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modify forward

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ly119399 3 years ago
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b1948cd81c
8 changed files with 340 additions and 13 deletions
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  1. +1
    -0
      modelscope/models/nlp/__init__.py
  2. +103
    -0
      modelscope/models/nlp/space/dialog_generation_model.py
  3. +1
    -0
      modelscope/pipelines/nlp/__init__.py
  4. +50
    -0
      modelscope/pipelines/nlp/space/dialog_generation_pipeline.py
  5. +1
    -0
      modelscope/preprocessors/__init__.py
  6. +50
    -0
      modelscope/preprocessors/space/dialog_generation_preprocessor.py
  7. +13
    -13
      modelscope/trainers/nlp/space/trainers/gen_trainer.py
  8. +121
    -0
      tests/pipelines/nlp/test_dialog_generation.py

+ 1
- 0
modelscope/models/nlp/__init__.py View File

@@ -1,3 +1,4 @@
from .sequence_classification_model import * # noqa F403
from .space.dialog_generation_model import * # noqa F403
from .space.dialog_intent_model import * # noqa F403
from .text_generation_model import * # noqa F403

+ 103
- 0
modelscope/models/nlp/space/dialog_generation_model.py View File

@@ -0,0 +1,103 @@
from typing import Any, Dict, Optional

from modelscope.trainers.nlp.space.trainers.gen_trainer import MultiWOZTrainer
from modelscope.utils.constant import Tasks
from ...base import Model, Tensor
from ...builder import MODELS
from .model.generator import Generator
from .model.model_base import ModelBase

__all__ = ['DialogGenerationModel']


@MODELS.register_module(
Tasks.dialog_generation, module_name=r'space-generation')
class DialogGenerationModel(Model):

def __init__(self, model_dir: str, *args, **kwargs):
"""initialize the test generation model from the `model_dir` path.

Args:
model_dir (str): the model path.
model_cls (Optional[Any], optional): model loader, if None, use the
default loader to load model weights, by default None.
"""

super().__init__(model_dir, *args, **kwargs)
self.model_dir = model_dir
self.text_field = kwargs.pop('text_field')
self.config = kwargs.pop('config')
self.generator = Generator.create(self.config, reader=self.text_field)
self.model = ModelBase.create(
model_dir=model_dir,
config=self.config,
reader=self.text_field,
generator=self.generator)

def to_tensor(array):
"""
numpy array -> tensor
"""
import torch
array = torch.tensor(array)
return array.cuda() if self.config.use_gpu else array

self.trainer = MultiWOZTrainer(
model=self.model,
to_tensor=to_tensor,
config=self.config,
reader=self.text_field,
evaluator=None)
self.trainer.load()

def forward(self, input: Dict[str, Tensor]) -> Dict[str, Tensor]:
"""return the result by the model

Args:
input (Dict[str, Any]): the preprocessed data

Returns:
Dict[str, np.ndarray]: results
Example:
{
'predictions': array([1]), # lable 0-negative 1-positive
'probabilities': array([[0.11491239, 0.8850876 ]], dtype=float32),
'logits': array([[-0.53860897, 1.5029076 ]], dtype=float32) # true value
}
"""
from numpy import array, float32
import torch

# turn_1 = {
# 'user': [
# 13, 1045, 2052, 2066, 1037, 10095, 2013, 3002, 2198, 1005,
# 1055, 2267, 2000, 10733, 12570, 21713, 4487, 15474, 1012, 7
# ]
# }
# old_pv_turn_1 = {}

turn_2 = {
'user':
[13, 1045, 2215, 2000, 2681, 2044, 2459, 1024, 2321, 1012, 7]
}
old_pv_turn_2 = {
'labels': [[
13, 1045, 2052, 2066, 1037, 10095, 2013, 3002, 2198, 1005,
1055, 2267, 2000, 10733, 12570, 21713, 4487, 15474, 1012, 7
]],
'resp': [
14, 1045, 2052, 2022, 3407, 2000, 2393, 2007, 2115, 5227, 1010,
2079, 2017, 2031, 1037, 2051, 2017, 2052, 2066, 2000, 2681,
2030, 7180, 2011, 1029, 8
],
'bspn': [
15, 43, 7688, 10733, 12570, 21713, 4487, 15474, 6712, 3002,
2198, 1005, 1055, 2267, 9
],
'db': [19, 24, 21, 20],
'aspn': [16, 43, 48, 2681, 7180, 10]
}

pv_turn = self.trainer.forward(turn=turn_2, old_pv_turn=old_pv_turn_2)

return pv_turn

+ 1
- 0
modelscope/pipelines/nlp/__init__.py View File

@@ -1,3 +1,4 @@
from .sequence_classification_pipeline import * # noqa F403
from .space.dialog_generation_pipeline import * # noqa F403
from .space.dialog_intent_pipeline import * # noqa F403
from .text_generation_pipeline import * # noqa F403

+ 50
- 0
modelscope/pipelines/nlp/space/dialog_generation_pipeline.py View File

@@ -0,0 +1,50 @@
from typing import Any, Dict, Optional

from modelscope.models.nlp import DialogGenerationModel
from modelscope.preprocessors import DialogGenerationPreprocessor
from modelscope.utils.constant import Tasks
from ...base import Pipeline, Tensor
from ...builder import PIPELINES

__all__ = ['DialogGenerationPipeline']


@PIPELINES.register_module(
Tasks.dialog_generation, module_name=r'space-generation')
class DialogGenerationPipeline(Pipeline):

def __init__(self, model: DialogGenerationModel,
preprocessor: DialogGenerationPreprocessor, **kwargs):
"""use `model` and `preprocessor` to create a nlp text classification pipeline for prediction

Args:
model (SequenceClassificationModel): a model instance
preprocessor (SequenceClassificationPreprocessor): a preprocessor instance
"""

super().__init__(model=model, preprocessor=preprocessor, **kwargs)
self.model = model

def postprocess(self, inputs: Dict[str, Tensor]) -> Dict[str, str]:
"""process the prediction results

Args:
inputs (Dict[str, Any]): _description_

Returns:
Dict[str, str]: the prediction results
"""

vocab_size = len(self.tokenizer.vocab)
pred_list = inputs['predictions']
pred_ids = pred_list[0][0].cpu().numpy().tolist()
for j in range(len(pred_ids)):
if pred_ids[j] >= vocab_size:
pred_ids[j] = 100
pred = self.tokenizer.convert_ids_to_tokens(pred_ids)
pred_string = ''.join(pred).replace(
'##',
'').split('[SEP]')[0].replace('[CLS]',
'').replace('[SEP]',
'').replace('[UNK]', '')
return {'pred_string': pred_string}

+ 1
- 0
modelscope/preprocessors/__init__.py View File

@@ -6,4 +6,5 @@ from .common import Compose
from .image import LoadImage, load_image
from .nlp import * # noqa F403
from .nlp import TextGenerationPreprocessor
from .space.dialog_generation_preprocessor import * # noqa F403
from .space.dialog_intent_preprocessor import * # noqa F403

+ 50
- 0
modelscope/preprocessors/space/dialog_generation_preprocessor.py View File

@@ -0,0 +1,50 @@
# Copyright (c) Alibaba, Inc. and its affiliates.

import os
import uuid
from typing import Any, Dict, Union

from modelscope.preprocessors.space.fields.gen_field import \
MultiWOZBPETextField
from modelscope.utils.config import Config
from modelscope.utils.constant import Fields, InputFields
from modelscope.utils.type_assert import type_assert
from ..base import Preprocessor
from ..builder import PREPROCESSORS

__all__ = ['DialogGenerationPreprocessor']


@PREPROCESSORS.register_module(Fields.nlp, module_name=r'space-generation')
class DialogGenerationPreprocessor(Preprocessor):

def __init__(self, model_dir: str, *args, **kwargs):
"""preprocess the data via the vocab.txt from the `model_dir` path

Args:
model_dir (str): model path
"""
super().__init__(*args, **kwargs)

self.model_dir: str = model_dir
self.config = Config.from_file(
os.path.join(self.model_dir, 'configuration.json'))
self.text_field = MultiWOZBPETextField(
self.model_dir, config=self.config)

@type_assert(object, str)
def __call__(self, data: str) -> Dict[str, Any]:
"""process the raw input data

Args:
data (str): a sentence
Example:
'you are so handsome.'

Returns:
Dict[str, Any]: the preprocessed data
"""

idx = self.text_field.get_ids(data)

return {'user_idx': idx}

+ 13
- 13
modelscope/trainers/nlp/space/trainers/gen_trainer.py View File

@@ -668,6 +668,11 @@ class MultiWOZTrainer(Trainer):

return

def _get_turn_doamin(self, constraint_ids, bspn_gen_ids):
# constraint_token = self.tokenizer.convert_ids_to_tokens(constraint_ids)
# bspn_token = self.tokenizer.convert_ids_to_tokens(bspn_gen_ids)
return []

def forward(self, turn, old_pv_turn):
with torch.no_grad():
first_turn = True if len(old_pv_turn) == 0 else False
@@ -678,7 +683,6 @@ class MultiWOZTrainer(Trainer):
batch = type(batch)(
map(lambda kv: (kv[0], self.to_tensor(kv[1])), batch.items()))
pv_turn = {}
print(batch)

outputs = self.func_model.infer(
inputs=batch,
@@ -687,29 +691,24 @@ class MultiWOZTrainer(Trainer):
max_gen_len=60)
generated_bs = outputs[0].cpu().numpy().tolist()
bspn_gen = self.decode_generated_bspn(generated_bs)
bspn_token = self.tokenizer.convert_ids_to_tokens(bspn_gen)
print(bspn_gen)
print(bspn_token)
turn_domain = []
for item in bspn_token:
if item.startswith('[') and item.endswith(']'):
turn_domain.append(item)

turn_domain = self._get_turn_doamin(old_pv_turn['constraint_ids'],
bspn_gen)
print(turn_domain)

db_result = self.reader.bspan_to_DBpointer(
self.tokenizer.decode(bspn_gen), ['[taxi]'])
self.tokenizer.decode(bspn_gen), turn_domain)
print(db_result)
book_result = 21
assert len(turn['db']) == 3
assert isinstance(db_result, str)
db = \
[self.reader.sos_db_id] + \
self.tokenizer.convert_tokens_to_ids([db_result]) + \
[book_result] + \
[self.reader.eos_db_id]
prompt_id = self.reader.sos_a_id

prev_input = torch.tensor(bspn_gen + db)
if self.func_model.use_gpu:
prev_input = prev_input.cuda()

outputs_db = self.func_model.infer(
inputs=batch,
start_id=prompt_id,
@@ -727,5 +726,6 @@ class MultiWOZTrainer(Trainer):
pv_turn['bspn'] = decoded['bspn']
pv_turn['db'] = None
pv_turn['aspn'] = None
pv_turn['constraint_ids'] = bspn_gen

return pv_turn

+ 121
- 0
tests/pipelines/nlp/test_dialog_generation.py View File

@@ -0,0 +1,121 @@
# Copyright (c) Alibaba, Inc. and its affiliates.
import os
import os.path as osp
import tempfile
import unittest

from modelscope.models.nlp import DialogGenerationModel
from modelscope.pipelines import DialogGenerationPipeline, pipeline
from modelscope.preprocessors import DialogGenerationPreprocessor


def merge(info, result):
return info


class DialogGenerationTest(unittest.TestCase):
test_case = {
'sng0073': {
'goal': {
'taxi': {
'info': {
'leaveat': '17:15',
'destination': 'pizza hut fen ditton',
'departure': "saint john's college"
},
'reqt': ['car', 'phone'],
'fail_info': {}
}
},
'log': [{
'user':
"i would like a taxi from saint john 's college to pizza hut fen ditton .",
'user_delex':
'i would like a taxi from [value_departure] to [value_destination] .',
'resp':
'what time do you want to leave and what time do you want to arrive by ?',
'sys':
'what time do you want to leave and what time do you want to arrive by ?',
'pointer': '0,0,0,0,0,0',
'match': '',
'constraint':
"[taxi] destination pizza hut fen ditton departure saint john 's college",
'cons_delex': '[taxi] destination departure',
'sys_act': '[taxi] [request] leave arrive',
'turn_num': 0,
'turn_domain': '[taxi]'
}, {
'user': 'i want to leave after 17:15 .',
'user_delex': 'i want to leave after [value_leave] .',
'resp':
'booking completed ! your taxi will be [value_car] contact number is [value_phone]',
'sys':
'booking completed ! your taxi will be blue honda contact number is 07218068540',
'pointer': '0,0,0,0,0,0',
'match': '',
'constraint':
"[taxi] destination pizza hut fen ditton departure saint john 's college leave 17:15",
'cons_delex': '[taxi] destination departure leave',
'sys_act': '[taxi] [inform] car phone',
'turn_num': 1,
'turn_domain': '[taxi]'
}, {
'user': 'thank you for all the help ! i appreciate it .',
'user_delex': 'thank you for all the help ! i appreciate it .',
'resp':
'you are welcome . is there anything else i can help you with today ?',
'sys':
'you are welcome . is there anything else i can help you with today ?',
'pointer': '0,0,0,0,0,0',
'match': '',
'constraint':
"[taxi] destination pizza hut fen ditton departure saint john 's college leave 17:15",
'cons_delex': '[taxi] destination departure leave',
'sys_act': '[general] [reqmore]',
'turn_num': 2,
'turn_domain': '[general]'
}, {
'user': 'no , i am all set . have a nice day . bye .',
'user_delex': 'no , i am all set . have a nice day . bye .',
'resp': 'you too ! thank you',
'sys': 'you too ! thank you',
'pointer': '0,0,0,0,0,0',
'match': '',
'constraint':
"[taxi] destination pizza hut fen ditton departure saint john 's college leave 17:15",
'cons_delex': '[taxi] destination departure leave',
'sys_act': '[general] [bye]',
'turn_num': 3,
'turn_domain': '[general]'
}]
}
}

def test_run(self):

modeldir = '/Users/yangliu/Desktop/space-dialog-generation'

preprocessor = DialogGenerationPreprocessor(model_dir=modeldir)
model = DialogGenerationModel(
model_dir=modeldir,
text_field=preprocessor.text_field,
config=preprocessor.config)
print(model.forward(None))
# pipeline = DialogGenerationPipeline(
# model=model, preprocessor=preprocessor)

# history_dialog_info = {}
# for step, item in enumerate(test_case['sng0073']['log']):
# user_question = item['user']
# print('user: {}'.format(user_question))
#
# # history_dialog_info = merge(history_dialog_info,
# # result) if step > 0 else {}
# result = pipeline(user_question, history=history_dialog_info)
# #
# # print('sys : {}'.format(result['pred_answer']))
print('test')


if __name__ == '__main__':
unittest.main()

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