Add model_server to lit-on-gcp,

lit_nlp/examples/gcp/ model_server.py

@@ -0,0 +1,88 @@
+r"""A model server for serving models on GCP via Gunicorn."""
+
+from collections.abc import Sequence
+import functools
+import os
+from typing import Optional
+from absl import app
+from absl import flags
+from lit_nlp import dev_server
+from lit_nlp.examples.prompt_debugging import models as prompt_debugging_models
+from lit_nlp.lib import serialize
+from lit_nlp.lib import wsgi_app
+
+_FLAGS = flags.FLAGS
+
+DEFAULT_DL_FRAMEWORK = 'kerasnlp'
+DEFAULT_DL_RUNTIME = 'tensorflow'
+DEFAULT_PRECISION = 'bfloat16'
+DEFAULT_SEQUENCE_LENGTH = 512
+DEFAULT_BATCH_SIZE = 1
+DEFAULT_MODELS = 'gemma_1.1_instruct_2b_en:/cns/je-d/home/mattdangerw/keras/gemma/gemma_1.1_instruct_2b_en/3/'
+
+
+def get_wsgi_app() -> wsgi_app.App:
+  """Return WSGI app for container-hosted demos."""
+
+  def wrap_handler(predict_fn):
+    @functools.wraps(predict_fn)
+    def _handler(app, request, environ):
+      data = serialize.from_json(request.data) if len(request.data) else None
+      inputs = data['inputs']
+      outputs = predict_fn(inputs)
+      response_body = serialize.to_json(outputs, simple=True)
+      return app.respond(request, response_body, 'application/json', 200)
+
+    return _handler
+
+  model_config = os.getenv('MODEL_CONFIG', DEFAULT_MODELS).split(',')
+  dl_framework = os.environ.get('DL_FRAMEWORK', DEFAULT_DL_FRAMEWORK)
+  dl_runtime = os.environ.get('DL_RUNTIME', DEFAULT_DL_RUNTIME)
+  precision = os.environ.get('PRECISION', DEFAULT_PRECISION)
+  batch_size = os.environ.get('BATCH_SIZE', DEFAULT_BATCH_SIZE)
+  sequence_length = os.environ.get('SEQUENCE_LENGTH', DEFAULT_SEQUENCE_LENGTH)
+
+  # Parse flags without calling app.run(main), to avoid conflict with
+  # gunicorn command line flags.
+  models = prompt_debugging_models.get_models(
+      models_config=model_config,
+      dl_framework=dl_framework,
+      dl_runtime=dl_runtime,
+      precision=precision,
+      batch_size=batch_size,
+      sequence_length=sequence_length,
+  )
+
+  if len(DEFAULT_MODELS) < 1:
+    raise ValueError('No models specified in DEFAULT_MODELS')
+  model_name = DEFAULT_MODELS[0].split(':')[0]
+
+  predict_model = models[model_name]
+  salience_model = models[f'{model_name}_salience']
+  tokenize_model = models[f'{model_name}_tokenize']
+
+  handlers = {
+      '/predict': predict_model.predict,
+      '/salience': salience_model.predict,
+      '/tokenize': tokenize_model.predict,
+  }
+
+  wrapped_handlers = {
+      endpoint: wrap_handler(endpoint_fn)
+      for endpoint, endpoint_fn in handlers.items()
+  }
+
+  return wsgi_app.App(
+      wrapped_handlers, project_root='gcp', index_file='index.html'
+  )
+
+
+def main(argv: Sequence[str]) -> Optional[dev_server.LitServerType]:
+  if len(argv) > 1:
+    raise app.UsageError('Too many command-line arguments.')
+
+  return get_wsgi_app()
+
+
+if __name__ == '__main__':
+  app.run(main)