feat: init ac

Author: Chamberlain0w0

example/gpt2/checkpoint_loader.cc

@@ -52,11 +52,22 @@ std::tuple<int32_t, infini_train::DataType> DetermineAndCheckVersion(const std::
         return {}; // Unreachable, but keeps compiler happy
     }
 }
+
+void ApplyRuntimeRecomputeConfig(nn::TransformerConfig *config, const nn::TransformerConfig &runtime_config) {
+    config->recompute_granularity = runtime_config.recompute_granularity;
+    config->recompute_method = runtime_config.recompute_method;
+    config->recompute_num_layers = runtime_config.recompute_num_layers;
+}
 } // namespace
 
 namespace gpt2 {
 
 std::shared_ptr<nn::TransformerModel> LoadFromLLMC(const std::string &filepath) {
+    return LoadFromLLMC(filepath, gpt2::GPT2Config());
+}
+
+std::shared_ptr<nn::TransformerModel> LoadFromLLMC(const std::string &filepath,
+                                                   const nn::TransformerConfig &runtime_config) {
     if (!std::filesystem::exists(filepath)) {
         LOG(FATAL) << "File not found: " << filepath;
     }
@@ -87,6 +98,7 @@ std::shared_ptr<nn::TransformerModel> LoadFromLLMC(const std::string &filepath)
     gpt2_config.n_layer = n_layer;
     gpt2_config.n_head = n_head;
     gpt2_config.n_embd = n_embd;
+    ApplyRuntimeRecomputeConfig(&gpt2_config, runtime_config);
     auto local_gpt2 = std::make_shared<nn::TransformerModel>(gpt2_config);
 
     LOG(INFO) << "magic: " << magic << " version: " << version << " block_size: " << block_size

example/gpt2/checkpoint_loader.h

@@ -3,10 +3,14 @@
 #include <memory>
 #include <string>
 
+#include "infini_train/include/nn/modules/transformer/transformer_config.h"
+
 namespace infini_train::nn {
 class TransformerModel;
 } // namespace infini_train::nn
 
 namespace gpt2 {
 std::shared_ptr<infini_train::nn::TransformerModel> LoadFromLLMC(const std::string &filepath);
+std::shared_ptr<infini_train::nn::TransformerModel>
+LoadFromLLMC(const std::string &filepath, const infini_train::nn::TransformerConfig &runtime_config);
 } // namespace gpt2

example/gpt2/main.cc

@@ -75,6 +75,12 @@ DEFINE_bool(sequence_parallel, false, "Whether to enable Sequence Parallel");
 DEFINE_uint32(pipeline_parallel, 1, "Pipeline Parallel world size, specified the number of PP stages.");
 DEFINE_uint32(virtual_pipeline_parallel, 1, "Number of chunks in PP stage.");
 
+// activation recompute
+DEFINE_bool(activation_recompute, false, "Enable activation recompute to trade compute for memory.");
+DEFINE_string(recompute_granularity, "full", "Activation recompute granularity: none|full|selective");
+DEFINE_string(recompute_method, "none", "Activation recompute method: none|uniform|block");
+DEFINE_uint32(recompute_num_layers, 0, "Number of transformer layers per recompute region for uniform/block methods.");
+
 // precision
 DEFINE_string(dtype, "float32", "precision used in training (float32/bfloat16)");
 // precision check
@@ -186,21 +192,24 @@ void Train(const nn::parallel::Rank &rank) {
     nn::TransformerConfig model_config = gpt2::GPT2Config();
     std::shared_ptr<nn::Module> model = nullptr;
 
-    if (!FLAGS_llmc_filepath.empty()) {
-        model = gpt2::LoadFromLLMC(FLAGS_llmc_filepath);
-    } else if (kModelToConfigs.count(FLAGS_model)) {
+    if (FLAGS_llmc_filepath.empty() && kModelToConfigs.count(FLAGS_model)) {
         model_config = kModelToConfigs.at(FLAGS_model);
+    }
+    nn::SetActivationRecomputeConfig(&model_config, FLAGS_activation_recompute, FLAGS_recompute_granularity,
+                                     FLAGS_recompute_method, static_cast<int64_t>(FLAGS_recompute_num_layers));
+
+    if (!FLAGS_llmc_filepath.empty()) {
+        model = gpt2::LoadFromLLMC(FLAGS_llmc_filepath, model_config);
+    } else {
         model = std::make_shared<nn::TransformerModel>(model_config);
     }
 
+    CHECK(model) << "GPT2 example expects GPT2 model.";
+
     model->To(device);
 
     utils::PrecisionChecker::BuildNameMap(model.get());
 
-    // Get chunk size before wrapping with LoRA (needed for PipelineParallel)
-    auto gpt2_model = std::dynamic_pointer_cast<nn::TransformerModel>(model);
-    CHECK(gpt2_model) << "GPT2 example expects GPT2 model.";
-
     // Apply LoRA using GetLoRAModel (in-place injection)
     bool lora_enabled = FLAGS_lora_rank > 0;
     if (lora_enabled) {

example/llama3/checkpoint_loader.cc

@@ -35,11 +35,22 @@ static std::mt19937 gen{kRandomSeed};
 namespace {
 constexpr int32_t kLLaMA3Magic = 20240803;
 constexpr int32_t kLLaMA3FP32Version = 3;
+
+void ApplyRuntimeRecomputeConfig(nn::TransformerConfig *config, const nn::TransformerConfig &runtime_config) {
+    config->recompute_granularity = runtime_config.recompute_granularity;
+    config->recompute_method = runtime_config.recompute_method;
+    config->recompute_num_layers = runtime_config.recompute_num_layers;
+}
 } // namespace
 
 namespace llama3 {
 
 std::shared_ptr<nn::TransformerModel> LoadFromLLMC(const std::string &filepath) {
+    return LoadFromLLMC(filepath, llama3::LLaMA3Config());
+}
+
+std::shared_ptr<nn::TransformerModel> LoadFromLLMC(const std::string &filepath,
+                                                   const nn::TransformerConfig &runtime_config) {
     if (!std::filesystem::exists(filepath)) {
         LOG(FATAL) << "File not found: " << filepath;
     }
@@ -80,6 +91,7 @@ std::shared_ptr<nn::TransformerModel> LoadFromLLMC(const std::string &filepath)
     llama3_config.use_scaled_rope = static_cast<bool>(use_scaled_rope);
     llama3_config.norm_eps = norm_eps;
     llama3_config.max_gen_batch_size = max_gen_bs;
+    ApplyRuntimeRecomputeConfig(&llama3_config, runtime_config);
     auto llama3 = std::make_shared<nn::TransformerModel>(llama3_config);
 
     // ========== pp_size：num_stages; vpp_size: num_chunks_per_stage ==========

example/llama3/checkpoint_loader.h

@@ -3,10 +3,14 @@
 #include <memory>
 #include <string>
 
+#include "infini_train/include/nn/modules/transformer/transformer_config.h"
+
 namespace infini_train::nn {
 class TransformerModel;
 } // namespace infini_train::nn
 
 namespace llama3 {
 std::shared_ptr<infini_train::nn::TransformerModel> LoadFromLLMC(const std::string &filepath);
+std::shared_ptr<infini_train::nn::TransformerModel>
+LoadFromLLMC(const std::string &filepath, const infini_train::nn::TransformerConfig &runtime_config);
 } // namespace llama3

example/llama3/main.cc

@@ -73,6 +73,12 @@ DEFINE_uint32(tensor_parallel, 1, "Tensor Parallel world size");
 DEFINE_bool(sequence_parallel, false, "Whether to enable Sequence Parallel");
 DEFINE_uint32(pipeline_parallel, 1, "Pipeline Parallel world size, specified the number of PP stages.");
 DEFINE_uint32(virtual_pipeline_parallel, 1, "Number of chunks in PP stage.");
+
+// activation recompute
+DEFINE_bool(activation_recompute, false, "Enable activation recompute to trade compute for memory.");
+DEFINE_string(recompute_granularity, "full", "Activation recompute granularity: none|full|selective");
+DEFINE_string(recompute_method, "none", "Activation recompute method: none|uniform|block");
+DEFINE_uint32(recompute_num_layers, 0, "Number of transformer layers per recompute region for uniform/block methods.");
 // precision
 DEFINE_string(dtype, "float32", "precision used in training (float32/bfloat16)");
 // precision check
@@ -171,12 +177,16 @@ void Train(const nn::parallel::Rank &rank) {
 
     nn::TransformerConfig model_config = llama3::LLaMA3Config();
     std::shared_ptr<nn::Module> model = nullptr;
+    nn::SetActivationRecomputeConfig(&model_config, FLAGS_activation_recompute, FLAGS_recompute_granularity,
+                                     FLAGS_recompute_method, static_cast<int64_t>(FLAGS_recompute_num_layers));
     if (!FLAGS_llmc_filepath.empty()) {
-        model = llama3::LoadFromLLMC(FLAGS_llmc_filepath);
+        model = llama3::LoadFromLLMC(FLAGS_llmc_filepath, model_config);
     } else {
         model = std::make_shared<nn::TransformerModel>(model_config);
     }
 
+    CHECK(model) << "LLaMA3 example expects LLaMA3 model.";
+
     model->To(device);
 
     utils::PrecisionChecker::BuildNameMap(model.get());
@@ -357,12 +367,20 @@ void Train(const nn::parallel::Rank &rank) {
                 autocast_guard.Disable();
 
                 LOG(INFO) << "Rank " << rank.GlobalRank() << ": finish loss forward";
+                auto [forward_used_mb, forward_reserved_mb] = impl->GetMemPoolPeakMB(device);
+                LOG(INFO) << std::format(
+                    "Rank {}: after forward (micro_step {}/{}), peak used: {:5d} MB | peak reserved: {:5d} MB",
+                    rank.GlobalRank(), micro_step + 1, grad_accum_steps, forward_used_mb, forward_reserved_mb);
 
                 auto loss_cpu = loss->To(Device());
                 lossf += static_cast<const float *>(loss_cpu.DataPtr())[0];
                 LOG(INFO) << "Rank " << rank.GlobalRank() << ": start backward";
                 loss->Backward();
                 LOG(INFO) << "Rank " << rank.GlobalRank() << ": finish backward";
+                auto [backward_used_mb, backward_reserved_mb] = impl->GetMemPoolPeakMB(device);
+                LOG(INFO) << std::format(
+                    "Rank {}: after backward (micro_step {}/{}), peak used: {:5d} MB | peak reserved: {:5d} MB",
+                    rank.GlobalRank(), micro_step + 1, grad_accum_steps, backward_used_mb, backward_reserved_mb);
             }
 
             optimizer->Step();

example/mnist/main.cc

@@ -66,7 +66,7 @@ int main(int argc, char *argv[]) {
             auto new_image = std::make_shared<Tensor>(image->To(device));
             auto new_label = std::make_shared<Tensor>(label->To(device));
 
-            auto outputs = network.Forward({new_image});
+            auto outputs = network({new_image});
             optimizer.ZeroGrad();
 
             auto loss = loss_fn.Forward({outputs[0], new_label});
@@ -101,7 +101,7 @@ int main(int argc, char *argv[]) {
         auto new_label = std::make_shared<Tensor>(label->To(device));
 
         auto label_cpu = label->To(cpu_device);
-        auto outputs = network.Forward({new_image});
+        auto outputs = network({new_image});
         auto output_cpu = outputs[0]->To(cpu_device);
         auto loss = loss_fn.Forward({outputs[0], new_label});
         auto loss_cpu = loss[0]->To(cpu_device);

infini_train/include/autocast.h

@@ -164,6 +164,24 @@ struct AutocastContext {
 // Global thread-local storage for autocast context
 inline thread_local AutocastContext tls_autocast_context;
 
+// Lightweight snapshot for autocast state
+struct AutocastState {
+    bool enabled = false;
+    Device::DeviceType device_type = Device::DeviceType::kCPU;
+    DataType autocast_dtype = DataType::kBFLOAT16;
+};
+
+inline AutocastState GetAutocastState() {
+    return AutocastState{tls_autocast_context.enabled, tls_autocast_context.device_type,
+                         tls_autocast_context.autocast_dtype};
+}
+
+inline void SetAutocastState(const AutocastState &state) {
+    tls_autocast_context.enabled = state.enabled;
+    tls_autocast_context.device_type = state.device_type;
+    tls_autocast_context.autocast_dtype = state.autocast_dtype;
+}
+
 // RAII guard to enable/disable autocast in a scope
 class AutocastGuard {
 public:

infini_train/include/autograd/function.h

@@ -2,6 +2,7 @@
 
 #include <functional>
 #include <memory>
+#include <optional>
 #include <utility>
 #include <vector>
 
@@ -21,6 +22,15 @@ class Function : public std::enable_shared_from_this<Function> {
     using FunctionPostHook = std::function<void(Function *, const std::vector<std::shared_ptr<Tensor>> &,
                                                 const std::vector<std::shared_ptr<Tensor>> &)>;
 
+    // Definition of hooks for saved_tensors, in alignment with torch.autograd.graph.saved_tensors_hooks
+    using SavedTensorPackHook = std::function<std::shared_ptr<void>(const std::shared_ptr<Tensor> &)>;
+    using SavedTensorUnpackHook = std::function<std::shared_ptr<Tensor>(const std::shared_ptr<void> &)>;
+
+    struct SavedTensorHooks {
+        SavedTensorPackHook pack;
+        SavedTensorUnpackHook unpack;
+    };
+
     static constexpr char kUndefinedType[] = "Undefined";
 
     Function() : type_(kUndefinedType) {}
@@ -34,7 +44,7 @@ class Function : public std::enable_shared_from_this<Function> {
     virtual std::vector<std::shared_ptr<Tensor>> Backward(const std::vector<std::shared_ptr<Tensor>> &grad_outputs) = 0;
 
     std::vector<std::shared_ptr<Tensor>> Apply(const std::vector<std::shared_ptr<Tensor>> &input_tensors);
-    virtual void BackwardPartial(const std::shared_ptr<Tensor> &grad_output, int idx);
+    virtual void BackwardPartial(std::shared_ptr<Tensor> grad_output, int idx);
 
     void IncreaseDependenciesNumber();
 
@@ -45,8 +55,33 @@ class Function : public std::enable_shared_from_this<Function> {
 
     const std::string &type() const { return type_; }
 
+    void SaveForBackward(const std::vector<std::shared_ptr<Tensor>> &tensors);
+    size_t SavedTensorsSize() const { return saved_tensors_.size(); }
+    std::shared_ptr<Tensor> GetSavedTensor(size_t index) const;
+    std::vector<std::shared_ptr<Tensor>> GetSavedTensors() const;
+
+    // RAII: Register pack/unpack hooks for saved_tensors, align with torch.autograd.graph.saved_tensors_hooks
+    class SavedTensorHooksGuard {
+    public:
+        explicit SavedTensorHooksGuard(SavedTensorHooks hooks);
+        ~SavedTensorHooksGuard();
+
+        SavedTensorHooksGuard(const SavedTensorHooksGuard &) = delete;
+        SavedTensorHooksGuard &operator=(const SavedTensorHooksGuard &) = delete;
+
+    private:
+        size_t depth_ = 0;
+    };
+
 protected:
-    std::vector<std::shared_ptr<Tensor>> saved_tensors_;
+    struct SavedTensorEntry {
+        // Tensor itself, used under default or reentrant version of recomputation
+        std::shared_ptr<Tensor> tensor;
+        // Function to recompute the target tensor, used under non-reentrant version of recomputation
+        std::shared_ptr<void> hook_state;
+        SavedTensorUnpackHook unpack;
+    };
+    std::vector<SavedTensorEntry> saved_tensors_;
     std::vector<bool> needs_input_grad_;
 
 private:

infini_train/include/autograd/grad_mode.h

@@ -8,10 +8,13 @@ class GradMode {
     // Whether to enable Autograd (enabled by default)
     static bool IsEnabled() { return grad_enabled_; }
     static void SetEnabled(bool enabled) { grad_enabled_ = enabled; }
+    static bool PropagateRequiresGrad() { return propagate_requires_grad_; }
+    static void SetPropagateRequiresGrad(bool enabled) { propagate_requires_grad_ = enabled; }
 
 private:
     // grad mode should be thread_local
     static thread_local bool grad_enabled_;
+    static thread_local bool propagate_requires_grad_;
 };
 
 // RAII: Disable grad (align with torch.no_grad)
@@ -34,4 +37,19 @@ class EnableGradGuard {
     bool prev_;
 };
 
+// RAII: Propagate requires_grad metadata while graph construction is disabled.
+// Used by non-reentrant checkpoint recomputation so downstream SetupContext
+// calls see the same needs_input_grad_ pattern as the original forward,
+// without wiring the recompute graph into the engine.
+class PropagateRequiresGradGuard {
+public:
+    PropagateRequiresGradGuard() : prev_(GradMode::PropagateRequiresGrad()) {
+        GradMode::SetPropagateRequiresGrad(true);
+    }
+    ~PropagateRequiresGradGuard() { GradMode::SetPropagateRequiresGrad(prev_); }
+
+private:
+    bool prev_;
+};
+
 } // namespace infini_train::autograd