未验证 提交 e5e52249 编写于 作者: L Leo Chen 提交者: GitHub

make gelu fp16 computing more robust (#29484)

上级 8094ac68
...@@ -36,10 +36,22 @@ struct GeluFunctor { ...@@ -36,10 +36,22 @@ struct GeluFunctor {
void operator()(Device d, X x, Out out, bool approximate) const { void operator()(Device d, X x, Out out, bool approximate) const {
if (approximate) { if (approximate) {
// gelu(x) = 0.5 * x * (1 + tanh(sqrt(2 / \pi) * (x + 0.044715 * x^{3}))) // gelu(x) = 0.5 * x * (1 + tanh(sqrt(2 / \pi) * (x + 0.044715 * x^{3})))
if (std::is_same<T, platform::float16>::value) {
VLOG(4) << "cast from float16 to float before computing";
auto casted_x = x.template cast<float>();
auto temp =
(static_cast<float>(M_2_SQRTPI * M_SQRT1_2) *
(casted_x + static_cast<float>(0.044715) * casted_x.cube()))
.tanh();
out.device(d) = (casted_x * static_cast<float>(0.5) *
(static_cast<float>(1) + temp))
.template cast<T>();
} else {
auto temp = (static_cast<T>(M_2_SQRTPI * M_SQRT1_2) * auto temp = (static_cast<T>(M_2_SQRTPI * M_SQRT1_2) *
(x + static_cast<T>(0.044715) * x.cube())) (x + static_cast<T>(0.044715) * x.cube()))
.tanh(); .tanh();
out.device(d) = x * static_cast<T>(0.5) * (static_cast<T>(1) + temp); out.device(d) = x * static_cast<T>(0.5) * (static_cast<T>(1) + temp);
}
} else { } else {
#if defined(PADDLE_WITH_MKLML) && !defined(_WIN32) && !defined(__APPLE__) && \ #if defined(PADDLE_WITH_MKLML) && !defined(_WIN32) && !defined(__APPLE__) && \
!defined(__OSX__) && !defined(PADDLE_WITH_CUDA) !defined(__OSX__) && !defined(PADDLE_WITH_CUDA)
...@@ -60,8 +72,17 @@ struct GeluFunctor { ...@@ -60,8 +72,17 @@ struct GeluFunctor {
} }
#else #else
// gelu(x) = 0.5 * x * (1 + erf(x / sqrt(2))) // gelu(x) = 0.5 * x * (1 + erf(x / sqrt(2)))
if (std::is_same<T, platform::float16>::value) {
VLOG(4) << "cast from float16 to float before computing";
auto casted_x = x.template cast<float>();
auto temp = (casted_x * static_cast<float>(M_SQRT1_2)).erf();
out.device(d) = (casted_x * static_cast<float>(0.5) *
(static_cast<float>(1) + temp))
.template cast<T>();
} else {
auto temp = (x * static_cast<T>(M_SQRT1_2)).erf(); auto temp = (x * static_cast<T>(M_SQRT1_2)).erf();
out.device(d) = x * static_cast<T>(0.5) * (static_cast<T>(1) + temp); out.device(d) = x * static_cast<T>(0.5) * (static_cast<T>(1) + temp);
}
#endif #endif
} }
} }
...@@ -72,6 +93,24 @@ struct GeluGradFunctor { ...@@ -72,6 +93,24 @@ struct GeluGradFunctor {
template <typename Device, typename X, typename dOut, typename dX> template <typename Device, typename X, typename dOut, typename dX>
void operator()(Device d, X x, dOut dout, dX dx, bool approximate) const { void operator()(Device d, X x, dOut dout, dX dx, bool approximate) const {
if (approximate) { if (approximate) {
if (std::is_same<T, platform::float16>::value) {
VLOG(4) << "cast from float16 to float before computing";
auto casted_x = x.template cast<float>();
auto casted_dout = dout.template cast<float>();
const float kAlpha = static_cast<float>(M_2_SQRTPI * M_SQRT1_2);
const float kBeta =
kAlpha * static_cast<float>(0.044715) * static_cast<float>(3);
const auto y =
(kAlpha *
((static_cast<float>(0.044715) * casted_x.cube()) + casted_x))
.tanh();
dx.device(d) = (static_cast<float>(0.5) * casted_dout *
(static_cast<float>(1) + y +
(casted_x - casted_x * y.square()) *
(kAlpha + kBeta * casted_x.square())))
.template cast<T>();
} else {
const T kAlpha = static_cast<T>(M_2_SQRTPI * M_SQRT1_2); const T kAlpha = static_cast<T>(M_2_SQRTPI * M_SQRT1_2);
const T kBeta = kAlpha * static_cast<T>(0.044715) * static_cast<T>(3); const T kBeta = kAlpha * static_cast<T>(0.044715) * static_cast<T>(3);
const auto y = const auto y =
...@@ -79,6 +118,7 @@ struct GeluGradFunctor { ...@@ -79,6 +118,7 @@ struct GeluGradFunctor {
dx.device(d) = static_cast<T>(0.5) * dout * dx.device(d) = static_cast<T>(0.5) * dout *
(static_cast<T>(1) + y + (static_cast<T>(1) + y +
(x - x * y.square()) * (kAlpha + kBeta * x.square())); (x - x * y.square()) * (kAlpha + kBeta * x.square()));
}
} else { } else {
#if defined(PADDLE_WITH_MKLML) && !defined(_WIN32) && !defined(__APPLE__) && \ #if defined(PADDLE_WITH_MKLML) && !defined(_WIN32) && !defined(__APPLE__) && \
!defined(__OSX__) && !defined(PADDLE_WITH_CUDA) !defined(__OSX__) && !defined(PADDLE_WITH_CUDA)
...@@ -117,6 +157,18 @@ struct GeluGradFunctor { ...@@ -117,6 +157,18 @@ struct GeluGradFunctor {
#else #else
// gelu_grad(x) = dout * 0.5 * (1 + erf(x / sqrt(2)) + x * sqrt(2 / pi) * // gelu_grad(x) = dout * 0.5 * (1 + erf(x / sqrt(2)) + x * sqrt(2 / pi) *
// exp(- x^2 / 2) // exp(- x^2 / 2)
if (std::is_same<T, platform::float16>::value) {
VLOG(4) << "cast from float16 to float before computing";
auto casted_x = x.template cast<float>();
auto casted_dout = dout.template cast<float>();
auto first = static_cast<float>(0.5) *
(static_cast<float>(1) +
((casted_x * static_cast<float>(M_SQRT1_2)).erf()));
auto second = static_cast<float>(0.5 * M_2_SQRTPI * M_SQRT1_2) *
casted_x *
(-static_cast<float>(0.5) * casted_x.square()).exp();
dx.device(d) = (casted_dout * (first + second)).template cast<T>();
} else {
auto first = auto first =
static_cast<T>(0.5) * static_cast<T>(0.5) *
(static_cast<T>(1) + ((x * static_cast<T>(M_SQRT1_2)).erf())); (static_cast<T>(1) + ((x * static_cast<T>(M_SQRT1_2)).erf()));
...@@ -124,6 +176,7 @@ struct GeluGradFunctor { ...@@ -124,6 +176,7 @@ struct GeluGradFunctor {
auto second = static_cast<T>(0.5 * M_2_SQRTPI * M_SQRT1_2) * x * auto second = static_cast<T>(0.5 * M_2_SQRTPI * M_SQRT1_2) * x *
(-static_cast<T>(0.5) * x.square()).exp(); (-static_cast<T>(0.5) * x.square()).exp();
dx.device(d) = dout * (first + second); dx.device(d) = dout * (first + second);
}
#endif #endif
} }
} }
......
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