[Perf] Use numpy zero-copy path for embedding float response serialization

[lokashrinav]

Summary

When encoding_format=float and ORJSONResponse is active, the /v1/embeddings response builder currently calls .tolist() on each embedding tensor, converting every float individually through Python. This PR replaces that with a zero-copy .numpy() view that ORJSON serializes natively.

Scope: Only the float-format OpenAI embedding response path is changed. Base64, bytes, Cohere, non-ORJSON fallback, model execution, scheduling, and KV cache are untouched.

Fallback: If .numpy() fails (bfloat16, float8, CUDA tensors), it falls back to the existing .tolist() path.

Benchmark results (Modal, T4 GPU, vLLM 0.20.1, intfloat/multilingual-e5-small)

Response construction microbenchmark (batch=128):

Embedding dim	.tolist() (ms)	.numpy() (ms)	Speedup
384	1.43	0.21	6.8x
768	3.97	0.22	18.2x
1536	8.67	0.22	39.9x
3072	14.63	0.21	68.8x

The new path is O(1) (zero-copy view) vs O(batch × dim) for .tolist().

E2E validation

Tested on Modal (T4, vLLM 0.20.1 with patch applied):

• Single and batch /v1/embeddings requests with encoding_format=float ✓
• encoding_format=base64 (unchanged path) ✓
• EmbeddingResponse.model_validate() on fast-path output ✓
• Float vs base64 cross-check (values match within tolerance) ✓
• Latency (batch-16): p50=26.9ms, p95=29.8ms

Test plan

• Unit tests for encode_pooling_output_float_or_ndarray (float32 → ndarray, fallback on TypeError)
• E2E server test with real model (intfloat/multilingual-e5-small, fp16)
• Upstream CI (pytest tests/entrypoints/pooling/test_utils.py)
• Existing embedding tests pass (pytest tests/entrypoints/pooling/embed/test_online.py)

[claude]

Claude Code Review

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[github-actions]

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[gemini-code-assist]

Code Review

This pull request introduces an optimization for embedding responses by utilizing ORJSONResponse to serialize NumPy arrays directly, which avoids the overhead of converting them to lists. It includes a new utility function and corresponding tests. The reviewer pointed out that the manual construction of the response dictionary could lead to inconsistencies with the OpenAI API protocol and recommended using existing Pydantic models to maintain the API contract while preserving performance benefits.

[lokashrinav]

Addressed in a647038 — the fast path now uses EmbeddingResponseData.model_dump() and EmbeddingResponse.model_dump() to build the response structure, then injects the numpy arrays into the resulting dicts. This keeps the response schema in sync with the Pydantic models while preserving the zero-copy serialization benefit.

Re-verified on Modal (T4 GPU, vLLM 0.20.1 + patch, intfloat/multilingual-e5-small):

• All unit tests pass
• All E2E tests pass (single float, batch float, base64, schema validation, float↔base64 cross-check)
• Performance unchanged (5–70x speedup in response construction)

[noooop]

Thanks for your improvement!

[mergify]

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[mergify]

Hi @lokashrinav, 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.

Tip

Is mypy failing?

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