kosmosx 0.0.4

Kosmos-X - PyTorch

Kosmos-X: Advanced Multi-Modality AI Model 🚀🌌

Kosmos-X is a modular Multi-Modality AI model designed to seamlessly process diverse forms of data, including:

• Images
• Videos
• Audio
• Any type

At lightspeed with ultra-reliability.

The unique strength of Kosmos-X lies in its ability to process extremely long sequences of these multi-modality inputs, with context lengths of up to 40,000+!

Model Roadmap

Help us create a Model Roadmap on Kosmos-X Figma

Ready for Training!

Kosmos-X is now ready for training, and we're actively seeking cloud providers or grant providers to collaborate in training this revolutionary model and eventually release it open source. If you're interested in learning more or supporting this endeavor, please feel free to email me at [e-mail].

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Appreciation

• All the creators in Agora, Join Agora the community of AI engineers changing the world with their creations.
• LucidRains for inspiring me to devote myself to open source AI

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Please note that this README.md has been recreated based on the provided information and may not fully reflect the actual content of the linked GitHub repository.

Installation

pip install kosmosx

Usage

import torch
from kosmosx.model import Kosmos

# Create a sample text token tensor
text_tokens = torch.randint(0, 32002, (1, 50), dtype=torch.long)

# Create a sample image tensor
images = torch.randn(1, 3, 224, 224)

# Instantiate the model
model = Kosmos()

# Pass the sample tensors to the model's forward function
output = model.forward(
    text_tokens=text_tokens,
    images=images
)

# Print the output from the model
print(f"Output: {output}")

Training

accelerate config

then: accelerate launch train_distributed.py

Get Involved

We're just at the beginning of our journey. As we continue to develop and refine Kosmos-X, we invite you to join us. Whether you're a developer, researcher, or simply an enthusiast, your insights and contributions can help shape the future of Kosmos-X.

Contributing to Kosmos-X

We are thrilled to invite you to be a part of the Kosmos-X project. This is not just an open source project but a community initiative, and we value your expertise and creativity. To show our appreciation, we have instituted a unique rewards system that directly compensates contributors from the revenue generated by the Kosmos-X API.

Why Contribute

Contributing to Kosmos-X not only enhances your skills and profile but also comes with financial rewards. When you contribute code, documentation, or any form of improvement to the Kosmos-X project, you are adding value. As such, we believe it's only fair that you share in the rewards.

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The model

KOSMOS-1 uses a decoder-only Transformer architecture based on Magneto (Foundation Transformers), i.e. an architecture that employs a so called sub-LN approach where layer normilization is added both before the attention module (pre-ln) and afterwards (post-ln) combining the advantages that either approaches have for language modelling and image understanding respectively. The model is also initialized according to a specific metric also described in the paper, allowing for more stable training at higher learning rates.

They encode images to image features using a CLIP VIT-L/14 model and use a perceiver resampler introduced in Flamingo to pool the image features from 256 -> 64 tokens. The image features are combined with the token embeddings by adding them to the input sequence surrounded by special tokens <image> and </image>. An example is <s> <image> image_features </image> text </s>. This allows image(s) to be interwoven with text in the same sequence.

We follow the hyperparameters described in the paper visible in the following image:

Details

Model (decoder)

We use the torchscale implementation of the decoder-only Transformer architecture from Foundation Transformers:

from torchscale.architecture.config import DecoderConfig
from torchscale.architecture.decoder import Decoder

config = DecoderConfig(
    decoder_layers=24,
    decoder_embed_dim=2048,
    decoder_ffn_embed_dim=8192,
    decoder_attention_heads=32,
    dropout=0.1,
    activation_fn="gelu",
    attention_dropout=0.1,
    vocab_size=32002,
    subln=True,                 # sub-LN approach
    xpos_rel_pos=True,          # rotary positional embeddings
    max_rel_pos=2048
)
decoder = Decoder(
    config,
    embed_tokens=embed,
    embed_positions=embed_positions,
    output_projection=output_projection
)

CLIP VIT-L/14

For the image model (CLIP VIT-L/14) we use a pretrained OpenClip model:

from transformers import CLIPModel
clip_model = CLIPModel.from_pretrained("laion/CLIP-ViT-L-14-laion2B-s32B-b82K").vision_model
# projects image to [batch_size, 256, 1024]
features = clip_model(pixel_values=images)["last_hidden_state"]

Perceiver Resampler

We follow the default hyperparams for the perceiver resampler as no hyperparams are given in the paper:

from flamingo_pytorch import PerceiverResampler
perceiver = PerceiverResampler(
    dim = 1024,
    depth = 2,
    dim_head = 64,
    heads = 8,
    num_latents = 64,
    num_media_embeds = 256
)
# projects image features to [batch_size, 64, 1024]
self.perceive(images).squeeze(1)

Because the model expects a hidden dimension of 2048, we use a nn.Linear layer to project the image features to the correct dimension and initialize it according to Magneto's initialization scheme:

image_proj = torch.nn.Linear(1024, 2048, bias=False)
torch.nn.init.normal_(
    image_proj.weight, mean=0, std=2048**-0.5
)
scaled_image_features = image_proj(image_features)

Tokenizer

The paper describes a SentencePiece with a vocabulary of 64007 tokens. For simplicity (as we don't have the training corpus available), we use the next best open-source alternative which is the pretrained T5-large tokenizer from HuggingFace. This tokenizer has a vocabulary of 32002 tokens.

from transformers import T5Tokenizer
tokenizer = T5Tokenizer.from_pretrained(
    "t5-large",
    additional_special_tokens=["<image>", "</image>"],
    extra_ids=0,
    model_max_length=1984 # 2048 - 64 (image features)
)

We then embed the tokens with a nn.Embedding layer. We actually use a bnb.nn.Embedding from bitandbytes which allows us to use 8-bit AdamW later.

import bitsandbytes as bnb
embed = bnb.nn.Embedding(
    32002,          # Num embeddings
    2048,           # Embedding dim
    padding_idx
)

For positional embeddings, we use:

from torchscale.component.embedding import PositionalEmbedding
embed_positions= PositionalEmbedding(
    2048,           # Num embeddings
    2048,           # Embedding dim
    padding_idx
)

Also, we add an output projection layer to project the hidden dimension to the vocabulary size and initialize it according to Magneto's initialization scheme:

output_projection = torch.nn.Linear(
    2048, 32002, bias=False
)
torch.nn.init.normal_(
    output_projection.weight, mean=0, std=2048**-0.5
)

Decoder changes

I had to make some slight changes to the decoder to allow it to accept already embedded features in the forward pass. This was necessary to allow the more complex input sequence described above. The changes are visible in the following diff in line 391 of torchscale/architecture/decoder.py:

+if kwargs.get("passed_x", None) is None:
+    x, _ = self.forward_embedding(
+        prev_output_tokens, token_embeddings, incremental_state
+    )
+else:
+    x = kwargs["passed_x"]

-x, _ = self.forward_embedding(
-    prev_output_tokens, token_embeddings, incremental_state
-)

Training

• We're actively seeking cloud providers or grant providers to train this all-new revolutionary model and release it open source, if you would like to learn more please email me at [e-mail]

TODO

• Integrate flash attention inside the torchscale/component/multihead_attention.py

• Integrate one write head is all you need

• Look into integrating qk_norm

• Look into integrating Falcon LLM model tokenizer if they allow special tokens

• Prepare datasets, training strategies, and infrastructure for massive production level traning

• Run tests and make sure trains well with all optimizations on small dataset