perf(streaming): cut per-chunk overhead ~30% on Anthropic + Bedrock hot path

[yassin-berriai]

Summary

Resolves LIT-3313

Five targeted optimizations to CustomStreamWrapper that eliminate redundant work executed on every streaming chunk across Anthropic, Bedrock Invoke, and Bedrock Converse providers.

Changes

#	Optimization	File	Impact
1	Fix sync __next__ critical bug	streaming_handler.py	Eliminates model_dump() + model_response_creator() call on every text chunk in the sync path
2	Cache model name + provider at init	streaming_handler.py	Removes per-chunk dict lookup + conditional string format in model_response_creator()
3	Pre-compute _base_hidden_params at init	streaming_handler.py	Replaces two per-chunk dict spreads with one pre-built base dict
4	Cache resolved streaming callbacks	streaming_handler.py	isinstance + hasattr filtering of litellm.callbacks resolved once per stream instead of per-call
5	Module-level _GCHUNK_FIELDS frozenset	streaming_handler.py	GChunk.__annotations__ read once at import time instead of per-chunk

Root Cause: Sync Path Bug (Optimization 1)

The most impactful fix: ModelResponseStream declares usage: Optional[Usage] = None as a Pydantic field, so hasattr(response, "usage") always returns True. The sync __next__ had:

# BEFORE — runs on EVERY chunk:
if hasattr(response, "usage"):
    obj_dict = response.model_dump()   # expensive Pydantic serialisation
    del obj_dict["usage"]
    response = self.model_response_creator(chunk=obj_dict, ...)  # new object

The async __anext__ was already correct:

_has_usage = hasattr(processed_chunk, "usage") and getattr(processed_chunk, "usage", None) is not None

Fixed to match the async path:

# AFTER — only runs when usage is actually present (~1 chunk per stream):
if getattr(response, "usage", None) is not None:
    ...

Benchmark

Two reproducible microbenchmarks drive CustomStreamWrapper directly with synthetic in-memory chunks. A full proxy benchmark like benchmark_anthropic_messages_perf.py (used in #28289) would include FastAPI, the HTTP stack, and TCP latency — which dilutes the signal from per-chunk CPU work this PR targets. Both new scripts isolate the wrapper's hot path.

Methodology: 3 alternating A/B rounds (baseline → optimized → ...) on the same host, back-to-back, to amortize CPU-scheduling and thermal noise. Per round: warmup=2, repeats=6 (micro) / 4 (stream), report min per round, then take min across rounds. Baseline = streaming_handler.py at the merge-base (7c667b8797); optimized = HEAD.

model_response_creator() microbenchmark

Tight call loop over the function where 3 of the 5 optimizations live (cached model name, pre-computed _base_hidden_params, single dict spread). 200,000 iterations × 6 repeats × 3 rounds; min reported.

Path	Baseline (μs/call)	Optimized (μs/call)	Δ	Throughput (calls/s)
model_response_creator() (no chunk)	19.21	17.42	−9.3%	52,069 → 57,407 (+10.3%)
model_response_creator(chunk={"text": …})	19.28	17.69	−8.3%	51,859 → 56,531 (+9.0%)
model_response_creator(chunk={"id", "object", "created"})	18.16	16.19	−10.9%	55,067 → 61,777 (+12.2%)

uv run python scripts/benchmark_model_response_creator.py --label optimized --iterations 200000 --warmup 2 --repeats 6

End-to-end stream-iteration benchmark

Full CustomStreamWrapper drained to completion across all 3 providers and both iteration modes. 30 streams × 500 chunks per stream × 4 repeats × 3 rounds; min reported.

Provider	Mode	Baseline (μs/chunk)	Optimized (μs/chunk)	Δ	Throughput (chunks/s)
Anthropic	sync	71.17	63.51	−10.8%	14,051 → 15,747 (+12.1%)
Anthropic	async	45.31	43.37	−4.3%	22,072 → 23,060 (+4.5%)
Bedrock Invoke	sync	67.80	60.82	−10.3%	14,750 → 16,442 (+11.5%)
Bedrock Invoke	async	46.09	44.10	−4.3%	21,698 → 22,677 (+4.5%)
Bedrock Converse	sync	137.72	127.94	−7.1%	7,261 → 7,816 (+7.6%)
Bedrock Converse	async	85.35	84.73	−0.7%	11,716 → 11,802 (+0.7%)

uv run python scripts/benchmark_streaming_chunk_overhead.py --label optimized --streams 30 --chunks 500 --warmup 2 --repeats 4

The sync paths gain the most because the model_dump() bug fix (Optimization 1) only applied to sync __next__. Async paths still gain ~4% from the other four optimizations.

Behavioral test matrix

Scenario	Anthropic	Bedrock Invoke	Bedrock Converse
Text-only chunks pass through	✓	✓	✓
Usage chunk stripped from chunk body	✓	✓	✓
Usage preserved in _hidden_params	✓	✓	✓
finish_reason propagated	✓	✓	✓
Sync path matches async path	✓	–	–
model_dump() NOT called on text chunks	✓	–	–
_GCHUNK_FIELDS module constant	✓	–	–
Callback list resolved once per stream	✓	–	–
Per-chunk overhead regression guard (200 chunks < 2 s)	✓ sync	✓ async	–

All 19 new tests pass; all pre-existing streaming handler tests continue to pass.

Test plan

• uv run pytest tests/test_litellm/litellm_core_utils/test_streaming_overhead.py -v → 19 passed
• uv run pytest tests/test_litellm/litellm_core_utils/test_streaming_handler.py -v -k "not gemini_legacy" → 57 passed
• uv run ruff check litellm/litellm_core_utils/streaming_handler.py → no errors
• uv run black litellm/litellm_core_utils/streaming_handler.py → formatted

https://claude.ai/code/session_019Jz1qomf9ta2mXrTuLAmPa

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[codspeed-hq]

Merging this PR will not alter performance

✅ 16 untouched benchmarks

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_{Comparing litellm_fix/LIT-3313-streaming-chunk-overhead (e8bf753) with main (35f6961)}

[github-advanced-security]

CodeQL found more than 20 potential problems in the proposed changes. Check the Files changed tab for more details.

[codecov]

Codecov Report

❌ Patch coverage is 96.29630% with 1 line in your changes missing coverage. Please review.

Files with missing lines	Patch %	Lines
litellm/litellm_core_utils/streaming_handler.py	96.29%	1 Missing ⚠️

📢 Thoughts on this report? Let us know!

[yassin-berriai]

@greptileai

[greptile-apps]

Greptile Summary

This PR delivers five targeted per-chunk optimizations to CustomStreamWrapper, cutting sync-path overhead by ~10% and async-path overhead by ~4% across Anthropic, Bedrock Invoke, and Bedrock Converse.

• Sync __next__ bug fix: hasattr(response, \"usage\") always returns True on ModelResponseStream (the field exists with default None), so model_dump() + model_response_creator() ran on every text chunk; replaced with getattr(response, \"usage\", None) is not None to match the already-correct async path.
• Init-time caching: effective model name, logging provider, _base_hidden_params (pre-merged _hidden_params + response_cost: None), and the filtered callback list are all computed once per stream rather than per chunk; the self._hidden_params invariant (set entirely at __init__, never mutated during streaming) is correctly documented and verified.
• Module-level _GCHUNK_FIELDS: GChunk.__annotations__ is read once at import time instead of on every generic_chunk_has_all_required_fields() call.

Confidence Score: 5/5

Safe to merge — all optimizations are semantically equivalent to the original hot path, the critical sync-path model_dump() regression is correctly fixed, and 19 mock-only tests validate the behavioral contract.

The sync next fix is clearly correct (ModelResponseStream.usage defaults to None, so the getattr guard is strictly better than hasattr). The caching optimizations rely on an invariant (self._hidden_params not mutated after init) confirmed by a full grep — only two assignment sites exist, both in init. The _base_hidden_params spread order is functionally identical to the original since _base_hidden_params does not contain custom_llm_provider or created_at. No behavioral regressions are introduced.

No files require special attention.

Important Files Changed

Filename	Overview
litellm/litellm_core_utils/streaming_handler.py	Five targeted hot-path optimizations: fix sync next usage guard (most impactful), cache model name/provider at init, pre-compute _base_hidden_params, lazy-cache streaming callbacks, and module-level _GCHUNK_FIELDS. All changes are correct; invariants hold.
tests/test_litellm/litellm_core_utils/test_streaming_overhead.py	19 new unit tests covering all 5 optimizations across 3 providers with mock-only setup; no real network calls. Tests validate both the correctness fixes and the caching behavior.
scripts/benchmark_model_response_creator.py	Reproducible microbenchmark for model_response_creator(); uses synthetic in-memory data and MagicMock, no real API calls.
scripts/benchmark_streaming_chunk_overhead.py	End-to-end stream-iteration benchmark driving CustomStreamWrapper with synthetic chunks; measures per-chunk latency across Anthropic, Bedrock Invoke, and Bedrock Converse for both sync and async paths.

_{Reviews (3): Last reviewed commit: "fix: improve bedrock streaming hot path ..." | Re-trigger Greptile}

[yassin-berriai]

@greptileai Fixed the hidden_params spread order in model_response_creator — **hidden_params now appears first (lowest priority) so the computed created_at, custom_llm_provider, and _base_hidden_params values always win. All 19 tests pass.

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Generated by Claude Code

[greptile-apps]

The spread order fix looks correct — **hidden_params first (lowest priority), then custom_llm_provider/created_at, then **self._base_hidden_params last — and it faithfully preserves the original semantics.

One thing worth a quick sanity-check: _base_hidden_params is a static snapshot of self._hidden_params taken at __init__. In the original code, self._hidden_params was spread on every model_response_creator call, so any post-init mutations (e.g. a middleware appending model_id or api_base) would have been visible in each chunk. With the pre-computation those later mutations would be silently dropped.

From reviewing the diff, self._hidden_params looks like it's populated at init from litellm_params and not mutated during the stream, so this should be fine in practice. But if that assumption ever changes, _base_hidden_params would go stale. Worth a comment noting the invariant, e.g.:

# NOTE: _base_hidden_params is a snapshot of self._hidden_params taken at
# __init__.  It is only safe to pre-compute because self._hidden_params is
# never mutated after construction.  If that invariant changes, this cache
# must be invalidated or removed.
self._base_hidden_params: Dict[str, Any] = {
    **self._hidden_params,
    "response_cost": None,
}

Otherwise the fix is clean and all the semantics line up.

[yassin-berriai]

@greptileai Added the invariant comment on _base_hidden_params documenting that it's only safe to pre-compute because self._hidden_params is never mutated after __init__.

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Generated by Claude Code