Initial commit

.flake8

@@ -0,0 +1,29 @@
+[flake8]
+# Suggested config from pytorch that we can adapt
+select = B,C,E,F,N,P,T4,W,B9,TOR0,TOR1,TOR2
+max-line-length = 120
+# C408 ignored because we like the dict keyword argument syntax
+# E501 is not flexible enough, we're using B950 instead
+# N812 ignored because import torch.nn.functional as F is PyTorch convention
+# N817 ignored because importing using acronyms is convention (DistributedDataParallel as DDP)
+# E731 allow usage of assigning lambda expressions
+# E701 let black auto-format statements on one line
+# E704 let black auto-format statements on one line
+ignore =
+    E203,E305,E402,E501,E721,E741,F405,F821,F841,F999,W503,W504,C408,E302,W291,E303,N812,N817,E731,E701,E704
+    # shebang has extra meaning in fbcode lints, so I think it's not worth trying
+    # to line this up with executable bit
+    EXE001,
+    # these ignores are from flake8-bugbear; please fix!
+    B007,B008,B950
+optional-ascii-coding = True
+exclude =
+    ./.git,
+    ./docs
+    ./build
+    ./scripts,
+    ./venv,
+    *.pyi
+    .pre-commit-config.yaml
+    *.md
+    .flake8

.gitignore

@@ -0,0 +1,4 @@
+__pycache__
+dist
+*.egg-info
+dev_requirements.txt

.pre-commit-config.yaml

@@ -0,0 +1,53 @@
+exclude: 'build'
+
+default_language_version:
+    python: python3
+
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: 6306a48f7dae5861702d573c9c247e4e9498e867
+    hooks:
+    -   id: trailing-whitespace
+    -   id: check-ast
+    -   id: check-merge-conflict
+    -   id: check-added-large-files
+        args: ['--maxkb=1000']
+    -   id: end-of-file-fixer
+        exclude: '^(.*\.svg)$'
+
+# Temporarily disabling this
+#    -   id: no-commit-to-branch
+#        args: ['--branch=main']
+
+-   repo: https://github.com/Lucas-C/pre-commit-hooks
+    rev: v1.5.4
+    hooks:
+    -   id: insert-license
+        files: \.py$|\.sh$
+        args:
+          - --license-filepath
+          - docs/license_header.txt
+
+-   repo: https://github.com/pycqa/flake8
+    rev: 34cbf8ef3950f43d09b85e2e45c15ae5717dc37b
+    hooks:
+    -   id: flake8
+        additional_dependencies:
+          - flake8-bugbear == 22.4.25
+          - pep8-naming == 0.12.1
+          - torchfix
+        args: ['--config=.flake8']
+
+-   repo: https://github.com/omnilib/ufmt
+    rev: v2.7.0
+    hooks:
+    -   id: ufmt
+        additional_dependencies:
+          - black == 24.4.2
+          - usort == 1.0.8
+
+# - repo: https://github.com/jsh9/pydoclint
+#   rev: d88180a8632bb1602a4d81344085cf320f288c5a
+#   hooks:
+#     - id: pydoclint
+#       args: [--config=pyproject.toml]

CODE_OF_CONDUCT.md

@@ -0,0 +1,80 @@
+# Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to make participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, sex characteristics, gender identity and expression,
+level of experience, education, socio-economic status, nationality, personal
+appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or
+advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies within all project spaces, and it also applies when
+an individual is representing the project or its community in public spaces.
+Examples of representing a project or community include using an official
+project e-mail address, posting via an official social media account, or acting
+as an appointed representative at an online or offline event. Representation of
+a project may be further defined and clarified by project maintainers.
+
+This Code of Conduct also applies outside the project spaces when there is a
+reasonable belief that an individual's behavior may have a negative impact on
+the project or its community.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team at <opensource-conduct@meta.com>. All
+complaints will be reviewed and investigated and will result in a response that
+is deemed necessary and appropriate to the circumstances. The project team is
+obligated to maintain confidentiality with regard to the reporter of an incident.
+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see
+https://www.contributor-covenant.org/faq

CONTRIBUTING.md

@@ -0,0 +1,36 @@
+# Contributing to Llama-Models
+We want to make contributing to this project as easy and transparent as
+possible.
+
+## Pull Requests
+We actively welcome your pull requests.
+
+1. Fork the repo and create your branch from `main`.
+2. If you've added code that should be tested, add tests.
+3. If you've changed APIs, update the documentation.
+4. Ensure the test suite passes.
+5. Make sure your code lints.
+6. If you haven't already, complete the Contributor License Agreement ("CLA").
+
+## Contributor License Agreement ("CLA")
+In order to accept your pull request, we need you to submit a CLA. You only need
+to do this once to work on any of Meta's open source projects.
+
+Complete your CLA here: <https://code.facebook.com/cla>
+
+## Issues
+We use GitHub issues to track public bugs. Please ensure your description is
+clear and has sufficient instructions to be able to reproduce the issue.
+
+Meta has a [bounty program](http://facebook.com/whitehat/info) for the safe
+disclosure of security bugs. In those cases, please go through the process
+outlined on that page and do not file a public issue.
+
+## Coding Style  
+* 2 spaces for indentation rather than tabs
+* 80 character line length
+* ...
+
+## License
+By contributing to Llama, you agree that your contributions will be licensed
+under the LICENSE file in the root directory of this source tree.

LICENSE

@@ -0,0 +1,48 @@
+ LLAMA 3.1 COMMUNITY LICENSE AGREEMENT
+
+ Llama 3.1 Version Release Date: July 23, 2024
+
+“Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.
+
+“Documentation” means the specifications, manuals and documentation accompanying Llama 3.1 distributed by Meta at https://llama.meta.com/doc/overview.
+
+“Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity's behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.
+
+“Llama 3.1” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.
+
+“Llama Materials” means, collectively, Meta's proprietary Llama 3.1 and Documentation (and any portion thereof) made available under this Agreement.
+
+“Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland)
+
+By clicking “I Accept” below or by using or distributing any portion or element of the Llama Materials, you agree to be bound by this Agreement.
+
+1. License Rights and Redistribution.
+    a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta's intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials.
+
+    b. Redistribution and Use.
+
+          i. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service (including another AI model) that contains any of them, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Llama” on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials or any outputs or results of the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include “Llama” at the beginning of any such AI model name.
+
+          ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you. 
+
+          iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.”
+
+          iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3_1/use-policy), which is hereby incorporated by reference into this Agreement.
+
+2. Additional Commercial Terms. If, on the Llama 3.1 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee's affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.
+
+3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.
+
+4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.
+
+5. Intellectual Property.
+
+    a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use “Llama” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta's brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/). All goodwill arising out of your use of the Mark will inure to the benefit of Meta.
+
+    b. Subject to Meta's ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications.
+
+    c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.1 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials.
+
+6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.
+
+7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. 

MANIFEST.in

@@ -0,0 +1 @@
+include llama_toolchain/data/*.yaml

README.md

@@ -0,0 +1,63 @@
+# llama-toolchain 
+
+This repo contains the API specifications for various components of the Llama Stack as well implementations for some of those APIs like model inference.
+The Stack consists of toolchain-apis and agentic-apis. This repo contains the toolchain-apis 
+
+## Installation and Setup ## 
+```bash
+mkdir -p ~/local
+cd ~/local
+git clone git@github.com:meta-llama/llama-toolchain.git
+
+conda create -n toolchain python=3.10 
+conda activate toolchain
+
+cd llama-toolchain
+pip install -e .
+```
+
+## Test with cli 
+We have built a llama cli to make it easy to configure / run parts of the toolchain 
+```
+llama --help
+
+usage: llama [-h] {download,inference,model,agentic_system} ...
+
+Welcome to the LLama cli
+
+options:
+  -h, --help            show this help message and exit
+
+subcommands:
+  {download,inference,model,agentic_system}
+```
+There are several subcommands to help get you started 
+
+## Start inference server that can run the llama models
+```bash
+llama inference configure
+llama inference start
+``` 
+
+
+## Test client
+```bash
+python -m llama_toolchain.inference.client localhost 5000
+
+Initializing client for http://localhost:5000
+User>hello world, help me out here
+Assistant> Hello! I'd be delighted to help you out. What's on your mind? Do you have a question, a problem, or just need someone to chat with? I'm all ears!
+```
+
+
+## Running FP8
+
+You need `fbgemm-gpu` package which requires torch >= 2.4.0 (currently only in nightly, but releasing shortly...).
+
+```bash
+ENV=fp8_env
+conda create -n $ENV python=3.10
+conda activate $ENV
+
+pip3 install -r fp8_requirements.txt
+```

docs/license_header.txt

@@ -0,0 +1,5 @@
+Copyright (c) Meta Platforms, Inc. and affiliates.
+All rights reserved.
+
+This source code is licensed under the terms described in the LICENSE file in
+the root directory of this source tree.

fp8_requirements.txt

@@ -0,0 +1,31 @@
+--extra-index-url https://download.pytorch.org/whl/nightly/cu121
+torch>=2.4.0.dev20240531,<2.4.1
+accelerate
+black==24.4.2
+codeshield
+fairscale
+fastapi
+fire
+flake8
+huggingface-hub
+httpx
+hydra-core
+hydra-zen
+json-strong-typing
+matplotlib
+omegaconf
+pandas
+Pillow
+pre-commit
+pydantic==1.10.13
+pydantic_core==2.18.2
+python-dotenv
+python-openapi
+requests
+tiktoken
+transformers
+ufmt==2.7.0
+usort==1.0.8
+uvicorn
+zmq
+fbgemm-gpu==0.8.0rc4

llama_toolchain/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.

llama_toolchain/cli/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.

llama_toolchain/cli/download.py

@@ -0,0 +1,97 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import argparse
+import os
+import textwrap
+from pathlib import Path
+
+from huggingface_hub import snapshot_download
+from huggingface_hub.utils import GatedRepoError, RepositoryNotFoundError
+
+from llama_toolchain.cli.subcommand import Subcommand
+from llama_toolchain.utils import DEFAULT_DUMP_DIR
+
+
+DEFAULT_CHECKPOINT_DIR = os.path.join(DEFAULT_DUMP_DIR, "checkpoints")
+
+
+class Download(Subcommand):
+    """Llama cli for downloading llama toolchain assets"""
+
+    def __init__(self, subparsers: argparse._SubParsersAction):
+        super().__init__()
+        self.parser = subparsers.add_parser(
+            "download",
+            prog="llama download",
+            description="Download a model from the Hugging Face Hub",
+            epilog=textwrap.dedent(
+                """\
+            # Here are some examples on how to use this command:
+
+            llama download --repo-id meta-llama/Llama-2-7b-hf --hf-token <HF_TOKEN>
+            llama download --repo-id meta-llama/Llama-2-7b-hf --output-dir /data/my_custom_dir --hf-token <HF_TOKEN>
+            HF_TOKEN=<HF_TOKEN> llama download --repo-id meta-llama/Llama-2-7b-hf
+
+            The output directory will be used to load models and tokenizers for inference.
+            """
+            ),
+            formatter_class=argparse.RawTextHelpFormatter,
+        )
+        self._add_arguments()
+        self.parser.set_defaults(func=self._run_download_cmd)
+
+    def _add_arguments(self):
+        self.parser.add_argument(
+            "repo_id",
+            type=str,
+            help="Name of the repository on Hugging Face Hub eg. llhf/Meta-Llama-3.1-70B-Instruct",
+        )
+        self.parser.add_argument(
+            "--hf-token",
+            type=str,
+            required=False,
+            default=os.getenv("HF_TOKEN", None),
+            help="Hugging Face API token. Needed for gated models like Llama2. Will also try to read environment variable `HF_TOKEN` as default.",
+        )
+        self.parser.add_argument(
+            "--ignore-patterns",
+            type=str,
+            required=False,
+            default="*.safetensors",
+            help="If provided, files matching any of the patterns are not downloaded. Defaults to ignoring "
+            "safetensors files to avoid downloading duplicate weights.",
+        )
+
+    def _run_download_cmd(self, args: argparse.Namespace):
+        model_name = args.repo_id.split("/")[-1]
+        output_dir = Path(DEFAULT_CHECKPOINT_DIR) / model_name
+        os.makedirs(output_dir, exist_ok=True)
+        try:
+            true_output_dir = snapshot_download(
+                args.repo_id,
+                local_dir=output_dir,
+                # "auto" will download to cache_dir and symlink files to local_dir
+                # avoiding unnecessary duplicate copies
+                local_dir_use_symlinks="auto",
+                ignore_patterns=args.ignore_patterns,
+                token=args.hf_token,
+            )
+        except GatedRepoError:
+            self.parser.error(
+                "It looks like you are trying to access a gated repository. Please ensure you "
+                "have access to the repository and have provided the proper Hugging Face API token "
+                "using the option `--hf-token` or by running `huggingface-cli login`."
+                "You can find your token by visiting https://huggingface.co/settings/tokens"
+            )
+        except RepositoryNotFoundError:
+            self.parser.error(
+                f"Repository '{args.repo_id}' not found on the Hugging Face Hub."
+            )
+        except Exception as e:
+            self.parser.error(e)
+
+        print(f"Successfully downloaded model to {true_output_dir}")

llama_toolchain/cli/inference/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.

llama_toolchain/cli/inference/configure.py

@@ -0,0 +1,91 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import argparse
+import os
+import textwrap
+
+from pathlib import Path
+
+import pkg_resources
+
+from llama_toolchain.cli.subcommand import Subcommand
+from llama_toolchain.utils import DEFAULT_DUMP_DIR
+
+
+CONFIGS_BASE_DIR = os.path.join(DEFAULT_DUMP_DIR, "configs")
+
+
+class InferenceConfigure(Subcommand):
+    """Llama cli for configuring llama toolchain configs"""
+
+    def __init__(self, subparsers: argparse._SubParsersAction):
+        super().__init__()
+        self.parser = subparsers.add_parser(
+            "configure",
+            prog="llama inference configure",
+            description="Configure llama toolchain inference configs",
+            epilog=textwrap.dedent(
+                """
+                Example:
+                    llama inference configure
+                """
+            ),
+            formatter_class=argparse.RawTextHelpFormatter,
+        )
+        self._add_arguments()
+        self.parser.set_defaults(func=self._run_inference_configure_cmd)
+
+    def _add_arguments(self):
+        pass
+
+    def read_user_inputs(self):
+        checkpoint_dir = input(
+            "Enter the checkpoint directory for the model (e.g., ~/.llama/checkpoints/Meta-Llama-3-8B/): "
+        )
+        model_parallel_size = input(
+            "Enter model parallel size (e.g., 1 for 8B / 8 for 70B and 405B): "
+        )
+        assert model_parallel_size.isdigit() and int(model_parallel_size) in {
+            1,
+            8,
+        }, "model parallel size must be 1 or 8"
+
+        return checkpoint_dir, model_parallel_size
+
+    def write_output_yaml(self, checkpoint_dir, model_parallel_size, yaml_output_path):
+        default_conf_path = pkg_resources.resource_filename(
+            "llama_toolchain", "data/default_inference_config.yaml"
+        )
+        with open(default_conf_path, "r") as f:
+            yaml_content = f.read()
+
+        yaml_content = yaml_content.format(
+            checkpoint_dir=checkpoint_dir,
+            model_parallel_size=model_parallel_size,
+        )
+
+        with open(yaml_output_path, "w") as yaml_file:
+            yaml_file.write(yaml_content.strip())
+
+        print(f"YAML configuration has been written to {yaml_output_path}")
+
+    def _run_inference_configure_cmd(self, args: argparse.Namespace) -> None:
+        checkpoint_dir, model_parallel_size = self.read_user_inputs()
+        checkpoint_dir = os.path.expanduser(checkpoint_dir)
+
+        assert (
+            Path(checkpoint_dir).exists() and Path(checkpoint_dir).is_dir()
+        ), f"{checkpoint_dir} does not exist or it not a directory"
+
+        os.makedirs(CONFIGS_BASE_DIR, exist_ok=True)
+        yaml_output_path = Path(CONFIGS_BASE_DIR) / "inference.yaml"
+
+        self.write_output_yaml(
+            checkpoint_dir,
+            model_parallel_size,
+            yaml_output_path,
+        )

llama_toolchain/cli/inference/inference.py

@@ -0,0 +1,36 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import argparse
+import textwrap
+
+from llama_toolchain.cli.inference.configure import InferenceConfigure
+from llama_toolchain.cli.inference.start import InferenceStart
+from llama_toolchain.cli.subcommand import Subcommand
+
+
+class InferenceParser(Subcommand):
+    """Llama cli for inference apis"""
+
+    def __init__(self, subparsers: argparse._SubParsersAction):
+        super().__init__()
+        self.parser = subparsers.add_parser(
+            "inference",
+            prog="llama inference",
+            description="Run inference on a llama model",
+            epilog=textwrap.dedent(
+                """
+                Example:
+                    llama inference start <options>
+                """
+            ),
+        )
+
+        subparsers = self.parser.add_subparsers(title="inference_subcommands")
+
+        # Add sub-commandsa
+        InferenceStart.create(subparsers)
+        InferenceConfigure.create(subparsers)

llama_toolchain/cli/inference/start.py

@@ -0,0 +1,57 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import argparse
+import textwrap
+
+from llama_toolchain.cli.subcommand import Subcommand
+
+from llama_toolchain.inference.server import main as inference_server_init
+
+
+class InferenceStart(Subcommand):
+    """Llama Inference cli for starting inference server"""
+
+    def __init__(self, subparsers: argparse._SubParsersAction):
+        super().__init__()
+        self.parser = subparsers.add_parser(
+            "start",
+            prog="llama inference start",
+            description="Start an inference server",
+            epilog=textwrap.dedent(
+                """
+                Example:
+                    llama inference start <options>
+                """
+            ),
+            formatter_class=argparse.RawTextHelpFormatter,
+        )
+        self._add_arguments()
+        self.parser.set_defaults(func=self._run_inference_start_cmd)
+
+    def _add_arguments(self):
+        self.parser.add_argument(
+            "--port",
+            type=int,
+            help="Port to run the server on. Defaults to 5000",
+            default=5000,
+        )
+        self.parser.add_argument(
+            "--disable-ipv6",
+            action="store_true",
+            help="Disable IPv6 support",
+            default=False,
+        )
+        self.parser.add_argument(
+            "--config", type=str, help="Path to config file", default="inference"
+        )
+
+    def _run_inference_start_cmd(self, args: argparse.Namespace) -> None:
+        inference_server_init(
+            config_path=args.config,
+            port=args.port,
+            disable_ipv6=args.disable_ipv6,
+        )

llama_toolchain/cli/llama.py

@@ -0,0 +1,58 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import argparse
+
+from llama_toolchain.cli.download import Download
+from llama_toolchain.cli.inference.inference import InferenceParser
+from llama_toolchain.cli.model.model import ModelParser
+
+
+class LlamaCLIParser:
+    """Defines CLI parser for Llama CLI"""
+
+    def __init__(self):
+        self.parser = argparse.ArgumentParser(
+            prog="llama",
+            description="Welcome to the LLama cli",
+            add_help=True,
+        )
+
+        # Default command is to print help
+        self.parser.set_defaults(func=lambda args: self.parser.print_help())
+
+        subparsers = self.parser.add_subparsers(title="subcommands")
+
+        # Add sub-commands
+        Download.create(subparsers)
+        InferenceParser.create(subparsers)
+        ModelParser.create(subparsers)
+
+        # Import sub-commands from agentic_system if they exist
+        try:
+            from llama_agentic_system.cli.subcommand_modules import SUBCOMMAND_MODULES
+
+            for module in SUBCOMMAND_MODULES:
+                module.create(subparsers)
+
+        except ImportError:
+            pass
+
+    def parse_args(self) -> argparse.Namespace:
+        return self.parser.parse_args()
+
+    def run(self, args: argparse.Namespace) -> None:
+        args.func(args)
+
+
+def main():
+    parser = LlamaCLIParser()
+    args = parser.parse_args()
+    parser.run(args)
+
+
+if __name__ == "__main__":
+    main()

llama_toolchain/cli/model/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.

llama_toolchain/cli/model/model.py

@@ -0,0 +1,35 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import argparse
+import textwrap
+
+from llama_toolchain.cli.model.template import ModelTemplate
+from llama_toolchain.cli.subcommand import Subcommand
+
+
+class ModelParser(Subcommand):
+    """Llama cli for model interface apis"""
+
+    def __init__(self, subparsers: argparse._SubParsersAction):
+        super().__init__()
+        self.parser = subparsers.add_parser(
+            "model",
+            prog="llama model",
+            description="Describe llama model interfaces",
+            epilog=textwrap.dedent(
+                """
+                Example:
+                    llama model <subcommand> <options>
+                """
+            ),
+        )
+
+        subparsers = self.parser.add_subparsers(title="model_subcommands")
+
+        # Add sub-commandsa
+        # ModelDescribe.create(subparsers)
+        ModelTemplate.create(subparsers)

llama_toolchain/cli/model/template.py

@@ -0,0 +1,57 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import argparse
+import textwrap
+
+from llama_models.llama3_1.api.interface import (
+    list_jinja_templates,
+    render_jinja_template,
+)
+
+from llama_toolchain.cli.subcommand import Subcommand
+
+
+class ModelTemplate(Subcommand):
+    """Llama model cli for describe a model template (message formats)"""
+
+    def __init__(self, subparsers: argparse._SubParsersAction):
+        super().__init__()
+        self.parser = subparsers.add_parser(
+            "template",
+            prog="llama model template",
+            description="Show llama model message formats",
+            epilog=textwrap.dedent(
+                """
+                Example:
+                    llama model template <options>
+                """
+            ),
+            formatter_class=argparse.RawTextHelpFormatter,
+        )
+        self._add_arguments()
+        self.parser.set_defaults(func=self._run_model_template_cmd)
+
+    def _add_arguments(self):
+        self.parser.add_argument(
+            "-m",
+            "--model-family",
+            type=str,
+            default="llama3_1",
+            help="Model Family (llama3_1, llama3_X, etc.)",
+        )
+        self.parser.add_argument(
+            "--template",
+            type=str,
+            help="Usecase template name (system_message, user_message, assistant_message, tool_message)...",
+            required=False,
+        )
+
+    def _run_model_template_cmd(self, args: argparse.Namespace) -> None:
+        if args.template:
+            render_jinja_template(args.template)
+        else:
+            list_jinja_templates()

llama_toolchain/cli/subcommand.py

@@ -0,0 +1,19 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+
+class Subcommand:
+    """All llama cli subcommands must inherit from this class"""
+
+    def __init__(self, *args, **kwargs):
+        pass
+
+    @classmethod
+    def create(cls, *args, **kwargs):
+        return cls(*args, **kwargs)
+
+    def _add_arguments(self):
+        pass

llama_toolchain/common/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.

llama_toolchain/common/deployment_types.py

@@ -0,0 +1,31 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from enum import Enum
+from typing import Dict, Optional
+
+from llama_models.llama3_1.api.datatypes import URL
+
+from pydantic import BaseModel
+
+from strong_typing.schema import json_schema_type
+
+
+@json_schema_type
+class RestAPIMethod(Enum):
+    GET = "GET"
+    POST = "POST"
+    PUT = "PUT"
+    DELETE = "DELETE"
+
+
+@json_schema_type
+class RestAPIExecutionConfig(BaseModel):
+    url: URL
+    method: RestAPIMethod
+    params: Optional[Dict[str, str]] = None
+    headers: Optional[Dict[str, str]] = None
+    body: Optional[Dict[str, str]] = None

llama_toolchain/common/training_types.py

@@ -0,0 +1,16 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from llama_models.llama3_1.api.datatypes import URL
+from pydantic import BaseModel
+from strong_typing.schema import json_schema_type
+
+
+@json_schema_type(schema={"description": "Checkpoint created during training runs"})
+class Checkpoint(BaseModel):
+    iters: int
+    path: URL
+    epoch: int

llama_toolchain/data/default_inference_config.yaml

@@ -0,0 +1,14 @@
+inference_config:
+  impl_config:
+    impl_type: "inline"
+    checkpoint_config:
+      checkpoint:
+        checkpoint_type: "pytorch"
+        checkpoint_dir: {checkpoint_dir}/
+        tokenizer_path: {checkpoint_dir}/tokenizer.model
+        model_parallel_size: {model_parallel_size}
+        quantization_format: bf16
+    quantization: null
+    torch_seed: null
+    max_seq_len: 16384
+    max_batch_size: 1

llama_toolchain/dataset/api/__init__.py

@@ -0,0 +1,8 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from .datatypes import *  # noqa: F401 F403
+from .endpoints import *  # noqa: F401 F403

llama_toolchain/dataset/api/datatypes.py

@@ -0,0 +1,34 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from enum import Enum
+from typing import Any, Dict, Optional
+
+from llama_models.llama3_1.api.datatypes import URL
+
+from pydantic import BaseModel
+
+from strong_typing.schema import json_schema_type
+
+
+@json_schema_type
+class TrainEvalDatasetColumnType(Enum):
+    dialog = "dialog"
+    text = "text"
+    media = "media"
+    number = "number"
+    json = "json"
+
+
+@json_schema_type
+class TrainEvalDataset(BaseModel):
+    """Dataset to be used for training or evaluating language models."""
+
+    # TODO(ashwin): figure out if we need to add an enum for a "dataset type"
+
+    columns: Dict[str, TrainEvalDatasetColumnType]
+    content_url: URL
+    metadata: Optional[Dict[str, Any]] = None

llama_toolchain/dataset/api/endpoints.py

@@ -0,0 +1,42 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Protocol
+
+from pydantic import BaseModel
+
+from pyopenapi import webmethod
+from strong_typing.schema import json_schema_type
+
+from .datatypes import *  # noqa: F403
+
+
+@json_schema_type
+class CreateDatasetRequest(BaseModel):
+    """Request to create a dataset."""
+
+    uuid: str
+    dataset: TrainEvalDataset
+
+
+class Datasets(Protocol):
+    @webmethod(route="/datasets/create")
+    def create_dataset(
+        self,
+        request: CreateDatasetRequest,
+    ) -> None: ...
+
+    @webmethod(route="/datasets/get")
+    def get_dataset(
+        self,
+        dataset_uuid: str,
+    ) -> TrainEvalDataset: ...
+
+    @webmethod(route="/datasets/delete")
+    def delete_dataset(
+        self,
+        dataset_uuid: str,
+    ) -> None: ...

llama_toolchain/evaluations/api/__init__.py

@@ -0,0 +1,8 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from .datatypes import *  # noqa: F401 F403
+from .endpoints import *  # noqa: F401 F403

llama_toolchain/evaluations/api/datatypes.py

@@ -0,0 +1,35 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from enum import Enum
+
+from pydantic import BaseModel
+
+
+class TextGenerationMetric(Enum):
+    perplexity = "perplexity"
+    rouge = "rouge"
+    bleu = "bleu"
+
+
+class QuestionAnsweringMetric(Enum):
+    em = "em"
+    f1 = "f1"
+
+
+class SummarizationMetric(Enum):
+    rouge = "rouge"
+    bleu = "bleu"
+
+
+class EvaluationJob(BaseModel):
+
+    job_uuid: str
+
+
+class EvaluationJobLogStream(BaseModel):
+
+    job_uuid: str

llama_toolchain/evaluations/api/endpoints.py

@@ -0,0 +1,99 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import List, Protocol
+
+from pydantic import BaseModel
+
+from pyopenapi import webmethod
+
+from llama_models.llama3_1.api.datatypes import *  # noqa: F403
+from .datatypes import *  # noqa: F403
+from llama_toolchain.dataset.api.datatypes import *  # noqa: F403
+from llama_toolchain.common.training_types import *  # noqa: F403
+
+
+class EvaluateTaskRequestCommon(BaseModel):
+    job_uuid: str
+    dataset: TrainEvalDataset
+
+    checkpoint: Checkpoint
+
+    # generation params
+    sampling_params: SamplingParams = SamplingParams()
+
+
+@json_schema_type
+class EvaluateTextGenerationRequest(EvaluateTaskRequestCommon):
+    """Request to evaluate text generation."""
+
+    metrics: List[TextGenerationMetric]
+
+
+@json_schema_type
+class EvaluateQuestionAnsweringRequest(EvaluateTaskRequestCommon):
+    """Request to evaluate question answering."""
+
+    metrics: List[QuestionAnsweringMetric]
+
+
+@json_schema_type
+class EvaluateSummarizationRequest(EvaluateTaskRequestCommon):
+    """Request to evaluate summarization."""
+
+    metrics: List[SummarizationMetric]
+
+
+class EvaluationJobStatusResponse(BaseModel):
+
+    job_uuid: str
+
+
+@json_schema_type
+class EvaluationJobArtifactsResponse(BaseModel):
+    """Artifacts of a evaluation job."""
+
+    job_uuid: str
+
+
+class Evaluations(Protocol):
+    @webmethod(route="/evaluate/text_generation/")
+    def post_evaluate_text_generation(
+        self,
+        request: EvaluateTextGenerationRequest,
+    ) -> EvaluationJob: ...
+
+    @webmethod(route="/evaluate/question_answering/")
+    def post_evaluate_question_answering(
+        self,
+        request: EvaluateQuestionAnsweringRequest,
+    ) -> EvaluationJob: ...
+
+    @webmethod(route="/evaluate/summarization/")
+    def post_evaluate_summarization(
+        self,
+        request: EvaluateSummarizationRequest,
+    ) -> EvaluationJob: ...
+
+    @webmethod(route="/evaluate/jobs")
+    def get_evaluation_jobs(self) -> List[EvaluationJob]: ...
+
+    @webmethod(route="/evaluate/job/status")
+    def get_evaluation_job_status(
+        self, job_uuid: str
+    ) -> EvaluationJobStatusResponse: ...
+
+    # sends SSE stream of logs
+    @webmethod(route="/evaluate/job/logs")
+    def get_evaluation_job_logstream(self, job_uuid: str) -> EvaluationJobLogStream: ...
+
+    @webmethod(route="/evaluate/job/cancel")
+    def cancel_evaluation_job(self, job_uuid: str) -> None: ...
+
+    @webmethod(route="/evaluate/job/artifacts")
+    def get_evaluation_job_artifacts(
+        self, job_uuid: str
+    ) -> EvaluationJobArtifactsResponse: ...

llama_toolchain/inference/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.

llama_toolchain/inference/api/__init__.py

@@ -0,0 +1,8 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from .datatypes import *  # noqa: F401 F403
+from .endpoints import *  # noqa: F401 F403

llama_toolchain/inference/api/config.py

@@ -0,0 +1,94 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from enum import Enum
+from typing import Literal, Optional, Union
+
+from hydra.core.config_store import ConfigStore
+
+from hydra_zen import builds
+from llama_models.llama3_1.api.datatypes import CheckpointQuantizationFormat
+
+from pydantic import BaseModel, Field
+from strong_typing.schema import json_schema_type
+from typing_extensions import Annotated
+
+from .datatypes import QuantizationConfig
+
+
+@json_schema_type
+class ImplType(Enum):
+    inline = "inline"
+    remote = "remote"
+
+
+@json_schema_type
+class CheckpointType(Enum):
+    pytorch = "pytorch"
+    huggingface = "huggingface"
+
+
+@json_schema_type
+class PytorchCheckpoint(BaseModel):
+    checkpoint_type: Literal[CheckpointType.pytorch.value] = (
+        CheckpointType.pytorch.value
+    )
+    checkpoint_dir: str
+    tokenizer_path: str
+    model_parallel_size: int
+    quantization_format: CheckpointQuantizationFormat = (
+        CheckpointQuantizationFormat.bf16
+    )
+
+
+@json_schema_type
+class HuggingFaceCheckpoint(BaseModel):
+    checkpoint_type: Literal[CheckpointType.huggingface.value] = (
+        CheckpointType.huggingface.value
+    )
+    repo_id: str  # or model_name ?
+    model_parallel_size: int
+    quantization_format: CheckpointQuantizationFormat = (
+        CheckpointQuantizationFormat.bf16
+    )
+
+
+@json_schema_type
+class ModelCheckpointConfig(BaseModel):
+    checkpoint: Annotated[
+        Union[PytorchCheckpoint, HuggingFaceCheckpoint],
+        Field(discriminator="checkpoint_type"),
+    ]
+
+
+@json_schema_type
+class InlineImplConfig(BaseModel):
+    impl_type: Literal[ImplType.inline.value] = ImplType.inline.value
+    checkpoint_config: ModelCheckpointConfig
+    quantization: Optional[QuantizationConfig] = None
+    torch_seed: Optional[int] = None
+    max_seq_len: int
+    max_batch_size: int = 1
+
+
+@json_schema_type
+class RemoteImplConfig(BaseModel):
+    impl_type: Literal[ImplType.remote.value] = ImplType.remote.value
+    url: str = Field(..., description="The URL of the remote module")
+
+
+@json_schema_type
+class InferenceConfig(BaseModel):
+    impl_config: Annotated[
+        Union[InlineImplConfig, RemoteImplConfig],
+        Field(discriminator="impl_type"),
+    ]
+
+
+InferenceHydraConfig = builds(InferenceConfig)
+
+cs = ConfigStore.instance()
+cs.store(name="inference_config", node=InferenceHydraConfig)

llama_toolchain/inference/api/datatypes.py

@@ -0,0 +1,72 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from enum import Enum
+from typing import List, Literal, Optional, Union
+
+from pydantic import BaseModel, Field
+
+from strong_typing.schema import json_schema_type
+from typing_extensions import Annotated
+
+from llama_models.llama3_1.api.datatypes import *  # noqa: F403
+
+
+class LogProbConfig(BaseModel):
+    top_k: Optional[int] = 0
+
+
+@json_schema_type
+class QuantizationType(Enum):
+    bf16 = "bf16"
+    fp8 = "fp8"
+
+
+@json_schema_type
+class Fp8QuantizationConfig(BaseModel):
+    type: Literal[QuantizationType.fp8.value] = QuantizationType.fp8.value
+
+
+@json_schema_type
+class Bf16QuantizationConfig(BaseModel):
+    type: Literal[QuantizationType.bf16.value] = QuantizationType.bf16.value
+
+
+QuantizationConfig = Annotated[
+    Union[Bf16QuantizationConfig, Fp8QuantizationConfig],
+    Field(discriminator="type"),
+]
+
+
+@json_schema_type
+class ChatCompletionResponseEventType(Enum):
+    start = "start"
+    complete = "complete"
+    progress = "progress"
+
+
+@json_schema_type
+class ToolCallParseStatus(Enum):
+    started = "started"
+    in_progress = "in_progress"
+    failure = "failure"
+    success = "success"
+
+
+@json_schema_type
+class ToolCallDelta(BaseModel):
+    content: Union[str, ToolCall]
+    parse_status: ToolCallParseStatus
+
+
+@json_schema_type
+class ChatCompletionResponseEvent(BaseModel):
+    """Chat completion response event."""
+
+    event_type: ChatCompletionResponseEventType
+    delta: Union[str, ToolCallDelta]
+    logprobs: Optional[List[TokenLogProbs]] = None
+    stop_reason: Optional[StopReason] = None

llama_toolchain/inference/api/endpoints.py

@@ -0,0 +1,123 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from .datatypes import *  # noqa: F403
+from typing import Optional, Protocol
+
+# this dependency is annoying and we need a forked up version anyway
+from pyopenapi import webmethod
+
+
+@json_schema_type
+class CompletionRequest(BaseModel):
+    model: PretrainedModel
+    content: InterleavedTextAttachment
+    sampling_params: Optional[SamplingParams] = SamplingParams()
+
+    stream: Optional[bool] = False
+    logprobs: Optional[LogProbConfig] = None
+    quantization_config: Optional[QuantizationConfig] = None
+
+
+@json_schema_type
+class CompletionResponse(BaseModel):
+    completion_message: CompletionMessage
+    logprobs: Optional[List[TokenLogProbs]] = None
+
+
+@json_schema_type
+class CompletionResponseStreamChunk(BaseModel):
+    """streamed completion response."""
+
+    delta: str
+    stop_reason: Optional[StopReason] = None
+    logprobs: Optional[List[TokenLogProbs]] = None
+
+
+@json_schema_type
+class BatchCompletionRequest(BaseModel):
+    model: PretrainedModel
+    content_batch: List[InterleavedTextAttachment]
+    sampling_params: Optional[SamplingParams] = SamplingParams()
+    logprobs: Optional[LogProbConfig] = None
+    quantization_config: Optional[QuantizationConfig] = None
+
+
+@json_schema_type
+class BatchCompletionResponse(BaseModel):
+    completion_message_batch: List[CompletionMessage]
+
+
+@json_schema_type
+class ChatCompletionRequest(BaseModel):
+    model: InstructModel
+    messages: List[Message]
+    sampling_params: Optional[SamplingParams] = SamplingParams()
+
+    # zero-shot tool definitions as input to the model
+    available_tools: Optional[List[ToolDefinition]] = Field(default_factory=list)
+
+    stream: Optional[bool] = False
+    logprobs: Optional[LogProbConfig] = None
+    quantization_config: Optional[QuantizationConfig] = None
+
+
+@json_schema_type
+class ChatCompletionResponseStreamChunk(BaseModel):
+    """SSE-stream of these events."""
+
+    event: ChatCompletionResponseEvent
+
+
+@json_schema_type
+class ChatCompletionResponse(BaseModel):
+    completion_message: CompletionMessage
+    logprobs: Optional[List[TokenLogProbs]] = None
+
+
+@json_schema_type
+class BatchChatCompletionRequest(BaseModel):
+    model: InstructModel
+    messages_batch: List[List[Message]]
+    sampling_params: Optional[SamplingParams] = SamplingParams()
+
+    # zero-shot tool definitions as input to the model
+    available_tools: Optional[List[ToolDefinition]] = Field(default_factory=list)
+
+    logprobs: Optional[LogProbConfig] = None
+    quantization_config: Optional[QuantizationConfig] = None
+
+
+@json_schema_type
+class BatchChatCompletionResponse(BaseModel):
+    completion_message_batch: List[CompletionMessage]
+
+
+class Inference(Protocol):
+
+    @webmethod(route="/inference/completion")
+    async def completion(
+        self,
+        request: CompletionRequest,
+    ) -> Union[CompletionResponse, CompletionResponseStreamChunk]: ...
+
+    @webmethod(route="/inference/chat_completion")
+    async def chat_completion(
+        self,
+        request: ChatCompletionRequest,
+    ) -> Union[ChatCompletionResponse, ChatCompletionResponseStreamChunk]: ...
+
+    @webmethod(route="/inference/batch_completion")
+    async def batch_completion(
+        self,
+        request: BatchCompletionRequest,
+    ) -> BatchCompletionResponse: ...
+
+    @webmethod(route="/inference/batch_chat_completion")
+    async def batch_chat_completion(
+        self,
+        request: BatchChatCompletionRequest,
+    ) -> BatchChatCompletionResponse: ...

llama_toolchain/inference/api_instance.py

@@ -0,0 +1,18 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from .api.config import ImplType, InferenceConfig
+
+
+async def get_inference_api_instance(config: InferenceConfig):
+    if config.impl_config.impl_type == ImplType.inline.value:
+        from .inference import InferenceImpl
+
+        return InferenceImpl(config.impl_config)
+
+    from .client import InferenceClient
+
+    return InferenceClient(config.impl_config.url)

llama_toolchain/inference/client.py

@@ -0,0 +1,85 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import asyncio
+import json
+from typing import AsyncGenerator
+
+import fire
+import httpx
+from termcolor import cprint
+
+from .api import (
+    ChatCompletionRequest,
+    ChatCompletionResponseStreamChunk,
+    CompletionRequest,
+    Inference,
+    InstructModel,
+    UserMessage,
+)
+from .event_logger import EventLogger
+
+
+class InferenceClient(Inference):
+    def __init__(self, base_url: str):
+        print(f"Initializing client for {base_url}")
+        self.base_url = base_url
+
+    async def initialize(self) -> None:
+        pass
+
+    async def shutdown(self) -> None:
+        pass
+
+    async def completion(self, request: CompletionRequest) -> AsyncGenerator:
+        raise NotImplementedError()
+
+    async def chat_completion(self, request: ChatCompletionRequest) -> AsyncGenerator:
+        async with httpx.AsyncClient() as client:
+            async with client.stream(
+                "POST",
+                f"{self.base_url}/inference/chat_completion",
+                data=request.json(),
+                headers={"Content-Type": "application/json"},
+                timeout=20,
+            ) as response:
+                async for line in response.aiter_lines():
+                    if line.startswith("data:"):
+                        data = line[len("data: ") :]
+                        try:
+                            yield ChatCompletionResponseStreamChunk(**json.loads(data))
+                        except Exception as e:
+                            print(data)
+                            print(f"Error with parsing or validation: {e}")
+
+
+async def run_main(host: str, port: int):
+    client = InferenceClient(f"http://{host}:{port}")
+
+    message = UserMessage(content="hello world, help me out here")
+    cprint(f"User>{message.content}", "green")
+    req = ChatCompletionRequest(
+        model=InstructModel.llama3_70b_chat,
+        messages=[message],
+        stream=True,
+    )
+    iterator = client.chat_completion(
+        ChatCompletionRequest(
+            model=InstructModel.llama3_8b_chat,
+            messages=[message],
+            stream=True,
+        )
+    )
+    async for log in EventLogger().log(iterator):
+        log.print()
+
+
+def main(host: str, port: int):
+    asyncio.run(run_main(host, port))
+
+
+if __name__ == "__main__":
+    fire.Fire(main)

llama_toolchain/inference/event_logger.py

@@ -0,0 +1,36 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from termcolor import cprint
+
+from llama_toolchain.inference.api import ChatCompletionResponseEventType
+
+
+class LogEvent:
+    def __init__(
+        self,
+        content: str = "",
+        end: str = "\n",
+        color="white",
+    ):
+        self.content = content
+        self.color = color
+        self.end = "\n" if end is None else end
+
+    def print(self, flush=True):
+        cprint(f"{self.content}", color=self.color, end=self.end, flush=flush)
+
+
+class EventLogger:
+    async def log(self, event_generator, stream=True):
+        async for chunk in event_generator:
+            event = chunk.event
+            if event.event_type == ChatCompletionResponseEventType.start:
+                yield LogEvent("Assistant> ", color="cyan", end="")
+            elif event.event_type == ChatCompletionResponseEventType.progress:
+                yield LogEvent(event.delta, color="yellow", end="")
+            elif event.event_type == ChatCompletionResponseEventType.complete:
+                yield LogEvent("")

llama_toolchain/inference/generation.py

@@ -0,0 +1,319 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
+
+import json
+import os
+import sys
+import time
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Generator, List, Optional
+
+import torch
+import torch.nn.functional as F
+from fairscale.nn.model_parallel.initialize import (
+    get_model_parallel_rank,
+    initialize_model_parallel,
+    model_parallel_is_initialized,
+)
+from llama_models.llama3_1.api.args import ModelArgs
+from llama_models.llama3_1.api.chat_format import ChatFormat, ModelInput
+from llama_models.llama3_1.api.datatypes import Message
+from llama_models.llama3_1.api.model import Transformer
+from llama_models.llama3_1.api.tokenizer import Tokenizer
+from termcolor import cprint
+
+from .api.config import CheckpointType, InlineImplConfig
+from .api.datatypes import QuantizationType
+
+
+@dataclass
+class TokenResult:
+    token: int
+    text: str
+    logprobs: Optional[List[float]] = None
+
+
+class Llama:
+    @staticmethod
+    def build(config: InlineImplConfig):
+        """
+        Build a Llama instance by initializing and loading a model checkpoint.
+
+        Note:
+            This method initializes the distributed process group, sets the device to CUDA,
+            and loads the pre-trained model and tokenizer.
+        """
+        checkpoint = config.checkpoint_config.checkpoint
+        if checkpoint.checkpoint_type != CheckpointType.pytorch.value:
+            raise NotImplementedError("HuggingFace checkpoints not supported yet")
+
+        if (
+            config.quantization
+            and config.quantization.type == QuantizationType.fp8.value
+        ):
+            from .quantization.loader import is_fbgemm_available
+
+            if not is_fbgemm_available():
+                raise ImportError("fbgemm-gpu is required for FP8 quantization")
+
+        if not torch.distributed.is_initialized():
+            torch.distributed.init_process_group("nccl")
+
+        model_parallel_size = checkpoint.model_parallel_size
+        if not model_parallel_is_initialized():
+            initialize_model_parallel(model_parallel_size)
+
+        local_rank = int(os.environ.get("LOCAL_RANK", 0))
+        torch.cuda.set_device(local_rank)
+
+        # seed must be the same in all processes
+        if config.torch_seed is not None:
+            torch.manual_seed(config.torch_seed)
+
+        if local_rank > 0:
+            sys.stdout = open(os.devnull, "w")
+
+        start_time = time.time()
+        ckpt_dir = checkpoint.checkpoint_dir
+        checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
+        assert len(checkpoints) > 0, f"no checkpoint files found in {ckpt_dir}"
+        assert model_parallel_size == len(
+            checkpoints
+        ), f"Loading a checkpoint for MP={len(checkpoints)} but world size is {model_parallel_size}"
+        ckpt_path = checkpoints[get_model_parallel_rank()]
+        state_dict = torch.load(ckpt_path, map_location="cpu", weights_only=True)
+        with open(Path(ckpt_dir) / "params.json", "r") as f:
+            params = json.loads(f.read())
+
+        # TODO(ashwin): this block is so we can load internal checkpoints without additional
+        # fuss. the final code should _not_ have this blurb
+        if "model" in params:
+            params = params["model"]
+
+        model_args: ModelArgs = ModelArgs(
+            max_seq_len=config.max_seq_len,
+            max_batch_size=config.max_batch_size,
+            **params,
+        )
+        tokenizer = Tokenizer(model_path=checkpoint.tokenizer_path)
+
+        assert (
+            model_args.vocab_size == tokenizer.n_words
+        ), f"model_args vocab = {model_args.vocab_size} but tokenizer vocab = {tokenizer.n_words}"
+
+        fp8 = (
+            config.quantization
+            and config.quantization.type == QuantizationType.fp8.value
+        )
+
+        if fp8:
+            # load on CPU in bf16 so that fp8 conversion does not find an
+            # unexpected (fp32, e.g.) datatype
+            torch.set_default_tensor_type(torch.BFloat16Tensor)
+
+        model = Transformer(model_args)
+
+        if fp8:
+            # load on CPU first since if we are doing fp8, we probably don't
+            # have enough memory on GPU for bf16
+            model.load_state_dict(state_dict, strict=False)
+
+        if torch.cuda.is_bf16_supported():
+            torch.set_default_tensor_type(torch.cuda.BFloat16Tensor)
+        else:
+            torch.set_default_tensor_type(torch.cuda.HalfTensor)
+
+        if not fp8:
+            model.load_state_dict(state_dict, strict=False)
+
+        if config.quantization:
+            from .quantization.loader import convert_to_quantized_model
+
+            model = convert_to_quantized_model(model, config)
+        else:
+            model = model.to("cuda")
+
+        print(f"Loaded in {time.time() - start_time:.2f} seconds")
+
+        return Llama(model, tokenizer, model_args)
+
+    def __init__(self, model: Transformer, tokenizer: Tokenizer, args: ModelArgs):
+        self.args = args
+        self.model = model
+        self.tokenizer = tokenizer
+        self.formatter = ChatFormat(tokenizer)
+
+    @torch.inference_mode()
+    def generate(
+        self,
+        model_input: ModelInput,
+        max_gen_len: int,
+        temperature: float = 0.6,
+        top_p: float = 0.9,
+        logprobs: bool = False,
+        echo: bool = False,
+        include_stop_token: bool = False,
+    ) -> Generator:
+        params = self.model.params
+
+        # cprint("Input to model -> " + self.tokenizer.decode(model_input.tokens), "red")
+        prompt_tokens = [model_input.tokens]
+
+        bsz = 1
+        assert bsz <= params.max_batch_size, (bsz, params.max_batch_size)
+
+        min_prompt_len = min(len(t) for t in prompt_tokens)
+        max_prompt_len = max(len(t) for t in prompt_tokens)
+
+        if max_prompt_len >= params.max_seq_len:
+            cprint(
+                f"Out of token budget {max_prompt_len} vs {params.max_seq_len}", "red"
+            )
+            return
+
+        total_len = min(max_gen_len + max_prompt_len, params.max_seq_len)
+        pad_id = self.tokenizer.pad_id
+        tokens = torch.full((bsz, total_len), pad_id, dtype=torch.long, device="cuda")
+        for k, t in enumerate(prompt_tokens):
+            tokens[k, : len(t)] = torch.tensor(t, dtype=torch.long, device="cuda")
+        if logprobs:
+            token_logprobs = torch.zeros_like(tokens, dtype=torch.float)
+
+        prev_pos = 0
+        eos_reached = torch.tensor([False] * bsz, device="cuda")
+        input_text_mask = tokens != pad_id
+        if min_prompt_len == total_len:
+            # TODO(ashwin): unify this branch with the one below and figure out multimodal crap
+            logits = self.model.forward(tokens, prev_pos)
+            token_logprobs = -F.cross_entropy(
+                input=logits.transpose(1, 2),
+                target=tokens,
+                reduction="none",
+                ignore_index=pad_id,
+            )
+
+        stop_tokens = torch.tensor(self.tokenizer.stop_tokens)
+
+        for cur_pos in range(min_prompt_len, total_len):
+            logits = self.model.forward(tokens[:, prev_pos:cur_pos], prev_pos)
+
+            if temperature > 0:
+                probs = torch.softmax(logits[:, -1] / temperature, dim=-1)
+                next_token = sample_top_p(probs, top_p)
+            else:
+                next_token = torch.argmax(logits[:, -1], dim=-1)
+
+            next_token = next_token.reshape(-1)
+            # only replace token if prompt has already been generated
+            next_token = torch.where(
+                input_text_mask[:, cur_pos], tokens[:, cur_pos], next_token
+            )
+            tokens[:, cur_pos] = next_token
+
+            target = tokens[:, prev_pos + 1 : cur_pos + 1]
+            if logprobs:
+                token_logprobs[:, prev_pos + 1 : cur_pos + 1] = -F.cross_entropy(
+                    input=logits.transpose(1, 2),
+                    target=tokens[:, prev_pos + 1 : cur_pos + 1],
+                    reduction="none",
+                    ignore_index=pad_id,
+                )
+            eos_reached |= (~input_text_mask[:, cur_pos]) & (
+                torch.isin(next_token, stop_tokens)
+            )
+            yield TokenResult(
+                token=next_token[0].item(),
+                text=self.tokenizer.decode(next_token.tolist()),
+                logprobs=(
+                    token_logprobs[:, prev_pos + 1 : cur_pos + 1][0].tolist()
+                    if logprobs
+                    else None
+                ),
+            )
+
+            prev_pos = cur_pos
+            if all(eos_reached):
+                break
+
+    def text_completion(
+        self,
+        prompt: str,
+        temperature: float = 0.6,
+        top_p: float = 0.9,
+        max_gen_len: Optional[int] = None,
+        logprobs: bool = False,
+        echo: bool = False,
+    ) -> Generator:
+        if (
+            max_gen_len is None
+            or max_gen_len == 0
+            or max_gen_len >= self.model.params.max_seq_len
+        ):
+            max_gen_len = self.model.params.max_seq_len - 1
+
+        prompt_tokens = self.tokenizer.encode(x, bos=True, eos=False)
+
+        yield from self.generate(
+            model_input=ModelInput(tokens=prompt_tokens),
+            max_gen_len=max_gen_len,
+            temperature=temperature,
+            top_p=top_p,
+            logprobs=logprobs,
+            echo=echo,
+        )
+
+    def chat_completion(
+        self,
+        messages: List[Message],
+        temperature: float = 0.6,
+        top_p: float = 0.9,
+        max_gen_len: Optional[int] = None,
+        logprobs: bool = False,
+    ) -> Generator:
+        if (
+            max_gen_len is None
+            or max_gen_len == 0
+            or max_gen_len >= self.model.params.max_seq_len
+        ):
+            max_gen_len = self.model.params.max_seq_len - 1
+
+        yield from self.generate(
+            model_input=self.formatter.encode_dialog_prompt(messages),
+            max_gen_len=max_gen_len,
+            temperature=temperature,
+            top_p=top_p,
+            logprobs=logprobs,
+            include_stop_token=True,
+        )
+
+
+def sample_top_p(probs, p):
+    """
+    Perform top-p (nucleus) sampling on a probability distribution.
+
+    Args:
+        probs (torch.Tensor): Probability distribution tensor.
+        p (float): Probability threshold for top-p sampling.
+
+    Returns:
+        torch.Tensor: Sampled token indices.
+
+    Note:
+        Top-p sampling selects the smallest set of tokens whose cumulative probability mass
+        exceeds the threshold p. The distribution is renormalized based on the selected tokens.
+    """
+    probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
+    probs_sum = torch.cumsum(probs_sort, dim=-1)
+    mask = probs_sum - probs_sort > p
+    probs_sort[mask] = 0.0
+    probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
+    next_token = torch.multinomial(probs_sort, num_samples=1)
+    next_token = torch.gather(probs_idx, -1, next_token)
+    return next_token

llama_toolchain/inference/inference.py

@@ -0,0 +1,159 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import AsyncGenerator
+
+from llama_models.llama3_1.api.datatypes import StopReason
+
+from .api.config import InlineImplConfig
+from .api.datatypes import (
+    ChatCompletionResponseEvent,
+    ChatCompletionResponseEventType,
+    ToolCallDelta,
+    ToolCallParseStatus,
+)
+from .api.endpoints import (
+    ChatCompletionRequest,
+    ChatCompletionResponseStreamChunk,
+    CompletionRequest,
+    Inference,
+)
+from .model_parallel import LlamaModelParallelGenerator
+
+
+class InferenceImpl(Inference):
+
+    def __init__(self, config: InlineImplConfig) -> None:
+        self.config = config
+
+    async def initialize(self) -> None:
+        self.generator = LlamaModelParallelGenerator(self.config)
+        self.generator.start()
+
+    async def shutdown(self) -> None:
+        self.generator.stop()
+
+    async def completion(self, request: CompletionRequest) -> AsyncGenerator:
+        raise NotImplementedError()
+
+    async def chat_completion(self, request: ChatCompletionRequest) -> AsyncGenerator:
+        yield ChatCompletionResponseStreamChunk(
+            event=ChatCompletionResponseEvent(
+                event_type=ChatCompletionResponseEventType.start,
+                delta="",
+            )
+        )
+
+        tokens = []
+        logprobs = []
+
+        stop_reason = None
+
+        buffer = ""
+        ipython = False
+
+        for token_result in self.generator.chat_completion(
+            messages=request.messages,
+            temperature=request.sampling_params.temperature,
+            top_p=request.sampling_params.top_p,
+            max_gen_len=request.sampling_params.max_tokens,
+            logprobs=request.logprobs,
+        ):
+            buffer += token_result.text
+            tokens.append(token_result.token)
+
+            if not ipython and buffer.startswith("<|python_tag|>"):
+                ipython = True
+                yield ChatCompletionResponseStreamChunk(
+                    event=ChatCompletionResponseEvent(
+                        event_type=ChatCompletionResponseEventType.progress,
+                        delta=ToolCallDelta(
+                            content="",
+                            parse_status=ToolCallParseStatus.started,
+                        ),
+                    )
+                )
+                buffer = buffer[len("<|python_tag|>") :]
+                continue
+
+            if not request.stream:
+                if request.logprobs:
+                    logprobs.append(token_result.logprob)
+
+                continue
+
+            if token_result.text == "<|eot_id|>":
+                stop_reason = StopReason.end_of_turn
+                text = ""
+            elif token_result.text == "<|eom_id|>":
+                stop_reason = StopReason.end_of_message
+                text = ""
+            else:
+                text = token_result.text
+
+            if ipython:
+                delta = ToolCallDelta(
+                    content=text,
+                    parse_status=ToolCallParseStatus.in_progress,
+                )
+            else:
+                delta = text
+            yield ChatCompletionResponseStreamChunk(
+                event=ChatCompletionResponseEvent(
+                    event_type=ChatCompletionResponseEventType.progress,
+                    delta=delta,
+                    stop_reason=stop_reason,
+                )
+            )
+
+        if stop_reason is None:
+            stop_reason = StopReason.out_of_tokens
+
+        # TODO(ashwin): parse tool calls separately here and report errors?
+        # if someone breaks the iteration before coming here we are toast
+        message = self.generator.formatter.decode_assistant_message(tokens, stop_reason)
+        if request.stream:
+            parsed_tool_calls = len(message.tool_calls) > 0
+            if ipython and not parsed_tool_calls:
+                yield ChatCompletionResponseStreamChunk(
+                    event=ChatCompletionResponseEvent(
+                        event_type=ChatCompletionResponseEventType.progress,
+                        delta=ToolCallDelta(
+                            content="",
+                            parse_status=ToolCallParseStatus.failure,
+                        ),
+                        stop_reason=stop_reason,
+                    )
+                )
+
+            for tool_call in message.tool_calls:
+                yield ChatCompletionResponseStreamChunk(
+                    event=ChatCompletionResponseEvent(
+                        event_type=ChatCompletionResponseEventType.progress,
+                        delta=ToolCallDelta(
+                            content=tool_call,
+                            parse_status=ToolCallParseStatus.success,
+                        ),
+                        stop_reason=stop_reason,
+                    )
+                )
+
+            yield ChatCompletionResponseStreamChunk(
+                event=ChatCompletionResponseEvent(
+                    event_type=ChatCompletionResponseEventType.complete,
+                    delta="",
+                    stop_reason=stop_reason,
+                )
+            )
+
+            # TODO(ashwin): what else do we need to send out here when everything finishes?
+        else:
+            yield ChatCompletionResponse(
+                content=message.content,
+                tool_calls=message.tool_calls,
+                stop_reason=stop_reason,
+                logprobs=logprobs if request.logprobs else None,
+            )

llama_toolchain/inference/model_parallel.py

@@ -0,0 +1,104 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from copy import deepcopy
+from dataclasses import dataclass
+from functools import partial
+from typing import Generator, List, Optional
+
+from llama_models.llama3_1.api.chat_format import ChatFormat
+from llama_models.llama3_1.api.datatypes import Message
+from llama_models.llama3_1.api.tokenizer import Tokenizer
+
+from .api.config import InlineImplConfig
+from .generation import Llama
+from .parallel_utils import ModelParallelProcessGroup
+
+
+@dataclass
+class InferenceArgs:
+    messages: List[Message]
+    temperature: float
+    top_p: float
+    max_gen_len: int
+    logprobs: bool
+
+
+class ModelRunner:
+    def __init__(self, llama):
+        self.llama = llama
+
+    # the `task` object is the same that is sent to `ModelParallelProcessGroup.run_inference()`
+    def __call__(self, task: InferenceArgs):
+        return self.llama.chat_completion(
+            task.messages,
+            task.temperature,
+            task.top_p,
+            task.max_gen_len,
+            task.logprobs,
+        )
+
+
+def init_model_cb(config: InlineImplConfig):
+    llama = Llama.build(config)
+    return ModelRunner(llama)
+
+
+class LlamaModelParallelGenerator:
+    """
+    This abstraction exists so
+     - we can run model parallel code without needing to run the CLIs via torchrun
+     - this also enables use model parallel code within a notebook context.
+
+    A Context Manager is used to ensure that the model parallel process is started and stopped
+    correctly. This does make the ergonomics a little awkward, because it isn't immediately
+    clear at the callsite why we need to use a context manager.
+    """
+
+    def __init__(self, config: InlineImplConfig):
+        self.config = config
+
+        # this is a hack because Agent's loop uses this to tokenize and check if input is too long
+        # while the tool-use loop is going
+        checkpoint = self.config.checkpoint_config.checkpoint
+        self.formatter = ChatFormat(Tokenizer(checkpoint.tokenizer_path))
+
+    def start(self):
+        self.__enter__()
+
+    def stop(self):
+        self.__exit__(None, None, None)
+
+    def __enter__(self):
+        checkpoint = self.config.checkpoint_config.checkpoint
+        self.group = ModelParallelProcessGroup(
+            checkpoint.model_parallel_size,
+            init_model_cb=partial(init_model_cb, self.config),
+        )
+        self.group.start()
+        return self
+
+    def __exit__(self, exc_type, exc_value, exc_traceback):
+        self.group.stop()
+
+    def chat_completion(
+        self,
+        messages: List[Message],
+        temperature: float = 0.6,
+        top_p: float = 0.9,
+        max_gen_len: Optional[int] = None,
+        logprobs: bool = False,
+    ) -> Generator:
+        req_obj = InferenceArgs(
+            messages=deepcopy(messages),
+            temperature=temperature,
+            top_p=top_p,
+            max_gen_len=max_gen_len,
+            logprobs=logprobs,
+        )
+
+        gen = self.group.run_inference(req_obj)
+        yield from gen

llama_toolchain/inference/parallel_utils.py

@@ -0,0 +1,265 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import multiprocessing
+import os
+import pickle
+import tempfile
+import time
+import uuid
+
+from typing import Callable, Generator
+
+import torch
+
+import zmq
+
+from fairscale.nn.model_parallel.initialize import (
+    get_model_parallel_group,
+    get_model_parallel_rank,
+    get_model_parallel_src_rank,
+)
+
+from torch.distributed.launcher.api import elastic_launch, LaunchConfig
+
+
+_END_SENTINEL = "__end_sentinel__"
+_CANCEL_SENTINEL = "__cancel_sentinel__"
+
+
+def mp_rank_0() -> bool:
+    return get_model_parallel_rank() == 0
+
+
+def retrieve_requests(reply_socket_url: str):
+    if mp_rank_0():
+        context = zmq.Context()
+        reply_socket = context.socket(zmq.ROUTER)
+        reply_socket.connect(reply_socket_url)
+
+        while True:
+            client_id, obj = maybe_get_work(reply_socket)
+            if obj is None:
+                time.sleep(0.01)
+                continue
+
+            reply_socket.send_multipart([client_id, pickle.dumps("YES READY")])
+            break
+
+    def send_obj(obj):
+        reply_socket.send_multipart([client_id, pickle.dumps(obj)])
+
+    while True:
+        tasks = [None]
+        if mp_rank_0():
+            client_id, task = maybe_get_work(reply_socket)
+            # there is still an unknown unclean GeneratorExit happening resulting in a
+            # cancel sentinel getting queued _after_ we have finished sending everything :/
+            # kind of a hack this is :/
+            if task != _CANCEL_SENTINEL:
+                tasks = [task]
+
+        torch.distributed.broadcast_object_list(
+            tasks,
+            src=get_model_parallel_src_rank(),
+            group=get_model_parallel_group(),
+        )
+
+        task = tasks[0]
+        if task is None:
+            time.sleep(0.1)
+        else:
+            try:
+                out = yield task
+                if out is None:
+                    break
+
+                for obj in out:
+                    updates = [None]
+                    if mp_rank_0():
+                        _, update = maybe_get_work(reply_socket)
+                        if update == _CANCEL_SENTINEL:
+                            updates = [update]
+                        else:
+                            # only send the update if it's not cancelled otherwise the object sits in the socket
+                            # and gets pulled in the next request lol
+                            send_obj(obj)
+
+                    torch.distributed.broadcast_object_list(
+                        updates,
+                        src=get_model_parallel_src_rank(),
+                        group=get_model_parallel_group(),
+                    )
+                    if updates[0] == _CANCEL_SENTINEL:
+                        print("quitting generation loop because request was cancelled")
+                        break
+
+                if mp_rank_0():
+                    send_obj(_END_SENTINEL)
+            except Exception as e:
+                print(f"[debug] got exception {e}")
+                import traceback
+
+                traceback.print_exc()
+                if mp_rank_0():
+                    send_obj(e)
+
+    if mp_rank_0():
+        send_obj("DONE")
+
+
+def maybe_get_work(sock: zmq.Socket):
+    message = None
+    client_id = None
+    try:
+        client_id, obj = sock.recv_multipart(zmq.NOBLOCK)
+        message = pickle.loads(obj)
+    except zmq.ZMQError as e:
+        if e.errno != zmq.EAGAIN:
+            raise e
+
+    return client_id, message
+
+
+def worker_process_entrypoint(
+    reply_socket_url: str,
+    init_model_cb: Callable,
+) -> None:
+    model = init_model_cb()
+    torch.distributed.barrier()
+    time.sleep(1)
+
+    # run the requests co-routine which retrieves requests from the socket
+    # and sends responses (we provide) back to the caller
+    req_gen = retrieve_requests(reply_socket_url)
+    result = None
+    while True:
+        try:
+            task = req_gen.send(result)
+            if isinstance(task, str) and task == _END_SENTINEL:
+                break
+
+            result = model(task)
+        except StopIteration:
+            break
+
+    print("[debug] worker process done")
+
+
+def launch_dist_group(
+    reply_socket_url: str,
+    model_parallel_size: int,
+    init_model_cb: Callable,
+    **kwargs,
+) -> None:
+    id = uuid.uuid4().hex
+    dist_url = f"file:///tmp/llama3_{id}_{time.time()}"
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        # TODO: track workers and if they terminate, tell parent process about it so cleanup can happen
+        launch_config = LaunchConfig(
+            max_nodes=1,
+            min_nodes=1,
+            nproc_per_node=model_parallel_size,
+            start_method="fork",
+            rdzv_backend="c10d",
+            rdzv_endpoint=os.path.join(tmpdir, "rdzv"),
+            rdzv_configs={"store_type": "file", "timeout": 90},
+            max_restarts=0,
+            monitor_interval=1,
+            run_id=str(uuid.uuid4()),
+        )
+        elastic_launch(launch_config, entrypoint=worker_process_entrypoint)(
+            reply_socket_url,
+            init_model_cb,
+        )
+
+
+def start_model_parallel_process(
+    model_parallel_size: int,
+    init_model_cb: Callable,
+    **kwargs,
+):
+    context = zmq.Context()
+    request_socket = context.socket(zmq.DEALER)
+
+    # Binding the request socket to a random port
+    request_socket.bind("tcp://127.0.0.1:0")
+
+    main_process_url = request_socket.getsockopt_string(zmq.LAST_ENDPOINT)
+
+    ctx = multiprocessing.get_context("fork")
+    process = ctx.Process(
+        target=launch_dist_group,
+        args=(
+            main_process_url,
+            model_parallel_size,
+            init_model_cb,
+        ),
+        kwargs=kwargs,
+    )
+    process.start()
+
+    # wait until the model is loaded; rank 0 will send a message to indicate it's ready
+
+    request_socket.send_pyobj("READY?")
+    response = request_socket.recv_pyobj()
+    print(f"Finished model load {response}")
+
+    return request_socket, process
+
+
+class ModelParallelProcessGroup:
+    def __init__(
+        self,
+        model_parallel_size: int,
+        init_model_cb: Callable,
+        **kwargs,
+    ):
+        self.model_parallel_size = model_parallel_size
+        self.init_model_cb = init_model_cb
+        self.started = False
+        self.running = False
+
+    def start(self):
+        assert not self.started, "process group already started"
+        self.request_socket, self.process = start_model_parallel_process(
+            self.model_parallel_size,
+            self.init_model_cb,
+        )
+        self.started = True
+
+    def stop(self):
+        assert self.started, "process group not started"
+        if self.process.is_alive():
+            self.request_socket.send_pyobj(_END_SENTINEL, zmq.NOBLOCK)
+            self.process.join()
+        self.started = False
+
+    def run_inference(self, request) -> Generator:
+        assert not self.running, "inference already running"
+
+        self.running = True
+        self.request_socket.send_pyobj(request)
+        try:
+            while True:
+                obj = self.request_socket.recv_pyobj()
+                if obj == _END_SENTINEL:
+                    break
+
+                if isinstance(obj, Exception):
+                    print(f"[debug] got exception {obj}")
+                    raise obj
+
+                yield obj
+        except GeneratorExit as e:
+            self.request_socket.send_pyobj(_CANCEL_SENTINEL)
+            while True:
+                obj = self.request_socket.recv_pyobj()
+                if obj == _END_SENTINEL:
+                    break
+        finally:
+            self.running = False

llama_toolchain/inference/quantization/fp8_impls.py

@@ -0,0 +1,184 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
+
+import collections
+from typing import Optional, Type
+
+try:
+    import fbgemm_gpu.experimental.gen_ai  # noqa: F401
+
+    print("Using efficient FP8 operators in FBGEMM.")
+except ImportError:
+    print("No efficient FP8 operators. Please install FBGEMM in fp8_requirements.txt.")
+    raise
+
+import torch
+from torch import nn, Tensor
+
+
+class Fp8ScaledWeights:
+    # TODO: Ugly trick so torch allows us to replace parameters
+    # with our custom Fp8Weights instance. Do this properly.
+    @property
+    def __class__(self) -> Type[nn.parameter.Parameter]:
+        return nn.Parameter
+
+    @property
+    def grad_fn(self) -> None:
+        return None
+
+
+# pyre-fixme[4]: Attribute annotation cannot be `Any`.
+# pyre-fixme[2]: Parameter annotation cannot be `Any`.
+class Fp8RowwiseWeights(
+    Fp8ScaledWeights,
+    collections.namedtuple(
+        "Fp8RowwiseWeights",
+        ["weight", "scale", "shape", "activation_scale_ub"],
+    ),
+):
+    pass
+
+
+def ffn_swiglu(
+    x: Tensor,
+    w1: Fp8RowwiseWeights,
+    w3: Fp8RowwiseWeights,
+    w2: Fp8RowwiseWeights,
+    num_tokens: Optional[Tensor] = None,
+    is_memory_bounded: bool = False,
+) -> Tensor:
+    if (
+        isinstance(w1, Fp8ScaledWeights)
+        and isinstance(w3, Fp8ScaledWeights)
+        and isinstance(w2, Fp8ScaledWeights)
+    ):
+        return ffn_swiglu_fp8_dynamic(
+            x, w1, w3, w2, w1.activation_scale_ub, num_tokens, is_memory_bounded
+        )
+
+    (B, T, D) = x.shape  # noqa: N806
+    (HD_L, D_) = w1.shape  # noqa: N806
+    assert D_ == D
+
+    assert isinstance(w1, Tensor)
+    assert isinstance(w3, Tensor)
+    x1 = x.view(B * T, D) @ w1.T
+    x2 = x.view(B * T, D) @ w3.T
+    z = torch.nn.functional.silu(x1) * x2
+    del x1, x2
+    assert isinstance(w2, Tensor)
+    return (z @ w2.T).view(B, T, D)
+
+
+@torch.inference_mode()
+def quantize_fp8(
+    w: Tensor,
+    fp8_activation_scale_ub: float,
+    output_device: Optional[torch.device] = None,
+) -> Fp8RowwiseWeights:
+    """Quantize [n, k] weight tensor.
+
+    Args:
+        w (Tensor): [n, k] input high precision tensor to quantize.
+        fp8_activation_scale_ub (float): Upper bound for activation max.
+    """
+    activation_scale_ub = torch.tensor(
+        [fp8_activation_scale_ub],
+        dtype=torch.float,
+        device="cuda",
+    )
+    wq, w_scale = torch.ops.fbgemm.quantize_fp8_per_row(w)
+    del w
+    return Fp8RowwiseWeights(
+        weight=wq,
+        scale=w_scale,
+        shape=wq.shape,
+        activation_scale_ub=activation_scale_ub,
+    )
+
+
+@torch.inference_mode()
+def load_fp8(
+    w: Tensor,
+    w_scale: Tensor,
+    fp8_activation_scale_ub: float,
+) -> Fp8RowwiseWeights:
+    """Load FP8 [n, k] weight tensor.
+
+    Args:
+        w (Tensor): [n, k] input FP8.
+        fp8_activation_scale_ub (float): Upper bound for activation max.
+    """
+    activation_scale_ub = torch.tensor(
+        [fp8_activation_scale_ub],
+        dtype=torch.float,
+        device="cuda",
+    )
+    return Fp8RowwiseWeights(
+        weight=w.to(torch.float8_e4m3fn).to(device="cuda"),
+        scale=w_scale.to(device="cuda"),
+        shape=w.shape,
+        activation_scale_ub=activation_scale_ub,
+    )
+
+
+def fc_fp8_dynamic(
+    x: Tensor,
+    w: Fp8RowwiseWeights,
+    activation_scale_ub: Optional[Tensor] = None,
+    num_tokens: Optional[Tensor] = None,
+    is_memory_bounded: bool = False,
+) -> Tensor:
+    """
+    Single w8a8 fc layer with dynamic row-wise scaling.
+    """
+    if isinstance(w, Fp8RowwiseWeights):
+        xq, x_scale = torch.ops.fbgemm.quantize_fp8_per_row(
+            x, num_tokens, activation_scale_ub
+        )
+        y = torch.ops.fbgemm.f8f8bf16_rowwise(
+            xq, w.weight, x_scale, w.scale, use_fast_accum=True
+        )
+    del xq
+    return y
+
+
+def ffn_swiglu_fp8_dynamic(
+    x: Tensor,
+    w1: Fp8RowwiseWeights,
+    w3: Fp8RowwiseWeights,
+    w2: Fp8RowwiseWeights,
+    activation_scale_ub: Optional[Tensor] = None,
+    num_tokens: Optional[Tensor] = None,
+    is_memory_bounded: bool = False,
+) -> Tensor:
+    (B, T, D) = x.shape  # noqa: N806
+    HD_L = w1.shape[0]  # noqa: N806
+    assert HD_L == w3.shape[0]
+    x1 = fc_fp8_dynamic(
+        x.view(B * T, D),
+        w1,
+        activation_scale_ub,
+        num_tokens,
+        is_memory_bounded,
+    )
+    x2 = fc_fp8_dynamic(
+        x.view(B * T, D),
+        w3,
+        activation_scale_ub,
+        num_tokens,
+        is_memory_bounded,
+    )
+    z = torch.nn.functional.silu(x1) * x2
+    del x1, x2
+
+    z_ = fc_fp8_dynamic(z, w2, activation_scale_ub, num_tokens, is_memory_bounded)
+
+    return z_.view(B, T, D)

llama_toolchain/inference/quantization/loader.py

@@ -0,0 +1,105 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
+
+import os
+from typing import Optional
+
+import torch
+
+from fairscale.nn.model_parallel.mappings import reduce_from_model_parallel_region
+from llama_models.llama3_1.api.model import Transformer, TransformerBlock
+
+from llama_toolchain.inference.api.config import (
+    CheckpointQuantizationFormat,
+    InlineImplConfig,
+)
+from llama_toolchain.inference.api.datatypes import QuantizationType
+
+from termcolor import cprint
+from torch import Tensor
+
+
+def is_fbgemm_available() -> bool:
+    try:
+        import fbgemm_gpu.experimental.gen_ai  # noqa: F401
+
+        return True
+    except ImportError:
+        return False
+
+
+def swiglu_wrapper(
+    self,
+    x: Tensor,
+):
+    from .fp8_impls import ffn_swiglu
+
+    out = ffn_swiglu(x, self.w1.weight, self.w3.weight, self.w2.weight)
+    return reduce_from_model_parallel_region(out)
+
+
+def convert_to_quantized_model(
+    model: Transformer,
+    config: InlineImplConfig,
+    fp8_activation_scale_ub: Optional[float] = 1200.0,
+) -> Transformer:
+    if config.quantization.type == QuantizationType.bf16.value:
+        return model
+
+    elif config.quantization.type != QuantizationType.fp8.value:
+        raise ValueError("Only FP8 quantization is supported")
+
+    from .fp8_impls import Fp8ScaledWeights, load_fp8, quantize_fp8
+
+    checkpoint = config.checkpoint_config.checkpoint
+    # Move weights to GPU with quantization
+    if checkpoint.quantization_format == CheckpointQuantizationFormat.fp8_mixed.value:
+        cprint("Loading fp8 scales...", "yellow")
+        fp8_scales_path = os.path.join(
+            checkpoint.checkpoint_dir, f"fp8_scales_{get_model_parallel_rank()}.pt"
+        )
+        assert os.path.isfile(
+            fp8_scales_path
+        ), f"fp8_scales_path not found for rank {get_model_parallel_rank()}"
+        fp8_scales = torch.load(fp8_scales_path, weights_only=True)
+
+        for block in model.layers:
+            if isinstance(block, TransformerBlock):
+                if block.layer_id == 0 or block.layer_id == (model.n_layers - 1):
+                    continue
+
+                block.feed_forward.forward = swiglu_wrapper.__get__(block.feed_forward)
+                for key in ("w1", "w3", "w2"):
+                    param = getattr(block.feed_forward, key)
+                    param.weight = load_fp8(
+                        param.weight,
+                        fp8_scales[
+                            f"{block.layer_id}_feed_forward.{key}_{get_model_parallel_rank()}"
+                        ],
+                        fp8_activation_scale_ub,
+                    )
+    else:
+        cprint("Quantizing fp8 weights from bf16...", "yellow")
+        for block in model.layers:
+            if isinstance(block, TransformerBlock):
+                if block.layer_id == 0 or block.layer_id == (model.n_layers - 1):
+                    continue
+                block.feed_forward.forward = swiglu_wrapper.__get__(block.feed_forward)
+                for key in ("w1", "w3", "w2"):
+                    param = getattr(block.feed_forward, key)
+                    param.weight = quantize_fp8(
+                        param.weight,
+                        fp8_activation_scale_ub,
+                        output_device=torch.device("cuda"),
+                    )
+
+    for _, parameter in model.named_parameters():
+        if not isinstance(parameter, Fp8ScaledWeights):
+            parameter.data = parameter.to(device="cuda")
+    return model

llama_toolchain/inference/quantization/scripts/build_conda.sh

@@ -0,0 +1,30 @@
+#!/bin/bash
+
+if [[ $# -ne 1 ]]; then
+    echo "Error: Please provide the name of CONDA environment you wish to create"
+    exit 1
+fi
+
+ENV_NAME=$1
+
+set -eu
+eval "$(conda shell.bash hook)"
+
+echo "Will build env (or overwrite) named '$ENV_NAME'"
+
+set -x
+
+run_build() {
+    # Set up the conda environment
+    yes | conda remove --name $ENV_NAME --all
+    yes | conda create -n $ENV_NAME python=3.10
+    conda activate $ENV_NAME
+
+    # PT nightly
+    pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu121
+
+    # install dependencies for `llama-agentic-system`
+    pip install -r fp8_requirements.txt
+}
+
+run_build

llama_toolchain/inference/quantization/scripts/quantize_checkpoint.py

@@ -0,0 +1,161 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
+
+import json
+import os
+import shutil
+import sys
+from pathlib import Path
+from typing import Optional
+
+import fire
+
+import torch
+from fairscale.nn.model_parallel.initialize import (
+    get_model_parallel_rank,
+    initialize_model_parallel,
+    model_parallel_is_initialized,
+)
+from fp8.fp8_impls import FfnQuantizeMode, quantize_fp8
+
+from llama.model import ModelArgs, Transformer, TransformerBlock
+from llama.tokenizer import Tokenizer
+from torch.nn.parameter import Parameter
+
+
+def main(
+    ckpt_dir: str,
+    tokenizer_path: str,
+    quantized_ckpt_dir: str,
+    max_seq_len: Optional[int] = 512,
+    max_batch_size: Optional[int] = 4,
+    model_parallel_size: Optional[int] = None,
+    ffn_quantize_mode: Optional[FfnQuantizeMode] = FfnQuantizeMode.FP8_ROWWISE,
+    fp8_activation_scale_ub: Optional[float] = 1200.0,
+    seed: int = 1,
+):
+    """ """
+    if not os.path.exists(quantized_ckpt_dir):
+        os.makedirs(quantized_ckpt_dir)
+        shutil.copy(
+            os.path.join(ckpt_dir, "params.json"),
+            os.path.join(quantized_ckpt_dir, "params.json"),
+        )
+        shutil.copy(
+            os.path.join(ckpt_dir, "tokenizer.model"),
+            os.path.join(quantized_ckpt_dir, "tokenizer.model"),
+        )
+
+    if not torch.distributed.is_initialized():
+        torch.distributed.init_process_group("nccl")
+        if not model_parallel_is_initialized():
+            if model_parallel_size is None:
+                model_parallel_size = int(os.environ.get("WORLD_SIZE", 1))
+            initialize_model_parallel(model_parallel_size)
+
+        local_rank = int(os.environ.get("LOCAL_RANK", 0))
+        torch.cuda.set_device(local_rank)
+
+        # seed must be the same in all processes
+        torch.manual_seed(seed)
+
+        if local_rank > 0:
+            sys.stdout = open(os.devnull, "w")
+
+        checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
+        assert len(checkpoints) > 0, f"no checkpoint files found in {ckpt_dir}"
+        assert model_parallel_size == len(
+            checkpoints
+        ), f"Loading a checkpoint for MP={len(checkpoints)} but world size is {model_parallel_size}"
+        ckpt_path = checkpoints[get_model_parallel_rank()]
+        checkpoint = torch.load(ckpt_path, map_location="cpu", weights_only=True)
+        with open(Path(ckpt_dir) / "params.json", "r") as f:
+            params = json.loads(f.read())
+
+        model_args: ModelArgs = ModelArgs(
+            max_seq_len=max_seq_len,
+            max_batch_size=max_batch_size,
+            **params,
+        )
+        tokenizer = Tokenizer(model_path=tokenizer_path)
+        assert (
+            model_args.vocab_size == tokenizer.n_words
+        ), f"model_args vocab = {model_args.vocab_size} but tokenizer vocab = {tokenizer.n_words}"
+
+        # load on CPU in bf16 so that fp8 conversion does not find an unexpected (fp32, e.g.) datatype
+        torch.set_default_tensor_type(torch.BFloat16Tensor)
+
+        model = Transformer(model_args)
+        model.load_state_dict(checkpoint, strict=False)
+
+        if torch.cuda.is_bf16_supported():
+            torch.set_default_tensor_type(torch.cuda.BFloat16Tensor)
+        else:
+            torch.set_default_tensor_type(torch.cuda.HalfTensor)
+
+        print(ckpt_path)
+        assert (
+            quantized_ckpt_dir is not None
+        ), "QUantized checkpoint directory should not be None"
+        fp8_scales = {}
+        for block in model.layers:
+            if isinstance(block, TransformerBlock):
+                if block.layer_id == 0 or block.layer_id == (model.n_layers - 1):
+                    continue
+
+                fp8_weight = quantize_fp8(
+                    block.feed_forward.w1.weight,
+                    fp8_activation_scale_ub,
+                    ffn_quantize_mode,
+                    output_device=torch.device("cpu"),
+                )
+                with torch.inference_mode():
+                    block.feed_forward.w1.weight = Parameter(fp8_weight.weight)
+                fp8_scales[
+                    f"{block.layer_id}_feed_forward.w1_{get_model_parallel_rank()}"
+                ] = fp8_weight.scale
+
+                fp8_weight = quantize_fp8(
+                    block.feed_forward.w3.weight,
+                    fp8_activation_scale_ub,
+                    ffn_quantize_mode,
+                    output_device=torch.device("cpu"),
+                )
+                with torch.inference_mode():
+                    block.feed_forward.w3.weight = Parameter(fp8_weight.weight)
+                fp8_scales[
+                    f"{block.layer_id}_feed_forward.w3_{get_model_parallel_rank()}"
+                ] = fp8_weight.scale
+
+                fp8_weight = quantize_fp8(
+                    block.feed_forward.w2.weight,
+                    fp8_activation_scale_ub,
+                    ffn_quantize_mode,
+                    output_device=torch.device("cpu"),
+                )
+                with torch.inference_mode():
+                    block.feed_forward.w2.weight = Parameter(fp8_weight.weight)
+                fp8_scales[
+                    f"{block.layer_id}_feed_forward.w2_{get_model_parallel_rank()}"
+                ] = fp8_weight.scale
+
+        fp8_scales_path = os.path.join(
+            quantized_ckpt_dir, f"fp8_scales_{get_model_parallel_rank()}.pt"
+        )
+        torch.save(fp8_scales, fp8_scales_path)
+
+        ckpt_path = os.path.join(
+            quantized_ckpt_dir,
+            "consolidated.{:02d}.pth".format(get_model_parallel_rank()),
+        )
+        torch.save(model.state_dict(), ckpt_path)
+
+
+if __name__ == "__main__":
+    fire.Fire(main)

llama_toolchain/inference/quantization/scripts/run_quantize_checkpoint.sh

@@ -0,0 +1,31 @@
+#!/bin/bash
+
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+set -euo pipefail
+set -x
+
+cd $(git rev-parse --show-toplevel)
+
+MASTER_HOST=$1
+RUN_ID=$2
+CKPT_DIR=$3
+QUANT_CKPT_DIR=$4
+TOKENIZER_PATH=$5
+NNODES=$6
+NPROC=$7
+
+echo $MASTER_HOST, $RUN_ID, $CKPT_DIR, $QUANT_CKPT_DIR
+
+NCCL_NET=Socket NCCL_SOCKET_IFNAME=eth TIKTOKEN_CACHE_DIR="" \
+  torchrun \
+   --nnodes=$NNODES --nproc_per_node=$NPROC \
+   --rdzv_id=$RUN_ID \
+   --rdzv_conf='timeout=120' \
+   --rdzv_backend=c10d \
+   --rdzv_endpoint="${MASTER_HOST}:29502" \
+   quantize_checkpoint.py $CKPT_DIR $TOKENIZER_PATH $QUANT_CKPT_DIR

llama_toolchain/inference/quantization/test_fp8.py

@@ -0,0 +1,76 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
+
+import unittest
+
+import torch
+
+from fp8_impls import ffn_swiglu_fp8_dynamic, FfnQuantizeMode, quantize_fp8
+from hypothesis import given, settings, strategies as st
+from torch import Tensor
+
+
+@unittest.skipIf(
+    not torch.cuda.is_available()
+    or torch.cuda.get_device_properties(torch.cuda.current_device()).major < 9,
+    "Skip when H100 is not available",
+)
+class FP8Tests(unittest.TestCase):
+    @settings(deadline=None)
+    @given(
+        D=st.sampled_from([4096, 8192]),
+        HD_L=st.sampled_from([1280, 2560]),
+        B=st.sampled_from([1, 2]),
+        T=st.sampled_from([2048, 4096]),
+        UB=st.sampled_from([1000, 10000]),
+    )
+    def test_fp8_ffn(
+        self,
+        D: int,  # noqa
+        HD_L: int,
+        B: int,
+        T: int,
+        UB: float,
+    ) -> None:
+        x = torch.randn(size=(B, T, D), dtype=torch.bfloat16, device="cuda") * 0.1
+        w1 = torch.randn(size=(HD_L, D), dtype=torch.bfloat16, device="cuda") * 0.01
+        w3 = torch.randn(size=(HD_L, D), dtype=torch.bfloat16, device="cuda") * 0.01
+        w2 = torch.randn(size=(D, HD_L), dtype=torch.bfloat16, device="cuda") * 0.1
+
+        x_q = quantize_fp8(x, UB, mode=FfnQuantizeMode.FP8_ROWWISE)
+        w1_q = quantize_fp8(w1, UB, mode=FfnQuantizeMode.FP8_ROWWISE)
+        w3_q = quantize_fp8(w3, UB, mode=FfnQuantizeMode.FP8_ROWWISE)
+        w2_q = quantize_fp8(w2, UB, mode=FfnQuantizeMode.FP8_ROWWISE)
+
+        def ref_ffn(x: Tensor, w1: Tensor, w3: Tensor, w2: Tensor) -> Tensor:
+            (B, T, D) = x.shape  # noqa: N806
+            (HD_L, D_) = w1.shape  # noqa: N806
+            assert D_ == D
+
+            x1 = x.view(B * T, D) @ w1.T
+            x2 = x.view(B * T, D) @ w3.T
+
+            z = torch.nn.functional.silu(x1) * x2
+            return (z @ w2.T).view(B, T, D).to(torch.bfloat16)
+
+        v = ffn_swiglu_fp8_dynamic(x, w1_q, w3_q, w2_q)
+
+        # Fake quant
+        x = x_q.weight.bfloat16() * x_q.scale.unsqueeze(-1)
+        w1 = w1_q.weight.bfloat16() * w1_q.scale.unsqueeze(-1)
+        w3 = w3_q.weight.bfloat16() * w3_q.scale.unsqueeze(-1)
+        w2 = w2_q.weight.bfloat16() * w2_q.scale.unsqueeze(-1)
+
+        v_ref = ref_ffn(x, w1, w3, w2)
+
+        torch.testing.assert_close(v_ref, v, atol=4.0e-3, rtol=4.0e-3)
+
+
+if __name__ == "__main__":
+    unittest.main()

llama_toolchain/inference/server.py

@@ -0,0 +1,119 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import asyncio
+import signal
+
+import fire
+
+from dotenv import load_dotenv
+
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.responses import StreamingResponse
+
+from hydra_zen import instantiate
+
+from llama_toolchain.utils import get_default_config_dir, parse_config
+from .api.endpoints import ChatCompletionRequest, ChatCompletionResponseStreamChunk
+
+from .api_instance import get_inference_api_instance
+
+
+load_dotenv()
+
+
+GLOBAL_CONFIG = None
+
+
+def get_config():
+    return GLOBAL_CONFIG
+
+
+def handle_sigint(*args, **kwargs):
+    print("SIGINT or CTRL-C detected. Exiting gracefully", args)
+    loop = asyncio.get_event_loop()
+    for task in asyncio.all_tasks(loop):
+        task.cancel()
+    loop.stop()
+
+
+app = FastAPI()
+
+
+@app.on_event("startup")
+async def startup():
+    global InferenceApiInstance
+
+    config = get_config()
+
+    inference_config = instantiate(config["inference_config"])
+    InferenceApiInstance = await get_inference_api_instance(
+        inference_config,
+    )
+    await InferenceApiInstance.initialize()
+
+
+@app.on_event("shutdown")
+async def shutdown():
+    global InferenceApiInstance
+
+    print("shutting down")
+    await InferenceApiInstance.shutdown()
+
+
+# there's a single model parallel process running serving the model. for now,
+# we don't support multiple concurrent requests to this process.
+semaphore = asyncio.Semaphore(1)
+
+
+@app.post(
+    "/inference/chat_completion", response_model=ChatCompletionResponseStreamChunk
+)
+def chat_completion(request: Request, exec_request: ChatCompletionRequest):
+    if semaphore.locked():
+        raise HTTPException(
+            status_code=429,
+            detail="Only a single concurrent request allowed right now.",
+        )
+
+    async def sse_generator(event_gen):
+        try:
+            async for event in event_gen:
+                yield f"data: {event.json()}\n\n"
+                await asyncio.sleep(0.01)
+        except asyncio.CancelledError:
+            print("Generator cancelled")
+            await event_gen.aclose()
+        finally:
+            semaphore.release()
+
+    async def event_gen():
+        async for event in InferenceApiInstance.chat_completion(exec_request):
+            yield event
+
+    return StreamingResponse(
+        sse_generator(event_gen()),
+        media_type="text/event-stream",
+    )
+
+
+def main(config_path: str, port: int = 5000, disable_ipv6: bool = False):
+    global GLOBAL_CONFIG
+    config_dir = get_default_config_dir()
+    GLOBAL_CONFIG = parse_config(config_dir, config_path)
+
+    signal.signal(signal.SIGINT, handle_sigint)
+
+    import uvicorn
+
+    # FYI this does not do hot-reloads
+    listen_host = "::" if not disable_ipv6 else "0.0.0.0"
+    print(f"Listening on {listen_host}:{port}")
+    uvicorn.run(app, host=listen_host, port=port)
+
+
+if __name__ == "__main__":
+    fire.Fire(main)

llama_toolchain/memory/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.

llama_toolchain/memory/api/__init__.py

@@ -0,0 +1,8 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from .datatypes import *  # noqa: F401 F403
+from .endpoints import *  # noqa: F401 F403

llama_toolchain/memory/api/datatypes.py

@@ -0,0 +1,25 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Any, Dict
+
+from pydantic import BaseModel
+
+from strong_typing.schema import json_schema_type
+
+
+@json_schema_type
+class MemoryBank(BaseModel):
+    memory_bank_id: str
+    memory_bank_name: str
+
+
+@json_schema_type
+class MemoryBankDocument(BaseModel):
+    document_id: str
+    content: bytes
+    metadata: Dict[str, Any]
+    mime_type: str

llama_toolchain/memory/api/endpoints.py

@@ -0,0 +1,61 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import List, Protocol
+
+from pyopenapi import webmethod
+
+from .datatypes import *  # noqa: F403
+
+
+class MemoryBanks(Protocol):
+    @webmethod(route="/memory_banks/create")
+    def post_create_memory_bank(
+        self,
+        bank_id: str,
+        bank_name: str,
+        documents: List[MemoryBankDocument],
+    ) -> None: ...
+
+    @webmethod(route="/memory_banks/list")
+    def get_memory_banks(self) -> List[MemoryBank]: ...
+
+    @webmethod(route="/memory_banks/get")
+    def get_memory_bank(self, bank_id: str) -> List[MemoryBank]: ...
+
+    @webmethod(route="/memory_banks/drop")
+    def delete_memory_bank(
+        self,
+        bank_id: str,
+    ) -> str: ...
+
+    @webmethod(route="/memory_bank/insert")
+    def post_insert_memory_documents(
+        self,
+        bank_id: str,
+        documents: List[MemoryBankDocument],
+    ) -> None: ...
+
+    @webmethod(route="/memory_bank/update")
+    def post_update_memory_documents(
+        self,
+        bank_id: str,
+        documents: List[MemoryBankDocument],
+    ) -> None: ...
+
+    @webmethod(route="/memory_bank/get")
+    def get_memory_documents(
+        self,
+        bank_id: str,
+        document_uuids: List[str],
+    ) -> List[MemoryBankDocument]: ...
+
+    @webmethod(route="/memory_bank/delete")
+    def delete_memory_documents(
+        self,
+        bank_id: str,
+        document_uuids: List[str],
+    ) -> List[str]: ...

llama_toolchain/models/api/endpoints.py

@@ -0,0 +1,14 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Protocol
+
+from pydantic import BaseModel  # noqa: F401
+
+from pyopenapi import webmethod  # noqa: F401
+
+
+class Models(Protocol): ...

llama_toolchain/post_training/api/__init__.py

@@ -0,0 +1,8 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from .datatypes import *  # noqa: F401 F403
+from .endpoints import *  # noqa: F401 F403

llama_toolchain/post_training/api/datatypes.py

@@ -0,0 +1,94 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from enum import Enum
+from typing import List
+
+from pydantic import BaseModel
+
+from strong_typing.schema import json_schema_type
+
+
+class OptimizerType(Enum):
+    adam = "adam"
+    adamw = "adamw"
+    sgd = "sgd"
+
+
+@json_schema_type
+class OptimizerConfig(BaseModel):
+    optimizer_type: OptimizerType
+    lr: float
+    lr_min: float
+    weight_decay: float
+
+
+@json_schema_type
+class TrainingConfig(BaseModel):
+    n_epochs: int
+    batch_size: int
+    shuffle: bool
+    n_iters: int
+
+    enable_activation_checkpointing: bool
+    memory_efficient_fsdp_wrap: bool
+    fsdp_cpu_offload: bool
+
+
+@json_schema_type
+class FinetuningAlgorithm(Enum):
+    full = "full"
+    lora = "lora"
+    qlora = "qlora"
+    dora = "dora"
+
+
+@json_schema_type
+class LoraFinetuningConfig(BaseModel):
+    lora_attn_modules: List[str]
+    apply_lora_to_mlp: bool
+    apply_lora_to_output: bool
+    rank: int
+    alpha: int
+
+
+@json_schema_type
+class QLoraFinetuningConfig(LoraFinetuningConfig):
+    pass
+
+
+@json_schema_type
+class DoraFinetuningConfig(LoraFinetuningConfig):
+    pass
+
+
+@json_schema_type
+class PostTrainingJobLogStream(BaseModel):
+    """Stream of logs from a finetuning job."""
+
+    job_uuid: str
+    log_lines: List[str]
+
+
+@json_schema_type
+class PostTrainingJobStatus(Enum):
+    running = "running"
+    completed = "completed"
+    failed = "failed"
+    scheduled = "scheduled"
+
+
+@json_schema_type
+class RLHFAlgorithm(Enum):
+    dpo = "dpo"
+
+
+@json_schema_type
+class DPOAlignmentConfig(BaseModel):
+    reward_scale: float
+    reward_clip: float
+    epsilon: float
+    gamma: float

llama_toolchain/post_training/api/endpoints.py

@@ -0,0 +1,129 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from datetime import datetime
+
+from typing import Any, Dict, List, Optional, Protocol
+
+from pydantic import BaseModel, Field
+
+from pyopenapi import webmethod
+from strong_typing.schema import json_schema_type
+
+from llama_models.llama3_1.api.datatypes import *  # noqa: F403
+from llama_toolchain.dataset.api.datatypes import *  # noqa: F403
+from llama_toolchain.common.training_types import *  # noqa: F403
+from .datatypes import *  # noqa: F403
+
+
+@json_schema_type
+class PostTrainingSFTRequest(BaseModel):
+    """Request to finetune a model."""
+
+    job_uuid: str
+
+    model: PretrainedModel
+    dataset: TrainEvalDataset
+    validation_dataset: TrainEvalDataset
+
+    algorithm: FinetuningAlgorithm
+    algorithm_config: Union[
+        LoraFinetuningConfig, QLoraFinetuningConfig, DoraFinetuningConfig
+    ]
+
+    optimizer_config: OptimizerConfig
+    training_config: TrainingConfig
+
+    # TODO: define these
+    hyperparam_search_config: Dict[str, Any]
+    logger_config: Dict[str, Any]
+
+
+@json_schema_type
+class PostTrainingRLHFRequest(BaseModel):
+    """Request to finetune a model."""
+
+    job_uuid: str
+
+    finetuned_model: URL
+
+    dataset: TrainEvalDataset
+    validation_dataset: TrainEvalDataset
+
+    algorithm: RLHFAlgorithm
+    algorithm_config: Union[DPOAlignmentConfig]
+
+    optimizer_config: OptimizerConfig
+    training_config: TrainingConfig
+
+    # TODO: define these
+    hyperparam_search_config: Dict[str, Any]
+    logger_config: Dict[str, Any]
+
+
+class PostTrainingJob(BaseModel):
+
+    job_uuid: str
+
+
+@json_schema_type
+class PostTrainingJobStatusResponse(BaseModel):
+    """Status of a finetuning job."""
+
+    job_uuid: str
+    status: PostTrainingJobStatus
+
+    scheduled_at: Optional[datetime] = None
+    started_at: Optional[datetime] = None
+    completed_at: Optional[datetime] = None
+
+    resources_allocated: Optional[Dict[str, Any]] = None
+
+    checkpoints: List[Checkpoint] = Field(default_factory=list)
+
+
+@json_schema_type
+class PostTrainingJobArtifactsResponse(BaseModel):
+    """Artifacts of a finetuning job."""
+
+    job_uuid: str
+    checkpoints: List[Checkpoint] = Field(default_factory=list)
+
+    # TODO(ashwin): metrics, evals
+
+
+class PostTraining(Protocol):
+    @webmethod(route="/post_training/supervised_fine_tune")
+    def post_supervised_fine_tune(
+        self,
+        request: PostTrainingSFTRequest,
+    ) -> PostTrainingJob: ...
+
+    @webmethod(route="/post_training/preference_optimize")
+    def post_preference_optimize(
+        self,
+        request: PostTrainingRLHFRequest,
+    ) -> PostTrainingJob: ...
+
+    @webmethod(route="/post_training/jobs")
+    def get_training_jobs(self) -> List[PostTrainingJob]: ...
+
+    # sends SSE stream of logs
+    @webmethod(route="/post_training/job/logs")
+    def get_training_job_logstream(self, job_uuid: str) -> PostTrainingJobLogStream: ...
+
+    @webmethod(route="/post_training/job/status")
+    def get_training_job_status(
+        self, job_uuid: str
+    ) -> PostTrainingJobStatusResponse: ...
+
+    @webmethod(route="/post_training/job/cancel")
+    def cancel_training_job(self, job_uuid: str) -> None: ...
+
+    @webmethod(route="/post_training/job/artifacts")
+    def get_training_job_artifacts(
+        self, job_uuid: str
+    ) -> PostTrainingJobArtifactsResponse: ...

llama_toolchain/reward_scoring/api/__init__.py

@@ -0,0 +1,8 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from .datatypes import *  # noqa: F401 F403
+from .endpoints import *  # noqa: F401 F403

llama_toolchain/reward_scoring/api/datatypes.py

@@ -0,0 +1,31 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import List
+
+from pydantic import BaseModel
+
+from strong_typing.schema import json_schema_type
+
+from llama_models.llama3_1.api.datatypes import *  # noqa: F403
+
+
+@json_schema_type
+class ScoredMessage(BaseModel):
+    message: Message
+    score: float
+
+
+@json_schema_type
+class DialogGenerations(BaseModel):
+    dialog: List[Message]
+    sampled_generations: List[Message]
+
+
+@json_schema_type
+class ScoredDialogGenerations(BaseModel):
+    dialog: List[Message]
+    scored_generations: List[ScoredMessage]

llama_toolchain/reward_scoring/api/endpoints.py

@@ -0,0 +1,33 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import List, Protocol, Union
+from .datatypes import *  # noqa: F403
+
+from pyopenapi import webmethod
+
+
+@json_schema_type
+class RewardScoringRequest(BaseModel):
+    """Request to score a reward function. A list of prompts and a list of responses per prompt."""
+
+    dialog_generations: List[DialogGenerations]
+    model: RewardModel
+
+
+@json_schema_type
+class RewardScoringResponse(BaseModel):
+    """Response from the reward scoring. Batch of (prompt, response, score) tuples that pass the threshold."""
+
+    scored_generations: List[ScoredDialogGenerations]
+
+
+class RewardScoring(Protocol):
+    @webmethod(route="/reward_scoring/score")
+    def post_score(
+        self,
+        request: RewardScoringRequest,
+    ) -> Union[RewardScoringResponse]: ...

llama_toolchain/safety/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.

llama_toolchain/safety/api/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.

llama_toolchain/safety/api/config.py

@@ -0,0 +1,25 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import List, Optional
+
+from pydantic import BaseModel
+
+
+class LlamaGuardShieldConfig(BaseModel):
+    model_dir: str
+    excluded_categories: List[str]
+    disable_input_check: bool = False
+    disable_output_check: bool = False
+
+
+class PromptGuardShieldConfig(BaseModel):
+    model_dir: str
+
+
+class SafetyConfig(BaseModel):
+    llama_guard_shield: Optional[LlamaGuardShieldConfig] = None
+    prompt_guard_shield: Optional[PromptGuardShieldConfig] = None

llama_toolchain/safety/api/datatypes.py

@@ -0,0 +1,60 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from enum import Enum
+from typing import Dict, Optional, Union
+
+from llama_models.llama3_1.api.datatypes import ToolParamDefinition
+
+from pydantic import BaseModel
+
+from strong_typing.schema import json_schema_type
+
+from llama_toolchain.common.deployment_types import RestAPIExecutionConfig
+
+
+@json_schema_type
+class BuiltinShield(Enum):
+    llama_guard = "llama_guard"
+    code_scanner_guard = "code_scanner_guard"
+    third_party_shield = "third_party_shield"
+    injection_shield = "injection_shield"
+    jailbreak_shield = "jailbreak_shield"
+
+
+ShieldType = Union[BuiltinShield, str]
+
+
+@json_schema_type
+class OnViolationAction(Enum):
+    IGNORE = 0
+    WARN = 1
+    RAISE = 2
+
+
+@json_schema_type
+class ShieldDefinition(BaseModel):
+    shield_type: ShieldType
+    description: Optional[str] = None
+    parameters: Optional[Dict[str, ToolParamDefinition]] = None
+    on_violation_action: OnViolationAction = OnViolationAction.RAISE
+    execution_config: Optional[RestAPIExecutionConfig] = None
+
+
+@json_schema_type
+class ShieldCall(BaseModel):
+    call_id: str
+    shield_type: ShieldType
+    arguments: Dict[str, str]
+
+
+@json_schema_type
+class ShieldResponse(BaseModel):
+    shield_type: ShieldType
+    # TODO(ashwin): clean this up
+    is_violation: bool
+    violation_type: Optional[str] = None
+    violation_return_message: Optional[str] = None

llama_toolchain/safety/shields/__init__.py

@@ -0,0 +1,35 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+# supress warnings and spew of logs from hugging face
+import transformers
+
+from .base import (  # noqa: F401
+    DummyShield,
+    OnViolationAction,
+    ShieldBase,
+    ShieldResponse,
+    TextShield,
+)
+from .code_scanner import CodeScannerShield  # noqa: F401
+from .contrib.third_party_shield import ThirdPartyShield  # noqa: F401
+from .llama_guard import LlamaGuardShield  # noqa: F401
+from .prompt_guard import (  # noqa: F401
+    InjectionShield,
+    JailbreakShield,
+    PromptGuardShield,
+)
+from .shield_runner import SafetyException, ShieldRunnerMixin  # noqa: F401
+
+transformers.logging.set_verbosity_error()
+
+import os
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+import warnings
+
+warnings.filterwarnings("ignore")

llama_toolchain/safety/shields/base.py

@@ -0,0 +1,71 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from abc import ABC, abstractmethod
+from typing import List, Union
+
+from llama_models.llama3_1.api.datatypes import Attachment, Message
+from llama_toolchain.safety.api.datatypes import *  # noqa: F403
+
+CANNED_RESPONSE_TEXT = "I can't answer that. Can I help with something else?"
+
+
+class ShieldBase(ABC):
+
+    def __init__(
+        self,
+        on_violation_action: OnViolationAction = OnViolationAction.RAISE,
+    ):
+        self.on_violation_action = on_violation_action
+
+    @abstractmethod
+    def get_shield_type(self) -> ShieldType:
+        raise NotImplementedError()
+
+    @abstractmethod
+    async def run(self, messages: List[Message]) -> ShieldResponse:
+        raise NotImplementedError()
+
+
+def message_content_as_str(message: Message) -> str:
+    def _to_str(content: Union[str, Attachment]) -> str:
+        if isinstance(content, str):
+            return content
+        elif isinstance(content, Attachment):
+            return f"File: {str(content.url)}"
+        else:
+            raise
+
+    if isinstance(message.content, list) or isinstance(message.content, tuple):
+        return "\n".join([_to_str(c) for c in message.content])
+    else:
+        return _to_str(message.content)
+
+
+# For shields that operate on simple strings
+class TextShield(ShieldBase):
+    def convert_messages_to_text(self, messages: List[Message]) -> str:
+        return "\n".join([message_content_as_str(m) for m in messages])
+
+    async def run(self, messages: List[Message]) -> ShieldResponse:
+        text = self.convert_messages_to_text(messages)
+        return await self.run_impl(text)
+
+    @abstractmethod
+    async def run_impl(self, text: str) -> ShieldResponse:
+        raise NotImplementedError()
+
+
+class DummyShield(TextShield):
+
+    def get_shield_type(self) -> ShieldType:
+        return "dummy"
+
+    async def run_impl(self, text: str) -> ShieldResponse:
+        # Dummy return LOW to test e2e
+        return ShieldResponse(
+            shield_type=BuiltinShield.third_party_shield, is_violation=False
+        )

llama_toolchain/safety/shields/code_scanner.py

@@ -0,0 +1,34 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from codeshield.cs import CodeShield
+from termcolor import cprint
+
+from .base import ShieldResponse, TextShield
+from llama_toolchain.safety.api.datatypes import *  # noqa: F403
+
+
+class CodeScannerShield(TextShield):
+
+    def get_shield_type(self) -> ShieldType:
+        return BuiltinShield.code_scanner_guard
+
+    async def run_impl(self, text: str) -> ShieldResponse:
+        cprint(f"Running CodeScannerShield on {text[50:]}", color="magenta")
+        result = await CodeShield.scan_code(text)
+        if result.is_insecure:
+            return ShieldResponse(
+                shield_type=BuiltinShield.code_scanner_guard,
+                is_violation=True,
+                violation_type=",".join(
+                    [issue.pattern_id for issue in result.issues_found]
+                ),
+                violation_return_message="Sorry, I found security concerns in the code.",
+            )
+        else:
+            return ShieldResponse(
+                shield_type=BuiltinShield.code_scanner_guard, is_violation=False
+            )

llama_toolchain/safety/shields/contrib/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.

llama_toolchain/safety/shields/contrib/third_party_shield.py

@@ -0,0 +1,38 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import sys
+from typing import List
+
+from llama_models.llama3_1.api.datatypes import Message
+
+parent_dir = "../.."
+sys.path.append(parent_dir)
+from llama_toolchain.safety.shields.base import (
+    OnViolationAction,
+    ShieldBase,
+    ShieldResponse,
+)
+
+_INSTANCE = None
+
+
+class ThirdPartyShield(ShieldBase):
+    @staticmethod
+    def instance(on_violation_action=OnViolationAction.RAISE) -> "ThirdPartyShield":
+        global _INSTANCE
+        if _INSTANCE is None:
+            _INSTANCE = ThirdPartyShield(on_violation_action)
+        return _INSTANCE
+
+    def __init__(
+        self,
+        on_violation_action: OnViolationAction = OnViolationAction.RAISE,
+    ):
+        super().__init__(on_violation_action)
+
+    async def run(self, messages: List[Message]) -> ShieldResponse:
+        super.run()  # will raise NotImplementedError

llama_toolchain/safety/shields/llama_guard.py

@@ -0,0 +1,252 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import re
+
+from string import Template
+from typing import List, Optional
+
+import torch
+from llama_models.llama3_1.api.datatypes import Message, Role
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from .base import CANNED_RESPONSE_TEXT, OnViolationAction, ShieldBase, ShieldResponse
+from llama_toolchain.safety.api.datatypes import *  # noqa: F403
+
+SAFE_RESPONSE = "safe"
+_INSTANCE = None
+
+CAT_VIOLENT_CRIMES = "Violent Crimes"
+CAT_NON_VIOLENT_CRIMES = "Non-Violent Crimes"
+CAT_SEX_CRIMES = "Sex Crimes"
+CAT_CHILD_EXPLOITATION = "Child Exploitation"
+CAT_DEFAMATION = "Defamation"
+CAT_SPECIALIZED_ADVICE = "Specialized Advice"
+CAT_PRIVACY = "Privacy"
+CAT_INTELLECTUAL_PROPERTY = "Intellectual Property"
+CAT_INDISCRIMINATE_WEAPONS = "Indiscriminate Weapons"
+CAT_HATE = "Hate"
+CAT_SELF_HARM = "Self-Harm"
+CAT_SEXUAL_CONTENT = "Sexual Content"
+CAT_ELECTIONS = "Elections"
+CAT_CODE_INTERPRETER_ABUSE = "Code Interpreter Abuse"
+
+
+SAFETY_CATEGORIES_TO_CODE_MAP = {
+    CAT_VIOLENT_CRIMES: "S1",
+    CAT_NON_VIOLENT_CRIMES: "S2",
+    CAT_SEX_CRIMES: "S3",
+    CAT_CHILD_EXPLOITATION: "S4",
+    CAT_DEFAMATION: "S5",
+    CAT_SPECIALIZED_ADVICE: "S6",
+    CAT_PRIVACY: "S7",
+    CAT_INTELLECTUAL_PROPERTY: "S8",
+    CAT_INDISCRIMINATE_WEAPONS: "S9",
+    CAT_HATE: "S10",
+    CAT_SELF_HARM: "S11",
+    CAT_SEXUAL_CONTENT: "S12",
+    CAT_ELECTIONS: "S13",
+    CAT_CODE_INTERPRETER_ABUSE: "S14",
+}
+
+
+DEFAULT_LG_V3_SAFETY_CATEGORIES = [
+    CAT_VIOLENT_CRIMES,
+    CAT_NON_VIOLENT_CRIMES,
+    CAT_SEX_CRIMES,
+    CAT_CHILD_EXPLOITATION,
+    CAT_DEFAMATION,
+    CAT_SPECIALIZED_ADVICE,
+    CAT_PRIVACY,
+    CAT_INTELLECTUAL_PROPERTY,
+    CAT_INDISCRIMINATE_WEAPONS,
+    CAT_HATE,
+    CAT_SELF_HARM,
+    CAT_SEXUAL_CONTENT,
+    CAT_ELECTIONS,
+    CAT_CODE_INTERPRETER_ABUSE,
+]
+
+PROMPT_TASK = "Task: Check if there is unsafe content in '$agent_type' messages in conversations according our safety policy with the below categories."
+
+SAFETY_CATEGORIES = """
+
+<BEGIN UNSAFE CONTENT CATEGORIES>
+$categories
+<END UNSAFE CONTENT CATEGORIES>
+"""
+
+PROMPT_CONVERSATION = """
+<BEGIN CONVERSATION>
+
+$conversations
+
+<END CONVERSATION>
+"""
+
+
+PROMPT_INSTRUCTIONS = """
+Provide your safety assessment for ONLY THE LAST $agent_type message in the above conversation:
+ - First line must read 'safe' or 'unsafe'.
+ - If unsafe, a second line must include a comma-separated list of violated categories."""
+
+
+PROMPT_TEMPLATE = Template(
+    f"{PROMPT_TASK}{SAFETY_CATEGORIES}{PROMPT_CONVERSATION}{PROMPT_INSTRUCTIONS}"
+)
+
+
+class LlamaGuardShield(ShieldBase):
+
+    @staticmethod
+    def instance(
+        on_violation_action=OnViolationAction.RAISE,
+        model_dir: str = None,
+        excluded_categories: List[str] = None,
+        disable_input_check: bool = False,
+        disable_output_check: bool = False,
+    ) -> "LlamaGuardShield":
+        global _INSTANCE
+        if _INSTANCE is None:
+            _INSTANCE = LlamaGuardShield(
+                on_violation_action,
+                model_dir,
+                excluded_categories,
+                disable_input_check,
+                disable_output_check,
+            )
+        return _INSTANCE
+
+    def __init__(
+        self,
+        on_violation_action: OnViolationAction = OnViolationAction.RAISE,
+        model_dir: str = None,
+        excluded_categories: List[str] = None,
+        disable_input_check: bool = False,
+        disable_output_check: bool = False,
+    ):
+        super().__init__(on_violation_action)
+
+        dtype = torch.bfloat16
+
+        assert model_dir is not None, "Llama Guard model_dir is None"
+
+        if excluded_categories is None:
+            excluded_categories = []
+
+        assert len(excluded_categories) == 0 or all(
+            x in SAFETY_CATEGORIES_TO_CODE_MAP.values() for x in excluded_categories
+        ), "Invalid categories in excluded categories. Expected format is ['S1', 'S2', ..]"
+
+        self.device = "cuda"
+        self.excluded_categories = excluded_categories
+        self.disable_input_check = disable_input_check
+        self.disable_output_check = disable_output_check
+
+        # load model
+        torch_dtype = torch.bfloat16
+        self.tokenizer = AutoTokenizer.from_pretrained(model_dir)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_dir, torch_dtype=torch_dtype, device_map=self.device
+        )
+
+    def get_shield_type(self) -> ShieldType:
+        return BuiltinShield.llama_guard
+
+    def check_unsafe_response(self, response: str) -> Optional[str]:
+        match = re.match(r"^unsafe\n(.*)$", response)
+        if match:
+            # extracts the unsafe code
+            extracted = match.group(1)
+            return extracted
+
+        return None
+
+    def get_safety_categories(self) -> List[str]:
+
+        excluded_categories = self.excluded_categories
+        if set(excluded_categories) == set(SAFETY_CATEGORIES_TO_CODE_MAP.values()):
+            excluded_categories = []
+
+        categories = []
+        for cat in DEFAULT_LG_V3_SAFETY_CATEGORIES:
+            cat_code = SAFETY_CATEGORIES_TO_CODE_MAP[cat]
+            if cat_code in excluded_categories:
+                continue
+            categories.append(f"{cat_code}: {cat}.")
+
+        return categories
+
+    def build_prompt(self, messages: List[Message]) -> str:
+
+        categories = self.get_safety_categories()
+        categories_str = "\n".join(categories)
+        conversations_str = "\n\n".join(
+            [f"{m.role.capitalize()}: {m.content}" for m in messages]
+        )
+        return PROMPT_TEMPLATE.substitute(
+            agent_type=messages[-1].role.capitalize(),
+            categories=categories_str,
+            conversations=conversations_str,
+        )
+
+    def get_shield_response(self, response: str) -> ShieldResponse:
+        if response == SAFE_RESPONSE:
+            return ShieldResponse(
+                shield_type=BuiltinShield.llama_guard, is_violation=False
+            )
+        unsafe_code = self.check_unsafe_response(response)
+        if unsafe_code:
+            unsafe_code_list = unsafe_code.split(",")
+            if set(unsafe_code_list).issubset(set(self.excluded_categories)):
+                return ShieldResponse(
+                    shield_type=BuiltinShield.llama_guard, is_violation=False
+                )
+            return ShieldResponse(
+                shield_type=BuiltinShield.llama_guard,
+                is_violation=True,
+                violation_type=unsafe_code,
+                violation_return_message=CANNED_RESPONSE_TEXT,
+            )
+
+        raise ValueError(f"Unexpected response: {response}")
+
+    async def run(self, messages: List[Message]) -> ShieldResponse:
+        if self.disable_input_check and messages[-1].role == Role.user.value:
+            return ShieldResponse(
+                shield_type=BuiltinShield.llama_guard, is_violation=False
+            )
+        elif self.disable_output_check and messages[-1].role == Role.assistant.value:
+            return ShieldResponse(
+                shield_type=BuiltinShield.llama_guard,
+                is_violation=False,
+            )
+        else:
+
+            prompt = self.build_prompt(messages)
+            llama_guard_input = {
+                "role": "user",
+                "content": prompt,
+            }
+            input_ids = self.tokenizer.apply_chat_template(
+                [llama_guard_input], return_tensors="pt", tokenize=True
+            ).to(self.device)
+            prompt_len = input_ids.shape[1]
+            output = self.model.generate(
+                input_ids=input_ids,
+                max_new_tokens=20,
+                output_scores=True,
+                return_dict_in_generate=True,
+                pad_token_id=0,
+            )
+            generated_tokens = output.sequences[:, prompt_len:]
+
+            response = self.tokenizer.decode(
+                generated_tokens[0], skip_special_tokens=True
+            )
+            response = response.strip()
+            shield_response = self.get_shield_response(response)
+            return shield_response

llama_toolchain/safety/shields/prompt_guard.py

@@ -0,0 +1,156 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from enum import auto, Enum
+from typing import List
+
+import torch
+
+from llama_models.llama3_1.api.datatypes import Message
+from termcolor import cprint
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+from .base import message_content_as_str, OnViolationAction, ShieldResponse, TextShield
+from llama_toolchain.safety.api.datatypes import *  # noqa: F403
+
+
+class PromptGuardShield(TextShield):
+
+    class Mode(Enum):
+        INJECTION = auto()
+        JAILBREAK = auto()
+
+    _instances = {}
+    _model_cache = None
+
+    @staticmethod
+    def instance(
+        model_dir: str,
+        threshold: float = 0.9,
+        temperature: float = 1.0,
+        mode: "PromptGuardShield.Mode" = Mode.JAILBREAK,
+        on_violation_action=OnViolationAction.RAISE,
+    ) -> "PromptGuardShield":
+        action_value = on_violation_action.value
+        key = (model_dir, threshold, temperature, mode, action_value)
+        if key not in PromptGuardShield._instances:
+            PromptGuardShield._instances[key] = PromptGuardShield(
+                model_dir=model_dir,
+                threshold=threshold,
+                temperature=temperature,
+                mode=mode,
+                on_violation_action=on_violation_action,
+            )
+        return PromptGuardShield._instances[key]
+
+    def __init__(
+        self,
+        model_dir: str,
+        threshold: float = 0.9,
+        temperature: float = 1.0,
+        mode: "PromptGuardShield.Mode" = Mode.JAILBREAK,
+        on_violation_action: OnViolationAction = OnViolationAction.RAISE,
+    ):
+        super().__init__(on_violation_action)
+        assert (
+            model_dir is not None
+        ), "Must provide a model directory for prompt injection shield"
+        if temperature <= 0:
+            raise ValueError("Temperature must be greater than 0")
+        self.device = "cuda"
+        if PromptGuardShield._model_cache is None:
+            # load model and tokenizer
+            tokenizer = AutoTokenizer.from_pretrained(model_dir)
+            model = AutoModelForSequenceClassification.from_pretrained(
+                model_dir, device_map=self.device
+            )
+            PromptGuardShield._model_cache = (tokenizer, model)
+
+        self.tokenizer, self.model = PromptGuardShield._model_cache
+        self.temperature = temperature
+        self.threshold = threshold
+        self.mode = mode
+
+    def get_shield_type(self) -> ShieldType:
+        return (
+            BuiltinShield.jailbreak_shield
+            if self.mode == self.Mode.JAILBREAK
+            else BuiltinShield.injection_shield
+        )
+
+    def convert_messages_to_text(self, messages: List[Message]) -> str:
+        return message_content_as_str(messages[-1])
+
+    async def run_impl(self, text: str) -> ShieldResponse:
+        # run model on messages and return response
+        inputs = self.tokenizer(text, return_tensors="pt")
+        inputs = {name: tensor.to(self.model.device) for name, tensor in inputs.items()}
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+        logits = outputs[0]
+        probabilities = torch.softmax(logits / self.temperature, dim=-1)
+        score_embedded = probabilities[0, 1].item()
+        score_malicious = probabilities[0, 2].item()
+        cprint(
+            f"Ran PromptGuardShield and got Scores: Embedded: {score_embedded}, Malicious: {score_malicious}",
+            color="magenta",
+        )
+
+        if self.mode == self.Mode.INJECTION and (
+            score_embedded + score_malicious > self.threshold
+        ):
+            return ShieldResponse(
+                shield_type=self.get_shield_type(),
+                is_violation=True,
+                violation_type=f"prompt_injection:embedded={score_embedded},malicious={score_malicious}",
+                violation_return_message="Sorry, I cannot do this.",
+            )
+        elif self.mode == self.Mode.JAILBREAK and score_malicious > self.threshold:
+            return ShieldResponse(
+                shield_type=self.get_shield_type(),
+                is_violation=True,
+                violation_type=f"prompt_injection:malicious={score_malicious}",
+                violation_return_message="Sorry, I cannot do this.",
+            )
+
+        return ShieldResponse(
+            shield_type=self.get_shield_type(),
+            is_violation=False,
+        )
+
+
+class JailbreakShield(PromptGuardShield):
+    def __init__(
+        self,
+        model_dir: str,
+        threshold: float = 0.9,
+        temperature: float = 1.0,
+        on_violation_action: OnViolationAction = OnViolationAction.RAISE,
+    ):
+        super().__init__(
+            model_dir=model_dir,
+            threshold=threshold,
+            temperature=temperature,
+            mode=PromptGuardShield.Mode.JAILBREAK,
+            on_violation_action=on_violation_action,
+        )
+
+
+class InjectionShield(PromptGuardShield):
+    def __init__(
+        self,
+        model_dir: str,
+        threshold: float = 0.9,
+        temperature: float = 1.0,
+        on_violation_action: OnViolationAction = OnViolationAction.RAISE,
+    ):
+        super().__init__(
+            model_dir=model_dir,
+            threshold=threshold,
+            temperature=temperature,
+            mode=PromptGuardShield.Mode.INJECTION,
+            on_violation_action=on_violation_action,
+        )

llama_toolchain/safety/shields/shield_runner.py

@@ -0,0 +1,52 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import asyncio
+from typing import List
+
+from llama_models.llama3_1.api.datatypes import Message, Role
+
+from .base import OnViolationAction, ShieldBase, ShieldResponse
+
+
+class SafetyException(Exception):  # noqa: N818
+    def __init__(self, response: ShieldResponse):
+        self.response = response
+        super().__init__(response.violation_return_message)
+
+
+class ShieldRunnerMixin:
+
+    def __init__(
+        self,
+        input_shields: List[ShieldBase] = None,
+        output_shields: List[ShieldBase] = None,
+    ):
+        self.input_shields = input_shields
+        self.output_shields = output_shields
+
+    async def run_shields(
+        self, messages: List[Message], shields: List[ShieldBase]
+    ) -> List[ShieldResponse]:
+        # some shields like llama-guard require the first message to be a user message
+        # since this might be a tool call, first role might not be user
+        if len(messages) > 0 and messages[0].role != Role.user.value:
+            # TODO(ashwin): we need to change the type of the message, this kind of modification
+            # is no longer appropriate
+            messages[0].role = Role.user.value
+
+        results = await asyncio.gather(*[s.run(messages) for s in shields])
+        for shield, r in zip(shields, results):
+            if r.is_violation:
+                if shield.on_violation_action == OnViolationAction.RAISE:
+                    raise SafetyException(r)
+                elif shield.on_violation_action == OnViolationAction.WARN:
+                    cprint(
+                        f"[Warn]{shield.__class__.__name__} raised a warning",
+                        color="red",
+                    )
+
+        return results

llama_toolchain/synthetic_data_generation/api/__init__.py

@@ -0,0 +1,8 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from .datatypes import *  # noqa: F401 F403
+from .endpoints import *  # noqa: F401 F403

llama_toolchain/synthetic_data_generation/api/datatypes.py

@@ -0,0 +1,18 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from enum import Enum
+
+
+class FilteringFunction(Enum):
+    """The type of filtering function."""
+
+    none = "none"
+    random = "random"
+    top_k = "top_k"
+    top_p = "top_p"
+    top_k_top_p = "top_k_top_p"
+    sigmoid = "sigmoid"

llama_toolchain/synthetic_data_generation/api/endpoints.py

@@ -0,0 +1,41 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Any, Dict, List, Optional, Protocol
+
+from pydantic import BaseModel
+
+from pyopenapi import webmethod
+from strong_typing.schema import json_schema_type
+
+from llama_models.llama3_1.api.datatypes import *  # noqa: F403
+from llama_toolchain.reward_scoring.api.datatypes import *  # noqa: F403
+from .datatypes import *  # noqa: F403
+
+
+@json_schema_type
+class SyntheticDataGenerationRequest(BaseModel):
+    """Request to generate synthetic data. A small batch of prompts and a filtering function"""
+
+    dialogs: List[Message]
+    filtering_function: FilteringFunction = FilteringFunction.none
+    model: Optional[RewardModel] = None
+
+
+@json_schema_type
+class SyntheticDataGenerationResponse(BaseModel):
+    """Response from the synthetic data generation. Batch of (prompt, response, score) tuples that pass the threshold."""
+
+    synthetic_data: List[ScoredDialogGenerations]
+    statistics: Optional[Dict[str, Any]] = None
+
+
+class SyntheticDataGeneration(Protocol):
+    @webmethod(route="/synthetic_data_generation/generate")
+    def post_generate(
+        self,
+        request: SyntheticDataGenerationRequest,
+    ) -> Union[SyntheticDataGenerationResponse]: ...

llama_toolchain/utils.py

@@ -0,0 +1,64 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import getpass
+import os
+from typing import Optional
+
+from hydra import compose, initialize, MissingConfigException
+from hydra.core.global_hydra import GlobalHydra
+
+from omegaconf import OmegaConf
+
+
+DEFAULT_DUMP_DIR = os.path.expanduser("~/.llama/")
+
+
+def get_root_directory():
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    while os.path.isfile(os.path.join(current_dir, "__init__.py")):
+        current_dir = os.path.dirname(current_dir)
+
+    return current_dir
+
+
+def get_default_config_dir():
+    return os.path.join(DEFAULT_DUMP_DIR, "configs")
+
+
+def parse_config(config_dir: str, config_path: Optional[str] = None) -> str:
+    # Configs can be
+    # 1. relative paths in {config_dir}/
+    # 2. or default to file {config_dir}/{user}.yaml
+    # 3. or ultimate default to {config_dir}/default.yaml
+
+    # Get the relative path from the current file to the config directory
+    current_file_directory = os.path.dirname(os.path.abspath(__file__))
+    relative_path = os.path.relpath(config_dir, current_file_directory)
+
+    GlobalHydra.instance().clear()
+    initialize(config_path=relative_path)
+
+    if config_path is None:
+        try:
+            user = getpass.getuser()
+            config_name = user
+        except MissingConfigException:
+            print(f"No user-specific {user}.yaml, using default")
+            config_name = "default"
+    else:
+        config_name = config_path
+
+    config_abs_path = os.path.abspath(os.path.join(config_dir, f"{config_name}.yaml"))
+    print(f"Loading config from : {config_abs_path}")
+    config = compose(config_name=config_name)
+
+    print("Yaml config:")
+    print("------------------------")
+    print(OmegaConf.to_yaml(config, resolve=True))
+    print("------------------------")
+
+    return config

pyproject.toml

@@ -0,0 +1,3 @@
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"

requirements.txt

@@ -0,0 +1,32 @@
+accelerate
+black==24.4.2
+blobfile
+codeshield
+fairscale
+fastapi
+fire
+flake8
+huggingface-hub
+httpx
+hydra-core
+hydra-zen
+json-strong-typing
+matplotlib
+omegaconf
+pandas
+Pillow
+pre-commit
+pydantic==1.10.13
+pydantic_core==2.18.2
+python-dotenv
+python-openapi
+requests
+tiktoken
+torch
+transformers
+ufmt==2.7.0
+usort==1.0.8
+uvicorn
+zmq
+
+llama_models[llama3_1] @ git+ssh://git@github.com/meta-llama/llama-models.git

setup.py

@@ -0,0 +1,32 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from setuptools import find_packages, setup
+
+
+# Function to read the requirements.txt file
+def read_requirements():
+    with open("requirements.txt") as req:
+        content = req.readlines()
+    return [line.strip() for line in content]
+
+
+setup(
+    name="llama_toolchain",
+    version="0.0.0.1",
+    author="Meta Llama",
+    author_email="llama-oss@meta.com",
+    description="Llama toolchain",
+    entry_points={"console_scripts": ["llama = llama_toolchain.cli.llama:main"]},
+    long_description=open("README.md").read(),
+    long_description_content_type="text/markdown",
+    url="https://github.com/meta-llama/llama-toolchain",
+    packages=find_packages(),
+    classifiers=[],
+    python_requires=">=3.10",
+    install_requires=read_requirements(),
+    include_package_data=True,
+)