forked from phoenix-oss/llama-stack-mirror
253 lines
9.2 KiB
Python
253 lines
9.2 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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import logging
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from typing import AsyncGenerator
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from huggingface_hub import AsyncInferenceClient, HfApi
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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_stack.apis.inference import * # noqa: F403
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from llama_stack.providers.utils.inference.augment_messages import (
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augment_messages_for_tools,
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)
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from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig
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logger = logging.getLogger(__name__)
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class _HfAdapter(Inference):
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client: AsyncInferenceClient
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max_tokens: int
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model_id: str
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def __init__(self) -> None:
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self.tokenizer = Tokenizer.get_instance()
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self.formatter = ChatFormat(self.tokenizer)
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async def shutdown(self) -> None:
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pass
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async def completion(
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self,
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model: str,
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content: InterleavedTextMedia,
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sampling_params: Optional[SamplingParams] = SamplingParams(),
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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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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]] = None,
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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 or [],
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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 = augment_messages_for_tools(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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options = self.get_chat_options(request)
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if not request.stream:
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response = await 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 in ["stop", "eos_token"]:
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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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async for response in await 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 TGIAdapter(_HfAdapter):
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async def initialize(self, config: TGIImplConfig) -> None:
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self.client = AsyncInferenceClient(model=config.url, token=config.api_token)
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endpoint_info = await self.client.get_endpoint_info()
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self.max_tokens = endpoint_info["max_total_tokens"]
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self.model_id = endpoint_info["model_id"]
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class InferenceAPIAdapter(_HfAdapter):
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async def initialize(self, config: InferenceAPIImplConfig) -> None:
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self.client = AsyncInferenceClient(
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model=config.model_id, token=config.api_token
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)
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endpoint_info = await self.client.get_endpoint_info()
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self.max_tokens = endpoint_info["max_total_tokens"]
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self.model_id = endpoint_info["model_id"]
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class InferenceEndpointAdapter(_HfAdapter):
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async def initialize(self, config: InferenceEndpointImplConfig) -> None:
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# Get the inference endpoint details
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api = HfApi(token=config.api_token)
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endpoint = api.get_inference_endpoint(config.endpoint_name)
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# Wait for the endpoint to be ready (if not already)
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endpoint.wait(timeout=60)
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# Initialize the adapter
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self.client = endpoint.async_client
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self.model_id = endpoint.repository
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self.max_tokens = int(
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endpoint.raw["model"]["image"]["custom"]["env"]["MAX_TOTAL_TOKENS"]
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)
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