Test(rollout): add unit tests for vllm_rollout helpers and mocked generate paths

[CalvinXKY]

Summary

Adds unit tests for slime.rollout.vllm_rollout, covering token preprocessing, vLLM /inference/v1/generate request/response handling, and core async paths (generate, generate_and_rm, abort) via mocks.

• New: tests/unit/rollout/test_vllm_rollout.py — 55 test cases marked with @pytest.mark.unit
• New: tests/unit/rollout/conftest.py — stubs for optional deps (vllm_router, PIL, transformers, aiohttp, pylatexenc) so tests collect on minimal dev machines
• Unchanged: tests/test_vllm_generate_endpoint.py — still used for real vLLM + GPU integration checks

Coverage

Pure helpers

• _coerce_flat_int_token_ids, _prepare_prompt_ids, _base_dataset_prompt_ids
• get_model_url
• _vllm_meta_from_generate_choice, _decode_vllm_routed_experts, _apply_vllm_routed_experts (including disabled / invalid-shape branches)
• _inference_generate_tokens_and_logprobs, _align_engine_tokens_and_logprobs
• _build_inference_sampling_params, _mm_render_response_to_generate_body
• _router_worker_urls, _resume_vllm_workers

Mocked async paths

• generate(): text rollout, partial-continuation budget, zero-budget truncation, consistent-hashing headers, multimodal render→generate, routed-experts assignment
• generate_and_rm(): skip completed samples, abort handling, custom generate hook
• generate_and_rm_group(): per-sample session_id assignment
• abort(): worker pause + resume

Out of scope (existing integration tests)

• generate_rollout_async / generate_rollout / eval_rollout*
• Live vLLM server + GPU E2E (tests/test_vllm_generate_endpoint.py)

Test plan

cd vime
pytest tests/unit/rollout/test_vllm_rollout.py -m unit -q

[gemini-code-assist]

Code Review

This pull request transitions the base Docker image to vllm/vllm-openai, updates the build process with necessary CUDA development headers and dependencies, and introduces a build-time smoke test. It also adds a comprehensive suite of unit tests and shared stubs for the vllm_rollout module. Feedback was provided regarding the inclusion of IPython in the production image, which may be unnecessary and increases the image size.

[gemini-code-assist]

The installation of IPython in the production Docker image seems unnecessary and increases the image size. Unless it is explicitly required for a runtime feature (e.g., interactive debugging within the container, which is usually discouraged for production), it should be removed to keep the image lean.

RUN pip install --no-deps "sglang==${SGLANG_VERSION}" sglang-router==0.3.2

[CalvinXKY]

No changes were made to the Dockerfile.