vendor/mage_flow/models/modules/mage_layers.py

import math
from typing import Any

import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusers.models.attention import FeedForward
from diffusers.models.embeddings import TimestepEmbedding
from diffusers.models.normalization import RMSNorm
from ._attn_backend import flash_attn_varlen_func
from torch import Tensor
from torch._dynamo import allow_in_graph as maybe_allow_in_graph


def apply_rotary_emb_mageflow(x: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
    """Apply complex rotary embeddings to `x` ([B, S, H, D]) using `freqs_cis`
    (the MageFlowEmbedRope 2D multi-scale RoPE, adjacent-pair complex convention)."""
    x_rotated = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
    freqs_cis = freqs_cis.unsqueeze(1)
    x_out = torch.view_as_real(x_rotated * freqs_cis).flatten(-2)
    return x_out.type_as(x)


def get_timestep_embedding(
    timesteps: torch.Tensor,
    embedding_dim: int,
    flip_sin_to_cos: bool = False,
    downscale_freq_shift: float = 1,
    scale: float = 1,
    max_period: int = 10000,
) -> torch.Tensor:
    """Sinusoidal timestep embeddings (DDPM convention).

    NOTE: kept vendored (not diffusers') because the frequency table is
    downcast to ``timesteps.dtype`` (bf16) here — the model was trained with
    this exact bf16 rounding, so diffusers' fp32 variant produces a slightly
    different embedding and degrades outputs.
    """
    assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"

    half_dim = embedding_dim // 2
    exponent = -math.log(max_period) * torch.arange(start=0, end=half_dim, dtype=torch.float32, device=timesteps.device)
    exponent = exponent / (half_dim - downscale_freq_shift)

    emb = torch.exp(exponent).to(timesteps.dtype)
    emb = timesteps[:, None].float() * emb[None, :]

    # scale embeddings
    emb = scale * emb

    # concat sine and cosine embeddings
    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)

    # flip sine and cosine embeddings
    if flip_sin_to_cos:
        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)

    # zero pad
    if embedding_dim % 2 == 1:
        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
    return emb


class Timesteps(nn.Module):
    def __init__(
        self,
        num_channels: int,
        flip_sin_to_cos: bool,
        downscale_freq_shift: float,
        scale: int = 1,
    ):
        super().__init__()
        self.num_channels = num_channels
        self.flip_sin_to_cos = flip_sin_to_cos
        self.downscale_freq_shift = downscale_freq_shift
        self.scale = scale

    def forward(self, timesteps: torch.Tensor) -> torch.Tensor:
        t_emb = get_timestep_embedding(
            timesteps,
            self.num_channels,
            flip_sin_to_cos=self.flip_sin_to_cos,
            downscale_freq_shift=self.downscale_freq_shift,
            scale=self.scale,
        )
        return t_emb


class MageFlowTimestepProjEmbeddings(nn.Module):
    def __init__(self, embedding_dim):
        super().__init__()

        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0, scale=1000)
        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)

    def forward(self, timestep, hidden_states):
        timesteps_proj = self.time_proj(timestep)
        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_states.dtype))  # (N, D)

        conditioning = timesteps_emb

        return conditioning


class MageFlowEmbedRope(nn.Module):
    def __init__(self, theta: int, axes_dim: list[int], scale_rope=False):
        super().__init__()
        self.theta = theta
        self.axes_dim = axes_dim
        pos_index = torch.arange(4096)
        neg_index = torch.arange(4096).flip(0) * -1 - 1
        self.pos_freqs = torch.cat(
            [
                self.rope_params(pos_index, self.axes_dim[0], self.theta),
                self.rope_params(pos_index, self.axes_dim[1], self.theta),
                self.rope_params(pos_index, self.axes_dim[2], self.theta),
            ],
            dim=1,
        )
        self.neg_freqs = torch.cat(
            [
                self.rope_params(neg_index, self.axes_dim[0], self.theta),
                self.rope_params(neg_index, self.axes_dim[1], self.theta),
                self.rope_params(neg_index, self.axes_dim[2], self.theta),
            ],
            dim=1,
        )

        # DO NOT USING REGISTER BUFFER HERE, IT WILL CAUSE COMPLEX NUMBERS LOSE ITS IMAGINARY PART
        self.scale_rope = scale_rope
        self.video_freq_cache = {}

    def rope_params(self, index, dim, theta=10000):
        """
        Args:
            index: [0, 1, 2, 3] 1D Tensor representing the position index of the token
        """
        assert dim % 2 == 0
        freqs = torch.outer(
            index,
            1.0 / torch.pow(theta, torch.arange(0, dim, 2).to(torch.float32).div(dim)),
        )
        freqs = torch.polar(torch.ones_like(freqs), freqs)
        return freqs

    def forward(
        self,
        video_fhw: tuple[int, int, int] | list[tuple[int, int, int]],
        device: torch.device,
        max_img_len: int = None,
    ) -> torch.Tensor:
        """Compute the vision RoPE frequencies (`vid_freqs`) for the packed image
        tokens. Text tokens are NOT rotated, so no text RoPE is computed.

        Args:
            video_fhw (`Tuple[int, int, int]` or `List[Tuple[int, int, int]]`):
                A list of 3 integers [frame, height, width] representing the shape of the video.
            device: (`torch.device`):
                The device on which to perform the RoPE computation.
        """
        if self.pos_freqs.device != device:
            self.pos_freqs = self.pos_freqs.to(device)
            self.neg_freqs = self.neg_freqs.to(device)

        if isinstance(video_fhw, list):
            video_fhw = video_fhw[0]
        if not isinstance(video_fhw, list):
            video_fhw = [video_fhw]

        vid_freqs = []
        for idx, fhw in enumerate(video_fhw):
            frame, height, width = fhw
            # RoPE frequencies are cached manually
            key = (frame, height, width, idx)
            if key not in self.video_freq_cache:
                self.video_freq_cache[key] = self._compute_video_freqs(frame, height, width, idx)
            vid_freqs.append(self.video_freq_cache[key].to(device))

        vid_freqs = torch.cat(vid_freqs, dim=0)

        if max_img_len is not None and vid_freqs.shape[0] < max_img_len:
            pad_len = max_img_len - vid_freqs.shape[0]
            vid_freqs = torch.nn.functional.pad(vid_freqs, (0, 0, 0, pad_len))

        return vid_freqs

    def _compute_video_freqs(self, frame: int, height: int, width: int, idx: int = 0) -> torch.Tensor:
        seq_lens = frame * height * width
        freqs_pos = self.pos_freqs.split([x // 2 for x in self.axes_dim], dim=1)
        freqs_neg = self.neg_freqs.split([x // 2 for x in self.axes_dim], dim=1)

        freqs_frame = freqs_pos[0][idx : idx + frame].view(frame, 1, 1, -1).expand(frame, height, width, -1)
        if self.scale_rope:
            freqs_height = torch.cat(
                [freqs_neg[1][-(height - height // 2) :], freqs_pos[1][: height // 2]],
                dim=0,
            )
            freqs_height = freqs_height.view(1, height, 1, -1).expand(frame, height, width, -1)
            freqs_width = torch.cat(
                [freqs_neg[2][-(width - width // 2) :], freqs_pos[2][: width // 2]],
                dim=0,
            )
            freqs_width = freqs_width.view(1, 1, width, -1).expand(frame, height, width, -1)
        else:
            freqs_height = freqs_pos[1][:height].view(1, height, 1, -1).expand(frame, height, width, -1)
            freqs_width = freqs_pos[2][:width].view(1, 1, width, -1).expand(frame, height, width, -1)

        freqs = torch.cat([freqs_frame, freqs_height, freqs_width], dim=-1).reshape(seq_lens, -1)
        return freqs.clone().contiguous()


class Attention(nn.Module):
    def __init__(
        self,
        query_dim: int,
        cross_attention_dim: int | None = None,
        heads: int = 8,
        kv_heads: int | None = None,
        dim_head: int = 64,
        dropout: float = 0.0,
        bias: bool = False,
        scale_qk: bool = True,
        added_kv_proj_dim: int | None = None,
        added_proj_bias: bool | None = True,
        out_bias: bool = True,
        eps: float = 1e-5,
        processor=None,
        out_dim: int = None,
        out_context_dim: int = None,
        elementwise_affine: bool = True,
    ):
        super().__init__()
        # logger.info(f"processor: {processor}")

        self.inner_dim = out_dim if out_dim is not None else dim_head * heads
        self.inner_kv_dim = self.inner_dim if kv_heads is None else dim_head * kv_heads
        self.query_dim = query_dim
        self.use_bias = bias
        self.is_cross_attention = cross_attention_dim is not None
        self.cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim
        self.fused_projections = False
        self.out_dim = out_dim if out_dim is not None else query_dim
        self.out_context_dim = out_context_dim if out_context_dim is not None else query_dim

        self.scale_qk = scale_qk
        self.scale = dim_head**-0.5 if self.scale_qk else 1.0

        self.heads = out_dim // dim_head if out_dim is not None else heads
        # for slice_size > 0 the attention score computation
        # is split across the batch axis to save memory
        # You can set_slice_size with `set_attention_slice`
        self.sliceable_head_dim = heads

        self.added_kv_proj_dim = added_kv_proj_dim

        # qk_norm is always "rms_norm" for MageFlow.
        self.norm_q = RMSNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
        self.norm_k = RMSNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)

        self.to_q = nn.Linear(query_dim, self.inner_dim, bias=bias)
        self.to_k = nn.Linear(self.cross_attention_dim, self.inner_kv_dim, bias=bias)
        self.to_v = nn.Linear(self.cross_attention_dim, self.inner_kv_dim, bias=bias)

        self.added_proj_bias = added_proj_bias
        if self.added_kv_proj_dim is not None:
            self.add_k_proj = nn.Linear(added_kv_proj_dim, self.inner_kv_dim, bias=added_proj_bias)
            self.add_v_proj = nn.Linear(added_kv_proj_dim, self.inner_kv_dim, bias=added_proj_bias)
            self.add_q_proj = nn.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias)
            self.norm_added_q = RMSNorm(dim_head, eps=eps)
            self.norm_added_k = RMSNorm(dim_head, eps=eps)
        else:
            self.add_q_proj = None
            self.add_k_proj = None
            self.add_v_proj = None
            self.norm_added_q = None
            self.norm_added_k = None

        self.to_out = nn.ModuleList([])
        self.to_out.append(nn.Linear(self.inner_dim, self.out_dim, bias=out_bias))
        self.to_out.append(nn.Dropout(dropout))

        self.to_add_out = nn.Linear(self.inner_dim, self.out_context_dim, bias=out_bias)

        self.set_processor(processor)

    def set_processor(self, processor) -> None:
        self.processor = processor

    def get_processor(self):
        return self.processor

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        txt_cu_lens: torch.Tensor | None = None,
        img_cu_lens: torch.Tensor | None = None,
        # ms_pe: tuple[torch.FloatTensor, torch.FloatTensor] | None = None,
        # pe: torch.FloatTensor | None = None,
        # freqs_cos: torch.Tensor | None = None,
        # freqs_sin: torch.Tensor | None = None,
        image_rotary_emb: torch.Tensor | None = None,
        **attention_kwargs,
    ) -> torch.Tensor:
        r"""
        The forward method of the `Attention` class.

        Args:
            hidden_states (`torch.Tensor`):
                The hidden states of the query.
            encoder_hidden_states (`torch.Tensor`, *optional*):
                The hidden states of the encoder.
            attention_mask (`torch.Tensor`, *optional*):
                The attention mask to use. If `None`, no mask is applied.
            **attention_kwargs:
                Additional keyword arguments to pass along to the attention.

        Returns:
            `torch.Tensor`: The output of the attention layer.
        """
        # The `Attention` class can call different attention processors / attention functions
        # here we simply pass along all tensors to the selected processor class
        # For standard processors that are defined here, `**attention_kwargs` is empty

        return self.processor(
            self,
            hidden_states,
            attention_mask=attention_mask,
            txt_cu_lens=txt_cu_lens,
            img_cu_lens=img_cu_lens,
            image_rotary_emb=image_rotary_emb,
            **attention_kwargs,
        )


class MageDoubleStreamAttnProcessor:
    """
    Attention processor for the Mage double-stream architecture, matching DoubleStreamLayerMegatron logic. This processor
    implements joint attention computation where text and image streams are processed together.
    """

    _attention_backend = None
    _parallel_config = None

    def __init__(self):
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError(
                "MageDoubleStreamAttnProcessor requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
            )

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,  # Image stream
        img_cu_lens: torch.LongTensor,
        attention_mask: torch.FloatTensor | None = None,
        encoder_hidden_states: torch.FloatTensor = None,  # Text stream
        txt_cu_lens: torch.LongTensor = None,
        image_rotary_emb: torch.Tensor | None = None,
        **kwargs,
    ) -> torch.FloatTensor:
        if encoder_hidden_states is None:
            raise ValueError("MageDoubleStreamAttnProcessor requires encoder_hidden_states (text stream)")

        # seq_txt = encoder_hidden_states.shape[1]

        # logger.info(f"hidden_states: {hidden_states.shape}")
        # logger.info(f"encoder_hidden_states: {encoder_hidden_states.shape}")

        # Compute QKV for image stream (sample projections). Experimental
        # runtimes may provide one fused projection; the ordinary checkpoint
        # path continues to use the three standard Linear modules.
        if getattr(attn, "to_qkv", None) is not None:
            img_query, img_key, img_value = attn.to_qkv(hidden_states).chunk(3, dim=-1)
        else:
            img_query = attn.to_q(hidden_states)
            img_key = attn.to_k(hidden_states)
            img_value = attn.to_v(hidden_states)

        # Compute QKV for text stream (context projections).
        if getattr(attn, "add_qkv_proj", None) is not None:
            txt_query, txt_key, txt_value = attn.add_qkv_proj(encoder_hidden_states).chunk(3, dim=-1)
        else:
            txt_query = attn.add_q_proj(encoder_hidden_states)
            txt_key = attn.add_k_proj(encoder_hidden_states)
            txt_value = attn.add_v_proj(encoder_hidden_states)

        # Reshape for multi-head attention
        img_query = img_query.unflatten(-1, (attn.heads, -1))
        img_key = img_key.unflatten(-1, (attn.heads, -1))
        img_value = img_value.unflatten(-1, (attn.heads, -1))

        txt_query = txt_query.unflatten(-1, (attn.heads, -1))
        txt_key = txt_key.unflatten(-1, (attn.heads, -1))
        txt_value = txt_value.unflatten(-1, (attn.heads, -1))

        # logger.info(
        #     f"img_query shape: {img_query.shape}, img_key shape: {img_key.shape}, img_value shape: {img_value.shape}"
        # )
        # logger.info(
        #     f"txt_query shape: {txt_query.shape}, txt_key shape: {txt_key.shape}, txt_value shape: {txt_value.shape}"
        # )

        if img_query.ndim == 4:
            img_query = img_query.flatten(0, 1)
            img_key = img_key.flatten(0, 1)
            img_value = img_value.flatten(0, 1)

        if txt_query.ndim == 4:
            txt_query = txt_query.flatten(0, 1)
            txt_key = txt_key.flatten(0, 1)
            txt_value = txt_value.flatten(0, 1)

        # Apply QK normalization
        if attn.norm_q is not None:
            img_query = attn.norm_q(img_query)
        if attn.norm_k is not None:
            img_key = attn.norm_k(img_key)
        if attn.norm_added_q is not None:
            txt_query = attn.norm_added_q(txt_query)
        if attn.norm_added_k is not None:
            txt_key = attn.norm_added_k(txt_key)

        # logger.info(f"txt_query shape: {txt_query.shape}, txt_key shape: {txt_key.shape}")
        # logger.info(f"freqs_cos shape: {freqs_cos.shape}, freqs_sin shape: {freqs_sin.shape}")

        # Apply 2D multi-scale RoPE (MageFlowEmbedRope) to image tokens
        img_freqs = image_rotary_emb
        img_query = apply_rotary_emb_mageflow(img_query, img_freqs)
        img_key = apply_rotary_emb_mageflow(img_key, img_freqs)
        # Concatenate for joint attention
        # Order: [text, image]
        # joint_query = torch.cat([txt_query, img_query], dim=1)
        # joint_key = torch.cat([txt_key, img_key], dim=1)
        # joint_value = torch.cat([txt_value, img_value], dim=1)

        # Calculate lengths
        img_lens = img_cu_lens[1:] - img_cu_lens[:-1]
        txt_lens = txt_cu_lens[1:] - txt_cu_lens[:-1]

        # Calculate joint cu_seqlens
        joint_lens = txt_lens + img_lens
        joint_cu_lens = torch.cat(
            [
                torch.zeros(1, dtype=torch.int32, device=joint_lens.device),
                torch.cumsum(joint_lens, dim=0, dtype=torch.int32),
            ],
            dim=0,
        )

        # logger.info(f"txt_lens: {txt_lens}, img_lens: {img_lens}")
        # logger.info(f"joint_lens: {joint_lens}, joint_cu_lens: {joint_cu_lens}")

        device = joint_lens.device
        batch_size = len(txt_lens)
        sample_indices = torch.arange(batch_size, device=device)

        txt_sample_ids = torch.repeat_interleave(sample_indices, txt_lens)
        img_sample_ids = torch.repeat_interleave(sample_indices, img_lens)

        txt_intra_pos = torch.arange(txt_query.shape[0], device=device) - txt_cu_lens[txt_sample_ids]
        img_intra_pos = torch.arange(img_query.shape[0], device=device) - img_cu_lens[img_sample_ids]

        txt_dest_indices = joint_cu_lens[txt_sample_ids] + txt_intra_pos
        img_dest_indices = joint_cu_lens[img_sample_ids] + txt_lens[img_sample_ids] + img_intra_pos

        total_tokens = joint_cu_lens[-1]
        joint_query = torch.empty((total_tokens, *txt_query.shape[1:]), dtype=txt_query.dtype, device=device)
        joint_key = torch.empty((total_tokens, *txt_key.shape[1:]), dtype=txt_key.dtype, device=device)
        joint_value = torch.empty((total_tokens, *txt_value.shape[1:]), dtype=txt_value.dtype, device=device)

        # logger.info(f"joint_query shape: {joint_query.shape}")
        # logger.info(f"joint_key shape: {joint_key.shape}")
        # logger.info(f"joint_value shape: {joint_value.shape}")
        # logger.info(f"txt_dest_indices shape: {txt_dest_indices.shape}")
        # logger.info(f"img_dest_indices shape: {img_dest_indices.shape}")

        joint_query[txt_dest_indices] = txt_query
        joint_query[img_dest_indices] = img_query

        joint_key[txt_dest_indices] = txt_key
        joint_key[img_dest_indices] = img_key

        joint_value[txt_dest_indices] = txt_value
        joint_value[img_dest_indices] = img_value

        max_seqlen = joint_lens.max().item()
        joint_attn_output = flash_attn_varlen_func(
            joint_query,
            joint_key,
            joint_value,
            cu_seqlens_q=joint_cu_lens,
            cu_seqlens_k=joint_cu_lens,
            max_seqlen_q=max_seqlen,
            max_seqlen_k=max_seqlen,
            dropout_p=0.0,
            softmax_scale=None,
            causal=False,
        )

        txt_attn_output = joint_attn_output[txt_dest_indices]
        img_attn_output = joint_attn_output[img_dest_indices]

        img_attn_output = img_attn_output.flatten(1, 2)  # (N, H, D) -> (N, H*D)
        img_attn_output = img_attn_output.to(joint_query.dtype)

        txt_attn_output = txt_attn_output.flatten(1, 2)  # (N, H, D) -> (N, H*D)
        txt_attn_output = txt_attn_output.to(joint_query.dtype)

        img_attn_output = attn.to_out[0](img_attn_output)
        if len(attn.to_out) > 1:
            img_attn_output = attn.to_out[1](img_attn_output)  # dropout

        txt_attn_output = attn.to_add_out(txt_attn_output)
        txt_attn_output = txt_attn_output.view(
            encoder_hidden_states.shape[0], encoder_hidden_states.shape[1], txt_attn_output.shape[-1]
        )

        return img_attn_output, txt_attn_output


@maybe_allow_in_graph
class MageFlowTransformerBlock(nn.Module):
    def __init__(
        self,
        dim: int,
        num_attention_heads: int,
        attention_head_dim: int,
        eps: float = 1e-6,
    ):
        super().__init__()

        self.dim = dim
        self.num_attention_heads = num_attention_heads
        self.attention_head_dim = attention_head_dim

        # Image processing modules
        self.img_mod = nn.Sequential(
            nn.SiLU(),
            nn.Linear(dim, 6 * dim, bias=True),  # For scale, shift, gate for norm1 and norm2
        )
        self.img_norm1 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
        self.attn = Attention(
            query_dim=dim,
            cross_attention_dim=None,  # Enable cross attention for joint computation
            added_kv_proj_dim=dim,  # Enable added KV projections for text stream
            dim_head=attention_head_dim,
            heads=num_attention_heads,
            out_dim=dim,
            bias=True,
            processor=MageDoubleStreamAttnProcessor(),
            eps=eps,
        )
        self.img_norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
        self.img_mlp = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")

        # Text processing modules
        self.txt_mod = nn.Sequential(
            nn.SiLU(),
            nn.Linear(dim, 6 * dim, bias=True),  # For scale, shift, gate for norm1 and norm2
        )
        self.txt_norm1 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
        # Text doesn't need separate attention - it's handled by img_attn joint computation
        self.txt_norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=eps)
        self.txt_mlp = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")

    def _modulate(self, x, mod_params, cu_lens=None, seq_lens=None):
        """Apply modulation to input tensor"""
        shift, scale, gate = mod_params.chunk(3, dim=-1)
        if cu_lens is not None:
            assert x.shape[0] == 1, "x must be of shape (1, *) when cu_lens is not None"
            x_flattened = x.view(-1, x.shape[-1])
            lengths = cu_lens[1:] - cu_lens[:-1]
            shift_t = shift.repeat_interleave(lengths, dim=0)
            scale_t = scale.repeat_interleave(lengths, dim=0)
            gate_t = gate.repeat_interleave(lengths, dim=0)

            x_flattened = x_flattened * (1 + scale_t) + shift_t
            x = x_flattened.view(x.shape)
            return x, gate_t
        else:
            return x * (1 + scale) + shift, gate

    def forward(
        self,
        hidden_states: torch.Tensor,
        encoder_hidden_states: torch.Tensor,
        # encoder_hidden_states_mask: torch.Tensor,
        temb: torch.Tensor | tuple[torch.Tensor, torch.Tensor],
        image_rotary_emb: torch.Tensor,
        # freqs_cos: torch.Tensor,
        # freqs_sin: torch.Tensor,
        txt_cu_lens: torch.Tensor,
        img_cu_lens: torch.Tensor,
        joint_attention_kwargs: dict[str, Any] | None = None,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        # Get modulation parameters for both streams
        # if isinstance(temb, tuple):
        #     temb_img, temb_txt = temb
        # else:
        #     temb_img = temb_txt = temb

        img_mod_params = self.img_mod(temb)  # [B, 6*dim]
        txt_mod_params = self.txt_mod(temb)  # [B, 6*dim]

        # logger.info(f"img_mod_params: {img_mod_params.shape}, txt_mod_params: {txt_mod_params.shape}")

        # if img_cu_lens is not None and txt_cu_lens is not None and hidden_states.ndim == 2:
        #     img_lens = img_cu_lens[1:] - img_cu_lens[:-1]
        #     txt_lens = txt_cu_lens[1:] - txt_cu_lens[:-1]
        #     img_mod_params = img_mod_params.repeat_interleave(img_lens, dim=0)
        #     txt_mod_params = txt_mod_params.repeat_interleave(txt_lens, dim=0)

        # Split modulation parameters for norm1 and norm2
        img_mod1, img_mod2 = img_mod_params.chunk(2, dim=-1)  # Each [B, 3*dim]
        txt_mod1, txt_mod2 = txt_mod_params.chunk(2, dim=-1)  # Each [B, 3*dim]

        # Process image stream - norm1 + modulation
        img_normed = self.img_norm1(hidden_states)
        img_modulated, img_gate1 = self._modulate(img_normed, img_mod1, cu_lens=img_cu_lens)

        # Process text stream - norm1 + modulation
        txt_normed = self.txt_norm1(encoder_hidden_states)
        txt_modulated, txt_gate1 = self._modulate(txt_normed, txt_mod1, cu_lens=txt_cu_lens)

        # Use MageDoubleStreamAttnProcessor for joint attention computation
        # This directly implements the DoubleStreamLayerMegatron logic:
        # 1. Computes QKV for both streams
        # 2. Applies QK normalization and RoPE
        # 3. Concatenates and runs joint attention
        # 4. Splits results back to separate streams
        joint_attention_kwargs = joint_attention_kwargs or {}
        # logger.info(f"img_modulated: {img_modulated}")
        # logger.info(f"txt_modulated: {txt_modulated}")
        attn_output = self.attn(
            hidden_states=img_modulated,  # Image stream (will be processed as "sample")
            encoder_hidden_states=txt_modulated,  # Text stream (will be processed as "context")
            # encoder_hidden_states_mask=encoder_hidden_states_mask,
            image_rotary_emb=image_rotary_emb,
            txt_cu_lens=txt_cu_lens,
            img_cu_lens=img_cu_lens,
            # freqs_cos=freqs_cos,
            # freqs_sin=freqs_sin,
            **joint_attention_kwargs,
        )
        # logger.info(f"attn_output: {attn_output}")

        # MageDoubleStreamAttnProcessor returns (img_output, txt_output) when encoder_hidden_states is provided
        img_attn_output, txt_attn_output = attn_output

        # Apply attention gates and add residual (like in Megatron)
        hidden_states = hidden_states + img_gate1 * img_attn_output
        encoder_hidden_states = encoder_hidden_states + txt_gate1 * txt_attn_output

        # Process image stream - norm2 + MLP
        img_normed2 = self.img_norm2(hidden_states)
        img_modulated2, img_gate2 = self._modulate(img_normed2, img_mod2, cu_lens=img_cu_lens)
        img_mlp_output = self.img_mlp(img_modulated2)
        hidden_states = hidden_states + img_gate2 * img_mlp_output

        # Process text stream - norm2 + MLP
        txt_normed2 = self.txt_norm2(encoder_hidden_states)
        txt_modulated2, txt_gate2 = self._modulate(txt_normed2, txt_mod2, cu_lens=txt_cu_lens)
        txt_mlp_output = self.txt_mlp(txt_modulated2)
        encoder_hidden_states = encoder_hidden_states + txt_gate2 * txt_mlp_output

        # Clip to prevent overflow for fp16
        if encoder_hidden_states.dtype == torch.float16:
            encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504)
        if hidden_states.dtype == torch.float16:
            hidden_states = hidden_states.clip(-65504, 65504)

        return encoder_hidden_states, hidden_states


class AdaLayerNormContinuous(nn.Module):
    r"""
    Adaptive normalization layer with a norm layer (layer_norm or rms_norm).

    Args:
        embedding_dim (`int`): Embedding dimension to use during projection.
        conditioning_embedding_dim (`int`): Dimension of the input condition.
        elementwise_affine (`bool`, defaults to `True`):
            Boolean flag to denote if affine transformation should be applied.
        eps (`float`, defaults to 1e-5): Epsilon factor.
        bias (`bias`, defaults to `True`): Boolean flag to denote if bias should be use.
        norm_type (`str`, defaults to `"layer_norm"`):
            Normalization layer to use. Values supported: "layer_norm", "rms_norm".
    """

    def __init__(
        self,
        embedding_dim: int,
        conditioning_embedding_dim: int,
        # NOTE: It is a bit weird that the norm layer can be configured to have scale and shift parameters
        # because the output is immediately scaled and shifted by the projected conditioning embeddings.
        # Note that AdaLayerNorm does not let the norm layer have scale and shift parameters.
        # However, this is how it was implemented in the original code, and it's rather likely you should
        # set `elementwise_affine` to False.
        elementwise_affine=True,
        eps=1e-5,
        bias=True,
        norm_type="layer_norm",
    ):
        super().__init__()
        self.silu = nn.SiLU()
        self.linear = nn.Linear(conditioning_embedding_dim, embedding_dim * 2, bias=bias)
        if norm_type == "layer_norm":
            self.norm = nn.LayerNorm(embedding_dim, eps, elementwise_affine, bias)
        elif norm_type == "rms_norm":
            self.norm = RMSNorm(embedding_dim, eps, elementwise_affine)
        else:
            raise ValueError(f"unknown norm_type {norm_type}")

    def forward(
        self, x: torch.Tensor, conditioning_embedding: torch.Tensor,
        cu_seqlens: torch.Tensor | None = None, seq_lens: torch.Tensor | None = None,
    ) -> torch.Tensor:
        # convert back to the original dtype in case `conditioning_embedding`` is upcasted to float32 (needed for
        # hunyuanDiT)
        emb = self.linear(self.silu(conditioning_embedding).to(x.dtype))
        if cu_seqlens is None:
            scale, shift = torch.chunk(emb, 2, dim=-1)
            x = self.norm(x) * (1 + scale) + shift
        else:
            sample_lens = cu_seqlens[1:] - cu_seqlens[:-1]
            flattened_x = x.view(-1, x.shape[-1])
            scale, shift = torch.chunk(emb, 2, dim=-1)
            scale_t = torch.repeat_interleave(scale, sample_lens, dim=0)
            shift_t = torch.repeat_interleave(shift, sample_lens, dim=0)
            flattened_x = self.norm(flattened_x) * (1 + scale_t) + shift_t
            x = flattened_x.view(x.shape)
        return x