elementwise op support fp16 (#45496)

paddle/phi/kernels/elementwise_kernel.cc

@@ -237,7 +237,8 @@ PD_REGISTER_KERNEL(remainder,
                    float,
                    double,
                    int,
-                   int64_t) {}
+                   int64_t,
+                   phi::dtype::float16) {}
 PD_REGISTER_KERNEL(
     floor_divide, KPS, ALL_LAYOUT, phi::FloorDivideKernel, int, int64_t) {}
 PD_REGISTER_KERNEL(elementwise_heaviside,
@@ -247,7 +248,8 @@ PD_REGISTER_KERNEL(elementwise_heaviside,
                    float,
                    double,
                    int,
-                   int64_t) {}
+                   int64_t,
+                   phi::dtype::float16) {}
 PD_REGISTER_KERNEL(elementwise_pow,
                    KPS,
                    ALL_LAYOUT,
@@ -255,7 +257,8 @@ PD_REGISTER_KERNEL(elementwise_pow,
                    float,
                    double,
                    int,
-                   int64_t) {}
+                   int64_t,
+                   phi::dtype::float16) {}
 
 #endif
 

paddle/phi/kernels/funcs/elementwise_functor.h

@@ -524,6 +524,19 @@ struct RemainderFunctor<
   }
 };
 
+template <>
+struct RemainderFunctor<dtype::float16> {
+  inline HOSTDEVICE dtype::float16 operator()(const dtype::float16 a,
+                                              const dtype::float16 b) const {
+    float b_float = static_cast<float>(b);
+    float res = fmod(static_cast<float>(a), b_float);
+    // Accoding to #PR26732: in dividen % divsor
+    // remainder shall have the same sign as divsor.
+    if ((res != 0.0f) && ((res < 0.0f) != (b_float < 0.0f))) res += b_float;
+    return static_cast<dtype::float16>(res);
+  }
+};
+
 template <typename T, typename Enable = void>
 struct InverseRemainderFunctor {
   inline HOSTDEVICE T operator()(const T a, const T b) const {
@@ -547,7 +560,7 @@ struct InverseRemainderFunctor<
 template <typename T>
 struct ElementwiseHeavisideFunctor {
   inline HOSTDEVICE T operator()(const T a, const T b) const {
-    return a == static_cast<T>(0) ? b : static_cast<T>(a > 0);
+    return a == static_cast<T>(0) ? b : static_cast<T>(a > static_cast<T>(0));
   }
 };
 
@@ -592,5 +605,16 @@ struct ElementwisePowFunctor {
     return std::pow(a, b);
   }
 };
+
+template <>
+struct ElementwisePowFunctor<dtype::float16> {
+  inline HOSTDEVICE dtype::float16 operator()(const dtype::float16 a,
+                                              const dtype::float16 b) const {
+    float f_a = static_cast<float>(a);
+    float f_b = static_cast<float>(b);
+    return static_cast<dtype::float16>(std::pow(f_a, f_b));
+  }
+};
+
 }  // namespace funcs
 }  // namespace phi

paddle/phi/kernels/gpu/elementwise_grad_kernel.cu

@@ -35,6 +35,7 @@ void MaximumGradKernel(const Context& dev_ctx,
                        DenseTensor* dx,
                        DenseTensor* dy) {
   const auto place = dev_ctx.GetPlace();
+
   if (dx != nullptr && dy != nullptr) {
     std::vector<const DenseTensor*> ins = {&x, &y, &dout};
     GetGradXAndYOut<ElementwiseType::kTernary, T>(
@@ -96,6 +97,7 @@ PD_REGISTER_KERNEL(fmax_grad,
                    float,
                    double,
                    int,
+                   phi::dtype::float16,
                    int64_t) {}
 
 PD_REGISTER_KERNEL(fmin_grad,
@@ -105,6 +107,7 @@ PD_REGISTER_KERNEL(fmin_grad,
                    float,
                    double,
                    int,
+                   phi::dtype::float16,
                    int64_t) {}
 
 PD_REGISTER_KERNEL(maximum_grad,
@@ -136,6 +139,7 @@ PD_REGISTER_KERNEL(elementwise_heaviside_grad,
                    float,
                    double,
                    int,
+                   phi::dtype::float16,
                    int64_t) {}
 
 PD_REGISTER_KERNEL(elementwise_pow_grad,
@@ -145,4 +149,5 @@ PD_REGISTER_KERNEL(elementwise_pow_grad,
                    float,
                    double,
                    int,
+                   phi::dtype::float16,
                    int64_t) {}

paddle/phi/kernels/impl/elementwise_grad_kernel_impl.h

@@ -15,6 +15,7 @@ limitations under the License. */
 #pragma once
 
 #include "paddle/phi/common/complex.h"
+#include "paddle/phi/common/float16.h"
 #include "paddle/phi/core/dense_tensor.h"
 #include "paddle/phi/core/tensor_utils.h"
 #include "paddle/phi/kernels/funcs/broadcast_function.h"
@@ -753,6 +754,20 @@ struct PowGradDX {
   }
 };
 
+template <>
+struct PowGradDX<dtype::float16> {
+  HOSTDEVICE dtype::float16 operator()(dtype::float16 x,
+                                       dtype::float16 y,
+                                       dtype::float16 out,
+                                       dtype::float16 dout) const {
+    float tmp_y = static_cast<float>(y);
+    float tmp_dout = static_cast<float>(dout);
+    float tmp_x = static_cast<float>(x);
+    float result = tmp_dout * tmp_y * std::pow(tmp_x, tmp_y - 1.0f);
+    return static_cast<dtype::float16>(result);
+  }
+};
+
 template <typename T, typename Enable = void>
 struct PowGradDY {
   HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
@@ -766,6 +781,21 @@ struct PowGradDY {
   }
 };
 
+template <>
+struct PowGradDY<dtype::float16, void> {
+  HOSTDEVICE dtype::float16 operator()(dtype::float16 x,
+                                       dtype::float16 y,
+                                       dtype::float16 out,
+                                       dtype::float16 dout) const {
+    float tmp_y = static_cast<float>(y);
+    float tmp_dout = static_cast<float>(dout);
+    float tmp_x = static_cast<float>(x);
+    float tmp_pow = std::pow(tmp_x, tmp_y);
+    float result = tmp_pow * tmp_dout * std::log(tmp_x);
+    return static_cast<dtype::float16>(result);
+  }
+};
+
 template <typename T, typename Context>
 void ElementwisePowGradKernel(const Context& dev_ctx,
                               const DenseTensor& x,

paddle/phi/kernels/kps/elementwise_kernel.cu

@@ -32,6 +32,7 @@ void MaximumKernel(const Context& dev_ctx,
   int axis = -1;
   MaximumRawKernel<T>(dev_ctx, x, y, axis, out);
 }
+
 // Create the definition of Minimum
 DEFINE_CUDA_ELEMENTWISE_OP(Minimum)
 template <typename T, typename Context>
@@ -92,11 +93,25 @@ using bfloat16 = phi::dtype::bfloat16;
 using complex64 = ::phi::dtype::complex<float>;
 using complex128 = ::phi::dtype::complex<double>;
 
-PD_REGISTER_KERNEL(
-    fmax, KPS, ALL_LAYOUT, phi::FMaxKernel, float, double, int, int64_t) {}
+PD_REGISTER_KERNEL(fmax,
+                   KPS,
+                   ALL_LAYOUT,
+                   phi::FMaxKernel,
+                   float,
+                   double,
+                   int,
+                   float16,
+                   int64_t) {}
 
-PD_REGISTER_KERNEL(
-    fmin, KPS, ALL_LAYOUT, phi::FMinKernel, float, double, int, int64_t) {}
+PD_REGISTER_KERNEL(fmin,
+                   KPS,
+                   ALL_LAYOUT,
+                   phi::FMinKernel,
+                   float,
+                   double,
+                   int,
+                   float16,
+                   int64_t) {}
 
 PD_REGISTER_KERNEL(maximum_raw,
                    KPS,
@@ -125,6 +140,7 @@ PD_REGISTER_KERNEL(remainder_raw,
                    float,
                    double,
                    int,
+                   float16,
                    int64_t) {}
 PD_REGISTER_KERNEL(floor_divide_raw,
                    KPS,
@@ -139,6 +155,7 @@ PD_REGISTER_KERNEL(elementwise_heaviside_raw,
                    float,
                    double,
                    int,
+                   float16,
                    int64_t) {}
 PD_REGISTER_KERNEL(elementwise_pow_raw,
                    KPS,
@@ -147,5 +164,6 @@ PD_REGISTER_KERNEL(elementwise_pow_raw,
                    float,
                    double,
                    int,
+                   float16,
                    int64_t) {}
 #endif

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

@@ -18,6 +18,13 @@ from op_test import OpTest
 import paddle
 
 
+def Heaviside_grad(x, y, dout):
+    tmp = np.zeros(x.shape).astype("float16")
+    dx = np.multiply(tmp, dout)
+    dy = np.multiply(np.equal(x, 0), dout).astype("float16")
+    return dx, dy
+
+
 class TestElementwiseOp(OpTest):
 
     def setUp(self):
@@ -152,6 +159,30 @@ class TestHeavisideAPI_int32(TestHeavisideAPI_float64):
         self.dtype = "int32"
 
 
+class TestHeavisideAPI_float16(OpTest):
+
+    def setUp(self):
+        self.dtype = np.float16
+        self.op_type = "elementwise_heaviside"
+        self.python_api = paddle.heaviside
+        self.inputs = {
+            'X': np.random.uniform(1, 2, [20, 5]).astype("float16"),
+            'Y': np.random.uniform(1, 2, [20, 5]).astype("float16")
+        }
+        self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X', 'Y'],
+                        'Out',
+                        user_defined_grads=Heaviside_grad(
+                            self.inputs['X'], self.inputs['Y'],
+                            1 / self.inputs['X'].size),
+                        check_eager=True)
+
+
 class TestHeavisideError(unittest.TestCase):
 
     def test_input(self):

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

@@ -89,6 +89,23 @@ class TestElementwiseModOpFloat(TestElementwiseModOp):
             self.check_output(check_eager=False)
 
 
+class TestElementwiseModOpFp16(TestElementwiseModOp):
+
+    def init_dtype(self):
+        self.dtype = np.float16
+
+    def init_input_output(self):
+        self.x = np.random.uniform(-1000, 1000, [10, 10]).astype(self.dtype)
+        self.y = np.random.uniform(-100, 100, [10, 10]).astype(self.dtype)
+        self.out = np.mod(self.x, self.y)
+
+    def test_check_output(self):
+        if self.attrs['axis'] == -1:
+            self.check_output(check_eager=True)
+        else:
+            self.check_output(check_eager=False)
+
+
 class TestElementwiseModOpDouble(TestElementwiseModOpFloat):
 
     def init_dtype(self):

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

@@ -20,6 +20,12 @@ import paddle.fluid as fluid
 import paddle
 
 
+def pow_grad(x, y, dout):
+    dx = dout * y * np.power(x, (y - 1))
+    dy = dout * np.log(x) * np.power(x, y)
+    return dx, dy
+
+
 class TestElementwisePowOp(OpTest):
 
     def setUp(self):
@@ -194,7 +200,6 @@ class TestElementwisePowGradOpInt(unittest.TestCase):
         # dy = dout * log(x) * pow(x, y)
         self.grad_y = (self.grad_res * np.log(self.x) *
                        (self.x**self.y)).astype("int")
-        print(self.grad_res, self.grad_x, self.grad_y)
 
     def test_grad(self):
         fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
@@ -205,7 +210,6 @@ class TestElementwisePowGradOpInt(unittest.TestCase):
             with fluid.dygraph.guard(place):
                 x = fluid.dygraph.to_variable(self.x, zero_copy=False)
                 y = fluid.dygraph.to_variable(self.y, zero_copy=False)
-                print(x, y)
                 x.stop_gradient = False
                 y.stop_gradient = False
                 res = x**y
@@ -216,5 +220,31 @@ class TestElementwisePowGradOpInt(unittest.TestCase):
         fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False})
 
 
+class TestElementwisePowOpFP16(OpTest):
+
+    def setUp(self):
+        self.op_type = "elementwise_pow"
+        self.python_api = paddle.pow
+        self.inputs = {
+            'X': np.random.uniform(1, 2, [20, 5]).astype("float16"),
+            'Y': np.random.uniform(1, 2, [20, 5]).astype("float16")
+        }
+        self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
+
+    def test_check_output(self):
+        if hasattr(self, 'attrs'):
+            self.check_output(check_eager=False)
+        else:
+            self.check_output(check_eager=True)
+
+    def test_check_grad(self):
+        self.check_grad(['X', 'Y'],
+                        'Out',
+                        user_defined_grads=pow_grad(self.inputs['X'],
+                                                    self.inputs['Y'],
+                                                    1 / self.inputs['X'].size),
+                        check_eager=True)
+
+
 if __name__ == '__main__':
     unittest.main()

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

@@ -209,3 +209,33 @@ class TestElementwiseFmax2Op(OpTest):
                         max_relative_error=0.005,
                         no_grad_set=set('Y'),
                         check_eager=True)
+
+
+class TestElementwiseFmax3Op(OpTest):
+    """TestElementwiseFmax3Op"""
+
+    def setUp(self):
+        """setUp"""
+        self.op_type = "elementwise_fmax"
+        self.python_api = paddle.fmax
+        # If x and y have the same value, the max() is not differentiable.
+        # So we generate test data by the following method
+        # to avoid them being too close to each other.
+        x = np.random.uniform(0.1, 1, [13, 17]).astype("float16")
+        sgn = np.random.choice([-1, 1], [13, 17]).astype("float16")
+        y = x + sgn * np.random.uniform(0.1, 1, [13, 17]).astype("float16")
+
+        self.inputs = {'X': x, 'Y': y}
+        self.outputs = {'Out': np.fmax(self.inputs['X'], self.inputs['Y'])}
+
+    def test_check_output(self):
+        """test_check_output"""
+        self.check_output(check_eager=True)
+
+    def test_check_grad_normal(self):
+        """test_check_grad_normal"""
+        self.check_grad(['X', 'Y'], 'Out', check_eager=True)
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -213,6 +213,32 @@ class TestElementwiseFmin2Op(OpTest):
                         check_eager=True)
 
 
+class TestElementwiseFmin3Op(OpTest):
+    """TestElementwiseFmin2Op"""
+
+    def setUp(self):
+        """setUp"""
+        self.op_type = "elementwise_fmin"
+        self.python_api = paddle.fmin
+        # If x and y have the same value, the min() is not differentiable.
+        # So we generate test data by the following method
+        # to avoid them being too close to each other.
+        x = np.random.uniform(1, 1, [13, 17]).astype("float16")
+        sgn = np.random.choice([-1, 1], [13, 17]).astype("float16")
+        y = x + sgn * np.random.uniform(1, 1, [13, 17]).astype("float16")
+
+        self.inputs = {'X': x, 'Y': y}
+        self.outputs = {'Out': np.fmin(self.inputs['X'], self.inputs['Y'])}
+
+    def test_check_output(self):
+        """test_check_output"""
+        self.check_output(check_eager=True)
+
+    def test_check_grad_normal(self):
+        """test_check_grad_normal"""
+        self.check_grad(['X', 'Y'], 'Out', check_eager=True)
+
+
 if __name__ == "__main__":
     paddle.enable_static()
     unittest.main()

python/paddle/tensor/math.py

@@ -352,7 +352,7 @@ def pow(x, y, name=None):
 
 
     Args:
-        x (Tensor): An N-D Tensor, the data type is float32, float64, int32 or int64.
+        x (Tensor): An N-D Tensor, the data type is float16, float32, float64, int32 or int64.
         y (float|int|Tensor): If it is an N-D Tensor, its data type should be the same as `x`.
         name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
     
@@ -762,8 +762,8 @@ def remainder(x, y, name=None):
         ``paddle.remainder`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
 
     Args:
-        x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
-        y (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
+        x (Tensor): the input tensor, it's data type should be float16, float32, float64, int32, int64.
+        y (Tensor): the input tensor, it's data type should be float16, float32, float64, int32, int64.
         name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
@@ -1003,8 +1003,8 @@ def fmax(x, y, name=None):
         ``paddle.fmax`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
 
     Args:
-        x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
-        y (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
+        x (Tensor): the input tensor, it's data type should be float16, float32, float64, int32, int64.
+        y (Tensor): the input tensor, it's data type should be float16, float32, float64, int32, int64.
         name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
@@ -1066,8 +1066,8 @@ def fmin(x, y, name=None):
         ``paddle.fmin`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting` .
 
     Args:
-        x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
-        y (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
+        x (Tensor): the input tensor, it's data type should be float16, float32, float64, int32, int64.
+        y (Tensor): the input tensor, it's data type should be float16, float32, float64, int32, int64.
         name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
@@ -4696,8 +4696,8 @@ def heaviside(x, y, name=None):
         ``paddle.heaviside`` supports broadcasting. If you want know more about broadcasting, please refer to :ref:`user_guide_broadcasting`.
 
     Args:
-        x (Tensor): The input tensor of Heaviside step function, it's data type should be float32, float64, int32 or int64.
-        y (Tensor): The tensor that determines a Heaviside step function, it's data type should be float32, float64, int32 or int64.
+        x (Tensor): The input tensor of Heaviside step function, it's data type should be float16, float32, float64, int32 or int64.
+        y (Tensor): The tensor that determines a Heaviside step function, it's data type should be float16, float32, float64, int32 or int64.
         name (str, optional): Name for the operation (optional, default is None). Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`.
 
     Returns: