Quantization

The Arm VGF delegate can be used to execute quantized models. To quantize a model so that is supported by this delegate, the VgfQuantizer should be used.

Currently the symmetric int8 config defined by executorch.backends.arm.quantizer.arm_quantizer.get_symmetric_quantization_config is the main config available to use with the VGF quantizer.

Supported Quantization Schemes

The quantization schemes supported by the VGF Backend are:

• 8-bit symmetric weights with 8-bit asymmetric activations (via the PT2E quantization flow).

  • Supports both static and dynamic activations

  • Supports per-channel and per-tensor schemes

Weight-only quantization is not currently supported on the VGF backend.

Quantization API

class VgfQuantizer(compile_spec: 'VgfCompileSpec') -> 'None'

Quantizer supported by the Arm Vgf backend.

Args:

• compile_spec: A VgfCompileSpec instance.

def VgfQuantizer.set_global(self, quantization_config: 'QuantizationConfig') -> 'TOSAQuantizer':

Set quantization_config for submodules that are not already annotated by name or type filters.

Args:

• quantization_config: The QuantizationConfig to set as global configuration.

def VgfQuantizer.set_io(self, quantization_config: 'QuantizationConfig') -> 'TOSAQuantizer':

Set quantization_config for input and output nodes.

Args:

• quantization_config: The QuantizationConfig to set for input and output nodes.

def VgfQuantizer.set_module_name(self, module_name: 'str', quantization_config: 'Optional[QuantizationConfig]') -> 'TOSAQuantizer':

Set quantization_config for a submodule with name: module_name, for example: quantizer.set_module_name(“blocks.sub”), it will quantize all supported operator/operator patterns in the submodule with this module name with the given quantization_config

Args:

• module_name: The name of the submodule to set the quantization config for.

• quantization_config: The QuantizationConfig to set for the submodule.

def VgfQuantizer.set_module_type(self, module_type: 'Callable', quantization_config: 'QuantizationConfig') -> 'TOSAQuantizer':

Set quantization_config for a submodule with type: module_type, for example: quantizer.set_module_name(Sub) or quantizer.set_module_name(nn.Linear), it will quantize all supported operator/operator patterns in the submodule with this module type with the given quantization_config.

Args:

• module_type: The type of the submodule to set the quantization config for.

• quantization_config: The QuantizationConfig to set for the submodule.

def VgfQuantizer.transform_for_annotation(self, model: 'GraphModule') -> 'GraphModule':

An initial pass for transforming the graph to prepare it for annotation. Currently transforms scalar values to tensor attributes.

Args:

• model: The model to transform. Returns: The transformed model.