From 3b1dfaf2787246d4e263dbe57e851bee41b450e6 Mon Sep 17 00:00:00 2001
From: Nick <nickwalton00@gmail.com>
Date: Sat, 2 Nov 2019 14:14:56 -0600
Subject: [PATCH] adding low mem trans

---
 generator/ctrl/model/low_mem_transformer.py | 139 ++++++++++++++++++++
 1 file changed, 139 insertions(+)
 create mode 100644 generator/ctrl/model/low_mem_transformer.py

diff --git a/generator/ctrl/model/low_mem_transformer.py b/generator/ctrl/model/low_mem_transformer.py
new file mode 100644
index 0000000..da4c19f
--- /dev/null
+++ b/generator/ctrl/model/low_mem_transformer.py
@@ -0,0 +1,139 @@
+import tensorflow as tf
+import numpy as np
+
+
+def angle_defn(pos, i, d_model_size):
+    angle_rates = 1 / np.power(10000, (2 * (i // 2)) / np.float32(d_model_size))
+    return pos * angle_rates
+
+
+def positional_encoding(position, d_model_size):
+    # create the sinusoidal pattern for the positional encoding
+    angle_rads = angle_defn(np.arange(position)[:, np.newaxis], np.arange(d_model_size)[np.newaxis, :], d_model_size)
+
+    sines = np.sin(angle_rads[:, 0::2])
+    cosines = np.cos(angle_rads[:, 1::2])
+
+    pos_encoding = tf.cast(np.concatenate([sines, cosines], axis=-1)[np.newaxis, ...], dtype=tf.float32)
+    return pos_encoding
+
+
+def scaled_dot_product_attention(q, k, v, mask):
+    # calculate attention
+    matmul_qk = tf.cast(tf.matmul(q, k, transpose_b=True), tf.float32)
+
+    dk = tf.cast(tf.shape(k)[-1], tf.float32)
+    scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)
+
+    if mask is not None:
+        scaled_attention_logits += (mask * -1e3)
+
+    attention_weights = tf.cast(tf.nn.softmax(scaled_attention_logits, axis=-1), tf.float16)
+    output = tf.matmul(attention_weights, v)
+    return output
+
+
+class MultiHeadAttention(tf.keras.layers.Layer):
+    def __init__(self, d_model_size, num_heads):
+        super(MultiHeadAttention, self).__init__()
+        self.num_heads = num_heads
+        self.d_model_size = d_model_size
+
+        self.depth = int(d_model_size / self.num_heads)
+
+        self.Wq = tf.keras.layers.Dense(d_model_size)
+        self.Wk = tf.keras.layers.Dense(d_model_size)
+        self.Wv = tf.keras.layers.Dense(d_model_size)
+
+        self.dense = tf.keras.layers.Dense(d_model_size)
+
+    def split_into_heads(self, x, batch_size):
+        x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))
+        return tf.transpose(x, perm=[0, 2, 1, 3])
+
+    def call(self, v, k, q, mask):
+        batch_size = tf.shape(q)[0]
+
+        q = self.Wq(q)
+        k = self.Wk(k)
+        v = self.Wv(v)
+
+        q = self.split_into_heads(q, batch_size)
+        k = self.split_into_heads(k, batch_size)
+        v = self.split_into_heads(v, batch_size)
+
+        scaled_attention = tf.transpose(scaled_dot_product_attention(q, k, v, mask), perm=[0, 2, 1, 3])
+        original_size_attention = tf.reshape(scaled_attention, (batch_size, -1, self.d_model_size))
+        output = self.dense(original_size_attention)
+
+        return output
+
+
+def point_wise_feed_forward_network(d_model_size, dff):
+    return tf.keras.Sequential([tf.keras.layers.Dense(dff, activation='relu'),
+                                tf.keras.layers.Dense(d_model_size)])
+
+
+class EncoderLayer(tf.keras.layers.Layer):
+    def __init__(self, d_model_size, num_heads, dff, rate=0.1):
+        super(EncoderLayer, self).__init__()
+
+        self.multi_head_attention = MultiHeadAttention(d_model_size, num_heads)
+        self.ffn = point_wise_feed_forward_network(d_model_size, dff)
+
+        self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
+        self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
+
+        self.dropout1 = tf.keras.layers.Dropout(rate)
+        self.dropout2 = tf.keras.layers.Dropout(rate)
+        self.to32 = lambda x: tf.cast(x, tf.float32)
+        self.to16 = lambda x: tf.cast(x, tf.float16)
+
+    def call(self, x, training, mask):
+        normed = self.to16(self.layernorm1(self.to32(x)))
+        attn_output = self.multi_head_attention(normed, normed, normed, mask)
+        attn_output = self.dropout1(attn_output, training=training)
+        out1 = x + attn_output
+
+        out2 = self.to16(self.layernorm2(self.to32(out1)))
+        ffn_output = self.ffn(out2)
+        ffn_output = self.dropout2(ffn_output, training=training)
+        out2 = out1 + ffn_output
+
+        return out2
+
+
+class Encoder(tf.keras.layers.Layer):
+    def __init__(self, num_layers=48, d_model_size=1280, num_heads=16, dff=8192, input_vocab_size=50000,
+                 rate=0.1, **kwargs):
+        super(Encoder, self).__init__()
+
+        self.d_model_size = d_model_size
+        self.num_layers = num_layers
+
+        self.pos_encoding = positional_encoding(input_vocab_size, self.d_model_size)
+
+        for i in range(num_layers):
+            setattr(self, "layer%i" % i, EncoderLayer(d_model_size, num_heads, dff, rate))
+
+        self.layernorm = tf.keras.layers.LayerNormalization(epsilon=1e-6)
+        self.dropout = tf.keras.layers.Dropout(rate)
+
+    def get_config(self):
+        base_config = super(Encoder, self).get_config()
+        return base_config
+
+    def call(self, x, training):
+        seq_len = tf.shape(x)[1]
+
+        mask = 1 - tf.linalg.band_part(tf.ones((seq_len, seq_len)), -1, 0)
+
+        x *= tf.math.sqrt(tf.cast(self.d_model_size, tf.float32))
+        x += self.pos_encoding[:, :seq_len, :]
+
+        x = self.dropout(x, training=training)
+        x = tf.cast(x, tf.float16)
+
+        for i in range(self.num_layers):
+            x = getattr(self, "layer%i" % i)(x, training, mask)
+        return self.layernorm(tf.cast(x, tf.float32))
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