37 lines
1.4 KiB
Python
37 lines
1.4 KiB
Python
from pathlib import Path
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from typing import List, Tuple
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import torch
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from src.common import DataType, Op
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from src.pytorch.base import TorchBase
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class DenseNetwork(torch.nn.Module):
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def __init__(self, input_dim: int, dtype: torch.dtype):
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super().__init__()
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self.dense = torch.nn.Sequential(
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*[torch.nn.Linear(input_dim, input_dim, dtype=dtype) for _ in range(5)])
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def forward(self, input_data: torch.Tensor) -> torch.Tensor:
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return self.dense(input_data)
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class TorchNNDenseX5Bench(TorchBase):
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def __init__(self, output_path: Path, data_type: DataType):
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super().__init__(output_path, Op.NN_DENSE_X5, data_type)
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self.tensor: torch.Tensor = None
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self.tensor_result: torch.Tensor = None
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self.network: torch.nn.Module = None
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def pre_experiment(self, experiment_args: Tuple[int, int]):
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batch_size, dimension = experiment_args
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self.tensor = torch.ones((batch_size, dimension), dtype=self.dtype, device=self.device, requires_grad=False)
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self.network = DenseNetwork(dimension, self.dtype).to(self.device)
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self.tensor_result = self.network(self.tensor)
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def experiment(self):
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self.tensor_result = self.network(self.tensor)
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def run(self, experiment_args: List[Tuple[int, int]], experiment_count: int):
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super().run(experiment_args, experiment_count)
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