A few updates (fastmachinelearning#29)

* Increase precision of weight printing

* Get class name differently for profiling. Import profiling in model/__init__ for easier import elsewhere

* Make optimizer passes configurable. API is with list in config file. Splitting hls_model.py into hls_model.py and hls_layers.py was necessary to remove circular import dependency from optimizers importing Layers and utilities, while hls_model now needs to import optimizer

* QKeras use of integer bits seems to differ a bit from ap_fixed. e.g. quantized_bits(4,0).max() is 1.0, whereas it would be 0.5 with ap_fixed. So, add 1 bit to the integer for ap_fixed types

* Add 1 bit elsewhere for QKeras, since they don't count the sign bit

hls4ml/converters/__init__.py

@@ -62,7 +62,6 @@ def convert_from_yaml_config(yamlConfig):
 
 def convert_from_keras_model(model, output_dir='my-hls-test', project_name='myproject',
     fpga_part='xcku115-flvb2104-2-i', clock_period=5, hls_config={}):
-
     config = create_vivado_config(output_dir=output_dir,
         project_name=project_name, fpga_part=fpga_part, clock_period=clock_period)
     config['KerasModel'] = model
@@ -82,5 +81,8 @@ def convert_from_keras_model(model, output_dir='my-hls-test', project_name='mypr
 
     if 'LayerType' in hls_config:
         config['HLSConfig']['LayerType'] = hls_config['LayerType']
+
+    if 'Optimizers' in hls_config:
+        config['HLSConfig']['Optimizers'] = hls_config['Optimizers']
 
     return keras_to_hls(config)

hls4ml/converters/keras/qkeras.py

@@ -32,7 +32,7 @@ def get_type(quantizer_config):
         else:
             return IntegerPrecisionType(width=width, signed=True)
     else:
-        return FixedPrecisionType(width=width, integer=integer, signed=True)
+        return FixedPrecisionType(width=width+1, integer=integer+1, signed=True)
 
 def get_quantizer_from_config(keras_layer, quantizer_var):
     quantizer_config = keras_layer['config']['{}_quantizer'.format(quantizer_var)]
@@ -81,8 +81,8 @@ def parse_qactivation_layer(keras_layer, input_names, input_shapes, data_reader,
 
     layer['class_name'] = 'Activation'
     layer['activation'] = act_class.replace('quantized_', '')
-    layer['bits'] = activation_config['config']['bits']
-    layer['integer'] = activation_config['config']['integer']
+    layer['bits'] = activation_config['config']['bits'] + 1
+    layer['integer'] = activation_config['config']['integer'] + 1
     #TODO this needs extra work in HLS model and HLS templates
 
     return layer, [shape for shape in input_shapes[0]]

hls4ml/converters/keras_to_hls.py

@@ -5,7 +5,6 @@
 import math
 
 from hls4ml.model import HLSModel
-from hls4ml.model.optimizer import optimize_model
 
 MAXMULT = 4096
 
@@ -478,8 +477,6 @@ def keras_to_hls_old(yamlConfig):
 
     print('Creating HLS model')
     hls_model = HLSModel(yamlConfig, reader, layer_list, input_layers, output_layers)
-    optimizers = ['eliminate_linear_activation', 'merge_batch_norm_quantized_tanh', 'quantize_dense_output', 'fuse_dense_batch_norm']
-    optimize_model(hls_model, optimizers)
     return hls_model
 
 def parse_default_keras_layer(keras_layer, input_names):
@@ -638,6 +635,4 @@ def keras_to_hls(config):
 
     print('Creating HLS model')
     hls_model = HLSModel(config, reader, layer_list, input_layers, output_layers)
-    optimizers = ['eliminate_linear_activation', 'merge_batch_norm_quantized_tanh', 'quantize_dense_output', 'fuse_dense_batch_norm']
-    optimize_model(hls_model, optimizers)
     return hls_model

hls4ml/converters/onnx_to_hls.py

@@ -5,7 +5,6 @@
 from onnx import optimizer, helper, numpy_helper, shape_inference
 
 from hls4ml.model import HLSModel
-from hls4ml.model.optimizer import optimize_model
 
 MAXMULT = 4096
 
@@ -374,6 +373,4 @@ def onnx_to_hls(yamlConfig):
 
     print('Creating HLS model')
     hls_model = HLSModel(yamlConfig, reader, layer_list, input_layers, output_layers)
-    optimizers = ['eliminate_linear_activation', 'merge_batch_norm_quantized_tanh', 'quantize_dense_output', 'fuse_dense_batch_norm']
-    optimize_model(hls_model, optimizers)
     return hls_model

hls4ml/converters/pytorch_to_hls.py

@@ -8,7 +8,6 @@
 import re
 
 from hls4ml.model import HLSModel
-from hls4ml.model.optimizer import optimize_model
 
 class PyTorchDataReader:
     def __init__(self, config):
@@ -101,6 +100,4 @@ def pytorch_to_hls(yamlConfig):
     reader = PyTorchDataReader(yamlConfig)
     print('Creating HLS model')
     hls_model = HLSModel(yamlConfig, reader, layer_list)
-    optimizers = ['eliminate_linear_activation', 'merge_batch_norm_quantized_tanh', 'quantize_dense_output', 'fuse_dense_batch_norm']
-    optimize_model(hls_model, optimizers)
     return hls_model

hls4ml/converters/tf_to_hls.py

@@ -8,7 +8,6 @@
 from tensorflow.python.framework import tensor_util
 
 from hls4ml.model import HLSModel
-from hls4ml.model.optimizer import optimize_model
 
 MAXMULT = 4096
 
@@ -371,6 +370,4 @@ def tf_to_hls(yamlConfig):
     reader = TFDataReader(graph)
     print('Creating HLS model')
     hls_model = HLSModel(yamlConfig, reader, layer_list, input_layers, output_layers)
-    optimizers = ['eliminate_linear_activation', 'merge_batch_norm_quantized_tanh', 'quantize_dense_output', 'fuse_biasadd', 'fuse_dense_batch_norm']
-    optimize_model(hls_model, optimizers)
     return hls_model

hls4ml/model/__init__.py

@@ -1,3 +1,4 @@
 from __future__ import absolute_import
 
-from .hls_model import HLSModel, HLSConfig
+from .hls_model import HLSModel, HLSConfig
+from . import profiling