Merge pull request #14868 from reyoung/feature/refine_w2v

Feature/refine w2v

cmake/external/python.cmake

@@ -18,8 +18,8 @@ ENDIF()
 
 INCLUDE(python_module)
 
-FIND_PACKAGE(PythonInterp ${PY_VERSION})
-FIND_PACKAGE(PythonLibs ${PY_VERSION})
+FIND_PACKAGE(PythonInterp ${PY_VERSION} REQUIRED)
+FIND_PACKAGE(PythonLibs ${PY_VERSION} REQUIRED)
 
 if(WIN32)
     execute_process(COMMAND "${PYTHON_EXECUTABLE}" "-c"
@@ -79,6 +79,5 @@ IF(PYTHONINTERP_FOUND)
         "please use pip to upgrade protobuf. pip install -U protobuf")
     ENDIF()
 ENDIF(PYTHONINTERP_FOUND)
-
 INCLUDE_DIRECTORIES(${PYTHON_INCLUDE_DIR})
 INCLUDE_DIRECTORIES(${PYTHON_NUMPY_INCLUDE_DIR})

paddle/fluid/operators/hierarchical_sigmoid_op.h

@@ -150,19 +150,27 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
                                                        label.data<int64_t>()));
     }
 
-    auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
-    auto pre_out_mat = EigenMatrix<T>::From(pre_out);
-    auto pre_out_grad_mat = EigenMatrix<T>::From(pre_out_grad);
-    auto out_grad_mat = EigenMatrix<T>::From(out_grad);
+    // softrelu derivative
 
-    Eigen::array<int, 2> bcast{1, static_cast<int>(pre_out_grad.dims()[1])};
+    auto blas = math::GetBlas<DeviceContext, T>(ctx);
 
-    // softrelu derivative
-    pre_out_grad_mat.device(place) =
-        static_cast<T>(1.0) - static_cast<T>(1.0) / pre_out_mat.exp();
+    auto* pre_out_grad_data = pre_out_grad.data<T>();
+    auto* pre_out_data = pre_out.data<T>();
+    auto n = pre_out.numel();
+    blas.VEXP(n, pre_out_data, pre_out_grad_data);
+    blas.VINV(n, pre_out_grad_data, pre_out_grad_data);
+    for (int64_t i = 0; i < n; ++i) {
+      pre_out_grad_data[i] = 1.0 - pre_out_grad_data[i];
+    }
     bit_code->Sub(&pre_out_grad);  // the gradient of clip(w * x + b)
-    pre_out_grad_mat.device(place) =
-        pre_out_grad_mat * out_grad_mat.broadcast(bcast);
+    auto* out_grad_data = out_grad.data<T>();
+
+    int64_t dim0 = pre_out_grad.dims()[0];
+    int64_t dim1 = pre_out_grad.dims()[1];
+    for (int64_t i = 0; i < dim0; ++i) {
+      T tmp = out_grad_data[i];
+      blas.SCAL(dim1, tmp, pre_out_grad_data + i * dim1);
+    }
     // TODO(guosheng): multiply pre_out_grad with subgradient of clipping to
     // be consistent with the clipping in forward.
 

paddle/fluid/operators/math/blas.h

@@ -181,6 +181,9 @@ class Blas {
               const framework::Tensor& mat_b, const MatDescriptor& dim_b,
               T alpha, framework::Tensor* mat_out, T beta) const;
 
+  template <typename T>
+  void VINV(int n, const T* a, T* y) const;
+
  private:
   const DeviceContext& context_;
 };
@@ -282,6 +285,11 @@ class BlasT : private Blas<DeviceContext> {
     Base()->template BatchedGEMM<T>(args...);
   }
 
+  template <typename... ARGS>
+  void VINV(ARGS... args) const {
+    Base()->template VINV<T>(args...);
+  }
+
  private:
   const Blas<DeviceContext>* Base() const {
     return static_cast<const Blas<DeviceContext>*>(this);

paddle/fluid/operators/math/blas_impl.h

@@ -118,6 +118,11 @@ struct CBlas<float> {
   static void VPOW(ARGS... args) {
     platform::dynload::vsPowx(args...);
   }
+
+  template <typename... ARGS>
+  static void VINV(ARGS... args) {
+    platform::dynload::vsInv(args...);
+  }
 };
 
 template <>
@@ -213,6 +218,11 @@ struct CBlas<double> {
   static void VPOW(ARGS... args) {
     platform::dynload::vdPowx(args...);
   }
+
+  template <typename... ARGS>
+  static void VINV(ARGS... args) {
+    platform::dynload::vdInv(args...);
+  }
 };
 
 #else
@@ -603,6 +613,17 @@ void Blas<DeviceContext>::MatMul(const framework::Tensor &mat_a,
         dim_a.stride_, dim_b.stride_);
   }
 }
+template <typename DeviceContext>
+template <typename T>
+void Blas<DeviceContext>::VINV(int n, const T *a, T *y) const {
+#ifdef PADDLE_WITH_MKLML
+  CBlas<T>::VINV(n, a, y);
+#else
+  for (int i = 0; i < n; ++i) {
+    y[i] = 1.0 / a[i];
+  }
+#endif
+}
 
 }  // namespace math
 }  // namespace operators

paddle/fluid/operators/math/matrix_bit_code.h

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+#include <map>
 #include <unordered_map>
 #include <utility>
 #include <vector>
@@ -22,6 +23,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/tensor.h"
 #include "paddle/fluid/operators/math/blas.h"
 #include "paddle/fluid/platform/device_context.h"
+#include "paddle/fluid/platform/variant.h"
 
 #if defined(_WIN32)
 #include <intrin.h>
@@ -98,24 +100,7 @@ inline int clz(const T& value) {
 
 inline size_t FindLastSet(size_t x) { return sizeof(size_t) * 8 - clz(x); }
 #endif  // !_WIN32
-// set a code interface to create multiple code
-class Code {
- public:
-  virtual ~Code() {}
-  virtual size_t calc_index(int bit) const = 0;
-  virtual bool calc_bit(int bit) const = 0;
-  virtual int get_length() const = 0;
-};
-// set a CodeTable interface to create multiple code table
-class CodeTable {
- public:
-  virtual std::unique_ptr<Code> get_code(int64_t code) const = 0;
-  virtual size_t size() const = 0;
-  virtual int get_max_code_length() const = 0;
-  virtual ~CodeTable() {}
-};
-
-class SimpleCode : public Code {
+class SimpleCode {
  public:
   SimpleCode(size_t code, size_t num_classes, const int64_t* ids)
       : c_(static_cast<size_t>(ids[code]) + num_classes) {}
@@ -137,16 +122,16 @@ class SimpleCode : public Code {
 };
 
 template <typename T>
-class CustomCode : public Code {
+class CustomCode {
  public:
   CustomCode(const framework::Tensor& ptable, const framework::Tensor& pcode,
-             const int64_t* ids, int index)
-      : ids_(ids), index_(index) {
-    ptable_ = ptable.Slice(index, index + 1);
-    pcode_ = pcode.Slice(index, index + 1);
+             const int64_t* ids, int index) {
+    seq_len_ = ptable.dims()[1];
+    ptable_data_ = ptable.data<T>() + seq_len_ * index;
+    pcode_data_ = pcode.data<T>() + seq_len_ * index;
   }
   /**
-   * Here the id of root shoud be 1 rather than 0, thus the encoding of class c
+   * Here the id of root should be 1 rather than 0, thus the encoding of class c
    * is `c + num_classes` and all siblings can get the same weight indice using
    * prefixes.
    * Weight index is the prefixes of encoding, thus leave out the right most
@@ -154,36 +139,37 @@ class CustomCode : public Code {
    * Binary classification path is the suffixes of encoding, thus leave out the
    * left most bit in calc_bit.
    */
-  size_t calc_index(int bit) const { return ptable_.data<T>()[bit]; }
-  bool calc_bit(int bit) const { return pcode_.data<T>()[bit]; }
-  int get_length() const {
-    int length = 0;
+  size_t calc_index(int bit) const { return ptable_data_[bit]; }
+  bool calc_bit(int bit) const { return pcode_data_[bit]; }
 
-    for (int i = 0; i < static_cast<int>(ptable_.dims()[1]); i++) {
-      if (ptable_.data<T>()[i] >= 0) {
-        length++;
-      } else {
-        return length;
-      }
+  // NOTE: this function is not thread-safe.
+  int get_length() const {
+    if (length_ < 0) {
+      auto len = seq_len_;
+      length_ =
+          static_cast<int>(std::find_if(ptable_data_, ptable_data_ + len,
+                                        [](const T& val) { return val < 0; }) -
+                           ptable_data_);
     }
-    return length;
+    return length_;
   }
 
  private:
-  framework::Tensor ptable_;
-  framework::Tensor pcode_;
-  const int64_t* ids_;
-  const int index_;
+  int64_t seq_len_;
+  const T* ptable_data_;
+  const T* pcode_data_;
+  mutable int length_{-1};
 };
 
-class SimpleCodeTable : public CodeTable {
+class SimpleCodeTable {
  public:
   SimpleCodeTable(size_t num_classes, const int64_t* ids)
       : num_classes_(num_classes), ids_(ids) {}
-  std::unique_ptr<Code> get_code(int64_t code) const {
-    std::unique_ptr<Code> coder(new SimpleCode(code, num_classes_, ids_));
-    return coder;
+
+  SimpleCode get_code(int64_t code) const {
+    return SimpleCode(code, num_classes_, ids_);
   }
+
   size_t size() const { return num_classes_; }
   int get_max_code_length() const { return FindLastSet(num_classes_ - 1); }
 
@@ -193,15 +179,14 @@ class SimpleCodeTable : public CodeTable {
 };
 
 template <typename T>
-class CustomCodeTable : public CodeTable {
+class CustomCodeTable {
  public:
   CustomCodeTable(const framework::Tensor& ptable,
                   const framework::Tensor& pcode, const int64_t* ids)
       : ptable_(ptable), pcode_(pcode), ids_(ids) {}
 
-  std::unique_ptr<Code> get_code(int64_t code) const {
-    std::unique_ptr<Code> coder(new CustomCode<T>(ptable_, pcode_, ids_, code));
-    return coder;
+  CustomCode<T> get_code(int64_t code) const {
+    return CustomCode<T>(ptable_, pcode_, ids_, code);
   }
 
   size_t size() const { return static_cast<size_t>(ptable_.dims()[1]); }
@@ -215,19 +200,21 @@ class CustomCodeTable : public CodeTable {
   const int64_t* ids_;
 };
 
+using CodeTable = boost::variant<SimpleCodeTable, CustomCodeTable<int64_t>>;
+
 template <typename T>
 class MatrixBitCodeFunctor {
  public:
   MatrixBitCodeFunctor(size_t num_classes, const int64_t* ids)
       : num_classes_(num_classes),
         ids_(ids),
-        code_table_(new SimpleCodeTable(num_classes, ids)) {}
+        code_table_(SimpleCodeTable(num_classes, ids)) {}
 
   MatrixBitCodeFunctor(const framework::Tensor& ptable,
                        const framework::Tensor& pcode, const int64_t* ids)
       : num_classes_(static_cast<size_t>(ptable.dims()[1])),
         ids_(ids),
-        code_table_(new CustomCodeTable<int64_t>(ptable, pcode, ids)) {}
+        code_table_(CustomCodeTable<int64_t>(ptable, pcode, ids)) {}
   /* For j < code_length
        tmat(i, j) += vec(0, index(i, j))
   */
@@ -277,7 +264,7 @@ class MatrixBitCodeFunctor {
 
   size_t num_classes_;
   const int64_t* ids_;
-  std::unique_ptr<CodeTable> code_table_;
+  CodeTable code_table_;
 };
 }  // namespace math
 }  // namespace operators

paddle/fluid/platform/dynload/mklml.h

@@ -82,6 +82,8 @@ extern void* mklml_dso_handle;
   __macro(vdSqr);                   \
   __macro(vsPowx);                  \
   __macro(vdPowx);                  \
+  __macro(vsInv);                   \
+  __macro(vdInv);                   \
   __macro(MKL_Set_Num_Threads)
 
 MKLML_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_MKLML_WRAP);