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vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe.rocm_moe_rdna

ROCm MoE kernel dispatcher.

Selects architecture-specific native HIP MoE kernels in priority order. Falls back to the Triton WNA16 path when no native kernel is available.

Functions:

  • is_supported –

    Check if a native ROCm MoE kernel is available for this config.

  • make_method –

    Create the native ROCm MoE method. Call only after is_supported().

is_supported(weight_quant)

Check if a native ROCm MoE kernel is available for this config.

Source code in vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/rocm_moe_rdna.py 
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def is_supported(weight_quant) -> bool:
    """Check if a native ROCm MoE kernel is available for this config."""
    if weight_quant.num_bits != 4:
        return False

    from vllm.platforms.rocm import on_gfx1100

    # RDNA3 (gfx1100). Future: add RDNA4 (gfx12x), CDNA (gfx94x), etc.
    return (
        on_gfx1100()
        and hasattr(torch.ops, "_rocm_C")
        and hasattr(torch.ops._rocm_C, "moe_gptq_gemm_rdna3")
    )

make_method(weight_quant, input_quant, moe_config)

Create the native ROCm MoE method. Call only after is_supported().

Source code in vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe/rocm_moe_rdna.py 
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def make_method(weight_quant, input_quant, moe_config):
    """Create the native ROCm MoE method. Call only after is_supported()."""
    from vllm.platforms.rocm import on_gfx1100

    if on_gfx1100():
        from .compressed_tensors_moe_wna16_rdna3 import (
            CompressedTensorsWNA16RDNA3MoEMethod,
        )

        logger.info_once(
            "Using CompressedTensorsWNA16RDNA3MoEMethod (native RDNA3 HIP kernel)"
        )
        return CompressedTensorsWNA16RDNA3MoEMethod(
            weight_quant, input_quant, moe_config
        )

    # Future: RDNA4, CDNA, etc.
    raise RuntimeError("is_supported() returned True but no kernel matched")