Do we need resume_from now that we have initial_state?

[torfjelde]

AbstractMCMC@5 has initial_state as a mechanism to "resume" a chain.

This then begs the question: do we even need resume_from that is present in many sampler impls in Turing.jl?

Turing.jl/src/mcmc/hmc.jl

Lines 92 to 93 in 39f5d5b

	resume_from=nothing,
	initial_state=DynamicPPL.loadstate(resume_from),

AFAIK the only place resume_from is actually overloaded is for Chains:

https://github.com/TuringLang/DynamicPPL.jl/blob/ff68206d4b34e230c7e444e91fc60297dd5a5bd0/ext/DynamicPPLMCMCChainsExt.jl#L18-L27

IMO we should replace this with a simple getstate or something from Chains and then just use initial_state everywhere to stay consistent with the AbstractMCMC interface.

[torfjelde]

@devmotion @yebai thoughts?

[yebai]

I don't think we need resume_from anymore.

[devmotion]

Yes, I think it's redundant and confusing, now that initial_state is an official AbstractMCMC keyword argument.

[torfjelde]

I see #2115 did something on this, but it doesn't seem to be "complete", e.g.

Turing.jl/src/mcmc/hmc.jl

Lines 100 to 115 in c29d36e

	if resume_from === nothing
	# If `nadapts` is `-1`, then the user called a convenience
	# constructor like `NUTS()` or `NUTS(0.65)`,
	# and we should set a default for them.
	if nadapts == -1
	_nadapts = min(1000, N ÷ 2)
	else
	_nadapts = nadapts
	end
	# If `discard_initial` is `-1`, then users did not specify the keyword argument.
	if discard_initial == -1
	_discard_initial = discard_adapt ? _nadapts : 0
	else
	_discard_initial = discard_initial
	end

is hit when resume_from === nothing which will be the case even if the user specifies initial_state.