├── diet_classifier
├── __init__.py
├── config.py
├── models.py
├── data.py
└── classifier.py
├── .dockerignore
├── Dockerfile
├── plot.py
├── nlu.yml
├── .editorconfig
├── Makefile
├── Pipfile
├── docker-compose.yml
├── README.md
├── train.py
├── .vscode
└── launch.json
├── .github
└── FUNDING.yml
├── infer.py
├── LICENSE
├── executor.py
├── .gitignore
├── plot.ipynb
└── Pipfile.lock
/diet_classifier/__init__.py:
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1 |
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/.dockerignore:
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1 | /lightning_logs
2 |
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/Dockerfile:
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1 | FROM python:3.9.9
2 |
3 | WORKDIR /opt/server
4 |
5 | RUN pip install pipenv
6 |
7 | COPY Pipfile Pipfile.lock /opt/server/
8 |
9 | RUN pipenv install --system --deploy
10 |
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/plot.py:
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1 | from docarray import Document, DocumentArray
2 |
3 | part2_docs = DocumentArray.load_binary('part-2-embeddings.bin', protocol='pickle-array', compress='gzip')
4 | part2_docs.plot_embeddings('embeddings', path='./plot')
5 |
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/nlu.yml:
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1 | nlu:
2 | - intent: greet
3 | examples:
4 | - Hello
5 | - Hi
6 | - Hey
7 | - intent: affirm
8 | examples:
9 | - "Yes"
10 | - "Yes, that's right"
11 | - intent: deny
12 | examples:
13 | - "No"
14 | - "No, that's wrong"
15 |
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/.editorconfig:
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1 | # EditorConfig is awesome: https://EditorConfig.org
2 |
3 | # top-most EditorConfig file
4 | root = true
5 |
6 | # Unix-style newlines with a newline ending every file
7 | [*]
8 | end_of_line = lf
9 | insert_final_newline = true
10 |
11 | [*.py]
12 | charset = utf-8
13 | indent_style = space
14 | indent_size = 4
15 |
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/diet_classifier/config.py:
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1 | from typing import NamedTuple
2 |
3 |
4 | class DIETClassifierConfig(NamedTuple):
5 | """
6 | Configuration for the DIET classifier.
7 | """
8 | # Model parameters
9 | num_intents: int
10 | sentence_feature_dimension: int = 1024
11 | embedding_dimension: int = 40
12 | num_negative_samples: int = 20
13 |
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/Makefile:
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1 | start-featurizer:
2 | docker-compose up -d featurizer
3 |
4 | infer:
5 | docker-compose run --rm diet-classifier python infer.py
6 |
7 | train:
8 | docker-compose run --rm diet-classifier python train.py
9 |
10 | train-debug:
11 | docker-compose run -p 5678:5678 --rm diet-classifier python -m debugpy --listen 0.0.0.0:5678 train.py
12 |
13 | clean-models:
14 | rm -rf lightning_logs
15 |
16 | clean-featurizer:
17 | docker-compose down -v
18 |
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/Pipfile:
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1 | [[source]]
2 | url = "https://pypi.org/simple"
3 | verify_ssl = true
4 | name = "pypi"
5 |
6 | [packages]
7 | torch = "==1.12.1"
8 | jina = "==3.7.12"
9 | transformers = "==4.21.1"
10 | pydantic = "*"
11 | numpy = "*"
12 | pytorch-lightning = "*"
13 | pyyaml = "*"
14 | joblib = "*"
15 | debugpy = "*"
16 | pandas = "*"
17 |
18 | [dev-packages]
19 | autopep8 = "*"
20 | torch-tb-profiler = "*"
21 | ipykernel = "*"
22 |
23 | [requires]
24 | python_version = "3.9"
25 |
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/docker-compose.yml:
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1 | version: '3.7'
2 |
3 | services:
4 | diet-classifier:
5 | # build: .
6 | image: diet-classifier:latest
7 | ports:
8 | - 5678:5678
9 | volumes:
10 | - ".:/opt/server"
11 | environment:
12 | - JINA_LOG_LEVEL=DEBUG
13 | featurizer:
14 | image: paddlenlp-featurizer:latest
15 | restart: always
16 | ports:
17 | - 8888:8888
18 | command: --port 8888
19 | environment:
20 | - JINA_LOG_LEVEL=DEBUG
21 | - OMP_NUM_THREADS=4
22 | # - MODEL_NAME=ernie-3.0-mini-zh
23 |
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/README.md:
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1 | # diet-classifier-pytorch
2 | PyTorch Implementation of Rasa's DIET Classifier.
3 |
4 | The reason we are working on this project is to set up a PyTorch implementation of the DIET classifier for chatbots, so we can use it on our own chatbot projects. This allows us to ship the model on different platforms since we can build the model weights to ONNX.
5 |
6 | [Blog Article Series - Part 1](https://www.divby0.io/posts/diet-classifier-part-1)
7 |
8 | [Blog Article Series - Part 2](https://www.divby0.io/posts/diet-classifier-part-2)
9 |
10 | # License
11 |
12 | [MIT](./LICENSE)
13 |
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/train.py:
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1 |
2 | import pytorch_lightning as pl
3 |
4 | from diet_classifier.config import DIETClassifierConfig
5 | from diet_classifier.classifier import DIETClassifier
6 | from diet_classifier.data import DIETClassifierDataModule
7 |
8 |
9 | datamodule = DIETClassifierDataModule(filename='intents202208.yml', batch_size=32)
10 | config = DIETClassifierConfig(
11 | num_intents=datamodule.num_intents,
12 | # sentence_feature_dimension=384,
13 | sentence_feature_dimension=768,
14 | )
15 | model = DIETClassifier(config)
16 |
17 | trainer = pl.Trainer(max_epochs=1000, log_every_n_steps=50)
18 | trainer.fit(model=model, datamodule=datamodule)
19 |
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/.vscode/launch.json:
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1 | {
2 | // Use IntelliSense to learn about possible attributes.
3 | // Hover to view descriptions of existing attributes.
4 | // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
5 | "version": "0.2.0",
6 | "configurations": [
7 | {
8 | "name": "Python: Remote Attach",
9 | "type": "python",
10 | "request": "attach",
11 | "connect": {
12 | "host": "localhost",
13 | "port": 5678
14 | },
15 | "pathMappings": [
16 | {
17 | "localRoot": "${workspaceFolder}",
18 | "remoteRoot": "."
19 | }
20 | ],
21 | "justMyCode": true
22 | }
23 | ]
24 | }
25 |
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/.github/FUNDING.yml:
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1 | # These are supported funding model platforms
2 |
3 | github: ['lhr0909', 'xanthous-tech']
4 | patreon: # Replace with a single Patreon username
5 | open_collective: # Replace with a single Open Collective username
6 | ko_fi: # Replace with a single Ko-fi username
7 | tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
8 | community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
9 | liberapay: # Replace with a single Liberapay username
10 | issuehunt: # Replace with a single IssueHunt username
11 | otechie: # Replace with a single Otechie username
12 | lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
13 | custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']
14 |
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/diet_classifier/models.py:
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1 | from typing import Tuple
2 | from torch import nn, Tensor
3 |
4 | from .config import DIETClassifierConfig
5 |
6 | class IntentClassifier(nn.Module):
7 | def __init__(self, config: DIETClassifierConfig):
8 | super().__init__()
9 | # Rasa's embedding layer is actually a "dense embedding layer" which is just a Keras dense layer
10 | # equivalent to a PyTorch Linear layer.
11 | self.sentence_embed = nn.Linear(config.sentence_feature_dimension, config.embedding_dimension)
12 | self.label_embed = nn.Linear(config.num_intents, config.embedding_dimension)
13 |
14 | def forward(self, sentence_features: Tensor, label_features: Tensor) -> Tuple[Tensor, Tensor]:
15 | sentence_embeddings = self.sentence_embed(sentence_features)
16 | label_embeddings = self.label_embed(label_features)
17 |
18 | return sentence_embeddings, label_embeddings
19 |
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/infer.py:
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1 | from jina import Flow
2 | from docarray import DocumentArray, Document
3 | from pathlib import Path
4 | import yaml
5 | from typing import Dict, Any, List
6 |
7 | from executor import DIETClassifierExecutor
8 |
9 | f = Flow(
10 | prefetch=1,
11 | ).add(
12 | # host='paddlenlp.default.api.senses.chat',
13 | # port=80,
14 | host='featurizer',
15 | port=8888,
16 | external=True,
17 | ).add(
18 | uses=DIETClassifierExecutor,
19 | uses_with={
20 | 'nlu_filename': 'intents202208.yml',
21 | 'model_path': './lightning_logs/version_0/checkpoints/epoch=999-step=10000.ckpt',
22 | # 'sentence_feature_dimension': 384,
23 | 'sentence_feature_dimension': 768,
24 | },
25 | )
26 |
27 | nlu_file = open(Path('intents202208.yml').resolve(), 'r')
28 | nlu: Dict[str, Any] = yaml.load(nlu_file, Loader=yaml.Loader)
29 | nlu_intents: List[Dict[str, Any]] = nlu.get('nlu', [])
30 |
31 | with f:
32 | inputs = DocumentArray([Document(text='你真烦'), Document(text='大家好')])
33 | outputs: DocumentArray = f.post('/', inputs)
34 | for doc in outputs:
35 | doc.summary()
36 |
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/LICENSE:
--------------------------------------------------------------------------------
1 | MIT License
2 |
3 | Copyright (c) 2022 Botisan AI
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 |
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/executor.py:
--------------------------------------------------------------------------------
1 | from typing import Any, Dict, List
2 | import yaml
3 | from pathlib import Path
4 | from jina import Executor, requests
5 | from docarray import DocumentArray, Document
6 | from docarray.score import NamedScore
7 | import torch
8 |
9 | from diet_classifier.config import DIETClassifierConfig
10 | from diet_classifier.classifier import DIETClassifier
11 |
12 | class DIETClassifierExecutor(Executor):
13 | def __init__(self, nlu_filename='nlu.yml', model_path='./lightning_logs', sentence_feature_dimension: int = 1024, **kwargs):
14 | super().__init__(**kwargs)
15 | self.nlu_filename = nlu_filename
16 | self.read_nlu_file()
17 | config = DIETClassifierConfig(num_intents=self.num_intents, sentence_feature_dimension=sentence_feature_dimension)
18 | self.model = DIETClassifier.load_from_checkpoint(Path(model_path).resolve(), config=config)
19 |
20 | def read_nlu_file(self):
21 | nlu_file = open(Path(self.nlu_filename).resolve(), 'r')
22 | nlu: Dict[str, Any] = yaml.load(nlu_file, Loader=yaml.Loader)
23 | self.nlu_intents: List[Dict[str, Any]] = nlu.get('nlu', [])
24 | self.num_intents = len(self.nlu_intents)
25 |
26 | @requests
27 | def request(self, docs: DocumentArray, **kwargs) -> DocumentArray:
28 | embeddings = docs['@c[2]'].embeddings
29 | result = self.model.predict(torch.tensor(embeddings))
30 | similarities = result.get('similarities')
31 | sentence_embeddings = result.get('sentence_embeddings')
32 | for i, doc in enumerate(docs):
33 | doc.embedding = sentence_embeddings[i].detach().numpy()
34 | for j in range(self.num_intents):
35 | score = similarities[i].detach().numpy()[j]
36 | intent = Document(text=self.nlu_intents[j]['intent'], modality='intent')
37 | intent.scores['confidence'] = NamedScore(value=score, description='confidence')
38 | doc.matches.append(intent)
39 | doc.matches = sorted(doc.matches, key=lambda d: d.scores['confidence'].value, reverse=True)
40 | return docs
41 |
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/diet_classifier/data.py:
--------------------------------------------------------------------------------
1 | from typing import Dict, Any, List
2 | from pathlib import Path
3 | import yaml
4 | import torch
5 | import numpy as np
6 | import pytorch_lightning as pl
7 | from torch.utils.data import DataLoader
8 | from jina import Flow
9 | from docarray import DocumentArray, Document
10 |
11 |
12 | class DIETClassifierDataModule(pl.LightningDataModule):
13 | def __init__(
14 | self,
15 | featurizer_host: str ='featurizer',
16 | featurizer_port: int = 8888,
17 | filename: str = 'nlu.yml',
18 | batch_size: int = 32,
19 | ) -> None:
20 | super().__init__()
21 | self.flow = Flow().add(host=featurizer_host, port=featurizer_port, external=True)
22 | self.filename = filename
23 | self.batch_size = batch_size
24 | self.read_nlu_file()
25 |
26 | def read_nlu_file(self):
27 | nlu_file = open(Path(self.filename).resolve(), 'r')
28 | nlu: Dict[str, Any] = yaml.load(nlu_file, Loader=yaml.Loader)
29 | self.nlu_intents: List[Dict[str, Any]] = nlu.get('nlu', [])
30 | self.num_intents = len(self.nlu_intents)
31 |
32 | def setup(self, stage: str):
33 | self.flow.start()
34 | self.read_nlu_file()
35 | intent_data_raw = []
36 | for i, intent in enumerate(self.nlu_intents):
37 | examples: List[str] = intent.get('examples', [])
38 | example_docs = [Document(text=sentence) for sentence in examples]
39 | for doc in example_docs:
40 | intent_data_raw.append((doc, i))
41 |
42 | self.intent_dataset = []
43 | examples_da = DocumentArray([d[0] for d in intent_data_raw])
44 | features_da: DocumentArray = self.flow.post('/', inputs=examples_da, show_progress=True, request_size=10)
45 |
46 | for i, feature in enumerate(features_da):
47 | src = torch.from_numpy(np.array(feature.chunks[2].embedding))
48 | tgt = torch.tensor(intent_data_raw[i][1])
49 | self.intent_dataset.append((src, tgt))
50 |
51 | def train_dataloader(self):
52 | return DataLoader(self.intent_dataset, batch_size=self.batch_size, shuffle=True)
53 |
54 | def teardown(self, stage: str):
55 | self.flow.close()
56 |
57 | if __name__ == '__main__':
58 | data_module = DIETClassifierDataModule()
59 | data_module.prepare_data()
60 |
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/.gitignore:
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1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
11 | build/
12 | develop-eggs/
13 | dist/
14 | downloads/
15 | eggs/
16 | .eggs/
17 | lib/
18 | lib64/
19 | parts/
20 | sdist/
21 | var/
22 | wheels/
23 | pip-wheel-metadata/
24 | share/python-wheels/
25 | *.egg-info/
26 | .installed.cfg
27 | *.egg
28 | MANIFEST
29 |
30 | # PyInstaller
31 | # Usually these files are written by a python script from a template
32 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
33 | *.manifest
34 | *.spec
35 |
36 | # Installer logs
37 | pip-log.txt
38 | pip-delete-this-directory.txt
39 |
40 | # Unit test / coverage reports
41 | htmlcov/
42 | .tox/
43 | .nox/
44 | .coverage
45 | .coverage.*
46 | .cache
47 | nosetests.xml
48 | coverage.xml
49 | *.cover
50 | *.py,cover
51 | .hypothesis/
52 | .pytest_cache/
53 |
54 | # Translations
55 | *.mo
56 | *.pot
57 |
58 | # Django stuff:
59 | *.log
60 | local_settings.py
61 | db.sqlite3
62 | db.sqlite3-journal
63 |
64 | # Flask stuff:
65 | instance/
66 | .webassets-cache
67 |
68 | # Scrapy stuff:
69 | .scrapy
70 |
71 | # Sphinx documentation
72 | docs/_build/
73 |
74 | # PyBuilder
75 | target/
76 |
77 | # Jupyter Notebook
78 | .ipynb_checkpoints
79 |
80 | # IPython
81 | profile_default/
82 | ipython_config.py
83 |
84 | # pyenv
85 | .python-version
86 |
87 | # pipenv
88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies
90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not
91 | # install all needed dependencies.
92 | #Pipfile.lock
93 |
94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow
95 | __pypackages__/
96 |
97 | # Celery stuff
98 | celerybeat-schedule
99 | celerybeat.pid
100 |
101 | # SageMath parsed files
102 | *.sage.py
103 |
104 | # Environments
105 | .env
106 | .venv
107 | env/
108 | venv/
109 | ENV/
110 | env.bak/
111 | venv.bak/
112 |
113 | # Spyder project settings
114 | .spyderproject
115 | .spyproject
116 |
117 | # Rope project settings
118 | .ropeproject
119 |
120 | # mkdocs documentation
121 | /site
122 |
123 | # mypy
124 | .mypy_cache/
125 | .dmypy.json
126 | dmypy.json
127 |
128 | # Pyre type checker
129 | .pyre/
130 |
131 | # Pytorch Lightning logs
132 | /lightning_logs
133 | /plot
134 |
135 | *.bin
136 | *.yml
137 |
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/diet_classifier/classifier.py:
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1 | from typing import Tuple, Dict, Any
2 | import torch
3 | from torch import optim, nn, Tensor
4 | import torch.nn.functional as F
5 | import pytorch_lightning as pl
6 |
7 | from .config import DIETClassifierConfig
8 | from .models import IntentClassifier
9 |
10 | class DIETClassifier(pl.LightningModule):
11 | def __init__(self, config: DIETClassifierConfig):
12 | super().__init__()
13 | self.config = config
14 | self.intent_classifier = IntentClassifier(config)
15 | self.loss = nn.CrossEntropyLoss()
16 |
17 | def forward(self, sentence_features: Tensor) -> Tuple[Tensor, Tensor]:
18 | label_features = self._get_label_features()
19 | return self.intent_classifier(
20 | sentence_features,
21 | label_features,
22 | )
23 |
24 | def predict(self, sentence_features: Tensor) -> Dict[str, Any]:
25 | sentence_embeddings, label_embeddings = self.forward(sentence_features)
26 | similarities = torch.mm(sentence_embeddings, label_embeddings.t())
27 | return {
28 | 'similarities': F.softmax(similarities, dim=-1),
29 | 'sentence_embeddings': sentence_embeddings,
30 | }
31 |
32 | def configure_optimizers(self):
33 | optimizer = optim.Adam(self.parameters(), lr=1e-3)
34 | return optimizer
35 |
36 | def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int):
37 | input_features, target_labels = batch
38 | input_embeddings, label_embeddings = self.forward(input_features)
39 | target_embeddings = label_embeddings[target_labels]
40 |
41 | loss, accuracy = self._calculate_loss_and_accuracy(
42 | input_embeddings,
43 | label_embeddings,
44 | target_embeddings,
45 | target_labels,
46 | )
47 |
48 | self.log('train_loss', loss)
49 | self.log('train_acc', accuracy)
50 |
51 | return loss
52 |
53 | def _calculate_loss_and_accuracy(
54 | self,
55 | input_embeddings: Tensor,
56 | label_embeddings: Tensor,
57 | target_embeddings: Tensor,
58 | target_labels: Tensor,
59 | ) -> Tuple[Tensor, Tensor]:
60 | (
61 | pos_input_embeddings,
62 | pos_label_embeddings,
63 | neg_input_embeddings,
64 | neg_label_embeddings,
65 | input_bad_negatives,
66 | label_bad_negatives,
67 | ) = self._sample_negatives(
68 | input_embeddings,
69 | label_embeddings,
70 | target_embeddings,
71 | target_labels,
72 | )
73 |
74 | # calculate similarities
75 | (
76 | sim_pos,
77 | sim_neg_input_label,
78 | sim_neg_label_label,
79 | sim_neg_input_input,
80 | sim_neg_label_input,
81 | ) = self._calculate_similarity(
82 | pos_input_embeddings,
83 | pos_label_embeddings,
84 | neg_input_embeddings,
85 | neg_label_embeddings,
86 | input_bad_negatives,
87 | label_bad_negatives,
88 | )
89 |
90 | accuracy = self._calculate_accuracy(sim_pos, sim_neg_input_label)
91 |
92 | loss = self._calculate_loss(
93 | sim_pos,
94 | sim_neg_input_label,
95 | sim_neg_label_label,
96 | sim_neg_input_input,
97 | sim_neg_label_input,
98 | )
99 |
100 | return loss, accuracy
101 |
102 | def _sample_negatives(
103 | self,
104 | input_embeddings: Tensor,
105 | label_embeddings: Tensor,
106 | target_embeddings: Tensor,
107 | target_labels: Tensor,
108 | ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]:
109 | pos_input_embeddings = input_embeddings.unsqueeze(-2)
110 | pos_label_embeddings = target_embeddings.unsqueeze(-2)
111 |
112 | # sample negative inputs
113 | neg_input_embeddings, input_bad_negatives = self._get_negatives(
114 | input_embeddings,
115 | target_labels,
116 | target_labels,
117 | )
118 | # sample negative labels
119 | neg_label_embeddings, label_bad_negatives = self._get_negatives(
120 | label_embeddings,
121 | self._get_all_labels(),
122 | target_labels,
123 | )
124 |
125 | return (
126 | pos_input_embeddings,
127 | pos_label_embeddings,
128 | neg_input_embeddings,
129 | neg_label_embeddings,
130 | input_bad_negatives,
131 | label_bad_negatives,
132 | )
133 |
134 | def _get_negatives(
135 | self,
136 | embeddings: Tensor,
137 | labels: Tensor,
138 | target_labels: Tensor,
139 | ):
140 | # batch_flatten
141 | embeddings_flattened = embeddings.view(-1, embeddings.shape[-1])
142 | labels_flattened = labels.view(-1, labels.shape[-1])
143 | target_labels_flattened = target_labels.view(-1, target_labels.shape[-1])
144 |
145 | total_candidates = embeddings_flattened.shape[0]
146 | target_size = target_labels_flattened.shape[1]
147 |
148 | # assign random indices as negative labels
149 | negative_indices = torch.randint(
150 | total_candidates,
151 | (target_size, self.config.num_negative_samples),
152 | )
153 |
154 | negative_embeddings = embeddings_flattened[negative_indices]
155 | negative_labels = labels_flattened.t()[negative_indices]
156 |
157 | bad_negatives = torch.eq(negative_labels, target_labels_flattened.t().unsqueeze(-1)).all(dim=-1)
158 |
159 | return negative_embeddings, bad_negatives
160 |
161 | def _calculate_similarity(
162 | self,
163 | pos_input_embeddings: Tensor,
164 | pos_label_embeddings: Tensor,
165 | neg_input_embeddings: Tensor,
166 | neg_label_embeddings: Tensor,
167 | input_bad_negatives: Tensor,
168 | label_bad_negatives: Tensor,
169 | ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
170 | neg_infinity = torch.tensor(-1e9)
171 |
172 | # inner dot product
173 | sim_pos = torch.sum(pos_input_embeddings * pos_label_embeddings, dim=-1)
174 | sim_neg_input_label = torch.sum(pos_input_embeddings * neg_label_embeddings, dim=-1) + neg_infinity * label_bad_negatives
175 | sim_neg_label_label = torch.sum(pos_label_embeddings * neg_label_embeddings, dim=-1) + neg_infinity * label_bad_negatives
176 | sim_neg_input_input = torch.sum(pos_input_embeddings * neg_input_embeddings, dim=-1) + neg_infinity * input_bad_negatives
177 | sim_neg_label_input = torch.sum(pos_label_embeddings * neg_input_embeddings, dim=-1) + neg_infinity * input_bad_negatives
178 |
179 | return sim_pos, sim_neg_input_label, sim_neg_label_label, sim_neg_input_input, sim_neg_label_input
180 |
181 | def _calculate_accuracy(
182 | self,
183 | sim_pos: Tensor,
184 | sim_neg: Tensor,
185 | ):
186 | max_all_sim = torch.cat([sim_pos, sim_neg], dim=-1).max(dim=-1).values
187 | return torch.eq(max_all_sim, sim_pos.squeeze(-1)).float().mean()
188 |
189 | def _calculate_loss(
190 | self,
191 | sim_pos: Tensor,
192 | sim_neg_input_label: Tensor,
193 | sim_neg_label_label: Tensor,
194 | sim_neg_input_input: Tensor,
195 | sim_neg_label_input: Tensor,
196 | ) -> Tensor:
197 | # concatenate all similarities
198 | logits = torch.cat([
199 | sim_pos,
200 | sim_neg_input_label,
201 | sim_neg_label_label,
202 | sim_neg_input_input,
203 | sim_neg_label_input,
204 | ], dim=-1)
205 |
206 | # create target labels, since the positive samples are always the first element
207 | # create all zeros
208 | target_labels = torch.zeros(logits.shape[0], dtype=torch.long)
209 |
210 | return self.loss(logits, target_labels)
211 |
212 | def _get_all_labels(self):
213 | return torch.arange(0, self.config.num_intents)
214 |
215 | def _get_label_features(self):
216 | return torch.eye(self.config.num_intents)
217 |
--------------------------------------------------------------------------------
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2 | "cells": [
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54 | "│ │\n",
55 | "╰────────────────────────────────────────────────────────────────────╯\n"
56 | ]
57 | },
58 | "metadata": {},
59 | "output_type": "display_data"
60 | }
61 | ],
62 | "source": [
63 | "from docarray import Document, DocumentArray\n",
64 | "\n",
65 | "part1_docs = DocumentArray.load_binary('part-1-embeddings.bin', protocol='pickle-array', compress='gzip')\n",
66 | "part1_docs.summary()"
67 | ]
68 | },
69 | {
70 | "cell_type": "code",
71 | "execution_count": 5,
72 | "metadata": {},
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84 | "│ Multimodal dataclass False │\n",
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118 | "│ matches \u001b[1m(\u001b[0m\u001b[32m'MatchArray'\u001b[0m,\u001b[1m)\u001b[0m \u001b[1;36m300\u001b[0m \u001b[3;91mFalse\u001b[0m │\n",
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120 | "│ │\n",
121 | "╰────────────────────────────────────────────────────────────────────╯\n"
122 | ]
123 | },
124 | "metadata": {},
125 | "output_type": "display_data"
126 | }
127 | ],
128 | "source": [
129 | "part2_docs = DocumentArray.load_binary('part-2-embeddings.bin', protocol='pickle-array', compress='gzip')\n",
130 | "part2_docs.summary()"
131 | ]
132 | }
133 | ],
134 | "metadata": {
135 | "kernelspec": {
136 | "display_name": "Python 3.9.9 ('diet-classifier-pytorch-tuzCfIY6')",
137 | "language": "python",
138 | "name": "python3"
139 | },
140 | "language_info": {
141 | "codemirror_mode": {
142 | "name": "ipython",
143 | "version": 3
144 | },
145 | "file_extension": ".py",
146 | "mimetype": "text/x-python",
147 | "name": "python",
148 | "nbconvert_exporter": "python",
149 | "pygments_lexer": "ipython3",
150 | "version": "3.9.9"
151 | },
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157 | }
158 | },
159 | "nbformat": 4,
160 | "nbformat_minor": 2
161 | }
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