Implement TF2 and add, mul and div benchmark

src/tf_2/matmul.py

@@ -0,0 +1,34 @@
+from pathlib import Path
+
+import tensorflow as tf
+
+from src.common import DataType, Op
+from src.tf_2.base import TFBase
+
+
+class TFMatmulBench(TFBase):
+    def __init__(self, output_path: Path):
+        super().__init__(output_path, Op.MATMUL)
+
+    def experiment(self, experiment_args: tuple[int, int], length: int, dtype: tf.DType, device: tf.device):
+        shape_1, shape_2 = experiment_args
+        with device:
+            tensor_1 = tf.ones(shape_1, dtype=dtype)
+            tensor_2 = tf.ones(shape_2, dtype=dtype)
+
+            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)