transformer-engine 1.9.0

Transformer Engine

Quickstart | Installation | User Guide | Examples | FP8 Convergence | Integrations | Release notes

Latest News

• [03/2024] Turbocharged Training: Optimizing the Databricks Mosaic AI stack with FP8

• [03/2024] FP8 Training Support in SageMaker Model Parallelism Library

• [12/2023] New NVIDIA NeMo Framework Features and NVIDIA H200

• [11/2023] Inflection-2: The Next Step Up

• [11/2023] Unleashing The Power Of Transformers With NVIDIA Transformer Engine

• [11/2023] Accelerating PyTorch Training Workloads with FP8

• [09/2023] Transformer Engine added to AWS DL Container for PyTorch Training

• [06/2023] Breaking MLPerf Training Records with NVIDIA H100 GPUs

• [04/2023] Benchmarking Large Language Models on NVIDIA H100 GPUs with CoreWeave (Part 1)

What is Transformer Engine?

Transformer Engine (TE) is a library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper GPUs, to provide better performance with lower memory utilization in both training and inference. TE provides a collection of highly optimized building blocks for popular Transformer architectures and an automatic mixed precision-like API that can be used seamlessly with your framework-specific code. TE also includes a framework agnostic C++ API that can be integrated with other deep learning libraries to enable FP8 support for Transformers.

As the number of parameters in Transformer models continues to grow, training and inference for architectures such as BERT, GPT and T5 become very memory and compute-intensive. Most deep learning frameworks train with FP32 by default. This is not essential, however, to achieve full accuracy for many deep learning models. Using mixed-precision training, which combines single-precision (FP32) with lower precision (e.g. FP16) format when training a model, results in significant speedups with minimal differences in accuracy as compared to FP32 training. With Hopper GPU architecture FP8 precision was introduced, which offers improved performance over FP16 with no degradation in accuracy. Although all major deep learning frameworks support FP16, FP8 support is not available natively in frameworks today.

TE addresses the problem of FP8 support by providing APIs that integrate with popular Large Language Model (LLM) libraries. It provides a Python API consisting of modules to easily build a Transformer layer as well as a framework-agnostic library in C++ including structs and kernels needed for FP8 support. Modules provided by TE internally maintain scaling factors and other values needed for FP8 training, greatly simplifying mixed precision training for users.

Highlights

• Easy-to-use modules for building Transformer layers with FP8 support

• Optimizations (e.g. fused kernels) for Transformer models

• Support for FP8 on NVIDIA Hopper and NVIDIA Ada GPUs

• Support for optimizations across all precisions (FP16, BF16) on NVIDIA Ampere GPU architecture generations and later

Examples

import torch
import transformer_engine.pytorch as te
from transformer_engine.common import recipe

# Set dimensions.
in_features = 768
out_features = 3072
hidden_size = 2048

# Initialize model and inputs.
model = te.Linear(in_features, out_features, bias=True)
inp = torch.randn(hidden_size, in_features, device="cuda")

# Create an FP8 recipe. Note: All input args are optional.
fp8_recipe = recipe.DelayedScaling(margin=0, fp8_format=recipe.Format.E4M3)

# Enable autocasting for the forward pass
with te.fp8_autocast(enabled=True, fp8_recipe=fp8_recipe):
    out = model(inp)

loss = out.sum()
loss.backward()

import flax
import jax
import jax.numpy as jnp
import transformer_engine.jax as te
import transformer_engine.jax.flax as te_flax
from transformer_engine.common import recipe

BATCH = 32
SEQLEN = 128
HIDDEN = 1024

# Initialize RNG and inputs.
rng = jax.random.PRNGKey(0)
init_rng, data_rng = jax.random.split(rng)
inp = jax.random.normal(data_rng, [BATCH, SEQLEN, HIDDEN], jnp.float32)

# Create an FP8 recipe. Note: All input args are optional.
fp8_recipe = recipe.DelayedScaling(margin=0, fp8_format=recipe.Format.HYBRID)

# Enable autocasting for the forward pass
with te.fp8_autocast(enabled=True, fp8_recipe=fp8_recipe):
    model = te_flax.DenseGeneral(features=HIDDEN)

    def loss_fn(params, other_vars, inp):
      out = model.apply({'params':params, **other_vars}, inp)
      return jnp.mean(out)

    # Initialize models.
    variables = model.init(init_rng, inp)
    other_variables, params = flax.core.pop(variables, 'params')

    # Construct the forward and backward function
    fwd_bwd_fn = jax.value_and_grad(loss_fn, argnums=(0, 1))

    for _ in range(10):
      loss, (param_grads, other_grads) = fwd_bwd_fn(params, other_variables, inp)

Installation

docker run --gpus all -it --rm nvcr.io/nvidia/pytorch:23.10-py3

pip install git+https://github.com/NVIDIA/TransformerEngine.git@stable

Breaking Changes

v1.7: Padding mask definition for PyTorch

In an effort to unify the definition and usage of the attention mask across all three frameworks in Transformer Engine, the padding mask has changed from True meaning inclusion of the corresponding position in attention to exclusion of that position in our PyTorch implementation. Since v1.7, all attention mask types follow the same definition where True means masking out the corresponding position and False means including that position in attention calculation.

An example of this change is,

# for a batch of 3 sequences where `a`s, `b`s and `c`s are the useful tokens
# and `0`s are the padding tokens,
[a, a, a, 0, 0,
 b, b, 0, 0, 0,
 c, c, c, c, 0]
# the padding mask for this batch before v1.7 is,
[ True,  True,  True, False, False,
  True,  True, False, False, False,
  True,  True,  True,  True, False]
# and for v1.7 onwards it should be,
[False, False, False,  True,  True,
 False, False,  True,  True,  True,
 False, False, False, False,  True]

FP8 Convergence

FP8 has been tested extensively across different model architectures and configurations and we found no significant difference between FP8 and BF16 training loss curves. FP8 has also been validated for accuracy on downstream LLM tasks (e.g. LAMBADA and WikiText). Below are examples of models tested for convergence across different frameworks.

Integrations

Transformer Engine has been integrated with popular LLM frameworks such as:

• DeepSpeed

• Hugging Face Accelerate

• Lightning

• MosaicML Composer

• NVIDIA JAX Toolbox

• NVIDIA Megatron-LM

• NVIDIA NeMo Framework

• Amazon SageMaker Model Parallel Library

• Levanter

• Hugging Face Nanotron - Coming soon!

• Colossal-AI - Coming soon!

• PeriFlow - Coming soon!

• GPT-NeoX - Coming soon!

Contributing

We welcome contributions to Transformer Engine! To contribute to Transformer Engine and make pull requests, follow the guidelines outlined in the CONTRIBUTING.rst guide.

Papers

• Attention original paper

• Megatron-LM tensor parallel

• Megatron-LM sequence parallel

• FP8 Formats for Deep Learning

Videos

• What’s New in Transformer Engine and FP8 Training | GTC 2024

• FP8 Training with Transformer Engine | GTC 2023

• FP8 for Deep Learning | GTC 2023

• Inside the Hopper Architecture