PyTorch conversion issues

[thesps]

Improper handling of PyTorch Linear layer with bias=False

When a PyTorch model has a Linear layer with bias=False, conversion crashes out when trying to access the non-existent bias data from the model. Minimal example to reproduce the issue:

import torch
import hls4ml
model = torch.nn.Sequential(torch.nn.Linear(1,1,bias=False))
config = hls4ml.utils.config_from_pytorch_model(model)
hls_model = hls4ml.converters.convert_from_pytorch_model(model, (1,1) hls_config=config)
hls_model = hls4ml.converters.convert_from_pytorch_model(model, hls_config=config)

End of error message:

~/Work/hls4ml/hls4ml-master/hls4ml/model/hls_layers.py in add_bias(self, quantizer)
    460
    461     def add_bias(self, quantizer=None):
--> 462         data = self.model.get_weights_data(self.name, 'bias')
    463         precision = None
    464         type_name = None

~/Work/hls4ml/hls4ml-master/hls4ml/model/hls_model.py in get_weights_data(self, layer_name, var_name)
    477
    478     def get_weights_data(self, layer_name, var_name):
--> 479         return self.reader.get_weights_data(layer_name, var_name)
    480
    481     def next_layer(self):

~/Work/hls4ml/hls4ml-master/hls4ml/converters/pytorch_to_hls.py in get_weights_data(self, layer_name, var_name)
     58             var_name = torch_paramap[var_name]
     59
---> 60         data = self.state_dict[layer_name + '.' + var_name].numpy().transpose() #Look at transpose when systhesis produce lousy results. Might need to remove it.
     61
     62         return data

KeyError: '0.bias'

Non-handling of ParametrizedActivation parameter

The parameter(s) of ParametrizedActivation layers, e.g. ELU, PReLU, LeakyReLU don't get propagated through the conversion. Simple demonstration case:

import torch
import hls4ml
model = torch.nn.Sequential(torch.nn.ELU(alpha=0.5))
hls4ml.utils.config_from_pytorch_model(model)
hls_model = hls4ml.converters.convert_from_pytorch_model(model, input_shape=[1,1])
print(list(hls_model.get_layers())[1].attributes)

Gives:

{'class_name': 'ELU', 'activation': 'ELU', 'name': '0', 'accum_t': <hls4ml.model.hls_layers.FixedPrecisionType object at 0x7fa619b242d0>, 'table_t': <hls4ml.model.hls_layers.FixedPrecisionType object at 0x7fa619f10750>, 'table_size': 1024}

The Keras equivalent also has the attribute activ_param: 0.5, and the absence of that gives wrong inference results.

Tasks (can be assigned / self-assigned)

• [ ] Develop simple test cases that currently fail (working on it)
• [ ] Fix bias=False handling
• [ ] Fix ParametrizedActivation handling