draft of Reader classes

paddle/framework/CMakeLists.txt

@@ -24,6 +24,8 @@ cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto)
 cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor paddle_memory)
 nv_test(lod_tensor_gpu_test SRCS lod_tensor_test.cu DEPS lod_tensor)
 
+cc_library(reader SRCS reader.cc DEPS lod_tensor ddim)
+
 cc_test(variable_test SRCS variable_test.cc)
 
 cc_library(threadpool SRCS threadpool.cc DEPS enforce)

paddle/framework/reader.cc

@@ -17,35 +17,100 @@
 namespace paddle {
 namespace framework {
 
-DDim Reader::shape(int idx) const {
+DDim Reader::shape(size_t idx) const {
   PADDLE_ENFORCE_LT(
       idx, shapes_.size(),
       "Cannot get the %d'th shape, 'shapes_' only has %d elements.", idx,
       shapes_.size());
+  return shapes_[idx];
 }
 
-int RandomReader::ReadNext(std::vector<LoDTensor>* outs) {
-  PADDLE_ENFORCE_EQ(
-      shapes_.size(), outs.size(),
-      "shapes_.size() is %d, while outs.size() is %d. They are not equal.",
-      shapes_.size(), outs.size());
-  std::minstd_rand engine;
-  unsigned int seed = std::random_device()();
-  engine.seed(seed);
-  std::uniform_real_distribution<float> dist(min_, max_);
-  for (int idx = 0; idx < shapes_.size(); ++idx) {
-    DDim shape = shapes_[idx];
-    LoDTensor* out = outs[idx];
-    int64_t numel = out->numel();
-    PADDLE_ENFORCE_EQ(product(shape), numel,
-                      "The product of %d'th shape is %lld, while the "
-                      "corresponding out's numel is %lld. They are not equal.",
-                      idx, product(shape), numel);
-    for (int64_t i = 0; i < numel, ++i) {
-      out[i] = dist(engine);
+std::vector<LoDTensor> ShuffleReader::ReadNext() {
+  if (iteration_pos_ >= buffer_.size()) {
+    // Reload buffer with new data
+    buffer_.clear();
+    for (int i = 0; i < buffer_size_; ++i) {
+      if (reader_->HasNext()) {
+        buffer_.push_back(reader_->ReadNext());
+      } else {
+        break;
+      }
     }
+    std::random_shuffle(buffer_.begin(), buffer_.end());
+    iteration_pos_ = 0;
   }
-  return 0;
+  if (buffer_.empty()) {
+    std::vector<LoDTensor> empty_res;
+    return empty_res;
+  }
+  return buffer_[iteration_pos_++];
+}
+
+std::vector<LoDTensor> BatchReader::ReadNext() {
+  buffer_.clear();
+  for (int i = 0; i < batch_size_; ++i) {
+    if (reader_->HasNext()) {
+      buffer_.push_back(reader_->ReadNext());
+    } else {
+      break;
+    }
+  }
+  // Concat instances
+  std::vector<LoDTensor> res;
+  if (buffer_.empty()) {
+    return res;
+  }
+  int out_num = buffer_[0].size();
+  res.reserve(out_num);
+  for (int j = 0; j < out_num; ++j) {
+    // Merge shape and check date type
+    std::type_index batch_type = buffer_[0][j].type();
+    DDim batch_shape = buffer_[0][j].dims();
+    for (size_t i = 1; i < buffer_.size(); ++i) {
+      std::type_index ins_type = buffer_[i][j].type();
+      DDim ins_shape = buffer_[i][j].dims();
+      PADDLE_ENFORCE_EQ(batch_type, ins_type);
+      PADDLE_ENFORCE_EQ(slice_ddim(batch_shape, 1, batch_shape.size()),
+                        slice_ddim(ins_shape, 1, ins_shape.size()));
+      PADDLE_ENFORCE_GT(ins_shape[0], 0);
+      batch_shape[0] += ins_shape[0];
+    }
+
+    LoDTensor out;
+    out.Resize(batch_shape);
+    out.mutable_data(platform::CPUPlace(), batch_type);
+    int64_t dst_offset = 0;
+
+    // Merge lod and data
+    LoD batch_lod;
+    std::vector<size_t> top_level_lod({0});
+    for (size_t i = 0; i < buffer_.size(); ++i) {
+      DDim ins_shape = buffer_[i][j].dims();
+      LoD ins_lod = buffer_[i][j].lod();
+      if (i == 0) {
+        batch_lod = ins_lod;
+      } else {
+        PADDLE_ENFORCE_EQ(batch_lod.size(), ins_lod.size());
+        for (size_t level_idx = 0; level_idx < batch_lod.size(); ++level_idx) {
+          auto& lod_level = batch_lod[level_idx];
+          for (size_t k = 1; k < ins_lod[level_idx].size(); ++k) {
+            lod_level.push_back(ins_lod[level_idx][k] + lod_level.back());
+          }
+        }
+      }
+      top_level_lod.push_back(
+          top_level_lod.back() +
+          (ins_lod.empty() ? ins_shape[0] : (ins_lod[0].size() - 1)));
+
+      Tensor dst = out.Slice(dst_offset, dst_offset + ins_shape[0]);
+      Copy(buffer_[i][j], platform::CPUPlace(), &dst);
+      dst_offset += ins_shape[0];
+    }
+    batch_lod.insert(batch_lod.begin(), top_level_lod);
+    out.set_lod(batch_lod);
+    res.push_back(out);
+  }
+  return res;
 }
 }  // namespace framework
 }  // namespace paddle

paddle/framework/reader.h

@@ -22,20 +22,61 @@ namespace framework {
 
 class Reader {
  public:
-  virtual int ReadNext(std::vector<LoDTensor>* outs) = 0;
-  DDim shape(int idx) const;
+  Reader() {}
+  explicit Reader(const std::vector<DDim>& shapes) : shapes_(shapes) {}
+
+  virtual std::vector<LoDTensor> ReadNext() = 0;
+  virtual bool HasNext() const = 0;
+
+  virtual DDim shape(size_t idx) const;
+  virtual std::vector<DDim> shapes() const { return shapes_; }
+
+  virtual ~Reader() {}
 
  private:
+  // set private to prevent directly access in decorators
+  // a decorator should access its underlying reader_'s shape, not its own.
   std::vector<DDim> shapes_;
 };
 
 // file readers
 
+template <typename T>
 class RandomReader : public Reader {
  public:
   RandomReader(const std::vector<DDim>& shapes, float min, float max)
-      : shapes_(shapes), min_(min), max_(max) {}
-  int ReadNext(std::vector<LoDTensor>* outs) override;
+      : Reader(shapes), min_(min), max_(max) {
+    PADDLE_ENFORCE_LE(min, max,
+                      "'min' should be less than or equal to 'max'.(%f vs %f)",
+                      min, max);
+  }
+
+  std::vector<LoDTensor> ReadNext() override {
+    std::minstd_rand engine;
+    unsigned int seed = std::random_device()();
+    engine.seed(seed);
+    std::uniform_real_distribution<float> dist(min_, max_);
+
+    std::vector<LoDTensor> res;
+    res.reserve(shapes().size());
+    for (const DDim& shape : shapes()) {
+      PADDLE_ENFORCE_GE(
+          shape.size(), 2,
+          "The rank of input data should be 2 at least.(Now it's %d)",
+          shape.size());
+      LoDTensor out;
+      out.Resize(shape);
+      T* data = out.mutable_data<T>(platform::CPUPlace());
+      int64_t numel = product(shape);
+      for (int64_t i = 0; i < numel; ++i) {
+        data[i] = dist(engine);
+      }
+      res.push_back(out);
+    }
+    return res;
+  }
+
+  bool HasNext() const override { return true; }
 
  private:
   float min_;
@@ -44,22 +85,40 @@ class RandomReader : public Reader {
 
 // decorators
 
-class BatchReader : public Reader {
+class ShuffleReader : public Reader {
  public:
-  BatchReader(const Reader* reader) : reader_(reader) {}
-  int ReadNext(std::vector<LoDTensor>* outs) override;
+  ShuffleReader(Reader* reader, int buffer_size)
+      : reader_(reader), buffer_size_(buffer_size), iteration_pos_(0) {
+    buffer_.reserve(buffer_size);
+  }
+  std::vector<LoDTensor> ReadNext() override;
+  bool HasNext() const override { return reader_->HasNext(); }
+
+  DDim shape(size_t idx) const override { return reader_->shape(idx); }
+  std::vector<DDim> shapes() const override { return reader_->shapes(); }
 
  private:
-  const Reader* reader_;
+  Reader* reader_;
+  int buffer_size_;
+  std::vector<std::vector<LoDTensor>> buffer_;
+  size_t iteration_pos_;
 };
 
-class ShuffleReader : public Reader {
+class BatchReader : public Reader {
  public:
-  ShuffleReader(const Reader* reader) : reader_(reader) {}
-  int ReadNext(std::vector<LoDTensor>* outs) override;
+  BatchReader(Reader* reader, int batch_size)
+      : reader_(reader), batch_size_(batch_size) {}
+  std::vector<LoDTensor> ReadNext() override;
+  bool HasNext() const override { return reader_->HasNext(); };
+
+  DDim shape(size_t idx) const override { return reader_->shape(idx); }
+  std::vector<DDim> shapes() const override { return reader_->shapes(); }
 
  private:
-  const Reader* reader_;
+  Reader* reader_;
+  int batch_size_;
+  std::vector<std::vector<LoDTensor>> buffer_;
 };
+
 }  // namespace framework
 }  // namespace paddle