[AMP] Add bfloat16 Support for `elementwise_pow` Op (#51888)

* Add bf16 support for elementwise_pow

* Update ut

paddle/phi/kernels/cpu/elementwise_grad_kernel.cc

@@ -105,4 +105,5 @@ PD_REGISTER_KERNEL(elementwise_pow_grad,
                    float,
                    double,
                    int,
-                   int64_t) {}
+                   int64_t,
+                   phi::dtype::bfloat16) {}

paddle/phi/kernels/cpu/elementwise_kernel.cc

@@ -166,7 +166,8 @@ PD_REGISTER_KERNEL(elementwise_pow_raw,
                    float,
                    double,
                    int,
-                   int64_t) {}
+                   int64_t,
+                   phi::dtype::bfloat16) {}
 
 PD_REGISTER_KERNEL(heaviside,
                    CPU,

paddle/phi/kernels/elementwise_kernel.cc

@@ -140,7 +140,8 @@ PD_REGISTER_KERNEL(elementwise_pow,
                    float,
                    double,
                    int,
-                   int64_t) {}
+                   int64_t,
+                   phi::dtype::bfloat16) {}
 
 PD_REGISTER_KERNEL(subtract,
                    CPU,
@@ -232,7 +233,8 @@ PD_REGISTER_KERNEL(elementwise_pow,
                    double,
                    int,
                    int64_t,
-                   phi::dtype::float16) {}
+                   phi::dtype::float16,
+                   phi::dtype::bfloat16) {}
 
 #endif
 

paddle/phi/kernels/funcs/elementwise_functor.h

@@ -23,6 +23,7 @@ limitations under the License. */
 
 #include "xpu/kernel/math_xpu2.h"  // pow()
 #endif
+#include "paddle/phi/common/amp_type_traits.h"
 
 namespace phi {
 namespace funcs {
@@ -585,68 +586,55 @@ struct InverseFloorDivideFunctor {
   }
 };
 
-template <typename T>
-struct ElementwisePowFunctor {
-  inline HOSTDEVICE T operator()(const T a, const T b) const {
-// TODO(wujionghao): A potential speed improvement is supporting different
-// types in C++.
 #if defined(__CUDA_ARCH__) || defined(__HIPCC__)
-    // On CUDAPlace, std::pow(3, 1) calls pow(float, float), and
-    // it will return a float number like 2.99... , which floor to 2
-    // when cast to int by default and it is wrong.
-    // Use llrint to cast it to the nearest integer, which is 3.
-    if (std::is_integral<T>::value) {
-      return std::llrint(
-          std::pow(static_cast<double>(a), static_cast<double>(b)));
-    }
+template <typename T, typename MPType>
+inline HOSTDEVICE typename std::enable_if<std::is_integral<T>::value, T>::type
+compute_pow(const T a, const T b) {
+  // TODO(wujionghao): A potential speed improvement is supporting different
+  // types in C++.
+  // On CUDAPlace, std::pow(3, 1) calls pow(float, float), and
+  // it will return a float number like 2.99... , which floor to 2
+  // when cast to int by default and it is wrong.
+  // Use llrint to cast it to the nearest integer, which is 3.
+  return std::llrint(std::pow(static_cast<double>(a), static_cast<double>(b)));
+}
+template <typename T, typename MPType>
+inline HOSTDEVICE typename std::enable_if<!std::is_integral<T>::value, T>::type
+compute_pow(const T a, const T b) {
+  MPType a_val = static_cast<MPType>(a);
+  MPType b_val = static_cast<MPType>(b);
+#ifdef PADDLE_WITH_XPU_KP
+  return static_cast<T>(pow(a_val, b_val));
 #endif
+  return static_cast<T>(std::pow(a_val, b_val));
+}
+#else
+template <typename T, typename MPType>
+inline HOSTDEVICE T compute_pow(const T a, const T b) {
+  MPType a_val = static_cast<MPType>(a);
+  MPType b_val = static_cast<MPType>(b);
 #ifdef PADDLE_WITH_XPU_KP
-    return pow(a, b);
+  return static_cast<T>(pow(a_val, b_val));
+#endif
+  return static_cast<T>(std::pow(a_val, b_val));
+}
 #endif
-    return std::pow(a, b);
-  }
-};
 
 template <typename T>
-struct ElementwiseInversePowFunctor {
+struct ElementwisePowFunctor {
+  using MPType = typename phi::dtype::MPTypeTrait<T>::Type;
   inline HOSTDEVICE T operator()(const T a, const T b) const {
-// TODO(wujionghao): A potential speed improvement is supporting different
-// types in C++.
-#if defined(__CUDA_ARCH__) || defined(__HIPCC__)
-    // On CUDAPlace, std::pow(3, 1) calls pow(float, float), and
-    // it will return a float number like 2.99... , which floor to 2
-    // when cast to int by default and it is wrong.
-    // Use llrint to cast it to the nearest integer, which is 3.
-    if (std::is_integral<T>::value) {
-      return std::llrint(
-          std::pow(static_cast<double>(b), static_cast<double>(a)));
-    }
-#endif
-#ifdef PADDLE_WITH_XPU_KP
-    return pow(b, a);
-#endif
-    return std::pow(b, a);
+    return compute_pow<T, MPType>(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));
+template <typename T>
+struct ElementwiseInversePowFunctor {
+  using MPType = typename phi::dtype::MPTypeTrait<T>::Type;
+  inline HOSTDEVICE T operator()(const T a, const T b) const {
+    return compute_pow<T, MPType>(b, a);
   }
 };
 
-template <>
-struct ElementwiseInversePowFunctor<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_b, f_a));
-  }
-};
 }  // namespace funcs
 }  // namespace phi

paddle/phi/kernels/gpu/elementwise_grad_kernel.cu

@@ -150,4 +150,5 @@ PD_REGISTER_KERNEL(elementwise_pow_grad,
                    double,
                    int,
                    phi::dtype::float16,
+                   phi::dtype::bfloat16,
                    int64_t) {}

paddle/phi/kernels/impl/elementwise_grad_kernel_impl.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 
+#include "paddle/phi/common/amp_type_traits.h"
 #include "paddle/phi/common/complex.h"
 #include "paddle/phi/common/float16.h"
 #include "paddle/phi/core/dense_tensor.h"
@@ -851,58 +852,65 @@ void HeavisideGradKernel(const Context& dev_ctx,
           HeavisideGradDy<T>());
 }
 
-template <typename T>
-struct PowGradDX {
-  HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
 #if defined(__CUDA_ARCH__) || defined(__HIPCC__)
-    if (std::is_integral<T>::value) {
-      return dout * y *
-             std::pow(static_cast<double>(x), static_cast<double>(y - 1));
-    }
+template <typename T, typename MPType>
+HOSTDEVICE typename std::enable_if<std::is_integral<T>::value, T>::type
+compute_pow_grad_dx(T x, T y, T out, T dout) {
+  return dout * y *
+         std::pow(static_cast<double>(x), static_cast<double>(y - 1));
+}
+template <typename T, typename MPType>
+HOSTDEVICE typename std::enable_if<!std::is_integral<T>::value, T>::type
+compute_pow_grad_dx(T x, T y, T out, T dout) {
+  MPType x_val = static_cast<MPType>(x);
+  MPType y_val = static_cast<MPType>(y);
+  return static_cast<T>(static_cast<MPType>(dout) * y_val *
+                        std::pow(x_val, y_val - 1));
+}
+template <typename T, typename MPType>
+HOSTDEVICE typename std::enable_if<std::is_integral<T>::value, T>::type
+compute_pow_grad_dy(T x, T y, T out, T dout) {
+  return dout * std::log(static_cast<double>(x)) *
+         std::pow(static_cast<double>(x), static_cast<double>(y));
+}
+template <typename T, typename MPType>
+HOSTDEVICE typename std::enable_if<!std::is_integral<T>::value, T>::type
+compute_pow_grad_dy(T x, T y, T out, T dout) {
+  MPType x_val = static_cast<MPType>(x);
+  MPType y_val = static_cast<MPType>(y);
+  return static_cast<T>(static_cast<MPType>(dout) * std::log(x_val) *
+                        std::pow(x_val, y_val));
+}
+#else
+template <typename T, typename MPType>
+HOSTDEVICE T compute_pow_grad_dx(T x, T y, T out, T dout) {
+  MPType x_val = static_cast<MPType>(x);
+  MPType y_val = static_cast<MPType>(y);
+  return static_cast<T>(static_cast<MPType>(dout) * y_val *
+                        std::pow(x_val, y_val - 1));
+}
+template <typename T, typename MPType>
+HOSTDEVICE T compute_pow_grad_dy(T x, T y, T out, T dout) {
+  MPType x_val = static_cast<MPType>(x);
+  MPType y_val = static_cast<MPType>(y);
+  return static_cast<T>(static_cast<MPType>(dout) * std::log(x_val) *
+                        std::pow(x_val, y_val));
+}
 #endif
-    return dout * y * std::pow(x, y - 1);
-  }
-};
 
-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>
+struct PowGradDX {
+  using MPType = typename phi::dtype::MPTypeTrait<T>::Type;
+  HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
+    return compute_pow_grad_dx<T, MPType>(x, y, out, dout);
   }
 };
 
 template <typename T, typename Enable = void>
 struct PowGradDY {
+  using MPType = typename phi::dtype::MPTypeTrait<T>::Type;
   HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
-#if defined(__CUDA_ARCH__) || defined(__HIPCC__)
-    if (std::is_integral<T>::value) {
-      return dout * std::log(static_cast<double>(x)) *
-             std::pow(static_cast<double>(x), static_cast<double>(y));
-    }
-#endif
-    return dout * std::log(x) * std::pow(x, y);
-  }
-};
-
-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);
+    return compute_pow_grad_dy<T, MPType>(x, y, out, dout);
   }
 };
 

paddle/phi/kernels/kps/elementwise_kernel.cu

@@ -181,5 +181,6 @@ PD_REGISTER_KERNEL(elementwise_pow_raw,
                    double,
                    int,
                    float16,
+                   bfloat16,
                    int64_t) {}
 #endif

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

@@ -15,7 +15,7 @@
 import unittest
 
 import numpy as np
-from op_test import OpTest, skip_check_grad_ci
+from op_test import OpTest, convert_float_to_uint16, skip_check_grad_ci
 
 import paddle
 import paddle.fluid as fluid
@@ -308,6 +308,7 @@ class TestElementwisePowGradOpInt(unittest.TestCase):
 class TestElementwisePowOpFP16(OpTest):
     def setUp(self):
         self.op_type = "elementwise_pow"
+        self.dtype = np.float16
         self.python_api = paddle.pow
         self.public_python_api = paddle.pow
         self.prim_op_type = "prim"
@@ -336,5 +337,30 @@ class TestElementwisePowOpFP16(OpTest):
         )
 
 
+class TestElementwisePowBF16Op(OpTest):
+    def setUp(self):
+        self.op_type = "elementwise_pow"
+        self.dtype = np.uint16
+        self.python_api = paddle.pow
+
+        x = np.random.uniform(0, 1, [20, 5]).astype(np.float32)
+        y = np.random.uniform(0, 1, [20, 5]).astype(np.float32)
+        out = np.power(x, y)
+        self.inputs = {
+            'X': convert_float_to_uint16(x),
+            'Y': convert_float_to_uint16(y),
+        }
+        self.outputs = {'Out': convert_float_to_uint16(out)}
+
+    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', check_eager=True)
+
+
 if __name__ == '__main__':
     unittest.main()