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🧠 Introduction: What Is torch.func?

In PyTorch, most models are built using stateful objects like nn.Module. While this is intuitive and powerful, functional programming can unlock more control, composability, and efficiency in advanced machine learning workflows.

That’s where torch.func comes in.

torch.func is a PyTorch module introduced to bring functional programming paradigms into the PyTorch ecosystem. It allows users to:

• Apply automatic differentiation (grad, vjp, jvp)
• Perform vectorization using vmap
• Use functional calls with stateless modules
• Enable pure functions, reducing side effects

This is especially useful in higher-order optimization, meta-learning, batched operations, and advanced gradient computations.

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🔧 Getting Started with torch.func

Before diving in, make sure you’re using PyTorch 2.0 or later, as torch.func is part of the newer functional APIs.

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🛠️ Code Examples Using torch.func

1. Stateless Model Execution with functional_call

import torch
import torch.nn as nn
from torch.func import functional_call

model = nn.Linear(2, 2)
x = torch.randn(1, 2)

# Extract model parameters
params = dict(model.named_parameters())

# Run model in a functional/stateless way
output = functional_call(model, params, (x,))
print("Functional output:", output)

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2. Automatic Differentiation with grad

from torch.func import grad

def f(x):
    return (x**2).sum()

# Compute gradient of f w.r.t x
grad_f = grad(f)
x = torch.tensor([1.0, 2.0, 3.0])
print("Gradient:", grad_f(x))

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3. Batching with vmap

from torch.func import vmap

def linear(x, w, b):
    return x @ w.T + b

# Batch inputs
xs = torch.randn(10, 3)
ws = torch.randn(10, 2, 3)
bs = torch.randn(10, 2)

# Apply vectorization
batched_output = vmap(linear)(xs, ws, bs)
print("Batched output:", batched_output.shape)

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📚 Common Methods in torch.func

Function	Description
grad(func)	Computes gradient of a scalar function
vmap(func)	Vectorizes a function over batch dimensions
functional_call(model, params, args)	Executes a model statelessly
jacrev(func)	Computes Jacobian via reverse-mode AD
jacfwd(func)	Computes Jacobian via forward-mode AD
jvp(func)	Computes Jacobian-vector product
vjp(func)	Computes vector-Jacobian product

These methods work seamlessly with PyTorch’s autograd and enable functional transformations similar to JAX.

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❗ Common Errors and Debugging Tips

❌ Error: TypeError: 'Linear' object is not callable

Fix: When using functional_call, ensure the third argument is a tuple of inputs:

pythonCopyEditfunctional_call(model, params, (x,))

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❌ Error: RuntimeError: grad can only be applied to scalar outputs

Cause: torch.func.grad requires the function to return a scalar (like loss).

Fix:

def f(x): return x.sum()  # or use .mean(), .norm(), etc.

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❌ Shape Mismatch in vmap

Tip: Ensure all batched inputs have the same leading dimensions:

pythonCopyEditvmap(fn)(batched_input1, batched_input2)

Check .shape of inputs if unexpected broadcasting occurs.

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❌ Using Stateful Layers with functional_call

functional_call expects pure functions. If your model uses BatchNorm or other layers that track state, you may need to pass buffers as well:

pythonCopyEditparams = dict(model.named_parameters())
buffers = dict(model.named_buffers())

Then pass both into functional_call.

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✅ People Also Ask (FAQ)

🔹 What is the torch function?

In general, the term “torch function” refers to any callable provided by the PyTorch library. This includes mathematical functions, neural network modules, tensor operations, and higher-order functions under torch.func.

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🔹 What is the function of the torch library?

PyTorch (torch) is a Python-based deep learning library used for:

• Building neural networks
• Automatic differentiation
• Tensor computation
• GPU acceleration
• Functional and modular modeling

It’s widely used in both research and production for training and deploying ML models.

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🔹 What does torch.where do?

torch.where(condition, x, y) returns elements chosen from x or y depending on the condition.

a = torch.tensor([1, 2, 3])
b = torch.tensor([4, 5, 6])
c = torch.where(a > 2, a, b)
# c = [4, 5, 3]

This is useful for conditional selection or masking.

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🔹 Is torch.func the same as torch.nn.functional?

No.

• torch.nn.functional provides stateless versions of PyTorch layers like F.relu, F.linear, etc.
• torch.func provides functional programming utilities like vmap, grad, and functional_call.

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🔹 When should I use torch.func?

Use torch.func when:

• You need per-sample gradients
• You want to apply batched operations efficiently
• You’re implementing meta-learning or higher-order optimization
• You need full control over function transformation and differentiation

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📌 Final Thoughts

torch.func marks a new era in PyTorch’s evolution by empowering developers with functional programming capabilities. It helps build stateless, composable, and differentiable functions—a crucial foundation for advanced ML workflows.