Let repack_stream optimizer inheirt original precision

hls4ml/backends/fpga/passes/repack_stream.py

@@ -59,6 +59,8 @@ def transform(self, model, node):
 
         # Insert new Repack node instead of Reshape
         repack_layer = model.make_node(Repack, 'repack_' + node.name, attrs, node.inputs.copy())
+        # As result_t attribute is not honored by type conversion, set it manually here
+        repack_layer.attributes[repack_layer.name].type = node.attributes[node.name].type
         model.replace_node(node, repack_layer)
 
         return True

test/pytest/test_repack_precision.py

@@ -0,0 +1,27 @@
+from tensorflow import keras
+
+from hls4ml.converters import convert_from_keras_model
+
+
+def test_repack_precision():
+    inp = keras.Input(shape=(3, 3), name='inp')
+    out = keras.layers.Reshape((3, 3), name='reshape')(inp)
+    out = keras.layers.Conv1D(2, 2, name='conv')(out)
+    model = keras.Model(inp, out)
+
+    layer_conf = {
+        'inp': {'Precision': 'fixed<20,10>'},
+        'reshape': {'Precision': 'fixed<20,10>'},
+        'conv': {'Precision': 'fixed<20,10>'},
+    }
+
+    hls_config = {'Model': {'Precision': 'fixed<2,1>', 'ReuseFactor': 1}, 'LayerName': layer_conf}
+
+    # Repack only happens in io_stream
+    model_hls = convert_from_keras_model(model, hls_config=hls_config, io_type='io_stream')
+    assert 'repack_reshape' in model_hls.graph, 'repack_reshape not found in graph'
+    repack_precision = model_hls.graph['repack_reshape'].attributes['result_t'].precision
+    assert repack_precision.integer == 10, 'Precision mismatch'
+    assert repack_precision.fractional == 10, 'Precision mismatch'
+    assert repack_precision.width == 20, 'Precision mismatch'
+    assert repack_precision.signed is True, 'Precision mismatch'