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docs: add ICML booth talk slides link #23

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docs: add ICML booth talk slides link

docs: add ICML booth talk slides link #23

Workflow file for this run

.github/workflows/e2e_ppo_trainer.yml at aa5df0c
name: e2e_ppo_trainer
on:
# Trigger the workflow on push or pull request,
# but only for the main branch
push:
branches:
- main
- v0.*
pull_request:
branches:
- main
- v0.*
paths:
- "**/*.py"
# Other entrypoints
- "!examples/**"
- "!tests/**"
- "!verl/trainer/main_*.py"
- "!verl/trainer/fsdp_sft_trainer.py"
# Recipes
- "!recipe/**"
# Megatron
- "!verl/workers/**/megatron_*.py"
# Entrypoints
- ".github/workflows/e2e_ppo_trainer.yml"
- "examples/data_preprocess/gsm8k.py"
- "examples/data_preprocess/geo3k.py"
- "tests/e2e/ppo_trainer"
- "verl/trainer/main_ppo.py"
- "verl/trainer/config/ppo_trainer.yaml"
# Cancel jobs on the same ref if a new one is triggered
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: ${{ github.ref != 'refs/heads/main' }}
# Declare permissions just read content.
permissions:
contents: read
jobs:
pre_commit_for_ppo:
runs-on: ubuntu-latest
strategy:
matrix:
python-version: ["3.12"]
steps:
- uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
with:
python-version: ${{ matrix.python-version }}
- name: Set ruff --output-format=github
run: |
sed -i 's/--output-format=full/--output-format=github/' .pre-commit-config.yaml
git add .pre-commit-config.yaml
- uses: pre-commit/action@v3.0.1
with:
extra_args: "" # Overriding default "--all-files"
e2e_ppo_trainer_vllm:
runs-on: [L20x8]
timeout-minutes: 40 # Increase this timeout value as needed
env:
HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
NO_PROXY: "localhost,127.0.0.1,hf-mirror.com"
HF_ENDPOINT: "https://hf-mirror.com"
HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable
container:
image: whatcanyousee/verl:ngc-cu124-vllm0.8.5-sglang0.4.6-mcore0.12.0-te2.3
options: --gpus all --shm-size=10g
steps:
- uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
with:
fetch-depth: 0
- name: Install the current repository
run: |
pip3 install --no-deps -e .[test,vllm]
- name: Prepare GSM8K dataset
run: |
ray stop --force
python3 examples/data_preprocess/gsm8k.py
# Function RM
- name: Running GSM8K E2E training tests on 8 L20 GPUs with rmpad using function rm with validation and saving (FSDP_SIZE=8)
run: |
ray stop --force
VAL_BEFORE_TRAIN=True TEST_FREQ=1 SAVE_FREQ=1 SAVE_HF_MODEL=True VERL_EXP_NAME="qwen2.5-0.5b-function-reward-minimal-fsdp8" bash tests/e2e/ppo_trainer/run_function_reward.sh
- name: Running GSM8K E2E training tests on 8 L20 GPUs with rmpad using function rm after resuming
run: |
ray stop --force
RESUME_MODE=auto VERL_EXP_NAME="qwen2.5-0.5b-function-reward-minimal-fsdp8" bash tests/e2e/ppo_trainer/run_function_reward.sh
- name: Test merging FSDP checkpoints (Qwen Actor)
run: |
exp_name="qwen2.5-0.5b-function-reward-minimal-fsdp8"
python scripts/model_merger.py test --backend fsdp --local_dir checkpoints/verl-test/${exp_name}/global_step_1/actor --test_hf_dir checkpoints/verl-test/${exp_name}/global_step_1/actor/huggingface
- name: Running GSM8K E2E training tests on 8 L20 GPUs with rmpad using function rm with validation and saving (DDP_SIZE=2, FSDP_SIZE=4)
run: |
ray stop --force
VAL_BEFORE_TRAIN=True TEST_FREQ=1 SAVE_FREQ=1 SAVE_HF_MODEL=True FSDP_SIZE=4 VERL_EXP_NAME="qwen2.5-0.5b-function-reward-minimal-ddp2-fsdp4" bash tests/e2e/ppo_trainer/run_function_reward.sh
- name: Test merging DDP+FSDP checkpoints (Qwen Actor)
run: |
exp_name="qwen2.5-0.5b-function-reward-minimal-ddp2-fsdp4"
python scripts/model_merger.py test --backend fsdp --local_dir checkpoints/verl-test/${exp_name}/global_step_1/actor --test_hf_dir checkpoints/verl-test/${exp_name}/global_step_1/actor/huggingface
- name: Running GSM8K E2E without rmpad using function rm
run: |
ray stop --force
RM_PAD=False bash tests/e2e/ppo_trainer/run_function_reward.sh
- name: Running GSM8K E2E training tests on 8 L20 GPUs with rmpad using function rm (GRPO)
run: |
ray stop --force
ADV_ESTIMATOR=grpo USE_KL=True bash tests/e2e/ppo_trainer/run_function_reward.sh
- name: Running GSM8K E2E training tests on 8 L20 GPUs with rmpad using function rm (ReMax)
run: |
ray stop --force
ADV_ESTIMATOR=remax USE_KL=True bash tests/e2e/ppo_trainer/run_function_reward.sh
- name: Running GSM8K E2E training tests on 8 L20 GPUs with rmpad using customized reward function
run: |
ray stop --force
CUSTOM_REWARD_FN=True bash tests/e2e/ppo_trainer/run_function_reward.sh
- name: Running GSM8K E2E training tests on 8 L20 GPUs with rmpad using function rm with in-reward kl and kl loss
run: |
ray stop --force
USE_KL=True bash tests/e2e/ppo_trainer/run_function_reward.sh
# LoRA tests
- name: Running GSM8K E2E training tests on 8 L20 GPUs with grpo lora using function rm with use_shm
run: |
ray stop --force
ADV_ESTIMATOR=grpo USE_SHM=True LORA_RANK=32 LOAD_FORMAT=safetensors bash tests/e2e/ppo_trainer/run_function_reward.sh
- name: Running GSM8K E2E training tests on 8 L20 GPUs with grpo lora using function rm with use_shm and layered_summon
run: |
ray stop --force
ADV_ESTIMATOR=grpo USE_SHM=True LORA_RANK=32 LOAD_FORMAT=safetensors LAYERED_SUMMON=True bash tests/e2e/ppo_trainer/run_function_reward.sh
# Model RM
- name: Running GRPO GSM8K E2E training tests with FSDP on 8 L20 GPUs (DeepSeek)
run: |
ray stop --force
MODEL_ID=deepseek-ai/deepseek-coder-1.3b-instruct bash tests/e2e/ppo_trainer/run_function_reward.sh
- name: Running GSM8K E2E with rmpad using model rm
run: |
ray stop --force
bash tests/e2e/ppo_trainer/run_model_reward.sh
- name: Running GSM8K E2E without rmpad using model rm
run: |
ray stop --force
RM_PAD=False bash tests/e2e/ppo_trainer/run_model_reward.sh
- name: Running GSM8K E2E with rmpad using model rm and ulysses sp=2
run: |
ray stop --force
SP_SIZE=2 bash tests/e2e/ppo_trainer/run_model_reward.sh
- name: Running GSM8K E2E with rmpad using model rm and dynamic batch size
run: |
ray stop --force
SEQ_BALANCE=True bash tests/e2e/ppo_trainer/run_model_reward.sh
- name: Running GSM8K E2E with rmpad using model rm with Liger Kernel enabled
run: |
ray stop --force
LIGER=True bash tests/e2e/ppo_trainer/run_model_reward.sh
e2e_ppo_trainer_vllm_vlm:
runs-on: [L20x8]
needs: pre_commit_for_ppo
timeout-minutes: 40 # Increase this timeout value as needed
env:
HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
NO_PROXY: "localhost,127.0.0.1,hf-mirror.com"
HF_ENDPOINT: "https://hf-mirror.com"
HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable
container:
image: hiyouga/verl:ngc-th2.6.0-cu126-vllm0.8.3-flashinfer0.2.2-cxx11abi0
options: --gpus all --shm-size=50g # Visual dataloader requires large memory
steps:
- uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
with:
fetch-depth: 0
- name: Install the current repository
run: |
pip3 install -e .[test,geo,vllm]
# Geo3k
- name: Prepare Geo3k dataset
run: |
ray stop --force
python3 examples/data_preprocess/geo3k.py
- name: Running Geo3k VLM E2E training tests on 8 L20 GPUs with rmpad using function rm
run: |
ray stop --force
TRAIN_FILES=$HOME/data/geo3k/train.parquet VAL_FILES=$HOME/data/geo3k/test.parquet \
MAX_PROMPT_LEN=1536 MAX_RESPONSE_LEN=1536 \
MODEL_ID=Qwen/Qwen2-VL-2B-Instruct \
ADV_ESTIMATOR=grpo RM_PAD=True USE_KL=True ENABLE_CHUNKED_PREFILL=False \
SP_SIZE=2 \
bash tests/e2e/ppo_trainer/run_function_reward.sh
e2e_ppo_trainer_sglang:
runs-on: [L20x8]
needs: pre_commit_for_ppo
timeout-minutes: 40 # Increase this timeout value as needed
env:
HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
NO_PROXY: "localhost,127.0.0.1,hf-mirror.com"
HF_ENDPOINT: "https://hf-mirror.com"
HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable
container:
image: whatcanyousee/verl:ngc-cu124-vllm0.8.5-sglang0.4.6.post5-mcore0.12.0-te2.3
options: --gpus all --shm-size=10g
steps:
- uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
with:
fetch-depth: 0
- name: Install the current repository
run: |
pip3 install -e .[test,gpu,sglang] --no-deps
- name: Prepare gsm8k dataset
run: |
ray stop --force
python3 examples/data_preprocess/gsm8k.py
- name: Running GSM8K E2E training tests on 8 L20 GPUs with rmpad using function rm and save ckpt
run: |
ray stop --force
ENGINE=sglang bash tests/e2e/ppo_trainer/run_function_reward.sh
e2e_ppo_trainer_sglang_multiturn_with_tool:
runs-on: [L20x8]
needs: pre_commit_for_ppo
timeout-minutes: 40 # Increase this timeout value as needed
env:
HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
NO_PROXY: "localhost,127.0.0.1,hf-mirror.com"
HF_ENDPOINT: "https://hf-mirror.com"
HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable
container:
image: whatcanyousee/verl:ngc-cu124-vllm0.8.5-sglang0.4.6.post5-mcore0.12.0-te2.3
options: --gpus all --shm-size=10g
steps:
- uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
with:
fetch-depth: 0
- name: Install the current repository
run: |
pip3 install -e .[test,gpu,sglang] --no-deps
- name: Prepare gsm8k dataset with tool
run: |
ray stop --force
python3 examples/data_preprocess/gsm8k_multiturn_w_tool.py --local_dir $HOME/data/gsm8k_verl_sgl_multi_turn_preprocessed
- name: Running GSM8K with tool E2E training tests on 8 L20 GPUs with rmpad using function rm and save ckpt with sglang
run: |
ray stop --force
bash tests/e2e/run_gsm8k_fsdp_sgl_multiturn_w_tool.sh
- name: Running GSM8K with tool E2E training tests with FSDP2
run: |
ray stop --force
FSDP_STRATEGY=fsdp2 bash tests/e2e/run_gsm8k_fsdp_sgl_multiturn_w_tool.sh
e2e_ppo_trainer_sglang_vlm:
runs-on: [L20x8]
needs: pre_commit_for_ppo
timeout-minutes: 40 # Increase this timeout value as needed
env:
HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
NO_PROXY: "localhost,127.0.0.1,hf-mirror.com"
HF_ENDPOINT: "https://hf-mirror.com"
HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable
container:
image: whatcanyousee/verl:ngc-cu124-vllm0.8.5-sglang0.4.6.post5-mcore0.12.0-te2.3
options: --gpus all --shm-size=50g # Visual dataloader requires large memory
steps:
- uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
with:
fetch-depth: 0
- name: Install the current repository
run: |
pip3 install -e .[test,geo,gpu,sglang]
# Geo3k
- name: Prepare Geo3k dataset
run: |
ray stop --force
python3 examples/data_preprocess/geo3k.py
- name: Running Geo3k VLM E2E training tests on 8 L20 GPUs with rmpad using function rm
run: |
ray stop --force
TRAIN_FILES=$HOME/data/geo3k/train.parquet VAL_FILES=$HOME/data/geo3k/test.parquet \
MAX_PROMPT_LEN=1536 MAX_RESPONSE_LEN=1536 \
MODEL_ID=Qwen/Qwen2-VL-2B-Instruct \
ADV_ESTIMATOR=grpo RM_PAD=True USE_KL=True ENABLE_CHUNKED_PREFILL=False \
ENGINE=sglang GPU_MEMORY_UTILIZATION=0.6 ACTOR_FSDP_PARAM_OFFLOAD=True \
ACTOR_FSDP_OPTIMIZER_OFFLOAD=True REF_FSDP_PARAM_OFFLOAD=True \
bash tests/e2e/ppo_trainer/run_function_reward.sh
e2e_ppo_trainer_fused_kernels_vllm:
runs-on: [L20x8]
needs: pre_commit_for_ppo
timeout-minutes: 40 # Increase this timeout value as needed
env:
HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
NO_PROXY: "localhost,127.0.0.1,hf-mirror.com"
HF_ENDPOINT: "https://hf-mirror.com"
HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable
container:
image: hiyouga/verl:ngc-th2.6.0-cu126-vllm0.8.3-flashinfer0.2.2-cxx11abi0
options: --gpus all --shm-size=50g # Visual dataloader requires large memory
steps:
- uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
with:
fetch-depth: 0
- name: Install the current repository
run: |
pip3 install -e .[test,geo,vllm]
# Geo3k
- name: Prepare Geo3k dataset
run: |
ray stop --force
python3 examples/data_preprocess/geo3k.py
- name: Running Geo3k VLM E2E with rmpad using fused kernel (Qwen2.5-VL)
run: |
ray stop --force
FUSED_KERNELS=True TRAIN_FILES=$HOME/data/geo3k/train.parquet VAL_FILES=$HOME/data/geo3k/test.parquet \
MAX_PROMPT_LEN=1536 MAX_RESPONSE_LEN=1536 \
MODEL_ID=Qwen/Qwen2.5-VL-3B-Instruct \
ADV_ESTIMATOR=grpo RM_PAD=True USE_KL=True ENABLE_CHUNKED_PREFILL=False \
GPU_MEMORY_UTILIZATION=0.6 ACTOR_FSDP_PARAM_OFFLOAD=True \
ACTOR_FSDP_OPTIMIZER_OFFLOAD=True REF_FSDP_PARAM_OFFLOAD=True \
bash tests/e2e/ppo_trainer/run_function_reward.sh
e2e_ppo_trainer_fused_kernels_sglang:
runs-on: [L20x8]
needs: pre_commit_for_ppo
timeout-minutes: 40 # Increase this timeout value as needed
env:
HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
NO_PROXY: "localhost,127.0.0.1,hf-mirror.com"
HF_ENDPOINT: "https://hf-mirror.com"
HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable
container:
image: whatcanyousee/verl:ngc-cu124-vllm0.8.5-sglang0.4.6.post5-mcore0.12.0-te2.3
options: --gpus all --shm-size=50g # Visual dataloader requires large memory
steps:
- uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
with:
fetch-depth: 0
- name: Install the current repository
run: |
pip3 install -e .[test,geo,gpu,sglang]
- name: Prepare Geo3k dataset
run: |
ray stop --force
python3 examples/data_preprocess/geo3k.py
- name: Running Geo3k VLM E2E with rmpad using fused kernel (Qwen2.5-VL)
run: |
ray stop --force
FUSED_KERNELS=True TRAIN_FILES=$HOME/data/geo3k/train.parquet VAL_FILES=$HOME/data/geo3k/test.parquet \
MAX_PROMPT_LEN=1536 MAX_RESPONSE_LEN=1536 \
MODEL_ID=Qwen/Qwen2.5-VL-3B-Instruct \
ADV_ESTIMATOR=grpo RM_PAD=True USE_KL=True ENABLE_CHUNKED_PREFILL=False \
ENGINE=sglang GPU_MEMORY_UTILIZATION=0.6 ACTOR_FSDP_PARAM_OFFLOAD=True \
ACTOR_FSDP_OPTIMIZER_OFFLOAD=True REF_FSDP_PARAM_OFFLOAD=True \
bash tests/e2e/ppo_trainer/run_function_reward.sh