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[megatron] chore: update example 671B script, no offline dist-ckpt needed any more #2945

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merged 3 commits into from
Aug 6, 2025
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[megatron] chore: update example 671B script, no offline dist-ckpt needed any more #2945

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f89040b
update 671B script
ISEEKYAN Aug 6, 2025
64f8544
update
ISEEKYAN Aug 6, 2025
e6d8d6b
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ISEEKYAN Aug 6, 2025
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59 changes: 33 additions & 26 deletions examples/grpo_trainer/run_deepseek671b_math_megatron.sh
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Original file line number Diff line number Diff line change
@@ -1,21 +1,19 @@
set -x

# 0. download the config
# only need to download the `configuration_deepseek.py`, `config.json`, `tokenizer_config.json`, `tokenizer.json` and `generation_config.json`
# remove the `quantization_config` in the `config.json`
# set `num_nextn_predict_layers=0` to disable MTP, which is not currently supported
# # 0. download HF checkpoint
# # remove the `quantization_config` in the `config.json`
# # set `num_nextn_predict_layers=0` to disable MTP, which is not currently supported
# huggingface-cli download deepseek-ai/DeepSeek-V3-0324

huggingface-cli download deepseek-ai/DeepSeek-V3-0324 configuration_deepseek.py config.json

# 1. download the dist_ckpt format model from https://huggingface.co/BearBiscuit05/dpsk-v3-671B-BF16-dist_ckpt/tree/main
# change the HF_MODEL_PATH and DIST_CKPT_PATH to your own path
DIST_CKPT_PATH="<path_to_dist_ckpt>"
# no offline dist checkpoint needed, now with mbridge>=0.13.0, we can directly init model from huggingface downloaded fp8 weights
# tested on docker://verlai/verl:app-verl0.5-vllm0.10.0-mcore0.13.0-te2.2
LLM="<path_to_dsv3_config>"
LLM=/lustre/fs1/portfolios/coreai/projects/coreai_dlalgo_mcore/checkpoints/DeepSeek-V3-0324_hf_verl


# 2. run the script
gsm8k_train_path=/data/gsm8k/train.parquet
gsm8k_test_path=/data/gsm8k/test.parquet
gsm8k_train_path=/root/data/gsm8k/train.parquet
gsm8k_test_path=/root/data/gsm8k/test.parquet
train_files=$gsm8k_train_path
test_files=$gsm8k_test_path

Expand All @@ -33,30 +31,32 @@ CRITIC_GRAD_OFFLOAD=${CRITIC_GRAD_OFFLOAD:-$COMMON_GRAD_OFFLOAD}
CRITIC_OPTIMIZER_OFFLOAD=${CRITIC_OPTIMIZER_OFFLOAD:-$COMMON_OPTIMIZER_OFFLOAD}
RM_PARAM_OFFLOAD=${RM_PARAM_OFFLOAD:-$COMMON_PARAM_OFFLOAD}

# 512 H20(96GB)
NODES=64
# 256 H100(80GB)
NODES=32
PP=16
TP=1
EP=32
EP=16
ETP=1
INFER_TP=32
# consider TP/ETP, and enable recompute if short of memory

# full recompute
# +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform \
# +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full \
# +actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 \

n_resp_per_prompt=4
max_prompt_length=2048
max_response_length=4096
use_dynamic_bsz=True
actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 10 / 10))
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high

The calculation * 10 / 10 is redundant as it's equivalent to multiplying by 1. This is confusing and should be removed for clarity. If a different ratio was intended, please correct it.

Suggested change
actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 10 / 10))
actor_ppo_max_token_len=$((max_prompt_length + max_response_length))

infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3))

# RAY_ADDRESS='auto' ray job submit --working-dir . --
python3 -m verl.trainer.main_ppo --config-path=./config --config-name='ppo_megatron_trainer'\
algorithm.adv_estimator=grpo \
data.train_files="$train_files" \
data.val_files="$test_files" \
data.train_batch_size=512 \
data.max_prompt_length=2048 \
data.max_response_length=4096 \
data.train_batch_size=128 \
data.max_prompt_length=$max_prompt_length \
data.max_response_length=$max_response_length \
data.filter_overlong_prompts=True \
data.truncation='error' \
actor_rollout_ref.model.path=$LLM \
Expand All @@ -81,8 +81,15 @@ python3 -m verl.trainer.main_ppo --config-path=./config --config-name='ppo_megat
trainer.nnodes=$NODES \
trainer.save_freq=-1 \
trainer.test_freq=5 \
+actor_rollout_ref.actor.megatron.override_transformer_config.num_layers_in_first_pipeline_stage=3 \
+actor_rollout_ref.actor.megatron.override_transformer_config.num_layers_in_last_pipeline_stage=2 \
actor_rollout_ref.model.use_fused_kernels=True \
actor_rollout_ref.actor.use_dynamic_bsz=${use_dynamic_bsz} \
actor_rollout_ref.ref.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \
actor_rollout_ref.rollout.log_prob_use_dynamic_bsz=${use_dynamic_bsz} \
actor_rollout_ref.actor.ppo_max_token_len_per_gpu=${actor_ppo_max_token_len} \
actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
+actor_rollout_ref.actor.megatron.override_transformer_config.num_layers_in_first_pipeline_stage=4 \
+actor_rollout_ref.actor.megatron.override_transformer_config.num_layers_in_last_pipeline_stage=1 \
actor_rollout_ref.actor.megatron.pipeline_model_parallel_size=$PP \
actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=$PP \
actor_rollout_ref.actor.megatron.tensor_model_parallel_size=$TP \
Expand All @@ -95,10 +102,10 @@ python3 -m verl.trainer.main_ppo --config-path=./config --config-name='ppo_megat
actor_rollout_ref.actor.megatron.optimizer_offload=${ACTOR_OPTIMIZER_OFFLOAD} \
actor_rollout_ref.actor.megatron.grad_offload=${ACTOR_GRAD_OFFLOAD} \
actor_rollout_ref.ref.megatron.param_offload=${REF_PARAM_OFFLOAD} \
actor_rollout_ref.actor.megatron.use_dist_checkpointing=True \
actor_rollout_ref.ref.megatron.use_dist_checkpointing=True \
actor_rollout_ref.actor.megatron.dist_checkpointing_path=$DIST_CKPT_PATH \
actor_rollout_ref.ref.megatron.dist_checkpointing_path=$DIST_CKPT_PATH \
+actor_rollout_ref.actor.megatron.override_transformer_config.recompute_method=uniform \
+actor_rollout_ref.actor.megatron.override_transformer_config.recompute_granularity=full \
+actor_rollout_ref.actor.megatron.override_transformer_config.recompute_num_layers=1 \
actor_rollout_ref.actor.megatron.use_mbridge=True \
trainer.default_local_dir=$CKPT_DIR \
trainer.val_before_train=False \
trainer.total_epochs=100 $@