[megatron] chore: clean legacy code path part 2, clean legacy CI

.github/workflows/e2e_ppo_trainer_megatron_sglang.yml

@@ -136,11 +136,6 @@ jobs:
           export VLLM_USE_V1=1
           ray start --head
           ENGINE=sglang MODE=async RESUME_MODE=auto MODEL_ID=deepseek-ai/deepseek-coder-1.3b-instruct TOTAL_TRAIN_STEPS=2 bash tests/special_e2e/run_ppo_trainer_megatron.sh
-      - name: Test Megatron checkpoints merging function (DeepSeek Actor and Critic)
-        run: |
-          exp_name="deepseek-coder-1.3b-instruct-megatron-gsm8k-minimal"
-          python -m verl.model_merger test --backend megatron --local_dir checkpoints/verl-test/${exp_name}/global_step_1/actor --test_hf_dir checkpoints/verl-test/${exp_name}/global_step_1/actor/huggingface
-          python -m verl.model_merger test --backend megatron --is-value-model --local_dir checkpoints/verl-test/${exp_name}/global_step_1/critic --test_hf_dir checkpoints/verl-test/${exp_name}/global_step_1/critic/huggingface
       - name: Profiling GRPO GSM8K E2E training tests with 3D parallelism on 8 L20 GPUs with Megatron (Deepseek)
         run: |
           ray stop --force
@@ -181,11 +176,6 @@ jobs:
         run: |
           ray stop --force
           ALL_OFFLOAD=True VAL_BEFORE_TRAIN=True TEST_FREQ=1 SAVE_FREQ=1 LR_WARMUP_STEPS=1 TOTAL_TRAIN_STEPS=2 MODEL_ID=Qwen/Qwen3-0.6B bash tests/special_e2e/run_ppo_trainer_megatron.sh
-      - name: Test Megatron checkpoints merging function (Qwen3 Actor and Critic)
-        run: |
-          exp_name="qwen3-0.6b-megatron-gsm8k-minimal"
-          python -m verl.model_merger test --backend megatron --tie-word-embedding --local_dir checkpoints/verl-test/${exp_name}/global_step_1/actor --test_hf_dir checkpoints/verl-test/${exp_name}/global_step_1/actor/huggingface
-          python -m verl.model_merger test --backend megatron --is-value-model --local_dir checkpoints/verl-test/${exp_name}/global_step_1/critic --test_hf_dir checkpoints/verl-test/${exp_name}/global_step_1/critic/huggingface
       - name: Running GSM8K E2E training tests with 3D parallelism on 8 L20 GPUs with FP8 rollout
         run: |
           ray stop --force

.github/workflows/e2e_ppo_trainer_megatron_sglang_2.yml

@@ -105,37 +105,6 @@ jobs:
           faas-url: "${{ env.DYNAMIC_RUNNER_ENDPOINT }}"
           mlp-image: "${{ env.IMAGE }}"
 
-  e2e_ppo_trainer_megatron-qwen2_5vl-3b:
-    needs: setup
-    runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"]
-    timeout-minutes: 60 # 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
-    steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
-        with:
-          fetch-depth: 0
-      - name: Install the current repository
-        run: |
-          pip3 install --no-deps -e .[test]
-      - name: Prepare Geo3k dataset
-        run: |
-          python3 examples/data_preprocess/geo3k.py --local_dataset_path ${HOME}/models/hf_data/hiyouga/geometry3k/
-      - name: Prepare dist_ckpt of Qwen2.5-VL-3B, only supports dist_ckpt
-        run: |
-          python3 scripts/converter_hf_to_mcore.py --hf_model_path ${HOME}/models/Qwen/Qwen2.5-VL-3B-Instruct --output_path checkpoints/verl-test/qwen2.5-vl-3b-megatron
-      - name: Running Geo3k E2E training tests with 3D parallelism on 8 L20 GPUs with Megatron (Qwen)
-        run: |
-          ray stop --force
-          ENGINE=sglang ROLLOUT_MODE=async TRAIN_FILES=${HOME}/data/geo3k/train.parquet VAL_FILES=${HOME}/data/geo3k/test.parquet MAX_PROMPT_LENGTH=1024 MAX_RESPONSE_LENGTH=2048  MODEL_ID=Qwen/Qwen2.5-VL-3B-Instruct ADV_ESTIMATOR=grpo USE_DYNAMIC_BSZ=False SKIP_SAVE_HF_MODEL=1 COMMON_PP=4 COMMON_VPP=null COMMON_CP=1 COMMON_TP=2 USE_DIST_CKPT=true DIST_CKPT_PATH=checkpoints/verl-test/qwen2.5-vl-3b-megatron bash tests/special_e2e/run_ppo_trainer_megatron.sh
-      - name: clean up
-        run: |
-          rm -rf checkpoints
-
   e2e_ppo_trainer_fsdp_sglang:
     needs: setup
     runs-on: [ "${{ needs.setup.outputs.runner-label || 'L20x8' }}" ]
@@ -221,7 +190,6 @@ jobs:
     needs:
       [
         setup,
-        e2e_ppo_trainer_megatron-qwen2_5vl-3b,
         e2e_ppo_trainer_fsdp-qwen2_5vl-3b,
         e2e_ppo_trainer_fsdp_sglang,
       ]

.github/workflows/e2e_ppo_trainer_megatron_vllm.yml

@@ -186,11 +186,6 @@ jobs:
         run: |
           ray stop --force
           ALL_OFFLOAD=True VAL_BEFORE_TRAIN=True TEST_FREQ=1 SAVE_FREQ=1 LR_WARMUP_STEPS=1 TOTAL_TRAIN_STEPS=2 MODEL_ID=Qwen/Qwen3-0.6B bash tests/special_e2e/run_ppo_trainer_megatron.sh
-      - name: Test Megatron checkpoints merging function (Qwen3 Actor and Critic)
-        run: |
-          exp_name="qwen3-0.6b-megatron-gsm8k-minimal"
-          python -m verl.model_merger test --backend megatron --tie-word-embedding --local_dir checkpoints/verl-test/${exp_name}/global_step_1/actor --test_hf_dir checkpoints/verl-test/${exp_name}/global_step_1/actor/huggingface
-          python -m verl.model_merger test --backend megatron --is-value-model --local_dir checkpoints/verl-test/${exp_name}/global_step_1/critic --test_hf_dir checkpoints/verl-test/${exp_name}/global_step_1/critic/huggingface
       - name: Running GSM8K E2E training tests with 3D parallelism on 8 L20 GPUs with FP8 rollout
         run: |
           ray stop --force

.github/workflows/e2e_ppo_trainer_megatron_vllm_2.yml

@@ -153,42 +153,6 @@ jobs:
         run: |
           rm -rf checkpoints
 
-  e2e_ppo_trainer_megatron-qwen2_5vl-3b:
-    needs: setup
-    runs-on: ["${{ needs.setup.outputs.runner-label || 'L20x8' }}"]
-    timeout-minutes: 60 # Increase this timeout value as needed
-    env:
-      HTTP_PROXY: ${{ secrets.PROXY_HTTP }}
-      HTTPS_PROXY: ${{ secrets.PROXY_HTTPS }}
-      NO_PROXY: "localhost,127.0.0.1,hf-mirror.com"
-      HF_ENDPOINT: "https://hf-mirror.com"
-      HF_HUB_ENABLE_HF_TRANSFER: "0" # This is more stable
-    steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
-        with:
-          fetch-depth: 0
-      - name: Install the current repository
-        run: |
-          pip3 install --no-deps -e .[test]
-          pip3 install transformers==$TRANSFORMERS_VERSION
-      - name: Prepare Geo3k dataset
-        run: |
-          python3 examples/data_preprocess/geo3k.py --local_dataset_path ${HOME}/models/hf_data/hiyouga/geometry3k/
-      - name: Prepare dist_ckpt of Qwen2.5-VL-3B, only supports dist_ckpt
-        run: |
-          python3 scripts/converter_hf_to_mcore.py --hf_model_path ${HOME}/models/Qwen/Qwen2.5-VL-3B-Instruct --output_path checkpoints/verl-test/qwen2.5-vl-3b-megatron
-      - name: Running Geo3k E2E training tests with 3D parallelism on 8 L20 GPUs with Megatron (Qwen)
-        run: |
-          ray stop --force
-          TRAIN_FILES=${HOME}/data/geo3k/train.parquet VAL_FILES=${HOME}/data/geo3k/test.parquet \
-          MAX_PROMPT_LENGTH=1024 MAX_RESPONSE_LENGTH=2048  MODEL_ID=Qwen/Qwen2.5-VL-3B-Instruct ADV_ESTIMATOR=grpo \
-          USE_DYNAMIC_BSZ=False USE_FUSED_KERNELS=True SKIP_SAVE_HF_MODEL=1 \
-          COMMON_PP=4 COMMON_VPP=null COMMON_CP=1 COMMON_TP=2 USE_DIST_CKPT=true \
-          DIST_CKPT_PATH=checkpoints/verl-test/qwen2.5-vl-3b-megatron bash tests/special_e2e/run_ppo_trainer_megatron.sh
-      - name: clean up
-        run: |
-          rm -rf checkpoints
-
   e2e_ppo_trainer_fsdp_vllm:
     needs: setup
     runs-on: [ "${{ needs.setup.outputs.runner-label || 'L20x8' }}" ]
@@ -330,7 +294,6 @@ jobs:
       [
         setup,
         e2e_ppo_trainer_megatron-moe-expert-parallel,
-        e2e_ppo_trainer_megatron-qwen2_5vl-3b,
         e2e_ppo_trainer_fsdp-qwen2_5vl-3b,
         e2e_ppo_trainer_fsdp_vllm,
       ]

.github/workflows/model.yml

@@ -48,7 +48,6 @@ on:
       # Entrypoints
       - ".github/workflows/model.yml"
       - "tests/special_distributed/test_fsdp_ckpt.py"
-      - "tests/special_distributed/test_mcore_config_converter.py"
       - "tests/special_distributed/test_tensor_dict.py"
       - "tests/models/**"
       - "tests/special_distributed/run_all.sh"
@@ -144,34 +143,6 @@ jobs:
         run: |
           STRATEGY=fsdp2 torchrun --nproc_per_node=8 tests/special_distributed/test_fsdp_ckpt.py
 
-  mcore_config_converter:
-    needs: setup
-    runs-on: [ "${{ needs.setup.outputs.runner-label || 'L20x8' }}" ]
-    timeout-minutes: 20 # 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
-    steps:
-      - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
-        with:
-          fetch-depth: 0
-      - name: Install the current repository
-        run: |
-          pip3 install -e .[test]
-#      - name: Download model config files
-#        run: |
-#          hf download Qwen/Qwen2.5-7B config.json --local-dir $HOME/configs/Qwen/Qwen2.5-7B
-#          hf download Qwen/Qwen3-8B config.json --local-dir $HOME/configs/Qwen/Qwen3-8B
-#          hf download deepseek-ai/deepseek-coder-1.3b-instruct config.json --local-dir $HOME/configs/deepseek-ai/deepseek-coder-1.3b-instruct
-#          hf download Qwen/Qwen2-57B-A14B config.json --local-dir $HOME/configs/Qwen/Qwen2-57B-A14B
-#          hf download Qwen/Qwen3-30B-A3B config.json --local-dir $HOME/configs/Qwen/Qwen3-30B-A3B
-#          hf download deepseek-ai/DeepSeek-V3-Base config.json --local-dir $HOME/configs/deepseek-ai/DeepSeek-V3-Base
-      - name: Running mcore config converter tests on 8 L20 GPUs
-        run: |
-          torchrun --nproc_per_node=8 tests/special_distributed/test_mcore_config_converter.py
 
   model_engine:
     needs: setup
@@ -206,7 +177,6 @@ jobs:
         setup,
         model_rmpad,
         model_rmpad_fsdp2_unstable,
-        mcore_config_converter,
         model_engine
       ]
     if: always()

docs/advance/checkpoint.rst

@@ -137,32 +137,8 @@ Current implementation use solution 2.
 HuggingFace to Megatron DistCheckpoint details
 ----------------------------------------------
 
-If your model is quite huge, we recommend you to use Megatron dist-checkpoint to load the model.
-Megatron dist-checkpoint supports loading with different kinds of model parallelism,
-and it is much faster than the original checkpoint loading.
-
-To convert original HuggingFace model to Megatron dist-checkpoint,
-you can use the ``scripts/converter_hf_to_mcore.py`` script. Large MoE models are temporarily supported with CPU initialization,
-which is a little slower. While we are working on a better solution to support large models.
-
-Example command to convert the model is as follows:
-
-.. code:: bash
-
-    python scripts/converter_hf_to_mcore.py \
-        --hf_model_path Qwen/Qwen1.5-MoE-A2.7B-Chat \
-        --output_path /mnt/disk/Qwen/Qwen1.5-MoE-A2.7B-Chat \
-        --use_cpu_initialization    # Only work for MoE models
-
-
-Example command to distributed convert the huge model like deepseekv3 671B is as follows:
-
-.. code:: bash
-
-    torchrun --nproc_per_node 1 --nnodes 8 --node_rank ${RANK} scripts/converter_hf_to_mcore.py \
-        --hf_model_path deepseek-ai/DeepSeek-V3 \
-        --output_path /mnt/disk/deepseek-ai/DeepSeek-V3 \
-        --use_cpu_initialization    # Only work for MoE models
+Through ``mbridge``, we can directly save the mcore model to huggingface format during training.
+No need to convert the model to Megatron dist-checkpoint format.
 
 Original Checkpoint Utils
 -------------------------