Merge pull request #307 from runpod-workers/fix/revert-0.20.0

revert to 0.20.2

.github/workflows/CI-readme_version_sync.yml

@@ -0,0 +1,71 @@
+name: CI | Sync vLLM version in READMEs
+
+on:
+  push:
+    branches: ["main"]
+    paths:
+      - "Dockerfile"
+
+  workflow_dispatch:
+
+permissions:
+  contents: write
+  pull-requests: write
+
+jobs:
+  sync_version:
+    runs-on: ubuntu-latest
+    name: Check README version matches Dockerfile and update if needed
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Extract vLLM version from Dockerfile and sync READMEs
+        run: |
+          echo "Extracting vLLM version from Dockerfile..."
+          dockerfile_version=$(grep -oP 'vllm(?:\[[\w,]+\])?==\K[\d.]+' Dockerfile | head -1)
+
+          if [ -z "$dockerfile_version" ]; then
+            echo "ERROR: Could not extract vLLM version from Dockerfile."
+            exit 1
+          fi
+          echo "Dockerfile vLLM version: $dockerfile_version"
+          echo "VLLM_VERSION=$dockerfile_version" >> $GITHUB_ENV
+
+          updated=0
+          for readme in README.md .runpod/README.md; do
+            if [ ! -f "$readme" ]; then
+              echo "Skipping $readme (not found)"
+              continue
+            fi
+
+            readme_version=$(grep -oP 'Current vLLM version: \[\K[\d.]+' "$readme" || echo "")
+            echo "$readme current version: ${readme_version:-not found}"
+
+            if [ "$readme_version" = "$dockerfile_version" ]; then
+              echo "$readme is already up to date."
+              continue
+            fi
+
+            echo "Updating $readme from $readme_version to $dockerfile_version..."
+            sed -i "s|Current vLLM version: \[${readme_version}\](https://github.com/vllm-project/vllm/releases/tag/v${readme_version})|Current vLLM version: [${dockerfile_version}](https://github.com/vllm-project/vllm/releases/tag/v${dockerfile_version})|g" "$readme"
+            updated=1
+          done
+
+          echo "UPDATED=$updated" >> $GITHUB_ENV
+
+      - name: Create Pull Request
+        if: env.UPDATED == '1'
+        uses: peter-evans/create-pull-request@v7
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}
+          commit-message: "docs: sync vLLM version to ${{ env.VLLM_VERSION }} in READMEs"
+          title: "docs: sync vLLM version to ${{ env.VLLM_VERSION }} in READMEs"
+          body: |
+            The vLLM version in the Dockerfile has been updated to `${{ env.VLLM_VERSION }}`.
+
+            This PR syncs the version badge/link in:
+            - `README.md`
+            - `.runpod/README.md`
+          branch: docs/sync-vllm-version-${{ env.VLLM_VERSION }}
+          labels: documentation

.runpod/README.md

@@ -6,7 +6,7 @@ Run LLMs using [vLLM](https://docs.vllm.ai) with an OpenAI-compatible API
 
 [![RunPod](https://api.runpod.io/badge/runpod-workers/worker-vllm)](https://www.runpod.io/console/hub/runpod-workers/worker-vllm)
 
-Current vLLM version: [0.20.2](https://github.com/vllm-project/vllm/releases/tag/v0.20.2)
+Current vLLM version: [0.22.1](https://github.com/vllm-project/vllm/releases/tag/v0.22.1)
 
 ---
 
@@ -33,6 +33,17 @@ All behaviour is controlled through environment variables:
 
 **Pass any vLLM engine arg** not listed above by setting an env var with the **UPPERCASED** field name (e.g. `MAX_MODEL_LEN=4096`, `ENABLE_CHUNKED_PREFILL=true`). The worker auto-discovers all `AsyncEngineArgs` fields from env. See the [vLLM engine args docs](https://docs.vllm.ai/en/latest/configuration/engine_args) for all available options.
 
+**Configuration file:** You can also supply a `config.yaml` instead of (or alongside) env vars. Mount it at `/vllm_config.yaml` in the container, or set `VLLM_CONFIG_FILE` to a custom path. Use the same key names as `vllm serve` — hyphens and underscores both work:
+
+```yaml
+model: meta-llama/Llama-3.1-8B-Instruct
+max-model-len: 8192
+gpu-memory-utilization: 0.90
+quantization: awq
+```
+
+Environment variables always override config file values.
+
 For complete configuration options, see the [full configuration documentation](https://github.com/runpod-workers/worker-vllm/blob/main/docs/configuration.md).
 
 ### Specify Transformers Version

.runpod/tests.json

@@ -5,7 +5,7 @@
       "input": {
         "prompt": "Write a short poem about artificial intelligence."
       },
-      "timeout": 30000
+      "timeout": 300000
     },
     {
       "name": "openai_messages_test",
@@ -26,11 +26,11 @@
           "temperature": 0.1
         }
       },
-      "timeout": 30000
+      "timeout": 300000
     }
   ],
   "config": {
-    "gpuTypeId": "NVIDIA GeForce RTX 4090",
+    "gpuTypeId": "NVIDIA L40",
     "gpuCount": 1,
     "env": [
       {
@@ -38,6 +38,6 @@
         "value": "HuggingFaceTB/SmolLM2-135M-Instruct"
       }
     ],
-    "allowedCudaVersions": ["12.9", "12.8", "12.7", "12.6", "12.5"]
+    "allowedCudaVersions": ["13.0"]
   }
 }

README.md

@@ -8,7 +8,7 @@ Deploy OpenAI-Compatible Blazing-Fast LLM Endpoints powered by the [vLLM](https:
 
 ![vLLM worker banner](https://image.runpod.ai/preview/vllm/vllm-banner.png)
 
-Current vLLM version: [0.20.2](https://github.com/vllm-project/vllm/releases/tag/v0.20.2)
+Current vLLM version: [0.22.1](https://github.com/vllm-project/vllm/releases/tag/v0.22.1)
 
 
 > Check out our Load Balancer implementation here: [vLLM Load Balancer](https://github.com/runpod-workers/vllm-loadbalancer-ep)
@@ -78,6 +78,20 @@ Configure worker-vllm using environment variables:
 
 Any env var whose name matches a valid `AsyncEngineArgs` field (uppercased) is applied automatically. Backward-compat aliases: `MODEL_NAME`, `TOKENIZER_NAME`, `MAX_CONTEXT_LEN_TO_CAPTURE`. This lets you configure any vLLM option without waiting for explicit worker support.
 
+### Configuration File (config.yaml)
+
+As an alternative to environment variables, you can supply a `config.yaml` file using the same key names as `vllm serve` (hyphens or underscores both work):
+
+```yaml
+model: meta-llama/Llama-3.1-8B-Instruct
+max-model-len: 8192
+gpu-memory-utilization: 0.90
+quantization: awq
+tensor-parallel-size: 2
+```
+
+Mount the file into the container at `/vllm_config.yaml`, or point to a custom path with the `VLLM_CONFIG_FILE` env var. Environment variables always take precedence over config file values.
+
 For the complete list of all available environment variables, examples, and detailed descriptions: **[Configuration](docs/configuration.md)**
 
 ### Specify Transformers Version

builder/requirements.txt

@@ -1,7 +1,7 @@
 ray
 pandas
 pyarrow
-runpod==1.9.0
+runpod==1.9.1
 huggingface-hub
 lmcache==0.4.5
 packaging>=24.2
@@ -11,5 +11,5 @@ pydantic-settings
 hf-transfer
 transformers>=5
 bitsandbytes>=0.45.0
-kernels
+kernels<0.15
 torch-c-dlpack-ext

configs/llama/llama3.1_8b_instruct.yaml

@@ -0,0 +1,10 @@
+model: meta-llama/Llama-3.1-8B-Instruct
+gpu-memory-utilization: 0.95
+max-model-len: 8192
+dtype: auto
+trust-remote-code: true
+quantization: fp8
+kv-cache-dtype: fp8
+enforce-eager: false
+enable-prefix-caching: true
+speculative-config: '{"model":"RedHatAI/Llama-3.1-8B-Instruct-speculator.eagle3","method":"eagle3","num_speculative_tokens":3}'

configs/qwen/qwen3_8b.yaml

@@ -0,0 +1,10 @@
+model: Qwen/Qwen3-8B
+gpu-memory-utilization: 0.95
+max-model-len: 8192
+dtype: auto
+trust-remote-code: true
+quantization: fp8
+kv-cache-dtype: fp8
+enforce-eager: false
+enable-prefix-caching: true
+speculative-config: '{"model":"RedHatAI/Qwen3-8B-speculator.eagle3","method":"eagle3","num_speculative_tokens":3}'

src/engine_args.py

@@ -404,6 +404,23 @@ def _resolve_cached_model_path(model_name: str) -> str:
     return resolved
 
 
+def _get_args_from_config_file() -> dict:
+    """Load engine args from a vLLM-style config.yaml.
+
+    Checks VLLM_CONFIG_FILE env var, then falls back to /vllm_config.yaml.
+    Keys use the same long-form names as vllm serve (hyphens converted to underscores).
+    """
+    import yaml
+    path = os.getenv("VLLM_CONFIG_FILE", "/vllm_config.yaml")
+    if not os.path.exists(path):
+        return {}
+    with open(path) as f:
+        raw = yaml.safe_load(f) or {}
+    normalized = {k.replace("-", "_"): v for k, v in raw.items()}
+    logging.info("Loaded engine args from config file %s: %s", path, list(normalized.keys()))
+    return normalized
+
+
 def get_local_args():
     """
     Retrieve local arguments from a JSON file.
@@ -429,6 +446,9 @@ def get_engine_args():
     # Start with worker custom defaults (only where we differ from vLLM)
     args = dict(DEFAULT_ARGS)
 
+    # Config file values sit above defaults but below env vars
+    args.update(_get_args_from_config_file())
+
     # Auto-discover: every AsyncEngineArgs field from env UPPERCASED (e.g. MAX_MODEL_LEN)
     args.update(_get_args_from_env_auto_discover())