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// Copyright 2023 Google LLC
// SPDX-License-Identifier: Apache-2.0
//
// Licensed 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.
// OrderedDemote2To is not supported by HWY_SCALAR.
#ifndef HWY_DISABLED_TARGETS
#define HWY_DISABLED_TARGETS HWY_SCALAR
#endif
#include "ops/ops.h"
#include <stddef.h>
#include <stdio.h>
#include <algorithm>
#include <array>
#include <cmath>
#include <functional>
#include <numeric>
#include <random>
#include <vector>
#include "compression/compress.h" // BF16
#include "gemma/common.h"
#include "gemma/configs.h"
#include "util/allocator.h"
#include "util/test_util.h"
#include "hwy/base.h"
#include "hwy/tests/hwy_gtest.h"
// clang-format off
#undef HWY_TARGET_INCLUDE
#define HWY_TARGET_INCLUDE "ops/ops_test.cc" // NOLINT
// clang-format on
#include "hwy/foreach_target.h" // IWYU pragma: keep
#include "hwy/highway.h"
// After highway.h
#include "ops/ops-inl.h"
#include "hwy/tests/test_util-inl.h"
HWY_BEFORE_NAMESPACE();
namespace gcpp {
namespace HWY_NAMESPACE {
namespace hn = hwy::HWY_NAMESPACE;
template <class Test>
struct ForeachCountAndMisalign {
template <typename T, class D>
HWY_NOINLINE void operator()(T /*unused*/, D d) const {
hwy::RandomState rng;
const size_t N = Lanes(d);
const size_t misalignments[3] = {0, N / 4, 3 * N / 5};
for (size_t count = 0; count < 2 * N; ++count) {
for (size_t ma : misalignments) {
for (size_t mb : misalignments) {
Test()(d, count, ma, mb, rng);
}
}
}
}
};
template <typename T>
T Random(hwy::RandomState& rng) {
const int32_t bits = static_cast<int32_t>(Random32(&rng)) & 1023;
const double val = (bits - 512) / 64.0;
// Clamp negative to zero for unsigned types.
return hwy::ConvertScalarTo<T>(
HWY_MAX(hwy::ConvertScalarTo<double>(hwy::LowestValue<T>()), val));
}
HWY_NOINLINE void SourceAddFrom(const float* HWY_RESTRICT other,
float* HWY_RESTRICT x, size_t size) {
for (size_t i = 0; i < size; ++i) {
x[i] += other[i];
}
}
HWY_NOINLINE void SourceMulBy(const float* HWY_RESTRICT other,
float* HWY_RESTRICT x, size_t size,
size_t max_pos) {
HWY_DASSERT(max_pos <= size);
for (size_t i = 0; i < max_pos; ++i) {
x[i] *= other[i];
}
}
HWY_NOINLINE void SourceMulByConst(float c, float* HWY_RESTRICT x, size_t size,
size_t max_pos) {
for (size_t i = 0; i < max_pos; ++i) {
x[i] *= c;
}
}
HWY_NOINLINE void SourceMulByConstAndAdd(float c, const float* HWY_RESTRICT x,
float* HWY_RESTRICT out, size_t size) {
for (size_t i = 0; i < size; ++i) {
out[i] += x[i] * c;
}
}
HWY_NOINLINE void SourceSoftmax(float* HWY_RESTRICT x, size_t size,
size_t mask_pos) {
HWY_DASSERT(size != 0);
HWY_DASSERT(mask_pos <= size);
float sum = 0.0;
const float maxval = *std::max_element(x, x + mask_pos);
for (size_t i = 0; i < mask_pos; ++i) {
x[i] = std::exp(x[i] - maxval);
sum += x[i];
}
const float scale = 1.0f / sum;
for (size_t i = 0; i < mask_pos; ++i) {
x[i] *= scale;
}
}
template <size_t k>
HWY_NOINLINE std::discrete_distribution<int> SourceCreateDistribution(
std::array<float, k>& top_k, float temperature) {
// re-normalize distribution
for (size_t i = 0; i < k; ++i) {
top_k[i] = exp(log(top_k[i]) / temperature);
}
float denominator = 0.0f;
for (size_t i = 0; i < k; ++i) {
denominator += top_k[i];
}
denominator = 1.0f / denominator;
MulByConst(denominator, top_k.data(), k);
return std::discrete_distribution<int>(std::begin(top_k), std::end(top_k));
}
struct TestAddFrom {
template <class D>
void operator()(D d, size_t count, size_t misalign_a, size_t misalign_b,
hwy::RandomState& rng) {
using T = hn::TFromD<D>;
hwy::AlignedFreeUniquePtr<T[]> px =
hwy::AllocateAligned<T>(HWY_MAX(1, misalign_a + count));
hwy::AlignedFreeUniquePtr<T[]> pe =
hwy::AllocateAligned<T>(HWY_MAX(1, misalign_a + count));
hwy::AlignedFreeUniquePtr<T[]> po =
hwy::AllocateAligned<T>(HWY_MAX(1, misalign_b + count));
HWY_ASSERT(px && pe && po);
T* x = px.get() + misalign_a;
T* e = pe.get() + misalign_a;
T* o = po.get() + misalign_b;
for (size_t i = 0; i < count; ++i) {
x[i] = Random<T>(rng);
e[i] = x[i];
o[i] = Random<T>(rng);
}
SourceAddFrom(o, e, count);
AddFrom(o, x, count);
hwy::AssertArraySimilar(e, x, count, hwy::TargetName(HWY_TARGET), __FILE__,
__LINE__);
}
};
struct TestMulBy {
template <class D>
void operator()(D d, size_t count, size_t misalign_a, size_t misalign_b,
hwy::RandomState& rng) {
using T = hn::TFromD<D>;
hwy::AlignedFreeUniquePtr<T[]> px =
hwy::AllocateAligned<T>(HWY_MAX(1, misalign_a + count));
hwy::AlignedFreeUniquePtr<T[]> pe =
hwy::AllocateAligned<T>(HWY_MAX(1, misalign_a + count));
hwy::AlignedFreeUniquePtr<T[]> po =
hwy::AllocateAligned<T>(HWY_MAX(1, misalign_b + count));
HWY_ASSERT(px && pe && po);
T* x = px.get() + misalign_a;
T* e = pe.get() + misalign_a;
T* o = po.get() + misalign_b;
for (size_t i = 0; i < count; ++i) {
x[i] = Random<T>(rng);
e[i] = x[i];
o[i] = Random<T>(rng);
}
SourceMulBy(o, e, count, count);
MulBy(o, x, count, count);
hwy::AssertArraySimilar(e, x, count, hwy::TargetName(HWY_TARGET), __FILE__,
__LINE__);
}
};
struct TestMulByConstAndAdd {
template <class D>
void operator()(D d, size_t count, size_t misalign_a, size_t misalign_b,
hwy::RandomState& rng) {
using T = hn::TFromD<D>;
hwy::AlignedFreeUniquePtr<T[]> px =
hwy::AllocateAligned<T>(HWY_MAX(1, misalign_a + count));
hwy::AlignedFreeUniquePtr<T[]> pe =
hwy::AllocateAligned<T>(HWY_MAX(1, misalign_a + count));
hwy::AlignedFreeUniquePtr<T[]> po =
hwy::AllocateAligned<T>(HWY_MAX(1, misalign_b + count));
HWY_ASSERT(px && pe && po);
T* x = px.get() + misalign_a;
T* e = pe.get() + misalign_a;
T* o = po.get() + misalign_b;
for (size_t i = 0; i < count; ++i) {
x[i] = Random<T>(rng);
e[i] = x[i];
o[i] = Random<T>(rng);
}
T constant = Random<T>(rng);
SourceMulByConstAndAdd(constant, o, e, count);
MulByConstAndAdd(constant, o, x, count);
hwy::AssertArraySimilar(e, x, count, hwy::TargetName(HWY_TARGET), __FILE__,
__LINE__);
}
};
struct TestMulByConst {
template <class D>
void operator()(D d, size_t count, size_t misalign_a, size_t misalign_b,
hwy::RandomState& rng) {
if (misalign_b == 0) return;
using T = hn::TFromD<D>;
hwy::AlignedFreeUniquePtr<T[]> px =
hwy::AllocateAligned<T>(HWY_MAX(1, misalign_a + count));
hwy::AlignedFreeUniquePtr<T[]> pe =
hwy::AllocateAligned<T>(HWY_MAX(1, misalign_a + count));
HWY_ASSERT(px && pe);
T* x = px.get() + misalign_a;
T* e = pe.get() + misalign_a;
for (size_t i = 0; i < count; ++i) {
x[i] = Random<T>(rng);
e[i] = x[i];
}
T constant = Random<T>(rng);
SourceMulByConst(constant, e, count, count);
MulByConst(constant, x, count, count);
hwy::AssertArraySimilar(e, x, count, hwy::TargetName(HWY_TARGET), __FILE__,
__LINE__);
}
};
struct TestSoftmax {
template <class D>
void operator()(D d, size_t count, size_t misalign_a, size_t misalign_b,
hwy::RandomState& rng) {
if (count == 0) return; // *Softmax would assert
if (misalign_b == 0) return;
using T = hn::TFromD<D>;
hwy::AlignedFreeUniquePtr<T[]> px =
hwy::AllocateAligned<T>(HWY_MAX(1, misalign_a + count));
hwy::AlignedFreeUniquePtr<T[]> pe =
hwy::AllocateAligned<T>(HWY_MAX(1, misalign_a + count));
HWY_ASSERT(px && pe);
T* x = px.get() + misalign_a;
T* e = pe.get() + misalign_a;
for (size_t i = 0; i < count; ++i) {
x[i] = Random<T>(rng);
e[i] = x[i];
}
SourceSoftmax(e, count, count);
Softmax(x, count, count);
T sum = 0.0f;
for (size_t i = 0; i < count; ++i) {
sum += x[i];
double rel = std::abs(x[i] - e[i]) / e[i];
ASSERT_LT(rel, 1e-6) << "Mismatch on coordinate " << i << " out of "
<< count;
}
ASSERT_NEAR(sum, 1.0, 1e-6);
}
};
template <size_t k>
struct TestCreateDistribution {
void operator()(hwy::RandomState& rng) {
std::array<float, k> x;
std::array<float, k> e;
for (size_t i = 0; i < k; ++i) {
x[i] = Random<float>(rng);
e[i] = x[i];
}
const float constant = Random<float>(rng);
auto expected = SourceCreateDistribution(e, constant);
auto output = create_distribution(x, constant);
AssertEqual(expected, output, hwy::TargetName(HWY_TARGET), __FILE__,
__LINE__);
}
};
void TestAllAddFrom() {
hn::ForPartialVectors<ForeachCountAndMisalign<TestAddFrom>>()(float());
}
void TestAllMulBy() {
hn::ForPartialVectors<ForeachCountAndMisalign<TestMulBy>>()(float());
}
void TestAllMulByConst() {
hn::ForPartialVectors<ForeachCountAndMisalign<TestMulByConst>>()(float());
}
void TestAllMulByConstAndAdd() {
hn::ForPartialVectors<ForeachCountAndMisalign<TestMulByConstAndAdd>>()(
float());
}
void TestAllSoftmax() {
hn::ForPartialVectors<ForeachCountAndMisalign<TestSoftmax>>()(float());
}
void TestAllCreateDistribution() {
TestCreateDistribution<2048>();
TestCreateDistribution<5000>();
}
void TestSigmoid() {
std::vector<float> values;
for (int i = -150; i <= 150; ++i) {
values.push_back(.1f * i);
}
std::vector<float> result = values;
Sigmoid(result.data(), result.size());
for (size_t i = 0; i < values.size(); i++) {
const float max_error = 0.00007;
float value = values[i];
float approx = result[i];
float expected = (1 / (1 + std::exp(-values[i])));
EXPECT_NEAR(approx, expected, max_error) << "Input: " << value;
}
}
static HWY_NOINLINE HWY_MAYBE_UNUSED void ScalarRopeAndMulBy(
const float mul, const float* HWY_RESTRICT x, size_t dim_qkv,
const float* HWY_RESTRICT inv_timescale, int pos,
float* HWY_RESTRICT x_out) {
HWY_DASSERT(dim_qkv % 2 == 0);
const size_t half_dim_qkv = dim_qkv / 2;
for (size_t dim = 0; dim < half_dim_qkv; ++dim) {
const float theta = StaticCast<float>(pos) * inv_timescale[dim];
const float cos_val = cosf(theta);
const float sin_val = sinf(theta);
const float x0 = x[dim];
const float x1 = x[dim + half_dim_qkv];
x_out[dim] = mul * (x0 * cos_val - x1 * sin_val);
x_out[dim + half_dim_qkv] = mul * (x0 * sin_val + x1 * cos_val);
}
}
void TestRopeAndMulBy() {
ModelConfig config = ConfigFromModel(Model::GEMMA2_9B);
int dim_qkv = config.layer_configs[0].qkv_dim;
RowVectorBatch<float> x(Extents2D(1, dim_qkv));
std::mt19937 gen;
gen.seed(0x12345678);
std::normal_distribution<float> r{0.0, 5.0};
auto random_float = [&r, &gen] { return r(gen); };
for (int i = 0; i < dim_qkv; ++i) {
x.All()[i] = random_float();
}
const float qmul = ChooseQueryScale(config);
const float kmul = 1.0;
std::vector<float> qexpected(dim_qkv);
std::vector<float> qactual(dim_qkv);
std::vector<float> kexpected(dim_qkv);
std::vector<float> kactual(dim_qkv);
RowVectorBatch<float> inv_timescale = gcpp::CreateInvTimescale(
config.layer_configs[0].qkv_dim,
config.layer_configs[0].post_qk == PostQKType::HalfRope);
// Assert VectorizedRope computation is same as regular rope at different pos.
for (int pos = 1; pos < 500; pos++) {
// Rope'd Q embeddings
ScalarRopeAndMulBy(qmul, x.Const(), dim_qkv, inv_timescale.Const(), pos,
qexpected.data());
RopeAndMulBy(qmul, x.Const(), dim_qkv, inv_timescale.Const(), pos,
qactual.data());
for (int i = 0; i < dim_qkv; ++i) {
EXPECT_NEAR(qactual[i], qexpected[i], 1e-4)
<< "qIndex:" << i << "qInput:" << qactual[i];
}
// Rope'd K embeddings
ScalarRopeAndMulBy(kmul, x.Const(), dim_qkv, inv_timescale.Const(), pos,
kexpected.data());
RopeAndMulBy(kmul, x.Const(), dim_qkv, inv_timescale.Const(), pos,
kactual.data());
for (int i = 0; i < dim_qkv; ++i) {
EXPECT_NEAR(kactual[i], kexpected[i], 1e-4)
<< "kIndex:" << i << "kInput:" << kactual[i];
}
}
}
template <typename T>
HWY_NOINLINE float ScalarSquaredL2(const T* HWY_RESTRICT a, size_t size) {
double sum = 0.0;
for (size_t i = 0; i < size; ++i) {
const float f = hwy::ConvertScalarTo<float>(a[i]);
sum += f * f;
}
return static_cast<float>(sum);
}
// Supports bf16 and f32 inputs/outputs, which can be in-place.
template <typename VecT, typename WeightT, typename OutT>
HWY_NOINLINE void ScalarRMSNorm(const VecT* x,
const WeightT* HWY_RESTRICT weight, OutT* out,
size_t size) {
constexpr float kEps = 1e-6f;
float ss = ScalarSquaredL2(x, size);
ss = 1.0f / sqrtf(ss / StaticCast<float>(size) + kEps);
for (size_t j = 0; j < size; j++) {
const float v = hwy::ConvertScalarTo<float>(x[j]);
const float w = hwy::ConvertScalarTo<float>(weight[j]);
// Note 1.0f centering here
out[j] = hwy::ConvertScalarTo<OutT>((1.0f + w) * (ss * v));
}
}
template <typename VecT, typename WeightT, typename OutT>
void TestRMSNorm(hwy::RandomState& rng) {
constexpr size_t kSize = 128;
HWY_ALIGN VecT vec[kSize];
HWY_ALIGN WeightT weight[kSize];
HWY_ALIGN OutT expected[kSize];
HWY_ALIGN OutT actual[kSize];
for (size_t i = 0; i < kSize; ++i) {
vec[i] = hwy::ConvertScalarTo<VecT>(RandomGaussian(rng));
weight[i] = hwy::ConvertScalarTo<WeightT>(RandomGaussian(rng));
}
ScalarRMSNorm(vec, weight, expected, kSize);
RMSNorm(vec, weight, actual, kSize);
for (size_t i = 0; i < kSize; i++) {
const float e = hwy::ConvertScalarTo<float>(expected[i]);
const float a = hwy::ConvertScalarTo<float>(actual[i]);
if (!IsNear(e, a, 1e-5f)) {
HWY_ABORT("RMSNorm %s %s %s mismatch at %zu: %E %E\n", TypeName<VecT>(),
TypeName<WeightT>(), TypeName<OutT>(), i, e, a);
}
}
}
void TestAllRMSNorm() {
hwy::RandomState rng;
TestRMSNorm<float, float, float>(rng);
TestRMSNorm<float, float, BF16>(rng);
TestRMSNorm<float, BF16, float>(rng);
TestRMSNorm<float, BF16, BF16>(rng);
TestRMSNorm<BF16, float, float>(rng);
TestRMSNorm<BF16, float, BF16>(rng);
TestRMSNorm<BF16, BF16, float>(rng);
TestRMSNorm<BF16, BF16, BF16>(rng);
}
void TestLayerNormSimple() {
const size_t kSize = 52;
std::vector<float> values(kSize);
// Alternating 1.0/-1.0, so mean=0.0, var=1.0, rsqrt(var+epsilon)=0.9999995
for (int i = 0; i < kSize; ++i) {
values[i] = (i % 2 == 0) ? 1.0f : -1.0f;
}
std::vector<float> scale(kSize, 1.2f);
std::vector<float> bias(kSize, 0.1f);
std::vector<float> result(kSize);
LayerNorm(values.data(), scale.data(), bias.data(), result.data(), kSize);
for (size_t i = 0; i < kSize; i++) {
const float max_error = 1e-6f;
float value = values[i];
float res = result[i];
// out = (x - 0.0) * 1.2 * 0.9999995 + 0.1 = 1.2999994 / -1.0999994;
float expected = (i % 2 == 0) ? 1.2999994f : -1.0999994f;
EXPECT_NEAR(res, expected, max_error) << "Input: " << value;
}
}
// Note: there is no vectorized implementation of LayerNorm yet. So this test
// currently only checks that the scalar version can be called for the below
// combinations of float/BF16 inputs and outputs.
template <typename VecT, typename WeightT, typename OutT>
void TestLayerNorm(hwy::RandomState& rng) {
constexpr size_t kSize = 128;
VecT vec[kSize];
WeightT weight[kSize];
WeightT bias[kSize];
OutT expected[kSize];
OutT actual[kSize];
for (size_t i = 0; i < kSize; ++i) {
vec[i] = hwy::ConvertScalarTo<VecT>(RandomGaussian(rng));
weight[i] = hwy::ConvertScalarTo<WeightT>(RandomGaussian(rng));
bias[i] = hwy::ConvertScalarTo<WeightT>(RandomGaussian(rng));
}
ScalarLayerNorm(vec, weight, bias, expected, kSize);
LayerNorm(vec, weight, bias, actual, kSize);
for (size_t i = 0; i < kSize; i++) {
const float e = hwy::ConvertScalarTo<float>(expected[i]);
const float a = hwy::ConvertScalarTo<float>(actual[i]);
if (!IsNear(e, a, 1e-5f)) {
HWY_ABORT("LayerNorm %s %s %s mismatch at %zu: %E %E\n", TypeName<VecT>(),
TypeName<WeightT>(), TypeName<OutT>(), i, e, a);
}
}
}
void TestAllLayerNorm() {
hwy::RandomState rng;
TestLayerNorm<float, float, float>(rng);
TestLayerNorm<float, float, BF16>(rng);
TestLayerNorm<float, BF16, float>(rng);
TestLayerNorm<float, BF16, BF16>(rng);
}
void TestSampleTopK() {
const size_t kSize = 52;
std::vector<float> logits(kSize);
// Create a vector going from -100 to -100+51=49 and take Softmax.
std::iota(logits.begin(), logits.end(), -100.0f);
Softmax(logits.data(), kSize);
std::mt19937 gen;
gen.seed(0x12345678);
float temperature = 1.0f;
// SampleTopK<1> should return the argmax.
std::function<bool(int, float)> accept_token;
int sample =
SampleTopK(logits.data(), /*k=*/1, kSize, gen, temperature, accept_token);
EXPECT_EQ(sample, 51); // Last is largest.
// Only accept even tokens, expect the last (largest) even index.
accept_token = [](int i, float) { return i % 2 == 0; };
sample =
SampleTopK(logits.data(), /*k=*/1, kSize, gen, temperature, accept_token);
EXPECT_EQ(sample, 50); // Last even index.
// Reset the logits to a positive, increasing sequence and take Softmax.
std::iota(logits.begin(), logits.end(), 1.0f);
Softmax(logits.data(), kSize);
// Sample from the top 3, expect one of the top 3 even indices.
for (int i = 0; i < 100; ++i) {
sample = SampleTopK(logits.data(), /*k=*/3, kSize, gen, temperature,
accept_token);
EXPECT_TRUE(sample == 50 || sample == 48 || sample == 46);
}
// Now set the temperature to 0.0f, which should always return the argmax,
// even for k=3.
temperature = 0.0f;
for (int i = 0; i < 100; ++i) {
sample = SampleTopK(logits.data(), /*k=*/3, kSize, gen, temperature,
accept_token);
EXPECT_EQ(sample, 50);
}
}
// NOLINTNEXTLINE(google-readability-namespace-comments)
} // namespace HWY_NAMESPACE
} // namespace gcpp
HWY_AFTER_NAMESPACE();
#if HWY_ONCE
namespace gcpp {
HWY_BEFORE_TEST(OpsTest);
HWY_EXPORT_AND_TEST_P(OpsTest, TestAllAddFrom);
HWY_EXPORT_AND_TEST_P(OpsTest, TestAllMulBy);
HWY_EXPORT_AND_TEST_P(OpsTest, TestAllMulByConst);
HWY_EXPORT_AND_TEST_P(OpsTest, TestAllMulByConstAndAdd);
HWY_EXPORT_AND_TEST_P(OpsTest, TestAllSoftmax);
HWY_EXPORT_AND_TEST_P(OpsTest, TestAllCreateDistribution);
HWY_EXPORT_AND_TEST_P(OpsTest, TestSigmoid);
HWY_EXPORT_AND_TEST_P(OpsTest, TestRopeAndMulBy);
HWY_EXPORT_AND_TEST_P(OpsTest, TestAllRMSNorm);
HWY_EXPORT_AND_TEST_P(OpsTest, TestAllLayerNorm);
HWY_EXPORT_AND_TEST_P(OpsTest, TestLayerNormSimple);
HWY_EXPORT_AND_TEST_P(OpsTest, TestSampleTopK);
HWY_AFTER_TEST();
} // namespace gcpp
#endif
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