Implement first rewrite (proof of concept)

* Define replacement function fully_connected layer
* Define RewriteConfiguration and Rewriter to integrate
  rewrite module into mlia optimize command
* Fix a bug in the ethos_u/data_collection.py file
* Fix a bug in join.py
* Remove diff_stats and use diff instead, added related
  changes around this to ensure e2e tests passing
* Add unit tests for all changes
* Fix bug in diff_stats function
  * The bug was caused by a dividing by numpy array
    of all zeros. The previous way of handling it
    did not consider the all zeros case but only
    dealt with partially zeros
  * unit tests added.
* Fix the bug in rewrite/core/graph_edit/join.py
  * Remove the possibility of passing None to append_relabel
    function because it is immutable
  * The bug happened when empty dictionary was passed in the
    append_relabel function and the function overwrites the
    reference of operator_map which caused the dictionary
    was not updated after the function call

Resolves: MLIA-749, MLIA-864, MLIA-866
Change-Id: I1ab426996232f182345e6e98033d5dcb32aea08c
Signed-off-by: Benjamin Klimczak <benjamin.klimczak@arm.com>

1 files changed, 69 insertions, 0 deletions

diff --git a/src/mlia/nn/rewrite/core/rewrite.py b/src/mlia/nn/rewrite/core/rewrite.py
index ab34b47..0d182df 100644
--- a/src/mlia/nn/rewrite/core/rewrite.py
+++ b/src/mlia/nn/rewrite/core/rewrite.py
@@ -3,11 +3,19 @@
 """Contains class Rewriter to replace a subgraph/layer of a model."""
 from __future__ import annotations
 
+import importlib
+import tempfile
 from dataclasses import dataclass
 from pathlib import Path
+from typing import Any
 
+from mlia.core.errors import ConfigurationError
 from mlia.nn.common import Optimizer
 from mlia.nn.common import OptimizerConfiguration
+from mlia.nn.rewrite.core.train import eval_in_dir
+from mlia.nn.rewrite.core.train import join_in_dir
+from mlia.nn.rewrite.core.train import train
+from mlia.nn.rewrite.core.train import train_in_dir
 from mlia.nn.tensorflow.config import TFLiteModel
 
 
@@ -33,10 +41,71 @@ class Rewriter(Optimizer):
         """Init Rewriter instance."""
         self.model = TFLiteModel(tflite_model_path)
         self.optimizer_configuration = optimizer_configuration
+        self.train_dir = ""
 
     def apply_optimization(self) -> None:
         """Apply the rewrite flow."""
 
+        def get_function(arg: str) -> Any:
+            module_name = ".".join(arg.split(".")[:-1])
+            fn_name = arg.split(".")[-1]
+            module = importlib.import_module(module_name)
+            return getattr(module, fn_name)
+
+        if self.optimizer_configuration.optimization_target == "fully_connected":
+            replace_function = "mlia.nn.rewrite.library.fc_layer.get_keras_model"
+        else:
+            raise ConfigurationError(
+                "Only fully_connected replacement is supported in rewrite module."
+            )
+
+        replace_fn = get_function(replace_function)
+
+        augmentation_preset = (None, None)
+        use_unmodified_model = True
+        tflite_model = self.model.model_path
+        tfrecord = str(self.optimizer_configuration.dataset)
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            tmp_output = Path(tmp_dir, "output.tflite")
+
+            if self.train_dir:
+                tmp_new = Path(tmp_dir, "new.tflite")
+                new_part = train_in_dir(
+                    train_dir=self.train_dir,
+                    baseline_dir=None,
+                    output_filename=tmp_new,
+                    replace_fn=replace_fn,
+                    augmentations=augmentation_preset,
+                    steps=32,
+                    learning_rate=1e-3,
+                    batch_size=1,
+                    verbose=True,
+                    show_progress=True,
+                )
+                eval_in_dir(self.train_dir, new_part[0])
+                join_in_dir(self.train_dir, new_part[0], str(tmp_output))
+            else:
+                if not self.optimizer_configuration.layers_to_optimize:
+                    raise ConfigurationError(
+                        "Input and output tensor names need to be set for rewrite."
+                    )
+                train(
+                    source_model=tflite_model,
+                    unmodified_model=tflite_model if use_unmodified_model else None,
+                    output_model=str(tmp_output),
+                    input_tfrec=str(tfrecord),
+                    replace_fn=replace_fn,
+                    input_tensors=[self.optimizer_configuration.layers_to_optimize[0]],
+                    output_tensors=[self.optimizer_configuration.layers_to_optimize[1]],
+                    augment=augmentation_preset,
+                    steps=32,
+                    learning_rate=1e-3,
+                    batch_size=1,
+                    verbose=True,
+                    show_progress=True,
+                )
+
     def get_model(self) -> TFLiteModel:
         """Return optimized model."""
         return self.model