File size: 3,437 Bytes
4b9fefd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | // test_rms_norm.cpp — verify aclnnRmsNorm against Python reference.
#include "acl_common.h"
#include "acl_runtime.h"
#include "aclnn_ops.h"
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <vector>
static float bf16_to_float(uint16_t x) {
uint32_t u = (uint32_t)x << 16;
float f; std::memcpy(&f, &u, 4);
return f;
}
static std::vector<uint8_t> read_file(const std::string& path) {
std::ifstream f(path, std::ios::binary | std::ios::ate);
if (!f) { fprintf(stderr, "open %s failed\n", path.c_str()); std::abort(); }
size_t sz = f.tellg();
f.seekg(0);
std::vector<uint8_t> v(sz);
f.read((char*)v.data(), sz);
return v;
}
int main() {
const std::string data = "tests/rms_norm_data";
// Parse shape.txt
int64_t N = 0, D = 0;
double eps = 1e-6;
{
std::ifstream f(data + "/shape.txt");
std::string line;
while (std::getline(f, line)) {
auto eq = line.find('=');
if (eq == std::string::npos) continue;
auto k = line.substr(0, eq);
auto v = line.substr(eq + 1);
if (k == "N") N = std::atoll(v.c_str());
else if (k == "D") D = std::atoll(v.c_str());
else if (k == "eps") eps = std::atof(v.c_str());
}
}
printf("Shape: N=%ld D=%ld eps=%g\n", N, D, eps);
AclRuntime rt;
rt.init(0);
auto x_host = read_file(data + "/x.bin");
auto gamma_host = read_file(data + "/gamma.bin");
auto y_ref_host = read_file(data + "/y_ref.bin");
DeviceBuffer x_dev(N * D * 2);
DeviceBuffer gamma_dev(D * 2);
DeviceBuffer y_dev(N * D * 2);
DeviceBuffer rstd_dev(N * 4); // F32
ACL_CHECK(aclrtMemcpy(x_dev.get(), x_host.size(), x_host.data(), x_host.size(), ACL_MEMCPY_HOST_TO_DEVICE));
ACL_CHECK(aclrtMemcpy(gamma_dev.get(), gamma_host.size(), gamma_host.data(), gamma_host.size(), ACL_MEMCPY_HOST_TO_DEVICE));
auto t_x = make_contig_tensor(x_dev.get(), ACL_BF16, {N, D});
auto t_gamma = make_contig_tensor(gamma_dev.get(), ACL_BF16, {D});
auto t_y = make_contig_tensor(y_dev.get(), ACL_BF16, {N, D});
auto t_rstd = make_contig_tensor(rstd_dev.get(), ACL_FLOAT, {N});
rms_norm(rt.stream(), t_x.get(), t_gamma.get(), eps, t_y.get(), t_rstd.get());
rt.sync();
std::vector<uint16_t> y_cxx(N * D);
ACL_CHECK(aclrtMemcpy(y_cxx.data(), N * D * 2, y_dev.get(), N * D * 2, ACL_MEMCPY_DEVICE_TO_HOST));
auto* y_ref = (const uint16_t*)y_ref_host.data();
// Compare
double l2_d = 0, l2_r = 0, max_abs = 0;
for (int i = 0; i < N * D; i++) {
float a = bf16_to_float(y_cxx[i]);
float b = bf16_to_float(y_ref[i]);
l2_d += (a-b)*(a-b);
l2_r += b*b;
if (std::abs(a-b) > max_abs) max_abs = std::abs(a-b);
}
double rel = std::sqrt(l2_d) / (std::sqrt(l2_r) + 1e-10);
printf("L2 diff = %.6f, L2 ref = %.6f, relative = %.6e, max abs = %.6f\n",
std::sqrt(l2_d), std::sqrt(l2_r), rel, max_abs);
printf("y_cxx[0, :8]: ");
for (int i = 0; i < 8; i++) printf("%.4f ", bf16_to_float(y_cxx[i]));
printf("\ny_ref[0, :8]: ");
for (int i = 0; i < 8; i++) printf("%.4f ", bf16_to_float(y_ref[i]));
printf("\n");
bool ok = rel < 1e-2;
printf("\n%s\n", ok ? "=== test_rms_norm PASS ===" : "=== test_rms_norm FAIL ===");
return ok ? 0 : 1;
}
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