def test_gpt2(model_name, ctx):
# test from pretrained
assert len(list_pretrained_gpt2()) > 0
with tempfile.TemporaryDirectory() as root, ctx:
cfg, tokenizer, params_path, lm_params_path =\
get_pretrained_gpt2(model_name, load_backbone=True, load_lm=True, root=root)
assert cfg.MODEL.vocab_size == len(tokenizer.vocab)
# test backbone
gpt2_model = GPT2Model.from_cfg(cfg)
gpt2_model.load_parameters(params_path)
# test lm model
gpt2_lm_model = GPT2ForLM(cfg)
gpt2_lm_model.load_parameters(lm_params_path)
# test forward
batch_size = 3
seq_length = 32
vocab_size = len(tokenizer.vocab)
input_ids = mx.np.array(np.random.randint(2, vocab_size,
(batch_size, seq_length)),
dtype=np.int32,
ctx=ctx)
logits, _ = gpt2_lm_model(input_ids,
gpt2_lm_model.init_states(batch_size, ctx))
mx.npx.waitall()
# test backward
label_smooth_loss = LabelSmoothCrossEntropyLoss(num_labels=vocab_size)
with mx.autograd.record():
logits, _ = gpt2_lm_model(
input_ids, gpt2_lm_model.init_states(batch_size, ctx))
loss = label_smooth_loss(logits, input_ids)
loss.backward()
mx.npx.waitall()
def calculate_metrics(args):
with open(args.file, encoding='utf-8') as of:
samples = of.read()
pattern = '=' * 40 + r' SAMPLE \d+ ' + '=' * 40 + '\n'
samples = re.split(pattern, samples)[1:]
num_samples = args.num_samples if args.num_samples else len(samples)
assert num_samples <= len(samples), \
f'The requested number of samples {num_samples} is greater than ' \
f'total number of samples {len(samples)}'
samples = samples[:num_samples]
num_bleu_samples = args.num_bleu_samples if args.num_bleu_samples else num_samples
assert num_bleu_samples <= num_samples, \
f'The requested number of samples {num_bleu_samples} for ' \
f'calculating self-BLEU is greater than number of samples {num_samples}.'
seed = args.seed if args.seed is not None else 0
random.seed(seed)
_, tokenizer, _, _ = get_pretrained_gpt2(load_backbone=False,
load_lm=False)
sample_ids = tokenizer.encode(samples, output_type=int)
if sample_ids[-1] == tokenizer.vocab.eos_id:
sample_ids.pop()
self_bleu4 = calculate_self_bleu4(sample_ids, num_bleu_samples)
zipf_coefficient = calculate_zipf_coefficient(sample_ids, tokenizer)
repetition = calculate_repetition(sample_ids)
print('Self BLEU 4: {}\n'
'Zipf coefficient: {}\n'
'Repetition: {}\n'.format(self_bleu4, zipf_coefficient, repetition))
def sample_gpt2(args):
ctx = mx.gpu(args.gpu) if args.gpu is not None else \
mx.cpu()
cfg, tokenizer, _, lm_params_path = get_pretrained_gpt2(
model_name=args.model_name, load_backbone=False, load_lm=True)
cfg.defrost()
cfg.MODEL.layout = args.layout
cfg.freeze()
if args.length is None:
args.length = cfg.MODEL.max_length
assert args.length <= cfg.MODEL.max_length, \
"Can't get samples longer than window size: {}".format(cfg.MODEL.max_length)
model = GPT2ForLM(cfg)
model.hybridize()
model.load_parameters(lm_params_path, ctx=ctx)
gpt2decoder = GPT2Decoder(model)
sampler = BeamSearchSampler(beam_size=1,
decoder=gpt2decoder,
eos_id=None,
vocab_size=cfg.MODEL.vocab_size,
max_length_a=0,
max_length_b=args.length,
min_length=1,
temperature=args.temperature,
sampling=True,
sampling_topp=args.top_p,
sampling_topk=args.top_k,
early_return=False)
start_states = gpt2decoder.init_states(args.batch_size, ctx)
while True:
raw_text = input('Model prompt >>> ')
while not raw_text:
print('Prompt should not be empty!')
raw_text = input("Model prompt >>> ")
context_tokens = tokenizer.encode(raw_text, output_type=int)
batch_axis = 0 if args.layout == 'NT' else 1
new_shape = (args.batch_size, len(context_tokens)) if args.layout == 'NT' else \
(len(context_tokens), args.batch_size)
start_input = mx.np.broadcast_to(
mx.np.expand_dims(mx.np.array(context_tokens, ctx=ctx),
batch_axis), new_shape)
generated = 0
while generated < args.nsamples:
samples, _, _ = sampler(start_input, start_states)
for i in range(args.batch_size):
generated += 1
ids = samples[i][0].asnumpy().tolist()
ids = ids[1:ids.index(-1)] if -1 in ids else \
ids[1:]
text = tokenizer.decode(ids)
print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40)
print(text)
print("=" * 80)
def sample_gpt2(args):
ctx = mx.gpu(args.gpu) if args.gpu is not None else \
mx.cpu()
cfg, tokenizer, _, lm_params_path = get_pretrained_gpt2(
model_name=args.model_name, load_backbone=False, load_lm=True)
cfg.defrost()
cfg.MODEL.layout = args.layout
cfg.freeze()
if args.length is None:
args.length = cfg.MODEL.max_length
assert args.length <= cfg.MODEL.max_length, \
"Can't get samples longer than window size: {}".format(cfg.MODEL.max_length)
model = GPT2ForLM(cfg)
model.hybridize()
model.load_parameters(lm_params_path, ctx=ctx)
gpt2decoder = GPT2Decoder(model)
sampler = BeamSearchSampler(beam_size=1,
decoder=gpt2decoder,
eos_id=None,
vocab_size=cfg.MODEL.vocab_size,
max_length_a=0,
max_length_b=args.length,
min_length=1,
temperature=args.temperature,
sampling=True,
sampling_topp=args.top_p,
sampling_topk=args.top_k,
early_return=False)
start_input = mx.np.full((args.batch_size, 1) if args.layout == 'NT' else
(1, args.batch_size),
tokenizer.vocab.eos_id,
ctx=ctx)
start_states = gpt2decoder.init_states(args.batch_size, ctx)
generated = 0
while args.nsamples <= 0 or generated < args.nsamples:
samples, _, _ = sampler(start_input, start_states)
for i in range(args.batch_size):
generated += 1
ids = samples[i][0].asnumpy().tolist()
ids = ids[1:ids.index(-1)] if -1 in ids else \
ids[1:]
text = tokenizer.decode(ids)
print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40)
print(text)
def calculate_metrics(args):
with open(args.file, encoding='utf-8') as of:
samples = of.read()
pattern = '=' * 40 + ' SAMPLE \d+ ' + '=' * 40 + '\n'
samples = re.split(pattern, samples)[1:]
samples = samples[:args.num_samples]
assert len(samples) == args.num_samples
_, tokenizer, _, _ = get_pretrained_gpt2(load_backbone=False,
load_lm=False)
sample_ids = tokenizer.encode(samples, output_type=int)
if sample_ids[-1] == tokenizer.vocab.eos_id:
sample_ids.pop()
sample_strs = tokenizer.encode(samples, output_type=str)
self_bleu4 = calculate_self_bleu4(sample_strs, args.num_bleu_samples)
zipf_coefficient = calculate_zipf_coefficient(sample_ids, tokenizer)
repetition = calculate_repetition(sample_ids)
print('Self BLEU 4: {}\n'
'Zipf coefficient: {}\n'
'Repetition: {}\n'.format(self_bleu4, zipf_coefficient, repetition))
