This is an automated email from the ASF dual-hosted git repository. liuyizhi pushed a commit to branch tvm_sync in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
commit 2ef7de0ec0072828e788976d1ec44e9438b96383 Author: Yizhi Liu <[email protected]> AuthorDate: Fri Jan 24 22:17:50 2020 -0800 upgrade enum according to updated tvm --- src/nnvm/plan_memory.cc | 2 -- src/nnvm/tvm_bridge.cc | 4 ++-- src/operator/numpy/np_elemwise_broadcast_logic_op.cc | 6 +++--- src/operator/tensor/elemwise_unary_op_pow.cc | 4 ++-- src/operator/tvmop/op_module.cc | 2 +- 5 files changed, 8 insertions(+), 10 deletions(-) diff --git a/src/nnvm/plan_memory.cc b/src/nnvm/plan_memory.cc index c89eefc..e061dab 100644 --- a/src/nnvm/plan_memory.cc +++ b/src/nnvm/plan_memory.cc @@ -26,7 +26,6 @@ #include <nnvm/pass.h> #include <nnvm/graph_attr_types.h> #include <nnvm/op_attr_types.h> -#include <nnvm/top/tensor.h> #include <mxnet/base.h> #include <memory> #include "graph_algorithm.h" @@ -36,7 +35,6 @@ namespace nnvm { namespace pass { namespace { - using namespace nnvm::top; // Return bytes of data flag. static int MXGetDTypeSize(int type_flag) { switch (type_flag) { diff --git a/src/nnvm/tvm_bridge.cc b/src/nnvm/tvm_bridge.cc index 0692998..17e05e3 100644 --- a/src/nnvm/tvm_bridge.cc +++ b/src/nnvm/tvm_bridge.cc @@ -73,7 +73,7 @@ class TVMFunctor { const NDArray& nd = static_cast<NDArray*>(args.values[i].v_handle)[0]; // We cannot set the value until - type_codes_[i] = kArrayHandle; + type_codes_[i] = kTVMDLTensorHandle; array_data_.push_back(nd); array_loc_.push_back(i); // check if there is read or mutate @@ -86,7 +86,7 @@ class TVMFunctor { mutate_vars->push_back(nd.var()); } } else { - CHECK_LT(args.type_codes[i], kTVMType) + CHECK_LT(args.type_codes[i], kTVMDataType) << "Only allow POD type in mxnet async call"; } } diff --git a/src/operator/numpy/np_elemwise_broadcast_logic_op.cc b/src/operator/numpy/np_elemwise_broadcast_logic_op.cc index 7e8951a..8395caf 100644 --- a/src/operator/numpy/np_elemwise_broadcast_logic_op.cc +++ b/src/operator/numpy/np_elemwise_broadcast_logic_op.cc @@ -95,7 +95,7 @@ struct TVMBinaryBroadcastCompute { values.resize(num_args); for (size_t i = 0; i < num_args; ++i) { tblobs[i] = PrependAxes(tblobs[i], ondim); - type_codes[i] = kArrayHandle; + type_codes[i] = kTVMDLTensorHandle; values[i].v_handle = const_cast<DLTensor*>(&(tblobs[i].dltensor())); } tvm::runtime::TVMArgs tvm_args(&values[0], &type_codes[0], tblobs.size()); @@ -200,7 +200,7 @@ struct TVMBinaryBroadcastScalarCompute { values.resize(num_args); // input tensor setup - type_codes[0] = kArrayHandle; + type_codes[0] = kTVMDLTensorHandle; values[0].v_handle = const_cast<DLTensor*>(&(tblobs[0].dltensor())); // scalar param @@ -208,7 +208,7 @@ struct TVMBinaryBroadcastScalarCompute { values[1].v_float64 = nnvm::get<double>(attrs.parsed); // output tensor - type_codes[2] = kArrayHandle; + type_codes[2] = kTVMDLTensorHandle; values[2].v_handle = const_cast<DLTensor*>(&(tblobs[1].dltensor())); tvm::runtime::TVMArgs tvm_args(&values[0], &type_codes[0], 3); diff --git a/src/operator/tensor/elemwise_unary_op_pow.cc b/src/operator/tensor/elemwise_unary_op_pow.cc index b4d3a4a..914cb820 100644 --- a/src/operator/tensor/elemwise_unary_op_pow.cc +++ b/src/operator/tensor/elemwise_unary_op_pow.cc @@ -224,7 +224,7 @@ The storage type of ``rsqrt`` output is always dense MXNET_OPERATOR_REGISTER_BINARY_WITH_SPARSE_CPU_DR( _backward_rsqrt, unary_bwd<mshadow_op::reciprocal_square_root_grad>) .set_attr<nnvm::FGradient>("FGradient", - [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) { + [](const nnvm::ObjectPtr& n, const std::vector<nnvm::NodeEntry>& ograds) { // NodeEntry{n} : y_grad * f'(x) // n->inputs[0] : y_grad // n->inputs[1] : x @@ -329,7 +329,7 @@ MXNET_OPERATOR_REGISTER_BINARY(_backward_rcbrt) ElemwiseBinaryOp::Compute<cpu, unary_bwd<mshadow_op::reciprocal_cube_root_grad>>) .set_attr<nnvm::FGradient>("FGradient", - [](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) { + [](const nnvm::ObjectPtr& n, const std::vector<nnvm::NodeEntry>& ograds) { // NodeEntry{n} : y_grad * f'(x) // n->inputs[0] : y_grad // n->inputs[1] : x diff --git a/src/operator/tvmop/op_module.cc b/src/operator/tvmop/op_module.cc index b45df5d..cdd7321 100644 --- a/src/operator/tvmop/op_module.cc +++ b/src/operator/tvmop/op_module.cc @@ -94,7 +94,7 @@ void TVMOpModule::Call(const std::string &func_name, type_codes.resize(args.size()); values.resize(args.size()); for (size_t i = 0; i < args.size(); ++i) { - type_codes[i] = kArrayHandle; + type_codes[i] = kTVMDLTensorHandle; values[i].v_handle = const_cast<DLTensor *>(&(args[i].dltensor())); }
