Multimodal emo and transformer #202
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| # Copyright (C) 2017 Beijing Didi Infinity Technology and Development Co.,Ltd. | ||
| # 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. | ||
| # ============================================================================== | ||
| """Transformer sub layers.""" | ||
| from absl import logging | ||
| import delta.compat as tf | ||
| import numpy as np | ||
| from delta.layers.base_layer import Layer | ||
| from delta.layers.utils_tf import shape_list | ||
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| #pylint: disable=invalid-name, too-many-instance-attributes, too-many-arguments | ||
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| class PositionEmbedding(Layer): | ||
| """ | ||
| PositionEmbedding represents the positional information of tokens | ||
| consisting of two optional types: constant(untrainable) and trainable. | ||
| """ | ||
| def __init__(self, max_len, embed_dim, use_const, name, **kwargs): | ||
| super().__init__(**kwargs) | ||
| self.max_len = max_len | ||
| self.embed_dim = embed_dim | ||
| self.use_const = use_const | ||
| self.pos_name = name | ||
| self.pos_embed = self.get_pos_embedding_matrix(self.max_len, | ||
| self.embed_dim, | ||
| self.use_const, | ||
| self.pos_name) | ||
| @staticmethod | ||
| def get_pos_embedding_matrix(max_len, embed_dim, use_const, name): | ||
| """ | ||
| generate position embedding matrix, two optional types: | ||
| constant(untrainable) and trainable. | ||
| Args: | ||
| max_len, embed_dim, use_const | ||
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| Return: | ||
| pos_embed: [max_len, embed_dim] | ||
| """ | ||
| # First part of the PE function: sin and cos argument | ||
| if use_const: | ||
| pos_embed = np.array([[ | ||
| pos / np.power(10000, (i - i % 2) / embed_dim) | ||
| for i in range(embed_dim) | ||
| ] for pos in range(max_len)]) | ||
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| # Second part, apply the cosine to even columns and sin to odds. | ||
| pos_embed[:, 0::2] = np.sin(pos_embed[:, 0::2]) # dim 2i | ||
| pos_embed[:, 1::2] = np.cos(pos_embed[:, 1::2]) # dim 2i+1 | ||
| pos_embed = pos_embed[np.newaxis, ...] | ||
| pos_embed = tf.cast(pos_embed, dtype=tf.float32) | ||
| else: | ||
| pos_embed = tf.get_variable( | ||
| name=name, | ||
| shape=[max_len, embed_dim], | ||
| initializer=tf.random_uniform_initializer(-0.1, 0.1)) | ||
| pos_embed = tf.expand_dims(pos_embed, 0) | ||
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| return pos_embed | ||
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| def call(self, inputs, training=None, mask=None): | ||
| """ | ||
| Args: | ||
| inputs: [batch_size, seq_x_len, embed_dim] | ||
| Return: | ||
| pos_embed: [batch_size, seq_x_len, embed_dim] | ||
| """ | ||
| seq_len = shape_list(inputs)[1] | ||
| pos_embed = self.pos_embed[:, :seq_len, :] | ||
| return pos_embed | ||
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| class PositionwiseFeedForward(Layer): | ||
| """ | ||
| A two-layer Feed-Forward-Network. | ||
| """ | ||
| def __init__(self, d_model, dff, act_func, **kwargs): | ||
| super().__init__(**kwargs) | ||
| self.dense1 = tf.keras.layers.Dense(dff, activation=act_func) | ||
| self.dense2 = tf.keras.layers.Dense(d_model) | ||
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| def call(self, inputs, training=None, mask=None): | ||
| """ | ||
| The implementation of PositionwiseFeedForward. | ||
| Args: | ||
| inputs: [batch_size, seq_x_len, d_model] | ||
| Return: | ||
| ffn: [batch_size, seq_x_len, d_model] | ||
| """ | ||
| ffn = self.dense2(self.dense1(inputs)) | ||
| return ffn | ||
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| class MultiHeadAttention(Layer): | ||
| """ | ||
| Multi-headed attention is based on "Attention | ||
| is all you Need" (https://arxiv.org/pdf/1706.03762.pdf). | ||
| """ | ||
| def __init__(self, hidden_size, num_heads, **kwargs): | ||
| super().__init__(**kwargs) | ||
| self.hidden_size, self.num_heads = hidden_size, num_heads | ||
| assert self.hidden_size % self.num_heads == 0 | ||
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| self.depth = self.hidden_size // self.num_heads | ||
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| self.wq = tf.keras.layers.Dense(self.hidden_size) | ||
| self.wk = tf.keras.layers.Dense(self.hidden_size) | ||
| self.wv = tf.keras.layers.Dense(self.hidden_size) | ||
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| self.dense = tf.keras.layers.Dense(self.hidden_size) | ||
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| def split_heads(self, x, batch_size): | ||
| """ | ||
| Split hidden_size into depth(hidden_size // num_heads) for | ||
| multi-head attention. | ||
| Args: | ||
| x: (batch_size, seq_len_x, hidden_size) | ||
| batch_size | ||
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| Returns: | ||
| split_x: (batch_size, num_heads, seq_len_x, depth) | ||
| """ | ||
| x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth)) | ||
| split_x = tf.transpose(x, perm=[0, 2, 1, 3]) | ||
| return split_x | ||
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| def call(self, inputs, training=None, mask=None): | ||
| """ | ||
| The implementation of Multi-headed attention. | ||
| Args: | ||
| inputs = (v, k, q) | ||
| q: (batch_size, seq_len_q, hidden_size) | ||
| k: (batch_size, seq_len_k, hidden_size) | ||
| v: (batch_size, seq_len_v, hidden_size) | ||
| mask: (batch_size, seq_len_q, seq_len_k) | ||
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| Returns: | ||
| output: (batch_size, seq_len_q, hidden_size) | ||
| attention_weights: (batch_size, num_heads, seq_len_q, seq_len_k) | ||
| """ | ||
| q, k, v = inputs | ||
| batch_size = tf.shape(q)[0] | ||
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| q = self.wq(q) # (batch_size, seq_len_q, hidden_size) | ||
| k = self.wk(k) # (batch_size, seq_len_k, hidden_size) | ||
| v = self.wv(v) # (batch_size, seq_len_v, hidden_size) | ||
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| q = self.split_heads(q, batch_size) # (batch_size, num_heads, seq_len_q, depth) | ||
| k = self.split_heads(k, batch_size) # (batch_size, num_heads, seq_len_k, depth) | ||
| v = self.split_heads(v, batch_size) # (batch_size, num_heads, seq_len_v, depth) | ||
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| # scaled_attention.shape == (batch_size, num_heads, seq_len_q, depth) | ||
| # attention_weights.shape == (batch_size, num_heads, seq_len_q, seq_len_k) | ||
| scaled_attention, attention_weights = self.scaled_dot_product_attention( | ||
| q, k, v, mask) | ||
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| scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3]) # (batch_size, seq_len_q, num_heads, depth) | ||
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| concat_attention = tf.reshape(scaled_attention, | ||
| (batch_size, -1, self.hidden_size)) # (batch_size, seq_len_q, hidden_size) | ||
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| output = self.dense(concat_attention) # (batch_size, seq_len_q, hidden_size) | ||
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| return output, attention_weights | ||
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| @staticmethod | ||
| def scaled_dot_product_attention(q, k, v, mask): | ||
| """ | ||
| The implementation of scaled attention. | ||
| Args: | ||
| v: (batch_size, seq_len_v, hidden_size) | ||
| k: (batch_size, seq_len_k, hidden_size) | ||
| q: (batch_size, seq_len_q, hidden_size) | ||
| mask: (batch_size, seq_len_q, seq_len_k) | ||
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| Returns: | ||
| output: (batch_size, seq_len_q, hidden_size) | ||
| attention_weights: (batch_size, num_heads, seq_len_q, seq_len_k) | ||
| """ | ||
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| matmul_qk = tf.matmul(q, k, transpose_b=True) # (batch_size, seq_len_q, seq_len_k) | ||
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| # Scaled | ||
| dk = tf.cast(tf.shape(k)[-1], tf.float32) | ||
| scaled_attention_logits = matmul_qk / tf.math.sqrt(dk) | ||
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| # Masked | ||
| if mask is not None: | ||
| scaled_attention_logits += (mask * -1e9) | ||
|
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| # Normalized | ||
| attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1) # (batch_size, seq_len_q, seq_len_k) | ||
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| # Weighted sum | ||
| output = tf.matmul(attention_weights, v) # (batch_size, seq_len_q, depth_v) | ||
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| return output, attention_weights |
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You can make it a class static method.