clean for range in test=develop

paddle/fluid/operators/optimizers/adam_op.h

@@ -227,8 +227,10 @@ struct SparseAdamFunctor {
   inline HOSTDEVICE void operator()(size_t i) const {
     auto row_idx =
         math::BinarySearch<int64_t>(rows_, row_count_, i / row_numel_);
-    T g = row_idx >= 0 ? grad_[row_idx * row_numel_ + i % row_numel_] : 0;
-    adam_update(i, g);
+    if (!(lazy_mode_ && row_idx < 0)) {
+      T g = row_idx >= 0 ? grad_[row_idx * row_numel_ + i % row_numel_] : 0;
+      adam_update(i, g);
+    }
   }
 };
 
@@ -359,19 +361,15 @@ class AdamOpKernel : public framework::OpKernel<T> {
           param_out.template mutable_data<T>(ctx.GetPlace()), rows, row_numel,
           grad_merge.rows().size(), lazy_mode);
       VLOG(3) << "lazy_mode :" << lazy_mode;
-      if (lazy_mode) {
-        std::vector<int64_t> id_vector;
+      if (lazy_mode && platform::is_cpu_place(ctx.GetPlace())) {
         size_t row_count = grad_merge.rows().size();
         std::vector<int64_t> cpu_rows(grad_merge.rows());
         for (size_t row_index = 0; row_index < row_count; ++row_index) {
           for (size_t offset = 0; offset < row_numel; ++offset) {
             size_t i = cpu_rows[row_index] * row_numel + offset;
-            id_vector.push_back(i);
+            functor.adam_update(i, grad_data[row_index * row_numel + offset]);
           }
         }
-        platform::ForRangeIn<DeviceContext> for_range_in(
-            static_cast<const DeviceContext&>(ctx.device_context()), id_vector);
-        for_range_in(functor);
       } else {
         platform::ForRange<DeviceContext> for_range(
             static_cast<const DeviceContext&>(ctx.device_context()),

paddle/fluid/platform/for_range.h

@@ -22,29 +22,6 @@ limitations under the License. */
 namespace paddle {
 namespace platform {
 
-template <typename DeviceContext>
-struct ForRangeIn {
-  ForRangeIn(const DeviceContext& dev_ctx, std::vector<int64_t> range);
-
-  template <typename Function>
-  void operator()(Function func) const;
-};
-
-template <>
-struct ForRangeIn<CPUDeviceContext> {
-  ForRangeIn(const CPUDeviceContext& dev_ctx, std::vector<int64_t> range)
-      : range_(range) {}
-
-  template <typename Function>
-  void operator()(Function func) const {
-    for (auto i : range_) {
-      func(i);
-    }
-  }
-
-  std::vector<int64_t> range_;
-};
-
 template <typename DeviceContext>
 struct ForRange {
   ForRange(const DeviceContext& dev_ctx, size_t limit);
@@ -106,35 +83,6 @@ struct ForRange<CUDADeviceContext> {
   int limit_;
 };
 
-template <typename T, typename Function>
-__global__ static void ForRangeInElemwiseOp(Function func, T* vector,
-                                            int vector_size) {
-  size_t idx = static_cast<size_t>(blockIdx.x * blockDim.x + threadIdx.x);
-  if (idx < vector_size) {
-    func(vector[idx]);
-  }
-}
-
-template <>
-struct ForRangeIn<CUDADeviceContext> {
-  ForRangeIn(const CUDADeviceContext& dev_ctx, std::vector<int64_t> range)
-      : dev_ctx_(dev_ctx), range_(range) {}
-
-  template <typename Function>
-  inline void operator()(Function func) const {
-    constexpr int num_threads = 1024;
-    int range_size = range_.size();
-    int block_size = range_size <= num_threads ? range_size : num_threads;
-    int grid_size = (range_.size() + num_threads - 1) / num_threads;
-
-    ForRangeInElemwiseOp<<<grid_size, block_size, 0, dev_ctx_.stream()>>>(
-        func, range_.CUDAData(dev_ctx_.GetPlace()), range_size);
-  }
-
-  const CUDADeviceContext& dev_ctx_;
-  framework::Vector<int64_t> range_;
-};
-
 #endif
 
 }  // namespace platform