phoenix/litellm
1
0
Fork 0
forked from phoenix/litellm-mirror
Code Pull requests Activity

(feat) parallel HF text completion + completion_with_retries show exception

Browse source
This commit is contained in:
ishaan-jaff 2023-11-06 17:58:06 -08:00
parent 7219fcb968
commit 2498d95dc5
1 changed files with 20 additions and 15 deletions
Download patch file Download diff file Expand all files Collapse all files

35
litellm/main.py
View file

@ -1412,8 +1412,8 @@ def completion_with_retries(*args, **kwargs):
""" """
try: try:
import tenacity import tenacity
except: except Exception as e:
raise Exception("tenacity import failed please run `pip install tenacity`") raise Exception(f"tenacity import failed please run `pip install tenacity`. Error{e}")
num_retries = kwargs.pop("num_retries", 3) num_retries = kwargs.pop("num_retries", 3)
retryer = tenacity.Retrying(stop=tenacity.stop_after_attempt(num_retries), reraise=True) retryer = tenacity.Retrying(stop=tenacity.stop_after_attempt(num_retries), reraise=True)
@ -1989,27 +1989,32 @@ def text_completion(
# processing prompt - users can pass raw tokens to OpenAI Completion() # processing prompt - users can pass raw tokens to OpenAI Completion()
if type(prompt) == list: if type(prompt) == list:
import concurrent.futures
tokenizer = tiktoken.encoding_for_model("text-davinci-003") tokenizer = tiktoken.encoding_for_model("text-davinci-003")
## if it's a 2d list - each element in the list is a text_completion() request ## if it's a 2d list - each element in the list is a text_completion() request
if len(prompt) > 0 and type(prompt[0]) == list: if len(prompt) > 0 and type(prompt[0]) == list:
responses = [None for x in prompt] # init responses responses = [None for x in prompt] # init responses
for i, individual_prompt in enumerate(prompt): def process_prompt(i, individual_prompt):
decoded_prompt = tokenizer.decode(individual_prompt) # type: ignore decoded_prompt = tokenizer.decode(individual_prompt)
all_params = {**kwargs, **optional_params} # combine optional params and kwargs all_params = {**kwargs, **optional_params}
response = text_completion( response = text_completion(
model = model, # type: ignore model=model,
prompt = decoded_prompt, # type: ignore prompt=decoded_prompt,
num_retries=3,# ensure this does not fail for the batch
*args, *args,
**all_params, **all_params,
) )
responses[i] = response["choices"][0] #print(response)
text_completion_response["id"] = response.get("id", None)
text_completion_response["id"] = response.get("id", None) text_completion_response["object"] = "text_completion"
text_completion_response["object"] = "text_completion" text_completion_response["created"] = response.get("created", None)
text_completion_response["created"] = response.get("created", None) text_completion_response["model"] = response.get("model", None)
text_completion_response["model"] = response.get("model", None) return response["choices"][0]
text_completion_response["choices"] = responses with concurrent.futures.ThreadPoolExecutor() as executor:
text_completion_response["usage"] = response.get("usage", None) futures = [executor.submit(process_prompt, i, individual_prompt) for i, individual_prompt in enumerate(prompt)]
for i, future in enumerate(concurrent.futures.as_completed(futures)):
responses[i] = future.result()
text_completion_response["choices"] = responses
return text_completion_response return text_completion_response
else: else: