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add ENABLE_EXPERT_PARALLEL engine arg for MoE models (#239) 
* enable expert parallel arg for moe models

* add ENABLE_EXPERT_PARALLEL to hub config
2025-11-17 19:25:19 +01:00

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# Configuration Reference
Complete guide to all environment variables and configuration options for worker-vllm.
## LLM Settings
| Variable | Default | Type/Choices | Description |
| ------------------------------ | ------------------- | ----------------------------------------------------------- | ------------------------------------------------------------------------------- |
| `MODEL_NAME` | 'facebook/opt-125m' | `str` | Name or path of the Hugging Face model to use. |
| `MODEL_REVISION` | 'main' | `str` | Model revision to load (default: main). |
| `TOKENIZER` | None | `str` | Name or path of the Hugging Face tokenizer to use. |
| `SKIP_TOKENIZER_INIT` | False | `bool` | Skip initialization of tokenizer and detokenizer. |
| `TOKENIZER_MODE` | 'auto' | ['auto', 'slow'] | The tokenizer mode. |
| `TRUST_REMOTE_CODE` | `False` | `bool` | Trust remote code from Hugging Face. |
| `DOWNLOAD_DIR` | None | `str` | Directory to download and load the weights. |
| `LOAD_FORMAT` | 'auto' | `str` | The format of the model weights to load. |
| `HF_TOKEN` | - | `str` | Hugging Face token for private and gated models. |
| `DTYPE` | 'auto' | ['auto', 'half', 'float16', 'bfloat16', 'float', 'float32'] | Data type for model weights and activations. |
| `KV_CACHE_DTYPE` | 'auto' | ['auto', 'fp8'] | Data type for KV cache storage. |
| `QUANTIZATION_PARAM_PATH` | None | `str` | Path to the JSON file containing the KV cache scaling factors. |
| `MAX_MODEL_LEN` | None | `int` | Model context length. |
| `GUIDED_DECODING_BACKEND` | 'outlines' | ['outlines', 'lm-format-enforcer'] | Which engine will be used for guided decoding by default. |
| `DISTRIBUTED_EXECUTOR_BACKEND` | None | ['ray', 'mp'] | Backend to use for distributed serving. |
| `WORKER_USE_RAY` | False | `bool` | Deprecated, use --distributed-executor-backend=ray. |
| `PIPELINE_PARALLEL_SIZE` | 1 | `int` | Number of pipeline stages. |
| `TENSOR_PARALLEL_SIZE` | 1 | `int` | Number of tensor parallel replicas. |
| `MAX_PARALLEL_LOADING_WORKERS` | None | `int` | Load model sequentially in multiple batches. |
| `RAY_WORKERS_USE_NSIGHT` | False | `bool` | If specified, use nsight to profile Ray workers. |
| `ENABLE_PREFIX_CACHING` | False | `bool` | Enables automatic prefix caching. |
| `DISABLE_SLIDING_WINDOW` | False | `bool` | Disables sliding window, capping to sliding window size. |
| `USE_V2_BLOCK_MANAGER` | False | `bool` | Use BlockSpaceMangerV2. |
| `NUM_LOOKAHEAD_SLOTS` | 0 | `int` | Experimental scheduling config necessary for speculative decoding. |
| `SEED` | 0 | `int` | Random seed for operations. |
| `NUM_GPU_BLOCKS_OVERRIDE` | None | `int` | If specified, ignore GPU profiling result and use this number of GPU blocks. |
| `MAX_NUM_BATCHED_TOKENS` | None | `int` | Maximum number of batched tokens per iteration. |
| `MAX_NUM_SEQS` | 256 | `int` | Maximum number of sequences per iteration. |
| `MAX_LOGPROBS` | 20 | `int` | Max number of log probs to return when logprobs is specified in SamplingParams. |
| `DISABLE_LOG_STATS` | False | `bool` | Disable logging statistics. |
| `QUANTIZATION` | None | ['awq', 'squeezellm', 'gptq', 'bitsandbytes'] | Method used to quantize the weights. |
| `ROPE_SCALING` | None | `dict` | RoPE scaling configuration in JSON format. |
| `ROPE_THETA` | None | `float` | RoPE theta. Use with rope_scaling. |
| `TOKENIZER_POOL_SIZE` | 0 | `int` | Size of tokenizer pool to use for asynchronous tokenization. |
| `TOKENIZER_POOL_TYPE` | 'ray' | `str` | Type of tokenizer pool to use for asynchronous tokenization. |
| `TOKENIZER_POOL_EXTRA_CONFIG` | None | `dict` | Extra config for tokenizer pool. |
## LoRA (Low-Rank Adaptation) Settings
| Variable | Default | Type | Description |
| --------------------------- | ------- | ------------------------------------------ | --------------------------------------------------------------------------------------------------------- |
| `ENABLE_LORA` | False | `bool` | If True, enable handling of LoRA adapters. |
| `MAX_LORAS` | 1 | `int` | Max number of LoRAs in a single batch. |
| `MAX_LORA_RANK` | 16 | `int` | Max LoRA rank. |
| `LORA_EXTRA_VOCAB_SIZE` | 256 | `int` | Maximum size of extra vocabulary for LoRA adapters. |
| `LORA_DTYPE` | 'auto' | ['auto', 'float16', 'bfloat16', 'float32'] | Data type for LoRA. |
| `LONG_LORA_SCALING_FACTORS` | None | `tuple` | Specify multiple scaling factors for LoRA adapters. |
| `MAX_CPU_LORAS` | None | `int` | Maximum number of LoRAs to store in CPU memory. |
| `FULLY_SHARDED_LORAS` | False | `bool` | Enable fully sharded LoRA layers. |
| `LORA_MODULES` | `[]` | `list[dict]` | Add lora adapters from Hugging Face `[{"name": "xx", "path": "xxx/xxxx", "base_model_name": "xxx/xxxx"}]` |
## Speculative Decoding Settings
| Variable | Default | Type/Choices | Description |
| ------------------------------------------------ | ------------------- | --------------------------------------------------- | ----------------------------------------------------------------------------------------- |
| `SCHEDULER_DELAY_FACTOR` | 0.0 | `float` | Apply a delay before scheduling next prompt. |
| `ENABLE_CHUNKED_PREFILL` | False | `bool` | Enable chunked prefill requests. |
| `SPECULATIVE_MODEL` | None | `str` | The name of the draft model to be used in speculative decoding. |
| `NUM_SPECULATIVE_TOKENS` | None | `int` | The number of speculative tokens to sample from the draft model. |
| `SPECULATIVE_DRAFT_TENSOR_PARALLEL_SIZE` | None | `int` | Number of tensor parallel replicas for the draft model. |
| `SPECULATIVE_MAX_MODEL_LEN` | None | `int` | The maximum sequence length supported by the draft model. |
| `SPECULATIVE_DISABLE_BY_BATCH_SIZE` | None | `int` | Disable speculative decoding if the number of enqueue requests is larger than this value. |
| `NGRAM_PROMPT_LOOKUP_MAX` | None | `int` | Max size of window for ngram prompt lookup in speculative decoding. |
| `NGRAM_PROMPT_LOOKUP_MIN` | None | `int` | Min size of window for ngram prompt lookup in speculative decoding. |
| `SPEC_DECODING_ACCEPTANCE_METHOD` | 'rejection_sampler' | ['rejection_sampler', 'typical_acceptance_sampler'] | Specify the acceptance method for draft token verification in speculative decoding. |
| `TYPICAL_ACCEPTANCE_SAMPLER_POSTERIOR_THRESHOLD` | None | `float` | Set the lower bound threshold for the posterior probability of a token to be accepted. |
| `TYPICAL_ACCEPTANCE_SAMPLER_POSTERIOR_ALPHA` | None | `float` | A scaling factor for the entropy-based threshold for token acceptance. |
## System Performance Settings
| Variable | Default | Type/Choices | Description |
| ------------------------------ | ------- | --------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
| `GPU_MEMORY_UTILIZATION` | `0.95` | `float` | Sets GPU VRAM utilization. |
| `MAX_PARALLEL_LOADING_WORKERS` | `None` | `int` | Load model sequentially in multiple batches, to avoid RAM OOM when using tensor parallel and large models. |
| `BLOCK_SIZE` | `16` | `8`, `16`, `32` | Token block size for contiguous chunks of tokens. |
| `SWAP_SPACE` | `4` | `int` | CPU swap space size (GiB) per GPU. |
| `ENFORCE_EAGER` | False | `bool` | Always use eager-mode PyTorch. If False(`0`), will use eager mode and CUDA graph in hybrid for maximal performance and flexibility. |
| `MAX_SEQ_LEN_TO_CAPTURE` | `8192` | `int` | Maximum context length covered by CUDA graphs. When a sequence has context length larger than this, we fall back to eager mode. |
| `DISABLE_CUSTOM_ALL_REDUCE` | `0` | `int` | Enables or disables custom all reduce. |
| `ENABLE_EXPERT_PARALLEL` | `False` | `bool` | Enable Expert Parallel for MoE models |
## Tokenizer Settings
| Variable | Default | Type/Choices | Description |
| ---------------------- | ------- | ----------------------------------- | ------------------------------------------------------------------------------------------------- |
| `TOKENIZER_NAME` | `None` | `str` | Tokenizer repository to use a different tokenizer than the model's default. |
| `TOKENIZER_REVISION` | `None` | `str` | Tokenizer revision to load. |
| `CUSTOM_CHAT_TEMPLATE` | `None` | `str` of single-line jinja template | Custom chat jinja template. [More Info](https://huggingface.co/docs/transformers/chat_templating) |
## Streaming & Batch Settings
The way this works is that the first request will have a batch size of `DEFAULT_MIN_BATCH_SIZE`, and each subsequent request will have a batch size of `previous_batch_size * DEFAULT_BATCH_SIZE_GROWTH_FACTOR`. This will continue until the batch size reaches `DEFAULT_BATCH_SIZE`. E.g. for the default values, the batch sizes will be `1, 3, 9, 27, 50, 50, 50, ...`. You can also specify this per request, with inputs `max_batch_size`, `min_batch_size`, and `batch_size_growth_factor`. This has nothing to do with vLLM's internal batching, but rather the number of tokens sent in each HTTP request from the worker.
| Variable | Default | Type/Choices | Description |
| ---------------------------------- | ------- | ------------ | --------------------------------------------------------------------------------------------------------- |
| `DEFAULT_BATCH_SIZE` | `50` | `int` | Default and Maximum batch size for token streaming to reduce HTTP calls. |
| `DEFAULT_MIN_BATCH_SIZE` | `1` | `int` | Batch size for the first request, which will be multiplied by the growth factor every subsequent request. |
| `DEFAULT_BATCH_SIZE_GROWTH_FACTOR` | `3` | `float` | Growth factor for dynamic batch size. |
## OpenAI Compatibility Settings
| Variable | Default | Type/Choices | Description |
| ----------------------------------- | ----------- | ---------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `RAW_OPENAI_OUTPUT` | `1` | boolean as `int` | Enables raw OpenAI SSE format string output when streaming. **Required** to be enabled (which it is by default) for OpenAI compatibility. |
| `OPENAI_SERVED_MODEL_NAME_OVERRIDE` | `None` | `str` | Overrides the name of the served model from model repo/path to specified name, which you will then be able to use the value for the `model` parameter when making OpenAI requests |
| `OPENAI_RESPONSE_ROLE` | `assistant` | `str` | Role of the LLM's Response in OpenAI Chat Completions. |
| `ENABLE_AUTO_TOOL_CHOICE` | `false` | `bool` | Enables automatic tool selection for supported models. Set to `true` to activate. |
| `TOOL_CALL_PARSER` | `None` | `str` | Specifies the parser for tool calls. Options: `mistral`, `hermes`, `llama3_json`, `llama4_json`, `llama4_pythonic`, `granite`, `granite-20b-fc`, `deepseek_v3`, `internlm`, `jamba`, `phi4_mini_json`, `pythonic` |
| `REASONING_PARSER` | `None` | `str` | Parser for reasoning-capable models (enables reasoning mode). Examples: `deepseek_r1`, `qwen3`, `granite`, `hunyuan_a13b`. Leave unset to disable. |
## Serverless & Concurrency Settings
| Variable | Default | Type/Choices | Description |
| ---------------------- | ------- | ------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `MAX_CONCURRENCY` | `30` | `int` | Max concurrent requests per worker. vLLM has an internal queue, so you don't have to worry about limiting by VRAM, this is for improving scaling/load balancing efficiency |
| `DISABLE_LOG_STATS` | False | `bool` | Enables or disables vLLM stats logging. |
| `DISABLE_LOG_REQUESTS` | False | `bool` | Enables or disables vLLM request logging. |
## Advanced Settings
| Variable | Default | Type | Description |
| --------------------------- | ------- | ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `MODEL_LOADER_EXTRA_CONFIG` | None | `dict` | Extra config for model loader. |
| `PREEMPTION_MODE` | None | `str` | If 'recompute', the engine performs preemption-aware recomputation. If 'save', the engine saves activations into the CPU memory as preemption happens. |
| `PREEMPTION_CHECK_PERIOD` | 1.0 | `float` | How frequently the engine checks if a preemption happens. |
| `PREEMPTION_CPU_CAPACITY` | 2 | `float` | The percentage of CPU memory used for the saved activations. |
| `DISABLE_LOGGING_REQUEST` | False | `bool` | Disable logging requests. |
| `MAX_LOG_LEN` | None | `int` | Max number of prompt characters or prompt ID numbers being printed in log. |
## Docker Build Arguments
These variables are used when building custom Docker images with models baked in:
| Variable | Default | Type | Description |
| --------------------- | ---------------- | ----- | ------------------------------------------------- |
| `BASE_PATH` | `/runpod-volume` | `str` | Storage directory for huggingface cache and model |
| `WORKER_CUDA_VERSION` | `12.1.0` | `str` | CUDA version for the worker image |
## Deprecated Variables
⚠️ **The following variables are deprecated and will be removed in future versions:**
| Old Variable | New Variable | Note |
| ---------------------------- | ------------------------ | --------------------- |
| `MAX_CONTEXT_LEN_TO_CAPTURE` | `MAX_SEQ_LEN_TO_CAPTURE` | Use new variable name |
| `kv_cache_dtype=fp8_e5m2` | `kv_cache_dtype=fp8` | Simplified fp8 format |
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