style

Author: calad0i

hls4ml/converters/keras_v3/hgq2/_base.py

@@ -17,7 +17,7 @@
 
 def extract_fixed_quantizer_config(q, tensor: 'KerasTensor', is_input: bool) -> dict[str, Any]:
     from hgq.quantizer.internal.fixed_point_quantizer import FixedPointQuantizerKBI, FixedPointQuantizerKIF
-    from keras.ops import convert_to_numpy
+    from keras import ops
 
     internal_q: FixedPointQuantizerKIF | FixedPointQuantizerKBI = q.quantizer
 
@@ -26,7 +26,7 @@ def extract_fixed_quantizer_config(q, tensor: 'KerasTensor', is_input: bool) ->
         raise ValueError(f"Tensor {tensor.name} has at least one dimension with no fixed size")
     k, i, f = internal_q.kif
     k, B, I = k, k + i + f, k + i  # type: ignore # noqa: E741
-    k, B, I = convert_to_numpy(k), convert_to_numpy(B), convert_to_numpy(I)  # noqa: E741
+    k, B, I = ops.convert_to_numpy(k), ops.convert_to_numpy(B), ops.convert_to_numpy(I)  # noqa: E741
     I = np.where(B > 0, I, 0)  # noqa: E741 # type: ignore
 
     k = np.broadcast_to(k.astype(np.int8), (1,) + shape)  # type: ignore
@@ -61,7 +61,7 @@ def override_io_tensor_confs(confs: tuple[dict[str, Any], ...], overrides: dict[
 
 
 @register
-class SQLayerHandler(KerasV3LayerHandler):
+class QLayerHandler(KerasV3LayerHandler):
     def __call__(
         self,
         layer: 'hgq.layers.QLayerBase',
@@ -101,23 +101,23 @@ def __call__(
         return *iq_confs, *ret, *oq_confs
 
     def load_weight(self, layer: 'Layer', key: str):
-        from keras.ops import convert_to_numpy
+        from keras import ops
 
         if hasattr(layer, f'q{key}'):
-            return convert_to_numpy(getattr(layer, f'q{key}'))
+            return ops.convert_to_numpy(getattr(layer, f'q{key}'))
         return super().load_weight(layer, key)
 
 
 @register
-class SQEinsumDenseHandler(SQLayerHandler, EinsumDenseHandler):
+class QEinsumDenseHandler(QLayerHandler, EinsumDenseHandler):
     handles = (
         'hgq.layers.core.einsum_dense.QEinsumDense',
         'hgq.layers.einsum_dense_batchnorm.QEinsumDenseBatchnorm',
     )
 
 
 @register
-class SQStandaloneQuantizerHandler(KerasV3LayerHandler):
+class QStandaloneQuantizerHandler(KerasV3LayerHandler):
     handles = ('hgq.quantizer.quantizer.Quantizer',)
 
     def handle(
@@ -132,7 +132,7 @@ def handle(
 
 
 @register
-class SQConvHandler(SQLayerHandler, ConvHandler):
+class QConvHandler(QLayerHandler, ConvHandler):
     handles = (
         'hgq.layers.conv.QConv1D',
         'hgq.layers.conv.QConv2D',
@@ -158,7 +158,7 @@ def handle(
 
 
 @register
-class SQDenseHandler(SQLayerHandler, DenseHandler):
+class QDenseHandler(QLayerHandler, DenseHandler):
     handles = ('hgq.layers.core.dense.QDense', 'hgq.layers.core.dense.QBatchNormDense')
 
     def handle(
@@ -177,12 +177,12 @@ def handle(
 
 
 @register
-class SQActivationHandler(SQLayerHandler, ActivationHandler):
+class QActivationHandler(QLayerHandler, ActivationHandler):
     handles = ('hgq.layers.activation.QActivation',)
 
 
 @register
-class SQBatchNormalizationHandler(SQLayerHandler):
+class QBatchNormalizationHandler(QLayerHandler):
     handles = ('hgq.layers.batch_normalization.QBatchNormalization',)
 
     def handle(
@@ -208,7 +208,7 @@ def handle(
 
 
 @register
-class SQMergeHandler(SQLayerHandler, MergeHandler):
+class QMergeHandler(QLayerHandler, MergeHandler):
     handles = (
         'hgq.layers.ops.merge.QAdd',
         'hgq.layers.ops.merge.QSubtract',

hls4ml/converters/keras_v3/hgq2/einsum.py

@@ -1,16 +1,16 @@
 import typing
-from typing import Sequence
+from collections.abc import Sequence
 
 from ..einsum_dense import strip_batch_dim
-from ._base import SQLayerHandler, register
+from ._base import QLayerHandler, register
 
 if typing.TYPE_CHECKING:
     import hgq
     from keras import KerasTensor
 
 
 @register
-class SQEinsumHandler(SQLayerHandler):
+class QEinsumHandler(QLayerHandler):
     handles = ('hgq.layers.ops.einsum.QEinsum',)
 
     def handle(

hls4ml/converters/keras_v3/hgq2/multi_head_attention.py

@@ -1,20 +1,20 @@
 import typing
+from collections.abc import Sequence
 from inspect import Signature
-from typing import Sequence
 
 import numpy as np
 
-from ._base import SQEinsumDenseHandler, SQLayerHandler, register
-from .einsum import SQEinsumHandler
-from .softmax import SQSoftmaxHandler
+from ._base import QEinsumDenseHandler, QLayerHandler, register
+from .einsum import QEinsumHandler
+from .softmax import QSoftmaxHandler
 
 if typing.TYPE_CHECKING:
     import hgq
     from keras import KerasTensor
 
 
 @register
-class SQMultiHeadAttentionHandler(SQLayerHandler):
+class QMultiHeadAttentionHandler(QLayerHandler):
     handles = ('hgq.layers.multi_head_attention.QMultiHeadAttention',)
 
     def handle(
@@ -99,9 +99,9 @@ def handle(
         tensor_pre_score = KerasTensor(name=f'{unique_name}_pre_score', shape=score_batch_shape)
         tensor_score = KerasTensor(name=f'{unique_name}_score', shape=score_batch_shape)
 
-        einsum_handler = SQEinsumHandler()
-        einsum_dense_handler = SQEinsumDenseHandler()
-        softmax_handler = SQSoftmaxHandler()
+        einsum_handler = QEinsumHandler()
+        einsum_dense_handler = QEinsumDenseHandler()
+        softmax_handler = QSoftmaxHandler()
 
         config_to_Q = einsum_dense_handler(to_Q, [tensor_q], [tensor_Q])
         config_to_K = einsum_dense_handler(to_K, [tensor_k], [tensor_K])

hls4ml/converters/keras_v3/hgq2/softmax.py

@@ -1,10 +1,10 @@
 import typing
+from collections.abc import Sequence
 from math import prod
-from typing import Sequence
 
 from hls4ml.model.types import FixedPrecisionType, RoundingMode, SaturationMode
 
-from ._base import SQLayerHandler, register
+from ._base import QLayerHandler, register
 
 if typing.TYPE_CHECKING:
     import hgq
@@ -40,7 +40,7 @@ def fixed_quantizer_to_hls4ml_t(q: 'FixedPointQuantizerBase', take_max=False):
 
 
 @register
-class SQSoftmaxHandler(SQLayerHandler):
+class QSoftmaxHandler(QLayerHandler):
     handles = ('hgq.layers.softmax.QSoftmax',)
 
     def handle(

hls4ml/converters/keras_v3/hgq2/unary_lut.py

@@ -1,12 +1,12 @@
 import typing
-from typing import Sequence
+from collections.abc import Sequence
 
 import numpy as np
 from quantizers import float_quantize, get_fixed_quantizer_np
 
 from hls4ml.model.types import FixedPrecisionType
 
-from ._base import KerasV3LayerHandler, SQLayerHandler, register
+from ._base import KerasV3LayerHandler, QLayerHandler, register
 
 if typing.TYPE_CHECKING:
     import hgq
@@ -18,7 +18,7 @@
 
 
 @register
-class SQUnaryLUTHandler(SQLayerHandler, KerasV3LayerHandler):
+class QUnaryLUTHandler(QLayerHandler, KerasV3LayerHandler):
     handles = ('hgq.layers.activation.QUnaryFunctionLUT',)
 
     def handle(