3953dcf784
## Summary
- Add automatic quantization format detection, eliminating the need to
manually specify `--quantization` when serving quantized models.
- The detection inspects only lightweight JSON files
(`quant_model_description.json` and `config.json`) at engine
initialization time, with no `.safetensors` reads.
- User-explicit `--quantization` flags are always respected;
auto-detection only applies when the flag is omitted.
## Details
**Detection priority:**
1. `quant_model_description.json` exists → `quantization="ascend"`
(ModelSlim)
2. `config.json` contains `"quant_method": "compressed-tensors"` →
`quantization="compressed-tensors"` (LLM-Compressor)
3. Neither → default float behavior
**Technical approach:**
Hooked into `NPUPlatform.check_and_update_config()` to run detection
after `VllmConfig.__post_init__`. Since `quant_config` is already `None`
at that point, we explicitly recreate it via
`VllmConfig._get_quantization_config()` to trigger the full quantization
initialization pipeline.
## Files Changed
| File | Description |
|------|-------------|
| `vllm_ascend/quantization/utils.py` | Added
`detect_quantization_method()` and `maybe_auto_detect_quantization()` |
| `vllm_ascend/platform.py` | Integrated auto-detection in
`check_and_update_config()` |
| `vllm_ascend/quantization/modelslim_config.py` | Improved error
handling for weight loading |
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
---------
Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
118 lines
4.2 KiB
Python
118 lines
4.2 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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# 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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# This file is a part of the vllm-ascend project.
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#
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from tests.e2e.conftest import VllmRunner
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from tests.e2e.model_utils import check_outputs_equal
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# fmt: off
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def test_qwen3_w8a8_quant():
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max_tokens = 5
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example_prompts = [
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"vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs."
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]
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vllm_target_outputs = [([
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85, 4086, 44, 374, 264, 1550, 42747, 628, 323, 4938, 72816, 44378, 323,
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13480, 4712, 369, 444, 10994, 82, 13, 1084, 374, 6188, 311, 387
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], 'vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. It is designed to be'
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)]
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# fmt: on
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with VllmRunner(
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"vllm-ascend/Qwen3-0.6B-W8A8",
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max_model_len=8192,
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gpu_memory_utilization=0.7,
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cudagraph_capture_sizes=[1, 2, 4, 8],
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quantization="ascend",
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) as vllm_model:
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vllm_quant_w8a8_outputs = vllm_model.generate_greedy(
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example_prompts, max_tokens)
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check_outputs_equal(
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outputs_0_lst=vllm_target_outputs,
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outputs_1_lst=vllm_quant_w8a8_outputs,
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name_0="vllm_target_outputs",
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name_1="vllm_quant_w8a8_outputs",
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)
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# fmt: off
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def test_qwen3_w8a8_quant_auto_detect():
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"""Test that ModelSlim quantization is auto-detected without --quantization.
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Uses the same W8A8 model as test_qwen3_w8a8_quant but omits the
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quantization parameter, verifying that the auto-detection in
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maybe_auto_detect_quantization() picks up quant_model_description.json
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and produces identical results.
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"""
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max_tokens = 5
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example_prompts = [
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"vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs."
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]
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vllm_target_outputs = [([
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85, 4086, 44, 374, 264, 1550, 42747, 628, 323, 4938, 72816, 44378, 323,
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13480, 4712, 369, 444, 10994, 82, 13, 1084, 374, 6188, 311, 387
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], 'vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. It is designed to be'
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)]
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# fmt: on
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with VllmRunner(
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"vllm-ascend/Qwen3-0.6B-W8A8",
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max_model_len=8192,
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gpu_memory_utilization=0.7,
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cudagraph_capture_sizes=[1, 2, 4, 8],
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) as vllm_model:
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vllm_quant_auto_detect_outputs = vllm_model.generate_greedy(
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example_prompts, max_tokens)
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check_outputs_equal(
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outputs_0_lst=vllm_target_outputs,
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outputs_1_lst=vllm_quant_auto_detect_outputs,
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name_0="vllm_target_outputs",
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name_1="vllm_quant_auto_detect_outputs",
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)
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# fmt: off
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def test_qwen3_dense_w8a16():
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max_tokens = 5
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example_prompts = [
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"vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs."
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]
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vllm_target_outputs = [([
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85, 4086, 44, 374, 264, 1550, 42747, 628, 323, 4938, 72816, 44378, 323,
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13480, 4712, 369, 444, 10994, 82, 13, 1084, 374, 6188, 311, 387
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], 'vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. It is designed to be'
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)]
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# fmt: on
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with VllmRunner(
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"vllm-ascend/Qwen3-0.6B-W8A16",
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max_model_len=8192,
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enforce_eager=False,
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gpu_memory_utilization=0.7,
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quantization="ascend",
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) as vllm_model:
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vllm_quant_w8a16_outputs = vllm_model.generate_greedy(
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example_prompts, max_tokens)
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check_outputs_equal(
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outputs_0_lst=vllm_target_outputs,
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outputs_1_lst=vllm_quant_w8a16_outputs,
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name_0="vllm_target_outputs",
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name_1="vllm_quant_w8a16_outputs",
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)
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