eformer 0.0.1

Embark on a journey of paralleled/unparalleled computational prowess with eformer in JAX

eformer (EasyDel Former)

eformer (EasyDel Former) is a utility library designed to simplify and enhance the development of machine learning models using JAX. It provides a collection of tools for sharding, custom PyTrees, quantization, and optimized operations, making it easier to build and scale models efficiently.

Features

• Sharding Utilities (escale): Tools for efficient sharding and distributed computation in JAX.
• Custom PyTrees (jaximus): Enhanced utilities for creating custom PyTrees and ArrayValue objects, updated from Equinox.
• Custom Calling (callib): A tool for custom function calls and direct integration with Triton kernels in JAX.
• Optimizer Factory: A flexible factory for creating and configuring optimizers like AdamW, Adafactor, Lion, and RMSProp.
• Custom Operations and Kernels:
  • Flash Attention 2 for GPUs/TPUs (via Triton and Pallas).
  • 8-bit and NF4 quantization for efficient model.
• Quantization Support: Tools for 8-bit and NF4 quantization, enabling memory-efficient model deployment.

Installation

You can install eformer via pip:

pip install eformer

Quick Start

Customizing Arrays With ArrayValue

import jax

from eformer.jaximus import ArrayValue, implicit
from eformer.ops.quantization.quantization_functions import (
 dequantize_row_q8_0,
 quantize_row_q8_0,
)

array = jax.random.normal(jax.random.key(0), (256, 64), "f2")


class Array8B(ArrayValue):
 scale: jax.Array
 weight: jax.Array

 def __init__(self, array: jax.Array):
  self.weight, self.scale = quantize_row_q8_0(array)

 def materialize(self):
  return dequantize_row_q8_0(self.weight, self.scale)


qarray = Array8B(array)


@jax.jit
@implicit
def sqrt(x):
 return jax.numpy.sqrt(x)


print(sqrt(qarray))
print(qarray)

Optimizer Factory

from eformer.optimizers import OptimizerFactory, SchedulerConfig, AdamWConfig

# Create an AdamW optimizer with a cosine scheduler
scheduler_config = SchedulerConfig(scheduler_type="cosine", learning_rate=1e-3, steps=1000)
optimizer, scheduler = OptimizerFactory.create("adamw", scheduler_config, AdamWConfig())

Quantization

from eformer.quantization import Array8B, ArrayNF4

# Quantize an array to 8-bit
qarray = Array8B(jax.random.normal(jax.random.key(0), (256, 64), "f2"))

# Quantize an array to NF4
n4array = ArrayNF4(jax.random.normal(jax.random.key(0), (256, 64), "f2"), 64)

Contributing

We welcome contributions! Please read our Contributing Guidelines to get started.

License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.