wehub-resource-sync
113 lines
4.0 KiB
Plaintext
113 lines
4.0 KiB
Plaintext
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/phi/kernels/gelu_kernel.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/common/amp_type_traits.h"
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/broadcast_function.h"
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#include "paddle/phi/kernels/gpu/gelu_funcs.h"
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COMMON_DECLARE_bool(use_fast_math);
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COMMON_DECLARE_bool(use_accuracy_compatible_kernel);
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namespace phi {
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template <typename T>
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struct GeluWithApproximateGradFunctor {
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using MT = typename MPTypeTrait<T>::Type;
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inline HOSTDEVICE T operator()(T arg_x, T arg_dout) {
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MT x = static_cast<MT>(arg_x);
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MT dout = static_cast<MT>(arg_dout);
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MT kBeta = M_SQRT2 * M_2_SQRTPI * static_cast<MT>(0.5);
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MT kKappa = static_cast<MT>(GELU_CONSTANT);
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auto x_sq = x * x;
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auto x_cube = x_sq * x;
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auto inner = kBeta * (x + kKappa * x_cube);
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auto tanh_inner = tanh(inner);
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auto left = static_cast<MT>(0.5) * x;
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auto right = static_cast<MT>(1) + tanh_inner;
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auto left_derivative = static_cast<MT>(0.5) * right;
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auto tanh_derivative = static_cast<MT>(1) - tanh_inner * tanh_inner;
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auto inner_derivative =
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kBeta * (static_cast<MT>(1) + static_cast<MT>(3) * kKappa * x_sq);
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auto right_derivative = left * tanh_derivative * inner_derivative;
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return static_cast<T>(dout * (left_derivative + right_derivative));
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}
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};
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template <typename T>
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struct GeluWithoutApproximateGradFunctor {
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using MT = typename MPTypeTrait<T>::Type;
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inline HOSTDEVICE T operator()(T arg_x, T arg_dout) {
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MT x = static_cast<MT>(arg_x);
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MT dout = static_cast<MT>(arg_dout);
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constexpr MT kBeta = M_2_SQRTPI * M_SQRT1_2 * MT(0.5);
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constexpr MT kAlpha = M_SQRT1_2;
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const MT cdf = MT(0.5) * (MT(1) + std::erf(x * kAlpha));
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const MT pdf = exp(static_cast<MT>(-0.5) * x * x) * kBeta;
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return static_cast<T>(dout * (cdf + x * pdf));
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}
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};
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template <typename T, typename Context>
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void GeluGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& out_grad,
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bool approximate,
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DenseTensor* x_grad) {
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dev_ctx.template Alloc<T>(x_grad);
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if (x_grad && x_grad->numel() == 0) {
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return;
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}
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std::vector<const DenseTensor*> ins = {&x, &out_grad};
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std::vector<DenseTensor*> outs = {x_grad};
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if (approximate) {
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#if defined(__NVCC__) || defined(__HIPCC__)
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if (std::is_same<T, dtype::float16>::value &&
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!FLAGS_use_accuracy_compatible_kernel) {
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size_t n = x.numel();
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const auto* x_ptr = reinterpret_cast<const __half*>(x.data<T>());
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const auto* y_g_ptr = reinterpret_cast<const __half*>(out_grad.data<T>());
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auto* x_g_ptr = reinterpret_cast<__half*>(x_grad->data<T>());
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if (TryLaunchFP16FastGeluBwdVectorizeCUDAKernel(
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dev_ctx, x_ptr, y_g_ptr, x_g_ptr, n)) {
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return;
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}
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}
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#endif
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using Functor = GeluWithApproximateGradFunctor<T>;
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funcs::ElementwiseKernel<T, Functor, 1>(dev_ctx, ins, &outs, Functor());
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} else {
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using Functor = GeluWithoutApproximateGradFunctor<T>;
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funcs::ElementwiseKernel<T, Functor, 1>(dev_ctx, ins, &outs, Functor());
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(gelu_grad,
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GPU,
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ALL_LAYOUT,
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phi::GeluGradKernel,
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float,
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double,
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phi::float16,
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phi::bfloat16) {}
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