[Model Runner V2] Account for prompt-logprobs memory

[malaiwah]

Summary

• include the Model Runner V2 prompt-logprobs logits/all-gather/top-k path in startup memory profiling on the last pipeline stage
• make the existing 1,024-row logits chunk an explicit validated VLLM_PROMPT_LOGPROBS_CHUNK_SIZE setting
• accumulate chunked-prefill prompt-logprob results in a preallocated CPU buffer instead of retaining every chunk on GPU
• warn when num_gpu_blocks_override exceeds the capacity implied by available KV memory

Closes #257.

Root cause and impact

A production TP4/DCP4 GLM-5.2 service had 218.81 MiB physically free per rank when four valid requests used prompt_logprobs=20. The V1 prompt-logprobs path attempted a 304 MiB allocation on every rank and killed EngineCore:

1024 prompt rows * 154880 vocabulary * 2 BF16 bytes = 302.5 MiB

The allocation is the full-vocabulary tensor-parallel logits all-gather performed before top-k selection. Startup memory profiling did not exercise this path, so KV sizing could consume the required headroom. Model Runner V2 is implemented beneath the V1 engine namespace at vllm/v1/worker/gpu/; the similarly named legacy runner is vllm/v1/worker/gpu_model_runner.py.

MRv2 also retained each prompt-logprobs result chunk on GPU until prompt completion and concatenated the full result on GPU. For long or concurrent prompts that memory grows across scheduler steps. This change adopts the bounded CPU-accumulation design already used by the legacy runner.

The observed 92.2% logical KV occupancy was workload context, not the cause of the missing physical memory: the GPU KV tensor is normally allocated at startup.

Duplicate-work check

• Reduce prompt logprobs memory usage vllm-project/vllm#45327 changes the legacy runner from a full log_softmax tensor to compute_topk_logprobs; this fork's MRv2 path already uses top-k and the PR does not profile its TP logits workspace or fix cross-step GPU retention.
• [Core] Preallocate sampler logits workspace during memory profiling vllm-project/vllm#37518 profiles sampler logits workspaces; its discussion explicitly notes that prompt logprobs bypass the sampler and remain a separate path.
• no open local-inference-lab/vllm PR references [Model Runner V2][OOM] Fixed 1024-token prompt_logprobs chunk can kill EngineCore #257 or implements this repair.

Validation

Passed locally on macOS/CPU:

.venv/bin/python -m pytest tests/v1/worker/test_prompt_logprobs.py tests/v1/core/test_kv_cache_utils.py::test_warns_when_num_gpu_blocks_override_exceeds_profiled_capacity -q
8 passed

Focused coverage verifies:

• invalid chunk settings fail closed
• every logits call stays within the configured row bound
• startup profiles the full batch and forwards max_logprobs=-1
• two scheduler steps preserve CPU-accumulated values and order
• only the last PP rank profiles prompt logprobs
• unsafe block overrides emit a warning

ruff check, ruff format, typos, SPDX, configuration validation, and the other applicable pre-commit hooks passed. The full pre-commit invocation also surfaced one pre-existing custom-branch mypy error at vllm/v1/worker/gpu/model_runner.py:1987 (bool | None passed to defer_copy_event: bool); this patch does not change that code.

GPU qualification is deliberately left pending in this draft. The planned gate is TP2/TP4 startup-capacity comparison plus concurrent prompt_logprobs=20, long chunked prefill, and MTP smoke tests on an authorized non-production host.

AI assistance disclosure

AI assistance was used for source auditing, implementation, and test drafting. The human submitter reviewed the incident evidence and is expected to review every changed line and the GPU qualification results before this PR is marked ready.

Summary by CodeRabbit

• New Features

  • Added configurable chunk sizing for prompt logprobs through an environment setting.
  • Improved prompt logprob processing to reduce GPU memory usage during large requests.

• Bug Fixes

  • Added warnings when configured GPU cache capacity exceeds profiled capacity, helping identify potential CUDA out-of-memory risks.
  • Improved validation and profiling for prompt logprob processing across supported execution modes.
  • Prevented prompt-logprob requests from reading externally cached prefix data while preserving newly generated cache state.

[coderabbitai]

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Review details

⚙️ Run configuration

Configuration used: Organization UI

Review profile: CHILL

Plan: Pro Plus

Run ID: 22754c37-30e7-41bc-9b05-608b52265b9e

📥 Commits

Reviewing files that changed from the base of the PR and between 1e164d4 and 63b77c8.

📒 Files selected for processing (2)

• tests/v1/core/test_scheduler.py
• vllm/v1/core/sched/scheduler.py

📝 Walkthrough

Walkthrough

Prompt-logprob processing now uses configurable chunk sizes, CPU accumulation, explicit logits cleanup, and startup profiling. Prompt-logprob requests skip external prefix-cache reads. KV-cache overrides warn when they exceed profiled capacity.

Changes

Prompt logprob memory handling

Layer / File(s)	Summary
Configurable chunking and CPU accumulation vllm/envs.py, vllm/v1/worker/gpu/sample/prompt_logprob.py, tests/v1/worker/test_prompt_logprobs.py	Prompt-logprob chunk size is configurable and validated. Results accumulate in preallocated CPU buffers. Intermediate logits are released after processing.
Runtime chunked model-runner path vllm/v1/worker/gpu_model_runner.py	The GPU model runner computes prompt logits in configurable chunks and copies each result into its output slice.
Startup prompt-logprob profiling vllm/v1/worker/gpu/model_runner.py, tests/v1/worker/test_prompt_logprobs.py	The GPU model runner profiles prompt-logprob workspace usage on the final pipeline rank. Tests cover profiling arguments and rank filtering.

KV-cache scheduling and capacity controls

Layer / File(s)	Summary
Prompt-logprob KV scheduling vllm/v1/core/sched/scheduler.py, tests/v1/core/test_scheduler.py	Prompt-logprob requests skip external KV prefix-cache matching while retaining connector state updates.
KV override capacity warning vllm/v1/core/kv_cache_utils.py, tests/v1/core/test_kv_cache_utils.py	KV-cache setup warns when num_gpu_blocks_override exceeds profiled capacity. The test verifies the warning.

Estimated code review effort: 4 (Complex) | ~60 minutes

Sequence Diagram(s)

sequenceDiagram
  participant ModelRunner
  participant PromptLogprobsWorker
  participant LogitsFunction
  participant CPUBuffer
  ModelRunner->>PromptLogprobsWorker: profile prompt-logprob workspace
  PromptLogprobsWorker->>LogitsFunction: compute configured-size logits chunks
  LogitsFunction-->>PromptLogprobsWorker: return chunk logits
  PromptLogprobsWorker->>CPUBuffer: copy each result slice
  PromptLogprobsWorker-->>ModelRunner: report profiled workspace usage

Loading

Possibly related issues

• local-inference-lab/vllm#264: The change covers chunked prompt-logprob memory handling but does not address unbounded upfront CPU allocation for prompt_logprobs=-1.

Suggested reviewers: njhill

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly identifies the primary change: Model Runner V2 prompt-logprobs memory accounting.
Linked Issues check	✅ Passed	The changes address issue #257 through startup profiling, configurable chunking, CPU accumulation, GPU release, and override-capacity warnings.
Out of Scope Changes check	✅ Passed	The V1 runner updates and regression tests directly support the prompt-logprobs memory objectives described for issue #257.

✨ Finishing Touches

🧪 Generate unit tests (beta)

• Create PR with unit tests

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Caution

Some comments are outside the diff and can’t be posted inline due to platform limitations.

⚠️ Outside diff range comments (1)

vllm/v1/worker/gpu/sample/prompt_logprob.py (1)

230-254: 🩺 Stability & Availability | 🟠 Major | 🏗️ Heavy lift

Release each chunk result before the next logits call.

del prompt_logits only releases the logits tensor. Lines 255-261 retain every compute_topk_logprobs result on GPU until the loop completes, then allocate a concatenated GPU tensor.

This can still exhaust GPU memory for long prompts, especially when prompt_logprobs=-1. Stream each chunk into the request CPU buffer before processing the next chunk. Add a CUDA regression that uses multiple chunks and full-vocabulary prompt logprobs.

🤖 Prompt for AI Agents

Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

In `@vllm/v1/worker/gpu/sample/prompt_logprob.py` around lines 230 - 254, Update
the chunk-processing loop in the prompt logprob function to copy each
compute_topk_logprobs result into a request CPU buffer before the next logits_fn
call, then release the chunk GPU tensors instead of retaining them in token_ids,
logprobs, and ranks. Preserve final output ordering and concatenation semantics,
and add a CUDA regression covering multiple chunks with num_prompt_logprobs=-1.

🤖 Prompt for all review comments with AI agents

Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

Outside diff comments:
In `@vllm/v1/worker/gpu/sample/prompt_logprob.py`:
- Around line 230-254: Update the chunk-processing loop in the prompt logprob
function to copy each compute_topk_logprobs result into a request CPU buffer
before the next logits_fn call, then release the chunk GPU tensors instead of
retaining them in token_ids, logprobs, and ranks. Preserve final output ordering
and concatenation semantics, and add a CUDA regression covering multiple chunks
with num_prompt_logprobs=-1.

---

ℹ️ Review info

⚙️ Run configuration

Configuration used: Organization UI

Review profile: CHILL

Plan: Pro Plus

Run ID: 56878e27-4d90-42fd-b08e-644f5187619b

📥 Commits

Reviewing files that changed from the base of the PR and between e2666d9 and 02fb59c.

📒 Files selected for processing (6)

• tests/v1/core/test_kv_cache_utils.py
• tests/v1/worker/test_prompt_logprobs.py
• vllm/envs.py
• vllm/v1/core/kv_cache_utils.py
• vllm/v1/worker/gpu/model_runner.py
• vllm/v1/worker/gpu/sample/prompt_logprob.py

[malaiwah]

V1 Model Runner chunking — follow-up commit

Added V1 _get_prompt_logprobs_dict chunking to complement the V2 PromptLogprobsWorker fix in the original commit.

Problem

The original PR #258 commit fixed the V2 PromptLogprobsWorker path by introducing VLLM_PROMPT_LOGPROBS_CHUNK_SIZE and chunking compute_logits + compute_logprobs in compute_prompt_logprobs_with_chunking. However, the V1 GPUModelRunner._get_prompt_logprobs_dict path in gpu_model_runner.py was not touched — it still materialized the full [num_logits, vocab_size] logits tensor in one shot:

logits = self.model.compute_logits(prompt_hidden_states)
logprobs = self.sampler.compute_logprobs(logits)  # log_softmax on full tensor

This OOMs on memory-dense profiles — reproduced on AIBoss (RTX 5090, r28 image) with a ~3,800-token chunked prompt + prompt_logprobs=1:

torch.OutOfMemoryError: CUDA out of memory. Tried to allocate ...
  File ".../vllm/v1/sample/sampler.py", line ...
    return logits.log_softmax(dim=-1, dtype=torch.float32)

EngineCore dies (EngineDeadError). See upstream vllm-project/vllm#14239 for the same bug.

Fix

Chunk the compute_logits + compute_logprobs + gather_logprobs loop in _get_prompt_logprobs_dict using the same VLLM_PROMPT_LOGPROBS_CHUNK_SIZE env var (default 1024) that the V2 path uses:

chunk_size = envs.VLLM_PROMPT_LOGPROBS_CHUNK_SIZE
for chunk_start in range(0, num_logits, chunk_size):
    chunk_end = min(chunk_start + chunk_size, num_logits)
    chunk_logits = self.model.compute_logits(
        prompt_hidden_states[chunk_start:chunk_end]
    )
    chunk_tgt = tgt_token_ids[chunk_start:chunk_end]
    chunk_logprobs = self.sampler.compute_logprobs(chunk_logits)
    del chunk_logits
    token_ids, logprobs, ranks, _ = self.sampler.gather_logprobs(
        chunk_logprobs, num_prompt_logprobs, chunk_tgt
    )
    del chunk_logprobs
    # ... GPU→CPU async copy per chunk

Each chunk's full-vocab logits tensor is freed (del) before the next chunk, bounding peak memory to [chunk_size, vocab_size] instead of [num_logits, vocab_size].

Note on production relevance

V1 is not used in the production stack (VLLM_USE_V2_MODEL_RUNNER=1). This fix is for consistency — so both the V1 and V2 prompt-logprobs paths are bounded by the same VLLM_PROMPT_LOGPROBS_CHUNK_SIZE guard, and deployments running without the V2 runner don't hit the OOM.

Verification (AIBoss, RTX 5090, r28 image)

Model: malaiwah/GLM-5.2-SIQ-Fruit-Instruct (GLM-5.2 MLA, 5B, TP1) with --max-num-batched-tokens 3072 --max-model-len 4096 --enable-chunked-prefill --kv-cache-dtype nvfp4_ds_mla.

Before fix (r28 baseline):

~3800-token prompt + prompt_logprobs=1
→ torch.OutOfMemoryError in logits.log_softmax
→ EngineDeadError (engine dies)

After fix (r28 + overlay):

~3800-token prompt + prompt_logprobs=1
→ finish_reason: stop
→ prompt_logprobs count: 3420 (3419 non-null entries)
→ no crash, no OOM

The same request that previously killed EngineCore now completes cleanly with all prompt logprobs returned.

[malaiwah]

AIBeast GPU qualification — r31 derivative + PR head 63b77c8

Hardware/runtime: 4x RTX PRO 6000 Blackwell 96 GB, driver 595.71.05, CUDA 13.2; GLM-5.2 EXL3-TR3-3.42bpw + online K6, TP4/DCP4/MTP3, dynamic NVFP4 MLA KV.

The production failure that motivated this PR had only 218.81 MiB physically free when the unprofiled 1,024-row GLM full-vocabulary logits path requested about 304 MiB, killing EngineCore. With this PR and VLLM_PROMPT_LOGPROBS_CHUNK_SIZE=128, the corresponding per-rank logits workspace is bounded to about 38 MiB, is startup-profiled, and long-prompt accumulation is moved to CPU.

GPU results:

• Exact KV pool: KV_CACHE_MEMORY_BYTES=4518907904 per rank = 520,192 logical tokens; the testing-only block override was removed so it cannot bypass memory accounting.
• 4 concurrent ~16,395-token requests with prompt_logprobs=20 all completed with exact prompt-logprob row counts; no worker restart, preemption, or OOM.
• After this first-use/JIT gate, ordinary warm physical headroom was about 1.00–1.10 GiB/rank.
• Cold 65,528 / 131,073-token prefills completed at 2,323.9 / 2,199.5 tok/s; minimum observed free memory was about 0.84 GiB/rank.
• Two independent near-maximum five-depth needle probes passed 5/5 at 517,178 and 517,176 actual tokens.
• Short correctness, strict structured output with reasoning, and required tool-call output all passed.
• Container remained at 0 restarts / OOMKilled=false.

A separate C8/4,096-row experiment OOMed and was rejected; that is an arena/activation tradeoff, not a regression from this PR. A C12 width-48 graph also proved too tight at near-maximum prefill, so production will keep the PP-favored 3,072-row scheduler with a safer C8/graph-32 envelope.

Conclusion: this PR does not merely add nominal static headroom. It converts a runtime-only, unprofiled ~304 MiB allocation plus cross-step GPU accumulation into a profiled, bounded ~38 MiB workspace with CPU accumulation, directly preventing the reproduced production crash while retaining the 520K serving envelope.