ERROR: [XFORM 203-504] Stop unrolling loop 'MultLoop' #904
Comments
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Vivado has an unroll limit of 4096 hardcoded that cannot be circumvented easily. The warning message ( |
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thanks for your answer @calad0i as you mentioned and i read the documentation of hls4ml and other answers i found that i should use the resource strategy and and some higher reuse factors so i tried reuse factor of 1024 for all layers and reuse factor of 704 for out put dense as it's the maximum reuse factor for this layer and i passed that error but i face new one : and also i tried reuse factor of 128 for all layers and 704 for out put dense and i faced this error its very important for me to solve this problem and i hope if you could help me guys @vloncar @jmduarte @thesps |
Is this homework assignment? 😄 Your model is too big, you need to reduce the number of filters, a lot. There may be something else going on since that loop shouldn't be unrolled, but even with that resolved I would not expect this model to work. |
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no it's a part of my project :) :) @vloncar or layer in model_pruned.layers:
if layer.__class__.__name__ in ['Conv1D', 'Dense']:
w = layer.get_weights()[0]
layersize = np.prod(w.shape)
print("{}: {}".format(layer.name, layersize)) # 0 = weights, 1 = biases
if layersize > 4096: # assuming that shape[0] is batch, i.e., 'None'
print("Layer {} is too large ({}), are you sure you want to train?".format(layer.name, layersize)) |
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Try with 10-50k parameters, not half a million. All weights will be stored on chip, so you can't really go large |
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thank you so much @vloncar i changed the model architecture and as you said i tried to reduce model parameters to less than 50K here is my new model: and the configuration that i used is : import hls4ml
import plotting
# First, the baseline model
hls_config = hls4ml.utils.config_from_keras_model(model_pruned, granularity='name')
# Set the precision and reuse factor for the full model
hls_config['Model']['Precision'] = 'ap_fixed<22,6>'
hls_config['Model']['ReuseFactor'] = 96
hls_config['Model']['Strategy'] = 'resource'
# Create an entry for each layer, here you can for instance change the strategy for a layer to 'resource'
# or increase the reuse factor individually for large layers.
# In this case, we designed the model to be small enough for a fully parallel implementation
# so we use the latency strategy and reuse factor of 1 for all layers.
for Layer in hls_config['LayerName'].keys():
hls_config['LayerName'][Layer]['Strategy'] = 'resource'
hls_config['LayerName'][Layer]['ReuseFactor'] = 96
hls_config['LayerName'][Layer]['Precision'] = 'ap_fixed<22,6>'
#hls_config['LayerName'][layer]['Trace'] = True
# If you want best numerical performance for high-accuray models, while the default latency strategy is faster but numerically more unstable
hls_config['LayerName']['output_softmax']['Strategy'] = 'Stable'
hls_config['LayerName']['dense_0']['ReuseFactor'] = 64
hls_config['LayerName']['dense_1']['ReuseFactor'] = 64
hls_config['LayerName']['output_dense']['ReuseFactor'] = 16
plotting.print_dict(hls_config)
cfg = hls4ml.converters.create_config(backend='Vivado')
cfg['IOType'] = 'io_stream' # Must set this if using CNNs!
cfg['HLSConfig'] = hls_config
cfg['KerasModel'] = model
cfg['OutputDir'] = 'pruned_cnn/'
cfg['XilinxPart'] = 'xcu250-figd2104-2L-e'
cfg['strategy'] = 'resource'
#cfg['Interface'] = 'axi_stream'
hls_model = hls4ml.converters.keras_to_hls(cfg)
hls_model.compile()the code passed the pre-synthesis part and all the errors disappeared but after two days the code is still running on synthesis part and it can't pass the following warning : should i stop the code and run with different reuse factor for conv layer ? the code is running on a 12th Gen Intel® Core™ i9-12900K × 24 cpu . |
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thank you @vloncar i could pass the synthesis by reducing the model parameters. 👍 :):):) |
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@vloncar regarding your suggestionto @behnamarefy , I also reduced the number of parameters in my model. However, I still encounter the same issue, as indicated in solution1.log. Additionally, here is the content of yml configuration that i used is : Model: "LeNet5_MNIST" the cmd used in terminal : |
hello i'm working on a cnn network and i tried to implement it using hls4ml0.7.1 and vivado2019.2 but by trying different reuse factor i get the this error :
here is my code :
i tried to pruned this network and i load the pruned network here is the whole code
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