b94d589769
### What this PR does / why we need it?
Following https://github.com/vllm-project/vllm-ascend/pull/5205, update
`hf_config` to `hf_text_config`.
Find more details at
https://github.com/vllm-project/vllm-ascend/pull/5205#issuecomment-3675417534
and
https://github.com/vllm-project/vllm-ascend/pull/5205#issuecomment-3677920872.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: release/v0.13.0
- vLLM main:
5fbfa8d9ef
Signed-off-by: shen-shanshan <467638484@qq.com>
294 lines
13 KiB
Python
294 lines
13 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 TYPE_CHECKING, Optional
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from vllm.logger import logger
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from vllm.triton_utils import HAS_TRITON
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if TYPE_CHECKING:
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from vllm.config import VllmConfig
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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: "VllmConfig"):
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additional_config = vllm_config.additional_config if vllm_config.additional_config is not None else {}
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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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finegrained_tp_config = additional_config.get("finegrained_tp_config",
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{})
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self.finegrained_tp_config = FinegrainedTPConfig(
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finegrained_tp_config, vllm_config)
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# Dump / PrecisionDebugger configuration
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self.dump_config_path = additional_config.get("dump_config_path", None)
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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.enable_shared_expert_dp = additional_config.get(
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"enable_shared_expert_dp",
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False) 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.multistream_overlap_gate = additional_config.get(
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"multistream_overlap_gate", 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.enable_cpu_binding = additional_config.get(
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"enable_cpu_binding", False)
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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_text_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 get_flashcomm2_config_and_validate
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self.flashcomm2_oproj_tensor_parallel_size, self.flashcomm2_oproj_shared = get_flashcomm2_config_and_validate(
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self, vllm_config)
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self.enable_npugraph_ex = additional_config.get(
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"enable_npugraph_ex", False)
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# We find that _npu_paged_attention still performs better than
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# npu_fused_infer_attention_score in some cases. We allow to execute
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# _npu_paged_attention in this cases. This should be removed once
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# npu_fused_infer_attention_score performs better on all scenarios.
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self.pa_shape_list = additional_config.get("pa_shape_list", [])
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self.enable_async_exponential = bool(
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additional_config.get("enable_async_exponential", False))
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self.enable_kv_nz = additional_config.get("enable_kv_nz", False)
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if self.enable_kv_nz:
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use_sparse = hasattr(vllm_config.model_config.hf_text_config,
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"index_topk")
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if not vllm_config.model_config.is_deepseek_mla or use_sparse:
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raise RuntimeError(
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"enable_kv_nz is only supported for mla currently.")
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if vllm_config.kv_transfer_config is None \
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or not vllm_config.kv_transfer_config.is_kv_consumer:
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raise NotImplementedError(
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"enable_kv_nz is only supported in pd scenario and can "
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"only be used in D node.")
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class FinegrainedTPConfig:
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"""
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Configuration Object for finegrained_tp_config from additional_config
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"""
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def __init__(self, finegrained_tp_config: dict, vllm_config):
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self.oproj_tensor_parallel_size = finegrained_tp_config.get(
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"oproj_tensor_parallel_size", 0)
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self.lmhead_tensor_parallel_size = finegrained_tp_config.get(
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"lmhead_tensor_parallel_size", 0)
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self.embedding_tensor_parallel_size = finegrained_tp_config.get(
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"embedding_tensor_parallel_size", 0)
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self.mlp_tensor_parallel_size = finegrained_tp_config.get(
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"mlp_tensor_parallel_size", 0)
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enabled_configs = []
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if self.oproj_tensor_parallel_size > 0:
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enabled_configs.append(
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f"oproj_tensor_parallel_size={self.oproj_tensor_parallel_size}"
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)
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# dummy_run does not run the entire attention module in eager mode,, so the o_proj tp split can only be used in graph mode.
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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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if self.lmhead_tensor_parallel_size > 0:
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enabled_configs.append(
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f"lmhead_tensor_parallel_size={self.lmhead_tensor_parallel_size}"
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)
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if self.embedding_tensor_parallel_size > 0:
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enabled_configs.append(
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f"embedding_tensor_parallel_size={self.embedding_tensor_parallel_size}"
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)
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if self.mlp_tensor_parallel_size > 0:
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enabled_configs.append(
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f"mlp_tensor_parallel_size={self.mlp_tensor_parallel_size}")
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module_tp_sizes = [
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self.oproj_tensor_parallel_size,
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self.lmhead_tensor_parallel_size,
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self.embedding_tensor_parallel_size,
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self.mlp_tensor_parallel_size,
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]
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for module_tp_size in module_tp_sizes:
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if module_tp_size > 0 and vllm_config.parallel_config.data_parallel_size % module_tp_size != 0:
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raise AssertionError(
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"module tp sizes must divide data_parallel_size")
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if any(size > 0 for size in module_tp_sizes) and enabled_configs:
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logger.info(
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f"finegrained_tp_config enabled: {', '.join(enabled_configs)}")
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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,
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fuse_norm_quant: bool = True,
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fuse_qknorm_rope: bool = False,
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**kwargs):
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"""
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Initialize the configuration.
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Args:
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fuse_norm_quant (bool): Whether to enable norm and quant fusion optimization.
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When set to True, the system will optimize norm and quant operations.
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Default: True
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fuse_qknorm_rope (bool): Whether to enable qknorm and rope fusion optimization.
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Default: False
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**kwargs: Additional optional parameters for forward compatibility and configuration extension.
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"""
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self.fuse_norm_quant = fuse_norm_quant
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self.fuse_qknorm_rope = HAS_TRITON or fuse_qknorm_rope
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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 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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