Fix logpdf_grad for transformed cont. dists.

Author: ztangent

src/modeling_library/dist_dsl/dist_dsl.jl

@@ -76,8 +76,11 @@ function logpdf_grad(d::CompiledDistWithArgs{T}, x::T, args...) where T
     base_arg_grads = [g for (i, g) in enumerate(base_grads[2:end])
                       if base_has_arg_grads[i]]
     argvec = collect(args)
-    eval_arg_grads = hcat([ReverseDiff.gradient(xs -> eval_arg(arg, xs), argvec)
-        for (i, arg) in enumerate(d.arglist) if base_has_arg_grads[i]]...)
+    if !isempty(argvec)
+        eval_arg_grads = [ReverseDiff.gradient(xs -> eval_arg(arg, xs), argvec) for
+                          (i, arg) in enumerate(d.arglist) if base_has_arg_grads[i]]
+        eval_arg_grads = reduce(hcat, eval_arg_grads)
+    end
 
     retval = [base_grads[1]]
     for i in 1:d.n_args
@@ -87,7 +90,7 @@ function logpdf_grad(d::CompiledDistWithArgs{T}, x::T, args...) where T
             push!(retval, nothing)
         end
     end
-    retval
+    return Tuple(retval)
 end
 
 function random(d::CompiledDistWithArgs{T}, args...)::T where T

src/modeling_library/dist_dsl/transformed_distribution.jl

@@ -41,14 +41,14 @@ function logpdf_grad(d::TransformedDistribution{T, U}, x::T, args...) where {T,
 
     if is_discrete(d.base) || !has_output_grad(d.base)
         # TODO: should this be nothing or 0?
-        [base_grad[1], fill(nothing, d.nArgs)..., base_grad[2:end]...]
+        return (base_grad[1], fill(nothing, d.nArgs)..., base_grad[2:end]...)
     else
         transformation_grad = d.backward_grad(x, args[1:d.nArgs]...)
         correction_grad = ReverseDiff.gradient(v -> logpdf_correction(d, v[1], v[2:end]), [x, args[1:d.nArgs]...])
         # TODO: Will this sort of thing work if the arguments w.r.t. which we are taking
         # gradients are themselves vector-valued?
         full_grad = (transformation_grad .* base_grad[1]) .+ correction_grad
-        [full_grad..., base_grad[2:end]...]
+        return (full_grad..., base_grad[2:end]...)
     end
 end
 
@@ -62,8 +62,8 @@ end
 
 function has_argument_grads(d::TransformedDistribution{T, U}) where {T, U}
     if is_discrete(d.base) || !has_output_grad(d.base)
-        [fill(false, d.nArgs)..., has_argument_grads(d.base)...]
+        (fill(false, d.nArgs)..., has_argument_grads(d.base)...)
     else
-        [fill(true, d.nArgs)..., has_argument_grads(d.base)...]
+        (fill(true, d.nArgs)..., has_argument_grads(d.base)...)
     end
 end