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import torch
from torch import einsum, nn
from einops import rearrange, repeat
from einops_exts import rearrange_many
from einops import rearrange
from typing import List, Optional, Tuple, Union
import torch.nn.functional as F
from transformers.modeling_outputs import CausalLMOutputWithPast
from dataclasses import dataclass
from transformers import CLIPVisionModel
import transformers
from .utils import num_params, getattr_recursive, stack_with_padding, get_anyres_image_grid_shape, unpad_image
class VisionTokenizer(nn.Module):
def __init__(self, dim_media, num_tokens_per_media):
super().__init__()
self.dim_media = dim_media
self.num_tokens_per_media = num_tokens_per_media
class PerceiverAttention(nn.Module):
def __init__(self, *, dim, dim_head=64, heads=8):
super().__init__()
self.scale = dim_head**-0.5
self.heads = heads
inner_dim = dim_head * heads
self.norm_media = nn.LayerNorm(dim)
self.norm_latents = nn.LayerNorm(dim)
self.to_q = nn.Linear(dim, inner_dim, bias=False)
self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
self.to_out = nn.Linear(inner_dim, dim, bias=False)
def forward(self, x, latents, vision_attn_masks=None):
"""
Args:
x (torch.Tensor): image features
shape (b, T, n1, D)
latent (torch.Tensor): latent features
shape (b, T, n2, D)
"""
x = self.norm_media(x)
latents = self.norm_latents(latents)
h = self.heads
q = self.to_q(latents)
kv_input = torch.cat((x, latents), dim=-2) # TODO: Change the shape of vision attention mask according to this.
if vision_attn_masks is not None:
vision_attn_masks = torch.cat((vision_attn_masks,
torch.ones((latents.shape[0], latents.shape[-2]), dtype=latents.dtype, device=latents.device)),
dim=-1)
k, v = self.to_kv(kv_input).chunk(2, dim=-1)
q, k, v = rearrange_many((q, k, v), "b t n (h d) -> b h t n d", h=h)
q = q * self.scale
# attention
sim = einsum("... i d, ... j d -> ... i j", q, k)
# Apply vision attention mask here.
# Reference: https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html#torch.nn.functional.scaled_dot_product_attention
if vision_attn_masks is not None:
attn_bias = torch.zeros((q.size(0), 1, 1, q.size(-2), k.size(-2)), dtype=q.dtype, device=q.device)
vision_attn_masks = repeat(vision_attn_masks, 'b n -> b 1 1 l n', l=q.size(-2))
attn_bias.masked_fill_(vision_attn_masks.logical_not(), float("-inf"))
sim += attn_bias
sim = sim - sim.amax(dim=-1, keepdim=True).detach()
attn = sim.softmax(dim=-1)
out = einsum("... i j, ... j d -> ... i d", attn, v)
out = rearrange(out, "b h t n d -> b t n (h d)", h=h)
return self.to_out(out)
def FeedForward(dim, mult=4):
inner_dim = int(dim * mult)
return nn.Sequential(
nn.LayerNorm(dim),
nn.Linear(dim, inner_dim, bias=False),
nn.GELU(),
nn.Linear(inner_dim, dim, bias=False),
)
class PerceiverResampler(VisionTokenizer):
def __init__(
self,
*,
dim,
dim_inner=None,
depth=6,
dim_head=96,
heads=16,
num_latents=128,
max_num_media=None,
max_num_frames=None,
ff_mult=4,
):
"""
Perceiver module which takes in image features and outputs image tokens.
Args:
dim (int): dimension of the incoming image features
dim_inner (int, optional): final dimension to project the incoming image features to;
also the final dimension of the outputted features. If None, no projection is used, and dim_inner = dim.
depth (int, optional): number of layers. Defaults to 6.
dim_head (int, optional): dimension of each head. Defaults to 64.
heads (int, optional): number of heads. Defaults to 8.
num_latents (int, optional): number of latent tokens to use in the Perceiver;
also corresponds to number of tokens per sequence to output. Defaults to 64.
max_num_media (int, optional): maximum number of media per sequence to input into the Perceiver
and keep positional embeddings for. If None, no positional embeddings are used.
max_num_frames (int, optional): maximum number of frames to input into the Perceiver
and keep positional embeddings for. If None, no positional embeddings are used.
ff_mult (int, optional): dimension multiplier for the feedforward network. Defaults to 4.
"""
if dim_inner is not None:
projection = nn.Linear(dim, dim_inner)
else:
projection = None
dim_inner = dim
super().__init__(dim_media=dim, num_tokens_per_media=num_latents)
self.projection = projection
self.latents = nn.Parameter(torch.randn(num_latents, dim))
# positional embeddings
self.frame_embs = (
nn.Parameter(torch.randn(max_num_frames, dim))
if exists(max_num_frames)
else None
)
self.media_time_embs = (
nn.Parameter(torch.randn(max_num_media, 1, dim))
if exists(max_num_media)
else None
)
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(
nn.ModuleList(
[
PerceiverAttention(
dim=dim, dim_head=dim_head, heads=heads
),
FeedForward(dim=dim, mult=ff_mult),
]
)
)
self.norm = nn.LayerNorm(dim)
def forward(self, x):
"""
Args:
x (torch.Tensor): image features
shape (b, T, F, v, D)
Returns:
shape (b, T, n, D) where n is self.num_latents
"""
b, T, F, v = x.shape[:4]
# frame and media time embeddings
if exists(self.frame_embs):
frame_embs = repeat(self.frame_embs[:F], "F d -> b T F v d", b=b, T=T, v=v)
x = x + frame_embs
x = rearrange(
x, "b T F v d -> b T (F v) d"
) # flatten the frame and spatial dimensions
if exists(self.media_time_embs):
x = x + self.media_time_embs[:T]
# blocks
latents = repeat(self.latents, "n d -> b T n d", b=b, T=T)
for attn, ff in self.layers:
latents = attn(x, latents) + latents
latents = ff(latents) + latents
if exists(self.projection):
return self.projection(self.norm(latents))
else:
return self.norm(latents)
class DecoupledEmbedding(nn.Embedding):
# Derived from https://pytorch.org/docs/stable/_modules/torch/nn/modules/sparse.html#Embedding
"""
Implements a decoupling of parameters to allow freezing (or not) a subset of the embeddings. In practise, the
regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `num_additional_embeddings` > 0,
then it will create `num_additional_embeddings` additional parameters that are always trained. If
`num_additional_embeddings=0`, then the module defaults back to the regular behavior of `nn.Embedding`.
"""
def __init__(
self,
max_original_id: int,
num_additional_embeddings: int = 0,
_weight: torch.Tensor = None,
num_original_embeddings: int = None,
embedding_dim: int = None,
partially_freeze=True,
device=None,
dtype=None,
pad_token_id=None,
) -> None:
"""
Args:
max_original_id (`int`):
The largest token id that should be embedded using the regular embedding (regular `weight`).
This is usually len(tokenizer) - 1 before additional tokens are added.
Note that this may not equal self.weight.shape[0]
num_additional_embeddings (`int`):
Number of additional tokens to initialize an Embedding matrix for (`additional_weight`).
_weight (`torch.Tensor`, *optional*, defaults to `None`): The regular weight tensor.
If provided, this sets the `num_original_embeddings` and `embedding_dim` parameters.
num_original_embeddings (`int`):
self.weight.shape[0]
embedding_dim (`int`):
The size of each embedding vector
partially_freeze: (`bool`, *optional*, defaults to `True`):
If `True`, the regular `weight` will be frozen. `additional_weight` is never frozen.
padding_idx (`int`, *optional*):
The padding index (needs to be less than num_embeddings)
Note: there are a lot of other parameters to initialize a standard `nn.Embedding` such as `padding_idx`,
`max_norm` or `norm_type`. We are not supporting these.
"""
# validate args
if pad_token_id is not None and pad_token_id > max_original_id:
raise ValueError(
f"pad_token_id must be <= max_original_id. Got {pad_token_id} and {max_original_id}."
+ "If the original tokenizer does not have a pad_token_id, use pad_token_id=None."
)
if _weight is not None:
assert (num_original_embeddings is None) or (
_weight.shape[0] == num_original_embeddings
), f"num_original_embeddings={num_original_embeddings} but _weight.shape[0]={_weight.shape[0]}"
assert (embedding_dim is None) or (
_weight.shape[1] == embedding_dim
), f"embedding_dim={embedding_dim} but _weight.shape[1]={_weight.shape[1]}"
num_original_embeddings = _weight.shape[0]
embedding_dim = _weight.shape[1]
else:
assert (
num_original_embeddings is not None
), "num_original_embeddings must be provided if _weight is not provided"
assert (
embedding_dim is not None
), "embedding_dim must be provided if _weight is not provided"
super().__init__(
num_embeddings=num_original_embeddings,
embedding_dim=embedding_dim,
device=device,
dtype=dtype,
padding_idx=pad_token_id,
_weight=_weight,
)
self.max_original_id = max_original_id
self.padding_idx = pad_token_id
self.num_additional_embeddings = num_additional_embeddings
if self.num_additional_embeddings > 0:
self.additional_embedding = nn.Embedding(
num_embeddings=self.num_additional_embeddings,
embedding_dim=embedding_dim,
device=device,
dtype=dtype,
)
self.set_requires_grad(
require_regular_grad=not partially_freeze, require_additional_grad=True
)
def set_requires_grad(self, require_regular_grad, require_additional_grad):
"""
Helper function to separately set the requires_grad flag for the regular weight and the additional weight.
"""
self.weight.requires_grad_(require_regular_grad)
self.additional_embedding.requires_grad_(require_additional_grad)
def forward(self, input_ids):
"""
we have 2 embeddings, with different indices - one pretrained self.weight and another
self.additional_embedding.weight that is being trained.
in order to make a lookup of the input ids, we:
1. find out the indices of the entries belonging to the 2nd embedding
2. extract those values while subtracting the size of the first embedding (num_embeddings), since the 2nd
embedding starts from 0 and not num_embeddings
3. perform the 2nd embedding lookup
4. now we handle the 1st embedding, we overwrite indices belonging to the 2nd embedding with a padding index
5. perform the 1st embedding lookup
6. now we overwrite the values in the 1st embedding lookup with the values of the 2nd embedding lookup
note: for the 1st embedding lookup we could have looked up only the low indices and not do the padding, but
then we have to create a new tensor and populate it with 2 tensors that are spread out across various indices -
i.e. not a simple concat - I haven't benchmarked the complex case if it's any faster, given that seqlens are
usually relatively short it's probably not faster or if faster not by much - but might be a good idea to
measure.
"""
if self.num_additional_embeddings == 0:
return F.embedding(input_ids, self.weight)
# Clone so that we don't modify the original input_ids later on
input_ids = input_ids.clone()
additional_vocab_indices = torch.where(input_ids > self.max_original_id)
input_ids_additional_vocab = input_ids[additional_vocab_indices]
additional_embeddings = self.additional_embedding(
input_ids_additional_vocab - self.max_original_id - 1
)
# for successful lookup replace input_ids with 0, the results of these will be discarded anyway
input_ids[additional_vocab_indices] = 0
full_vector = F.embedding(input_ids, self.weight)
# overwrite the records with high indices
full_vector[additional_vocab_indices] = additional_embeddings
return full_vector
def extra_repr(self) -> str:
return "num_original_embeddings={}, num_additional_embeddings={}, embedding_dim={}, partially_freeze={}".format(
self.max_original_id + 1,
self.num_additional_embeddings,
self.embedding_dim,
(not self.weight.requires_grad),
)
class DecoupledLinear(nn.Linear):
# Derived from https://pytorch.org/docs/stable/_modules/torch/nn/modules/linear.html#Linear
"""
Implements a decoupling of parameters to allow freezing (or not) a subset of the parameters. In practise, the
regular `weight` can be trained or frozen (i.e. `partially_freeze=True`), and if `additional_out_features` > 0,
then it will create `additional_out_features * in_features` additional parameters that are always trained. If
`additional_out_features=0`, then the module defaults back to the regular behavior of `nn.Linear`.
"""
def __init__(
self,
max_original_id: int,
additional_out_features: int = 0,
_weight: torch.Tensor = None,
_bias: torch.Tensor = None,
in_features: int = None,
original_out_features: int = None,
bias: bool = True,
partially_freeze: bool = True,
device=None,
dtype=None,
) -> None:
"""
Args:
max_original_id (`int`): The largest token id that should be extracted from the regular weight.
This is usually len(tokenizer) - 1 before additional tokens are added.
Note that this may not equal original_out_features - 1
_weight: torch.Tensor, *optional*, defaults to `None`. The regular weight tensor.
If provided, this sets the `in_features` and `original_out_features` parameters.
_bias: torch.Tensor, *optional*, defaults to `None`. The regular bias tensor.
in_features: int. Input hidden size.
original_out_features: int. Original out_features of the language model's get_output_embeddings() function.
additional_out_features: int. Number of additional trainable dimensions.
bias: bool. Whether to include a bias term.
partially_freeze: bool, *optional*, defaults to `True`): If `True`, the regular `weight` will be frozen.
"""
# argument validation
if _weight is not None:
assert (_weight.shape[0] == original_out_features) or (
original_out_features is None
), f"original_out_features={original_out_features} but _weight.shape[0]={_weight.shape[0]}"
assert (_weight.shape[1] == in_features) or (
in_features is None
), f"in_features={in_features} but _weight.shape[1]={_weight.shape[1]}"
in_features = _weight.shape[1]
original_out_features = _weight.shape[0]
else:
assert (
in_features is not None
), "in_features must be provided if _weight is not provided"
assert (
original_out_features is not None
), "original_out_features must be provided if _weight is not provided"
if _bias is not None:
assert bias is True, "bias must be True if _bias is provided"
# initialize original linear
super().__init__(
in_features,
original_out_features,
bias,
device,
dtype)
# set weight and bias manually
if _weight is not None:
self.weight = nn.Parameter(_weight)
if _bias is not None:
self.bias = nn.Parameter(_bias)
self.in_features = in_features
self.original_out_features = original_out_features
self.max_original_id = max_original_id
# initialize additional linear
self.additional_out_features = additional_out_features
self.has_bias = bias
if additional_out_features > 0:
self.additional_fc = nn.Linear(
in_features=in_features,
out_features=additional_out_features,
bias=self.has_bias,
device=device,
dtype=dtype,
)
self.set_requires_grad(
require_regular_grad=not partially_freeze, require_additional_grad=True
)
def set_requires_grad(self, require_regular_grad, require_additional_grad):
"""
Helper function to separately set the requires_grad flag for the regular weight and the additional weight.
"""
self.weight.requires_grad_(require_regular_grad)
if self.has_bias:
self.bias.requires_grad_(require_regular_grad)
self.additional_fc.requires_grad_(require_additional_grad)
def forward(self, input: torch.Tensor) -> torch.Tensor:
output = F.linear(input, self.weight, self.bias)
output = output[..., : self.max_original_id + 1]
if self.additional_out_features > 0:
additional_features = F.linear(
input, self.additional_fc.weight, self.additional_fc.bias
)
output = torch.cat((output, additional_features), -1)
return output
def extra_repr(self) -> str:
"""Overwriting `nn.Linear.extra_repr` to include new parameters."""
return "in_features={}, out_features={}, additional_out_features={}, bias={}, partially_freeze={}".format(
self.in_features,
self.max_original_id + 1,
self.additional_out_features,
self.bias is not None,
(not self.weight.requires_grad or not self.bias.requires_grad),
)
class VLM(nn.Module):
"""
Generic vision-language model (VLM) class.
A VLM consists of four components:
1. A vision encoder that extracts features from pixels, e.g. CLIP
input: (B, T_img, F, C, H, W)
output: (B, T_img, F, v, d)
2. A vision tokenizer that converts these features to visual token-like embeddings, e.g. Perceiver, or a linear projection head
input: (B, T_img, F, v, d)
output: (B, T_img, n, d)
3. A fusion method that allows the language model to attend to these tokens, e.g. cross-attention, or placing the tokens directly in the language model's input sequence
4. A language model
"""
def __init__(
self,
vision_encoder: nn.Module,
vision_tokenizer: nn.Module,
lang_model: nn.Module,
initial_tokenizer_len: int,
pad_token_id: int,
gradient_checkpointing: bool = False,
):
"""
Args:
vision_encoder (nn.Module): e.g. CLIP
vision_tokenizer (nn.Module): e.g. PerceiverResampler
lang_model (nn.Module): e.g. MPT
initial_tokenizer_len (int): size of the original tokenizer vocab
pad_token_id (int): id of the pad token
gradient_checkpointing (bool, optional): Whether to use gradient checkpointing. Defaults to False.
"""
super().__init__()
# save dimension information
self.lang_embedding_dim = lang_model.get_input_embeddings().weight.shape[1]
if hasattr(lang_model.config, "d_model"):
self.lang_hidden_dim = lang_model.config.d_model # mpt uses d_model
else:
self.lang_hidden_dim = lang_model.config.hidden_size
self.vis_embedding_dim = vision_tokenizer.dim_media
self.num_tokens_per_vis = vision_tokenizer.num_tokens_per_media
# core components
self.vision_encoder = vision_encoder
self.vision_tokenizer = vision_tokenizer
self.lang_model = lang_model
# lm embeddings
self.pad_token_id = pad_token_id
self.initial_tokenizer_len = initial_tokenizer_len
input_embeds = DecoupledEmbedding(
max_original_id=initial_tokenizer_len - 1,
num_additional_embeddings=len(self.special_tokens),
_weight=self.lang_model.get_input_embeddings().weight,
pad_token_id=self.pad_token_id,
)
if hasattr(input_embeds, "additional_embedding"):
input_embeds.additional_embedding.weight.data.normal_(
mean=0.0,
std=self.lang_model.config.initializer_range
if hasattr(self.lang_model.config, "initializer_range")
else 0.02,
)
self.lang_model.set_input_embeddings(input_embeds)
out_embeds = DecoupledLinear(
max_original_id=initial_tokenizer_len - 1,
additional_out_features=len(self.special_tokens),
_weight=self.lang_model.get_output_embeddings().weight,
_bias=self.lang_model.get_output_embeddings().bias if hasattr(self.lang_model.get_output_embeddings(), "bias") else None,
)
if hasattr(out_embeds, "additional_fc"):
out_embeds.additional_fc.weight.data.normal_(
mean=0.0,
std=self.lang_model.config.initializer_range
if hasattr(self.lang_model.config, "initializer_range")
else 0.02,
)
self.lang_model.set_output_embeddings(out_embeds)
# gradient checkpointing
self.vision_tokenizer._use_gradient_checkpointing = gradient_checkpointing
def forward(
self,
vision_x: Optional[torch.Tensor],
lang_x: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
past_key_values: Optional[
List[Union[torch.Tensor, Tuple[torch.Tensor]]]
] = None,
past_media_locations: Optional[torch.Tensor] = None,
past_vision_tokens: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = False,
**kwargs,
):
"""
Args:
vision_x: Vision input
shape (B, T_img, F, C, H, W) with F=1
only F = 1 is supported (single-frame videos)
if T_img > the number of media tokens in the corresponding input_ids (lang_x),
only the first number of media tokens in lang_x are used
lang_x: Language input ids, with media tokens denoting where
visual media should be inserted.
shape (B, T_txt)
attention_mask: Attention mask. Defaults to None.
labels: Labels. Defaults to None.
shape (B, T_txt)
past_key_values (Tuple[torch.Tensor]], optional): Past key value pairs for each of the T_txt previous tokens in the language model. Defaults to None.
list of length = number of decoder layers in the LM
exact implementation depends on LM, see Hugging Face docs
past_media_locations (torch.Tensor, optional): boolean mask denoting which of the previous T_txt tokens were media tokens. Defaults to None.
shape (B, T_txt)
past_vision_tokens (torch.Tensor, optional): Previous vision tokens. Defaults to None.
use_cache (Optional[bool], optional): Whether to use cache. Defaults to False.
If True, includes key_values, media_locations, and vision_tokens in the output.
"""
assert not (past_vision_tokens is None) ^ (
past_media_locations is None
), "past_vision_tokens and past_media_locations must both be None or both be not None"
# convert pixels to vision tokens
if vision_x is not None:
vision_features = self._encode_vision_x(vision_x=vision_x)
vision_tokens = self.vision_tokenizer(vision_features)
else:
vision_tokens = None
# fuse the vision and language tokens
new_inputs = self._prepare_inputs_for_forward(
vision_tokens=vision_tokens,
lang_x=lang_x,
attention_mask=attention_mask,
labels=labels,
past_key_values=past_key_values,
past_media_locations=past_media_locations,
padding_side="right",
past_vision_tokens=past_vision_tokens,
)
output = self.lang_model(
**new_inputs,
use_cache=use_cache,
past_key_values=past_key_values,
**kwargs,
)
# postprocessing may be needed, e.g. to remove extra tokens from logits that were inserted into the language stream
# or to add the past_vision_tokens and past_media_locations to the output
output = self._postprocess_outputs_from_forward(
output=output,
lang_x=lang_x,
vision_tokens=vision_tokens,
use_cache=use_cache,
past_vision_tokens=past_vision_tokens,
past_media_locations=past_media_locations,
)
# postforward hooks
self._post_forward_hook()
return output
def _encode_vision_x_anyres(self, samples, device):
image_raw = samples["image"] # list of patch list in of shape [1, N_patch, C, H, W]
image_sizes = samples["image_size"]
# concate list of patches into one big patch for any res encoding.
images = [x.squeeze(0) for x in image_raw] # [N_patch, C, H, W]
image = torch.cat(images, dim=0) # [\sum{B}{N_patch_i}, C, H, W]
image = image.to(device)
with torch.no_grad():
if self.vision_encoder.__class__.__name__ == "TimmModel":
image_embeds = self.vision_encoder.trunk.forward_features(image)
elif self.vision_encoder.__class__.__name__ == 'CLIPVisionModel':
image_embeds = self.vision_encoder(image).last_hidden_state
else:
image_embeds = self.vision_encoder(image)[1] # OpenCLIP returns tuples
if isinstance(self.vision_encoder, CLIPVisionModel):
base_img_size = self.vision_encoder.config.image_size
else:
base_img_size = self.vision_encoder.image_size[0]
if self.vision_encoder.__class__.__name__ == "TimmModel":
grid_size = self.vision_encoder.trunk.patch_embed.grid_size
elif self.vision_encoder.__class__.__name__ == 'CLIPVisionModel':
grid_size_base = self.vision_encoder.config.image_size // self.vision_encoder.config.patch_size
grid_size = (grid_size_base, grid_size_base)
else:
grid_size = self.vision_encoder.grid_size
height, width = grid_size
if not image_embeds.shape[1] == height * width:
assert image_embeds.shape[1] == height * width + 1 # For vision encoders that has [CLS] token.
image_embeds = image_embeds[:, 1:, :] # Drop the cls token for each patch.
n_vis_token_per_patch = image_embeds.shape[1]
# Split encoded patches and merge patch features
# 1. Get the raw sizes from samples, and split the image embeds [\sum_{B}(N_patch_i), N_tok(16*16), C]
split_sizes = [image.shape[0] for image in images]
image_embeds = torch.split(image_embeds, split_sizes, dim=0)
# 2. For each image (consist of a list of patches), merge the patches spatially (of shape [C, n_patch_height, n_patch_width])
new_image_embeds = []
patch_attn_masks = []
max_n_img_token = -1
for idx, patch_embeds in enumerate(image_embeds):
if patch_embeds.shape[0] > 1:
# 3. Flatten the patch features and get [C, n_patch_height * (n_patch_width+1)]
base_patch_embeds = patch_embeds[0] # TODO: prepend the CLS token for th base patch embeds (of the resized entire image).
patch_embeds = patch_embeds[1:]
assert height * width == base_patch_embeds.shape[0]
num_patch_width, num_patch_height = get_anyres_image_grid_shape(image_sizes[idx],
[[base_img_size,base_img_size*2],
[base_img_size*2,base_img_size],
[base_img_size*2,base_img_size*2],
[base_img_size*3,base_img_size],
[base_img_size,base_img_size*3]],
base_img_size) # Hardcoded grid_pinpoints.
patch_embeds = patch_embeds.view(num_patch_height, num_patch_width, height, width, -1)
patch_embeds = patch_embeds.permute(4, 0, 2, 1, 3).contiguous()
patch_embeds = patch_embeds.flatten(1, 2).flatten(2, 3)
# TODO: add an option that return masked patch_embeds instead of trimmed.
patch_embeds, patch_attn_mask = unpad_image(patch_embeds, image_sizes[idx], self.anyres_patch_sampling)
if hasattr(self, 'image_newline'):
patch_embeds = torch.cat((
patch_embeds,
self.image_newline[:, None, None].expand(*patch_embeds.shape[:-1], 1)
), dim=-1)
if self.anyres_patch_sampling:
patch_embeds = patch_embeds.view(-1, num_patch_height, num_patch_width, height*width)
patch_embeds = patch_embeds.flatten(1, 2).permute(1, 2, 0)
assert patch_attn_mask is not None
patch_attn_mask = patch_attn_mask.view(num_patch_height, num_patch_width, height*width)
patch_attn_mask = patch_attn_mask.flatten(0, 1)
patch_embeds = torch.cat((base_patch_embeds.unsqueeze(0), patch_embeds), dim=0)
patch_attn_mask = torch.cat((torch.ones(n_vis_token_per_patch, device=patch_embeds.device).unsqueeze(0), patch_attn_mask), dim=0)
else:
patch_embeds = patch_embeds.flatten(1, 2).transpose(0, 1)
patch_embeds = torch.cat((base_patch_embeds, patch_embeds), dim=0)
else:
patch_embeds = patch_embeds[0].unsqueeze(0) if self.anyres_patch_sampling else patch_embeds[0]
patch_attn_mask = torch.ones(n_vis_token_per_patch, device=patch_embeds.device).unsqueeze(0) if self.anyres_patch_sampling else None
if hasattr(self, 'image_newline'):
patch_embeds = torch.cat((
patch_embeds,
self.image_newline[None]
), dim=0)
if not self.anyres_patch_sampling:
max_n_img_token = max(patch_embeds.shape[0], max_n_img_token)
new_image_embeds.append(patch_embeds)
patch_attn_masks.append(patch_attn_mask)
if self.anyres_patch_sampling:
# Return individual patches for independent token downsampling.
return new_image_embeds, patch_attn_masks
# 4. Pad and concat the list of image_embeds [N_tok_i, C] together into a batch. Also modify the query attention mask.
image_embeds = []
image_atts = []
for image_embed in new_image_embeds:
n_img_token = image_embed.shape[0]
img_attn = torch.ones((max_n_img_token), dtype=torch.long, device=image_embed.device)
if n_img_token < max_n_img_token:
padded_embed = torch.zeros((max_n_img_token, image_embed.shape[-1]), dtype=image_embed.dtype, device=image_embed.device)
padded_embed[:n_img_token, :] = image_embed
img_attn[n_img_token:] = 0 # Mask out the padded entries.
else:
padded_embed = image_embed
image_embeds.append(padded_embed)
image_atts.append(img_attn)
image_embeds = torch.stack(image_embeds, dim=0) # Shape [B, N_tok_longest, C_dim]
image_atts = torch.stack(image_atts, dim=0) # Shape [B, N_tok_longest, C_dim]
# TODO: reshape image_embeds and image_atts to "b T F v d"
image_embeds = image_embeds[:, None, None, :, :]
# image_atts = image_atts[:, None, None, :, :]
return image_embeds, image_atts
def _encode_vision_x(self, vision_x: torch.Tensor):
"""
Compute media tokens from vision input by passing it through vision encoder and conditioning language model.
Args:
vision_x: Vision input
shape (B, T_img, F, C, H, W)
Images in the same chunk are collated along T_img, and frames are collated along F
Currently only F=1 is supported (single-frame videos)
rearrange code based on https://github.com/dhansmair/flamingo-mini
"""
assert vision_x.ndim == 6, "vision_x should be of shape (b, T_img, F, C, H, W)"
b, T, F = vision_x.shape[:3]
vision_x = rearrange(vision_x, "b T F c h w -> (b T F) c h w")
with torch.no_grad():
if self.vision_encoder.__class__.__name__ == "TimmModel":
vision_x = self.vision_encoder.trunk.forward_features(vision_x)
elif self.vision_encoder.__class__.__name__ == 'CLIPVisionModel':
vision_x = self.vision_encoder(vision_x).last_hidden_state
else:
vision_x = self.vision_encoder(vision_x)[1] # OpenCLIP returns tuples
vision_x = rearrange(vision_x, "(b T F) v d -> b T F v d", b=b, T=T, F=F)
return vision_x
def _concat_vision_cache(
self, lang_x, vision_tokens, past_vision_tokens, past_media_locations, use_cache
):
"""
Helper function to include the past vision tokens and past media locations in the output.
"""
if use_cache:
if past_media_locations is not None and past_vision_tokens is not None:
if vision_tokens is not None:
updated_vision_tokens = torch.cat(
[
past_vision_tokens,
vision_tokens,
],
dim=1,
)
else:
updated_vision_tokens = past_vision_tokens
updated_media_locations = torch.cat(
[
past_media_locations,
lang_x == self.media_token_id,
],
dim=1,
)
else:
updated_vision_tokens = vision_tokens
updated_media_locations = lang_x == self.media_token_id
else:
updated_vision_tokens = None
updated_media_locations = None
return updated_vision_tokens, updated_media_locations
def generate(
self,
vision_x: torch.Tensor,
lang_x: torch.Tensor,
attention_mask: torch.Tensor = None,
past_key_values: Optional[
List[Union[torch.Tensor, Tuple[torch.Tensor]]]
] = None,
past_media_locations: Optional[torch.Tensor] = None,
past_vision_tokens: Optional[torch.Tensor] = None,
**kwargs,
):
"""
Generate text conditioned on vision and language inputs.
Args:
vision_x (torch.Tensor): Vision input
shape (B, T_img, F, C, H, W)
see documentation for forward
lang_x (torch.Tensor): Language input
shape (B, T_txt)
attention_mask (torch.Tensor, optional): Attention mask. Defaults to None.
**kwargs: see generate documentation in Hugging Face CausalLM models.
Returns:
torch.Tensor: lang_x with generated tokens appended to it
"""
num_beams = kwargs.pop("num_beams", 1)
# convert pixels to vision tokens
if vision_x is not None:
vision_features = self._encode_vision_x(vision_x=vision_x)
vision_tokens = self.vision_tokenizer(vision_features)
else:
vision_tokens = None
# fuse the vision and language tokens
# for xattn, vision_x and media_location are repeat_interleaved s.t.
# the total batch size is B * num_beams
new_inputs = self._prepare_inputs_for_forward(
vision_tokens=vision_tokens,
lang_x=lang_x,
attention_mask=attention_mask,
past_key_values=past_key_values,
past_media_locations=past_media_locations,
past_vision_tokens=past_vision_tokens,
padding_side="left",
num_beams=num_beams,
)
output = self.lang_model.generate(
**new_inputs,
past_key_values=past_key_values,
num_beams=num_beams,
use_cache=True,
**kwargs,
)
self._post_forward_hook()
return output
@property
def num_trainable_params(self):
"""Print the number of trainable parameters"""
return num_params(self, filter_to_trainable=True)
def set_trainable(self):
"""
Freeze appropriate parameters in the model.
"""
raise NotImplementedError
def group_params_by_weight_decay(self):
"""
Return a tuple of (params to optimize w/ weight decay, params to optimize w/o weight decay)
"""
params_with_wd, params_without_wd = [], []
for n, p in self.named_parameters():
if p.requires_grad:
if self._should_apply_weight_decay(n):
params_with_wd.append(p)
else:
params_without_wd.append(p)
return params_with_wd, params_without_wd
def _should_apply_weight_decay(self, parameter_name):
"""
Return whether weight decay should be applied to a parameter.
"""
raise NotImplementedError
@property
def special_tokens(self):
"""
Returns a dict mapping from the attribute name of a special token to its string format,
e.g. "media_token": "<image>"
"""
assert (
"media_token" in self._special_tokens
), "VLMs need to request that the tokenizer add a media_token and call set_special_token_ids to set self.media_token_id"
return self._special_tokens
@property
def special_token_ids(self):
"""
Returns a list of the special token ids
"""
return [getattr(self, f"{att_name}_id") for att_name in self.special_tokens]
def set_special_token_ids(self, string_to_ids):
"""
Args:
string_to_ids (dict): mapping from token string to id
"""
assert set(self.special_tokens.values()).issubset(set(string_to_ids.keys()))
for att_name, token_str in self.special_tokens.items():
token_id = string_to_ids[token_str]
setattr(self, f"{att_name}_id", token_id)
setattr(self.lang_model, f"{att_name}_id", token_id)
def init_gradient_checkpointing(self):
from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
checkpoint_wrapper,
CheckpointWrapper,
CheckpointImpl,
apply_activation_checkpointing,
)
from functools import partial
non_reentrant_wrapper = partial(
checkpoint_wrapper,
checkpoint_impl=CheckpointImpl.NO_REENTRANT,
)
apply_activation_checkpointing(
self,
checkpoint_wrapper_fn=non_reentrant_wrapper,
check_fn=lambda m: getattr(m, "_use_gradient_checkpointing", False)
and not isinstance(m, CheckpointWrapper),
)
@dataclass
class VLMOutputWithPast(CausalLMOutputWithPast):
"""
VLMOutputWithPast is a wrapper around CausalLMOutputWithPast that adds the following attributes:
past_media_locations: Optional[torch.Tensor] = None,
past_vision_tokens: Optional[torch.Tensor] = None,
"""
past_media_locations: Optional[torch.Tensor] = None
past_vision_tokens: Optional[torch.Tensor] = None
def exists(val):
return val is not None
def FeedForward(dim, mult=4):
inner_dim = int(dim * mult)
return nn.Sequential(
nn.LayerNorm(dim),
nn.Linear(dim, inner_dim, bias=False),
nn.GELU(),
nn.Linear(inner_dim, dim, bias=False),
)
class VLMWithLanguageStream(VLM):
"""
VLM that fuses modalities by inserting vision tokens directly into the language stream.
"""
def __init__(
self,
vision_encoder: nn.Module,
vision_tokenizer: nn.Module,
lang_model: nn.Module,
initial_tokenizer_len: int,
pad_token_id: int,
decoder_layers_attr_name: str = None,
gradient_checkpointing: bool = False,
):
super().__init__(
vision_encoder=vision_encoder,
vision_tokenizer=vision_tokenizer,
lang_model=lang_model,
initial_tokenizer_len=initial_tokenizer_len,
pad_token_id=pad_token_id,
gradient_checkpointing=gradient_checkpointing,
)
self.decoder_layers_attr_name = decoder_layers_attr_name
if decoder_layers_attr_name is not None:
for block in getattr_recursive(self.lang_model, self.decoder_layers_attr_name):
block._use_gradient_checkpointing = gradient_checkpointing
def _prepare_inputs_for_forward(
self,
vision_tokens: torch.Tensor,
lang_x: torch.Tensor,
attention_mask: torch.Tensor,
labels: torch.Tensor = None,
past_key_values=None,
past_media_locations: torch.Tensor = None,
past_vision_tokens: torch.Tensor = None,
padding_side: str = "left",
num_beams: int = 1,
):
"""
Insert the vision tokens directly into the language stream/
This requires us to modify the input_ids, attention_mask, and labels.
"""
if past_key_values is not None:
past_len = past_key_values[0][0].shape[2]
assert attention_mask.shape[1] == past_len + lang_x.shape[1], (
"Attention_mask must be as long as the entire past len (including image tokens) and current input IDs. "
+ "Check that you've expanded the attention mask to account for past image tokens."
)
if vision_tokens is None:
return {
"input_ids": lang_x,
"attention_mask": attention_mask,
"labels": labels,
}
# get the language embeddings
lang_embeds = self.lang_model.get_input_embeddings()(lang_x)
# build up the multimodal embeddings
B = lang_x.shape[0]
has_labels = labels is not None
multimodal_embeds = []
multimodal_attention_mask = []
multimodal_labels = [] if has_labels else None
for i in range(B):
# get index of <image> tokens in lang_x[i]
image_token_idxs = torch.where(lang_x[i] == self.media_token_id)[0]
if len(image_token_idxs) == 0:
multimodal_embeds.append(lang_embeds[i].clone())
multimodal_attention_mask.append(attention_mask[i].clone())
if has_labels:
multimodal_labels.append(labels[i].clone())
continue
# # since an image is represented by self.num_tokens_per_vis tokens, we need to offset the image_token_idxs
# for j, img_idx in enumerate(image_token_idxs):
# image_token_idxs[j] += (self.num_tokens_per_vis - 1) * j
# loop through the image_token_idxs and insert the vision tokens
new_embed = lang_embeds[i].clone()
new_attention_mask = (
attention_mask[i].clone() if attention_mask is not None else None
)
if has_labels:
new_label = labels[i].clone()
for img_num, img_idx in enumerate(image_token_idxs):
new_embed = torch.cat(
(
new_embed[:img_idx],
vision_tokens[i][img_num],
new_embed[img_idx + self.num_tokens_per_vis :],
),
dim=0,
)
new_attention_mask = torch.cat(
(
new_attention_mask[:img_idx],
torch.ones(self.num_tokens_per_vis, dtype=torch.long).to(
attention_mask.device
),
new_attention_mask[img_idx + self.num_tokens_per_vis :],
),
dim=0,
)
if has_labels:
new_label = torch.cat(
(
new_label[:img_idx],
torch.ones(self.num_tokens_per_vis, dtype=torch.long).to(
labels.device
)
* -100,
new_label[img_idx + self.num_tokens_per_vis :],
),
dim=0,
)
multimodal_embeds.append(new_embed)
multimodal_attention_mask.append(new_attention_mask)
if has_labels:
multimodal_labels.append(new_label)
# stack
multimodal_embeds = stack_with_padding(
multimodal_embeds,
padding_value=self.pad_token_id,
padding_side=padding_side,
)
multimodal_attention_mask = stack_with_padding(
multimodal_attention_mask,
padding_value=0,
padding_side=padding_side,
)
if has_labels:
multimodal_labels = stack_with_padding(
multimodal_labels,
padding_value=-100,
padding_side=padding_side,
)
return {
"inputs_embeds": multimodal_embeds,
"attention_mask": multimodal_attention_mask,
"labels": multimodal_labels,
}
def _postprocess_outputs_from_forward(
self,
output: CausalLMOutputWithPast,
lang_x: torch.Tensor,
vision_tokens: torch.Tensor,
past_vision_tokens: torch.Tensor,
past_media_locations: torch.Tensor,
use_cache: bool = False,
):
# Include the past vision tokens and past media locations in the output
updated_vision_tokens, updated_media_locations = self._concat_vision_cache(
lang_x=lang_x,
vision_tokens=vision_tokens,
past_vision_tokens=past_vision_tokens,
past_media_locations=past_media_locations,
use_cache=use_cache,
)
# return logits that are the same shape as the original input_ids
logits = output.logits
batch_logits = []
B, T_txt = lang_x.shape
for i in range(B):
sequence_logits = []
logits_j = 0
for j in range(T_txt):
if lang_x[i, j] != self.media_token_id:
sequence_logits.append(logits[i, logits_j])
logits_j += 1
else:
# append the logit for the first image token, then skip over the rest
# note: the model actually learns to predict <im_patch>, not <image>
sequence_logits.append(logits[i, logits_j])
logits_j += self.num_tokens_per_vis
sequence_logits = torch.stack(sequence_logits, dim=0) # (B, vocab_size)
batch_logits.append(sequence_logits)
batch_logits = torch.stack(batch_logits, dim=0) # (B, T_txt, vocab_size)
# The final logits shape should be the same as the original input_ids shape
assert batch_logits.shape[:2] == (B, T_txt)
# assemble the output
output = VLMOutputWithPast(
loss=output.loss,
logits=batch_logits,
past_key_values=output.past_key_values,
hidden_states=output.hidden_states,
attentions=output.attentions,
past_media_locations=updated_media_locations,
past_vision_tokens=updated_vision_tokens,
)
return output
def _post_forward_hook(self):
pass
@property
def num_params_per_module(self):
"""Print the number of parameters per module in the model"""
return "\n".join(
[
f"Vision encoder: {num_params(self.vision_encoder):,} parameters",
f"Vision tokenizer: {num_params(self.vision_tokenizer):,} parameters",
f"Language model: {num_params(self.lang_model):,} parameters",
]
)
@property
def num_trainable_params_per_module(self):
"""Print the number of trainable parameters per module in the model"""
return "\n".join(
[
f"Vision encoder: {num_params(self.vision_encoder, filter_to_trainable=True):,} trainable parameters",
f"Vision tokenizer: {num_params(self.vision_tokenizer, filter_to_trainable=True):,} trainable parameters",
f"Language model: {num_params(self.lang_model, filter_to_trainable=True):,} trainable parameters",
]
)
class Kosmos(VLMWithLanguageStream):
def __init__(
self,
vision_encoder: nn.Module,
vision_tokenizer: nn.Module,
lang_model: nn.Module,
initial_tokenizer_len: int,
pad_token_id: int,
decoder_layers_attr_name: str = None,
gradient_checkpointing: bool = False,
):
"""
Args:
vision_encoder (nn.Module): HF CLIPModel
lang_encoder (nn.Module): HF causal language model
vis_feature_dim (int): final dimension of the visual features outputted by the vision_encoder
initial_tokenizer_len (int): size of the tokenizer vocab
padding_token_id (int): id of the padding token. None if no padding token; then a padding token
will be inserted into self.special_tokens, which factory.py fills after creating new tokens
decoder_layers_attr_name (str, optional): name of the decoder layers attribute. Defaults to None.
gradient_checkpointing (bool, optional): whether to use gradient checkpointing. Defaults to False.
"""
self._special_tokens = {
"media_token": "<image>",
"image_placeholder_token": "<image placeholder>",
"end_of_trunk_token": "<|endofchunk|>"
}
super().__init__(
vision_encoder=vision_encoder,
vision_tokenizer=vision_tokenizer,
lang_model=lang_model,
initial_tokenizer_len=initial_tokenizer_len,
gradient_checkpointing=gradient_checkpointing,
decoder_layers_attr_name=decoder_layers_attr_name,
pad_token_id=pad_token_id
)
# def set_trainable(self):
# """
# Unfreeze everything except the vision_encoder
# """
# self.requires_grad_(True)
# self.vision_encoder.requires_grad_(False)
def set_trainable(self, unfreeze_vision_encoder: bool = False):
"""
Unfreeze everything except the vision_encoder
"""
self.requires_grad_(True)
self.vision_encoder.requires_grad_(unfreeze_vision_encoder)
def _should_apply_weight_decay(self, parameter_name):
"""
Kosmos applies 0.01 weight deacy to everything
"""
return True
def generate(
self,
vision_x: torch.Tensor,
lang_x: torch.Tensor,
attention_mask: torch.Tensor = None,
past_key_values: Optional[
List[Union[torch.Tensor, Tuple[torch.Tensor]]]
] = None,
past_media_locations: Optional[torch.Tensor] = None,
past_vision_tokens: Optional[torch.Tensor] = None,
**kwargs
):
"""
Generate text conditioned on vision and language inputs.
Args:
vision_x (torch.Tensor): Vision input
shape (B, T_img, F, C, H, W)
see documentation for forward
lang_x (torch.Tensor): Language input
shape (B, T_txt)
attention_mask (torch.Tensor, optional): Attention mask. Defaults to None.
**kwargs: see generate documentation in Hugging Face CausalLM models.
Returns:
torch.Tensor: lang_x with generated tokens appended to it
"""
num_beams = kwargs.pop("num_beams", 1)
# convert pixels to vision tokens
if vision_x is not None:
vision_features = self._encode_vision_x(vision_x=vision_x)
vision_tokens = self.vision_tokenizer(vision_features)
else:
vision_tokens = None
# fuse the vision and language tokens
# for xattn, vision_x and media_location are repeat_interleaved s.t.
# the total batch size is B * num_beams
new_inputs = self._prepare_inputs_for_forward(
vision_tokens=vision_tokens,
lang_x=lang_x,
attention_mask=attention_mask,
past_key_values=past_key_values,
past_media_locations=past_media_locations,
past_vision_tokens=past_vision_tokens,
padding_side="left",
num_beams=num_beams,
)
if transformers.__version__ == '4.41.0.dev0':
output = self.lang_model.generate(
**new_inputs,
num_beams=num_beams,
use_cache=True,
eos_token_id=self.end_of_trunk_token_id,
**kwargs)
else:
output = self.lang_model.generate(
**new_inputs,
past_key_values=past_key_values,
num_beams=num_beams,
use_cache=True,
eos_token_id=self.end_of_trunk_token_id,
**kwargs)
return output