mirror of
https://github.com/labmlai/annotated_deep_learning_paper_implementations.git
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221 lines
7.3 KiB
Python
221 lines
7.3 KiB
Python
"""
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---
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title: Fuzzy Tiling Activation Experiment
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summary: >
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Training a transformer with FTA in FFN on Tiny Shakespeare.
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---
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# [Fuzzy Tiling Activation](index.html) Experiment
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[](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/activations/fta/experiment.ipynb)
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[](https://www.comet.ml/labml/fta/69be11f83693407f82a86dcbb232bcfe?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&viewId=rlJOpXDGtL8zbkcX66R77P5me&xAxis=step)
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Here we train a transformer that uses [Fuzzy Tiling Activation](index.html) in the
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[Feed-Forward Network](../../transformers/feed_forward.html).
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We use it for a language model and train it on Tiny Shakespeare dataset
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for demonstration.
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However, this is probably not the ideal task for FTA, and we
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believe FTA is more suitable for modeling data with continuous variables.
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"""
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import copy
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import torch
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import torch.nn as nn
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from labml import experiment
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from labml.configs import option
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from labml_helpers.module import Module
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from labml_nn.activations.fta import FTA
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from labml_nn.experiments.nlp_autoregression import NLPAutoRegressionConfigs
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from labml_nn.transformers import MultiHeadAttention, TransformerLayer
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from labml_nn.transformers.utils import subsequent_mask
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class FeedForwardFTA(nn.Module):
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"""
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## FFN module with [FTA](index.html) activation
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"""
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def __init__(self, d_model: int, d_ff: int,
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activation: FTA,
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dropout: float = 0.1):
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"""
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* `d_model` is the number of features in a token embedding
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* `d_ff` is the number of features in the hidden layer of the FFN
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* `activation` is FTA activation module
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* `dropout` is dropout probability for the hidden layer
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"""
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super().__init__()
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# Layer one parameterized by weight $W_1$ and bias $b_1$
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self.layer1 = nn.Linear(d_model, d_ff)
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# Layer two parameterized by weight $W_1$ and bias $b_1$
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self.layer2 = nn.Linear(d_ff * activation.expansion_factor, d_model)
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# Hidden layer dropout
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self.dropout = nn.Dropout(dropout)
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# Activation function $f$
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self.activation = activation
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def forward(self, x: torch.Tensor):
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# $f(x W_1 + b_1)$
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x = self.activation(self.layer1(x))
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# Apply dropout
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x = self.dropout(x)
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#
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return self.layer2(x)
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class AutoregressiveTransformer(Module):
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"""
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## Auto-Regressive model
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This is an autoregressive transformer model that uses Feed-Forward Networks with
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(Fuzzy Tiling Activations)(index.html).
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"""
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def __init__(self, n_tokens: int, d_model: int, n_layers: int, layer: TransformerLayer):
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"""
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:param n_tokens: is the number of tokens in the vocabulary
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:param d_model: is the embedding size
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:param n_layers: is the number of transformer layers
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:param layer: is the layer. We use `n_layers` copies of this for the transformer.
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"""
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super().__init__()
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# Transformer with `n_layers` layers
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self.transformer_layers = nn.ModuleList([copy.deepcopy(layer) for _ in range(n_layers)])
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# Token embedding layer
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self.emb = nn.Embedding(n_tokens, d_model)
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# Readout layer
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self.readout = nn.Linear(d_model, n_tokens)
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# The mask will be initialized on the first call
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self.mask = None
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def forward(self, x: torch.Tensor):
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"""
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:param x: are the input tokens of shape `[seq_len, batch_size]`
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"""
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# Create auto-regressive mask
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if self.mask is None or self.mask.size(0) != len(x):
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# Subsequent mask, will mask out tokens from seeing future tokens
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self.mask = subsequent_mask(len(x)).to(x.device)
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# Get the token embeddings
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x = self.emb(x)
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# Transformer encoder
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for layer in self.transformer_layers:
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x = layer(x=x, mask=self.mask)
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# Get logits
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x = self.readout(x)
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# Return results
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return x, None
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class Configs(NLPAutoRegressionConfigs):
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"""
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## Configurations
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This inherits from
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[`NLPAutoRegressionConfigs`](../../experiments/nlp_autoregression.html#NLPAutoRegressionConfigs)
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"""
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# Model
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model: AutoregressiveTransformer
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# Number of layers
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n_layers: int = 4
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# $\alpha$ and $\beta$ for DeepNorm
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deep_norm_alpha: float
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deep_norm_beta: float
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# Number of heads in the attention
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n_heads: int = 4
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# Embedding size
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d_model: int = 256
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# Size of each attention head
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d_k: int = 16
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# Feed forward layer size
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d_ff: int = 256
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# FTA
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fta_lower_limit: float = -1.
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fta_upper_limit: float = +1.
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fta_delta: float = 0.2
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fta_eta: float = 0.05
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@option(Configs.model)
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def _model(c: Configs):
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"""
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#### Initialize the model
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"""
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# Create FTA activation module
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fta = FTA(c.fta_lower_limit, c.fta_upper_limit, c.fta_delta, c.fta_eta)
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# Create the transformer.
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# We re-use [`TransformerLayer`](../../transformers/models.html#TransformerLayer) and
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# [`MultiHeadAttention`](../../transformers/mha.html) implementations.
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m = AutoregressiveTransformer(c.n_tokens, c.d_model, c.n_layers,
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TransformerLayer(d_model=c.d_model,
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feed_forward=FeedForwardFTA(d_model=c.d_model,
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d_ff=c.d_ff,
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activation=fta,
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dropout=0.1),
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self_attn=MultiHeadAttention(c.n_heads, c.d_model,
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dropout_prob=0.0),
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dropout_prob=0.0))
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# Move to the device
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return m.to(c.device)
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def main():
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"""
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#### Create and run the experiment
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"""
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# Create experiment
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experiment.create(name="fta", writers={'screen', 'comet', 'labml'})
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# Create configs
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conf = Configs()
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# Override configurations
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experiment.configs(conf, {
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# Use character level tokenizer
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'tokenizer': 'character',
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# Prompt separator is blank
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'prompt_separator': '',
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# Starting prompt for sampling
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'prompt': 'It is ',
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# Use Tiny Shakespeare dataset
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'text': 'tiny_shakespeare',
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# Use a context size of $256$
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'seq_len': 256,
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# Train for 32 epochs
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'epochs': 32,
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# Batch size $16$
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'batch_size': 16,
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# Switch between training and validation for $10$ times per epoch
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'inner_iterations': 10,
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# Adam optimizer with no warmup
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'optimizer.optimizer': 'Adam',
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'optimizer.learning_rate': 3e-4,
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})
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# Set model(s) for saving and loading
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experiment.add_pytorch_models({'model': conf.model})
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# Start the experiment
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with experiment.start():
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# Run training
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conf.run()
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#
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if __name__ == '__main__':
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main()
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