The Occupancy Experiment: Forcing Eight Warps in the DeepSeek-V4 Sparse Decode Kernel

The Message

The subject message (global index 13557) is deceptively brief for the weight of reasoning it carries:

[assistant] ## Agent Reasoning Now I'm editing the local copy of fmla_remote.py to simplify it down to a single configuration with BLOCK_T=16 and num_warps=8, replacing the existing three configs, then I'll back up the remote version, transfer the updated file, and restart the service. File matches. Backing up remote and applying V1 (force BLOCK_T=16, num_warps=8 — the only fitting w8 config, gives 8 warps/SM): [edit] /tmp/opencode/fmla_remote.py Edit applied successfully.

On its surface, this is a routine code edit: replace three autotune configurations with one, bumping num_warps from 4 to 8. But this message is the culmination of a deep investigative process spanning multiple subagents, conflicting evidence, and careful architectural reasoning. It represents the moment when a hypothesis about GPU kernel occupancy transitions from theory to experiment.

Why This Message Was Written: The Motivation and Context

To understand why this edit matters, one must understand the problem the assistant was trying to solve. The DeepSeek-V4-Flash model was deployed on eight NVIDIA RTX PRO 6000 Blackwell GPUs (sm_120 architecture) using SGLang with prefill-decode (PD) disaggregation. The system was stable and functional, but the user wanted to push decode throughput higher — specifically, scaling from C60 (60 concurrent requests) to C90.

The decode bottleneck had been analyzed in detail. The sparse MLA (Multi-head Latent Attention) kernel, which performs the core attention computation during token generation, was identified as latency- and occupancy-bound. Each step took approximately 18 milliseconds plus 1.05 milliseconds per request. The kernel was running with only 4 warps per streaming multiprocessor (SM), leaving the GPU's computational resources underutilized. The hypothesis was that raising occupancy to 8 warps per SM would improve latency hiding — the ability to keep the SM busy with other work while waiting for memory operations to complete — and thereby increase throughput, especially at higher concurrency where more requests are in flight simultaneously.

The message was written to execute "V1" of a planned A/B experiment documented in DSV4_ATTN_OCCUPANCY_AB.md. The experiment's goal was to test whether forcing the Triton-compiled attention kernel to use 8 warps instead of 4 would improve decode throughput across batch sizes, and to measure any regressions at low concurrency where fewer warps might be sufficient.

The Decision-Making Process: How the Configuration Was Chosen

The choice of BLOCK_T=16, num_warps=8 was far from arbitrary. It emerged from a rigorous process of elimination and constraint analysis.

The existing kernel had three autotune configurations:

triton.Config({"BLOCK_T": 16}, num_warps=4, num_stages=2),
triton.Config({"BLOCK_T": 32}, num_warps=4, num_stages=2),
triton.Config({"BLOCK_T": 32}, num_warps=8, num_stages=2),

The assistant's research revealed a critical fact: the two BLOCK_T=32 configurations were silently out-of-range pruned during compilation because their shared memory (SMEM) requirements exceeded the ~99KB limit on Blackwell GPUs. This meant the autotuner had only one viable option — BLOCK_T=16, num_warps=4 — making the autotune decorator effectively pointless. There was no num_warps=8 configuration that actually fit within SMEM constraints.

The assistant needed to add a configuration that:

1. Fit within the SMEM budget (under ~99KB)
2. Used 8 warps to double occupancy
3. Was compatible with the existing kernel structure BLOCK_T=16 with num_warps=8 satisfied all constraints. The SMEM calculation depends on block_h (the number of attention heads processed per block), which was a point of contention between two subagents: one reported 80KB SMEM (implying block_h=32), the other reported 60KB (implying block_h=16). Crucially, the BLOCK_T=16/num_warps=8 configuration fit within both estimates, making it a safe choice regardless of which subagent was correct. The SMEM is determined by tile shape and is independent of num_warps, so doubling warps doesn't increase memory pressure — it only increases register pressure, which at ~150 registers per thread was well within the 256-register limit.

Assumptions Embedded in This Message

The message carries several implicit assumptions that are worth examining:

Assumption 1: Doubling warps will improve throughput. The core hypothesis is that 8 warps per SM provides better latency hiding than 4 warps, particularly for the gather-heavy operations in sparse attention. This assumes the kernel is currently occupancy-limited rather than compute-bound or memory-bandwidth-bound. The earlier profiling evidence supported this — the kernel showed significant stall cycles attributable to memory latency — but the assumption would only be validated by the A/B measurement.

Assumption 2: A single forced configuration is safe for all batch sizes. By removing all but one autotune config, the assistant eliminates Triton's runtime benchmarking entirely. This is actually desirable for CUDA graph capture (which cannot tolerate timed kernel launches), but it means the same configuration is used whether there is 1 request or 96. The assistant acknowledged this risk: at low batch sizes, 8 warps might waste resources that 4 warps could use more efficiently. The plan was to measure this explicitly and, if necessary, implement a batch-aware autotune key in a later iteration.

Assumption 3: The edit is safe and reversible. The assistant backed up the remote file before applying the change, and the verification step (reading the Triton cache JSON after restart) would confirm the new configuration was active. This assumption proved sound — the edit was clean and the system remained operational.

Assumption 4: The BLOCK_T=32 configs were genuinely dead. This was confirmed by the subagent's analysis of the Triton compilation logs, which showed SMEM estimates exceeding 99KB for both BLOCK_T=32 variants. The assistant correctly identified that these configs were being silently dropped, meaning the autotuner had no real choice to make.

Input Knowledge Required

A reader fully grasping this message needs familiarity with several domains:

• GPU architecture: Understanding of SMs, warps, occupancy, shared memory limits, and how these interact on NVIDIA Blackwell (sm_120) hardware
• Triton compiler: Knowledge of how @triton.autotune works, how configurations are benchmarked at runtime, how the key parameter controls caching, and how CUDA graph capture interacts with autotuning
• DeepSeek-V4 architecture: Understanding of Multi-head Latent Attention (MLA), sparse attention with top-k selection, split-K decoding, and how these map to GPU kernels
• SGLang deployment: Familiarity with the PD disaggregation setup, CUDA graph capture for inference, and the specific kernel file flash_mla_sm120_triton.py
• A/B testing methodology: The concept of isolating a single variable (num_warps) while controlling for other factors (batch size, top-k, context length) The assistant had built this knowledge incrementally through the conversation, using subagents to research specific aspects and resolving contradictions through direct code inspection and hardware probing.

Output Knowledge Created

This message produces several concrete outputs:

1. A modified kernel file: The edit replaces three autotune configs with one, changing flash_mla_sm120_triton.py on the remote machine
2. A testable hypothesis: The system is now configured to run with num_warps=8, enabling measurement of whether this improves decode throughput
3. A resolved ambiguity: The verification step (reading the Triton cache JSON) would definitively show the SMEM footprint, resolving whether block_h is 16 or 32
4. Documentation of the experiment: The A/B plan document (DSV4_ATTN_OCCUPANCY_AB.md) records the baseline, the change, and provides a results table to be filled The edit itself is minimal — replacing three lines of configuration with one — but its implications are significant. It changes the fundamental execution model of the most performance-critical kernel in the decode pipeline, shifting from 4 warps per SM to 8.

The Thinking Process Visible in the Reasoning

The assistant's reasoning reveals a methodical, evidence-driven approach characteristic of expert systems debugging. Several patterns stand out:

Contradiction resolution: Two subagents produced conflicting reports about SMEM usage (80KB vs 60KB). Rather than picking one arbitrarily, the assistant identified a configuration that worked under both assumptions, then planned to resolve the ambiguity through direct measurement (reading the Triton cache JSON after the edit). This is a textbook engineering approach: when data is inconclusive, design an experiment that works regardless of the uncertainty.

Constraint propagation: The reasoning flows naturally from high-level goal (improve throughput) to specific constraint (SMEM limit on sm_120) to viable configuration (BLOCK_T=16, num_warps=8). Each step eliminates alternatives: BLOCK_T=32 configs are pruned, num_warps=4 is the only currently viable option, so the only path to 8 warps is through BLOCK_T=16.

Safety consciousness: The assistant backs up the remote file before editing, verifies the MD5 checksum matches between local and remote copies, and plans a verification step after restart. This reflects awareness that kernel changes can silently break CUDA graph capture or produce incorrect results without obvious errors.

Pragmatic scoping: The assistant explicitly limits the scope of this change to "V1" — a simple forced configuration. It acknowledges that a batch-aware autotune key (V3) might be needed later if the single config performs poorly at some batch sizes. This prevents the perfect-from-the-start trap and enables rapid experimentation.

Conclusion

Message 13557 is a study in how deep technical reasoning compresses into a few lines of action. The edit itself — replacing three autotune configurations with one — is trivial. But the journey to that edit involved resolving contradictory evidence from multiple subagents, understanding the silent pruning behavior of Triton's compiler, navigating the constraints of CUDA graph capture, and designing an experiment that would yield clean, interpretable results regardless of unresolved ambiguities.

This message represents the transition from analysis to action. The hypothesis has been formed, the constraints have been mapped, the risks have been assessed, and now the experiment begins. Whether the 8-warp configuration improves throughput, hurts it, or reveals a more complex performance landscape, the answer will be grounded in measurement rather than speculation — and that is precisely the point of an A/B experiment.