fix(bit_exact): add produce_kif handler for ZeroPadding1D/2D

hls4ml/model/optimizer/passes/bit_exact.py

@@ -35,6 +35,8 @@
     Reshape,
     Softmax,
     Transpose,
+    ZeroPadding1D,
+    ZeroPadding2D,
 )
 from hls4ml.model.optimizer import ModelOptimizerPass, OptimizerPass
 from hls4ml.model.optimizer.passes.hgq_proxy_model import FixedPointQuantizer, UnaryLUT
@@ -677,6 +679,36 @@ def _(layer: Embedding):
     return k, i, f
 
 
+@_produce_kif.register
+def _(layer: ZeroPadding1D):
+    assert layer.attributes['data_format'] == 'channels_last', 'Only channels_last format is supported'
+    k_in, i_in, f_in = get_input_kifs(layer)[0]
+    pad_left = int(layer.attributes['pad_left'])
+    pad_right = int(layer.attributes['pad_right'])
+    # channels_last: kif shape is (in_width, n_chan); pad axis 0.
+    pad_shape = ((pad_left, pad_right),) + ((0, 0),) * (k_in.ndim - 1)
+    k = np.pad(k_in, pad_shape, mode='constant', constant_values=0)
+    i = np.pad(i_in, pad_shape, mode='constant', constant_values=0)
+    f = np.pad(f_in, pad_shape, mode='constant', constant_values=0)
+    return k.astype(np.int16), i, f
+
+
+@_produce_kif.register
+def _(layer: ZeroPadding2D):
+    assert layer.attributes['data_format'] == 'channels_last', 'Only channels_last format is supported'
+    k_in, i_in, f_in = get_input_kifs(layer)[0]
+    pad_top = int(layer.attributes['pad_top'])
+    pad_bottom = int(layer.attributes['pad_bottom'])
+    pad_left = int(layer.attributes['pad_left'])
+    pad_right = int(layer.attributes['pad_right'])
+    # channels_last: kif shape is (in_height, in_width, n_chan); pad axes 0 and 1.
+    pad_shape = ((pad_top, pad_bottom), (pad_left, pad_right)) + ((0, 0),) * (k_in.ndim - 2)
+    k = np.pad(k_in, pad_shape, mode='constant', constant_values=0)
+    i = np.pad(i_in, pad_shape, mode='constant', constant_values=0)
+    f = np.pad(f_in, pad_shape, mode='constant', constant_values=0)
+    return k.astype(np.int16), i, f
+
+
 def kif_arrs_to_ints(arr: tuple[np.ndarray, np.ndarray, np.ndarray]):
     return tuple(int(np.max(a)) for a in arr)
 

test/pytest/test_bit_exact_zeropadding.py

@@ -0,0 +1,80 @@
+"""Bit-exact precision propagation through ZeroPadding1D/2D.
+
+ZeroPadding inserts exact zeros, so the bit-exact flow must propagate the
+input kif unchanged (padded with zeros) through the layer. Placing a plain
+Keras ZeroPadding layer between two HGQ2 quantized convolutions triggers the
+bit_exact pass and exercises the ZeroPadding _produce_kif handlers.
+"""
+
+from pathlib import Path
+
+import keras
+import numpy as np
+import pytest
+
+from hls4ml.converters import convert_from_keras_model
+
+try:
+    from hgq.config import QuantizerConfigScope
+    from hgq.layers import QConv1D, QConv2D
+    from hgq.utils import trace_minmax
+except ImportError:
+    pytest.skip('HGQ2 is not installed', allow_module_level=True)
+
+from keras.layers import Input, ZeroPadding1D, ZeroPadding2D  # noqa: E402
+
+test_root_path = Path(__file__).parent
+
+
+@pytest.mark.parametrize('backend', ['Vivado', 'Vitis', 'oneAPI'])
+@pytest.mark.parametrize('io_type', ['io_parallel'])
+def test_bit_exact_zeropadding1d(test_case_id, backend, io_type):
+    """ZeroPadding1D between two quantized Conv1D layers must convert via the
+    bit_exact flow and reproduce the quantized Keras output exactly."""
+    with QuantizerConfigScope(f0=4, i0=4):
+        inp = Input((16, 8))
+        x = QConv1D(4, 1, name='c0')(inp)
+        x = ZeroPadding1D(padding=(2, 0))(x)
+        out = QConv1D(4, 3, padding='valid', name='c1')(x)
+        model = keras.Model(inp, out)
+
+    data = np.random.default_rng(0).standard_normal((1000, 16, 8)).astype(np.float32)
+    r_keras = trace_minmax(model, data, return_results=True)
+
+    precision = 'ac_fixed<2,0>' if backend == 'oneAPI' else 'ap_fixed<1,0>'
+    hls_config = {'Model': {'Precision': precision, 'ReuseFactor': 1, 'Strategy': 'latency'}}
+    output_dir = str(test_root_path / test_case_id)
+    hls_model = convert_from_keras_model(
+        model, backend=backend, output_dir=output_dir, hls_config=hls_config, io_type=io_type
+    )
+    hls_model.compile()
+
+    r_hls = hls_model.predict(data).reshape(r_keras.shape)
+    np.testing.assert_array_equal(r_keras, r_hls)
+
+
+@pytest.mark.parametrize('backend', ['Vivado', 'Vitis', 'oneAPI'])
+@pytest.mark.parametrize('io_type', ['io_parallel'])
+def test_bit_exact_zeropadding2d(test_case_id, backend, io_type):
+    """ZeroPadding2D between two quantized Conv2D layers must convert via the
+    bit_exact flow and reproduce the quantized Keras output exactly."""
+    with QuantizerConfigScope(f0=4, i0=4):
+        inp = Input((8, 8, 4))
+        x = QConv2D(8, 1, name='c0')(inp)
+        x = ZeroPadding2D(padding=((1, 1), (1, 1)))(x)
+        out = QConv2D(8, 3, padding='valid', name='c1')(x)
+        model = keras.Model(inp, out)
+
+    data = np.random.default_rng(1).standard_normal((500, 8, 8, 4)).astype(np.float32)
+    r_keras = trace_minmax(model, data, return_results=True)
+
+    precision = 'ac_fixed<2,0>' if backend == 'oneAPI' else 'ap_fixed<1,0>'
+    hls_config = {'Model': {'Precision': precision, 'ReuseFactor': 1, 'Strategy': 'latency'}}
+    output_dir = str(test_root_path / test_case_id)
+    hls_model = convert_from_keras_model(
+        model, backend=backend, output_dir=output_dir, hls_config=hls_config, io_type=io_type
+    )
+    hls_model.compile()
+
+    r_hls = hls_model.predict(data).reshape(r_keras.shape)
+    np.testing.assert_array_equal(r_keras, r_hls)