See The following user guide for install and using of cytnx:
https://kaihsinwu.gitlab.io/Cytnx_doc/install.html
Cytnx_v0.5.pdf (dated 07/25/2020)
[v0.7.2]
v0.7.2
1. [Pending][GPU] Get/Set elements on GPU is still down.
v0.7.3
1. [Fix] bug for Get slice does not reduce when dim=1.
2. [Enhance] checking the memory alloc failing for EL.
3. [Change] remove Tensor init assignment op from initializer_list, for conflict with UniTensor init.
4. [Enhance] print information for Symmetric UniTensor.
5. [Enhance] linalg::ExpM/ExpH support for symmetric UniTensor.
6. [Enhance] add UniTensor.get_blocks_qnums() for corresponding qnums for current blocks.
7. [Enhance][Safety] add UniTensor.get_blocks_(silent=false) with "silent" option by default pop-up a warning when UniTensor is non-contiguous.
8. [Enhance] add operator* and operator*= for combineBond.
9. [Enhance] add support for Symmetric UniTensor with is_diag=true.
10. [Fix] remove the dtype & device option for arange(Nelem). Use .astype() .to() instead.
11. [Fix] reshape() without postfix const causing error when reshape with const Tensor.
12. [Enhance][Experiment] add Lstsq for least square calculation. [PR]
13. [Fix][C++] minor issue related to laterial argument passing by variables cannot properly resolved on C++
14. [Enhance] Diag now support rank-1 Tensor as input for constructing a diagonal tensor with input as diagonal elements.
15. [Enhance] Add c++ example for DMRG (Ke)
16. [Fix] Bug fixed in DMRG code and updated to the latest features.
17. [Fix] Bug in UniTensor do svd with rowrank=1 and the first rank has dimension=1.
v0.7.2
1. [Enhance] Add Tensor.set with Scalar
2. [Enhance][C++] Add Tensor initialize assignment op from initializer_list
3. [Enhance][C++] Add Storage initialize assignment op from vector & initializer list
4. [Fix] bug for set partial elements on Tensor with slicing issue.
5. [Fix][DenseUniTensor] set_rowrank cannot set full rank issue #24
v0.7.1
1. [Enhance] Finish UniTensor arithmetic.
2. [Fix] bug when using Tensor.get() accessing only single element
3. [Enhance] Add default argument is_U = True and is_vT = True for Svd_truncate() python API
v0.7
1. [Enhance] add binary op. -Tensor.
2. [Enhance] New introduce Scalar class, generic scalar placeholder.
3. [Enhance][expr] Storage.at(), Storage.back(), Storage.get_item() can now without specialization. The return is Scalar class.
4. [Enhance] Storage.get_item, Storage.set_item
5. [Enhance] Scalar, iadd,isub,imul,idiv
6. [Important] Storage.resize will match the behavior of vector, new elements are set to zero!
7. [Enhance] Scalar +,-,*,/ finished
8. [Enhance] add Histogram class and stat namespace.
9. [Enhance] add fstream option for Tofile
10. [Enhance] return self when UniTensor.set_name
11. [Enhance] return self when UniTensor.set_label(s)
12. [Enhance] return self when UniTensor.set_rowrank
13. [Fatal!][Fix] fix bug of wrong answer in Tensor slice for non-contiguous Tensor, with faster internal kernel
14. [Warning] Slice of GPU Tensor is now off-line for further inspection.
15. [Fix] bug causing crash when print non-contiguous Uint64 Tensor
16. [Fatal!][Fix] fix bug of wrong answer in Tensor set-element with slice for non-contiguous Tensor.
17. [Enhance] Network on the fly construction.
18. [Enhance] Scalar: Add on TN. TN.item()
19. [Fix] bug in Mod interanlly calling Cpr fixed.
20. [Enhance] All operation related to TN <-> Scalar
21. [Enhance] Reduce RTTR overhead.
v0.6.5
1. [Fix] Bug in UniTensor _Load
2. [Enhance] Improve stability in Lanczos_ER
3. [Enhance] Move _SII to stack.
4. [Enhance] Add LinOp operator() for mv_elem
5. [Enhance] Add c++ API fast link to cutt
6. [Enhance] Add Fromfile/Tofile for load/save binary files @ Tensor/Storage
7. [Enhance] Add linspace generator
8. [Fix] Bug in Div for fast Blas call bug
9. [Enhance] Add Tensor.append(Storage) if Tensor is rank-2 and dimension match.
10. [Enhance] Add algo namespace
11. [Enhance] Add Sort-@cpu
12. [Enhance] add Storage.numpy() for pythonAPI
13. [Enhance] add Tensor.from_storage() for python API
v0.6.4
1. [Enhance] Add option mv_elem for Tensordot, which actually move elements in input tensor. This is beneficial when same tensordot is called multiple times.
2. [Enhance] Add option cacheL, cacheR to Contract of unitensor. which mv the elements of input tensors to the matmul handy position.
3. [Enhance] optimize Network contraction policy to reduce contiguous permute, with is_clone argument when PutUniTensor.
4. [Enhance] Add Lanczos_Gnd for fast get ground state and it's eigen value (currently only real float).
5. [Enhance] Add Tridiag python API, and option is_row
6. [Enhance] C++ API storage add .back<>() function.
7. [Enhance] C++ API storage fix from_vector() for bool type.
8. [Enhance] Change Network Launch optimal=True behavior. if user order is given, optimal will not have effect.
9. [Enhance] Add example/iDMRG/dmrg_optim.py for better performace with Lanczos_Gnd and Network cache.
10. [Fix] wrong error message in linalg::Cpr
11. [Fix] reshape() on a already contiguous Tensor will resulting as the change in original tensor, which should not happened.
https://kaihsin.github.io/Cytnx/docs/html/index.html
* Storage [binded]
* Tensor [binded]
* Accessor [c++ only]
* Bond [binded]
* Symmetry [binded]
* CyTensor [binded]
* Network [binded]
Benefit from both side.
One can do simple prototype on python side
and easy transfer to C++ with small effort!
// c++ version:
#include "cytnx.hpp"
cytnx::Tensor A({3,4,5},cytnx::Type.Double,cytnx::Device.cpu) # python version:
import cytnx
A = cytnx.Tensor((3,4,5),dtype=cytnx.Type.Double,device=cytnx.Device.cpu) The avaliable types are :
| cytnx type | c++ type | Type object
|------------------|----------------------|--------------------
| cytnx_double | double | Type.Double
| cytnx_float | float | Type.Float
| cytnx_uint64 | uint64_t | Type.Uint64
| cytnx_uint32 | uint32_t | Type.Uint32
| cytnx_uint16 | uint16_t | Type.Uint16
| cytnx_int64 | int64_t | Type.Int64
| cytnx_int32 | int32_t | Type.Int32
| cytnx_int16 | int16_t | Type.Int16
| cytnx_complex128 | std::complex<double> | Type.ComplexDouble
| cytnx_complex64 | std::complex<float> | Type.ComplexFloat
| cytnx_bool | bool | Type.Bool
* Memory container with GPU/CPU support.
maintain type conversions (type casting btwn Storages)
and moving btwn devices.
* Generic type object, the behavior is very similar to python.
Storage A(400,Type.Double);
for(int i=0;i<400;i++)
A.at<double>(i) = i;
Storage B = A; // A and B share same memory, this is similar as python
Storage C = A.to(Device.cuda+0); * A tensor, API very similar to numpy and pytorch.
* simple moving btwn CPU and GPU:
Tensor A({3,4},Type.Double,Device.cpu); // create tensor on CPU (default)
Tensor B({3,4},Type.Double,Device.cuda+0); // create tensor on GPU with gpu-id=0
Tensor C = B; // C and B share same memory.
// move A to gpu
Tensor D = A.to(Device.cuda+0);
// inplace move A to gpu
A.to_(Device.cuda+0); * Type conversion in between avaliable:
Tensor A({3,4},Type.Double);
Tensor B = A.astype(Type.Uint64); // cast double to uint64_t * vitual swap and permute. All the permute and swap will not change the underlying memory
* Use Contiguous() when needed to actual moving the memory layout.
Tensor A({3,4,5,2},Type.Double);
A.permute_(0,3,1,2); // this will not change the memory, only the shape info is changed.
cout << A.is_contiguous() << endl; // this will be false!
A.contiguous_(); // call Configuous() to actually move the memory.
cout << A.is_contiguous() << endl; // this will be true! * access single element using .at
Tensor A({3,4,5},Type.Double);
double val = A.at<double>(0,2,2); * access elements with python slices similarity:
typedef Accessor ac;
Tensor A({3,4,5},Type.Double);
Tensor out = A(0,":","1:4");
// equivalent to python: out = A[0,:,1:4]
* extension of Tensor, specifically design for Tensor network simulation.
* See Intro slide for more details
Tensor A({3,4,5},Type.Double);
UniTensor tA = UniTensor(A,2); // convert directly.
UniTensor tB = UniTensor({Bond(3),Bond(4),Bond(5)},{},2); // init from scratch. See example/ folder or documentation for how to use API
See example/iTEBD folder for implementation on iTEBD algo.
See example/DMRG folder for implementation on DMRG algo.
See example/iDMRG folder for implementation on iDMRG algo.
See example/HOTRG folder for implementation on HOTRG algo for classical system.
See example/ED folder for implementation using LinOp & Lanczos.
func | inplace | CPU | GPU | callby tn | Tn | CyTn (xlinalg)
--------------|-----------|-----|------|-------------|----|-------
Add | x | Y | Y | Y | Y | Y
Sub | x | Y | Y | Y | Y | Y
Mul | x | Y | Y | Y | Y | Y
Div | x | Y | Y | Y | Y | Y
Cpr | x | Y | Y | Y | Y | x
--------------|-----------|-----|------|-------------|----|-------
+,+=[tn] | x | Y | Y | Y (Add_) | Y | Y
-,-=[tn] | x | Y | Y | Y (Sub_) | Y | Y
*,*=[tn] | x | Y | Y | Y (Mul_) | Y | Y
/,/=[tn] | x | Y | Y | Y (Div_) | Y | Y
==[tn] | x | Y | Y | Y (Cpr_) | Y | x
--------------|-----------|-----|------|-------------|----|-------
Svd | x | Y | Y | Y | Y | Y
*Svd_truncate| x | Y | Y | N | Y | Y
InvM | InvM_ | Y | Y | Y | Y | N
Inv | Inv _ | Y | Y | Y | Y | N
Conj | Conj_ | Y | Y | Y | Y | Y
--------------|-----------|-----|------|-------------|----|-------
Exp | Exp_ | Y | Y | Y | Y | N
Expf | Expf_ | Y | Y | Y | Y | N
Eigh | x | Y | Y | Y | Y | N
*ExpH | x | Y | Y | N | Y | Y
*ExpM | x | Y | N | N | Y | Y
--------------|-----------|-----|------|-------------|----|-------
Matmul | x | Y | Y | N | Y | N
Diag | x | Y | Y | N | Y | N
*Tensordot | x | Y | Y | N | Y | N
Outer | x | Y | Y | N | Y | N
Vectordot | x | Y | .Y | N | Y | N
--------------|-----------|-----|------|-------------|----|-------
Tridiag | x | Y | N | N | Y | N
Kron | x | Y | N | N | Y | N
Norm | x | Y | Y | Y | Y | N
*Dot | x | Y | Y | N | Y | N
Eig | x | Y | N | N | Y | N
--------------|-----------|-----|------|-------------|----|-------
Pow | Pow_ | Y | Y | Y | Y | Y
Abs | Abs_ | Y | N | Y | Y | N
Qr | x | Y | N | N | Y | Y
Qdr | x | Y | N | N | Y | Y
Det | x | Y | N | N | Y | N
--------------|-----------|-----|------|-------------|----|-------
Min | x | Y | N | Y | Y | N
Max | x | Y | N | Y | Y | N
*Trace | x | Y | N | Y | Y | Y
Mod | x | Y | Y | Y | Y | Y
Matmul_dg | x | Y | Y | N | Y | N
--------------|-----------|-----|------|-------------|----|-------
*Tensordot_dg | x | Y | Y | N | Y | N
iterative solver:
Lanczos_ER
* this is a high level linalg
^ this is temporary disable
. this is floating point type only
Tensor: zeros(), ones(), arange(), identity(), eye()
Tensor: pauli(), spin()
func | Tn | Stor | CPU | GPU
-----------------------------------------------------
*Make_normal() | Y | Y | Y | Y
*Make_uniform() | Y | Y | Y | N
^normal() | Y | x | Y | Y
^uniform() | Y | x | Y | N
* this is initializer
^ this is generator
[Note] The difference of initializer and generator is that initializer is used to initialize the Tensor, and generator generates a new Tensor.
Kai-Hsin Wu (Boston Univ.) kaihsinwu@gmail.com
Ying-Jer Kao (NTU, Taiwan): setuptool, cmake
Yen-Hsin Wu (NTU, Taiwan): Network optimization
Yu-Hsueh Chen (NTU, Taiwan): example, and testing
Po-Kwan Wu (OSU): Icon optimization
Wen-Han Kao (UMN, USA) : testing of conda install
Ke Hsu (NTU, Taiwan): Lstsq, linalg funcitons and examples
* example/DMRG:
https://www.tensors.net/dmrg
* hptt library:
https://github.com/springer13/hptt
KHW whould like to thanks for the following contributor(s) for invaluable contribution to the library
* PoChung Chen (NCHU, Taiwan) : testing, and bug reporting