feat: CNN model start + rude concept of training
This commit is contained in:
Robin Meersman
parent
d6c8bf4a13
commit
947aba31ee
6 changed files with 383 additions and 1 deletions
46
CNN-model/cnn.py
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46
CNN-model/cnn.py
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import torch
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import torch.nn as nn
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from torch import Tensor
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from torch.nn.functional import softmax
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class CausalConv1d(nn.Conv1d):
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def __init__(self, input_channels, output_channels, kernel_size, **kwargs):
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super().__init__(input_channels, output_channels, kernel_size, padding=kernel_size-1, **kwargs)
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def forward(self, input: Tensor) -> Tensor:
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return super().forward(input)
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class CNNPredictor(nn.Module):
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def __init__(
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self,
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vocab_size=256,
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context_length=128,
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num_layers=3,
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hidden_dim=128,
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kernel_size=3,
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dropout_prob=0.1,
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use_batchnorm=False
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):
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super().__init__()
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self.embedding = nn.Embedding(vocab_size, hidden_dim)
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layers = []
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in_channels = hidden_dim
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for _ in range(num_layers):
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out_channels = hidden_dim
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layers.append(CausalConv1d(in_channels, out_channels, kernel_size))
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if use_batchnorm:
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layers.append(nn.BatchNorm1d(out_channels))
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layers.append(nn.ReLU())
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layers.append(nn.Dropout(dropout_prob))
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in_channels = out_channels
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self.network = nn.Sequential(*layers)
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self.output_layer = nn.Linear(hidden_dim, vocab_size)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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emdedding = self.embedding(x) # B, L, H
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emdedding = emdedding.transpose(1, 2) # B, H, L
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prediction = self.network(emdedding)
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last_prediction = prediction[:, :, -1]
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return softmax(self.output_layer(last_prediction), dim=-1) # convert output of linear layer to prob. distr.
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11
CNN-model/data_utils.py
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11
CNN-model/data_utils.py
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import torch
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from torch.utils.data import TensorDataset
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def make_context_pairs(data: bytes, context_length: int) -> TensorDataset:
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data = torch.tensor(data, dtype=torch.uint8)
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sample_count = data.shape[0] - context_length
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x = data.unfold(0, context_length, 1)[:sample_count]
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y = data[context_length:]
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return TensorDataset(x, y)
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35
CNN-model/main_cnn.py
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35
CNN-model/main_cnn.py
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import torch
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import torch.nn as nn
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import torch.functional as F
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import optuna.trial as tr
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from torch.utils.data import DataLoader
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from optuna_trial import create_model
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from data_utils import make_context_pairs
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# hyper parameters
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context_length = 128
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def train_and_eval(
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model: nn.Module,
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training_data: bytes,
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validation_data: bytes,
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batch_size: int,
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epochs: int = 100,
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learning_rate: float = 1e-3,
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device: torch.device = torch.device("cpu")
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):
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model.to(device)
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optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
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training_loader = DataLoader(make_context_pairs(training_data, context_length=context_length))
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validation_loader= DataLoader(make_context_pairs(validation_data, context_length=context_length))
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for epoch in range(epochs):
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model.train()
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def objective_function(trial: tr.Trial):
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model = create_model(trial)
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if __name__ == "__main__":
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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19
CNN-model/optuna_trial.py
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19
CNN-model/optuna_trial.py
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import optuna.trial as tr
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from cnn import CNNPredictor
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def create_model(trial: tr.Trial, vocab_size: int = 256, context_length: int = 128):
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num_layers = trial.suggest_int("num_layers", 1, 6)
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hidden_dim = trial.suggest_int("hidden_dim", 64, 512, log=True)
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kernel_size = trial.suggest_int("kernel_size", 2, 7)
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dropout_prob = trial.suggest_float("dropout_prob", 0.1, 0.5)
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use_batchnorm = trial.suggest_categorical("use_batchnorm", [True, False])
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return CNNPredictor(
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vocab_size=vocab_size,
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context_length=context_length,
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num_layers=num_layers,
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hidden_dim=hidden_dim,
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kernel_size=kernel_size,
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dropout_prob=dropout_prob,
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use_batchnorm=use_batchnorm
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)
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@ -5,5 +5,7 @@ description = "Add your description here"
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readme = "README.md"
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requires-python = ">=3.12"
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dependencies = [
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"numpy>=2.3.4",
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"optuna>=4.5.0",
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"torch>=2.9.0",
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]
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271
uv.lock
generated
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uv.lock
generated
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@ -2,6 +2,41 @@ version = 1
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revision = 3
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requires-python = ">=3.12"
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{ url = "https://files.pythonhosted.org/packages/9c/5e/6a29fa884d9fb7ddadf6b69490a9d45fded3b38541713010dad16b77d015/sqlalchemy-2.0.44-py3-none-any.whl", hash = "sha256:19de7ca1246fbef9f9d1bff8f1ab25641569df226364a0e40457dc5457c54b05", size = 1928718, upload-time = "2025-10-10T15:29:45.32Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "sympy"
|
||||
version = "1.14.0"
|
||||
|
|
@ -331,6 +588,18 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/fc/29/bd361e0cbb2c79ce6450f42643aaf6919956f89923a50571b0ebfe92d142/torch-2.9.0-cp314-cp314t-win_amd64.whl", hash = "sha256:695ba920f234ad4170c9c50e28d56c848432f8f530e6bc7f88fcb15ddf338e75", size = 109503850, upload-time = "2025-10-15T15:50:24.118Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tqdm"
|
||||
version = "4.67.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "colorama", marker = "sys_platform == 'win32'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/a8/4b/29b4ef32e036bb34e4ab51796dd745cdba7ed47ad142a9f4a1eb8e0c744d/tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2", size = 169737, upload-time = "2024-11-24T20:12:22.481Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/d0/30/dc54f88dd4a2b5dc8a0279bdd7270e735851848b762aeb1c1184ed1f6b14/tqdm-4.67.1-py3-none-any.whl", hash = "sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2", size = 78540, upload-time = "2024-11-24T20:12:19.698Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "triton"
|
||||
version = "3.5.0"
|
||||
|
|
|
|||
Reference in a new issue