From fe392ce8e714e616b5ab5b8a519d3eb84623273d Mon Sep 17 00:00:00 2001 From: Kevin Cheng Date: Mon, 18 Oct 2021 21:51:55 +0000 Subject: 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 Change-Id: Iddc446db4b356ba2f36ea4a79b7220b9cfc2aa4e --- reference_model/src/ops/data_layout.cc | 57 +++++++++++++++++++++++---------- reference_model/src/ops/data_layout.h | 4 +-- reference_model/src/ops/ewise_binary.cc | 42 ++++++++---------------- reference_model/src/ops/ewise_binary.h | 34 +++++++++++--------- 4 files changed, 73 insertions(+), 64 deletions(-) (limited to 'reference_model/src/ops') 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 @@ OpPad::OpPad(SubgraphTraverser* sgt_, uint64_t id_) : GraphNode(sgt_, Op_PAD, id_) { - setRequiredOperands(2, 1); + setRequiredOperands(1, 1); setRequiredRank(0, 6); INIT_QINFO(Pad); + INIT_ATTRIBUTE(Pad); } template @@ -159,9 +160,22 @@ int OpPad::checkTensorAttributes() return 1; } - in = dynamic_cast*>(inputs[0]); - out = dynamic_cast*>(outputs[0]); - paddings = dynamic_cast>*>(inputs[1]); + in = dynamic_cast*>(inputs[0]); + out = dynamic_cast*>(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 @@ int OpPad::checkTensorAttributes() template int OpPad::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 @@ OpTranspose::OpTranspose(SubgraphTraverser* sgt_, uint64_t id_) : GraphNode(sgt_, Op_TRANSPOSE, id_) { - setRequiredOperands(2, 1); + setRequiredOperands(1, 1); setRequiredRank(0, 6); + + INIT_ATTRIBUTE(Transpose); } template @@ -634,9 +656,10 @@ int OpTranspose::checkTensorAttributes() return 1; } - in = dynamic_cast*>(inputs[0]); - out = dynamic_cast*>(outputs[0]); - perm_tensor = dynamic_cast>*>(inputs[1]); + in = dynamic_cast*>(inputs[0]); + out = dynamic_cast*>(outputs[0]); + + ASSERT_MEM(in && out); return 0; } @@ -646,7 +669,7 @@ int OpTranspose::eval() { 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 @@ protected: Eigen::array, Rank> paddings_array; TosaReference::TensorTemplate* in; TosaReference::TensorTemplate* out; - TosaReference::TensorTemplate>* paddings; TosaPadQuantInfo* qinfo; + TosaPadAttribute* attribute; }; template @@ -207,8 +207,8 @@ public: protected: Eigen::array perm_array; + TosaTransposeAttribute* attribute; TosaReference::TensorTemplate* in; - TosaReference::TensorTemplate>* perm_tensor; TosaReference::TensorTemplate* 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 @@ OpTable::OpTable(SubgraphTraverser* sgt_, uint64_t id_) : GraphNode(sgt_, Op_TABLE, id_) { - setRequiredOperands(2, 1); + setRequiredOperands(1, 1); setRequiredRank(0, 6); + + INIT_ATTRIBUTE(Table); } template @@ -509,36 +511,18 @@ int OpTable::checkTensorAttributes() return 1; } - if (inputs[1]->getRank() != 1) - { - printNodeValidationError("OpTable: Table must be rank 1 tensor"); - return 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); - if (inputs[0]->getDtype() == DType_INT8) + for (uint32_t i = 0; i < TableNumEntries; i++) { - 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"); + table[i] = (TableEigenType)attribute->table()[i]; } - in = dynamic_cast*>(inputs[0]); - table = dynamic_cast*>(inputs[1]); - out = dynamic_cast*>(outputs[0]); + in = dynamic_cast*>(inputs[0]); + out = dynamic_cast*>(outputs[0]); - ASSERT_MEM(in && table && out); + ASSERT_MEM(in && out); return 0; } @@ -552,7 +536,7 @@ int OpTable::eval() this->out->getTensor() = this->in->getTensor().unaryExpr([this](InEigenType in) -> OutEigenType { int32_t input_truncated = std::min(std::max(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 @@ int OpTable::eval() 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 @@ public: 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::type; - using TableEigenType = typename GetEigenType::type; - using OutEigenType = typename GetEigenType::type; - using TIn = Eigen::Tensor; - using TTable = Eigen::Tensor; - using TOut = Eigen::Tensor; - 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::value; - static constexpr int32_t QInMax = GetQMax::value; - static constexpr int32_t QOutMin = GetQMin::value; - static constexpr int32_t QOutMax = GetQMax::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::type; + using TableEigenType = typename GetEigenType::type; + using OutEigenType = typename GetEigenType::type; + using TIn = Eigen::Tensor; + using TTable = Eigen::Tensor; + using TOut = Eigen::Tensor; + 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::value; + static constexpr int32_t QInMax = GetQMax::value; + static constexpr int32_t QOutMin = GetQMin::value; + static constexpr int32_t QOutMax = GetQMax::value; protected: TosaReference::TensorTemplate* in; - TosaReference::TensorTemplate* table; TosaReference::TensorTemplate* out; + TosaTableAttribute* attribute; + std::array table; }; }; // namespace TosaReference -- cgit v1.2.1