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Zain Hasan 2024-09-29 11:56:29 -07:00 • committed by GitHub
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17
llama_stack/providers/adapters/inference/bedrock/__init__.py Normal file
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@ -0,0 +1,17 @@
# 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 .bedrock import BedrockInferenceAdapter
from .config import BedrockConfig
async def get_adapter_impl(config: BedrockConfig, _deps):
assert isinstance(config, BedrockConfig), f"Unexpected config type: {type(config)}"
impl = BedrockInferenceAdapter(config)
await impl.initialize()
return impl

457
llama_stack/providers/adapters/inference/bedrock/bedrock.py Normal file
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@ -0,0 +1,457 @@
# 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 * # noqa: F403
import boto3
from botocore.client import BaseClient
from botocore.config import Config
from llama_models.llama3.api.chat_format import ChatFormat
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.adapters.inference.bedrock.config import BedrockConfig
# mapping of Model SKUs to ollama models
BEDROCK_SUPPORTED_MODELS = {
"Meta-Llama3.1-8B-Instruct": "meta.llama3-1-8b-instruct-v1:0",
"Meta-Llama3.1-70B-Instruct": "meta.llama3-1-70b-instruct-v1:0",
"Meta-Llama3.1-405B-Instruct": "meta.llama3-1-405b-instruct-v1:0",
}
class BedrockInferenceAdapter(Inference):
@staticmethod
def _create_bedrock_client(config: BedrockConfig) -> BaseClient:
retries_config = {
k: v
for k, v in dict(
total_max_attempts=config.total_max_attempts,
mode=config.retry_mode,
).items()
if v is not None
}
config_args = {
k: v
for k, v in dict(
region_name=config.region_name,
retries=retries_config if retries_config else None,
connect_timeout=config.connect_timeout,
read_timeout=config.read_timeout,
).items()
if v is not None
}
boto3_config = Config(**config_args)
session_args = {
k: v
for k, v in dict(
aws_access_key_id=config.aws_access_key_id,
aws_secret_access_key=config.aws_secret_access_key,
aws_session_token=config.aws_session_token,
region_name=config.region_name,
profile_name=config.profile_name,
).items()
if v is not None
}
boto3_session = boto3.session.Session(**session_args)
return boto3_session.client("bedrock-runtime", config=boto3_config)
def __init__(self, config: BedrockConfig) -> None:
self._config = config
self._client = BedrockInferenceAdapter._create_bedrock_client(config)
tokenizer = Tokenizer.get_instance()
self.formatter = ChatFormat(tokenizer)
@property
def client(self) -> BaseClient:
return self._client
async def initialize(self) -> None:
pass
async def shutdown(self) -> None:
self.client.close()
async def completion(
self,
model: str,
content: InterleavedTextMedia,
sampling_params: Optional[SamplingParams] = SamplingParams(),
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
) -> Union[CompletionResponse, CompletionResponseStreamChunk]:
raise NotImplementedError()
@staticmethod
def resolve_bedrock_model(model_name: str) -> str:
model = resolve_model(model_name)
assert (
model is not None
and model.descriptor(shorten_default_variant=True)
in BEDROCK_SUPPORTED_MODELS
), (
f"Unsupported model: {model_name}, use one of the supported models: "
f"{','.join(BEDROCK_SUPPORTED_MODELS.keys())}"
)
return BEDROCK_SUPPORTED_MODELS.get(
model.descriptor(shorten_default_variant=True)
)
@staticmethod
def _bedrock_stop_reason_to_stop_reason(bedrock_stop_reason: str) -> StopReason:
if bedrock_stop_reason == "max_tokens":
return StopReason.out_of_tokens
return StopReason.end_of_turn
@staticmethod
def _builtin_tool_name_to_enum(tool_name_str: str) -> Union[BuiltinTool, str]:
for builtin_tool in BuiltinTool:
if builtin_tool.value == tool_name_str:
return builtin_tool
else:
return tool_name_str
@staticmethod
def _bedrock_message_to_message(converse_api_res: Dict) -> Message:
stop_reason = BedrockInferenceAdapter._bedrock_stop_reason_to_stop_reason(
converse_api_res["stopReason"]
)
bedrock_message = converse_api_res["output"]["message"]
role = bedrock_message["role"]
contents = bedrock_message["content"]
tool_calls = []
text_content = []
for content in contents:
if "toolUse" in content:
tool_use = content["toolUse"]
tool_calls.append(
ToolCall(
tool_name=BedrockInferenceAdapter._builtin_tool_name_to_enum(
tool_use["name"]
),
arguments=tool_use["input"] if "input" in tool_use else None,
call_id=tool_use["toolUseId"],
)
)
elif "text" in content:
text_content.append(content["text"])
return CompletionMessage(
role=role,
content=text_content,
stop_reason=stop_reason,
tool_calls=tool_calls,
)
@staticmethod
def _messages_to_bedrock_messages(
messages: List[Message],
) -> Tuple[List[Dict], Optional[List[Dict]]]:
bedrock_messages = []
system_bedrock_messages = []
user_contents = []
assistant_contents = None
for message in messages:
role = message.role
content_list = (
message.content
if isinstance(message.content, list)
else [message.content]
)
if role == "ipython" or role == "user":
if not user_contents:
user_contents = []
if role == "ipython":
user_contents.extend(
[
{
"toolResult": {
"toolUseId": message.call_id,
"content": [
{"text": content} for content in content_list
],
}
}
]
)
else:
user_contents.extend(
[{"text": content} for content in content_list]
)
if assistant_contents:
bedrock_messages.append(
{"role": "assistant", "content": assistant_contents}
)
assistant_contents = None
elif role == "system":
system_bedrock_messages.extend(
[{"text": content} for content in content_list]
)
elif role == "assistant":
if not assistant_contents:
assistant_contents = []
assistant_contents.extend(
[
{
"text": content,
}
for content in content_list
]
+ [
{
"toolUse": {
"input": tool_call.arguments,
"name": (
tool_call.tool_name
if isinstance(tool_call.tool_name, str)
else tool_call.tool_name.value
),
"toolUseId": tool_call.call_id,
}
}
for tool_call in message.tool_calls
]
)
if user_contents:
bedrock_messages.append({"role": "user", "content": user_contents})
user_contents = None
else:
# Unknown role
pass
if user_contents:
bedrock_messages.append({"role": "user", "content": user_contents})
if assistant_contents:
bedrock_messages.append(
{"role": "assistant", "content": assistant_contents}
)
if system_bedrock_messages:
return bedrock_messages, system_bedrock_messages
return bedrock_messages, None
@staticmethod
def get_bedrock_inference_config(sampling_params: Optional[SamplingParams]) -> Dict:
inference_config = {}
if sampling_params:
param_mapping = {
"max_tokens": "maxTokens",
"temperature": "temperature",
"top_p": "topP",
}
for k, v in param_mapping.items():
if getattr(sampling_params, k):
inference_config[v] = getattr(sampling_params, k)
return inference_config
@staticmethod
def _tool_parameters_to_input_schema(
tool_parameters: Optional[Dict[str, ToolParamDefinition]]
) -> Dict:
input_schema = {"type": "object"}
if not tool_parameters:
return input_schema
json_properties = {}
required = []
for name, param in tool_parameters.items():
json_property = {
"type": param.param_type,
}
if param.description:
json_property["description"] = param.description
if param.required:
required.append(name)
json_properties[name] = json_property
input_schema["properties"] = json_properties
if required:
input_schema["required"] = required
return input_schema
@staticmethod
def _tools_to_tool_config(
tools: Optional[List[ToolDefinition]], tool_choice: Optional[ToolChoice]
) -> Optional[Dict]:
if not tools:
return None
bedrock_tools = []
for tool in tools:
tool_name = (
tool.tool_name
if isinstance(tool.tool_name, str)
else tool.tool_name.value
)
tool_spec = {
"toolSpec": {
"name": tool_name,
"inputSchema": {
"json": BedrockInferenceAdapter._tool_parameters_to_input_schema(
tool.parameters
),
},
}
}
if tool.description:
tool_spec["toolSpec"]["description"] = tool.description
bedrock_tools.append(tool_spec)
tool_config = {
"tools": bedrock_tools,
}
if tool_choice:
tool_config["toolChoice"] = (
{"any": {}}
if tool_choice.value == ToolChoice.required
else {"auto": {}}
)
return tool_config
async def chat_completion(
self,
model: str,
messages: List[Message],
sampling_params: Optional[SamplingParams] = SamplingParams(),
# zero-shot tool definitions as input to the model
tools: Optional[List[ToolDefinition]] = None,
tool_choice: Optional[ToolChoice] = ToolChoice.auto,
tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
) -> (
AsyncGenerator
): # Union[ChatCompletionResponse, ChatCompletionResponseStreamChunk]:
bedrock_model = BedrockInferenceAdapter.resolve_bedrock_model(model)
inference_config = BedrockInferenceAdapter.get_bedrock_inference_config(
sampling_params
)
tool_config = BedrockInferenceAdapter._tools_to_tool_config(tools, tool_choice)
bedrock_messages, system_bedrock_messages = (
BedrockInferenceAdapter._messages_to_bedrock_messages(messages)
)
converse_api_params = {
"modelId": bedrock_model,
"messages": bedrock_messages,
}
if inference_config:
converse_api_params["inferenceConfig"] = inference_config
# Tool use is not supported in streaming mode
if tool_config and not stream:
converse_api_params["toolConfig"] = tool_config
if system_bedrock_messages:
converse_api_params["system"] = system_bedrock_messages
if not stream:
converse_api_res = self.client.converse(**converse_api_params)
output_message = BedrockInferenceAdapter._bedrock_message_to_message(
converse_api_res
)
yield ChatCompletionResponse(
completion_message=output_message,
logprobs=None,
)
else:
converse_stream_api_res = self.client.converse_stream(**converse_api_params)
event_stream = converse_stream_api_res["stream"]
for chunk in event_stream:
if "messageStart" in chunk:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.start,
delta="",
)
)
elif "contentBlockStart" in chunk:
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=ToolCallDelta(
content=ToolCall(
tool_name=chunk["contentBlockStart"]["toolUse"][
"name"
],
call_id=chunk["contentBlockStart"]["toolUse"][
"toolUseId"
],
),
parse_status=ToolCallParseStatus.started,
),
)
)
elif "contentBlockDelta" in chunk:
if "text" in chunk["contentBlockDelta"]["delta"]:
delta = chunk["contentBlockDelta"]["delta"]["text"]
else:
delta = ToolCallDelta(
content=ToolCall(
arguments=chunk["contentBlockDelta"]["delta"][
"toolUse"
]["input"]
),
parse_status=ToolCallParseStatus.success,
)
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.progress,
delta=delta,
)
)
elif "contentBlockStop" in chunk:
# Ignored
pass
elif "messageStop" in chunk:
stop_reason = (
BedrockInferenceAdapter._bedrock_stop_reason_to_stop_reason(
chunk["messageStop"]["stopReason"]
)
)
yield ChatCompletionResponseStreamChunk(
event=ChatCompletionResponseEvent(
event_type=ChatCompletionResponseEventType.complete,
delta="",
stop_reason=stop_reason,
)
)
elif "metadata" in chunk:
# Ignored
pass
else:
# Ignored
pass

55
llama_stack/providers/adapters/inference/bedrock/config.py Normal file
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@ -0,0 +1,55 @@
# 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 * # noqa: F403
from llama_models.schema_utils import json_schema_type
from pydantic import BaseModel, Field
@json_schema_type
class BedrockConfig(BaseModel):
aws_access_key_id: Optional[str] = Field(
default=None,
description="The AWS access key to use. Default use environment variable: AWS_ACCESS_KEY_ID",
)
aws_secret_access_key: Optional[str] = Field(
default=None,
description="The AWS secret access key to use. Default use environment variable: AWS_SECRET_ACCESS_KEY",
)
aws_session_token: Optional[str] = Field(
default=None,
description="The AWS session token to use. Default use environment variable: AWS_SESSION_TOKEN",
)
region_name: Optional[str] = Field(
default=None,
description="The default AWS Region to use, for example, us-west-1 or us-west-2."
"Default use environment variable: AWS_DEFAULT_REGION",
)
profile_name: Optional[str] = Field(
default=None,
description="The profile name that contains credentials to use."
"Default use environment variable: AWS_PROFILE",
)
total_max_attempts: Optional[int] = Field(
default=None,
description="An integer representing the maximum number of attempts that will be made for a single request, "
"including the initial attempt. Default use environment variable: AWS_MAX_ATTEMPTS",
)
retry_mode: Optional[str] = Field(
default=None,
description="A string representing the type of retries Boto3 will perform."
"Default use environment variable: AWS_RETRY_MODE",
)
connect_timeout: Optional[float] = Field(
default=60,
description="The time in seconds till a timeout exception is thrown when attempting to make a connection. "
"The default is 60 seconds.",
)
read_timeout: Optional[float] = Field(
default=60,
description="The time in seconds till a timeout exception is thrown when attempting to read from a connection."
"The default is 60 seconds.",
)

12
llama_stack/providers/adapters/inference/fireworks/fireworks.py
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@ -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
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@ -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)

27
llama_stack/providers/adapters/inference/tgi/__init__.py
View file

@ -4,21 +4,26 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from .config import TGIImplConfig
from .tgi import InferenceEndpointAdapter, TGIAdapter
from typing import Union
from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig
from .tgi import InferenceAPIAdapter, InferenceEndpointAdapter, TGIAdapter
async def get_adapter_impl(config: TGIImplConfig, _deps):
assert isinstance(config, TGIImplConfig), f"Unexpected config type: {type(config)}"
if config.url is not None:
impl = TGIAdapter(config)
elif config.is_inference_endpoint():
impl = InferenceEndpointAdapter(config)
async def get_adapter_impl(
config: Union[InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig],
_deps,
):
if isinstance(config, TGIImplConfig):
impl = TGIAdapter()
elif isinstance(config, InferenceAPIImplConfig):
impl = InferenceAPIAdapter()
elif isinstance(config, InferenceEndpointImplConfig):
impl = InferenceEndpointAdapter()
else:
raise ValueError(
"Invalid configuration. Specify either an URL or HF Inference Endpoint details (namespace and endpoint name)."
f"Invalid configuration. Expected 'TGIAdapter', 'InferenceAPIImplConfig' or 'InferenceEndpointImplConfig'. Got {type(config)}."
)
await impl.initialize()
await impl.initialize(config)
return impl

34
llama_stack/providers/adapters/inference/tgi/config.py
View file

@ -12,18 +12,32 @@ from pydantic import BaseModel, Field
@json_schema_type
class TGIImplConfig(BaseModel):
url: Optional[str] = Field(
default=None,
description="The URL for the local TGI endpoint (e.g., http://localhost:8080)",
url: str = Field(
description="The URL for the TGI endpoint (e.g. 'http://localhost:8080')",
)
api_token: Optional[str] = Field(
default=None,
description="The HF token for Hugging Face Inference Endpoints (will default to locally saved token if not provided)",
)
hf_endpoint_name: Optional[str] = Field(
default=None,
description="The name of the Hugging Face Inference Endpoint : can be either in the format of '{namespace}/{endpoint_name}' (namespace can be the username or organization name) or just '{endpoint_name}' if logged into the same account as the namespace",
description="A bearer token if your TGI endpoint is protected.",
)
def is_inference_endpoint(self) -> bool:
return self.hf_endpoint_name is not None
@json_schema_type
class InferenceEndpointImplConfig(BaseModel):
endpoint_name: str = Field(
description="The name of the Hugging Face Inference Endpoint in the format of '{namespace}/{endpoint_name}' (e.g. 'my-cool-org/meta-llama-3-1-8b-instruct-rce'). Namespace is optional and will default to the user account if not provided.",
)
api_token: Optional[str] = Field(
default=None,
description="Your Hugging Face user access token (will default to locally saved token if not provided)",
)
@json_schema_type
class InferenceAPIImplConfig(BaseModel):
model_id: str = Field(
description="The model ID of the model on the Hugging Face Hub (e.g. 'meta-llama/Meta-Llama-3.1-70B-Instruct')",
)
api_token: Optional[str] = Field(
default=None,
description="Your Hugging Face user access token (will default to locally saved token if not provided)",
)

121
llama_stack/providers/adapters/inference/tgi/tgi.py
View file

@ -5,52 +5,33 @@
# the root directory of this source tree.
from typing import Any, AsyncGenerator, Dict
import logging
from typing import AsyncGenerator
import requests
from huggingface_hub import HfApi, InferenceClient
from huggingface_hub import AsyncInferenceClient, HfApi
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
from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig
logger = logging.getLogger(__name__)
class TGIAdapter(Inference):
def __init__(self, config: TGIImplConfig) -> None:
self.config = config
class _HfAdapter(Inference):
client: AsyncInferenceClient
max_tokens: int
model_id: str
def __init__(self) -> None:
self.tokenizer = Tokenizer.get_instance()
self.formatter = ChatFormat(self.tokenizer)
@property
def client(self) -> InferenceClient:
return InferenceClient(model=self.config.url, token=self.config.api_token)
def _get_endpoint_info(self) -> Dict[str, Any]:
return {
**self.client.get_endpoint_info(),
"inference_url": self.config.url,
}
async def initialize(self) -> None:
try:
info = self._get_endpoint_info()
if "model_id" not in info:
raise RuntimeError("Missing model_id in model info")
if "max_total_tokens" not in info:
raise RuntimeError("Missing max_total_tokens in model info")
self.max_tokens = info["max_total_tokens"]
self.inference_url = info["inference_url"]
except Exception as e:
import traceback
traceback.print_exc()
raise RuntimeError(f"Error initializing TGIAdapter: {e}") from e
async def shutdown(self) -> None:
pass
@ -95,7 +76,7 @@ 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)
@ -109,7 +90,7 @@ class TGIAdapter(Inference):
options = self.get_chat_options(request)
if not request.stream:
response = self.client.text_generation(
response = await self.client.text_generation(
prompt=prompt,
stream=False,
details=True,
@ -145,7 +126,7 @@ class TGIAdapter(Inference):
stop_reason = None
tokens = []
for response in self.client.text_generation(
async for response in await self.client.text_generation(
prompt=prompt,
stream=True,
details=True,
@ -237,46 +218,36 @@ class TGIAdapter(Inference):
)
class InferenceEndpointAdapter(TGIAdapter):
def __init__(self, config: TGIImplConfig) -> None:
super().__init__(config)
self.config.url = self._construct_endpoint_url()
class TGIAdapter(_HfAdapter):
async def initialize(self, config: TGIImplConfig) -> None:
self.client = AsyncInferenceClient(model=config.url, token=config.api_token)
endpoint_info = await self.client.get_endpoint_info()
self.max_tokens = endpoint_info["max_total_tokens"]
self.model_id = endpoint_info["model_id"]
def _construct_endpoint_url(self) -> str:
hf_endpoint_name = self.config.hf_endpoint_name
assert hf_endpoint_name.count("/") <= 1, (
"Endpoint name must be in the format of 'namespace/endpoint_name' "
"or 'endpoint_name'"
class InferenceAPIAdapter(_HfAdapter):
async def initialize(self, config: InferenceAPIImplConfig) -> None:
self.client = AsyncInferenceClient(
model=config.model_id, token=config.api_token
)
if "/" not in hf_endpoint_name:
hf_namespace: str = self.get_namespace()
endpoint_path = f"{hf_namespace}/{hf_endpoint_name}"
else:
endpoint_path = hf_endpoint_name
return f"https://api.endpoints.huggingface.cloud/v2/endpoint/{endpoint_path}"
endpoint_info = await self.client.get_endpoint_info()
self.max_tokens = endpoint_info["max_total_tokens"]
self.model_id = endpoint_info["model_id"]
def get_namespace(self) -> str:
return HfApi().whoami()["name"]
@property
def client(self) -> InferenceClient:
return InferenceClient(model=self.inference_url, token=self.config.api_token)
class InferenceEndpointAdapter(_HfAdapter):
async def initialize(self, config: InferenceEndpointImplConfig) -> None:
# Get the inference endpoint details
api = HfApi(token=config.api_token)
endpoint = api.get_inference_endpoint(config.endpoint_name)
def _get_endpoint_info(self) -> Dict[str, Any]:
headers = {
"accept": "application/json",
"authorization": f"Bearer {self.config.api_token}",
}
response = requests.get(self.config.url, headers=headers)
response.raise_for_status()
endpoint_info = response.json()
return {
"inference_url": endpoint_info["status"]["url"],
"model_id": endpoint_info["model"]["repository"],
"max_total_tokens": int(
endpoint_info["model"]["image"]["custom"]["env"]["MAX_TOTAL_TOKENS"]
),
}
# Wait for the endpoint to be ready (if not already)
endpoint.wait(timeout=60)
async def initialize(self) -> None:
await super().initialize()
# Initialize the adapter
self.client = endpoint.async_client
self.model_id = endpoint.repository
self.max_tokens = int(
endpoint.raw["model"]["image"]["custom"]["env"]["MAX_TOTAL_TOKENS"]
)

2
llama_stack/providers/adapters/inference/together/__init__.py
View file

@ -4,7 +4,7 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from .config import TogetherImplConfig, TogetherHeaderExtractor
from .config import TogetherImplConfig
async def get_adapter_impl(config: TogetherImplConfig, _deps):

11
llama_stack/providers/adapters/inference/together/config.py
View file

@ -4,17 +4,8 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from pydantic import BaseModel, Field
from llama_models.schema_utils import json_schema_type
from llama_stack.distribution.request_headers import annotate_header
class TogetherHeaderExtractor(BaseModel):
api_key: annotate_header(
"X-LlamaStack-Together-ApiKey", str, "The API Key for the request"
)
from pydantic import BaseModel, Field
@json_schema_type

30
llama_stack/providers/adapters/inference/together/together.py
View file

@ -15,14 +15,20 @@ 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.distribution.request_headers import get_request_provider_data
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/Meta-Llama-3.1-8B-Instruct-Turbo",
"Llama3.1-70B-Instruct": "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
"Llama3.1-405B-Instruct": "meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",
"Llama3.2-3B-Instruct": "meta-llama/Llama-3.2-3B-Instruct-Turbo",
"Llama3.2-11B-Vision-Instruct": "meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo",
"Llama3.2-90B-Vision-Instruct": "meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo",
}
@ -95,6 +101,16 @@ class TogetherInferenceAdapter(Inference):
stream: Optional[bool] = False,
logprobs: Optional[LogProbConfig] = None,
) -> AsyncGenerator:
together_api_key = None
provider_data = get_request_provider_data()
if provider_data is None or not provider_data.together_api_key:
raise ValueError(
'Pass Together API Key in the header X-LlamaStack-ProviderData as { "together_api_key": <your api key>}'
)
together_api_key = provider_data.together_api_key
client = Together(api_key=together_api_key)
# wrapper request to make it easier to pass around (internal only, not exposed to API)
request = ChatCompletionRequest(
model=model,
@ -110,11 +126,11 @@ 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
r = self.client.chat.completions.create(
r = client.chat.completions.create(
model=together_model,
messages=self._messages_to_together_messages(messages),
stream=False,
@ -149,7 +165,7 @@ class TogetherInferenceAdapter(Inference):
ipython = False
stop_reason = None
for chunk in self.client.chat.completions.create(
for chunk in client.chat.completions.create(
model=together_model,
messages=self._messages_to_together_messages(messages),
stream=True,

18
llama_stack/providers/adapters/safety/bedrock/__init__.py Normal file
View file

@ -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 typing import Any
from .config import BedrockSafetyConfig
async def get_adapter_impl(config: BedrockSafetyConfig, _deps) -> Any:
from .bedrock import BedrockSafetyAdapter
impl = BedrockSafetyAdapter(config)
await impl.initialize()
return impl

109
llama_stack/providers/adapters/safety/bedrock/bedrock.py Normal file
View file

@ -0,0 +1,109 @@
# 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 traceback
from typing import Any, Dict, List
from .config import BedrockSafetyConfig
from llama_stack.apis.safety import * # noqa
from llama_models.llama3.api.datatypes import * # noqa: F403
import json
import logging
import boto3
logger = logging.getLogger(__name__)
class BedrockSafetyAdapter(Safety):
def __init__(self, config: BedrockSafetyConfig) -> None:
self.config = config
async def initialize(self) -> None:
if not self.config.aws_profile:
raise RuntimeError(
f"Missing boto_client aws_profile in model info::{self.config}"
)
try:
print(f"initializing with profile --- > {self.config}::")
self.boto_client_profile = self.config.aws_profile
self.boto_client = boto3.Session(
profile_name=self.boto_client_profile
).client("bedrock-runtime")
except Exception as e:
raise RuntimeError(f"Error initializing BedrockSafetyAdapter: {e}") from e
async def shutdown(self) -> None:
pass
async def run_shield(
self, shield_type: str, messages: List[Message], params: Dict[str, Any] = None
) -> RunShieldResponse:
"""This is the implementation for the bedrock guardrails. The input to the guardrails is to be of this format
```content = [
{
"text": {
"text": "Is the AB503 Product a better investment than the S&P 500?"
}
}
]```
However the incoming messages are of this type UserMessage(content=....) coming from
https://github.com/meta-llama/llama-models/blob/main/models/llama3/api/datatypes.py
They contain content, role . For now we will extract the content and default the "qualifiers": ["query"]
"""
try:
logger.debug(f"run_shield::{params}::messages={messages}")
if "guardrailIdentifier" not in params:
raise RuntimeError(
"Error running request for BedrockGaurdrails:Missing GuardrailID in request"
)
if "guardrailVersion" not in params:
raise RuntimeError(
"Error running request for BedrockGaurdrails:Missing guardrailVersion in request"
)
# - convert the messages into format Bedrock expects
content_messages = []
for message in messages:
content_messages.append({"text": {"text": message.content}})
logger.debug(
f"run_shield::final:messages::{json.dumps(content_messages, indent=2)}:"
)
response = self.boto_client.apply_guardrail(
guardrailIdentifier=params.get("guardrailIdentifier"),
guardrailVersion=params.get("guardrailVersion"),
source="OUTPUT", # or 'INPUT' depending on your use case
content=content_messages,
)
logger.debug(f"run_shield:: response: {response}::")
if response["action"] == "GUARDRAIL_INTERVENED":
user_message = ""
metadata = {}
for output in response["outputs"]:
# guardrails returns a list - however for this implementation we will leverage the last values
user_message = output["text"]
for assessment in response["assessments"]:
# guardrails returns a list - however for this implementation we will leverage the last values
metadata = dict(assessment)
return SafetyViolation(
user_message=user_message,
violation_level=ViolationLevel.ERROR,
metadata=metadata,
)
except Exception:
error_str = traceback.format_exc()
logger.error(
f"Error in apply_guardrails:{error_str}:: RETURNING None !!!!!"
)
return None

16
llama_stack/providers/adapters/safety/bedrock/config.py Normal file
View file

@ -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 pydantic import BaseModel, Field
class BedrockSafetyConfig(BaseModel):
"""Configuration information for a guardrail that you want to use in the request."""
aws_profile: str = Field(
default="default",
description="The profile on the machine having valid aws credentials. This will ensure separation of creation to invocation",
)

18
llama_stack/providers/adapters/safety/together/__init__.py Normal file
View file

@ -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 .config import TogetherProviderDataValidator, TogetherSafetyConfig # noqa: F401
async def get_adapter_impl(config: TogetherSafetyConfig, _deps):
from .together import TogetherSafetyImpl
assert isinstance(
config, TogetherSafetyConfig
), f"Unexpected config type: {type(config)}"
impl = TogetherSafetyImpl(config)
await impl.initialize()
return impl

26
llama_stack/providers/adapters/safety/together/config.py Normal file
View file

@ -0,0 +1,26 @@
# 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 Optional
from llama_models.schema_utils import json_schema_type
from pydantic import BaseModel, Field
class TogetherProviderDataValidator(BaseModel):
together_api_key: str
@json_schema_type
class TogetherSafetyConfig(BaseModel):
url: str = Field(
default="https://api.together.xyz/v1",
description="The URL for the Together AI server",
)
api_key: Optional[str] = Field(
default=None,
description="The Together AI API Key (default for the distribution, if any)",
)

99
llama_stack/providers/adapters/safety/together/together.py Normal file
View file

@ -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 llama_models.sku_list import resolve_model
from together import Together
from llama_models.llama3.api.datatypes import * # noqa: F403
from llama_stack.apis.safety import (
RunShieldResponse,
Safety,
SafetyViolation,
ViolationLevel,
)
from llama_stack.distribution.request_headers import get_request_provider_data
from .config import TogetherSafetyConfig
SAFETY_SHIELD_TYPES = {
"Llama-Guard-3-8B": "meta-llama/Meta-Llama-Guard-3-8B",
"Llama-Guard-3-11B-Vision": "meta-llama/Llama-Guard-3-11B-Vision-Turbo",
}
def shield_type_to_model_name(shield_type: str) -> str:
if shield_type == "llama_guard":
shield_type = "Llama-Guard-3-8B"
model = resolve_model(shield_type)
if (
model is None
or not model.descriptor(shorten_default_variant=True) in SAFETY_SHIELD_TYPES
or model.model_family is not ModelFamily.safety
):
raise ValueError(
f"{shield_type} is not supported, please use of {','.join(SAFETY_SHIELD_TYPES.keys())}"
)
return SAFETY_SHIELD_TYPES.get(model.descriptor(shorten_default_variant=True))
class TogetherSafetyImpl(Safety):
def __init__(self, config: TogetherSafetyConfig) -> None:
self.config = config
async def initialize(self) -> None:
pass
async def run_shield(
self, shield_type: str, messages: List[Message], params: Dict[str, Any] = None
) -> RunShieldResponse:
together_api_key = None
provider_data = get_request_provider_data()
if provider_data is None or not provider_data.together_api_key:
raise ValueError(
'Pass Together API Key in the header X-LlamaStack-ProviderData as { "together_api_key": <your api key>}'
)
together_api_key = provider_data.together_api_key
model_name = shield_type_to_model_name(shield_type)
# messages can have role assistant or user
api_messages = []
for message in messages:
if message.role in (Role.user.value, Role.assistant.value):
api_messages.append({"role": message.role, "content": message.content})
violation = await get_safety_response(
together_api_key, model_name, api_messages
)
return RunShieldResponse(violation=violation)
async def get_safety_response(
api_key: str, model_name: str, messages: List[Dict[str, str]]
) -> Optional[SafetyViolation]:
client = Together(api_key=api_key)
response = client.chat.completions.create(messages=messages, model=model_name)
if len(response.choices) == 0:
return None
response_text = response.choices[0].message.content
if response_text == "safe":
return None
parts = response_text.split("\n")
if len(parts) != 2:
return None
if parts[0] == "unsafe":
return SafetyViolation(
violation_level=ViolationLevel.ERROR,
user_message="unsafe",
metadata={"violation_type": parts[1]},
)
return None

550
llama_stack/providers/impls/ios/inference/LocalInferenceImpl.xcodeproj/project.pbxproj Normal file
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ENABLE_MODULE_VERIFIER = YES;
GENERATE_INFOPLIST_FILE = YES;
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defaultConfigurationIsVisible = 0;
defaultConfigurationName = Release;
};
/* End XCConfigurationList section */
/* Begin XCRemoteSwiftPackageReference section */
5CAF3DD62CA485740029CD2B /* XCRemoteSwiftPackageReference "llama-stack-client-swift" */ = {
isa = XCRemoteSwiftPackageReference;
repositoryURL = "https://github.com/meta-llama/llama-stack-client-swift";
requirement = {
branch = main;
kind = branch;
};
};
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isa = XCRemoteSwiftPackageReference;
repositoryURL = "https://github.com/pytorch/executorch";
requirement = {
branch = latest;
kind = branch;
};
};
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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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isa = XCSwiftPackageProductDependency;
package = 5CAF3DD62CA485740029CD2B /* XCRemoteSwiftPackageReference "llama-stack-client-swift" */;
productName = LlamaStackClient;
};
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productName = Stencil;
};
/* End XCSwiftPackageProductDependency section */
};
rootObject = 5CCBC5FF2CA1F04A00E958D0 /* Project object */;
}

7
llama_stack/providers/impls/ios/inference/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/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>

9
llama_stack/providers/impls/ios/inference/LocalInferenceImpl/LocalInference.h Normal file
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@ -0,0 +1,9 @@
#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/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/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/LocalInferenceImpl/PromptTemplate.swift Normal file
View file

@ -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/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/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
```

1
llama_stack/providers/impls/ios/inference/executorch Submodule

@ -0,0 +1 @@
Subproject commit 9b6d4b4a7b9b8f811bb6b269b0c2ce254e3a0c1b

15
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,
@ -627,7 +638,7 @@ class ChatAgent(ShieldRunnerMixin):
memory_bank = await self.memory_api.create_memory_bank(
name=f"memory_bank_{session_id}",
config=VectorMemoryBankConfig(
embedding_model="sentence-transformer/all-MiniLM-L6-v2",
embedding_model="all-MiniLM-L6-v2",
chunk_size_in_tokens=512,
),
)

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

23
llama_stack/providers/impls/meta_reference/inference/config.py
View file

@ -7,16 +7,17 @@
from typing import Optional
from llama_models.datatypes import * # noqa: F403
from llama_models.sku_list import all_registered_models, resolve_model
from llama_models.sku_list import resolve_model
from llama_stack.apis.inference import * # noqa: F401, F403
from pydantic import BaseModel, Field, field_validator
from llama_stack.providers.utils.inference import supported_inference_models
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
@ -27,12 +28,7 @@ class MetaReferenceImplConfig(BaseModel):
@field_validator("model")
@classmethod
def validate_model(cls, model: str) -> str:
permitted_models = [
m.descriptor()
for m in all_registered_models()
if m.model_family == ModelFamily.llama3_1
or m.core_model_id == CoreModelId.llama_guard_3_8b
]
permitted_models = supported_inference_models()
if model not in permitted_models:
model_list = "\n\t".join(permitted_models)
raise ValueError(
@ -42,14 +38,9 @@ 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

85
llama_stack/providers/impls/meta_reference/inference/generation.py
View file

@ -24,26 +24,36 @@ 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(), (
f"Could not find checkpoint dir: {checkpoint_dir}."
f"Please download model using `llama download {model.descriptor()}`"
f"Could not find checkpoints in: {model_local_dir(model.descriptor())}. "
f"Please download model using `llama download --model-id {model.descriptor()}`"
)
return str(checkpoint_dir)
@ -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(

7
llama_stack/providers/impls/meta_reference/inference/quantization/loader.py
View file

@ -14,6 +14,10 @@ import torch
from fairscale.nn.model_parallel.mappings import reduce_from_model_parallel_region
from llama_models.llama3.api.model import Transformer, TransformerBlock
from termcolor import cprint
from torch import Tensor
from llama_stack.apis.inference import QuantizationType
from llama_stack.apis.inference.config import (
@ -21,9 +25,6 @@ from llama_stack.apis.inference.config import (
MetaReferenceImplConfig,
)
from termcolor import cprint
from torch import Tensor
def is_fbgemm_available() -> bool:
try:

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(

145
llama_stack/providers/impls/meta_reference/safety/shields/llama_guard.py
View file

@ -9,7 +9,7 @@ import re
from string import Template
from typing import List, Optional
from llama_models.llama3.api.datatypes import Message, Role
from llama_models.llama3.api.datatypes import * # noqa: F403
from llama_stack.apis.inference import * # noqa: F403
from .base import CANNED_RESPONSE_TEXT, OnViolationAction, ShieldBase, ShieldResponse
@ -66,9 +66,18 @@ DEFAULT_LG_V3_SAFETY_CATEGORIES = [
CAT_SELF_HARM,
CAT_SEXUAL_CONTENT,
CAT_ELECTIONS,
CAT_CODE_INTERPRETER_ABUSE,
]
MODEL_TO_SAFETY_CATEGORIES_MAP = {
CoreModelId.llama_guard_3_8b.value: (
DEFAULT_LG_V3_SAFETY_CATEGORIES + [CAT_CODE_INTERPRETER_ABUSE]
),
CoreModelId.llama_guard_3_1b.value: DEFAULT_LG_V3_SAFETY_CATEGORIES,
CoreModelId.llama_guard_3_11b_vision.value: DEFAULT_LG_V3_SAFETY_CATEGORIES,
}
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 = """
@ -117,6 +126,9 @@ 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', ..]"
if model not in MODEL_TO_SAFETY_CATEGORIES_MAP:
raise ValueError(f"Unsupported model: {model}")
self.model = model
self.inference_api = inference_api
self.excluded_categories = excluded_categories
@ -137,20 +149,110 @@ class LlamaGuardShield(ShieldBase):
if set(excluded_categories) == set(SAFETY_CATEGORIES_TO_CODE_MAP.values()):
excluded_categories = []
categories = []
for cat in DEFAULT_LG_V3_SAFETY_CATEGORIES:
final_categories = []
all_categories = MODEL_TO_SAFETY_CATEGORIES_MAP[self.model]
for cat in all_categories:
cat_code = SAFETY_CATEGORIES_TO_CODE_MAP[cat]
if cat_code in excluded_categories:
continue
categories.append(f"{cat_code}: {cat}.")
final_categories.append(f"{cat_code}: {cat}.")
return categories
return final_categories
def validate_messages(self, messages: List[Message]) -> None:
if len(messages) == 0:
raise ValueError("Messages must not be empty")
if messages[0].role != Role.user.value:
raise ValueError("Messages must start with user")
if len(messages) >= 2 and (
messages[0].role == Role.user.value and messages[1].role == Role.user.value
):
messages = messages[1:]
for i in range(1, len(messages)):
if messages[i].role == messages[i - 1].role:
raise ValueError(
f"Messages must alternate between user and assistant. Message {i} has the same role as message {i-1}"
)
return messages
async def run(self, messages: List[Message]) -> ShieldResponse:
messages = self.validate_messages(messages)
if self.disable_input_check and messages[-1].role == Role.user.value:
return ShieldResponse(is_violation=False)
elif self.disable_output_check and messages[-1].role == Role.assistant.value:
return ShieldResponse(
is_violation=False,
)
if self.model == CoreModelId.llama_guard_3_11b_vision.value:
shield_input_message = self.build_vision_shield_input(messages)
else:
shield_input_message = self.build_text_shield_input(messages)
# 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=[shield_input_message],
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
def build_text_shield_input(self, messages: List[Message]) -> UserMessage:
return UserMessage(content=self.build_prompt(messages))
def build_vision_shield_input(self, messages: List[Message]) -> UserMessage:
conversation = []
most_recent_img = None
for m in messages[::-1]:
if isinstance(m.content, str):
conversation.append(m)
elif isinstance(m.content, ImageMedia):
if most_recent_img is None and m.role == Role.user.value:
most_recent_img = m.content
conversation.append(m)
elif isinstance(m.content, list):
content = []
for c in m.content:
if isinstance(c, str):
content.append(c)
elif isinstance(c, ImageMedia):
if most_recent_img is None and m.role == Role.user.value:
most_recent_img = c
content.append(c)
else:
raise ValueError(f"Unknown content type: {c}")
conversation.append(UserMessage(content=content))
else:
raise ValueError(f"Unknown content type: {m.content}")
prompt = []
if most_recent_img is not None:
prompt.append(most_recent_img)
prompt.append(self.build_prompt(conversation[::-1]))
return UserMessage(content=prompt)
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]
[
f"{m.role.capitalize()}: {interleaved_text_media_as_str(m.content)}"
for m in messages
]
)
return PROMPT_TEMPLATE.substitute(
agent_type=messages[-1].role.capitalize(),
@ -159,6 +261,7 @@ class LlamaGuardShield(ShieldBase):
)
def get_shield_response(self, response: str) -> ShieldResponse:
response = response.strip()
if response == SAFE_RESPONSE:
return ShieldResponse(is_violation=False)
unsafe_code = self.check_unsafe_response(response)
@ -173,31 +276,3 @@ class LlamaGuardShield(ShieldBase):
)
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(is_violation=False)
elif self.disable_output_check and messages[-1].role == Role.assistant.value:
return ShieldResponse(
is_violation=False,
)
else:
prompt = self.build_prompt(messages)
# 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

23
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",
],
@ -47,11 +48,29 @@ def available_providers() -> List[ProviderSpec]:
api=Api.inference,
adapter=AdapterSpec(
adapter_id="tgi",
pip_packages=["huggingface_hub"],
pip_packages=["huggingface_hub", "aiohttp"],
module="llama_stack.providers.adapters.inference.tgi",
config_class="llama_stack.providers.adapters.inference.tgi.TGIImplConfig",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_id="hf::serverless",
pip_packages=["huggingface_hub", "aiohttp"],
module="llama_stack.providers.adapters.inference.tgi",
config_class="llama_stack.providers.adapters.inference.tgi.InferenceAPIImplConfig",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_id="hf::endpoint",
pip_packages=["huggingface_hub", "aiohttp"],
module="llama_stack.providers.adapters.inference.tgi",
config_class="llama_stack.providers.adapters.inference.tgi.InferenceEndpointImplConfig",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
@ -72,7 +91,7 @@ def available_providers() -> List[ProviderSpec]:
],
module="llama_stack.providers.adapters.inference.together",
config_class="llama_stack.providers.adapters.inference.together.TogetherImplConfig",
header_extractor_class="llama_stack.providers.adapters.inference.together.TogetherHeaderExtractor",
provider_data_validator="llama_stack.providers.adapters.safety.together.TogetherProviderDataValidator",
),
),
]

29
llama_stack/providers/registry/safety.py
View file

@ -6,7 +6,13 @@
from typing import List
from llama_stack.distribution.datatypes import * # noqa: F403
from llama_stack.distribution.datatypes import (
AdapterSpec,
Api,
InlineProviderSpec,
ProviderSpec,
remote_provider_spec,
)
def available_providers() -> List[ProviderSpec]:
@ -34,4 +40,25 @@ def available_providers() -> List[ProviderSpec]:
config_class="llama_stack.providers.adapters.safety.sample.SampleConfig",
),
),
remote_provider_spec(
api=Api.safety,
adapter=AdapterSpec(
adapter_id="bedrock",
pip_packages=["boto3"],
module="llama_stack.providers.adapters.safety.bedrock",
config_class="llama_stack.providers.adapters.safety.bedrock.BedrockSafetyConfig",
),
),
remote_provider_spec(
api=Api.safety,
adapter=AdapterSpec(
adapter_id="together",
pip_packages=[
"together",
],
module="llama_stack.providers.adapters.safety.together",
config_class="llama_stack.providers.adapters.safety.together.TogetherSafetyConfig",
provider_data_validator="llama_stack.providers.adapters.safety.together.TogetherProviderDataValidator",
),
),
]

28
llama_stack/providers/utils/inference/__init__.py
View file

@ -3,3 +3,31 @@
#
# 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 llama_models.datatypes import * # noqa: F403
from llama_models.sku_list import all_registered_models
def is_supported_safety_model(model: Model) -> bool:
if model.quantization_format != CheckpointQuantizationFormat.bf16:
return False
model_id = model.core_model_id
return model_id in [
CoreModelId.llama_guard_3_8b,
CoreModelId.llama_guard_3_1b,
CoreModelId.llama_guard_3_11b_vision,
]
def supported_inference_models() -> List[str]:
return [
m.descriptor()
for m in all_registered_models()
if (
m.model_family in {ModelFamily.llama3_1, ModelFamily.llama3_2}
or is_supported_safety_model(m)
)
]

172
llama_stack/providers/utils/inference/augment_messages.py Normal file
View file

@ -0,0 +1,172 @@
# 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
from llama_stack.providers.utils.inference import supported_inference_models
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.descriptor() not in supported_inference_models():
cprint(f"Unsupported inference model? {model.descriptor()}", 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

84
llama_stack/providers/utils/inference/prepare_messages.py
View file

@ -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