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#
# Copyright (c) 2026 audio-embeddings contributors
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
from collections.abc import Mapping
from typing import Any, Optional, Tuple
import torch
import torch.nn as nn
from timm.layers import build_sincos2d_pos_embed
from .rope import RotaryEmbedding1D
from .rope import RotaryEmbedding2D
from .transformer import build_norm_layer
from .transformer import FullAttentionResidual
from .transformer import RoPEBlock
def vit_config_with_patch_geometry(
config: Mapping[str, Any],
*,
img_size: tuple[int, int],
patch_size: tuple[int, int],
) -> dict[str, Any]:
"""Return a ViT config aligned with the patch embedding's actual geometry."""
resolved = dict(config)
resolved["img_size"] = tuple(img_size)
resolved["patch_size"] = tuple(patch_size)
resolved.setdefault("rope_mode", "auto")
return resolved
class ViT(nn.Module):
"""
Vision Transformer with support for RoPE and 2D positional embeddings.
Args:
embed_dim (int): Embedding dimension.
depth (int): Number of transformer blocks.
num_heads (int): Number of attention heads.
mlp_ratio (float): Ratio of MLP hidden dim to embedding dim.
qkv_bias (bool): Enable bias for QKV projections.
drop_rate (float): Dropout rate.
attn_drop_rate (float): Attention dropout rate.
drop_path_rate (float): Stochastic depth rate.
norm_layer (nn.Module): Normalization layer.
norm_eps (float | None): Explicit normalization epsilon; None preserves
the normalization implementation's default.
act_layer (nn.Module): Activation layer.
num_patches (int): Total number of patches (used for learnable/sincos pos embed).
img_size (tuple[int, int]): Input image size (H, W).
patch_size (tuple[int, int]): Patch size (H, W).
pos_embed_type (str): Type of positional embedding ("rope", "sincos", "learnable").
rope_mode (str | None): RoPE axes ("2d" or temporal "1d"). ``None``
or ``"auto"`` selects 1D when the patch height equals the image height,
otherwise 2D.
"""
def __init__(
self,
embed_dim: int = 768,
depth: int = 12,
num_heads: int = 12,
mlp_ratio: float = 4.0,
mlp_type: str = "gelu_mlp",
qkv_bias: bool = True,
proj_bias: bool = True,
mlp_bias: bool = True,
qk_norm: bool = False,
qk_norm_type: str = "layernorm",
drop_rate: float = 0.0,
attn_drop_rate: float = 0.0,
drop_path_rate: float = 0.0,
norm_layer: nn.Module | None = None,
norm_type: str = "layernorm",
act_layer: nn.Module = nn.GELU,
num_patches: int = 128,
img_size: tuple[int, int] = (128, 256),
patch_size: tuple[int, int] = (16, 16),
pos_embed_type: str = "rope",
rope_mode: str | None = "2d",
residual_type: str = "standard",
norm_eps: float | None = None,
):
super().__init__()
self.embed_dim = embed_dim
self.num_patches = num_patches
self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
self.pos_embed_type = pos_embed_type
requested_rope_mode = (
"auto" if rope_mode is None else rope_mode.strip().lower().replace("-", "_")
)
if requested_rope_mode == "auto":
self.rope_mode = "1d" if patch_size[0] == img_size[0] else "2d"
else:
self.rope_mode = requested_rope_mode
self.norm_type = norm_type
self.mlp_type = mlp_type
self.residual_type = residual_type.strip().lower().replace("-", "_")
if self.residual_type not in {"standard", "full_attnres"}:
raise ValueError(
f"Unknown residual_type={residual_type!r}; expected 'standard' "
"or 'full_attnres'"
)
# Positional Embeddings
if pos_embed_type == "rope":
head_dim = embed_dim // num_heads
if self.rope_mode == "2d":
self.rope = RotaryEmbedding2D(dim=head_dim, max_res=self.grid_size)
elif self.rope_mode == "1d":
self.rope = RotaryEmbedding1D(
dim=head_dim,
max_seq_len=self.grid_size[1],
)
else:
raise ValueError(
f"Unknown rope_mode: {rope_mode!r}; expected 'auto', '1d', or '2d'"
)
self.pos_embed = None
elif pos_embed_type == "sincos":
self.rope = None
# build_sincos2d_pos_embed(feat_shape, dim, ...)
# We assume grid_size matches num_patches
pos_embed = build_sincos2d_pos_embed(self.grid_size, embed_dim)
self.register_buffer("pos_embed", pos_embed.unsqueeze(0)) # [1, N, D]
elif pos_embed_type == "learnable":
self.rope = None
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
nn.init.trunc_normal_(self.pos_embed, std=0.02)
else:
raise ValueError(f"Unknown pos_embed_type: {pos_embed_type}")
# Stochastic Depth
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
self.blocks = nn.ModuleList(
[
RoPEBlock(
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
mlp_type=mlp_type,
qkv_bias=qkv_bias,
proj_bias=proj_bias,
mlp_bias=mlp_bias,
qk_norm=qk_norm,
qk_norm_type=qk_norm_type,
proj_drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[i],
norm_type=norm_type,
norm_layer=norm_layer,
norm_eps=norm_eps,
act_layer=act_layer,
rope=self.rope,
residual_type=self.residual_type,
)
for i in range(depth)
]
)
self.norm = build_norm_layer(
dim=embed_dim,
norm_type=norm_type,
norm_layer=norm_layer,
norm_eps=norm_eps,
)
self.output_residual = (
FullAttentionResidual(embed_dim)
if self.residual_type == "full_attnres"
else None
)
self.apply(self._init_weights)
def _init_weights(self, m: nn.Module) -> None:
if isinstance(m, nn.Linear):
nn.init.trunc_normal_(m.weight, std=0.02)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.RMSNorm):
nn.init.constant_(m.weight, 1.0)
def forward(
self,
x: torch.Tensor,
pos_ids: Optional[torch.Tensor] = None,
add_pos_embed: bool = True,
grid_size: Optional[Tuple[int, int]] = None,
) -> torch.Tensor:
"""
Forward pass.
Args:
x (torch.Tensor): Input tensor [B, N, D].
pos_ids (Optional[torch.Tensor]): Positional indices [B, N] or [N].
add_pos_embed (bool): Whether to add positional embeddings (for non-RoPE).
grid_size (Optional[Tuple[int, int]]): Grid size for RoPE/PosEmbed.
Returns:
torch.Tensor: Output tensor [B, N, D].
"""
# Determine grid size
if grid_size is None:
if pos_ids is None:
# Infer from x assuming full sequence
B, N, D = x.shape
H_grid = self.grid_size[0]
W_grid = N // H_grid
current_grid_size = (H_grid, W_grid)
else:
# Cannot infer, use default (might be wrong if variable length)
current_grid_size = self.grid_size
else:
current_grid_size = grid_size
if self.pos_embed_type != "rope" and add_pos_embed:
if pos_ids is not None:
# Select positional embeddings
if pos_ids.ndim == 1:
# Shared pos_ids across batch
pos_embed = self.pos_embed[:, pos_ids, :] # [1, N_subset, D]
else:
# Different pos_ids per sample
pos_embed = self.pos_embed.expand(x.shape[0], -1, -1)
pos_embed = torch.gather(
pos_embed,
1,
pos_ids.unsqueeze(-1).expand(-1, -1, self.embed_dim),
)
x = x + pos_embed
else:
# Assume full sequence
if x.shape[1] == self.num_patches:
x = x + self.pos_embed
elif (
self.pos_embed is not None and x.shape[1] <= self.pos_embed.shape[1]
):
x = x + self.pos_embed[:, : x.shape[1], :]
# For RoPE, we need pos_ids. If not provided, generate them.
if self.pos_embed_type == "rope" and pos_ids is None:
device = x.device
# We need to generate pos_ids for the current grid
# If we inferred current_grid_size, we should use it.
# pos_ids should be 0..N-1
B, N, D = x.shape
pos_ids = torch.arange(N, device=device)
if self.residual_type == "full_attnres":
values = [x]
for block in self.blocks:
if block.attention_residual is None or block.mlp_residual is None:
raise RuntimeError(
"Full AttnRes block is missing depth aggregators"
)
attention_input = block.attention_residual(values)
values.append(
block.attention_output(
attention_input,
pos_ids=pos_ids,
grid_size=current_grid_size,
)
)
mlp_input = block.mlp_residual(values)
values.append(block.mlp_output(mlp_input))
if self.output_residual is None:
raise RuntimeError("Full AttnRes ViT is missing its output aggregator")
x = self.output_residual(values)
else:
for block in self.blocks:
x = block(x, pos_ids=pos_ids, grid_size=current_grid_size)
x = self.norm(x)
return x
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