[Whisper] Enable CUDA graph support and timestamp for whisper model

[JustinTong0323]

Motivation

Previously, Whisper used a custom bmm + mask cross-attention implementation with a Python-side _encoder_cache dict, which was incompatible with CUDA graph capture/replay. This PR enables CUDA graph for Whisper, achieving 36% throughput improvement with identical accuracy.

Related: #21161

Modifications

• Cross-attention → RadixAttention: Replaced manual BMM cross-attention with native RadixAttention path. During prefill, encoder KV is projected and saved to the KV pool; during decode, k=None, v=None triggers cached KV read from the pool.
• Removed _encoder_cache dict: Encoder outputs now flow through the native encoder-decoder cache managed by scheduler/attention backends.
• Auto-select flashinfer backend: Encoder-decoder models require flashinfer for cross-attention support (trtllm_mha/fa3 lack encoder_out_cache_loc handling).
• Auto-disable radix cache: Encoder token padding conflicts with prefix caching, causing assert len(req.prefix_indices) == 0 failures.
• Fixed CUDA graph capture: Set encoder_len_fill_value to max_source_positions (1500 for Whisper) so cross-attention kernels are included in the captured graph. Previously was 0, causing cross-attention to be skipped during capture.
• Fixed FlashInfer decode cross-attention planning: update_cross_attention was passing decoder seq_lens_cpu for cross-attention wrapper, causing global_override_indptr_cpu to override KV length from 1500 to ~5. Now correctly uses encoder_lens for cross-attention.
• Position embedding OOB protection: Clamp position_ids to max_target_positions - 1 to prevent warmup from exceeding the 448-entry position embedding table.
• Added encoder_out_cache_loc to trtllm_mha backend: For forward compatibility.

Accuracy Tests

# launch server (no manual flags needed - auto-configured)
sglang serve --model-path openai/whisper-large-v3

# benchmark
python benchmark/asr/bench_sglang.py \
    --base-url http://127.0.0.1:30000 \
    --model openai/whisper-large-v3 \
    --api-type transcription \
    --language en \
    --concurrency 1

Config	WER	Avg Latency	Throughput
CUDA graph (ours)	12.77%	0.297s	3.26 req/s
No CUDA graph (baseline)	12.77%	0.406s	2.40 req/s

WER is identical between CUDA graph and non-CUDA-graph modes (12.7690% on earnings22 dataset, 511 samples).

Benchmarking

Metric	No CUDA Graph	CUDA Graph	Improvement
WER	12.77%	12.77%	identical
Avg Latency	0.406s	0.297s	-27%
Median Latency	0.376s	0.267s	-29%
P95 Latency	0.837s	0.601s	-28%
Throughput	2.40 req/s	3.26 req/s	+36%
Token Throughput	44.71 tok/s	60.72 tok/s	+36%

Dataset: D4nt3/esb-datasets-earnings22-validation-tiny-filtered (511 samples), concurrency=1, model=openai/whisper-large-v3.

Checklist

• Format your code according to the Format code with pre-commit.
• Add unit tests according to the Run and add unit tests.
• Provide accuracy and speed benchmark results.
• Follow the SGLang code style guidance.

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SGLang vs vLLM: Whisper Serving Benchmark Report

Environment

Item	Detail
GPU	NVIDIA B200 (183 GB), single-GPU serving (CUDA_VISIBLE_DEVICES=0)
Model	openai/whisper-large-v3 (1.55B params)
Dataset	D4nt3/esb-datasets-earnings22-validation-tiny-filtered (511 samples, earnings call transcriptions, <30s each)
SGLang	0.5.9 — branch feat/whisper-cudagraph, CUDA graph enabled (52 batch sizes)
vLLM	0.18.0 — FA4, CUDA graph enabled (decode only, 51 batch sizes), nvidia-cutlass-dsl==4.4.2
Torch	SGLang: 2.9.1 / vLLM: 2.10.0

Launch Commands

# SGLang
CUDA_VISIBLE_DEVICES=0 python -m sglang.launch_server \
  --model-path openai/whisper-large-v3 --port 30000

# vLLM
CUDA_VISIBLE_DEVICES=0 vllm serve openai/whisper-large-v3 --port 30000

Benchmark Command

python benchmark/asr/bench_sglang.py \
  --base-url http://localhost:30000 \
  --model openai/whisper-large-v3 \
  --api-type transcription \
  --concurrency 64           # or 1 for single-request test

SGLang benchmarks include --language en; vLLM benchmarks omit it due to a vLLM bug where language=en produces garbled output. SGLang defaults to en when language is not specified, so the comparison is equivalent.

---

1. Accuracy Tests (SGLang w/ CUDA Graph)

Test audio: LibriSpeech sample (Narsil/asr_dummy/1.flac)

Test	Result
Basic transcription	PASS — correct full output
Consistency (3 sequential identical requests)	PASS — all 3 outputs identical
Quality (keyword check: stew, dinner, turnips, carrots, potatoes, mutton)	PASS — 6/6 keywords found
CUDA graph capture	52 batch sizes captured, disable_cuda_graph=False

Transcription output:

" he hoped there would be stew for dinner turnips and carrots and bruised potatoes and fat mutton pieces to be ladled out in thick peppered flour fattened sauce"

---

2. Benchmark: High Concurrency (64 concurrent requests, 511 samples)

Metric	SGLang (CUDA graph)	vLLM 0.18.0	Delta
WER ↓	12.78%	51.22%	4.0x better
Avg Latency ↓	0.516s	1.206s	2.3x faster
Median Latency ↓	0.469s	0.606s	1.3x faster
P95 Latency ↓	1.103s	7.451s	6.8x faster
Throughput ↑	48.82 req/s	22.56 req/s	2.2x higher
Token Throughput ↑	910.07 tok/s	493.29 tok/s	1.8x higher
Total Time ↓	10.47s	22.65s	2.2x faster

Sample Predictions Comparison (High Concurrency)

Sample 2 — SGLang complete, vLLM truncated:

REF:    so within fiscal year 2021 say 120 a 100 depending on what the micro will do
        and next year it is not necessarily payable in q one is we will look at what
        the cash flows for 2022 look like
SGLang: so within fiscal year 2021 say $120000 $100000 depending on what the macro
        will do and next year it is not necessarily payable in q one is we will look
        at what the cash flows for 2022 look like                                      ✓
vLLM:   so within fiscal year 2021 say $120000 $100000 depending on what the macro
        will do and next year it is not necessarily payable in q one is     ← TRUNCATED

Sample 7 — vLLM cross-attention corruption (repetition):

REF:    essentially a transformer that was allocated to a future project we we have now retrofitted
SGLang: essentially a transformer that was allocated to a future project
        we now have retrofitted                                                         ✓
vLLM:   essentially a transformer that was allocated to a future project we now
        this this this this this this this this this this this this this this
        this this this this                                        ← REPETITION/GARBAGE

Sample 9 — vLLM cross-language contamination:

REF:    i think you mentioned generation was impacted by a transformer upgrade
SGLang: i think you mentioned a generation was impacted by a transformer outfit        ✓
vLLM:   i think you mentioned a generation was impacted by a transformer outfit
        i i i i i i i i i i i i i i i i抽抽抽抽抽抽抽抽抽抽抽抽抽抽抽抽抽抽抽抽抽抽抽
                                                   ← REPETITION + CROSS-LANGUAGE GARBAGE

---

3. Benchmark: Single Request (concurrency=1, 50 samples)

Metric	SGLang (CUDA graph)	vLLM 0.18.0	Delta
WER ↓	13.23%	14.84%	1.1x better
Avg Latency ↓	0.097s	0.136s	1.4x faster
Median Latency ↓	0.083s	0.126s	1.5x faster
P95 Latency ↓	0.206s	0.217s	1.1x
Throughput ↑	8.00 req/s	6.08 req/s	1.3x higher
Token Throughput ↑	173.05 tok/s	133.58 tok/s	1.3x higher

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