Disable multithreading when seq_ends is passed as a tuple

examples/controlled.jl

@@ -94,7 +94,7 @@ function StatsAPI.fit!(
     fb_storage::HMMs.ForwardBackwardStorage,
     obs_seq::AbstractVector,
     control_seq::AbstractVector;
-    seq_ends::AbstractVector{Int},
+    seq_ends,
 ) where {T}
     (; γ, ξ) = fb_storage
     N = length(hmm)

examples/interfaces.jl

@@ -186,7 +186,7 @@ function StatsAPI.fit!(
     hmm::PriorHMM,
     fb_storage::HiddenMarkovModels.ForwardBackwardStorage,
     obs_seq::AbstractVector;
-    seq_ends::AbstractVector{Int},
+    seq_ends,
 )
     ## initialize to defaults without observations
     hmm.init .= 0

examples/temporal.jl

@@ -109,7 +109,7 @@ function StatsAPI.fit!(
     fb_storage::HMMs.ForwardBackwardStorage,
     obs_seq::AbstractVector,
     control_seq::AbstractVector;
-    seq_ends::AbstractVector{Int},
+    seq_ends,
 ) where {T}
     (; γ, ξ) = fb_storage
     L, N = period(hmm), length(hmm)

libs/HMMTest/src/HMMTest.jl

@@ -2,6 +2,7 @@ module HMMTest
 
 using BenchmarkTools: @ballocated
 using HiddenMarkovModels
+using HiddenMarkovModels: AbstractVectorOrNTuple
 import HiddenMarkovModels as HMMs
 using HMMBase: HMMBase
 using JET: @test_opt, @test_call

libs/HMMTest/src/allocations.jl

@@ -3,7 +3,7 @@ function test_allocations(
     rng::AbstractRNG,
     hmm::AbstractHMM,
     control_seq::AbstractVector;
-    seq_ends::AbstractVector{Int},
+    seq_ends::AbstractVectorOrNTuple{Int},
     hmm_guess::Union{Nothing,AbstractHMM}=nothing,
 )
     @testset "Allocations" begin

libs/HMMTest/src/coherence.jl

@@ -55,7 +55,7 @@ function test_coherent_algorithms(
     rng::AbstractRNG,
     hmm::AbstractHMM,
     control_seq::AbstractVector;
-    seq_ends::AbstractVector{Int},
+    seq_ends::AbstractVectorOrNTuple{Int},
     hmm_guess::Union{Nothing,AbstractHMM}=nothing,
     atol::Real=0.05,
     init::Bool=true,

libs/HMMTest/src/jet.jl

@@ -3,7 +3,7 @@ function test_type_stability(
     rng::AbstractRNG,
     hmm::AbstractHMM,
     control_seq::AbstractVector;
-    seq_ends::AbstractVector{Int},
+    seq_ends::AbstractVectorOrNTuple{Int},
     hmm_guess::Union{Nothing,AbstractHMM}=nothing,
 )
     @testset "Type stability" begin

src/inference/baum_welch.jl

@@ -22,7 +22,7 @@ function baum_welch!(
     hmm::AbstractHMM,
     obs_seq::AbstractVector,
     control_seq::AbstractVector;
-    seq_ends::AbstractVector{Int},
+    seq_ends::AbstractVectorOrNTuple{Int},
     atol::Real,
     max_iterations::Integer,
     loglikelihood_increasing::Bool,
@@ -55,7 +55,7 @@ function baum_welch(
     hmm_guess::AbstractHMM,
     obs_seq::AbstractVector,
     control_seq::AbstractVector=Fill(nothing, length(obs_seq));
-    seq_ends::AbstractVector{Int}=Fill(length(obs_seq), 1),
+    seq_ends::AbstractVectorOrNTuple{Int}=(length(obs_seq),),
     atol=1e-5,
     max_iterations=100,
     loglikelihood_increasing=true,
@@ -73,7 +73,7 @@ function baum_welch(
         seq_ends,
         atol,
         max_iterations,
-        loglikelihood_increasing=false,
+        loglikelihood_increasing,
     )
     return hmm, logL_evolution
 end
@@ -85,7 +85,7 @@ function StatsAPI.fit!(
     fb_storage::ForwardBackwardStorage,
     obs_seq::AbstractVector,
     control_seq::AbstractVector;
-    seq_ends::AbstractVector{Int},
+    seq_ends::AbstractVectorOrNTuple{Int},
 )
     return fit!(hmm, fb_storage, obs_seq; seq_ends)
 end

src/inference/chainrules.jl

@@ -4,7 +4,7 @@ function _params_and_loglikelihoods(
     hmm::AbstractHMM,
     obs_seq::Vector,
     control_seq::AbstractVector=Fill(nothing, length(obs_seq));
-    seq_ends::AbstractVector{Int}=Fill(length(obs_seq), 1),
+    seq_ends::AbstractVectorOrNTuple{Int}=(length(obs_seq),),
 )
     init = initialization(hmm)
     trans_by_time = mapreduce(_dcat, eachindex(control_seq)) do t
@@ -22,7 +22,7 @@ function ChainRulesCore.rrule(
     hmm::AbstractHMM,
     obs_seq::AbstractVector,
     control_seq::AbstractVector=Fill(nothing, length(obs_seq));
-    seq_ends::AbstractVector{Int}=Fill(length(obs_seq), 1),
+    seq_ends::AbstractVectorOrNTuple{Int}=(length(obs_seq),),
 )
     _, pullback = rrule_via_ad(
         rc, _params_and_loglikelihoods, hmm, obs_seq, control_seq; seq_ends

src/inference/forward.jl

@@ -16,7 +16,35 @@
     c::Vector{R}
 end
 
+"""
+$(TYPEDEF)
+
+# Fields
+
+Only the fields with a description are part of the public API.
+
+$(TYPEDFIELDS)
+"""
+struct ForwardBackwardStorage{R,M<:AbstractMatrix{R}}
+    "posterior state marginals `γ[i,t] = ℙ(X[t]=i | Y[1:T])`"
+    γ::Matrix{R}
+    "posterior transition marginals `ξ[t][i,j] = ℙ(X[t]=i, X[t+1]=j | Y[1:T])`"
+    ξ::Vector{M}
+    "one loglikelihood per observation sequence"
+    logL::Vector{R}
+    B::Matrix{R}
+    α::Matrix{R}
+    c::Vector{R}
+    β::Matrix{R}
+    Bβ::Matrix{R}
+end
+
 Base.eltype(::ForwardStorage{R}) where {R} = R
+Base.eltype(::ForwardBackwardStorage{R}) where {R} = R
+
+const ForwardOrForwardBackwardStorage{R} = Union{
+    ForwardStorage{R},ForwardBackwardStorage{R}
+}
 
 """
 $(SIGNATURES)
@@ -25,7 +53,7 @@
     hmm::AbstractHMM,
     obs_seq::AbstractVector,
     control_seq::AbstractVector;
-    seq_ends::AbstractVector{Int},
+    seq_ends::AbstractVectorOrNTuple{Int},
 )
     N, T, K = length(hmm), length(obs_seq), length(seq_ends)
     R = eltype(hmm, obs_seq[1], control_seq[1])
@@ -40,7 +68,7 @@
 $(SIGNATURES)
 """
 function forward!(
-    storage,
+    storage::ForwardOrForwardBackwardStorage,
     hmm::AbstractHMM,
     obs_seq::AbstractVector,
     control_seq::AbstractVector,
@@ -88,16 +116,23 @@
 $(SIGNATURES)
 """
 function forward!(
-    storage,
+    storage::ForwardOrForwardBackwardStorage,
     hmm::AbstractHMM,
     obs_seq::AbstractVector,
     control_seq::AbstractVector;
-    seq_ends::AbstractVector{Int},
+    seq_ends::AbstractVectorOrNTuple{Int},
 )
     (; logL) = storage
-    @threads for k in eachindex(seq_ends)
-        t1, t2 = seq_limits(seq_ends, k)
-        logL[k] = forward!(storage, hmm, obs_seq, control_seq, t1, t2;)
+    if seq_ends isa NTuple
+        for k in eachindex(seq_ends)
+            t1, t2 = seq_limits(seq_ends, k)
+            logL[k] = forward!(storage, hmm, obs_seq, control_seq, t1, t2;)
+        end
+    else
+        @threads for k in eachindex(seq_ends)
+            t1, t2 = seq_limits(seq_ends, k)
+            logL[k] = forward!(storage, hmm, obs_seq, control_seq, t1, t2;)
+        end
     end
     return nothing
 end
@@ -113,7 +148,7 @@
     hmm::AbstractHMM,
     obs_seq::AbstractVector,
     control_seq::AbstractVector=Fill(nothing, length(obs_seq));
-    seq_ends::AbstractVector{Int}=Fill(length(obs_seq), 1),
+    seq_ends::AbstractVectorOrNTuple{Int}=(length(obs_seq),),
 )
     storage = initialize_forward(hmm, obs_seq, control_seq; seq_ends)
     forward!(storage, hmm, obs_seq, control_seq; seq_ends)

src/inference/forward_backward.jl

@@ -1,36 +1,11 @@
-"""
-$(TYPEDEF)
-
-# Fields
-
-Only the fields with a description are part of the public API.
-
-$(TYPEDFIELDS)
-"""
-struct ForwardBackwardStorage{R,M<:AbstractMatrix{R}}
-    "posterior state marginals `γ[i,t] = ℙ(X[t]=i | Y[1:T])`"
-    γ::Matrix{R}
-    "posterior transition marginals `ξ[t][i,j] = ℙ(X[t]=i, X[t+1]=j | Y[1:T])`"
-    ξ::Vector{M}
-    "one loglikelihood per observation sequence"
-    logL::Vector{R}
-    B::Matrix{R}
-    α::Matrix{R}
-    c::Vector{R}
-    β::Matrix{R}
-    Bβ::Matrix{R}
-end
-
-Base.eltype(::ForwardBackwardStorage{R}) where {R} = R
-
 """
 $(SIGNATURES)
 """
 function initialize_forward_backward(
     hmm::AbstractHMM,
     obs_seq::AbstractVector,
     control_seq::AbstractVector;
-    seq_ends::AbstractVector{Int},
+    seq_ends::AbstractVectorOrNTuple{Int},
     transition_marginals=true,
 )
     N, T, K = length(hmm), length(obs_seq), length(seq_ends)
@@ -100,20 +75,29 @@
 $(SIGNATURES)
 """
 function forward_backward!(
-    storage::ForwardBackwardStorage{R},
+    storage::ForwardBackwardStorage,
     hmm::AbstractHMM,
     obs_seq::AbstractVector,
     control_seq::AbstractVector;
-    seq_ends::AbstractVector{Int},
+    seq_ends::AbstractVectorOrNTuple{Int},
     transition_marginals::Bool=true,
-) where {R}
+)
     (; logL) = storage
-    @threads for k in eachindex(seq_ends)
-        t1, t2 = seq_limits(seq_ends, k)
-        logL[k] = forward_backward!(
-            storage, hmm, obs_seq, control_seq, t1, t2; transition_marginals
-        )
+    if seq_ends isa NTuple
+        for k in eachindex(seq_ends)
+            t1, t2 = seq_limits(seq_ends, k)
+            logL[k] = forward_backward!(
+                storage, hmm, obs_seq, control_seq, t1, t2; transition_marginals
+            )
+        end
+    else
+        @threads for k in eachindex(seq_ends)
+            t1, t2 = seq_limits(seq_ends, k)
+            logL[k] = forward_backward!(
+                storage, hmm, obs_seq, control_seq, t1, t2; transition_marginals
+            )
+        end
     end
     return nothing
 end
 
@@ -128,7 +112,7 @@
     hmm::AbstractHMM,
     obs_seq::AbstractVector,
     control_seq::AbstractVector=Fill(nothing, length(obs_seq));
-    seq_ends::AbstractVector{Int}=Fill(length(obs_seq), 1),
+    seq_ends::AbstractVectorOrNTuple{Int}=(length(obs_seq),),
 )
     transition_marginals = false
     storage = initialize_forward_backward(

src/inference/logdensity.jl

@@ -7,7 +7,7 @@ function DensityInterface.logdensityof(
     hmm::AbstractHMM,
     obs_seq::AbstractVector,
     control_seq::AbstractVector=Fill(nothing, length(obs_seq));
-    seq_ends::AbstractVector{Int}=Fill(length(obs_seq), 1),
+    seq_ends::AbstractVectorOrNTuple{Int}=(length(obs_seq),),
 )
     _, logL = forward(hmm, obs_seq, control_seq; seq_ends)
     return sum(logL)
@@ -23,7 +23,7 @@ function joint_logdensityof(
     obs_seq::AbstractVector,
     state_seq::AbstractVector,
     control_seq::AbstractVector=Fill(nothing, length(obs_seq));
-    seq_ends::AbstractVector{Int}=Fill(length(obs_seq), 1),
+    seq_ends::AbstractVectorOrNTuple{Int}=(length(obs_seq),),
 )
     R = eltype(hmm, obs_seq[1], control_seq[1])
     logL = zero(R)

src/inference/viterbi.jl

@@ -26,7 +26,7 @@ function initialize_viterbi(
     hmm::AbstractHMM,
     obs_seq::AbstractVector,
     control_seq::AbstractVector;
-    seq_ends::AbstractVector{Int},
+    seq_ends::AbstractVectorOrNTuple{Int},
 )
     N, T, K = length(hmm), length(obs_seq), length(seq_ends)
     R = eltype(hmm, obs_seq[1], control_seq[1])
@@ -85,12 +85,19 @@ function viterbi!(
     hmm::AbstractHMM,
     obs_seq::AbstractVector,
     control_seq::AbstractVector;
-    seq_ends::AbstractVector{Int},
+    seq_ends::AbstractVectorOrNTuple{Int},
 ) where {R}
     (; logL) = storage
-    @threads for k in eachindex(seq_ends)
-        t1, t2 = seq_limits(seq_ends, k)
-        logL[k] = viterbi!(storage, hmm, obs_seq, control_seq, t1, t2;)
+    if seq_ends isa NTuple
+        for k in eachindex(seq_ends)
+            t1, t2 = seq_limits(seq_ends, k)
+            logL[k] = viterbi!(storage, hmm, obs_seq, control_seq, t1, t2;)
+        end
+    else
+        @threads for k in eachindex(seq_ends)
+            t1, t2 = seq_limits(seq_ends, k)
+            logL[k] = viterbi!(storage, hmm, obs_seq, control_seq, t1, t2;)
+        end
     end
     return nothing
 end
@@ -106,7 +113,7 @@ function viterbi(
     hmm::AbstractHMM,
     obs_seq::AbstractVector,
     control_seq::AbstractVector=Fill(nothing, length(obs_seq));
-    seq_ends::AbstractVector{Int}=Fill(length(obs_seq), 1),
+    seq_ends::AbstractVectorOrNTuple{Int}=(length(obs_seq),),
 )
     storage = initialize_viterbi(hmm, obs_seq, control_seq; seq_ends)
     viterbi!(storage, hmm, obs_seq, control_seq; seq_ends)

src/types/hmm.jl

@@ -61,7 +61,7 @@ function StatsAPI.fit!(
     hmm::HMM,
     fb_storage::ForwardBackwardStorage,
     obs_seq::AbstractVector;
-    seq_ends::AbstractVector{Int},
+    seq_ends::AbstractVectorOrNTuple{Int},
 )
     (; γ, ξ) = fb_storage
     # Fit states

src/utils/limits.jl

@@ -3,7 +3,7 @@ $(SIGNATURES)
 
 Return a tuple `(t1, t2)` giving the begin and end indices of subsequence `k` within a set of sequences ending at `seq_ends`.
 """
-function seq_limits(seq_ends::AbstractVector{Int}, k::Integer)
+function seq_limits(seq_ends::AbstractVectorOrNTuple{Int}, k::Integer)
     if k == 1
         return 1, seq_ends[k]
     else

src/utils/linalg.jl

@@ -1,3 +1,5 @@
+const AbstractVectorOrNTuple{T} = Union{AbstractVector{T},NTuple{N,T}} where {N}
+
 sum_to_one!(x) = ldiv!(sum(x), x)
 
 mynonzeros(x::AbstractArray) = x