github/DB-GPT
1
0
Fork 0
mirror of https://github.com/csunny/DB-GPT.git synced 2025-08-08 03:44:14 +00:00
Code Issues Packages Projects Releases Wiki Activity

fix: remove generate track

Browse Source
This commit is contained in:
csunny 2023-05-31 14:25:06 +08:00
parent 4fb7ed5a4b
commit c84cf762cd
1 changed files with 19 additions and 19 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

38
pilot/model/guanaco_llm.py
View File

@ -32,30 +32,30 @@ def guanaco_generate_output(model, tokenizer, params, device, context_len=2048,
"max_new_tokens": max_new_tokens,
}
if stream_output:
# Stream the reply 1 token at a time.
# This is based on the trick of using 'stopping_criteria' to create an iterator,
# from https://github.com/oobabooga/text-generation-webui/blob/ad37f396fc8bcbab90e11ecf17c56c97bfbd4a9c/modules/text_generation.py#L216-L243.
# if stream_output:
# # Stream the reply 1 token at a time.
# # This is based on the trick of using 'stopping_criteria' to create an iterator,
# # from https://github.com/oobabooga/text-generation-webui/blob/ad37f396fc8bcbab90e11ecf17c56c97bfbd4a9c/modules/text_generation.py#L216-L243.
def generate_with_callback(callback=None, **kwargs):
kwargs.setdefault("stopping_criteria", transformers.StoppingCriteriaList())
kwargs["stopping_criteria"].append(Stream(callback_func=callback))
with torch.no_grad():
model.generate(**kwargs)
# def generate_with_callback(callback=None, **kwargs):
# kwargs.setdefault("stopping_criteria", transformers.StoppingCriteriaList())
# kwargs["stopping_criteria"].append(Stream(callback_func=callback))
# with torch.no_grad():
# model.generate(**kwargs)
def generate_with_streaming(**kwargs):
return Iteratorize(generate_with_callback, kwargs, callback=None)
# def generate_with_streaming(**kwargs):
# return Iteratorize(generate_with_callback, kwargs, callback=None)
with generate_with_streaming(**generate_params) as generator:
for output in generator:
# new_tokens = len(output) - len(input_ids[0])
decoded_output = tokenizer.decode(output)
# with generate_with_streaming(**generate_params) as generator:
# for output in generator:
# # new_tokens = len(output) - len(input_ids[0])
# decoded_output = tokenizer.decode(output)
if output[-1] in [tokenizer.eos_token_id]:
break
# if output[-1] in [tokenizer.eos_token_id]:
# break
yield decoded_output.split("### Response:")[-1].strip()
return # early return for stream_output
# yield decoded_output.split("### Response:")[-1].strip()
# return # early return for stream_output
with torch.no_grad():
generation_output = model.generate(