Support FP16 mean (#38289)

* mean first version

* fix scalar mean

* add fp16 dtype for api

paddle/fluid/operators/kernel_primitives/functor_primitives.h

@@ -14,7 +14,10 @@
 
 #pragma once
 
+#include "paddle/fluid/operators/amp/fp16_type_traits.h"
 #include "paddle/fluid/platform/eigen_ext.h"
+#include "paddle/fluid/platform/enforce.h"
+#include "paddle/fluid/platform/float16.h"
 
 namespace paddle {
 namespace operators {
@@ -74,16 +77,20 @@ struct IdentityFunctor {
  */
 template <typename Tx, typename Ty = Tx>
 struct DivideFunctor {
-  HOSTDEVICE inline DivideFunctor() { n_inv = static_cast<Tx>(1.0f); }
+ private:
+  using MPType = typename ::paddle::operators::details::MPTypeTrait<Tx>::Type;
+
+ public:
+  HOSTDEVICE inline DivideFunctor() { n_inv = static_cast<MPType>(1.0f); }
 
-  HOSTDEVICE explicit inline DivideFunctor(int n) : n_inv((Tx)(1.0 / n)) {}
+  HOSTDEVICE explicit inline DivideFunctor(int n) : n_inv((MPType)(1.0 / n)) {}
 
   HOSTDEVICE inline Ty operator()(const Tx& x) const {
-    return static_cast<Ty>(x * n_inv);
+    return static_cast<Ty>(static_cast<MPType>(x) * n_inv);
   }
 
  private:
-  Tx n_inv;
+  MPType n_inv;
 };
 
 /**

paddle/fluid/operators/mean_op.cu

@@ -18,30 +18,23 @@ 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/kernel_primitives/functor_primitives.h"
 #include "paddle/fluid/operators/mean_op.h"
+#include "paddle/fluid/operators/reduce_ops/reduce_op.cu.h"
 #include "paddle/fluid/platform/device/gpu/gpu_primitives.h"
 #include "paddle/fluid/platform/float16.h"
 
 namespace paddle {
 namespace operators {
 
-template <typename T>
-struct DivideFunctor {
-  HOSTDEVICE explicit inline DivideFunctor(int n)
-      : n_inv(static_cast<T>(1.0 / n)) {}
-
-  HOSTDEVICE inline T operator()(const T& x) const { return x * n_inv; }
-
- private:
-  T n_inv;
-};
-
 template <typename T>
 __global__ void MeanRunKernel(const T* in_data, T* out_data, int N) {
+  using MT = typename details::MPTypeTrait<T>::Type;
   int idx = blockDim.x * blockIdx.x + threadIdx.x;
-  T data = in_data[0];
+  auto data = static_cast<MT>(in_data[0]);
   for (; idx < N; idx += blockDim.x * gridDim.x) {
-    out_data[idx] = data / (static_cast<T>(N));
+    out_data[idx] = static_cast<T>(data / (static_cast<MT>(N)));
   }
 }
 
@@ -52,27 +45,29 @@ class MeanCUDAKernel : public framework::OpKernel<T> {
     auto* input = context.Input<Tensor>("X");
     auto* output = context.Output<Tensor>("Out");
 
-    output->mutable_data<T>(context.GetPlace());
-    auto size_prob = input->numel();
     const T* in_data = input->data<T>();
     T* out_data = output->mutable_data<T>(context.GetPlace());
+    auto numel = input->numel();
+    auto rank = input->dims().size();
+    auto place = context.GetPlace();
     auto stream = context.cuda_device_context().stream();
 
-    DivideFunctor<T> transformer(size_prob);
-    cub::TransformInputIterator<T, DivideFunctor<T>, const T*> trans_x(
-        in_data, transformer);
-    size_t temp_storage_bytes = 0;
+    if (rank == 0) {  // scalar
+      auto gpu_place = BOOST_GET(platform::CUDAPlace, place);
+      memory::Copy(gpu_place, out_data, gpu_place, in_data, numel * sizeof(T),
+                   stream);
+      return;
+    }
 
-    auto err = cub::DeviceReduce::Sum(nullptr, temp_storage_bytes, trans_x,
-                                      out_data, size_prob, stream);
-    PADDLE_ENFORCE_GPU_SUCCESS(err);
-    framework::Tensor tmp;
-    auto* temp_storage = tmp.mutable_data<uint8_t>(
-        framework::make_ddim({static_cast<int64_t>(temp_storage_bytes)}),
-        context.GetPlace());
-    err = cub::DeviceReduce::Sum(temp_storage, temp_storage_bytes, trans_x,
-                                 out_data, size_prob, stream);
-    PADDLE_ENFORCE_GPU_SUCCESS(err);
+    using MT = typename details::MPTypeTrait<T>::Type;
+    using Div = kernel_primitives::DivideFunctor<T, MT>;
+    std::vector<int> reduce_dims;
+    reduce_dims.reserve(rank);
+    for (decltype(rank) i = 0; i < rank; ++i) {
+      reduce_dims.push_back(i);
+    }
+    TensorReduceFunctorImpl<T, T, kernel_primitives::AddFunctor, Div>(
+        *input, output, Div(numel), reduce_dims, stream);
   }
 };
 

paddle/fluid/operators/reduce_ops/reduce_functor_op.h

@@ -77,7 +77,7 @@ struct CustomSub {
 
 template <typename Tx, typename Ty = Tx>
 struct CustomMean {
-  using Transformer = kps::DivideFunctor<Tx>;
+  using Transformer = kps::DivideFunctor<Tx, Ty>;
 
   inline Ty initial() { return static_cast<Ty>(0.0f); }
 

paddle/fluid/operators/reduce_ops/reduce_mean_op.cu

@@ -19,5 +19,7 @@
 REGISTER_OP_CUDA_KERNEL(
     reduce_mean,
     ops::ReduceCudaKernel<bool, kps::AddFunctor, kps::DivideFunctor>,
+    ops::ReduceCudaKernel<paddle::platform::float16, kps::AddFunctor,
+                          kps::DivideFunctor>,
     ops::ReduceCudaKernel<float, kps::AddFunctor, kps::DivideFunctor>,
     ops::ReduceCudaKernel<double, kps::AddFunctor, kps::DivideFunctor>);

paddle/fluid/operators/reduce_ops/reduce_mean_op.h

@@ -35,5 +35,18 @@ struct MeanGradFunctor {
   }
 };
 
+// TODO(zengjinle): Should refine the numeric stability of FP16 reduce_mean
+// and reduce_mean_grad later.
+struct FP16MeanGradFunctor {
+  template <typename DeviceContext, typename X, typename Y, typename DX,
+            typename DY, typename Dim>
+  void operator()(const DeviceContext& place, X* x, Y* y, DX* dx, DY* dy,
+                  const Dim& dim, int size) {
+    dx->device(place) = (dy->template cast<float>().broadcast(dim) /
+                         dx->template cast<float>().constant(size))
+                            .template cast<platform::float16>();
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/reduce_ops/reduce_mean_op.part.cu

@@ -20,6 +20,12 @@ using CUDAReduceMeanGradKernel =
     ops::ReduceGradKernel<paddle::platform::CUDADeviceContext, T,
                           ops::MeanGradFunctor, true>;
 
+using FP16CUDAReduceMeanGradKernel =
+    ops::ReduceGradKernel<paddle::platform::CUDADeviceContext,
+                          paddle::platform::float16, ops::FP16MeanGradFunctor,
+                          true>;
+
 REGISTER_OP_CUDA_KERNEL(reduce_mean_grad, CUDAReduceMeanGradKernel<bool>,
+                        FP16CUDAReduceMeanGradKernel,
                         CUDAReduceMeanGradKernel<float>,
                         CUDAReduceMeanGradKernel<double>);

paddle/fluid/operators/reduce_ops/reduce_op.cu.h

@@ -38,7 +38,9 @@ namespace cub = hipcub;
 #include "paddle/fluid/operators/kernel_primitives/kernel_primitives.h"
 #include "paddle/fluid/platform/device/gpu/gpu_device_function.h"
 #include "paddle/fluid/platform/device/gpu/gpu_info.h"
+#include "paddle/fluid/platform/enforce.h"
 #include "paddle/fluid/platform/fast_divmod.h"
+#include "paddle/fluid/string/string_helper.h"
 
 // Reduce split or not, Whether to use ReduceHigherDim
 #define REDUCE_SPLIT_BOUNDARY 512
@@ -814,11 +816,42 @@ static void LaunchReduceKernel(const Tx* x_data, Ty* y_data,
   }
 }
 
+template <typename Tx, typename Ty, template <typename> class ReduceOp,
+          typename TransformOp>
+static typename std::enable_if<!std::is_same<Tx, platform::float16>::value,
+                               void>::type
+CubTensorReduceFunctorImpl(const Tx* x_data, Ty* y_data,
+                           const TransformOp& transform, int reduce_num,
+                           const platform::Place& place, gpuStream_t stream) {
+  auto reducer = ReduceOp<Ty>();
+  cub::TransformInputIterator<Ty, TransformOp, const Tx*> trans_x(x_data,
+                                                                  transform);
+  size_t temp_storage_bytes = 0;
+  cub::DeviceReduce::Reduce(nullptr, temp_storage_bytes, trans_x, y_data,
+                            reduce_num, reducer, reducer.initial(), stream);
+  framework::Tensor tmp;
+  auto* temp_storage = tmp.mutable_data<uint8_t>(
+      framework::make_ddim({static_cast<int64_t>(temp_storage_bytes)}), place);
+  cub::DeviceReduce::Reduce(temp_storage, temp_storage_bytes, trans_x, y_data,
+                            reduce_num, reducer, reducer.initial(), stream);
+}
+
+template <typename Tx, typename Ty, template <typename> class ReduceOp,
+          typename TransformOp>
+static typename std::enable_if<std::is_same<Tx, platform::float16>::value,
+                               void>::type
+CubTensorReduceFunctorImpl(const Tx* x_data, Ty* y_data,
+                           const TransformOp& transform, int reduce_num,
+                           const platform::Place& place, gpuStream_t stream) {
+  PADDLE_THROW(platform::errors::InvalidArgument(
+      "Tx should not be float16 when using cub::DeviceReduce::Reduce()."));
+}
+
 template <typename Tx, typename Ty, template <typename> class ReduceOp,
           typename TransformOp>
 void TensorReduceFunctorImpl(const framework::Tensor& x, framework::Tensor* y,
                              const TransformOp& transform,
-                             std::vector<int> origin_reduce_dims,
+                             const std::vector<int>& origin_reduce_dims,
                              gpuStream_t stream) {
   auto x_dim = framework::vectorize<int>(x.dims());
   auto config = ReduceConfig<Ty>(origin_reduce_dims, x_dim);
@@ -848,25 +881,11 @@ void TensorReduceFunctorImpl(const framework::Tensor& x, framework::Tensor* y,
   }
 
   config.SetOutputData(y_data, x.place(), &tmp);
-  bool use_cub_reduce = (config.reduce_num == numel) &&
-                        (!std::is_same<Tx, paddle::platform::float16>::value);
+  constexpr bool kIsTxFP16 = std::is_same<Tx, paddle::platform::float16>::value;
+  bool use_cub_reduce = config.reduce_num == numel && !kIsTxFP16;
   if (use_cub_reduce) {
-    // launch CUB::Reduce
-    auto reducer = ReduceOp<Ty>();
-    cub::TransformInputIterator<Ty, TransformOp, const Tx*> trans_x(x_data,
-                                                                    transform);
-    size_t temp_storage_bytes = 0;
-    cub::DeviceReduce::Reduce(nullptr, temp_storage_bytes, trans_x, y_data,
-                              config.reduce_num, reducer, reducer.initial(),
-                              stream);
-    framework::Tensor tmp;
-    auto* temp_storage = tmp.mutable_data<uint8_t>(
-        framework::make_ddim({static_cast<int64_t>(temp_storage_bytes)}),
-        x.place());
-    cub::DeviceReduce::Reduce(temp_storage, temp_storage_bytes, trans_x, y_data,
-                              config.reduce_num, reducer, reducer.initial(),
-                              stream);
-
+    CubTensorReduceFunctorImpl<Tx, Ty, ReduceOp, TransformOp>(
+        x_data, y_data, transform, config.reduce_num, x.place(), stream);
     return;
   }
 

paddle/fluid/operators/reduce_ops/reduce_op.h

@@ -703,7 +703,7 @@ class ReduceCudaKernel : public framework::OpKernel<T> {
     std::vector<int> reduce_dims =
         GetReduceDim(dims, input->dims().size(), reduce_all);
     int reduce_num = 1;
-    for (int i = 0; i < input->dims().size(); i++) {
+    for (auto i : reduce_dims) {
       reduce_num *= (input->dims())[i];
     }
     gpuStream_t stream = context.cuda_device_context().stream();
@@ -713,8 +713,10 @@ class ReduceCudaKernel : public framework::OpKernel<T> {
           TensorReduceFunc<T, ReduceOp, TransformOp>(
               *input, output, reduce_dims, reduce_num, stream));
     } else {
-      TensorReduceFunctorImpl<T, T, ReduceOp, TransformOp<T, T>>(
-          *input, output, TransformOp<T, T>(reduce_num), reduce_dims, stream);
+      using MPType = typename details::MPTypeTrait<T>::Type;
+      TensorReduceFunctorImpl<T, T, ReduceOp, TransformOp<T, MPType>>(
+          *input, output, TransformOp<T, MPType>(reduce_num), reduce_dims,
+          stream);
     }
   }
 };

python/paddle/fluid/tests/unittests/test_mean_op.py

@@ -63,17 +63,25 @@ class TestMeanOpError(unittest.TestCase):
 class TestFP16MeanOp(TestMeanOp):
     def init_dtype_type(self):
         self.dtype = np.float16
+        self.__class__.no_need_check_grad = True
 
     def test_check_output(self):
         place = core.CUDAPlace(0)
         if core.is_float16_supported(place):
-            self.check_output_with_place(place, atol=2e-3)
+            self.check_output_with_place(place)
 
     def test_checkout_grad(self):
         place = core.CUDAPlace(0)
         if core.is_float16_supported(place):
-            self.check_grad_with_place(
-                place, ['X'], 'Out', max_relative_error=0.8)
+            with fluid.dygraph.guard():
+                x_np = np.random.random((10, 10)).astype(self.dtype)
+                x = paddle.to_tensor(x_np)
+                x.stop_gradient = False
+                y = fluid.layers.mean(x)
+                dx = paddle.grad(y, x)[0].numpy()
+                dx_expected = self.dtype(1.0 / np.prod(x_np.shape)) * np.ones(
+                    x_np.shape).astype(self.dtype)
+                self.assertTrue(np.array_equal(dx, dx_expected))
 
 
 @OpTestTool.skip_if_not_cpu_bf16()
@@ -98,6 +106,14 @@ def ref_reduce_mean(x, axis=None, keepdim=False, reduce_all=False):
     return np.mean(x, axis=axis, keepdims=keepdim)
 
 
+def ref_reduce_mean_grad(x, axis, dtype):
+    if reduce_all:
+        axis = list(range(x.ndim))
+
+    shape = [x.shape[i] for i in axis]
+    return (1.0 / np.prod(shape) * np.ones(shape)).astype(dtype)
+
+
 class TestReduceMeanOp(OpTest):
     def setUp(self):
         self.op_type = 'reduce_mean'
@@ -105,11 +121,13 @@ class TestReduceMeanOp(OpTest):
         self.shape = [2, 3, 4, 5]
         self.axis = [0]
         self.keepdim = False
-        self.reduce_all = False
         self.set_attrs()
 
         np.random.seed(10)
         x_np = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
+        if not hasattr(self, "reduce_all"):
+            self.reduce_all = (not self.axis) or len(self.axis) == len(x_np)
+
         out_np = ref_reduce_mean(x_np, self.axis, self.keepdim, self.reduce_all)
         self.inputs = {'X': x_np}
         self.outputs = {'Out': out_np}
@@ -119,14 +137,39 @@ class TestReduceMeanOp(OpTest):
             'reduce_all': self.reduce_all
         }
 
+        if self.dtype == 'float16':
+            self.__class__.no_need_check_grad = True
+
     def set_attrs(self):
         pass
 
     def test_check_output(self):
+        if self.dtype != 'float16':
             self.check_output()
+        else:
+            if not core.is_compiled_with_cuda():
+                return
+            place = paddle.CUDAPlace(0)
+            self.check_output_with_place(place=place)
 
     def test_check_grad(self):
+        if self.dtype != 'float16':
             self.check_grad(['X'], ['Out'])
+        else:
+            return
+            if not core.is_compiled_with_cuda():
+                return
+            place = paddle.CUDAPlace(0)
+            if core.is_float16_supported(place):
+                return
+            with fluid.dygraph.guard(place=place):
+                x = paddle.tensor(self.inputs['X'])
+                y = paddle.mean(
+                    x, axis=self.attrs['dim'], keepdim=self.attrs['keep_dim'])
+                dx = paddle.grad(y, x)[0].numpy()
+                dx_expected = ref_reduce_mean_grad(
+                    self.inputs['X'], self.attrs['dim'], self.dtype)
+                self.assertTrue(np.array_equal(dx, dx_expected))
 
 
 class TestReduceMeanOpDefaultAttrs(TestReduceMeanOp):
@@ -146,47 +189,101 @@ class TestReduceMeanOpFloat32(TestReduceMeanOp):
         self.dtype = 'float32'
 
 
+class TestReduceMeanOpFloat16(TestReduceMeanOp):
+    def set_attrs(self):
+        self.dtype = 'float16'
+
+
 class TestReduceMeanOpShape1D(TestReduceMeanOp):
     def set_attrs(self):
         self.shape = [100]
 
 
+class TestReduceMeanOpShape1DFP16(TestReduceMeanOp):
+    def set_attrs(self):
+        self.shape = [100]
+        self.dtype = 'float16'
+
+
 class TestReduceMeanOpShape6D(TestReduceMeanOp):
     def set_attrs(self):
         self.shape = [2, 3, 4, 5, 6, 7]
 
 
+class TestReduceMeanOpShape6DFP16(TestReduceMeanOp):
+    def set_attrs(self):
+        self.shape = [2, 3, 4, 5, 6, 7]
+        self.dtype = 'float16'
+
+
 class TestReduceMeanOpAxisAll(TestReduceMeanOp):
     def set_attrs(self):
         self.axis = [0, 1, 2, 3]
 
 
+class TestReduceMeanOpAxisAllFP16(TestReduceMeanOp):
+    def set_attrs(self):
+        self.axis = [0, 1, 2, 3]
+        self.dtype = 'float16'
+
+
 class TestReduceMeanOpAxisTuple(TestReduceMeanOp):
     def set_attrs(self):
         self.axis = (0, 1, 2)
 
 
+class TestReduceMeanOpAxisTupleFP16(TestReduceMeanOp):
+    def set_attrs(self):
+        self.axis = (0, 1, 2)
+        self.dtype = 'float16'
+
+
 class TestReduceMeanOpAxisNegative(TestReduceMeanOp):
     def set_attrs(self):
         self.axis = [-2, -1]
 
 
+class TestReduceMeanOpAxisNegativeFP16(TestReduceMeanOp):
+    def set_attrs(self):
+        self.axis = [-2, -1]
+        self.dtype = 'float16'
+
+
 class TestReduceMeanOpKeepdimTrue1(TestReduceMeanOp):
     def set_attrs(self):
         self.keepdim = True
 
 
+class TestReduceMeanOpKeepdimTrue1FP16(TestReduceMeanOp):
+    def set_attrs(self):
+        self.keepdim = True
+        self.dtype = 'float16'
+
+
 class TestReduceMeanOpKeepdimTrue2(TestReduceMeanOp):
     def set_attrs(self):
         self.axis = [0, 1, 2, 3]
         self.keepdim = True
 
 
+class TestReduceMeanOpKeepdimTrue2FP16(TestReduceMeanOp):
+    def set_attrs(self):
+        self.axis = [0, 1, 2, 3]
+        self.keepdim = True
+        self.dtype = 'float16'
+
+
 class TestReduceMeanOpReduceAllTrue(TestReduceMeanOp):
     def set_attrs(self):
         self.reduce_all = True
 
 
+class TestReduceMeanOpReduceAllTrueFP16(TestReduceMeanOp):
+    def set_attrs(self):
+        self.reduce_all = True
+        self.dtype = 'float16'
+
+
 class TestMeanAPI(unittest.TestCase):
     # test paddle.tensor.stat.mean
 

python/paddle/tensor/stat.py

@@ -92,7 +92,8 @@ def mean(x, axis=None, keepdim=False, name=None):
         return _C_ops.reduce_mean(x, 'dim', axis, 'keep_dim', keepdim,
                                   'reduce_all', reduce_all)
 
-    check_variable_and_dtype(x, 'x/input', ['uint16', 'float32', 'float64'],
+    check_variable_and_dtype(x, 'x/input',
+                             ['uint16', 'float16', 'float32', 'float64'],
                              'mean/reduce_mean')
     check_type(axis, 'axis/dim', (int, list, tuple), 'mean/reduce_mean')
     if isinstance(axis, (list, tuple)):