Pranjal Swarup
0497b7934f
- Create a local copy for ROCm/rccl-tests for our examples.
- Update argument parsing to no longer use getopt_long.
- Workaround for Dyninst instrumentation.
---------
Co-authored-by: David Galiffi <David.Galiffi@amd.com>
[ROCm/rocprofiler-systems commit: 4e5029221b]
237 строки
7.8 KiB
C++
237 строки
7.8 KiB
C++
/*************************************************************************
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* Copyright (c) 2016-2022, NVIDIA CORPORATION. All rights reserved.
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* Modifications Copyright (c) 2020-2022 Advanced Micro Devices, Inc. All rights reserved.
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*
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* See LICENSE.txt for license information
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************************************************************************/
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/* Generate parameters for our error bound model of floating point average
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* (sum of scaled values) by sampling sums of random sequences for each
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* floating point type.
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*
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* The model has parameters "coef" and "power", where for two floats a & b,
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* they are close enough if and only if:
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* abs(intBits(a) - intBits(b)) <= 1 + coef*pow(rank_n, power);
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*
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* Where intBits(x) is the reinterpretation of the float bitpattern as an integer.
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*
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* Compile with:
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* nvcc -gencode=arch=compute_80,code=sm_80
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*/
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#include <algorithm>
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#include <cmath>
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#include <cstdint>
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#include <cstdio>
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#include <cuda_fp16.h>
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#include <hip/hip_bfloat16.h>
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using std::uint32_t;
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using std::uint64_t;
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using bfloat16 = hip_bfloat16;
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template <typename T>
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struct float_traits;
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template <>
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struct float_traits<float>
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{
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static constexpr int mantissa_bits = 23;
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static constexpr int exponent_bits = 8;
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using uint_t = uint32_t;
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__device__ static float make(double x) { return (float) x; }
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__device__ static float make(uint64_t x) { return (float) x; }
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__device__ static double todouble(float x) { return x; }
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__device__ static float add(float a, float b) { return a + b; }
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__device__ static float mul(float a, float b) { return a * b; }
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};
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template <>
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struct float_traits<double>
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{
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static constexpr int mantissa_bits = 52;
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static constexpr int exponent_bits = 11;
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using uint_t = uint64_t;
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__device__ static double make(double x) { return x; }
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__device__ static double make(uint64_t x) { return (double) x; }
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__device__ static double todouble(double x) { return x; }
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__device__ static double add(double a, double b) { return a + b; }
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__device__ static double mul(double a, double b) { return a * b; }
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};
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template <>
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struct float_traits<__half>
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{
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static constexpr int mantissa_bits = 10;
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static constexpr int exponent_bits = 5;
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using uint_t = uint16_t;
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__device__ static __half make(double x) { return __float2half((float) x); }
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__device__ static __half make(uint64_t x) { return __int2half_rn(x); }
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__device__ static double todouble(__half x) { return __half2float(x); }
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__device__ static __half add(__half a, __half b) { return __hadd(a, b); }
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__device__ static __half mul(__half a, __half b) { return __hmul(a, b); }
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};
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template <>
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struct float_traits<bfloat16>
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{
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static constexpr int mantissa_bits = 7;
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static constexpr int exponent_bits = 8;
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using uint_t = uint16_t;
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__device__ static bfloat16 make(double x) { return bfloat16(x); }
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__device__ static bfloat16 make(uint64_t x) { return bfloat16(x); }
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__device__ static double todouble(bfloat16 x) { return double(x); }
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__device__ static bfloat16 add(bfloat16 a, bfloat16 b)
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{
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return bfloat16(__hadd((float) a, (float) b));
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}
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__device__ static bfloat16 mul(bfloat16 a, bfloat16 b)
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{
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return bfloat16(__hmul((float) a, (float) b));
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}
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};
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template <typename F>
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__device__ int
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compare(F a, F b)
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{
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union
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{
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typename float_traits<F>::uint_t ua;
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F fa;
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};
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union
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{
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typename float_traits<F>::uint_t ub;
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F fb;
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};
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ua = 0;
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ub = 0;
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fa = a;
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fb = b;
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// std::printf("bits(%1.10f)=%x bits(%1.10f)=%x\n", fa, ua, fb, ub);
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return ua < ub ? ub - ua : ua - ub;
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}
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struct xoshiro256ss
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{
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uint64_t s[4];
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__device__ xoshiro256ss(int seed)
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{
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constexpr uint64_t src[4] = { 0xbb99e851d1f545cc, 0xbfc4022389ca40cb,
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0xe84aff5cb1914af5, 0x845999858284de77 };
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for(int i = 0; i < 4; i++)
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s[i] = src[i] + (seed + i) * 0xb45de8a52fdb65d3;
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}
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__device__ uint64_t operator()()
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{
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auto rol64 = [](uint64_t x, int k) { return (x << k) | (x >> (64 - k)); };
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uint64_t const result = rol64(s[1] * 5, 7) * 9;
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uint64_t const t = s[1] << 17;
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s[2] ^= s[0];
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s[3] ^= s[1];
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s[1] ^= s[2];
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s[0] ^= s[3];
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s[2] ^= t;
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s[3] = rol64(s[3], 45);
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return result;
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}
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};
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static __device__ int
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__reduce_max_sync(unsigned int mask, int value)
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{
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// We ignore mask, since all bits are set when calling them in the
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// test code below.
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int width = warpSize;
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for(unsigned int i = warpSize; i; i >>= 1)
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{
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value = max(__shfl_down(value, i, width), value);
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}
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return value;
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}
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template <typename F>
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__global__ void
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kernel()
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{
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using traits = float_traits<F>;
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constexpr int samps = 4 << 10;
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__shared__ F accf[samps];
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__shared__ double accd[samps];
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xoshiro256ss rng(threadIdx.x);
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float expo_avg = 1;
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for(int pass = 0; pass < 2; pass++)
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{
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F scalar = traits::make(1.0 / (3.14159 + .5 * threadIdx.x));
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int err_max = 0;
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float coef = 0;
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double expo_sum = 0;
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int expo_n = 0;
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int max_ranks =
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std::is_same<F, float>::value ? 16 << 10 : 1 << traits::mantissa_bits;
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for(int round = 0; round < 1 + (16 << 10) / max_ranks; round++)
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{
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// for(int round=0; round < 2; round++) {
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for(int i = threadIdx.x; i < samps; i += blockDim.x)
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{
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accf[i] = (F) 0;
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accd[i] = 0;
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}
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__syncthreads();
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for(int r = 0; r < max_ranks; r++)
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{
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int err = 0;
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for(int i = threadIdx.x; i < samps; i += blockDim.x)
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{
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constexpr uint64_t m = (1ll << traits::mantissa_bits) - 1;
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double d = std::is_same<F, float>::value ? double(rng() & m) : 1.0;
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F f = traits::make(d);
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accf[i] = traits::add(accf[i], traits::mul(scalar, f));
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accd[i] += traits::todouble(f);
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// if(threadIdx.x==0 && std::is_same<F,half>::value) std::printf("
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// r=%d f=%f\n", r, traits::todouble(accf[i]));
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int e = compare(accf[i], traits::mul(scalar, traits::make(accd[i])));
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err = err > e ? err : e;
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}
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err = __reduce_max_sync(-1u, err);
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err_max = err_max > err ? err_max : err;
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if(r >= 2)
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{
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// err = 1 + coef*pow(r,expo)
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float c = float(err - 1) / powf(float(r), expo_avg);
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coef = coef > c ? coef : c;
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}
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if(r >= 2)
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{
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double expo = log2f(1 + err_max) / log2f(r);
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expo_sum += expo;
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expo_n++;
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// if(threadIdx.x==0 && std::is_same<F,half>::value) std::printf("
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// r=%d err=%d errmax=%d expo=%f sum=%f n=%d\n", r, err, err_max,
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// expo, expo_sum, expo_n);
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}
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}
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}
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if(pass == 0)
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expo_avg = expo_sum / expo_n;
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else if(threadIdx.x == 0)
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printf(" coef=%1.10f expo=%1.10f\n", coef, expo_avg);
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}
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}
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int
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main()
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{
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std::printf("type=float:\n");
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kernel<float><<<1, 32>>>();
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cudaDeviceSynchronize();
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std::printf("\ntype=half:\n");
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kernel<half><<<1, 32>>>();
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cudaDeviceSynchronize();
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std::printf("\ntype=bfloat16:\n");
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kernel<bfloat16><<<1, 32>>>();
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cudaDeviceSynchronize();
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return 0;
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}
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