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infer_reddit_jokes_sw_tf_ver2_gpt.py
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infer_reddit_jokes_sw_tf_ver2_gpt.py
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# Import the libraries. #
import time
import numpy as np
import pandas as pd
import pickle as pkl
import tensorflow as tf
import tf_ver2_gpt_keras as gpt
import byte_pair_encoding as bpe
from gpt_utils import (
compute_kl_div, bp_kl_decode)
# Model Parameters. #
seq_length = 51
num_heads = 4
num_layers = 3
prob_keep = 0.9
hidden_size = 256
ffwd_size = 4*hidden_size
warmup_flag = True
cooling_step = 100
model_data_ckpt_dir = "TF_Models/gpt_data_subword_reddit"
model_bgrd_ckpt_dir = "TF_Models/gpt_bgrd_subword_reddit"
# Load the data. #
tmp_pkl_file = "../../Data/reddit_jokes/"
tmp_pkl_file += "reddit_jokes_subword_v1.pkl"
with open(tmp_pkl_file, "rb") as tmp_load_file:
full_data = pkl.load(tmp_load_file)
subword_vocab = pkl.load(tmp_load_file)
idx_2_subword = pkl.load(tmp_load_file)
subword_2_idx = pkl.load(tmp_load_file)
vocab_size = len(subword_2_idx)
print("Vocabulary Size:", str(vocab_size) + ".")
# Set the number of threads to use. #
tf.config.threading.set_intra_op_parallelism_threads(2)
tf.config.threading.set_inter_op_parallelism_threads(2)
SOS_token = subword_2_idx["<SOS>"]
EOS_token = subword_2_idx["<EOS>"]
PAD_token = subword_2_idx["<PAD>"]
UNK_token = subword_2_idx["<UNK>"]
print("Total of", str(len(full_data)), "rows loaded.")
# Build the GPT model. #
print("Building the GPT Model.")
start_time = time.time()
# For the data model. #
data_model = gpt.GPTDecoder(
num_layers, num_heads, hidden_size,
ffwd_size, vocab_size, seq_length,
rate1=1.0-prob_keep, rate2=1.0-prob_keep)
data_optimizer = tf.keras.optimizers.Adam(
beta_1=0.9, beta_2=0.98, epsilon=1.0e-9)
# For the background model. #
bgrd_model = gpt.GPTDecoder(
num_layers, num_heads, hidden_size,
ffwd_size, vocab_size, seq_length,
rate1=1.0-prob_keep, rate2=1.0-prob_keep)
bgrd_optimizer = tf.keras.optimizers.Adam(
beta_1=0.9, beta_2=0.98, epsilon=1.0e-9)
elapsed_time = (time.time()-start_time) / 60
print("GPT Models Built", "(" + str(elapsed_time), "mins).")
# Create the model checkpoint. #
ckpt_data = tf.train.Checkpoint(
d_step=tf.Variable(0),
data_model=data_model,
data_optimizer=data_optimizer)
ckpt_bgrd = tf.train.Checkpoint(
b_step=tf.Variable(0),
bgrd_model=bgrd_model,
bgrd_optimizer=bgrd_optimizer)
manager_data = tf.train.CheckpointManager(
ckpt_data, model_data_ckpt_dir, max_to_keep=1)
manager_bgrd = tf.train.CheckpointManager(
ckpt_bgrd, model_bgrd_ckpt_dir, max_to_keep=1)
ckpt_data.restore(manager_data.latest_checkpoint)
if manager_data.latest_checkpoint:
print("Model restored from {}".format(
manager_data.latest_checkpoint))
else:
print("Error: No data model checkpoint found.")
ckpt_bgrd.restore(manager_bgrd.latest_checkpoint)
if manager_bgrd.latest_checkpoint:
print("Model restored from {}".format(
manager_bgrd.latest_checkpoint))
else:
print("Error: No bgrd model checkpoint found.")
# GPT model inference. #
tmp_test_in = np.zeros(
[1, seq_length], dtype=np.int32)
# Print the GPT model summary. #
tmp_outputs = data_model(
tmp_test_in, training=True)
print(data_model.summary())
del tmp_outputs
# Warmup learning schedule. #
n_iter = ckpt_data.d_step.numpy().astype(np.int32)
b_iter = ckpt_bgrd.b_step.numpy().astype(np.int32)
print("-" * 50)
print("GPT Model Inference",
"(" + str(n_iter),
"data iterations,",
str(b_iter) + " bgrd iterations).")
print("-" * 50)
while True:
tmp_prompt = input("Enter prompt: ")
tmp_prompt = tmp_prompt.lower().strip()
if tmp_prompt == "":
break
else:
tmp_test_in[:, :] = PAD_token
tmp_i_index = bpe.bp_encode(
tmp_prompt, subword_vocab, subword_2_idx)
in_phrase = bpe.bp_decode(
tmp_i_index, idx_2_subword)
in_phrase = " ".join(
in_phrase).replace("<", "").replace(">", "")
n_tokens = len(tmp_i_index)
tmp_test_in[0, :n_tokens] = tmp_i_index
# Infer the generated sequence. #
tmp_data_infer = data_model.infer(
tmp_test_in[:, :n_tokens])
tmp_bgrd_infer = bgrd_model.infer(
tmp_test_in[:, :n_tokens])
# Compute the KL-Divergence. #
tmp_kl_div = compute_kl_div(
data_model, bgrd_model,
tmp_data_infer.numpy()[:, :-1])[0]
tmp_seq = list(tmp_data_infer.numpy()[0])
kl_tuple = [(
tmp_seq[x], tmp_kl_div[x]) \
for x in range(seq_length+1)]
kl_display = bp_kl_decode(kl_tuple, idx_2_subword)
# Decode the subwords. #
gen_phrase = bpe.bp_decode(
tmp_data_infer[0].numpy(), idx_2_subword)
gen_phrase = " ".join(
gen_phrase).replace("<", "").replace(">", "")
bgrd_phrase = bpe.bp_decode(
tmp_bgrd_infer[0].numpy(), idx_2_subword)
bgrd_phrase = " ".join(
bgrd_phrase).replace("<", "").replace(">", "")
del tmp_i_index, n_tokens
print("")
print("Input Phrase:")
print(in_phrase)
print("Generated Phrase (Data):")
print(gen_phrase)
print("Generated Phrase (Bgrd):")
print(bgrd_phrase)
print("Actual Phrase:")
print(in_phrase)
print("KL-Divergence Scores:")
print(kl_display)
print("-" * 50)