nn (Neural Networks)
Neural Network classes¤
BatchNorm
¤
BatchNorm(
sz: int,
eps=1e-05,
affine=True,
track_running_stats=True,
momentum=0.1,
)
Applies Batch Normalization over a 2D or 3D input.
- Described: https://paperswithcode.com/method/batch-normalization
- Paper: https://arxiv.org/abs/1502.03167v3
See: Tensor.batchnorm
norm = nn.BatchNorm(3)
t = Tensor.rand(2, 3, 4, 4)
print(t.mean().item(), t.std().item())
0.4598855674266815 0.3118361234664917
t = norm(t)
print(t.mean().item(), t.std().item())
0.45988306403160095 0.3118346035480499
Source code in tinygrad/nn/__init__.py
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Conv1d
¤
Conv1d(
in_channels: int,
out_channels: int,
kernel_size: int,
stride=1,
padding: Union[int, str] = 0,
dilation=1,
groups=1,
bias=True,
) -> Conv2d
Applies a 1D convolution over an input signal composed of several input planes.
See: https://pytorch.org/docs/stable/generated/torch.nn.Conv1d
conv = nn.Conv1d(1, 1, 3)
t = Tensor.rand(1, 1, 4)
print(t.numpy())
[[[0.633 0.4219 0.9435 0.3781]]]
t = conv(t)
print(t.numpy())
[[[-0.2079 -0.2278]]]
Source code in tinygrad/nn/__init__.py
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Conv2d
¤
Conv2d(
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple[int, ...]],
stride=1,
padding: Union[int, Tuple[int, ...], str] = 0,
dilation=1,
groups=1,
bias=True,
)
Applies a 2D convolution over an input signal composed of several input planes.
See: https://pytorch.org/docs/stable/generated/torch.nn.Conv2d
conv = nn.Conv2d(1, 1, 3)
t = Tensor.rand(1, 1, 4, 4)
print(t.numpy())
[[[[0.7504 0.9585 0.6272 0.49 ]
[0.9784 0.9519 0.9723 0.5912]
[0.0482 0.3065 0.4264 0.1904]
[0.4743 0.3874 0.1868 0.7578]]]]
t = conv(t)
print(t.numpy())
[[[[ 0.1475 0.1675]
[-0.1956 -0.2561]]]]
Source code in tinygrad/nn/__init__.py
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ConvTranspose1d
¤
ConvTranspose1d(
in_channels: int,
out_channels: int,
kernel_size: int,
stride=1,
padding=0,
output_padding=0,
dilation=1,
groups=1,
bias=True,
) -> ConvTranspose2d
Applies a 1D transposed convolution operator over an input signal composed of several input planes.
See: https://pytorch.org/docs/stable/generated/torch.nn.ConvTranspose1d
conv = nn.ConvTranspose1d(1, 1, 3)
t = Tensor.rand(1, 1, 4)
print(t.numpy())
[[[0.2403 0.403 0.4781 0.2091]]]
t = conv(t)
print(t.numpy())
[[[-0.0604 -0.2259 -0.2446 -0.1018 0.1197 0.1139]]]
Source code in tinygrad/nn/__init__.py
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ConvTranspose2d
¤
ConvTranspose2d(
in_channels: int,
out_channels: int,
kernel_size: Union[int, Tuple[int, ...]],
stride=1,
padding=0,
output_padding=0,
dilation=1,
groups=1,
bias=True,
)
Bases: Conv2d
Applies a 2D transposed convolution operator over an input image.
See: https://pytorch.org/docs/stable/generated/torch.nn.ConvTranspose2d
conv = nn.ConvTranspose2d(1, 1, 3)
t = Tensor.rand(1, 1, 4, 4)
print(t.numpy())
[[[[0.5208 0.5191 0.8096 0.7397]
[0.255 0.4553 0.7458 0.8506]
[0.5975 0.048 0.0388 0.0133]
[0.9676 0.6126 0.6517 0.321 ]]]]
t = conv(t)
print(t.numpy())
[[[[ 0.112 0.1477 0.0018 0.0356 0.1276 0.0893]
[ 0.1019 -0.0619 -0.1297 -0.2355 0.028 0.1468]
[ 0.2219 0.1047 0.041 -0.0494 -0.1139 0.3697]
[ 0.0283 0.0428 0.1037 0.1349 -0.0846 0.2416]
[ 0.2771 -0.3053 0.1214 0.0577 0.1939 0.2492]
[ 0.5039 0.0781 0.298 0.1529 0.1699 0.245 ]]]]
Source code in tinygrad/nn/__init__.py
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Linear
¤
Applies a linear transformation to the incoming data.
See: https://pytorch.org/docs/stable/generated/torch.nn.Linear
lin = nn.Linear(3, 4)
t = Tensor.rand(2, 3)
print(t.numpy())
[[0.2356 0.205 0.6857]
[0.2688 0.3793 0.1747]]
t = lin(t)
print(t.numpy())
[[-0.0564 0.1003 0.57 0.1524]
[-0.1655 -0.1321 0.3016 -0.0316]]
Source code in tinygrad/nn/__init__.py
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GroupNorm
¤
Applies Group Normalization over a mini-batch of inputs.
- Described: https://paperswithcode.com/method/group-normalization
- Paper: https://arxiv.org/abs/1803.08494v3
norm = nn.GroupNorm(2, 12)
t = Tensor.rand(2, 12, 4, 4) * 2 + 1
print(t.mean().item(), t.std().item())
2.034907579421997 0.5706599354743958
t = norm(t)
print(t.mean().item(), t.std().item())
1.2917772096443514e-07 1.001288652420044
Source code in tinygrad/nn/__init__.py
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InstanceNorm
¤
InstanceNorm(num_features: int, eps=1e-05, affine=True)
Applies Instance Normalization over a mini-batch of inputs.
- Described: https://paperswithcode.com/method/instance-normalization
- Paper: https://arxiv.org/abs/1607.08022v3
norm = nn.InstanceNorm(3)
t = Tensor.rand(2, 3, 4, 4) * 2 + 1
print(t.mean().item(), t.std().item())
1.988901138305664 0.6165143251419067
t = norm(t)
print(t.mean().item(), t.std().item())
3.130856907063162e-09 1.0052341222763062
Source code in tinygrad/nn/__init__.py
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LayerNorm
¤
Applies Layer Normalization over a mini-batch of inputs.
- Described: https://paperswithcode.com/method/layer-normalization
- Paper: https://arxiv.org/abs/1607.06450v1
norm = nn.LayerNorm(3)
t = Tensor.rand(2, 5, 3) * 2 + 1
print(t.mean().item(), t.std().item())
1.7113289833068848 0.5231955051422119
t = norm(t)
print(t.mean().item(), t.std().item())
-2.5924123292497825e-07 1.017027497291565
Source code in tinygrad/nn/__init__.py
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LayerNorm2d
¤
Bases: LayerNorm
Applies Layer Normalization over a mini-batch of 2D inputs.
See: LayerNorm
norm = nn.LayerNorm2d(3)
t = Tensor.rand(2, 3, 4, 4) * 2 + 1
print(t.mean().item(), t.std().item())
1.912937879562378 0.5994055867195129
t = norm(t)
print(t.mean().item(), t.std().item())
-1.442572425958133e-07 1.0052019357681274
Source code in tinygrad/nn/__init__.py
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RMSNorm
¤
RMSNorm(dim: int, eps=1e-06)
Applies Root Mean Square Normalization to input.
- Described: https://paperswithcode.com/method/rmsnorm
- Paper: https://arxiv.org/abs/1910.07467
norm = nn.RMSNorm(4)
t = Tensor.arange(12, dtype=dtypes.float).reshape(3, 4)
print(t.numpy())
[[ 0. 1. 2. 3.]
[ 4. 5. 6. 7.]
[ 8. 9. 10. 11.]]
print(norm(t).numpy())
[[0. 0.5345 1.069 1.6036]
[0.7127 0.8909 1.069 1.2472]
[0.8363 0.9409 1.0454 1.15 ]]
Source code in tinygrad/nn/__init__.py
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Embedding
¤
A simple lookup table that stores embeddings of a fixed dictionary and size.
See: https://pytorch.org/docs/stable/generated/torch.nn.Embedding
emb = nn.Embedding(10, 3)
print(emb(Tensor([1, 2, 3, 1])).numpy())
[[ 0.2066 -0.5046 -0.3256]
[ 0.2658 -0.6113 0.172 ]
[ 0.6762 0.5657 0.0801]
[ 0.2066 -0.5046 -0.3256]]
Source code in tinygrad/nn/__init__.py
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LSTMCell
¤
A long short-term memory (LSTM) cell.
Parameters:
-
input_size
(int
) –The number of expected features in the input
x
-
hidden_size
(int
) –The number of features in the hidden state
h
-
bias
(bool
, default:True
) –If
False
, then the layer does not use bias weightsb_ih
andb_hh
Source code in tinygrad/nn/__init__.py
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Optimizers¤
SGD
¤
SGD(
params: List[Tensor],
lr=0.001,
momentum=0.0,
weight_decay=0.0,
nesterov=False,
classic=False,
)
Stochastic Gradient Descent (SGD) optimizer with optional momentum and weight decay.
classic
is a boolean flag that determines whether to use the popular momentum update rule or the classic momentum update rule.
- Described: https://paperswithcode.com/method/sgd
Source code in tinygrad/nn/optim.py
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LARS
¤
LARS(
params: List[Tensor],
lr=0.001,
momentum=0.9,
weight_decay=0.0001,
nesterov=False,
classic=True,
tcoef=0.001,
)
Bases: Optimizer
Layer-wise Adaptive Rate Scaling (LARS) optimizer with optional momentum and weight decay.
- Described: https://paperswithcode.com/method/lars
- Paper: https://arxiv.org/abs/1708.03888v3
Source code in tinygrad/nn/optim.py
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AdamW
¤
AdamW optimizer with optional weight decay.
- Described: https://paperswithcode.com/method/adamw
- Paper: https://arxiv.org/abs/1711.05101v3
Source code in tinygrad/nn/optim.py
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Adam
¤
Adam optimizer.
- Described: https://paperswithcode.com/method/adam
- Paper: https://arxiv.org/abs/1412.6980
Source code in tinygrad/nn/optim.py
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LAMB
¤
Bases: Optimizer
LAMB optimizer with optional weight decay.
- Described: https://paperswithcode.com/method/lamb
- Paper: https://arxiv.org/abs/1904.00962
Source code in tinygrad/nn/optim.py
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Load/Save¤
safe_load
¤
Loads a .safetensor file from disk, returning the state_dict.
state_dict = nn.state.safe_load("test.safetensor")
Source code in tinygrad/nn/state.py
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safe_save
¤
Saves a state_dict to disk in a .safetensor file with optional metadata.
t = Tensor([1, 2, 3])
nn.state.safe_save({'t':t}, "test.safetensor")
Source code in tinygrad/nn/state.py
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get_state_dict
¤
Returns a state_dict of the object, with optional prefix.
class Net:
def __init__(self):
self.l1 = nn.Linear(4, 5)
self.l2 = nn.Linear(5, 6)
net = Net()
print(nn.state.get_state_dict(net).keys())
dict_keys(['l1.weight', 'l1.bias', 'l2.weight', 'l2.bias'])
Source code in tinygrad/nn/state.py
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get_parameters
¤
class Net:
def __init__(self):
self.l1 = nn.Linear(4, 5)
self.l2 = nn.Linear(5, 6)
net = Net()
print(len(nn.state.get_parameters(net)))
4
Source code in tinygrad/nn/state.py
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load_state_dict
¤
load_state_dict(
model,
state_dict: Dict[str, Tensor],
strict=True,
verbose=True,
consume=False,
) -> None
Loads a state_dict into a model.
class Net:
def __init__(self):
self.l1 = nn.Linear(4, 5)
self.l2 = nn.Linear(5, 6)
net = Net()
state_dict = nn.state.get_state_dict(net)
nn.state.load_state_dict(net, state_dict)
Source code in tinygrad/nn/state.py
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torch_load
¤
Loads a torch .pth file from disk.
state_dict = nn.state.torch_load("test.pth")
Source code in tinygrad/nn/state.py
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