Gemini

The open source implementation of Gemini, the model that will "eclipse ChatGPT", it seems to work by directly taking in all modalities without an encoder for some kind which means that the encoding is built into the modal.

The input sequences for Gemini consist of texts, audio, images, and videos. These inputs are transformed into tokens, which are then processed by a transformer. Subsequently, conditional decoding takes place to generate image outputs.

Interestingly, the architecture of Gemini bears resemblance to Fuyu's architecture but is expanded to encompass multiple modalities. Instead of utilizing a visual transformer (vit) encoder, Gemini simply feeds image embeddings directly into the transformer.

For Gemini, the token inputs will likely be indicated by special modality tokens such as [IMG], , [AUDIO], or . Codi, a component of Gemini, also employs conditional generation and makes use of the tokenized outputs.

To implement this model effectively, I intend to initially focus on the image embeddings to ensure their smooth integration. Subsequently, I will proceed with incorporating audio embeddings and then video embeddings.

Install

pip3 install gemini-torch

Usage

Gemini Transformer Usage

Base transformer
Multi Grouped Query Attn / flash attn
rope
alibi
xpos
qk norm
no pos embeds
kv cache

import torch 
from gemini_torch import Gemini

# Initialize the model
model = Gemini(
    num_tokens=50432,
    max_seq_len=8192,
    dim=2560,
    depth=32,
    dim_head=128,
    heads=24,
    use_abs_pos_emb=False,
    alibi_pos_bias=True,
    alibi_num_heads=12,
    rotary_xpos=True,
    attn_flash=True,
    attn_kv_heads=2,
    qk_norm=True,
    attn_qk_norm=True,
    attn_qk_norm_dim_scale=True,
)

# Initialize the randint
x = torch.randint(0, 50432, (1, 8192))

# Apply model to y
y = model(x)

# Print logits
print(y)

Multi-Modal with Imgs

Img processing through a specially crafted module that takes in img -> patches it -> then reshapes to the shape of the text tensors, [B, seqlen, dim] -> align with text tokens

import torch
from gemini_torch.model import Gemini

# Initialize model
model = Gemini(
    num_tokens=50432,
    max_seq_len=8192,
    dim=2560,
    depth=32,
    dim_head=128,
    heads=24,
    use_abs_pos_emb=False,
    alibi_pos_bias=True,
    alibi_num_heads=12,
    rotary_xpos=True,
    attn_flash=True,
    attn_kv_heads=2,
    qk_norm=True,
    attn_qk_norm=True,
    attn_qk_norm_dim_scale=True,
)

# Text shape: [batch, seq_len, dim]
text = torch.randint(0, 50432, (1, 8192))

# Img shape: [batch, channels, height, width]
img = torch.randn(1, 3, 256, 256)

# Apply model to text and img
y = model(text, img)

# Output shape: [batch, seq_len, dim]
print(y.shape)

`ImgToTransformer`

takes in img -> patches -> reshapes to [B, SEQLEN, Dim] to align with transformer

import torch
from gemini_torch.utils import ImgToTransformer

# Example usage
num_patches = 16
patch_size = 16
transformer_dim = 512
img_channels = 3
seq_len = 50000
reduced_dim = 256  # Reduced dimension after dimensionality reduction

model = ImgToTransformer(
    num_patches, patch_size, transformer_dim, img_channels, seq_len, reduced_dim
)

# Dummy image input [BATCH, CHANNELS, HEIGHT, WIDTH]
dummy_img = torch.randn(1, 3, 64, 64)  # Batch size of 1, 64x64 RGB image

# Forward pass
seq_space_output = model(dummy_img)
print(seq_space_output.shape)  # Expected shape: [1, 50000, 256]

References

Combine Reinforcment learning with modular pretrained transformer, multi-modal capabilities, image, audio,
self improving mechanisms like robocat
PPO? or MPO
get good at backtracking and exploring alternative paths
speculative decoding
Algorithm of Thoughts
RLHF
Gemini Report
Gemini Landing Page

Todo

Implement the img feature embedder and align imgs with text and pass into transformer
Implement the audio processing by making an audio processor that intakes in audio embeddings and reshapes it to match language embeddings dimension shape [B, SEQLEN, Dim]
Do the same for video

junqiangchen / Gemini