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import math
import pytest
import torch
import torch.nn.functional as F
from einops import rearrange
from flash_attn.losses.cross_entropy import CrossEntropyLoss
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("lse_square_scale", [0.0, 1e-2])
@pytest.mark.parametrize("return_z_loss", [False, True])
# @pytest.mark.parametrize("lse_square_scale", [1e-2])
@pytest.mark.parametrize("logit_scale", [1.0, 0.7])
# @pytest.mark.parametrize("logit_scale", [1.0])
@pytest.mark.parametrize("smoothing", [0.0, 0.9])
# @pytest.mark.parametrize("smoothing", [0.0])
@pytest.mark.parametrize("vocab_size", [50257, 128 * 1024]) # test vocab larger than 64k for split
# @pytest.mark.parametrize("vocab_size", [12])
def test_cross_entropy_loss(
vocab_size, smoothing, logit_scale, lse_square_scale, return_z_loss, 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 = 1 if dtype == torch.float32 else 4 # Otherwise OOM
seqlen = 4096 if lse_square_scale == 0.0 and logit_scale == 1.0 else 1024 # Otherwise OOM
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)
if batch_size * seqlen > 10:
y[torch.randperm(batch_size * seqlen)[:10]] = -100
model_pt = torch.nn.CrossEntropyLoss(label_smoothing=smoothing)
model = CrossEntropyLoss(
label_smoothing=smoothing,
logit_scale=logit_scale,
lse_square_scale=lse_square_scale,
return_z_loss=return_z_loss,
inplace_backward=inplace_backward,
)
if return_z_loss:
out, out_z_loss = model(x, y)
else:
out = model(x, y)
x_pt_scaled = (x_pt.float() * logit_scale) if logit_scale != 1.0 else x_pt.float()
out_pt = model_pt(x_pt_scaled, y)
if lse_square_scale > 0.0:
lse_pt = torch.logsumexp(x_pt_scaled, dim=-1)
z_loss_pt = lse_square_scale * (lse_pt[y != -100] ** 2).mean()
if return_z_loss:
assert torch.allclose(out_z_loss, z_loss_pt, rtol=rtol, atol=atol)
out_pt += z_loss_pt
assert torch.allclose(out, out_pt, rtol=1e-5, atol=1e-6)
g = torch.randn_like(out)
out_pt.backward(g)
out.backward(g)
assert torch.allclose(x.grad, x_pt.grad, rtol=rtol, atol=atol)
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