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""" Mixed precision inference """
from typing import Any, Callable, Union, Tuple, List
import onnxruntime as ort
from aimet_common.utils import AimetLogger
from aimet_common.amp.utils import (
visualize_quantizer_group_sensitivity,
visualize_pareto_curve,
CANDIDATE_WITH_DTYPE,
AMPSearchAlgo,
)
from aimet_onnx.quantsim import QuantizationSimModel
from aimet_onnx.amp.mixed_precision_algo import GreedyMixedPrecisionAlgo
from aimet_onnx.amp.quantizer_groups import QuantizerGroup
logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.MixedPrecision)
# pylint: disable=too-many-arguments
[docs]
def choose_mixed_precision(sim: QuantizationSimModel,
candidates: List[CANDIDATE_WITH_DTYPE],
eval_callback_for_phase1: Callable[[ort.InferenceSession], float],
eval_callback_for_phase2: Callable[[ort.InferenceSession], float],
allowed_accuracy_drop: Union[None, float],
results_dir: str, clean_start: bool,
forward_pass_callback: Callable[[ort.InferenceSession], Any],
use_all_amp_candidates: bool = False, phase1_optimize: bool = True,
amp_search_algo: AMPSearchAlgo = AMPSearchAlgo.Binary) -> \
Union[List[Tuple[int, float, QuantizerGroup, int]], None]:
"""
High-level API to perform in place Mixed Precision evaluation on the given sim model. A pareto list is created and
a curve for Accuracy vs BitOps is saved under the results directory
:param sim: Quantized sim model
:param candidates: List of tuples for all possible bitwidth values for activations and parameters
Suppose the possible combinations are-
((Activation bitwidth - 8, Activation data type - int), (Parameter bitwidth - 16, parameter data type - int))
((Activation bitwidth - 16, Activation data type - float), (Parameter bitwidth - 16, parameter data type - float))
candidates will be [((8, QuantizationDataType.int), (16, QuantizationDataType.int)),
((16, QuantizationDataType.float), (16, QuantizationDataType.float))]
:param eval_callback_for_phase1: Callable object used to measure sensitivity of each
quantizer group during phase 1. The phase 1 involves finding accuracy list/sensitivity of each
module. Therefore, a user might want to run the phase 1 with a smaller dataset
:param eval_callback_for_phase2: Callale object used to get accuracy of quantized model
for phase 2 calculations. The phase 2 involves finding pareto front curve
:param allowed_accuracy_drop: Maximum allowed drop in accuracy from FP32 baseline. The pareto front curve is plotted only till the point where the allowable
accuracy drop is met. To get a complete plot for picking points on the curve, the user
can set the allowable accuracy drop to None.
:param results_dir: Path to save results and cache intermediate results
:param clean_start: If true, any cached information from previous runs will be deleted prior to starting the
mixed-precision analysis. If false, prior cached information will be used if applicable. Note
it is the user's responsibility to set this flag to true if anything in the model or
quantization parameters changes compared to the previous run.
:param forward_pass_callback: Callable object used to compute quantization encodings
:param use_all_amp_candidates: Using the “supported_kernels” field in the config file (under defaults
and op_type sections), a list of supported candidates can be specified. All the AMP candidates
which are passed through the “candidates” field may not be supported based on the data passed
through “supported_kernels”. When the field “use_all_amp_candidates” is set to True, the AMP
algorithm will ignore the "supported_kernels" in the config file and continue to use all candidates.
:phase1_optimize: If user set this parameter to false then phase1 default logic will be executed else optimized logic will be executed.
:param amp_search_algo: A valid value from the Enum AMPSearchAlgo. Defines the search algorithm to be used for
the phase 2 of AMP.
:return: Pareto front list containing information including Bitops, QuantizerGroup candidates and
corresponding eval scores. The Pareto front list can be used for plotting a pareto front curve which
provides information regarding how bit ops vary w.r.t. accuracy. If the allowable accuracy drop is set to
100% then a user can use the pareto front curve to pick points and re-run,
None if we early exit the mixed precision algorithm.
"""
mixed_precision_algo = GreedyMixedPrecisionAlgo(sim, candidates, eval_callback_for_phase1,
eval_callback_for_phase2, results_dir, clean_start,
forward_pass_callback, use_all_amp_candidates, phase1_optimize)
mixed_precision_algo.run(allowed_accuracy_drop, amp_search_algo)
if mixed_precision_algo.accuracy_list is not None and mixed_precision_algo.pareto_list is not None:
# Print mixed precision stats
logger.info(mixed_precision_algo)
# Visualize quantizer group sensitivity
visualize_quantizer_group_sensitivity(mixed_precision_algo.accuracy_list,
mixed_precision_algo.baseline_candidate,
mixed_precision_algo.fp32_accuracy,
results_dir=results_dir)
# Create pareto list curve
visualize_pareto_curve(mixed_precision_algo.pareto_list, results_dir)
return mixed_precision_algo.pareto_list
return None