"""
Translate from OpenAI's `/v1/chat/completions` to Sagemaker's `/invoke`
In the Huggingface TGI format.
"""
import json
import time
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
from httpx._models import Headers, Response
import litellm
from litellm.litellm_core_utils.asyncify import asyncify
from litellm.litellm_core_utils.prompt_templates.factory import (
custom_prompt,
prompt_factory,
)
from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
from litellm.types.llms.openai import AllMessageValues
from litellm.types.utils import ModelResponse, Usage
from ..common_utils import SagemakerError
if TYPE_CHECKING:
from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
LiteLLMLoggingObj = _LiteLLMLoggingObj
else:
LiteLLMLoggingObj = Any
class SagemakerConfig(BaseConfig):
"""
Reference: https://d-uuwbxj1u4cnu.studio.us-west-2.sagemaker.aws/jupyter/default/lab/workspaces/auto-q/tree/DemoNotebooks/meta-textgeneration-llama-2-7b-SDK_1.ipynb
"""
max_new_tokens: Optional[int] = None
top_p: Optional[float] = None
temperature: Optional[float] = None
return_full_text: Optional[bool] = None
def __init__(
self,
max_new_tokens: Optional[int] = None,
top_p: Optional[float] = None,
temperature: Optional[float] = None,
return_full_text: Optional[bool] = None,
) -> None:
locals_ = locals().copy()
for key, value in locals_.items():
if key != "self" and value is not None:
setattr(self.__class__, key, value)
@classmethod
def get_config(cls):
return super().get_config()
def get_error_class(
self, error_message: str, status_code: int, headers: Union[dict, Headers]
) -> BaseLLMException:
return SagemakerError(
message=error_message, status_code=status_code, headers=headers
)
def get_supported_openai_params(self, model: str) -> List:
return ["stream", "temperature", "max_tokens", "top_p", "stop", "n"]
def map_openai_params(
self,
non_default_params: dict,
optional_params: dict,
model: str,
drop_params: bool,
) -> dict:
for param, value in non_default_params.items():
if param == "temperature":
if value == 0.0 or value == 0:
# hugging face exception raised when temp==0
# Failed: Error occurred: HuggingfaceException - Input validation error: `temperature` must be strictly positive
if not non_default_params.get(
"aws_sagemaker_allow_zero_temp", False
):
value = 0.01
optional_params["temperature"] = value
if param == "top_p":
optional_params["top_p"] = value
if param == "n":
optional_params["best_of"] = value
optional_params["do_sample"] = (
True # Need to sample if you want best of for hf inference endpoints
)
if param == "stream":
optional_params["stream"] = value
if param == "stop":
optional_params["stop"] = value
if param == "max_tokens":
# HF TGI raises the following exception when max_new_tokens==0
# Failed: Error occurred: HuggingfaceException - Input validation error: `max_new_tokens` must be strictly positive
if value == 0:
value = 1
optional_params["max_new_tokens"] = value
non_default_params.pop("aws_sagemaker_allow_zero_temp", None)
return optional_params
def _transform_prompt(
self,
model: str,
messages: List,
custom_prompt_dict: dict,
hf_model_name: Optional[str],
) -> str:
if model in custom_prompt_dict:
# check if the model has a registered custom prompt
model_prompt_details = custom_prompt_dict[model]
prompt = custom_prompt(
role_dict=model_prompt_details.get("roles", None),
initial_prompt_value=model_prompt_details.get(
"initial_prompt_value", ""
),
final_prompt_value=model_prompt_details.get("final_prompt_value", ""),
messages=messages,
)
elif hf_model_name in custom_prompt_dict:
# check if the base huggingface model has a registered custom prompt
model_prompt_details = custom_prompt_dict[hf_model_name]
prompt = custom_prompt(
role_dict=model_prompt_details.get("roles", None),
initial_prompt_value=model_prompt_details.get(
"initial_prompt_value", ""
),
final_prompt_value=model_prompt_details.get("final_prompt_value", ""),
messages=messages,
)
else:
if hf_model_name is None:
if "llama-2" in model.lower(): # llama-2 model
if "chat" in model.lower(): # apply llama2 chat template
hf_model_name = "meta-llama/Llama-2-7b-chat-hf"
else: # apply regular llama2 template
hf_model_name = "meta-llama/Llama-2-7b"
hf_model_name = (
hf_model_name or model
) # pass in hf model name for pulling it's prompt template - (e.g. `hf_model_name="meta-llama/Llama-2-7b-chat-hf` applies the llama2 chat template to the prompt)
prompt: str = prompt_factory(model=hf_model_name, messages=messages) # type: ignore
return prompt
def transform_request(
self,
model: str,
messages: List[AllMessageValues],
optional_params: dict,
litellm_params: dict,
headers: dict,
) -> dict:
inference_params = optional_params.copy()
stream = inference_params.pop("stream", False)
data: Dict = {"parameters": inference_params}
if stream is True:
data["stream"] = True
custom_prompt_dict = (
litellm_params.get("custom_prompt_dict", None) or litellm.custom_prompt_dict
)
hf_model_name = litellm_params.get("hf_model_name", None)
prompt = self._transform_prompt(
model=model,
messages=messages,
custom_prompt_dict=custom_prompt_dict,
hf_model_name=hf_model_name,
)
data["inputs"] = prompt
return data
async def async_transform_request(
self,
model: str,
messages: List[AllMessageValues],
optional_params: dict,
litellm_params: dict,
headers: dict,
) -> dict:
return await asyncify(self.transform_request)(
model, messages, optional_params, litellm_params, headers
)
def transform_response(
self,
model: str,
raw_response: Response,
model_response: ModelResponse,
logging_obj: LiteLLMLoggingObj,
request_data: dict,
messages: List[AllMessageValues],
optional_params: dict,
litellm_params: dict,
encoding: str,
api_key: Optional[str] = None,
json_mode: Optional[bool] = None,
) -> ModelResponse:
completion_response = raw_response.json()
## LOGGING
logging_obj.post_call(
input=messages,
api_key="",
original_response=completion_response,
additional_args={"complete_input_dict": request_data},
)
prompt = request_data["inputs"]
## RESPONSE OBJECT
try:
if isinstance(completion_response, list):
completion_response_choices = completion_response[0]
else:
completion_response_choices = completion_response
completion_output = ""
if "generation" in completion_response_choices:
completion_output += completion_response_choices["generation"]
elif "generated_text" in completion_response_choices:
completion_output += completion_response_choices["generated_text"]
# check if the prompt template is part of output, if so - filter it out
if completion_output.startswith(prompt) and "<s>" in prompt:
completion_output = completion_output.replace(prompt, "", 1)
model_response.choices[0].message.content = completion_output # type: ignore
except Exception:
raise SagemakerError(
message=f"LiteLLM Error: Unable to parse sagemaker RAW RESPONSE {json.dumps(completion_response)}",
status_code=500,
)
## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here.
prompt_tokens = len(encoding.encode(prompt))
completion_tokens = len(
encoding.encode(model_response["choices"][0]["message"].get("content", ""))
)
model_response.created = int(time.time())
model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
setattr(model_response, "usage", usage)
return model_response
def validate_environment(
self,
headers: Optional[dict],
model: str,
messages: List[AllMessageValues],
optional_params: dict,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
) -> dict:
headers = {"Content-Type": "application/json"}
if headers is not None:
headers = {"Content-Type": "application/json", **headers}
return headers
