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(fix) litellm.text_completion raises a non-blocking error on simple usage (#6546)

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* unit test test_huggingface_text_completion_logprobs

* fix return TextCompletionHandler convert_chat_to_text_completion

* fix hf rest api

* fix test_huggingface_text_completion_logprobs

* fix linting errors

* fix importLiteLLMResponseObjectHandler

* fix test for LiteLLMResponseObjectHandler

* fix test text completion
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Ishaan Jaff 2024-11-05 05:17:48 +05:30 • committed by GitHub
parent 67ddf55ebd
commit 58ce30acee
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6 changed files with 374 additions and 111 deletions
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71
litellm/utils.py
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@ -71,6 +71,7 @@ from litellm.litellm_core_utils.get_llm_provider_logic import (
)
from litellm.litellm_core_utils.llm_request_utils import _ensure_extra_body_is_safe
from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import (
LiteLLMResponseObjectHandler,
_handle_invalid_parallel_tool_calls,
convert_to_model_response_object,
convert_to_streaming_response,
@ -8388,76 +8389,6 @@ def get_valid_models() -> List[str]:
return [] # NON-Blocking
# used for litellm.text_completion() to transform HF logprobs to OpenAI.Completion() format
def transform_logprobs(hf_response):
# Initialize an empty list for the transformed logprobs
transformed_logprobs = []
# For each Hugging Face response, transform the logprobs
for response in hf_response:
# Extract the relevant information from the response
response_details = response["details"]
top_tokens = response_details.get("top_tokens", {})
# Initialize an empty list for the token information
token_info = {
"tokens": [],
"token_logprobs": [],
"text_offset": [],
"top_logprobs": [],
}
for i, token in enumerate(response_details["prefill"]):
# Extract the text of the token
token_text = token["text"]
# Extract the logprob of the token
token_logprob = token["logprob"]
# Add the token information to the 'token_info' list
token_info["tokens"].append(token_text)
token_info["token_logprobs"].append(token_logprob)
# stub this to work with llm eval harness
top_alt_tokens = {"": -1, "": -2, "": -3} # noqa: F601
token_info["top_logprobs"].append(top_alt_tokens)
# For each element in the 'tokens' list, extract the relevant information
for i, token in enumerate(response_details["tokens"]):
# Extract the text of the token
token_text = token["text"]
# Extract the logprob of the token
token_logprob = token["logprob"]
top_alt_tokens = {}
temp_top_logprobs = []
if top_tokens != {}:
temp_top_logprobs = top_tokens[i]
# top_alt_tokens should look like this: { "alternative_1": -1, "alternative_2": -2, "alternative_3": -3 }
for elem in temp_top_logprobs:
text = elem["text"]
logprob = elem["logprob"]
top_alt_tokens[text] = logprob
# Add the token information to the 'token_info' list
token_info["tokens"].append(token_text)
token_info["token_logprobs"].append(token_logprob)
token_info["top_logprobs"].append(top_alt_tokens)
# Add the text offset of the token
# This is computed as the sum of the lengths of all previous tokens
token_info["text_offset"].append(
sum(len(t["text"]) for t in response_details["tokens"][:i])
)
# Add the 'token_info' list to the 'transformed_logprobs' list
transformed_logprobs = token_info
return transformed_logprobs
def print_args_passed_to_litellm(original_function, args, kwargs):
try:
# we've already printed this for acompletion, don't print for completion