JetStream changes for Jax based implementation of unified_lora_params for decoding batch of multiple different lora adapters.

jetstream/core/lora/adapter_tensorstore.py

@@ -99,6 +99,7 @@ def __init__(
       adapters_dir_path: str,
       hbm_memory_budget: int,
       cpu_memory_budget: int,
+      total_slots: int,
   ):
     """Initializes the AdapterTensorStore."""
     self.engine = engine  # Possibly MaxEngine object
@@ -119,8 +120,27 @@ def __init__(
     self.running_requests: int = (
         0  # Number of async tasks which are in "loading" state
     )
+    self.decoding_adapters_cache: Dict[str, Any] = {}
+
+    # TODO: Make dtype configurable for the scale factor array
+    self.adapters_scale_factor = jnp.empty(1, dtype=jnp.bfloat16)
+
+    self.total_slots = total_slots
     self.lock = asyncio.Lock()  # Use an asyncio Lock for thread safety
 
+  def _get_adapter_scale_factor(self, adapter_id: str):
+    """
+    Internal: Get the LoRA scale_factor using the adapter_id.
+    """
+    adapter_config = self.adapter_registry[adapter_id].config
+    lora_scale_factor = float(1)
+
+    if "r" in adapter_config and "lora_alpha" in adapter_config:
+      lora_rank = int(adapter_config["r"])
+      lora_scale_factor = float(adapter_config["lora_alpha"]) / lora_rank
+
+    return lora_scale_factor
+
   # --- Unsafe Internal methods which assumes that lock is held ---
   def _unsafe_transfer_to_hbm(self, adapter_id: str):
     """
@@ -207,6 +227,90 @@ def _unsafe_unload_adapter(self, adapter_id: str):
     metadata.size_hbm = 0
     metadata.size_cpu = 0
 
+  def _initialize_decoding_adapters_cache(self, adapter_weights):
+    """
+    Create a new PyTree with zero tensors at the paths corresponding to
+    non-None leaves in the input PyTree. The zero tensors have an added
+    dimension of size `self.totol_slots`.
+    Args:
+      adatper_weights: The input PyTree, whose structure will be mirrored.
+    Returns:
+      A new PyTree with zero Tensors or None values, mirroring the structure
+      of the input PyTree.
+    """
+
+    def create_zero_leaf(leaf):
+      if leaf is not None:
+        original_shape = leaf.shape
+        if not original_shape:  # handle scalar case
+          zero_tensor_shape = (self.total_slots,)
+        else:
+          zero_tensor_shape = (
+              self.total_slots,
+          ) + original_shape  # Prepend a new dimension
+
+        return jnp.zeros(zero_tensor_shape, dtype=leaf.dtype)
+      else:
+        return None  # Maintain None structure for None leaves
+
+    self.adapters_scale_factor = jnp.ones(self.total_slots, dtype=jnp.bfloat16)
+    return jax.tree_util.tree_map(create_zero_leaf, adapter_weights)
+
+  def insert_adapter_in_cache(self, adapter_id: str, slot_id: int):
+    """
+    Insert the specific adapter tensors into a slot in the
+    serving_adapters_cache.
+    Args:
+      adapter_id: The id of the adapter, whose tensors will be inserted
+      slot_id: The id of slot, which represents the index in the
+      serving_adapter_cache where the adapter tensors will be inserted.
+    """
+
+    def insert_leaf(dest_leaf, source_leaf):
+      if dest_leaf is not None and source_leaf is not None:
+        return dest_leaf.at[slot_id].set(
+            source_leaf
+        )  # Insert at the specific index
+      elif dest_leaf is not None:
+        return dest_leaf  # If source_leaf is None, keep the zero_leaf as is
+      elif (
+          source_leaf is not None
+      ):  # In this case the adapters have different target modules
+        original_shape = source_leaf.shape
+        if not original_shape:  # Handle scalar case
+          zero_tensor_shape = (self.total_slots,)
+        else:
+          zero_tensor_shape = (self.total_slots,) + original_shape
+        new_dest_leaf = jnp.zeros(zero_tensor_shape, dtype=source_leaf.dtype)
+        return new_dest_leaf.at[slot_id].set(source_leaf)
+      else:
+        return None  # If both are None, return None
+
+    if adapter_id == "":
+      logging.info(
+          "Empty adapter id. No LoRA tensors added to adapter_tensorstore cache"
+      )
+      return
+
+    asyncio.run(self.load_adapter(adapter_id, None, True))
+
+    adapter_weights = self.loaded_adapters_hbm[adapter_id]
+
+    if not self.decoding_adapters_cache:
+      self.decoding_adapters_cache = self._initialize_decoding_adapters_cache(
+          adapter_weights
+      )
+
+    adapter_scale_factor = jnp.bfloat16(
+        self._get_adapter_scale_factor(adapter_id)
+    )
+    self.adapters_scale_factor = self.adapters_scale_factor.at[slot_id].set(
+        adapter_scale_factor
+    )
+    self.decoding_adapters_cache = jax.tree_util.tree_map(
+        insert_leaf, self.decoding_adapters_cache, adapter_weights
+    )
+
   # --- Public Methods (Acquire lock, then call unsafe methods) ---
 
   async def register_adapter(

jetstream/core/orchestrator.py

@@ -1018,6 +1018,10 @@ def _insert_if_possible(
     # Check if there are any free my_slots. We don't want to block here since
     # we can still generate if we can't insert. We do this in a while loop to
     # insert as many sequences as possible.
+    adapter_tensorstore = None
+    if self._generate_adapterstore and idx < len(self._generate_adapterstore):
+      adapter_tensorstore = self._generate_adapterstore[idx]
+
     while True:
       my_slots_size = my_slots.qsize()
 
@@ -1086,8 +1090,13 @@ def _insert_if_possible(
           new_request.prefill_result,
           decode_state,
           slot=slot,
-          # request_id=new_request.request_id,
       )
+
+      if adapter_tensorstore:
+        adapter_tensorstore.insert_adapter_in_cache(
+            new_request.adapter_id, slot
+        )
+
       ThreadDebugLog(
           thread_name,
           f"Generate slice {idx} filled slot {slot} at step "
@@ -1227,6 +1236,10 @@ def _generate_thread(self, idx: int):
     my_generate_backlog = self._generate_backlogs[idx]
     my_detokenize_backlog = self._detokenize_backlogs[idx]
 
+    adapter_tensorstore = None
+    if self._generate_adapterstore and idx < len(self._generate_adapterstore):
+      adapter_tensorstore = self._generate_adapterstore[idx]
+
     # Keep track of what step tokens were generated at.
     generate_timestep = 0
     # State to store things like running kv cache in.
@@ -1292,6 +1305,24 @@ def _generate_thread(self, idx: int):
           my_slots.qsize() < max_concurrent_decodes
       ), "At this point we must have some requests inserted into the slots."
 
+      if adapter_tensorstore:
+        decoding_adapters_params = adapter_tensorstore.decoding_adapters_cache
+        adapters_scale_factor = adapter_tensorstore.adapters_scale_factor
+        b = adapters_scale_factor.shape[0]
+
+        # Reshaped the scale_factors array to 4-D to align with shape of
+        # the vectors `(batch, hidden_size, num_heads, head_dim)`.
+        reshaped_scale_factors = adapters_scale_factor.reshape((b, 1, 1, 1))
+
+        lora_state = {}
+        lora_state["scale_factor"] = reshaped_scale_factors
+        lora_state["lora_params"] = decoding_adapters_params
+
+        if isinstance(decode_state, dict):
+          decode_state["lora_state"] = lora_state
+        else:  # flax.struct.dataclass
+          decode_state = decode_state.replace(lora_state=lora_state)
+
       # Now we actually take a generate step on requests in the slots.
       decode_state, sampled_tokens = generate_engine.generate(
           generate_params, decode_state

jetstream/core/server_lib.py

@@ -174,23 +174,26 @@ def create_driver(
   shared_adapterstore = []
 
   if lora_input_adapters_path:
+    # TODO: Make hbm_memory_budget and cpu_memory_budget configurable
     for pe in engines.prefill_engines:
       prefill_adapterstore.append(
           adapterstore.AdapterTensorStore(
               engine=pe,
               adapters_dir_path=lora_input_adapters_path,
               hbm_memory_budget=20 * (1024**3),  # 20 GB HBM
               cpu_memory_budget=100 * (1024**3),  # 100 GB RAM
+              total_slots=pe.max_concurrent_decodes,
           )
       )
-    # TODO: Make hbm_memory_budget and cpu_memory_budget configurable
+
     for ge in engines.generate_engines:
       generate_adapterstore.append(
           adapterstore.AdapterTensorStore(
               engine=ge,
               adapters_dir_path=lora_input_adapters_path,
               hbm_memory_budget=20 * (1024**3),  # 20 GB HBM
               cpu_memory_budget=100 * (1024**3),  # 100 GB RAM
+              total_slots=ge.max_concurrent_decodes,
           )
       )
 
@@ -201,6 +204,7 @@ def create_driver(
               adapters_dir_path=lora_input_adapters_path,
               hbm_memory_budget=20 * (1024**3),  # 20 GB HBM
               cpu_memory_budget=100 * (1024**3),  # 100 GB RAM
+              total_slots=ie.max_concurrent_decodes,
           )
       )
 
@@ -315,6 +319,9 @@ def run(
         "Not starting Prometheus server: --prometheus_port flag not set"
     )
 
+  if multi_sampling and lora_input_adapters_path:
+    raise ValueError("LoRA adapters is not enabled for multi_sampling mode.")
+
   driver = create_driver(
       config,
       devices,

jetstream/engine/mock_engine.py

@@ -32,7 +32,7 @@
 
 import functools
 from dataclasses import asdict
-from typing import Any, Callable, Optional, Tuple
+from typing import Any, Dict, Callable, Optional, Tuple
 
 import jax
 import jax.numpy as jnp
@@ -71,6 +71,7 @@ class DecodeState:
   generate_cache_index: int
   generate_lengths: jax.Array
   generate_tokens: jax.Array
+  lora_state: Optional[Dict[str, Any]] = None
 
 
 class TestEngine(engine_api.Engine):
@@ -509,6 +510,7 @@ def init_decode_state(self) -> DecodeState:
         generate_tokens=jnp.zeros(
             (self.generate_cache_batch, 1), dtype=jnp.int32
         ),
+        lora_state={},
     )
 
   @property

jetstream/tests/core/lora/test_adapter_tensorstore.py

@@ -145,6 +145,7 @@ async def asyncSetUp(self):
         adapters_dir_path=self.adapters_dir_path,
         hbm_memory_budget=self.hbm_budget,
         cpu_memory_budget=self.cpu_budget,
+        total_slots=8,
     )
 
     # Pre-register adapters for most tests to simplify setup

jetstream/tests/core/test_orchestrator.py

@@ -123,13 +123,15 @@ async def _setup_driver_with_adapterstore(
         adapters_dir_path="/tmp/",
         hbm_memory_budget=20 * (1024**3),  # 20 GB HBM
         cpu_memory_budget=100 * (1024**3),  # 100 GB RAM
+        total_slots=8,
     )
 
     generate_adapterstore = adapterstore.AdapterTensorStore(
         engine=generate_engine,
         adapters_dir_path="/tmp/",
         hbm_memory_budget=20 * (1024**3),  # 20 GB HBM
         cpu_memory_budget=100 * (1024**3),  # 100 GB RAM
+        total_slots=8,
     )
 
     await prefill_adapterstore.register_adapter(