# ------------------------------------------------------------------------------
# FILE: roles/deploy-vllm/tasks/dependencies.yml
# PHASE 1: Python 3.10+, vLLM >=0.5.0, PyTorch+CUDA, verify nvidia-smi.
#
# Pitfall (homelab-llm-serving skill): vLLM bundles its own CUDA 12.x wheels —
# do NOT apt-install a system cuda-toolkit, it's not required and may not even
# be in default apt repos on Ubuntu. pip install vllm is sufficient.
#
# Idempotent: venv creation and pip install are both check-then-act; a second
# run against an already-provisioned host is a no-op (verified via molecule-
# style manual second-run test, see README.md Testing section).
# ------------------------------------------------------------------------------

- name: Verify nvidia-smi is present and a GPU is visible
  ansible.builtin.command: nvidia-smi --query-gpu=name,memory.total,driver_version --format=csv,noheader
  register: vllm_nvidia_smi
  changed_when: false

- name: Report detected GPU
  ansible.builtin.debug:
    msg: "GPU detected: {{ vllm_nvidia_smi.stdout }}"

- name: Fail fast if nvidia-smi reports no GPU
  ansible.builtin.fail:
    msg: "nvidia-smi returned no GPU rows — cannot deploy vLLM without a CUDA-visible GPU."
  when: vllm_nvidia_smi.stdout | trim | length == 0

- name: Ensure system Python {{ vllm_python_min_version }}+ is present
  ansible.builtin.command: "python3 -c 'import sys; assert sys.version_info >= (3, 10), sys.version'"
  register: vllm_python_version_check
  changed_when: false
  failed_when: vllm_python_version_check.rc != 0

- name: Ensure python3-venv is installed
  ansible.builtin.apt:
    name: python3-venv
    state: present
    update_cache: true
    cache_valid_time: 3600
  become: true

- name: Create dedicated vLLM Python venv
  ansible.builtin.command:
    cmd: "python3 -m venv {{ vllm_venv_path }}"
    creates: "{{ vllm_venv_path }}/bin/python"
  become: true
  become_user: "{{ vllm_venv_owner }}"

- name: Upgrade pip/wheel inside the venv
  ansible.builtin.pip:
    name:
      - pip
      - wheel
    state: latest
    virtualenv: "{{ vllm_venv_path }}"
  become: true
  become_user: "{{ vllm_venv_owner }}"

# setuptools is deliberately NOT upgraded to "latest" here — vLLM pins
# setuptools<81.0.0,>=77.0.3 as a transitive dependency. Forcing it to latest
# (84.x as of this writing) causes an install/uninstall flip-flop with the
# next task on every single run (upgrade to 84.x here, vLLM's pip install
# downgrades it back to satisfy its own pin) — a genuine non-idempotency bug
# caught during second-run testing (t_ca1af9fb, 2026-08-31). Let vLLM's own
# pip install resolve setuptools to whatever version it needs.

- name: Install vLLM ({{ vllm_version_spec }})
  ansible.builtin.pip:
    name: "{{ vllm_version_spec }}"
    state: present
    virtualenv: "{{ vllm_venv_path }}"
  become: true
  become_user: "{{ vllm_venv_owner }}"
  register: vllm_pip_install
  # vLLM + deps (torch, etc.) is a large download — allow generous time.
  async: 1800
  poll: 30

- name: Install huggingface_hub (provides the `hf` CLI for model downloads)
  ansible.builtin.pip:
    name: "huggingface_hub"
    state: present
    virtualenv: "{{ vllm_venv_path }}"
  become: true
  become_user: "{{ vllm_venv_owner }}"

- name: Verify vLLM is importable and report version
  ansible.builtin.command:
    cmd: "{{ vllm_venv_path }}/bin/python -c 'import vllm; print(vllm.__version__)'"
  register: vllm_version_check
  changed_when: false

- name: Report vLLM version
  ansible.builtin.debug:
    msg: "vLLM version installed: {{ vllm_version_check.stdout }}"

- name: Verify torch reports CUDA available
  ansible.builtin.command:
    cmd: "{{ vllm_venv_path }}/bin/python -c 'import torch; print(torch.cuda.is_available(), torch.version.cuda)'"
  register: vllm_torch_cuda_check
  changed_when: false

- name: Report torch/CUDA status
  ansible.builtin.debug:
    msg: "torch.cuda.is_available(), torch.version.cuda = {{ vllm_torch_cuda_check.stdout }}"

- name: Warn if CUDA is not available to torch
  ansible.builtin.debug:
    msg: >-
      WARNING: torch reports CUDA unavailable inside the vLLM venv. Serving will
      fall back to CPU (unusable for 32B-class models). Check nvidia driver /
      CUDA wheel compatibility before proceeding to Phase 2.
  when: "'True' not in vllm_torch_cuda_check.stdout"