Integrate disaggregated serving with JetStream (#117)

* add diaggregated server with ray support

* add run_server wity ray

* format

Author: FanhaiLu1

jetstream_pt/ray_engine.py

@@ -1,5 +1,5 @@
 from collections import defaultdict
-from typing import Any, Iterable, Optional, Union
+from typing import Any, Iterable, Optional, Union, Tuple, List
 
 import numpy as np
 import ray
@@ -180,7 +180,9 @@ def create_pytorch_ray_engine(
     decode_pod_slice_name: str = None,
     enable_jax_profiler: bool = False,
     jax_profiler_port: int = 9999,
-) -> Any:
+) -> Union[
+    PyTorchRayEngine, Tuple[List[PyTorchRayEngine], List[PyTorchRayEngine]]
+]:
 
   # Return tuple as reponse: issues/107
   supported_models = ["llama-2", "llama-3", "gemma"]
@@ -254,4 +256,4 @@ def create_pytorch_ray_engine(
       is_disaggregated=is_disaggregated,
       pod_slice_name=decode_pod_slice_name,
   )
-  return (prefill_engine, decode_engine)
+  return ([prefill_engine], [decode_engine])

run_interactive_disaggregated.py

@@ -94,25 +94,27 @@ def create_disaggregated_engines():
   os.environ["TF_CPP_MIN_LOG_LEVEL"] = "0"
 
   start = time.perf_counter()
-  prefill_engine, decode_engine = ray_engine.create_pytorch_ray_engine(
-      model_name=_MODEL_NAME.value,
-      tokenizer_path=_TOKENIZER_PATH.value,
-      ckpt_path=_CKPT_PATH.value,
-      bf16_enable=True,
-      param_size=_SIZE.value,
-      context_length=_CONTEXT_LENGTH.value,
-      batch_size=_BATCH_SIZE.value,
-      quantize_weights=_QUANTIZE_WEIGHTS.value,
-      quantize_kv=_QUANTIZE_KV_CACHE.value,
-      max_cache_length=_MAX_CACHE_LENGTH.value,
-      sharding_config=_SHARDING_CONFIG.value,
-      is_disaggregated=_IS_DISAGGREGATED.value,
-      num_hosts=_NUM_HOSTS.value,
-      decode_pod_slice_name=_DECODE_POD_SLICE_NAME.value,
+  prefill_engine_list, decode_engine_list = (
+      ray_engine.create_pytorch_ray_engine(
+          model_name=_MODEL_NAME.value,
+          tokenizer_path=_TOKENIZER_PATH.value,
+          ckpt_path=_CKPT_PATH.value,
+          bf16_enable=True,
+          param_size=_SIZE.value,
+          context_length=_CONTEXT_LENGTH.value,
+          batch_size=_BATCH_SIZE.value,
+          quantize_weights=_QUANTIZE_WEIGHTS.value,
+          quantize_kv=_QUANTIZE_KV_CACHE.value,
+          max_cache_length=_MAX_CACHE_LENGTH.value,
+          sharding_config=_SHARDING_CONFIG.value,
+          is_disaggregated=_IS_DISAGGREGATED.value,
+          num_hosts=_NUM_HOSTS.value,
+          decode_pod_slice_name=_DECODE_POD_SLICE_NAME.value,
+      )
   )
 
   print("Initialize engine", time.perf_counter() - start)
-  return (prefill_engine, decode_engine)
+  return (prefill_engine_list[0], decode_engine_list[0])
 
 
 # pylint: disable-next=all

run_server_with_ray.py

@@ -37,6 +37,14 @@
 flags.DEFINE_bool("enable_jax_profiler", False, "enable jax profiler")
 flags.DEFINE_integer("jax_profiler_port", 9999, "port of JAX profiler server")
 
+flags.DEFINE_bool(
+    "is_disaggregated", False, "Disaggregated serving if it's True"
+)
+
+flags.DEFINE_integer("num_hosts", 4, "Number of TPU host", required=False)
+
+flags.DEFINE_string("decode_pod_slice_name", "", "Decode pod slice name")
+
 
 def create_engine():
   """create a pytorch engine"""
@@ -64,6 +72,37 @@ def create_engine():
   return engine
 
 
+def create_disaggregated_engine():
+  """create a pytorch engine"""
+  jax.config.update("jax_default_prng_impl", "unsafe_rbg")
+  os.environ["TF_CPP_MIN_LOG_LEVEL"] = "0"
+
+  start = time.perf_counter()
+  prefill_engine_list, decode_engine_list = (
+      ray_engine.create_pytorch_ray_engine(
+          model_name=FLAGS.model_name,
+          tokenizer_path=FLAGS.tokenizer_path,
+          ckpt_path=FLAGS.checkpoint_path,
+          bf16_enable=FLAGS.bf16_enable,
+          param_size=FLAGS.size,
+          context_length=FLAGS.context_length,
+          batch_size=FLAGS.batch_size,
+          quantize_weights=FLAGS.quantize_weights,
+          quantize_kv=FLAGS.quantize_kv_cache,
+          max_cache_length=FLAGS.max_cache_length,
+          sharding_config=FLAGS.sharding_config,
+          enable_jax_profiler=FLAGS.enable_jax_profiler,
+          jax_profiler_port=FLAGS.jax_profiler_port,
+          is_disaggregated=FLAGS.is_disaggregated,
+          num_hosts=FLAGS.num_hosts,
+          decode_pod_slice_name=FLAGS.decode_pod_slice_name,
+      )
+  )
+
+  print("Initialize engine", time.perf_counter() - start)
+  return (prefill_engine_list, decode_engine_list)
+
+
 # pylint: disable-next=all
 def main(argv: Sequence[str]):
   del argv
@@ -74,12 +113,24 @@ def main(argv: Sequence[str]):
 
   print(f"devices: {devices}")
 
-  engine = create_engine()
+  if FLAGS.is_disaggregated:
+    prefill_engine_list, decode_engine_list = create_disaggregated_engine()
+    chips = int(len(devices) / 2)
+    server_config = ServerConfig(
+        prefill_slices=(f"tpu={chips}",),
+        prefill_engine_create_fns=(lambda a: prefill_engine_list[0],),
+        generate_slices=(f"tpu={chips}",),
+        generate_engine_create_fns=(lambda a: decode_engine_list[0],),
+        is_ray_backend=True,
+    )
+
+  else:
+    engine = create_engine()
+    server_config = ServerConfig(
+        interleaved_slices=(f"tpu={len(devices)}",),
+        interleaved_engine_create_fns=(lambda a: engine,),
+    )
 
-  server_config = ServerConfig(
-      interleaved_slices=(f"tpu={len(devices)}",),
-      interleaved_engine_create_fns=(lambda a: engine,),
-  )
   print(f"server_config: {server_config}")
 
   jetstream_server = server_lib.run(