@tensorflow/micro
Remove extraneous tensor copy operation after first invocation of condition subgraph.
Move copy of operator inputs to outputs, such that it occurs before the first invocation of the condition subgraph. This preserves the operator inputs when one or more of them is the output of DECODE, and alternate decompression memory is in use. This is because the output of DECODE is for immediate consumption by the next operator in the graph (WHILE), yet it is possible for the WHILE subgraph invocations to share memory with the original DECODE output.
Update the unit test for multiple invocations of the condition and body subgraphs.
When copying tensors between operator inputs/outputs and subgraph inputs/outputs, check if the source and destination tensors share memory.
bug=fixes #3632
@tensorflow/micro
Set the alternate profiler in the benchmark tool prior to the `Prepare` phase (before calling `MicroInterpreter::AllocateTensors`). This is because the DECODE operator requires the alternate `MicroProfilerInterface` to already be initialized during the `Prepare` phase.
Previously the alternate profiler was only required during the `Eval` phase with the legacy compression code. This fix does not change the functionality of the benchmark tool with respect to the legacy compression.
bug=#3628
Add spec types, YAML parser support, and plugin stubs for Huffman and
Pruning compression methods. The plugins raise CompressionError when
invoked, to be replaced with working implementations later.
BUG=part of #3256
Implement LutCompressor using the Compressor protocol. Lookup table
compression replaces tensor values with indices into a table of unique
values, producing packed indices and ancillary data in the format
expected by the TFLM DECODE kernel.
Supports per-tensor and per-channel compression, sizes value tables to
actual unique count, and handles unquantized tensors.
BUG=part of #3256
- Adapt build logic to not build float support since TFLM does not use
the CMSIS-NN CMake path.
- Zero-initialize the cmsis_nn_lstm_context used by the int8 and int16
unidirectional sequence LSTM paths so optional fields such as hidden_state do
not contain stack garbage.
Change-Id: I5b44a3a231eb19023e004a5652b207930853648f
Signed-off-by: Måns Nilsson <mans.nilsson@arm.com>
Define the plugin interface for compression methods. Each compressor
implements the Compressor protocol with a compress() method that returns
encoded data and ancillary data.
BUG=part of #3256
Replace model_facade with model_editor in compress.py and tests.
model_editor provides a cleaner API with better buffer and metadata
handling.
Update BUILD dependencies accordingly.
BUG=part of #3256
Implement unified module for creating, reading, and modifying TFLite
models with a clean API. The module eliminates manual index tracking
and buffer management through automatic bookkeeping, supporting both
declarative and imperative construction styles.
Wrapper classes (Tensor, Operator, Subgraph, Model) hold the underlying
flatbuffer T objects as backing storage rather than copying fields into
dataclasses. This ensures all schema fields are preserved during
read-modify-write cycles, even fields not explicitly handled by
model_editor. Future schema additions will be preserved automatically.
Add comprehensive test coverage including field preservation tests that
verify unhandled schema fields survive read-modify-write.
BUG=part of #3256
Add a tensor_type module that holds the single mapping from a TFLite
TensorType to a numpy dtype, and convert view.py to use it. The mapping
was inlined in view.py; centralizing it gives the compression tooling
one place to maintain as more callers need to read tensor buffers as
numpy arrays.
tensor_type.to_numpy() raises ValueError for types with no clean numpy
equivalent (STRING, RESOURCE, VARIANT, BFLOAT16, and the sub-byte
integer types) instead of silently returning a wrong dtype. Only types
with an unambiguous little-endian numpy representation are mapped.
BUG=part of #3256
Python targets in this repository import one another under the
"tflite_micro" package namespace, which //:tflite_micro_shim synthesizes
at import time. The shim is required under Bzlmod, where the main
repository's runfiles root is the fixed name "_main" rather than the
module name, so the "tflite_micro" prefix no longer resolves on its own.
Every such target therefore had to list //:tflite_micro_shim in its
deps, which was repetitive and easy to forget.
Add tflm_py_library, tflm_py_test, and tflm_py_binary wrappers in a new
//python:py_rules.bzl that inject the shim dependency automatically,
following the naming convention of the existing tflm_cc_* wrappers, and
document the shim's rationale there. Convert every target that
previously listed the shim to the corresponding wrapper and drop the
explicit dependency. The dependency graph is unchanged; only the means
by which the shim is attached differs.
* Fix Python module shadowing in Docker CI
Applies legacy_create_init = False to Python rules in BUILD files to prevent Bazel from automatically generating __init__.py files. This prevents the root 'signal/' directory (which contains C++ kernels but no Python source) from being treated as a Python package, which was shadowing the standard library 'signal' module and causing psutil failures in the tflm-ci Docker container.
* Fix directory path in Docker workflows
