Add EmbeddingGemma to generative

ai_edge_torch/generative/examples/embedding_gemma/convert_embedding_gemma_to_litert.py

@@ -0,0 +1,58 @@
+# Copyright 2025 The AI Edge Torch Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Example of converting a Gemma3 model to multi-signature tflite model."""
+
+from absl import app
+from ai_edge_torch.generative.examples.embedding_gemma import embedding_gemma
+from ai_edge_torch.generative.utilities import converter
+from ai_edge_torch.generative.utilities import loader
+
+flags = converter.define_conversion_flags(
+    'embedding_gemma',
+    default_mask_as_input=False,
+    default_transpose_kv_cache=False,
+)
+
+_NORMALIZE_OUTPUT = flags.DEFINE_bool(
+    'normalize_output', True, 'Whether to normalize the output with L2 norm.'
+)
+
+_SEQ_LEN = flags.DEFINE_integer(
+    'seq_len', 2048, 'The sequence length of the model.'
+)
+
+
+def main(_):
+  checkpoint_path = flags.FLAGS.checkpoint_path
+  pytorch_model = embedding_gemma.build_embedding_gemma(
+      checkpoint_path,
+      normalize_output=_NORMALIZE_OUTPUT.value,
+      custom_loader=loader.maybe_get_custom_loader(
+          checkpoint_path, flags.FLAGS.custom_checkpoint_loader
+      ),
+      mask_cache_size=converter.get_mask_cache_size_from_flags(),
+  )
+  embedding_gemma.convert_to_litert(
+      pytorch_model,
+      output_path=flags.FLAGS.output_path,
+      output_name_prefix=flags.FLAGS.output_name_prefix,
+      prefill_seq_len=_SEQ_LEN.value,
+      quantize=flags.FLAGS.quantize,
+  )
+
+
+if __name__ == '__main__':
+  app.run(main)