MLBEDSW-3301: Vela fails ungracefully when reading string buffers
When encountering a sparse string buffer, Vela fails
both due to missing a mapping for a Numpy string type
and also for not being able to read sparse buffers.
The failing line is attempting to reshape a [100]
buffer into a [3, 5] tensor which does not work due
to Vela treating the buffer as non-sparse.
The solution here is to simply not do the reshape
for string buffers (which all appear to be sparse)
since it is not something that will be supported in
the future anyway.
The related operator can then be pushed to the CPU
as expected.
Signed-off-by: Dwight Lidman <dwight.lidman@arm.com>
Change-Id: Iea0af6cd60a691f975209014b6aa098dde8d6a4b
diff --git a/ethosu/vela/test/test_tflite_reader.py b/ethosu/vela/test/test_tflite_reader.py
index 23abb4a..14c9b20 100644
--- a/ethosu/vela/test/test_tflite_reader.py
+++ b/ethosu/vela/test/test_tflite_reader.py
@@ -18,9 +18,11 @@
from unittest.mock import MagicMock
from unittest.mock import patch
+import numpy as np
import pytest
from ethosu.vela.operation import Op
+from ethosu.vela.tflite.TensorType import TensorType
from ethosu.vela.tflite_reader import TFLiteSubgraph
@@ -79,3 +81,34 @@
assert len(created_op.inputs) == expected
assert created_op.outputs[0].name == "tensor_{}".format(output)
assert inputs[-1] != -1 or not created_op.inputs[-1]
+
+ string_buffer_testdata = [
+ (np.array([np.random.randint(256) for _ in range(100)], dtype=np.uint8), [3, 5]),
+ (np.array([np.random.randint(256) for _ in range(100)], dtype=np.int16), [10, 10]),
+ (np.array([np.random.randint(256) for _ in range(100)], dtype=np.float32), [100]),
+ (np.array([], dtype=np.int8), [30]),
+ ]
+
+ @pytest.mark.parametrize("buffer, tens_shape", string_buffer_testdata)
+ def test_parse_tensor_with_string_buffer(self, buffer, tens_shape):
+ tens_data = MagicMock()
+ tens_data.ShapeAsNumpy = MagicMock(return_value=np.array(tens_shape), dtype=np.int32)
+ tens_data.Name = MagicMock(return_value=b"test_data")
+ tens_data.Type = MagicMock(return_value=TensorType.STRING)
+ tens_data.Quantization = MagicMock(return_value=None)
+ tens_data.Buffer = MagicMock(return_value=0)
+
+ tfl_sg = MagicMock()
+ tfl_sg.Name = MagicMock(return_value=b"test_sg")
+ tfl_sg.TensorsLength = MagicMock(return_value=0)
+ tfl_sg.OperatorsLength = MagicMock(return_value=0)
+ tfl_sg.OutputsAsNumpy = MagicMock(return_value=[])
+ tfl_sg.InputsAsNumpy = MagicMock(return_value=[])
+
+ graph = MagicMock()
+ graph.buffers = [buffer]
+
+ subgraph = TFLiteSubgraph(graph, tfl_sg)
+
+ tens = subgraph.parse_tensor(tens_data)
+ assert tens.values is None