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alt text

What's new

v0.3
1. Three type of UniTensor can be construct. [symmetry][tagged][untagged] 
2. The Bond can be tagged with bra/ket or untagged (regular)
3. Now change the storage of symmetry to blockform. the dense storage for symmetry is deprecated.
4. Add braketform() to coalesce the bra/ket with row/col(in/out)
5. rowrank defines the row/col space. 
6. Enhanced Print_diagram
7. Seperate Contiguous() and Contiguous\_() for inplace and return type function.
8. change behavior of GetBlock and PutBlock on a non-symm tensor. in this version, it will return a rank-2 matrix.
9. Add GetValidQnums() for symmetry tensor
10. For symmtry, the bond order is relevant. The default fusion tree is in order of ((((0,1),2),3),4 ....
11. Add UniTensor.dtype() [@property]
12. Add UniTensor.device() [@property]
13. nn , some linalg can only accept non-symm, untagged tensor. 

v0.3.1    
1. Fix cannot create rank-0 UniTensor bug.

v0.3.2
1. Fix Permute() will print the tensor elements bug.

v0.3.3
1. Remove the private args in UniTensor.__init__, in place, they are move to private member function __mac() 
2. Fix UniTensor.__pow__ does not return anything bug. 
3. Remove unrelated arguments in documentation
4. Change the order of functions appears in documentation Tor10.UniTensor

v0.3.4
1. Fix Svd, Svd_truncate, Qr, Qdr crash bug. Originated from the variable change of v0.3.3a 
2. From 1., iTEBD.py cannot work is fixed. 

v0.3.5
1. Fix create Rank-0 Tensor crash bug, also change to real rank-0 tensor
2. Fix Reshape_() labels are not set bug, and remove redundancy code in the Reshape_()
3. Add View() and View_, which is the same functionality as pytorch 
4. Can now create rank-0 tensor from torch.tensor(). 

v0.3.6
1. Exchange BRA and KET. KET is now row-space and BRA is now col-space
2. Change `N_rowrank` to `rowrank`

Release version

v0.3.6 alpha

Known issue

1. HOSVD is not functional
2. Contract for UniTensors with symmetry cannot have the bonds in two tensors all are unique labels.

Requirements

pytorch>=1.0
numpy  >=1.15
sphinx >=1.8.2
sphinx_rtd_theme >=0.4.2

Documentation:

https://kaihsinwu.gitlab.io/tor10

Code naming principle:

1) the functions start with "_" are the private function that should not be call directly by user.

Feature:

1. Create Tensor:
    * support multiple precisions.        
    * support devices (cpu and gpu are trivial)
    * preserve the similar api for Bond 
    * can serve as regular generic Tensor or physical tensor (with bra-ket tagged)

       ## create a rank-2 Physical Tensor with no symmetry 
       bds = [ Bond(3,BD_BRA), Bond(4,BD_KET)]
       A = UniTensor(bds,label=[2,4],N_inbond=1,dtype=torch.float64,device=torch.device("cpu"))

       ## create a rank-2 generic Tensor 
       bds = [ Bond(4), Bond(6) ]
       B = UniTensor(bds,N_inbond=1)

       ## Moving to GPU:
       A.to(torch.device("cuda:0"))
2. Tensor :
    * vitual swap and permute. All the permute and swap will not change the underlying memory
    * Use Contiguous() or Contiguous_() when needed to actual moving the memory layout.

        A.Contiguous()
3. Multiple Symmetries:
    * Support arbitrary numbers and types of symmetry.
    * Currently support U1 and Zn (with arbitrary n). 

        #> Multiple mix symmetry: U1 x Z2 x Z4
        bd_sym_mix = Tor10.Bond(3,qnums=[[-2,0,0],
                                         [-1,1,3],
                                         [ 1,0,2]],
                                 sym_types=[Tor10.Symmetry.U1(),
                                            Tor10.Symmetry.Zn(2),
                                            Tor10.Symmetry.Zn(4)],
                                  Tor10.BD_BRA)
4. Network :
    * See documentation for how to use network.

5. Autograd mechanism:
    The Tor10 now support the autograd functionality. The Contract, Matmul etc will automatically contruct the gradient flow for UniTensor that has [requires_grad=True]
    
    * See documentation for further details


6. Easy coordinate with pytorch for Neural-Network:
    We provide Tor10.nn that can easy cooperate with pytorch.nn.Module to perform neural-network tasks.

        import torch
        import Tor10
        class Model(torch.nn.Module):
            def __init__(self):
                super(Model,self).__init__()
                ## Customize and register the parameter.
                self.P1 = Tor10.nn.Parameter(Tor10.UniTensor(bonds=[Tor10.Bond(2),Tor10.Bond(2)],rowrank=1))
                self.P2 = Tor10.nn.Parameter(Tor10.UniTensor(bonds=[Tor10.Bond(2),Tor10.Bond(2)],rowrank=1))
 
            def forward(self,x):
                y = Tor10.Matmul(Tor10.Matmul(x,self.P1),self.P2)
                return y

        md = Model()
        print(list(md.parameters()))
        ## Output:
        #    [Parameter containing:
        #    tensor([[0., 0.],
        #            [0., 0.]], dtype=torch.float64, requires_grad=True), Parameter containing:
        #    tensor([[0., 0.],
        #            [0., 0.]], dtype=torch.float64, requires_grad=True)]
    * See documentation for further details


See test.py for further detail application functions.

Example:

See iTEBD.py for an simple example of using iTEBD algo. to calculate the 1D-transverse field Ising model 
See iTEBD_gpu.py for an simple example of the same algo accelerated with GPU. 

See example.py for elementary usage.

Developers:

* Kai-Hsin Wu     kaihsinwu@gmail.com

* Jing-Jer Yen 
* Yen-Hsin Wu