#!/usr/bin/env python
# ******************************************************************************
# Copyright 2022 Brainchip Holdings Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ******************************************************************************
__all__ = ["recording", "TensorRecorder", "FixedPointRecorder", "QFloatRecorder",
"NonTrackVariable", "NonTrackFixedPointVariable"]
import os
import tensorflow as tf
from contextlib import contextmanager
from ..tensors import FixedPoint, QFloat
RECORDING_ENV = "RECORDING_ENABLED"
[docs]@contextmanager
def recording(enable):
"""Enable or disable recording.
Args:
enable (bool): True to enable recording, False to disable it
"""
value = "1" if enable else "0"
_prev_state = os.environ.get(RECORDING_ENV, None)
try:
os.environ[RECORDING_ENV] = value
yield
finally:
# Recover default value
if _prev_state is not None:
os.environ[RECORDING_ENV] = _prev_state
else:
os.environ.pop(RECORDING_ENV)
class BaseNonTrackVariable():
"""Base interface class for temporary variables that should be tracked only during the call and
which does not require to be serialized within the layer.
"""
def __init__(self, name=""):
self._name = name
self._var = None
@property
def var(self):
raise NotImplementedError
@tf.function
def set_var(self, new_value):
raise NotImplementedError
@tf.function
def init_var(self):
raise NotImplementedError
def reset_var(self):
raise NotImplementedError
[docs]class NonTrackVariable(BaseNonTrackVariable):
"""A wrapper class for the temporary Tensor variables that should be tracked only during the
call and which does not require to be serialized within the layer.
"""
@property
def var(self):
return self._var
@tf.function
def set_var(self, new_value):
self._var.assign(new_value)
@tf.function
def init_var(self, init_value, validate_shape=False):
"""Function that creates and initializes a variable, if it doesn't exist. This variable will
be integrated in the layer graph and tracked (but not within the main layer variables).
See pattern defined here: https://www.tensorflow.org/guide/function#creating_tfvariables
Args:
init_value (tf.Tensor): Tensor, or Python object convertible to a Tensor which is the
initial value for the Variable. The initial value must have a shape specified
unless validate_shape is set to False.
validate_shape (bool, optional): If False, allows the variable to be initialized with a
value of unknown shape. If True the shape of initial_value must be known.
Defaults to False.
"""
if self._var is None:
self._var = tf.Variable(init_value, trainable=False,
validate_shape=validate_shape,
name=self._name,
synchronization=tf.VariableSynchronization.ON_READ)
def reset_var(self):
""" Reset internal var."""
if self._var is not None:
self._var.assign_add(-self._var)
[docs]class NonTrackFixedPointVariable(BaseNonTrackVariable):
"""A wrapper class for the temporary FixedPoint variables that should be tracked only during
the call and which does not require to be serialized within the layer.
"""
def __init__(self, name=""):
super().__init__(name)
self._frac_bits = None
self._value_bits = None
@property
def var(self):
return None if self._var is None else FixedPoint(self._var, self._value_bits,
self._frac_bits)
def promote(self, target_value_bits):
return self.var.promote(target_value_bits)
def expand(self, target_value_bits):
return self.var.expand(target_value_bits)
@tf.function
def set_var(self, new_value):
assert (isinstance(new_value, FixedPoint))
self._var.assign(new_value.values)
self._frac_bits.assign(new_value.frac_bits)
self._value_bits = new_value.value_bits
@tf.function
def init_var(self, init_value, validate_shape=False):
"""Function that creates and initializes a variable, if it doesn't exist. This variable will
be integrated in the layer graph and tracked (but not within the main layer variables).
See pattern defined here: https://www.tensorflow.org/guide/function#creating_tfvariables
Args:
init_value (FixedPoint): initial FixedPoint variable.
validate_shape (bool, optional): If False, allows the variable to be initialized with a
value of unknown shape. If True the shape of initial_value must be known.
Defaults to False.
"""
assert (isinstance(init_value, FixedPoint))
if self._var is None:
self._var = tf.Variable(init_value.values, trainable=False,
validate_shape=validate_shape,
name=self._name,
synchronization=tf.VariableSynchronization.ON_READ)
self._frac_bits = tf.Variable(init_value.frac_bits, trainable=False,
validate_shape=validate_shape,
name=self._name + "/frac_bits",
synchronization=tf.VariableSynchronization.ON_READ)
self._value_bits = init_value.value_bits
def reset_var(self):
""" Reset internal var."""
if self._var is not None:
self._var.assign_add(-self._var)
self._frac_bits.assign_add(-self._frac_bits)
[docs]class TensorRecorder(NonTrackVariable):
"""Wrapper class to store and retrieve a tf.Tensor extracted from a graph.
This is mainly used to recover FixedPoint alignment shift information.
"""
@property
def name(self):
return self._name if self._name is not None else "record"
@property
def recording(self):
"""Flag to specify if the object is in recording mode or not.
Returns:
bool: True if recording mode is enabled, False otherwise.
"""
value = os.environ.get(RECORDING_ENV, "0")
return (value == "1")
@property
def value(self):
"""Get the recorded value.
Returns:
tf.Tensor: value of the stored record or None.
"""
return None if self._var is None else self._var.value()
def __call__(self, inputs):
"""Record the values of the inputs if recording is True.
Args:
inputs (tf.Tensor): new values.
Returns:
tf.Tensor: the inputs.
"""
self.init_var(tf.zeros_like(inputs), True)
if self.recording:
# Store the new values
self.set_var(inputs)
return inputs
[docs]class FixedPointRecorder():
"""Wrapper class to store and retrieve a FixedPoint extracted from a graph.
This is mainly used to recover FixedPoint quantized weights.
"""
def __init__(self, name=""):
self._value_bits = None
self._name = name
if name != "":
name += "/"
self._values = TensorRecorder(name + "values/record")
self._frac_bits = TensorRecorder(name + "frac_bits/record")
@property
def name(self):
return self._name if self._name is not None else "record"
@property
def value(self):
"""Get the recorded value.
Returns:
:obj:`FixedPoint`: value of the stored record or None.
"""
return None if self._value_bits is None else FixedPoint(self._values.value,
self._value_bits,
self._frac_bits.value)
@property
def recording(self):
return self._values.recording
def __call__(self, inputs):
"""Record the values of the inputs if recording is True.
Args:
inputs (:obj:`FixedPoint`): new values.
Returns:
:obj:`FixedPoint`: the inputs.
"""
if self.recording:
self._value_bits = inputs.value_bits
self._values(inputs.values)
self._frac_bits(inputs.frac_bits)
return inputs
[docs]class QFloatRecorder():
"""Wrapper class to store and retrieve a QFloat extracted from a graph.
This is mainly used to recover QFloat quantized weights.
"""
def __init__(self, name=""):
self._fp = FixedPointRecorder(name)
scales_name = "scales/record"
if name != "":
scales_name = name + "/" + scales_name
self._scales = TensorRecorder(scales_name)
@property
def name(self):
return self._fp.name
@property
def value(self):
"""Get the recorded value.
Returns:
:obj:`QFloat`: value of the stored record or None.
"""
return None if self._fp.value is None else QFloat(self._fp.value, self._scales.value)
@property
def recording(self):
return self._fp.recording
def __call__(self, inputs):
"""Record the values of the inputs if recording is True.
Args:
inputs (:obj:`QFloat`): new values.
Returns:
:obj:`QFloat`: the inputs.
"""
self._fp(inputs.fp)
self._scales(inputs.scales)
return inputs