Fixes pytorch#135447.

When the 3rd from last dimension is 2^16 or greater, MPSGraph returns 0 for padgradient.
To work around this, we break the problematic dimension into chunks with chunk size being
no greater than 2^16 - 1.

Test case for nn.ReplicationPad1d:
```
    shape = [65739, 2, 4]
    x_cpu = torch.randn(shape, device='cpu', requires_grad=True)
    x_mps = x_cpu.clone().detach().to('mps').requires_grad_(True)
    model = torch.nn.ReplicationPad1d((1, 1))

    out_cpu = model(x_cpu)
    out_mps = model(x_mps)

    # backward
    g_cpu = torch.randn_like(out_cpu)
    g_mps = g_cpu.clone().detach().to('mps').requires_grad_(False)
    out_cpu.backward(g_cpu)
    out_mps.backward(g_mps)

    print(f"{((x_cpu.grad - x_mps.grad.cpu()).abs() > 1e-5).sum() = }")

    # Expected Output:
    # ((x_cpu.grad - x_mps.grad.cpu()).abs() > 1e-5).sum() = tensor(0)
```

Test case for nn.ReplicationPad2d,
```
    shape = [2, 65739, 2, 4]
    x_cpu = torch.randn(shape, device='cpu', requires_grad=True)
    x_mps = x_cpu.clone().detach().to('mps').requires_grad_(True)
    model = torch.nn.ReplicationPad2d((1, 1, 1, 1))

    out_cpu = model(x_cpu)
    out_mps = model(x_mps)

    # backward
    g_cpu = torch.randn_like(out_cpu)
    g_mps = g_cpu.clone().detach().to('mps').requires_grad_(False)
    out_cpu.backward(g_cpu)
    out_mps.backward(g_mps)

    print(f"{((x_cpu.grad - x_mps.grad.cpu()).abs() > 1e-5).sum() = }")

    # Expected Output:
    # ((x_cpu.grad - x_mps.grad.cpu()).abs() > 1e-5).sum() = tensor(0)
```

These tests produce expected output with this workaround.