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Support for Llama3.2 models and Swift SDK (#98)

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Ashwin Bharambe 2024-09-25 10:29:58 -07:00 • committed by GitHub
parent 95abbf576b
commit 56aed59eb4
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56 changed files with 3745 additions and 630 deletions
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85
llama_stack/apis/agents/client.py
View file

@ -94,14 +94,16 @@ class AgentsClient(Agents):
print(f"Error with parsing or validation: {e}")
async def _run_agent(api, tool_definitions, user_prompts, attachments=None):
async def _run_agent(
api, model, tool_definitions, tool_prompt_format, user_prompts, attachments=None
):
agent_config = AgentConfig(
model="Meta-Llama3.1-8B-Instruct",
model=model,
instructions="You are a helpful assistant",
sampling_params=SamplingParams(temperature=1.0, top_p=0.9),
sampling_params=SamplingParams(temperature=0.6, top_p=0.9),
tools=tool_definitions,
tool_choice=ToolChoice.auto,
tool_prompt_format=ToolPromptFormat.function_tag,
tool_prompt_format=tool_prompt_format,
enable_session_persistence=False,
)
@ -130,7 +132,8 @@ async def _run_agent(api, tool_definitions, user_prompts, attachments=None):
log.print()
async def run_main(host: str, port: int):
async def run_llama_3_1(host: str, port: int):
model = "Llama3.1-8B-Instruct"
api = AgentsClient(f"http://{host}:{port}")
tool_definitions = [
@ -167,10 +170,11 @@ async def run_main(host: str, port: int):
"Write code to check if a number is prime. Use that to check if 7 is prime",
"What is the boiling point of polyjuicepotion ?",
]
await _run_agent(api, tool_definitions, user_prompts)
await _run_agent(api, model, tool_definitions, ToolPromptFormat.json, user_prompts)
async def run_rag(host: str, port: int):
async def run_llama_3_2_rag(host: str, port: int):
model = "Llama3.2-3B-Instruct"
api = AgentsClient(f"http://{host}:{port}")
urls = [
@ -206,12 +210,71 @@ async def run_rag(host: str, port: int):
"Tell me briefly about llama3 and torchtune",
]
await _run_agent(api, tool_definitions, user_prompts, attachments)
await _run_agent(
api, model, tool_definitions, ToolPromptFormat.json, user_prompts, attachments
)
def main(host: str, port: int, rag: bool = False):
fn = run_rag if rag else run_main
asyncio.run(fn(host, port))
async def run_llama_3_2(host: str, port: int):
model = "Llama3.2-3B-Instruct"
api = AgentsClient(f"http://{host}:{port}")
# zero shot tools for llama3.2 text models
tool_definitions = [
FunctionCallToolDefinition(
function_name="get_boiling_point",
description="Get the boiling point of a imaginary liquids (eg. polyjuice)",
parameters={
"liquid_name": ToolParamDefinition(
param_type="str",
description="The name of the liquid",
required=True,
),
"celcius": ToolParamDefinition(
param_type="bool",
description="Whether to return the boiling point in Celcius",
required=False,
),
},
),
FunctionCallToolDefinition(
function_name="make_web_search",
description="Search the web / internet for more realtime information",
parameters={
"query": ToolParamDefinition(
param_type="str",
description="the query to search for",
required=True,
),
},
),
]
user_prompts = [
"Who are you?",
"what is the 100th prime number?",
"Who was 44th President of USA?",
# multiple tool calls in a single prompt
"What is the boiling point of polyjuicepotion and pinkponklyjuice?",
]
await _run_agent(
api, model, tool_definitions, ToolPromptFormat.python_list, user_prompts
)
def main(host: str, port: int, run_type: str):
assert run_type in [
"tools_llama_3_1",
"tools_llama_3_2",
"rag_llama_3_2",
], f"Invalid run type {run_type}, must be one of tools_llama_3_1, tools_llama_3_2, rag_llama_3_2"
fn = {
"tools_llama_3_1": run_llama_3_1,
"tools_llama_3_2": run_llama_3_2,
"rag_llama_3_2": run_llama_3_2_rag,
}
asyncio.run(fn[run_type](host, port))
if __name__ == "__main__":

36
llama_stack/apis/inference/client.py
View file

@ -10,6 +10,10 @@ from typing import Any, AsyncGenerator, List, Optional
import fire
import httpx
from llama_models.llama3.api.datatypes import ImageMedia, URL
from PIL import Image as PIL_Image
from pydantic import BaseModel
from llama_models.llama3.api import * # noqa: F403
@ -105,7 +109,7 @@ async def run_main(host: str, port: int, stream: bool):
)
cprint(f"User>{message.content}", "green")
iterator = client.chat_completion(
model="Meta-Llama3.1-8B-Instruct",
model="Llama3.1-8B-Instruct",
messages=[message],
stream=stream,
)
@ -113,8 +117,34 @@ async def run_main(host: str, port: int, stream: bool):
log.print()
def main(host: str, port: int, stream: bool = True):
asyncio.run(run_main(host, port, stream))
async def run_mm_main(host: str, port: int, stream: bool, path: str):
client = InferenceClient(f"http://{host}:{port}")
with open(path, "rb") as f:
img = PIL_Image.open(f).convert("RGB")
message = UserMessage(
content=[
ImageMedia(image=URL(uri=f"file://{path}")),
# ImageMedia(image=img),
"Describe this image in two sentences",
],
)
cprint(f"User>{message.content}", "green")
iterator = client.chat_completion(
model="Llama3.2-11B-Vision-Instruct",
messages=[message],
stream=stream,
)
async for log in EventLogger().log(iterator):
log.print()
def main(host: str, port: int, stream: bool = True, mm: bool = False, file: str = None):
if mm:
asyncio.run(run_mm_main(host, port, stream, file))
else:
asyncio.run(run_main(host, port, stream))
if __name__ == "__main__":

2
llama_stack/apis/memory_banks/memory_banks.py
View file

@ -7,11 +7,11 @@
from typing import List, Optional, Protocol
from llama_models.schema_utils import json_schema_type, webmethod
from pydantic import BaseModel, Field
from llama_stack.apis.memory import MemoryBankType
from llama_stack.distribution.datatypes import GenericProviderConfig
from pydantic import BaseModel, Field
@json_schema_type

5
llama_stack/apis/safety/client.py
View file

@ -51,6 +51,11 @@ class SafetyClient(Safety):
),
headers={
"Content-Type": "application/json",
"X-LlamaStack-ProviderData": json.dumps(
{
"together_api_key": "1882f9a484fc7c6ce3e4dc90272d5db52346c93838daab3d704803181f396b22"
}
),
},
timeout=20,
)

4
llama_stack/cli/download.py
View file

@ -44,7 +44,7 @@ def setup_download_parser(parser: argparse.ArgumentParser) -> None:
parser.add_argument(
"--source",
choices=["meta", "huggingface"],
required=True,
default="meta",
)
parser.add_argument(
"--model-id",
@ -116,7 +116,7 @@ def _hf_download(
"You can find your token by visiting https://huggingface.co/settings/tokens"
)
except RepositoryNotFoundError:
parser.error(f"Repository '{args.repo_id}' not found on the Hugging Face Hub.")
parser.error(f"Repository '{repo_id}' not found on the Hugging Face Hub.")
except Exception as e:
parser.error(e)

4
llama_stack/cli/model/model.py
View file

@ -9,7 +9,7 @@ import argparse
from llama_stack.cli.model.describe import ModelDescribe
from llama_stack.cli.model.download import ModelDownload
from llama_stack.cli.model.list import ModelList
from llama_stack.cli.model.template import ModelTemplate
from llama_stack.cli.model.prompt_format import ModelPromptFormat
from llama_stack.cli.subcommand import Subcommand
@ -30,5 +30,5 @@ class ModelParser(Subcommand):
# Add sub-commands
ModelDownload.create(subparsers)
ModelList.create(subparsers)
ModelTemplate.create(subparsers)
ModelPromptFormat.create(subparsers)
ModelDescribe.create(subparsers)

116
llama_stack/cli/model/prompt_format.py Normal file
View file

@ -0,0 +1,116 @@
# 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 subprocess
import textwrap
from io import StringIO
from llama_models.datatypes import CoreModelId, is_multimodal, model_family, ModelFamily
from llama_stack.cli.subcommand import Subcommand
class ModelPromptFormat(Subcommand):
"""Llama model cli for describe a model prompt format (message formats)"""
def __init__(self, subparsers: argparse._SubParsersAction):
super().__init__()
self.parser = subparsers.add_parser(
"prompt-format",
prog="llama model prompt-format",
description="Show llama model message formats",
epilog=textwrap.dedent(
"""
Example:
llama model prompt-format <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-name",
type=str,
default="llama3_1",
help="Model Family (llama3_1, llama3_X, etc.)",
)
def _run_model_template_cmd(self, args: argparse.Namespace) -> None:
import pkg_resources
# Only Llama 3.1 and 3.2 are supported
supported_model_ids = [
m
for m in CoreModelId
if model_family(m) in {ModelFamily.llama3_1, ModelFamily.llama3_2}
]
model_str = "\n".join([m.value for m in supported_model_ids])
try:
model_id = CoreModelId(args.model_name)
except ValueError:
raise argparse.ArgumentTypeError(
f"{args.model_name} is not a valid Model. Choose one from --\n{model_str}"
) from None
if model_id not in supported_model_ids:
raise argparse.ArgumentTypeError(
f"{model_id} is not a valid Model. Choose one from --\n {model_str}"
) from None
llama_3_1_file = pkg_resources.resource_filename(
"llama_models", "llama3_1/prompt_format.md"
)
llama_3_2_text_file = pkg_resources.resource_filename(
"llama_models", "llama3_2/text_prompt_format.md"
)
llama_3_2_vision_file = pkg_resources.resource_filename(
"llama_models", "llama3_2/vision_prompt_format.md"
)
if model_family(model_id) == ModelFamily.llama3_1:
with open(llama_3_1_file, "r") as f:
content = f.read()
elif model_family(model_id) == ModelFamily.llama3_2:
if is_multimodal(model_id):
with open(llama_3_2_vision_file, "r") as f:
content = f.read()
else:
with open(llama_3_2_text_file, "r") as f:
content = f.read()
render_markdown_to_pager(content)
def render_markdown_to_pager(markdown_content: str):
from rich.console import Console
from rich.markdown import Markdown
from rich.style import Style
from rich.text import Text
class LeftAlignedHeaderMarkdown(Markdown):
def parse_header(self, token):
level = token.type.count("h")
content = Text(token.content)
header_style = Style(color="bright_blue", bold=True)
header = Text(f"{'#' * level} ", style=header_style) + content
self.add_text(header)
# Render the Markdown
md = LeftAlignedHeaderMarkdown(markdown_content)
# Capture the rendered output
output = StringIO()
console = Console(file=output, force_terminal=True, width=100) # Set a fixed width
console.print(md)
rendered_content = output.getvalue()
# Pipe to pager
pager = subprocess.Popen(["less", "-R"], stdin=subprocess.PIPE)
pager.communicate(input=rendered_content.encode())

113
llama_stack/cli/model/template.py
View file

@ -1,113 +0,0 @@
# 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 termcolor import colored
from llama_stack.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 _prompt_type(self, value):
from llama_models.llama3.api.datatypes import ToolPromptFormat
try:
return ToolPromptFormat(value.lower())
except ValueError:
raise argparse.ArgumentTypeError(
f"{value} is not a valid ToolPromptFormat. Choose from {', '.join(t.value for t in ToolPromptFormat)}"
) from None
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(
"--name",
type=str,
help="Usecase template name (system_message, user_message, assistant_message, tool_message)...",
required=False,
)
self.parser.add_argument(
"--format",
type=str,
help="ToolPromptFormat (json or function_tag). This flag is used to print the template in a specific formats.",
required=False,
default="json",
)
self.parser.add_argument(
"--raw",
action="store_true",
help="If set to true, don't pretty-print into a table. Useful to copy-paste.",
)
def _run_model_template_cmd(self, args: argparse.Namespace) -> None:
from llama_models.llama3.api.interface import (
list_jinja_templates,
render_jinja_template,
)
from llama_stack.cli.table import print_table
if args.name:
tool_prompt_format = self._prompt_type(args.format)
template, tokens_info = render_jinja_template(args.name, tool_prompt_format)
rendered = ""
for tok, is_special in tokens_info:
if is_special:
rendered += colored(tok, "yellow", attrs=["bold"])
else:
rendered += tok
if not args.raw:
rendered = rendered.replace("\n", "↵\n")
print_table(
[
(
"Name",
colored(template.template_name, "white", attrs=["bold"]),
),
("Template", rendered),
("Notes", template.notes),
],
separate_rows=True,
)
else:
print("Template: ", template.template_name)
print("=" * 40)
print(rendered)
else:
templates = list_jinja_templates()
headers = ["Role", "Template Name"]
print_table(
[(t.role, t.template_name) for t in templates],
headers,
)

5
llama_stack/distribution/control_plane/__init__.py
View file

@ -1,5 +0,0 @@
# 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.

30
llama_stack/distribution/start_container.sh
View file

@ -8,6 +8,7 @@
DOCKER_BINARY=${DOCKER_BINARY:-docker}
DOCKER_OPTS=${DOCKER_OPTS:-}
LLAMA_CHECKPOINT_DIR=${LLAMA_CHECKPOINT_DIR:-}
set -euo pipefail
@ -37,10 +38,25 @@ port="$1"
shift
set -x
$DOCKER_BINARY run $DOCKER_OPTS -it \
-p $port:$port \
-v "$yaml_config:/app/config.yaml" \
$docker_image \
python -m llama_stack.distribution.server.server \
--yaml_config /app/config.yaml \
--port $port "$@"
if [ -n "$LLAMA_CHECKPOINT_DIR" ]; then
$DOCKER_BINARY run $DOCKER_OPTS -it \
-p $port:$port \
-v "$yaml_config:/app/config.yaml" \
-v "$LLAMA_CHECKPOINT_DIR:/root/.llama" \
--gpus=all \
$docker_image \
python -m llama_stack.distribution.server.server \
--yaml_config /app/config.yaml \
--port $port "$@"
fi
if [ -z "$LLAMA_CHECKPOINT_DIR" ]; then
$DOCKER_BINARY run $DOCKER_OPTS -it \
-p $port:$port \
-v "$yaml_config:/app/config.yaml" \
$docker_image \
python -m llama_stack.distribution.server.server \
--yaml_config /app/config.yaml \
--port $port "$@"
fi

12
llama_stack/providers/adapters/inference/fireworks/fireworks.py
View file

@ -15,14 +15,16 @@ from llama_models.llama3.api.tokenizer import Tokenizer
from llama_models.sku_list import resolve_model
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.providers.utils.inference.prepare_messages import prepare_messages
from llama_stack.providers.utils.inference.augment_messages import (
augment_messages_for_tools,
)
from .config import FireworksImplConfig
FIREWORKS_SUPPORTED_MODELS = {
"Meta-Llama3.1-8B-Instruct": "fireworks/llama-v3p1-8b-instruct",
"Meta-Llama3.1-70B-Instruct": "fireworks/llama-v3p1-70b-instruct",
"Meta-Llama3.1-405B-Instruct": "fireworks/llama-v3p1-405b-instruct",
"Llama3.1-8B-Instruct": "fireworks/llama-v3p1-8b-instruct",
"Llama3.1-70B-Instruct": "fireworks/llama-v3p1-70b-instruct",
"Llama3.1-405B-Instruct": "fireworks/llama-v3p1-405b-instruct",
}
@ -106,7 +108,7 @@ class FireworksInferenceAdapter(Inference):
logprobs=logprobs,
)
messages = prepare_messages(request)
messages = augment_messages_for_tools(request)
# accumulate sampling params and other options to pass to fireworks
options = self.get_fireworks_chat_options(request)

12
llama_stack/providers/adapters/inference/ollama/ollama.py
View file

@ -16,14 +16,16 @@ from llama_models.sku_list import resolve_model
from ollama import AsyncClient
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.providers.utils.inference.prepare_messages import prepare_messages
from llama_stack.providers.utils.inference.augment_messages import (
augment_messages_for_tools,
)
# TODO: Eventually this will move to the llama cli model list command
# mapping of Model SKUs to ollama models
OLLAMA_SUPPORTED_SKUS = {
# "Meta-Llama3.1-8B-Instruct": "llama3.1",
"Meta-Llama3.1-8B-Instruct": "llama3.1:8b-instruct-fp16",
"Meta-Llama3.1-70B-Instruct": "llama3.1:70b-instruct-fp16",
# "Llama3.1-8B-Instruct": "llama3.1",
"Llama3.1-8B-Instruct": "llama3.1:8b-instruct-fp16",
"Llama3.1-70B-Instruct": "llama3.1:70b-instruct-fp16",
}
@ -115,7 +117,7 @@ class OllamaInferenceAdapter(Inference):
logprobs=logprobs,
)
messages = prepare_messages(request)
messages = augment_messages_for_tools(request)
# accumulate sampling params and other options to pass to ollama
options = self.get_ollama_chat_options(request)
ollama_model = self.resolve_ollama_model(request.model)

6
llama_stack/providers/adapters/inference/tgi/tgi.py
View file

@ -14,7 +14,9 @@ from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.datatypes import StopReason
from llama_models.llama3.api.tokenizer import Tokenizer
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.providers.utils.inference.prepare_messages import prepare_messages
from llama_stack.providers.utils.inference.augment_messages import (
augment_messages_for_tools,
)
from .config import TGIImplConfig
@ -95,7 +97,7 @@ class TGIAdapter(Inference):
logprobs=logprobs,
)
messages = prepare_messages(request)
messages = augment_messages_for_tools(request)
model_input = self.formatter.encode_dialog_prompt(messages)
prompt = self.tokenizer.decode(model_input.tokens)

12
llama_stack/providers/adapters/inference/together/together.py
View file

@ -15,14 +15,16 @@ from llama_models.sku_list import resolve_model
from together import Together
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.providers.utils.inference.prepare_messages import prepare_messages
from llama_stack.providers.utils.inference.augment_messages import (
augment_messages_for_tools,
)
from .config import TogetherImplConfig
TOGETHER_SUPPORTED_MODELS = {
"Meta-Llama3.1-8B-Instruct": "meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
"Meta-Llama3.1-70B-Instruct": "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
"Meta-Llama3.1-405B-Instruct": "meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",
"Llama3.1-8B-Instruct": "meta-llama/Llama-3.1-8B-Instruct-Turbo",
"Llama3.1-70B-Instruct": "meta-llama/Llama-3.1-70B-Instruct-Turbo",
"Llama3.1-405B-Instruct": "meta-llama/Llama-3.1-405B-Instruct-Turbo",
}
@ -110,7 +112,7 @@ class TogetherInferenceAdapter(Inference):
# accumulate sampling params and other options to pass to together
options = self.get_together_chat_options(request)
together_model = self.resolve_together_model(request.model)
messages = prepare_messages(request)
messages = augment_messages_for_tools(request)
if not request.stream:
# TODO: might need to add back an async here

548
llama_stack/providers/impls/ios/inference/LocalInference/LocalInference.xcodeproj/project.pbxproj Normal file
View file

@ -0,0 +1,548 @@
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7
llama_stack/providers/impls/ios/inference/LocalInference/LocalInference.xcodeproj/project.xcworkspace/contents.xcworkspacedata generated Normal file
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@ -0,0 +1,7 @@
<?xml version="1.0" encoding="UTF-8"?>
<Workspace
version = "1.0">
<FileRef
location = "self:">
</FileRef>
</Workspace>

8
llama_stack/providers/impls/ios/inference/LocalInference/LocalInference.xcodeproj/project.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist Normal file
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<?xml version="1.0" encoding="UTF-8"?>
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<key>IDEDidComputeMac32BitWarning</key>
<true/>
</dict>
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16
llama_stack/providers/impls/ios/inference/LocalInference/LocalInference/LocalInference.h Normal file
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@ -0,0 +1,16 @@
//
// LocalInference.h
// LocalInference
//
// Created by Dalton Flanagan on 9/23/24.
//
#import <Foundation/Foundation.h>
//! Project version number for LocalInference.
FOUNDATION_EXPORT double LocalInferenceVersionNumber;
//! Project version string for LocalInference.
FOUNDATION_EXPORT const unsigned char LocalInferenceVersionString[];
// In this header, you should import all the public headers of your framework using statements like #import <LocalInference/PublicHeader.h>

167
llama_stack/providers/impls/ios/inference/LocalInference/LocalInference/LocalInference.swift Normal file
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@ -0,0 +1,167 @@
import Foundation
import LLaMARunner
import LlamaStackClient
class RunnerHolder: ObservableObject {
var runner: Runner?
}
public class LocalInference: Inference {
private var runnerHolder = RunnerHolder()
private let runnerQueue: DispatchQueue
public init (queue: DispatchQueue) {
runnerQueue = queue
}
public func loadModel(modelPath: String, tokenizerPath: String, completion: @escaping (Result<Void, Error>) -> Void) {
runnerHolder.runner = runnerHolder.runner ?? Runner(
modelPath: modelPath,
tokenizerPath: tokenizerPath
)
runnerQueue.async {
let runner = self.runnerHolder.runner
do {
try runner!.load()
completion(.success(()))
} catch let loadError {
print("error: " + loadError.localizedDescription)
completion(.failure(loadError))
}
}
}
public func chatCompletion(request: Components.Schemas.ChatCompletionRequest) -> AsyncStream<Components.Schemas.ChatCompletionResponseStreamChunk> {
return AsyncStream { continuation in
runnerQueue.async {
do {
var tokens: [String] = []
let prompt = try encodeDialogPrompt(messages: prepareMessages(request: request))
var stopReason: Components.Schemas.StopReason? = nil
var buffer = ""
var ipython = false
var echoDropped = false
try self.runnerHolder.runner?.generate(prompt, sequenceLength: 4096) { token in
buffer += token
// HACK: Workaround until LlamaRunner exposes echo param
if (!echoDropped) {
if (buffer.hasPrefix(prompt)) {
buffer = String(buffer.dropFirst(prompt.count))
echoDropped = true
}
return
}
tokens.append(token)
if !ipython && (buffer.starts(with: "<|python_tag|>") || buffer.starts(with: "[") ) {
ipython = true
continuation.yield(
Components.Schemas.ChatCompletionResponseStreamChunk(
event: Components.Schemas.ChatCompletionResponseEvent(
delta: .ToolCallDelta(Components.Schemas.ToolCallDelta(
content: .case1(""),
parse_status: Components.Schemas.ToolCallParseStatus.started
)
),
event_type: .progress
)
)
)
if (buffer.starts(with: "<|python_tag|>")) {
buffer = String(buffer.dropFirst("<|python_tag|>".count))
}
}
// TODO: Non-streaming lobprobs
var text = ""
if token == "<|eot_id|>" {
stopReason = Components.Schemas.StopReason.end_of_turn
} else if token == "<|eom_id|>" {
stopReason = Components.Schemas.StopReason.end_of_message
} else {
text = token
}
var delta: Components.Schemas.ChatCompletionResponseEvent.deltaPayload
if ipython {
delta = .ToolCallDelta(Components.Schemas.ToolCallDelta(
content: .case1(text),
parse_status: .in_progress
))
} else {
delta = .case1(text)
}
if stopReason == nil {
continuation.yield(
Components.Schemas.ChatCompletionResponseStreamChunk(
event: Components.Schemas.ChatCompletionResponseEvent(
delta: delta,
event_type: .progress
)
)
)
}
}
if stopReason == nil {
stopReason = Components.Schemas.StopReason.out_of_tokens
}
let message = decodeAssistantMessage(tokens: tokens.joined(), stopReason: stopReason!)
// TODO: non-streaming support
let didParseToolCalls = message.tool_calls.count > 0
if ipython && !didParseToolCalls {
continuation.yield(
Components.Schemas.ChatCompletionResponseStreamChunk(
event: Components.Schemas.ChatCompletionResponseEvent(
delta: .ToolCallDelta(Components.Schemas.ToolCallDelta(content: .case1(""), parse_status: .failure)),
event_type: .progress
)
// TODO: stopReason
)
)
}
for toolCall in message.tool_calls {
continuation.yield(
Components.Schemas.ChatCompletionResponseStreamChunk(
event: Components.Schemas.ChatCompletionResponseEvent(
delta: .ToolCallDelta(Components.Schemas.ToolCallDelta(
content: .ToolCall(toolCall),
parse_status: .success
)),
event_type: .progress
)
// TODO: stopReason
)
)
}
continuation.yield(
Components.Schemas.ChatCompletionResponseStreamChunk(
event: Components.Schemas.ChatCompletionResponseEvent(
delta: .case1(""),
event_type: .complete
)
// TODO: stopReason
)
)
}
catch (let error) {
print("Inference error: " + error.localizedDescription)
}
}
}
}
}

235
llama_stack/providers/impls/ios/inference/LocalInference/LocalInference/Parsing.swift Normal file
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import Foundation
import LlamaStackClient
func encodeHeader(role: String) -> String {
return "<|start_header_id|>\(role)<|end_header_id|>\n\n"
}
func encodeDialogPrompt(messages: [Components.Schemas.ChatCompletionRequest.messagesPayloadPayload]) -> String {
var prompt = ""
prompt.append("<|begin_of_text|>")
for message in messages {
let msg = encodeMessage(message: message)
prompt += msg
}
prompt.append(encodeHeader(role: "assistant"))
return prompt
}
func getRole(message: Components.Schemas.ChatCompletionRequest.messagesPayloadPayload) -> String {
switch (message) {
case .UserMessage(let m):
return m.role.rawValue
case .SystemMessage(let m):
return m.role.rawValue
case .ToolResponseMessage(let m):
return m.role.rawValue
case .CompletionMessage(let m):
return m.role.rawValue
}
}
func encodeMessage(message: Components.Schemas.ChatCompletionRequest.messagesPayloadPayload) -> String {
var prompt = encodeHeader(role: getRole(message: message))
switch (message) {
case .CompletionMessage(let m):
if (m.tool_calls.count > 0) {
prompt += "<|python_tag|>"
}
default:
break
}
func _processContent(_ content: Any) -> String {
func _process(_ c: Any) {
if let str = c as? String {
prompt += str
}
}
if let str = content as? String {
_process(str)
} else if let list = content as? [Any] {
for c in list {
_process(c)
}
}
return ""
}
switch (message) {
case .UserMessage(let m):
prompt += _processContent(m.content)
case .SystemMessage(let m):
prompt += _processContent(m.content)
case .ToolResponseMessage(let m):
prompt += _processContent(m.content)
case .CompletionMessage(let m):
prompt += _processContent(m.content)
}
var eom = false
switch (message) {
case .UserMessage(let m):
switch (m.content) {
case .case1(let c):
prompt += _processContent(c)
case .case2(let c):
prompt += _processContent(c)
}
case .CompletionMessage(let m):
// TODO: Support encoding past tool call history
// for t in m.tool_calls {
// _processContent(t.)
//}
eom = m.stop_reason == Components.Schemas.StopReason.end_of_message
case .SystemMessage(_):
break
case .ToolResponseMessage(_):
break
}
if (eom) {
prompt += "<|eom_id|>"
} else {
prompt += "<|eot_id|>"
}
return prompt
}
func prepareMessages(request: Components.Schemas.ChatCompletionRequest) throws -> [Components.Schemas.ChatCompletionRequest.messagesPayloadPayload] {
var existingMessages = request.messages
var existingSystemMessage: Components.Schemas.ChatCompletionRequest.messagesPayloadPayload?
// TODO: Existing system message
var messages: [Components.Schemas.ChatCompletionRequest.messagesPayloadPayload] = []
let defaultGen = SystemDefaultGenerator()
let defaultTemplate = defaultGen.gen()
var sysContent = ""
// TODO: Built-in tools
sysContent += try defaultTemplate.render()
messages.append(.SystemMessage(Components.Schemas.SystemMessage(
content: .case1(sysContent),
role: .system))
)
if request.tools?.isEmpty == false {
// TODO: Separate built-ins and custom tools (right now everything treated as custom)
let toolGen = FunctionTagCustomToolGenerator()
let toolTemplate = try toolGen.gen(customTools: request.tools!)
let tools = try toolTemplate.render()
messages.append(.UserMessage(Components.Schemas.UserMessage(
content: .case1(tools),
role: .user)
))
}
messages.append(contentsOf: existingMessages)
return messages
}
struct FunctionCall {
let name: String
let params: [String: Any]
}
public func maybeExtractCustomToolCalls(input: String) -> [Components.Schemas.ToolCall] {
guard input.hasPrefix("[") && input.hasSuffix("]") else {
return []
}
do {
let trimmed = input.trimmingCharacters(in: CharacterSet(charactersIn: "[]"))
let calls = trimmed.components(separatedBy: "),").map { $0.hasSuffix(")") ? $0 : $0 + ")" }
var result: [Components.Schemas.ToolCall] = []
for call in calls {
guard let nameEndIndex = call.firstIndex(of: "("),
let paramsStartIndex = call.firstIndex(of: "{"),
let paramsEndIndex = call.lastIndex(of: "}") else {
return []
}
let name = String(call[..<nameEndIndex]).trimmingCharacters(in: .whitespacesAndNewlines)
let paramsString = String(call[paramsStartIndex...paramsEndIndex])
guard let data = paramsString.data(using: .utf8),
let params = try? JSONSerialization.jsonObject(with: data, options: []) as? [String: Any] else {
return []
}
var props: [String : Components.Schemas.ToolCall.argumentsPayload.additionalPropertiesPayload] = [:]
for (param_name, param) in params {
switch (param) {
case let value as String:
props[param_name] = .case1(value)
case let value as Int:
props[param_name] = .case2(value)
case let value as Double:
props[param_name] = .case3(value)
case let value as Bool:
props[param_name] = .case4(value)
default:
return []
}
}
result.append(
Components.Schemas.ToolCall(
arguments: .init(additionalProperties: props),
call_id: UUID().uuidString,
tool_name: .case2(name) // custom_tool
)
)
}
return result.isEmpty ? [] : result
} catch {
return []
}
}
func decodeAssistantMessage(tokens: String, stopReason: Components.Schemas.StopReason) -> Components.Schemas.CompletionMessage {
var content = tokens
let roles = ["user", "system", "assistant"]
for role in roles {
let headerStr = encodeHeader(role: role)
if content.hasPrefix(headerStr) {
content = String(content.dropFirst(encodeHeader(role: role).count))
}
}
if content.hasPrefix("<|python_tag|>") {
content = String(content.dropFirst("<|python_tag|>".count))
}
if content.hasSuffix("<|eot_id|>") {
content = String(content.dropLast("<|eot_id|>".count))
} else {
content = String(content.dropLast("<|eom_id|>".count))
}
return Components.Schemas.CompletionMessage(
content: .case1(content),
role: .assistant,
stop_reason: stopReason,
tool_calls: maybeExtractCustomToolCalls(input: content)
)
}

12
llama_stack/providers/impls/ios/inference/LocalInference/LocalInference/PromptTemplate.swift Normal file
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import Foundation
import Stencil
public struct PromptTemplate {
let template: String
let data: [String: Any]
public func render() throws -> String {
let template = Template(templateString: self.template)
return try template.render(self.data)
}
}

91
llama_stack/providers/impls/ios/inference/LocalInference/LocalInference/SystemPrompts.swift Normal file
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import Foundation
import LlamaStackClient
func convertToNativeSwiftType(_ value: Any) -> Any {
switch value {
case let number as NSNumber:
if CFGetTypeID(number) == CFBooleanGetTypeID() {
return number.boolValue
}
if floor(number.doubleValue) == number.doubleValue {
return number.intValue
}
return number.doubleValue
case let string as String:
return string
case let array as [Any]:
return array.map(convertToNativeSwiftType)
case let dict as [String: Any]:
return dict.mapValues(convertToNativeSwiftType)
case is NSNull:
return NSNull()
default:
return value
}
}
public class SystemDefaultGenerator {
public init() {}
public func gen() -> PromptTemplate {
let templateStr = """
Cutting Knowledge Date: December 2023
Today Date: {{ today }}
"""
let dateFormatter = DateFormatter()
dateFormatter.dateFormat = "dd MMMM yyyy"
return PromptTemplate(
template: templateStr,
data: ["today": dateFormatter.string(from: Date())]
)
}
}
public class FunctionTagCustomToolGenerator {
public init() {}
public func gen(customTools: [Components.Schemas.ToolDefinition]) throws -> PromptTemplate {
// TODO: required params
// TODO: {{#unless @last}},{{/unless}}
let templateStr = """
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.
If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]
You SHOULD NOT include any other text in the response.
Here is a list of functions in JSON format that you can invoke.
[
{% for t in custom_tools %}
{
"name": "{{t.tool_name}}",
"description": "{{t.description}}",
"parameters": {
"type": "dict",
"properties": { {{t.parameters}} }
}
{{/let}}
{% endfor -%}
]
"""
let encoder = JSONEncoder()
return PromptTemplate(
template: templateStr,
data: ["custom_tools": try customTools.map {
let data = try encoder.encode($0)
let obj = try JSONSerialization.jsonObject(with: data)
return convertToNativeSwiftType(obj)
}]
)
}
}

541
llama_stack/providers/impls/ios/inference/LocalInference/LocalInferenceImpl.xcodeproj/project.pbxproj Normal file
View file

@ -0,0 +1,541 @@
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defaultConfigurationIsVisible = 0;
defaultConfigurationName = Release;
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/* End XCConfigurationList section */
/* Begin XCLocalSwiftPackageReference section */
5CADC7182CA471CC007662D2 /* XCLocalSwiftPackageReference "internal-llama-stack-client-swift" */ = {
isa = XCLocalSwiftPackageReference;
relativePath = "internal-llama-stack-client-swift";
};
/* End XCLocalSwiftPackageReference section */
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5CCBC6732CA1F45800E958D0 /* XCRemoteSwiftPackageReference "executorch" */ = {
isa = XCRemoteSwiftPackageReference;
repositoryURL = "https://github.com/pytorch/executorch";
requirement = {
branch = latest;
kind = branch;
};
};
5CCBC6912CA1F7D000E958D0 /* XCRemoteSwiftPackageReference "Stencil" */ = {
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repositoryURL = "https://github.com/stencilproject/Stencil";
requirement = {
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minimumVersion = 0.15.1;
};
};
/* End XCRemoteSwiftPackageReference section */
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isa = XCSwiftPackageProductDependency;
productName = LlamaStackClient;
};
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7
llama_stack/providers/impls/ios/inference/LocalInference/LocalInferenceImpl.xcodeproj/project.xcworkspace/contents.xcworkspacedata generated Normal file
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@ -0,0 +1,7 @@
<?xml version="1.0" encoding="UTF-8"?>
<Workspace
version = "1.0">
<FileRef
location = "self:">
</FileRef>
</Workspace>

8
llama_stack/providers/impls/ios/inference/LocalInference/LocalInferenceImpl.xcodeproj/project.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist Normal file
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@ -0,0 +1,8 @@
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
<key>IDEDidComputeMac32BitWarning</key>
<true/>
</dict>
</plist>

16
llama_stack/providers/impls/ios/inference/LocalInference/LocalInferenceImpl/LocalInference.h Normal file
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@ -0,0 +1,16 @@
//
// LocalInference.h
// LocalInference
//
// Created by Dalton Flanagan on 9/23/24.
//
#import <Foundation/Foundation.h>
//! Project version number for LocalInference.
FOUNDATION_EXPORT double LocalInferenceVersionNumber;
//! Project version string for LocalInference.
FOUNDATION_EXPORT const unsigned char LocalInferenceVersionString[];
// In this header, you should import all the public headers of your framework using statements like #import <LocalInference/PublicHeader.h>

167
llama_stack/providers/impls/ios/inference/LocalInference/LocalInferenceImpl/LocalInference.swift Normal file
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@ -0,0 +1,167 @@
import Foundation
import LLaMARunner
import LlamaStackClient
class RunnerHolder: ObservableObject {
var runner: Runner?
}
public class LocalInference: Inference {
private var runnerHolder = RunnerHolder()
private let runnerQueue: DispatchQueue
public init (queue: DispatchQueue) {
runnerQueue = queue
}
public func loadModel(modelPath: String, tokenizerPath: String, completion: @escaping (Result<Void, Error>) -> Void) {
runnerHolder.runner = runnerHolder.runner ?? Runner(
modelPath: modelPath,
tokenizerPath: tokenizerPath
)
runnerQueue.async {
let runner = self.runnerHolder.runner
do {
try runner!.load()
completion(.success(()))
} catch let loadError {
print("error: " + loadError.localizedDescription)
completion(.failure(loadError))
}
}
}
public func chatCompletion(request: Components.Schemas.ChatCompletionRequest) -> AsyncStream<Components.Schemas.ChatCompletionResponseStreamChunk> {
return AsyncStream { continuation in
runnerQueue.async {
do {
var tokens: [String] = []
let prompt = try encodeDialogPrompt(messages: prepareMessages(request: request))
var stopReason: Components.Schemas.StopReason? = nil
var buffer = ""
var ipython = false
var echoDropped = false
try self.runnerHolder.runner?.generate(prompt, sequenceLength: 4096) { token in
buffer += token
// HACK: Workaround until LlamaRunner exposes echo param
if (!echoDropped) {
if (buffer.hasPrefix(prompt)) {
buffer = String(buffer.dropFirst(prompt.count))
echoDropped = true
}
return
}
tokens.append(token)
if !ipython && (buffer.starts(with: "<|python_tag|>") || buffer.starts(with: "[") ) {
ipython = true
continuation.yield(
Components.Schemas.ChatCompletionResponseStreamChunk(
event: Components.Schemas.ChatCompletionResponseEvent(
delta: .ToolCallDelta(Components.Schemas.ToolCallDelta(
content: .case1(""),
parse_status: Components.Schemas.ToolCallParseStatus.started
)
),
event_type: .progress
)
)
)
if (buffer.starts(with: "<|python_tag|>")) {
buffer = String(buffer.dropFirst("<|python_tag|>".count))
}
}
// TODO: Non-streaming lobprobs
var text = ""
if token == "<|eot_id|>" {
stopReason = Components.Schemas.StopReason.end_of_turn
} else if token == "<|eom_id|>" {
stopReason = Components.Schemas.StopReason.end_of_message
} else {
text = token
}
var delta: Components.Schemas.ChatCompletionResponseEvent.deltaPayload
if ipython {
delta = .ToolCallDelta(Components.Schemas.ToolCallDelta(
content: .case1(text),
parse_status: .in_progress
))
} else {
delta = .case1(text)
}
if stopReason == nil {
continuation.yield(
Components.Schemas.ChatCompletionResponseStreamChunk(
event: Components.Schemas.ChatCompletionResponseEvent(
delta: delta,
event_type: .progress
)
)
)
}
}
if stopReason == nil {
stopReason = Components.Schemas.StopReason.out_of_tokens
}
let message = decodeAssistantMessage(tokens: tokens.joined(), stopReason: stopReason!)
// TODO: non-streaming support
let didParseToolCalls = message.tool_calls.count > 0
if ipython && !didParseToolCalls {
continuation.yield(
Components.Schemas.ChatCompletionResponseStreamChunk(
event: Components.Schemas.ChatCompletionResponseEvent(
delta: .ToolCallDelta(Components.Schemas.ToolCallDelta(content: .case1(""), parse_status: .failure)),
event_type: .progress
)
// TODO: stopReason
)
)
}
for toolCall in message.tool_calls {
continuation.yield(
Components.Schemas.ChatCompletionResponseStreamChunk(
event: Components.Schemas.ChatCompletionResponseEvent(
delta: .ToolCallDelta(Components.Schemas.ToolCallDelta(
content: .ToolCall(toolCall),
parse_status: .success
)),
event_type: .progress
)
// TODO: stopReason
)
)
}
continuation.yield(
Components.Schemas.ChatCompletionResponseStreamChunk(
event: Components.Schemas.ChatCompletionResponseEvent(
delta: .case1(""),
event_type: .complete
)
// TODO: stopReason
)
)
}
catch (let error) {
print("Inference error: " + error.localizedDescription)
}
}
}
}
}

235
llama_stack/providers/impls/ios/inference/LocalInference/LocalInferenceImpl/Parsing.swift Normal file
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@ -0,0 +1,235 @@
import Foundation
import LlamaStackClient
func encodeHeader(role: String) -> String {
return "<|start_header_id|>\(role)<|end_header_id|>\n\n"
}
func encodeDialogPrompt(messages: [Components.Schemas.ChatCompletionRequest.messagesPayloadPayload]) -> String {
var prompt = ""
prompt.append("<|begin_of_text|>")
for message in messages {
let msg = encodeMessage(message: message)
prompt += msg
}
prompt.append(encodeHeader(role: "assistant"))
return prompt
}
func getRole(message: Components.Schemas.ChatCompletionRequest.messagesPayloadPayload) -> String {
switch (message) {
case .UserMessage(let m):
return m.role.rawValue
case .SystemMessage(let m):
return m.role.rawValue
case .ToolResponseMessage(let m):
return m.role.rawValue
case .CompletionMessage(let m):
return m.role.rawValue
}
}
func encodeMessage(message: Components.Schemas.ChatCompletionRequest.messagesPayloadPayload) -> String {
var prompt = encodeHeader(role: getRole(message: message))
switch (message) {
case .CompletionMessage(let m):
if (m.tool_calls.count > 0) {
prompt += "<|python_tag|>"
}
default:
break
}
func _processContent(_ content: Any) -> String {
func _process(_ c: Any) {
if let str = c as? String {
prompt += str
}
}
if let str = content as? String {
_process(str)
} else if let list = content as? [Any] {
for c in list {
_process(c)
}
}
return ""
}
switch (message) {
case .UserMessage(let m):
prompt += _processContent(m.content)
case .SystemMessage(let m):
prompt += _processContent(m.content)
case .ToolResponseMessage(let m):
prompt += _processContent(m.content)
case .CompletionMessage(let m):
prompt += _processContent(m.content)
}
var eom = false
switch (message) {
case .UserMessage(let m):
switch (m.content) {
case .case1(let c):
prompt += _processContent(c)
case .case2(let c):
prompt += _processContent(c)
}
case .CompletionMessage(let m):
// TODO: Support encoding past tool call history
// for t in m.tool_calls {
// _processContent(t.)
//}
eom = m.stop_reason == Components.Schemas.StopReason.end_of_message
case .SystemMessage(_):
break
case .ToolResponseMessage(_):
break
}
if (eom) {
prompt += "<|eom_id|>"
} else {
prompt += "<|eot_id|>"
}
return prompt
}
func prepareMessages(request: Components.Schemas.ChatCompletionRequest) throws -> [Components.Schemas.ChatCompletionRequest.messagesPayloadPayload] {
var existingMessages = request.messages
var existingSystemMessage: Components.Schemas.ChatCompletionRequest.messagesPayloadPayload?
// TODO: Existing system message
var messages: [Components.Schemas.ChatCompletionRequest.messagesPayloadPayload] = []
let defaultGen = SystemDefaultGenerator()
let defaultTemplate = defaultGen.gen()
var sysContent = ""
// TODO: Built-in tools
sysContent += try defaultTemplate.render()
messages.append(.SystemMessage(Components.Schemas.SystemMessage(
content: .case1(sysContent),
role: .system))
)
if request.tools?.isEmpty == false {
// TODO: Separate built-ins and custom tools (right now everything treated as custom)
let toolGen = FunctionTagCustomToolGenerator()
let toolTemplate = try toolGen.gen(customTools: request.tools!)
let tools = try toolTemplate.render()
messages.append(.UserMessage(Components.Schemas.UserMessage(
content: .case1(tools),
role: .user)
))
}
messages.append(contentsOf: existingMessages)
return messages
}
struct FunctionCall {
let name: String
let params: [String: Any]
}
public func maybeExtractCustomToolCalls(input: String) -> [Components.Schemas.ToolCall] {
guard input.hasPrefix("[") && input.hasSuffix("]") else {
return []
}
do {
let trimmed = input.trimmingCharacters(in: CharacterSet(charactersIn: "[]"))
let calls = trimmed.components(separatedBy: "),").map { $0.hasSuffix(")") ? $0 : $0 + ")" }
var result: [Components.Schemas.ToolCall] = []
for call in calls {
guard let nameEndIndex = call.firstIndex(of: "("),
let paramsStartIndex = call.firstIndex(of: "{"),
let paramsEndIndex = call.lastIndex(of: "}") else {
return []
}
let name = String(call[..<nameEndIndex]).trimmingCharacters(in: .whitespacesAndNewlines)
let paramsString = String(call[paramsStartIndex...paramsEndIndex])
guard let data = paramsString.data(using: .utf8),
let params = try? JSONSerialization.jsonObject(with: data, options: []) as? [String: Any] else {
return []
}
var props: [String : Components.Schemas.ToolCall.argumentsPayload.additionalPropertiesPayload] = [:]
for (param_name, param) in params {
switch (param) {
case let value as String:
props[param_name] = .case1(value)
case let value as Int:
props[param_name] = .case2(value)
case let value as Double:
props[param_name] = .case3(value)
case let value as Bool:
props[param_name] = .case4(value)
default:
return []
}
}
result.append(
Components.Schemas.ToolCall(
arguments: .init(additionalProperties: props),
call_id: UUID().uuidString,
tool_name: .case2(name) // custom_tool
)
)
}
return result.isEmpty ? [] : result
} catch {
return []
}
}
func decodeAssistantMessage(tokens: String, stopReason: Components.Schemas.StopReason) -> Components.Schemas.CompletionMessage {
var content = tokens
let roles = ["user", "system", "assistant"]
for role in roles {
let headerStr = encodeHeader(role: role)
if content.hasPrefix(headerStr) {
content = String(content.dropFirst(encodeHeader(role: role).count))
}
}
if content.hasPrefix("<|python_tag|>") {
content = String(content.dropFirst("<|python_tag|>".count))
}
if content.hasSuffix("<|eot_id|>") {
content = String(content.dropLast("<|eot_id|>".count))
} else {
content = String(content.dropLast("<|eom_id|>".count))
}
return Components.Schemas.CompletionMessage(
content: .case1(content),
role: .assistant,
stop_reason: stopReason,
tool_calls: maybeExtractCustomToolCalls(input: content)
)
}

12
llama_stack/providers/impls/ios/inference/LocalInference/LocalInferenceImpl/PromptTemplate.swift Normal file
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@ -0,0 +1,12 @@
import Foundation
import Stencil
public struct PromptTemplate {
let template: String
let data: [String: Any]
public func render() throws -> String {
let template = Template(templateString: self.template)
return try template.render(self.data)
}
}

91
llama_stack/providers/impls/ios/inference/LocalInference/LocalInferenceImpl/SystemPrompts.swift Normal file
View file

@ -0,0 +1,91 @@
import Foundation
import LlamaStackClient
func convertToNativeSwiftType(_ value: Any) -> Any {
switch value {
case let number as NSNumber:
if CFGetTypeID(number) == CFBooleanGetTypeID() {
return number.boolValue
}
if floor(number.doubleValue) == number.doubleValue {
return number.intValue
}
return number.doubleValue
case let string as String:
return string
case let array as [Any]:
return array.map(convertToNativeSwiftType)
case let dict as [String: Any]:
return dict.mapValues(convertToNativeSwiftType)
case is NSNull:
return NSNull()
default:
return value
}
}
public class SystemDefaultGenerator {
public init() {}
public func gen() -> PromptTemplate {
let templateStr = """
Cutting Knowledge Date: December 2023
Today Date: {{ today }}
"""
let dateFormatter = DateFormatter()
dateFormatter.dateFormat = "dd MMMM yyyy"
return PromptTemplate(
template: templateStr,
data: ["today": dateFormatter.string(from: Date())]
)
}
}
public class FunctionTagCustomToolGenerator {
public init() {}
public func gen(customTools: [Components.Schemas.ToolDefinition]) throws -> PromptTemplate {
// TODO: required params
// TODO: {{#unless @last}},{{/unless}}
let templateStr = """
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.
If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]
You SHOULD NOT include any other text in the response.
Here is a list of functions in JSON format that you can invoke.
[
{% for t in custom_tools %}
{
"name": "{{t.tool_name}}",
"description": "{{t.description}}",
"parameters": {
"type": "dict",
"properties": { {{t.parameters}} }
}
{{/let}}
{% endfor -%}
]
"""
let encoder = JSONEncoder()
return PromptTemplate(
template: templateStr,
data: ["custom_tools": try customTools.map {
let data = try encoder.encode($0)
let obj = try JSONSerialization.jsonObject(with: data)
return convertToNativeSwiftType(obj)
}]
)
}
}

109
llama_stack/providers/impls/ios/inference/LocalInference/README.md Normal file
View file

@ -0,0 +1,109 @@
# LocalInference
LocalInference provides a local inference implementation powered by [executorch](https://github.com/pytorch/executorch/).
Llama Stack currently supports on-device inference for iOS with Android coming soon. You can run on-device inference on Android today using [executorch](https://github.com/pytorch/executorch/tree/main/examples/demo-apps/android/LlamaDemo), PyTorch’s on-device inference library.
## Installation
We're working on making LocalInference easier to set up. For now, you'll need to import it via `.xcframework`:
1. Clone the executorch submodule in this repo and its dependencies: `git submodule update --init --recursive`
1. Install [Cmake](https://cmake.org/) for the executorch build`
1. Drag `LocalInference.xcodeproj` into your project
1. Add `LocalInference` as a framework in your app target
1. Add a package dependency on https://github.com/pytorch/executorch (branch latest)
1. Add all the kernels / backends from executorch (but not exectuorch itself!) as frameworks in your app target:
- backend_coreml
- backend_mps
- backend_xnnpack
- kernels_custom
- kernels_optimized
- kernels_portable
- kernels_quantized
1. In "Build Settings" > "Other Linker Flags" > "Any iOS Simulator SDK", add:
```
-force_load
$(BUILT_PRODUCTS_DIR)/libkernels_optimized-simulator-release.a
-force_load
$(BUILT_PRODUCTS_DIR)/libkernels_custom-simulator-release.a
-force_load
$(BUILT_PRODUCTS_DIR)/libkernels_quantized-simulator-release.a
-force_load
$(BUILT_PRODUCTS_DIR)/libbackend_xnnpack-simulator-release.a
-force_load
$(BUILT_PRODUCTS_DIR)/libbackend_coreml-simulator-release.a
-force_load
$(BUILT_PRODUCTS_DIR)/libbackend_mps-simulator-release.a
```
1. In "Build Settings" > "Other Linker Flags" > "Any iOS SDK", add:
```
-force_load
$(BUILT_PRODUCTS_DIR)/libkernels_optimized-simulator-release.a
-force_load
$(BUILT_PRODUCTS_DIR)/libkernels_custom-simulator-release.a
-force_load
$(BUILT_PRODUCTS_DIR)/libkernels_quantized-simulator-release.a
-force_load
$(BUILT_PRODUCTS_DIR)/libbackend_xnnpack-simulator-release.a
-force_load
$(BUILT_PRODUCTS_DIR)/libbackend_coreml-simulator-release.a
-force_load
$(BUILT_PRODUCTS_DIR)/libbackend_mps-simulator-release.a
```
## Preparing a model
1. Prepare a `.pte` file [following the executorch docs](https://github.com/pytorch/executorch/blob/main/examples/models/llama2/README.md#step-2-prepare-model)
2. Bundle the `.pte` and `tokenizer.model` file into your app
## Using LocalInference
1. Instantiate LocalInference with a DispatchQueue. Optionally, pass it into your agents service:
```swift
init () {
runnerQueue = DispatchQueue(label: "org.meta.llamastack")
inferenceService = LocalInferenceService(queue: runnerQueue)
agentsService = LocalAgentsService(inference: inferenceService)
}
```
2. Before making any inference calls, load your model from your bundle:
```swift
let mainBundle = Bundle.main
inferenceService.loadModel(
modelPath: mainBundle.url(forResource: "llama32_1b_spinquant", withExtension: "pte"),
tokenizerPath: mainBundle.url(forResource: "tokenizer", withExtension: "model"),
completion: {_ in } // use to handle load failures
)
```
3. Make inference calls (or agents calls) as you normally would with LlamaStack:
```
for await chunk in try await agentsService.initAndCreateTurn(
messages: [
.UserMessage(Components.Schemas.UserMessage(
content: .case1("Call functions as needed to handle any actions in the following text:\n\n" + text),
role: .user))
]
) {
```
## Troubleshooting
If you receive errors like "missing package product" or "invalid checksum", try cleaning the build folder and resetting the Swift package cache:
(Opt+Click) Product > Clean Build Folder Immediately
```
rm -rf \
~/Library/org.swift.swiftpm \
~/Library/Caches/org.swift.swiftpm \
~/Library/Caches/com.apple.dt.Xcode \
~/Library/Developer/Xcode/DerivedData
```

13
llama_stack/providers/impls/meta_reference/agents/agent_instance.py
View file

@ -398,7 +398,11 @@ class ChatAgent(ShieldRunnerMixin):
color = "yellow"
else:
color = None
cprint(f"{str(msg)}", color=color)
if len(str(msg)) > 1000:
msg_str = f"{str(msg)[:500]}...<more>...{str(msg)[-500:]}"
else:
msg_str = str(msg)
cprint(f"{msg_str}", color=color)
step_id = str(uuid.uuid4())
yield AgentTurnResponseStreamChunk(
@ -466,6 +470,13 @@ class ChatAgent(ShieldRunnerMixin):
stop_reason = event.stop_reason
stop_reason = stop_reason or StopReason.out_of_tokens
# If tool calls are parsed successfully,
# if content is not made null the tool call str will also be in the content
# and tokens will have tool call syntax included twice
if tool_calls:
content = ""
message = CompletionMessage(
content=content,
stop_reason=stop_reason,

3
llama_stack/providers/impls/meta_reference/agents/rag/context_retriever.py
View file

@ -10,13 +10,14 @@ from jinja2 import Template
from llama_models.llama3.api import * # noqa: F403
from termcolor import cprint # noqa: F401
from llama_stack.apis.agents import (
DefaultMemoryQueryGeneratorConfig,
LLMMemoryQueryGeneratorConfig,
MemoryQueryGenerator,
MemoryQueryGeneratorConfig,
)
from termcolor import cprint # noqa: F401
from llama_stack.apis.inference import * # noqa: F403

13
llama_stack/providers/impls/meta_reference/inference/config.py
View file

@ -16,7 +16,7 @@ from pydantic import BaseModel, Field, field_validator
class MetaReferenceImplConfig(BaseModel):
model: str = Field(
default="Meta-Llama3.1-8B-Instruct",
default="Llama3.1-8B-Instruct",
description="Model descriptor from `llama model list`",
)
quantization: Optional[QuantizationConfig] = None
@ -30,7 +30,7 @@ class MetaReferenceImplConfig(BaseModel):
permitted_models = [
m.descriptor()
for m in all_registered_models()
if m.model_family == ModelFamily.llama3_1
if m.model_family in {ModelFamily.llama3_1, ModelFamily.llama3_2}
or m.core_model_id == CoreModelId.llama_guard_3_8b
]
if model not in permitted_models:
@ -42,14 +42,9 @@ class MetaReferenceImplConfig(BaseModel):
@property
def model_parallel_size(self) -> int:
# HUGE HACK ALERT: this will be fixed when we move inference configuration
# HACK ALERT: this will be fixed when we move inference configuration
# to ModelsRegistry and we can explicitly ask for `model_parallel_size`
# as configuration there
gpu_count = 1
resolved = resolve_model(self.model)
assert resolved is not None
descriptor = resolved.descriptor().lower()
if "-70b" in descriptor or "-405b" in descriptor:
gpu_count = 8
return gpu_count
return resolved.pth_file_count

81
llama_stack/providers/impls/meta_reference/inference/generation.py
View file

@ -24,21 +24,31 @@ from fairscale.nn.model_parallel.initialize import (
)
from llama_models.llama3.api.args import ModelArgs
from llama_models.llama3.api.chat_format import ChatFormat, ModelInput
from llama_models.llama3.api.datatypes import Message, ToolPromptFormat
from llama_models.llama3.api.datatypes import (
InterleavedTextMedia,
Message,
ToolPromptFormat,
)
from llama_models.llama3.api.tokenizer import Tokenizer
from llama_models.llama3.reference_impl.model import Transformer
from llama_models.llama3.reference_impl.multimodal.model import (
CrossAttentionTransformer,
)
from llama_models.sku_list import resolve_model
from termcolor import cprint
from llama_stack.apis.inference import QuantizationType
from llama_stack.distribution.utils.model_utils import model_local_dir
from termcolor import cprint
from .config import MetaReferenceImplConfig
def model_checkpoint_dir(model) -> str:
checkpoint_dir = Path(model_local_dir(model.descriptor()))
if not Path(checkpoint_dir / "consolidated.00.pth").exists():
paths = [Path(checkpoint_dir / f"consolidated.{ext}") for ext in ["pth", "00.pth"]]
if not any(p.exists() for p in paths):
checkpoint_dir = checkpoint_dir / "original"
assert checkpoint_dir.exists(), (
@ -134,7 +144,11 @@ class Llama:
# 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 model_args.vision_chunk_size > 0:
model = CrossAttentionTransformer(model_args)
model.setup_cache(model_args.max_batch_size, torch.bfloat16)
else:
model = Transformer(model_args)
model.load_state_dict(state_dict, strict=False)
model = convert_to_quantized_model(model, config)
else:
@ -142,7 +156,11 @@ class Llama:
torch.set_default_tensor_type(torch.cuda.BFloat16Tensor)
else:
torch.set_default_tensor_type(torch.cuda.HalfTensor)
model = Transformer(model_args)
if model_args.vision_chunk_size > 0:
model = CrossAttentionTransformer(model_args)
model.setup_cache(model_args.max_batch_size, torch.bfloat16)
else:
model = Transformer(model_args)
model.load_state_dict(state_dict, strict=False)
print(f"Loaded in {time.time() - start_time:.2f} seconds")
@ -167,7 +185,11 @@ class Llama:
) -> Generator:
params = self.model.params
# cprint("Input to model -> " + self.tokenizer.decode(model_input.tokens), "red")
# input_tokens = [
# self.formatter.vision_token if t == 128256 else t
# for t in model_input.tokens
# ]
# cprint("Input to model -> " + self.tokenizer.decode(input_tokens), "red")
prompt_tokens = [model_input.tokens]
bsz = 1
@ -183,6 +205,21 @@ class Llama:
return
total_len = min(max_gen_len + max_prompt_len, params.max_seq_len)
is_vision = isinstance(self.model, CrossAttentionTransformer)
if is_vision:
images = model_input.vision.images if model_input.vision is not None else []
mask = model_input.vision.mask if model_input.vision is not None else []
# the method works for bsz > 1 so add a batch dimension
xattn_caches, cross_attention_masks, full_text_row_masked_out_mask = (
self.model.compute_vision_tokens_masks(
batch_images=[images],
batch_masks=[mask],
total_len=total_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):
@ -206,7 +243,19 @@ class Llama:
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 is_vision:
position_ids = torch.arange(
prev_pos, cur_pos, dtype=torch.long, device="cuda"
)
logits = self.model.forward(
position_ids,
tokens,
cross_attention_masks,
full_text_row_masked_out_mask,
xattn_caches,
)
else:
logits = self.model.forward(tokens[:, prev_pos:cur_pos], prev_pos)
if temperature > 0:
probs = torch.softmax(logits[:, -1] / temperature, dim=-1)
@ -222,6 +271,18 @@ class Llama:
tokens[:, cur_pos] = next_token
target = tokens[:, prev_pos + 1 : cur_pos + 1]
if is_vision:
# the logits space (num_classes) is designed to never contain a media_token
# however our input token stream does contain them. we need to nuke them here
# or else the CUDA kernels will crash with an illegal memory access
vision_tokens = [self.tokenizer.special_tokens["<|image|>"], 128256]
masks = [target.eq(t) for t in vision_tokens]
if len(masks) > 1:
mask = torch.logical_or(*masks)
else:
mask = masks[0]
target[mask] = 0
if logprobs:
token_logprobs[:, prev_pos + 1 : cur_pos + 1] = -F.cross_entropy(
input=logits.transpose(1, 2),
@ -248,7 +309,7 @@ class Llama:
def text_completion(
self,
prompt: str,
content: InterleavedTextMedia,
temperature: float = 0.6,
top_p: float = 0.9,
max_gen_len: Optional[int] = None,
@ -262,10 +323,10 @@ class Llama:
):
max_gen_len = self.model.params.max_seq_len - 1
prompt_tokens = self.tokenizer.encode(prompt, bos=True, eos=False)
model_input = self.formatter.encode_content(content)
yield from self.generate(
model_input=ModelInput(tokens=prompt_tokens),
model_input=model_input,
max_gen_len=max_gen_len,
temperature=temperature,
top_p=top_p,

10
llama_stack/providers/impls/meta_reference/inference/inference.py
View file

@ -21,7 +21,9 @@ from llama_stack.apis.inference import (
ToolCallDelta,
ToolCallParseStatus,
)
from llama_stack.providers.utils.inference.prepare_messages import prepare_messages
from llama_stack.providers.utils.inference.augment_messages import (
augment_messages_for_tools,
)
from .config import MetaReferenceImplConfig
from .model_parallel import LlamaModelParallelGenerator
@ -57,7 +59,7 @@ class MetaReferenceInferenceImpl(Inference):
model: str,
messages: List[Message],
sampling_params: Optional[SamplingParams] = SamplingParams(),
tools: Optional[List[ToolDefinition]] = [],
tools: Optional[List[ToolDefinition]] = None,
tool_choice: Optional[ToolChoice] = ToolChoice.auto,
tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
stream: Optional[bool] = False,
@ -70,14 +72,14 @@ class MetaReferenceInferenceImpl(Inference):
model=model,
messages=messages,
sampling_params=sampling_params,
tools=tools,
tools=tools or [],
tool_choice=tool_choice,
tool_prompt_format=tool_prompt_format,
stream=stream,
logprobs=logprobs,
)
messages = prepare_messages(request)
messages = augment_messages_for_tools(request)
model = resolve_model(request.model)
if model is None:
raise RuntimeError(

4
llama_stack/providers/impls/meta_reference/safety/__init__.py
View file

@ -7,11 +7,11 @@
from .config import SafetyConfig
async def get_provider_impl(config: SafetyConfig, _deps):
async def get_provider_impl(config: SafetyConfig, deps):
from .safety import MetaReferenceSafetyImpl
assert isinstance(config, SafetyConfig), f"Unexpected config type: {type(config)}"
impl = MetaReferenceSafetyImpl(config)
impl = MetaReferenceSafetyImpl(config, deps)
await impl.initialize()
return impl

5
llama_stack/providers/impls/meta_reference/safety/config.py
View file

@ -31,7 +31,10 @@ class LlamaGuardShieldConfig(BaseModel):
permitted_models = [
m.descriptor()
for m in safety_models()
if m.core_model_id == CoreModelId.llama_guard_3_8b
if (
m.core_model_id
in {CoreModelId.llama_guard_3_8b, CoreModelId.llama_guard_3_11b_vision}
)
]
if model not in permitted_models:
raise ValueError(

28
llama_stack/providers/impls/meta_reference/safety/safety.py
View file

@ -7,8 +7,10 @@
from llama_models.sku_list import resolve_model
from llama_stack.distribution.utils.model_utils import model_local_dir
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.apis.safety import * # noqa: F403
from llama_models.llama3.api.datatypes import * # noqa: F403
from llama_stack.distribution.datatypes import Api
from llama_stack.providers.impls.meta_reference.safety.shields.base import (
OnViolationAction,
@ -34,20 +36,11 @@ def resolve_and_get_path(model_name: str) -> str:
class MetaReferenceSafetyImpl(Safety):
def __init__(self, config: SafetyConfig) -> None:
def __init__(self, config: SafetyConfig, deps) -> None:
self.config = config
self.inference_api = deps[Api.inference]
async def initialize(self) -> None:
shield_cfg = self.config.llama_guard_shield
if shield_cfg is not None:
model_dir = resolve_and_get_path(shield_cfg.model)
_ = LlamaGuardShield.instance(
model_dir=model_dir,
excluded_categories=shield_cfg.excluded_categories,
disable_input_check=shield_cfg.disable_input_check,
disable_output_check=shield_cfg.disable_output_check,
)
shield_cfg = self.config.prompt_guard_shield
if shield_cfg is not None:
model_dir = resolve_and_get_path(shield_cfg.model)
@ -91,11 +84,18 @@ class MetaReferenceSafetyImpl(Safety):
def get_shield_impl(self, typ: MetaReferenceShieldType) -> ShieldBase:
cfg = self.config
if typ == MetaReferenceShieldType.llama_guard:
cfg = cfg.llama_guard_shield
assert (
cfg.llama_guard_shield is not None
cfg is not None
), "Cannot use LlamaGuardShield since not present in config"
model_dir = resolve_and_get_path(cfg.llama_guard_shield.model)
return LlamaGuardShield.instance(model_dir=model_dir)
return LlamaGuardShield(
model=cfg.model,
inference_api=self.inference_api,
excluded_categories=cfg.excluded_categories,
disable_input_check=cfg.disable_input_check,
disable_output_check=cfg.disable_output_check,
)
elif typ == MetaReferenceShieldType.jailbreak_shield:
assert (
cfg.prompt_guard_shield is not None

78
llama_stack/providers/impls/meta_reference/safety/shields/llama_guard.py
View file

@ -9,9 +9,8 @@ import re
from string import Template
from typing import List, Optional
import torch
from llama_models.llama3.api.datatypes import Message, Role
from transformers import AutoModelForCausalLM, AutoTokenizer
from llama_stack.apis.inference import * # noqa: F403
from .base import CANNED_RESPONSE_TEXT, OnViolationAction, ShieldBase, ShieldResponse
@ -100,39 +99,17 @@ PROMPT_TEMPLATE = Template(
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,
model: str,
inference_api: Inference,
excluded_categories: List[str] = None,
disable_input_check: bool = False,
disable_output_check: bool = False,
on_violation_action: OnViolationAction = OnViolationAction.RAISE,
):
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 = []
@ -140,18 +117,12 @@ class LlamaGuardShield(ShieldBase):
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.model = model
self.inference_api = inference_api
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 check_unsafe_response(self, response: str) -> Optional[str]:
match = re.match(r"^unsafe\n(.*)$", response)
if match:
@ -212,26 +183,21 @@ class LlamaGuardShield(ShieldBase):
)
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)
# TODO: llama-stack inference protocol has issues with non-streaming inference code
content = ""
async for chunk in self.inference_api.chat_completion(
model=self.model,
messages=[
UserMessage(content=prompt),
],
stream=True,
):
event = chunk.event
if event.event_type == ChatCompletionResponseEventType.progress:
assert isinstance(event.delta, str)
content += event.delta
content = content.strip()
shield_response = self.get_shield_response(content)
return shield_response

12
llama_stack/providers/registry/inference.py
View file

@ -20,6 +20,7 @@ def available_providers() -> List[ProviderSpec]:
"fairscale",
"fbgemm-gpu==0.8.0",
"torch",
"torchvision",
"transformers",
"zmq",
],
@ -75,15 +76,4 @@ def available_providers() -> List[ProviderSpec]:
header_extractor_class="llama_stack.providers.adapters.inference.together.TogetherHeaderExtractor",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_id="bedrock",
pip_packages=[
"boto3",
],
module="llama_stack.providers.adapters.inference.bedrock",
config_class="llama_stack.providers.adapters.inference.bedrock.BedrockConfig",
),
),
]

6
llama_stack/providers/registry/safety.py
View file

@ -21,13 +21,15 @@ def available_providers() -> List[ProviderSpec]:
api=Api.safety,
provider_id="meta-reference",
pip_packages=[
"accelerate",
"codeshield",
"torch",
"transformers",
"torch --index-url https://download.pytorch.org/whl/cpu",
],
module="llama_stack.providers.impls.meta_reference.safety",
config_class="llama_stack.providers.impls.meta_reference.safety.SafetyConfig",
api_dependencies=[
Api.inference,
],
),
remote_provider_spec(
api=Api.safety,

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llama_stack/providers/utils/inference/augment_messages.py Normal file
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@ -0,0 +1,170 @@
# 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_models.llama3.api.datatypes import * # noqa: F403
from llama_stack.apis.inference import * # noqa: F403
from llama_models.datatypes import ModelFamily
from llama_models.llama3.prompt_templates import (
BuiltinToolGenerator,
FunctionTagCustomToolGenerator,
JsonCustomToolGenerator,
PythonListCustomToolGenerator,
SystemDefaultGenerator,
)
from llama_models.sku_list import resolve_model
def augment_messages_for_tools(request: ChatCompletionRequest) -> List[Message]:
"""Reads chat completion request and augments the messages to handle tools.
For eg. for llama_3_1, add system message with the appropriate tools or
add user messsage for custom tools, etc.
"""
model = resolve_model(request.model)
if model is None:
cprint(f"Could not resolve model {request.model}", color="red")
return request.messages
if model.model_family not in [ModelFamily.llama3_1, ModelFamily.llama3_2]:
cprint(f"Model family {model.model_family} not llama 3_1 or 3_2", color="red")
return request.messages
if model.model_family == ModelFamily.llama3_1 or (
model.model_family == ModelFamily.llama3_2 and is_multimodal(model)
):
# llama3.1 and llama3.2 multimodal models follow the same tool prompt format
return augment_messages_for_tools_llama_3_1(request)
elif model.model_family == ModelFamily.llama3_2:
return augment_messages_for_tools_llama_3_2(request)
else:
return request.messages
def augment_messages_for_tools_llama_3_1(
request: ChatCompletionRequest,
) -> List[Message]:
assert request.tool_choice == ToolChoice.auto, "Only `ToolChoice.auto` supported"
existing_messages = request.messages
existing_system_message = None
if existing_messages[0].role == Role.system.value:
existing_system_message = existing_messages.pop(0)
assert (
existing_messages[0].role != Role.system.value
), "Should only have 1 system message"
messages = []
default_gen = SystemDefaultGenerator()
default_template = default_gen.gen()
sys_content = ""
tool_template = None
if request.tools:
tool_gen = BuiltinToolGenerator()
tool_template = tool_gen.gen(request.tools)
sys_content += tool_template.render()
sys_content += "\n"
sys_content += default_template.render()
if existing_system_message:
# TODO: this fn is needed in many places
def _process(c):
if isinstance(c, str):
return c
else:
return "<media>"
sys_content += "\n"
if isinstance(existing_system_message.content, str):
sys_content += _process(existing_system_message.content)
elif isinstance(existing_system_message.content, list):
sys_content += "\n".join(
[_process(c) for c in existing_system_message.content]
)
messages.append(SystemMessage(content=sys_content))
has_custom_tools = any(isinstance(dfn.tool_name, str) for dfn in request.tools)
if has_custom_tools:
if request.tool_prompt_format == ToolPromptFormat.json:
tool_gen = JsonCustomToolGenerator()
elif request.tool_prompt_format == ToolPromptFormat.function_tag:
tool_gen = FunctionTagCustomToolGenerator()
else:
raise ValueError(
f"Non supported ToolPromptFormat {request.tool_prompt_format}"
)
custom_tools = [t for t in request.tools if isinstance(t.tool_name, str)]
custom_template = tool_gen.gen(custom_tools)
messages.append(UserMessage(content=custom_template.render()))
# Add back existing messages from the request
messages += existing_messages
return messages
def augment_messages_for_tools_llama_3_2(
request: ChatCompletionRequest,
) -> List[Message]:
assert request.tool_choice == ToolChoice.auto, "Only `ToolChoice.auto` supported"
existing_messages = request.messages
existing_system_message = None
if existing_messages[0].role == Role.system.value:
existing_system_message = existing_messages.pop(0)
assert (
existing_messages[0].role != Role.system.value
), "Should only have 1 system message"
messages = []
sys_content = ""
custom_tools, builtin_tools = [], []
for t in request.tools:
if isinstance(t.tool_name, str):
custom_tools.append(t)
else:
builtin_tools.append(t)
tool_template = None
if builtin_tools:
tool_gen = BuiltinToolGenerator()
tool_template = tool_gen.gen(builtin_tools)
sys_content += tool_template.render()
sys_content += "\n"
custom_tools = [dfn for dfn in request.tools if isinstance(dfn.tool_name, str)]
if custom_tools:
if request.tool_prompt_format != ToolPromptFormat.python_list:
raise ValueError(
f"Non supported ToolPromptFormat {request.tool_prompt_format}"
)
tool_gen = PythonListCustomToolGenerator()
tool_template = tool_gen.gen(custom_tools)
sys_content += tool_template.render()
sys_content += "\n"
if existing_system_message:
sys_content += interleaved_text_media_as_str(
existing_system_message.content, sep="\n"
)
messages.append(SystemMessage(content=sys_content))
# Add back existing messages from the request
messages += existing_messages
return messages

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@ -1,84 +0,0 @@
# 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.api.datatypes import * # noqa: F403
from llama_stack.apis.inference import * # noqa: F403
from llama_models.llama3.prompt_templates import (
BuiltinToolGenerator,
FunctionTagCustomToolGenerator,
JsonCustomToolGenerator,
SystemDefaultGenerator,
)
def prepare_messages(request: ChatCompletionRequest) -> List[Message]:
assert request.tool_choice == ToolChoice.auto, "Only `ToolChoice.auto` supported"
existing_messages = request.messages
existing_system_message = None
if existing_messages[0].role == Role.system.value:
existing_system_message = existing_messages.pop(0)
assert (
existing_messages[0].role != Role.system.value
), "Should only have 1 system message"
messages = []
default_gen = SystemDefaultGenerator()
default_template = default_gen.gen()
sys_content = ""
tool_template = None
if request.tools:
tool_gen = BuiltinToolGenerator()
tool_template = tool_gen.gen(request.tools)
sys_content += tool_template.render()
sys_content += "\n"
sys_content += default_template.render()
if existing_system_message:
# TODO: this fn is needed in many places
def _process(c):
if isinstance(c, str):
return c
else:
return "<media>"
sys_content += "\n"
if isinstance(existing_system_message.content, str):
sys_content += _process(existing_system_message.content)
elif isinstance(existing_system_message.content, list):
sys_content += "\n".join(
[_process(c) for c in existing_system_message.content]
)
messages.append(SystemMessage(content=sys_content))
has_custom_tools = any(isinstance(dfn.tool_name, str) for dfn in request.tools)
if has_custom_tools:
if request.tool_prompt_format == ToolPromptFormat.json:
tool_gen = JsonCustomToolGenerator()
elif request.tool_prompt_format == ToolPromptFormat.function_tag:
tool_gen = FunctionTagCustomToolGenerator()
else:
raise ValueError(
f"Non supported ToolPromptFormat {request.tool_prompt_format}"
)
custom_tools = [t for t in request.tools if isinstance(t.tool_name, str)]
custom_template = tool_gen.gen(custom_tools)
messages.append(UserMessage(content=custom_template.render()))
# Add back existing messages from the request
messages += existing_messages
return messages