happyz
/
gemma.cpp
mirror of https://github.com/google/gemma.cpp.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
gemma.cpp/gemma/backward_test.cc

271 lines
9.0 KiB
C++
Raw Blame History

// 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.
#ifndef HWY_DISABLED_TARGETS
#define HWY_DISABLED_TARGETS HWY_SCALAR
#endif
#include <stddef.h>
#include <algorithm>
#include <array>
#include <complex>
#include <random>
#include <vector>
#include "compression/compress.h"
#include "hwy/aligned_allocator.h"
#include "hwy/base.h"
#include "hwy/contrib/thread_pool/thread_pool.h"
#include "gemma/backward_scalar.h"
#include "gemma/forward_scalar.h"
#include "gemma/gemma.h"
#include "gemma/sampler.h"
#include "gemma/test_util.h"
#include "gemma/weights.h"
// clang-format off
#undef HWY_TARGET_INCLUDE
#define HWY_TARGET_INCLUDE "gemma/backward_test.cc" //NOLINT
// clang-format on
#include "hwy/foreach_target.h" // IWYU pragma: keep
#include "hwy/highway.h"
#include "hwy/tests/test_util-inl.h"
// After highway.h
#include "gemma/backward-inl.h"
#include "gemma/forward-inl.h"
#include "gemma/ops.h"
HWY_BEFORE_NAMESPACE();
namespace gcpp {
namespace HWY_NAMESPACE {
namespace hn = hwy::HWY_NAMESPACE;
void TestMatMulVJP() {
static const size_t kRows = 8;
static const size_t kCols = 64;
static const size_t kTokens = 5;
hwy::ThreadPool pool(8);
std::mt19937 gen(42);
HWY_ALIGN std::array<float, kRows * kCols> weights;
HWY_ALIGN std::array<float, kTokens * kCols> x;
HWY_ALIGN std::array<float, kTokens * kRows> dy;
HWY_ALIGN std::array<float, kRows * kCols> grad;
HWY_ALIGN std::array<float, kTokens * kCols> dx;
HWY_ALIGN std::array<float, kRows * kCols> grad_scalar;
HWY_ALIGN std::array<float, kTokens * kCols> dx_scalar;
using TC = std::complex<double>;
std::array<TC, kRows * kCols> c_weights;
std::array<TC, kTokens * kCols> c_x;
std::array<TC, kTokens * kRows> c_y;
for (int iter = 0; iter < 10; ++iter) {
RandInit(weights, 1.0f * (1 << iter), gen);
RandInit(x, 1.0f * (1 << iter), gen);
RandInit(dy, 1.0f, gen);
Complexify(weights, c_weights);
Complexify(x, c_x);
auto func = [&]() {
MatMulT(c_weights.data(), c_x.data(), c_y.data(), kRows, kCols, kTokens);
return DotT(dy.data(), c_y.data(), kTokens * kRows);
};
memset(&grad, 0, sizeof(grad));
MatMulVJP<kCols, kRows>(weights, x.data(), dy.data(), kTokens,
grad, dx.data(), pool);
TestGradient(dx, c_x, func, 5e-5, 5e-5, __LINE__);
TestGradient(grad, c_weights, func, 5e-5, 5e-5, __LINE__);
memset(&grad_scalar, 0, sizeof(grad_scalar));
MatMulVJPT(weights.data(), x.data(), dy.data(), grad_scalar.data(),
dx_scalar.data(), kRows, kCols, kTokens);
TestNear(dx, dx_scalar, 0, 0, __LINE__);
TestNear(grad, grad_scalar, 0, 0, __LINE__);
}
}
void TestMultiHeadMatMulVJP() {
static const size_t kRows = 2;
static const size_t kCols = 16;
static const size_t kHeads = 4;
static const size_t kTokens = 3;
hwy::ThreadPool pool(8);
std::mt19937 gen(42);
HWY_ALIGN std::array<float, kRows * kCols * kHeads> weights;
HWY_ALIGN std::array<float, kTokens * kCols * kHeads> x;
HWY_ALIGN std::array<float, kRows * kCols * kHeads> grad;
HWY_ALIGN std::array<float, kTokens * kCols * kHeads> dx;
HWY_ALIGN std::array<float, kTokens * kRows> dy;
HWY_ALIGN std::array<float, kRows * kCols * kHeads> grad_scalar;
HWY_ALIGN std::array<float, kTokens * kCols * kHeads> dx_scalar;
using TC = std::complex<double>;
std::array<TC, kRows * kCols * kHeads> c_weights;
std::array<TC, kTokens * kCols * kHeads> c_x;
std::array<TC, kTokens * kRows> c_y;
for (int iter = 0; iter < 10; ++iter) {
RandInit(weights, 1.0f * (1 << iter), gen);
RandInit(x, 1.0f * (1 << iter), gen);
RandInit(dy, 1.0f, gen);
Complexify(weights, c_weights);
Complexify(x, c_x);
auto func = [&]() {
MultiHeadMatMul(c_weights.data(), c_x.data(), c_y.data(), kHeads, kRows,
kCols, kTokens);
return DotT(dy.data(), c_y.data(), kTokens * kRows);
};
memset(&grad, 0, sizeof(grad));
MultiHeadMatMulVJP<kHeads, kCols, kRows>(
weights, x.data(), dy.data(), kTokens, grad, dx.data(), pool);
TestGradient(dx, c_x, func, 5e-5, 5e-5, __LINE__);
TestGradient(grad, c_weights, func, 5e-5, 5e-5, __LINE__);
memset(&grad_scalar, 0, sizeof(grad_scalar));
MultiHeadMatMulVJPT(weights.data(), x.data(), dy.data(), grad_scalar.data(),
dx_scalar.data(), kHeads, kRows, kCols, kTokens);
TestNear(dx, dx_scalar, 0, 0, __LINE__);
TestNear(grad, grad_scalar, 0, 0, __LINE__);
}
}
void TestRMSNormVJP() {
static const size_t K = 2;
static const size_t N = 64;
hwy::ThreadPool pool(8);
std::mt19937 gen(42);
HWY_ALIGN std::array<float, N> weights;
HWY_ALIGN std::array<float, K * N> x;
HWY_ALIGN std::array<float, N> grad;
HWY_ALIGN std::array<float, K * N> dx;
HWY_ALIGN std::array<float, K * N> dy;
HWY_ALIGN std::array<float, N> grad_scalar;
HWY_ALIGN std::array<float, K * N> dx_scalar;
using TC = std::complex<double>;
std::array<TC, N> c_weights;
std::array<TC, K * N> c_x;
std::array<TC, K * N> c_y;
for (int iter = 0; iter < 10; ++iter) {
RandInit(weights, 1.0f * (1 << iter), gen);
RandInit(x, 1.0f * (1 << iter), gen);
RandInit(dy, 1.0f, gen);
Complexify(weights, c_weights);
Complexify(x, c_x);
auto func = [&]() {
RMSNormT(c_weights.data(), c_x.data(), c_y.data(), N, K);
return DotT(dy.data(), c_y.data(), K * N);
};
memset(&grad, 0, sizeof(grad));
RMSNormVJP(weights.data(), x.data(), dy.data(), N, K, grad.data(),
dx.data(), pool);
TestGradient(dx, c_x, func, 5e-5, 5e-5, __LINE__);
TestGradient(grad, c_weights, func, 5e-5, 5e-5, __LINE__);
memset(&grad_scalar, 0, sizeof(grad_scalar));
RMSNormVJPT(weights.data(), x.data(), dy.data(), grad_scalar.data(),
dx_scalar.data(), N, K);
TestNear(dx, dx_scalar, 0, 2e-5, __LINE__);
TestNear(grad, grad_scalar, 0, 2e-5, __LINE__);
}
}
struct TestConfig {
static constexpr int kSeqLen = 24;
static constexpr int kVocabSize = 16;
static constexpr int kModelDim = 32;
static constexpr int kHeads = 3;
static constexpr int kQKVDim = 16;
static constexpr int kFFHiddenDim = 64;
static constexpr std::array<LayerAttentionType, 2> kLayerConfig =
FixedLayerConfig<2>(LayerAttentionType::kGemma);
static constexpr int kLayers = kLayerConfig.size();
static constexpr bool kAbsolutePE = false;
static constexpr bool kPostNormScale = false;
static constexpr int kKVHeads = 1;
static constexpr int kConv1dWidth = 0;
static constexpr bool kFFBiases = false;
static constexpr bool kSoftmaxAttnOutputBiases = false;
static constexpr int kGemmaLayers = kLayers;
static constexpr int kGriffinLayers = 0;
static constexpr int kNumTensorScales = 0;
};
void TestEndToEnd() {
std::mt19937 gen(42);
hwy::ThreadPool pool(0);
WeightsWrapper<float, TestConfig> weights;
WeightsWrapper<float, TestConfig> grad;
ActivationsWrapper<float, TestConfig> forward0;
ActivationsWrapper<float, TestConfig> forward1;
ActivationsWrapper<float, TestConfig> backward;
using TC = std::complex<double>;
WeightsWrapper<TC, TestConfig> c_weights;
ForwardPass<TC, TestConfig> c_forward;
ReverseSequenceSampler training_task({0, 0, 1, 1});
std::vector<Prompt> batch = training_task.SampleBatch(10, gen);
for (const Prompt& prompt : batch) {
ReverseSequenceSampler::LogPrompt(prompt);
RandInit(weights.get(), 1.0f, gen);
float loss0 = CrossEntropyLossForwardPass(
prompt, weights.get(), forward0.get());
float loss1 = CrossEntropyLossForwardPass<TestConfig, WeightsF, LayerF>(
prompt.tokens, prompt.context_size, weights.get(), forward1.get(),
pool);
EXPECT_NEAR(loss1, loss0, std::abs(loss0) * 1e-5);
grad.clear();
CrossEntropyLossBackwardPass(
prompt, weights.get(), forward1.get(), grad.get(), backward.get(),
pool);
Complexify(weights.get(), c_weights.get());
auto func = [&]() {
return CrossEntropyLossForwardPass(prompt, c_weights.get(), c_forward);
};
TestGradient(grad.get(), c_weights.get(), func, 2e-3f);
}
}
// NOLINTNEXTLINE(google-readability-namespace-comments)
} // namespace HWY_NAMESPACE
} // namespace gcpp
HWY_AFTER_NAMESPACE();
#if HWY_ONCE
namespace gcpp {
HWY_BEFORE_TEST(BackwardTest);
HWY_EXPORT_AND_TEST_P(BackwardTest, TestMatMulVJP);
HWY_EXPORT_AND_TEST_P(BackwardTest, TestMultiHeadMatMulVJP);
HWY_EXPORT_AND_TEST_P(BackwardTest, TestRMSNormVJP);
HWY_EXPORT_AND_TEST_P(BackwardTest, TestEndToEnd);
#ifdef HWY_AFTER_TEST
HWY_AFTER_TEST();
#endif
} // namespace gcpp
#endif
Reference in New Issue View Git Blame Copy Permalink