Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Remove dead code related to namespaces #602

Merged
merged 1 commit into from
Nov 13, 2024
Merged

Remove dead code related to namespaces #602

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 2 additions & 2 deletions lib/axon.ex
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3875,7 +3875,7 @@ defmodule Axon do
## `init_fn`

The `init_fn` receives two arguments, the input template and
an optional map with initial parameters for layers or namespaces:
an optional map with initial parameters for layers:

{init_fn, predict_fn} = Axon.build(model)
init_fn.(Nx.template({1, 1}, {:f, 32}), %{"dense_0" => dense_params})
Expand Down Expand Up @@ -3968,7 +3968,7 @@ defmodule Axon do
purposes.

You may optionally specify initial parameters for some layers or
namespaces by passing a partial parameter map:
by passing a partial parameter map:

Axon.trace_init(model, %{"dense_0" => dense_params})

Expand Down
92 changes: 0 additions & 92 deletions lib/axon/compiler.ex
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -687,98 +687,6 @@ defmodule Axon.Compiler do
{id, model_funs, cache, op_counts, block_cache, model_state_meta}
end

defp recur_model_funs(
%Axon.Node{id: id, op: :namespace, name: name_fn, parent: [parent]},
nodes,
{cache, op_counts, block_cache, model_state_meta},
config
) do
name = name_fn.(:namespace, op_counts)
# To ensure that a namespace always has the same layer names,
# we reset op_counts, input layers always belong to the global
# namespace, so we include those regardless
input_count = op_counts[:input] || 0
namespace_op_counts = %{input: input_count}
namespace_model_state_meta = %{parameters: %{}, state: %{}, frozen_parameters: %{}}

# All of the children of this namespace belong to it, so
# we forward this name to the namespace, but everything after
# it belongs to whatever namespace we're currently in
{parent_id, {cache, namespace_op_counts, block_cache, namespace_model_state_meta}} =
to_model_funs(
parent,
nodes,
{cache, namespace_op_counts, block_cache, namespace_model_state_meta},
config
)

# Update the global op_count of input layers, since they
# are a global operation regardless of where they are
input_count = namespace_op_counts[:input] || 0
op_counts = Map.put(op_counts, :input, input_count)

# Update the model state meta to include the namespace model state meta
model_state_meta =
model_state_meta
|> Map.update!(:parameters, &Map.put(&1, name, namespace_model_state_meta[:parameters]))
|> Map.update!(:state, &Map.put(&1, name, namespace_model_state_meta[:state]))
|> Map.update!(
:frozen_parameters,
&Map.put(&1, name, namespace_model_state_meta[:frozen_parameters])
)

# The function just returns the result of it's child,
# or parent depending on how you view the tree
predict_fun = fn params, inputs, state, cache, result_cache, fn_stacktrace ->
# We're only concerned with this namespaces parameters, so we pair
# down parameters first given the namespace
namespace_params = params[name]

# TODO: How should hooks be handled here?
# TODO: I think we can actually handle parameter freezing and access
# better here by only forwarding params[namespace] to the child function
{out, {state, result_cache}} =
call_predict_cache(
parent_id,
namespace_params,
inputs,
state,
cache,
result_cache,
fn_stacktrace
)

state =
if map_size(state) == 0 do
state
else
%{name => state}
end

{out, {state, result_cache}}
end

init_fun = fn template, cache, result_cache, fn_stacktrace, keys ->
{_parent_template, {namespace_params, result_cache}} =
call_init_cache(parent_id, template, %{}, cache, result_cache, fn_stacktrace, keys)

params =
if namespace_params == %{} do
%{}
else
%{name => namespace_params}
end

{pred_expr, {_, result_cache}} =
predict_fun.(params, template, %{}, cache, result_cache, fn_stacktrace)

{Nx.to_template(pred_expr), {params, result_cache}}
end

model_funs = %{predict: predict_fun, init: init_fun}
{id, model_funs, cache, op_counts, block_cache, model_state_meta}
end

defp recur_model_funs(
%Axon.Node{
id: id,
Expand Down
Loading