Merge branch 'fastmachinelearning:main' into main

dgburnette · Apr 19, 2024 · b02d224 · b02d224
2 parents c2ed5fb + 1616caf
commit b02d224
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Showing 12 changed files with 756 additions and 29 deletions.
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
@@ -6,6 +6,8 @@ stages:
 generator:
   stage: generate
   image: python:3.8-alpine
+  variables:
+    N_TESTS_PER_YAML: 5
   tags:
     - k8s-default
   before_script:

diff --git a/hls4ml/backends/fpga/fpga_backend.py b/hls4ml/backends/fpga/fpga_backend.py
@@ -34,8 +34,10 @@
     ExponentPrecisionType,
     FixedPrecisionType,
     IntegerPrecisionType,
+    PrecisionType,
     RoundingMode,
     SaturationMode,
+    UnspecifiedPrecisionType,
     XnorPrecisionType,
 )
 from hls4ml.writer import get_writer
@@ -290,9 +292,12 @@ def get_valid_conv_partition_splits(self, out_height, out_width):
 
     @classmethod
     def convert_precision_string(cls, precision):
-        if isinstance(precision, IntegerPrecisionType) or isinstance(precision, FixedPrecisionType):
+        if isinstance(precision, PrecisionType):
             return precision
 
+        if precision.lower() == 'auto':
+            return cls._convert_auto_type(precision)
+
         if precision.startswith('ac_'):
             return cls._convert_ac_type(precision)
         else:
@@ -366,6 +371,13 @@ def _convert_ac_type(cls, precision):
         elif 'int' in precision:
             return IntegerPrecisionType(width, signed)
 
+    @classmethod
+    def _convert_auto_type(cls, precision):
+        '''
+        Convert a "auto" precision string into the UnspecifiedPrecisionType
+        '''
+        return UnspecifiedPrecisionType()
+
     def product_type(self, data_T, weight_T):
         '''
         Helper function to determine which product implementation to use during inference

diff --git a/hls4ml/backends/quartus/quartus_backend.py b/hls4ml/backends/quartus/quartus_backend.py
@@ -73,6 +73,7 @@ def _register_flows(self):
             'quartus:inplace_stream_flatten',
             'quartus:skip_softmax',
             'quartus:fix_softmax_table_size',
+            'infer_precision_types',
         ]
         optimization_flow = register_flow('optimize', optimization_passes, requires=[init_flow], backend=self.name)
 

diff --git a/hls4ml/backends/vivado/passes/convolution_templates.py b/hls4ml/backends/vivado/passes/convolution_templates.py
@@ -285,6 +285,7 @@ def format(self, node):
         params['nzeros'] = node.get_weights('depthwise').nzeros
         params['index'] = str(node.index) + '_depthwise'
         params['weight_t'] = node.get_weights('depthwise').type
+        params['bias_t'] = node.get_weights('zero_bias').type
         params['fill_fn'] = 'FillConv1DBuffer'
 
         if node.get_attr('unscaled'):

diff --git a/hls4ml/backends/vivado/vivado_backend.py b/hls4ml/backends/vivado/vivado_backend.py
@@ -110,6 +110,7 @@ def _register_flows(self):
             'vivado:inplace_stream_flatten',
             'vivado:skip_softmax',
             'vivado:fix_softmax_table_size',
+            'infer_precision_types',
         ]
         optimization_flow = register_flow('optimize', optimization_passes, requires=[init_flow], backend=self.name)
 

diff --git a/hls4ml/model/optimizer/__init__.py b/hls4ml/model/optimizer/__init__.py
@@ -33,6 +33,7 @@
 register_flow(
     'convert',
     [
+        'infer_precision_types',
         'channels_last_converter',
         'fuse_bias_add',
         'remove_useless_transpose',
@@ -51,6 +52,7 @@
         'fuse_consecutive_batch_normalization',
         'fuse_batch_normalization',
         'replace_multidimensional_dense_with_conv',
+        'infer_precision_types',
         'set_precision_concat',
     ],
     requires=['convert'],