rm pooling precision override in favor of fastmachinelearning#855

calad0i · Apr 18, 2024 · dcf805e · dcf805e
1 parent 6806aca
commit dcf805e
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diff --git a/hls4ml/backends/catapult/catapult_backend.py b/hls4ml/backends/catapult/catapult_backend.py
@@ -17,8 +17,6 @@
     Embedding,
     GarNet,
     GarNetStack,
-    GlobalPooling1D,
-    GlobalPooling2D,
     Layer,
     Pooling1D,
     Pooling2D,
@@ -30,7 +28,6 @@
 from hls4ml.model.optimizer import get_backend_passes, layer_optimizer
 from hls4ml.model.types import FixedPrecisionType, IntegerPrecisionType, NamedType, PackedType
 from hls4ml.report import parse_catapult_report
-from hls4ml.utils.fixed_point_utils import ceil_log2
 
 
 class CatapultBackend(FPGABackend):
@@ -409,37 +406,14 @@ def init_depconv2d(self, layer):
             dw_output_t = NamedType(dw_out_name, dw_out_precision)
         layer.set_attr('dw_output_t', dw_output_t)
 
-    def _set_pooling_accum_t(self, layer, pool_size):
-        extra_bits = ceil_log2(pool_size)
-        accum_t = layer.get_attr('accum_t')
-        accum_t.precision.width += extra_bits * 2
-        if isinstance(accum_t.precision, FixedPrecisionType):
-            accum_t.precision.integer += extra_bits
-
     @layer_optimizer(Pooling1D)
     def init_pooling1d(self, layer):
-        pool_size = layer.get_attr('pool_width')
-        self._set_pooling_accum_t(layer, pool_size)
-
         layer.set_attr('implementation', layer.model.config.get_conv_implementation(layer).lower())
 
     @layer_optimizer(Pooling2D)
     def init_pooling2d(self, layer):
-        pool_size = layer.get_attr('pool_height') * layer.get_attr('pool_width')
-        self._set_pooling_accum_t(layer, pool_size)
-
         layer.set_attr('implementation', layer.model.config.get_conv_implementation(layer).lower())
 
-    @layer_optimizer(GlobalPooling1D)
-    def init_global_pooling1d(self, layer):
-        pool_size = layer.get_attr('n_in')
-        self._set_pooling_accum_t(layer, pool_size)
-
-    @layer_optimizer(GlobalPooling2D)
-    def init_global_pooling2d(self, layer):
-        pool_size = layer.get_attr('in_height') * layer.get_attr('in_width')
-        self._set_pooling_accum_t(layer, pool_size)
-
     @layer_optimizer(Softmax)
     def init_softmax(self, layer):
         if layer.model.config.get_config_value('IOType') == 'io_parallel':

diff --git a/hls4ml/backends/vivado/vivado_backend.py b/hls4ml/backends/vivado/vivado_backend.py
@@ -17,8 +17,6 @@
     Embedding,
     GarNet,
     GarNetStack,
-    GlobalPooling1D,
-    GlobalPooling2D,
     Layer,
     Pooling1D,
     Pooling2D,
@@ -30,7 +28,6 @@
 from hls4ml.model.optimizer import get_backend_passes, layer_optimizer
 from hls4ml.model.types import FixedPrecisionType, IntegerPrecisionType, NamedType, PackedType
 from hls4ml.report import parse_vivado_report
-from hls4ml.utils.fixed_point_utils import ceil_log2
 
 
 class VivadoBackend(FPGABackend):
@@ -376,52 +373,14 @@ def init_depconv2d(self, layer):
         )  # TODO Once we have SeparableConv implementation for io_parallel this should be set properly
         layer.set_attr('implementation', layer.model.config.get_conv_implementation(layer).lower())
 
-    def _set_pooling_accum_t(self, layer: Layer, pool_size):
-        extra_bits = ceil_log2(pool_size)
-
-        input_layer_name = layer.inputs[0]
-        input_t = layer.model.get_layer_output_variable(input_layer_name).type
-        accum_t = layer.attributes['accum_t']
-        pool_op = layer.attributes['pool_op'].lower()
-
-        accum_t.name = f'{layer.name}_accum'
-        # accum_t.name was likely model_default, change to avoid override parameters for other by chance
-
-        accum_t = layer.get_attr('accum_t')
-
-        if pool_op == 'max':
-            accum_t.precision = input_t.precision
-        else:
-            # Average pool
-            if isinstance(accum_t.precision, FixedPrecisionType):
-                accum_t.precision.integer = input_t.precision.integer + extra_bits
-            accum_t.precision.width = input_t.precision.width + extra_bits * 2
-            accum_t.precision.signed = input_t.precision.signed
-
     @layer_optimizer(Pooling1D)
     def init_pooling1d(self, layer):
-        pool_size = layer.get_attr('pool_width')
-        self._set_pooling_accum_t(layer, pool_size)
-
         layer.set_attr('implementation', layer.model.config.get_conv_implementation(layer).lower())
 
     @layer_optimizer(Pooling2D)
     def init_pooling2d(self, layer):
-        pool_size = layer.get_attr('pool_height') * layer.get_attr('pool_width')
-        self._set_pooling_accum_t(layer, pool_size)
-
         layer.set_attr('implementation', layer.model.config.get_conv_implementation(layer).lower())
 
-    @layer_optimizer(GlobalPooling1D)
-    def init_global_pooling1d(self, layer):
-        pool_size = layer.get_attr('n_in')
-        self._set_pooling_accum_t(layer, pool_size)
-
-    @layer_optimizer(GlobalPooling2D)
-    def init_global_pooling2d(self, layer):
-        pool_size = layer.get_attr('in_height') * layer.get_attr('in_width')
-        self._set_pooling_accum_t(layer, pool_size)
-
     @layer_optimizer(Softmax)
     def init_softmax(self, layer):
         if layer.model.config.get_config_value('IOType') == 'io_parallel':