Build SOTA AI Models 80% faster with modular, high-performance, and scalable building blocks!
After building out thousands of neural nets and facing the same annoying bottlenecks of chaotic codebases with no modularity and low performance modules, Zeta needed to be born to enable me and others to quickly prototype, train, and optimize the latest SOTA neural nets and deploy them into production.
Zeta places a radical emphasis on useability, modularity, and performance. Zeta is now currently employed in 100s of models across my github and across others. Get started below and LMK if you want my help building any model, I'm here for you 😊 💜
$ pip3 install -U zetascale
Creating a model empowered with the aforementioned breakthrough research features is a breeze. Here's how to quickly materialize the renowned Flash Attention
import torch
from zeta.nn import FlashAttention
q = torch.randn(2, 4, 6, 8)
k = torch.randn(2, 4, 10, 8)
v = torch.randn(2, 4, 10, 8)
attention = FlashAttention(causal=False, dropout=0.1, flash=True)
output = attention(q, k, v)
print(output.shape)- Powers Transformer models
import torch
from zeta.nn import SwiGLUStacked
x = torch.randn(5, 10)
swiglu = SwiGLUStacked(10, 20)
swiglu(x).shapeRelativePositionBiasquantizes the distance between two positions into a certain number of buckets and then uses an embedding to get the relative position bias. This mechanism aids in the attention mechanism by providing biases based on relative positions between the query and key, rather than relying solely on their absolute positions.
import torch
from torch import nn
from zeta.nn import RelativePositionBias
# Initialize the RelativePositionBias module
rel_pos_bias = RelativePositionBias()
# Example 1: Compute bias for a single batch
bias_matrix = rel_pos_bias(1, 10, 10)
# Example 2: Utilize in conjunction with an attention mechanism
# NOTE: This is a mock example, and may not represent an actual attention mechanism's complete implementation.
class MockAttention(nn.Module):
def __init__(self):
super().__init__()
self.rel_pos_bias = RelativePositionBias()
def forward(self, queries, keys):
bias = self.rel_pos_bias(queries.size(0), queries.size(1), keys.size(1))
# Further computations with bias in the attention mechanism...
return None # Placeholder
# Example 3: Modify default configurations
custom_rel_pos_bias = RelativePositionBias(
bidirectional=False, num_buckets=64, max_distance=256, num_heads=8
)The FeedForward module performs a feedforward operation on the input tensor x. It consists of a multi-layer perceptron (MLP) with an optional activation function and LayerNorm. Used in most language, multi-modal, and modern neural networks.
import torch
from zeta.nn import FeedForward
model = FeedForward(256, 512, glu=True, post_act_ln=True, dropout=0.2)
x = torch.randn(1, 256)
output = model(x)
print(output.shape)- The BitLinear module performs linear transformation on the input data, followed by quantization and dequantization. The quantization process is performed using the absmax_quantize function, which quantizes the input tensor based on the absolute maximum value, from the paper
import torch
from torch import nn
import zeta.quant as qt
class MyModel(nn.Module):
def __init__(self):
super().__init__()
self.linear = qt.BitLinear(10, 20)
def forward(self, x):
return self.linear(x)
# Initialize the model
model = MyModel()
# Create a random tensor of size (128, 10)
input = torch.randn(128, 10)
# Perform the forward pass
output = model(input)
# Print the size of the output
print(output.size()) # torch.Size([128, 20])- This is an implementation of the multi-modal Palm-E model using a decoder llm as the backbone with an VIT image encoder to process vision, it's very similiar to GPT4, Kosmos, RTX2, and many other multi-modality model architectures
import torch
from zeta.structs import (
AutoregressiveWrapper,
Decoder,
Encoder,
Transformer,
ViTransformerWrapper,
)
class PalmE(torch.nn.Module):
"""
PalmE is a transformer architecture that uses a ViT encoder and a transformer decoder.
Args:
image_size (int): Size of the image.
patch_size (int): Size of the patch.
encoder_dim (int): Dimension of the encoder.
encoder_depth (int): Depth of the encoder.
encoder_heads (int): Number of heads in the encoder.
num_tokens (int): Number of tokens.
max_seq_len (int): Maximum sequence length.
decoder_dim (int): Dimension of the decoder.
decoder_depth (int): Depth of the decoder.
decoder_heads (int): Number of heads in the decoder.
alibi_num_heads (int): Number of heads in the alibi attention.
attn_kv_heads (int): Number of heads in the attention key-value projection.
use_abs_pos_emb (bool): Whether to use absolute positional embeddings.
cross_attend (bool): Whether to cross attend in the decoder.
alibi_pos_bias (bool): Whether to use positional bias in the alibi attention.
rotary_xpos (bool): Whether to use rotary positional embeddings.
attn_flash (bool): Whether to use attention flash.
qk_norm (bool): Whether to normalize the query and key in the attention layer.
Returns:
torch.Tensor: The output of the model.
Usage:
img = torch.randn(1, 3, 256, 256)
text = torch.randint(0, 20000, (1, 1024))
model = PalmE()
output = model(img, text)
print(output)
"""
def __init__(
self,
image_size=256,
patch_size=32,
encoder_dim=512,
encoder_depth=6,
encoder_heads=8,
num_tokens=20000,
max_seq_len=1024,
decoder_dim=512,
decoder_depth=6,
decoder_heads=8,
alibi_num_heads=4,
attn_kv_heads=2,
use_abs_pos_emb=False,
cross_attend=True,
alibi_pos_bias=True,
rotary_xpos=True,
attn_flash=True,
qk_norm=True,
):
super().__init__()
# vit architecture
self.encoder = ViTransformerWrapper(
image_size=image_size,
patch_size=patch_size,
attn_layers=Encoder(
dim=encoder_dim, depth=encoder_depth, heads=encoder_heads
),
)
# palm model architecture
self.decoder = Transformer(
num_tokens=num_tokens,
max_seq_len=max_seq_len,
use_abs_pos_emb=use_abs_pos_emb,
attn_layers=Decoder(
dim=decoder_dim,
depth=decoder_depth,
heads=decoder_heads,
cross_attend=cross_attend,
alibi_pos_bias=alibi_pos_bias,
alibi_num_heads=alibi_num_heads,
rotary_xpos=rotary_xpos,
attn_kv_heads=attn_kv_heads,
attn_flash=attn_flash,
qk_norm=qk_norm,
),
)
# autoregressive wrapper to enable generation of tokens
self.decoder = AutoregressiveWrapper(self.decoder)
def forward(self, img: torch.Tensor, text: torch.Tensor):
"""Forward pass of the model."""
try:
encoded = self.encoder(img, return_embeddings=True)
return self.decoder(text, context=encoded)
except Exception as error:
print(f"Failed in forward method: {error}")
raise
# Usage with random inputs
img = torch.randn(1, 3, 256, 256)
text = torch.randint(0, 20000, (1, 1024))
# Initiliaze the model
model = PalmE()
output = model(img, text)
print(output)Unet is a famous convolutional neural network architecture originally used for biomedical image segmentation but soon became the backbone of the generative AI Mega-revolution. The architecture comprises two primary pathways: downsampling and upsampling, followed by an output convolution. Due to its U-shape, the architecture is named U-Net. Its symmetric architecture ensures that the context (from downsampling) and the localization (from upsampling) are captured effectively.
import torch
from zeta.nn import Unet
# Initialize the U-Net model
model = Unet(n_channels=1, n_classes=2)
# Random input tensor with dimensions [batch_size, channels, height, width]
x = torch.randn(1, 1, 572, 572)
# Forward pass through the model
y = model(x)
# Output
print(f"Input shape: {x.shape}")
print(f"Output shape: {y.shape}")The VisionEmbedding class is designed for converting images into patch embeddings, making them suitable for processing by transformer-based models. This class plays a crucial role in various computer vision tasks and enables the integration of vision data into transformer architectures!
import torch
from zeta.nn import VisionEmbedding
# Create an instance of VisionEmbedding
vision_embedding = VisionEmbedding(
img_size=224,
patch_size=16,
in_chans=3,
embed_dim=768,
contain_mask_token=True,
prepend_cls_token=True,
)
# Load an example image (3 channels, 224x224)
input_image = torch.rand(1, 3, 224, 224)
# Perform image-to-patch embedding
output = vision_embedding(input_image)
# The output now contains patch embeddings, ready for input to a transformer model- Niva focuses on weights of certain layers (specified by quantize_layers). Ideal for models where runtime activation is variable. 👁️ Example Layers: nn.Embedding, nn.LSTM.
import torch
from zeta import niva
# Load a pre-trained model
model = YourModelClass()
# Quantize the model dynamically, specifying layers to quantize
niva(
model=model,
model_path="path_to_pretrained_model_weights.pt",
output_path="quantized_model.pt",
quant_type="dynamic",
quantize_layers=[nn.Linear, nn.Conv2d],
dtype=torch.qint8,
)- Increase model speed by 2x with this module that fuses together 2 hyper-optimized dense ops from bits and bytes and a gelu together!
import torch
from zeta.nn import FusedDenseGELUDense
x = torch.randn(1, 512)
model = FusedDenseGELUDense(512, 1024)
out = model(x)
out.shape- FusedDropoutLayerNorm is a fused kernel of dropout and layernorm to speed up FFNs or MLPS by 2X
import torch
from torch import nn
from zeta.nn import FusedDropoutLayerNorm
# Initialize the module
model = FusedDropoutLayerNorm(dim=512)
# Create a sample input tensor
x = torch.randn(1, 512)
# Forward pass
output = model(x)
# Check output shape
print(output.shape) # Expected: torch.Size([1, 512])- Pytorch implementation of the new SSM model architecture Mamba
import torch
from zeta.nn import MambaBlock
# Initialize Mamba
block = MambaBlock(dim=64, depth=1)
# Random input
x = torch.randn(1, 10, 64)
# Apply the model to the block
y = block(x)
print(y.shape)
# torch.Size([1, 10, 64])import torch
from zeta.nn import Film
# Initialize the Film layer
film_layer = Film(dim=128, hidden_dim=64, expanse_ratio=4)
# Create some dummy data for conditions and hiddens
conditions = torch.randn(10, 128) # Batch size is 10, feature size is 128
hiddens = torch.randn(
10, 1, 128
) # Batch size is 10, sequence length is 1, feature size is 128
# Pass the data through the Film layer
modulated_features = film_layer(conditions, hiddens)
# Print the shape of the output
print(modulated_features.shape) # Should be [10, 1, 128]- A single wrapper for torch.fx, torch.script, torch.compile, dynamic quantization, mixed precision through torch.amp, with execution time metrics all in once place!
import torch
from zeta.nn import hyper_optimize
@hyper_optimize(
torch_fx=False,
torch_script=False,
torch_compile=True,
quantize=True,
mixed_precision=True,
enable_metrics=True,
)
def model(x):
return x @ x
out = model(torch.randn(1, 3, 32, 32))
print(out)Direct Policy Optimization employed for many RLHF applications for LLMs.
import torch
from torch import nn
from zeta.rl import DPO
# Define a simple policy model
class PolicyModel(nn.Module):
def __init__(self, input_dim, output_dim):
super().__init__()
self.fc = nn.Linear(input_dim, output_dim)
def forward(self, x):
return self.fc(x)
input_dim = 10
output_dim = 5
policy_model = PolicyModel(input_dim, output_dim)
# Initialize DPO with the policy model
dpo_model = DPO(model=policy_model, beta=0.1)
# Sample preferred and unpreferred sequences
preferred_seq = torch.randint(0, output_dim, (3, input_dim))
unpreferred_seq = torch.randint(0, output_dim, (3, input_dim))
# Compute loss
loss = dpo_model(preferred_seq, unpreferred_seq)
print(loss)Train or finetune any model on any cluster in 1 click with zetacloud, just pass in your file and the GPU type and quantity you want! To gain access first pip install zetascale then run zeta -h in the terminal. Here is the docs for more
- Flexible Pricing with pooling from many clouds
- Easy Deployment with 1 click
- Various options for cloud providers!
Zetacloud CLI
options:
-h, --help show this help message and exit
-t TASK_NAME, --task_name TASK_NAME
Task name
-c CLUSTER_NAME, --cluster_name CLUSTER_NAME
Cluster name
-cl CLOUD, --cloud CLOUD
Cloud provider
-g GPUS, --gpus GPUS GPUs
-f FILENAME, --filename FILENAME
Filename
-s, --stop Stop flag
-d, --down Down flag
-sr, --status_report Status report flag
- A simple run example code would be like:
zeta -f train.py -g A100:8All classes must have documentation if you see a class or function without documentation then please report it to me at kye@apac.ai,
Documentation is at zeta.apac.ai
You should install the pre-commit hooks with pre-commit install. This will run the linter, mypy, and a subset of the tests on every commit.
For more examples on how to run the full test suite please refer to the CI workflow.
Some examples of running tests locally:
python3 -m pip install -e '.[testing]' # install extra deps for testing
python3 -m pytest tests/ # whole test suiteJoin our growing community around the world, for real-time support, ideas, and discussions on how to build better models 😊
- View our official Docs
- Chat live with us on Discord
- Follow us on Twitter
- Connect with us on LinkedIn
- Visit us on YouTube
- Join the Swarms community on Discord!
Want to train a custom AI model for a real-world task like General Multi-Modal Models, Facial Recognitions, Drug Discovery, Humanoid Robotics? I'll help you create the model architecture then train the model and then optimize it to meet your quality assurance standards.
Book a 1-on-1 Session with Kye here., the Creator, to discuss any issues, provide feedback, or explore how we can improve Zeta for you or help you build your own custom models!
The easiest way to contribute is to pick any issue with the good first issue tag 💪. Read the Contributing guidelines here. Bug Report? File here | Feature Request? File here
Zeta is an open-source project, and contributions are VERY welcome. If you want to contribute, you can create new features, fix bugs, or improve the infrastructure. Please refer to the CONTRIBUTING.md and our contributing board to participate in Roadmap discussions!
Help us accelerate our backlog by supporting us financially! Note, we're an open source corporation and so all the revenue we generate is through donations at the moment ;)
- Apache