Add LoRA support to AI Edge Transformers.

PiperOrigin-RevId: 704425190
google-ai-edge · Dec 20, 2024 · 1672654 · 1672654
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commit 1672654
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Showing 16 changed files with 984 additions and 124 deletions.
diff --git a/ai_edge_torch/generative/examples/gemma/convert_gemma1_to_tflite.py b/ai_edge_torch/generative/examples/gemma/convert_gemma1_to_tflite.py
@@ -29,10 +29,15 @@
     os.path.join(pathlib.Path.home(), 'Downloads/llm_data/gemma-2b'),
     'The path to the model checkpoint, or directory holding the checkpoint.',
 )
-_TFLITE_PATH = flags.DEFINE_string(
-    'tflite_path',
+_OUTPUT_PATH = flags.DEFINE_string(
+    'output_path',
     '/tmp/',
-    'The tflite file path to export.',
+    'The path to export the tflite model.',
+)
+_OUTPUT_NAME_PREFIX = flags.DEFINE_string(
+    'output_name_prefix',
+    'gemma',
+    'The prefix of the output tflite model name.',
 )
 _PREFILL_SEQ_LENS = flags.DEFINE_multi_integer(
     'prefill_seq_lens',
@@ -49,19 +54,24 @@
     True,
     'Whether the model should be quantized.',
 )
+_LORA_RANKS = flags.DEFINE_multi_integer(
+    'lora_ranks',
+    None,
+    'If set, the model will be converted with the provided list of LoRA ranks.',
+)
 
 
 def main(_):
   pytorch_model = gemma1.build_2b_model(
       _CHECKPOINT_PATH.value, kv_cache_max_len=_KV_CACHE_MAX_LEN.value
   )
-  quant_suffix = 'q8' if _QUANTIZE.value else 'f32'
-  output_filename = f'gemma_{quant_suffix}_ekv{_KV_CACHE_MAX_LEN.value}.tflite'
   converter.convert_to_tflite(
       pytorch_model,
-      tflite_path=os.path.join(_TFLITE_PATH.value, output_filename),
+      output_path=_OUTPUT_PATH.value,
+      output_name_prefix=_OUTPUT_NAME_PREFIX.value,
       prefill_seq_len=_PREFILL_SEQ_LENS.value,
       quantize=_QUANTIZE.value,
+      lora_ranks=_LORA_RANKS.value,
       export_config=ExportConfig(),
   )
 

diff --git a/ai_edge_torch/generative/examples/gemma/convert_gemma2_to_tflite.py b/ai_edge_torch/generative/examples/gemma/convert_gemma2_to_tflite.py
@@ -29,10 +29,15 @@
     os.path.join(pathlib.Path.home(), 'Downloads/llm_data/gemma2-2b'),
     'The path to the model checkpoint, or directory holding the checkpoint.',
 )
-_TFLITE_PATH = flags.DEFINE_string(
-    'tflite_path',
+_OUTPUT_PATH = flags.DEFINE_string(
+    'output_path',
     '/tmp/',
-    'The tflite file path to export.',
+    'The path to export the tflite model.',
+)
+_OUTPUT_NAME_PREFIX = flags.DEFINE_string(
+    'output_name_prefix',
+    'gemma2',
+    'The prefix of the output tflite model name.',
 )
 _PREFILL_SEQ_LENS = flags.DEFINE_multi_integer(
     'prefill_seq_lens',
@@ -49,19 +54,24 @@
     True,
     'Whether the model should be quantized.',
 )
+_LORA_RANKS = flags.DEFINE_multi_integer(
+    'lora_ranks',
+    None,
+    'If set, the model will be converted with the provided list of LoRA ranks.',
+)
 
 
 def main(_):
   pytorch_model = gemma2.build_2b_model(
       _CHECKPOINT_PATH.value, kv_cache_max_len=_KV_CACHE_MAX_LEN.value
   )
-  quant_suffix = 'q8' if _QUANTIZE.value else 'f32'
-  output_filename = f'gemma2_{quant_suffix}_ekv{_KV_CACHE_MAX_LEN.value}.tflite'
   converter.convert_to_tflite(
       pytorch_model,
-      tflite_path=os.path.join(_TFLITE_PATH.value, output_filename),
+      output_path=_OUTPUT_PATH.value,
+      output_name_prefix=_OUTPUT_NAME_PREFIX.value,
       prefill_seq_len=_PREFILL_SEQ_LENS.value,
       quantize=_QUANTIZE.value,
+      lora_ranks=_LORA_RANKS.value,
       export_config=ExportConfig(),
   )
 

diff --git a/ai_edge_torch/generative/examples/llama/convert_to_tflite.py b/ai_edge_torch/generative/examples/llama/convert_to_tflite.py
@@ -35,10 +35,15 @@
     os.path.join(pathlib.Path.home(), 'Downloads/llm_data/llama'),
     'The path to the model checkpoint, or directory holding the checkpoint.',
 )
-_TFLITE_PATH = flags.DEFINE_string(
-    'tflite_path',
+_OUTPUT_PATH = flags.DEFINE_string(
+    'output_path',
     '/tmp/',
-    'The tflite file path to export.',
+    'The path to export the tflite model.',
+)
+_OUTPUT_NAME_PREFIX = flags.DEFINE_string(
+    'output_name_prefix',
+    'llama',
+    'The prefix of the output tflite model name.',
 )
 _PREFILL_SEQ_LENS = flags.DEFINE_multi_integer(
     'prefill_seq_lens',
@@ -55,6 +60,11 @@
     True,
     'Whether the model should be quantized.',
 )
+_LORA_RANKS = flags.DEFINE_multi_integer(
+    'lora_ranks',
+    None,
+    'If set, the model will be converted with the provided list of LoRA ranks.',
+)
 
 _BUILDER = {
     '1b': llama.build_1b_model,
@@ -66,13 +76,13 @@ def main(_):
   pytorch_model = _BUILDER[_MODEL_SIZE.value](
       _CHECKPOINT_PATH.value, kv_cache_max_len=_KV_CACHE_MAX_LEN.value
   )
-  quant_suffix = 'q8' if _QUANTIZE.value else 'f32'
-  output_filename = f'llama_{_MODEL_SIZE.value}_{quant_suffix}_ekv{_KV_CACHE_MAX_LEN.value}.tflite'
   converter.convert_to_tflite(
       pytorch_model,
-      tflite_path=os.path.join(_TFLITE_PATH.value, output_filename),
+      output_path=_OUTPUT_PATH.value,
+      output_name_prefix=_OUTPUT_NAME_PREFIX.value,
       prefill_seq_len=_PREFILL_SEQ_LENS.value,
       quantize=_QUANTIZE.value,
+      lora_ranks=_LORA_RANKS.value,
       export_config=ExportConfig(),
   )
 

diff --git a/ai_edge_torch/generative/examples/openelm/convert_to_tflite.py b/ai_edge_torch/generative/examples/openelm/convert_to_tflite.py
@@ -29,10 +29,15 @@
     os.path.join(pathlib.Path.home(), 'Downloads/llm_data/openelm'),
     'The path to the model checkpoint, or directory holding the checkpoint.',
 )
-_TFLITE_PATH = flags.DEFINE_string(
-    'tflite_path',
+_OUTPUT_PATH = flags.DEFINE_string(
+    'output_path',
     '/tmp/',
-    'The tflite file path to export.',
+    'The path to export the tflite model.',
+)
+_OUTPUT_NAME_PREFIX = flags.DEFINE_string(
+    'output_name_prefix',
+    'openelm',
+    'The prefix of the output tflite model name.',
 )
 _PREFILL_SEQ_LENS = flags.DEFINE_multi_integer(
     'prefill_seq_lens',
@@ -49,22 +54,24 @@
     True,
     'Whether the model should be quantized.',
 )
+_LORA_RANKS = flags.DEFINE_multi_integer(
+    'lora_ranks',
+    None,
+    'If set, the model will be converted with the provided list of LoRA ranks.',
+)
 
 
 def main(_):
   pytorch_model = openelm.build_model(
       _CHECKPOINT_PATH.value, kv_cache_max_len=_KV_CACHE_MAX_LEN.value
   )
-  quant_suffix = 'q8' if _QUANTIZE.value else 'f32'
-  output_filename = (
-      f'openelm_{quant_suffix}_ekv{_KV_CACHE_MAX_LEN.value}.tflite'
-  )
-
   converter.convert_to_tflite(
       pytorch_model,
-      tflite_path=os.path.join(_TFLITE_PATH.value, output_filename),
+      output_path=_OUTPUT_PATH.value,
+      output_name_prefix=_OUTPUT_NAME_PREFIX.value,
       prefill_seq_len=_PREFILL_SEQ_LENS.value,
       quantize=_QUANTIZE.value,
+      lora_ranks=_LORA_RANKS.value,
       export_config=ExportConfig(),
   )
 

diff --git a/ai_edge_torch/generative/examples/paligemma/convert_to_tflite.py b/ai_edge_torch/generative/examples/paligemma/convert_to_tflite.py
@@ -34,10 +34,15 @@
     os.path.join(pathlib.Path.home(), 'Downloads/llm_data/paligemma-3b-224'),
     'The path to the model checkpoint, or directory holding the checkpoint.',
 )
-_TFLITE_PATH = flags.DEFINE_string(
-    'tflite_path',
+_OUTPUT_PATH = flags.DEFINE_string(
+    'output_path',
     '/tmp/',
-    'The tflite file path to export.',
+    'The path to export the tflite model.',
+)
+_OUTPUT_NAME_PREFIX = flags.DEFINE_string(
+    'output_name_prefix',
+    'paligemma',
+    'The prefix of the output tflite model name.',
 )
 _PREFILL_SEQ_LEN = flags.DEFINE_integer(
     'prefill_seq_len',
@@ -65,11 +70,10 @@ def main(_):
   pytorch_model = paligemma.build_model(
       _CHECKPOINT_PATH.value, kv_cache_max_len=_KV_CACHE_MAX_LEN.value
   )
-  quant_suffix = 'q8' if _QUANTIZE.value else 'f32'
-  output_filename = f'paligemma_{quant_suffix}_seq{_PREFILL_SEQ_LEN.value}_ekv{_KV_CACHE_MAX_LEN.value}.tflite'
   converter.convert_to_tflite(
       pytorch_model,
-      tflite_path=os.path.join(_TFLITE_PATH.value, output_filename),
+      output_path=_OUTPUT_PATH.value,
+      output_name_prefix=_OUTPUT_NAME_PREFIX.value,
       prefill_seq_len=_PREFILL_SEQ_LEN.value,
       pixel_values_size=torch.Size(_PIXEL_VALUES_SIZE.value),
       quantize=_QUANTIZE.value,

diff --git a/ai_edge_torch/generative/examples/phi/convert_phi3_to_tflite.py b/ai_edge_torch/generative/examples/phi/convert_phi3_to_tflite.py
@@ -26,13 +26,18 @@
 
 _CHECKPOINT_PATH = flags.DEFINE_string(
     'checkpoint_path',
-    os.path.join(pathlib.Path.home(), 'Downloads/llm_data/phi3'),
+    os.path.join(pathlib.Path.home(), 'Downloads/llm_data/gemma-2b'),
     'The path to the model checkpoint, or directory holding the checkpoint.',
 )
-_TFLITE_PATH = flags.DEFINE_string(
-    'tflite_path',
+_OUTPUT_PATH = flags.DEFINE_string(
+    'output_path',
     '/tmp/',
-    'The tflite file path to export.',
+    'The path to export the tflite model.',
+)
+_OUTPUT_NAME_PREFIX = flags.DEFINE_string(
+    'output_name_prefix',
+    'phi3',
+    'The prefix of the output tflite model name.',
 )
 _PREFILL_SEQ_LENS = flags.DEFINE_multi_integer(
     'prefill_seq_lens',
@@ -49,19 +54,24 @@
     True,
     'Whether the model should be quantized.',
 )
+_LORA_RANKS = flags.DEFINE_multi_integer(
+    'lora_ranks',
+    None,
+    'If set, the model will be converted with the provided list of LoRA ranks.',
+)
 
 
 def main(_):
   pytorch_model = phi3.build_model(
       _CHECKPOINT_PATH.value, kv_cache_max_len=_KV_CACHE_MAX_LEN.value
   )
-  quant_suffix = 'q8' if _QUANTIZE.value else 'f32'
-  output_filename = f'phi3_{quant_suffix}_ekv{_KV_CACHE_MAX_LEN.value}.tflite'
   converter.convert_to_tflite(
       pytorch_model,
-      tflite_path=os.path.join(_TFLITE_PATH.value, output_filename),
+      output_path=_OUTPUT_PATH.value,
+      output_name_prefix=_OUTPUT_NAME_PREFIX.value,
       prefill_seq_len=_PREFILL_SEQ_LENS.value,
       quantize=_QUANTIZE.value,
+      lora_ranks=_LORA_RANKS.value,
       export_config=ExportConfig(),
   )
 

diff --git a/ai_edge_torch/generative/examples/phi/convert_to_tflite.py b/ai_edge_torch/generative/examples/phi/convert_to_tflite.py
@@ -29,10 +29,15 @@
     os.path.join(pathlib.Path.home(), 'Downloads/llm_data/phi2'),
     'The path to the model checkpoint, or directory holding the checkpoint.',
 )
-_TFLITE_PATH = flags.DEFINE_string(
-    'tflite_path',
+_OUTPUT_PATH = flags.DEFINE_string(
+    'output_path',
     '/tmp/',
-    'The tflite file path to export.',
+    'The path to export the tflite model.',
+)
+_OUTPUT_NAME_PREFIX = flags.DEFINE_string(
+    'output_name_prefix',
+    'phi2',
+    'The prefix of the output tflite model name.',
 )
 _PREFILL_SEQ_LENS = flags.DEFINE_multi_integer(
     'prefill_seq_lens',
@@ -49,19 +54,24 @@
     True,
     'Whether the model should be quantized.',
 )
+_LORA_RANKS = flags.DEFINE_multi_integer(
+    'lora_ranks',
+    None,
+    'If set, the model will be converted with the provided list of LoRA ranks.',
+)
 
 
 def main(_):
   pytorch_model = phi2.build_model(
       _CHECKPOINT_PATH.value, kv_cache_max_len=_KV_CACHE_MAX_LEN.value
   )
-  quant_suffix = 'q8' if _QUANTIZE.value else 'f32'
-  output_filename = f'phi2_{quant_suffix}_ekv{_KV_CACHE_MAX_LEN.value}.tflite'
   converter.convert_to_tflite(
       pytorch_model,
-      tflite_path=os.path.join(_TFLITE_PATH.value, output_filename),
+      output_path=_OUTPUT_PATH.value,
+      output_name_prefix=_OUTPUT_NAME_PREFIX.value,
       prefill_seq_len=_PREFILL_SEQ_LENS.value,
       quantize=_QUANTIZE.value,
+      lora_ranks=_LORA_RANKS.value,
       export_config=ExportConfig(),
   )
 

diff --git a/ai_edge_torch/generative/examples/qwen/convert_to_tflite.py b/ai_edge_torch/generative/examples/qwen/convert_to_tflite.py
@@ -35,10 +35,15 @@
     os.path.join(pathlib.Path.home(), 'Downloads/llm_data/qwen'),
     'The path to the model checkpoint, or directory holding the checkpoint.',
 )
-_TFLITE_PATH = flags.DEFINE_string(
-    'tflite_path',
+_OUTPUT_PATH = flags.DEFINE_string(
+    'output_path',
     '/tmp/',
-    'The tflite file path to export.',
+    'The path to export the tflite model.',
+)
+_OUTPUT_NAME_PREFIX = flags.DEFINE_string(
+    'output_name_prefix',
+    'qwen',
+    'The prefix of the output tflite model name.',
 )
 _PREFILL_SEQ_LENS = flags.DEFINE_multi_integer(
     'prefill_seq_lens',
@@ -55,6 +60,12 @@
     True,
     'Whether the model should be quantized.',
 )
+_LORA_RANKS = flags.DEFINE_multi_integer(
+    'lora_ranks',
+    None,
+    'If set, the model will be converted with the provided list of LoRA ranks.',
+)
+
 
 _BUILDER = {
     '0.5b': qwen.build_0_5b_model,
@@ -67,16 +78,13 @@ def main(_):
   pytorch_model = _BUILDER[_MODEL_SIZE.value](
       _CHECKPOINT_PATH.value, kv_cache_max_len=_KV_CACHE_MAX_LEN.value
   )
-  quant_suffix = 'q8' if _QUANTIZE.value else 'f32'
-  model_size = _MODEL_SIZE.value.replace('.', '_')
-  output_filename = (
-      f'qwen_{model_size}_{quant_suffix}_ekv{_KV_CACHE_MAX_LEN.value}.tflite'
-  )
   converter.convert_to_tflite(
       pytorch_model,
-      tflite_path=os.path.join(_TFLITE_PATH.value, output_filename),
+      output_path=_OUTPUT_PATH.value,
+      output_name_prefix=_OUTPUT_NAME_PREFIX.value,
       prefill_seq_len=_PREFILL_SEQ_LENS.value,
       quantize=_QUANTIZE.value,
+      lora_ranks=_LORA_RANKS.value,
       export_config=ExportConfig(),
   )