63adbedb7a
### What this PR does / why we need it?
This patch purpose to add the `update_max_model_len` interface.
- vLLM version: v0.14.0
- vLLM main:
d68209402d
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
101 lines
3.7 KiB
Python
101 lines
3.7 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/tests/entrypoints/llm/test_guided_generate.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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import torch
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from vllm.config import ModelConfig, VllmConfig
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from vllm.v1.core.kv_cache_utils import get_kv_cache_configs
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from vllm.v1.kv_cache_interface import FullAttentionSpec
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def new_kv_cache_spec(
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block_size=16,
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num_kv_heads=2,
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head_size=64,
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dtype=torch.float32,
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page_size_padded=None,
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sliding_window=None,
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attention_chunk_size=None,
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):
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return FullAttentionSpec(
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block_size=block_size,
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num_kv_heads=num_kv_heads,
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head_size=head_size,
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dtype=dtype,
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page_size_padded=page_size_padded,
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sliding_window=sliding_window,
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attention_chunk_size=attention_chunk_size,
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)
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def test_auto_fit_max_model_len():
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"""Test that max_model_len=-1 auto-fits to available NPU memory."""
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# Create config with original_max_model_len=-1 to trigger auto-fit
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model_config = ModelConfig(max_model_len=1024)
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# Simulate the user passing -1 by setting original_max_model_len
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model_config.original_max_model_len = -1
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vllm_config = VllmConfig(model_config=model_config)
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# block_size * 2 * head_size * num_kv_heads * dtype_size
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mem_per_block_per_layer = 16 * 2 * 64 * 4 * 2 # 16KB per block per layer
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kv_cache_specs = {
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"layer_1": new_kv_cache_spec(),
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"layer_2": new_kv_cache_spec(),
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}
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# With enough memory, max_model_len stays at the derived max
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large_available_memory = mem_per_block_per_layer * 2 * 1024 # plenty of memory
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_kv_cache_configs = get_kv_cache_configs(
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vllm_config, [kv_cache_specs], [large_available_memory]
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)
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assert vllm_config.model_config.max_model_len == 1024
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# Reset for next test
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model_config = ModelConfig(max_model_len=1024)
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model_config.original_max_model_len = -1
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vllm_config = VllmConfig(model_config=model_config)
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# With limited memory, max_model_len should be reduced
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# Need memory for at least max_model_len tokens
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# 32 blocks worth of memory for 2 layers = can fit 32*16=512 tokens
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limited_memory = mem_per_block_per_layer * 2 * 32
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_kv_cache_configs = get_kv_cache_configs(
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vllm_config, [kv_cache_specs], [limited_memory]
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)
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# Should be reduced to fit in memory
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assert vllm_config.model_config.max_model_len < 1024
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assert vllm_config.model_config.max_model_len > 0
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def test_auto_fit_max_model_len_not_triggered():
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"""Test that auto-fit is not triggered when original_max_model_len is not -1."""
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model_config = ModelConfig(max_model_len=16)
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# original_max_model_len should be None by default, not -1
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vllm_config = VllmConfig(model_config=model_config)
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mem_per_block_per_layer = 16 * 2 * 64 * 4 * 2
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kv_cache_specs = {
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"layer_1": new_kv_cache_spec(),
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"layer_2": new_kv_cache_spec(),
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}
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# This should work normally without auto-fit
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_kv_cache_configs = get_kv_cache_configs(
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vllm_config, [kv_cache_specs], [mem_per_block_per_layer * 2 * 32]
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)
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assert vllm_config.model_config.max_model_len == 16
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