v0.3.0: OpenAI Compatibility, Dynamic Stream Batching, Refactor, Erro…

…r Catching

.gitignore

@@ -2,4 +2,5 @@
 runpod.toml
 *.pyc
 .env
-test/*
+test/*
+vllm-base/vllm-*

Dockerfile

@@ -1,5 +1,5 @@
 ARG WORKER_CUDA_VERSION=11.8.0
-FROM runpod/worker-vllm:base-0.2.2-cuda${WORKER_CUDA_VERSION} AS vllm-base
+FROM runpod/worker-vllm:base-0.3.0-cuda${WORKER_CUDA_VERSION} AS vllm-base
 
 RUN apt-get update -y \
     && apt-get install -y python3-pip

builder/requirements.txt

@@ -2,7 +2,7 @@ hf_transfer
 ray
 pandas
 pyarrow
-runpod==1.5.3
+runpod==1.6.2
 huggingface-hub
 packaging
 typing-extensions==4.7.1

src/config.py

@@ -0,0 +1,51 @@
+import os
+from dotenv import load_dotenv
+from utils import count_physical_cores 
+from torch.cuda import device_count
+
+class EngineConfig:
+    def __init__(self):
+        load_dotenv()
+        self.model_name_or_path, self.hf_home, self.model_revision = self._get_local_or_env("/local_model_path.txt", "MODEL_NAME")
+        self.tokenizer_name_or_path, _, self.tokenizer_revision = self._get_local_or_env("/local_tokenizer_path.txt", "TOKENIZER_NAME")
+        self.tokenizer_name_or_path = self.tokenizer_name_or_path or self.model_name_or_path
+        self.quantization = self._get_quantization()
+        self.config = self._initialize_config()
+
+    def _get_local_or_env(self, local_path, env_var):
+        if os.path.exists(local_path):
+            with open(local_path, "r") as file:
+                return file.read().strip(), None, None
+        return os.getenv(env_var), os.getenv("HF_HOME"), os.getenv(f"{env_var}_REVISION")
+
+    def _get_quantization(self):
+        quantization = os.getenv("QUANTIZATION", "").lower()
+        return quantization if quantization in ["awq", "squeezellm", "gptq"] else None
+
+    def _initialize_config(self):
+        args = {
+            "model": self.model_name_or_path,
+            "revision": self.model_revision,
+            "download_dir": self.hf_home,
+            "quantization": self.quantization,
+            "load_format": os.getenv("LOAD_FORMAT", "auto"),
+            "dtype": os.getenv("DTYPE", "half" if self.quantization else "auto"),
+            "tokenizer": self.tokenizer_name_or_path,
+            "tokenizer_revision": self.tokenizer_revision,
+            "disable_log_stats": bool(int(os.getenv("DISABLE_LOG_STATS", 1))),
+            "disable_log_requests": bool(int(os.getenv("DISABLE_LOG_REQUESTS", 1))),
+            "trust_remote_code": bool(int(os.getenv("TRUST_REMOTE_CODE", 0))),
+            "gpu_memory_utilization": float(os.getenv("GPU_MEMORY_UTILIZATION", 0.95)),
+            "max_parallel_loading_workers": None if device_count() > 1 or not os.getenv("MAX_PARALLEL_LOADING_WORKERS") else int(os.getenv("MAX_PARALLEL_LOADING_WORKERS")),
+            "max_model_len": int(os.getenv("MAX_MODEL_LENGTH")) if os.getenv("MAX_MODEL_LENGTH") else None,
+            "tensor_parallel_size": device_count(),
+            "seed": int(os.getenv("SEED")) if os.getenv("SEED") else None,
+            "kv_cache_dtype": os.getenv("KV_CACHE_DTYPE"),
+            "block_size": int(os.getenv("BLOCK_SIZE")) if os.getenv("BLOCK_SIZE") else None,
+            "swap_space": int(os.getenv("SWAP_SPACE")) if os.getenv("SWAP_SPACE") else None,
+            "max_context_len_to_capture": int(os.getenv("MAX_CONTEXT_LEN_TO_CAPTURE")) if os.getenv("MAX_CONTEXT_LEN_TO_CAPTURE") else None,
+            "disable_custom_all_reduce": bool(int(os.getenv("DISABLE_CUSTOM_ALL_REDUCE", 0))),
+            "enforce_eager": bool(int(os.getenv("ENFORCE_EAGER", 0)))
+        }
+
+        return {k: v for k, v in args.items() if v is not None}

src/constants.py

@@ -1,7 +1,9 @@
 from typing import Union
 
-DEFAULT_BATCH_SIZE = 30
+DEFAULT_BATCH_SIZE = 50
 DEFAULT_MAX_CONCURRENCY = 300
+DEFAULT_BATCH_SIZE_GROWTH_FACTOR = 3
+DEFAULT_MIN_BATCH_SIZE = 1
 
 SAMPLING_PARAM_TYPES = {
     "n": int,
@@ -25,4 +27,4 @@
     "skip_special_tokens": bool,
     "spaces_between_special_tokens": bool,
     "include_stop_str_in_output": bool
-}
+}