Quantization

The Arm Ethos-U delegate only supports the execution of quantized models. To quantize a model so that is supported by this delegate, the EthosUQuantizer 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 Ethos-U quantizer.

Supported Quantization Schemes

The Arm Ethos-U delegate supports the following quantization schemes:

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

• Limited support for 16-bit quantization with 16-bit activations and 8-bit weights (a.k.a 16x8 quantization). This is under development.

Quantization API

class EthosUQuantizer(compile_spec: 'EthosUCompileSpec') -> 'None'

Quantizer supported by the Arm Ethos-U backend.

Args:

• compile_spec (EthosUCompileSpec): Backend compile specification for Ethos-U targets.

def EthosUQuantizer.quantize_with_submodules(self, model: 'GraphModule', calibration_samples: 'list[tuple]', is_qat: 'bool' = False):

Quantizes a GraphModule in a way such that conditional submodules are handled properly.

Args:

• model (GraphModule): The model to quantize.

• calibration_samples (list[tuple]): A list of inputs to used to calibrate the model during quantization. To properly calibrate a model with submodules, at least one sample per code path is needed.

• is_qat (bool): Whether to do quantization aware training or not.

Returns:

• GraphModule: The quantized model.

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

Set quantization_config for submodules not matched by other filters.

Args:

• quantization_config (QuantizationConfig): Configuration to apply to modules that are not captured by name or type filters.

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

Set quantization_config for input and output nodes.

Args:

• quantization_config (QuantizationConfig): Configuration describing activation quantization for model inputs and outputs.

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

Set quantization_config for submodules with a given module name.

For example, calling set_module_name(“blocks.sub”) quantizes supported patterns for that submodule with the provided quantization_config.

Args:

• module_name (str): Fully qualified module name to configure.

• quantization_config (QuantizationConfig): Configuration to apply to the named submodule.

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

Set quantization_config for submodules with a given module type.

For example, calling set_module_type(Sub) quantizes supported patterns in each Sub instance with the provided quantization_config.

Args:

• module_type (Callable): Type whose submodules should use the provided quantization configuration.

• quantization_config (QuantizationConfig): Configuration to apply to submodules of the given type.

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

Transform the graph to prepare it for quantization annotation.

Currently transforms scalar values to tensor attributes.

Args:

• model (GraphModule): Model whose graph will be transformed.

Returns:

• GraphModule: Transformed model prepared for annotation.