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triton/lib/codegen/pass.cc

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#include "triton/codegen/pass.h"
#include "llvm/IR/Constants.h"
#include "llvm/IR/LegacyPassManager.h"
#include "llvm/IR/Module.h"
#include "llvm/IR/Verifier.h"
#include "llvm/IRReader/IRReader.h"
#include "llvm/Linker/Linker.h"
#include "llvm/Support/SourceMgr.h"
#include "llvm/Transforms/IPO.h"
#include "llvm/Transforms/IPO/PassManagerBuilder.h"
#include "triton/codegen/analysis/align.h"
#include "triton/codegen/analysis/allocation.h"
#include "triton/codegen/analysis/axes.h"
#include "triton/codegen/analysis/liveness.h"
#include "triton/codegen/analysis/swizzle.h"
#include "triton/codegen/selection/generator.h"
#include "triton/codegen/transform/coalesce.h"
#include "triton/codegen/transform/cts.h"
#include "triton/codegen/transform/dce.h"
#include "triton/codegen/transform/disassociate.h"
#include "triton/codegen/transform/inline.h"
#include "triton/codegen/transform/membar.h"
#include "triton/codegen/transform/peephole.h"
#include "triton/codegen/transform/pipeline.h"
#include "triton/codegen/transform/prefetch.h"
#include "triton/ir/function.h"
#include "triton/ir/module.h"
#include "triton/ir/print.h"
namespace triton {
namespace codegen {
static void link_extern_libs(const ExternLibMap& user_extern_lib_map,
const ExternLibMap& target_extern_lib_map,
ir::module& ir, llvm::LLVMContext& ctx,
std::unique_ptr<llvm::Module>& llvm) {
for (const auto& iter : target_extern_lib_map) {
auto &lib_name = iter.first;
if (user_extern_lib_map.count(lib_name) != 0 &&
user_extern_lib_map.at(lib_name)->path() != "") {
// If the user specified a path for this library, use it.
user_extern_lib_map.at(lib_name)->install(ctx, llvm);
} else {
// Otherwise, use the default path.
iter.second->install(ctx, llvm);
}
}
std::set<llvm::StringRef> function_names;
for (auto& func : ir.get_function_list()) {
function_names.insert(func->get_name());
}
llvm::legacy::PassManager pass;
pass.add(llvm::createInternalizePass([&](const llvm::GlobalValue& v) -> bool {
if (function_names.count(v.getName()) != 0) {
// Preserve global functions
return true;
}
// Internalize all device functions
return false;
}));
llvm::legacy::PassManager pm;
pm.add(llvm::createVerifierPass());
pm.run(*llvm);
llvm::PassManagerBuilder builder;
builder.OptLevel = 3;
builder.SizeLevel = 0;
builder.populateModulePassManager(pass);
pass.run(*llvm);
}
// TODO:
// There should be a proper pass manager there!
std::unique_ptr<llvm::Module> add_passes_to_emit_bin(
ir::module& ir, llvm::LLVMContext& ctx, codegen::target* target,
int num_warps, int num_stages, int& shared_static,
const ExternLibMap& extern_lib_map) {
// generate llvm code
std::string name = ir.get_function_list()[0]->get_name();
std::unique_ptr<llvm::Module> llvm(new llvm::Module(name, ctx));
// optimizations
bool has_sm80 = target->as_nvidia() && target->as_nvidia()->sm() >= 80;
// create passes
codegen::analysis::align align;
codegen::transform::inliner inliner;
codegen::analysis::axes axes;
codegen::transform::pipeline pipeline(has_sm80, num_stages);
codegen::transform::disassociate disassociate;
codegen::analysis::layouts layouts(&axes, &align, num_warps, target);
codegen::transform::cts cts(&layouts, has_sm80);
codegen::analysis::liveness liveness(&layouts);
codegen::analysis::swizzle swizzle(&layouts, target);
codegen::analysis::allocation allocation(&liveness);
codegen::transform::dce dce;
codegen::transform::peephole peephole(target, &layouts);
codegen::transform::coalesce coalesce(&align, &layouts, has_sm80);
codegen::transform::prefetch prefetch_s(target);
codegen::transform::membar barriers(&liveness, &layouts, &allocation,
&prefetch_s, target);
codegen::generator isel(&axes, &layouts, &align, &allocation, &swizzle,
target, num_warps);
// run passes
inliner.run(ir);
dce.run(ir);
peephole.run(ir);
dce.run(ir);
pipeline.run(ir);
dce.run(ir);
// ir.print(std::cout);
disassociate.run(ir);
dce.run(ir);
align.run(ir);
axes.run(ir);
layouts.run(ir);
peephole.run(ir);
dce.run(ir);
if (target->is_gpu()) cts.run(ir);
align.run(ir);
axes.run(ir);
layouts.run(ir);
coalesce.run(ir);
dce.run(ir);
align.run(ir);
dce.run(ir);
if (target->is_gpu()) cts.run(ir);
dce.run(ir);
align.run(ir);
axes.run(ir);
layouts.run(ir);
peephole.run(ir);
dce.run(ir);
align.run(ir);
axes.run(ir);
layouts.run(ir);
swizzle.run(ir);
// std::cout << "---" << std::endl;
// ir.print(std::cout);
// std::cout << "---" << std::endl;
// ir.print(std::cout);
liveness.run(ir);
allocation.run(ir);
prefetch_s.run(ir);
barriers.run(ir);
// exit(1);
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// ir.print(std::cout);
isel.visit(ir, *llvm);
shared_static = allocation.allocated_size();
Better NVIDIA Pascal GPU Support (#827) This PR clarifies which features are supported on P100 via its tests, though Pascal is not officially and fully supported by Triton. ## What this PR does - Skip unsupported tests on P100. - Atomic RMW - `tl.dot()` (perhaps not all patterns, but basically most `tl.dot()` tests do not work on P100). - Add an explicit error if shared memory size >= 64K on P100. - Otherwise it causes `Invalid CUDA argument` error at `cuLaunchKernel()`, but this error is not very straightforward to understand. Instead of this generic CUDA argument error, this PR makes Triton show an error during codegen when `sm < 70`. This check happens in C/C++ so won't add an overhead in Triton's Python runtime. - 3 tests (see below) are currently failing, but these are not marked as skipped because any codegen update in the future can change the kernel size of the other tests. - This change won't affect Triton-MLIR. Hopefully Triton-MLIR's generic `tl.dot()` implementation would support P100. Importantly, Triton passed all the other tests on P100. Though this support is not official, it is great for, for example, PyTorch's TorchDynamo/Inductor, which can use Triton (without `tl.dot()`) for its backend (https://github.com/pytorch/torchdynamo/issues/1591). ### Results on P100 (Google Cloud) ```sh $ pytest test/unit ... ================================================================================== short test summary info ================================================================================== FAILED test/unit/language/test_core.py::test_reduce2d[argmin-float32-shape99-1] - RuntimeError: Device does not support shared memory of 65536bytes FAILED test/unit/language/test_core.py::test_reduce2d[argmax-float32-shape113-1] - RuntimeError: Device does not support shared memory of 65536bytes FAILED test/unit/language/test_core.py::test_permute[float32-shape5-perm5] - RuntimeError: Device does not support shared memory of 67584bytes ================================================================== 3 failed, 3824 passed, 952 skipped in 470.90s (0:07:50) ================================================================== ``` <details><summary> <b>Environment Details (collapsed)</b></summary> <p> ### VM details (Google Cloud) https://cloud.google.com/ ``` # You need a paid account (free trial does not cover GPUs) Google Cloud -> New Project -> Compute-Engine -> VM Instance Machine: GPU: NVIDIA Tesla P100 x 1 CPU: 2 vCPUs, 7.5GB memory Boot disk: OS: Ubuntu 18.04 LTS Disk: 40GB (cannot build Triton on the default 10GB disk) - When I tried, about $1.2 per hour. - US instances were full when I tried. I used Asia or Australia. - Needed a paid account (GPU is not covered by free trial) - Needed quota request for any GPU instance (by default, no GPU instance is allowed). Needed to wait an hour for approval ``` ### Reproducer ```sh ## 1. Install CUDA and a driver # Update the apt key (https://developer.nvidia.com/blog/updating-the-cuda-linux-gpg-repository-key/) sudo apt-key del 7fa2af80 wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-keyring_1.0-1_all.deb # Download CUDA as instructed wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600 sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /" sudo apt-get update sudo apt-get -y install cuda # Are you using P100? nvidia-smi | grep "Tesla P100" ## 2. Setup the build environment sudo apt update sudo apt install -y build-essential wget git libz-dev wget https://repo.anaconda.com/archive/Anaconda3-2022.05-Linux-x86_64.sh bash Anaconda3-2022.05-Linux-x86_64.sh -b -p $(pwd)/anaconda3 eval "$($(pwd)/anaconda3/bin/conda shell.bash hook)" conda create -y --name triton_base conda activate triton_base conda install -y cmake setuptools ## 3. Build Triton git clone https://github.com/openai/triton.git cd triton/python pip3 install -e '.[tests]' ## 4. Test pytest test/unit ``` ### Environment ```sh $ nvidia-smi +-----------------------------------------------------------------------------+ | NVIDIA-SMI 520.61.05 Driver Version: 520.61.05 CUDA Version: 11.8 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+======================+======================| | 0 Tesla P100-PCIE... On | 00000000:00:04.0 Off | 0 | | N/A 36C P0 25W / 250W | 0MiB / 16384MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ ``` </p></details>
2022-11-03 00:11:52 -07:00
if (target->as_nvidia() && target->as_nvidia()->sm() < 70) {
// sm < 70 (Pascal) has little shared memory resource.
// Instead of having "Error: Invalid argument" on launching a kernel, let's throw an error here.
if (shared_static >= 65536) {
throw std::runtime_error("Device does not support shared memory of " + std::to_string(shared_static) + "bytes");
}
}
if (isel.get_extern_lib_map().size() > 0) {
// If there's any extern lib calls,
// we need to link them in.
link_extern_libs(extern_lib_map, isel.get_extern_lib_map(), ir, ctx, llvm);
}
return llvm;
}
} // namespace codegen
} // namespace triton