mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-26 11:14:04 +00:00
Litellm merge pr (#7161)
* build: merge branch * test: fix openai naming * fix(main.py): fix openai renaming * style: ignore function length for config factory * fix(sagemaker/): fix routing logic * fix: fix imports * fix: fix override
This commit is contained in:
Krish Dholakia
GitHub
parent
d5aae81c6d
commit
350cfc36f7
88 changed files with 3617 additions and 4421 deletions
|
|
@ -1,246 +0,0 @@
|
|||
import json
|
||||
import os
|
||||
import time
|
||||
import types
|
||||
from enum import Enum
|
||||
from typing import Callable, Optional
|
||||
|
||||
import requests # type: ignore
|
||||
|
||||
import litellm
|
||||
from litellm.utils import ModelResponse, Usage
|
||||
|
||||
|
||||
class NLPCloudError(Exception):
|
||||
def __init__(self, status_code, message):
|
||||
self.status_code = status_code
|
||||
self.message = message
|
||||
super().__init__(
|
||||
self.message
|
||||
) # Call the base class constructor with the parameters it needs
|
||||
|
||||
|
||||
class NLPCloudConfig:
|
||||
"""
|
||||
Reference: https://docs.nlpcloud.com/#generation
|
||||
|
||||
- `max_length` (int): Optional. The maximum number of tokens that the generated text should contain.
|
||||
|
||||
- `length_no_input` (boolean): Optional. Whether `min_length` and `max_length` should not include the length of the input text.
|
||||
|
||||
- `end_sequence` (string): Optional. A specific token that should be the end of the generated sequence.
|
||||
|
||||
- `remove_end_sequence` (boolean): Optional. Whether to remove the `end_sequence` string from the result.
|
||||
|
||||
- `remove_input` (boolean): Optional. Whether to remove the input text from the result.
|
||||
|
||||
- `bad_words` (list of strings): Optional. List of tokens that are not allowed to be generated.
|
||||
|
||||
- `temperature` (float): Optional. Temperature sampling. It modulates the next token probabilities.
|
||||
|
||||
- `top_p` (float): Optional. Top P sampling. Below 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.
|
||||
|
||||
- `top_k` (int): Optional. Top K sampling. The number of highest probability vocabulary tokens to keep for top k filtering.
|
||||
|
||||
- `repetition_penalty` (float): Optional. Prevents the same word from being repeated too many times.
|
||||
|
||||
- `num_beams` (int): Optional. Number of beams for beam search.
|
||||
|
||||
- `num_return_sequences` (int): Optional. The number of independently computed returned sequences.
|
||||
"""
|
||||
|
||||
max_length: Optional[int] = None
|
||||
length_no_input: Optional[bool] = None
|
||||
end_sequence: Optional[str] = None
|
||||
remove_end_sequence: Optional[bool] = None
|
||||
remove_input: Optional[bool] = None
|
||||
bad_words: Optional[list] = None
|
||||
temperature: Optional[float] = None
|
||||
top_p: Optional[float] = None
|
||||
top_k: Optional[int] = None
|
||||
repetition_penalty: Optional[float] = None
|
||||
num_beams: Optional[int] = None
|
||||
num_return_sequences: Optional[int] = None
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
max_length: Optional[int] = None,
|
||||
length_no_input: Optional[bool] = None,
|
||||
end_sequence: Optional[str] = None,
|
||||
remove_end_sequence: Optional[bool] = None,
|
||||
remove_input: Optional[bool] = None,
|
||||
bad_words: Optional[list] = None,
|
||||
temperature: Optional[float] = None,
|
||||
top_p: Optional[float] = None,
|
||||
top_k: Optional[int] = None,
|
||||
repetition_penalty: Optional[float] = None,
|
||||
num_beams: Optional[int] = None,
|
||||
num_return_sequences: Optional[int] = None,
|
||||
) -> None:
|
||||
locals_ = locals()
|
||||
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 {
|
||||
k: v
|
||||
for k, v in cls.__dict__.items()
|
||||
if not k.startswith("__")
|
||||
and not isinstance(
|
||||
v,
|
||||
(
|
||||
types.FunctionType,
|
||||
types.BuiltinFunctionType,
|
||||
classmethod,
|
||||
staticmethod,
|
||||
),
|
||||
)
|
||||
and v is not None
|
||||
}
|
||||
|
||||
|
||||
def validate_environment(api_key):
|
||||
headers = {
|
||||
"accept": "application/json",
|
||||
"content-type": "application/json",
|
||||
}
|
||||
if api_key:
|
||||
headers["Authorization"] = f"Token {api_key}"
|
||||
return headers
|
||||
|
||||
|
||||
def completion(
|
||||
model: str,
|
||||
messages: list,
|
||||
api_base: str,
|
||||
model_response: ModelResponse,
|
||||
print_verbose: Callable,
|
||||
encoding,
|
||||
api_key,
|
||||
logging_obj,
|
||||
optional_params: dict,
|
||||
litellm_params=None,
|
||||
logger_fn=None,
|
||||
default_max_tokens_to_sample=None,
|
||||
):
|
||||
headers = validate_environment(api_key)
|
||||
|
||||
## Load Config
|
||||
config = litellm.NLPCloudConfig.get_config()
|
||||
for k, v in config.items():
|
||||
if (
|
||||
k not in optional_params
|
||||
): # completion(top_k=3) > togetherai_config(top_k=3) <- allows for dynamic variables to be passed in
|
||||
optional_params[k] = v
|
||||
|
||||
completion_url_fragment_1 = api_base
|
||||
completion_url_fragment_2 = "/generation"
|
||||
model = model
|
||||
text = " ".join(message["content"] for message in messages)
|
||||
|
||||
data = {
|
||||
"text": text,
|
||||
**optional_params,
|
||||
}
|
||||
|
||||
completion_url = completion_url_fragment_1 + model + completion_url_fragment_2
|
||||
|
||||
## LOGGING
|
||||
logging_obj.pre_call(
|
||||
input=text,
|
||||
api_key=api_key,
|
||||
additional_args={
|
||||
"complete_input_dict": data,
|
||||
"headers": headers,
|
||||
"api_base": completion_url,
|
||||
},
|
||||
)
|
||||
## COMPLETION CALL
|
||||
response = requests.post(
|
||||
completion_url,
|
||||
headers=headers,
|
||||
data=json.dumps(data),
|
||||
stream=optional_params["stream"] if "stream" in optional_params else False,
|
||||
)
|
||||
if "stream" in optional_params and optional_params["stream"] is True:
|
||||
return clean_and_iterate_chunks(response)
|
||||
else:
|
||||
## LOGGING
|
||||
logging_obj.post_call(
|
||||
input=text,
|
||||
api_key=api_key,
|
||||
original_response=response.text,
|
||||
additional_args={"complete_input_dict": data},
|
||||
)
|
||||
print_verbose(f"raw model_response: {response.text}")
|
||||
## RESPONSE OBJECT
|
||||
try:
|
||||
completion_response = response.json()
|
||||
except Exception:
|
||||
raise NLPCloudError(message=response.text, status_code=response.status_code)
|
||||
if "error" in completion_response:
|
||||
raise NLPCloudError(
|
||||
message=completion_response["error"],
|
||||
status_code=response.status_code,
|
||||
)
|
||||
else:
|
||||
try:
|
||||
if len(completion_response["generated_text"]) > 0:
|
||||
model_response.choices[0].message.content = ( # type: ignore
|
||||
completion_response["generated_text"]
|
||||
)
|
||||
except Exception:
|
||||
raise NLPCloudError(
|
||||
message=json.dumps(completion_response),
|
||||
status_code=response.status_code,
|
||||
)
|
||||
|
||||
## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here.
|
||||
prompt_tokens = completion_response["nb_input_tokens"]
|
||||
completion_tokens = completion_response["nb_generated_tokens"]
|
||||
|
||||
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 clean_and_iterate_chunks(response):
|
||||
# def process_chunk(chunk):
|
||||
# print(f"received chunk: {chunk}")
|
||||
# cleaned_chunk = chunk.decode("utf-8")
|
||||
# # Perform further processing based on your needs
|
||||
# return cleaned_chunk
|
||||
|
||||
|
||||
# for line in response.iter_lines():
|
||||
# if line:
|
||||
# yield process_chunk(line)
|
||||
def clean_and_iterate_chunks(response):
|
||||
buffer = b""
|
||||
|
||||
for chunk in response.iter_content(chunk_size=1024):
|
||||
if not chunk:
|
||||
break
|
||||
|
||||
buffer += chunk
|
||||
while b"\x00" in buffer:
|
||||
buffer = buffer.replace(b"\x00", b"")
|
||||
yield buffer.decode("utf-8")
|
||||
buffer = b""
|
||||
|
||||
# No more data expected, yield any remaining data in the buffer
|
||||
if buffer:
|
||||
yield buffer.decode("utf-8")
|
||||
|
||||
|
||||
def embedding():
|
||||
# logic for parsing in - calling - parsing out model embedding calls
|
||||
pass
|
||||
Loading…
Add table
Add a link
Reference in a new issue