2be0fe2691
### What this PR does / why we need it? This patch adds support for the xlite graph wrapper to vllm_ascend. Xlite provides operator implementations of the transformer network on Ascend hardware. For details about xlite, please refer to the following link: https://gitee.com/openeuler/GVirt/blob/master/xlite/README.md The latest performance comparison data between xlite and the default aclgraph mode is as follows: ## Qwen3 32B TPS 910B3(A2) Online Inference Performance Comparison - aclgraph: main(c4a71fc6) - xlite-full: main(c4a71fc6) + xlite-full - xlite-decode-only: main(c4a71fc6) + xlite-decode-only - diff1: Performance comparison between xlite-full and aclgraph - diff2: Performance comparison between xlite-decode-only and aclgraph ### Does this PR introduce _any_ user-facing change? Enable the xlite graph mode by setting xlite_graph_config: --additional-config='{"xlite_graph_config": {"enabled": true}}' # Enabled for decode only --additional-config='{"xlite_graph_config": {"enabled": true, "full_mode": true}}' # Enabled for prefill and decode - vLLM version: v0.12.0 - vLLM main:ad32e3e19c--------- Signed-off-by: lulina <lina.lulina@huawei.com> Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
421 lines
18 KiB
Python
421 lines
18 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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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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from typing import Optional
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from uuid import uuid4
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from vllm.logger import logger
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TORCHAIR_MODEL_LIST = ["deepseek", "pangu", "kimi_k2", "qwen"]
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def _check_torchair_supported(model_type: str):
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for supported_model in TORCHAIR_MODEL_LIST:
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if supported_model in model_type.lower():
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return True
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return False
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def check_kv_extra_config(vllm_config):
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def _check(name: str, config: dict):
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tp_key = "tp_size"
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dp_key = "dp_size"
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if tp_key in config:
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config_tp = config[tp_key]
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vllm_tp = vllm_config.parallel_config.tensor_parallel_size
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if config_tp != vllm_tp:
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raise ValueError(
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f"KV transfer '{name}' config has a conflicting tensor parallel size. "
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f"Expected {vllm_tp}, but got {config_tp}.")
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if dp_key in config:
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config_dp = config[dp_key]
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vllm_dp = vllm_config.parallel_config.data_parallel_size
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if config_dp != vllm_dp:
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raise ValueError(
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f"KV transfer '{name}' config has a conflicting data parallel size. "
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f"Expected {vllm_dp}, but got {config_dp}.")
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if vllm_config.kv_transfer_config.is_kv_producer:
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_check(
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"prefill",
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vllm_config.kv_transfer_config.get_from_extra_config(
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"prefill", {}))
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if vllm_config.kv_transfer_config.is_kv_consumer:
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_check(
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"decode",
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vllm_config.kv_transfer_config.get_from_extra_config("decode", {}))
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class AscendConfig:
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"""
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Configuration Object for additional_config from vllm.configs.
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"""
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def __init__(self, vllm_config):
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additional_config = vllm_config.additional_config if vllm_config.additional_config is not None else {}
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torchair_graph_config = additional_config.get("torchair_graph_config",
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{})
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self.torchair_graph_config = TorchairGraphConfig(
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torchair_graph_config, vllm_config, additional_config)
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xlite_graph_config = additional_config.get("xlite_graph_config", {})
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self.xlite_graph_config = XliteGraphConfig(xlite_graph_config,
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vllm_config)
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ascend_compilation_config = additional_config.get(
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"ascend_compilation_config", {})
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self.ascend_compilation_config = AscendCompilationConfig(
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**ascend_compilation_config)
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# Dump / PrecisionDebugger configuration
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dump_config_path = additional_config.get("dump_config", None)
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self.dump_config = DumpConfig(dump_config_path)
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weight_prefetch_config = additional_config.get(
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"weight_prefetch_config", {})
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self.weight_prefetch_config = WeightPrefetchConfig(
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weight_prefetch_config)
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# Todo: Once https://github.com/vllm-project/vllm/issues/22246 is merged in vllm. Remove this config
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self.expert_map_path = additional_config.get("expert_map_path", None)
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self.eplb_policy_type = additional_config.get("eplb_policy_type", 1)
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self.expert_map_record_path = additional_config.get(
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"expert_map_record_path",
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None) # Provide path to export expert map
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self.init_redundancy_expert = additional_config.get(
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"init_redundancy_expert", 0)
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self.dynamic_eplb = additional_config.get("dynamic_eplb", False)
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self.num_iterations_eplb_update = additional_config.get(
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"num_iterations_eplb_update", 400)
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self.gate_eplb = additional_config.get("gate_eplb", False)
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self.num_wait_worker_iterations = additional_config.get(
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"num_wait_worker_iterations", 30)
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self.chunked_prefill_for_mla = additional_config.get(
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"chunked_prefill_for_mla", False)
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self.enable_shared_expert_dp = additional_config.get(
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"enable_shared_expert_dp", False
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) and not self.torchair_graph_config.enabled and vllm_config.parallel_config.enable_expert_parallel
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if self.enable_shared_expert_dp:
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from vllm_ascend.utils import enable_sp
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assert enable_sp(vllm_config=vllm_config,
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enable_shared_expert_dp=True)
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self.multistream_overlap_shared_expert = additional_config.get(
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"multistream_overlap_shared_expert", False)
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self.recompute_scheduler_enable = additional_config.get(
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"recompute_scheduler_enable", False)
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self.lmhead_tensor_parallel_size = additional_config.get(
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"lmhead_tensor_parallel_size", None)
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if self.lmhead_tensor_parallel_size is not None:
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logger.info(
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f"Enable lmhead_tensor_parallel_size={self.lmhead_tensor_parallel_size} in pure DP scenario"
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)
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if vllm_config.parallel_config.tensor_parallel_size != 1:
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raise AssertionError(
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"lmhead_tensor_parallel_size is only supported in the pure DP scenario"
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)
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self.oproj_tensor_parallel_size = additional_config.get(
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"oproj_tensor_parallel_size", None)
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if self.oproj_tensor_parallel_size is not None:
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logger.info(
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f"Enable oproj_tensor_parallel_size={self.oproj_tensor_parallel_size} in pure DP scenario"
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)
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if vllm_config.parallel_config.tensor_parallel_size != 1:
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raise AssertionError(
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"oproj_tensor_parallel_size is only supported in the pure DP scenario"
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)
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if vllm_config.model_config.enforce_eager is True:
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raise AssertionError(
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"oproj_tensor_parallel_size is only supported in graph mode"
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)
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if vllm_config.kv_transfer_config is None or not vllm_config.kv_transfer_config.is_kv_consumer:
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raise AssertionError(
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"oproj_tensor_parallel_size is only supported in pd scenario and can only be used in D node."
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)
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self.enable_cpu_binding = additional_config.get(
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"enable_cpu_binding", False)
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if vllm_config.kv_transfer_config is not None:
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check_kv_extra_config(vllm_config)
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self.pd_tp_ratio = 1
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self.pd_head_ratio = 1
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self.num_head_replica = 1
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if vllm_config.kv_transfer_config is not None and not vllm_config.model_config.is_deepseek_mla:
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prefill_tp_size = vllm_config.kv_transfer_config.get_from_extra_config(
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"prefill", {"tp_size": 1})["tp_size"]
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decode_tp_size = vllm_config.kv_transfer_config.get_from_extra_config(
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"decode", {"tp_size": 1})["tp_size"]
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assert prefill_tp_size % decode_tp_size == 0, "Prefill TP size must be divisible by Decode TP size."
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self.pd_tp_ratio = prefill_tp_size // decode_tp_size
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if self.pd_tp_ratio > 1:
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try:
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# only support Qwen model now
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# TODO: use a more robust method to get kv_head_num
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num_kv_head = vllm_config.model_config.hf_config.num_key_value_heads
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self.num_head_replica = prefill_tp_size // num_kv_head if prefill_tp_size >= num_kv_head else 1
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prefill_tp_size = min(prefill_tp_size, num_kv_head)
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decode_tp_size = min(decode_tp_size, num_kv_head)
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self.pd_head_ratio = prefill_tp_size // decode_tp_size
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except Exception:
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raise AssertionError(
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"Can not get num_key_value_heads from model_config")
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if self.pd_tp_ratio == 0:
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raise AssertionError(
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"Only support P node tp size lagger then D node tp size")
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self.SLO_limits_for_dynamic_batch = additional_config.get(
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"SLO_limits_for_dynamic_batch", -1)
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from vllm_ascend.utils import \
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get_flashcomm2_oproj_tp_size_and_validate_config
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self.flashcomm2_oproj_tensor_parallel_size = get_flashcomm2_oproj_tp_size_and_validate_config(
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self, vllm_config)
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kv_cfg = vllm_config.kv_transfer_config
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if kv_cfg is not None and not getattr(kv_cfg, "_engine_id_patched",
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False):
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kv_cfg.engine_id = f"{kv_cfg.engine_id}-{uuid4().hex}"
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kv_cfg._engine_id_patched = True
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class AscendCompilationConfig:
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"""
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Configuration for controlling the behavior of Ascend graph optimization.
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This class provides a way to configure graph fusion optimizations.
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These configurations directly impact the performance and behavior of models
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deployed on Ascend platforms.
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"""
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def __init__(self, enable_quantization_fusion: bool = True, **kwargs):
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"""
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Initialize the configuration.
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Args:
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enable_quantization_fusion (bool): Whether to enable quantization fusion optimization.
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When set to True, the system will optimize quantization-related operations,
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reducing the number of quantization/dequantization nodes.
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Default: True
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**kwargs: Additional optional parameters for forward compatibility and configuration extension.
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"""
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self.enable_quantization_fusion = enable_quantization_fusion
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# Add more compilation related configs here as needed
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class TorchairGraphConfig:
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"""
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Configuration Object for torchair_graph_config from additional_config
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"""
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def __init__(self, torchair_graph_config, vllm_config, additional_config):
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self.enabled = torchair_graph_config.get("enabled", False)
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self.mode = torchair_graph_config.get("mode", '')
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self.use_cached_graph = torchair_graph_config.get(
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"use_cached_graph", False)
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self.use_cached_kv_cache_bytes = torchair_graph_config.get(
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"use_cached_kv_cache_bytes", False)
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self.graph_batch_sizes = torchair_graph_config.get(
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"graph_batch_sizes", [])
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self.graph_batch_sizes_init = torchair_graph_config.get(
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"graph_batch_sizes_init", False)
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self.enable_multistream_mla = torchair_graph_config.get(
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"enable_multistream_mla", False)
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self.enable_view_optimize = torchair_graph_config.get(
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"enable_view_optimize", True)
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self.enable_frozen_parameter = torchair_graph_config.get(
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"enable_frozen_parameter", True)
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self.enable_kv_nz = torchair_graph_config.get("enable_kv_nz", False)
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self.enable_super_kernel = torchair_graph_config.get(
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"enable_super_kernel", False)
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if not isinstance(self.graph_batch_sizes, list):
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raise TypeError("graph_batch_sizes must be list[int]")
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if self.graph_batch_sizes_init and len(self.graph_batch_sizes) > 0:
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raise ValueError(
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"graph_batch_sizes_init is only valid when graph_batch_sizes is empty"
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)
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if not self.enabled:
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if self.mode:
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raise RuntimeError(
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"mode is valid only when Torchair graph mode is enabled")
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if self.use_cached_graph:
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raise RuntimeError(
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"use_cached_graph is valid only when Torchair graph mode is enabled"
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)
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if self.use_cached_kv_cache_bytes:
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raise RuntimeError(
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"use_cached_kv_cache_bytes is valid only when Torchair graph mode is enabled"
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)
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if self.graph_batch_sizes:
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raise RuntimeError(
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"graph_batch_sizes is valid only when Torchair graph mode is enabled"
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)
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if self.graph_batch_sizes_init:
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raise RuntimeError(
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"graph_batch_sizes_init is valid only when Torchair graph mode is enabled"
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)
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if self.enable_multistream_mla:
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raise RuntimeError(
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"enable_multistream_mla is valid only when Torchair graph mode is enabled"
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)
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if self.enable_kv_nz:
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raise RuntimeError(
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"enable_kv_nz is valid only when Torchair graph mode is enabled"
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)
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if self.enable_super_kernel:
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raise RuntimeError(
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"enable_super_kernel is valid only when Torchair graph mode is enabled"
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)
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if self.enable_super_kernel:
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if vllm_config.parallel_config.tensor_parallel_size != 1:
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raise RuntimeError(
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"enable_super_kernel is valid only when tensor_parallel_size is 1"
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)
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if not additional_config.get("multistream_overlap_shared_expert",
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False):
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raise RuntimeError(
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"enable_super_kernel is valid only when multistream_overlap_shared_expert is enabled"
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)
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if self.use_cached_kv_cache_bytes and not self.use_cached_graph:
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raise RuntimeError(
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"use_cached_kv_cache_bytes is valid only when Torchair graph mode and use_cached_graph are enabled"
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)
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class XliteGraphConfig:
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"""
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Configuration Object for xlite_graph_config from additional_config
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"""
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def __init__(self, xlite_graph_config, vllm_config):
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self.enabled = xlite_graph_config.get("enabled", False)
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self.full_mode = xlite_graph_config.get("full_mode", False)
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if self.enabled:
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if bool(vllm_config.speculative_config):
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raise RuntimeError(
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"Xlite graph mode is not compatible with speculative decoding. Please disable speculative decoding."
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)
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if vllm_config.parallel_config.pipeline_parallel_size > 1:
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raise RuntimeError(
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"Xlite graph mode is not compatible with pipeline parallelism. Please set pipeline_parallel_size to 1."
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)
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if vllm_config.cache_config.block_size != 128:
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raise RuntimeError(
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"Xlite graph mode is only compatible with block_size of 128. Please set block_size to 128."
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)
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class DumpConfig:
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"""
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Configuration object for dump/PrecisionDebugger settings.
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"""
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def __init__(self, dump_config_path: Optional[str] = None):
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# enable_dump is True when dump_cfg exists and config_path is not empty
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self.enable_dump: bool = bool(dump_config_path)
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# Path to msprobe config json; may be None.
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self.config_path: Optional[str] = dump_config_path
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class WeightPrefetchConfig:
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"""
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Configuration Object for weight_prefetch_config from additional_config
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"""
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prefetch_ratio: dict = {
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"attn": {
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"qkv": 1.0,
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"o": 1.0,
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},
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"moe": {
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"gate_up": 0.8
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}
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}
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def __init__(self, weight_prefetch_config: dict):
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self.enabled = weight_prefetch_config.get("enabled", False)
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self.prefetch_ratio = weight_prefetch_config.get(
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"prefetch_ratio", self.prefetch_ratio)
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_ASCEND_CONFIG: Optional[AscendConfig] = None
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def init_ascend_config(vllm_config):
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additional_config = vllm_config.additional_config if vllm_config.additional_config is not None else {}
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refresh = additional_config.get("refresh",
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False) if additional_config else False
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global _ASCEND_CONFIG
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if _ASCEND_CONFIG is not None and not refresh:
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return _ASCEND_CONFIG
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_ASCEND_CONFIG = AscendConfig(vllm_config)
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return _ASCEND_CONFIG
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def clear_ascend_config():
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global _ASCEND_CONFIG
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_ASCEND_CONFIG = None
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def get_ascend_config():
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global _ASCEND_CONFIG
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if _ASCEND_CONFIG is None:
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raise RuntimeError(
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"Ascend config is not initialized. Please call init_ascend_config first."
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)
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return _ASCEND_CONFIG
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def check_ascend_config(vllm_config, enforce_eager):
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ascend_config = get_ascend_config()
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# for eager mode
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if enforce_eager:
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# torchair_graph cannot be enabled with eager mode.
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if ascend_config.torchair_graph_config.enabled:
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raise RuntimeError(
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"Can't enable graph mode and eager mode at the same time. Please set `enforce_eager=False` if you attempt to enable NPU graph mode."
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)
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# for graph mode
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else:
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# torchair_graph case
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if ascend_config.torchair_graph_config.enabled:
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# torchair_graph is supported for deepseek/pangu/qwen model only.
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if vllm_config.model_config:
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model_type = vllm_config.model_config.hf_config.model_type
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if not _check_torchair_supported(model_type):
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raise NotImplementedError(
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"Torchair graph mode only works with following model types:"
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f"{TORCHAIR_MODEL_LIST}.")
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if ascend_config.enable_shared_expert_dp:
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logger.warning(
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"enable_shared_expert_dp is not supported for torchair graph mode currently, "
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"it has been disabled automatically.")
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# aclgraph case
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else:
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if ascend_config.ascend_compilation_config.enable_quantization_fusion:
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logger.info(
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"Quantization fusion enabled! op fusion on quantization are expected. "
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)
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if vllm_config.model_config:
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model_type = vllm_config.model_config.hf_config.model_type
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if "qwen" not in model_type:
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logger.warning(
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"ACL Graph is currently experimental. Please "
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"raise an issue on https://github.com/vllm-project/vllm-ascend/issues"
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" if you encourage any Error")
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