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""" Top-level API to AIMET compression library """
from typing import Union, Tuple
import torch
from aimet_common.defs import CostMetric, CompressionScheme, EvalFunction, CompressionStats
from aimet_common.bokeh_plots import BokehServerSession
from aimet_torch.defs import SpatialSvdParameters, WeightSvdParameters, ChannelPruningParameters
from aimet_torch.compression_factory import CompressionFactory
[docs]class ModelCompressor:
""" AIMET model compressor: Enables model compression using various schemes """
# Too many arguments in this function, disabling pylint for now
[docs] @staticmethod
def compress_model(model: torch.nn.Module, eval_callback: EvalFunction, eval_iterations,
input_shape: Tuple,
compress_scheme: CompressionScheme, cost_metric: CostMetric,
parameters: Union[SpatialSvdParameters,
WeightSvdParameters,
ChannelPruningParameters],
trainer=None, visualization_url=None) -> Tuple[torch.nn.Module, CompressionStats]:
"""
Compress a given model using the specified parameters
:param model: Model to compress
:param eval_callback: Evaluation callback. Expected signature is evaluate(model, iterations, use_cuda).
Expected to return an accuracy metric.
:param eval_iterations: Iterations to run evaluation for
:param trainer: Training Class: Contains a callable, train_model, which takes model, layer which is being fine
tuned and an optional parameter train_flag as a parameter
None: If per layer fine tuning is not required while creating the final compressed model
:param input_shape: Shape of the input tensor for model
:param compress_scheme: Compression scheme. See the enum for allowed values
:param cost_metric: Cost metric to use for the compression-ratio (either mac or memory)
:param parameters: Compression parameters specific to given compression scheme
:param visualization_url: url the user will need to input where visualizations will appear
:return: A tuple of the compressed model, and compression statistics
"""
# pylint:disable=too-many-arguments
# If no url is passed in, then do not create a bokeh server session
if not visualization_url:
bokeh_session = None
else:
# create a bokeh session to publish visualizations to the server document for compression
bokeh_session = BokehServerSession(url=visualization_url, session_id="compression")
# put model in eval mode. This is important because otherwise running a forward pass can change module buffers
# e.g. for batchnorm layers that can affect model evaluation results
if trainer is not None:
trainer.train_model(model, model, train_flag=False)
model = model.eval()
if parameters.multiplicity < 1:
raise ValueError('Rounding Multiplicity should be greater than 1')
if compress_scheme == CompressionScheme.spatial_svd:
algo = CompressionFactory.create_spatial_svd_algo(model, eval_callback, eval_iterations,
input_shape, cost_metric, parameters, bokeh_session)
elif compress_scheme == CompressionScheme.weight_svd:
algo = CompressionFactory.create_weight_svd_algo(model, eval_callback, eval_iterations,
input_shape, cost_metric, parameters, bokeh_session)
elif compress_scheme == CompressionScheme.channel_pruning:
algo = CompressionFactory.create_channel_pruning_algo(model, eval_callback, eval_iterations,
input_shape, cost_metric, parameters, bokeh_session)
else:
raise ValueError("Compression scheme not supported: {}".format(compress_scheme))
compressed_layer_db, stats = algo.compress_model(cost_metric, trainer)
return compressed_layer_db.model, stats