pyramid_dit/modeling_normalization.py

import numbers
from typing import Dict, Optional, Tuple

import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from diffusers.utils import is_torch_version


if is_torch_version(">=", "2.1.0"):
    LayerNorm = nn.LayerNorm
else:
    # Has optional bias parameter compared to torch layer norm
    # TODO: replace with torch layernorm once min required torch version >= 2.1
    class LayerNorm(nn.Module):
        def __init__(self, dim, eps: float = 1e-5, elementwise_affine: bool = True, bias: bool = True):
            super().__init__()

            self.eps = eps

            if isinstance(dim, numbers.Integral):
                dim = (dim,)

            self.dim = torch.Size(dim)

            if elementwise_affine:
                self.weight = nn.Parameter(torch.ones(dim))
                self.bias = nn.Parameter(torch.zeros(dim)) if bias else None
            else:
                self.weight = None
                self.bias = None

        def forward(self, input):
            return F.layer_norm(input, self.dim, self.weight, self.bias, self.eps)


class RMSNorm(nn.Module):
    def __init__(self, dim, eps: float, elementwise_affine: bool = True):
        super().__init__()

        self.eps = eps

        if isinstance(dim, numbers.Integral):
            dim = (dim,)

        self.dim = torch.Size(dim)

        if elementwise_affine:
            self.weight = nn.Parameter(torch.ones(dim))
        else:
            self.weight = None

    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.eps)

        if self.weight is not None:
            # convert into half-precision if necessary
            if self.weight.dtype in [torch.float16, torch.bfloat16]:
                hidden_states = hidden_states.to(self.weight.dtype)
            hidden_states = hidden_states * self.weight

        hidden_states = hidden_states.to(input_dtype)

        return hidden_states


class AdaLayerNormContinuous(nn.Module):
    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 = 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_with_pad(self, x: torch.Tensor, conditioning_embedding: torch.Tensor, hidden_length=None) -> torch.Tensor:
        assert hidden_length is not None
        
        emb = self.linear(self.silu(conditioning_embedding).to(x.dtype))
        batch_emb = torch.zeros_like(x).repeat(1, 1, 2)

        i_sum = 0
        num_stages = len(hidden_length)
        for i_p, length in enumerate(hidden_length):
            batch_emb[:, i_sum:i_sum+length] = emb[i_p::num_stages][:,None]
            i_sum += length

        batch_scale, batch_shift = torch.chunk(batch_emb, 2, dim=2)
        x = self.norm(x) * (1 + batch_scale) + batch_shift
        return x

    def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor, hidden_length=None) -> torch.Tensor:
        # convert back to the original dtype in case `conditioning_embedding`` is upcasted to float32 (needed for hunyuanDiT)
        if hidden_length is not None:
            return self.forward_with_pad(x, conditioning_embedding, hidden_length)
        emb = self.linear(self.silu(conditioning_embedding).to(x.dtype))
        scale, shift = torch.chunk(emb, 2, dim=1)
        x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
        return x


class AdaLayerNormZero(nn.Module):
    r"""
    Norm layer adaptive layer norm zero (adaLN-Zero).

    Parameters:
        embedding_dim (`int`): The size of each embedding vector.
        num_embeddings (`int`): The size of the embeddings dictionary.
    """

    def __init__(self, embedding_dim: int, num_embeddings: Optional[int] = None):
        super().__init__()
        if num_embeddings is not None:
            self.emb = CombinedTimestepLabelEmbeddings(num_embeddings, embedding_dim)
        else:
            self.emb = None

        self.silu = nn.SiLU()
        self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True)
        self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6)

    def forward_with_pad(
        self,
        x: torch.Tensor,
        timestep: Optional[torch.Tensor] = None,
        class_labels: Optional[torch.LongTensor] = None,
        hidden_dtype: Optional[torch.dtype] = None,
        emb: Optional[torch.Tensor] = None,
        hidden_length: Optional[torch.Tensor] = None,
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
        # hidden_length: [[20, 30], [30, 40], [50, 60]]
        # x: [bs, seq_len, dim]
        if self.emb is not None:
            emb = self.emb(timestep, class_labels, hidden_dtype=hidden_dtype)

        emb = self.linear(self.silu(emb))
        batch_emb = torch.zeros_like(x).repeat(1, 1, 6)
    
        i_sum = 0
        num_stages = len(hidden_length)
        for i_p, length in enumerate(hidden_length):
            batch_emb[:, i_sum:i_sum+length] = emb[i_p::num_stages][:,None]
            i_sum += length

        batch_shift_msa, batch_scale_msa, batch_gate_msa, batch_shift_mlp, batch_scale_mlp, batch_gate_mlp = batch_emb.chunk(6, dim=2)
        x = self.norm(x) * (1 + batch_scale_msa) + batch_shift_msa
        return x, batch_gate_msa, batch_shift_mlp, batch_scale_mlp, batch_gate_mlp

    def forward(
        self,
        x: torch.Tensor,
        timestep: Optional[torch.Tensor] = None,
        class_labels: Optional[torch.LongTensor] = None,
        hidden_dtype: Optional[torch.dtype] = None,
        emb: Optional[torch.Tensor] = None,
        hidden_length: Optional[torch.Tensor] = None,
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
        if hidden_length is not None:
            return self.forward_with_pad(x, timestep, class_labels, hidden_dtype, emb, hidden_length)
        if self.emb is not None:
            emb = self.emb(timestep, class_labels, hidden_dtype=hidden_dtype)
        emb = self.linear(self.silu(emb))
        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.chunk(6, dim=1)
        x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp