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Runtime error
pseudotensor
commited on
Commit
•
b38cab2
1
Parent(s):
8a46296
Update with h2oGPT hash c0762b9528f67797cf2d2ec3a99ae7880d324fec
Browse files
app.py
CHANGED
@@ -2,10 +2,8 @@ import functools
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import inspect
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import sys
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import os
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-
import time
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import traceback
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import typing
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import filelock
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from utils import set_seed, flatten_list, clear_torch_cache, system_info_print, zip_data, save_generate_output
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SEED = 1236
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@@ -60,7 +58,7 @@ def main(
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llama_type: bool = None,
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debug: bool = False,
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-
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share: bool = True,
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local_files_only: bool = False,
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resume_download: bool = True,
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@@ -114,7 +112,7 @@ def main(
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if is_hf:
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# must override share if in spaces
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share = False
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-
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# get defaults
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model_lower = base_model.lower()
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@@ -182,7 +180,7 @@ def main(
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if not eval_sharegpt_as_output:
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model, tokenizer, device = get_model(**locals())
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model_state = [model, tokenizer, device, base_model]
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fun = partial(evaluate, model_state, debug=debug, chat=chat,
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else:
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assert eval_sharegpt_prompts_only > 0
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@@ -816,7 +814,7 @@ body.dark{background:linear-gradient(#0d0d0d,#333333);}"""
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file_output = gr.File()
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# Get flagged data
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zip_data1 = functools.partial(zip_data, root_dirs=['flagged_data_points', kwargs['
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zip_btn.click(zip_data1, inputs=None, outputs=file_output)
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def check_admin_pass(x):
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@@ -1143,7 +1141,7 @@ body.dark{background:linear-gradient(#0d0d0d,#333333);}"""
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input_args_list = ['model_state']
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inputs_kwargs_list = ['debug', 'chat', '
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def get_inputs_list(inputs_dict, model_lower):
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@@ -1206,7 +1204,7 @@ def evaluate(
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src_lang=None,
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tgt_lang=None,
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debug=False,
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-
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chat=False,
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hard_stop_list=None,
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sanitize_bot_response=True,
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@@ -1269,7 +1267,7 @@ def evaluate(
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# encounters = [prompt.count(human) + 1, prompt.count(bot) + 1]
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# stopping only starts once output is beyond prompt
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# 1 human is enough to trigger, but need 2 bots, because very first view back will be bot we added
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stop_words = [human, bot]
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encounters = [1, 2]
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elif prompt_type == 'instruct_vicuna':
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# even below is not enough, generic strings and many ways to encode
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@@ -1300,6 +1298,9 @@ def evaluate(
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# avoid padding in front of tokens
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if tokenizer.pad_token:
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stop_words_ids = [x[1:] if x[0] == tokenizer.pad_token_id and len(x) > 1 else x for x in stop_words_ids]
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stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids, encounters=encounters)])
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else:
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stopping_criteria = StoppingCriteriaList()
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@@ -1420,14 +1421,16 @@ def evaluate(
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raise StopIteration
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yield prompter.get_response(decoded_output, prompt=inputs_decoded,
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sanitize_bot_response=sanitize_bot_response)
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if
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save_generate_output(output=decoded_output, base_model=base_model,
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return
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else:
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outputs = model.generate(**gen_kwargs)
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outputs = [decoder(s) for s in outputs.sequences]
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yield prompter.get_response(outputs, prompt=inputs_decoded,
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sanitize_bot_response=sanitize_bot_response)
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def get_generate_params(model_lower, chat,
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import inspect
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import sys
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import os
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import traceback
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import typing
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from utils import set_seed, flatten_list, clear_torch_cache, system_info_print, zip_data, save_generate_output
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SEED = 1236
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llama_type: bool = None,
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debug: bool = False,
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save_dir: str = None,
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share: bool = True,
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local_files_only: bool = False,
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resume_download: bool = True,
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if is_hf:
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# must override share if in spaces
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share = False
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save_dir = os.getenv('SAVE_DIR', save_dir)
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# get defaults
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model_lower = base_model.lower()
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if not eval_sharegpt_as_output:
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model, tokenizer, device = get_model(**locals())
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model_state = [model, tokenizer, device, base_model]
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fun = partial(evaluate, model_state, debug=debug, chat=chat, save_dir=save_dir)
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else:
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assert eval_sharegpt_prompts_only > 0
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file_output = gr.File()
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# Get flagged data
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zip_data1 = functools.partial(zip_data, root_dirs=['flagged_data_points', kwargs['save_dir']])
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zip_btn.click(zip_data1, inputs=None, outputs=file_output)
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def check_admin_pass(x):
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input_args_list = ['model_state']
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inputs_kwargs_list = ['debug', 'chat', 'save_dir', 'hard_stop_list', 'sanitize_bot_response', 'model_state0']
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def get_inputs_list(inputs_dict, model_lower):
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src_lang=None,
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tgt_lang=None,
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debug=False,
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save_dir=None,
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chat=False,
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hard_stop_list=None,
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sanitize_bot_response=True,
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# encounters = [prompt.count(human) + 1, prompt.count(bot) + 1]
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# stopping only starts once output is beyond prompt
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# 1 human is enough to trigger, but need 2 bots, because very first view back will be bot we added
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stop_words = [human, bot, '\n' + human, '\n' + bot]
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encounters = [1, 2]
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elif prompt_type == 'instruct_vicuna':
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# even below is not enough, generic strings and many ways to encode
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# avoid padding in front of tokens
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if tokenizer.pad_token:
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stop_words_ids = [x[1:] if x[0] == tokenizer.pad_token_id and len(x) > 1 else x for x in stop_words_ids]
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# handle fake \n added
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stop_words_ids = [x[1:] if y[0] == '\n' else x for x,y in zip(stop_words_ids, stop_words)]
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# build stopper
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stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids, encounters=encounters)])
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else:
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stopping_criteria = StoppingCriteriaList()
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raise StopIteration
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yield prompter.get_response(decoded_output, prompt=inputs_decoded,
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sanitize_bot_response=sanitize_bot_response)
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if save_dir and decoded_output:
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save_generate_output(output=decoded_output, base_model=base_model, save_dir=save_dir)
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else:
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outputs = model.generate(**gen_kwargs)
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outputs = [decoder(s) for s in outputs.sequences]
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yield prompter.get_response(outputs, prompt=inputs_decoded,
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sanitize_bot_response=sanitize_bot_response)
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if save_dir and outputs and len(outputs) >= 1:
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decoded_output = prompt + outputs[0]
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save_generate_output(output=decoded_output, base_model=base_model, save_dir=save_dir)
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def get_generate_params(model_lower, chat,
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utils.py
CHANGED
@@ -118,33 +118,31 @@ def _zip_data(root_dirs=None, zip_path='data.zip', base_dir='./'):
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return "data.zip"
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def save_generate_output(output=None, base_model=None,
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try:
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return _save_generate_output(output=output, base_model=base_model,
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except Exception as e:
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traceback.print_exc()
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print('Exception in saving: %s' % str(e))
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def _save_generate_output(output=None, base_model=None,
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"""
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Save conversation to .json, row by row
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Appends if file exists
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"""
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assert
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os.remove(as_file)
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os.makedirs(json_file_path, exist_ok=True)
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json_file_file = os.path.join(json_file_path, 'save.json')
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import json
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if output[-10:] == '\n\n<human>:':
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# remove trailing <human>:
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output = output[:-10]
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with filelock.FileLock("
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# lock logging in case have concurrency
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with open(
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# just add [ at start, and ] at end, and have proper JSON dataset
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f.write(
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" " + json.dumps(
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return "data.zip"
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def save_generate_output(output=None, base_model=None, save_dir=None):
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try:
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return _save_generate_output(output=output, base_model=base_model, save_dir=save_dir)
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except Exception as e:
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traceback.print_exc()
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print('Exception in saving: %s' % str(e))
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def _save_generate_output(output=None, base_model=None, save_dir=None):
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"""
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Save conversation to .json, row by row.
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json_file_path is path to final JSON file. If not in ., then will attempt to make directories.
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Appends if file exists
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"""
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assert save_dir, "save_dir must be provided"
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if os.path.exists(save_dir) and not os.path.isdir(save_dir):
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raise RuntimeError("save_dir already exists and is not a directory!")
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os.makedirs(save_dir, exist_ok=True)
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import json
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if output[-10:] == '\n\n<human>:':
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# remove trailing <human>:
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output = output[:-10]
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with filelock.FileLock("save_dir.lock"):
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# lock logging in case have concurrency
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with open(os.path.join(save_dir, "history.json"), "a") as f:
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# just add [ at start, and ] at end, and have proper JSON dataset
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f.write(
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" " + json.dumps(
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