Note

Click here to download the full example code

# Example using all extensions

Basic example showing how compute the gradient, and and other quantities with BackPACK, on a linear model for MNIST.

Let’s start by loading some dummy data and extending the model

```
from torch import rand
from torch.nn import CrossEntropyLoss, Flatten, Linear, Sequential
from backpack import backpack, extend
from backpack.extensions import (
GGNMP,
HMP,
KFAC,
KFLR,
KFRA,
PCHMP,
BatchDiagGGNExact,
BatchDiagGGNMC,
BatchDiagHessian,
BatchGrad,
BatchL2Grad,
DiagGGNExact,
DiagGGNMC,
DiagHessian,
SqrtGGNExact,
SqrtGGNMC,
SumGradSquared,
Variance,
)
from backpack.utils.examples import load_one_batch_mnist
X, y = load_one_batch_mnist(batch_size=512)
model = Sequential(Flatten(), Linear(784, 10))
lossfunc = CrossEntropyLoss()
model = extend(model)
lossfunc = extend(lossfunc)
```

```
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz
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Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz
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Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz
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Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz
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Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw
```

## First order extensions

Batch gradients

```
loss = lossfunc(model(X), y)
with backpack(BatchGrad()):
loss.backward()
for name, param in model.named_parameters():
print(name)
print(".grad.shape: ", param.grad.shape)
print(".grad_batch.shape: ", param.grad_batch.shape)
```

```
1.weight
.grad.shape: torch.Size([10, 784])
.grad_batch.shape: torch.Size([512, 10, 784])
1.bias
.grad.shape: torch.Size([10])
.grad_batch.shape: torch.Size([512, 10])
```

Variance

```
loss = lossfunc(model(X), y)
with backpack(Variance()):
loss.backward()
for name, param in model.named_parameters():
print(name)
print(".grad.shape: ", param.grad.shape)
print(".variance.shape: ", param.variance.shape)
```

```
1.weight
.grad.shape: torch.Size([10, 784])
.variance.shape: torch.Size([10, 784])
1.bias
.grad.shape: torch.Size([10])
.variance.shape: torch.Size([10])
```

Second moment/sum of gradients squared

```
loss = lossfunc(model(X), y)
with backpack(SumGradSquared()):
loss.backward()
for name, param in model.named_parameters():
print(name)
print(".grad.shape: ", param.grad.shape)
print(".sum_grad_squared.shape: ", param.sum_grad_squared.shape)
```

```
1.weight
.grad.shape: torch.Size([10, 784])
.sum_grad_squared.shape: torch.Size([10, 784])
1.bias
.grad.shape: torch.Size([10])
.sum_grad_squared.shape: torch.Size([10])
```

L2 norm of individual gradients

```
loss = lossfunc(model(X), y)
with backpack(BatchL2Grad()):
loss.backward()
for name, param in model.named_parameters():
print(name)
print(".grad.shape: ", param.grad.shape)
print(".batch_l2.shape: ", param.batch_l2.shape)
```

```
1.weight
.grad.shape: torch.Size([10, 784])
.batch_l2.shape: torch.Size([512])
1.bias
.grad.shape: torch.Size([10])
.batch_l2.shape: torch.Size([512])
```

It’s also possible to ask for multiple quantities at once

```
loss = lossfunc(model(X), y)
with backpack(BatchGrad(), Variance(), SumGradSquared(), BatchL2Grad()):
loss.backward()
for name, param in model.named_parameters():
print(name)
print(".grad.shape: ", param.grad.shape)
print(".grad_batch.shape: ", param.grad_batch.shape)
print(".variance.shape: ", param.variance.shape)
print(".sum_grad_squared.shape: ", param.sum_grad_squared.shape)
print(".batch_l2.shape: ", param.batch_l2.shape)
```

```
1.weight
.grad.shape: torch.Size([10, 784])
.grad_batch.shape: torch.Size([512, 10, 784])
.variance.shape: torch.Size([10, 784])
.sum_grad_squared.shape: torch.Size([10, 784])
.batch_l2.shape: torch.Size([512])
1.bias
.grad.shape: torch.Size([10])
.grad_batch.shape: torch.Size([512, 10])
.variance.shape: torch.Size([10])
.sum_grad_squared.shape: torch.Size([10])
.batch_l2.shape: torch.Size([512])
```

## Second order extensions

Diagonal of the generalized Gauss-Newton and its Monte-Carlo approximation

```
loss = lossfunc(model(X), y)
with backpack(DiagGGNExact(), DiagGGNMC(mc_samples=1)):
loss.backward()
for name, param in model.named_parameters():
print(name)
print(".grad.shape: ", param.grad.shape)
print(".diag_ggn_mc.shape: ", param.diag_ggn_mc.shape)
print(".diag_ggn_exact.shape: ", param.diag_ggn_exact.shape)
```

```
1.weight
.grad.shape: torch.Size([10, 784])
.diag_ggn_mc.shape: torch.Size([10, 784])
.diag_ggn_exact.shape: torch.Size([10, 784])
1.bias
.grad.shape: torch.Size([10])
.diag_ggn_mc.shape: torch.Size([10])
.diag_ggn_exact.shape: torch.Size([10])
```

Per-sample diagonal of the generalized Gauss-Newton and its Monte-Carlo approximation

```
loss = lossfunc(model(X), y)
with backpack(BatchDiagGGNExact(), BatchDiagGGNMC(mc_samples=1)):
loss.backward()
for name, param in model.named_parameters():
print(name)
print(".diag_ggn_mc_batch.shape: ", param.diag_ggn_mc_batch.shape)
print(".diag_ggn_exact_batch.shape: ", param.diag_ggn_exact_batch.shape)
```

```
1.weight
.diag_ggn_mc_batch.shape: torch.Size([512, 10, 784])
.diag_ggn_exact_batch.shape: torch.Size([512, 10, 784])
1.bias
.diag_ggn_mc_batch.shape: torch.Size([512, 10])
.diag_ggn_exact_batch.shape: torch.Size([512, 10])
```

KFAC, KFRA and KFLR

```
loss = lossfunc(model(X), y)
with backpack(KFAC(mc_samples=1), KFLR(), KFRA()):
loss.backward()
for name, param in model.named_parameters():
print(name)
print(".grad.shape: ", param.grad.shape)
print(".kfac (shapes): ", [kfac.shape for kfac in param.kfac])
print(".kflr (shapes): ", [kflr.shape for kflr in param.kflr])
print(".kfra (shapes): ", [kfra.shape for kfra in param.kfra])
```

```
1.weight
.grad.shape: torch.Size([10, 784])
.kfac (shapes): [torch.Size([10, 10]), torch.Size([784, 784])]
.kflr (shapes): [torch.Size([10, 10]), torch.Size([784, 784])]
.kfra (shapes): [torch.Size([10, 10]), torch.Size([784, 784])]
1.bias
.grad.shape: torch.Size([10])
.kfac (shapes): [torch.Size([10, 10])]
.kflr (shapes): [torch.Size([10, 10])]
.kfra (shapes): [torch.Size([10, 10])]
```

Diagonal Hessian and per-sample diagonal Hessian

```
loss = lossfunc(model(X), y)
with backpack(DiagHessian(), BatchDiagHessian()):
loss.backward()
for name, param in model.named_parameters():
print(name)
print(".grad.shape: ", param.grad.shape)
print(".diag_h.shape: ", param.diag_h.shape)
print(".diag_h_batch.shape: ", param.diag_h_batch.shape)
```

```
1.weight
.grad.shape: torch.Size([10, 784])
.diag_h.shape: torch.Size([10, 784])
.diag_h_batch.shape: torch.Size([512, 10, 784])
1.bias
.grad.shape: torch.Size([10])
.diag_h.shape: torch.Size([10])
.diag_h_batch.shape: torch.Size([512, 10])
```

Matrix square root of the generalized Gauss-Newton or its Monte-Carlo approximation

```
loss = lossfunc(model(X), y)
with backpack(SqrtGGNExact(), SqrtGGNMC(mc_samples=1)):
loss.backward()
for name, param in model.named_parameters():
print(name)
print(".grad.shape: ", param.grad.shape)
print(".sqrt_ggn_exact.shape: ", param.sqrt_ggn_exact.shape)
print(".sqrt_ggn_mc.shape: ", param.sqrt_ggn_mc.shape)
```

```
1.weight
.grad.shape: torch.Size([10, 784])
.sqrt_ggn_exact.shape: torch.Size([10, 512, 10, 784])
.sqrt_ggn_mc.shape: torch.Size([1, 512, 10, 784])
1.bias
.grad.shape: torch.Size([10])
.sqrt_ggn_exact.shape: torch.Size([10, 512, 10])
.sqrt_ggn_mc.shape: torch.Size([1, 512, 10])
```

## Block-diagonal curvature products

Curvature-matrix product (`MP`

) extensions provide functions
that multiply with the block diagonal of different curvature matrices, such as

the Hessian (

`HMP`

)the generalized Gauss-Newton (

`GGNMP`

)the positive-curvature Hessian (

`PCHMP`

)

Multiply a random vector with curvature blocks.

```
V = 1
for name, param in model.named_parameters():
vec = rand(V, *param.shape)
print(name)
print(".grad.shape: ", param.grad.shape)
print("vec.shape: ", vec.shape)
print(".hmp(vec).shape: ", param.hmp(vec).shape)
print(".ggnmp(vec).shape: ", param.ggnmp(vec).shape)
print(".pchmp_clip(vec).shape: ", param.pchmp_clip(vec).shape)
print(".pchmp_abs(vec).shape: ", param.pchmp_abs(vec).shape)
```

```
1.weight
.grad.shape: torch.Size([10, 784])
vec.shape: torch.Size([1, 10, 784])
.hmp(vec).shape: torch.Size([1, 10, 784])
.ggnmp(vec).shape: torch.Size([1, 10, 784])
.pchmp_clip(vec).shape: torch.Size([1, 10, 784])
.pchmp_abs(vec).shape: torch.Size([1, 10, 784])
1.bias
.grad.shape: torch.Size([10])
vec.shape: torch.Size([1, 10])
.hmp(vec).shape: torch.Size([1, 10])
.ggnmp(vec).shape: torch.Size([1, 10])
.pchmp_clip(vec).shape: torch.Size([1, 10])
.pchmp_abs(vec).shape: torch.Size([1, 10])
```

Multiply a collection of three vectors (a matrix) with curvature blocks.

```
V = 3
for name, param in model.named_parameters():
vec = rand(V, *param.shape)
print(name)
print(".grad.shape: ", param.grad.shape)
print("vec.shape: ", vec.shape)
print(".hmp(vec).shape: ", param.hmp(vec).shape)
print(".ggnmp(vec).shape: ", param.ggnmp(vec).shape)
print(".pchmp_clip(vec).shape: ", param.pchmp_clip(vec).shape)
print(".pchmp_abs(vec).shape: ", param.pchmp_abs(vec).shape)
```

```
1.weight
.grad.shape: torch.Size([10, 784])
vec.shape: torch.Size([3, 10, 784])
.hmp(vec).shape: torch.Size([3, 10, 784])
.ggnmp(vec).shape: torch.Size([3, 10, 784])
.pchmp_clip(vec).shape: torch.Size([3, 10, 784])
.pchmp_abs(vec).shape: torch.Size([3, 10, 784])
1.bias
.grad.shape: torch.Size([10])
vec.shape: torch.Size([3, 10])
.hmp(vec).shape: torch.Size([3, 10])
.ggnmp(vec).shape: torch.Size([3, 10])
.pchmp_clip(vec).shape: torch.Size([3, 10])
.pchmp_abs(vec).shape: torch.Size([3, 10])
```

**Total running time of the script:** ( 0 minutes 2.199 seconds)