mollusk/jitty-scripts
1
1
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Add: julia-0.6.2

Browse Source 
Former-commit-id: ccc667cf67d569f3fb3df39aa57c2134755a7551
This commit is contained in:
mollusk
2018-02-10 10:27:19 -07:00
parent 94220957d7
commit 019f8e3064
723 changed files with 276164 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

424
julia-0.6.2/share/julia/base/asyncmap.jl Normal file
View File

@@ -0,0 +1,424 @@
# This file is a part of Julia. License is MIT: https://julialang.org/license
using Base.Iterators.Enumerate
"""
asyncmap(f, c...; ntasks=0, batch_size=nothing)
Uses multiple concurrent tasks to map `f` over a collection (or multiple
equal length collections). For multiple collection arguments, `f` is
applied elementwise.
`ntasks` specifies the number of tasks to run concurrently.
Depending on the length of the collections, if `ntasks` is unspecified,
up to 100 tasks will be used for concurrent mapping.
`ntasks` can also be specified as a zero-arg function. In this case, the
number of tasks to run in parallel is checked before processing every element and a new
task started if the value of `ntasks_func()` is less than the current number
of tasks.
If `batch_size` is specified, the collection is processed in batch mode. `f` must
then be a function that must accept a `Vector` of argument tuples and must
return a vector of results. The input vector will have a length of `batch_size` or less.
The following examples highlight execution in different tasks by returning
the `object_id` of the tasks in which the mapping function is executed.
First, with `ntasks` undefined, each element is processed in a different task.
```
julia> tskoid() = object_id(current_task());
julia> asyncmap(x->tskoid(), 1:5)
5-element Array{UInt64,1}:
0x6e15e66c75c75853
0x440f8819a1baa682
0x9fb3eeadd0c83985
0xebd3e35fe90d4050
0x29efc93edce2b961
julia> length(unique(asyncmap(x->tskoid(), 1:5)))
5
```
With `ntasks=2` all elements are processed in 2 tasks.
```
julia> asyncmap(x->tskoid(), 1:5; ntasks=2)
5-element Array{UInt64,1}:
0x027ab1680df7ae94
0xa23d2f80cd7cf157
0x027ab1680df7ae94
0xa23d2f80cd7cf157
0x027ab1680df7ae94
julia> length(unique(asyncmap(x->tskoid(), 1:5; ntasks=2)))
2
```
With `batch_size` defined, the mapping function needs to be changed to accept an array
of argument tuples and return an array of results. `map` is used in the modified mapping
function to achieve this.
```
julia> batch_func(input) = map(x->string("args_tuple: ", x, ", element_val: ", x[1], ", task: ", tskoid()), input)
batch_func (generic function with 1 method)
julia> asyncmap(batch_func, 1:5; ntasks=2, batch_size=2)
5-element Array{String,1}:
"args_tuple: (1,), element_val: 1, task: 9118321258196414413"
"args_tuple: (2,), element_val: 2, task: 4904288162898683522"
"args_tuple: (3,), element_val: 3, task: 9118321258196414413"
"args_tuple: (4,), element_val: 4, task: 4904288162898683522"
"args_tuple: (5,), element_val: 5, task: 9118321258196414413"
```
!!! note
Currently, all tasks in Julia are executed in a single OS thread co-operatively. Consequently,
`ayncmap` is beneficial only when the mapping function involves any I/O - disk, network, remote
worker invocation, etc.
"""
function asyncmap(f, c...; ntasks=0, batch_size=nothing)
return async_usemap(f, c...; ntasks=ntasks, batch_size=batch_size)
end
function async_usemap(f, c...; ntasks=0, batch_size=nothing)
ntasks = verify_ntasks(c[1], ntasks)
batch_size = verify_batch_size(batch_size)
if batch_size !== nothing
exec_func = batch -> begin
# extract the Refs from the input tuple
batch_refs = map(x->x[1], batch)
# and the args tuple....
batched_args = map(x->x[2], batch)
results = f(batched_args)
foreach(x -> (batch_refs[x[1]].x = x[2]), enumerate(results))
end
else
exec_func = (r,args) -> (r.x = f(args...))
end
chnl, worker_tasks = setup_chnl_and_tasks(exec_func, ntasks, batch_size)
return wrap_n_exec_twice(chnl, worker_tasks, ntasks, exec_func, c...)
end
batch_size_err_str(batch_size) = string("batch_size must be specified as a positive integer. batch_size=", batch_size)
function verify_batch_size(batch_size)
if batch_size === nothing
return batch_size
elseif isa(batch_size, Number)
batch_size = Int(batch_size)
batch_size < 1 && throw(ArgumentError(batch_size_err_str(batch_size)))
return batch_size
else
throw(ArgumentError(batch_size_err_str(batch_size)))
end
end
function verify_ntasks(iterable, ntasks)
if !((isa(ntasks, Number) && (ntasks >= 0)) || isa(ntasks, Function))
err = string("ntasks must be specified as a positive integer or a 0-arg function. ntasks=", ntasks)
throw(ArgumentError(err))
end
if ntasks == 0
chklen = iteratorsize(iterable)
if (chklen == HasLength()) || (chklen == HasShape())
ntasks = max(1,min(100, length(iterable)))
else
ntasks = 100
end
end
return ntasks
end
function wrap_n_exec_twice(chnl, worker_tasks, ntasks, exec_func, c...)
# The driver task, creates a Ref object and writes it and the args tuple to
# the communication channel for processing by a free worker task.
push_arg_to_channel = (x...) -> (r=Ref{Any}(nothing); put!(chnl,(r,x));r)
if isa(ntasks, Function)
map_f = (x...) -> begin
# check number of tasks every time, and start one if required.
# number_tasks > optimal_number is fine, the other way around is inefficient.
if length(worker_tasks) < ntasks()
start_worker_task!(worker_tasks, exec_func, chnl)
end
push_arg_to_channel(x...)
end
else
map_f = push_arg_to_channel
end
maptwice(map_f, chnl, worker_tasks, c...)
end
function maptwice(wrapped_f, chnl, worker_tasks, c...)
# first run, returns a collection of Refs
asyncrun_excp = nothing
local asyncrun
try
asyncrun = map(wrapped_f, c...)
catch ex
if isa(ex,InvalidStateException)
# channel could be closed due to exceptions in the async tasks,
# we propagate those errors, if any, over the `put!` failing
# in asyncrun due to a closed channel.
asyncrun_excp = ex
else
rethrow(ex)
end
end
# close channel and wait for all worker tasks to finish
close(chnl)
# check and throw any exceptions from the worker tasks
foreach(x->(v=wait(x); isa(v, Exception) && throw(v)), worker_tasks)
# check if there was a genuine problem with asyncrun
(asyncrun_excp !== nothing) && throw(asyncrun_excp)
if isa(asyncrun, Ref)
# scalar case
return asyncrun.x
else
# second run, extract values from the Refs and return
return map(ref->ref.x, asyncrun)
end
end
function setup_chnl_and_tasks(exec_func, ntasks, batch_size=nothing)
if isa(ntasks, Function)
nt = ntasks()
# start at least one worker task.
if nt == 0
nt = 1
end
else
nt = ntasks
end
# Use an unbuffered channel for communicating with the worker tasks. In the event
# of an error in any of the worker tasks, the channel is closed. This
# results in the `put!` in the driver task failing immediately.
chnl = Channel(0)
worker_tasks = []
foreach(_ -> start_worker_task!(worker_tasks, exec_func, chnl, batch_size), 1:nt)
yield()
return (chnl, worker_tasks)
end
function start_worker_task!(worker_tasks, exec_func, chnl, batch_size=nothing)
t = @schedule begin
retval = nothing
try
if isa(batch_size, Number)
while isopen(chnl)
# The mapping function expects an array of input args, as it processes
# elements in a batch.
batch_collection=Any[]
n = 0
for exec_data in chnl
push!(batch_collection, exec_data)
n += 1
(n == batch_size) && break
end
if n > 0
exec_func(batch_collection)
end
end
else
for exec_data in chnl
exec_func(exec_data...)
end
end
catch e
close(chnl)
retval = e
end
retval
end
push!(worker_tasks, t)
end
# Special handling for some types.
function asyncmap(f, s::AbstractString; kwargs...)
s2 = Array{Char,1}(length(s))
asyncmap!(f, s2, s; kwargs...)
return convert(String, s2)
end
# map on a single BitArray returns a BitArray if the mapping function is boolean.
function asyncmap(f, b::BitArray; kwargs...)
b2 = async_usemap(f, b; kwargs...)
if eltype(b2) == Bool
return BitArray(b2)
end
return b2
end
# TODO: Optimize for sparse arrays
# For now process as regular arrays and convert back
function asyncmap(f, s::AbstractSparseArray...; kwargs...)
sa = map(Array, s)
return sparse(asyncmap(f, sa...; kwargs...))
end
mutable struct AsyncCollector
f
results
enumerator::Enumerate
ntasks
batch_size
nt_check::Bool # check number of tasks on every iteration
AsyncCollector(f, r, en::Enumerate, ntasks, batch_size) = new(f, r, en, ntasks, batch_size, isa(ntasks, Function))
end
"""
AsyncCollector(f, results, c...; ntasks=0, batch_size=nothing) -> iterator
Returns an iterator which applies `f` to each element of `c` asynchronously
and collects output into `results`.
Keyword args `ntasks` and `batch_size` have the same behavior as in
[`asyncmap()`](@ref). If `batch_size` is specified, `f` must
be a function which operates on an array of argument tuples.
!!! note
`next(::AsyncCollector, state) -> (nothing, state)`. A successful return
from `next` indicates that the next element from the input collection is
being processed asynchronously. It blocks until a free worker task becomes
available.
!!! note
`for _ in AsyncCollector(f, results, c...; ntasks=1) end` is equivalent to
`map!(f, results, c...)`.
"""
function AsyncCollector(f, results, c...; ntasks=0, batch_size=nothing)
AsyncCollector(f, results, enumerate(zip(c...)), ntasks, batch_size)
end
mutable struct AsyncCollectorState
chnl::Channel
worker_tasks::Array{Task,1}
enum_state # enumerator state
end
function start(itr::AsyncCollector)
itr.ntasks = verify_ntasks(itr.enumerator, itr.ntasks)
itr.batch_size = verify_batch_size(itr.batch_size)
if itr.batch_size !== nothing
exec_func = batch -> begin
# extract indexes from the input tuple
batch_idxs = map(x->x[1], batch)
# and the args tuple....
batched_args = map(x->x[2], batch)
results = f(batched_args)
foreach(x -> (itr.results[batch_idxs[x[1]]] = x[2]), enumerate(results))
end
else
exec_func = (i,args) -> (itr.results[i]=itr.f(args...))
end
chnl, worker_tasks = setup_chnl_and_tasks((i,args) -> (itr.results[i]=itr.f(args...)), itr.ntasks, itr.batch_size)
return AsyncCollectorState(chnl, worker_tasks, start(itr.enumerator))
end
function done(itr::AsyncCollector, state::AsyncCollectorState)
if !isopen(state.chnl) || done(itr.enumerator, state.enum_state)
close(state.chnl)
# wait for all tasks to finish
foreach(x->(v=wait(x); isa(v, Exception) && throw(v)), state.worker_tasks)
empty!(state.worker_tasks)
return true
else
return false
end
end
function next(itr::AsyncCollector, state::AsyncCollectorState)
if itr.nt_check && (length(state.worker_tasks) < itr.ntasks())
start_worker_task!(state.worker_tasks, itr.f, state.chnl)
end
# Get index and mapped function arguments from enumeration iterator.
(i, args), state.enum_state = next(itr.enumerator, state.enum_state)
put!(state.chnl, (i, args))
return (nothing, state)
end
"""
AsyncGenerator(f, c...; ntasks=0, batch_size=nothing) -> iterator
Apply `f` to each element of `c` using at most `ntasks` asynchronous tasks.
Keyword args `ntasks` and `batch_size` have the same behavior as in
[`asyncmap()`](@ref). If `batch_size` is specified, `f` must
be a function which operates on an array of argument tuples.
!!! note
`collect(AsyncGenerator(f, c...; ntasks=1))` is equivalent to
`map(f, c...)`.
"""
mutable struct AsyncGenerator
collector::AsyncCollector
end
function AsyncGenerator(f, c...; ntasks=0)
AsyncGenerator(AsyncCollector(f, Dict{Int,Any}(), c...; ntasks=ntasks))
end
mutable struct AsyncGeneratorState
i::Int
collector_state::AsyncCollectorState
end
start(itr::AsyncGenerator) = AsyncGeneratorState(0, start(itr.collector))
# Done when source async collector is done and all results have been consumed.
function done(itr::AsyncGenerator, state::AsyncGeneratorState)
done(itr.collector, state.collector_state) && isempty(itr.collector.results)
end
function next(itr::AsyncGenerator, state::AsyncGeneratorState)
state.i += 1
results_dict = itr.collector.results
while !haskey(results_dict, state.i)
if done(itr.collector, state.collector_state)
# `done` waits for async tasks to finish. if we do not have the index
# we are looking for, it is an error.
!haskey(results_dict, state.i) && error("Error processing index ", i)
break;
end
_, state.collector_state = next(itr.collector, state.collector_state)
end
r = results_dict[state.i]
delete!(results_dict, state.i)
return (r, state)
end
# pass-through iterator traits to the iterable
# on which the mapping function is being applied
iteratorsize(itr::AsyncGenerator) = iteratorsize(itr.collector.enumerator)
size(itr::AsyncGenerator) = size(itr.collector.enumerator)
length(itr::AsyncGenerator) = length(itr.collector.enumerator)
"""
asyncmap!(f, results, c...; ntasks=0, batch_size=nothing)
Like [`asyncmap()`](@ref), but stores output in `results` rather than
returning a collection.
"""
function asyncmap!(f, r, c1, c...; ntasks=0, batch_size=nothing)
foreach(identity, AsyncCollector(f, r, c1, c...; ntasks=ntasks, batch_size=batch_size))
r
end