mirror of
https://github.com/meta-llama/llama-stack.git
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5712566061
* [1/n] migrate inference/chat_completion * migrate inference/completion * inference/completion * inference regenerate openapi spec * safety api * migrate agentic system * migrate apis without implementations * re-generate openapi spec * remove hack from openapi generator * fix inference * fix inference * openapi generator rerun * Simplified Telemetry API and tying it to logger (#57) * Simplified Telemetry API and tying it to logger * small update which adds a METRIC type * move span events one level down into structured log events --------- Co-authored-by: Ashwin Bharambe <ashwin@meta.com> * fix api to work with openapi generator * fix agentic calling inference * together adapter inference * update inference adapters --------- Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com> Co-authored-by: Ashwin Bharambe <ashwin@meta.com>
295 lines
11 KiB
Python
295 lines
11 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from typing import Any, AsyncGenerator, Dict
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import requests
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from huggingface_hub import HfApi, InferenceClient
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from llama_models.llama3.api.chat_format import ChatFormat
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from llama_models.llama3.api.datatypes import StopReason
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from llama_models.llama3.api.tokenizer import Tokenizer
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from llama_toolchain.inference.api import * # noqa: F403
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from llama_toolchain.inference.prepare_messages import prepare_messages
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from .config import TGIImplConfig
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HF_SUPPORTED_MODELS = {
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"Meta-Llama3.1-8B-Instruct": "meta-llama/Meta-Llama-3.1-8B-Instruct",
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"Meta-Llama3.1-70B-Instruct": "meta-llama/Meta-Llama-3.1-70B-Instruct",
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"Meta-Llama3.1-405B-Instruct": "meta-llama/Meta-Llama-3.1-405B-Instruct",
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}
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class TGIAdapter(Inference):
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def __init__(self, config: TGIImplConfig) -> None:
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self.config = config
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self.tokenizer = Tokenizer.get_instance()
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self.formatter = ChatFormat(self.tokenizer)
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@property
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def client(self) -> InferenceClient:
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return InferenceClient(model=self.config.url, token=self.config.api_token)
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def _get_endpoint_info(self) -> Dict[str, Any]:
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return {
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**self.client.get_endpoint_info(),
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"inference_url": self.config.url,
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}
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async def initialize(self) -> None:
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try:
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info = self._get_endpoint_info()
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if "model_id" not in info:
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raise RuntimeError("Missing model_id in model info")
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if "max_total_tokens" not in info:
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raise RuntimeError("Missing max_total_tokens in model info")
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self.max_tokens = info["max_total_tokens"]
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model_id = info["model_id"]
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model_name = next(
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(name for name, id in HF_SUPPORTED_MODELS.items() if id == model_id),
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None,
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)
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if model_name is None:
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raise RuntimeError(
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f"TGI is serving model: {model_id}, use one of the supported models: {', '.join(HF_SUPPORTED_MODELS.values())}"
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)
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self.model_name = model_name
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self.inference_url = info["inference_url"]
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except Exception as e:
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import traceback
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traceback.print_exc()
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raise RuntimeError(f"Error initializing TGIAdapter: {e}") from e
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async def shutdown(self) -> None:
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pass
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async def completion(self, request: CompletionRequest) -> AsyncGenerator:
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raise NotImplementedError()
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def get_chat_options(self, request: ChatCompletionRequest) -> dict:
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options = {}
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if request.sampling_params is not None:
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for attr in {"temperature", "top_p", "top_k", "max_tokens"}:
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if getattr(request.sampling_params, attr):
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options[attr] = getattr(request.sampling_params, attr)
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return options
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async def chat_completion(
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self,
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model: str,
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messages: List[Message],
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sampling_params: Optional[SamplingParams] = SamplingParams(),
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tools: Optional[List[ToolDefinition]] = list(),
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tool_choice: Optional[ToolChoice] = ToolChoice.auto,
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tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
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stream: Optional[bool] = False,
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logprobs: Optional[LogProbConfig] = None,
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) -> AsyncGenerator:
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request = ChatCompletionRequest(
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model=model,
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messages=messages,
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sampling_params=sampling_params,
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tools=tools,
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tool_choice=tool_choice,
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tool_prompt_format=tool_prompt_format,
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stream=stream,
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logprobs=logprobs,
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)
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messages = prepare_messages(request)
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model_input = self.formatter.encode_dialog_prompt(messages)
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prompt = self.tokenizer.decode(model_input.tokens)
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input_tokens = len(model_input.tokens)
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max_new_tokens = min(
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request.sampling_params.max_tokens or (self.max_tokens - input_tokens),
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self.max_tokens - input_tokens - 1,
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)
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print(f"Calculated max_new_tokens: {max_new_tokens}")
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assert (
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request.model == self.model_name
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), f"Model mismatch, expected {self.model_name}, got {request.model}"
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options = self.get_chat_options(request)
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if not request.stream:
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response = self.client.text_generation(
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prompt=prompt,
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stream=False,
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details=True,
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max_new_tokens=max_new_tokens,
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stop_sequences=["<|eom_id|>", "<|eot_id|>"],
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**options,
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)
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stop_reason = None
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if response.details.finish_reason:
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if response.details.finish_reason == "stop":
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stop_reason = StopReason.end_of_turn
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elif response.details.finish_reason == "length":
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stop_reason = StopReason.out_of_tokens
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completion_message = self.formatter.decode_assistant_message_from_content(
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response.generated_text,
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stop_reason,
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)
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yield ChatCompletionResponse(
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completion_message=completion_message,
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logprobs=None,
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)
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else:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.start,
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delta="",
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)
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)
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buffer = ""
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ipython = False
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stop_reason = None
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tokens = []
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for response in self.client.text_generation(
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prompt=prompt,
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stream=True,
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details=True,
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max_new_tokens=max_new_tokens,
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stop_sequences=["<|eom_id|>", "<|eot_id|>"],
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**options,
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):
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token_result = response.token
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buffer += token_result.text
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tokens.append(token_result.id)
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if not ipython and buffer.startswith("<|python_tag|>"):
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ipython = True
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=ToolCallDelta(
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content="",
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parse_status=ToolCallParseStatus.started,
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),
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)
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)
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buffer = buffer[len("<|python_tag|>") :]
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continue
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if token_result.text == "<|eot_id|>":
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stop_reason = StopReason.end_of_turn
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text = ""
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elif token_result.text == "<|eom_id|>":
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stop_reason = StopReason.end_of_message
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text = ""
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else:
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text = token_result.text
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if ipython:
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delta = ToolCallDelta(
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content=text,
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parse_status=ToolCallParseStatus.in_progress,
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)
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else:
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delta = text
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if stop_reason is None:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=delta,
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stop_reason=stop_reason,
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)
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)
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if stop_reason is None:
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stop_reason = StopReason.out_of_tokens
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# parse tool calls and report errors
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message = self.formatter.decode_assistant_message(tokens, stop_reason)
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parsed_tool_calls = len(message.tool_calls) > 0
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if ipython and not parsed_tool_calls:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=ToolCallDelta(
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content="",
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parse_status=ToolCallParseStatus.failure,
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),
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stop_reason=stop_reason,
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)
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)
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for tool_call in message.tool_calls:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=ToolCallDelta(
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content=tool_call,
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parse_status=ToolCallParseStatus.success,
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),
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stop_reason=stop_reason,
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)
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)
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.complete,
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delta="",
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stop_reason=stop_reason,
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)
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)
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class InferenceEndpointAdapter(TGIAdapter):
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def __init__(self, config: TGIImplConfig) -> None:
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super().__init__(config)
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self.config.url = self._construct_endpoint_url()
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def _construct_endpoint_url(self) -> str:
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hf_endpoint_name = self.config.hf_endpoint_name
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assert hf_endpoint_name.count("/") <= 1, (
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"Endpoint name must be in the format of 'namespace/endpoint_name' "
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"or 'endpoint_name'"
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)
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if "/" not in hf_endpoint_name:
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hf_namespace: str = self.get_namespace()
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endpoint_path = f"{hf_namespace}/{hf_endpoint_name}"
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else:
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endpoint_path = hf_endpoint_name
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return f"https://api.endpoints.huggingface.cloud/v2/endpoint/{endpoint_path}"
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def get_namespace(self) -> str:
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return HfApi().whoami()["name"]
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@property
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def client(self) -> InferenceClient:
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return InferenceClient(model=self.inference_url, token=self.config.api_token)
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def _get_endpoint_info(self) -> Dict[str, Any]:
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headers = {
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"accept": "application/json",
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"authorization": f"Bearer {self.config.api_token}",
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}
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response = requests.get(self.config.url, headers=headers)
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response.raise_for_status()
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endpoint_info = response.json()
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return {
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"inference_url": endpoint_info["status"]["url"],
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"model_id": endpoint_info["model"]["repository"],
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"max_total_tokens": int(
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endpoint_info["model"]["image"]["custom"]["env"]["MAX_TOTAL_TOKENS"]
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),
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}
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async def initialize(self) -> None:
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await super().initialize()
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