Merge branch 'main' into yuya/bug-hunter-gpt3-alias-20260718

Author: yaoyu-33

.dev.commit

@@ -1 +1 @@
-b3b1e2d793c596c2fa61076d001ab93f294e4e87
+2f2f8ebaef90f4d62a18a43808398223b1fc5809

.github/scripts/test_docker_dependency_layers.sh

@@ -252,9 +252,18 @@ if ! grep -Fq \
   echo "Bridge's lock metadata must preserve the MCore TE extra" >&2
   exit 1
 fi
-if grep -q 'transformer-engine @ git+https://github.com/NVIDIA/TransformerEngine.git@' pyproject.toml || \
-  grep -q '^name = "transformer-engine"$' pyproject.toml; then
-  echo "Bridge must inherit the TransformerEngine source and metadata from the selected MCore ref" >&2
+if grep -q 'transformer-engine @ git+https://github.com/NVIDIA/TransformerEngine.git@' pyproject.toml; then
+  echo "Bridge must inherit the TransformerEngine source from the selected MCore ref" >&2
+  exit 1
+fi
+
+if ! grep -Fq '    NVTE_SKIP_SUBMODULE_CHECKS_DURING_BUILD=1 \' "$dockerfile"; then
+  echo "CI builds must disable TransformerEngine's recursive build-time submodule fetch" >&2
+  exit 1
+fi
+if grep -R -qE 'git submodule update.*--recursive|git submodule update --init --recursive' \
+  "$dockerfile" docker .github/actions; then
+  echo "CI build surfaces must not fetch recursive submodules at build time" >&2
   exit 1
 fi
 

.github/workflows/verl-e2e-weekly.yml

@@ -130,6 +130,7 @@ jobs:
               cd /workspace/verl
               git checkout "$VERL_REF"
               git rev-parse HEAD
+              python3 -m pip install uv
               bash "$SCRIPT_PATH"
             '
 

.main.commit

@@ -1 +1 @@
-2a75ac12c54ba6a42b34024756820bf819a2e25f
+731b791469004f8fdcb896e65d610d6b1b0ebb32

3rdparty/Megatron-LM

@@ -11,6 +11,8 @@
 </div>
 
 ## 📣 News
+- [08/19/2026] **Megatron Bridge 0.6.0 released!** Highlights include GLM-5.2 with cuDNN fused DSA for 128K THD-packed context-parallel training, new LLM/VLM/diffusion models, more reliable VLM data and sequence packing, improved checkpoint and LoRA workflows, unified training and conversion launchers, and refreshed performance recipes. Huge thanks to our community contributors! See the [full release notes](https://github.com/NVIDIA-NeMo/Megatron-Bridge/releases/tag/v0.6.0).
+
 - [08/11/2026] **NVIDIA Nemotron 3.5 Lightning is released!** Read the technical blog [here](https://developer.nvidia.com/blog/nvidia-nemotron-3-5-lightning-delivers-fast-accurate-specialized-task-execution-for-long-running-agents/). See the [Nemotron 3.5 Lightning README](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/main/examples/models/nemotron/nemotron_3/lightning/README.md) for details.
 
 - [07/31/2026] **[K-EXAONE-2](https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B) is now supported**! Day-0 support for LG AI Research's K-EXAONE-2 (750B-A37B MoE) is now available on the [k-exaone-2 branch](https://github.com/NVIDIA-NeMo/Megatron-Bridge/tree/k-exaone-2). Users can convert checkpoints, run inference, fine-tune with SFT or PEFT (LoRA), and use the NVFP4 inference-optimized checkpoint released alongside the model. See the branch documentation for setup instructions and examples.

README.md

@@ -55,6 +55,7 @@ ENV PATH="/opt/venv/bin:/usr/local/bin:$PATH" \
     UV_VERSION="0.7.2" \
     UV_HTTP_TIMEOUT=120 \
     NVTE_BUILD_NUM_PHILOX_ROUNDS=3 \
+    NVTE_SKIP_SUBMODULE_CHECKS_DURING_BUILD=1 \
     HYBRID_EP_MULTINODE=1 \
     RDMA_CORE_HOME=/opt/rdma-core/build \
     LD_LIBRARY_PATH="/usr/local/cuda/lib64/:$LD_LIBRARY_PATH"
@@ -192,7 +193,7 @@ COPY --chown=1000:1000 . /opt/Megatron-Bridge
 # prepared environment, and uses /nemo_run for runtime outputs. Keep those paths owned by the
 # existing unprivileged base-image user instead of relying on a root final user.
 RUN install -d -o 1000 -g 1000 -m 0755 /nemo_run /home/ubuntu/.cache && \
-    chown 1000:1000 /opt/Megatron-Bridge
+    chown -R 1000:1000 /opt/Megatron-Bridge
 
 ENV HOME=/home/ubuntu
 USER 1000:1000

docker/Dockerfile.ci

@@ -13,10 +13,12 @@
 # limitations under the License.
 
 # Build layer variables
-# Supported mode: FW_DEP_BUILDER=base, FW_BASE_FINAL=fw_toolkit_builder
+# Supported mode: FW_DEP_BUILDER=base, FW_BASE_FINAL=fw_toolkit_builder,
+# VLLM_WHEEL_SRC=vllm_wheel_build|vllm_wheel_none
 # TRT-LLM stages are not present in this Dockerfile.
 ARG FW_DEP_BUILDER
 ARG FW_BASE_FINAL
+ARG VLLM_WHEEL_SRC=vllm_wheel_build
 
 ARG NEMO_FW_BASE_IMAGE
 FROM ${NEMO_FW_BASE_IMAGE} AS base
@@ -31,7 +33,7 @@ ENV NVIDIA_PRODUCT_NAME="NeMo Framework" \
 ##
 ##############################################################################
 
-FROM base AS vllm_wheel
+FROM base AS vllm_wheel_build
 
 ARG VLLM_VERSION=v0.14.1
 ARG MAX_JOBS=4
@@ -47,6 +49,13 @@ RUN mkdir -p /src/vllm && \
     pip install -r requirements/build/cuda.txt && \
     pip wheel --no-deps --no-build-isolation -v .
 
+# /src/vllm must exist for the install step's bind mount.
+FROM base AS vllm_wheel_none
+
+RUN mkdir -p /src/vllm
+
+FROM ${VLLM_WHEEL_SRC} AS vllm_wheel
+
 ##############################################################################
 ##
 ## Top layer of FW_BASE container
@@ -127,10 +136,14 @@ RUN --mount=type=bind,source=docker/common/install_nsys.sh,target=/opt/install_n
 # Copy Wheels
 RUN --mount=type=bind,from=vllm_wheel,source=/src/vllm/,target=/tmp/vllm/ \
     --mount=type=bind,source=docker/patches/vllm.patch,target=/opt/vllm.patch \
+    if ! compgen -G "/tmp/vllm/vllm*.whl" >/dev/null; then \
+    echo "No vLLM wheel produced; skipping vLLM install."; \
+    else \
     pip install /tmp/vllm/vllm*.whl && \
     pushd /usr/local/lib/python3.12/dist-packages/vllm && \
     patch -p1 < /opt/vllm.patch && \
-    popd
+    popd; \
+    fi
 
 ##############################################################################
 ##

docker/Dockerfile.fw_base

@@ -132,6 +132,7 @@ docker build \
 | `FW_BASE_FINAL` | Output stage. `trtllm_install` (with TRT-LLM) or `fw_toolkit_builder` (without) |
 | `UV_VERSION` | uv version to install |
 | `VLLM_VERSION` | vLLM git tag to build |
+| `VLLM_WHEEL_SRC` | Stage supplying the vLLM wheel. `vllm_wheel_build` (default) builds it from source; `vllm_wheel_none` skips both the build and the install |
 | `TRT_LLM_COMMIT` | TensorRT-LLM git commit or tag |
 | `TRT_LLM_VERSION` | TensorRT-LLM version string embedded as an image environment variable |
 | `TRT_VER` | TensorRT version for the TRT-LLM install scripts |

docker/README.md

@@ -76,24 +76,46 @@ Translate your RL framework config into Megatron Bridge's `ConfigContainer` for
 
 ```python
 import torch
+from megatron.bridge import AutoBridge
 from megatron.bridge.training.config import (
     ConfigContainer,
     TrainingConfig,
     OptimizerConfig,
     SchedulerConfig,
     DistributedDataParallelConfig,
     CheckpointConfig,
+    LoggerConfig,
     TokenizerConfig,
 )
 from nemo_rl.models.policy import PolicyConfig  # or your own policy cfg type
 
 # Example: map your RL config to Megatron config
 def build_megatron_config(rl_cfg: PolicyConfig, pretrained_ckpt_dir: str) -> ConfigContainer:
-    model_cfg = rl_cfg["megatron_cfg"].copy()
+    bridge = AutoBridge.from_hf_pretrained(rl_cfg["model_name"])
+    model_cfg = bridge.to_megatron_provider(load_weights=False)
+
+    # Keep framework-owned optimizer/scheduler/DDP mappings out of the model
+    # provider. Apply only provider fields, with Bridge validating each name.
+    provider_fields = (
+        "tensor_model_parallel_size",
+        "pipeline_model_parallel_size",
+        "context_parallel_size",
+        "expert_model_parallel_size",
+        "expert_tensor_parallel_size",
+        "sequence_parallel",
+        "recompute_granularity",
+        "recompute_method",
+        "recompute_num_layers",
+    )
+    model_overrides = {name: rl_cfg["megatron_cfg"][name] for name in provider_fields if name in rl_cfg["megatron_cfg"]}
+
     # Precision
-    dtype = rl_cfg["precision"]
-    model_cfg["bf16"] = dtype == "bfloat16"
-    model_cfg["fp16"] = dtype == "float16"
+    dtype = {
+        "float32": torch.float32,
+        "bfloat16": torch.bfloat16,
+        "float16": torch.float16,
+    }[rl_cfg["precision"]]
+    model_cfg.apply_overrides_and_finalize(dtype=dtype, overrides=model_overrides)
 
     checkpoint = CheckpointConfig(
         save_interval=100,
@@ -130,17 +152,19 @@ def build_megatron_config(rl_cfg: PolicyConfig, pretrained_ckpt_dir: str) -> Con
         tokenizer_model=rl_cfg["model_name"],
     )
 
-    return ConfigContainer(
+    cfg = ConfigContainer(
         model=model_cfg,
         checkpoint=checkpoint,
-        logger=None,
+        logger=LoggerConfig(),
         train=train,
         optimizer=opt,
         ddp=ddp,
         scheduler=sch,
         dataset=None,
         tokenizer=tokenizer,
     )
+    cfg.validate()
+    return cfg
 ```
 
 Initialize Megatron-Core using a helper similar to `setup_megatron_model` from NeMo-RL: