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One way to integrate into existing CUDA infrastructure #2

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One way to integrate into existing CUDA infrastructure #2

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2ad706f
use cuptrs instead of normal pointers
Mar 4, 2020
1138070
add using
Mar 4, 2020
2b63147
ptr{cvoid} -> ptr{T}
Mar 4, 2020
25df3fa
converting to a CuArray
Mar 4, 2020
fa013f2
add Artifacts
Mar 11, 2020
8afd2fb
Merge branch 'master' into dg/cu
DhairyaLGandhi Mar 11, 2020
5639e48
Merge branch 'master' into dg/cu
DhairyaLGandhi Mar 11, 2020
5777545
Merge branch 'master' of https://github.com/dhairyagandhi96/Torch.jl …
Mar 11, 2020
8e04282
add arch to artifacts
Mar 11, 2020
0fa5bdf
add correct checksums
Mar 11, 2020
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10 changes: 6 additions & 4 deletions Artifacts.toml
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Original file line number Diff line number Diff line change
@@ -1,17 +1,19 @@
[[torch]]
git-tree-sha1 = "21fba0f64d7e6dce93146b84ba73226f4f0f0983"
os = "macos"
arch = "x86_64"
git-tree-sha1 = "21fba0f64d7e6dce93146b84ba73226f4f0f0983"
os = "macos"

[[torch.download]]
sha256 = "21fba0f64d7e6dce93146b84ba73226f4f0f0983"
sha256 = "84e9112b442ee1e3dc9e078d9066a855a2344ec566616cffbff1662e08cd8bf7"
url = "https://download.pytorch.org/libtorch/cpu/libtorch-macos-1.4.0.zip"

[[torch]]
arch = "x86_64"
git-tree-sha1 = "42bc9814d06d949098f5ab218be61adaa3e29c06"
os = "linux"

[[torch.download]]
sha256 = "42bc9814d06d949098f5ab218be61adaa3e29c06"
sha256 = "f214bfde532877aa5d4e0803e51a28fa8edd97b6a44b6615f75a70352b6b542e"
url = "https://download.pytorch.org/libtorch/cu101/libtorch-cxx11-abi-shared-with-deps-1.4.0.zip"


17 changes: 17 additions & 0 deletions Manifest.toml
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Expand Up @@ -9,6 +9,23 @@ git-tree-sha1 = "5b08ed6036d9d3f0ee6369410b830f8873d4024c"
uuid = "b99e7846-7c00-51b0-8f62-c81ae34c0232"
version = "0.5.8"

[[CEnum]]
git-tree-sha1 = "62847acab40e6855a9b5905ccb99c2b5cf6b3ebb"
uuid = "fa961155-64e5-5f13-b03f-caf6b980ea82"
version = "0.2.0"

[[CUDAapi]]
deps = ["Libdl", "Logging"]
git-tree-sha1 = "d7ceadd8f821177d05b897c0517e94633db535fe"
uuid = "3895d2a7-ec45-59b8-82bb-cfc6a382f9b3"
version = "3.1.0"

[[CUDAdrv]]
deps = ["CEnum", "CUDAapi", "Printf"]
git-tree-sha1 = "01e90fa34e25776bc7c8661183d4519149ebfe59"
uuid = "c5f51814-7f29-56b8-a69c-e4d8f6be1fde"
version = "6.0.0"

[[Codecs]]
deps = ["Test"]
git-tree-sha1 = "70885e5e038cba1c4c17a84ad6c40756e10a4fb5"
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1 change: 1 addition & 0 deletions Project.toml
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Expand Up @@ -4,6 +4,7 @@ authors = ["dhairyagandhi96 "]
version = "0.1.0"

[deps]
CUDAdrv = "c5f51814-7f29-56b8-a69c-e4d8f6be1fde"
Libdl = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
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5 changes: 5 additions & 0 deletions README.md
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Expand Up @@ -22,5 +22,10 @@ tip = tensor(ip, dev = 0) # 0 => GPU:0 in Torch
tresnet(tip);
```

## Making a CuArray
```julia
CuArray{Float32,4}(tip.ptr, size(tip), false)::CuArray
```

## Acknowledgements
Takes a lot of inspiration from existing such projects - ocaml-torch for generating the wrappers.
1 change: 1 addition & 0 deletions src/Torch.jl
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Expand Up @@ -4,6 +4,7 @@ export Tensor, tensor, Scalar

using YAML
using Clang
using CUDAdrv
using ZygoteRules
using ZygoteRules: @adjoint
using NNlib
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8 changes: 5 additions & 3 deletions src/tensor.jl
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Expand Up @@ -18,11 +18,13 @@ async_free!(x) = let x = x, ptr = x.ptr, oid = objectid(x)
end

mutable struct Tensor{T, N} <: AbstractArray{T,N}
ptr::Ptr
ptr::Union{Ptr,CuPtr}
device::Int

function Tensor{T,N}(ptr::Ptr, dev::Int) where {T,N}
obj = new(ptr, dev)
function Tensor{T,N}(ptr::Union{Ptr,CuPtr}, dev::Int) where {T,N}
cuptr = convert(CuPtr{Cvoid}, Base.bitcast(UInt, ptr))
cuptr = convert(CuPtr{T}, cuptr)
obj = new(cuptr, dev)
finalizer(async_free!, obj)
# TURN_ON_LOGGING == true && (logdict[ptr] = (size(obj), stacktrace()))
obj
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1,900 changes: 950 additions & 950 deletions src/wrap/libdoeye_caml_generated.jl
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2 changes: 1 addition & 1 deletion src/wrap/libtorch_common.jl
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@@ -1,7 +1,7 @@
# Automatically generated using Clang.jl


const tensor = Ptr{Cvoid}
const tensor = CuPtr{Cvoid}
const optimizer = Ptr{Cvoid}
const scalar = Ptr{Cvoid}
const module_t = Ptr{Cvoid}
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