atropos/example_trainer/README.md

GRPO Example Trainer

This directory contains an example script (grpo.py) demonstrating how to integrate a custom training loop with the Atropos API for reinforcement learning using the GRPO (Group Relative Policy Optimization) algorithm.

Training Modes

The trainer supports three weight synchronization modes:

Mode	Description	Sync Latency	Best For
Legacy (none)	Save checkpoints, restart vLLM	~30-60 seconds	Simple setups, debugging
Shared vLLM (shared_vllm)	Direct shared memory updates	~0 ms	Production, maximum throughput
LoRA (lora_only)	Train adapters, hot-swap	~1-5 seconds	Memory-constrained, fast iteration

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Quick Start with GSM8k

Prerequisites

# Install dependencies
pip install -r example_trainer/requirements.txt

# Install GSM8k environment dependencies
pip install datasets latex2sympy2_extended math_verify

Architecture Overview

┌─────────────────────────────────────────────────────────────────┐
│                         Training Setup                          │
│                                                                 │
│  ┌─────────────┐    ┌─────────────┐    ┌─────────────────────┐ │
│  │ GSM8k Env   │───▶│ Atropos API │◀───│ GRPO Trainer        │ │
│  │ (problems)  │    │ (batching)  │    │ (optimization)      │ │
│  └─────────────┘    └─────────────┘    └─────────────────────┘ │
│         │                                        │              │
│         │                                        │              │
│         ▼                                        ▼              │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │              vLLM Inference Server                       │   │
│  │         (generates rollouts for scoring)                 │   │
│  └─────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────┘

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Mode 1: Legacy (Checkpoint + Restart)

This is the simplest mode. The trainer periodically saves checkpoints and restarts vLLM.

Step-by-Step Guide

Terminal 1: Start the Atropos API

cd atropos
run-api

Terminal 2: Start the GSM8k Environment

cd atropos
python environments/gsm8k_server.py serve --slurm False

Terminal 3: Start the GRPO Trainer

cd atropos
python example_trainer/grpo.py \
  --model-name Qwen/Qwen2.5-3B-Instruct \
  --weight-bridge-mode none \
  --training-steps 100 \
  --vllm-restart-interval 10 \
  --batch-size 2 \
  --gradient-accumulation-steps 16 \
  --lr 1e-5 \
  --use-wandb \
  --wandb-project gsm8k-grpo

What Happens

1. Trainer loads Qwen/Qwen2.5-3B-Instruct into GPU memory
2. Trainer launches vLLM server on port 9001
3. GSM8k env sends problems → vLLM generates solutions → scores sent to API
4. Trainer fetches scored batches from API, computes GRPO loss, updates weights
5. Every 10 steps: save checkpoint → kill vLLM → restart vLLM with new weights
6. Repeat until done

Pros & Cons

• Simple, works out of the box
• Easy to debug

• 30-60 second sync latency per restart
• 2x GPU memory (trainer + vLLM both load model)

---

Mode 2: Shared vLLM Bridge (In-Place Updates)

This mode shares GPU tensors between trainer and vLLM. Updates happen instantly.

Step-by-Step Guide

Terminal 1: Start the Atropos API

cd atropos
run-api

Terminal 2: Set up environment variables and start vLLM with bridge support

cd atropos
export LOGDIR=/tmp/atropos_bridge
export NUM_INFERENCE_NODES=0  # Single-node local mode
export MASTER_ADDR=localhost
export MASTER_PORT=26756

mkdir -p $LOGDIR

# Start the custom vLLM server with bridge endpoints
python example_trainer/vllm_api_server.py \
  --model Qwen/Qwen2.5-3B-Instruct \
  --port 9001 \
  --gpu-memory-utilization 0.45

Terminal 3: Start the GSM8k Environment

cd atropos
python environments/gsm8k_server.py serve --slurm False

Terminal 4: Start the GRPO Trainer in shared mode

cd atropos
export LOGDIR=/tmp/atropos_bridge
export NUM_INFERENCE_NODES=0
export MASTER_ADDR=localhost
export MASTER_PORT=26756

python example_trainer/grpo.py \
  --model-name Qwen/Qwen2.5-3B-Instruct \
  --weight-bridge-mode shared_vllm \
  --trainer-rank 0 \
  --world-size 1 \
  --num-inference-nodes 0 \
  --training-steps 100 \
  --batch-size 2 \
  --gradient-accumulation-steps 16 \
  --lr 1e-5 \
  --use-wandb \
  --wandb-project gsm8k-grpo-shared

What Happens (Local Mode - num_inference_nodes=0)

1. vLLM server starts on port 9001
2. Trainer initializes bridge in LOCAL MODE (HTTP-based, no NCCL)
3. Trainer loads its own model copy and trains normally
4. After each optimizer.step():
   • bridge.notify_update() sends HTTP POST to vLLM
   • Periodic checkpoint saves sync weights to disk
5. Much simpler than distributed mode!

What Happens (Distributed Mode - num_inference_nodes>0)

1. vLLM server starts, writes parameter mapping to $LOGDIR/vllm_bridge_config.json
2. Trainer reads mapping, joins NCCL process group with vLLM
3. Trainer's model parameters point to vLLM's GPU tensors (shared memory)
4. Training loop:
   • Forward pass uses shared weights
   • optimizer.step() modifies shared tensors in-place
   • bridge.notify_update() broadcasts via Gloo
   • vLLM immediately uses new weights for next inference
5. No restarts needed!

Environment Variables

Variable	Description	Example
LOGDIR	Directory for bridge coordination files	/tmp/atropos_bridge
NUM_INFERENCE_NODES	Number of vLLM nodes (0 = local)	0
MASTER_ADDR	Rendezvous address	localhost
MASTER_PORT	Rendezvous port	26756

Pros & Cons

• ~0ms sync latency (instant updates)
• 1x GPU memory (shared tensors)
• Maximum training throughput

• More complex setup
• Requires compatible vLLM version

---

Mode 3: LoRA Adapters (Hot-Swap)

This mode trains only LoRA adapter weights. Much smaller checkpoints, faster iteration.

Step-by-Step Guide

Terminal 1: Start the Atropos API

cd atropos
run-api

Terminal 2: Start the GSM8k Environment

cd atropos
python environments/gsm8k_server.py serve --slurm False

Terminal 3: Start the GRPO Trainer in LoRA mode

cd atropos
python example_trainer/grpo.py \
  --model-name Qwen/Qwen2.5-3B-Instruct \
  --weight-bridge-mode lora_only \
  --lora-r 16 \
  --lora-alpha 32 \
  --lora-dropout 0.05 \
  --lora-target-modules q_proj v_proj \
  --training-steps 100 \
  --vllm-restart-interval 20 \
  --batch-size 2 \
  --gradient-accumulation-steps 16 \
  --lr 1e-4 \
  --use-wandb \
  --wandb-project gsm8k-grpo-lora

What Happens

1. Trainer loads base model, wraps with LoRA adapters (PEFT)
2. Only adapter parameters are trainable (~0.1% of total)
3. Training loop updates adapter weights only
4. Every N steps: save adapter checkpoint (small, ~10-50MB)
5. vLLM can hot-swap adapters via /lora/load endpoint

LoRA Configuration

Option	Default	Description
--lora-r	16	Rank of low-rank matrices
--lora-alpha	32	Scaling factor (typically 2x rank)
--lora-dropout	0.05	Dropout for regularization
--lora-target-modules	q_proj v_proj	Which layers to adapt

Common Target Module Combinations

# Minimal (fastest training)
--lora-target-modules q_proj v_proj

# Attention only
--lora-target-modules q_proj k_proj v_proj o_proj

# Full (most expressive)
--lora-target-modules q_proj k_proj v_proj o_proj gate_proj up_proj down_proj

Pros & Cons

• Much faster training (fewer parameters)
• Tiny checkpoints (~10-50MB vs ~6GB)
• Can hot-swap adapters without full restart
• Lower GPU memory (base model frozen)

• Less expressive than full fine-tuning
• May need higher learning rate

---

Configuration Reference

All CLI Options

python example_trainer/grpo.py --help

Core Training Options

Option	Default	Description
--model-name	(required)	HuggingFace model ID
--lr	1e-5	Learning rate
--training-steps	10	Total optimization steps
--batch-size	2	Micro-batch size
--gradient-accumulation-steps	32	Gradient accumulation
--seq-len	2048	Max sequence length
--save-path	trained_model_checkpoints	Checkpoint directory

vLLM Options

Option	Default	Description
--vllm-port	9001	vLLM server port
--vllm-restart-interval	3	Steps between syncs

Weight Bridge Options

Option	Default	Description
--weight-bridge-mode	none	none, shared_vllm, or lora_only
--trainer-rank	0	Distributed rank
--world-size	1	Total processes
--init-method	env://	PyTorch distributed init
--num-inference-nodes	0	Number of vLLM nodes

Logging Options

Option	Default	Description
--use-wandb	False	Enable W&B logging
--wandb-project	None	W&B project name
--wandb-group	None	W&B group name

---

Troubleshooting

"CUDA out of memory"

Try reducing:

--batch-size 1 \
--gradient-accumulation-steps 64 \
--seq-len 1024

Or use LoRA mode which uses less memory.

"Connection refused" to Atropos API

Make sure the API is running:

run-api  # In a separate terminal

vLLM fails to start

Check if port 9001 is in use:

lsof -i :9001

Kill existing processes or use a different port:

--vllm-port 9002

Bridge mode: "Parameter mapping file not found"

Ensure $LOGDIR is set and vLLM server is running:

export LOGDIR=/tmp/atropos_bridge
ls $LOGDIR/vllm_bridge_config.json

LoRA mode: "PEFT library not available"

Install PEFT:

pip install peft

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Files in This Directory

File	Description
grpo.py	Main trainer script with all modes
vllm_api_server.py	Custom vLLM server with bridge endpoints
vllm_weight_bridge.py	Shared memory bridge implementation
requirements.txt	Python dependencies
README.md	This documentation

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Example Runs

Quick Test (Legacy Mode)

# Minimal test to verify setup works
python example_trainer/grpo.py \
  --model-name Qwen/Qwen2.5-0.5B-Instruct \
  --training-steps 5 \
  --batch-size 1 \
  --gradient-accumulation-steps 4

Full GSM8k Training (LoRA Mode)

# Recommended for single-GPU training
python example_trainer/grpo.py \
  --model-name Qwen/Qwen2.5-3B-Instruct \
  --weight-bridge-mode lora_only \
  --lora-r 32 \
  --lora-alpha 64 \
  --training-steps 500 \
  --batch-size 2 \
  --gradient-accumulation-steps 32 \
  --lr 5e-5 \
  --use-wandb \
  --wandb-project gsm8k-lora

Production (Shared vLLM Mode)

# Maximum throughput setup
export LOGDIR=/tmp/atropos_bridge
export NUM_INFERENCE_NODES=0

python example_trainer/grpo.py \
  --model-name Qwen/Qwen2.5-3B-Instruct \
  --weight-bridge-mode shared_vllm \
  --training-steps 1000 \
  --batch-size 4 \
  --gradient-accumulation-steps 16 \
  --lr 1e-5 \
  --use-wandb \
  --wandb-project gsm8k-shared