numba max reduction is 10x slower than sum on the same array

pt.max reduces at about a tenth the speed of pt.sum through the same CAReduce codegen on the same contiguous array, and unlike sum it loses to numpy rather than beating it. Every axis configuration I tried, full and partial.

import time

import numpy as np
import pytensor
import pytensor.tensor as pt

x_np = np.random.default_rng(0).normal(size=(200, 200, 200))
x = pt.tensor("x", shape=x_np.shape)


def bench(fn, *args):
    fn(*args)
    start = time.perf_counter()
    for _ in range(10):
        fn(*args)
    return (time.perf_counter() - start) / 10 * 1000


for name, op in (("max", pt.max), ("sum", pt.sum)):
    print(name, round(bench(pytensor.function([x], op(x), trust_input=True), x_np), 2), "ms")

print("numpy max", round(bench(x_np.max), 2), "ms")
print("numpy sum", round(bench(x_np.sum), 2), "ms")

# max 7.31 ms   sum 0.72 ms   numpy max 0.72 ms   numpy sum 0.96 ms

Start by running the Python benchmark in the issue, comparing pt.max and pt.sum through the CAReduce codegen on full and partial axes. Trace the reduction entry points and generated execution path to identify why max is slower; done means max approaches NumPy performance and no longer has the reported gap with sum across the tested axis configurations.