Fix cuda

src/arraymancer/tensor/backend/cublas.nim

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import  nimcuda/[cublas_v2, cublas_api],
+import  nimcuda/cuda12_5/[cublas_v2, cublas_api],
         ./cuda_global_state,
         ./cuda
 

src/arraymancer/tensor/backend/cuda.nim

@@ -14,21 +14,18 @@
 
 import  ../data_structure,
         ./global_config,
-        nimcuda/[nimcuda, cuda_runtime_api, driver_types]
+        nimcuda/cuda12_5/[check, cuda_runtime_api, driver_types]
 
-export nimcuda, cuda_runtime_api, driver_types
+export check, cuda_runtime_api, driver_types
 
 # Data structures to ease interfacing with Cuda and kernels
 
-proc cudaMalloc*[T](size: Natural): ptr T {.noSideEffect, inline.}=
+proc cudaMalloc*[T](size: Natural): ptr UncheckedArray[T] {.noSideEffect, inline.}=
   ## Internal proc.
   ## Wrap CudaMAlloc(var pointer, size) -> Error_code
-  let s = size * sizeof(T)
+  let s = csize_t(size * sizeof(T))
   check cudaMalloc(cast[ptr pointer](addr result), s)
 
-proc deallocCuda*[T](p: ref[ptr T]) {.noSideEffect.}=
-  if not p[].isNil:
-    check cudaFree(p[])
 
 
 # ##############################################################
@@ -38,7 +35,7 @@ proc newCudaStorage*[T: SomeFloat](length: int): CudaStorage[T] {.noSideEffect.}
   result.Flen = length
   new(result.Fref_tracking, deallocCuda)
   result.Fdata = cast[ptr UncheckedArray[T]](cudaMalloc[T](result.Flen))
-  result.Fref_tracking[] = result.Fdata
+  result.Fref_tracking.value = result.Fdata
 
 # #########################################################
 # # Sending tensor layout to Cuda Kernel
@@ -70,7 +67,9 @@ type
   ## Using arrays instead of seq avoids having to indicate __restrict__ everywhere to indicate no-aliasing
   ## We also prefer stack allocated array sice the data will be used at every single loop iteration to compute elements position.
   ## Ultimately it avoids worrying about deallocation too
-  CudaLayoutArray = ref[ptr cint]
+  CudaLayoutArrayObj* = object
+    value*: ptr UncheckedArray[cint]
+  CudaLayoutArray* = ref CudaLayoutArrayObj
 
 
   CudaTensorLayout [T: SomeFloat] = object
@@ -88,6 +87,11 @@ type
     data*: ptr T              # Data on Cuda device
     len*: cint                # Number of elements allocated in memory
 
+
+proc deallocCuda*(p: CudaLayoutArray) {.noSideEffect.}=
+  if not p.value.isNil:
+    check cudaFree(p.value)
+
 proc layoutOnDevice*[T:SomeFloat](t: CudaTensor[T]): CudaTensorLayout[T] {.noSideEffect.}=
   ## Store a CudaTensor shape, strides, etc information on the GPU
   #
@@ -103,8 +107,8 @@ proc layoutOnDevice*[T:SomeFloat](t: CudaTensor[T]): CudaTensorLayout[T] {.noSid
   new result.shape, deallocCuda
   new result.strides, deallocCuda
 
-  result.shape[] = cudaMalloc[cint](MAXRANK)
-  result.strides[] = cudaMalloc[cint](MAXRANK)
+  result.shape.value = cudaMalloc[cint](MAXRANK)
+  result.strides.value = cudaMalloc[cint](MAXRANK)
 
   var
     tmp_shape: array[MAXRANK, cint] # CudaLayoutArray
@@ -116,6 +120,6 @@ proc layoutOnDevice*[T:SomeFloat](t: CudaTensor[T]): CudaTensorLayout[T] {.noSid
 
 
   # TODO: use streams and async
-  let size = t.rank * sizeof(cint)
-  check cudaMemCpy(result.shape[], addr tmp_shape[0], size, cudaMemcpyHostToDevice)
-  check cudaMemCpy(result.strides[], addr tmp_strides[0], size, cudaMemcpyHostToDevice)
+  let size = csize_t(t.rank * sizeof(cint))
+  check cudaMemCpy(result.shape.value, addr tmp_shape[0], size, cudaMemcpyHostToDevice)
+  check cudaMemCpy(result.strides.value, addr tmp_strides[0], size, cudaMemcpyHostToDevice)

src/arraymancer/tensor/backend/cuda_global_state.nim

@@ -12,7 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import nimcuda/[nimcuda, cuda_runtime_api, cublas_v2, cublas_api]
+import nimcuda/cuda12_5/[check, cuda_runtime_api, cublas_v2, cublas_api,
+                         driver_types]
 
 # ###################################################
 # Global Cuda and CuBLAS state

src/arraymancer/tensor/data_structure.nim

@@ -16,13 +16,19 @@ import
   ../laser/dynamic_stack_arrays,
   ../laser/tensor/datatypes,
   nimblas,
+  nimcuda/cuda12_5/[cuda_runtime_api, check],
   # Standard library
   std/[complex]
 
 export nimblas.OrderType, complex
 export datatypes, dynamic_stack_arrays
 
 type
+  CudaTensorRefTrackerObj*[T: SomeFloat] = object
+    value*: ptr UncheckedArray[T]
+
+  CudaTensorRefTracker*[T] = ref CudaTensorRefTrackerObj[T]
+
   CudaStorage*[T: SomeFloat] = object
     ## Opaque seq-like structure for storage on the Cuda backend.
     ##
@@ -31,7 +37,7 @@ type
     # TODO: Forward declaring this and making this completely private prevent assignment in newCudaStorage from working
     Flen*: int
     Fdata*: ptr UncheckedArray[T]
-    Fref_tracking*: ref[ptr UncheckedArray[T]] # We keep ref tracking for the GC in a separate field to avoid double indirection.
+    Fref_tracking*: CudaTensorRefTracker[T] # We keep ref tracking for the GC in a separate field to avoid double indirection.
 
   CudaTensor*[T: SomeFloat] = object
     ## Tensor data structure stored on Nvidia GPU (Cuda)
@@ -73,6 +79,12 @@ type
 
   AnyTensor*[T] = Tensor[T] or CudaTensor[T] or ClTensor[T]
 
+
+proc deallocCuda*[T](p: CudaTensorRefTracker[T]) {.noSideEffect.}=
+  if not p.value.isNil:
+    check cudaFree(p.value)
+
+
 # ###############
 # Field accessors
 # ###############

src/arraymancer/tensor/init_cuda.nim

@@ -40,15 +40,16 @@ proc cuda*[T:SomeFloat](t: Tensor[T]): CudaTensor[T] {.noinit.}=
                         cudaMemcpyHostToDevice,
                         cudaStream0) # cudaStream0 is a cudaStream_t global var
 
-proc cpu*[T:SomeFloat](t: CudaTensor[T]): Tensor[T] {.noSideEffect, noinit.}=
+proc cpu*[T:SomeFloat](t: CudaTensor[T]): Tensor[T] {.noinit.}=
   ## Convert a tensor on a Cuda device to a tensor on Cpu.
   # We use blocking copy in this case to make sure
   # all data is available for future computation
 
   result.shape = t.shape
   result.strides = t.strides
   result.offset = t.offset
-  result.data = newSeqUninit[T](t.storage.Flen) # We copy over all the memory allocated
+
+  allocCpuStorage result.storage, t.storage.Flen
 
   let size = csize_t(t.storage.Flen * sizeof(T))
 

src/arraymancer/tensor/private/p_kernels_interface_cuda.nim

@@ -31,8 +31,8 @@ template cuda_assign_binding(kernel_name: string, binding_name: untyped)=
   proc `binding_name`[T: SomeFloat](
     blocksPerGrid, threadsPerBlock: cint,
     rank, len: cint,
-    dst_shape, dst_strides: ptr cint, dst_offset: cint, dst_data: ptr T,
-    src_shape, src_strides: ptr cint, src_offset: cint, src_data: ptr T
+    dst_shape, dst_strides: ptr UncheckedArray[cint], dst_offset: cint, dst_data: ptr T,
+    src_shape, src_strides: ptr UncheckedArray[cint], src_offset: cint, src_data: ptr T
   ) {.importcpp: import_string, noSideEffect.}
 
 
@@ -86,9 +86,9 @@ template cuda_assign_call*[T: SomeFloat](
   kernel_name[T](
     CUDA_HOF_TPB, CUDA_HOF_BPG,
     src.rank, dst.len, # Note: small shortcut, in this case len and size are the same
-    dst.shape[], dst.strides[],
+    dst.shape.value, dst.strides.value,
     dst.offset, dst.data,
-    src.shape[], src.strides[],
+    src.shape.value, src.strides.value,
     src.offset, src.data
   )
 
@@ -106,9 +106,9 @@ template cuda_binary_binding(kernel_name: string, binding_name: untyped)=
   proc `binding_name`[T: SomeFloat](
     blocksPerGrid, threadsPerBlock: cint,
     rank, len: cint,
-    dst_shape, dst_strides: ptr cint, dst_offset: cint, dst_data: ptr T,
-    a_shape, a_strides: ptr cint, a_offset: cint, a_data: ptr T,
-    b_shape, b_strides: ptr cint, b_offset: cint, b_data: ptr T
+    dst_shape, dst_strides: ptr UncheckedArray[cint], dst_offset: cint, dst_data: ptr T,
+    a_shape, a_strides: ptr UncheckedArray[cint], a_offset: cint, a_data: ptr T,
+    b_shape, b_strides: ptr UncheckedArray[cint], b_offset: cint, b_data: ptr T
   ) {.importcpp: import_string, noSideEffect.}
 
 
@@ -170,11 +170,11 @@ template cuda_binary_call*[T: SomeFloat](
   kernel_name(
     CUDA_HOF_TPB, CUDA_HOF_BPG,
     src_a.rank, dst.len, # Note: small shortcut, in this case len and size are the same
-    dst.shape[], dst.strides[],
+    dst.shape.value, dst.strides.value,
     dst.offset, dst.data,
-    src_a.shape[], src_a.strides[],
+    src_a.shape.value, src_a.strides.value,
     src_a.offset, src_a.data,
-    src_b.shape[], src_b.strides[],
+    src_b.shape.value, src_b.strides.value,
     src_b.offset, src_b.data
   )
 
@@ -193,8 +193,8 @@ template cuda_rscal_binding(kernel_name: string, binding_name: untyped)=
   proc `binding_name`[T: SomeFloat](
     blocksPerGrid, threadsPerBlock: cint,
     rank, len: cint,
-    dst_shape, dst_strides: ptr cint, dst_offset: cint, dst_data: ptr T,
-    src_shape, src_strides: ptr cint, src_offset: cint, src_data: ptr T,
+    dst_shape, dst_strides: ptr UncheckedArray[cint], dst_offset: cint, dst_data: ptr T,
+    src_shape, src_strides: ptr UncheckedArray[cint], src_offset: cint, src_data: ptr T,
     beta: T
   ) {.importcpp: import_string, noSideEffect.}
 
@@ -252,9 +252,9 @@ template cuda_rscal_call*[T: SomeFloat](
   kernel_name[T](
     CUDA_HOF_TPB, CUDA_HOF_BPG,
     src.rank, dst.len, # Note: small shortcut, in this case len and size are the same
-    dst.shape[], dst.strides[],
+    dst.shape.value, dst.strides.value,
     dst.offset, dst.data,
-    src.shape[], src.strides[],
+    src.shape.value, src.strides.value,
     src.offset, src.data,
     beta
     )
@@ -274,9 +274,9 @@ template cuda_lscal_binding(kernel_name: string, binding_name: untyped)=
   proc `binding_name`[T: SomeFloat](
     blocksPerGrid, threadsPerBlock: cint,
     rank, len: cint,
-    dst_shape, dst_strides: ptr cint, dst_offset: cint, dst_data: ptr T,
+    dst_shape, dst_strides: ptr UncheckedArray[cint], dst_offset: cint, dst_data: ptr T,
     alpha: T,
-    src_shape, src_strides: ptr cint, src_offset: cint, src_data: ptr T,
+    src_shape, src_strides: ptr UncheckedArray[cint], src_offset: cint, src_data: ptr T,
   ) {.importcpp: import_string, noSideEffect.}
 
 
@@ -332,10 +332,10 @@ template cuda_lscal_call*[T: SomeFloat](
   kernel_name[T](
     CUDA_HOF_TPB, CUDA_HOF_BPG,
     src.rank, dst.len, # Note: small shortcut, in this case len and size are the same
-    dst.shape[], dst.strides[],
+    dst.shape.value, dst.strides.value,
     dst.offset, dst.data,
     alpha,
-    src.shape[], src.strides[],
+    src.shape.value, src.strides.value,
     src.offset, src.data
     )
 
@@ -352,7 +352,7 @@ template cuda_assignscal_binding(kernel_name: string, binding_name: untyped)=
   proc `binding_name`[T: SomeFloat](
     blocksPerGrid, threadsPerBlock: cint,
     rank, len: cint,
-    dst_shape, dst_strides: ptr cint, dst_offset: cint, dst_data: ptr T,
+    dst_shape, dst_strides: ptr UncheckedArray[cint], dst_offset: cint, dst_data: ptr T,
     scalar: T
   ) {.importcpp: import_string, noSideEffect.}
 
@@ -402,7 +402,7 @@ template cuda_assignscal_call*[T: SomeFloat](
   kernel_name[T](
     CUDA_HOF_TPB, CUDA_HOF_BPG,
     dst.rank, dst.len, # Note: small shortcut, in this case len and size are the same
-    dst.shape[], dst.strides[],
+    dst.shape.value, dst.strides.value,
     dst.offset, dst.data,
     val
   )

src/arraymancer/tensor/shapeshifting_cuda.nim

@@ -33,14 +33,17 @@ proc transpose*(t: CudaTensor): CudaTensor {.noSideEffect.}=
 
 cuda_assign_glue("cuda_asContiguous", "CopyOp", cuda_asContiguous)
 
-proc asContiguous*[T: SomeFloat](t: CudaTensor[T], layout: OrderType = colMajor, force: bool = false):
-  CudaTensor[T] {.noSideEffect.}=
+proc asContiguous*[T: SomeFloat](t: CudaTensor[T], layout: OrderType = rowMajor, force: bool = false):
+  CudaTensor[T] {.noSideEffect, error: "NOT WORKING RIGHT NOW TODO: FIX".}=
   ## Transform a tensor with general striding to a Tensor with contiguous layout.
   ##
   ## By default CudaTensor will be colMajor (contrary to a cpu tensor).
   ##
   ## By default nothing is done if the tensor is already contiguous (C Major or F major)
   ## The "force" parameter can force re-ordering to a specific layout
+  # TODO: fix. this proc always outputs rowmajor, no matter the input.
+  # probably has to do with all the cuda tensors being colmajor by default,
+  # plus probably some double-negative of two bugs making the other procs work.
 
   if t.isContiguous and not force:
     return t

tests/tensor/test_accessors_slicer_cuda.nim

@@ -17,7 +17,7 @@ import ../../src/arraymancer
 import std / unittest
 
 
-testSuite "CUDA: Testing indexing and slice syntax":
+suite "CUDA: Testing indexing and slice syntax":
   const
     a = @[1, 2, 3, 4, 5]
     b = @[1, 2, 3, 4, 5]

tests/tensor/test_broadcasting_cuda.nim

@@ -15,7 +15,7 @@
 import ../../src/arraymancer
 import std / [unittest, sugar, sequtils]
 
-testSuite "CUDA: Shapeshifting - broadcasting and non linear algebra elementwise operations":
+suite "CUDA: Shapeshifting - broadcasting and non linear algebra elementwise operations":
   test "Tensor element-wise multiplication (Hadamard product) and division":
     block:
       let u = @[-4, 0, 9].toTensor().asType(float32).cuda

tests/tensor/test_init_cuda.nim

@@ -16,7 +16,7 @@ import ../../src/arraymancer
 import std / unittest
 
 
-testSuite "Cuda init":
+suite "Cuda init":
   test "Clone function":
     let a = [ 7, 4, 3, 1, 8, 6,
               8, 1, 6, 2, 6, 6,

tests/tensor/test_operators_blas_cuda.nim

@@ -16,7 +16,7 @@
 import ../../src/arraymancer
 import std / [unittest, sugar]
 
-testSuite "CUDA CuBLAS backend (Basic Linear Algebra Subprograms)":
+suite "CUDA CuBLAS backend (Basic Linear Algebra Subprograms)":
   test "GEMM - General Matrix to Matrix Multiplication":
     ## TODO: test with slices
     let a = [[1.0,2,3],