def forward(self, input):
"""Method to perform forward propagations.
Parameters
----------
input : torch.Tensor
Input tensor.
Returns
-------
torch.Tensor
Output tensor.
"""
if self.forward_legacy_enabled:
return torch.nn.functional.conv1d(
input.to(self.device),
self.weight.to(self.device),
bias=self.bias,
stride=self.stride,
padding=self.padding,
)
else:
output_dim = (
int(
(input.shape[2] - self.kernel_size[0] + 2 * self.padding[0])
/ self.stride[0]
)
+ 1
)
out = torch.zeros((input.shape[0], self.out_channels, output_dim)).to(
self.device
)
if not all(item == 0 for item in self.padding):
input = nn.functional.pad(input, pad=(self.padding[0], self.padding[0]))
if self.max_input_voltage is not None:
assert (
type(self.max_input_voltage) == int
or type(self.max_input_voltage) == float
) and self.max_input_voltage > 0, (
"The maximum input voltage (max_input_voltage) must be >0."
)
input = convert_range(
input,
input.min(),
input.max(),
-self.max_input_voltage,
self.max_input_voltage,
)
for batch in range(input.shape[0]):
unfolded_batch_input = (
input[batch]
.unfold(-1, size=self.kernel_size[0], step=self.stride[0])
.permute(1, 0, 2)
.reshape(-1, self.in_channels * self.kernel_size[0])
)
unfolded_batch_input_shape = unfolded_batch_input.shape
if hasattr(self, "non_linear"):
if self.tile_shape is not None:
tiles_map = self.crossbars[0].tiles_map
crossbar_shape = (
self.crossbars[0].rows,
self.crossbars[0].columns,
)
else:
tiles_map = None
crossbar_shape = None
if hasattr(self, "simulate"):
nl = False
else:
nl = True
out_ = (
self.crossbar_operation(
self.crossbars,
lambda crossbar, input_: simulate_matmul(
unfolded_batch_input,
crossbar,
nl=nl,
tiles_map=tiles_map,
crossbar_shape=crossbar_shape,
max_input_voltage=self.max_input_voltage,
ADC_resolution=self.ADC_resolution,
ADC_overflow_rate=self.ADC_overflow_rate,
quant_method=self.quant_method,
),
input_=unfolded_batch_input,
)
.to(self.device)
.T
)
else:
if self.tile_shape is not None:
(
unfolded_batch_input_tiles,
unfolded_batch_input_tiles_map,
) = gen_tiles(unfolded_batch_input, self.tile_shape, input=True)
crossbar_shape = (
self.crossbars[0].rows,
self.crossbars[0].columns,
)
tiles_map = self.crossbars[0].tiles_map
out_ = tile_matmul(
unfolded_batch_input_tiles,
unfolded_batch_input_tiles_map,
unfolded_batch_input_shape,
self.crossbar_operation(
self.crossbars,
lambda crossbar: crossbar.conductance_matrix,
),
tiles_map,
crossbar_shape,
self.ADC_resolution,
self.ADC_overflow_rate,
self.quant_method,
).T
else:
out_ = torch.matmul(
unfolded_batch_input,
self.crossbar_operation(
self.crossbars,
lambda crossbar: crossbar.conductance_matrix,
),
).T
if self.quant_method is not None:
out_ = memtorch.bh.Quantize.quantize(
out_,
quant=self.ADC_resolution,
overflow_rate=self.ADC_overflow_rate,
quant_method=self.quant_method,
)
out[batch] = out_.view(size=(1, self.out_channels, output_dim))
out = self.transform_output(out)
if self.bias is not None:
out += self.bias.view(-1, 1).expand_as(out)
return out
def forward(self, input):
"""Method to perform forward propagations.
Parameters
----------
input : torch.Tensor
Input tensor.
Returns
-------
torch.Tensor
Output tensor.
"""
if self.forward_legacy_enabled:
return torch.nn.functional.conv3d(input.to(self.device),
self.weight.to(self.device),
bias=self.bias,
stride=self.stride,
padding=self.padding)
else:
output_dim = [0, 0, 0]
output_dim[0] = int(
(input.shape[2] - self.kernel_size[0] + 2 * self.padding[0]) /
self.stride[0]) + 1
output_dim[1] = int(
(input.shape[3] - self.kernel_size[1] + 2 * self.padding[1]) /
self.stride[1]) + 1
output_dim[2] = int(
(input.shape[4] - self.kernel_size[2] + 2 * self.padding[2]) /
self.stride[2]) + 1
out = torch.zeros(
(input.shape[0], self.out_channels, output_dim[0],
output_dim[1], output_dim[2])).to(self.device)
for batch in range(input.shape[0]):
if not all(item == 0 for item in self.padding):
batch_input = nn.functional.pad(
input[batch],
pad=(self.padding[2], self.padding[2], self.padding[1],
self.padding[1], self.padding[0],
self.padding[0]))
else:
batch_input = input[batch]
if self.max_input_voltage is not None:
assert (
type(self.max_input_voltage) == int
or type(self.max_input_voltage) == float
) and self.max_input_voltage > 0, 'The maximum input voltage (max_input_voltage) must be >0.'
batch_input = batch_input = convert_range(
batch_input, batch_input.min(), batch_input.max(),
-self.max_input_voltage, self.max_input_voltage)
unfolded_batch_input = batch_input.unfold(1, self.kernel_size[0], self.stride[0]).unfold(2, self.kernel_size[1], self.stride[1]).unfold(3, self.kernel_size[2], self.stride[2]) \
.permute(1, 2, 3, 0, 4, 5, 6).reshape(-1, self.in_channels * self.kernel_size[0] * self.kernel_size[1] * self.kernel_size[2])
unfolded_batch_input_shape = unfolded_batch_input.shape
if hasattr(self, 'non_linear'):
if self.tile_shape is not None:
tiles_map = self.crossbars[0].tiles_map
crossbar_shape = (self.crossbars[0].rows,
self.crossbars[0].columns)
else:
tiles_map = None
crossbar_shape = None
if hasattr(self, 'simulate'):
nl = False
else:
nl = True
out_ = self.crossbar_operation(self.crossbars, lambda crossbar, input_: simulate_matmul(unfolded_batch_input, crossbar, nl=nl, \
tiles_map=tiles_map, crossbar_shape=crossbar_shape, max_input_voltage=self.max_input_voltage,
ADC_resolution=self.ADC_resolution, ADC_overflow_rate=self.ADC_overflow_rate,
quant_method=self.quant_method), input_=unfolded_batch_input).to(self.device).T
else:
if self.tile_shape is not None:
unfolded_batch_input_tiles, unfolded_batch_input_tiles_map = gen_tiles(
unfolded_batch_input, self.tile_shape, input=True)
crossbar_shape = (self.crossbars[0].rows,
self.crossbars[0].columns)
tiles_map = self.crossbars[0].tiles_map
out_ = tile_matmul(unfolded_batch_input_tiles, unfolded_batch_input_tiles_map, unfolded_batch_input_shape, \
self.crossbar_operation(self.crossbars, lambda crossbar: crossbar.conductance_matrix), tiles_map, crossbar_shape,
self.ADC_resolution, self.ADC_overflow_rate, self.quant_method).T
else:
out_ = torch.matmul(
unfolded_batch_input,
self.crossbar_operation(
self.crossbars, lambda crossbar: crossbar.
conductance_matrix)).T
if self.quant_method is not None:
out_ = memtorch.bh.Quantize.quantize(
out_,
bits=self.ADC_resolution,
overflow_rate=self.ADC_overflow_rate,
quant_method=self.quant_method)
out[batch] = out_.view(size=(1, self.out_channels,
*output_dim))
out = self.transform_output(out)
if not self.bias is None:
out[batch] += self.bias.data.view(-1, 1, 1,
1).expand_as(out[batch])
return out
def forward(self, input):
"""Method to perform forward propagations.
Parameters
----------
input : torch.Tensor
Input tensor.
Returns
-------
torch.Tensor
Output tensor.
"""
if self.forward_legacy_enabled:
out = torch.matmul(input.to(self.device),
self.weight.data.T.to(self.device))
if self.bias is not None:
out += self.bias.view(1, -1).expand_as(out)
return out
else:
input_shape = input.shape
if self.max_input_voltage is not None:
assert (
type(self.max_input_voltage) == int
or type(self.max_input_voltage) == float
) and self.max_input_voltage > 0, 'The maximum input voltage (max_input_voltage) must be >0.'
input = input = convert_range(input, input.min(), input.max(),
-self.max_input_voltage,
self.max_input_voltage)
if hasattr(self, 'non_linear'):
if self.tile_shape is not None:
tiles_map = self.crossbars[0].tiles_map
crossbar_shape = self.weight.data.shape
else:
tiles_map = None
crossbar_shape = None
if hasattr(self, 'simulate'):
nl = False
else:
nl = True
out = self.crossbar_operation(self.crossbars, lambda crossbar, input_: simulate_matmul(input, crossbar, nl=nl, \
tiles_map=tiles_map, crossbar_shape=crossbar_shape, max_input_voltage=self.max_input_voltage,
ADC_resolution=self.ADC_resolution, ADC_overflow_rate=self.ADC_overflow_rate,
quant_method=self.quant_method), input_=input).to(self.device)
else:
if self.tile_shape is not None:
input_tiles, input_tiles_map = gen_tiles(input,
self.tile_shape,
input=True)
crossbar_shape = (self.crossbars[0].rows,
self.crossbars[0].columns)
tiles_map = self.crossbars[0].tiles_map
out = tile_matmul(input_tiles, input_tiles_map, input_shape, self.crossbar_operation(self.crossbars, \
lambda crossbar: crossbar.conductance_matrix), tiles_map, crossbar_shape,
self.ADC_resolution, self.ADC_overflow_rate, self.quant_method)
else:
out = torch.matmul(
input.to(self.device),
self.crossbar_operation(
self.crossbars,
lambda crossbar: crossbar.conductance_matrix))
if self.quant_method is not None:
out = memtorch.bh.Quantize.quantize(
out,
bits=self.ADC_resolution,
overflow_rate=self.ADC_overflow_rate,
quant_method=self.quant_method)
out = self.transform_output(out)
if self.bias is not None:
out += self.bias.data.view(1, -1).expand_as(out)
return out