diff --git a/3rdparty/nvbench/l2_cache_flush.h b/3rdparty/nvbench/l2_cache_flush.h new file mode 100644 index 000000000000..3d0211564535 --- /dev/null +++ b/3rdparty/nvbench/l2_cache_flush.h @@ -0,0 +1,74 @@ +/* + * Copyright 2021 NVIDIA Corporation + * + * Licensed under the Apache License, Version 2.0 with the LLVM exception + * (the "License"); you may not use this file except in compliance with + * the License. + * + * You may obtain a copy of the License at + * + * http://llvm.org/foundation/relicensing/LICENSE.txt + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * \file l2_cache_flush.h + * \brief Functions to flush L2 cache using CUDA's API, adopted from nvbench. + */ +#ifndef L2_CACHE_FLUSH_H_ +#define L2_CACHE_FLUSH_H_ + +#include +#include +#include + +namespace tvm { +namespace runtime { + +#define CUDA_CALL(func) \ + { \ + cudaError_t e = (func); \ + ICHECK(e == cudaSuccess || e == cudaErrorCudartUnloading) \ + << "CUDA: " << cudaGetErrorString(e); \ + } + +class L2Flush { + public: + L2Flush() : initialized_(false), l2_size_(0), l2_buffer_(nullptr) {} + + ~L2Flush() { + if (l2_size_ > 0) { + CUDA_CALL(cudaFree(l2_buffer_)); + } + } + + void Flush(cudaStream_t stream) { + if (!initialized_) { + // initialize l2_buffer_ and l2_size_ + initialized_ = true; + int device_id; + CUDA_CALL(cudaGetDevice(&device_id)); + CUDA_CALL(cudaDeviceGetAttribute(&l2_size_, cudaDevAttrL2CacheSize, device_id)); + if (l2_size_ > 0) { + CUDA_CALL(cudaMalloc(reinterpret_cast(&l2_buffer_), l2_size_)); + } + } + if (l2_size_ > 0) { + CUDA_CALL(cudaMemsetAsync(l2_buffer_, 0, l2_size_, stream)); + } + } + + static L2Flush* ThreadLocal(); + + private: + bool initialized_ = false; + int l2_size_; + int* l2_buffer_; +}; + +} // namespace runtime +} // namespace tvm + +#endif // L2_CACHE_FLUSH_H_ diff --git a/LICENSE b/LICENSE index 6524d530deca..fbc11be2deb5 100644 --- a/LICENSE +++ b/LICENSE @@ -212,6 +212,7 @@ Apache Software Foundation License 2.0 3rdparty/dlpack 3rdparty/dmlc-core 3rdparty/OpenCL-Headers +3rdparty/nvbench (with LLVM exception) BSD 2-clause License @@ -234,7 +235,6 @@ MIT License 3rdparty/cma 3rdparty/compiler-rt/builtin_fp16.h - The Unlicense ------------- diff --git a/licenses/LICENSE.l2_cache_flush.txt b/licenses/LICENSE.l2_cache_flush.txt new file mode 100644 index 000000000000..bd8b243dfa02 --- /dev/null +++ b/licenses/LICENSE.l2_cache_flush.txt @@ -0,0 +1,218 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ +#include "../../../3rdparty/nvbench/l2_cache_flush.h" + +#include +#include +#include + +#include "cuda_common.h" + +namespace tvm { + +namespace runtime { + +typedef dmlc::ThreadLocalStore L2FlushStore; + +L2Flush* L2Flush::ThreadLocal() { return L2FlushStore::Get(); } + +TVM_REGISTER_GLOBAL("l2_cache_flush_cuda").set_body([](TVMArgs args, TVMRetValue* rv) { + ICHECK(L2Flush::ThreadLocal() != nullptr) << "L2Flush::ThreadLocal do not exist."; + cudaStream_t stream = CUDAThreadEntry::ThreadLocal()->stream; + L2Flush::ThreadLocal()->Flush(stream); +}); + +} // namespace runtime +} // namespace tvm diff --git a/tests/python/unittest/test_evaluator_with_preproc.py b/tests/python/unittest/test_evaluator_with_preproc.py new file mode 100644 index 000000000000..fc6eec25b8da --- /dev/null +++ b/tests/python/unittest/test_evaluator_with_preproc.py @@ -0,0 +1,60 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +import tvm +from tvm import te +from tvm.script import tir as T +import tvm.testing +import numpy as np +import pytest + + +@T.prim_func +def matmul(a: T.handle, b: T.handle, c: T.handle) -> None: + A = T.match_buffer(a, [128, 128]) + B = T.match_buffer(b, [128, 128]) + C = T.match_buffer(c, [128, 128]) + for i, j, k in T.grid(128, 128, 128): + with T.block("matmul"): + vi, vj, vk = T.axis.remap("SSR", [i, j, k]) + with T.init(): + C[vi, vj] = 0.0 + C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj] + + +@tvm.testing.requires_cuda +@pytest.mark.parametrize("f_preproc", ["", "l2_cache_flush_cuda"]) +def test_time_evalutor_with_preproc(f_preproc: str): + mod = tvm.IRModule.from_expr(matmul) + sch = tvm.tir.Schedule(mod) + blk = sch.get_block("matmul") + i, j, k = sch.get_loops(blk) + sch.bind(i, "blockIdx.x") + sch.bind(j, "threadIdx.x") + f = tvm.build(sch.mod["main"], target="cuda") + dev = tvm.cuda(0) + evaluator = f.time_evaluator(f.entry_name, dev, repeat=1000, number=1, f_preproc=f_preproc) + + a = tvm.nd.array(np.random.rand(128, 128).astype("float32"), device=dev) + b = tvm.nd.array(np.random.rand(128, 128).astype("float32"), device=dev) + c = tvm.nd.array(np.zeros((128, 128)).astype("float32"), device=dev) + args = [a, b, c] + print("Evaluator (f_preproc={}):\t{:.5f}ms".format(f_preproc, evaluator(*args).mean * 1000)) + + +if __name__ == "__main__": + test_time_evalutor_with_preproc("l2_cache_flush_cuda")