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Some Shape Related Fusions #19832

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merged 5 commits into from
Mar 12, 2024
Merged

Some Shape Related Fusions #19832

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faa8259
shape related fusion
centwang Mar 8, 2024
5803bef
add ut
centwang Mar 8, 2024
e94effd
resolve comments, fix build error
centwang Mar 9, 2024
4b19595
refactor
centwang Mar 10, 2024
a2bb985
remove shape gather constant folding for this pr
centwang Mar 11, 2024
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55 changes: 55 additions & 0 deletions onnxruntime/core/optimizer/common_subexpression_elimination.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@
#include "common_subexpression_elimination.h"
#include "core/optimizer/utils.h"
#include "core/graph/graph_utils.h"
#include "core/framework/tensorprotoutils.h"

#include <memory>
#include <type_traits>
Expand Down Expand Up @@ -170,6 +171,32 @@ bool AreRangesEqual(const Range& lhs, const Range& rhs) {
std::equal(lhs.begin(), lhs.end(), rhs.begin());
}

// Check if two tensor attributes are equal scalar tensors, mainly to support ConstantOfShape Op.
// Currently support float, float16 and int64 data types, and requires the data are raw data in TensorProto.
bool AreScalarTensorAttributeEqual(const ONNX_NAMESPACE::TensorProto& lhs_t, const ONNX_NAMESPACE::TensorProto& rhs_t) {
if (!(utils::HasDataType(lhs_t) && utils::HasDataType(rhs_t) && lhs_t.data_type() == rhs_t.data_type() &&
(lhs_t.data_type() == onnx::TensorProto_DataType_FLOAT ||
lhs_t.data_type() == onnx::TensorProto_DataType_FLOAT16 ||
lhs_t.data_type() == onnx::TensorProto_DataType_INT64) &&
lhs_t.dims_size() == 1 && rhs_t.dims_size() == 1 && lhs_t.dims()[0] == 1 && rhs_t.dims()[0] == 1 &&
utils::HasRawData(lhs_t) && utils::HasRawData(rhs_t))) {
return false;
}
const void* lhs_value = lhs_t.raw_data().data();
const void* rhs_value = rhs_t.raw_data().data();
switch (lhs_t.data_type()) {
case onnx::TensorProto_DataType_FLOAT:
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return *reinterpret_cast<const float*>(lhs_value) == *reinterpret_cast<const float*>(rhs_value);
case onnx::TensorProto_DataType_FLOAT16:
return *reinterpret_cast<const MLFloat16*>(lhs_value) == *reinterpret_cast<const MLFloat16*>(rhs_value);
case onnx::TensorProto_DataType_INT64:
return *reinterpret_cast<const int64_t*>(lhs_value) == *reinterpret_cast<const int64_t*>(rhs_value);
default:
break;
}
return false;
}

bool AreEqual(const ONNX_NAMESPACE::AttributeProto& lhs, const ONNX_NAMESPACE::AttributeProto& rhs) {
if (&lhs == &rhs) {
return true;
Expand All @@ -193,6 +220,7 @@ bool AreEqual(const ONNX_NAMESPACE::AttributeProto& lhs, const ONNX_NAMESPACE::A
case onnx::AttributeProto_AttributeType_STRINGS:
return AreRangesEqual(lhs.strings(), rhs.strings());
case onnx::AttributeProto_AttributeType_TENSOR:
return AreScalarTensorAttributeEqual(lhs.t(), rhs.t());
case onnx::AttributeProto_AttributeType_GRAPH:
case onnx::AttributeProto_AttributeType_SPARSE_TENSOR:
case onnx::AttributeProto_AttributeType_TYPE_PROTO:
Expand All @@ -207,6 +235,31 @@ bool AreEqual(const ONNX_NAMESPACE::AttributeProto& lhs, const ONNX_NAMESPACE::A
return false;
}

// Support scalar float/int64/fp16 tensor attribute only for now, and requires data is raw data in TensorProto.
std::size_t GetTensorAttributeHash(const ONNX_NAMESPACE::TensorProto& attr_t) {
std::size_t hash = 0;
if (utils::HasDataType(attr_t) && attr_t.dims_size() == 1 && attr_t.dims()[0] == 1 && utils::HasRawData(attr_t)) {
int data_type = attr_t.data_type();
switch (data_type) {
case onnx::TensorProto_DataType_FLOAT:
UpdateHash(data_type, hash);
UpdateHash(*reinterpret_cast<const float*>(attr_t.raw_data().data()), hash);
break;
case onnx::TensorProto_DataType_FLOAT16:
UpdateHash(data_type, hash);
UpdateHash(static_cast<float>(*reinterpret_cast<const MLFloat16*>(attr_t.raw_data().data())), hash);
break;
case onnx::TensorProto_DataType_INT64:
UpdateHash(data_type, hash);
UpdateHash(*reinterpret_cast<const int64_t*>(attr_t.raw_data().data()), hash);
break;
default:
break;
}
}
return hash;
}

std::size_t GetAttributeHash(const ONNX_NAMESPACE::AttributeProto& attr) {
std::size_t hash = 0;
UpdateHash(
Expand All @@ -233,6 +286,8 @@ std::size_t GetAttributeHash(const ONNX_NAMESPACE::AttributeProto& attr) {
UpdateHashWithContainer(attr.strings(), hash);
break;
case onnx::AttributeProto_AttributeType_TENSOR:
UpdateHash(attr.t(), &GetTensorAttributeHash, hash);
break;
case onnx::AttributeProto_AttributeType_GRAPH:
case onnx::AttributeProto_AttributeType_SPARSE_TENSOR:
case onnx::AttributeProto_AttributeType_TYPE_PROTO:
Expand Down
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