[v1] Expose num_prompt_tokens in CommonAttentionMetadata

[asadafa123]

Summary

Add num_prompt_tokens (per-request original prompt length) to CommonAttentionMetadata so that model layers can access it during the forward pass via get_forward_context().

Motivation

Dual-cache RoPE implementations like LongRoPE's SplitByLength need to select SHORT vs LONG cache based on each request's total prompt length, not the current chunk's positions.max(). Under chunked prefill (max_num_batched_tokens < prompt_length), a long sequence is split into multiple chunks. Early chunks have positions.max() < len0 (the SHORT/LONG threshold), causing them to incorrectly use the SHORT cache, while later chunks use the LONG cache. This produces mismatched RoPE embeddings in the KV cache and destroys model output quality for long contexts.

The existing Phi3LongRoPEScaledRotaryEmbedding avoids this by making an init-time decision based on max_model_len, but this forces all requests to use the same cache regardless of their actual prompt length. With num_prompt_tokens available in the forward context, RoPE implementations can make per-sequence decisions — matching the behavior of TRT-LLM's per-sequence original_prompt_length selection in its attention kernels.

Changes

• vllm/v1/attention/backend.py: Add optional num_prompt_tokens: torch.Tensor | None field to CommonAttentionMetadata, with handling in unpadded().
• vllm/v1/worker/gpu_model_runner.py: Pass the already-computed num_prompt_tokens_cpu tensor when constructing CommonAttentionMetadata.

The data already exists in InputBatch.num_prompt_tokens (set once at add_request time). This change simply threads it through to where model code can read it.

Impact

• No behavioral change for existing models — the field defaults to None and is only read by models that opt in.
• Enables correct per-sequence LongRoPE cache selection under chunked prefill without monkey-patching.

[github-actions]

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[gemini-code-assist]

Code Review

This pull request introduces num_prompt_tokens to CommonAttentionMetadata to support dual-cache RoPE implementations during chunked prefill. A review comment identifies a performance concern where a CPU tensor is passed to the metadata; it is recommended to transfer this tensor to the GPU to prevent host-device synchronization and potential graph breaks during execution.

[gemini-code-assist]

The num_prompt_tokens field is being assigned a CPU tensor (num_prompt_tokens_cpu). In CommonAttentionMetadata, fields without the _cpu suffix are expected to be device tensors. Since this metadata is intended for use in model layers (like RoPE) during the forward pass, using a CPU tensor will cause host-device synchronizations or graph breaks in torch.compile, leading to significant performance degradation. This should be a GPU tensor to allow efficient device-side access. Since the source tensor in InputBatch is pinned, you can use a non-blocking transfer here, or ideally, use a persistent GPU buffer if one is available in GPUModelRunner.

Suggested change

	num_prompt_tokens=num_prompt_tokens_cpu,
	num_prompt_tokens=num_prompt_tokens_cpu.to(device=self.device, non_blocking=True),