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feat: multi-GPU support for 4x RTX 5090 (PCIe DDP, BF16)

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Hardware analysis:
  4x RTX 5090 32GB without NVLink is fully sufficient.
  PCIe 5.0 all-reduce overhead <1% of step time for MacBERT-large (340M params).
  BF16 mixed precision gives ~2x throughput vs FP32 on 5090.

Module B (Detector) — full 4-GPU DDP via Accelerate:
  - DistributedSampler with per-epoch shuffling (correct DDP data split)
  - BF16 autocast via accelerator.mixed_precision
  - Gradient accumulation handled by accelerator.accumulate()
  - Only rank-0 saves checkpoints and logs to wandb
  - accelerator.gather_for_metrics() for correct multi-GPU validation
  - per_gpu_batch_size=32, effective_batch = 32×4 = 128

Module C (Intervention) — hybrid parallel strategy:
  - Stage 1 (BC warm-up): all 4 GPUs via Accelerate DDP
    TensorDataset broadcast from rank-0 to all processes
  - Stage 2 (PPO): GPU-0 only — env-agent loop is inherently sequential
  - Detector preprocessing: distributed across all 4 GPUs via shard split
    + all_gather_object to collect results on rank-0

Configs updated:
  detector_config.yaml:    per_gpu_batch_size=32, gradient_accumulation_steps=1,
                           mixed_precision=bf16, num_workers=4
  intervention_config.yaml: BC per_gpu_batch_size=256, PPO batch_size=256

Launch scripts added:
  scripts/run_detector.sh         — single command: 4-GPU detector training
  scripts/run_intervention.sh     — single command: hybrid BC+PPO training
  scripts/run_full_pipeline.sh    — end-to-end pipeline steps 1-5

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
wangyu
2026-05-09 17:56:13 +08:00
parent 4a0e71fb23
commit b4be3983b7
7 changed files with 637 additions and 184 deletions
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340
scripts/train_intervention.py
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@@ -2,19 +2,33 @@
Step 4: Train Module C — RL Intervention Policy (PPO).
Two-stage training:
Stage 1: Behavior cloning warm-up from a_recommend labels
Stage 2: PPO fine-tuning with multi-objective reward
Stage 1 (BC warm-up): behavior cloning on all 4 GPUs via Accelerate DDP
Stage 2 (PPO fine-tuning): single-GPU (GPU-0) offline RL — inherently sequential
Usage:
python scripts/train_intervention.py --config configs/intervention_config.yaml \
--train-data data/processed/train.jsonl
Preprocessing (detector inference) is distributed across all 4 GPUs.
Usage (4 GPUs):
accelerate launch --num_processes=4 --mixed_precision=bf16 \\
scripts/train_intervention.py --config configs/intervention_config.yaml \\
--train-data data/processed/train.jsonl
Usage (single GPU):
accelerate launch --num_processes=1 \\
scripts/train_intervention.py --config configs/intervention_config.yaml
"""
import argparse
import os
import yaml
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from pathlib import Path
from torch.utils.data import DataLoader, TensorDataset, DistributedSampler
from transformers import AutoTokenizer
from accelerate import Accelerator
from accelerate.utils import set_seed
from src.data.dataset import load_jsonl
from src.models.detector import CompanionRiskDetector
@@ -30,10 +44,122 @@ import wandb
def get_obs_dim(detector_hidden: int) -> int:
"""Compute observation vector dimension."""
return 1 + NUM_RISK_LEVELS + NUM_PRIMARY + detector_hidden * 2 + 1
def distributed_preprocess(
raw_samples,
detector,
tokenizer,
accelerator,
binary_threshold: float = 0.5,
):
"""
Distribute detector inference across all GPUs.
Each process handles its shard of the dataset; results are gathered
on the main process.
"""
n = len(raw_samples)
rank = accelerator.process_index
world = accelerator.num_processes
# Each process takes its contiguous shard
start = (n * rank) // world
end = (n * (rank + 1)) // world
local_samples = raw_samples[start:end]
accelerator.print(
f"Preprocessing: rank {rank} handles samples {start}–{end} "
f"({len(local_samples)} samples)"
)
local_processed = preprocess_samples_with_detector(
local_samples,
detector,
tokenizer,
device=str(accelerator.device),
binary_threshold=binary_threshold,
)
# Gather on main process via object lists
all_shards = [None] * world
torch.distributed.all_gather_object(all_shards, local_processed)
if accelerator.is_main_process:
processed = []
for shard in all_shards:
processed.extend(shard)
return processed
return []
def run_bc_warmup(
agent: InterventionAgent,
obs_tensor: torch.Tensor,
action_tensor: torch.Tensor,
cfg: dict,
accelerator: Accelerator,
):
"""
Stage 1: Behavior cloning on all GPUs.
Returns the updated agent weights (synced automatically via DDP).
"""
bc_cfg = cfg.get("behavior_cloning", {})
per_gpu_bs = bc_cfg.get("per_gpu_batch_size", 256)
n_epochs = bc_cfg.get("epochs", 5)
lr = bc_cfg.get("lr", 1e-3)
dataset = TensorDataset(obs_tensor, action_tensor)
sampler = None
if accelerator.num_processes > 1:
sampler = DistributedSampler(
dataset,
num_replicas=accelerator.num_processes,
rank=accelerator.process_index,
shuffle=True,
)
loader = DataLoader(
dataset,
batch_size=per_gpu_bs,
sampler=sampler,
shuffle=(sampler is None),
pin_memory=True,
drop_last=False,
)
optimizer = optim.Adam(agent.parameters(), lr=lr)
agent, optimizer, loader = accelerator.prepare(agent, optimizer, loader)
losses = []
for epoch in range(n_epochs):
if accelerator.num_processes > 1:
loader.sampler.set_epoch(epoch)
epoch_loss = 0.0
agent.train()
for obs_batch, act_batch in loader:
loss = accelerator.unwrap_model(agent).behavior_clone_loss(
obs_batch, act_batch
)
accelerator.backward(loss)
optimizer.step()
optimizer.zero_grad()
epoch_loss += loss.item()
avg_loss = epoch_loss / max(len(loader), 1)
losses.append(avg_loss)
accelerator.print(f"[BC] Epoch {epoch + 1}/{n_epochs}, Loss: {avg_loss:.4f}")
if cfg["logging"]["use_wandb"] and accelerator.is_main_process:
accelerator.log({"bc/loss": avg_loss, "bc/epoch": epoch + 1})
# Return the unwrapped agent (weights are consistent across all processes)
return accelerator.unwrap_model(agent), losses
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--config", default="configs/intervention_config.yaml")
@@ -43,109 +169,163 @@ def main():
with open(args.config) as f:
cfg = yaml.safe_load(f)
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Device: {device}")
set_seed(42)
# ── Accelerator for BC stage ─────────────────────────────────────────
bc_cfg = cfg.get("behavior_cloning", {})
accelerator = Accelerator(
mixed_precision=bc_cfg.get("mixed_precision", "bf16"),
gradient_accumulation_steps=1,
log_with="wandb" if cfg["logging"]["use_wandb"] else None,
)
accelerator.print(
f"Running on {accelerator.num_processes} GPU(s), "
f"mixed_precision={accelerator.mixed_precision}"
)
if cfg["logging"]["use_wandb"]:
wandb.init(
project=cfg["logging"]["project"],
name=cfg["logging"]["run_name"],
accelerator.init_trackers(
project_name=cfg["logging"]["project"],
config=cfg,
init_kwargs={"wandb": {"name": cfg["logging"]["run_name"]}},
)
# Load detector
# ── Load detector (shared weights, each process loads its own copy) ──
detector_cfg = cfg["detector"]
tokenizer = AutoTokenizer.from_pretrained(detector_cfg["model_name"])
detector = CompanionRiskDetector(
model_name=detector_cfg["model_name"],
hidden_size=detector_cfg["hidden_size"],
).to(device)
).to(accelerator.device)
ckpt_path = detector_cfg["checkpoint"]
if Path(ckpt_path).exists():
detector.load_state_dict(torch.load(ckpt_path, map_location=device))
print(f"Detector loaded from {ckpt_path}")
detector.load_state_dict(
torch.load(ckpt_path, map_location=accelerator.device)
)
accelerator.print(f"Detector loaded from {ckpt_path}")
else:
print(f"[WARN] Detector checkpoint not found at {ckpt_path}. Using random weights.")
accelerator.print(f"[WARN] No detector checkpoint at {ckpt_path}. Using random weights.")
detector.eval()
# Pre-process training data through the detector
print(f"Loading training data: {args.train_data}")
# ── Distributed preprocessing ────────────────────────────────────────
accelerator.print(f"Loading: {args.train_data}")
raw_samples = load_jsonl(args.train_data)
print(f"Preprocessing {len(raw_samples)} samples with detector...")
accelerator.print(f"Preprocessing {len(raw_samples)} samples across {accelerator.num_processes} GPU(s)...")
processed = preprocess_samples_with_detector(
raw_samples,
detector,
tokenizer,
device=device,
binary_threshold=cfg.get("evaluation", {}).get("binary_threshold", 0.5),
)
binary_threshold = cfg.get("evaluation", {}).get("binary_threshold", 0.5)
if accelerator.num_processes > 1:
# Use distributed preprocessing
processed = distributed_preprocess(
raw_samples, detector, tokenizer, accelerator, binary_threshold
)
else:
processed = preprocess_samples_with_detector(
raw_samples, detector, tokenizer,
device=str(accelerator.device),
binary_threshold=binary_threshold,
)
detector_hidden = detector_cfg["hidden_size"]
obs_dim = get_obs_dim(detector_hidden)
print(f"Observation dimension: {obs_dim}")
accelerator.print(f"Observation dim: {obs_dim}")
# Build the RL agent
agent_cfg = cfg["agent"]
agent = InterventionAgent(
detector_hidden=detector_hidden,
state_hidden=agent_cfg["state_hidden"],
dropout=agent_cfg["dropout"],
).to(device)
trainer = PPOTrainer(
agent=agent,
obs_dim=obs_dim,
lr=cfg["ppo"]["lr"],
clip_eps=cfg["ppo"]["clip_eps"],
entropy_coef=cfg["ppo"]["entropy_coef"],
value_coef=cfg["ppo"]["value_coef"],
max_grad_norm=cfg["ppo"]["max_grad_norm"],
gamma=cfg["ppo"]["gamma"],
gae_lambda=cfg["ppo"]["gae_lambda"],
n_epochs=cfg["ppo"]["n_epochs"],
batch_size=cfg["ppo"]["batch_size"],
buffer_size=cfg["ppo"]["n_rollout_steps"],
device=device,
use_wandb=cfg["logging"]["use_wandb"],
)
# Stage 1: Behavior cloning warm-up
bc_cfg = cfg.get("behavior_cloning", {})
# ── Stage 1: Behavior Cloning (all GPUs) ────────────────────────────
if bc_cfg.get("enabled", True):
print("\n=== Stage 1: Behavior Cloning Warm-up ===")
obs_tensor, action_tensor = build_bc_tensors(processed, device=device)
trainer.behavior_cloning_warmup(
obs_tensor,
action_tensor,
n_epochs=bc_cfg.get("epochs", 5),
lr=bc_cfg.get("lr", 1e-3),
accelerator.print("\n=== Stage 1: Behavior Cloning Warm-up (all GPUs) ===")
# Build BC tensors on main process, broadcast to others
if accelerator.is_main_process:
obs_tensor, action_tensor = build_bc_tensors(processed, device="cpu")
else:
obs_tensor = torch.zeros(1, obs_dim)
action_tensor = torch.zeros(1, dtype=torch.long)
if accelerator.num_processes > 1:
# Broadcast tensor sizes from rank 0
size_tensor = torch.tensor([obs_tensor.shape[0]], dtype=torch.long)
torch.distributed.broadcast(size_tensor, src=0)
n_samples = size_tensor.item()
if not accelerator.is_main_process:
obs_tensor = torch.zeros(n_samples, obs_dim)
action_tensor = torch.zeros(n_samples, dtype=torch.long)
# Broadcast data from rank 0 to all processes
torch.distributed.broadcast(obs_tensor, src=0)
torch.distributed.broadcast(action_tensor, src=0)
obs_tensor = obs_tensor.to(accelerator.device)
action_tensor = action_tensor.to(accelerator.device)
agent = InterventionAgent(
detector_hidden=detector_hidden,
state_hidden=cfg["agent"]["state_hidden"],
dropout=cfg["agent"]["dropout"],
)
# Stage 2: PPO fine-tuning
print("\n=== Stage 2: PPO Fine-tuning ===")
env_cfg = cfg.get("environment", {})
env = CompanionEnv(
samples=processed,
detector_hidden=detector_hidden,
reward_weights=cfg.get("reward"),
max_turns=env_cfg.get("max_turns", 20),
)
agent, _ = run_bc_warmup(agent, obs_tensor, action_tensor, cfg, accelerator)
output_cfg = cfg["output"]
Path(output_cfg["checkpoint_dir"]).mkdir(parents=True, exist_ok=True)
else:
agent = InterventionAgent(
detector_hidden=detector_hidden,
state_hidden=cfg["agent"]["state_hidden"],
dropout=cfg["agent"]["dropout"],
)
trainer.train(
env=env,
total_timesteps=cfg["ppo"]["total_timesteps"],
n_rollout_steps=cfg["ppo"]["n_rollout_steps"],
checkpoint_dir=output_cfg["checkpoint_dir"],
save_interval=output_cfg.get("save_interval", 10_000),
)
# ── Stage 2: PPO (main process only — inherently sequential) ─────────
accelerator.wait_for_everyone()
print("Training complete.")
if accelerator.is_main_process:
accelerator.print("\n=== Stage 2: PPO Fine-tuning (GPU-0 only) ===")
# Move agent to GPU-0
device = accelerator.device
agent = agent.to(device)
ppo_cfg = cfg["ppo"]
trainer = PPOTrainer(
agent=agent,
obs_dim=obs_dim,
lr=ppo_cfg["lr"],
clip_eps=ppo_cfg["clip_eps"],
entropy_coef=ppo_cfg["entropy_coef"],
value_coef=ppo_cfg["value_coef"],
max_grad_norm=ppo_cfg["max_grad_norm"],
gamma=ppo_cfg["gamma"],
gae_lambda=ppo_cfg["gae_lambda"],
n_epochs=ppo_cfg["n_epochs"],
batch_size=ppo_cfg["batch_size"],
buffer_size=ppo_cfg["n_rollout_steps"],
device=str(device),
use_wandb=cfg["logging"]["use_wandb"],
)
env_cfg = cfg.get("environment", {})
env = CompanionEnv(
samples=processed,
detector_hidden=detector_hidden,
reward_weights=cfg.get("reward"),
max_turns=env_cfg.get("max_turns", 20),
)
output_cfg = cfg["output"]
Path(output_cfg["checkpoint_dir"]).mkdir(parents=True, exist_ok=True)
trainer.train(
env=env,
total_timesteps=ppo_cfg["total_timesteps"],
n_rollout_steps=ppo_cfg["n_rollout_steps"],
checkpoint_dir=output_cfg["checkpoint_dir"],
save_interval=output_cfg.get("save_interval", 10_000),
)
accelerator.print("Training complete.")
if cfg["logging"]["use_wandb"]:
accelerator.end_training()
if __name__ == "__main__":