modify the pruning from reading mask to specify sparsity_ratio

paddle/parameter/ParameterUpdaterHook.cpp

@@ -19,130 +19,31 @@ limitations under the License. */
 #include <mutex>
 #include <thread>
 #include <unordered_map>
+#include <vector>
 
 #include "paddle/math/Vector.h"
 #include "paddle/parameter/Parameter.h"
 #include "paddle/utils/Flags.h"
 #include "paddle/utils/Util.h"
 
-using std::vector;
-using std::pair;
-
 namespace paddle {
 
 /**
  * The static pruning hook
- *
- * Static means user load a mask map before training started. This map will
- * define which link/weight between neural is disabled.
+ * Static means user specific a sparsity_ratio map before training started. The
+ * network will
+ * hold the sparsity_ratio maximum numbers of parameters, and cut off the rest.
  */
-class StaticPruningHook : public IParameterUpdaterHook {
-public:
-  /**
-   * The Mask Map Header.
-   * The map file started with this header.
-   *
-   * In Version 0, reset file will be:
-   *  contains header.size bit, each bit means such weight is enabled or not.
-   *    if bit is 1, then such weight is enabled.
-   *  at end, the file will round to byte, and the low bits of end byte will be
-   *  filled by zero.
-   *
-   */
-  struct StaticMaskHeader {
-    uint32_t version;
-    size_t size;
-  } __attribute__((__packed__));
-
-  explicit StaticPruningHook(const std::string& mask_filename) : initCount_(0) {
-    bool ok = this->loadMaskFile(mask_filename);
-    if (!ok) {
-      LOG(WARNING) << "Fail to load mask file " << mask_filename
-                   << " in current directory, searching in init_model_path";
-      std::string combineMaskFilename =
-          path::join(FLAGS_init_model_path, mask_filename);
-      CHECK(this->loadMaskFile(combineMaskFilename))
-          << "Cannot load " << mask_filename << " in ./" << mask_filename
-          << " and " << combineMaskFilename;
-    }
-    VLOG(3) << mask_filename << " mask size = " << this->mask_.size();
-  }
 
-  void update(Parameter* para) {
-    updateThreadChecker_.check();
-    auto& vec = para->getBuf(PARAMETER_GRADIENT);
-    if (vec) {
-      vec->dotMul(*maskVec_);
-    }
-  }
-
-  void init(Parameter* para) {
-    size_t initCount = this->initCount_.fetch_add(1);
-    CHECK_EQ(initCount, 0UL) << "Currently the StaticPruningHook must invoke "
-                                "in same ParamterUpdater";
-    VLOG(3) << "Initialize Parameter " << para;
-    SetDevice device(para->getDeviceId());
-
-    auto maskVec = Vector::create(this->mask_.size(), false);
-    {  // Initialize maskVec with float mask vector
-      real* dataPtr = maskVec->getData();
-      size_t i = 0;
-      for (bool m : mask_) {
-        dataPtr[i++] = m ? 1.0 : 0.0;
-      }
-    }
-
-    // Currently just use a mask vector for hack.
-    // @TODO(yuyang18): Implemented the mask operation in vector.
-    if (para->useGpu()) {
-      maskVec_ = Vector::create(this->mask_.size(), para->useGpu());
-      maskVec_->copyFrom(*maskVec);
-    } else {
-      maskVec_ = maskVec;
-    }
-
-    auto& vec = para->getBuf(PARAMETER_VALUE);
-    vec->dotMul(*maskVec_);
-  }
-
-private:
-  bool loadMaskFile(const std::string& mask_filename) {
-    std::ifstream fin;
-    fin.open(mask_filename);
-    if (fin.is_open()) {
-      StaticMaskHeader header;
-      fin.read(reinterpret_cast<char*>(&header), sizeof(StaticMaskHeader));
-      CHECK_EQ(header.version, 0UL);
-      mask_.resize(header.size);
-      uint8_t buf;
-      for (size_t i = 0; i < header.size; ++i, buf <<= 1) {
-        if (i % 8 == 0) {
-          fin.read(reinterpret_cast<char*>(&buf), sizeof(uint8_t));
-        }
-        mask_[i] = buf & 0x80;
-      }
-      fin.close();
-      return true;
-    } else {
-      return false;
-    }
-  }
-
-  SameThreadChecker updateThreadChecker_;
-  std::atomic<size_t> initCount_;
-  VectorPtr maskVec_;
-  std::vector<bool> mask_;
-};
-
-class DynamicPruningHook : public IParameterUpdaterHook {
+class StaticPruningHook : public IParameterUpdaterHook {
 public:
-  explicit DynamicPruningHook(const ParameterUpdaterHookConfig& hookConfig)
+  explicit StaticPruningHook(const ParameterUpdaterHookConfig& hookConfig)
       : initCount_(0) {
     sparsityRatio_ = hookConfig.sparsity_ratio();
   }
 
-  static bool sortPairAscend(const pair<real, size_t>& pair1,
-                             const pair<real, size_t>& pair2) {
+  static bool sortPairAscend(const std::pair<real, size_t>& pair1,
+                             const std::pair<real, size_t>& pair2) {
     return pair1.first > pair2.first;
   }
 
@@ -162,7 +63,7 @@ public:
 
     VectorPtr vecCpu = Vector::create(para->getSize(), false);
     vecCpu->copyFrom(*vec);
-    vector<pair<real, size_t>> param;
+    std::vector<std::pair<real, size_t>> param;
 
     for (size_t i = 0; i < para->getSize(); i++)
       param.push_back(std::make_pair(fabs(vecCpu->getData()[i]), i));
@@ -175,7 +76,7 @@ public:
   void init(Parameter* para) {
     generateMask(para);
     size_t initCount = this->initCount_.fetch_add(1);
-    CHECK_EQ(initCount, 0UL) << "Currently the DynamicPruningHook must invoke "
+    CHECK_EQ(initCount, 0UL) << "Currently the StaticPruningHook must invoke "
                                 "in same ParamterUpdater";
     VLOG(3) << "Initialize Parameter " << para;
     SetDevice device(para->getDeviceId());
@@ -234,16 +135,9 @@ static WeakKVCache<std::pair<std::string, int>,
 static IParameterUpdaterHook* createImpl(
     const ParameterUpdaterHookConfig& config) {
   auto& type = config.type();
-  if (type == "pruning_static") {
-    if (config.has_purning_mask_filename())
-      return new StaticPruningHook(config.purning_mask_filename());
-    else
-      LOG(FATAL) << "There must be mask_filename parameter for " << type
-                 << " Hook";
-
-  } else if (type == "pruning") {
+  if (type == "pruning") {
     if (config.has_sparsity_ratio())
-      return new DynamicPruningHook(config);
+      return new StaticPruningHook(config);
     else
       LOG(FATAL) << "There must be sparsity_ratio parameter for " << type
                  << " Hook";

proto/ParameterConfig.proto

@@ -26,8 +26,7 @@ enum ParameterInitStrategy {
 
 message ParameterUpdaterHookConfig {
   required string type = 1;
-  //hook type such as  'pruning', 'pruning_static'
-  optional string purning_mask_filename = 2;
+  //hook type such as  'pruning'
   optional double sparsity_ratio = 3;
 }
 

python/paddle/trainer/config_parser.py

@@ -3171,14 +3171,7 @@ def Layer(name, type, **xargs):
 
 @config_func
 def ParameterHook(type, **kwargs):
-    if type == 'pruning_static':
-        hook = ParameterUpdaterHookConfig()
-        hook.type = type
-        mask_filename = kwargs.get('mask_filename', None)
-        assert mask_filename is not None
-        hook.pruning_mask_filename = mask_filename
-        return hook
-    elif type == 'pruning':
+    if type == 'pruning':
         hook = ParameterUpdaterHookConfig()
         hook.type = type
         sparsity_ratio = kwargs.get('sparsity_ratio', None)

python/paddle/trainer_config_helpers/attrs.py

@@ -64,32 +64,24 @@ class HookAttribute(object):
     here paddle/parameter/ParameterUpdaterHook.cpp
     NOTE: IT IS A HIGH LEVEL USER INTERFACE.
 		
-    :param  type: Hook type, eg: 'pruning', 'pruning_static'
+    :param  type: Hook type, eg: 'pruning'
     :type type: string
 
-    :param mask_file: Must be specified if hook type is 'pruning_static', 
-					  the network reads the mask from the file to determine which parameters should be cut off
-    :type mask_file: string
-
     :param sparsity_ratio: Must be specified if hook type is 'pruning',
 	                       the network will hold the sparsity_ratio maximum parameters, and cut off the rest. 
     :type sparsity_ratio: float number between 0 and 1
 	
     """
 
-    def __init__(self, type, mask_filename=None, sparsity_ratio=None):
+    def __init__(self, type, sparsity_ratio=None):
         self.type = type
-        self.mask_filename = mask_filename
         self.sparsity_ratio = sparsity_ratio
         assert is_compatible_with(self.sparsity_ratio,
                                   float), 'sparisity_ratio must be float type'
         assert self.sparsity_ratio <= 1 and self.sparsity_ratio >= 0, 'sparisity must be a flaot between [0, 1] '
 
     def __call__(self):
-        return ParameterHook(
-            self.type,
-            mask_filename=self.mask_filename,
-            sparsity_ratio=self.sparsity_ratio)
+        return ParameterHook(self.type, sparsity_ratio=self.sparsity_ratio)
 
 
 class ParameterAttribute(object):