# Backend and Delegate

Audience: Vendors, Backend Delegate developers, who are interested in integrating their own compilers and hardware as part of ExecuTorch

Backend delegation is an entry point for backends to process and execute PyTorch
programs to leverage performance and efficiency benefits of specialized
backends and hardware, while still providing PyTorch users with an experience
close to that of the PyTorch runtime.

At a high level, the entry point for backends is defined by 2 components:

- An IR to represent the program: **Edge Dialect** (which is produced through
    the `to_edge` API)
- A couple of interfaces for backends to implement:
    - Ahead-of-Time (AOT)
        - Program preprocessing (e.g. ahead of time compilation, transformation, optimization...).
    - Runtime
        - Program initialization (e.g. runtime compilation).
        - Program execution.
        - (optional) Program destroy (e.g. release backend owned resource).

## Backend Interfaces

A delegate backend implementation is composed of:

1) An ahead-of-time preprocessing interface
2) A runtime initialization and execution interface


### Ahead-of-Time Preprocessing

For the AOT preprocessing, backends are given an edge dialect program,
a list of compile specs specifying the values needed for compilation, and are
expected to return a compiled blob, or binary contains the desired program to be
run in the backend, and profiling information. During serialization, the
compiled blob will be serialized as part of the `.pte` file, and directly loaded to the device. The
API looks something like:

```python
def preprocess(
    edge_program: ExportedProgram,
    compile_specs: List[CompileSpec],
) -> PreprocessResult:
```

A demo of the preprocess function is implemented
[here](https://github.com/pytorch/executorch/blob/main/exir/backend/test/backend_with_compiler_demo.py).
The demo loops through the nodes in the graph module of the `edge_program` and
serializes the `add`, `mul`, and `sin` instructions into a string, which is later
parsed and executed at runtime.

### Runtime Initialization and Execution

During the runtime, the compiled blob from the `preprocess` function will be
loaded and passed directly to the backend's custom `init` function. This
function is responsible for further processing the compiled unit, as well as
perform any backend initialization. The backend's custom `execute` function will
then be called to execute the handle produced by `init`. And finally, if
destroying is required for some backend, backends can implement a `destroy`
function which will be called when the program is out of its lifespan.

```cpp
// Runtime initialization
__ET_NODISCARD virtual Result<DelegateHandle*> init(
    BackendInitContext& context,
    FreeableBuffer* processed,
    ArrayRef<CompileSpec> compile_specs);

// Runtime execution
__ET_NODISCARD virtual Error execute(
    BackendExecutionContext& context,
    DelegateHandle* handle,
    EValue** args);

// [optional] Runtime destroy. Destroy the resource held by the backend
virtual void destroy(__ET_UNUSED DelegateHandle* handle);
```

Once the backend is ready, they can then be registered via the `register_backend` API:
```cpp
__ET_NODISCARD Error register_backend(const Backend& backend);
```

`register_backend` can be statically registered like the following
```cpp
namespace {
auto cls = BackendWithCompiler();
Backend backend{"BackendWithCompilerDemo", &cls};
static auto success_with_compiler = register_backend(backend);
} // namespace
```


## Error Messages

If there is an error in the backend, for example, if there is any operator that
is not supported by the backend, a debug handler can be thrown. It can surface
back to the Python frontend with the source code information. Below is an
example where the `tan` operator is not supported in `BackendWithCompilerDemo`
backend.

A problematic program:
```python
class TanModule(torch.nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        return torch.tan(x)

tan_module = TanModule()
model_inputs = (torch.ones(1),)
edgeir_m = exir.capture(tan_module, model_inputs).to_edge()
lowered_tan_module = to_backend(
    "BackendWithCompilerDemo", edgeir_m, []
)

class CompositeModelWithTan(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.lowered_tan = lowered_tan_module

    def forward(self, x):
        output_from_submodule = self.lowered_tan(x)
        return output_from_submodule

composite_model_with_tan = CompositeModelWithTan()
model_inputs = (torch.ones(1),)

composite_model_with_tan(*model_inputs)

exec_prog = (
    exir.capture(composite_model_with_tan, model_inputs).to_edge().to_executorch()
)

buff = exec_prog.buffer
model_inputs = torch.ones(1)

# Load and init the program in executor
executorch_module = _load_for_executorch_from_buffer(buff)

# Expect to throw with debug handler here.
model_outputs = executorch_module.forward([model_inputs])
```

It's expected to capture debug handler like `instruction
demo::tan_default<debug_handle>1 is not supported, debug handler is: 1`


## Common Questions

**1. How can we get data in backend.preprocess?**

The graph module being preprocessed is a lifted graph, this means that static
data like weights and biases are supplied as inputs to the graph. However, we
can access the weights and biases ahead-of-time through the exported program. To
access these parameters from a given node, we can use the function `get_params`
provided in  `torch/_export/utils.py`

**2. How can we embed the data (like weight/bias) to the backend?**

It's common that backend have some ways optimize the const data. In this case,
we'd need to tag the placeholder node which are also the state in the
partitioner, and during backend.preprocess, we can follow the description in the
first question to get the weight.

**3. How can we run the lowered module in Python with the specific backend?**

We haven't added the support yet but that's the plan!

**4. Should we expect to see `get_attr` nodes in the edge dialect program?**

`get_attr` nodes will only show up for submodules used for control flow or
delegation. It won't hold any data.

**5. Can we delegate to multiple backends?**

Yes! There are two ways to do this:

*Option 1: Run to_backend multiple times for different backends*

If we have two backends, backend_1 and backend_2, and they have their own
parititioners: backend_1_parititioner and backend_2_partitioner, we can run it
like:

```python
# Will first lower nodes to backend_1 depending on the backend_1_parititioner depending on partitioner algorithm
exported_program_backend_1 = to_backend(exported_program, backend_1_parititioner)
# For the rest of nodes, they will be lowered to backend_2 depending on backend_2_parititioner
exported_program_backend_1_and_2 = to_backend(exported_program_backend_1, backend_2_parititioner)
```

A more concrete example be found
[here](https://github.com/pytorch/executorch/blob/main/exir/backend/test/demos/test_xnnpack_qnnpack.py).
In this example,
qnnpack is one backend and xnnpack is another backend. We haven't open-sourced
these two backends delegates yet, and this example won't run out of box. It can
be used as a reference to see how it can be done.

This option is easy to try becuase usually all backends will implement their own
parititioner. However this option may get different results if we change the
order of to_backend call. If we want to have a better control on the nodes, like
which backend they should go, option 2 is better.

*Option 2: Have a partitioner which partitions for different backends*

Another option is to create a customized partitioner, say partitioner
`backend_1_2_partitioner`, and inside the partitioner logic,

```python
class Backend_1_2_Partitioner(Partitioner):
    """
    Partitions all add/mul nodes regardless of order for Backend2
    """

    def __init__(self) -> None:
        self.delegation_spec_1 = DelegationSpec("Backend1", [])
        self.delegation_spec_2 = DelegationSpec("Backend2", [])
        self.partition_tags = {}

    def partition(
        self, edge_graph_module: torch.fx.GraphModule
    ) -> torch.fx.GraphModule:

        # Tag all nodes in the first partiton to backend 1
        node_to_backend_1 = ... # some logic to select the nodes from the graph
        delegation_tag = f"backend2_tag{partitioner_1.id}"
        node.meta["delegation_tag"] = delegation_tag
        self.partition_tags[delegation_tag] = self.delegation_spec_1

        # Tag all nodes in the first partiton to backend 2
        node_to_backend_2 = ... # some logic to select the nodes from the graph
        delegation_tag = f"backend2_tag{partitioner_2.id}"
        node.meta["delegation_tag"] = delegation_tag
        self.partition_tags[delegation_tag] = self.delegation_spec_2
        return edge_graph_module
```

**6. Is there an easy way to write a partitioner?**

We provide some helper partitioners
[here](./compiler-custom-compiler-passes.md) to make it easy to find
nodes from decomposed operators.