vLLM example with mistral7B model

examples/large_models/vllm/mistral/Readme.md

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+# Example showing inference with vLLM with mistralai/Mistral-7B-v0.1 model
+
+This is an example showing how to integrate [vLLM](https://github.com/vllm-project/vllm) with TorchServe and run inference on `mistralai/Mistral-7B-v0.1` model.
+vLLM achieves high throughput using PagedAttention. More details can be found [here](https://vllm.ai/)
+
+Install vLLM with the following
+
+```
+pip install -r requirements.txt
+```
+### Step 1: Login to HuggingFace
+
+Login with a HuggingFace account
+```
+huggingface-cli login
+# or using an environment variable
+huggingface-cli login --token $HUGGINGFACE_TOKEN
+```
+
+```bash
+python ../Download_model.py --model_path model --model_name mistralai/Mistral-7B-v0.1
+```
+Model will be saved in the following path, `mistralai/Mistral-7B-v0.1`.
+
+### Step 2: Generate MAR file
+
+Add the downloaded path to " model_path:" in `model-config.yaml` and run the following.
+
+```bash
+torch-model-archiver --model-name mistral7b --version 1.0 --handler custom_handler.py --config-file model-config.yaml -r requirements.txt --archive-format tgz
+```
+
+### Step 3: Add the mar file to model store
+
+```bash
+mkdir model_store
+mv mistral7b.tar.gz model_store
+```
+
+### Step 3: Start torchserve
+
+Update config.properties and start torchserve
+
+```bash
+torchserve --start --ncs --ts-config config.properties --model-store model_store --models mistral7b.tar.gz
+```
+
+### Step 4: Run inference
+
+```bash
+curl -v "http://localhost:8080/predictions/mistral7b" -T sample_text.txt
+```
+
+results in the following output
+```
+Mayonnaise is made of eggs, oil, vinegar, salt and pepper. Using an electric blender, combine all the ingredients and beat at high speed for 4 to 5 minutes.
+
+Try it with some mustard and paprika mixed in, and a bit of sweetener if you like. But use real mayonnaise or it isn’t the same. Marlou
+
+What in the world is mayonnaise?
+```

examples/large_models/vllm/mistral/config.properties

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+inference_address=http://127.0.0.1:8080
+management_address=http://127.0.0.1:8081
+metrics_address=http://127.0.0.1:8082
+enable_envvars_config=true
+install_py_dep_per_model=true

examples/large_models/vllm/mistral/custom_handler.py

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+import logging
+from abc import ABC
+
+import torch
+import vllm
+from vllm import LLM, SamplingParams
+
+from ts.context import Context
+from ts.torch_handler.base_handler import BaseHandler
+
+logger = logging.getLogger(__name__)
+logger.info("vLLM version %s", vllm.__version__)
+
+
+class LlamaHandler(BaseHandler, ABC):
+    """
+    Transformers handler class for sequence, token classification and question answering.
+    """
+
+    def __init__(self):
+        super(LlamaHandler, self).__init__()
+        self.max_new_tokens = None
+        self.tokenizer = None
+        self.initialized = False
+
+    def initialize(self, ctx: Context):
+        """In this initialize function, the HF large model is loaded and
+        partitioned using DeepSpeed.
+        Args:
+            ctx (context): It is a JSON Object containing information
+            pertaining to the model artifacts parameters.
+        """
+        model_dir = ctx.system_properties.get("model_dir")
+        self.max_new_tokens = int(ctx.model_yaml_config["handler"]["max_new_tokens"])
+        model_name = ctx.model_yaml_config["handler"]["model_name"]
+        model_path = ctx.model_yaml_config["handler"]["model_path"]
+        seed = int(ctx.model_yaml_config["handler"]["manual_seed"])
+        torch.manual_seed(seed)
+
+        self.model = LLM(model=model_path)
+
+        logger.info("Model %s loaded successfully", ctx.model_name)
+        self.initialized = True
+
+    def preprocess(self, requests):
+        """
+        Basic text preprocessing, based on the user's choice of application mode.
+        Args:
+            requests (list): A list of dictionaries with a "data" or "body" field, each
+                            containing the input text to be processed.
+        Returns:
+            tuple: A tuple with two tensors: the batch of input ids and the batch of
+                attention masks.
+        """
+        input_texts = [data.get("data") or data.get("body") for data in requests]
+        # return torch.as_tensor(input_texts, device=self.device)
+        input_texts = [
+            input_text.decode("utf-8")
+            for input_text in input_texts
+            if isinstance(input_text, (bytes, bytearray))
+        ]
+        return input_texts
+
+    def inference(self, input_batch):
+        """
+        Predicts the class (or classes) of the received text using the serialized transformers
+        checkpoint.
+        Args:
+            input_batch (tuple): A tuple with two tensors: the batch of input ids and the batch
+                                of attention masks, as returned by the preprocess function.
+        Returns:
+            list: A list of strings with the predicted values for each input text in the batch.
+        """
+        logger.info(f"Input text is {input_batch}")
+        sampling_params = SamplingParams(max_tokens=self.max_new_tokens)
+        outputs = self.model.generate(input_batch, sampling_params=sampling_params)
+
+        logger.info("Generated text: %s", outputs)
+        return outputs
+
+    def postprocess(self, inference_output):
+        """Post Process Function converts the predicted response into Torchserve readable format.
+        Args:
+            inference_output (list): It contains the predicted response of the input text.
+        Returns:
+            (list): Returns a list of the Predictions and Explanations.
+        """
+        return [inference_output[0].outputs[0].text]

examples/large_models/vllm/mistral/model-config.yaml

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+# TorchServe frontend parameters
+minWorkers: 1
+maxWorkers: 1
+maxBatchDelay: 100
+responseTimeout: 1200
+deviceType: "gpu"
+
+handler:
+    model_name: "mistralai/Mistral-7B-v0.1"
+    model_path: "/home/ubuntu/serve/examples/large_models/vllm/mistral/model/models--mistralai--Mistral-7B-v0.1/snapshots/5e9c98b96d071dce59368012254c55b0ec6f8658"
+    max_new_tokens: 100
+    manual_seed: 40
+    fast_kernels: True

examples/large_models/vllm/mistral/requirements.txt

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+vllm

examples/large_models/vllm/mistral/sample_text.txt

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+what is the recipe of mayonnaise?

ts_scripts/spellcheck_conf/wordlist.txt

@@ -1137,3 +1137,6 @@ Naver
 FlashAttention
 GenAI
 prem
+vLLM
+mistralai
+PagedAttention