Answers from the documentation; checking which model is on duty
Hi - I answer from the OpenSmartRoute documentation: routing, the API, plans and quotas, self-hosting. Ask away, or open a support ticket if you need a person.
Grounded in the docs - follow a source before acting on it.
Long-Context Training on TPUs with MaxText Adds New Parallel Strategie - OpenSmartRoute
Introduction - what long-context training is and why it matters
Long-context training allows models to process very large sequences of tokens. These sequences can be millions of tokens long. This capability is important for tasks like reading entire code repositories or hours of video transcripts. It also helps models reason through complex multi-step tasks. Longer context windows give models a better working memory. This improves their ability to understand and generate detailed, coherent responses.
Traditionally, models could only handle a few hundred tokens at once. Increasing the context window size has been a major goal in AI research. Larger windows enable models to handle more information at once. This makes them more useful for real-world applications. It also allows models to learn from longer sequences during training. As a result, long-context training is becoming a foundation of frontier AI models.
Background - growth of context window sizes and training challenges
Over the past few years, context window sizes have grown significantly. Early models used windows of around 128,000 to 200,000 tokens. Now, models are being trained with windows of 1 million to 2 million tokens. Some models are even arriving with the ability to process 10 million tokens. This growth reflects the demand for models that can handle more complex data.
Training at these lengths introduces new challenges. As sequences grow longer, the amount of computation needed increases dramatically. The attention mechanism, which is core to many models, becomes a bottleneck. Attention FLOPs (floating point operations) grow quadratically with sequence length. This makes training very slow and expensive. Managing memory and communication also becomes harder. These challenges require new strategies to keep training feasible.
Attention bottlenecks - compute, memory, and communication limits
Attention is a key operation in large language models. It involves every token attending to every other token. When sequences are long, this process becomes very costly. The number of FLOPs needed grows quadratically with sequence length. This means that doubling the length quadruples the compute needed. Attention dominates the training step at very long sequences.
Stanford's CS146S course now emphasizes AI-driven coding practices, open-source contributions, and architecture principles, reflecting recent advances in autonomous coding.
Memory is another limiting factor. Activations, keys, and values for each token take up space. As sequences grow, these volumes increase. Storing all of them on each chip can exhaust high-bandwidth memory (HBM). This can cause out-of-memory errors and slowdowns. Communication between chips also becomes a problem. Moving large amounts of data across chips adds latency and reduces efficiency. These issues make training at multi-million-token lengths difficult without new methods.
MaxText framework - new support for context parallelism strategies
Google AI has introduced support for long-context training in MaxText, an open-source framework for training models. MaxText now supports four different strategies for context parallelism on Cloud TPU hardware. These strategies help split long sequences across multiple chips. This reduces memory load and improves efficiency.
MaxText uses a kernel called Splash Attention from Tokamax. This kernel handles attention operations efficiently, skipping masked blocks and packed sequences. The framework also leverages a high-speed inter-chip interconnect called the 3D inter-chip interconnect (ICI). This allows sequences to span entire slices of TPU chips while overlapping communication with compute. These improvements enable models to process sequences up to 14 million tokens long.
Parallel strategies explained - All-gather, Ulysses, Ring Attention, USP
Four main strategies support context parallelism in MaxText. Each has different tradeoffs in memory, compute, and communication.
The simplest is All-gather. Each chip keeps a chunk of the sequence. At each layer, chips gather keys and values from all other chips. This allows each chip to attend locally. However, because of causal masking, later tokens need larger prefixes. Balancing compute load across chips is necessary.
Ulysses, or All-to-All, changes how data is shared. Each chip holds the full sequence for some attention heads. Keys and values are rotated across chips using All-to-All communication. This keeps attention memory constant regardless of the number of chips. It does not need token load balancing but cannot split across more chips than the number of attention heads.
Ring Attention, or P2P, moves away from full data gathering. Keys and values travel around a ring of chips. Each chip attends to its own chunk and the chunks from neighbors. This reduces memory needs further and overlaps communication with compute. However, it requires explicit load balancing and can reduce kernel efficiency.
Unified Sequence Parallelism (USP) combines All-to-All and Ring Attention. It maps these strategies onto two axes of the chip mesh. This allows longer chunks and fewer communication hops. It improves scalability for very long sequences and larger models.
Performance benchmarks - results on TPU 5p and TPU 7
MaxText benchmarks show strong performance on TPU hardware. Using a model like Llama 3.1 8B, it achieves 70% Model FLOPs Utilization (MFU) on TPU 5p for sequences up to 4 million tokens. This indicates high efficiency at long sequences.
On TPU 7 chips, the same model can process sequences up to 14 million tokens. The MFU remains around 50% across all sequence lengths below 6 million tokens. TPU 7's larger memory and bandwidth help sustain higher utilization. Ring Attention, in particular, performs well on TPU 7, enabling very long sequences with good efficiency.
These results show that the right strategy and hardware can handle extremely long sequences efficiently. The choice of strategy depends on the model size, sequence length, and hardware capabilities.
Tradeoffs and strategy selection - memory, compute, and model shape
Choosing the best context parallelism strategy depends on several factors. When the gathered key-value (KV) data fits in high-bandwidth memory, All-gather is usually the simplest and fastest option. It is suitable for models with smaller KV sizes or when the sequence is short enough.
If the KV data no longer fits in memory, the ring family of strategies becomes more suitable. Ring Attention (P2P) moves data around the ring, reducing memory needs. It is ideal for very long sequences or models with many attention heads.
The number of chips and the model's attention head count also influence strategy choice. When the KV data cannot fit in memory, a hybrid approach like USP can be effective. It combines the benefits of All-to-All and ring strategies to extend sequence length further.
Memory management is crucial. When running low on memory, offloading activations and other data to host memory can help. MaxText supports moving data tensor by tensor, hiding transfers behind compute. This keeps Model FLOPs Utilization stable even at very long sequences.
Future directions - broader model support and kernel tuning
Looking ahead, support for more model types is planned. Models that combine sliding-window and global layers, such as Gemma, could use different strategies for each part. Support for linear attention mechanisms like Gated Delta Net (GDN) and Kimi Delta Attention (KDA) is also being considered.
Further tuning of kernels and backward passes aims to narrow the efficiency gap between different strategies. This will help models run more smoothly at various sequence lengths and hardware configurations. The goal is to make long-context training more accessible and scalable across different models and setups.
How OpenSmartRoute helps
Teams routing AI requests through OpenSmartRoute can benefit from these advancements. The platform can direct requests to models optimized for long sequences, using the best-fit strategy for each case. It scores models based on quality, cost, speed, and safety, helping teams choose the most efficient option.
OpenSmartRoute can also manage models with different context lengths and memory needs. It supports local and cloud deployment, ensuring requests stay within cost and safety rules. The platform learns from outcomes, increasing traffic to well-performing models. This makes it easier to deploy and scale long-context models effectively.
How it compares - what existed before, what this changes and what stays the same
Before this development, training very long sequences on TPUs was limited by memory and compute bottlenecks. Traditional attention mechanisms, like the standard all-gather approach, required each chip to hold the full sequence's keys and values. This limited sequence length because memory and bandwidth could not keep up.
Existing solutions used model parallelism, but they faced challenges with communication overhead and memory use. As sequence lengths grew, training became slower and more expensive. Some models could only handle a few hundred thousand tokens at once.
This new approach introduces multiple context-parallel strategies supported by MaxText. These strategies include All-gather, Ulysses (All-to-All), ring attention (P2P), and hybrid methods like USP. They use the Tokamax Splash Attention kernel and leverage the 3D inter-chip interconnect (ICI). This allows sequences to span millions of tokens without overwhelming memory or bandwidth.
The main change is that these strategies enable training at multi-million-token lengths efficiently. They overlap communication with compute and stream keys and values instead of materializing them fully. As a result, longer context windows are now feasible for training large models.
What stays the same is the core attention mechanism. The strategies modify how keys and values are shared across chips, not how attention itself works. Standard attention principles remain, but the implementation becomes more scalable. The support for longer sequences does not alter the fundamental model architecture but enhances training capabilities.
Questions this leaves open - what the source does not say and how a reader can check it
The source does not specify the exact limits of sequence length for different models or hardware configurations. It mentions benchmarks with Llama 3.1 8B, but does not detail how other models perform or how the strategies scale with larger models.
It does not explain how these strategies affect training speed in real-world scenarios. While it reports Model FLOPs Utilization (MFU) percentages, it does not give precise training times or throughput numbers for different sequence lengths.
The impact on model quality is not discussed. Longer sequences can improve performance on certain tasks, but the source does not evaluate the quality of models trained with these methods versus traditional approaches.
It also leaves open how these strategies integrate with other training techniques, such as mixed precision or gradient checkpointing. These are common in large-scale training but are not mentioned here.
To check these points, a reader can look for additional benchmarks or case studies from Google or other research groups. They can also review the MaxText documentation and experiment with the open-source code. Testing different models and sequence lengths on TPUs will reveal how well these strategies perform in practice.
Furthermore, exploring how these methods interact with other training optimizations can help understand their broader impact. Keeping an eye on updates from Google and related research papers will provide more insights into the capabilities and limitations of long-context training.
Pierre Fabre and XtalPi announced a partnership to discover new small-molecule drugs. XtalPi provides AI and robotics, while Pierre Fabre handles selection and development.
