fix compile problem on windows and some invalid argument check

Fix the compile problem in norm op in the windows env, add delete some invalid argument

paddle/fluid/operators/p_norm_op.cc

@@ -64,18 +64,19 @@ class PnormOp : public framework::OperatorWithKernel {
     OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "p_norm");
     OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "p_norm");
     auto porder = ctx->Attrs().Get<float>("porder");
-    PADDLE_ENFORCE_NE(porder, 0,
-                      platform::errors::InvalidArgument(
-                          "The input porder of p_norm is not support for "
-                          "porder == 0, INFINITY, -INFINITY now."));
     PADDLE_ENFORCE_NE(porder, INFINITY,
-                      platform::errors::InvalidArgument(
+                      platform::errors::Unimplemented(
                           "The input porder of p_norm is not support for "
                           "porder == 0, INFINITY, -INFINITY now."));
     PADDLE_ENFORCE_NE(porder, -INFINITY,
-                      platform::errors::InvalidArgument(
+                      platform::errors::Unimplemented(
                           "The input porder of p_norm is not support for "
                           "porder == 0, INFINITY, -INFINITY now."));
+    PADDLE_ENFORCE_GT(porder, 0.0f,
+                      platform::errors::InvalidArgument(
+                          "The input porder of p_norm is not support for "
+                          "porder <= 0, But received porder=%f.",
+                          porder));
     auto xdim = ctx->GetInputDim("X");
     int axis = ctx->Attrs().Get<int>("axis");
     bool keepdim = ctx->Attrs().Get<bool>("keepdim");

paddle/fluid/operators/p_norm_op.cu

@@ -44,6 +44,9 @@ __global__ void Pnorm(const T* x, const int pre,
   typedef cub::BlockReduce<T, BlockDim> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   int num = pre * post;
+  auto porder_t = static_cast<T>(porder);
+  auto porder_inv = static_cast<T>(1.0 / porder);
+
   for (int i = blockIdx.x; i < num; i += gridDim.x) {
     int base = (i / post) * post * axis_n + (i % post);
 
@@ -51,12 +54,12 @@ __global__ void Pnorm(const T* x, const int pre,
     __shared__ T norm;
     for (int j = threadIdx.x; j < axis_n; j += blockDim.x) {
       const T x_ij = x[base + j * post];
-      sum += inline_pow(inline_abs(x_ij), porder);
+      sum += inline_pow(inline_abs(x_ij), porder_t);
     }
     T reduce_result = BlockReduce(temp_storage).Sum(sum);
 
     if (threadIdx.x == 0) {
-      norm = inline_pow(reduce_result, 1.0f / porder);
+      norm = inline_pow(reduce_result, porder_inv);
       out_norm[i] = norm;
     }
     __syncthreads();
@@ -100,6 +103,7 @@ __global__ void PnormGradient(const T* x, const T* x_norm, const T* y_grad,
   __shared__ typename BlockReduce::TempStorage temp_storage_sum;
   // dx = (x/pnorm_broadcast).pow(p-1) * norm_dy.broadcast * sign(x)
   int num = pre * post;
+  auto porder_grad = static_cast<T>(porder - 1.0f);
   for (int i = blockIdx.x; i < num; i += gridDim.x) {
     T sum = 0.0;
     __shared__ T row_sum;
@@ -128,8 +132,8 @@ __global__ void PnormGradient(const T* x, const T* x_norm, const T* y_grad,
       int index = base + j * post;
       const T x_ij = inline_abs(x[index]);
       const T dy_ij = y_grad[index];
-      x_grad[index] = inline_pow(x_ij, porder - 1.0f) /
-                      (inline_pow(pnorm_i, porder - 1.0f) + eps) * yout_i *
+      x_grad[index] = inline_pow(x_ij, porder_grad) /
+                      (inline_pow(pnorm_i, porder_grad) + eps) * yout_i *
                       inline_sign(x[index]);
     }
   }