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apache / mxnet / refs/heads/Zha0q1-patch-6 / . / src / c_api / 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 "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));
};
typedef decltype(gpu_alloc) alloc_type_gpu;
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));
};
typedef decltype(sparse_alloc) alloc_type_sparse;
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 &regOp = 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));
opCallDestroyOpState_t callDestroyOpState =
get_func<opCallDestroyOpState_t>(lib, const_cast<char*>(MXLIB_OPCALLDESTROYOPSTATE_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
size_t 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
size_t 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
size_t 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
size_t extra_inputs = 0;
if (attrs.dict.count(MX_STR_EXTRA_INPUTS) > 0)
extra_inputs = std::stoi(attrs.dict.at(MX_STR_EXTRA_INPUTS));
size_t 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 (size_t 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 (size_t 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 (size_t 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 (size_t 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 (size_t 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 (size_t 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 (size_t i = 0; i < num_inputs; i++) {
callFree(mod_inshapes[i]);
}
callFree(mod_inshapes);
callFree(outdims);
for (size_t 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
size_t 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);
bool res = mxnet::op::DefaultSubgraphOpShape(attrs, sg_in_shapes, out_shape);
// assign modified input shapes to ShapeVector
for (unsigned i = 0; i < sg_in_shapes->size(); ++i) {
SHAPE_ASSIGN_CHECK(*in_shape, i, sg_in_shapes->at(i));
}
return res;
};
// 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
size_t extra_inputs = 0;
if (attrs.dict.count(MX_STR_EXTRA_INPUTS) > 0)
extra_inputs = std::stoi(attrs.dict.at(MX_STR_EXTRA_INPUTS));
size_t 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
size_t 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);
bool res = mxnet::op::DefaultSubgraphOpType(attrs, sg_in_types, out_type);
// copy and assign modified input types
for (size_t i = 0; i < sg_in_types->size(); i++) {
TYPE_ASSIGN_CHECK(*in_type, i, sg_in_types->at(i));
}
return res;
};
// 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
size_t extra_inputs = 0;
if (attrs.dict.count(MX_STR_EXTRA_INPUTS) > 0)
extra_inputs = std::stoi(attrs.dict.at(MX_STR_EXTRA_INPUTS));
size_t 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());
int retval = callInferSType(stype_fp, attr_keys.data(), attr_vals.data(), attr_keys.size(),
instypes.data(), num_inputs,
outstypes.data(), out_stypes->size());
std::string msgs = getExtensionMsgs(msgSize, msgGet);
CHECK(retval) << "Error calling InferSType for custom operator '" << name_str << "'"
<< msgs;
// copy and assign modified input storage types from custom op to MXNet memory.
for (size_t i = 0; i < num_inputs; i++) {
STORAGE_TYPE_ASSIGN_CHECK(*in_stypes, i, instypes[i]);
}
// copy and assign output storage types from custom op to MXNet memory.
for (size_t i = 0; i < out_stypes->size(); i++) {
STORAGE_TYPE_ASSIGN_CHECK(*out_stypes, i, outstypes[i]);
}
// assign dispatch mode
DISPATCH_MODE_ASSIGN_CHECK(dispatch_mode, 0, DispatchMode::kFComputeEx);
return true;
}
};
// lambda function to set storage types for subgraph ops
auto infer_subgraph_storage_type = [=](const nnvm::NodeAttrs& attrs,
const int dev_mask,
DispatchMode* dispatch_mode,
std::vector<int>* in_stypes,
std::vector<int>* out_stypes) {
// get extra inputs, if exists
size_t 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_stypes->begin();
auto in_last = in_first + in_stypes->size() - extra_inputs;
std::vector<int> *sg_in_stypes = new std::vector<int>(in_first, in_last);
bool res = mxnet::op::DefaultSubgraphOpStorageType(attrs, dev_mask, dispatch_mode,
sg_in_stypes, out_stypes);
// copy and assign modified input storage types
for (size_t i = 0; i < sg_in_stypes->size(); i++) {
STORAGE_TYPE_ASSIGN_CHECK(*in_stypes, i, sg_in_stypes->at(i));
}
return res;
};
// FGradient register lambda
auto grad_reg = [=](const nnvm::ObjectPtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
// create node for gradient
auto p = nnvm::Node::Create();
std::string grad_name = "_backward_" + name_str;
p->attrs.op = nnvm::Op::Get(grad_name.c_str());
p->attrs.name = n->attrs.name + "_backward";
// copy attributes and subgraphs
p->attrs.dict = n->attrs.dict;
for (auto s : n->attrs.subgraphs)
p->attrs.subgraphs.push_back(s);
// set control dependency and attr parser
p->control_deps.emplace_back(n);
if (p->op()->attr_parser != nullptr) {
p->op()->attr_parser(&(p->attrs));
}
// gradient inputs: copy gradients first
std::vector<nnvm::NodeEntry> heads(ograds.begin(), ograds.end());
// copy inputs second
for (auto& h : n->inputs) {
heads.push_back(h);
}
// gradient inputs: copy outputs last
uint32_t n_out = n->num_outputs();
for (uint32_t i = 0; i < n_out; ++i) {
heads.emplace_back(n, i, 0);
}
// set inputs to gradient node
p->inputs = heads;
CHECK_EQ(p->num_inputs(), p->inputs.size())
<< "Number of inputs to operator " << grad_name << " (" << p->num_inputs()
<< ") does not match the actual number of inputs provided to operator "
<< p->attrs.name << " (" << p->inputs.size() << ").";
// create output node entries
return mxnet::op::CreateNodeEntries(p);
};
auto resc_req = [=](const NodeAttrs& attrs) {
return std::vector<ResourceRequest>{ResourceRequest::kTempSpace,
ResourceRequest::kParallelRandom};
};
// library author should implement and return a 'state' which points to an instance
// in lambda we create OpStatePtr using the returned 'state'
auto create_opstate = [=] (const NodeAttrs& attrs,
Context ctx,
const std::vector<TShape>& in_shapes,
const std::vector<int>& in_types) {
// 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());
}
// string repr of supported context for custom library, currently only "cpu" and "gpu"
const char* ctx_str = ctx.dev_mask() == Context::kCPU ? "cpu" : "gpu";
std::vector<uint32_t*> inshapes(in_shapes.size());
std::vector<int> indims(in_shapes.size());
// determine amount of memory needed to store all the input shapes
size_t buff_size = 0;
for (size_t i = 0; i < in_shapes.size(); ++i)
buff_size += in_shapes[i].ndim();
// copy input shapes to raw memory layout
std::vector<uint32_t> inbuff(buff_size);
uint32_t *ptr = inbuff.data();
for (size_t i = 0; i < in_shapes.size(); ++i) {
inshapes[i] = ptr;
indims[i] = in_shapes[i].ndim();
for (int j = 0; j < in_shapes[i].ndim(); ++j, ++ptr) {
*ptr = static_cast<uint32_t>(in_shapes[i][j]);
}
}
// 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());
}
// create a pointer to hold custom op state object
// only create one stateful op depending on passing context
// user can add new supported context and call to custom library
void* state_op_inst = nullptr;
if (ctx.dev_mask() == Context::kCPU) {
CHECK(createop_map.count("cpu") > 0)
<< "CPU CreateOpState not implemented for '" << name_str << "'";
int retval = callCreateOpState(createop_map.at("cpu"), attr_keys.data(), attr_vals.data(),
attr_keys.size(), ctx_str, ctx.real_dev_id(),
inshapes.data(), indims.data(),
in_shapes.size(), in_types.data(), &state_op_inst);
std::string msgs = getExtensionMsgs(msgSize, msgGet);
CHECK(retval) << "Error calling CreateOpState CPU for custom operator '" << name_str << "'"
<< msgs;
} else if (ctx.dev_mask() == Context::kGPU) {
CHECK(createop_map.count("gpu") > 0)
<< "GPU CreateOpState not implemented for '" << name_str << "'";
int retval = callCreateOpState(createop_map.at("gpu"), attr_keys.data(), attr_vals.data(),
attr_keys.size(), ctx_str, ctx.real_dev_id(),
inshapes.data(), indims.data(),
in_shapes.size(), in_types.data(), &state_op_inst);
std::string msgs = getExtensionMsgs(msgSize, msgGet);
CHECK(retval) << "Error calling CreateOpState GPU for custom operator '" << name_str << "'"
<< msgs;
}
std::string msgs = getExtensionMsgs(msgSize, msgGet);
CHECK(state_op_inst != nullptr)
<< "Error custom library failed to create stateful operator '" << name_str << "'" << msgs;
CustomStatefulOp* state_op = reinterpret_cast<CustomStatefulOp*>(state_op_inst);
if (!state_op->wasCreated() && !state_op->ignore_warn)
LOG(INFO) << "WARNING! Custom stateful op " << state_op_inst << " was created without "
<< "calling CustomStatefulOp::create(). Please ensure this object was "
<< "allocated with 'new' since it will be destructed with 'delete'. "
<< "To suppress this message without calling CustomStatefulOp::create() "
<< "set ignore_warn to 'true' on custom stateful op instance.";
return OpStatePtr::Create<CustomStatefulOpWrapper>(state_op, callDestroyOpState);
};
/* -------------- BELOW IS THE REGISTRATION FOR CUSTOM OPERATORS --------------- */
registerOp(name, name_str, isSubgraphOp, resc_req, attr_parser, num_inputs, num_outputs,
num_inouts, infer_type, infer_shape, infer_storage_type, mutate_inputs,
num_subgraph_inputs, infer_subgraph_type, infer_subgraph_shape,
infer_subgraph_storage_type, create_opstate, grad_reg, mutate_fp,
createop_map, forward_ctx_map, backward_ctx_map, callFComp, callFStatefulComp,
msgSize, msgGet);
}
}
void registerPartitioners(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));
partCallSupportedOps_t callSupportedOps =
get_func<partCallSupportedOps_t>(lib, const_cast<char*>(MXLIB_PARTCALLSUPPORTEDOPS_STR));
partCallCreateSelector_t callCreateSelector =
get_func<partCallCreateSelector_t>(lib, const_cast<char*>(MXLIB_PARTCALLCREATESELECTOR_STR));
partCallSelect_t callSelect =
get_func<partCallSelect_t>(lib, const_cast<char*>(MXLIB_PARTCALLSELECT_STR));
partCallSelectInput_t callSelectInput =
get_func<partCallSelectInput_t>(lib, const_cast<char*>(MXLIB_PARTCALLSELECTINPUT_STR));
partCallSelectOutput_t callSelectOutput =
get_func<partCallSelectOutput_t>(lib, const_cast<char*>(MXLIB_PARTCALLSELECTOUTPUT_STR));
partCallFilter_t callFilter =
get_func<partCallFilter_t>(lib, const_cast<char*>(MXLIB_PARTCALLFILTER_STR));
partCallReset_t callReset =
get_func<partCallReset_t>(lib, const_cast<char*>(MXLIB_PARTCALLRESET_STR));
partCallReviewSubgraph_t callReviewSubgraph =
get_func<partCallReviewSubgraph_t>(lib, const_cast<char*>(MXLIB_PARTCALLREVIEWSUBGRAPH_STR));
// get number of partitioners registered in the library
partRegSize_t partRegSize = get_func<partRegSize_t>(lib,
const_cast<char*>(MXLIB_PARTREGSIZE_STR));
int numParts = partRegSize();
if (verbose) LOG(INFO) << "Found " << numParts << " partitioners in library";
/*
* Get all custom partitioners implementation from custom library
* loop and register each partitioner in the library to NNVM
*/
partRegGetCount_t partRegGetCount = get_func<partRegGetCount_t>(lib,
const_cast<char*>(MXLIB_PARTREGGETCOUNT_STR));
partRegGet_t partRegGet = get_func<partRegGet_t>(lib, const_cast<char*>(MXLIB_PARTREGGET_STR));
for (int i = 0; i < numParts; i++) {
const char* name;
// get custom partitioner strategy count from the dynamic library
int count = partRegGetCount(i, &name);
CHECK(count > 0) << "Error loading '" << name
<< "' custom partitioner, no strategies defined";
std::string name_str(name);
if (verbose) LOG(INFO) << "\tPartitioner[" << i << "] " << name;
mxnet::op::SubgraphBackendRegistry::Get()->__REGISTER_BACKEND__(name);
for (int j = 0; j < count; j++) {
const char* strategy;
// function pointers holding implementation from custom library
supportedOps_t supportedOps_fp = nullptr;
createSelector_t createSelector_fp = nullptr;
reviewSubgraph_t reviewSubgraph_fp = nullptr;
// name of subgraph op
const char* op_name = nullptr;
// get custom partitioner strategy from the dynamic library
partRegGet(i, j, &strategy, &supportedOps_fp, &createSelector_fp,
&reviewSubgraph_fp, &op_name);
// validate custom partitioner functions from the dynamic library
if (supportedOps_fp == nullptr && createSelector_fp == nullptr)
LOG(ERROR) << "Error loading '" << name << "' custom partitioner strategy '"
<< strategy << "', must implement supportedOps or createSelector";
std::string strategy_str(strategy);
std::string op_name_str(op_name);
if (verbose) LOG(INFO) << "\t\tStrategy[" << j << "] " << strategy_str
<< " subgraphOp: '" << op_name_str << "'";
mxnet::op::SubgraphBackendRegistry::Get()->__REGISTER_CUSTOM_PROPERTY__
(name_str, std::make_shared<mxnet::op::CustomSubgraphProperty>
(strategy_str, callSupportedOps, supportedOps_fp, callCreateSelector,
createSelector_fp, callSelect, callSelectInput, callSelectOutput,
callFilter, callReset, callReviewSubgraph, reviewSubgraph_fp, callFree,
op_name_str));
}
}
}
void registerPasses(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));
passCallGraphPass_t callGraphPass =
get_func<passCallGraphPass_t>(lib, const_cast<char*>(MXLIB_PASSCALLGRAPHPASS_STR));
// get number of passes registered in the library
partRegSize_t passRegSize = get_func<passRegSize_t>(lib,
const_cast<char*>(MXLIB_PASSREGSIZE_STR));
int numPasses = passRegSize();
if (verbose) LOG(INFO) << "Found " << numPasses << " graph passes in library";
/*
* Get all custom pass implementation from custom library
* loop and register each pass in the library to NNVM
*/
passRegGet_t passRegGet = get_func<passRegGet_t>(lib, const_cast<char*>(MXLIB_PASSREGGET_STR));
for (int i = 0; i < numPasses; i++) {
const char* name;
// function pointers holding implementation from custom library
graphPass_t pass_fp = nullptr;
// main function to get custom pass implemenation from the custom library
passRegGet(i, &pass_fp, &name);
if (verbose) LOG(INFO) << "\tGraph Pass [" << i << "] " << name;
auto pass_lambda = [=] (nnvm::Graph&& g) {
// get pass name
const char* pass_name = g.GetAttr<const char*>("pass_name");
// get options
const std::unordered_map<std::string, std::string>& options_map =
g.GetAttr<const std::unordered_map<std::string, std::string>>("options_map");
// convert options_map_ to char* to pass to backend library
std::vector<const char*> opt_keys, opt_vals;
for (auto& kv : options_map) {
opt_keys.push_back(kv.first.c_str());
opt_vals.push_back(kv.second.c_str());
}
// get input args and arg names
std::vector<std::string> in_arg_names = g.GetAttr<std::vector<std::string>>("in_arg_names");
std::vector<std::string> in_aux_names = g.GetAttr<std::vector<std::string>>("in_aux_names");
NDArray **in_args_ptr = g.GetAttr<NDArray**>("in_args");
NDArray **in_aux_ptr = g.GetAttr<NDArray**>("in_aux");
// get shapes/types
mxnet::ShapeVector shapes;
if (g.HasAttr("shape"))
shapes = g.GetAttr<mxnet::ShapeVector>("shape");
std::vector<int> dtypes;
if (g.HasAttr("dtype"))
dtypes = g.GetAttr<std::vector<int> >("dtype");
g.attrs.clear();
const nnvm::IndexedGraph& indexed_graph = g.indexed_graph();
// set shape attrs for each node in the graph
if (shapes.size() > 0) {
for (unsigned nid = 0; nid < indexed_graph.num_nodes(); nid++) {
nnvm::Node* node = const_cast<nnvm::Node*>(indexed_graph[nid].source);
std::stringstream ss;
ss << "[";
// set the output shapes for this node
for (unsigned oid = 0; oid < node->num_outputs(); oid++) {
const uint32_t out_entry_id = indexed_graph.entry_id(nid, oid);
mxnet::TShape& shape = shapes[out_entry_id];
ss << shape;
if (oid < node->num_outputs()-1) ss << ",";
}
ss << "]";
node->attrs.dict[MX_STR_SHAPE] = ss.str();
}
}
// set dtype attrs for each node in the graph
if (dtypes.size() > 0) {
for (unsigned nid = 0; nid < indexed_graph.num_nodes(); nid++) {
nnvm::Node* node = const_cast<nnvm::Node*>(indexed_graph[nid].source);
std::stringstream ss;
ss << "[";
// set the output dtypes for this node
for (unsigned oid = 0; oid < node->num_outputs(); oid++) {
const uint32_t out_entry_id = indexed_graph.entry_id(nid, oid);
int dtype = dtypes[out_entry_id];
ss << dtype;
if (oid < node->num_outputs()-1) ss << ",";
}
ss << "]";
node->attrs.dict[MX_STR_DTYPE] = ss.str();
}
}
std::vector<const char*> arg_names, aux_names;
std::vector<void*> arg_data, aux_data;
std::vector<const int64_t*> arg_shapes, aux_shapes;
std::vector<int> arg_dims, aux_dims;
std::vector<int> arg_types, aux_types;
std::vector<size_t> arg_verIDs, aux_verIDs;
std::vector<const char*> arg_dev_type, aux_dev_type;
std::vector<int> arg_dev_id, aux_dev_id;
// convert input args
for (size_t i=0; i < in_arg_names.size(); i++) {
if (in_args_ptr[i] != nullptr) {
arg_names.push_back(in_arg_names[i].c_str());
const NDArray &in_arg = *(in_args_ptr[i]);
#if MXNET_USE_MKLDNN == 1
// reorder data if in MKLDNN format
if (in_arg.IsMKLDNNData()) {
in_arg.Reorder2DefaultAsync();
in_arg.WaitToRead();
}
#endif
// pull out parts of NDArray to send to backend
arg_data.push_back(in_arg.data().dptr_);
arg_shapes.push_back(in_arg.shape().data());
arg_dims.push_back(in_arg.shape().ndim());
arg_types.push_back(in_arg.dtype());
arg_verIDs.push_back(in_arg.version());
const char* arg_ctx_str = in_arg.ctx().dev_mask() == Context::kCPU ? "cpu" : "gpu";
arg_dev_type.push_back(arg_ctx_str);
arg_dev_id.push_back(in_arg.ctx().real_dev_id());
}
}
// convert input aux
for (size_t i=0; i < in_aux_names.size(); i++) {
if (in_aux_ptr[i] != nullptr) {
aux_names.push_back(in_aux_names[i].c_str());
const auto &in_aux = *(in_aux_ptr[i]);
#if MXNET_USE_MKLDNN == 1
// reorder data if in MKLDNN format
if (in_aux.IsMKLDNNData()) {
in_aux.Reorder2DefaultAsync();
in_aux.WaitToRead();
}
#endif
// pull out parts of NDArray to send to backend
aux_data.push_back(in_aux.data().dptr_);
aux_shapes.push_back(in_aux.shape().data());
aux_dims.push_back(in_aux.shape().ndim());
aux_types.push_back(in_aux.dtype());
aux_verIDs.push_back(in_aux.version());
const char* aux_ctx_str = in_aux.ctx().dev_mask() == Context::kCPU ? "cpu" : "gpu";
aux_dev_type.push_back(aux_ctx_str);
aux_dev_id.push_back(in_aux.ctx().real_dev_id());
}
}
// convert graph to string
std::string in_json = nnvm::pass::SaveJSON(g);
std::vector<std::string> new_arg_names, new_aux_names;
std::vector<NDArray*> new_args, new_aux;
// create lambda that captures stream & resource objects
// this temp workspace holds memory allocated by custom library via OpResource
auto ndarray_alloc = [&](const mxnet::TShape &shape, Context ctx, int dtype,
std::string name, bool isArg) {
NDArray* arr = new NDArray(shape, ctx, false, dtype);
if (isArg) {
new_args.push_back(arr);
new_arg_names.push_back(name);
} else {
new_aux.push_back(arr);
new_aux_names.push_back(name);
}
return arr;
};
// 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(ndarray_alloc) alloc_type_ndarray;
auto ndarray_malloc = [](const void* _ndarray_alloc, const int64_t* shapes, int num_shapes,
const char* dev_str, int dev_id, int dtype, const char* name,
int isArg, void** data) {
mxnet::TShape shape(num_shapes, 0);
for (int i = 0; i < num_shapes; i++)
shape[i] = shapes[i];
int dev_type = -1;
if (strcmp(dev_str, "cpu") == 0)
dev_type = kCPU;
else
dev_type = kGPU;
Context ctx = Context::Create(static_cast<Context::DeviceType>(dev_type), dev_id);
// cast the void* argument to the type for the cpu_alloc lambda function
const alloc_type_ndarray* ndalloc = static_cast<const alloc_type_ndarray*>(_ndarray_alloc);
// call cpu_alloc to actually allocate memory and return the pointer
NDArray* arr = (*ndalloc)(shape, ctx, dtype, name, isArg);
*data = arr->data().dptr_;
};
char* out_json;
int retval = callGraphPass(pass_fp, in_json.c_str(), &out_json, opt_keys.data(),
opt_vals.data(), opt_keys.size(), pass_name,
arg_names.data(), arg_names.size(), arg_data.data(),
arg_shapes.data(), arg_dims.data(), arg_types.data(),
arg_verIDs.data(), arg_dev_type.data(),
arg_dev_id.data(), aux_names.data(), aux_names.size(),
aux_data.data(), aux_shapes.data(), aux_dims.data(),
aux_types.data(), aux_verIDs.data(),
aux_dev_type.data(), aux_dev_id.data(),
ndarray_malloc, &ndarray_alloc);
std::string msgs = getExtensionMsgs(msgSize, msgGet);
CHECK(retval) << "Error calling graph pass for '" << pass_name << "'" << msgs;
std::string out_string(out_json);
nnvm::Graph out_graph = nnvm::pass::LoadJSON(out_string);
out_graph.attrs["new_args"] = std::make_shared<nnvm::any>(new_args);
out_graph.attrs["new_arg_names"] = std::make_shared<nnvm::any>(new_arg_names);
out_graph.attrs["new_aux"] = std::make_shared<nnvm::any>(new_aux);
out_graph.attrs["new_aux_names"] = std::make_shared<nnvm::any>(new_aux_names);
callFree(out_json);
return out_graph;
};
nnvm::PassFunctionReg& pass = dmlc::Registry<nnvm::PassFunctionReg>::Get()->__REGISTER__(name);
pass.set_body(pass_lambda);
pass.set_change_graph(true);
}
}
/*!
* \brief Loads dynamic custom library and initializes it
* \param path library path
*/
int MXLoadLib(const char *path, unsigned verbose) {
API_BEGIN();
void *lib = LibraryInitializer::Get()->lib_load(path);
if (!lib)
LOG(FATAL) << "Unable to load library";
// check that library and MXNet use same version of library API
mxnet::ext::opVersion_t opVersion =
get_func<mxnet::ext::opVersion_t>(lib, const_cast<char*>(MXLIB_OPVERSION_STR));
int libVersion = opVersion();
if (MX_LIBRARY_VERSION != libVersion)
LOG(FATAL) << "Library version (" << libVersion << ") does not match MXNet version ("
<< MX_LIBRARY_VERSION << ")";
// get error messaging APIs
mxnet::ext::msgSize_t msgSize =
get_func<mxnet::ext::msgSize_t>(lib, const_cast<char*>(MXLIB_MSGSIZE_STR));
mxnet::ext::msgGet_t msgGet =
get_func<mxnet::ext::msgGet_t>(lib, const_cast<char*>(MXLIB_MSGGET_STR));
// initialize library by passing MXNet version
mxnet::ext::initialize_t initialize =
get_func<mxnet::ext::initialize_t>(lib, const_cast<char*>(MXLIB_INITIALIZE_STR));
if (!initialize(static_cast<int>(MXNET_VERSION))) {
std::string msgs = getExtensionMsgs(msgSize, msgGet);
LOG(FATAL) << "Library failed to initialize" << msgs;
}
// find ops, partitioners, and passes in library
registerOperators(lib, verbose, msgSize, msgGet);
registerPartitioners(lib, verbose, msgSize, msgGet);
registerPasses(lib, verbose, msgSize, msgGet);
API_END();
}
int MXLibInfoFeatures(const struct LibFeature **lib_features, size_t *size) {
using namespace features;
API_BEGIN();
LibInfo* lib_info = LibInfo::getInstance();
*lib_features = lib_info->getFeatures().data();
*size = lib_info->getFeatures().size();
API_END();
}
int MXRandomSeed(int seed) {
API_BEGIN();
mxnet::RandomSeed(seed);
API_END();
}
int MXRandomSeedContext(int seed, int dev_type, int dev_id) {
API_BEGIN();
Context ctx = Context::Create(static_cast<Context::DeviceType>(dev_type), dev_id);
mxnet::RandomSeed(ctx, seed);
API_END();
}
int MXNotifyShutdown() {
API_BEGIN();
mxnet::op::custom::CustomOperator::Get()->Stop();
Engine::Get()->NotifyShutdown();
Engine::Get()->WaitForAll();
API_END();
}
int MXSetNumOMPThreads(int thread_num) {
API_BEGIN();
omp_set_num_threads(thread_num);
API_END();
}
int MXEngineSetBulkSize(int bulk_size, int* prev_bulk_size) {
API_BEGIN();
*prev_bulk_size = Engine::Get()->set_bulk_size(bulk_size);
API_END();
}
int MXGetGPUCount(int* out) {
API_BEGIN();
*out = Context::GetGPUCount();
API_END();
}
// Deprecated: use MXGetGPUMemoryInformation64() instead.
int MXGetGPUMemoryInformation(int dev, int *free_mem, int *total_mem) {
API_BEGIN();
uint64_t free_mem64 = 0UL;
uint64_t total_mem64 = 0UL;
Context::GetGPUMemoryInformation(dev, &free_mem64, &total_mem64);
*free_mem = static_cast<int>(free_mem64);
*total_mem = static_cast<int>(total_mem64);
API_END();
}
int MXGetGPUMemoryInformation64(int dev, uint64_t *free_mem, uint64_t *total_mem) {
API_BEGIN();
Context::GetGPUMemoryInformation(dev, free_mem, total_mem);
API_END();
}
int MXGetVersion(int *out) {
API_BEGIN();
*out = static_cast<int>(MXNET_VERSION);
API_END();
}
#if MXNET_USE_TVM_OP
int MXLoadTVMOp(const char *libpath) {
API_BEGIN();
tvm::runtime::TVMOpModule::Get()->Load(libpath);
API_END();
}
int MXLoadTVMConfig(ConfigSpaces config) {
API_BEGIN();
for (int k = 0; k < config.spaces_size; ++k) {
tvm::runtime::TVMOpConfig& entry = ::dmlc::Registry<tvm::runtime::TVMOpConfig>::Get()
->__REGISTER_OR_GET__(std::string(config.spaces_key[k]));
const ConfigSpace& c = config.spaces_val[k];
for (int i = 0; i < c.entity_map_size; ++i) {
entry.add_entity(std::string(c.entity_map_key[i]), c.entity_map_val[i].val);
}
for (int i = 0; i < c.space_map_size; ++i) {
std::string name = std::string(c.space_map_key[i]);
std::vector<int> entities;
for (int j = 0; j < c.space_map_val[i].entities_size; ++j) {
int val = c.space_map_val[i].entities[j].val;
entities.push_back(val);
}
entry.add_space(name, entities);
}
}
API_END();
}
#endif // MXNET_USE_TVM_OP
int MXNDArrayCreateNone(NDArrayHandle *out) {
API_BEGIN();
*out = new NDArray();
API_END();
}
template<typename DataType>
void CreateNDArray(const DataType* shape,
int ndim,
int dev_type,
int dev_id,
int delay_alloc,
int dtype,
NDArrayHandle* out) {
mxnet::TShape requested_shape = mxnet::TShape(shape, shape + ndim);
if (!features::is_enabled(features::INT64_TENSOR_SIZE)) {
CHECK_LT(requested_shape.Size(), (int64_t{1} << 31) - 1) <<
"[CreateNDArray] Size of tensor you are trying to allocate is larger than "
"2^31 elements. Please build with flag USE_INT64_TENSOR_SIZE=1";
}
*out = new NDArray(requested_shape,
Context::Create(static_cast<Context::DeviceType>(dev_type), dev_id),
delay_alloc != 0, dtype);
}
int MXNDArrayCreate(const uint32_t *shape,
uint32_t ndim,
int dev_type,
int dev_id,
int delay_alloc,
NDArrayHandle *out) {
API_BEGIN();
*out = new NDArray(mxnet::TShape(shape, shape + ndim),
Context::Create(static_cast<Context::DeviceType>(dev_type), dev_id),
delay_alloc != 0);
API_END();
}
int MXNDArrayCreateEx64(const int64_t *shape,
int ndim,
int dev_type,
int dev_id,
int delay_alloc,
int dtype,
NDArrayHandle *out) {
API_BEGIN();
CreateNDArray<int64_t>(shape, ndim, dev_type, dev_id, delay_alloc, dtype, out);
API_END();
}
int MXNDArrayCreateEx(const uint32_t *shape,
uint32_t ndim,
int dev_type,
int dev_id,
int delay_alloc,
int dtype,
NDArrayHandle *out) {
API_BEGIN();
CreateNDArray<uint32_t>(shape, static_cast<int>(ndim), dev_type, dev_id, delay_alloc, dtype, out);
API_END();
}
template<typename DType>
void CreateSparseNDArray(int storage_type,
const DType *shape,
int ndim,
int dev_type,
int dev_id,
int delay_alloc,
int dtype,
uint32_t num_aux,
int *aux_type,
int *aux_ndims,
const DType *aux_shape,
NDArrayHandle *out) {
std::vector<int> aux_types;
mxnet::ShapeVector aux_shapes;
auto shape_start = aux_shape;
for (size_t i = 0; i < num_aux; i++) {
// types
aux_types.push_back(aux_type[i]);
// shapes
aux_shapes.emplace_back(shape_start, shape_start + aux_ndims[i]);
shape_start += aux_ndims[i];
}
*out = new NDArray(
NDArrayStorageType(storage_type),
mxnet::TShape(shape, shape + ndim),
Context::Create(static_cast<Context::DeviceType>(dev_type), dev_id),
delay_alloc != 0,
dtype, aux_types, aux_shapes);
}
int MXNDArrayCreateSparseEx(int storage_type,
const uint32_t *shape,
uint32_t ndim,
int dev_type,
int dev_id,
int delay_alloc,
int dtype,
uint32_t num_aux,
int *aux_type,
uint32_t *aux_ndims,
const uint32_t *aux_shape,
NDArrayHandle *out) {
API_BEGIN();
CreateSparseNDArray<uint32_t>(storage_type, shape, static_cast<int>(ndim), dev_type, dev_id,
delay_alloc, dtype, num_aux, aux_type,
reinterpret_cast<int *>(aux_ndims), aux_shape, out);
API_END();
}
int MXNDArrayCreateSparseEx64(int storage_type,
const int64_t *shape,
int ndim,
int dev_type,
int dev_id,
int delay_alloc,
int dtype,
uint32_t num_aux,
int *aux_type,
int *aux_ndims,
const int64_t *aux_shape,
NDArrayHandle *out) {
API_BEGIN();
CreateSparseNDArray<int64_t>(storage_type, shape, static_cast<int>(ndim), dev_type, dev_id,
delay_alloc, dtype, num_aux, aux_type,
reinterpret_cast<int *>(aux_ndims), aux_shape, out);
API_END();
}
int MXNDArrayLoadFromRawBytes(const void *buf,
size_t size,
NDArrayHandle *out) {
NDArray *ptr = nullptr;
API_BEGIN();
dmlc::MemoryFixedSizeStream strm((void*)buf, size); // NOLINT(*)
ptr = new NDArray();
if (!ptr->Load(&strm)) {
throw dmlc::Error("Invalid NDArray serialization format");
}
*out = ptr;
API_END_HANDLE_ERROR(delete ptr);
}
int MXNDArraySaveRawBytes(NDArrayHandle handle,
size_t *out_size,
const char **out_buf) {
MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
API_BEGIN();
ret->ret_str.resize(0);
dmlc::MemoryStringStream strm(&ret->ret_str);
static_cast<NDArray*>(handle)->Save(&strm);
*out_size = ret->ret_str.length();
*out_buf = ret->ret_str.c_str();
API_END();
}
int MXNDArraySyncCopyFromCPU(NDArrayHandle handle,
const void *data,
size_t size) {
API_BEGIN();
static_cast<NDArray*>(handle)->SyncCopyFromCPU(data, size);
API_END();
}
int MXNDArraySyncCopyToCPU(NDArrayHandle handle,
void *data,
size_t size) {
API_BEGIN();
static_cast<NDArray*>(handle)->SyncCopyToCPU(data, size);
API_END();
}
/*!
* \brief Copy src.data() to dst.data() if i = -1, else dst.aux_data(i) if i >= 0
* This function blocks. Do not use it in performance critical code.
* \param handle_dst handle of a dst ndarray whose data/aux_data has been allocated
* \param handle_src handle of a src ndarray which has default storage type
* \param i dst data blob indicator
*/
int MXNDArraySyncCopyFromNDArray(NDArrayHandle handle_dst,
const NDArrayHandle handle_src,
const int i) {
API_BEGIN();
NDArray* dst = static_cast<NDArray*>(handle_dst);
NDArray* src = static_cast<NDArray*>(handle_src);
dst->SyncCopyFromNDArray(*src, -1, i);
API_END();
}
int MXNDArraySyncCheckFormat(NDArrayHandle handle, const bool full_check) {
API_BEGIN();
NDArray *arr = static_cast<NDArray*>(handle);
arr->SyncCheckFormat(full_check);
API_END();
}
int MXNDArrayWaitToRead(NDArrayHandle handle) {
API_BEGIN();
static_cast<NDArray*>(handle)->WaitToRead();
API_END();
}
int MXNDArrayWaitToWrite(NDArrayHandle handle) {
API_BEGIN();
static_cast<NDArray*>(handle)->WaitToWrite();
API_END();
}
int MXNDArrayWaitAll() {
API_BEGIN();
Engine::Get()->WaitForAll();
API_END();
}
int MXNDArraySave(const char* fname,
uint32_t num_args,
NDArrayHandle* args,
const char** keys) {
API_BEGIN();
std::vector<NDArray> data(num_args);
std::vector<std::string> names;
for (uint32_t i = 0; i < num_args; ++i) {
data[i] = *static_cast<NDArray*>(args[i]);
}
if (keys != nullptr) {
names.resize(num_args);
for (uint32_t i = 0; i < num_args; ++i) {
names[i] = keys[i];
}
}
{
std::unique_ptr<dmlc::Stream> fo(dmlc::Stream::Create(fname, "w"));
mxnet::NDArray::Save(fo.get(), data, names);
}
API_END();
}
int MXNDArrayLoad(const char* fname,
uint32_t *out_size,
NDArrayHandle** out_arr,
uint32_t *out_name_size,
const char*** out_names) {
MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
ret->ret_vec_str.clear();
API_BEGIN();
std::vector<NDArray> data;
std::vector<std::string> &names = ret->ret_vec_str;
{
std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname, "r"));
mxnet::NDArray::Load(fi.get(), &data, &names);
}
ret->ret_handles.resize(data.size());
for (size_t i = 0; i < data.size(); ++i) {
NDArray *ptr = new NDArray();
*ptr = data[i];
ret->ret_handles[i] = ptr;
}
ret->ret_vec_charp.resize(names.size());
for (size_t i = 0; i < names.size(); ++i) {
ret->ret_vec_charp[i] = names[i].c_str();
}
*out_size = static_cast<uint32_t>(data.size());
*out_arr = dmlc::BeginPtr(ret->ret_handles);
*out_name_size = static_cast<uint32_t>(names.size());
*out_names = dmlc::BeginPtr(ret->ret_vec_charp);
API_END();
}
int MXNDArrayLoadFromBuffer(const void *ndarray_buffer,
size_t size,
uint32_t *out_size,
NDArrayHandle** out_arr,
uint32_t *out_name_size,
const char*** out_names) {
MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
ret->ret_vec_str.clear();
API_BEGIN();
CHECK_NOTNULL(ndarray_buffer);
std::vector<NDArray> data;
std::vector<std::string> &names = ret->ret_vec_str;
{
std::unique_ptr<dmlc::MemoryFixedSizeStream> fi(new dmlc::MemoryFixedSizeStream(
const_cast<void*>(ndarray_buffer), size));
mxnet::NDArray::Load(fi.get(), &data, &names);
}
ret->ret_handles.resize(data.size());
for (size_t i = 0; i < data.size(); ++i) {
NDArray *ptr = new NDArray();
*ptr = data[i];
ret->ret_handles[i] = ptr;
}
ret->ret_vec_charp.resize(names.size());
for (size_t i = 0; i < names.size(); ++i) {
ret->ret_vec_charp[i] = names[i].c_str();
}
*out_size = static_cast<uint32_t>(data.size());
*out_arr = dmlc::BeginPtr(ret->ret_handles);
*out_name_size = static_cast<uint32_t>(names.size());
*out_names = dmlc::BeginPtr(ret->ret_vec_charp);
API_END();
}
int MXNDArrayFree(NDArrayHandle handle) {
API_BEGIN();
delete static_cast<NDArray*>(handle);
API_END();
}
template<typename dtype>
void SliceArray(NDArrayHandle handle, dtype slice_begin, dtype slice_end, NDArray* ptr,
NDArrayHandle* out) {
*ptr = static_cast<NDArray*>(handle)->SliceWithRecord(slice_begin, slice_end);
*out = ptr;
}
int MXNDArraySlice(NDArrayHandle handle,
uint32_t slice_begin,
uint32_t slice_end,
NDArrayHandle *out) {
NDArray *ptr = new NDArray();
API_BEGIN();
SliceArray<uint32_t>(handle, slice_begin, slice_end, ptr, out);
API_END_HANDLE_ERROR(delete ptr);
}
int MXNDArraySlice64(NDArrayHandle handle,
int64_t slice_begin,
int64_t slice_end,
NDArrayHandle *out) {
NDArray *ptr = new NDArray();
API_BEGIN();
SliceArray<int64_t>(handle, slice_begin, slice_end, ptr, out);
API_END_HANDLE_ERROR(delete ptr);
}
int MXNDArrayAt(NDArrayHandle handle,
uint32_t idx,
NDArrayHandle *out) {
NDArray *ptr = new NDArray();
API_BEGIN();
*ptr = static_cast<NDArray*>(handle)->AtWithRecord(idx);
*out = ptr;
API_END_HANDLE_ERROR(delete ptr);
}
int MXNDArrayAt64(NDArrayHandle handle,
int64_t idx,
NDArrayHandle *out) {
NDArray *ptr = new NDArray();
API_BEGIN();
*ptr = static_cast<NDArray*>(handle)->AtWithRecord(idx);
*out = ptr;
API_END_HANDLE_ERROR(delete ptr);
}
MXNET_DLL int MXNDArrayReshape(NDArrayHandle handle,
int ndim,
int *dims,
NDArrayHandle *out) {
NDArray *ptr = new NDArray();
API_BEGIN();
NDArray *arr = static_cast<NDArray*>(handle);
mxnet::TShape new_shape(dims, dims+ndim);
int size = 1;
int pos = -1;
for (int i = 0; i < ndim; ++i) {
int dim = dims[i];
if (dim == -1) {
CHECK_EQ(pos, -1)
<< "Invalid new shape " << new_shape
<< ": more than one dimensions are -1";
pos = i;
} else {
if (dim == 0) {
CHECK_LT(i, arr->shape().ndim())
<< "Invalid new shape " << new_shape
<< ": 0 dimension exceeds original shape " << arr->shape();
dim = arr->shape()[i];
}
size *= dim;
new_shape[i] = dim;
}
}
if (pos >= 0) {
new_shape[pos] = arr->shape().Size() / size;
}
*ptr = arr->ReshapeWithRecord(new_shape);
*out = ptr;
API_END_HANDLE_ERROR(delete ptr);
}
MXNET_DLL int MXNDArrayReshape64(NDArrayHandle handle,
int ndim,
dim_t *dims,
bool reverse,
NDArrayHandle *out) {
NDArray *ptr = new NDArray();
API_BEGIN();
NDArray *arr = static_cast<NDArray*>(handle);
mxnet::Tuple<dim_t> shape(dims, dims+ndim);
mxnet::TShape new_shape = mxnet::op::InferReshapeShape(shape, arr->shape(), reverse);
*ptr = arr->ReshapeWithRecord(new_shape);
*out = ptr;
API_END_HANDLE_ERROR(delete ptr);
}
int MXNDArrayGetStorageType(NDArrayHandle handle,
int *out_storage_type) {
API_BEGIN();
NDArray *arr = static_cast<NDArray*>(handle);
if (!arr->is_none()) {
*out_storage_type = arr->storage_type();
} else {
*out_storage_type = kUndefinedStorage;
}
API_END();
}
int MXNDArrayGetShape(NDArrayHandle handle,
uint32_t *out_dim,
const uint32_t **out_pdata) {
MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
API_BEGIN();
NDArray *arr = static_cast<NDArray*>(handle);
if (!arr->is_none()) {
const mxnet::TShape &s = arr->shape();
*out_dim = s.ndim();
std::vector<uint32_t>& buffer = ret->arg_shape_buffer;
buffer.resize(s.ndim());
nnvm::ShapeTypeCast(s.begin(), s.end(), buffer.data());
*out_pdata = buffer.data();
} else {
*out_dim = 0;
}
API_END();
}
template<typename dtype>
inline void GetShape(NDArrayHandle handle, const dtype** out_pdata, int* out_dim,
MXAPIThreadLocalEntry<dtype>* ret) {
NDArray* arr = static_cast<NDArray*>(handle);
if (!arr->is_none()) {
if (!features::is_enabled(features::INT64_TENSOR_SIZE)) {
CHECK_LT(arr->shape().Size(), (int64_t{1} << 31) - 1) <<
"[Get Shape] Size of tensor you are trying to allocate is larger than "
"2^31 elements. Please build with flag USE_INT64_TENSOR_SIZE=1";
}
mxnet::TShape s = arr->shape();
if (!Imperative::Get()->is_np_shape()) {
common::ConvertToLegacyShape(&s);
}
*out_dim = s.ndim();
if (s.ndim() >= 0) {
std::vector<dtype> &buffer = ret->arg_shape_buffer_ex;
buffer.resize(s.ndim());
mxnet::ShapeTypeCast(s.begin(), s.end(), buffer.data());
*out_pdata = buffer.data();
}
} else {
if (Imperative::Get()->is_np_shape()) {
*out_dim = -1;
} else {
*out_dim = 0;
}
}
}
int MXNDArrayGetShapeEx(NDArrayHandle handle,
int *out_dim,
const int **out_pdata) {
MXAPIThreadLocalEntry<> *ret = MXAPIThreadLocalStore<>::Get();
API_BEGIN();
GetShape<int>(handle, out_pdata, out_dim, ret);
API_END();
}
int MXNDArrayGetShapeEx64(NDArrayHandle handle,
int *out_dim,
const int64_t **out_pdata) {
MXAPIThreadLocalEntry<int64_t> *ret = MXAPIThreadLocalStore<int64_t>::Get();
API_BEGIN();
GetShape<int64_t>(handle, out_pdata, out_dim, ret);
API_END();
}
int MXNDArrayGetData(NDArrayHandle handle,
void **out_pdata) {
API_BEGIN();
NDArray *arr = static_cast<NDArray*>(handle);
#if MXNET_USE_MKLDNN == 1
if (arr->IsMKLDNNData()) {
arr->Reorder2DefaultAsync();
arr->WaitToRead();
}
#endif
if (!arr->is_none()) {
*out_pdata = arr->data().dptr_;
} else {
*out_pdata = nullptr;
}
API_END();
}
int MXNDArrayToDLPack(NDArrayHandle handle,
DLManagedTensorHandle *out_dlpack) {
API_BEGIN();
NDArray *arr = static_cast<NDArray*>(handle);
*out_dlpack = arr->ToDLPack();
API_END();
}
int MXNDArrayFromDLPack(DLManagedTensorHandle dlpack,
NDArrayHandle *out_handle) {
return MXNDArrayFromDLPackEx(dlpack, false, out_handle);
}
int MXNDArrayFromDLPackEx(DLManagedTensorHandle dlpack,
const bool transient_handle,
NDArrayHandle *out_handle) {
API_BEGIN();
*out_handle = new NDArray(NDArray::FromDLPack(
static_cast<DLManagedTensor*>(dlpack),
transient_handle));
API_END();
}
int MXNDArrayCallDLPackDeleter(DLManagedTensorHandle dlpack) {
API_BEGIN();
if (dlpack != nullptr) {
DLManagedTensor *p_dlpack = static_cast<DLManagedTensor*>(dlpack);
p_dlpack->deleter(p_dlpack);
}
API_END();
}
int MXNDArrayGetDType(NDArrayHandle handle,
int *out_dtype) {
API_BEGIN();
NDArray *arr = static_cast<NDArray*>(handle);
if (!arr->is_none()) {
*out_dtype = arr->dtype();
} else {
*out_dtype = -1;
}
API_END();
}
int MXNDArrayGetAuxType(NDArrayHandle handle,
uint32_t i,
int *out_type) {
API_BEGIN();
NDArray *arr = static_cast<NDArray*>(handle);
*out_type = arr->aux_type(i);
API_END();
}
/*!
* \brief Get a deep copy of the ith aux data blob
* in the form of an NDArray of default storage type.
* This function blocks. Do not use it in performance critical code.
*/
int MXNDArrayGetAuxNDArray(NDArrayHandle handle,
uint32_t i,
NDArrayHandle *out) {
API_BEGIN();
NDArray *arr = static_cast<NDArray*>(handle);
*out = new NDArray(arr->aux_ndarray(i));
API_END();
}
/*!
* \brief Get a deep copy of the data blob
* in the form of an NDArray of default storage type.
* This function blocks. Do not use it in performance critical code.
*/
int MXNDArrayGetDataNDArray(NDArrayHandle handle,
NDArrayHandle *out) {
API_BEGIN();
NDArray *arr = static_cast<NDArray*>(handle);
*out = new NDArray(arr->data_ndarray());
API_END();
}
int MXNDArrayGetContext(NDArrayHandle handle,
int *out_dev_type,
int *out_dev_id) {
API_BEGIN();
NDArray *arr = static_cast<NDArray*>(handle);
if (!arr->is_none()) {
const Context &ctx = arr->ctx();
*out_dev_type = ctx.dev_type;
*out_dev_id = ctx.dev_id;
} else {
*out_dev_type = 0;
*out_dev_id = 0;
}
API_END();
}
int MXNDArrayGetGrad(NDArrayHandle handle, NDArrayHandle *out) {
API_BEGIN();
NDArray *arr = static_cast<NDArray*>(handle);
NDArray ret = arr->grad();
if (ret.is_none()) {
*out = nullptr;
} else {
*out = new NDArray(ret);
}
API_END();
}
int MXNDArrayDetach(NDArrayHandle handle, NDArrayHandle *out) {
API_BEGIN();
NDArray *arr = static_cast<NDArray*>(handle);
*out = new NDArray(arr->Detach());
API_END();
}
int MXNDArraySetGradState(NDArrayHandle handle, int state) {
API_BEGIN();
NDArray *arr = static_cast<NDArray*>(handle);
arr->set_fresh_out_grad(static_cast<bool>(state));
API_END();
}
int MXNDArrayGetGradState(NDArrayHandle handle, int *out) {
API_BEGIN();
NDArray *arr = static_cast<NDArray*>(handle);
*out = arr->fresh_out_grad();
API_END();
}
int MXListFunctions(uint32_t *out_size,
FunctionHandle **out_array) {
API_BEGIN();
auto &vec = dmlc::Registry<NDArrayFunctionReg>::List();
*out_size = static_cast<uint32_t>(vec.size());
*out_array = (FunctionHandle*)(dmlc::BeginPtr(vec)); // NOLINT(*)
API_END();
}
int MXGetFunction(const char *name,
FunctionHandle *out) {
API_BEGIN();
*out = dmlc::Registry<NDArrayFunctionReg>::Find(name);
API_END();
}
int MXFuncGetInfo(FunctionHandle fun,
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) {
return MXAPIGetFunctionRegInfo(static_cast<const NDArrayFunctionReg *>(fun),
name, description, num_args,
arg_names, arg_type_infos, arg_descriptions,
return_type);
}
int MXFuncDescribe(FunctionHandle fun,
uint32_t *num_use_vars,
uint32_t *num_scalars,
uint32_t *num_mutate_vars,
int *type_mask) {
API_BEGIN();
auto *f = static_cast<const NDArrayFunctionReg*>(fun);
*num_use_vars = f->num_use_vars;
*num_scalars = f->num_scalars;
*num_mutate_vars = f->num_mutate_vars;
*type_mask = f->type_mask;
API_END();
}
int MXFuncInvoke(FunctionHandle fun,
NDArrayHandle *use_vars,
float *scalar_args,
NDArrayHandle *mutate_vars) {
API_BEGIN();
auto *f = static_cast<const NDArrayFunctionReg*>(fun);
f->body((NDArray**)(use_vars), // NOLINT(*)
scalar_args,
(NDArray**)(mutate_vars), // NOLINT(*)
0,
nullptr,
nullptr);
API_END();
}
int MXFuncInvokeEx(FunctionHandle fun,
NDArrayHandle *use_vars,
float *scalar_args,
NDArrayHandle *mutate_vars,
int num_params,
char **param_keys,
char **param_vals) {
API_BEGIN();
auto *f = static_cast<const NDArrayFunctionReg*>(fun);
f->body((NDArray**)(use_vars), // NOLINT(*)
scalar_args,
(NDArray**)(mutate_vars), // NOLINT(*)
num_params,
param_keys,
param_vals);
API_END();
}
//--------------------------------------------
// Part 5: IO Interface
//--------------------------------------------
int MXListDataIters(uint32_t *out_size,
DataIterCreator **out_array) {
API_BEGIN();
auto &vec = dmlc::Registry<DataIteratorReg>::List();
*out_size = static_cast<uint32_t>(vec.size());
*out_array = (DataIterCreator*)(dmlc::BeginPtr(vec)); // NOLINT(*)
API_END();
}
int MXDataIterGetIterInfo(DataIterCreator creator,
const char **name,
const char **description,
uint32_t *num_args,
const char ***arg_names,
const char ***arg_type_infos,
const char ***arg_descriptions) {
DataIteratorReg *e = static_cast<DataIteratorReg *>(creator);
return MXAPIGetFunctionRegInfo(e, name, description, num_args,
arg_names, arg_type_infos, arg_descriptions,
nullptr);
}
int MXDataIterCreateIter(DataIterCreator creator,
uint32_t num_param,
const char **keys,
const char **vals,
DataIterHandle *out) {
IIterator<DataBatch> *iter = nullptr;
API_BEGIN();
DataIteratorReg *e = static_cast<DataIteratorReg *>(creator);
iter = e->body();
std::vector<std::pair<std::string, std::string> > kwargs;
for (uint32_t i = 0; i < num_param; ++i) {
kwargs.push_back({std::string(keys[i]), std::string(vals[i])});
}
iter->Init(kwargs);
*out = iter;
API_END_HANDLE_ERROR(delete iter);
}
int MXDataIterFree(DataIterHandle handle) {
API_BEGIN();
delete static_cast<IIterator<DataBatch> *>(handle);
API_END();
}
int MXDataIterBeforeFirst(DataIterHandle handle) {
API_BEGIN();
static_cast<IIterator<DataBatch>* >(handle)->BeforeFirst();
API_END();
}
int MXDataIterNext(DataIterHandle handle, int *out) {
API_BEGIN();
*out = static_cast<IIterator<DataBatch>* >(handle)->Next();
API_END();
}
int MXDataIterGetLabel(DataIterHandle handle, NDArrayHandle *out) {
API_BEGIN();
const DataBatch& db = static_cast<IIterator<DataBatch>* >(handle)->Value();
NDArray* pndarray = new NDArray();
// temp hack to make label 1D
// TODO(tianjun) make label 1D when label_width=0
mxnet::TShape shape = db.data[1].shape();
if (shape.ndim() > 1 && shape[1] == 1) {
*pndarray = db.data[1].Reshape(mshadow::Shape1(shape[0]));
} else {
*pndarray = db.data[1];
}
*out = pndarray;
API_END();
}
int MXDataIterGetIndex(DataIterHandle handle, uint64_t **out_index, uint64_t *out_size) {
API_BEGIN();
const DataBatch& db = static_cast<IIterator<DataBatch>* >(handle)->Value();
*out_size = db.index.size();
*out_index = const_cast<uint64_t*>(db.index.data());
API_END();
}
int MXDataIterGetData(DataIterHandle handle, NDArrayHandle *out) {
API_BEGIN();
const DataBatch& db = static_cast<IIterator<DataBatch>* >(handle)->Value();
NDArray* pndarray = new NDArray();
*pndarray = db.data[0];
*out = pndarray;
API_END();
}
int MXDataIterGetPadNum(DataIterHandle handle, int *pad) {
API_BEGIN();
const DataBatch& db = static_cast<IIterator<DataBatch>* >(handle)->Value();
*pad = db.num_batch_padd;
API_END();
}
int MXKVStoreCreate(const char *type,
KVStoreHandle *out) {
API_BEGIN();
*out = KVStore::Create(type);
API_END();
}
int MXKVStoreSetGradientCompression(KVStoreHandle handle, uint32_t num_params,
const char** keys, const char** vals) {
API_BEGIN();
std::vector<std::pair<std::string, std::string> > params;
for (uint32_t i = 0; i < num_params; ++i) {
std::pair<std::string, std::string> p;
p.first = keys[i];
p.second = vals[i];
params.push_back(p);
}
static_cast<KVStore*>(handle)->SetGradientCompression(params);
API_END();
}
int MXKVStoreFree(KVStoreHandle handle) {
API_BEGIN();
delete static_cast<KVStore*>(handle);
API_END();
}
int MXKVStoreInit(KVStoreHandle handle,
uint32_t num,
const int* keys,
NDArrayHandle* vals) {
API_BEGIN();
std::vector<int> v_keys(num);
std::vector<NDArray> v_vals(num);
for (uint32_t i = 0; i < num; ++i) {
v_keys[i] = keys[i];
v_vals[i] = *static_cast<NDArray*>(vals[i]);
}
static_cast<KVStore*>(handle)->Init(v_keys, v_vals);
API_END();
}
int MXKVStoreInitEx(KVStoreHandle handle,
uint32_t num,
const char** keys,
NDArrayHandle* vals) {
API_BEGIN();
std::vector<std::string> v_keys(num);
std::vector<NDArray> v_vals(num);
for (uint32_t i = 0; i < num; ++i) {
v_keys[i] = keys[i];
v_vals[i] = *static_cast<NDArray*>(vals[i]);
}
static_cast<KVStore*>(handle)->Init(v_keys, v_vals);
API_END();
}
int MXKVStorePush(KVStoreHandle handle,
uint32_t num,
const int* keys,
NDArrayHandle* vals,
int priority) {
API_BEGIN();
std::vector<int> v_keys(num);
std::vector<NDArray> v_vals(num);
for (uint32_t i = 0; i < num; ++i) {
v_keys[i] = keys[i];
v_vals[i] = *static_cast<NDArray*>(vals[i]);
}
static_cast<KVStore*>(handle)->Push(v_keys, v_vals, priority);
API_END();
}
int MXKVStorePushEx(KVStoreHandle handle,
uint32_t num,
const char** keys,
NDArrayHandle* vals,
int priority) {
API_BEGIN();
std::vector<std::string> v_keys(num);
std::vector<NDArray> v_vals(num);
for (uint32_t i = 0; i < num; ++i) {
v_keys[i] = keys[i];
v_vals[i] = *static_cast<NDArray*>(vals[i]);
}
static_cast<KVStore*>(handle)->Push(v_keys, v_vals, priority);
API_END();
}
int MXKVStorePull(KVStoreHandle handle,
uint32_t num,
const int* keys,
NDArrayHandle* vals,
int priority) {
API_BEGIN();
std::vector<int> v_keys(num);
std::vector<NDArray*> v_vals(num);
for (uint32_t i = 0; i < num; ++i) {
v_keys[i] = keys[i];
v_vals[i] = static_cast<NDArray*>(vals[i]);
}
static_cast<KVStore*>(handle)->Pull(v_keys, v_vals, priority, true);
API_END();
}
int MXKVStorePullEx(KVStoreHandle handle,
uint32_t num,
const char** keys,
NDArrayHandle* vals,
int priority) {
API_BEGIN();
std::vector<std::string> v_keys(num);
std::vector<NDArray*> v_vals(num);
for (uint32_t i = 0; i < num; ++i) {
v_keys[i] = keys[i];
v_vals[i] = static_cast<NDArray*>(vals[i]);
}
static_cast<KVStore*>(handle)->Pull(v_keys, v_vals, priority, true);
API_END();
}
int MXKVStoreBroadcast(KVStoreHandle handle,
mx_uint vnum,
const int* vkeys,
mx_uint onum,
const int* okeys,
NDArrayHandle* vals,
NDArrayHandle* outs,
int priority) {
API_BEGIN();
std::vector<int> v_vkeys(vnum);
std::vector<int> v_okeys(onum);
std::vector<NDArray> v_vals(vnum);
std::vector<NDArray*> v_outs(onum);
for (mx_uint i = 0; i < vnum; ++i) {
v_vkeys[i] = vkeys[i];
v_vals[i] = *static_cast<NDArray*>(vals[i]);
}
for (mx_uint i = 0; i < onum; ++i) {
v_okeys[i] = okeys[i];
v_outs[i] = static_cast<NDArray*>(outs[i]);
}
static_cast<KVStore*>(handle)->Broadcast(v_vkeys, v_okeys, v_vals, v_outs,
priority);
API_END();
}
int MXKVStoreBroadcastEx(KVStoreHandle handle,
mx_uint vnum,
const char** vkeys,
mx_uint onum,
const char** okeys,
NDArrayHandle* vals,
NDArrayHandle* outs,
int priority) {
API_BEGIN();
std::vector<std::string> v_vkeys(vnum);
std::vector<std::string> v_okeys(onum);
std::vector<NDArray> v_vals(vnum);
std::vector<NDArray*> v_outs(onum);
for (mx_uint i = 0; i < vnum; ++i) {
v_vkeys[i] = vkeys[i];
v_vals[i] = *static_cast<NDArray*>(vals[i]);
}
for (mx_uint i = 0; i < onum; ++i) {
v_okeys[i] = okeys[i];
v_outs[i] = static_cast<NDArray*>(outs[i]);
}
static_cast<KVStore*>(handle)->Broadcast(v_vkeys, v_okeys, v_vals, v_outs,
priority);
API_END();
}
int MXKVStorePushPull(KVStoreHandle handle,
mx_uint vnum,
const int* vkeys,
mx_uint onum,
const int* okeys,
NDArrayHandle* vals,
NDArrayHandle* outs,
int priority) {
API_BEGIN();
std::vector<int> v_vkeys(vnum);
std::vector<int> v_okeys(onum);
std::vector<NDArray> v_vals(vnum);
std::vector<NDArray*> v_outs(onum);
for (mx_uint i = 0; i < vnum; ++i) {
v_vkeys[i] = vkeys[i];
v_vals[i] = *static_cast<NDArray*>(vals[i]);
}
for (mx_uint i = 0; i < onum; ++i) {
v_okeys[i] = okeys[i];
v_outs[i] = static_cast<NDArray*>(outs[i]);
}
static_cast<KVStore*>(handle)->PushPull(v_vkeys, v_okeys, v_vals, v_outs,
priority);
API_END();
}
int MXKVStorePushPullEx(KVStoreHandle handle,
mx_uint vnum,
const char** vkeys,
mx_uint onum,
const char** okeys,
NDArrayHandle* vals,
NDArrayHandle* outs,
int priority) {
API_BEGIN();
std::vector<std::string> v_vkeys(vnum);
std::vector<std::string> v_okeys(onum);
std::vector<NDArray> v_vals(vnum);
std::vector<NDArray*> v_outs(onum);
for (mx_uint i = 0; i < vnum; ++i) {
v_vkeys[i] = vkeys[i];
v_vals[i] = *static_cast<NDArray*>(vals[i]);
}
for (mx_uint i = 0; i < onum; ++i) {
v_okeys[i] = okeys[i];
v_outs[i] = static_cast<NDArray*>(outs[i]);
}
static_cast<KVStore*>(handle)->PushPull(v_vkeys, v_okeys, v_vals, v_outs,
priority);
API_END();
}
int MXKVStorePullWithSparse(KVStoreHandle handle,
uint32_t num,
const int* keys,
NDArrayHandle* vals,
int priority,
bool ignore_sparse) {
API_BEGIN();
std::vector<int> v_keys(num);
std::vector<NDArray*> v_vals(num);
for (uint32_t i = 0; i < num; ++i) {
v_keys[i] = keys[i];
v_vals[i] = static_cast<NDArray*>(vals[i]);
}
static_cast<KVStore*>(handle)->Pull(v_keys, v_vals, priority, ignore_sparse);
API_END();
}
int MXKVStorePullWithSparseEx(KVStoreHandle handle,
uint32_t num,
const char** keys,
NDArrayHandle* vals,
int priority,
bool ignore_sparse) {
API_BEGIN();
std::vector<std::string> v_keys(num);
std::vector<NDArray*> v_vals(num);
for (uint32_t i = 0; i < num; ++i) {
v_keys[i] = keys[i];
v_vals[i] = static_cast<NDArray*>(vals[i]);
}
static_cast<KVStore*>(handle)->Pull(v_keys, v_vals, priority, ignore_sparse);
API_END();
}
int MXKVStorePullRowSparse(KVStoreHandle handle,
uint32_t num,
const int* keys,
NDArrayHandle* vals,
const NDArrayHandle* row_ids,
int priority) {
API_BEGIN();
std::vector<int> v_keys(num);
std::vector<std::pair<NDArray*, NDArray>> v_val_rowids(num);
for (uint32_t i = 0; i < num; ++i) {
v_keys[i] = keys[i];
v_val_rowids[i] = std::make_pair(static_cast<NDArray*>(vals[i]),
*static_cast<NDArray*>(row_ids[i]));
}
static_cast<KVStore*>(handle)->PullRowSparse(v_keys, v_val_rowids, priority);
API_END();
}
int MXKVStorePullRowSparseEx(KVStoreHandle handle,
uint32_t num,
const char** keys,
NDArrayHandle* vals,
const NDArrayHandle* row_ids,
int priority) {
API_BEGIN();
std::vector<std::string> v_keys(num);
std::vector<std::pair<NDArray*, NDArray>> v_val_rowids(num);
for (uint32_t i = 0; i < num; ++i) {
v_keys[i] = keys[i];
v_val_rowids[i] = std::make_pair(static_cast<NDArray*>(vals[i]),
*static_cast<NDArray*>(row_ids[i]));
}
static_cast<KVStore*>(handle)->PullRowSparse(v_keys, v_val_rowids, priority);
API_END();
}
void MXKVStoreSetUpdaterImpl(KVStoreHandle handle,
MXKVStoreUpdater updater,
void* updater_handle) {
MXKVStoreUpdater * updater_temp = updater;
void* updater_handle_temp = updater_handle;
std::function<void(int, const NDArray&, NDArray*)> updt
= [updater_temp, updater_handle_temp](int key, const NDArray& recv, NDArray* local) {
NDArray* recv_copy = new NDArray();
*recv_copy = recv;
NDArray* local_copy = new NDArray();
*local_copy = *local;
updater_temp(key, recv_copy, local_copy, updater_handle_temp);
};
static_cast<KVStore*>(handle)->set_updater(updt);
}
int MXKVStoreSetUpdater(KVStoreHandle handle,
MXKVStoreUpdater updater,
void* updater_handle) {
API_BEGIN();
MXKVStoreSetUpdaterImpl(handle, updater, updater_handle);
API_END();
}
int MXKVStoreSetUpdaterEx(KVStoreHandle handle,
MXKVStoreUpdater updater,
MXKVStoreStrUpdater str_updater,
void* updater_handle) {
API_BEGIN();
// set updater with int keys
MXKVStoreSetUpdaterImpl(handle, updater, updater_handle);
// set updater with string keys
MXKVStoreStrUpdater * updater_temp = str_updater;
void* updater_handle_temp = updater_handle;
std::function<void(const std::string&, const NDArray&, NDArray*)> updt
= [updater_temp, updater_handle_temp]
(const std::string& key, const NDArray& recv, NDArray* local) {
NDArray* recv_copy = new NDArray();
*recv_copy = recv;
NDArray* local_copy = new NDArray();
*local_copy = *local;
updater_temp(key.c_str(), recv_copy, local_copy, updater_handle_temp);
};
static_cast<KVStore*>(handle)->set_updater(updt);
API_END();
}
int MXKVStoreGetRank(KVStoreHandle handle, int *rank) {
API_BEGIN();
*rank = static_cast<KVStore*>(handle)->get_rank();
API_END();
}
int MXKVStoreGetGroupSize(KVStoreHandle handle, int *size) {
API_BEGIN();
*size = static_cast<KVStore*>(handle)->get_group_size();
API_END();
}
int MXKVStoreBarrier(KVStoreHandle handle) {
API_BEGIN();
static_cast<KVStore*>(handle)->Barrier();
API_END();
}
int MXKVStoreSetBarrierBeforeExit(KVStoreHandle handle,
const int barrier_before_exit) {
API_BEGIN();
static_cast<KVStore*>(handle)->set_barrier_before_exit(barrier_before_exit);
API_END();
}
int MXInitPSEnv(uint32_t num_vars,
const char **keys,
const char **vals) {
API_BEGIN();
std::unordered_map<std::string, std::string> kwargs;
for (uint32_t i = 0; i < num_vars; ++i) {
kwargs[std::string(keys[i])] = std::string(vals[i]);
}
KVStore::InitPSEnv(kwargs);
API_END();
}
int MXKVStoreIsWorkerNode(int *ret) {
API_BEGIN();
*ret = KVStore::IsWorkerNode();
API_END();
}
int MXKVStoreIsServerNode(int *ret) {
API_BEGIN();
*ret = KVStore::IsServerNode();
API_END();
}
int MXKVStoreIsSchedulerNode(int *ret) {
API_BEGIN();
*ret = KVStore::IsSchedulerNode();
API_END();
}
int MXKVStoreRunServer(KVStoreHandle handle,
MXKVStoreServerController controller,
void *controller_handle) {
API_BEGIN();
MXKVStoreServerController *controller_temp = controller;
void *controller_handle_temp = controller_handle;
auto ctrl = [controller_temp, controller_handle_temp](int head, const std::string& body) {
controller_temp(head, body.c_str(), controller_handle_temp);
};
static_cast<KVStore*>(handle)->RunServer(ctrl);
API_END();
}
int MXKVStoreSendCommmandToServers(KVStoreHandle handle,
int cmd_id,
const char* cmd_body) {
API_BEGIN();
static_cast<KVStore*>(handle)->SendCommandToServers(
cmd_id, std::string(cmd_body));
API_END();
}
int MXKVStoreGetType(KVStoreHandle handle,
const char** type) {
API_BEGIN();
*CHECK_NOTNULL(type) = static_cast<KVStore*>(handle)->type().c_str();
API_END();
}
int MXKVStoreGetNumDeadNode(KVStoreHandle handle,
const int node_id,
int *number,
const int timeout_sec) {
API_BEGIN();
*number = static_cast<KVStore*>(handle)->get_num_dead_node(node_id, timeout_sec);
API_END();
}
struct MXRecordIOContext {
dmlc::RecordIOWriter *writer;
dmlc::RecordIOReader *reader;
dmlc::Stream *stream;
std::string *read_buff;
};
int MXRecordIOWriterCreate(const char *uri,
RecordIOHandle *out) {
API_BEGIN();
dmlc::Stream *stream = dmlc::Stream::Create(uri, "w");
MXRecordIOContext *context = new MXRecordIOContext;
context->writer = new dmlc::RecordIOWriter(stream);
context->reader = nullptr;
context->stream = stream;
context->read_buff = nullptr;
*out = reinterpret_cast<RecordIOHandle>(context);
API_END();
}
int MXRecordIOWriterFree(RecordIOHandle handle) {
API_BEGIN();
MXRecordIOContext *context =
reinterpret_cast<MXRecordIOContext*>(handle);
delete context->writer;
delete context->stream;
delete context;
API_END();
}
int MXRecordIOWriterWriteRecord(RecordIOHandle handle,
const char *buf, size_t size) {
API_BEGIN();
MXRecordIOContext *context =
reinterpret_cast<MXRecordIOContext*>(handle);
context->writer->WriteRecord(reinterpret_cast<const void*>(buf), size);
API_END();
}
int MXRecordIOWriterTell(RecordIOHandle handle, size_t *pos) {
API_BEGIN();
MXRecordIOContext *context =
reinterpret_cast<MXRecordIOContext*>(handle);
*pos = context->writer->Tell();
API_END();
}
int MXRecordIOReaderCreate(const char *uri,
RecordIOHandle *out) {
API_BEGIN();
dmlc::Stream *stream = dmlc::Stream::Create(uri, "r");
MXRecordIOContext *context = new MXRecordIOContext;
context->reader = new dmlc::RecordIOReader(stream);
context->writer = nullptr;
context->stream = stream;
context->read_buff = new std::string();
*out = reinterpret_cast<RecordIOHandle>(context);
API_END();
}
int MXRecordIOReaderFree(RecordIOHandle handle) {
API_BEGIN();
MXRecordIOContext *context =
reinterpret_cast<MXRecordIOContext*>(handle);
delete context->reader;
delete context->stream;
delete context->read_buff;
delete context;
API_END();
}
int MXRecordIOReaderReadRecord(RecordIOHandle handle,
char const **buf, size_t *size) {
API_BEGIN();
MXRecordIOContext *context =
reinterpret_cast<MXRecordIOContext*>(handle);
if (context->reader->NextRecord(context->read_buff)) {
*buf = context->read_buff->c_str();
*size = context->read_buff->size();
} else {
*buf = nullptr;
*size = 0;
}
API_END();
}
int MXRecordIOReaderSeek(RecordIOHandle handle, size_t pos) {
API_BEGIN();
MXRecordIOContext *context =
reinterpret_cast<MXRecordIOContext*>(handle);
context->reader->Seek(pos);
API_END();
}
int MXRecordIOReaderTell(RecordIOHandle handle, size_t *pos) {
API_BEGIN();
MXRecordIOContext *context =
reinterpret_cast<MXRecordIOContext*>(handle);
*pos = context->reader->Tell();
API_END();
}
int MXRtcCreate(char* name, uint32_t num_input, uint32_t num_output,
char** input_names, char** output_names,
NDArrayHandle* inputs, NDArrayHandle* outputs,
char* kernel, RtcHandle *out) {
API_BEGIN();
LOG(FATAL) << "Old rtc API is deprecated. Please use CudaModule";
API_END();
}
int MXRtcPush(RtcHandle handle, uint32_t num_input, uint32_t num_output,
NDArrayHandle* inputs, NDArrayHandle* outputs,
uint32_t gridDimX,
uint32_t gridDimY,
uint32_t gridDimZ,
uint32_t blockDimX,
uint32_t blockDimY,
uint32_t blockDimZ) {
API_BEGIN();
LOG(FATAL) << "Old rtc API is deprecated. Please use CudaModule";
API_END();
}
int MXRtcFree(RtcHandle handle) {
API_BEGIN();
LOG(FATAL) << "Old rtc API is deprecated. Please use CudaModule";
API_END();
}
int MXCustomOpRegister(const char* op_type, CustomOpPropCreator creator) {
API_BEGIN();
mxnet::op::custom::CustomOperator::Get()->Register(op_type, creator);
API_END();
}
int MXRtcCudaModuleCreate(const char* source, int num_options,
const char** options, int num_exports,
const char** exports, CudaModuleHandle *out) {
API_BEGIN();
#if MXNET_USE_CUDA && MXNET_ENABLE_CUDA_RTC
std::vector<std::string> str_opts;
for (int i = 0; i < num_options; ++i) str_opts.emplace_back(options[i]);
std::vector<std::string> str_exports;
for (int i = 0; i < num_exports; ++i) str_exports.emplace_back(exports[i]);
*out = new rtc::CudaModule(source, str_opts, str_exports);
#else
LOG(FATAL) << "Compile with USE_CUDA=1 and ENABLE_CUDA_RTC=1 to have CUDA runtime compilation.";
#endif
API_END();
}
int MXRtcCudaModuleFree(CudaModuleHandle handle) {
API_BEGIN();
#if MXNET_USE_CUDA && MXNET_ENABLE_CUDA_RTC
delete reinterpret_cast<rtc::CudaModule*>(handle);
#else
LOG(FATAL) << "Compile with USE_CUDA=1 and ENABLE_CUDA_RTC=1 to have CUDA runtime compilation.";
#endif
API_END();
}
int MXRtcCudaKernelCreate(CudaModuleHandle handle, const char* name, int num_args,
int* is_ndarray, int* is_const, int* arg_types,
CudaKernelHandle *out) {
API_BEGIN();
#if MXNET_USE_CUDA && MXNET_ENABLE_CUDA_RTC
auto module = reinterpret_cast<rtc::CudaModule*>(handle);
std::vector<rtc::CudaModule::ArgType> signature;
for (int i = 0; i < num_args; ++i) {
signature.push_back(rtc::CudaModule::ArgType{
static_cast<bool>(is_ndarray[i]), static_cast<bool>(is_const[i]),
static_cast<mshadow::TypeFlag>(arg_types[i])});
}
auto kernel = module->GetKernel(name, signature);
*out = new std::shared_ptr<rtc::CudaModule::Kernel>(kernel);
#else
LOG(FATAL) << "Compile with USE_CUDA=1 and ENABLE_CUDA_RTC=1 to have CUDA runtime compilation.";
#endif
API_END();
}
int MXRtcCudaKernelFree(CudaKernelHandle handle) {
API_BEGIN();
#if MXNET_USE_CUDA && MXNET_ENABLE_CUDA_RTC
delete reinterpret_cast<std::shared_ptr<rtc::CudaModule::Kernel>*>(handle);
#else
LOG(FATAL) << "Compile with USE_CUDA=1 and ENABLE_CUDA_RTC=1 to have CUDA runtime compilation.";
#endif
API_END();
}
int MXRtcCudaKernelCall(CudaKernelHandle handle, int dev_id, void** args,
uint32_t grid_dim_x, uint32_t grid_dim_y,
uint32_t grid_dim_z, uint32_t block_dim_x,
uint32_t block_dim_y, uint32_t block_dim_z,
uint32_t shared_mem) {
API_BEGIN();
#if MXNET_USE_CUDA && MXNET_ENABLE_CUDA_RTC
auto kernel = reinterpret_cast<std::shared_ptr<rtc::CudaModule::Kernel>*>(handle);
const auto& signature = (*kernel)->signature();
std::vector<dmlc::any> any_args;
for (size_t i = 0; i < signature.size(); ++i) {
if (signature[i].is_ndarray) {
any_args.emplace_back(*static_cast<NDArray*>(args[i]));
} else {
MSHADOW_TYPE_SWITCH(signature[i].dtype, DType, {
any_args.emplace_back(*static_cast<DType*>(args[i]));
});
}
}
(*kernel)->Launch(Context::GPU(dev_id), any_args, grid_dim_x, grid_dim_y,
grid_dim_z, block_dim_x, block_dim_y, block_dim_z, shared_mem);
#else
LOG(FATAL) << "Compile with USE_CUDA=1 and ENABLE_CUDA_RTC=1 to have CUDA runtime compilation.";
#endif
API_END();
}
int MXNDArrayGetSharedMemHandle(NDArrayHandle handle, int* shared_pid, int* shared_id) {
API_BEGIN();
NDArray* arr = reinterpret_cast<NDArray*>(handle);
Storage::Handle shandle;
if (arr->ctx().dev_type == Context::kCPUShared) {
arr->WaitToRead();
shandle = arr->storage_handle();
Storage::Get()->SharedIncrementRefCount(shandle);
} else {
NDArray new_arr(arr->shape(), Context::CPUShared(0), false, arr->dtype());
CopyFromTo(*arr, new_arr);
new_arr.WaitToRead();
shandle = new_arr.storage_handle();
Storage::Get()->SharedIncrementRefCount(shandle);
}
*shared_pid = shandle.shared_pid;
*shared_id = shandle.shared_id;
API_END();
}
int MXNDArrayCreateFromSharedMem(int shared_pid, int shared_id, const uint32_t *shape,
uint32_t ndim, int dtype, NDArrayHandle *out) {
API_BEGIN();
*out = new NDArray(shared_pid, shared_id, mxnet::TShape(shape, shape + ndim), dtype);
API_END();
}
int MXNDArrayCreateFromSharedMemEx(int shared_pid, int shared_id, const int *shape,
int ndim, int dtype, NDArrayHandle *out) {
API_BEGIN();
*out = new NDArray(shared_pid, shared_id, mxnet::TShape(shape, shape + ndim), dtype);
API_END();
}
typedef Engine::VarHandle VarHandle;
typedef Engine::CallbackOnComplete CallbackOnComplete;
void AssertValidNumberVars(int num_const_vars, int num_mutable_vars) {
CHECK_GE(num_const_vars, 0) << "Non-negative number of const vars expected.";
CHECK_GE(num_mutable_vars, 0) << "Non-negative number of mutable vars expected.";
}
int MXEnginePushAsync(EngineAsyncFunc async_func, void* func_param,
EngineFuncParamDeleter deleter, ContextHandle ctx_handle,
EngineVarHandle const_vars_handle, int num_const_vars,
EngineVarHandle mutable_vars_handle, int num_mutable_vars,
EngineFnPropertyHandle prop_handle, int priority,
const char* opr_name, bool wait) {
API_BEGIN();
auto exec_ctx = *static_cast<const Context*>(ctx_handle);
auto const_vars = static_cast<VarHandle*>(const_vars_handle);
auto mutable_vars = static_cast<VarHandle*>(mutable_vars_handle);
auto prop = FnProperty::kNormal;
if (prop_handle) {
prop = *static_cast<const FnProperty*>(prop_handle);
}
Engine::AsyncFn exec_fn;
if (deleter == nullptr) {
exec_fn = [async_func, func_param](RunContext rctx,
CallbackOnComplete on_complete) {
async_func(&rctx, &on_complete, func_param);
};
} else {
// Wrap func_param in a shared_ptr with deleter such that deleter
// will be called when the lambda goes out of scope.
std::shared_ptr<void> shared_func_param(func_param, deleter);
exec_fn = [async_func, shared_func_param](RunContext rctx,
CallbackOnComplete on_complete) {
async_func(&rctx, &on_complete, shared_func_param.get());
};
}
AssertValidNumberVars(num_const_vars, num_mutable_vars);
std::vector<VarHandle> const_var_vec(const_vars, const_vars + num_const_vars);
std::vector<VarHandle> mutable_var_vec(mutable_vars, mutable_vars + num_mutable_vars);
Engine::Get()->PushAsync(exec_fn, exec_ctx, const_var_vec, mutable_var_vec,
prop, priority, opr_name, wait);
API_END();
}
int MXEnginePushSync(EngineSyncFunc sync_func, void* func_param,
EngineFuncParamDeleter deleter, ContextHandle ctx_handle,
EngineVarHandle const_vars_handle, int num_const_vars,
EngineVarHandle mutable_vars_handle, int num_mutable_vars,
EngineFnPropertyHandle prop_handle, int priority,
const char* opr_name) {
API_BEGIN();
auto exec_ctx = *static_cast<const Context*>(ctx_handle);
auto const_vars = static_cast<VarHandle*>(const_vars_handle);
auto mutable_vars = static_cast<VarHandle*>(mutable_vars_handle);
auto prop = FnProperty::kNormal;
if (prop_handle) {
prop = *static_cast<const FnProperty*>(prop_handle);
}
Engine::SyncFn exec_fn;
if (deleter == nullptr) {
exec_fn = [sync_func, func_param](RunContext rctx) {
sync_func(&rctx, func_param);
};
} else {
// Wrap func_param in a shared_ptr with deleter such that deleter
// will be called when the lambda goes out of scope.
std::shared_ptr<void> shared_func_param(func_param, deleter);
exec_fn = [sync_func, shared_func_param](RunContext rctx) {
sync_func(&rctx, shared_func_param.get());
};
}
AssertValidNumberVars(num_const_vars, num_mutable_vars);
std::vector<VarHandle> const_var_vec(const_vars, const_vars + num_const_vars);
std::vector<VarHandle> mutable_var_vec(mutable_vars, mutable_vars + num_mutable_vars);
Engine::Get()->PushSync(exec_fn, exec_ctx, const_var_vec, mutable_var_vec,
prop, priority, opr_name);
API_END();
}
int MXEnginePushAsyncND(EngineAsyncFunc async_func, void* func_param,
EngineFuncParamDeleter deleter, ContextHandle ctx_handle,
NDArrayHandle* const_nds_handle, int num_const_nds,
NDArrayHandle* mutable_nds_handle, int num_mutable_nds,
EngineFnPropertyHandle prop_handle, int priority,
const char* opr_name, bool wait) {
API_BEGIN();
NDArray** const_nds = reinterpret_cast<NDArray**>(const_nds_handle);
NDArray** mutable_nds = reinterpret_cast<NDArray**>(mutable_nds_handle);
std::vector<VarHandle> const_var_vec(num_const_nds);
for (int i = 0; i < num_const_nds; ++i) const_var_vec[i] = const_nds[i]->var();
std::vector<VarHandle> mutable_var_vec(num_mutable_nds);
for (int i = 0; i < num_mutable_nds; ++i) mutable_var_vec[i] = mutable_nds[i]->var();
return MXEnginePushAsync(async_func, func_param, deleter, ctx_handle,
const_var_vec.data(), num_const_nds,
mutable_var_vec.data(), num_mutable_nds,
prop_handle, priority, opr_name, wait);
API_END();
}
int MXEnginePushSyncND(EngineSyncFunc sync_func, void* func_param,
EngineFuncParamDeleter deleter, ContextHandle ctx_handle,
NDArrayHandle* const_nds_handle, int num_const_nds,
NDArrayHandle* mutable_nds_handle, int num_mutable_nds,
EngineFnPropertyHandle prop_handle, int priority,
const char* opr_name) {
API_BEGIN();
NDArray** const_nds = reinterpret_cast<NDArray**>(const_nds_handle);
NDArray** mutable_nds = reinterpret_cast<NDArray**>(mutable_nds_handle);
std::vector<VarHandle> const_var_vec(num_const_nds);
for (int i = 0; i < num_const_nds; ++i) const_var_vec[i] = const_nds[i]->var();
std::vector<VarHandle> mutable_var_vec(num_mutable_nds);
for (int i = 0; i < num_mutable_nds; ++i) mutable_var_vec[i] = mutable_nds[i]->var();
return MXEnginePushSync(sync_func, func_param, deleter, ctx_handle,
const_var_vec.data(), num_const_nds,
mutable_var_vec.data(), num_mutable_nds,
prop_handle, priority, opr_name);
API_END();
}
int MXStorageEmptyCache(int dev_type, int dev_id) {
API_BEGIN();
Context ctx = Context::Create(static_cast<Context::DeviceType>(dev_type), dev_id);
Storage::Get()->ReleaseAll(ctx);
API_END();
}
int MXShallowCopyNDArray(NDArrayHandle src_handle, NDArrayHandle* out) {
NDArray* ret = nullptr;
API_BEGIN();
NDArray* src_array = static_cast<NDArray*>(src_handle);
ret = new NDArray(*src_array);
*out = ret;
API_END_HANDLE_ERROR(delete ret);
}