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fixing optional param mapping

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This commit is contained in:
Krrish Dholakia 2023-10-02 14:14:30 -07:00
parent 7cec308a2c
commit 8b60d797e1
6 changed files with 32 additions and 21 deletions
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litellm/__pycache__/main.cpython-311.pyc
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11
litellm/main.py
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@ -202,6 +202,7 @@ def completion(
- If 'mock_response' is provided, a mock completion response is returned for testing or debugging.
"""
######### unpacking kwargs #####################
args = locals()
return_async = kwargs.get('return_async', False)
mock_response = kwargs.get('mock_response', None)
api_key = kwargs.get('api_key', None)
@ -216,9 +217,8 @@ def completion(
metadata = kwargs.get('metadata', None)
fallbacks = kwargs.get('fallbacks', [])
######## end of unpacking kwargs ###########
args = locals()
openai_params = ["functions", "function_call", "temperature", "temperature", "top_p", "n", "stream", "stop", "max_tokens", "presence_penalty", "frequency_penalty", "logit_bias", "user", "metadata"]
litellm_params = ["caching", "return_async", "mock_response", "api_key", "api_version", "api_base", "force_timeout", "logger_fn", "verbose", "custom_llm_provider", "litellm_logging_obj", "litellm_call_id", "use_client", "id", "metadata", "fallbacks"]
litellm_params = ["acompletion", "caching", "return_async", "mock_response", "api_key", "api_version", "api_base", "force_timeout", "logger_fn", "verbose", "custom_llm_provider", "litellm_logging_obj", "litellm_call_id", "use_client", "id", "metadata", "fallbacks"]
default_params = openai_params + litellm_params
non_default_params = {k: v for k,v in kwargs.items() if k not in default_params} # model-specific params - pass them straight to the model/provider
if mock_response:
@ -797,7 +797,7 @@ def completion(
logging_obj=logging
)
# fake palm streaming
if stream == True:
if "stream" in optional_params and optional_params["stream"] == True:
# fake streaming for palm
resp_string = model_response["choices"][0]["message"]["content"]
response = CustomStreamWrapper(
@ -836,7 +836,6 @@ def completion(
if k not in optional_params:
optional_params[k] = v
print(f"optional_params: {optional_params}")
## LOGGING
logging.pre_call(input=prompt, api_key=None, additional_args={"complete_input_dict": optional_params})
@ -979,7 +978,7 @@ def completion(
logging_obj=logging
)
if stream==True: ## [BETA]
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
# "SageMaker is currently not supporting streaming responses."
@ -1009,7 +1008,7 @@ def completion(
)
if stream == True:
if "stream" in optional_params and optional_params["stream"] == True:
# don't try to access stream object,
response = CustomStreamWrapper(
iter(model_response), model, custom_llm_provider="bedrock", logging_obj=logging

4
litellm/tests/test_async_fn.py
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@ -19,10 +19,8 @@ async def test_get_response():
response = await acompletion(model="gpt-3.5-turbo", messages=messages)
except Exception as e:
pass
return response
# response = asyncio.run(test_get_response())
response = asyncio.run(test_get_response())
# print(response)
@pytest.mark.asyncio

7
litellm/tests/test_completion.py
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@ -47,7 +47,6 @@ def test_completion_claude():
print(response.response_ms)
except Exception as e:
pytest.fail(f"Error occurred: {e}")
test_completion_claude()
def test_completion_claude_max_tokens():
try:
@ -531,7 +530,7 @@ def test_completion_openai_with_more_optional_params():
except Exception as e:
pytest.fail(f"Error occurred: {e}")
test_completion_openai_with_more_optional_params()
# test_completion_openai_with_more_optional_params()
# def test_completion_openai_azure_with_functions():
# function1 = [
# {
@ -916,7 +915,8 @@ def test_completion_bedrock_ai21():
def test_completion_with_fallbacks():
fallbacks = ["gpt-3.5-turb", "gpt-3.5-turbo", "command-nightly"]
print(f"RUNNING TEST COMPLETION WITH FALLBACKS - test_completion_with_fallbacks")
fallbacks = ["gpt-3.5-turbo", "gpt-3.5-turbo", "command-nightly"]
try:
response = completion(
model="bad-model", messages=messages, force_timeout=120, fallbacks=fallbacks
@ -926,6 +926,7 @@ def test_completion_with_fallbacks():
except Exception as e:
pytest.fail(f"Error occurred: {e}")
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')}")

31
litellm/utils.py
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@ -1072,13 +1072,15 @@ def get_optional_params( # use the openai defaults
optional_params["stop"] = stop #TG AI expects a list, example ["\n\n\n\n","<|endoftext|>"]
elif custom_llm_provider == "palm":
## check if unsupported param passed in
supported_params = ["temperature", "top_p"]
supported_params = ["temperature", "top_p", "stream"]
_check_valid_arg(supported_params=supported_params)
if temperature:
optional_params["temperature"] = temperature
if top_p:
optional_params["top_p"] = top_p
if stream:
optional_params["stream"] = stream
elif (
custom_llm_provider == "vertex_ai"
):
@ -1104,7 +1106,7 @@ def get_optional_params( # use the openai defaults
return_full_text: If True, input text will be part of the output generated text. If specified, it must be boolean. The default value for it is False.
"""
## check if unsupported param passed in
supported_params = ["temperature", "max_tokens"]
supported_params = ["temperature", "max_tokens", "stream"]
_check_valid_arg(supported_params=supported_params)
if max_tokens:
@ -1113,13 +1115,15 @@ def get_optional_params( # use the openai defaults
optional_params["temperature"] = temperature
if top_p:
optional_params["top_p"] = top_p
if stream:
optional_params["stream"] = stream
else:
## check if unsupported param passed in
supported_params = []
_check_valid_arg(supported_params=supported_params)
elif custom_llm_provider == "bedrock":
if "ai21" in model:
supported_params = ["max_tokens", "temperature", "stop", "top_p"]
supported_params = ["max_tokens", "temperature", "stop", "top_p", "stream"]
_check_valid_arg(supported_params=supported_params)
# params "maxTokens":200,"temperature":0,"topP":250,"stop_sequences":[],
# https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=j2-ultra
@ -1131,8 +1135,10 @@ def get_optional_params( # use the openai defaults
optional_params["stop_sequences"] = stop
if top_p:
optional_params["topP"] = top_p
if stream:
optional_params["stream"] = stream
elif "anthropic" in model:
supported_params = ["max_tokens", "temperature", "stop", "top_p"]
supported_params = ["max_tokens", "temperature", "stop", "top_p", "stream"]
_check_valid_arg(supported_params=supported_params)
# anthropic params on bedrock
# \"max_tokens_to_sample\":300,\"temperature\":0.5,\"top_p\":1,\"stop_sequences\":[\"\\\\n\\\\nHuman:\"]}"
@ -1146,8 +1152,10 @@ def get_optional_params( # use the openai defaults
optional_params["top_p"] = top_p
if stop:
optional_params["stop_sequences"] = stop
if stream:
optional_params["stream"] = stream
elif "amazon" in model: # amazon titan llms
supported_params = ["max_tokens", "temperature", "stop", "top_p"]
supported_params = ["max_tokens", "temperature", "stop", "top_p", "stream"]
_check_valid_arg(supported_params=supported_params)
# see https://us-west-2.console.aws.amazon.com/bedrock/home?region=us-west-2#/providers?model=titan-large
if max_tokens:
@ -1158,7 +1166,8 @@ def get_optional_params( # use the openai defaults
optional_params["stopSequences"] = stop
if top_p:
optional_params["topP"] = top_p
if stream:
optional_params["stream"] = stream
elif model in litellm.aleph_alpha_models:
supported_params = ["max_tokens", "stream", "top_p", "temperature", "presence_penalty", "frequency_penalty", "n", "stop"]
_check_valid_arg(supported_params=supported_params)
@ -3431,13 +3440,15 @@ def completion_with_split_tests(models={}, messages=[], use_client=False, overri
return litellm.completion(model=selected_llm, messages=messages, use_client=use_client, **kwargs)
def completion_with_fallbacks(**kwargs):
print(f"kwargs inside completion_with_fallbacks: {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"]] + kwargs["fallbacks"]
del kwargs["fallbacks"] # remove fallbacks so it's not recursive
fallbacks = [kwargs["model"]] + nested_kwargs["fallbacks"]
del nested_kwargs["fallbacks"] # remove fallbacks so it's not recursive
while response == None and time.time() - start_time < 45:
for model in fallbacks:
@ -3466,8 +3477,10 @@ def completion_with_fallbacks(**kwargs):
if kwargs.get("model"):
del kwargs["model"]
print(f"trying to make completion call with model: {model}")
kwargs = {**kwargs, **nested_kwargs} # combine the openai + litellm params at the same level
response = litellm.completion(**kwargs, model=model)
print(f"response: {response}")
if response != None:
return response