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…g defects Lets VLLM_BATCH_INVARIANT=1 reach the Qwen GatedDeltaNet layers, and makes every request-dependent step in that path dispatch per request so its arithmetic does not depend on which neighbours share the engine step. Review fixes from vllm-project#45819: 1. prefill_query_start_loc_cpu is declared with the other prefill fields. A decode-only batch previously raised UnboundLocalError, with batch invariance disabled too. 2. GDNAttentionBackend.supports_batch_invariance() returns True, so the selector stops rejecting VLLM_BATCH_INVARIANT=1 before execution. Scoped to the implementation that provides it: GatedDeltaNetAttention gains a supports_batch_invariant flag defaulting to False, and only QwenGatedDeltaNetAttention opts in, so Kimi, OLMo and Bailing raise NotImplementedError at init rather than silently returning non-invariant output. 3. non_spec_query_start_loc_cpu is declared on GDNAttentionMetadata and populated in build(). The projection and convolution paths read it instead of calling .tolist() on a CUDA tensor, removing the host-device sync from both. It is only computed when batch invariance is on. 4. CUDA graph padding rows are preserved. Rows past the last real request are carried through the projection and the convolution as one trailing segment, so the row count still matches num_tokens and the residual and z shapes downstream are unchanged. Also fixes a latent NameError: the batch-invariant prefill path called prepare_chunk_indices and prepare_chunk_offsets without importing them, so that branch could not execute at all. Per-request dispatch now covers all four request-dependent sites. The two decode sites, the fused recurrence and the convolution state update, were missing and are added here. Speculative decoding is rejected at init in the metadata builder and in the layer instead of part-way through model execution. GDN models are detected from layer_types rather than a model_type allowlist, covering qwen3_next, qwen3_5_text, qwen3_5_moe_text and the multimodal ForConditionalGeneration wrappers without enumerating them. Measured on H100 NVL (SM90), Qwen/Qwen3.5-0.8B, vLLM v0.31.0: this takes bs=1 against bs=16 logprob agreement from unreachable, the selector used to refuse to start, to 15 of 16 prompts bitwise identical. One prompt still diverges, reproducibly and at the same step and magnitude, which is below the level request dispatch can reach. A non-GDN control on the same setup is 16 of 16. Full invariance needs the shared recurrent arithmetic explored in vllm-project#60033; this change establishes the gate that work depends on. Signed-off-by: Yuval Luria <yluria@redhat.com> Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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@yewentao256 could you take another look? This supersedes #49827: I have reviewed the current diff, rebased it onto main and trimmed the scheduler restrictions/non-BIC changes. Fresh H100/TP1 checks pass for mixed prefill/decode, arbitrary partitions and public Qwen serving with cache off/on (200 requests, 6,400 complete logit hashes). The description includes the measured performance cost and validation limits; Blackwell and MRV2 remain unvalidated. Intel CI #14141 has a KV-planning optimized-Python subprocess timeout and a quantization case whose ModelOpt NVFP4 source is unsupported on XPU. Those tests and the supported-format declaration are unchanged by this diff. Could someone with Intel CI access compare the two failing cases on base The normalization overlap with #57280 is disclosed in Related Work; happy to coordinate that during review. GEMM #60034 and TP #50299 remain separate follow-ups. |
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Thanks for the work!
Please do the following:
- shrink the diff, idealy < 200 LOC including the tests
- full reproduce command line, error log in main and the same command line works in current PR
- e2e accuracy using
lm_eval... - e2e benchmark using
vllm bench serve...
When doing reproduce / e2e test, please attach with raw output log in case some agents fake the data. DO NOT use AI generated Table here
For example:
pytest tests/v1/kv_connector/extract_hidden_states_integration/test_extraction.py::test_extract_hidden_states_tp2
# main
| (EngineCore pid=1580) File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 1070, in __init__
| (EngineCore pid=1580) super().__init__(
| (EngineCore pid=1580) File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 160, in __init__
| (EngineCore pid=1580) self.scheduler: SchedulerInterface = Scheduler(
| (EngineCore pid=1580) ^^^^^^^^^^
| (EngineCore pid=1580) File "/usr/local/lib/python3.12/dist-packages/vllm/v1/core/sched/async_scheduler.py", line 14, in __init__
| (EngineCore pid=1580) super().__init__(*args, **kwargs)
| (EngineCore pid=1580) File "/usr/local/lib/python3.12/dist-packages/vllm/v1/core/sched/scheduler.py", line 277, in __init__
| (EngineCore pid=1580) self.kv_cache_manager = KVCacheManager(
| (EngineCore pid=1580) ^^^^^^^^^^^^^^^
| (EngineCore pid=1580) File "/usr/local/lib/python3.12/dist-packages/vllm/v1/core/kv_cache_manager.py", line 151, in __init__
| (EngineCore pid=1580) self.coordinator = get_kv_cache_coordinator(
| (EngineCore pid=1580) ^^^^^^^^^^^^^^^^^^^^^^^^^
| (EngineCore pid=1580) File "/usr/local/lib/python3.12/dist-packages/vllm/v1/core/kv_cache_coordinator.py", line 891, in get_kv_cache_coordinator
| (EngineCore pid=1580) return HybridKVCacheCoordinator(
| (EngineCore pid=1580) ^^^^^^^^^^^^^^^^^^^^^^^^^
| (EngineCore pid=1580) File "/usr/local/lib/python3.12/dist-packages/vllm/v1/core/kv_cache_coordinator.py", line 563, in __init__
| (EngineCore pid=1580) assert all(
| (EngineCore pid=1580) ^^^^
| (EngineCore pid=1580) AssertionError: Each KV cache group's real block_size must be divisible by hash_block_size. block_sizes=[544, 544, 544, 544, 181], hash_block_size=98464
| FAILED
| v1/kv_connector/extract_hidden_states_integration/test_extraction.py::test_extract_hidden_states_tp2
# Now
================= 1 passed, 23 warnings in 174.57s (0:02:54) =================="lm_eval --model local-completions --model_args "base_url=http://127.0.0.1:9256/v1/completions,model=$MODEL,num_concurrent=1024" --tasks gsm8k --trust_remote_code"
# Now
|Tasks|Version| Filter |n-shot| Metric | |Value | |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.9212|± |0.0074|
| | |strict-match | 5|exact_match|↑ |0.9212|± |0.0074|
# main
|Tasks|Version| Filter |n-shot| Metric | |Value | |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.9151|± |0.0077|
| | |strict-match | 5|exact_match|↑ |0.9158|± |0.0076|
`lm_eval run --model local-chat-completions --model_args base_url=http://127.0.0.1:9256/v1/chat/completions,model=$MODEL,num_concurrent=1024,seed=256 --tasks aime25 --trust_remote_code --apply_chat_template`
# now
|Tasks |Version|Filter|n-shot| Metric | |Value | |Stderr|
|------|------:|------|-----:|-----------|---|-----:|---|-----:|
|aime25| 0|none | 0|exact_match|↑ |0.7333|± |0.0821|
# main
|Tasks |Version|Filter|n-shot| Metric | |Value | |Stderr|
|------|------:|------|-----:|-----------|---|-----:|---|-----:|
|aime25| 0|none | 0|exact_match|↑ |0.7333|± |0.0821|vllm bench serve --model zai-org/GLM-5.2-FP8 --dataset-name random --host 127.0.0.1 --port 9256 --random-input-len 8192 --random-output-len 1 --request-rate inf --num-prompts 128 --num-warmups 16
# Now
============ Serving Benchmark Result ============
Successful requests: 128
Failed requests: 0
Benchmark duration (s): 3.08
Total input tokens: 1048576
Total generated tokens: 128
Request throughput (req/s): 41.50
Output token throughput (tok/s): 41.50
Peak output token throughput (tok/s): 76.00
Peak concurrent requests: 128.00
Total token throughput (tok/s): 340041.81
---------------Time to First Token----------------
Mean TTFT (ms): 1669.15
Median TTFT (ms): 1669.02
P99 TTFT (ms): 2724.38
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 0.00
Median TPOT (ms): 0.00
P99 TPOT (ms): 0.00
---------------Inter-token Latency----------------
Mean ITL (ms): 0.00
Median ITL (ms): 0.00
P99 ITL (ms): 0.00
==================================================
# main
============ Serving Benchmark Result ============
Successful requests: 128
Failed requests: 0
Benchmark duration (s): 3.23
Total input tokens: 1048576
Total generated tokens: 128
Request throughput (req/s): 39.63
Output token throughput (tok/s): 39.63
Peak output token throughput (tok/s): 50.00
Peak concurrent requests: 128.00
Total token throughput (tok/s): 324671.20
---------------Time to First Token----------------
Mean TTFT (ms): 1668.69
Median TTFT (ms): 1649.00
P99 TTFT (ms): 3126.43
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 0.00
Median TPOT (ms): 0.00
P99 TPOT (ms): 0.00
---------------Inter-token Latency----------------
Mean ITL (ms): 0.00
Median ITL (ms): 0.00
P99 ITL (ms): 0.00
==================================================For batch invariance, it is better to test using Qwen series model, with the current unit test script for e2e test
5 of 6 tasks
Port the experiment to upstream cb4f016 for review. Remove private capture hooks and enable the shared recurrence under BIC. Retain compiler, normalization and scheduler dependencies pending review and clean-tree end-to-end validation. Co-authored-by: OpenAI Codex Signed-off-by: Charlie Masters <charlie.masters@hcompany.ai>
Remove scheduler arithmetic-grid restrictions and non-BIC packed-route changes. Keep the shared recurrent path and its mixed/partitioned-state regression tests. Fix the online CUDA skip marker import. Co-authored-by: OpenAI Codex Signed-off-by: Charlie Masters <charlie.masters@hcompany.ai>
Pass the same five tensors by name so mypy can resolve the call signature without treating the unpacked generator as potentially supplying later arguments. Co-authored-by: OpenAI Codex Signed-off-by: Charlie Masters <charlie.masters@hcompany.ai>
Reuse the upstream online invariance suite and retain the exact recurrent partition and state-reload regression. Co-authored-by: Codex <noreply@openai.com> Signed-off-by: Charlie Masters <charlie.masters@hcompany.ai>
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@yewentao256 I've pushed the rebase and test cleanup in The fresh H100/TP1/BF16 run is complete:
Exact commands and native output are below. Server settings were eager MRV1, FLASH_ATTN, prefix cache off, concurrency eight and a 128-token scheduling budget. Both checkouts used the same dependencies and wheel This matrix does not establish TP/EP, MRV2, Blackwell corectness (that will be adressed in follow up PRs). The normalization overlap with #57280 also remains open. Could you take another look? Reproducer: exact commands and native error/pass outputSame command in each matching checkout/environment: cd /root/gdn-rebase-base
export PATH=/root/gdn-rebase-base/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-base
export VLLM_BATCH_INVARIANT=1
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/python -m pytest tests/v1/determinism/test_online_batch_invariance.py -k 'bs1_vs_bsN and FLASH_ATTN' -v -s --tb=shortcd /root/gdn-rebase-pr
export PATH=/root/gdn-rebase-pr/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-pr
export VLLM_BATCH_INVARIANT=1
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/python -m pytest tests/v1/determinism/test_online_batch_invariance.py -k 'bs1_vs_bsN and FLASH_ATTN' -v -s --tb=shortBase native error excerpt: PR native pytest output: Kernel, compiler and NIXL regressionscd /root/gdn-rebase-pr
export PATH=/root/gdn-rebase-pr/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-pr
export VLLM_BATCH_INVARIANT=0
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/python -m pytest tests/kernels/mamba/test_gdn_forward_core_split.py -v -scd /root/gdn-rebase-pr
export PATH=/root/gdn-rebase-pr/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-pr
export VLLM_BATCH_INVARIANT=1
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/python -m pytest tests/compile/test_config.py -k bic_compiler -v -scd /root/gdn-rebase-pr
export PATH=/root/gdn-rebase-pr/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-pr
export VLLM_BATCH_INVARIANT=0
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/python -m pytest tests/v1/kv_connector/unit/test_nixl_connector_hma.py -k test_derive_mamba_conv_split -v -sFull GSM8K: exact commands and native lm_eval outputbase-normal cd /root/gdn-rebase-base
export PATH=/root/gdn-rebase-base/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-base
export VLLM_BATCH_INVARIANT=0
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/lm_eval --model local-completions --model_args base_url=http://127.0.0.1:8020/v1/completions,model=Qwen/Qwen3.5-0.8B,num_concurrent=8,tokenized_requests=False,max_length=8192,seed=42 --tasks gsm8k --num_fewshot 5 --seed 42 --gen_kwargs max_gen_toks=256,temperature=0,do_sample=False --output_path /root/gdn-durable-20261022-attempt2/base-normal-trial0/lm-eval --log_samplespr-bic cd /root/gdn-rebase-pr
export PATH=/root/gdn-rebase-pr/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-pr
export VLLM_BATCH_INVARIANT=1
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/lm_eval --model local-completions --model_args base_url=http://127.0.0.1:8020/v1/completions,model=Qwen/Qwen3.5-0.8B,num_concurrent=8,tokenized_requests=False,max_length=8192,seed=42 --tasks gsm8k --num_fewshot 5 --seed 42 --gen_kwargs max_gen_toks=256,temperature=0,do_sample=False --output_path /root/gdn-durable-20261022-attempt2/pr-bic-trial0/lm-eval --log_samplesServing benchmarks: exact commands and all 18 native outputsThree rounds, with mode order base-normal / pr-normal / pr-bic in rounds 0 and 2, reversed in round 1. Each mode starts a fresh server. Native outputs for every repetition are included, not just the best run. Prefill uses 128 requests at 1024 input / 1 output token; mixed uses 64 requests at 1024 / 128. Four warmups, fixed lengths, seed 42, forced output length and concurrency eight. Upload checkpoints run outside measured benchmark commands. Server command (identical across modes): .venv/bin/vllm serve Qwen/Qwen3.5-0.8B --host 127.0.0.1 --port 8020 --dtype bfloat16 --max-model-len 8192 --max-num-seqs 8 --max-num-batched-tokens 128 --gpu-memory-utilization 0.4 --attention-backend FLASH_ATTN --enforce-eager --no-enable-prefix-cachingMode environments and complete client commands appear below; base-normal-trial0-prefill cd /root/gdn-rebase-base
export PATH=/root/gdn-rebase-base/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-base
export VLLM_BATCH_INVARIANT=0
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/vllm bench serve --model Qwen/Qwen3.5-0.8B --host 127.0.0.1 --port 8020 --dataset-name random --random-input-len 1024 --random-output-len 1 --random-range-ratio 0 --request-rate inf --max-concurrency 8 --num-prompts 128 --num-warmups 4 --ignore-eos --seed 42 --save-result --result-dir /root/gdn-durable-20261022-attempt2/base-normal-trial0 --result-filename prefill.jsonbase-normal-trial0-mixed cd /root/gdn-rebase-base
export PATH=/root/gdn-rebase-base/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-base
export VLLM_BATCH_INVARIANT=0
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/vllm bench serve --model Qwen/Qwen3.5-0.8B --host 127.0.0.1 --port 8020 --dataset-name random --random-input-len 1024 --random-output-len 128 --random-range-ratio 0 --request-rate inf --max-concurrency 8 --num-prompts 64 --num-warmups 4 --ignore-eos --seed 42 --save-result --result-dir /root/gdn-durable-20261022-attempt2/base-normal-trial0 --result-filename mixed.jsonpr-normal-trial0-prefill cd /root/gdn-rebase-pr
export PATH=/root/gdn-rebase-pr/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-pr
export VLLM_BATCH_INVARIANT=0
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/vllm bench serve --model Qwen/Qwen3.5-0.8B --host 127.0.0.1 --port 8020 --dataset-name random --random-input-len 1024 --random-output-len 1 --random-range-ratio 0 --request-rate inf --max-concurrency 8 --num-prompts 128 --num-warmups 4 --ignore-eos --seed 42 --save-result --result-dir /root/gdn-durable-20261022-attempt2/pr-normal-trial0 --result-filename prefill.jsonpr-normal-trial0-mixed cd /root/gdn-rebase-pr
export PATH=/root/gdn-rebase-pr/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-pr
export VLLM_BATCH_INVARIANT=0
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/vllm bench serve --model Qwen/Qwen3.5-0.8B --host 127.0.0.1 --port 8020 --dataset-name random --random-input-len 1024 --random-output-len 128 --random-range-ratio 0 --request-rate inf --max-concurrency 8 --num-prompts 64 --num-warmups 4 --ignore-eos --seed 42 --save-result --result-dir /root/gdn-durable-20261022-attempt2/pr-normal-trial0 --result-filename mixed.jsonpr-bic-trial0-prefill cd /root/gdn-rebase-pr
export PATH=/root/gdn-rebase-pr/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-pr
export VLLM_BATCH_INVARIANT=1
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/vllm bench serve --model Qwen/Qwen3.5-0.8B --host 127.0.0.1 --port 8020 --dataset-name random --random-input-len 1024 --random-output-len 1 --random-range-ratio 0 --request-rate inf --max-concurrency 8 --num-prompts 128 --num-warmups 4 --ignore-eos --seed 42 --save-result --result-dir /root/gdn-durable-20261022-attempt2/pr-bic-trial0 --result-filename prefill.jsonpr-bic-trial0-mixed cd /root/gdn-rebase-pr
export PATH=/root/gdn-rebase-pr/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-pr
export VLLM_BATCH_INVARIANT=1
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/vllm bench serve --model Qwen/Qwen3.5-0.8B --host 127.0.0.1 --port 8020 --dataset-name random --random-input-len 1024 --random-output-len 128 --random-range-ratio 0 --request-rate inf --max-concurrency 8 --num-prompts 64 --num-warmups 4 --ignore-eos --seed 42 --save-result --result-dir /root/gdn-durable-20261022-attempt2/pr-bic-trial0 --result-filename mixed.jsonbase-normal-trial1-prefill cd /root/gdn-rebase-base
export PATH=/root/gdn-rebase-base/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-base
export VLLM_BATCH_INVARIANT=0
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/vllm bench serve --model Qwen/Qwen3.5-0.8B --host 127.0.0.1 --port 8020 --dataset-name random --random-input-len 1024 --random-output-len 1 --random-range-ratio 0 --request-rate inf --max-concurrency 8 --num-prompts 128 --num-warmups 4 --ignore-eos --seed 42 --save-result --result-dir /root/gdn-durable-20261022-attempt2/base-normal-trial1 --result-filename prefill.jsonbase-normal-trial1-mixed cd /root/gdn-rebase-base
export PATH=/root/gdn-rebase-base/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-base
export VLLM_BATCH_INVARIANT=0
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/vllm bench serve --model Qwen/Qwen3.5-0.8B --host 127.0.0.1 --port 8020 --dataset-name random --random-input-len 1024 --random-output-len 128 --random-range-ratio 0 --request-rate inf --max-concurrency 8 --num-prompts 64 --num-warmups 4 --ignore-eos --seed 42 --save-result --result-dir /root/gdn-durable-20261022-attempt2/base-normal-trial1 --result-filename mixed.jsonpr-normal-trial1-prefill cd /root/gdn-rebase-pr
export PATH=/root/gdn-rebase-pr/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-pr
export VLLM_BATCH_INVARIANT=0
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/vllm bench serve --model Qwen/Qwen3.5-0.8B --host 127.0.0.1 --port 8020 --dataset-name random --random-input-len 1024 --random-output-len 1 --random-range-ratio 0 --request-rate inf --max-concurrency 8 --num-prompts 128 --num-warmups 4 --ignore-eos --seed 42 --save-result --result-dir /root/gdn-durable-20261022-attempt2/pr-normal-trial1 --result-filename prefill.jsonpr-normal-trial1-mixed cd /root/gdn-rebase-pr
export PATH=/root/gdn-rebase-pr/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-pr
export VLLM_BATCH_INVARIANT=0
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/vllm bench serve --model Qwen/Qwen3.5-0.8B --host 127.0.0.1 --port 8020 --dataset-name random --random-input-len 1024 --random-output-len 128 --random-range-ratio 0 --request-rate inf --max-concurrency 8 --num-prompts 64 --num-warmups 4 --ignore-eos --seed 42 --save-result --result-dir /root/gdn-durable-20261022-attempt2/pr-normal-trial1 --result-filename mixed.jsonpr-bic-trial1-prefill cd /root/gdn-rebase-pr
export PATH=/root/gdn-rebase-pr/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-pr
export VLLM_BATCH_INVARIANT=1
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/vllm bench serve --model Qwen/Qwen3.5-0.8B --host 127.0.0.1 --port 8020 --dataset-name random --random-input-len 1024 --random-output-len 1 --random-range-ratio 0 --request-rate inf --max-concurrency 8 --num-prompts 128 --num-warmups 4 --ignore-eos --seed 42 --save-result --result-dir /root/gdn-durable-20261022-attempt2/pr-bic-trial1 --result-filename prefill.jsonpr-bic-trial1-mixed cd /root/gdn-rebase-pr
export PATH=/root/gdn-rebase-pr/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-pr
export VLLM_BATCH_INVARIANT=1
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/vllm bench serve --model Qwen/Qwen3.5-0.8B --host 127.0.0.1 --port 8020 --dataset-name random --random-input-len 1024 --random-output-len 128 --random-range-ratio 0 --request-rate inf --max-concurrency 8 --num-prompts 64 --num-warmups 4 --ignore-eos --seed 42 --save-result --result-dir /root/gdn-durable-20261022-attempt2/pr-bic-trial1 --result-filename mixed.jsonbase-normal-trial2-prefill cd /root/gdn-rebase-base
export PATH=/root/gdn-rebase-base/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-base
export VLLM_BATCH_INVARIANT=0
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/vllm bench serve --model Qwen/Qwen3.5-0.8B --host 127.0.0.1 --port 8020 --dataset-name random --random-input-len 1024 --random-output-len 1 --random-range-ratio 0 --request-rate inf --max-concurrency 8 --num-prompts 128 --num-warmups 4 --ignore-eos --seed 42 --save-result --result-dir /root/gdn-durable-20261022-attempt2/base-normal-trial2 --result-filename prefill.jsonbase-normal-trial2-mixed cd /root/gdn-rebase-base
export PATH=/root/gdn-rebase-base/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-base
export VLLM_BATCH_INVARIANT=0
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/vllm bench serve --model Qwen/Qwen3.5-0.8B --host 127.0.0.1 --port 8020 --dataset-name random --random-input-len 1024 --random-output-len 128 --random-range-ratio 0 --request-rate inf --max-concurrency 8 --num-prompts 64 --num-warmups 4 --ignore-eos --seed 42 --save-result --result-dir /root/gdn-durable-20261022-attempt2/base-normal-trial2 --result-filename mixed.jsonpr-normal-trial2-prefill cd /root/gdn-rebase-pr
export PATH=/root/gdn-rebase-pr/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-pr
export VLLM_BATCH_INVARIANT=0
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/vllm bench serve --model Qwen/Qwen3.5-0.8B --host 127.0.0.1 --port 8020 --dataset-name random --random-input-len 1024 --random-output-len 1 --random-range-ratio 0 --request-rate inf --max-concurrency 8 --num-prompts 128 --num-warmups 4 --ignore-eos --seed 42 --save-result --result-dir /root/gdn-durable-20261022-attempt2/pr-normal-trial2 --result-filename prefill.jsonpr-normal-trial2-mixed cd /root/gdn-rebase-pr
export PATH=/root/gdn-rebase-pr/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-pr
export VLLM_BATCH_INVARIANT=0
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/vllm bench serve --model Qwen/Qwen3.5-0.8B --host 127.0.0.1 --port 8020 --dataset-name random --random-input-len 1024 --random-output-len 128 --random-range-ratio 0 --request-rate inf --max-concurrency 8 --num-prompts 64 --num-warmups 4 --ignore-eos --seed 42 --save-result --result-dir /root/gdn-durable-20261022-attempt2/pr-normal-trial2 --result-filename mixed.jsonpr-bic-trial2-prefill cd /root/gdn-rebase-pr
export PATH=/root/gdn-rebase-pr/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-pr
export VLLM_BATCH_INVARIANT=1
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/vllm bench serve --model Qwen/Qwen3.5-0.8B --host 127.0.0.1 --port 8020 --dataset-name random --random-input-len 1024 --random-output-len 1 --random-range-ratio 0 --request-rate inf --max-concurrency 8 --num-prompts 128 --num-warmups 4 --ignore-eos --seed 42 --save-result --result-dir /root/gdn-durable-20261022-attempt2/pr-bic-trial2 --result-filename prefill.jsonpr-bic-trial2-mixed cd /root/gdn-rebase-pr
export PATH=/root/gdn-rebase-pr/.venv/bin:/root/.local/bin:/usr/local/cuda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/root/.local/bin
export PYTHONPATH=/root/gdn-rebase-pr
export VLLM_BATCH_INVARIANT=1
export VLLM_USE_V2_MODEL_RUNNER=0
export CUDA_VISIBLE_DEVICES=0
export VLLM_TEST_MODEL=Qwen/Qwen3.5-0.8B
export VLLM_TEST_SERVER_ARGS='--max-num-batched-tokens 128 --enforce-eager --dtype bfloat16 --gpu-memory-utilization 0.4 --no-enable-prefix-caching'
.venv/bin/vllm bench serve --model Qwen/Qwen3.5-0.8B --host 127.0.0.1 --port 8020 --dataset-name random --random-input-len 1024 --random-output-len 128 --random-range-ratio 0 --request-rate inf --max-concurrency 8 --num-prompts 64 --num-warmups 4 --ignore-eos --seed 42 --save-result --result-dir /root/gdn-durable-20261022-attempt2/pr-bic-trial2 --result-filename mixed.json |
ZJY0516
reviewed
Oct 22, 2026
| class QwenGatedDeltaNetAttention(GatedDeltaNetAttention): | ||
| @property | ||
| def mamba_type(self) -> MambaAttentionBackendEnum: | ||
| return MambaAttentionBackendEnum.QWEN_GDN_ATTN |
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I don't like the name, it will be confusing between QWEN_GDN_ATTN and GDN_ATTN
yewentao256
reviewed
Oct 25, 2026
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| output, _ = fused_recurrent_gated_delta_rule( | ||
| q=q.unsqueeze(0), | ||
| k=k.unsqueeze(0), | ||
| v=v.unsqueeze(0), | ||
| g=g.unsqueeze(0), | ||
| beta=beta.unsqueeze(0), | ||
| initial_state=ssm_state, | ||
| inplace_final_state=True, | ||
| cu_seqlens=non_spec_query_start_loc, | ||
| ssm_state_indices=repeated_indices, | ||
| use_qk_l2norm_in_kernel=True, | ||
| ) |
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Please take a look at this AI generated comment:
**[P2] Ensure bitwise replay with the default BF16 SSM cache**
The recurrent kernel accumulates state in FP32 within a sequence, but stores it to `ssm_state` after each token. When a prefill is split across scheduler steps, the next invocation reloads the rounded cache state.
By default, `gated_delta_net_state_dtype()` uses BF16 for BF16 models, so a single 97-token prefill and a 23+74 split may no longer be bitwise identical.
The new partition regression test uses `torch.randn(...)` without a dtype, which makes the SSM state FP32 and avoids this rounding.
Could we verify the exact partition comparison with a BF16 SSM cache? If it diverges, the BIC path may need to require FP32 recurrent-state storage.| ssm_state_indices=repeated_indices, | ||
| use_qk_l2norm_in_kernel=True, | ||
| ) | ||
| core_attn_out[:num_actual_tokens] = output.squeeze(0) |
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**[P2] Handle or reject prefix-cache checkpoints in the BIC path**
When `mamba_cache_mode="align"` and the FlashInfer GDN prefill backend are enabled, the metadata builder can still produce `attn_metadata.checkpoint` and `flashinfer_checkpoint_plan`.
However, this new early return bypasses the existing `_checkpoint_exporter.export()` path, so the intermediate convolution and recurrent checkpoint states are never written.
Could we either preserve checkpoint export or explicitly reject this configuration when batch invariance is enabled? Otherwise, a subsequent prefix-cache hit may restore an uninitialized state.
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| repeated_indices = ( | ||
| indices[:, None].expand(-1, max(1, num_actual_tokens)).contiguous() | ||
| ) |
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Might affect performance for additional memory allocation each time, do we have other method?
This was referenced Oct 31, 2026
sfeng33
added
the
batch-invariance
yuvalluria
added a commit
to yuvalluria/vllm
that referenced
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Nov 5, 2026
…g defects Lets VLLM_BATCH_INVARIANT=1 reach the Qwen GatedDeltaNet layers, and makes every request-dependent step in that path dispatch per request so its arithmetic does not depend on which neighbours share the engine step. Review fixes from vllm-project#45819: 1. prefill_query_start_loc_cpu is declared with the other prefill fields. A decode-only batch previously raised UnboundLocalError, with batch invariance disabled too. 2. GDNAttentionBackend.supports_batch_invariance() returns True, so the selector stops rejecting VLLM_BATCH_INVARIANT=1 before execution. Scoped to the implementation that provides it: GatedDeltaNetAttention gains a supports_batch_invariant flag defaulting to False, and only QwenGatedDeltaNetAttention opts in, so Kimi, OLMo and Bailing raise NotImplementedError at init rather than silently returning non-invariant output. 3. non_spec_query_start_loc_cpu is declared on GDNAttentionMetadata and populated in build(). The projection and convolution paths read it instead of calling .tolist() on a CUDA tensor, removing the host-device sync from both. It is only computed when batch invariance is on. 4. CUDA graph padding rows are preserved. Rows past the last real request are carried through the projection and the convolution as one trailing segment, so the row count still matches num_tokens and the residual and z shapes downstream are unchanged. Also fixes a latent NameError: the batch-invariant prefill path called prepare_chunk_indices and prepare_chunk_offsets without importing them, so that branch could not execute at all. Per-request dispatch now covers all four request-dependent sites. The two decode sites, the fused recurrence and the convolution state update, were missing and are added here. Speculative decoding is rejected at init in the metadata builder and in the layer instead of part-way through model execution. GDN models are detected from layer_types rather than a model_type allowlist, covering qwen3_next, qwen3_5_text, qwen3_5_moe_text and the multimodal ForConditionalGeneration wrappers without enumerating them. Measured on H100 NVL (SM90), Qwen/Qwen3.5-0.8B, vLLM v0.31.0: this takes bs=1 against bs=16 logprob agreement from unreachable, the selector used to refuse to start, to 15 of 16 prompts bitwise identical. One prompt still diverges, reproducibly and at the same step and magnitude, which is below the level request dispatch can reach. A non-GDN control on the same setup is 16 of 16. Full invariance needs the shared recurrent arithmetic explored in vllm-project#60033; this change establishes the gate that work depends on. Signed-off-by: Yuval Luria <yluria@redhat.com> Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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Overview
Enable batch-invariant Qwen GatedDeltaNet prefill, cached continuation and decode under
VLLM_BATCH_INVARIANT=1by sharing recurrent arithmetic. Supersedes #49827 and addresses the prefill-partitioning concerns in #48613. AI assistance was used.Claims
The diff is reduced from +421/-25 to +205/-22 including tests. The bespoke HTTP test and overlapping mock-heavy test were removed; the existing online suite and exact partition/state regression remain. This is still above the reviewer's ideal 200-line size.
Validation
Tested head
1cdaeab837, based on upstream main snapshot11a8d571d2, on 22 October. This is not a claim about a later main revision.H100, public
Qwen/Qwen3.5-0.8B, TP1, BF16, eager MRV1, FLASH_ATTN, prefix cache off. Base and PR use matching dependencies: torch 2.14.0+cu130, Triton 3.8.0, FlashInfer 0.7.2, transformers 5.20.2 and lm_eval 0.4.13. Wheela6c898e2ahas identical native build inputs and CUDA/common requirements to the pinned base. Remote source checks after validation match the pushed patch.Exact commands and native output, including all 18 serving measurements. The summaries below do not replace those raw outputs.
Correctness
The same existing online Qwen test fails on the base snapshot with
RuntimeError: VLLM batch_invariant mode is not supported for GDN_ATTN.and passes on this PR: 1 passed, 2 deselected. The base error establishes the unsupported gate, not a numeric failure of an enabled base kernel.Additional regressions: 10 kernel tests, 1 compiler/cache-key test and 11 NIXL state-layout tests passed. Partition tests compare output, convolution state and recurrent state exactly, including state reload. Applicable pre-commit checks, including mypy and CI test tethering, passed on the same source.
The online test checks output tokens and selected-token logprobs; it is not full-vocabulary logit-hash validation. Older cache-on/compiled/4B experiments from the previous head are not substituted for this head's evidence.
Accuracy
Full five-shot GSM8K via
lm_eval: all 1,319 questions in base-normal and PR-BIC modes, seed 42, greedy generation capped at 256 tokens, concurrency eight. Document, prompt and target hashes match across modes, with complete nonempty responses.No observed aggregate accuracy drop on this run, not statistical equivalence or broad quality validation. Generated text differs on 417 questions; BIC is not expected to reproduce ordinary batch-variable arithmetic.
Serving Performance
Unprofiled
vllm bench serve, three fresh-server rounds each for base normal, PR normal and PR BIC, alternating mode order. Concurrency eight, scheduling token budget 128, four warmups, seed 42, fixed lengths and forced output length. All 18 runs passed equal-work checks. Artifact uploads occurred outside measured benchmark commands.This measures the combined BIC cost, including global GEMM behavior, not GDN-only overhead. Three repetitions are not a significance test; all repetition results and native TTFT/TPOT/ITL output are in the linked comment. Accuracy-run duration is not used as throughput evidence.
Details
Why Shared Recurrence
Chunked prefill and recurrent decode implement the same mathematical operation but can produce different floating-point results. Scheduler batching and prefill partitioning can therefore change a request's output. Opening the backend support flag alone does not resolve that.
In BIC mode this change shares recurrent arithmetic across non-speculative Qwen GDN prefill, continuation and decode, preserving convolution/recurrent state across arbitrary tested partitions. It also adds a Qwen-specific backend identity/support gate, includes the Inductor BIC policy in the compilation cache configuration, and makes normalization row tiling independent of batch size. XPU/NIXL references account for the backend identity. No scheduler chunk-grid restriction is added.
Related Work And Open Review Points
QWEN_GDN_ATTNversusGDN_ATTNnaming is under review following @ZJY0516's comment; no naming change has been made yet.Out Of Scope And Limits
TP/EP, MRV2, Blackwell, multi-node, prefix-cache-on serving and live disaggregated state transfer are not validated by this matrix. Speculative decoding is unsupported. Long prefills may be slower under the recurrent route.
GEMM performance (#60034) and TP collective work (#50299) remain separate follow-ups; they do not establish multi-GPU correctness for this PR. No universal determinism or merge readiness is claimed.
Local validation does not replace upstream CI. Older Intel failures discussed in the thread belong to earlier heads; this update does not claim to fix or clear them, and no unrelated CI repairs are bundled here.
Pull Request Checklist
/pr-checklistskill.