Add unit test

Author: jeejeelee

tests/lora/test_minicpmv.py

@@ -1,13 +1,9 @@
 from typing import List
 
-import pytest
-
 import vllm
 from vllm.assets.image import ImageAsset
 from vllm.lora.request import LoRARequest
 
-from ..utils import multi_gpu_test
-
 MODEL_PATH = "openbmb/MiniCPM-Llama3-V-2_5"
 
 PROMPT_TEMPLATE = (
@@ -73,23 +69,3 @@ def test_minicpmv_lora(minicpmv_lora_files):
     output2 = do_sample(llm, minicpmv_lora_files, lora_id=2)
     for i in range(len(EXPECTED_OUTPUT)):
         assert output2[i] == EXPECTED_OUTPUT[i]
-
-
-@multi_gpu_test(num_gpus=4)
-@pytest.mark.parametrize("fully_sharded", [True, False])
-@pytest.mark.parametrize("tp", [2, 4])
-def test_minicpmv_tensor_parallel(minicpmv_lora_files, fully_sharded, tp):
-    llm = vllm.LLM(
-        MODEL_PATH,
-        enable_lora=True,
-        max_num_seqs=16,
-        max_loras=4,
-        max_lora_rank=64,
-        tensor_parallel_size=tp,
-        trust_remote_code=True,
-        fully_sharded_loras=fully_sharded,
-    )
-    output_tp = do_sample(llm, minicpmv_lora_files, lora_id=1)
-
-    for i in range(len(EXPECTED_OUTPUT)):
-        assert output_tp[i] == EXPECTED_OUTPUT[i]

tests/lora/test_minicpmv_tp.py

@@ -0,0 +1,99 @@
+from typing import List
+
+import pytest
+
+import vllm
+from vllm.assets.image import ImageAsset
+from vllm.lora.request import LoRARequest
+
+from ..utils import multi_gpu_test
+
+MODEL_PATH = "openbmb/MiniCPM-Llama3-V-2_5"
+
+PROMPT_TEMPLATE = (
+    "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n"
+    "(<image>./</image>)\nWhat is in the image?<|eot_id|>"
+    "<|start_header_id|>assistant<|end_header_id|>\n\n"
+)
+
+IMAGE_ASSETS = [
+    ImageAsset("stop_sign"),
+    ImageAsset("cherry_blossom"),
+]
+
+# After fine-tuning with LoRA, all generated content should start begin `A`.
+EXPECTED_OUTPUT = [
+    "A red and white stop sign with a Chinese archway in the background featuring red lanterns and gold accents.",  # noqa: E501
+    "A pink cherry blossom tree with a blue sky in the background.",
+]
+
+
+def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> List[str]:
+    sampling_params = vllm.SamplingParams(
+        temperature=0,
+        max_tokens=256,
+        stop_token_ids=[128001, 128009],  # eos_id, eot_id
+    )
+
+    inputs = [
+        {
+            "prompt": PROMPT_TEMPLATE,
+            "multi_modal_data": {"image": asset.pil_image},
+        }
+        for asset in IMAGE_ASSETS
+    ]
+
+    outputs = llm.generate(
+        inputs,
+        sampling_params,
+        lora_request=LoRARequest(str(lora_id), lora_id, lora_path)
+        if lora_id
+        else None,
+    )
+    # Print the outputs.
+    generated_texts: List[str] = []
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text.strip()
+        generated_texts.append(generated_text)
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+    return generated_texts
+
+@multi_gpu_test(num_gpus=2)
+@pytest.mark.parametrize("fully_sharded", [True, False])
+def test_minicpmv_tp2(minicpmv_lora_files, fully_sharded):
+    llm = vllm.LLM(
+        MODEL_PATH,
+        enable_lora=True,
+        max_num_seqs=16,
+        max_loras=4,
+        max_lora_rank=64,
+        tensor_parallel_size=2,
+        trust_remote_code=True,
+        fully_sharded_loras=fully_sharded,
+    )
+
+    output_tp = do_sample(llm, minicpmv_lora_files, lora_id=1)
+
+    for i in range(len(EXPECTED_OUTPUT)):
+        assert output_tp[i] == EXPECTED_OUTPUT[i]
+
+
+@multi_gpu_test(num_gpus=4)
+@pytest.mark.parametrize("fully_sharded", [True, False])
+def test_minicpmv_tp4(minicpmv_lora_files, fully_sharded):
+    llm = vllm.LLM(
+        MODEL_PATH,
+        enable_lora=True,
+        max_num_seqs=16,
+        max_loras=4,
+        max_lora_rank=64,
+        tensor_parallel_size=4,
+        trust_remote_code=True,
+        fully_sharded_loras=fully_sharded,
+    )
+
+    output_tp = do_sample(llm, minicpmv_lora_files, lora_id=1)
+
+    for i in range(len(EXPECTED_OUTPUT)):
+        assert output_tp[i] == EXPECTED_OUTPUT[i]