feat - refactor fmha python in cudagraph & adapt pymodel mla cudagraph #463
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Nancheng-11
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| py_forward_method_(inputs); | ||
| auto attn_pyobj = graph_instances_[key].mem_hold_.attn_pyobj_; | ||
| attn_pyobj.attr("prepare")(inputs); | ||
| py_forward_method_(inputs, attn_pyobj); |
rtp_llm/models_py/utils/debug.py
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| data=data, | ||
| start_row=0, | ||
| start_col=0, | ||
| m=1, |
| .def(pybind11::init<>()) | ||
| .def_readwrite("kv_cache_offset", &TRTAttn::kv_cache_offset) | ||
| .def( | ||
| "__cpp_ptr__", |
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cudagraph调试的时候对比地址用的
| from rtp_llm.ops.compute_ops import PyModelInputs | ||
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| class AttnPyObj: |
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感觉不需要他，直接把方法放到fmhabase里？
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| def support_cuda_graph(self) -> bool: | ||
| return False | ||
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| def prepare_cuda_graph(self, attn_inputs: PyAttentionInputs): |
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我放在cudagraph里显式调用哇
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