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feat: add ReplaySSM state modes for GDN and KDA - #1585
No actionable comments were generated in the recent review.
Walkthrough
This pull request adds Native, Compact, and Replay SSM state modes for GDN and KDA. It integrates state preparation, recurrent execution, MTP commit receipts, checkpoint transfer, startup validation, autotuning, benchmarks, and tests. It also adjusts several model execution paths.
Changes: SSM state modes and MTP integration
Layer / File(s)
Summary
Shared Conv/SSM state resources lightllm/common/attention_state/bounded_att/*, lightllm/common/attention_state/commit.py, lightllm/common/ssm_types.py
Shared resources allocate Native, Compact, or Replay state and manage accepted-state access, reset, page operations, and CPU, DP, PD, and tensor-parallel transfers.
SSM execution, replay, and tuning lightllm/common/attention_state/bounded_att/ssm/*, lightllm/common/basemodel/triton_kernel/linear_att/*, lightllm/common/triton_utils/autotuner.py
Native dispatches recurrent kernels. Compact records verification inputs for accepted-prefix replay. Replay stores and folds request history and materializes accepted state. Autotuning resolves run configurations and records CUDA out-of-memory tuning failures.
Linear attention integration lightllm/common/basemodel/attention/linear/*, lightllm/common/basemodel/cuda_graph.py, lightllm/common/attention_state/bounded_att/ssm/forward.py
GDN and KDA use shared prefill and decode wrappers. Per-layer forward objects provide state indices and recurrent metadata. Warmup and CUDA Graph paths copy these objects.
MTP receipt validation and commits lightllm/common/attention_state/manager.py, lightllm/common/attention_state/commit.py, lightllm/server/router/model_infer/*, lightllm/common/basemodel/*
Model outputs publish state commits. MTP verification passes request-partition batches in a context. The state manager validates the context before applying commits.
SSM configuration and model wiring lightllm/server/api_cli.py, lightllm/server/api_start.py, lightllm/server/core/objs/start_args_type.py, lightllm/models/glm5_next/*, lightllm/models/qwen3next/model.py
CLI and startup arguments select SSM modes and replay settings. Startup applies defaults and validates mode, model, dtype, MTP, capacity, and replay constraints.
Benchmarks and validation test/benchmark/kernels/*, unit_tests/**/*
Benchmarks cover KDA state cycles and streamed ReplaySSM service requests. Tests cover state contracts, replay execution, CUDA Graph behavior, transfers, startup validation, and model integration.
Additional model execution adjustments
Layer / File(s)
Summary
Model execution and cache paths lightllm/models/glm5_next/indexer.py, lightllm/models/llama/layer_infer/post_layer_infer.py, lightllm/models/qwen3next/layer_infer/transformer_layer_infer.py, lightllm/server/router/model_infer/infer_batch.py
GLM5 clamps zero pool lengths for scheduling metadata and logits. Llama adds a single-rank CUDA BF16 logits path. Qwen3Next passes the original strided tensor to linear_norm. Hybrid-state saving returns when the radix cache is absent.
sequenceDiagram
participant LinearDecodeAttState
participant ModelOutput
participant verify_mtp_tokens
participant AttentionStateManager
LinearDecodeAttState->>ModelOutput: publish state commits
ModelOutput->>verify_mtp_tokens: pass commits in request batches
verify_mtp_tokens->>AttentionStateManager: commit accepted lengths with context
AttentionStateManager->>AttentionStateManager: validate context and state commits
AttentionStateManager->>LinearDecodeAttState: apply validated commits
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Merge Risk: Low · previous review 38d9a
GLM5 configured with parallel replay fails during initialization. Use another replay mode until startup validation rejects this combination.
Pre-merge checks: 4 · 1
Failed checks: 1 warning
Check name
Status
Explanation
Resolution
Docstring Coverage
Docstring coverage is 23.64% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documenta…
Write docstrings for the functions missing them to satisfy the coverage threshold.
Passed checks: 4 passed
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Status
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Description Check
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Title check
The title clearly and concisely describes the main change: adding ReplaySSM state modes for GDN and KDA.
Linked Issues check
Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check
Check skipped because no linked issues were found for this pull request.
Full details: Docstring Coverage
Explanation
Docstring coverage is 23.64% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentation-only edits and deletions within surviving functions are included; unchanged diff context and fully deleted functions are excluded. Analyzed 110 functions across 54 files. (1 skipped: 1 unsupported.)
Resolution
Write docstrings for the functions missing them to satisfy the coverage threshold.
Finishing Touches
Generate unit tests (beta)
Create a new PR
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Files skipped from review as they are similar to previous changes: 1
docs/replay_ssm.md
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sufubao
changed the title
feat: add ReplaySSM state modes and optimize GDN MTP decoding
feat: add ReplaySSM state modes for GDN and KDA
Nov 3, 2026
set -o pipefail
printf'%s\n''--- validator call context ---'
nl -ba lightllm/server/api_start.py | sed -n '320,385p'printf'%s\n''--- ConvSSMState ---'
nl -ba lightllm/common/attention_state/bounded_att/conv_ssm_state.py | sed -n '1,120p'printf'%s\n''--- StartArgs data type fields and defaults ---'
nl -ba lightllm/server/core/objs/start_args_type.py | sed -n '220,280p'printf'%s\n''--- data_type resolution references ---'
rg -n -F -- 'args.data_type' lightllm/server lightllm/utils lightllm/common | head -100
rg -n -F -- 'data_type =' lightllm/server lightllm/utils lightllm/common | head -100
Repository: ModelTC/LightLLM
Length of output: 14150
Script executed:
set -o pipefail
printf'%s\n''--- startup dtype resolution ---'
nl -ba lightllm/server/api_start.py | sed -n '385,425p'printf'%s\n''--- ConvSSMStateConfig declaration and producers ---'
rg -n -F -- 'ConvSSMStateConfig' lightllm/common lightllm/models | head -100
rg -n -F -- 'conv_dtype=' lightllm/common lightllm/models | head -100
printf'%s\n''--- linear attention dtype configuration ---'
rg -n -F -- 'ssm_dtype' lightllm/common lightllm/models | head -100
rg -n -F -- 'conv_dtype' lightllm/common lightllm/models | head -100
Repository: ModelTC/LightLLM
Length of output: 6414
Script executed:
set -o pipefail
printf'%s\n''--- GLM5 state construction ---'
nl -ba lightllm/models/glm5_next/model.py | sed -n '35,90p'printf'%s\n''--- Qwen3Next state construction ---'
nl -ba lightllm/models/qwen3next/model.py | sed -n '42,90p'printf'%s\n''--- algorithm declarations and model mapping ---'
rg -n -F -- 'algorithm = ' lightllm/models/glm5_next lightllm/models/qwen3next lightllm/common/attention
rg -n -F -- 'glm5_next_text' lightllm | head -50
Repository: ModelTC/LightLLM
Length of output: 5926
Reject parallel replay for KDA models during startup.
GLM5 uses create_kda_state, which passes kda=True to ReplaySSMState. Parallel replay asserts not kda, but startup accepts GLM5 as a valid GDN/KDA model without a parallel-mode restriction. A KDA configuration can therefore pass startup validation and fail later during state construction.
Add a GDN-only check for compute_mode="parallel" after computing the model type.
Proposed fix
- is_gdn_or_kda = get_model_type(args.model_dir) in (+ model_type = get_model_type(args.model_dir)+ is_gdn_or_kda = model_type in (
"glm5_next",
"glm5_next_text",
@@
assert is_gdn_or_kda, "Non-native SSM state modes currently require a GDN or KDA model"
+ assert config.get("compute_mode") != "parallel" or model_type not in (+ "glm5_next",+ "glm5_next_text",+ ), "Parallel ReplaySSM requires a GDN model; KDA is not supported"
Add a test case for glm5_next with compute_mode="parallel".
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Review comment at @lightllm/server/api_start.py around lines 68 - 70:
Update the startup validation for config["compute_mode"] so parallel mode is
rejected for KDA models, including GLM5, before ReplaySSM state construction;
keep parallel mode available for supported GDN models. Use the model type
already determined by get_model_type to distinguish KDA from GDN, and add
coverage for GLM5 with parallel mode.
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sufubao
changed the title
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
为 GDN 和 GLM-5.3-FLASH KDA 增加可选的
compact/replaySSM 状态存储，减少 MTP 的逐 token 状态快照，默认保持native。支持普通 decode、固定/动态 MTP、checkpoint、CPU cache 和 PD 恢复。ConvSSMState统一 GDN/KDA 的状态生命周期和传输，各模式独立管理计算与接受提交。SSMForward保存执行索引和内核配置，StateCommit将 compact 的提交配置绑定到产生它的 forward 和 microbatch；CUDA Graph 显式复制元数据并隔离预热配置。保留 GDN conv+pack 和 BF16 MTP 的 parallel/derived Replay 路径；native/compact 逐 token 舍入，replay 按历史折叠舍入。GDN 普通/MTP decode 全程使用逻辑执行层号选择 Conv/SSM 状态，避免 SenseNova Loop 共享物理权重时串层。从
decode_att()入口覆盖对齐 Loop、非对齐 Loop 和无 Loop 对照：修复前 4 个 CPU 用例失败，修复后 6 个 CPU 用例及 6 个真实 GPU prefill→decode 用例通过。另补上 checkpoint 测试独立运行所需的启动参数 fixture。基于 upstream/main
269ab62d0的本地验证：test_mtp_fused_recurrent_equiv.py的 7 项 native MTP 逐位等价断言在独立 upstream/main base 和当前代码上均失败，输出最大绝对差均为 0.000488–0.000977。两个 kernel 和该测试与 base 逐字一致，保留原断言；GPU 回归并非全绿。未执行全量 autotune 候选及大跨度索引矩阵、完整模型服务、服务级 microbatch overlap、吞吐/延迟/显存对比或 GSM8K。上述 GPU 数值与状态验证不能替代这些验收。
Summary by CodeRabbit
New Features
Bug Fixes