alpa-projects/alpa - Githubissues

Note: Alpa is not actively maintained currently. It is available as a research artifact. The core algorithm in Alpa has been merged into XLA, which is still being maintained. https://github.com/openxla/xla/tree/main/xla/hlo/experimental/auto_sharding

Documentation | Slack

Alpa is a system for training and serving large-scale neural networks.

Scaling neural networks to hundreds of billions of parameters has enabled dramatic breakthroughs such as GPT-3, but training and serving these large-scale neural networks require complicated distributed system techniques. Alpa aims to automate large-scale distributed training and serving with just a few lines of code.

The key features of Alpa include:

💻 Automatic Parallelization. Alpa automatically parallelizes users' single-device code on distributed clusters with data, operator, and pipeline parallelism.

🚀 Excellent Performance. Alpa achieves linear scaling on training models with billions of parameters on distributed clusters.

✨ Tight Integration with Machine Learning Ecosystem. Alpa is backed by open-source, high-performance, and production-ready libraries such as Jax, XLA, and Ray.

Serving

The code below shows how to use huggingface/transformers interface and Alpa distributed backend for large model inference. Detailed documentation is in Serving OPT-175B using Alpa.

from transformers import AutoTokenizer
from llm_serving.model.wrapper import get_model

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-2.7b")
tokenizer.add_bos_token = False

# Load the model. Alpa automatically downloads the weights to the specificed path
model = get_model(model_name="alpa/opt-2.7b", path="~/opt_weights/")

# Generate
prompt = "Paris is the capital city of"

input_ids = tokenizer(prompt, return_tensors="pt").input_ids
output = model.generate(input_ids=input_ids, max_length=256, do_sample=True)
generated_string = tokenizer.batch_decode(output, skip_special_tokens=True)

print(generated_string)

Training

Use Alpa's decorator @parallelize to scale your single-device training code to distributed clusters. Check out the documentation site and examples folder for installation instructions, tutorials, examples, and more.

import alpa

# Parallelize the training step in Jax by simply using a decorator
@alpa.parallelize
def train_step(model_state, batch):
    def loss_func(params):
        out = model_state.forward(params, batch["x"])
        return jnp.mean((out - batch["y"]) ** 2)

    grads = grad(loss_func)(model_state.params)
    new_model_state = model_state.apply_gradient(grads)
    return new_model_state

# The training loop now automatically runs on your designated cluster
model_state = create_train_state()
for batch in data_loader:
    model_state = train_step(model_state, batch)

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