fix bug for big number; float->double and code refine

Author: chengduoZH

paddle/fluid/operators/math/concat.cu

@@ -70,7 +70,7 @@ __global__ void KernelConcat(T** inputs, const int input_col,
                              const int output_rows, const int output_cols,
                              T* output) {
   int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
-  float inv_input_col = 1.0 / input_col;
+  double inv_input_col = 1.0 / input_col;
   for (; tid_x < output_cols; tid_x += blockDim.x * gridDim.x) {
     int split = tid_x * inv_input_col;
     int in_offset = tid_x - split * input_col;
@@ -113,7 +113,7 @@ __global__ void KernelConcatGrad(const T* input, const int input_row,
                                  const int input_col, const int output_cols,
                                  T** outputs) {
   int tid_x = blockIdx.x * blockDim.x + threadIdx.x;
-  float inv_input_col = 1.0 / input_col;
+  double inv_input_col = 1.0 / input_col;
   for (; tid_x < input_col; tid_x += blockDim.x * gridDim.x) {
     int split = tid_x * inv_input_col;
     int in_offset = tid_x - split * input_col;
@@ -145,8 +145,8 @@ class ConcatFunctor<platform::CUDADeviceContext, T> {
     int cols = input[0].numel() / rows;
     int out_rows = rows, out_cols = 0;
 
-    paddle::framework::Vector<int16_t> inputs_data(num * sizeof(T*) / 2);
-    paddle::framework::Vector<int> inputs_cols(num + 1);
+    framework::Vector<int16_t> inputs_data(num * sizeof(T*) / 2);
+    framework::Vector<int> inputs_cols(num + 1);
     inputs_cols[0] = 0;
     T** inputs_ptr = reinterpret_cast<T**>(inputs_data.data());
 
@@ -168,15 +168,14 @@ class ConcatFunctor<platform::CUDADeviceContext, T> {
     // computation
     // set the thread block and grid according to CurrentDeviceId
     const int kThreadsPerBlock = 1024;
-    int block_cols = std::min(out_cols, kThreadsPerBlock);
-    int block_rows = std::max(kThreadsPerBlock / block_cols, 1);
+    int block_cols = kThreadsPerBlock;
+    if (out_cols < kThreadsPerBlock) {  // block_cols is aligned by 32.
+      block_cols = ((out_cols + 31) >> 5) << 5;
+    }
+    int block_rows = kThreadsPerBlock / block_cols;
     dim3 block_size = dim3(block_cols, block_rows, 1);
 
-    int dev_id = paddle::platform::GetCurrentDeviceId();
-    int multi_process = paddle::platform::GetCUDAMultiProcessors(dev_id);
-    int max_threads_per_mp =
-        paddle::platform::GetCUDAMaxThreadsPerMultiProcessor(dev_id);
-    int max_threads = multi_process * max_threads_per_mp;
+    int max_threads = context.GetMaxPhysicalThreadCount();
     int max_blocks = std::max(max_threads / kThreadsPerBlock, 1);
 
     int grid_cols =
@@ -218,8 +217,8 @@ class ConcatGradFunctor<platform::CUDADeviceContext, T> {
     int input_col = 0;
     bool sameShape = true;
 
-    paddle::framework::Vector<int16_t> outputs_data(num * sizeof(T*) / 2);
-    paddle::framework::Vector<int> outputs_cols(num + 1);
+    framework::Vector<int16_t> outputs_data(num * sizeof(T*) / 2);
+    framework::Vector<int> outputs_cols(num + 1);
     outputs_cols[0] = 0;
     T** outputs_ptr = reinterpret_cast<T**>(outputs_data.data());
 
@@ -239,12 +238,20 @@ class ConcatGradFunctor<platform::CUDADeviceContext, T> {
 
     // computation
     const int kThreadsPerBlock = 1024;
-    int block_cols = std::min(input_col, kThreadsPerBlock);
-    int block_rows = std::max(kThreadsPerBlock / block_cols, 1);
+    int block_cols = kThreadsPerBlock;
+    if (input_col < kThreadsPerBlock) {  // block_cols is aligned by 32.
+      block_cols = ((input_col + 31) >> 5) << 5;
+    }
+    int block_rows = kThreadsPerBlock / block_cols;
     dim3 block_size = dim3(block_cols, block_rows, 1);
 
-    int grid_cols = (input_col + block_cols - 1) / block_cols;
-    int grid_rows = (input_row + block_rows - 1) / block_rows;
+    int max_threads = context.GetMaxPhysicalThreadCount();
+    int max_blocks = std::max(max_threads / kThreadsPerBlock, 1);
+
+    int grid_cols =
+        std::min((input_col + block_cols - 1) / block_cols, max_blocks);
+    int grid_rows =
+        std::min(max_blocks / grid_cols, std::max(input_row / block_rows, 1));
     dim3 grid_size = dim3(grid_cols, grid_rows, 1);
 
     if (sameShape) {

paddle/fluid/platform/device_context.cc

@@ -121,6 +121,8 @@ class EigenCudaStreamDevice : public Eigen::StreamInterface {
 
 CUDADeviceContext::CUDADeviceContext(CUDAPlace place) : place_(place) {
   SetDeviceId(place_.device);
+  multi_process = GetCUDAMultiProcessors(place_.device);
+  max_threads_per_mp = GetCUDAMaxThreadsPerMultiProcessor(place_.device);
   PADDLE_ENFORCE(cudaStreamCreate(&stream_));
   eigen_stream_.reset(new EigenCudaStreamDevice());
   eigen_stream_->Reinitialize(&stream_, place);
@@ -154,6 +156,10 @@ void CUDADeviceContext::Wait() const {
   PADDLE_ENFORCE(cudaGetLastError());
 }
 
+int CUDADeviceContext::GetMaxPhysicalThreadCount() const {
+  return multi_process * max_threads_per_mp;
+}
+
 Eigen::GpuDevice* CUDADeviceContext::eigen_device() const {
   return eigen_device_.get();
 }

paddle/fluid/platform/device_context.h

@@ -79,6 +79,9 @@ class CUDADeviceContext : public DeviceContext {
   /*! \brief  Return place in the device context. */
   Place GetPlace() const override;
 
+  /*! \brief  Return the max physical thread count in the device context */
+  int GetMaxPhysicalThreadCount() const;
+
   /*! \brief  Return eigen device in the device context. */
   Eigen::GpuDevice* eigen_device() const;
 
@@ -100,6 +103,9 @@ class CUDADeviceContext : public DeviceContext {
   cudaStream_t stream_;
   cudnnHandle_t cudnn_handle_;
   cublasHandle_t cublas_handle_;
+
+  int multi_process;
+  int max_threads_per_mp;
 };
 
 template <>