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from typing import List
import torch
from torch import Tensor
from torch._ops import ops
__all__ = ['FloatFunctional', 'FXFloatFunctional', 'QFunctional']
class FloatFunctional(torch.nn.Module):
r"""State collector class for float operations.
The instance of this class can be used instead of the ``torch.`` prefix for
some operations. See example usage below.
.. note::
This class does not provide a ``forward`` hook. Instead, you must use
one of the underlying functions (e.g. ``add``).
Examples::
>>> f_add = FloatFunctional()
>>> a = torch.tensor(3.0)
>>> b = torch.tensor(4.0)
>>> f_add.add(a, b) # Equivalent to ``torch.add(a, b)``
Valid operation names:
- add
- cat
- mul
- add_relu
- add_scalar
- mul_scalar
"""
def __init__(self):
super(FloatFunctional, self).__init__()
self.activation_post_process = torch.nn.Identity()
def forward(self, x):
raise RuntimeError("FloatFunctional is not intended to use the " +
"'forward'. Please use the underlying operation")
r"""Operation equivalent to ``torch.add(Tensor, Tensor)``"""
def add(self, x: Tensor, y: Tensor) -> Tensor:
r = torch.add(x, y)
r = self.activation_post_process(r)
return r
r"""Operation equivalent to ``torch.add(Tensor, float)``"""
def add_scalar(self, x: Tensor, y: float) -> Tensor:
r = torch.add(x, y)
# Note: this operation is not observed because the observation is not
# needed for the quantized op.
return r
r"""Operation equivalent to ``torch.mul(Tensor, Tensor)``"""
def mul(self, x: Tensor, y: Tensor) -> Tensor:
r = torch.mul(x, y)
r = self.activation_post_process(r)
return r
r"""Operation equivalent to ``torch.mul(Tensor, float)``"""
def mul_scalar(self, x: Tensor, y: float) -> Tensor:
r = torch.mul(x, y)
# Note: this operation is not observed because the observation is not
# needed for the quantized op.
return r
r"""Operation equivalent to ``torch.cat``"""
def cat(self, x: List[Tensor], dim: int = 0) -> Tensor:
r = torch.cat(x, dim=dim)
r = self.activation_post_process(r)
return r
r"""Operation equivalent to ``relu(torch.add(x,y))``"""
def add_relu(self, x: Tensor, y: Tensor) -> Tensor:
r = torch.add(x, y)
r = torch.nn.functional.relu(r)
r = self.activation_post_process(r)
return r
class FXFloatFunctional(torch.nn.Module):
r""" module to replace FloatFunctional module before FX graph mode quantization,
since activation_post_process will be inserted in top level module directly
Valid operation names:
- add
- cat
- mul
- add_relu
- add_scalar
- mul_scalar
"""
def forward(self, x):
raise RuntimeError("FloatFunctional is not intended to use the " +
"'forward'. Please use the underlying operation")
r"""Operation equivalent to ``torch.add(Tensor, Tensor)``"""
def add(self, x: Tensor, y: Tensor) -> Tensor:
r = torch.add(x, y)
return r
r"""Operation equivalent to ``torch.add(Tensor, float)``"""
def add_scalar(self, x: Tensor, y: float) -> Tensor:
r = torch.add(x, y)
return r
r"""Operation equivalent to ``torch.mul(Tensor, Tensor)``"""
def mul(self, x: Tensor, y: Tensor) -> Tensor:
r = torch.mul(x, y)
return r
r"""Operation equivalent to ``torch.mul(Tensor, float)``"""
def mul_scalar(self, x: Tensor, y: float) -> Tensor:
r = torch.mul(x, y)
return r
r"""Operation equivalent to ``torch.cat``"""
def cat(self, x: List[Tensor], dim: int = 0) -> Tensor:
r = torch.cat(x, dim=dim)
return r
r"""Operation equivalent to ``relu(torch.add(x,y))``"""
def add_relu(self, x: Tensor, y: Tensor) -> Tensor:
r = torch.add(x, y)
r = torch.nn.functional.relu(r)
return r
class QFunctional(torch.nn.Module):
r"""Wrapper class for quantized operations.
The instance of this class can be used instead of the
``torch.ops.quantized`` prefix. See example usage below.
.. note::
This class does not provide a ``forward`` hook. Instead, you must use
one of the underlying functions (e.g. ``add``).
Examples::
>>> q_add = QFunctional()
>>> # xdoctest: +SKIP
>>> a = torch.quantize_per_tensor(torch.tensor(3.0), 1.0, 0, torch.qint32)
>>> b = torch.quantize_per_tensor(torch.tensor(4.0), 1.0, 0, torch.qint32)
>>> q_add.add(a, b) # Equivalent to ``torch.ops.quantized.add(a, b, 1.0, 0)``
Valid operation names:
- add
- cat
- mul
- add_relu
- add_scalar
- mul_scalar
"""
def __init__(self):
super(QFunctional, self).__init__()
self.scale = 1.0
self.zero_point = 0
self.activation_post_process = torch.nn.Identity()
def _save_to_state_dict(self, destination, prefix, keep_vars):
super(QFunctional, self)._save_to_state_dict(destination, prefix, keep_vars)
destination[prefix + 'scale'] = torch.tensor(self.scale)
destination[prefix + 'zero_point'] = torch.tensor(self.zero_point)
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
missing_keys, unexpected_keys, error_msgs):
self.scale = float(state_dict.pop(prefix + 'scale'))
self.zero_point = int(state_dict.pop(prefix + 'zero_point'))
super(QFunctional, self)._load_from_state_dict(state_dict, prefix, local_metadata, False,
missing_keys, unexpected_keys, error_msgs)
def _get_name(self):
return 'QFunctional'
def extra_repr(self):
return 'scale={}, zero_point={}'.format(
self.scale, self.zero_point
)
def forward(self, x):
raise RuntimeError("Functional is not intended to use the " +
"'forward'. Please use the underlying operation")
r"""Operation equivalent to ``torch.ops.quantized.add``"""
def add(self, x: Tensor, y: Tensor) -> Tensor:
r = ops.quantized.add(x, y, scale=self.scale, zero_point=self.zero_point)
r = self.activation_post_process(r)
return r
r"""Operation equivalent to ``torch.ops.quantized.add(Tensor, float)``"""
def add_scalar(self, x: Tensor, y: float) -> Tensor:
r = ops.quantized.add_scalar(x, y)
# Note: this operation is not observed because the observation is not
# needed for the quantized op.
return r
r"""Operation equivalent to ``torch.ops.quantized.mul(Tensor, Tensor)``"""
def mul(self, x: Tensor, y: Tensor) -> Tensor:
r = ops.quantized.mul(x, y, scale=self.scale, zero_point=self.zero_point)
r = self.activation_post_process(r)
return r
r"""Operation equivalent to ``torch.ops.quantized.mul(Tensor, float)``"""
def mul_scalar(self, x: Tensor, y: float) -> Tensor:
r = ops.quantized.mul_scalar(x, y)
# Note: this operation is not observed because the observation is not
# needed for the quantized op.
return r
r"""Operation equivalent to ``torch.ops.quantized.cat``"""
def cat(self, x: List[Tensor], dim: int = 0) -> Tensor:
r = ops.quantized.cat(x, scale=self.scale, zero_point=self.zero_point, dim=dim)
r = self.activation_post_process(r)
return r
r"""Operation equivalent to ``torch.ops.quantized.add_relu``"""
def add_relu(self, x: Tensor, y: Tensor) -> Tensor:
r = ops.quantized.add_relu(x, y, scale=self.scale, zero_point=self.zero_point)
r = self.activation_post_process(r)
return r
@classmethod
def from_float(cls, mod):
assert type(mod) == FloatFunctional,\
"QFunctional.from_float expects an instance of FloatFunctional"
scale, zero_point = mod.activation_post_process.calculate_qparams() # type: ignore[operator]
new_mod = QFunctional()
new_mod.scale = float(scale)
new_mod.zero_point = int(zero_point)
return new_mod
|
