wehub-resource-sync
135 lines
3.8 KiB
C++
135 lines
3.8 KiB
C++
// Copyright (c) 2023 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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#pragma once
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#include <string>
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#include "paddle/common/enforce.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/backends/gpu/gpu_device_function.h"
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#include "paddle/phi/backends/gpu/gpu_dnn.h"
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#include "paddle/phi/common/amp_type_traits.h"
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#include "paddle/phi/common/data_type.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/load_store_util.h"
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#include "paddle/phi/kernels/gpu/gelu_funcs.h"
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// for windows build
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#define M_SQRT1_2 0.70710678118654752440
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namespace phi {
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namespace fusion {
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template <typename T>
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struct FastGeluFunctor {
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inline __device__ T operator()(const T x) const {
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return phi::GeluFwd<T, true>(x);
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}
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};
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template <typename T>
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struct GeluComputeType;
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template <>
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struct GeluComputeType<phi::bfloat16> {
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using Type = float;
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};
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template <>
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struct GeluComputeType<phi::float16> {
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using Type = float;
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};
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template <>
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struct GeluComputeType<float> {
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using Type = float;
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};
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template <typename T>
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using GeluType = typename GeluComputeType<T>::Type;
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using funcs::DequantLoad;
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using funcs::LoadFunc;
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using funcs::QuantStore;
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using funcs::StoreFunc;
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template <typename T>
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struct BaseActivationFunctor {
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using ELEMENT_TYPE = T;
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using AttrPair = std::vector<std::pair<const char *, float *>>;
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AttrPair GetAttrs() { return AttrPair(); }
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};
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// For windows build
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template <typename T>
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struct CudaSwishFunctor : public BaseActivationFunctor<T> {
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using MT = typename MPTypeTrait<T>::Type;
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MT one = static_cast<MT>(1.0f);
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float beta = 1.0;
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typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
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return {{"beta", &beta}};
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}
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// swish(x) = x / (1 + exp(-beta * x))
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__device__ __forceinline__ T operator()(const T arg_x) const {
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MT x = static_cast<MT>(arg_x);
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MT b = static_cast<MT>(beta);
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return static_cast<T>(x / (one + exp(-b * x)));
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}
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};
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// TODO(lzc): transfer to phi::funcs
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template <typename T>
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struct GeluFunctor {
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inline __host__ __device__ T operator()(const T x) const {
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using U = GeluType<T>;
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const U casted_x = static_cast<U>(x);
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const U temp = erf(casted_x * static_cast<U>(M_SQRT1_2));
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const U out = (casted_x * static_cast<U>(0.5) * (static_cast<U>(1) + temp));
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return static_cast<T>(out);
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}
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};
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template <typename T>
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struct ReluFunctor {
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inline __host__ __device__ T operator()(const T x) const {
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T zero = static_cast<T>(0.0);
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return x > zero ? x : zero;
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}
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};
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inline gpuError_t GetNumBlocks(int64_t n, int *num_blocks) {
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constexpr int kBlockSize = 128;
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constexpr int kNumWaves = 16;
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const int device_id = backends::gpu::GetCurrentDeviceId();
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const int sm_count = backends::gpu::GetGPUMultiProcessors(device_id);
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const int max_thread_per_multiprocessor =
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backends::gpu::GetGPUMaxThreadsPerMultiProcessor(device_id);
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int64_t num_blocks_candidate = std::min<int64_t>(
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(n + kBlockSize - 1) / kBlockSize,
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sm_count * max_thread_per_multiprocessor / kBlockSize * kNumWaves);
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PADDLE_ENFORCE_LE_INT_MAX(num_blocks_candidate, "num_blocks_candidate");
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*num_blocks = std::max<int>(1, static_cast<int>(num_blocks_candidate));
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return gpuSuccess;
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}
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} // namespace fusion
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} // namespace phi
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