fix(serve): reject empty/falsy prompt input in format_prompts and AsyncEngine.generate

[SuperMarioYL]

fix(serve): reject empty/falsy prompt input in format_prompts and AsyncEngine.generate

Motivation

Passing an empty/falsy prompt ('', [], {}, ()) to the offline SDK path
(AsyncEngine.generate / pipe.stream_infer) does not raise a clear error. The
prompt is not None, so it slips past the messages is not None XOR guard in
AsyncEngine.generate (async_engine.py:501). The falsy messages then takes the
input_ids else-branch, leaving input_ids at its default None, and the request
later crashes in len(input_ids) with a confusing:

TypeError: object of type 'NoneType' has no len()

at async_engine.py:566 (and the same shape at :424). The HTTP path already
filters empty messages (api_server.py:443-445), so this only affects users of the
offline SDK / pipeline API who pass malformed input — they get an opaque
TypeError deep inside the engine instead of an actionable error at the boundary.

A regression test (tests/test_lmdeploy/serve/test_empty_prompt_guard.py) reproduces
the real generate() path (no model/GPU required) and is red on main (raises
TypeError) / green on this branch (raises a clear ValueError).

Why guard format_prompts + generate() rather than a one-line assert in
pipeline.stream_infer? MultimodalProcessor.format_prompts is the single
pipeline-layer chokepoint that every offline caller (stream_infer/infer/
__call__/chat via _request_generator, see pipeline.py:123,170,343-378)
passes through, so guarding it once fixes the whole offline path. generate() is
the direct-SDK backstop reachable without going through pipeline at all (the
reproduction exercises it directly). A guard only in stream_infer would miss
direct generate() callers and would be strictly redundant with the
format_prompts guard — hence the two non-redundant layers, no third.

Modification

• lmdeploy/serve/processors/multimodal.py: add a shared
  MultimodalProcessor.validate_prompt @staticmethod that rejects None and the
  empty str/list/tuple/dict shapes with a clear ValueError; call it at the top of
  format_prompts (root-cause boundary). A (prompt, image) multimodal pair has
  len == 2 and is intentionally not rejected.
• lmdeploy/serve/core/async_engine.py: call validate_prompt in generate()
  immediately after the existing XOR guard, covering both the messages and
  input_ids falsy shapes (direct-SDK backstop).
• tests/test_lmdeploy/serve/test_empty_prompt_guard.py: new regression test
  (red-on-main / green-on-branch) exercising the real generate() path via
  AsyncEngine.__new__ + minimal mocks (no model/GPU), plus pure-function
  format_prompts('' / [] / None) assertions.

Scope is intentionally offline-SDK only: the HTTP (serve/openai,
serve/anthropic), TurboMind C++, PyTorch engine, pipeline.py, and the reward/ppl
paths are untouched.

BC-breaking (Optional)

No. The change only converts a previously-crashing input (TypeError deep inside
the engine) into an early, clear ValueError at the input boundary. All valid
(non-empty) input flows through unchanged. Downstream projects passing valid prompts
see no behavioral change.

Use cases (Optional)

Offline SDK users who accidentally pass an empty prompt (pipe(''),
engine.generate(messages=''), an empty batch) now get an actionable
ValueError: ... at the boundary instead of an opaque TypeError: object of type 'NoneType' has no len() from inside the engine.

Checklist

1. Lint: ruff check on all changed files — all checks passed (line-length 120,
   E/F/I/W/UP, py310), per .pre-commit-config.yaml.
2. Tests: python -m pytest tests/test_lmdeploy/serve/test_empty_prompt_guard.py -q → 7 passed (green on branch; 7 failed on main); python -m pytest tests/test_lmdeploy/serve/test_session_cleanup.py -q → 9 passed (regression for
   the touched generate() path, no regressions).
3. No new/downstream dependency introduced.
4. No documentation change needed (no user-facing API surface added — only a clearer
   error for previously-crashing invalid input).