mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
273 lines
8.9 KiB
Python
273 lines
8.9 KiB
Python
import base64
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import json
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import logging
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import subprocess
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import textwrap
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import time
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from typing import Any, Dict, List, Mapping, Optional
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import requests
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models.llms import LLM
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from langchain_core.pydantic_v1 import Extra, Field, root_validator
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from langchain_core.utils import get_from_dict_or_env
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logger = logging.getLogger(__name__)
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DEFAULT_NUM_TRIES = 10
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DEFAULT_SLEEP_TIME = 4
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class Beam(LLM):
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"""Beam API for gpt2 large language model.
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To use, you should have the ``beam-sdk`` python package installed,
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and the environment variable ``BEAM_CLIENT_ID`` set with your client id
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and ``BEAM_CLIENT_SECRET`` set with your client secret. Information on how
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to get this is available here: https://docs.beam.cloud/account/api-keys.
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The wrapper can then be called as follows, where the name, cpu, memory, gpu,
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python version, and python packages can be updated accordingly. Once deployed,
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the instance can be called.
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Example:
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.. code-block:: python
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llm = Beam(model_name="gpt2",
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name="langchain-gpt2",
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cpu=8,
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memory="32Gi",
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gpu="A10G",
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python_version="python3.8",
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python_packages=[
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"diffusers[torch]>=0.10",
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"transformers",
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"torch",
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"pillow",
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"accelerate",
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"safetensors",
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"xformers",],
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max_length=50)
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llm._deploy()
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call_result = llm._call(input)
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"""
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model_name: str = ""
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name: str = ""
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cpu: str = ""
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memory: str = ""
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gpu: str = ""
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python_version: str = ""
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python_packages: List[str] = []
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max_length: str = ""
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url: str = ""
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"""model endpoint to use"""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Holds any model parameters valid for `create` call not
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explicitly specified."""
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beam_client_id: str = ""
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beam_client_secret: str = ""
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app_id: Optional[str] = None
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class Config:
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"""Configuration for this pydantic config."""
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extra = Extra.forbid
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@root_validator(pre=True)
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def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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"""Build extra kwargs from additional params that were passed in."""
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all_required_field_names = {field.alias for field in cls.__fields__.values()}
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extra = values.get("model_kwargs", {})
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for field_name in list(values):
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if field_name not in all_required_field_names:
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if field_name in extra:
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raise ValueError(f"Found {field_name} supplied twice.")
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logger.warning(
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f"""{field_name} was transferred to model_kwargs.
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Please confirm that {field_name} is what you intended."""
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)
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extra[field_name] = values.pop(field_name)
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values["model_kwargs"] = extra
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return values
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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beam_client_id = get_from_dict_or_env(
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values, "beam_client_id", "BEAM_CLIENT_ID"
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)
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beam_client_secret = get_from_dict_or_env(
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values, "beam_client_secret", "BEAM_CLIENT_SECRET"
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)
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values["beam_client_id"] = beam_client_id
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values["beam_client_secret"] = beam_client_secret
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return values
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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"""Get the identifying parameters."""
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return {
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"model_name": self.model_name,
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"name": self.name,
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"cpu": self.cpu,
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"memory": self.memory,
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"gpu": self.gpu,
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"python_version": self.python_version,
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"python_packages": self.python_packages,
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"max_length": self.max_length,
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"model_kwargs": self.model_kwargs,
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}
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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return "beam"
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def app_creation(self) -> None:
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"""Creates a Python file which will contain your Beam app definition."""
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script = textwrap.dedent(
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"""\
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import beam
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# The environment your code will run on
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app = beam.App(
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name="{name}",
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cpu={cpu},
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memory="{memory}",
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gpu="{gpu}",
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python_version="{python_version}",
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python_packages={python_packages},
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)
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app.Trigger.RestAPI(
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inputs={{"prompt": beam.Types.String(), "max_length": beam.Types.String()}},
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outputs={{"text": beam.Types.String()}},
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handler="run.py:beam_langchain",
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)
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"""
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)
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script_name = "app.py"
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with open(script_name, "w") as file:
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file.write(
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script.format(
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name=self.name,
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cpu=self.cpu,
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memory=self.memory,
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gpu=self.gpu,
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python_version=self.python_version,
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python_packages=self.python_packages,
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)
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)
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def run_creation(self) -> None:
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"""Creates a Python file which will be deployed on beam."""
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script = textwrap.dedent(
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"""
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import os
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import transformers
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from transformers import GPT2LMHeadModel, GPT2Tokenizer
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model_name = "{model_name}"
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def beam_langchain(**inputs):
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prompt = inputs["prompt"]
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length = inputs["max_length"]
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tokenizer = GPT2Tokenizer.from_pretrained(model_name)
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model = GPT2LMHeadModel.from_pretrained(model_name)
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encodedPrompt = tokenizer.encode(prompt, return_tensors='pt')
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outputs = model.generate(encodedPrompt, max_length=int(length),
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do_sample=True, pad_token_id=tokenizer.eos_token_id)
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output = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(output) # noqa: T201
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return {{"text": output}}
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"""
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)
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script_name = "run.py"
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with open(script_name, "w") as file:
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file.write(script.format(model_name=self.model_name))
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def _deploy(self) -> str:
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"""Call to Beam."""
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try:
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import beam # type: ignore
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if beam.__path__ == "":
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raise ImportError
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except ImportError:
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raise ImportError(
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"Could not import beam python package. "
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"Please install it with `curl "
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"https://raw.githubusercontent.com/slai-labs"
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"/get-beam/main/get-beam.sh -sSfL | sh`."
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)
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self.app_creation()
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self.run_creation()
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process = subprocess.run(
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"beam deploy app.py", shell=True, capture_output=True, text=True
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)
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if process.returncode == 0:
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output = process.stdout
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logger.info(output)
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lines = output.split("\n")
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for line in lines:
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if line.startswith(" i Send requests to: https://apps.beam.cloud/"):
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self.app_id = line.split("/")[-1]
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self.url = line.split(":")[1].strip()
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return self.app_id
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raise ValueError(
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f"""Failed to retrieve the appID from the deployment output.
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Deployment output: {output}"""
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)
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else:
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raise ValueError(f"Deployment failed. Error: {process.stderr}")
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@property
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def authorization(self) -> str:
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if self.beam_client_id:
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credential_str = self.beam_client_id + ":" + self.beam_client_secret
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else:
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credential_str = self.beam_client_secret
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return base64.b64encode(credential_str.encode()).decode()
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def _call(
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self,
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prompt: str,
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stop: Optional[list] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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"""Call to Beam."""
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url = "https://apps.beam.cloud/" + self.app_id if self.app_id else self.url
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payload = {"prompt": prompt, "max_length": self.max_length}
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payload.update(kwargs)
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headers = {
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"Accept": "*/*",
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"Accept-Encoding": "gzip, deflate",
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"Authorization": "Basic " + self.authorization,
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"Connection": "keep-alive",
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"Content-Type": "application/json",
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}
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for _ in range(DEFAULT_NUM_TRIES):
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request = requests.post(url, headers=headers, data=json.dumps(payload))
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if request.status_code == 200:
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return request.json()["text"]
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time.sleep(DEFAULT_SLEEP_TIME)
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logger.warning("Unable to successfully call model.")
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return ""
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