Describe the bug

_wait_one_response in tokenizer_manager.py consumes only the last element of state.out_list on each wakeup:

When handle_loop processes multiple scheduler batches before _wait_one_response is scheduled (i.e. several ZMQ messages are buffered), intermediate streaming outputs accumulate in out_list. Only the last one is kept and the rest are silently discarded.

The race in _wait_one_response exists unconditionally, but its severity depends on which output the consumer reads.

• Cumulative text consumers: each output dict carries cumulative decoded text (output_str[sent_offset:]). If intermediate chunks are dropped, out_list[-1] still contains all text up to that point. The client sees a larger than usual chunk but no data loss.
• Token ID consumers: output_ids in each output dict are disjoint deltas - they contain only the new tokens since the last chunk. Dropping a chunk permanently loses those token IDs with no way to recover them. This is silent, unrecoverable data loss, regardless of whether --skip-tokenizer-init is set.
  In other words, _wait_one_response was never designed to handle disjoint streaming outputs safely. The cumulative text path happened to mask the race, but any consumer reading output_ids from the stream has no safety.
  Additionally, --skip-tokenizer-init removes the detokenizer process, which normally acts as a natural throttle (tokenizer.batch_decode() is CPU-bound) and makes ZMQ buffer accumulation rare. Without it, messages arrive at wire speed and accumulation under load becomes routine - making the race significantly easier to trigger.

Symptoms

• Example of corrupted streaming output from DeepSeek-V3.2 (tool-calling response):

The file prerequests.sh exists in the repository root. Now let's **creatory (create a directory for)** modules.

**<｜DSML｜function_c>**
<｜DSML｜invoke name="add_entry">
...
</｜DSML｜invoke>
</｜DSML｜function_calls>

• completion_tokens reported by SGLang is higher than the number of tokens the client actually received
• P99 inter-token latency spikes on affected chunks

Why Dynamo's default configuration amplifies the problem

Dynamo forces --skip-tokenizer-init by default, which has a compounding effect on this bug.

Beyond amplifying this race, --skip-tokenizer-init has additional costs in Dynamo:

• No structured output enforcement. SGLang's grammar-guided decoding and JSON mode require the tokenizer to be initialized. With --skip-tokenizer-init, the schema is never forwarded to SGLang — constrained decoding is silently disabled. (cc @ishandhanani there is PR from my co-worker in dynamo: feat: sglang guided decoding support ai-dynamo/dynamo#6620)
• Single tokenizer worker bottleneck. --skip-tokenizer-init also prevents using --tokenizer-worker-num > 1. In our production data, the single tokenizer worker relaying requests over ZMQ becomes a throughput bottleneck under high concurrency. Re-enabling tokenizer init would unlock multi-worker scaling.

A possible middle ground: a new SGLang flag (e.g. --skip-detokenization or something) that initializes the tokenizer (enabling structured output and multi-worker) but skips the detokenization pass for output chunks, leaving that to the external consumer.

Reproduction

This race is sensitive to system-level timing. In our experience, it reproduces reliably in a k8s production environment (+Dynamo) under sustained load, but we were unable to trigger it in a local setup with the same SGLang + Dynamo frontend configuration in a single container - likely because differences in network stack and container overhead affect the exact back-pressure dynamics that cause ZMQ message accumulation.

Observed in production (Dynamo + SGLang, detokenizer enabled (for SO compatability)):
466 affected requests in 20 minutes under max_concurrency=5 (DeepSeek-V3.2)

• Backlogs of up to 14 queued chunks per wakeup
• A few percent (~1-3%) of requests showed a token count mismatch

cc @ishandhanani

Environment

Dynamo + SGLang