> For the complete documentation index, see [llms.txt](https://docs.zama.org/concrete-ml/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zama.org/concrete-ml/1.2/developer-guide/api/concrete.ml.torch.hybrid_model.md).

# concrete.ml.torch.hybrid\_model.md

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L0)

## module `concrete.ml.torch.hybrid_model`

Implement the conversion of a torch model to a hybrid fhe/torch inference.

### **Global Variables**

* **MAX\_BITWIDTH\_BACKWARD\_COMPATIBLE**

***

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### function `tuple_to_underscore_str`

```python
tuple_to_underscore_str(tup: Tuple) → str
```

Convert a tuple to a string representation.

**Args:**

* `tup` (Tuple): a tuple to change into string representation

**Returns:**

* `str`: a string representing the tuple

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L54)

### function `underscore_str_to_tuple`

```python
underscore_str_to_tuple(tup: str) → Tuple
```

Convert a a string representation of a tuple to a tuple.

**Args:**

* `tup` (str): a string representing the tuple

**Returns:**

* `Tuple`: a tuple to change into string representation

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L67)

### function `convert_conv1d_to_linear`

```python
convert_conv1d_to_linear(layer_or_module)
```

Convert all Conv1D layers in a module or a Conv1D layer itself to nn.Linear.

**Args:**

* `layer_or_module` (nn.Module or Conv1D): The module which will be recursively searched for Conv1D layers, or a Conv1D layer itself.

**Returns:**

* `nn.Module or nn.Linear`: The updated module with Conv1D layers converted to Linear layers, or the Conv1D layer converted to a Linear layer.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L33)

### class `HybridFHEMode`

Simple enum for different modes of execution of HybridModel.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L107)

### class `RemoteModule`

A wrapper class for the modules to be evaluated remotely with FHE.

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L110)

#### method `__init__`

```python
__init__(
    module: Optional[Module] = None,
    server_remote_address: Optional[str] = None,
    module_name: Optional[str] = None,
    model_name: Optional[str] = None,
    verbose: int = 0
)
```

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L216)

#### method `forward`

```python
forward(x: Tensor) → Union[Tensor, QuantTensor]
```

Forward pass of the remote module.

To change the behavior of this forward function one must change the fhe\_local\_mode attribute. Choices are:

* disable: forward using torch module
* remote: forward with fhe client-server
* simulate: forward with local fhe simulation
* calibrate: forward for calibration

**Args:**

* `x` (torch.Tensor): The input tensor.

**Returns:**

* `(torch.Tensor)`: The output tensor.

**Raises:**

* `ValueError`: if local\_fhe\_mode is not supported

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L132)

#### method `init_fhe_client`

```python
init_fhe_client(
    path_to_client: Optional[Path] = None,
    path_to_keys: Optional[Path] = None
)
```

Set the clients keys.

**Args:**

* `path_to_client` (str): Path where the client.zip is located.
* `path_to_keys` (str): Path where keys are located.

**Raises:**

* `ValueError`: if anything goes wrong with the server.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L277)

#### method `remote_call`

```python
remote_call(x: Tensor) → Tensor
```

Call the remote server to get the private module inference.

**Args:**

* `x` (torch.Tensor): The input tensor.

**Returns:**

* `torch.Tensor`: The result of the FHE computation

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L338)

### class `HybridFHEModel`

Convert a model to a hybrid model.

This is done by converting targeted modules by RemoteModules. This will modify the model in place.

**Args:**

* `model` (nn.Module): The model to modify (in-place modification)
* `module_names` (Union\[str, List\[str]]): The module name(s) to replace with FHE server.
* `server_remote_address)`: The remote address of the FHE server
* `model_name` (str): Model name identifier
* `verbose` (int): If logs should be printed when interacting with FHE server

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L352)

#### method `__init__`

```python
__init__(
    model: Module,
    module_names: Union[str, List[str]],
    server_remote_address=None,
    model_name: str = 'model',
    verbose: int = 0
)
```

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L454)

#### method `compile_model`

```python
compile_model(
    x: Tensor,
    n_bits: Union[int, Dict[str, int]] = 8,
    rounding_threshold_bits: Optional[int] = None,
    p_error: Optional[float] = None,
    configuration: Optional[Configuration] = None
)
```

Compiles the specific layers to FHE.

**Args:**

* `x` (torch.Tensor): The input tensor for the model. This is used to run the model once for calibration.
* `n_bits` (int): The bit precision for quantization during FHE model compilation. Default is 8.
* `rounding_threshold_bits` (int): The number of bits to use for rounding threshold during FHE model compilation. Default is 8.
* `p_error` (float): Error allowed for each table look-up in the circuit.
* `configuration` (Configuration): A concrete Configuration object specifying the FHE encryption parameters. If not specified, a default configuration is used.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L437)

#### method `init_client`

```python
init_client(
    path_to_clients: Optional[Path] = None,
    path_to_keys: Optional[Path] = None
)
```

Initialize client for all remote modules.

**Args:**

* `path_to_clients` (Optional\[Path]): Path to the client.zip files.
* `path_to_keys` (Optional\[Path]): Path to the keys folder.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L561)

#### method `publish_to_hub`

```python
publish_to_hub()
```

Allow the user to push the model and FHE required files to HF Hub.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L537)

#### method `save_and_clear_private_info`

```python
save_and_clear_private_info(path: Path, via_mlir=False)
```

Save the PyTorch model to the provided path and also saves the corresponding FHE circuit.

**Args:**

* `path` (Path): The directory where the model and the FHE circuit will be saved.
* `via_mlir` (bool): if fhe circuits should be serialized using via\_mlir option useful for cross-platform (compile on one architecture and run on another)

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L565)

#### method `set_fhe_mode`

```python
set_fhe_mode(hybrid_fhe_mode: Union[str, HybridFHEMode])
```

Set Hybrid FHE mode for all remote modules.

**Args:**

* `hybrid_fhe_mode` (Union\[str, HybridFHEMode]): Hybrid FHE mode to set to all remote modules.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L576)

### class `LoggerStub`

Placeholder type for a typical logger like the one from loguru.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L579)

#### method `info`

```python
info(msg: str)
```

Placholder function for logger.info.

**Args:**

* `msg` (str): the message to output

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L618)

### class `HybridFHEModelServer`

Hybrid FHE Model Server.

This is a class object to server FHE models serialized using HybridFHEModel.

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L624)

#### method `__init__`

```python
__init__(key_path: Path, model_dir: Path, logger: Optional[LoggerStub])
```

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L771)

#### method `add_key`

```python
add_key(key: bytes, model_name: str, module_name: str, input_shape: str)
```

Add public key.

**Arguments:**

* `key` (bytes): public key
* `model_name` (str): model name
* `module_name` (str): name of the module in the model
* `input_shape` (str): input shape of said module

**Returns:** Dict\[str, str] - uid: uid a personal uid

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L693)

#### method `check_inputs`

```python
check_inputs(
    model_name: str,
    module_name: Optional[str],
    input_shape: Optional[str]
)
```

Check that the given configuration exist in the compiled models folder.

**Args:**

* `model_name` (str): name of the model
* `module_name` (Optional\[str]): name of the module in the model
* `input_shape` (Optional\[str]): input shape of the module

**Raises:**

* `ValueError`: if the given configuration does not exist.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L795)

#### method `compute`

```python
compute(
    model_input: bytes,
    uid: str,
    model_name: str,
    module_name: str,
    input_shape: str
)
```

Compute the circuit over encrypted input.

**Arguments:**

* `model_input` (bytes): input of the circuit
* `uid` (str): uid of the public key to use
* `model_name` (str): model name
* `module_name` (str): name of the module in the model
* `input_shape` (str): input shape of said module

**Returns:**

* `bytes`: the result of the circuit

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L667)

#### method `dump_key`

```python
dump_key(key_bytes: bytes, uid: Union[UUID, str]) → None
```

Dump a public key to a stream.

**Args:**

* `key_bytes` (bytes): stream to dump the public serialized key to
* `uid` (Union\[str, uuid.UUID]): uid of the public key to dump

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L677)

#### method `get_circuit`

```python
get_circuit(model_name, module_name, input_shape)
```

Get circuit based on model name, module name and input shape.

**Args:**

* `model_name` (str): name of the model
* `module_name` (str): name of the module in the model
* `input_shape` (str): input shape of the module

**Returns:**

* `FHEModelServer`: a fhe model server of the given module of the given model for the given shape

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L749)

#### method `get_client`

```python
get_client(model_name: str, module_name: str, input_shape: str)
```

Get client.

**Args:**

* `model_name` (str): name of the model
* `module_name` (str): name of the module in the model
* `input_shape` (str): input shape of the module

**Returns:**

* `Path`: the path to the correct client

**Raises:**

* `ValueError`: if client couldn't be found

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L724)

#### method `list_modules`

```python
list_modules(model_name: str)
```

List all modules in a model.

**Args:**

* `model_name` (str): name of the model

**Returns:** Dict\[str, Dict\[str, Dict]]

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L736)

#### method `list_shapes`

```python
list_shapes(model_name: str, module_name: str)
```

List all modules in a model.

**Args:**

* `model_name` (str): name of the model
* `module_name` (str): name of the module in the model

**Returns:** Dict\[str, Dict]

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.2.x/src/concrete/ml/torch/hybrid_model.py#L656)

#### method `load_key`

```python
load_key(uid: Union[str, UUID]) → bytes
```

Load a public key from the key path in the file system.

**Args:**

* `uid` (Union\[str, uuid.UUID]): uid of the public key to load

**Returns:**

* `bytes`: the bytes of the public key


---

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