Merge branch 'windows/build' into windows/online

test=develop

paddle/fluid/operators/math/cpu_vec.h

@@ -33,11 +33,11 @@ namespace math {
 #define SIGMOID_THRESHOLD_MIN -40.0
 #define SIGMOID_THRESHOLD_MAX 13.0
 
-#define AVX_FLOAT_BLOCK 8
+#define YMM_FLOAT_BLOCK 8
 #define AVX_DOUBLE_BLOCK 4
-#define AVX2_FLOAT_BLOCK 8
+#define YMM_FLOAT_BLOCK 8
 #define AVX2_DOUBLE_BLOCK 4
-#define AVX512_FLOAT_BLOCK 16
+#define ZMM_FLOAT_BLOCK 16
 #define AVX512_DOUBLE_BLOCK 8
 
 template <typename T>
@@ -88,7 +88,7 @@ template <>
 inline void vec_scal<float, platform::jit::avx>(const int n, const float a,
                                                 const float* x, float* y) {
 #ifdef __AVX__
-  constexpr int block = AVX_FLOAT_BLOCK;
+  constexpr int block = YMM_FLOAT_BLOCK;
   if (n < block) {
     vec_scal<float, platform::jit::isa_any>(n, a, x, y);
     return;
@@ -142,7 +142,7 @@ template <>
 inline void vec_bias_sub<float, platform::jit::avx>(const int n, const float a,
                                                     const float* x, float* y) {
 #ifdef __AVX__
-  constexpr int block = AVX_FLOAT_BLOCK;
+  constexpr int block = YMM_FLOAT_BLOCK;
   if (n < block) {
     vec_bias_sub<float, platform::jit::isa_any>(n, a, x, y);
     return;
@@ -200,7 +200,7 @@ inline void vec_cross<float, platform::jit::avx>(const int n, const float* x,
                                                  const float* y, const float* z,
                                                  float* out) {
 #ifdef __AVX__
-  constexpr int block = AVX_FLOAT_BLOCK;
+  constexpr int block = YMM_FLOAT_BLOCK;
   if (n < block) {
     vec_cross<float, platform::jit::isa_any>(n, x, y, z, out);
     return;
@@ -257,7 +257,7 @@ template <>
 inline void vec_add_bias<float, platform::jit::avx>(const int n, const float a,
                                                     const float* x, float* y) {
 #ifdef __AVX__
-  constexpr int block = AVX_FLOAT_BLOCK;
+  constexpr int block = YMM_FLOAT_BLOCK;
   if (n < block) {
     vec_add_bias<float, platform::jit::isa_any>(n, a, x, y);
     return;
@@ -326,7 +326,7 @@ template <>
 inline void vec_sigmoid<float, platform::jit::avx>(const int n, const float* x,
                                                    float* y) {
 #ifdef __AVX__
-  constexpr int block = AVX_FLOAT_BLOCK;
+  constexpr int block = YMM_FLOAT_BLOCK;
   if (n < block) {
     vec_sigmoid<float, platform::jit::isa_any>(n, x, y);
     return;
@@ -415,7 +415,7 @@ template <>
 inline void vec_relu<float, platform::jit::avx>(const int n, const float* x,
                                                 float* y) {
 #ifdef __AVX__
-  constexpr int block = AVX_FLOAT_BLOCK;
+  constexpr int block = YMM_FLOAT_BLOCK;
   if (n < block * 4) {
     vec_relu<float, platform::jit::isa_any>(n, x, y);
     return;

paddle/fluid/operators/math/jit_code.cc

@@ -41,7 +41,7 @@ void VXXJitCode::generate() {
   } else if (scalar_index_ == 2) {
     vbroadcastss(ymm_src2, ptr[param2]);
   }
-  for (int i = 0; i < num_ / AVX_FLOAT_BLOCK; ++i) {
+  for (int i = 0; i < num_ / YMM_FLOAT_BLOCK; ++i) {
     if (scalar_index_ != 1) {
       vmovups(ymm_src1, ptr[param1 + offset]);
     }
@@ -57,9 +57,9 @@ void VXXJitCode::generate() {
       vmaxps(ymm_dst, ymm_zero, ymm_dst);
     }
     vmovups(ptr[param3 + offset], ymm_dst);
-    offset += sizeof(float) * AVX_FLOAT_BLOCK;
+    offset += sizeof(float) * YMM_FLOAT_BLOCK;
   }
-  int rest = num_ % AVX_FLOAT_BLOCK;
+  int rest = num_ % YMM_FLOAT_BLOCK;
   if (rest >= 4) {
     if (scalar_index_ != 1) {
       vmovups(xmm_src1, ptr[param1 + offset]);
@@ -118,18 +118,237 @@ void VXXJitCode::generate() {
   ret();
 }
 
-bool ReluJitCode::init(int d) { return MayIUse(avx); }
+#define ALIGN32 __attribute__((aligned(32)))
+#define EXP_HIG 88.3762626647949f
+#define EXP_LOW -88.3762626647949f
+#define CEPHES_LOG2EF 1.44269504088896341
+#define CEPHES_EXP_C1 0.693359375
+#define CEPHES_EXP_C2 -2.12194440e-4
+#define CEPHES_EXP_P0 1.9875691500E-4
+#define CEPHES_EXP_P1 1.3981999507E-3
+#define CEPHES_EXP_P2 8.3334519073E-3
+#define CEPHES_EXP_P3 4.1665795894E-2
+#define CEPHES_EXP_P4 1.6666665459E-1
+#define CEPHES_EXP_P5 5.0000001201E-1
 
-void ReluJitCode::generate() {
-  int offset = 0;
+#define REPEAT_8TIMES(val) val, val, val, val, val, val, val, val
+
+#define OFFSET_EXP_ONE 0 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_TWO 1 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_0P5 2 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_HIG 3 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_LOW 4 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_LOG2EF 5 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_C1 6 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_C2 7 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_P0 8 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_P1 9 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_P2 10 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_P3 11 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_P4 12 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_P5 13 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_EXP_MAX_INPUT 14 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_SIGMOID_MAX 15 * YMM_FLOAT_BLOCK * sizeof(float)
+#define OFFSET_SIGMOID_MIN 16 * YMM_FLOAT_BLOCK * sizeof(float)
+
+static const float exp_float_consts[] ALIGN32 = {
+    REPEAT_8TIMES(1.f),
+    REPEAT_8TIMES(2.f),
+    REPEAT_8TIMES(0.5f),
+    REPEAT_8TIMES(EXP_HIG),
+    REPEAT_8TIMES(EXP_LOW),
+    REPEAT_8TIMES(CEPHES_LOG2EF),
+    REPEAT_8TIMES(CEPHES_EXP_C1),
+    REPEAT_8TIMES(CEPHES_EXP_C2),
+    REPEAT_8TIMES(CEPHES_EXP_P0),
+    REPEAT_8TIMES(CEPHES_EXP_P1),
+    REPEAT_8TIMES(CEPHES_EXP_P2),
+    REPEAT_8TIMES(CEPHES_EXP_P3),
+    REPEAT_8TIMES(CEPHES_EXP_P4),
+    REPEAT_8TIMES(CEPHES_EXP_P5),
+    REPEAT_8TIMES(EXP_MAX_INPUT),
+    REPEAT_8TIMES(SIGMOID_THRESHOLD_MAX),
+    REPEAT_8TIMES(SIGMOID_THRESHOLD_MIN)};
+
+static const int exp_int_0x7f[] ALIGN32 = {REPEAT_8TIMES(0x7f)};
+static int g_tmp_mem[16] ALIGN32 = {0};
+
+bool VActJitCode::init(int d, operand_type type) {
+  bool ok = MayIUse(avx);
+  if (type == operand_type::relu) {
+    return ok;
+  } else if (type == operand_type::exp) {
+    // exp is slower than mkl when d >= 256
+    return ok && d % 8 == 0 && d < 256;
+  } else {
+    // TODO(TJ): support more
+    return ok && d % 8 == 0;
+  }
+}
+
+void VActJitCode::relu_ymm(ymm_t& ymm_dst, ymm_t& ymm_src, ymm_t& ymm_zero) {
+  vmaxps(ymm_dst, ymm_zero, ymm_src);
+}
+
+void VActJitCode::exp_ymm(ymm_t& ymm_dst, ymm_t& ymm_src, int fx_idx,
+                          int fy_idx, int mask_idx, int tmp_idx) {
+  assert(ymm_src.getIdx() != ymm_dst.getIdx());  // TODO(TJ): use enfore
+  // check all idx can not equal
+  ymm_t ymm_fx = ymm_t(fx_idx);
+  ymm_t ymm_fy = ymm_t(fy_idx);
+  ymm_t ymm_mask = ymm_t(mask_idx);
+  ymm_t ymm_tmp = ymm_t(tmp_idx);
+  reg64_t reg_ptr_global = rax;
+  push(reg_ptr_global);
+  mov(reg_ptr_global, reinterpret_cast<size_t>(exp_float_consts));
+  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_HIG]);
+  vminps(ymm_src, ymm_src, ymm_tmp);
+  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_LOW]);
+  vmaxps(ymm_src, ymm_src, ymm_tmp);
+  // express exp(x) as exp(g + n*log(2))
+  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_LOG2EF]);
+  vmulps(ymm_fx, ymm_src, ymm_tmp);
+  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_0P5]);
+  vaddps(ymm_fx, ymm_fx, ymm_tmp);
+  vroundps(ymm_fy, ymm_fx, 0x01);
+  // if greater, substract 1
+  vcmpgtps(ymm_mask, ymm_fy, ymm_fx);
+  vmovaps(ymm_tmp, ptr[reg_ptr_global]);
+  vandps(ymm_mask, ymm_mask, ymm_tmp);
+  vsubps(ymm_fx, ymm_fy, ymm_mask);
+  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_C1]);
+  vmulps(ymm_fy, ymm_fx, ymm_tmp);
+  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_C2]);
+  ymm_t ymm_z = ymm_t(ymm_mask.getIdx());
+  vmulps(ymm_z, ymm_fx, ymm_tmp);
+  vsubps(ymm_src, ymm_src, ymm_fy);
+  vsubps(ymm_src, ymm_src, ymm_z);
+  vmulps(ymm_z, ymm_src, ymm_src);
+  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_P0]);
+  vmulps(ymm_dst, ymm_src, ymm_tmp);
+  for (size_t i = OFFSET_EXP_P1; i < OFFSET_EXP_P5;
+       i += (YMM_FLOAT_BLOCK * sizeof(float))) {
+    vmovaps(ymm_tmp, ptr[reg_ptr_global + i]);  // P1~P4
+    vaddps(ymm_dst, ymm_dst, ymm_tmp);
+    vmulps(ymm_dst, ymm_dst, ymm_src);
+  }
+  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_P5]);
+  vaddps(ymm_dst, ymm_dst, ymm_tmp);
+  vmulps(ymm_dst, ymm_dst, ymm_z);
+  vaddps(ymm_dst, ymm_dst, ymm_src);
+  vmovaps(ymm_tmp, ptr[reg_ptr_global]);
+  vaddps(ymm_dst, ymm_dst, ymm_tmp);
+  // build 2^n
+  ymm_t ymm_int = ymm_fx;
+  vcvttps2dq(ymm_int, ymm_fx);
+  mov(reg_ptr_global, reinterpret_cast<size_t>(exp_int_0x7f));
+  vmovdqa(ymm_tmp, ptr[reg_ptr_global]);
+  if (MayIUse(avx2)) {
+    vpaddd(ymm_int, ymm_int, ymm_tmp);
+    vpslld(ymm_int, ymm_int, 23);
+  } else if (MayIUse(avx)) {
+    xmm_t xtmp1 = xmm_t(ymm_int.getIdx());
+    xmm_t xtmp2 = xmm_t(ymm_tmp.getIdx());
+    reg64_t reg_ptr_tmp = reg_ptr_global;
+    mov(reg_ptr_tmp, reinterpret_cast<size_t>(g_tmp_mem));
+    vmovdqa(ptr[reg_ptr_tmp], ymm_int);
+    vmovdqa(ptr[reg_ptr_tmp + YMM_FLOAT_BLOCK * sizeof(float)], ymm_tmp);
+    vpaddd(xtmp1, xtmp1, xtmp2);
+    vpslld(xtmp1, xtmp1, 23);
+    vmovdqa(ptr[reg_ptr_tmp], xtmp1);
+    // next 128bits
+    vmovdqa(xtmp1, ptr[reg_ptr_tmp + 4 /*xmm float block*/ * sizeof(float)]);
+    vmovdqa(xtmp2,
+            ptr[reg_ptr_tmp +
+                (YMM_FLOAT_BLOCK + 4 /*xmm float block*/) * sizeof(float)]);
+    vpaddd(xtmp1, xtmp1, xtmp2);
+    vpslld(xtmp1, xtmp1, 23);
+    vmovdqa(ptr[reg_ptr_tmp + 4 /*xmm float block*/ * sizeof(float)], xtmp1);
+    // load out
+    vmovdqa(ymm_int, ptr[reg_ptr_tmp]);
+  }
+  vmulps(ymm_dst, ymm_dst, ymm_int);
+  pop(reg_ptr_global);
+}
+
+void VActJitCode::sigmoid_ymm(ymm_t& ymm_dst, ymm_t& ymm_src, int fx_idx,
+                              int fy_idx, int mask_idx, int tmp_idx) {
+  // y = 1 / (1 + e^-x)
+  ymm_t ymm_tmp = ymm_t(tmp_idx);
+  reg64_t reg_ptr_global = rax;
+  push(reg_ptr_global);
+  mov(reg_ptr_global, reinterpret_cast<size_t>(exp_float_consts));
+  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_SIGMOID_MAX]);
+  vminps(ymm_src, ymm_src, ymm_tmp);
+  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_SIGMOID_MIN]);
+  vmaxps(ymm_src, ymm_src, ymm_tmp);
+  vxorps(ymm_tmp, ymm_tmp, ymm_tmp);
+  vsubps(ymm_src, ymm_tmp, ymm_src);
+  exp_ymm(ymm_dst, ymm_src, fx_idx, fy_idx, mask_idx, tmp_idx);
+  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_ONE]);
+  vaddps(ymm_dst, ymm_dst, ymm_tmp);
+  vdivps(ymm_dst, ymm_tmp, ymm_dst);
+  pop(reg_ptr_global);
+}
+
+void VActJitCode::tanh_ymm(ymm_t& ymm_dst, ymm_t& ymm_src, int fx_idx,
+                           int fy_idx, int mask_idx, int tmp_idx) {
+  // y = 2 / (1 + e^(-2x)) - 1
+  ymm_t ymm_tmp = ymm_t(tmp_idx);
+  ymm_t ymm_zero = ymm_t(mask_idx);
+  reg64_t reg_ptr_global = rax;
+  push(reg_ptr_global);
+  mov(reg_ptr_global, reinterpret_cast<size_t>(exp_float_consts));
+  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_TWO]);
   vxorps(ymm_zero, ymm_zero, ymm_zero);
-  for (int i = 0; i < num_ / AVX_FLOAT_BLOCK; ++i) {
+  vsubps(ymm_tmp, ymm_zero, ymm_tmp);
+  vmulps(ymm_src, ymm_src, ymm_tmp);
+  exp_ymm(ymm_dst, ymm_src, fx_idx, fy_idx, mask_idx, tmp_idx);
+  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_ONE]);
+  vaddps(ymm_dst, ymm_dst, ymm_tmp);
+  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_TWO]);
+  vdivps(ymm_dst, ymm_tmp, ymm_dst);
+  vmovaps(ymm_tmp, ptr[reg_ptr_global + OFFSET_EXP_ONE]);
+  vsubps(ymm_dst, ymm_dst, ymm_tmp);
+  pop(reg_ptr_global);
+}
+
+void VActJitCode::generate() {
+  xmm_t xmm_zero = xmm_t(2);
+  ymm_t ymm_zero = ymm_t(2);
+  if (type_ == operand_type::relu) {
+    vxorps(ymm_zero, ymm_zero, ymm_zero);
+  }
+  int offset = 0;
+  for (int i = 0; i < num_ / YMM_FLOAT_BLOCK; ++i) {
     vmovups(ymm_src, ptr[param1 + offset]);
-    vmaxps(ymm_dst, ymm_zero, ymm_src);
+    switch (type_) {
+      case operand_type::relu:
+        relu_ymm(ymm_dst, ymm_src, ymm_zero);
+        break;
+      case operand_type::exp:
+        exp_ymm(ymm_dst, ymm_src, 2, 3, 4, 5);
+        break;
+      case operand_type::sigmoid:
+        sigmoid_ymm(ymm_dst, ymm_src, 2, 3, 4, 5);
+        break;
+      case operand_type::tanh:
+        tanh_ymm(ymm_dst, ymm_src, 2, 3, 4, 5);
+        break;
+      case operand_type::identity:
+        break;
+      default:
+        break;
+    }
     vmovups(ptr[param2 + offset], ymm_dst);
-    offset += sizeof(float) * AVX_FLOAT_BLOCK;
+    offset += sizeof(float) * YMM_FLOAT_BLOCK;
   }
-  int rest = num_ % AVX_FLOAT_BLOCK;
+  if (type_ != operand_type::relu) {
+    // TODO(TJ): remove me
+    ret();
+    return;
+  }
+  int rest = num_ % YMM_FLOAT_BLOCK;
   if (rest >= 4) {
     vmovups(xmm_src, ptr[param1 + offset]);
     vmaxps(xmm_dst, xmm_zero, xmm_src);
@@ -151,6 +370,7 @@ void ReluJitCode::generate() {
   }
   ret();
 }
+
 }  // namespace gen
 }  // namespace jitkernel
 }  // namespace math

paddle/fluid/operators/math/jit_code.h

@@ -29,7 +29,16 @@ using ymm_t = const Xbyak::Ymm;
 using zmm_t = const Xbyak::Zmm;
 using Label = Xbyak::Label;
 
-typedef enum { mul = 0, add } operand_type;
+typedef enum {
+  mul = 0,
+  add,
+  sub,
+  relu,
+  exp,
+  sigmoid,
+  tanh,
+  identity
+} operand_type;
 
 // function: vec = Operand(vec(or scalar), vec(or scalar)) (maybe with relu)
 class VXXJitCode : public JitCode {
@@ -85,26 +94,65 @@ class VXXJitCode : public JitCode {
   ymm_t ymm_zero = ymm_t(3);
 };
 
-class ReluJitCode : public JitCode {
+class VActJitCode : public JitCode {
  public:
-  DECLARE_JIT_CODE(ReluJitCode);
-  explicit ReluJitCode(int d, size_t code_size = 256 * 1024,
+  const char* name() const override {
+    std::string base = "VActJitCode";
+    switch (type_) {
+      case operand_type::relu:
+        base += "_Relu";
+        break;
+      case operand_type::exp:
+        base += "_Exp";
+        break;
+      case operand_type::sigmoid:
+        base += "_Sigmoid";
+        break;
+      case operand_type::tanh:
+        base += "_Tanh";
+        break;
+      case operand_type::identity:
+        base += "_Identity";
+        break;
+      default:
+        break;
+    }
+    return base.c_str();
+  }
+
+  explicit VActJitCode(int d, operand_type type, size_t code_size = 256 * 1024,
                        void* code_ptr = nullptr)
-      : JitCode(code_size, code_ptr), num_(d) {}
-  static bool init(int d);
+      : JitCode(code_size, code_ptr), num_(d), type_(type) {}
+  static bool init(int d, operand_type type);
   void generate() override;
 
- private:
+ protected:
+  // compute relu with ymm
+  void relu_ymm(const Xbyak::Ymm& dst, const Xbyak::Ymm& src,
+                const Xbyak::Ymm& zero);
+
+  // compute exp with ymm
+  void exp_ymm(const Xbyak::Ymm& dst, const Xbyak::Ymm& src, int fx_idx = 2,
+               int fy_idx = 3, int mask_idx = 4, int tmp_idx = 5);
+
+  // compute sigmoid with ymm
+  void sigmoid_ymm(const Xbyak::Ymm& dst, const Xbyak::Ymm& src, int fx_idx = 2,
+                   int fy_idx = 3, int mask_idx = 4, int tmp_idx = 5);
+
+  // compute tanh with ymm
+  void tanh_ymm(const Xbyak::Ymm& dst, const Xbyak::Ymm& src, int fx_idx = 2,
+                int fy_idx = 3, int mask_idx = 4, int tmp_idx = 5);
+
+ protected:
   int num_;
+  operand_type type_;
   reg64_t param1{abi_param1};
   reg64_t param2{abi_param2};
 
-  xmm_t xmm_zero = xmm_t(0);
-  xmm_t xmm_src = xmm_t(1);
-  xmm_t xmm_dst = xmm_t(1);
+  xmm_t xmm_src = xmm_t(0);
+  ymm_t ymm_src = ymm_t(0);
 
-  ymm_t ymm_zero = ymm_t(0);
-  ymm_t ymm_src = ymm_t(1);
+  xmm_t xmm_dst = xmm_t(1);
   ymm_t ymm_dst = ymm_t(1);
 };
 

paddle/fluid/operators/math/jit_kernel.h

@@ -29,9 +29,9 @@ namespace jitkernel {
 #define SIGMOID_THRESHOLD_MIN -40.0
 #define SIGMOID_THRESHOLD_MAX 13.0
 #define EXP_MAX_INPUT 40.0
-#define AVX_FLOAT_BLOCK 8
-#define AVX2_FLOAT_BLOCK 8
-#define AVX512_FLOAT_BLOCK 16
+#define XMM_FLOAT_BLOCK 4
+#define YMM_FLOAT_BLOCK 8
+#define ZMM_FLOAT_BLOCK 16
 
 typedef enum { kLT8, kEQ8, kGT8LT16, kEQ16, kGT16 } jit_block;
 
@@ -97,39 +97,23 @@ class VAddBiasKernel : public Kernel {
 template <typename T>
 class VActKernel : public Kernel {
  public:
-  virtual void ComputeDeprecated(const T *x, T *y) const = 0;
+  void (*Compute)(const T *, T *, int);
 };
 
 template <typename T>
-class VReluKernel : public VActKernel<T> {
- public:
-  virtual void ComputeDeprecated(const T *x, T *y) const = 0;
-  void (*Compute)(const T *, T *, int);
-};
+class VReluKernel : public VActKernel<T> {};
 
 template <typename T>
-class VIdentityKernel : public VActKernel<T> {
- public:
-  virtual void ComputeDeprecated(const T *x, T *y) const = 0;
-};
+class VIdentityKernel : public VActKernel<T> {};
 
 template <typename T>
-class VExpKernel : public VActKernel<T> {
- public:
-  virtual void ComputeDeprecated(const T *x, T *y) const = 0;
-};
+class VExpKernel : public VActKernel<T> {};
 
 template <typename T>
-class VSigmoidKernel : public VActKernel<T> {
- public:
-  virtual void ComputeDeprecated(const T *x, T *y) const = 0;
-};
+class VSigmoidKernel : public VActKernel<T> {};
 
 template <typename T>
-class VTanhKernel : public VActKernel<T> {
- public:
-  virtual void ComputeDeprecated(const T *x, T *y) const = 0;
-};
+class VTanhKernel : public VActKernel<T> {};
 
 template <typename T>
 class LSTMKernel : public Kernel {

paddle/fluid/operators/math/jit_kernel_blas.cc

@@ -25,10 +25,6 @@ limitations under the License. */
 #include "paddle/fluid/platform/dynload/mklml.h"
 #endif
 
-#ifdef __AVX__
-#include <immintrin.h>
-#endif
-
 namespace paddle {
 namespace operators {
 namespace math {
@@ -128,23 +124,16 @@ void VScalMKL<double>(const double* a, const double* x, double* y, int n) {
 
 #endif
 
-#define DECLARE_STATIC_FUNC                                 \
-  static inline std::string name(int d) {                   \
-    PADDLE_THROW("DType should be either float or double"); \
-  }                                                         \
-  static inline bool useJIT(int d) { return false; }        \
-  static inline bool useMKL(int d) { return false; }
-
 /* VMUL JitKernel */
 template <typename T>
 class VMulKernelImpl : public VMulKernel<T> {
  public:
-  DECLARE_STATIC_FUNC;
+  JITKERNEL_DECLARE_STATIC_FUNC;
   explicit VMulKernelImpl(int d) : VMulKernel<T>() {
 #ifdef PADDLE_WITH_XBYAK
     if (useJIT(d)) {
       // roughly estimate the size of code
-      size_t sz = 96 + d / AVX_FLOAT_BLOCK * 4 * 8;
+      size_t sz = 96 + d / YMM_FLOAT_BLOCK * 4 * 8;
       jitcode_.reset(new gen::VXXJitCode(d, gen::operand_type::mul, 0, false,
                                          sz > 4096 ? sz : 4096));
       this->Compute =
@@ -191,11 +180,11 @@ bool VMulKernelImpl<double>::useMKL(int d) {
 template <typename T>
 class VAddKernelImpl : public VAddKernel<T> {
  public:
-  DECLARE_STATIC_FUNC;
+  JITKERNEL_DECLARE_STATIC_FUNC;
   explicit VAddKernelImpl(int d) : VAddKernel<T>() {
 #ifdef PADDLE_WITH_XBYAK
     if (useJIT(d)) {
-      size_t sz = 96 + d / AVX_FLOAT_BLOCK * 4 * 8;
+      size_t sz = 96 + d / YMM_FLOAT_BLOCK * 4 * 8;
       jitcode_.reset(new gen::VXXJitCode(d, gen::operand_type::add, 0, false,
                                          sz > 4096 ? sz : 4096));
       this->Compute =
@@ -241,11 +230,11 @@ bool VAddKernelImpl<double>::useMKL(int d) {
 template <typename T>
 class VAddReluKernelImpl : public VAddReluKernel<T> {
  public:
-  DECLARE_STATIC_FUNC;
+  JITKERNEL_DECLARE_STATIC_FUNC;
   explicit VAddReluKernelImpl(int d) : VAddReluKernel<T>() {
 #ifdef PADDLE_WITH_XBYAK
     if (useJIT(d)) {
-      size_t sz = 96 + d / AVX_FLOAT_BLOCK * 4 * 8;
+      size_t sz = 96 + d / YMM_FLOAT_BLOCK * 4 * 8;
       jitcode_.reset(new gen::VXXJitCode(d, gen::operand_type::add, 0, true,
                                          sz > 4096 ? sz : 4096));
       this->Compute =
@@ -273,11 +262,11 @@ bool VAddReluKernelImpl<float>::useJIT(int d) {
 template <typename T>
 class VScalKernelImpl : public VScalKernel<T> {
  public:
-  DECLARE_STATIC_FUNC;
+  JITKERNEL_DECLARE_STATIC_FUNC;
   explicit VScalKernelImpl(int d) : VScalKernel<T>() {
 #ifdef PADDLE_WITH_XBYAK
     if (useJIT(d)) {
-      size_t sz = 96 + d / AVX_FLOAT_BLOCK * 4 * 8;
+      size_t sz = 96 + d / YMM_FLOAT_BLOCK * 4 * 8;
       jitcode_.reset(new gen::VXXJitCode(d, gen::operand_type::mul, 1, false,
                                          sz > 4096 ? sz : 4096));
       this->Compute =
@@ -322,11 +311,11 @@ bool VScalKernelImpl<double>::useMKL(int d) {
 template <typename T>
 class VAddBiasKernelImpl : public VAddBiasKernel<T> {
  public:
-  DECLARE_STATIC_FUNC;
+  JITKERNEL_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;
+      size_t sz = 96 + d / YMM_FLOAT_BLOCK * 4 * 8;
       jitcode_.reset(new gen::VXXJitCode(d, gen::operand_type::add, 1, false,
                                          sz > 4096 ? sz : 4096));
       this->Compute =
@@ -355,15 +344,15 @@ bool VAddBiasKernelImpl<float>::useJIT(int d) {
 template <typename T>
 class VReluKernelImpl : public VReluKernel<T> {
  public:
-  DECLARE_STATIC_FUNC;
+  JITKERNEL_DECLARE_STATIC_FUNC;
   explicit VReluKernelImpl(int d) : VReluKernel<T>() {
-    this->num_ = d;  // TODO(TJ): remove me when ComputeDeprecated done
 #ifdef PADDLE_WITH_XBYAK
     if (useJIT(d)) {
-      size_t sz = 96 /*init*/ +
-                  d / AVX_FLOAT_BLOCK * 4 /* instructions*/ *
-                      8 /*everage byte for each instruction*/;
-      jitcode_.reset(new gen::ReluJitCode(d, sz > 4096 ? sz : 4096));
+      size_t sz = 96 /* init size */ +
+                  d / YMM_FLOAT_BLOCK * 4 /* instructions */ *
+                      8 /* average bytes for each instruction */;
+      jitcode_.reset(new gen::VActJitCode(d, gen::operand_type::relu,
+                                          sz > 4096 ? sz : 4096));
       this->Compute = jitcode_->getCode<void (*)(const T*, T*, int)>();
       return;
     }
@@ -371,24 +360,32 @@ class VReluKernelImpl : public VReluKernel<T> {
 
     this->Compute = VReluRefer<T>;
   }
-  void ComputeDeprecated(const T* x, T* y) const override {
-    VReluRefer(x, y, this->num_);
-  }
 #ifdef PADDLE_WITH_XBYAK
 
  private:
-  std::unique_ptr<gen::ReluJitCode> jitcode_{nullptr};
+  std::unique_ptr<gen::VActJitCode> jitcode_{nullptr};
 #endif
 };
 
 #ifdef PADDLE_WITH_XBYAK
 template <>
 bool VReluKernelImpl<float>::useJIT(int d) {
-  return gen::ReluJitCode::init(d);
+  return gen::VActJitCode::init(d, gen::operand_type::relu);
 }
 #endif
 
-#undef DECLARE_STATIC_FUNC
+template <typename T>
+inline void VIdentityRefer(const T* x, T* y, int n) {}
+
+/* An empty JitKernel */
+template <typename T>
+class VIdentityKernelImpl : public VIdentityKernel<T> {
+ public:
+  JITKERNEL_DECLARE_STATIC_FUNC;
+  explicit VIdentityKernelImpl(int d) : VIdentityKernel<T>() {
+    this->Compute = VIdentityRefer<T>;
+  }
+};
 
 REGISTER_JITKERNEL(vmul, VMulKernel);
 REGISTER_JITKERNEL(vadd, VAddKernel);
@@ -396,16 +393,7 @@ REGISTER_JITKERNEL(vaddrelu, VAddReluKernel);
 REGISTER_JITKERNEL(vscal, VScalKernel);
 REGISTER_JITKERNEL(vaddbias, VAddBiasKernel);
 REGISTER_JITKERNEL(vrelu, VReluKernel);
-
-/* An empty JitKernel */
-template <typename T, platform::jit::cpu_isa_t isa, jit_block>
-class VIdentityKernelImpl : public VIdentityKernel<T> {
- public:
-  explicit VIdentityKernelImpl(int d) : VIdentityKernel<T>() { this->num_ = d; }
-  void ComputeDeprecated(const T* x, T* y) const override {}
-};
-
-REGISTER_JITKERNEL_DEPRECATED(videntity, VIdentityKernel);
+REGISTER_JITKERNEL(videntity, VIdentityKernel);
 
 }  // namespace jitkernel
 }  // namespace math

paddle/fluid/operators/math/jit_kernel_crf_decode.cc

@@ -105,14 +105,14 @@ class CRFDecodeKernelImpl : public CRFDecodeKernel<T> {
       int tag_num)                                                             \
       : CRFDecodeKernel<float>() {                                             \
     this->num_ = tag_num;                                                      \
-    this->end_ = this->num_ / AVX_FLOAT_BLOCK;                                 \
-    this->rest_ = this->num_ % AVX_FLOAT_BLOCK;                                \
+    this->end_ = this->num_ / YMM_FLOAT_BLOCK;                                 \
+    this->rest_ = this->num_ % YMM_FLOAT_BLOCK;                                \
   }                                                                            \
   template <>                                                                  \
   void CRFDecodeKernelImpl<float, jit::avx, block>::Compute(                   \
       const int seq_len, const float* x, const float* w, float* alpha,         \
       int* track) const {                                                      \
-    INIT_ALPHA(AVX_FLOAT_BLOCK)                                                \
+    INIT_ALPHA(YMM_FLOAT_BLOCK)                                                \
     /* Use the column-major strategy to get the location of maximum score.*/   \
     int seq_offset = 0;                                                        \
     constexpr int state_trans_base_idx = 2;                                    \
@@ -150,7 +150,7 @@ class CRFDecodeKernelImpl : public CRFDecodeKernel<T> {
           max_score = _mm256_max_ps(max_score, score_v);                       \
           trans_offset += this->num_;                                          \
         }                                                                      \
-        UPDATE_ALPHA(AVX_FLOAT_BLOCK)                                          \
+        UPDATE_ALPHA(YMM_FLOAT_BLOCK)                                          \
       }                                                                        \
       seq_offset += this->num_;                                                \
     }                                                                          \
@@ -161,14 +161,14 @@ class CRFDecodeKernelImpl : public CRFDecodeKernel<T> {
   CRFDecodeKernelImpl<float, isa, block>::CRFDecodeKernelImpl(int tag_num)     \
       : CRFDecodeKernel<float>() {                                             \
     this->num_ = tag_num;                                                      \
-    this->end_ = this->num_ / AVX2_FLOAT_BLOCK;                                \
-    this->rest_ = this->num_ % AVX2_FLOAT_BLOCK;                               \
+    this->end_ = this->num_ / YMM_FLOAT_BLOCK;                                 \
+    this->rest_ = this->num_ % YMM_FLOAT_BLOCK;                                \
   }                                                                            \
   template <>                                                                  \
   void CRFDecodeKernelImpl<float, isa, block>::Compute(                        \
       const int seq_len, const float* x, const float* w, float* alpha,         \
       int* track) const {                                                      \
-    INIT_ALPHA(AVX2_FLOAT_BLOCK)                                               \
+    INIT_ALPHA(YMM_FLOAT_BLOCK)                                                \
     /* Use the column-major strategy to get the location of maximum score.*/   \
     int seq_offset = 0;                                                        \
     constexpr int state_trans_base_idx = 2;                                    \
@@ -196,7 +196,7 @@ class CRFDecodeKernelImpl : public CRFDecodeKernel<T> {
           max_score = _mm256_max_ps(max_score, score_v);                       \
           trans_offset += this->num_;                                          \
         }                                                                      \
-        UPDATE_ALPHA(AVX2_FLOAT_BLOCK)                                         \
+        UPDATE_ALPHA(YMM_FLOAT_BLOCK)                                          \
       }                                                                        \
       seq_offset += this->num_;                                                \
     }                                                                          \
@@ -208,14 +208,14 @@ class CRFDecodeKernelImpl : public CRFDecodeKernel<T> {
       int tag_num)                                                             \
       : CRFDecodeKernel<float>() {                                             \
     this->num_ = tag_num;                                                      \
-    this->end_ = this->num_ / AVX512_FLOAT_BLOCK;                              \
-    this->rest_ = this->num_ % AVX512_FLOAT_BLOCK;                             \
+    this->end_ = this->num_ / ZMM_FLOAT_BLOCK;                                 \
+    this->rest_ = this->num_ % ZMM_FLOAT_BLOCK;                                \
   }                                                                            \
   template <>                                                                  \
   void CRFDecodeKernelImpl<float, jit::avx512f, block>::Compute(               \
       const int seq_len, const float* x, const float* w, float* alpha,         \
       int* track) const {                                                      \
-    INIT_ALPHA(AVX512_FLOAT_BLOCK)                                             \
+    INIT_ALPHA(ZMM_FLOAT_BLOCK)                                                \
     /* Use the column-major strategy to get the location of maximum score.*/   \
     int seq_offset = 0;                                                        \
     constexpr int state_trans_base_idx = 2;                                    \
@@ -250,7 +250,7 @@ class CRFDecodeKernelImpl : public CRFDecodeKernel<T> {
                                                        this->num_ + j_offset), \
                             max_j);                                            \
         /* Calculate the offset of next step*/                                 \
-        j_offset += AVX512_FLOAT_BLOCK;                                        \
+        j_offset += ZMM_FLOAT_BLOCK;                                           \
         if (j == this->end_ - 1) {                                             \
           if (this->rest_ > 0) {                                               \
             j_offset += last_offset;                                           \

paddle/fluid/operators/math/jit_kernel_macro.h

@@ -15,12 +15,20 @@ limitations under the License. */
 #pragma once
 #include <string>
 #include "paddle/fluid/platform/cpu_info.h"
+#include "paddle/fluid/platform/enforce.h"
 
 namespace paddle {
 namespace operators {
 namespace math {
 namespace jitkernel {
 
+#define JITKERNEL_DECLARE_STATIC_FUNC                       \
+  static inline std::string name(int d) {                   \
+    PADDLE_THROW("DType should be either float or double"); \
+  }                                                         \
+  static inline bool useJIT(int d) { return false; }        \
+  static inline bool useMKL(int d) { return false; }
+
 #define JITKERNEL_DEFINE_NAME(ker_key, ker_class)    \
   template <>                                        \
   std::string ker_class##Impl<float>::name(int d) {  \
@@ -87,13 +95,13 @@ namespace jitkernel {
 namespace jit = platform::jit;
 // TODO(TJ): below defines are deprecated, would be remove recently
 #define SEARCH_BLOCK(macro_, ker, dtype, isa)              \
-  if (d < AVX_FLOAT_BLOCK) {                                  \
+  if (d < YMM_FLOAT_BLOCK) {                               \
     macro_(ker, dtype, isa, kLT8);                         \
-  } else if (d == AVX_FLOAT_BLOCK) {                          \
+  } else if (d == YMM_FLOAT_BLOCK) {                       \
     macro_(ker, dtype, isa, kEQ8);                         \
-  } else if (d > AVX_FLOAT_BLOCK && d < AVX512_FLOAT_BLOCK) { \
+  } else if (d > YMM_FLOAT_BLOCK && d < ZMM_FLOAT_BLOCK) { \
     macro_(ker, dtype, isa, kGT8LT16);                     \
-  } else if (d == AVX512_FLOAT_BLOCK) {                       \
+  } else if (d == ZMM_FLOAT_BLOCK) {                       \
     macro_(ker, dtype, isa, kEQ16);                        \
   } else {                                                 \
     macro_(ker, dtype, isa, kGT16);                        \

paddle/fluid/operators/math/jit_kernel_rnn.cc

@@ -175,26 +175,26 @@ class LSTMKernelImpl : public LSTMKernel<T> {
   void ComputeCtHt(T* gates, const T* ct_1, T* ct, T* ht, const T* wp_data,
                    T* checked) const override {
     // gates: W_ch, W_ih, W_fh, W_oh
-    act_gate_d3_->ComputeDeprecated(gates + d_, gates + d_);
+    act_gate_d3_->Compute(gates + d_, gates + d_, d3_);
 
     /* C_t = C_t-1 * fgated + cand_gated * igated */
-    act_cand_d_->ComputeDeprecated(gates, gates);
+    act_cand_d_->Compute(gates, gates, d_);
     vmul_d_->Compute(gates, gates + d_, gates + d_, d_);
     vmul_d_->Compute(ct_1, gates + d2_, gates + d2_, d_);
     vadd_d_->Compute(gates + d_, gates + d2_, ct, d_);
 
     /* H_t = act_cell(C_t) * ogated */
-    act_cell_d_->ComputeDeprecated(ct, gates + d2_);
+    act_cell_d_->Compute(ct, gates + d2_, d_);
     vmul_d_->Compute(gates + d2_, gates + d3_, ht, d_);
   }
   void ComputeC1H1(T* gates, T* ct, T* ht, const T* wp_data) const override {
     /* C_t = igated * cgated*/
-    act_gate_d_->ComputeDeprecated(gates + d_, gates + d_);
-    act_cand_d_->ComputeDeprecated(gates, gates);
+    act_gate_d_->Compute(gates + d_, gates + d_, d_);
+    act_cand_d_->Compute(gates, gates, d_);
     vmul_d_->Compute(gates, gates + d_, ct, d_);
     /* H_t = act_cell(C_t) * ogated */
-    act_gate_d_->ComputeDeprecated(gates + d3_, gates + d3_);
-    act_cell_d_->ComputeDeprecated(ct, gates + d2_);
+    act_gate_d_->Compute(gates + d3_, gates + d3_, d_);
+    act_cell_d_->Compute(ct, gates + d2_, d_);
     vmul_d_->Compute(gates + d2_, gates + d3_, ht, d_);
   }
 
@@ -292,32 +292,32 @@ class PeepholeKernelImpl : public LSTMKernel<T> {
     vmul_d_->Compute(wp_data, ct_1, checked, d_);
     vmul_d_->Compute(wp_data + d_, ct_1, checked + d_, d_);
     vadd_d2_->Compute(checked, gates + d_, gates + d_, d2_);
-    act_gate_d2_->ComputeDeprecated(gates + d_, gates + d_);
+    act_gate_d2_->Compute(gates + d_, gates + d_, d2_);
     /* C_t = C_t-1 * fgated + cand_gated * igated*/
-    act_cand_d_->ComputeDeprecated(gates, gates);
+    act_cand_d_->Compute(gates, gates, d_);
     vmul_d_->Compute(gates, gates + d_, gates + d_, d_);
     vmul_d_->Compute(ct_1, gates + d2_, gates + d2_, d_);
     vadd_d_->Compute(gates + d_, gates + d2_, ct, d_);
     /* get ogated*/
     vmul_d_->Compute(wp_data + d2_, ct, gates + d_, d_);
     vadd_d_->Compute(gates + d_, gates + d3_, gates + d3_, d_);
-    act_gate_d_->ComputeDeprecated(gates + d3_, gates + d3_);
+    act_gate_d_->Compute(gates + d3_, gates + d3_, d_);
     /* H_t = act_cell(C_t) * ogated */
-    act_cell_d_->ComputeDeprecated(ct, gates + d2_);
+    act_cell_d_->Compute(ct, gates + d2_, d_);
     vmul_d_->Compute(gates + d2_, gates + d3_, ht, d_);
   }
 
   void ComputeC1H1(T* gates, T* ct, T* ht, const T* wp_data) const override {
     /* C_t = igated * cgated*/
-    act_gate_d_->ComputeDeprecated(gates + d_, gates + d_);
-    act_cand_d_->ComputeDeprecated(gates, gates);
+    act_gate_d_->Compute(gates + d_, gates + d_, d_);
+    act_cand_d_->Compute(gates, gates, d_);
     vmul_d_->Compute(gates, gates + d_, ct, d_);
     /* get outgated, put W_oc * C_t on igated */
     vmul_d_->Compute(wp_data + d2_, ct, gates + d_, d_);
     vadd_d_->Compute(gates + d_, gates + d3_, gates + d3_, d_);
     /* H_t = act_cell(C_t) * ogated */
-    act_gate_d_->ComputeDeprecated(gates + d3_, gates + d3_);
-    act_cell_d_->ComputeDeprecated(ct, gates + d2_);
+    act_gate_d_->Compute(gates + d3_, gates + d3_, d_);
+    act_cell_d_->Compute(ct, gates + d2_, d_);
     vmul_d_->Compute(gates + d2_, gates + d3_, ht, d_);
   }
 
@@ -376,20 +376,20 @@ class GRUKernelImpl : public GRUKernel<T> {
   }
 
   void ComputeH1(T* gates, T* ht) const override {
-    act_gate_d_->ComputeDeprecated(gates, gates);
-    act_state_d_->ComputeDeprecated(gates + d2_, gates + d2_);
+    act_gate_d_->Compute(gates, gates, d_);
+    act_state_d_->Compute(gates + d2_, gates + d2_, d_);
     vmul_d_->Compute(gates, gates + d2_, ht, d_);
   }
 
   void ComputeHtPart1(T* gates, const T* ht_1, T* ht) const override {
     // W: {W_update, W_reset; W_state}
-    act_gate_d2_->ComputeDeprecated(gates, gates);
+    act_gate_d2_->Compute(gates, gates, d2_);
     vmul_d_->Compute(ht_1, gates + d_, ht, d_);
   }
 
   void ComputeHtPart2(T* gates, const T* ht_1, T* ht) const override {
     T* y = gates + d2_;
-    act_state_d_->ComputeDeprecated(y, y);
+    act_state_d_->Compute(y, y, d_);
     // out = zt*ht~ + (1-zt)*ht_1
     for (int i = 0; i < d_; ++i) {
       ht[i] = gates[i] * y[i] + (static_cast<T>(1) - gates[i]) * ht_1[i];

paddle/fluid/operators/math/jit_kernel_test.cc

@@ -181,7 +181,8 @@ TEST(JitKernel, vexp) {
 
     auto ttgts = GetCurrentUS();
     for (int i = 0; i < repeat; ++i) {
-      ker->ComputeDeprecated(x_data, ztgt_data);
+      // ker->Compute(x_data, ztgt_data);
+      ker->Compute(x_data, ztgt_data, d);
     }
     auto ttgte = GetCurrentUS();
 
@@ -222,7 +223,7 @@ void vsigmoid_better(
     y[i] = (x[i] < min) ? min : ((x[i] > max) ? max : x[i]);
     y[i] = 0.f - y[i];
   }
-  vexp->ComputeDeprecated(y, y);
+  vexp->Compute(y, y, n);
   for (int i = 0; i < n; ++i) {
     y[i] = 1.f / (1.f + y[i]);
   }
@@ -253,7 +254,7 @@ TEST(JitKernel, vsigmoid) {
     auto trefe = GetCurrentUS();
     auto ttgts = GetCurrentUS();
     for (int i = 0; i < repeat; ++i) {
-      ker->ComputeDeprecated(x_data, ztgt_data);
+      ker->Compute(x_data, ztgt_data, d);
     }
     auto ttgte = GetCurrentUS();
 
@@ -287,7 +288,7 @@ void vtanh_better(
     const int n, const float* x, float* y) {
   const float a = 2.f, b = -1.f;
   vscal->Compute(&a, x, y, n);
-  vsigmoid->ComputeDeprecated(y, y);
+  vsigmoid->Compute(y, y, n);
   vscal->Compute(&a, y, y, n);
   vaddbias->Compute(&b, y, y, n);
 }
@@ -321,7 +322,7 @@ TEST(JitKernel, vtanh) {
     auto trefe = GetCurrentUS();
     auto ttgts = GetCurrentUS();
     for (int i = 0; i < repeat; ++i) {
-      ker->ComputeDeprecated(x_data, ztgt_data);
+      ker->Compute(x_data, ztgt_data, d);
     }
     auto ttgte = GetCurrentUS();
 
@@ -344,8 +345,8 @@ void lstm_ctht_ref(
     const std::shared_ptr<
         const paddle::operators::math::jitkernel::VExpKernel<float>>& vexp_1,
     const int d, float* gates, const float* ct_1, float* ct, float* ht) {
-  vsigmoid_3d->ComputeDeprecated(gates + d, gates + d);
-  vtanh_d->ComputeDeprecated(gates, gates);
+  vsigmoid_3d->Compute(gates + d, gates + d, 3 * d);
+  vtanh_d->Compute(gates, gates, d);
   const float *i = gates + d, *f = gates + d * 2, *o = gates + d * 3;
   const float min = SIGMOID_THRESHOLD_MIN;
   const float max = SIGMOID_THRESHOLD_MAX;
@@ -355,7 +356,7 @@ void lstm_ctht_ref(
     // H_t = act_cell(C_t) * ogated
     float tmp = ct[k] * 2;
     tmp = 0.f - ((tmp < min) ? min : ((tmp > max) ? max : tmp));
-    vexp_1->ComputeDeprecated(&tmp, &tmp);
+    vexp_1->Compute(&tmp, &tmp, 1);
     tmp = 2.f / (1.f + tmp) - 1.f;
     ht[k] = tmp * o[k];
   }
@@ -373,13 +374,13 @@ void lstm_ctht_better(
         const paddle::operators::math::jitkernel::VAddKernel<float>>& vadd_d,
     const int d, float* gates, const float* ct_1, float* ct, float* ht) {
   int d2 = d * 2;
-  vsigmoid_3d->ComputeDeprecated(gates + d, gates + d);
-  vtanh_d->ComputeDeprecated(gates, gates);
+  vsigmoid_3d->Compute(gates + d, gates + d, 3 * d);
+  vtanh_d->Compute(gates, gates, d);
   vmul_d->Compute(gates, gates + d, gates + d, d);
   vmul_d->Compute(ct_1, gates + d2, gates + d2, d);
   vadd_d->Compute(gates + d, gates + d2, ct, d);
   /* H_t = act_cell(C_t) * ogated */
-  vtanh_d->ComputeDeprecated(ct, gates + d2);
+  vtanh_d->Compute(ct, gates + d2, d);
   vmul_d->Compute(gates + d2, gates + d * 3, ht, d);
 }
 
@@ -736,7 +737,7 @@ void vaddrelu_better(
         const paddle::operators::math::jitkernel::VReluKernel<float>>& vrelu,
     const float* x, const float* y, float* z, int d) {
   vadd->Compute(x, y, z, d);
-  vrelu->ComputeDeprecated(z, z);
+  vrelu->Compute(z, z, d);
 }
 
 TEST(JitKernel, vaddrelu) {

python/paddle/fluid/tests/book/test_label_semantic_roles.py

@@ -38,7 +38,7 @@ depth = 8
 mix_hidden_lr = 1e-3
 
 IS_SPARSE = True
-PASS_NUM = 1
+PASS_NUM = 2
 BATCH_SIZE = 10
 
 embedding_name = 'emb'
@@ -196,7 +196,7 @@ def train(use_cuda, save_dirname=None, is_local=True):
                         print("second per batch: " + str((time.time(
                         ) - start_time) / batch_id))
                     # Set the threshold low to speed up the CI test
-                    if float(cost) < 60.0:
+                    if float(cost) < 80.0:
                         if save_dirname is not None:
                             # TODO(liuyiqun): Change the target to crf_decode
                             fluid.io.save_inference_model(save_dirname, [
@@ -208,6 +208,10 @@ def train(use_cuda, save_dirname=None, is_local=True):
 
                 batch_id = batch_id + 1
 
+        raise RuntimeError(
+            "This model should save_inference_model and return, but not reach here, please check!"
+        )
+
     if is_local:
         train_loop(fluid.default_main_program())
     else: