Add BUILD rules and update copy.bara.sky for toy models.

PiperOrigin-RevId: 678471108

ai_edge_torch/generative/examples/test_models/convert_toy_model.py

@@ -0,0 +1,105 @@
+# Copyright 2024 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.
+# ==============================================================================
+# A toy example which has a single-layer transformer block.
+from absl import app
+import ai_edge_torch
+from ai_edge_torch import lowertools
+from ai_edge_torch.generative.examples.test_models import toy_model
+from ai_edge_torch.generative.examples.test_models import toy_model_with_kv_cache
+from ai_edge_torch.generative.layers import kv_cache as kv_utils
+import torch
+
+KV_CACHE_MAX_LEN = 100
+
+
+def convert_toy_model(_) -> None:
+  """Converts a toy model to tflite."""
+  model = toy_model.ToySingleLayerModel(toy_model.get_model_config())
+  idx = torch.unsqueeze(torch.arange(0, KV_CACHE_MAX_LEN), 0)
+  input_pos = torch.arange(0, KV_CACHE_MAX_LEN)
+  print('running an inference')
+  print(
+      model.forward(
+          idx,
+          input_pos,
+      )
+  )
+
+  # Convert model to tflite.
+  print('converting model to tflite')
+  edge_model = ai_edge_torch.convert(
+      model,
+      (
+          idx,
+          input_pos,
+      ),
+  )
+  edge_model.export('/tmp/toy_model.tflite')
+
+
+def _export_stablehlo_mlir(model, args):
+  ep = torch.export.export(model, args)
+  return lowertools.exported_program_to_mlir_text(ep)
+
+
+def convert_toy_model_with_kv_cache(_) -> None:
+  """Converts a toy model with kv cache to tflite."""
+  dump_mlir = False
+
+  config = toy_model_with_kv_cache.get_model_config()
+  model = toy_model_with_kv_cache.ToyModelWithKVCache(config)
+  model.eval()
+  print('running an inference')
+  kv = kv_utils.KVCache.from_model_config(config)
+
+  tokens, input_pos = toy_model_with_kv_cache.get_sample_prefill_inputs()
+  decode_token, decode_input_pos = (
+      toy_model_with_kv_cache.get_sample_decode_inputs()
+  )
+  print(model.forward(tokens, input_pos, kv))
+
+  if dump_mlir:
+    mlir_text = _export_stablehlo_mlir(model, (tokens, input_pos, kv))
+    with open('/tmp/toy_model_with_external_kv.stablehlo.mlir', 'w') as f:
+      f.write(mlir_text)
+
+  # Convert model to tflite with 2 signatures (prefill + decode).
+  print('converting toy model to tflite with 2 signatures (prefill + decode)')
+  edge_model = (
+      ai_edge_torch.signature(
+          'prefill',
+          model,
+          sample_kwargs={
+              'tokens': tokens,
+              'input_pos': input_pos,
+              'kv_cache': kv,
+          },
+      )
+      .signature(
+          'decode',
+          model,
+          sample_kwargs={
+              'tokens': decode_token,
+              'input_pos': decode_input_pos,
+              'kv_cache': kv,
+          },
+      )
+      .convert()
+  )
+  edge_model.export('/tmp/toy_external_kv_cache.tflite')
+
+
+if __name__ == '__main__':
+  app.run(convert_toy_model)

ai_edge_torch/generative/examples/test_models/toy_model.py

@@ -15,13 +15,12 @@
 # A toy example which has a single-layer transformer block.
 from typing import Tuple
 
-import ai_edge_torch
+from ai_edge_torch.generative.layers import builder
 from ai_edge_torch.generative.layers.attention import TransformerBlock
 import ai_edge_torch.generative.layers.attention_utils as attn_utils
-import ai_edge_torch.generative.layers.builder as builder
 import ai_edge_torch.generative.layers.model_config as cfg
 import torch
-import torch.nn as nn
+from torch import nn
 
 RoPECache = Tuple[torch.Tensor, torch.Tensor]
 KV_CACHE_MAX_LEN = 100
@@ -149,31 +148,3 @@ def get_model_config() -> cfg.ModelConfig:
       final_norm_config=norm_config,
   )
   return config
-
-
-def define_and_run() -> None:
-  model = ToySingleLayerModel(get_model_config())
-  idx = torch.unsqueeze(torch.arange(0, KV_CACHE_MAX_LEN), 0)
-  input_pos = torch.arange(0, KV_CACHE_MAX_LEN)
-  print('running an inference')
-  print(
-      model.forward(
-          idx,
-          input_pos,
-      )
-  )
-
-  # Convert model to tflite.
-  print('converting model to tflite')
-  edge_model = ai_edge_torch.convert(
-      model,
-      (
-          idx,
-          input_pos,
-      ),
-  )
-  edge_model.export('/tmp/toy_model.tflite')
-
-
-if __name__ == '__main__':
-  define_and_run()

ai_edge_torch/generative/examples/test_models/toy_model_with_kv_cache.py

@@ -17,15 +17,14 @@
 
 from typing import Tuple
 
-import ai_edge_torch
-from ai_edge_torch import lowertools
+from absl import app
 from ai_edge_torch.generative.layers import attention
 from ai_edge_torch.generative.layers import builder
 from ai_edge_torch.generative.layers import kv_cache as kv_utils
 import ai_edge_torch.generative.layers.attention_utils as attn_utils
 import ai_edge_torch.generative.layers.model_config as cfg
 import torch
-import torch.nn as nn
+from torch import nn
 
 RoPECache = Tuple[torch.Tensor, torch.Tensor]
 
@@ -87,11 +86,6 @@ def forward(
     return {'logits': self.lm_head(x), 'kv_cache': updated_kv_cache}
 
 
-def _export_stablehlo_mlir(model, args):
-  ep = torch.export.export(model, args)
-  return lowertools.exported_program_to_mlir_text(ep)
-
-
 def get_model_config() -> cfg.ModelConfig:
   attn_config = cfg.AttentionConfig(
       num_heads=32,
@@ -133,51 +127,3 @@ def get_sample_decode_inputs() -> Tuple[torch.Tensor, torch.Tensor]:
   tokens = torch.tensor([[1]], dtype=torch.int)
   input_pos = torch.tensor([10])
   return tokens, input_pos
-
-
-def define_and_run() -> None:
-  dump_mlir = False
-
-  config = get_model_config()
-  model = ToyModelWithExternalKV(config)
-  model.eval()
-  print('running an inference')
-  kv = kv_utils.KVCache.from_model_config(config)
-
-  tokens, input_pos = get_sample_prefill_inputs()
-  decode_token, decode_input_pos = get_sample_decode_inputs()
-  print(model.forward(tokens, input_pos, kv))
-
-  if dump_mlir:
-    mlir_text = _export_stablehlo_mlir(model, (tokens, input_pos, kv))
-    with open('/tmp/toy_model_with_external_kv.stablehlo.mlir', 'w') as f:
-      f.write(mlir_text)
-
-  # Convert model to tflite with 2 signatures (prefill + decode).
-  print('converting toy model to tflite with 2 signatures (prefill + decode)')
-  edge_model = (
-      ai_edge_torch.signature(
-          'prefill',
-          model,
-          sample_kwargs={
-              'tokens': tokens,
-              'input_pos': input_pos,
-              'kv_cache': kv,
-          },
-      )
-      .signature(
-          'decode',
-          model,
-          sample_kwargs={
-              'tokens': decode_token,
-              'input_pos': decode_input_pos,
-              'kv_cache': kv,
-          },
-      )
-      .convert()
-  )
-  edge_model.export('/tmp/toy_external_kv_cache.tflite')
-
-
-if __name__ == '__main__':
-  define_and_run()