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

100.0%
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)

loss = lossfunc(model(X), y)

with backpack(
    HMP(),
    GGNMP(),
    PCHMP(savefield="pchmp_clip", modify="clip"),
    PCHMP(savefield="pchmp_abs", modify="abs"),
):
    loss.backward()

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 2.265 seconds)

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