Update tests.json

update vllm

.runpod/tests.json

@@ -15,18 +15,6 @@
             {
                 "key": "MODEL_NAME",
                 "value": "facebook/opt-350m"
-            },
-            {
-                "key": "HF_TOKEN",
-                "value": "hf_dummy_token_for_testing_purposes_only"
-            },
-            {
-                "key": "MAX_MODEL_LEN",
-                "value": "8192"
-            },
-            {
-                "key": "GPU_MEMORY_UTILIZATION",
-                "value": "0.95"
             }
         ],
         "allowedCudaVersions": [

README.md

@@ -18,9 +18,9 @@ Deploy OpenAI-Compatible Blazing-Fast LLM Endpoints powered by the [vLLM](https:
 ### 1. UI for Deploying vLLM Worker on RunPod console:
 ![Demo of Deploying vLLM Worker on RunPod console with new UI](media/ui_demo.gif)
 
-### 2. Worker vLLM `v2.4.0` with vLLM `0.8.4` now available under `stable` tags 
+### 2. Worker vLLM `v2.5.0` with vLLM `0.8.5` now available under `stable` tags 
 
-Update v2.3.0 is now available, use the image tag `runpod/worker-v1-vllm:v2.4.0stable-cuda12.1.0`.
+Update v2.5.0 is now available, use the image tag `runpod/worker-v1-vllm:v2.5.0stable-cuda12.1.0`.
 
 ### 3. OpenAI-Compatible [Embedding Worker](https://github.com/runpod-workers/worker-infinity-embedding) Released
 Deploy your own OpenAI-compatible Serverless Endpoint on RunPod with multiple embedding models and fast inference for RAG and more! 
@@ -38,9 +38,8 @@ Worker vLLM is now cached on all RunPod machines, resulting in near-instant depl
     - [Environment Variables](#environment-variables)
       - [LLM Settings](#llm-settings)
       - [Tokenizer Settings](#tokenizer-settings)
-      - [Tensor Parallelism (Multi-GPU) Settings](#tensor-parallelism-multi-gpu-settings)
+      - [System and Parallelism Settings](#system-and-parallelism-settings)
-      - [System Settings](#system-settings)
+      - [Streaming Batch Size Settings](#streaming-batch-size-settings)
-      - [Streaming Batch Size](#streaming-batch-size)
       - [OpenAI Settings](#openai-settings)
       - [Serverless Settings](#serverless-settings)
   - [Option 2: Build Docker Image with Model Inside](#option-2-build-docker-image-with-model-inside)
@@ -82,7 +81,7 @@ Below is a summary of the available RunPod Worker images, categorized by image s
 
 | CUDA Version | Stable Image Tag                  | Development Image Tag             | Note                                                        |
 |--------------|-----------------------------------|-----------------------------------|----------------------------------------------------------------------|
-| 12.1.0       | `runpod/worker-v1-vllm:v2.4.0stable-cuda12.1.0` | `runpod/worker-v1-vllm:v2.4.0dev-cuda12.1.0` | When creating an Endpoint, select CUDA Version 12.3, 12.2 and 12.1 in the filter. |
+| 12.1.0       | `runpod/worker-v1-vllm:v2.5.0stable-cuda12.1.0` | `runpod/worker-v1-vllm:v2.5.0dev-cuda12.1.0` | When creating an Endpoint, select CUDA Version 12.3, 12.2 and 12.1 in the filter. |
 
 
 
@@ -91,9 +90,10 @@ Below is a summary of the available RunPod Worker images, categorized by image s
 #### Prerequisites
 - RunPod Account
 
-#### Environment Variables/Settings
+#### Environment Variables
 > Note:  `0` is equivalent to `False` and `1` is equivalent to `True` for boolean as int values.
 
+#### LLM Settings
 | `Name`                                    | `Default`             | `Type/Choices`                             | `Description` |
 |-------------------------------------------|-----------------------|--------------------------------------------|---------------|
 | `MODEL_NAME`                                   | 'facebook/opt-125m'   | `str`                                      | Name or path of the Hugging Face model to use. |
@@ -157,11 +157,20 @@ Below is a summary of the available RunPod Worker images, categorized by image s
 | `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. |
-**Tokenizer Settings**
+
+
+#### Tokenizer Settings
+
+| `Name`                                    | `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) |
-**System, GPU, and Tensor Parallelism(Multi-GPU) Settings**
+
+#### System and Parallelism Settings
+
+| `Name`                                    | `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. |
@@ -169,16 +178,31 @@ Below is a summary of the available RunPod Worker images, categorized by image s
 | `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. |
-**Streaming Batch Size Settings**:  
+
+
+#### Streaming Batch Size 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
+
+
+| `Name`                                    | `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. |
-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 |
+
-**OpenAI Settings**
+#### OpenAI Settings
+
+| `Name`                                    | `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. |
-**Serverless Settings**
+
+#### Serverless Settings
+
+| `Name`                                    | `Default`             | `Type/Choices`                             | `Description` |
+|-------------------------------------------|-----------------------|--------------------------------------------|---------------|
 | `MAX_CONCURRENCY`                   | `300`                | `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. |

worker-config.json

@@ -1,5 +1,66 @@
 {
   "versions": {
+    "0.8.5": {
+      "imageName": "runpod/worker-v1-vllm:v2.5.0stable-cuda12.1.0",
+      "minimumCudaVersion": "12.1",
+      "categories": [
+        {
+          "title": "LLM Settings",
+          "settings": [
+            "TOKENIZER", "TOKENIZER_MODE", "SKIP_TOKENIZER_INIT", "TRUST_REMOTE_CODE",
+            "DOWNLOAD_DIR", "LOAD_FORMAT", "DTYPE", "KV_CACHE_DTYPE", "QUANTIZATION_PARAM_PATH",
+            "MAX_MODEL_LEN", "GUIDED_DECODING_BACKEND", "DISTRIBUTED_EXECUTOR_BACKEND",
+            "WORKER_USE_RAY", "RAY_WORKERS_USE_NSIGHT", "PIPELINE_PARALLEL_SIZE",
+            "TENSOR_PARALLEL_SIZE", "MAX_PARALLEL_LOADING_WORKERS", "ENABLE_PREFIX_CACHING",
+            "DISABLE_SLIDING_WINDOW", "NUM_LOOKAHEAD_SLOTS",
+            "SEED", "NUM_GPU_BLOCKS_OVERRIDE", "MAX_NUM_BATCHED_TOKENS", "MAX_NUM_SEQS",
+            "MAX_LOGPROBS", "DISABLE_LOG_STATS", "QUANTIZATION", "ROPE_SCALING", "ROPE_THETA",
+            "TOKENIZER_POOL_SIZE", "TOKENIZER_POOL_TYPE", "TOKENIZER_POOL_EXTRA_CONFIG",
+            "ENABLE_LORA", "MAX_LORAS", "MAX_LORA_RANK", "LORA_EXTRA_VOCAB_SIZE",
+            "LORA_DTYPE", "LONG_LORA_SCALING_FACTORS", "MAX_CPU_LORAS", "FULLY_SHARDED_LORAS",
+            "DEVICE", "SCHEDULER_DELAY_FACTOR", "ENABLE_CHUNKED_PREFILL", "SPECULATIVE_MODEL",
+            "NUM_SPECULATIVE_TOKENS", "SPECULATIVE_DRAFT_TENSOR_PARALLEL_SIZE",
+            "SPECULATIVE_MAX_MODEL_LEN", "SPECULATIVE_DISABLE_BY_BATCH_SIZE",
+            "NGRAM_PROMPT_LOOKUP_MAX", "NGRAM_PROMPT_LOOKUP_MIN", "SPEC_DECODING_ACCEPTANCE_METHOD",
+            "TYPICAL_ACCEPTANCE_SAMPLER_POSTERIOR_THRESHOLD", "TYPICAL_ACCEPTANCE_SAMPLER_POSTERIOR_ALPHA",
+            "MODEL_LOADER_EXTRA_CONFIG", "PREEMPTION_MODE", "PREEMPTION_CHECK_PERIOD",
+            "PREEMPTION_CPU_CAPACITY", "MAX_LOG_LEN", "DISABLE_LOGGING_REQUEST",
+            "ENABLE_AUTO_TOOL_CHOICE", "TOOL_CALL_PARSER"
+          ]
+        },
+        {
+          "title": "Tokenizer Settings",
+          "settings": [
+            "TOKENIZER_NAME", "TOKENIZER_REVISION", "CUSTOM_CHAT_TEMPLATE"
+          ]
+        },
+        {
+          "title": "System Settings",
+          "settings": [
+            "GPU_MEMORY_UTILIZATION", "MAX_PARALLEL_LOADING_WORKERS", "BLOCK_SIZE",
+            "SWAP_SPACE", "ENFORCE_EAGER", "MAX_SEQ_LEN_TO_CAPTURE", "DISABLE_CUSTOM_ALL_REDUCE"
+          ]
+        },
+        {
+          "title": "Streaming Settings",
+          "settings": [
+            "DEFAULT_BATCH_SIZE", "DEFAULT_MIN_BATCH_SIZE", "DEFAULT_BATCH_SIZE_GROWTH_FACTOR"
+          ]
+        },
+        {
+          "title": "OpenAI Settings",
+          "settings": [
+            "RAW_OPENAI_OUTPUT", "OPENAI_RESPONSE_ROLE", "OPENAI_SERVED_MODEL_NAME_OVERRIDE"
+          ]
+        },
+        {
+          "title": "Serverless Settings",
+          "settings": [
+            "MAX_CONCURRENCY", "DISABLE_LOG_STATS", "DISABLE_LOG_REQUESTS"
+          ]
+        }
+      ]
+    },
     "0.8.4": {
       "imageName": "runpod/worker-v1-vllm:v2.4.0stable-cuda12.1.0",
       "minimumCudaVersion": "12.1",