feat: Add TensorRT Edge-LLM AttentionPlugin backend support#4013
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feat: Add TensorRT Edge-LLM AttentionPlugin backend support#4013
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@zewenli98 please review |
narendasan
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Jan 14, 2026
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| This example uses a custom TensorRT plugin shared library (``libNvInfer_edgellm_plugin.so``) | ||
| that replaces standard transformer attention operations and RoPE computations with optimized | ||
| CUDA kernels. The plugin source code is available at (internal access only): |
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@chohk88 can you change this to external links?
narendasan
reviewed
Jan 14, 2026
| - kv_cache_start_idx: [B] starting index in KV cache (required for release version) | ||
| """ | ||
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| @torch.library.custom_op("xqa::attn", mutates_args=()) |
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lets call the op tensorrt_edge_llm::xqa_attn
narendasan
reviewed
Jan 14, 2026
| - kv_cache_start_idx: [B] starting index in KV cache (required for release version) | ||
| """ | ||
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| @torch.library.custom_op("xqa::attn", mutates_args=()) |
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Same thing here: tensorrt_edge_llm::xqa_attn
narendasan
reviewed
Jan 14, 2026
| nkv: int, | ||
| d: int, | ||
| ) -> Tuple[torch.Tensor, torch.Tensor]: | ||
| batch_size = qkv.shape[0] |
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Is it possible to provide a valid implementation here easily? could we lift the kernel from the .so?
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This would be a P1/P2 sort of thing, but I think it would be good for the sake of completeness
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Description
This PR adds TensorRT Edge-LLM AttentionPlugin backend support as an alternative to the default SDPA lowering, providing 1.7x ~ 3.3x performance improvement for LLM inference.
Supported Models: Llama 3.x (3.1 and 3.2), Qwen 2.5, Qwen 3, Qwen3.1
This is a temporary solution for the initial implementation. The fork contains Torch-TRT compatibility Python runtime support that is not yet available in the official NVIDIA TensorRT-Edge-LLM repository.
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Checklist: