def gpt2_pred_input(params, text=None):
from models.gpt2 import encoder
enc = encoder.get_encoder(params["encoder_path"])
tokens = enc.encode(text)
if len(tokens) > 1024:
tokens = tokens[:1024]
t = tf.broadcast_to(tokens, [params["batch_size"], len(tokens)])
dataset = tf.data.Dataset.from_tensors(t)
return dataset
def gpt2_predict(network, text, params):
logger = logging.getLogger('tensorflow')
enc = encoder.get_encoder(params["encoder_path"])
predictions = network.predict(input_fn=partial(gpt2_pred_input, text=text))
with tf.gfile.Open(params["predict_path"], "a") as f:
for i, p in enumerate(predictions):
p = p["tokens"]
text = enc.decode(p)
f.write("=" * 40 + " SAMPLE " + str(i) + " " + "=" * 40 + "\n")
f.write(text)
f.write("\n" + "=" * 80 + "\n")
logger.info("=" * 40 + " SAMPLE " + str(i) + " " + "=" * 40 + "\n")
logger.info(text)
logger.info("\n" + "=" * 80 + "\n")
# Non TPU setup
if not predict_mode:
params["batch_size"] = params["train_batch_size"]
else:
params["batch_size"] = params["predict_batch_size"]
run_config = tf.estimator.RunConfig(
model_dir=params["model_path"],
session_config=sess_config,
)
network = tf.estimator.Estimator(model_fn=gpt2_model,
config=run_config,
params=params)
enc = encoder.get_encoder(params["encoder_path"])
while True:
with ai_integration.get_next_input(
inputs_schema={"text": {
"type": "text"
}}) as inputs_dict:
# If an exception happens in this 'with' block, it will be sent back to the ai_integration library
text = inputs_dict['text']
if isinstance(text, bytes):
text = text.decode('utf-8')
predictions = network.predict(
input_fn=partial(gpt2_pred_input, text=text))
p = next(predictions) # return just the first one
p = p["tokens"]
back_prop=False,
)
return [final]
#return tf.concat([context, final], axis=-1)
#token = thunk(context)
#return token
sample_op = tft.tpu_shard(thunk2, inputs=[[context]*8, [length]*8])
r(sample_op, {context: [enc.encode("Hello, my name is")], length: 1}, options=config_pb2.RunOptions(report_tensor_allocations_upon_oom=True))
from transformers import GPT2TokenizerFast
enc = GPT2TokenizerFast.from_pretrained('gpt2')
enc = encoder.get_encoder()
params = gpt2.default_hparams()
context = tf.placeholder(tf.int32, shape=[1, None], name="tokens")
fwd = gpt2_rev.model(params=params, X=context, reuse=tf1.AUTO_REUSE)
params = gpt2_rev.default_hparams()
context_fixed = tf1.placeholder(tf.int32, shape=[1, params['n_ctx']+1], name="tokens")
output_fixed = gpt2_rev.model_grad(params=params, X=context_fixed[:, :-1], labels=context_fixed[:, 1:], reuse=tf1.AUTO_REUSE)
context = tf1.placeholder(tf.int32, shape=[1, None], name="tokens")
fwd = gpt2_rev.model(params=params, X=context, reuse=tf1.AUTO_REUSE)
length = tf1.placeholder(tf.int32, shape=[], name="length")
samp = sample.sample_sequence(params=params, length=length, context=context, batch_size=1, temperature=0.7, top_k=40)
#output = gpt2_rev.model_grad(params=params, X=context[..., :-1], labels=context[..., 1:], reuse=tf1.AUTO_REUSE)