Merge pull request #116 from fjebaker/fergus/frozen-cache

Frozen parameters in parameter cache

Project.toml

@@ -1,7 +1,7 @@
 name = "SpectralFitting"
 uuid = "f2c56810-742e-4b72-8bf4-27af3bb81a12"
 authors = ["Fergus Baker <fergusbkr@gmail.com>"]
-version = "0.5.8"
+version = "0.5.9"
 
 [deps]
 ConstructionBase = "187b0558-2788-49d3-abe0-74a17ed4e7c9"

src/abstract-models.jl

@@ -378,9 +378,5 @@ function make_diff_parameter_cache(
     N = isnothing(param_diff_cache_size) ? length(vals) : param_diff_cache_size
     diffcache = DiffCache(vals, ForwardDiff.pickchunksize(N))
 
-    # embed current parameter values inside of the dual cache
-    # else all frozens will be zero
-    get_tmp(diffcache, ForwardDiff.Dual(one(eltype(vals)))) .= vals
-
-    ParameterCache(free_mask, diffcache)
+    ParameterCache(free_mask, diffcache, vals[.!free_mask])
 end

src/param-cache.jl

@@ -1,9 +1,10 @@
 # these should work for both single models and composite models
 # so that the parameters can all be allocated in one go
 # for multi model: give a view to each cache
-struct ParameterCache{M<:AbstractArray,V}
+struct ParameterCache{M<:AbstractArray,V,T<:Number}
     free_mask::M # bit vector or a view into one
     parameters::V
+    frozen_values::Vector{T}
 end
 
 function _make_free_mask(params::AbstractArray{<:FitParam})
@@ -16,15 +17,20 @@ end
 
 function ParameterCache(params::AbstractArray{<:FitParam})
     free_mask = _make_free_mask(params)
-    ParameterCache(free_mask, map(get_value, params))
+    frozen = params[.!free_mask]
+    ParameterCache(free_mask, map(get_value, params), map(get_value, frozen))
 end
 
-function _update_conditional!(parameters, mask, new_parameters, condition)
+function _update_conditional!(parameters, mask, new_parameters, frozen)
     j::Int = 1
+    k::Int = 1
     for (i, free) in enumerate(mask)
-        if condition(free)
+        if free
             parameters[i] = new_parameters[j]
             j += 1
+        else
+            parameters[i] = frozen[k]
+            k += 1
         end
     end
 end
@@ -35,15 +41,13 @@ _get_parameters(cache::ParameterCache{M,V}, params) where {M<:AbstractArray,V<:D
 
 function update_free_parameters!(cache::ParameterCache, params)
     @assert count(cache.free_mask) == length(params)
-    _update_conditional!(_get_parameters(cache, params), cache.free_mask, params, ==(true))
+    _update_conditional!(
+        _get_parameters(cache, params),
+        cache.free_mask,
+        params,
+        cache.frozen_values,
+    )
     cache
 end
 
-function update_frozen_parameters!(cache::ParameterCache, params)
-    parameters = _get_parameters(cache, params)
-    @assert length(parameters) - count(cache.free_mask) == length(params)
-    _update_conditional!(parameters, cache.free_mask, params, ==(false))
-    cache
-end
-
-export ParameterCache, update_free_parameters!, update_frozen_parameters!
+export ParameterCache, update_free_parameters!

test/parameters/test-free-frozen.jl

@@ -7,5 +7,6 @@ model = DummyMultiplicative() * PowerLaw(a = FitParam(4.0, frozen = true))
 cache = make_parameter_cache(model)
 SpectralFitting.update_free_parameters!(cache, [2.0, 0.5])
 @test cache.parameters == [2.0, 4.0, 0.5, 5.0]
-SpectralFitting.update_frozen_parameters!(cache, [50.0, 0.0])
-@test cache.parameters == [2.0, 50.0, 0.5, 0.0]
+
+
+# TODO: test to check that auto diff gradients with various sizes can be propagated correctly