[Model] Add AWQ quantization support for InternVL2 model by Isotr0py · Pull Request #7187 · vllm-project/vllm (original) (raw)
FILL IN THE PR DESCRIPTION HERE
FIX #xxxx (link existing issues this PR will resolve)
- This PR add support for AWQ quantized InternVL2 model like OpenGVLab/InternVL2-2B-AWQ etc
TODO
- Add AWQ support for InternVL2 model
- Add quantized VLM test
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Let's see whether the 26B awq test can work on CI.
The 26B AWQ model test costs about 22GB on my local environment with an RTX3090Ti:
$ pytest -s -v tests/models/test_internvl.py::test_large_models
================================================================================== test session starts ===================================================================================
platform linux -- Python 3.12.3, pytest-8.3.2, pluggy-1.5.0 -- /root/miniconda3/bin/python
cachedir: .pytest_cache
rootdir: /root/autodl-tmp/vllm
configfile: pyproject.toml
plugins: shard-0.1.2, rerunfailures-14.0, forked-1.6.0, asyncio-0.23.8, anyio-4.4.0
asyncio: mode=Mode.STRICT
collected 1 item
Running 1 items in this shard: tests/models/test_internvl.py::test_large_models[128-half-expected_outputs0-/root/autodl-tmp/InternVL2-26B-AWQ]
tests/models/test_internvl.py::test_large_models[128-half-expected_outputs0-/root/autodl-tmp/InternVL2-26B-AWQ] WARNING 08-09 11:08:27 config.py:1483] Casting torch.bfloat16 to torch.float16. WARNING 08-09 11:08:27 config.py:286] awq quantization is not fully optimized yet. The speed can be slower than non-quantized models. INFO 08-09 11:08:27 llm_engine.py:176] Initializing an LLM engine (v0.5.4) with config: model='/root/autodl-tmp/InternVL2-26B-AWQ', speculative_config=None, tokenizer='/root/autodl-tmp/InternVL2-26B-AWQ', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.float16, max_seq_len=1024, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=awq, enforce_eager=True, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None), seed=0, served_model_name=/root/autodl-tmp/InternVL2-26B-AWQ, use_v2_block_manager=False, enable_prefix_caching=False) WARNING 08-09 11:08:27 tokenizer.py:137] Using a slow tokenizer. This might cause a significant slowdown. Consider using a fast tokenizer instead. INFO 08-09 11:08:27 model_runner.py:721] Starting to load model /root/autodl-tmp/InternVL2-26B-AWQ... Loading pt checkpoint shards: 0% Completed | 0/13 [00:00<?, ?it/s] Loading pt checkpoint shards: 8% Completed | 1/13 [00:00<00:09, 1.21it/s] Loading pt checkpoint shards: 15% Completed | 2/13 [00:02<00:12, 1.15s/it] Loading pt checkpoint shards: 23% Completed | 3/13 [00:03<00:12, 1.22s/it] Loading pt checkpoint shards: 31% Completed | 4/13 [00:04<00:11, 1.26s/it] Loading pt checkpoint shards: 38% Completed | 5/13 [00:06<00:10, 1.29s/it] Loading pt checkpoint shards: 46% Completed | 6/13 [00:07<00:09, 1.32s/it] Loading pt checkpoint shards: 54% Completed | 7/13 [00:08<00:06, 1.07s/it] Loading pt checkpoint shards: 62% Completed | 8/13 [00:09<00:05, 1.08s/it] Loading pt checkpoint shards: 69% Completed | 9/13 [00:10<00:04, 1.14s/it] Loading pt checkpoint shards: 77% Completed | 10/13 [00:11<00:03, 1.13s/it] Loading pt checkpoint shards: 85% Completed | 11/13 [00:12<00:02, 1.02s/it] Loading pt checkpoint shards: 92% Completed | 12/13 [00:13<00:01, 1.04s/it] Loading pt checkpoint shards: 100% Completed | 13/13 [00:14<00:00, 1.07s/it] Loading pt checkpoint shards: 100% Completed | 13/13 [00:14<00:00, 1.12s/it]
INFO 08-09 11:08:45 model_runner.py:733] Loading model weights took 21.7726 GB WARNING 08-09 11:08:45 model_runner.py:898] Computed max_num_seqs (min(256, 2048 // 3328)) to be less than 1. Setting it to the minimum value of 1. WARNING 08-09 11:08:46 tokenizer.py:137] Using a slow tokenizer. This might cause a significant slowdown. Consider using a fast tokenizer instead. INFO 08-09 11:08:51 gpu_executor.py:102] # GPU blocks: 209, # CPU blocks: 1365
It's strange that the CI will OOM on L4 with 24G VRAM refer to CI log.
The 26B AWQ model test costs about 22GB on my local environment with an RTX3090Ti:
$ pytest -s -v tests/models/test_internvl.py::test_large_models ================================================================================== test session starts =================================================================================== platform linux -- Python 3.12.3, pytest-8.3.2, pluggy-1.5.0 -- /root/miniconda3/bin/python cachedir: .pytest_cache rootdir: /root/autodl-tmp/vllm configfile: pyproject.toml plugins: shard-0.1.2, rerunfailures-14.0, forked-1.6.0, asyncio-0.23.8, anyio-4.4.0 asyncio: mode=Mode.STRICT collected 1 item
Running 1 items in this shard: tests/models/test_internvl.py::test_large_models[128-half-expected_outputs0-/root/autodl-tmp/InternVL2-26B-AWQ]tests/models/test_internvl.py::test_large_models[128-half-expected_outputs0-/root/autodl-tmp/InternVL2-26B-AWQ] WARNING 08-09 11:08:27 config.py:1483] Casting torch.bfloat16 to torch.float16. WARNING 08-09 11:08:27 config.py:286] awq quantization is not fully optimized yet. The speed can be slower than non-quantized models. INFO 08-09 11:08:27 llm_engine.py:176] Initializing an LLM engine (v0.5.4) with config: model='/root/autodl-tmp/InternVL2-26B-AWQ', speculative_config=None, tokenizer='/root/autodl-tmp/InternVL2-26B-AWQ', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.float16, max_seq_len=1024, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=awq, enforce_eager=True, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None), seed=0, served_model_name=/root/autodl-tmp/InternVL2-26B-AWQ, use_v2_block_manager=False, enable_prefix_caching=False) WARNING 08-09 11:08:27 tokenizer.py:137] Using a slow tokenizer. This might cause a significant slowdown. Consider using a fast tokenizer instead. INFO 08-09 11:08:27 model_runner.py:721] Starting to load model /root/autodl-tmp/InternVL2-26B-AWQ... Loading pt checkpoint shards: 0% Completed | 0/13 [00:00<?, ?it/s] Loading pt checkpoint shards: 8% Completed | 1/13 [00:00<00:09, 1.21it/s] Loading pt checkpoint shards: 15% Completed | 2/13 [00:02<00:12, 1.15s/it] Loading pt checkpoint shards: 23% Completed | 3/13 [00:03<00:12, 1.22s/it] Loading pt checkpoint shards: 31% Completed | 4/13 [00:04<00:11, 1.26s/it] Loading pt checkpoint shards: 38% Completed | 5/13 [00:06<00:10, 1.29s/it] Loading pt checkpoint shards: 46% Completed | 6/13 [00:07<00:09, 1.32s/it] Loading pt checkpoint shards: 54% Completed | 7/13 [00:08<00:06, 1.07s/it] Loading pt checkpoint shards: 62% Completed | 8/13 [00:09<00:05, 1.08s/it] Loading pt checkpoint shards: 69% Completed | 9/13 [00:10<00:04, 1.14s/it] Loading pt checkpoint shards: 77% Completed | 10/13 [00:11<00:03, 1.13s/it] Loading pt checkpoint shards: 85% Completed | 11/13 [00:12<00:02, 1.02s/it] Loading pt checkpoint shards: 92% Completed | 12/13 [00:13<00:01, 1.04s/it] Loading pt checkpoint shards: 100% Completed | 13/13 [00:14<00:00, 1.07s/it] Loading pt checkpoint shards: 100% Completed | 13/13 [00:14<00:00, 1.12s/it]
INFO 08-09 11:08:45 model_runner.py:733] Loading model weights took 21.7726 GB WARNING 08-09 11:08:45 model_runner.py:898] Computed max_num_seqs (min(256, 2048 // 3328)) to be less than 1. Setting it to the minimum value of 1. WARNING 08-09 11:08:46 tokenizer.py:137] Using a slow tokenizer. This might cause a significant slowdown. Consider using a fast tokenizer instead. INFO 08-09 11:08:51 gpu_executor.py:102] # GPU blocks: 209, # CPU blocks: 1365
It's strange that the CI will OOM on L4 with 24G VRAM refer to CI log.
vLLM only uses 90% of the GPU memory by default. You can try setting gpu_memory_utilization to a higher value in your test. If this doesn't work, we might have to test the model in distributed tests which can split it over multiple GPUs.
I have passed gpu_memory_utilization=1.0 and cpu_offload_gb=4 to vllm_runner but it still keeps OOM. I agree that we can add this model to distributed tests.
@DarkLight1337 The VLM tests have passed again. Can you please take a look at this? Thanks!
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Thanks for enabling this!
Alvant pushed a commit to compressa-ai/vllm that referenced this pull request
Signed-off-by: Alvant alvasian@yandex.ru
LeiWang1999 pushed a commit to LeiWang1999/vllm-bitblas that referenced this pull request
Signed-off-by: LeiWang1999 leiwang1999@outlook.com
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