diff --git a/Dockerfile b/Dockerfile index 65986a3..49b5d0d 100644 --- a/Dockerfile +++ b/Dockerfile @@ -15,10 +15,16 @@ 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="main" +ARG TOKENIZER_REVISION="main" 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" \ diff --git a/README.md b/README.md index eaabb6b..e89ef1d 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: `main`). - `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`). @@ -67,8 +66,12 @@ 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: `main`). + - `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`). @@ -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/src/download_model.py b/src/download_model.py index 12850f9..02d0e26 100644 --- a/src/download_model.py +++ b/src/download_model.py @@ -1,11 +1,16 @@ import os +import shutil import logging from huggingface_hub import snapshot_download -from vllm.model_executor.weight_utils import prepare_hf_model_weights +from vllm.model_executor.weight_utils import Disabledtqdm, prepare_hf_model_weights if __name__ == "__main__": model = os.getenv("MODEL_NAME") download_dir = os.getenv("HF_HOME") + tokenizer = os.getenv("TOKENIZER_NAME", model) + model_revision = os.getenv("MODEL_REVISION", "main") + tokenizer_revision = os.getenv("TOKENIZER_REVISION", "main") + if not model or not download_dir: raise ValueError(f"Must specify model and download_dir. Model: {model}, download_dir: {download_dir}") @@ -14,22 +19,41 @@ if __name__ == "__main__": logging.info(f"Downloading model {model} to {download_dir}") - hf_folder, hf_weights_files, use_safetensors = prepare_hf_model_weights( + model_folder, hf_weights_files, use_safetensors = prepare_hf_model_weights( model_name_or_path=model, cache_dir=download_dir, - ) - - snapshot_download( - model, - cache_dir=download_dir, - allow_patterns=[ - "*token*", - "config.json", - ] + revision=model_revision, ) - logging.info(f"Finished downloading model {model} to {download_dir}") + model_extras_folder = snapshot_download(model, + allow_patterns=[ + "*token*", + "*.json" + ], + cache_dir=download_dir + "/extras", + tqdm_class=Disabledtqdm, + revision=model_revision) + + # Move extras to hf_folder + for f in os.listdir(model_extras_folder): + shutil.move(model_extras_folder + "/" + f, model_folder + "/" + f) # Wrie hf_folder to file with open("/local_model_path.txt", "w") as f: - f.write(hf_folder) \ No newline at end of file + f.write(model_folder) + + logging.info(f"Finished downloading model {model} to {download_dir}") + + if tokenizer != model: + logging.info(f"Downloading tokenizer {tokenizer} to {download_dir}") + tokenizer_folder = snapshot_download(tokenizer, + cache_dir=download_dir, + tqdm_class=Disabledtqdm, + revision=tokenizer_revision) + with open("/local_tokenizer_path.txt", "w") as f: + f.write(tokenizer_folder) + + logging.info(f"Finished downloading tokenizer {tokenizer} to {download_dir}") + + + diff --git a/src/engine.py b/src/engine.py index 356fee3..dedf370 100644 --- a/src/engine.py +++ b/src/engine.py @@ -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_name_or_path"]) 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)) @@ -166,6 +166,9 @@ class vLLMEngine: def _initialize_config(self): quantization = self._get_quantization() model, download_dir = self._get_model_name_and_path() + tokenizer_name_or_path = self._get_tokenizer_name_or_path() + if not tokenizer_name_or_path: + tokenizer_name_or_path = model return { "model": model, @@ -173,7 +176,7 @@ class vLLMEngine: "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, "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))), @@ -209,6 +212,14 @@ class vLLMEngine: else: model, download_dir = os.getenv("MODEL_NAME"), os.getenv("HF_HOME") return model, download_dir + + def _get_tokenizer_name_or_path(self): + if os.path.exists("/local_tokenizer_path.txt"): + tokenizer_name_or_path = open("/local_tokenizer_path.txt", "r").read().strip() + logging.info("Using local tokenizer at %s", tokenizer_name_or_path) + else: + tokenizer_name_or_path = os.getenv("TOKENIZER_NAME") + return tokenizer_name_or_path def _get_num_gpu_shard(self): num_gpu_shard = int(os.getenv("TENSOR_PARALLEL_SIZE", 1))