[BugFix] Fix data parallel (#940)

### What this PR does / why we need it?
With this PR, we can migrate to the native `data_parallel.py` in vllm
examples and remove the version in vllm-ascend.

At present, `ASCEND_RT_VISIBLE_DEVICES` introduces considerable
difficulties; therefore, we must employ a temporary workaround and
manually specify the device.

Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>

vllm_ascend/patch/platform/patch_0_9_0/__init__.py

@@ -14,3 +14,4 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+import vllm_ascend.patch.platform.patch_0_9_0.patch_distributed  # noqa

vllm_ascend/patch/platform/patch_0_9_0/patch_distributed.py

@@ -0,0 +1,116 @@
+import torch
+from torch.distributed import ProcessGroup
+from torch.distributed.distributed_c10d import (Backend, PrefixStore,
+                                                _get_default_timeout,
+                                                is_nccl_available)
+from torch.distributed.rendezvous import rendezvous
+from vllm.distributed import utils
+
+
+def stateless_init_torch_distributed_process_group(
+        host: str, port: int, rank: int, world_size: int,
+        backend: str) -> ProcessGroup:
+    """
+    A replacement for `torch.distributed.init_process_group` that does not
+    pollute the global state. The created ProcessGroup object can be used for
+    some operations such as `allreduce`, because it does not depend on the
+    global rank. However, some operations such as `broadcast` cannot be used
+    because it depends on the global rank.
+
+    # TODO: ask for help from PyTorch team if we need the `broadcast` operation.
+
+    This function is useful when we are not sure about the total number of
+    processes in the process group. For example, we may have process
+    1, 2, ..., 8 who want to communicate, and process 9 might be the same
+    process as process 1, or it might be a different process; process 10
+    might be the same process as process 5, or it might be a different process.
+    In this case, how can we reliably form a communication channel within
+    process 9 and 10, without affecting the communication channel within
+    process 1, 2, ..., 8?
+
+    One possible solution is to figure out if process 9 and 10 are the same
+    as process 1 and 5 beforehand, and then form a communication channel
+    based on the information, adjusting the ranks and world_size etc. However,
+    figuring out the information is not always easy, and it will interfere
+    with the main communication channel.
+
+    Our solution is to always form a communication channel with process 1, 2,
+    ..., 8, and then use this function to form another communication channel
+    with process 9 and 10. This way, regardless of whether process 9 and 10
+    are the same as process 1 and 5, the main communication channel is
+    always formed with process 1, 2, ..., 8, and the additional communication
+    channel is formed with process 9 and 10.
+    """
+    init_method = f"tcp://{host}:{port}"
+    backend = Backend(backend)  # it is basically string
+    timeout = _get_default_timeout(backend)
+
+    store, rank, world_size = next(
+        rendezvous(init_method, rank, world_size, timeout=timeout))
+    store.set_timeout(timeout)
+
+    group_rank = rank
+    group_size = world_size
+
+    # Use a PrefixStore to avoid accidental overrides of keys used by
+    # different systems (e.g. RPC) in case the store is multi-tenant.
+    prefix_store = PrefixStore(init_method, store)
+
+    # TODO(Yizhou): The reason we need to set options while vllm does not
+    # seems to be related to the version of PyTorch. In the latest version,
+    # there is no need to set options. While in the older version, 2.5.1
+    # specifically, we need to set options.
+    options = ProcessGroup.Options(backend=backend)
+    pg: ProcessGroup = ProcessGroup(
+        prefix_store,
+        group_rank,
+        group_size,
+        options,
+    )
+    if backend == "gloo":
+        from torch.distributed.distributed_c10d import ProcessGroupGloo
+        backend_class = ProcessGroupGloo(prefix_store,
+                                         group_rank,
+                                         group_size,
+                                         timeout=timeout)
+        backend_type = ProcessGroup.BackendType.GLOO
+        device = torch.device("cpu")
+    elif backend == "nccl":
+        assert is_nccl_available()
+        from torch.distributed.distributed_c10d import ProcessGroupNCCL
+
+        backend_options = ProcessGroupNCCL.Options()
+        backend_options._timeout = timeout
+
+        backend_class = ProcessGroupNCCL(prefix_store, group_rank, group_size,
+                                         backend_options)
+        backend_type = ProcessGroup.BackendType.NCCL
+        device = torch.device("cuda")
+    elif backend == "hccl":
+        from torch.distributed import is_hccl_available
+        assert is_hccl_available()
+        from torch_npu._C._distributed_c10d import ProcessGroupHCCL
+        backend_options = ProcessGroupHCCL.Options()
+        backend_options._timeout = timeout
+        backend_class = ProcessGroupHCCL(prefix_store, group_rank, group_size,
+                                         backend_options)
+        device = torch.device("npu")
+        backend_class._set_sequence_number_for_group()
+        backend_type = ProcessGroup.BackendType.CUSTOM
+        pg._register_backend(device, backend_type, backend_class)
+        return pg
+    else:
+        raise RuntimeError(f"Unsupported torch distributed backend: {backend}")
+
+    # TODO(Yizhou): Like we mentioned above, _set_default_backend is not
+    # implemented in the 2.5.1 version of PyTorch. But we need to set it
+    # after the latest version is released.
+    # pg._set_default_backend(backend_type)
+    backend_class._set_sequence_number_for_group()
+
+    pg._register_backend(device, backend_type, backend_class)
+
+    return pg
+
+
+utils.stateless_init_torch_distributed_process_group = stateless_init_torch_distributed_process_group

vllm_ascend/patch/platform/patch_common/patch_distributed.py

@@ -17,16 +17,14 @@
 # Adapted from vllm/model_executor/models/qwen2_vl.py
 # This file is a part of the vllm-ascend project.
 
-import torch
 import vllm
 import vllm.distributed
 import vllm.envs as envs
 from torch.distributed import ProcessGroup
-from torch.distributed.distributed_c10d import (Backend, PrefixStore,
+from vllm.config import ParallelConfig, VllmConfig
-                                                _get_default_timeout,
+from vllm.distributed.utils import \
-                                                is_nccl_available)
+    stateless_init_torch_distributed_process_group
-from torch.distributed.rendezvous import rendezvous
+from vllm.v1.engine.core import DPEngineCoreProc
-from vllm.config import ParallelConfig
 
 
 def ascend_destroy_model_parallel():
@@ -48,112 +46,6 @@ def ascend_destroy_model_parallel():
     destory_ascend_model_parallel()
 
 
-def stateless_init_torch_distributed_process_group(
-        host: str, port: int, rank: int, world_size: int,
-        backend: str) -> ProcessGroup:
-    """
-    A replacement for `torch.distributed.init_process_group` that does not
-    pollute the global state. The created ProcessGroup object can be used for
-    some operations such as `allreduce`, because it does not depend on the
-    global rank. However, some operations such as `broadcast` cannot be used
-    because it depends on the global rank.
-
-    # TODO: ask for help from PyTorch team if we need the `broadcast` operation.
-
-    This function is useful when we are not sure about the total number of
-    processes in the process group. For example, we may have process
-    1, 2, ..., 8 who want to communicate, and process 9 might be the same
-    process as process 1, or it might be a different process; process 10
-    might be the same process as process 5, or it might be a different process.
-    In this case, how can we reliably form a communication channel within
-    process 9 and 10, without affecting the communication channel within
-    process 1, 2, ..., 8?
-
-    One possible solution is to figure out if process 9 and 10 are the same
-    as process 1 and 5 beforehand, and then form a communication channel
-    based on the information, adjusting the ranks and world_size etc. However,
-    figuring out the information is not always easy, and it will interfere
-    with the main communication channel.
-
-    Our solution is to always form a communication channel with process 1, 2,
-    ..., 8, and then use this function to form another communication channel
-    with process 9 and 10. This way, regardless of whether process 9 and 10
-    are the same as process 1 and 5, the main communication channel is
-    always formed with process 1, 2, ..., 8, and the additional communication
-    channel is formed with process 9 and 10.
-    """
-    init_method = f"tcp://{host}:{port}"
-    backend = Backend(backend)  # it is basically string
-    timeout = _get_default_timeout(backend)
-
-    store, rank, world_size = next(
-        rendezvous(init_method, rank, world_size, timeout=timeout))
-    store.set_timeout(timeout)
-
-    group_rank = rank
-    group_size = world_size
-
-    # Use a PrefixStore to avoid accidental overrides of keys used by
-    # different systems (e.g. RPC) in case the store is multi-tenant.
-    prefix_store = PrefixStore(init_method, store)
-
-    # TODO(Yizhou): The reason we need to set options while vllm does not
-    # seems to be related to the version of PyTorch. In the latest version,
-    # there is no need to set options. While in the older version, 2.5.1
-    # specifically, we need to set options.
-    options = ProcessGroup.Options(backend=backend)
-    pg: ProcessGroup = ProcessGroup(
-        prefix_store,
-        group_rank,
-        group_size,
-        options,
-    )
-    if backend == "gloo":
-        from torch.distributed.distributed_c10d import ProcessGroupGloo
-        backend_class = ProcessGroupGloo(prefix_store,
-                                         group_rank,
-                                         group_size,
-                                         timeout=timeout)
-        backend_type = ProcessGroup.BackendType.GLOO
-        device = torch.device("cpu")
-    elif backend == "nccl":
-        assert is_nccl_available()
-        from torch.distributed.distributed_c10d import ProcessGroupNCCL
-
-        backend_options = ProcessGroupNCCL.Options()
-        backend_options._timeout = timeout
-
-        backend_class = ProcessGroupNCCL(prefix_store, group_rank, group_size,
-                                         backend_options)
-        backend_type = ProcessGroup.BackendType.NCCL
-        device = torch.device("cuda")
-    elif backend == "hccl":
-        from torch.distributed import is_hccl_available
-        assert is_hccl_available()
-        from torch_npu._C._distributed_c10d import ProcessGroupHCCL
-        backend_options = ProcessGroupHCCL.Options()
-        backend_options._timeout = timeout
-        backend_class = ProcessGroupHCCL(prefix_store, group_rank, group_size,
-                                         backend_options)
-        device = torch.device("npu")
-        backend_class._set_sequence_number_for_group()
-        backend_type = ProcessGroup.BackendType.CUSTOM
-        pg._register_backend(device, backend_type, backend_class)
-        return pg
-    else:
-        raise RuntimeError(f"Unsupported torch distributed backend: {backend}")
-
-    # TODO(Yizhou): Like we mentioned above, _set_default_backend is not
-    # implemented in the 2.5.1 version of PyTorch. But we need to set it
-    # after the latest version is released.
-    # pg._set_default_backend(backend_type)
-    backend_class._set_sequence_number_for_group()
-
-    pg._register_backend(device, backend_type, backend_class)
-
-    return pg
-
-
 def parallel_config_get_dp_port(self) -> int:
     """
     We might need to initialize process groups in multiple
@@ -171,7 +63,7 @@ def parallel_config_get_dp_port(self) -> int:
     return port
 
 
-def ascend_stateless_init_dp_group(self) -> "ProcessGroup":
+def stateless_init_dp_group(self) -> "ProcessGroup":
     # TODO(Yizhou): Currently we have to set the backend to gloo
     # because in vllm.config.ParallelConfig.has_unfinished_dp the
     # device is set to cpu. We need to fix this in the future.
@@ -187,6 +79,21 @@ def ascend_stateless_init_dp_group(self) -> "ProcessGroup":
     return dp_group
 
 
+def _init_data_parallel(self, vllm_config: VllmConfig):
+    # Configure NPUs and stateless process group for data parallel.
+    dp_rank = vllm_config.parallel_config.data_parallel_rank
+    dp_size = vllm_config.parallel_config.data_parallel_size
+    local_dp_rank = vllm_config.parallel_config.data_parallel_rank_local
+
+    assert dp_size > 1
+    assert 0 <= local_dp_rank <= dp_rank < dp_size
+
+    self.local_dp_rank = local_dp_rank
+    self.dp_group = vllm_config.parallel_config.stateless_init_dp_group()
+    self.current_wave = 0
+
+
 vllm.distributed.parallel_state.destroy_model_parallel = ascend_destroy_model_parallel
+DPEngineCoreProc._init_data_parallel = _init_data_parallel
 ParallelConfig.get_next_dp_init_port = parallel_config_get_dp_port
-ParallelConfig.stateless_init_dp_group = ascend_stateless_init_dp_group
+ParallelConfig.stateless_init_dp_group = stateless_init_dp_group

vllm_ascend/platform.py

@@ -18,10 +18,13 @@
 import gc
 import logging
 import os
+from datetime import timedelta
 from typing import TYPE_CHECKING, Optional, Tuple
 
 import torch
 import vllm.envs as envs
+from torch.distributed import ProcessGroup
+from torch.distributed.distributed_c10d import PrefixStore
 from vllm.logger import logger
 from vllm.platforms import Platform, PlatformEnum
 
@@ -262,3 +265,45 @@ class NPUPlatform(Platform):
         Get piecewise backend class for piecewise graph.
         """
         return "vllm_ascend.compilation.piecewise_backend.NPUPiecewiseBackend"  # noqa
+
+    @classmethod
+    def stateless_init_device_torch_dist_pg(
+        cls,
+        backend: str,
+        prefix_store: PrefixStore,
+        group_rank: int,
+        group_size: int,
+        timeout: timedelta,
+    ) -> ProcessGroup:
+        from torch.distributed import is_hccl_available
+        from torch_npu._C._distributed_c10d import ProcessGroupHCCL
+
+        assert is_hccl_available()
+
+        # TODO(Yizhou): The reason we need to set options while vllm does not
+        # seems to be related to the version of PyTorch. In the latest version,
+        # there is no need to set options. While in the older version, 2.5.1
+        # specifically, we need to set options.
+        options = ProcessGroup.Options(backend=backend)
+        pg: ProcessGroup = ProcessGroup(
+            prefix_store,
+            group_rank,
+            group_size,
+            options,
+        )
+
+        backend_options = ProcessGroupHCCL.Options()
+        backend_options._timeout = timeout
+
+        backend_class = ProcessGroupHCCL(prefix_store, group_rank, group_size,
+                                         backend_options)
+        device = torch.device("npu")
+        # TODO(Yizhou): Like we mentioned above, _set_default_backend is not
+        # implemented in the 2.5.1 version of PyTorch. But we need to set it
+        # after the latest version is released.
+        # pg._set_default_backend(backend_type)
+        backend_class._set_sequence_number_for_group()
+        backend_type = ProcessGroup.BackendType.CUSTOM
+
+        pg._register_backend(device, backend_type, backend_class)
+        return pg

vllm_ascend/worker/worker_v1.py

@@ -74,6 +74,13 @@ class NPUWorker(WorkerBase):
                          rank=rank,
                          distributed_init_method=distributed_init_method,
                          is_driver_worker=is_driver_worker)
+
+        # NOTE(Yizhou): Since we do not set ASCEND_RT_VISIBLE_DEVICES in
+        # vllm_ascend, we need to set the device id manually.
+        local_dp_rank = self.vllm_config.parallel_config.data_parallel_rank_local
+        world_size = self.vllm_config.parallel_config.world_size
+        self.local_rank_across_dp = local_dp_rank * world_size + self.local_rank
+
         # Try to import mindie_turbo to accelerate vLLM inference.
         try_register_lib(
             "mindie_turbo",
@@ -112,7 +119,7 @@ class NPUWorker(WorkerBase):
 
     def init_device(self):
         if self.device_config.device.type == "npu":
-            self.device = torch.device(f"npu:{self.local_rank}")
+            self.device = torch.device(f"npu:{self.local_rank_across_dp}")
             NPUPlatform.set_device(self.device)
             NPUPlatform.empty_cache()
             self.init_npu_memory = NPUPlatform.mem_get_info()[0]