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@@ -1,282 +1,105 @@
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---
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---
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name: clean-startup-log
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name: clean-startup-log
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description: Clean up noisy startup warnings and spurious prints in SGLang server logs. Use when users ask to clean up unwanted warnings, deprecation messages, or third-party noise in the server startup output.
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description: Audit SGLang startup logs, save evidence, and propose cleanup for user review. With no arguments, run Qwen3-8B at TP1 and TP2 plus gpt-oss-20b at TP1.
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disable-model-invocation: true
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disable-model-invocation: true
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---
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---
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# Clean Up SGLang Server Startup Logs
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# Audit SGLang Startup Logs
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Goal: ensure the server startup log is clean and minimal, with no spurious warnings, deprecation messages, or unformatted prints from third-party libraries.
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The default outcome is saved logs and a findings report. Apply runtime changes
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only after the user selects them. A request to edit this skill does not itself
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## Workflow
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launch servers.
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### 1. Launch a server and capture the log
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## Default runs
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```bash
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A bare `$clean-startup-log` invocation runs these cases sequentially without
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uv run sglang serve --model-path Qwen/Qwen3-8B 2>&1 | tee /tmp/startup_log.txt
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asking for commands. Explicit commands, models, or TP sizes replace this matrix.
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```
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| Case / log filename | Command |
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Wait until the server prints `The server is fired up and ready to roll!`, then Ctrl-C.
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|---|---|
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| `qwen3-8b-tp1.log` | `uv run sglang serve --model-path Qwen/Qwen3-8B` |
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For TP>1 testing:
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| `qwen3-8b-tp2.log` | `uv run sglang serve --model-path Qwen/Qwen3-8B --tp 2` |
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```bash
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| `gpt-oss-20b-tp1.log` | `uv run sglang serve --model-path openai/gpt-oss-20b` |
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uv run sglang serve --model-path Qwen/Qwen3-8B --tp 2 2>&1 | tee /tmp/startup_log.txt
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```
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These cover dense, tensor-parallel, and MoE/hybrid sliding-window attention
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startup. Reuse complete captures from the current audit when code and environment
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For MoE / hybrid-SWA models (e.g. gpt-oss), test separately — they exercise different code paths:
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have not changed.
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```bash
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uv run sglang serve --model-path openai/gpt-oss-20b 2>&1 | tee /tmp/startup_log.txt
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## Capture logs
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```
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1. Check the checkout, free GPUs, and ports once. Use the requested command when
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### 2. Compare against the clean reference log
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resources are free; otherwise select free GPUs with `CUDA_VISIBLE_DEVICES` and
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an unused `--port`, recording the adjustments. Leave existing servers alone.
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Read `/tmp/startup_log.txt` and compare it against the reference log at the bottom of this file. Identify lines that:
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2. Create a unique directory with `mktemp -d /tmp/sglang-startup-audit-XXXXXX`.
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Save raw stdout and stderr together in a separate log for each case, for
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- Do NOT have the `[timestamp]` or `[timestamp TPx]` logger prefix
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example with `set -o pipefail` and `COMMAND 2>&1 | tee LOG_PATH`. Record commands,
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- Contain `WARNING`, `deprecated`, `is deprecated`, or similar noise
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GPU IDs, ports, commit, relevant overrides, and readiness status.
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- Are printed by third-party libraries (transformers, torchao, NCCL, Gloo, tqdm, etc.)
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3. Wait for `The server is fired up and ready to roll!`, then stop that server and
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- Are duplicate/redundant with information already logged by SGLang
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its workers before the next case. First runs can spend many minutes downloading
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- Appear multiple times due to `ModelConfig` being constructed in multiple processes
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weights or compiling FlashInfer kernels; check download/compiler activity
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before treating a quiet log as a hang. Preserve partial logs for failed or
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### 3. Classify each noisy line
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stalled starts and report the last stage. Continue independent cases when possible.
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4. Preserve the user's logging configuration, including `NCCL_DEBUG`. If NCCL
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For each noisy line, determine:
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verbosity needs explaining, inspect relevant shell settings, `NCCL_CONF_FILE`,
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and `/etc/nccl.conf`. Do not override intentional diagnostics or recommend
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| Category | Action |
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`NCCL_DEBUG=WARN` solely because the output is long. Avoid full environment dumps.
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|----------|--------|
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| **SGLang code using wrong API** | Fix the SGLang code (e.g., replace deprecated API with new one) |
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## Investigate efficiently
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| **SGLang code logging at wrong level** | Change log level (e.g., warning -> debug for non-actionable messages) |
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| **Duplicated across processes** | Downgrade to debug — info logged in one process becomes noise in 3-4 |
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- Scan for deprecations, duplicate handler output, unrelated import failures,
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| **Third-party lib prints at import time** | Suppress the logger or redirect stdout during that import |
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unformatted prints, and unexpected warnings. Read representative excerpts and
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| **C-level print from .so library** | Redirect fd 1 during the specific C call, or accept it if too invasive |
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counts instead of repeatedly dumping `server_args`, progress redraws, or NCCL
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| **Real warning the user should see** | Keep it |
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diagnostics. Normalize carriage returns for analysis only; preserve raw logs.
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- Trace each candidate to its actual emitter with focused `rg` searches. Inspect
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### 4. Present findings before fixing
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its log level: SGLang's formatter may omit severity. Group shared signatures
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across cases and distinguish handler duplication from separate GPU/process calls.
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List all noisy lines with their source and proposed fix. Ask the user to review before making changes.
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- Repetition, WARNING severity, or a different third-party format alone does not
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establish a cleanup need. Consider whether the message explains configuration,
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### 5. Apply fixes and verify
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progress, resource use, or an operational limitation.
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- Consult [noise-source hints](references/noise-sources.md) only for a matching
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After approval, apply fixes one at a time, re-launch the server, and verify each fix works.
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signature or an unresolved emitter. Verify current code rather than trusting
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historical line numbers, fix status, or assumptions about unrelated models.
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## Key Architecture: Why Logs Repeat
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## Accepted output
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`ModelConfig` is constructed **3-4 times** during startup across different processes:
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1. Main process: `ServerArgs.__post_init__()` → `get_model_config()` → `ModelConfig()`
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Preserve these reviewed messages unless the user requests a different policy:
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2. Scheduler subprocess: `Scheduler.init_model_config()` → `ModelConfig.from_server_args()`
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3. Scheduler subprocess: `TpModelWorker._init_model_config()` → `ModelConfig.from_server_args()`
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- NCCL diagnostics enabled by the user's environment or host configuration.
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4. Main process: `TokenizerManager.init_model_config()` → `ModelConfig.from_server_args()`
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- NUMA permission warnings, including one check per GPU in TP runs.
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- GPT-OSS MXFP4 backend-selection warnings and default page-size selection warnings.
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Similarly, `get_tokenizer()` is called **5 times** across processes:
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- `Init Unified Radix Cache. Components: ... Tree Core: ...`, tree-cache summaries,
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1. `resolve_auto_parsers` (main) — `template_detection.py`
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SWA allocation details, and per-rank memory/timing records.
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2. `Scheduler.init_tokenizer()` (scheduler subprocess) — `scheduler.py`
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- Useful progress bars, warmup HTTP access logs, uv synchronization messages,
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3. `DetokenizerManager` (detokenizer subprocess) — `detokenizer_manager.py`
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isolated NCCL/Gloo startup lines, and one timestamped HF authentication warning.
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4. `TpModelWorker.__init__()` (scheduler subprocess) — `tp_worker.py`
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5. `TokenizerManager` (main) — `tokenizer_manager.py`
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These can appear in a clean startup log. Do not repeatedly propose the declined
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NUMA deduplication, backend/page-size level changes, or NCCL verbosity override.
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Any `logger.info()` or `logger.warning()` in `ModelConfig.__init__()` or `get_tokenizer()` will appear 3-5 times. **Keep these at `logger.debug()`.**
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Keep real operational warnings visible: for example, a Harmony vocabulary failure
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can disable `/v1/responses` even when server readiness and `/generate` succeed.
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## Known Noise Sources and Fixes (from past sessions)
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## Report before changing code
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### 1. torchao "Skipping import of cpp extensions due to incompatible torch version"
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Return a compact run table with readiness status and clickable raw-log links.
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- **Source:** `torchao/__init__.py` — printed via `logger.warning()` when torch version < 2.11.0
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For each actual cleanup candidate, give an exact representative message, affected
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- **Trigger:** `sglang/__init__.py` -> `_apply_hf_patches()` -> `_patch_removed_symbols()` -> `from transformers.models.llama import modeling_llama` -> deep import chain -> `transformers/quantizers/auto.py` -> `from .quantizer_torchao import TorchAoHfQuantizer` -> imports torchao
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cases/counts, source file/function, and specific proposed behavior. Distinguish
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- **Fix:** In `hf_transformers_patches.py::_patch_removed_symbols()`, temporarily set the `torchao` logger level to `ERROR` around the `modeling_llama` import:
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confirmed findings from suspicions and operational failures from logging noise.
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```python
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_torchao_logger = logging.getLogger("torchao")
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If there are no actionable cleanup findings, say the logs are clean and no
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_prev_level = _torchao_logger.level
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further cleanup is needed. Otherwise, ask which numbered changes to adopt and
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_torchao_logger.setLevel(logging.ERROR)
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wait for the user's selections before editing runtime code or preparing patches.
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try:
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Honor existing approvals and declined items without asking again.
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from transformers.models.llama import modeling_llama
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finally:
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## Apply selected changes
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_torchao_logger.setLevel(_prev_level)
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```
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- Batch compatible approved edits, then verify affected cases once. Repeat a
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startup only for a new change, failure, or unresolved concern; do not relaunch
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### 2. "`torch_dtype` is deprecated! Use `dtype` instead!" (PARTIALLY FIXED)
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after every one-line edit. Save verification logs separately from baselines.
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- Preserve useful warnings and application handlers. HF can warn during early
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- **Source:** `transformers/configuration_utils.py` — the `torch_dtype` property warns via `logger.warning_once()`
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CLI model detection before `configure_logger()`, and spawned processes have
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- **Trigger:** Model files accessing `config.torch_dtype` instead of `config.dtype`
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independent logger state. Keep `configure_hf_hub_logger()` in both
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- **Fix applied so far:** Only `models/gpt_oss.py` (lines 222, 471) — tested with `openai/gpt-oss-20b`.
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`suppress_noisy_warnings()` and `configure_logger()`; make repeated setup safe.
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- **Remaining files that still use `config.torch_dtype`** (fix each only after testing with the corresponding model):
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- Keep the legacy compiled-kernel cache migration notice at DEBUG. Avoid broad
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- `models/bailing_moe.py` (line 302)
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library-level suppression or fd redirection for a narrow logging problem.
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- `models/llada2.py` (line 313)
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- Run relevant formatting and focused existing checks. Add tests only when they
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- `models/qwen3_next.py` (lines 192, 209)
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verify meaningful behavior, not a log-level spelling. Report changes and
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- `models/qwen3_5.py` (line 245)
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verification; create branches, commits, and PRs when requested.
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- `models/nano_nemotron_vl.py` (lines 79, 102, 284)
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- `models/llava.py` (lines 732, 734-737)
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- `model_loader/loader.py` (line 649)
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- **Note:** `common.py` was already fixed in a prior session. If new model files are added with `config.torch_dtype`, the warning will reappear — grep for `\.torch_dtype` to find them.
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- **Important:** Only change `config.torch_dtype` → `config.dtype` for models you have actually tested. The `dtype` property should return the same value, but verify per-model to avoid regressions.
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### 3. "`BaseImageProcessorFast` is deprecated"
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- **Source:** `transformers/utils/import_utils.py` — the lazy module `__getattr__` warns when `BaseImageProcessorFast` is accessed
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- **Trigger:** `base_processor.py` and `ernie45_vl.py` have `from transformers import BaseImageProcessorFast` at top level. These are imported eagerly via `tokenizer_manager.py` -> `multimodal_processor.py` -> `base_processor.py`, even for non-multimodal models.
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- **Fix:** Replace `from transformers import BaseImageProcessorFast` with `from transformers import BaseImageProcessor` and update all `isinstance(..., BaseImageProcessorFast)` checks to `isinstance(..., BaseImageProcessor)`
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### 4. "No platform detected. Using base SRTPlatform with defaults."
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- **Source:** `sglang/srt/platforms/__init__.py` — `logger.warning()`
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- **Fix:** Change to `logger.debug()` — this is expected on machines without a platform plugin and not actionable.
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### 5. `NCCL version 2.27.7+cuda13.0`
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- **Source:** C-level print from `libnccl.so` during `ncclCommInitRank()` call
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- **Status:** Accepted as-is. SGLang already logs the version via `sglang is using nccl==X.Y.Z`. The C-level print cannot be suppressed without redirecting stdout fd, which is too invasive. `NCCL_DEBUG=WARN` does not suppress it in NCCL 2.27+.
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### 6. `[Gloo] Rank X is connected to Y peer ranks`
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- **Source:** C++ Gloo library print during process group init
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- **Status:** Accepted as-is. From C++ code inside PyTorch's Gloo backend.
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### 7. `torchao SyntaxWarning: invalid escape sequence`
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- **Source:** `torchao/quantization/quant_api.py` — a raw string with unescaped `\.`
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- **Status:** Upstream torchao bug. Cannot fix from SGLang side.
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### 8. tqdm progress bars (e.g., `Multi-thread loading shards`, `Capturing batches`)
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- **Status:** These are expected and useful. They show progress during weight loading and CUDA graph capture. Keep them.
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### 9. CUTE_DSL "Unexpected error during package walk" — double-logged (FIXED)
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- **Source:** `nvidia-cutlass-dsl` package at `.venv/.../cutlass/cutlass_dsl/cutlass.py`, line 391. Logger named `CUTE_DSL` with its own `StreamHandler`.
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- **Trigger:** During CUDA graph capture, cutlass DSL walks packages and hits an unexpected error for `cutlass.cute.experimental`.
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- **Root cause of double-logging:** The CUTE_DSL logger has `propagate=True` (default), so the warning is emitted by both the CUTE_DSL handler (with its format) and the root logger (SGLang's format).
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- **Fix applied:** In `entrypoints/engine.py`, changed `CUTE_DSL_LOG_LEVEL` from `"30"` (WARNING) to `"40"` (ERROR). This suppresses the WARNING at both the CUTE_DSL logger and root propagation levels. The env var controls both `logger.setLevel()` and `console_handler.setLevel()` in cutlass's `setup_log()`.
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### 10. ModelConfig init logs repeated 3x (FIXED)
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- **Lines:** `"Downcasting torch.float32 to ..."`, `"Hybrid swa model: ..."`, `"DeepGemm is enabled but ..."`
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- **Source:** `configs/model_config.py` — `_get_and_verify_dtype()` (line 1457), `_derive_hybrid_model()` (line 497), `_verify_quantization()` (line 1236)
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- **Root cause:** `ModelConfig.__init__()` is called 3-4 times in different processes (see "Key Architecture" above). Each construction fires the same log lines.
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- **Fix applied:** Downgraded all three from `logger.info()`/`logger.warning()` to `logger.debug()`. The dtype is already visible in `server_args` and `Load weight end`. Hybrid SWA info appears in `Tree cache initialized`. DeepGemm is not actionable.
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### 11. Tokenizer retry/fallback messages repeated 3-4x (FIXED)
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- **Lines:** `"Tokenizer loaded as generic TokenizersBackend ... retrying"`, `"Loading tokenizer ... directly as PreTrainedTokenizerFast"`, `"Tokenizer for ... loaded as generic TokenizersBackend. Set --trust-remote-code"`
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- **Source:** `utils/hf_transformers/tokenizer.py` — `_resolve_tokenizers_backend()` (line 215), `_load_tokenizer_by_declared_class()` (line 110), final warning (line 244)
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- **Root cause:** 5 separate `get_tokenizer()` calls across processes (see "Key Architecture" above). Each produces 3 log lines. Concurrent subprocess launches cause interleaved/doubled output.
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- **Fix applied:** Downgraded all three from `logger.warning()`/`logger.info()` to `logger.debug()`.
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### 12. Template detection logs — 5 lines consolidated to 1 (FIXED)
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- **Lines:** `"Detected reasoning config '...' from template rule '...'"`, `"Detected reasoning parser '...' from template rule '...'"`, `"Detected tool-call parser '...' from template rule '...'"`, `"Auto-detected reasoning parser: ..."`, `"Auto-detected tool-call parser: ..."`
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- **Source:** `managers/template_detection.py` (lines 337, 370) logged each detection rule match. `managers/template_manager.py` (lines 177-182) logged summary lines that duplicated the detection logs.
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- **Fix applied:** Removed per-rule logs from `template_detection.py`. Consolidated the 5 lines in `template_manager.py` into a single summary: `"Auto-detected template features: reasoning_config=..., reasoning_parser=..., tool_call_parser=..."`
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### 13. KV cache dtype logged separately from allocation (FIXED)
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- **Lines:** `"Using KV cache dtype: torch.bfloat16"` then `"KV Cache is allocated. #tokens: ..., K size: ..., V size: ..."`
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- **Source:** `model_executor/model_runner.py` (line 2217) and `mem_cache/memory_pool.py` (line 740)
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- **Fix applied:** Removed the standalone dtype log from `model_runner.py`. Added `dtype` field to the allocation log in `memory_pool.py`: `"KV Cache is allocated. dtype: torch.bfloat16, #tokens: ..., K size: ..., V size: ..."`
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### 14. CUTLASS backend warning — B200 → SM100, warning → info (FIXED)
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- **Line:** `"CUTLASS backend is disabled when piecewise cuda graph is enabled due to TMA descriptor initialization issues on B200."`
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- **Source:** `layers/attention/flashinfer_backend.py` (line 249)
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- **Fix applied:** Changed "B200" to "SM100 GPUs" (the condition checks `is_sm100_supported()` which matches SM10x, not just B200). Downgraded from `logger.warning()` to `logger.info()` since it's an expected automatic fallback.
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### 15. `max_total_num_tokens` and `Tree cache initialized` log ordering
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- **Issue:** `max_total_num_tokens=...` appears before `Tree cache initialized:...` even though tree cache is conceptually part of memory setup.
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- **Root cause:** `max_total_num_tokens` is logged inside `init_model_worker()` (scheduler.py:972), which runs before `build_kv_cache()` (scheduler.py:425) where tree cache is created.
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- **Status:** Not fixed — reordering was reverted. Acceptable as-is.
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### 16. `Ignore import error when loading sglang.srt.models.midashenglm`
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- **Source:** `models/registry.py` (line 109) — `logger.warning()` during `import_model_classes()` which iterates all model modules via `pkgutil.iter_modules`
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- **Trigger:** The `midashenglm` model depends on `torchaudio`, which fails to load
|
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- **Status:** Should be downgraded to `logger.debug()` — not actionable when loading an unrelated model. Same pattern exists in `managers/multimodal_processor.py`, `dllm/algorithm/__init__.py`, `multimodal_gen/runtime/models/registry.py`.
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### 17. `Multiple NUMA nodes found for GPU X`
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|
- **Source:** `utils/numa_utils.py` (line 112) — `logger.warning()`
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- **Status:** Could be downgraded to `logger.info()`. The situation is handled gracefully ("Using the first one") and not actionable.
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### 18. Warmup `/model_info` access log
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- **Source:** Uvicorn access log, triggered by SGLang's own warmup at `entrypoints/http_server.py` (line 1877)
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- **Status:** SGLang talking to itself. Could suppress uvicorn access logger during warmup, or exclude `/model_info` from warmup access logging.
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## Investigation Techniques
|
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|
|
### Trace what triggers an import
|
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|
|
```python
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|
|
import sys
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|
|
_real_import = __builtins__.__import__
|
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|
|
def _tracing_import(name, *args, **kwargs):
|
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|
|
|
if 'TARGET_MODULE' in name:
|
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|
|
import traceback
|
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|
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|
|
print(f'=== Importing {name} ===')
|
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|
|
traceback.print_stack()
|
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|
|
return _real_import(name, *args, **kwargs)
|
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|
|
|
__builtins__.__import__ = _tracing_import
|
|
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|
|
```
|
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|
|
### Trace what triggers a logger warning
|
|
|
|
|
|
|
|
```python
|
|
|
|
|
|
|
|
import logging, traceback
|
|
|
|
|
|
|
|
class TraceHandler(logging.Handler):
|
|
|
|
|
|
|
|
def emit(self, record):
|
|
|
|
|
|
|
|
if 'SEARCH_STRING' in record.getMessage():
|
|
|
|
|
|
|
|
traceback.print_stack()
|
|
|
|
|
|
|
|
h = TraceHandler()
|
|
|
|
|
|
|
|
h.setLevel(logging.WARNING)
|
|
|
|
|
|
|
|
logging.getLogger('TARGET_LOGGER_NAME').addHandler(h)
|
|
|
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
### Find C-level prints in .so files
|
|
|
|
|
|
|
|
```bash
|
|
|
|
|
|
|
|
strings /path/to/library.so | grep "SEARCH_STRING"
|
|
|
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
### Find all config.torch_dtype accesses (for deprecation warning)
|
|
|
|
|
|
|
|
```bash
|
|
|
|
|
|
|
|
grep -rn '\.torch_dtype' python/sglang/srt/models/ python/sglang/srt/model_loader/ python/sglang/srt/utils/hf_transformers/
|
|
|
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
## Reference: Clean Startup Log (TP=1, Qwen3-8B)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
```
|
|
|
|
|
|
|
|
[2026-05-24 00:52:39] Attention backend not specified. Use trtllm_mha backend by default.
|
|
|
|
|
|
|
|
[2026-05-24 00:52:39] TensorRT-LLM MHA only supports page_size of 16, 32 or 64, changing page_size from None to 64.
|
|
|
|
|
|
|
|
[2026-05-24 00:52:40] server_args=ServerArgs(model_path='Qwen/Qwen3-8B', ...)
|
|
|
|
|
|
|
|
[2026-05-24 00:52:40] Multiple NUMA nodes found for GPU 0: [...]. Using the first one.
|
|
|
|
|
|
|
|
[2026-05-24 00:52:42] Using default HuggingFace chat template with detected content format: string
|
|
|
|
|
|
|
|
[2026-05-24 00:52:42] Auto-detected template features: reasoning_config=..., reasoning_parser=qwen3, tool_call_parser=qwen
|
|
|
|
|
|
|
|
[2026-05-24 00:52:50] Init torch distributed begin.
|
|
|
|
|
|
|
|
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
|
|
|
|
|
|
|
|
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
|
|
|
|
|
|
|
|
[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0
|
|
|
|
|
|
|
|
[2026-05-24 00:52:50] Init torch distributed ends. elapsed=0.21 s, mem usage=0.10 GB
|
|
|
|
|
|
|
|
[2026-05-24 00:52:51] Load weight begin. avail mem=275.75 GB
|
|
|
|
|
|
|
|
[2026-05-24 00:52:51] Found local HF snapshot for Qwen/Qwen3-8B at ...; skipping download.
|
|
|
|
|
|
|
|
Multi-thread loading shards: 100% Completed | 5/5 [00:01<00:00, 2.62it/s]
|
|
|
|
|
|
|
|
[2026-05-24 00:52:54] Load weight end. elapsed=2.62 s, type=Qwen3ForCausalLM, avail mem=260.48 GB, mem usage=15.28 GB.
|
|
|
|
|
|
|
|
[2026-05-24 00:52:54] KV Cache is allocated. dtype: torch.bfloat16, #tokens: 1707904, K size: 117.28 GB, V size: 117.28 GB
|
|
|
|
|
|
|
|
[2026-05-24 00:52:54] Memory pool end. avail mem=25.28 GB
|
|
|
|
|
|
|
|
[2026-05-24 00:52:54] CUTLASS backend is disabled when piecewise cuda graph is enabled due to TMA descriptor initialization issues on SM100 GPUs. Using auto backend instead for stability.
|
|
|
|
|
|
|
|
[2026-05-24 00:52:54] Capture cuda graph begin. This can take up to several minutes. avail mem=24.16 GB
|
|
|
|
|
|
|
|
[2026-05-24 00:52:54] Capture cuda graph bs [1, 2, 4, ...]
|
|
|
|
|
|
|
|
Capturing batches (bs=1 avail_mem=23.56 GB): 100% | 52/52 [00:05<00:00, 10.36it/s]
|
|
|
|
|
|
|
|
[2026-05-24 00:53:00] Capture cuda graph end. Time elapsed: 5.38 s. mem usage=0.60 GB. avail mem=23.56 GB.
|
|
|
|
|
|
|
|
[2026-05-24 00:53:00] Capture piecewise CUDA graph begin. avail mem=23.56 GB
|
|
|
|
|
|
|
|
[2026-05-24 00:53:00] Capture cuda graph num tokens [4, 8, 12, ...]
|
|
|
|
|
|
|
|
Compiling num tokens (num_tokens=4): 100% | 74/74 [00:09<00:00, 7.44it/s]
|
|
|
|
|
|
|
|
Capturing num tokens (num_tokens=4 avail_mem=21.24 GB): 100% | 74/74 [00:07<00:00, 10.44it/s]
|
|
|
|
|
|
|
|
[2026-05-24 00:53:18] Capture piecewise CUDA graph end. Time elapsed: 18.18 s. mem usage=2.32 GB. avail mem=21.24 GB.
|
|
|
|
|
|
|
|
[2026-05-24 00:53:20] Tree cache initialized: source=default impl=RadixCache hybrid_swa=False hybrid_ssm=False hierarchical=False streaming_wrapped=False
|
|
|
|
|
|
|
|
[2026-05-24 00:53:20] max_total_num_tokens=1707904, chunked_prefill_size=16384, max_prefill_tokens=16384, max_running_requests=4096, context_len=40960, available_gpu_mem=21.24 GB
|
|
|
|
|
|
|
|
[2026-05-24 00:53:20] INFO: Started server process [1964249]
|
|
|
|
|
|
|
|
[2026-05-24 00:53:20] INFO: Waiting for application startup.
|
|
|
|
|
|
|
|
[2026-05-24 00:53:20] Using default chat sampling params from model generation config: {'temperature': 0.6, 'top_k': 20, 'top_p': 0.95}
|
|
|
|
|
|
|
|
[2026-05-24 00:53:20] INFO: Application startup complete.
|
|
|
|
|
|
|
|
[2026-05-24 00:53:20] INFO: Uvicorn running on http://127.0.0.1:30000 (Press CTRL+C to quit)
|
|
|
|
|
|
|
|
[2026-05-24 00:53:21] Prefill batch, #new-seq: 1, #new-token: 64, ...
|
|
|
|
|
|
|
|
[2026-05-24 00:53:21] INFO: 127.0.0.1:... - "POST /generate HTTP/1.1" 200 OK
|
|
|
|
|
|
|
|
[2026-05-24 00:53:21] The server is fired up and ready to roll!
|
|
|
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Note: `[Gloo]` messages and tqdm progress bars are acceptable. The key is no warnings or deprecation messages from transformers, torchao, or other third-party libraries. The `CUTLASS backend is disabled` message is now `info` level, not a warning.
|
|
|
|
|
|
|
|
Lianmin Zheng