Support numpy dense

cmake/flags.cmake

@@ -64,7 +64,9 @@ set(COMMON_FLAGS
     -Wdelete-non-virtual-dtor
     -Wno-unused-parameter
     -Wno-error=literal-suffix
-    -Wno-error=unused-local-typedefs)
+    -Wno-error=unused-local-typedefs
+    -Wno-error=unused-function  # Warnings in Numpy Header.
+)
 
 foreach(flag ${COMMON_FLAGS})
     safe_set_cflag(CMAKE_C_FLAGS ${flag})

demo/mnist/data/get_mnist_data.sh

@@ -1,6 +1,6 @@
 #!/usr/bin/env sh
 # This scripts downloads the mnist data and unzips it.
-
+set -e
 DIR="$( cd "$(dirname "$0")" ; pwd -P )"
 rm -rf "$DIR/raw_data"
 mkdir "$DIR/raw_data"

paddle/gserver/dataproviders/DataProvider.cpp

@@ -57,7 +57,8 @@ void BufferBatch::clone(DataBatch* srcBatch, bool useGpu) {
   }
 }
 
-DoubleBuffer::DoubleBuffer(DataProvider* dataPool, bool useGpu,
+DoubleBuffer::DoubleBuffer(DataProvider *dataPool,
+                           bool useGpu,
                            int64_t batchSize) {
   batchSize_ = batchSize;
   dataPool_ = dataPool;
@@ -110,6 +111,9 @@ void DoubleBuffer::removeOneBatch(DataBatch* dataBatch) {
 }
 
 void DoubleBuffer::insertOneBatch(DataBatch* batch) {
+  while (!bufferQueue_->waitNotEmptyFor(2 /* seconds */)) {  // time out
+    if (stopping_) return;
+  }
   BufferBatch* bufBatch = bufferQueue_->dequeue();
   // clone and copy the data from an Threadlocal Variable
   bufBatch->clone(batch, useGpu_);
@@ -138,7 +142,7 @@ void DoubleBuffer::asyncLoadBatch() {
         actualSize = dataPool_->getNextBatchInternal(batchSize_, &newBatch);
       }
       insertOneBatch(&newBatch);
-    } while (actualSize > 0);
+    } while (actualSize > 0 && !stopping_);
   }
 }
 

paddle/gserver/dataproviders/DataProvider.h

@@ -259,7 +259,9 @@ typedef Queue<BufferBatch*> BufferBatchQueue;
 
 class DoubleBuffer {
 public:
-  DoubleBuffer(DataProvider* dataPool, bool useGpu, int64_t batchSize = 0);
+  DoubleBuffer(DataProvider* dataPool,
+               bool useGpu,
+               int64_t batchSize = 0);
   virtual ~DoubleBuffer();
   void removeOneBatch(DataBatch* dataBatch);
 
@@ -349,7 +351,6 @@ class DataProvider {
    */
   virtual void reset() {
     if (doubleBuffer_ != nullptr) {
-      LOG(INFO) << "the double-buffer is starting ...";
       doubleBuffer_->startAsyncLoad();
     }
   }

paddle/gserver/dataproviders/PyDataProvider2.cpp

@@ -18,9 +18,16 @@ limitations under the License. */
 #include <stdlib.h>
 #include <unordered_set>
 #include <list>
+#include <Python.h>
+#include <numpy/numpyconfig.h>
+#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
+#include <numpy/ndarrayobject.h>
 
 #include "DataProvider.h"
+
 #include "paddle/utils/PythonUtil.h"
+#include "paddle/utils/Locks.h"
+#include "paddle/utils/Stat.h"
 
 namespace paddle {
 
@@ -202,7 +209,10 @@ class PyDataProvider2 : public DataProvider {
   PyDataProvider2(const DataConfig& config,
                   const ModelConfig& modelConfig,
                   bool useGpu)
-    :DataProvider(config, useGpu), callingContextCreated_(2) {
+    :DataProvider(config, useGpu),
+      callingContextCreated_(2) {
+    if (PyArray_API == NULL)
+      import_array();
     auto& args = config.load_data_args();
     PyObjectPtr kwargs = PyObjectPtr(PyDict_New());
     if (!args.empty()) {
@@ -454,6 +464,7 @@ class PyDataProvider2 : public DataProvider {
   std::condition_variable pushCV_;
   std::condition_variable pullCV_;
   std::mutex mtx_;
+
   ThreadBarrier callingContextCreated_;
   std::unique_ptr<IPyDataProviderCache> cache_;
 
@@ -496,8 +507,8 @@ class PyDataProvider2 : public DataProvider {
    * Resetting the PyDataProvider. May start reading thread here.
    */
   virtual void reset() {
-    DataProvider::reset();
     resetImpl(true);
+    DataProvider::reset();
   }
 
   /**
@@ -518,6 +529,7 @@ class PyDataProvider2 : public DataProvider {
    * Loading a batch of data.
    */
   int64_t getNextBatchInternal(int64_t size_, DataBatch *batch) {
+    REGISTER_TIMER("PyDP2.getNextBatchInternal")
     CHECK_GE(size_, 0);
     size_t size = (size_t) size_;
     if (loadThread_) {  // loading from thread should wait for data pool ready.
@@ -698,10 +710,22 @@ class DenseScanner: public IFieldScanner {
    */
   virtual void fill(Argument &argument, PyObject *obj) {
     real* dat = argument.value->getData() + height_ * headerPtr_->dim;
-    py::SequenceHelper s(obj);
-    // TODO(yuyang18): Here we can use AVX or SSE to accelerate memory copy.
-    for (size_t i=0; i < headerPtr_->dim; ++i) {
-      dat[i] = (real) s.getDouble(i);
+    if (PyArray_Check(obj)) {
+        auto dtype = PyArray_DTYPE((PyArrayObject*)obj);
+        if (dtype->type == 'f' && dtype->elsize == sizeof(real)) {
+            real * data = (real*)PyArray_DATA((PyArrayObject*)obj);
+            auto sz = PyArray_SIZE((PyArrayObject*)obj);
+            std::copy(data, data + sz, dat);
+        } else {
+            LOG(FATAL) << "You should yield float" << sizeof(real) * 8
+                       << " array";
+        }
+     } else {
+        py::SequenceHelper s(obj);
+        // TODO(yuyang18): Here we can use AVX or SSE to accelerate memory copy.
+        for (size_t i=0; i < headerPtr_->dim; ++i) {
+          dat[i] = (real) s.getDouble(i);
+        }
     }
     ++height_;
   }

paddle/utils/Queue.h

@@ -135,6 +135,21 @@ class Queue {
     queueCV_.wait(lock, [this]() { return numElements_ == 0; });
   }
 
+  /**
+   * @brief wait queue is not empty at most for some seconds.
+   * @param seconds wait time limit.
+   * @return true if queue is not empty. false if timeout.
+   */
+  bool waitNotEmptyFor(int seconds) {
+    std::unique_lock<std::mutex> lock(queueLock_);
+    return queueCV_.wait_for(
+          lock,
+          std::chrono::seconds(seconds),
+          [this] {
+      return numElements_ != 0;
+    });
+  }
+
 private:
   std::deque<T> elements_;
   int numElements_;

python/paddle/trainer_config_helpers/data_sources.py

@@ -84,6 +84,7 @@ def py_data2(files, load_data_module, load_data_object, load_data_args,
             data.load_data_module = load_data_module
             data.load_data_object = load_data_object
             data.load_data_args = load_data_args
+            data.async_load_data = True
             return data
         data_cls = py_data2