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docs/source/inference_tutorials/sagemaker/deploy-llama-3-3-70b.mdx
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| <!--- | ||
| Copyright 2023 The HuggingFace Team. All rights reserved. | ||
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| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
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| http://www.apache.org/licenses/LICENSE-2.0 | ||
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| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
| --> | ||
| # Deploy Llama 3.3 70B on AWS Inferentia2 | ||
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| _There is a notebook version of that tutorial [here](https://github.com/huggingface/optimum-neuron/blob/main/notebooks/sagemaker/deploy-llama-3-3-70b.ipynb)._ | ||
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| In this tutorial you will learn how to deploy [/meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) model on AWS Inferentia2 with Hugging Face Optimum on Amazon SageMaker. We are going to use the Hugging Face TGI Neuron Container, a purpose-built Inference Container to easily deploy LLMs on AWS Inferentia2 powered by[ Text Generation Inference](https://huggingface.co/docs/text-generation-inference/index) and [Optimum Neuron](https://huggingface.co/docs/optimum-neuron/index). | ||
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| We will cover how to: | ||
| 1. [Setup development environment](#1-setup-development-environment) | ||
| 2. [Retrieve the new Hugging Face TGI Neuron DLC](#2-retrieve-the-new-hugging-face-tgi-neuron-dlc) | ||
| 3. [Deploy Llama 3.3 70B to inferentia2](#3-deploy-llama-33-70b-to-inferentia2) | ||
| 4. [Clean up](#5-clean-up) | ||
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| Lets get started! 🚀 | ||
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| [AWS inferentia (Inf2)](https://aws.amazon.com/de/ec2/instance-types/inf2/) are purpose-built EC2 for deep learning (DL) inference workloads. Here are the different instances of the Inferentia2 family. | ||
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| | instance size | accelerators | Neuron Cores | accelerator memory | vCPU | CPU Memory | on-demand price ($/h) | | ||
| | ------------- | ------------ | ------------ | ------------------ | ---- | ---------- | --------------------- | | ||
| | inf2.xlarge | 1 | 2 | 32 | 4 | 16 | 0.76 | | ||
| | inf2.8xlarge | 1 | 2 | 32 | 32 | 128 | 1.97 | | ||
| | inf2.24xlarge | 6 | 12 | 192 | 96 | 384 | 6.49 | | ||
| | inf2.48xlarge | 12 | 24 | 384 | 192 | 768 | 12.98 | | ||
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| ## 1. Setup development environment | ||
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| For this tutorial, we are going to use a Notebook Instance in Amazon SageMaker with the Python 3 (ipykernel) and the `sagemaker` python SDK to deploy Llama 3.3 70B to a SageMaker inference endpoint. | ||
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| Make sur you have the latest version of the SageMaker SDK installed. | ||
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| ```bash | ||
| !pip install sagemaker --upgrade --quiet | ||
| ``` | ||
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| Then, instantiate the sagemaker role and session. | ||
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| ```python | ||
| import sagemaker | ||
| import boto3 | ||
| sess = sagemaker.Session() | ||
| # sagemaker session bucket -> used for uploading data, models and logs | ||
| # sagemaker will automatically create this bucket if it not exists | ||
| sagemaker_session_bucket=None | ||
| if sagemaker_session_bucket is None and sess is not None: | ||
| # set to default bucket if a bucket name is not given | ||
| sagemaker_session_bucket = sess.default_bucket() | ||
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| try: | ||
| role = sagemaker.get_execution_role() | ||
| except ValueError: | ||
| iam = boto3.client('iam') | ||
| role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn'] | ||
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| sess = sagemaker.Session(default_bucket=sagemaker_session_bucket) | ||
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| print(f"sagemaker role arn: {role}") | ||
| print(f"sagemaker session region: {sess.boto_region_name}") | ||
| ``` | ||
| ## 2. Retrieve the latest Hugging Face TGI Neuron DLC | ||
| The latest Hugging Face TGI Neuron DLCs can be used to run inference on AWS Inferentia2. You can use the `get_huggingface_llm_image_uri` method of the `sagemaker` SDK to retrieve the appropriate Hugging Face TGI Neuron DLC URI based on your desired `backend`, `session`, `region`, and `version`. You can find the latest version of the container [here](https://huggingface.co/docs/optimum-neuron/containers), if not yet added to the SageMaker SDK. | ||
| ```python | ||
| from sagemaker.huggingface import get_huggingface_llm_image_uri | ||
| # retrieve the llm image uri | ||
| llm_image = get_huggingface_llm_image_uri( | ||
| "huggingface-neuronx", | ||
| ) | ||
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| print(f"llm image uri: {llm_image}") | ||
| ``` | ||
| ## 3. Deploy Llama 3.3 70B to Inferentia2 | ||
| At the time of writing, [AWS Inferentia2 does not support dynamic shapes for inference](https://awsdocs-neuron.readthedocs-hosted.com/en/v2.6.0/general/arch/neuron-features/dynamic-shapes.html#neuron-dynamic-shapes), which means that we need to specify our sequence length and batch size ahead of time. | ||
| To make it easier for customers to utilize the full power of Inferentia2, we created a [neuron model cache](https://huggingface.co/docs/optimum-neuron/guides/cache_system), which contains pre-compiled configurations for the most popular LLMs, including Llama 3.3 70B. | ||
| This means we don't need to compile the model ourselves, but we can use the pre-compiled model from the cache. You can find compiled/cached configurations on the [Hugging Face Hub](https://huggingface.co/aws-neuron/optimum-neuron-cache/tree/main/inference-cache-config). If your desired configuration is not yet cached, you can compile it yourself using the [Optimum CLI](https://huggingface.co/docs/optimum-neuron/guides/export_model) or open a request at the [Cache repository](https://huggingface.co/aws-neuron/optimum-neuron-cache/discussions). | ||
| **Deploying Llama 3.3 70B to a SageMaker Endpoint** | ||
| Before deploying the model to Amazon SageMaker, we must define the TGI Neuron endpoint configuration. We need to make sure the following additional parameters are defined: | ||
| - `HF_NUM_CORES`: Number of Neuron Cores used for the compilation. | ||
| - `HF_BATCH_SIZE`: The batch size that was used to compile the model. | ||
| - `HF_SEQUENCE_LENGTH`: The sequence length that was used to compile the model. | ||
| - `HF_AUTO_CAST_TYPE`: The auto cast type that was used to compile the model. | ||
| We still need to define traditional TGI parameters with: | ||
| - `HF_MODEL_ID`: The Hugging Face model ID. | ||
| - `HF_TOKEN`: The Hugging Face API token to access gated models. | ||
| - `MAX_BATCH_SIZE`: The maximum batch size that the model can handle, equal to the batch size used for compilation. | ||
| - `MAX_INPUT_LENGTH`: The maximum input length that the model can handle. | ||
| - `MAX_TOTAL_TOKENS`: The maximum total tokens the model can generate, equal to the sequence length used for compilation. | ||
| Optionnaly, you can configure the endpoint to support chat templates: | ||
| - `MESSAGES_API_ENABLED`: Enable Messages API | ||
| **Select the right instance type** | ||
| Llama 3.3 70B is a large model and requires a lot of memory. We are going to use the `inf2.48xlarge` instance type, which has 192 vCPUs and 384 GB of accelerator memory. The `inf2.48xlarge` instance comes with 12 Inferentia2 accelerators that include 24 Neuron Cores. If you want to find the cached configurations for Llama 3.3 70B, you can find them [here](https://huggingface.co/aws-neuron/optimum-neuron-cache/blob/main/inference-cache-config/llama3-70b.json#L16). In our case we will use a batch size of 4 and a sequence length of 4096. | ||
| Before we can deploy Llama 3.3 70B to Inferentia2, we need to make sure we have the necessary permissions to access the model. You can request access to the model [here](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) and create a User access token following this [guide](https://huggingface.co/docs/hub/en/security-tokens). | ||
| After that we can create our endpoint configuration and deploy the model to Amazon SageMaker. | ||
| ```python | ||
| from sagemaker.huggingface import HuggingFaceModel | ||
| # sagemaker config | ||
| instance_type = "ml.inf2.48xlarge" | ||
| health_check_timeout=2400 # additional time to load the model | ||
| volume_size=512 # size in GB of the EBS volume | ||
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| # Define Model and Endpoint configuration parameter | ||
| config = { | ||
| "HF_MODEL_ID": "meta-llama/Meta-Llama-3-70B-Instruct", | ||
| "HF_NUM_CORES": "24", # number of neuron cores | ||
| "HF_AUTO_CAST_TYPE": "bf16", # dtype of the model | ||
| "MAX_BATCH_SIZE": "4", # max batch size for the model | ||
| "MAX_INPUT_TOKENS": "4000", # max length of input text | ||
| "MAX_TOTAL_TOKENS": "4096", # max length of generated text | ||
| "MESSAGES_API_ENABLED": "true", # Enable the messages API | ||
| "HF_TOKEN": "<REPLACE WITH YOUR TOKEN>", | ||
| } | ||
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| assert config["HF_TOKEN"] != "<REPLACE WITH YOUR TOKEN>", "Please replace '<REPLACE WITH YOUR TOKEN>' with your Hugging Face Hub API token" | ||
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| # create HuggingFaceModel with the image uri | ||
| llm_model = HuggingFaceModel( | ||
| role=role, | ||
| image_uri=llm_image, | ||
| env=config | ||
| ) | ||
| ``` | ||
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| After we have created the `HuggingFaceModel` we can deploy it to Amazon SageMaker using the `deploy` method. We will deploy the model with the `ml.inf2.48xlarge` instance type. TGI will automatically distribute and shard the model across all Inferentia devices. | ||
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| ```python | ||
| # deactivate warning since model is compiled | ||
| llm_model._is_compiled_model = True | ||
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| llm = llm_model.deploy( | ||
| initial_instance_count=1, | ||
| instance_type=instance_type, | ||
| container_startup_health_check_timeout=health_check_timeout, | ||
| volume_size=volume_size | ||
| ) | ||
| ``` | ||
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| SageMaker will now create our endpoint and deploy the model to it. It takes around 30-40 minutes, we are working on improving the deployment time. | ||
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| After our endpoint is deployed we can run inference on it. We will use the `predict` method from the `predictor` to run inference on our endpoint. We can inference with different parameters to impact the generation. Parameters can be defined as in the `parameters` attribute of the payload. You can find supported parameters in the [here](https://huggingface.co/docs/text-generation-inference/messages_api). | ||
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| The Messages API allows us to interact with the model in a conversational way. We can define the role of the message and the content. The role can be either `system`,`assistant` or `user`. The `system` role is used to provide context to the model and the `user` role is used to ask questions or provide input to the model. | ||
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| ```python | ||
| # Prompt to generate | ||
| messages=[ | ||
| { "role": "system", "content": "You are a helpful assistant." }, | ||
| { "role": "user", "content": "What is deep learning?" } | ||
| ] | ||
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| # Generation arguments | ||
| parameters = { | ||
| "top_p": 0.6, | ||
| "temperature": 0.9, | ||
| "max_tokens": 50, | ||
| "stop": ["<|eot_id|>"], | ||
| } | ||
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| chat = llm.predict({"messages" :messages, **parameters,"steam":True}) | ||
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| print(chat["choices"][0]["message"]["content"].strip()) | ||
| ``` | ||
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| ## 4. Clean up | ||
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| To clean up, we can delete the model and endpoint. | ||
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| ```python | ||
| llm.delete_model() | ||
| llm.delete_endpoint() | ||
| ``` |
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