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Paddle compiler #35
base: paddle_compiler
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Paddle compiler #35
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The first pr for paddle compiler.
Pass() {} | ||
virtual ~Pass() {}; | ||
virtual bool run(void *ir) = 0; | ||
virtual std::string name() const = 0; |
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不大明白为什么要把name
设计为一个纯虚函数?作为构造函数参数不更好么?
class Pass {
public:
Pass(const std::string& name) : name_(name) {}
virtual ~Pass() =default;
const std::string& name() { return name_;}
private:
std::string name_;
};
class SubPass : public Pass {
public:
SubPass() : Pass("SubPass") {}
};
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Hello,构造参数就要为每个对象分配 name 空间,虚函数的用意是一个类只需要一个常量 string,constexpr 也可以?
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不管有没有name
,虚函数意味着总是得给对象分配空间的,多个name
感觉没啥负担
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另外,constexpr应该不行,但const std::string& name
应该可以,只要保证name
只会在构造函数中被赋值且不会被修改。引用的负担也不大
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这块我想过,比较下来还是虚函数最好,name严格说都不算pass的本质属性,另外name()不频繁调用,对象只包含一个虚函数表指针。
PASSDEF_ALL(PASSDEF_ID_) | ||
}; | ||
|
||
#define INC(name) +1 |
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这个应该是\
而不是+1
吧?另外这个name
好像没有用到唉?
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这个 是 +1,展开之后相当于 +1 +1 +1 ... = number of pass classes
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受教了,还没这么用过
// Pass id enum is used as key for dispatching pass classes | ||
enum class PassId { | ||
PASS_NA, | ||
PASSDEF_ALL(PASSDEF_ID_) |
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感觉是不是弄复杂了。。。这儿用宏感觉没啥必要唉?
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只需要维护 PASSDEF_ALL 那个宏即可。。
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这宏太多,有点头晕😂
__macro(ATest) | ||
|
||
#define PASSDEF_ID(pass) PASS_##pass | ||
#define PASSDEF_ID_(pass) PASS_##pass, |
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为个逗号加个宏感觉冗余了吧?这样感觉反而容易用混淆
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不然在 enum PassId 里面展开缺逗号。。
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这我再改下
class PASSDEF_CLASS(pass); | ||
PASSDEF_ALL(DECL) | ||
#undef DECL | ||
|
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如果已经包含了 pass class 的头文件,这是不是把已经定义的类屏蔽掉?
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对的,会覆盖掉
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是的,这块需要改掉。
* 1. add interface for fft; 2. add data type predicate; 3. fix paddle.roll. * add fft c2c cufft kernel * implement argument checking & op calling parts for fft_c2c and fftn_c2c * add operator and opmaker definitions * only register float and double for cpu. * add common code for implementing FFT, add pocketfft as a dependency * add fft c2c cufft kernel function * fix bugs in python interface * add support for c2r, r2c operators, op makers, kernels and kernel functors. * test and fix bugs * 1. fft_c2c function: add support for onesided=False; 2. add complex<float>, complex<double> support for concat and flip. * 1. fft: fix python api bugs; 2. shape_op: add support for complex data types. * fft c2c cufft kernel done with complie and link * fix shape_op, add mkl placeholder * remove mkl * complete fft c2c in gpu * 1. implement mkl-based fft, FFTC2CFunctor and common function exec_fft; 2. change the design, add input and output typename as template parameter for all FFTFunctors, update pocketfft-based implementation. * complete fft c2c on gpu in ND * complete fft c2c on gpu in ND * complete fft c2c backward in ND * fix MKL-based implementation * Add frame op and CPU/GPU kernels. * Add frame op forward unittest. * Add frame op forward unittest. * Remove axis parameter in FrameFunctor. * Add frame op grad CPU/GPU kernels and unittest. * Add frame op grad CPU/GPU kernels and unittest. * Update doc string. * Update after review and remove librosa requirement in unittest. * Update grad kernel. * add fft_c2r op * Remove data allocation in TransCompute function. * add fft r2c onesided with cpu(pocketfft/mkl) and gpu * last fft c2r functor * fix C2R and R2C for cufft, becase the direction is not an option in these cases. * add fft r2c onesided with cpu(pocketfft/mkl) and gpu * fix bugs in python APIs * fix fft_c2r grad kernal * fix bugs in python APIs * add cuda fft c2r grad kernal functor * clean code * fix fft_c2r python API * fill fft r2c result with conjugate symmetry (#19) fill fft r2c result with conjugate symmetry * add placeholder for unittests (#24) * simple parameterize test function by auto generate test case from parm list (#25) * miscellaneous fixes for python APIs (#26) * add placeholder for unittests * resize fft inputs before computation is n or s is provided. * add complex kernels for pad and pad_grad * simplify argument checking. * add type promotion * add int to float or complex promotion * fix output data type for static mode * fix fft's input dtype dispatch, import fft to paddle * fix typos in axes checking (#27) * fix typos in axes checking * fix argument checking (#28) * fix argument checking * Add C2R Python layer normal and abnormal use cases (#29) * documents and single case * test c2r case * New C2R Python layer normal and exception use cases * complete rfft,rfft2,rfftn,ihfft,ihfft2,ihfftn unittest and doc string (#30) * Documentation of the common interfaces of c2r and c2c (#31) * Documentation of the common interfaces of c2r and c2c * clean c++ code (#32) * clean code * Add numpy-based implementation of spectral ops (#33) * add numpy reference implementation of spectral ops * Add fft_c2r numpy based implementation for unittest. (#34) * add fft_c2r numpy implementation * Add deframe op and stft/istft api. (#23) * Add frame api * Add deframe op and kernels. * Add stft and istft apis. * Add deframe api. Update stft and istft apis. * Fix bug in frame_from_librosa function when input dims >= 3 * Rename deframe to overlap_add. * Update istft. * Update after code review. * Add overlap_add op and stft/istft api unittest (#35) * Add overlap_add op unittest. * Register complex kernels of squeeze/unsquuze op. * Add stft/istft api unittest. * Add unittest for fft helper functions (#36) * add unittests for fft helper functions. add complex kernel for roll op. * complete static graph unittest for all public api (#37) * Unittest of op with FFT C2C, C2R and r2c added (#38) * documents and single case * test c2r case * New C2R Python layer normal and exception use cases * Documentation of the common interfaces of c2r and c2c * Unittest of op with FFT C2C, C2R and r2c added Co-authored-by: lijiaqi <lijiaqi0612@163.com> * add fft related options to CMakeLists.txt * fix typos and clean code (#39) * fix invisible character in mkl branch and fix error in error message * clean code: remove docstring from unittest for signal.py. * always convert numpy array to paddle.Tensor to avoid comparing numpy dtype with paddle dtype. (#40) * always convert numpy array to paddle.Tensor to avoid comparing numpy dtype with paddle dtype. * fix CI Errors: numpy dtype comparison, thrust when cuda is not available (#41) 1. always convert numpy array to paddle.Tensor to avoid comparing numpy dtype with paddle dtype. 2. promote floating point tensor to complex tensor ior fft_c2c and fft_c2r; 3. fix unittest to catch UnImplementedError and RuntimeError; 4. fix compile error by avoid using thrust when cuda is not available. 5. fix sample code, use paddle.fft instead of paddle.tensor.fft * remove inclusion of thrust, add __all__ list for fft (#42) * Add api doc and update unittest. (#43) * Add doc strings. * Update overlap_add op unittest * fix MKL-based FFT implementation (#44) * fix MKL-based FFT implementation, MKL CDFT's FORWARD DOMAIN is always REAL for R2C and C2R * remove code for debug (#45) * use dynload for cufft (#46) * use std::ptrdiff_t as datatype of stride (instead of int64_t) to avoid argument mismatch on some platforms. * add complex support for fill_zeros_like * use dynload for cufft * Update doc and unittest. (#47) * Add doc of frame op and overlap_add op. * Update unittest. * use dynload for cufft (#48) 1. use dynload for cufft 2. fix unittest; 3. temporarily disable Rocm. * fix conflicts and merge upstream (#49) fix conflicts and merge upstream * fix compile error: only link dyload_cuda when cuda is available (PaddlePaddle#50) * fix compile error: only link dyload_cuda when cuda is available * fix dynload for cufft on windows (PaddlePaddle#51) 1. fix dynload for cufft on windows; 2. fix unittests. * add NOMINMAX to compile on windows (PaddlePaddle#52) add NOMINMAX to compile on windows * explicitly specify capture mode for lambdas (PaddlePaddle#55) explicitly specify capture mode for lambdas * fix fft sample (PaddlePaddle#53) * fix fft sample * update scipy and numpy version for unittests of fft (PaddlePaddle#56) update scipy and numpy version for unittests of fft * Add static graph unittests of frame and overlap_add api. (PaddlePaddle#57) * Remove cache of cuFFT & Disable ONEMKL (PaddlePaddle#59) 1. replace numpy.fft with scipy.fft as numpy<1.20 not support ortho norm 2. remove cache of cufft plans; 3. enhance error checking. 4. default WITH_ONEMKL to OFF Co-authored-by: jeff41404 <jeff41404@gmail.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming9.bjyz.baidu.com> Co-authored-by: KP <109694228@qq.com> Co-authored-by: lijiaqi <lijiaqi0612@163.com> Co-authored-by: Xiaoxu Chen <chenxx_id@163.com> Co-authored-by: lijiaqi0612 <33169170+lijiaqi0612@users.noreply.github.com>
PR types
New Features
PR changes
Describe
Included are files for declaring and building Piano optimization passes. Auxiliary utility classes are also given for future adoption.
The base pass class is defined in pass.h, along with macros for exhausting all
registered
pass classes. Byregistration
we loosely refer to a pass class 1) whose pass name is included PASSDEF_ALL macro and 2) whose forward declaration is included in pass.hThe instantiate a pass object, be advised to use the
make_pass
macro with pass name and the compiler context pointer as parameters.We intentionally decide not to rely on a registration table for sake of simplicity and performance.