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add allgather fusion

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pull/9298/head
alouhahaha 4 years ago
parent 0e6d06c463
commit 7265a49400
4 changed files with 74 additions and 60 deletions
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18
mindspore/ccsrc/backend/kernel_compiler/hccl/hccl_kernel.cc
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@ -133,8 +133,22 @@ const std::vector<size_t> &HcclKernel::GetOutputSizeList() const {
if (!output_size_list_.empty()) {
return output_size_list_;
}
for (ulong i = 0; i < hccl_data_type_list_.size(); ++i) {
if (!HcomUtil::GetHcclOpSize(hccl_data_type_list_[i], hccl_kernel_output_shape_list_[i], &size)) {
auto cnode = anf_node_->cast<CNodePtr>();
auto op_name = AnfAlgo::GetCNodeName(cnode);
int64_t rank_size = 1;
if (AnfAlgo::HasNodeAttr(kAttrRankSize, cnode)) {
rank_size = AnfAlgo::GetNodeAttr<int64_t>(cnode, kAttrRankSize);
}
int64_t fusion = 0;
if (AnfAlgo::HasNodeAttr(kAttrFusion, cnode)) {
fusion = AnfAlgo::GetNodeAttr<int64_t>(cnode, kAttrFusion);
}
ulong loop_size = hccl_data_type_list_.size();
if (op_name == kAllGatherOpName && fusion >= 1) {
loop_size *= rank_size;
}
for (ulong i = 0; i < loop_size; ++i) {
if (!HcomUtil::GetHcclOpSize(hccl_data_type_list_[0], hccl_kernel_output_shape_list_[i], &size)) {
MS_LOG(ERROR) << "GetHcclOpOutputSize failed";
}
output_size_list_.push_back(size);

7
mindspore/ccsrc/backend/kernel_compiler/hccl/hcom_util.cc
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@ -127,7 +127,12 @@ bool HcomUtil::GetHcomCount(const AnfNodePtr &anf_node, const vector<HcclDataTyp
total_size = total_size + block_size;
} else {
if (AnfAlgo::GetCNodeName(anf_node) == kAllGatherOpName) {
block_size = input_size;
auto cnode = anf_node->cast<CNodePtr>();
if (AnfAlgo::HasNodeAttr(kAttrFusion, cnode) && AnfAlgo::GetNodeAttr<int64_t>(anf_node, kAttrFusion)) {
block_size = (input_size + align_size - 1 + filled_size) / align_size * align_size;
} else {
block_size = input_size;
}
} else {
block_size = (input_size + align_size - 1 + filled_size) / align_size * align_size;
}

66
mindspore/ccsrc/backend/optimizer/ascend/enhancer/concat_outputs_for_all_gather.cc
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@ -20,58 +20,33 @@
namespace mindspore {
namespace opt {
namespace {
void AddOutputs(const AnfNodePtr &node, int64_t rank_size) {
MS_EXCEPTION_IF_NULL(node);
auto origin_abstract = node->abstract();
MS_EXCEPTION_IF_NULL(origin_abstract);
auto tuple_abstract = origin_abstract->cast<abstract::AbstractTuplePtr>();
MS_EXCEPTION_IF_NULL(tuple_abstract);
auto &origin_abstracts = tuple_abstract->elements();
AbstractBasePtrList abstract_list;
std::vector<TypeId> outputs_device_type;
std::vector<std::string> outputs_device_format;
for (int64_t i = 0; i < rank_size; ++i) {
for (size_t j = 0; j < origin_abstracts.size(); ++j) {
abstract_list.push_back(origin_abstracts[j]);
outputs_device_type.push_back(AnfAlgo::GetOutputDeviceDataType(node, j));
outputs_device_format.push_back(AnfAlgo::GetOutputFormat(node, j));
}
}
// Update abstract
auto new_abstracts = std::make_shared<abstract::AbstractTuple>(abstract_list);
node->set_abstract(new_abstracts);
// Update kernel build info
auto builder =
std::make_shared<kernel::KernelBuildInfo::KernelBuildInfoBuilder>(AnfAlgo::GetSelectKernelBuildInfo(node));
builder->SetOutputsDeviceType(outputs_device_type);
builder->SetOutputsFormat(outputs_device_format);
AnfAlgo::SetSelectKernelBuildInfo(builder->Build(), node.get());
}
} // namespace
AnfNodePtr ConcatOutputsForAllGather::InsertConcatForOutput(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
const std::vector<AnfNodePtr> &new_tuple_getitems,
int64_t rank_size) const {
MS_EXCEPTION_IF_NULL(func_graph);
std::vector<AnfNodePtr> make_tuple_inputs;
std::vector<AnfNodePtr> make_tuple_inputs{NewValueNode(std::make_shared<Primitive>(prim::kPrimMakeTuple->name()))};
size_t inputs_size = AnfAlgo::GetInputTensorNum(node);
for (size_t i = 0; i < inputs_size; ++i) {
for (size_t j = 0, idx = i; j < LongToSize(rank_size); ++j, idx += inputs_size) {
std::vector<AnfNodePtr> concat_inputs{NewValueNode(std::make_shared<Primitive>(prim::kPrimConcat->name()))};
std::vector<AnfNodePtr> concat_inputs{NewValueNode(std::make_shared<Primitive>(prim::kPrimConcat->name()))};
for (size_t j = 0, idx = i; j < IntToSize(rank_size); ++j, idx += inputs_size) {
concat_inputs.push_back(new_tuple_getitems[idx]);
auto concat = func_graph->NewCNode(concat_inputs);
MS_EXCEPTION_IF_NULL(concat);
MS_EXCEPTION_IF_NULL(new_tuple_getitems[idx]);
concat->set_abstract(new_tuple_getitems[idx]->abstract());
AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(static_cast<int64_t>(0)), concat);
AnfAlgo::SetNodeAttr(kAttrInputNums, MakeValue(rank_size), concat);
std::vector<int64_t> dyn_input_size{rank_size};
AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_size), concat);
kernel_select_->SelectKernel(concat);
make_tuple_inputs.push_back(concat);
}
auto concat = func_graph->NewCNode(concat_inputs);
MS_EXCEPTION_IF_NULL(concat);
MS_EXCEPTION_IF_NULL(new_tuple_getitems[i]);
auto dtypes = {AnfAlgo::GetOutputInferDataType(new_tuple_getitems[i], 0)};
std::vector<size_t> shape = AnfAlgo::GetOutputInferShape(new_tuple_getitems[i], 0);
shape[0] *= rank_size;
std::vector<std::vector<size_t>> shapes = {shape};
AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, concat.get());
AnfAlgo::SetNodeAttr(kAttrAxis, MakeValue(static_cast<int64_t>(0)), concat);
AnfAlgo::SetNodeAttr(kAttrInputNums, MakeValue(rank_size), concat);
std::vector<int64_t> dyn_input_size{rank_size};
AnfAlgo::SetNodeAttr(kAttrDynInputSizes, MakeValue(dyn_input_size), concat);
kernel_select_->SelectKernel(concat);
make_tuple_inputs.push_back(concat);
}
auto make_tuple = func_graph->NewCNode(make_tuple_inputs);
return make_tuple;
}
@ -94,8 +69,11 @@ const AnfNodePtr ConcatOutputsForAllGather::Process(const FuncGraphPtr &func_gra
if (fusion <= 0) {
return nullptr;
}
if (AnfAlgo::HasNodeAttr("fused", cnode)) {
return nullptr;
}
AnfAlgo::SetNodeAttr("fused", MakeValue(true), node);
auto rank_size = AnfAlgo::GetNodeAttr<int64_t>(node, kAttrRankSize);
AddOutputs(node, rank_size);
std::vector<AnfNodePtr> new_outputs;
CreateMultipleOutputsOfAnfNode(func_graph, node, AnfAlgo::GetOutputTensorNum(node), &new_outputs);
return InsertConcatForOutput(func_graph, node, new_outputs, rank_size);

43
mindspore/ccsrc/backend/optimizer/pass/communication_op_fusion.cc
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@ -46,15 +46,23 @@ kernel::KernelBuildInfoPtr GenerateKernelBuildInfo(const CommunicationOpInfo &co
kernel::KernelBuildInfo::KernelBuildInfoBuilder builder;
for (size_t idx = start_index; idx <= end_index; ++idx) {
auto cnode = communication_op_info.communication_op_nodes[idx];
int64_t rank_size = 1;
if (AnfAlgo::HasNodeAttr(kAttrRankSize, cnode) && AnfAlgo::GetCNodeName(cnode) == kAllGatherOpName) {
rank_size = AnfAlgo::GetNodeAttr<int64_t>(cnode, kAttrRankSize);
}
MS_EXCEPTION_IF_NULL(cnode);
for (size_t input_index = 0; input_index < AnfAlgo::GetInputTensorNum(cnode); ++input_index) {
inputs_device_format.push_back(AnfAlgo::GetInputFormat(cnode, input_index));
inputs_device_type.push_back(AnfAlgo::GetInputDeviceDataType(cnode, input_index));
}
for (size_t output_index = 0; output_index < AnfAlgo::GetOutputTensorNum(cnode); ++output_index) {
outputs_device_format.push_back(AnfAlgo::GetOutputFormat(cnode, output_index));
outputs_device_type.push_back(AnfAlgo::GetOutputDeviceDataType(cnode, output_index));
outputs_shape.push_back(AnfAlgo::GetOutputInferShape(cnode, output_index));
for (size_t rank_index = 0; rank_index < IntToSize(rank_size); ++rank_index) {
for (size_t output_index = 0; output_index < AnfAlgo::GetOutputTensorNum(cnode); ++output_index) {
outputs_device_format.push_back(AnfAlgo::GetOutputFormat(cnode, output_index));
outputs_device_type.push_back(AnfAlgo::GetOutputDeviceDataType(cnode, output_index));
std::vector<size_t> shape = AnfAlgo::GetOutputInferShape(cnode, output_index);
shape[0] /= rank_size;
outputs_shape.push_back(AnfAlgo::GetOutputInferShape(cnode, output_index));
}
}
builder.SetFusionType(AnfAlgo::GetFusionType(cnode));
builder.SetProcessor(AnfAlgo::GetProcessor(cnode));
@ -182,18 +190,27 @@ AnfNodePtr CommunicationOpFusion::CreateFusedCommunicationOp(const FuncGraphPtr
auto kernel_info = std::make_shared<device::KernelInfo>();
MS_EXCEPTION_IF_NULL(kernel_info);
fused_node->set_kernel_info(kernel_info);
AbstractBasePtrList abstract_list;
for (size_t idx = start_index; idx <= end_index; ++idx) {
auto cnode = communication_op_info.communication_op_nodes[idx];
MS_EXCEPTION_IF_NULL(cnode);
abstract_list.push_back(cnode->abstract());
auto final_node = communication_op_info.communication_op_nodes[end_index];
size_t node_num = end_index - start_index + 1;
int64_t rank_size = 1;
if (AnfAlgo::HasNodeAttr(kAttrRankSize, final_node) && AnfAlgo::GetCNodeName(final_node) == kAllGatherOpName) {
rank_size = AnfAlgo::GetNodeAttr<int64_t>(final_node, kAttrRankSize);
}
size_t output_num = node_num * rank_size;
std::vector<TypeId> dtypes(output_num, AnfAlgo::GetOutputInferDataType(final_node, 0));
std::vector<std::vector<size_t>> shapes;
for (size_t i = 0; i < IntToSize(rank_size); ++i) {
for (size_t idx = start_index; idx <= end_index; ++idx) {
auto cnode = communication_op_info.communication_op_nodes[idx];
MS_EXCEPTION_IF_NULL(cnode);
std::vector<size_t> shape = AnfAlgo::GetOutputInferShape(cnode, 0);
shape[0] /= rank_size;
shapes.push_back(shape);
}
}
AnfAlgo::SetOutputInferTypeAndShape(dtypes, shapes, fused_node.get());
auto kernel_build_info = GenerateKernelBuildInfo(communication_op_info, start_index, end_index);
AnfAlgo::SetSelectKernelBuildInfo(kernel_build_info, fused_node.get());
auto abstract_tuple = std::make_shared<abstract::AbstractTuple>(abstract_list);
MS_EXCEPTION_IF_NULL(abstract_tuple);
fused_node->set_abstract(abstract_tuple);
auto final_node = communication_op_info.communication_op_nodes[end_index];
AnfAlgo::CopyNodeAttr(kAttrFusion, final_node, fused_node);
AnfAlgo::CopyNodeAttr(kAttrOp, final_node, fused_node);
AnfAlgo::CopyNodeAttr(kAttrGroup, final_node, fused_node);

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