[CI] Upgrade vllm to 0.9.1 (#1165)
1. upgrade vllm to 0.9.1. 0.9.0 is not supported for main branch now. keep doc to 0.9.0 until we release the first 0.9.1 release. 2. disable V0 test for PR 3. move actionlint check to lint job Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
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wangxiyuan
@@ -24,9 +24,9 @@
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# each worker's `__init__` function.
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#
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# Then in each kind of patch, there are three folders:
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# - patch_0_9_0: contains the patches applied when vllm version is 0.9.0.
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# - patch_0_9_1: contains the patches applied when vllm version is 0.9.1.
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# - patch_main: contains the patches applied when vllm version is main branch.
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# - patch_common: contains the patches applied in both 0.9.0 and main branch.
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# - patch_common: contains the patches applied in both 0.9.1 and main branch.
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#
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# Once a new patch is added in vllm-ascend, please add the patch description into this file as well.
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# ----------------------------------------------------------------------------------
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@@ -35,17 +35,6 @@
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# --------------------------------
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# * Platform Patch:
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# =================
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# ** File: platform/patch_0_9_0/patch_distributed.py**
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.distributed.utils.stateless_init_torch_distributed_process_group()`
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# Why:
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# vllm distributed use gloo backend by default to initialize stateless process group, but we want to use hccl here
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# How：
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# Add hccl backend to the `stateless_init_torch_distributed_process_group`
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# Related PR (if no, explain why):
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# https://github.com/vllm-project/vllm/pull/18763
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# Future Plan:
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# Remove this patch once vllm is upgraded to 0.9.1
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# ** File: platform/patch_common/patch_distributed.py**
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# 1. `vllm.distributed.parallel_state.destroy_model_parallel()`
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@@ -17,8 +17,8 @@
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from vllm_ascend.utils import vllm_version_is
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# Import specific patches for different versions
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if vllm_version_is("0.9.0"):
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from vllm_ascend.patch.platform import patch_0_9_0 # noqa: F401
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if vllm_version_is("0.9.1"):
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from vllm_ascend.patch.platform import patch_0_9_1 # noqa: F401
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from vllm_ascend.patch.platform import patch_common # noqa: F401
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else:
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from vllm_ascend.patch.platform import patch_common # noqa: F401
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@@ -1,116 +0,0 @@
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import torch
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from torch.distributed import ProcessGroup
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from torch.distributed.distributed_c10d import (Backend, PrefixStore,
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_get_default_timeout,
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is_nccl_available)
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from torch.distributed.rendezvous import rendezvous
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from vllm.distributed import utils
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def stateless_init_torch_distributed_process_group(
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host: str, port: int, rank: int, world_size: int,
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backend: str) -> ProcessGroup:
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"""
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A replacement for `torch.distributed.init_process_group` that does not
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pollute the global state. The created ProcessGroup object can be used for
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some operations such as `allreduce`, because it does not depend on the
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global rank. However, some operations such as `broadcast` cannot be used
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because it depends on the global rank.
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# TODO: ask for help from PyTorch team if we need the `broadcast` operation.
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This function is useful when we are not sure about the total number of
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processes in the process group. For example, we may have process
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1, 2, ..., 8 who want to communicate, and process 9 might be the same
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process as process 1, or it might be a different process; process 10
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might be the same process as process 5, or it might be a different process.
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In this case, how can we reliably form a communication channel within
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process 9 and 10, without affecting the communication channel within
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process 1, 2, ..., 8?
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One possible solution is to figure out if process 9 and 10 are the same
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as process 1 and 5 beforehand, and then form a communication channel
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based on the information, adjusting the ranks and world_size etc. However,
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figuring out the information is not always easy, and it will interfere
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with the main communication channel.
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Our solution is to always form a communication channel with process 1, 2,
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..., 8, and then use this function to form another communication channel
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with process 9 and 10. This way, regardless of whether process 9 and 10
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are the same as process 1 and 5, the main communication channel is
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always formed with process 1, 2, ..., 8, and the additional communication
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channel is formed with process 9 and 10.
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"""
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init_method = f"tcp://{host}:{port}"
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backend = Backend(backend) # it is basically string
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timeout = _get_default_timeout(backend)
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store, rank, world_size = next(
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rendezvous(init_method, rank, world_size, timeout=timeout))
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store.set_timeout(timeout)
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group_rank = rank
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group_size = world_size
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# Use a PrefixStore to avoid accidental overrides of keys used by
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# different systems (e.g. RPC) in case the store is multi-tenant.
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prefix_store = PrefixStore(init_method, store)
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# TODO(Yizhou): The reason we need to set options while vllm does not
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# seems to be related to the version of PyTorch. In the latest version,
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# there is no need to set options. While in the older version, 2.5.1
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# specifically, we need to set options.
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options = ProcessGroup.Options(backend=backend)
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pg: ProcessGroup = ProcessGroup(
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prefix_store,
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group_rank,
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group_size,
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options,
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)
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if backend == "gloo":
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from torch.distributed.distributed_c10d import ProcessGroupGloo
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backend_class = ProcessGroupGloo(prefix_store,
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group_rank,
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group_size,
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timeout=timeout)
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backend_type = ProcessGroup.BackendType.GLOO
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device = torch.device("cpu")
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elif backend == "nccl":
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assert is_nccl_available()
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from torch.distributed.distributed_c10d import ProcessGroupNCCL
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backend_options = ProcessGroupNCCL.Options()
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backend_options._timeout = timeout
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backend_class = ProcessGroupNCCL(prefix_store, group_rank, group_size,
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backend_options)
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backend_type = ProcessGroup.BackendType.NCCL
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device = torch.device("cuda")
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elif backend == "hccl":
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from torch.distributed import is_hccl_available
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assert is_hccl_available()
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from torch_npu._C._distributed_c10d import ProcessGroupHCCL
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backend_options = ProcessGroupHCCL.Options()
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backend_options._timeout = timeout
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backend_class = ProcessGroupHCCL(prefix_store, group_rank, group_size,
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backend_options)
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device = torch.device("npu")
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backend_class._set_sequence_number_for_group()
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backend_type = ProcessGroup.BackendType.CUSTOM
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pg._register_backend(device, backend_type, backend_class)
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return pg
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else:
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raise RuntimeError(f"Unsupported torch distributed backend: {backend}")
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# TODO(Yizhou): Like we mentioned above, _set_default_backend is not
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# implemented in the 2.5.1 version of PyTorch. But we need to set it
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# after the latest version is released.
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# pg._set_default_backend(backend_type)
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backend_class._set_sequence_number_for_group()
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pg._register_backend(device, backend_type, backend_class)
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return pg
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utils.stateless_init_torch_distributed_process_group = stateless_init_torch_distributed_process_group
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@@ -18,8 +18,8 @@
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from vllm_ascend.utils import vllm_version_is
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# Import specific patches for different versions
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if vllm_version_is("0.9.0"):
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from vllm_ascend.patch.worker import patch_0_9_0 # noqa: F401
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if vllm_version_is("0.9.1"):
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from vllm_ascend.patch.worker import patch_0_9_1 # noqa: F401
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from vllm_ascend.patch.worker import patch_common # noqa: F401
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else:
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from vllm_ascend.patch.worker import patch_common # noqa: F401
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@@ -14,4 +14,3 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import vllm_ascend.patch.platform.patch_0_9_0.patch_distributed # noqa
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