Gpu Dropout kernel fix

mindspore/ccsrc/kernel/gpu/cuda_impl/dropout_impl.cu

@@ -19,10 +19,10 @@
 #include "include/cuda_runtime.h"
 
 __global__ void DropoutForwardKernel(const float *input, float *mask, float *output, size_t num_count,
-                                     float drop_prob) {
-  float scale = 1.f / drop_prob;
+                                     float keep_prob) {
+  float scale = 1.f / keep_prob;
   for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < num_count; i += blockDim.x * gridDim.x) {
-    mask[i] = mask[i] > drop_prob;
+    mask[i] = mask[i] <= keep_prob;
     output[i] = scale * input[i] * mask[i];
   }
 }
@@ -34,8 +34,8 @@ void DropoutForward(const float *input, float *mask, float *output, size_t num_c
 }
 
 __global__ void DropoutBackwardKernel(const float *dy, const float *mask, float *dx, size_t num_count,
-                                      float drop_prob) {
-  float scale = 1.f / (1.f - drop_prob);
+                                      float keep_prob) {
+  float scale = 1.f / keep_prob;
   for (size_t i = blockIdx.x * blockDim.x + threadIdx.x; i < num_count; i += blockDim.x * gridDim.x) {
     dx[i] = scale * dy[i] * mask[i];
   }

mindspore/ccsrc/kernel/gpu/cuda_impl/dropout_impl.cuh

@@ -18,9 +18,9 @@
 #define MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_DROPOUT_H_
 
 #include "device/gpu/cuda_common.h"
-void DropoutForward(const float *input, float *mask, float *output, size_t num_count, float drop_prob,
+void DropoutForward(const float *input, float *mask, float *output, size_t num_count, float keep_prob,
                     cudaStream_t cuda_stream);
-void DropoutBackward(const float *dy, const float *mask, float *dx, size_t num_count, float drop_prob,
+void DropoutBackward(const float *dy, const float *mask, float *dx, size_t num_count, float keep_prob,
                      cudaStream_t cuda_stream);
 
 #endif  // MINDSPORE_CCSRC_KERNEL_GPU_CUDA_IMPL_DROPOUT_H_

mindspore/ccsrc/kernel/gpu/nn/dropout_gpu_kernel.cc

@@ -23,7 +23,7 @@ DropoutGpuFwdKernel::DropoutGpuFwdKernel()
     : cudnn_handle_(nullptr),
       is_null_input_(false),
       num_count_(0),
-      drop_prob_(0.0),
+      keep_prob_(0.0),
       states_init_(false),
       mask_generator_(nullptr) {}
 
@@ -54,7 +54,7 @@ bool DropoutGpuFwdKernel::Init(const CNodePtr &kernel_node) {
   for (size_t x : input_shape) {
     num_count_ *= x;
   }
-  drop_prob_ = GetValue<float>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("drop_prob"));
+  keep_prob_ = GetValue<float>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("keep_prob"));
 
   InitSizeLists();
   return true;
@@ -92,7 +92,7 @@ bool DropoutGpuFwdKernel::Launch(const std::vector<AddressPtr> &inputs, const st
   }
 
   curandGenerateUniform(mask_generator_, mask, num_count_);
-  DropoutForward(input, mask, output, num_count_, drop_prob_, reinterpret_cast<cudaStream_t>(stream_ptr));
+  DropoutForward(input, mask, output, num_count_, keep_prob_, reinterpret_cast<cudaStream_t>(stream_ptr));
 
   return true;
 }

mindspore/ccsrc/kernel/gpu/nn/dropout_gpu_kernel.h

@@ -52,7 +52,7 @@ class DropoutGpuFwdKernel : public GpuKernel {
   cudnnHandle_t cudnn_handle_;
   bool is_null_input_;
   size_t num_count_;
-  float drop_prob_;
+  float keep_prob_;
   bool states_init_;
   curandGenerator_t mask_generator_;
   std::vector<size_t> input_size_list_;

mindspore/ccsrc/kernel/gpu/nn/dropout_grad_kernel.cc

@@ -20,7 +20,7 @@
 namespace mindspore {
 namespace kernel {
 DropoutGradGpuFwdKernel::DropoutGradGpuFwdKernel()
-    : cudnn_handle_(nullptr), is_null_input_(false), num_count_(0), drop_prob_(0.0) {}
+    : cudnn_handle_(nullptr), is_null_input_(false), num_count_(0), keep_prob_(0.0) {}
 
 DropoutGradGpuFwdKernel::~DropoutGradGpuFwdKernel() { DestroyResource(); }
 
@@ -50,7 +50,7 @@ bool DropoutGradGpuFwdKernel::Init(const CNodePtr &kernel_node) {
   for (size_t x : input_shape) {
     num_count_ *= x;
   }
-  drop_prob_ = GetValue<float>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("drop_prob"));
+  keep_prob_ = GetValue<float>(AnfAlgo::GetCNodePrimitive(kernel_node)->GetAttr("keep_prob"));
 
   InitSizeLists();
   return true;
@@ -84,7 +84,7 @@ bool DropoutGradGpuFwdKernel::Launch(const std::vector<AddressPtr> &inputs, cons
   auto *mask = reinterpret_cast<float *>(inputs[1]->addr);
   auto *dx = reinterpret_cast<float *>(outputs[0]->addr);
 
-  DropoutBackward(dy, mask, dx, num_count_, drop_prob_, reinterpret_cast<cudaStream_t>(stream_ptr));
+  DropoutBackward(dy, mask, dx, num_count_, keep_prob_, reinterpret_cast<cudaStream_t>(stream_ptr));
 
   return true;
 }

mindspore/ccsrc/kernel/gpu/nn/dropout_grad_kernel.h

@@ -45,7 +45,7 @@ class DropoutGradGpuFwdKernel : public GpuKernel {
   cudnnHandle_t cudnn_handle_;
   bool is_null_input_;
   size_t num_count_;
-  float drop_prob_;
+  float keep_prob_;
   std::vector<size_t> input_size_list_;
   std::vector<size_t> output_size_list_;
   std::vector<size_t> workspace_size_list_;

mindspore/ops/_grad/grad_nn_ops.py

@@ -675,7 +675,7 @@ def get_bprop_binary_cross_entropy(self):
 @bprop_getters.register(P.Dropout)
 def get_bprop_dropout(self):
     """Grad definition for `Dropout` operation."""
-    grad = P.DropoutGrad(self.drop_prob)
+    grad = P.DropoutGrad(self.keep_prob)
 
     def bprop(x, out, dout):
         _, mask = out

mindspore/ops/operations/nn_ops.py

@@ -3227,7 +3227,8 @@ class Dropout(PrimitiveWithInfer):
     During training, randomly zeroes some of the elements of the input tensor with probability.
 
     Args:
-        drop_prob (float): probability of an element to be zeroed. Default: 0.
+        keep_prob (float): The keep rate, between 0 and 1, e.g. keep_prob = 0.9,
+          means dropping out 10% of input units.
 
     Inputs:
         - **shape** (tuple[int]) - The shape of target mask.
@@ -3236,14 +3237,14 @@ class Dropout(PrimitiveWithInfer):
         Tensor, the value of generated mask for input shape.
 
     Examples:
-        >>> dropout = P.Dropout(drop_prob=0.5)
+        >>> dropout = P.Dropout(keep_prob=0.5)
         >>> in = Tensor((20, 16, 50, 50))
         >>> out = dropout(in)
     """
 
     @prim_attr_register
-    def __init__(self, drop_prob=0):
-        self.drop_prob = validator.check_number_range("drop_prob", drop_prob, 0, 1, Rel.INC_BOTH, self.name)
+    def __init__(self, keep_prob=0.5):
+        self.keep_prob = validator.check_number_range("keep_prob", keep_prob, 0, 1, Rel.INC_RIGHT, self.name)
 
     def infer_shape(self, x_shape):
         validator.check_integer("x_shape", len(x_shape), 1, Rel.GE, self.name)
@@ -3262,7 +3263,8 @@ class DropoutGrad(PrimitiveWithInfer):
     of the input tensor with probability.
 
     Args:
-        drop_prob (float): probability of an element to be zeroed. Default: 0.
+        keep_prob (float): The keep rate, between 0 and 1, e.g. keep_prob = 0.9,
+          means dropping out 10% of input units.
 
     Inputs:
         - **shape** (tuple[int]) - The shape of target mask.
@@ -3271,14 +3273,14 @@ class DropoutGrad(PrimitiveWithInfer):
         Tensor, the value of generated mask for input shape.
 
     Examples:
-        >>> dropout_grad = P.DropoutGrad(drop_prob=0.5)
+        >>> dropout_grad = P.DropoutGrad(keep_prob=0.5)
         >>> in = Tensor((20, 16, 50, 50))
         >>> out = dropout_grad(in)
     """
 
     @prim_attr_register
-    def __init__(self, drop_prob=0):
-        self.drop_prob = validator.check_number_range("drop_prob", drop_prob, 0, 1, Rel.INC_BOTH, self.name)
+    def __init__(self, keep_prob=0.5):
+        self.keep_prob = validator.check_number_range("keep_prob", keep_prob, 0, 1, Rel.INC_RIGHT, self.name)
 
     def infer_shape(self, dy_shape, mask_shape):
         return dy_shape