Add support for qkeras v3

docs/intro/setup.rst

@@ -54,6 +54,7 @@ The following Python packages are all optional and are only required if you inte
 
 * Quantization support
    * `QKeras <https://github.com/fastmachinelearning/qkeras>`_: based on Keras v2. See `frontend/keras <../frontend/keras.html>`_ for more details
+   * `QKeras-v3 <https://github.com/fastmachinelearning/qkeras-v3>`_: based on Keras v3. See `frontend/keras <../frontend/keras.html>`_ for more details
    * `HGQ <https://github.com/calad0i/HGQ>`_: Based on Keras v2. See `advanced/HGQ <../advanced/hgq.html>`_ for more details.
    * `HGQ2 <https://github.com/calad0i/HGQ2>`_: Based on Keras v3. See `advanced/HGQ2 <../advanced/hgq.html>`_ for more details.
    * `Brevitas <https://xilinx.github.io/brevitas/>`_: Based on PyTorch. See `frontend/pytorch <../frontend/pytorch.html>`_ for more details.
@@ -197,6 +198,9 @@ Optional Dependencies
    # For QKeras frontend
    pip install hls4ml[qkeras]
 
+   # For QKeras-v3 frontend
+   pip install hls4ml[qkeras-v3]
+
    # For Quartus report parsing
    pip install hls4ml[quartus-report]
 

docs/intro/status.rst

@@ -25,6 +25,7 @@ Frontend support:
     * HGQ
   * Keras v3
 
+    * QKeras-v3
     * HGQ2
 * PyTorch
 * ONNX
@@ -59,6 +60,8 @@ A summary of the on-going status of the ``hls4ml`` tool is in the table below.
 +-----------------------+-----+-----+--------------+--------+--------+-----+
 | QKeras                | ✅  | ✅  | ✅           | ✅     | N/A    | N/A |
 +-----------------------+-----+-----+--------------+--------+--------+-----+
+| QKeras-v3             | ✅  | ✅  | ✅           | ✅      | ✅    | N/A |
++-----------------------+-----+-----+--------------+--------+--------+-----+
 | HGQ                   | ✅  | ✅  | N/A          | N/A    | N/A    | N/A |
 +-----------------------+-----+-----+--------------+--------+--------+-----+
 | Keras v3              | ✅  | ✅  | ✅           | N/A    | ✅     | ❌  |

hls4ml/converters/keras_v3/__init__.py

@@ -5,6 +5,7 @@
     hgq2,  # noqa: F401
     merge,  # noqa: F401
     pooling,  # noqa: F401
+    qkeras,  # noqa: F401
     recurrent,  # noqa: F401
 )
 from ._base import registry as layer_handlers

hls4ml/converters/keras_v3/qkeras/__init__.py

@@ -0,0 +1 @@
+from . import activation, layer, utils

hls4ml/converters/keras_v3/qkeras/activation.py

@@ -0,0 +1,35 @@
+from typing import Any
+
+from hls4ml.converters.utils import IsolatedLayerReader
+
+from ..core import KerasV3LayerHandler
+from .utils import set_default_config
+
+
+class QKerasQActivationHandler(KerasV3LayerHandler):
+    handles = ('qkeras.qlayers.QActivation', 'QActivation')
+
+    def handle(
+        self,
+        layer,
+        in_tensors,
+        out_tensors,
+    ) -> tuple[dict[str, Any], ...]:
+
+        config = layer.get_config()
+        layer_dict = {'config': config, 'class_name': layer.__class__.__name__}
+
+        reader = IsolatedLayerReader(layer)
+        input_shapes = [list(t.shape) for t in in_tensors]
+        input_names = [t.name for t in in_tensors]
+
+        from hls4ml.converters.keras_v2_to_hls import layer_handlers as v2_layer_handlers
+
+        v2_handler = v2_layer_handlers.get(layer.__class__.__name__)
+        if v2_handler is None:
+            raise ValueError(f'No v2 handler found for {layer.__class__.__name__}')
+
+        hls_conf, _ = v2_handler(layer_dict, input_names, input_shapes, reader)
+        hls_conf = set_default_config(hls_conf, self.default_config)
+
+        return (hls_conf,)

hls4ml/converters/keras_v3/qkeras/layer.py

@@ -0,0 +1,49 @@
+from hls4ml.converters.utils import IsolatedLayerReader
+
+from ..core import KerasV3LayerHandler
+from .utils import set_default_config
+
+
+class QKerasV3LayerHandler(KerasV3LayerHandler):
+    handles = (
+        'qkeras.qlayers.QDense',
+        'qkeras.qconvolutional.QConv1D',
+        'qkeras.qconvolutional.QConv2D',
+        'qkeras.qconvolutional.QDepthwiseConv2D',
+        'qkeras.qconv2d_batchnorm.QConv2DBatchnorm',
+    )
+
+    def handle(self, layer, in_tensors, out_tensors):
+        config = layer.get_config()
+        layer_dict = {'config': config, 'class_name': layer.__class__.__name__}
+
+        reader = IsolatedLayerReader(layer)
+        input_shapes = [list(t.shape) for t in in_tensors]
+        input_names = [t.name for t in in_tensors]
+
+        from hls4ml.converters.keras_v2_to_hls import layer_handlers as v2_layer_handlers
+
+        v2_handler = v2_layer_handlers.get(layer.__class__.__name__)
+        if v2_handler is None:
+            raise ValueError(f'No v2 handler found for {layer.__class__.__name__}')
+
+        ret, _ = v2_handler(layer_dict, input_names, input_shapes, reader)
+        ret = set_default_config(ret, self.default_config)
+
+        activation = config.get('activation')
+        if activation not in (None, 'linear'):
+            from hls4ml.converters.keras.qkeras import get_activation_quantizer
+
+            activation_config = get_activation_quantizer(layer_dict, input_names)
+            intermediate_tensor_name = f'{out_tensors[0].name}_activation'
+            ret['output_keras_tensor_names'] = [intermediate_tensor_name]
+            activation_config.update(
+                {
+                    'name': f'{layer.name}_activation',
+                    'input_keras_tensor_names': [intermediate_tensor_name],
+                    'output_keras_tensor_names': [out_tensors[0].name],
+                }
+            )
+            return ret, activation_config
+
+        return ret

hls4ml/converters/keras_v3/qkeras/utils.py

@@ -0,0 +1,5 @@
+def set_default_config(hls_conf, default_config):
+    for key, value in default_config.items():
+        if key not in hls_conf.keys():
+            hls_conf[key] = value
+    return hls_conf

hls4ml/converters/keras_v3_to_hls.py

@@ -4,8 +4,7 @@
 from types import FunctionType
 from typing import Any
 
-import numpy as np
-
+from hls4ml.converters.utils import IsolatedLayerReader
 from hls4ml.model import ModelGraph
 
 if typing.TYPE_CHECKING:
@@ -238,15 +237,7 @@ def v2_call(
         config = layer.get_config()
         layer_dict = {'config': config, 'class_name': layer.__class__.__name__}
 
-        class IsolatedLayerReader:
-            def get_weights_data(self, layer_name, var_name):
-                assert layer_name == layer.name, f'Processing {layer.name}, but handler tried to read {layer_name}'
-                for w in layer.weights:
-                    if var_name in w.name:
-                        return np.array(w)
-                return None
-
-        reader = IsolatedLayerReader()
+        reader = IsolatedLayerReader(layer)
         input_shapes = [list(t.shape) for t in inp_tensors]
         input_names = [t.name for t in inp_tensors]
         output_names = [t.name for t in out_tensors]

hls4ml/converters/utils.py

@@ -1,5 +1,7 @@
 import math
 
+import numpy as np
+
 
 def parse_data_format(input_shape, data_format='channels_last'):
     """Parses the given input shape according to the specified data format.
@@ -287,3 +289,15 @@ def compute_padding_2d_pytorch(
             pad_left = pad_width
 
     return (out_height, out_width, pad_top, pad_bottom, pad_left, pad_right)
+
+
+class IsolatedLayerReader:
+    def __init__(self, layer):
+        self.layer = layer
+
+    def get_weights_data(self, layer_name, var_name):
+        assert layer_name == self.layer.name, f'Processing {self.layer.name}, but handler tried to read {layer_name}'
+        for w in self.layer.weights:
+            if var_name in w.name:
+                return np.array(w)
+        return None

hls4ml/model/quantizers.py

@@ -127,7 +127,6 @@ def __init__(self, config):
     def __call__(self, data):
         data = np.array(data, dtype='float32')
         return self.quantizer_fn(data).numpy()
-        # return self.quantizer_fn(data)
 
     def _get_type(self, quantizer_config):
         width = quantizer_config['config']['bits']
@@ -170,7 +169,9 @@ def __init__(self, config, xnor=False):
 
     def __call__(self, data):
         data = np.array(data, dtype='float32')
-        y = self.quantizer_fn(data).numpy()
+        y = self.quantizer_fn(data)
+        if hasattr(y, 'numpy'):
+            y = y.numpy()
         return self.binary_quantizer(y)
 
     def serialize_state(self):

hls4ml/model/types.py

@@ -5,6 +5,7 @@
 higher-dimensional tensors, which are defined as arrays or FIFO streams in the generated code.
 """
 
+import math
 from enum import Enum
 
 import numpy as np
@@ -837,7 +838,7 @@ def _format(self):
 
     def __iter__(self):
         data = self._format()
-        self._iterator = iter(data.reshape((np.product(data.shape[:-1]), 2)))
+        self._iterator = iter(data.reshape((math.prod(data.shape[:-1]), 2)))
         return self
 
     def __next__(self):

pyproject.toml

@@ -49,6 +49,7 @@ optional-dependencies.qkeras = [
   "tensorflow>=2.8,<=2.14.1",
   "tensorflow-model-optimization<=0.7.5",
 ]
+optional-dependencies.qkeras-v3 = [ "qkeras-v3" ]
 optional-dependencies.quartus-report = [ "calmjs-parse", "tabulate" ]
 optional-dependencies.sr = [ "sympy>=1.13.1" ]
 optional-dependencies.testing = [
@@ -72,6 +73,11 @@ optional-dependencies.testing-keras3 = [
   "keras>=3.10",
   "tensorflow>=2.15",
 ]
+optional-dependencies.testing-qkeras-v3 = [
+  "keras==3.14.1",
+  "qkeras-v3",
+  "tensorflow>=2.21",
+]
 urls.Homepage = "https://fastmachinelearning.org/hls4ml"
 scripts.hls4ml = "hls4ml.cli:main"
 entry-points.pytest_randomly.random_seeder = "hls4ml:reseed"

test/pytest/ci-template.yml

@@ -50,3 +50,9 @@
   variables:
     CONDA_ENV: "hls4ml-testing-keras3"
     EXTRA_DEPS: "[da,testing,testing-keras3,sr]"
+
+.pytest-qkeras-v3-only:
+  extends: .pytest
+  variables:
+    CONDA_ENV: "hls4ml-testing-keras3"
+    EXTRA_DEPS: "[qkeras-v3]"

test/pytest/generate_ci_yaml.py

@@ -36,6 +36,7 @@
     'test_multiout_onnx',
     'test_keras_v3_profiling',
 }
+QKERAS3_LIST = {'test_qkerasV3'}
 
 # Test files to split by individual test cases
 # Value = chunk size per CI job
@@ -79,7 +80,7 @@ def generate_test_yaml(test_root='.'):
     test_paths = [
         path
         for path in test_root.glob('**/test_*.py')
-        if path.stem not in (BLACKLIST | LONGLIST | set(SPLIT_BY_TEST_CASE.keys()) | KERAS3_LIST)
+        if path.stem not in (BLACKLIST | LONGLIST | set(SPLIT_BY_TEST_CASE.keys()) | KERAS3_LIST | QKERAS3_LIST)
     ]
     need_example_models = [uses_example_model(path) for path in test_paths]
 
@@ -140,6 +141,21 @@ def generate_test_yaml(test_root='.'):
         diff_yml = yaml.safe_load(template.format(name, '.pytest-keras3-only', test_files, batch_need_example_model))
         yml.update(diff_yml)
 
+    qkeras3_paths = [path for path in test_root.glob('**/test_*.py') if path.stem in QKERAS3_LIST]
+    qkeras3_need_examples = [uses_example_model(path) for path in qkeras3_paths]
+
+    qk3_idxs = list(range(len(qkeras3_need_examples)))
+    qk3_idxs = sorted(qk3_idxs, key=lambda i: f'{qkeras3_need_examples[i]}_{path_to_name(qkeras3_paths[i])}')
+
+    for batch_idxs in batched(qk3_idxs, n_test_files_per_yml):
+        batch_paths: list[Path] = [qkeras3_paths[i] for i in batch_idxs]
+        names = [path_to_name(path) for path in batch_paths]
+        name = 'qkerasV3'
+        test_files = ' '.join([str(path.relative_to(test_root)) for path in batch_paths])
+        batch_need_example_model = int(any([qkeras3_need_examples[i] for i in batch_idxs]))
+        diff_yml = yaml.safe_load(template.format(name, '.pytest-qkeras-v3-only', test_files, batch_need_example_model))
+        yml.update(diff_yml)
+
     return yml