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refactor: add black formatting

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Krrish Dholakia 2023-12-25 14:10:38 +05:30
parent b87d630b0a
commit 4905929de3
156 changed files with 19723 additions and 10869 deletions
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238
litellm/llms/aleph_alpha.py
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@ -8,17 +8,21 @@ import litellm
from litellm.utils import ModelResponse, Choices, Message, Usage
import httpx
class AlephAlphaError(Exception):
def __init__(self, status_code, message):
self.status_code = status_code
self.message = message
self.request = httpx.Request(method="POST", url="https://api.aleph-alpha.com/complete")
self.request = httpx.Request(
method="POST", url="https://api.aleph-alpha.com/complete"
)
self.response = httpx.Response(status_code=status_code, request=self.request)
super().__init__(
self.message
) # Call the base class constructor with the parameters it needs
class AlephAlphaConfig():
class AlephAlphaConfig:
"""
Reference: https://docs.aleph-alpha.com/api/complete/
@ -42,13 +46,13 @@ class AlephAlphaConfig():
- `repetition_penalties_include_prompt`, `repetition_penalties_include_completion`, `use_multiplicative_presence_penalty`,`use_multiplicative_frequency_penalty`,`use_multiplicative_sequence_penalty` (boolean, nullable; default value: false): Various settings that adjust how the repetition penalties are applied.
- `penalty_bias` (string, nullable): Text used in addition to the penalized tokens for repetition penalties.
- `penalty_bias` (string, nullable): Text used in addition to the penalized tokens for repetition penalties.
- `penalty_exceptions` (string[], nullable): Strings that may be generated without penalty.
- `penalty_exceptions_include_stop_sequences` (boolean, nullable; default value: true): Include all stop_sequences in penalty_exceptions.
- `best_of` (integer, nullable; default value: 1): The number of completions will be generated on the server side.
- `best_of` (integer, nullable; default value: 1): The number of completions will be generated on the server side.
- `n` (integer, nullable; default value: 1): The number of completions to return.
@ -68,87 +72,101 @@ class AlephAlphaConfig():
- `completion_bias_inclusion_first_token_only`, `completion_bias_exclusion_first_token_only` (boolean; default value: false): Consider only the first token for the completion_bias_inclusion/exclusion.
- `contextual_control_threshold` (number, nullable): Control over how similar tokens are controlled.
- `contextual_control_threshold` (number, nullable): Control over how similar tokens are controlled.
- `control_log_additive` (boolean; default value: true): Method of applying control to attention scores.
"""
maximum_tokens: Optional[int]=litellm.max_tokens # aleph alpha requires max tokens
minimum_tokens: Optional[int]=None
echo: Optional[bool]=None
temperature: Optional[int]=None
top_k: Optional[int]=None
top_p: Optional[int]=None
presence_penalty: Optional[int]=None
frequency_penalty: Optional[int]=None
sequence_penalty: Optional[int]=None
sequence_penalty_min_length: Optional[int]=None
repetition_penalties_include_prompt: Optional[bool]=None
repetition_penalties_include_completion: Optional[bool]=None
use_multiplicative_presence_penalty: Optional[bool]=None
use_multiplicative_frequency_penalty: Optional[bool]=None
use_multiplicative_sequence_penalty: Optional[bool]=None
penalty_bias: Optional[str]=None
penalty_exceptions_include_stop_sequences: Optional[bool]=None
best_of: Optional[int]=None
n: Optional[int]=None
logit_bias: Optional[dict]=None
log_probs: Optional[int]=None
stop_sequences: Optional[list]=None
tokens: Optional[bool]=None
raw_completion: Optional[bool]=None
disable_optimizations: Optional[bool]=None
completion_bias_inclusion: Optional[list]=None
completion_bias_exclusion: Optional[list]=None
completion_bias_inclusion_first_token_only: Optional[bool]=None
completion_bias_exclusion_first_token_only: Optional[bool]=None
contextual_control_threshold: Optional[int]=None
control_log_additive: Optional[bool]=None
maximum_tokens: Optional[
int
] = litellm.max_tokens # aleph alpha requires max tokens
minimum_tokens: Optional[int] = None
echo: Optional[bool] = None
temperature: Optional[int] = None
top_k: Optional[int] = None
top_p: Optional[int] = None
presence_penalty: Optional[int] = None
frequency_penalty: Optional[int] = None
sequence_penalty: Optional[int] = None
sequence_penalty_min_length: Optional[int] = None
repetition_penalties_include_prompt: Optional[bool] = None
repetition_penalties_include_completion: Optional[bool] = None
use_multiplicative_presence_penalty: Optional[bool] = None
use_multiplicative_frequency_penalty: Optional[bool] = None
use_multiplicative_sequence_penalty: Optional[bool] = None
penalty_bias: Optional[str] = None
penalty_exceptions_include_stop_sequences: Optional[bool] = None
best_of: Optional[int] = None
n: Optional[int] = None
logit_bias: Optional[dict] = None
log_probs: Optional[int] = None
stop_sequences: Optional[list] = None
tokens: Optional[bool] = None
raw_completion: Optional[bool] = None
disable_optimizations: Optional[bool] = None
completion_bias_inclusion: Optional[list] = None
completion_bias_exclusion: Optional[list] = None
completion_bias_inclusion_first_token_only: Optional[bool] = None
completion_bias_exclusion_first_token_only: Optional[bool] = None
contextual_control_threshold: Optional[int] = None
control_log_additive: Optional[bool] = None
def __init__(self,
maximum_tokens: Optional[int]=None,
minimum_tokens: Optional[int]=None,
echo: Optional[bool]=None,
temperature: Optional[int]=None,
top_k: Optional[int]=None,
top_p: Optional[int]=None,
presence_penalty: Optional[int]=None,
frequency_penalty: Optional[int]=None,
sequence_penalty: Optional[int]=None,
sequence_penalty_min_length: Optional[int]=None,
repetition_penalties_include_prompt: Optional[bool]=None,
repetition_penalties_include_completion: Optional[bool]=None,
use_multiplicative_presence_penalty: Optional[bool]=None,
use_multiplicative_frequency_penalty: Optional[bool]=None,
use_multiplicative_sequence_penalty: Optional[bool]=None,
penalty_bias: Optional[str]=None,
penalty_exceptions_include_stop_sequences: Optional[bool]=None,
best_of: Optional[int]=None,
n: Optional[int]=None,
logit_bias: Optional[dict]=None,
log_probs: Optional[int]=None,
stop_sequences: Optional[list]=None,
tokens: Optional[bool]=None,
raw_completion: Optional[bool]=None,
disable_optimizations: Optional[bool]=None,
completion_bias_inclusion: Optional[list]=None,
completion_bias_exclusion: Optional[list]=None,
completion_bias_inclusion_first_token_only: Optional[bool]=None,
completion_bias_exclusion_first_token_only: Optional[bool]=None,
contextual_control_threshold: Optional[int]=None,
control_log_additive: Optional[bool]=None) -> None:
def __init__(
self,
maximum_tokens: Optional[int] = None,
minimum_tokens: Optional[int] = None,
echo: Optional[bool] = None,
temperature: Optional[int] = None,
top_k: Optional[int] = None,
top_p: Optional[int] = None,
presence_penalty: Optional[int] = None,
frequency_penalty: Optional[int] = None,
sequence_penalty: Optional[int] = None,
sequence_penalty_min_length: Optional[int] = None,
repetition_penalties_include_prompt: Optional[bool] = None,
repetition_penalties_include_completion: Optional[bool] = None,
use_multiplicative_presence_penalty: Optional[bool] = None,
use_multiplicative_frequency_penalty: Optional[bool] = None,
use_multiplicative_sequence_penalty: Optional[bool] = None,
penalty_bias: Optional[str] = None,
penalty_exceptions_include_stop_sequences: Optional[bool] = None,
best_of: Optional[int] = None,
n: Optional[int] = None,
logit_bias: Optional[dict] = None,
log_probs: Optional[int] = None,
stop_sequences: Optional[list] = None,
tokens: Optional[bool] = None,
raw_completion: Optional[bool] = None,
disable_optimizations: Optional[bool] = None,
completion_bias_inclusion: Optional[list] = None,
completion_bias_exclusion: Optional[list] = None,
completion_bias_inclusion_first_token_only: Optional[bool] = None,
completion_bias_exclusion_first_token_only: Optional[bool] = None,
contextual_control_threshold: Optional[int] = None,
control_log_additive: Optional[bool] = None,
) -> None:
locals_ = locals()
for key, value in locals_.items():
if key != 'self' and value is not None:
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}
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):
@ -160,6 +178,7 @@ def validate_environment(api_key):
headers["Authorization"] = f"Bearer {api_key}"
return headers
def completion(
model: str,
messages: list,
@ -177,9 +196,11 @@ def completion(
headers = validate_environment(api_key)
## Load Config
config = litellm.AlephAlphaConfig.get_config()
for k, v in config.items():
if k not in optional_params: # completion(top_k=3) > aleph_alpha_config(top_k=3) <- allows for dynamic variables to be passed in
config = litellm.AlephAlphaConfig.get_config()
for k, v in config.items():
if (
k not in optional_params
): # completion(top_k=3) > aleph_alpha_config(top_k=3) <- allows for dynamic variables to be passed in
optional_params[k] = v
completion_url = api_base
@ -188,21 +209,17 @@ def completion(
if "control" in model: # follow the ###Instruction / ###Response format
for idx, message in enumerate(messages):
if "role" in message:
if idx == 0: # set first message as instruction (required), let later user messages be input
if (
idx == 0
): # set first message as instruction (required), let later user messages be input
prompt += f"###Instruction: {message['content']}"
else:
if message["role"] == "system":
prompt += (
f"###Instruction: {message['content']}"
)
prompt += f"###Instruction: {message['content']}"
elif message["role"] == "user":
prompt += (
f"###Input: {message['content']}"
)
prompt += f"###Input: {message['content']}"
else:
prompt += (
f"###Response: {message['content']}"
)
prompt += f"###Response: {message['content']}"
else:
prompt += f"{message['content']}"
else:
@ -215,24 +232,27 @@ def completion(
## LOGGING
logging_obj.pre_call(
input=prompt,
api_key=api_key,
additional_args={"complete_input_dict": data},
)
input=prompt,
api_key=api_key,
additional_args={"complete_input_dict": data},
)
## COMPLETION CALL
response = requests.post(
completion_url, headers=headers, data=json.dumps(data), stream=optional_params["stream"] if "stream" in optional_params else False
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"] == True:
return response.iter_lines()
else:
## LOGGING
logging_obj.post_call(
input=prompt,
api_key=api_key,
original_response=response.text,
additional_args={"complete_input_dict": data},
)
input=prompt,
api_key=api_key,
original_response=response.text,
additional_args={"complete_input_dict": data},
)
print_verbose(f"raw model_response: {response.text}")
## RESPONSE OBJECT
completion_response = response.json()
@ -247,18 +267,23 @@ def completion(
for idx, item in enumerate(completion_response["completions"]):
if len(item["completion"]) > 0:
message_obj = Message(content=item["completion"])
else:
else:
message_obj = Message(content=None)
choice_obj = Choices(finish_reason=item["finish_reason"], index=idx+1, message=message_obj)
choice_obj = Choices(
finish_reason=item["finish_reason"],
index=idx + 1,
message=message_obj,
)
choices_list.append(choice_obj)
model_response["choices"] = choices_list
except:
raise AlephAlphaError(message=json.dumps(completion_response), status_code=response.status_code)
raise AlephAlphaError(
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 = len(
encoding.encode(prompt)
)
## 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"]["content"])
)
@ -268,11 +293,12 @@ def completion(
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens
total_tokens=prompt_tokens + completion_tokens,
)
model_response.usage = usage
return model_response
def embedding():
# logic for parsing in - calling - parsing out model embedding calls
pass