def test_init_fail(self, bit_width_init, min_bit_width_init):
"""
Test that BitWidthParameter init fails when bit_width is less than min_bit_width
"""
if bit_width_init < min_bit_width_init:
BitWidthParameter(bit_width_init, min_bit_width=min_bit_width_init)
else:
pytest.skip('Skip expected legal cases')
def bit_width_parameter(bit_width_init, min_bit_width_init,
override_pretrained):
"""
Learned bit-width with default arguments module
"""
if bit_width_init < min_bit_width_init:
pytest.xfail('bit_width cannot be smaller than min_bit_width')
module = BitWidthParameter(
bit_width_init,
min_bit_width=min_bit_width_init,
override_pretrained_bit_width=override_pretrained)
return module
def _weight_quant_init_impl(bit_width: Optional[int],
quant_type: QuantType,
narrow_range: bool,
scaling_override: Optional[nn.Module],
restrict_scaling_type: RestrictValueType,
scaling_const: float,
scaling_stats_op: StatsOp,
scaling_impl_type: ScalingImplType,
scaling_stats_reduce_dim: Optional[int],
scaling_shape: Tuple[int, ...],
scaling_min_val: Optional[float],
bit_width_impl_type: Optional[BitWidthImplType],
restrict_bit_width_type: Optional[RestrictValueType],
min_overall_bit_width: Optional[int],
max_overall_bit_width: Optional[int],
bit_width_impl_override: Optional[Union[BitWidthConst, BitWidthParameter]],
scaling_stats_input_view_shape_impl: StatsInputViewShapeImpl,
scaling_stats_input_concat_dim: int,
ternary_threshold: Optional[float],
scaling_stats_sigma: Optional[float],
tracked_parameter_list: List[torch.nn.Parameter],
zero_hw_sentinel: torch.Tensor,
override_pretrained_bit_width: bool):
if quant_type == QuantType.FP:
tensor_quant = IdentityQuant()
else:
if scaling_impl_type != ScalingImplType.OVERRIDE and scaling_override is not None:
raise Exception("Overriding scaling requires to set ScalingImplType to OVERRIDE explicitly.")
if scaling_impl_type == ScalingImplType.OVERRIDE and scaling_override is None:
raise Exception("Overriding scaling requires to pass a scaling impl module.")
if scaling_impl_type == ScalingImplType.OVERRIDE and scaling_override is not None:
scaling_impl = scaling_override
elif scaling_impl_type == ScalingImplType.STATS \
or scaling_impl_type == ScalingImplType.AFFINE_STATS \
or scaling_impl_type == ScalingImplType.PARAMETER_FROM_STATS:
stats_scaling = ParameterStatsScaling(stats_op=scaling_stats_op,
restrict_scaling_type=restrict_scaling_type,
tracked_parameter_list=tracked_parameter_list,
stats_input_view_shape_impl=scaling_stats_input_view_shape_impl,
stats_input_concat_dim=scaling_stats_input_concat_dim,
sigma=scaling_stats_sigma,
scaling_min_val=scaling_min_val,
stats_reduce_dim=scaling_stats_reduce_dim,
stats_output_shape=scaling_shape,
affine=scaling_impl_type == ScalingImplType.AFFINE_STATS)
if scaling_impl_type == ScalingImplType.PARAMETER_FROM_STATS:
if quant_type == QuantType.BINARY or quant_type == QuantType.TERNARY:
raise Exception("Parameter from stats scaling is currently not supported for binary/ternary")
scaling_init = stats_scaling(zero_hw_sentinel).detach()
scaling_impl = StandaloneScaling(scaling_init=scaling_init,
parameter_shape=scaling_shape,
restrict_scaling_type=restrict_scaling_type,
is_parameter=True,
scaling_min_val=scaling_min_val)
else:
scaling_impl = stats_scaling
elif scaling_impl_type == ScalingImplType.CONST or scaling_impl_type == ScalingImplType.HE:
if scaling_impl_type == ScalingImplType.HE:
scaling_const = 0.0
for param in tracked_parameter_list: # takes average of He scaling over parameter list
two_dim_param = param.view(param.shape[0], -1)
scaling_const += math.sqrt(2.0 / two_dim_param.shape[1])
scaling_const /= len(tracked_parameter_list)
scaling_init = torch.tensor(scaling_const)
scaling_impl = StandaloneScaling(scaling_init=scaling_init,
parameter_shape=SCALING_SCALAR_SHAPE,
restrict_scaling_type=restrict_scaling_type,
is_parameter=False,
scaling_min_val=None)
else:
raise Exception("Scaling type {} not supported for weight quantization"
.format(str(scaling_impl_type)))
if bit_width == 1 and quant_type == QuantType.BINARY:
tensor_quant = BinaryQuant(scaling_impl=scaling_impl)
elif bit_width == 2 and quant_type == QuantType.TERNARY:
tensor_quant = TernaryQuant(scaling_impl=scaling_impl, threshold=ternary_threshold)
elif bit_width >= 2 and quant_type == QuantType.INT:
if bit_width_impl_override is None:
if (bit_width_impl_type is None
or bit_width is None
or restrict_bit_width_type is None):
raise Exception("Bit width is not defined properly")
if bit_width_impl_type == BitWidthImplType.CONST:
tensor_clamp_impl = TensorClampSte()
bit_width_impl = BitWidthConst(bit_width, restrict_bit_width_type)
elif bit_width_impl_type == BitWidthImplType.PARAMETER:
tensor_clamp_impl = TensorClamp()
bit_width_impl = BitWidthParameter(bit_width_init=bit_width,
restrict_bit_width_type=restrict_bit_width_type,
min_overall_bit_width=min_overall_bit_width,
max_overall_bit_width=max_overall_bit_width,
override_pretrained=override_pretrained_bit_width)
else:
raise Exception("Bit width type {} not supported for weight quantization."
.format(str(bit_width_impl_type)))
else:
tensor_clamp_impl = TensorClamp()
bit_width_impl = bit_width_impl_override
float_to_int_impl = RestrictValue(restrict_value_type=RestrictValueType.INT,
float_to_int_impl_type=FloatToIntImplType.ROUND,
min_val=None)
int_scaling_impl = IntScaling(narrow_range,
signed=True,
restrict_scaling_type=restrict_scaling_type)
tensor_quant = RescalingIntQuant(narrow_range=narrow_range,
signed=True,
scaling_impl=scaling_impl,
int_scaling_impl=int_scaling_impl,
tensor_clamp_impl=tensor_clamp_impl,
msb_clamp_bit_width_impl=bit_width_impl,
float_to_int_impl=float_to_int_impl,
runtime=False)
else:
raise Exception('Unsupported weight quantization: {} bit width, {} quantization.'
.format(bit_width, str(quant_type)))
return tensor_quant
def __init__(self,
activation_impl: Module,
bit_width: int,
signed: bool,
narrow_range: bool,
min_val: float,
max_val: float,
quant_type: QuantType,
float_to_int_impl_type: FloatToIntImplType,
scaling_override: Optional[Module],
scaling_impl_type: ScalingImplType,
scaling_per_channel: bool,
scaling_min_val: Optional[float],
scaling_stats_sigma: Optional[float],
scaling_stats_op: Optional[StatsOp],
scaling_stats_buffer_momentum: Optional[float],
scaling_stats_permute_dims: Optional[Tuple],
per_channel_broadcastable_shape: Optional[Tuple[int, ...]],
min_overall_bit_width: Optional[int],
max_overall_bit_width: Optional[int],
bit_width_impl_override: Module,
bit_width_impl_type: BitWidthImplType,
restrict_bit_width_type: RestrictValueType,
restrict_scaling_type: RestrictValueType,
override_pretrained_bit_width: bool):
super(ActivationQuantProxy, self).__init__()
if not signed and min_val != 0.0:
raise Exception("Min val has to be 0.0 when quantization is unsigned.")
if scaling_per_channel and per_channel_broadcastable_shape is None:
raise Exception("Per channel scaling requires to specify number of channels.")
if quant_type == QuantType.FP:
tensor_quant = IdentityQuant()
else:
if scaling_impl_type != ScalingImplType.OVERRIDE and scaling_override is not None:
raise Exception("Overriding scaling requires to set ScalingImplType to OVERRIDE explicitly.")
if scaling_impl_type == ScalingImplType.OVERRIDE and scaling_override is None:
raise Exception("Overriding scaling requires to pass a scaling impl module.")
if scaling_per_channel:
scaling_shape = per_channel_broadcastable_shape
else:
scaling_shape = SCALING_SCALAR_SHAPE
if scaling_impl_type == ScalingImplType.OVERRIDE and scaling_override is not None:
scaling_impl = scaling_override
runtime = False
elif scaling_impl_type == ScalingImplType.CONST or scaling_impl_type == ScalingImplType.PARAMETER:
scaling_init = RescalingIntQuant.scaling_init_from_min_max(min_val, max_val)
scaling_impl = StandaloneScaling(is_parameter=scaling_impl_type == ScalingImplType.PARAMETER,
parameter_shape=scaling_shape,
restrict_scaling_type=restrict_scaling_type,
scaling_init=scaling_init,
scaling_min_val=scaling_min_val)
runtime = False
elif scaling_impl_type == ScalingImplType.STATS or scaling_impl_type == ScalingImplType.AFFINE_STATS:
if scaling_per_channel and not scaling_stats_op == StatsOp.MAX_AVE:
scaling_stats_input_view_shape_impl = StatsInputViewShapeImpl.OVER_OUTPUT_CHANNELS
scaling_stats_reduce_dim = 1
elif scaling_per_channel and scaling_stats_op == StatsOp.MAX_AVE:
raise Exception("Can't do per channel scaling with MAX AVE statistics.")
elif not scaling_per_channel and scaling_stats_op == StatsOp.MAX_AVE:
scaling_stats_input_view_shape_impl = StatsInputViewShapeImpl.OVER_OUTPUT_CHANNELS
scaling_stats_reduce_dim = 1
else: # not scaling_per_channel
scaling_stats_input_view_shape_impl = StatsInputViewShapeImpl.OVER_TENSOR
scaling_stats_reduce_dim = None
scaling_stats_permute_dims = None
stats_buffer_init = RescalingIntQuant.scaling_init_from_min_max(min_val, max_val).item()
scaling_impl = RuntimeStatsScaling(stats_op=scaling_stats_op,
restrict_scaling_type=restrict_scaling_type,
stats_input_view_shape_impl=scaling_stats_input_view_shape_impl,
stats_output_shape=scaling_shape,
sigma=scaling_stats_sigma,
scaling_min_val=scaling_min_val,
stats_reduce_dim=scaling_stats_reduce_dim,
stats_buffer_momentum=scaling_stats_buffer_momentum,
stats_buffer_init=stats_buffer_init,
stats_permute_dims=scaling_stats_permute_dims,
affine=scaling_impl_type == ScalingImplType.AFFINE_STATS)
runtime = True
else:
raise Exception("Scaling type {} not supported for int runtime quantization"
.format(str(scaling_impl_type)))
if quant_type == QuantType.BINARY:
if not signed:
raise Exception("Binary activation supports only signed activations")
tensor_quant = ClampedBinaryQuant(scaling_impl=scaling_impl)
elif quant_type == QuantType.INT:
if bit_width_impl_override is None:
if bit_width_impl_type is None or bit_width is None or restrict_bit_width_type is None:
raise Exception("Bit width is not defined properly")
if bit_width_impl_type == BitWidthImplType.CONST:
tensor_clamp_impl = TensorClamp() # If it's const, don't pass gradients to clipped values
msb_clamp_bit_width_impl = BitWidthConst(bit_width, restrict_bit_width_type)
elif bit_width_impl_type == BitWidthImplType.PARAMETER:
tensor_clamp_impl = TensorClamp() # if it's learned, I pass gradients to the bit width
msb_clamp_bit_width_impl = BitWidthParameter(bit_width,
min_overall_bit_width,
max_overall_bit_width,
restrict_bit_width_type,
override_pretrained_bit_width)
else:
raise Exception("Bit width type {} not supported for weight quantization"
.format(str(bit_width_impl_type)))
else:
msb_clamp_bit_width_impl = bit_width_impl_override
tensor_clamp_impl = TensorClamp() # if there is an override, it's learned
float_to_int_impl = RestrictValue(restrict_value_type=RestrictValueType.INT,
float_to_int_impl_type=float_to_int_impl_type,
min_val=None)
int_scaling_impl = IntScaling(narrow_range,
signed=signed,
restrict_scaling_type=restrict_scaling_type)
tensor_quant = RescalingIntQuant(signed=signed,
narrow_range=narrow_range,
scaling_impl=scaling_impl,
int_scaling_impl=int_scaling_impl,
tensor_clamp_impl=tensor_clamp_impl,
msb_clamp_bit_width_impl=msb_clamp_bit_width_impl,
float_to_int_impl=float_to_int_impl,
runtime=runtime)
else:
raise Exception("Quantization type {} not supported for activations.".format(quant_type))
self.fused_activation_quant_proxy = FusedActivationQuantProxy(activation_impl, tensor_quant)
self.scaling_impl_type = scaling_impl_type # needed to switch between different scaling modes
def bit_width_parameter_defaults(bit_width_init):
"""
Learned bit-width with default arguments module
"""
module = BitWidthParameter(bit_width_init)
return module