[Bugfix] Treat max_tokens as output upper bound, not input-space reservation by YuvrajSinghBhadoria2 · Pull Request #57035 · vllm-project/vllm

YuvrajSinghBhadoria2 · 2026-09-15T15:54:51Z

Why are these changes needed?

TokenizeParams.max_input_tokens was defined as max_total_tokens - max_output_tokens, treating the client's requested max_tokens as a hard reservation off the model's context window. Per the OpenAI API spec — and how Zed/Factory/opencode-style clients actually use it — max_tokens is an upper bound on generation, not a reservation. The real generation budget is already clamped downstream by get_max_tokens() in vllm/entrypoints/serve/utils/api_utils.py based on actual post-tokenization prompt length, so the output side stays bounded regardless.

Example: model context 128000, prompt 62466 tokens, max_tokens: 65535

before: rejected (62466 > 128000-65535 = 62465)
after: prompt accepted, sampling max_tokens clamped to 65534

Why this is a fix at the source, not whack-a-mole

The property feeds seven consumers. Patching only one (e.g. _token_len_check alone) left get_encode_kwargs capping the tokenizer at max_input_tokens + 1, silently truncating prompts for left-truncation chat models and breaking prefix-cache locality. Fixing the property at the source corrects all seven consistently.

What stays correct

__post_init__ still rejects max_output_tokens > max_total_tokens (a genuine bug)
_token_len_check / _text_len_check still reject genuine context overflow; their messages are updated to reflect that the rejection means input alone exceeds the context, not input+output
with_kwargs now preserves the original max_output_tokens when no explicit max_length is passed, instead of recomputing it to 0 (this was flagged on the existing POC PR)
get_max_tokens() continues to clamp sampling_params.max_tokens to max_model_len - input_length

Tests

tests/renderers/test_completions.py::TestMaxTokensNotReservation (7 tests):

large max_tokens no longer eats the prompt budget (the reported symptom)
genuine prompt overflow is still rejected
truncate_prompt_tokens=-1 / pad_prompt_tokens=-1 map to max_total_tokens, not max_total - max_output
with_kwargs preserves/re-derives the output budget correctly

All pre-existing renderer tests run with max_output_tokens=0, so their behavior is unchanged.

Related PR

#42482 is an open proof-of-concept for the same fix. This PR additionally addresses the functional regression (output budget reset in with_kwargs) and the now-inconsistent validation messages that its bot review called out.

github-actions · 2026-09-15T15:55:16Z

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jperezdealgaba · 2026-10-09T08:03:54Z

Can you convert this into review instead of draft mode please? The changes make sense

claude

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

DarkLight1337

I think this makes sense API-wise. cc @njhill

DarkLight1337 · 2026-10-09T09:05:07Z

@claude review

YuvrajSinghBhadoria2 · 2026-10-09T09:34:58Z

Fixed in f5f3718.

EmbeddingTokenizeParamsMixin.build_tok_params encoded the max_embed_len cap by setting max_output_tokens = max_model_len - max_embed_len, relying on the old max_input_tokens == max_total_tokens - max_output_tokens formula. Since max_input_tokens now returns max_total_tokens directly, that cap was silently dropped for the non-chunked path, letting prompts up to the full model context through where max_embed_len was configured lower.

Now sets max_total_tokens = max_embed_len directly, matching what the enable_chunked_processing branch above already does, and drops the max_output_tokens_param string since it no longer describes anything real. Tests in tests/entrypoints/pooling/test_embed_max_embed_len.py cover the chunked and non-chunked paths plus the unset-max_embed_len fallback.

On verification: pre-commit passes locally on both files. I could not run the test suite locally — vLLM requires torch 2.13.0, which has no x86_64 macOS wheel, so the suite needs CI.

DarkLight1337

Thanks, LGTM now

DarkLight1337 · 2026-10-09T09:45:15Z

/ci run

github-actions · 2026-10-09T09:46:56Z

❌ This PR is 1 commit behind upstream main. Your branch must contain every commit currently on upstream main. No new CI build was started. Merge or rebase onto the latest main, then rerun /ci run. To test this branch at your own risk, use /ci run --allow-stale.

mergify · 2026-10-09T09:51:00Z

Hi @YuvrajSinghBhadoria2, the pre-commit checks have failed. Please run:

uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-files

Then, commit the changes and push to your branch.

For future commits, pre-commit will run automatically on changed files before each commit.

Fixes #42474. TokenizeParams.max_input_tokens was defined as max_total_tokens - max_output_tokens, treating the client's requested max_tokens as a hard reservation off the model context. The actual output budget is already clamped downstream by get_max_tokens() based on real post-tokenization prompt length, so max_tokens must only act as an upper bound on generation. Requests with a large max_tokens (e.g. agent/IDE clients that use a ceiling like 65535) were rejected even when the prompt itself fit the context. More than a one-line property change because the property feeds seven consumers: - _token_len_check / _text_len_check: messages now reflect that rejection means genuine prompt overflow, not prompt+output overflow. - __post_init__: truncate_prompt_tokens check message no longer claims max_input = max_total - max_output. - with_kwargs: preserves the original max_output_tokens when no explicit max_length is passed, instead of recomputing it to 0. - get_encode_kwargs / -1 pad/truncate sentinels inherit the corrected value automatically. Adds regression tests covering the reported scenario, the -1 sentinels, genuine-overflow rejection, and with_kwargs budget preservation. Signed-off-by: YuvrajSinghBhadoria2 <245782664+YuvrajSinghBhadoria2@users.noreply.github.com> Signed-off-by: YuvrajSinghBhadoria2 <yuvrajsinghbhado2030@gmail.com>

EmbeddingTokenizeParamsMixin.build_tok_params encoded the max_embed_len cap by setting max_output_tokens = max_model_len - max_embed_len and relying on max_input_tokens == max_total_tokens - max_output_tokens. Now that max_input_tokens returns max_total_tokens directly, that cap is dropped for the non-chunked path, letting prompts up to the full model context through. Set max_total_tokens = max_embed_len instead, matching the chunked branch, and drop the now-meaningless max_output_tokens_param error-message string. Signed-off-by: YuvrajSinghBhadoria2 <yuvrajsinghbhado2030@gmail.com>

Addresses review comment asking for these tests to live alongside the other embed protocol tests. Signed-off-by: YuvrajSinghBhadoria2 <yuvrajsinghbhado2030@gmail.com>

mypy rejected pooling_target=; PoolerConfig takes seq_pooling_type. Signed-off-by: YuvrajSinghBhadoria2 <yuvrajsinghbhado2030@gmail.com>

DarkLight1337 · 2026-10-09T15:11:10Z

Please fix DCO

With max_input_tokens now equal to max_total_tokens, resolving the -1 sentinel to it truncates a prompt to the full context length. get_max_tokens() then clamps the sampling budget to 0, so an over-long prompt silently becomes a zero-token generation instead of a truncation that can actually generate. Add max_truncation_tokens, which holds one token back for generative requests, and use it in the two places that resolve the sentinel (get_encode_kwargs and _truncation_slice). Pooling requests set max_output_tokens = 0 and emit an embedding rather than generated tokens, so they keep the full input budget. _token_len_check still validates against max_input_tokens, so a prompt that cannot fit at all is still rejected. This also fixes two of the three entrypoints CI failures on their own, since reserving a token returns the rendered prompts to what those tests expect: - TestInitializeToolSessions::test_harmony_tool_followup_preserves_render_params renders [3, 4] again. The remaining two are re-baselined: - test_online_renderer_applies_responses_token_budget_to_harmony_prompt now truncates to 4 of 5 tokens rather than 3. - TestMaxTokensNotReservation::test_negative_truncation_uses_max_total expects 99 rather than 100. - test_serving_chat_mistral_token_ids_prompt_is_validated asserted that a 95 token prompt with max_tokens=10 against max_model_len=100 raises, which the new semantics make a legal request. It now asserts the request is accepted and that the generation budget is clamped to the remaining context. Genuine overflow stays covered by test_serving_chat_mistral_token_ids_prompt_too_long_is_rejected. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Signed-off-by: Yuvraj Singh Bhadoria <yuvrajsinghbhado2030@gmail.com>

YuvrajSinghBhadoria2 · 2026-10-09T15:21:38Z

Implemented per @DarkLight1337's answer — reserve one output token when truncate_prompt_tokens=-1 truncates a prompt, so an over-long prompt can't leave a zero-token generation budget.

Scoped to generative requests: pooling sets max_output_tokens = 0 and emits an embedding rather than generated tokens, so there's no output budget to protect and it keeps the full input length.

This fixes test_harmony_tool_followup_preserves_render_params on its own — the reserve returns the rendered prompt to [3, 4]. The other two needed re-baselining:

test_online_renderer_applies_responses_token_budget_to_harmony_prompt now truncates to 4 of 5 tokens rather than 3.
test_serving_chat_mistral_token_ids_prompt_is_validated asserted that a 95-token prompt with max_tokens=10 against max_model_len=100 raises. That's now a legal request, so the test asserts it's accepted and that the generation budget is clamped to the remaining context. Genuine overflow is still covered by test_serving_chat_mistral_token_ids_prompt_too_long_is_rejected.

TestMaxTokensNotReservation::test_negative_truncation_uses_max_total also needed updating (100 → 99) since it asserts the truncated length directly.

_token_len_check still validates against max_input_tokens, so a prompt that can't fit at all is still rejected — only the truncation cap moved.

Note that only 4 tests in the suite pass truncate_prompt_tokens=-1, and truncation defaults to "disabled" in the Responses API, so the default request path never reaches the changed code.

YuvrajSinghBhadoria2 · 2026-10-09T15:23:54Z

Please /ci run

jperezdealgaba · 2026-10-09T15:24:53Z

/ci run

github-actions · 2026-10-09T15:25:14Z

❌ This PR is 12 commits behind upstream main. Your branch must contain every commit currently on upstream main. No new CI build was started. Merge or rebase onto the latest main, then rerun /ci run. To test this branch at your own risk, use /ci run --allow-stale.

DarkLight1337 · 2026-10-09T15:26:04Z

/ci run

github-actions · 2026-10-09T15:26:27Z

✅ Triggered Buildkite CI #93872 for commit a8ba4599ad7a.

## What does this PR do? README only. Tells OpenClaw users to set `context_window` to the model server's maximum context length, and explains how OpenClaw uses it and `max_output_tokens`. Follow-up to NVIDIA-NeMo#4476, which lets sandbox sessions use `max_output_tokens`; there is no issue. Why: when `context_window` is unset, OpenClaw assumes 200,000 tokens. On a server with a smaller limit, it keeps requesting its full output limit and does not compact in time, so long episodes can send requests the server rejects. Gym's `vllm_model` returns an empty reply, not an error, by default, and the run ends early. Example, worked out from the code and not measured in a run: released vLLM (up to 0.31) rejects a call when prompt + requested output > `max_model_len`. At 131,072 with no `context_window`, calls fail once the prompt passes 122,880 tokens (131,072 − 8,192 default), or 98,304 with `max_output_tokens: 32768` (131,072 − 32,768). With `context_window: 131072`, OpenClaw lowers each call's output to fit the room left by its estimate of the input; the estimate can undercount code or JSON. Unreleased vLLM main (vllm-project/vllm#57035) lowers the output instead of rejecting, but still rejects a prompt longer than `max_model_len`. The sandbox section now also says that the request field is rejected but the agent config field applies, and that a `max_output_tokens` above `context_window` is rejected (the check from NVIDIA-NeMo#4476). ## Validation - Read the OpenClaw npm package at 2026.6.11 (pinned in `configs/openclaw_agent.yaml`) and at 2026.6.35 (the pin in NVIDIA-NeMo#4082). Both match the README: - `contextWindow` defaults to 200,000 and `maxTokens` to min(8,192, `contextWindow`) (`dist/io-*.js`); - each call requests at most min(`maxTokens`, `contextWindow` − estimated input − 1), where the estimate is characters / 4 × 1.25. This applies to any base URL other than OpenAI's or Azure OpenAI's, which covers Gym's model server (`dist/openai-transport-stream-*.js`); - two in-run limits are computed from `contextWindow`. One is the context guard: once a run passes its limit, OpenClaw compacts and retries (`installToolResultContextGuard` in `dist/selection-*.js`). The other is the cap on each tool result's size (`resolveLiveToolResultMaxChars` in `dist/tool-result-truncation-*.js`). - Gym code on main: `_build_openclaw_config` writes `contextWindow` and `maxTokens` only when they are set, and `vllm_model` returns an empty `length` completion on a context-length error unless `propagate_context_overflow_errors` is true. - Open PRs touching OpenClaw: NVIDIA-NeMo#3902 and NVIDIA-NeMo#2733 edit this README, and NVIDIA-NeMo#4082 moves the pin to 2026.6.35. `git merge-tree` shows this PR adds no conflicts with any of them. - `pre-commit run --all-files`: passed. ## Rollouts N/A: README-only change, with no code or config changes. ## Compatibility and benchmark impact None from this PR. Following the advice can change rollouts with long episodes or long tool results, because `context_window` also sets when OpenClaw compacts and how much of a tool result it keeps. ## Checklist - [x] I have read the [contributing guidelines](https://github.com/NVIDIA-NeMo/Gym/blob/main/CONTRIBUTING.md). - [x] The change is focused; unrelated "drive-by" edits are tracked as separate issues/PRs. - [x] Tests added or updated and pass locally, or N/A for docs-only / non-code changes (so CI unit/server checks pass when applicable). N/A: docs-only. - [x] Documentation added or updated, or N/A with justification. - [x] Pre-commit checks pass locally (`pre-commit run --all-files`) (so CI lint/format checks pass). - [x] New source files include the required Apache-2.0 SPDX header. N/A: no new files. - [x] All commits have DCO sign-off (`git commit -s`) (so the DCO check passes). Signed-off-by: Abhishree <athittenaman@nvidia.com>

…pper bound clamped to the context left after the prompt (vllm-project#57035), so keep the room the client left for the answer (requested max_tokens - budget); before, a long prompt let the thinking fill the whole clamped output and the turn had no answer. Lets gx10-guard stop counting tokens itself

Bumps vLLM 0.31.0 -> 0.32.0. ## Dependencies - `vllm` 0.32.0 wheel. torch stays 2.13.0, flashinfer stays 0.7.0.post1. - `transformers` 5.6.2 -> 5.19.0 (vLLM 0.32 requires `>=5.16.1,<5.20`; 5.19.0 is what vLLM's CI tests). The `transformers==5.6.2` override is removed. `tokenizers` 0.22.2 -> 0.23.3. - `nvidia-cutlass-dsl` override 4.7.1 -> 4.8.0 (exact pin in vLLM 0.32). `tokenspeed-mla` 0.2.15, `humming-kernels` 0.1.17, `instanttensor` 0.2.2, `xgrammar` 0.2.8 come from vLLM; `humming-kernels` and `instanttensor` are exempt from the 7-day cutoff. - `ifbench` is excluded from the workspace: it pins `setuptools<78`, vLLM 0.32 needs `>=78.1.1`. No config or test references it. ## Patches deleted (fixed upstream in 0.32) - `monkey_patch_shm_buffer_ownership`: vllm-project/vllm#61000. - `monkey_patch_tokenize_params_validation`: vllm-project/vllm#57035 (`max_tokens` is an upper bound, not reserved from the context). - `monkey_patch_nano_v3_reasoning_parser`: upstream `nemotron_v3` parser. The Nemotron 3 Nano / 3.5 auto-detection now picks `nemotron_v3`. Configs that set `reasoning_parser = "nano_v3"` must rename it. - DeepSeek V4 `layer_types` shim (`utils/transformers_compat.py` and its vLLM patch): transformers >= 5.15 knows the V4 layer types. ## Breakages fixed - `serving_tokens.py`: `GenerateResponseChoice` is gone and `GenerateResponse` is now a discriminated union (vllm-project/vllm#58588). Subclass `GenerateTokensChoice` / `GenerateTokensResponse` and check `isinstance` against `GenerateTokensResponse`. - `prefill_logprobs`: validate with `GenerateTokensResponse` (the union alias has no `model_validate_json`). - `_patch_lora_key_prefix`: re-copied from 0.32's `LoRAModel.from_local_checkpoint`; tensorizer support was removed upstream (vllm-project/vllm#60024). - NemotronH config: transformers 5.19 renames legacy layer types (`mamba` → `linear_attention`, `attention` → `full_attention`) by setting `config.layer_types`, which was a read-only property. `layer_types` is now an `attribute_map` alias of `layers_block_type` (as upstream), and the model and conversion code use the new names. Hub configs with legacy `layers_block_type` or `hybrid_override_pattern` load as before. - Qwen3.5 tests: `from_config` through the auto class now swaps a composite config for its text config; the tests build VLMs with `Qwen3_5ForCausalLM`, as the trainer does. - `monkey_patch_kv_xfer_finished_tolerate_freed`: synced the copied method with 0.32 (`_set_kv_fetch_stage(req, KV_FETCH_COMPLETED_WAITING)`). Still needed on 0.32 (fixes land in 0.33 or are not merged): DeepSeek V4 C128 boundary, DeepSeek V4 `attn_sink` loading, GPT-OSS weight loading, KV-transfer freed assert, MiniMax think-end, routed experts with P/D connectors. Every remaining patch target still exists in 0.32; targets whose source changed were checked against the new source. ## Validation CPU: `pytest tests/unit -m "not gpu"` 854 passed; ruff clean. Import/attribute check of every `vllm` import and monkey-patch target in `src/` and `tests/` against 0.32: no missing names; patched methods whose source changed were reviewed. GPU (H200, max 2 nodes; 0.31 baselines use main's lock at the same base commit, same nodes): | Check | 0.32 | 0.31 | |---|---|---| | `tests/unit -m gpu` (NemotronH, Qwen3.5, Mamba CP, DSv4 kernels, …) | pass after the fixes above¹ | – | | Integration: reverse_text, reverse_text_moe, reverse_text_lora, reverse_text_rl_opd, reverse_text_rl_sft, reverse_text_sft, reverse_text_sft_lora, alphabet_sort | pass² | – | | Qwen3-30B-A3B RL, bf16, NCCL (15 steps, median step / mismatch KL) | 15.3 s / 0.028 | 14.9 s / 0.025 | | Qwen3-30B-A3B RL, online fp8 (`fp8_per_block`), NCCL | 15.4 s / 0.061 | 16.4 s / 0.063 | | Qwen3-30B-A3B RL, bf16, filesystem broadcast (trainer step) | 156 s / 0.030 | 155 s / 0.020 | | GLM-4.5-Air RL, router replay, 1 train + 1 infer node (10 steps, median step / mismatch KL / reward) | 23.4 s / 0.0028 / 0.49→0.65 | 24.7 s / 0.0036 / 0.44→0.66 | | Qwen3-30B-A3B P/D (NIXL 1P1D, vllm-router 0.2.3) vs colocated, router replay + sampling masks, batch-invariant: identical tokens, rows, masks, logprobs | c1 10/10, c10 20/20, uint16 c1 10/10 / c10 20/20, decode preemption 20/20 | same as 0.31 results | | GLM-4.5-Air (TP4) P/D vs prefill server queried directly (same node), batch-invariant: identical tokens and rows | 0/10 (decode diverges); 10/10 with `disable_custom_all_reduce = true`³ | 10/10 | | Long multi-turn prompt (14.4k prompt + `max_tokens` 4096, `max_model_len` 16384), plain and router-replay server | 200, prompt not truncated; `/generate` 15.9k + 4096 → 200, capped at `max_model_len`; 16.4k prompt → 400 | same | | DeepSeek-V4-Flash TP8+EP serving smoke | loads, coherent completion | – | ¹ The DSv4 `cudnn_flashmla` kernel tests fail the same way on main when vLLM's DSv4 test loads first (FlashMLA symbol clash); with #4139 applied they pass on 0.32 (123 passed). ² Each integration module's `test_dashboard` needs `playwright install --with-deps`, which fails on our nodes; all other tests pass. ³ 0.32 uses the custom all-reduce under `VLLM_BATCH_INVARIANT=1` (vllm-project/vllm#58623). Two TP>1 servers then no longer decode bit-identically, so P/D only matches colocated serving bit-for-bit with `disable_custom_all_reduce = true`. prime-rl does not set batch invariance; RL is unaffected (GLM row above). Not covered: Qwen3.5/Qwen3.8 PLE reload (no checkpoint on disk); GLM-4.5-Air disaggregated RL (needs 3 nodes). Router-replay requests need `top_k > 0` and `temperature > 0` (sampling-mask replay), on 0.31 and 0.32 alike.  --- > [!NOTE] > **Medium Risk** > Touches core inference dependencies and removes several runtime patches; misalignment with vLLM 0.32 APIs or Nemotron parser changes could break rollouts or serving until configs are updated. > > **Overview** > **Upgrades the inference stack from vLLM 0.31.0 to 0.32.0**, with matching pins: `transformers` 5.19.0, `nvidia-cutlass-dsl` 4.8.0, and direct wheel URLs. `ifbench` is dropped from the uv workspace because its `setuptools<78` pin conflicts with vLLM 0.32. > > Several **vLLM workarounds are removed** now that upstream fixed them: SHM buffer ownership on dequeue, relaxed `TokenizeParams` prompt-length checks, the custom `nano_v3` reasoning parser, and the DeepSeek V4 `layer_types` transformers shim (`transformers_compat.py`). **Nemotron 3 Nano / 3.5** auto-detection now uses the built-in **`nemotron_v3`** parser instead of `nano_v3`. > > **API follow-ups for 0.32:** token-in/token-out serving and `prefill_logprobs` use `GenerateTokensChoice` / `GenerateTokensResponse` instead of the old `GenerateResponse*` types. The LoRA checkpoint patch is re-synced from 0.32 (tensorizer path removed). The KV-transfer “freed request” patch adds `_set_kv_fetch_stage(..., KV_FETCH_COMPLETED_WAITING)` to match upstream. > > **Trainer Nemotron-H** aligns layer-type names with newer transformers (`linear_attention`, `full_attention`) via `attribute_map`, while still accepting legacy `mamba` / `attention` in `layers_block_type`. > > <sup>Reviewed by [Cursor Bugbot](https://cursor.com/bugbot) for commit f92627e. Bugbot is set up for automated code reviews on this repo. Configure [here](https://www.cursor.com/dashboard/bugbot).</sup>

…rvation (vllm-project#57035) Signed-off-by: YuvrajSinghBhadoria2 <245782664+YuvrajSinghBhadoria2@users.noreply.github.com> Signed-off-by: YuvrajSinghBhadoria2 <yuvrajsinghbhado2030@gmail.com> Signed-off-by: Yuvraj Singh Bhadoria <yuvrajsinghbhado2030@gmail.com> Co-authored-by: YuvrajSinghBhadoria2 <245782664+YuvrajSinghBhadoria2@users.noreply.github.com> Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk> Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

… guards Pins: transformers 5.18.0 (upstream 0.32 requires >=5.16.1,<5.20.0, so the old 5.15.0 pin had every 0.32 gate running unsupported) and torch 2.13.0+gfx906, because 2.15.dev does not exist for this GPU -- so upstream's torch-2.15 fusion restorations and static FP8 fusion are out of reach here, recorded rather than worked around. max_tokens (vllm-project#57035 upper bound): no reservation assumptions in either harness. _bench_gfx906.py uses ignore_eos=True and divides by emitted token ids; _bench_chat_serve.py uses ignore_eos and divides by usage.completion_tokens. Both now emit max_tokens_cfg, and the HTTP client warns on <95% of the requested count or a missing usage block, so a compat layer dropping ignore_eos cannot look like a throughput regression. Smoke-verified: 64/64 on both paths.

vLLM v0.32 (vllm-project/vllm#57035) clamps the output budget to the room left after the prompt instead of rejecting prompt plus max_tokens over the maximum model length. In front of such servers the reservation rejects requests the pod would serve with fewer output tokens, so the README names the servers that reject the overflow and limits the recommendation to them. Signed-off-by: satyamg1620 <Satyam.Gupta.3@ibm.com>

YuvrajSinghBhadoria2 force-pushed the fix/max-tokens-not-reservation branch from 6dd8bfc to d3c3fd6 Compare September 15, 2026 16:00

This was referenced Oct 1, 2026

[Frontend] Report model_context_window_exceeded on /v1/messages #59616

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[Frontend] Return Anthropic's prompt-too-long error on /v1/messages #59617

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YuvrajSinghBhadoria2 marked this pull request as ready for review October 9, 2026 08:18

YuvrajSinghBhadoria2 requested review from DarkLight1337 and njhill as code owners October 9, 2026 08:18

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Comment thread vllm/renderers/params.py Resolved

YuvrajSinghBhadoria2 requested a review from noooop as a code owner October 9, 2026 09:34

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Comment thread tests/entrypoints/pooling/test_embed_max_embed_len.py Resolved

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YuvrajSinghBhadoria2 changed the title ~~Treat max_tokens as output upper bound, not input-space reservation~~ [Bugfix] Treat max_tokens as output upper bound, not input-space reservation Oct 9, 2026

mergify Bot added the bug Something isn't working label Oct 9, 2026

YuvrajSinghBhadoria2 and others added 4 commits October 9, 2026 15:38

Move max_embed_len tests into embed/test_protocol.py

1815beb

Addresses review comment asking for these tests to live alongside the other embed protocol tests. Signed-off-by: YuvrajSinghBhadoria2 <yuvrajsinghbhado2030@gmail.com>

Fix PoolerConfig field name in max_embed_len tests

5e46c4b

mypy rejected pooling_target=; PoolerConfig takes seq_pooling_type. Signed-off-by: YuvrajSinghBhadoria2 <yuvrajsinghbhado2030@gmail.com>

YuvrajSinghBhadoria2 requested a review from AndreasKaratzas as a code owner October 9, 2026 15:07

YuvrajSinghBhadoria2 force-pushed the fix/max-tokens-not-reservation branch from e7de8ab to 49c587b Compare October 9, 2026 15:14

Merge branch 'main' into fix/max-tokens-not-reservation

a8ba459

DarkLight1337 merged commit 1a3a7a8 into vllm-project:main Oct 9, 2026
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athitten mentioned this pull request Oct 12, 2026

docs(openclaw): recommend setting context_window NVIDIA-NeMo/Gym#4547

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faresobeid mentioned this pull request Oct 18, 2026

chore: bump vLLM to 0.32.0 PrimeIntellect-ai/prime-rl#4140

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richard-for-bugen Bot mentioned this pull request Oct 19, 2026

[Rust Frontend] Truncate prompts against max_model_len like Python BugenZhao/vllm#11

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This was referenced Oct 19, 2026

docs(upstream): v0.32.0 triage — KV cache tour and stack-relevant changes jsboige/vllm#120

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feat(vllm): bump to v0.32.0 — KV 429 319 (+1.1%), full-window 400 eliminated jsboige/vllm#122

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BugenZhao mentioned this pull request Oct 20, 2026

[Rust Frontend] Truncate prompts against max_model_len like Python #62642

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unverbraucht mentioned this pull request Oct 20, 2026

[P32-8] Post-merge pins: transformers/torch divergence and max_tokens upper bound KIntegrated/vllm-gfx906-mobydick#66

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ghost91- mentioned this pull request Oct 22, 2026

[Feature] Treat max_tokens as an upper bound when the prompt fits the context sgl-project/sglang#45841

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richard-for-bugen Bot mentioned this pull request Oct 26, 2026

[Rust Frontend] Reject max_tokens above max_model_len like Python BugenZhao/vllm#62

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BugenZhao mentioned this pull request Oct 27, 2026

[Rust Frontend] Reject max_tokens above max_model_len like Python #63964

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satyamg1620 mentioned this pull request Oct 31, 2026

contextlengthaware: reserve the output token cap in the routing length llm-d/llm-d-router#3215

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YuvrajSinghBhadoria2 commented Sep 15, 2026

Why are these changes needed?

Why this is a fix at the source, not whack-a-mole

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