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#!/usr/bin/env python3 -u
from collections import namedtuple
import math
import torch
from torch.nn.utils.rnn import pad_sequence
from fairseq import checkpoint_utils, options, tasks, utils
from eet.fairseq.transformer import EETTransformerDecoder
Batch = namedtuple('Batch', 'ids src_tokens src_lengths')
def make_batches(lines, task, max_positions, encode_fn):
tokens = [task.source_dictionary.encode_line(encode_fn(line),
add_if_not_exist=False,
append_eos=False,
reverse_order=True).long()
for line in lines]
lengths = [t.numel() for t in tokens]
tokens = pad_sequence(tokens, batch_first=True,
padding_value=1).flip(dims=(1,))
return Batch(ids=torch.arange(len(tokens)),
src_tokens=tokens,
src_lengths=torch.tensor(lengths))
def encode_fn(x_str):
x_str = "</s> " + x_str
return x_str
def decode_fn(x):
x = x.replace(" ", "")
return x
def eos_token_filter(sent):
return True
def post_precess(line):
if "<" in line:
line = line.split("<")[0]
return line
class Inference(object):
def __init__(self, model_path, data_path, eet_batch_size):
parser = options.get_generation_parser(default_task="language_modeling")
args = options.parse_args_and_arch(parser)
args.data = data_path
args.path = model_path
self.args = args
# generate parameter
args.beam = 1 # don't change
args.min_len = 5
args.max_len_b = 30
args.lenpen = 1.0
args.sampling = True
# args.sampling_topp = 0.7
args.sampling_topk = 10
args.temperature = 0.8
args.no_repeat_ngram_size = 1
args.fp16 = True
# Setup task, e.g., translation
task = tasks.setup_task(args)
self.task = task
# Set dictionaries
self.src_dict = task.source_dictionary
self.tgt_dict = task.target_dictionary
use_cuda = torch.cuda.is_available() and not args.cpu
self.use_cuda = use_cuda
model_path = args.path
checkpoint = torch.load(model_path.replace("best.pt", "best_part_1.pt"))
checkpoint["model"].update(torch.load(model_path.replace("best.pt", "best_part_2.pt")))
checkpoint["model"].update(torch.load(model_path.replace("best.pt", "best_part_3.pt")))
torch.save(checkpoint, model_path)
state = torch.load(args.path, map_location=torch.device("cpu"))
cfg_args = eval(str(state["cfg"]))["model"]
del cfg_args["_name"]
keys_list = []
values_list = []
for key,value in cfg_args.items() :
keys_list.append(key)
values_list.append(value)
Model_args = namedtuple("Model_args", keys_list)
model_args = Model_args._make(values_list)
del state
eet_seq_len = 512 # max seqence length
eet_batch_size = eet_batch_size
data_type = torch.float16
eet_config = {"data_type":data_type,
"max_batch":eet_batch_size,
"full_seq_len":eet_seq_len}
print(model_args)
eet_model = EETTransformerDecoder.from_fairseq_pretrained(model_id_or_path = args.path,
dictionary = self.src_dict,args=model_args,
config = eet_config,
no_encoder_attn = True)
self.models = [eet_model]
# Initialize generator
self.generator = task.build_generator(self.models, args)
# Load alignment dictionary for unknown word replacement
# (None if no unknown word replacement, empty if no path to align dictionary)
self.align_dict = utils.load_align_dict(args.replace_unk)
self.max_positions = 1024
self.eos_index = self.tgt_dict.eos()
self.pad_index = self.tgt_dict.pad()
def __call__(self, inputs, append_right_eos=True):
results = []
start_id = 0
batch = make_batches(inputs, self.task, self.max_positions, encode_fn)
inputs_str = inputs
src_tokens = batch.src_tokens
src_lengths = batch.src_lengths
# a new paragraph always
if src_tokens[0][-1].item() != self.eos_index and append_right_eos:
src_tokens = torch.cat([src_tokens, src_tokens.new_ones(src_tokens.size(0), 1) * self.eos_index], dim=1)
src_lengths += 1
if self.use_cuda:
src_tokens = src_tokens.cuda()
src_lengths = src_lengths.cuda()
sample = {
'net_input': {
'src_tokens': src_tokens,
'src_lengths': src_lengths,
},
}
translations = self.task.inference_step(self.generator, self.models, sample)
for i, (id, hypos) in enumerate(zip(batch.ids.tolist(), translations)):
results.append((start_id + id, src_tokens[i], hypos))
# sort output to match input order
final_results = []
for id, src_tokens, hypos in sorted(results, key=lambda x: x[0]):
# Process top predictions
tmp_res = []
for hypo in hypos[:min(len(hypos), self.args.nbest)]:
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo['tokens'].int().cpu()[len(src_tokens)-1:],
src_str=None,
alignment=hypo['alignment'],
align_dict=self.align_dict,
tgt_dict=self.tgt_dict)
detok_hypo_str = decode_fn(hypo_str)
if eos_token_filter(detok_hypo_str):
detok_hypo_str = post_precess(detok_hypo_str)
score = hypo['score'] / math.log(2) # convert to base 2
tmp_res.append([detok_hypo_str, score])
final_results.append(tmp_res)
return final_results
class Dialogue(object):
def __init__(self, inference_model=None, max_dialogue_history=6):
self.inference_model = inference_model
self.max_dialogue_history = max_dialogue_history
self.dialogue_history = []
def get_repsonse(self, input_text):
self.dialogue_history.append(input_text.strip())
model_inp = ""
for idx, x in enumerate(self.dialogue_history[-self.max_dialogue_history:]):
if idx % 2 == 0:
model_inp += " <0> " + " ".join(list(x))
else:
model_inp += " <1> " + " ".join(list(x))
if idx % 2 == 0:
model_inp += " <1>"
else:
model_inp += " <0>"
# generate 5 candidates
text = self.inference_model([model_inp]*5, append_right_eos=False)
response = [x[0][0] for x in text]
# response rank according to length
response = sorted(response, key=lambda x:len(set(x)))
# overlap-score
overlap = [[len(set(x) & set(model_inp)) * len(x), x] for x in response[-4:-1]]
overlap = sorted(overlap, key=lambda x:x[0])
final_response = overlap[-2][1]
self.dialogue_history.append(final_response)
return final_response
def clear_dialogue_history(self):
self.dialogue_history = []
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