[ROCm][CI][Bugfix] Fixing the Multi-Modal Models Test (Extended) 1 group (#30013)

Signed-off-by: Andreas Karatzas <akaratza@amd.com>

.buildkite/test-amd.yaml

@@ -987,7 +987,8 @@ steps:
   commands:
   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-mm-small.txt --tp-size=1
 
-- label: Multi-Modal Models Test (Extended) 1
+- label: Multi-Modal Models Test (Extended) 1 # 60min
+  timeout_in_minutes: 120
   mirror_hardwares: [amdexperimental]
   agent_pool: mi325_1
   # grade: Blocking
@@ -1011,7 +1012,8 @@ steps:
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
     - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=0) and not core_model'
 
-- label: Multi-Modal Models Test (Extended) 3
+- label: Multi-Modal Models Test (Extended) 3 # 75min
+  timeout_in_minutes: 150
   mirror_hardwares: [amdexperimental]
   agent_pool: mi325_1
   # grade: Blocking

tests/models/multimodal/generation/conftest.py

@@ -2,6 +2,8 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """Pytest configuration for vLLM tests."""
 
+import warnings
+
 import torch
 
 from vllm.platforms import current_platform
@@ -14,6 +16,20 @@ def pytest_configure(config):
     if not current_platform.is_rocm():
         return
 
+    skip_patterns = ["test_granite_speech.py"]
+    if any(pattern in str(arg) for arg in config.args for pattern in skip_patterns):
+        # Skip disabling SDP for Granite Speech tests on ROCm
+        return
+
+    # Disable Flash/MemEfficient SDP on ROCm to avoid HF Transformers
+    # accuracy issues
+    # TODO: Remove once ROCm SDP accuracy issues are resolved on HuggingFace
     torch.backends.cuda.enable_flash_sdp(False)
     torch.backends.cuda.enable_mem_efficient_sdp(False)
     torch.backends.cuda.enable_math_sdp(True)
+    warnings.warn(
+        "ROCm: Disabled flash_sdp and mem_efficient_sdp, enabled math_sdp "
+        "to avoid HuggingFace Transformers accuracy issues",
+        UserWarning,
+        stacklevel=1,
+    )

tests/models/multimodal/generation/test_common.py

@@ -403,12 +403,13 @@ VLM_TEST_SETTINGS = {
         # So, we need to reduce the number of tokens for the test to pass.
         max_tokens=8,
         num_logprobs=10,
+        auto_cls=AutoModelForCausalLM,
         marks=[large_gpu_mark(min_gb=32)],
     ),
     "glm4_1v": VLMTestInfo(
         models=["zai-org/GLM-4.1V-9B-Thinking"],
         test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
-        prompt_formatter=lambda img_prompt: f"<|user|>\n{img_prompt}<|assistant|>",
+        prompt_formatter=lambda img_prompt: f"[gMASK]<|user|>\n{img_prompt}<|assistant|>\n",  # noqa: E501
         img_idx_to_prompt=lambda idx: "<|begin_of_image|><|image|><|end_of_image|>",
         video_idx_to_prompt=lambda idx: "<|begin_of_video|><|video|><|end_of_video|>",
         max_model_len=2048,
@@ -423,6 +424,7 @@ VLM_TEST_SETTINGS = {
         models=["zai-org/GLM-4.1V-9B-Thinking"],
         # GLM4.1V require include video metadata for input
         test_type=VLMTestType.CUSTOM_INPUTS,
+        prompt_formatter=lambda vid_prompt: f"[gMASK]<|user|>\n{vid_prompt}<|assistant|>\n",  # noqa: E501
         max_model_len=4096,
         max_num_seqs=2,
         auto_cls=AutoModelForImageTextToText,
@@ -737,7 +739,13 @@ VLM_TEST_SETTINGS = {
         max_model_len=8192,
         max_num_seqs=2,
         auto_cls=AutoModelForImageTextToText,
-        marks=[large_gpu_mark(min_gb=48)],
+        marks=[
+            large_gpu_mark(min_gb=48),
+            pytest.mark.skipif(
+                current_platform.is_rocm(),
+                reason="Model produces a vector of <UNK> output in HF on ROCm",
+            ),
+        ],
     ),
     "qwen_vl": VLMTestInfo(
         models=["Qwen/Qwen-VL"],

tests/models/multimodal/generation/test_granite_speech.py

@@ -8,6 +8,7 @@ from transformers import AutoModelForSpeechSeq2Seq
 
 from vllm.logprobs import SampleLogprobs
 from vllm.lora.request import LoRARequest
+from vllm.platforms import current_platform
 
 from ....conftest import AudioTestAssets, HfRunner, PromptAudioInput, VllmRunner
 from ...registry import HF_EXAMPLE_MODELS
@@ -34,6 +35,12 @@ audio_lora_path = MODEL_NAME
 models = [MODEL_NAME]
 
 
+@pytest.fixture(autouse=True)
+def set_attention_backend_for_rocm(monkeypatch):
+    if current_platform.is_rocm():
+        monkeypatch.setenv("VLLM_ATTENTION_BACKEND", "TRITON_ATTN")
+
+
 def run_test(
     hf_runner: type[HfRunner],
     vllm_runner: type[VllmRunner],
@@ -111,8 +118,12 @@ def run_test(
 
 
 @pytest.mark.parametrize("model", models)
-@pytest.mark.parametrize("dtype", ["bfloat16"])
-@pytest.mark.parametrize("max_model_len", [2048])
+@pytest.mark.parametrize(
+    "dtype", ["float16"] if current_platform.is_rocm() else ["bfloat16"]
+)
+@pytest.mark.parametrize(
+    "max_model_len", [512] if current_platform.is_rocm() else [2048]
+)
 @pytest.mark.parametrize("max_tokens", [128])
 @pytest.mark.parametrize("num_logprobs", [10])
 def test_models(

tests/models/multimodal/generation/test_pixtral.py

@@ -15,6 +15,7 @@ from transformers import AutoProcessor
 from vllm import SamplingParams, TextPrompt, TokensPrompt
 from vllm.logprobs import Logprob, SampleLogprobs
 from vllm.multimodal import MultiModalDataBuiltins
+from vllm.platforms import current_platform
 
 from ....utils import VLLM_PATH, large_gpu_test
 from ...utils import check_logprobs_close
@@ -165,6 +166,15 @@ def load_outputs_w_logprobs(filename: "StrPath") -> OutputsLogprobs:
 def test_chat(
     vllm_runner, max_model_len: int, model: str, dtype: str, local_asset_server
 ) -> None:
+    if (
+        model == MISTRAL_SMALL_3_1_ID
+        and max_model_len == 65536
+        and current_platform.is_rocm()
+    ):
+        pytest.skip(
+            "OOM on ROCm: 24B model with 65536 context length exceeds GPU memory"
+        )
+
     EXPECTED_CHAT_LOGPROBS = load_outputs_w_logprobs(FIXTURE_LOGPROBS_CHAT[model])
     with vllm_runner(
         model,

tests/models/multimodal/generation/vlm_utils/custom_inputs.py

@@ -140,7 +140,7 @@ def video_with_metadata_glm4_1v():
     metadata = VIDEO_ASSETS[0].metadata
     question = "Describe the video."
     video_prompt = "<|begin_of_video|><|video|><|end_of_video|>"
-    formatted_prompt = f"<|user|>\n{video_prompt}{question}<|assistant|>\n"
+    formatted_prompt = f"[gMASK]<|user|>\n{video_prompt}{question}<|assistant|>\n"
 
     scales = [0.1, 0.2, 0.25]
     video_input = [

tests/models/multimodal/generation/vlm_utils/model_utils.py

@@ -25,6 +25,7 @@ from transformers import (
 from transformers.video_utils import VideoMetadata
 
 from vllm.logprobs import SampleLogprobs
+from vllm.platforms import current_platform
 from vllm.utils.collection_utils import is_list_of
 
 from .....conftest import HfRunner, ImageAsset, ImageTestAssets
@@ -366,6 +367,40 @@ def gemma3_vllm_to_hf_output(vllm_output: RunnerOutput, model: str) -> RunnerOut
 
 def glm4v_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
     """Patches and returns an instance of the HfRunner to use for GLM4V."""
+    if current_platform.is_rocm():
+        import types
+
+        config = hf_model.model.config
+        if hasattr(config, "num_layers") and not hasattr(config, "num_hidden_layers"):
+            config.num_hidden_layers = config.num_layers
+        config.output_hidden_states = True
+
+        def patched_prepare_cache(
+            self, generation_config, model_kwargs, *args, **kwargs
+        ):
+            model_kwargs["past_key_values"] = None
+            model_kwargs["use_cache"] = False
+            return model_kwargs
+
+        hf_model.model._prepare_cache_for_generation = types.MethodType(
+            patched_prepare_cache, hf_model.model
+        )
+        original_generate = hf_model.model.generate
+
+        def patched_generate(*args, **kwargs):
+            kwargs["output_hidden_states"] = True
+            kwargs["return_dict_in_generate"] = True
+            return original_generate(*args, **kwargs)
+
+        hf_model.model.generate = patched_generate
+        original_forward = hf_model.model.forward
+
+        def patched_forward(*args, **kwargs):
+            kwargs["output_hidden_states"] = True
+            return original_forward(*args, **kwargs)
+
+        hf_model.model.forward = patched_forward
+
     hf_processor = hf_model.processor
 
     def processor(*args, text="", images=None, **kwargs):
@@ -406,7 +441,15 @@ def glm4_1v_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
         if videos is not None and is_list_of(videos, tuple):
             # If videos is a list of tuples, we assume each tuple contains
             # (video_array, metadata) as in the case of GLM4.1V.
-            video_metadata = [[VideoMetadata(**video[1])] for video in videos]
+            # Filter out 'do_sample_frames' as it's not a valid VideoMetadata arg
+            video_metadata = [
+                [
+                    VideoMetadata(
+                        **{k: v for k, v in video[1].items() if k != "do_sample_frames"}
+                    )
+                ]
+                for video in videos
+            ]
             videos = [[video[0]] for video in videos]
         else:
             video_metadata = None

tests/models/multimodal/pooling/conftest.py

@@ -0,0 +1,24 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Pytest configuration for vLLM pooling tests."""
+
+import os
+import warnings
+
+from vllm.platforms import current_platform
+
+
+def pytest_collection_modifyitems(config, items):
+    """Set FLEX_ATTENTION backend for SigLIP tests on ROCm."""
+    if not current_platform.is_rocm():
+        return
+
+    siglip_tests = [item for item in items if "test_siglip" in item.nodeid]
+
+    if siglip_tests:
+        os.environ["VLLM_ATTENTION_BACKEND"] = "FLEX_ATTENTION"
+        warnings.warn(
+            "ROCm: Set VLLM_ATTENTION_BACKEND=FLEX_ATTENTION for SigLIP tests",
+            UserWarning,
+            stacklevel=1,
+        )

tests/models/registry.py

@@ -667,6 +667,10 @@ _MULTIMODAL_EXAMPLE_MODELS = {
         "moonshotai/Kimi-VL-A3B-Instruct",
         extras={"thinking": "moonshotai/Kimi-VL-A3B-Thinking"},
         trust_remote_code=True,
+        max_transformers_version="4.53.3",
+        transformers_version_reason="HF model uses deprecated transformers API "
+        "(PytorchGELUTanh, DynamicCache.seen_tokens, and more). See: "
+        "https://huggingface.co/moonshotai/Kimi-VL-A3B-Instruct/discussions/31",
     ),
     "LightOnOCRForConditionalGeneration": _HfExamplesInfo(
         "lightonai/LightOnOCR-1B",

vllm/v1/attention/backends/flex_attention.py

@@ -31,6 +31,7 @@ from vllm.logger import init_logger
 from vllm.model_executor.layers.batch_invariant import (
     vllm_is_batch_invariant,
 )
+from vllm.platforms import current_platform
 from vllm.utils.math_utils import cdiv
 from vllm.utils.torch_utils import is_torch_equal_or_newer
 from vllm.v1.attention.backends.utils import (
@@ -927,7 +928,18 @@ def get_kernel_options(
 
         if torch.cuda.is_available():
             device_props = torch.cuda.get_device_properties()
-            max_shared_memory = device_props.shared_memory_per_block_optin
+            # ROCm doesn't expose shared_memory_per_block_optin attribute
+            # AMD GPUs typically have 64KB LDS (Local Data Share) per workgroup
+            if hasattr(device_props, "shared_memory_per_block_optin"):
+                max_shared_memory = device_props.shared_memory_per_block_optin
+            elif current_platform.is_rocm():
+                # ROCm fallback: use 64KB
+                max_shared_memory = 65536
+            else:
+                raise RuntimeError(
+                    "Unable to determine shared memory size on this hardware."
+                )
+
             if max_shared_memory < 144 * 1024:
                 block_m_candidate = ensure_divisible(
                     max(1, block_m_candidate // 2), block_m