40 lines
1.5 KiB
Python
40 lines
1.5 KiB
Python
import pytest
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import torch
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import triton
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import triton._C.libtriton.triton as _triton
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@pytest.mark.parametrize("M, N, dtype, mode",
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[
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(M, N, dtype, mode) for M in [1024, 821]
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for N in [512, 857, 1871, 2089, 8573, 31000]
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for dtype in ['bfloat16', 'float16', 'float32']
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for mode in ['forward', 'backward']
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]
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)
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def test_op(M, N, dtype, mode):
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cc = _triton.runtime.cc(_triton.runtime.backend.CUDA, torch.cuda.current_device())
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if cc < 80 and dtype == "bfloat16":
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pytest.skip("Only test bfloat16 on devices with sm >= 80")
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dtype = {'bfloat16': torch.bfloat16, 'float16': torch.float16, 'float32': torch.float32}[dtype]
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# create inputs
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x = torch.randn(M, N, dtype=dtype, device='cuda', requires_grad=True)
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idx = 4 + torch.ones(M, dtype=torch.int64, device='cuda')
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# forward pass
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tt_y = triton.ops.cross_entropy(x, idx)
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th_y = torch.nn.CrossEntropyLoss(reduction="none")(x, idx)
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if mode == 'forward':
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triton.testing.assert_almost_equal(th_y, tt_y)
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# backward pass
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elif mode == 'backward':
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dy = torch.randn_like(tt_y)
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# triton backward
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tt_y.backward(dy)
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tt_dx = x.grad.clone()
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# torch backward
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x.grad.zero_()
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th_y.backward(dy)
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th_dx = x.grad.clone()
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triton.testing.assert_almost_equal(th_dx, tt_dx)
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