[ROCm][CI][Bugfix] Multi-Modal Model Support Fixes and Attention Backend Improvements (#30270)

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

.buildkite/test-amd.yaml

@@ -964,7 +964,7 @@ steps:
     - pytest -v -s models/multimodal/processing
 
 - label: Multi-Modal Models Test (Standard) # 60min
-  timeout_in_minutes: 80
+  timeout_in_minutes: 100
   mirror_hardwares: [amdexperimental]
   agent_pool: mi325_1
   # grade: Blocking
@@ -973,13 +973,15 @@ steps:
   - vllm/
   - tests/models/multimodal
   commands:
+    - export MIOPEN_DEBUG_CONV_DIRECT=0
+    - export MIOPEN_DEBUG_CONV_GEMM=0
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
     - pip freeze | grep -E 'torch'
     - pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/processing
     - cd .. && VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model  # Otherwise, mp_method="spawn" doesn't work
 
-- label: Multi-Modal Accuracy Eval (Small Models) # 150min - 180min
+- label: Multi-Modal Accuracy Eval (Small Models) # 5min
-  timeout_in_minutes: 180
+  timeout_in_minutes: 10
   mirror_hardwares: [amdexperimental, amdproduction]
   agent_pool: mi325_1
   # grade: Blocking
@@ -989,7 +991,9 @@ steps:
   - vllm/inputs/
   - vllm/v1/core/
   commands:
-  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-mm-small.txt --tp-size=1
+  - export MIOPEN_DEBUG_CONV_DIRECT=0
+  - export MIOPEN_DEBUG_CONV_GEMM=0
+  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-mm-small.txt
 
 - label: Multi-Modal Models Test (Extended) 1 # 60min
   timeout_in_minutes: 120
@@ -1001,10 +1005,13 @@ steps:
   - vllm/
   - tests/models/multimodal
   commands:
+    - export MIOPEN_DEBUG_CONV_DIRECT=0
+    - export MIOPEN_DEBUG_CONV_GEMM=0
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
     - pytest -v -s models/multimodal -m 'not core_model' --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/processing
 
-- label: Multi-Modal Models Test (Extended) 2
+- label: Multi-Modal Models Test (Extended) 2 #60min
+  timeout_in_minutes: 120
   mirror_hardwares: [amdexperimental]
   agent_pool: mi325_1
   # grade: Blocking
@@ -1013,6 +1020,8 @@ steps:
   - vllm/
   - tests/models/multimodal
   commands:
+    - export MIOPEN_DEBUG_CONV_DIRECT=0
+    - export MIOPEN_DEBUG_CONV_GEMM=0
     - 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'
 
@@ -1026,6 +1035,8 @@ steps:
   - vllm/
   - tests/models/multimodal
   commands:
+    - export MIOPEN_DEBUG_CONV_DIRECT=0
+    - export MIOPEN_DEBUG_CONV_GEMM=0
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
     - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=1) and not core_model'
 

tests/models/multimodal/conftest.py

@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Pytest configuration for vLLM tests."""
+"""Pytest configuration for vLLM multimodal tests."""
 
 import warnings
 
@@ -9,16 +9,13 @@ import torch
 from vllm.platforms import current_platform
 
 
-def pytest_configure(config):
+def pytest_collection_modifyitems(config, items):
-    """Disable Flash/MemEfficient SDP on ROCm to avoid HF
+    """Configure ROCm-specific settings based on collected tests."""
-    Transformers accuracy issues.
-    """
     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

tests/models/multimodal/generation/test_common.py

@@ -173,6 +173,13 @@ VLM_TEST_SETTINGS = {
         auto_cls=AutoModelForImageTextToText,
         vllm_output_post_proc=model_utils.qwen2_vllm_to_hf_output,
         patch_hf_runner=model_utils.qwen3_vl_patch_hf_runner,
+        vllm_runner_kwargs={
+            "attention_config": {
+                "backend": "ROCM_AITER_FA",
+            },
+        }
+        if current_platform.is_rocm()
+        else None,
         image_size_factors=[(0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
         marks=[
             pytest.mark.core_model,
@@ -253,8 +260,19 @@ VLM_TEST_SETTINGS = {
         image_size_factors=[(0.25, 0.2, 0.15)],
         vllm_runner_kwargs={
             "model_impl": "transformers",
+            # TODO: [ROCm] Revert this once issue #30167 is resolved
+            **(
+                {
+                    "mm_processor_kwargs": {
+                        "min_pixels": 256 * 28 * 28,
+                        "max_pixels": 1280 * 28 * 28,
+                    },
+                }
+                if current_platform.is_rocm()
+                else {}
+            ),
         },
-        marks=[large_gpu_mark(min_gb=32)],
+        marks=[large_gpu_mark(min_gb=80 if current_platform.is_rocm() else 32)],
     ),
     #### Extended model tests
     "aria": VLMTestInfo(
@@ -645,7 +663,17 @@ VLM_TEST_SETTINGS = {
         hf_output_post_proc=model_utils.minimax_vl_01_hf_output,
         patch_hf_runner=model_utils.minimax_vl_01_patch_hf_runner,
         auto_cls=AutoModelForImageTextToText,
-        marks=[large_gpu_mark(min_gb=80)],
+        marks=[
+            large_gpu_mark(min_gb=80),
+            # TODO: [ROCm] Fix pickle issue with ROCm spawn and tp>1
+            pytest.mark.skipif(
+                current_platform.is_rocm(),
+                reason=(
+                    "ROCm: Model too large for single GPU; "
+                    "multi-GPU blocked by HF _LazyConfigMapping pickle issue with spawn"
+                ),
+            ),
+        ],
     ),
     "molmo": VLMTestInfo(
         models=["allenai/Molmo-7B-D-0924"],

tests/models/multimodal/generation/test_granite_speech.py

@@ -39,7 +39,7 @@ models = [MODEL_NAME]
 def granite_speech_attention_config():
     """Return attention config for Granite Speech tests on ROCm."""
     if current_platform.is_rocm():
-        return {"backend": "TRITON_ATTN"}
+        return {"backend": "ROCM_AITER_FA"}
     return None
 
 

tests/models/multimodal/pooling/conftest.py

@@ -1,18 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Pytest configuration for vLLM pooling tests."""
-
-import pytest
-
-from vllm.platforms import current_platform
-
-
-@pytest.fixture
-def siglip_attention_config():
-    """Return attention config for SigLIP tests on ROCm.
-
-    On ROCm, SigLIP tests require FLEX_ATTENTION backend.
-    """
-    if current_platform.is_rocm():
-        return {"backend": "FLEX_ATTENTION"}
-    return None

vllm/model_executor/models/transformers/multimodal.py

@@ -22,6 +22,7 @@ from typing import TYPE_CHECKING
 import torch
 
 from vllm.config.utils import getattr_iter
+from vllm.logger import init_logger
 from vllm.model_executor.models.interfaces import SupportsMRoPE, SupportsMultiModal
 from vllm.model_executor.models.utils import WeightsMapper
 from vllm.multimodal import MultiModalKwargsItems
@@ -36,6 +37,7 @@ from vllm.multimodal.inputs import (
 from vllm.multimodal.parse import ImageProcessorItems, MultiModalDataItems
 from vllm.multimodal.processing import BaseMultiModalProcessor, BaseProcessingInfo
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
 
 if TYPE_CHECKING:
@@ -52,6 +54,8 @@ DYNAMIC_ARG_DIMS = {
     "inputs_embeds": 0,
 }
 
+logger = init_logger(__name__)
+
 
 class MultiModalProcessingInfo(BaseProcessingInfo):
     def get_supported_mm_limits(self):
@@ -345,8 +349,29 @@ class MultiModalMixin(SupportsMultiModal, SupportsMRoPE):
 
         num_image_patches = kwargs.pop("num_image_patches")
         kwargs.pop("token_type_ids", None)  # used only in `forward`
+
         if pixel_values is not None:
-            vision_embeddings = self.model.get_image_features(pixel_values, **kwargs)
+            # ROCm: Force math SDP backend for vision encoder to avoid accuracy issues
+            # with flash_sdp and mem_efficient_sdp
+            if current_platform.is_rocm():
+                # TODO: [ROCm] Fix accuracy issues with flash backend
+                logger.debug(
+                    "ROCm platform detected. Forcing math SDP backend "
+                    "for vision encoder. Currently ROCm platform has "
+                    "accuracy issues with `flash_sdp` and"
+                    "`mem_efficient_sdp` backends. See issue: "
+                    "https://github.com/vllm-project/vllm/issues/30167"
+                )
+                with torch.nn.attention.sdpa_kernel(
+                    backends=[torch.nn.attention.SDPBackend.MATH]
+                ):
+                    vision_embeddings = self.model.get_image_features(
+                        pixel_values, **kwargs
+                    )
+            else:
+                vision_embeddings = self.model.get_image_features(
+                    pixel_values, **kwargs
+                )
 
             if isinstance(vision_embeddings, torch.Tensor):
                 if vision_embeddings.ndim == 2:
@@ -364,6 +389,11 @@ class MultiModalMixin(SupportsMultiModal, SupportsMRoPE):
                 ]
 
             return vision_embeddings
+        else:
+            logger.debug(
+                "No pixel values or image embeddings provided for multimodal embedding."
+            )
+            return None
 
     def get_mrope_input_positions(
         self,

vllm/platforms/rocm.py

@@ -8,6 +8,7 @@ from typing import TYPE_CHECKING, Optional
 import torch
 
 import vllm.envs as envs
+from vllm.attention.backends.abstract import AttentionType
 from vllm.attention.backends.registry import AttentionBackendEnum
 from vllm.logger import init_logger
 from vllm.utils.torch_utils import cuda_device_count_stateless
@@ -204,7 +205,7 @@ class RocmPlatform(Platform):
             assert block_size == 1, (
                 "Sparse MLA backend on ROCm only supports block size 1 for now."
             )
-            logger.info_once("Using Sparse MLA backend on V1 engine.")
+            logger.info_once("Using Sparse MLA backend.")
             return AttentionBackendEnum.ROCM_AITER_MLA_SPARSE.get_path()
 
         if attn_selector_config.use_mla:
@@ -239,16 +240,16 @@ class RocmPlatform(Platform):
             return AttentionBackendEnum.FLEX_ATTENTION.get_path()
 
         if selected_backend == AttentionBackendEnum.TRITON_ATTN:
-            logger.info("Using Triton Attention backend on V1 engine.")
+            logger.info("Using Triton Attention backend.")
             return AttentionBackendEnum.TRITON_ATTN.get_path()
 
         if selected_backend == AttentionBackendEnum.ROCM_ATTN:
-            logger.info("Using Rocm Attention backend on V1 engine.")
+            logger.info("Using Rocm Attention backend.")
             return AttentionBackendEnum.ROCM_ATTN.get_path()
 
         if selected_backend == AttentionBackendEnum.ROCM_AITER_FA:
             if on_gfx9():
-                logger.info("Using Aiter Flash Attention backend on V1 engine.")
+                logger.info("Using Aiter Flash Attention backend.")
                 return AttentionBackendEnum.ROCM_AITER_FA.get_path()
             else:
                 raise ValueError(
@@ -257,25 +258,25 @@ class RocmPlatform(Platform):
                 )
 
         if selected_backend == AttentionBackendEnum.ROCM_AITER_UNIFIED_ATTN:
-            logger.info("Using Aiter Unified Attention backend on V1 engine.")
+            logger.info("Using Aiter Unified Attention backend.")
             return AttentionBackendEnum.ROCM_AITER_UNIFIED_ATTN.get_path()
 
         # Handle automatic backend selection based on environment variables
         if selected_backend is None:
             # Priority 1: Check for AITER Unified Attention (must check before MHA)
             if envs.VLLM_ROCM_USE_AITER and envs.VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION:
-                logger.info("Using Aiter Unified Attention backend on V1 engine.")
+                logger.info("Using Aiter Unified Attention backend.")
                 return AttentionBackendEnum.ROCM_AITER_UNIFIED_ATTN.get_path()
 
             # Priority 2: Check for AITER MHA (Flash Attention)
             # Only use if explicitly enabled (not just VLLM_ROCM_USE_AITER=1)
             if envs.VLLM_ROCM_USE_AITER and envs.VLLM_ROCM_USE_AITER_MHA and on_gfx9():
-                logger.info("Using Aiter Flash Attention backend on V1 engine.")
+                logger.info("Using Aiter Flash Attention backend.")
                 return AttentionBackendEnum.ROCM_AITER_FA.get_path()
 
             # Priority 3: Check for ROCM_ATTN (prefill-decode split)
             if envs.VLLM_V1_USE_PREFILL_DECODE_ATTENTION:
-                logger.info("Using Rocm Attention backend on V1 engine.")
+                logger.info("Using Rocm Attention backend.")
                 return AttentionBackendEnum.ROCM_ATTN.get_path()
 
             # Priority 4: Check for AITER enabled without specific flags
@@ -285,11 +286,19 @@ class RocmPlatform(Platform):
                 and on_gfx9()
                 and envs.VLLM_ROCM_USE_AITER_MHA is not False
             ):
-                logger.info("Using Aiter Flash Attention backend on V1 engine.")
+                logger.info("Using Aiter Flash Attention backend.")
                 return AttentionBackendEnum.ROCM_AITER_FA.get_path()
 
+            # Priority 5: If model is Encoder-only self-attention type
+            if (
+                attn_selector_config.attn_type is not None
+                and attn_selector_config.attn_type == AttentionType.ENCODER_ONLY
+            ):
+                logger.info("Using FlexAttention backend.")
+                return AttentionBackendEnum.FLEX_ATTENTION.get_path()
+
             # Default: Triton Unified Attention
-            logger.info("Using Triton Attention backend on V1 engine.")
+            logger.info("Using Triton Attention backend.")
             return AttentionBackendEnum.TRITON_ATTN.get_path()
 
         raise RuntimeError(
@@ -324,14 +333,19 @@ class RocmPlatform(Platform):
 
         from vllm._aiter_ops import rocm_aiter_ops
 
-        if rocm_aiter_ops.is_mha_enabled():
+        if rocm_aiter_ops.is_enabled():
-            # Note: AITER FA is only supported for Qwen-VL models.
+            logger.info_once("Using AITER Flash Attention backend for ViT model.")
-            # TODO: Add support for other VL models in their model class.
             return AttentionBackendEnum.ROCM_AITER_FA
 
-        if on_gfx9() and find_spec("flash_attn") is not None:
+        if (
+            on_gfx9()
+            and find_spec("flash_attn") is not None
+            and (dtype == torch.float16 or dtype == torch.bfloat16)
+        ):
+            logger.info_once("Using Flash Attention backend for ViT model.")
             return AttentionBackendEnum.FLASH_ATTN
 
+        logger.info_once("Using Torch SDPA backend for ViT model.")
         return AttentionBackendEnum.TORCH_SDPA
 
     @classmethod