a5f33590d3
### What this PR does / why we need it? This PR supports torchair graph mode with non-mla backend on both 800IA2 and 300I Duo platforms. The main change is to add `attention_v1_torchair.py` to support specific attention related operations that are required by torchair. ### Does this PR introduce _any_ user-facing change? Before this PR, vLLM-Ascend only allows deepseek to use torchair. Now we can also use it with pangu. Besides, we add a support model list to control which type of models that can use torchair. ### How was this patch tested? We have test it with PanguProMoE on both 800IA2 and 300I Duo platforms, and model generates answer normally. --------- Signed-off-by: angazenn <zengyanjia@huawei.com> Signed-off-by: tianyitang <tangtianyi4@huawei.com> Co-authored-by: angazenn <zengyanjia@huawei.com> Co-authored-by: tianyitang <tangtianyi4@huawei.com>
138 lines
4.4 KiB
Python
138 lines
4.4 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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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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# Adapted from vllm/model_executor/models/qwen2_vl.py
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# This file is a part of the vllm-ascend project.
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import torch
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import vllm
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import vllm.distributed
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import vllm.envs as envs
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from vllm.config import ParallelConfig
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from vllm_ascend.utils import is_310p
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def ascend_destroy_model_parallel():
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"""Set the groups to none and destroy them."""
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from vllm.distributed.parallel_state import _DP, _PP, _TP
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if _TP:
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_TP.destroy()
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_TP = None
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if _PP:
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_PP.destroy()
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_PP = None
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if _DP:
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_DP.destroy()
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_DP = None
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from vllm_ascend.distributed.parallel_state import \
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destory_ascend_model_parallel
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destory_ascend_model_parallel()
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def parallel_config_get_dp_port(self) -> int:
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"""
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We might need to initialize process groups in multiple
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processes that is related to data parallelism,
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e.g. both in the worker and in the engine, which
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can live in different processes. To avoid port conflicts, we
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increment the port number each time we need to initialize a
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new process group related to data parallelism.
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"""
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answer = self.data_parallel_master_port
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self.data_parallel_master_port += 1
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# NOTE: Get port from envs directly when using torchrun
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port = envs.VLLM_DP_MASTER_PORT if envs.VLLM_DP_MASTER_PORT else answer
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return port
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vllm.distributed.parallel_state.destroy_model_parallel = ascend_destroy_model_parallel
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ParallelConfig.get_next_dp_init_port = parallel_config_get_dp_port
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class NullHandle:
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def __init__(self):
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pass
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def wait(self):
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pass
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def communication_adaptation_310p():
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def broadcast310p_wrapper(fn):
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def broadcast310p(tensor, src, group=None, async_op=False):
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if tensor.device == torch.device('cpu'):
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return fn(tensor, src, group, async_op)
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rank = torch.distributed.get_rank(group)
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world_size = torch.distributed.get_world_size(group)
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tensor_list = [torch.empty_like(tensor) for _ in range(world_size)]
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tensor_list[rank] = tensor
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torch.distributed.all_gather(tensor_list, tensor, group=group)
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tensor[...] = tensor_list[src]
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if async_op:
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return NullHandle()
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else:
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return None
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return broadcast310p
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torch.distributed.broadcast = broadcast310p_wrapper(
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torch.distributed.broadcast)
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torch.distributed.distributed_c10d.broadcast = broadcast310p_wrapper(
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torch.distributed.distributed_c10d.broadcast)
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def all_reduce_wrapper_310p(fn):
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def all_reduce(
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tensor,
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op=torch.distributed.ReduceOp.SUM,
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group=None,
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async_op=False,
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):
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if tensor.dtype != torch.int64:
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return fn(tensor, op, group, async_op)
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rank = torch.distributed.get_rank(group)
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world_size = torch.distributed.get_world_size(group)
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tensor_list = [torch.empty_like(tensor) for _ in range(world_size)]
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tensor_list[rank] = tensor
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torch.distributed.all_gather(tensor_list, tensor, group=group)
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if op == torch.distributed.ReduceOp.SUM:
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return torch.stack(tensor_list).sum(0)
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elif op == torch.distributed.ReduceOp.MAX:
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return torch.tensor(
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torch.stack(tensor_list).cpu().numpy().max(0),
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device=tensor.device,
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)
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else:
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raise RuntimeError(f"not implement op {op}")
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return all_reduce
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torch.distributed.all_reduce = all_reduce_wrapper_310p(
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torch.distributed.all_reduce)
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torch.distributed.distributed_c10d.all_reduce = all_reduce_wrapper_310p(
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torch.distributed.distributed_c10d.all_reduce)
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if is_310p():
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communication_adaptation_310p()
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