mirror of
https://github.com/allenai/olmocr.git
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275 lines
8.5 KiB
Bash
Executable File
275 lines
8.5 KiB
Bash
Executable File
#!/bin/bash
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set -e
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# Parse command line arguments
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MODEL_NAME="Qwen/Qwen2.5-VL-7B-Instruct"
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SKIP_DOCKER_BUILD=false
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PREEMPTIBLE=false
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MAX_TRAIN_SAMPLES=""
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MAX_EVAL_SAMPLES=""
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NUM_EPOCHS=1
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LEARNING_RATE="1e-6"
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BATCH_SIZE=1
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GRAD_ACCUM_STEPS=4
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USE_WANDB=false
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WANDB_PROJECT="olmocr-grpo"
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WANDB_RUN_NAME=""
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while [[ $# -gt 0 ]]; do
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case $1 in
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--model)
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MODEL_NAME="$2"
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shift 2
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;;
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--skip-docker-build)
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SKIP_DOCKER_BUILD=true
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shift
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;;
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--preemptible)
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PREEMPTIBLE=true
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shift
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;;
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--max-train-samples)
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MAX_TRAIN_SAMPLES="$2"
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shift 2
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;;
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--max-eval-samples)
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MAX_EVAL_SAMPLES="$2"
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shift 2
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;;
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--num-epochs)
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NUM_EPOCHS="$2"
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shift 2
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;;
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--learning-rate)
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LEARNING_RATE="$2"
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shift 2
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;;
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--batch-size)
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BATCH_SIZE="$2"
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shift 2
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;;
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--grad-accum-steps)
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GRAD_ACCUM_STEPS="$2"
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shift 2
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;;
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--use-wandb)
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USE_WANDB=true
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shift
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;;
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--wandb-project)
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WANDB_PROJECT="$2"
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shift 2
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;;
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--wandb-run-name)
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WANDB_RUN_NAME="$2"
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shift 2
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;;
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*)
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echo "Unknown option: $1"
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echo "Usage: $0 [options]"
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echo "Options:"
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echo " --model MODEL_NAME Model to use (default: Qwen/Qwen2.5-VL-7B-Instruct)"
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echo " --skip-docker-build Skip Docker build"
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echo " --preemptible Use preemptible instances"
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echo " --max-train-samples N Max training samples"
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echo " --max-eval-samples N Max evaluation samples"
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echo " --num-epochs N Number of training epochs (default: 1)"
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echo " --learning-rate LR Learning rate (default: 1e-6)"
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echo " --batch-size N Batch size per device (default: 1)"
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echo " --grad-accum-steps N Gradient accumulation steps (default: 4)"
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echo " --use-wandb Enable W&B logging"
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echo " --wandb-project NAME W&B project name"
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echo " --wandb-run-name NAME W&B run name"
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exit 1
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;;
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esac
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done
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echo "Model: $MODEL_NAME"
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echo "Preemptible: $PREEMPTIBLE"
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echo "Use W&B: $USE_WANDB"
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# Use conda environment Python if available, otherwise use system Python
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if [ -n "$CONDA_PREFIX" ]; then
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PYTHON="$CONDA_PREFIX/bin/python"
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echo "Using conda Python from: $CONDA_PREFIX"
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else
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PYTHON="python"
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echo "Warning: No conda environment detected, using system Python"
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fi
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# Get version from version.py
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VERSION=$($PYTHON -c 'import olmocr.version; print(olmocr.version.VERSION)')
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echo "OlmOCR version: $VERSION"
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# Get first 10 characters of git hash
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GIT_HASH=$(git rev-parse HEAD | cut -c1-10)
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echo "Git hash: $GIT_HASH"
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# Get current git branch name
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GIT_BRANCH=$(git rev-parse --abbrev-ref HEAD)
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echo "Git branch: $GIT_BRANCH"
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# Create full image tag
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IMAGE_TAG="olmocr-grpo-${VERSION}-${GIT_HASH}"
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echo "Building Docker image with tag: $IMAGE_TAG"
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# Build and push Docker image if not skipping
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if [ "$SKIP_DOCKER_BUILD" = false ]; then
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echo "Building Docker image..."
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docker build --platform linux/amd64 -f ./Dockerfile -t $IMAGE_TAG .
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# Push image to beaker
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echo "Trying to push image to Beaker..."
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if ! beaker image create --workspace ai2/oe-data-pdf --name $IMAGE_TAG $IMAGE_TAG 2>/dev/null; then
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echo "Warning: Beaker image with tag $IMAGE_TAG already exists. Using existing image."
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fi
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else
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echo "Skipping Docker build as requested"
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fi
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# Get Beaker username
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BEAKER_USER=$(beaker account whoami --format json | jq -r '.[0].name')
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echo "Beaker user: $BEAKER_USER"
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# Create Python script to run beaker experiment
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cat << 'EOF' > /tmp/run_grpo_experiment.py
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import sys
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from beaker import Beaker, ExperimentSpec, TaskSpec, TaskContext, ResultSpec, TaskResources, ImageSource, Priority, Constraints, EnvVar, DataMount
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# Get parameters from command line
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image_tag = sys.argv[1]
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beaker_user = sys.argv[2]
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git_branch = sys.argv[3]
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git_hash = sys.argv[4]
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model_name = sys.argv[5]
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preemptible = sys.argv[6] == "true"
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max_train_samples = sys.argv[7]
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max_eval_samples = sys.argv[8]
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num_epochs = sys.argv[9]
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learning_rate = sys.argv[10]
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batch_size = sys.argv[11]
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grad_accum_steps = sys.argv[12]
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use_wandb = sys.argv[13] == "true"
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wandb_project = sys.argv[14]
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wandb_run_name = sys.argv[15]
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# Initialize Beaker client
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b = Beaker.from_env(default_workspace="ai2/olmocr")
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# Build the training command
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commands = [
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# Install dependencies
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"pip install .[train]",
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"pip install trl wandb",
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"pip install transformers==4.55.2", # Updated for GRPO compatibility
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"pip install flash-attn==2.8.0.post2 --no-build-isolation",
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"pip install s5cmd",
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# Sync the bench data from S3
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"echo 'Syncing bench data from S3...'",
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"mkdir -p /data/olmOCR-bench",
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"s5cmd sync 's3://ai2-oe-data/jakep/olmocr/olmOCR-bench-snapshot-082225/*' /data/olmOCR-bench/",
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# Build GRPO training command
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"echo 'Starting GRPO training...'",
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]
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# Build the python command with all parameters
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grpo_cmd = [
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"python -m olmocr.train.grpo_train",
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"--train_bench_data_folder /data/olmOCR-bench/bench_data",
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"--eval_bench_data_folder /data/olmOCR-bench/bench_data", # Using same data for now
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f"--model_name {model_name}",
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"--output_dir /weka/oe-training-default/olmocr-grpo-checkpoints",
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f"--num_train_epochs {num_epochs}",
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f"--learning_rate {learning_rate}",
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f"--per_device_train_batch_size {batch_size}",
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f"--per_device_eval_batch_size {batch_size}",
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f"--gradient_accumulation_steps {grad_accum_steps}",
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]
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# Add optional parameters
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if max_train_samples:
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grpo_cmd.append(f"--max_train_samples {max_train_samples}")
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if max_eval_samples:
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grpo_cmd.append(f"--max_eval_samples {max_eval_samples}")
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if use_wandb:
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grpo_cmd.append("--use_wandb")
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grpo_cmd.append(f"--wandb_project {wandb_project}")
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if wandb_run_name:
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grpo_cmd.append(f"--wandb_run_name {wandb_run_name}")
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# Add the GRPO command to the commands list
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commands.append(" ".join(grpo_cmd))
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# Build task spec
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task_spec = TaskSpec(
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name="olmocr-grpo-training",
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image=ImageSource(beaker=f"{beaker_user}/{image_tag}"),
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command=[
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"bash", "-c",
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" && ".join(commands)
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],
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context=TaskContext(
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priority=Priority.normal,
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preemptible=preemptible,
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),
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resources=TaskResources(
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gpu_count=1,
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shared_memory="10GiB"
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),
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constraints=Constraints(cluster=["ai2/titan-cirrascale"]),
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result=ResultSpec(path="/noop-results"),
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env_vars=[
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EnvVar(name="LOG_FILTER_TYPE", value="local_rank0_only"),
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EnvVar(name="OMP_NUM_THREADS", value="8"),
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EnvVar(name="BEAKER_USER_ID", value=beaker_user),
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EnvVar(name="AWS_ACCESS_KEY_ID", secret="ALLENNLP_AWS_ACCESS_KEY_ID"),
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EnvVar(name="AWS_SECRET_ACCESS_KEY", secret="ALLENNLP_AWS_SECRET_ACCESS_KEY"),
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EnvVar(name="WANDB_API_KEY", secret="JAKE_WANDB_API_KEY"),
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],
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datasets=[
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DataMount.new(mount_path="/weka/oe-data-default", weka="oe-data-default"),
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DataMount.new(mount_path="/weka/oe-training-default", weka="oe-training-default"),
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]
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)
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# Create experiment spec
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experiment_spec = ExperimentSpec(
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description=f"OlmOCR GRPO Training - Model: {model_name}, Branch: {git_branch}, Commit: {git_hash}",
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budget="ai2/oe-base",
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tasks=[task_spec],
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)
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# Create the experiment
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experiment = b.experiment.create(spec=experiment_spec, workspace="ai2/olmocr")
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print(f"Created GRPO training experiment: {experiment.id}")
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print(f"View at: https://beaker.org/ex/{experiment.id}")
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EOF
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# Run the Python script to create the experiment
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echo "Creating Beaker GRPO experiment..."
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$PYTHON /tmp/run_grpo_experiment.py \
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"$IMAGE_TAG" \
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"$BEAKER_USER" \
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"$GIT_BRANCH" \
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"$GIT_HASH" \
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"$MODEL_NAME" \
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"$PREEMPTIBLE" \
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"$MAX_TRAIN_SAMPLES" \
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"$MAX_EVAL_SAMPLES" \
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"$NUM_EPOCHS" \
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"$LEARNING_RATE" \
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"$BATCH_SIZE" \
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"$GRAD_ACCUM_STEPS" \
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"$USE_WANDB" \
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"$WANDB_PROJECT" \
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"$WANDB_RUN_NAME"
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# Clean up temporary file
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rm /tmp/run_grpo_experiment.py
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echo "GRPO training experiment submitted successfully!" |