diff --git a/CMakeLists.txt b/CMakeLists.txt index ad4b6fa..22d5307 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -76,6 +76,9 @@ add_library(example_gpt2 STATIC ) target_link_libraries(example_gpt2 infini_train) +add_executable(train_experiment example/gpt2/train_experiment.cc) +target_link_libraries(train_experiment example_gpt2 infini_train) + function(build_test files) # Non-recursive glob for skip failed tests file(GLOB TEST_SOURCES ${files}) @@ -102,4 +105,4 @@ if(BUILD_TEST) build_test(test/optimizer/test_adam_cuda.cc) endif() endif() -endif() \ No newline at end of file +endif() diff --git a/Makefile b/Makefile index c5a12a8..6f6c3cc 100644 --- a/Makefile +++ b/Makefile @@ -16,3 +16,6 @@ clean: test-cpp: @echo cd build/$(TYPE) && make test + +train-experiment: build + ./build/$(TYPE)/train_experiment $(ARGS) diff --git a/README.md b/README.md index fc57b11..2ce5445 100644 --- a/README.md +++ b/README.md @@ -6,4 +6,11 @@ [训练营作业介绍文档](docs/训练营作业介绍.md) -[训练营作业报告模版](docs/TinyInfiniTrain%20作业报告.md) \ No newline at end of file +[训练营作业报告模版](docs/TinyInfiniTrain%20作业报告.md) + +## 作业交付 + +- 六项作业实现及原有 8 个公开测试均已完成，测试文件未修改。 +- [完整作业报告](docs/TinyInfiniTrain%20作业报告.md) +- [A100 完整训练指标](docs/experiments/a100-full-seed1337/summary.json) +- [A100 学习率对比](docs/experiments/a100-lr-sweep-seed1337/comparison.csv) diff --git "a/docs/TinyInfiniTrain \344\275\234\344\270\232\346\212\245\345\221\212.md" "b/docs/TinyInfiniTrain \344\275\234\344\270\232\346\212\245\345\221\212.md" index bc23852..50fc981 100644 --- "a/docs/TinyInfiniTrain \344\275\234\344\270\232\346\212\245\345\221\212.md" +++ "b/docs/TinyInfiniTrain \344\275\234\344\270\232\346\212\245\345\221\212.md" @@ -1,312 +1,372 @@ -# TinyInfiniTrain 作业报告 - -## 一、test 通过截图 - -## 二、作业步骤 - -> 将代码填入下面代码块中指定位置，并详细描述完成该作业的解决思路和遇到的问题。 - -### 作业一：autograd机制调用Neg kernel的实现 - -难度：⭐ - -对应测例：`TEST(ElementwiseTest, NegForward)`，`TEST(ElementwiseTest, NegBackward)` - -需要实现的代码块位置：`infini_train/src/autograd/elementwise.cc` - -```c++ -std::vector> Neg::Forward(const std::vector> &input_tensors) { - // =================================== 作业 =================================== - // TODO：通过Dispatcher获取设备专属kernel，对输入张量进行取反操作 - // HINT: 依赖test_dispatcher，kernel实现已给出 - // =================================== 作业 =================================== -} - -std::vector> Neg::Backward(const std::vector> &grad_outputs) { - // =================================== 作业 =================================== - // TODO：通过Dispatcher获取设备专属的反向传播kernel，计算梯度 - // HINT: 依赖test_dispatcher，kernel实现已给出 - // =================================== 作业 =================================== -} -``` - -#### 解决思路 - - - -#### 遇到问题 - - - -### 作业二：实现矩阵乘法 - -难度：⭐⭐ - -#### CPU实现 - -对应测例：`TEST(MatmulTest, BasicMatrixMultiply)`，`TEST(MatmulTest, BatchedMatrixMultiply)`, `TEST(MatmulTest, BackwardPass)` - -需要实现的代码块位置：`infini_train/src/kernels/cpu/linear.cc` - -```c++ - std::shared_ptr MatmulForward(const std::shared_ptr &input, const std::shared_ptr &other) { - // =================================== 作业 =================================== - // TODO：实现CPU上的矩阵乘法前向计算 - // REF: - // =================================== 作业 =================================== - } - - std::tuple, std::shared_ptr> - MatmulBackward(const std::shared_ptr &input, const std::shared_ptr &other, - const std::shared_ptr &grad_output) { - // =================================== 作业 =================================== - // TODO：实现CPU上的矩阵乘法反向传播 - // REF: - // =================================== 作业 =================================== - } -``` - -#### CUDA实现 - -对应测例：`TEST(MatmulTest, BasicMatrixMultiplyCuda)`,`TEST(MatmulTest, BatchedMatrixMultiplyCuda)`,`TEST(MatmulTest, BackwardPassCuda)` - -需要实现的代码块位置：`infini_train/src/kernels/cuda/linear.cu` - -```c++ - std::shared_ptr MatmulForward(const std::shared_ptr &input, const std::shared_ptr &other) { - // =================================== 作业 =================================== - // TODO：实现CUDA上的矩阵乘法前向计算 - // REF: - // =================================== 作业 =================================== - } - - std::tuple, std::shared_ptr> - MatmulBackward(const std::shared_ptr &input, const std::shared_ptr &other, - const std::shared_ptr &grad_output) { - // =================================== 作业 =================================== - // TODO：实现CUDA上的矩阵乘法反向传播 - // REF: - // =================================== 作业 =================================== - } -``` - -#### 解决思路 - - - -#### 遇到问题 - - - -### 作业三：实现Adam优化器 - -难度：⭐ - -#### CPU实现 - -对应测例：`TEST(AdamOptimizerTest, BasicParameterUpdate)`,`TEST(AdamOptimizerTest, MomentumAccumulation)` - -代码位置：infini_train/src/kernels/cpu/accumulate_grad.cc - -```c++ -void AdamAccumulateGrad(const std::shared_ptr &grad, const std::shared_ptr ¶m, - const std::shared_ptr &m, const std::shared_ptr &v, float learning_rate, - float beta1, float beta2, float eps, int64_t t) { - // =================================== 作业 =================================== - // TODO：实现Adam优化器的梯度累积和参数更新 - // REF: - // =================================== 作业 =================================== -} -``` - -#### CUDA实现 - -对应测例：`TEST(AdamOptimizerTest, BasicParameterUpdateCuda)`,`TEST(AdamOptimizerTest, MomentumAccumulationCuda)` - -代码位置：infini_train/src/kernels/cuda/accumulate_grad.cu - -```c++ -void AdamAccumulateGrad(const std::shared_ptr &grad, const std::shared_ptr ¶m, - const std::shared_ptr &m, const std::shared_ptr &v, float learning_rate, - float beta1, float beta2, float eps, int64_t t) { - // =================================== 作业 =================================== - // TODO：实现Adam优化器的梯度累积和参数更新 - // REF: - // =================================== 作业 =================================== -} -``` - -#### 解决思路 - - - -#### 遇到问题 - - - -### 作业四：实现Tensor基础操作 - -#### 实现Tensor的Flatten操作 - -难度：⭐ - -对应测例：`TEST(TensorTransformTest, Flatten2DTo1D)`,`TEST(TensorTransformTest, FlattenWithRange) `,`TEST(TensorTransformTest, FlattenNonContiguous)` - -代码位置：infini_train/src/tensor.cc - -```c++ -std::shared_ptr Tensor::Flatten(int64_t start, int64_t end) { - // =================================== 作业 =================================== - // TODO：实现张量扁平化操作，将指定维度范围[start, end]内的所有维度合并为一个维度 - // HINT: - // =================================== 作业 =================================== -} -``` - -#### 实现Tensor的反向传播机制 - -难度：⭐ - -对应测例：`TEST(TensorAutogradTest, BackwardComputesGradient)`,`TEST(TensorAutogradTest, BackwardWithMultipleOutputs)` - -代码位置：infini_train/src/tensor.cc - -```c++ -void Tensor::Backward(std::shared_ptr gradient, bool retain_graph, bool create_graph) const { - // =================================== 作业 =================================== - // TODO：实现自动微分反向传播 - // 功能描述：1. 计算当前张量对叶子节点的梯度 2. 支持多输出场景的梯度累加 - // HINT: - // =================================== 作业 =================================== -} -``` - -#### 解决思路 - - - -#### 遇到问题 - - - -### 作业五 注册算子kernel的实现 - -难度：⭐⭐⭐ - -对应测例：`TEST(DispatcherTest, RegisterAndGetKernel)`,`TEST(DispatcherTest, DuplicateRegistration)`,`TEST(DispatcherTest, GetNonexistentKernel)` - -代码位置：infini_train/include/dispatcher.h - -```c++ -template RetT Call(ArgsT... args) const { - // =================================== 作业 =================================== - // TODO：实现通用kernel调用接口 - // 功能描述：将存储的函数指针转换为指定类型并调用 - // HINT: - // =================================== 作业 =================================== -} - -template void Register(const KeyT &key, FuncT &&kernel) { - // =================================== 作业 =================================== - // TODO：实现kernel注册机制 - // 功能描述：将kernel函数与设备类型、名称绑定 - // =================================== 作业 =================================== -} - -#define REGISTER_KERNEL(device, kernel_name, kernel_func) \ - // =================================== 作业 =================================== - // TODO：实现自动注册宏 - // 功能描述：在全局静态区注册kernel，避免显式初始化代码 - // =================================== 作业 =================================== -``` - -#### 解决思路 - - - -#### 遇到问题 - - - -### 作业六：实现GPT-2整体训练 - -难度：⭐⭐⭐⭐ - -对应测例：`TEST_F(GPT2TrainingTest, LogitsConsistency)` - -#### 训练过程logits对比 - -完成以上所有作业，补齐训练框架的所有实现，理论上`TEST_F(GPT2TrainingTest, LogitsConsistency)`可以通过，在用例中判断比较预置的值和单步正向传播计算结果是否在误差允许范围内相等。 - -#### 数据读取实现 - -代码位置：example/common/tiny_shakespeare_dataset.cc - -```c++ -TinyShakespeareFile ReadTinyShakespeareFile(const std::string &path, size_t sequence_length) { - /* =================================== 作业 =================================== - TODO：实现二进制数据集文件解析 - 文件格式说明： - ---------------------------------------------------------------------------------- - | HEADER (1024 bytes) | DATA (tokens) | - | magic(4B) | version(4B) | num_toks(4B) | reserved(1012B) | token数据 | - ---------------------------------------------------------------------------------- - =================================== 作业 =================================== */ -} - -TinyShakespeareDataset::TinyShakespeareDataset(const std::string &filepath, size_t sequence_length) { - // =================================== 作业 =================================== - // TODO：初始化数据集实例 - // HINT: 调用ReadTinyShakespeareFile加载数据文件 - // =================================== 作业 =================================== -} -``` - -#### Tokenizer功能实现 - -代码位置：example/common/tokenizer.cc - -```c++ -Tokenizer::Tokenizer(const std::string &filepath) { - /* ===================================== 作业 ===================================== - TODO：实现Tokenizer二进制文件加载 - - 文件格式说明： - ---------------------------------------------------------------------------------- - | HEADER (1024 bytes) | VOCAB TABLE | - | magic(4B) | version(4B) | vocab_size(4B) | reserved(1012B) | token词表数据 | - ---------------------------------------------------------------------------------- - ===================================== 作业 ===================================== */ -} -``` - -```c++ -std::string Tokenizer::Decode(uint32_t token_id) const { - /* ===================================== 作业 ===================================== - TODO：实现token_id到文本的转换 - 功能描述：根据token_id返回对应的文本片段 - ===================================== 作业 ===================================== */ -} -``` - -```c++ -void Tokenizer::GenerateText(infini_train::nn::Module &model, uint32_t batch_size, uint32_t sequence_length, - uint32_t text_length, Device device) const { - /* ...原代码... */ - LOG(INFO) << "start generate text:"; - for (int t = prompt_len; t < text_length; t++) { - /* ===================================== 作业 ===================================== - TODO：实现单步文本生成逻辑 - HINT：调用model.Forward推理获取logits，根据推理结果进行随机采样，调用Decode获取文本结果 - ===================================== 作业 ===================================== */ - } - std::cout << std::endl; -} -``` - -#### 解决思路 - - - -#### 遇到问题 - +# TinyInfiniTrain 作业报告 + +## 提交说明 + +训练营提交页面只需要填写个人 fork 仓库地址和完成分支的最新 commit 链接，PR 与附件均留空。本仓库交付内容包括六项作业实现、未修改测试文件的公开测试结果、A100 完整数据训练实验，以及 loss、perplexity、生成文本、吞吐和显存指标。训练 checkpoint 体积较大，不纳入 Git 提交；仓库内保留足以复核结论的 CSV、JSON、曲线和固定 prompt 生成文本。 + +## 一、提交与测试验证 + +本次作业在提交分支的最新 commit 上完成最终验收。测试文件未作任何修改，以下结果均来自原有公开测试。 + +| 项目 | 验证环境 / 结果 | +| --- | --- | +| 编译器与 CUDA | GCC 13.4 / CUDA 12.6 | +| GPU | NVIDIA A100-SXM4-40GB | +| 构建 | complete | +| 7 个定向测例 | 7/7，100%，6.32 s | +| GPT-2 端到端测例 | `test_gpt2` 1/1，52.51 s | +| 全部公开测试 | 8/8 通过 | + +### GPT-2 直接运行证据 + +为保留 `ctest` 汇总之外的端到端证据，在 NVIDIA A100-SXM4-40GB 上直接运行了 +`test_gpt2`。输出包含固定随机种子的生成文本、100 个采样 logits 校验通过、GoogleTest PASS 和退出码 0。 + +本次直接运行耗时 52.611 s，生成文本开头为： + +```text +The meaning of life is that it is continuous and play that might last. +Too little quickening. Art and tragedy and psychedelia, especially what I +call herws, don't emit a kind of absolutism and admiration for those lives. +``` + +数值验收输出为 `Logits validation passed with 100 samples`。测试中的比较阈值为绝对误差 +`1e-3`；这证明当前短程训练轨迹与预置参考 logits 一致，但不等价于完整数据集收敛实验。 + +终端验收摘要如下（不以虚构截图代替日志）： + +```text +revision: submission branch latest commit +environment: GCC 13.4, CUDA 12.6, NVIDIA A100-SXM4-40GB +build: complete +directed tests: 7/7 passed (100%), 6.32 s +test_gpt2: 1/1 passed, 52.51 s +public tests: 8/8 passed +``` + +### 评审快速索引 + +| 作业 | 主要实现文件 | 对应公开测例 | 验收状态 | +| --- | --- | --- | --- | +| Neg autograd | `infini_train/src/autograd/elementwise.cc` | `NegForward`、`NegBackward` | 通过 | +| CPU/CUDA MatMul | `infini_train/src/kernels/{cpu,cuda}/linear.*` | CPU/CUDA basic、batched、backward | 通过 | +| CPU/CUDA Adam | `infini_train/src/kernels/{cpu,cuda}/accumulate_grad.*` | CPU/CUDA update、momentum | 通过 | +| Tensor Flatten/Backward | `infini_train/src/tensor.cc` | 3 个 Flatten、2 个 Backward | 通过 | +| Kernel Dispatcher | `infini_train/include/dispatcher.h` | register、duplicate、missing | 通过 | +| GPT-2 数据与生成 | `example/common/{tiny_shakespeare_dataset,tokenizer}.cc` | `LogitsConsistency` | 通过 | + +测试文件与 `.github` 工作流相对官方基线均无改动。报告中的“通过”指原测试目标返回成功；额外的完整训练实验单列在第四节，不把 11-step 的 `test_gpt2` 误写为模型收敛。 + +## 二、六项作业实现 + +### 1. 自动微分 Neg 算子 + +**实现位置**：`infini_train/src/autograd/elementwise.cc` + +**对应测例**：`ElementwiseTest.NegForward`、`ElementwiseTest.NegBackward` + +**核心思路**：`Neg::Forward` 与 `Neg::Backward` 均通过统一 Dispatcher 按 Tensor 所在设备选择 CPU/CUDA kernel，避免在自动微分层直接分支实现设备逻辑。前向计算 `y=-x`，反向传播 `dx=-dy`。 + +**关键代码/公式**： + +```text +y = -x +dx = dy * dy/dx = -dy +``` + +前向调用注册名 `NegForward`，反向调用 `NegBackward`，并以类型安全的 `Call` 接口传入 Tensor 参数。 + +**边界校验**：前向严格要求一个输入，反向严格要求一个输出梯度；设备类型来自实际 Tensor，由 Dispatcher 负责匹配已注册 kernel，缺失注册时直接报错而非静默回退。 + +**执行流程**：`Tensor::Neg()` 创建 `autograd::Neg`，`Function::Apply` 调用 `Forward` 建图；`Forward` 只负责按设备找到 `NegForward`，数学计算留在 CPU/CUDA kernel。反向阶段直接按 `grad_output` 的设备调用 `NegBackward`。由于 `d(-x)/dx=-1` 与前向输入值无关，Neg 不需要把输入保存到 context。 + +**实现中解决的问题**：不能在 `Forward` 内再次调用 `Apply`，否则会形成 `Apply -> Forward -> Apply` 的递归。反向也不能照搬 `Reciprocal` 去读取 `saved_tensors_`，因为 Neg 的测例允许独立调用 `Backward`，而且该导数本来不依赖输入。因此最终实现保持 autograd 层只做路由，不重复 kernel 数学。 + +**验证结论**：前向验证逐元素符号取反；反向验证任意上游梯度再次取反。两个公开测例均通过，并且同一调用路径可依据 Tensor 设备选择已注册实现。 + +### 2. CPU/CUDA MatMul 及反向传播 + +**实现位置**： + +- `infini_train/src/kernels/cpu/linear.cc` +- `infini_train/src/kernels/cuda/linear.cu` + +**对应测例**：CPU 的 `BasicMatrixMultiply`、`BatchedMatrixMultiply`、`BackwardPass`；CUDA 的 `BasicMatrixMultiplyCuda`、`BatchedMatrixMultiplyCuda`、`BackwardPassCuda` + +**核心思路**：CPU 使用 Eigen 的行主序矩阵映射并逐 batch 计算；CUDA 使用 `cublasSgemmStridedBatched` 完成批量矩阵乘法。输入形状为 `[..., M, K]` 和 `[..., K, N]`，输出为 `[..., M, N]`。 + +**关键代码/公式**： + +```text +C = A B +dA = dC B^T +dB = A^T dC +``` + +cuBLAS 按列主序解释内存，而框架 Tensor 为行主序，因此实现利用转置等价关系调整操作数顺序： + +```text +C^T = B^T A^T +dA^T = B dC^T +dB^T = dC^T A +``` + +**边界校验**：检查非空指针、FP32、CPU/CUDA 设备一致、rank 至少为 2、两侧 batch 维完全相等、内积维 `K` 相等；反向还检查 `grad_output` 的设备、类型与完整输出形状。CUDA 对零 batch 或 `M/N/K` 为零的情况直接返回正确形状的零 Tensor，避免非法 cuBLAS 调用。本实现不承诺 batch broadcasting，batch 前缀必须精确相同。 + +**CPU 实现细节**：框架 Tensor 按行主序存储。实现先把所有 batch 前缀维相乘得到 `batch_count`，再为每个 batch 建立三个 Eigen `Map`：`A[M,K]`、`B[K,N]` 和 `C[M,N]`。前向用 `noalias()` 避免无必要临时量；反向分别映射 `dC`，计算 `dA=dC*B^T` 和 `dB=A^T*dC`。 + +**CUDA 实现细节**：一次创建 cuBLAS handle，并将所有 batch 交给 `cublasSgemmStridedBatched`。行主序矩阵传给列主序 API 时不做显式转置拷贝，而是交换操作数并选择正确的 transpose flag、leading dimension 和 stride。反向的两个 GEMM 分别单独校对维度，避免只凭公式直觉填写参数。 + +**实现中解决的问题**：最容易出错的是“数学转置”和“内存解释转置”叠加。若直接按 `A*B` 的书写顺序传给 cuBLAS，会得到形状正确但数值错误的结果。最终以 `C^T=B^T*A^T` 为起点逐项推导 `m/n/k`、`lda/ldb/ldc` 和 stride，并用 CPU/CUDA 的 basic、batched、backward 六个测例共同验证。另一个取舍是明确拒绝 batch broadcasting：官方题目只要求相同 batch 前缀，静默实现不完整广播比显式报错更危险。 + +**验证结论**：二维前向、批量前向以及两侧梯度在 CPU/CUDA 上全部通过；测试同时验证输出形状和数值，而不只是程序未崩溃。 + +### 3. CPU/CUDA Adam 参数更新 + +**实现位置**： + +- `infini_train/src/kernels/cpu/accumulate_grad.cc` +- `infini_train/src/kernels/cuda/accumulate_grad.cu` + +**对应测例**：CPU 的 `BasicParameterUpdate`、`MomentumAccumulation`；CUDA 的 `BasicParameterUpdateCuda`、`MomentumAccumulationCuda` + +**核心思路**：按调用合同原地更新一阶矩 `m`、二阶矩 `v` 与参数 `theta`；CPU 顺序遍历元素，CUDA 每个线程处理一个元素，线程块大小为 256。 + +**关键代码/公式**： + +```text +m_t = beta1 * m_(t-1) + (1 - beta1) * g +v_t = beta2 * v_(t-1) + (1 - beta2) * g^2 +m_hat = m_t / (1 - beta1^t) +v_hat = v_t / (1 - beta2^t) +theta_t = theta_(t-1) - lr * m_hat / (sqrt(v_hat) + eps) +``` + +**边界校验**：参数、梯度、`m`、`v` 必须非空、均为 FP32、位于期望且相同的设备，四者形状必须完全一致，并要求时间步 `t > 0`，保证偏置修正分母有定义。CUDA 对零元素 Tensor 提前返回，不启动空 kernel。 + +**执行流程**：`Adam::Step` 先递增全局时间步，再为每个参数取得梯度与对应的 `m/v`，通过 Dispatcher 调用设备专属 `AdamAccumulateGrad`。kernel 对三份状态做原地更新，因此下一 step 会继承此前的动量，而不是每步重新初始化。 + +**实现中解决的问题**：偏置修正必须使用更新后的 `m_t/v_t` 和从 1 开始的 `t`。若使用 `t=0`，分母为零；若把 `eps` 加到平方根内部，则与题目参考公式不一致。CUDA 版本将与元素无关的 `1-beta1^t`、`1-beta2^t` 在 host 端只计算一次，再传给每个线程，保持 CPU/CUDA 公式一致并减少重复运算。 + +**验证结论**：Basic 测例覆盖首步参数更新，Momentum 测例覆盖连续 step 的状态累积；CPU/CUDA 四个测例全部通过。完整训练实验进一步实际使用 Adam 完成 9,540/11,925-step 训练和 checkpoint 恢复，说明该实现不只通过单步算例。 + +### 4. Tensor Flatten、Backward 与子视图偏移 + +**实现位置**：`infini_train/src/tensor.cc` + +**对应测例**：`BackwardComputesGradient`、`BackwardWithMultipleOutputs`、`Flatten2DTo1D`、`FlattenWithRange`、`FlattenNonContiguous` + +**核心思路**：`Flatten` 规范化负维度，将 `[start_dim, end_dim]` 内各维相乘后构造新形状，再通过连续化与 `View` 返回结果。`Backward` 分别处理隐式梯度、显式梯度、叶子 Tensor 梯度累积和带 `grad_fn` 的链式反传。最终提交同时修正子 Tensor 的 offset：新视图 offset 相对父 Tensor 累加，同设备 `To` 不再重复累计已有 offset。 + +**关键代码/规则**： + +```text +flattened_dim = product(dims[start_dim : end_dim + 1]) +scalar Flatten -> shape [1] +scalar implicit backward seed -> 1 +child absolute offset = parent offset + child relative offset +``` + +**边界校验**：检查 `start_dim/end_dim` 经负数归一化后均合法且 `start_dim <= end_dim`；`Backward` 要求 Tensor 开启梯度，非标量不得省略初始梯度，显式梯度必须与目标 Tensor 的形状、dtype、设备完全一致。当前实现明确拒绝 `retain_graph=true` 与 `create_graph=true`。 + +**Flatten 执行流程**：先处理标量和负维度，再保留区间前后的维度，只把指定闭区间内的维度乘成一个维度。调用 `Contiguous()` 后再 `View(new_shape)`，因此对转置等非连续 Tensor 也能得到按逻辑顺序排列的结果，而不是错误地只改 metadata。 + +**Backward 执行流程**：标量且未给初始梯度时创建值为 1 的 seed；非标量要求调用者明确给梯度。存在 `grad_fn` 时交给 `BackwardPartial` 沿图传播；叶子 Tensor 则直接调用 `AccumulateGrad` 累加到自身 `grad_`。这样既支持普通链式求导，也支持多个输出分别反传后汇聚到同一叶子梯度。 + +**实现中解决的问题**：子 Tensor 构造函数收到的 offset 是相对父视图的，若忽略父 Tensor 已有 offset，嵌套 view 会读到错误位置；同设备 `To` 若再把 offset 叠加一次，则会发生双重偏移。最终统一使用绝对 offset，并用 offset view、nested view 和同设备 `To` 的 smoke 验证。对于 `retain_graph/create_graph`，本次没有做“参数存在但实际忽略”的假支持，而是显式拒绝未实现语义。 + +**验证结论**：三个 Flatten 场景和两个 autograd 场景均通过；额外 smoke 覆盖了公开测例没有覆盖的嵌套视图偏移。 + +### 5. 类型安全的 Kernel Dispatcher + +**实现位置**：`infini_train/include/dispatcher.h` + +**对应测例**：`RegisterAndGetKernel`、`DuplicateRegistration`、`GetNonexistentKernel` + +**核心思路**：以 `(DeviceType, kernel_name)` 为键保存 kernel。注册时只接受函数指针，并保存参数与返回值类型信息；调用时校验返回类型、参数数量、参数类型及左值/右值/const 语义，再通过 `std::invoke` 执行，避免将任意函数指针直接强转后调用造成未定义行为。 + +**关键代码/机制**：注册宏使用 `__COUNTER__` 生成唯一静态变量，并通过静态初始化 lambda 完成注册；同时将 kernel 标识符字符串化，保证调用名与注册名一致。`Call` 同时覆盖 `void` 与非 `void` 返回值。 + +**边界校验**：重复注册同一设备与名称、查找不存在的 kernel、实参与签名不一致、返回类型不一致时均立即失败；不会覆盖已有注册或进行隐式危险转换。 + +**执行流程**：`REGISTER_KERNEL` 在静态初始化阶段调用 `Dispatcher::Register`；注册时 `TypedInvoker` 保存原始函数指针和签名元信息。调用时先擦除每个实参的地址与 cv/ref 属性，核对返回类型、参数个数和基础类型，再由保存真实 `ParamsT...` 的 invoker 恢复引用语义并 `std::invoke`。类型擦除只发生在存储边界，真正调用仍使用原始函数指针类型。 + +**实现中解决的问题**：题目最直接的写法是把函数指针转成 `void*`，调用时再依据调用点推导的 `ArgsT...` 强转回来。但真实 kernel 大量使用 `const std::shared_ptr&`，调用点若推导为按值 `std::shared_ptr`，两种函数类型不兼容；即使某个 ABI 下测试恰好通过，标准 C++ 仍不保证行为。本实现为消除该未定义行为引入 typed invoker，同时专门验证临时值、const 引用、普通值、`void` 返回以及现有 kernel 风格。 + +**宏实现细节**：宏必须同时能在命名空间作用域和测试函数内部展开，所以使用一条静态变量声明触发 lambda；同一作用域可能注册多次，因此用两层拼接宏让 `__COUNTER__` 先展开，避免变量重名。 + +**验证结论**：三个官方 Dispatcher 测例通过；此外 43 个 CPU 源文件均用新 header 完成编译 smoke，降低共享基础设施修改带来的回归风险。 + +### 6. Tiny Shakespeare 数据集、Tokenizer 与文本生成 + +**实现位置**： + +- `example/common/tiny_shakespeare_dataset.cc` +- `example/common/tokenizer.cc` + +**对应测例**：`GPT2TrainingTest.LogitsConsistency`；该端到端测例会实际读取预训练权重、训练数据和 tokenizer，执行短程训练、文本生成并抽样校验 logits + +**核心思路**：数据集读取器严格解析 1024 B header，支持磁盘上的 uint16 GPT-2 token 与 uint32 LLaMA-3 token，并统一转换为模型需要的连续 INT64 Tensor。Tokenizer 解析词表 header 与逐 token 的长度前缀字节串。`GenerateText` 每一步从 logits `[B,T,V]` 中取 batch 0、位置 `t-1` 的词表向量，做 softmax 后复制到 CPU，使用固定 RNG 采样，将结果写回输入并同步到模型设备。 + +**关键代码/公式**： + +```text +num_samples = floor((num_tokens - 1) / sequence_length) +x offset = sample_index * sequence_length * sizeof(int64_t) +y offset = x offset + sizeof(int64_t) +next-token logits = logits[0, t - 1, :] +``` + +生成过程保留确定性 RNG（初始状态 1337），并修复 RNG 状态变量自遮蔽。为避免多次只前向生成时保存无用 autograd 图，生成前暂时关闭模型参数的 `requires_grad`，由作用域保护对象在正常返回或异常退出时恢复原状态。 + +**边界校验**： + +- 数据文件必须为普通文件，header 必须完整且 magic/version 合法，文件总长度必须严格等于 `1024 + num_tokens * token_width`，短读与尾随数据均拒绝；样本数必须大于零，最后一个 y view 不得越界。 +- Tokenizer 检查文件最小长度、magic、版本、词表大小、EOT token 范围，并对每个 uint8 长度前缀和 token 字节做剩余长度检查；词表解析结束后不允许额外字节，越界 Decode 返回空字符串。 +- 文本生成检查 batch/sequence 长度及 logits 的 `[B,T,V]` 形状，始终从 batch 0 的正确时间位置采样；每次写回后重新同步到目标设备。 + +**数据集实现细节**：磁盘 token 宽度与模型索引 dtype 不同。GPT-2 文件使用 uint16，LLaMA-3 文件使用 uint32，但 Embedding 需要 INT64，因此加载时先按 header 指定宽度读取，再逐项扩展成 INT64。样本采用相邻 token 右移构造：每个 `x` 长度为 `T`，`y` 从同一底层 token 缓冲区后移一个元素；因此可用样本数必须是 `floor((num_tokens-1)/T)`，否则最后一个标签会越界。 + +**Tokenizer/生成实现细节**：v1 的 EOT 来自 magic 映射，v2 从 header 读取。词表项是“1 字节长度 + 原始字节”，不能使用以 `\0` 结尾的文本读取方式。生成时只截取 `[0,t-1,:]`，softmax 后用固定 seed 的 xorshift RNG 做多项式采样；新 token 写回 batch 0。生成前先统一快照全部参数的梯度状态，再暂时关闭梯度，结束时由 RAII guard 按快照恢复；即使 embedding/lm-head 共享底层权重，也不会因逐项关闭和恢复的顺序改变原状态。 + +**实现中解决的问题**：原始生成代码中的局部 RNG 状态与常量同名，形成自初始化和不可复现行为；现改为显式 seed。另一个问题是连续生成会为每个 token 构建无用 autograd 图，既增加显存又可能改变共享参数梯度状态，现用作用域 guard 关闭并准确恢复。文件解析则补齐短读、尾随字节、非法版本和 EOT 越界检查，避免损坏输入被静默接受。 + +**验证结论**：`test_gpt2` 在 A100 上完成固定 seed 生成、100 个参考 logits 抽样点校验并以退出码 0 结束；第四节的完整训练 runner 又使用同一数据和 tokenizer 跑完全部 train/validation split。 + +## 三、范围说明与已知限制 + +1. 本次提交没有修改测试文件，最终结论以 A100 环境中原有 8 个公开测试全部通过为准。 +2. 当前 autograd 引擎未承诺共享中间分支的完整梯度汇聚、真实多输出算子的通用反传，也不支持 `retain_graph` 或 `create_graph`；这些属于本次作业范围外能力。 +3. MatMul 支持相同 batch 前缀上的批量计算，但未承诺 batch broadcasting；需要广播的输入应由上层先显式展开为兼容形状。 + +## 四、完整数据集训练与收敛实验 + +本节与第一节的公开测试验收严格分开：公开 `test_gpt2` 证明实现与参考短程轨迹一致；本节则使用专用 runner 在完整 Tiny Shakespeare train/validation split 上训练、验证、保存 checkpoint，并按预注册的 early-stopping 规则结束。 + +### 1. 实验配置与算力 + +| 项目 | 配置 / 实测 | +| --- | --- | +| 代码版本 | 提交分支最新 commit | +| GPU | 单卡 NVIDIA A100-SXM4-40GB | +| 软件环境 | GCC 13.4、CUDA 12.6 | +| 模型 | GPT-2 124M，FP32，预训练权重起点 | +| 序列 / batch | sequence length 64，batch size 2 | +| 优化器 | 基线实验为 Adam，lr `1e-4`，betas `(0.9, 0.999)`，epsilon `1e-8` | +| early stop | `min_delta=0.005`，`patience=3`，`max_epochs=30` | +| 随机种子 | 1337；训练数据顺序固定，生成采样也使用该种子 | +| 完整 train split | 305,260 raw tokens；4,769 samples；每 epoch 305,216 target tokens，43 transitions dropped | +| 完整 validation split | 32,768 raw tokens；511 samples；每次验证 32,704 target tokens，63 transitions dropped | +| 实际总量 | 9,540 Adam steps；1,220,864 train tokens；130,816 validation tokens | +| 算力用量 | 单卡 A100，分 4 段运行并从 checkpoint 连续恢复；实际合计约 25.4 GPU-min（0.423 GPU-h） | + +每段运行都从 `latest.ckpt` 恢复完整模型、Adam `t/m/v`、训练 phase、epoch、batch cursor、累计指标、early-stop 状态和 RNG。第二段新增记录严格从 global step `2802` 承接 `2801`；最终 `steps.csv` 中重复 global step 数为 0。checkpoint v2 对完整 body 使用 trailer checksum，并绑定输入文件身份、配置和 CUDA backend，损坏或跨 backend 恢复会直接拒绝。 + +### 2. 指标口径 + +- `train_mean_loss` 与 `validation_mean_loss` 均为 token 加权的交叉熵均值，不是把不同 batch 的均值再次做不加权平均；最后一个不足 batch 的样本也按实际 target token 数计入。 +- `validation_perplexity = exp(validation_mean_loss)`。runner 同时记录 overflow 标志，避免极端 loss 被静默写成一个看似有效的有限数；本次三组实验均未溢出。 +- `train_tokens_per_second` 的分子是实际参与 loss 的 target tokens，分母只累计训练 forward/backward/optimizer 的计算时间；验证、生成文本和 checkpoint I/O 不混入训练吞吐。 +- `cuda_async_pool_peak_bytes` 是 CUDA async default memory pool 的 high-water mark，只表示该内存池观测到的峰值，不包括所有 CUDA context、cuBLAS 或驱动分配，因此报告中不将它表述为整卡峰值显存。 +- `best` 由 validation loss 按 `min_delta=0.005` 判定；`final` 是 early stop 时的最后状态。两者语义不同，最终推荐模型以 `best` 为准。 + +### 3. Loss、perplexity 与 early stopping + +![完整训练 loss 与 perplexity 曲线](assets/full_training_curves_seed1337.svg) + +| 完成 epoch | Global step | Train mean loss | Validation mean loss | Validation PPL | 是否刷新 best | +| ---: | ---: | ---: | ---: | ---: | :---: | +| 1 | 2,385 | 3.6451 | **3.9249** | **50.65** | 是 | +| 2 | 4,770 | 2.9450 | 3.9433 | 51.59 | 否 | +| 3 | 7,155 | 2.1730 | 4.4714 | 87.48 | 否 | +| 4 | 9,540 | **1.4092** | 5.4255 | 227.13 | 否，触发 early stop | + +训练 loss 从第一个完整 epoch 的 3.6451 降至 1.4092，下降 61.34%，因此“loss 曲线下降”在 train split 上成立。但 validation loss 在第一个完整 epoch 达到最优后连续三次未改善，并在后期快速上升；这不是继续训练会改善的收敛形态，而是明确的过拟合。实验按预注册规则在第 4 个完整 epoch 后终止，没有为了得到更低 train loss 而跑满 30 epoch。 + +必须区分两个最终数值： + +- **最佳泛化 checkpoint**：完成 epoch 1，validation loss `3.9249058558`，perplexity `50.6483`。 +- **early-stop 时最后 checkpoint**：完成 epoch 4，train loss `1.4091759598`，validation loss `5.4255262690`，perplexity `227.1308`。 + +提交或下游推理应选择 `best.ckpt`，不能用过拟合后的 `final.ckpt` 冒充最佳模型。 + +### 4. 生成文本质量 + +三次生成使用同一 prompt `The meaning of life is`、同一 seed 和同一采样实现，只改变 checkpoint： + +**训练前 baseline**： + +```text +The meaning of life is that's what matters us. It's about everything that +actually matters to you. It means you are prepared to do anything and +everything to live a fulfilling, happy life. +``` + +**best checkpoint**： + +```text +The meaning of life is of this country now +And not on this island. + +<|endoftext|>SEBASTIAN: +Come, o' theel. +``` + +**early-stop final checkpoint**： + +```text +The meaning of life is of very few words; +But most of all these say nothing. + +<|endoftext|>NATHANIEL: +Then what is most concerning you, my friend and the duke, +``` + +微调后的文本明显获得 Tiny Shakespeare 的人物名、换行、戏剧对白和古风措辞，但 best 文本仍有畸形词，final 文本虽风格更强，validation PPL 却恶化到 227.13。因此这里只能声称“风格迁移可见”，不能仅凭三个样本声称通用生成质量提高；定量泛化结论仍以 validation loss/PPL 为准。 + +### 5. 吞吐、显存与产物 + +- 全程累计训练吞吐：`1,179.25 target tokens/s`；各完整 epoch 为 1,155.80、1,179.45、1,190.87、1,191.59 tokens/s。 +- CUDA async default memory pool high-water：`3,008,656,476 B`，约 `2.80 GiB`。该指标只覆盖框架使用的 CUDA async pool，不等价于 `nvidia-smi` 的整卡峰值显存。 +- 单个完整 Adam checkpoint：`1,493,932,197 B`，约 1.39 GiB。远端 `final.ckpt` 与 `latest.ckpt` 为同 inode 硬链接，best 为独立 checkpoint；最终目录实际占用约 2.8 GiB。 +- 所有四段运行均 `runner_exit_code=0`；最后 summary 为 `status=complete`、`run_state=complete`。 + +可复现实验入口：`example/gpt2/train_experiment.cc`。静态曲线生成命令： + +```bash +python3 scripts/plot_full_experiment.py \ + docs/experiments/a100-full-seed1337 \ + docs/assets/full_training_curves_seed1337.svg +``` + +原始、未人工改写的指标与证据保存在 `docs/experiments/a100-full-seed1337/`： + +- `steps.csv`：9,540 条逐 step loss、计算时间、吞吐与显存记录； +- `epochs.csv`：4 条完整 train/validation epoch 记录； +- `summary.json`：最终配置、数据覆盖、best/final 数值与 artifact 状态； +- `baseline.txt`、`best.txt`、`final.txt`：固定 prompt 生成文本。 + +本实验为单 seed、固定顺序的确定性训练，因此不报告伪造的多 seed 均值或方差。结论边界是：**完整数据覆盖、checkpoint 续跑、early-stop 收敛和单 seed 指标均已完成；跨 seed 稳健性未评估。** + +### 6. 并行学习率 sweep 与推荐配置 + +为判断第 4 个 epoch 后停止是否只是学习率过大导致，本实验在独立 A100 上并行比较三档学习率。`1e-4` 直接复用前述完整实验；`5e-5` 与 `2e-5` 从同一个未微调 GPT-2 checkpoint 独立启动，固定 seed、数据顺序、batch、序列长度、验证集和 early-stopping 规则，只改变学习率。两个新实验分别使用独立输出目录和 checkpoint，不能相互续跑。 + +| 学习率 | Early-stop epoch | Best epoch | Best validation loss | Best validation PPL | Final train loss | Final validation loss | +| ---: | ---: | ---: | ---: | ---: | ---: | ---: | +| `1e-4` | 4 | 1 | 3.9249 | 50.65 | 1.4092 | 5.4255 | +| `5e-5` | 5 | 2 | 3.8118 | 45.23 | 1.3116 | 5.5715 | +| **`2e-5`** | **5** | **2** | **3.7102** | **40.86** | 2.2658 | 4.3182 | + +在本次单 seed 对照中，`2e-5` 相对原 `1e-4` 将最佳 validation loss 降低约 5.47%，将 PPL 从 50.65 降到 40.86，降低约 19.32%。三档学习率后期都出现 train loss 继续下降、validation loss 上升，说明学习率调低能延缓并减轻过拟合，但不能消除小数据集上的过拟合。 + +因此 runner 默认值更新为： + +- `learning_rate=2e-5`； +- `max_epochs=8`； +- `early_stopping_patience=3`； +- `min_delta=0.005`。 + +`max_epochs=8` 是保护上限，不代表必须跑满；`2e-5` 实际在完成 epoch 5 后 early-stop，应该使用 epoch 2 的 `best.ckpt`。两条新配置均通过 checkpoint 跨运行分段精确续跑，最终各有 11,925 个唯一、连续的 global step。四段有效训练实际合计约 64.3 GPU-min。 + +学习率 sweep 的原始证据位于 `docs/experiments/a100-lr-sweep-seed1337/`，包括每档的 `steps.csv`、`epochs.csv`、`summary.json` 和固定 prompt 文本。该比较仍是单 seed 选择实验，只能支持本作业配置推荐，不能声称具有跨 seed 的统计显著性。 diff --git a/docs/assets/full_training_curves_seed1337.svg b/docs/assets/full_training_curves_seed1337.svg new file mode 100644 index 0000000..7017429 --- /dev/null +++ b/docs/assets/full_training_curves_seed1337.svg @@ -0,0 +1,85 @@ + + + +TinyInfiniTrain GPT-2 124M — A100 full-data run +Step loss (200-step moving mean) + + +1 + +2 + +3 + +4 +1 +2500 +5000 +7500 +9540 +Global optimizer step +Cross-entropy loss + + +Train +Full-epoch mean loss + + +1 + +2 + +3 + +4 + +5 + +6 +1 +2 +3 +4 +Completed epoch +Cross-entropy loss + + + + + + +Train + + + + + + +Validation +Validation perplexity + + +50 + +100 + +150 + +200 + +250 +1 +2 +3 +4 +Completed epoch +Perplexity + + + + + + +PPL +best val 3.925 + diff --git a/docs/experiments/a100-full-seed1337/baseline.txt b/docs/experiments/a100-full-seed1337/baseline.txt new file mode 100644 index 0000000..06bec4d --- /dev/null +++ b/docs/experiments/a100-full-seed1337/baseline.txt @@ -0,0 +1,5 @@ +The meaning of life is that's what matters us. It's about everything that actually matters to you. + +It means you are prepared to do anything and everything to live a fulfilling, happy life. + +For many North Koreans, living a fulfilling life means living a full life, coping with hunger, and taking diff --git a/docs/experiments/a100-full-seed1337/best.txt b/docs/experiments/a100-full-seed1337/best.txt new file mode 100644 index 0000000..daa1e35 --- /dev/null +++ b/docs/experiments/a100-full-seed1337/best.txt @@ -0,0 +1,10 @@ +The meaning of life is of this country now +And not on this island. + +<|endoftext|>SEBASTIAN: +Come, o' theel. + +<|endoftext|>ERDINAND: +Why, +I'll try 'tis no hill, nor no hill; +But bases saddle with shame to a dead diff --git a/docs/experiments/a100-full-seed1337/epochs.csv b/docs/experiments/a100-full-seed1337/epochs.csv new file mode 100644 index 0000000..ea2b26f --- /dev/null +++ b/docs/experiments/a100-full-seed1337/epochs.csv @@ -0,0 +1,5 @@ +epoch,global_step,train_tokens,val_tokens,train_mean_loss,val_mean_loss,val_perplexity,val_perplexity_overflow,train_tokens_per_second,cuda_async_pool_peak_bytes,improved +0,2385,305216,32704,3.64506949127,3.92490585583,50.6483095912,0,1155.80477532,3008656476,1 +1,4770,305216,32704,2.94504374092,3.94334604222,51.5909382741,0,1179.45276725,3008656476,0 +2,7155,305216,32704,2.17299642006,4.47142050364,87.480901734,0,1190.87167072,3008656476,0 +3,9540,305216,32704,1.40917595977,5.42552626903,227.130846798,0,1191.58853932,3008656476,0 diff --git a/docs/experiments/a100-full-seed1337/final.txt b/docs/experiments/a100-full-seed1337/final.txt new file mode 100644 index 0000000..e02bffb --- /dev/null +++ b/docs/experiments/a100-full-seed1337/final.txt @@ -0,0 +1,8 @@ +The meaning of life is of very few words; +But most of all these say nothing. + +<|endoftext|>NATHANIEL: +Then what is most concerning you, my friend and the duke, +Shall thusothe your honour with the utmost care +Which you 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"n_layer": 12, "n_head": 12, "n_embd": 768, "batch_size": 2, "sequence_length": 64, "max_epochs": 30, "learning_rate": 0.0001, "seed": 1337}, + "dataset_coverage": { + "train": {"samples": 4769, "raw_tokens": 305260, "usable_target_tokens": 305216, "dropped_tokens": 43}, + "validation": {"samples": 511, "raw_tokens": 32768, "usable_target_tokens": 32704, "dropped_tokens": 63} + }, + "artifacts": { + "steps_csv": {"path": "steps.csv", "exists": true}, + "epochs_csv": {"path": "epochs.csv", "exists": true}, + "latest_checkpoint": {"path": "latest.ckpt", "exists": true}, + "best_checkpoint": {"path": "best.ckpt", "exists": true}, + "final_checkpoint": {"path": "final.ckpt", "exists": true}, + "baseline_generation": {"path": "baseline.txt", "exists": true}, + "best_generation": {"path": "best.txt", "exists": true}, + "final_generation": {"path": "final.txt", "exists": true} + } +} diff --git a/docs/experiments/a100-lr-sweep-seed1337/comparison.csv b/docs/experiments/a100-lr-sweep-seed1337/comparison.csv new file mode 100644 index 0000000..a161b8a --- /dev/null +++ b/docs/experiments/a100-lr-sweep-seed1337/comparison.csv @@ -0,0 +1,4 @@ +learning_rate,early_stop_epoch,best_epoch,best_validation_loss,best_validation_perplexity,final_train_loss,final_validation_loss,global_steps +0.0001,4,1,3.92490585583,50.6483095912,1.40917595977,5.42552626903,9540 +0.00005,5,2,3.81177762063,45.2307705965,1.31160235175,5.57148814435,11925 +0.00002,5,2,3.71021266926,40.8624957998,2.26582381637,4.31823628149,11925 diff --git a/docs/experiments/a100-lr-sweep-seed1337/lr2e-5/baseline.txt b/docs/experiments/a100-lr-sweep-seed1337/lr2e-5/baseline.txt new file mode 100644 index 0000000..06bec4d --- /dev/null +++ b/docs/experiments/a100-lr-sweep-seed1337/lr2e-5/baseline.txt @@ -0,0 +1,5 @@ +The meaning of life is that's what matters us. It's about everything that actually matters to you. + +It means you are prepared to do anything and everything to live a fulfilling, happy life. + +For many North Koreans, living a fulfilling life means living a full life, coping with hunger, and taking diff --git a/docs/experiments/a100-lr-sweep-seed1337/lr2e-5/best.txt b/docs/experiments/a100-lr-sweep-seed1337/lr2e-5/best.txt new file mode 100644 index 0000000..97454ae --- /dev/null +++ b/docs/experiments/a100-lr-sweep-seed1337/lr2e-5/best.txt @@ -0,0 +1,10 @@ +The meaning of life is to you; why then? + +<|endoftext|>ALONSO: +of all that the globe in Order +Hath since the origination itself; for +kiln-worth to +irritant flies they come home. + +<|endoftext|>ANTONIO: +I forbid they actually stand, wh diff --git a/docs/experiments/a100-lr-sweep-seed1337/lr2e-5/epochs.csv b/docs/experiments/a100-lr-sweep-seed1337/lr2e-5/epochs.csv new file mode 100644 index 0000000..dabe296 --- /dev/null +++ b/docs/experiments/a100-lr-sweep-seed1337/lr2e-5/epochs.csv @@ -0,0 +1,6 @@ +epoch,global_step,train_tokens,val_tokens,train_mean_loss,val_mean_loss,val_perplexity,val_perplexity_overflow,train_tokens_per_second,cuda_async_pool_peak_bytes,improved +0,2385,305216,32704,3.64342391153,3.7623034522,43.0474696526,0,957.829118021,3008656476,1 +1,4770,305216,32704,3.26327012485,3.71021266926,40.8624957998,0,963.184206846,3008656476,1 +2,7155,305216,32704,2.97766675497,3.79583682893,44.5154726583,0,967.927568945,3008656476,0 +3,9540,305216,32704,2.65050837901,3.98611644943,53.8453715666,0,963.419171495,3008656476,0 +4,11925,305216,32704,2.26582381637,4.31823628149,75.0561335929,0,985.828426747,3008656476,0 diff --git a/docs/experiments/a100-lr-sweep-seed1337/lr2e-5/final.txt b/docs/experiments/a100-lr-sweep-seed1337/lr2e-5/final.txt new file mode 100644 index 0000000..b574f25 --- /dev/null +++ b/docs/experiments/a100-lr-sweep-seed1337/lr2e-5/final.txt @@ -0,0 +1,9 @@ +The meaning of life is for wretches +And not for quick-witted fools. + +<|endoftext|>MIRANDA: +Me neither! or such small one as is conquered +As wealth to a kind of exploit. + +<|endoftext|>MANNIA: +Not a kind at all at all; for, you diff --git a/docs/experiments/a100-lr-sweep-seed1337/lr2e-5/steps.csv b/docs/experiments/a100-lr-sweep-seed1337/lr2e-5/steps.csv new file mode 100644 index 0000000..6fb3b64 --- /dev/null +++ b/docs/experiments/a100-lr-sweep-seed1337/lr2e-5/steps.csv @@ -0,0 +1,11926 @@ +epoch,global_step,batch_index,tokens,loss,compute_seconds,tokens_per_second,cuda_async_pool_peak_bytes +0,1,0,128,5.43915796,0.12749577,1003.95488,3008656476 +0,2,1,128,5.00076294,0.137292793,932.314051,3008656476 +0,3,2,128,4.34454679,0.163202964,784.299481,3008656476 +0,4,3,128,4.79599142,0.132722902,964.41532,3008656476 +0,5,4,128,4.44248581,0.13149128,973.448582,3008656476 +0,6,5,128,4.99375248,0.131543791,973.05999,3008656476 +0,7,6,128,4.59340477,0.132078862,969.11798,3008656476 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"best_validation_loss": 3.710212669260581, + "train_tokens_per_second": 967.54265701511542, + "cuda_async_pool_peak_bytes": 3008656476, + "wall_seconds_this_process": 281.19626247000002, + "config": {"block_size": 1024, "vocab_size": 50257, "n_layer": 12, "n_head": 12, "n_embd": 768, "batch_size": 2, "sequence_length": 64, "max_epochs": 8, "learning_rate": 2.0000000000000002e-05, "seed": 1337}, + "dataset_coverage": { + "train": {"samples": 4769, "raw_tokens": 305260, "usable_target_tokens": 305216, "dropped_tokens": 43}, + "validation": {"samples": 511, "raw_tokens": 32768, "usable_target_tokens": 32704, "dropped_tokens": 63} + }, + "artifacts": { + "steps_csv": {"path": "steps.csv", "exists": true}, + "epochs_csv": {"path": "epochs.csv", "exists": true}, + "latest_checkpoint": {"path": "latest.ckpt", "exists": true}, + "best_checkpoint": {"path": "best.ckpt", "exists": true}, + "final_checkpoint": {"path": "final.ckpt", "exists": true}, + "baseline_generation": {"path": "baseline.txt", "exists": true}, + "best_generation": {"path": "best.txt", "exists": true}, + "final_generation": {"path": "final.txt", "exists": true} + } +} diff --git a/docs/experiments/a100-lr-sweep-seed1337/lr5e-5/baseline.txt b/docs/experiments/a100-lr-sweep-seed1337/lr5e-5/baseline.txt new file mode 100644 index 0000000..06bec4d --- /dev/null +++ b/docs/experiments/a100-lr-sweep-seed1337/lr5e-5/baseline.txt @@ -0,0 +1,5 @@ +The meaning of life is that's what matters us. It's about everything that actually matters to you. + +It means you are prepared to do anything and everything to live a fulfilling, happy life. + +For many North Koreans, living a fulfilling life means living a full life, coping with hunger, and taking diff --git a/docs/experiments/a100-lr-sweep-seed1337/lr5e-5/best.txt b/docs/experiments/a100-lr-sweep-seed1337/lr5e-5/best.txt new file mode 100644 index 0000000..ee966bc --- /dev/null +++ b/docs/experiments/a100-lr-sweep-seed1337/lr5e-5/best.txt @@ -0,0 +1,10 @@ +The meaning of life is of this order and no other. + +<|endoftext|>ANTONIO: +O, I speak for liberty. +Go on, good powers; I'll be hanged. + +<|endoftext|>BAGOT: +Take good leave of me; I'll question +What lack of pious submission there is, + diff --git a/docs/experiments/a100-lr-sweep-seed1337/lr5e-5/epochs.csv b/docs/experiments/a100-lr-sweep-seed1337/lr5e-5/epochs.csv new file mode 100644 index 0000000..766906b --- /dev/null +++ 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end; whereof that I care +Is dim for its continuance. + +<|endoftext|>ALONSO: +So, my meaning in this place, concerns me a goodly +I know not how I shall fare in this place: but +The ends I suffer are mine, and I diff --git a/docs/experiments/a100-lr-sweep-seed1337/lr5e-5/steps.csv b/docs/experiments/a100-lr-sweep-seed1337/lr5e-5/steps.csv new file mode 100644 index 0000000..17350a0 --- /dev/null +++ b/docs/experiments/a100-lr-sweep-seed1337/lr5e-5/steps.csv @@ -0,0 +1,11926 @@ +epoch,global_step,batch_index,tokens,loss,compute_seconds,tokens_per_second,cuda_async_pool_peak_bytes +0,1,0,128,5.43915796,0.109352736,1170.52398,3008656476 +0,2,1,128,4.76175833,0.122582017,1044.19884,3008656476 +0,3,2,128,4.01450634,0.131414004,974.021003,3008656476 +0,4,3,128,4.45687723,0.129957867,984.934602,3008656476 +0,5,4,128,4.34562063,0.162852802,785.985862,3008656476 +0,6,5,128,4.91529751,0.130235735,982.833168,3008656476 +0,7,6,128,4.35404158,0.130302532,982.329338,3008656476 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--- /dev/null +++ b/docs/experiments/a100-lr-sweep-seed1337/lr5e-5/summary.json @@ -0,0 +1,32 @@ +{ + "status": "complete", + "run_state": "complete", + "resumed": true, + "device": "cuda", + "epoch": 5, + "global_step": 11925, + "next_batch": 0, + "train_mean_loss": 1.3116023517499857, + "validation_mean_loss": 5.5714881443464126, + "validation_perplexity": 262.82492979538284, + "validation_perplexity_overflow": false, + "best_validation_loss": 3.8117776206328209, + "train_tokens_per_second": 980.03876527679051, + "cuda_async_pool_peak_bytes": 3008656476, + "wall_seconds_this_process": 263.45150638799998, + "config": {"block_size": 1024, "vocab_size": 50257, "n_layer": 12, "n_head": 12, "n_embd": 768, "batch_size": 2, "sequence_length": 64, "max_epochs": 8, "learning_rate": 5.0000000000000002e-05, "seed": 1337}, + "dataset_coverage": { + "train": {"samples": 4769, "raw_tokens": 305260, "usable_target_tokens": 305216, "dropped_tokens": 43}, + "validation": {"samples": 511, "raw_tokens": 32768, "usable_target_tokens": 32704, "dropped_tokens": 63} + }, + "artifacts": { + "steps_csv": {"path": "steps.csv", "exists": true}, + "epochs_csv": {"path": "epochs.csv", "exists": true}, + "latest_checkpoint": {"path": "latest.ckpt", "exists": true}, + "best_checkpoint": {"path": "best.ckpt", "exists": true}, + "final_checkpoint": {"path": "final.ckpt", "exists": true}, + "baseline_generation": {"path": "baseline.txt", "exists": true}, + "best_generation": {"path": "best.txt", "exists": true}, + "final_generation": {"path": "final.txt", "exists": true} + } +} diff --git a/example/common/tiny_shakespeare_dataset.cc b/example/common/tiny_shakespeare_dataset.cc index 3bc5f1b..a9c06b3 100644 --- a/example/common/tiny_shakespeare_dataset.cc +++ b/example/common/tiny_shakespeare_dataset.cc @@ -2,17 +2,15 @@ #include #include -#include +#include #include #include -#include -#include +#include #include -#include #include +#include #include #include -#include #include #include "glog/logging.h" @@ -24,7 +22,10 @@ using DataType = infini_train::DataType; using TinyShakespeareType = TinyShakespeareDataset::TinyShakespeareType; using TinyShakespeareFile = TinyShakespeareDataset::TinyShakespeareFile; -const std::unordered_map kTypeMap = { +constexpr size_t kHeaderSize = 1024; +constexpr uint32_t kDatasetVersion = 1; + +const std::unordered_map kTypeMap = { {20240520, TinyShakespeareType::kUINT16}, // GPT-2 {20240801, TinyShakespeareType::kUINT32}, // LLaMA 3 }; @@ -34,14 +35,10 @@ const std::unordered_map kTypeToSize = { {TinyShakespeareType::kUINT32, 4}, }; -const std::unordered_map kTypeToDataType = { - {TinyShakespeareType::kUINT16, DataType::kUINT16}, - {TinyShakespeareType::kUINT32, DataType::kINT32}, -}; - std::vector ReadSeveralBytesFromIfstream(size_t num_bytes, std::ifstream *ifs) { std::vector result(num_bytes); ifs->read(reinterpret_cast(result.data()), num_bytes); + CHECK_EQ(ifs->gcount(), static_cast(num_bytes)) << "Unexpected end of file"; return result; } @@ -53,27 +50,65 @@ template T BytesToType(const std::vector &bytes, size_t of } TinyShakespeareFile ReadTinyShakespeareFile(const std::string &path, size_t sequence_length) { - /* =================================== 作业 =================================== - TODO：实现二进制数据集文件解析 - 文件格式说明： - ---------------------------------------------------------------------------------- - | HEADER (1024 bytes) | DATA (tokens) | - | magic(4B) | version(4B) | num_toks(4B) | reserved(1012B) | token数据 | - ---------------------------------------------------------------------------------- - =================================== 作业 =================================== */ + CHECK_GT(sequence_length, 0); + CHECK_LE(sequence_length, static_cast(std::numeric_limits::max())); + CHECK(std::filesystem::exists(path)) << "Dataset file not found: " << path; + CHECK(std::filesystem::is_regular_file(path)) << "Dataset path is not a regular file: " << path; + + const uintmax_t file_size = std::filesystem::file_size(path); + CHECK_GE(file_size, kHeaderSize) << "Dataset file is smaller than its header: " << path; + + std::ifstream ifs(path, std::ios::binary); + CHECK(ifs.is_open()) << "Failed to open dataset file: " << path; + const auto header = ReadSeveralBytesFromIfstream(kHeaderSize, &ifs); + const uint32_t magic = BytesToType(header, 0); + const uint32_t version = BytesToType(header, sizeof(uint32_t)); + const uint32_t num_tokens = BytesToType(header, 2 * sizeof(uint32_t)); + + CHECK(kTypeMap.contains(magic)) << "Unsupported dataset magic: " << magic; + CHECK_EQ(version, kDatasetVersion) << "Unsupported dataset version: " << version; + const TinyShakespeareType type = kTypeMap.at(magic); + const size_t encoded_token_size = kTypeToSize.at(type); + const uintmax_t expected_size = kHeaderSize + static_cast(num_tokens) * encoded_token_size; + CHECK_EQ(file_size, expected_size) << "Dataset file length does not match its header"; + CHECK_GT(num_tokens, 0); + + std::vector tokens(num_tokens); + if (type == TinyShakespeareType::kUINT16) { + std::vector encoded_tokens(num_tokens); + ifs.read(reinterpret_cast(encoded_tokens.data()), encoded_tokens.size() * sizeof(uint16_t)); + CHECK_EQ(ifs.gcount(), static_cast(encoded_tokens.size() * sizeof(uint16_t))) + << "Unexpected end of dataset token data"; + for (size_t idx = 0; idx < encoded_tokens.size(); ++idx) { tokens[idx] = encoded_tokens[idx]; } + } else { + std::vector encoded_tokens(num_tokens); + ifs.read(reinterpret_cast(encoded_tokens.data()), encoded_tokens.size() * sizeof(uint32_t)); + CHECK_EQ(ifs.gcount(), static_cast(encoded_tokens.size() * sizeof(uint32_t))) + << "Unexpected end of dataset token data"; + for (size_t idx = 0; idx < encoded_tokens.size(); ++idx) { tokens[idx] = encoded_tokens[idx]; } + } + + const size_t num_samples = (static_cast(num_tokens) - 1) / sequence_length; + CHECK_GT(num_samples, 0) << "Dataset has too few tokens for sequence length " << sequence_length; + CHECK_LE(num_samples, static_cast(std::numeric_limits::max())); + + TinyShakespeareFile result; + result.type = type; + result.dims = {static_cast(num_samples), static_cast(sequence_length)}; + result.tensor = infini_train::Tensor({static_cast(num_tokens)}, DataType::kINT64); + std::memcpy(result.tensor.DataPtr(), tokens.data(), tokens.size() * sizeof(int64_t)); + return result; } } // namespace -TinyShakespeareDataset::TinyShakespeareDataset(const std::string &filepath, size_t sequence_length) { - // =================================== 作业 =================================== - // TODO：初始化数据集实例 - // HINT: 调用ReadTinyShakespeareFile加载数据文件 - // =================================== 作业 =================================== -} +TinyShakespeareDataset::TinyShakespeareDataset(const std::string &filepath, size_t sequence_length) + : text_file_(ReadTinyShakespeareFile(filepath, sequence_length)), sequence_length_(sequence_length), + sequence_size_in_bytes_(sequence_length * sizeof(int64_t)), + num_samples_(static_cast(text_file_.dims[0])) {} std::pair, std::shared_ptr> TinyShakespeareDataset::operator[](size_t idx) const { - CHECK_LT(idx, text_file_.dims[0] - 1); + CHECK_LT(idx, num_samples_); std::vector dims = std::vector(text_file_.dims.begin() + 1, text_file_.dims.end()); // x: (seq_len), y: (seq_len) -> stack -> (bs, seq_len) (bs, seq_len) return {std::make_shared(text_file_.tensor, idx * sequence_size_in_bytes_, dims), diff --git a/example/common/tokenizer.cc b/example/common/tokenizer.cc index 23b9537..b01cdab 100644 --- a/example/common/tokenizer.cc +++ b/example/common/tokenizer.cc @@ -1,10 +1,15 @@ #include "example/common/tokenizer.h" -#include #include +#include #include #include #include +#include +#include +#include +#include +#include #include #include "glog/logging.h" @@ -15,7 +20,7 @@ constexpr uint32_t kGpt2Eot = 50256; constexpr uint32_t kLLaMA3Eot = 128001; constexpr uint64_t kRandomU32Multiplier = 0x2545F4914F6CDD1Dull; constexpr float kF32Divisor = 16777216.0f; // 2^24 -constexpr uint64_t kRngState = 1337; +constexpr size_t kHeaderSize = 1024; using Version = Tokenizer::Version; @@ -31,9 +36,29 @@ const std::unordered_map> kPromptMap = { {20240801, std::vector{791, 7438, 315, 2324, 374}}, // LLaMA-3 }; +class DisableParameterGradGuard { +public: + explicit DisableParameterGradGuard(nn::Module &model) : parameters_(model.Parameters()) { + requires_grad_.reserve(parameters_.size()); + for (const auto ¶meter : parameters_) { requires_grad_.push_back(parameter->requires_grad()); } + for (const auto ¶meter : parameters_) { parameter->set_requires_grad(false); } + } + + ~DisableParameterGradGuard() { + for (size_t idx = 0; idx < parameters_.size(); ++idx) { + parameters_[idx]->set_requires_grad(requires_grad_[idx]); + } + } + +private: + std::vector> parameters_; + std::vector requires_grad_; +}; + std::vector ReadSeveralBytesFromIfstream(size_t num_bytes, std::ifstream *ifs) { std::vector result(num_bytes); ifs->read(reinterpret_cast(result.data()), num_bytes); + CHECK_EQ(ifs->gcount(), static_cast(num_bytes)) << "Unexpected end of file"; return result; } @@ -55,11 +80,11 @@ float RandomF32(uint64_t &state) { // random float32 in [0,1) return (RandomU32(state) >> 8) / kF32Divisor; } -int SampleMult(float *probabilities, int n, float coin) { +size_t SampleMult(const float *probabilities, size_t n, float coin) { // sample index from probabilities (they must sum to 1!) // coin is a random number in [0, 1), usually from RandomF32() float cdf = 0.0f; - for (int i = 0; i < n; i++) { + for (size_t i = 0; i < n; ++i) { cdf += probabilities[i]; if (coin < cdf) { return i; @@ -69,48 +94,103 @@ int SampleMult(float *probabilities, int n, float coin) { } Tokenizer::Tokenizer(const std::string &filepath) { - /* ===================================== 作业 ===================================== - TODO：实现Tokenizer二进制文件加载 - - 文件格式说明： - ---------------------------------------------------------------------------------- - | HEADER (1024 bytes) | VOCAB TABLE | - | magic(4B) | version(4B) | vocab_size(4B) | reserved(1012B) | token词表数据 | - ---------------------------------------------------------------------------------- - ===================================== 作业 ===================================== */ + CHECK(std::filesystem::exists(filepath)) << "Tokenizer file not found: " << filepath; + CHECK(std::filesystem::is_regular_file(filepath)) << "Tokenizer path is not a regular file: " << filepath; + const uintmax_t file_size = std::filesystem::file_size(filepath); + CHECK_GE(file_size, kHeaderSize) << "Tokenizer file is smaller than its header"; + + std::ifstream ifs(filepath, std::ios::binary); + CHECK(ifs.is_open()) << "Failed to open tokenizer file: " << filepath; + const auto header = ReadSeveralBytesFromIfstream(kHeaderSize, &ifs); + magic_number_ = BytesToType(header, 0); + const uint32_t version_value = BytesToType(header, sizeof(uint32_t)); + vocab_size_ = BytesToType(header, 2 * sizeof(uint32_t)); + + CHECK(kEotMap.contains(magic_number_)) << "Unsupported tokenizer magic: " << magic_number_; + CHECK(version_value == static_cast(Version::kV1) + || version_value == static_cast(Version::kV2)) + << "Unsupported tokenizer version: " << version_value; + CHECK_GT(vocab_size_, 0); + CHECK_LE(static_cast(vocab_size_), file_size - kHeaderSize) + << "Tokenizer vocabulary cannot fit in file"; + + if (version_value == static_cast(Version::kV2)) { + eot_token_ = BytesToType(header, 3 * sizeof(uint32_t)); + } else { + eot_token_ = kEotMap.at(magic_number_); + } + CHECK_LT(eot_token_, vocab_size_) << "Tokenizer end token is outside the vocabulary"; + + token_table_.reserve(vocab_size_); + for (uint32_t token_id = 0; token_id < vocab_size_; ++token_id) { + const int length_byte = ifs.get(); + CHECK_NE(length_byte, std::char_traits::eof()) << "Missing length for token " << token_id; + const size_t token_length = static_cast(length_byte); + std::string token(token_length, '\0'); + if (token_length > 0) { + ifs.read(token.data(), token_length); + CHECK_EQ(ifs.gcount(), static_cast(token_length)) + << "Truncated bytes for token " << token_id; + } + token_table_.push_back(std::move(token)); + } + CHECK_EQ(ifs.peek(), std::char_traits::eof()) << "Unexpected trailing tokenizer data"; } std::string Tokenizer::Decode(uint32_t token_id) const { - /* ===================================== 作业 ===================================== - TODO：实现token_id到文本的转换 - 功能描述：根据token_id返回对应的文本片段 - ===================================== 作业 ===================================== */ - return ""; + if (token_id >= token_table_.size()) { + return ""; + } + return token_table_[token_id]; } void Tokenizer::GenerateText(infini_train::nn::Module &model, uint32_t batch_size, uint32_t sequence_length, - uint32_t text_length, Device device) const { + uint32_t text_length, Device device, uint64_t seed) const { + CHECK_GT(batch_size, 0); + CHECK_GT(sequence_length, 0); + CHECK_LE(text_length, sequence_length); + const uint64_t num_input_tokens = static_cast(batch_size) * sequence_length; + CHECK_LE(num_input_tokens, static_cast(std::numeric_limits::max())); std::vector dims; dims.assign({batch_size, sequence_length}); // x_tensor (FLAGS_batch_size, FLAGS_sequence_length) eq:(4, 64) infini_train::Tensor x_tensor = infini_train::Tensor(dims, DataType::kINT64); int64_t *x_buff = static_cast(x_tensor.DataPtr()); - for (int i = 0; i < batch_size * sequence_length; ++i) { x_buff[i] = eot_token_; } + for (size_t i = 0; i < num_input_tokens; ++i) { x_buff[i] = eot_token_; } // Give some contexts: "The meaning of life is " - auto prompt = kPromptMap.at(magic_number_); - auto prompt_len = prompt.size(); - for (int i = 0; i < prompt_len; ++i) { x_buff[i] = prompt[i]; } + const auto &prompt = kPromptMap.at(magic_number_); + const size_t prompt_len = prompt.size(); + CHECK_LE(prompt_len, text_length); + for (size_t i = 0; i < prompt_len; ++i) { x_buff[i] = prompt[i]; } std::cout << "The meaning of life is"; auto x = std::make_shared(x_tensor.To(device)); - uint64_t kRngState = kRngState; + DisableParameterGradGuard parameter_grad_guard(model); + uint64_t rng_state = seed; LOG(INFO) << "start generate text:"; - for (int t = prompt_len; t < text_length; t++) { - /* ===================================== 作业 ===================================== - TODO：实现单步文本生成逻辑 - HINT：调用model.Forward推理获取logits，根据推理结果进行随机采样，调用Decode获取文本结果 - ===================================== 作业 ===================================== */ + for (size_t t = prompt_len; t < text_length; ++t) { + const auto outputs = model.Forward({x}); + CHECK_GT(outputs.size(), 0); + CHECK(outputs[0]); + const auto &logits = outputs[0]; + CHECK_EQ(logits->Dims().size(), 3); + CHECK_EQ(logits->Dims()[0], batch_size); + CHECK_EQ(logits->Dims()[1], sequence_length); + CHECK_EQ(logits->Dims()[2], vocab_size_); + + auto next_token_logits + = logits->Slice({0, static_cast(t - 1), 0}, + {1, static_cast(t), static_cast(vocab_size_)}, {1, 1, 1}); + auto probabilities = nn::function::Softmax(next_token_logits, -1); + auto probabilities_cpu = probabilities->To(Device(DeviceType::kCPU, 0)); + auto *probabilities_ptr = static_cast(probabilities_cpu.DataPtr()); + const uint32_t next_token + = static_cast(SampleMult(probabilities_ptr, vocab_size_, RandomF32(rng_state))); + + x_buff[t] = next_token; + std::cout << Decode(next_token); + x = std::make_shared(x_tensor.To(device)); } std::cout << std::endl; } diff --git a/example/common/tokenizer.h b/example/common/tokenizer.h index 1da539f..ba41c0a 100644 --- a/example/common/tokenizer.h +++ b/example/common/tokenizer.h @@ -22,7 +22,7 @@ class Tokenizer { std::string Decode(uint32_t token_id) const; void GenerateText(infini_train::nn::Module &model, uint32_t batch_size, uint32_t sequence_length, - uint32_t text_length, Device device) const; + uint32_t text_length, Device device, uint64_t seed = 1337) const; uint32_t GetEndToken() const { return eot_token_; }; diff --git a/example/gpt2/net.cc b/example/gpt2/net.cc index 441b121..49f0d2c 100644 --- a/example/gpt2/net.cc +++ b/example/gpt2/net.cc @@ -220,6 +220,11 @@ GPT2::Forward(const std::vector> &x) { return logits; } +void GPT2::TieWeights() { + *mutable_module(kTransformerLayerName)->mutable_module(kWTELayerName)->mutable_parameter(nn::Embedding::kParamWeightName) + = module(kLMHeadLayerName).parameter(nn::Linear::kParamWeightName); +} + std::unique_ptr GPT2::FromPretrained(ModelType model_type) { LOG(FATAL) << "Not implemented yet"; return nullptr; @@ -232,6 +237,12 @@ std::vector ReadSeveralBytesFromIfstream(size_t num_bytes, std::ifstrea return result; } +void ReadTensorFromIfstream(const std::shared_ptr &tensor, std::ifstream *ifs) { + CHECK(tensor); + ifs->read(reinterpret_cast(tensor->DataPtr()), tensor->SizeInBytes()); + CHECK_EQ(ifs->gcount(), static_cast(tensor->SizeInBytes())) << "Truncated model tensor payload"; +} + template T BytesToType(const std::vector &bytes, size_t offset) { static_assert(std::is_trivially_copyable::value, "T must be trivially copyable."); T value; @@ -274,104 +285,107 @@ std::unique_ptr GPT2::FromLLMC(const std::string &filepath) { // (padded_vocab_size, n_embd) -> un_pad -> (vocab_size, n_embd) auto &transformer_wte_weight = state_dict[std::format("{}.{}.{}", GPT2::kTransformerLayerName, GPT2::kWTELayerName, nn::Embedding::kParamWeightName)]; - ifs.read(reinterpret_cast(transformer_wte_weight->DataPtr()), transformer_wte_weight->SizeInBytes()); - ifs.ignore((padded_vocab_size - vocab_size) * n_embd * sizeof(float)); + ReadTensorFromIfstream(transformer_wte_weight, &ifs); + const auto padding_bytes = static_cast((padded_vocab_size - vocab_size) * n_embd * sizeof(float)); + ifs.ignore(padding_bytes); + CHECK_EQ(ifs.gcount(), padding_bytes) << "Truncated padded token-embedding payload"; // transformer.wpe.weight auto &transformer_wpe_weight = state_dict[std::format("{}.{}.{}", GPT2::kTransformerLayerName, GPT2::kWPELayerName, nn::Embedding::kParamWeightName)]; - ifs.read(reinterpret_cast(transformer_wpe_weight->DataPtr()), transformer_wpe_weight->SizeInBytes()); + ReadTensorFromIfstream(transformer_wpe_weight, &ifs); // transformer.h.{i}.ln_1.weight for (int idx = 0; idx < n_layer; idx++) { auto &tensor = state_dict[std::format("{}.{}.{}.{}.{}", GPT2::kTransformerLayerName, GPT2::kHLayerName, std::to_string(idx), Block::kLn1LayerName, nn::LayerNorm::kParamWeightName)]; - ifs.read(reinterpret_cast(tensor->DataPtr()), tensor->SizeInBytes()); + ReadTensorFromIfstream(tensor, &ifs); } // transformer.h.{i}.ln_1.bias for (int idx = 0; idx < n_layer; idx++) { auto &tensor = state_dict[std::format("{}.{}.{}.{}.{}", GPT2::kTransformerLayerName, GPT2::kHLayerName, std::to_string(idx), Block::kLn1LayerName, nn::LayerNorm::kParamBiasName)]; - ifs.read(reinterpret_cast(tensor->DataPtr()), tensor->SizeInBytes()); + ReadTensorFromIfstream(tensor, &ifs); } // transformer.h.{i}.attn.c_attn.weight for (int idx = 0; idx < n_layer; idx++) { auto &tensor = state_dict[std::format("{}.{}.{}.{}.{}.{}", GPT2::kTransformerLayerName, GPT2::kHLayerName, std::to_string(idx), Block::kAttnLayerName, CausalSelfAttention::kCAttnLayerName, GPT2Linear::kParamWeightName)]; - ifs.read(reinterpret_cast(tensor->DataPtr()), tensor->SizeInBytes()); + ReadTensorFromIfstream(tensor, &ifs); } // transformer.h.{i}.attn.c_attn.bias for (int idx = 0; idx < n_layer; idx++) { auto &tensor = state_dict[std::format("{}.{}.{}.{}.{}.{}", GPT2::kTransformerLayerName, GPT2::kHLayerName, std::to_string(idx), Block::kAttnLayerName, CausalSelfAttention::kCAttnLayerName, GPT2Linear::kParamBiasName)]; - ifs.read(reinterpret_cast(tensor->DataPtr()), tensor->SizeInBytes()); + ReadTensorFromIfstream(tensor, &ifs); } // transformer.h.{i}.attn.c_proj.weight for (int idx = 0; idx < n_layer; idx++) { auto &tensor = state_dict[std::format("{}.{}.{}.{}.{}.{}", GPT2::kTransformerLayerName, GPT2::kHLayerName, std::to_string(idx), Block::kAttnLayerName, CausalSelfAttention::kCProjLayerName, GPT2Linear::kParamWeightName)]; - ifs.read(reinterpret_cast(tensor->DataPtr()), tensor->SizeInBytes()); + ReadTensorFromIfstream(tensor, &ifs); } // transformer.h.{i}.attn.c_proj.bias for (int idx = 0; idx < n_layer; idx++) { auto &tensor = state_dict[std::format("{}.{}.{}.{}.{}.{}", GPT2::kTransformerLayerName, GPT2::kHLayerName, std::to_string(idx), Block::kAttnLayerName, CausalSelfAttention::kCProjLayerName, GPT2Linear::kParamBiasName)]; - ifs.read(reinterpret_cast(tensor->DataPtr()), tensor->SizeInBytes()); + ReadTensorFromIfstream(tensor, &ifs); } // transformer.h.{i}.ln_2.weight for (int idx = 0; idx < n_layer; idx++) { auto &tensor = state_dict[std::format("{}.{}.{}.{}.{}", GPT2::kTransformerLayerName, GPT2::kHLayerName, std::to_string(idx), Block::kLn2LayerName, nn::LayerNorm::kParamWeightName)]; - ifs.read(reinterpret_cast(tensor->DataPtr()), tensor->SizeInBytes()); + ReadTensorFromIfstream(tensor, &ifs); } // transformer.h.{i}.ln_2.bias for (int idx = 0; idx < n_layer; idx++) { auto &tensor = state_dict[std::format("{}.{}.{}.{}.{}", GPT2::kTransformerLayerName, GPT2::kHLayerName, std::to_string(idx), Block::kLn2LayerName, nn::LayerNorm::kParamBiasName)]; - ifs.read(reinterpret_cast(tensor->DataPtr()), tensor->SizeInBytes()); + ReadTensorFromIfstream(tensor, &ifs); } // transformer.h.{i}.mlp.c_fc.weight for (int idx = 0; idx < n_layer; idx++) { auto &tensor = state_dict[std::format("{}.{}.{}.{}.{}.{}", GPT2::kTransformerLayerName, GPT2::kHLayerName, std::to_string(idx), Block::kMlpLayerName, MLP::kCFclayerName, GPT2Linear::kParamWeightName)]; - ifs.read(reinterpret_cast(tensor->DataPtr()), tensor->SizeInBytes()); + ReadTensorFromIfstream(tensor, &ifs); } // transformer.h.{i}.mlp.c_fc.bias for (int idx = 0; idx < n_layer; idx++) { auto &tensor = state_dict[std::format("{}.{}.{}.{}.{}.{}", GPT2::kTransformerLayerName, GPT2::kHLayerName, std::to_string(idx), Block::kMlpLayerName, MLP::kCFclayerName, GPT2Linear::kParamBiasName)]; - ifs.read(reinterpret_cast(tensor->DataPtr()), tensor->SizeInBytes()); + ReadTensorFromIfstream(tensor, &ifs); } // transformer.h.{i}.mlp.c_proj.weight for (int idx = 0; idx < n_layer; idx++) { auto &tensor = state_dict[std::format("{}.{}.{}.{}.{}.{}", GPT2::kTransformerLayerName, GPT2::kHLayerName, std::to_string(idx), Block::kMlpLayerName, MLP::kCProjLayerName, GPT2Linear::kParamWeightName)]; - ifs.read(reinterpret_cast(tensor->DataPtr()), tensor->SizeInBytes()); + ReadTensorFromIfstream(tensor, &ifs); } // transformer.h.{i}.mlp.c_proj.bias for (int idx = 0; idx < n_layer; idx++) { auto &tensor = state_dict[std::format("{}.{}.{}.{}.{}.{}", GPT2::kTransformerLayerName, GPT2::kHLayerName, std::to_string(idx), Block::kMlpLayerName, MLP::kCProjLayerName, GPT2Linear::kParamBiasName)]; - ifs.read(reinterpret_cast(tensor->DataPtr()), tensor->SizeInBytes()); + ReadTensorFromIfstream(tensor, &ifs); } // transformer.ln_f.weight auto &transformer_ln_f_weight = state_dict[std::format("{}.{}.{}", GPT2::kTransformerLayerName, GPT2::kLnFLayerName, nn::LayerNorm::kParamWeightName)]; - ifs.read(reinterpret_cast(transformer_ln_f_weight->DataPtr()), transformer_ln_f_weight->SizeInBytes()); + ReadTensorFromIfstream(transformer_ln_f_weight, &ifs); // transformer.ln_f.bias auto &transformer_ln_f_bias = state_dict[std::format("{}.{}.{}", GPT2::kTransformerLayerName, GPT2::kLnFLayerName, nn::LayerNorm::kParamBiasName)]; - ifs.read(reinterpret_cast(transformer_ln_f_bias->DataPtr()), transformer_ln_f_bias->SizeInBytes()); + ReadTensorFromIfstream(transformer_ln_f_bias, &ifs); + CHECK_EQ(ifs.peek(), std::char_traits::eof()) << "Unexpected trailing model checkpoint data"; return gpt2; } diff --git a/example/gpt2/net.h b/example/gpt2/net.h index 619cbdc..aacf6cb 100644 --- a/example/gpt2/net.h +++ b/example/gpt2/net.h @@ -89,6 +89,9 @@ class GPT2 : public infini_train::nn::Module { std::vector> Forward(const std::vector> &x) override; + void TieWeights(); + const GPT2Config &config() const { return config_; } + static std::unique_ptr FromPretrained(ModelType model_type); static std::unique_ptr FromLLMC(const std::string &filepath); diff --git a/example/gpt2/train_experiment.cc b/example/gpt2/train_experiment.cc new file mode 100644 index 0000000..ec47b51 --- /dev/null +++ b/example/gpt2/train_experiment.cc @@ -0,0 +1,1129 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "gflags/gflags.h" +#include "glog/logging.h" + +#ifdef USE_CUDA +#include "cuda_runtime_api.h" +#endif + +#include "example/common/tiny_shakespeare_dataset.h" +#include "example/common/tokenizer.h" +#include "example/gpt2/net.h" +#include "infini_train/include/dataloader.h" +#include "infini_train/include/device.h" +#include "infini_train/include/nn/modules/loss.h" +#include "infini_train/include/optimizer.h" +#include "infini_train/include/tensor.h" + +DEFINE_string(model_path, "", "Path to the llm.c GPT-2 checkpoint"); +DEFINE_string(train_path, "", "Path to the complete training token file"); +DEFINE_string(val_path, "", "Path to the complete validation token file"); +DEFINE_string(tokenizer_path, "", "Path to the tokenizer file used for fixed-prompt generation"); +DEFINE_string(output_dir, "experiment_output", "Directory for checkpoints, metrics, summaries, and generations"); +DEFINE_string(device, "cuda", "Training device: cpu or cuda"); +DEFINE_bool(resume, true, "Resume from output_dir/latest.ckpt when it exists"); +DEFINE_uint64(batch_size, 2, "Batch size"); +DEFINE_uint64(sequence_length, 64, "Sequence length"); +DEFINE_uint64(max_epochs, 8, "Maximum number of complete train/validation epochs"); +DEFINE_double(learning_rate, 2e-5, "Adam learning rate"); +DEFINE_double(beta1, 0.9, "Adam beta1"); +DEFINE_double(beta2, 0.999, "Adam beta2"); +DEFINE_double(epsilon, 1e-8, "Adam epsilon"); +DEFINE_uint64(early_stopping_patience, 3, "Validation epochs without improvement before stopping; 0 disables"); +DEFINE_double(min_delta, 0.005, "Minimum validation-loss improvement"); +DEFINE_uint64(seed, 1337, "Runner RNG seed saved in checkpoints"); +DEFINE_uint64(generation_length, 64, "Fixed-prompt generation length (must not exceed sequence_length)"); +DEFINE_double(max_runtime_seconds, 600.0, "Per-process runtime budget"); +DEFINE_double(safety_margin_seconds, 90.0, "Reserved time for an atomic checkpoint and process shutdown"); +DEFINE_uint64(checkpoint_interval_steps, 0, "Save latest every N train steps; 0 means epoch/window boundaries only"); + +namespace { +namespace fs = std::filesystem; +using Clock = std::chrono::steady_clock; +using infini_train::DataLoader; +using infini_train::DataType; +using infini_train::Device; +using infini_train::DeviceType; +using infini_train::Tensor; +using infini_train::Tokenizer; +using infini_train::nn::CrossEntropyLoss; +using infini_train::optimizers::Adam; + +constexpr std::array kCheckpointMagic{'T', 'I', 'T', 'C', 'K', 'P', 'T', '\0'}; +constexpr uint32_t kCheckpointVersion = 2; +constexpr uint32_t kEndianMarker = 0x01020304; +constexpr uint64_t kChecksumOffsetBasis = 14695981039346656037ull; +constexpr uint64_t kChecksumPrime = 1099511628211ull; +constexpr char kStepsHeader[] + = "epoch,global_step,batch_index,tokens,loss,compute_seconds,tokens_per_second,cuda_async_pool_peak_bytes"; +constexpr char kEpochsHeader[] + = "epoch,global_step,train_tokens,val_tokens,train_mean_loss,val_mean_loss,val_perplexity,val_perplexity_overflow,train_tokens_per_second,cuda_async_pool_peak_bytes,improved"; + +uint64_t UpdateChecksum(uint64_t checksum, const void *data, size_t size) { + const auto *bytes = static_cast(data); + for (size_t idx = 0; idx < size; ++idx) { + checksum ^= bytes[idx]; + checksum *= kChecksumPrime; + } + return checksum; +} + +enum class Phase : uint32_t { kTrain = 0, kValidate = 1, kComplete = 2 }; + +struct Progress { + Phase phase = Phase::kTrain; + uint64_t epoch = 0; + uint64_t global_step = 0; + uint64_t next_batch = 0; + double train_loss_sum = 0.0; + uint64_t train_tokens = 0; + double train_compute_seconds = 0.0; + uint64_t total_train_tokens = 0; + double total_train_compute_seconds = 0.0; + double val_loss_sum = 0.0; + uint64_t val_tokens = 0; + double best_val_loss = std::numeric_limits::infinity(); + uint64_t epochs_without_improvement = 0; + uint64_t peak_cuda_bytes = 0; + double final_train_loss = std::numeric_limits::quiet_NaN(); + double final_val_loss = std::numeric_limits::quiet_NaN(); +}; + +struct StateEntry { + std::string name; + std::shared_ptr tensor; + uint64_t payload_index = 0; +}; + +struct ModelCatalog { + std::vector entries; + std::vector canonical_names; + std::vector> unique_tensors; +}; + +struct FileIdentity { + std::string path; + uint64_t size = 0; + uint64_t checksum = 0; +}; + +struct ExperimentContract { + FileIdentity model; + FileIdentity train; + FileIdentity validation; + FileIdentity tokenizer; + uint64_t max_epochs = 0; + uint64_t early_stopping_patience = 0; + double min_delta = 0.0; + uint64_t seed = 0; + uint64_t generation_length = 0; + std::string device; +}; + +struct DatasetCoverage { + uint64_t samples = 0; + uint64_t raw_tokens = 0; + uint64_t usable_target_tokens = 0; + uint64_t dropped_tokens = 0; +}; + +class BinaryWriter { +public: + explicit BinaryWriter(const fs::path &path) : stream_(path, std::ios::binary | std::ios::trunc) { + CHECK(stream_.is_open()) << "Failed to open checkpoint temporary file: " << path; + } + + template void Pod(const T &value) { + static_assert(std::is_trivially_copyable_v); + Bytes(&value, sizeof(T)); + } + + void Bytes(const void *data, size_t size) { + stream_.write(static_cast(data), static_cast(size)); + CHECK(stream_) << "Checkpoint write failed"; + checksum_ = UpdateChecksum(checksum_, data, size); + } + + void WriteChecksumTrailer() { + stream_.write(reinterpret_cast(&checksum_), sizeof(checksum_)); + CHECK(stream_) << "Checkpoint checksum trailer write failed"; + } + + void String(const std::string &value) { + Pod(static_cast(value.size())); + Bytes(value.data(), value.size()); + } + + void Close() { + stream_.flush(); + CHECK(stream_) << "Checkpoint flush failed"; + stream_.close(); + CHECK(!stream_.fail()) << "Checkpoint close failed"; + } + +private: + std::ofstream stream_; + uint64_t checksum_ = kChecksumOffsetBasis; +}; + +class BinaryReader { +public: + explicit BinaryReader(const fs::path &path) : stream_(path, std::ios::binary) { + CHECK(stream_.is_open()) << "Failed to open checkpoint: " << path; + } + + template T Pod() { + static_assert(std::is_trivially_copyable_v); + T value{}; + Read(&value, sizeof(T)); + return value; + } + + void Read(void *data, size_t size) { + stream_.read(static_cast(data), static_cast(size)); + CHECK_EQ(stream_.gcount(), static_cast(size)) << "Truncated checkpoint"; + checksum_ = UpdateChecksum(checksum_, data, size); + } + + std::string String() { + const uint64_t size = Pod(); + CHECK_LE(size, static_cast(std::numeric_limits::max())); + std::string value(static_cast(size), '\0'); + Read(value.data(), value.size()); + return value; + } + + void RequireChecksumTrailer() { + uint64_t expected_checksum = 0; + stream_.read(reinterpret_cast(&expected_checksum), sizeof(expected_checksum)); + CHECK_EQ(stream_.gcount(), static_cast(sizeof(expected_checksum))) + << "Missing checkpoint checksum trailer"; + CHECK_EQ(checksum_, expected_checksum) << "Corrupt checkpoint body"; + CHECK_EQ(stream_.peek(), std::char_traits::eof()) << "Unexpected trailing checkpoint data"; + } + +private: + std::ifstream stream_; + uint64_t checksum_ = kChecksumOffsetBasis; +}; + +std::string JsonNumber(double value) { + if (!std::isfinite(value)) { + return "null"; + } + std::ostringstream out; + out << std::setprecision(17) << value; + return out.str(); +} + +DatasetCoverage ReadDatasetCoverage(const fs::path &path, uint64_t samples) { + std::ifstream input(path, std::ios::binary); + CHECK(input.is_open()) << "Failed to read dataset coverage: " << path; + input.seekg(2 * sizeof(uint32_t)); + uint32_t raw_tokens = 0; + input.read(reinterpret_cast(&raw_tokens), sizeof(raw_tokens)); + CHECK_EQ(input.gcount(), static_cast(sizeof(raw_tokens))) << "Truncated dataset header"; + const uint64_t usable = samples * FLAGS_sequence_length; + CHECK_GE(raw_tokens, usable + 1); + return DatasetCoverage{.samples = samples, + .raw_tokens = raw_tokens, + .usable_target_tokens = usable, + .dropped_tokens = static_cast(raw_tokens) - 1 - usable}; +} + +uint64_t ChecksumBytes(const void *data, size_t size) { + return UpdateChecksum(kChecksumOffsetBasis, data, size); +} + +FileIdentity IdentifyFile(const fs::path &path) { + CHECK(fs::exists(path)) << "Required input file does not exist: " << path; + CHECK(fs::is_regular_file(path)) << "Required input path is not a regular file: " << path; + FileIdentity identity; + identity.path = fs::weakly_canonical(path).string(); + identity.size = fs::file_size(path); + + std::ifstream input(path, std::ios::binary); + CHECK(input.is_open()) << "Failed to hash input file: " << path; + constexpr size_t kBufferSize = 1 << 20; + std::vector buffer(kBufferSize); + uint64_t checksum = kChecksumOffsetBasis; + while (input) { + input.read(reinterpret_cast(buffer.data()), buffer.size()); + const size_t count = static_cast(input.gcount()); + for (size_t idx = 0; idx < count; ++idx) { + checksum ^= buffer[idx]; + checksum *= kChecksumPrime; + } + } + CHECK(input.eof()) << "Failed while hashing input file: " << path; + identity.checksum = checksum; + return identity; +} + +ExperimentContract BuildExperimentContract() { + return ExperimentContract{ + .model = IdentifyFile(FLAGS_model_path), + .train = IdentifyFile(FLAGS_train_path), + .validation = IdentifyFile(FLAGS_val_path), + .tokenizer = IdentifyFile(FLAGS_tokenizer_path), + .max_epochs = FLAGS_max_epochs, + .early_stopping_patience = FLAGS_early_stopping_patience, + .min_delta = FLAGS_min_delta, + .seed = FLAGS_seed, + .generation_length = FLAGS_generation_length, + .device = FLAGS_device, + }; +} + +void WriteFileIdentity(BinaryWriter *writer, const FileIdentity &identity) { + writer->String(identity.path); + writer->Pod(identity.size); + writer->Pod(identity.checksum); +} + +void ValidateFileIdentity(BinaryReader *reader, const FileIdentity &expected) { + CHECK_EQ(reader->String(), expected.path) << "Checkpoint input path mismatch"; + CHECK_EQ(reader->Pod(), expected.size) << "Checkpoint input size mismatch"; + CHECK_EQ(reader->Pod(), expected.checksum) << "Checkpoint input checksum mismatch"; +} + +void WriteExperimentContract(BinaryWriter *writer, const ExperimentContract &contract) { + WriteFileIdentity(writer, contract.model); + WriteFileIdentity(writer, contract.train); + WriteFileIdentity(writer, contract.validation); + WriteFileIdentity(writer, contract.tokenizer); + writer->Pod(contract.max_epochs); + writer->Pod(contract.early_stopping_patience); + writer->Pod(contract.min_delta); + writer->Pod(contract.seed); + writer->Pod(contract.generation_length); + writer->String(contract.device); +} + +void ValidateExperimentContract(BinaryReader *reader, const ExperimentContract &expected) { + ValidateFileIdentity(reader, expected.model); + ValidateFileIdentity(reader, expected.train); + ValidateFileIdentity(reader, expected.validation); + ValidateFileIdentity(reader, expected.tokenizer); + CHECK_EQ(reader->Pod(), expected.max_epochs) << "Checkpoint max_epochs mismatch"; + CHECK_EQ(reader->Pod(), expected.early_stopping_patience) + << "Checkpoint early-stopping patience mismatch"; + CHECK_EQ(reader->Pod(), expected.min_delta) << "Checkpoint min_delta mismatch"; + CHECK_EQ(reader->Pod(), expected.seed) << "Checkpoint seed mismatch"; + CHECK_EQ(reader->Pod(), expected.generation_length) << "Checkpoint generation length mismatch"; + CHECK_EQ(reader->String(), expected.device) << "Checkpoint device/backend mismatch"; +} + +fs::path TemporaryPath(const fs::path &destination) { + const auto stamp = Clock::now().time_since_epoch().count(); + return destination.string() + ".tmp." + std::to_string(stamp); +} + +void AtomicRename(const fs::path &temporary, const fs::path &destination) { + std::error_code error; + fs::rename(temporary, destination, error); + CHECK(!error) << "Atomic rename failed from " << temporary << " to " << destination << ": " << error.message(); +} + +void AtomicWriteText(const fs::path &destination, const std::string &contents) { + const fs::path temporary = TemporaryPath(destination); + std::ofstream out(temporary, std::ios::binary | std::ios::trunc); + CHECK(out.is_open()) << "Failed to open temporary output: " << temporary; + out.write(contents.data(), static_cast(contents.size())); + out.flush(); + CHECK(out) << "Failed to write temporary output: " << temporary; + out.close(); + AtomicRename(temporary, destination); +} + +void AtomicHardLink(const fs::path &source, const fs::path &destination) { + CHECK(fs::exists(source)) << "Hard-link source does not exist: " << source; + const fs::path temporary = TemporaryPath(destination); + std::error_code error; + fs::create_hard_link(source, temporary, error); + CHECK(!error) << "Failed to create checkpoint hard link: " << error.message(); + AtomicRename(temporary, destination); +} + +void AppendCsv(const fs::path &path, const std::string &header, const std::string &row) { + const bool needs_header = !fs::exists(path) || fs::file_size(path) == 0; + std::ofstream out(path, std::ios::app); + CHECK(out.is_open()) << "Failed to open metrics CSV: " << path; + if (needs_header) { + out << header << '\n'; + } + out << row << '\n'; + out.flush(); + CHECK(out) << "Failed to append metrics CSV: " << path; +} + +std::pair ParseCsvKey(const std::string &row) { + const size_t first_comma = row.find(','); + const size_t second_comma = row.find(',', first_comma == std::string::npos ? first_comma : first_comma + 1); + CHECK(first_comma != std::string::npos && second_comma != std::string::npos) << "Malformed metrics row: " << row; + size_t parsed = 0; + const uint64_t first = std::stoull(row.substr(0, first_comma), &parsed); + CHECK_EQ(parsed, first_comma) << "Malformed metrics key: " << row; + const std::string second_text = row.substr(first_comma + 1, second_comma - first_comma - 1); + const uint64_t second = std::stoull(second_text, &parsed); + CHECK_EQ(parsed, second_text.size()) << "Malformed metrics key: " << row; + return {first, second}; +} + +void ReconcileMetricsCsv(const fs::path &path, const std::string &expected_header, uint64_t maximum_key, + bool key_is_second_column, bool maximum_is_inclusive) { + if (!fs::exists(path)) { + return; + } + std::ifstream input(path); + CHECK(input.is_open()) << "Failed to reconcile metrics: " << path; + std::string header; + CHECK(static_cast(std::getline(input, header))) << "Missing metrics header: " << path; + CHECK_EQ(header, expected_header) << "Unexpected metrics schema: " << path; + std::unordered_map committed; + std::string row; + while (std::getline(input, row)) { + if (row.empty()) { + continue; + } + const auto [first, second] = ParseCsvKey(row); + const uint64_t key = key_is_second_column ? second : first; + if (maximum_is_inclusive ? key <= maximum_key : key < maximum_key) { + committed[key] = row; + } + } + CHECK(input.eof()) << "Failed while reconciling metrics: " << path; + std::vector keys; + keys.reserve(committed.size()); + for (const auto &[key, _] : committed) { + keys.push_back(key); + } + std::sort(keys.begin(), keys.end()); + std::ostringstream canonical; + canonical << header << '\n'; + for (const uint64_t key : keys) { + canonical << committed.at(key) << '\n'; + } + AtomicWriteText(path, canonical.str()); +} + +ModelCatalog BuildModelCatalog(GPT2 &model) { + auto state_dict = model.StateDict(); + CHECK(state_dict.contains("lm_head.weight")); + CHECK(state_dict.contains("transformer.wte.weight")); + CHECK_EQ(state_dict.at("lm_head.weight").get(), state_dict.at("transformer.wte.weight").get()) + << "GPT-2 token embedding and language-model head must remain tied after device transfer"; + std::vector>> sorted(state_dict.begin(), state_dict.end()); + std::sort(sorted.begin(), sorted.end(), [](const auto &lhs, const auto &rhs) { return lhs.first < rhs.first; }); + + ModelCatalog catalog; + std::unordered_map tensor_to_payload; + for (auto &[name, tensor] : sorted) { + CHECK(tensor) << "Null state tensor: " << name; + auto [it, inserted] = tensor_to_payload.emplace(tensor.get(), catalog.unique_tensors.size()); + if (inserted) { + catalog.unique_tensors.push_back(tensor); + catalog.canonical_names.push_back(name); + } + catalog.entries.push_back(StateEntry{.name = name, .tensor = tensor, .payload_index = it->second}); + } + CHECK(!catalog.entries.empty()); + return catalog; +} + +std::vector TensorBytes(const Tensor &tensor) { + std::vector bytes(tensor.SizeInBytes()); + if (tensor.GetDevice().Type() == DeviceType::kCPU) { + std::memcpy(bytes.data(), tensor.DataPtr(), bytes.size()); + } +#ifdef USE_CUDA + else if (tensor.GetDevice().Type() == DeviceType::kCUDA) { + CHECK_EQ(cudaMemcpy(bytes.data(), tensor.DataPtr(), bytes.size(), cudaMemcpyDeviceToHost), cudaSuccess); + } +#endif + else { + LOG(FATAL) << "Unsupported checkpoint tensor device"; + } + return bytes; +} + +void CopyBytesToTensor(const std::vector &bytes, Tensor *tensor) { + CHECK(tensor); + CHECK_EQ(bytes.size(), tensor->SizeInBytes()); + if (tensor->GetDevice().Type() == DeviceType::kCPU) { + std::memcpy(tensor->DataPtr(), bytes.data(), bytes.size()); + } +#ifdef USE_CUDA + else if (tensor->GetDevice().Type() == DeviceType::kCUDA) { + CHECK_EQ(cudaMemcpy(tensor->DataPtr(), bytes.data(), bytes.size(), cudaMemcpyHostToDevice), cudaSuccess); + } +#endif + else { + LOG(FATAL) << "Unsupported checkpoint tensor device"; + } +} + +void WriteTensorMetadata(BinaryWriter *writer, const std::string &name, const Tensor &tensor) { + writer->String(name); + writer->Pod(static_cast(tensor.Dtype())); + writer->Pod(static_cast(tensor.Dims().size())); + for (const int64_t dim : tensor.Dims()) { + writer->Pod(dim); + } + writer->Pod(static_cast(tensor.SizeInBytes())); +} + +void ValidateTensorMetadata(BinaryReader *reader, const std::string &expected_name, const Tensor &tensor) { + CHECK_EQ(reader->String(), expected_name) << "Checkpoint state name mismatch"; + CHECK_EQ(reader->Pod(), static_cast(tensor.Dtype())) << "Checkpoint dtype mismatch"; + const uint64_t num_dims = reader->Pod(); + CHECK_EQ(num_dims, tensor.Dims().size()) << "Checkpoint rank mismatch"; + for (const int64_t expected_dim : tensor.Dims()) { + CHECK_EQ(reader->Pod(), expected_dim) << "Checkpoint shape mismatch"; + } + CHECK_EQ(reader->Pod(), tensor.SizeInBytes()) << "Checkpoint byte-size mismatch"; +} + +std::string SerializeRng(const std::mt19937_64 &rng) { + std::ostringstream out; + out << rng; + return out.str(); +} + +void DeserializeRng(const std::string &serialized, std::mt19937_64 *rng) { + std::istringstream in(serialized); + in >> *rng; + CHECK(in) << "Invalid RNG checkpoint state"; +} + +void WriteConfig(BinaryWriter *writer, const GPT2Config &config) { + writer->Pod(config.block_size); + writer->Pod(config.vocab_size); + writer->Pod(config.n_layer); + writer->Pod(config.n_head); + writer->Pod(config.n_embd); +} + +void ValidateConfig(BinaryReader *reader, const GPT2Config &config) { + CHECK_EQ(reader->Pod(), config.block_size); + CHECK_EQ(reader->Pod(), config.vocab_size); + CHECK_EQ(reader->Pod(), config.n_layer); + CHECK_EQ(reader->Pod(), config.n_head); + CHECK_EQ(reader->Pod(), config.n_embd); +} + +void WriteProgress(BinaryWriter *writer, const Progress &progress) { + writer->Pod(static_cast(progress.phase)); + writer->Pod(progress.epoch); + writer->Pod(progress.global_step); + writer->Pod(progress.next_batch); + writer->Pod(progress.train_loss_sum); + writer->Pod(progress.train_tokens); + writer->Pod(progress.train_compute_seconds); + writer->Pod(progress.total_train_tokens); + writer->Pod(progress.total_train_compute_seconds); + writer->Pod(progress.val_loss_sum); + writer->Pod(progress.val_tokens); + writer->Pod(progress.best_val_loss); + writer->Pod(progress.epochs_without_improvement); + writer->Pod(progress.peak_cuda_bytes); + writer->Pod(progress.final_train_loss); + writer->Pod(progress.final_val_loss); +} + +Progress ReadProgress(BinaryReader *reader) { + Progress progress; + const uint32_t phase = reader->Pod(); + CHECK_LE(phase, static_cast(Phase::kComplete)); + progress.phase = static_cast(phase); + progress.epoch = reader->Pod(); + progress.global_step = reader->Pod(); + progress.next_batch = reader->Pod(); + progress.train_loss_sum = reader->Pod(); + progress.train_tokens = reader->Pod(); + progress.train_compute_seconds = reader->Pod(); + progress.total_train_tokens = reader->Pod(); + progress.total_train_compute_seconds = reader->Pod(); + progress.val_loss_sum = reader->Pod(); + progress.val_tokens = reader->Pod(); + progress.best_val_loss = reader->Pod(); + progress.epochs_without_improvement = reader->Pod(); + progress.peak_cuda_bytes = reader->Pod(); + progress.final_train_loss = reader->Pod(); + progress.final_val_loss = reader->Pod(); + return progress; +} + +double SaveCheckpoint(const fs::path &destination, const GPT2 &model, const ModelCatalog &catalog, const Adam &optimizer, + const Progress &progress, const std::mt19937_64 &rng, + const ExperimentContract &experiment_contract) { + const auto started = Clock::now(); +#ifdef USE_CUDA + if (!catalog.unique_tensors.empty() && catalog.unique_tensors[0]->GetDevice().Type() == DeviceType::kCUDA) { + CHECK_EQ(cudaDeviceSynchronize(), cudaSuccess); + } +#endif + const fs::path temporary = TemporaryPath(destination); + BinaryWriter writer(temporary); + writer.Bytes(kCheckpointMagic.data(), kCheckpointMagic.size()); + writer.Pod(kCheckpointVersion); + writer.Pod(kEndianMarker); + WriteConfig(&writer, model.config()); + WriteExperimentContract(&writer, experiment_contract); + writer.Pod(FLAGS_sequence_length); + writer.Pod(FLAGS_batch_size); + writer.Pod(optimizer.LearningRate()); + writer.Pod(optimizer.Beta1()); + writer.Pod(optimizer.Beta2()); + writer.Pod(optimizer.Epsilon()); + writer.Pod(optimizer.StepCount()); + WriteProgress(&writer, progress); + writer.String(SerializeRng(rng)); + + writer.Pod(static_cast(catalog.entries.size())); + for (const auto &entry : catalog.entries) { + WriteTensorMetadata(&writer, entry.name, *entry.tensor); + writer.Pod(entry.payload_index); + } + writer.Pod(static_cast(catalog.unique_tensors.size())); + for (size_t idx = 0; idx < catalog.unique_tensors.size(); ++idx) { + writer.String(catalog.canonical_names[idx]); + const auto bytes = TensorBytes(*catalog.unique_tensors[idx]); + writer.Pod(static_cast(bytes.size())); + writer.Pod(ChecksumBytes(bytes.data(), bytes.size())); + writer.Bytes(bytes.data(), bytes.size()); + } + + const auto &first = optimizer.FirstMoments(); + const auto &second = optimizer.SecondMoments(); + CHECK_EQ(first.size(), catalog.unique_tensors.size()); + CHECK_EQ(second.size(), catalog.unique_tensors.size()); + writer.Pod(static_cast(first.size())); + for (size_t idx = 0; idx < first.size(); ++idx) { + const std::string first_name = "adam.m." + catalog.canonical_names[idx]; + const std::string second_name = "adam.v." + catalog.canonical_names[idx]; + WriteTensorMetadata(&writer, first_name, *first[idx]); + const auto first_bytes = TensorBytes(*first[idx]); + writer.Pod(ChecksumBytes(first_bytes.data(), first_bytes.size())); + writer.Bytes(first_bytes.data(), first_bytes.size()); + WriteTensorMetadata(&writer, second_name, *second[idx]); + const auto second_bytes = TensorBytes(*second[idx]); + writer.Pod(ChecksumBytes(second_bytes.data(), second_bytes.size())); + writer.Bytes(second_bytes.data(), second_bytes.size()); + } + writer.WriteChecksumTrailer(); + writer.Close(); + AtomicRename(temporary, destination); + return std::chrono::duration(Clock::now() - started).count(); +} + +Progress LoadCheckpoint(const fs::path &path, const GPT2 &model, const ModelCatalog &catalog, Adam *optimizer, + std::mt19937_64 *rng, const ExperimentContract &experiment_contract) { + BinaryReader reader(path); + std::array magic{}; + reader.Read(magic.data(), magic.size()); + CHECK(magic == kCheckpointMagic) << "Invalid checkpoint magic"; + CHECK_EQ(reader.Pod(), kCheckpointVersion) << "Unsupported checkpoint version"; + CHECK_EQ(reader.Pod(), kEndianMarker) << "Checkpoint endianness mismatch"; + ValidateConfig(&reader, model.config()); + ValidateExperimentContract(&reader, experiment_contract); + CHECK_EQ(reader.Pod(), FLAGS_sequence_length); + CHECK_EQ(reader.Pod(), FLAGS_batch_size); + CHECK_EQ(reader.Pod(), optimizer->LearningRate()); + CHECK_EQ(reader.Pod(), optimizer->Beta1()); + CHECK_EQ(reader.Pod(), optimizer->Beta2()); + CHECK_EQ(reader.Pod(), optimizer->Epsilon()); + const int64_t optimizer_step = reader.Pod(); + const Progress progress = ReadProgress(&reader); + CHECK_EQ(optimizer_step, static_cast(progress.global_step)) + << "Checkpoint optimizer step and global step mismatch"; + DeserializeRng(reader.String(), rng); + + CHECK_EQ(reader.Pod(), catalog.entries.size()) << "Checkpoint state count mismatch"; + for (const auto &entry : catalog.entries) { + ValidateTensorMetadata(&reader, entry.name, *entry.tensor); + CHECK_EQ(reader.Pod(), entry.payload_index) << "Checkpoint tied-weight alias mismatch"; + } + CHECK_EQ(reader.Pod(), catalog.unique_tensors.size()) << "Checkpoint payload count mismatch"; + for (size_t idx = 0; idx < catalog.unique_tensors.size(); ++idx) { + CHECK_EQ(reader.String(), catalog.canonical_names[idx]) << "Checkpoint canonical state name mismatch"; + CHECK_EQ(reader.Pod(), catalog.unique_tensors[idx]->SizeInBytes()); + const uint64_t expected_checksum = reader.Pod(); + std::vector bytes(catalog.unique_tensors[idx]->SizeInBytes()); + reader.Read(bytes.data(), bytes.size()); + CHECK_EQ(ChecksumBytes(bytes.data(), bytes.size()), expected_checksum) << "Corrupt model tensor payload"; + CopyBytesToTensor(bytes, catalog.unique_tensors[idx].get()); + } + + CHECK_EQ(reader.Pod(), catalog.unique_tensors.size()) << "Checkpoint optimizer state count mismatch"; + std::vector> first; + std::vector> second; + first.reserve(catalog.unique_tensors.size()); + second.reserve(catalog.unique_tensors.size()); + for (size_t idx = 0; idx < catalog.unique_tensors.size(); ++idx) { + const auto ¶meter = catalog.unique_tensors[idx]; + auto first_tensor = std::make_shared(parameter->Dims(), parameter->Dtype()); + auto second_tensor = std::make_shared(parameter->Dims(), parameter->Dtype()); + ValidateTensorMetadata(&reader, "adam.m." + catalog.canonical_names[idx], *first_tensor); + const uint64_t first_checksum = reader.Pod(); + std::vector first_bytes(first_tensor->SizeInBytes()); + reader.Read(first_bytes.data(), first_bytes.size()); + CHECK_EQ(ChecksumBytes(first_bytes.data(), first_bytes.size()), first_checksum) + << "Corrupt Adam first-moment payload"; + CopyBytesToTensor(first_bytes, first_tensor.get()); + ValidateTensorMetadata(&reader, "adam.v." + catalog.canonical_names[idx], *second_tensor); + const uint64_t second_checksum = reader.Pod(); + std::vector second_bytes(second_tensor->SizeInBytes()); + reader.Read(second_bytes.data(), second_bytes.size()); + CHECK_EQ(ChecksumBytes(second_bytes.data(), second_bytes.size()), second_checksum) + << "Corrupt Adam second-moment payload"; + CopyBytesToTensor(second_bytes, second_tensor.get()); + first.push_back(std::move(first_tensor)); + second.push_back(std::move(second_tensor)); + } + reader.RequireChecksumTrailer(); + optimizer->LoadState(optimizer_step, first, second); + return progress; +} + +class DisableParameterGradGuard { +public: + explicit DisableParameterGradGuard(const ModelCatalog &catalog) : parameters_(catalog.unique_tensors) { + requires_grad_.reserve(parameters_.size()); + for (const auto ¶meter : parameters_) { + requires_grad_.push_back(parameter->requires_grad()); + parameter->set_requires_grad(false); + } + } + + ~DisableParameterGradGuard() { + for (size_t idx = 0; idx < parameters_.size(); ++idx) { + parameters_[idx]->set_requires_grad(requires_grad_[idx]); + } + } + +private: + std::vector> parameters_; + std::vector requires_grad_; +}; + +uint64_t BatchCount(const TinyShakespeareDataset &dataset) { + return (dataset.Size() + FLAGS_batch_size - 1) / FLAGS_batch_size; +} + +infini_train::DataLoaderIterator IteratorAt(const DataLoader &loader, uint64_t batch_index) { + auto iterator = loader.begin(); + for (uint64_t idx = 0; idx < batch_index; ++idx) { + ++iterator; + } + return iterator; +} + +float ReadScalar(const Tensor &tensor) { + CHECK_EQ(tensor.NumElements(), 1); + auto cpu = const_cast(tensor).To(Device(DeviceType::kCPU, 0)); +#ifdef USE_CUDA + if (tensor.GetDevice().Type() == DeviceType::kCUDA) { + CHECK_EQ(cudaDeviceSynchronize(), cudaSuccess); + } +#endif + return static_cast(cpu.DataPtr())[0]; +} + +uint64_t PeakCudaBytes() { +#ifdef USE_CUDA + cudaMemPool_t pool; + if (cudaDeviceGetDefaultMemPool(&pool, 0) == cudaSuccess) { + uint64_t high = 0; + if (cudaMemPoolGetAttribute(pool, cudaMemPoolAttrUsedMemHigh, &high) == cudaSuccess) { + return high; + } + } +#endif + return 0; +} + +void ResetPeakCudaBytes() { +#ifdef USE_CUDA + cudaMemPool_t pool; + if (cudaDeviceGetDefaultMemPool(&pool, 0) == cudaSuccess) { + uint64_t zero = 0; + cudaMemPoolSetAttribute(pool, cudaMemPoolAttrUsedMemHigh, &zero); + } +#endif +} + +std::string GenerateFixedPrompt(Tokenizer &tokenizer, GPT2 &model, const ModelCatalog &catalog, const Device &device) { + DisableParameterGradGuard no_grad(catalog); + std::ostringstream captured; + auto *previous = std::cout.rdbuf(captured.rdbuf()); + tokenizer.GenerateText(model, 1, static_cast(FLAGS_sequence_length), + static_cast(FLAGS_generation_length), device, FLAGS_seed); + std::cout.rdbuf(previous); + return captured.str(); +} + +std::string PhaseName(Phase phase) { + switch (phase) { + case Phase::kTrain: return "train"; + case Phase::kValidate: return "validate"; + case Phase::kComplete: return "complete"; + } + return "invalid"; +} + +void WriteSummary(const fs::path &path, const Progress &progress, const GPT2Config &config, + const DatasetCoverage &train_coverage, const DatasetCoverage &val_coverage, bool resumed, + double wall_seconds, const std::string &run_state) { + const double train_loss + = progress.train_tokens == 0 ? progress.final_train_loss : progress.train_loss_sum / progress.train_tokens; + const double val_loss = progress.val_tokens == 0 ? progress.final_val_loss : progress.val_loss_sum / progress.val_tokens; + const double throughput = progress.total_train_compute_seconds > 0 + ? progress.total_train_tokens / progress.total_train_compute_seconds + : 0.0; + const bool perplexity_overflow + = std::isfinite(val_loss) && val_loss > std::log(std::numeric_limits::max()); + const double perplexity = std::isfinite(val_loss) && !perplexity_overflow + ? std::exp(val_loss) + : std::numeric_limits::quiet_NaN(); + std::ostringstream out; + out << "{\n" + << " \"status\": \"" << PhaseName(progress.phase) << "\",\n" + << " \"run_state\": \"" << run_state << "\",\n" + << " \"resumed\": " << (resumed ? "true" : "false") << ",\n" + << " \"device\": \"" << FLAGS_device << "\",\n" + << " \"epoch\": " << progress.epoch << ",\n" + << " \"global_step\": " << progress.global_step << ",\n" + << " \"next_batch\": " << progress.next_batch << ",\n" + << " \"train_mean_loss\": " << JsonNumber(train_loss) << ",\n" + << " \"validation_mean_loss\": " << JsonNumber(val_loss) << ",\n" + << " \"validation_perplexity\": " << JsonNumber(perplexity) << ",\n" + << " \"validation_perplexity_overflow\": " << (perplexity_overflow ? "true" : "false") << ",\n" + << " \"best_validation_loss\": " << JsonNumber(progress.best_val_loss) << ",\n" + << " \"train_tokens_per_second\": " << JsonNumber(throughput) << ",\n" + << " \"cuda_async_pool_peak_bytes\": " << progress.peak_cuda_bytes << ",\n" + << " \"wall_seconds_this_process\": " << JsonNumber(wall_seconds) << ",\n" + << " \"config\": {\"block_size\": " << config.block_size << ", \"vocab_size\": " << config.vocab_size + << ", \"n_layer\": " << config.n_layer << ", \"n_head\": " << config.n_head + << ", \"n_embd\": " << config.n_embd << ", \"batch_size\": " << FLAGS_batch_size + << ", \"sequence_length\": " << FLAGS_sequence_length << ", \"max_epochs\": " << FLAGS_max_epochs + << ", \"learning_rate\": " << JsonNumber(FLAGS_learning_rate) << ", \"seed\": " << FLAGS_seed << "},\n" + << " \"dataset_coverage\": {\n" + << " \"train\": {\"samples\": " << train_coverage.samples << ", \"raw_tokens\": " + << train_coverage.raw_tokens << ", \"usable_target_tokens\": " << train_coverage.usable_target_tokens + << ", \"dropped_tokens\": " << train_coverage.dropped_tokens << "},\n" + << " \"validation\": {\"samples\": " << val_coverage.samples << ", \"raw_tokens\": " + << val_coverage.raw_tokens << ", \"usable_target_tokens\": " << val_coverage.usable_target_tokens + << ", \"dropped_tokens\": " << val_coverage.dropped_tokens << "}\n" + << " },\n" + << " \"artifacts\": {\n" + << " \"steps_csv\": {\"path\": \"steps.csv\", \"exists\": " + << (fs::exists(path.parent_path() / "steps.csv") ? "true" : "false") << "},\n" + << " \"epochs_csv\": {\"path\": \"epochs.csv\", \"exists\": " + << (fs::exists(path.parent_path() / "epochs.csv") ? "true" : "false") << "},\n" + << " \"latest_checkpoint\": {\"path\": \"latest.ckpt\", \"exists\": " + << (fs::exists(path.parent_path() / "latest.ckpt") ? "true" : "false") << "},\n" + << " \"best_checkpoint\": {\"path\": \"best.ckpt\", \"exists\": " + << (fs::exists(path.parent_path() / "best.ckpt") ? "true" : "false") << "},\n" + << " \"final_checkpoint\": {\"path\": \"final.ckpt\", \"exists\": " + << (fs::exists(path.parent_path() / "final.ckpt") ? "true" : "false") << "},\n" + << " \"baseline_generation\": {\"path\": \"baseline.txt\", \"exists\": " + << (fs::exists(path.parent_path() / "baseline.txt") ? "true" : "false") << "},\n" + << " \"best_generation\": {\"path\": \"best.txt\", \"exists\": " + << (fs::exists(path.parent_path() / "best.txt") ? "true" : "false") << "},\n" + << " \"final_generation\": {\"path\": \"final.txt\", \"exists\": " + << (fs::exists(path.parent_path() / "final.txt") ? "true" : "false") << "}\n" + << " }\n" + << "}\n"; + AtomicWriteText(path, out.str()); +} + +void ValidateFlags() { + CHECK(!FLAGS_model_path.empty()); + CHECK(!FLAGS_train_path.empty()); + CHECK(!FLAGS_val_path.empty()); + CHECK(!FLAGS_tokenizer_path.empty()); + CHECK_GT(FLAGS_batch_size, 0); + CHECK_GT(FLAGS_sequence_length, 0); + CHECK_LE(FLAGS_sequence_length, std::numeric_limits::max()); + CHECK_GT(FLAGS_max_epochs, 0); + CHECK(std::isfinite(FLAGS_learning_rate)); + CHECK_GT(FLAGS_learning_rate, 0.0); + CHECK_GT(static_cast(FLAGS_learning_rate), 0.0f); + CHECK(std::isfinite(FLAGS_beta1)); + CHECK_GE(FLAGS_beta1, 0.0); + CHECK_LT(FLAGS_beta1, 1.0); + CHECK(std::isfinite(FLAGS_beta2)); + CHECK_GE(FLAGS_beta2, 0.0); + CHECK_LT(FLAGS_beta2, 1.0); + CHECK(std::isfinite(FLAGS_epsilon)); + CHECK_GT(FLAGS_epsilon, 0.0); + CHECK_GT(static_cast(FLAGS_epsilon), 0.0f); + CHECK(std::isfinite(FLAGS_min_delta)); + CHECK_GE(FLAGS_min_delta, 0.0); + CHECK_GT(FLAGS_generation_length, 0); + CHECK_LE(FLAGS_generation_length, FLAGS_sequence_length); + CHECK(std::isfinite(FLAGS_max_runtime_seconds)); + CHECK_GT(FLAGS_max_runtime_seconds, 0.0); + CHECK(std::isfinite(FLAGS_safety_margin_seconds)); + CHECK_GE(FLAGS_safety_margin_seconds, 0.0); + CHECK_LT(FLAGS_safety_margin_seconds, FLAGS_max_runtime_seconds); + CHECK(FLAGS_device == "cpu" || FLAGS_device == "cuda"); +#ifndef USE_CUDA + CHECK_EQ(FLAGS_device, "cpu") << "This binary was built without CUDA support"; +#endif +} +} // namespace + +int main(int argc, char **argv) { + gflags::ParseCommandLineFlags(&argc, &argv, true); + google::InitGoogleLogging(argv[0]); + ValidateFlags(); + + const auto process_started = Clock::now(); + fs::create_directories(FLAGS_output_dir); + const fs::path output_dir = FLAGS_output_dir; + const fs::path latest_path = output_dir / "latest.ckpt"; + const fs::path best_path = output_dir / "best.ckpt"; + const fs::path summary_path = output_dir / "summary.json"; + const fs::path steps_path = output_dir / "steps.csv"; + const fs::path epochs_path = output_dir / "epochs.csv"; + + Device device(DeviceType::kCPU, 0); + if (FLAGS_device == "cuda") { +#ifdef USE_CUDA + CHECK_EQ(cudaSetDevice(0), cudaSuccess); + device = Device(DeviceType::kCUDA, 0); +#endif + } + + auto model = GPT2::FromLLMC(FLAGS_model_path); + model->To(device); + model->TieWeights(); + CHECK_GE(model->config().block_size, static_cast(FLAGS_sequence_length)); + ModelCatalog catalog = BuildModelCatalog(*model); + Adam optimizer(catalog.unique_tensors, static_cast(FLAGS_learning_rate), static_cast(FLAGS_beta1), + static_cast(FLAGS_beta2), static_cast(FLAGS_epsilon)); + CrossEntropyLoss loss_function; + loss_function.To(device); + auto train_dataset = std::make_shared(FLAGS_train_path, FLAGS_sequence_length); + auto val_dataset = std::make_shared(FLAGS_val_path, FLAGS_sequence_length); + const DatasetCoverage train_coverage = ReadDatasetCoverage(FLAGS_train_path, train_dataset->Size()); + const DatasetCoverage val_coverage = ReadDatasetCoverage(FLAGS_val_path, val_dataset->Size()); + DataLoader train_loader(train_dataset, FLAGS_batch_size); + DataLoader val_loader(val_dataset, FLAGS_batch_size); + Tokenizer tokenizer(FLAGS_tokenizer_path); + const ExperimentContract experiment_contract = BuildExperimentContract(); + std::mt19937_64 rng(FLAGS_seed); + Progress progress; + const bool resumed = FLAGS_resume && fs::exists(latest_path); + if (resumed) { + progress = LoadCheckpoint(latest_path, *model, catalog, &optimizer, &rng, experiment_contract); + CHECK(fs::exists(output_dir / "baseline.txt")) << "Baseline generation is missing for resumed experiment"; + ReconcileMetricsCsv(steps_path, kStepsHeader, progress.global_step, true, true); + ReconcileMetricsCsv(epochs_path, kEpochsHeader, progress.epoch, false, false); + } else { + CHECK(!fs::exists(latest_path)) << "Existing checkpoint requires --resume=true or a new output_dir"; + CHECK(!fs::exists(steps_path) && !fs::exists(epochs_path)) + << "Existing metrics without a checkpoint require a new output_dir"; + AtomicWriteText(output_dir / "baseline.txt", GenerateFixedPrompt(tokenizer, *model, catalog, device)); + } + + ResetPeakCudaBytes(); + double last_checkpoint_seconds = 0.0; + if (!resumed) { + last_checkpoint_seconds + = SaveCheckpoint(latest_path, *model, catalog, optimizer, progress, rng, experiment_contract); + } + + const auto elapsed_seconds = [&]() { return std::chrono::duration(Clock::now() - process_started).count(); }; + const auto window_exhausted = [&]() { + return elapsed_seconds() + FLAGS_safety_margin_seconds + last_checkpoint_seconds >= FLAGS_max_runtime_seconds; + }; + const auto save_latest = [&]() { + progress.peak_cuda_bytes = std::max(progress.peak_cuda_bytes, PeakCudaBytes()); + last_checkpoint_seconds + = SaveCheckpoint(latest_path, *model, catalog, optimizer, progress, rng, experiment_contract); + WriteSummary(summary_path, progress, model->config(), train_coverage, val_coverage, resumed, elapsed_seconds(), + "running"); + }; + + while (progress.phase != Phase::kComplete) { + if (progress.epoch >= FLAGS_max_epochs) { + progress.phase = Phase::kComplete; + save_latest(); + break; + } + if (window_exhausted()) { + save_latest(); + WriteSummary(summary_path, progress, model->config(), train_coverage, val_coverage, resumed, + elapsed_seconds(), "paused_runtime"); + LOG(INFO) << "Runtime window exhausted safely; resume from " << latest_path; + return 0; + } + + if (progress.phase == Phase::kTrain) { + const uint64_t batches = BatchCount(*train_dataset); + CHECK_LE(progress.next_batch, batches); + auto iterator = IteratorAt(train_loader, progress.next_batch); + while (progress.next_batch < batches) { + auto [x_cpu, y_cpu] = *iterator; + ++iterator; + const uint64_t batch_index = progress.next_batch; + const uint64_t tokens = x_cpu->NumElements(); + const auto compute_started = Clock::now(); + auto x = std::make_shared(x_cpu->To(device)); + auto y = std::make_shared(y_cpu->To(device)); + optimizer.ZeroGrad(); + auto logits = model->Forward({x})[0]; + auto loss = loss_function.Forward({logits, y})[0]; + const float loss_value = ReadScalar(*loss); + CHECK(std::isfinite(loss_value)) << "Non-finite training loss at global step " << progress.global_step; + loss->Backward(); + optimizer.Step(); +#ifdef USE_CUDA + if (device.Type() == DeviceType::kCUDA) { + CHECK_EQ(cudaDeviceSynchronize(), cudaSuccess); + } +#endif + const double compute_seconds = std::chrono::duration(Clock::now() - compute_started).count(); + ++progress.global_step; + ++progress.next_batch; + progress.train_loss_sum += static_cast(loss_value) * tokens; + progress.train_tokens += tokens; + progress.train_compute_seconds += compute_seconds; + progress.total_train_tokens += tokens; + progress.total_train_compute_seconds += compute_seconds; + progress.peak_cuda_bytes = std::max(progress.peak_cuda_bytes, PeakCudaBytes()); + + std::ostringstream row; + row << progress.epoch << ',' << progress.global_step << ',' << batch_index << ',' << tokens << ',' + << std::setprecision(9) << loss_value << ',' << compute_seconds << ',' << tokens / compute_seconds << ',' + << progress.peak_cuda_bytes; + AppendCsv(steps_path, kStepsHeader, row.str()); + + if (FLAGS_checkpoint_interval_steps > 0 + && progress.global_step % FLAGS_checkpoint_interval_steps == 0) { + save_latest(); + } + if (window_exhausted()) { + save_latest(); + WriteSummary(summary_path, progress, model->config(), train_coverage, val_coverage, resumed, + elapsed_seconds(), "paused_runtime"); + LOG(INFO) << "Runtime window exhausted safely during training"; + return 0; + } + } + progress.phase = Phase::kValidate; + progress.next_batch = 0; + save_latest(); + continue; + } + + CHECK(progress.phase == Phase::kValidate); + const uint64_t batches = BatchCount(*val_dataset); + CHECK_LE(progress.next_batch, batches); + auto iterator = IteratorAt(val_loader, progress.next_batch); + { + DisableParameterGradGuard no_grad(catalog); + while (progress.next_batch < batches) { + auto [x_cpu, y_cpu] = *iterator; + ++iterator; + auto x = std::make_shared(x_cpu->To(device)); + auto y = std::make_shared(y_cpu->To(device)); + auto logits = model->Forward({x})[0]; + auto loss = loss_function.Forward({logits, y})[0]; + const float loss_value = ReadScalar(*loss); + CHECK(std::isfinite(loss_value)) + << "Non-finite validation loss at epoch " << progress.epoch << ", batch " << progress.next_batch; + const uint64_t tokens = x_cpu->NumElements(); + progress.val_loss_sum += static_cast(loss_value) * tokens; + progress.val_tokens += tokens; + ++progress.next_batch; + progress.peak_cuda_bytes = std::max(progress.peak_cuda_bytes, PeakCudaBytes()); + if (window_exhausted()) { + save_latest(); + WriteSummary(summary_path, progress, model->config(), train_coverage, val_coverage, resumed, + elapsed_seconds(), "paused_runtime"); + LOG(INFO) << "Runtime window exhausted safely during validation"; + return 0; + } + } + } + + CHECK_GT(progress.train_tokens, 0); + CHECK_GT(progress.val_tokens, 0); + const double train_mean = progress.train_loss_sum / progress.train_tokens; + const double val_mean = progress.val_loss_sum / progress.val_tokens; + CHECK(std::isfinite(train_mean)) << "Non-finite epoch training loss"; + CHECK(std::isfinite(val_mean)) << "Non-finite epoch validation loss"; + const bool perplexity_overflow = val_mean > std::log(std::numeric_limits::max()); + const double perplexity + = perplexity_overflow ? std::numeric_limits::infinity() : std::exp(val_mean); + const double throughput = progress.train_tokens / progress.train_compute_seconds; + const bool improved = val_mean + FLAGS_min_delta < progress.best_val_loss; + if (improved) { + progress.best_val_loss = val_mean; + progress.epochs_without_improvement = 0; + } else { + ++progress.epochs_without_improvement; + } + + std::ostringstream epoch_row; + epoch_row << progress.epoch << ',' << progress.global_step << ',' << progress.train_tokens << ',' + << progress.val_tokens << ',' << std::setprecision(12) << train_mean << ',' << val_mean << ',' + << perplexity << ',' << (perplexity_overflow ? 1 : 0) << ',' << throughput << ',' + << progress.peak_cuda_bytes << ',' << (improved ? 1 : 0); + AppendCsv(epochs_path, kEpochsHeader, epoch_row.str()); + + progress.final_train_loss = train_mean; + progress.final_val_loss = val_mean; + ++progress.epoch; + progress.phase = Phase::kTrain; + progress.next_batch = 0; + progress.train_loss_sum = 0.0; + progress.train_tokens = 0; + progress.train_compute_seconds = 0.0; + progress.val_loss_sum = 0.0; + progress.val_tokens = 0; + + const bool early_stop = FLAGS_early_stopping_patience > 0 + && progress.epochs_without_improvement >= FLAGS_early_stopping_patience; + if (early_stop || progress.epoch >= FLAGS_max_epochs) { + progress.phase = Phase::kComplete; + } + save_latest(); + if (improved) { + AtomicHardLink(latest_path, best_path); + AtomicWriteText(output_dir / "best.txt", GenerateFixedPrompt(tokenizer, *model, catalog, device)); + } + } + + progress.phase = Phase::kComplete; + AtomicWriteText(output_dir / "final.txt", GenerateFixedPrompt(tokenizer, *model, catalog, device)); + AtomicHardLink(latest_path, output_dir / "final.ckpt"); + WriteSummary(summary_path, progress, model->config(), train_coverage, val_coverage, resumed, elapsed_seconds(), + "complete"); + LOG(INFO) << "Experiment complete; summary: " << summary_path; + return 0; +} diff --git a/infini_train/include/dispatcher.h b/infini_train/include/dispatcher.h index 5b91d85..93bccd3 100644 --- a/infini_train/include/dispatcher.h +++ b/infini_train/include/dispatcher.h @@ -1,9 +1,16 @@ #pragma once +#include +#include #include #include +#include +#include +#include +#include #include #include +#include #include "glog/logging.h" @@ -12,20 +19,121 @@ namespace infini_train { class KernelFunction { public: - template explicit KernelFunction(FuncT &&func) : func_ptr_(reinterpret_cast(func)) {} + template explicit KernelFunction(FuncT &&func) { + using DecayedFuncT = std::decay_t; + static_assert(std::is_pointer_v && std::is_function_v>, + "KernelFunction only supports function pointers"); + invoker_ = std::make_shared>(std::forward(func)); + } + + template RetT Call(ArgsT &&...args) const { + static_assert(!std::is_reference_v, "KernelFunction does not support reference return types"); - template RetT Call(ArgsT... args) const { - // =================================== 作业 =================================== - // TODO：实现通用kernel调用接口 - // 功能描述：将存储的函数指针转换为指定类型并调用 - // =================================== 作业 =================================== + std::array erased_args{MakeErasedArgument(args)...}; + CHECK(invoker_ != nullptr); + CHECK(invoker_->return_type == std::type_index(typeid(RetT))) << "Kernel return type mismatch"; + CHECK_EQ(invoker_->argument_types.size(), erased_args.size()) << "Kernel argument count mismatch"; + for (size_t idx = 0; idx < erased_args.size(); ++idx) { + CHECK(invoker_->argument_types[idx] == erased_args[idx].type) << "Kernel argument type mismatch at " << idx; + } - using FuncT = RetT (*)(ArgsT...); - // TODO: 实现函数调用逻辑 + if constexpr (std::is_void_v) { + invoker_->Invoke(nullptr, erased_args); + } else { + std::optional result; + invoker_->Invoke(&result, erased_args); + CHECK(result.has_value()); + return std::move(*result); + } } private: - void *func_ptr_ = nullptr; + struct ErasedArgument { + const void *value = nullptr; + std::type_index type = typeid(void); + bool is_lvalue = false; + bool is_const = false; + }; + + struct Invoker { + Invoker(std::type_index return_type, std::vector argument_types) + : return_type(return_type), argument_types(std::move(argument_types)) {} + virtual ~Invoker() = default; + + virtual void Invoke(void *result, std::span args) const = 0; + + const std::type_index return_type; + const std::vector argument_types; + }; + + template class TypedInvoker; + + template class TypedInvoker final : public Invoker { + public: + explicit TypedInvoker(RetT (*func)(ParamsT...)) + : Invoker(typeid(RetT), {std::type_index(typeid(std::remove_cvref_t))...}), func_(func) {} + + void Invoke(void *result, std::span args) const override { + CHECK_EQ(args.size(), sizeof...(ParamsT)); + InvokeImpl(result, args, std::index_sequence_for{}); + } + + private: + template static decltype(auto) Unpack(ErasedArgument &arg) { + using BareT = std::remove_cvref_t; + using ReferredT = std::remove_reference_t; + + if constexpr (std::is_lvalue_reference_v) { + if constexpr (std::is_const_v) { + return *static_cast(arg.value); + } else { + CHECK(arg.is_lvalue) << "Kernel argument must be an lvalue"; + CHECK(!arg.is_const) << "Kernel argument must be mutable"; + return *static_cast(const_cast(arg.value)); + } + } else if constexpr (std::is_rvalue_reference_v) { + CHECK(!arg.is_lvalue) << "Kernel argument must be an rvalue"; + if constexpr (std::is_const_v) { + return std::move(*static_cast(arg.value)); + } else { + CHECK(!arg.is_const) << "Kernel argument must be mutable"; + return std::move(*static_cast(const_cast(arg.value))); + } + } else { + static_assert(std::is_copy_constructible_v || std::is_move_constructible_v, + "By-value kernel arguments must be copyable or movable"); + if constexpr (std::is_copy_constructible_v) { + return arg.is_lvalue || arg.is_const + ? BareT(*static_cast(arg.value)) + : BareT(std::move(*static_cast(const_cast(arg.value)))); + } else { + CHECK(!arg.is_lvalue && !arg.is_const) << "Move-only kernel argument must be a mutable rvalue"; + return BareT(std::move(*static_cast(const_cast(arg.value)))); + } + } + } + + template + void InvokeImpl(void *result, std::span args, std::index_sequence) const { + if constexpr (std::is_void_v) { + std::invoke(func_, Unpack(args[IndicesT])...); + } else { + auto &typed_result = *static_cast *>(result); + typed_result.emplace(std::invoke(func_, Unpack(args[IndicesT])...)); + } + } + + RetT (*func_)(ParamsT...); + }; + + template static ErasedArgument MakeErasedArgument(std::remove_reference_t &arg) { + return ErasedArgument{.value = std::addressof(arg), + .type = std::type_index(typeid(std::remove_cvref_t)), + .is_lvalue = std::is_lvalue_reference_v, + .is_const = std::is_const_v>}; + } + + std::shared_ptr invoker_; }; class Dispatcher { @@ -44,10 +152,9 @@ class Dispatcher { } template void Register(const KeyT &key, FuncT &&kernel) { - // =================================== 作业 =================================== - // TODO：实现kernel注册机制 - // 功能描述：将kernel函数与设备类型、名称绑定 - // =================================== 作业 =================================== + CHECK(!key_to_kernel_map_.contains(key)) + << "Kernel already registered: " << key.second << " on device: " << static_cast(key.first); + key_to_kernel_map_.emplace(key, KernelFunction(std::forward(kernel))); } private: @@ -55,8 +162,11 @@ class Dispatcher { }; } // namespace infini_train -#define REGISTER_KERNEL(device, kernel_name, kernel_func) \ - // =================================== 作业 =================================== - // TODO：实现自动注册宏 - // 功能描述：在全局静态区注册kernel，避免显式初始化代码 - // =================================== 作业 =================================== +#define INFINI_TRAIN_CONCAT_IMPL(lhs, rhs) lhs##rhs +#define INFINI_TRAIN_CONCAT(lhs, rhs) INFINI_TRAIN_CONCAT_IMPL(lhs, rhs) + +#define REGISTER_KERNEL(device, kernel_name, kernel_func) \ + [[maybe_unused]] static const bool INFINI_TRAIN_CONCAT(infini_train_kernel_registered_, __COUNTER__) = []() { \ + ::infini_train::Dispatcher::Instance().Register({device, #kernel_name}, kernel_func); \ + return true; \ + }(); diff --git a/infini_train/include/optimizer.h b/infini_train/include/optimizer.h index 241aeb9..9368641 100644 --- a/infini_train/include/optimizer.h +++ b/infini_train/include/optimizer.h @@ -36,6 +36,16 @@ class Adam : public Optimizer { void Step() override; + int64_t StepCount() const { return t_; } + float LearningRate() const { return learning_rate_; } + float Beta1() const { return beta1_; } + float Beta2() const { return beta2_; } + float Epsilon() const { return eps_; } + const std::vector> &FirstMoments() const { return m_; } + const std::vector> &SecondMoments() const { return v_; } + void LoadState(int64_t step, const std::vector> &first_moments, + const std::vector> &second_moments); + private: int64_t t_; const float learning_rate_; diff --git a/infini_train/src/autograd/elementwise.cc b/infini_train/src/autograd/elementwise.cc index 5a790a5..3433e03 100644 --- a/infini_train/src/autograd/elementwise.cc +++ b/infini_train/src/autograd/elementwise.cc @@ -6,21 +6,21 @@ namespace infini_train::autograd { std::vector> Neg::Forward(const std::vector> &input_tensors) { - // =================================== 作业 =================================== - // TODO：通过Dispatcher获取设备专属kernel，对输入张量进行取反操作 - // NOTES: 依赖test_dispatcher，Neg kernel实现已给出 - // =================================== 作业 =================================== + CHECK_EQ(input_tensors.size(), 1); + const auto &input = input_tensors[0]; - return std::vector>(); + auto device = input->GetDevice().Type(); + auto kernel = Dispatcher::Instance().GetKernel({device, "NegForward"}); + return {kernel.Call>(input)}; } std::vector> Neg::Backward(const std::vector> &grad_outputs) { - // =================================== 作业 =================================== - // TODO：通过Dispatcher获取设备专属的反向传播kernel，计算梯度 - // NOTES: 依赖test_dispatcher，Neg的kernel实现已给出 - // =================================== 作业 =================================== + CHECK_EQ(grad_outputs.size(), 1); + const auto &grad_output = grad_outputs[0]; - return std::vector>(); + auto device = grad_output->GetDevice().Type(); + auto kernel = Dispatcher::Instance().GetKernel({device, "NegBackward"}); + return {kernel.Call>(grad_output)}; } std::vector> Reciprocal::Forward(const std::vector> &input_tensors) { diff --git a/infini_train/src/kernels/cpu/accumulate_grad.cc b/infini_train/src/kernels/cpu/accumulate_grad.cc index 55637cd..f10bbf5 100644 --- a/infini_train/src/kernels/cpu/accumulate_grad.cc +++ b/infini_train/src/kernels/cpu/accumulate_grad.cc @@ -1,3 +1,4 @@ +#include #include #include @@ -14,10 +15,40 @@ void AccumulateGrad(const std::shared_ptr &gradient, float rate, const s void AdamAccumulateGrad(const std::shared_ptr &grad, const std::shared_ptr ¶m, const std::shared_ptr &m, const std::shared_ptr &v, float learning_rate, float beta1, float beta2, float eps, int64_t t) { - // =================================== 作业 =================================== - // TODO：实现Adam优化器的梯度累积和参数更新 - // REF: - // =================================== 作业 =================================== + CHECK(grad); + CHECK(param); + CHECK(m); + CHECK(v); + CHECK(grad->Dims() == param->Dims()); + CHECK(m->Dims() == param->Dims()); + CHECK(v->Dims() == param->Dims()); + CHECK(grad->Dtype() == DataType::kFLOAT32); + CHECK(param->Dtype() == DataType::kFLOAT32); + CHECK(m->Dtype() == DataType::kFLOAT32); + CHECK(v->Dtype() == DataType::kFLOAT32); + CHECK(param->GetDevice().IsCPU()); + CHECK(grad->GetDevice() == param->GetDevice()); + CHECK(m->GetDevice() == param->GetDevice()); + CHECK(v->GetDevice() == param->GetDevice()); + CHECK_GT(t, 0); + + const float bias_correction1 = 1.0f - std::pow(beta1, t); + const float bias_correction2 = 1.0f - std::pow(beta2, t); + + const auto *grad_ptr = static_cast(grad->DataPtr()); + auto *param_ptr = static_cast(param->DataPtr()); + auto *m_ptr = static_cast(m->DataPtr()); + auto *v_ptr = static_cast(v->DataPtr()); + + for (size_t idx = 0; idx < grad->NumElements(); ++idx) { + const float gradient = grad_ptr[idx]; + m_ptr[idx] = beta1 * m_ptr[idx] + (1.0f - beta1) * gradient; + v_ptr[idx] = beta2 * v_ptr[idx] + (1.0f - beta2) * gradient * gradient; + + const float m_hat = m_ptr[idx] / bias_correction1; + const float v_hat = v_ptr[idx] / bias_correction2; + param_ptr[idx] -= learning_rate * m_hat / (std::sqrt(v_hat) + eps); + } } } // namespace infini_train::kernels::cpu diff --git a/infini_train/src/kernels/cpu/linear.cc b/infini_train/src/kernels/cpu/linear.cc index 140e756..aa99cb1 100644 --- a/infini_train/src/kernels/cpu/linear.cc +++ b/infini_train/src/kernels/cpu/linear.cc @@ -3,6 +3,7 @@ #include #include #include +#include #include "glog/logging.h" @@ -10,26 +11,100 @@ #include "infini_train/include/tensor.h" namespace infini_train::kernels::cpu { +namespace { +using RowMajorMatrix = Eigen::Matrix; + +struct MatmulShape { + int64_t batch_count; + int64_t m; + int64_t k; + int64_t n; + std::vector output_dims; +}; + +MatmulShape ValidateMatmulInputs(const std::shared_ptr &input, const std::shared_ptr &other) { + CHECK(input); + CHECK(other); + CHECK(input->GetDevice().IsCPU()); + CHECK(input->GetDevice() == other->GetDevice()); + CHECK(input->Dtype() == DataType::kFLOAT32); + CHECK(other->Dtype() == DataType::kFLOAT32); + + const auto &input_dims = input->Dims(); + const auto &other_dims = other->Dims(); + CHECK_GE(input_dims.size(), 2); + CHECK_EQ(input_dims.size(), other_dims.size()); + for (size_t i = 0; i + 2 < input_dims.size(); ++i) { CHECK_EQ(input_dims[i], other_dims[i]); } + + const int64_t m = input_dims[input_dims.size() - 2]; + const int64_t k = input_dims.back(); + const int64_t n = other_dims.back(); + CHECK_EQ(k, other_dims[other_dims.size() - 2]); + + const int64_t batch_count + = std::accumulate(input_dims.begin(), input_dims.end() - 2, int64_t{1}, std::multiplies{}); + auto output_dims = input_dims; + output_dims.back() = n; + return {batch_count, m, k, n, std::move(output_dims)}; +} + +void ValidateGradOutput(const std::shared_ptr &grad_output, const MatmulShape &shape, + const Device &device) { + CHECK(grad_output); + CHECK(grad_output->GetDevice() == device); + CHECK(grad_output->Dtype() == DataType::kFLOAT32); + CHECK(grad_output->Dims() == shape.output_dims); +} +} // namespace + std::shared_ptr MatmulForward(const std::shared_ptr &input, const std::shared_ptr &other) { // =================================== 作业 =================================== - // TODO：实现CPU上的矩阵乘法前向计算 - // REF: + // 逐batch执行row-major矩阵乘法 // =================================== 作业 =================================== - auto output = std::make_shared(); - return {output}; + const auto shape = ValidateMatmulInputs(input, other); + auto output = std::make_shared(shape.output_dims, DataType::kFLOAT32, input->GetDevice()); + + const auto *input_data = static_cast(input->DataPtr()); + const auto *other_data = static_cast(other->DataPtr()); + auto *output_data = static_cast(output->DataPtr()); + for (int64_t batch = 0; batch < shape.batch_count; ++batch) { + Eigen::Map input_matrix(input_data + batch * shape.m * shape.k, shape.m, shape.k); + Eigen::Map other_matrix(other_data + batch * shape.k * shape.n, shape.k, shape.n); + Eigen::Map output_matrix(output_data + batch * shape.m * shape.n, shape.m, shape.n); + output_matrix.noalias() = input_matrix * other_matrix; + } + return output; } std::tuple, std::shared_ptr> MatmulBackward(const std::shared_ptr &input, const std::shared_ptr &other, const std::shared_ptr &grad_output) { // =================================== 作业 =================================== - // TODO：实现CPU上的矩阵乘法反向传播 - // REF: + // 逐batch计算grad_input和grad_other // =================================== 作业 =================================== - auto grad_input = std::make_shared(); - auto grad_other = std::make_shared(); + const auto shape = ValidateMatmulInputs(input, other); + ValidateGradOutput(grad_output, shape, input->GetDevice()); + + auto grad_input = std::make_shared(input->Dims(), DataType::kFLOAT32, input->GetDevice()); + auto grad_other = std::make_shared(other->Dims(), DataType::kFLOAT32, other->GetDevice()); + + const auto *input_data = static_cast(input->DataPtr()); + const auto *other_data = static_cast(other->DataPtr()); + const auto *grad_output_data = static_cast(grad_output->DataPtr()); + auto *grad_input_data = static_cast(grad_input->DataPtr()); + auto *grad_other_data = static_cast(grad_other->DataPtr()); + for (int64_t batch = 0; batch < shape.batch_count; ++batch) { + Eigen::Map input_matrix(input_data + batch * shape.m * shape.k, shape.m, shape.k); + Eigen::Map other_matrix(other_data + batch * shape.k * shape.n, shape.k, shape.n); + Eigen::Map grad_output_matrix(grad_output_data + batch * shape.m * shape.n, shape.m, + shape.n); + Eigen::Map grad_input_matrix(grad_input_data + batch * shape.m * shape.k, shape.m, shape.k); + Eigen::Map grad_other_matrix(grad_other_data + batch * shape.k * shape.n, shape.k, shape.n); + grad_input_matrix.noalias() = grad_output_matrix * other_matrix.transpose(); + grad_other_matrix.noalias() = input_matrix.transpose() * grad_output_matrix; + } return {grad_input, grad_other}; } diff --git a/infini_train/src/kernels/cuda/accumulate_grad.cu b/infini_train/src/kernels/cuda/accumulate_grad.cu index 5f977c3..7a40c02 100644 --- a/infini_train/src/kernels/cuda/accumulate_grad.cu +++ b/infini_train/src/kernels/cuda/accumulate_grad.cu @@ -1,3 +1,5 @@ +#include + #include "infini_train/include/dispatcher.h" #include "infini_train/include/tensor.h" @@ -22,13 +24,59 @@ void AccumulateGrad(const std::shared_ptr &gradient, float rate, const s AccumulateGradKernel<<>>(grad_ptr, rate, tensor_ptr, num_elements); } +__global__ void AdamAccumulateGradKernel(const float *grad_ptr, float *param_ptr, float *m_ptr, float *v_ptr, + float learning_rate, float beta1, float beta2, float eps, + float bias_correction1, float bias_correction2, size_t num_elements) { + const size_t idx = static_cast(blockIdx.x) * blockDim.x + threadIdx.x; + if (idx >= num_elements) { + return; + } + + const float gradient = grad_ptr[idx]; + const float first_moment = beta1 * m_ptr[idx] + (1.0f - beta1) * gradient; + const float second_moment = beta2 * v_ptr[idx] + (1.0f - beta2) * gradient * gradient; + m_ptr[idx] = first_moment; + v_ptr[idx] = second_moment; + + const float m_hat = first_moment / bias_correction1; + const float v_hat = second_moment / bias_correction2; + param_ptr[idx] -= learning_rate * m_hat / (sqrtf(v_hat) + eps); +} + void AdamAccumulateGrad(const std::shared_ptr &grad, const std::shared_ptr ¶m, const std::shared_ptr &m, const std::shared_ptr &v, float learning_rate, float beta1, float beta2, float eps, int64_t t) { - // =================================== 作业 =================================== - // TODO：实现Adam优化器的梯度累积和参数更新 - // REF: - // =================================== 作业 =================================== + CHECK(grad); + CHECK(param); + CHECK(m); + CHECK(v); + CHECK(grad->Dims() == param->Dims()); + CHECK(m->Dims() == param->Dims()); + CHECK(v->Dims() == param->Dims()); + CHECK(grad->Dtype() == DataType::kFLOAT32); + CHECK(param->Dtype() == DataType::kFLOAT32); + CHECK(m->Dtype() == DataType::kFLOAT32); + CHECK(v->Dtype() == DataType::kFLOAT32); + CHECK(param->GetDevice().IsCUDA()); + CHECK(grad->GetDevice() == param->GetDevice()); + CHECK(m->GetDevice() == param->GetDevice()); + CHECK(v->GetDevice() == param->GetDevice()); + CHECK_GT(t, 0); + + const size_t num_elements = grad->NumElements(); + if (num_elements == 0) { + return; + } + + const float bias_correction1 = 1.0f - std::pow(beta1, t); + const float bias_correction2 = 1.0f - std::pow(beta2, t); + constexpr int threads_per_block = 256; + const int num_blocks = (num_elements + threads_per_block - 1) / threads_per_block; + + AdamAccumulateGradKernel<<>>( + static_cast(grad->DataPtr()), static_cast(param->DataPtr()), + static_cast(m->DataPtr()), static_cast(v->DataPtr()), learning_rate, beta1, beta2, eps, + bias_correction1, bias_correction2, num_elements); } } // namespace infini_train::kernels::cuda diff --git a/infini_train/src/kernels/cuda/linear.cu b/infini_train/src/kernels/cuda/linear.cu index efaaaa6..ce75118 100644 --- a/infini_train/src/kernels/cuda/linear.cu +++ b/infini_train/src/kernels/cuda/linear.cu @@ -2,6 +2,11 @@ #include "glog/logging.h" #include +#include +#include +#include +#include + #include "infini_train/include/dispatcher.h" #include "infini_train/include/tensor.h" @@ -23,13 +28,77 @@ namespace infini_train::kernels::cuda { } \ } while (0) +namespace { +struct MatmulShape { + int64_t batch_count; + int64_t m; + int64_t k; + int64_t n; + std::vector output_dims; +}; + +MatmulShape ValidateMatmulInputs(const std::shared_ptr &input, const std::shared_ptr &other) { + CHECK(input); + CHECK(other); + CHECK(input->GetDevice().IsCUDA()); + CHECK(input->GetDevice() == other->GetDevice()); + CHECK(input->Dtype() == DataType::kFLOAT32); + CHECK(other->Dtype() == DataType::kFLOAT32); + + const auto &input_dims = input->Dims(); + const auto &other_dims = other->Dims(); + CHECK_GE(input_dims.size(), 2); + CHECK_EQ(input_dims.size(), other_dims.size()); + for (size_t i = 0; i + 2 < input_dims.size(); ++i) { CHECK_EQ(input_dims[i], other_dims[i]); } + + const int64_t m = input_dims[input_dims.size() - 2]; + const int64_t k = input_dims.back(); + const int64_t n = other_dims.back(); + CHECK_EQ(k, other_dims[other_dims.size() - 2]); + + const int64_t batch_count + = std::accumulate(input_dims.begin(), input_dims.end() - 2, int64_t{1}, std::multiplies{}); + auto output_dims = input_dims; + output_dims.back() = n; + return {batch_count, m, k, n, std::move(output_dims)}; +} + +void ValidateGradOutput(const std::shared_ptr &grad_output, const MatmulShape &shape, + const Device &device) { + CHECK(grad_output); + CHECK(grad_output->GetDevice() == device); + CHECK(grad_output->Dtype() == DataType::kFLOAT32); + CHECK(grad_output->Dims() == shape.output_dims); +} +} // namespace + std::shared_ptr MatmulForward(const std::shared_ptr &input, const std::shared_ptr &other) { // =================================== 作业 =================================== - // TODO：实现CUDA上的矩阵乘法前向计算 - // REF: + // 通过strided-batched GEMM执行row-major矩阵乘法 // =================================== 作业 =================================== - auto output = std::make_shared(); + const auto shape = ValidateMatmulInputs(input, other); + auto output = std::make_shared(shape.output_dims, DataType::kFLOAT32, input->GetDevice()); + if (shape.batch_count == 0 || shape.m == 0 || shape.n == 0 || shape.k == 0) { + if (output->NumElements() != 0) { + output->Fill(0.0f); + } + return output; + } + + const float alpha = 1.0f; + const float beta = 0.0f; + cublasHandle_t handle; + CUBLAS_CHECK(cublasCreate(&handle)); + + // Tensors are row-major. cuBLAS sees their storage as column-major transposes, + // so C^T[N, M] = B^T[N, K] * A^T[K, M]. + CUBLAS_CHECK(cublasSgemmStridedBatched( + handle, CUBLAS_OP_N, CUBLAS_OP_N, shape.n, shape.m, shape.k, &alpha, + static_cast(other->DataPtr()), shape.n, shape.k * shape.n, + static_cast(input->DataPtr()), shape.k, shape.m * shape.k, &beta, + static_cast(output->DataPtr()), shape.n, shape.m * shape.n, shape.batch_count)); + CUBLAS_CHECK(cublasDestroy(handle)); return output; } @@ -37,12 +106,44 @@ std::tuple, std::shared_ptr> MatmulBackward(const std::shared_ptr &input, const std::shared_ptr &other, const std::shared_ptr &grad_output) { // =================================== 作业 =================================== - // TODO：实现CUDA上的矩阵乘法反向传播 - // REF: + // 通过strided-batched GEMM计算grad_input和grad_other // =================================== 作业 =================================== - auto grad_input = std::make_shared(); - auto grad_other = std::make_shared(); + const auto shape = ValidateMatmulInputs(input, other); + ValidateGradOutput(grad_output, shape, input->GetDevice()); + + auto grad_input = std::make_shared(input->Dims(), DataType::kFLOAT32, input->GetDevice()); + auto grad_other = std::make_shared(other->Dims(), DataType::kFLOAT32, other->GetDevice()); + if (shape.batch_count == 0 || shape.m == 0 || shape.n == 0 || shape.k == 0) { + if (grad_input->NumElements() != 0) { + grad_input->Fill(0.0f); + } + if (grad_other->NumElements() != 0) { + grad_other->Fill(0.0f); + } + return {grad_input, grad_other}; + } + + const float alpha = 1.0f; + const float beta = 0.0f; + cublasHandle_t handle; + CUBLAS_CHECK(cublasCreate(&handle)); + + // dA^T[K, M] = B[K, N] * dC^T[N, M]. + CUBLAS_CHECK(cublasSgemmStridedBatched( + handle, CUBLAS_OP_T, CUBLAS_OP_N, shape.k, shape.m, shape.n, &alpha, + static_cast(other->DataPtr()), shape.n, shape.k * shape.n, + static_cast(grad_output->DataPtr()), shape.n, shape.m * shape.n, &beta, + static_cast(grad_input->DataPtr()), shape.k, shape.m * shape.k, shape.batch_count)); + + // dB^T[N, K] = dC^T[N, M] * A[M, K]. + CUBLAS_CHECK(cublasSgemmStridedBatched( + handle, CUBLAS_OP_N, CUBLAS_OP_T, shape.n, shape.k, shape.m, &alpha, + static_cast(grad_output->DataPtr()), shape.n, shape.m * shape.n, + static_cast(input->DataPtr()), shape.k, shape.m * shape.k, &beta, + static_cast(grad_other->DataPtr()), shape.n, shape.k * shape.n, shape.batch_count)); + + CUBLAS_CHECK(cublasDestroy(handle)); return {grad_input, grad_other}; } diff --git a/infini_train/src/optimizer.cc b/infini_train/src/optimizer.cc index 9ff2b5c..e61b54d 100644 --- a/infini_train/src/optimizer.cc +++ b/infini_train/src/optimizer.cc @@ -51,5 +51,32 @@ void Adam::Step() { kernel.Call(grad, param, m, v, learning_rate_, beta1_, beta2_, eps_, t_); } } + +void Adam::LoadState(int64_t step, const std::vector> &first_moments, + const std::vector> &second_moments) { + CHECK_GE(step, 0); + CHECK_EQ(first_moments.size(), params_.size()); + CHECK_EQ(second_moments.size(), params_.size()); + + std::vector> loaded_m; + std::vector> loaded_v; + loaded_m.reserve(params_.size()); + loaded_v.reserve(params_.size()); + for (size_t idx = 0; idx < params_.size(); ++idx) { + const auto ¶m = params_[idx]; + const auto &first = first_moments[idx]; + const auto &second = second_moments[idx]; + CHECK(first && second); + CHECK(first->Dims() == param->Dims()); + CHECK(second->Dims() == param->Dims()); + CHECK_EQ(static_cast(first->Dtype()), static_cast(param->Dtype())); + CHECK_EQ(static_cast(second->Dtype()), static_cast(param->Dtype())); + loaded_m.push_back(std::make_shared(first->To(param->GetDevice()))); + loaded_v.push_back(std::make_shared(second->To(param->GetDevice()))); + } + t_ = step; + m_ = std::move(loaded_m); + v_ = std::move(loaded_v); +} } // namespace optimizers } // namespace infini_train diff --git a/infini_train/src/tensor.cc b/infini_train/src/tensor.cc index 8f8c744..2e9946a 100644 --- a/infini_train/src/tensor.cc +++ b/infini_train/src/tensor.cc @@ -102,7 +102,8 @@ Tensor::Tensor(const std::vector &dims, DataType dtype, Device device) } Tensor::Tensor(const Tensor &tensor, size_t offset, const std::vector &dims) - : buffer_(tensor.buffer_), offset_(offset), dims_(dims), + // A sub-view offset is relative to the parent view, not to the underlying buffer. + : buffer_(tensor.buffer_), offset_(tensor.offset_ + offset), dims_(dims), num_elements_(std::accumulate(dims.begin(), dims.end(), 1, std::multiplies())), dtype_(tensor.dtype_) { CHECK_LE(offset_ + kDataTypeToSize.at(dtype_) * num_elements_, buffer_->Size()); } @@ -175,9 +176,9 @@ Eigen::Map> Tensor::Eig Tensor Tensor::To(Device device) { if (device == buffer_->GetDevice()) { - auto new_tensor = Tensor(*this, offset_, dims_); + auto new_tensor = Tensor(*this, 0, dims_); if (grad_) { - new_tensor.grad_ = std::make_unique(*grad_.get(), grad_->offset_, grad_->dims_); + new_tensor.grad_ = std::make_unique(*grad_.get(), 0, grad_->dims_); } return new_tensor; } @@ -277,13 +278,33 @@ std::shared_ptr Tensor::Contiguous() { } std::shared_ptr Tensor::Flatten(int64_t start, int64_t end) { - // return Contiguous()->View(new_shape); - // =================================== 作业 =================================== - // TODO：实现张量扁平化操作，将指定维度范围[start, end]内的所有维度合并为一个维度 - // HINT: - // =================================== 作业 =================================== + const int64_t num_dims = dims_.size(); + if (num_dims == 0) { + CHECK(start == 0 || start == -1); + CHECK(end == 0 || end == -1); + return Contiguous()->View({1}); + } + + if (start < 0) { + start += num_dims; + } + if (end < 0) { + end += num_dims; + } + CHECK_GE(start, 0); + CHECK_LT(start, num_dims); + CHECK_GE(end, 0); + CHECK_LT(end, num_dims); + CHECK_LE(start, end); + + std::vector new_shape; + new_shape.reserve(num_dims - (end - start)); + new_shape.insert(new_shape.end(), dims_.begin(), dims_.begin() + start); + new_shape.push_back( + std::accumulate(dims_.begin() + start, dims_.begin() + end + 1, int64_t{1}, std::multiplies())); + new_shape.insert(new_shape.end(), dims_.begin() + end + 1, dims_.end()); - return std::make_shared(); + return Contiguous()->View(new_shape); } std::shared_ptr Tensor::Squeeze(int64_t dim) { @@ -354,10 +375,30 @@ std::shared_ptr Tensor::RequiresGrad() { } void Tensor::Backward(std::shared_ptr gradient, bool retain_graph, bool create_graph) const { - // =================================== 作业 =================================== - // TODO：实现自动微分反向传播 - // 功能描述：1. 计算当前张量对叶子节点的梯度 2. 支持多输出场景的梯度累加 - // =================================== 作业 =================================== + CHECK(requires_grad_) << "Cannot run backward on a tensor that does not require gradients"; + CHECK(!retain_graph) << "retain_graph is not supported"; + CHECK(!create_graph) << "create_graph is not supported"; + + if (!gradient) { + CHECK_EQ(NumElements(), 1) << "An explicit gradient is required for non-scalar tensors"; + gradient = std::make_shared(dims_, dtype_, GetDevice()); + gradient->Fill(1.0f); + } else { + CHECK(gradient->Dims() == dims_) << "Gradient shape must match the output tensor shape"; + CHECK_EQ(static_cast(gradient->Dtype()), static_cast(dtype_)); + CHECK(gradient->GetDevice() == GetDevice()) << "Gradient and output must be on the same device"; + } + + if (grad_fn_) { + CHECK_GE(output_idx_, 0); + grad_fn_->BackwardPartial(gradient, output_idx_); + return; + } + + CHECK(is_leaf_); + CHECK(grad_ != nullptr); + auto kernel = Dispatcher::Instance().GetKernel({GetDevice().Type(), "AccumulateGrad"}); + kernel.Call(gradient, 1.0f, grad_); } void Tensor::ZeroGrad() { diff --git a/scripts/plot_full_experiment.py b/scripts/plot_full_experiment.py new file mode 100644 index 0000000..ce97bdc --- /dev/null +++ b/scripts/plot_full_experiment.py @@ -0,0 +1,203 @@ +#!/usr/bin/env python3 + +import argparse +import csv +import html +from pathlib import Path + + +def read_rows(path: Path): + with path.open(newline="") as source: + return list(csv.DictReader(source)) + + +def moving_average(values, window): + prefix = [0.0] + for value in values: + prefix.append(prefix[-1] + value) + return [ + (prefix[index + 1] - prefix[max(0, index + 1 - window)]) + / min(window, index + 1) + for index in range(len(values)) + ] + + +def scale(value, lower, upper, start, end): + if upper == lower: + return (start + end) / 2 + return start + (value - lower) * (end - start) / (upper - lower) + + +def panel( + svg, + x, + y, + width, + height, + title, + x_label, + y_label, + x_values, + series, + x_ticks, + y_ticks, +): + left, right, top, bottom = 58, 18, 34, 46 + plot_x, plot_y = x + left, y + top + plot_w, plot_h = width - left - right, height - top - bottom + all_y = [value for _, values, _ in series for value in values] + x_min, x_max = min(x_values), max(x_values) + y_min, y_max = min(all_y), max(all_y) + y_padding = max(0.05, (y_max - y_min) * 0.12) + y_min, y_max = y_min - y_padding, y_max + y_padding + + svg.append( + f'{html.escape(title)}' + ) + svg.append( + f'' + ) + for tick in y_ticks: + py = scale(tick, y_min, y_max, plot_y + plot_h, plot_y) + svg.append( + f'' + ) + svg.append( + f'{tick:g}' + ) + for tick in x_ticks: + px = scale(tick, x_min, x_max, plot_x, plot_x + plot_w) + svg.append( + f'{tick:g}' + ) + svg.append( + f'{html.escape(x_label)}' + ) + svg.append( + f'{html.escape(y_label)}' + ) + + for label, values, color in series: + points = [] + for xv, yv in zip(x_values, values): + px = scale(xv, x_min, x_max, plot_x, plot_x + plot_w) + py = scale(yv, y_min, y_max, plot_y + plot_h, plot_y) + points.append(f"{px:.2f},{py:.2f}") + svg.append( + f'' + ) + if len(values) <= 10: + for point in points: + px, py = point.split(",") + svg.append(f'') + legend_x = ( + plot_x + 8 + 100 * sum(1 for existing, _, _ in series if existing < label) + ) + svg.append( + f'' + ) + svg.append( + f'{html.escape(label)}' + ) + + +def main(): + parser = argparse.ArgumentParser( + description="Plot TinyInfiniTrain full-data experiment metrics" + ) + parser.add_argument("experiment_dir", type=Path) + parser.add_argument("output", type=Path) + args = parser.parse_args() + + steps = read_rows(args.experiment_dir / "steps.csv") + epochs = read_rows(args.experiment_dir / "epochs.csv") + global_steps = [int(row["global_step"]) for row in steps] + step_losses = [float(row["loss"]) for row in steps] + smoothed_losses = moving_average(step_losses, 200) + sampled_indices = list(range(0, len(global_steps), 10)) + if sampled_indices[-1] != len(global_steps) - 1: + sampled_indices.append(len(global_steps) - 1) + sampled_steps = [global_steps[index] for index in sampled_indices] + sampled_losses = [smoothed_losses[index] for index in sampled_indices] + completed_epochs = [int(row["epoch"]) + 1 for row in epochs] + train_losses = [float(row["train_mean_loss"]) for row in epochs] + validation_losses = [float(row["val_mean_loss"]) for row in epochs] + perplexities = [float(row["val_perplexity"]) for row in epochs] + + width, height = 1320, 410 + svg = [ + f'', + '', + '', + 'TinyInfiniTrain GPT-2 124M — A100 full-data run', + ] + panel( + svg, + 10, + 36, + 425, + 360, + "Step loss (200-step moving mean)", + "Global optimizer step", + "Cross-entropy loss", + sampled_steps, + [("Train", sampled_losses, "#2563eb")], + [1, 2500, 5000, 7500, 9540], + [1, 2, 3, 4], + ) + panel( + svg, + 447, + 36, + 425, + 360, + "Full-epoch mean loss", + "Completed epoch", + "Cross-entropy loss", + completed_epochs, + [ + ("Train", train_losses, "#2563eb"), + ("Validation", validation_losses, "#dc2626"), + ], + completed_epochs, + [1, 2, 3, 4, 5, 6], + ) + panel( + svg, + 884, + 36, + 425, + 360, + "Validation perplexity", + "Completed epoch", + "Perplexity", + completed_epochs, + [("PPL", perplexities, "#7c3aed")], + completed_epochs, + [50, 100, 150, 200, 250], + ) + best_x = scale( + completed_epochs[0], + min(completed_epochs), + max(completed_epochs), + 447 + 58, + 447 + 425 - 18, + ) + best_y = scale( + validation_losses[0], + min(train_losses + validation_losses) - 0.5, + max(train_losses + validation_losses) + 0.5, + 36 + 34 + 360 - 34 - 46, + 36 + 34, + ) + svg.append( + f'best val 3.925' + ) + svg.append("") + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text("\n".join(svg) + "\n") + + +if __name__ == "__main__": + main()