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Pepijn
b6fb536460
After the recipe fix (target=${subtask} at every frame) the model
can still reach low text_loss by reading the answer off the plan in
the prompt: at training the prompt contains the 6-step plan, and the
current subtask is one of those steps, so the model just learns
"active step N matches subtask N" and never needs to look at the
image. Symptom at inference: subtask string is set but never updates
because the model isn't really conditioning on the visual progress.
Drop plan and memory with p=0.50 each — half of training frames the
prompt is just "${task}" (constant for this dataset) + visual prefix,
which is the only place the answer can come from. Forces the LM head
to actually use vision.
``subtask_dropout`` stays at 0.20 because subtask isn't in the
high-level prompt anymore (recipe fix removed the "Current subtask:
X" message); the knob still affects other sub-recipes that reference
it as context.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
81 lines
3.1 KiB
Bash
81 lines
3.1 KiB
Bash
#!/bin/bash
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#SBATCH --job-name=smolvla2-hirobot
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#SBATCH --partition=hopper-prod
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#SBATCH --qos=high
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#SBATCH --time=48:00:00
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#SBATCH --ntasks=1
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#SBATCH --gpus-per-task=8
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# SmolVLA2 training on an annotated dataset.
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#
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# The high_level_subtask recipe (recipes/smolvla2_hirobot.yaml) was
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# fixed in PR3 to supervise the LM head with the *current* active
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# subtask span at every frame, not the next-span target which is
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# empty on stable phases. With the old recipe the head learned to
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# emit ``\n`` on every chunk boundary; the new one supervises a
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# real, scene-grounded string at every frame.
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#
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# Two regularisers are still on:
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#
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# * --dataset.image_transforms.enable=true: torchvision-v2
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# ColorJitter + SharpnessJitter + RandomAffine per frame; default
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# envelope (brightness ±20% etc).
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# * --policy.{plan,memory,subtask}_dropout_prob: randomly drop the
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# context messages carrying the named recipe binding so the model
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# handles missing/stale context. Mirrors Pi0.7 §V.E.
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set -euo pipefail
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cd "${LEROBOT_ROOT:-$HOME/lerobot}"
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export PATH="$HOME/miniconda3/bin:$HOME/.local/bin:$PATH"
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export LD_LIBRARY_PATH="$HOME/miniconda3/lib:${LD_LIBRARY_PATH:-}"
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export NCCL_TIMEOUT="${NCCL_TIMEOUT:-1800}"
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export HF_HUB_DOWNLOAD_TIMEOUT="${HF_HUB_DOWNLOAD_TIMEOUT:-120}"
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export WANDB_INIT_TIMEOUT="${WANDB_INIT_TIMEOUT:-300}"
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DATASET="${DATASET:-pepijn223/super_poulain_full_tool3}"
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POLICY_REPO_ID="${POLICY_REPO_ID:-pepijn223/smolvla2_hirobot_super_poulain_tool6}"
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JOB_NAME="${JOB_NAME:-smolvla2-hirobot-super-poulain-tool6}"
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NUM_PROCESSES="${NUM_PROCESSES:-8}"
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BATCH_SIZE="${BATCH_SIZE:-32}"
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STEPS="${STEPS:-2000}"
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RUN_ID="${SLURM_JOB_ID:-$(date +%Y%m%d_%H%M%S)}"
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OUTPUT_DIR="${OUTPUT_DIR:-/fsx/pepijn/outputs/train/smolvla2_hirobot_super_poulain_tool3_${STEPS}_${RUN_ID}}"
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echo "Training smolvla2 on $DATASET"
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echo " GPUs: $NUM_PROCESSES"
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echo " batch: $BATCH_SIZE / GPU (global=$((NUM_PROCESSES * BATCH_SIZE)))"
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echo " steps: $STEPS"
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echo " output: $OUTPUT_DIR"
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echo " augmentation: image_transforms ON, prompt dropout {plan:0.50 memory:0.50 subtask:0.20}"
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accelerate launch --multi_gpu --num_processes="$NUM_PROCESSES" \
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-m lerobot.scripts.lerobot_train \
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--policy.type=smolvla2 \
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--policy.recipe_path=recipes/smolvla2_hirobot.yaml \
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--dataset.repo_id="$DATASET" \
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--dataset.revision=main \
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--dataset.video_backend=pyav \
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--output_dir="$OUTPUT_DIR" \
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--job_name="$JOB_NAME" \
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--policy.repo_id="$POLICY_REPO_ID" \
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--policy.compile_model=false \
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--policy.device=cuda \
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--policy.tokenizer_max_length=512 \
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--steps="$STEPS" \
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--policy.scheduler_decay_steps="$STEPS" \
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--batch_size="$BATCH_SIZE" \
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--wandb.enable=true \
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--wandb.disable_artifact=true \
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--wandb.project=hirobot \
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--log_freq=100 \
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--save_freq="$STEPS" \
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--num_workers=0 \
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--dataset.image_transforms.enable=true \
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--dataset.image_transforms.max_num_transforms=3 \
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--dataset.image_transforms.random_order=true \
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--policy.plan_dropout_prob=0.50 \
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--policy.memory_dropout_prob=0.50 \
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--policy.subtask_dropout_prob=0.20
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