20100a7254
This PR merges the `triton-mlir` branch, in which we have been quietly rewriting the Triton backend from scratch to increase maintainability, stability and ultimately performance. Changes to the runtime are minimal, and this new version aims to remain backward-compatible with the previous commit. The legacy backend is now officially deprecated, but can still be accessed via the `legacy-backend` tag. Co-authored-by: Keren Zhou <kerenzhou@openai.com> Co-authored-by: Yan Chunwei <yanchunwei@outlook.com> Co-authored-by: goostavz <109190422+goostavz@users.noreply.github.com> Co-authored-by: Shintaro Iwasaki <siwasaki@fb.com> Co-authored-by: Yan Da <dyanab@connect.ust.hk> Co-authored-by: Jun Yang <yangjunpro@gmail.com> Co-authored-by: Ian Bearman <ianb@microsoft.com> Co-authored-by: Jason Ansel <jansel@jansel.net> Co-authored-by: Qingyi Liu <qingyil@nvidia.com> Co-authored-by: ben-zhang-609 <110140741+ben-zhang-609@users.noreply.github.com> Co-authored-by: Chenggang Zhao <lyricz@yeah.net> Co-authored-by: ben-zhang-609 <benzh609@gmail.com> Co-authored-by: dongdongl <dongdongl@nvidia.com>
57 lines
1.4 KiB
Python
57 lines
1.4 KiB
Python
import torch
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from torch.testing import assert_close
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import triton
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import triton.language as tl
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torch_type = {
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"bool": torch.bool,
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'int8': torch.int8,
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'uint8': torch.uint8,
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'int16': torch.int16,
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"int32": torch.int32,
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'int64': torch.long,
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'float16': torch.float16,
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'bfloat16': torch.bfloat16,
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"float32": torch.float32,
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"float64": torch.float64
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}
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def get_tensor(shape, data_type, b_positive=False):
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x = None
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if data_type.startswith('int'):
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x = torch.arange(0, shape[0], dtype=torch_type[data_type], device='cuda')
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else:
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x = torch.arange(0, shape[0], dtype=torch_type[data_type], device='cuda')
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return x
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# @pytest.mark.parametrize('data_type',
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# [("int8"),
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# ('int16'),
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# ('int32'),
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# ("int64"),
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# ('float16'),
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# ("float32"),
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# ("float64")])
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def printf(data_type):
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@triton.jit
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def kernel(X, Y, BLOCK: tl.constexpr):
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x = tl.load(X + tl.arange(0, BLOCK))
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tl.printf("", x)
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tl.store(Y + tl.arange(0, BLOCK), x)
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shape = (128, )
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# limit the range of integers so that the sum does not overflow
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x = get_tensor(shape, data_type)
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y = torch.zeros(shape, dtype=x.dtype, device="cuda")
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kernel[(1,)](x, y, BLOCK=shape[0])
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assert_close(y, x)
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printf("float16")
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printf("int8")
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