vllm/tests/models/multimodal/conftest.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Pytest configuration for vLLM multimodal tests."""

import warnings

import torch

from vllm.platforms import current_platform


def pytest_collection_modifyitems(config, items):
    """Configure ROCm-specific settings based on collected tests."""
    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):
        return

    # Disable Flash/MemEfficient SDP on ROCm to avoid HF Transformers
    # accuracy issues: https://github.com/vllm-project/vllm/issues/30167
    # 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,
    )


def patch_hf_vision_attn_for_rocm(model):
    """Force SDPA for HF vision encoders on ROCm.

    HF's flash_attention_2 has accuracy issues on ROCm that bypass
    torch.backends.cuda settings. This forces SDPA which then uses
    math_sdp via the pytest_collection_modifyitems settings.
    """
    if not current_platform.is_rocm():
        return

    inner = getattr(model, "model", model)

    if hasattr(inner, "vision_embedding"):
        vit = inner.vision_embedding[0]
        for layer in vit.encoder.layers:
            if hasattr(layer, "self_attn"):
                layer.self_attn.vision_config._attn_implementation = "sdpa"