Strangely, Matrix Multiplications Run Faster When Given "Predictable" Data! https://www.thonking.ai/p/strangely-matrix-multiplications

mm_weird.py

import torch
torch.set_default_device('cuda')
from triton.testing import do_bench
from collections import defaultdict
from functools import partial
import random
random.seed(0)

def get_flops(A, B):
    ms = do_bench(lambda: torch.mm(A, B))
    flops = A.shape[0] * A.shape[1] * B.shape[1] * 2
    return (1e3/ms) * flops

M = 8192
N = 8192
K = 8192
def get_tensors(f):
    A = f(M, K, dtype=torch.bfloat16)
    B = f(N, K, dtype=torch.bfloat16).t()
    return A, B

def one_bit_random(*shape, dtype=torch.bfloat16):
    x = torch.randn(*shape, dtype=dtype)
    x = (x.view(torch.int16) & 0b1000).to(dtype=dtype)
    return x

def sparse(*shape, dtype=torch.bfloat16):
    x = torch.randn(*shape, dtype=dtype)
    x = torch.where(x < 0, 0, x)
    return x

original_setups = [
    ("randn", torch.randn),
    ("twos", lambda *shape, dtype: torch.full(shape, fill_value=2, dtype=dtype)),
    ("sparse", sparse),
    ("one bit", one_bit_random),
    ("rand", torch.rand),
    ("zeros", torch.zeros),
]
results = defaultdict(list)
setups = list(original_setups)
ITERS = 10
for _ in range(ITERS):
    random.shuffle(setups)
    for name, f in setups:
        results[name].append(get_flops(*get_tensors(f)))

def median(x):
    x = sorted(x)
    if len(x) % 2 == 0:
        return (x[len(x)//2] + x[(len(x) - 1)//2])/2
    else:
        return x[len(x)//2]

for name, _ in original_setups:
    print(f"{name}: {median(results[name])/1e12}")

sustainable_clock_speed.py

import torch
torch.set_default_device('cuda')
from triton.testing import do_bench
from collections import defaultdict
from functools import partial
import random
import subprocess
random.seed(0)

def set_gpu_limits(ref_sm_clock=1810, power_limit=330):
    subprocess.check_output([
        "sudo",
        "nvidia-smi",
        "-i",
        "0",
        f"--lock-gpu-clocks={ref_sm_clock},{ref_sm_clock}",
    ])
    subprocess.check_output([
        "sudo",
        "nvidia-smi",
        "-i",
        "0",
        f"-pl={power_limit}",
    ])
def get_flops(A, B):
    ms = do_bench(lambda: torch.mm(A, B))
    flops = A.shape[0] * A.shape[1] * B.shape[1] * 2
    return (1e3/ms) * flops

M = 8192
N = 8192
K = 8192
def get_tensors(f):
    A = f(M, K, dtype=torch.bfloat16)
    B = f(N, K, dtype=torch.bfloat16).t()
    return A, B

def one_bit_random(*shape, dtype=torch.bfloat16):
    x = torch.randn(*shape, dtype=dtype)
    x = (x.view(torch.int16) & 0b1000).to(dtype=dtype)
    return x

def sparse(*shape, dtype=torch.bfloat16):
    x = torch.randn(*shape, dtype=dtype)
    x = torch.where(torch.rand_like(x) > 0.1, 0, x)
    return x

def checkerboard(*shape, dtype=torch.bfloat16):
    x = torch.randn(*shape, dtype=dtype)
    x = torch.where((torch.arange(shape[0]).view(1, -1) - torch.arange(shape[1]).view(-1, 1)) % 2 == 0, x, 0)
    return x

def ternary(*shape, dtype=torch.bfloat16):
    x = torch.randint(low=-1, high=2, size=shape, dtype=torch.bfloat16)
    return x

original_setups = [
    # ("zeros", torch.zeros),
    ("randn", torch.randn),
    # ("checkerboard", checkerboard),
    # ("sparse", sparse),
    # ("rand", torch.rand),
    # ("ternary", ternary),
    # ("one bit", one_bit_random),
    # ("all_pi", lambda *shape, dtype: torch.full(shape, fill_value=3.1415926535897932384626, dtype=dtype)),
    # ("twos", lambda *shape, dtype: torch.full(shape, fill_value=2, dtype=dtype)),
]
def get_results(clocks, power):
    set_gpu_limits(clocks, power)
    results = defaultdict(list)
    setups = list(original_setups)
    ITERS = 10
    for _ in range(ITERS):
        random.shuffle(setups)
        for name, f in setups:
            results[name].append(get_flops(*get_tensors(f)))

    def median(x):
        x = sorted(x)
        if len(x) % 2 == 0:
            return (x[len(x)//2] + x[(len(x) - 1)//2])/2
        else:
            return x[len(x)//2]

    # for name, _ in original_setups:
    #     print(f"{name}: {median(results[name])/1e12}")
    # print(median(results['zeros']) / median(results["randn"]))
    return median(results['randn'])

start_clocks = 1980 # H100
for power in reversed([150, 200, 250, 300, 350, 400, 450, 500]):
    max_clocks = 1980 # H100
    start_flops = get_results(max_clocks, power)
    for clocks in range(start_clocks, 200, -100):
        # print(power, clocks)
        cur_flops = get_results(clocks, power)
        if cur_flops < start_flops * 0.9:
            print("Done: ", power, clocks)
            start_clocks = clocks
            break

Yeah I reran it on an A100:

Am I obtuse or isn't this just GPU speculative execution in action?

@photomz If you haven't seen it, this is the associated article: https://www.thonking.ai/p/strangely-matrix-multiplications

But speculative execution is an interesting thought - it's definitely a phenomenon that looks "fairly similar" on the surface, and I thought a bit about it.

But:

1. I'm not actually sure GPUs do speculative execution? Since GPUs are generally focused on parallel execution, they usually have much shallower "pipes" than CPUs do.
2. Matrix multiplications do not typically have any branching on data. Generally, I've seen speculative execution interact with branch prediction to result in differing performance based off of the input data. But in this case, there is no branching! The GPU executes the exact same assembly instructions in the exact same order, regardless of what the input data is.
3. And related to the above, I'd have a hard time seeing how randn and rand would lead to differing speculative execution behavior on the GPUs.