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# Owner(s): ["module: unknown"]
from typing import Dict, Any, Tuple
from torch.ao.pruning import BaseSparsifier
import torch
import torch.nn.functional as F
from torch import nn
class ImplementedSparsifier(BaseSparsifier):
def __init__(self, **kwargs: Dict[str, Any]) -> None:
super().__init__(defaults=kwargs)
def update_mask(self, module: nn.Module, tensor_name: str, **kwargs: Dict[str, Any]) -> None:
module.parametrizations.weight[0].mask[0] = 0 # type: ignore[index, union-attr]
linear_state = self.state['linear1.weight']
linear_state['step_count'] = linear_state.get('step_count', 0) + 1
class MockSparseLinear(nn.Linear):
"""
This class is a MockSparseLinear class to check convert functionality.
It is the same as a normal Linear layer, except with a different type, as
well as an additional from_dense method.
"""
@classmethod
def from_dense(cls, mod: nn.Linear) -> 'MockSparseLinear':
"""
"""
linear = cls(mod.in_features,
mod.out_features)
return linear
def rows_are_subset(subset_tensor: torch.Tensor, superset_tensor: torch.Tensor) -> bool:
"""
Checks to see if all rows in subset tensor are present in the superset tensor
"""
i = 0
for row in subset_tensor:
while i < len(superset_tensor):
if not torch.equal(row, superset_tensor[i]):
i += 1
else:
break
else:
return False
return True
class SimpleLinear(nn.Module):
r"""Model with only Linear layers without biases, some wrapped in a Sequential,
some following the Sequential. Used to test basic pruned Linear-Linear fusion."""
def __init__(self) -> None:
super().__init__()
self.seq = nn.Sequential(
nn.Linear(7, 5, bias=False),
nn.Linear(5, 6, bias=False),
nn.Linear(6, 4, bias=False),
)
self.linear1 = nn.Linear(4, 4, bias=False)
self.linear2 = nn.Linear(4, 10, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.seq(x)
x = self.linear1(x)
x = self.linear2(x)
return x
class LinearBias(nn.Module):
r"""Model with only Linear layers, alternating layers with biases,
wrapped in a Sequential. Used to test pruned Linear-Bias-Linear fusion."""
def __init__(self) -> None:
super().__init__()
self.seq = nn.Sequential(
nn.Linear(7, 5, bias=True),
nn.Linear(5, 6, bias=False),
nn.Linear(6, 3, bias=True),
nn.Linear(3, 3, bias=True),
nn.Linear(3, 10, bias=False),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.seq(x)
return x
class LinearActivation(nn.Module):
r"""Model with only Linear layers, some with bias, some in a Sequential and some following.
Activation functions modules in between each Linear in the Sequential, and each outside layer.
Used to test pruned Linear(Bias)-Activation-Linear fusion."""
def __init__(self) -> None:
super().__init__()
self.seq = nn.Sequential(
nn.Linear(7, 5, bias=True),
nn.ReLU(),
nn.Linear(5, 6, bias=False),
nn.Tanh(),
nn.Linear(6, 4, bias=True),
)
self.linear1 = nn.Linear(4, 3, bias=True)
self.act1 = nn.ReLU()
self.linear2 = nn.Linear(3, 10, bias=False)
self.act2 = nn.Tanh()
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.seq(x)
x = self.linear1(x)
x = self.act1(x)
x = self.linear2(x)
x = self.act2(x)
return x
class LinearActivationFunctional(nn.Module):
r"""Model with only Linear layers, some with bias, some in a Sequential and some following.
Activation functions modules in between each Linear in the Sequential, and functional
activationals are called in between each outside layer.
Used to test pruned Linear(Bias)-Activation-Linear fusion."""
def __init__(self) -> None:
super().__init__()
self.seq = nn.Sequential(
nn.Linear(7, 5, bias=True),
nn.ReLU(),
nn.Linear(5, 6, bias=False),
nn.ReLU(),
nn.Linear(6, 4, bias=True),
)
self.linear1 = nn.Linear(4, 3, bias=True)
self.linear2 = nn.Linear(3, 8, bias=False)
self.linear3 = nn.Linear(8, 10, bias=False)
self.act1 = nn.ReLU()
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.seq(x)
x = self.linear1(x)
x = F.relu(x)
x = self.linear2(x)
x = F.relu(x)
x = self.linear3(x)
x = F.relu(x)
return x
class SimpleConv2d(nn.Module):
r"""Model with only Conv2d layers, all without bias, some in a Sequential and some following.
Used to test pruned Conv2d-Conv2d fusion."""
def __init__(self) -> None:
super().__init__()
self.seq = nn.Sequential(
nn.Conv2d(1, 32, 3, 1, bias=False),
nn.Conv2d(32, 64, 3, 1, bias=False),
)
self.conv2d1 = nn.Conv2d(64, 48, 3, 1, bias=False)
self.conv2d2 = nn.Conv2d(48, 52, 3, 1, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.seq(x)
x = self.conv2d1(x)
x = self.conv2d2(x)
return x
class Conv2dBias(nn.Module):
r"""Model with only Conv2d layers, some with bias, some in a Sequential and some outside.
Used to test pruned Conv2d-Bias-Conv2d fusion."""
def __init__(self) -> None:
super().__init__()
self.seq = nn.Sequential(
nn.Conv2d(1, 32, 3, 1, bias=True),
nn.Conv2d(32, 32, 3, 1, bias=True),
nn.Conv2d(32, 64, 3, 1, bias=False),
)
self.conv2d1 = nn.Conv2d(64, 48, 3, 1, bias=True)
self.conv2d2 = nn.Conv2d(48, 52, 3, 1, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.seq(x)
x = self.conv2d1(x)
x = self.conv2d2(x)
return x
class Conv2dActivation(nn.Module):
r"""Model with only Conv2d layers, some with bias, some in a Sequential and some following.
Activation function modules in between each Sequential layer, functional activations called
in-between each outside layer.
Used to test pruned Conv2d-Bias-Activation-Conv2d fusion."""
def __init__(self) -> None:
super().__init__()
self.seq = nn.Sequential(
nn.Conv2d(1, 32, 3, 1, bias=True),
nn.ReLU(),
nn.Conv2d(32, 64, 3, 1, bias=True),
nn.Tanh(),
nn.Conv2d(64, 64, 3, 1, bias=False),
nn.ReLU(),
)
self.conv2d1 = nn.Conv2d(64, 48, 3, 1, bias=False)
self.conv2d2 = nn.Conv2d(48, 52, 3, 1, bias=True)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.seq(x)
x = self.conv2d1(x)
x = F.relu(x)
x = self.conv2d2(x)
x = F.hardtanh(x)
return x
class Conv2dPadBias(nn.Module):
r"""Model with only Conv2d layers, all with bias and some with padding > 0,
some in a Sequential and some following. Activation function modules in between each layer.
Used to test that bias is propagated correctly in the special case of
pruned Conv2d-Bias-(Activation)Conv2d fusion, when the second Conv2d layer has padding > 0."""
def __init__(self) -> None:
super().__init__()
self.seq = nn.Sequential(
nn.Conv2d(1, 32, 3, 1, padding=1, bias=True),
nn.ReLU(),
nn.Conv2d(32, 32, 3, 1, bias=False),
nn.ReLU(),
nn.Conv2d(32, 32, 3, 1, padding=1, bias=True),
nn.ReLU(),
nn.Conv2d(32, 32, 3, 1, padding=1, bias=True),
nn.ReLU(),
nn.Conv2d(32, 64, 3, 1, bias=True),
nn.Tanh(),
)
self.conv2d1 = nn.Conv2d(64, 48, 3, 1, padding=1, bias=True)
self.act1 = nn.ReLU()
self.conv2d2 = nn.Conv2d(48, 52, 3, 1, padding=1, bias=True)
self.act2 = nn.Tanh()
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.seq(x)
x = self.conv2d1(x)
x = self.act1(x)
x = self.conv2d2(x)
x = self.act2(x)
return x
class Conv2dPool(nn.Module):
r"""Model with only Conv2d layers, all with bias, some in a Sequential and some following.
Activation function modules in between each layer, Pool2d modules in between each layer.
Used to test pruned Conv2d-Pool2d-Conv2d fusion."""
def __init__(self) -> None:
super().__init__()
self.seq = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=3, padding=1, bias=True),
nn.MaxPool2d(kernel_size=2, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(32, 64, kernel_size=3, padding=1, bias=True),
nn.Tanh(),
nn.AvgPool2d(kernel_size=2, stride=2, padding=1),
)
self.conv2d1 = nn.Conv2d(64, 48, kernel_size=3, padding=1, bias=True)
self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2, padding=1)
self.af1 = nn.ReLU()
self.conv2d2 = nn.Conv2d(48, 52, kernel_size=3, padding=1, bias=True)
self.conv2d3 = nn.Conv2d(52, 52, kernel_size=3, padding=1, bias=True)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.seq(x)
x = self.conv2d1(x)
x = self.maxpool(x)
x = self.af1(x)
x = self.conv2d2(x)
x = F.avg_pool2d(x, kernel_size=2, stride=2, padding=1)
x = F.relu(x)
x = self.conv2d3(x)
return x
class Conv2dPoolFlattenFunctional(nn.Module):
r"""Model with Conv2d layers, all with bias, some in a Sequential and some following, and then a Pool2d
and a functional Flatten followed by a Linear layer.
Activation functions and Pool2ds in between each layer also.
Used to test pruned Conv2d-Pool2d-Flatten-Linear fusion."""
def __init__(self) -> None:
super().__init__()
self.seq = nn.Sequential(
nn.Conv2d(1, 3, kernel_size=3, padding=1, bias=True),
nn.MaxPool2d(kernel_size=2, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(3, 5, kernel_size=3, padding=1, bias=True),
nn.Tanh(),
nn.AvgPool2d(kernel_size=2, stride=2, padding=1),
)
self.conv2d1 = nn.Conv2d(5, 7, kernel_size=3, padding=1, bias=True)
self.af1 = nn.ReLU()
self.conv2d2 = nn.Conv2d(7, 11, kernel_size=3, padding=1, bias=True)
self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(11, 13, bias=True)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.seq(x)
x = self.conv2d1(x)
x = F.max_pool2d(x, kernel_size=2, stride=2, padding=1)
x = self.af1(x)
x = self.conv2d2(x)
x = self.avg_pool(x)
x = torch.flatten(x, 1) # test functional flatten
x = self.fc(x)
return x
class Conv2dPoolFlatten(nn.Module):
r"""Model with Conv2d layers, all with bias, some in a Sequential and some following, and then a Pool2d
and a Flatten module followed by a Linear layer.
Activation functions and Pool2ds in between each layer also.
Used to test pruned Conv2d-Pool2d-Flatten-Linear fusion."""
def __init__(self) -> None:
super().__init__()
self.seq = nn.Sequential(
nn.Conv2d(1, 3, kernel_size=3, padding=1, bias=True),
nn.MaxPool2d(kernel_size=2, stride=2, padding=1),
nn.ReLU(),
nn.Conv2d(3, 5, kernel_size=3, padding=1, bias=True),
nn.Tanh(),
nn.AvgPool2d(kernel_size=2, stride=2, padding=1),
)
self.conv2d1 = nn.Conv2d(5, 7, kernel_size=3, padding=1, bias=True)
self.af1 = nn.ReLU()
self.conv2d2 = nn.Conv2d(7, 11, kernel_size=3, padding=1, bias=True)
self.avg_pool = nn.AdaptiveAvgPool2d((2, 2))
self.flatten = nn.Flatten()
self.fc = nn.Linear(44, 13, bias=True)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.seq(x)
x = self.conv2d1(x)
x = F.max_pool2d(x, kernel_size=2, stride=2, padding=1)
x = self.af1(x)
x = self.conv2d2(x)
x = self.avg_pool(x)
x = self.flatten(x)
x = self.fc(x)
return x
class LSTMLinearModel(nn.Module):
"""Container module with an encoder, a recurrent module, and a linear."""
def __init__(
self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int
) -> None:
super().__init__()
self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers)
self.linear = nn.Linear(hidden_dim, output_dim)
def forward(self, input: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
output, _hidden = self.lstm(input)
decoded = self.linear(output)
return decoded, output
class LSTMLayerNormLinearModel(nn.Module):
"""Container module with an LSTM, a LayerNorm, and a linear."""
def __init__(
self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int
) -> None:
super().__init__()
self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers)
self.norm = nn.LayerNorm(hidden_dim)
self.linear = nn.Linear(hidden_dim, output_dim)
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
x, state = self.lstm(x)
x = self.norm(x)
x = self.linear(x)
return x, state
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