The Autotune Cache That Wasn't: Tracing Triton's In-Memory Kernel Selection on Blackwell GPUs

Introduction

In the intricate dance of deploying large language models on cutting-edge hardware, few moments are as satisfying as the convergence of multiple investigative threads into a single, clarifying insight. Message 31 of this opencode session captures exactly such a moment. The assistant, deep in the trenches of deploying the GLM-5-NVFP4 model on NVIDIA's RTX PRO 6000 Blackwell GPUs, has been painstakingly tracing why the Triton autotuner selects a particular configuration for the _mma_sparse_decode_split_kernel. After establishing that the BT32 (block size 32) configurations exceed the per-thread-block shared memory cap of 99 KB on compute capability 12.0 (SM120), and that the autotuner prunes such configurations by assigning them infinite latency, the assistant reaches for the final piece of evidence: the persisted autotune disk cache. What it finds—or rather, does not find—becomes the pivot point for a deeper understanding of how Triton's autotuning lifecycle actually works in production.

This message is a study in investigative reasoning: the moment when a negative result (the absence of a file) transforms into positive knowledge about system behavior, reshaping the investigator's mental model of how kernel selection operates across process restarts.

The Investigation So Far

To appreciate the significance of message 31, we must understand the terrain the assistant has been navigating. The problem space involves deploying the GLM-5-NVFP4 model using SGLang on a machine equipped with eight RTX PRO 6000 Blackwell GPUs. The assistant has been investigating the sparse decode kernel's autotuning behavior, specifically why the Triton compiler selects a configuration with BLOCK_T=16 and 4-bit quantization (BT16/w4) over alternatives with BLOCK_T=32 (BT32/w4 and BT32/w8).

The earlier messages in this segment established a critical hardware constraint: the Blackwell SM120 architecture has a per-thread-block shared memory opt-in cap of 99 KB (101376 bytes), as confirmed by both the NVIDIA Blackwell Tuning Guide and CUTLASS source code. The BT32 configurations require 104704 bytes (102.25 KB), exceeding this cap by 3328 bytes. When the Triton autotuner attempts to launch these kernels, the CUDA runtime throws an OutOfResources error, which the autotuner catches and translates into an infinite latency score, effectively pruning the configuration from consideration. The BT16/w4 configuration, requiring only 61568 bytes, fits comfortably within the cap and is selected by default.

But this conclusion rested on an implicit assumption about when and how the autotuner makes its selection. The assistant had been searching for the .autotune.json file—the persisted disk cache that Triton uses to store autotuning results across process invocations—expecting to find definitive evidence of which configuration won and which were pruned.

The Missing File: A Negative Result with Positive Implications

The bash command executed in this message tells the story succinctly:

find /root/.triton -name "*mma_sparse_decode_split*autotune*.json" 2>/dev/null

The result: nothing. No autotune.json files exist for the split kernel. The assistant's reasoning immediately pivots to accommodate this new information:

"There's no autotune.json file, so the disk cache isn't being used—the autotune results are computed fresh in memory during each process startup via warmup."

This is the central insight of the message. The assistant had been operating under the assumption that Triton's autotuning results were persisted to disk, meaning the kernel selection could survive process restarts. The absence of the cache file reveals a different reality: the autotuner's cache_results parameter defaults to False, and in this deployment, no explicit configuration has overridden it. The compiled cubins (CUDA binaries) are cached to avoid recompilation, but the timing benchmarks and configuration selection are recomputed from scratch every time the SGLang server starts.

The Significance of In-Memory Autotuning

This distinction between cached cubins and in-memory autotuning is subtle but crucial. When the assistant writes "the compiled cubins stay cached to avoid recompilation, but the timing and kernel selection get recalculated every restart," it is drawing a sharp boundary between two phases of the Triton compilation pipeline.

The first phase—compilation—translates the Triton kernel source into CUDA binaries (cubins). This is expensive, taking seconds per configuration, and benefits enormously from disk caching. The second phase—autotuning—benchmarks each compiled configuration by launching the kernel and measuring latency. This is relatively cheap (milliseconds per launch) because the cubins are already compiled and loaded. The autotuner selects the configuration with the minimum measured latency, with infinity representing pruned configurations.

Because the autotuning happens in-memory, each process startup triggers a fresh round of benchmarking during the warmup phase. The assistant correctly identifies that this warmup is orchestrated by the FullCudaGraphBackend, which runs eager warmup forwards before each captured CUDA graph bucket. During these warmup passes, the autotuner encounters each key for the first time, triggers benchmarking, caches the result in memory, and selects the winning configuration. By the time the actual CUDA graph capture runs, the configuration is already resolved, making the capture safe from autotuner-induced synchronization.

The Robustness of the Conclusion

One of the most impressive aspects of this message is the assistant's confidence in its conclusion despite the missing evidence. Rather than treating the absent cache file as a dead end, the assistant recognizes that the SMEM-cap logic combined with the OutOfResources pruning behavior is independently conclusive:

"I don't strictly need the autotune.json since the SMEM-cap logic and OOR pruning are conclusive."

This is a mature research posture. The assistant has built a chain of reasoning that stands on multiple independent pillars: the hardware specification (99 KB cap from NVIDIA documentation), the measured shared memory requirements (61568 vs 104704 bytes), the autotuner's error handling (catching OutOfResources and assigning infinity), and now the understanding that even without disk persistence, the same selection logic applies every time. The BT32 configurations cannot launch on SM120 hardware regardless of when or how the autotuner runs, so BT16/w4 is the only viable option in every scenario.

The Thinking Process: From Verification to Discovery

The reasoning in this message follows a clear arc. It begins with a verification goal: find the autotune.json file to confirm which configuration was selected. The negative result triggers a re-evaluation of the mental model. The assistant considers why the cache file might be missing and arrives at the correct explanation: cache_results=False by default, no explicit knob to enable it.

The reasoning then extrapolates the implications: if selection is in-memory, then every restart re-benchmarks. But since the cubins are cached, this re-benchmarking is cheap. And since BT32 always exceeds the SMEM cap, the outcome is deterministic—BT16/w4 wins every time. This means the deployment is stable across restarts, even though the selection mechanism is more ephemeral than initially assumed.

The assistant then pivots to the next logical question: if the autotuning is in-memory and happens during warmup, how does the backend call site handle the batch dimension? The bash command at the end of the message searches for batch-related parameters in deepseek_v4_backend.py, looking for a natural scalar to key on for V3 of the kernel. This forward-looking move shows that the assistant is already thinking about how to extend the analysis—if batch size were added to the autotune key, would the warmup-before-capture pattern still guarantee safety?

Assumptions and Their Validity

The message operates on several assumptions, all of which appear sound:

  1. The autotune.json naming convention: The assistant assumes the cache file follows the pattern *mma_sparse_decode_split*autotune*.json. This is consistent with Triton's naming scheme where the cache file is named after the kernel function with .autotune.json appended.
  2. The default value of cache_results: The assistant assumes cache_results=False is the default. This is correct for Triton 3.6.0, where the Autotuner class initializes with cache_results=None and the disk cache is only written when explicitly enabled via knobs.
  3. The cubin caching behavior: The assistant assumes that compiled cubins are cached independently of autotuning results. This is correct—Triton caches compiled binaries in ~/.triton/cache by their hash, while autotuning results are a separate concern.
  4. The warmup-before-capture pattern: The assistant assumes that the FullCudaGraphBackend runs warmup forwards before each captured bucket, which triggers autotuning safely outside the graph capture. This was established in earlier reasoning and is consistent with the SGLang codebase. One potential subtlety the assistant does not explicitly address: could the absence of the autotune.json file also be explained by a different cache directory or a cleanup mechanism? The assistant searches /root/.triton which is the default Triton cache root. If the SGLang server runs as a different user or uses a custom TRITON_CACHE_DIR, the cache files could be elsewhere. However, the assistant has been operating on this machine throughout the session and has established that Triton is using the default cache location, so this concern is unlikely.

Input and Output Knowledge

Input knowledge required to understand this message includes:

The Broader Context

This message sits at a transition point in the investigation. The assistant has definitively answered the original question—why BT16/w4 is selected—and has now uncovered a deeper layer of complexity about how that selection persists (or doesn't) across process lifecycles. The bash command at the end of the message opens a new line of inquiry into the backend call site, specifically how the batch dimension is handled and whether it could be used as an additional autotune key.

The assistant's earlier reasoning (from the context messages) had explored the possibility of adding batch size to the autotune key to enable batch-aware configuration selection. The discovery that autotuning is in-memory and recomputed at every startup has implications for this design: if batch were added to the key, each unique batch size encountered during warmup would trigger a fresh round of benchmarking. With approximately 20 captured buckets, this would mean up to 20 eager benchmark runs at startup—safe but potentially slow. The assistant's investigation of the backend call site in this message is the first step toward understanding whether such a change is feasible.

Conclusion

Message 31 is a masterclass in investigative reasoning under uncertainty. The assistant sets out to verify a hypothesis by finding a specific file, encounters a negative result, and instead of treating it as a dead end, uses it to refine the mental model of how the system actually works. The absence of the autotune.json file becomes the key piece of evidence that reveals the in-memory nature of Triton's autotuning in this deployment.

This discovery has practical implications: it means that every SGLang server restart triggers a fresh round of kernel benchmarking, but because the cubins are cached and the hardware constraints are deterministic, the outcome is always the same. The BT32 configurations cannot run on Blackwell SM120 hardware, and BT16/w4 is selected by process of elimination rather than by performance comparison. The assistant's confidence in this conclusion is well-founded, resting on multiple independent lines of evidence: hardware specifications, measured shared memory usage, autotuner error handling, and now the lifecycle of autotuning results themselves.

The message also demonstrates the importance of following the evidence wherever it leads. The assistant could have stopped at the SMEM-cap analysis and declared victory. Instead, it pursued the disk cache verification, and in doing so, uncovered a more nuanced understanding of how Triton operates in production. This is the kind of deep, systematic investigation that separates surface-level debugging from genuine system understanding.