# ******************************************************************************
# Copyright 2017-2019 Intel Corporation
#
# 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.
# ******************************************************************************
# pylint: disable=no-member
"""
Quantization ops
"""
from __future__ import absolute_import, division, print_function, unicode_literals
from enum import Enum, auto
import logging
import json
import copy
import torch
from torch import nn
from torch.nn import functional as F
logger = logging.getLogger(__name__)
[docs]def get_dynamic_scale(x, bits, with_grad=False):
"""Calculate dynamic scale for quantization from input by taking the
maximum absoulute value from x and number of bits"""
with torch.set_grad_enabled(with_grad):
threshold = x.abs().max()
return get_scale(bits, threshold)
[docs]def get_scale(bits, threshold):
"""Calculate scale for quantization according to some constant and number of bits"""
return calc_max_quant_value(bits) / threshold
[docs]def calc_max_quant_value(bits):
"""Calculate the maximum symmetric quantized value according to number of bits"""
return 2**(bits - 1) - 1
[docs]def quantize(input, scale, bits):
"""Do linear quantization to input according to a scale and number of bits"""
thresh = calc_max_quant_value(bits)
return input.mul(scale).round().clamp(-thresh, thresh)
[docs]def dequantize(input, scale):
"""linear dequantization according to some scale"""
return input.div(scale)
# TODO(ofir) future work, implement a layer that uses this function that gives a more comfortable
[docs]class FakeLinearQuantizationWithSTE(torch.autograd.Function):
"""Simulates error caused by quantization. Uses Straight-Through Estimator for Back prop"""
[docs] @staticmethod
def forward(ctx, input, scale, bits=8):
"""fake quantize input according to scale and number of bits, dequantize
quantize(input))"""
return dequantize(quantize(input, scale, bits), scale)
[docs] @staticmethod
def backward(ctx, grad_output):
"""Calculate estimated gradients for fake quantization using
Straigh-Through Estimator (STE) according to:
https://openreview.net/pdf?id=B1ae1lZRb"""
return grad_output, None, None
[docs]class QuantizationMode(Enum):
NONE = auto()
DYNAMIC = auto()
EMA = auto()
_fake_quantize = FakeLinearQuantizationWithSTE.apply
[docs]class QuantizedLinear(nn.Linear):
"""Linear layer with quantization aware training capability"""
def __init__(self, *args, activation_bits=8, weight_bits=8,
requantize_output=True, ema_decay=0.9999, start_step=0, mode='none',
**kwargs):
super().__init__(*args, **kwargs)
if activation_bits < 2 or weight_bits < 2:
raise ValueError(
f"activation_bits={activation_bits} and weight_bits="
f"{weight_bits} must be higher than 1 ")
self.activation_bits = activation_bits
self.weight_bits = weight_bits
self.accumulation_bits = 32
self.ema_decay = ema_decay
self.requantize_output = requantize_output
self.start_step = start_step
self.mode = QuantizationMode[mode.upper()]
self.register_buffer('_step', torch.zeros(1))
self.register_buffer('input_thresh', torch.zeros(1))
self.register_buffer('output_thresh', torch.zeros(1))
[docs] def training_quantized_forward(self, input):
"""fake quantized forward, fake quantizes weights and activations,
learn quantization ranges if quantization mode is EMA.
This function should only be used while training"""
assert self.training, "should only be called when training"
if self.mode == QuantizationMode.EMA:
self._update_ema(self.input_thresh, input.detach())
input_scale = self._get_input_scale(input)
out = F.linear(_fake_quantize(input, input_scale, self.activation_bits),
self.fake_quantized_weight, self.bias)
if self.requantize_output:
if self.mode == QuantizationMode.EMA:
self._update_ema(self.output_thresh, out.detach())
out = _fake_quantize(
out, self._get_output_scale(out), self.activation_bits)
return out
[docs] @classmethod
def from_config(cls, *args, config=None, **kwargs):
"""Initialize quantized layer from config"""
keys = ['weight_bits', 'start_step', 'mode',
'activation_bits', 'requantize_output', 'ema_decay']
return cls(*args, **kwargs, **{k: getattr(config, k) for k in keys})
[docs] def inference_quantized_forward(self, input):
"""Simulate quantized inference. quantize input and perform calculation with only integer numbers.
This function should only be used while doing inference"""
assert not self.training, "should only be called when not training"
input_scale = self._get_input_scale(input)
dequantize_scale = self.weight_scale * input_scale
quantized_input = quantize(input, input_scale, self.activation_bits)
out = F.linear(quantized_input, self.quantized_weight,
self.get_quantized_bias(dequantize_scale))
out = dequantize(out, dequantize_scale)
if self.requantize_output:
output_scale = self._get_output_scale(out)
out = dequantize(quantize(out, output_scale, self.activation_bits), output_scale)
return out
[docs] def forward(self, input):
if self.mode == QuantizationMode.NONE:
return super().forward(input)
if self.training:
if self._step >= self.start_step:
out = self.training_quantized_forward(input)
else:
out = super().forward(input)
self._step += 1
else:
out = self.inference_quantized_forward(input)
return out
[docs] def get_quantized_bias(self, scale):
try:
bias = quantize(self.bias, scale, self.accumulation_bits)
except AttributeError:
bias = None
return bias
@property
def fake_quantized_weight(self):
return _fake_quantize(self.weight, self.weight_scale, self.weight_bits)
@property
def quantized_weight(self):
return quantize(self.weight, self.weight_scale, self.weight_bits)
@property
def weight_scale(self):
return get_dynamic_scale(self.weight, self.weight_bits)
def _get_input_scale(self, input):
return self._get_activation_scale(input, self.input_thresh)
def _get_output_scale(self, output):
return self._get_activation_scale(output, self.output_thresh)
def _get_activation_scale(self, activation, threshold):
if self.mode == QuantizationMode.DYNAMIC:
scale = get_dynamic_scale(activation, self.activation_bits)
elif self.mode == QuantizationMode.EMA:
scale = get_scale(self.activation_bits, threshold)
return scale
def _update_ema(self, ema, input, reduce_fn=lambda x: x.abs().max()):
"""Update exponential moving average (EMA) of activations thresholds.
the reduce_fn calculates the current threshold from the input tensor"""
assert self._step >= self.start_step
if self._step == self.start_step:
ema.fill_(reduce_fn(input))
else:
ema.sub_((1 - self.ema_decay) * (ema - reduce_fn(input)))
[docs]class QuantizedEmbedding(nn.Embedding):
"""Embedding layer with quantization aware training capability"""
def __init__(self, *args, weight_bits=8, start_step=0, mode='none', **kwargs):
super().__init__(*args, **kwargs)
if weight_bits < 2:
raise ValueError(
f"weight_bits={weight_bits} must be higher than 1 ")
self.weight_bits = weight_bits
self.mode = QuantizationMode[mode.upper()]
self.start_step = start_step
self.register_buffer('_step', torch.zeros(1))
[docs] def forward(self, input):
if self.mode == QuantizationMode.NONE:
return super().forward(input)
if self._step >= self.start_step:
out = self.quantized_forward(input)
else:
out = super().forward(input)
if self.training:
self._step += 1
return out
[docs] @classmethod
def from_config(cls, *args, config=None, **kwargs):
"""Initialize quantized layer from config"""
keys = ['weight_bits', 'start_step', 'mode']
return cls(*args, **kwargs, **{k: getattr(config, k) for k in keys})
[docs] def quantized_forward(self, input):
"""Return quantized embeddings"""
return F.embedding(
input, self.fake_quantized_weight, self.padding_idx, self.max_norm,
self.norm_type, self.scale_grad_by_freq, self.sparse)
@property
def fake_quantized_weight(self):
return _fake_quantize(self.weight, self.weight_scale, self.weight_bits)
@property
def weight_scale(self):
return get_dynamic_scale(self.weight, self.weight_bits)
[docs]class QuantizationConfig(object):
"""Quantization Configuration Object"""
def __init__(self,
activation_bits=8,
weight_bits=8,
mode='none',
start_step=0,
ema_decay=0.9999,
requantize_output=True
):
self.activation_bits = activation_bits
self.weight_bits = weight_bits
self.mode = mode
self.start_step = start_step
self.ema_decay = ema_decay
self.requantize_output = requantize_output
[docs] @classmethod
def from_dict(cls, json_object):
"""Constructs a `QuantizationConfig` from a Python dictionary of parameters."""
config = QuantizationConfig()
for key, value in json_object.items():
config.__dict__[key] = value
return config
def __repr__(self):
return str(self.to_json_string())
[docs] def to_json_string(self):
"""Serializes this instance to a JSON string."""
return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
[docs] def to_dict(self):
"""Serializes this instance to a Python dictionary."""
output = copy.deepcopy(self.__dict__)
return output