Add decode req pool #6980
Add decode req pool #6980
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Hello @ByronHsu, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
Summary of Changes
Hello team, gemini-code-assist here to provide a summary of this pull request. This PR introduces a new memory pool mechanism specifically for the "decode" disaggregation mode. The core idea is to create a DecodeReqToTokenPool that can pre-allocate memory slots for requests beyond the limit of currently running requests. This aims to utilize available memory more effectively to potentially unblock prefill operations, especially when the number of running requests is low but there's free memory capacity. The existing ReqToTokenPool is retained for other disaggregation modes.
Highlights
- New Memory Pool: Introduces
DecodeReqToTokenPoolinpython/sglang/srt/disaggregation/decode.py. - Pre-allocation Strategy: The new pool pre-allocates memory slots (
pre_alloc_size) in addition to the slots formax_num_reqs, allowing more requests to be ready for prefill when memory is available. - Conditional Usage: The
ModelRunneris updated to useDecodeReqToTokenPoolonly when thedisaggregation_modeis set to "decode". - Pre-allocation Size Logic: A specific logic is added to calculate
pre_alloc_size, setting it tomax_num_reqs * 2ifmax_num_reqsis 32 or less, and 0 otherwise.
Changelog
- python/sglang/srt/disaggregation/decode.py
- Added
DecodeReqToTokenPoolclass with methods for allocation, deallocation, writing, and checking availability. - Imported
Unionfor type hinting. - Imported
TorchMemorySaverAdapterfor memory management context.
- Added
- python/sglang/srt/model_executor/model_runner.py
- Modified
init_memory_poolto conditionally initializeself.req_to_token_pool. - Uses
DecodeReqToTokenPoolifserver_args.disaggregation_modeis "decode". - Calculates a
pre_alloc_sizeforDecodeReqToTokenPoolbased onmax_num_reqs.
- Modified
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Code Review
This pull request introduces DecodeReqToTokenPool, a specialized version of ReqToTokenPool designed to handle pre-allocated requests in a disaggregated decoding setup. The new class is well-documented and the integration into ModelRunner seems correct for the described purpose.
I've identified a couple of areas for potential improvement regarding maintainability and efficiency, detailed in the comments below.
Additionally, for future pull requests, please consider filling out the PR description template. This helps reviewers understand the motivation and changes more quickly. The checklist in the PR description also appears to be unchecked.
Overall, the changes are clear and address a specific need in the disaggregation architecture.
Summary of Findings
- Efficiency of
allocinDecodeReqToTokenPool: Theallocmethod inpython/sglang/srt/disaggregation/decode.pyuses list slicing, which can be O(N) and potentially inefficient for large lists. Usingcollections.dequemight offer better performance characteristics (O(1) for popleft/append). - Magic Numbers in
pre_alloc_sizeCalculation: The calculation ofpre_alloc_sizeinpython/sglang/srt/model_executor/model_runner.pyuses hardcoded numbers (2 and 32). These should ideally be named constants or configurable parameters for better maintainability. - Missing Type Hints (Low Severity): The
writemethod inpython/sglang/srt/disaggregation/decode.pyis missing type hints for itsindicesandvaluesparameters. This was not commented on directly due to review settings prioritizing medium severity and above.
Merge Readiness
The pull request introduces a useful DecodeReqToTokenPool for managing pre-allocated requests. The code is generally clear and well-structured. However, there are a couple of medium-severity issues related to potential efficiency in list operations and the use of magic numbers that should be addressed to improve maintainability and performance.
I recommend addressing these points before merging. As an AI reviewer, I am not authorized to approve pull requests; please ensure further review and approval from other maintainers.
| def alloc(self, need_size: int) -> List[int]: | ||
| if need_size > len(self.free_slots): | ||
| return None | ||
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| select_index = self.free_slots[:need_size] | ||
| self.free_slots = self.free_slots[need_size:] | ||
| return select_index |
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The alloc method currently uses list slicing (self.free_slots[:need_size] and self.free_slots = self.free_slots[need_size:]) to manage self.free_slots. If self.free_slots can become very large (e.g., thousands of entries, which is possible given max_num_reqs can be large) and alloc is called frequently, these O(N) list operations (where N is the length of free_slots) could potentially become a performance bottleneck.
Have you considered using collections.deque for self.free_slots? A deque would allow for O(1) appends (for free) and O(1) popleft operations. Allocating need_size items would then be O(need_size), which could be more efficient than O(N) list slicing if N is large and need_size is relatively small. This would require changing the type of self.free_slots in __init__ and clear as well.
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| # subscribe memory for pre-allocated requests | ||
| # if max_num_reqs <= 32, we pre-allocate 2x requests | ||
| pre_alloc_size = max_num_reqs * 2 if max_num_reqs <= 32 else 0 |
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The numbers 2 (multiplier) and 32 (threshold for max_num_reqs) used to calculate pre_alloc_size are magic numbers. While the comment explains the heuristic (2x for small max_num_reqs), it would improve maintainability and configurability if these were defined as named constants (e.g., at the module level or as part of ServerArgs if they need to be configurable).
Could these be refactored into constants? For example:
# At module level or in a config class
_PRE_ALLOC_SIZE_MULTIPLIER = 2
_MAX_NUM_REQS_THRESHOLD_FOR_PRE_ALLOC = 32
# In the function
pre_alloc_size = (max_num_reqs * _PRE_ALLOC_SIZE_MULTIPLIER
if max_num_reqs <= _MAX_NUM_REQS_THRESHOLD_FOR_PRE_ALLOC
else 0)| pre_alloc_size = max_num_reqs * 2 if max_num_reqs <= 32 else 0 | |
| pre_alloc_size = max_num_reqs * 2 if max_num_reqs <= 32 else 0 # TODO: Consider making 2 and 32 named constants or configurable |
Merge branch 'sgl_20250610_sync_tag047 of git@code.alipay.com:Theta/SGLang.git into main https://code.alipay.com/Theta/SGLang/pull_requests/52 Reviewed-by: 剑川 <jianchuan.gys@antgroup.com> * [Bugfix] Fix slice operation when chunk size mismatch (sgl-project#6697) * [Bugfix] Fix ChatCompletion endpoint of mini_lb when stream is set (sgl-project#6703) * [CI] Fix setup of disaggregation with different tp (sgl-project#6706) * [PD] Remove Unnecessary Exception Handling for FastQueue.get() (sgl-project#6712) * Fuse routed_scaling_factor in DeepSeek (sgl-project#6710) * Overlap two kernels in DeepSeek with communication (sgl-project#6711) * Minor refactor two-batch overlap (sgl-project#6682) * Speed up when having padding tokens two-batch overlap (sgl-project#6668) * [Feature] Support Flashinfer fp8 blockwise GEMM kernel on Blackwell (sgl-project#6479) * Fix LoRA bench (sgl-project#6719) * temp * Fix PP for Qwen3 MoE (sgl-project#6709) * [feat] triton kernel for get_last_loc (sgl-project#6676) * [fix] more mem for draft_extend cuda_graph (sgl-project#6726) * [PD] bug fix: Update status if nixl receiver send a a dummy req. (sgl-project#6720) * Tune memory arguments on B200 (sgl-project#6718) * Add DeepSeek-R1-0528 function call chat template (sgl-project#6725) * refactor(tool call): Fix BaseFormatDetector tool_index issue and refactor `parse_streaming_increment` (sgl-project#6715) * Add draft extend CUDA graph for Triton backend (sgl-project#6705) * refactor apply_w8a8_block_fp8_linear in fp (sgl-project#6545) * [PD] Support completion endpoint (sgl-project#6729) * PD Rust LB (PO2) (sgl-project#6437) * Super tiny enable sole usage of expert distribution metrics and update doc (sgl-project#6680) * Support picking variants of EPLB algorithms (sgl-project#6728) * Support tuning DeepEP configs (sgl-project#6742) * [test] add ut and bm for get_last_loc (sgl-project#6746) * Fix mem_fraction_static for AMD CI (sgl-project#6748) * [fix][RL] Fix DeepSeekV3ForCausalLM.post_load_weights for multiple update weight (sgl-project#6265) * Improve EPLB logical to physical dispatch map (sgl-project#6727) * Update DeepSeek-R1-0528 function call chat template (sgl-project#6765) * [PD] Optimize time out logic and add env var doc for mooncake (sgl-project#6761) * Fix aiohttp 'Chunk too big' in bench_serving (sgl-project#6737) * Support sliding window in triton backend (sgl-project#6509) * Fix shared experts fusion error (sgl-project#6289) * Fix one bug in the grouped-gemm triton kernel (sgl-project#6772) * update llama4 chat template and pythonic parser (sgl-project#6679) * feat(tool call): Enhance Llama32Detector for improved JSON parsing in non-stream (sgl-project#6784) * Support token-level quantization for EP MoE (sgl-project#6782) * Temporarily lower mmlu threshold for triton sliding window backend (sgl-project#6785) * ci: relax test_function_call_required (sgl-project#6786) * Add intel_amx backend for Radix Attention for CPU (sgl-project#6408) * Fix incorrect LoRA weight loading for fused gate_up_proj (sgl-project#6734) * fix(PD-disaggregation): Can not get local ip (sgl-project#6792) * [FIX] mmmu bench serving result display error (sgl-project#6525) (sgl-project#6791) * Bump torch to 2.7.0 (sgl-project#6788) * chore: bump sgl-kernel v0.1.5 (sgl-project#6794) * Improve profiler and integrate profiler in bench_one_batch_server (sgl-project#6787) * chore: upgrade sgl-kernel v0.1.5 (sgl-project#6795) * [Minor] Always append newline after image token when parsing chat message (sgl-project#6797) * Update CI tests for Llama4 models (sgl-project#6421) * [Feat] Enable PDL automatically on Hopper architecture (sgl-project#5981) * chore: update blackwell docker (sgl-project#6800) * misc: cache is_hopper_arch (sgl-project#6799) * Remove contiguous before Flashinfer groupwise fp8 gemm (sgl-project#6804) * Correctly abort the failed grammar requests & Improve the handling of abort (sgl-project#6803) * [EP] Add cuda kernel for moe_ep_pre_reorder (sgl-project#6699) * Add draft extend CUDA graph for flashinfer backend (sgl-project#6805) * Refactor CustomOp to avoid confusing bugs (sgl-project#5382) * Tiny log prefill time (sgl-project#6780) * Tiny fix EPLB assertion about rebalancing period and recorder window size (sgl-project#6813) * Add simple utility to dump tensors for debugging (sgl-project#6815) * Fix profiles do not have consistent names (sgl-project#6811) * Speed up rebalancing when using non-static dispatch algorithms (sgl-project#6812) * [1/2] Add Kernel support for Cutlass based Fused FP4 MoE (sgl-project#6093) * [Router] Fix k8s Service Discovery (sgl-project#6766) * Add CPU optimized kernels for topk and rope fusions (sgl-project#6456) * fix new_page_count_next_decode (sgl-project#6671) * Fix wrong weight reference in dynamic EPLB (sgl-project#6818) * Minor add metrics to expert location updater (sgl-project#6816) * [Refactor] Rename `n_share_experts_fusion` as `num_fused_shared_experts` (sgl-project#6735) * [FEAT] Add transformers backend support (sgl-project#5929) * [fix] recover auto-dispatch for rmsnorm and rope (sgl-project#6745) * fix ep_moe_reorder kernel bugs (sgl-project#6858) * [Refactor] Multimodal data processing for VLM (sgl-project#6659) * Decoder-only Scoring API (sgl-project#6460) * feat: add dp-rank to KV events (sgl-project#6852) * Set `num_fused_shared_experts` as `num_shared_experts` when shared_experts fusion is not disabled (sgl-project#6736) * Fix one missing arg in DeepEP (sgl-project#6878) * Support LoRA in TestOpenAIVisionServer and fix fused kv_proj loading bug. (sgl-project#6861) * support 1 shot allreduce in 1-node and 2-node using mscclpp (sgl-project#6277) * Fix Qwen3MoE missing token padding optimization (sgl-project#6820) * Tiny update error hints (sgl-project#6846) * Support layerwise rebalancing experts (sgl-project#6851) * Tiny allow profiler API to auto create directory (sgl-project#6865) * Support Blackwell DeepEP docker images (sgl-project#6868) * [EP] Add cuda kernel for moe_ep_post_reorder (sgl-project#6837) * [theta]merge 0605 * oai: fix openAI client error with single request via batch api (sgl-project#6170) * [PD] Fix potential perf spike caused by tracker gc and optimize doc (sgl-project#6764) * Use deepgemm instead of triton for fused_qkv_a_proj_with_mqa (sgl-project#6890) * [CUTLASS-FP4-MOE] Introduce CutlassMoEParams class for easy initialization of Cutlass Grouped Gems Metadata (sgl-project#6887) * bugfix(OAI): Fix image_data processing for jinja chat templates (sgl-project#6877) * [CPU] enable CI for PRs, add Dockerfile and auto build task (sgl-project#6458) * AITER backend extension and workload optimizations (sgl-project#6838) * [theta]merge * [theta]merge * [Feature] Support Flashinfer fmha on Blackwell (sgl-project#6930) * Fix a bug in abort & Improve docstrings for abort (sgl-project#6931) * Tiny support customize DeepEP max dispatch tokens per rank (sgl-project#6934) * Sync the changes on cuda graph runners (sgl-project#6932) * [PD] Optimize transfer queue forward logic for dummy rank (sgl-project#6922) * [Refactor] image data process in bench_serving (sgl-project#6879) * [fix] logical_to_all_physical_map index 256 is out of bounds in EP parallel. (sgl-project#6767) * Add triton fused moe kernel config for E=257 on B200 (sgl-project#6939) * [sgl-kernel] update deepgemm (sgl-project#6942) * chore: bump sgl-kernel v0.1.6 (sgl-project#6943) * Minor compile fused topk (sgl-project#6944) * [Bugfix] pipeline parallelism and Eagle Qwen2 (sgl-project#6910) * Tiny re-introduce profile id logging (sgl-project#6912) * Add triton version as a fused_moe_triton config search key to avoid performace decrease in different Triton version (sgl-project#5955) * reduce torch.zeros overhead in moe align block size kernel (sgl-project#6369) * chore: upgrade sgl-kernel v0.1.6 (sgl-project#6945) * add fbgemm moe grouped gemm kernel benchmark (sgl-project#6924) * [Docker] Add docker file for SGL Router (sgl-project#6915) * Disabling mixed chunked prefill when eagle is enabled (sgl-project#6874) * Add canary for EPLB rebalancing (sgl-project#6895) * Refactor global_server_args_dict (sgl-project#6866) * Fuse routed scaling factor in topk_reduce kernel (sgl-project#6220) * Update server timeout time in AMD CI. (sgl-project#6953) * [misc] add is_cpu() (sgl-project#6950) * Add H20 fused MoE kernel tuning configs for DeepSeek-R1/V3 (sgl-project#6885) * Add a CUDA kernel for fusing mapping and weighted sum for MoE. (sgl-project#6916) * chore: bump sgl-kernel v0.1.6.post1 (sgl-project#6955) * chore: upgrade sgl-kernel v0.1.6.post1 (sgl-project#6957) * [DeepseekR1-FP4] Add Support for nvidia/DeepSeekR1-FP4 model (sgl-project#6853) * Revert "Fuse routed scaling factor in topk_reduce kernel (sgl-project#6220)" (sgl-project#6968) * [AMD] Add more tests to per-commit-amd (sgl-project#6926) * chore: bump sgl-kernel v0.1.7 (sgl-project#6963) * Slightly improve the sampler to skip unnecessary steps (sgl-project#6956) * rebase h20 fused_moe config (sgl-project#6966) * Fix CI and triton moe Configs (sgl-project#6974) * Remove unnecessary kernels of num_token_non_padded (sgl-project#6965) * Extend cuda graph capture bs for B200 (sgl-project#6937) * Fuse routed scaling factor in deepseek (sgl-project#6970) * Sync cuda graph runners (sgl-project#6976) * Fix draft extend ut stability with flush cache (sgl-project#6979) * Fix triton sliding window test case (sgl-project#6981) * Fix expert distribution dumping causes OOM (sgl-project#6967) * Minor remove one kernel for DeepSeek (sgl-project#6977) * [perf][sgl-kernel] extend cutlass_mla_decode to support num_head < 128 (sgl-project#6929) * Enable more unit tests for AMD CI. (sgl-project#6983) * Use torch.compile to fuse flash attention decode metadata preparation (sgl-project#6973) * Eliminate stream sync to speed up LoRA batch init (sgl-project#6960) * support qwen3 emebedding (sgl-project#6990) * Fix torch profiler bugs for bench_offline_throughput.py (sgl-project#6557) * chore: upgrade flashinfer v0.2.6.post1 jit (sgl-project#6958) * cleanup tmp dir (sgl-project#7007) * chore: update pr test xeon (sgl-project#7008) * Fix cutlass MLA gets almost zero accuracy (sgl-project#6998) * Update amd nightly models CI. (sgl-project#6992) * feat: add direct routing strategy to DP worker (sgl-project#6884) * Fallback to lower triton version for unfound fused moe configs (sgl-project#7013) * Fix torchvision version for Blackwell (sgl-project#7015) * Simplify prepare_extend_after_decode (sgl-project#6987) * Migrate to assertEqual (sgl-project#6741) * Fix torch version in blackwell dockerfile (sgl-project#7017) * chore: update pr test xeon (sgl-project#7018) * Update default settings for blackwell (sgl-project#7023) * Support both approximate and exact expert distribution collection (sgl-project#6964) * Add decode req pool (sgl-project#6980) * [theta]merge 0610 * [theta]merge 0610 * [CI] Add CI workflow for sgl-router docker build (sgl-project#7027) * Fix fused_moe triton configs (sgl-project#7029) * CPU: map changes from developing branch in sgl-kernel (sgl-project#6833) * chore: bump v0.4.7 (sgl-project#7038) * Update README.md (sgl-project#7040)
Motivation
Modifications
Checklist