def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=False,
cast_func=void_cast_func,
n_train_sample=1):
BitCenterLayer.__init__(
self,
fp_functional=F.conv2d,
lp_functional=bit_center_conv2d,
bias=bias,
cast_func=cast_func,
n_train_sample=n_train_sample)
Conv2d.__init__(
self,
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=bias)
# weight_delta is the delta tensor in the algorithm while weight_lp is the cached
# lp version of weight offset
self.setup_bit_center_vars()
self.cuda()
self.reset_parameters_bit_center()
self.register_backward_hook(self.update_grad_output_cache)
def __init__(self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple[int, int]],
stride: Union[int, Tuple[int, int]] = 1,
padding: Union[int, Tuple[int, int]] = 0,
dilation: Union[int, Tuple[int, int]] = 1,
groups: int = 1,
bias: bool = True,
padding_type: str = 'standard',
weight_quant: Union[
WeightQuantProxyProtocol,
Type[Injector]] = Int8WeightPerTensorFloat,
bias_quant: Union[BiasQuantProxyProtocol,
Type[Injector]] = FloatBias,
input_quant: Union[ActQuantProxyProtocol,
Type[Injector]] = None,
output_quant: Union[ActQuantProxyProtocol,
Type[Injector]] = None,
return_quant_tensor: bool = False,
**kwargs) -> None:
Conv2d.__init__(self,
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=bias)
QuantWBIOL.__init__(self,
weight=self.weight,
bias=self.bias,
weight_quant=weight_quant,
bias_quant=bias_quant,
input_quant=input_quant,
output_quant=output_quant,
return_quant_tensor=return_quant_tensor,
**kwargs)
assert self.padding_mode == 'zeros'
assert not (padding_type == 'same' and padding != 0)
self.padding_type = padding_type
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple[int, int]],
stride: Union[int, Tuple[int, int]] = 1,
padding: Union[int, Tuple[int, int]] = 0,
padding_type: PaddingType = PaddingType.STANDARD,
dilation: Union[int, Tuple[int, int]] = 1,
groups: int = 1,
bias: bool = True,
bias_quant_type: QuantType = QuantType.FP,
bias_narrow_range: bool = False,
bias_bit_width: int = None,
weight_quant_override: WeightQuantProxy = None,
weight_quant_type: QuantType = QuantType.FP,
weight_narrow_range: bool = False,
weight_scaling_override: Optional[Module] = None,
weight_bit_width_impl_override: Union[BitWidthParameter,
BitWidthConst] = None,
weight_bit_width_impl_type: BitWidthImplType = BitWidthImplType.
CONST,
weight_restrict_bit_width_type:
RestrictValueType = RestrictValueType.INT,
weight_bit_width: int = 32,
weight_min_overall_bit_width: Optional[int] = 2,
weight_max_overall_bit_width: Optional[int] = None,
weight_scaling_impl_type: ScalingImplType = ScalingImplType.STATS,
weight_scaling_const: Optional[float] = None,
weight_scaling_stats_op: StatsOp = StatsOp.MAX,
weight_scaling_per_output_channel: bool = False,
weight_ternary_threshold: float = 0.5,
weight_restrict_scaling_type: RestrictValueType = RestrictValueType
.LOG_FP,
weight_scaling_stats_sigma: float = 3.0,
weight_scaling_min_val: float = SCALING_MIN_VAL,
weight_override_pretrained_bit_width: bool = False,
compute_output_scale: bool = False,
compute_output_bit_width: bool = False,
return_quant_tensor: bool = False) -> None:
QuantLayer.__init__(self,
compute_output_scale=compute_output_scale,
compute_output_bit_width=compute_output_bit_width,
return_quant_tensor=return_quant_tensor)
Conv2d.__init__(self,
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=bias)
if weight_quant_type == QuantType.FP and compute_output_bit_width:
raise Exception(
"Computing output bit width requires enabling quantization")
if bias_quant_type != QuantType.FP and not (compute_output_scale and
compute_output_bit_width):
raise Exception(
"Quantizing bias requires to compute output scale and output bit width"
)
self.per_elem_ops = 2 * self.kernel_size[0] * self.kernel_size[1] * (
in_channels // groups)
self.padding_type = padding_type
self.weight_reg = WeightReg()
if weight_quant_override is not None:
self.weight_quant = weight_quant_override
self.weight_quant.add_tracked_parameter(self.weight)
else:
weight_scaling_stats_input_concat_dim = 1
if weight_scaling_per_output_channel:
weight_stats_input_view_shape_impl = StatsInputViewShapeImpl.OVER_OUTPUT_CHANNELS
weight_scaling_shape = self.per_output_channel_broadcastable_shape
weight_scaling_stats_reduce_dim = 1
else:
weight_stats_input_view_shape_impl = StatsInputViewShapeImpl.OVER_TENSOR
weight_scaling_shape = SCALING_SCALAR_SHAPE
weight_scaling_stats_reduce_dim = None
if weight_scaling_stats_op == StatsOp.MAX_AVE:
weight_stats_input_view_shape_impl = StatsInputViewShapeImpl.OVER_OUTPUT_CHANNELS
weight_scaling_stats_reduce_dim = 1
self.weight_quant = WeightQuantProxy(
bit_width=weight_bit_width,
quant_type=weight_quant_type,
narrow_range=weight_narrow_range,
scaling_override=weight_scaling_override,
restrict_scaling_type=weight_restrict_scaling_type,
scaling_const=weight_scaling_const,
scaling_stats_op=weight_scaling_stats_op,
scaling_impl_type=weight_scaling_impl_type,
scaling_stats_reduce_dim=weight_scaling_stats_reduce_dim,
scaling_shape=weight_scaling_shape,
bit_width_impl_type=weight_bit_width_impl_type,
bit_width_impl_override=weight_bit_width_impl_override,
restrict_bit_width_type=weight_restrict_bit_width_type,
min_overall_bit_width=weight_min_overall_bit_width,
max_overall_bit_width=weight_max_overall_bit_width,
tracked_parameter_list_init=self.weight,
ternary_threshold=weight_ternary_threshold,
scaling_stats_input_view_shape_impl=
weight_stats_input_view_shape_impl,
scaling_stats_input_concat_dim=
weight_scaling_stats_input_concat_dim,
scaling_stats_sigma=weight_scaling_stats_sigma,
scaling_min_val=weight_scaling_min_val,
override_pretrained_bit_width=
weight_override_pretrained_bit_width)
self.bias_quant = BiasQuantProxy(quant_type=bias_quant_type,
bit_width=bias_bit_width,
narrow_range=bias_narrow_range)
