def __init__(self, convolutional_layer, memristor_model, memristor_model_params, mapping_routine=naive_map, transistor=False, programming_routine=None, p_l=None, scheme=memtorch.bh.Scheme.DoubleColumn, *args, **kwargs):
assert isinstance(convolutional_layer, nn.Conv1d), 'convolutional_layer is not an instance of nn.Conv1d.'
self.device = torch.device('cpu' if 'cpu' in memtorch.__version__ else 'cuda')
super(Conv1d, self).__init__(convolutional_layer.in_channels, convolutional_layer.out_channels, convolutional_layer.kernel_size, **kwargs)
self.padding = convolutional_layer.padding
self.stride = convolutional_layer.stride
self.weight.data = convolutional_layer.weight.data
if convolutional_layer.bias is not None:
self.bias.data = convolutional_layer.bias.data
self.zero_grad()
self.weight.requires_grad = False
if convolutional_layer.bias is not None:
self.bias.requires_grad = False
self.crossbars, self.crossbar_operation = init_crossbar(weights=self.weight,
memristor_model=memristor_model,
memristor_model_params=memristor_model_params,
transistor=transistor,
mapping_routine=mapping_routine,
programming_routine=programming_routine,
p_l=p_l,
scheme=scheme)
self.transform_output = lambda x: x
print('Patched %s -> %s' % (convolutional_layer, self))
def __init__(self, linear_layer, memristor_model, memristor_model_params, mapping_routine=naive_map, transistor=True, programming_routine=None, programming_routine_params={}, p_l=None, scheme=memtorch.bh.Scheme.DoubleColumn, **kwargs):
assert isinstance(linear_layer, nn.Linear), 'linear_layer is not an instance of nn.Linear.'
self.device = torch.device('cpu' if 'cpu' in memtorch.__version__ else 'cuda')
self.scheme = scheme
self.forward_legacy_enabled = True
super(Linear, self).__init__(linear_layer.in_features, linear_layer.out_features, **kwargs)
self.weight.data = linear_layer.weight.data
if linear_layer.bias is not None:
self.bias.data = linear_layer.bias.data
else:
self.bias = None
self.zero_grad()
self.weight.requires_grad = False
if linear_layer.bias is not None:
self.bias.requires_grad = False
self.crossbars, self.crossbar_operation = init_crossbar(weights=self.weight,
memristor_model=memristor_model,
memristor_model_params=memristor_model_params,
transistor=transistor,
mapping_routine=mapping_routine,
programming_routine=programming_routine,
programming_routine_params=programming_routine_params,
p_l=p_l,
scheme=scheme)
self.transform_output = lambda x: x
print('Patched %s -> %s' % (linear_layer, self))
def __init__(self,
convolutional_layer,
memristor_model,
memristor_model_params,
mapping_routine=naive_map,
transistor=True,
programming_routine=None,
programming_routine_params={},
p_l=None,
scheme=memtorch.bh.Scheme.DoubleColumn,
tile_shape=None,
max_input_voltage=None,
ADC_resolution=None,
ADC_overflow_rate=0.,
quant_method=None,
verbose=True,
*args,
**kwargs):
assert isinstance(
convolutional_layer,
nn.Conv3d), 'convolutional_layer is not an instance of nn.Conv3d.'
self.device = torch.device('cpu' if 'cpu' in
memtorch.__version__ else 'cuda')
self.scheme = scheme
self.tile_shape = tile_shape
self.max_input_voltage = max_input_voltage
self.ADC_resolution = ADC_resolution
self.ADC_overflow_rate = ADC_overflow_rate
if quant_method in memtorch.bh.Quantize.quant_methods:
self.quant_method = quant_method
else:
self.quant_method = None
if quant_method is not None:
assert ADC_resolution is not None and type(
ADC_resolution
) == int and ADC_resolution > 0, 'ADC resolution is invalid.'
assert ADC_overflow_rate is not None, 'ADC_overflow_rate must be specified if quant_method is not None.'
self.verbose = verbose
self.forward_legacy_enabled = True
super(Conv3d, self).__init__(convolutional_layer.in_channels,
convolutional_layer.out_channels,
convolutional_layer.kernel_size, **kwargs)
self.padding = convolutional_layer.padding
self.stride = convolutional_layer.stride
self.weight.data = convolutional_layer.weight.data
if convolutional_layer.bias is not None:
self.bias.data = convolutional_layer.bias.data
self.zero_grad()
self.weight.requires_grad = False
if convolutional_layer.bias is not None:
self.bias.requires_grad = False
self.crossbars, self.crossbar_operation = init_crossbar(
weights=self.weight,
memristor_model=memristor_model,
memristor_model_params=memristor_model_params,
transistor=transistor,
mapping_routine=mapping_routine,
programming_routine=programming_routine,
programming_routine_params=programming_routine_params,
p_l=p_l,
scheme=scheme,
tile_shape=tile_shape)
self.transform_output = lambda x: x
if verbose:
print('Patched %s -> %s' % (convolutional_layer, self))
def __init__(self,
linear_layer,
memristor_model,
memristor_model_params,
mapping_routine=naive_map,
transistor=True,
programming_routine=None,
programming_routine_params={},
p_l=None,
scheme=memtorch.bh.Scheme.DoubleColumn,
tile_shape=None,
max_input_voltage=None,
ADC_resolution=None,
ADC_overflow_rate=0.0,
quant_method=None,
verbose=True,
*args,
**kwargs):
assert isinstance(
linear_layer,
nn.Linear), "linear_layer is not an instance of nn.Linear."
self.device = torch.device("cpu" if "cpu" in
memtorch.__version__ else "cuda")
self.scheme = scheme
self.tile_shape = tile_shape
self.max_input_voltage = max_input_voltage
self.ADC_resolution = ADC_resolution
self.ADC_overflow_rate = ADC_overflow_rate
if quant_method in memtorch.bh.Quantize.quant_methods:
self.quant_method = quant_method
else:
self.quant_method = None
if quant_method is not None:
assert (ADC_resolution is not None and type(ADC_resolution) == int
and ADC_resolution > 0), "ADC resolution is invalid."
assert (
ADC_overflow_rate is not None
), "ADC_overflow_rate must be specified if quant_method is not None."
self.verbose = verbose
self.forward_legacy_enabled = True
super(Linear, self).__init__(linear_layer.in_features,
linear_layer.out_features, **kwargs)
self.weight.data = linear_layer.weight.data
if linear_layer.bias is not None:
self.bias.data = linear_layer.bias.data
else:
self.bias = None
self.zero_grad()
self.weight.requires_grad = False
if linear_layer.bias is not None:
self.bias.requires_grad = False
self.crossbars, self.crossbar_operation = init_crossbar(
weights=self.weight,
memristor_model=memristor_model,
memristor_model_params=memristor_model_params,
transistor=transistor,
mapping_routine=mapping_routine,
programming_routine=programming_routine,
programming_routine_params=programming_routine_params,
p_l=p_l,
scheme=scheme,
tile_shape=tile_shape,
)
self.transform_output = lambda x: x
if verbose:
print("Patched %s -> %s" % (linear_layer, self))
