rszper commented on code in PR #27075:
URL: https://github.com/apache/beam/pull/27075#discussion_r1226913777
##########
examples/notebooks/beam-ml/run_inference_generative_AI.ipynb:
##########
@@ -0,0 +1,323 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "code",
+ "source": [
+ "# @title ###### Licensed to the Apache Software Foundation (ASF),
Version 2.0 (the \"License\")\n",
+ "\n",
+ "# Licensed to the Apache Software Foundation (ASF) under one\n",
+ "# or more contributor license agreements. See the NOTICE file\n",
+ "# distributed with this work for additional information\n",
+ "# regarding copyright ownership. The ASF licenses this file\n",
+ "# to you under the Apache License, Version 2.0 (the\n",
+ "# \"License\"); you may not use this file except in compliance\n",
+ "# with the License. You may obtain a copy of the License at\n",
+ "#\n",
+ "# http://www.apache.org/licenses/LICENSE-2.0\n";,
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing,\n",
+ "# software distributed under the License is distributed on an\n",
+ "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+ "# KIND, either express or implied. See the License for the\n",
+ "# specific language governing permissions and limitations\n",
+ "# under the License"
+ ],
+ "metadata": {
+ "cellView": "form",
+ "id": "IVkpU8HZ1eyz"
+ },
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Use RunInference for Generative AI\n",
+ "\n",
+ "<table align=\"left\">\n",
+ " <td>\n",
+ " <a target=\"_blank\"
href=\"https://colab.research.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_generative_ai.ipynb\";><img
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\";
/>Run in Google Colab</a>\n",
+ " </td>\n",
+ " <td>\n",
+ " <a target=\"_blank\"
href=\"https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_generative_ai.ipynb\";><img
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\";
/>View source on GitHub</a>\n",
+ " </td>\n",
+ "</table>\n"
+ ],
+ "metadata": {
+ "id": "kH8SORNim8on"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "This notebook shows how to use the Apache Beam
[RunInference](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.RunInference)
transform for generative AI tasks. It uses a large language model (LLM) from
the [Hugging Face Model Hub](https://huggingface.co/models).\n",
+ "\n",
+ "This notebook demonstrates the following steps:\n",
+ "- Load and save a model from the Hugging Face Model Hub.\n",
+ "- Use the PyTorch model handler for RunInference.\n",
+ "\n",
+ "For more information about using RunInference, see [Get started with
AI/ML pipelines](https://beam.apache.org/documentation/ml/overview/) in the
Apache Beam documentation."
+ ],
+ "metadata": {
+ "id": "7N2XzwoA0k4L"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Install the Apache Beam SDK and dependencies\n",
+ "\n",
+ "Use the following code to install the Apache Beam Python SDK,
PyTorch, and Transformers."
+ ],
+ "metadata": {
+ "id": "nhf_lOeEsO1C"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "wS9a3Y0oZ_l5"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install apache_beam[gcp]==2.48.0\n",
+ "!pip install torch\n",
+ "!pip install transformers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Use the following code to import dependencies\n",
+ "\n",
+ "**Important**: If an error occurs, restart your runtime."
+ ],
+ "metadata": {
+ "id": "I7vMsFGW16bZ"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import os\n",
+ "import apache_beam as beam\n",
+ "from apache_beam.options.pipeline_options import PipelineOptions\n",
+ "from apache_beam.ml.inference.base import PredictionResult\n",
+ "from apache_beam.ml.inference.base import RunInference\n",
+ "from apache_beam.ml.inference.pytorch_inference import
make_tensor_model_fn\n",
+ "from apache_beam.ml.inference.pytorch_inference import
PytorchModelHandlerTensor\n",
+ "import torch\n",
+ "from transformers import AutoConfig\n",
+ "from transformers import AutoModelForSeq2SeqLM\n",
+ "from transformers import AutoTokenizer\n",
+ "from transformers.tokenization_utils import PreTrainedTokenizer\n",
+ "\n",
+ "\n",
+ "MAX_RESPONSE_TOKENS = 256\n",
+ "\n",
+ "model_name = \"google/flan-t5-small\"\n",
+ "state_dict_path = \"saved_model\""
+ ],
+ "metadata": {
+ "id": "uhbOYUzvbOSc"
+ },
+ "execution_count": 3,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Download and save the model\n",
+ "This notebook uses the [auto
classes](https://huggingface.co/docs/transformers/model_doc/auto) from Hugging
Face to instantly load the model in memory. Later, the model is saved to the
path defined previously."
+ ],
+ "metadata": {
+ "id": "yRls3LmxswrC"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "model = AutoModelForSeq2SeqLM.from_pretrained(\n",
+ " model_name, torch_dtype=torch.bfloat16\n",
+ " )\n",
+ "\n",
+ "directory = os.path.dirname(state_dict_path)\n",
+ "torch.save(model.state_dict(), state_dict_path)"
+ ],
+ "metadata": {
+ "id": "PKhkiQFJe44n"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Define utitlity functions\n",
+ "The input and output for the
[`google/flan-t5-small`](https://huggingface.co/google/flan-t5-small) model are
token tensors. These utility functions are used for the conversion of text to
token tensors and then back to text.\n"
+ ],
+ "metadata": {
+ "id": "7TSqb3l1s7F7"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "def to_tensors(input_text: str, tokenizer) -> torch.Tensor:\n",
+ " \"\"\"Encodes input text into token tensors.\n",
+ " Args:\n",
+ " input_text: Input text for the LLM model.\n",
+ " tokenizer: Tokenizer for the LLM model.\n",
+ " Returns: Tokenized input tokens.\n",
+ " \"\"\"\n",
+ " return tokenizer(input_text,
return_tensors=\"pt\").input_ids[0]\n",
+ "\n",
+ "\n",
+ "def from_tensors(result: PredictionResult, tokenizer) -> str:\n",
+ " \"\"\"Decodes output token tensors into text.\n",
+ " Args:\n",
+ " result: Prediction results from the RunInference
transform.\n",
+ " tokenizer: Tokenizer for the LLM model.\n",
+ " Returns: The model's response as text.\n",
+ " \"\"\"\n",
+ " output_tokens = result.inference\n",
+ " return tokenizer.decode(output_tokens, skip_special_tokens=True)"
+ ],
+ "metadata": {
+ "id": "OeTMbaLidnBe"
+ },
+ "execution_count": 5,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Load the tokenizer\n",
Review Comment:
```suggestion
"# Load the tokenizer.\n",
```
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