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# Transformer building blocks for the MiniMax H3 visual VAE ViT decoder.
import math
import os
import torch
import torch.nn as nn
from typing import Optional
from diffusers.utils import logging
from diffusers.utils.torch_utils import maybe_allow_in_graph
from .attention import Attention
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
def _env_flag(name, default="0"):
value = os.environ.get(name, default)
return str(value).strip().lower() in ("1", "true", "yes", "on")
def _env_optional_bool(name, default=""):
value = str(os.environ.get(name, default)).strip().lower()
if value in ("", "default", "auto", "none", "unset"):
return None
return value not in ("0", "false", "no", "off", "disabled")
def _vit_torch_compile_kwargs(prefix):
kwargs = {}
backend = os.environ.get(f"{prefix}_BACKEND", "inductor").strip()
mode = os.environ.get(f"{prefix}_MODE", "reduce-overhead").strip()
if backend and backend.lower() not in ("default", "none"):
kwargs["backend"] = backend
if mode and mode.lower() not in ("default", "none"):
kwargs["mode"] = mode
kwargs["fullgraph"] = _env_flag(f"{prefix}_FULLGRAPH", "0")
dynamic = _env_optional_bool(f"{prefix}_DYNAMIC")
if dynamic is not None:
kwargs["dynamic"] = dynamic
return kwargs
def _vit_norm_input(module, hidden_states):
if _env_flag("MINIMAX_H3_VAE_DECODER_VIT_FP32_NORM", "1"):
return hidden_states.float()
return hidden_states.to(getattr(module.weight, "dtype", hidden_states.dtype))
class FeedForward(nn.Module):
def __init__(
self,
dim: int,
dim_out: Optional[int] = None,
mult: int = 4,
activation_fn: str = "silu",
bias: bool = True,
use_gated: bool = True,
glu_balanced: bool = False,
):
super().__init__()
ratio = 2 / 3 if (use_gated and glu_balanced) else 1
inner_dim = round(dim * mult * ratio)
dim_out = dim_out if dim_out is not None else dim
self.use_gated = use_gated
if use_gated:
self.w1 = nn.Linear(dim, inner_dim * 2, bias=bias)
else:
self.w1 = nn.Linear(dim, inner_dim, bias=bias)
if activation_fn == "silu":
self.act_fn = nn.SiLU()
elif activation_fn == "gelu":
self.act_fn = nn.GELU()
elif activation_fn == "gelu-approximate":
self.act_fn = nn.GELU(approximate="tanh")
else:
raise ValueError(f"Unsupported activation function: {activation_fn}")
self.w2 = nn.Linear(inner_dim, dim_out, bias=bias)
self._compile_forward_enabled = _env_flag(
"MINIMAX_H3_VAE_DECODER_VIT_FF_TORCH_COMPILE", "0"
)
self._compile_forward_fatal = _env_flag(
"MINIMAX_H3_VAE_DECODER_VIT_FF_TORCH_COMPILE_FATAL", "0"
)
self._compiled_forward = None
def _forward_impl(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.w1(hidden_states)
if self.use_gated:
gate, hidden_states = hidden_states.chunk(2, dim=-1)
hidden_states = self.act_fn(gate) * hidden_states
else:
hidden_states = self.act_fn(hidden_states)
hidden_states = self.w2(hidden_states)
return hidden_states
def _get_forward_impl(self):
if not self._compile_forward_enabled:
return self._forward_impl
if self._compiled_forward is not None:
return self._compiled_forward
if not hasattr(torch, "compile"):
message = "torch.compile is unavailable; falling back to eager ViT FeedForward"
if self._compile_forward_fatal:
raise RuntimeError(message)
logger.warning(f"[ViTFeedForward] {message}")
self._compile_forward_enabled = False
return self._forward_impl
kwargs = _vit_torch_compile_kwargs("MINIMAX_H3_VAE_DECODER_VIT_FF_TORCH_COMPILE")
try:
self._compiled_forward = torch.compile(self._forward_impl, **kwargs)
logger.info(f"[ViTFeedForward] torch.compile enabled kwargs={kwargs}")
except Exception as exc:
if self._compile_forward_fatal:
raise
logger.warning(
f"[ViTFeedForward] torch.compile setup failed: {type(exc).__name__}: {exc}; "
"falling back to eager"
)
self._compile_forward_enabled = False
self._compiled_forward = None
return self._forward_impl
return self._compiled_forward
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
forward_impl = self._get_forward_impl()
try:
return forward_impl(hidden_states)
except Exception as exc:
if (
self._compile_forward_enabled
and self._compiled_forward is not None
and forward_impl is self._compiled_forward
and not self._compile_forward_fatal
):
logger.warning(
f"[ViTFeedForward] compiled forward failed: {type(exc).__name__}: {exc}; "
"disabling compile and retrying eager"
)
self._compile_forward_enabled = False
self._compiled_forward = None
return self._forward_impl(hidden_states)
raise
class RotaryEmbeddingND(nn.Module):
def __init__(self, dim, rotary_base=10000, n_dim=3, use_angle=False):
super().__init__()
self.dim = dim
self.n_dim = n_dim
if dim % (2 * n_dim) != 0:
raise ValueError(
f"head_dim {dim} must be divisible by 2 * n_dim {2 * n_dim}"
)
if use_angle:
self.angle_scale = 2.0 * math.pi
else:
self.angle_scale = 1.0
inv_freq = 1 / rotary_base ** torch.arange(
0, 1, 2 * n_dim / dim, dtype=torch.float32
)
self.register_buffer("inv_freq", inv_freq, persistent=False)
def forward(self, img_ids):
B, N, D = img_ids.shape
if D != self.n_dim:
raise ValueError(f"Expected {self.n_dim} dimensions, got {D}")
with torch.autocast("cuda", enabled=False):
angles = (
self.angle_scale
* img_ids[:, :, :, None]
* self.inv_freq.to(img_ids.device)[None, None, None, :]
)
angles = angles.flatten(2, 3)
angles = angles.tile(2)
angles = angles.unsqueeze(2)
cos = torch.cos(angles)
sin = torch.sin(angles)
return cos.to(dtype=img_ids.dtype), sin.to(dtype=img_ids.dtype)
@maybe_allow_in_graph
class TransformerBlock(nn.Module):
def __init__(
self,
heads: int,
dim_head: int,
embed_dim: Optional[int] = None,
ffn_glu_balanced: bool = False,
norm_type: str = "layer_norm",
norm_affine: bool = True,
qk_norm_type: str = "rms_norm",
qk_norm_affine: bool = False,
ffn_activation_fn: str = "silu",
ffn_use_gated: bool = True,
use_scale: bool = True,
bias: bool = True,
eps: float = 1e-5,
**kwargs,
):
super().__init__()
dim = embed_dim if embed_dim is not None else dim_head * heads
self.use_scale = use_scale
if norm_type == "layer_norm":
norm_class = nn.LayerNorm
elif norm_type == "rms_norm":
norm_class = nn.RMSNorm
else:
raise ValueError(f"unknown norm_type {norm_type}")
self.norm1 = norm_class(
dim,
elementwise_affine=norm_affine,
eps=eps,
)
self.attn = Attention(
heads=heads,
dim_head=dim_head,
embed_dim=dim,
qk_norm_type=qk_norm_type,
qk_norm_affine=qk_norm_affine,
bias=bias,
eps=eps,
**kwargs,
)
if use_scale:
self.scale1 = nn.Parameter(torch.zeros(dim))
self.norm2 = norm_class(
dim,
elementwise_affine=norm_affine,
eps=eps,
)
self.ff = FeedForward(
dim=dim,
activation_fn=ffn_activation_fn,
bias=bias,
use_gated=ffn_use_gated,
glu_balanced=ffn_glu_balanced,
)
if use_scale:
self.scale2 = nn.Parameter(torch.zeros(dim))
def forward(
self,
hidden_states: torch.FloatTensor,
rotary_pos_emb: Optional[torch.FloatTensor] = None,
pack_info: dict = {},
):
norm_hidden_states = self.norm1(_vit_norm_input(self.norm1, hidden_states)).to(hidden_states.dtype)
attn_output = self.attn(norm_hidden_states, rotary_pos_emb, pack_info)
if self.use_scale:
hidden_states = hidden_states + attn_output * self.scale1
else:
hidden_states = hidden_states + attn_output
norm_hidden_states = self.norm2(_vit_norm_input(self.norm2, hidden_states)).to(hidden_states.dtype)
ff_output = self.ff(norm_hidden_states)
if self.use_scale:
hidden_states = hidden_states + ff_output * self.scale2
else:
hidden_states = hidden_states + ff_output
return hidden_states
|