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https://github.com/labmlai/annotated_deep_learning_paper_implementations.git
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auto regression common exp
This commit is contained in:
Varuna Jayasiri
@ -9,21 +9,13 @@ summary: This is training code with notes for a basic auto-regressive transforme
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This trains a simple [transformer](../../) model for auto-regression.
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"""
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from typing import Callable, Any
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import torch
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import torch.nn as nn
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from torchtext.data.utils import get_tokenizer
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from labml import lab, experiment, monit, tracker, logger
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from labml import experiment
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from labml.configs import option
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from labml.logger import Text
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from labml.utils.pytorch import get_modules
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from labml_helpers.datasets.text import TextDataset, SequentialDataLoader, TextFileDataset
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from labml_helpers.metrics.accuracy import Accuracy
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from labml_helpers.module import Module
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from labml_helpers.optimizer import OptimizerConfigs
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from labml_helpers.train_valid import SimpleTrainValidConfigs, BatchIndex
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from labml_nn.experiments.nlp_autoregression import NLPAutoRegressionConfigs
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from labml_nn.transformers import Encoder, Generator, TransformerConfigs
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from labml_nn.transformers.utils import subsequent_mask
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@ -64,23 +56,10 @@ class AutoregressiveModel(Module):
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# Embed the tokens (`src`) and run it through the the transformer
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res = self.encoder(self.src_embed(src), self.src_mask)
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# Generate logits of the next token
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return self.generator(res)
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return self.generator(res), None
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class CrossEntropyLoss(Module):
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"""
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Cross entropy loss
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"""
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def __init__(self):
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super().__init__()
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self.loss = nn.CrossEntropyLoss()
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def __call__(self, outputs, targets):
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return self.loss(outputs.view(-1, outputs.shape[-1]), targets.view(-1))
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class Configs(SimpleTrainValidConfigs):
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class Configs(NLPAutoRegressionConfigs):
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"""
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## Configurations
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@ -89,141 +68,8 @@ class Configs(SimpleTrainValidConfigs):
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transformer: TransformerConfigs
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model: AutoregressiveModel
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text: TextDataset
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batch_size: int = 20
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seq_len: int = 32
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n_tokens: int
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tokenizer: Callable = 'character'
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is_save_models = True
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prompt: str
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prompt_separator: str
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is_save_ff_input = False
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optimizer: torch.optim.Adam = 'transformer_optimizer'
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accuracy = Accuracy()
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loss_func = CrossEntropyLoss()
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def init(self):
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# Create a configurable optimizer.
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# Parameters like learning rate can be changed by passing a dictionary when starting the experiment.
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optimizer = OptimizerConfigs()
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optimizer.parameters = self.model.parameters()
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optimizer.d_model = self.transformer.d_model
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optimizer.optimizer = 'Noam'
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self.optimizer = optimizer
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# Create a sequential data loader for training
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self.train_loader = SequentialDataLoader(text=self.text.train,
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dataset=self.text,
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batch_size=self.batch_size,
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seq_len=self.seq_len)
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# Create a sequential data loader for validation
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self.valid_loader = SequentialDataLoader(text=self.text.valid,
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dataset=self.text,
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batch_size=self.batch_size,
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seq_len=self.seq_len)
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self.state_modules = [self.accuracy]
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def sample(self):
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"""
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Sampling function to generate samples periodically while training
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"""
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prompt = self.prompt
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log = [(prompt, Text.subtle)]
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# Sample 25 tokens
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for i in monit.iterate('Sample', 25):
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# Tokenize the prompt
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data = self.text.text_to_i(prompt).unsqueeze(-1)
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data = data.to(self.device)
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# Get the model output
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output = self.model(data)
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# Get the model prediction (greedy)
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output = output.argmax(dim=-1).squeeze()
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# Add the prediction to prompt
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prompt += self.prompt_separator + self.text.itos[output[-1]]
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# Add the prediction for logging
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log += [(self.prompt_separator + self.text.itos[output[-1]], Text.value)]
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logger.log(log)
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def step(self, batch: Any, batch_idx: BatchIndex):
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"""
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This method is called for each batch
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"""
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self.model.train(self.mode.is_train)
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# Get data and target labels
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data, target = batch[0].to(self.model.device), batch[1].to(self.model.device)
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if self.mode.is_train:
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tracker.add_global_step(data.shape[0] * data.shape[1])
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# Run the model
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output = self.model(data)
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# Calculate loss
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loss = self.loss_func(output, target)
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# Calculate accuracy
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self.accuracy(output, target)
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# Log the loss
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tracker.add("loss.", loss)
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# If we are in training mode, calculate the gradients
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if self.mode.is_train:
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loss.backward()
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self.optimizer.step()
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if batch_idx.is_last:
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tracker.add('model', self.model)
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self.optimizer.zero_grad()
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tracker.save()
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@option(Configs.tokenizer)
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def basic_english():
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"""
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Basic english tokenizer
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We use character level tokenizer in this experiment.
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You can switch by setting,
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```
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'tokenizer': 'basic_english',
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```
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as the configurations dictionary when starting the experiment.
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"""
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return get_tokenizer('basic_english')
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def character_tokenizer(x: str):
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return list(x)
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@option(Configs.tokenizer)
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def character():
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"""
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Character level tokenizer
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"""
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return character_tokenizer
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@option(Configs.text)
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def tiny_shakespeare(c: Configs):
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"""
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Initialize/load tiny shakespeare dataset
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This dataset is from Andrej Karpathy's [char-rnn](https://github.com/karpathy/char-rnn) project.
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"""
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return TextFileDataset(
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lab.get_data_path() / 'tiny_shakespeare.txt', c.tokenizer,
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url='https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt')
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@option(Configs.model)
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@ -256,7 +102,7 @@ def transformer_c(c: Configs):
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def main():
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# Create experiment
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experiment.create(name="knn_lm", comment='', writers={'tensorboard', 'sqlite', 'screen'})
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experiment.create(name="knn_lm")
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# Create configs
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conf = Configs()
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# Load configurations
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@ -267,6 +113,10 @@ def main():
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'prompt': 'It is ',
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'text': 'tiny_shakespeare',
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'optimizer.optimizer': 'Noam',
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'optimizer.learning_rate': 1.,
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'optimizer.d_model': 256,
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'seq_len': 1024,
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'epochs': 128,
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'batch_size': 6,
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