diff --git a/modal-deploy/sunflower-grpo-inference-vllm/.dockerignore b/modal-deploy/sunflower-grpo-inference-vllm/.dockerignore new file mode 100644 index 00000000..af560216 --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/.dockerignore @@ -0,0 +1,29 @@ +.venv +__pycache__ +*.pyc +*.pyo +*.pyd +.pytest_cache +.mypy_cache +.ruff_cache +.coverage +.env +.env.* +.git +.gitignore +.DS_Store +cloudrun/ +sunflower_grpo_combined_inference.ipynb +sunflower_grpo_vllm_modal.py +client.py +main.py +README.md +Dockerfile +.dockerignore + +# Web: ship source, not build artifacts or deps. +web/node_modules +web/.next +web/out +web/.env +web/.env.* diff --git a/modal-deploy/sunflower-grpo-inference-vllm/.python-version b/modal-deploy/sunflower-grpo-inference-vllm/.python-version new file mode 100644 index 00000000..e4fba218 --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/modal-deploy/sunflower-grpo-inference-vllm/Dockerfile b/modal-deploy/sunflower-grpo-inference-vllm/Dockerfile new file mode 100644 index 00000000..ddbec901 --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/Dockerfile @@ -0,0 +1,61 @@ +# syntax=docker/dockerfile:1.6 + +# ---------- Stage 1: build the Next.js static frontend ---------- +FROM node:22-slim AS web-builder + +ENV NODE_ENV=production \ + NEXT_TELEMETRY_DISABLED=1 + +WORKDIR /web + +COPY web/package.json web/package-lock.json* ./ +RUN npm install --no-audit --no-fund + +COPY web/ ./ +# Produces /web/out via `BUILD_MODE=export next build`. +RUN npm run build + + +# ---------- Stage 2: python deps via uv ---------- +FROM python:3.12-slim AS py-builder + +ENV UV_COMPILE_BYTECODE=1 \ + UV_LINK_MODE=copy \ + UV_PYTHON_DOWNLOADS=never \ + PYTHONDONTWRITEBYTECODE=1 \ + PYTHONUNBUFFERED=1 + +COPY --from=ghcr.io/astral-sh/uv:0.5.11 /uv /uvx /usr/local/bin/ + +WORKDIR /app + +COPY pyproject.toml uv.lock ./ + +RUN --mount=type=cache,target=/root/.cache/uv \ + uv sync --frozen --no-install-project --no-dev + +COPY fastapi_app.py ./ + + +# ---------- Stage 3: runtime ---------- +FROM python:3.12-slim AS runtime + +ENV PYTHONDONTWRITEBYTECODE=1 \ + PYTHONUNBUFFERED=1 \ + PATH="/app/.venv/bin:$PATH" \ + PORT=8080 + +RUN groupadd --system --gid 1001 app \ + && useradd --system --uid 1001 --gid app --home-dir /app --shell /sbin/nologin app + +WORKDIR /app + +COPY --from=py-builder --chown=app:app /app/.venv /app/.venv +COPY --from=py-builder --chown=app:app /app/fastapi_app.py /app/fastapi_app.py +COPY --from=web-builder --chown=app:app /web/out /app/web/out + +USER app + +EXPOSE 8080 + +CMD ["sh", "-c", "uvicorn fastapi_app:app --host 0.0.0.0 --port ${PORT}"] diff --git a/modal-deploy/sunflower-grpo-inference-vllm/Makefile b/modal-deploy/sunflower-grpo-inference-vllm/Makefile new file mode 100644 index 00000000..237d7a21 --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/Makefile @@ -0,0 +1,43 @@ +# Local dev helpers for the integrated frontend + backend app. +# +# Usage: +# make install # one-time: npm install + uv sync +# make web # build the Next.js static frontend (produces web/out/) +# make serve # build frontend, then run uvicorn with --reload on :8000 +# make dev-web # hot-reload UI on :3000 (proxies API to :8000 via rewrites) +# make dev-api # backend only on :8000 with --reload (no static mount) +# make clean # remove generated frontend artifacts + +PORT ?= 8000 +HOST ?= 0.0.0.0 + +.DEFAULT_GOAL := help + +.PHONY: help +help: + @awk 'BEGIN {FS = ":.*##"; printf "Targets:\n"} /^[a-zA-Z0-9_.-]+:.*##/ {printf " \033[36m%-10s\033[0m %s\n", $$1, $$2}' $(MAKEFILE_LIST) + +.PHONY: install +install: ## One-time: npm install (in web/) + uv sync + cd web && npm install + uv sync + +.PHONY: web +web: ## Build the Next.js static frontend → web/out/ + cd web && npm run build + +.PHONY: serve +serve: web ## Build frontend, then run uvicorn with --reload on $(PORT) + uv run uvicorn fastapi_app:app --host $(HOST) --port $(PORT) --reload + +.PHONY: dev-api +dev-api: ## Backend only with --reload (no static mount) + uv run uvicorn fastapi_app:app --host $(HOST) --port $(PORT) --reload + +.PHONY: dev-web +dev-web: ## Next.js dev server on :3000 (proxies API to http://localhost:$(PORT)) + cd web && BACKEND_URL=http://localhost:$(PORT) npm run dev + +.PHONY: clean +clean: ## Remove generated frontend artifacts + rm -rf web/.next web/out diff --git a/modal-deploy/sunflower-grpo-inference-vllm/README.md b/modal-deploy/sunflower-grpo-inference-vllm/README.md new file mode 100644 index 00000000..74da68a8 --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/README.md @@ -0,0 +1,559 @@ +# Sunflower 14B GRPO — Modal + vLLM Deployment + +Serves the **`jq/sunflower-14b-bs64-lr1e-4-250919`** base model with the +**`jq/sunflower-14b-grpo-combined`** LoRA adapter on [Modal](https://modal.com) +using **vLLM**, exposing both blocking and streaming HTTP endpoints. + +Files: + +- [sunflower_grpo_vllm_modal.py](sunflower_grpo_vllm_modal.py) — Modal app (image, volume, vLLM class, FastAPI endpoints). +- [fastapi_app.py](fastapi_app.py) — FastAPI proxy app; also mounts the static frontend at `/`. +- [web/](web/) — Next.js 15 frontend (static export) that the backend serves at `/`. +- [client.py](client.py) — CLI client for HTTP, streaming, and direct Modal calls. +- [Dockerfile](Dockerfile) — three-stage build (Node → Python deps → runtime) producing a single image. +- [cloudrun/](cloudrun/) — Terraform module + Makefile for deploying the image to GCP Cloud Run. +- [sunflower_grpo_combined_inference.ipynb](sunflower_grpo_combined_inference.ipynb) — original reference notebook. + +--- + +## 1. Prerequisites + +```bash +uv init +uv sync +source .venv/bin/activate +uv add modal requests +uv run modal setup # one-time auth +``` + +#### Output +``` +https://modal.com/token-flow/tf-k381a71vs2CEqGnmHUSP48 + +Web authentication finished successfully! +Token is connected to the sb-modal-ws workspace. +Verifying token against https://api.modal.com +Token verified successfully! +Token written to /Users/username/.modal.toml in profile sb-modal-ws. +``` + +You also need a Hugging Face token with access to the gated repos: + +```bash +uv run modal secret create huggingface-secret HF_TOKEN=hf_xxx +``` + +(Name must be `huggingface` — that's what the app references.) + +## 2. Cache the model weights into a Modal Volume (one-time) + +This snapshot-downloads the base model + LoRA adapter into a persistent +`sunflower-grpo-models` volume so cold starts don't re-pull weights. + +```bash +uv run modal run sunflower_grpo_vllm_modal.py::download_model +``` + +## 3. Smoke-test locally via Modal + +Runs a few notebook prompts (Luganda, Runyankole, general Q&A) plus a streaming demo: + +```bash +uv run modal run sunflower_grpo_vllm_modal.py +``` + +## 4. Deploy + +```bash +uv run modal deploy sunflower_grpo_vllm_modal.py +``` + +This deploys: + +- The `SunflowerVLLM` class (callable via `modal.Cls.from_name(...)`). +- A FastAPI ASGI app `web` exposing: + - `POST /generate` — JSON `{response: "..."}` + - `POST /generate_stream` — Server-Sent Events streaming `{delta: "..."}` chunks + - `GET /health` + +After deploy, Modal prints a URL like +`https://--sunflower-grpo-vllm-web.modal.run`. + +## 5. Test the deployment with the client + +```bash +export SUNFLOWER_URL=https://--sunflower-grpo-vllm-web.modal.run + +# Blocking HTTP call +uv run client.py http "Translate to luganda: I am watching an Arsenal game right now" 0.1 + +uv run client.py http "Who is Sunbird AI, what do they do?" + +# Streaming SSE call +uv run client.py stream "Translate to runyakole: Good morning, how are you today?" 0.2 + +uv run client.py stream "Who is Sunbird AI, what do they do?" + +# Direct Modal SDK call (no HTTP) +uv run client.py modal "Who is Sunbird AI, what do they do?" +``` + +Or with `curl`: + +```bash +curl -X POST $SUNFLOWER_URL/generate \ + -H 'content-type: application/json' \ + -d '{"instruction":"Translate to luganda: To promote standardisation in the planning, acquisition, implementation, delivery, support and maintenance of information technology equipment and services, to ensure uniformity in quality, adequacy and reliability of information technology usage throughout Uganda;","temperature":0.2}' + +curl -N -X POST $SUNFLOWER_URL/generate_stream \ + -H 'content-type: application/json' \ + -d '{"instruction":"Translate to luganda: To promote standardisation in the planning, acquisition, implementation, delivery, support and maintenance of information technology equipment and services, to ensure uniformity in quality, adequacy and reliability of information technology usage throughout Uganda;","temperature":0.2}' +``` + +## 6. Run the standalone FastAPI proxy app + +[fastapi_app.py](fastapi_app.py) is a thin **HTTP proxy** sitting in front of +the Modal-hosted `web` endpoint. The Modal app already exposes +`/generate`, `/generate_stream`, and `/health` directly — the proxy is there so +we can layer **auth, rate limiting, access logging, custom domains, and +request shaping** without redeploying the inference service. + +### Endpoints + +| Method | Path | Description | +|--------|---------------------------|-------------------------------------------------------------------| +| GET | `/health` | Liveness for the proxy + readiness probe of the upstream | +| POST | `/generate` | Blocking generation (GRPO LoRA) → `{"response": "..."}` | +| POST | `/generate_stream` | SSE stream of `{"delta": "..."}` chunks (GRPO LoRA) then `[DONE]` | +| POST | `/generate_openai` | Blocking generation via OpenAI-compatible Sunflower-14B server | +| POST | `/generate_openai_stream` | SSE stream via OpenAI-compatible server (same `{"delta": "..."}` shape) | +| POST | `/generate_production` | Forwards to the production Sunbird AI API for A/B comparison | + +Request body for `/generate` and `/generate_stream`: + +```json +{ + "instruction": "Translate to luganda: To promote standardisation in the planning, acquisition, implementation, delivery, support and maintenance of information technology equipment and services, to ensure uniformity in quality, adequacy and reliability of information technology usage throughout Uganda;", + "temperature": 0.6, + "max_tokens": 1024 +} +``` + +Request body for `/generate_production` (forwarded as form-encoded to +`https://api.sunbird.ai/tasks/sunflower_simple`): + +```json +{ + "instruction": "translate from english to luganda: To promote standardisation in the planning, acquisition, implementation, delivery, support and maintenance of information technology equipment and services, to ensure uniformity in quality, adequacy and reliability of information technology usage throughout Uganda;", + "model_type": "qwen", + "temperature": 0.1, + "system_message": "" +} +``` + +### Features + +- **Pure HTTP proxy** built on `httpx.AsyncClient` — no Modal SDK dependency. +- **Rate limiting** (`slowapi`) — defaults to **100 requests/minute** and + **1000 requests/day** per client IP. Returns HTTP 429 when exceeded. +- **Access logging** with per-request `x-request-id` header and timing. +- **Streaming passthrough** of upstream SSE (no buffering). +- All defaults configurable via env vars. + +### Configuration (env vars) + +| Variable | Default | Notes | +|--------------------------|------------------------------------------------------------------|-------| +| `SUNFLOWER_UPSTREAM_URL` | `https://sb-modal-ws--sunflower-grpo-vllm-web.modal.run` | Modal `web` endpoint to proxy (GRPO LoRA). | +| `UPSTREAM_TIMEOUT` | `600` | Per-request timeout (seconds). | +| `SUNBIRD_PROD_URL` | `https://api.sunbird.ai` | Base URL of the production Sunbird AI API. | +| `SUNBIRD_PROD_TOKEN` | _(unset)_ | Bearer token for the production API. Required for `/generate_production`. | +| `SUNFLOWER_OPENAI_URL` | `https://sb-modal-ws--sunflower-14b-openai-serve.modal.run/v1` | OpenAI-compatible vLLM upstream (must include `/v1`). | +| `VLLM_API_KEY` | _(unset)_ | Bearer token for the OpenAI-compatible server. Required if the upstream was deployed with the `vllm-api-key` Modal secret. | +| `SUNFLOWER_OPENAI_MODEL` | `Sunbird/Sunflower-14B` | Model id sent to the OpenAI server. | +| `SUNFLOWER_SYSTEM_MESSAGE` | _(built-in Sunflower system prompt)_ | Override the system message prepended to OpenAI chat requests. | +| `RATE_LIMIT_PER_MINUTE` | `100/minute` | Per-IP per-minute limit. | +| `RATE_LIMIT_PER_DAY` | `1000/day` | Per-IP per-day limit. | +| `RATE_LIMIT_STORAGE_URI` | `memory://` | Use `redis://...` for multi-replica deployments. | +| `LOG_LEVEL` | `INFO` | Standard Python logging level. | + +### Run locally + +```bash +uv add fastapi uvicorn httpx slowapi +uv run uvicorn fastapi_app:app --host 0.0.0.0 --port 8000 --reload +``` + +Optional: custom limits + production comparison: + +```bash +export RATE_LIMIT_PER_MINUTE=60/minute +export RATE_LIMIT_PER_DAY=500/day +export SUNBIRD_PROD_TOKEN=prod_access_token_here # enables /generate_production +``` + +### Test it + +```bash +curl http://localhost:8000/health + +curl -X POST http://localhost:8000/generate \ + -H 'content-type: application/json' \ + -d '{"instruction":"Translate to luganda: To promote standardisation in the planning, acquisition, implementation, delivery, support and maintenance of information technology equipment and services, to ensure uniformity in quality, adequacy and reliability of information technology usage throughout Uganda;","temperature":0.2}' + +curl -N -X POST http://localhost:8000/generate_stream \ + -H 'content-type: application/json' \ + -d '{"instruction":"Translate to luganda: To promote standardisation in the planning, acquisition, implementation, delivery, support and maintenance of information technology equipment and services, to ensure uniformity in quality, adequacy and reliability of information technology usage throughout Uganda;","temperature":0.2}' + +# Compare against production Sunbird AI API +curl -X POST http://localhost:8000/generate_production \ + -H 'content-type: application/json' \ + -d '{ + "instruction":"translate from english to luganda: To promote standardisation in the planning, acquisition, implementation, delivery, support and maintenance of information technology equipment and services, to ensure uniformity in quality, adequacy and reliability of information technology usage throughout Uganda;", + "model_type":"qwen", + "temperature":0.1, + "system_message":"" + }' + +# --- OpenAI-compatible Sunflower-14B upstream -------------------------------- +# Translation → temperature 0.3 +curl -X POST http://localhost:8000/generate_openai \ + -H 'content-type: application/json' \ + -d '{ + "instruction":"Translate to luganda: To promote standardisation in the planning, acquisition, implementation, delivery, support and maintenance of information technology equipment and services, to ensure uniformity in quality, adequacy and reliability of information technology usage throughout Uganda;", + "temperature":0.3, + "max_tokens":1024 + }' | jq + +# Non-translation → temperature 0.6 +curl -X POST http://localhost:8000/generate_openai \ + -H 'content-type: application/json' \ + -d '{ + "instruction":"Who is Sunbird AI, what do they do?", + "temperature":0.6, + "max_tokens":1024 + }' | jq + +# Streaming, translation → temperature 0.3 +curl -N -X POST http://localhost:8000/generate_openai_stream \ + -H 'content-type: application/json' \ + -d '{ + "instruction":"Translate to runyankole: Good morning, how are you today?", + "temperature":0.3, + "max_tokens":512 + }' + +# Streaming, non-translation → temperature 0.6 +curl -N -X POST http://localhost:8000/generate_openai_stream \ + -H 'content-type: application/json' \ + -d '{ + "instruction":"Write a short poem about sunflowers.", + "temperature":0.6, + "max_tokens":512 + }' +``` + +Or point [client.py](client.py) at the local proxy: + +```bash +export SUNFLOWER_URL=http://localhost:8000 +uv run client.py http "Translate to luganda: To promote standardisation in the planning, acquisition, implementation, delivery, support and maintenance of information technology equipment and services, to ensure uniformity in quality, adequacy and reliability of information technology usage throughout Uganda;" 0.2 +uv run client.py stream "Translate to luganda: To promote standardisation in the planning, acquisition, implementation, delivery, support and maintenance of information technology equipment and services, to ensure uniformity in quality, adequacy and reliability of information technology usage throughout Uganda;" 0.2 +``` + +### Notes on rate limiting + +- The default `memory://` backend keeps counters per-process. If you run + multiple uvicorn workers or replicas, set `RATE_LIMIT_STORAGE_URI` to a + Redis URL (e.g. `redis://localhost:6379`) so all workers share state. +- `slowapi` uses the client IP via `get_remote_address`. Behind a reverse + proxy / load balancer, ensure `X-Forwarded-For` is forwarded and consider + swapping the key function to read it. + +## 7. Dockerize + deploy (frontend + backend in one image) to GCP Cloud Run (Terraform) + +The single container ships both the FastAPI proxy **and** a static Next.js +frontend (translation + free-chat A/B comparison UI). FastAPI mounts the +pre-built `web/out/` at `/`, so everything lives on the same origin — no +CORS, no second service. + +Layout: + +- [Dockerfile](Dockerfile) — three-stage: `node:22-slim` builds `web/`, `python:3.12-slim` installs deps via `uv`, runtime stage ships both. +- [web/](web/) — Next.js 15 App Router + Tailwind. Built with `BUILD_MODE=export next build` into `web/out/`. +- [cloudrun/](cloudrun/) — Terraform module (local state) + `Makefile` for build/push/apply. + +### 7.0 Local development + +**Integrated mode (recommended for running the app, single terminal)** — +FastAPI serves both the API and the built frontend at `http://localhost:8000/`. + +```bash +make install # one-time: npm install in web/, uv sync +make serve # builds web/out/ then runs uvicorn --reload on :8000 +``` + +`uvicorn --reload` auto-reloads when `fastapi_app.py` changes; re-run +`make web` (or `make serve`) after editing anything under `web/` to rebuild +the static bundle. + +**Hot-reload mode (two terminals, only needed when iterating on the UI)** — +Next.js dev server on `:3000` proxies API calls to FastAPI on `:8000`. + +```bash +# Terminal 1 +make dev-api # backend only on :8000 + +# Terminal 2 +make dev-web # next dev on :3000 (rewrites /health, /generate* to :8000) +# → http://localhost:3000 +``` + +`next.config.ts` rewrites are gated on `BUILD_MODE !== "export"`, so they +power the dev proxy but have no effect on the production static build. + +### 7.1 Prerequisites + +```bash +# Tools +gcloud --version # >= 450 +terraform version # >= 1.6 +docker --version # with buildx +node --version # >= 20 (only needed for local dev; the image build uses its own Node) +npm --version + +# Auth (one-time) +make -C cloudrun auth +``` + +### 7.2 App / infra configuration + +Defaults, matching the request above: + +``` +APP=sunflower-grpo-test +PROJECT_ID=sb-gcp-project-01 +REGION=europe-west1 +PORT=8080 +REPO=sunflower-grpo-test +TAG=${REGION}-docker.pkg.dev/${PROJECT_ID}/${REPO}/${APP} +``` + +Copy the example tfvars and set the production bearer token: + +```bash +cp cloudrun/terraform.tfvars.example cloudrun/terraform.tfvars +# edit cloudrun/terraform.tfvars → set sunbird_prod_token +``` + +### 7.3 One-shot deploy + +From inside [cloudrun/](cloudrun/): + +```bash +make tf-init # first time only +make deploy # build+push linux/amd64 image, then terraform apply +make url # print the Cloud Run URL +``` + +`make deploy` uses the short git SHA as the image tag (and also pushes +`:latest`), and passes it to Terraform via `-var image_tag=...`. + +### 7.4 Manual steps (if you'd rather not use `make deploy`) + +```bash +# From repo root (so the build context sees fastapi_app.py, pyproject.toml, uv.lock) +cd modal-deploy/sunflower-grpo-inference-vllm + +TAG=$(git rev-parse --short HEAD) +IMAGE=europe-west1-docker.pkg.dev/sb-gcp-project-01/sunflower-grpo-test/sunflower-grpo-test + +# Build + push +docker buildx build --platform linux/amd64 \ + -t $IMAGE:$TAG -t $IMAGE:latest --push . + +# Apply +cd cloudrun +terraform apply -var="image_tag=$TAG" +``` + +### 7.5 Test the Cloud Run service + +Pick up the service URL from Terraform (or substitute the one Terraform +prints, e.g. `https://sunflower-grpo-test--ew.a.run.app`): + +```bash +export SUNFLOWER_URL=$(cd cloudrun && terraform output -raw service_url) +``` + +#### 7.5.1 Liveness + upstream probe + +```bash +curl -sS $SUNFLOWER_URL/health | jq +``` + +Expected: + +```json +{ + "status": "ok", + "upstream": "https://sb-modal-ws--sunflower-grpo-vllm-web.modal.run", + "upstream_status": "ok" +} +``` + +#### 7.5.2 Blocking generation — `POST /generate` + +```bash +curl -sS -X POST $SUNFLOWER_URL/generate \ + -H 'content-type: application/json' \ + -d '{ + "instruction":"Translate to luganda: To promote standardisation in the planning, acquisition, implementation, delivery, support and maintenance of information technology equipment and services, to ensure uniformity in quality, adequacy and reliability of information technology usage throughout Uganda;", + "temperature":0.2, + "max_tokens":1024 + }' | jq +``` + +Returns `{"response": "..."}`. + +#### 7.5.3 Streaming generation — `POST /generate_stream` + +`-N` disables curl output buffering so you see SSE chunks as they arrive. + +```bash +curl -N -sS -X POST $SUNFLOWER_URL/generate_stream \ + -H 'content-type: application/json' \ + -d '{ + "instruction":"Translate to luganda: To promote standardisation in the planning, acquisition, implementation, delivery, support and maintenance of information technology equipment and services, to ensure uniformity in quality, adequacy and reliability of information technology usage throughout Uganda;", + "temperature":0.2, + "max_tokens":1024 + }' +``` + +Stream is a sequence of `data: {"delta": "..."}` lines terminated with +`data: [DONE]`. + +#### 7.5.4 OpenAI-compatible Sunflower-14B — `POST /generate_openai` and `/generate_openai_stream` + +Both forward to the `sunflower-14b-openai` Modal app. Translation prompts +should run cooler (`temperature: 0.3`) than free-form generation (`0.6`). + +```bash +# Blocking, translation → temperature 0.3 +curl -sS -X POST $SUNFLOWER_URL/generate_openai \ + -H 'content-type: application/json' \ + -d '{ + "instruction":"Translate to luganda: To promote standardisation in the planning, acquisition, implementation, delivery, support and maintenance of information technology equipment and services, to ensure uniformity in quality, adequacy and reliability of information technology usage throughout Uganda;", + "temperature":0.3, + "max_tokens":1024 + }' | jq + +# Blocking, non-translation → temperature 0.6 +curl -sS -X POST $SUNFLOWER_URL/generate_openai \ + -H 'content-type: application/json' \ + -d '{ + "instruction":"Who is Sunbird AI, what do they do?", + "temperature":0.6, + "max_tokens":1024 + }' | jq + +# Streaming, translation → temperature 0.3 +curl -N -sS -X POST $SUNFLOWER_URL/generate_openai_stream \ + -H 'content-type: application/json' \ + -d '{ + "instruction":"Translate to runyankole: Good morning, how are you today?", + "temperature":0.3, + "max_tokens":512 + }' + +# Streaming, non-translation → temperature 0.6 +curl -N -sS -X POST $SUNFLOWER_URL/generate_openai_stream \ + -H 'content-type: application/json' \ + -d '{ + "instruction":"Write a short poem about sunflowers.", + "temperature":0.6, + "max_tokens":512 + }' +``` + +Stream wire format matches `/generate_stream` — `data: {"delta": "..."}` lines +terminated with `data: [DONE]` — so frontend readers written against the GRPO +stream work unchanged. + +#### 7.5.5 Production comparison — `POST /generate_production` + +Proxied to `https://api.sunbird.ai/tasks/sunflower_simple` using +`SUNBIRD_PROD_TOKEN` configured on the Cloud Run service. + +```bash +curl -sS -X POST $SUNFLOWER_URL/generate_production \ + -H 'content-type: application/json' \ + -d '{ + "instruction":"translate from english to luganda: To promote standardisation in the planning, acquisition, implementation, delivery, support and maintenance of information technology equipment and services, to ensure uniformity in quality, adequacy and reliability of information technology usage throughout Uganda;", + "model_type":"qwen", + "temperature":0.1, + "system_message":"" + }' | jq +``` + +Returns the production API's JSON body unchanged. + +#### 7.5.6 Rate-limit sanity check + +Defaults are **100 req/min** and **1000 req/day** per IP. Firing 105 quick +requests should produce a mix of `200` and `429` responses: + +```bash +for i in $(seq 1 105); do + curl -s -o /dev/null -w "%{http_code}\n" $SUNFLOWER_URL/health +done | sort | uniq -c +``` + +Every response also carries an `x-request-id` header from the proxy — grab +it with `-D -` to correlate with logs (`make -C cloudrun logs`). + +### 7.6 Tail logs + +```bash +make -C cloudrun logs +``` + +### 7.7 Notes + +- **State** is local (`cloudrun/terraform.tfstate`). Commit nothing from + `cloudrun/` other than `*.tf`, `terraform.tfvars.example`, the `Makefile`, + and `.gitignore`. +- **Secrets**: `sunbird_prod_token` is passed to Cloud Run as a plain env var + via tfvars. If you later want to rotate without re-applying, migrate it to + Secret Manager + `env.value_source.secret_key_ref`. +- **Region**: `europe-west1` for both Artifact Registry and Cloud Run, so + image pulls stay in-region. +- **Platform**: the image is built `--platform linux/amd64` — required when + building from an Apple Silicon Mac. +- **Frontend build**: happens inside the Docker build's Node stage, so you + don't need Node installed to ship. Re-runs any time `web/**` changes. +- **UI modes**: the landing page starts in **Chat** mode (free-form + instruction → A/B against production). Clicking **Translate** swaps the + input to From / To language pickers + text area; **Exit translation** + returns to chat. Both modes fire `/generate_stream` (GRPO, streaming) and + `/generate_production` (blocking) in parallel; two panels show results + side-by-side with per-panel latency. + +## 8. Configuration knobs + +Edit constants at the top of [sunflower_grpo_vllm_modal.py](sunflower_grpo_vllm_modal.py): + +| Constant | Default | Notes | +|------------------|------------------|-------| +| `GPU_TYPE` | `A100-40GB` | Alternatives: `A100-80GB`, `L40S` (48 GB), `H100`. | +| `MAX_MODEL_LEN` | `1024` | Increase for longer context (more KV cache). | +| `MAX_LORA_RANK` | `32` | Must be ≥ adapter rank. | +| `scaledown_window` | `300` (5 min) | Idle time before container spins down. | +| `@modal.concurrent(max_inputs=8)` | `8` | vLLM batches concurrent requests on one warm replica. | + +## 9. Troubleshooting + +- **OOM at engine init** — drop `gpu_memory_utilization` to `0.85` or move to `A100-80GB` / `L40S`. +- **Gated repo 401** — confirm the `huggingface` Modal secret has a token with access to the two `jq/...` repos. +- **Streaming hangs** — use `curl -N` (no buffering) and ensure your client reads `text/event-stream` line-by-line. +- **Re-download weights** — `modal volume rm sunflower-grpo-models && modal run sunflower_grpo_vllm_modal.py::download_model`. diff --git a/modal-deploy/sunflower-grpo-inference-vllm/client.py b/modal-deploy/sunflower-grpo-inference-vllm/client.py new file mode 100644 index 00000000..be34fde4 --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/client.py @@ -0,0 +1,97 @@ +""" +Client for the deployed Sunflower GRPO vLLM Modal app. + +Two transports are supported: + +1. HTTP (FastAPI endpoints exposed by `web` ASGI app): + python client.py http "Translate to luganda: Good morning" + python client.py stream "Translate to luganda: Good morning" + +2. Direct Modal class call (no HTTP, faster locally if you have modal CLI auth): + python client.py modal "Translate to luganda: Good morning" + +The HTTP base URL is auto-discovered via `modal app list` style URL convention, +or you can set SUNFLOWER_URL explicitly. +""" + +from __future__ import annotations + +import json +import os +import sys + +import requests + + +def _base_url() -> str: + url = os.environ.get("SUNFLOWER_URL") + if not url: + raise SystemExit( + "Set SUNFLOWER_URL to the deployed `web` endpoint, e.g.\n" + " export SUNFLOWER_URL=https://--sunflower-grpo-vllm-web.modal.run" + ) + return url.rstrip("/") + + +def call_http(instruction: str, temperature: float = 0.6) -> None: + r = requests.post( + f"{_base_url()}/generate", + json={"instruction": instruction, "temperature": temperature}, + timeout=300, + ) + if not r.ok: + print(f"HTTP {r.status_code}: {r.text}") + r.raise_for_status() + print(r.json()["response"]) + + +def call_stream(instruction: str, temperature: float = 0.6) -> None: + with requests.post( + f"{_base_url()}/generate_stream", + json={"instruction": instruction, "temperature": temperature}, + stream=True, + timeout=300, + ) as r: + r.raise_for_status() + for raw in r.iter_lines(decode_unicode=True): + if not raw or not raw.startswith("data: "): + continue + payload = raw[len("data: "):] + if payload == "[DONE]": + break + try: + print(json.loads(payload)["delta"], end="", flush=True) + except json.JSONDecodeError: + pass + print() + + +def call_modal(instruction: str, temperature: float = 0.6) -> None: + """Invoke the deployed Modal class directly via the Modal SDK.""" + import modal + + cls = modal.Cls.from_name("sunflower-grpo-vllm", "SunflowerVLLM") + print(cls().generate.remote(instruction, temperature)) + + +def main() -> None: + if len(sys.argv) < 3: + print("Usage: python client.py [http|stream|modal] '' [temperature]") + sys.exit(1) + + mode = sys.argv[1] + instruction = sys.argv[2] + temperature = float(sys.argv[3]) if len(sys.argv) > 3 else 0.6 + + if mode == "http": + call_http(instruction, temperature) + elif mode == "stream": + call_stream(instruction, temperature) + elif mode == "modal": + call_modal(instruction, temperature) + else: + raise SystemExit(f"Unknown mode: {mode}") + + +if __name__ == "__main__": + main() diff --git a/modal-deploy/sunflower-grpo-inference-vllm/cloudrun/.gitignore b/modal-deploy/sunflower-grpo-inference-vllm/cloudrun/.gitignore new file mode 100644 index 00000000..b1f6e158 --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/cloudrun/.gitignore @@ -0,0 +1,12 @@ +.terraform/ +.terraform.lock.hcl +terraform.tfstate +terraform.tfstate.backup +*.tfvars +!terraform.tfvars.example +crash.log +override.tf +override.tf.json +*_override.tf +*_override.tf.json +terraform.tfstate.*.backup diff --git a/modal-deploy/sunflower-grpo-inference-vllm/cloudrun/Makefile b/modal-deploy/sunflower-grpo-inference-vllm/cloudrun/Makefile new file mode 100644 index 00000000..a6493425 --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/cloudrun/Makefile @@ -0,0 +1,106 @@ +# Build/push/deploy helpers for the Cloud Run proxy. +# +# Usage: +# make auth # gcloud auth + Artifact Registry docker auth +# make build # build linux/amd64 image locally +# make push # push image to Artifact Registry +# make tf-init # terraform init +# make tf-plan # terraform plan (requires terraform.tfvars) +# make tf-apply # terraform apply +# make deploy # build + push + tf-apply +# make url # print the service URL +# make logs # tail recent Cloud Run logs + +APP ?= sunflower-grpo-test +PROJECT_ID ?= sb-gcp-project-01 +REGION ?= europe-west1 +PORT ?= 8080 +REPO ?= sunflower-grpo-test + +# Tagging strategy: +# - clean working tree → +# - dirty working tree → - (so Terraform sees a real change) +# - no git → +GIT_SHA := $(shell git rev-parse --short HEAD 2>/dev/null) +GIT_DIRTY := $(shell git status --porcelain 2>/dev/null) +TS := $(shell date -u +%Y%m%d%H%M%S) +ifeq ($(GIT_SHA),) + AUTO_TAG := $(TS) +else ifeq ($(GIT_DIRTY),) + AUTO_TAG := $(GIT_SHA) +else + AUTO_TAG := $(GIT_SHA)-$(TS) +endif +TAG ?= $(AUTO_TAG) + +IMAGE := $(REGION)-docker.pkg.dev/$(PROJECT_ID)/$(REPO)/$(APP) +IMAGE_TAG := $(IMAGE):$(TAG) +IMAGE_LATEST := $(IMAGE):latest + +# fastapi_app.py + Dockerfile live one level up from this Makefile. +BUILD_CONTEXT := .. + +.DEFAULT_GOAL := help + +.PHONY: help +help: + @awk 'BEGIN {FS = ":.*##"; printf "Targets:\n"} /^[a-zA-Z0-9_.-]+:.*##/ {printf " \033[36m%-14s\033[0m %s\n", $$1, $$2}' $(MAKEFILE_LIST) + +.PHONY: auth +auth: ## gcloud auth + configure docker for Artifact Registry + gcloud auth login + gcloud config set project $(PROJECT_ID) + gcloud auth configure-docker $(REGION)-docker.pkg.dev --quiet + +.PHONY: build +build: ## Build linux/amd64 image + docker buildx build \ + --platform linux/amd64 \ + -t $(IMAGE_TAG) \ + -t $(IMAGE_LATEST) \ + --load \ + $(BUILD_CONTEXT) + +.PHONY: push +push: ## Push both SHA and latest tags + docker push $(IMAGE_TAG) + docker push $(IMAGE_LATEST) + +.PHONY: build-push +build-push: ## Build + push in one buildx invocation (no local load) + docker buildx build \ + --platform linux/amd64 \ + -t $(IMAGE_TAG) \ + -t $(IMAGE_LATEST) \ + --push \ + $(BUILD_CONTEXT) + +.PHONY: tf-init +tf-init: ## terraform init + terraform init + +.PHONY: tf-plan +tf-plan: ## terraform plan + terraform plan -var="image_tag=$(TAG)" + +.PHONY: tf-apply +tf-apply: ## terraform apply + terraform apply -auto-approve -var="image_tag=$(TAG)" + +.PHONY: tf-destroy +tf-destroy: ## terraform destroy + terraform destroy -var="image_tag=$(TAG)" + +.PHONY: deploy +deploy: build-push tf-apply ## Build, push, and apply in one shot + +.PHONY: url +url: ## Print the Cloud Run service URL + @terraform output -raw service_url + +.PHONY: logs +logs: ## Tail recent Cloud Run logs + gcloud run services logs read $(APP) \ + --region $(REGION) \ + --project $(PROJECT_ID) \ + --limit 100 diff --git a/modal-deploy/sunflower-grpo-inference-vllm/cloudrun/main.tf b/modal-deploy/sunflower-grpo-inference-vllm/cloudrun/main.tf new file mode 100644 index 00000000..0946ae62 --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/cloudrun/main.tf @@ -0,0 +1,142 @@ +provider "google" { + project = var.project_id + region = var.region +} + +locals { + image = "${var.region}-docker.pkg.dev/${var.project_id}/${var.repo}/${var.app}:${var.image_tag}" +} + +# --------------------------------------------------------------------------- +# Enable required APIs +# --------------------------------------------------------------------------- +resource "google_project_service" "run" { + service = "run.googleapis.com" + disable_on_destroy = false +} + +resource "google_project_service" "artifactregistry" { + service = "artifactregistry.googleapis.com" + disable_on_destroy = false +} + +# --------------------------------------------------------------------------- +# Artifact Registry repository +# --------------------------------------------------------------------------- +resource "google_artifact_registry_repository" "repo" { + location = var.region + repository_id = var.repo + description = "Container images for ${var.app}" + format = "DOCKER" + + depends_on = [google_project_service.artifactregistry] +} + +# --------------------------------------------------------------------------- +# Cloud Run v2 service +# --------------------------------------------------------------------------- +resource "google_cloud_run_v2_service" "app" { + name = var.app + location = var.region + ingress = "INGRESS_TRAFFIC_ALL" + deletion_protection = var.deletion_protection + + template { + scaling { + min_instance_count = var.min_instances + max_instance_count = var.max_instances + } + + timeout = "${var.request_timeout_seconds}s" + + containers { + image = local.image + + ports { + container_port = var.port + } + + resources { + limits = { + cpu = var.cpu + memory = var.memory + } + } + + # NOTE: PORT is a reserved Cloud Run env var and is automatically set by + # the runtime to match `ports.container_port` — setting it here is + # rejected by the API. + env { + name = "SUNFLOWER_UPSTREAM_URL" + value = var.sunflower_upstream_url + } + env { + name = "SUNBIRD_PROD_URL" + value = var.sunbird_prod_url + } + env { + name = "SUNBIRD_PROD_TOKEN" + value = var.sunbird_prod_token + } + env { + name = "SUNFLOWER_OPENAI_URL" + value = var.sunflower_openai_url + } + env { + name = "VLLM_API_KEY" + value = var.vllm_api_key + } + env { + name = "SUNFLOWER_OPENAI_MODEL" + value = var.sunflower_openai_model + } + env { + name = "SUNFLOWER_SYSTEM_MESSAGE" + value = var.sunflower_system_message + } + env { + name = "UPSTREAM_TIMEOUT" + value = tostring(var.upstream_timeout) + } + env { + name = "RATE_LIMIT_PER_MINUTE" + value = var.rate_limit_per_minute + } + env { + name = "RATE_LIMIT_PER_DAY" + value = var.rate_limit_per_day + } + + startup_probe { + initial_delay_seconds = 5 + period_seconds = 5 + timeout_seconds = 3 + failure_threshold = 10 + tcp_socket { + port = var.port + } + } + } + } + + traffic { + type = "TRAFFIC_TARGET_ALLOCATION_TYPE_LATEST" + percent = 100 + } + + depends_on = [ + google_project_service.run, + google_artifact_registry_repository.repo, + ] +} + +# --------------------------------------------------------------------------- +# Public access (allUsers → run.invoker) +# --------------------------------------------------------------------------- +resource "google_cloud_run_v2_service_iam_member" "public" { + count = var.allow_public ? 1 : 0 + location = google_cloud_run_v2_service.app.location + name = google_cloud_run_v2_service.app.name + role = "roles/run.invoker" + member = "allUsers" +} diff --git a/modal-deploy/sunflower-grpo-inference-vllm/cloudrun/outputs.tf b/modal-deploy/sunflower-grpo-inference-vllm/cloudrun/outputs.tf new file mode 100644 index 00000000..79527d93 --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/cloudrun/outputs.tf @@ -0,0 +1,22 @@ +output "service_url" { + description = "Public URL of the Cloud Run service." + value = google_cloud_run_v2_service.app.uri +} + +output "image" { + description = "Full image reference deployed to Cloud Run." + value = local.image +} + +output "repository" { + description = "Artifact Registry repository path." + value = google_artifact_registry_repository.repo.id +} + +output "region" { + value = var.region +} + +output "project_id" { + value = var.project_id +} diff --git a/modal-deploy/sunflower-grpo-inference-vllm/cloudrun/terraform.tfvars.example b/modal-deploy/sunflower-grpo-inference-vllm/cloudrun/terraform.tfvars.example new file mode 100644 index 00000000..08b6f6bf --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/cloudrun/terraform.tfvars.example @@ -0,0 +1,31 @@ +project_id = "sb-gcp-project-01" +region = "europe-west1" +app = "sunflower-grpo-test" +repo = "sunflower-grpo-test" +port = 8080 + +# Bump this (or pass via -var) to deploy a new image. +image_tag = "latest" + +sunflower_upstream_url = "https://sb-modal-ws--sunflower-grpo-vllm-web.modal.run" +sunbird_prod_url = "https://api.sunbird.ai" +sunbird_prod_token = "REPLACE_WITH_PROD_BEARER_TOKEN" + +# OpenAI-compatible Sunflower-14B upstream (sunflower_14b_openai_vllm_modal.py). +# Must include the trailing `/v1`. +sunflower_openai_url = "https://sb-modal-ws--sunflower-14b-openai-serve.modal.run/v1" +# Same value as Modal secret `vllm-api-key`. Generate with scripts/generate_api_key.sh. +vllm_api_key = "REPLACE_WITH_VLLM_API_KEY" +sunflower_openai_model = "Sunbird/Sunflower-14B" + +upstream_timeout = 600 +rate_limit_per_minute = "100/minute" +rate_limit_per_day = "1000/day" + +cpu = "1" +memory = "1Gi" +min_instances = 0 +max_instances = 5 + +allow_public = true +deletion_protection = false diff --git a/modal-deploy/sunflower-grpo-inference-vllm/cloudrun/variables.tf b/modal-deploy/sunflower-grpo-inference-vllm/cloudrun/variables.tf new file mode 100644 index 00000000..cdc55ab0 --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/cloudrun/variables.tf @@ -0,0 +1,138 @@ +variable "project_id" { + type = string + description = "GCP project ID." + default = "sb-gcp-project-01" +} + +variable "region" { + type = string + description = "GCP region for Cloud Run and Artifact Registry." + default = "europe-west1" +} + +variable "app" { + type = string + description = "Cloud Run service name / image name." + default = "sunflower-grpo-test" +} + +variable "repo" { + type = string + description = "Artifact Registry repository name." + default = "sunflower-grpo-test" +} + +variable "image_tag" { + type = string + description = "Image tag to deploy (e.g. the git SHA or `latest`)." + default = "latest" +} + +variable "port" { + type = number + description = "Container port the app listens on." + default = 8080 +} + +variable "sunflower_upstream_url" { + type = string + description = "Upstream Modal web URL used by the proxy." + default = "https://sb-modal-ws--sunflower-grpo-vllm-web.modal.run" +} + +variable "sunbird_prod_url" { + type = string + description = "Production Sunbird AI API base URL." + default = "https://api.sunbird.ai" +} + +variable "sunbird_prod_token" { + type = string + description = "Bearer token for the production Sunbird AI API. Passed as an env var on the Cloud Run service." + sensitive = true +} + +variable "sunflower_openai_url" { + type = string + description = "Base URL of the OpenAI-compatible Sunflower-14B vLLM deployment on Modal. Must include the trailing `/v1`." + default = "https://sb-modal-ws--sunflower-14b-openai-serve.modal.run/v1" +} + +variable "vllm_api_key" { + type = string + description = "Bearer token accepted by the OpenAI-compatible vLLM server (the same value stored in Modal secret `vllm-api-key`). Empty string disables auth on the proxy side." + sensitive = true + default = "" +} + +variable "sunflower_openai_model" { + type = string + description = "Model id sent to the OpenAI-compatible upstream. Must match one of the values in `--served-model-name`." + default = "Sunbird/Sunflower-14B" +} + +variable "sunflower_system_message" { + type = string + description = "System prompt prepended to every /generate_openai* request." + default = "You are Sunflower, a helpful assistant made by Sunbird AI who understands all Ugandan languages. You specialise in accurate translations, explanations, summaries and other language tasks." +} + +variable "upstream_timeout" { + type = number + description = "Per-request upstream timeout in seconds." + default = 600 +} + +variable "rate_limit_per_minute" { + type = string + description = "slowapi per-IP per-minute limit." + default = "100/minute" +} + +variable "rate_limit_per_day" { + type = string + description = "slowapi per-IP per-day limit." + default = "1000/day" +} + +variable "cpu" { + type = string + description = "Cloud Run CPU allocation per container." + default = "1" +} + +variable "memory" { + type = string + description = "Cloud Run memory allocation per container." + default = "1Gi" +} + +variable "min_instances" { + type = number + description = "Minimum number of Cloud Run instances." + default = 0 +} + +variable "max_instances" { + type = number + description = "Maximum number of Cloud Run instances." + default = 5 +} + +variable "request_timeout_seconds" { + type = number + description = "Cloud Run request timeout in seconds." + default = 600 +} + +variable "allow_public" { + type = bool + description = "If true, binds `allUsers` as run.invoker (public service)." + default = true +} + +variable "deletion_protection" { + type = bool + description = "Cloud Run v2 deletion_protection flag. Defaults to false for this test service; set true for production to prevent Terraform from destroying/recreating it." + default = false +} diff --git a/modal-deploy/sunflower-grpo-inference-vllm/cloudrun/versions.tf b/modal-deploy/sunflower-grpo-inference-vllm/cloudrun/versions.tf new file mode 100644 index 00000000..23c7ce36 --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/cloudrun/versions.tf @@ -0,0 +1,10 @@ +terraform { + required_version = ">= 1.6" + + required_providers { + google = { + source = "hashicorp/google" + version = "~> 6.10" + } + } +} diff --git a/modal-deploy/sunflower-grpo-inference-vllm/fastapi_app.py b/modal-deploy/sunflower-grpo-inference-vllm/fastapi_app.py new file mode 100644 index 00000000..d2ad9c54 --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/fastapi_app.py @@ -0,0 +1,381 @@ +""" +Standalone FastAPI proxy in front of the deployed Modal `web` endpoint. + +Why a proxy? + The Modal app already exposes /generate, /generate_stream, /health at + https://--sunflower-grpo-vllm-web.modal.run. This wrapper sits + in front of it so we can add auth, rate-limiting, logging, custom domains, + request shaping, etc. without touching the inference deployment. + +Run locally: + uv add fastapi uvicorn httpx + uv run uvicorn fastapi_app:app --host 0.0.0.0 --port 8000 --reload + +Configuration (env vars): + SUNFLOWER_UPSTREAM_URL Upstream Modal web URL (no trailing slash). + Default: https://sb-modal-ws--sunflower-grpo-vllm-web.modal.run + UPSTREAM_TIMEOUT Per-request timeout in seconds. Default: 600. + SUNBIRD_PROD_URL Production Sunbird AI API base URL. + Default: https://api.sunbird.ai + SUNBIRD_PROD_TOKEN Bearer token for the production API (required for + /generate_production). +""" + +from __future__ import annotations + +import json +import logging +import os +import time +import uuid +from contextlib import asynccontextmanager +from pathlib import Path +from typing import AsyncIterator + +import httpx +from fastapi import FastAPI, HTTPException, Request +from fastapi.responses import StreamingResponse +from fastapi.staticfiles import StaticFiles +from pydantic import BaseModel, Field +from slowapi import Limiter +from slowapi.errors import RateLimitExceeded +from slowapi.middleware import SlowAPIMiddleware +from slowapi.util import get_remote_address +from starlette.responses import JSONResponse +from dotenv import load_dotenv + +load_dotenv(override=True) + +# --------------------------------------------------------------------------- +# Config +# --------------------------------------------------------------------------- +UPSTREAM_URL = os.environ.get( + "SUNFLOWER_UPSTREAM_URL", + "https://sb-modal-ws--sunflower-grpo-vllm-web.modal.run", +).rstrip("/") +UPSTREAM_TIMEOUT = float(os.environ.get("UPSTREAM_TIMEOUT", "600")) +SUNBIRD_PROD_URL = os.environ.get("SUNBIRD_PROD_URL", "https://api.sunbird.ai").rstrip("/") +SUNBIRD_PROD_TOKEN = os.environ.get("SUNBIRD_PROD_TOKEN") + +# OpenAI-compatible vLLM deployment (sunflower_14b_openai_vllm_modal.py). +# Base URL must include `/v1`. +SUNFLOWER_OPENAI_URL = os.environ.get( + "SUNFLOWER_OPENAI_URL", + "https://sb-modal-ws--sunflower-14b-openai-serve.modal.run/v1", +).rstrip("/") +SUNFLOWER_OPENAI_API_KEY = os.environ.get("VLLM_API_KEY", "") +SUNFLOWER_OPENAI_MODEL = os.environ.get( + "SUNFLOWER_OPENAI_MODEL", "Sunbird/Sunflower-14B" +) +SUNFLOWER_SYSTEM_MESSAGE = os.environ.get( + "SUNFLOWER_SYSTEM_MESSAGE", + "You are Sunflower, a helpful assistant made by Sunbird AI who understands " + "all Ugandan languages. You specialise in accurate translations, " + "explanations, summaries and other language tasks.", +) +RATE_LIMIT_PER_MINUTE = os.environ.get("RATE_LIMIT_PER_MINUTE", "100/minute") +RATE_LIMIT_PER_DAY = os.environ.get("RATE_LIMIT_PER_DAY", "1000/day") +# Optional Redis-backed storage for multi-process / multi-replica deploys. +RATE_LIMIT_STORAGE_URI = os.environ.get("RATE_LIMIT_STORAGE_URI", "memory://") + +limiter = Limiter( + key_func=get_remote_address, + storage_uri=RATE_LIMIT_STORAGE_URI, + default_limits=[RATE_LIMIT_PER_MINUTE, RATE_LIMIT_PER_DAY], +) + +# --------------------------------------------------------------------------- +# Logging +# --------------------------------------------------------------------------- +logging.basicConfig( + level=os.environ.get("LOG_LEVEL", "INFO"), + format="%(asctime)s [%(levelname)s] %(name)s :: %(message)s", +) +log = logging.getLogger("sunflower-proxy") + + +# --------------------------------------------------------------------------- +# Schemas +# --------------------------------------------------------------------------- +class GenerateRequest(BaseModel): + instruction: str = Field(..., description="User prompt / instruction") + temperature: float = Field(0.6, ge=0.0, le=1.0) + max_tokens: int = Field(1024, ge=1, le=4096) + + +class GenerateResponse(BaseModel): + response: str + + +class ProductionRequest(BaseModel): + instruction: str = Field(..., description="User prompt / instruction") + model_type: str = Field("qwen", description="Production model type, e.g. `qwen`") + temperature: float = Field(0.1, ge=0.0, le=1.0) + system_message: str = Field("", description="Optional system message override") + + +# --------------------------------------------------------------------------- +# Lifespan: shared httpx client +# --------------------------------------------------------------------------- +@asynccontextmanager +async def lifespan(app: FastAPI): + app.state.http = httpx.AsyncClient( + base_url=UPSTREAM_URL, + timeout=httpx.Timeout(UPSTREAM_TIMEOUT, connect=15.0), + ) + app.state.prod_http = httpx.AsyncClient( + base_url=SUNBIRD_PROD_URL, + timeout=httpx.Timeout(UPSTREAM_TIMEOUT, connect=15.0), + ) + openai_headers = {"Content-Type": "application/json"} + if SUNFLOWER_OPENAI_API_KEY: + openai_headers["Authorization"] = f"Bearer {SUNFLOWER_OPENAI_API_KEY}" + app.state.openai_http = httpx.AsyncClient( + base_url=SUNFLOWER_OPENAI_URL, + timeout=httpx.Timeout(UPSTREAM_TIMEOUT, connect=15.0), + headers=openai_headers, + ) + log.info( + "Proxy ready, upstream=%s prod_upstream=%s openai_upstream=%s", + UPSTREAM_URL, + SUNBIRD_PROD_URL, + SUNFLOWER_OPENAI_URL, + ) + try: + yield + finally: + await app.state.http.aclose() + await app.state.prod_http.aclose() + await app.state.openai_http.aclose() + + +app = FastAPI( + title="Sunflower GRPO Inference Proxy", + description="HTTP proxy in front of the Modal-hosted Sunflower vLLM deployment.", + version="0.1.0", + lifespan=lifespan, +) + +# Rate limiting (slowapi) ----------------------------------------------------- +app.state.limiter = limiter +app.add_middleware(SlowAPIMiddleware) + + +def _rate_limit_handler(request: Request, exc: RateLimitExceeded) -> JSONResponse: + return JSONResponse( + status_code=429, + content={ + "detail": f"Rate limit exceeded: {exc.detail}", + "limits": [RATE_LIMIT_PER_MINUTE, RATE_LIMIT_PER_DAY], + }, + ) + + +app.add_exception_handler(RateLimitExceeded, _rate_limit_handler) + + +# --------------------------------------------------------------------------- +# Middleware: request id + access log +# --------------------------------------------------------------------------- +@app.middleware("http") +async def access_log(request: Request, call_next): + rid = request.headers.get("x-request-id", uuid.uuid4().hex[:12]) + started = time.perf_counter() + log.info("→ %s %s rid=%s", request.method, request.url.path, rid) + try: + response = await call_next(request) + except Exception: + log.exception("✗ unhandled error rid=%s", rid) + raise + elapsed_ms = (time.perf_counter() - started) * 1000 + response.headers["x-request-id"] = rid + log.info( + "← %s %s status=%d rid=%s %.1fms", + request.method, + request.url.path, + response.status_code, + rid, + elapsed_ms, + ) + return response + + +# --------------------------------------------------------------------------- +# Endpoints +# --------------------------------------------------------------------------- +@app.get("/health") +async def health(): + """Liveness for the proxy + readiness probe of the upstream.""" + client: httpx.AsyncClient = app.state.http + upstream_status: str + try: + r = await client.get("/health", timeout=10.0) + upstream_status = "ok" if r.status_code == 200 else f"http_{r.status_code}" + except Exception as e: + upstream_status = f"unreachable: {e.__class__.__name__}" + return { + "status": "ok", + "upstream": UPSTREAM_URL, + "upstream_status": upstream_status, + } + + +@app.post("/generate", response_model=GenerateResponse) +async def generate(req: GenerateRequest): + client: httpx.AsyncClient = app.state.http + try: + r = await client.post("/generate", json=req.model_dump()) + except httpx.HTTPError as e: + raise HTTPException(status_code=502, detail=f"Upstream error: {e}") from e + if r.status_code >= 400: + raise HTTPException(status_code=r.status_code, detail=r.text) + return r.json() + + +@app.post("/generate_stream") +async def generate_stream(req: GenerateRequest): + client: httpx.AsyncClient = app.state.http + + async def passthrough() -> AsyncIterator[bytes]: + try: + async with client.stream( + "POST", "/generate_stream", json=req.model_dump() + ) as upstream: + if upstream.status_code >= 400: + body = await upstream.aread() + raise HTTPException(status_code=upstream.status_code, detail=body.decode()) + async for chunk in upstream.aiter_raw(): + if chunk: + yield chunk + except httpx.HTTPError as e: + log.exception("upstream stream error") + raise HTTPException(status_code=502, detail=f"Upstream error: {e}") from e + + return StreamingResponse(passthrough(), media_type="text/event-stream") + + +@app.post("/generate_production") +async def generate_production(req: ProductionRequest): + """Forward to the production Sunbird AI API for side-by-side comparison. + + The upstream expects form-encoded (`application/x-www-form-urlencoded`) + data with a bearer token distinct from this proxy's own API_KEY. + """ + if not SUNBIRD_PROD_TOKEN: + raise HTTPException( + status_code=500, + detail="SUNBIRD_PROD_TOKEN is not configured on the proxy", + ) + client: httpx.AsyncClient = app.state.prod_http + try: + r = await client.post( + "/tasks/sunflower_simple", + headers={ + "accept": "application/json", + "Authorization": f"Bearer {SUNBIRD_PROD_TOKEN}", + }, + data={ + "instruction": req.instruction, + "model_type": req.model_type, + "temperature": str(req.temperature), + "system_message": req.system_message, + }, + ) + except httpx.HTTPError as e: + raise HTTPException( + status_code=502, detail=f"Production upstream error: {e}" + ) from e + if r.status_code >= 400: + raise HTTPException(status_code=r.status_code, detail=r.text) + return r.json() + + +# --------------------------------------------------------------------------- +# OpenAI-compatible Sunflower-14B upstream +# --------------------------------------------------------------------------- +def _openai_chat_body(req: GenerateRequest, *, stream: bool) -> dict: + return { + "model": SUNFLOWER_OPENAI_MODEL, + "messages": [ + {"role": "system", "content": SUNFLOWER_SYSTEM_MESSAGE}, + {"role": "user", "content": req.instruction}, + ], + "temperature": req.temperature, + "max_tokens": req.max_tokens, + "stream": stream, + } + + +@app.post("/generate_openai", response_model=GenerateResponse) +async def generate_openai(req: GenerateRequest): + """Blocking chat completion against the OpenAI-compatible Sunflower-14B server.""" + client: httpx.AsyncClient = app.state.openai_http + try: + r = await client.post("/chat/completions", json=_openai_chat_body(req, stream=False)) + except httpx.HTTPError as e: + raise HTTPException(status_code=502, detail=f"OpenAI upstream error: {e}") from e + if r.status_code >= 400: + raise HTTPException(status_code=r.status_code, detail=r.text) + data = r.json() + try: + text = data["choices"][0]["message"]["content"] + except (KeyError, IndexError, TypeError) as e: + raise HTTPException(status_code=502, detail=f"Unexpected upstream payload: {e}") from e + return {"response": text} + + +@app.post("/generate_openai_stream") +async def generate_openai_stream(req: GenerateRequest): + """Stream token deltas from the OpenAI-compatible server as `{delta: ...}` SSE events. + + Wire format matches /generate_stream so the frontend can consume either upstream + without branching. + """ + client: httpx.AsyncClient = app.state.openai_http + + async def passthrough() -> AsyncIterator[bytes]: + try: + async with client.stream( + "POST", "/chat/completions", json=_openai_chat_body(req, stream=True) + ) as upstream: + if upstream.status_code >= 400: + body = await upstream.aread() + raise HTTPException(status_code=upstream.status_code, detail=body.decode()) + async for line in upstream.aiter_lines(): + if not line.startswith("data:"): + continue + payload = line[5:].strip() + if payload == "[DONE]": + yield b"data: [DONE]\n\n" + return + try: + chunk = json.loads(payload) + delta = chunk["choices"][0].get("delta", {}).get("content") + except (json.JSONDecodeError, KeyError, IndexError): + continue + if delta: + yield f"data: {json.dumps({'delta': delta})}\n\n".encode() + except httpx.HTTPError as e: + log.exception("openai upstream stream error") + raise HTTPException(status_code=502, detail=f"OpenAI upstream error: {e}") from e + + return StreamingResponse(passthrough(), media_type="text/event-stream") + + +# --------------------------------------------------------------------------- +# Static frontend (Next.js static export → web/out/). Mounted last so all +# explicit API routes above take precedence over the SPA fallback. +# --------------------------------------------------------------------------- +_STATIC_DIR = Path(__file__).parent / "web" / "out" +if _STATIC_DIR.is_dir(): + app.mount( + "/", + StaticFiles(directory=_STATIC_DIR, html=True), + name="frontend", + ) + log.info("Mounted static frontend from %s", _STATIC_DIR) +else: + log.info( + "No static frontend at %s — run `npm run build` in web/ to generate it.", + _STATIC_DIR, + ) diff --git a/modal-deploy/sunflower-grpo-inference-vllm/main.py b/modal-deploy/sunflower-grpo-inference-vllm/main.py new file mode 100644 index 00000000..43242833 --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/main.py @@ -0,0 +1,6 @@ +def main(): + print("Hello from sunflower-grpo-inference-vllm!") + + +if __name__ == "__main__": + main() diff --git a/modal-deploy/sunflower-grpo-inference-vllm/openai_client.py b/modal-deploy/sunflower-grpo-inference-vllm/openai_client.py new file mode 100644 index 00000000..c3131c47 --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/openai_client.py @@ -0,0 +1,260 @@ +"""Client for the Sunflower-14B OpenAI-compatible vLLM deployment on Modal. + +Usage: + # one-shot with streaming (default) + uv run python openai_client.py --prompt "Translate to luganda: Good morning" + + # disable streaming + uv run python openai_client.py --no-stream --prompt "Who is Sunbird AI?" + + # interactive chat + uv run python openai_client.py --chat + + # override model / workspace / api key + uv run python openai_client.py --model sunflower-14b --api-key "$VLLM_API_KEY" + +The client auto-resolves the Modal URL: + https://[-]---.modal.run/v1 +and auto-loads VLLM_API_KEY from `.env` (next to this file) if --api-key is not set. +""" + +from __future__ import annotations + +import argparse +import os +from pathlib import Path + +import modal +from openai import OpenAI + + +class Colors: + GREEN = "\033[0;32m" + RED = "\033[0;31m" + BLUE = "\033[0;34m" + GRAY = "\033[0;90m" + BOLD = "\033[1m" + END = "\033[0m" + + +DEFAULT_SYSTEM_PROMPT = ( + "You are Sunflower, a helpful assistant made by Sunbird AI who understands " + "all Ugandan languages. You specialise in accurate translations, " + "explanations, summaries and other language tasks." +) +DEFAULT_PROMPT = "Translate to luganda: I am watching an Arsenal game right now." + + +def load_dotenv_key(name: str) -> str | None: + """Minimal .env reader: return the value for `name` if present in ./.env.""" + env_path = Path(__file__).resolve().parent / ".env" + if not env_path.exists(): + return None + for line in env_path.read_text().splitlines(): + line = line.strip() + if not line or line.startswith("#") or "=" not in line: + continue + key, _, value = line.partition("=") + if key.strip() == name: + return value.strip().strip('"').strip("'") + return None + + +def get_completion(client: OpenAI, model_id: str, messages: list[dict], args): + completion_args = { + "model": model_id, + "messages": messages, + "frequency_penalty": args.frequency_penalty, + "max_tokens": args.max_tokens, + "n": args.n, + "presence_penalty": args.presence_penalty, + "seed": args.seed, + "stop": args.stop, + "stream": args.stream, + "temperature": args.temperature, + "top_p": args.top_p, + } + completion_args = {k: v for k, v in completion_args.items() if v is not None} + try: + return client.chat.completions.create(**completion_args) + except Exception as e: + print(Colors.RED, f"Error during API call: {e}", Colors.END, sep="") + return None + + +def main(): + parser = argparse.ArgumentParser(description="Sunflower-14B OpenAI client") + + parser.add_argument( + "--model", + type=str, + default=None, + help="Model id to use. Defaults to the first model advertised by /v1/models.", + ) + parser.add_argument( + "--workspace", + type=str, + default=None, + help="Modal workspace (defaults to current profile).", + ) + parser.add_argument( + "--environment", + type=str, + default=None, + help="Modal environment (defaults to current environment).", + ) + parser.add_argument( + "--app-name", + type=str, + default="sunflower-14b-openai", + help="Modal App name serving the OpenAI-compatible API.", + ) + parser.add_argument( + "--function-name", + type=str, + default="serve", + help="Modal Function name. Append '-dev' for a `modal serve`d function.", + ) + parser.add_argument( + "--base-url", + type=str, + default=None, + help="Override the resolved base URL (e.g. http://localhost:8000/v1).", + ) + parser.add_argument( + "--api-key", + type=str, + default=None, + help="VLLM_API_KEY. Defaults to env VLLM_API_KEY or the value in ./.env.", + ) + + # Completion parameters + parser.add_argument("--max-tokens", type=int, default=1024) + parser.add_argument("--temperature", type=float, default=0.2) + parser.add_argument("--top-p", type=float, default=0.9) + parser.add_argument("--frequency-penalty", type=float, default=0) + parser.add_argument("--presence-penalty", type=float, default=0) + parser.add_argument( + "--n", + type=int, + default=1, + help="Number of completions. Streaming / chat mode require n=1.", + ) + parser.add_argument("--stop", type=str, default=None) + parser.add_argument("--seed", type=int, default=None) + + # Prompting + parser.add_argument("--prompt", type=str, default=DEFAULT_PROMPT) + parser.add_argument("--system-prompt", type=str, default=DEFAULT_SYSTEM_PROMPT) + + # UI options + parser.add_argument( + "--no-stream", + dest="stream", + action="store_false", + help="Disable streaming of response chunks.", + ) + parser.add_argument( + "--chat", action="store_true", help="Enable interactive chat mode." + ) + + args = parser.parse_args() + + api_key = ( + args.api_key + or os.environ.get("VLLM_API_KEY") + or load_dotenv_key("VLLM_API_KEY") + or "EMPTY" + ) + + if args.base_url: + base_url = args.base_url.rstrip("/") + else: + workspace = args.workspace or modal.config._profile + environment = args.environment or modal.config.config.get("environment") + prefix = workspace + (f"-{environment}" if environment else "") + base_url = ( + f"https://{prefix}--{args.app_name}-{args.function_name}.modal.run/v1" + ) + + client = OpenAI(api_key=api_key, base_url=base_url) + + if args.model: + model_id = args.model + print( + Colors.BOLD, + f"Using model {model_id}. First call may trigger a cold boot.", + Colors.END, + sep="", + ) + else: + print( + Colors.BOLD, + f"Listing models at {client.base_url} ...", + Colors.END, + sep="", + ) + model = client.models.list().data[0] + model_id = model.id + print(Colors.BOLD, f"Using {model_id}", Colors.END, sep="") + + messages = [{"role": "system", "content": args.system_prompt}] + print(Colors.GRAY + f"[system]: {args.system_prompt}" + Colors.END) + + if args.chat: + print( + Colors.GREEN + + Colors.BOLD + + "\nEntering chat mode. Type 'bye' to end the conversation." + + Colors.END + ) + MAX_HISTORY = 10 + while True: + user_input = input("\nYou: ") + if user_input.lower() in {"bye", "exit", "quit"}: + break + + if len(messages) > MAX_HISTORY: + messages = messages[:1] + messages[-MAX_HISTORY + 1 :] + + messages.append({"role": "user", "content": user_input}) + response = get_completion(client, model_id, messages, args) + if not response: + continue + + print(Colors.BLUE + "\n[bot]: ", end="") + if args.stream: + assistant_message = "" + for chunk in response: + delta = chunk.choices[0].delta.content + if delta: + print(delta, end="", flush=True) + assistant_message += delta + print(Colors.END) + else: + assistant_message = response.choices[0].message.content + print(assistant_message + Colors.END) + + messages.append({"role": "assistant", "content": assistant_message}) + else: + messages.append({"role": "user", "content": args.prompt}) + print(Colors.GREEN + f"\nYou: {args.prompt}" + Colors.END) + response = get_completion(client, model_id, messages, args) + if not response: + return + + if args.stream: + print(Colors.BLUE + "\n[bot]: ", end="") + for chunk in response: + delta = chunk.choices[0].delta.content + if delta: + print(delta, end="", flush=True) + print(Colors.END) + else: + for i, choice in enumerate(response.choices): + label = f"[bot] Choice {i + 1}: " if len(response.choices) > 1 else "[bot]: " + print(Colors.BLUE + f"\n{label}{choice.message.content}" + Colors.END) + + +if __name__ == "__main__": + main() diff --git a/modal-deploy/sunflower-grpo-inference-vllm/pyproject.toml b/modal-deploy/sunflower-grpo-inference-vllm/pyproject.toml new file mode 100644 index 00000000..c6bf73c3 --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/pyproject.toml @@ -0,0 +1,16 @@ +[project] +name = "sunflower-grpo-inference-vllm" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "fastapi>=0.135.3", + "httpx>=0.28.1", + "modal>=1.4.1", + "openai>=2.32.0", + "python-dotenv>=1.2.2", + "requests>=2.33.1", + "slowapi>=0.1.9", + "uvicorn>=0.44.0", +] diff --git a/modal-deploy/sunflower-grpo-inference-vllm/scripts/deploy.sh b/modal-deploy/sunflower-grpo-inference-vllm/scripts/deploy.sh new file mode 100755 index 00000000..16c3ae6d --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/scripts/deploy.sh @@ -0,0 +1,36 @@ +#!/usr/bin/env bash +# Deploy the Sunflower 14B OpenAI-compatible vLLM app to Modal. +# +# Usage: +# ./scripts/deploy.sh # deploy the OpenAI app (default) +# ./scripts/deploy.sh grpo # deploy the GRPO LoRA app instead +# ./scripts/deploy.sh both # deploy both apps sequentially +set -euo pipefail + +HERE="$(cd "$(dirname "$0")" && pwd)" +ROOT="$(cd "${HERE}/.." && pwd)" + +OPENAI_APP="${ROOT}/sunflower_14b_openai_vllm_modal.py" +GRPO_APP="${ROOT}/sunflower_grpo_vllm_modal.py" + +if ! command -v uv >/dev/null 2>&1; then + echo "error: uv not found. Install with: curl -LsSf https://astral.sh/uv/install.sh | sh" >&2 + exit 1 +fi + +deploy() { + local target="$1" + if [[ ! -f "${target}" ]]; then + echo "error: app file not found: ${target}" >&2 + exit 1 + fi + echo ">>> uv run modal deploy ${target}" + uv run modal deploy "${target}" +} + +case "${1:-openai}" in + openai) deploy "${OPENAI_APP}" ;; + grpo) deploy "${GRPO_APP}" ;; + both) deploy "${OPENAI_APP}"; deploy "${GRPO_APP}" ;; + *) echo "usage: $0 [openai|grpo|both]" >&2; exit 2 ;; +esac diff --git a/modal-deploy/sunflower-grpo-inference-vllm/scripts/generate_api_key.sh b/modal-deploy/sunflower-grpo-inference-vllm/scripts/generate_api_key.sh new file mode 100755 index 00000000..732994ba --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/scripts/generate_api_key.sh @@ -0,0 +1,53 @@ +#!/usr/bin/env bash +# Generate a 32-character URL-safe API key for the vLLM OpenAI server. +# +# Usage: +# ./scripts/generate_api_key.sh # print the key +# ./scripts/generate_api_key.sh --create # create the Modal secret `vllm-api-key` +# ./scripts/generate_api_key.sh --update # overwrite the existing Modal secret +# ./scripts/generate_api_key.sh --env # append VLLM_API_KEY=... to .env +set -euo pipefail + +SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)" +ROOT_DIR="$(cd "${SCRIPT_DIR}/.." && pwd)" +ENV_FILE="${ROOT_DIR}/.env" + +SECRET_NAME="vllm-api-key" + +# 24 random bytes -> 32 base64url chars (no padding). +KEY="$(openssl rand -base64 24 | tr '+/' '-_' | tr -d '=\n')" + +case "${1:-}" in + --create) + uv run modal secret create "${SECRET_NAME}" "VLLM_API_KEY=${KEY}" + echo + echo "Created Modal secret '${SECRET_NAME}'. Save this key — it will not be shown again:" + echo " VLLM_API_KEY=${KEY}" + ;; + --update) + uv run modal secret create --force "${SECRET_NAME}" "VLLM_API_KEY=${KEY}" + echo + echo "Updated Modal secret '${SECRET_NAME}'. Save this key — it will not be shown again:" + echo " VLLM_API_KEY=${KEY}" + ;; + --env) + touch "${ENV_FILE}" + # Drop any existing VLLM_API_KEY line, then append the new one. + if grep -q '^VLLM_API_KEY=' "${ENV_FILE}"; then + tmp="$(mktemp)" + grep -v '^VLLM_API_KEY=' "${ENV_FILE}" > "${tmp}" + mv "${tmp}" "${ENV_FILE}" + fi + echo "VLLM_API_KEY=${KEY}" >> "${ENV_FILE}" + echo "Wrote VLLM_API_KEY to ${ENV_FILE}" + echo " VLLM_API_KEY=${KEY}" + ;; + "") + echo "${KEY}" + ;; + *) + echo "unknown flag: $1" >&2 + echo "usage: $0 [--create|--update|--env]" >&2 + exit 2 + ;; +esac diff --git a/modal-deploy/sunflower-grpo-inference-vllm/sunflower_14b_openai_vllm_modal.py b/modal-deploy/sunflower-grpo-inference-vllm/sunflower_14b_openai_vllm_modal.py new file mode 100644 index 00000000..1719c96e --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/sunflower_14b_openai_vllm_modal.py @@ -0,0 +1,175 @@ +""" +Modal deployment of Sunbird/Sunflower-14B as an OpenAI-compatible vLLM server. + +Serves the standard OpenAI REST surface: + - GET /v1/models + - POST /v1/chat/completions (supports stream=true for SSE) + - POST /v1/completions (supports stream=true for SSE) + - GET /health + +Deploy: + # 1. Create required secrets (one-time) + modal secret create huggingface-secret HF_TOKEN= + modal secret create vllm-api-key VLLM_API_KEY= # optional, see AUTH below + + # 2. Deploy + modal deploy sunflower_14b_openai_vllm_modal.py + +Test: + export BASE="https://--sunflower-14b-openai-serve.modal.run" + export KEY="" + + curl -s "$BASE/v1/chat/completions" \ + -H "Authorization: Bearer $KEY" \ + -H "content-type: application/json" \ + -d '{ + "model": "Sunbird/Sunflower-14B", + "messages": [{"role":"user","content":"Translate to luganda: Good morning"}], + "stream": false + }' | jq . + +Streaming (SSE): + curl -N "$BASE/v1/chat/completions" \ + -H "Authorization: Bearer $KEY" \ + -H "content-type: application/json" \ + -d '{ + "model": "Sunbird/Sunflower-14B", + "messages": [{"role":"user","content":"Tell me a short story"}], + "stream": true + }' + +Python client: + from openai import OpenAI + client = OpenAI(base_url=f"{BASE}/v1", api_key=KEY) + resp = client.chat.completions.create( + model="Sunbird/Sunflower-14B", + messages=[{"role": "user", "content": "Hello"}], + stream=True, + ) + for chunk in resp: + print(chunk.choices[0].delta.content or "", end="", flush=True) + +AUTH: + If the `vllm-api-key` secret exists and defines VLLM_API_KEY, the server + requires `Authorization: Bearer $VLLM_API_KEY` on every request. + To run without auth, remove the secret from the `secrets=` list below + (note: the endpoint will then be publicly reachable). +""" + +from __future__ import annotations + +import modal + +# --------------------------------------------------------------------------- +# Config +# --------------------------------------------------------------------------- +APP_NAME = "sunflower-14b-openai" +MODEL_NAME = "Sunbird/Sunflower-14B" +MODEL_REVISION = "main" +SERVED_MODEL_ALIAS = "sunflower-14b" # extra alias clients may send as `model=` + +VLLM_PORT = 8000 +MAX_MODEL_LEN = 4096 +GPU_SPEC = "L40S:1" # 48GB. Alternatives: "H100:1" (80GB), "A100-80GB:1" (80GB) + +# FAST_BOOT=True -> --enforce-eager (faster cold start, slower steady-state) +# FAST_BOOT=False -> --no-enforce-eager (slower cold start, CUDA graphs + torch.compile) +FAST_BOOT = True + +MINUTE = 60 + +# --------------------------------------------------------------------------- +# Image +# --------------------------------------------------------------------------- +vllm_image = ( + modal.Image.from_registry( + "nvidia/cuda:12.4.0-devel-ubuntu22.04", add_python="3.12" + ) + .entrypoint([]) + .pip_install( + "vllm==0.11.0", + "huggingface_hub[hf_transfer]>=0.34.0,<1.0", + "transformers>=4.55.2,<5.0", + "hf-transfer>=0.1.8", + ) + .env( + { + "HF_HUB_ENABLE_HF_TRANSFER": "1", + "VLLM_USE_V1": "1", + } + ) +) + +# --------------------------------------------------------------------------- +# Volumes for model + compilation caches (HF cache path + vLLM compile cache) +# --------------------------------------------------------------------------- +hf_cache_vol = modal.Volume.from_name( + "sunflower-14b-hf-cache", create_if_missing=True +) +vllm_cache_vol = modal.Volume.from_name( + "sunflower-14b-vllm-cache", create_if_missing=True +) + +hf_secret = modal.Secret.from_name("huggingface-secret") +api_key_secret = modal.Secret.from_name("vllm-api-key") + +app = modal.App(APP_NAME) + + +# --------------------------------------------------------------------------- +# OpenAI-compatible vLLM server +# --------------------------------------------------------------------------- +@app.function( + image=vllm_image, + gpu=GPU_SPEC, + volumes={ + "/root/.cache/huggingface": hf_cache_vol, + "/root/.cache/vllm": vllm_cache_vol, + }, + secrets=[hf_secret, api_key_secret], + scaledown_window=15 * MINUTE, + timeout=24 * 60 * MINUTE, + min_containers=0, +) +@modal.concurrent(max_inputs=32) +@modal.web_server(port=VLLM_PORT, startup_timeout=30 * MINUTE) +def serve() -> None: + """Boot vLLM's built-in OpenAI-compatible API server as a subprocess.""" + import os + import subprocess + + cmd = [ + "vllm", + "serve", + MODEL_NAME, + "--revision", + MODEL_REVISION, + "--served-model-name", + MODEL_NAME, + SERVED_MODEL_ALIAS, + "--host", + "0.0.0.0", + "--port", + str(VLLM_PORT), + "--tensor-parallel-size", + "1", + "--max-model-len", + str(MAX_MODEL_LEN), + "--dtype", + "bfloat16", + "--gpu-memory-utilization", + "0.90", + "--uvicorn-log-level", + "info", + ] + + # enforce-eager disables both Torch compilation and CUDA graph capture. + # Default is --no-enforce-eager. See --compilation-config for tighter control. + cmd.append("--enforce-eager" if FAST_BOOT else "--no-enforce-eager") + + api_key = os.environ.get("VLLM_API_KEY") + if api_key: + cmd.extend(["--api-key", api_key]) + + print("Launching:", " ".join(cmd)) + subprocess.Popen(cmd) diff --git a/modal-deploy/sunflower-grpo-inference-vllm/sunflower_grpo_combined_inference.ipynb b/modal-deploy/sunflower-grpo-inference-vllm/sunflower_grpo_combined_inference.ipynb new file mode 100644 index 00000000..6ff992bb --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/sunflower_grpo_combined_inference.ipynb @@ -0,0 +1,477 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b7f721e8", + "source": "# Sunflower 14B GRPO Combined — Inference Notebook\n\nThis notebook runs inference on the `jq/sunflower-14b-bs64-lr1e-4-250919` base model combined with the `jq/sunflower-14b-grpo-combined` LoRA adapter, using **Unsloth + vLLM** for fast generation. It demonstrates:\n\n1. Environment and dependency setup\n2. Loading the base model and LoRA adapter\n3. Running translation prompts across Ugandan languages (Luganda, Runyankole, Lusoga, Acholi, Ateso)\n4. Running general-knowledge and reasoning prompts\n\n## Setup / Dependency Installation\n\nThe commented commands below reinstall PyTorch, Transformers, vLLM, Unsloth and related CUDA 13.0 wheels. Uncomment and run once per fresh environment.", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f742ef7-1f77-43ec-8327-ef8edcc50f4f", + "metadata": {}, + "outputs": [], + "source": [ + "# !uv pip install --force-reinstall --no-cache-dir \\\n", + "# torchaudio torchvision torchcodec \\\n", + "# --index-url https://download.pytorch.org/whl/cu130\n", + "\n", + "# !uv pip install --force-reinstall transformers torch accelerate vllm unsloth huggingface_hub bitsandbytes \"xformers==0.0.33.post1\" --torch-backend=cu130\n", + "\n", + "# uv pip install --force-reinstall https://github.com/vllm-project/vllm/releases/download/v0.17.1/vllm-0.17.1+cu130-cp38-abi3-manylinux_2_35_x86_64.whl" + ] + }, + { + "cell_type": "markdown", + "id": "604a052e", + "source": "## Imports and Environment Flags\n\n- `FastLanguageModel` (Unsloth) loads the base model with vLLM fast-inference enabled.\n- `LLM` / `SamplingParams` / `LoRARequest` come from vLLM for generation and LoRA hot-swapping.\n- `UNSLOTH_VLLM_STANDBY=0` avoids a known crash when standby mode is enabled.\n- `FLASHINFER_DISABLE_VERSION_CHECK=1` skips a strict FlashInfer version check.", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ffe5ba6-f04f-4778-89a6-46f23e53be2e", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from unsloth import FastLanguageModel\n", + "import torch\n", + "from huggingface_hub import snapshot_download, login\n", + "from vllm import LLM, SamplingParams\n", + "from vllm.lora.request import LoRARequest\n", + "\n", + "os.environ[\"UNSLOTH_VLLM_STANDBY\"] = \"0\" # Causes crashes when set to 1\n", + "os.environ[\"FLASHINFER_DISABLE_VERSION_CHECK\"] =\"1\"" + ] + }, + { + "cell_type": "markdown", + "id": "c5f381f7", + "source": "## Authenticate with Hugging Face\n\nRequired because the base model and adapter repositories are gated. Prompts for an access token interactively.", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "id": "52a89ded-7763-42fc-b4cc-5cdb439aaaee", + "metadata": {}, + "outputs": [], + "source": [ + "login()" + ] + }, + { + "cell_type": "markdown", + "id": "40104335", + "source": "## Model Paths and System Prompt\n\n- `BASE_MODEL_PATH` — the SFT base checkpoint.\n- `GRPO_LORA_PATH` — the GRPO-trained LoRA adapter merged at inference time.\n- `SYSTEM_MESSAGE` — the Sunflower persona prompt prepended to every chat request.", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "id": "922da038-188f-459c-91c8-ad81fcc9843d", + "metadata": {}, + "outputs": [], + "source": [ + "BASE_MODEL_PATH = \"jq/sunflower-14b-bs64-lr1e-4-250919\"\n", + "GRPO_LORA_PATH = \"jq/sunflower-14b-grpo-combined\"\n", + "SYSTEM_MESSAGE = \"\"\"You are Sunflower, a helpful assistant made by Sunbird AI who understands all Ugandan languages. You specialise in accurate translations, \n", + "explanations, summaries and other language tasks.\"\"\"" + ] + }, + { + "cell_type": "markdown", + "id": "d91142f8", + "source": "## Download the GRPO LoRA Adapter\n\n`snapshot_download` pulls the full adapter repository (weights + `adapter_config.json`) to a local cache directory. The resulting path is passed to `LoRARequest` later.", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "id": "119d8bca-9947-443e-b23f-13ed32ff7f74", + "metadata": {}, + "outputs": [], + "source": [ + "model_lora_path = snapshot_download(repo_id=GRPO_LORA_PATH)" + ] + }, + { + "cell_type": "markdown", + "id": "df611416-2b0d-403f-bf19-ba0b5c9222fa", + "metadata": {}, + "source": [ + "## Inference with Base Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98dbac0c-b598-432e-affa-50562f74182c", + "metadata": {}, + "outputs": [], + "source": [ + "max_seq_length = 1024\n", + "lora_rank = 32 \n", + "\n", + "model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name = BASE_MODEL_PATH,\n", + " max_seq_length = max_seq_length,\n", + " load_in_4bit = False, # False for LoRA 16bit\n", + " fast_inference = True, # Enable vLLM fast inference\n", + " max_lora_rank = lora_rank,\n", + " gpu_memory_utilization = 0.9, # Reduce if out of memory\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "de5a01a6", + "source": "### Example instruction (English → Luganda translation)", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1931b6f-cdda-4a9e-8bff-101923131e37", + "metadata": {}, + "outputs": [], + "source": [ + "instruction = \"Translate to luganda: I am watching an Arsenal game right now\"" + ] + }, + { + "cell_type": "markdown", + "id": "6a14d565", + "source": "### Build the chat prompt and sampling configuration\n\nThe tokenizer's chat template formats the system + user messages into the model's expected prompt format. `SamplingParams` controls decoding: temperature 0.6, top-k 50, up to 1024 generated tokens.", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5ce346e8-efa3-4b29-9220-ccf67c0c6421", + "metadata": {}, + "outputs": [], + "source": [ + "messages = [\n", + " {\"role\": \"system\", \"content\": SYSTEM_MESSAGE},\n", + " {\"role\": \"user\", \"content\": instruction},\n", + "]\n", + "\n", + "prompt = tokenizer.apply_chat_template(\n", + " messages,\n", + " add_generation_prompt = True, # Must add for generation\n", + " tokenize = False,\n", + ")\n", + "sampling_params = SamplingParams(\n", + " temperature = 0.6,\n", + " top_k = 50,\n", + " max_tokens = 1024,\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "48b407d7", + "source": "### Run generation with the LoRA adapter attached\n\n`fast_generate` routes the request through vLLM. `LoRARequest` tells vLLM to dynamically load and apply the downloaded adapter for this call — the base weights stay untouched, so adapters can be swapped per request.", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "id": "185b8b93-182c-403b-84e1-d9aa8c222758", + "metadata": {}, + "outputs": [], + "source": [ + "output = model.fast_generate(\n", + " [prompt],\n", + " sampling_params = sampling_params,\n", + " lora_request = LoRARequest(\n", + " lora_name=\"adapter\", # any unique name/identifier\n", + " lora_int_id=1, # unique integer ID\n", + " lora_path=model_lora_path, # local dir with adapter_config.json etc.\n", + " ),\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "6da770f3", + "source": "### Inspect raw output and extract the generated text\n\n`output` is a list of vLLM `RequestOutput` objects; `output[0].outputs[0].text` is the decoded completion string.", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36cb2dfa-863d-4049-8cbf-dafc41d7e9b2", + "metadata": {}, + "outputs": [], + "source": [ + "output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7a5540a9-0bcf-4c06-8091-62dd3f334cfe", + "metadata": {}, + "outputs": [], + "source": [ + "response = output[0].outputs[0].text" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "063335e0-88d6-4683-a381-b2820b177abd", + "metadata": {}, + "outputs": [], + "source": [ + "response" + ] + }, + { + "cell_type": "markdown", + "id": "4210889e-b8bd-4c6d-b15c-15ef469e69a8", + "metadata": {}, + "source": [ + "## Inference with Lora Adapter using vllm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2b8f1b3-6d14-4136-85f6-66b98d758aa9", + "metadata": {}, + "outputs": [], + "source": "def generate_response(instruction, temperature=0.6):\n messages = [\n {\"role\": \"system\", \"content\": SYSTEM_MESSAGE},\n {\"role\": \"user\", \"content\": instruction},\n ]\n \n prompt = tokenizer.apply_chat_template(\n messages,\n add_generation_prompt = True, # Must add for generation\n tokenize = False,\n )\n sampling_params = SamplingParams(\n temperature = temperature,\n top_k = 50,\n max_tokens = 1024,\n )\n output = model.fast_generate(\n [prompt],\n sampling_params = sampling_params,\n lora_request = LoRARequest(\n lora_name=\"adapter\", # any unique name/identifier\n lora_int_id=1, # unique integer ID\n lora_path=model_lora_path, # local dir with adapter_config.json etc.\n ),\n )\n response = output[0].outputs[0].text\n return response" + }, + { + "cell_type": "markdown", + "id": "77032b1a", + "source": "### Helper: `generate_response(instruction)`\n\nWraps prompt formatting, sampling, and LoRA-enabled vLLM generation into a single reusable function used by the test cells below.\n\n### Translation tests across Ugandan languages\nThe following cells translate the same English sentence into Luganda, Runyankole, Lusoga, Acholi, and Ateso to spot-check cross-lingual quality.", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3c3d19c-872d-4603-99ef-875e54a50da5", + "metadata": {}, + "outputs": [], + "source": "instruction = \"Translate to luganda: The winner of Sunday's Carabao Cup Final will gain qualification to the Europa Conference League play-off round next season.\"\nres = generate_response(instruction, temperature=0.1)\nres" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c7dc723-fad2-41cd-a83d-fe76d4864e09", + "metadata": {}, + "outputs": [], + "source": "instruction = \"Translate to runyakole: The winner of Sunday's Carabao Cup Final will gain qualification to the Europa Conference League play-off round next season.\"\nres = generate_response(instruction, temperature=0.1)\nres" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b91958a-795b-4505-b03f-047ab942bbbe", + "metadata": {}, + "outputs": [], + "source": "instruction = \"Translate to lusoga: The winner of Sunday's Carabao Cup Final will gain qualification to the Europa Conference League play-off round next season.\"\nres = generate_response(instruction, temperature=0.3)\nres" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2a2638db-19ff-4a3f-8e74-0ddf9eeaf26c", + "metadata": {}, + "outputs": [], + "source": "instruction = \"Translate to acholi: The winner of Sunday's Carabao Cup Final will gain qualification to the Europa Conference League play-off round next season.\"\nres = generate_response(instruction, temperature=0.3)\nres" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4308643d-91ad-46c6-a575-4ad35dfd240a", + "metadata": {}, + "outputs": [], + "source": "instruction = \"Translate to ateso: The winner of Sunday's Carabao Cup Final will gain qualification to the Europa Conference League play-off round next season.\"\nres = generate_response(instruction, temperature=0.1)\nres" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0138b42-18ca-43b7-8375-d287a536bae0", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "422bf62a", + "source": "## General Knowledge Q&A\n\nNon-translation prompts to probe the model's open-domain knowledge and its grounding in Sunbird AI / Sunflower identity.", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ff4b8f1-4808-4890-b656-dfed91833709", + "metadata": {}, + "outputs": [], + "source": [ + "instruction = \"Who is Sunbird AI, what do they do?\"\n", + "res = generate_response(instruction)\n", + "res" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44756f66-925d-4a83-97b9-9b9fd3a62c29", + "metadata": {}, + "outputs": [], + "source": [ + "instruction = \"What is Sunflower\"\n", + "res = generate_response(instruction)\n", + "res" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e76dad01-5de6-43a1-b61e-d3ffd13a3e94", + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import display, Markdown" + ] + }, + { + "cell_type": "markdown", + "id": "8be83d6c", + "source": "### Pretty-print responses as Markdown\n\n`display(Markdown(res))` renders formatting (lists, bold, etc.) inline for easier review of longer answers.", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f86344e-c2f0-4bbe-a1c9-af5da027d057", + "metadata": {}, + "outputs": [], + "source": [ + "display(Markdown(res))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "299b8f3d-ffef-4d46-954b-e5cc9128cb9a", + "metadata": {}, + "outputs": [], + "source": [ + "instruction = \"Uganda yafuna ddi ebwetogoze?\"\n", + "res = generate_response(instruction)\n", + "display(Markdown(res))" + ] + }, + { + "cell_type": "markdown", + "id": "fd54ba17", + "source": "### Ugandan-language questions and long-form translations\n\nMixes Luganda-language questions (understanding + response in Luganda), knowledge cut-off probes, and longer English→Luganda passages to stress-test fluency on extended context.", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6250fb55-116b-422d-8c01-1f819b9d42cd", + "metadata": {}, + "outputs": [], + "source": [ + "instruction = \"Uganda yakabera neba pulezidenti bameka, era bebani?\"\n", + "res = generate_response(instruction)\n", + "display(Markdown(res))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e026d5ef-3e04-4188-bf1e-5eb8efd1c14e", + "metadata": {}, + "outputs": [], + "source": [ + "instruction = \"When is Luwum day is Uganda?\"\n", + "res = generate_response(instruction)\n", + "display(Markdown(res))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb2d3a83-8105-4db4-9e56-c0e7ccf83fa2", + "metadata": {}, + "outputs": [], + "source": "instruction = \"\"\"Translate to Luganda: The English Football League Cup, often referred to as the League Cup and officially known as the Carabao Cup for sponsorship reasons,\nis an annual knockout competition in men's domestic football in England\"\"\"\nres = generate_response(instruction, temperature=0.1)\ndisplay(Markdown(res))" + }, + { + "cell_type": "code", + "execution_count": null, + "id": "523c0857-05ec-4004-a039-a7788f7a7f5e", + "metadata": {}, + "outputs": [], + "source": [ + "instruction = \"\"\"What is the current weather now?\"\"\"\n", + "res = generate_response(instruction)\n", + "display(Markdown(res))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d503c0ff-d36f-425f-9e98-4bd7809828d5", + "metadata": {}, + "outputs": [], + "source": [ + "instruction = \"\"\"What was the result from the Caraboa cup final of Arsenal Vs Manchester City?\"\"\"\n", + "res = generate_response(instruction)\n", + "display(Markdown(res))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a58d722a-3612-4a44-a3d9-56fd10f9456b", + "metadata": {}, + "outputs": [], + "source": [ + "instruction = \"\"\"When is Arsenal playing Sporting Lisbon in the Champions league\"\"\"\n", + "res = generate_response(instruction)\n", + "display(Markdown(res))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "03fbfc13-ddeb-4982-b319-ca3b7fbe7edf", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python3 (main venv)", + "language": "python", + "name": "main" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/modal-deploy/sunflower-grpo-inference-vllm/sunflower_grpo_vllm_modal.py b/modal-deploy/sunflower-grpo-inference-vllm/sunflower_grpo_vllm_modal.py new file mode 100644 index 00000000..d73d34ac --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/sunflower_grpo_vllm_modal.py @@ -0,0 +1,280 @@ +""" +Modal deployment of Sunflower 14B GRPO Combined (base + LoRA adapter) using vLLM. + +Base model: jq/sunflower-14b-bs64-lr1e-4-250919 +LoRA adapter: jq/sunflower-14b-grpo-combined + +Endpoints exposed: + - POST /generate -> JSON {response: "..."} + - POST /generate_stream -> text/event-stream (SSE), token-by-token +Also exposes Modal class methods callable via `.remote()` from Python clients. + +Deploy: + modal run sunflower_grpo_vllm_modal.py::download_model # one-time weight cache + modal deploy sunflower_grpo_vllm_modal.py +""" + +from __future__ import annotations + +import json +import os + +import modal + +# --------------------------------------------------------------------------- +# Constants +# --------------------------------------------------------------------------- +APP_NAME = "sunflower-grpo-vllm" +BASE_MODEL_REPO = "jq/sunflower-14b-bs64-lr1e-4-250919" +LORA_REPO = "jq/sunflower-14b-grpo-combined" + +MODELS_DIR = "/models" +BASE_MODEL_DIR = f"{MODELS_DIR}/base" +LORA_DIR = f"{MODELS_DIR}/lora" + +MAX_MODEL_LEN = 1024 +MAX_LORA_RANK = 32 +GPU_TYPE = "A100-40GB" # alternatives: "A100-80GB", "L40S" (48GB), "H100" + +# FAST_BOOT=True -> enforce_eager=True (faster cold start, slower steady-state) +# FAST_BOOT=False -> enforce_eager=False (slower cold start, CUDA graphs + torch.compile) +FAST_BOOT = True + +SYSTEM_MESSAGE = ( + "You are Sunflower, a helpful assistant made by Sunbird AI who understands all " + "Ugandan languages. You specialise in accurate translations, explanations, " + "summaries and other language tasks." +) + +# --------------------------------------------------------------------------- +# Modal image, volume, secrets +# --------------------------------------------------------------------------- +vllm_image = ( + modal.Image.debian_slim(python_version="3.12") + .pip_install( + "vllm==0.11.0", + "huggingface_hub[hf_transfer]>=0.34.0,<1.0", + "transformers>=4.55.2,<5.0", + "fastapi[standard]==0.115.4", + ) + .env( + { + "HF_HUB_ENABLE_HF_TRANSFER": "1", + "VLLM_USE_V1": "1", + } + ) +) + +models_volume = modal.Volume.from_name( + "sunflower-grpo-models", create_if_missing=True +) +hf_secret = modal.Secret.from_name("huggingface-secret") + +app = modal.App(APP_NAME) + + +# --------------------------------------------------------------------------- +# One-time weight download into the volume +# --------------------------------------------------------------------------- +@app.function( + image=vllm_image, + volumes={MODELS_DIR: models_volume}, + secrets=[hf_secret], + timeout=60 * 60, +) +def download_model() -> None: + """Snapshot-download the base model and LoRA adapter into the persistent volume.""" + from huggingface_hub import snapshot_download + + os.makedirs(BASE_MODEL_DIR, exist_ok=True) + os.makedirs(LORA_DIR, exist_ok=True) + + print(f"Downloading base model {BASE_MODEL_REPO} -> {BASE_MODEL_DIR}") + snapshot_download( + repo_id=BASE_MODEL_REPO, + local_dir=BASE_MODEL_DIR, + token=os.environ.get("HF_TOKEN"), + ) + + print(f"Downloading LoRA adapter {LORA_REPO} -> {LORA_DIR}") + snapshot_download( + repo_id=LORA_REPO, + local_dir=LORA_DIR, + token=os.environ.get("HF_TOKEN"), + ) + + models_volume.commit() + print("Done. Weights cached in volume `sunflower-grpo-models`.") + + +# --------------------------------------------------------------------------- +# vLLM serving class +# --------------------------------------------------------------------------- +@app.cls( + image=vllm_image, + gpu=GPU_TYPE, + volumes={MODELS_DIR: models_volume}, + secrets=[hf_secret], + scaledown_window=300, + timeout=60 * 30, +) +@modal.concurrent(max_inputs=8) +class SunflowerVLLM: + @modal.enter() + def load(self) -> None: + from transformers import AutoTokenizer + from vllm.engine.arg_utils import AsyncEngineArgs + from vllm.v1.engine.async_llm import AsyncLLM + from vllm.lora.request import LoRARequest + + print("Loading vLLM AsyncLLM engine...") + engine_args = AsyncEngineArgs( + model=BASE_MODEL_DIR, + enable_lora=True, + max_lora_rank=MAX_LORA_RANK, + max_loras=1, + max_model_len=MAX_MODEL_LEN, + gpu_memory_utilization=0.90, + dtype="bfloat16", + enforce_eager=FAST_BOOT, + ) + self.engine = AsyncLLM.from_engine_args(engine_args) + self.tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_DIR) + self.lora_request = LoRARequest( + lora_name="sunflower-grpo", + lora_int_id=1, + lora_path=LORA_DIR, + ) + print("vLLM engine ready.") + + # -- helpers ---------------------------------------------------------- + def _build_prompt(self, instruction: str) -> str: + messages = [ + {"role": "system", "content": SYSTEM_MESSAGE}, + {"role": "user", "content": instruction}, + ] + return self.tokenizer.apply_chat_template( + messages, add_generation_prompt=True, tokenize=False + ) + + def _sampling(self, temperature: float, max_tokens: int): + from vllm import SamplingParams + + return SamplingParams( + temperature=temperature, + top_k=50, + max_tokens=max_tokens, + ) + + # -- non-streaming method -------------------------------------------- + @modal.method() + async def generate( + self, + instruction: str, + temperature: float = 0.6, + max_tokens: int = 1024, + ) -> str: + prompt = self._build_prompt(instruction) + sampling = self._sampling(temperature, max_tokens) + request_id = f"req-{os.urandom(4).hex()}" + final_text = "" + async for out in self.engine.generate( + prompt, sampling, request_id, lora_request=self.lora_request + ): + final_text = out.outputs[0].text + return final_text + + # -- streaming method (yields delta strings) ------------------------- + @modal.method() + async def generate_stream( + self, + instruction: str, + temperature: float = 0.6, + max_tokens: int = 1024, + ): + prompt = self._build_prompt(instruction) + sampling = self._sampling(temperature, max_tokens) + request_id = f"req-{os.urandom(4).hex()}" + previous = "" + async for out in self.engine.generate( + prompt, sampling, request_id, lora_request=self.lora_request + ): + text = out.outputs[0].text + delta = text[len(previous):] + previous = text + if delta: + yield delta + + +# --------------------------------------------------------------------------- +# HTTP endpoints (FastAPI via Modal) +# --------------------------------------------------------------------------- +web_image = vllm_image + + +@app.function(image=web_image, secrets=[hf_secret]) +@modal.asgi_app() +def web(): + from fastapi import Body, FastAPI + from fastapi.responses import StreamingResponse + + api = FastAPI(title="Sunflower GRPO vLLM") + + @api.post("/generate") + async def generate( + instruction: str = Body(...), + temperature: float = Body(0.6), + max_tokens: int = Body(1024), + ): + text = await SunflowerVLLM().generate.remote.aio( + instruction, temperature, max_tokens + ) + return {"response": text} + + @api.post("/generate_stream") + async def generate_stream( + instruction: str = Body(...), + temperature: float = Body(0.6), + max_tokens: int = Body(1024), + ): + async def event_source(): + async for delta in SunflowerVLLM().generate_stream.remote_gen.aio( + instruction, temperature, max_tokens + ): + yield f"data: {json.dumps({'delta': delta})}\n\n" + yield "data: [DONE]\n\n" + + return StreamingResponse(event_source(), media_type="text/event-stream") + + @api.get("/health") + def health(): + return {"status": "ok"} + + return api + + +# --------------------------------------------------------------------------- +# Local entrypoint: smoke-test parity with the notebook +# --------------------------------------------------------------------------- +@app.local_entrypoint() +def main(): + prompts = [ + ("Translate to luganda: I am watching an Arsenal game right now", 0.1), + ( + "Translate to runyakole: The winner of Sunday's Carabao Cup Final will " + "gain qualification to the Europa Conference League play-off round next season.", + 0.1, + ), + ("Who is Sunbird AI, what do they do?", 0.6), + ] + cls = SunflowerVLLM() + for instr, temp in prompts: + print(f"\n>>> {instr}") + print(cls.generate.remote(instr, temp)) + + print("\n--- streaming demo ---") + for delta in cls.generate_stream.remote_gen( + "Translate to luganda: Good morning, how are you today?", 0.2 + 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components; +@tailwind utilities; + +@layer base { + :root { + --background: 0 0% 100%; + --foreground: 240 10% 3.9%; + --card: 0 0% 100%; + --card-foreground: 240 10% 3.9%; + --muted: 240 4.8% 95.9%; + --muted-foreground: 240 3.8% 46.1%; + --border: 240 5.9% 90%; + --input: 0 0% 100%; + --ring: 20 95% 55%; + --accent: 20 95% 55%; + --accent-foreground: 0 0% 100%; + --destructive: 0 72.2% 50.6%; + --destructive-foreground: 0 0% 98%; + } + + .dark { + --background: 240 10% 3.9%; + --foreground: 0 0% 98%; + --card: 240 6% 10%; + --card-foreground: 0 0% 98%; + --muted: 240 3.7% 15.9%; + --muted-foreground: 240 5% 64.9%; + --border: 240 3.7% 15.9%; + --input: 240 10% 3.9%; + --ring: 20 95% 55%; + --accent: 20 95% 55%; + --accent-foreground: 0 0% 100%; + --destructive: 0 62.8% 50%; + --destructive-foreground: 0 0% 98%; + } + + html, body { + height: 100%; + } + + body { + background-color: hsl(var(--background)); + color: hsl(var(--foreground)); + } +} + +/* Custom scrollbar, theme-aware */ +::-webkit-scrollbar { + width: 10px; + height: 10px; +} +::-webkit-scrollbar-track { + background: hsl(var(--background)); +} +::-webkit-scrollbar-thumb { + background: hsl(var(--border)); + border-radius: 6px; +} +::-webkit-scrollbar-thumb:hover { + background: hsl(var(--muted-foreground) / 0.4); +} diff --git a/modal-deploy/sunflower-grpo-inference-vllm/web/app/layout.tsx b/modal-deploy/sunflower-grpo-inference-vllm/web/app/layout.tsx new file mode 100644 index 00000000..f3784e47 --- /dev/null +++ b/modal-deploy/sunflower-grpo-inference-vllm/web/app/layout.tsx @@ -0,0 +1,35 @@ +import "./globals.css"; +import type { Metadata } from "next"; +import { Inter } from "next/font/google"; +import { ThemeProvider } from "@/components/theme-provider"; + +const inter = Inter({ + subsets: ["latin"], + variable: "--font-sans", +}); + +export const metadata: Metadata = { + title: "Sunflower GRPO · Sunbird AI", + description: + "A/B comparison UI for the Sunflower GRPO (test) model against the production Sunbird AI API.", +}; + +// Applied in before hydration to avoid a flash of the wrong theme. +const NO_FLASH_SCRIPT = `(function(){try{var s=localStorage.getItem('sunflower-theme');var d=s==='dark'||(!s&&window.matchMedia('(prefers-color-scheme: dark)').matches);var r=document.documentElement;r.classList.toggle('dark',d);r.style.colorScheme=d?'dark':'light';}catch(e){}})();`; + +export default function RootLayout({ + children, +}: { + children: React.ReactNode; +}) { + return ( + + +