forked from jiuyuan/InfiniTensor
fix ReduceMean and element_wise (#90)
* feat: 导出 getPerfTime 到 python Signed-off-by: YdrMaster <ydrml@hotmail.com> * fix parsing of ReduceMean * ReduceMean axes defaults to None * fix ElementWiseCudnn with shape broadcasting * fix format --------- Signed-off-by: YdrMaster <ydrml@hotmail.com> Co-authored-by: YdrMaster <ydrml@hotmail.com>
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@ -101,7 +101,11 @@ class GraphHandlerObj {
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inline void data_malloc() { g->dataMalloc(); }
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inline void tune() { g->getRuntime()->run(g, true); }
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inline void run() { g->getRuntime()->run(g); }
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inline double get_perf_time() { return g->getRuntime()->getPerfTime(g); }
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};
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} // namespace infini
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@ -476,7 +476,8 @@ class OnnxStub:
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tensors[node.output[0]] = self.handler.reduce_mean(
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tensors[node.input[0]],
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tensors.get(node.output[0]),
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tensors[node.input[1]] if len(node.input) > 1 else None,
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# NOTE(constroy): `axes` is an attribute until opset version 13.
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next((attr.ints for attr in node.attribute if attr.name == "axes"), None),
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next((attr.i for attr in node.attribute if attr.name == "keepdims"))
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!= 0,
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)
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@ -823,9 +824,15 @@ class OnnxStub:
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def optimize(self) -> None:
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self.handler.optimize()
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def tune(self) -> None:
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self.handler.tune()
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def run(self) -> None:
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self.handler.run()
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def get_perf_time(self) -> float:
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self.handler.get_perf_time()
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def from_onnx(model: ModelProto, runtime):
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stub = OnnxStub(model, runtime)
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@ -325,7 +325,10 @@ void init_graph_builder(py::module &m) {
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.def("optimize", &Handler::optimize, policy::automatic)
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.def("operators", &Handler::operators, policy::move)
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.def("data_malloc", &Handler::data_malloc, policy::automatic)
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.def("run", &Handler::run, policy::automatic);
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.def("get_perf_time", &Handler::get_perf_time, policy::automatic)
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.def("tune", &Handler::tune, policy::automatic)
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.def("run", &Handler::run, policy::automatic)
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.def("get_perf_time", &Handler::get_perf_time, policy::automatic);
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}
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} // namespace infini
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@ -14,14 +14,25 @@ class ElementWiseCudnn : public CudaKernelWithoutConfig {
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auto op = as<ElementWiseObj>(_op);
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auto context = dynamic_cast<const CudaRuntimeObj *>(_context);
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void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
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void *const bData = (op->getInputs(1)->getRawDataPtr<void *>());
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void *const cData = (op->getOutput()->getRawDataPtr<void *>());
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auto aTensor = op->getInputs(0);
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auto bTensor = op->getInputs(1);
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auto cTensor = op->getOutput();
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// cudnnOpTensor only allows B to be broadcasted.
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if (aTensor->getDims() != cTensor->getDims()) {
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swap(aTensor, bTensor);
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}
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IT_ASSERT(aTensor->getDims() == cTensor->getDims(),
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"Shape does not match.");
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void *const aData = (aTensor->getRawDataPtr<void *>());
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void *const bData = (bTensor->getRawDataPtr<void *>());
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void *const cData = (cTensor->getRawDataPtr<void *>());
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cudnnTensorDescriptor_t aDesc, bDesc, cDesc;
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auto a_dim = op->getInputs(0)->getDims();
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auto b_dim = op->getInputs(1)->getDims();
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auto c_dim = op->getOutput()->getDims();
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auto a_dim = aTensor->getDims();
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auto b_dim = bTensor->getDims();
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auto c_dim = cTensor->getDims();
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if (a_dim.size() > 4 || b_dim.size() > 4 || c_dim.size() > 4)
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IT_TODO_HALT();
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