ConvolutedDog opened a new pull request, #19565:
URL: https://github.com/apache/tvm/pull/19565
Fix CUDA lowering of standard TIR math intrinsics so they use precise CUDA
math functions by default instead of fast-math `__*f` functions. This fixes
the default behavior reported in #19546, where operators such as `tirx.exp`
could lower to `__expf` even though fast math was not explicitly requested.
This change adds a CUDA target attribute, `enable_fast_math`, which defaults
to `false`. When the attribute is unset or false, standard math intrinsics
lower through the normal CUDA math rule, for example `expf`, `logf`, `sinf`,
`cosf`, `powf`, and `rsqrtf` for `float32`. When users explicitly enable the
attribute on the target, the lowering pass also checks the
`cuda.fastmath.FLowerIntrinsic` rules before the normal CUDA lowering rules.
Users can opt in to fast math by constructing a CUDA target with the
attribute:
```py
tvm.target.Target({"kind": "cuda", "enable_fast_math": True})
target = tvm.target.Target({
"tag": "nvidia/nvidia-a100",
"enable_fast_math": True,
})
```
The fast-math lowering path currently covers the CUDA math operators
registered with `cuda.fastmath.FLowerIntrinsic`: `tirx.exp`, `tirx.exp10`,
`tirx.log`, `tirx.log2`, `tirx.log10`, `tirx.tan`, `tirx.cos`, `tirx.sin`,
`tirx.tanh`, and `tirx.pow`.
`tirx.rsqrt` is also registered for CUDA lowering so it maps to the CUDA
reciprocal-square-root intrinsic instead of being legalized as `1 / sqrt(x)`.
Add CUDA codegen tests
`tests/python/codegen/test_target_codegen_cuda_fastmath.py` that check the
lowered IR, generated CUDA source, and runtime results for the supported math
intrinsics across floating point dtypes and both default and fast-math targets.
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