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Test moving DistributedRunContext instance into shared provider layer

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(with purpose error to verify it's being built properly)
This commit is contained in:
Ryan Hill 2021-05-13 00:42:54 -07:00
parent 741e09a882
commit 0a59bc3902
3 changed files with 15 additions and 4 deletions
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3
onnxruntime/core/framework/provider_bridge_ort.cc
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@ -62,6 +62,7 @@ Status LongformerAttentionBase__CheckInputs(const LongformerAttentionBase* p, co
#include "orttraining/training_ops/cpu/controlflow/yield.h"
#include "orttraining/training_ops/cpu/loss/softmax_cross_entropy_loss.h"
#include "orttraining/training_ops/cpu/tensor/split.h"
#include "orttraining/core/framework/distributed_run_context.h"
#endif
#if defined(USE_CUDA) && defined(ORT_USE_NCCL) && defined(USE_NCCL_P2P)
#include "orttraining/training_ops/cuda/communication/nccl_service.h"
@ -879,6 +880,8 @@ struct ProviderHostImpl : ProviderHost {
void contrib__GetPermutationAndShape(bool ncd_to_ndc, const TensorShape& tensor_shape, std::vector<int64_t>& new_shape, std::vector<size_t>& permutations) override { contrib::GetPermutationAndShape(ncd_to_ndc, tensor_shape, new_shape, permutations); }
Status contrib__PrepareForTrainingCompute(const TensorShape& input_shape, int num_outputs, int64_t& axis, int& before_dims, int& after_dims_including_split_axis, int& after_dims_excluding_split, std::vector<int64_t>& split_sizes) override { return contrib::PrepareForTrainingCompute(input_shape, num_outputs, axis, before_dims, after_dims_including_split_axis, after_dims_excluding_split, split_sizes); }
Status contrib__YieldOp__Compute(const contrib::YieldOp* p, OpKernelContext* context) override { return p->YieldOp::Compute(context); }
training::DistributedRunContext& GetDistributedRunContextInstance() override { return training::DistributedRunContext::GetInstance(); }
#endif
#endif
} provider_host_;

6
onnxruntime/core/providers/shared_library/provider_interfaces.h
View file

@ -46,6 +46,10 @@ class PassThrough;
class YieldOp;
} // namespace contrib
namespace training {
class DistributedRunContext;
}
template <typename T, typename TResult>
struct IteratorHolder {
IteratorHolder(std::unique_ptr<T>&& p) : p_{std::move(p)} {}
@ -784,6 +788,8 @@ struct ProviderHost {
virtual void contrib__GetPermutationAndShape(bool ncd_to_ndc, const TensorShape& tensor_shape, std::vector<int64_t>& new_shape, std::vector<size_t>& permutations) = 0;
virtual Status contrib__PrepareForTrainingCompute(const TensorShape& input_shape, int num_outputs, int64_t& axis, int& before_dims, int& after_dims_including_split_axis, int& after_dims_excluding_split, std::vector<int64_t>& split_sizes) = 0;
virtual Status contrib__YieldOp__Compute(const contrib::YieldOp* p, OpKernelContext* context) = 0;
virtual training::DistributedRunContext& GetDistributedRunContextInstance() = 0;
#endif
#endif
};

10
orttraining/orttraining/core/framework/distributed_run_context.h
View file

@ -33,8 +33,7 @@ struct WorkerGroup {
std::string ToString() const {
std::stringstream msg;
msg << "group_type: " << group_type << ", group_id: " << group_id <<
", rank in group:" << rank_in_group << ", world-rank:" << ranks.at(rank_in_group);
msg << "group_type: " << group_type << ", group_id: " << group_id << ", rank in group:" << rank_in_group << ", world-rank:" << ranks.at(rank_in_group);
msg << ", ranks: [";
for (size_t i = 0; i < ranks.size(); ++i) {
msg << ranks.at(i);
@ -81,10 +80,13 @@ class DistributedRunContext {
config.pipeline_stage_size);
}
#ifndef SHARED_PROVIDER
static DistributedRunContext& GetInstance() {
return DistributedRunContext::GetOrCreateInstance();
}
#else
DistributedRunContext& GetInstance() { return Provider_Gethost()->GetDistributedRunContextInstance; }
#endif
/* SHORTCUT FUNCTIONS START */
static DistributedRunConfig& RunConfig() {
@ -121,7 +123,7 @@ class DistributedRunContext {
return DistributedRunContext::GetInstance().GetWorkerGroup(group_type).group_id;
}
static std::vector<int32_t> GetRanks(WorkerGroupType group_type){
static std::vector<int32_t> GetRanks(WorkerGroupType group_type) {
return DistributedRunContext::GetInstance().GetWorkerGroup(group_type).ranks;
}