277 lines
9.6 KiB
Python
277 lines
9.6 KiB
Python
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# mypy: ignore-errors
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from __future__ import annotations
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import io
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import json
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import logging
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from typing import TYPE_CHECKING, Any
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import boto3 # type: ignore
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from llama_index.core.base.llms.generic_utils import (
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completion_response_to_chat_response,
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stream_completion_response_to_chat_response,
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)
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from llama_index.core.bridge.pydantic import Field
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from llama_index.core.llms import (
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CompletionResponse,
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CustomLLM,
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LLMMetadata,
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)
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from llama_index.core.llms.callbacks import (
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llm_chat_callback,
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llm_completion_callback,
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)
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if TYPE_CHECKING:
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from collections.abc import Sequence
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from llama_index.callbacks import CallbackManager
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from llama_index.llms import (
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ChatMessage,
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ChatResponse,
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ChatResponseGen,
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CompletionResponseGen,
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)
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logger = logging.getLogger(__name__)
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class LineIterator:
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r"""A helper class for parsing the byte stream input from TGI container.
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The output of the model will be in the following format:
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```
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b'data:{"token": {"text": " a"}}\n\n'
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b'data:{"token": {"text": " challenging"}}\n\n'
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b'data:{"token": {"text": " problem"
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b'}}'
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...
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```
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While usually each PayloadPart event from the event stream will contain a byte array
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with a full json, this is not guaranteed and some of the json objects may be split
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across PayloadPart events. For example:
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```
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{'PayloadPart': {'Bytes': b'{"outputs": '}}
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{'PayloadPart': {'Bytes': b'[" problem"]}\n'}}
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```
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This class accounts for this by concatenating bytes written via the 'write' function
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and then exposing a method which will return lines (ending with a '\n' character)
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within the buffer via the 'scan_lines' function. It maintains the position of the
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last read position to ensure that previous bytes are not exposed again. It will
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also save any pending lines that doe not end with a '\n' to make sure truncations
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are concatinated
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"""
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def __init__(self, stream: Any) -> None:
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"""Line iterator initializer."""
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self.byte_iterator = iter(stream)
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self.buffer = io.BytesIO()
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self.read_pos = 0
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def __iter__(self) -> Any:
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"""Self iterator."""
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return self
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def __next__(self) -> Any:
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"""Next element from iterator."""
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while True:
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self.buffer.seek(self.read_pos)
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line = self.buffer.readline()
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if line and line[-1] == ord("\n"):
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self.read_pos += len(line)
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return line[:-1]
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try:
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chunk = next(self.byte_iterator)
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except StopIteration:
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if self.read_pos < self.buffer.getbuffer().nbytes:
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continue
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raise
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if "PayloadPart" not in chunk:
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logger.warning("Unknown event type=%s", chunk)
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continue
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self.buffer.seek(0, io.SEEK_END)
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self.buffer.write(chunk["PayloadPart"]["Bytes"])
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class SagemakerLLM(CustomLLM):
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"""Sagemaker Inference Endpoint models.
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To use, you must supply the endpoint name from your deployed
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Sagemaker model & the region where it is deployed.
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To authenticate, the AWS client uses the following methods to
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automatically load credentials:
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https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
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If a specific credential profile should be used, you must pass
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the name of the profile from the ~/.aws/credentials file that is to be used.
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Make sure the credentials / roles used have the required policies to
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access the Sagemaker endpoint.
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See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html
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"""
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endpoint_name: str = Field(description="")
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temperature: float = Field(description="The temperature to use for sampling.")
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max_new_tokens: int = Field(description="The maximum number of tokens to generate.")
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context_window: int = Field(
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description="The maximum number of context tokens for the model."
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)
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messages_to_prompt: Any = Field(
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description="The function to convert messages to a prompt.", exclude=True
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)
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completion_to_prompt: Any = Field(
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description="The function to convert a completion to a prompt.", exclude=True
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)
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generate_kwargs: dict[str, Any] = Field(
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default_factory=dict, description="Kwargs used for generation."
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)
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model_kwargs: dict[str, Any] = Field(
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default_factory=dict, description="Kwargs used for model initialization."
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)
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verbose: bool = Field(description="Whether to print verbose output.")
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_boto_client: Any = boto3.client(
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"sagemaker-runtime",
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) # TODO make it an optional field
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def __init__(
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self,
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endpoint_name: str | None = "",
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temperature: float = 0.1,
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max_new_tokens: int = 512, # to review defaults
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context_window: int = 2048, # to review defaults
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messages_to_prompt: Any = None,
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completion_to_prompt: Any = None,
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callback_manager: CallbackManager | None = None,
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generate_kwargs: dict[str, Any] | None = None,
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model_kwargs: dict[str, Any] | None = None,
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verbose: bool = True,
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) -> None:
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"""SagemakerLLM initializer."""
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model_kwargs = model_kwargs or {}
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model_kwargs.update({"n_ctx": context_window, "verbose": verbose})
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messages_to_prompt = messages_to_prompt or {}
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completion_to_prompt = completion_to_prompt or {}
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generate_kwargs = generate_kwargs or {}
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generate_kwargs.update(
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{"temperature": temperature, "max_tokens": max_new_tokens}
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)
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super().__init__(
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endpoint_name=endpoint_name,
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temperature=temperature,
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context_window=context_window,
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max_new_tokens=max_new_tokens,
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messages_to_prompt=messages_to_prompt,
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completion_to_prompt=completion_to_prompt,
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callback_manager=callback_manager,
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generate_kwargs=generate_kwargs,
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model_kwargs=model_kwargs,
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verbose=verbose,
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)
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@property
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def inference_params(self):
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# TODO expose the rest of params
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return {
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"do_sample": True,
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"top_p": 0.7,
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"temperature": self.temperature,
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"top_k": 50,
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"max_new_tokens": self.max_new_tokens,
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}
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@property
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def metadata(self) -> LLMMetadata:
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"""Get LLM metadata."""
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return LLMMetadata(
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context_window=self.context_window,
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num_output=self.max_new_tokens,
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model_name="Sagemaker LLama 2",
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)
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@llm_completion_callback()
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def complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
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self.generate_kwargs.update({"stream": False})
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is_formatted = kwargs.pop("formatted", False)
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if not is_formatted:
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prompt = self.completion_to_prompt(prompt)
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request_params = {
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"inputs": prompt,
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"stream": False,
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"parameters": self.inference_params,
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}
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resp = self._boto_client.invoke_endpoint(
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EndpointName=self.endpoint_name,
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Body=json.dumps(request_params),
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ContentType="application/json",
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)
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response_body = resp["Body"]
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response_str = response_body.read().decode("utf-8")
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response_dict = json.loads(response_str)
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return CompletionResponse(
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text=response_dict[0]["generated_text"][len(prompt) :], raw=resp
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)
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@llm_completion_callback()
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def stream_complete(self, prompt: str, **kwargs: Any) -> CompletionResponseGen:
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def get_stream():
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text = ""
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request_params = {
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"inputs": prompt,
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"stream": True,
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"parameters": self.inference_params,
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}
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resp = self._boto_client.invoke_endpoint_with_response_stream(
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EndpointName=self.endpoint_name,
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Body=json.dumps(request_params),
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ContentType="application/json",
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)
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event_stream = resp["Body"]
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start_json = b"{"
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stop_token = "<|endoftext|>"
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first_token = True
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for line in LineIterator(event_stream):
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if line != b"" and start_json in line:
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data = json.loads(line[line.find(start_json) :].decode("utf-8"))
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special = data["token"]["special"]
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stop = data["token"]["text"] == stop_token
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if not special and not stop:
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delta = data["token"]["text"]
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# trim the leading space for the first token if present
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if first_token:
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delta = delta.lstrip()
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first_token = False
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text += delta
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yield CompletionResponse(delta=delta, text=text, raw=data)
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return get_stream()
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@llm_chat_callback()
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def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
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prompt = self.messages_to_prompt(messages)
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completion_response = self.complete(prompt, formatted=True, **kwargs)
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return completion_response_to_chat_response(completion_response)
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@llm_chat_callback()
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def stream_chat(
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self, messages: Sequence[ChatMessage], **kwargs: Any
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) -> ChatResponseGen:
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prompt = self.messages_to_prompt(messages)
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completion_response = self.stream_complete(prompt, formatted=True, **kwargs)
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return stream_completion_response_to_chat_response(completion_response)
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