[Codegen][CUDA] Fix: cuda codegen vectorize cast #7561
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kongroo
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Laurawly
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Mar 2, 2021
| } | ||
| if (!fail) { | ||
| return; | ||
| } |
| @@ -511,8 +511,8 @@ def check(t0, t1): | |||
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| # schedule | |||
| s = tvm.te.create_schedule(C.op) | |||
| ob, ib = s[C].split(s[C].op.axis[0], nparts=32) | |||
| _, iib = s[C].split(ib, factor=4) | |||
| ob, ib = s[C].split(s[C].op.axis[0], nparts=n // factor) | |||
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We can also directly say factor=factor here.
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like this?
ob, ib = s[C].split(s[C].op.axis[0], factor=factor)
# _, iib = s[C].split(ib, factor=factor)
s[C].vectorize(ib)
vinx13
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Mar 2, 2021
trevor-m
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May 6, 2021
* fix: cuda codegen vectorize cast * style: fix python coding style * fix: missing break * refactor: directly split by factor Co-authored-by: jiangchengquan <[email protected]>
trevor-m
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May 11, 2021
* fix: cuda codegen vectorize cast * style: fix python coding style * fix: missing break * refactor: directly split by factor Co-authored-by: jiangchengquan <[email protected]>
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Data types such as float32x8 and int32x8 are not supported in CUDA, which will result in errors like "Cannot convert type float32x8 to CUDA type!" in code generation. I tried to fix this by storing 2 32-bits values in 1 64-bits value.
Could you please help review this fix? @Laurawly