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Fix jaxtyping shape mismatch for multimodal inputs in Gemma3nTransformer #624

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Fix jaxtyping shape mismatch for multimodal inputs in Gemma3nTransformer #624

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25 changes: 19 additions & 6 deletions gemma/gm/nn/gemma3n/_transformer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -227,13 +227,13 @@ def __call__( # pytype: disable=signature-mismatch
*,
images: UInt8['*B N H W C'] | UInt8['*B H W C'] | None = None,
# TODO(epot): Cleanup and simplify the API.
positions: Int['*B L'] | None = None,
positions: Int['*B L_with_mm'] | None = None,
positions_offset: Int['*B'] | None = None,
cache: _config.Cache | None = None,
# During training and pre-filling, the attention mask is `*B L L`
# When sampling (after prefilling), tokens are decoded one by one,
# so the attention mask is `*B 1 cache_length`
attention_mask: Bool['*B L cache_length'] | None = None,
attention_mask: Bool['*B L_with_mm cache_length'] | None = None,
return_last_only: bool | None = None,
return_hidden_states: bool | None = None,
) -> Output: # Output['*B']
Expand Down Expand Up @@ -375,8 +375,8 @@ def _encode_and_get_inputs(
*,
tokens: Int['B L_no_mm'],
images: UInt8['B H W C'] | UInt8['B N H W C'] | None = None,
attention_mask: Bool['B L_no_mm cache_length'] | None = None,
positions: Int['B L_no_mm'] | None = None,
attention_mask: Bool['B L_with_mm cache_length'] | None = None,
positions: Int['B L_with_mm'] | None = None,
positions_offset: Int['B'] | None = None,
) -> _Inputs:
"""Encode the text tokens, eventually including the vision embeddings."""
Expand All @@ -400,6 +400,7 @@ def _encode_and_get_inputs(
# Currently, The placeholders are required so the mask, positions are
# correctly computed.
x = self.embedder.encode(inputs.tokens_with_mm)
seq_len_with_mm = inputs.tokens_with_mm.shape[1]

# Encode the vision tokens and merge them with the text embeddings.
if inputs.images is not None:
Expand All @@ -422,15 +423,27 @@ def _encode_and_get_inputs(
# it's the user responsibility to correctly take into account the extra
# tokens inserted for the images.
# This is what the `gm.text.Sampler` implementation does.
# if positions is None:
# positions = _pos_utils.build_positions_from_mask(inputs.inputs_mask)
# # For multi-turn, during the pre-fill phase, the positions should be
# # shifted to take into account the previous turns.
# if positions_offset is not None:
# positions += positions_offset[..., None]
if positions is None:
# Build correct positions aligned with MM-expanded tokens
positions = _pos_utils.build_positions_from_mask(inputs.inputs_mask)
# For multi-turn, during the pre-fill phase, the positions should be
# shifted to take into account the previous turns.
if positions_offset is not None:
positions += positions_offset[..., None]
else:
if positions.shape[1] != seq_len_with_mm:
# Expand positions using inputs_mask (safe + correct)
positions = _pos_utils.build_positions_from_mask(inputs.inputs_mask)

if attention_mask is None:
attention_mask = inputs.attention_mask
else:
if attention_mask.shape[1] != seq_len_with_mm:
attention_mask = inputs.attention_mask

return _Inputs(
embeddings=x,
Expand Down