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"""Quant Analyzer"""
import os
import contextlib
from typing import Tuple, List, Type, Generator
from aimet_common.quant_analyzer import export_stats_histogram_plot
from aimet_common.utils import AimetLogger
from aimet_torch import utils
from aimet_torch._base.quant_analyzer import QuantAnalyzerBase
from aimet_torch.v1.tensor_quantizer import TensorQuantizer, StaticGridTensorQuantizer
from aimet_torch.v1.qc_quantize_op import QcQuantizeWrapper
from aimet_torch.v1.qc_quantize_recurrent import QcQuantizeRecurrent
from aimet_torch.v1.quantsim import QuantizationSimModel
from aimet_torch.v1.batch_norm_fold import fold_all_batch_norms
_logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.QuantAnalyzer)
DEFAULT_BOKEH_FIGURE_HEIGHT = 300
[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 _enable_disable_quantizers(quantizers: List[TensorQuantizer], enabled: bool):
"""
For given list of quantizers, set (enable/disable) quantizer's enabled.
:param quantizers: List of quantizers.
:param enabled: Enabled flag.
"""
for quantizer in quantizers:
quantizer.enabled = enabled
def _create_and_export_stats_histogram_plot(
self,
quantizer: StaticGridTensorQuantizer,
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.
"""
os.makedirs(results_dir, exist_ok=True)
histograms = quantizer.get_stats_histogram()
encodings = quantizer.encoding
if not isinstance(encodings, List):
encodings = [encodings]
for index, (histogram, encoding) in enumerate(zip(histograms, encodings)):
export_stats_histogram_plot(
histogram, encoding, results_dir, title=f"{title}_{index}"
)
@staticmethod
def patch_quantsim_to_store_histogram(_):
"""
Placeholder function to prevent patching v1 quantsim
"""
@staticmethod
def _get_quantsim_cls() -> Type[QuantizationSimModel]:
return QuantizationSimModel
@staticmethod
def _get_quant_wrapper_type() -> Tuple[Type]:
return (QcQuantizeWrapper, QcQuantizeRecurrent)
@staticmethod
def _is_quantizer_enabled(quantizer: TensorQuantizer):
return quantizer.enabled
@staticmethod
def _get_quantizer_encodings(quantizer: TensorQuantizer):
if quantizer.encoding and not isinstance(quantizer.encoding, List):
return [quantizer.encoding]
return quantizer.encoding
@classmethod
@contextlib.contextmanager
def _disable_param_quantizers(cls, sim: QuantizationSimModel):
enabled_param_quantizers = cls._get_enabled_param_quantizers(sim)
cls._enable_disable_quantizers(enabled_param_quantizers, enabled=False)
yield
cls._enable_disable_quantizers(enabled_param_quantizers, enabled=True)
@classmethod
@contextlib.contextmanager
def _disable_activation_quantizers(cls, sim: QuantizationSimModel):
enabled_activation_quantizers = cls._get_enabled_activation_quantizers(sim)
cls._enable_disable_quantizers(enabled_activation_quantizers, enabled=False)
yield
cls._enable_disable_quantizers(enabled_activation_quantizers, enabled=True)
@staticmethod
def _disable_quant_wrapper(module: QcQuantizeWrapper):
return utils.disable_all_quantizers(module)
@staticmethod
def _get_quantized_modules(
sim: QuantizationSimModel,
) -> Generator[QcQuantizeWrapper, None, None]:
for module in sim.model.modules():
if isinstance(module, (QcQuantizeWrapper, QcQuantizeRecurrent)):
yield module
@staticmethod
def _fold_all_batch_norms(*args, **kwargs):
return fold_all_batch_norms(*args, **kwargs)