Support torchrun and SPMD-style offline inference (vllm-project#12071)

Signed-off-by: youkaichao <[email protected]>
Signed-off-by: Isotr0py <[email protected]>

Author: youkaichao

.buildkite/test-pipeline.yaml

@@ -463,6 +463,7 @@ steps:
   - vllm/worker/worker.py
   - vllm/worker/model_runner.py
   commands:
+  - torchrun --nproc-per-node=2 distributed/test_torchrun_example.py
   - pytest -v -s ./compile/test_basic_correctness.py
   - pytest -v -s ./compile/test_wrapper.py
   - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'

examples/offline_inference/torchrun_example.py

@@ -0,0 +1,64 @@
+"""
+experimental support for tensor-parallel inference with torchrun,
+see https://github.com/vllm-project/vllm/issues/11400 for
+the motivation and use case for this example.
+run the script with `torchrun --nproc-per-node=2 torchrun_example.py`,
+the argument 2 should match the `tensor_parallel_size` below.
+see `tests/distributed/test_torchrun_example.py` for the unit test.
+"""
+
+from vllm import LLM, SamplingParams
+
+# Create prompts, the same across all ranks
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+
+# Create sampling parameters, the same across all ranks
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+# Use `distributed_executor_backend="external_launcher"` so that
+# this llm engine/instance only creates one worker.
+llm = LLM(
+    model="facebook/opt-125m",
+    tensor_parallel_size=2,
+    distributed_executor_backend="external_launcher",
+)
+
+outputs = llm.generate(prompts, sampling_params)
+
+# all ranks will have the same outputs
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, "
+          f"Generated text: {generated_text!r}")
+"""
+Further tips:
+
+1. to communicate control messages across all ranks, use the cpu group,
+a PyTorch ProcessGroup with GLOO backend.
+
+```python
+from vllm.distributed.parallel_state import get_world_group
+cpu_group = get_world_group().cpu_group
+torch_rank = dist.get_rank(group=cpu_group)
+if torch_rank == 0:
+    # do something for rank 0, e.g. saving the results to disk.
+```
+
+2. to communicate data across all ranks, use the model's device group,
+a PyTorch ProcessGroup with NCCL backend.
+```python
+from vllm.distributed.parallel_state import get_world_group
+device_group = get_world_group().device_group
+```
+
+3. to access the model directly in every rank, use the following code:
+```python
+llm.llm_engine.model_executor.driver_worker.worker.model_runner.model
+```
+"""

tests/distributed/test_torchrun_example.py

@@ -0,0 +1,56 @@
+# unit test for `examples/offline_inference/torchrun_example.py`
+
+import random
+
+import torch.distributed as dist
+
+from vllm import LLM, SamplingParams
+from vllm.distributed.parallel_state import get_world_group
+
+# Create prompts
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+# set different `gpu_memory_utilization` and `swap_space` for different ranks,
+# to test if all ranks agree on the same kv cache configuration.
+llm = LLM(model="facebook/opt-125m",
+          tensor_parallel_size=2,
+          distributed_executor_backend="external_launcher",
+          gpu_memory_utilization=random.uniform(0.7, 0.9),
+          swap_space=random.randint(1, 4))
+
+outputs = llm.generate(prompts, sampling_params)
+
+cpu_group = get_world_group().cpu_group
+
+torch_rank = dist.get_rank(group=cpu_group)
+
+
+def test_consistent_across_ranks(obj):
+    if torch_rank == 0:
+        dist.broadcast_object_list([obj], src=0, group=cpu_group)
+    else:
+        container = [None]
+        dist.broadcast_object_list(container, src=0, group=cpu_group)
+        assert container[0] == obj
+
+
+test_consistent_across_ranks(
+    llm.llm_engine.vllm_config.cache_config.num_cpu_blocks)
+test_consistent_across_ranks(
+    llm.llm_engine.vllm_config.cache_config.num_gpu_blocks)
+
+# all ranks should have the same outputs
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    test_consistent_across_ranks(prompt)
+    test_consistent_across_ranks(generated_text)
+    print(f"Rank {torch_rank}, Prompt: {prompt!r}, "
+          f"Generated text: {generated_text!r}")

tests/engine/test_multiproc_workers.py

@@ -22,7 +22,7 @@ def worker_method(self, worker_input: Any) -> Tuple[int, Any]:
             # simulate error case
             raise worker_input
 
-        return self.rank, input
+        return self.rpc_rank, input
 
 
 def _start_workers() -> Tuple[List[ProcessWorkerWrapper], WorkerMonitor]:

vllm/config.py

@@ -1338,14 +1338,15 @@ def _verify_args(self) -> None:
         from vllm.executor.executor_base import ExecutorBase
         from vllm.platforms import current_platform
         if self.distributed_executor_backend not in (
-                "ray", "mp", "uni", None) and not (isinstance(
+                "ray", "mp", "uni",
+                "external_launcher", None) and not (isinstance(
                     self.distributed_executor_backend, type) and issubclass(
                         self.distributed_executor_backend, ExecutorBase)):
             raise ValueError(
                 "Unrecognized distributed executor backend "
                 f"{self.distributed_executor_backend}. Supported "
-                "values are 'ray', 'mp' 'uni', or custom ExecutorBase"
-                " subclass.")
+                "values are 'ray', 'mp' 'uni', 'external_launcher' or"
+                " custom ExecutorBase subclass.")
         if self.use_ray:
             from vllm.executor import ray_utils
             ray_utils.assert_ray_available()

vllm/engine/arg_utils.py

@@ -388,7 +388,7 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
         # Parallel arguments
         parser.add_argument(
             '--distributed-executor-backend',
-            choices=['ray', 'mp'],
+            choices=['ray', 'mp', 'uni', 'external_launcher'],
             default=EngineArgs.distributed_executor_backend,
             help='Backend to use for distributed model '
             'workers, either "ray" or "mp" (multiprocessing). If the product '

vllm/engine/llm_engine.py

@@ -457,6 +457,11 @@ def _get_executor_cls(cls,
                 # JAX-style, single-process, multi-device executor.
                 from vllm.executor.uniproc_executor import UniProcExecutor
                 executor_class = UniProcExecutor
+            elif distributed_executor_backend == "external_launcher":
+                # executor with external launcher
+                from vllm.executor.uniproc_executor import (  # noqa
+                    ExecutorWithExternalLauncher)
+                executor_class = ExecutorWithExternalLauncher
         else:
             from vllm.executor.uniproc_executor import UniProcExecutor
             executor_class = UniProcExecutor

vllm/executor/ray_distributed_executor.py

@@ -172,7 +172,7 @@ def _init_workers_ray(self, placement_group: "PlacementGroup",
                     scheduling_strategy=scheduling_strategy,
                     **ray_remote_kwargs,
                 )(RayWorkerWrapper).remote(vllm_config=self.vllm_config,
-                                           rank=rank)
+                                           rpc_rank=rank)
             else:
                 worker = ray.remote(
                     num_cpus=0,
@@ -181,7 +181,7 @@ def _init_workers_ray(self, placement_group: "PlacementGroup",
                     scheduling_strategy=scheduling_strategy,
                     **ray_remote_kwargs,
                 )(RayWorkerWrapper).remote(vllm_config=self.vllm_config,
-                                           rank=rank)
+                                           rpc_rank=rank)
             worker_metadata.append(
                 RayWorkerMetaData(worker=worker, created_rank=rank))
             rank += 1
@@ -204,7 +204,7 @@ def _init_workers_ray(self, placement_group: "PlacementGroup",
                     # as the resource holder for the driver process.
                     self.driver_dummy_worker = worker
                     self.driver_worker = RayWorkerWrapper(
-                        vllm_config=self.vllm_config, rank=0)
+                        vllm_config=self.vllm_config, rpc_rank=0)
                     worker_metadata.pop(i)
                     break
 

vllm/executor/uniproc_executor.py

@@ -1,5 +1,10 @@
+import os
 from typing import Any, Dict, List, Optional, Tuple
 
+import torch
+import torch.distributed as dist
+
+import vllm.envs as envs
 from vllm.executor.executor_base import ExecutorBase
 from vllm.logger import init_logger
 from vllm.utils import get_distributed_init_method, get_ip, get_open_port
@@ -16,7 +21,7 @@ def _init_executor(self) -> None:
         """Initialize the worker and load the model.
         """
         self.driver_worker = WorkerWrapperBase(vllm_config=self.vllm_config,
-                                               rank=0)
+                                               rpc_rank=0)
         distributed_init_method = get_distributed_init_method(
             get_ip(), get_open_port())
         local_rank = 0
@@ -55,3 +60,77 @@ def check_health(self) -> None:
 
 
 UniProcExecutorAsync = UniProcExecutor
+
+
+class ExecutorWithExternalLauncher(UniProcExecutor):
+    """An executor that uses external launchers to launch engines,
+    specially designed for torchrun-compatible launchers, for
+    offline inference with tensor parallelism.
+
+    see https://github.com/vllm-project/vllm/issues/11400 for
+    the motivation, and examples/offline_inference/torchrun_example.py
+    for the usage example.
+
+    The key idea: although it is tensor-parallel inference, we only
+    create one worker per executor, users will launch multiple
+    engines with torchrun-compatible launchers, and all these engines
+    work together to process the same prompts. When scheduling is
+    deterministic, all the engines will generate the same outputs,
+    and they don't need to synchronize the states with each other.
+    """
+    uses_ray: bool = False
+
+    def _init_executor(self) -> None:
+        """Initialize the worker and load the model.
+        """
+        assert self.vllm_config.parallel_config.pipeline_parallel_size == 1, \
+            ("ExecutorWithExternalLauncher does not "
+            "support pipeline parallelism.")
+        assert self.vllm_config.scheduler_config.delay_factor == 0.0, \
+            ("ExecutorWithExternalLauncher needs deterministic "
+            "execution, so it"
+            "does not support delay_factor in scheduling")
+        assert not envs.VLLM_USE_V1, \
+            ("V1 architecture cannot guarantee deterministic execution, "
+            "so it is not supported in ExecutorWithExternalLauncher.")
+        self.driver_worker = WorkerWrapperBase(vllm_config=self.vllm_config,
+                                               rpc_rank=0)
+        # engines are launched in torchrun-compatible launchers
+        # so we can use the env:// method.
+        # required env vars:
+        # - RANK
+        # - MASTER_ADDR
+        # - MASTER_PORT
+        distributed_init_method = "env://"
+        rank = int(os.environ["RANK"])
+        local_rank = rank
+        is_driver_worker = True
+        kwargs = dict(
+            vllm_config=self.vllm_config,
+            local_rank=local_rank,
+            rank=rank,
+            distributed_init_method=distributed_init_method,
+            is_driver_worker=is_driver_worker,
+        )
+        self.collective_rpc("init_worker", args=([kwargs], ))
+        self.collective_rpc("init_device")
+        self.collective_rpc("load_model")
+
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
+        """
+        Determine the number of available KV blocks.
+        Add an additional all_reduce to get the min across all ranks.
+        Note that even if we have the same `gpu_memory_utilization` and 
+        `swap_space`, the available memory in every rank might still 
+        differ because NCCL can take different amounts of memory in 
+        different ranks. Therefore, it is necessary to test if all ranks 
+        agree on the same KV cache configuration.
+        """
+        a, b = super().determine_num_available_blocks()
+        from vllm.distributed.parallel_state import get_world_group
+        cpu_group = get_world_group().cpu_group
+        a_tensor = torch.tensor([a], device="cpu", dtype=torch.int64)
+        b_tensor = torch.tensor([b], device="cpu", dtype=torch.int64)
+        dist.all_reduce(a_tensor, group=cpu_group, op=dist.ReduceOp.MIN)
+        dist.all_reduce(b_tensor, group=cpu_group, op=dist.ReduceOp.MIN)
+        return a_tensor.item(), b_tensor.item()

vllm/lora/layers.py

@@ -940,8 +940,8 @@ def soft_cap(self):
         return self.base_layer.soft_cap
 
     @property
-    def use_gather(self):
-        return self.base_layer.use_gather
+    def use_all_gather(self):
+        return self.base_layer.use_all_gather
 
     @property
     def org_vocab_size(self):