Method optimize

ppq/quantization/optim/parameters.py:172–215

(
        self,
        graph: BaseGraph,
        dataloader: Iterable,
        executor: TorchExecutor,
        **kwargs
    )

        super().__init__(name='PPQ Parameter Quantization Pass')

    def optimize(
        self,
        graph: BaseGraph,
        dataloader: Iterable,
        executor: TorchExecutor,
        **kwargs
    ) -> None:
        # build observer and hook for each quantable operation
        hooks, observers, state_records = {}, {}, {}
        for op_name, operation in graph.operations.items():
            if not isinstance(operation, QuantableOperation): continue

            for config, var in operation.config_with_variable:
                # deactivate non-parameter variable quantization just for now
                if not var.is_parameter:
                    state_records[config] = config.state
                    config.state = QuantizationStates.FP32
                elif self._method is not None:
                    # override quantizer's setting if necessary
                    config.observer_algorithm = self._method

            observer = OperationObserver(
                operation=executor._graph.operations[op_name],
                monitor_outputs=False, monitor_inputs=False)
            observers[op_name] = observer
            hooks[op_name]     = observer.hook

        # dummy forward, quant all parameter.
        assert isinstance(executor, TorchExecutor), \
            'ParameterQuantizePass Only support TorchExecutor now.'
        executor.dummy_forward(hooks=hooks)

        # render quantization config, restore non-parameter quantization state
        for op_name, operation in graph.operations.items():
            if not isinstance(operation, QuantableOperation): continue

            for cfg, var in operation.config_with_variable:
                if not var.is_parameter:
                    cfg.state = state_records[cfg]

            observer = observers[op_name]
            assert isinstance(observer, OperationObserver)
            observer.render_quantization_config()
            observer.report()