biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

[Misc] Auto detect bitsandbytes pre-quantized models (#16027)

Browse Source 
Signed-off-by: Tristan Leclercq <tristanleclercq@gmail.com>
This commit is contained in:
Tristan Leclercq
2025-04-05 08:30:45 +02:00
committed by GitHub
parent 63375f0cdb
commit 4285e423a6
3 changed files with 16 additions and 7 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
tests/quantization/test_bitsandbytes.py
View File

@@ -41,7 +41,7 @@ def test_load_4bit_bnb_model(hf_runner, vllm_runner, example_prompts,
hf_model_kwargs = {"load_in_4bit": True}
validate_generated_texts(hf_runner, vllm_runner, example_prompts[:1],
model_name, hf_model_kwargs)
model_name, False, hf_model_kwargs)
@pytest.mark.skipif(not is_quant_method_supported("bitsandbytes"),
@@ -53,7 +53,7 @@ def test_load_pre_quant_4bit_bnb_model(hf_runner, vllm_runner, example_prompts,
model_name, description) -> None:
validate_generated_texts(hf_runner, vllm_runner, example_prompts[:1],
model_name)
model_name, True)
@pytest.mark.skipif(not is_quant_method_supported("bitsandbytes"),
@@ -65,7 +65,7 @@ def test_load_8bit_bnb_model(hf_runner, vllm_runner, example_prompts,
model_name, description) -> None:
validate_generated_texts(hf_runner, vllm_runner, example_prompts[:1],
model_name)
model_name, True)
@pytest.mark.skipif(torch.cuda.device_count() < 2,
@@ -82,6 +82,7 @@ def test_load_tp_4bit_bnb_model(hf_runner, vllm_runner, example_prompts,
vllm_runner,
example_prompts[:1],
model_name,
False,
hf_model_kwargs,
vllm_tp_size=2)
@@ -128,13 +129,14 @@ def validate_generated_texts(hf_runner,
vllm_runner,
prompts,
model_name,
pre_quant=False,
hf_model_kwargs=None,
vllm_tp_size=1):
# NOTE: run vLLM first, as it requires a clean process
# when using distributed inference
with vllm_runner(model_name,
quantization='bitsandbytes',
quantization=None if pre_quant else 'bitsandbytes',
tensor_parallel_size=vllm_tp_size,
enforce_eager=False) as llm:
vllm_outputs = llm.generate_greedy(prompts, 8)