def test_pretrained_generate_use_cache_equality(self):
model = ReformerModelWithLMHead.from_pretrained("google/reformer-crime-and-punishment").to(torch_device)
tokenizer = ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment")
model.eval()
input_ids = tokenizer.encode("A few months later", return_tensors="pt").to(torch_device)
output_ids_with_cache = model.generate(input_ids, max_length=130, num_hashes=8, use_cache=False)
output_ids_without_cache = model.generate(input_ids, max_length=130, num_hashes=8, use_cache=True)
output_with_cache = tokenizer.decode(output_ids_with_cache[0])
output_without_cache = tokenizer.decode(output_ids_without_cache[0])
self.assertEqual(output_with_cache, output_without_cache)
def test_pretrained_generate_crime_and_punish(self):
model = ReformerModelWithLMHead.from_pretrained("google/reformer-crime-and-punishment").to(torch_device)
tokenizer = ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment")
model.eval()
input_ids = tokenizer.encode("A few months later", return_tensors="pt").to(torch_device)
output_ids = model.generate(
input_ids, max_length=50, num_beams=4, early_stopping=True, do_sample=False, num_hashes=8
)
output_text = tokenizer.decode(output_ids[0])
self.assertEqual(
output_text,
"A few months later state expression in his ideas, at the first entrance. He was positively for an inst",
)
def test_model_from_pretrained(self):
for model_name in REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = ReformerModelWithLMHead.from_pretrained(model_name)
self.assertIsNotNone(model)
# Decoding
def decode(outputs_ids):
decoded_outputs = []
o = outputs_ids.tolist() if torch.is_tensor(
outputs_ids) else outputs_ids
for output_ids in o:
# transform id back to char IDs < 2 are simply transformed to ""
decoded_outputs.append("".join(
[chr(x - 2) if x > 1 else "" for x in output_ids]))
return decoded_outputs
from transformers import ReformerModelWithLMHead, ReformerForMaskedLM
# transformers.ReformerModel - raw hidden states
# ReformerForMaskedLM - UGH THIS IS WHAT I WANT
# ReformerModelWithLMHead - next token prediction ONLY
model = ReformerModelWithLMHead.from_pretrained("google/reformer-enwik8")
encoded, attention_masks = encode(
["In 1965, Brooks left IBM to found the Department of"])
x = model.generate(encoded, do_sample=True, max_length=150)
d = decode(x)
input_ids, attention_masks = encode(
["In 1965, Brooks left IBM to found the Department of"])
#i,a = input_ids.to("cuda"), attention_masks.to("cuda")
sentence = "The quick brown fox jumps over the lazy dog."
input_ids, attention_masks = encode([sentence])
attention_masks[0, 37] = attention_masks[0, 19] = attention_masks[0,
27] = 0
i, a = input_ids, attention_masks
f = model.forward(input_ids=i,
from pydantic import BaseModel, Field
from transformers import ReformerModelWithLMHead, ReformerTokenizer
tokenizer = ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment')
model = ReformerModelWithLMHead.from_pretrained('google/reformer-crime-and-punishment')
##
# GPT-2 generator.
# Make java code!.
def mk_crime_punish(text, length, how_many, top_p, top_k, do_sample):
try:
input_ids = tokenizer.encode(text, return_tensors='pt')
min_length = len(input_ids.tolist()[0])
length += min_length
length = length if length > 0 else 1
top_k = top_k if top_k > 0 else 10
top_p = top_p if top_p > 0 else 0.5
# model generating
sample_outputs = model.generate(input_ids, pad_token_id=50256,
do_sample=do_sample,
max_length=length,
top_p=top_p,
top_k=top_k,
num_return_sequences=how_many)
result = dict()