Actually, no! everything looks fine to me. I even ran your code and it works fine:

x = torch.tensor([[[10., 20., 30.]],

                [[20., 30., 40.]],

                [[30., 40., 50.]],

                [[40., 50., 60.]],

                [[50., 60., 70.]],

                [[60., 70., 80.]]])
y = torch.ones(6, 1)

train = data.TensorDataset(x, y)
trainloader = data.DataLoader(train, batch_size=len(x), shuffle=False)

model = nn.Sequential(
        nn.Conv1d(1,64,2),
        nn.ReLU(),
        nn.MaxPool1d(2),
        nn.Flatten(start_dim=1),
        nn.Linear(64,50),
        nn.ReLU(),
        nn.Linear(50, 1)
        )

for batch in trainloader:
    x_batch, y_batch = batch
    print(x_batch.shape)
    print(model(x_batch).shape)

You may have missed something? like the way you use x after batching to feed to model? Can you show your train loop?
PS. when you set batch_size=len(your whole data), you will get only 1 batch which contains a tensor [6, 1, 3]. You need to set batch_size=1 if you want 6x[1, 1, 3] tensors.