/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/operators/math/jit_kernel.h" #include // for exp #include #include "paddle/fluid/operators/math/jit_kernel_macro.h" #ifdef PADDLE_WITH_XBYAK #include "paddle/fluid/operators/math/jit_code.h" #endif #ifdef PADDLE_WITH_MKLML #include "paddle/fluid/platform/dynload/mklml.h" #endif #ifdef __AVX__ #include #endif namespace paddle { namespace operators { namespace math { namespace jitkernel { namespace jit = platform::jit; // TODO(TJ): move refer codes to one file // Refer code only focus on correctness template void VExpRefer(const T* x, T* y, int n) { for (int i = 0; i < n; ++i) { y[i] = std::exp(x[i]); } } template void VSigmoidRefer(const T* x, T* y, int n) { // y = 1 / (1 + e^-x) const T min = SIGMOID_THRESHOLD_MIN; const T max = SIGMOID_THRESHOLD_MAX; for (int i = 0; i < n; ++i) { T tmp = (x[i] < min) ? min : ((x[i] > max) ? max : x[i]); y[i] = static_cast(1) / (static_cast(1) + std::exp(-tmp)); } } template void VTanhRefer(const T* x, T* y, int n) { // y = 2 * sigmoid(2x) - 1 for (int i = 0; i < n; ++i) { y[i] = static_cast(2) * x[i]; } VSigmoidRefer(y, y, n); for (int i = 0; i < n; ++i) { y[i] = static_cast(2) * y[i] - static_cast(1); } } #ifdef PADDLE_WITH_MKLML // try to use MKL to speedup template void VExpMKL(const T* x, T* y, int n); template <> void VExpMKL(const float* x, float* y, int n) { platform::dynload::vsExp(n, x, y); } template <> void VExpMKL(const double* x, double* y, int n) { platform::dynload::vdExp(n, x, y); } template void VSigmoidMKL(const T* x, T* y, int n) { const T min = SIGMOID_THRESHOLD_MIN; const T max = SIGMOID_THRESHOLD_MAX; for (int i = 0; i < n; ++i) { y[i] = (x[i] < min) ? min : ((x[i] > max) ? max : x[i]); y[i] = static_cast(0) - y[i]; } VExpMKL(y, y, n); for (int i = 0; i < n; ++i) { y[i] = static_cast(1) / (static_cast(1) + y[i]); } } template void VTanhMKL(const T* x, T* y, int n) { for (int i = 0; i < n; ++i) { y[i] = static_cast(2) * x[i]; } VSigmoidMKL(y, y, n); for (int i = 0; i < n; ++i) { y[i] = static_cast(2) * y[i] - static_cast(1); } } #endif /* VExp JitKernel */ template class VExpKernelImpl : public VExpKernel { public: JITKERNEL_DECLARE_STATIC_FUNC; explicit VExpKernelImpl(int d) : VExpKernel() { #ifdef PADDLE_WITH_XBYAK if (useJIT(d)) { size_t sz = 96 + d / YMM_FLOAT_BLOCK * 70 * 8; jitcode_.reset(new gen::VActJitCode(d, gen::operand_type::exp, sz > 4096 ? sz : 4096)); this->Compute = jitcode_->getCode(); return; } #endif #ifdef PADDLE_WITH_MKLML if (useMKL(d)) { this->Compute = VExpMKL; return; } #endif this->Compute = VExpRefer; } #ifdef PADDLE_WITH_XBYAK private: std::unique_ptr jitcode_{nullptr}; #endif }; #ifdef PADDLE_WITH_XBYAK template <> bool VExpKernelImpl::useJIT(int d) { return gen::VActJitCode::init(d, gen::operand_type::exp); } #endif #ifdef PADDLE_WITH_MKLML template <> bool VExpKernelImpl::useMKL(int d) { return d > 512; } template <> bool VExpKernelImpl::useMKL(int d) { return true; } #endif /* VSigmoid JitKernel */ template class VSigmoidKernelImpl : public VSigmoidKernel { public: JITKERNEL_DECLARE_STATIC_FUNC; explicit VSigmoidKernelImpl(int d) : VSigmoidKernel() { #ifdef PADDLE_WITH_XBYAK if (useJIT(d)) { size_t sz = 96 + d / YMM_FLOAT_BLOCK * 82 * 8; jitcode_.reset(new gen::VActJitCode(d, gen::operand_type::sigmoid, sz > 4096 ? sz : 4096)); this->Compute = jitcode_->getCode(); return; } #endif #ifdef PADDLE_WITH_MKLML // strictly it's a better impl with MKL, then is refer if (useMKL(d)) { this->Compute = VSigmoidMKL; return; } #endif this->Compute = VSigmoidRefer; } #ifdef PADDLE_WITH_XBYAK private: std::unique_ptr jitcode_{nullptr}; #endif }; #ifdef PADDLE_WITH_XBYAK template <> bool VSigmoidKernelImpl::useJIT(int d) { return gen::VActJitCode::init(d, gen::operand_type::sigmoid); } #endif #ifdef PADDLE_WITH_MKLML template <> bool VSigmoidKernelImpl::useMKL(int d) { return d > 512; } template <> bool VSigmoidKernelImpl::useMKL(int d) { return true; } #endif /* VTanh JitKernel */ template class VTanhKernelImpl : public VTanhKernel { public: JITKERNEL_DECLARE_STATIC_FUNC; explicit VTanhKernelImpl(int d) : VTanhKernel() { #ifdef PADDLE_WITH_XBYAK if (useJIT(d)) { size_t sz = 96 + d / YMM_FLOAT_BLOCK * 84 * 8; jitcode_.reset(new gen::VActJitCode(d, gen::operand_type::tanh, sz > 4096 ? sz : 4096)); this->Compute = jitcode_->getCode(); return; } #endif #ifdef PADDLE_WITH_MKLML // strictly it's a better impl with MKL, then is refer if (useMKL(d)) { this->Compute = VTanhMKL; return; } #endif this->Compute = VTanhRefer; } #ifdef PADDLE_WITH_XBYAK private: std::unique_ptr jitcode_{nullptr}; #endif }; #ifdef PADDLE_WITH_XBYAK template <> bool VTanhKernelImpl::useJIT(int d) { return gen::VActJitCode::init(d, gen::operand_type::tanh); } #endif #ifdef PADDLE_WITH_MKLML template <> bool VTanhKernelImpl::useMKL(int d) { return d > 512; } template <> bool VTanhKernelImpl::useMKL(int d) { return true; } #endif REGISTER_JITKERNEL(vexp, VExpKernel); REGISTER_JITKERNEL(vsigmoid, VSigmoidKernel); REGISTER_JITKERNEL(vtanh, VTanhKernel); namespace detail { #define ALIGN32 __attribute__((aligned(32))) #define _PS256_CONST(Name, Val) \ static const float _ps256_##Name[8] ALIGN32 = {Val, Val, Val, Val, \ Val, Val, Val, Val} #define _PI256_CONST(Name, Val) \ static const int _pi256_##Name[8] ALIGN32 = {Val, Val, Val, Val, \ Val, Val, Val, Val} _PI256_CONST(0x7f, 0x7f); _PS256_CONST(one, 1.f); _PS256_CONST(0p5, 0.5f); _PS256_CONST(exp_hi, 88.3762626647949f); _PS256_CONST(exp_lo, -88.3762626647949f); _PS256_CONST(cephes_LOG2EF, 1.44269504088896341); _PS256_CONST(cephes_exp_C1, 0.693359375); _PS256_CONST(cephes_exp_C2, -2.12194440e-4); _PS256_CONST(cephes_exp_p0, 1.9875691500E-4); _PS256_CONST(cephes_exp_p1, 1.3981999507E-3); _PS256_CONST(cephes_exp_p2, 8.3334519073E-3); _PS256_CONST(cephes_exp_p3, 4.1665795894E-2); _PS256_CONST(cephes_exp_p4, 1.6666665459E-1); _PS256_CONST(cephes_exp_p5, 5.0000001201E-1); typedef union imm_xmm_union { __m256i imm; __m128i xmm[2]; } imm_xmm_union; #define COPY_IMM_TO_XMM(imm_, xmm0_, xmm1_) \ { \ imm_xmm_union u ALIGN32; \ u.imm = imm_; \ xmm0_ = u.xmm[0]; \ xmm1_ = u.xmm[1]; \ } #define COPY_XMM_TO_IMM(xmm0_, xmm1_, imm_) \ { \ imm_xmm_union u ALIGN32; \ u.xmm[0] = xmm0_; \ u.xmm[1] = xmm1_; \ imm_ = u.imm; \ } #define AVX2_BITOP_USING_SSE2(fn) \ static inline __m256i avx2_mm256_##fn(__m256i x, int y) { \ /* use SSE2 to perform the bitop AVX2 */ \ __m128i x1, x2; \ __m256i ret; \ COPY_IMM_TO_XMM(x, x1, x2); \ x1 = _mm_##fn(x1, y); \ x2 = _mm_##fn(x2, y); \ COPY_XMM_TO_IMM(x1, x2, ret); \ return ret; \ } #define AVX2_INTOP_USING_SSE2(fn) \ static inline __m256i avx2_mm256_add_epi32(__m256i x, __m256i y) { \ /* use SSE2 to perform the AVX2 integer operation */ \ __m128i x1, x2; \ __m128i y1, y2; \ __m256i ret; \ COPY_IMM_TO_XMM(x, x1, x2); \ COPY_IMM_TO_XMM(y, y1, y2); \ x1 = _mm_##fn(x1, y1); \ x2 = _mm_##fn(x2, y2); \ COPY_XMM_TO_IMM(x1, x2, ret); \ return ret; \ } AVX2_BITOP_USING_SSE2(slli_epi32); AVX2_INTOP_USING_SSE2(add_epi32); #define AVXEXP_BASE \ __m256 tmp = _mm256_setzero_ps(), fx; \ __m256 one = *reinterpret_cast(_ps256_one); \ __m256i imm0; \ x = _mm256_min_ps(x, *reinterpret_cast(_ps256_exp_hi)); \ x = _mm256_max_ps(x, *reinterpret_cast(_ps256_exp_lo)); \ /* express exp(x) as exp(g + n*log(2)) */ \ fx = _mm256_mul_ps(x, \ *reinterpret_cast(_ps256_cephes_LOG2EF)); \ fx = _mm256_add_ps(fx, *reinterpret_cast(_ps256_0p5)); \ tmp = _mm256_floor_ps(fx); \ /* if greater, substract 1 */ \ __m256 mask = _mm256_cmp_ps(tmp, fx, _CMP_GT_OS); \ mask = _mm256_and_ps(mask, one); \ fx = _mm256_sub_ps(tmp, mask); \ tmp = _mm256_mul_ps(fx, \ *reinterpret_cast(_ps256_cephes_exp_C1)); \ __m256 z = _mm256_mul_ps( \ fx, *reinterpret_cast(_ps256_cephes_exp_C2)); \ x = _mm256_sub_ps(x, tmp); \ x = _mm256_sub_ps(x, z); \ z = _mm256_mul_ps(x, x); \ __m256 y = *reinterpret_cast(_ps256_cephes_exp_p0); \ y = _mm256_mul_ps(y, x); \ y = _mm256_add_ps(y, \ *reinterpret_cast(_ps256_cephes_exp_p1)); \ y = _mm256_mul_ps(y, x); \ y = _mm256_add_ps(y, \ *reinterpret_cast(_ps256_cephes_exp_p2)); \ y = _mm256_mul_ps(y, x); \ y = _mm256_add_ps(y, \ *reinterpret_cast(_ps256_cephes_exp_p3)); \ y = _mm256_mul_ps(y, x); \ y = _mm256_add_ps(y, \ *reinterpret_cast(_ps256_cephes_exp_p4)); \ y = _mm256_mul_ps(y, x); \ y = _mm256_add_ps(y, \ *reinterpret_cast(_ps256_cephes_exp_p5)); \ y = _mm256_mul_ps(y, z); \ y = _mm256_add_ps(y, x); \ y = _mm256_add_ps(y, one); \ /* build 2^n */ \ imm0 = _mm256_cvttps_epi32(fx) __m256 ExpAVX(__m256 x) { AVXEXP_BASE; // two AVX2 instructions using SSE2 imm0 = avx2_mm256_add_epi32(imm0, *reinterpret_cast(_pi256_0x7f)); imm0 = avx2_mm256_slli_epi32(imm0, 23); __m256 pow2n = _mm256_castsi256_ps(imm0); y = _mm256_mul_ps(y, pow2n); return y; } #ifdef __AVX2__ __m256 ExpAVX2(__m256 x) { AVXEXP_BASE; // two AVX2 instructions imm0 = _mm256_add_epi32(imm0, *reinterpret_cast(_pi256_0x7f)); imm0 = _mm256_slli_epi32(imm0, 23); __m256 pow2n = _mm256_castsi256_ps(imm0); y = _mm256_mul_ps(y, pow2n); return y; } #endif } // namespace detail } // namespace jitkernel } // namespace math } // namespace operators } // namespace paddle