@@ -122,7 +122,7 @@ Status ShapeInferenceState::AwaitShapesReady(const GraphExecutionContext &contex | |||
GE_CHECK_NOTNULL(input_desc); | |||
int64_t tensor_size = -1; | |||
(void) TensorUtils::GetSize(*src_tensor_desc, tensor_size); | |||
GELOGD("[%s] Update input shape [%u] with shape: [%s] and ori_shape: [%s]", | |||
GELOGD("[%s] Update input shape [%u] with shape: [%s] and ori_shape: [%s], index = %zu", | |||
node_item.NodeName().c_str(), | |||
idx, | |||
src_tensor_desc->GetShape().ToString().c_str(), | |||
@@ -71,7 +71,7 @@ Status ShapeInferenceEngine::InferShape(NodeState &node_state) { | |||
std::lock_guard<std::mutex> lk(mu_); | |||
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."); | |||
"Invoke InferShapeAndType failed."); | |||
RECORD_SHAPE_INFERENCE_EVENT(execution_context_, node_item.NodeName().c_str(), "[InferShapeAndType] End"); | |||
} | |||
@@ -229,66 +229,87 @@ Status ShapeInferenceEngine::UpdatePeerNodeShape(const Node &node) { | |||
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(); | |||
auto dim_num = dims.size(); | |||
if (shape.IsUnknownShape()) { | |||
if (!fallback_with_range) { | |||
GELOGE(INTERNAL_ERROR, "[%s] Shape of output[%zu] is still unknown after shape inference. shape = [%s]", | |||
node_item.NodeName().c_str(), | |||
output_index, | |||
shape.ToString().c_str()); | |||
return INTERNAL_ERROR; | |||
} | |||
GELOGD("[%s] Calc output[%zu] size by range", node_item.NodeName().c_str(), output_index); | |||
std::vector<std::pair<int64_t, int64_t>> shape_range; | |||
GE_CHK_GRAPH_STATUS_RET(tensor_desc->GetShapeRange(shape_range), | |||
"[$s] Failed to get shape range for output: %zu", | |||
node_item.NodeName().c_str(), | |||
output_index); | |||
if (shape_range.size() != dim_num) { | |||
GELOGE(INTERNAL_ERROR, "[%s] Number of shape ranges (%zu) mismatches that of dims (%zu), index = %zu", | |||
node_item.NodeName().c_str(), | |||
shape_range.size(), | |||
dim_num, | |||
output_index); | |||
return INTERNAL_ERROR; | |||
} | |||
for (size_t dim_index = 0; dim_index < dim_num; ++dim_index) { | |||
if (dims[dim_index] == ge::UNKNOWN_DIM) { | |||
dims[dim_index] = shape_range[dim_index].second; | |||
} | |||
} | |||
} | |||
uint32_t type_size = 0; | |||
if (!TypeUtils::GetDataTypeLength(tensor_desc->GetDataType(), type_size)) { | |||
GELOGE(INTERNAL_ERROR, "Failed to get data type size"); | |||
return INTERNAL_ERROR; | |||
} | |||
int64_t tensor_size = type_size; | |||
for (const auto &dim : dims) { | |||
GE_CHECK_GE(dim, 0); | |||
GE_CHK_STATUS_RET(Int64MulCheckOverflow(tensor_size, dim), | |||
"[%s] Shape size overflow, shape = [%s]", | |||
node_item.NodeName().c_str(), | |||
shape.ToString().c_str()); | |||
tensor_size *= dim; | |||
} | |||
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); | |||
GE_CHK_STATUS_RET(CheckInt64AddOverflow(tensor_size, kAlignment - 1), | |||
"[%s] Output[%zu] Tensor size too large, shape = [%s]", | |||
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, | |||
shape.ToString().c_str()); | |||
tensor_size = (tensor_size + kAlignment - 1) / kAlignment * kAlignment; | |||
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); | |||
} | |||
@@ -37,6 +37,8 @@ class ShapeInferenceEngine { | |||
static Status CalcOutputTensorSizes(const NodeItem &node_item, bool fallback_with_range = false); | |||
private: | |||
static Status CanonicalizeShape(GeTensorDesc &tensor_desc, std::vector<int64_t> &shape, bool fallback_with_range); | |||
static Status CalcTensorSize(DataType data_type, const std::vector<int64_t> &shape, int64_t &tensor_size); | |||
static Status UpdatePeerNodeShape(const Node &node); | |||
Status AwaitDependentNodes(NodeState &node_state); | |||
@@ -127,12 +127,7 @@ Status NodeItem::Create(const NodePtr &node, std::unique_ptr<NodeItem> &node_ite | |||
return SUCCESS; | |||
} | |||
Status NodeItem::Init() { | |||
GE_CHECK_LE(op_desc->GetInputsSize(), INT32_MAX); | |||
GE_CHECK_LE(op_desc->GetOutputsSize(), INT32_MAX); | |||
num_inputs = static_cast<int>(op_desc->GetInputsSize()); | |||
num_outputs = static_cast<int>(op_desc->GetOutputsSize()); | |||
void NodeItem::ResolveOptionalInputs() { | |||
if (op_desc->GetAllInputsSize() != op_desc->GetInputsSize()) { | |||
has_optional_inputs = true; | |||
for (size_t i = 0; i < op_desc->GetAllInputsSize(); ++i) { | |||
@@ -144,7 +139,18 @@ Status NodeItem::Init() { | |||
} | |||
} | |||
} | |||
} | |||
Status NodeItem::InitInputsAndOutputs() { | |||
GE_CHECK_LE(op_desc->GetInputsSize(), INT32_MAX); | |||
GE_CHECK_LE(op_desc->GetOutputsSize(), INT32_MAX); | |||
num_inputs = static_cast<int>(op_desc->GetInputsSize()); | |||
num_outputs = static_cast<int>(op_desc->GetOutputsSize()); | |||
ResolveOptionalInputs(); | |||
return SUCCESS; | |||
} | |||
Status NodeItem::ResolveDynamicState() { | |||
(void) AttrUtils::GetBool(op_desc, ATTR_NAME_FORCE_UNKNOWN_SHAPE, is_dynamic); | |||
GELOGD("node name = %s, is_dynamic = %d.", this->node_name.c_str(), is_dynamic); | |||
if (!is_dynamic) { | |||
@@ -152,42 +158,54 @@ Status NodeItem::Init() { | |||
"[%s] Failed to get shape status.", | |||
node->GetName().c_str()); | |||
} | |||
return SUCCESS; | |||
} | |||
if (is_dynamic) { | |||
for (int i = 0; i < num_inputs; ++i) { | |||
const auto &input_desc = MutableInputDesc(i); | |||
GE_CHECK_NOTNULL(input_desc); | |||
if (input_desc->MutableShape().IsUnknownShape()) { | |||
is_input_shape_static_.push_back(false); | |||
} else { | |||
num_static_input_shapes++; | |||
is_input_shape_static_.push_back(true); | |||
GELOGD("[%s] The shape of input[%d] is static. shape = [%s]", | |||
NodeName().c_str(), i, input_desc->MutableShape().ToString().c_str()); | |||
} | |||
Status NodeItem::ResolveStaticInputsAndOutputs() { | |||
for (int i = 0; i < num_inputs; ++i) { | |||
const auto &input_desc = MutableInputDesc(i); | |||
GE_CHECK_NOTNULL(input_desc); | |||
if (input_desc->MutableShape().IsUnknownShape()) { | |||
is_input_shape_static_.push_back(false); | |||
} else { | |||
num_static_input_shapes++; | |||
is_input_shape_static_.push_back(true); | |||
GELOGD("[%s] The shape of input[%d] is static. shape = [%s]", | |||
NodeName().c_str(), i, input_desc->MutableShape().ToString().c_str()); | |||
} | |||
} | |||
for (int i = 0; i < num_outputs; ++i) { | |||
const auto &output_desc = op_desc->MutableOutputDesc(i); | |||
GE_CHECK_NOTNULL(output_desc); | |||
if (output_desc->MutableShape().IsUnknownShape()) { | |||
is_output_shape_static = false; | |||
break; | |||
} | |||
for (int i = 0; i < num_outputs; ++i) { | |||
const auto &output_desc = op_desc->MutableOutputDesc(i); | |||
GE_CHECK_NOTNULL(output_desc); | |||
if (output_desc->MutableShape().IsUnknownShape()) { | |||
is_output_shape_static = false; | |||
break; | |||
} | |||
} | |||
if (is_output_shape_static) { | |||
GE_CHK_STATUS_RET_NOLOG(ShapeInferenceEngine::CalcOutputTensorSizes(*this)); | |||
} | |||
if (is_output_shape_static) { | |||
GE_CHK_STATUS_RET_NOLOG(ShapeInferenceEngine::CalcOutputTensorSizes(*this)); | |||
} | |||
return SUCCESS; | |||
} | |||
if (IsControlOp() || node_type == PARTITIONEDCALL) { | |||
shape_inference_type = DEPEND_COMPUTE; | |||
} else { | |||
int32_t unknown_shape_type_val = 0; | |||
(void) AttrUtils::GetInt(op_desc, ::ge::ATTR_NAME_UNKNOWN_SHAPE_TYPE, unknown_shape_type_val); | |||
shape_inference_type = static_cast<UnknowShapeOpType>(unknown_shape_type_val); | |||
} | |||
void NodeItem::ResolveUnknownShapeType() { | |||
if (IsControlOp() || node_type == PARTITIONEDCALL) { | |||
shape_inference_type = DEPEND_COMPUTE; | |||
} else { | |||
int32_t unknown_shape_type_val = 0; | |||
(void) AttrUtils::GetInt(op_desc, ::ge::ATTR_NAME_UNKNOWN_SHAPE_TYPE, unknown_shape_type_val); | |||
shape_inference_type = static_cast<UnknowShapeOpType>(unknown_shape_type_val); | |||
} | |||
} | |||
Status NodeItem::Init() { | |||
GE_CHK_STATUS_RET_NOLOG(InitInputsAndOutputs()); | |||
GE_CHK_STATUS_RET_NOLOG(ResolveDynamicState()); | |||
if (is_dynamic) { | |||
ResolveUnknownShapeType(); | |||
GE_CHK_STATUS_RET_NOLOG(ResolveStaticInputsAndOutputs()); | |||
GE_CHK_STATUS_RET(ParseFusedSubgraph(*this), "[%s] Failed to parse fused subgraph", node_name.c_str()); | |||
} | |||
@@ -103,6 +103,11 @@ struct NodeItem { | |||
private: | |||
explicit NodeItem(NodePtr node); | |||
Status Init(); | |||
Status InitInputsAndOutputs(); | |||
void ResolveOptionalInputs(); | |||
Status ResolveDynamicState(); | |||
Status ResolveStaticInputsAndOutputs(); | |||
void ResolveUnknownShapeType(); | |||
std::vector<bool> is_input_shape_static_; | |||
std::vector<uint32_t> input_desc_indices_; | |||