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fixes to get optional params

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This commit is contained in:
Krrish Dholakia 2023-10-02 14:44:11 -07:00
parent 0ae93d1112
commit 8c48af11c2
9 changed files with 86 additions and 90 deletions
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litellm/__pycache__/main.cpython-311.pyc
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litellm/__pycache__/utils.cpython-311.pyc
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72
litellm/llms/palm.py
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@ -56,48 +56,44 @@ def completion(
## COMPLETION CALL
response = palm.chat(messages=prompt)
## LOGGING
logging_obj.post_call(
input=prompt,
api_key="",
original_response=response,
additional_args={"complete_input_dict": {}},
)
print_verbose(f"raw model_response: {response}")
## RESPONSE OBJECT
completion_response = response.last
if "stream" in optional_params and optional_params["stream"] == True:
return response.iter_lines()
if "error" in completion_response:
raise PalmError(
message=completion_response["error"],
status_code=response.status_code,
)
else:
## LOGGING
logging_obj.post_call(
input=prompt,
api_key="",
original_response=response,
additional_args={"complete_input_dict": {}},
)
print_verbose(f"raw model_response: {response}")
## RESPONSE OBJECT
completion_response = response.last
try:
model_response["choices"][0]["message"]["content"] = completion_response
except:
raise PalmError(message=json.dumps(completion_response), status_code=response.status_code)
if "error" in completion_response:
raise PalmError(
message=completion_response["error"],
status_code=response.status_code,
)
else:
try:
model_response["choices"][0]["message"]["content"] = completion_response
except:
raise PalmError(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)
)
completion_tokens = len(
encoding.encode(model_response["choices"][0]["message"]["content"])
)
## 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"])
)
model_response["created"] = time.time()
model_response["model"] = "palm/" + model
model_response["usage"] = {
"prompt_tokens": prompt_tokens,
"completion_tokens": completion_tokens,
"total_tokens": prompt_tokens + completion_tokens,
}
return model_response
model_response["created"] = time.time()
model_response["model"] = "palm/" + model
model_response["usage"] = {
"prompt_tokens": prompt_tokens,
"completion_tokens": completion_tokens,
"total_tokens": prompt_tokens + completion_tokens,
}
return model_response
def embedding():
# logic for parsing in - calling - parsing out model embedding calls

77
litellm/llms/sagemaker.py
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@ -6,6 +6,7 @@ import time
from typing import Callable
from litellm.utils import ModelResponse, get_secret
import sys
from copy import deepcopy
class SagemakerError(Exception):
def __init__(self, status_code, message):
@ -60,9 +61,12 @@ def completion(
)
else:
prompt += f"{message['content']}"
# pop streaming if it's in the optional params as 'stream' raises an error with sagemaker
inference_params = deepcopy(optional_params)
inference_params.pop("stream", None)
data = {
"inputs": prompt,
"parameters": optional_params
"parameters": inference_params
}
## LOGGING
@ -79,46 +83,43 @@ def completion(
CustomAttributes="accept_eula=true",
)
response = response["Body"].read().decode("utf8")
if "stream" in optional_params and optional_params["stream"] == True:
return response.iter_lines()
## LOGGING
logging_obj.post_call(
input=prompt,
api_key="",
original_response=response,
additional_args={"complete_input_dict": data},
)
print_verbose(f"raw model_response: {response}")
## RESPONSE OBJECT
completion_response = json.loads(response)
if "error" in completion_response:
raise SagemakerError(
message=completion_response["error"],
status_code=response.status_code,
)
else:
## LOGGING
logging_obj.post_call(
input=prompt,
api_key="",
original_response=response,
additional_args={"complete_input_dict": data},
)
print_verbose(f"raw model_response: {response}")
## RESPONSE OBJECT
completion_response = json.loads(response)
if "error" in completion_response:
raise SagemakerError(
message=completion_response["error"],
status_code=response.status_code,
)
else:
try:
model_response["choices"][0]["message"]["content"] = completion_response[0]["generation"]
except:
raise SagemakerError(message=json.dumps(completion_response), status_code=response.status_code)
try:
model_response["choices"][0]["message"]["content"] = completion_response[0]["generation"]
except:
raise SagemakerError(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)
)
completion_tokens = len(
encoding.encode(model_response["choices"][0]["message"]["content"])
)
## 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"])
)
model_response["created"] = time.time()
model_response["model"] = model
model_response["usage"] = {
"prompt_tokens": prompt_tokens,
"completion_tokens": completion_tokens,
"total_tokens": prompt_tokens + completion_tokens,
}
return model_response
model_response["created"] = time.time()
model_response["model"] = model
model_response["usage"] = {
"prompt_tokens": prompt_tokens,
"completion_tokens": completion_tokens,
"total_tokens": prompt_tokens + completion_tokens,
}
return model_response
def embedding():
# logic for parsing in - calling - parsing out model embedding calls

1
litellm/main.py
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@ -977,7 +977,6 @@ def completion(
encoding=encoding,
logging_obj=logging
)
if "stream" in optional_params and optional_params["stream"]==True: ## [BETA]
# sagemaker does not support streaming as of now so we're faking streaming:
# https://discuss.huggingface.co/t/streaming-output-text-when-deploying-on-sagemaker/39611

2
litellm/tests/test_completion.py
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@ -926,7 +926,7 @@ def test_completion_with_fallbacks():
except Exception as e:
pytest.fail(f"Error occurred: {e}")
test_completion_with_fallbacks()
# test_completion_with_fallbacks()
# def test_completion_with_fallbacks_multiple_keys():
# print(f"backup key 1: {os.getenv('BACKUP_OPENAI_API_KEY_1')}")
# print(f"backup key 2: {os.getenv('BACKUP_OPENAI_API_KEY_2')}")

2
litellm/tests/test_streaming.py
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@ -709,7 +709,7 @@ def test_completion_sagemaker_stream():
except Exception as e:
pytest.fail(f"Error occurred: {e}")
# test_completion_sagemaker_stream()
test_completion_sagemaker_stream()
# test on openai completion call
def test_openai_text_completion_call():

14
litellm/utils.py
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@ -977,9 +977,9 @@ def get_optional_params( # use the openai defaults
raise ValueError("LiteLLM.Exception: Function calling is not supported by this provider")
def _check_valid_arg(supported_params):
print(f"checking params for {model}")
print(f"params passed in {passed_params}")
print(f"non-default params passed in {non_default_params}")
print_verbose(f"checking params for {model}")
print_verbose(f"params passed in {passed_params}")
print_verbose(f"non-default params passed in {non_default_params}")
unsupported_params = [k for k in non_default_params.keys() if k not in supported_params]
if unsupported_params:
raise ValueError("LiteLLM.Exception: Unsupported parameters passed: {}".format(', '.join(unsupported_params)))
@ -1225,7 +1225,6 @@ def get_optional_params( # use the openai defaults
for k in passed_params.keys():
if k not in default_params.keys():
optional_params[k] = passed_params[k]
print(f"final params going to model: {optional_params}")
return optional_params
def get_llm_provider(model: str, custom_llm_provider: Optional[str] = None):
@ -3441,14 +3440,15 @@ def completion_with_split_tests(models={}, messages=[], use_client=False, overri
def completion_with_fallbacks(**kwargs):
print(f"kwargs inside completion_with_fallbacks: {kwargs}")
nested_kwargs = kwargs.pop("kwargs")
nested_kwargs = kwargs.pop("kwargs", {})
response = None
rate_limited_models = set()
model_expiration_times = {}
start_time = time.time()
original_model = kwargs["model"]
fallbacks = [kwargs["model"]] + nested_kwargs["fallbacks"]
del nested_kwargs["fallbacks"] # remove fallbacks so it's not recursive
fallbacks = [kwargs["model"]] + nested_kwargs.get("fallbacks", [])
if "fallbacks" in nested_kwargs:
del nested_kwargs["fallbacks"] # remove fallbacks so it's not recursive
while response == None and time.time() - start_time < 45:
for model in fallbacks:

2
pyproject.toml
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@ -1,6 +1,6 @@
[tool.poetry]
name = "litellm"
version = "0.1.811"
version = "0.1.812"
description = "Library to easily interface with LLM API providers"
authors = ["BerriAI"]
license = "MIT License"