# -*- mode: python -*-
# =============================================================================
# @@-COPYRIGHT-START-@@
#
# Copyright (c) 2018, Qualcomm Innovation Center, Inc. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software
# without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
#
# SPDX-License-Identifier: BSD-3-Clause
#
# @@-COPYRIGHT-END-@@
# =============================================================================
"""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