@bh/model-validation #1324
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| #include "model_companion_parser.h" | ||
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| #include <cstddef> | ||
| #include <cstdint> | ||
| #include <format> | ||
| #include <optional> | ||
| #include <stdexcept> | ||
| #include <string> | ||
| #include <vector> | ||
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| #include <executorch/runtime/core/tag.h> | ||
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| namespace { | ||
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| template <typename T> | ||
| T unwrap(const std::string &ctx, executorch::runtime::Result<T> result) { | ||
| if (!result.ok()) { | ||
| throw std::runtime_error( | ||
| std::format("{}: {}", ctx, executorch::runtime::to_string(result.error()))); | ||
| } | ||
| return std::move(result.get()); | ||
| } | ||
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| } // namespace | ||
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| namespace rnexecutorch::core::model { | ||
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| std::optional<std::vector<tensor::ShapeConstraint>> | ||
| parseDynamicInputShapes(executorch::extension::Module &module, const std::string &methodName) { | ||
| using executorch::aten::ScalarType; | ||
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| const auto getDynamicShapesMethodName = std::format("get_dynamic_dims_{}", methodName); | ||
| const auto ctx = getDynamicShapesMethodName + ": "; | ||
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| auto methodNames = unwrap(ctx + "failed to get method names", module.method_names()); | ||
| if (methodName == getDynamicShapesMethodName || | ||
| !methodNames.contains(getDynamicShapesMethodName)) { | ||
| return std::nullopt; | ||
| } | ||
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| auto methodMeta = unwrap(std::format("{}failed to get meta for method '{}'", ctx, methodName), | ||
| module.method_meta(methodName)); | ||
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| size_t expectedTensorInputs = 0; | ||
| for (size_t i = 0; i < methodMeta.num_inputs(); ++i) { | ||
| auto tag = unwrap(std::format("{}failed to get tag for input [{}]", ctx, i), methodMeta.input_tag(i)); | ||
| if (tag == executorch::runtime::Tag::Tensor) { | ||
| expectedTensorInputs++; | ||
| } | ||
| } | ||
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| auto result = unwrap(ctx + "failed to execute", module.execute(getDynamicShapesMethodName)); | ||
| if (result.size() != expectedTensorInputs) { | ||
| throw std::runtime_error(std::format("{}number of outputs returned ({}) does not match the number of " | ||
| "tensor inputs declared by method '{}' ({})", | ||
| ctx, result.size(), methodName, expectedTensorInputs)); | ||
| } | ||
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| std::vector<tensor::ShapeConstraint> dynamicShapes; | ||
| dynamicShapes.reserve(methodMeta.num_inputs()); | ||
| size_t tensorIndex = 0; | ||
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| for (size_t i = 0; i < methodMeta.num_inputs(); ++i) { | ||
| auto tag = unwrap(std::format("{}failed to get tag for input [{}]", ctx, i), | ||
| methodMeta.input_tag(i)); | ||
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| if (tag != executorch::runtime::Tag::Tensor) { | ||
| dynamicShapes.emplace_back(); | ||
| continue; | ||
| } | ||
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| const auto &out = result.at(tensorIndex); | ||
| if (!out.isTensor()) { | ||
| throw std::runtime_error(std::format("{}output[{}] is not a tensor", ctx, tensorIndex)); | ||
| } | ||
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| auto inputMeta = unwrap(std::format("{}failed to get tensor meta for input [{}]", ctx, i), | ||
| methodMeta.input_tensor_meta(i)); | ||
| const auto rank = inputMeta.sizes().size(); | ||
| const auto shapeTensor = out.toTensor(); | ||
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| if (shapeTensor.dim() != 2 || shapeTensor.size(1) != 3 || | ||
| shapeTensor.size(0) != static_cast<ssize_t>(rank) || | ||
| shapeTensor.scalar_type() != ScalarType::Int) { | ||
| throw std::runtime_error(std::format("{}output[{}] expected to be a 2D int32_t tensor of shape [{}, 3]", | ||
| ctx, tensorIndex, rank)); | ||
| } | ||
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| const auto *shape = shapeTensor.const_data_ptr<int32_t>(); | ||
| tensor::SymbolicShape symbolicShape; | ||
| symbolicShape.reserve(rank); | ||
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| for (size_t axis = 0; axis < rank; ++axis) { | ||
| const auto minDim = shape[axis * 3 + 0]; | ||
| const auto maxDim = shape[axis * 3 + 1]; | ||
| const auto step = shape[axis * 3 + 2]; | ||
| if (minDim < 0 || maxDim < minDim || step < 1) { | ||
| throw std::runtime_error(std::format("{}output[{}], axis {} is invalid: " | ||
| "expected 0 <= min <= max and step >= 1 but got [{}, {}, {}]", | ||
| ctx, tensorIndex, axis, minDim, maxDim, step)); | ||
| } | ||
| if (maxDim > inputMeta.sizes()[axis]) { | ||
| throw std::runtime_error(std::format("{}output[{}], axis {} max dimension ({}) " | ||
| "exceeds model metadata upper limit ({})", | ||
| ctx, tensorIndex, axis, maxDim, inputMeta.sizes()[axis])); | ||
| } | ||
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| symbolicShape.emplace_back(tensor::RangeDim{.min = minDim, .max = maxDim, .step = step}); | ||
| } | ||
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| dynamicShapes.emplace_back(std::move(symbolicShape)); | ||
| ++tensorIndex; | ||
| } | ||
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| return dynamicShapes; | ||
| } | ||
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| std::optional<std::vector<tensor::ShapeConstraint>> | ||
| parseEnumeratedInputShapes(executorch::extension::Module &module, const std::string &methodName) { | ||
| using executorch::aten::ScalarType; | ||
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| const auto getEnumeratedShapesMethodName = std::format("get_enumerated_dims_{}", methodName); | ||
| const auto ctx = getEnumeratedShapesMethodName + ": "; | ||
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| auto methodNames = unwrap(ctx + "failed to get method names", module.method_names()); | ||
| if (methodName == getEnumeratedShapesMethodName || | ||
| !methodNames.contains(getEnumeratedShapesMethodName)) { | ||
| return std::nullopt; | ||
| } | ||
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| auto methodMeta = unwrap(std::format("{}failed to get meta for method '{}'", ctx, methodName), | ||
| module.method_meta(methodName)); | ||
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| size_t expectedTensorInputs = 0; | ||
| for (size_t i = 0; i < methodMeta.num_inputs(); ++i) { | ||
| auto tag = unwrap(std::format("{}failed to get tag for input [{}]", ctx, i), | ||
| methodMeta.input_tag(i)); | ||
| if (tag == executorch::runtime::Tag::Tensor) { | ||
| expectedTensorInputs++; | ||
| } | ||
| } | ||
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| auto result = unwrap(ctx + "failed to execute", module.execute(getEnumeratedShapesMethodName)); | ||
| if (result.size() != expectedTensorInputs) { | ||
| throw std::runtime_error(std::format("{}number of outputs returned ({}) does not match the number of " | ||
| "tensor inputs declared by method '{}' ({})", | ||
| ctx, result.size(), methodName, expectedTensorInputs)); | ||
| } | ||
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Comment on lines
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Member
There was a problem hiding this comment. Maybe we should abstract common part of these two into function in anonymous helper? |
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| std::vector<tensor::ShapeConstraint> enumeratedShapes; | ||
| enumeratedShapes.reserve(methodMeta.num_inputs()); | ||
| size_t tensorIndex = 0; | ||
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| for (size_t i = 0; i < methodMeta.num_inputs(); ++i) { | ||
| auto tag = unwrap(std::format("{}failed to get tag for input [{}]", ctx, i), | ||
| methodMeta.input_tag(i)); | ||
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| if (tag != executorch::runtime::Tag::Tensor) { | ||
| enumeratedShapes.emplace_back(); | ||
| continue; | ||
| } | ||
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| const auto &out = result.at(tensorIndex); | ||
| if (!out.isTensor()) { | ||
| throw std::runtime_error(std::format("{}output[{}] is not a tensor", ctx, tensorIndex)); | ||
| } | ||
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| auto inputMeta = unwrap(std::format("{}failed to get tensor meta for input [{}]", ctx, i), | ||
| methodMeta.input_tensor_meta(i)); | ||
| const auto rank = inputMeta.sizes().size(); | ||
| const auto shapeTensor = out.toTensor(); | ||
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| if (shapeTensor.dim() != 2 || | ||
| shapeTensor.size(1) != static_cast<ssize_t>(rank) || | ||
| shapeTensor.scalar_type() != ScalarType::Int) { | ||
| throw std::runtime_error(std::format("{}output[{}] expected to be a 2D int32_t tensor of shape [num_shapes, {}]", | ||
| ctx, tensorIndex, rank)); | ||
| } | ||
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| const auto numShapes = static_cast<size_t>(shapeTensor.size(0)); | ||
| const auto *shapeData = shapeTensor.const_data_ptr<int32_t>(); | ||
| std::vector<tensor::SymbolicShape> alternatives; | ||
| alternatives.reserve(numShapes); | ||
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| for (size_t s = 0; s < numShapes; ++s) { | ||
| tensor::SymbolicShape singleShape; | ||
| singleShape.reserve(rank); | ||
| for (size_t axis = 0; axis < rank; ++axis) { | ||
| const auto dimVal = shapeData[s * rank + axis]; | ||
| if (dimVal < 0) { | ||
| throw std::runtime_error(std::format("{}output[{}], shape {}, axis {} is invalid: " | ||
| "dimension cannot be negative ({})", | ||
| ctx, tensorIndex, s, axis, dimVal)); | ||
| } | ||
| singleShape.emplace_back(dimVal); | ||
| } | ||
| alternatives.push_back(std::move(singleShape)); | ||
| } | ||
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| enumeratedShapes.emplace_back(std::move(alternatives)); | ||
| ++tensorIndex; | ||
| } | ||
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| return enumeratedShapes; | ||
| } | ||
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| } // namespace rnexecutorch::core::model | ||
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How about the analogical check for minDim?