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#
# For licensing see accompanying LICENSE file.
# Copyright (C) 2024 Apple Inc. All Rights Reserved.
#
import math
from typing import Optional, Sequence
import torch
from torch import Tensor, nn
from mobileclip.modules.common.transformer import (
PositionalEmbedding,
TransformerEncoder,
get_normalization_layer,
)
from mobileclip.modules.text.repmixer import RepMixerBlock
from mobileclip import logger
class TextTransformer(nn.Module):
def __init__(self, cfg: dict, projection_dim: int, *args, **kwargs) -> None:
super().__init__()
model_dim = cfg["dim"]
no_scale_embedding = cfg.get("no_scale_embedding", False)
no_pos_embedding = cfg.get("no_pos_embedding", False)
embed_dropout = cfg.get("embed_dropout", 0.0)
norm_layer = cfg["norm_layer"]
variant = cfg["model_name"]
self.vocab_size = cfg["vocab_size"]
self.projection_dim = projection_dim
# Token embedding layer
self.embedding_layer = nn.Embedding(
embedding_dim=model_dim, num_embeddings=self.vocab_size
)
self.embed_scale = 1.0 if no_scale_embedding else model_dim**-0.5
# Context length
context_length = cfg["context_length"]
assert (
context_length is not None
), "Context length can't be None. Please set value accordingly."
self.positional_embedding = (
None
if no_pos_embedding
else PositionalEmbedding(
num_embeddings=context_length, embedding_dim=model_dim
)
)
self.embedding_dropout = nn.Dropout(p=embed_dropout)
# Transformer layer
n_transformer_layers = cfg["n_transformer_layers"]
# FFN multipliers for transformer layer
ffn_multipliers = cfg["ffn_multiplier_per_layer"]
if isinstance(ffn_multipliers, (float, int)):
ffn_multipliers = [ffn_multipliers] * n_transformer_layers
if not isinstance(ffn_multipliers, Sequence):
logger.error(
"{} expects FFN multipliers as a list, whose length is the same as"
" number of transformer layers. Got: {}".format(
self.__class__.__name__, type(ffn_multipliers)
)
)
elif (
isinstance(ffn_multipliers, Sequence)
and len(ffn_multipliers) != n_transformer_layers
):
logger.error(
"We need FFN multiplier for each transformer layer. Got {} ffn"
" multipliers while number of transformer layers = {}".format(
len(ffn_multipliers), n_transformer_layers
)
)
ffn_dims = [
int(math.ceil(model_dim * ffn_mult / 16.0) * 16.0)
for ffn_mult in ffn_multipliers
]
# Heads for transformer layers
mha_heads = cfg["n_heads_per_layer"]
if isinstance(mha_heads, int):
mha_heads = [mha_heads] * n_transformer_layers
if not isinstance(mha_heads, Sequence):
logger.error(
"{} expects MHA heads as a list, whose length is the same as number of "
"transformer layers. Got: {}".format(
self.__class__.__name__, type(mha_heads)
)
)
elif isinstance(mha_heads, Sequence) and len(mha_heads) != n_transformer_layers:
logger.error(
"{} needs MHA heads for each transformer layer. Got {} mha heads while"
" number of transformer layers = {}".format(
self.__class__.__name__, len(mha_heads), n_transformer_layers
)
)
if variant == "base":
self.transformer = nn.ModuleList(
[
TransformerEncoder(
embed_dim=model_dim,
num_heads=mha_heads[layer_idx],
ffn_latent_dim=ffn_dims[layer_idx],
transformer_norm_layer=norm_layer,
)
for layer_idx in range(n_transformer_layers)
]
)
elif variant == "mct":
self.transformer = nn.ModuleList([RepMixerBlock(dim=model_dim)])
self.transformer.extend(
[
TransformerEncoder(
embed_dim=model_dim,
num_heads=mha_heads[layer_idx],
ffn_latent_dim=ffn_dims[layer_idx],
transformer_norm_layer=norm_layer,
)
for layer_idx in range(n_transformer_layers)
]
)
self.transformer.extend([RepMixerBlock(dim=model_dim)])
else:
raise ValueError("Unrecognized text encoder variant {}".format(variant))
self.final_layer_norm = get_normalization_layer(
num_features=model_dim, norm_type=norm_layer
)
self.projection_layer = nn.Parameter(
torch.empty(model_dim, self.projection_dim)
)
self.model_dim = model_dim
self.causal_masking = cfg["causal_masking"]
def forward_embedding(self, text_tokens: Tensor) -> Tensor:
"""Return text embedding for all tokens.
Args:
text_tokens: a tensor of token indices. Shape: [batch_size, context_length]
Returns:
A tensor of [batch_size, context_length, hidden_dim].
"""
# [batch_size, context_length] --> [batch_size, context_length, hidden_dim]
token_emb = self.embedding_layer(text_tokens)
seq_len = token_emb.shape[1]
if self.positional_embedding is not None:
token_emb = token_emb + self.positional_embedding(seq_len).to(
token_emb.dtype
)
token_emb = self.embedding_dropout(token_emb)
return token_emb
def build_attention_mask(self, context_length: int, batch_size: int) -> Tensor:
"""Build causal attention mask [batch_size, context_length, context_length]."""
# Build mask with full attention between the tokens
# pytorch uses additive attention mask; fill with -inf
mask = torch.empty(context_length, context_length)
mask.fill_(float("-inf"))
mask.triu_(1) # zero out the lower diagonal
mask = mask.unsqueeze(0) # add dummy batch dimension
mask = mask.expand(batch_size, -1, -1)
return mask
def encode_text(
self,
text_tokens: Tensor,
key_padding_mask: Optional[Tensor] = None,
return_all_tokens: bool = False,
*args,
**kwargs
) -> Tensor:
"""Return text token embeddings.
Args:
text_tokens: a tensor of token indices. Shape: [batch_size, context_length]
key_padding_mask: a tensor of boolean values as the padding mask.
Shape: [batch_size, context_length]
return_all_tokens: a boolean flag to return all tokens, defaults to False
to return only EOT token embedding.
Returns:
A tensor of [batch_size, context_length, hidden_dim] if return_all_tokens is
True, otherwise a tensor of [batch_size, hidden_dim].
"""
# Discrete tokens to continuous embeddings
# [batch_size, context_length] --> [batch_size, context_length, hidden_dim]
token_emb = self.forward_embedding(text_tokens)
# [1, context_length, context_length]
attn_mask = None
if self.causal_masking:
attn_mask = self.build_attention_mask(
context_length=text_tokens.shape[1], batch_size=text_tokens.shape[0]
)
attn_mask = attn_mask.to(device=token_emb.device, dtype=token_emb.dtype)
key_padding_mask = None
for layer in self.transformer:
token_emb = layer(
token_emb,
key_padding_mask=key_padding_mask,
attn_mask=attn_mask,
)
# Apply layer norm
token_emb = self.final_layer_norm(token_emb)
if return_all_tokens:
return token_emb
# Take features from the eot embedding (eot_token is the highest number in each sequence)
token_emb = token_emb[
torch.arange(text_tokens.shape[0]), text_tokens.argmax(dim=-1)
]
token_emb = token_emb @ self.projection_layer
return token_emb
def forward(
self,
text_tokens: Tensor,
key_padding_mask: Optional[Tensor] = None,
return_all_tokens: bool = False,
*args,
**kwargs
) -> Tensor:
# Image-text pair data with single caption
# [B, CL] --> [B, d]
text_tokens = self.encode_text(
text_tokens=text_tokens,
key_padding_mask=key_padding_mask,
return_all_tokens=return_all_tokens,
*args,
**kwargs
)
return text_tokens
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