m53297601
/
graphengine
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from '6eeed0e394'
${ noResults }
graphengine/ge/hybrid/executor/worker/shape_inference_engine.cc

315 lines
14 KiB
Raw Blame History

/**
* Copyright 2019-2020 Huawei Technologies Co., Ltd
*
* Licensed 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.
*/
#include "hybrid/executor/worker/shape_inference_engine.h"
#include "graph/shape_refiner.h"
#include "graph/utils/node_utils.h"
#include "graph/utils/tensor_utils.h"
#include "graph/utils/type_utils.h"
#include "common/math/math_util.h"
#include "hybrid/node_executor/node_executor.h"
namespace ge {
namespace {
const int kAlignment = 32;
}
namespace hybrid {
ShapeInferenceEngine::ShapeInferenceEngine(GraphExecutionContext *execution_context, SubgraphContext *subgraph_context)
: execution_context_(execution_context),
subgraph_context_(subgraph_context) {
}
Status ShapeInferenceEngine::InferShape(NodeState &node_state) {
// Wait for all input shape become valid
GE_CHK_STATUS_RET_NOLOG(node_state.GetShapeInferenceState().AwaitShapesReady(*execution_context_));
auto &node_item = *node_state.GetNodeItem();
// Wait for "const input nodes" if node's shape inference function requires any.
// Even if output shape is static, there are cases that the const-input will be used in OpTiling and Execution
GE_CHK_STATUS_RET_NOLOG(AwaitDependentNodes(node_state));
if (node_item.is_output_shape_static) {
return SUCCESS;
}
if (node_item.fused_subgraph != nullptr) {
GE_CHK_STATUS_RET_NOLOG(InferShapeForSubgraph(node_item, *node_item.fused_subgraph));
GE_CHK_STATUS_RET_NOLOG(CalcOutputTensorSizes(node_item));
return SUCCESS;
}
// Skip shape inference for node of type DEPEND_COMPUTE
if (node_item.shape_inference_type == DEPEND_COMPUTE) {
GELOGD("[%s] Skipping node with unknown shape type DEPEND_COMPUTE", node_item.NodeName().c_str());
return SUCCESS;
}
// Clear shape range in case shape inference func forgot to do it
if (node_item.shape_inference_type == DEPEND_SHAPE_RANGE) {
// in case InferFunc forgot to reset output shape
for (auto &output_desc : node_item.op_desc->GetAllOutputsDescPtr()) {
output_desc->SetShape(GeShape({UNKNOWN_DIM_NUM}));
}
}
// Do shape inference
GELOGD("[%s] Start to invoke InferShapeAndType", node_item.NodeName().c_str());
{
RECORD_SHAPE_INFERENCE_EVENT(execution_context_, node_item.NodeName().c_str(), "[InferShapeAndType] Start");
GE_CHK_STATUS_RET(ShapeRefiner::InferShapeAndTypeForRunning(node_item.node, true),
"Invoke InferShapeAndType failed.");
RECORD_SHAPE_INFERENCE_EVENT(execution_context_, node_item.NodeName().c_str(), "[InferShapeAndType] End");
}
// update output tensor sizes after shape inference
// error if shape is still unknown and not of type DEPEND_SHAPE_RANGE
RECORD_COMPILE_EVENT(execution_context_, node_item.NodeName().c_str(), "[CalcOpRunningParam] Start");
GE_CHK_STATUS_RET_NOLOG(CalcOutputTensorSizes(node_item, node_item.shape_inference_type == DEPEND_SHAPE_RANGE));
RECORD_COMPILE_EVENT(execution_context_, node_item.NodeName().c_str(), "[CalcOpRunningParam] End");
GELOGD("[%s] [HybridTrace] After shape inference. Node = %s",
node_item.NodeName().c_str(),
node_item.DebugString().c_str());
GELOGD("[%s] InferShapeAndType finished successfully.", node_item.NodeName().c_str());
return SUCCESS;
}
Status ShapeInferenceEngine::AwaitDependentNodes(NodeState &node_state) {
auto &node_item = *node_state.GetNodeItem();
for (auto &src_node : node_item.dependents_for_shape_inference) {
GELOGI("[%s] Start to wait for data dependent node: %s",
node_item.NodeName().c_str(),
src_node->GetName().c_str());
RECORD_SHAPE_INFERENCE_EVENT(execution_context_,
node_item.NodeName().c_str(),
"[AwaitNodeDone] [%s] Start",
src_node->GetName().c_str());
HYBRID_CHK_STATUS_RET(subgraph_context_->Await(src_node), "[%s] Await node failed.", src_node->GetName().c_str());
RECORD_SHAPE_INFERENCE_EVENT(execution_context_,
node_item.NodeName().c_str(),
"[AwaitNodeDone] [%s] End",
src_node->GetName().c_str());
GELOGI("[%s] Done waiting node.", src_node->GetName().c_str());
}
return SUCCESS;
}
Status ShapeInferenceEngine::PropagateOutputShapes(const NodeItem &node_item) {
if (node_item.is_output_shape_static) {
return SUCCESS;
}
// output shape will not be valid until compute is done.
bool shape_is_future =
node_item.shape_inference_type == DEPEND_SHAPE_RANGE || node_item.shape_inference_type == DEPEND_COMPUTE;
GELOGD("[%s] Start to propagate output shapes. shape_type = %d",
node_item.NodeName().c_str(),
node_item.shape_inference_type);
RECORD_SHAPE_INFERENCE_EVENT(execution_context_, node_item.NodeName().c_str(), "[PropagateOutputShapes] Start");
// propagate each output
for (int i = 0; i < node_item.num_outputs; ++i) {
auto output_desc = node_item.op_desc->MutableOutputDesc(i);
auto &output_nodes = node_item.outputs[i];
// propagate output to all sub-inputs
for (auto &dst_input_index_and_node : output_nodes) {
auto &dst_node_item = dst_input_index_and_node.second;
auto dst_node_state = subgraph_context_->GetOrCreateNodeState(dst_node_item);
GE_CHECK_NOTNULL(dst_node_state);
GELOGI("[%s] Update dst node [%s], input index = %d",
node_item.NodeName().c_str(),
dst_node_item->NodeName().c_str(),
dst_input_index_and_node.first);
// in case type 3 and 4, shape will be valid after computing is done
auto &infer_state = dst_node_state->GetShapeInferenceState();
if (shape_is_future) {
ShapeFuture future(node_item.node, i, subgraph_context_);
infer_state.UpdateInputShapeFuture(dst_input_index_and_node.first,
std::move(future));
} else {
GE_CHK_STATUS_RET_NOLOG(infer_state.UpdateInputShape(dst_input_index_and_node.first, *output_desc));
}
}
}
RECORD_SHAPE_INFERENCE_EVENT(execution_context_, node_item.NodeName().c_str(), "[PropagateOutputShapes] End");
GELOGD("[%s] Propagating output shapes finished successfully.", node_item.NodeName().c_str());
return SUCCESS;
}
Status ShapeInferenceEngine::InferShapeForSubgraph(const NodeItem &node_item, const FusedSubgraph &fused_subgraph) {
GELOGD("[%s] Start to infer shape by fused subgraph", node_item.NodeName().c_str());
for (auto &it : fused_subgraph.input_mapping) {
auto parent_tensor_desc = node_item.MutableInputDesc(it.first);
GE_CHECK_NOTNULL(parent_tensor_desc);
GELOGD("Start to update shape by input[%d]", it.first);
GELOGD("Update shape to [%s]", parent_tensor_desc->GetShape().ToString().c_str());
GELOGD("Update original shape to [%s]", parent_tensor_desc->GetOriginShape().ToString().c_str());
for (auto &tensor_desc : it.second) {
tensor_desc->SetShape(parent_tensor_desc->GetShape());
tensor_desc->SetOriginShape(parent_tensor_desc->GetOriginShape());
}
}
for (auto &node : fused_subgraph.nodes) {
GELOGD("[%s] Start to invoke InferShapeAndType", node->GetName().c_str());
GE_CHK_STATUS_RET(ShapeRefiner::InferShapeAndType(node));
GELOGD("[%s] Done invoking InferShapeAndType", node->GetName().c_str());
GE_CHK_STATUS_RET(UpdatePeerNodeShape(*node),
"[%s] Failed to update shapes of peer node.",
node->GetName().c_str());
}
for (auto &it : fused_subgraph.output_mapping) {
int parent_output_idx = it.first;
const auto &op_desc = it.second;
GELOGD("Update parent output[%d] by [%s]", parent_output_idx, op_desc->GetName().c_str());
auto input_desc = op_desc->MutableInputDesc(0);
GE_CHECK_NOTNULL(input_desc);
auto parent_output_tensor_desc = node_item.MutableOutputDesc(parent_output_idx);
GE_CHECK_NOTNULL(parent_output_tensor_desc);
GELOGD("Update shape to [%s]", input_desc->GetShape().ToString().c_str());
GELOGD("Update original shape to [%s]", input_desc->GetOriginShape().ToString().c_str());
parent_output_tensor_desc->SetOriginShape(input_desc->GetOriginShape());
parent_output_tensor_desc->SetShape(input_desc->GetShape());
}
GELOGD("[%s] Done shape inference by subgraph successfully.", node_item.NodeName().c_str());
return SUCCESS;
}
Status ShapeInferenceEngine::UpdatePeerNodeShape(const Node &node) {
auto op_desc = node.GetOpDesc();
for (const auto &out_anchor : node.GetAllOutDataAnchors()) {
auto output_tensor = op_desc->MutableOutputDesc(out_anchor->GetIdx());
for (const auto &peer_anchor : out_anchor->GetPeerInDataAnchors()) {
auto peer_node = peer_anchor->GetOwnerNode();
GE_CHECK_NOTNULL(peer_node);
auto peer_op_desc = peer_node->GetOpDesc();
GE_CHECK_NOTNULL(peer_op_desc);
auto peer_input_desc = peer_op_desc->MutableInputDesc(peer_anchor->GetIdx());
if (peer_input_desc == nullptr) {
GELOGE(GRAPH_FAILED, "peer_input_desc is nullptr");
continue;
}
GELOGI("Peer input op desc name is %s, need to flush: shape size is %zu, datatype is %d, original datatype is %d",
peer_anchor->GetOwnerNode()->GetOpDesc()->GetName().c_str(),
output_tensor->GetShape().GetDimNum(), output_tensor->GetDataType(),
output_tensor->GetOriginDataType());
peer_input_desc->SetOriginShape(output_tensor->GetOriginShape());
peer_input_desc->SetShape(output_tensor->GetShape());
GELOGI("Peer input op desc name is %s, shape size is %zu, datatype is %d, original datatype is %d",
peer_anchor->GetOwnerNode()->GetOpDesc()->GetName().c_str(),
peer_input_desc->GetShape().GetDimNum(), peer_input_desc->GetDataType(),
peer_input_desc->GetOriginDataType());
}
}
return SUCCESS;
}
Status ShapeInferenceEngine::CanonicalizeShape(GeTensorDesc &tensor_desc,
std::vector<int64_t> &shape,
bool fallback_with_range) {
const auto &tensor_shape = tensor_desc.MutableShape();
if (tensor_shape.IsUnknownShape()) {
if (!fallback_with_range) {
GELOGE(INTERNAL_ERROR, "Output shape is still unknown after shape inference. shape = [%s]",
tensor_shape.ToString().c_str());
return INTERNAL_ERROR;
}
GELOGD("Calc output size by range");
std::vector<std::pair<int64_t, int64_t>> shape_range;
GE_CHK_GRAPH_STATUS_RET(tensor_desc.GetShapeRange(shape_range), "Failed to get shape range");
if (shape_range.size() != shape.size()) {
GELOGE(INTERNAL_ERROR, "Number of shape ranges (%zu) mismatches that of dims (%zu)",
shape_range.size(),
shape.size());
return INTERNAL_ERROR;
}
for (size_t dim_index = 0; dim_index < shape.size(); ++dim_index) {
if (shape[dim_index] == ge::UNKNOWN_DIM) {
shape[dim_index] = shape_range[dim_index].second;
}
}
GELOGD("After canonicalization, shape = [%s], before = [%s]",
GeShape(shape).ToString().c_str(),
tensor_shape.ToString().c_str());
}
return SUCCESS;
}
Status ShapeInferenceEngine::CalcTensorSize(DataType data_type,
const std::vector<int64_t> &shape,
int64_t &tensor_size) {
GELOGD("To calc tensor size by shape = [%s]", GeShape(shape).ToString().c_str());
uint32_t type_size;
if (!TypeUtils::GetDataTypeLength(data_type, type_size)) {
GELOGE(INTERNAL_ERROR, "Failed to get data type size");
return INTERNAL_ERROR;
}
tensor_size = type_size;
for (const auto &dim : shape) {
GE_CHECK_GE(dim, 0);
GE_CHK_STATUS_RET(Int64MulCheckOverflow(tensor_size, dim),
"Shape size overflow, shape = [%s]",
GeShape(shape).ToString().c_str());
tensor_size *= dim;
}
GE_CHK_STATUS_RET(CheckInt64AddOverflow(tensor_size, kAlignment - 1),
"Tensor size is too large: %ld, shape = [%s]",
tensor_size,
GeShape(shape).ToString().c_str());
tensor_size = (tensor_size + kAlignment - 1) / kAlignment * kAlignment;
return SUCCESS;
}
Status ShapeInferenceEngine::CalcOutputTensorSizes(const NodeItem &node_item, bool fallback_with_range) {
auto op_desc = node_item.GetOpDesc();
for (size_t output_index = 0; output_index < op_desc->GetOutputsSize(); ++output_index) {
auto tensor_desc = op_desc->MutableOutputDesc(output_index);
GE_CHECK_NOTNULL(tensor_desc);
const auto &shape = tensor_desc->MutableShape();
// modify on copy
auto dims = shape.GetDims();
GE_CHK_STATUS_RET(CanonicalizeShape(*tensor_desc, dims, fallback_with_range),
"[%s] Failed to canonicalize shape for output %zu",
node_item.NodeName().c_str(),
output_index);
int64_t tensor_size;
GE_CHK_STATUS_RET(CalcTensorSize(tensor_desc->GetDataType(), dims, tensor_size),
"[%s] Failed to calc tensor size for output %zu",
node_item.NodeName().c_str(),
output_index);
GELOGD("[%s] Tensor size of output %zu = %ld", node_item.NodeName().c_str(), output_index, tensor_size);
(void) TensorUtils::SetSize(*tensor_desc, tensor_size);
}
return SUCCESS;
}
} // namespace hybrid
} // namespace ge
Reference in new issue View Git Blame Copy Permalink