Deepdive: pytorch profiler, standford transformer, XTuner, Luminal, DeepFaceLive

July 17, 2024 admin

The PyTorch Profiler analyzes deep learning models’ performance by collecting timing and resource usage stats, helping identify bottlenecks and optimize memory and execution. Stanford’s CS25 lecture series, “Transformers United V4,” covers state-of-the-art transformer research and applications. XTuner offers a flexible toolkit for fine-tuning large models, supporting various algorithms and high training throughput. Luminal optimizes deep learning performance with ahead-of-time compilation and efficient execution on CUDA/Metal APIs. DeepFaceLive allows real-time face swaps from video streams, with options to train custom models and animate static faces.

Pytorch Profiler
Standford lecture on transformer
XTuner
Luminal
DeepFaceLive

Pytorch Profiler

The PyTorch profiler is a powerful tool that helps analyze the performancecharacteristics of your deep learning models. It works by collecting detailed timing information and resource usage statistics while your model is running, allowing you to identify bottlenecks and optimize performance.

You should use the profiler when you need to understand where your model’s execution time is being spent, whether on CPU or GPU operations, or when you want to optimize memory usage or identify potential areas for parallelization.

It’s particularly useful when working with large, complex models or when you need to deploy your models on resource-constrained devices.

You can use it to:

Identify slow operations or sections of code that could be optimized or replaced with more efficient alternatives.
Understand memory usage patterns and potential sources of memory leaks.
Analyze the impact of different input data or model architecture choices on performance.

import torch
import torch.nn as nn
import torch.profiler

# Define a simple model
model = nn.Sequential(nn.Linear(100, 50),

                      nn.ReLU(), 
                      nn.Linear(50, 10)).cuda()

# Dummy input
input_tensor = torch.randn(32, 100).cuda()

# Set up the profiler
with torch.profiler.profile(
    activities=[torch.profiler.ProfilerActivity.CPU, 
                torch.profiler.ProfilerActivity.CUDA],
    record_shapes=True,
    profile_memory=True,
    with_stack=True) as prof:

    # Run the model
    output = model(input_tensor)

# Print the profiler results
print(prof.key_averages().table(sort_by="cuda_time_total"))

Standford lecture on transformer

Stanford’s first CS25 lecture, Transformers United V4, was just uploaded on Youtube. You can also attend it live on Zoom.

It features leading researchers discussing cutting-edge topics such as:

Large language model architectures (GPT, Gemini)
Creative applications (DALL-E for generating art, Sora)
Applications in biology, neuroscience, robotics, complex games

The series aims to provide an in-depth technical look at the state-of-the-art in transformer research. Scheduled speakers include experts who will dive into advanced concepts like:

Model alignment techniques (reinforcement learning, RLHF)
Sparse mixtures of experts architectures (Mixtral)
Developing precision language models for edge computing (U-LaMPS)
New training objectives for large language models
Code generation with language models (StarCoder)

The technical lectures offer insights into the latest transformer innovations from researchers at places like OpenAI, Google, NVIDIA, the Allen Institute, Mistral AI, Drexel University, Zhipu AI and more.

Stanford CS25: V1 I Transformers United: DL Models that have revolutionized NLP, CV, RL

Stanford CS25: V1 I Transformers in Language: The development of GPT Models, GPT3

Stanford CS25: V1 I Transformers in Vision: Tackling problems in Computer Vision

Stanford CS25: V1 I Decision Transformer: Reinforcement Learning via Sequence Modeling

Stanford CS25: V1 I Mixture of Experts (MoE) paradigm and the Switch Transformer

Stanford CS25: V1 I DeepMind's Perceiver and Perceiver IO: new data family architecture

Stanford CS25: V1 I Self Attention and Non-parametric transformers (NPTs)

Stanford CS25: V1 I Transformer Circuits, Induction Heads, In-Context Learning

Stanford CS25: V1 I Audio Research: Transformers for Applications in Audio, Speech, Music

Stanford CS25: V2 I Represent part-whole hierarchies in a neural network, Geoff Hinton

Stanford CS25: V2 I Introduction to Transformers w/ Andrej Karpathy

Stanford CS25: V2 I Language and Human Alignment

Stanford CS25: V2 I Emergent Abilities and Scaling in LLMs

Stanford CS25: V2 I Strategic Games

Stanford CS25: V2 I Robotics and Imitation Learning

XTuner

XTuner is an efficient, flexible and full-featured toolkit for fine-tuning large models. It supports different kinds of fine-tuning (continuous pre-training, instruction fine-tuning, agent fine-tuning) and various training algorithms (QLoRA, LoRA, full parameter).

It also supports pre-training and fine-tuning on almost all GPUs. It offers a high training throughput by automatically dispatching high-performance operators, such as FlashAttention and Triton kernels.

GitHub

Luminal

Luminal, a deep learning library, helps you achieve high performance by compiling everything ahead-of-time. It enables you to build and execute static computation graphs, optimizing performance. With minimalistic architecture and native support for CUDA/Metal APIs, it ensures efficient execution. Tested against Pytorch, it validates correctness. Luminal aims to be the fastest ML framework, achieving speeds of 15-25 tokens per second on M-series Macbooks.

GitHub

DeepFaceLive

LLaMA DeepFaceLive is a library that allows to perform face swaps in real time a from computer streaming using a webcam or directly from a video call.

The repository shows a list of faces the swap can be performed with. It also offers the possibility to train your own face model for better results.

There is also a Face Animator module in the DeepFaceLive repository that allows to control a static face picture using video or your own face from the camera.

GitHub