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# pylint: disable=redefined-builtin
""" Float quantizers """
import contextlib
import functools
from typing import Optional, List, Dict
import math
import torch
from aimet_torch.v2.quantization.encoding_analyzer import EncodingAnalyzer, _flag_extreme_min_max
from aimet_torch.v2.quantization.base import QuantizerBase
from aimet_torch.v2.quantization.float import FloatEncoding
from aimet_torch.v2.utils import StatisticsNotFoundError, patch_attr
from aimet_torch.fp_quantization import fake_cast_to_ieee_float
__all__ = ['QuantizeDequantize', 'FloatQuantizeDequantize']
def _ieee_float_max_representable_value(exponent_bits, mantissa_bits):
exponent_max = 2 ** exponent_bits - 1
exponent_bias = exponent_max // 2
return (2 - 2**-mantissa_bits) * 2 ** (exponent_max - exponent_bias - 1)
_IEEE_FLOAT16_EXPONENT_BITS = 5
_IEEE_FLOAT16_MANTISSA_BITS = 10
assert _ieee_float_max_representable_value(_IEEE_FLOAT16_EXPONENT_BITS, _IEEE_FLOAT16_MANTISSA_BITS) == \
torch.finfo(torch.float16).max
_BFLOAT16_EXPONENT_BITS = 8
_BFLOAT16_MANTISSA_BITS = 7
assert _ieee_float_max_representable_value(_BFLOAT16_EXPONENT_BITS, _BFLOAT16_MANTISSA_BITS) == \
torch.finfo(torch.bfloat16).max
[docs]class FloatQuantizeDequantize(QuantizerBase): # pylint: disable=abstract-method
r"""
Simulates quantization by fake-casting the input
If dtype is provided, this is equivalent to
.. math::
out = x.to(dtype).to(x.dtype) \\
If the exponent and mantissa bits are provided, this is equivalent to
.. math::
out = \left\lceil\frac{x_c}{scale}\right\rfloor * scale
where
.. math::
x_c &= clamp(x, -max, max) \\
bias &= 2^{exponent} - \log_2(max) + \log_2(2 - 2^{-mantissa}) - 1 \\
scale &= 2 ^ {\left\lfloor \log_2 |x_c| + bias \right\rfloor - mantissa - bias} \\
The IEEE standard computes the maximum representable value by
.. math::
max = (2 - 2^{-mantissa}) * 2^{(\left\lfloor 0.5 * exponent\_max \right\rfloor)} \\
where
.. math::
exponent\_max = 2^{exponent} - 1 \\
Args:
exponent_bits (int): Number of exponent bits to simulate
mantissa_bits (int): Number of mantissa bits to simulate
dtype (torch.dtype): torch.dtype to simulate. This argument is mutually exclusive with exponent_bits and mantissa_bits.
encoding_analyzer (EncodingAnalyzer): If specified, the maximum value to represent will be determined dynamically based on the input statistics for finer precision.
Examples:
>>> import aimet_torch.v2.quantization as Q
>>> input = torch.tensor([[ 1.8998, -0.0947],[-1.0891, -0.1727]])
>>> qdq = Q.float.FloatQuantizeDequantize(mantissa_bits=7, exponent_bits=8)
>>> # Unlike AffineQuantizer, FloatQuantizer is initialized without calling compute_encodings()
>>> qdq.is_initialized()
True
>>> qdq.is_bfloat16()
True
>>> qdq.bitwidth
16
>>> qdq(input)
tensor([[ 1.8984, -0.0947], [-1.0859, -0.1729]])
>>> from aimet_torch.v2.quantization.encoding_analyzer import MinMaxEncodingAnalyzer
>>> encoding_analyzer = MinMaxEncodingAnalyzer(shape=[])
>>> qdq = Q.float.FloatQuantizeDequantize(dtype=torch.float16, encoding_analyzer=encoding_analyzer)
>>> qdq.is_float16()
True
>>> qdq.bitwidth
16
>>> qdq(input)
tensor([[ 1.8994, -0.0947], [-1.0889, -0.1727]])
"""
maxval: torch.Tensor
def __init__(self,
exponent_bits: int = None,
mantissa_bits: int = None,
dtype: torch.dtype = None,
encoding_analyzer: EncodingAnalyzer = None):
super().__init__()
if dtype is None:
if exponent_bits is None or mantissa_bits is None:
raise ValueError('Neither "dtype" nor "exponent/mantissa_bits" was specified.')
if dtype is not None:
if exponent_bits is not None or mantissa_bits is not None:
raise ValueError(
'Argument "dtype" is mutually exclusive with "exponent/mantissa_bits".')
if dtype not in (torch.half, torch.float16, torch.bfloat16):
raise ValueError(
f"Float quantizer only supports torch.float16 and torch.bfloat16. Got {dtype}.")
if dtype in (torch.half, torch.float16):
exponent_bits = _IEEE_FLOAT16_EXPONENT_BITS
mantissa_bits = _IEEE_FLOAT16_MANTISSA_BITS
else:
exponent_bits = _BFLOAT16_EXPONENT_BITS
mantissa_bits = _BFLOAT16_MANTISSA_BITS
self.exponent_bits = exponent_bits
self.mantissa_bits = mantissa_bits
self.encoding_analyzer = encoding_analyzer
if self.encoding_analyzer:
shape = self.encoding_analyzer.observer.shape
maxval = _ieee_float_max_representable_value(exponent_bits, mantissa_bits)
self.register_buffer('maxval', torch.full(shape, maxval))
else:
self.register_buffer('maxval', None)
def get_extra_state(self):
extra_state_dict = {}
extra_state_dict['exponent_bits'] = torch.tensor(self.exponent_bits)
extra_state_dict['mantissa_bits'] = torch.tensor(self.mantissa_bits)
super_extra_state = super().get_extra_state()
extra_state_dict.update(super_extra_state)
return extra_state_dict
def set_extra_state(self, state):
self.exponent_bits = state['exponent_bits'].item()
self.mantissa_bits = state['mantissa_bits'].item()
super().set_extra_state(state)
def load_state_dict(self, state_dict, strict: bool = True):
if 'maxval' in state_dict:
if self.maxval is None:
del self.maxval
self.register_buffer('maxval', state_dict['maxval'])
elif self.maxval is not None:
del self.maxval
self.register_buffer('maxval', None)
ret = super().load_state_dict(state_dict, strict)
return ret
@property
def bitwidth(self):
"""
Returns bitwidth of the quantizer
"""
return self.exponent_bits + self.mantissa_bits + 1
def is_float16(self):
"""
Returns true if current configuration simulates IEEE float16
"""
return self.exponent_bits == _IEEE_FLOAT16_EXPONENT_BITS and \
self.mantissa_bits == _IEEE_FLOAT16_MANTISSA_BITS
def is_bfloat16(self):
"""
Returns true if current configuration simulates bfloat16
"""
return self.exponent_bits == _BFLOAT16_EXPONENT_BITS and \
self.mantissa_bits == _BFLOAT16_MANTISSA_BITS
def get_legacy_encodings(self) -> Optional[List[Dict]]:
"""
:meta private:
"""
return [{'bitwidth': self.bitwidth, 'dtype': 'float'}]
def set_legacy_encodings(self, encodings: List[Dict]):
"""
:meta private:
Set encodings represented in the same format as the output of get_legacy_encodings as below:
[
{'bitwidth': int, 'dtype': str},
...
]
"""
if encodings[0]['bitwidth'] != 16:
raise RuntimeError(f"{self.__class__} can only import 16-bit legay encodings.")
self.exponent_bits = 5
self.mantissa_bits = 10
def get_encoding(self) -> Optional[FloatEncoding]:
if self.is_initialized():
return FloatEncoding(self.mantissa_bits, self.exponent_bits, self.maxval)
return None
@contextlib.contextmanager
def compute_encodings(self):
"""
Observe inputs and update quantization parameters based on the input statistics.
During ``compute_encodings`` is enabled, the quantizer forward pass performs
dynamic quantization using the batch statistics.
"""
if not self.encoding_analyzer or not self._allow_overwrite:
yield
return
original_forward = self.forward
@functools.wraps(original_forward)
def forward_wrapper(input):
input = input.as_subclass(torch.Tensor)
batch_statistics = self.encoding_analyzer.update_stats(input)
num_steps = math.pow(2, self.bitwidth) - 1
dynamic_min, dynamic_max =\
self.encoding_analyzer.compute_encodings_from_stats(batch_statistics,
num_steps,
is_symmetric=False)
dynamic_absmax = torch.maximum(dynamic_min.abs(), dynamic_max.abs())
dynamic_absmax = dynamic_absmax.to(dtype=self.maxval.dtype,
device=self.maxval.device).expand_as(self.maxval)
with patch_attr(self, 'maxval', dynamic_absmax):
return original_forward(input)
self.encoding_analyzer.reset_stats()
try:
with patch_attr(self, 'forward', forward_wrapper):
yield
except: # pylint: disable=try-except-raise
raise
else:
try:
num_steps = math.pow(2, self.bitwidth) - 1
min, max = self.encoding_analyzer.compute_encodings(num_steps,
is_symmetric=False)
_flag_extreme_min_max(min, max)
except StatisticsNotFoundError:
return
if min is None or max is None:
return
absmax = torch.maximum(min.abs(), max.abs()).expand_as(self.maxval)
with torch.no_grad():
self.maxval.copy_(absmax)
def forward(self, input: torch.Tensor):
"""
:param input: Input to quantize and dequantize
:return: Quantize-dequantized output
"""
maxval = self.maxval
exponent_bits = self.exponent_bits
mantissa_bits = self.mantissa_bits
if maxval is None:
if self.is_float16() or self.is_bfloat16():
# Fast forward using type casting
orig_dtype = input.dtype
dtype = torch.float16 if self.is_float16() else torch.bfloat16
return input.to(dtype).to(orig_dtype)
maxval = _ieee_float_max_representable_value(exponent_bits, mantissa_bits)
# Subclasses of torch.Tensor with custom __torch_function__ (in our case, QuantizedTensorBase)
# is known to introduce substantial CPU overhead.
# Cast types of the inputs to plain torch.Tensor for faster execution.
return fake_cast_to_ieee_float(input.as_subclass(torch.Tensor), maxval, exponent_bits, mantissa_bits)
def extra_repr(self):
"""
:meta private:
"""
return f'exponent_bits={self.exponent_bits}, mantissa_bits={self.mantissa_bits}'
[docs]class QuantizeDequantize(FloatQuantizeDequantize):
r"""
Alias of FloatQuantizeDequantize
"""