- replace explicit
optimizer*
procedures by genericoptimizer
with a typed first argumentoptimizer(SGD, ...)
,optimizer(Adam, ...)
etc. (PR #557)
- replace
shallowCopy
bymove
under ARC/ORC (PR #562)
- rewrote neural network DSL to support custom non-macro layers and composition of multiple models (PR #548)
- syntactic changes to the neural network DSL (PR #548)
- autograd context is no longer specified at network definition. It is only used when instantiating a model. E.g.:
network FizzBuzzNet: ...
instead ofnetwork ctx, FizzBuzzNet: ...
- there is no
Input
layer anymore. All input variables are specified at the beginning of theforward
declaration - in/output-shapes of layers are generally described as
seq[int]
now, but this depends on the concrete layer type - layer descriptions behave like functions, so function parameters can be specifically named and potentially reordered or omitted. E.g.:
Conv2D(@[1, 28, 28], out_channels = 20, kernel_size = (5, 5))
- the
Conv2D
layer expects the kernel size as aSize2D
(integer tuple of size 2), instead of passing height and width as separate arguments - when using an
out_shape
function of a previous layer to describe aLinear
layer, one has to useout_shape[0]
for the number of input features. E.g.:Linear(fl.out_shape[0], 500)
instead ofLinear(fl.out_shape, 500)
GRU
has been replaced byGRULayer
, which has now a different description signature:(num_input_features, hidden_size, stacked_layers)
instead of([seq_len, Batch, num_input_features], hidden_size, stacked_layers)
- autograd context is no longer specified at network definition. It is only used when instantiating a model. E.g.:
- Remove cursor annotation for CPU Tensor again as it causes memory leaks (PR #555)
- disable OpenMP in reducing contexts if ARC/ORC is used as it leads to segfaults (PR #555)
- Add cursor annotation for CPU Tensor fixing #535 (PR #533)
- Add
toFlatSeq
(flatten data and export it as a seq) andtoSeq1D
which export a rank-1 Tensor into aseq
(PR #533) - add test for
syevr
(PR #552) - introduce CI for Nim 1.6 and switch to Github Action task for Nim binaries (PR #551)
- fixes
einsum
in more general generic contexts by replacing the logic introduced in PR #539 by a rewrite from typed AST tountyped
. This makes it work in (all?) generic contexts, PR #545 - add element wise exponentiation
^.
for scalar base to all elements of a tensor, i.e.2^.t
for a tensort
, thanks to @asnt PR #546.
- fixes
einsum
in generic contexts by allowingnnkCall
andnnkOpenSymChoice
, PR #539 - add test for
einsum
showing cross product, PR #538 - add test for display of uninitialized tensor, PR #540
- allow
CustomMetric
as user defined metric indistances
, PR #541. User must provide their owndistance
procedure for the metric with signature:proc distance*(metric: typedesc[CustomMetric], v, w: Tensor[float]): float
- disable AVX512 support by default. Add the
-d:avx512
compilation flag to activate it. Note: this activates it for all CPUs as it hands-mavx512dq
to gcc / clang!
- further fix undeclared identifier issues present for certain
generics context, in this case for the
|
identifier when slicing - fix printing of uninitialized tensors. Instead of crashing these now print as "Unitialized Tensor[T] ...".
- fix CSV parsing regression (still used
Fdata
field access) and improved efficiency of the parser
- fix autograd code after changes in the Nim compiler from version 1.4 on. Requires to keep the procedure signatures using the base type and then convert to the specific type in the procedure (#528)
- fixes an issue when creating a laser tensor within a generic, in
which case one might see "undeclared identifier
rank
"
- remove the MNIST download test from the default test cases, as it is too flaky for a CI
- fix issue #523 by fixing how
map_inline
works for rank > 1 tensors. Access the data storage at correct indices instead of using the regular tensor[]
accessor (which requires N indices for a rank N tensor)
- change least squares wrapper around
gelsd
to have workspace computed by LAPACK and setrcond
to default-1
. But we also make it an argument for users to change (PR #520)
- add k-d tree (PR #447)
- add DBSCAN clustering (PR #413 / PR #518), thanks to @abieler
argsort
now has atoCopy
argument to avoid sorting the argument tensor in place if not desiredcumsum
,cumprod
now use axis 0 by default if no axis given- as part of DBSCAN + k-d tree a
distances.nim
submodule was added that adds Manhattan, Minkowski, Euclidean and Jaccard distances nonzero
was added to get indices of non zero elements in a tensor
- fix memory allocation to not zero initialize the memory for tensors
(which we do manually). This made
newTensorUninit
not do what it was supposed to (PR #517). - add
vandermonde
matrix constructor (PR #519) - change
rcond
argument togelsd
for linear least squares solver to use simpleepsilon
(PR #519)
- fixes issue #459, ambiguity in
tanh
activation layer - fixes issue #514, make all examples compile again
- compile all examples during CI to avoid further regressions at a compilation level (thanks @Anuken)
Hotfix release fixing CUDA tensor printing. The code as pushed in #509 was broken.
This release is named after "Memories of Ice" (2001), the third book of Steven Erikson epic dark fantasy masterpiece "The Malazan Book of the Fallen".
Changes :
- Add
toUnsafeView
as replacement ofdataArray
to return aptr UncheckedArray
- Doc generation fixes
cumsum
,cumprod
- Fix least square solver
- Fix mean square error backpropagation
- Adapt to upstream symbol resolution changes
- Basic Graph Convolution Network
- Slicing tutorial revamp
- n-dimensional tensor pretty printing
- Compilation fixes to handle Nim v1.0 to Nim devel
Thanks to @Vindaar for maintaining the repo, the docs, pretty-printing and answering many many questions on Discord while I took a step back. Thanks to @filipeclduarte for the cumsum/cumprod, @Clonkk for updating raw data accesses, @struggle for finding a bug in mean square error backprop, @timotheecour for spreading new upstream requirements downstream and @anon767 for Graph Neural Network.
Changes :
- Fancy Indexing (#434)
argmax_max
obsolete assert to 1D/2D removed. It refered to issues #183 and merged PR #171
Deprecation
- The dot in broadcasting and elementwise operators has changed place. This was not propagated to logical comparison
Use
==.
,!=.
,<=.
,<.
,>=.
,>.
instead of the previous order.==
. This allows the broadcasting operators to have the same precedence as the natural operators. This also align Arraymancer with other Nim packages: Manu and NumericalNim
Overview (TODO)
This release integrates part of the Laser Backend (https://github.com/numforge/laser) that has been brewing since the end of 2018. The new backend provides the following features:
- Tensors can now either be a view over a memory buffer or manage the memory (like before).
The "view" allows zero-copy with libraries using the same
multi-dimensional array/tensor memory layout in particular Numpy,
PyTorch and Tensorflow or even image libraries. This can be achieved
using the new
fromBuffer
procedures to create a tensor. - strings, ref types and types with non-trivial destructors will still always own and manager their memory buffer.
Trivial types (plain-old data) like integers, floats or complex can use the zero-copy scheme
by setting
isMemOwner
to false and then pointraw_buffer
to the preallocated buffer. In that case, the memory must be freed manually to avoid memory leaks. - To keep the benefits of enforcing (im)mutabilility via the type system, procedures like
dataArray
that used to return raw pointers have been deleted or deprecated in favor of routines that returnRawImmutableView
andRawMutableView
with only appropriate indexing or mutable indexing defined. This is an improvement over raw pointers. Note that at the moment there is no scheme like a borrow-checker to prevent users from using them even after the buffer has been invalidated (borrow-checking). In the futurelent
will be used to provide borrow-checking security.
Breaking changes
- In the past, it was mentioned in the README that Arraymancer supported up to 6 dimensions.
In reality up to 7 dimensions was possible. It has now been changed to 6 by default.
It is now possible to configure this via a compiler define
LASER_MAXRANK
For examplenim c -d:LASER_MAXRANK=16 path/to/app
to support up to 16 dimensions. ornim c -d:LASER_MAXRANK=2 path/to/app
if only 2 dimensions are ever needed and we want to save on stack space and optimize memory cache accesses. - The CpuStorage data structure has been completely refactored
- The routines
data
,data=
andtoRawSeq
that used to return theseq
backing the Tensor have been changed in a backward-incompatible way. They now return the canonical row-major representation of a tensor. With the change to a view and decoupling with a lower-level pointer based backend, Arraymancer does not track anymore the whole reserved memory and so cannot return the raw in-memory storage of the tensor. They have been deprecated. - Some procedures now have side-effects inherited from Nim's
allocShared
variable
from theautograd
modulesolve
from thelinear_algebra
module
io_hdf5
is not imported automatically anymore if the module is installed. The reason for this is that the HDF5 library runs code in global scope to initialize the HDF5 library. This means dead code elimination does not work and a binary will always depend on the HDF5 shared library if thenimhdf5
is installed, even if not used. Simply import usingimport arraymancer/io/io_hdf5
.
Deprecation
MetadataArray
is nowMetadata
dataArray
has been deprecated in favor on mutability-safeunsafe_raw_offset
This release is named after "Windwalkers" (2004, French "La Horde du Contrevent"), by Alain Damasio.
Changes:
- The
symeig
proc to compute eigenvectors of a symmetric matrix now accepts an "uplo" char parameter. This allows to fill only the Upper or Lower part of the matrix, the other half is not used in computation. - Added
svd_randomized
, a fast and accurate SVD approximation via random sampling. This is the standard driver for large scale SVD applications as SVD on large matrices is very slow. pca
now uses the randomized SVD instead of computing the covariance matrix. It can now efficiently deal with large scale problems. It now accepts acenter
,n_oversamples
andn_power_iters
arguments. Note thatpca
without centering is equivalent to a truncated SVD.- LU decomposition has been added
- QR decomposition has been added
hilbert
has been introduced. It creates the famous ill-conditioned Hilbert matrix. The matrix is suitable to stress test decompositions.- The
arange
procedure has been introduced. It creates evenly spaced value within a specified range and step - The ordering of arguments to error functions has been converted to
(y_pred, y_target)
(from (y_target, y_pred)), enabling the syntaxy_pred.accuracy_score(y)
. All existing error functions in Arraymancer were commutative w.r.t. to arguments so existing code will keep working. - a
solve
procedure has been added to solve linear system of equations represented as matrices. - a
softmax
layer has been added to the autograd and neural networks complementing the SoftmaxCrossEntropy layer which fused softmax + Negative-loglikelihood. - The stochastic gradient descent now has a version with Momentum
Bug fixes:
gemm
could crash when the result was column major.- The automatic fusion of matrix multiplication with matrix addition
(A * X) + b
could update the b matrix. - Complex converters do not pollute the global namespace and do not
prevent string covnersion via
$
of number types due to ambiguous call. - in-place division has been fixed, a typo made it into substraction.
- A conflict between NVIDIA "nanosecond" and Nim times module "nanosecond" preventing CUDA compilation has been fixed
Breaking
- In
symeig
, theeigenvectors
argument is now calledreturn_eigenvectors
. - In
symeig
with slice, the newuplo
precedes the slice argument. - pca input "nb_components" has been renamed "n_components".
- pca output tuple used the names (results, components). It has been renamed to (projected, components).
- A
pca
overload that projected a data matrix on already existing principal axes was removed. Simply multiply the mean-centered data matrix with the loadings instead. - Complex converters were removed. This prevents hard to debug and workaround implicit conversion bug in downstream library. If necessary, users can reimplement converters themselves. This also provides a 20% boost in Arraymancer compilation times
Deprecation:
- The syntax gemm(A, B, C) is now deprecated. Use explicit "gemm(1.0, A, B, 0.0, C)" instead. Arguably not zero-ing C could also be a reasonable default.
- The dot in broadcasting and elementwise operators has changed place
Use
+.
,*.
,/.
,-.
,^.
,+.=
,*.=
,/.=
,-.=
,^.=
instead of the previous order.+
and.+=
. This allows the broadcasting operators to have the same precedence as the natural operators. This also align Arraymancer with other Nim packages: Manu and NumericalNim
Thanks to @dynalagreen for the SGD with Momentum, @xcokazaki for spotting the in-place division typo, @Vindaar for fixing the automatic matrix multiplication and addition fusion, @Imperator26 for the Softmax layer, @brentp for reviewing and augmenting the SVD and PCA API, @auxym for the linear equation solver and @berquist for the reordering all error functions to the new API. Thanks @b3liever for suggesting the dot change to solve the precedence issue in broadcasting and elementwise operators.
Changes affecting backward compatibility:
- None
Changes:
- 0.20.x compatibility (commit 0921190)
- Complex support
Einsum
- Naive whitespace tokenizer for NLP
- Preview of Laser backend for matrix multiplication without SIMD autodetection (already 5x faster on integer matrix multiplication)
Fix:
- Fix height/width order when reading an image in tensor
Thanks to @chimez for the complex support and updating for 0.20, @metasyn for the tokenizer, @xcokazaki for the image dimension fix and @Vindaar for the einsum implemention
This release is named after "Sign of the Unicorn" (1975), the third book of Roger Zelazny masterpiece "The Chronicles of Amber".
Changes affecting backward compatibility:
- PCA has been split into 2
- The old PCA with input
pca(x: Tensor, nb_components: int)
now returns a tuple of result and principal components tensors in descending order instead of just a result - A new PCA
pca(x: Tensor, principal_axes: Tensor)
will project the input x on the principal axe supplied
- The old PCA with input
Changes:
-
Datasets:
- MNIST is now autodownloaded and cached
- Added IMDB Movie Reviews dataset
-
IO:
- Numpy file format support
- Image reading and writing support (jpg, bmp, png, tga)
- HDF5 reading and writing
-
Machine learning
- Kmeans clustering
-
Neural network and autograd:
- Support substraction, sum and stacking in neural networks
- Recurrent NN: GRUCell, GRU and Fused Stacked GRU support
- The NN declarative lang now supports GRU
- Added Embedding layer with up to 3D input tensors [batch_size, sequence_length, features] or [sequence_length, batch_size, features]. Indexing can be done with any sized integers, byte or chars and enums.
- Sparse softmax cross-entropy now supports target tensors with indices of type: any size integers, byte, chars or enums.
- Added ADAM optimiser (Adaptative Moment Estimation)
- Added Hadamard product backpropagation (Elementwise matrix multiply)
- Added Xavier Glorot, Kaiming He and Yann Lecun weight initialisations
- The NN declarative lang automatically initialises weights with the following scheme:
- Linear and Convolution: Kaiming (suitable for Relu activation)
- GRU: Xavier (suitable for the internal tanh and sigmoid)
- Embedding: Not supported in declarative lang at the moment
-
Tensors:
- Add tensor splitting and chunking
- Fancy indexing via
index_select
- division broadcasting, scalar division and multiplication broadcasting
- High-dimensional
toSeq
exports
-
End-to-end Examples:
- Sequence/mini time-series classification example using RNN
- Training and text generation example with Shakespeare and Jane Austen work. This can be applied to any text-based dataset (including blog posts, Latex papers and code). It should contain at least 700k characters (0.7 MB), this is considered small already.
-
Important fixes:
- Convolution shape inference on non-unit strided convolutions
- Support the future OpenMP changes from nim#devel
- GRU: inference was squeezing all singleton dimensions instead of just the "layer" dimension.
- Autograd: remove pointers to avoid pointing to wrong memory when the garbage collector moves it under pressure. This unfortunately comes at the cost of more GC pressure, this will be addressed in the future.
- Autograd: remove all methods. They caused issues with generic instantiation and object variants.
Special thanks to @metasyn (MNIST caching, IMDB dataset, Kmeans) and @Vindaar (HDF5 support and the example of using Arraymancer + Plot.ly) for their large contributions on this release.
Ecosystem:
-
Using Arraymancer + Plotly for NN training visualisation: https://github.com/Vindaar/NeuralNetworkLiveDemo
-
Monocle, proof-of-concept data visualisation in Nim using Vega. Hopefully allowing this kind of visualisation in the future:
and compatibility with the Vega ecosystem, especially the Tableau-like Voyager.
-
Agent Smith, reinforcement learning framework. Currently it wraps the
Arcade Learning Environment
for practicing reinforcement learning on Atari games. In the future it will wrap Starcraft 2 AI bindings and provides a high-level interface and examples to reinforcement learning algorithms. -
Laser, the future Arraymancer backend which provides:
- SIMD intrinsics
- OpenMP templates with fine-grained control
- Runtime CPU features detection for ARM and x86
- A proof-of-concept JIT Assembler
- A raw minimal tensor type which can work as a view to arbitrary buffers
- Loop fusion macros for iteration on an arbitrary number of tensors. As far as I know it should provide the fastest multi-threaded iteration scheme on strided tensors all languages and libraries included.
- Optimized reductions, exponential and logarithm functions reaching 4x to 10x the speed of naively compiled for loops
- Optimised parallel strided matrix multiplication reaching 98% of OpenBLAS performance
- This is a generic implementation that can also be used for integers
- It will support preprocessing (relu_backward, tanh_backward, sigmoid_backward) and epilogue (relu, tanh, sigmoid, bias addition) operation fusion to avoid looping an extra time with a memory bandwidth bound pass.
- Convolutions will be optimised with a preprocessing pass fused into matrix multiplication. Traditional
im2col
solutions can only reach 16% of matrix multiplication efficiency on the common deep learning filter sizes - State-of-the art random distributions and random sampling implementations for stochastic algorithms, text generation and reinforcement learning.
Future breaking changes.
-
Arraymancer backend will switch to
Laser
for next version. Impact:- At a low-level CPU tensors will become a view on top of a pointer+len fon old data types instead of using the default Nim seqs. This will enable plenty of no-copy use cases and even using memory-mapped tensors for out-of-core processing. However libraries relying on teh very low-level representation of tensors will break. The future type is already implemented in Laser.
- Tensors of GC-allocated types like seq, string and references will keep using Nim seqs.
- While it was possible to use the Javascript backend by modifying the iteration scheme this will not be possible at all. Use JS->C FFI or WebAssembly compilation instead.
- The inline iteration templates
map_inline
,map2_inline
,map3_inline
,apply_inline
,apply2_inline
,apply3_inline
,reduce_inline
,fold_inline
,fold_axis_inline
will be removed and replace byforEach
andforEachStaged
with the following syntax:
forEach x in a, y in b, z in c: x += y * z
Both will work with an arbitrary number of tensors and will generate 2x to 3x more compact code wile being about 30% more efficient for strided iteration. Furthermore
forEachStaged
will allow precise control of the parallelisation strategy including pre-loop and post-loop synchronisation with thread-local variables, locks, atomics and barriers. The existing higer-order functionsmap
,map2
,apply
,apply2
,fold
,reduce
will not be impacted. For small inlinable functions it will be recommended to use theforEach
macro to remove function call overhead (Yyou can't inline a proc parameter). -
The neural network domain specific language will use less magic for the
forward
proc. Currently the neural net domain specific language only allows the typeVariable[T]
for inputs and the result. This prevents its use with embedding layers which also requires an index input. Furthermore this prevents usingtuple[output, hidden: Variable]
result type which is very useful to pass RNNs hidden state for generative neural networks (for example text sequence or time-series). So unfortunately the syntax will go from the currentforward x, y:
shortcut to classic Nimproc forward[T](x, y: Variable[T]): Variable[T]
-
Once CuDNN GRU is implemented, the GRU layer might need some adjustments to give the same results on CPU and Nvidia's GPU and allow using GPU trained weights on CPU and vice-versa.
Thanks:
- metasyn: Datasets and Kmeans clustering
- vindaar: HDF5 support and Plot.ly demo
- bluenote10: toSeq exports
- andreaferetti: Adding axis parameter to Mean layer autograd
- all the contributors of fixes in code and documentation
- the Nim community for the encouragements
This release is named after "The Name of the Wind" (2007), the first book of Patrick Rothfuss masterpiece "The Kingkiller Chronicle".
Changes:
-
Core:
- OpenCL tensors are now available! However Arraymancer will naively select the first backend available. It can be CPU, it can be GPU. They support basic and broadcasted operations (Addition, matrix multiplication, elementwise multiplication, ...)
- Addition of an
argmax
andargmax_max
procs.
-
Datasets:
- Loading the MNIST dataset from http://yann.lecun.com/exdb/mnist/
- Reading and writing from CSV
-
Linear algebra:
- Least squares solver
- Eigenvalues and eigenvectors decomposition for symmetric matrices
-
Machine Learning
- Principal Component Analysis (PCA)
-
Statistics
- Computation of covariance matrices
-
Neural network
- Introduction of a short intuitive syntax to build neural networks! (A blend of Keras and PyTorch).
- Maxpool2D layer
- Mean Squared Error loss
- Tanh and softmax activation functions
-
Examples and tutorials
- Digit recognition using Convolutional Neural Net
- Teaching Fizzbuzz to a neural network
-
Tooling
- Plotting tensors through Python
Several updates linked to Nim rapid development and several bugfixes.
Thanks:
- Bluenote10 for the CSV writing proc and the tensor plotting tool
- Miran for benchmarking
- Manguluka for tanh
- Vindaar for bugfixing
- Every participants in RFCs
- And you user of the library.
This release is named after "Wizard's First Rule" (1994), the first book of Terry Goodkind masterpiece "The Sword of Truth".
I am very excited to announce the third release of Arraymancer which includes numerous improvements, features and (unfortunately!) breaking changes. Warning ⚠: Deprecated ALL procs will be removed next release due to deprecated spam and to reduce maintenance burden.
Changes:
-
Very Breaking
- Tensors uses reference semantics now:
let a = b
will share data by default and copies must be made explicitly.- There is no need to use
unsafe
proc to avoid copies especially for slices. - Unsafe procs are deprecated and will be removed leading to a smaller and simpler codebase and API/documentation.
- Tensors and CudaTensors now works the same way.
- Use
clone
to do copies. - Arraymancer now works like Numpy and Julia, making it easier to port code.
- Unfortunately it makes it harder to debug unexpected data sharing.
- There is no need to use
- Tensors uses reference semantics now:
-
Breaking (?)
- The max number of dimensions supported has been reduced from 8 to 7 to reduce cache misses. Note, in deep learning the max number of dimensions needed is 6 for 3D videos: [batch, time, color/feature channels, Depth, Height, Width]
-
Documentation
- Documentation has been completely revamped and is available here: https://mratsim.github.io/Arraymancer/
-
Huge performance improvements
- Use non-initialized seq
- shape and strides are now stored on the stack
- optimization via inlining all higher-order functions
apply_inline
,map_inline
,fold_inline
andreduce_inline
templates are available.
- all higher order functions are parallelized through OpenMP
- integer matrix multiplication uses SIMD, loop unrolling, restrict and 64-bit alignment
- prevent false sharing/cache contention in OpenMP reduction
- remove temporary copies in several proc
- runtime checks/exception are now behind
unlikely
A*B + C
andC+=A*B
are automatically fused in one operation- do not initialized result tensors
-
Neural network:
- Added
linear
,sigmoid_cross_entropy
,softmax_cross_entropy
layers - Added Convolution layer
- Added
-
Shapeshifting:
- Added
unsqueeze
andstack
- Added
-
Math:
- Added
min
,max
,abs
,reciprocal
,negate
and in-placemnegate
andmreciprocal
- Added
-
Statistics:
- Added variance and standard deviation
-
Broadcasting
- Added
.^
(broadcasted exponentiation)
- Added
-
Cuda:
- Support for convolution primitives: forward and backward
- Broadcasting ported to Cuda
-
Examples
- Added perceptron learning
xor
function example
- Added perceptron learning
-
Precision
- Arraymancer uses
ln1p
(ln(1 + x)
) andexp1m
procs (exp(1 - x)
) where appropriate to avoid catastrophic cancellation
- Arraymancer uses
-
Deprecated
- Version 0.3.1 with the ALL deprecated proc removed will be released in a week. Due to issue nim-lang/Nim#6436,
even using non-deprecated proc like
zeros
,ones
,newTensor
you will get a deprecated warning. newTensor
,zeros
,ones
arguments have been changed fromzeros([5, 5], int)
tozeros[int]([5, 5])
- All
unsafe
proc are now default and deprecated.
- Version 0.3.1 with the ALL deprecated proc removed will be released in a week. Due to issue nim-lang/Nim#6436,
even using non-deprecated proc like
This release is named after "The Colour of Magic" (1983), the first book of Terry Pratchett masterpiece "Discworld".
I am very excited to announce the second release of Arraymancer which includes numerous improvements blablabla
...
Without further ado:
-
community
- There is a Gitter room!
-
Breaking
shallowCopy
is nowunsafeView
and acceptslet
arguments- Element-wise multiplication is now
.*
instead of|*|
- vector dot product is now
dot
instead of.*
-
Deprecated
- All tensor initialization proc have their
Backend
parameter deprecated. fmap
is nowmap
agg
andagg_in_place
are nowfold
and nothing (too bad!)
- All tensor initialization proc have their
-
Initial support for Cuda !!!
- All linear algebra operations are supported
- Slicing (read-only) is supported
- Transforming a slice to a new contiguous Tensor is supported
-
Tensors
- Introduction of
unsafe
operations that works without copy:unsafeTranspose
,unsafeReshape
,unsafebroadcast
,unsafeBroadcast2
,unsafeContiguous
, - Implicit broadcasting via
.+, .*, ./, .-
and their in-place equivalent.+=, .-=, .*=, ./=
- Several shapeshifting operations:
squeeze
,at
and theirunsafe
version. - New property:
size
- Exporting:
export_tensor
andtoRawSeq
- Reduce and reduce on axis
- Introduction of
-
Ecosystem:
- I express my deep thanks to @edubart for testing Arraymancer, contributing new functions, and improving its overall performance. He built arraymancer-demos and arraymancer-vision,check those out you can load images in Tensor and do logistic regression on those!
Also thanks to the Nim community on IRC/Gitter, they are a tremendous help (yes Varriount, Yardanico, Zachary, Krux). I probably would have struggled a lot more without the guidance of Andrea's code for Cuda in his neo and nimcuda library. And obviously Araq and Dom for Nim which is an amazing language for performance, productivity, safety and metaprogramming.
This release is named after "Magician: Apprentice" (1982), the first book of Raymond E. Feist masterpiece "The Riftwar Cycle".
First public release.
Include:
- converting from deep nested proc or array
- Slicing, and slice mutation
- basic linear algebra operations,
- reshaping, broadcasting, concatenating,
- universal functions
- iterators (in-place, axis, inline and closure versions)
- BLAS and BLIS support for fast linear algebra