def nnlm_modelpredict(D_simi,
D_next,
config_predict,
inputStr='怎么了',
maxNext=3,
maxChoice=10,
num=5):
prefix, punc = findMaxMatch(inputStr, D_simi, D_next, config_predict)
if punc == '':
punc = '，'
if len(prefix) == 0:
return []
output = []
for ii in range(num + 5):
if len(output) == num:
break
s0 = prefix
S = []
#S.append(inputStr)
lastsent = s0
for i in range(maxNext):
if s0 in D_next:
p = [float(tt) for tt in D_next[s0]['probs']]
w = D_next[s0]['words']
t = random.choices(w[:maxChoice], p[:maxChoice])[0]
if t != lastsent:
S.append(t)
lastsent = t
s0 = t
elif s0 in D_simi:
p = [float(tt) for tt in D_simi[s0]['probs']]
w = D_simi[s0]['words']
t0 = random.choices(w, p)[0]
p = [float(tt) for tt in D_next[t0]['probs']]
w = D_next[t0]['words']
t = random.choices(w[:maxChoice], p[:maxChoice])[0]
if t != lastsent:
S.append(t)
lastsent = t
s0 = t
else:
break
S = inputStr + punc + '，'.join(S)
if S not in output:
output.append(S)
if len(output) >= num:
break
output = postprocess(output,
inputStr,
config_predict,
sentEndcontent=False,
removeHighFreqWords=False)
output = dropDuplicateContent(output)
return output
def generating(app,
prefix,
model,
config,
tokenizer,
device,
config_predict,
quick=False,
num=5,
continue_writing=False,
removeHighFreqWords=False,
batchGenerating=False,
gpu='0',
onlyMax=False,
fast_pattern=False):
#print("start:",prefix)
#os.environ["CUDA_VISIBLE_DEVICES"] = gpu
torch.cuda.set_device(int(gpu))
prefix0 = prefix
if config_predict.prefixTrim:
prefix = sentTriming(prefix0)
if len(prefix) == 0:
prefix = prefix0
punc = '.,?!;\t 。，？！；'
global a
a = app
if config_predict.fast_pattern:
fast_pattern = True
n_ctx = model.config.n_ctx
length = config['length']
nsamples = num
temperature = config['temperature']
topk = config['topk']
topp = config['topp']
quick_pattern = quick
repetition_penalty = config['repetition_penalty']
if length == -1:
length = model.config.n_ctx
raw_text = prefix
context_tokens = tokenizer.convert_tokens_to_ids(
tokenizer.tokenize(raw_text))
t0 = time.time()
if batchGenerating:
S = []
if onlyMax:
outs = sample_sequence_batch_max(
model,
context_tokens,
length,
n_ctx,
tokenizer,
nsamples=2,
temperature=temperature,
top_k=topk,
top_p=topp,
repitition_penalty=repetition_penalty,
device=device)
else:
if fast_pattern:
outs = fast_sample_sequence_batch(
model,
context_tokens,
length,
nsamples=nsamples,
temperature=temperature,
top_k=topk,
repitition_penalty=repetition_penalty,
device=device)
else:
outs = sample_sequence_batch_opti(
model,
context_tokens,
length,
n_ctx,
tokenizer,
nsamples,
temperature=temperature,
top_k=topk,
top_p=topp,
repitition_penalty=repetition_penalty,
device=device)
#print('model predict all time:%0.4f' % (t1 - t0))
for out in outs:
tmptext = untokenization(out, config, tokenizer, punc,
continue_writing)
S.append(tmptext)
#print('model untokenization time:%0.4f' % (t2 - t1))
else:
S = []
for _ in range(nsamples):
out = generate(n_ctx=n_ctx,
model=model,
context=context_tokens,
length=length,
is_fast_pattern=fast_pattern,
tokenizer=tokenizer,
is_quick=quick_pattern,
temperature=temperature,
top_k=topk,
top_p=topp,
repitition_penalty=repetition_penalty,
device=device)
tmptext = untokenization(out, config, tokenizer, punc,
continue_writing)
S.append(tmptext)
t1 = time.time()
if config_predict.prefixTrim:
S = [prefix0 + s[len(prefix):] for s in S]
S = postprocess(S,
prefix0,
config_predict,
removeHighFreqWords=removeHighFreqWords)
S = dropDuplicateContent(S)
t2 = time.time()
#print('text generating and posprocess time:%0.4f and %0.4f' % (t1 - t0,t2-t1))
return S
def generating(app,
prefix,
model,
config,
tokenizer,
device,
config_predict,
quick=False,
num=5,
continue_writing=False,
removeHighFreqWords=False,
batchGenerating=False,
gpu='0',
onlyMax=False,
maxNb=20,
style=''):
#print("start:",prefix)
#os.environ["CUDA_VISIBLE_DEVICES"] = gpu
if len(prefix) == 0 or len(prefix) > model.config.n_ctx:
return []
if gpu:
torch.cuda.set_device(int(gpu))
device = "cuda" if torch.cuda.is_available() else "cpu"
else:
device = 'cpu'
#if style=='prose':
#prefix = prefix[0] + prefix
prefix0 = prefix
if config_predict.prefixTrim:
prefix = sentTriming(prefix0)
if len(prefix) == 0:
prefix = prefix0
punc = '.,?!;\t 。，？！；'
global a
a = app
fast_pattern = config_predict.fast_pattern
n_ctx = model.config.n_ctx
len_prefix = len(prefix)
if len_prefix < 5:
max_genlen = 20
elif len_prefix < 10:
max_genlen = 25
else:
max_genlen = config['length']
length = min(max_genlen, n_ctx - len_prefix - 1)
nsamples = num
maxNb = max(nsamples, maxNb)
temperature = config['temperature']
topk = config['topk']
topp = config['topp']
quick_pattern = quick
repetition_penalty = config['repetition_penalty']
if length == -1:
length = model.config.n_ctx
raw_text = '[MASK]' + prefix
context_tokens = tokenizer.convert_tokens_to_ids(
tokenizer.tokenize(raw_text))
if batchGenerating:
S = []
if onlyMax:
outs = sample_sequence_batch_max(
model,
context_tokens,
length,
n_ctx,
tokenizer,
nsamples=2,
temperature=temperature,
top_k=topk,
top_p=topp,
repitition_penalty=repetition_penalty,
device=device)
else:
if fast_pattern:
outs = fast_sample_sequence_batch(
model,
context_tokens,
length,
nsamples=maxNb,
temperature=temperature,
top_k=topk,
repitition_penalty=repetition_penalty,
device=device)
else:
outs = sample_sequence_batch_opti(
model,
context_tokens,
length,
n_ctx,
tokenizer,
maxNb,
temperature=temperature,
top_k=topk,
top_p=topp,
repitition_penalty=repetition_penalty,
device=device)
for out in outs:
tmptext = untokenization(out, config, tokenizer, punc,
continue_writing)
S.append(tmptext)
else:
S = []
for _ in range(maxNb):
out = generate(n_ctx=n_ctx,
model=model,
context=context_tokens,
length=length,
is_fast_pattern=fast_pattern,
tokenizer=tokenizer,
is_quick=quick_pattern,
temperature=temperature,
top_k=topk,
top_p=topp,
repitition_penalty=repetition_penalty,
device=device)
tmptext = untokenization(out, config, tokenizer, punc,
continue_writing)
S.append(tmptext)
if config_predict.prefixTrim:
S = [prefix0 + s[len(prefix):] for s in S]
S = postprocess(S,
prefix0,
config_predict,
removeHighFreqWords=removeHighFreqWords)
S = dropDuplicateContent(S)
if config_predict.resort:
if len(S) > 0:
S = resort(prefix0, S, config_predict)
S = S[:nsamples]
#if style == 'prose':
#S = [r[1:] for r in S]
return S
def testFun(app,
prefix,
model,
config,
tokenizer,
device,
config_predict,
quick=False,
num=5,
continue_writing=False,
removeHighFreqWords=False,
batchGenerating=False,
gpu='0',
onlyMax=False,
maxNb=20):
#print("start:",prefix)
#os.environ["CUDA_VISIBLE_DEVICES"] = gpu
if len(prefix) == 0 or len(prefix) > model.config.n_ctx:
return []
if gpu:
torch.cuda.set_device(int(gpu))
device = "cuda" if torch.cuda.is_available() else "cpu"
else:
device = 'cpu'
#print("use device:%s" % device)
prefix0 = prefix
if config_predict.prefixTrim:
prefix = sentTriming(prefix0)
if len(prefix) == 0:
prefix = prefix0
punc = '.,?!;\t 。，？！；'
global a
a = app
fast_pattern = config_predict.fast_pattern
n_ctx = model.config.n_ctx
len_prefix = len(prefix)
if len_prefix < 5:
max_genlen = 5 * len_prefix
elif len_prefix < 10:
max_genlen = 3 * len_prefix
else:
max_genlen = config['length']
length = min(max_genlen, n_ctx - len_prefix - 1)
nsamples = num
maxNb = max(nsamples, maxNb)
temperature = config['temperature']
topk = config['topk']
topp = config['topp']
quick_pattern = quick
repetition_penalty = config['repetition_penalty']
if length == -1:
length = model.config.n_ctx
raw_text = prefix
context_tokens = tokenizer.convert_tokens_to_ids(
tokenizer.tokenize(raw_text))
t0 = time.time()
S = []
rev_repitition_penalty = 1.0 / repetition_penalty
inputs = [context_tokens] * nsamples
inputs = torch.tensor(inputs, dtype=torch.long, device=device)
_, past = model(inputs[:, :-1], None)[:2]
prev = inputs[:, -1].view(-1, 1)
context = context_tokens
generate = [[t for t in context] for _ in range(nsamples)]
A0 = []
A1 = []
for kk in range(len(generate)):
for jj in range(len(generate[kk])):
A0.append(kk)
A1.append(generate[kk][jj])
with torch.no_grad():
for i in range(nsamples):
output = model(prev, past=past)
output, past = output[:2]
output = output.squeeze(1)
output[A0, A1] *= rev_repitition_penalty
output /= temperature
filtered_logits = top_k_top_p_filtering(output,
top_k=topk,
top_p=0)
next_token = torch.multinomial(torch.softmax(filtered_logits,
dim=-1),
num_samples=1)
prev = next_token
NT_np = next_token.cpu().numpy()
for ii in range(nsamples):
generate[ii].append(NT_np[ii][0])
A0.append(ii)
A1.append(NT_np[ii])
outs = generate
for out in outs:
tmptext = untokenization(out, config, tokenizer, punc,
continue_writing)
S.append(tmptext)
return S
outs = fast_sample_sequence_batch(model,
context_tokens,
length,
nsamples=maxNb,
temperature=temperature,
top_k=topk,
repitition_penalty=repetition_penalty,
device=device)
return outs
for out in outs:
tmptext = untokenization(out, config, tokenizer, punc,
continue_writing)
S.append(tmptext)
t1 = time.time()
if config_predict.prefixTrim:
S = [prefix0 + s[len(prefix):] for s in S]
S = postprocess(S,
prefix0,
config_predict,
removeHighFreqWords=removeHighFreqWords)
S = dropDuplicateContent(S)
if config_predict.resort:
if len(S) > 0:
S = resort(prefix0, S, config_predict)
t2 = time.time()
#print('text generating and posprocess time:%0.4f and %0.4f' % (t1 - t0,t2-t1))
S = S[:nsamples]
return S