Fix accumulate_param_gradients! for Map and Unfold.

Author: ztangent

src/gen_fn_interface.jl

@@ -356,8 +356,9 @@ If an argument is not annotated with `(grad)`, the corresponding value in
 Also increment the gradient accumulators for the trainable parameters \$Θ\$ of
 the function by:
 ```math
-∇_Θ \\left( \\log P(t; x) + J \\right)
+s * ∇_Θ \\left( \\log P(t; x) + J \\right)
 ```
+where \$s\$ is `scale_factor`.
 """
 function accumulate_param_gradients!(trace, retgrad, scale_factor)
     error("Not implemented")

src/modeling_library/map/backprop.jl

@@ -35,7 +35,7 @@ function choice_gradients(trace::VectorTrace{MapType,T,U}, selection::Selection,
     ((arg_grad...,), value_choices, gradient_choices)
 end
 
-function accumulate_param_gradients!(trace::VectorTrace{MapType,T,U}, retval_grad) where {T,U}
+function accumulate_param_gradients!(trace::VectorTrace{MapType,T,U}, retval_grad, scale_factor) where {T,U}
 
     args = get_args(trace)
     n_args = length(args)
@@ -54,7 +54,7 @@ function accumulate_param_gradients!(trace::VectorTrace{MapType,T,U}, retval_gra
     for key=1:len
         subtrace = trace.subtraces[key]
         kernel_retval_grad = (retval_grad == nothing) ? nothing : retval_grad[key]
-        kernel_arg_grad::Tuple = accumulate_param_gradients!(subtrace, kernel_retval_grad)
+        kernel_arg_grad::Tuple = accumulate_param_gradients!(subtrace, kernel_retval_grad, scale_factor)
         for (i, grad, has_grad) in zip(1:n_args, kernel_arg_grad, has_grads)
             if has_grad
                 arg_grad[i][key] = grad

src/modeling_library/unfold/backprop.jl

@@ -50,7 +50,7 @@ function choice_gradients(trace::VectorTrace{UnfoldType,T,U}, selection::Selecti
     ((nothing, kernel_arg_grads[2], map(_sum, params_grad)...), value_choices, gradient_choices)
 end
 
-function accumulate_param_gradients!(trace::VectorTrace{UnfoldType,T,U}, retval_grad) where {T,U}
+function accumulate_param_gradients!(trace::VectorTrace{UnfoldType,T,U}, retval_grad, scale_factor) where {T,U}
     kernel_has_grads = has_argument_grads(trace.gen_fn.kernel)
     if kernel_has_grads[1]
         error("Cannot differentiate with respect to index in unfold")
@@ -76,7 +76,7 @@ function accumulate_param_gradients!(trace::VectorTrace{UnfoldType,T,U}, retval_
         if state_has_grad
             kernel_retval_grad = fold_sum(kernel_retval_grad, kernel_arg_grads[2])
         end
-        kernel_arg_grads = accumulate_param_gradients!(subtrace, kernel_retval_grad)
+        kernel_arg_grads = accumulate_param_gradients!(subtrace, kernel_retval_grad, scale_factor)
         @assert kernel_arg_grads[1] == nothing
         state_has_grad || @assert kernel_arg_grads[2] == nothing
         for (i, (grad, has_grad)) in enumerate(zip(kernel_arg_grads[3:end], params_has_grad))