diff options
author | Jerry Ge <jerry.ge@arm.com> | 2024-01-26 16:56:55 +0000 |
---|---|---|
committer | Jerry Ge <jerry.ge@arm.com> | 2024-02-22 18:22:41 +0000 |
commit | 20ab3df3d3100af68c47825846eee31925ff592d (patch) | |
tree | b032f4e6cbab7edbe5b3a02fadd1621a3e51216f /reference_model/src/tensor.cc | |
parent | c7bfa58c76e73aac772f714d8ae04cc875715689 (diff) | |
download | reference_model-20ab3df3d3100af68c47825846eee31925ff592d.tar.gz |
Save Int16/UINT16 test outputs to native dtypes
* Int16/UInt16 reference outputs were previously saved to INT32
* Save those in their native dtypes and updated other affected code
Signed-off-by: Jerry Ge <jerry.ge@arm.com>
Change-Id: I0c3b7fba096a8cb1ddabef20ad13498b8f46d36f
Diffstat (limited to 'reference_model/src/tensor.cc')
-rw-r--r-- | reference_model/src/tensor.cc | 423 |
1 files changed, 415 insertions, 8 deletions
diff --git a/reference_model/src/tensor.cc b/reference_model/src/tensor.cc index 27f21f3..16020cf 100644 --- a/reference_model/src/tensor.cc +++ b/reference_model/src/tensor.cc @@ -353,6 +353,8 @@ int TosaReference::Tensor::writeToNpyFile(const char* filename) const half_float::half* f16databuf = nullptr; uint8_t* ui8databuf = nullptr; int8_t* i8databuf = nullptr; + int16_t* i16databuf = nullptr; + uint16_t* ui16databuf = nullptr; int32_t* i32databuf = nullptr; int64_t* i64databuf = nullptr; bool* bdatabuf = nullptr; @@ -444,19 +446,32 @@ int TosaReference::Tensor::writeToNpyFile(const char* filename) const free(i8databuf); break; case TOSA_REF_TYPE_INT16: + i16databuf = (int16_t*)calloc(sizeof(int16_t), elements); + ASSERT_MEM(i16databuf); + + if (getTensorValueInt16(elements, i16databuf)) + { + free(i16databuf); + return 1; + } + + nperror = NumpyUtilities::writeToNpyFile(filename, shape, i16databuf); + + free(i16databuf); + break; case TOSA_REF_TYPE_UINT16: - i32databuf = (int32_t*)calloc(sizeof(int32_t), elements); - ASSERT_MEM(i32databuf); + ui16databuf = (uint16_t*)calloc(sizeof(uint16_t), elements); + ASSERT_MEM(ui16databuf); - if (getTensorValueInt32(elements, i32databuf)) + if (getTensorValueUInt16(elements, ui16databuf)) { - free(i32databuf); + free(ui16databuf); return 1; } - nperror = NumpyUtilities::writeToNpyFile(filename, shape, i32databuf); + nperror = NumpyUtilities::writeToNpyFile(filename, shape, ui16databuf); - free(i32databuf); + free(ui16databuf); break; case TOSA_REF_TYPE_INT48: case TOSA_REF_TYPE_SHAPE: @@ -761,6 +776,31 @@ int TosaReference::Tensor::readfromVector(const ArrayProxy<int8_t> vals) return 0; } +int TosaReference::Tensor::readfromVector(const ArrayProxy<uint16_t> vals) +{ + uint32_t elements = getElementCount(); + switch (getDtype()) + { + case TOSA_REF_TYPE_INT16: + case TOSA_REF_TYPE_UINT16: + if (vals.size() != elements) + { + WARNING("The input size (%ld) doesn't match the number of elements (%d) assigned to the tensor.", + vals.size(), elements); + return -1; + } + + setTensorValueUInt16(elements, vals.data()); + break; + default: + WARNING("The input type doesn't match the data type assigned to the tensor (%s).", + EnumNameTOSAREFTYPE(getDtype())); + return -2; + } + setIsValid(); + return 0; +} + int TosaReference::Tensor::readfromVector(const ArrayProxy<int16_t> vals) { uint32_t elements = getElementCount(); @@ -985,6 +1025,31 @@ int TosaReference::Tensor::writeToVector(ArrayProxy<int8_t> vals) return 0; } +int TosaReference::Tensor::writeToVector(ArrayProxy<uint16_t> vals) +{ + uint32_t elements = getElementCount(); + + switch (getDtype()) + { + case TOSA_REF_TYPE_INT16: + case TOSA_REF_TYPE_UINT16: + if (vals.size() != elements) + { + WARNING("The output size (%ld) doesn't match the number of elements (%d) assigned to the tensor.", + vals.size(), elements); + return -1; + } + + getTensorValueUInt16(elements, vals.data()); + break; + default: + WARNING("The output type doesn't match the data type assigned to the tensor (%s).", + EnumNameTOSAREFTYPE(getDtype())); + return -2; + } + return 0; +} + int TosaReference::Tensor::writeToVector(ArrayProxy<int16_t> vals) { uint32_t elements = getElementCount(); @@ -1841,9 +1906,161 @@ int TosaReference::Tensor6<int32_t>::setTensorValueInt8(const size_t bufLen, con } template <class T> +int TosaReference::TensorTemplate<T>::setTensorValueUInt16(const size_t bufLen, const uint16_t* vals) +{ + FATAL_ERROR("TensorTemplate<T>::setTensorValueUInt16 should not be called. " + "Implement template specialization version."); + return 0; +} + +template <> +int TosaReference::Tensor0<int32_t>::setTensorValueUInt16(const size_t bufLen, const uint16_t* vals) +{ + ASSERT_MSG(bufLen == getElementCount(), "Total elements must match"); + + (*tensor)(0) = static_cast<int32_t>(vals[0]); + + return 0; +} + +template <> +int TosaReference::Tensor1<int32_t>::setTensorValueUInt16(const size_t bufLen, const uint16_t* vals) +{ + uint32_t idx = 0; + + ASSERT_MSG(bufLen == getElementCount(), "Total elements must match"); + + for (int i0 = 0; i0 < shape[0]; i0++) + { + (*tensor)(i0) = static_cast<int32_t>(vals[idx++]); + } + + return 0; +} + +template <> +int TosaReference::Tensor2<int32_t>::setTensorValueUInt16(const size_t bufLen, const uint16_t* vals) +{ + uint32_t idx = 0; + + ASSERT_MSG(bufLen == getElementCount(), "Total elements must match"); + + for (int i0 = 0; i0 < shape[0]; i0++) + { + for (int i1 = 0; i1 < shape[1]; i1++) + { + (*tensor)(i0, i1) = static_cast<int32_t>(vals[idx++]); + } + } + + return 0; +} + +template <> +int TosaReference::Tensor3<int32_t>::setTensorValueUInt16(const size_t bufLen, const uint16_t* vals) +{ + uint32_t idx = 0; + + ASSERT_MSG(bufLen == getElementCount(), "Total elements must match"); + + for (int i0 = 0; i0 < shape[0]; i0++) + { + for (int i1 = 0; i1 < shape[1]; i1++) + { + for (int i2 = 0; i2 < shape[2]; i2++) + { + (*tensor)(i0, i1, i2) = static_cast<int32_t>(vals[idx++]); + } + } + } + + return 0; +} + +template <> +int TosaReference::Tensor4<int32_t>::setTensorValueUInt16(const size_t bufLen, const uint16_t* vals) +{ + uint32_t idx = 0; + + ASSERT_MSG(bufLen == getElementCount(), "Total elements must match"); + + for (int i0 = 0; i0 < shape[0]; i0++) + { + for (int i1 = 0; i1 < shape[1]; i1++) + { + for (int i2 = 0; i2 < shape[2]; i2++) + { + for (int i3 = 0; i3 < shape[3]; i3++) + { + (*tensor)(i0, i1, i2, i3) = static_cast<int32_t>(vals[idx++]); + } + } + } + } + + return 0; +} + +template <> +int TosaReference::Tensor5<int32_t>::setTensorValueUInt16(const size_t bufLen, const uint16_t* vals) +{ + uint32_t idx = 0; + + ASSERT_MSG(bufLen == getElementCount(), "Total elements must match"); + + for (int i0 = 0; i0 < shape[0]; i0++) + { + for (int i1 = 0; i1 < shape[1]; i1++) + { + for (int i2 = 0; i2 < shape[2]; i2++) + { + for (int i3 = 0; i3 < shape[3]; i3++) + { + for (int i4 = 0; i4 < shape[4]; i4++) + { + (*tensor)(i0, i1, i2, i3, i4) = static_cast<int32_t>(vals[idx++]); + } + } + } + } + } + + return 0; +} + +template <> +int TosaReference::Tensor6<int32_t>::setTensorValueUInt16(const size_t bufLen, const uint16_t* vals) +{ + uint32_t idx = 0; + + ASSERT_MSG(bufLen == getElementCount(), "Total elements must match"); + + for (int i0 = 0; i0 < shape[0]; i0++) + { + for (int i1 = 0; i1 < shape[1]; i1++) + { + for (int i2 = 0; i2 < shape[2]; i2++) + { + for (int i3 = 0; i3 < shape[3]; i3++) + { + for (int i4 = 0; i4 < shape[4]; i4++) + { + for (int i5 = 0; i5 < shape[5]; i5++) + { + (*tensor)(i0, i1, i2, i3, i4, i5) = static_cast<int32_t>(vals[idx++]); + } + } + } + } + } + } + return 0; +} + +template <class T> int TosaReference::TensorTemplate<T>::setTensorValueInt16(const size_t bufLen, const int16_t* vals) { - FATAL_ERROR("TensorTemplate<T>::setTensorValueInt32 should not be called. " + FATAL_ERROR("TensorTemplate<T>::setTensorValueInt16 should not be called. " "Implement template specialization version."); return 0; } @@ -3211,9 +3428,199 @@ int TosaReference::Tensor6<int32_t>::getTensorValueInt8(const size_t bufLen, int } template <class T> +int TosaReference::TensorTemplate<T>::getTensorValueUInt16(const size_t bufLen, uint16_t* vals) const +{ + FATAL_ERROR("TensorTemplate<T>::getTensorValueUInt16 should not be called. " + "Implement template specialization version."); + return 0; +} + +template <> +int TosaReference::Tensor0<int32_t>::getTensorValueUInt16(const size_t bufLen, uint16_t* vals) const +{ + int totalVals = 1; + + ASSERT_MSG((size_t)totalVals == bufLen, "Output buffer and tensor size do not match"); + + vals[0] = (*tensor)(0); + + return 0; +} + +template <> +int TosaReference::Tensor1<int32_t>::getTensorValueUInt16(const size_t bufLen, uint16_t* vals) const +{ + uint32_t idx = 0; + int totalVals = 1; + + for (size_t i = 0; i < shape.size(); i++) + { + totalVals *= shape[i]; + } + + ASSERT_MSG((size_t)totalVals == bufLen, "Output buffer and tensor size do not match"); + + for (int i0 = 0; i0 < shape[0]; i0++) + { + vals[idx++] = (*tensor)(i0); + } + + return 0; +} + +template <> +int TosaReference::Tensor2<int32_t>::getTensorValueUInt16(const size_t bufLen, uint16_t* vals) const +{ + uint32_t idx = 0; + int totalVals = 1; + + for (size_t i = 0; i < shape.size(); i++) + { + totalVals *= shape[i]; + } + + ASSERT_MSG((size_t)totalVals == bufLen, "Output buffer and tensor size do not match"); + + for (int i0 = 0; i0 < shape[0]; i0++) + { + for (int i1 = 0; i1 < shape[1]; i1++) + { + vals[idx++] = (*tensor)(i0, i1); + } + } + + return 0; +} + +template <> +int TosaReference::Tensor3<int32_t>::getTensorValueUInt16(const size_t bufLen, uint16_t* vals) const +{ + uint32_t idx = 0; + int totalVals = 1; + + for (size_t i = 0; i < shape.size(); i++) + { + totalVals *= shape[i]; + } + + ASSERT_MSG((size_t)totalVals == bufLen, "Output buffer and tensor size do not match"); + + for (int i0 = 0; i0 < shape[0]; i0++) + { + for (int i1 = 0; i1 < shape[1]; i1++) + { + for (int i2 = 0; i2 < shape[2]; i2++) + { + vals[idx++] = (*tensor)(i0, i1, i2); + } + } + } + + return 0; +} + +template <> +int TosaReference::Tensor4<int32_t>::getTensorValueUInt16(const size_t bufLen, uint16_t* vals) const +{ + uint32_t idx = 0; + int totalVals = 1; + + for (size_t i = 0; i < shape.size(); i++) + { + totalVals *= shape[i]; + } + + ASSERT_MSG((size_t)totalVals == bufLen, "Output buffer and tensor size do not match"); + + for (int i0 = 0; i0 < shape[0]; i0++) + { + for (int i1 = 0; i1 < shape[1]; i1++) + { + for (int i2 = 0; i2 < shape[2]; i2++) + { + for (int i3 = 0; i3 < shape[3]; i3++) + { + vals[idx++] = (*tensor)(i0, i1, i2, i3); + } + } + } + } + + return 0; +} + +template <> +int TosaReference::Tensor5<int32_t>::getTensorValueUInt16(const size_t bufLen, uint16_t* vals) const +{ + uint32_t idx = 0; + int totalVals = 1; + + for (size_t i = 0; i < shape.size(); i++) + { + totalVals *= shape[i]; + } + + ASSERT_MSG((size_t)totalVals == bufLen, "Output buffer and tensor size do not match"); + + for (int i0 = 0; i0 < shape[0]; i0++) + { + for (int i1 = 0; i1 < shape[1]; i1++) + { + for (int i2 = 0; i2 < shape[2]; i2++) + { + for (int i3 = 0; i3 < shape[3]; i3++) + { + for (int i4 = 0; i4 < shape[4]; i4++) + { + vals[idx++] = (*tensor)(i0, i1, i2, i3, i4); + } + } + } + } + } + + return 0; +} + +template <> +int TosaReference::Tensor6<int32_t>::getTensorValueUInt16(const size_t bufLen, uint16_t* vals) const +{ + uint32_t idx = 0; + int totalVals = 1; + + for (size_t i = 0; i < shape.size(); i++) + { + totalVals *= shape[i]; + } + + ASSERT_MSG((size_t)totalVals == bufLen, "Output buffer and tensor size do not match"); + + for (int i0 = 0; i0 < shape[0]; i0++) + { + for (int i1 = 0; i1 < shape[1]; i1++) + { + for (int i2 = 0; i2 < shape[2]; i2++) + { + for (int i3 = 0; i3 < shape[3]; i3++) + { + for (int i4 = 0; i4 < shape[4]; i4++) + { + for (int i5 = 0; i5 < shape[5]; i5++) + { + vals[idx++] = (*tensor)(i0, i1, i2, i3, i4, i5); + } + } + } + } + } + } + return 0; +} + +template <class T> int TosaReference::TensorTemplate<T>::getTensorValueInt16(const size_t bufLen, int16_t* vals) const { - FATAL_ERROR("TensorTemplate<T>::getTensorValueInt32 should not be called. " + FATAL_ERROR("TensorTemplate<T>::getTensorValueInt16 should not be called. " "Implement template specialization version."); return 0; } |