test_cross_entropy.py

import math

import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
from flash_attn.losses.cross_entropy import CrossEntropyLossApex

is_sm8x = torch.cuda.get_device_capability("cuda")[0] >= 8


@pytest.mark.parametrize(
    "dtype", [torch.float16, torch.float32] + ([torch.bfloat16] if is_sm8x else [])
)
# @pytest.mark.parametrize('dtype', [torch.float16])
@pytest.mark.parametrize("inplace_backward", [False, True])
# @pytest.mark.parametrize('inplace_backward', [False])
@pytest.mark.parametrize("smoothing", [0.0, 0.9])
@pytest.mark.parametrize("vocab_size", [50257])
def test_cross_entropy_loss_apex(vocab_size, smoothing, inplace_backward, dtype):
    device = "cuda"
    rtol, atol = (1e-5, 1e-6) if dtype == torch.float32 else (1e-3, 1e-4)
    # set seed
    torch.random.manual_seed(0)
    batch_size = 8
    seqlen = 128
    x_pt = torch.randn(
        batch_size * seqlen, vocab_size, device=device, dtype=dtype, requires_grad=True
    )
    x = x_pt.detach().clone().requires_grad_()
    y = torch.randint(0, vocab_size, (batch_size * seqlen,), dtype=torch.long, device=device)
    y[torch.randperm(batch_size * seqlen)[:10]] = -100
    model_pt = torch.nn.CrossEntropyLoss(label_smoothing=smoothing)
    model = CrossEntropyLossApex(label_smoothing=smoothing, inplace_backward=inplace_backward)
    out = model(x, y)
    out_pt = model_pt(x_pt.float(), y)
    assert torch.allclose(out, out_pt, rtol=rtol, atol=atol)

    g = torch.randn_like(out)
    out_pt.backward(g)
    out.backward(g)
    assert torch.allclose(x.grad, x_pt.grad, rtol=rtol, atol=atol)