diff --git a/Dockerfile b/Dockerfile index 65986a3..0790c31 100644 --- a/Dockerfile +++ b/Dockerfile @@ -10,15 +10,18 @@ RUN --mount=type=cache,target=/root/.cache/pip \ python3 -m pip install --upgrade pip && \ python3 -m pip install --upgrade -r /requirements.txt -# Add source files -COPY src /src - # Setup for Option 2: Building the Image with the Model included ARG MODEL_NAME="" +ARG TOKENIZER_NAME="" ARG BASE_PATH="/runpod-volume" ARG QUANTIZATION="" +ARG MODEL_REVISION="" +ARG TOKENIZER_REVISION="" ENV MODEL_NAME=$MODEL_NAME \ + MODEL_REVISION=$REVISION \ + TOKENIZER_NAME=$TOKENIZER_NAME \ + TOKENIZER_REVISION=$TOKENIZER_REVISION \ BASE_PATH=$BASE_PATH \ QUANTIZATION=$QUANTIZATION \ HF_DATASETS_CACHE="${BASE_PATH}/huggingface-cache/datasets" \ @@ -27,14 +30,19 @@ ENV MODEL_NAME=$MODEL_NAME \ HF_TRANSFER=1 ENV PYTHONPATH="/:/vllm-installation" - + +COPY builder/download_model.py /download_model.py RUN --mount=type=secret,id=HF_TOKEN,required=false \ if [ -f /run/secrets/HF_TOKEN ]; then \ export HF_TOKEN=$(cat /run/secrets/HF_TOKEN); \ fi && \ if [ -n "$MODEL_NAME" ]; then \ - python3 /src/download_model.py; \ + python3 /download_model.py; \ fi +# Add source files +COPY src /src + + # Start the handler -CMD ["python3", "/src/handler.py"] +CMD ["python3", "/src/handler.py"] \ No newline at end of file diff --git a/README.md b/README.md index eaabb6b..2fb7bad 100644 --- a/README.md +++ b/README.md @@ -58,8 +58,7 @@ Development Image: ```runpod/worker-vllm:dev``` **Optional**: - LLM Settings: - - `TOKENIZER_NAME`: Tokenizer repository if you would like to use a different tokenizer than the one that comes with the model. (default: `None`) - - `CUSTOM_CHAT_TEMPLATE`: Custom chat jinja template, read more about Hugging Face chat templates [here](https://huggingface.co/docs/transformers/chat_templating). (default: `None`) + - `MODEL_REVISION`: Model revision to load (default: `None`). - `MAX_MODEL_LENGTH`: Maximum number of tokens for the engine to be able to handle. (default: maximum supported by the model) - `BASE_PATH`: Storage directory where huggingface cache and model will be located. (default: `/runpod-volume`, which will utilize network storage if you attach it or create a local directory within the image if you don't) - `LOAD_FORMAT`: Format to load model in (default: `auto`). @@ -67,15 +66,19 @@ Development Image: ```runpod/worker-vllm:dev``` - `QUANTIZATION`: AWQ (`awq`), SqueezeLLM (`squeezellm`) or GPTQ (`gptq`) Quantization. The specified Model Repo must be of a quantized model. (default: `None`) - `TRUST_REMOTE_CODE`: Trust remote code for Hugging Face (default: `0`) -- Tensor Parallelism: +- Tokenizer Settings: + - `TOKENIZER_NAME`: Tokenizer repository if you would like to use a different tokenizer than the one that comes with the model. (default: `None`, which uses the model's tokenizer) + - `TOKENIZER_REVISION`: Tokenizer revision to load (default: `None`). + - `CUSTOM_CHAT_TEMPLATE`: Custom chat jinja template, read more about Hugging Face chat templates [here](https://huggingface.co/docs/transformers/chat_templating). (default: `None`) +- Tensor Parallelism: Note that the more GPUs you split a model's weights accross, the slower it will be due to inter-GPU communication overhead. If you can fit the model on a single GPU, it is recommended to do so. - `TENSOR_PARALLEL_SIZE`: Number of GPUs to shard the model across (default: `1`). - If you are having issues loading your model with Tensor Parallelism, try decreasing `VLLM_CPU_FRACTION` (default: `1`). - System Settings: - `GPU_MEMORY_UTILIZATION`: GPU VRAM utilization (default: `0.98`). - - `MAX_PARALLEL_LOADING_WORKERS`: Maximum number of parallel workers for loading models (default: `number of available CPU cores` if `TENSOR_PARALLEL_SIZE` is `1`, otherwise `None`). + - `MAX_PARALLEL_LOADING_WORKERS`: Maximum number of parallel workers for loading models, for non-Tensor Parallel only. (default: `number of available CPU cores` if `TENSOR_PARALLEL_SIZE` is `1`, otherwise `None`). - Serverless Settings: @@ -96,9 +99,12 @@ To build an image with the model baked in, you must specify the following docker - **Required** - `MODEL_NAME` - **Optional** + - `MODEL_REVISION`: Model revision to load (default: `main`). - `BASE_PATH`: Storage directory where huggingface cache and model will be located. (default: `/runpod-volume`, which will utilize network storage if you attach it or create a local directory within the image if you don't. If your intention is to bake the model into the image, you should set this to something like `/models` to make sure there are no issues if you were to accidentally attach network storage.) - `QUANTIZATION` - `WORKER_CUDA_VERSION`: `11.8.0` or `12.1.0` (default: `11.8.0` due to a small amount of workers not having CUDA 12.1 support yet. `12.1.0` is recommended for optimal performance). + - `TOKENIZER_NAME`: Tokenizer repository if you would like to use a different tokenizer than the one that comes with the model. (default: `None`, which uses the model's tokenizer) + - `TOKENIZER_REVISION`: Tokenizer revision to load (default: `main`). For the remaining settings, you may apply them as environment variables when running the container. Supported environment variables are listed in the [Environment Variables](#environment-variables) section. diff --git a/builder/download_model.py b/builder/download_model.py new file mode 100644 index 0000000..caf4a42 --- /dev/null +++ b/builder/download_model.py @@ -0,0 +1,50 @@ +import os +import shutil +from huggingface_hub import snapshot_download +from vllm.model_executor.weight_utils import prepare_hf_model_weights, Disabledtqdm + +def download_extras_or_tokenizer(model_name, cache_dir, revision, extras=False): + """Download model or tokenizer and prepare its weights, returning the local folder path.""" + pattern = ["*token*", "*.json"] if extras else None + extra_dir = "/extras" if extras else "" + folder = snapshot_download( + model_name, + cache_dir=cache_dir + extra_dir, + revision=revision, + tqdm_class=Disabledtqdm, + allow_patterns=pattern if extras else None, + ignore_patterns=["*.safetensors", "*.bin", "*.pt"] if not extras else None + ) + return folder + +def move_files(src_dir, dest_dir): + """Move files from source to destination directory.""" + for f in os.listdir(src_dir): + src_path = os.path.join(src_dir, f) + dst_path = os.path.join(dest_dir, f) + shutil.copy2(src_path, dst_path) + os.remove(src_path) + +if __name__ == "__main__": + model, download_dir = os.getenv("MODEL_NAME"), os.getenv("HF_HOME") + tokenizer = os.getenv("TOKENIZER_NAME", model) + revisions = { + "model": os.getenv("MODEL_REVISION") or None, + "tokenizer": os.getenv("TOKENIZER_REVISION") or None + } + + if not model or not download_dir: + raise ValueError(f"Must specify model and download_dir. Model: {model}, download_dir: {download_dir}") + + os.makedirs(download_dir, exist_ok=True) + model_folder, hf_weights_files, use_safetensors = prepare_hf_model_weights(model_name_or_path=model, revision=revisions["model"], cache_dir=download_dir) + model_extras_folder = download_extras_or_tokenizer(model, download_dir, revisions["model"], extras=True) + move_files(model_extras_folder, model_folder) + + with open("/local_model_path.txt", "w") as f: + f.write(model_folder) + + if tokenizer != model: + tokenizer_folder = download_extras_or_tokenizer(tokenizer, download_dir, revisions["tokenizer"]) + with open("/local_tokenizer_path.txt", "w") as f: + f.write(tokenizer_folder) diff --git a/src/download_model.py b/src/download_model.py deleted file mode 100644 index 086a2a0..0000000 --- a/src/download_model.py +++ /dev/null @@ -1,25 +0,0 @@ -import os -import logging -from vllm.model_executor.weight_utils import prepare_hf_model_weights - -if __name__ == "__main__": - model = os.getenv("MODEL_NAME") - download_dir = os.getenv("HF_HOME") - if not model or not download_dir: - raise ValueError(f"Must specify model and download_dir. Model: {model}, download_dir: {download_dir}") - - if not os.path.exists(download_dir): - os.makedirs(download_dir) - - logging.info(f"Downloading model {model} to {download_dir}") - - hf_folder, hf_weights_files, use_safetensors = prepare_hf_model_weights( - model_name_or_path=model, - cache_dir=download_dir, - ) - - logging.info(f"Finished downloading model {model} to {download_dir}") - - # Wrie hf_folder to file - with open("/local_model_path.txt", "w") as f: - f.write(hf_folder) \ No newline at end of file diff --git a/src/engine.py b/src/engine.py index b518d7a..ea5befd 100644 --- a/src/engine.py +++ b/src/engine.py @@ -13,8 +13,8 @@ from dotenv import load_dotenv class Tokenizer: - def __init__(self, model_name): - self.tokenizer = AutoTokenizer.from_pretrained(model_name) + def __init__(self, tokenizer_name_or_path, tokenizer_revision): + self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path, revision=tokenizer_revision) self.custom_chat_template = os.getenv("CUSTOM_CHAT_TEMPLATE") self.has_chat_template = bool(self.tokenizer.chat_template) or bool(self.custom_chat_template) if self.custom_chat_template and isinstance(self.custom_chat_template, str): @@ -41,7 +41,7 @@ class vLLMEngine: load_dotenv() # For local development self.config = self._initialize_config() logging.info("vLLM config: %s", self.config) - self.tokenizer = Tokenizer(os.getenv("TOKENIZER_NAME", os.getenv("MODEL_NAME"))) + self.tokenizer = Tokenizer(self.config["tokenizer"], self.config["tokenizer_revision"]) self.llm = self._initialize_llm() if engine is None else engine self.openai_engine = self._initialize_openai() self.max_concurrency = int(os.getenv("MAX_CONCURRENCY", DEFAULT_MAX_CONCURRENCY)) @@ -60,7 +60,6 @@ class vLLMEngine: yield batch async def generate_vllm(self, llm_input, validated_sampling_params, batch_size, stream, apply_chat_template, request_id: str) -> AsyncGenerator[dict, None]: - if apply_chat_template or isinstance(llm_input, list): llm_input = self.tokenizer.apply_chat_template(llm_input) validated_sampling_params = SamplingParams(**validated_sampling_params) @@ -145,15 +144,20 @@ class vLLMEngine: def _initialize_config(self): quantization = self._get_quantization() - model, download_dir = self._get_model_name_and_path() + model, download_dir, model_revision = self._get_model_info() + tokenizer_name_or_path, tokenizer_revision = self._get_tokenizer_info() + if not tokenizer_name_or_path: + tokenizer_name_or_path = model return { "model": model, + "revision": model_revision, "download_dir": download_dir, "quantization": quantization, "load_format": os.getenv("LOAD_FORMAT", "auto"), "dtype": "half" if quantization else "auto", - "tokenizer": os.getenv("TOKENIZER_NAME"), + "tokenizer": tokenizer_name_or_path, + "tokenizer_revision": 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))), @@ -181,14 +185,22 @@ class vLLMEngine: return None else: return int(os.getenv("MAX_PARALLEL_LOADING_WORKERS", count_physical_cores())) - - def _get_model_name_and_path(self): + + def _get_model_info(self): if os.path.exists("/local_model_path.txt"): - model, download_dir = open("/local_model_path.txt", "r").read().strip(), None + model, download_dir, revision = open("/local_model_path.txt", "r").read().strip(), None, None logging.info("Using local model at %s", model) else: - model, download_dir = os.getenv("MODEL_NAME"), os.getenv("HF_HOME") - return model, download_dir + model, download_dir, revision = os.getenv("MODEL_NAME"), os.getenv("HF_HOME"), os.getenv("MODEL_REVISION") or None + return model, download_dir, revision + + def _get_tokenizer_info(self): + if os.path.exists("/local_tokenizer_path.txt"): + tokenizer_name_or_path, revision = open("/local_tokenizer_path.txt", "r").read().strip(), None + logging.info("Using local tokenizer at %s", tokenizer_name_or_path) + else: + tokenizer_name_or_path, revision = os.getenv("TOKENIZER_NAME"), os.getenv("TOKENIZER_REVISION") or None + return tokenizer_name_or_path, revision def _get_num_gpu_shard(self): num_gpu_shard = int(os.getenv("TENSOR_PARALLEL_SIZE", 1))