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fix(vivado): Derive softmax layer config name from activation attribute #1525

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Merged
JanFSchulte merged 1 commit into from
Aug 12, 2026
+15 −5
Merged

fix(vivado): Derive softmax layer config name from activation attribute #1525

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2 changes: 1 addition & 1 deletion hls4ml/backends/vivado/passes/core_templates.py
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Original file line number Diff line number Diff line change
Expand Up @@ -347,7 +347,7 @@ def format(self, node):
use_multidim = node.get_attr('n_inner', 1) > 1 or node.get_attr('n_outer', 1) > 1
use_multidim = use_multidim and node.model.config.get_config_value('IOType') == 'io_parallel'
params['activation'] = 'softmax' if not use_multidim else 'softmax_multidim'
params['config'] = f'softmax_config{node.index}'
params['config'] = '{}_config{}'.format(node.get_attr('activation'), node.index)

return self.template.format(**params)

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18 changes: 14 additions & 4 deletions test/pytest/test_softmax.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,7 @@ def generate_data(input_shape):
return np.clip(d, -32, 31)


@pytest.mark.parametrize('softmax_impl', ['activation', 'standalone'])
@pytest.mark.parametrize('backend', ['Vivado', 'Vitis', 'Quartus', 'Catapult'])
@pytest.mark.parametrize('strategy', ['stable', 'latency', 'argmax'])
@pytest.mark.parametrize(
Expand All @@ -35,10 +36,15 @@ def generate_data(input_shape):
('16,6', (8, 8, 3), '18,8', 'io_stream', False),
],
)
def test_softmax(test_case_id, backend, strategy, generate_data, input_bits, input_shape, table_bits, io_type, custom_accum):
def test_softmax(
test_case_id, softmax_impl, backend, strategy, generate_data, input_bits, input_shape, table_bits, io_type, custom_accum
):
X = generate_data
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Activation(input_shape=input_shape, activation='softmax', name='softmax'))
if softmax_impl == 'activation':
model.add(tf.keras.layers.Activation(input_shape=input_shape, activation='softmax', name='softmax'))
else:
model.add(tf.keras.layers.Softmax(input_shape=input_shape, name='softmax'))
model.compile()

table_type = f'fixed<{table_bits}, RND, SAT>'
Expand Down Expand Up @@ -73,12 +79,16 @@ def test_softmax(test_case_id, backend, strategy, generate_data, input_bits, inp
assert acc_hls4ml >= 0.98


@pytest.mark.parametrize('softmax_impl', ['activation', 'standalone'])
@pytest.mark.parametrize('backend', ['Vivado', 'Vitis', 'Quartus', 'Catapult'])
@pytest.mark.parametrize('io_type', ['io_parallel', 'io_stream'])
def test_softmax_skipped(test_case_id, backend, io_type):
def test_softmax_skipped(test_case_id, softmax_impl, backend, io_type):
X = np.random.rand(100, 10)
dense = tf.keras.layers.Dense(14, input_shape=(10,), name='dense')
softmax = tf.keras.layers.Activation(activation='softmax', name='softmax')
if softmax_impl == 'activation':
softmax = tf.keras.layers.Activation(activation='softmax', name='softmax')
else:
softmax = tf.keras.layers.Softmax(name='softmax')
model = tf.keras.models.Sequential([dense, softmax])
model.compile()

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