Added experimental support for Ideogram 4
This commit is contained in:
Jaret Burkett
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"""Ideogram4 transformer backbone.
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Ported from the reference ``modeling_ideogram4.py`` for ai-toolkit. The
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transformer consumes Qwen3-VL hidden states and flow-matching noise tokens
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(packed into a single sequence) and produces velocity predictions on the image
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latent tokens.
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"""
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from __future__ import annotations
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import math
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from dataclasses import dataclass
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.utils.checkpoint import checkpoint
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# Per-token role indicators used inside the packed sequence.
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SEQUENCE_PADDING_INDICATOR = -1
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OUTPUT_IMAGE_INDICATOR = 2
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LLM_TOKEN_INDICATOR = 3
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# Image grid coordinates start at this offset so they never collide with text
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# token indices (text positions start at 0 and never exceed max_text_tokens).
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IMAGE_POSITION_OFFSET = 65536
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# Layers of Qwen3-VL whose hidden states are concatenated and fed to the transformer.
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QWEN3_VL_ACTIVATION_LAYERS = (0, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 35)
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@dataclass
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class Ideogram4Config:
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emb_dim: int = 4608
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num_layers: int = 34
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num_heads: int = 18
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intermediate_size: int = 12288
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adanln_dim: int = 512
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# Latent dimension after patchification: ae_channels (32) * patch_size**2 (4) = 128.
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in_channels: int = 128
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# Hidden size of Qwen3-VL-8B-Instruct multiplied by the number of extracted layers.
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# Qwen3-VL hidden size = 4096
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llm_features_dim: int = 4096 * len(QWEN3_VL_ACTIVATION_LAYERS)
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rope_theta: int = 5_000_000
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mrope_section: tuple[int, ...] = (24, 20, 20)
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norm_eps: float = 1e-5
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def _rotate_half(x: torch.Tensor) -> torch.Tensor:
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half = x.shape[-1] // 2
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x1 = x[..., :half]
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x2 = x[..., half:]
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return torch.cat((-x2, x1), dim=-1)
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def _apply_rotary_pos_emb(
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q: torch.Tensor,
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k: torch.Tensor,
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cos: torch.Tensor,
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sin: torch.Tensor,
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) -> tuple[torch.Tensor, torch.Tensor]:
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# q, k: (B, num_heads, L, head_dim); cos/sin: (B, L, head_dim).
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cos = cos.unsqueeze(1)
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sin = sin.unsqueeze(1)
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q_embed = (q * cos) + (_rotate_half(q) * sin)
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k_embed = (k * cos) + (_rotate_half(k) * sin)
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return q_embed, k_embed
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class Ideogram4MRoPE(nn.Module):
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inv_freq: torch.Tensor
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def __init__(
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self,
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head_dim: int,
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base: int,
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mrope_section: tuple[int, ...],
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) -> None:
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super().__init__()
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inv_freq = 1.0 / (
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base ** (torch.arange(0, head_dim, 2, dtype=torch.float32) / head_dim)
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)
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self.mrope_section = tuple(mrope_section)
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self.head_dim = head_dim
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@torch.no_grad()
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def forward(self, position_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
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# position_ids: (B, L, 3) of int.
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assert position_ids.ndim == 3 and position_ids.shape[-1] == 3
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batch_size, seq_len, _ = position_ids.shape
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# (3, B, inv_freq_size, L)
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pos = position_ids.permute(2, 0, 1).to(dtype=torch.float32)
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inv_freq = self.inv_freq.to(dtype=torch.float32)[None, None, :, None].expand(
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3, batch_size, -1, 1
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)
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freqs = inv_freq @ pos.unsqueeze(2)
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freqs = freqs.transpose(2, 3) # (3, B, L, inv_freq_size)
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# interleaved mrope: pull H freqs into idx 1 mod 3, W freqs into idx 2 mod 3.
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freqs_t = freqs[0].clone()
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for axis, offset in ((1, 1), (2, 2)):
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length = self.mrope_section[axis] * 3
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idx = torch.arange(offset, length, 3, device=freqs_t.device)
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freqs_t[..., idx] = freqs[axis][..., idx]
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emb = torch.cat((freqs_t, freqs_t), dim=-1)
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return emb.cos(), emb.sin()
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class Ideogram4RMSNorm(nn.Module):
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def __init__(self, dim: int, eps: float = 1e-6) -> None:
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super().__init__()
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self.weight = nn.Parameter(torch.ones(dim))
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self.eps = eps
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return F.rms_norm(x, self.weight.shape, self.weight, self.eps)
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class Ideogram4Attention(nn.Module):
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def __init__(self, hidden_size: int, num_heads: int, eps: float = 1e-5) -> None:
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super().__init__()
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assert hidden_size % num_heads == 0
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self.hidden_size = hidden_size
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self.num_heads = num_heads
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self.head_dim = hidden_size // num_heads
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self.qkv = nn.Linear(hidden_size, hidden_size * 3, bias=False)
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self.norm_q = Ideogram4RMSNorm(self.head_dim, eps=eps)
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self.norm_k = Ideogram4RMSNorm(self.head_dim, eps=eps)
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self.o = nn.Linear(hidden_size, hidden_size, bias=False)
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def forward(
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self,
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x: torch.Tensor,
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attn_mask: torch.Tensor,
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cos: torch.Tensor,
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sin: torch.Tensor,
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) -> torch.Tensor:
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batch_size, seq_len, _ = x.shape
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qkv = self.qkv(x)
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qkv = qkv.view(batch_size, seq_len, 3, self.num_heads, self.head_dim)
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q, k, v = qkv.unbind(dim=2)
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q = self.norm_q(q)
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k = self.norm_k(k)
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# SDPA expects (B, num_heads, L, head_dim).
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q = q.transpose(1, 2)
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k = k.transpose(1, 2)
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v = v.transpose(1, 2)
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q, k = _apply_rotary_pos_emb(q, k, cos, sin)
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out = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
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out = out.transpose(1, 2).reshape(batch_size, seq_len, self.hidden_size)
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return self.o(out)
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class Ideogram4MLP(nn.Module):
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def __init__(self, dim: int, hidden_dim: int) -> None:
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super().__init__()
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self.w1 = nn.Linear(dim, hidden_dim, bias=False)
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self.w2 = nn.Linear(hidden_dim, dim, bias=False)
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self.w3 = nn.Linear(dim, hidden_dim, bias=False)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.w2(F.silu(self.w1(x)) * self.w3(x))
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class Ideogram4TransformerBlock(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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num_heads: int,
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norm_eps: float,
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adanln_dim: int,
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) -> None:
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super().__init__()
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self.attention = Ideogram4Attention(hidden_size, num_heads, eps=1e-5)
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self.feed_forward = Ideogram4MLP(hidden_size, intermediate_size)
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self.attention_norm1 = Ideogram4RMSNorm(hidden_size, eps=norm_eps)
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self.ffn_norm1 = Ideogram4RMSNorm(hidden_size, eps=norm_eps)
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self.attention_norm2 = Ideogram4RMSNorm(hidden_size, eps=norm_eps)
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self.ffn_norm2 = Ideogram4RMSNorm(hidden_size, eps=norm_eps)
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self.adaln_modulation = nn.Linear(adanln_dim, 4 * hidden_size, bias=True)
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def forward(
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self,
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x: torch.Tensor,
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attn_mask: torch.Tensor,
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cos: torch.Tensor,
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sin: torch.Tensor,
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adaln_input: torch.Tensor,
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) -> torch.Tensor:
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mod = self.adaln_modulation(adaln_input)
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scale_msa, gate_msa, scale_mlp, gate_mlp = mod.chunk(4, dim=-1)
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gate_msa = torch.tanh(gate_msa)
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gate_mlp = torch.tanh(gate_mlp)
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scale_msa = 1.0 + scale_msa
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scale_mlp = 1.0 + scale_mlp
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attn_out = self.attention(
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self.attention_norm1(x) * scale_msa,
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attn_mask=attn_mask,
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cos=cos,
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sin=sin,
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)
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x = x + gate_msa * self.attention_norm2(attn_out)
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x = x + gate_mlp * self.ffn_norm2(
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self.feed_forward(self.ffn_norm1(x) * scale_mlp)
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)
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return x
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def _sinusoidal_embedding(
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t: torch.Tensor, dim: int, scale: float = 1e4
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) -> torch.Tensor:
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t = t.to(torch.float32)
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half = dim // 2
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freq = math.log(scale) / (half - 1)
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freq = torch.exp(torch.arange(half, dtype=torch.float32, device=t.device) * -freq)
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emb = t.unsqueeze(-1) * freq
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emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
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if dim % 2 == 1:
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emb = F.pad(emb, (0, 1))
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return emb
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class Ideogram4EmbedScalar(nn.Module):
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def __init__(self, dim: int, input_range: tuple[float, float]) -> None:
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super().__init__()
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self.dim = dim
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self.range_min, self.range_max = input_range
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assert self.range_max > self.range_min
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self.mlp_in = nn.Linear(dim, dim, bias=True)
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self.mlp_out = nn.Linear(dim, dim, bias=True)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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# x is shape (..., 1) or (...,) holding a scalar per token.
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x = x.to(torch.float32)
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scaled = 1e4 * (x - self.range_min) / (self.range_max - self.range_min)
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emb = _sinusoidal_embedding(scaled, self.dim)
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emb = emb.to(self.mlp_in.weight.dtype)
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emb = F.silu(self.mlp_in(emb))
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return self.mlp_out(emb)
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class Ideogram4FinalLayer(nn.Module):
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def __init__(self, hidden_size: int, out_channels: int, adanln_dim: int) -> None:
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super().__init__()
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self.norm_final = nn.LayerNorm(hidden_size, eps=1e-6, elementwise_affine=False)
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self.linear = nn.Linear(hidden_size, out_channels, bias=True)
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self.adaln_modulation = nn.Linear(adanln_dim, hidden_size, bias=True)
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def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
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scale = 1.0 + self.adaln_modulation(F.silu(c))
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return self.linear(self.norm_final(x) * scale)
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class Ideogram4Transformer2DModel(nn.Module):
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"""Ideogram 4 flow-matching transformer."""
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def __init__(self, config: Ideogram4Config) -> None:
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super().__init__()
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self.config = config
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self.gradient_checkpointing = False
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head_dim = config.emb_dim // config.num_heads
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self.input_proj = nn.Linear(config.in_channels, config.emb_dim, bias=True)
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self.llm_cond_norm = Ideogram4RMSNorm(config.llm_features_dim, eps=1e-6)
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self.llm_cond_proj = nn.Linear(
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config.llm_features_dim, config.emb_dim, bias=True
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)
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self.t_embedding = Ideogram4EmbedScalar(config.emb_dim, input_range=(0.0, 1.0))
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self.adaln_proj = nn.Linear(config.emb_dim, config.adanln_dim, bias=True)
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self.embed_image_indicator = nn.Embedding(2, config.emb_dim)
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self.rotary_emb = Ideogram4MRoPE(
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head_dim=head_dim,
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base=config.rope_theta,
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mrope_section=config.mrope_section,
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)
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self.layers = nn.ModuleList(
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[
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Ideogram4TransformerBlock(
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hidden_size=config.emb_dim,
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intermediate_size=config.intermediate_size,
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num_heads=config.num_heads,
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norm_eps=config.norm_eps,
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adanln_dim=config.adanln_dim,
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)
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for _ in range(config.num_layers)
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]
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)
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self.final_layer = Ideogram4FinalLayer(
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hidden_size=config.emb_dim,
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out_channels=config.in_channels,
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adanln_dim=config.adanln_dim,
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)
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@property
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def device(self) -> torch.device:
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return next(self.parameters()).device
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@property
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def dtype(self) -> torch.dtype:
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return next(self.parameters()).dtype
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def enable_gradient_checkpointing(self):
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self.gradient_checkpointing = True
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def disable_gradient_checkpointing(self):
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self.gradient_checkpointing = False
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def forward(
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self,
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*,
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llm_features: torch.Tensor,
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x: torch.Tensor,
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t: torch.Tensor,
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position_ids: torch.Tensor,
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segment_ids: torch.Tensor,
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indicator: torch.Tensor,
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) -> torch.Tensor:
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"""Velocity prediction.
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Args:
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llm_features: (B, L, llm_features_dim) Qwen3-VL conditioning features.
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x: (B, L, in_channels) noise tokens.
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t: (B,) or (B, L) flow-matching time in [0, 1].
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position_ids: (B, L, 3) (t, h, w) positions for MRoPE.
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segment_ids: (B, L) sample id within a packed batch.
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indicator: (B, L) per-token role: LLM_TOKEN_INDICATOR or OUTPUT_IMAGE_INDICATOR.
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Returns:
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(B, L, in_channels) velocity prediction in float32. Only the positions
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with ``indicator == OUTPUT_IMAGE_INDICATOR`` are meaningful.
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"""
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batch_size, seq_len, in_channels = x.shape
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assert in_channels == self.config.in_channels
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param_dtype = self.input_proj.weight.dtype
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x = x.to(param_dtype)
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t = t.to(param_dtype)
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llm_features = llm_features.to(param_dtype)
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indicator = indicator.to(torch.long)
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llm_token_mask = (indicator == LLM_TOKEN_INDICATOR).to(x.dtype).unsqueeze(-1)
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output_image_mask = (
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(indicator == OUTPUT_IMAGE_INDICATOR).to(x.dtype).unsqueeze(-1)
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)
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llm_features = llm_features * llm_token_mask
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x = x * output_image_mask
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x = self.input_proj(x) * output_image_mask
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# Keep shape (B, 1, ...) when t is per-sample so downstream adaln_modulation
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# projections don't pay for L identical copies.
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t_cond = self.t_embedding(t)
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if t.dim() == 1:
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t_cond = t_cond.unsqueeze(1)
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adaln_input = F.silu(self.adaln_proj(t_cond))
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llm_features = self.llm_cond_norm(llm_features)
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llm_features = self.llm_cond_proj(llm_features) * llm_token_mask
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h = x + llm_features
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image_indicator_embedding = self.embed_image_indicator(
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(indicator == OUTPUT_IMAGE_INDICATOR).to(torch.long)
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)
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h = h + image_indicator_embedding
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cos, sin = self.rotary_emb(position_ids)
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cos = cos.to(h.dtype)
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sin = sin.to(h.dtype)
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# Block-diagonal mask from segment ids: (B, 1, L, L), True = attend.
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attn_mask = (segment_ids.unsqueeze(2) == segment_ids.unsqueeze(1)).unsqueeze(1)
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for layer in self.layers:
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if self.gradient_checkpointing and torch.is_grad_enabled():
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h = checkpoint(
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layer, h, attn_mask, cos, sin, adaln_input, use_reentrant=False
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)
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else:
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h = layer(h, attn_mask, cos, sin, adaln_input)
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out = self.final_layer(h, c=adaln_input)
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return out.to(torch.float32)
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