[E2E][MM] Add e2e tests for InternVL model

Signed-off-by: gcanlin <[email protected]>

Author: gcanlin

.github/workflows/_e2e_test.yaml

@@ -101,6 +101,7 @@ jobs:
           pytest -sv tests/e2e/singlecard/test_quantization.py
           pytest -sv tests/e2e/singlecard/test_sampler.py
           pytest -sv tests/e2e/singlecard/test_vlm.py
+          pytest -sv tests/e2e/singlecard/multi-modal/test_internvl.py
 
           # ------------------------------------ v1 spec decode test ------------------------------------ #
           pytest -sv tests/e2e/singlecard/spec_decode_v1/test_v1_mtp_correctness.py

tests/e2e/singlecard/multi-modal/test_internvl.py

@@ -0,0 +1,92 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# This file is a part of the vllm-ascend project.
+#
+
+import os
+
+# Set spawn method before any torch/NPU imports to avoid fork issues
+os.environ.setdefault('VLLM_WORKER_MULTIPROC_METHOD', 'spawn')
+
+import pytest
+from vllm.assets.image import ImageAsset
+
+from tests.e2e.conftest import VllmRunner
+from tests.e2e.model_utils import check_outputs_equal
+
+MODELS = [
+    "OpenGVLab/InternVL2-8B",
+    "OpenGVLab/InternVL2_5-8B",
+    "OpenGVLab/InternVL3-8B",
+    "OpenGVLab/InternVL3_5-8B",
+]
+
+
+@pytest.mark.parametrize("model", MODELS)
+def test_internvl_basic(model: str):
+    """Test basic InternVL2 inference with single image."""
+    # Load test image
+    image = ImageAsset("cherry_blossom").pil_image.convert("RGB")
+
+    # InternVL uses chat template format
+    # Format: <|im_start|>user\n<image>\nQUESTION<|im_end|>\n<|im_start|>assistant\n
+    questions = [
+        "What is the content of this image?",
+        "Describe this image in detail.",
+    ]
+
+    # Build prompts with InternVL2 chat template
+    prompts = [
+        f"<|im_start|>user\n<image>\n{q}<|im_end|>\n<|im_start|>assistant\n"
+        for q in questions
+    ]
+    images = [image] * len(prompts)
+
+    outputs = {}
+    for enforce_eager, mode in [(False, "eager"), (True, "graph")]:
+        with VllmRunner(
+                model,
+                max_model_len=8192,
+                limit_mm_per_prompt={"image": 4},
+                enforce_eager=enforce_eager,
+                dtype="bfloat16",
+        ) as vllm_model:
+            generated_outputs = vllm_model.generate_greedy(
+                prompts=prompts,
+                images=images,
+                max_tokens=128,
+            )
+
+            assert len(generated_outputs) == len(prompts), \
+                f"Expected {len(prompts)} outputs, got {len(generated_outputs)} in {mode} mode"
+
+            for i, (_, output_str) in enumerate(generated_outputs):
+                assert output_str, \
+                    f"{mode.capitalize()} mode output {i} should not be empty. Prompt: {prompts[i]}"
+                assert len(output_str.strip()) > 0, \
+                    f"{mode.capitalize()} mode Output {i} should have meaningful content"
+
+            outputs[mode] = generated_outputs
+
+    eager_outputs = outputs["eager"]
+    graph_outputs = outputs["graph"]
+
+    check_outputs_equal(
+        outputs_0_lst=eager_outputs,
+        outputs_1_lst=graph_outputs,
+        name_0="eager mode",
+        name_1="graph mode",
+        similarity_threshold=0.50,
+    )