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"""Quant Analyzer for AIMET v2"""
import os
import contextlib
from collections import namedtuple
from typing import Tuple, List, Type, Optional, Generator
import torch
from aimet_common.quant_analyzer import export_stats_histogram_plot
from aimet_torch._base.quant_analyzer import QuantAnalyzerBase
from aimet_torch.v2.quantsim import QuantizationSimModel
from aimet_torch.v2.nn.base import BaseQuantizationMixin
from aimet_torch.v2.quantization.base import QuantizerBase
from aimet_torch.v2.quantization.encoding_analyzer import _HistogramObserver, _Histogram
from aimet_torch.v2.batch_norm_fold import fold_all_batch_norms
V1Encoding = namedtuple("V1Encoding", ["min", "max"])
[docs]
class QuantAnalyzer(QuantAnalyzerBase):
"""
QuantAnalyzer tool provides
1) model sensitivity to weight and activation quantization
2) per layer sensitivity analysis
3) per layer encoding (min - max range)
4) per PDF analysis and
5) per layer MSE analysis
"""
@staticmethod
def _get_quantsim_cls() -> Type[QuantizationSimModel]:
return QuantizationSimModel
@staticmethod
def _get_quant_wrapper_type() -> Tuple[Type]:
return (BaseQuantizationMixin,)
def _create_and_export_stats_histogram_plot(
self,
quantizer: QuantizerBase,
results_dir: str,
title: str,
):
"""
For given quantizer, create and export histogram (PDF) of statistics in html format.
:param quantizer: Quantizer.
:param results_dir: Directory to save the results.
:param title: Title of the plot.
"""
if isinstance(quantizer.encoding_analyzer.observer, _HistogramObserver):
os.makedirs(results_dir, exist_ok=True)
v2_histograms = quantizer.encoding_analyzer.observer.get_stats()
histograms = self._convert_to_v1_histograms(v2_histograms)
encodings = self._get_quantizer_encodings(quantizer)
for index, (histogram, encoding) in enumerate(zip(histograms, encodings)):
export_stats_histogram_plot(
histogram, encoding, results_dir, title=f"{title}_{index}"
)
@staticmethod
def _enable_disable_quantizers(quantizers: List[QuantizerBase], enabled: bool):
"""
For given list of quantizers, set (enable/disable) quantizer's enabled.
:param quantizers: List of quantizers.
:param enabled: Enabled flag.
"""
raise RuntimeError("Changing enabled attribute is not allowed in quantsim v2")
@classmethod
def _disable_param_quantizers(cls, sim: QuantizationSimModel):
# pylint: disable=protected-access
ctx = contextlib.ExitStack()
for quant_wrapper in cls._get_quantized_modules(sim):
ctx.enter_context(quant_wrapper._remove_param_quantizers())
return ctx
@classmethod
def _disable_activation_quantizers(cls, sim: QuantizationSimModel):
# pylint: disable=protected-access
ctx = contextlib.ExitStack()
for quant_wrapper in cls._get_quantized_modules(sim):
ctx.enter_context(quant_wrapper._remove_activation_quantizers())
return ctx
@staticmethod
def _disable_quant_wrapper(module: BaseQuantizationMixin):
# pylint: disable=protected-access
return module._remove_all_quantizers()
@staticmethod
def _convert_to_v1_histograms(histograms: List[_Histogram]) -> List:
v1_histograms = []
for hist in histograms:
assert hist is not None, "Cannot find histogram data in quantizer"
hist_sum = torch.sum(hist.histogram).item()
v1_hist = []
for bin_edge, hist_value in zip(hist.bin_edges, hist.histogram):
v1_hist.append((bin_edge.item(), hist_value.item() / hist_sum))
v1_histograms.append(v1_hist)
return v1_histograms
@staticmethod
def _is_quantizer_enabled(quantizer: Optional[QuantizerBase]):
return quantizer is not None
@classmethod
def _get_quantizer_encodings(cls, quantizer: QuantizerBase) -> Optional[List]:
v1_encodings = []
encoding = quantizer.get_encodings()
if not encoding:
return None
flatten_min = encoding.min.flatten()
flatten_max = encoding.max.flatten()
for encoding_min, encoding_max in zip(flatten_min, flatten_max):
v1_encodings.append(
V1Encoding(min=encoding_min.item(), max=encoding_max.item())
)
return v1_encodings
@staticmethod
def _get_quantized_modules(
sim: QuantizationSimModel,
) -> Generator[BaseQuantizationMixin, None, None]:
for module in sim.model.modules():
if isinstance(module, BaseQuantizationMixin):
yield module
@staticmethod
def _fold_all_batch_norms(*args, **kwargs):
return fold_all_batch_norms(*args, **kwargs)