[Bug] input_embeddings are omitted from prefix-cache identity, causing wrong KV reuse

Checklist

• 1. I have searched related issues but cannot get the expected help.
• 2. The bug has not been fixed in the latest version.
• 3. Please note that if the bug-related issue you submitted lacks corresponding environment info and a minimal reproducible demo, it will be challenging for us to reproduce and resolve the issue, reducing the likelihood of receiving feedback.

Describe the bug

With PyTorch prefix caching enabled, input_embeddings content is not included in the prefix-cache trie identity. Two requests can have identical token IDs and embedding spans but different embedding values. The second request then reuses KV computed from the first request's embeddings.

The relevant path is:

• pytorch/messages.py:875-902: get_prefix_cache_extra_identity() indexes multimodal_spans, but not embedding content.
• pytorch/messages.py:954-962: _update_embeddings() stores embeddings in history_embeddings without adding them to the trie key identity.
• pytorch/messages.py:904-936: embedding ranges are considered only for cache boundary safety; embedding content is not compared or hashed.
• pytorch/engine/engine.py:496-513: requests accept and propagate input_embeddings.
• pytorch/models/qwen2.py:339-344: the model replaces token embeddings with the supplied values before the forward pass.

Therefore, request 1 with embeddings E1 can populate a cache entry that request 2 with the same token IDs and different embeddings E2 treats as a valid hit. This is a silent correctness bug: no exception is raised, and the output differs from a cold computation using E2. Text-only requests without input_embeddings are not affected by this specific path.

Reproduction

Use the reproducer f4_embeddings_demo.py from the reporter's verification checkout. It uses the production PyTorch engine and request path, with only the input_embeddings field added to the upstream ADD_MESSAGE payload.

Requirements: a PyTorch-capable GPU, the Qwen2.5-0.5B-Instruct model (or an equivalent supported model), and a checkout containing the code above. Set the MODEL constant in the script to the local model path, then run:

python verify/f4_embeddings_demo.py

The script performs three runs with the same token IDs:

run1: tokens + E1 (cold)
run2: tokens + E2=-E1 (identity-complete cache would MISS)
      prefix-cache hit; 448 tokens reused from run1
evict trie KV
run3: tokens + E2 (cold recompute)

O1 vs O3: first_diff=1 (E content matters)
O2 vs O3: first_diff=1
RESULT: EMBEDDINGS-BLIND REUSE CONFIRMED

O2 is the warm result for E2, but it differs from the clean E2 result O3, proving that the hit reused KV computed from E1.

Environment

The e2e reproduction was run on Linux with the PyTorch backend, prefix caching enabled, tp=1, model Qwen2.5-0.5B-Instruct, and 8 NVIDIA RTX 3090 GPUs available on the verification host. The recorded run used the lmdeploy 0.16.0+ Python sources at commit 2928f477; the same relevant path remains unchanged in the current checkout checked above. Run python -m lmdeploy check_env on the target host for the complete environment.

Error traceback

None. The incorrect cache hit and output difference are silent.

Identified reason and suggested fix

Include an identity for every embedding span, such as (start, end, hash(embedding content)), in the prefix-cache key. The matching logic must use the same embedding-aware identity, and a regression test should verify that same-token requests with different embedding values miss the cache and match their respective cold outputs.