tests/test_rms_norm.py

import random

import pytest
import torch

import activation

from .utils import assert_close, opcheck

DTYPES = [torch.float, torch.bfloat16, torch.half]
NUM_TOKENS = [7, 83, 256, 2048]  # Arbitrary values for testing
D = [1, 7, 512, 13824]  # Arbitrary values for testing
SEEDS = [0]
CUDA_DEVICES = [
    f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)
]


@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
@pytest.mark.parametrize("d", D)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("seed", SEEDS)
@pytest.mark.parametrize("device", CUDA_DEVICES)
def test_rms_norm(
    num_tokens: int,
    d: int,
    dtype: torch.dtype,
    seed: int,
    device: str,
) -> None:
    random.seed(seed)
    torch.manual_seed(seed)
    torch.set_default_device(device)

    x = torch.randn(num_tokens, d, dtype=dtype, requires_grad=True)
    weight = torch.randn(d, dtype=dtype, requires_grad=True)
    eps = 1e-05

    x.retain_grad()
    weight.retain_grad()
    # To separate gradient computation, clone the inputs

    x_ref = x.detach().clone().requires_grad_(True)
    weight_ref = weight.detach().clone().requires_grad_(True)

    torch_layer = torch.nn.RMSNorm(d, eps=eps, dtype=dtype)
    torch_layer.weight = torch.nn.Parameter(weight_ref)

    op = activation.ops.rms_norm
    fn = activation.rms_norm
    layer = activation.layers.RMSNorm(d, eps=eps, dtype=dtype)
    layer.weight = torch.nn.Parameter(weight)

    opcheck(op, (x, weight, eps))

    out = fn(x, weight, eps)
    mod_out = layer(x)
    ref_out = torch_layer(x_ref)

    assert_close(out, ref_out)
    assert_close(mod_out, out, atol=0.0, rtol=0.0)

    # test backward pass
    out_grad = torch.randn_like(out)
    out_grad = out_grad / out_grad.norm()

    ref_out.backward(out_grad)
    mod_out.backward(out_grad)

    assert_close(x.grad, x_ref.grad)
    assert_close(layer.weight.grad, torch_layer.weight.grad, rtol=0.05)