Commit e5f7998f authored by jameskrw's avatar jameskrw
Browse files

minor

parent 35a9064b

scripts/exps/representation/maniskill/free_think/env_config.yaml

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env1:

    env_name: primitive_skill 

    env_config:

        render_mode: vision

        prompt_format: free_think

        use_accuracy_reward: false

        env_id: "AlignTwoCube" # AlignTwoCube,PlaceTwoCube,PutAppleInDrawer,StackThreeCube

    train_size: 10000  

    test_size: 8

env2:

    env_name: primitive_skill 

    env_config:

        render_mode: vision

        prompt_format: free_think

        use_accuracy_reward: false

        env_id: "PlaceTwoCube" # AlignTwoCube,PlaceTwoCube,PutAppleInDrawer,StackThreeCube

    train_size: 10000  

    test_size: 8

env3:

    env_name: primitive_skill 

    env_config:

        render_mode: vision

        prompt_format: free_think

        use_accuracy_reward: false

        env_id: "PutAppleInDrawer" # AlignTwoCube,PlaceTwoCube,PutAppleInDrawer,StackThreeCube

    train_size: 10000  

    test_size: 8

env4:

    env_name: primitive_skill 

    env_config:

        render_mode: vision

        prompt_format: free_think

        use_accuracy_reward: false

        env_id: "StackThreeCube" # AlignTwoCube,PlaceTwoCube,PutAppleInDrawer,StackThreeCube

    train_size: 10000  

    test_size: 8

scripts/exps/representation/maniskill/free_think/run.sh

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set -x





export VLLM_ATTENTION_BACKEND=XFORMERS

export PYTHONHASHSEED=0



SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"



# run 

# python -m vagen.server.server server.port=5001

# in a tmux session first



# Extract experiment name from the path

# This will take the last 3 parts of the path: format/sokoban/free_think

EXPERIMENT_NAME=$(echo $SCRIPT_DIR | rev | cut -d'/' -f1-3 | rev | tr '/' '-')



echo "Experiment name: $EXPERIMENT_NAME"

# run 

# python -m vagen.server.server server.port=5001

# in a tmux session first

python -m vagen.env.create_dataset \

    --yaml_path "$SCRIPT_DIR/env_config.yaml" \

    --train_path "data/$EXPERIMENT_NAME/train.parquet" \

    --test_path "data/$EXPERIMENT_NAME/test.parquet" \



# max_trajectory_length = max_prompt_length + max_response_length



python3 -m vagen.trainer.main_ppo \

    algorithm.adv_estimator=masked_gae \

    algorithm.high_level_gamma=0.95 \

    data.train_files=data/$EXPERIMENT_NAME/train.parquet \

    data.val_files=data/$EXPERIMENT_NAME/test.parquet \

    data.train_batch_size=64 \

    data.val_batch_size=32 \

    data.max_prompt_length=1024 \

    data.max_response_length=150 \

    data.max_trajectory_length=3600 \

    data.image_key=images \

    data.truncation=error \

    actor_rollout_ref.model.path=Qwen/Qwen2.5-VL-3B-Instruct \

    actor_rollout_ref.actor.optim.lr=1e-6 \

    actor_rollout_ref.model.use_remove_padding=True \

    actor_rollout_ref.actor.ppo_mini_batch_size=32 \

    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \

    actor_rollout_ref.actor.use_kl_loss=False \

    actor_rollout_ref.actor.kl_loss_coef=0.001 \

    actor_rollout_ref.actor.kl_loss_type=mse \

    actor_rollout_ref.model.enable_gradient_checkpointing=True \

    actor_rollout_ref.actor.fsdp_config.param_offload=True \

    actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \

    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \

    actor_rollout_ref.rollout.tensor_model_parallel_size=4 \

    actor_rollout_ref.rollout.name=vllm \

    actor_rollout_ref.rollout.gpu_memory_utilization=0.3 \

    actor_rollout_ref.rollout.enable_chunked_prefill=False \

    actor_rollout_ref.rollout.enforce_eager=False \

    actor_rollout_ref.rollout.free_cache_engine=False \

    actor_rollout_ref.rollout.n=1 \

    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \

    actor_rollout_ref.ref.fsdp_config.param_offload=True \

    actor_rollout_ref.rollout.top_p=0.95 \

    actor_rollout_ref.rollout.temperature=0.7 \

    critic.optim.lr=1e-5 \

    critic.model.use_remove_padding=True \

    critic.model.path=Qwen/Qwen2.5-VL-3B-Instruct \

    critic.model.enable_gradient_checkpointing=True \

    critic.ppo_micro_batch_size_per_gpu=1 \

    critic.model.fsdp_config.param_offload=False \

    critic.model.fsdp_config.optimizer_offload=False \

    algorithm.kl_ctrl.kl_coef=0.001 \

    trainer.critic_warmup=0 \

    trainer.logger=['console','wandb'] \

    trainer.project_name='vagen_new' \

    trainer.experiment_name=$EXPERIMENT_NAME \

    trainer.n_gpus_per_node=4 \

    trainer.nnodes=1 \

    trainer.save_freq=90 \

    trainer.test_freq=20 \

    trainer.total_training_steps=200 \

    rollout_manager.max_turns=3 \

    rollout_manager.window_size=3 \

    rollout_manager.use_multi_turn_reward=False \

    rollout_manager.use_loss_mask=True \

    rollout_manager.use_gae_mask=True \

    trainer.val_before_train=True \

    trainer.val_generations_to_log_to_wandb=8 \

    rollout_manager.n_trajectory=2 \

    rollout_manager.use_service=True \

    rollout_manager.timeout=240 \

    rollout_manager.base_url="http://localhost:5001" \

    2>&1 | tee $EXPERIMENT_NAME.log

scripts/exps/representation/maniskill/free_think/run_tmux.sh

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#!/bin/bash

set -e  # Exit immediately if a command exits with a non-zero status



# Interactive input for port and CUDA devices

read -p "Enter port number (default: 5000): " PORT_INPUT

PORT=${PORT_INPUT:-5000}



read -p "Enter CUDA devices (default: 0,1,2,3): " CUDA_DEVICES

CUDA_DEVICES=${CUDA_DEVICES:-0,1,2,3}



# Get the directory of the script

SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"



# Extract experiment name from the path

# This will take the last 3 parts of the path: format/sokoban/free_think

EXPERIMENT_NAME=$(echo $SCRIPT_DIR | rev | cut -d'/' -f1-3 | rev | tr '/' '-')

echo "Experiment name: $EXPERIMENT_NAME"



# Find available session names

find_available_session() {

  local base_name=$1

  local count=0

  

  while tmux has-session -t "${base_name}${count}" 2>/dev/null; do

    count=$((count+1))

  done

  

  echo "${base_name}${count}"

}



# Create session names

SERVER_SESSION=$(find_available_session "server")

TRAIN_SESSION=$(find_available_session "train")



echo "Creating tmux sessions: $SERVER_SESSION and $TRAIN_SESSION"

echo "Using port: $PORT"

echo "Using CUDA devices: $CUDA_DEVICES"



# Create directories if they don't exist

mkdir -p "data/$EXPERIMENT_NAME"



# Create server session

tmux new-session -d -s "$SERVER_SESSION"

# Configure server session with conda and environment variables

tmux send-keys -t "$SERVER_SESSION" "conda activate vagen" C-m

tmux send-keys -t "$SERVER_SESSION" "export CUDA_VISIBLE_DEVICES=$CUDA_DEVICES" C-m

tmux send-keys -t "$SERVER_SESSION" "export VLLM_ATTENTION_BACKEND=XFORMERS" C-m

tmux send-keys -t "$SERVER_SESSION" "export PYTHONHASHSEED=0" C-m

# Start the server

tmux send-keys -t "$SERVER_SESSION" "python -m vagen.server.server server.port=$PORT" C-m



# Wait for server to start

echo "Waiting for server to start on port $PORT..."

sleep 10  # Adjust as needed



# Create training session

tmux new-session -d -s "$TRAIN_SESSION"

# Configure training session with conda and environment variables

tmux send-keys -t "$TRAIN_SESSION" "cd $SCRIPT_DIR" C-m

tmux send-keys -t "$TRAIN_SESSION" "conda activate vagen" C-m

tmux send-keys -t "$TRAIN_SESSION" "export CUDA_VISIBLE_DEVICES=$CUDA_DEVICES" C-m

tmux send-keys -t "$TRAIN_SESSION" "export VLLM_ATTENTION_BACKEND=XFORMERS" C-m

tmux send-keys -t "$TRAIN_SESSION" "export PYTHONHASHSEED=0" C-m



# First create the dataset

tmux send-keys -t "$TRAIN_SESSION" "python -m vagen.env.create_dataset \\

    --yaml_path \"$SCRIPT_DIR/env_config.yaml\" \\

    --train_path \"data/$EXPERIMENT_NAME/train.parquet\" \\

    --test_path \"data/$EXPERIMENT_NAME/test.parquet\"" C-m



# Then start the training

tmux send-keys -t "$TRAIN_SESSION" "python3 -m vagen.trainer.main_ppo \\

    algorithm.adv_estimator=masked_gae \\

    algorithm.high_level_gamma=0.95 \\

    data.train_files=data/$EXPERIMENT_NAME/train.parquet \\

    data.val_files=data/$EXPERIMENT_NAME/test.parquet \\

    data.train_batch_size=64 \\

    data.val_batch_size=32 \\

    data.max_prompt_length=1024 \\

    data.max_response_length=150 \\

    data.max_trajectory_length=3600 \\

    data.image_key=images \\

    data.truncation=error \\

    actor_rollout_ref.model.path=Qwen/Qwen2.5-VL-3B-Instruct \\

    actor_rollout_ref.actor.optim.lr=1e-6 \\

    actor_rollout_ref.model.use_remove_padding=True \\

    actor_rollout_ref.actor.ppo_mini_batch_size=32 \\

    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \\

    actor_rollout_ref.actor.use_kl_loss=False \\

    actor_rollout_ref.actor.kl_loss_coef=0.001 \\

    actor_rollout_ref.actor.kl_loss_type=mse \\

    actor_rollout_ref.model.enable_gradient_checkpointing=True \\

    actor_rollout_ref.actor.fsdp_config.param_offload=True \\

    actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \\

    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \\

    actor_rollout_ref.rollout.tensor_model_parallel_size=4 \\

    actor_rollout_ref.rollout.name=vllm \\

    actor_rollout_ref.rollout.gpu_memory_utilization=0.3 \\

    actor_rollout_ref.rollout.enable_chunked_prefill=False \\

    actor_rollout_ref.rollout.enforce_eager=False \\

    actor_rollout_ref.rollout.free_cache_engine=False \\

    actor_rollout_ref.rollout.n=1 \\

    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \\

    actor_rollout_ref.ref.fsdp_config.param_offload=True \\

    actor_rollout_ref.rollout.top_p=0.95 \\

    actor_rollout_ref.rollout.temperature=0.7 \\

    critic.optim.lr=1e-5 \\

    critic.model.use_remove_padding=True \\

    critic.model.path=Qwen/Qwen2.5-VL-3B-Instruct \\

    critic.model.enable_gradient_checkpointing=True \\

    critic.ppo_micro_batch_size_per_gpu=1 \\

    critic.model.fsdp_config.param_offload=False \\

    critic.model.fsdp_config.optimizer_offload=False \\

    algorithm.kl_ctrl.kl_coef=0.001 \\

    trainer.critic_warmup=0 \\

    trainer.logger=['console','wandb'] \\

    trainer.project_name='vagen_new' \\

    trainer.experiment_name=$EXPERIMENT_NAME \\

    trainer.n_gpus_per_node=4 \\

    trainer.nnodes=1 \\

    trainer.save_freq=90 \\

    trainer.test_freq=20 \\

    trainer.total_training_steps=200 \\

    rollout_manager.max_turns=3 \\

    rollout_manager.window_size=3 \\

    rollout_manager.use_multi_turn_reward=False \\

    rollout_manager.use_loss_mask=True \\

    rollout_manager.use_gae_mask=True \\

    trainer.val_before_train=True \\

    trainer.val_generations_to_log_to_wandb=8 \\

    rollout_manager.n_trajectory=2 \\

    rollout_manager.use_service=True \\

    rollout_manager.timeout=240 \\

    rollout_manager.base_url=\"http://localhost:$PORT\" \\

    2>&1 | tee $EXPERIMENT_NAME.log" C-m



echo "-----------------------------------------"

echo "Configuration Summary:"

echo "Port: $PORT"

echo "CUDA Devices: $CUDA_DEVICES"

echo "Server Session: $SERVER_SESSION"

echo "Training Session: $TRAIN_SESSION"

echo "-----------------------------------------"

echo "To attach to server session: tmux attach-session -t $SERVER_SESSION"

echo "To attach to training session: tmux attach-session -t $TRAIN_SESSION"

echo "NOTE: The sessions will remain active. To detach from a session use Ctrl+B followed by D"
 
 No newline at end of file

scripts/exps/representation/maniskill/grounding/env_config.yaml

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env1:

    env_name: primitive_skill 

    env_config:

        render_mode: vision

        prompt_format: grounding

        use_accuracy_reward: false

        env_id: "AlignTwoCube" # AlignTwoCube,PlaceTwoCube,PutAppleInDrawer,StackThreeCube

    train_size: 10000  

    test_size: 8

env2:

    env_name: primitive_skill 

    env_config:

        render_mode: vision

        prompt_format: grounding

        use_accuracy_reward: false

        env_id: "PlaceTwoCube" # AlignTwoCube,PlaceTwoCube,PutAppleInDrawer,StackThreeCube

    train_size: 10000  

    test_size: 8

env3:

    env_name: primitive_skill 

    env_config:

        render_mode: vision

        prompt_format: grounding

        use_accuracy_reward: false

        env_id: "PutAppleInDrawer" # AlignTwoCube,PlaceTwoCube,PutAppleInDrawer,StackThreeCube

    train_size: 10000  

    test_size: 8

env4:

    env_name: primitive_skill 

    env_config:

        render_mode: vision

        prompt_format: grounding

        use_accuracy_reward: false

        env_id: "StackThreeCube" # AlignTwoCube,PlaceTwoCube,PutAppleInDrawer,StackThreeCube

    train_size: 10000  

    test_size: 8

scripts/exps/representation/maniskill/grounding/run.sh

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set -x





export VLLM_ATTENTION_BACKEND=XFORMERS

export PYTHONHASHSEED=0



SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"



# run 

# python -m vagen.server.server server.port=5001

# in a tmux session first



# Extract experiment name from the path

# This will take the last 3 parts of the path: format/sokoban/free_think

EXPERIMENT_NAME=$(echo $SCRIPT_DIR | rev | cut -d'/' -f1-3 | rev | tr '/' '-')



echo "Experiment name: $EXPERIMENT_NAME"

# run 

# python -m vagen.server.server server.port=5001

# in a tmux session first

python -m vagen.env.create_dataset \

    --yaml_path "$SCRIPT_DIR/env_config.yaml" \

    --train_path "data/$EXPERIMENT_NAME/train.parquet" \

    --test_path "data/$EXPERIMENT_NAME/test.parquet" \



# max_trajectory_length = max_prompt_length + max_response_length



python3 -m vagen.trainer.main_ppo \

    algorithm.adv_estimator=masked_gae \

    algorithm.high_level_gamma=0.95 \

    data.train_files=data/$EXPERIMENT_NAME/train.parquet \

    data.val_files=data/$EXPERIMENT_NAME/test.parquet \

    data.train_batch_size=64 \

    data.val_batch_size=32 \

    data.max_prompt_length=1024 \

    data.max_response_length=150 \

    data.max_trajectory_length=3600 \

    data.image_key=images \

    data.truncation=error \

    actor_rollout_ref.model.path=Qwen/Qwen2.5-VL-3B-Instruct \

    actor_rollout_ref.actor.optim.lr=1e-6 \

    actor_rollout_ref.model.use_remove_padding=True \

    actor_rollout_ref.actor.ppo_mini_batch_size=32 \

    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=1 \

    actor_rollout_ref.actor.use_kl_loss=False \

    actor_rollout_ref.actor.kl_loss_coef=0.001 \

    actor_rollout_ref.actor.kl_loss_type=mse \

    actor_rollout_ref.model.enable_gradient_checkpointing=True \

    actor_rollout_ref.actor.fsdp_config.param_offload=True \

    actor_rollout_ref.actor.fsdp_config.optimizer_offload=True \

    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \

    actor_rollout_ref.rollout.tensor_model_parallel_size=4 \

    actor_rollout_ref.rollout.name=vllm \

    actor_rollout_ref.rollout.gpu_memory_utilization=0.3 \

    actor_rollout_ref.rollout.enable_chunked_prefill=False \

    actor_rollout_ref.rollout.enforce_eager=False \

    actor_rollout_ref.rollout.free_cache_engine=False \

    actor_rollout_ref.rollout.n=1 \

    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=1 \

    actor_rollout_ref.ref.fsdp_config.param_offload=True \

    actor_rollout_ref.rollout.top_p=0.95 \

    actor_rollout_ref.rollout.temperature=0.7 \

    critic.optim.lr=1e-5 \

    critic.model.use_remove_padding=True \

    critic.model.path=Qwen/Qwen2.5-VL-3B-Instruct \

    critic.model.enable_gradient_checkpointing=True \

    critic.ppo_micro_batch_size_per_gpu=1 \

    critic.model.fsdp_config.param_offload=False \

    critic.model.fsdp_config.optimizer_offload=False \

    algorithm.kl_ctrl.kl_coef=0.001 \

    trainer.critic_warmup=0 \

    trainer.logger=['console','wandb'] \

    trainer.project_name='vagen_new' \

    trainer.experiment_name=$EXPERIMENT_NAME \

    trainer.n_gpus_per_node=4 \

    trainer.nnodes=1 \

    trainer.save_freq=90 \

    trainer.test_freq=20 \

    trainer.total_training_steps=200 \

    rollout_manager.max_turns=3 \

    rollout_manager.window_size=3 \

    rollout_manager.use_multi_turn_reward=False \

    rollout_manager.use_loss_mask=True \

    rollout_manager.use_gae_mask=True \

    trainer.val_before_train=True \

    trainer.val_generations_to_log_to_wandb=8 \

    rollout_manager.n_trajectory=2 \

    rollout_manager.use_service=True \

    rollout_manager.timeout=240 \

    rollout_manager.base_url="http://localhost:5001" \

    2>&1 | tee $EXPERIMENT_NAME.log

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