From 6e1ee7fb5789f67202882ca36d49c7406b2b3c51 Mon Sep 17 00:00:00 2001
From: tensor-tang <tangjian03@baidu.com>
Date: Tue, 29 Jan 2019 05:51:31 +0000
Subject: [PATCH] cache softmax kernel func

test=develop
---
 paddle/fluid/operators/jit/helper.h        | 23 ++++++----
 paddle/fluid/operators/jit/more/mix/mix.cc | 53 ++++------------------
 paddle/fluid/operators/math/softmax_impl.h |  5 +-
 3 files changed, 28 insertions(+), 53 deletions(-)

diff --git a/paddle/fluid/operators/jit/helper.h b/paddle/fluid/operators/jit/helper.h
index 7bdc45779b7..7e8049c0e1d 100644
--- a/paddle/fluid/operators/jit/helper.h
+++ b/paddle/fluid/operators/jit/helper.h
@@ -118,26 +118,33 @@ typename KernelTuples::func_type Get(
   return GetRefer<KT, KernelTuples>();
 }
 
-template <KernelType KT, typename KernelTuples>
-class KernelFuncsCache {
+template <KernelType KT, typename KernelTuples, typename PlaceType>
+class KernelFuncs {
  public:
-  KernelFuncsCache() = default;
-  static KernelFuncsCache& Instance() {
-    static thread_local KernelFuncsCache<KT, KernelTuples> g_func_cache;
+  KernelFuncs() = default;
+  static KernelFuncs& Cache() {
+    static thread_local KernelFuncs<KT, KernelTuples, PlaceType> g_func_cache;
     return g_func_cache;
   }
 
   bool Has(int key) const { return funcs_.find(key) != funcs_.end(); }
 
-  typename KernelTuples::func_type At(int key) { return funcs_.at(key); }
-
   void Insert(int key, typename KernelTuples::func_type func) {
     funcs_.emplace(key, func);
   }
 
+  typename KernelTuples::func_type At(int key) {
+    if (Has(key)) {
+      return funcs_.at(key);
+    }
+    auto func = Get<KT, KernelTuples, PlaceType>(key);
+    Insert(key, func);
+    return func;
+  }
+
  private:
   std::unordered_map<int, typename KernelTuples::func_type> funcs_;
-  DISABLE_COPY_AND_ASSIGN(KernelFuncsCache);
+  DISABLE_COPY_AND_ASSIGN(KernelFuncs);
 };
 
 const char* to_string(KernelType kt);
diff --git a/paddle/fluid/operators/jit/more/mix/mix.cc b/paddle/fluid/operators/jit/more/mix/mix.cc
index 0f42ac158ca..0036d1c238b 100644
--- a/paddle/fluid/operators/jit/more/mix/mix.cc
+++ b/paddle/fluid/operators/jit/more/mix/mix.cc
@@ -49,49 +49,16 @@ void VTanh(const T* x, T* y, int n) {
 }
 
 void Softmax(const T* x, T* y, int n, int bs) {
-  typename XRNTuples<T>::func_type compute_hmax{nullptr};
-  typename XRNTuples<T>::func_type compute_hsum{nullptr};
-  typename AXYNTuples<T>::func_type compute_vscal{nullptr};
-  typename AXYNTuples<T>::func_type compute_vaddbias{nullptr};
-  typename XYNTuples<T>::func_type compute_vexp{nullptr};
-
-  if (!KernelFuncsCache<kHMax, XRNTuples<T>>::Instance().Has(n)) {
-    compute_hmax = Get<kHMax, XRNTuples<T>, platform::CPUPlace>(n);
-    KernelFuncsCache<kHMax, XRNTuples<T>>::Instance().Insert(n, compute_hmax);
-  } else {
-    compute_hmax = KernelFuncsCache<kHMax, XRNTuples<T>>::Instance().At(n);
-  }
-
-  if (!KernelFuncsCache<kHSum, XRNTuples<T>>::Instance().Has(n)) {
-    compute_hsum = Get<kHSum, XRNTuples<T>, platform::CPUPlace>(n);
-    KernelFuncsCache<kHSum, XRNTuples<T>>::Instance().Insert(n, compute_hsum);
-  } else {
-    compute_hsum = KernelFuncsCache<kHSum, XRNTuples<T>>::Instance().At(n);
-  }
-
-  if (!KernelFuncsCache<kVScal, AXYNTuples<T>>::Instance().Has(n)) {
-    compute_vscal = Get<kVScal, AXYNTuples<T>, platform::CPUPlace>(n);
-    KernelFuncsCache<kVScal, AXYNTuples<T>>::Instance().Insert(n,
-                                                               compute_vscal);
-  } else {
-    compute_vscal = KernelFuncsCache<kVScal, AXYNTuples<T>>::Instance().At(n);
-  }
-
-  if (!KernelFuncsCache<kVAddBias, AXYNTuples<T>>::Instance().Has(n)) {
-    compute_vaddbias = Get<kVAddBias, AXYNTuples<T>, platform::CPUPlace>(n);
-    KernelFuncsCache<kVAddBias, AXYNTuples<T>>::Instance().Insert(
-        n, compute_vaddbias);
-  } else {
-    compute_vaddbias =
-        KernelFuncsCache<kVAddBias, AXYNTuples<T>>::Instance().At(n);
-  }
-
-  if (!KernelFuncsCache<kVExp, XYNTuples<T>>::Instance().Has(n)) {
-    compute_vexp = Get<KernelType::kVExp, XYNTuples<T>, platform::CPUPlace>(n);
-    KernelFuncsCache<kVExp, XYNTuples<T>>::Instance().Insert(n, compute_vexp);
-  } else {
-    compute_vexp = KernelFuncsCache<kVExp, XYNTuples<T>>::Instance().At(n);
-  }
+  auto compute_hmax =
+      KernelFuncs<kHMax, XRNTuples<T>, platform::CPUPlace>::Cache().At(n);
+  auto compute_hsum =
+      KernelFuncs<kHSum, XRNTuples<T>, platform::CPUPlace>::Cache().At(n);
+  auto compute_vscal =
+      KernelFuncs<kVScal, AXYNTuples<T>, platform::CPUPlace>::Cache().At(n);
+  auto compute_vaddbias =
+      KernelFuncs<kVAddBias, AXYNTuples<T>, platform::CPUPlace>::Cache().At(n);
+  auto compute_vexp =
+      KernelFuncs<kVExp, XYNTuples<T>, platform::CPUPlace>::Cache().At(n);
 
   for (int i = 0; i < bs; ++i) {
     T scalar;
diff --git a/paddle/fluid/operators/math/softmax_impl.h b/paddle/fluid/operators/math/softmax_impl.h
index 1ff9ff684fc..a1cb3f97282 100644
--- a/paddle/fluid/operators/math/softmax_impl.h
+++ b/paddle/fluid/operators/math/softmax_impl.h
@@ -82,8 +82,9 @@ class SoftmaxFunctor<DeviceContext, float, true, enable_if_CPU<DeviceContext>> {
     const int kClassDim = 1;
     // 2D data. Batch x C
     auto compute_softmax =
-        jit::Get<jit::kSoftmax, jit::SoftmaxTuples<float>, platform::CPUPlace>(
-            in_dims[kClassDim]);
+        jit::KernelFuncs<jit::kSoftmax, jit::SoftmaxTuples<float>,
+                         platform::CPUPlace>::Cache()
+            .At(in_dims[kClassDim]);
     compute_softmax(in_data, out_data, in_dims[kClassDim], in_dims[kBatchDim]);
   }
 };
-- 
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