40 lines
1.4 KiB
Python
40 lines
1.4 KiB
Python
import math
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import torch
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import torch.nn.functional as F
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import pytest
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from einops import rearrange
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from src.losses.cross_entropy_apex import CrossEntropyLossApex
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is_sm8x = torch.cuda.get_device_capability('cuda')[0] >= 8
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@pytest.mark.parametrize('dtype', [torch.float16, torch.float32] + ([torch.bfloat16] if is_sm8x else []))
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# @pytest.mark.parametrize('dtype', [torch.float16])
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@pytest.mark.parametrize('inplace_backward', [False, True])
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# @pytest.mark.parametrize('inplace_backward', [False])
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@pytest.mark.parametrize('vocab_size', [50257])
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def test_cross_entropy_loss_apex(vocab_size, inplace_backward, dtype):
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device = 'cuda'
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rtol, atol = (1e-5, 1e-6) if dtype == torch.float32 else (1e-3, 1e-4)
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# set seed
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torch.random.manual_seed(0)
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batch_size = 8
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seqlen = 128
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x_pt = torch.randn(batch_size * seqlen, vocab_size, device=device, dtype=dtype, requires_grad=True)
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x = x_pt.detach().clone().requires_grad_()
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y = torch.randint(0, vocab_size, (batch_size * seqlen,), dtype=torch.long, device=device)
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y[torch.randperm(batch_size * seqlen)[:10]] = -100
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model_pt = torch.nn.CrossEntropyLoss()
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model = CrossEntropyLossApex(inplace_backward=inplace_backward)
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out = model(x, y)
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out_pt = model_pt(x_pt.float(), y)
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assert torch.allclose(out, out_pt, rtol=rtol, atol=atol)
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g = torch.randn_like(out)
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out_pt.backward(g)
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out.backward(g)
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assert torch.allclose(x.grad, x_pt.grad, rtol=rtol, atol=atol)
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