[Bugfix][V1] Chunk prompt logprob computation to bound peak VRAM

[mganczarenko]

Problem

_get_prompt_logprobs_dict materializes a [num_logits, vocab_size] fp32 tensor in one shot. For large-vocab MoE models on long 5-shot lm_eval prompts (e.g. inclusionAI/Ling-lite-1.5, vocab=64000, ~2000 prompt tokens), peak VRAM reaches ~2.4 GiB and triggers:

RuntimeError: level_zero backend failed with error: 39 (UR_RESULT_ERROR_OUT_OF_DEVICE_MEMORY)

on both TP workers, crashing the engine mid-eval.

Fix

Process prompt logprobs in chunks of 1024 tokens:

• Peak per chunk: 1024 × vocab_size × 6 bytes ≈ 375 MiB
• Previously: ~2000 × 64000 × 6 bytes ≈ 2.4 GiB in one shot

Each chunk computes compute_logits → compute_logprobs → gather_logprobs independently and copies results GPU→CPU async, so memory is freed between chunks.

Verification

Tested on Intel B70 (2×XPU, 30.3 GiB/card), inclusionAI/Ling-lite-1.5 (BailingMoeForCausalLM, 31.3 GB, vocab=64000):

	Before	After
lm_eval MMLU 5-shot	❌ OOM at mmlu_professional_accounting (error 39)	✅ Full run completes
MMLU 5-shot acc	—	71.56%
Peak VRAM per batch	~2.4 GiB	~375 MiB/chunk

lm_eval --model vllm \
  --model_args pretrained=inclusionAI/Ling-lite-1.5,tensor_parallel_size=2,dtype=bfloat16,trust_remote_code=True,max_model_len=4096,gpu_memory_utilization=0.85 \
  --tasks mmlu --num_fewshot 5 --batch_size auto

Relation to upstream PRs

This PR is not a duplicate of upstream work. Two upstream PRs address the same root problem via a different approach (top-k reuse: vllm-project#44886, vllm-project#45327). This fix is the chunking approach, developed independently for the XPU test image before those PRs existed in our sync window. The approaches are complementary — top-k reuse avoids materializing full vocab when num_logprobs is small; chunking bounds memory regardless of top-k count.

AI assistance

This fix was developed with Claude Code assistance (Sonnet 4.6). All changed lines reviewed and tested by the submitter end-to-end on Intel XPU hardware.

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