gen_models.py

#!/usr/bin/python
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import torch
import torch.nn as nn


class SumModule(nn.Module):
    def __init__(self, device):
        super(SumModule, self).__init__()
        self.device = device

    def forward(self, INPUT0, INPUT1):
        INPUT0 = INPUT0.to(self.device)
        INPUT1 = INPUT1.to(self.device)
        print(
            "SumModule - INPUT0 device: {}, INPUT1 device: {}\n".format(
                INPUT0.device, INPUT1.device
            )
        )
        return INPUT0 + INPUT1


class DiffModule(nn.Module):
    def __init__(self, device):
        super(DiffModule, self).__init__()
        self.device = device

    def forward(self, INPUT0, INPUT1):
        INPUT0 = INPUT0.to(self.device)
        INPUT1 = INPUT1.to(self.device)
        print(
            "DiffModule - INPUT0 device: {}, INPUT1 device: {}\n".format(
                INPUT0.device, INPUT1.device
            )
        )
        return INPUT0 - INPUT1


class TestModel(nn.Module):
    def __init__(self, device0, device1):
        super(TestModel, self).__init__()
        self.device0 = device0
        self.device1 = device1

        self.layer1 = SumModule(self.device0)
        self.layer2 = DiffModule(self.device1)

    def forward(self, INPUT0, INPUT1):
        op0 = self.layer1(INPUT0, INPUT1)
        op1 = self.layer2(INPUT0, INPUT1)
        return op0, op1


if torch.cuda.device_count() < 4:
    print("Need at least 4 GPUs to run this test")
    exit(1)

devices = [("cuda:2", "cuda:0"), ("cpu", "cuda:3")]
model_names = ["libtorch_multi_gpu", "libtorch_multi_device"]

for device_pair, model_name in zip(devices, model_names):
    model = TestModel(device_pair[0], device_pair[1])
    model_path = "models/" + model_name + "/1/model.pt"
    scripted_model = torch.jit.script(model)
    scripted_model.save(model_path)