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# SPDX-FileCopyrightText: Copyright 2023, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Save subgraph data."""
# pylint: disable=too-many-locals
from __future__ import annotations
import math
import os
from contextlib import ExitStack
from pathlib import Path
import tensorflow as tf
from rich.progress import track
from mlia.nn.rewrite.core.utils.numpy_tfrecord import numpytf_count
from mlia.nn.rewrite.core.utils.numpy_tfrecord import numpytf_read
from mlia.nn.rewrite.core.utils.numpy_tfrecord import NumpyTFWriter
from mlia.nn.rewrite.core.utils.parallel import ParallelTFLiteModel
from mlia.nn.tensorflow.config import NameToTensorMap
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
DEQUANT_SUFFIX = "_dequant"
def dequantized_path(filename: str | Path) -> Path:
"""Append the de-quantization suffix to the given filename."""
path = Path(filename)
path = Path(path.parent, f"{path.stem}{DEQUANT_SUFFIX}{path.suffix}")
return path
def record_model(
input_filename: str | Path,
model_filename: str | Path,
output_filename: str | Path,
batch_size: int = 0,
show_progress: bool = False,
num_procs: int = 1,
num_threads: int = 0,
dequantize_output: bool = False,
) -> None:
"""Model recorder.
num_procs: 0 => detect real cores on system
num_threads: 0 => TFLite impl. specific setting, usually 3
dequantize: True => de-quantize the recorded output before saving
"""
model = ParallelTFLiteModel(model_filename, num_procs, num_threads, batch_size)
if not batch_size:
batch_size = (
model.num_procs * model.batch_size
) # automatically batch to the minimum effective size if not specified
total = numpytf_count(input_filename)
dataset = numpytf_read(input_filename)
if batch_size > 1:
# Collapse batch-size 1 items into batch-size n.
dataset = dataset.map(
lambda d: {k: tf.squeeze(v, axis=0) for k, v in d.items()}
)
dataset = dataset.batch(batch_size, drop_remainder=False)
total = int(math.ceil(total / batch_size))
with ExitStack() as stack:
writer = stack.enter_context(NumpyTFWriter(output_filename))
writer_dequant = None
if dequantize_output:
dequant_path = dequantized_path(output_filename)
writer_dequant = stack.enter_context(NumpyTFWriter(dequant_path))
def write(writer: NumpyTFWriter, data: NameToTensorMap) -> None:
"""Write the data using the given NumpyTFWriter instance."""
if batch_size > 1:
for i in range(batch_size):
# Expand the batches and recreate each dict as a
# batch-size 1 item for the tfrec output
recreated_dict = {
k: v[i : i + 1] # noqa: E203
for k, v in data.items()
if i < v.shape[0]
}
if recreated_dict:
writer.write(recreated_dict)
else:
writer.write(data)
for _, named_x in enumerate(
track(dataset.as_numpy_iterator(), total=total, disable=not show_progress)
):
named_y = model(named_x)
write(writer, named_y)
if dequantize_output:
assert writer_dequant
named_y_dequant = model.dequantize_outputs(named_y)
write(writer_dequant, named_y_dequant)
model.close()
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