add text embedding implementation

README.md

@@ -1,3 +1,42 @@
-# bert-embedding
+# BERT Text Embedding Operator (Pytorch)
 
-This is another test repo
+Authors: Kyle He
+
+## Overview
+
+This operator transforms text into embedding using BERT[1], which stands for 
+Bidirectional Encoder Representations from Transformers. 
+
+## Interface
+
+```python
+__call__(self, text: str)
+```
+
+**Args:**
+
+- audio_path:
+  - the text to be embedded
+  - supported types: str
+
+**Returns:**
+
+The Operator returns a tuple Tuple[('embs', numpy.ndarray)] containing following fields:
+
+- embs:
+  - embeddings of the text
+  - data type: `numpy.ndarray`
+  - shape: 768
+
+## Requirements
+
+You can get the required python package by [requirements.txt](./requirements.txt).
+
+## How it works
+
+The `towhee/torch-bert` Operator is based on Huggingface[2].
+
+## Reference
+
+[1]. https://arxiv.org/pdf/1810.04805.pdf
+[2]. https://huggingface.co/docs/transformers

__init__.py

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+# Copyright 2021 Zilliz. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

requirements.txt

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+torch
+numpy
+bertviz
+transformers

torch_bert.py

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+from bertviz.transformers_neuron_view import BertModel, BertConfig
+from transformers import BertTokenizer
+from typing import NamedTuple
+import numpy
+import torch
+
+from towhee.operator import Operator
+
+
+class TorchBert(Operator):
+    """
+    Text to embedding using BERT
+    """
+    def __init__(self, max_length: int = 256, framework: str = 'pytorch') -> None:
+        super().__init__()
+        config = BertConfig.from_pretrained("bert-base-cased", output_attentions=True, output_hidden_states=True,
+                                            return_dict=True)
+        self.tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
+        config.max_position_embeddings = max_length
+        self.max_length = max_length
+        model = BertModel(config)
+        self.model = model.eval()
+
+    def __call__(self, text: str) -> NamedTuple('Outputs', [('embs', numpy.ndarray)]):
+        inputs = self.tokenizer(text, truncation=True, padding=True, max_length=self.max_length,
+                                return_tensors='pt')
+        f1 = torch.index_select(self.model.embeddings.word_embeddings.weight, 0,
+                                inputs['input_ids'][0])  # words embeddings
+        + torch.index_select(self.model.embeddings.position_embeddings.weight, 0,
+                             torch.tensor(range(inputs['input_ids'][0].size(0))).long())  # pos embeddings
+        + torch.index_select(self.model.embeddings.token_type_embeddings.weight, 0,
+                             inputs['token_type_ids'][0])  # token embeddings
+        # single example normalization
+        ex1 = f1[0, :]
+        ex1_mean = ex1.mean()
+        ex1_std = (ex1 - ex1_mean).pow(2).mean()
+        norm_embedding = ((ex1 - ex1_mean) / torch.sqrt(ex1_std + 1e-12))
+        norm_embedding_centered = self.model.embeddings.LayerNorm.weight * norm_embedding \
+                                + self.model.embeddings.LayerNorm.bias
+        Outputs = NamedTuple('Outputs', [('embs', numpy.ndarray)])
+        return Outputs(norm_embedding_centered.detach().numpy())