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from typing import Optional
import torch
import torch.nn.intrinsic as nni
from torch.ao.nn.sparse.quantized import linear
from torch.ao.nn.sparse.quantized.utils import LinearBlockSparsePattern
from torch.ao.nn.quantized.modules.utils import _quantize_weight, hide_packed_params_repr
__all__ = ['Linear']
class Linear(torch.nn.Module):
r"""
A dynamically quantized sparse linear module with float tensor as inputs and outputs.
"""
_version = 1
_op_type = "sparse_dynamic"
_FLOAT_MODULE = torch.nn.Linear
def __init__(self, in_features, out_features, row_block_size, col_block_size, bias=True, dtype=torch.qint8):
super().__init__()
if dtype != torch.qint8:
raise NotImplementedError("Only QINT8 is supported for Sparse Quantized Linear Dynamic")
self.in_features = in_features
self.out_features = out_features
if bias:
bias = torch.zeros(self.out_features, dtype=torch.float)
else:
bias = None
qweight = torch._empty_affine_quantized([out_features, in_features],
scale=1, zero_point=0, dtype=torch.qint8)
self._packed_params = linear.LinearPackedParams(row_block_size=row_block_size,
col_block_size=col_block_size,
dtype=dtype)
self._packed_params.set_weight_bias(qweight, bias, row_block_size, col_block_size)
def _get_name(self):
return 'SparseQuantizedDynamicLinear'
def extra_repr(self):
return 'in_features={}, out_features={}, qscheme={}'.format(
self.in_features, self.out_features, self.weight().qscheme()
)
def __repr__(self):
return hide_packed_params_repr(self, linear.LinearPackedParams)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return torch.ops.sparse.qlinear_dynamic(x, self._packed_params._packed_params)
def _save_to_state_dict(self, destination, prefix, keep_vars):
super()._save_to_state_dict(destination, prefix, keep_vars)
destination[prefix + 'op_type'] = self._op_type
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
missing_keys, unexpected_keys, error_msgs):
op_type = int(state_dict[prefix + 'op_type'])
assert op_type == 'sparse', \
"Cannot load from op_type [{}], expecting [{}]".format(op_type, self._op_type)
state_dict.pop(prefix + 'op_type')
version = local_metadata.get('version', None)
assert version <= self._version
# Is this code valid? In old quantization it seemed to be used to load
# older model
weight = state_dict.pop(prefix + 'weight')
bias = state_dict.pop(prefix + 'bias')
state_dict.update({prefix + '_packed_params.weight': weight,
prefix + '_packed_params.bias': bias})
super()._load_from_state_dict(
state_dict, prefix, local_metadata, False,
missing_keys, unexpected_keys, error_msgs)
def _weight_bias(self):
return self._packed_params._weight_bias()
def weight(self):
return self._weight_bias()[0]
def bias(self):
return self._weight_bias()[1]
def set_weight_bias(self, w: torch.Tensor, b: Optional[torch.Tensor],
row_block_size: Optional[int], col_block_size: Optional[int]) -> None:
assert row_block_size is not None and col_block_size is not None
self.out_features = w.shape[0]
self.in_features = w.shape[1]
self._packed_params.set_weight_bias(w, b, row_block_size, col_block_size)
@classmethod
def from_float(cls, mod):
r"""Create a quantized sparse dynamic module from a float module.
We only care about the convert at this stage, no need for observers just yet.
"""
assert type(mod) == cls._FLOAT_MODULE, ' nnq.' + cls.__name__ + '.from_float only works for ' + \
cls._FLOAT_MODULE.__name__
# TODO: Need to add options to qconfig to avoid the calibration.
# TODO: Add calibration for the sparsity
assert hasattr(mod, 'qconfig'), 'Input float module must have qconfig defined'
if type(mod) == nni.LinearReLU:
mod = mod[0]
if mod.qconfig is not None and mod.qconfig.weight is not None:
weight_observer = mod.qconfig.weight()
else:
# We have the circular import issues if we import the qconfig in the beginning of this file:
# https://github.com/pytorch/pytorch/pull/24231. The current workaround is to postpone the
# import until we need it.
from torch.ao.quantization.qconfig import default_dynamic_qconfig
weight_observer = default_dynamic_qconfig.weight()
# It is important to multiply by the mask BEFORE calling the `weight_observer`
# TODO (zaf): Mask might not be part of the qconfig (T83295194)
weight = mod.weight
if getattr(mod.qconfig, 'mask', False):
weight = mod.qconfig.mask * mod.weight
weight_observer(weight)
dtype = weight_observer.dtype
assert dtype == torch.qint8, 'Weight observer must have dtype torch.qint8'
w_sc, w_zp = weight_observer.calculate_qparams()
if isinstance(w_zp, torch.Tensor):
assert not torch.any(w_zp.bool()), "All weight zero points must map to 0"
else:
assert w_zp == 0, 'Weight zero point must map to 0'
qweight = _quantize_weight(weight.float(), weight_observer)
row_block_size, col_block_size = LinearBlockSparsePattern.block_size()
qlinear = cls(mod.in_features,
mod.out_features,
row_block_size,
col_block_size,
dtype=dtype)
qlinear.set_weight_bias(qweight, mod.bias, row_block_size, col_block_size)
return qlinear
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