Shaunak/vllm specdec metrics

docker/Dockerfile

@@ -114,7 +114,8 @@ fi
 # The venv is symlinked to avoid bloating the layer size
 uv sync --link-mode symlink --locked --no-install-project
 uv sync --link-mode symlink --locked --extra vllm --no-install-project
-uv sync --link-mode symlink --locked --extra sglang --no-install-project
+# Skipped: sgl-kernel build broken (DeepGEMM ref 54f99a8 missing upstream)
+# uv sync --link-mode symlink --locked --extra sglang --no-install-project
 uv sync --link-mode symlink --locked --extra mcore --no-install-project
 uv sync --link-mode symlink --locked --extra automodel --no-install-project
 uv sync --link-mode symlink --locked --all-groups --no-install-project

docs/repro-spec-decode-build.md

@@ -0,0 +1,264 @@
+# Reproduction Report: NeMo-RL v0.5.0 + vLLM Speculative Decoding with Draft Models
+
+**Date:** 2026-02-10
+**Author:** Shaunak J
+**Host:** viking-prod-228 (8x NVIDIA H20, computelab-sc-01 cluster)
+
+## Overview
+
+This report documents how to build a custom NeMo-RL Docker image that includes vLLM speculative decoding with draft models (vLLM PR #24322), and how to run GRPO training with it.
+
+## Source Components
+
+| Component | Source | Commit / Version | How Obtained |
+|-----------|--------|-------------------|--------------|
+| NeMo-RL | `https://github.com/NVIDIA-NeMo/RL.git` | `946862e7` (HEAD of `main` on 2026-02-10) | Already cloned locally |
+| vLLM (custom) | `https://github.com/vllm-project/vllm.git` | `4a5299c93ff97c26def537b92562df5ada530fea` | Merge commit of PR #24322 |
+| Precompiled vLLM wheel | `https://wheels.vllm.ai` | Same commit as above | Auto-downloaded during build |
+| Base Docker image | `nvcr.io/nvidia/cuda-dl-base:25.05-cuda12.9-devel-ubuntu24.04` | Pulled by Dockerfile | From NGC registry |
+
+## Commit Hash Provenance
+
+### `946862e7` — NeMo-RL
+
+The latest commit on the NeMo-RL `main` branch at the time of this build:
+
+```
+946862e7 docs: add release runs to front page readme for 0.5 (#1879)
+```
+
+This is the same codebase as the `nvcr.io/nvidia/nemo-rl:v0.5.0` release.
+
+### `4a5299c93ff97c26def537b92562df5ada530fea` — vLLM
+
+The merge commit of [vllm-project/vllm#24322](https://github.com/vllm-project/vllm/pull/24322) ("feat: spec decode with draft models"), merged on 2026-01-19. Retrieved via:
+
+```bash
+gh pr view 24322 --repo vllm-project/vllm --json mergeCommit
+# => 4a5299c93ff97c26def537b92562df5ada530fea
+```
+
+The precompiled wheel for this commit exists at:
+```
+https://wheels.vllm.ai/4a5299c93ff97c26def537b92562df5ada530fea/vllm-0.14.0rc2.dev156%2Bg4a5299c93-cp38-abi3-manylinux_2_31_x86_64.whl
+```
+
+This was verified by HTTP HEAD request returning 200.
+
+### Replaced defaults in `tools/build-custom-vllm.sh`
+
+The build script had two hardcoded defaults that were written by the NeMo-RL team for their v0.10-era vLLM pin:
+
+- `GIT_REF` default: `cc99baf14dacc2497d0c5ed84e076ef2c37f6a4d` — the previous vLLM commit pin
+- `VLLM_WHEEL_COMMIT` default: `862f2ef893d9751db0a92bd2d4ae0e3d9677872f` — chosen as `git merge-base --fork-point origin/main tags/v0.10.0`
+
+Both were replaced with the PR #24322 merge commit. The wheel URL template was also updated because vLLM changed their naming convention from `vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl` to `vllm-<version>-cp38-abi3-manylinux_2_31_x86_64.whl`.
+
+## Local Modifications
+
+### 1. `tools/build-custom-vllm.sh`
+
+**Line 24** — Changed default `GIT_REF`:
+```diff
+-GIT_REF=${2:-cc99baf14dacc2497d0c5ed84e076ef2c37f6a4d}
++GIT_REF=${2:-4a5299c93ff97c26def537b92562df5ada530fea}
+```
+
+**Lines 27-28** — Changed default wheel commit and URL pattern, made env var overridable:
+```diff
+-VLLM_WHEEL_COMMIT=${3:-862f2ef893d9751db0a92bd2d4ae0e3d9677872f}  # use full commit hash from the main branch
+-export VLLM_PRECOMPILED_WHEEL_LOCATION="https://wheels.vllm.ai/${VLLM_WHEEL_COMMIT}/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl"
++VLLM_WHEEL_COMMIT=${3:-4a5299c93ff97c26def537b92562df5ada530fea}  # merge commit of vllm PR #24322 (spec decode with draft models)
++export VLLM_PRECOMPILED_WHEEL_LOCATION="${VLLM_PRECOMPILED_WHEEL_LOCATION:-https://wheels.vllm.ai/${VLLM_WHEEL_COMMIT}/vllm-0.14.0rc2.dev156%2Bg4a5299c93-cp38-abi3-manylinux_2_31_x86_64.whl}"
+```
+
+### 2. `docker/Dockerfile`
+
+**Line 117** — Commented out sglang extra (broken upstream dependency):
+```diff
+-uv sync --link-mode symlink --locked --extra sglang --no-install-project
++# Skipped: sgl-kernel build broken (DeepGEMM ref 54f99a8 missing upstream)
++# uv sync --link-mode symlink --locked --extra sglang --no-install-project
+```
+
+**Reason:** The `sgl-kernel` build fails because it tries to fetch DeepGEMM at commit `54f99a8af537b3c6eb4819b69907ccbe2b600792` which no longer exists in the upstream repo. This only affects the sglang inference backend, not vLLM.
+
+## Step-by-Step Reproduction
+
+### Prerequisites
+
+- Docker with buildx support
+- GPU node with NVIDIA drivers
+- ~100 GB disk space for the build
+- Internet access (to pull base image, clone vLLM, download wheel, download HF models)
+
+### 1. Clone and prepare the source
+
+```bash
+git clone https://github.com/NVIDIA-NeMo/RL.git
+cd RL
+git checkout 946862e7
+
+# Initialize submodules (required for Megatron-LM, Automodel, etc.)
+git submodule update --init --depth 1
+```
+
+### 2. Apply patches to build-custom-vllm.sh
+
+```bash
+# Update GIT_REF default to PR #24322 merge commit
+sed -i 's|GIT_REF=${2:-cc99baf14dacc2497d0c5ed84e076ef2c37f6a4d}|GIT_REF=${2:-4a5299c93ff97c26def537b92562df5ada530fea}|' \
+  tools/build-custom-vllm.sh
+
+# Update VLLM_WHEEL_COMMIT default
+sed -i 's|VLLM_WHEEL_COMMIT=${3:-862f2ef893d9751db0a92bd2d4ae0e3d9677872f}.*|VLLM_WHEEL_COMMIT=${3:-4a5299c93ff97c26def537b92562df5ada530fea}  # merge commit of vllm PR #24322|' \
+  tools/build-custom-vllm.sh
+
+# Update wheel URL pattern and make it overridable
+sed -i 's|^export VLLM_PRECOMPILED_WHEEL_LOCATION="https://wheels.vllm.ai/${VLLM_WHEEL_COMMIT}/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl"|export VLLM_PRECOMPILED_WHEEL_LOCATION="${VLLM_PRECOMPILED_WHEEL_LOCATION:-https://wheels.vllm.ai/${VLLM_WHEEL_COMMIT}/vllm-0.14.0rc2.dev156%2Bg4a5299c93-cp38-abi3-manylinux_2_31_x86_64.whl}"|' \
+  tools/build-custom-vllm.sh
+```
+
+### 3. Apply patch to Dockerfile
+
+```bash
+sed -i 's|^uv sync --link-mode symlink --locked --extra sglang --no-install-project$|# Skipped: sgl-kernel build broken (DeepGEMM ref missing upstream)\n# uv sync --link-mode symlink --locked --extra sglang --no-install-project|' \
+  docker/Dockerfile
+```
+
+### 4. Build the Docker image
+
+```bash
+docker buildx build \
+  --build-arg BUILD_CUSTOM_VLLM=1 \
+  --target release \
+  --build-context nemo-rl=. \
+  -f docker/Dockerfile \
+  --tag nemo-rl:v0.5.0-spec-decode \
+  .
+```
+
+This takes approximately 20-30 minutes. The build will:
+1. Pull the CUDA base image
+2. Install system dependencies, uv, Python 3.12
+3. Clone vLLM at commit `4a5299c` and build it with the precompiled wheel
+4. Sync all dependency extras (vllm, mcore, automodel) except sglang
+5. Prefetch Ray worker virtual environments
+6. Generate container fingerprint
+
+### 5. Save the image (optional)
+
+```bash
+docker save nemo-rl:v0.5.0-spec-decode | gzip > nemo-rl-v0.5.0-spec-decode.tar.gz
+```
+
+The resulting image is approximately 21 GB compressed.
+
+To load on another machine:
+```bash
+docker load < nemo-rl-v0.5.0-spec-decode.tar.gz
+```
+
+### 6. Run the container
+
+```bash
+docker run --gpus all --ipc=host \
+  --ulimit memlock=-1 --ulimit stack=67108864 \
+  -v /path/to/your/scratch:/path/to/your/scratch \
+  -it nemo-rl:v0.5.0-spec-decode bash
+```
+
+### 7. Create the training config
+
+Save as `grpo-qwen3-4b-spec-decode-1n8g.yaml`:
+
+```yaml
+defaults: ../../grpo_math_1B.yaml
+grpo:
+  max_num_steps: 200
+  num_prompts_per_step: 32
+  num_generations_per_prompt: 16
+checkpointing:
+  checkpoint_dir: results/grpo-qwen3-4b-spec-decode-1n8g
+policy:
+  model_name: Qwen/Qwen3-4B
+  tokenizer:
+    name: Qwen/Qwen3-4B
+  train_global_batch_size: 512
+  train_micro_batch_size: 1
+  logprob_batch_size: 1
+  max_total_sequence_length: 2048
+  dynamic_batching:
+    enabled: true
+  sequence_packing:
+    enabled: false
+  make_sequence_length_divisible_by: 1
+  generation:
+    max_new_tokens: 1024
+    vllm_cfg:
+      tensor_parallel_size: 1
+      gpu_memory_utilization: 0.85
+      max_model_len: 2048
+    vllm_kwargs:
+      speculative_config:
+        method: draft_model
+        model: Qwen/Qwen3-0.6B
+        num_speculative_tokens: 5
+        draft_tensor_parallel_size: 1
+data:
+  max_input_seq_length: 1024
+logger:
+  log_dir: logs/grpo-qwen3-4b-spec-decode-1n8g
+  wandb_enabled: false
+  tensorboard_enabled: true
+cluster:
+  gpus_per_node: 8
+```
+
+**Model pairing note:** The target and draft models must have the same vocabulary size. Qwen3 models all use vocab_size=151936 across all sizes, so any Qwen3 pair works. Qwen2.5 models have inconsistent vocab sizes (7B+ use 152064, smaller models use 151936), which prevents cross-size draft pairing.
+
+### 8. Run GRPO training
+
+```bash
+python examples/run_grpo.py \
+  --config /path/to/grpo-qwen3-4b-spec-decode-1n8g.yaml \
+  ++logger.log_dir=/path/to/your/scratch/logs/grpo-qwen3-4b-spec-decode \
+  ++checkpointing.checkpoint_dir=/path/to/your/scratch/results/grpo-qwen3-4b-spec-decode
+```
+
+**Important:** Use `python` (not `uv run`) inside the container, as custom vLLM containers use frozen environments. See `docs/guides/use-custom-vllm.md` for details.
+
+## Verified Results
+
+Training was verified running on viking-prod-228 (8x H20 GPUs). Key metrics from the first 3 steps:
+
+| Metric | Step 1 | Step 2 | Step 3 |
+|--------|--------|--------|--------|
+| Loss | -0.035 | -0.014 | -0.016 |
+| Avg Reward | 0.340 | 0.272 | 0.229 |
+| Mean Gen Length | 1008 | 1015 | 1001 |
+| E2E Tokens/sec (total) | 2488 | 2564 | 2555 |
+| Step Time | 228s | 224s | 224s |
+| Generation % of step | 51.5% | 46.2% | 46.2% |
+
+vLLM confirmed speculative decoding active:
+```
+Initializing a V1 LLM engine (v0.14.0rc2.dev156+g4a5299c93.d20260210)
+  speculative_config=SpeculativeConfig(method='draft_model', model='Qwen/Qwen3-0.6B', num_spec_tokens=5)
+Loading drafter model...
+Starting to load draft model Qwen/Qwen3-0.6B. TP=1, rank=0
+Model loading took 8.67 GiB memory
+```
+
+## Known Issues
+
+1. **sglang extra is disabled** — `sgl-kernel` cannot build due to a missing DeepGEMM commit upstream. This does not affect vLLM-based inference or training.
+2. **Async scheduling disabled with draft model spec decode** — vLLM logs: "Async scheduling not supported with draft_model-based speculative decoding and will be disabled." This is expected behavior from the vLLM implementation.
+3. **`min_p`, `logit_bias`, `min_tokens` unsupported with spec decode** — vLLM warning, not an issue for standard GRPO training.
+
+## File Locations (on viking-prod-228)
+
+- Source: `/home/scratch.shaunakj_other/Development/RL/`
+- Saved image: `/home/scratch.shaunakj_other/Development/nemo-rl-v0.5.0-spec-decode.tar.gz`
+- Training logs: `/tmp/logs/grpo-qwen3-4b-spec-decode/exp_001/`
+- Training config: `/home/scratch.shaunakj_other/Development/RL/examples/configs/recipes/llm/grpo-qwen2.5-7b-spec-decode-1n8g.yaml`

examples/configs/recipes/llm/grpo-qwen2.5-7b-spec-decode-1n8g.yaml

@@ -0,0 +1,46 @@
+defaults: ../../grpo_math_1B.yaml
+grpo:
+  max_num_steps: 200
+  num_prompts_per_step: 32
+  num_generations_per_prompt: 16
+checkpointing:
+  checkpoint_dir: results/grpo-qwen2.5-7b-spec-decode-1n8g
+policy:
+  model_name: Qwen/Qwen3-4B
+  tokenizer:
+    name: Qwen/Qwen3-4B
+  train_global_batch_size: 512
+  train_micro_batch_size: 1
+  logprob_batch_size: 1
+  max_total_sequence_length: 2048
+  dynamic_batching:
+    enabled: true
+  sequence_packing:
+    enabled: false
+  make_sequence_length_divisible_by: 1
+  generation:
+    max_new_tokens: 1024
+    vllm_cfg:
+      tensor_parallel_size: 1
+      gpu_memory_utilization: 0.85
+      max_model_len: 2048
+      async_engine: true
+      enable_vllm_metrics_logger: true
+      vllm_metrics_logger_interval: 0.5
+    vllm_kwargs:
+      speculative_config:
+        method: draft_model
+        model: Qwen/Qwen3-0.6B
+        num_speculative_tokens: 5
+        draft_tensor_parallel_size: 1
+data:
+  max_input_seq_length: 1024
+logger:
+  log_dir: logs/grpo-qwen2.5-7b-spec-decode-1n8g
+  wandb_enabled: false
+  tensorboard_enabled: true
+  wandb:
+    project: nemo-rl
+    name: grpo-qwen2.5-7b-spec-decode-1n8g
+cluster:
+  gpus_per_node: 8

examples/configs/recipes/llm/grpo-qwen3-14b-draft0.6b-spec-decode-1n8g.yaml

@@ -0,0 +1,43 @@
+defaults: /opt/nemo-rl/examples/configs/grpo_math_1B.yaml
+grpo:
+  max_num_steps: 200
+  num_prompts_per_step: 32
+  num_generations_per_prompt: 16
+checkpointing:
+  checkpoint_dir: results/grpo-qwen3-14b-draft0.6b-spec-decode-1n8g
+policy:
+  model_name: Qwen/Qwen3-14B
+  tokenizer:
+    name: Qwen/Qwen3-14B
+  train_global_batch_size: 512
+  train_micro_batch_size: 1
+  logprob_batch_size: 1
+  max_total_sequence_length: 2048
+  dynamic_batching:
+    enabled: true
+  sequence_packing:
+    enabled: false
+  make_sequence_length_divisible_by: 1
+  generation:
+    max_new_tokens: 1024
+    vllm_cfg:
+      tensor_parallel_size: 2
+      gpu_memory_utilization: 0.80
+      max_model_len: 2048
+      async_engine: true
+      enable_vllm_metrics_logger: true
+      vllm_metrics_logger_interval: 0.5
+    vllm_kwargs:
+      speculative_config:
+        method: draft_model
+        model: Qwen/Qwen3-0.6B
+        num_speculative_tokens: 5
+        draft_tensor_parallel_size: 2
+data:
+  max_input_seq_length: 1024
+logger:
+  log_dir: logs/grpo-qwen3-14b-draft0.6b-spec-decode-1n8g
+  wandb_enabled: false
+  tensorboard_enabled: true
+cluster:
+  gpus_per_node: 8