enable crf decoding intrinsic code

paddle/fluid/operators/jit/README.md

@@ -19,6 +19,10 @@ PaddlePaddle/Paddle/paddle/fluid/
     │   ├── ...
     │   ├── mkl/
     │   │   └── ...
+    │   ├── mkldnn/
+    │   │   └── ...
+    │   ├── intrinsic/
+    │   │   └── ...
     │   └── openblas/
     │       └── ...
     └── refer/

paddle/fluid/operators/jit/more/intrinsic/CMakeLists.txt

@@ -6,4 +6,3 @@ set(JIT_KERNEL_DEPS ${JIT_KERNEL_DEPS} jit_kernel_intrinsic PARENT_SCOPE)
 
 # use mkl kernels by name and type
 USE_JITKERNEL_MORE(crfdecoding, intrinsic)
-USE_JITKERNEL_MORE(layernorm, intrinsic)

paddle/fluid/operators/jit/more/intrinsic/crf_decoding.cc

@@ -13,7 +13,7 @@
  * limitations under the License. */
 
 #include "paddle/fluid/operators/jit/more/intrinsic/crf_decoding.h"
-#include "paddle/fluid/operators/jit/refer/refer.h"
+#include <limits>
 #include "paddle/fluid/operators/jit/registry.h"
 #include "paddle/fluid/platform/cpu_info.h"
 
@@ -21,118 +21,151 @@ namespace paddle {
 namespace operators {
 namespace jit {
 namespace more {
-namespace mkl {
-
-template <>
-void VMul<float>(const float* x, const float* y, float* z, int n) {
-  platform::dynload::vsMul(n, x, y, z);
-}
-
-template <>
-void VMul<double>(const double* x, const double* y, double* z, int n) {
-  platform::dynload::vdMul(n, x, y, z);
-}
-
-template <>
-void VAdd<float>(const float* x, const float* y, float* z, int n) {
-  platform::dynload::vsAdd(n, x, y, z);
-}
-
-template <>
-void VAdd<double>(const double* x, const double* y, double* z, int n) {
-  platform::dynload::vdAdd(n, x, y, z);
-}
-
-template <>
-void VScal<float>(const float* a, const float* x, float* y, int n) {
-  if (x == y) {
-    platform::dynload::cblas_sscal(n, *a, y, 1);
+namespace intrinsic {
+
+void CRFDecoding(const int seq_len, const float* x, const float* w,
+                 float* alpha, int* track, int tag_num) {
+  const int step_size =
+      platform::MayIUse(platform::avx512f) ? ZMM_FLOAT_BLOCK : YMM_FLOAT_BLOCK;
+  const int end = tag_num / step_size;
+  const int rest = tag_num % step_size;
+  /* Setup the alpha initial value.*/
+  int i_offset = 0;
+  int last_offset = rest - step_size;
+  for (int i = 0; i <= end; ++i) {
+#ifdef __AVX512F__
+    // Declare the variable for the content of weights, input and alpha values.
+    __m512 w_content, x_content, alpha_content;
+    // Load the relevant data into the variables from un-aligned address.
+    w_content = _mm512_loadu_ps(w + i_offset);
+    x_content = _mm512_loadu_ps(x + i_offset);
+    alpha_content = _mm512_add_ps(w_content, x_content);
+    // Save the alpha value.
+    _mm512_storeu_ps(alpha_value + i_offset, alpha_content);
+#else
+    // AVX or AVX2
+    // weights, input and alpha values.
+    __m256 w_content, x_content, alpha_content;
+    // Load the relevant data into the variables from un-aligned address.
+    w_content = _mm256_loadu_ps(w + i_offset);
+    x_content = _mm256_loadu_ps(x + i_offset);
+    alpha_content = _mm256_add_ps(w_content, x_content);
+    _mm256_storeu_ps(alpha + i_offset, alpha_content);
+#endif
+    i_offset += step_size;
+    if (i == end - 1) {
+      if (rest > 0) {
+        i_offset += last_offset;
       } else {
-    refer::VScal<float>(a, x, y, n);
+        break;
       }
-}
-
-template <>
-void VScal<double>(const double* a, const double* x, double* y, int n) {
-  if (x == y) {
-    platform::dynload::cblas_dscal(n, *a, y, 1);
+    }
+  }
+  // Use the column-major strategy to get the location of maximum score.
+  int seq_offset = 0;
+  constexpr int state_trans_base_idx = 2;
+  for (int k = 1; k < seq_len; ++k) {
+    int j_offset = 0;
+    for (int j = 0; j <= end; ++j) {
+/* Initialize the variables of maximum score and location.*/
+#ifdef __AVX512F__
+      __m512 max_score = _mm512_set1_ps(-std::numeric_limits<float>::max());
+      __m512i max_j = _mm512_setzero_si512();
+#else
+      __m256 max_score = _mm256_set1_ps(-std::numeric_limits<float>::max());
+      __m256i max_j = _mm256_set1_epi32(0);
+#endif
+      /* Calculate the offset of transition_weights.*/
+      int trans_offset = state_trans_base_idx * tag_num + j_offset;
+      for (int i = 0; i < tag_num; ++i) {
+/* Initalize the content of alpha variable with related offset.*/
+#ifdef __AVX512F__
+        __m512 alpha_content = _mm512_set1_ps(*(alpha + seq_offset + i));
+        /* Obtain the content of weights from un-aligned address.*/
+        __m512 w_content = _mm512_loadu_ps(w + trans_offset);
+        __m512 score_v = _mm512_add_ps(alpha_content, w_content);
+        __mmask16 mask = _mm512_cmp_ps_mask(score_v, max_score, _CMP_GT_OS);
+        /* AVX512 instructions.*/
+        max_j = _mm512_mask_set1_epi32(max_j, mask, i);
+        /* Update the max_score value.*/
+        max_score = _mm512_max_ps(max_score, score_v);
+
+#else
+        __m256 alpha_content = _mm256_broadcast_ss(alpha + seq_offset + i);
+        /* Obtain the content of weights from un-aligned address.*/
+        __m256 w_content = _mm256_loadu_ps(w + trans_offset);
+        __m256 score_v = _mm256_add_ps(alpha_content, w_content);
+        __m256 mask = _mm256_cmp_ps(score_v, max_score, _CMP_GT_OS);
+/* According to the mask value, update the index of the max_score.*/
+#ifdef __AVX2__
+        max_j = _mm256_or_si256(
+            _mm256_andnot_si256((__m256i)mask, max_j),
+            _mm256_and_si256((__m256i)mask, _mm256_set1_epi32(i)));
+#else
+        __m128i lo_max_j = _mm256_extractf128_si256(max_j, 0);
+        __m128i hi_max_j = _mm256_extractf128_si256(max_j, 1);
+        __m128i lo_mask =
+            _mm256_extractf128_si256(*(__m256i*)&mask, 0);  // NOLINT
+        __m128i hi_mask =
+            _mm256_extractf128_si256(*(__m256i*)&mask, 1);  // NOLINT
+        lo_max_j = _mm_andnot_si128(lo_mask, lo_max_j);
+        hi_max_j = _mm_andnot_si128(hi_mask, hi_max_j);
+        lo_mask = _mm_and_si128(lo_mask, _mm_set1_epi32(i));
+        hi_mask = _mm_and_si128(hi_mask, _mm_set1_epi32(i));
+        lo_max_j = _mm_or_si128(lo_mask, lo_max_j);
+        hi_max_j = _mm_or_si128(hi_mask, hi_max_j);
+        max_j = _mm256_insertf128_si256(max_j, lo_max_j, 0);
+        max_j = _mm256_insertf128_si256(max_j, hi_max_j, 1);
+#endif
+        /* Update the max_score value.*/
+        max_score = _mm256_max_ps(max_score, score_v);
+
+#endif
+
+        trans_offset += tag_num;
+      }
+/* Update the alpha and track values. */
+#ifdef __AVX512F__
+      __m512 x_content =
+          _mm512_loadu_ps(x + seq_offset + this->num_ + j_offset);
+      max_score = _mm512_add_ps(max_score, x_content);
+      _mm512_storeu_ps(alpha + seq_offset + this->num_ + j_offset, max_score);
+      _mm512_storeu_si512(reinterpret_cast<__m512i*>(track + seq_offset +
+                                                     this->num_ + j_offset),
+                          max_j);
+#else
+      __m256 x_content = _mm256_loadu_ps(x + seq_offset + tag_num + j_offset);
+      max_score = _mm256_add_ps(max_score, x_content);
+      _mm256_storeu_ps(alpha + seq_offset + tag_num + j_offset, max_score);
+      _mm256_storeu_si256(
+          reinterpret_cast<__m256i*>(track + seq_offset + tag_num + j_offset),
+          max_j);
+#endif
+
+      /* Calculate the offset of next step*/
+      j_offset += step_size;
+      if (j == end - 1) {
+        if (rest > 0) {
+          j_offset += last_offset;
         } else {
-    refer::VScal<double>(a, x, y, n);
+          break;
+        }
+      }
+    }
+    seq_offset += tag_num;
   }
 }
 
-template <>
-void VExp<float>(const float* x, float* y, int n) {
-  platform::dynload::vsExp(n, x, y);
-}
-
-template <>
-void VExp<double>(const double* x, double* y, int n) {
-  platform::dynload::vdExp(n, x, y);
-}
-
-// TODO(TJ): tuning me carefully on AVX, AVX2 and AVX512
-template <>
-bool VMulKernel<float>::UseMe(int d) const {
-  return platform::MayIUse(platform::avx512f) && d > 512;
-}
-
-template <>
-bool VAddKernel<float>::UseMe(int d) const {
-  return platform::MayIUse(platform::avx512f) && d > 512;
-}
-
-template <>
-bool VScalKernel<float>::UseMe(int d) const {
-  return platform::MayIUse(platform::avx512f) && d > 512;
-}
-
-template <>
-bool VExpKernel<float>::UseMe(int d) const {
-  return d > 7;
-}
-
-template <>
-bool VSigmoidKernel<float>::UseMe(int d) const {
-  return d > 7;
-}
-
-template <>
-bool VTanhKernel<float>::UseMe(int d) const {
-  return d > 7;
+bool CRFDecodingKernel::UseMe(int d) const {
+  return platform::MayIUse(platform::avx);
 }
 
-#define AWALYS_USE_ME_WITH_DOUBLE(func)           \
-  template <>                                     \
-  bool func##Kernel<double>::UseMe(int d) const { \
-    return true;                                  \
-  }
-
-AWALYS_USE_ME_WITH_DOUBLE(VMul);
-AWALYS_USE_ME_WITH_DOUBLE(VAdd);
-AWALYS_USE_ME_WITH_DOUBLE(VScal);
-AWALYS_USE_ME_WITH_DOUBLE(VExp);
-AWALYS_USE_ME_WITH_DOUBLE(VSigmoid);
-AWALYS_USE_ME_WITH_DOUBLE(VTanh);
-
-#undef AWALYS_USE_ME_WITH_DOUBLE
-}  // namespace mkl
+}  // namespace intrinsic
 }  // namespace more
 }  // namespace jit
 }  // namespace operators
 }  // namespace paddle
 
-namespace mkl = paddle::operators::jit::more::mkl;
-
-#define REGISTER_MKL_KERNEL(key, func)                        \
-  REGISTER_JITKERNEL_MORE(key, mkl, mkl::func##Kernel<float>, \
-                          mkl::func##Kernel<double>)
-
-REGISTER_MKL_KERNEL(vmul, VMul);
-REGISTER_MKL_KERNEL(vadd, VAdd);
-REGISTER_MKL_KERNEL(vscal, VScal);
-REGISTER_MKL_KERNEL(vexp, VExp);
-REGISTER_MKL_KERNEL(vsigmoid, VSigmoid);
-REGISTER_MKL_KERNEL(vtanh, VTanh);
+namespace intrinsic = paddle::operators::jit::more::intrinsic;
 
-#undef REGISTER_MKL_KERNEL
+REGISTER_JITKERNEL_MORE(crfdecoding, intrinsic, intrinsic::CRFDecodingKernel);

paddle/fluid/operators/jit/more/intrinsic/crf_decoding.h

@@ -21,68 +21,18 @@ namespace paddle {
 namespace operators {
 namespace jit {
 namespace more {
-namespace mkl {
+namespace intrinsic {
 
-template <typename T>
-void VMul(const T* x, const T* y, T* z, int n);
+void CRFDecoding(const int seq_len, const float* x, const float* w,
+                 float* alpha, int* track, int tag_num);
 
-template <typename T>
-void VAdd(const T* x, const T* y, T* z, int n);
+class CRFDecodingKernel : public KernelImpl<CRFDecodingTuples<float>> {
+ public:
+  CRFDecodingKernel() { this->func = CRFDecoding; }
+  bool UseMe(typename CRFDecodingTuples<float>::attr_type) const override;
+};
 
-template <typename T>
-void VScal(const T* a, const T* x, T* y, int n);
-
-template <typename T>
-void VExp(const T* x, T* y, int n);
-
-template <typename T>
-void VSigmoid(const T* x, T* y, int n) {
-  const T min = SIGMOID_THRESHOLD_MIN;
-  const T max = SIGMOID_THRESHOLD_MAX;
-  for (int i = 0; i < n; ++i) {
-    y[i] = (x[i] < min) ? min : ((x[i] > max) ? max : x[i]);
-    y[i] = static_cast<T>(0) - y[i];
-  }
-  VExp(y, y, n);
-  for (int i = 0; i < n; ++i) {
-    y[i] = static_cast<T>(1) / (static_cast<T>(1) + y[i]);
-  }
-}
-
-template <typename T>
-void VTanh(const T* x, T* y, int n) {
-  for (int i = 0; i < n; ++i) {
-    y[i] = static_cast<T>(2) * x[i];
-  }
-  VSigmoid(y, y, n);
-  for (int i = 0; i < n; ++i) {
-    y[i] = static_cast<T>(2) * y[i] - static_cast<T>(1);
-  }
-}
-
-#define DECLARE_MKL_KERNEL(name, tuples)                      \
-  template <typename T>                                       \
-  class name##Kernel : public KernelImpl<tuples<T>> {         \
-   public:                                                    \
-    name##Kernel() { this->func = name<T>; }                  \
-    bool UseMe(typename tuples<T>::attr_type) const override; \
-  }
-
-// XYZN
-DECLARE_MKL_KERNEL(VMul, XYZNTuples);
-DECLARE_MKL_KERNEL(VAdd, XYZNTuples);
-
-// AXYN
-DECLARE_MKL_KERNEL(VScal, AXYNTuples);
-
-// XYN
-DECLARE_MKL_KERNEL(VExp, XYNTuples);
-DECLARE_MKL_KERNEL(VSigmoid, XYNTuples);
-DECLARE_MKL_KERNEL(VTanh, XYNTuples);
-
-#undef DECLARE_MKL_KERNEL
-
-}  // namespace mkl
+}  // namespace intrinsic
 }  // namespace more
 }  // namespace jit
 }  // namespace operators