Julia: deprecate mx.empty, replace it with UndefInitializer constructor

julia/NEWS.md

@@ -1,11 +1,10 @@
-# v0.4.0 (#TBD)
+# v1.5.0 (#TBD)
 
 * Following material from `mx` module got exported (#TBD):
     * `NDArray`
         * `clip()`
         * `clip!()`
         * `context()`
-        * `empty()`
         * `expand_dims()`
         * `@inplace`
         * `σ()`
@@ -113,6 +112,16 @@
    3.0
   ```
 
+* `mx.empty` is deprecated and replaced by `UndefInitializer` constructor. (#TBD)
+
+  E.g.
+  ```julia
+  julia> NDArray(undef, 2, 5)
+  2×5 NDArray{Float32,2} @ CPU0:
+   -21260.344f0     1.674986f19    0.00016893122f0  1.8363f-41  0.0f0
+        3.0763f-41  1.14321726f27  4.24219f-8       0.0f0       0.0f0
+  ```
+
 * A port of Python's `autograd` for `NDArray` (#274)
 
 * `size(x, dims...)` is supported now. (#TBD)

julia/docs/src/user-guide/overview.md

@@ -73,9 +73,13 @@ operators in Julia directly.
 
 The followings are common ways to create `NDArray` objects:
 
-- `mx.empty(shape[, context])`: create on uninitialized array of a
-  given shape on a specific device. For example,
-  `mx.empty(2, 3)`, `mx.((2, 3), mx.gpu(2))`.
+- `NDArray(undef, shape...; ctx = context, writable = true)`:
+  create an uninitialized array of a given shape on a specific device.
+  For example,
+  `NDArray(undef, 2, 3)`, `NDArray(undef, 2, 3, ctx = mx.gpu(2))`.
+- `NDArray(undef, shape; ctx = context, writable = true)`
+- `NDArray{T}(undef, shape...; ctx = context, writable = true)`:
+  create an uninitialized with the given type `T`.
 - `mx.zeros(shape[, context])` and `mx.ones(shape[, context])`:
   similar to the Julia's built-in `zeros` and `ones`.
 - `mx.copy(jl_arr, context)`: copy the contents of a Julia `Array` to
@@ -101,11 +105,11 @@ shows a way to set the contents of an `NDArray`.
 ```@repl
 using MXNet
 mx.srand(42)
-a = mx.empty(2, 3)
+a = NDArray(undef, 2, 3)
 a[:] = 0.5              # set all elements to a scalar
 a[:] = rand(size(a))    # set contents with a Julia Array
 copy!(a, rand(size(a))) # set value by copying a Julia Array
-b = mx.empty(size(a))
+b = NDArray(undef, size(a))
 b[:] = a                # copying and assignment between NDArrays
 ```
 
@@ -175,7 +179,7 @@ function inplace_op()
   grad   = mx.ones(SHAPE, CTX)
 
   # pre-allocate temp objects
-  grad_lr = mx.empty(SHAPE, CTX)
+  grad_lr = NDArray(undef, SHAPE, ctx = CTX)
 
   for i = 1:N_REP
     copy!(grad_lr, grad)
@@ -234,7 +238,7 @@ shape = (2, 3)
 key   = 3
 
 mx.init!(kv, key, mx.ones(shape) * 2)
-a = mx.empty(shape)
+a = NDArray(undef, shape)
 mx.pull!(kv, key, a) # pull value into a
 a
 ```

julia/src/MXNet.jl

@@ -53,7 +53,6 @@ export NDArray,
        clip,
        clip!,
        context,
-       empty,
        expand_dims,
        @inplace,
        # activation funcs

julia/src/deprecated.jl

@@ -169,3 +169,28 @@ import Base: sum, maximum, minimum, prod, cat
 
 import Statistics: mean
 @deprecate mean(x::NDArray, dims) mean(x, dims = dims)
+
+# replaced by UndefInitializer
+function empty(::Type{T}, dims::NTuple{N,Int}, ctx::Context = cpu()) where {N,T<:DType}
+  @warn("`mx.empty(T, dims, ctx)` is deprecated, " *
+        "use `NDArray{T,N}(undef, dims; ctx = ctx)` instead.")
+  NDArray{T,N}(undef, dims; ctx = ctx)
+end
+
+function empty(::Type{T}, dims::Int...) where {T<:DType}
+  @warn("`mx.empty(T, dims...)` is deprecated, " *
+        "use `NDArray{T,N}(undef, dims...)` instead.")
+  NDArray{T,N}(undef, dims...)
+end
+
+function empty(dims::NTuple{N,Int}, ctx::Context = cpu()) where N
+  @warn("`mx.empty(dims, ctx)` is deprecated, " *
+        "use `NDArray(undef, dims; ctx = ctx)` instead.")
+  NDArray(undef, dims; ctx = ctx)
+end
+
+function empty(dims::Int...)
+  @warn("`mx.empty(dims...)` is deprecated, " *
+        "use `NDArray(undef, dims...)` instead.")
+  NDArray(undef, dims...)
+end

julia/src/io.jl

@@ -360,7 +360,7 @@ function ArrayDataProvider(data, label; batch_size::Int = 0, shuffle::Bool = fal
   function gen_batch_nds(arrs :: Vector{Array{MX_float}}, bsize :: Int)
     map(arrs) do arr
       shape = size(arr)
-      empty(shape[1:end-1]..., bsize)
+      NDArray(undef, shape[1:end-1]..., bsize)
     end
   end
 

julia/src/kvstore.jl

@@ -128,7 +128,7 @@ One can use ``barrier()`` to sync all workers.
 julia> kv = KVStore(:local)
 mx.KVStore @ local
 
-julia> x = mx.empty(2, 3);
+julia> x = NDArray(undef, 2, 3);
 
 julia> init!(kv, 3, x)
 
@@ -161,11 +161,11 @@ julia> x
 ```jldoctest
 julia> keys = [4, 5];
 
-julia> init!(kv, keys, [empty(2, 3), empty(2, 3)])
+julia> init!(kv, keys, [NDArray(undef, 2, 3), NDArray(undef, 2, 3)])
 
 julia> push!(kv, keys, [x, x])
 
-julia> y, z = empty(2, 3), empty(2, 3);
+julia> y, z = NDArray(undef, 2, 3), NDArray(undef, 2, 3);
 
 julia> pull!(kv, keys, [y, z])
 ```
@@ -279,7 +279,7 @@ julia> init!(kv, 42, mx.ones(2, 3))
 
 julia> push!(kv, 42, mx.ones(2, 3))
 
-julia> x = empty(2, 3);
+julia> x = NDArray(undef, 2, 3);
 
 julia> pull!(kv, 42, x)
 

julia/src/model.jl

@@ -122,7 +122,7 @@ function init_model(self::FeedForward, initializer::AbstractInitializer; overwri
         delete!(self.arg_params, name)
       end
     end
-    arg_params[name] = empty(shape)
+    arg_params[name] = NDArray(undef, shape)
   end
 
   for (name, shape) in zip(aux_names, aux_shapes)
@@ -135,7 +135,7 @@ function init_model(self::FeedForward, initializer::AbstractInitializer; overwri
         delete!(self.aux_params, name)
       end
     end
-    aux_params[name] = empty(shape)
+    aux_params[name] = NDArray(undef, shape)
   end
 
   for (k,v) in arg_params
@@ -463,8 +463,8 @@ function fit(self::FeedForward, optimizer::AbstractOptimizer, data::AbstractData
   # set up output and labels in CPU for evaluation metric
   output_shapes = [tuple(size(x)[1:end-1]...,batch_size) for x in train_execs[1].outputs]
   cpu_dev = Context(CPU)
-  cpu_output_arrays = [empty(shape, cpu_dev) for shape in output_shapes]
-  cpu_label_arrays  = [empty(shape, cpu_dev) for (name,shape) in provide_label(data)]
+  cpu_output_arrays = [NDArray(undef, shape, ctx = cpu_dev) for shape in output_shapes]
+  cpu_label_arrays  = [NDArray(undef, shape, ctx = cpu_dev) for (name,shape) in provide_label(data)]
 
   # invoke callbacks on epoch 0
   _invoke_callbacks(self, opts.callbacks, op_state, AbstractEpochCallback)

julia/src/ndarray.jl

@@ -110,20 +110,47 @@ mutable struct NDArray{T,N}
   handle   :: MX_NDArrayHandle
   writable :: Bool
 
-  NDArray{T,N}(handle, writable = true) where {T,N} = new(handle, writable)
+  NDArray{T,N}(handle::MX_NDArrayHandle, writable::Bool = true) where {T,N} =
+    new(handle, writable)
 end
 
+# UndefInitializer constructors
+NDArray{T,N}(::UndefInitializer, dims::NTuple{N,Integer};
+             writable = true, ctx::Context = cpu()) where {T,N} =
+  NDArray{T,N}(_ndarray_alloc(T, dims, ctx, false), writable)
+NDArray{T,N}(::UndefInitializer, dims::Vararg{Integer,N}; kw...) where {T,N} =
+  NDArray{T,N}(undef, dims; kw...)
+
+NDArray{T}(::UndefInitializer, dims::NTuple{N,Integer}; kw...) where {T,N} =
+  NDArray{T,N}(undef, dims; kw...)
+NDArray{T}(::UndefInitializer, dims::Vararg{Integer,N}; kw...) where {T,N} =
+  NDArray{T,N}(undef, dims; kw...)
+
+NDArray(::UndefInitializer, dims::NTuple{N,Integer}; kw...) where {N} =
+  NDArray{DEFAULT_DTYPE,N}(undef, dims; kw...)
+NDArray(::UndefInitializer, dims::Vararg{Integer,N}; kw...) where {N} =
+  NDArray{DEFAULT_DTYPE,N}(undef, dims; kw...)
+
 NDArray(x::AbstractArray{<:DType}) = copy(collect(x), cpu())
 NDArray(x::Array{<:DType})         = copy(x, cpu())
+
 NDArray(::Type{T}, x::AbstractArray) where {T<:DType} =
   copy(convert(AbstractArray{T}, x), cpu())
+
 NDArray(handle, writable = true) =
   NDArray{eltype(handle), ndims(handle)}(handle, writable)
 
 # type aliases
 const NDArrayOrReal = Union{NDArray,Real}
 const VecOfNDArray = AbstractVector{<:NDArray}
 
+Base.unsafe_convert(::Type{MX_handle}, x::NDArray) =
+  Base.unsafe_convert(MX_handle, x.handle)
+Base.convert(T::Type{MX_handle}, x::NDArray) = Base.unsafe_convert(T, x)
+Base.cconvert(T::Type{MX_handle}, x::NDArray) = Base.unsafe_convert(T, x)
+
+MX_handle(x::NDArray) = Base.convert(MX_handle, x)
+
 function Base.show(io::IO, x::NDArray)
   print(io, "NDArray(")
   Base.show(io, try_get_shared(x, sync = :read))
@@ -139,13 +166,6 @@ function Base.show(io::IO, ::MIME{Symbol("text/plain")}, x::NDArray{T,N}) where
   Base.print_array(io, try_get_shared(x, sync = :read))
 end
 
-Base.unsafe_convert(::Type{MX_handle}, x::NDArray) =
-  Base.unsafe_convert(MX_handle, x.handle)
-Base.convert(T::Type{MX_handle}, x::NDArray) = Base.unsafe_convert(T, x)
-Base.cconvert(T::Type{MX_handle}, x::NDArray) = Base.unsafe_convert(T, x)
-
-MX_handle(x::NDArray) = Base.convert(MX_handle, x)
-
 ################################################################################
 # NDArray functions exported to the users
 ################################################################################
@@ -163,34 +183,14 @@ function context(x::NDArray)
 end
 
 """
-    empty(DType, dims[, ctx::Context = cpu()])
-    empty(DType, dims)
-    empty(DType, dim1, dim2, ...)
-
-Allocate memory for an uninitialized `NDArray` with a specified type.
-"""
-empty(::Type{T}, dims::NTuple{N,Int}, ctx::Context = cpu()) where {N,T<:DType} =
-  NDArray{T,N}(_ndarray_alloc(T, dims, ctx, false))
-empty(::Type{T}, dims::Int...) where {T<:DType} = empty(T, dims)
-
-"""
-    empty(dims::Tuple[, ctx::Context = cpu()])
-    empty(dim1, dim2, ...)
-
-Allocate memory for an uninitialized `NDArray` with specific shape of type Float32.
-"""
-empty(dims::NTuple{N,Int}, ctx::Context = cpu()) where N =
-  NDArray(_ndarray_alloc(dims, ctx, false))
-empty(dims::Int...) = empty(dims)
-
-"""
-    similar(x::NDArray)
+    similar(x::NDArray; writable, ctx)
 
 Create an `NDArray` with similar shape, data type,
 and context with the given one.
 Note that the returned `NDArray` is uninitialized.
 """
-Base.similar(x::NDArray{T}) where {T} = empty(T, size(x), context(x))
+Base.similar(x::NDArray{T,N}; writable = x.writable, ctx = context(x)) where {T,N} =
+  NDArray{T,N}(undef, size(x)...; writable = writable, ctx = ctx)
 
 """
     zeros([DType], dims, [ctx::Context = cpu()])
@@ -200,7 +200,7 @@ Base.similar(x::NDArray{T}) where {T} = empty(T, size(x), context(x))
 Create zero-ed `NDArray` with specific shape and type.
 """
 function zeros(::Type{T}, dims::NTuple{N,Int}, ctx::Context = cpu()) where {N,T<:DType}
-  x = empty(T, dims, ctx)
+  x = NDArray{T}(undef, dims..., ctx = ctx)
   x[:] = zero(T)
   x
 end
@@ -222,7 +222,7 @@ Base.zeros(x::NDArray)::typeof(x) = zeros_like(x)
 Create an `NDArray` with specific shape & type, and initialize with 1.
 """
 function ones(::Type{T}, dims::NTuple{N,Int}, ctx::Context = cpu()) where {N,T<:DType}
-  arr = empty(T, dims, ctx)
+  arr = NDArray{T}(undef, dims..., ctx = ctx)
   arr[:] = one(T)
   arr
 end
@@ -504,10 +504,10 @@ copy(x::NDArray{T,D}, ctx::Context) where {T,D} =
 
 # Create copy: Julia Array -> NDArray in a given context
 copy(x::Array{T}, ctx::Context) where {T<:DType} =
-  copy!(empty(T, size(x), ctx), x)
+  copy!(NDArray{T}(undef, size(x); ctx = ctx), x)
 
 copy(x::AbstractArray, ctx::Context) =
-  copy!(empty(eltype(x), size(x), ctx), collect(x))
+  copy!(NDArray{eltype(x)}(undef, size(x); ctx = ctx), collect(x))
 
 """
     convert(::Type{Array{<:Real}}, x::NDArray)
@@ -866,8 +866,8 @@ end
 
 Create an `NDArray` filled with the value `x`, like `Base.fill`.
 """
-function fill(x, dims::NTuple{N,Integer}, ctx::Context=cpu()) where N
-  arr = empty(typeof(x), dims, ctx)
+function fill(x::T, dims::NTuple{N,Integer}, ctx::Context = cpu()) where {T,N}
+  arr = NDArray{T}(undef, dims, ctx = ctx)
   arr[:] = x
   arr
 end

julia/src/random.jl

@@ -23,12 +23,12 @@ Samples are uniformly distributed over the half-open interval [low, high)
 (includes low, but excludes high).
 
 ```julia
-julia> mx.rand!(empty(2, 3))
+julia> mx.rand!(NDArray(undef, 2, 3))
 2×3 mx.NDArray{Float32,2} @ CPU0:
  0.385748   0.839275  0.444536
  0.0879585  0.215928  0.104636
 
-julia> mx.rand!(empty(2, 3), low = 1, high = 10)
+julia> mx.rand!(NDArray(undef, 2, 3), low = 1, high = 10)
 2×3 mx.NDArray{Float32,2} @ CPU0:
  6.6385   4.18888  2.07505
  8.97283  2.5636   1.95586
@@ -56,8 +56,8 @@ julia> mx.rand(2, 2; low = 1, high = 10)
  9.81258  3.58068
 ```
 """
-rand(dims::Int...; low = 0, high = 1, context = cpu()) =
-  rand!(empty(dims, context), low = low, high = high)
+rand(dims::Integer...; low = 0, high = 1, context = cpu()) =
+  rand!(NDArray(undef, dims, ctx = context), low = low, high = high)
 
 """
     randn!(x::NDArray; μ = 0, σ = 1)
@@ -73,7 +73,7 @@ randn!(x::NDArray; μ = 0, σ = 1) =
 Draw random samples from a normal (Gaussian) distribution.
 """
 randn(dims::Int...; μ = 0, σ = 1, context = cpu()) =
-  randn!(empty(dims, context), μ = μ, σ = σ)
+  randn!(NDArray(undef, dims, ctx = context), μ = μ, σ = σ)
 
 """
     seed!(seed::Int)

julia/test/unittest/bind.jl

@@ -33,10 +33,10 @@ function test_arithmetic(::Type{T}, uf, gf) where T <: mx.DType
   ret = uf(lhs, rhs)
   @test mx.list_arguments(ret) == [:lhs, :rhs]
 
-  lhs_arr  = mx.NDArray(rand(T, shape))
-  rhs_arr  = mx.NDArray(rand(T, shape))
-  lhs_grad = mx.empty(T, shape)
-  rhs_grad = mx.empty(T, shape)
+  lhs_arr  = NDArray(rand(T, shape))
+  rhs_arr  = NDArray(rand(T, shape))
+  lhs_grad = NDArray{T}(undef, shape)
+  rhs_grad = NDArray{T}(undef, shape)
 
   exec2 = mx.bind(ret, mx.Context(mx.CPU), [lhs_arr, rhs_arr], args_grad=[lhs_grad, rhs_grad])
   exec3 = mx.bind(ret, mx.Context(mx.CPU), [lhs_arr, rhs_arr])

julia/test/unittest/io.jl

@@ -38,8 +38,8 @@ function test_mnist()
   n_batch = 0
   for batch in mnist_provider
     if n_batch == 0
-      data_array  = mx.empty(28,28,1,batch_size)
-      label_array = mx.empty(batch_size)
+      data_array  = NDArray(undef, 28, 28, 1, batch_size)
+      label_array = NDArray(undef, batch_size)
       # have to use "for i=1:1" to get over the legacy "feature" of using
       # [ ] to do concatenation in Julia
       data_targets = [[(1:batch_size, data_array)] for i = 1:1]

julia/test/unittest/kvstore.jl

@@ -47,7 +47,7 @@ function test_single_kv_pair()
 
   kv = init_kv()
   mx.push!(kv, 3, mx.ones(SHAPE))
-  val = mx.empty(SHAPE)
+  val = NDArray(undef, SHAPE)
   mx.pull!(kv, 3, val)
   @test maximum(abs.(copy(val) .- 1)) == 0
 end