Skip to content

P
Paddle

BaiXuePrincess / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
提交 3d928d4f 编写于 作者: T tensor-tang
浏览文件
操作

refine and seepdup

上级 77fc42d2
隐藏空白更改
内联 并排
Showing with 103 addition and 108 deletion
paddle/fluid/operators/math/jit_kernel.cc
浏览文件 @ 3d928d4f
......@@ -35,29 +35,6 @@ const std::shared_ptr<Kernel> KernelPool::Get(const std::string& key) const {
return kers_.at(key);
}
#define DEFINE_WITH_DTYPE(ker_key, ker_class, ker_dtype, dtype_key) \
template <> \
const std::shared_ptr<ker_class<ker_dtype>> \
KernelPool::Get<ker_class<ker_dtype>>(int d) { \
std::string key = #ker_key #dtype_key + std::to_string(d); \
if (kers_.find(key) == kers_.end()) { \
auto p = std::make_shared<ker_class<ker_dtype>>(d); \
kers_.insert({key, std::dynamic_pointer_cast<Kernel>(p)}); \
return p; \
} \
return std::dynamic_pointer_cast<ker_class<ker_dtype>>(kers_.at(key)); \
}
#define REGISTER_BLAS_JITKERNEL(ker_key, ker_class) \
DEFINE_WITH_DTYPE(ker_key, ker_class, float, f); \
DEFINE_WITH_DTYPE(ker_key, ker_class, double, d)
REGISTER_BLAS_JITKERNEL(vmul, VMulKernel);
REGISTER_BLAS_JITKERNEL(vadd, VAddKernel);
#undef REGISTER_BLAS_JITKERNEL
#undef DEFINE_WITH_DTYPE
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 @ 3d928d4f
......@@ -40,7 +40,7 @@ typedef enum { kLT8, kEQ8, kGT8LT16, kEQ16, kGT16 } jit_block;
class Kernel {
public:
Kernel() {}
Kernel() = default;
virtual ~Kernel() = default;
private:
......@@ -66,15 +66,13 @@ class KernelPool {
template <typename T>
class VMulKernel : public Kernel {
public:
explicit VMulKernel(int n);
void (*Compute)(const int n, const T *, const T *, T *);
virtual void Compute(const int n, const T *x, const T *y, T *z) = 0;
};
template <typename T>
class VAddKernel : public Kernel {
public:
explicit VAddKernel(int n);
void (*Compute)(const int n, const T *, const T *, T *);
virtual void Compute(const int n, const T *x, const T *y, T *z) = 0;
};
template <typename T>
......
paddle/fluid/operators/math/jit_kernel_blas.cc
浏览文件 @ 3d928d4f
......@@ -29,17 +29,21 @@ namespace jitkernel {
namespace jit = platform::jit;
#define NEW_IMPL(src, t, isa, k) \
p = std::dynamic_pointer_cast<src<t>>( \
std::make_shared<src##Impl<t, isa, k>>())
#define SEARCH_BLOCK(src, t, isa) \
if (d < AVX_FLOAT_BLOCK) { \
Compute = src<t, isa, kLT8>; \
NEW_IMPL(src, t, isa, kLT8); \
} else if (d == AVX_FLOAT_BLOCK) { \
Compute = src<t, isa, kEQ8>; \
NEW_IMPL(src, t, isa, kEQ8); \
} else if (d > AVX_FLOAT_BLOCK && d < AVX512_FLOAT_BLOCK) { \
Compute = src<t, isa, kGT8LT16>; \
NEW_IMPL(src, t, isa, kGT8LT16); \
} else if (d == AVX512_FLOAT_BLOCK) { \
Compute = src<t, isa, kEQ16>; \
NEW_IMPL(src, t, isa, kEQ16); \
} else { \
Compute = src<t, isa, kGT16>; \
NEW_IMPL(src, t, isa, kGT16); \
}
#define SEARCH_ISA_BLOCK(src, t) \
......@@ -53,6 +57,24 @@ namespace jit = platform::jit;
SEARCH_BLOCK(src, t, jit::isa_any); \
}
#define DEFINE_WITH_DTYPE(ker_key, ker_class, ker_dtype, dtype_key) \
template <> \
const std::shared_ptr<ker_class<ker_dtype>> \
KernelPool::Get<ker_class<ker_dtype>>(int d) { \
std::string key = #ker_key #dtype_key + std::to_string(d); \
if (kers_.find(key) == kers_.end()) { \
std::shared_ptr<ker_class<ker_dtype>> p; \
SEARCH_ISA_BLOCK(ker_class, ker_dtype); \
kers_.insert({key, std::dynamic_pointer_cast<Kernel>(p)}); \
return p; \
} \
return std::dynamic_pointer_cast<ker_class<ker_dtype>>(kers_.at(key)); \
}
#define REGISTER_BLAS_JITKERNEL(ker_key, ker_class) \
DEFINE_WITH_DTYPE(ker_key, ker_class, float, f); \
DEFINE_WITH_DTYPE(ker_key, ker_class, double, d)
// do not include lt8, eq8, eq16
#define FOR_EACH_COMMON_BLOCK(macro_, isa) \
macro_(isa, kGT8LT16) macro_(isa, kGT16)
......@@ -73,132 +95,130 @@ namespace jit = platform::jit;
FOR_EACH_ALL_BLOCK(macro_, jit::avx) \
FOR_EACH_ALL_BLOCK(macro_, jit::isa_any)
#define BIND_KERNEL_WITH_DTYPE(ker_class, ker_func, ker_dtype) \
template <> \
ker_class<ker_dtype>::ker_class(int d) { \
SEARCH_ISA_BLOCK(ker_func, ker_dtype); \
}
#define BIND_KERNEL(ker_class, ker_func) \
BIND_KERNEL_WITH_DTYPE(ker_class, ker_func, float); \
BIND_KERNEL_WITH_DTYPE(ker_class, ker_func, double)
/* VMUL JitKernel */
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) {
for (int i = 0; i < n; ++i) {
z[i] = x[i] * y[i];
class VMulKernelImpl : public VMulKernel<T> {
public:
void Compute(const int n, const T* x, const T* y, T* z) override {
for (int i = 0; i < n; ++i) {
z[i] = x[i] * y[i];
}
}
}
};
#ifdef PADDLE_WITH_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_FLOAT(isa, block) \
template <> \
void VMulKernelImpl<float, isa, block>::Compute(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, double* z) { \
platform::dynload::vdMul(n, x, y, z); \
#define VMUL_MKL_DOUBLE(isa, block) \
template <> \
void VMulKernelImpl<double, isa, block>::Compute( \
const int n, const double* x, const double* y, double* z) { \
platform::dynload::vdMul(n, x, y, z); \
}
FOR_EACH_ISA_COMMON_BLOCK(VMUL_MKL_FLOAT)
FOR_EACH_ISA_ALL_BLOCK(VMUL_MKL_DOUBLE)
FOR_EACH_ISA_COMMON_BLOCK(VMUL_MKL_FLOAT);
FOR_EACH_ISA_ALL_BLOCK(VMUL_MKL_DOUBLE);
#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); \
#define VMUL_INTRI8_FLOAT(isa) \
template <> \
void VMulKernelImpl<float, isa, kEQ8>::Compute(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); \
}
// avx > for > mkl
#ifdef __AVX__
VMUL_INTRI8_FLOAT(jit::avx);
#endif
// avx2 > for > mkl
#ifdef __AVX2__
VMUL_INTRI8_FLOAT(jit::avx2)
VMUL_INTRI8_FLOAT(jit::avx2);
#endif
#ifdef __AVX512F__
VMUL_INTRI8_FLOAT(jit::avx512f);
#endif
// TODO(TJ): test and complete avx512
// TODO(TJ): eq16 test and complete avx512
#undef VMUL_INTRI8_FLOAT
#undef VMUL_MKL_FLOAT
#undef VMUL_MKL_DOUBLE
/* VADD */
/* VADD JitKernel */
template <typename T, platform::jit::cpu_isa_t isa, jit_block>
static void VAddCompute(const int n, const T* x, const T* y, T* z) {
for (int i = 0; i < n; ++i) {
z[i] = x[i] + y[i];
class VAddKernelImpl : public VAddKernel<T> {
public:
void Compute(const int n, const T* x, const T* y, T* z) override {
for (int i = 0; i < n; ++i) {
z[i] = x[i] + y[i];
}
}
}
};
#ifdef PADDLE_WITH_MKLML
#define VADD_MKL_FLOAT(isa, block) \
template <> \
void VAddCompute<float, isa, block>(const int n, const float* x, \
const float* y, float* z) { \
platform::dynload::vsAdd(n, x, y, z); \
#define VADD_MKL_FLOAT(isa, block) \
template <> \
void VAddKernelImpl<float, isa, block>::Compute(const int n, const float* x, \
const float* y, float* z) { \
platform::dynload::vsAdd(n, x, y, z); \
}
#define VADD_MKL_DOUBLE(isa, block) \
template <> \
void VAddCompute<double, isa, block>(const int n, const double* x, \
const double* y, double* z) { \
platform::dynload::vdAdd(n, x, y, z); \
#define VADD_MKL_DOUBLE(isa, block) \
template <> \
void VAddKernelImpl<double, isa, block>::Compute( \
const int n, const double* x, const double* y, double* z) { \
platform::dynload::vdAdd(n, x, y, z); \
}
FOR_EACH_ISA_COMMON_BLOCK(VADD_MKL_FLOAT)
FOR_EACH_ISA_ALL_BLOCK(VADD_MKL_DOUBLE)
FOR_EACH_ISA_COMMON_BLOCK(VADD_MKL_FLOAT);
FOR_EACH_ISA_ALL_BLOCK(VADD_MKL_DOUBLE);
#endif
/// eq8
#define VADD_INTRI8_FLOAT(isa) \
template <> \
void VAddCompute<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_add_ps(tmpx, tmpy); \
_mm256_storeu_ps(z, tmpx); \
#define VADD_INTRI8_FLOAT(isa) \
template <> \
void VAddKernelImpl<float, isa, kEQ8>::Compute(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_add_ps(tmpx, tmpy); \
_mm256_storeu_ps(z, tmpx); \
}
#ifdef __AVX__
VADD_INTRI8_FLOAT(jit::avx)
VADD_INTRI8_FLOAT(jit::avx);
#endif
#ifdef __AVX2__
VADD_INTRI8_FLOAT(jit::avx2)
VADD_INTRI8_FLOAT(jit::avx2);
#endif
#ifdef __AVX512F__
VADD_INTRI8_FLOAT(jit::avx512f);
#endif
// TODO(TJ): test and complete avx512
// TODO(TJ): eq16 test and complete avx512
#undef VADD_INTRI8_FLOAT
#undef VADD_MKL_FLOAT
#undef VADD_MKL_DOUBLE
BIND_KERNEL(VMulKernel, VMulCompute);
BIND_KERNEL(VAddKernel, VAddCompute);
REGISTER_BLAS_JITKERNEL(vmul, VMulKernel);
REGISTER_BLAS_JITKERNEL(vadd, VAddKernel);
#undef BIND_KERNEL
#undef BIND_KERNEL_WITH_DTYPE
#undef FOR_EACH_ISA_ALL_BLOCK
#undef FOR_EACH_ALL_BLOCK
#undef FOR_EACH_ISA_COMMON_BLOCK
#undef FOR_EACH_COMMON_BLOCK
#undef REGISTER_BLAS_JITKERNEL
#undef DEFINE_WITH_DTYPE
#undef SEARCH_ISA_BLOCK
#undef SEARCH_BLOCK
#undef NEW_IMPL
} // namespace jitkernel
} // namespace math
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或