Enhancement of PyTorch connector

edoaltamura commented 6 months ago

What should we add?

Given the demand for robust coupling with PyTorch, we propose to enhance the module https://github.com/qiskit-community/qiskit-machine-learning/blob/60f2c2e1b6469b0ba878d2026c662cb5f98af50f/qiskit_machine_learning/connectors/torch_connector.py#L56 in two phases.

Phase 1 will involve no or very minor changes to the user-facing API. The changes will focus on refractoring the backend connector, bug patches, and compatibility robustness.

[x] Fixing compatibility with PyTorch versions 2.x, possibly in qiskit_machine_learning/optionals.py
[x] Refactoring _TorchNNFunction and other definitions in torch_connector.py
[ ] Refractoring code that involves https://github.com/qiskit-community/qiskit-machine-learning/blob/60f2c2e1b6469b0ba878d2026c662cb5f98af50f/qiskit_machine_learning/connectors/torch_connector.py#L215 into modular units based on _optionals.HAS_SPARSE.require_now("SparseArray")

Phase 2 will focus on upgrading the inter-functionality with PyTorch and may involve changes in API and UX. These changes are likely to be incrementally introduced after the version 0.8 roll-out, leading up to a stable 1.0.

[ ] Adding version compatibility between Qiskit machine-learning and PyTorch, with relevant deprecation warnings and supported features
[ ] Improving the test coverage
[ ] Improved documentation

Jrbiltmore commented 6 months ago

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Refactoring _TorchNNFunction and other definitions in torch_connector.py Refractoring code that involves https://github.com/qiskit-community/qiskit-machine-learning/blob/60f2c2e1b6469b0ba878d2026c662cb5f98af50f/qiskit_machine_learning/connectors/torch_connector.py#L215 into modular units based on _optionals.HAS_SPARSE.require_now("SparseArray") Adding version compatibility between Qiskit machine-learning and PyTorch, with relevant deprecation warnings and supported features Improving the test coverage

—Reply to this email directly, view it on GitHub, or unsubscribe.You are receiving this because you are subscribed to this thread.Message ID: @.***>

edoaltamura commented 3 months ago

This problem could be broken down into two steps.

Refactoring `_TorchNNFunction` as a standalone function

This is currently nested within TorchConnector. To improve modularity, we can move it outside and make it a separate function to use within TorchConnector:

def torch_nn_function_forward(
    input_data: Tensor,
    weights: Tensor,
    neural_network: NeuralNetwork,
    sparse: bool
) -> Tensor:
    """Forward pass computation."""
    if input_data.shape[-1] != neural_network.num_inputs:
        raise QiskitMachineLearningError(
            f"Invalid input dimension! Received {input_data.shape} and "
            + f"expected input compatible to {neural_network.num_inputs}"
        )

    result = neural_network.forward(
        input_data.detach().cpu().numpy(), weights.detach().cpu().numpy()
    )
    if sparse:
        if not neural_network.sparse:
            raise RuntimeError("TorchConnector configured as sparse, the network must be sparse as well")

        # Handle sparse output
        # (Implementation of sparse handling here)
    else:
        if neural_network.sparse:
            # Handle conversion to dense if necessary
            # (Implementation of dense handling here)
        result_tensor = torch.as_tensor(result, dtype=torch.float)

    if len(input_data.shape) == 1:
        result_tensor = result_tensor[0]

    result_tensor = result_tensor.to(input_data.device)
    return result_tensor

def torch_nn_function_backward(
    ctx: Any,
    grad_output: Tensor
) -> Tuple:
    """Backward pass computation."""
    input_data, weights = ctx.saved_tensors
    neural_network = ctx.neural_network

    if input_data.shape[-1] != neural_network.num_inputs:
        raise QiskitMachineLearningError(
            f"Invalid input dimension! Received {input_data.shape} and "
            + f" expected input compatible to {neural_network.num_inputs}"
        )

    # (Implementation of backward pass here)

    return input_grad, weights_grad, None, None

Restructuring `TorchConnector`

Now, TorchConnector will use these functions for its forward and backward passes. So we can write the rest as:

class TorchConnector(Module):
    def __init__(
        self,
        neural_network: NeuralNetwork,
        initial_weights: np.ndarray | Tensor | None = None,
        sparse: bool | None = None,
    ):
        super().__init__()
        self._neural_network = neural_network
        if sparse is None:
            sparse = self._neural_network.sparse

        self._sparse = sparse

        if self._sparse and not self._neural_network.sparse:
            raise QiskitMachineLearningError(
                "TorchConnector configured as sparse, the network must be sparse as well"
            )

        weight_param = torch.nn.Parameter(torch.zeros(neural_network.num_weights))
        self.register_parameter("weight", weight_param)
        self._weights = weight_param

        if initial_weights is None:
            self._weights.data.uniform_(-1, 1)
        else:
            self._weights.data = torch.tensor(initial_weights, dtype=torch.float)

    @property
    def neural_network(self) -> NeuralNetwork:
        return self._neural_network

    @property
    def weight(self) -> Tensor:
        return self._weights

    @property
    def sparse(self) -> bool | None:
        return self._sparse

    def forward(self, input_data: Tensor | None = None) -> Tensor:
        input_ = input_data if input_data is not None else torch.zeros(0)
        return torch_nn_function_forward(
            input_, self._weights, self._neural_network, self._sparse
        )

    def backward(self, ctx: Any, grad_output: Tensor) -> Tuple:
        return torch_nn_function_backward(ctx, grad_output)

Other points to note when updating

Replace Tuple with Python 3.12 compatible tuple typing syntax
Create dummy torch.<attribute>'s when Torch is not installed. E.g. result_tensor = torch.as_tensor(result, dtype=torch.float) must have a corresponding
```
if _optionals.HAS_TORCH:
# ...
else:
def f(): pass
torch.as_tensor = f
torch.float = float
```

qiskit-community / qiskit-machine-learning