62835a0979
This PR adds an automatic memory alignment mechanism in the Triton runtime. Specifically, the JIT compiler detects the alignment (in bytes) of each pointer argument as well as the largest power of two divisor (between 1 and 16) of each integer argument. Proper .aligned and .multipleof attributes are then added to the Triton-IR on-the-fly for all auto-tunable kernels. There is a cache that remembers all the kernels compiled for each possible configuration. This PR also includes substantial cleaning of the Python API. This adds 2-3us overhead, mostly due to accessing integer #defines from the auto-tuned compilation options. The previous solution was slightly faster but hacky and potentially unsafe, so this is preferred for now.
32 lines
825 B
C++
32 lines
825 B
C++
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#include "triton/driver/device.h"
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#include "triton/driver/stream.h"
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#include <ATen/cuda/CUDAContext.h>
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#include <cuda_runtime_api.h>
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#include <torch/extension.h>
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namespace torch_utils {
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uint64_t cu_device(int64_t dev_id) {
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CUdevice handle;
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triton::driver::dispatch::cuDeviceGet(&handle, dev_id);
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return (uint64_t)handle;
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}
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uint64_t cu_stream(int64_t dev_id) {
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return (uint64_t)c10::cuda::getCurrentCUDAStream(dev_id).stream();
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}
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void set_device(int64_t dev_id) {
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if (dev_id >= 0)
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C10_CUDA_CHECK(cudaSetDevice(dev_id));
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}
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} // namespace torch_utils
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void init_torch_utils(pybind11::module &m) {
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pybind11::module subm = m.def_submodule("torch_utils");
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subm.def("cu_device", &torch_utils::cu_device);
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subm.def("cu_stream", &torch_utils::cu_stream);
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subm.def("set_device", &torch_utils::set_device);
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} |