# Naive use of CUDA for DMRG leads to MethodError

**URL:** <https://itensor.discourse.group/t/naive-use-of-cuda-for-dmrg-leads-to-methoderror/2094>\
**Category:** ITensor Julia Questions\
**Created:** [October 29, 2024, 5:52pm UTC](https://itensor.discourse.group/t/naive-use-of-cuda-for-dmrg-leads-to-methoderror/2094 "2024-10-29T17:52:47Z")\
**Posts on this page:** 5\
**Page:** 1

<div class="post-metadata">

**Author:** ![tibidabo](https://avatars.discourse-cdn.com/v4/letter/t/bcef8e/32.png) [@tibidabo](https://itensor.discourse.group/u/tibidabo)\
**Post date:** [October 29, 2024, 5:52pm UTC](https://itensor.discourse.group/t/naive-use-of-cuda-for-dmrg-leads-to-methoderror/2094/1 "2024-10-29T17:52:47Z")

</div>

Hello ITensors team,  
I would like to solve larger MPO’s with the build in DMRG on an NVIDIA GPU.

I was guessing that following your [documentation](https://itensor.github.io/ITensors.jl/dev/RunningOnGPUs.html) you could do it in the following naive way.

Is the build in DMRG even supposed to be compatible with GPU use? If this is the case could you point me to a solution of my error.

Thank you very much,  
Tibidabo

Minimal code example:

```julia
using ITensors
using CUDA

let
# Define the simple model
N = 10
sites = siteinds("S=1/2", N)
os = OpSum()
for j in 1:N-1
    os += "Sx", j, "Sx", j+1
    os += "Sy", j, "Sy", j+1
    os += "Sz", j, "Sz", j+1
end
H = MPO(os, sites)
Hcu = cu(H)

# Perform DMRG calculation
psi0 = randomMPS(sites, 10)
energy, psi = dmrg(Hcu, psi0, nsweeps=5, maxdim=200)
println("Ground state energy on NVIDIA GPU with CUDA: $energy")

end

```

Which leads to the following error

```bash
ERROR: MethodError: no method matching DenseArray{ComplexF64, 3}(::UndefInitializer, ::Tuple{Int64, Int64, Int64})
The type `DenseArray{ComplexF64, 3}` exists, but no method is defined for this combination of argument types when trying to construct it.

Closest candidates are:
  (::Type{<:AbstractArray})(::NDTensors.AllocateData.UndefInitializer, ::Tuple)
   @ NDTensors ~/.julia/packages/NDTensors/EHP8P/src/lib/AllocateData/src/initializers.jl:16
  (::Type{<:AbstractArray})(::NDTensors.NeverAlias, ::AbstractArray)
   @ ITensors ~/.julia/packages/ITensors/gRmgt/src/itensor.jl:673
  (::Type{<:AbstractArray})(::NDTensors.AllowAlias, ::AbstractArray)
   @ ITensors ~/.julia/packages/ITensors/gRmgt/src/itensor.jl:677

Stacktrace:
  [1] similar(arraytype::Type{DenseVector{ComplexF64}}, dims::Tuple{Index{Int64}, Index{Int64}, Index{Int64}})
    @ NDTensors ~/.julia/packages/NDTensors/EHP8P/src/abstractarray/similar.jl:10
  [2] similar(storagetype::Type{NDTensors.Dense{…}}, dims::Tuple{Index{…}, Index{…}, Index{…}})
    @ NDTensors ~/.julia/packages/NDTensors/EHP8P/src/tensorstorage/similar.jl:40
  [3] similar(tensortype::Type{NDTensors.DenseTensor{…}}, dims::Tuple{Index{…}, Index{…}, Index{…}})
    @ NDTensors ~/.julia/packages/NDTensors/EHP8P/src/tensor/similar.jl:22
  [4] contraction_output(tensor1::NDTensors.DenseTensor{…}, tensor2::NDTensors.DenseTensor{…}, indsR::Tuple{…})
    @ NDTensors ~/.julia/packages/NDTensors/EHP8P/src/dense/tensoralgebra/contract.jl:3
  [5] contraction_output(tensor1::NDTensors.DenseTensor{…}, labelstensor1::Tuple{…}, tensor2::NDTensors.DenseTensor{…}, labelstensor2::Tuple{…}, labelsoutput_tensor::Tuple{…})
    @ NDTensors ~/.julia/packages/NDTensors/EHP8P/src/tensoroperations/generic_tensor_operations.jl:62
  [6] contract(tensor1::NDTensors.DenseTensor{…}, labelstensor1::Tuple{…}, tensor2::NDTensors.DenseTensor{…}, labelstensor2::Tuple{…}, labelsoutput_tensor::Tuple{…})
    @ NDTensors ~/.julia/packages/NDTensors/EHP8P/src/tensoroperations/generic_tensor_operations.jl:108
  [7] contract(::Type{…}, tensor1::NDTensors.DenseTensor{…}, labels_tensor1::Tuple{…}, tensor2::NDTensors.DenseTensor{…}, labels_tensor2::Tuple{…})
    @ NDTensors ~/.julia/packages/NDTensors/EHP8P/src/tensoroperations/generic_tensor_operations.jl:91
  [8] contract
    @ ~/.julia/packages/SimpleTraits/l1ZsK/src/SimpleTraits.jl:331 [inlined]
  [9] _contract(A::NDTensors.DenseTensor{…}, B::NDTensors.DenseTensor{…})
    @ ITensors ~/.julia/packages/ITensors/gRmgt/src/tensor_operations/tensor_algebra.jl:3
 [10] _contract(A::ITensor, B::ITensor)
    @ ITensors ~/.julia/packages/ITensors/gRmgt/src/tensor_operations/tensor_algebra.jl:9
 [11] contract(A::ITensor, B::ITensor)
    @ ITensors ~/.julia/packages/ITensors/gRmgt/src/tensor_operations/tensor_algebra.jl:74
 [12] *
    @ ~/.julia/packages/ITensors/gRmgt/src/tensor_operations/tensor_algebra.jl:61 [inlined]
 [13] *
    @ ./operators.jl:596 [inlined]
 [14] _makeR!(P::ProjMPO, psi::MPS, k::Int64)
    @ ITensors.ITensorMPS ~/.julia/packages/ITensors/gRmgt/src/lib/ITensorMPS/src/abstractprojmpo/abstractprojmpo.jl:177
 [15] makeR!
    @ ~/.julia/packages/ITensors/gRmgt/src/lib/ITensorMPS/src/abstractprojmpo/abstractprojmpo.jl:186 [inlined]
 [16] position!
    @ ~/.julia/packages/ITensors/gRmgt/src/lib/ITensorMPS/src/abstractprojmpo/abstractprojmpo.jl:204 [inlined]
 [17] 
    @ ITensors.ITensorMPS ~/.julia/packages/ITensors/gRmgt/src/lib/ITensorMPS/src/dmrg.jl:202
 [18] dmrg
    @ ~/.julia/packages/ITensors/gRmgt/src/lib/ITensorMPS/src/dmrg.jl:158 [inlined]
 [19] #dmrg#509
    @ ~/.julia/packages/ITensors/gRmgt/src/lib/ITensorMPS/src/dmrg.jl:28 [inlined]
 [20] dmrg
    @ ~/.julia/packages/ITensors/gRmgt/src/lib/ITensorMPS/src/dmrg.jl:21 [inlined]
 [21] #dmrg#515
    @ ~/.julia/packages/ITensors/gRmgt/src/lib/ITensorMPS/src/dmrg.jl:388 [inlined]
 [22] top-level scope
    @ ~/cqed-tensor-network-study/quick_tests/test_gpus/test_itensors_gpu.jl:19
Some type information was truncated. Use `show(err)` to see complete types.

```

My Environment  
NVIDIA GeForce RTX 4090

```julia
julia> versioninfo()
Julia Version 1.11.1
Commit 8f5b7ca12ad (2024-10-16 10:53 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 16 × Intel(R) Core(TM) i9-14900KF
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (ORCJIT, alderlake)
Threads: 1 default, 0 interactive, 1 GC (on 16 virtual cores)
Environment:
  JULIA_EDITOR = code
  JULIA_NUM_THREADS = 

(@v1.11) pkg> status
Status `~/.julia/environments/v1.11/Project.toml`
  [13e28ba4] AppleAccelerate v0.4.0
  [052768ef] CUDA v5.5.2
⌃ [9136182c] ITensors v0.6.22
  [33e6dc65] MKL v0.7.0
  [856f044c] MKL_jll v2024.2.0+0
  [8e850b90] libblastrampoline_jll v5.11.0+0
Info Packages marked with ⌃ have new versions available and may be upgradable.

```

---

<div class="post-metadata">

**Author:** ![mtfishman](https://yyz2.discourse-cdn.com/free1/user_avatar/itensor.discourse.group/mtfishman/32/11_2.png) [@mtfishman](https://itensor.discourse.group/u/mtfishman)\
**Post date:** [October 29, 2024, 5:54pm UTC](https://itensor.discourse.group/t/naive-use-of-cuda-for-dmrg-leads-to-methoderror/2094/2 "2024-10-29T17:54:50Z")

</div>

You should move the MPS state to GPU as well, i.e.:

```julia
psi0 = cu(random_mps(sites; linkdims=10))

```

(also note my use of `random_mps` and the `linkdims` keyword argument which is the latest syntax.)

---

<div class="post-metadata">

**Author:** ![tibidabo](https://avatars.discourse-cdn.com/v4/letter/t/bcef8e/32.png) [@tibidabo](https://itensor.discourse.group/u/tibidabo)\
**Post date:** [October 29, 2024, 6:57pm UTC](https://itensor.discourse.group/t/naive-use-of-cuda-for-dmrg-leads-to-methoderror/2094/3 "2024-10-29T18:57:04Z")

</div>

Thank you very much! It is running now as accepted.

---

<div class="post-metadata">

**Author:** ![mtfishman](https://yyz2.discourse-cdn.com/free1/user_avatar/itensor.discourse.group/mtfishman/32/11_2.png) [@mtfishman](https://itensor.discourse.group/u/mtfishman)\
**Post date:** [October 29, 2024, 7:13pm UTC](https://itensor.discourse.group/t/naive-use-of-cuda-for-dmrg-leads-to-methoderror/2094/4 "2024-10-29T19:13:40Z")

</div>

For context, we don’t support mixed operations between CPU and GPU tensors, we require that you move all data to GPU if you want to run it on GPU. Otherwise it is ambiguous where to put the results of tensor operations, i.e. how do we know if you want the calculation to end up on CPU or GPU if there is a mixture?

In the particular case of DMRG, maybe if the MPO is on GPU it is “obvious” the user wants the calculation to run on GPU, but we don’t want to hardcode assumptions about that in high-level code like DMRG.

---

<div class="post-metadata">

**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/itensor/original/1X/d3072b13e047cd06df7f3981547c0917d940dfa4.png) [@system](https://itensor.discourse.group/u/system)\
**Post date:** [November 8, 2024, 7:14pm UTC](https://itensor.discourse.group/t/naive-use-of-cuda-for-dmrg-leads-to-methoderror/2094/5 "2024-11-08T19:14:12Z")

</div>

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