add profileing and small conv3d repoducer

conv3d.py

@@ -0,0 +1,46 @@
+import torch
+import time
+
+configs = [
+	[128, 128, 3, 1],
+	[256, 256, 3, 1],
+	[512, 512, 3, 1],
+	[128, 256, 1, 1],
+	[512, 512, 3, (2, 2, 2)],
+	[256, 256, 3, (2, 2, 2)],
+	[128, 3, 3, 1]
+]
+
+inputs = [
+	[1, 128, 67, 258, 258],
+	[1, 256, 35, 130, 130],
+	[1, 512, 35, 130, 130],
+	[1, 128, 67, 258, 258],
+	[1, 512, 35, 130, 130],
+	[1, 256, 27, 258, 258],
+	[1, 128, 67, 258, 258],
+]
+
+
+def conv3dbenchmark(configs: list[list[int]], inputs: list[list[int]], repeat: int, dtype: torch.dtype, device: torch.device):
+	modules = list()
+	assert len(inputs) == len(configs)
+
+	for config in configs:
+		modules.append(torch.nn.Conv3d(config[0], config[1], config[2], stride=config[3]).to(device, dtype))
+
+	for i in range(len(modules)):
+		x = torch.randn(inputs[i]).to(device, dtype)
+		print(f"Running Conv3d config: {configs[i]} input: {inputs[i]} type: {dtype}")
+		start = time.perf_counter()
+		for n in range(repeat):
+			modules[i].forward(x)
+		torch.cuda.synchronize(device)
+		print(f"Time {(time.perf_counter() - start) / repeat} seconds\n")
+
+
+if __name__ == "__main__":
+	device = torch.device(0)
+
+	conv3dbenchmark(configs, inputs, 5, torch.bfloat16, device)
+	conv3dbenchmark(configs, inputs, 5, torch.float16, device)