add tests for einsumdense

test/pytest/test_einsum_dense.py

@@ -0,0 +1,57 @@
+from pathlib import Path
+
+import keras
+import numpy as np
+import pytest
+
+from hls4ml.converters import convert_from_keras_model
+
+if keras.__version__ < '3.0.0':
+    pytest.skip('Only keras v3 is supported for now', allow_module_level=True)
+
+from keras.api.layers import EinsumDense, Input
+
+test_root_path = Path(__file__).parent
+
+
+@pytest.mark.parametrize('strategy', ['latency'])
+@pytest.mark.parametrize('io_type', ['io_parallel'])
+@pytest.mark.parametrize('backend', ['Vivado', 'Vitis'])
+@pytest.mark.parametrize(
+    'operation',
+    [
+        # eq, inp, out
+        ('bi,j->bij', (8,), (8, 7), None),
+        ('bi,j->bij', (8,), (8, 7), 'i'),
+        ('bi,j->bij', (8,), (8, 7), 'j'),
+        ('bi,io->bo', (8,), 7, None),
+        ('...i,oi->...o', (4, 3), (5,), None),
+        ('...abcd,bcde->...aeb', (5, 4, 3, 2), (5, 6, 4), None),
+        ('...abcd,bcde->...aeb', (5, 4, 3, 2), (5, 6, 4), 'aeb'),
+        ('...abcd,bcde->...aeb', (5, 4, 3, 2), (5, 6, 4), 'ab'),
+        ('...abcd,bcde->...aeb', (5, 4, 3, 2), (5, 6, 4), 'a'),
+    ],
+)
+def test_einsum_dense(backend, io_type, strategy, operation):
+    eq, inp_shape, out_shape, bias_axes = operation
+    model = keras.Sequential(
+        [Input(inp_shape), EinsumDense(eq, output_shape=out_shape, bias_axes=bias_axes, name='einsum_dense')]
+    )
+
+    if bias_axes is not None:
+        layer = model.get_layer('einsum_dense')
+        layer.bias.assign(keras.ops.convert_to_tensor(np.random.rand(*layer.bias.shape)))
+
+    data = np.random.rand(1000, *inp_shape)
+    eq_name = eq.replace(',', '_').replace('->', '_') + ('' if bias_axes is None else f'_{bias_axes}')
+    output_dir = str(test_root_path / f'hls4mlprj_einsum_dense_{eq_name}_{backend}_{io_type}_{strategy}')
+    hls_config = {'Model': {'Precision': 'ap_fixed<32,8>', 'ReuseFactor': 1}, 'Strategy': strategy}
+    model_hls = convert_from_keras_model(
+        model, backend=backend, output_dir=output_dir, hls_config=hls_config, io_type=io_type
+    )
+
+    model_hls.compile()
+    r_keras = model.predict(data, verbose=0, batch_size=1000)  # type: ignore
+    r_hls = model_hls.predict(data).reshape(r_keras.shape)  # type: ignore
+
+    np.testing.assert_allclose(r_hls, r_keras, atol=2e-6, rtol=0)