af5600420b
This patch adds and exception to the nvlink wrapper tool to not pass empty cubin files to the nvlink job. If an empty file is passed to nvlink it will cause an error indicating that the file could not be opened. This would occur if the user tried to link object files that contained offloading code with a file that didnt. This will act as a workaround until the new OpenMP offloading driver becomes the default. Reviewed By: jdoerfert Differential Revision: https://reviews.llvm.org/D117777
82 lines
3.4 KiB
C++
82 lines
3.4 KiB
C++
// REQUIRES: clang-driver
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// REQUIRES: x86-registered-target
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// REQUIRES: nvptx-registered-target
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// See the steps to create a fat archive are given at the end of the file.
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// Given a FatArchive, clang-offload-bundler should be called to create a
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// device specific archive, which should be passed to clang-nvlink-wrapper.
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// RUN: %clang -O2 -### -fopenmp -fopenmp-targets=nvptx64-nvidia-cuda %s -L%S/Inputs/openmp_static_device_link -lFatArchive 2>&1 | FileCheck %s
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// CHECK: clang{{.*}}"-cc1"{{.*}}"-triple" "nvptx64-nvidia-cuda"{{.*}}"-target-cpu" "[[GPU:sm_[0-9]+]]"{{.*}}"-o" "[[HOSTBC:.*.s]]" "-x" "c++"{{.*}}.cpp
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// CHECK: clang-offload-bundler" "-unbundle" "-type=a" "-inputs={{.*}}/Inputs/openmp_static_device_link/libFatArchive.a" "-targets=openmp-nvptx64-nvidia-cuda-[[GPU]]" "-outputs=[[DEVICESPECIFICARCHIVE:.*.a]]" "-allow-missing-bundles"
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// CHECK: clang-nvlink-wrapper{{.*}}"-o" "{{.*}}.out" "-arch" "[[GPU]]" "{{.*}}[[DEVICESPECIFICARCHIVE]]"
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// RUN: not %clang -fopenmp -fopenmp-targets=nvptx64-nvidia-cuda %s %S/Inputs/openmp_static_device_link/empty.o --libomptarget-nvptx-bc-path=%S/Inputs/openmp_static_device_link/lib.bc 2>&1 | FileCheck %s --check-prefix=EMPTY
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// EMPTY-NOT: Could not open input file
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#ifndef HEADER
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#define HEADER
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#define N 10
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#pragma omp declare target
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// Functions defined in Fat Archive.
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extern "C" void func_present(float *, float *, unsigned);
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#ifdef MISSING
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// Function not defined in the fat archive.
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extern "C" void func_missing(float *, float *, unsigned);
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#endif
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#pragma omp end declare target
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int main() {
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float in[N], out[N], sum = 0;
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unsigned i;
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#pragma omp parallel for
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for (i = 0; i < N; ++i) {
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in[i] = i;
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}
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func_present(in, out, N); // Returns out[i] = a[i] * 0
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#ifdef MISSING
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func_missing(in, out, N); // Should throw an error here
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#endif
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#pragma omp parallel for reduction(+ \
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: sum)
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for (i = 0; i < N; ++i)
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sum += out[i];
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if (!sum)
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return 0;
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return sum;
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}
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#endif
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/***********************************************
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Steps to create Fat Archive (libFatArchive.a)
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************************************************
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***************** File: func_1.c ***************
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void func_present(float* in, float* out, unsigned n){
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unsigned i;
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#pragma omp target teams distribute parallel for map(to: in[0:n]) map(from: out[0:n])
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for(i=0; i<n; ++i){
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out[i] = in[i] * 0;
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}
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}
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*************************************************
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1. Compile source file(s) to generate object file(s)
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clang -O2 -fopenmp -fopenmp-targets=amdgcn-amd-amdhsa -Xopenmp-target=amdgcn-amd-amdhsa -march=gfx906 -c func_1.c -o func_1_gfx906.o
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clang -O2 -fopenmp -fopenmp-targets=amdgcn-amd-amdhsa -Xopenmp-target=amdgcn-amd-amdhsa -march=gfx908 -c func_1.c -o func_1_gfx908.o
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clang -O2 -fopenmp -fopenmp-targets=amdgcn-amd-amdhsa -Xopenmp-target=amdgcn-amd-amdhsa -march=gfx906 -c func_2.c -o func_2_gfx906.o
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clang -O2 -fopenmp -fopenmp-targets=amdgcn-amd-amdhsa -Xopenmp-target=amdgcn-amd-amdhsa -march=gfx908 -c func_2.c -o func_2_gfx908.o
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clang -O2 -fopenmp -fopenmp-targets=nvptx64-nvidia-cuda -c func_1.c -o func_1_nvptx.o
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clang -O2 -fopenmp -fopenmp-targets=nvptx64-nvidia-cuda -c func_2.c -o func_2_nvptx.o
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2. Create a fat archive by combining all the object file(s)
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llvm-ar cr libFatArchive.a func_1_gfx906.o func_1_gfx908.o func_2_gfx906.o func_2_gfx908.o func_1_nvptx.o func_2_nvptx.o
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************************************************/
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