[Hardware][TPU] Refactor TPU backend (#5831)

vllm/executor/tpu_executor.py

@@ -1,4 +1,4 @@
-from typing import List, Set, Tuple
+from typing import Any, Dict, List, Optional, Set, Tuple
 
 import torch
 
@@ -26,30 +26,46 @@ class TPUExecutor(ExecutorBase):
             self.model_config.dtype = torch.bfloat16
 
         # Instantiate the worker and load the model to the device.
-        self._init_worker()
+        self.driver_worker = self._create_worker()
-
-    def _init_worker(self):
-        from vllm.worker.tpu_worker import TPUWorker
-
-        assert self.parallel_config.world_size == 1, (
-            "TPUExecutor currently only supports a single TPU chip.")
-        distributed_init_method = get_distributed_init_method(
-            get_ip(), get_open_port())
-        self.driver_worker = TPUWorker(
-            self.model_config,
-            self.parallel_config,
-            self.scheduler_config,
-            self.device_config,
-            self.cache_config,
-            self.load_config,
-            self.vision_language_config,
-            local_rank=0,
-            rank=0,
-            distributed_init_method=distributed_init_method,
-        )
         self.driver_worker.init_device()
         self.driver_worker.load_model()
 
+    def _get_worker_kwargs(
+        self,
+        local_rank: int = 0,
+        rank: int = 0,
+        distributed_init_method: Optional[str] = None,
+    ) -> Dict[str, Any]:
+        """Return worker init args for a given rank."""
+        if distributed_init_method is None:
+            distributed_init_method = get_distributed_init_method(
+                get_ip(), get_open_port())
+        return dict(
+            model_config=self.model_config,
+            parallel_config=self.parallel_config,
+            scheduler_config=self.scheduler_config,
+            device_config=self.device_config,
+            cache_config=self.cache_config,
+            load_config=self.load_config,
+            local_rank=local_rank,
+            rank=rank,
+            distributed_init_method=distributed_init_method,
+            vision_language_config=self.vision_language_config,
+            is_driver_worker=rank == 0,
+        )
+
+    def _create_worker(
+        self,
+        local_rank: int = 0,
+        rank: int = 0,
+        distributed_init_method: Optional[str] = None,
+    ):
+        from vllm.worker.tpu_worker import TPUWorker
+
+        worker = TPUWorker(**self._get_worker_kwargs(local_rank, rank,
+                                                     distributed_init_method))
+        return worker
+
     def initialize_cache(
         self,
         num_gpu_blocks: int,

vllm/worker/tpu_model_runner.py

@@ -33,6 +33,7 @@ class TPUModelRunner:
         cache_config: CacheConfig,
         load_config: LoadConfig,
         vision_language_config: Optional[VisionLanguageConfig] = None,
+        is_driver_worker: bool = False,
     ):
         self.model_config = model_config
         self.parallel_config = parallel_config
@@ -41,6 +42,7 @@ class TPUModelRunner:
         self.cache_config = cache_config
         self.load_config = load_config
         self.vision_language_config = vision_language_config
+        self.is_driver_worker = is_driver_worker
 
         self.block_size = self.cache_config.block_size
         self.max_num_blocks_per_seq = (self.model_config.max_model_len //
@@ -373,6 +375,8 @@ class TPUModelRunner:
         inputs = self.prepare_inputs(seq_group_metadata_list)
         next_token_ids = self.model(inputs[0], inputs[1], kv_caches,
                                     *inputs[2:])
+        if not self.is_driver_worker:
+            return []
         next_token_ids = next_token_ids.cpu().tolist()
 
         i = 0

vllm/worker/tpu_worker.py

@@ -34,6 +34,7 @@ class TPUWorker(LoraNotSupportedWorkerBase):
         local_rank: int,
         rank: int,
         distributed_init_method: str,
+        is_driver_worker: bool,
     ) -> None:
         self.model_config = model_config
         self.parallel_config = parallel_config
@@ -45,6 +46,7 @@ class TPUWorker(LoraNotSupportedWorkerBase):
         self.local_rank = local_rank
         self.rank = rank
         self.distributed_init_method = distributed_init_method
+        self.is_driver_worker = is_driver_worker
 
         assert self.device_config.device_type == "tpu"
         if self.cache_config.cache_dtype == "auto":
@@ -53,10 +55,14 @@ class TPUWorker(LoraNotSupportedWorkerBase):
             self.cache_dtype = STR_DTYPE_TO_TORCH_DTYPE[
                 self.cache_config.cache_dtype]
 
-        self.model_runner = TPUModelRunner(model_config, parallel_config,
+        self.model_runner = TPUModelRunner(model_config,
-                                           scheduler_config, device_config,
+                                           parallel_config,
-                                           cache_config, load_config,
+                                           scheduler_config,
-                                           vision_language_config)
+                                           device_config,
+                                           cache_config,
+                                           load_config,
+                                           vision_language_config,
+                                           is_driver_worker=is_driver_worker)
 
     def init_device(self) -> None:
         os.environ["PJRT_DEVICE"] = "TPU"
@@ -175,16 +181,13 @@ class TPUWorker(LoraNotSupportedWorkerBase):
 
     def execute_model(
         self,
-        execute_model_req: Optional[ExecuteModelRequest] = None
+        execute_model_req: Optional[ExecuteModelRequest] = None,
     ) -> List[SamplerOutput]:
-        if execute_model_req is None:
+        if not self.is_driver_worker:
-            return []
+            self._execute_model_non_driver()
-
-        seq_group_metadata_list = execute_model_req.seq_group_metadata_list
-        num_seq_groups = len(seq_group_metadata_list)
-        if num_seq_groups == 0:
             return []
 
+        assert execute_model_req is not None
         # Currently, TPUWorker does not support swapping.
         # TODO(woosuk): Support block copying.
         assert len(execute_model_req.blocks_to_swap_in) == 0, (
@@ -193,6 +196,16 @@ class TPUWorker(LoraNotSupportedWorkerBase):
             "Swapping is not supported for the TPU backend.")
         assert len(execute_model_req.blocks_to_copy) == 0
 
+        seq_group_metadata_list = execute_model_req.seq_group_metadata_list
+        assert len(seq_group_metadata_list) > 0
         output = self.model_runner.execute_model(seq_group_metadata_list,
                                                  self.tpu_cache)
         return [output]
+
+    def start_worker_execution_loop(self) -> None:
+        while self._execute_model_non_driver():
+            pass
+
+    def _execute_model_non_driver(self) -> bool:
+        self.model_runner.execute_model(None, self.tpu_cache)
+        return True