What is torch.compile() and how does it speed up PyTorch model execution?

Introduced in PyTorch 2.0, torch.compile() performs just-in-time compilation of a model. Instead of executing each tensor operation eagerly (PyTorch's default), it captures the computation graph, fuses operations, and generates optimised kernels — primarily reducing GPU memory round-trips.

import torch
import torch.nn as nn
import time

model = nn.Sequential(
    nn.Linear(1024, 1024), nn.GELU(),
    nn.Linear(1024, 512),  nn.GELU(),
    nn.Linear(512, 10),
).cuda()

# Compile the model — wraps it, does NOT change the API
compiled_model = torch.compile(model)

x = torch.randn(256, 1024).cuda()

# First call triggers compilation (slow — may take 10-60 seconds)
out = compiled_model(x)

# Subsequent calls use the compiled, optimised kernels (fast)
for _ in range(5):
    out = compiled_model(x)

# Compilation modes — trade compile time for runtime speed
model_default = torch.compile(model)                              # balanced
model_reduce  = torch.compile(model, mode="reduce-overhead")      # less Python overhead
model_max     = torch.compile(model, mode="max-autotune")         # slowest compile, fastest run

# Benchmark comparison
def benchmark(fn, x, n=100):
    for _ in range(5): fn(x)            # warmup
    torch.cuda.synchronize()
    start = time.time()
    for _ in range(n): fn(x)
    torch.cuda.synchronize()
    return time.time() - start

eager_time    = benchmark(model, x)
compiled_time = benchmark(compiled_model, x)
print(f"Eager: {eager_time:.3f}s, Compiled: {compiled_time:.3f}s")

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