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# #quantization.fuse_modules

horizon_plugin_pytorch.quantization.fuse_modules (model, modules_to_fuse, inplace=False, fuser_func=, fuse_custom_config_dict=None)

Fuses a list of modules into a single module.

Fuses only the following sequence of modules:

conv, bn;

conv, bn, relu;

conv, relu;

conv, bn, add;

conv, bn, add, relu;

conv, add;

conv, add, relu;

linear, bn;

linear, bn, relu;

linear, relu;

linear, bn, add;

linear, bn, add, relu;

linear, add;

linear, add, relu.

For these sequences, the first element in the output module list performs the fused operation. The rest of the elements are set to nn.Identity()

Parameters:

model – Model containing the modules to be fused

modules_to_fuse – list of list of module names to fuse. Can also be a list of strings if there is only a single list of modules to fuse.

inplace – bool specifying if fusion happens in place on the model, by default a new model is returned

fuser_func – Function that takes in a list of modules and outputs a list of fused modules of the same length. For example, fuser_func([convModule, BNModule]) returns the list [ConvBNModule, nn.Identity()] Defaults to torch.ao.quantization.fuse_known_modules

fuse_custom_config_dict – custom configuration for fusion

`# Example of fuse_custom_config_dict fuse_custom_config_dict = { # Additional fuser_method mapping "additional_fuser_method_mapping": { (torch.nn.Conv2d, torch.nn.BatchNorm2d): fuse_conv_bn }, }`

Returns: model with fused modules. A new copy is created if inplace=True.

Examples:

`>>> # xdoctest: +SKIP >>> m = M().eval() >>> # m is a module containing the sub-modules below >>> modules_to_fuse = [ ['conv1', 'bn1', 'relu1'], ['submodule.conv', 'submodule.relu']] >>> fused_m = fuse_modules( m, modules_to_fuse) >>> output = fused_m(input) >>> m = M().eval() >>> # Alternately provide a single list of modules to fuse >>> modules_to_fuse = ['conv1', 'bn1', 'relu1'] >>> fused_m = fuse_modules( m, modules_to_fuse) >>> output = fused_m(input)`
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