torch/quantization/quantize_fx.py

from .fx import Fuser  # noqa: F401
from .fx import Quantizer  # noqa: F401
from torch.fx import GraphModule

def _check_is_graph_module(model):
    if not isinstance(model, GraphModule):
        raise ValueError(
            'input model must be a GraphModule, ' +
            'please run torch.fx.symbolic_trace on your model before using ' +
            'quantize_fx. Got type:' + str(type(model)))

def fuse_fx(graph_module, inplace=False):
    r""" Fuse modules in preparation for quantization

    Args:
        graph_module: GraphModule object from symbolic tracing (torch.fx.symbolic_trace)
    """
    _check_is_graph_module(graph_module)
    fuser = Fuser()
    return fuser.fuse(graph_module, inplace)

def _prepare_fx(graph_module, qconfig_dict, inplace, is_dynamic_quant):
    _check_is_graph_module(graph_module)

    quantizer = Quantizer()
    prepare = quantizer.prepare_dynamic if is_dynamic_quant else quantizer.prepare
    prepared = prepare(graph_module, qconfig_dict, inplace)
    return prepared

def prepare_fx(graph_module, qconfig_dict, inplace=False):
    r""" Prepare a model for post training static quantization or
    qantization aware training, not for public use.

    Args:
      graph_module: model from symbolic_tracing (torch.fx.symbolic_trace), must be
      an eval model
      qconfig_dict: see :func:`~torch.quantization.quantize_fx`

    Return:
      A GraphModule with observer or fake quant modules, ready for
      calibration or quantization aware training
    """
    return _prepare_fx(graph_module, qconfig_dict, inplace, is_dynamic_quant=False)

def prepare_static_fx(graph_module, qconfig_dict, inplace=False):
    assert not graph_module.training, 'prepare_static_fx only works for models in ' + \
        'eval mode'
    return prepare_fx(graph_moduel, qconfig_dict, inplace)

def prepare_qat_fx(graph_module, qconfig_dict, inplace=False):
    r""" Prepare a model for quantization aware training
    Args:
      graph_module: model from symbolic_tracing (torch.fx.symbolic_trace), must be
      a train model
      qconfig_dict: see :func:`~torch.quantization.quantize_fx`

    Return:
      A GraphModule with observer or fake quant modules, ready for
      calibration or quantization aware training
    """
    assert graph_module.training, 'prepare_qat_fx only works for models in ' + \
        'train mode'
    return prepare_fx(graph_moduel, qconfig_dict, inplace)

def prepare_dynamic_fx(graph_module, qconfig_dict, inplace=False):
    r""" Prepare a model for post training dynamic quantization
    """
    return _prepare_fx(graph_module, qconfig_dict, inplace, True)

def _convert_fx(graph_module, inplace=False, debug=False, is_dynamic_quant=False):
    _check_is_graph_module(graph_module)
    quantizer = Quantizer()
    return quantizer.convert(graph_module, inplace, debug, is_dynamic_quant)

def convert_fx(graph_module, inplace=False, debug=False):
    r""" Convert a calibrated or trained model to a quantized model
    """
    return _convert_fx(graph_module, inplace, debug, is_dynamic_quant=False)

convert_static_fx = convert_fx
convert_qat_fx = convert_fx

def convert_dynamic_fx(graph_module, inplace=False, debug=False):
    return _convert_fx(graph_module, inplace, debug, is_dynamic_quant=True)

def _quantize_fx(model, qconfig_dict, run_fn=None, run_args=None, inplace=False,
                 debug=False, is_dynamic_quant=False):
    assert not model.training, 'quantize_fx is only used for post training ' + \
        'quantization(eval mode), for quantization aware training please use ' + \
        'prepare_qat_fx and convert_qat_fx.'

    if is_dynamic_quant:
        model = prepare_dynamic_fx(model, qconfig_dict, inplace)
        # TODO: change inplace to True since the model is already copied in
        # prepare
        model = convert_dynamic_fx(model, False, debug)
    else:
        assert run_fn, "Must provide calibration function for post training static quantization"
        assert run_args, "Must provide calibration dataset for post training static quantization"
        model = prepare_fx(model, qconfig_dict, inplace)
        run_fn(model, *run_args)
        # TODO: change inplace to True since the model is already copied in
        # prepare
        model = convert_fx(model, False, debug)

    return model


def quantize_fx(model, qconfig_dict, run_fn, run_args, inplace=False, debug=False):
    r"""Quantize the input float symbolically traced GraphModule model with
    post training static quantization

    First it will prepare the model for calibration, then it calls
    `run_fn` which will run the calibration step, after that we will
    convert the model to a quantized model.

    Args:
        `model`: input float TorchScript model
        `qconfig_dict`: qconfig_dict is a dictionary with names of sub modules as key and
        qconfig for that module as value, empty key means the qconfig will be applied
        to whole model unless it’s overwritten by more specific configurations, the
        qconfig for each module is either found in the dictionary or fallback to
         the qconfig of parent module.

        Right now qconfig_dict is the only way to configure how the model is quantized,
        and it is done in the granularity of module, that is, we only support one type
        of qconfig for each torch.nn.Module, and the qconfig for sub module will
        override the qconfig for parent module, empty string means global configuration.
        `run_fn`: a calibration function for calibrating the prepared model
        `run_args`: positional arguments for `run_fn`
        `inplace`: carry out model transformations in-place, the original module is
        mutated
        `debug`: flag for producing a debug friendly model (preserve weight attribute)

    Return:
        Quantized TorchSciprt model.

    Example:
    ```python
    import torch
    from torch.quantization import get_default_qconfig
    from torch.quantization import quantize_fx

    graph_module = torch.fx.symbolic_trace(float_model.eval())
    qconfig = get_default_qconfig('fbgemm')
    def calibrate(model, data_loader):
        model.eval()
        with torch.no_grad():
            for image, target in data_loader:
                model(image)

    quantized_model = quantize_fx(
        graph_module,
        {'': qconfig},
        calibrate,
        [data_loader_test])
    ```
    """
    return _quantize_fx(
        model, qconfig_dict, run_fn, run_args, inplace, debug, is_dynamic_quant=False)

def quantize_dynamic_fx(model, qconfig_dict, inplace=False, debug=False):
    r"""Quantize the input float symbolically traced GraphModule model with
    post training dynamic quantization.
    Currently only qint8 quantization of torch.nn.Linear is supported.

    Args:
        `model`: input float TorchScript model
        `qconfig_dict`: qconfig_dict is a dictionary with names of sub modules as key and
        qconfig for that module as value, please see detailed
        descriptions in :func:`~torch.quantization.quantize_fx`
        `inplace`: carry out model transformations in-place, the original module is
        mutated
        `debug`: flag for producing a debug friendly model (preserve weight attribute)

    Return:
        Quantized TorchSciprt model.

    Example:
    ```python
    import torch
    from torch.quantization import per_channel_dynamic_qconfig
    from torch.quantization import quantize_dynmiac_fx

    graph_module = torch.fx.symbolic_trace(float_model.eval())
    qconfig = get_default_qconfig('fbgemm')
    def calibrate(model, data_loader):
        model.eval()
        with torch.no_grad():
            for image, target in data_loader:
                model(image)

    quantized_model = quantize_dynamic_fx(
        graph_module,
        {'': qconfig},
        calibrate,
        [data_loader_test])
    ```
    """
    return _quantize_fx(
        model, qconfig_dict, inplace=inplace, debug=debug, is_dynamic_quant=True)