support fp16 (#35888)

paddle/fluid/operators/elementwise/elementwise_max_op.cu

@@ -41,12 +41,16 @@ namespace ops = paddle::operators;
 
 REGISTER_OP_CUDA_KERNEL(
     elementwise_max,
+    ops::ElementwiseMaxKernel<paddle::platform::CUDADeviceContext,
+                              paddle::platform::float16>,
     ops::ElementwiseMaxKernel<paddle::platform::CUDADeviceContext, float>,
     ops::ElementwiseMaxKernel<paddle::platform::CUDADeviceContext, double>,
     ops::ElementwiseMaxKernel<paddle::platform::CUDADeviceContext, int>,
     ops::ElementwiseMaxKernel<paddle::platform::CUDADeviceContext, int64_t>);
 REGISTER_OP_CUDA_KERNEL(
     elementwise_max_grad,
+    ops::ElementwiseMaxGradKernel<paddle::platform::CUDADeviceContext,
+                                  paddle::platform::float16>,
     ops::ElementwiseMaxGradKernel<paddle::platform::CUDADeviceContext, float>,
     ops::ElementwiseMaxGradKernel<paddle::platform::CUDADeviceContext, double>,
     ops::ElementwiseMaxGradKernel<paddle::platform::CUDADeviceContext, int>,

paddle/fluid/operators/elementwise/elementwise_max_op.h

@@ -39,14 +39,14 @@ class ElementwiseMaxKernel : public framework::OpKernel<T> {
 template <typename T>
 struct MaxGradDx {
   HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
-    return dout * (x > y);
+    return dout * static_cast<T>(x > y);
   }
 };
 
 template <typename T>
 struct MaxGradDy {
   HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
-    return dout * (x <= y);
+    return dout * static_cast<T>(x <= y);
   }
 };
 

paddle/fluid/operators/p_norm_op.cu

@@ -20,7 +20,9 @@ limitations under the License. */
 #include <hipcub/hipcub.hpp>
 namespace cub = hipcub;
 #endif
+#include "paddle/fluid/operators/amp/fp16_type_traits.h"
 #include "paddle/fluid/operators/p_norm_op.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace paddle {
 namespace operators {
@@ -30,12 +32,23 @@ __device__ __forceinline__ int sgn(T val) {
   return (T(0) < val) - (val < T(0));
 }
 
+__device__ __forceinline__ platform::float16 inline_abs(platform::float16 x) {
+  return static_cast<platform::float16>(abs(static_cast<float>(x)));
+}
 __device__ __forceinline__ float inline_abs(float x) { return abs(x); }
 __device__ __forceinline__ double inline_abs(double x) { return abs(x); }
 
+__device__ __forceinline__ int inline_sign(platform::float16 x) {
+  return sgn<platform::float16>(x);
+}
 __device__ __forceinline__ int inline_sign(float x) { return sgn<float>(x); }
 __device__ __forceinline__ int inline_sign(double x) { return sgn<double>(x); }
 
+__device__ __forceinline__ platform::float16 inline_pow(
+    platform::float16 base, platform::float16 exponent) {
+  return static_cast<platform::float16>(
+      pow(static_cast<float>(base), static_cast<float>(exponent)));
+}
 __device__ __forceinline__ float inline_pow(float base, float exponent) {
   return pow(base, exponent);
 }
@@ -47,21 +60,23 @@ template <typename T, int BlockDim>
 __global__ void Pnorm(const T* x, const int pre,
                       const int axis_n,  // dim in axis
                       const int post, float porder, T* out_norm) {
-  typedef cub::BlockReduce<T, BlockDim> BlockReduce;
+  using MT = typename details::MPTypeTrait<T>::Type;
+  typedef cub::BlockReduce<MT, 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);
+  auto porder_t = static_cast<MT>(porder);
+  auto porder_inv = static_cast<MT>(1.0 / porder);
 
   for (int i = blockIdx.x; i < num; i += gridDim.x) {
     int base = (i / post) * post * axis_n + (i % post);
-    T sum = 0.0;
+    MT sum = static_cast<MT>(0.0);
     for (int j = threadIdx.x; j < axis_n; j += blockDim.x) {
-      const T x_ij = x[base + j * post];
+      const MT x_ij = static_cast<MT>(x[base + j * post]);
       sum += inline_pow(inline_abs(x_ij), porder_t);
     }
-    T reduce_result = BlockReduce(temp_storage).Sum(sum);
-    if (threadIdx.x == 0) out_norm[i] = inline_pow(reduce_result, porder_inv);
+    MT reduce_result = BlockReduce(temp_storage).Sum(sum);
+    if (threadIdx.x == 0)
+      out_norm[i] = static_cast<T>(inline_pow(reduce_result, porder_inv));
   }
 }
 
@@ -69,18 +84,19 @@ template <typename T, int BlockDim>
 __global__ void ZeorNorm(const T* x, const int pre,
                          const int axis_n,  // dim in axis
                          const int post, T* out_norm) {
-  typedef cub::BlockReduce<T, BlockDim> BlockReduce;
+  using MT = typename details::MPTypeTrait<T>::Type;
+  typedef cub::BlockReduce<MT, BlockDim> BlockReduce;
   __shared__ typename BlockReduce::TempStorage temp_storage;
   int num = pre * post;
   for (int i = blockIdx.x; i < num; i += gridDim.x) {
     int base = (i / post) * post * axis_n + (i % post);
-    T sum = 0.0;
+    MT sum = static_cast<MT>(0.0);
     for (int j = threadIdx.x; j < axis_n; j += blockDim.x) {
-      const T x_ij = x[base + j * post];
-      sum += static_cast<T>(x_ij != 0);
+      const MT x_ij = static_cast<MT>(x[base + j * post]);
+      sum += static_cast<MT>(static_cast<double>(x_ij) != 0);
     }
-    T reduce_result = BlockReduce(temp_storage).Sum(sum);
-    if (threadIdx.x == 0) out_norm[i] = reduce_result;
+    MT reduce_result = BlockReduce(temp_storage).Sum(sum);
+    if (threadIdx.x == 0) out_norm[i] = static_cast<T>(reduce_result);
   }
 }
 
@@ -172,27 +188,29 @@ __global__ void PnormGradient(const T* x, const T* x_norm, const T* y_grad,
                               const float porder, const int pre,
                               const int axis_n, const int post, const T eps,
                               T* x_grad) {
+  using MT = typename details::MPTypeTrait<T>::Type;
   // 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);
+  auto porder_grad = static_cast<MT>(porder - 1.0f);
   for (int i = blockIdx.x; i < num; i += gridDim.x) {
-    __shared__ T pnorm_i;
-    __shared__ T yout_i;
+    __shared__ MT pnorm_i;
+    __shared__ MT yout_i;
 
     auto base = (i / post) * post * axis_n + (i % post);
 
     if (threadIdx.x == 0) {
-      pnorm_i = x_norm[i];
-      yout_i = y_grad[i];
+      pnorm_i = static_cast<MT>(x_norm[i]);
+      yout_i = static_cast<MT>(y_grad[i]);
     }
     __syncthreads();
 
     for (int j = threadIdx.x; j < axis_n; j += blockDim.x) {
       int index = base + j * post;
-      const T x_ij = inline_abs(x[index]);
-      x_grad[index] = inline_pow(x_ij, porder_grad) /
-                      (inline_pow(pnorm_i, porder_grad) + eps) * yout_i *
-                      inline_sign(x[index]);
+      const MT x_ij = static_cast<MT>(inline_abs(x[index]));
+      x_grad[index] = static_cast<T>(
+          inline_pow(x_ij, porder_grad) /
+          (inline_pow(pnorm_i, porder_grad) + static_cast<MT>(eps)) * yout_i *
+          static_cast<MT>(inline_sign(x[index])));
     }
   }
 }
@@ -216,7 +234,7 @@ __global__ void InfNormGradient(const T* x, const T* x_norm, const T* y_grad,
       int index = base + j * post;
       const T x_ij = inline_abs(x[index]);
       if (x_ij == pnorm_i) {
-        x_grad[index] = inline_sign(x[index]) * yout_i;
+        x_grad[index] = static_cast<T>(inline_sign(x[index])) * yout_i;
       } else {
         x_grad[index] = static_cast<T>(0);
       }
@@ -278,7 +296,11 @@ class PnormGradCUDAKernel : public framework::OpKernel<T> {
 namespace ops = paddle::operators;
 using CUDA = paddle::platform::CUDADeviceContext;
 
-REGISTER_OP_CUDA_KERNEL(p_norm, ops::PnormCUDAKernel<CUDA, float>,
+REGISTER_OP_CUDA_KERNEL(p_norm,
+                        ops::PnormCUDAKernel<CUDA, paddle::platform::float16>,
+                        ops::PnormCUDAKernel<CUDA, float>,
                         ops::PnormCUDAKernel<CUDA, double>);
-REGISTER_OP_CUDA_KERNEL(p_norm_grad, ops::PnormGradCUDAKernel<CUDA, float>,
+REGISTER_OP_CUDA_KERNEL(
+    p_norm_grad, ops::PnormGradCUDAKernel<CUDA, paddle::platform::float16>,
+    ops::PnormGradCUDAKernel<CUDA, float>,
     ops::PnormGradCUDAKernel<CUDA, double>);

python/paddle/nn/functional/norm.py

@@ -86,7 +86,8 @@ def normalize(x, p=2, axis=1, epsilon=1e-12, name=None):
 
     check_type(p, 'p', (float, int), 'normalize')
     check_type(axis, 'axis', (int), 'normalize')
-    check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'normalize')
+    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
+                             'normalize')
     if len(x.shape) == 1 and axis != 0 and axis != -1:
         raise ValueError(
             "Axis must be 0 or -1 when x is a 1-D tensor, but received axis = {}".