Delete eva_clip/flux/modules/layers.py

eva_clip/flux/modules/layers.py

@@ -1,253 +0,0 @@
-import math
-from dataclasses import dataclass
-
-import torch
-from einops import rearrange
-from torch import Tensor, nn
-
-from flux.math import attention, rope
-
-
-class EmbedND(nn.Module):
-    def __init__(self, dim: int, theta: int, axes_dim: list[int]):
-        super().__init__()
-        self.dim = dim
-        self.theta = theta
-        self.axes_dim = axes_dim
-
-    def forward(self, ids: Tensor) -> Tensor:
-        n_axes = ids.shape[-1]
-        emb = torch.cat(
-            [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
-            dim=-3,
-        )
-
-        return emb.unsqueeze(1)
-
-
-def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
-    """
-    Create sinusoidal timestep embeddings.
-    :param t: a 1-D Tensor of N indices, one per batch element.
-                      These may be fractional.
-    :param dim: the dimension of the output.
-    :param max_period: controls the minimum frequency of the embeddings.
-    :return: an (N, D) Tensor of positional embeddings.
-    """
-    t = time_factor * t
-    half = dim // 2
-    freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(
-        t.device
-    )
-
-    args = t[:, None].float() * freqs[None]
-    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-    if dim % 2:
-        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-    if torch.is_floating_point(t):
-        embedding = embedding.to(t)
-    return embedding
-
-
-class MLPEmbedder(nn.Module):
-    def __init__(self, in_dim: int, hidden_dim: int):
-        super().__init__()
-        self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
-        self.silu = nn.SiLU()
-        self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)
-
-    def forward(self, x: Tensor) -> Tensor:
-        return self.out_layer(self.silu(self.in_layer(x)))
-
-
-class RMSNorm(torch.nn.Module):
-    def __init__(self, dim: int):
-        super().__init__()
-        self.scale = nn.Parameter(torch.ones(dim))
-
-    def forward(self, x: Tensor):
-        x_dtype = x.dtype
-        x = x.float()
-        rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6)
-        return (x * rrms).to(dtype=x_dtype) * self.scale
-
-
-class QKNorm(torch.nn.Module):
-    def __init__(self, dim: int):
-        super().__init__()
-        self.query_norm = RMSNorm(dim)
-        self.key_norm = RMSNorm(dim)
-
-    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]:
-        q = self.query_norm(q)
-        k = self.key_norm(k)
-        return q.to(v), k.to(v)
-
-
-class SelfAttention(nn.Module):
-    def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False):
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.norm = QKNorm(head_dim)
-        self.proj = nn.Linear(dim, dim)
-
-    def forward(self, x: Tensor, pe: Tensor) -> Tensor:
-        qkv = self.qkv(x)
-        q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
-        q, k = self.norm(q, k, v)
-        x = attention(q, k, v, pe=pe)
-        x = self.proj(x)
-        return x
-
-
-@dataclass
-class ModulationOut:
-    shift: Tensor
-    scale: Tensor
-    gate: Tensor
-
-
-class Modulation(nn.Module):
-    def __init__(self, dim: int, double: bool):
-        super().__init__()
-        self.is_double = double
-        self.multiplier = 6 if double else 3
-        self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)
-
-    def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut]:
-        out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
-
-        return (
-            ModulationOut(*out[:3]),
-            ModulationOut(*out[3:]) if self.is_double else None,
-        )
-
-
-class DoubleStreamBlock(nn.Module):
-    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False):
-        super().__init__()
-
-        mlp_hidden_dim = int(hidden_size * mlp_ratio)
-        self.num_heads = num_heads
-        self.hidden_size = hidden_size
-        self.img_mod = Modulation(hidden_size, double=True)
-        self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
-
-        self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.img_mlp = nn.Sequential(
-            nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
-            nn.GELU(approximate="tanh"),
-            nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
-        )
-
-        self.txt_mod = Modulation(hidden_size, double=True)
-        self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
-
-        self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.txt_mlp = nn.Sequential(
-            nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
-            nn.GELU(approximate="tanh"),
-            nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
-        )
-
-    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor) -> tuple[Tensor, Tensor]:
-        img_mod1, img_mod2 = self.img_mod(vec)
-        txt_mod1, txt_mod2 = self.txt_mod(vec)
-
-        # prepare image for attention
-        img_modulated = self.img_norm1(img)
-        img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
-        img_qkv = self.img_attn.qkv(img_modulated)
-        img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
-        img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
-
-        # prepare txt for attention
-        txt_modulated = self.txt_norm1(txt)
-        txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
-        txt_qkv = self.txt_attn.qkv(txt_modulated)
-        txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
-        txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
-
-        # run actual attention
-        q = torch.cat((txt_q, img_q), dim=2)
-        k = torch.cat((txt_k, img_k), dim=2)
-        v = torch.cat((txt_v, img_v), dim=2)
-
-        attn = attention(q, k, v, pe=pe)
-        txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
-
-        # calculate the img bloks
-        img = img + img_mod1.gate * self.img_attn.proj(img_attn)
-        img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
-
-        # calculate the txt bloks
-        txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn)
-        txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
-        return img, txt
-
-
-class SingleStreamBlock(nn.Module):
-    """
-    A DiT block with parallel linear layers as described in
-    https://arxiv.org/abs/2302.05442 and adapted modulation interface.
-    """
-
-    def __init__(
-        self,
-        hidden_size: int,
-        num_heads: int,
-        mlp_ratio: float = 4.0,
-        qk_scale: float = None,
-    ):
-        super().__init__()
-        self.hidden_dim = hidden_size
-        self.num_heads = num_heads
-        head_dim = hidden_size // num_heads
-        self.scale = qk_scale or head_dim**-0.5
-
-        self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
-        # qkv and mlp_in
-        self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
-        # proj and mlp_out
-        self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
-
-        self.norm = QKNorm(head_dim)
-
-        self.hidden_size = hidden_size
-        self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-
-        self.mlp_act = nn.GELU(approximate="tanh")
-        self.modulation = Modulation(hidden_size, double=False)
-
-    def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor:
-        mod, _ = self.modulation(vec)
-        x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
-        qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
-
-        q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
-        q, k = self.norm(q, k, v)
-
-        # compute attention
-        attn = attention(q, k, v, pe=pe)
-        # compute activation in mlp stream, cat again and run second linear layer
-        output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
-        return x + mod.gate * output
-
-
-class LastLayer(nn.Module):
-    def __init__(self, hidden_size: int, patch_size: int, out_channels: int):
-        super().__init__()
-        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
-        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
-
-    def forward(self, x: Tensor, vec: Tensor) -> Tensor:
-        shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
-        x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
-        x = self.linear(x)
-        return x