pytorch/torch/quantization/utils.py

"""
Utils shared by different modes of quantization (eager/graph)
"""
import warnings

import torch
from .quant_type import QuantType, quant_type_to_str

def get_combined_dict(default_dict, additional_dict):
    d = default_dict.copy()
    d.update(additional_dict)
    return d

def is_per_tensor(qscheme):
    return qscheme == torch.per_tensor_affine or \
        qscheme == torch.per_tensor_symmetric

def is_per_channel(qscheme):
    return qscheme in [torch.per_channel_affine,
                       torch.per_channel_affine_float_qparams,
                       torch.per_channel_symmetric]

def get_qparam_dict(observer_or_fake_quant):
    qscheme = observer_or_fake_quant.qscheme if hasattr(observer_or_fake_quant, "qscheme") else None
    dtype = observer_or_fake_quant.dtype
    qparams = {"qscheme": qscheme, "dtype": dtype}

    if not qscheme:
        return qparams

    if is_per_tensor(qscheme):
        qscheme = torch.per_tensor_affine
    elif is_per_channel(qscheme):
        # change symmetric to affine since we do not have symmetric
        # quantized Tensor
        if qscheme == torch.per_channel_symmetric:
            qscheme = torch.per_channel_affine
        qparams["axis"] = observer_or_fake_quant.ch_axis
    else:
        raise RuntimeError(f"Unrecognized qscheme: {qscheme}")
    # update qscheme, since we don't have symmetric quant qscheme
    # in quantized Tensor
    qparams["qscheme"] = qscheme

    scale, zero_point = observer_or_fake_quant.calculate_qparams()
    qparams["scale"] = scale
    qparams["zero_point"] = zero_point

    return qparams


def get_swapped_custom_module_class(custom_module, custom_module_class_mapping, qconfig):
    """ Get the observed/quantized custom module class that we need
    to swap `custom_module` to
    Input:
        custom_module: input, can be an instance of either a float or observed custom module
        custom_module_class_mapping: the float to observed or observed to quantized custom module class mapping
        qconfig: qconfig configured for the custom module

    Output:
        corresponding observed/quantized custom module class for input custom module instance
    """
    quant_type = get_quant_type(qconfig)
    quant_type_str = quant_type_to_str(quant_type)
    class_mapping = custom_module_class_mapping.get(quant_type_str, {})
    assert type(custom_module) in class_mapping, "did not find corresponding observed " \
        "module class for {} in mapping: {}".format(type(custom_module), class_mapping)
    return class_mapping[type(custom_module)]

def activation_dtype(qconfig):
    assert qconfig is not None
    activation = qconfig.activation()
    return activation.dtype

def weight_dtype(qconfig):
    assert qconfig is not None
    weight = qconfig.weight()
    return weight.dtype

def activation_is_statically_quantized(qconfig):
    """ Given a qconfig, decide if the activation needs to be
    quantized or not, this includes quantizing to quint8, qint8 and float16
    """
    return activation_dtype(qconfig) in [torch.quint8, torch.qint8, torch.float16]

def activation_is_int8_quantized(qconfig):
    """ Given a qconfig, decide if the activation needs to be
    quantized to int8 or not, this includes quantizing to quint8, qint8
    """
    return activation_dtype(qconfig) in [torch.quint8, torch.qint8]

def weight_is_quantized(qconfig):
    """ Given a qconfig, decide if the weight needs to be
    quantized or not
    """
    return weight_dtype(qconfig) in [torch.quint8, torch.qint8, torch.float16]

def weight_is_statically_quantized(qconfig):
    """ Given a qconfig, decide if the weight needs to be statically
    quantized or not
    """
    return weight_dtype(qconfig) in [torch.quint8, torch.qint8]

def get_qconfig_dtypes(qconfig):
    r""" returns the qconfig tuple for qconfig:
    (activation_dtype, weight_dtype, activation_compute_dtype)
    """
    assert qconfig is not None
    activation = qconfig.activation()
    weight = qconfig.weight()
    compute_dtype = activation.compute_dtype if hasattr(activation, 'compute_dtype') else None
    return (activation.dtype, weight.dtype, compute_dtype)

def get_quant_type(qconfig):
    assert qconfig is not None
    activation = qconfig.activation()
    weight = qconfig.weight()
    static_dtypes = [torch.quint8, torch.qint8]
    if weight.dtype in static_dtypes:
        if activation.dtype in static_dtypes:
            return QuantType.STATIC
        elif hasattr(activation, 'compute_dtype') and activation.compute_dtype in static_dtypes:
            return QuantType.DYNAMIC
        else:
            return QuantType.WEIGHT_ONLY

    if weight.dtype == torch.float16:
        if activation.dtype == torch.float:
            return QuantType.DYNAMIC
        elif activation.dtype == torch.float16:
            return QuantType.STATIC

    raise Exception("Unrecognized dtype combination in get_quant_type: activation({}),"
                    "weight({})".format(activation.dtype, weight.dtype))

def check_min_max_valid(min_val: torch.Tensor, max_val: torch.Tensor) -> bool:
    """ Checks if the given minimum and maximum values are valid, meaning that
    they exist and the min value is less than the max value.
    """
    if min_val.numel() == 0 or max_val.numel() == 0:
        warnings.warn(
            "must run observer before calling calculate_qparams. " +
            "Returning default values."
        )
        return False

    if min_val.dim() == 0 or max_val.dim() == 0:
        if min_val == float("inf") and max_val == float("-inf"):
            warnings.warn(
                "must run observer before calling calculate_qparams. " +
                "Returning default values."
            )

            return False

        assert min_val <= max_val, "min {} should be less than max {}".format(
            min_val, max_val
        )
    else:
        assert torch.all(
            min_val <= max_val
        ), "min {} should be less than max {}".format(min_val, max_val)

    return True