fix(dataloader): track distributed progress by global batch

example/gpt2/main.cc

@@ -372,15 +372,19 @@ void Train(const nn::parallel::Rank &rank) {
     start_step = resume_result.global_step;
     size_t consumed_batches = resume_result.consumed_batches;
 
-    // TODO(jym): Replace with Sampler abstraction when available.
-    // Skip dataloader to resume from the correct batch position.
-    if (consumed_batches > 0) {
-        size_t start = train_iter.BatchIndex();
-        // Each rank processes every ddp_world_size-th batch starting from its own rank.
-        // num_skips calculates how many ++ iterations to reach the saved batch position.
-        size_t num_skips = (consumed_batches - start) / ddp_world_size;
-        for (size_t i = 0; i < num_skips; ++i) { ++train_iter; }
-    }
+    // consumed_batches is the number of global dataloader batches consumed across cyclic epochs.
+    train_iter.SeekGlobalBatch(consumed_batches % train_loader.NumGlobalBatches());
+    auto next_train_batch = [&]() {
+        auto batch = *train_iter;
+        // if we are trying to overfit a single batch, we reset the loader here by commenting out the line below
+        // TODO(dcj): support dataloader.reset() later
+        ++train_iter;
+        if (train_iter == train_loader.end()) {
+            train_iter = train_loader.begin();
+        }
+        ++consumed_batches;
+        return batch;
+    };
 
     auto save_checkpoint = [&](const std::filesystem::path &save_dir, int64_t global_step) {
         SaveCheckpoint({
@@ -454,11 +458,7 @@ void Train(const nn::parallel::Rank &rank) {
                 infini_train::AutocastGuard autocast_guard(device.type(), dtype);
 
                 // (bs, seq_len), (bs, seq_len)
-                auto [x, y] = *train_iter;
-                // if we are trying to overfit a single batch, we reset the loader here by commenting out the line below
-                // TODO(dcj): support dataloader.reset() later
-                ++train_iter;
-                consumed_batches = train_iter.BatchIndex();
+                auto [x, y] = next_train_batch();
                 x = std::make_shared<Tensor>(x->To(device));
                 y = std::make_shared<Tensor>(y->To(device));
 
@@ -488,11 +488,7 @@ void Train(const nn::parallel::Rank &rank) {
                 scheduler->Step();
             }
         } else {
-            auto [x, y] = *train_iter;
-            // if we are trying to overfit a single batch, we reset the loader here by commenting out the line below
-            // TODO(dcj): support dataloader.reset() later
-            ++train_iter;
-            consumed_batches = train_iter.BatchIndex();
+            auto [x, y] = next_train_batch();
             x = std::make_shared<Tensor>(x->To(device));
             y = std::make_shared<Tensor>(y->To(device));
 

example/llama3/main.cc

@@ -354,15 +354,19 @@ void Train(const nn::parallel::Rank &rank) {
     start_step = resume_result.global_step;
     size_t consumed_batches = resume_result.consumed_batches;
 
-    // TODO(jym): Replace with Sampler abstraction when available.
-    // Skip dataloader to resume from the correct batch position.
-    if (consumed_batches > 0) {
-        size_t start = train_iter.BatchIndex();
-        // Each rank processes every ddp_world_size-th batch starting from its own rank.
-        // num_skips calculates how many ++ iterations to reach the saved batch position.
-        size_t num_skips = (consumed_batches - start) / ddp_world_size;
-        for (size_t i = 0; i < num_skips; ++i) { ++train_iter; }
-    }
+    // consumed_batches is the number of global dataloader batches consumed across cyclic epochs.
+    train_iter.SeekGlobalBatch(consumed_batches % train_loader.NumGlobalBatches());
+    auto next_train_batch = [&]() {
+        auto batch = *train_iter;
+        // if we are trying to overfit a single batch, we reset the loader here by commenting out the line below
+        // TODO(dcj): support dataloader.reset() later
+        ++train_iter;
+        if (train_iter == train_loader.end()) {
+            train_iter = train_loader.begin();
+        }
+        ++consumed_batches;
+        return batch;
+    };
 
     auto save_checkpoint = [&](const std::filesystem::path &save_dir, int64_t global_step) {
         SaveCheckpoint({
@@ -434,11 +438,7 @@ void Train(const nn::parallel::Rank &rank) {
                 infini_train::AutocastGuard autocast_guard(device.type(), dtype);
 
                 // (bs, seq_len), (bs, seq_len)
-                auto [x, y] = *train_iter;
-                // if we are trying to overfit a single batch, we reset the loader here by commenting out the line below
-                // TODO(dcj): support dataloader.reset() later
-                ++train_iter;
-                consumed_batches = train_iter.BatchIndex();
+                auto [x, y] = next_train_batch();
                 x = std::make_shared<Tensor>(x->To(device));
                 y = std::make_shared<Tensor>(y->To(device));
 
@@ -467,11 +467,7 @@ void Train(const nn::parallel::Rank &rank) {
                 scheduler->Step();
             }
         } else {
-            auto [x, y] = *train_iter;
-            // if we are trying to overfit a single batch, we reset the loader here by commenting out the line below
-            // TODO(dcj): support dataloader.reset() later
-            ++train_iter;
-            consumed_batches = train_iter.BatchIndex();
+            auto [x, y] = next_train_batch();
             x = std::make_shared<Tensor>(x->To(device));
             y = std::make_shared<Tensor>(y->To(device));
 

infini_train/include/dataloader.h

@@ -12,7 +12,7 @@ class Tensor;
 namespace infini_train {
 class DataLoaderIterator {
 public:
-    DataLoaderIterator(const Dataset &dataset, size_t batch_size, size_t batch_idx, size_t max_batch_idx,
+    DataLoaderIterator(const Dataset &dataset, size_t batch_size, size_t global_batch_idx, size_t num_global_batches,
                        size_t ddp_rank = 0, size_t ddp_world_size = 1);
 
     std::pair<std::shared_ptr<Tensor>, std::shared_ptr<Tensor>> operator*() const;
@@ -24,13 +24,14 @@ class DataLoaderIterator {
     friend bool operator!=(const DataLoaderIterator &lhs, const DataLoaderIterator &rhs);
     friend bool operator==(const DataLoaderIterator &lhs, const DataLoaderIterator &rhs);
 
-    size_t BatchIndex() const;
+    size_t GlobalBatchIndex() const;
+    DataLoaderIterator &SeekGlobalBatch(size_t global_batch_idx);
 
 private:
     const Dataset *dataset_ = nullptr; // not owned
     size_t batch_size_ = 0;
-    size_t batch_idx_ = 0;
-    size_t max_batch_idx_ = 0;
+    size_t global_batch_idx_ = 0;
+    size_t num_global_batches_ = 0;
     size_t ddp_rank_ = 0;
     size_t ddp_world_size_ = 1;
 };
@@ -42,10 +43,12 @@ class DataLoader {
     virtual DataLoaderIterator begin() const;
     virtual DataLoaderIterator end() const;
 
+    size_t NumGlobalBatches() const;
+
 protected:
     std::shared_ptr<Dataset> dataset_;
     size_t batch_size_ = 0;
-    size_t max_batch_idx_ = 0;
+    size_t num_global_batches_ = 0;
 };
 
 class DistributedDataLoader : public DataLoader {

infini_train/src/dataloader.cc

@@ -3,6 +3,7 @@
 #include <algorithm>
 #include <cstddef>
 #include <cstring>
+#include <functional>
 #include <numeric>
 #include <utility>
 
@@ -13,8 +14,14 @@
 
 namespace infini_train {
 namespace {
+size_t CheckedCeilDiv(size_t numerator, size_t denominator) {
+    CHECK_GT(denominator, 0);
+    return (numerator + denominator - 1) / denominator;
+}
+
 // TODO(dcj): Use official stack implementation later.
 std::shared_ptr<Tensor> Stack(const std::vector<std::shared_ptr<Tensor>> &tensors) {
+    CHECK(!tensors.empty()) << "Cannot stack an empty batch. Check DataLoader iterator end handling.";
     const int batch_size = tensors.size();
     const auto &dims = tensors[0]->Dims();
     const int stacked_dim = std::accumulate(dims.begin(), dims.end(), 1, std::multiplies<int64_t>());
@@ -35,21 +42,31 @@ std::shared_ptr<Tensor> Stack(const std::vector<std::shared_ptr<Tensor>> &tensor
 }
 } // namespace
 
-DataLoaderIterator::DataLoaderIterator(const Dataset &dataset, size_t batch_size, size_t batch_idx,
-                                       size_t max_batch_idx, size_t ddp_rank, size_t ddp_world_size)
-    : dataset_(&dataset), batch_size_(batch_size), batch_idx_(batch_idx), max_batch_idx_(max_batch_idx),
-      ddp_rank_(ddp_rank), ddp_world_size_(ddp_world_size){};
+DataLoaderIterator::DataLoaderIterator(const Dataset &dataset, size_t batch_size, size_t global_batch_idx,
+                                       size_t num_global_batches, size_t ddp_rank, size_t ddp_world_size)
+    : dataset_(&dataset), batch_size_(batch_size), global_batch_idx_(global_batch_idx),
+      num_global_batches_(num_global_batches), ddp_rank_(ddp_rank), ddp_world_size_(ddp_world_size){};
 
 std::pair<std::shared_ptr<Tensor>, std::shared_ptr<Tensor>> DataLoaderIterator::operator*() const {
     /*
       0,         1,            ..., x,                  ...
       [0, bs-1], [bs, 2*bs-1], ..., [x*bs, (x+1)*bs-1], ...
                                     ^
-                                    batch_idx
+                                    global_batch_idx
     */
     std::vector<std::shared_ptr<Tensor>> data_vec;
     std::vector<std::shared_ptr<Tensor>> label_vec;
-    for (int idx = batch_idx_ * batch_size_; idx < (batch_idx_ + 1) * batch_size_ && idx < dataset_->Size(); ++idx) {
+    CHECK_LT(global_batch_idx_, num_global_batches_)
+        << "Cannot dereference DataLoader end iterator. global_batch_idx=" << global_batch_idx_
+        << ", num_global_batches=" << num_global_batches_ << ", ddp_rank=" << ddp_rank_
+        << ", ddp_world_size=" << ddp_world_size_;
+    const size_t start_idx = (global_batch_idx_ * ddp_world_size_ + ddp_rank_) * batch_size_;
+    CHECK_LT(start_idx, dataset_->Size())
+        << "DataLoader batch starts past dataset end. global_batch_idx=" << global_batch_idx_
+        << ", start_idx=" << start_idx << ", dataset_size=" << dataset_->Size() << ", batch_size=" << batch_size_
+        << ", ddp_rank=" << ddp_rank_ << ", ddp_world_size=" << ddp_world_size_;
+    const size_t end_idx = std::min(start_idx + batch_size_, dataset_->Size());
+    for (size_t idx = start_idx; idx < end_idx; ++idx) {
         auto &&[data, label] = dataset_->operator[](idx);
         data_vec.push_back(std::move(data));
         label_vec.push_back(std::move(label));
@@ -58,7 +75,7 @@ std::pair<std::shared_ptr<Tensor>, std::shared_ptr<Tensor>> DataLoaderIterator::
 }
 
 DataLoaderIterator &DataLoaderIterator::operator++() {
-    batch_idx_ = std::min(batch_idx_ + ddp_world_size_, max_batch_idx_);
+    global_batch_idx_ = std::min(global_batch_idx_ + 1, num_global_batches_);
     return *this;
 }
 
@@ -68,36 +85,60 @@ DataLoaderIterator DataLoaderIterator::operator++(int) {
     return tmp;
 }
 
-bool operator<(const DataLoaderIterator &lhs, const DataLoaderIterator &rhs) { return lhs.batch_idx_ < rhs.batch_idx_; }
+bool operator<(const DataLoaderIterator &lhs, const DataLoaderIterator &rhs) {
+    return lhs.global_batch_idx_ < rhs.global_batch_idx_;
+}
 
 bool operator!=(const DataLoaderIterator &lhs, const DataLoaderIterator &rhs) {
-    return lhs.batch_idx_ != rhs.batch_idx_;
+    return lhs.global_batch_idx_ != rhs.global_batch_idx_;
 }
 
 bool operator==(const DataLoaderIterator &lhs, const DataLoaderIterator &rhs) {
-    return lhs.batch_idx_ == rhs.batch_idx_;
+    return lhs.global_batch_idx_ == rhs.global_batch_idx_;
 }
 
-size_t DataLoaderIterator::BatchIndex() const { return batch_idx_; }
+size_t DataLoaderIterator::GlobalBatchIndex() const { return global_batch_idx_; }
+
+DataLoaderIterator &DataLoaderIterator::SeekGlobalBatch(size_t global_batch_idx) {
+    CHECK_LE(global_batch_idx, num_global_batches_)
+        << "Cannot seek past DataLoader end. global_batch_idx=" << global_batch_idx
+        << ", num_global_batches=" << num_global_batches_;
+    global_batch_idx_ = global_batch_idx;
+    return *this;
+}
 
 DataLoader::DataLoader(const std::shared_ptr<Dataset> &dataset, size_t batch_size)
-    : dataset_(dataset), batch_size_(batch_size), max_batch_idx_((dataset_->Size() + batch_size_ - 1) / batch_size_) {}
+    : dataset_(dataset), batch_size_(batch_size), num_global_batches_(CheckedCeilDiv(dataset_->Size(), batch_size_)) {}
 
-DataLoaderIterator DataLoader::begin() const { return DataLoaderIterator(*dataset_, batch_size_, 0, max_batch_idx_); }
+DataLoaderIterator DataLoader::begin() const {
+    return DataLoaderIterator(*dataset_, batch_size_, 0, num_global_batches_);
+}
 
 DataLoaderIterator DataLoader::end() const {
-    return DataLoaderIterator(*dataset_, batch_size_, max_batch_idx_, max_batch_idx_);
+    return DataLoaderIterator(*dataset_, batch_size_, num_global_batches_, num_global_batches_);
 }
 
+size_t DataLoader::NumGlobalBatches() const { return num_global_batches_; }
+
 DistributedDataLoader::DistributedDataLoader(const std::shared_ptr<Dataset> &dataset, size_t batch_size,
                                              size_t ddp_rank, size_t ddp_world_size)
-    : DataLoader(dataset, batch_size), ddp_rank_(ddp_rank), ddp_world_size_(ddp_world_size) {}
+    : DataLoader(dataset, batch_size), ddp_rank_(ddp_rank), ddp_world_size_(ddp_world_size) {
+    CHECK_GT(ddp_world_size_, 0);
+    CHECK_LT(ddp_rank_, ddp_world_size_);
+    const size_t global_batch_size = ddp_world_size_ * batch_size_;
+    CHECK_GE(dataset_->Size(), global_batch_size)
+        << "DistributedDataLoader needs at least one full global batch. dataset_size=" << dataset_->Size()
+        << ", global_batch_size=" << global_batch_size << " (" << batch_size_ << " per rank * " << ddp_world_size_
+        << " ranks). Reduce batch size/world size or use a larger dataset.";
+    num_global_batches_ = dataset_->Size() / global_batch_size;
+}
 
 DataLoaderIterator DistributedDataLoader::begin() const {
-    return DataLoaderIterator(*dataset_, batch_size_, ddp_rank_, max_batch_idx_, ddp_rank_, ddp_world_size_);
+    return DataLoaderIterator(*dataset_, batch_size_, 0, num_global_batches_, ddp_rank_, ddp_world_size_);
 }
 
 DataLoaderIterator DistributedDataLoader::end() const {
-    return DataLoaderIterator(*dataset_, batch_size_, max_batch_idx_, max_batch_idx_, ddp_rank_, ddp_world_size_);
+    return DataLoaderIterator(*dataset_, batch_size_, num_global_batches_, num_global_batches_, ddp_rank_,
+                              ddp_world_size_);
 }
 } // namespace infini_train

tests/CMakeLists.txt

@@ -7,6 +7,9 @@ include(${CMAKE_SOURCE_DIR}/cmake/test_macros.cmake)
 # Common test utilities
 add_subdirectory(common)
 
+# DataLoader tests
+add_subdirectory(dataloader)
+
 # Tensor tests
 add_subdirectory(tensor)
 

tests/dataloader/CMakeLists.txt

@@ -0,0 +1,8 @@
+# ==========================================================================
+# DataLoader tests
+# ==========================================================================
+
+infini_train_add_test(test_dataloader
+  SOURCES test_dataloader.cc
+  LABELS cpu
+)