e1ab6d318e
More and more config options are added to additional_config. This PR provide a new AscendConfig to manage these config options by an easier way to make code cleaner and readable. This PR also added the `additional_config` doc for users. Added the test_ascend_config.py to make sure the new AscendConfig works as expect. TODO: Add e2e test with torchair and deepseek once the CI resource is available. Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
84 lines
3.2 KiB
Python
84 lines
3.2 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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# Adapted from vllm-project/vllm/vllm/worker/model_runner.py
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# Copyright 2023 The vLLM team.
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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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#
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from typing import Any, List
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import torch
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from vllm.utils import is_pin_memory_available
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from vllm.worker.cache_engine import CacheEngine
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from vllm_ascend.ascend_config import get_ascend_config
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def allocate_kv_cache(
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self,
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num_blocks: int,
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device: str,
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) -> List[Any]:
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"""Allocates KV cache on the specified device."""
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kv_cache_shape = self.attn_backend.get_kv_cache_shape(
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num_blocks, self.block_size, self.num_kv_heads, self.head_size)
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pin_memory = is_pin_memory_available() if device == "cpu" else False
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kv_cache: List[Any] = []
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ascend_config = get_ascend_config()
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if ascend_config.torchair_graph_config.enabled:
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# Align entries so they are 256 byte aligned for better performance
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# Primarily targets MLA as this typically only ends up having entries
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# be 128 byte aligned.
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alloc_shape = kv_cache_shape
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for _ in range(self.num_attention_layers):
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# null block in CpuGpuBlockAllocator requires at least that
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# block to be zeroed-out.
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# We zero-out everything for simplicity.
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layer_kv_cache_nope = torch.zeros(
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alloc_shape[:-1] +
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(self.model_config.hf_text_config.kv_lora_rank, ),
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dtype=self.dtype,
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pin_memory=pin_memory,
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device=device)
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layer_kv_cache_pe = torch.zeros(
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alloc_shape[:-1] +
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(self.model_config.hf_text_config.qk_rope_head_dim, ),
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dtype=self.dtype,
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pin_memory=pin_memory,
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device=device)
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# view back to (TOTAL_PAGES, PAGE_SIZE, entry_shape...) for cases
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# when entry_shape is higher than 1D
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kv_cache.append((layer_kv_cache_nope, layer_kv_cache_pe))
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else:
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for _ in range(self.num_attention_layers):
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# null block in CpuGpuBlockAllocator requires at least that
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# block to be zeroed-out.
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# We zero-out everything for simplicity.
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layer_kv_cache = torch.zeros(kv_cache_shape,
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dtype=self.dtype,
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pin_memory=pin_memory,
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device=device)
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# view back to (TOTAL_PAGES, PAGE_SIZE, entry_shape...) for cases
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# when entry_shape is higher than 1D
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kv_cache.append(layer_kv_cache)
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return kv_cache
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CacheEngine._allocate_kv_cache = allocate_kv_cache
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