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# SPDX-License-Identifier: Apache-2.0
#
# Adapted from https://github.com/ideogram-oss/ideogram4 (src/ideogram4/modeling_ideogram4.py),
# licensed under Apache-2.0. Modified by Tenstorrent as the PyTorch reference for the tt-metal port.
"""Ideogram4 transformer backbone.
The transformer consumes Qwen3-VL embeddings and flow-matching noise tokens to
produce velocity predictions on image latents.
"""
from __future__ import annotations
import math
from dataclasses import dataclass
import torch
import torch.nn as nn
import torch.nn.functional as F
from .constants import LLM_TOKEN_INDICATOR, OUTPUT_IMAGE_INDICATOR, QWEN3_VL_ACTIVATION_LAYERS
@dataclass
class Ideogram4Config:
emb_dim: int = 4608
num_layers: int = 34
num_heads: int = 18
intermediate_size: int = 12288
adanln_dim: int = 512
# Latent dimension after patchification: ae_channels (32) * patch_size**2 (4) = 128.
in_channels: int = 128
# Hidden size of Qwen3-VL-8B-Instruct multiplied by the number of layers we extract
# Qwen3-VL hidden size = 4096
llm_features_dim: int = 4096 * len(QWEN3_VL_ACTIVATION_LAYERS)
rope_theta: int = 5_000_000
mrope_section: tuple[int, ...] = (24, 20, 20)
norm_eps: float = 1e-5
def _rotate_half(x: torch.Tensor) -> torch.Tensor:
half = x.shape[-1] // 2
x1 = x[..., :half]
x2 = x[..., half:]
return torch.cat((-x2, x1), dim=-1)
def _apply_rotary_pos_emb(
q: torch.Tensor,
k: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
# q, k: (B, num_heads, L, head_dim); cos/sin: (B, L, head_dim).
cos = cos.unsqueeze(1)
sin = sin.unsqueeze(1)
q_embed = (q * cos) + (_rotate_half(q) * sin)
k_embed = (k * cos) + (_rotate_half(k) * sin)
return q_embed, k_embed
class Ideogram4MRoPE(nn.Module):
inv_freq: torch.Tensor
def __init__(
self,
head_dim: int,
base: int,
mrope_section: tuple[int, ...],
) -> None:
super().__init__()
inv_freq = 1.0 / (base ** (torch.arange(0, head_dim, 2, dtype=torch.float32) / head_dim))
self.register_buffer("inv_freq", inv_freq, persistent=False)
self.mrope_section = tuple(mrope_section)
self.head_dim = head_dim
@torch.no_grad()
def forward(self, position_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
# position_ids: (B, L, 3) of int.
assert position_ids.ndim == 3 and position_ids.shape[-1] == 3
batch_size, seq_len, _ = position_ids.shape
# (3, B, inv_freq_size, L)
pos = position_ids.permute(2, 0, 1).to(dtype=torch.float32) # type: ignore[arg-type]
inv_freq = self.inv_freq.to(dtype=torch.float32)[None, None, :, None].expand(
3, batch_size, -1, 1
) # type: ignore[index]
freqs = inv_freq @ pos.unsqueeze(2)
freqs = freqs.transpose(2, 3) # (3, B, L, inv_freq_size)
# interleaved mrope: pull H freqs into idx 1 mod 3, W freqs into idx 2 mod 3.
freqs_t = freqs[0].clone()
for axis, offset in ((1, 1), (2, 2)):
length = self.mrope_section[axis] * 3
idx = torch.arange(offset, length, 3, device=freqs_t.device)
freqs_t[..., idx] = freqs[axis][..., idx]
emb = torch.cat((freqs_t, freqs_t), dim=-1)
return emb.cos(), emb.sin()
class Ideogram4RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6) -> None:
super().__init__()
self.weight = nn.Parameter(torch.ones(dim))
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.Tensor:
return F.rms_norm(x, self.weight.shape, self.weight, self.eps)
class Ideogram4Attention(nn.Module):
def __init__(self, hidden_size: int, num_heads: int, eps: float = 1e-5) -> None:
super().__init__()
assert hidden_size % num_heads == 0
self.hidden_size = hidden_size
self.num_heads = num_heads
self.head_dim = hidden_size // num_heads
self.qkv = nn.Linear(hidden_size, hidden_size * 3, bias=False)
self.norm_q = Ideogram4RMSNorm(self.head_dim, eps=eps)
self.norm_k = Ideogram4RMSNorm(self.head_dim, eps=eps)
self.o = nn.Linear(hidden_size, hidden_size, bias=False)
def forward(
self,
x: torch.Tensor,
segment_ids: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
) -> torch.Tensor:
batch_size, seq_len, _ = x.shape
qkv = self.qkv(x)
qkv = qkv.view(batch_size, seq_len, 3, self.num_heads, self.head_dim)
q, k, v = qkv.unbind(dim=2)
q = self.norm_q(q)
k = self.norm_k(k)
# SDPA expects (B, num_heads, L, head_dim).
q = q.transpose(1, 2)
k = k.transpose(1, 2)
v = v.transpose(1, 2)
q, k = _apply_rotary_pos_emb(q, k, cos, sin)
# Block-diagonal mask from segment ids: (B, 1, L, L), True = attend.
attn_mask = (segment_ids.unsqueeze(2) == segment_ids.unsqueeze(1)).unsqueeze(1)
out = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
out = out.transpose(1, 2).reshape(batch_size, seq_len, self.hidden_size)
return self.o(out)
class Ideogram4MLP(nn.Module):
def __init__(self, dim: int, hidden_dim: int) -> None:
super().__init__()
self.w1 = nn.Linear(dim, hidden_dim, bias=False)
self.w2 = nn.Linear(hidden_dim, dim, bias=False)
self.w3 = nn.Linear(dim, hidden_dim, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.w2(F.silu(self.w1(x)) * self.w3(x))
class Ideogram4TransformerBlock(nn.Module):
def __init__(
self,
hidden_size: int,
intermediate_size: int,
num_heads: int,
norm_eps: float,
adanln_dim: int,
) -> None:
super().__init__()
self.attention = Ideogram4Attention(hidden_size, num_heads, eps=1e-5)
self.feed_forward = Ideogram4MLP(hidden_size, intermediate_size)
self.attention_norm1 = Ideogram4RMSNorm(hidden_size, eps=norm_eps)
self.ffn_norm1 = Ideogram4RMSNorm(hidden_size, eps=norm_eps)
self.attention_norm2 = Ideogram4RMSNorm(hidden_size, eps=norm_eps)
self.ffn_norm2 = Ideogram4RMSNorm(hidden_size, eps=norm_eps)
self.adaln_modulation = nn.Linear(adanln_dim, 4 * hidden_size, bias=True)
def forward(
self,
x: torch.Tensor,
segment_ids: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
adaln_input: torch.Tensor,
) -> torch.Tensor:
mod = self.adaln_modulation(adaln_input)
scale_msa, gate_msa, scale_mlp, gate_mlp = mod.chunk(4, dim=-1)
gate_msa = torch.tanh(gate_msa)
gate_mlp = torch.tanh(gate_mlp)
scale_msa = 1.0 + scale_msa
scale_mlp = 1.0 + scale_mlp
attn_out = self.attention(
self.attention_norm1(x) * scale_msa,
segment_ids=segment_ids,
cos=cos,
sin=sin,
)
x = x + gate_msa * self.attention_norm2(attn_out)
x = x + gate_mlp * self.ffn_norm2(self.feed_forward(self.ffn_norm1(x) * scale_mlp))
return x
def _sinusoidal_embedding(t: torch.Tensor, dim: int, scale: float = 1e4) -> torch.Tensor:
t = t.to(torch.float32)
half = dim // 2
freq = math.log(scale) / (half - 1)
freq = torch.exp(torch.arange(half, dtype=torch.float32, device=t.device) * -freq) # type: ignore[assignment]
emb = t.unsqueeze(-1) * freq
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
if dim % 2 == 1:
emb = F.pad(emb, (0, 1))
return emb
class Ideogram4EmbedScalar(nn.Module):
def __init__(self, dim: int, input_range: tuple[float, float]) -> None:
super().__init__()
self.dim = dim
self.range_min, self.range_max = input_range
assert self.range_max > self.range_min
self.mlp_in = nn.Linear(dim, dim, bias=True)
self.mlp_out = nn.Linear(dim, dim, bias=True)
def forward(self, x: torch.Tensor) -> torch.Tensor:
# x is shape (..., 1) or (...,) holding a scalar per token.
x = x.to(torch.float32)
scaled = 1e4 * (x - self.range_min) / (self.range_max - self.range_min)
emb = _sinusoidal_embedding(scaled, self.dim)
emb = emb.to(self.mlp_in.weight.dtype)
emb = F.silu(self.mlp_in(emb))
return self.mlp_out(emb)
class Ideogram4FinalLayer(nn.Module):
def __init__(self, hidden_size: int, out_channels: int, adanln_dim: int) -> None:
super().__init__()
self.norm_final = nn.LayerNorm(hidden_size, eps=1e-6, elementwise_affine=False)
self.linear = nn.Linear(hidden_size, out_channels, bias=True)
self.adaln_modulation = nn.Linear(adanln_dim, hidden_size, bias=True)
def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
scale = 1.0 + self.adaln_modulation(F.silu(c))
return self.linear(self.norm_final(x) * scale)
class Ideogram4Transformer(nn.Module):
"""Ideogram 4 flow-matching transformer."""
def __init__(self, config: Ideogram4Config) -> None:
super().__init__()
self.config = config
head_dim = config.emb_dim // config.num_heads
self.input_proj = nn.Linear(config.in_channels, config.emb_dim, bias=True)
self.llm_cond_norm = Ideogram4RMSNorm(config.llm_features_dim, eps=1e-6)
self.llm_cond_proj = nn.Linear(config.llm_features_dim, config.emb_dim, bias=True)
self.t_embedding = Ideogram4EmbedScalar(config.emb_dim, input_range=(0.0, 1.0))
self.adaln_proj = nn.Linear(config.emb_dim, config.adanln_dim, bias=True)
self.embed_image_indicator = nn.Embedding(2, config.emb_dim)
self.rotary_emb = Ideogram4MRoPE(
head_dim=head_dim,
base=config.rope_theta,
mrope_section=config.mrope_section,
)
self.layers = nn.ModuleList(
[
Ideogram4TransformerBlock(
hidden_size=config.emb_dim,
intermediate_size=config.intermediate_size,
num_heads=config.num_heads,
norm_eps=config.norm_eps,
adanln_dim=config.adanln_dim,
)
for _ in range(config.num_layers)
]
)
self.final_layer = Ideogram4FinalLayer(
hidden_size=config.emb_dim,
out_channels=config.in_channels,
adanln_dim=config.adanln_dim,
)
@property
def device(self) -> torch.device:
return next(self.parameters()).device
def forward(
self,
*,
llm_features: torch.Tensor,
x: torch.Tensor,
t: torch.Tensor,
position_ids: torch.Tensor,
segment_ids: torch.Tensor,
indicator: torch.Tensor,
) -> torch.Tensor:
"""Velocity prediction.
Args:
llm_features: (B, L, llm_features_dim) Qwen3-VL conditioning features.
x: (B, L, in_channels) noise tokens.
t: (B,) or (B, L) flow-matching time in [0, 1].
position_ids: (B, L, 3) (t, h, w) positions for MRoPE.
segment_ids: (B, L) sample id within a packed batch.
indicator: (B, L) per-token role: LLM_TOKEN_INDICATOR or OUTPUT_IMAGE_INDICATOR.
Returns:
(B, L, in_channels) velocity prediction in float32. Only the positions
with ``indicator == OUTPUT_IMAGE_INDICATOR`` are meaningful.
"""
batch_size, seq_len, in_channels = x.shape
assert in_channels == self.config.in_channels
param_dtype = self.input_proj.weight.dtype
x = x.to(param_dtype)
t = t.to(param_dtype)
llm_features = llm_features.to(param_dtype)
indicator = indicator.to(torch.long)
llm_token_mask = (indicator == LLM_TOKEN_INDICATOR).to(x.dtype).unsqueeze(-1)
output_image_mask = (indicator == OUTPUT_IMAGE_INDICATOR).to(x.dtype).unsqueeze(-1)
llm_features = llm_features * llm_token_mask
x = x * output_image_mask
x = self.input_proj(x) * output_image_mask
# Keep shape (B, 1, ...) when t is per-sample so downstream adaln_modulation
# projections don't pay for L identical copies.
t_cond = self.t_embedding(t)
if t.dim() == 1:
t_cond = t_cond.unsqueeze(1)
adaln_input = F.silu(self.adaln_proj(t_cond))
llm_features = self.llm_cond_norm(llm_features)
llm_features = self.llm_cond_proj(llm_features) * llm_token_mask
h = x + llm_features
image_indicator_embedding = self.embed_image_indicator((indicator == OUTPUT_IMAGE_INDICATOR).to(torch.long))
h = h + image_indicator_embedding
cos, sin = self.rotary_emb(position_ids)
cos = cos.to(h.dtype)
sin = sin.to(h.dtype)
for layer in self.layers:
h = layer(h, segment_ids=segment_ids, cos=cos, sin=sin, adaln_input=adaln_input)
out = self.final_layer(h, c=adaln_input)
return out.to(torch.float32)
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