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fix: deprecation of tree_flatten / tree_unflatten #21

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fix: deprecation of tree_flatten / tree_unflatten #21

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26 changes: 13 additions & 13 deletions precondition/distributed_shampoo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2166,7 +2166,7 @@ def sharded_init_fn(params):
Args:
params: the parameters that should be updated.
"""
params_flat, treedef = jax.tree_flatten(params)
params_flat, treedef = jax.tree_util.tree_flatten(params)
# Find max size to pad to.
max_size = 0
for param in params_flat:
Expand Down Expand Up @@ -2227,7 +2227,7 @@ def sharded_init_fn(params):
index_start,
sizes))

local_stats = jax.tree_unflatten(treedef, local_stats_flat)
local_stats = jax.tree_util.tree_unflatten(treedef, local_stats_flat)
to_pad = -len(padded_statistics) % num_devices_for_pjit
if max_size == 0:
to_pad = num_devices_for_pjit
Expand Down Expand Up @@ -2286,9 +2286,9 @@ def sharded_init_partition_spec_fn(params, params_partition_spec,
partition_spec_for_statistics: PartitionSpec for the statistics.
"""
# Parallel lists of spec, and params.
param_pspec_flat, _ = jax.tree_flatten(
param_pspec_flat, _ = jax.tree_util.tree_flatten(
params_partition_spec, is_leaf=lambda x: x is None)
params_flat, treedef = jax.tree_flatten(params)
params_flat, treedef = jax.tree_util.tree_flatten(params)
assert param_pspec_flat
assert params_flat
# Step is replicated across cores.
Expand Down Expand Up @@ -2333,7 +2333,7 @@ def sharded_init_partition_spec_fn(params, params_partition_spec,
index_start,
sizes))

local_stats = jax.tree_unflatten(treedef, local_stats_flat)
local_stats = jax.tree_util.tree_unflatten(treedef, local_stats_flat)
global_stats = GlobalShardedParameterStats(partition_spec_for_statistics, # pytype: disable=wrong-arg-types # numpy-scalars
partition_spec_for_statistics,
jax.sharding.PartitionSpec())
Expand All @@ -2349,7 +2349,7 @@ def sharded_init_shape_and_dtype_fn(params):
params: A pytree with params.
"""
# Parallel lists of spec, and params.
params_flat, treedef = jax.tree_flatten(params)
params_flat, treedef = jax.tree_util.tree_flatten(params)
assert params_flat
# Step is replicated across cores.
# None means cores.
Expand Down Expand Up @@ -2395,7 +2395,7 @@ def sharded_init_shape_and_dtype_fn(params):
sizes,
))

local_stats = jax.tree_unflatten(treedef, local_stats_flat)
local_stats = jax.tree_util.tree_unflatten(treedef, local_stats_flat)
max_statistics_size = _max_statistics_size_from_params(params_flat)
to_pad = -num_statistics % num_devices_for_pjit
num_statistics += to_pad
Expand Down Expand Up @@ -2427,7 +2427,7 @@ def sharded_update_fn(grads, state, params):
Returns:
A tuple containing the new parameters and the new optimizer state.
"""
params_flat, treedef = jax.tree_flatten(params)
params_flat, treedef = jax.tree_util.tree_flatten(params)
grads_flat = treedef.flatten_up_to(grads)

global_stats = state.stats.global_stats
Expand All @@ -2450,7 +2450,7 @@ def sharded_update_fn(grads, state, params):
new_stats_flat, params_flat)
updates_flat, new_stats_flat = list(zip(*outputs)) if outputs else ((), ())

updates = jax.tree_unflatten(treedef, updates_flat)
updates = jax.tree_util.tree_unflatten(treedef, updates_flat)
new_local_stats_flat = []
for new_stat, local_stat in zip(new_stats_flat, local_stats_flat):
new_local_stats_flat.append(
Expand Down Expand Up @@ -2563,7 +2563,7 @@ def _update_preconditioners():
if generate_training_metrics:
new_local_stats_flat = _add_metrics_into_local_stats(
new_local_stats_flat, metrics, ~perform_step)
new_local_stats = jax.tree_unflatten(treedef, new_local_stats_flat)
new_local_stats = jax.tree_util.tree_unflatten(treedef, new_local_stats_flat)
errors = metrics.inverse_pth_root_errors
errors = errors.reshape((-1, 1, 1))
predicate = jnp.logical_or(
Expand Down Expand Up @@ -3630,7 +3630,7 @@ def update_fn(grads, state, params):
Returns:
A tuple containing the new parameters and the new optimizer state.
"""
params_flat, treedef = jax.tree_flatten(params)
params_flat, treedef = jax.tree_util.tree_flatten(params)
stats_flat = treedef.flatten_up_to(state.stats)
grads_flat = treedef.flatten_up_to(grads)
stats_grads = grads_flat
Expand All @@ -3646,8 +3646,8 @@ def update_fn(grads, state, params):
new_stats_flat, params_flat)
updates_flat, new_stats_flat = list(zip(*outputs)) if outputs else ((), ())

updates = jax.tree_unflatten(treedef, updates_flat)
new_stats = jax.tree_unflatten(treedef, new_stats_flat)
updates = jax.tree_util.tree_unflatten(treedef, updates_flat)
new_stats = jax.tree_util.tree_unflatten(treedef, new_stats_flat)

new_state = ShampooState(count=state.count + 1, stats=new_stats)
return updates, new_state
Expand Down