Cytnx 
Install
See The following user guide for install and using of cytnx:
https://kaihsinwu.gitlab.io/Cytnx_doc/install.html
Intro slide
Cytnx_v0.5.pdf (dated 07/25/2020)
News
[v0.9.x]
Implementation of new data structure for symmetric UniTensor, which different from previous version
Current dev Version:
v0.9.2
v0.9.1
1. [New] Add additional argument share_mem for Tensor.numpy() python API.
2. [Fix] UniTensor.at() python API not properly wrapped.
3. [Fix] Bug in testing for BlockUniTensor.
4. [Fix] Bug in UniTensor print info (duplicate name, is_diag=true BlockUniTensor dimension display)
5. [Change] Svd now using gesdd instead of gesvd.
6. [New] Add linalg.Gesvd function, along with Gesvd_truncate.
7. [Fix] Strict casting rule cause compiling fail when compile with icpc
8. [New] Add additional argument for Network.PutUniTensor to match the label.
9. [Fix] Network TOUT string lbl bug
10. [Fix] #156 storage python wrapper cause not return.
11. [Add] linalg.Gemm/Gemm_()
12. [Add] UniTensor.normalize()/normalize_()
v0.9
1. [New] New Network file format (removing the rowrank requirement
2. [New] label can now be string, instead of integer.
3. [New] UniTensor with symmetry are now faster.
API Documentation:
https://kaihsin.github.io/Cytnx/docs/html/index.html
User Guide [under construction!]:
Objects:
* Storage [binded]
* Tensor [binded]
* Accessor [c++ only]
* Bond [binded]
* Symmetry [binded]
* CyTensor [binded]
* Network [binded]
Feature:
Python x C++
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)1. All the Storage and Tensor can now have mulitple type support.
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
2. Storage
* 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); 3. Tensor
* 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]
4. UniTensor
* 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. Examples
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.
Avaliable linear-algebra function (Keep updating):
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
Container Generators
Tensor: zeros(), ones(), arange(), identity(), eye()
Physics category
Tensor: pauli(), spin()
Random
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.
Developers & Maintainers
[Creator and Project manager]
Kai-Hsin Wu (Boston Univ.) kaihsinwu@gmail.com
Chang Teng Lin (NTU, Taiwan): major maintainer and developer
Ke Hsu (NTU, Taiwan): major maintainer and developer
Hao Ti (NTU, Taiwan): documentation and linalg
Ying-Jer Kao (NTU, Taiwan): setuptool, cmake
Contributors
Yen-Hsin Wu (NTU, Taiwan)
Po-Kwan Wu (OSU)
Wen-Han Kao (UMN, USA)
Yu-Hsueh Chen (NTU, Taiwan)
PoChung Chen (NCHU, Taiwan)
References
* example/DMRG:
https://www.tensors.net/dmrg
* hptt library:
https://github.com/springer13/hptt