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# Copyright 2024 EPFL and Apple Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import partial
from typing import Dict, List, Optional, Tuple, Union
import torch
import torch.nn as nn
from einops import repeat
from .fm_utils import build_1d_sincos_posemb, build_2d_sincos_posemb, pair
class SequenceDecoderEmbedding(nn.Module):
"""Embedding module for sequence inputs, like captions or a sequence of objects.
Args:
vocab_size: Vocabulary size
max_length: Maximum number of tokens in the sequence
dim_tokens: Dimension of output tokens. Can be set using init method.
sincos_pos_emb: Set to True (default) to use fixed 1D sin-cos positional embeddings
padding_idx: Padding index for word embedding
share_embedding: Set to True to share input and output embedding weights
"""
def __init__(self,
vocab_size: int,
max_length: int,
dim_tokens: Optional[int] = None,
sincos_pos_emb: bool = True,
max_sincos_pos_emb: int = 512,
padding_idx: int = 0,
share_embedding: bool = True,
**kwargs):
super().__init__()
self.vocab_size = vocab_size
self.max_length = max_length
self.dim_tokens = dim_tokens
self.sincos_pos_emb = sincos_pos_emb
self.padding_idx = padding_idx
self.max_sincos_pos_emb = max_sincos_pos_emb
self.share_embedding = share_embedding
if self.dim_tokens is not None:
self.init(dim_tokens=dim_tokens)
def init(self, dim_tokens: int = 768, init_std=0.02):
"""
Initialize parts of embedding module that are dependent on dimension of tokens.
Should be called when setting up FourM.
Args:
dim_tokens: Dimension of tokens
init_std: Standard deviation of init
"""
self.dim_tokens = dim_tokens
# Task embedding identifying from which task a given token comes from
# Fixed-size positional embeddings. Can be interpolated to different input sizes
if self.sincos_pos_emb:
if self.max_length > self.max_sincos_pos_emb:
raise ValueError(f"Max length ({self.max_length}) is greater than the number of posembs ({self.max_sincos_pos_emb}")
# Get all posembs, than truncate up to max length
pos_emb = build_1d_sincos_posemb(max_len=self.max_sincos_pos_emb, embed_dim=self.dim_tokens)[:self.max_length]
self.register_buffer("pos_emb", pos_emb)
else:
self.pos_emb = nn.Parameter(torch.zeros(1, self.max_length, self.dim_tokens))
nn.init.normal_(self.pos_emb, std=init_std)
self.mod_emb = nn.Parameter(torch.zeros(1, 1, self.dim_tokens))
nn.init.normal_(self.mod_emb, std=init_std)
# Token embedding
self.token_emb = nn.Embedding(num_embeddings=self.vocab_size, embedding_dim=self.dim_tokens, padding_idx=self.padding_idx)
# Output projection layer
self.to_logits = nn.Linear(self.dim_tokens, self.vocab_size, bias=False)
if self.share_embedding:
# Share input and output embedding weights
self.to_logits.weight = self.token_emb.weight
@torch.jit.ignore
def no_weight_decay(self):
return set()
def forward_embed(self, d: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Forward pass through embedding module, transforming sequence of ids to sequence of embeddings.
Creates corresponding modality and positional embeddings and adds them to the dict.
Args:
d (Dict[str, torch.Tensor]): Modality dict, with at least the following keys:
- 'tensor' (torch.Tensor): Token sequence for each batch. Shape (B, L) where B is the batch size and L is the sequence length.
- 'target_mask' (torch.Tensor): Mask for valid tokens in the target sequence (set to 0 for valid tokens and 1 otherwise). Shape (B, L).
Returns:
Dict[str, torch.Tensor]: Modality dict with added keys:
- 'x' (torch.Tensor): Embedded token sequence. Shape (B, L, D) where D is the embedding dimension.
- 'emb' (torch.Tensor): Sum of positional and modality embeddings for the target sequence. Shape (B, L, D).
- 'ids' (torch.Tensor): Original token sequence from input dict. Shape (B, L).
"""
ids = d['tensor']
B = ids.shape[0]
assert self.dim_tokens is not None, 'Need to call init(dim_tokens) function first'
# Map to embedding
x = self.token_emb(ids)
expanded_pos_emb = repeat(self.pos_emb, "() n d -> b n d", b=B)
# Target pos encoding
target_mask = d['target_mask']
target_pos_id = (~target_mask).int().cumsum(dim=1) - 1
target_pos_id[target_mask] = 0
# Sometimes target sequence is over max length, it will be truncated in decoder
target_pos_id[target_pos_id >= self.max_length] = 0
target_pos_emb = torch.gather(expanded_pos_emb, dim=1, index=repeat(target_pos_id, "b n -> b n d", d=expanded_pos_emb.shape[2]))
target_pos_emb[target_mask] = 0
x_emb = target_pos_emb + self.mod_emb
d['x'] = x
d['emb'] = x_emb
d['ids'] = d['tensor']
return d
def forward_logits(self, x: torch.Tensor) -> torch.Tensor:
"""
Forward pass through output projection layer, transforming sequence of embeddings to logits.
Args:
x (torch.Tensor): Output tokens from the decoder. Shape (B, M, D)
Returns:
torch.Tensor: Logits for each token in the sequence. Shape (B, M, V)
"""
logits = self.to_logits(x)
return logits
class ImageTokenDecoderEmbedding(nn.Module):
"""Embedding module for tokenized spatial inputs.
Args:
vocab_size: Vocabulary size
patch_size: Int or tuple of the patch size over the full image size.
dim_tokens: Dimension of output tokens. Can be set using init method.
sincos_pos_emb: Set to True (default) to use fixed 2D sin-cos positional embeddings
image_size: Default image size. Used to initialize size of positional embeddings.
share_embedding: Set to True to share input and output embedding weights
"""
def __init__(self,
vocab_size: int,
patch_size: Union[int, Tuple[int,int]] = 16,
dim_tokens: Optional[int] = None,
sincos_pos_emb: bool = True,
image_size: Union[int, Tuple[int]] = 224,
share_embedding: bool = True,
**kwargs):
super().__init__()
self.vocab_size = vocab_size
self.patch_size = pair(patch_size)
self.dim_tokens = dim_tokens
self.sincos_pos_emb = sincos_pos_emb
self.image_size = pair(image_size)
self.num_patches = (self.image_size[0] // self.patch_size[0]) * (self.image_size[1] // self.patch_size[1])
self.share_embedding = share_embedding
if self.dim_tokens is not None:
self.init(dim_tokens=dim_tokens)
def init(self, dim_tokens: int = 768, init_std=0.02):
"""
Initialize parts of module that are dependent on dimension of tokens.
Should be called when setting up FourM.
Args:
dim_tokens: Dimension of tokens
init_std: Standard deviation of init
"""
self.dim_tokens = dim_tokens
# Task embedding identifying from which task a given token comes from
# Fixed-size positional embeddings. Can be interpolated to different input sizes
h_posemb = self.image_size[0] // self.patch_size[0]
w_posemb = self.image_size[1] // self.patch_size[1]
if self.sincos_pos_emb:
pos_emb = build_2d_sincos_posemb(h=h_posemb, w=w_posemb, embed_dim=self.dim_tokens)
self.register_buffer("pos_emb", pos_emb)
else:
self.pos_emb = nn.Parameter(torch.zeros(1, (h_posemb * w_posemb), self.dim_tokens))
nn.init.normal_(self.pos_emb, std=init_std)
self.mod_emb = nn.Parameter(torch.zeros(1, 1, self.dim_tokens))
nn.init.normal_(self.mod_emb, std=init_std)
# Token embedding (not needed if only masked tokens are given as input, but can be useful to train Token Critic)
self.token_emb = nn.Embedding(num_embeddings=self.vocab_size, embedding_dim=self.dim_tokens)
# Output projection layer
self.to_logits = nn.Linear(self.dim_tokens, self.vocab_size, bias=False)
if self.share_embedding:
# Share input and output embedding weights
self.to_logits.weight = self.token_emb.weight
@torch.jit.ignore
def no_weight_decay(self):
return set()
def forward_embed(self, d: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Forward pass through the embedding module, transforming tokenized spatial inputs to embeddings.
Creates corresponding modality and positional embeddings and adds them to the dict.
Args:
d (Dict[str, torch.Tensor]): Modality dict, with at least the following key:
- 'tensor' (torch.Tensor): Modality tokens for each batch (e.g. from tokenized images). Shape (B, H, W) where B is the batch size, H and W are height and width after tokenization.
Returns:
Dict[str, torch.Tensor]: Modality dict with added keys:
- 'x' (torch.Tensor): Embedded token sequence, which is replaced by mask tokens in the 4M decoder. Shape (B, H*W, D) where D is the embedding dimension.
- 'emb' (torch.Tensor): Sum of positional and modality embeddings for the token sequence. Shape (B, H*W, D).
- 'ids' (torch.Tensor): Reshaped token sequence from input dict, flattened in the spatial dimensions. Shape (B, H*W).
"""
ids = d['tensor']
B = ids.shape[0]
ids = ids.reshape(B, -1)
# Map to embedding
x = self.token_emb(ids)
# Create positional embedding + modality embedding
x_emb = repeat(self.pos_emb + self.mod_emb, '() n d -> b n d', b=B)
d['x'] = x
d['emb'] = x_emb
d['ids'] = ids
return d
def forward_logits(self, x: torch.Tensor) -> torch.Tensor:
"""
Forward pass through output projection layer, transforming sequence of embeddings to logits.
Args:
x (torch.Tensor): Output tokens from the decoder. Shape (B, M, D)
Returns:
torch.Tensor: Logits for each token in the sequence. Shape (B, M, V)
"""
logits = self.to_logits(x)
return logits
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