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mindspore
提交 79d1e465 编写于 作者: M mindspore-ci-bot 提交者： Gitee
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!702 add buffer fusion bnupdate eltwise pass 

Merge pull request !702 from Etone.Chan/r0.2
上级 a04e8486 4e39354d
展开全部 隐藏空白更改
内联 并排
Showing with 202 addition and 1409 deletion
mindspore/_akg/__init__.py
浏览文件 @ 79d1e465
......@@ -16,6 +16,8 @@
from __future__ import absolute_import as _abs
import sys
import os
from .op_build import op_build
from .message import compilewithjson
def AKGAddPath():
"""_akg add path."""
......@@ -58,6 +60,3 @@ class AKGMetaPathLoader:
sys.meta_path.insert(0, AKGMetaPathFinder())
from .op_build import op_build
from .message import compilewithjson
mindspore/ccsrc/kernel/tbe/tbe_kernel_build.cc
浏览文件 @ 79d1e465
......@@ -722,8 +722,7 @@ bool TbeKernelBuild::GenFusionComputeOutputJson(const mindspore::CNodePtr &cnode
std::vector<nlohmann::json> *output_desc_list) {
auto output_size = AnfAlgo::GetOutputTensorNum(cnode);
if (AnfAlgo::HasNodeAttr(kAttrOutputUsedNum, cnode)) {
// wait anther pr: auto output_used_nums = AnfAlgo::GetNodeAttr<std::vector<int>>(cnode, kAttrOutputUsedNum);
auto output_used_nums = {SizeToInt(AnfAlgo::GetNodeAttr<std::size_t>(cnode, kAttrOutputUsedNum))};
auto output_used_nums = AnfAlgo::GetNodeAttr<std::vector<int>>(cnode, kAttrOutputUsedNum);
MS_LOG(INFO) << "This node's output has been reused, node name: " << cnode->fullname_with_scope();
if (output_used_nums.size() != output_size) {
MS_LOG(INFO) << "Fusion error: output tenor num(" << output_size << ")"
......
mindspore/ccsrc/pre_activate/ascend/buffer_fusion/buffer_fusion.cc
浏览文件 @ 79d1e465
......@@ -17,6 +17,7 @@
#include <vector>
#include <tuple>
#include <utility>
#include <unordered_set>
#include <unordered_map>
#include <deque>
......@@ -282,11 +283,17 @@ kernel::KernelBuildInfoPtr CreateFusionOpKernelInfo(const std::vector<AnfNodePtr
// outputs format and data type
std::vector<std::string> outputs_format;
std::vector<TypeId> outputs_data_type;
for (size_t index = 0; index < outputs_list.size(); ++index) {
for (size_t idx = 0; idx < AnfAlgo::GetOutputTensorNum(outputs_list[index]); ++idx) {
auto kernel_with_index = AnfAlgo::VisitKernel(outputs_list[index], idx);
outputs_format.push_back(AnfAlgo::GetOutputFormat(kernel_with_index.first, kernel_with_index.second));
outputs_data_type.push_back(AnfAlgo::GetOutputDeviceDataType(kernel_with_index.first, kernel_with_index.second));
for (const auto &output : outputs_list) {
if (AnfAlgo::GetCNodeName(output) == prim::kPrimTupleGetItem->name()) {
auto tuple_getitem = output->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(tuple_getitem);
outputs_format.push_back(AnfAlgo::GetOutputFormat(
tuple_getitem->input(1), IntToSize(GetValue<int>(GetValueNode(tuple_getitem->input(2))))));
outputs_data_type.push_back(AnfAlgo::GetOutputDeviceDataType(
tuple_getitem->input(1), IntToSize(GetValue<int>(GetValueNode(tuple_getitem->input(2))))));
} else {
outputs_format.push_back(AnfAlgo::GetOutputFormat(output, 0));
outputs_data_type.push_back(AnfAlgo::GetOutputDeviceDataType(output, 0));
}
}
builder.SetInputsFormat(inputs_format);
......@@ -320,32 +327,35 @@ AnfNodePtr CreateTupleGetItem(const AnfNodePtr &buffer_fusion_kernel, session::K
return tuple_item;
}
void ReplaceOldNode(const std::vector<AnfNodePtr> &outputs_list, const AnfNodePtr &buffer_fusion_kernel,
session::KernelGraph *kernel_graph) {
void ReplaceInputNodeInOtherFusionScope(std::unordered_map<int32_t, BufferFusionInfo_t> *buffer_fusion_infos,
int32_t fusion_id, const AnfNodePtr &output_item,
const AnfNodePtr &replace_item) {
for (int32_t id = fusion_id + 1; id <= SizeToInt(buffer_fusion_infos->size()); ++id) {
auto itr = std::find((*buffer_fusion_infos)[id].inputs_list.begin(), (*buffer_fusion_infos)[id].inputs_list.end(),
output_item);
if (itr != (*buffer_fusion_infos)[id].inputs_list.end()) {
MS_LOG(DEBUG) << "replace input of other pattern, id = " << id;
*itr = replace_item;
}
}
}
void ReplaceOldNode(std::unordered_map<int32_t, BufferFusionInfo_t> *buffer_fusion_infos, int32_t fusion_id,
const AnfNodePtr &buffer_fusion_kernel, session::KernelGraph *kernel_graph) {
MS_EXCEPTION_IF_NULL(kernel_graph);
auto manager = kernel_graph->manager();
MS_EXCEPTION_IF_NULL(manager);
if (outputs_list.size() == 1) { // single output
(void)manager->Replace(outputs_list[0], buffer_fusion_kernel);
auto buffer_fusion_info = (*buffer_fusion_infos)[fusion_id];
if (buffer_fusion_info.outputs_list.size() == 1) { // single output
(void)manager->Replace(buffer_fusion_info.outputs_list[0], buffer_fusion_kernel);
ReplaceInputNodeInOtherFusionScope(buffer_fusion_infos, fusion_id, buffer_fusion_info.outputs_list[0],
buffer_fusion_kernel);
} else { // multiple output
size_t real_idx = 0;
for (size_t index = 0; index < outputs_list.size(); ++index) {
if (AnfAlgo::GetOutputTensorNum(outputs_list[index]) == 1) {
auto tuple_item = CreateTupleGetItem(buffer_fusion_kernel, kernel_graph, real_idx++);
(void)manager->Replace(outputs_list[index], tuple_item);
} else {
std::vector<AnfNodePtr> make_tuple_inputs;
AbstractBasePtrList abstract_list;
make_tuple_inputs.push_back(NewValueNode(prim::kPrimMakeTuple));
for (size_t idx = 0; idx < AnfAlgo::GetOutputTensorNum(outputs_list[index]); ++idx) {
auto tuple_item = CreateTupleGetItem(buffer_fusion_kernel, kernel_graph, real_idx++);
abstract_list.push_back(tuple_item->abstract());
make_tuple_inputs.push_back(tuple_item);
}
AnfNodePtr make_tuple = kernel_graph->NewCNode(make_tuple_inputs);
make_tuple->set_abstract(std::make_shared<abstract::AbstractTuple>(abstract_list));
(void)manager->Replace(outputs_list[index], make_tuple);
}
for (size_t index = 0; index < buffer_fusion_info.outputs_list.size(); ++index) {
auto tuple_item = CreateTupleGetItem(buffer_fusion_kernel, kernel_graph, index);
(void)manager->Replace(buffer_fusion_info.outputs_list[index], tuple_item);
ReplaceInputNodeInOtherFusionScope(buffer_fusion_infos, fusion_id, buffer_fusion_info.outputs_list[index],
tuple_item);
}
}
}
......@@ -406,38 +416,67 @@ void CheckCurrentNodeIsInput(const CNodePtr &node, const int32_t &cur_fusion_id,
}
}
void InsertNode(const AnfNodePtr &node, std::vector<AnfNodePtr> *list) {
MS_EXCEPTION_IF_NULL(list);
if (std::find(list->begin(), list->end(), node) == list->end()) {
(void)list->insert(list->end(), node);
void GetFusionScopeComputeNodeList(session::KernelGraph *kernel_graph,
std::unordered_map<int32_t, BufferFusionInfo_t> *buffer_fusion_infos) {
MS_EXCEPTION_IF_NULL(buffer_fusion_infos);
auto nodes = TopoSort(kernel_graph->get_return());
for (auto &node : nodes) {
MS_EXCEPTION_IF_NULL(node);
if (AnfAlgo::IsRealCNodeKernel(node) && AnfAlgo::HasNodeAttr(kOpAttrFusionId, node)) {
auto fusion_id = AnfAlgo::GetNodeAttr<int32_t>(node, kOpAttrFusionId);
(*buffer_fusion_infos)[fusion_id].anf_nodes.push_back(node);
}
}
}
void CheckCurrentNodeIsOutput(const CNodePtr &node, const int32_t &cur_fusion_id,
std::unordered_map<int32_t, BufferFusionInfo_t> *buffer_fusion_infos) {
MS_EXCEPTION_IF_NULL(node);
void GetFusionScopeOutputNodeList(session::KernelGraph *kernel_graph,
std::unordered_map<int32_t, BufferFusionInfo_t> *buffer_fusion_infos) {
MS_EXCEPTION_IF_NULL(kernel_graph);
MS_EXCEPTION_IF_NULL(buffer_fusion_infos);
for (auto &input : node->inputs()) {
MS_EXCEPTION_IF_NULL(input);
if (AnfAlgo::IsRealCNodeKernel(input) && AnfAlgo::HasNodeAttr(kOpAttrFusionId, input)) {
auto fusion_id = AnfAlgo::GetNodeAttr<int32_t>(input, kOpAttrFusionId);
if (buffer_fusion_infos->find(fusion_id) == buffer_fusion_infos->end()) {
BufferFusionInfo_t buffer_fusion_info;
(*buffer_fusion_infos)[fusion_id] = buffer_fusion_info;
}
if (fusion_id != cur_fusion_id) {
InsertNode(input, &((*buffer_fusion_infos)[fusion_id].outputs_list));
}
} else if (input->isa<CNode>()) {
for (auto &input_in : input->cast<CNodePtr>()->inputs()) {
if (AnfAlgo::IsRealCNodeKernel(input_in) && AnfAlgo::HasNodeAttr(kOpAttrFusionId, input_in)) {
auto fusion_id = AnfAlgo::GetNodeAttr<int32_t>(input_in, kOpAttrFusionId);
if (buffer_fusion_infos->find(fusion_id) == buffer_fusion_infos->end()) {
BufferFusionInfo_t buffer_fusion_info;
(*buffer_fusion_infos)[fusion_id] = buffer_fusion_info;
auto manager = kernel_graph->manager();
MS_EXCEPTION_IF_NULL(manager);
for (auto &buffer_fusion_info : *buffer_fusion_infos) {
auto fusion_id = buffer_fusion_info.first;
auto fusion_info = buffer_fusion_info.second;
for (const auto &node : fusion_info.anf_nodes) {
if (AnfAlgo::GetOutputTensorNum(node) == 1) {
for (auto use_node : manager->node_users()[node]) {
if (std::find(fusion_info.anf_nodes.begin(), fusion_info.anf_nodes.end(), use_node.first) ==
fusion_info.anf_nodes.end()) {
(*buffer_fusion_infos)[fusion_id].outputs_list.push_back(node);
break;
}
}
} else {
int prev_idx = 0;
std::vector<AnfNodePtr> tuple_getitem_nodes;
std::transform(manager->node_users()[node].begin(), manager->node_users()[node].end(),
std::back_inserter(tuple_getitem_nodes),
[](const std::pair<AnfNodePtr, int> &use_node) { return use_node.first; });
std::sort(tuple_getitem_nodes.begin(), tuple_getitem_nodes.end(),
[](const AnfNodePtr &node1, const AnfNodePtr &node2) {
auto getitem1 = node1->cast<CNodePtr>();
auto getitem2 = node2->cast<CNodePtr>();
auto output_idx1 = GetValue<int>(GetValueNode(getitem1->input(2)));
auto output_idx2 = GetValue<int>(GetValueNode(getitem2->input(2)));
return output_idx1 < output_idx2;
});
for (auto getitem : tuple_getitem_nodes) {
auto getitem_ptr = getitem->cast<CNodePtr>();
auto input2 = getitem_ptr->input(2);
auto output_idx = GetValue<int>(GetValueNode(input2));
for (int stub_idx = prev_idx; stub_idx < output_idx; ++stub_idx) {
auto stub_node = CreateTupleGetItem(node, kernel_graph, IntToSize(stub_idx));
(*buffer_fusion_infos)[fusion_id].outputs_list.push_back(stub_node);
}
if (fusion_id != cur_fusion_id) {
InsertNode(input_in, &((*buffer_fusion_infos)[fusion_id].outputs_list));
prev_idx = output_idx + 1;
for (auto item_use_node : manager->node_users()[getitem]) {
if (std::find(fusion_info.anf_nodes.begin(), fusion_info.anf_nodes.end(), item_use_node.first) ==
fusion_info.anf_nodes.end()) {
(*buffer_fusion_infos)[fusion_id].outputs_list.push_back(getitem);
break;
}
}
}
}
......@@ -445,15 +484,72 @@ void CheckCurrentNodeIsOutput(const CNodePtr &node, const int32_t &cur_fusion_id
}
}
void GetFusionScopeNodeList(const session::KernelGraph &kernel_graph,
std::unordered_map<int32_t, BufferFusionInfo_t> *buffer_fusion_infos) {
MS_EXCEPTION_IF_NULL(buffer_fusion_infos);
auto nodes = TopoSort(kernel_graph.get_return());
for (auto &node : nodes) {
MS_EXCEPTION_IF_NULL(node);
if (AnfAlgo::IsRealCNodeKernel(node) && AnfAlgo::HasNodeAttr(kOpAttrFusionId, node)) {
auto fusion_id = AnfAlgo::GetNodeAttr<int32_t>(node, kOpAttrFusionId);
(*buffer_fusion_infos)[fusion_id].anf_nodes.push_back(node);
void MatchConvBnreduce(const CNodePtr &cnode, const session::KernelGraph &kernel_graph,
std::unordered_set<AnfNodePtr> *fused_set, FusedNodeRecord *candidate_fusion) {
MS_EXCEPTION_IF_NULL(cnode);
MS_EXCEPTION_IF_NULL(fused_set);
MS_EXCEPTION_IF_NULL(candidate_fusion);
auto manager = kernel_graph.manager();
MS_EXCEPTION_IF_NULL(manager);
auto conv = cnode->input(1);
if (conv->isa<CNode>() && AnfAlgo::GetCNodeName(conv) == prim::kPrimConv2D->name()) {
std::vector<int> output_used_num{SizeToInt(manager->node_users()[conv].size())};
AnfAlgo::SetNodeAttr(kAttrOutputUsedNum, MakeValue(output_used_num), conv);
std::unordered_set<AnfNodePtr> record{cnode, conv};
candidate_fusion->push_back(record);
fused_set->insert(record.begin(), record.end());
}
}
void MatchBnupdateRelu(const CNodePtr &cnode, const AnfNodePtr &relu_input, const session::KernelGraph &kernel_graph,
std::unordered_set<AnfNodePtr> *fused_set, FusedNodeRecord *candidate_fusion) {
MS_EXCEPTION_IF_NULL(cnode);
MS_EXCEPTION_IF_NULL(fused_set);
MS_EXCEPTION_IF_NULL(candidate_fusion);
auto manager = kernel_graph.manager();
MS_EXCEPTION_IF_NULL(manager);
auto getitem = relu_input->cast<CNodePtr>();
auto bnupdate = getitem->input(1);
if (bnupdate->isa<CNode>() && AnfAlgo::GetCNodeName(bnupdate) == kBNTrainingUpdateOpName) {
std::vector<int> output_used_num(AnfAlgo::GetOutputTensorNum(bnupdate), 0);
for (auto out_getitem : manager->node_users()[bnupdate]) {
auto out_getitem_ptr = out_getitem.first->cast<CNodePtr>();
auto input2 = out_getitem_ptr->input(2);
auto output_idx = GetValue<int>(GetValueNode(input2));
output_used_num[output_idx] = SizeToInt(manager->node_users()[out_getitem.first].size());
}
AnfAlgo::SetNodeAttr(kAttrOutputUsedNum, MakeValue(output_used_num), bnupdate);
std::unordered_set<AnfNodePtr> record{cnode, bnupdate};
candidate_fusion->push_back(record);
fused_set->insert(record.begin(), record.end());
}
}
void MatchBnupdateAddRelu(const CNodePtr &cnode, const AnfNodePtr &relu_input, const session::KernelGraph &kernel_graph,
std::unordered_set<AnfNodePtr> *fused_set, FusedNodeRecord *candidate_fusion) {
MS_EXCEPTION_IF_NULL(cnode);
MS_EXCEPTION_IF_NULL(fused_set);
MS_EXCEPTION_IF_NULL(candidate_fusion);
auto manager = kernel_graph.manager();
MS_EXCEPTION_IF_NULL(manager);
auto add = relu_input->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(add);
auto tuple_getitem = add->input(1);
if (tuple_getitem->isa<CNode>() && AnfAlgo::GetCNodeName(tuple_getitem) == prim::kPrimTupleGetItem->name()) {
auto getitem = tuple_getitem->cast<CNodePtr>();
auto bnupdate = getitem->input(1);
if (bnupdate->isa<CNode>() && AnfAlgo::GetCNodeName(bnupdate) == kBNTrainingUpdateOpName) {
std::vector<int> output_used_num(AnfAlgo::GetOutputTensorNum(bnupdate), 0);
for (auto out_getitem : manager->node_users()[bnupdate]) {
auto out_getitem_ptr = out_getitem.first->cast<CNodePtr>();
auto input2 = out_getitem_ptr->input(2);
auto output_idx = GetValue<int>(GetValueNode(input2));
output_used_num[output_idx] = SizeToInt(manager->node_users()[out_getitem.first].size());
}
AnfAlgo::SetNodeAttr(kAttrOutputUsedNum, MakeValue(output_used_num), bnupdate);
std::unordered_set<AnfNodePtr> record{cnode, relu_input, bnupdate};
candidate_fusion->push_back(record);
fused_set->insert(record.begin(), record.end());
}
}
}
......@@ -470,15 +566,14 @@ void MatchOpNamePattern(const session::KernelGraph &kernel_graph, std::unordered
auto cnode = node->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(cnode);
if (AnfAlgo::GetCNodeName(cnode) == kBNTrainingReduceOpName) {
auto conv = cnode->input(1);
if (conv->isa<CNode>() && AnfAlgo::GetCNodeName(conv) == prim::kPrimConv2D->name()) {
auto manager = kernel_graph.manager();
MS_EXCEPTION_IF_NULL(manager);
auto &users = manager->node_users();
AnfAlgo::SetNodeAttr(kAttrOutputUsedNum, MakeValue(users[conv].size()), conv);
std::unordered_set<AnfNodePtr> record({cnode, conv});
candidate_fusion->push_back(record);
fused_set->insert(record.begin(), record.end());
MatchConvBnreduce(cnode, kernel_graph, fused_set, candidate_fusion);
} else if (AnfAlgo::GetCNodeName(cnode) == kReluV2OpName ||
AnfAlgo::GetCNodeName(cnode) == prim::kPrimRelu->name()) {
auto relu_input = cnode->input(1);
if (relu_input->isa<CNode>() && AnfAlgo::GetCNodeName(relu_input) == prim::kPrimTensorAdd->name()) {
MatchBnupdateAddRelu(cnode, relu_input, kernel_graph, fused_set, candidate_fusion);
} else if (relu_input->isa<CNode>() && AnfAlgo::GetCNodeName(relu_input) == prim::kPrimTupleGetItem->name()) {
MatchBnupdateRelu(cnode, relu_input, kernel_graph, fused_set, candidate_fusion);
}
}
}
......@@ -536,27 +631,23 @@ void MatchFusionTypePattern(const session::KernelGraph &kernel_graph, std::unord
}
} // namespace
void BufferFusion::GetBufferFusionInfo(const session::KernelGraph &kernel_graph,
void BufferFusion::GetBufferFusionInfo(session::KernelGraph *kernel_graph,
std::unordered_map<int32_t, BufferFusionInfo_t> *buffer_fusion_infos) const {
MS_EXCEPTION_IF_NULL(buffer_fusion_infos);
std::vector<AnfNodePtr> node_list = TopoSort(kernel_graph.get_return());
std::vector<AnfNodePtr> node_list = TopoSort(kernel_graph->get_return());
for (auto &node : node_list) {
if (!AnfAlgo::IsRealCNodeKernel(node)) {
continue;
}
int32_t cur_fusion_id = -1;
auto cnode = node->cast<CNodePtr>();
MS_EXCEPTION_IF_NULL(cnode);
if (AnfAlgo::HasNodeAttr(kOpAttrFusionId, cnode)) {
cur_fusion_id = AnfAlgo::GetNodeAttr<int32_t>(cnode, kOpAttrFusionId);
auto cur_fusion_id = AnfAlgo::GetNodeAttr<int32_t>(cnode, kOpAttrFusionId);
CheckCurrentNodeIsInput(cnode, cur_fusion_id, buffer_fusion_infos);
}
// Check if current node is output
CheckCurrentNodeIsOutput(cnode, cur_fusion_id, buffer_fusion_infos);
}
GetFusionScopeNodeList(kernel_graph, buffer_fusion_infos);
GetFusionScopeComputeNodeList(kernel_graph, buffer_fusion_infos);
GetFusionScopeOutputNodeList(kernel_graph, buffer_fusion_infos);
for (auto &buffer_fusion_info : *buffer_fusion_infos) {
buffer_fusion_info.second.kernel_build_info =
CreateFusionOpKernelInfo(buffer_fusion_info.second.inputs_list_in, buffer_fusion_info.second.inputs_list,
......@@ -569,7 +660,7 @@ bool BufferFusion::FuseBufferFusionPattern(session::KernelGraph *kernel_graph) c
bool change = false;
std::unordered_map<int32_t, BufferFusionInfo_t> buffer_fusion_infos;
buffer_fusion_infos.clear();
GetBufferFusionInfo(*kernel_graph, &buffer_fusion_infos);
GetBufferFusionInfo(kernel_graph, &buffer_fusion_infos);
std::vector<mindspore::kernel::FusionScopeInfo> fusion_scope_infos;
for (auto &buffer_fusion_info : buffer_fusion_infos) {
......@@ -600,7 +691,7 @@ bool BufferFusion::FuseBufferFusionPattern(session::KernelGraph *kernel_graph) c
MS_LOG(DEBUG) << "fusion id: " << fusion_id << ", fusion op compiling failed";
continue;
}
change = ReplaceFusionOp(buffer_fusion_infos[fusion_id], kernel_mods[fusion_id], kernel_graph);
change = ReplaceFusionOp(&buffer_fusion_infos, fusion_id, kernel_mods[fusion_id], kernel_graph);
}
MS_LOG(DEBUG) << "End Buffer Fusion";
return change;
......@@ -630,8 +721,10 @@ bool BufferFusion::MatchBufferFusionPattern(const session::KernelGraph &kernel_g
return true;
}
bool BufferFusion::ReplaceFusionOp(const BufferFusionInfo_t &buffer_fusion_info, const kernel::KernelModPtr &kernel_ptr,
bool BufferFusion::ReplaceFusionOp(std::unordered_map<int32_t, BufferFusionInfo_t> *buffer_fusion_infos,
int32_t fusion_id, const kernel::KernelModPtr &kernel_ptr,
session::KernelGraph *kernel_graph) const {
auto buffer_fusion_info = (*buffer_fusion_infos)[fusion_id];
auto buffer_fusion = CreateFusionOp(buffer_fusion_info.inputs_list, buffer_fusion_info.outputs_list,
buffer_fusion_info.anf_nodes, kernel_graph);
AnfAlgo::SetSelectKernelBuildInfo(buffer_fusion_info.kernel_build_info, buffer_fusion.get());
......@@ -651,7 +744,7 @@ bool BufferFusion::ReplaceFusionOp(const BufferFusionInfo_t &buffer_fusion_info,
AnfAlgo::SetOutputInferTypeAndShape(types, shapes, buffer_fusion.get());
AnfAlgo::SetKernelMod(kernel_ptr, buffer_fusion.get());
// replace node
ReplaceOldNode(buffer_fusion_info.outputs_list, buffer_fusion, kernel_graph);
ReplaceOldNode(buffer_fusion_infos, fusion_id, buffer_fusion, kernel_graph);
return true;
}
......
mindspore/ccsrc/pre_activate/ascend/buffer_fusion/buffer_fusion.h
浏览文件 @ 79d1e465
......@@ -44,10 +44,10 @@ class BufferFusion : public Pass {
bool Run(const FuncGraphPtr &graph) override;
private:
void GetBufferFusionInfo(const session::KernelGraph &kernel_graph,
void GetBufferFusionInfo(session::KernelGraph *kernel_graph,
std::unordered_map<int32_t, BufferFusionInfo_t> *buffer_fusion_infos) const;
bool ReplaceFusionOp(const BufferFusionInfo_t &buffer_fusion_info, const kernel::KernelModPtr &kernel_ptr,
session::KernelGraph *kernel_graph) const;
bool ReplaceFusionOp(std::unordered_map<int32_t, BufferFusionInfo_t> *buffer_fusion_infos, int32_t fusion_id,
const kernel::KernelModPtr &kernel_ptr, session::KernelGraph *kernel_graph) const;
bool MatchBufferFusionPattern(const session::KernelGraph &kernel_graph) const;
bool FuseBufferFusionPattern(session::KernelGraph *kernel_graph) const;
};
......
mindspore/nn/optim/ftrl.py
浏览文件 @ 79d1e465
......@@ -104,7 +104,7 @@ class FTRL(Optimizer):
self.lr_power = lr_power
self.reciprocal_scale = 1.0 / loss_scale
self.weight_decay = weight_decay
self.decay_tf = tuple((lambda:True)() for x in self.parameters)
self.decay_tf = tuple((lambda: True)() for x in self.parameters)
self.hyper_map = C.HyperMap()
self.opt = P.ApplyFtrl(use_locking=use_locking)
self.one = Tensor(1, mstype.int32)
......@@ -118,5 +118,6 @@ class FTRL(Optimizer):
if self.reciprocal_scale != 1.0:
grads = self.hyper_map(F.partial(grad_scale, self.reciprocal_scale), grads)
lr = self.learning_rate
success = self.hyper_map(F.partial(ftrl_opt, self.opt, lr, self.l1, self.l2, self.lr_power), linear, grads, params, moments)
success = self.hyper_map(F.partial(ftrl_opt, self.opt, lr, self.l1, self.l2, self.lr_power), linear, grads,
params, moments)
return success
mindspore/ops/operations/nn_ops.py
浏览文件 @ 79d1e465
......@@ -2063,7 +2063,7 @@ class LSTM(PrimitiveWithInfer):
return (y_shape, h_shape, c_shape, reserved_shape, state_shape)
def infer_dtype(self, x_dtype, h_dtype, c_dtype, w_dtype):
args = {'x': x_dtype, 'h': h_dtype, 'c': c_dtype, 'w': w_dtype}
args = {'x': x_dtype, 'h': h_dtype, 'c': c_dtype, 'w': w_dtype}
validator.check_tensor_type_same(args, (mstype.float32, mstype.float16), self.name)
return (x_dtype, x_dtype, x_dtype, x_dtype, x_dtype)
......@@ -2670,8 +2670,8 @@ class ConfusionMulGrad(PrimitiveWithInfer):
"""
@prim_attr_register
def __init__(self, axis = (), keep_dims = False):
self.init_prim_io_names(inputs = ["input0", "input1", "input2"], outputs = ["output0", "output1"])
def __init__(self, axis=(), keep_dims=False):
self.init_prim_io_names(inputs=["input0", "input1", "input2"], outputs=["output0", "output1"])
self.axis_ = validator.check_value_type("axis", axis, [int, tuple, list], self.name)
self.keep_dims_ = validator.check_value_type("keep_dims", keep_dims, [bool], self.name)
......
mindspore/train/callback.py
浏览文件 @ 79d1e465
......@@ -689,7 +689,7 @@ class TimeMonitor(Callback):
def epoch_begin(self, run_context):
self.epoch_time = time.time()
def epoch_end(self, run_context):
epoch_mseconds = (time.time() - self.epoch_time) * 1000
per_step_mseconds = epoch_mseconds / self.data_size
......@@ -701,4 +701,3 @@ class TimeMonitor(Callback):
def step_end(self, run_context):
step_mseconds = (time.time() - self.step_time) * 1000
print('step time', step_mseconds, flush=True)
mindspore/train/model.py
浏览文件 @ 79d1e465
......@@ -130,17 +130,17 @@ class Model:
if self._optimizer:
if self._loss_scale_manager_set:
network = amp.build_train_network(network,
self._optimizer,
self._loss_fn,
level=self._amp_level,
loss_scale_manager=self._loss_scale_manager,
keep_batchnorm_fp32=self._keep_bn_fp32)
self._optimizer,
self._loss_fn,
level=self._amp_level,
loss_scale_manager=self._loss_scale_manager,
keep_batchnorm_fp32=self._keep_bn_fp32)
else:
network = amp.build_train_network(network,
self._optimizer,
self._loss_fn,
level=self._amp_level,
keep_batchnorm_fp32=self._keep_bn_fp32)
self._optimizer,
self._loss_fn,
level=self._amp_level,
keep_batchnorm_fp32=self._keep_bn_fp32)
elif self._loss_fn:
network = nn.WithLossCell(network, self._loss_fn)
# If need to check if loss_fn is not None, but optimizer is None
......@@ -280,7 +280,7 @@ class Model:
# remove later to deal with loop sink
if need_wrap:
self._train_network = nn.DataWrapper(self._train_network, *(dataset_helper.types_shapes()),
train_dataset.__ME_INITED__)
train_dataset.__ME_INITED__)
cb_params.train_network = self._train_network
self._train_network.set_train()
......@@ -449,7 +449,7 @@ class Model:
# remove later to deal with loop sink
if need_wrap:
self._eval_network = nn.DataWrapper(self._eval_network, *(dataset_helper.types_shapes()),
valid_dataset.__ME_INITED__)
valid_dataset.__ME_INITED__)
self._eval_network.set_train(mode=False)
self._eval_network.phase = 'eval'
......
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