Merge pull request fastmachinelearning#909 from calad0i/fp_write_length_fix

Fix writer precision when fp bits >= 14

Author: jmitrevs

hls4ml/model/types.py

@@ -559,14 +559,9 @@ def update_precision(self, new_precision):
         if isinstance(new_precision, (IntegerPrecisionType, XnorPrecisionType, ExponentPrecisionType)):
             self.precision_fmt = '{:.0f}'
         elif isinstance(new_precision, FixedPrecisionType):
-            if new_precision.fractional > 0:
-                # Use str to represent the float with digits, get the length
-                # to right of decimal point
-                lsb = 2**-new_precision.fractional
-                decimal_spaces = len(str(lsb).split('.')[1])
-                self.precision_fmt = f'{{:.{decimal_spaces}f}}'
-            else:
-                self.precision_fmt = '{:.0f}'
+            decimal_spaces = max(0, new_precision.fractional)
+            self.precision_fmt = f'{{:.{decimal_spaces}f}}'
+
         else:
             raise RuntimeError(f"Unexpected new precision type: {new_precision}")
 

test/pytest/test_weight_writer.py

@@ -0,0 +1,33 @@
+from glob import glob
+from pathlib import Path
+
+import keras
+import numpy as np
+import pytest
+
+import hls4ml
+
+test_root_path = Path(__file__).parent
+test_root_path = Path('/tmp/trash')
+
+
+@pytest.mark.parametrize('k', [0, 1])
+@pytest.mark.parametrize('i', [4, 8, 10])
+@pytest.mark.parametrize('f', [-2, 0, 2, 7, 14])
+def test_weight_writer(k, i, f):
+    k, b, i = k, k + i + f, k + i
+    w = np.array([[np.float32(2.0**-f)]])
+    u = '' if k else 'u'
+    dtype = f'{u}fixed<{b}, {i}>'
+    hls_config = {'LayerName': {'dense': {'Precision': {'weight': dtype}}}}
+    model = keras.Sequential([keras.layers.Dense(1, input_shape=(1,), name='dense')])
+    model.layers[0].kernel.assign(keras.backend.constant(w))
+    output_dir = str(test_root_path / f'hls4ml_prj_test_weight_writer_{dtype}')
+    model_hls = hls4ml.converters.convert_from_keras_model(model, hls_config=hls_config, output_dir=output_dir)
+    model_hls.write()
+    w_paths = glob(str(Path(output_dir) / 'firmware/weights/w*.txt'))
+    print(w_paths[0])
+    assert len(w_paths) == 1
+    w_loaded = np.loadtxt(w_paths[0], delimiter=',').reshape(1, 1)
+    print(f'{w[0,0]:.14}', f'{w_loaded[0,0]:.14}')
+    assert np.all(w == w_loaded)