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Removing batch dimension from default layout maps for Gemma and Llama #2035

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Removing batch dimension from default layout maps for Gemma and Llama #2035

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26 changes: 7 additions & 19 deletions keras_hub/src/models/gemma/gemma_backbone.py
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Original file line number Diff line number Diff line change
Expand Up @@ -213,11 +213,7 @@ def get_config(self):
return config

@staticmethod
def get_layout_map(
device_mesh,
model_parallel_dim_name="model",
data_parallel_dim_name="batch",
):
def get_layout_map(device_mesh, model_parallel_dim_name="model"):
"""Get a `keras.distribution.LayoutMap` for model parallel distribution.

The returned `LayoutMap` contains the sharding spec for the gemma
Expand Down Expand Up @@ -257,8 +253,6 @@ def get_layout_map(
distribution.
model_parallel_dim_name: The axis name of the device mesh, where
the weights should be partition on.
data_parallel_dim_name: The axis name of the device mesh, where
the data should be partition on.
Return:
`keras.distribution.LayoutMap` that contains the sharding spec
for all the model weights.
Expand Down Expand Up @@ -286,31 +280,25 @@ def get_layout_map(
f"{model_parallel_dim_name} is not found in the "
f"device_mesh.axis_names. {device_mesh.axis_name=}"
)
if data_parallel_dim_name not in device_mesh.axis_names:
raise ValueError(
f"{data_parallel_dim_name} is not found in the "
f"device_mesh.axis_names. {device_mesh.axis_name=}"
)
# Note that it is possible to further config the mesh to be 3D, eg
# (data, seq, model). We leave it as 2D for now for simplicity.
data_dim = data_parallel_dim_name
model_dim = model_parallel_dim_name
# The sharding config is based on the Gemma team training config.
# See https://arxiv.org/abs/2403.08295
layout_map = keras.distribution.LayoutMap(device_mesh)
layout_map["token_embedding/embeddings"] = (model_dim, data_dim)
layout_map["token_embedding/embeddings"] = (model_dim, None)
layout_map["decoder_block.*attention.*(query|key|value).kernel"] = (
model_dim,
data_dim,
None,
None,
)
layout_map["decoder_block.*attention_output.kernel"] = (
model_dim,
None,
data_dim,
None,
)
layout_map["decoder_block.*ffw_gating.kernel"] = (data_dim, model_dim)
layout_map["decoder_block.*ffw_gating_2.kernel"] = (data_dim, model_dim)
layout_map["decoder_block.*ffw_linear.kernel"] = (model_dim, data_dim)
layout_map["decoder_block.*ffw_gating.kernel"] = (None, model_dim)
layout_map["decoder_block.*ffw_gating_2.kernel"] = (None, model_dim)
layout_map["decoder_block.*ffw_linear.kernel"] = (model_dim, None)

return layout_map
24 changes: 7 additions & 17 deletions keras_hub/src/models/llama/llama_backbone.py
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Original file line number Diff line number Diff line change
Expand Up @@ -178,11 +178,7 @@ def get_config(self):
return config

@staticmethod
def get_layout_map(
device_mesh,
model_parallel_dim_name="model",
data_parallel_dim_name="batch",
):
def get_layout_map(device_mesh, model_parallel_dim_name="model"):
"""Get a `keras.distribution.LayoutMap` for model parallel distribution.

The returned `LayoutMap` contains the sharding spec for the Llama
Expand Down Expand Up @@ -260,44 +256,38 @@ def get_layout_map(
f"{model_parallel_dim_name} is not found in the "
f"device_mesh.axis_names. {device_mesh.axis_name=}"
)
if data_parallel_dim_name not in device_mesh.axis_names:
raise ValueError(
f"{data_parallel_dim_name} is not found in the "
f"device_mesh.axis_names. {device_mesh.axis_name=}"
)
# Note that it is possible to further config the mesh to be 3D, eg
# (data, seq, model). We leave it as 2D for now for simplicity.
data_dim = data_parallel_dim_name
model_dim = model_parallel_dim_name
# The sharding config is based on the Gemma team training config.
# See https://arxiv.org/abs/2403.08295
layout_map = keras.distribution.LayoutMap(device_mesh)
layout_map["token_embedding/embeddings"] = (model_dim, data_dim)
layout_map["token_embedding/embeddings"] = (model_dim, None)
layout_map[
"transformer_layer.*self_attention.*(query|key|value).kernel"
] = (
model_dim,
data_dim,
None,
None,
)
layout_map["transformer_layer.*attention_output.kernel"] = (
model_dim,
None,
data_dim,
None,
)
layout_map[
"transformer_layer.*feedforward_intermediate_dense.kernel"
] = (
data_dim,
None,
model_dim,
)
layout_map["transformer_layer.*feedforward_gate_dense.kernel"] = (
data_dim,
None,
model_dim,
)
layout_map["transformer_layer.*feedforward_output_dense.kernel"] = (
model_dim,
data_dim,
None,
)

return layout_map
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