def setup_for_interactive(self):
self.get_args()
self.load_state_dict()
self.load_model()
self.commands = Commands()
self.wiki = Wikipedia()
## this is not used but is required for bot software... ##
self.output_lang = Lang('lang')
for i in range(len(self.enc.encoder.items())):
self.output_lang.addWord(self.enc.decode([i]))
### this file may be launched from parent dir!! ###
aiml_file = aiml01
if os.path.isfile(aiml01):
aiml_file = aiml01
else:
aiml_file = aiml02
print(aiml_file, 'aiml')
try:
self.kernel = aimlb.Kernel()
self.kernel.verbose(False)
self.kernel.learn(aiml_file)
self.k = aiml.Kernel()
self.k.verbose(False)
self.k.learn(aiml_file)
except:
print('no aiml')
pass
## do this also with each input... ##
self.prepare_common()
def setup_for_interactive(self):
self.get_args()
self.load_state_dict()
self.load_model()
self.commands = Commands()
## this is not used but is required for bot software...
self.output_lang = Lang('lang')
for i in range(len(self.enc.encoder.items())):
self.output_lang.addWord(self.enc.decode([i]))
## do this also with each input...
self.prepare_common()
def setup_for_interactive(self):
self.get_args()
self.load_state_dict()
self.load_model()
self.commands = Commands()
## this is not used but is required for bot software...
self.output_lang = Lang('lang')
for i in range(len(self.enc.encoder.items())):
self.output_lang.addWord(self.enc.decode([i]))
self.kernel = aiml.Kernel()
self.kernel.verbose(False)
self.kernel.learn("../data/std_startup.xml")
## do this also with each input...
self.prepare_common()
class NMT:
def __init__(self):
self.args = None
self.get_args()
self.state_dict = None
self.config = None
self.device = None
self.model = None
self.enc = None
self.hp_config = None
self.output_lang = None
self.commands = None
self.kernel = None
self.wiki = None
self.common = ''
self.common_pre = ''
self.common_wiki = ''
self.previous_sentences = []
self.sentences_formatted = ''
self.gather_sentences = False
self.recent_in = ''
self.recent_text = ''
self.save_num = 20
self.save_on_failure = False
self.use_common = True
self.reply_aiml = None
self.reply_aiml_dupes = 1
self.token_limit = 1024
self.q_string = ['Q: ']
self.a_string = ['A: ']
self.name = 'Jane'
if True:
self.q_string = ['Q: ', 'Q :', 'Q.']
self.a_string = ['A: ', 'A :', self.name + ':', 'A.']
def setup_for_interactive(self):
self.get_args()
self.load_state_dict()
self.load_model()
self.commands = Commands()
self.wiki = Wikipedia()
## this is not used but is required for bot software... ##
self.output_lang = Lang('lang')
for i in range(len(self.enc.encoder.items())):
self.output_lang.addWord(self.enc.decode([i]))
### this file may be launched from parent dir!! ###
aiml_file = aiml01
if os.path.isfile(aiml01):
aiml_file = aiml01
else:
aiml_file = aiml02
print(aiml_file, 'aiml')
try:
self.kernel = aimlb.Kernel()
self.kernel.verbose(False)
self.kernel.learn(aiml_file)
self.k = aiml.Kernel()
self.k.verbose(False)
self.k.learn(aiml_file)
except:
print('no aiml')
pass
## do this also with each input... ##
self.prepare_common()
def prepare_common(self):
self.common = ''
self.common_pre = ''
#self.common_wiki = ''
a_chars = self.a_string[0]
q_chars = self.q_string[0]
now = datetime.datetime.now()
time = now.strftime("%I:%M %p")
date = now.strftime("%B %d, %Y")
name = self.name
profession = 'student'
location = 'New York'
mood = 'happy'
#key_action_string = '\n ' + a_chars + 'play media.\n'
self.common += ' '
#self.common += q_chars + 'Hello?\n '
if self.reply_aiml is None:
#if self.common_pre == '':
self.common_pre += a_chars + 'Hello. Hi' + '.\n '
self.common += q_chars + 'What is your name?\n '
self.common += a_chars + 'My name is ' + name + '.\n '
self.common += q_chars + 'What time is it?\n '
self.common += a_chars + 'The time is ' + time + ' ' + date + '.\n\n '
#self.common += q_chars + 'What is your job?\n '
self.common += a_chars + 'My job is as a ' + profession + '.\n '
#self.common += q_chars + 'Where are you?\n '
self.common += a_chars + "I am in " + location + '. \n '
#self.common += q_chars + 'How do you feel?\n '
self.common += a_chars + "I feel " + mood + '. \n '
r = self.kernel.respond(self.recent_in)
if r != "":
self.common += '\n '
#self.common += q_chars + self.recent_in + '\n '
self.common += a_chars + r + ". \n "
if self.reply_aiml is not None:
self.common += '\n ' + self.reply_aiml + '\n '
def get_sentence(self, i):
in_01 = i
## aiml and rule based stuff ##
prep_copy_boolean = False
k = i.replace("'", '').replace('?', '').replace('.',
'').replace('!', '')
#print(k,'k')
k = str(k)
r = self.k.respond(k)
url = self.detect_url(r)
z = ''
if url and self.args.apps == True:
print(url)
if url == self.wiki.url_search:
self.wiki.set_topic(r[len(url):])
z = self.wiki.get_text()
self.common_wiki = z
if z == "":
r = 'ok'
i = ''
if url == self.wiki.url_stop and self.common_wiki != "":
self.common_wiki = ''
r = 'ok'
i = ''
if url == self.wiki.url_stop and self.common_wiki == "":
r = 'ok'
i = ''
elif url and url != self.wiki.url_stop:
i = ''
r = ''
elif url and url == self.wiki.url_stop and self.common_wiki == "":
i = ''
r = 'ok'
self.common_wiki = ''
r = r.strip()
r = r.replace('\n', '.').replace('\t', '')
if r.strip() != "" and False:
self.reply_aiml = ''
for _ in range(self.reply_aiml_dupes):
#self.reply_aiml += self.q_string[0] + i + '? \n '
self.reply_aiml += self.a_string[0] + r + '\n\n '
#self.reply_aiml += r + '\n\n '
else:
self.reply_aiml = None
prep_copy_boolean = True
if self.use_common:
self.recent_in = i
i = self.q_string[0] + i + '?'
if self.reply_aiml is None:
s = self.sentences_formatted
else:
s = ''
#i = ''
self.prepare_common()
if self.common_wiki != '':
#print('here 1',i)
self.common_pre = ''
self.common = ''
self.common_wiki = ' '.join(
self.common_wiki.split(' ')
[:self.token_limit // 2 -
len(i.split(' '))]) # -(len(i.split(' ')) + 800)])
#print(self.common_wiki, 'here 2', s)
s = ''
pass
i = self.common_wiki + ' ' + self.common_pre + '\n' + s + "\n" + self.common + '\n' + i
print('', "+" * 10, '\n', i, '\n', '+' * 10)
print(len(i.split(' ')), 'tokens')
### make sure history never gets too big ###
if self.common_wiki == '':
z = i.split(' ')
z = z[max(len(z) - (int(1024 * 3 / 4)), 0):]
i = ' '.join(z)
i = self.prepare_input(i)
self.args.text = i
text = self.text_generator()
#self.recent_text = text
if not self.args.quiet or True: print(text)
text = self.prepare_output(text)
text = re.sub(endoftext, '', text)
'''
print("text g >>", text)
print("text k >>", r)
k_score = self.kernel.bert_score()
g_score = self.kernel.bert_compare(text,in_01)
print('g', g_score, '- k', k_score)
if k_score > g_score and r != '':
text = r
print(text, '<< choose')
'''
print(text, "<<")
self.recent_text = text
if not self.args.no_recent:
self.prep_recent(prep_copy_boolean or True)
################
## if you want to launch apps !!
if self.args.apps is True:
strip = True
if url or len(self.common_wiki) > 2:
if url is None: url = ''
self.recent_in = 'find ' + url
strip = False
elif self.commands.is_command(self.recent_in):
self.commands.do_command(self.recent_in, strip)
return text
def loop(self):
while True:
try:
i = input("> ")
self.get_sentence(i)
except EOFError:
print()
exit()
except KeyboardInterrupt:
print()
exit()
def prepare_input(self, i):
self.random_seed()
if False:
i = self.q_string[0] + i + '?'
else:
i = i + "?"
#print(i)
return i
def prepare_output(self, i):
char_end = ['?', '!']
contains_junk = False
char_junk = [i for i in '{}@^&#']
out = []
for ii in i:
if ii.strip() != "" or ii == ' ':
if ii not in ['*']:
out.append(ii)
elif len(out) > 1:
break
if ii in char_end:
break
if ii in char_junk:
contains_junk = True
break
i = ''.join(out)
i = re.sub('^[\s]{2,}', '', i) ## remove space if it is included
i = i.strip()
for z in self.a_string:
z = z.lower()
if i.lower().startswith(z): i = i[len(z):]
for z in self.q_string:
z = z.lower()
if i.lower().startswith(z): i = i[len(z):]
if len(i.split('?')) > 1:
i = i.split('?')[0]
if len(i.split('!')) > 1:
i = i.split('!')[0]
start = i[:]
num = 0
default = ''
while num < 5:
i = start[:]
out = []
for ii in i.split(' '):
out.append(ii)
if (ii.endswith('[') or ii.endswith('.') or ii.endswith('!')
or ii.endswith('?')
) and len(ii) > 1 and ii.count('.') >= 1:
break
i = ' '.join(out)
if num == 0: default = i
num += 1
if i.strip() == '': i = default
i = re.sub('[;]', '', i)
if contains_junk is True:
i = ''
if self.gather_sentences:
i = i.strip()
for z in self.q_string + self.a_string:
z = z.lower()
if i.lower().startswith(z): i = i[len(z):]
i = re.sub('[?!]', ' ', i)
## long sentences with comma ##
slen = self.args.length
sout = ''
if len(i.split(' ')) > slen // 2 and ',' in i:
for x in i:
if x != ',':
sout += x
elif x == ',':
break
i = sout
return i
def prep_recent(self, prep_copy_boolean=True):
self.recent_in = self.q_string[0] + self.recent_in.strip('.').lower()
self.recent_text = self.a_string[0] + self.recent_text.strip(
'.').lower()
y = 'yes'
n = 'no'
for a in self.previous_sentences:
a = a.replace('.', '')
if (self.recent_text is not None
and len(self.recent_text.split(' ')) == 1
and self.recent_text.lower() in a.lower().split(' ')):
if y not in self.recent_text.lower(
) and n not in self.recent_text.lower():
self.recent_text = None
if self.recent_in is not None and len(
self.recent_in.split(' ')) == 1 and self.recent_in.lower(
) in a.lower().split(' '):
self.recent_in = None
if self.recent_text is not None and self.recent_text.lower().strip(
) == a.lower().strip():
if y not in self.recent_text.lower(
) and n not in self.recent_text.lower():
self.recent_text = None
if self.recent_in is not None and self.recent_in.lower().strip(
) == a.lower().strip():
self.recent_in = None
if not prep_copy_boolean:
self.recent_in = None
self.recent_text = None
if self.recent_in is not None and self.recent_text is not None and 'time' not in self.recent_in and 'name' not in self.recent_in:
self.previous_sentences.extend([self.recent_in, self.recent_text])
if self.save_num > -1:
self.previous_sentences = self.previous_sentences[-self.save_num:]
#print(self.previous_sentences)
s = ''
for k in self.previous_sentences:
k = k.strip().strip('.').strip('\n')
for z in self.a_string + self.q_string:
z = z.lower()
if k.lower().startswith(z) and False: k = k[len(z):]
if len(k) > 0:
s += k + '.\n'
#s = ['---'] + s + ['---']
self.sentences_formatted = s
def detect_url(self, txt):
urls = re.findall(
'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+',
txt)
print(len(urls), 'urls')
if len(urls) > 0:
return urls[0]
else:
return None
#########################################
def random_seed(self):
seed = random.randint(0, 2147483647)
np.random.seed(seed)
torch.random.manual_seed(seed)
torch.cuda.manual_seed(seed)
pass
def get_encoder(self):
print(self.args.source_file, '##')
source_path = self.args.source_file.split('/')[:-1]
source_path = '/'.join(source_path) + '/'
print(source_path, 'for vocab')
with open(realpath + '/' + source_path + '/encoder.json', 'r') as f:
encoder = json.load(f)
print('encoder.json')
f.close()
with open(realpath + '/' + source_path + '/vocab.bpe',
'r',
encoding="utf-8") as ff:
bpe_data = ff.read()
print('bpe data')
ff.close()
bpe_merges = [
tuple(merge_str.split())
for merge_str in bpe_data.split('\n')[1:-1]
]
return Encoder(
encoder=encoder,
bpe_merges=bpe_merges,
)
def get_config(self):
print(self.args.source_file)
source_path = self.args.source_file.split('/')[:-1]
source_path = '/'.join(source_path) + '/'
print(source_path, 'source path', realpath, 'real path')
if '774M' in source_path:
print('774M', 'model specific configs')
#self.use_common = False
self.args.temperature = 1e-4
self.args.top_k = 100
if os.path.isfile(realpath + '/' + source_path + '/config.json'):
print(realpath + '/' + source_path, 'config.json')
with open(realpath + '/' + source_path + '/config.json', 'r') as f:
hp_config = json.load(f)
print(hp_config, 'before')
self.config = GPT2Config(
vocab_size_or_config_json_file=hp_config['vocab_size'],
n_embd=hp_config['n_embd'],
n_layer=hp_config['n_layer'],
n_head=hp_config['n_head'],
# intermediate_size=self.intermediate_size,
# hidden_act=self.hidden_act,
# hidden_dropout_prob=self.hidden_dropout_prob,
# attention_probs_dropout_prob=self.attention_probs_dropout_prob,
n_positions=hp_config['n_positions'],
n_ctx=hp_config['n_ctx']
# type_vocab_size=self.type_vocab_size,
# initializer_range=self.initializer_range
)
else:
self.config = GPT2Config()
print(self.config)
def get_args(self):
parser = argparse.ArgumentParser()
parser.add_argument("--text", type=str, required=False)
parser.add_argument("--quiet", type=bool, default=True)
parser.add_argument("--nsamples", type=int, default=1)
parser.add_argument('--unconditional',
action='store_true',
help='If true, unconditional generation.')
parser.add_argument("--batch_size", type=int, default=-1)
parser.add_argument("--length", type=int, default=25)
parser.add_argument("--temperature", type=float, default=1e-4)
parser.add_argument("--top_k", type=int, default=40)
parser.add_argument("--apps", type=bool, required=False, default=True)
parser.add_argument(
"--source_file", type=str, required=False, default=location01
) #'../data/tf_gpt2_data/117M/converted/pytorch_model.bin')
parser.add_argument("--no-recent",
type=bool,
default=False,
help="Do not show model recent q and a.")
self.args = parser.parse_args()
def load_model(self):
if self.args.quiet is False or True:
print(self.args, 'args')
if self.args.batch_size == -1:
self.args.batch_size = 1
assert self.args.nsamples % self.args.batch_size == 0
seed = random.randint(0, 2147483647)
np.random.seed(seed)
torch.random.manual_seed(seed)
torch.cuda.manual_seed(seed)
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.get_config()
#self.get_args()
# Load Model
self.enc = self.get_encoder()
if self.config is None:
print('change config')
self.config = GPT2Config()
self.model = GPT2LMHeadModel(self.config)
self.model = load_weight(self.model, self.state_dict)
self.model.to(self.device)
self.model.eval()
print(self.config, 'config')
def text_generator(self):
if self.args.length == -1:
self.args.length = self.config.n_ctx // 2
elif self.args.length > self.config.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" %
self.config.n_ctx)
if self.args.quiet is False: print(self.args.text)
context_tokens = self.enc.encode(self.args.text)
generated = 0
for _ in range(self.args.nsamples // self.args.batch_size):
out = sample_sequence(model=self.model,
length=self.args.length,
context=context_tokens
if not self.args.unconditional else None,
start_token=self.enc.encoder['<|endoftext|>']
if self.args.unconditional else None,
batch_size=self.args.batch_size,
temperature=self.args.temperature,
top_k=self.args.top_k,
device=self.device)
out = out[:, len(context_tokens):].tolist()
for i in range(self.args.batch_size):
generated += 1
text = self.enc.decode(out[i])
if self.args.quiet is False:
print("=" * 40 + " SAMPLE " + str(generated) + " " +
"=" * 40)
print(text)
return text
def load_state_dict(self):
p = realpath + '/' + self.args.source_file
print(p)
if os.path.exists(p):
self.state_dict = torch.load(
p
) #, map_location='cpu') # if not torch.cuda.is_available() else None)
print('load p', p)
else:
print('Please download gpt2-pytorch_model.bin')
sys.exit()
class NMT:
def __init__(self):
self.args = None
self.state_dict = None
self.config = None
self.device = None
self.model = None
self.enc = None
self.hp_config = None
self.output_lang = None
self.commands = None
self.common = ''
self.previous_sentences = []
self.gather_sentences = False
self.recent_in = ''
self.recent_text = ''
self.save_num = 10
self.save_on_failure = False
self.use_common = True
self.q_string = ['Q: ']
self.a_string = ['A: ']
self.name = 'Jane'
if True:
self.q_string = [ 'Q: ', 'Q :', 'Q.']
self.a_string = [ 'A: ', 'A :', self.name+':', 'A.']
def setup_for_interactive(self):
self.get_args()
self.load_state_dict()
self.load_model()
self.commands = Commands()
## this is not used but is required for bot software...
self.output_lang = Lang('lang')
for i in range(len(self.enc.encoder.items())):
self.output_lang.addWord(self.enc.decode([i]))
## do this also with each input...
self.prepare_common()
def prepare_common(self):
self.common = ''
a_chars = '' #self.a_string[0]
q_chars = self.q_string[0]
now = datetime.datetime.now()
time = now.strftime("%I:%M %p")
date = now.strftime("%B %d, %Y")
name = self.name
profession = 'student'
location = 'New York'
key_action_string = '\n ' + a_chars + 'play media.\n'
key_phrases = [
'Play music? ' + key_action_string,
'Play movies? ' + key_action_string,
'Play radio? ' + key_action_string,
'Play any song? ' + key_action_string,
'Play any video? ' + key_action_string,
'Play any movie? ' + key_action_string,
'Play a song? ' + key_action_string,
'Play a video? ' + key_action_string,
'Play a movie? ' + key_action_string,
]## doesn't work??
#self.common += self.a_string[0] + 'I am ' + self.a_string[0] + '. \n '
self.common += a_chars + 'A: Hello' + '.\n '
self.common += a_chars + 'My name is ' + name + '.\n '
self.common += a_chars + 'The time is ' + time + ' ' + date + '.\n '
self.common += a_chars + 'My job is as a ' + profession + '.\n '
self.common += a_chars + "I am in " + location + '. \n'
if self.args.apps and False:
self.common +=' ' + ' '.join([q_chars + i for i in key_phrases])
def get_sentence(self, i):
a_chars = '' # self.a_string[0]
q_chars = '' # self.q_string[0]
if self.use_common:
self.recent_in = i
if self.save_num > -1:
self.previous_sentences = self.previous_sentences[-self.save_num:]
s = []
for k in self.previous_sentences :
k = k.strip().strip('..')
if not k.endswith('?'):
k = a_chars + k + '.\n'
else:
k = q_chars + k + '\n'
pass
s.append(k)
i = '\n\n' + self.q_string[0] + i.capitalize() # + '\n' + endoftext
s.append(i)
self.prepare_common()
i = self.common + "\n" + "\n" + ' ' + ' '.join(s)
print('',"+" * 10, '\n', i, '\n','+' * 10)
i = self.prepare_input(i)
self.args.text = i
text = self.text_generator()
self.recent_text = text
if not self.args.quiet or True: print(text)
text = self.prepare_output(text)
text = re.sub(endoftext, '', text)
print(text,"<")
## if you want to launch apps !!
if self.args.apps is True:
if self.commands.is_command(self.recent_in):
self.commands.do_command(self.recent_in)
#self.previous_sentences = []
return text
def loop(self):
while True:
try:
i = input("> ")
self.get_sentence(i)
except EOFError:
print()
exit()
except KeyboardInterrupt:
print()
exit()
def prepare_input(self, i):
self.random_seed()
if True:
i = self.q_string[0] + i + '?'
else:
i = i + "?"
return i
def prepare_output(self, i):
char_end = ['?','!']
contains_junk = False
char_junk = [i for i in '{[]}@$%^&#']
out = []
for ii in i:
if ii.strip() != "" or ii == ' ':
if ii not in ['*']:
out.append(ii)
elif len(out) > 1:
break
if ii in char_end:
break
if ii in char_junk:
contains_junk = True
break
i = ''.join(out)
i = i.strip()
for z in self.a_string:
z = z.lower()
if i.lower().startswith(z): i = i[len(z):]
for z in self.q_string:
z = z.lower()
if i.lower().startswith(z): i = i[len(z):]
start = i[:]
num = 0
default = ''
while num < 5:
i = start[:]
out = []
for ii in i.split(' '):
out.append(ii)
if (ii.endswith('.') or ii.endswith('!') or ii.endswith('?')) and len(ii) > 1 and ii.count('.') >= 1:
break
i = ' '.join(out)
if num == 0: default = i
if (i.strip() + '.' not in self.previous_sentences or len(start) <= 1) and len(i.strip()) > 0:
if not self.args.quiet: print('take first:', i.strip())
break
else:
if i.strip() == '':
i = ' '
if not self.args.quiet: print('take next:', '-'+i.strip()+'-')
start = start[len(i):]
num += 1
if i.strip() == '': i = default
i = re.sub('[;]','',i)
if contains_junk is True:
i = ''
if self.gather_sentences:
i = i.strip()
for z in self.q_string:
z = z.lower()
if i.lower().startswith(z): i = i[len(z):]
i = re.sub('[?!]', ' ', i)
#if self.recent_in.strip() + '?' not in self.previous_sentences or True:
self.previous_sentences.append(self.recent_in.strip() + "? " + i)
#elif self.save_on_failure:
# self.recent_text = re.sub('[\n]',' ', self.recent_text)
# l = self.a_string + self.q_string + ['Q.', 'A.']
# for k in l:
# self.recent_text = re.sub(k, '', self.recent_text)
# self.previous_sentences.append(self.recent_in.strip() + '?' + self.recent_text + '\n')
return i
#########################################
def random_seed(self):
seed = random.randint(0, 2147483647)
np.random.seed(seed)
torch.random.manual_seed(seed)
torch.cuda.manual_seed(seed)
pass
def get_encoder(self):
print(self.args.source_file)
source_path = self.args.source_file.split('/')[:-1]
source_path = '/'.join(source_path) + '/'
print(source_path)
with open(realpath + '/' + source_path + '/encoder.json', 'r') as f:
encoder = json.load(f)
with open(realpath + '/' + source_path + '/vocab.bpe', 'r', encoding="utf-8") as f:
bpe_data = f.read()
bpe_merges = [tuple(merge_str.split()) for merge_str in bpe_data.split('\n')[1:-1]]
return Encoder(
encoder=encoder,
bpe_merges=bpe_merges,
)
def get_config(self):
print(self.args.source_file)
source_path = self.args.source_file.split('/')[:-1]
source_path = '/'.join(source_path) + '/'
print(source_path)
if '774M' in source_path :
print('774M', 'model specific configs')
#self.use_common = False
self.args.temperature = 1e-10
self.args.top_k = 100
if os.path.isfile(realpath + '/' + source_path + '/config.json'):
with open(realpath + '/' + source_path + '/config.json', 'r') as f:
hp_config = json.load(f)
print(hp_config)
self.config = GPT2Config(
vocab_size_or_config_json_file=hp_config['vocab_size'],
n_embd=hp_config['n_embd'],
n_layer=hp_config['n_layer'],
n_head=hp_config['n_head'],
# intermediate_size=self.intermediate_size,
# hidden_act=self.hidden_act,
# hidden_dropout_prob=self.hidden_dropout_prob,
# attention_probs_dropout_prob=self.attention_probs_dropout_prob,
n_positions=hp_config['n_positions'],
n_ctx=hp_config['n_ctx']
# type_vocab_size=self.type_vocab_size,
# initializer_range=self.initializer_range
)
print(self.config)
def get_args(self ):
parser = argparse.ArgumentParser()
parser.add_argument("--text", type=str, required=False)
parser.add_argument("--quiet", type=bool, default=True)
parser.add_argument("--nsamples", type=int, default=1)
parser.add_argument('--unconditional', action='store_true', help='If true, unconditional generation.')
parser.add_argument("--batch_size", type=int, default=-1)
parser.add_argument("--length", type=int, default=25)
parser.add_argument("--temperature", type=float, default=0.0001)
parser.add_argument("--top_k", type=int, default=40)
parser.add_argument("--apps", type=bool, required=False, default=False)
parser.add_argument("--source_file", type=str, required=False, default='torch_gpt2/GPT2/gpt2-pytorch_model.bin')
self.args = parser.parse_args()
def load_model(self):
if self.args.quiet is False:
print(self.args)
if self.args.batch_size == -1:
self.args.batch_size = 1
assert self.args.nsamples % self.args.batch_size == 0
seed = random.randint(0, 2147483647)
np.random.seed(seed)
torch.random.manual_seed(seed)
torch.cuda.manual_seed(seed)
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.get_config()
# Load Model
self.enc = self.get_encoder()
if self.config is None: self.config = GPT2Config()
self.model = GPT2LMHeadModel(self.config)
self.model = load_weight(self.model, self.state_dict)
self.model.to(self.device)
self.model.eval()
print(self.config)
def text_generator(self):
if self.args.length == -1:
self.args.length = self.config.n_ctx // 2
elif self.args.length > self.config.n_ctx:
raise ValueError("Can't get samples longer than window size: %s" % self.config.n_ctx)
if self.args.quiet is False: print(self.args.text)
context_tokens = self.enc.encode(self.args.text)
generated = 0
for _ in range(self.args.nsamples // self.args.batch_size):
out = sample_sequence(
model=self.model, length=self.args.length,
context=context_tokens if not self.args.unconditional else None,
start_token=self.enc.encoder['<|endoftext|>'] if self.args.unconditional else None,
batch_size=self.args.batch_size,
temperature=self.args.temperature, top_k=self.args.top_k, device=self.device
)
out = out[:, len(context_tokens):].tolist()
for i in range(self.args.batch_size):
generated += 1
text = self.enc.decode(out[i])
if self.args.quiet is False:
print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40)
print(text)
return text
def load_state_dict(self):
print(self.args.source_file)
source_path = self.args.source_file.split('/')[:-1]
source_path = '/'.join(source_path) + '/'
print(source_path, 2)
p = realpath + '/' + self.args.source_file #'./torch_gpt2/gpt2-pytorch_model.bin'
#p = realpath + '/' + source_path + '/' + self.args.source_file
print(p)
if os.path.exists(p):
self.state_dict = torch.load(p, map_location='cpu' if not torch.cuda.is_available() else None)
#self.text_generator(state_dict)
else:
print('Please download gpt2-pytorch_model.bin')
sys.exit()
return self.state_dict