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https://github.com/saymrwulf/onnxruntime.git
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Merged PR 6668707: Fix Resize float16 ROI tensor (part 2)
The previous [PR](https://microsoft.visualstudio.com/WindowsAI/_git/onnxruntime/pullrequest/6668481) had an issue, in place transform 🤦♀️ (surprised it passed the test). Related work items: #33939079
This commit is contained in:
Dwayne Robinson
parent
3e5d636f6c
commit
52203e06dd
2 changed files with 127 additions and 127 deletions
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@ -301,7 +301,7 @@ constexpr static std::array<SupportedTensorDataTypes, 2> supportedTypeListLogica
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constexpr static std::array<SupportedTensorDataTypes, 2> supportedTypeListLogicalComparison9 = /* A&B,C */ { SupportedTensorDataTypes::Float16to32|SupportedTensorDataTypes::Ints8to64, SupportedTensorDataTypes::Bool };
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constexpr static std::array<SupportedTensorDataTypes, 1> supportedTypeListSigned = { SupportedTensorDataTypes::Float16to32 | SupportedTensorDataTypes::Int64 | SupportedTensorDataTypes::Int32 | SupportedTensorDataTypes::Int16 | SupportedTensorDataTypes::Int8 };
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constexpr static std::array<SupportedTensorDataTypes, 1> supportedTypeListRange = {SupportedTensorDataTypes::Int16|SupportedTensorDataTypes::Int32|SupportedTensorDataTypes::Int64|SupportedTensorDataTypes::Float32};
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constexpr static std::array<SupportedTensorDataTypes, 2> supportedTypeListResize11 = {SupportedTensorDataTypes::Float16to32, SupportedTensorDataTypes::Float16to32 /* float32 ROI read by CPU */};
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constexpr static std::array<SupportedTensorDataTypes, 2> supportedTypeListResize11 = {SupportedTensorDataTypes::Float16to32, SupportedTensorDataTypes::Float16to32 /* ROI read by CPU */};
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constexpr static std::array<SupportedTensorDataTypes, 3> supportedTypeListInteger = {SupportedTensorDataTypes::Int8|SupportedTensorDataTypes::UInt8, SupportedTensorDataTypes::Int8|SupportedTensorDataTypes::UInt8, SupportedTensorDataTypes::Int32 };
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constexpr static std::array<SupportedTensorDataTypes, 1> supportedTypeListInteger8 = {SupportedTensorDataTypes::Int8|SupportedTensorDataTypes::UInt8 };
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constexpr static std::array<SupportedTensorDataTypes, 2> supportedTypeListRoiAlign = {SupportedTensorDataTypes::Float16to32, SupportedTensorDataTypes::Int32|SupportedTensorDataTypes::Int64 };
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@ -32,130 +32,6 @@ namespace OperatorHelper
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}
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}
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void ReadCpuLocalTensorIntoInt32(
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const MLOperatorTensor& tensor,
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std::vector<int32_t>& result
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)
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{
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result.clear();
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ML_CHECK_VALID_ARGUMENT(tensor.IsCpuData(), "Tensor must be CPU Tensor.");
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const std::vector<uint32_t>& tensorDimensions = tensor.GetShape();
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const uint32_t elementCount = ComputeElementCountFromDimensions(tensorDimensions);
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switch (tensor.GetTensorDataType())
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{
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case MLOperatorTensorDataType::Int32:
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{
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const int32_t* data = tensor.GetData<int32_t>();
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result.assign(data, data + elementCount);
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}
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break;
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case MLOperatorTensorDataType::Int64:
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{
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const int64_t* data = tensor.GetData<int64_t>();
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result.reserve(elementCount);
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// Use clamped cast rather than static_cast/narrow_cast,
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// because it's not uncommon for a model to specify a
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// 64-bit INTMAX constant as a sentinel value to mean
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// the largest possible value (even though the actual
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// dimension values come nowhere close to that, far
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// less than 32-bit INTMAX).
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for (auto d : gsl::make_span(data, data + elementCount))
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{
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result.push_back(clamp_cast<int32_t>(d));
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}
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}
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break;
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default:
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ML_INVALID_ARGUMENT("Expecting CPU local tensor of type int32 or int64.");
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break;
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}
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}
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void ReadCpuLocalTensorIntoFloat32(
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const MLOperatorTensor& tensor,
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std::vector<float>& result
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)
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{
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result.clear();
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ML_CHECK_VALID_ARGUMENT(tensor.IsCpuData(), "Tensor must be CPU Tensor.");
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const std::vector<uint32_t>& tensorDimensions = tensor.GetShape();
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const uint32_t elementCount = ComputeElementCountFromDimensions(tensorDimensions);
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result.resize(elementCount);
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switch (tensor.GetTensorDataType())
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{
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case MLOperatorTensorDataType::Float16:
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{
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const onnxruntime::MLFloat16* data = tensor.GetData<onnxruntime::MLFloat16>();
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std::transform(result.begin(), result.end(), result.begin(), [](auto v) {return static_cast<float>(v); });
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}
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break;
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case MLOperatorTensorDataType::/*Float32*/Float:
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{
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const float* data = tensor.GetData<float>();
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result.assign(data, data + elementCount);
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}
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break;
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case MLOperatorTensorDataType::/*Float64*/Double:
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{
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const double* data = tensor.GetData<double>();
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std::transform(result.begin(), result.end(), result.begin(), [](auto v) {return static_cast<float>(v); });
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}
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break;
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case MLOperatorTensorDataType::Int32:
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{
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const int32_t* data = tensor.GetData<int32_t>();
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std::transform(result.begin(), result.end(), result.begin(), [](auto v) {return static_cast<float>(v); });
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}
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break;
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case MLOperatorTensorDataType::UInt32:
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{
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const uint32_t* data = tensor.GetData<uint32_t>();
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std::transform(result.begin(), result.end(), result.begin(), [](auto v) {return static_cast<float>(v); });
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}
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break;
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case MLOperatorTensorDataType::Int64:
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{
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const int64_t* data = tensor.GetData<int64_t>();
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std::transform(result.begin(), result.end(), result.begin(), [](auto v) {return static_cast<float>(v); });
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}
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break;
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case MLOperatorTensorDataType::UInt64:
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{
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const uint64_t* data = tensor.GetData<uint64_t>();
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std::transform(result.begin(), result.end(), result.begin(), [](auto v) {return static_cast<float>(v); });
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}
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break;
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default:
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ML_INVALID_ARGUMENT("Expecting CPU local tensor of type float32.");
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break;
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}
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}
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void DowncastDimensions(gsl::span<const int64_t> inputDimensions, std::vector<DimensionType>& outputDimensions)
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{
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outputDimensions.reserve(inputDimensions.size());
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outputDimensions.clear();
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for (int64_t dim : inputDimensions)
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{
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outputDimensions.push_back(gsl::narrow_cast<uint32_t>(std::clamp<int64_t>(dim, INT32_MIN, INT32_MAX)));
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}
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}
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float CastFloat16ToFloat32(uint16_t input)
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{
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// Promote float16m10e5s1 to float32m23e8s1.
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@ -244,6 +120,130 @@ namespace OperatorHelper
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}
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#pragma warning(pop)
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void ReadCpuLocalTensorIntoInt32(
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const MLOperatorTensor& tensor,
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std::vector<int32_t>& result
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)
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{
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result.clear();
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ML_CHECK_VALID_ARGUMENT(tensor.IsCpuData(), "Tensor must be CPU Tensor.");
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const std::vector<uint32_t>& tensorDimensions = tensor.GetShape();
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const uint32_t elementCount = ComputeElementCountFromDimensions(tensorDimensions);
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switch (tensor.GetTensorDataType())
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{
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case MLOperatorTensorDataType::Int32:
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{
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const int32_t* data = tensor.GetData<int32_t>();
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result.assign(data, data + elementCount);
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}
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break;
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case MLOperatorTensorDataType::Int64:
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{
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const int64_t* data = tensor.GetData<int64_t>();
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result.reserve(elementCount);
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// Use clamped cast rather than static_cast/narrow_cast,
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// because it's not uncommon for a model to specify a
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// 64-bit INTMAX constant as a sentinel value to mean
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// the largest possible value (even though the actual
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// dimension values come nowhere close to that, far
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// less than 32-bit INTMAX).
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for (auto d : gsl::make_span(data, data + elementCount))
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{
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result.push_back(clamp_cast<int32_t>(d));
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}
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}
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break;
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default:
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ML_INVALID_ARGUMENT("Expecting CPU local tensor of type int32 or int64.");
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break;
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}
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}
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void ReadCpuLocalTensorIntoFloat32(
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const MLOperatorTensor& tensor,
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std::vector<float>& result
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)
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{
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result.clear();
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ML_CHECK_VALID_ARGUMENT(tensor.IsCpuData(), "Tensor must be CPU Tensor.");
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const std::vector<uint32_t>& tensorDimensions = tensor.GetShape();
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const uint32_t elementCount = ComputeElementCountFromDimensions(tensorDimensions);
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result.resize(elementCount);
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switch (tensor.GetTensorDataType())
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{
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case MLOperatorTensorDataType::Float16:
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{
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const uint16_t* data = reinterpret_cast<const uint16_t*>(tensor.GetByteData());
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std::transform(data, data + elementCount, result.begin(), CastFloat16ToFloat32);
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}
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break;
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case MLOperatorTensorDataType::/*Float32*/Float:
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{
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const float* data = tensor.GetData<float>();
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result.assign(data, data + elementCount);
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}
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break;
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case MLOperatorTensorDataType::/*Float64*/Double:
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{
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const double* data = tensor.GetData<double>();
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std::transform(data, data + elementCount, result.begin(), [](auto v) {return static_cast<float>(v); });
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}
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break;
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case MLOperatorTensorDataType::Int32:
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{
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const int32_t* data = tensor.GetData<int32_t>();
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std::transform(data, data + elementCount, result.begin(), [](auto v) {return static_cast<float>(v); });
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}
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break;
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case MLOperatorTensorDataType::UInt32:
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{
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const uint32_t* data = tensor.GetData<uint32_t>();
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std::transform(data, data + elementCount, result.begin(), [](auto v) {return static_cast<float>(v); });
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}
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break;
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case MLOperatorTensorDataType::Int64:
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{
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const int64_t* data = tensor.GetData<int64_t>();
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std::transform(data, data + elementCount, result.begin(), [](auto v) {return static_cast<float>(v); });
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}
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break;
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case MLOperatorTensorDataType::UInt64:
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{
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const uint64_t* data = tensor.GetData<uint64_t>();
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std::transform(data, data + elementCount, result.begin(), [](auto v) {return static_cast<float>(v); });
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}
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break;
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default:
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ML_INVALID_ARGUMENT("Expecting CPU local tensor of type float32.");
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break;
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}
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}
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void DowncastDimensions(gsl::span<const int64_t> inputDimensions, std::vector<DimensionType>& outputDimensions)
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{
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outputDimensions.reserve(inputDimensions.size());
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outputDimensions.clear();
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for (int64_t dim : inputDimensions)
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{
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outputDimensions.push_back(gsl::narrow_cast<uint32_t>(std::clamp<int64_t>(dim, INT32_MIN, INT32_MAX)));
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}
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}
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int64_t IsFloatDataType(MLOperatorTensorDataType tensorDataType)
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{
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switch (tensorDataType)
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@ -1132,7 +1132,7 @@ namespace OperatorHelper
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ML_CHECK_VALID_ARGUMENT(inputCount + 1 == m_components.size(), "Mismatch between input tensor count and string equation component count.");
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ML_CHECK_VALID_ARGUMENT(outputCount == 1, "EinSum expects exactly 1 output tensor.");
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std::vector<uint32_t> labelSizes(m_labelIndices.size(), static_cast<uint32_t>(INT_MIN));
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std::vector<uint32_t> labelSizes(m_labelIndices.size(), UINT_MAX);
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// Read every input tensor, comparing labels to ensure consistent sizes from the equation parsed earlier.
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for (uint32_t i = 0; i < inputCount; ++i)
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@ -1153,7 +1153,7 @@ namespace OperatorHelper
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uint32_t labelIndex = labelIndices[j];
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assert(labelIndex < labelSizes.size());
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if (labelSizes[labelIndex] == static_cast<uint32_t>(INT_MIN))
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if (labelSizes[labelIndex] == UINT_MAX)
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{
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labelSizes[labelIndex] = dimensionSize;
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}
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