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compiler.py
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compiler.py
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import os
import time
import tqdm
import ujson
import random
import subprocess
import dsp
from datasets.fingerprint import Hasher
if os.environ.get('DSP_NOTEBOOK_CACHEDIR'):
training_data_directory = os.path.join(os.environ.get('DSP_NOTEBOOK_CACHEDIR'), 'compiler')
else:
training_data_directory = 'cache/compiler'
compilations_assumed_to_exist={'ft-zvEdzQVQ5xwlxvNPrxl6kpnw': 'ada:ft-stanfordpraglab-2023-02-09-19-50-49'}
def openai_check_finetune(jobname):
if dsp.settings.force_reuse_cached_compilation and jobname in compilations_assumed_to_exist:
return compilations_assumed_to_exist[jobname]
command = f"""openai api fine_tunes.get -i {jobname}"""
print(command)
result = subprocess.run(command.split(), stdout=subprocess.PIPE)
output = result.stdout.decode("utf-8").strip()
try:
output = ujson.loads(output)
if output['status'] == 'succeeded':
return output['fine_tuned_model']
if output['status'] in ['pending', 'running']:
print(f'Compiling, run ```openai api fine_tunes.follow -i {jobname}``` for details...')
time.sleep(60)
return openai_check_finetune(jobname)
except:
pass
return False
def convert_to_training_point2(y, inputs, outputs, template):
assert len(inputs) + len(outputs) == len(template.fields)
y_ = dsp.Example(**{f: y[f] for f in inputs}, demos=[])
prompt = template(y_, show_guidelines=False)
completion = y[outputs[0]]
output_fields = template.fields[len(inputs):]
for field in output_fields[1:]:
completion += f"\n\n{field.name} " + y[field.output_variable]
completion = " " + completion + " </s>"
return {'prompt': prompt, 'completion': completion}
def simulate(program, input_examples):
training_data = []
for input_example in tqdm.tqdm(input_examples):
prediction = program(input_example)
if prediction is not None:
# assert len(prediction.compiling_stages) == 2, "TMP"
for stage in prediction.compiling_stages:
name, template, inputs, outputs = stage['name'], stage['template'], stage['inputs'], stage['outputs']
training_data.append(convert_to_training_point2(prediction.get(name), inputs, outputs, template))
r = random.Random(0)
r.shuffle(training_data)
return training_data
def openai_finetune_(name, target):
training_data_path = name_to_path(name)
# Launch the fine-tune on the path
command = f"""openai api fine_tunes.create -t {training_data_path} -m {target} --n_epochs 4 --learning_rate_multiplier 0.05 --no_check_if_files_exist"""
print(command)
# command = """python script.py"""
process = subprocess.Popen(command.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE)
while line := process.stdout.readline().decode().strip():
if 'created fine-tune:' in line.lower():
jobname = line.split()[-1]
break
# if 'costs $' in line.lower():
# cost = line.split()[-1]
# break
# assert cost[0] == '$'
# if float(cost[1:]) > 300:
# print(f'Got cost {cost} -- you may wanna cancel the job: openai api fine_tunes.cancel -i {jobname}')
# print(cost)
print(jobname)
# Block until it's done
ft = openai_check_finetune(jobname)
assert ft, ft
# Return its name
return (jobname, ft)
def openai_finetune(name, target):
print(name)
training_data_path = name_to_path(name)
training_data_path += '.model'
# if path + stuff exists, load the tuple from it
try:
with open(training_data_path) as f:
jobname, ft = ujson.loads(f.readline())
if openai_check_finetune(jobname):
return jobname, ft
except:
pass
jobname, ft = openai_finetune_(name, target)
with open(training_data_path, 'w') as f:
f.write(ujson.dumps((jobname, ft)) + '\n')
return jobname, ft
def name_to_path(name):
if not os.path.exists(training_data_directory):
os.makedirs(training_data_directory)
training_data_path = os.path.join(training_data_directory, f'{name}.jsonl')
return training_data_path
# 3. Check that the output file name has status "success" (not deleted or non-existent). Otherwise, re-call with n = n+1.
def finetune(training_data, target):
name = Hasher.hash(training_data)
training_data_path = name_to_path(name)
with open(training_data_path, 'w') as f:
for line in training_data:
f.write(ujson.dumps(line) + '\n')
jobname, ft = openai_finetune(name, target)
print(ft)
ft = dsp.GPT3(model=ft, stop=" </s>")
return ft
# 4. Return updated program.
def compile(program, examples, target='ada'):
training_data = simulate(program, examples)
compiled_lm = finetune(training_data, target=target)
def compiled_program(*args, **kwargs):
with dsp.settings.context(compiled_lm=compiled_lm, compiling=False):
return program(*args, **kwargs)
compiled_program.lm = compiled_lm
return compiled_program