worker changes for pp

Signed-off-by: Chenyaaang <chenyangli@google.com>

Author: Chenyaaang

tpu_inference/worker/tpu_worker.py

@@ -10,6 +10,7 @@
 import jaxtyping
 import vllm.envs as vllm_envs
 from vllm.config import VllmConfig, set_current_vllm_config
+from vllm.distributed import get_pp_group
 from vllm.distributed.kv_transfer import (ensure_kv_transfer_initialized,
                                           has_kv_transfer_group)
 from vllm.distributed.parallel_state import (ensure_model_parallel_initialized,
@@ -23,10 +24,13 @@
 from vllm.v1.outputs import DraftTokenIds, ModelRunnerOutput
 
 from tpu_inference import envs, utils
+from tpu_inference.distributed import jax_parallel_state
 from tpu_inference.distributed.utils import (get_host_ip, get_kv_transfer_port,
                                              get_node_id)
 from tpu_inference.layers.common.sharding import ShardingConfigManager
 from tpu_inference.logger import init_logger
+from tpu_inference.models.jax.jax_intermediate_tensor import \
+    JaxIntermediateTensors
 from tpu_inference.runner.kv_cache import get_rpa_page_size_bytes
 from tpu_inference.runner.tpu_runner import TPUModelRunner
 
@@ -41,13 +45,17 @@
 
 class TPUWorker:
 
-    def __init__(self,
-                 vllm_config: VllmConfig,
-                 local_rank: int,
-                 rank: int,
-                 distributed_init_method: str,
-                 is_driver_worker: bool = False,
-                 devices=None):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        local_rank: int,
+        rank: int,
+        distributed_init_method: str,
+        is_driver_worker: bool = False,
+        devices=None,
+        ip: str = "localhost",
+        prev_worker_ip: str = "localhost",
+    ):
         # If we use vLLM's model implementation in PyTorch, we should set it
         # with torch version of the dtype.
         impl = envs.MODEL_IMPL_TYPE
@@ -74,6 +82,9 @@ def __init__(self,
         self.devices = devices if devices is not None else []
         self.device_ranks = set(device.id for device in self.devices
                                 if isinstance(device, jaxlib._jax.Device))
+        self.ip = ip
+        self.prev_worker_ip = prev_worker_ip
+        self.pp_world_size = self.parallel_config.pipeline_parallel_size
 
         if self.model_config.trust_remote_code:
             # note: lazy import to avoid importing torch before initializing
@@ -86,14 +97,20 @@ def __init__(self,
         # TPU Worker is initialized. The profiler server needs to start after
         # MP runtime is initialized.
         self.profile_dir = None
-        if vllm_envs.VLLM_TORCH_PROFILER_DIR and self.rank < 1:
+        if envs.VLLM_TORCH_PROFILER_DIR and self.rank < 1 and self.pp_world_size == 1:
             if not self.devices or 0 in self.device_ranks:
                 # For TPU, we can only have 1 active profiler session for 1 profiler
                 # server. So we only profile on rank0.
                 self.profile_dir = vllm_envs.VLLM_TORCH_PROFILER_DIR
                 logger.info("Profiling enabled. Traces will be saved to: %s",
                             self.profile_dir)
 
+        # For PP, we use MPMD so we want to profile every worker.
+        if self.pp_world_size > 1 and envs.VLLM_TORCH_PROFILER_DIR:
+            self.profile_dir = os.path.join(envs.VLLM_TORCH_PROFILER_DIR,
+                                            f"rank_{self.rank}")
+            os.makedirs(self.profile_dir, exist_ok=True)
+
         use_jax_profiler_server = os.getenv("USE_JAX_PROFILER_SERVER", False)
         # Only one instance of profiler is allowed
         if use_jax_profiler_server and self.rank < 1:
@@ -105,18 +122,49 @@ def __init__(self,
                 )
                 jax.profiler.start_server(jax_profiler_server_port)
 
+        self.step_counter = 0
+
     def initialize_cache(self, num_gpu_blocks: int,
                          num_cpu_blocks: int) -> None:
         self.cache_config.num_gpu_blocks = num_gpu_blocks
         self.cache_config.num_cpu_blocks = num_cpu_blocks
 
     def init_device(self):
+        # set tpu visible devices for Jax runtime in single host PP.
+        multihost_backend = os.environ.get("TPU_MULTIHOST_BACKEND", "").lower()
+        if multihost_backend != "ray" and self.parallel_config.pipeline_parallel_size > 1:
+            # Note: Below is the setting for v6e8 host (8 chips of v6e)
+            # There are 2 ways of subslicing a v6e:
+            # 1) 2 slices with 4 TPU chips each, we can do PP=2, TP=1/2/3/4
+            # 2) 1 chip for each subslice, with at most 8 subslices,
+            #    we can do TP=1, PP=1/2/3/4/5/6/7/8
+            # Replace with your own topology.
+
+            tpu_ports = [
+                jax_parallel_state.BASE_JAX_PORT + i
+                for i in range(self.pp_world_size)
+            ]
+            os.environ["TPU_PROCESS_ADDRESSES"] = ",".join(
+                [f"localhost:{port}" for port in tpu_ports])
+            os.environ["TPU_PROCESS_PORT"] = f"{tpu_ports[self.rank]}"
+            os.environ["CLOUD_TPU_TASK_ID"] = f"{self.rank}"
+
+            # first way of subslicing.
+            # os.environ["TPU_PROCESS_BOUNDS"] = "1,1,1"
+            # os.environ["TPU_CHIPS_PER_PROCESS_BOUNDS"] = f"1,4,1"
+            # os.environ["TPU_VISIBLE_CHIPS"] = "0,1,2,3" if self.rank == 0 else "4,5,6,7"
+
+            # second way of subslicing.
+            os.environ["TPU_PROCESS_BOUNDS"] = f"1,{self.pp_world_size},1"
+            os.environ["TPU_CHIPS_PER_PROCESS_BOUNDS"] = "1,1,1"
+            os.environ["TPU_VISIBLE_CHIPS"] = f"{self.rank}"
+
         if not self.devices:
             sharding_config: ShardingConfigManager = self.vllm_config.sharding_config
             device_indexes = sharding_config.device_indexes
             if device_indexes is not None and len(device_indexes) > 0:
                 # Enforcing the devices sequence to be consistent with the specified device indexes
-                all_devices = jax.devices()
+                all_devices = jax.local_devices()
                 device_dict = {device.id: device for device in all_devices}
                 self.devices = []
                 for device_index in device_indexes:
@@ -127,10 +175,12 @@ def init_device(self):
                             f"jax.devices() with IDs {list(device_dict.keys())}!"
                         )
                     self.devices.append(device)
+                assert len(self.devices) >= sharding_config.total_devices
                 self.devices = self.devices[:sharding_config.total_devices]
             else:
-                self.devices = jax.devices()[:sharding_config.total_devices]
-
+                assert jax.local_device_count() >= sharding_config.total_devices
+                self.devices = jax.local_devices()[:sharding_config.
+                                                   total_devices]
         # Initialize the vLLM distribution layer as a single chip environment,
         # we'll swap the model's parallel modules with TPU SPMD equivalents.
         with set_current_vllm_config(self.vllm_config):
@@ -146,15 +196,30 @@ def init_device(self):
                 tensor_model_parallel_size=1,
                 pipeline_model_parallel_size=1,
             )
+
+        jax_parallel_state.init_pp_distributed_environment(
+            self.ip,
+            self.rank,
+            self.parallel_config.pipeline_parallel_size,
+            self.devices[0],
+            need_pp=self.parallel_config.pipeline_parallel_size > 1)
+
         ensure_kv_transfer_initialized(self.vllm_config)
-        self.model_runner = TPUModelRunner(self.vllm_config, self.devices)
+        self.model_runner = TPUModelRunner(self.vllm_config, self.devices,
+                                           self.rank, self.rank == 0,
+                                           self.rank == self.pp_world_size - 1)
         logger.info(f"Init worker | "
                     f"rank={self.rank} | "
                     f"node_id={get_node_id()} | "
                     f"is_driver_worker={self.is_driver_worker} | "
                     f"hbm={utils.hbm_usage_gb(self.devices)}GiB")
         vllm_utils.report_usage_stats(self.vllm_config)
 
+    def initialize_pp_transfer_connect(self):
+        if self.rank == 0:
+            return
+        jax_parallel_state.connect(self.prev_worker_ip, self.rank - 1)
+
     def determine_available_memory(self) -> int:
         gpu_memory_utilization = self.cache_config.gpu_memory_utilization
         hbm_usage = utils.hbm_usage_bytes(self.devices)
@@ -194,14 +259,39 @@ def execute_model(
         # deliberate, temporary compromise for the same reasons outlined in
         # the `get_kv_cache_spec` method.
 
-        output = self.model_runner.execute_model(scheduler_output)
-
-        # With a connector, the scheduler expects output from all workers
-        # TODO(mrjunwan): Figure out if this is ok after https://github.com/vllm-project/vllm/pull/26866
-        if has_kv_transfer_group():
-            return output
-
-        return output if self.is_driver_worker else None
+        if self.parallel_config.pipeline_parallel_size == 1 or self.rank == 0:
+            intermediate_tensors = None
+        else:
+            # receive intermediate tensors
+            uuid = self.model_runner.get_uuid_for_jax_transfer(
+                scheduler_output, self.rank - 1, self.step_counter)
+            # TODO: this method might only works for vllm model, not sure about jax models.
+            tensor_spec = self.model_runner.get_intermediate_tensor_spec(
+                scheduler_output.total_num_scheduled_tokens)
+            intermediate_tensors_dict = get_pp_group().recv_tensor_dict(
+                uuid, tensor_spec)
+            intermediate_tensors = JaxIntermediateTensors(
+                intermediate_tensors_dict)
+
+        output = self.model_runner.execute_model(scheduler_output,
+                                                 intermediate_tensors)
+
+        if isinstance(output, JaxIntermediateTensors):
+            assert self.parallel_config.pipeline_parallel_size > 1
+            assert not get_pp_group().is_last_rank
+            # send intermediate tensors
+            uuid = self.model_runner.get_uuid_for_jax_transfer(
+                scheduler_output, self.rank, self.step_counter)
+            get_pp_group().send_tensor_dict(uuid, output.tensors)
+            self.step_counter += 1
+            return None
+        else:
+            self.step_counter += 1
+            # With a connector, the scheduler expects output from all workers
+            # TODO(mrjunwan): Figure out if this is ok after https://github.com/vllm-project/vllm/pull/26866
+            if has_kv_transfer_group():
+                return output
+            return output if self.is_driver_worker else None
 
     def sample_tokens(self,
                       grammar_output: GrammarOutput) -> ModelRunnerOutput: