add vaddbias jitcode

test=develop

paddle/fluid/operators/math/jit_code.h

@@ -31,16 +31,26 @@ using Label = Xbyak::Label;
 
 typedef enum { mul = 0, add } operand_type;
 
-// function: vec = Operand(vec(scalar), vec(scalar)) (maybe with relu)
+// function: vec = Operand(vec(or scalar), vec(or scalar)) (maybe with relu)
 class VXXJitCode : public JitCode {
  public:
   const char* name() const override {
     std::string base = "VXXJitCode";
+    if (scalar_index_ == 1) {
+      base += "_Scalar";
+    } else {
+      base += "_Vec";
+    }
     if (type_ == operand_type::mul) {
       base += "_Mul";
     } else if (type_ == operand_type::add) {
       base += "_Add";
     }
+    if (scalar_index_ == 2) {
+      base += "_Scalar";
+    } else {
+      base += "_Vec";
+    }
     base += (with_relu_ ? "_Relu" : "");
     return base.c_str();
   }

paddle/fluid/operators/math/jit_kernel.h

@@ -83,13 +83,15 @@ class VAddReluKernel : public Kernel {
 template <typename T>
 class VScalKernel : public Kernel {
  public:
+  // y = a.*x
   void (*Compute)(const T *, const T *, T *, int);
 };
 
 template <typename T>
 class VAddBiasKernel : public Kernel {
  public:
-  virtual void Compute(const T a, const T *x, T *y) const = 0;
+  // y = a.+x
+  void (*Compute)(const T *, const T *, T *, int);
 };
 
 template <typename T>

paddle/fluid/operators/math/jit_kernel_blas.cc

@@ -60,6 +60,13 @@ void VScalRefer(const T* a, const T* x, T* y, int n) {
   }
 }
 
+template <typename T>
+void VAddBiasRefer(const T* a, const T* x, T* y, int n) {
+  for (int i = 0; i < n; ++i) {
+    y[i] = a[0] + x[i];
+  }
+}
+
 #ifdef PADDLE_WITH_MKLML
 template <typename T>
 void VMulMKL(const T* x, const T* y, T* z, int n);
@@ -300,62 +307,46 @@ bool VScalKernelImpl<double>::useMKL(int d) {
 }
 #endif
 
-#undef DECLARE_STATIC_FUNC
-
-REGISTER_JITKERNEL(vmul, VMulKernel);
-REGISTER_JITKERNEL(vadd, VAddKernel);
-REGISTER_JITKERNEL(vscal, VScalKernel);
-REGISTER_JITKERNEL(vaddrelu, VAddReluKernel);
-
 /* VAddBias JitKernel */
-template <typename T, platform::jit::cpu_isa_t isa, jit_block>
+template <typename T>
 class VAddBiasKernelImpl : public VAddBiasKernel<T> {
  public:
-  explicit VAddBiasKernelImpl(int d) : VAddBiasKernel<T>() { this->num_ = d; }
-  void Compute(const T a, const T* x, T* y) const override {
-    for (int i = 0; i < this->num_; ++i) {
-      y[i] = x[i] + a;
+  DECLARE_STATIC_FUNC;
+  explicit VAddBiasKernelImpl(int d) : VAddBiasKernel<T>() {
+#ifdef PADDLE_WITH_XBYAK
+    if (useJIT(d)) {
+      size_t sz = 96 + d / AVX_FLOAT_BLOCK * 4 * 8;
+      jitcode_.reset(new gen::VXXJitCode(d, gen::operand_type::add, 1, false,
+                                         sz > 4096 ? sz : 4096));
+      this->Compute =
+          jitcode_->getCode<void (*)(const T*, const T*, T*, int)>();
+      return;
     }
-  }
-};
-
-#define INTRI8_FLOAT(isa)                              \
-  template <>                                          \
-  void VAddBiasKernelImpl<float, isa, kEQ8>::Compute(  \
-      const float a, const float* x, float* y) const { \
-    __m256 tmp = _mm256_loadu_ps(x);                   \
-    tmp = _mm256_add_ps(tmp, _mm256_set1_ps(a));       \
-    _mm256_storeu_ps(y, tmp);                          \
-  }
+#endif
 
-#define INTRI16_FLOAT(isa)                             \
-  template <>                                          \
-  void VAddBiasKernelImpl<float, isa, kEQ16>::Compute( \
-      const float a, const float* x, float* y) const { \
-    __m256 tmp0 = _mm256_loadu_ps(x);                  \
-    __m256 tmp1 = _mm256_loadu_ps(x + 8);              \
-    tmp0 = _mm256_add_ps(tmp0, _mm256_set1_ps(a));     \
-    tmp1 = _mm256_add_ps(tmp1, _mm256_set1_ps(a));     \
-    _mm256_storeu_ps(y, tmp0);                         \
-    _mm256_storeu_ps(y + 8, tmp1);                     \
+    this->Compute = VAddBiasRefer<T>;
   }
+#ifdef PADDLE_WITH_XBYAK
 
-#ifdef __AVX__
-INTRI8_FLOAT(jit::avx);
-INTRI16_FLOAT(jit::avx);
-#endif
-#ifdef __AVX2__
-INTRI8_FLOAT(jit::avx2);
-INTRI16_FLOAT(jit::avx2);
+ private:
+  std::unique_ptr<gen::VXXJitCode> jitcode_{nullptr};
 #endif
-#ifdef __AVX512F__
-INTRI8_FLOAT(jit::avx512f);
-INTRI16_FLOAT(jit::avx512f);
+};
+
+#ifdef PADDLE_WITH_XBYAK
+template <>
+bool VAddBiasKernelImpl<float>::useJIT(int d) {
+  return gen::VXXJitCode::init(d, 1);
+}
 #endif
-// TODO(TJ): eq16 test and complete avx512
 
-#undef INTRI8_FLOAT
-#undef INTRI16_FLOAT
+#undef DECLARE_STATIC_FUNC
+
+REGISTER_JITKERNEL(vmul, VMulKernel);
+REGISTER_JITKERNEL(vadd, VAddKernel);
+REGISTER_JITKERNEL(vaddrelu, VAddReluKernel);
+REGISTER_JITKERNEL(vscal, VScalKernel);
+REGISTER_JITKERNEL(vaddbias, VAddBiasKernel);
 
 /* VRelu JitKernel */
 template <typename T, platform::jit::cpu_isa_t isa, jit_block>
@@ -466,7 +457,6 @@ class VIdentityKernelImpl : public VIdentityKernel<T> {
   void Compute(const T* x, T* y) const override {}
 };
 
-REGISTER_JITKERNEL_DEPRECATED(vaddb, VAddBiasKernel);
 REGISTER_JITKERNEL_DEPRECATED(vrelu, VReluKernel);
 REGISTER_JITKERNEL_DEPRECATED(videntity, VIdentityKernel);
 

paddle/fluid/operators/math/jit_kernel_exp.cc

@@ -409,11 +409,11 @@ class VTanhKernelImpl : public VTanhKernel<T> {
     vaddbias_ = KernelPool::Instance().template Get<VAddBiasKernel<T>>(d);
   }
   void Compute(const T* x, T* y) const override {
-    const T a = static_cast<T>(2);
+    const T a = static_cast<T>(2), b = static_cast<T>(-1);
     vscal_->Compute(&a, x, y, this->num_);
     vsigmoid_->Compute(y, y);
     vscal_->Compute(&a, y, y, this->num_);
-    vaddbias_->Compute(static_cast<T>(-1), y, y);
+    vaddbias_->Compute(&b, y, y, this->num_);
   }
 
  private:
@@ -473,11 +473,11 @@ class VTanhKernelImpl : public VTanhKernel<T> {
     _mm256_storeu_ps(y, tmp);                                                 \
     x += AVX_FLOAT_BLOCK;                                                     \
     y += AVX_FLOAT_BLOCK;                                                     \
-    const float a = 2.f;                                                      \
+    const float a = 2.f, b = -1.f;                                            \
     vscal_->Compute(&a, x, y, this->num_);                                    \
     vsigmoid_->Compute(y, y);                                                 \
     vscal_->Compute(&a, y, y, this->num_);                                    \
-    vaddbias_->Compute(-1.f, y, y);                                           \
+    vaddbias_->Compute(&b, y, y, this->num_);                                 \
   }
 
 #define INTRI_GT16_FLOAT(isa, expisa)                                         \
@@ -504,11 +504,11 @@ class VTanhKernelImpl : public VTanhKernel<T> {
     }                                                                         \
     x += this->end_;                                                          \
     y += this->end_;                                                          \
-    const float a = 2.f;                                                      \
+    const float a = 2.f, b = -1.f;                                            \
     vscal_->Compute(&a, x, y, this->num_);                                    \
     vsigmoid_->Compute(y, y);                                                 \
     vscal_->Compute(&a, y, y, this->num_);                                    \
-    vaddbias_->Compute(-1.f, y, y);                                           \
+    vaddbias_->Compute(&b, y, y, this->num_);                                 \
   }
 
 #ifndef __WIN32

paddle/fluid/operators/math/jit_kernel_test.cc

@@ -128,7 +128,7 @@ TEST(JitKernel, vaddbias) {
     auto trefe = GetCurrentUS();
     auto ttgts = GetCurrentUS();
     for (int i = 0; i < repeat; ++i) {
-      ker->Compute(a, x_data, ztgt_data);
+      ker->Compute(&a, x_data, ztgt_data, d);
     }
     auto ttgte = GetCurrentUS();
 
@@ -281,11 +281,11 @@ void vtanh_better(
         const paddle::operators::math::jitkernel::VAddBiasKernel<float>>&
         vaddbias,
     const int n, const float* x, float* y) {
-  const float tmp1 = 2.f;
-  vscal->Compute(&tmp1, x, y, n);
+  const float a = 2.f, b = -1.f;
+  vscal->Compute(&a, x, y, n);
   vsigmoid->Compute(y, y);
-  vscal->Compute(&tmp1, y, y, n);
-  vaddbias->Compute(-1.f, y, y);
+  vscal->Compute(&a, y, y, n);
+  vaddbias->Compute(&b, y, y, n);
 }
 
 TEST(JitKernel, vtanh) {