1 files changed, 121 insertions, 1 deletions
diff --git a/tests/test_backend_vela_performance.py b/tests/test_backend_vela_performance.py
index df2ce08..5800630 100644
--- a/tests/test_backend_vela_performance.py
+++ b/tests/test_backend_vela_performance.py
@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright 2022-2023, Arm Limited and/or its affiliates.
+# SPDX-FileCopyrightText: Copyright 2022-2024, Arm Limited and/or its affiliates.
 # SPDX-License-Identifier: Apache-2.0
 """Tests for module vela/performance."""
 from pathlib import Path
@@ -8,6 +8,9 @@ import pytest
 
 from mlia.backend.vela.compiler import optimize_model
 from mlia.backend.vela.performance import estimate_performance
+from mlia.backend.vela.performance import layer_metrics
+from mlia.backend.vela.performance import LayerwisePerfInfo
+from mlia.backend.vela.performance import parse_layerwise_perf_csv
 from mlia.backend.vela.performance import PerformanceMetrics
 from mlia.target.ethos_u.config import EthosUConfiguration
 
@@ -22,6 +25,123 @@ def test_estimate_performance(test_tflite_model: Path) -> None:
     assert isinstance(perf_metrics, PerformanceMetrics)
 
 
+def test_estimate_performance_csv_parser_called(
+    monkeypatch: pytest.MonkeyPatch, test_tflite_model: Path
+) -> None:
+    """Test that estimate_performance from backend.vela.performance is called."""
+    target_config = EthosUConfiguration.load_profile("ethos-u55-256")
+    csv_file_name = target_config.compiler_options.output_dir / (
+        test_tflite_model.stem + "_per-layer.csv"
+    )
+    mock = MagicMock()
+    monkeypatch.setattr("mlia.backend.vela.performance.parse_layerwise_perf_csv", mock)
+    estimate_performance(test_tflite_model, target_config.compiler_options)
+    mock.assert_called_with(vela_csv_file=csv_file_name, metrics=layer_metrics)
+
+
+LAYERWISE_TMP_DATA_STR = """
+TFLite_operator,NNG Operator,SRAM Usage,Peak%,Op Cycles,Network%,NPU,SRAM AC,DRAM AC,OnFlash AC,OffFlash AC,MAC Count,Network%,Util%,Name
+CONV_2D,Conv2DBias,11936,54.65201465201465,7312.0,17.648194632168373,7312.0,2000.0,0.0,0.0,0.0,73008,8.653353814644136,3.9002666849015313,sequential/conv1/Relu;sequential/conv1/Conv2D
+MAX_POOL_2D,MaxPool,10944,50.10989010989011,2992.0,7.22147132651091,1330.0,2992.0,0.0,0.0,0.0,6912,0.819252432155658,0.9024064171122994,sequential/max_pooling2d/MaxPool
+""".strip()
+
+LAYERWISE_TMP_DATA_MISSING_HEADER_STR = """
+TFLite_operator,NNG Operator,Peak%,Op Cycles,Network%,NPU,SRAM AC,DRAM AC,OnFlash AC,OffFlash AC,MAC Count,Network%,Util%,Name
+CONV_2D,Conv2DBias,54.65201465201465,7312.0,17.648194632168373,7312.0,2000.0,0.0,0.0,0.0,73008,8.653353814644136,3.9002666849015313,sequential/conv1/Relu;sequential/conv1/Conv2D
+MAX_POOL_2D,MaxPool,50.10989010989011,2992.0,7.22147132651091,1330.0,2992.0,0.0,0.0,0.0,6912,0.819252432155658,0.9024064171122994,sequential/max_pooling2d/MaxPool
+""".strip()
+
+LAYERWISE_MULTI_HEADER_TMP_DATA_STR = """
+TFLite_operator,NNG Operator,SRAM Usage,Peak%,Op Cycles,Network%,NPU,SRAM AC,DRAM AC,OnFlash AC,OffFlash AC,MAC Count,Network%,Util%,Name
+CONV_2D,Conv2DBias,11936,54.65201465201465,7312.0,17.648194632168373,7312.0,2000.0,0.0,0.0,0.0,73008,8.653353814644136,3.9002666849015313,sequential/conv1/Relu;sequential/conv1/Conv2D
+TFLite_operator,NNG Operator,SRAM Usage,Peak%,Op Cycles,Network%,NPU,SRAM AC,DRAM AC,OnFlash AC,OffFlash AC,MAC Count,Network%,Util%,Name
+MAX_POOL_2D,MaxPool,10944,50.10989010989011,2992.0,7.22147132651091,1330.0,2992.0,0.0,0.0,0.0,6912,0.819252432155658,0.9024064171122994,sequential/max_pooling2d/MaxPool
+""".strip()
+
+
+TMP_DATA_EXPECTED_STRING = "\
+Name: sequential/conv1/Relu;sequential/conv1/Conv2D, \
+TFLite_operator: CONV_2D, \
+SRAM Usage: 11936, \
+Op Cycles: 7312, \
+NPU: 7312, \
+SRAM AC: 2000, \
+DRAM AC: 0, \
+OnFlash AC: 0, \
+OffFlash AC: 0, \
+MAC Count: 73008, \
+Util%: 3.9002666849015313, \
+\
+Name: sequential/max_pooling2d/MaxPool, \
+TFLite_operator: MAX_POOL_2D, \
+SRAM Usage: 10944, \
+Op Cycles: 2992, \
+NPU: 1330, \
+SRAM AC: 2992, \
+DRAM AC: 0, \
+OnFlash AC: 0, \
+OffFlash AC: 0, \
+MAC Count: 6912, \
+Util%: 0.9024064171122994, \
+"
+
+
+@pytest.mark.parametrize(
+    "input_csv_content, expected_output",
+    [
+        (LAYERWISE_TMP_DATA_STR, TMP_DATA_EXPECTED_STRING),
+        (
+            LAYERWISE_MULTI_HEADER_TMP_DATA_STR,
+            TMP_DATA_EXPECTED_STRING,
+        ),
+    ],
+)
+def test_estimate_performance_parse_layerwise_csv_file(
+    test_csv_file: Path, input_csv_content: str, expected_output: str
+) -> None:
+    """Test that parsing a csv file produces a LayerwisePerfInfo object."""
+    with open(test_csv_file, "w", encoding="utf8") as csv_file:
+        csv_file.write(input_csv_content)
+    layerwise_object = parse_layerwise_perf_csv(test_csv_file, layer_metrics)
+    strings_to_check_layerwise_object = repr(layerwise_object)
+    assert isinstance(layerwise_object, LayerwisePerfInfo)
+    assert expected_output == strings_to_check_layerwise_object
+
+
+def test_estimate_performance_parse_layerwise_csv_file_with_missing_headers(
+    test_csv_file: Path,
+) -> None:
+    """Test that ensures a KeyError
+    is raised when a csv file is parsed with missing headers.
+    """
+    with open(test_csv_file, "w", encoding="utf8") as csv_file:
+        csv_file.write(LAYERWISE_TMP_DATA_MISSING_HEADER_STR)
+    with pytest.raises(KeyError, match="Generated CSV missing expected headers"):
+        parse_layerwise_perf_csv(test_csv_file, layer_metrics)
+
+
+def test_estimate_performance_parse_layerwise_csv_file_missing_file() -> None:
+    """Test that ensures a FileNotFoundError
+    is raised when a non-existent csv file is parsed.
+    """
+    with pytest.raises(
+        FileNotFoundError, match="CSV File not found at missing_file.csv"
+    ):
+        parse_layerwise_perf_csv(Path("missing_file.csv"), layer_metrics)
+
+
+def test_estimate_performance_parse_layerwise_empty_csv_file(
+    empty_test_csv_file: Path,
+) -> None:
+    """Test that ensures that if an empty csv file
+    is parsed, we return an empty layerwise object.
+    """
+    empty_test_csv_file.touch()
+    layerwise_object = parse_layerwise_perf_csv(empty_test_csv_file, layer_metrics)
+    assert isinstance(layerwise_object, LayerwisePerfInfo)
+    assert len(layerwise_object.layerwise_info) == 0
+
+
 def test_estimate_performance_already_optimized(
     tmp_path: Path, test_tflite_model: Path
 ) -> None: