# Error with conserved quantum numbers and ITensorGPU **URL:** \ **Category:** ITensor Julia Questions\ **Tags:** julia, dmrg\ **Created:** [August 12, 2022, 9:38pm UTC](https://itensor.discourse.group/t/error-with-conserved-quantum-numbers-and-itensorgpu/335 "2022-08-12T21:38:49Z")\ **Posts on this page:** 4\ **Page:** 1
**Author:** ![apateonas](https://yyz2.discourse-cdn.com/free1/user_avatar/itensor.discourse.group/apateonas/32/101_2.png) [@apateonas](https://itensor.discourse.group/u/apateonas)\ **Post date:** [August 12, 2022, 9:38pm UTC](https://itensor.discourse.group/t/error-with-conserved-quantum-numbers-and-itensorgpu/335/1 "2022-08-12T21:38:49Z")
Hello, I am working on getting GPU acceleration working for my DMRG studies, and am running into an error when using the `ITensorGPU` library. DMRG runs fine with and without GPU acceleration when I set `conserve_qns = false`. When I set `conserve_qns = true`, DMRG runs fine without GPU acceleration but gets a `DimensionMismatch` error with GPU acceleration. Here is the error I am seeing: ```auto ERROR: LoadError: DimensionMismatch("new dimensions (5, 3, 3) must be consistent with array size (9,)") Stacktrace: [1] reshape(a::CUDA.CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}, dims::Tuple{Int64, Int64, Int64}) @ CUDA ~/.julia/packages/CUDA/5jdFl/src/array.jl:665 [2] reshape @ ./reshapedarray.jl:116 [inlined] [3] permute!(B::NDTensors.DenseTensor{Float64, 3, Tuple{Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}}, NDTensors.Dense{Float64, CUDA.CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}}}, A::NDTensors.DenseTensor{Float64, 3, Tuple{Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}}, NDTensors.Dense{Float64, CUDA.CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}}}) @ ITensorGPU ~/.julia/packages/ITensorGPU/QvjZs/src/tensor/cudense.jl:532 [4] #1 @ ~/.julia/packages/ITensorGPU/QvjZs/src/tensor/cudense.jl:61 [inlined] [5] permutedims!! @ ~/.julia/packages/ITensorGPU/QvjZs/src/tensor/cudense.jl:65 [inlined] [6] permutedims!! @ ~/.julia/packages/ITensorGPU/QvjZs/src/tensor/cudense.jl:63 [inlined] [7] permutedims @ ~/.julia/packages/NDTensors/c2BpJ/src/dense.jl:438 [inlined] [8] permutedims @ ~/.julia/packages/ITensors/OjQuG/src/itensor.jl:1786 [inlined] [9] _permute(as::NDTensors.NeverAlias, T::NDTensors.DenseTensor{Float64, 3, Tuple{Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}}, NDTensors.Dense{Float64, CUDA.CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}}}, new_inds::Tuple{Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}}) @ ITensors ~/.julia/packages/ITensors/OjQuG/src/itensor.jl:1791 [10] permute(as::NDTensors.NeverAlias, T::ITensor, new_inds::Tuple{Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}}) @ ITensors ~/.julia/packages/ITensors/OjQuG/src/itensor.jl:1795 [11] #permute#223 @ ~/.julia/packages/ITensors/OjQuG/src/itensor.jl:1776 [inlined] [12] permute(T::ITensor, new_inds::Tuple{Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}, Index{Vector{Pair{QN, Int64}}}}) @ ITensors ~/.julia/packages/ITensors/OjQuG/src/itensor.jl:1761 [13] permute(M::MPO, #unused#::Tuple{typeof(linkind), typeof(siteinds), typeof(linkind)}) @ ITensors ~/.julia/packages/ITensors/OjQuG/src/mps/dmrg.jl:14 [14] #dmrg#990 @ ~/.julia/packages/ITensors/OjQuG/src/mps/dmrg.jl:45 [inlined] [15] dmrg @ ~/.julia/packages/ITensors/OjQuG/src/mps/dmrg.jl:41 [inlined] ``` Is this a bug in `ITensorGPU`, or is there possibly something missing in my code? ---
**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:** [August 12, 2022, 9:40pm UTC](https://itensor.discourse.group/t/error-with-conserved-quantum-numbers-and-itensorgpu/335/2 "2022-08-12T21:40:45Z")
Yes, unfortunately our GPU backend doesn’t work for QN conserving tensors right now. It will be a bit of a project to get it working and we haven’t had the time yet but hopefully we will support it soon! ---
**Author:** ![apateonas](https://yyz2.discourse-cdn.com/free1/user_avatar/itensor.discourse.group/apateonas/32/101_2.png) [@apateonas](https://itensor.discourse.group/u/apateonas)\ **Post date:** [August 12, 2022, 9:43pm UTC](https://itensor.discourse.group/t/error-with-conserved-quantum-numbers-and-itensorgpu/335/3 "2022-08-12T21:43:30Z")
Hi @mtfishman Thank you for getting back to me. That’s a bummer! It would be great to add that to `ITensorGPU` documentation so that others do not run into this. Any idea on when QN conserving tensors might be added to the GPU backend? I am potentially interested in helping with the effort. Thank you! Roman ---
**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:** [August 12, 2022, 10:09pm UTC](https://itensor.discourse.group/t/error-with-conserved-quantum-numbers-and-itensorgpu/335/4 "2022-08-12T22:09:38Z")
I think it wouldn’t be too hard to get some version working. A strategy would be to work through primitive operations like tensor contraction and SVD which DMRG relies on, try running the code using QN ITensors allocated on GPU and work through the errors you come across. So for example run the code: ```julia using ITensors using ITensorGPU i = Index([QN(0) => 2, QN(1) => 2]) j = sim(i) k = sim(i) A = cu(randomITensor(i, dag(j))) B = cu(randomITensor(j, dag(k))) A * B svd(A, i) ``` and work through the errors that come up. I think the bigger challenge will be making it efficient, since there aren’t libraries for performing block sparse tensor contractions on GPU. For the CPU code we basically just loop over the QN blocks and perform dense contractions of blocks (with optional multithreading over block contractions that can be done in parallel), but for many small blocks I think this will be inefficient on GPU if done naively.