feat: Add VLM support

dev/math-vista/math-vista.ipynb

@@ -0,0 +1,207 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46a6ad6d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96d51078",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%html\n",
+    "<style>\n",
+    ".cell-output-ipywidget-background {\n",
+    "    background-color: transparent !important;\n",
+    "}\n",
+    ":root {\n",
+    "    --jp-widgets-color: var(--vscode-editor-foreground);\n",
+    "    --jp-widgets-font-size: var(--vscode-editor-font-size);\n",
+    "}  \n",
+    "</style>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7dd70e04",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import polars as pl\n",
+    "\n",
+    "splits = {\n",
+    "    \"testmini\": \"data/testmini-00000-of-00001-725687bf7a18d64b.parquet\",\n",
+    "    \"test\": \"data/test-*.parquet\",\n",
+    "}\n",
+    "df = pl.read_parquet(\"hf://datasets/AI4Math/MathVista/\" + splits[\"testmini\"]).sample(\n",
+    "    fraction=1.0, shuffle=True, seed=42\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81e02b97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Iterator, TypedDict, cast\n",
+    "\n",
+    "\n",
+    "class DecodedImage(TypedDict):\n",
+    "    bytes: bytes\n",
+    "\n",
+    "\n",
+    "class Scenario(TypedDict):\n",
+    "    pid: int\n",
+    "    question: str\n",
+    "    answer: str\n",
+    "    image: str\n",
+    "    decoded_image: DecodedImage\n",
+    "\n",
+    "\n",
+    "val_scenarios = cast(list[Scenario], df.head(64).to_dicts())\n",
+    "train_scenarios_iter = cast(Iterator[Scenario], df.tail(-64).iter_rows(named=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9287d8fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "\n",
+    "import art\n",
+    "from art.local import LocalBackend\n",
+    "\n",
+    "model = art.TrainableModel(\n",
+    "    name=\"002\",\n",
+    "    project=\"math-vista\",\n",
+    "    base_model=\"Qwen/Qwen2.5-VL-7B-Instruct\",\n",
+    ")\n",
+    "backend = LocalBackend()\n",
+    "await model.register(backend)\n",
+    "client = model.openai_client()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c92b4b11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "async def rollout(scenario: Scenario) -> art.Trajectory:\n",
+    "    image_path = f\"/tmp/{scenario['image']}\"\n",
+    "\n",
+    "    import os\n",
+    "\n",
+    "    os.makedirs(os.path.dirname(image_path), exist_ok=True)\n",
+    "\n",
+    "    with open(image_path, \"wb\") as f:\n",
+    "        f.write(scenario[\"decoded_image\"][\"bytes\"])\n",
+    "\n",
+    "    trajectory = art.Trajectory(messages_and_choices=[], reward=0.0)\n",
+    "    trajectory.messages_and_choices = [\n",
+    "        {\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": [\n",
+    "                {\n",
+    "                    \"type\": \"text\",\n",
+    "                    \"text\": scenario[\"question\"]\n",
+    "                    + \"\\n\\nNote: Provide your answer in a LaTeX box.\",\n",
+    "                },\n",
+    "                {\"type\": \"image_url\", \"image_url\": {\"url\": f\"file://{image_path}\"}},\n",
+    "            ],\n",
+    "        }\n",
+    "    ]\n",
+    "    chat_completion = await client.chat.completions.create(\n",
+    "        model=model.name, messages=trajectory.messages()\n",
+    "    )\n",
+    "    choice = chat_completion.choices[0]\n",
+    "    trajectory.messages_and_choices.append(choice)\n",
+    "    content = choice.message.content\n",
+    "    assert content is not None\n",
+    "    if matches := list(re.finditer(r\"\\\\boxed\\{(.*?)\\}\", content, re.DOTALL)):\n",
+    "        match = matches[-1]\n",
+    "        answer = match.group(1)\n",
+    "        if answer.lower() == scenario[\"answer\"].lower():\n",
+    "            trajectory.reward = 1.0\n",
+    "    return trajectory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "359e530d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import asyncio\n",
+    "import itertools\n",
+    "\n",
+    "SCENARIOS_PER_STEP = 8\n",
+    "TRAJECTORY_GROUP_SIZE = 8\n",
+    "start = await model.get_step()\n",
+    "train_scenarios_iter = itertools.cycle(train_scenarios_iter)\n",
+    "for _ in range(start * SCENARIOS_PER_STEP):\n",
+    "    next(train_scenarios_iter)\n",
+    "\n",
+    "for i in range(start, 1000):\n",
+    "    train_scenarios = [next(train_scenarios_iter) for _ in range(SCENARIOS_PER_STEP)]\n",
+    "    val_trajectories, train_trajectory_groups = await asyncio.gather(\n",
+    "        art.gather_trajectories(\n",
+    "            (rollout(scenario) for scenario in val_scenarios),\n",
+    "            pbar_desc=\"gather(val)\",\n",
+    "            max_exceptions=32,\n",
+    "        ),\n",
+    "        art.gather_trajectory_groups(\n",
+    "            (\n",
+    "                art.TrajectoryGroup(\n",
+    "                    rollout(scenario) for _ in range(TRAJECTORY_GROUP_SIZE)\n",
+    "                )\n",
+    "                for scenario in train_scenarios\n",
+    "            ),\n",
+    "            pbar_desc=\"gather(train)\",\n",
+    "            max_exceptions=32,\n",
+    "        ),\n",
+    "    )\n",
+    "    await model.log(val_trajectories)\n",
+    "    await model.train(train_trajectory_groups)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

dev/math-vista/math-vista.py

@@ -0,0 +1,136 @@
+import argparse
+import asyncio
+import itertools
+import os
+import re
+from typing import Iterator, TypedDict, cast
+
+import polars as pl
+
+import art
+from art.local import LocalBackend
+
+
+class DecodedImage(TypedDict):
+    bytes: bytes
+
+
+class Scenario(TypedDict):
+    pid: int
+    question: str
+    answer: str
+    image: str
+    decoded_image: DecodedImage
+
+
+async def main(model_name: str, steps: int) -> None:
+    # Load and shuffle the dataset
+    df = pl.read_parquet(
+        "hf://datasets/AI4Math/MathVista/data/testmini-00000-of-00001-725687bf7a18d64b.parquet"
+    ).sample(fraction=1.0, shuffle=True, seed=42)
+
+    val_scenarios = cast(list[Scenario], df.head(64).to_dicts())
+    train_scenarios_iter = cast(Iterator[Scenario], df.tail(-64).iter_rows(named=True))
+
+    # Initialize trainable model and backend
+    model = art.TrainableModel(
+        name=model_name,
+        project="math-vista",
+        base_model="Qwen/Qwen2.5-VL-7B-Instruct",
+    )
+
+    async def rollout(scenario: Scenario) -> art.Trajectory:
+        image_path = f"/tmp/{scenario['image']}"
+        os.makedirs(os.path.dirname(image_path), exist_ok=True)
+        with open(image_path, "wb") as f:
+            f.write(scenario["decoded_image"]["bytes"])
+
+        trajectory = art.Trajectory(messages_and_choices=[], reward=0.0)
+        trajectory.messages_and_choices = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "text",
+                        "text": scenario["question"]
+                        + "\n\nNote: Provide your answer in a LaTeX box.",
+                    },
+                    {"type": "image_url", "image_url": {"url": f"file://{image_path}"}},
+                ],
+            }
+        ]
+
+        chat_completion = await client.chat.completions.create(
+            model=model.name, messages=trajectory.messages()
+        )
+        choice = chat_completion.choices[0]
+        trajectory.messages_and_choices.append(choice)
+        content = choice.message.content
+        assert content is not None
+
+        if matches := list(re.finditer(r"\\boxed\{(.*?)\}", content, re.DOTALL)):
+            match = matches[-1]
+            answer = match.group(1)
+            if answer.lower() == scenario["answer"].lower():
+                trajectory.reward = 1.0
+        return trajectory
+
+    SCENARIOS_PER_STEP = 8
+    TRAJECTORY_GROUP_SIZE = 8
+
+    with LocalBackend() as backend:
+        await model.register(backend)
+        client = model.openai_client()
+
+        start = await model.get_step()
+        train_scenarios_iter = itertools.cycle(train_scenarios_iter)
+        for _ in range(start * SCENARIOS_PER_STEP):
+            next(train_scenarios_iter)
+
+        # Training loop
+        for _ in range(start, steps):
+            train_scenarios = [
+                next(train_scenarios_iter) for _ in range(SCENARIOS_PER_STEP)
+            ]
+            val_trajectories, train_trajectory_groups = await asyncio.gather(
+                art.gather_trajectories(
+                    (rollout(scenario) for scenario in val_scenarios),
+                    pbar_desc="gather(val)",
+                    max_exceptions=32,
+                ),
+                art.gather_trajectory_groups(
+                    (
+                        art.TrajectoryGroup(
+                            rollout(scenario) for _ in range(TRAJECTORY_GROUP_SIZE)
+                        )
+                        for scenario in train_scenarios
+                    ),
+                    pbar_desc="gather(train)",
+                    max_exceptions=32,
+                ),
+            )
+            await model.log(val_trajectories)
+            await model.train(train_trajectory_groups)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Minimal MathVista trainer script")
+    parser.add_argument(
+        "-n",
+        "--name",
+        required=True,
+        help="Run/model name to use for the TrainableModel",
+    )
+    parser.add_argument(
+        "-s",
+        "--steps",
+        type=int,
+        default=1000,
+        help="Number of training steps to run",
+    )
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    asyncio.run(main(args.name, args.steps))