def __init__(self,
num_features,
eps=1e-5,
momentum=0.1,
restrict_value_type: RestrictValueType = RestrictValueType.FP,
impl_type: ScalingImplType = ScalingImplType.STATS,
bias_quant_type: QuantType = QuantType.FP,
bias_narrow_range: bool = False,
bias_bit_width: int = None):
QuantLayer.__init__(self,
compute_output_scale=False,
compute_output_bit_width=False,
return_quant_tensor=False)
nn.Module.__init__(self)
if bias_quant_type != QuantType.FP and not (self.compute_output_scale and self.compute_output_bit_width):
raise Exception("Quantizing bias requires to compute output scale and output bit width")
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
if impl_type == ScalingImplType.PARAMETER_FROM_STATS:
self.running_mean = None
self.running_var = None
elif impl_type == ScalingImplType.STATS:
self.register_buffer('running_mean', torch.zeros(num_features))
self.register_buffer('running_var', torch.ones(num_features))
else:
raise Exception("Scaling mode not supported")
self.eps = eps
self.momentum = momentum
self.impl_type = impl_type
self.num_features = num_features
self.restrict_value_type = restrict_value_type
self.bias_quant = BiasQuantProxy(quant_type=bias_quant_type,
narrow_range=bias_narrow_range,
bit_width=bias_bit_width)
self.restrict_weight = RestrictValue(restrict_value_type=restrict_value_type,
float_to_int_impl_type=FloatToIntImplType.ROUND,
min_val=None)
self.restrict_scaling_preprocess = RestrictValue.restrict_value_op(restrict_value_type,
restrict_value_op_impl_type=
RestrictValueOpImplType.TORCH_MODULE)
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple[int]],
stride: Union[int, Tuple[int]] = 1,
padding: Union[int, Tuple[int]] = 0,
output_padding: Union[int, Tuple[int]] = 0,
padding_type: PaddingType = PaddingType.STANDARD,
dilation: Union[int, Tuple[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,
deterministic: 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)
ConvTranspose1d.__init__(self,
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
output_padding=output_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"
)
if torch.backends.cudnn.benchmark:
torch.backends.cudnn.deterministic = deterministic
# self.per_elem_ops = 2 * self.kernel_size[0] * (in_channels // groups) # TO DO: Implement op_count
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)
def __init__(self,
in_features: int,
out_features: int,
bias: bool,
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_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_override: Optional[Module] = 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_scaling_min_val: float = SCALING_MIN_VAL,
weight_ternary_threshold: float = 0.5,
weight_restrict_scaling_type: RestrictValueType = RestrictValueType.LOG_FP,
weight_scaling_stats_sigma: float = 3.0,
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)
Linear.__init__(self,
in_features=in_features,
out_features=out_features,
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 * in_features
self.weight_reg = WeightReg()
if weight_quant_override is not None:
self.weight_quant = weight_quant_override
self.weight_quant.add_tracked_tensor(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.out_features, 1)
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
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,
narrow_range=bias_narrow_range,
bit_width=bias_bit_width)
def __init__(
self,
num_features,
bias_quant_type: QuantType = QuantType.FP,
bias_narrow_range: bool = False,
bias_bit_width: int = None,
weight_quant_type: QuantType = QuantType.FP,
weight_quant_override: nn.Module = None,
weight_narrow_range: bool = False,
weight_scaling_override: Optional[nn.Module] = None,
weight_bit_width: int = 32,
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_restrict_scaling_type: RestrictValueType = RestrictValueType
.LOG_FP,
weight_scaling_stats_sigma: float = 3.0,
weight_scaling_min_val: float = SCALING_MIN_VAL,
compute_output_scale: bool = False,
compute_output_bit_width: bool = False,
return_quant_tensor: bool = False):
QuantLayer.__init__(self,
compute_output_scale=compute_output_scale,
compute_output_bit_width=compute_output_bit_width,
return_quant_tensor=return_quant_tensor)
ScaleBias.__init__(self, num_features)
if bias_quant_type != QuantType.FP and not self.compute_output_scale:
raise Exception("Quantizing bias requires to compute output scale")
if bias_quant_type != QuantType.FP and bias_bit_width is None and not self.compute_output_bit_width:
raise Exception(
"Quantizing bias requires a bit-width, either computed or defined"
)
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_stats_op == StatsOp.MAX_AVE:
assert not weight_scaling_per_output_channel
weight_stats_input_view_shape_impl = StatsInputViewShapeImpl.OVER_OUTPUT_CHANNELS
weight_scaling_shape = SCALING_SCALAR_SHAPE
weight_scaling_stats_reduce_dim = None
else:
if weight_scaling_per_output_channel:
weight_stats_input_view_shape_impl = StatsInputViewShapeImpl.OVER_OUTPUT_CHANNELS
weight_scaling_shape = (num_features, 1)
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
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=BitWidthImplType.CONST,
bit_width_impl_override=None,
restrict_bit_width_type=RestrictValueType.INT,
min_overall_bit_width=None,
max_overall_bit_width=None,
tracked_parameter_list_init=self.weight,
ternary_threshold=None,
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=None)
self.bias_quant = BiasQuantProxy(quant_type=bias_quant_type,
narrow_range=bias_narrow_range,
bit_width=bias_bit_width)
