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Harrison/self hosted runhouse (#1154)
Co-authored-by: Donny Greenberg <dongreenberg2@gmail.com> Co-authored-by: John Dagdelen <jdagdelen@users.noreply.github.com> Co-authored-by: Harrison Chase <harrisonchase@Harrisons-MBP.attlocal.net> Co-authored-by: Andrew White <white.d.andrew@gmail.com> Co-authored-by: Peng Qu <82029664+pengqu123@users.noreply.github.com> Co-authored-by: Matt Robinson <mthw.wm.robinson@gmail.com> Co-authored-by: jeff <tangj1122@gmail.com> Co-authored-by: Harrison Chase <harrisonchase@Harrisons-MacBook-Pro.local> Co-authored-by: zanderchase <zander@unfold.ag> Co-authored-by: Charles Frye <cfrye59@gmail.com> Co-authored-by: zanderchase <zanderchase@gmail.com> Co-authored-by: Shahriar Tajbakhsh <sh.tajbakhsh@gmail.com> Co-authored-by: Stefan Keselj <skeselj@princeton.edu> Co-authored-by: Francisco Ingham <fpingham@gmail.com> Co-authored-by: Dhruv Anand <105786647+dhruv-anand-aintech@users.noreply.github.com> Co-authored-by: cragwolfe <cragcw@gmail.com> Co-authored-by: Anton Troynikov <atroyn@users.noreply.github.com> Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com> Co-authored-by: Oliver Klingefjord <oliver@klingefjord.com> Co-authored-by: blob42 <contact@blob42.xyz> Co-authored-by: blob42 <spike@w530> Co-authored-by: Enrico Shippole <henryshippole@gmail.com> Co-authored-by: Ibis Prevedello <ibiscp@gmail.com> Co-authored-by: jped <jonathanped@gmail.com> Co-authored-by: Justin Torre <justintorre75@gmail.com> Co-authored-by: Ivan Vendrov <ivan@anthropic.com> Co-authored-by: Sasmitha Manathunga <70096033+mmz-001@users.noreply.github.com> Co-authored-by: Ankush Gola <9536492+agola11@users.noreply.github.com> Co-authored-by: Matt Robinson <mrobinson@unstructuredai.io> Co-authored-by: Jeff Huber <jeffchuber@gmail.com> Co-authored-by: Akshay <64036106+akshayvkt@users.noreply.github.com> Co-authored-by: Andrew Huang <jhuang16888@gmail.com> Co-authored-by: rogerserper <124558887+rogerserper@users.noreply.github.com> Co-authored-by: seanaedmiston <seane999@gmail.com> Co-authored-by: Hasegawa Yuya <52068175+Hase-U@users.noreply.github.com> Co-authored-by: Ivan Vendrov <ivendrov@gmail.com> Co-authored-by: Chen Wu (吴尘) <henrychenwu@cmu.edu> Co-authored-by: Dennis Antela Martinez <dennis.antela@gmail.com> Co-authored-by: Maxime Vidal <max.vidal@hotmail.fr> Co-authored-by: Rishabh Raizada <110235735+rishabh-ti@users.noreply.github.com>
This commit is contained in:
Harrison Chase
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31
docs/ecosystem/runhouse.md
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31
docs/ecosystem/runhouse.md
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@ -0,0 +1,31 @@
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# Runhouse
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This page covers how to use the [Runhouse](https://github.com/run-house/runhouse) ecosystem within LangChain.
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It is broken into three parts: installation and setup, LLMs, and Embeddings.
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## Installation and Setup
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- Install the Python SDK with `pip install runhouse`
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- If you'd like to use on-demand cluster, check your cloud credentials with `sky check`
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## Self-hosted LLMs
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For a basic self-hosted LLM, you can use the `SelfHostedHuggingFaceLLM` class. For more
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custom LLMs, you can use the `SelfHostedPipeline` parent class.
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```python
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from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
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```
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For a more detailed walkthrough of the Self-hosted LLMs, see [this notebook](../modules/llms/integrations/self_hosted_examples.ipynb)
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## Self-hosted Embeddings
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There are several ways to use self-hosted embeddings with LangChain via Runhouse.
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For a basic self-hosted embedding from a Hugging Face Transformers model, you can use
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the `SelfHostedEmbedding` class.
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```python
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from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
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```
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For a more detailed walkthrough of the Self-hosted Embeddings, see [this notebook](../modules/utils/combine_docs_examples/embeddings.ipynb)
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##
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@ -27,6 +27,8 @@ The examples here are all "how-to" guides for how to integrate with various LLM
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`Anthropic <./integrations/anthropic_example.html>`_: Covers how to use Anthropic models with Langchain.
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`Self-Hosted Models (via Runhouse) <./integrations/self_hosted_examples.html>`_: Covers how to run models on existing or on-demand remote compute with Langchain.
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.. toctree::
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:maxdepth: 1
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296
docs/modules/llms/integrations/self_hosted_examples.ipynb
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296
docs/modules/llms/integrations/self_hosted_examples.ipynb
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@ -0,0 +1,296 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "9597802c",
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"metadata": {},
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"source": [
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"# Self-Hosted Models via Runhouse\n",
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"This example goes over how to use LangChain and [Runhouse](https://github.com/run-house/runhouse) to interact with models hosted on your own GPU, or on-demand GPUs on AWS, GCP, AWS, or Lambda.\n",
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"\n",
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"For more information, see [Runhouse](https://github.com/run-house/runhouse) or the [Runhouse docs](https://runhouse-docs.readthedocs-hosted.com/en/latest/)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6fb585dd",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM\n",
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"from langchain import PromptTemplate, LLMChain\n",
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"import runhouse as rh"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "06d6866e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# For an on-demand A100 with GCP, Azure, or Lambda\n",
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"gpu = rh.cluster(name=\"rh-a10x\", instance_type=\"A100:1\", use_spot=False)\n",
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"\n",
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"# For an on-demand A10G with AWS (no single A100s on AWS)\n",
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"# gpu = rh.cluster(name='rh-a10x', instance_type='g5.2xlarge', provider='aws')\n",
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"\n",
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"# For an existing cluster\n",
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"# gpu = rh.cluster(ips=['<ip of the cluster>'], \n",
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"# ssh_creds={'ssh_user': '...', 'ssh_private_key':'<path_to_key>'},\n",
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"# name='rh-a10x')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "035dea0f",
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"metadata": {},
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"outputs": [],
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"source": [
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"template = \"\"\"Question: {question}\n",
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"\n",
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"Answer: Let's think step by step.\"\"\"\n",
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"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3f3458d9",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = SelfHostedHuggingFaceLLM(model_id=\"gpt2\", hardware=gpu, model_reqs=[\"pip:./\", \"transformers\", \"torch\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "a641dbd9",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm_chain = LLMChain(prompt=prompt, llm=llm)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"id": "6fb6fdb2",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"INFO | 2023-02-17 05:42:23,537 | Running _generate_text via gRPC\n",
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"INFO | 2023-02-17 05:42:24,016 | Time to send message: 0.48 seconds\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"\"\\n\\nLet's say we're talking sports teams who won the Super Bowl in the year Justin Beiber\""
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]
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},
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"execution_count": 31,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
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"\n",
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"llm_chain.run(question)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c88709cd",
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"metadata": {},
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"source": [
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"You can also load more custom models through the SelfHostedHuggingFaceLLM interface:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "22820c5a",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"llm = SelfHostedHuggingFaceLLM(\n",
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" model_id=\"google/flan-t5-small\",\n",
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" task=\"text2text-generation\",\n",
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" hardware=gpu,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"id": "1528e70f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"INFO | 2023-02-17 05:54:21,681 | Running _generate_text via gRPC\n",
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"INFO | 2023-02-17 05:54:21,937 | Time to send message: 0.25 seconds\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'berlin'"
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]
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},
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"execution_count": 39,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"llm(\"What is the capital of Germany?\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7a0c3746",
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"metadata": {},
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"source": [
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"Using a custom load function, we can load a custom pipeline directly on the remote hardware:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"id": "893eb1d3",
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"metadata": {},
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"outputs": [],
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"source": [
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"def load_pipeline():\n",
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" from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline # Need to be inside the fn in notebooks\n",
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" model_id = \"gpt2\"\n",
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" tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
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" model = AutoModelForCausalLM.from_pretrained(model_id)\n",
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" pipe = pipeline(\n",
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" \"text-generation\", model=model, tokenizer=tokenizer, max_new_tokens=10\n",
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" )\n",
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" return pipe\n",
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"\n",
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"def inference_fn(pipeline, prompt, stop = None):\n",
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" return pipeline(prompt)[0][\"generated_text\"][len(prompt):]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "087d50dc",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"llm = SelfHostedHuggingFaceLLM(model_load_fn=load_pipeline, hardware=gpu, inference_fn=inference_fn)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"id": "feb8da8e",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"INFO | 2023-02-17 05:42:59,219 | Running _generate_text via gRPC\n",
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"INFO | 2023-02-17 05:42:59,522 | Time to send message: 0.3 seconds\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'john w. bush'"
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]
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},
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"execution_count": 36,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"llm(\"Who is the current US president?\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "af08575f",
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"metadata": {},
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"source": [
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"You can send your pipeline directly over the wire to your model, but this will only work for small models (<2 Gb), and will be pretty slow:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d23023b9",
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"metadata": {},
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"outputs": [],
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"source": [
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"pipeline = load_pipeline()\n",
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"llm = SelfHostedPipeline.from_pipeline(\n",
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" pipeline=pipeline, hardware=gpu, model_reqs=model_reqs\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fcb447a1",
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"metadata": {},
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"source": [
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"Instead, we can also send it to the hardware's filesystem, which will be much faster."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7206b7d6",
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"metadata": {},
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"outputs": [],
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"source": [
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"rh.blob(pickle.dumps(pipeline), path=\"models/pipeline.pkl\").save().to(gpu, path=\"models\")\n",
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"\n",
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"llm = SelfHostedPipeline.from_pipeline(pipeline=\"models/pipeline.pkl\", hardware=gpu)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.15"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -313,13 +313,156 @@
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"query_result = embeddings.embed_query(text)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "eec4efda",
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"metadata": {},
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"source": [
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"## Self Hosted Embeddings\n",
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"Let's load the SelfHostedEmbeddings, SelfHostedHuggingFaceEmbeddings, and SelfHostedHuggingFaceInstructEmbeddings classes."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a961cdb5",
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"id": "d338722a",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"from langchain.embeddings import (\n",
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" SelfHostedEmbeddings, \n",
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" SelfHostedHuggingFaceEmbeddings, \n",
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" SelfHostedHuggingFaceInstructEmbeddings\n",
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")\n",
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"import runhouse as rh"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "146559e8",
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"# For an on-demand A100 with GCP, Azure, or Lambda\n",
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"gpu = rh.cluster(name=\"rh-a10x\", instance_type=\"A100:1\", use_spot=False)\n",
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"\n",
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"# For an on-demand A10G with AWS (no single A100s on AWS)\n",
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"# gpu = rh.cluster(name='rh-a10x', instance_type='g5.2xlarge', provider='aws')\n",
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"\n",
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"# For an existing cluster\n",
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"# gpu = rh.cluster(ips=['<ip of the cluster>'], \n",
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"# ssh_creds={'ssh_user': '...', 'ssh_private_key':'<path_to_key>'},\n",
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"# name='my-cluster')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1230f7df",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings = SelfHostedHuggingFaceEmbeddings(hardware=gpu)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "2684e928",
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"metadata": {},
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"outputs": [],
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"source": [
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"text = \"This is a test document.\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1dc5e606",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"query_result = embeddings.embed_query(text)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "cef9cc54",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"And similarly for SelfHostedHuggingFaceInstructEmbeddings:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "81a17ca3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"embeddings = SelfHostedHuggingFaceInstructEmbeddings(hardware=gpu)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "5a33d1c8",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now let's load an embedding model with a custom load function:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "c4af5679",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def get_pipeline():\n",
|
||||
" from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline # Must be inside the function in notebooks\n",
|
||||
" model_id = \"facebook/bart-base\"\n",
|
||||
" tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
|
||||
" model = AutoModelForCausalLM.from_pretrained(model_id)\n",
|
||||
" return pipeline(\"feature-extraction\", model=model, tokenizer=tokenizer)\n",
|
||||
"\n",
|
||||
"def inference_fn(pipeline, prompt):\n",
|
||||
" # Return last hidden state of the model\n",
|
||||
" if isinstance(prompt, list):\n",
|
||||
" return [emb[0][-1] for emb in pipeline(prompt)] \n",
|
||||
" return pipeline(prompt)[0][-1]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8654334b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"embeddings = SelfHostedEmbeddings(\n",
|
||||
" model_load_fn=get_pipeline, \n",
|
||||
" hardware=gpu,\n",
|
||||
" model_reqs=[\"./\", \"torch\", \"transformers\"],\n",
|
||||
" inference_fn=inference_fn\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "fc1bfd0f",
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"query_result = embeddings.embed_query(text)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
@ -338,7 +481,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.1"
|
||||
"version": "3.10.9"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
|
||||
@ -9,6 +9,11 @@ from langchain.embeddings.huggingface import (
|
||||
)
|
||||
from langchain.embeddings.huggingface_hub import HuggingFaceHubEmbeddings
|
||||
from langchain.embeddings.openai import OpenAIEmbeddings
|
||||
from langchain.embeddings.self_hosted import SelfHostedEmbeddings
|
||||
from langchain.embeddings.self_hosted_hugging_face import (
|
||||
SelfHostedHuggingFaceEmbeddings,
|
||||
SelfHostedHuggingFaceInstructEmbeddings,
|
||||
)
|
||||
from langchain.embeddings.tensorflow_hub import TensorflowHubEmbeddings
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
@ -20,6 +25,9 @@ __all__ = [
|
||||
"HuggingFaceHubEmbeddings",
|
||||
"TensorflowHubEmbeddings",
|
||||
"HuggingFaceInstructEmbeddings",
|
||||
"SelfHostedEmbeddings",
|
||||
"SelfHostedHuggingFaceEmbeddings",
|
||||
"SelfHostedHuggingFaceInstructEmbeddings",
|
||||
]
|
||||
|
||||
|
||||
|
||||
103
langchain/embeddings/self_hosted.py
Normal file
103
langchain/embeddings/self_hosted.py
Normal file
@ -0,0 +1,103 @@
|
||||
"""Running custom embedding models on self-hosted remote hardware."""
|
||||
from typing import Any, Callable, List
|
||||
|
||||
from pydantic import BaseModel, Extra
|
||||
|
||||
from langchain.embeddings.base import Embeddings
|
||||
from langchain.llms import SelfHostedPipeline
|
||||
|
||||
|
||||
def _embed_documents(pipeline: Any, *args: Any, **kwargs: Any) -> List[List[float]]:
|
||||
"""Inference function to send to the remote hardware.
|
||||
|
||||
Accepts a sentence_transformer model_id and
|
||||
returns a list of embeddings for each document in the batch.
|
||||
"""
|
||||
return pipeline(*args, **kwargs)
|
||||
|
||||
|
||||
class SelfHostedEmbeddings(SelfHostedPipeline, Embeddings, BaseModel):
|
||||
"""Runs custom embedding models on self-hosted remote hardware.
|
||||
|
||||
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
|
||||
and Lambda, as well as servers specified
|
||||
by IP address and SSH credentials (such as on-prem, or another
|
||||
cloud like Paperspace, Coreweave, etc.).
|
||||
|
||||
To use, you should have the ``runhouse`` python package installed.
|
||||
|
||||
Example using a model load function:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain.embeddings import SelfHostedEmbeddings
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
|
||||
import runhouse as rh
|
||||
|
||||
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
|
||||
def get_pipeline():
|
||||
model_id = "facebook/bart-large"
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
||||
model = AutoModelForCausalLM.from_pretrained(model_id)
|
||||
return pipeline("feature-extraction", model=model, tokenizer=tokenizer)
|
||||
embeddings = SelfHostedEmbeddings(
|
||||
model_load_fn=get_pipeline,
|
||||
hardware=gpu
|
||||
model_reqs=["./", "torch", "transformers"],
|
||||
)
|
||||
Example passing in a pipeline path:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain.embeddings import SelfHostedHFEmbeddings
|
||||
import runhouse as rh
|
||||
from transformers import pipeline
|
||||
|
||||
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
|
||||
pipeline = pipeline(model="bert-base-uncased", task="feature-extraction")
|
||||
rh.blob(pickle.dumps(pipeline),
|
||||
path="models/pipeline.pkl").save().to(gpu, path="models")
|
||||
embeddings = SelfHostedHFEmbeddings.from_pipeline(
|
||||
pipeline="models/pipeline.pkl",
|
||||
hardware=gpu,
|
||||
model_reqs=["./", "torch", "transformers"],
|
||||
)
|
||||
"""
|
||||
|
||||
inference_fn: Callable = _embed_documents
|
||||
"""Inference function to extract the embeddings on the remote hardware."""
|
||||
inference_kwargs: Any = None
|
||||
"""Any kwargs to pass to the model's inference function."""
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
|
||||
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
"""Compute doc embeddings using a HuggingFace transformer model.
|
||||
|
||||
Args:
|
||||
texts: The list of texts to embed.s
|
||||
|
||||
Returns:
|
||||
List of embeddings, one for each text.
|
||||
"""
|
||||
texts = list(map(lambda x: x.replace("\n", " "), texts))
|
||||
embeddings = self.client(self.pipeline_ref, texts)
|
||||
if not isinstance(embeddings, list):
|
||||
return embeddings.tolist()
|
||||
return embeddings
|
||||
|
||||
def embed_query(self, text: str) -> List[float]:
|
||||
"""Compute query embeddings using a HuggingFace transformer model.
|
||||
|
||||
Args:
|
||||
text: The text to embed.
|
||||
|
||||
Returns:
|
||||
Embeddings for the text.
|
||||
"""
|
||||
text = text.replace("\n", " ")
|
||||
embeddings = self.client(self.pipeline_ref, text)
|
||||
if not isinstance(embeddings, list):
|
||||
return embeddings.tolist()
|
||||
return embeddings
|
||||
171
langchain/embeddings/self_hosted_hugging_face.py
Normal file
171
langchain/embeddings/self_hosted_hugging_face.py
Normal file
@ -0,0 +1,171 @@
|
||||
"""Wrapper around HuggingFace embedding models for self-hosted remote hardware."""
|
||||
import importlib
|
||||
import logging
|
||||
from typing import Any, Callable, List, Optional
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from langchain.embeddings.self_hosted import SelfHostedEmbeddings
|
||||
|
||||
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
|
||||
DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-large"
|
||||
DEFAULT_EMBED_INSTRUCTION = "Represent the document for retrieval: "
|
||||
DEFAULT_QUERY_INSTRUCTION = (
|
||||
"Represent the question for retrieving supporting documents: "
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _embed_documents(client: Any, *args: Any, **kwargs: Any) -> List[List[float]]:
|
||||
"""Inference function to send to the remote hardware.
|
||||
|
||||
Accepts a sentence_transformer model_id and
|
||||
returns a list of embeddings for each document in the batch.
|
||||
"""
|
||||
return client.encode(*args, **kwargs)
|
||||
|
||||
|
||||
def load_embedding_model(model_id: str, instruct: bool = False, device: int = 0) -> Any:
|
||||
"""Load the embedding model."""
|
||||
if not instruct:
|
||||
import sentence_transformers
|
||||
|
||||
client = sentence_transformers.SentenceTransformer(model_id)
|
||||
else:
|
||||
from InstructorEmbedding import INSTRUCTOR
|
||||
|
||||
client = INSTRUCTOR(model_id)
|
||||
|
||||
if importlib.util.find_spec("torch") is not None:
|
||||
import torch
|
||||
|
||||
cuda_device_count = torch.cuda.device_count()
|
||||
if device < -1 or (device >= cuda_device_count):
|
||||
raise ValueError(
|
||||
f"Got device=={device}, "
|
||||
f"device is required to be within [-1, {cuda_device_count})"
|
||||
)
|
||||
if device < 0 and cuda_device_count > 0:
|
||||
logger.warning(
|
||||
"Device has %d GPUs available. "
|
||||
"Provide device={deviceId} to `from_model_id` to use available"
|
||||
"GPUs for execution. deviceId is -1 for CPU and "
|
||||
"can be a positive integer associated with CUDA device id.",
|
||||
cuda_device_count,
|
||||
)
|
||||
|
||||
client = client.to(device)
|
||||
return client
|
||||
|
||||
|
||||
class SelfHostedHuggingFaceEmbeddings(SelfHostedEmbeddings, BaseModel):
|
||||
"""Runs sentence_transformers embedding models on self-hosted remote hardware.
|
||||
|
||||
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
|
||||
and Lambda, as well as servers specified
|
||||
by IP address and SSH credentials (such as on-prem, or another cloud
|
||||
like Paperspace, Coreweave, etc.).
|
||||
|
||||
To use, you should have the ``runhouse`` python package installed.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain.embeddings import SelfHostedHuggingFaceEmbeddings
|
||||
import runhouse as rh
|
||||
model_name = "sentence-transformers/all-mpnet-base-v2"
|
||||
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
|
||||
hf = SelfHostedHuggingFaceEmbeddings(model_name=model_name, hardware=gpu)
|
||||
"""
|
||||
|
||||
client: Any #: :meta private:
|
||||
model_id: str = DEFAULT_MODEL_NAME
|
||||
"""Model name to use."""
|
||||
model_reqs: List[str] = ["./", "sentence_transformers", "torch"]
|
||||
"""Requirements to install on hardware to inference the model."""
|
||||
hardware: Any
|
||||
"""Remote hardware to send the inference function to."""
|
||||
model_load_fn: Callable = load_embedding_model
|
||||
"""Function to load the model remotely on the server."""
|
||||
load_fn_kwargs: Optional[dict] = None
|
||||
"""Key word arguments to pass to the model load function."""
|
||||
inference_fn: Callable = _embed_documents
|
||||
"""Inference function to extract the embeddings."""
|
||||
|
||||
def __init__(self, **kwargs: Any):
|
||||
"""Initialize the remote inference function."""
|
||||
load_fn_kwargs = kwargs.pop("load_fn_kwargs", {})
|
||||
load_fn_kwargs["model_id"] = load_fn_kwargs.get("model_id", DEFAULT_MODEL_NAME)
|
||||
load_fn_kwargs["instruct"] = load_fn_kwargs.get("instruct", False)
|
||||
load_fn_kwargs["device"] = load_fn_kwargs.get("device", 0)
|
||||
super().__init__(load_fn_kwargs=load_fn_kwargs, **kwargs)
|
||||
|
||||
|
||||
class SelfHostedHuggingFaceInstructEmbeddings(SelfHostedHuggingFaceEmbeddings):
|
||||
"""Runs InstructorEmbedding embedding models on self-hosted remote hardware.
|
||||
|
||||
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
|
||||
and Lambda, as well as servers specified
|
||||
by IP address and SSH credentials (such as on-prem, or another
|
||||
cloud like Paperspace, Coreweave, etc.).
|
||||
|
||||
To use, you should have the ``runhouse`` python package installed.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain.embeddings import SelfHostedHuggingFaceInstructEmbeddings
|
||||
import runhouse as rh
|
||||
model_name = "hkunlp/instructor-large"
|
||||
gpu = rh.cluster(name='rh-a10x', instance_type='A100:1')
|
||||
hf = SelfHostedHuggingFaceInstructEmbeddings(
|
||||
model_name=model_name, hardware=gpu)
|
||||
"""
|
||||
|
||||
model_id: str = DEFAULT_INSTRUCT_MODEL
|
||||
"""Model name to use."""
|
||||
embed_instruction: str = DEFAULT_EMBED_INSTRUCTION
|
||||
"""Instruction to use for embedding documents."""
|
||||
query_instruction: str = DEFAULT_QUERY_INSTRUCTION
|
||||
"""Instruction to use for embedding query."""
|
||||
model_reqs: List[str] = ["./", "InstructorEmbedding", "torch"]
|
||||
"""Requirements to install on hardware to inference the model."""
|
||||
|
||||
def __init__(self, **kwargs: Any):
|
||||
"""Initialize the remote inference function."""
|
||||
load_fn_kwargs = kwargs.pop("load_fn_kwargs", {})
|
||||
load_fn_kwargs["model_id"] = load_fn_kwargs.get(
|
||||
"model_id", DEFAULT_INSTRUCT_MODEL
|
||||
)
|
||||
load_fn_kwargs["instruct"] = load_fn_kwargs.get("instruct", True)
|
||||
load_fn_kwargs["device"] = load_fn_kwargs.get("device", 0)
|
||||
super().__init__(load_fn_kwargs=load_fn_kwargs, **kwargs)
|
||||
|
||||
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
"""Compute doc embeddings using a HuggingFace instruct model.
|
||||
|
||||
Args:
|
||||
texts: The list of texts to embed.
|
||||
|
||||
Returns:
|
||||
List of embeddings, one for each text.
|
||||
"""
|
||||
instruction_pairs = []
|
||||
for text in texts:
|
||||
instruction_pairs.append([self.embed_instruction, text])
|
||||
embeddings = self.client(self.pipeline_ref, instruction_pairs)
|
||||
return embeddings.tolist()
|
||||
|
||||
def embed_query(self, text: str) -> List[float]:
|
||||
"""Compute query embeddings using a HuggingFace instruct model.
|
||||
|
||||
Args:
|
||||
text: The text to embed.
|
||||
|
||||
Returns:
|
||||
Embeddings for the text.
|
||||
"""
|
||||
instruction_pair = [self.query_instruction, text]
|
||||
embedding = self.client(self.pipeline_ref, [instruction_pair])[0]
|
||||
return embedding.tolist()
|
||||
@ -14,6 +14,8 @@ from langchain.llms.nlpcloud import NLPCloud
|
||||
from langchain.llms.openai import AzureOpenAI, OpenAI
|
||||
from langchain.llms.petals import Petals
|
||||
from langchain.llms.promptlayer_openai import PromptLayerOpenAI
|
||||
from langchain.llms.self_hosted import SelfHostedPipeline
|
||||
from langchain.llms.self_hosted_hugging_face import SelfHostedHuggingFaceLLM
|
||||
|
||||
__all__ = [
|
||||
"Anthropic",
|
||||
@ -28,6 +30,8 @@ __all__ = [
|
||||
"HuggingFacePipeline",
|
||||
"AI21",
|
||||
"AzureOpenAI",
|
||||
"SelfHostedPipeline",
|
||||
"SelfHostedHuggingFaceLLM",
|
||||
"PromptLayerOpenAI",
|
||||
]
|
||||
|
||||
@ -44,4 +48,6 @@ type_to_cls_dict: Dict[str, Type[BaseLLM]] = {
|
||||
"petals": Petals,
|
||||
"huggingface_pipeline": HuggingFacePipeline,
|
||||
"azure": AzureOpenAI,
|
||||
"self_hosted": SelfHostedPipeline,
|
||||
"self_hosted_hugging_face": SelfHostedHuggingFaceLLM,
|
||||
}
|
||||
|
||||
212
langchain/llms/self_hosted.py
Normal file
212
langchain/llms/self_hosted.py
Normal file
@ -0,0 +1,212 @@
|
||||
"""Run model inference on self-hosted remote hardware."""
|
||||
import importlib.util
|
||||
import logging
|
||||
import pickle
|
||||
from typing import Any, Callable, List, Mapping, Optional
|
||||
|
||||
from pydantic import BaseModel, Extra
|
||||
|
||||
from langchain.llms.base import LLM
|
||||
from langchain.llms.utils import enforce_stop_tokens
|
||||
|
||||
logger = logging.getLogger()
|
||||
|
||||
|
||||
def _generate_text(
|
||||
pipeline: Any,
|
||||
prompt: str,
|
||||
*args: Any,
|
||||
stop: Optional[List[str]] = None,
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
"""Inference function to send to the remote hardware.
|
||||
|
||||
Accepts a pipeline callable (or, more likely,
|
||||
a key pointing to the model on the cluster's object store)
|
||||
and returns text predictions for each document
|
||||
in the batch.
|
||||
"""
|
||||
text = pipeline(prompt, *args, **kwargs)
|
||||
if stop is not None:
|
||||
text = enforce_stop_tokens(text, stop)
|
||||
return text
|
||||
|
||||
|
||||
def _send_pipeline_to_device(pipeline: Any, device: int) -> Any:
|
||||
"""Send a pipeline to a device on the cluster."""
|
||||
if isinstance(pipeline, str):
|
||||
with open(pipeline, "rb") as f:
|
||||
pipeline = pickle.load(f)
|
||||
|
||||
if importlib.util.find_spec("torch") is not None:
|
||||
import torch
|
||||
|
||||
cuda_device_count = torch.cuda.device_count()
|
||||
if device < -1 or (device >= cuda_device_count):
|
||||
raise ValueError(
|
||||
f"Got device=={device}, "
|
||||
f"device is required to be within [-1, {cuda_device_count})"
|
||||
)
|
||||
if device < 0 and cuda_device_count > 0:
|
||||
logger.warning(
|
||||
"Device has %d GPUs available. "
|
||||
"Provide device={deviceId} to `from_model_id` to use available"
|
||||
"GPUs for execution. deviceId is -1 for CPU and "
|
||||
"can be a positive integer associated with CUDA device id.",
|
||||
cuda_device_count,
|
||||
)
|
||||
|
||||
pipeline.device = torch.device(device)
|
||||
pipeline.model = pipeline.model.to(pipeline.device)
|
||||
return pipeline
|
||||
|
||||
|
||||
class SelfHostedPipeline(LLM, BaseModel):
|
||||
"""Run model inference on self-hosted remote hardware.
|
||||
|
||||
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
|
||||
and Lambda, as well as servers specified
|
||||
by IP address and SSH credentials (such as on-prem, or another
|
||||
cloud like Paperspace, Coreweave, etc.).
|
||||
|
||||
To use, you should have the ``runhouse`` python package installed.
|
||||
|
||||
Example for custom pipeline and inference functions:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain.llms import SelfHostedPipeline
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
|
||||
import runhouse as rh
|
||||
|
||||
def load_pipeline():
|
||||
tokenizer = AutoTokenizer.from_pretrained("gpt2")
|
||||
model = AutoModelForCausalLM.from_pretrained("gpt2")
|
||||
return pipeline(
|
||||
"text-generation", model=model, tokenizer=tokenizer,
|
||||
max_new_tokens=10
|
||||
)
|
||||
def inference_fn(pipeline, prompt, stop = None):
|
||||
return pipeline(prompt)[0]["generated_text"]
|
||||
|
||||
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
|
||||
llm = SelfHostedPipeline(
|
||||
model_load_fn=load_pipeline,
|
||||
hardware=gpu,
|
||||
model_reqs=model_reqs, inference_fn=inference_fn
|
||||
)
|
||||
Example for <2GB model (can be serialized and sent directly to the server):
|
||||
.. code-block:: python
|
||||
|
||||
from langchain.llms import SelfHostedPipeline
|
||||
import runhouse as rh
|
||||
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
|
||||
my_model = ...
|
||||
llm = SelfHostedPipeline.from_pipeline(
|
||||
pipeline=my_model,
|
||||
hardware=gpu,
|
||||
model_reqs=["./", "torch", "transformers"],
|
||||
)
|
||||
Example passing model path for larger models:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain.llms import SelfHostedPipeline
|
||||
import runhouse as rh
|
||||
import pickle
|
||||
from transformers import pipeline
|
||||
|
||||
generator = pipeline(model="gpt2")
|
||||
rh.blob(pickle.dumps(generator), path="models/pipeline.pkl"
|
||||
).save().to(gpu, path="models")
|
||||
llm = SelfHostedPipeline.from_pipeline(
|
||||
pipeline="models/pipeline.pkl",
|
||||
hardware=gpu,
|
||||
model_reqs=["./", "torch", "transformers"],
|
||||
)
|
||||
"""
|
||||
|
||||
pipeline_ref: Any #: :meta private:
|
||||
client: Any #: :meta private:
|
||||
inference_fn: Callable = _generate_text #: :meta private:
|
||||
"""Inference function to send to the remote hardware."""
|
||||
hardware: Any
|
||||
"""Remote hardware to send the inference function to."""
|
||||
model_load_fn: Callable
|
||||
"""Function to load the model remotely on the server."""
|
||||
load_fn_kwargs: Optional[dict] = None
|
||||
"""Key word arguments to pass to the model load function."""
|
||||
model_reqs: List[str] = ["./", "torch"]
|
||||
"""Requirements to install on hardware to inference the model."""
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
|
||||
def __init__(self, **kwargs: Any):
|
||||
"""Init the pipeline with an auxiliary function.
|
||||
|
||||
The load function must be in global scope to be imported
|
||||
and run on the server, i.e. in a module and not a REPL or closure.
|
||||
Then, initialize the remote inference function.
|
||||
"""
|
||||
super().__init__(**kwargs)
|
||||
try:
|
||||
import runhouse as rh
|
||||
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import runhouse python package. "
|
||||
"Please install it with `pip install runhouse`."
|
||||
)
|
||||
|
||||
remote_load_fn = rh.function(fn=self.model_load_fn).to(
|
||||
self.hardware, reqs=self.model_reqs
|
||||
)
|
||||
_load_fn_kwargs = self.load_fn_kwargs or {}
|
||||
self.pipeline_ref = remote_load_fn.remote(**_load_fn_kwargs)
|
||||
|
||||
self.client = rh.function(fn=self.inference_fn).to(
|
||||
self.hardware, reqs=self.model_reqs
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_pipeline(
|
||||
cls,
|
||||
pipeline: Any,
|
||||
hardware: Any,
|
||||
model_reqs: Optional[List[str]] = None,
|
||||
device: int = 0,
|
||||
**kwargs: Any,
|
||||
) -> LLM:
|
||||
"""Init the SelfHostedPipeline from a pipeline object or string."""
|
||||
if not isinstance(pipeline, str):
|
||||
logger.warning(
|
||||
"Serializing pipeline to send to remote hardware. "
|
||||
"Note, it can be quite slow"
|
||||
"to serialize and send large models with each execution. "
|
||||
"Consider sending the pipeline"
|
||||
"to the cluster and passing the path to the pipeline instead."
|
||||
)
|
||||
|
||||
load_fn_kwargs = {"pipeline": pipeline, "device": device}
|
||||
return cls(
|
||||
load_fn_kwargs=load_fn_kwargs,
|
||||
model_load_fn=_send_pipeline_to_device,
|
||||
hardware=hardware,
|
||||
model_reqs=["transformers", "torch"] + (model_reqs or []),
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Mapping[str, Any]:
|
||||
"""Get the identifying parameters."""
|
||||
return {
|
||||
**{"hardware": self.hardware},
|
||||
}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
return "self_hosted_llm"
|
||||
|
||||
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
|
||||
return self.client(pipeline=self.pipeline_ref, prompt=prompt, stop=stop)
|
||||
202
langchain/llms/self_hosted_hugging_face.py
Normal file
202
langchain/llms/self_hosted_hugging_face.py
Normal file
@ -0,0 +1,202 @@
|
||||
"""Wrapper around HuggingFace Pipeline API to run on self-hosted remote hardware."""
|
||||
import importlib.util
|
||||
import logging
|
||||
from typing import Any, Callable, List, Mapping, Optional
|
||||
|
||||
from pydantic import BaseModel, Extra
|
||||
|
||||
from langchain.llms.self_hosted import SelfHostedPipeline
|
||||
from langchain.llms.utils import enforce_stop_tokens
|
||||
|
||||
DEFAULT_MODEL_ID = "gpt2"
|
||||
DEFAULT_TASK = "text-generation"
|
||||
VALID_TASKS = ("text2text-generation", "text-generation")
|
||||
|
||||
logger = logging.getLogger()
|
||||
|
||||
|
||||
def _generate_text(
|
||||
pipeline: Any,
|
||||
prompt: str,
|
||||
*args: Any,
|
||||
stop: Optional[List[str]] = None,
|
||||
**kwargs: Any,
|
||||
) -> str:
|
||||
"""Inference function to send to the remote hardware.
|
||||
|
||||
Accepts a Hugging Face pipeline (or more likely,
|
||||
a key pointing to such a pipeline on the cluster's object store)
|
||||
and returns generated text.
|
||||
"""
|
||||
response = pipeline(prompt, *args, **kwargs)
|
||||
if pipeline.task == "text-generation":
|
||||
# Text generation return includes the starter text.
|
||||
text = response[0]["generated_text"][len(prompt) :]
|
||||
elif pipeline.task == "text2text-generation":
|
||||
text = response[0]["generated_text"]
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Got invalid task {pipeline.task}, "
|
||||
f"currently only {VALID_TASKS} are supported"
|
||||
)
|
||||
if stop is not None:
|
||||
text = enforce_stop_tokens(text, stop)
|
||||
return text
|
||||
|
||||
|
||||
def _load_transformer(
|
||||
model_id: str = DEFAULT_MODEL_ID,
|
||||
task: str = DEFAULT_TASK,
|
||||
device: int = 0,
|
||||
model_kwargs: Optional[dict] = None,
|
||||
) -> Any:
|
||||
"""Inference function to send to the remote hardware.
|
||||
|
||||
Accepts a huggingface model_id and returns a pipeline for the task.
|
||||
"""
|
||||
from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer
|
||||
from transformers import pipeline as hf_pipeline
|
||||
|
||||
_model_kwargs = model_kwargs or {}
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs)
|
||||
|
||||
try:
|
||||
if task == "text-generation":
|
||||
model = AutoModelForCausalLM.from_pretrained(model_id, **_model_kwargs)
|
||||
elif task == "text2text-generation":
|
||||
model = AutoModelForSeq2SeqLM.from_pretrained(model_id, **_model_kwargs)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Got invalid task {task}, "
|
||||
f"currently only {VALID_TASKS} are supported"
|
||||
)
|
||||
except ImportError as e:
|
||||
raise ValueError(
|
||||
f"Could not load the {task} model due to missing dependencies."
|
||||
) from e
|
||||
|
||||
if importlib.util.find_spec("torch") is not None:
|
||||
import torch
|
||||
|
||||
cuda_device_count = torch.cuda.device_count()
|
||||
if device < -1 or (device >= cuda_device_count):
|
||||
raise ValueError(
|
||||
f"Got device=={device}, "
|
||||
f"device is required to be within [-1, {cuda_device_count})"
|
||||
)
|
||||
if device < 0 and cuda_device_count > 0:
|
||||
logger.warning(
|
||||
"Device has %d GPUs available. "
|
||||
"Provide device={deviceId} to `from_model_id` to use available"
|
||||
"GPUs for execution. deviceId is -1 for CPU and "
|
||||
"can be a positive integer associated with CUDA device id.",
|
||||
cuda_device_count,
|
||||
)
|
||||
|
||||
pipeline = hf_pipeline(
|
||||
task=task,
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
device=device,
|
||||
model_kwargs=_model_kwargs,
|
||||
)
|
||||
if pipeline.task not in VALID_TASKS:
|
||||
raise ValueError(
|
||||
f"Got invalid task {pipeline.task}, "
|
||||
f"currently only {VALID_TASKS} are supported"
|
||||
)
|
||||
return pipeline
|
||||
|
||||
|
||||
class SelfHostedHuggingFaceLLM(SelfHostedPipeline, BaseModel):
|
||||
"""Wrapper around HuggingFace Pipeline API to run on self-hosted remote hardware.
|
||||
|
||||
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
|
||||
and Lambda, as well as servers specified
|
||||
by IP address and SSH credentials (such as on-prem, or another cloud
|
||||
like Paperspace, Coreweave, etc.).
|
||||
|
||||
To use, you should have the ``runhouse`` python package installed.
|
||||
|
||||
Only supports `text-generation` and `text2text-generation` for now.
|
||||
|
||||
Example using from_model_id:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain.llms import SelfHostedHuggingFaceLLM
|
||||
import runhouse as rh
|
||||
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
|
||||
hf = SelfHostedHuggingFaceLLM(
|
||||
model_id="google/flan-t5-large", task="text2text-generation",
|
||||
hardware=gpu
|
||||
)
|
||||
Example passing fn that generates a pipeline (bc the pipeline is not serializable):
|
||||
.. code-block:: python
|
||||
|
||||
from langchain.llms import SelfHostedHuggingFaceLLM
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
|
||||
import runhouse as rh
|
||||
|
||||
def get_pipeline():
|
||||
model_id = "gpt2"
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
||||
model = AutoModelForCausalLM.from_pretrained(model_id)
|
||||
pipe = pipeline(
|
||||
"text-generation", model=model, tokenizer=tokenizer
|
||||
)
|
||||
return pipe
|
||||
hf = SelfHostedHuggingFaceLLM(
|
||||
model_load_fn=get_pipeline, model_id="gpt2", hardware=gpu)
|
||||
"""
|
||||
|
||||
model_id: str = DEFAULT_MODEL_ID
|
||||
"""Hugging Face model_id to load the model."""
|
||||
task: str = DEFAULT_TASK
|
||||
"""Hugging Face task (either "text-generation" or "text2text-generation")."""
|
||||
device: int = 0
|
||||
"""Device to use for inference. -1 for CPU, 0 for GPU, 1 for second GPU, etc."""
|
||||
model_kwargs: Optional[dict] = None
|
||||
"""Key word arguments to pass to the model."""
|
||||
hardware: Any
|
||||
"""Remote hardware to send the inference function to."""
|
||||
model_reqs: List[str] = ["./", "transformers", "torch"]
|
||||
"""Requirements to install on hardware to inference the model."""
|
||||
model_load_fn: Callable = _load_transformer
|
||||
"""Function to load the model remotely on the server."""
|
||||
inference_fn: Callable = _generate_text #: :meta private:
|
||||
"""Inference function to send to the remote hardware."""
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
|
||||
def __init__(self, **kwargs: Any):
|
||||
"""Construct the pipeline remotely using an auxiliary function.
|
||||
|
||||
The load function needs to be importable to be imported
|
||||
and run on the server, i.e. in a module and not a REPL or closure.
|
||||
Then, initialize the remote inference function.
|
||||
"""
|
||||
load_fn_kwargs = {
|
||||
"model_id": kwargs.get("model_id", DEFAULT_MODEL_ID),
|
||||
"task": kwargs.get("task", DEFAULT_TASK),
|
||||
"device": kwargs.get("device", 0),
|
||||
"model_kwargs": kwargs.get("model_kwargs", None),
|
||||
}
|
||||
super().__init__(load_fn_kwargs=load_fn_kwargs, **kwargs)
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Mapping[str, Any]:
|
||||
"""Get the identifying parameters."""
|
||||
return {
|
||||
**{"model_id": self.model_id},
|
||||
**{"model_kwargs": self.model_kwargs},
|
||||
}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
return "selfhosted_huggingface_pipeline"
|
||||
|
||||
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
|
||||
return self.client(pipeline=self.pipeline_ref, prompt=prompt, stop=stop)
|
||||
96
tests/integration_tests/embeddings/test_self_hosted.py
Normal file
96
tests/integration_tests/embeddings/test_self_hosted.py
Normal file
@ -0,0 +1,96 @@
|
||||
"""Test self-hosted embeddings."""
|
||||
from typing import Any
|
||||
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
|
||||
|
||||
from langchain.embeddings import (
|
||||
SelfHostedEmbeddings,
|
||||
SelfHostedHuggingFaceEmbeddings,
|
||||
SelfHostedHuggingFaceInstructEmbeddings,
|
||||
)
|
||||
|
||||
|
||||
def get_remote_instance() -> Any:
|
||||
"""Get remote instance for testing."""
|
||||
import runhouse as rh
|
||||
|
||||
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1", use_spot=False)
|
||||
gpu.install_packages(["pip:./"])
|
||||
return gpu
|
||||
|
||||
|
||||
def test_self_hosted_huggingface_embedding_documents() -> None:
|
||||
"""Test self-hosted huggingface embeddings."""
|
||||
documents = ["foo bar"]
|
||||
gpu = get_remote_instance()
|
||||
embedding = SelfHostedHuggingFaceEmbeddings(hardware=gpu)
|
||||
output = embedding.embed_documents(documents)
|
||||
assert len(output) == 1
|
||||
assert len(output[0]) == 768
|
||||
|
||||
|
||||
def test_self_hosted_huggingface_embedding_query() -> None:
|
||||
"""Test self-hosted huggingface embeddings."""
|
||||
document = "foo bar"
|
||||
gpu = get_remote_instance()
|
||||
embedding = SelfHostedHuggingFaceEmbeddings(hardware=gpu)
|
||||
output = embedding.embed_query(document)
|
||||
assert len(output) == 768
|
||||
|
||||
|
||||
def test_self_hosted_huggingface_instructor_embedding_documents() -> None:
|
||||
"""Test self-hosted huggingface instruct embeddings."""
|
||||
documents = ["foo bar"]
|
||||
gpu = get_remote_instance()
|
||||
embedding = SelfHostedHuggingFaceInstructEmbeddings(hardware=gpu)
|
||||
output = embedding.embed_documents(documents)
|
||||
assert len(output) == 1
|
||||
assert len(output[0]) == 768
|
||||
|
||||
|
||||
def test_self_hosted_huggingface_instructor_embedding_query() -> None:
|
||||
"""Test self-hosted huggingface instruct embeddings."""
|
||||
query = "foo bar"
|
||||
gpu = get_remote_instance()
|
||||
embedding = SelfHostedHuggingFaceInstructEmbeddings(hardware=gpu)
|
||||
output = embedding.embed_query(query)
|
||||
assert len(output) == 768
|
||||
|
||||
|
||||
def get_pipeline() -> Any:
|
||||
"""Get pipeline for testing."""
|
||||
model_id = "facebook/bart-base"
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
||||
model = AutoModelForCausalLM.from_pretrained(model_id)
|
||||
return pipeline("feature-extraction", model=model, tokenizer=tokenizer)
|
||||
|
||||
|
||||
def inference_fn(pipeline: Any, prompt: str) -> Any:
|
||||
"""Inference function for testing."""
|
||||
# Return last hidden state of the model
|
||||
if isinstance(prompt, list):
|
||||
return [emb[0][-1] for emb in pipeline(prompt)]
|
||||
return pipeline(prompt)[0][-1]
|
||||
|
||||
|
||||
def test_self_hosted_embedding_documents() -> None:
|
||||
"""Test self-hosted huggingface instruct embeddings."""
|
||||
documents = ["foo bar"] * 2
|
||||
gpu = get_remote_instance()
|
||||
embedding = SelfHostedEmbeddings(
|
||||
model_load_fn=get_pipeline, hardware=gpu, inference_fn=inference_fn
|
||||
)
|
||||
output = embedding.embed_documents(documents)
|
||||
assert len(output) == 2
|
||||
assert len(output[0]) == 50265
|
||||
|
||||
|
||||
def test_self_hosted_embedding_query() -> None:
|
||||
"""Test self-hosted custom embeddings."""
|
||||
query = "foo bar"
|
||||
gpu = get_remote_instance()
|
||||
embedding = SelfHostedEmbeddings(
|
||||
model_load_fn=get_pipeline, hardware=gpu, inference_fn=inference_fn
|
||||
)
|
||||
output = embedding.embed_query(query)
|
||||
assert len(output) == 50265
|
||||
105
tests/integration_tests/llms/test_self_hosted_llm.py
Normal file
105
tests/integration_tests/llms/test_self_hosted_llm.py
Normal file
@ -0,0 +1,105 @@
|
||||
"""Test Self-hosted LLMs."""
|
||||
import pickle
|
||||
from typing import Any, List, Optional
|
||||
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
|
||||
|
||||
from langchain.llms import SelfHostedHuggingFaceLLM, SelfHostedPipeline
|
||||
|
||||
model_reqs = ["pip:./", "transformers", "torch"]
|
||||
|
||||
|
||||
def get_remote_instance() -> Any:
|
||||
"""Get remote instance for testing."""
|
||||
import runhouse as rh
|
||||
|
||||
return rh.cluster(name="rh-a10x", instance_type="A100:1", use_spot=False)
|
||||
|
||||
|
||||
def test_self_hosted_huggingface_pipeline_text_generation() -> None:
|
||||
"""Test valid call to self-hosted HuggingFace text generation model."""
|
||||
gpu = get_remote_instance()
|
||||
llm = SelfHostedHuggingFaceLLM(
|
||||
model_id="gpt2",
|
||||
task="text-generation",
|
||||
model_kwargs={"n_positions": 1024},
|
||||
hardware=gpu,
|
||||
model_reqs=model_reqs,
|
||||
)
|
||||
output = llm("Say foo:") # type: ignore
|
||||
assert isinstance(output, str)
|
||||
|
||||
|
||||
def test_self_hosted_huggingface_pipeline_text2text_generation() -> None:
|
||||
"""Test valid call to self-hosted HuggingFace text2text generation model."""
|
||||
gpu = get_remote_instance()
|
||||
llm = SelfHostedHuggingFaceLLM(
|
||||
model_id="google/flan-t5-small",
|
||||
task="text2text-generation",
|
||||
hardware=gpu,
|
||||
model_reqs=model_reqs,
|
||||
)
|
||||
output = llm("Say foo:") # type: ignore
|
||||
assert isinstance(output, str)
|
||||
|
||||
|
||||
def load_pipeline() -> Any:
|
||||
"""Load pipeline for testing."""
|
||||
model_id = "gpt2"
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_id)
|
||||
model = AutoModelForCausalLM.from_pretrained(model_id)
|
||||
pipe = pipeline(
|
||||
"text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
|
||||
)
|
||||
return pipe
|
||||
|
||||
|
||||
def inference_fn(pipeline: Any, prompt: str, stop: Optional[List[str]] = None) -> str:
|
||||
"""Inference function for testing."""
|
||||
return pipeline(prompt)[0]["generated_text"]
|
||||
|
||||
|
||||
def test_init_with_local_pipeline() -> None:
|
||||
"""Test initialization with a self-hosted HF pipeline."""
|
||||
gpu = get_remote_instance()
|
||||
pipeline = load_pipeline()
|
||||
llm = SelfHostedPipeline.from_pipeline(
|
||||
pipeline=pipeline,
|
||||
hardware=gpu,
|
||||
model_reqs=model_reqs,
|
||||
inference_fn=inference_fn,
|
||||
)
|
||||
output = llm("Say foo:") # type: ignore
|
||||
assert isinstance(output, str)
|
||||
|
||||
|
||||
def test_init_with_pipeline_path() -> None:
|
||||
"""Test initialization with a self-hosted HF pipeline."""
|
||||
gpu = get_remote_instance()
|
||||
pipeline = load_pipeline()
|
||||
import runhouse as rh
|
||||
|
||||
rh.blob(pickle.dumps(pipeline), path="models/pipeline.pkl").save().to(
|
||||
gpu, path="models"
|
||||
)
|
||||
llm = SelfHostedPipeline.from_pipeline(
|
||||
pipeline="models/pipeline.pkl",
|
||||
hardware=gpu,
|
||||
model_reqs=model_reqs,
|
||||
inference_fn=inference_fn,
|
||||
)
|
||||
output = llm("Say foo:") # type: ignore
|
||||
assert isinstance(output, str)
|
||||
|
||||
|
||||
def test_init_with_pipeline_fn() -> None:
|
||||
"""Test initialization with a self-hosted HF pipeline."""
|
||||
gpu = get_remote_instance()
|
||||
llm = SelfHostedPipeline(
|
||||
model_load_fn=load_pipeline,
|
||||
hardware=gpu,
|
||||
model_reqs=model_reqs,
|
||||
inference_fn=inference_fn,
|
||||
)
|
||||
output = llm("Say foo:") # type: ignore
|
||||
assert isinstance(output, str)
|
||||
Loading…
Reference in New Issue
Block a user