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// Copyright 2019 Google LLC
//
// This source code is licensed under the BSD-style license found in the
// LICENSE file in the root directory of this source tree.
#pragma once
#include <gtest/gtest.h>
#include <algorithm>
#include <cassert>
#include <cmath>
#include <cstddef>
#include <cstdlib>
#include <functional>
#include <random>
#include <vector>
#include <fp16.h>
#include <xnnpack.h>
#include <xnnpack/AlignedAllocator.h>
#include <xnnpack/params-init.h>
#include <xnnpack/params.h>
static inline bool is_fp16_zero(uint16_t x) {
const uint16_t two_x = x + x;
return two_x == 0;
}
class SpMMMicrokernelTester {
public:
enum class Variant {
Native,
Scalar,
};
inline SpMMMicrokernelTester& mr(size_t mr) {
this->mr_ = mr;
return *this;
}
inline size_t mr() const {
return this->mr_;
}
inline SpMMMicrokernelTester& nr(size_t nr) {
this->nr_ = nr;
return *this;
}
inline size_t nr() const {
return this->nr_;
}
inline SpMMMicrokernelTester& m(size_t m) {
this->m_ = m;
return *this;
}
inline size_t m() const {
return this->m_;
}
inline SpMMMicrokernelTester& n(size_t n) {
this->n_ = n;
return *this;
}
inline size_t n() const {
return this->n_;
}
inline SpMMMicrokernelTester& k(size_t k) {
this->k_ = k;
return *this;
}
inline size_t k() const {
return this->k_;
}
inline SpMMMicrokernelTester& output_stride(size_t output_stride) {
assert(output_stride != 0);
this->output_stride_ = output_stride;
return *this;
}
inline size_t output_stride() const {
if (this->output_stride_ == 0) {
return m();
} else {
assert(this->output_stride_ >= m());
return this->output_stride_;
}
}
inline SpMMMicrokernelTester& sparsity(float sparsity) {
this->sparsity_ = sparsity;
return *this;
}
inline float sparsity() const {
return this->sparsity_;
}
inline SpMMMicrokernelTester& qmin(uint8_t qmin) {
this->qmin_ = qmin;
return *this;
}
inline uint8_t qmin() const {
return this->qmin_;
}
inline SpMMMicrokernelTester& qmax(uint8_t qmax) {
this->qmax_ = qmax;
return *this;
}
inline uint8_t qmax() const {
return this->qmax_;
}
inline SpMMMicrokernelTester& iterations(size_t iterations) {
this->iterations_ = iterations;
return *this;
}
inline size_t iterations() const {
return this->iterations_;
}
void Test(xnn_f32_spmm_minmax_ukernel_function spmm, Variant variant = Variant::Native) const {
ASSERT_GE(m(), 1);
ASSERT_GE(n(), 1);
ASSERT_GE(k(), 1);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng);
auto prng = std::bind(std::uniform_real_distribution<float>(), rng);
std::vector<float, AlignedAllocator<float, 64>> input(k() * m());
// Think of b as (n/nr + n % nr) x k, expansion happens later.
const size_t ncols = n() / nr() + n() % nr();
std::vector<float> b(ncols * k());
std::vector<float> bias(n());
// Number of non-zero weights per N (output channel).
std::vector<uint32_t> nmap(n());
// Mapping from index of non-zero weight to increment of K (input channel) following this index.
std::vector<int32_t> dmap(n() * k());
std::vector<float> w(n() * k() + n());
std::vector<float> output((n() - 1) * output_stride() + m());
std::vector<float> output_ref(n() * m());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(f32rng));
std::generate(b.begin(), b.end(), std::ref(f32rng));
std::generate(bias.begin(), bias.end(), std::ref(f32rng));
std::fill(output.begin(), output.end(), nanf(""));
std::fill(output_ref.begin(), output_ref.end(), 0.0f);
std::fill(nmap.begin(), nmap.end(), 0);
std::fill(dmap.begin(), dmap.end(), 0);
std::fill(w.begin(), w.end(), 0.0f);
for (float& b_value : b) {
if (prng() <= sparsity()) {
b_value = 0.0f;
}
}
uint32_t nnz = 0;
uint32_t wcnt = 0;
size_t last_kk = 0;
bool first_nzz = true;
size_t first_kk = 0;
for (size_t nn = 0; nn < n() / nr(); nn++) {
for (size_t i = 0; i < nr(); ++i)
w[wcnt++] = bias[nr() * nn + i];
for (size_t kk = 0; kk < k(); kk++) {
if (b[nn * k() + kk] != 0.0f) {
// Every non-zero actually corresponds to nr adjacent non-zeros.
for (size_t i = 0; i < nr(); ++i)
w[wcnt++] = b[nn * k() + kk] + static_cast<float>(i);
// Skip the very first non-zero weight as we record only the difference.
if (first_nzz) {
first_kk = kk;
} else {
const int32_t increment = int32_t(kk - last_kk) * int32_t(m() * sizeof(float));
dmap[nnz++] = increment;
}
last_kk = kk;
first_nzz = false;
nmap[nn] += 1;
}
}
}
// now we've constructed the matrix for the blocked part and switch to the
// leftovers, which we do as nr=1 always.
for (size_t nn = n() / nr(); nn < ncols; nn++) {
w[wcnt++] = bias[(n() / nr()) * nr() + (nn - n() / nr())];
for (size_t kk = 0; kk < k(); kk++) {
if (b[nn * k() + kk] != 0.0f) {
// Every non-zero actually corresponds to nr adjacent non-zeros.
w[wcnt++] = b[nn * k() + kk];
// Skip the very first non-zero weight as we record only the difference.
if (first_nzz) {
first_kk = kk;
} else {
const int32_t increment = int32_t(kk - last_kk) * int32_t(m() * sizeof(float));
dmap[nnz++] = increment;
}
last_kk = kk;
first_nzz = false;
nmap[nn] += 1;
}
}
}
// In the end, we must return input pointer to the initial value.
const int64_t increment = int32_t(first_kk - last_kk) * int32_t(m() * sizeof(float));
dmap[nnz++] = increment;
// Generate expanded b which will be used in reference calculation.
// Everywhere there is input non-zero in the original we copy it and add an
// adjacent non-zero with incremented weight value.
std::vector<float> b_full(n() * k());
if (nr() == 1) {
b_full = b;
}
else {
for (size_t nn = 0; nn < n() / nr(); nn++) {
for (size_t kk = 0; kk < k(); kk++) {
if (b[nn * k() + kk] != 0.0f) {
for (size_t i = 0; i < nr(); ++i)
b_full[nr() * nn * k() + i * k() + kk] = b[nn * k() + kk] + static_cast<float>(i);
}
}
}
for (size_t nn = n() / nr(); nn < ncols; nn++) {
for (size_t kk = 0; kk < k(); kk++) {
if (b[nn * k() + kk] != 0.0f) {
b_full[nr() * (n() / nr()) * k() + (nn - n() / nr()) * k() + kk] = b[nn * k() + kk];
}
}
}
}
for (size_t oc = 0; oc < n(); oc++) {
for (size_t pxb = 0; pxb < m(); pxb++) {
output_ref[oc * m() + pxb] = bias[oc];
for (size_t ic = 0; ic < k(); ic++) {
output_ref[oc * m() + pxb] += input[ic * m() + pxb] * b_full[oc * k() + ic];
}
}
}
// Micro-kernel can access one element beyond w and dmap for software pipelining.
w.resize(wcnt + 1);
dmap.resize(nnz + 1);
// Compute clamping parameters.
const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend());
const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend());
const float output_min = accumulated_min + (accumulated_max - accumulated_min) / 255.0f * float(qmin());
const float output_max = accumulated_max - (accumulated_max - accumulated_min) / 255.0f * float(255 - qmax());
// Clamp reference results.
for (float& output_value : output_ref) {
output_value = std::min(std::max(output_value, output_min), output_max);
}
// Prepare parameters.
xnn_f32_minmax_params params = { };
switch (variant) {
case Variant::Native:
params = xnn_init_f32_minmax_params(output_min, output_max);
break;
case Variant::Scalar:
params = xnn_init_scalar_f32_minmax_params(output_min, output_max);
break;
}
spmm(m() * sizeof(float), n(),
input.data() + first_kk * m(),
w.data(), dmap.data(), nmap.data(),
output.data(), output_stride() * sizeof(float),
&params);
// Validate micro-kernel outputs.
for (size_t i = 0; i < m(); i++) {
for (size_t j = 0; j < n(); j++) {
ASSERT_NEAR(
output[j * output_stride() + i],
output_ref[j * m() + i],
std::abs(output_ref[j * m() + i]) * 1.0e-6f)
<< "at M index " << i << " / " << m() << " (tile " << mr() << ")"
<< ", N index " << j << " / " << n() << " (tile " << nr() << ")"
<< ", K = " << k();
}
}
}
}
void Test(xnn_f16_spmm_minmax_ukernel_function spmm) const {
ASSERT_GE(m(), 1);
ASSERT_GE(n(), 1);
ASSERT_GE(k(), 1);
std::random_device random_device;
auto rng = std::mt19937(random_device());
auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng);
auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng);
auto prng = std::bind(std::uniform_real_distribution<float>(), rng);
std::vector<uint16_t, AlignedAllocator<uint16_t, 64>> input(k() * m());
// Think of b as (n/nr + n % nr) x k, expansion happens later.
const size_t ncols = n() / nr() + n() % nr();
std::vector<uint16_t> b(ncols * k());
std::vector<uint16_t> bias(n());
// Number of non-zero weights per N (output channel).
std::vector<uint32_t> nmap(n());
// Mapping from index of non-zero weight to increment of K (input channel) following this index.
std::vector<int32_t> dmap(n() * k());
std::vector<uint16_t> w(n() * k() + n());
std::vector<uint16_t> output((n() - 1) * output_stride() + m());
std::vector<float> output_ref(n() * m());
for (size_t iteration = 0; iteration < iterations(); iteration++) {
std::generate(input.begin(), input.end(), std::ref(f16rng));
std::generate(b.begin(), b.end(), std::ref(f16rng));
std::generate(bias.begin(), bias.end(), std::ref(f16rng));
std::fill(output.begin(), output.end(), 0xC000);
std::fill(output_ref.begin(), output_ref.end(), 0.0f);
std::fill(nmap.begin(), nmap.end(), 0);
std::fill(dmap.begin(), dmap.end(), 0);
std::fill(w.begin(), w.end(), 0);
for (uint16_t& b_value : b) {
if (prng() <= sparsity()) {
b_value = 0;
}
}
uint32_t nnz = 0;
uint32_t wcnt = 0;
size_t last_kk = 0;
bool first_nzz = true;
size_t first_kk = 0;
for (size_t nn = 0; nn < n() / nr(); nn++) {
for (size_t i = 0; i < nr(); ++i)
w[wcnt++] = bias[nr() * nn + i];
for (size_t kk = 0; kk < k(); kk++) {
if (!is_fp16_zero(b[nn * k() + kk])) {
// Every non-zero actually corresponds to nr adjacent non-zeros.
for (size_t i = 0; i < nr(); ++i)
w[wcnt++] = fp16_ieee_from_fp32_value(fp16_ieee_to_fp32_value(b[nn * k() + kk]) + static_cast<float>(i));
// Skip the very first non-zero weight as we record only the difference.
if (first_nzz) {
first_kk = kk;
} else {
const int32_t increment = int32_t(kk - last_kk) * int32_t(m() * sizeof(uint16_t));
dmap[nnz++] = increment;
}
last_kk = kk;
first_nzz = false;
nmap[nn] += 1;
}
}
}
// now we've constructed the matrix for the blocked part and switch to the
// leftovers, which we do as nr=1 always.
for (size_t nn = n() / nr(); nn < ncols; nn++) {
w[wcnt++] = bias[(n() / nr()) * nr() + (nn - n() / nr())];
for (size_t kk = 0; kk < k(); kk++) {
if (!is_fp16_zero(b[nn * k() + kk])) {
// Every non-zero actually corresponds to nr adjacent non-zeros.
w[wcnt++] = b[nn * k() + kk];
// Skip the very first non-zero weight as we record only the difference.
if (first_nzz) {
first_kk = kk;
} else {
const int32_t increment = int32_t(kk - last_kk) * int32_t(m() * sizeof(uint16_t));
dmap[nnz++] = increment;
}
last_kk = kk;
first_nzz = false;
nmap[nn] += 1;
}
}
}
// In the end, we must return input pointer to the initial value.
const int64_t increment = int32_t(first_kk - last_kk) * int32_t(m() * sizeof(uint16_t));
dmap[nnz++] = increment;
// Generate expanded b which will be used in reference calculation.
// Everywhere there is input non-zero in the original we copy it and add an
// adjacent non-zero with incremented weight value.
std::vector<uint16_t> b_full(n() * k());
if (nr() == 1) {
b_full = b;
}
else {
for (size_t nn = 0; nn < n() / nr(); nn++) {
for (size_t kk = 0; kk < k(); kk++) {
if (b[nn * k() + kk] != 0.0f) {
for (size_t i = 0; i < nr(); ++i)
b_full[nr() * nn * k() + i * k() + kk] = fp16_ieee_from_fp32_value(
fp16_ieee_to_fp32_value(b[nn * k() + kk]) + static_cast<float>(i));
}
}
}
for (size_t nn = n() / nr(); nn < ncols; nn++) {
for (size_t kk = 0; kk < k(); kk++) {
if (b[nn * k() + kk] != 0.0f) {
b_full[nr() * (n() / nr()) * k() + (nn - n() / nr()) * k() + kk] = b[nn * k() + kk];
}
}
}
}
for (size_t oc = 0; oc < n(); oc++) {
for (size_t pxb = 0; pxb < m(); pxb++) {
output_ref[oc * m() + pxb] = fp16_ieee_to_fp32_value(bias[oc]);
for (size_t ic = 0; ic < k(); ic++) {
output_ref[oc * m() + pxb] += fp16_ieee_to_fp32_value(input[ic * m() + pxb]) * fp16_ieee_to_fp32_value(b_full[oc * k() + ic]);
}
}
}
// Micro-kernel can access one element beyond w and dmap for software pipelining.
w.resize(wcnt + 1);
dmap.resize(nnz + 1);
// Compute clamping parameters.
const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend());
const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend());
const float output_min = accumulated_min + (accumulated_max - accumulated_min) / 255.0f * float(qmin());
const float output_max = accumulated_max - (accumulated_max - accumulated_min) / 255.0f * float(255 - qmax());
// Clamp reference results.
for (float& output_value : output_ref) {
output_value = std::min(std::max(output_value, output_min), output_max);
}
// Prepare parameters.
xnn_f16_scaleminmax_params params;
params.scale = UINT16_C(0x3C00) /* 1.0 */;
params.max = fp16_ieee_from_fp32_value(output_max);
params.min = fp16_ieee_from_fp32_value(output_min);
spmm(m() * sizeof(uint16_t), n(),
input.data() + first_kk * m(),
w.data(), dmap.data(), nmap.data(),
output.data(), output_stride() * sizeof(uint16_t),
&params);
// Validate micro-kernel outputs.
for (size_t i = 0; i < m(); i++) {
for (size_t j = 0; j < n(); j++) {
ASSERT_NEAR(
fp16_ieee_to_fp32_value(output[j * output_stride() + i]),
output_ref[j * m() + i],
std::max(1.0e-4f, std::abs(output_ref[j * m() + i]) * 1.0e-2f))
<< "at M index " << i << " / " << m() << " (tile " << mr() << ")"
<< ", N index " << j << " / " << n() << " (tile " << nr() << ")"
<< ", K = " << k();
}
}
}
}
private:
size_t mr_{1};
size_t nr_{1};
size_t m_{1};
size_t n_{1};
size_t k_{1};
size_t output_stride_{0};
float sparsity_{0.5f};
uint8_t qmin_{0};
uint8_t qmax_{255};
size_t iterations_{1};
};