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c_api.cc
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/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
/*!
* Copyright (c) 2015 by Contributors
* \file c_api.cc
* \brief C API of mxnet
*/
#include <vector>
#include <sstream>
#include <string>
#include <mutex>
#include <memory>
#include <functional>
#include <unordered_map>
#include <utility>
#include "dmlc/base.h"
#include "dmlc/logging.h"
#include "dmlc/io.h"
#include "dmlc/memory_io.h"
#include "dmlc/recordio.h"
#include "dmlc/omp.h"
#include "mxnet/base.h"
#include "mxnet/ndarray.h"
#include "mxnet/operator.h"
#include "mxnet/io.h"
#include "mxnet/c_api.h"
#include "mxnet/kvstore.h"
#include "mxnet/rtc.h"
#include "mxnet/storage.h"
#include "mxnet/libinfo.h"
#include "mxnet/imperative.h"
#include "mxnet/lib_api.h"
#include "../initialize.h"
#include "./c_api_common.h"
#include "../operator/custom/custom-inl.h"
#include "../operator/operator_common.h"
#include "../operator/subgraph/common.h"
#include "../operator/tensor/matrix_op-inl.h"
#include "../operator/tvmop/op_module.h"
#include "../operator/subgraph/partitioner/custom_subgraph_property.h"
#include "../operator/subgraph/subgraph_property.h"
#include "../common/utils.h"
#include "../profiler/profiler.h"
#include "nnvm/pass_functions.h"
using namespace mxnet;
// Internal function to get the information
// from function registry
// Used to implement MXSymbolGetAtomicSymbolInfo and MXFuncGetInfo
template<typename FunRegType>
inline int MXAPIGetFunctionRegInfo(const FunRegType *e,
const char **name,
const char **description,
uint32_t *num_args,
const char ***arg_names,
const char ***arg_type_infos,
const char ***arg_descriptions,
const char **return_type) {
MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
API_BEGIN();
*name = e->name.c_str();
*description = e->description.c_str();
*num_args = static_cast<uint32_t>(e->arguments.size());
if (return_type) *return_type = e->return_type.c_str();
ret->ret_vec_charp.clear();
for (size_t i = 0; i < e->arguments.size(); ++i) {
ret->ret_vec_charp.push_back(e->arguments[i].name.c_str());
}
for (size_t i = 0; i < e->arguments.size(); ++i) {
ret->ret_vec_charp.push_back(e->arguments[i].type_info_str.c_str());
}
for (size_t i = 0; i < e->arguments.size(); ++i) {
ret->ret_vec_charp.push_back(e->arguments[i].description.c_str());
}
*arg_names = dmlc::BeginPtr(ret->ret_vec_charp);
*arg_type_infos = dmlc::BeginPtr(ret->ret_vec_charp) + e->arguments.size();
*arg_descriptions = dmlc::BeginPtr(ret->ret_vec_charp) + (e->arguments.size() * 2);
API_END();
}
// NOTE: return value is added in API_END
std::string getExtensionMsgs(mxnet::ext::msgSize_t msgSize,
mxnet::ext::msgGet_t msgGet) {
std::string str;
if (msgSize() > 0) {
str = "\nExtension Traceback:\n";
for (int i = 0; i < msgSize(); i++) {
const char* tmp;
msgGet(i, &tmp);
// format: [i] message
str += std::string("\t[") + std::to_string(i) + std::string("] ")
+ std::string(tmp) + std::string("\n");
}
}
return str;
}
/*!
* \brief Common compute function dispatcher for forward/backward and stateful forward/backward
* state_ptr will be nullptr for regular ops; fcomp_fp is nullptr for stateful ops
*/
void CustomFComputeDispatcher(const std::string op_name,
const mxnet::ext::opCallFComp_t callFComp,
const mxnet::ext::fcomp_t fcomp_fp,
const nnvm::NodeAttrs* attrs,
const mxnet::ext::opCallFStatefulComp_t callFStatefulComp,
int stateful_forward_flag,
const OpStatePtr* state_ptr,
const OpContext& ctx,
const std::vector<NDArray>& inputs,
const std::vector<OpReqType>& req,
const std::vector<NDArray>& outputs,
mxnet::ext::msgSize_t msgSize,
mxnet::ext::msgGet_t msgGet) {
using namespace mxnet::ext;
std::vector<void*> in_data, out_data;
std::vector<const int64_t*> in_shapes, out_shapes;
std::vector<int> in_dims, out_dims;
std::vector<int> in_types, out_types;
std::vector<size_t> in_verIDs, out_verIDs;
std::vector<const char*> in_dev_type, out_dev_type;
std::vector<int> in_dev_id, out_dev_id;
std::vector<NDArray> conv_mkl; // converted NDArrays from MKLDNN format
// Extra data for sparse inputs and outputs.
std::vector<int> in_stypes(inputs.size(), 0), out_stypes(outputs.size(), 0);
std::vector<void*> in_indices(inputs.size(), nullptr), out_indices(outputs.size(), nullptr);
std::vector<void*> in_indptr(inputs.size(), nullptr), out_indptr(outputs.size(), nullptr);
std::vector<int64_t> in_indices_shapes(inputs.size(), 0), out_indices_shapes(outputs.size(), 0);
std::vector<int64_t> in_indptr_shapes(inputs.size(), 0), out_indptr_shapes(outputs.size(), 0);
// convert inputs/outpus NDArray to C types to be passed to lib_api.h
for (size_t i = 0; i < inputs.size(); i++) {
NDArray const* in_nd = &(inputs[i]);
#if MXNET_USE_MKLDNN == 1
// reorder data if in MKLDNN format
if (in_nd->IsMKLDNNData()) {
// convert from MKLDNN
conv_mkl.push_back(in_nd->Reorder2Default());
in_nd = &(conv_mkl.back());
}
#endif
// pull out parts to pass over to library
in_data.push_back(in_nd->data().dptr_);
in_shapes.push_back(in_nd->shape().data());
in_dims.push_back(in_nd->shape().ndim());
in_types.push_back(in_nd->dtype());
in_verIDs.push_back(in_nd->version());
// string repr of supported context for custom library, currently only "cpu" and "gpu"
const char* ctx_str = in_nd->ctx().dev_mask() == Context::kCPU ? "cpu" : "gpu";
in_dev_type.push_back(ctx_str);
in_dev_id.push_back(in_nd->ctx().real_dev_id());
if (inputs[i].storage_type() == mxnet::kRowSparseStorage) {
in_stypes[i] = 1;
in_indices[i] = inputs[i].aux_data(rowsparse::kIdx).dptr_;
in_indices_shapes[i] = inputs[i].aux_shape(rowsparse::kIdx).Size();
} else if (inputs[i].storage_type() == mxnet::kCSRStorage) {
in_stypes[i] = 2;
in_indices[i] = inputs[i].aux_data(csr::kIdx).dptr_;
in_indptr[i] = inputs[i].aux_data(csr::kIndPtr).dptr_;
in_indices_shapes[i] = inputs[i].aux_shape(csr::kIdx).Size();
in_indptr_shapes[i] = inputs[i].aux_shape(csr::kIndPtr).Size();
}
}
for (size_t i = 0; i < outputs.size(); i++) {
out_data.push_back(outputs[i].data().dptr_);
out_shapes.push_back(outputs[i].shape().data());
out_dims.push_back(outputs[i].shape().ndim());
out_types.push_back(outputs[i].dtype());
out_verIDs.push_back(outputs[i].version());
const char* ctx_str = outputs[i].ctx().dev_mask() == Context::kCPU ? "cpu" : "gpu";
out_dev_type.push_back(ctx_str);
out_dev_id.push_back(outputs[i].ctx().real_dev_id());
if (outputs[i].storage_type() == mxnet::kRowSparseStorage) {
out_stypes[i] = 1;
out_indices[i] = outputs[i].aux_data(rowsparse::kIdx).dptr_;
out_indices_shapes[i] = outputs[i].aux_shape(rowsparse::kIdx).Size();
} else if (outputs[i].storage_type() == mxnet::kCSRStorage) {
out_stypes[i] = 2;
out_indices[i] = outputs[i].aux_data(csr::kIdx).dptr_;
out_indptr[i] = outputs[i].aux_data(csr::kIndPtr).dptr_;
out_indices_shapes[i] = outputs[i].aux_shape(csr::kIdx).Size();
out_indptr_shapes[i] = outputs[i].aux_shape(csr::kIndPtr).Size();
}
}
// get memory resource and mxnet backend streams
CHECK(ctx.requested.size() >= 2)
<< "Custom operator should register at least memory resource and parallel random resource";
const Resource &resource = ctx.requested.at(0);
mshadow::Stream<mxnet::cpu> *cpu_stream = ctx.get_stream<mxnet::cpu>();
mshadow::Stream<mxnet::gpu> *gpu_stream = ctx.get_stream<mxnet::gpu>();
// create lambda that captures stream & resource objects
// this temp workspace holds memory allocated by custom library via OpResource
auto cpu_alloc = [&](int size) {
mshadow::Tensor<mxnet::cpu, 1, char> workspace =
resource.get_space_typed<mxnet::cpu, 1, char>(mshadow::Shape1(size), cpu_stream);
return workspace.dptr_;
};
auto gpu_alloc = [&](int size) {
mshadow::Tensor<mxnet::gpu, 1, char> workspace =
resource.get_space_typed<mxnet::gpu, 1, char>(mshadow::Shape1(size), gpu_stream);
return workspace.dptr_;
};
// create lambda that allocates memory for sparse and
// returns allocated arrays for data, indices and indptr.
auto sparse_alloc = [&](int index, int indices_len, int idxptr_len,
void** data, int64_t** indices, int64_t** indptr) {
if (idxptr_len == 0) {
// Row Sparse
outputs[index].CheckAndAlloc({mshadow::Shape1(indices_len)});
*data = outputs[index].data().dptr_;
*indices = reinterpret_cast<int64_t*>(outputs[index].aux_data(rowsparse::kIdx).dptr_);
} else {
// CSR
outputs[index].CheckAndAlloc({mshadow::Shape1(idxptr_len), mshadow::Shape1(indices_len)});
*data = outputs[index].data().dptr_;
*indices = reinterpret_cast<int64_t*>(outputs[index].aux_data(csr::kIdx).dptr_);
*indptr = reinterpret_cast<int64_t*>(outputs[index].aux_data(csr::kIndPtr).dptr_);
}
};
// create no-capture lambda so that we can cast it to function pointer
// lambda with captures cannot be cast to function pointer and pass to lib_api.h
// this needs to be a lambda function so that we can do the decltype cast
typedef decltype(cpu_alloc) alloc_type_cpu;
auto cpu_malloc = [](void* _cpu_alloc, int size) {
// cast the void* argument to the type for the cpu_alloc lambda function
alloc_type_cpu* cpualloc = static_cast<alloc_type_cpu*>(_cpu_alloc);
// call cpu_alloc to actually allocate memory and return the pointer
return static_cast<void*>((*cpualloc)(size));
};
using alloc_type_gpu = decltype(gpu_alloc);
auto gpu_malloc = [](void* _gpu_alloc, int size) {
alloc_type_gpu* gpualloc = static_cast<alloc_type_gpu*>(_gpu_alloc);
return static_cast<void*>((*gpualloc)(size));
};
using alloc_type_sparse = decltype(sparse_alloc);
auto sparse_malloc = [](void* _sparse_alloc, int index, int indices_len, int idxptr_len,
void** data, int64_t** indices, int64_t** indptr) {
alloc_type_sparse* sparsealloc = static_cast<alloc_type_sparse*>(_sparse_alloc);
(*sparsealloc)(index, indices_len, idxptr_len, data, indices, indptr);
};
// get actual cudaStream_t out of mxnet gpu stream and pass to lib_api.h
void *cuda_stream = nullptr;
#if MXNET_USE_CUDA
if ((inputs.size() > 0 && inputs[0].ctx().dev_mask() == Context::kGPU) ||
(outputs.size() > 0 && outputs[0].ctx().dev_mask() == Context::kGPU)) {
cuda_stream = static_cast<void*>(gpu_stream->stream_);
}
#endif
// get mxnet initialized and seeded RNG states and pass to lib_api.h
void *rng_cpu_states = nullptr, *rng_gpu_states = nullptr;
using mxnet::common::random::RandGenerator;
RandGenerator<cpu, float> *pgen_cpu = ctx.requested.at(1).get_parallel_random<cpu, float>();
rng_cpu_states = pgen_cpu->GetStates();
#if MXNET_USE_CUDA
RandGenerator<gpu, float> *pgen_gpu = ctx.requested.at(1).get_parallel_random<gpu, float>();
rng_gpu_states = pgen_gpu->GetStates();
#endif
CHECK((fcomp_fp != nullptr && state_ptr == nullptr)
|| (fcomp_fp == nullptr && state_ptr != nullptr))
<< "Can only register either regular op or stateful op for '" << op_name << "'";
if (fcomp_fp != nullptr) {
// convert attributes to vector of char*
std::vector<const char*> attr_keys, attr_vals;
for (auto &kv : attrs->dict) {
attr_keys.push_back(kv.first.c_str());
attr_vals.push_back(kv.second.c_str());
}
// call fcompute function
int retval = callFComp(fcomp_fp, attr_keys.data(), attr_vals.data(), attr_keys.size(),
in_shapes.data(), in_dims.data(), in_data.data(), in_types.data(),
in_verIDs.data(), in_dev_type.data(), in_dev_id.data(), in_data.size(),
out_shapes.data(), out_dims.data(), out_data.data(), out_types.data(),
out_verIDs.data(), out_dev_type.data(), out_dev_id.data(),
out_data.size(),
cpu_malloc, &cpu_alloc, gpu_malloc, &gpu_alloc, cuda_stream,
sparse_malloc, &sparse_alloc, in_stypes.data(), out_stypes.data(),
in_indices.data(), out_indices.data(), in_indptr.data(),
out_indptr.data(),
in_indices_shapes.data(), out_indices_shapes.data(),
in_indptr_shapes.data(), out_indptr_shapes.data(),
rng_cpu_states, rng_gpu_states);
std::string msgs = getExtensionMsgs(msgSize, msgGet);
CHECK(retval) << "Error calling FCompute for custom operator '" << op_name << "'" << msgs;
}
if (state_ptr != nullptr) {
// retrieve op state object created from CreateOpState
CustomStatefulOpWrapper& op = state_ptr->get_state<CustomStatefulOpWrapper>();
CustomStatefulOp* state_op_inst = op.get_instance();
std::string msgs = getExtensionMsgs(msgSize, msgGet);
CHECK(state_op_inst != nullptr)
<< "Error custom stateful operator is null for operator '" << op_name << "'" << msgs;
// call fcompute function
int retval = callFStatefulComp(stateful_forward_flag, state_op_inst,
in_shapes.data(), in_dims.data(), in_data.data(),
in_types.data(),
in_verIDs.data(), in_dev_type.data(), in_dev_id.data(),
in_data.size(),
out_shapes.data(), out_dims.data(), out_data.data(),
out_types.data(),
out_verIDs.data(), out_dev_type.data(), out_dev_id.data(),
out_data.size(),
cpu_malloc, &cpu_alloc, gpu_malloc, &gpu_alloc, cuda_stream,
sparse_malloc, &sparse_alloc, in_stypes.data(),
out_stypes.data(), in_indices.data(), out_indices.data(),
in_indptr.data(), out_indptr.data(),
in_indices_shapes.data(), out_indices_shapes.data(),
in_indptr_shapes.data(), out_indptr_shapes.data(),
rng_cpu_states, rng_gpu_states);
msgs = getExtensionMsgs(msgSize, msgGet);
CHECK(retval) << "Error calling FStatefulCompute for custom operator '" << op_name << "'"
<< msgs;
}
}
template <typename RescReq, typename AttrParser, typename NumInputs, typename NumOutputs,
typename NumInOuts,
typename InferType, typename InferShape, typename InferSType, typename MutateInputs,
typename SubgraphNumInputs, typename SubgraphInferType, typename SubgraphInferShape,
typename SubgraphInferSType, typename CreateOpState, typename GradReg>
void registerOp(const char* name, const std::string& name_str, bool isSubgraphOp,
RescReq resc_req, AttrParser attr_parser, NumInputs num_inputs,
NumOutputs num_outputs, NumInOuts num_inouts, InferType infer_type,
InferShape infer_shape, InferSType infer_storage_type,
MutateInputs mutate_inputs, SubgraphNumInputs num_subgraph_inputs,
SubgraphInferType infer_subgraph_type, SubgraphInferShape infer_subgraph_shape,
SubgraphInferSType infer_subgraph_storage_type, CreateOpState create_opstate,
GradReg grad_reg, mxnet::ext::mutateInputs_t mutate_fp,
const std::unordered_map<std::string, mxnet::ext::createOpState_t> &createop_map,
const std::unordered_map<std::string, mxnet::ext::fcomp_t> &forward_ctx_map,
const std::unordered_map<std::string, mxnet::ext::fcomp_t> &backward_ctx_map,
mxnet::ext::opCallFComp_t callFComp,
mxnet::ext::opCallFStatefulComp_t callFStatefulComp,
mxnet::ext::msgSize_t msgSize,
mxnet::ext::msgGet_t msgGet) {
using namespace mxnet::ext;
// check if operator is already registered
const nnvm::Op *regOpPtr = dmlc::Registry<nnvm::Op>::Get()->Find(name);
nnvm::Op ®Op = dmlc::Registry<nnvm::Op>::Get()->__REGISTER_OR_GET__(name);
int plevel = 10;
if (regOpPtr != nullptr) {
// overwrite registration of existing op with custom op
regOp.arguments.clear();
// set attribute with higher plevel (11) to allow re-registering once
// TODO(samskalicky): enable constant overwriting of registertion multiple times
plevel++;
}
// define supported resources for both subgraph ops and regular ops
regOp.set_attr<FResourceRequest>("FResourceRequest", resc_req, plevel);
if (!isSubgraphOp) {
regOp.set_attr_parser(attr_parser);
regOp.set_num_inputs(num_inputs);
regOp.set_num_outputs(num_outputs);
regOp.set_attr<nnvm::FInferType>("FInferType", infer_type, plevel);
regOp.set_attr<FInferStorageType>("FInferStorageType", infer_storage_type, plevel);
regOp.set_attr<mxnet::FInferShape>("FInferShape", infer_shape, plevel);
// optionally add fmutate inputs if user specified a function
if (mutate_fp != nullptr)
regOp.set_attr<nnvm::FMutateInputs>("FMutateInputs", mutate_inputs, plevel);
} else {
using namespace mxnet::op;
regOp.set_num_inputs(num_subgraph_inputs);
regOp.set_num_outputs(DefaultSubgraphOpNumOutputs);
regOp.set_attr<nnvm::FInferType>("FInferType", infer_subgraph_type, plevel);
regOp.set_attr<mxnet::FInferShape>("FInferShape", infer_subgraph_shape, plevel);
regOp.set_attr<FInferStorageType>("FInferStorageType",
infer_subgraph_storage_type, plevel);
regOp.set_attr<nnvm::FMutateInputs>("FMutateInputs",
DefaultSubgraphOpMutableInputs, plevel);
}
// optionally add stateful forward
if (createop_map.size() != 0) {
regOp.set_attr<FCreateOpState>("FCreateOpState", create_opstate, plevel);
auto fstate_forward = [=](const OpStatePtr& state_ptr,
const OpContext& ctx,
const std::vector<NDArray>& inputs,
const std::vector<OpReqType>& req,
const std::vector<NDArray>& outputs) {
CustomFComputeDispatcher(name_str, nullptr, nullptr, nullptr,
callFStatefulComp, 1, &state_ptr, ctx, inputs, req, outputs,
msgSize, msgGet);
};
if (createop_map.count("cpu") > 0)
regOp.set_attr<FStatefulComputeEx>("FStatefulComputeEx<cpu>", fstate_forward, plevel);
if (createop_map.count("gpu") > 0)
regOp.set_attr<FStatefulComputeEx>("FStatefulComputeEx<gpu>", fstate_forward, plevel);
} else {
auto forward_lambda = [=](const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<NDArray>& inputs,
const std::vector<OpReqType>& req,
const std::vector<NDArray>& outputs) {
if (ctx.run_ctx.ctx.dev_mask() == Context::kCPU) {
CHECK_GT(forward_ctx_map.count("cpu"), 0);
fcomp_t fcomp = forward_ctx_map.at("cpu");
CustomFComputeDispatcher(name_str, callFComp, fcomp, &attrs,
nullptr, 0, nullptr, ctx, inputs, req, outputs, msgSize, msgGet);
} else if (ctx.run_ctx.ctx.dev_mask() == Context::kGPU) {
CHECK_GT(forward_ctx_map.count("gpu"), 0);
fcomp_t fcomp = forward_ctx_map.at("gpu");
CustomFComputeDispatcher(name_str, callFComp, fcomp, &attrs,
nullptr, 0, nullptr, ctx, inputs, req, outputs, msgSize, msgGet);
}
};
if (forward_ctx_map.count("cpu") > 0)
regOp.set_attr<FComputeEx>("FComputeEx<cpu>", forward_lambda, plevel);
if (forward_ctx_map.count("gpu") > 0)
regOp.set_attr<FComputeEx>("FComputeEx<gpu>", forward_lambda, plevel);
}
// optionally add fgradient if user specified a function, or for stateful ops
if (backward_ctx_map.size() != 0 || createop_map.size() != 0) {
std::string grad_name = "_backward_" + name_str;
nnvm::Op &gradOp = dmlc::Registry<nnvm::Op>::Get()->__REGISTER_OR_GET__(grad_name);
regOp.set_attr<nnvm::FGradient>("FGradient", grad_reg, plevel);
gradOp.set_attr<nnvm::TIsBackward>("TIsBackward", true, plevel);
gradOp.set_attr<FInferStorageType>("FInferStorageType", infer_storage_type, plevel);
gradOp.set_attr<FResourceRequest>("FResourceRequest", resc_req, plevel);
if (!isSubgraphOp) {
// register attr parser and standard functions for non-subgraph ops
gradOp.set_attr_parser(attr_parser);
gradOp.set_num_inputs(num_inouts);
gradOp.set_num_outputs(num_inputs);
} else {
// for subgraph ops use special functions that do not invoke attr_parser
using namespace mxnet::op;
auto grad_inouts = [=](const nnvm::NodeAttrs& attrs) {
// for backward passes, inputs + outputs + input gradients (one for each output)
uint32_t cnt = num_subgraph_inputs(attrs);
cnt += 2 * DefaultSubgraphOpNumOutputs(attrs);
return cnt;
};
gradOp.set_num_inputs(grad_inouts);
gradOp.set_num_outputs(num_subgraph_inputs);
}
if (createop_map.size() != 0) {
// for stateful operators
gradOp.set_attr<bool>("TIsLayerOpBackward", true, plevel);
auto fstate_backward = [=](const OpStatePtr& state_ptr,
const OpContext& ctx,
const std::vector<NDArray>& inputs,
const std::vector<OpReqType>& req,
const std::vector<NDArray>& outputs) {
CustomFComputeDispatcher(name_str, nullptr, nullptr, nullptr,
callFStatefulComp, 0, &state_ptr, ctx, inputs, req, outputs,
msgSize, msgGet);
};
gradOp.set_attr<FStatefulComputeEx>("FStatefulComputeEx<cpu>", fstate_backward, plevel);
gradOp.set_attr<FStatefulComputeEx>("FStatefulComputeEx<gpu>", fstate_backward, plevel);
} else {
// for stateless operators
if (backward_ctx_map.count("cpu") > 0) {
fcomp_t fcomp_back_cpu = backward_ctx_map.at("cpu");
auto backward_cpu_lambda = [=](const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<NDArray>& inputs,
const std::vector<OpReqType>& req,
const std::vector<NDArray>& outputs) {
CustomFComputeDispatcher(name_str, callFComp, fcomp_back_cpu, &attrs,
nullptr, 0, nullptr, ctx, inputs, req, outputs, msgSize, msgGet);
};
gradOp.set_attr<FComputeEx>("FComputeEx<cpu>", backward_cpu_lambda, plevel);
}
if (backward_ctx_map.count("gpu") > 0) {
fcomp_t fcomp_back_gpu = backward_ctx_map.at("gpu");
auto backward_gpu_lambda = [=](const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<NDArray>& inputs,
const std::vector<OpReqType>& req,
const std::vector<NDArray>& outputs) {
CustomFComputeDispatcher(name_str, callFComp, fcomp_back_gpu, &attrs,
nullptr, 0, nullptr, ctx, inputs, req, outputs, msgSize, msgGet);
};
gradOp.set_attr<FComputeEx>("FComputeEx<gpu>", backward_gpu_lambda, plevel);
}
}
}
regOp.add_argument("data", "NDArray[]", "Source inputs");
}
void registerOperators(void *lib, int verbose, mxnet::ext::msgSize_t msgSize,
mxnet::ext::msgGet_t msgGet) {
using namespace mxnet::ext;
// get C type interface functions
opCallFree_t callFree = get_func<opCallFree_t>(lib, const_cast<char*>(MXLIB_OPCALLFREE_STR));
opCallParseAttrs_t callParseAttrs =
get_func<opCallParseAttrs_t>(lib, const_cast<char*>(MXLIB_OPCALLPARSEATTRS_STR));
opCallInferShape_t callInferShape =
get_func<opCallInferShape_t>(lib, const_cast<char*>(MXLIB_OPCALLINFERSHAPE_STR));
opCallInferType_t callInferType =
get_func<opCallInferType_t>(lib, const_cast<char*>(MXLIB_OPCALLINFERTYPE_STR));
opCallInferSType_t callInferSType =
get_func<opCallInferSType_t>(lib, const_cast<char*>(MXLIB_OPCALLINFERSTYPE_STR));
opCallFComp_t callFComp =
get_func<opCallFComp_t>(lib, const_cast<char*>(MXLIB_OPCALLFCOMP_STR));
opCallMutateInputs_t callMutateInputs =
get_func<opCallMutateInputs_t>(lib, const_cast<char*>(MXLIB_OPCALLMUTATEINPUTS_STR));
opCallCreateOpState_t callCreateOpState =
get_func<opCallCreateOpState_t>(lib, const_cast<char*>(MXLIB_OPCALLCREATEOPSTATE_STR));
opCallFStatefulComp_t callFStatefulComp =
get_func<opCallFStatefulComp_t>(lib, const_cast<char*>(MXLIB_OPCALLFSTATEFULCOMP_STR));
// get number of operators registered in the library
opRegSize_t opRegSize = get_func<opRegSize_t>(lib, const_cast<char*>(MXLIB_OPREGSIZE_STR));
int numOps = opRegSize();
if (verbose) LOG(INFO) << "Found " << numOps << " operators in library";
/*
* Get all custom operators implementation from custom library
* loop and register each operator in the library to NNVM
*/
opRegGet_t opRegGet = get_func<opRegGet_t>(lib, const_cast<char*>(MXLIB_OPREGGET_STR));
for (int i = 0; i < numOps; i++) {
const char* name;
// function pointers holding implementation from custom library
parseAttrs_t parse_fp = nullptr;
inferType_t type_fp = nullptr;
inferSType_t stype_fp = nullptr;
inferShape_t shape_fp = nullptr;
// optional attributes
mutateInputs_t mutate_fp = nullptr;
bool isSubgraphOp = false;
int _isSubgraphOp = 0;
// lists of forward and backward function associated with each context
const char **forward_ctx, **backward_ctx, **createop_ctx;
fcomp_t *forward_fcomp, *backward_fcomp;
createOpState_t *createop_fp;
int forward_count, backward_count, createop_count;
// main function to get custom operator implemenation from the custom library
opRegGet(i, &name, &_isSubgraphOp,
&forward_ctx, &forward_fcomp, &forward_count,
&backward_ctx, &backward_fcomp, &backward_count,
&createop_ctx, &createop_fp, &createop_count,
&parse_fp, &type_fp, &stype_fp, &shape_fp, &mutate_fp);
// construct maps of context to forward/backward custom library function
std::unordered_map<std::string, fcomp_t> forward_ctx_map;
std::unordered_map<std::string, fcomp_t> backward_ctx_map;
std::unordered_map<std::string, createOpState_t> createop_map;
for (int i=0; i < forward_count; i++) {
std::string ctx_str(forward_ctx[i]);
forward_ctx_map[ctx_str] = forward_fcomp[i];
}
for (int i=0; i < backward_count; i++) {
std::string ctx_str(backward_ctx[i]);
backward_ctx_map[ctx_str] = backward_fcomp[i];
}
for (int i=0; i < createop_count; i++) {
std::string ctx_str(createop_ctx[i]);
createop_map[ctx_str] = createop_fp[i];
}
// set bool, dont pass bool across ABI boundary
isSubgraphOp = _isSubgraphOp;
// validate custom operator functions from the dynamic library
if (!isSubgraphOp) {
CHECK(parse_fp != nullptr) << "Error loading '" << name
<< "' custom op, ParseAttrs function was not set.";
CHECK(forward_ctx_map.size() != 0 || createop_map.size() != 0)
<< "Error loading '" << name
<< "' custom op, Forward or CreateOpState function was not set.";
CHECK(type_fp != nullptr) << "Error loading '" << name
<< "' custom op, InferType function was not set.";
CHECK(shape_fp != nullptr) << "Error loading '" << name
<< "' custom op, InferShape function was not set.";
} else {
CHECK(createop_map.size() != 0) << "Error loading '" << name
<< "' custom subgraph op, CreateOpState function was not set.";
}
if (verbose) LOG(INFO) << "\tOp[" << i << "] " << name;
if (verbose && isSubgraphOp) LOG(INFO) << "\t\tisSubgraphOp";
std::string name_str(name);
/*
* Below are a series of lambda functions that will be registered in the NNVM op registration
* Each one has the standard MXNet signature and converts to types supported by externally
* registered operators.
*/
// lambda function to call parse attributes
auto attr_parser = [=](const NodeAttrs* attrs) {
// convert attributes to vector of char
std::vector<const char*> attr_keys, attr_vals;
for (auto &kv : attrs->dict) {
attr_keys.push_back(kv.first.c_str());
attr_vals.push_back(kv.second.c_str());
}
// convert subgraph symbol from node attributes to char*
std::string subgraph_json;
if (!attrs->subgraphs.empty()) {
nnvm::Graph g;
g.outputs = attrs->subgraphs[0].get()->outputs;
subgraph_json = nnvm::pass::SaveJSON(g);
attr_keys.push_back(MX_STR_SUBGRAPH_SYM_JSON);
attr_vals.push_back(subgraph_json.c_str());
}
int num_in = -1;
int num_out = -1;
int retval = callParseAttrs(parse_fp, attr_keys.data(), attr_vals.data(), attr_keys.size(),
&num_in, &num_out);
std::string msgs = getExtensionMsgs(msgSize, msgGet);
CHECK(retval) << "Error calling ParseAttrs for custom operator '" << name_str << "'" << msgs;
// return type void
};
// lambda function to call parse attributes and return the number of inputs
auto num_inputs = [=](const NodeAttrs& attrs) {
// convert attributes to vector of char
std::vector<const char*> attr_keys, attr_vals;
for (auto &kv : attrs.dict) {
attr_keys.push_back(kv.first.c_str());
attr_vals.push_back(kv.second.c_str());
}
int num_in = -1;
int num_out = -1;
int retval = callParseAttrs(parse_fp, attr_keys.data(), attr_vals.data(), attr_keys.size(),
&num_in, &num_out);
std::string msgs = getExtensionMsgs(msgSize, msgGet);
CHECK(retval) << "Error calling ParseAttrs::num_inputs for custom operator '" << name_str
<< "'" << msgs;
// get extra inputs, if exists
int extra_inputs = 0;
if (attrs.dict.count(MX_STR_EXTRA_INPUTS) > 0)
extra_inputs = std::stoi(attrs.dict.at(MX_STR_EXTRA_INPUTS));
return num_in + extra_inputs;
};
// lambda function to call parse attributes and return the number of inputs for subgraph ops
auto num_subgraph_inputs = [=](const NodeAttrs& attrs) {
// get number of inputs for subgraph
int num_in = mxnet::op::DefaultSubgraphOpNumInputs(attrs);
// get extra inputs, if exists
int extra_inputs = 0;
if (attrs.dict.count(MX_STR_EXTRA_INPUTS) > 0)
extra_inputs = std::stoi(attrs.dict.at(MX_STR_EXTRA_INPUTS));
return num_in + extra_inputs;
};
// lambda function to call parse attributes and return the number of outputs
auto num_outputs = [=](const NodeAttrs& attrs) {
// convert attributes to vector of char*
std::vector<const char*> attr_keys, attr_vals;
for (auto &kv : attrs.dict) {
attr_keys.push_back(kv.first.c_str());
attr_vals.push_back(kv.second.c_str());
}
int num_in = -1;
int num_out = -1;
int retval = callParseAttrs(parse_fp, attr_keys.data(), attr_vals.data(), attr_keys.size(),
&num_in, &num_out);
std::string msgs = getExtensionMsgs(msgSize, msgGet);
CHECK(retval) << "Error calling ParseAttrs::num_outputs for custom operator '" << name_str
<< "'" << msgs;
return num_out;
};
// lambda function to call parse attributes and return the number of inputs and outputs
// for backward computation
auto num_inouts = [=](const NodeAttrs& attrs) {
// convert attributes to vector of char*
std::vector<const char*> attr_keys, attr_vals;
for (auto &kv : attrs.dict) {
attr_keys.push_back(kv.first.c_str());
attr_vals.push_back(kv.second.c_str());
}
int num_in = -1;
int num_out = -1;
int retval = callParseAttrs(parse_fp, attr_keys.data(), attr_vals.data(), attr_keys.size(),
&num_in, &num_out);
std::string msgs = getExtensionMsgs(msgSize, msgGet);
CHECK(retval) << "Error calling ParseAttrs::num_outputs for custom operator '" << name_str
<< "'" << msgs;
// for backward passes, inputs + outputs + input gradients (one for each output)
// get extra inputs, if exists
int extra_inputs = 0;
if (attrs.dict.count(MX_STR_EXTRA_INPUTS) > 0)
extra_inputs = std::stoi(attrs.dict.at(MX_STR_EXTRA_INPUTS));
return num_in + extra_inputs + 2 * num_out;
};
// lambda function to call infer shape
auto infer_shape = [=] (const nnvm::NodeAttrs& attrs,
mxnet::ShapeVector *in_shape,
mxnet::ShapeVector *out_shape) {
// convert attributes to vector of char*
std::vector<const char*> attr_keys, attr_vals;
for (auto &kv : attrs.dict) {
attr_keys.push_back(kv.first.c_str());
attr_vals.push_back(kv.second.c_str());
}
// get extra inputs, if exists
int extra_inputs = 0;
if (attrs.dict.count(MX_STR_EXTRA_INPUTS) > 0)
extra_inputs = std::stoi(attrs.dict.at(MX_STR_EXTRA_INPUTS));
int num_inputs = in_shape->size() - extra_inputs;
std::vector<uint32_t*> inshapes(num_inputs);
std::vector<int> indims(num_inputs);
// determine amount of memory needed to store all the input shapes
size_t buff_size = 0;
for (size_t i = 0; i < num_inputs; ++i)
buff_size += (*in_shape)[i].ndim();
// copy input shapes from ShapeVector to raw memory layout
std::vector<uint32_t> inbuff(buff_size);
uint32_t *ptr = inbuff.data();
for (size_t i = 0; i < num_inputs; ++i) {
inshapes[i] = ptr;
indims[i] = (*in_shape)[i].ndim();
for (int j = 0; j < (*in_shape)[i].ndim(); ++j, ++ptr) {
*ptr = static_cast<uint32_t>((*in_shape)[i][j]);
}
}
// modified input shapes will be allocated by infer shape function
uint32_t** mod_inshapes = nullptr;
int* mod_indims = nullptr;
// output shapes will be allocated by infer shape function
uint32_t** outshapes = nullptr;
int* outdims = nullptr;
int retval = callInferShape(shape_fp, attr_keys.data(), attr_vals.data(), attr_keys.size(),
inshapes.data(), indims.data(), num_inputs,
&mod_inshapes, &mod_indims,
&outshapes, &outdims, out_shape->size());
std::string msgs = getExtensionMsgs(msgSize, msgGet);
CHECK(retval) << "Error calling InferShape for custom operator '" << name_str << "'" << msgs;
std::vector<uint32_t*> in_shapes(num_inputs);
// determine amount of memory needed to store all the modified input shapes
buff_size = 0;
for (unsigned i = 0; i < num_inputs; i++) {
buff_size += mod_indims[i];
}
// copy modified input shapes from custom op memory to MXNet memory
std::vector<uint32_t> mod_inbuff(buff_size);
ptr = mod_inbuff.data();
for (unsigned i = 0; i < num_inputs; ++i) {
in_shapes[i] = ptr;
for (int j = 0; j < mod_indims[i]; ++j, ++ptr) {
*ptr = static_cast<uint32_t>(mod_inshapes[i][j]);
}
}
// assign modified input shapes to ShapeVector
for (unsigned i = 0; i < num_inputs; ++i) {
SHAPE_ASSIGN_CHECK(*in_shape, i,
mxnet::TShape(in_shapes[i], in_shapes[i]+mod_indims[i]));
}
std::vector<uint32_t*> out_shapes(out_shape->size());
// determine amount of memory needed to store all the output shapes
buff_size = 0;
for (unsigned i = 0; i < out_shape->size(); i++) {
buff_size += outdims[i];
}
// copy output shapes from custom op memory to MXNet memory
std::vector<uint32_t> outbuff(buff_size);
ptr = outbuff.data();
for (unsigned i = 0; i < out_shape->size(); ++i) {
out_shapes[i] = ptr;
for (int j = 0; j < outdims[i]; ++j, ++ptr) {
*ptr = static_cast<uint32_t>(outshapes[i][j]);
}
}
// assign output shapes to ShapeVector
for (unsigned i = 0; i < out_shape->size(); ++i) {
SHAPE_ASSIGN_CHECK(*out_shape, i,
mxnet::TShape(out_shapes[i], out_shapes[i]+outdims[i]));
}
// free memory used by custom op to allocate shapes/dims
callFree(mod_indims);
for (unsigned i = 0; i < num_inputs; i++) {
callFree(mod_inshapes[i]);
}
callFree(mod_inshapes);
callFree(outdims);
for (unsigned i = 0; i < out_shape->size(); i++) {
callFree(outshapes[i]);
}
callFree(outshapes);
return true;
};
// lambda function to call infer shape for subgraph ops
auto infer_subgraph_shape = [=] (const nnvm::NodeAttrs& attrs,
mxnet::ShapeVector *in_shape,
mxnet::ShapeVector *out_shape) {
// convert attributes to vector of char*
std::vector<const char*> attr_keys, attr_vals;
for (auto &kv : attrs.dict) {
attr_keys.push_back(kv.first.c_str());
attr_vals.push_back(kv.second.c_str());
}
// get extra inputs, if exists
int extra_inputs = 0;
if (attrs.dict.count(MX_STR_EXTRA_INPUTS) > 0)
extra_inputs = std::stoi(attrs.dict.at(MX_STR_EXTRA_INPUTS));
auto in_first = in_shape->begin();
auto in_last = in_first + in_shape->size() - extra_inputs;
mxnet::ShapeVector *sg_in_shapes = new mxnet::ShapeVector(in_first, in_last);
return mxnet::op::DefaultSubgraphOpShape(attrs, sg_in_shapes, out_shape);
};
// lambda function to call infer type
auto infer_type = [=] (const nnvm::NodeAttrs& attrs,
std::vector<int> *in_type,
std::vector<int> *out_type) {
// convert attributes to vector of char*
std::vector<const char*> attr_keys, attr_vals;
for (auto &kv : attrs.dict) {
attr_keys.push_back(kv.first.c_str());
attr_vals.push_back(kv.second.c_str());
}
// get extra inputs, if exists
int extra_inputs = 0;
if (attrs.dict.count(MX_STR_EXTRA_INPUTS) > 0)
extra_inputs = std::stoi(attrs.dict.at(MX_STR_EXTRA_INPUTS));
int num_inputs = in_type->size() - extra_inputs;
// copy input types from in_type
std::vector<int> intypes(*in_type);
// output types will be populated by inferType function
std::vector<int> outtypes(out_type->size());
int retval = callInferType(type_fp, attr_keys.data(), attr_vals.data(), attr_keys.size(),
intypes.data(), num_inputs,
outtypes.data(), out_type->size());
std::string msgs = getExtensionMsgs(msgSize, msgGet);
CHECK(retval) << "Error calling InferType for custom operator '" << name_str << "'" << msgs;
// copy and assign modified input types from custom op to MXNet memory
for (size_t i = 0; i < num_inputs; i++) {
TYPE_ASSIGN_CHECK(*in_type, i, intypes[i]);
}
// copy and assign output types from custom op to MXNet memory
for (size_t i = 0; i < out_type->size(); i++) {
TYPE_ASSIGN_CHECK(*out_type, i, outtypes[i]);
}
return true;
};
// lambda function to call infer type for subgraph ops
auto infer_subgraph_type = [=] (const nnvm::NodeAttrs& attrs,
std::vector<int> *in_type,
std::vector<int> *out_type) {
// convert attributes to vector of char*
std::vector<const char*> attr_keys, attr_vals;
for (auto &kv : attrs.dict) {
attr_keys.push_back(kv.first.c_str());
attr_vals.push_back(kv.second.c_str());
}
// get extra inputs, if exists
int extra_inputs = 0;
if (attrs.dict.count(MX_STR_EXTRA_INPUTS) > 0)
extra_inputs = std::stoi(attrs.dict.at(MX_STR_EXTRA_INPUTS));
auto in_first = in_type->begin();
auto in_last = in_first + in_type->size() - extra_inputs;
std::vector<int> *sg_in_types = new std::vector<int>(in_first, in_last);
return mxnet::op::DefaultSubgraphOpType(attrs, sg_in_types, out_type);
};
// lambda function to convert from external mutate_inputs to internal MXNet types
auto mutate_inputs = [=](const nnvm::NodeAttrs& attrs) {
// convert attributes to vector of char*
std::vector<const char*> attr_keys, attr_vals;
for (auto &kv : attrs.dict) {
attr_keys.push_back(kv.first.c_str());
attr_vals.push_back(kv.second.c_str());
}
// C type placeholder for mutate input indices vector
int* mutate_indices = nullptr;
int indices_size = 0;
// call mutate inputs function
int retval = callMutateInputs(mutate_fp, attr_keys.data(), attr_vals.data(), attr_keys.size(),
&mutate_indices, &indices_size);
std::string msgs = getExtensionMsgs(msgSize, msgGet);
CHECK(retval) << "Error calling MutateInputs for custom operator '" << name_str << "'"
<< msgs;
std::vector<uint32_t> mutate_indices_list(indices_size);
for (int i=0; i < indices_size; i++) {
mutate_indices_list[i] = static_cast<uint32_t>(mutate_indices[i]);
}
return mutate_indices_list;
};
// lambda function to set storage types
auto infer_storage_type = [=](const nnvm::NodeAttrs& attrs,
const int dev_mask,
DispatchMode* dispatch_mode,
std::vector<int>* in_stypes,
std::vector<int>* out_stypes) {
if (stype_fp == nullptr) {
// InferSType is not defined in customized lib.
CHECK(mxnet::common::ContainsOnlyStorage(*in_stypes, mxnet::kDefaultStorage))
<< "Error input tensors are not dense for custom operator '" << name_str << "'";
// set outputs as dense
return op::storage_type_assign(out_stypes, mxnet::kDefaultStorage,
dispatch_mode, DispatchMode::kFComputeEx);
} else {
// InferSType is defined in customized lib.
// convert attributes to vector of char*
std::vector<const char*> attr_keys, attr_vals;
for (auto kv : attrs.dict) {
attr_keys.push_back(kv.first.c_str());
attr_vals.push_back(kv.second.c_str());
}
// get extra inputs, if exists
int extra_inputs = 0;
if (attrs.dict.count(MX_STR_EXTRA_INPUTS) > 0)
extra_inputs = std::stoi(attrs.dict.at(MX_STR_EXTRA_INPUTS));
int num_inputs = in_stypes->size() - extra_inputs;
// copy input types from in_stype
std::vector<int> instypes(*in_stypes);
// output types will be populated by inferType function
std::vector<int> outstypes(out_stypes->size());