Implement TF2 and add, mul and div benchmark

src/pytorch/matmul.py

@@ -0,0 +1,33 @@
+from pathlib import Path
+
+import torch
+
+from src.common import DataType, Op
+from src.pytorch.base import TorchBase
+
+
+class TorchMatmulBench(TorchBase):
+    def __init__(self, output_path: Path):
+        super().__init__(output_path, Op.MATMUL)
+
+    def experiment(self, experiment_args: tuple[int, int], length: int, dtype: torch.dtype, device: torch.device):
+        shape_1, shape_2 = experiment_args
+        tensor_1 = torch.ones(shape_1, dtype=dtype, device=device, requires_grad=False)
+        tensor_2 = torch.ones(shape_2, dtype=dtype, device=device, requires_grad=False)
+
+        for _ in range(length):
+            _ = tensor_1 @ tensor_2
+
+    def name(self, experiment_args: tuple[int, int]) -> str:
+        shape_1, shape_2 = experiment_args
+        return f'{shape_1[0]}x{shape_1[1]} @ {shape_2[0]}x{shape_2[1]}'
+
+    def mop(self, experiment_args: tuple[int, int]) -> float:
+        shape_1, shape_2 = experiment_args
+        return (shape_1[0] * shape_2[1] / 1000_000) * 2 * (shape_1[1] - 1)
+
+    def run(self,
+            experiment_args: list[tuple[tuple[int, int], tuple[int, int]]],
+            experiment_count: int,
+            data_type: DataType):
+        super().run(experiment_args, experiment_count, data_type)