Changes for 0.23.0 release
- update serialization_lib hash
- PAD:
1. make padding as an attribute instead of tensor.
2. add pad_const_int (for non-float type) / pad_const_fp (for float type)
- TRANSPOSE: make perm as an attribute instead of tensor
- TABLE: make table as attribute instead of tensor
- update examples/ tests
Signed-off-by: Kevin Cheng <kevin.cheng@arm.com>
Change-Id: Iddc446db4b356ba2f36ea4a79b7220b9cfc2aa4e
diff --git a/reference_model/src/ops/data_layout.cc b/reference_model/src/ops/data_layout.cc
index 674227b..05a11e0 100644
--- a/reference_model/src/ops/data_layout.cc
+++ b/reference_model/src/ops/data_layout.cc
@@ -128,10 +128,11 @@
uint64_t id_)
: GraphNode(sgt_, Op_PAD, id_)
{
- setRequiredOperands(2, 1);
+ setRequiredOperands(1, 1);
setRequiredRank(0, 6);
INIT_QINFO(Pad);
+ INIT_ATTRIBUTE(Pad);
}
template <int Rank, DType Dtype>
@@ -159,9 +160,22 @@
return 1;
}
- in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
- out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
- paddings = dynamic_cast<TosaReference::TensorTemplate<ETensor2<int32_t>>*>(inputs[1]);
+ in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
+ out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
+ ASSERT_MEM(in && out);
+
+ // padding in spec is 2D array in shape of [Rank, 2]
+ // Reference model implement this as 1D array of [Rank * 2], with ordering:
+ // [Rank0_front, Rank0_back, Rank1_front, Rank1_back, ..., Rank(N-1)_front, Rank(N-1)_back]
+ ERROR_IF(attribute->padding().size() != (Rank * 2), "OpPad: padding length needs to be (rank(input1) * 2)");
+
+ for (int i = 0; i < Rank; i++)
+ {
+ int32_t pad_front = attribute->padding()[2 * i];
+ int32_t pad_back = attribute->padding()[2 * i + 1];
+ ERROR_IF((pad_front < 0) || (pad_back < 0), "OpPad: padding can't be smaller than 0");
+ paddings_array[i] = std::make_pair(pad_front, pad_back);
+ }
if (this->qinfo && Dtype != DType_INT8)
{
@@ -174,18 +188,24 @@
template <int Rank, DType Dtype>
int OpPad<Rank, Dtype>::eval()
{
- // Move this to
- for (int i = 0; i < Rank; i++)
+ InEigenType pad_value = 0;
+
+ switch (Dtype)
{
- ERROR_IF((paddings->getTensor()(i, 0) < 0) || (paddings->getTensor()(i, 1) < 0),
- "OpPad: padding can't be smaller than 0");
- paddings_array[i] = std::make_pair(paddings->getTensor()(i, 0), paddings->getTensor()(i, 1));
+ case DType_BOOL:
+ case DType_INT8:
+ case DType_INT16:
+ case DType_INT32:
+ pad_value = (InEigenType)attribute->pad_const_int();
+ break;
+ case DType_FLOAT:
+ pad_value = (InEigenType)attribute->pad_const_fp();
+ break;
}
- InEigenType pad_value = 0;
- if (this->qinfo)
+ if (this->qinfo && Dtype == DType_INT8)
{
- pad_value = (InEigenType)this->qinfo->input_zp();
+ pad_value += (InEigenType)this->qinfo->input_zp();
}
this->out->getTensor() = this->in->getTensor().pad(this->paddings_array, pad_value);
@@ -602,8 +622,10 @@
uint64_t id_)
: GraphNode(sgt_, Op_TRANSPOSE, id_)
{
- setRequiredOperands(2, 1);
+ setRequiredOperands(1, 1);
setRequiredRank(0, 6);
+
+ INIT_ATTRIBUTE(Transpose);
}
template <int Rank, DType Dtype>
@@ -634,9 +656,10 @@
return 1;
}
- in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
- out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
- perm_tensor = dynamic_cast<TosaReference::TensorTemplate<ETensor1<int32_t>>*>(inputs[1]);
+ in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
+ out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
+
+ ASSERT_MEM(in && out);
return 0;
}
@@ -646,7 +669,7 @@
{
for (int32_t d = 0; d < Rank; d++)
{
- perm_array[d] = this->perm_tensor->getTensor().data()[d];
+ perm_array[d] = attribute->perm()[d];
ERROR_IF(perm_array[d] < 0 or perm_array[d] >= Rank, "OpTranspose: index out of boundary");
}
diff --git a/reference_model/src/ops/data_layout.h b/reference_model/src/ops/data_layout.h
index 9f44fc7..bad88e4 100644
--- a/reference_model/src/ops/data_layout.h
+++ b/reference_model/src/ops/data_layout.h
@@ -63,8 +63,8 @@
Eigen::array<std::pair<ptrdiff_t, ptrdiff_t>, Rank> paddings_array;
TosaReference::TensorTemplate<TIn>* in;
TosaReference::TensorTemplate<TOut>* out;
- TosaReference::TensorTemplate<Eigen::Tensor<int32_t, 2>>* paddings;
TosaPadQuantInfo* qinfo;
+ TosaPadAttribute* attribute;
};
template <int InRank, int OutRank, DType Dtype>
@@ -207,8 +207,8 @@
protected:
Eigen::array<int, Rank> perm_array;
+ TosaTransposeAttribute* attribute;
TosaReference::TensorTemplate<TIn>* in;
- TosaReference::TensorTemplate<ETensor1<int32_t>>* perm_tensor;
TosaReference::TensorTemplate<TOut>* out;
};
}; // namespace TosaReference
diff --git a/reference_model/src/ops/ewise_binary.cc b/reference_model/src/ops/ewise_binary.cc
index 6808604..415cd1c 100644
--- a/reference_model/src/ops/ewise_binary.cc
+++ b/reference_model/src/ops/ewise_binary.cc
@@ -490,8 +490,10 @@
uint64_t id_)
: GraphNode(sgt_, Op_TABLE, id_)
{
- setRequiredOperands(2, 1);
+ setRequiredOperands(1, 1);
setRequiredRank(0, 6);
+
+ INIT_ATTRIBUTE(Table);
}
template <int Rank, DType InDtype>
@@ -509,36 +511,18 @@
return 1;
}
- if (inputs[1]->getRank() != 1)
+ ERROR_IF(inputs[0]->getDtype() != InDtype, "OpTable: Unexpected input type");
+ ERROR_IF(attribute->table().size() != TableNumEntries, "OpTable: table attribute size must be %u", TableNumEntries);
+
+ for (uint32_t i = 0; i < TableNumEntries; i++)
{
- printNodeValidationError("OpTable: Table must be rank 1 tensor");
- return 1;
+ table[i] = (TableEigenType)attribute->table()[i];
}
- if (inputs[0]->getDtype() == DType_INT8)
- {
- if (inputs[1]->getElementCount() != 256 || inputs[1]->getDtype() != DType_INT8)
- {
- printNodeValidationError("OpTable: Table must be INT8[256] if input is INT8");
- return 1;
- }
- ERROR_IF(outputs[0]->getDtype() != DType_INT8, "OpTable: output tensor must be INT8");
- }
- else if (inputs[0]->getDtype() == DType_INT16)
- {
- if (inputs[1]->getElementCount() != 513 || inputs[1]->getDtype() != DType_INT16)
- {
- printNodeValidationError("OpTable: Table must be INT16[513] if input is INT16");
- return 1;
- }
- ERROR_IF(outputs[0]->getDtype() != DType_INT32, "OpTable: output tensor must be INT32");
- }
+ in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
+ out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
- in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
- table = dynamic_cast<TosaReference::TensorTemplate<TTable>*>(inputs[1]);
- out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
-
- ASSERT_MEM(in && table && out);
+ ASSERT_MEM(in && out);
return 0;
}
@@ -552,7 +536,7 @@
this->out->getTensor() = this->in->getTensor().unaryExpr([this](InEigenType in) -> OutEigenType {
int32_t input_truncated = std::min<int32_t>(std::max<int32_t>(in, QInMin), QInMax);
int32_t index = input_truncated - QInMin;
- int32_t value = this->table->getTensor()(index);
+ int32_t value = table[index];
return value;
});
@@ -568,8 +552,8 @@
int32_t frac = (input_truncated)&0x7F; // 7-bit fraction
// 3. interpolate, generate 16.7 (23-bit) output
- int32_t base = this->table->getTensor()(index);
- int32_t next = this->table->getTensor()(index + 1);
+ int32_t base = table[index];
+ int32_t next = table[index + 1];
int32_t value = (base << 7) + (next - base) * frac;
return value;
diff --git a/reference_model/src/ops/ewise_binary.h b/reference_model/src/ops/ewise_binary.h
index fd4d408..373dfb8 100644
--- a/reference_model/src/ops/ewise_binary.h
+++ b/reference_model/src/ops/ewise_binary.h
@@ -184,26 +184,28 @@
virtual int checkTensorAttributes();
virtual int eval();
- static constexpr DType TableDtype = (InDtype == DType_INT8) ? DType_INT8 : DType_INT16;
- static constexpr DType OutDtype = (InDtype == DType_INT8) ? DType_INT8 : DType_INT32;
- using InEigenType = typename GetEigenType<InDtype>::type;
- using TableEigenType = typename GetEigenType<TableDtype>::type;
- using OutEigenType = typename GetEigenType<OutDtype>::type;
- using TIn = Eigen::Tensor<InEigenType, Rank>;
- using TTable = Eigen::Tensor<TableEigenType, 1>;
- using TOut = Eigen::Tensor<OutEigenType, Rank>;
- static constexpr int32_t IntegerBits = 9;
- static constexpr int32_t FractionBits = 7;
- static constexpr int32_t NumTableEntries = (1 << IntegerBits);
- static constexpr int32_t QInMin = GetQMin<InDtype>::value;
- static constexpr int32_t QInMax = GetQMax<InDtype>::value;
- static constexpr int32_t QOutMin = GetQMin<OutDtype>::value;
- static constexpr int32_t QOutMax = GetQMax<OutDtype>::value;
+ static constexpr DType TableDtype = (InDtype == DType_INT8) ? DType_INT8 : DType_INT16;
+ static constexpr DType OutDtype = (InDtype == DType_INT8) ? DType_INT8 : DType_INT32;
+ static constexpr uint32_t TableNumEntries = (InDtype == DType_INT8) ? 256 : 513;
+ using InEigenType = typename GetEigenType<InDtype>::type;
+ using TableEigenType = typename GetEigenType<TableDtype>::type;
+ using OutEigenType = typename GetEigenType<OutDtype>::type;
+ using TIn = Eigen::Tensor<InEigenType, Rank>;
+ using TTable = Eigen::Tensor<TableEigenType, 1>;
+ using TOut = Eigen::Tensor<OutEigenType, Rank>;
+ static constexpr int32_t IntegerBits = 9;
+ static constexpr int32_t FractionBits = 7;
+ static constexpr int32_t NumTableEntries = (1 << IntegerBits);
+ static constexpr int32_t QInMin = GetQMin<InDtype>::value;
+ static constexpr int32_t QInMax = GetQMax<InDtype>::value;
+ static constexpr int32_t QOutMin = GetQMin<OutDtype>::value;
+ static constexpr int32_t QOutMax = GetQMax<OutDtype>::value;
protected:
TosaReference::TensorTemplate<TIn>* in;
- TosaReference::TensorTemplate<TTable>* table;
TosaReference::TensorTemplate<TOut>* out;
+ TosaTableAttribute* attribute;
+ std::array<TableEigenType, TableNumEntries> table;
};
}; // namespace TosaReference