mlable 0.5.0

Tensorflow libs: layers, blocks, optimizers, etc.

MLable

Tensorflow libs:

• layers:
  • reshaping:
    • Divide
    • Merge
  • embedding:
    • RotaryPositionalEmbedding
  • transformer:
    • CachedMultiHeadAttention
    • FeedForwardGate
• metrics:
  • CategoricalGroupAccuracy

Installation

The package is available on pypi:

pip install -U mlable

Layers

Divide

Relative reshaping layers that divides a given axis and multiplies another by the same factor:

import mlable.layers.reshaping

__x = tf.ones(shape=(2, 4, 6, 8))
__l = mlable.layers.reshaping.Divide(
    input_axis=2, # relative to the NEW shape / rank
    output_axis=-1, # same
    factor=3,
    insert=False,) # whether to create a new axis

list(__l(__x).shape)
# [2, 4, 2, 24]

Merge

Relative reshaping layers that merges two axes:

import mlable.layers.reshaping

__x = tf.ones(shape=(2, 4, 6, 8))
__l = mlable.layers.reshaping.Merge(
    left_axis=1,
    right_axis=-1,
    left=False,) # whether to merge into the left axis

list(__l(__x).shape)
# [2, 6, 32]

CachedMultiHeadAttention

This layer subclasses the regular MultiHeadAttention and adds a cache.

It has the same parameters:

import mlable.layers.transformer

mlable.layers.transformer.CachedMultiHeadAttention(
    num_heads,
    key_dim,
    value_dim=None,
    dropout=0.0,
    use_bias=True,
    output_shape=None,
    attention_axes=None,
    kernel_initializer='glorot_uniform',
    bias_initializer='zeros',
    kernel_regularizer=None,
    bias_regularizer=None,
    activity_regularizer=None,
    kernel_constraint=None,
    bias_constraint=None,
    **kwargs)

And its call function has the following arguments:

mlable.layers.transformer.CachedMultiHeadAttention.call(
    query,
    value,
    key=None,
    cache=None,
    step=None,
    training=False,
    attention_mask=None,
    return_attention_scores=False,
    use_causal_mask=True,)

FeedForwardGate

A typical feed-forward layer with GELU activation:

import mlable.layers.transformer

__x = tf.ones(shape=(2, 3, 5), dtype=tf.dtypes.float32)
__l = mlable.layers.transformer.FeedForwardGate(
    input_dim=256,
    hidden_dim=1024)

__l(__x)

RotaryPositionalEmbedding

Tensorflow implementation of RoPE:

import mlable.layers.embedding

__x = tf.ones(shape=(2, 3, 5))
__l = mlable.layers.embedding.RotaryPositionalEmbedding(
    sequence_axis=1, # position along this axis
    feature_axis=-1, # output axis
    max_wavelength=10_000, # see the paper
    scaling_factor=1.) # see the paper

__l(inputs=__x, offset=2) # the offset is typically used to perform iterative decoding during inference

Metrics

CategoricalGroupAccuracy

Hierarchical models should not be scored on individual predictions but on their combination.

For example, tokun is a byte level autoencoder. It predicts probabilities for each byte of the output, like 0 in the UTF-32-BE encoding of "a" (0, 0, 0, 97).

A prediction of (0, 0, 0, 98) for "a" has 3 correct byte out of 4, but the prediction is actually "b".

In this case the byte accuracy is 75% while the character accuracy is 0%. Having several hierarchies of scoring helps with training and evaluation.

import mlable.metrics

byte_accuracy = mlable.metrics.CategoricalGroupAccuracy(group=1, name='byte_accuracy')
character_accuracy = mlable.metrics.CategoricalGroupAccuracy(group=4, name='character_accuracy')
token_accuracy = mlable.metrics.CategoricalGroupAccuracy(group=64, name='token_accuracy')

Credits

Andrej Karpathy reconnected my ML synapses with micrograd.

License

Licensed under the aGPLv3.