def _parse_hls_config(self):
hls_config = self.config['HLSConfig']
self.optimizers = hls_config.get('Optimizers')
if 'SkipOptimizers' in hls_config:
if self.optimizers is not None:
raise Exception(
'Invalid optimizer configuration, please use either "Optimizers" or "SkipOptimizers".'
)
skip_optimizers = hls_config.get('SkipOptimizers')
selected_optimizers = get_available_passes()
for opt in skip_optimizers:
try:
selected_optimizers.remove(opt)
except ValueError:
pass
self.optimizers = selected_optimizers
model_cfg = hls_config.get('Model')
if model_cfg is not None:
precision_cfg = model_cfg.get('Precision')
if precision_cfg is not None:
if isinstance(precision_cfg, dict):
for var, precision in precision_cfg.items():
self.model_precision[var] = precision
else:
self.model_precision[
'default'] = precision_cfg # Default precision for everything
self.model_rf = model_cfg.get('ReuseFactor')
self.model_strategy = model_cfg.get('Strategy', 'Latency')
self.model_compression = bool(model_cfg.get('Compression', 0))
layer_type_cfg = hls_config.get('LayerType')
if layer_type_cfg is not None:
for layer_type, layer_cfg in layer_type_cfg.items():
precision_cfg = layer_cfg.get('Precision')
if isinstance(precision_cfg, dict):
for var, precision in precision_cfg.items():
self.layer_type_precision[layer_type.lower() + '_' +
var] = precision
else:
self.layer_type_precision[layer_type.lower() +
'_default'] = precision_cfg
rf = layer_cfg.get('ReuseFactor')
if rf is not None:
self.layer_type_rf[layer_type.lower()] = rf
strategy = layer_cfg.get('Strategy')
if strategy is not None:
self.layer_type_strategy[layer_type.lower()] = strategy
compression = layer_cfg.get('Compression')
if compression is not None:
self.layer_type_compression[layer_type.lower()] = bool(
compression)
layer_name_cfg = hls_config.get('LayerName')
if layer_name_cfg is not None:
for layer_name, layer_cfg in layer_name_cfg.items():
precision_cfg = layer_cfg.get('Precision')
if isinstance(precision_cfg, dict):
for var, precision in precision_cfg.items():
self.layer_name_precision[layer_name.lower() + '_' +
var] = precision
else:
self.layer_name_precision[layer_name.lower() +
'_default'] = precision_cfg
rf = layer_cfg.get('ReuseFactor')
if rf is not None:
self.layer_name_rf[layer_name.lower()] = rf
strategy = layer_cfg.get('Strategy')
if strategy is not None:
self.layer_name_strategy[layer_name.lower()] = strategy
compression = layer_cfg.get('Compression')
if compression is not None:
self.layer_name_compression[layer_name.lower()] = bool(
compression)
def _parse_hls_config(self):
hls_config = self.config['HLSConfig']
self.flows = hls_config.get('Flows')
if self.flows is None:
self.flows = [self.backend.get_default_flow()]
# TODO this is now effectively broken
self.optimizers = hls_config.get('Optimizers')
if 'SkipOptimizers' in hls_config:
if self.optimizers is not None:
raise Exception(
'Invalid optimizer configuration, please use either "Optimizers" or "SkipOptimizers".'
)
skip_optimizers = hls_config.get('SkipOptimizers')
selected_optimizers = get_available_passes()
for opt in skip_optimizers:
try:
selected_optimizers.remove(opt)
except ValueError:
pass
self.optimizers = selected_optimizers
model_cfg = hls_config.get('Model')
if model_cfg is not None:
precision_cfg = model_cfg.get('Precision')
if precision_cfg is not None:
if isinstance(precision_cfg, dict):
for var, precision in precision_cfg.items():
self.model_precision[var] = precision
else:
self.model_precision[
'default'] = precision_cfg # Default precision for everything
self.model_bf = model_cfg.get(
'BramFactor', np.inf) # Weight threshold to be external BRAM
self.model_rf = model_cfg.get('ReuseFactor')
self.model_targ_cycles = model_cfg.get('TargetCycles')
self.model_conv_implementation = model_cfg.get(
'ConvImplementation', 'LineBuffer')
self.model_strategy = model_cfg.get('Strategy', 'Latency')
self.model_compression = bool(model_cfg.get('Compression', 0))
layer_type_cfg = hls_config.get('LayerType')
if layer_type_cfg is not None:
for layer_type, layer_cfg in layer_type_cfg.items():
precision_cfg = layer_cfg.get('Precision')
if isinstance(precision_cfg, dict):
for var, precision in precision_cfg.items():
self.layer_type_precision[layer_type.lower() + '_' +
var] = precision
else:
self.layer_type_precision[layer_type.lower() +
'_default'] = precision_cfg
rf = layer_cfg.get('ReuseFactor')
if rf is not None:
self.layer_type_rf[layer_type.lower()] = rf
targ_cycles = layer_cfg.get('TargetCycles')
if targ_cycles is not None:
self.layer_type_targ_cycles[
layer_type.lower()] = targ_cycles
strategy = layer_cfg.get('Strategy')
if strategy is not None:
self.layer_type_strategy[layer_type.lower()] = strategy
conv_implementation = layer_cfg.get('ConvImplementation')
if conv_implementation is not None:
self.layer_type_conv_implementation[
layer_type.lower()] = conv_implementation
compression = layer_cfg.get('Compression')
if compression is not None:
self.layer_type_compression[layer_type.lower()] = bool(
compression)
layer_name_cfg = hls_config.get('LayerName')
if layer_name_cfg is not None:
for layer_name, layer_cfg in layer_name_cfg.items():
precision_cfg = layer_cfg.get('Precision')
if isinstance(precision_cfg, dict):
for var, precision in precision_cfg.items():
self.layer_name_precision[layer_name.lower() + '_' +
var] = precision
else:
self.layer_name_precision[layer_name.lower() +
'_default'] = precision_cfg
rf = layer_cfg.get('ReuseFactor')
if rf is not None:
self.layer_name_rf[layer_name.lower()] = rf
targ_cycles = layer_cfg.get('TargetCycles')
if targ_cycles is not None:
self.layer_name_targ_cycles[
layer_name.lower()] = targ_cycles
strategy = layer_cfg.get('Strategy')
if strategy is not None:
self.layer_name_strategy[layer_name.lower()] = strategy
conv_implementation = layer_cfg.get('ConvImplementation')
if conv_implementation is not None:
self.layer_name_conv_implementation[
layer_name.lower()] = conv_implementation
compression = layer_cfg.get('Compression')
if compression is not None:
self.layer_name_compression[layer_name.lower()] = bool(
compression)