def test_crossbar(shape):
memristor_model = memtorch.bh.memristor.LinearIonDrift
memristor_model_params = {'time_series_resolution': 1e-3}
crossbar = memtorch.bh.crossbar.Crossbar(memristor_model, memristor_model_params, shape)
conductance_matrix = naive_map(torch.zeros(shape).uniform_(0, 1),
memristor_model().r_on, memristor_model().r_off,
memtorch.bh.crossbar.Scheme.SingleColumn)
crossbar.write_conductance_matrix(conductance_matrix)
if sys.version_info > (3, 6):
crossbar.update(from_devices=False, parallelize=True)
assert torch.all(torch.isclose(conductance_matrix.T[:, :], crossbar.conductance_matrix.cpu()[:, :], atol=1e-5))
assert crossbar.devices[0][0].g == crossbar.conductance_matrix[0][0].item()
crossbar.update(from_devices=False, parallelize=False)
assert crossbar.devices[0][0].g == crossbar.conductance_matrix[0][0].item()
inputs = torch.zeros(shape).uniform_(0, 1)
assert torch.all(torch.isclose(simulate_matmul(inputs, crossbar.devices).float(),
torch.matmul(inputs, conductance_matrix.T).float(), rtol=1e-2))
assert torch.all(torch.isclose(simulate_matmul(inputs, crossbar.devices, parallelize=True).float(),
torch.matmul(inputs, conductance_matrix.T).float(), rtol=1e-2))
programming_signal = gen_programming_signal(1, 1e-2, 1e-2, 1, memristor_model_params['time_series_resolution'])
assert type(programming_signal) == tuple
with pytest.raises(AssertionError):
gen_programming_signal(1, 1e-4, 1e-4, 1, memristor_model_params['time_series_resolution'])
point = (0, 0)
row, column = point
conductance_to_program = random.uniform(1 / crossbar.devices[row][column].r_off, 1 / crossbar.devices[row][column].r_on)
crossbar.devices = naive_program(crossbar, (row, column), conductance_to_program, rel_tol=0.01)
assert math.isclose(conductance_to_program, crossbar.devices[row][column].g, abs_tol=1e-4)
with pytest.raises(AssertionError):
naive_program(crossbar, (row, column), -1)
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 not self.bias is None:
out += self.bias.view(1, -1).expand_as(out)
return out
else:
if hasattr(self, 'non_linear'):
input = convert_range(input, input.min(), input.max(), -1, 1)
input = input.cpu().detach().numpy()
if hasattr(self, 'simulate'):
out = torch.tensor(
self.transform_output(
self.crossbar_operation(
self.crossbars,
lambda crossbar, input: simulate_matmul(
input, crossbar.devices, nl=False),
input))).to(self.device)
else:
out = torch.tensor(
self.transform_output(
self.crossbar_operation(
self.crossbars,
lambda crossbar, input: simulate_matmul(
input, crossbar.devices, nl=True),
input))).to(self.device)
else:
out = torch.matmul(
input.to(self.device),
self.crossbar_operation(
self.crossbars,
lambda crossbar: crossbar.conductance_matrix))
out = self.transform_output(out)
if not self.bias is 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:
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:
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
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 all(item == 0 for item in self.padding):
batch_input = input[batch]
else:
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]),
mode="constant",
value=0)
channel_idx = 0
for channel in range(self.in_channels):
batch_channel_input = batch_input[channel].unsqueeze(
0).unsqueeze(0)
unfolded_batch_channel_input = batch_channel_input.unfold(
2, self.kernel_size[0], self.stride[0]).unfold(
3, self.kernel_size[1],
self.stride[1]).unfold(4, self.kernel_size[2],
self.stride[2])
unfolded_batch_channel_input = unfolded_batch_channel_input.reshape(
-1, self.kernel_size[0] * self.kernel_size[1] *
self.kernel_size[2])
if hasattr(self, 'non_linear'):
unfolded_batch_channel_input = convert_range(
unfolded_batch_channel_input,
unfolded_batch_channel_input.min(),
unfolded_batch_channel_input.max(), -1,
1).squeeze(0)
unfolded_batch_channel_input = unfolded_batch_channel_input.transpose(
1, 0).cpu().detach().numpy()
if hasattr(self, 'simulate'):
nl = False
else:
nl = True
if self.scheme == memtorch.bh.Scheme.DoubleColumn:
out[batch, :, :, :, :] += self.transform_output(
self.crossbar_operation(
self.crossbars,
lambda crossbar, input_: simulate_matmul(
input_,
crossbar.devices.transpose(1, 0),
nl=nl),
input_=unfolded_batch_channel_input.T,
idx=(channel_idx, channel_idx + 1))).view(
self.out_channels, output_dim[0],
output_dim[1],
output_dim[2]).to(self.device)
elif self.scheme == memtorch.bh.Scheme.SingleColumn:
out[batch, :, :, :, :] += self.transform_output(
self.crossbar_operation(
self.crossbars,
lambda crossbar, input_: simulate_matmul(
input_,
crossbar.devices.transpose(1, 0),
nl=nl),
input_=unfolded_batch_channel_input.T,
idx=channel_idx)).view(
self.out_channels, output_dim[0],
output_dim[1],
output_dim[2]).to(self.device)
else:
raise Exception('Scheme is currently unsupported.')
else:
if self.scheme == memtorch.bh.Scheme.DoubleColumn:
out[batch, :, :, :, :] += self.transform_output(
torch.matmul(
self.crossbar_operation(
self.crossbars, lambda crossbar:
crossbar.conductance_matrix,
(channel_idx, channel_idx + 1)),
unfolded_batch_channel_input.T)).view(
self.out_channels, output_dim[0],
output_dim[1], output_dim[2])
channel_idx += 2
elif self.scheme == memtorch.bh.Scheme.SingleColumn:
out[batch, :, :, :, :] += self.transform_output(
torch.matmul(
self.crossbar_operation(
self.crossbars, lambda crossbar:
crossbar.conductance_matrix,
channel_idx),
unfolded_batch_channel_input.T)).view(
self.out_channels, output_dim[0],
output_dim[1], output_dim[2])
channel_idx += 1
else:
raise Exception('Scheme is currently unsupported.')
if not self.bias is None:
out[batch] += self.bias.view(-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.
"""
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,
output_dim)).to(self.device)
for batch in range(input.shape[0]):
unfolded_batch_input = torch.nn.functional.unfold(
input[batch, :, :, :].unsqueeze(0),
kernel_size=self.kernel_size,
padding=self.padding)
if hasattr(self, 'non_linear'):
unfolded_batch_input = convert_range(
unfolded_batch_input, unfolded_batch_input.min(),
unfolded_batch_input.max(), -1, 1).squeeze(0)
unfolded_batch_input = unfolded_batch_input.transpose(
1, 0).cpu().detach().numpy()
if hasattr(self, 'simulate'):
out_ = torch.tensor(
self.transform_output(
self.crossbar_operation(
self.crossbars,
lambda crossbar, input: simulate_matmul(
input,
crossbar.devices.transpose(1, 0),
nl=False),
unfolded_batch_input))).to(self.device)
else:
out_ = torch.tensor(
self.transform_output(
self.crossbar_operation(
self.crossbars,
lambda crossbar, input: simulate_matmul(
input,
crossbar.devices.transpose(1, 0),
nl=True),
unfolded_batch_input))).to(self.device)
else:
out_ = self.transform_output(
torch.matmul(
self.crossbar_operation(
self.crossbars,
lambda crossbar: crossbar.conductance_matrix),
unfolded_batch_input))
if not self.bias is None:
out_ += self.bias.view(-1, 1).expand_as(out_)
out[batch] = out_.view(size=(1, self.out_channels, output_dim,
output_dim))
return out
def test_crossbar(shape):
device = torch.device("cpu" if "cpu" in memtorch.__version__ else "cuda")
memristor_model = memtorch.bh.memristor.LinearIonDrift
memristor_model_params = {"time_series_resolution": 1e-3}
pos_crossbar = memtorch.bh.crossbar.Crossbar(
memristor_model, memristor_model_params, shape
)
pos_crossbar.devices[0][0].g = 1 / pos_crossbar.r_off_mean
pos_crossbar.devices[0][1].g = 1 / pos_crossbar.r_on_mean
neg_crossbar = copy.deepcopy(pos_crossbar)
pos_conductance_matrix, neg_conductance_matrix = naive_map(
torch.zeros(shape).uniform_(0, 1),
memristor_model().r_on,
memristor_model().r_off,
memtorch.bh.crossbar.Scheme.DoubleColumn,
)
pos_crossbar.write_conductance_matrix(
pos_conductance_matrix, transistor=False, programming_routine=naive_program
)
neg_crossbar.write_conductance_matrix(
pos_conductance_matrix, transistor=False, programming_routine=naive_program
)
crossbar = memtorch.bh.crossbar.Crossbar(
memristor_model, memristor_model_params, shape
)
conductance_matrix = naive_map(
torch.zeros(shape).uniform_(0, 1),
memristor_model().r_on,
memristor_model().r_off,
memtorch.bh.crossbar.Scheme.SingleColumn,
)
crossbar.write_conductance_matrix(conductance_matrix)
if sys.version_info > (3, 6):
crossbar.update(from_devices=False, parallelize=True)
assert torch.all(
torch.isclose(
conductance_matrix.T[:, :],
crossbar.conductance_matrix.cpu()[:, :],
atol=1e-5,
)
)
assert crossbar.devices[0][0].g == crossbar.conductance_matrix[0][0].item()
crossbar.update(from_devices=False, parallelize=False)
assert crossbar.devices[0][0].g == crossbar.conductance_matrix[0][0].item()
inputs = torch.zeros(shape).uniform_(0, 1)
assert torch.all(
torch.isclose(
simulate_matmul(inputs, crossbar).float(),
torch.matmul(inputs, conductance_matrix.T).float().to(device),
rtol=1e-1,
)
)
programming_signal = gen_programming_signal(
1, 1e-3, 0, 1, memristor_model_params["time_series_resolution"]
)
assert type(programming_signal) == tuple
with pytest.raises(AssertionError):
gen_programming_signal(
1, 1e-4, 0, 1, memristor_model_params["time_series_resolution"]
)
point = (0, 0)
row, column = point
conductance_to_program = random.uniform(
1 / crossbar.devices[row][column].r_off, 1 / crossbar.devices[row][column].r_on
)
crossbar.devices = naive_program(
crossbar, (row, column), conductance_to_program, rel_tol=0.01
)
assert math.isclose(
conductance_to_program, crossbar.devices[row][column].g, abs_tol=1e-4
)
with pytest.raises(AssertionError):
naive_program(crossbar, (row, column), -1)
