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提交 eeff268a 编写于 作者: T tensor-tang
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clean and refine kernels

上级 dee5d35c
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Showing with 241 addition and 195 deletion
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @ eeff268a
......@@ -76,5 +76,5 @@ if(WITH_GPU)
endif()
cc_test(concat_test SRCS concat_test.cc DEPS concat)
cc_test(cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info)
cc_library(jit_kernel SRCS jit_kernel.cc DEPS cpu_info cblas)
cc_library(jit_kernel SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_lstm.cc DEPS cpu_info cblas)
cc_test(jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel)
paddle/fluid/operators/math/jit_kernel.cc
浏览文件 @ eeff268a
......@@ -13,17 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/math/jit_kernel.h"
#include <functional>
#include <string>
#include "paddle/fluid/operators/math/cpu_vec.h"
#ifdef PADDLE_WITH_MKLML
#include "paddle/fluid/platform/dynload/mklml.h"
#endif
#ifdef __AVX__
#include <immintrin.h>
#endif
namespace paddle {
namespace operators {
......@@ -36,115 +26,6 @@ KernelPool& KernelPool::Instance() {
static KernelPool g_jit_kernels;
return g_jit_kernels;
}
#define SEARCH_BLOCK(src, t, isa) \
if (d < AVX_FLOAT_BLOCK) { \
Compute = src<t, isa, kLT8>; \
} else if (d == AVX_FLOAT_BLOCK) { \
Compute = src<t, isa, kEQ8>; \
} else if (d > AVX_FLOAT_BLOCK && d < AVX512_FLOAT_BLOCK) { \
Compute = src<t, isa, kGT8LT16>; \
} else if (d == AVX512_FLOAT_BLOCK) { \
Compute = src<t, isa, kEQ16>; \
} else { \
Compute = src<t, isa, kGT16>; \
}
#define SEARCH_ISA_BLOCK(src, t) \
if (jit::MayIUse(jit::avx512f)) { \
SEARCH_BLOCK(src, t, jit::avx512f); \
} else if (jit::MayIUse(jit::avx2)) { \
SEARCH_BLOCK(src, t, jit::avx2); \
} else if (jit::MayIUse(jit::avx)) { \
SEARCH_BLOCK(src, t, jit::avx); \
} else { \
SEARCH_BLOCK(src, t, jit::isa_any); \
}
// do not include lt8, eq8, eq16
#define FOR_EACH_COMMON_BLOCK(macro_, isa) \
macro_(isa, kGT8LT16) macro_(isa, kGT16)
#define FOR_EACH_ISA_COMMON_BLOCK(macro_) \
FOR_EACH_BLOCK(macro_, jit::avx512f) \
FOR_EACH_BLOCK(macro_, jit::avx2) \
FOR_EACH_BLOCK(macro_, jit::avx) \
FOR_EACH_BLOCK(macro_, jit::any)
#define VMUL_ANY \
for (int i = 0; i < n; ++i) { \
z[i] = x[i] * y[i]; \
}
template <typename T, platform::jit::cpu_isa_t isa, jit_block>
static void VMulCompute(const int n, const T* x, const T* y, T* z) {
VMUL_ANY
}
#ifdef PADDLE_USE_MKLML
#define DEFINE_VMUL_COMPUTE_FLOAT(isa, block) \
template <> \
void VMulCompute<float, isa, block>(const int n, const float* x, \
const float* y, float* z) { \
platform::dynload::vsMul(n, x, y, z); \
}
#define DEFINE_VMUL_COMPUTE_DOUBLE(isa, block) \
template <> \
void VMulCompute<double, isa, block>(const int n, const double* x, \
const double* y, float* z) { \
platform::dynload::vdMul(n, x, y, z); \
}
FOR_EACH_ISA_COMMON_BLOCK(DEFINE_VMUL_COMPUTE_FLOAT)
FOR_EACH_ISA_COMMON_BLOCK(DEFINE_VMUL_COMPUTE_DOUBLE)
DEFINE_VMUL_COMPUTE_FLOAT(jit::avx, kLT8)
DEFINE_VMUL_COMPUTE_FLOAT(jit::avx, kEQ16)
#endif
// mkl > avx > for, ">" means better
#ifdef PADDLE_USE_MKLML
DEFINE_VMUL_COMPUTE_FLOAT(jit::avx, kEQ8)
#elif defined __AVX__
template <>
void VMulCompute<float, jit::avx, kEQ8>(const int n, const float* x,
const float* y, float* z) {
__m256 tmpx, tmpy;
tmpx = _mm256_loadu_ps(x);
tmpy = _mm256_loadu_ps(y);
tmpx = _mm256_mul_ps(tmpx, tmpy);
_mm256_storeu_ps(z, tmpx);
}
#endif
// avx2 > mkl > for
#ifdef __AVX2__
template <>
void VMulCompute<float, jit::avx2, kEQ8>(const int n, const float* x,
const float* y, float* z) {
__m256 tmpx, tmpy;
tmpx = _mm256_loadu_ps(x);
tmpy = _mm256_loadu_ps(y);
tmpx = _mm256_mul_ps(tmpx, tmpy);
_mm256_storeu_ps(z, tmpx);
}
#elif defined PADDLE_USE_MKLML
DEFINE_VMUL_COMPUTE_FLOAT(jit::avx2, kEQ8)
#endif
// TODO(TJ): test and complete avx512
#undef DEFINE_VMUL_COMPUTE_FLOAT
#undef DEFINE_VMUL_COMPUTE_DOUBLE
#undef VMUL_ANY
template <>
VMulKernel<float>::VMulKernel(int d) {
SEARCH_ISA_BLOCK(VMulCompute, float);
}
template <>
VMulKernel<double>::VMulKernel(int d) {
SEARCH_ISA_BLOCK(VMulCompute, double);
}
template <>
const std::shared_ptr<VMulKernel<float>> KernelPool::Get<VMulKernel<float>>(
......@@ -170,52 +51,6 @@ const std::shared_ptr<VMulKernel<double>> KernelPool::Get<VMulKernel<double>>(
return std::dynamic_pointer_cast<VMulKernel<double>>(kers_.at(key));
}
template <>
LSTMKernel<float>::LSTMKernel(int d, const std::string& act_gate_str,
const std::string& act_cand_str,
const std::string& act_cell_str)
: Kernel(), d_(d) {
d2_ = d * 2;
d3_ = d * 3;
if (platform::jit::MayIUse(platform::jit::avx512f)) {
math::VecActivations<float, platform::jit::avx512f> act_functor;
act_gate_ = act_functor(act_gate_str);
act_cell_ = act_functor(act_cell_str);
act_cand_ = act_functor(act_cand_str);
} else if (platform::jit::MayIUse(platform::jit::avx2)) {
math::VecActivations<float, platform::jit::avx2> act_functor;
act_gate_ = act_functor(act_gate_str);
act_cell_ = act_functor(act_cell_str);
act_cand_ = act_functor(act_cand_str);
} else if (platform::jit::MayIUse(platform::jit::avx)) {
math::VecActivations<float, platform::jit::avx> act_functor;
act_gate_ = act_functor(act_gate_str);
act_cell_ = act_functor(act_cell_str);
act_cand_ = act_functor(act_cand_str);
// ComputeCtHt = [&](float*gates,const float*ct_1,float*ct, float*ht) {
// // gates: W_ch, W_ih, W_fh, W_oh
// act_gate(d3_, gates + d_, gates + d_);
// /* C_t = C_t-1 * fgated + cand_gated * igated */
// act_cand(d_, gates, gates);
// blas.VMUL(d_, gates, gates + d_, gates + d_);
// blas.VMUL(d_, ct_1, gates + d2_, gates + d2_);
// blas.VADD(d_, gates + d_, gates + d2_, ct);
// /* H_t = act_cell(C_t) * ogated */
// act_cell(d_, ct, gates + d2_);
// blas.VMUL(d_, gates + d2_, gates + d3_, ht)
// GET_Ct(ct_1, gates, ct);
// GET_Ht(ct, gates, ht);
// };
} else {
math::VecActivations<float, platform::jit::isa_any> act_functor;
act_gate_ = act_functor(act_gate_str);
act_cell_ = act_functor(act_cell_str);
act_cand_ = act_functor(act_cand_str);
}
}
template <>
const std::shared_ptr<LSTMKernel<float>>
KernelPool::Get<LSTMKernel<float>, int, const std::string&, const std::string&,
......
paddle/fluid/operators/math/jit_kernel.h
浏览文件 @ eeff268a
......@@ -87,5 +87,3 @@ class LSTMKernel : public Kernel {
} // namespace math
} // namespace operators
} // namespace paddle
#include "paddle/fluid/operators/math/jit_kernel_impl.h"
paddle/fluid/operators/math/jit_kernel_blas.cc 0 → 100644
浏览文件 @ eeff268a
/* 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 <string>
#ifdef PADDLE_WITH_MKLML
#include "paddle/fluid/platform/dynload/mklml.h"
#endif
#ifdef __AVX__
#include <immintrin.h>
#endif
namespace paddle {
namespace operators {
namespace math {
namespace jitkernel {
namespace jit = platform::jit;
#define SEARCH_BLOCK(src, t, isa) \
if (d < AVX_FLOAT_BLOCK) { \
Compute = src<t, isa, kLT8>; \
} else if (d == AVX_FLOAT_BLOCK) { \
Compute = src<t, isa, kEQ8>; \
} else if (d > AVX_FLOAT_BLOCK && d < AVX512_FLOAT_BLOCK) { \
Compute = src<t, isa, kGT8LT16>; \
} else if (d == AVX512_FLOAT_BLOCK) { \
Compute = src<t, isa, kEQ16>; \
} else { \
Compute = src<t, isa, kGT16>; \
}
#define SEARCH_ISA_BLOCK(src, t) \
if (jit::MayIUse(jit::avx512f)) { \
SEARCH_BLOCK(src, t, jit::avx512f); \
} else if (jit::MayIUse(jit::avx2)) { \
SEARCH_BLOCK(src, t, jit::avx2); \
} else if (jit::MayIUse(jit::avx)) { \
SEARCH_BLOCK(src, t, jit::avx); \
} else { \
SEARCH_BLOCK(src, t, jit::isa_any); \
}
// do not include lt8, eq8, eq16
#define FOR_EACH_COMMON_BLOCK(macro_, isa) \
macro_(isa, kGT8LT16) macro_(isa, kGT16)
#define FOR_EACH_ISA_COMMON_BLOCK(macro_) \
FOR_EACH_COMMON_BLOCK(macro_, jit::avx512f) \
FOR_EACH_COMMON_BLOCK(macro_, jit::avx2) \
FOR_EACH_COMMON_BLOCK(macro_, jit::avx) \
FOR_EACH_COMMON_BLOCK(macro_, jit::any)
#define FOR_EACH_ALL_BLOCK(macro_, isa) \
macro_(isa, kLT8) macro_(isa, kEQ8) macro_(isa, kGT8LT16) macro_(isa, kEQ16) \
macro_(isa, kGT16)
#define FOR_EACH_ISA_ALL_BLOCK(macro_) \
FOR_EACH_ALL_BLOCK(macro_, jit::avx512f) \
FOR_EACH_ALL_BLOCK(macro_, jit::avx2) \
FOR_EACH_ALL_BLOCK(macro_, jit::avx) \
FOR_EACH_ALL_BLOCK(macro_, jit::any)
/* VMUL JitKernel */
#define VMUL_ANY \
for (int i = 0; i < n; ++i) { \
z[i] = x[i] * y[i]; \
}
template <typename T, platform::jit::cpu_isa_t isa, jit_block>
static void VMulCompute(const int n, const T* x, const T* y, T* z) {
VMUL_ANY
}
#ifdef PADDLE_USE_MKLML
#define VMUL_MKL_FLOAT(isa, block) \
template <> \
void VMulCompute<float, isa, block>(const int n, const float* x, \
const float* y, float* z) { \
platform::dynload::vsMul(n, x, y, z); \
}
#define VMUL_MKL_DOUBLE(isa, block) \
template <> \
void VMulCompute<double, isa, block>(const int n, const double* x, \
const double* y, float* z) { \
platform::dynload::vdMul(n, x, y, z); \
}
FOR_EACH_ISA_COMMON_BLOCK(VMUL_MKL_FLOAT)
FOR_EACH_ISA_ALL_BLOCK(VMUL_MKL_DOUBLE)
#endif
/// lt8
#ifdef PADDLE_USE_MKLML
VMUL_MKL_FLOAT(jit::avx, kLT8)
#endif
/// eq8
#define VMUL_INTRI8_FLOAT(isa) \
template <> \
void VMulCompute<float, isa, kEQ8>(const int n, const float* x, \
const float* y, float* z) { \
__m256 tmpx, tmpy; \
tmpx = _mm256_loadu_ps(x); \
tmpy = _mm256_loadu_ps(y); \
tmpx = _mm256_mul_ps(tmpx, tmpy); \
_mm256_storeu_ps(z, tmpx); \
}
// mkl > avx > for, ">" means better
#ifdef PADDLE_USE_MKLML
VMUL_MKL_FLOAT(jit::avx, kEQ8)
#elif defined __AVX__
VMUL_INTRI8_FLOAT(jit::avx)
#endif
// avx2 > mkl > for
#ifdef __AVX2__
VMUL_INTRI8_FLOAT(jit::avx2)
#elif defined PADDLE_USE_MKLML
VMUL_MKL_FLOAT(jit::avx2, kEQ8)
#endif
// TODO(TJ): test and complete avx512
/// eq16
#ifdef PADDLE_USE_MKLML
// TODO(TJ): test and complete me
VMUL_MKL_FLOAT(jit::avx, kEQ16)
VMUL_MKL_FLOAT(jit::avx2, kEQ16)
VMUL_MKL_FLOAT(jit::avx512f, kEQ16)
#endif
#define USE_VMUL_KERNEL(T, func) \
template <> \
VMulKernel<T>::VMulKernel(int d) { \
SEARCH_ISA_BLOCK(func, T); \
}
USE_VMUL_KERNEL(float, VMulCompute);
USE_VMUL_KERNEL(double, VMulCompute);
#undef VMUL_ANY
#undef VMUL_INTRI8_FLOAT
#undef VMUL_MKL_FLOAT
#undef VMUL_MKL_DOUBLE
#undef USE_VMUL_KERNEL
} // namespace jitkernel
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/math/jit_kernel_impl.h 已删除 100644 → 0
浏览文件 @ dee5d35c
/* 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. */
#pragma once
#include <functional>
#include <map>
#include <string>
#include "paddle/fluid/platform/cpu_info.h"
namespace paddle {
namespace operators {
namespace math {
namespace jitkernel {} // namespace jitkernel
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/math/jit_kernel_lstm.cc 0 → 100644
浏览文件 @ eeff268a
/* 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 <functional>
#include <string>
#include "paddle/fluid/operators/math/cpu_vec.h"
namespace paddle {
namespace operators {
namespace math {
namespace jitkernel {
namespace jit = platform::jit;
template <>
LSTMKernel<float>::LSTMKernel(int d, const std::string& act_gate_str,
const std::string& act_cand_str,
const std::string& act_cell_str)
: Kernel(), d_(d) {
d2_ = d * 2;
d3_ = d * 3;
if (platform::jit::MayIUse(platform::jit::avx512f)) {
math::VecActivations<float, platform::jit::avx512f> act_functor;
act_gate_ = act_functor(act_gate_str);
act_cell_ = act_functor(act_cell_str);
act_cand_ = act_functor(act_cand_str);
} else if (platform::jit::MayIUse(platform::jit::avx2)) {
math::VecActivations<float, platform::jit::avx2> act_functor;
act_gate_ = act_functor(act_gate_str);
act_cell_ = act_functor(act_cell_str);
act_cand_ = act_functor(act_cand_str);
} else if (platform::jit::MayIUse(platform::jit::avx)) {
math::VecActivations<float, platform::jit::avx> act_functor;
act_gate_ = act_functor(act_gate_str);
act_cell_ = act_functor(act_cell_str);
act_cand_ = act_functor(act_cand_str);
// ComputeCtHt = [&](float*gates,const float*ct_1,float*ct, float*ht) {
// // gates: W_ch, W_ih, W_fh, W_oh
// act_gate(d3_, gates + d_, gates + d_);
// /* C_t = C_t-1 * fgated + cand_gated * igated */
// act_cand(d_, gates, gates);
// blas.VMUL(d_, gates, gates + d_, gates + d_);
// blas.VMUL(d_, ct_1, gates + d2_, gates + d2_);
// blas.VADD(d_, gates + d_, gates + d2_, ct);
// /* H_t = act_cell(C_t) * ogated */
// act_cell(d_, ct, gates + d2_);
// blas.VMUL(d_, gates + d2_, gates + d3_, ht)
// GET_Ct(ct_1, gates, ct);
// GET_Ht(ct, gates, ht);
// };
} else {
math::VecActivations<float, platform::jit::isa_any> act_functor;
act_gate_ = act_functor(act_gate_str);
act_cell_ = act_functor(act_cell_str);
act_cand_ = act_functor(act_cand_str);
}
}
} // namespace jitkernel
} // namespace math
} // namespace operators
} // namespace paddle
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