SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

better error messages

Browse Source
This commit is contained in:
thomwolf
2019-10-10 11:31:01 +02:00
parent 43a237f15e
commit a5997dd81a
4 changed files with 57 additions and 40 deletions
Download Patch File Download Diff File Expand all files Collapse all files

29
examples/run_generation.py
View File

@@ -107,7 +107,8 @@ def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')
return logits
def sample_sequence(model, length, context, num_samples=1, temperature=1, top_k=0, top_p=0.0, repetition_penalty=1.0, is_xlnet=False, xlm_lang=None, device='cpu'):
def sample_sequence(model, length, context, num_samples=1, temperature=1, top_k=0, top_p=0.0, repetition_penalty=1.0,
is_xlnet=False, is_xlm_mlm=False, xlm_mask_token=None, xlm_lang=None, device='cpu'):
context = torch.tensor(context, dtype=torch.long, device=device)
context = context.unsqueeze(0).repeat(num_samples, 1)
generated = context
@@ -125,10 +126,16 @@ def sample_sequence(model, length, context, num_samples=1, temperature=1, top_k=
target_mapping[0, 0, -1] = 1.0 # predict last token
inputs = {'input_ids': input_ids, 'perm_mask': perm_mask, 'target_mapping': target_mapping}
if is_xlm_mlm and xlm_mask_token:
# XLM MLM models are direct models (predict same token, not next token)
# => need one additional dummy token in the input (will be masked and guessed)
input_ids = torch.cat((generated, torch.full((1, 1), xlm_mask_token, dtype=torch.long, device=device)), dim=1)
inputs = {'input_ids': input_ids}
if xlm_lang is not None:
inputs["langs"] = torch.tensor([xlm_lang] * inputs["input_ids"].shape[1], device=device).view(1, -1)
outputs = model(**inputs) # Note: we could also use 'past' with GPT-2/Transfo-XL/XLNet (cached hidden-states)
outputs = model(**inputs) # Note: we could also use 'past' with GPT-2/Transfo-XL/XLNet/CTRL (cached hidden-states)
next_token_logits = outputs[0][0, -1, :] / (temperature if temperature > 0 else 1.)
# reptition penalty from CTRL (https://arxiv.org/abs/1909.05858)
@@ -167,10 +174,7 @@ def main():
parser.add_argument('--stop_token', type=str, default=None,
help="Token at which text generation is stopped")
args = parser.parse_args()
if args.model_type in ["ctrl"]:
if args.temperature > 0.7 :
print('CTRL typically works better with lower temperatures (and lower top_k).')
args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
@@ -191,6 +195,10 @@ def main():
args.length = MAX_LENGTH # avoid infinite loop
print(args)
if args.model_type in ["ctrl"]:
if args.temperature > 0.7 :
logger.info('CTRL typically works better with lower temperatures (and lower top_k).')
while True:
xlm_lang = None
# XLM Language usage detailed in the issues #1414
@@ -204,6 +212,13 @@ def main():
language = input("Using XLM. Select language in " + str(list(tokenizer.lang2id.keys())) + " >>> ")
xlm_lang = tokenizer.lang2id[language]
# XLM masked-language modeling (MLM) models need masked token (see details in sample_sequence)
is_xlm_mlm = args.model_type in ["xlm"] and 'mlm' in args.model_name_or_path
if is_xlm_mlm:
xlm_mask_token = tokenizer.mask_token_id
else:
xlm_mask_token = None
raw_text = args.prompt if args.prompt else input("Model prompt >>> ")
if args.model_type in ["transfo-xl", "xlnet"]:
# Models with memory likes to have a long prompt for short inputs.
@@ -218,6 +233,8 @@ def main():
top_p=args.top_p,
repetition_penalty=args.repetition_penalty,
is_xlnet=bool(args.model_type == "xlnet"),
is_xlm_mlm=is_xlm_mlm,
xlm_mask_token=xlm_mask_token,
xlm_lang=xlm_lang,
device=args.device,
)