127 lines
4.6 KiB
Python
127 lines
4.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import importlib.util
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from typing import TYPE_CHECKING
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import torch
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import torch.distributed as dist
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from vllm.forward_context import get_forward_context
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from vllm.logger import init_logger
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from .base_device_communicator import All2AllManagerBase, Cache
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logger = init_logger(__name__)
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if TYPE_CHECKING:
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from vllm.model_executor.layers.fused_moe.layer import FusedMoE
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else:
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FusedMoE = None
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class NaiveAll2AllManager(All2AllManagerBase):
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"""
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A naive implementation of all2all communication.
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It uses all-reduce under the hood, which is not
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efficient at all. The main purpose is for testing and
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debugging.
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"""
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def __init__(self, cpu_group):
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super().__init__(cpu_group)
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def naive_multicast(self, x: torch.Tensor,
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cu_tokens_across_dp_cpu: torch.Tensor):
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assert (len(x.shape) == 2)
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buffer = torch.empty((cu_tokens_across_dp_cpu[-1], x.size(1)),
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device=x.device,
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dtype=x.dtype)
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start = 0 if self.dp_rank == 0 else cu_tokens_across_dp_cpu[
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self.dp_rank - 1]
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end = cu_tokens_across_dp_cpu[self.dp_rank]
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buffer[start:end, :].copy_(x)
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for idx in range(self.dp_world_size):
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start = 0 if idx == 0 else cu_tokens_across_dp_cpu[idx - 1]
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end = cu_tokens_across_dp_cpu[idx]
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self.dp_group.broadcast(buffer[start:end, :], idx)
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return buffer
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def dispatch(self, hidden_states: torch.Tensor,
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router_logits: torch.Tensor):
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cu_tokens_across_dp_cpu = get_forward_context(
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).dp_metadata.cu_tokens_across_dp_cpu
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hidden_states = self.naive_multicast(hidden_states,
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cu_tokens_across_dp_cpu)
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router_logits = self.naive_multicast(router_logits,
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cu_tokens_across_dp_cpu)
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return hidden_states, router_logits
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def combine(self, hidden_states: torch.Tensor) -> torch.Tensor:
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cu_tokens_across_dp_cpu = get_forward_context(
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).dp_metadata.cu_tokens_across_dp_cpu
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start = 0 if self.dp_rank == 0 else cu_tokens_across_dp_cpu[
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self.dp_rank - 1]
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end = cu_tokens_across_dp_cpu[self.dp_rank]
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all_hidden_states = self.dp_group.all_reduce(hidden_states)
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hidden_states = all_hidden_states[start:end, :]
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return hidden_states
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def destroy(self):
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pass
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class PPLXAll2AllManager(All2AllManagerBase):
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"""
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All2All communication based on PPLX kernels.
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"""
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def __init__(self, cpu_group):
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has_pplx = importlib.util.find_spec("pplx_kernels") is not None
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assert has_pplx, "pplx_kernels not found. Please follow https://github.com/vllm-project/vllm/blob/main/tools/ep_kernels/README.md to install pplx_kernels." # noqa
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super().__init__(cpu_group)
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if self.internode:
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# inter-node communication needs nvshmem,
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# intra-node communication uses p2p mapping directly
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from pplx_kernels.nvshmem import (nvshmem_alloc_empty_unique_id,
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nvshmem_get_unique_id,
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nvshmem_init)
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logger.debug(
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"Initialize NVSHMEM for pplx_kernels: "
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"rank=%d, world size=%d", self.rank, self.world_size)
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uid = nvshmem_get_unique_id(
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) if self.rank == 0 else nvshmem_alloc_empty_unique_id()
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dist.broadcast(uid,
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src=dist.get_process_group_ranks(self.cpu_group)[0],
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group=self.cpu_group)
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logger.debug("PPLX NVSHMEM UID = %s", uid)
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nvshmem_init(uid, self.rank, self.world_size)
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self.handle_cache = Cache()
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def get_handle(self, kwargs):
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import pplx_kernels as pplx
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return self.handle_cache.get_or_create(
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kwargs, pplx.AllToAll.internode
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if self.internode else pplx.AllToAll.intranode)
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def dispatch(self, hidden_states: torch.Tensor,
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router_logits: torch.Tensor):
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raise NotImplementedError
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def combine(self, hidden_states: torch.Tensor) -> torch.Tensor:
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raise NotImplementedError
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def destroy(self):
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with self.handle_cache._lock:
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for _, handle in self.handle_cache._cache.items():
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handle.destroy()
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if self.internode:
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from pplx_kernels.nvshmem import nvshmem_finalize
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logger.debug("PPLX NVSHMEM finalize")
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nvshmem_finalize()
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