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"""Top-level API for aimet compression library"""
from typing import Union, Tuple, Callable
import tensorflow as tf
from aimet_common.defs import (
CostMetric,
CompressionScheme,
EvalFunction,
CompressionStats,
)
from aimet_common.bokeh_plots import BokehServerSession
from aimet_tensorflow.keras.utils.graph_saver import (
keras_wrapper_func,
keras_save_and_load_graph,
keras_remove_hanging_nodes,
)
from aimet_tensorflow.keras.defs import SpatialSvdParameters
from aimet_tensorflow.keras.compression_factory import CompressionFactory
[docs]
class ModelCompressor:
"""aimet model compressor: Enables model compression using various schemes"""
# pylint: disable=too-many-arguments
[docs]
@staticmethod
def compress_model(
model: tf.keras.Model,
eval_callback: EvalFunction,
eval_iterations,
compress_scheme: CompressionScheme,
cost_metric: CostMetric,
parameters: Union[SpatialSvdParameters],
trainer: Callable = None,
visualization_url: str = None,
) -> Tuple[tf.keras.Model, CompressionStats]:
"""
Compress a given model using the specified parameters
:param model: Model, represented by a tf.keras.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 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 trainer: Training function
None: If per layer fine-tuning is not required while creating the final compressed model
:param visualization_url: url the user will need to input where visualizations will appear
:return: A tuple of the compressed model session, and compression statistics
"""
# 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"
)
if parameters.multiplicity < 1:
raise ValueError("Rounding Multiplicity should be greater than 1")
# wrapper_func saves and reloads the graph before evaluation
# In Keras after making changes to the graph you must save and reload, then evaluate
eval_callback = keras_wrapper_func(eval_callback)
if compress_scheme == CompressionScheme.spatial_svd:
algo = CompressionFactory.create_spatial_svd_algo(
model,
eval_callback,
eval_iterations,
cost_metric,
parameters,
bokeh_session,
)
elif compress_scheme == CompressionScheme.weight_svd:
raise NotImplementedError(
"Not yet implemented for: {}".format(compress_scheme)
)
elif compress_scheme == CompressionScheme.channel_pruning:
raise NotImplementedError(
"Not yet implemented for: {}".format(compress_scheme)
)
else:
raise ValueError(
"Compression scheme not supported: {}".format(compress_scheme)
)
compressed_layer_db, stats = algo.compress_model(cost_metric, trainer)
# In keras after making changes to the model you must save and reload, then evaluate
tmp_dir = "./data/saved_model"
updated_model = keras_save_and_load_graph(tmp_dir, compressed_layer_db.model)
# Remove the hanging nodes
updated_model = keras_remove_hanging_nodes(updated_model)
return updated_model, stats