features with the high-level representation.
details = self.hierarchy.dechunk(high, trace)
return high, details, trace
class Fusion(nn.Module):
def __init__(self, config):
super().__init__()
width = config.stages["fusion"][0][0]
encoders = sorted(k for k in config.stages if k.endswith("_encoder"))
self.high_projections = nn.ModuleDict(
{k: nn.Linear(config.stages[k][-1][0], width, bias=False) for k in encoders}
)
self.detail_projections = nn.ModuleDict(
{k: nn.Linear(config.stages[k][0][0], width, bias=False) for k in encoders}
)
self.modality_embedding = nn.Embedding(5, width)
self.hierarchy = Hierarchy("fusion", config)
def forward(self, features, context):
tokens = []
for name, (high, detail, _) in features.items():
i = list(self.high_projections).index(name)
token = self.high_projections[name](high) + self.detail_projections[name](
detail.mean(1, keepdim=True)
)
tokens.append(token + self.modality_embedding.weight[i])
return self.hierarchy(torch.cat(tokens, dim=1), context)[0]
class Decoder(nn.Module):
def __init__(self, name, config, output_width):
super().__init__()
self.input_projection = nn.Linear(
config.stages["world_model"][-1][0], config.stages[name][0][0], bias=False
)
self.hierarchy = Hierarchy(name, config)
self.output_projection = nn.Linear(config.stages[name][-1][0], output_width, bias=False)
def forward(self, x, context):
x = self.input_projection(x)
return self.output_projection(self.hierarchy(x, context, causal=True)[0])
@dataclass
class OmniOutput(ModelOutput):
next_token_logits: torch.Tensor | None = None
audio_codes: torch.Tensor | None = None
image_latents: torch.Tensor | None = None
video_latents: torch.Tensor | None = None
memory_state: torch.Tensor | None = None
class OmniModel(PreTrainedModel):
config_class = OmniConfig
def __init__(self, config):
super().__init__(config)
for name in (k for k in config.stages if k.endswith("_encoder")):
setattr(self, name, Encoder(name, config))
self.fusion = Fusion(config)
self.world_input = nn.Linear(
config.stages["fusion"][-1][0], config.stages["world_model"][0][0], bias=False
)
self.world_model = Hierarchy("world_model", config, adaptive=True)
self.world_detail_projection = nn.Linear(
config.stages["world_model"][0][0], config.stages["world_model"][-1][0], bias=False
)
self.text_head = nn.Linear(
config.stages["world_model"][-1][0], config.vocab_size, bias=False
)
self.audio_decoder = Decoder(
"audio_decoder", config, config.audio_codebooks * config.audio_codebook_size
)
self.image_decoder = Decoder("image_decoder", config, 64)
self.video_decoder = Decoder("video_decoder", config, 128)
self.memory_input = nn.Linear(
config.stages["world_model"][-1][0], config.stages["memory_control"][0][0], bias=False
)
self.memory_control = Hierarchy("memory_control", config)
self.memory_output = nn.Linear(
config.stages["memory_control"][-1][0], config.stages["fusion"][0][0], bias=False
)
self.post_init()
def _init_weights(self, module):
if isinstance(module, (nn.Linear, nn.Embedding, nn.Conv2d, nn.Conv3d)):
nn.init.normal_(module.weight, std=0.02)
if getattr(module, "bias", None) is not None:
nn.init.zeros_(module.bias)
def forward(
self,
input_ids,
pixel_values=None,
audio_values=None,
video_values=None,
eeg_values=None,
eeg_adjacency=None,
task_vector=None,
memory_state=None,
output_modalities=(),
):
if input_ids.ndim != 2 or input_ids.shape[0] != 1 or input_ids.shape[1] == 0:
raise ValueError("Reference execution requires one nonempty observed prefix per call")
context = (
self.text_head.weight.new_zeros(1, self.config.context_dim)
if task_vector is None
else task_vector
)
if context.shape != (1, self.config.context_dim):
raise ValueError("Invalid task_vector shape")
values = {
"text_encoder": input_ids,
"image_encoder": pixel_values,
"audio_encoder": audio_values,
"video_encoder": video_values,
"eeg_encoder": eeg_values,
}
features = {
name: getattr(self, name)(value, context, eeg_adjacency)
for name, value in values.items()
if value is not None
}
fused = self.fusion(features, context)
if memory_state is not None:
fused = fused + self.memory_output(memory_state)[:, None]
world, trace = self.world_model(self.world_input(fused), context)
detail = self.world_model.dechunk(world, trace)
world = world + self.world_detail_projection(detail.mean(1, keepdim=True))
summary = world.mean(1)
memory = self.memory_control(self.memory_input(summary[:, None]), context)[0][:, 0]
outputs = {}
if set(output_modalities) - {"audio", "image", "video"}:
raise ValueError("Unknown output modality")
for name in output_modalities:
output = getattr(self, name + "_decoder")(world, context)
key = "audio_codes" if name == "audio" else name + "_latents"
outputs[key] = output
return OmniOutput(next_token_logits=self.text_head(summary), memory_state=memory, **outputs)
@torch.no_grad()
def generate(self, input_ids, max_new_tokens=8, **kwargs):
for _ in range(max_new_tokens):
token = self(input_ids, **kwargs).next_token_logits.argmax(-1, keepdim=True)
input_ids = torch.cat((input_ids, token), dim=-1)
return input_ids
OmniModel.register_for_auto_class("AutoModel")
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