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fixed order of parameters in stateless_apply in JAX distributed example #458

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merged 5 commits into from
Jul 12, 2023
Merged

fixed order of parameters in stateless_apply in JAX distributed example #458

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5e2c7a3
added Jax distributed training exammple using a Keras model
martin-gorner Jun 21, 2023
a7040a8
fixed file formatting
martin-gorner Jun 22, 2023
266fb0e
fixed file formatting
martin-gorner Jun 22, 2023
ded4269
the order of arguments in stateless_appply has changed. Fixed example.
martin-gorner Jul 12, 2023
209b989
Merge branch 'main' into mgorner-distrib-example
martin-gorner Jul 12, 2023
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10 changes: 2 additions & 8 deletions examples/demo_jax_distributed.py
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Original file line number Diff line number Diff line change
Expand Up @@ -157,13 +157,7 @@ def make_model():
# data will be split along the batch axis
data_mesh = Mesh(devices, axis_names=("batch",)) # naming axes of the mesh
# naming axes of the sharded partition
data_sharding = NamedSharding(
data_mesh,
P(
"batch",
),
)

data_sharding = NamedSharding(data_mesh,P("batch",),)
# all variables will be replicated on all devices
var_mesh = Mesh(devices, axis_names=("_"))
# in NamedSharding, axes that are not mentioned are replicated (all axes here)
Expand Down Expand Up @@ -275,7 +269,7 @@ def train_step(train_state, x, y):
)

trainable_variables, optimizer_variables = optimizer.stateless_apply(
grads, train_state.trainable_variables, train_state.optimizer_variables
train_state.optimizer_variables, grads, train_state.trainable_variables
)

return loss_value, TrainingState(
Expand Down