A Nim frontend for pytorch, aiming to be mostly auto-generated and internally using ATen.

after building ATEN via https://github.com/fragcolor-xyz/nimtorch/issues/5#issuecomment-427937945:

ATEN=/tmp/d11/Users/timothee/git_clone/nim/pytorch/built/output/lib nim cpp -r torch.nim
error: unknown type name 'constexpr'

ATEN=/tmp/d11/Users/timothee/git_clone/nim/pytorch/built/output nim cpp -r --passC:-std=c++11 torch.nim
/Users/timothee/.cache/nim/torch_d/torch_tensors.cpp:206:14: error: no matching constructor for initialization of 'at::IntList' (aka 'ArrayRef<long long>')
        at::IntList temp(((long*) ((&self[((NI) 0)]))), selfLen_0);
                    ^    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/tmp/d11/Users/timothee/git_clone/nim/pytorch/built/output/include/ATen/core/ArrayRef.h:67:13: note: candidate constructor not viable: no known conversion from 'long *' to 'const long long *' for 1st argument
  constexpr ArrayRef(const T* data, size_t length)

Download ATen binaries or build it (instructions under)

=> no OSX option

ATen build instructions

cd docker && cd docker-aten-native

is that a typo?

is my best bet to try to follow instructions from https://github.com/pytorch/pytorch or https://github.com/pytorch/pytorch/tree/master/aten ?

Using the latest nimtorch, I'm getting a SIGSEGV when trying to compile one of the examples (test_xor).

I'm not sure if this is a compiler bug or a problem with Aten?

Dockerfile:

FROM continuumio/miniconda

RUN conda install -y -c fragcolor nimtorch

ADD test.nim .

docker build -t nimtorch_conda .
docker run --rm nimtorch_conda nim c test.nim
#Hint: used config file '/opt/conda/config/nim.cfg' [Conf]
#Hint: used config file '/opt/conda/config/config.nims' [Conf]
#Hint: system [Processing]
#Hint: widestrs [Processing]
#Hint: io [Processing]
#Hint: test [Processing]
#Hint: torch [Processing]
#Hint: macros [Processing]
#Hint: cpp [Processing]
#Hint: nimline [Processing]
#Hint: tables [Processing]
#Hint: hashes [Processing]
#Hint: strutils [Processing]
#Hint: parseutils [Processing]
#Hint: math [Processing]
#Hint: bitops [Processing]
#Hint: algorithm [Processing]
#Hint: unicode [Processing]
#Hint: os [Processing]
#Hint: pathnorm [Processing]
#Hint: osseps [Processing]
#Hint: posix [Processing]
#Hint: times [Processing]
#Hint: options [Processing]
#Hint: typetraits [Processing]
#Hint: torch_cpp [Processing]
#Hint: tensors [Processing]
#Hint: sequtils [Processing]
#Hint: sets [Processing]
#Hint: strformat [Processing]
#Hint: tensor_ops [Processing]
#Hint: autograd_macro [Processing]
#Hint: autograd_backward [Processing]
#Hint: nn [Processing]
#Hint: modules [Processing]
#Hint: init [Processing]
#Hint: python_helpers [Processing]
#Hint: functional [Processing]
#SIGSEGV: Illegal storage access. (Attempt to read from nil?)

test.nim:

import torch
import torch/[nn, optim]

let inputs = torch.tensor([
  [0.0, 0.0],
  [0.0, 1.0],
  [1.0, 0.0],
  [1.0, 1.0],
])

let targets = torch.tensor([
  [0.0],
  [1.0],
  [1.0],
  [0.0],
])

let
  fc1 = nn.Linear(2, 4)
  fc2 = nn.Linear(4, 1)
  loss_fn = nn.MSELoss()
  optimizer = optim.SGD(fc1.parameters & fc2.parameters , lr = 0.01, momentum = 0.1)

set_num_threads(1)

when defined gperftools:
  discard ProfilerStart("test_xor.log")

for i in 0 ..< 50000:
  optimizer.zero_grad()

  let predictions = inputs.fc1.relu.fc2.sigmoid

  let loss = loss_fn(predictions, targets)
  loss.backward()
  optimizer.step()

  if i mod 5000 == 0:
    print(loss)

when defined gperftools:
  ProfilerStop()

$ nim --version Nim Compiler Version 0.18.1 [Windows: amd64] Compiled at 2018-08-18 Copyright (c) 2006-2018 by Andreas Rumpf

git hash: b5171f57ef00bffb12387d7daf3487c5e07645f9 active boot switches: -d:release

$ nimble install nimtorch Prompt: nimtorch not found in any local packages.json, check internet for updated packages? [y/N] y Answer: Downloading Official package list Success Package list downloaded. Tip: 3 messages have been suppressed, use --verbose to show them. Error: Package not found.

C:/Program Files/mingw-w64/x86_64-8.1.0-posix-seh-rt_v6-rev0/mingw64/bin/../lib/gcc/x86_64-w64-mingw32/8.1.0/../../../../x86_64-w64-mingw32/bin/ld.exe: cannot find -lC:\Users\vsagar200\work\bin\nim-0.19.0_x64\ATen-windows10-cpu\lib\ATen_cpu.lib
C:/Program Files/mingw-w64/x86_64-8.1.0-posix-seh-rt_v6-rev0/mingw64/bin/../lib/gcc/x86_64-w64-mingw32/8.1.0/../../../../x86_64-w64-mingw32/bin/ld.exe: cannot find -lC:\Users\vsagar200\work\bin\nim-0.19.0_x64\ATen-windows10-cpu\lib\cpuinfo.lib
collect2.exe: error: ld returned 1 exit status
Error: execution of an external program failed: 'g++.exe   -o C:\Users\vsagar200\work\soft\nimtorch\tests\test_xor.exe  C:\Users\vsagar200\nimcache\test_xor_d\torch_test_xor.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\stdlib_system.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\torch_torch.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\fragments_cpp.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\stdlib_macros.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\stdlib_tables.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\stdlib_hashes.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\stdlib_math.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\stdlib_strutils.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\stdlib_parseutils.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\stdlib_bitops.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\stdlib_algorithm.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\stdlib_unicode.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\stdlib_ospaths.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\stdlib_winlean.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\stdlib_dynlib.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\torch_torch_cpp.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\stdlib_os.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\stdlib_times.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\stdlib_options.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\stdlib_typetraits.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\stdlib_strformat.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\torch_tensors.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\stdlib_sequtils.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\stdlib_sets.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\torch_tensor_ops.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\torch_autograd_macro.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\torch_autograd_backward.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\torch_nn.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\torch_init.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\torch_python_helpers.cpp.o C:\Users\vsagar200\nimcache\test_xor_d\torch_optim.cpp.o  -LC:\Users\vsagar200\work\bin\nim-0.19.0_x64\ATen-windows10-cpu\lib -LC:\Users\vsagar200\work\bin\nim-0.19.0_x64\ATen-windows10-cpu\lib64 -lC:\Users\vsagar200\work\bin\nim-0.19.0_x64\ATen-windows10-cpu\lib\ATen_cpu.lib -lC:\Users\vsagar200\work\bin\nim-0.19.0_x64\ATen-windows10-cpu\lib\cpuinfo.lib '

Although the ATEN path is correctly set. Tried on nim 0.18.1 and 0.19.0 the .lib files present at the same location the error is saying it cannot find.

thanks for the library. are there any plans to have Aten distributed with nim-torch? This will make it easier to build command-line tools that depend on it.

moved to here the discussion started here https://github.com/fragcolor-xyz/nimtorch/issues/6#issuecomment-430511494 to keep each topic separate

@sinkingsugar

Nimble has a major flaw, which is it applies all the packages it has on every project you have by default. That's my major concern that easy can create a mess if not considered.

I will give you an example exactly with nimtorch: Have nimtorch installed as nimble package Also work on the repository Did not run nimble develop Rename a file in the repository Forget to update the module import myrenamedfile into import newlocation/myrenamedfile Build will succeed yet will use import myrenamedfile from nimble this time..

but it all works if you run nimble develop, right? I think that's expected and I don't see a limitation here (in the sense of making it impossible to do certain things). if you really feel something is ill-designed with nimble it really should be a bug report in https://github.com/nim-lang/nimble/issues/ otherwise it will never get fixed (if there's anything to fix).

That being said, one possibility would be (and that's doable, not a fundamental flaw IMO): if you call nimble build inside a local package foo, nimble could remove from search path an installed package named foo

fragcolor-xyz/nimtorch now has a Chat Room on Gitter

@sinkingsugar has just created a chat room. You can visit it here: https://gitter.im/nimtorch/Lobby.

This pull-request adds this badge to your README.md:

If my aim is a little off, please let me know.

Happy chatting.

PS: Click here if you would prefer not to receive automatic pull-requests from Gitter in future.

Hi,

I think a working binding to pytorch from nim could be very valuable to support the use of nim in data science. This project seems to be inactive for a long time now and it is not using the current version of pytorch - any chance that it will be updated?

Would it be possible or even advisable to import a pth or pkl, which was trained using FastAI, into NimTorch, for the purpose of exposing in a backend written in Nim (for efficiency and speed)?

add nimble install fragments to this part of the readme As a beginner I wasted an hour trying to figure out what did I do wrong, turns out it was just a package error

I am trying to use nimtorch (I am new to pytorch as well). I am struggling to run a first example.

I have installed in Windows 10 like this:

conda create -n aten -c fragcolor aten=2019.02.16.2841
nimble install fragments
nimble install torch@#head

And then I tried to compile the code from here:

import torch
import torch/[nn, optim]

let
  inputs = torch.tensor([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]])
  targets = torch.tensor([[0.0], [1.0], [1.0], [0.0]])

let
  fc1 = nn.Linear(2, 4)
  fc2 = nn.Linear(4, 1)
  loss_fn = nn.MSELoss()
  optimizer = optim.SGD(fc1.parameters & fc2.parameters, lr = 0.01, momentum = 0.1)

for i in 0 ..< 50000:
  optimizer.zero_grad()

  var predictions = fc1(inputs).relu()
  predictions = fc2(predictions).sigmoid()

  let loss = loss_fn(predictions, targets)
  loss.backward()
  discard optimizer.step()

  if i mod 5000 == 0:
    print(loss)

I compile by doing:

c:> conda activate aten
c.> nim cpp ex01
....
C:\Users\mantielero\Documents\src\torch\ex01.nim(22, 25) Error: expression 'step(optimizer)' has no type (or is ambiguous)

which is the line:

discard optimizer.step()

What am I doing wrong?

Its been a while since the last commit, pls dont tell me nimtorch is dead, its wonderful, and i want to start using it, and libraries like this would pump up nim's popularity, are you leaving it for a while but planning on taking it back or just completely abandoned?