benibus commented on code in PR #8510: URL: https://github.com/apache/arrow/pull/8510#discussion_r1126907080
########## cpp/src/arrow/extension/fixed_shape_tensor.cc: ########## @@ -0,0 +1,299 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "arrow/extension/fixed_shape_tensor.h" + +#include "arrow/array/array_nested.h" +#include "arrow/array/array_primitive.h" +#include "arrow/json/rapidjson_defs.h" // IWYU pragma: keep +#include "arrow/tensor.h" +#include "arrow/util/logging.h" +#include "arrow/util/sort.h" + +#include <rapidjson/document.h> +#include <rapidjson/writer.h> + +namespace rj = arrow::rapidjson; + +namespace arrow { +namespace extension { + +bool FixedShapeTensorType::ExtensionEquals(const ExtensionType& other) const { + if (extension_name() != other.extension_name()) { + return false; + } + const auto& other_ext = static_cast<const FixedShapeTensorType&>(other); + + auto is_permutation_trivial = [](const std::vector<int64_t>& permutation) { + for (size_t i = 1; i < permutation.size(); ++i) { + if (permutation[i - 1] + 1 != permutation[i]) { + return false; + } + } + return true; + }; + const bool permutation_equivalent = + (permutation_ == other_ext.permutation()) || + ((permutation_.empty() && is_permutation_trivial(other_ext.permutation())) && + (is_permutation_trivial(permutation_) || other_ext.permutation().empty())); + + return storage_type()->Equals(other_ext.storage_type()) && + shape_ == other_ext.shape() && dim_names_ == other_ext.dim_names() && + permutation_equivalent; +} + +std::string FixedShapeTensorType::Serialize() const { + rj::Document document; + document.SetObject(); + rj::Document::AllocatorType& allocator = document.GetAllocator(); + + rj::Value shape(rj::kArrayType); + for (auto v : shape_) { + shape.PushBack(v, allocator); + } + document.AddMember(rj::Value("shape", allocator), shape, allocator); + + if (!permutation_.empty()) { + rj::Value permutation(rj::kArrayType); + for (auto v : permutation_) { + permutation.PushBack(v, allocator); + } + document.AddMember(rj::Value("permutation", allocator), permutation, allocator); + } + + if (!dim_names_.empty()) { + rj::Value dim_names(rj::kArrayType); + for (std::string v : dim_names_) { + dim_names.PushBack(rj::Value{}.SetString(v.c_str(), allocator), allocator); + } + document.AddMember(rj::Value("dim_names", allocator), dim_names, allocator); + } + + rj::StringBuffer buffer; + rj::Writer<rj::StringBuffer> writer(buffer); + document.Accept(writer); + return buffer.GetString(); +} + +Result<std::shared_ptr<DataType>> FixedShapeTensorType::Deserialize( + std::shared_ptr<DataType> storage_type, const std::string& serialized_data) const { + if (storage_type->id() != Type::FIXED_SIZE_LIST) { + return Status::Invalid("Expected FixedSizeList storage type, got ", + storage_type->ToString()); + } + auto value_type = + internal::checked_pointer_cast<FixedSizeListType>(storage_type)->value_type(); + rj::Document document; + if (document.Parse(serialized_data.data(), serialized_data.length()).HasParseError() || + !document.HasMember("shape") || !document["shape"].IsArray()) { + return Status::Invalid("Invalid serialized JSON data: ", serialized_data); + } + + std::vector<int64_t> shape; + for (auto& x : document["shape"].GetArray()) { + shape.emplace_back(x.GetInt64()); + } + std::vector<int64_t> permutation; + if (document.HasMember("permutation")) { + for (auto& x : document["permutation"].GetArray()) { + permutation.emplace_back(x.GetInt64()); + } + if (shape.size() != permutation.size()) { + return Status::Invalid("Invalid permutation"); + } + } + std::vector<std::string> dim_names; + if (document.HasMember("dim_names")) { + for (auto& x : document["dim_names"].GetArray()) { + dim_names.emplace_back(x.GetString()); + } + if (shape.size() != dim_names.size()) { + return Status::Invalid("Invalid dim_names"); + } + } + + return fixed_shape_tensor(value_type, shape, permutation, dim_names); +} + +std::shared_ptr<Array> FixedShapeTensorType::MakeArray( + std::shared_ptr<ArrayData> data) const { + return std::make_shared<ExtensionArray>(data); +} + +Result<std::shared_ptr<Array>> FixedShapeTensorType::MakeArray( + std::shared_ptr<Tensor> tensor) const { + auto permutation = internal::ArgSort(tensor->strides()); + std::reverse(permutation.begin(), permutation.end()); + if (permutation[0] != 0) { + return Status::Invalid( + "Only first-major tensors can be zero-copy converted to arrays"); + } + + auto cell_shape = tensor->shape(); + cell_shape.erase(cell_shape.begin()); + if (cell_shape != shape_) { + return Status::Invalid("Expected cell shape does not match input tensor shape"); + } + + permutation.erase(permutation.begin()); + for (auto& x : permutation) { + x--; + } + + auto ext_type = internal::checked_pointer_cast<ExtensionType>( + fixed_shape_tensor(tensor->type(), cell_shape, permutation, tensor->dim_names())); + + std::shared_ptr<FixedSizeListArray> arr; + std::shared_ptr<Array> value_array; + switch (tensor->type_id()) { + case Type::UINT8: { + value_array = std::make_shared<UInt8Array>(tensor->size(), tensor->data()); + break; + } + case Type::INT8: { + value_array = std::make_shared<Int8Array>(tensor->size(), tensor->data()); + break; + } + case Type::UINT16: { + value_array = std::make_shared<UInt16Array>(tensor->size(), tensor->data()); + break; + } + case Type::INT16: { + value_array = std::make_shared<Int16Array>(tensor->size(), tensor->data()); + break; + } + case Type::UINT32: { + value_array = std::make_shared<UInt32Array>(tensor->size(), tensor->data()); + break; + } + case Type::INT32: { + value_array = std::make_shared<Int32Array>(tensor->size(), tensor->data()); + break; + } + case Type::UINT64: { + value_array = std::make_shared<Int64Array>(tensor->size(), tensor->data()); + break; + } + case Type::INT64: { + value_array = std::make_shared<Int64Array>(tensor->size(), tensor->data()); + break; + } + case Type::HALF_FLOAT: { + value_array = std::make_shared<HalfFloatArray>(tensor->size(), tensor->data()); + break; + } + case Type::FLOAT: { + value_array = std::make_shared<FloatArray>(tensor->size(), tensor->data()); + break; + } + case Type::DOUBLE: { + value_array = std::make_shared<DoubleArray>(tensor->size(), tensor->data()); + break; + } + default: { + return Status::NotImplemented("Unsupported tensor type: ", + tensor->type()->ToString()); + } + } + arr = std::make_shared<FixedSizeListArray>(ext_type->storage_type(), tensor->shape()[0], + value_array); + auto ext_data = arr->data(); + ext_data->type = ext_type; + return MakeArray(ext_data); +} + +Result<std::shared_ptr<Tensor>> FixedShapeTensorType::ToTensor( + std::shared_ptr<Array> arr) { + // To convert an array of n dimensional tensors to a n+1 dimensional tensor we + // interpret the array's length as the first dimension the new tensor. Further, we + // define n+1 dimensional tensor's strides by front appending a new stride to the n + // dimensional tensor's strides. + + ARROW_CHECK(is_tensor_supported(this->value_type_->id())); + + ARROW_DCHECK_EQ(arr->null_count(), 0) << "Null values not supported in tensors."; + auto ext_arr = internal::checked_pointer_cast<FixedSizeListArray>( + internal::checked_pointer_cast<ExtensionArray>(arr)->storage()); + + std::vector<int64_t> shape = this->shape(); + shape.insert(shape.begin(), 1, arr->length()); + + std::vector<int64_t> tensor_strides = this->strides(); + tensor_strides.insert(tensor_strides.begin(), 1, arr->length() * tensor_strides[0]); + + std::shared_ptr<Buffer> buffer = ext_arr->values()->data()->buffers[1]; + return *Tensor::Make(ext_arr->value_type(), buffer, shape, tensor_strides, dim_names()); Review Comment: It might be best to let `Tensor::Make` recompute the strides here (based on the `shape`). We would get a multiplication overflow check baked in. You'd just have to pass an empty vector. Also, would `permutation` play a role in this conversion? i.e. would `shape` potentially need to permuted before passing it to the `Tensor` to reflect a row-major layout in the buffer`? (actually not sure) ########## cpp/src/arrow/extension/fixed_shape_tensor.cc: ########## @@ -0,0 +1,299 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "arrow/extension/fixed_shape_tensor.h" + +#include "arrow/array/array_nested.h" +#include "arrow/array/array_primitive.h" +#include "arrow/json/rapidjson_defs.h" // IWYU pragma: keep +#include "arrow/tensor.h" +#include "arrow/util/logging.h" +#include "arrow/util/sort.h" + +#include <rapidjson/document.h> +#include <rapidjson/writer.h> + +namespace rj = arrow::rapidjson; + +namespace arrow { +namespace extension { + +bool FixedShapeTensorType::ExtensionEquals(const ExtensionType& other) const { + if (extension_name() != other.extension_name()) { + return false; + } + const auto& other_ext = static_cast<const FixedShapeTensorType&>(other); + + auto is_permutation_trivial = [](const std::vector<int64_t>& permutation) { + for (size_t i = 1; i < permutation.size(); ++i) { + if (permutation[i - 1] + 1 != permutation[i]) { + return false; + } + } + return true; + }; + const bool permutation_equivalent = + (permutation_ == other_ext.permutation()) || + ((permutation_.empty() && is_permutation_trivial(other_ext.permutation())) && + (is_permutation_trivial(permutation_) || other_ext.permutation().empty())); + + return storage_type()->Equals(other_ext.storage_type()) && + shape_ == other_ext.shape() && dim_names_ == other_ext.dim_names() && + permutation_equivalent; +} + +std::string FixedShapeTensorType::Serialize() const { + rj::Document document; + document.SetObject(); + rj::Document::AllocatorType& allocator = document.GetAllocator(); + + rj::Value shape(rj::kArrayType); + for (auto v : shape_) { + shape.PushBack(v, allocator); + } + document.AddMember(rj::Value("shape", allocator), shape, allocator); + + if (!permutation_.empty()) { + rj::Value permutation(rj::kArrayType); + for (auto v : permutation_) { + permutation.PushBack(v, allocator); + } + document.AddMember(rj::Value("permutation", allocator), permutation, allocator); + } + + if (!dim_names_.empty()) { + rj::Value dim_names(rj::kArrayType); + for (std::string v : dim_names_) { + dim_names.PushBack(rj::Value{}.SetString(v.c_str(), allocator), allocator); + } + document.AddMember(rj::Value("dim_names", allocator), dim_names, allocator); + } + + rj::StringBuffer buffer; + rj::Writer<rj::StringBuffer> writer(buffer); + document.Accept(writer); + return buffer.GetString(); +} + +Result<std::shared_ptr<DataType>> FixedShapeTensorType::Deserialize( + std::shared_ptr<DataType> storage_type, const std::string& serialized_data) const { + if (storage_type->id() != Type::FIXED_SIZE_LIST) { + return Status::Invalid("Expected FixedSizeList storage type, got ", + storage_type->ToString()); + } + auto value_type = + internal::checked_pointer_cast<FixedSizeListType>(storage_type)->value_type(); + rj::Document document; + if (document.Parse(serialized_data.data(), serialized_data.length()).HasParseError() || + !document.HasMember("shape") || !document["shape"].IsArray()) { + return Status::Invalid("Invalid serialized JSON data: ", serialized_data); + } + + std::vector<int64_t> shape; + for (auto& x : document["shape"].GetArray()) { + shape.emplace_back(x.GetInt64()); + } + std::vector<int64_t> permutation; + if (document.HasMember("permutation")) { + for (auto& x : document["permutation"].GetArray()) { + permutation.emplace_back(x.GetInt64()); + } + if (shape.size() != permutation.size()) { + return Status::Invalid("Invalid permutation"); + } + } + std::vector<std::string> dim_names; + if (document.HasMember("dim_names")) { + for (auto& x : document["dim_names"].GetArray()) { + dim_names.emplace_back(x.GetString()); + } + if (shape.size() != dim_names.size()) { + return Status::Invalid("Invalid dim_names"); + } + } + + return fixed_shape_tensor(value_type, shape, permutation, dim_names); +} + +std::shared_ptr<Array> FixedShapeTensorType::MakeArray( + std::shared_ptr<ArrayData> data) const { + return std::make_shared<ExtensionArray>(data); +} + +Result<std::shared_ptr<Array>> FixedShapeTensorType::MakeArray( + std::shared_ptr<Tensor> tensor) const { + auto permutation = internal::ArgSort(tensor->strides()); + std::reverse(permutation.begin(), permutation.end()); + if (permutation[0] != 0) { + return Status::Invalid( + "Only first-major tensors can be zero-copy converted to arrays"); + } + + auto cell_shape = tensor->shape(); + cell_shape.erase(cell_shape.begin()); + if (cell_shape != shape_) { + return Status::Invalid("Expected cell shape does not match input tensor shape"); + } + + permutation.erase(permutation.begin()); + for (auto& x : permutation) { + x--; + } + + auto ext_type = internal::checked_pointer_cast<ExtensionType>( + fixed_shape_tensor(tensor->type(), cell_shape, permutation, tensor->dim_names())); Review Comment: Wouldn't we override the new extension type with `this->dim_names()` rather than the tensor's? Keep in mind that a 3D input tensor would (potentially) have 3 `dim_names`, but the `shape` we'd be passing to the new type would only have 2 dimensions - so there would be a mismatch (which would be detected in `Make`). ########## cpp/src/arrow/extension/fixed_shape_tensor.cc: ########## @@ -0,0 +1,299 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "arrow/extension/fixed_shape_tensor.h" + +#include "arrow/array/array_nested.h" +#include "arrow/array/array_primitive.h" +#include "arrow/json/rapidjson_defs.h" // IWYU pragma: keep +#include "arrow/tensor.h" +#include "arrow/util/logging.h" +#include "arrow/util/sort.h" + +#include <rapidjson/document.h> +#include <rapidjson/writer.h> + +namespace rj = arrow::rapidjson; + +namespace arrow { +namespace extension { + +bool FixedShapeTensorType::ExtensionEquals(const ExtensionType& other) const { + if (extension_name() != other.extension_name()) { + return false; + } + const auto& other_ext = static_cast<const FixedShapeTensorType&>(other); + + auto is_permutation_trivial = [](const std::vector<int64_t>& permutation) { + for (size_t i = 1; i < permutation.size(); ++i) { + if (permutation[i - 1] + 1 != permutation[i]) { + return false; + } + } + return true; + }; + const bool permutation_equivalent = + (permutation_ == other_ext.permutation()) || + ((permutation_.empty() && is_permutation_trivial(other_ext.permutation())) && + (is_permutation_trivial(permutation_) || other_ext.permutation().empty())); + + return storage_type()->Equals(other_ext.storage_type()) && + shape_ == other_ext.shape() && dim_names_ == other_ext.dim_names() && + permutation_equivalent; +} + +std::string FixedShapeTensorType::Serialize() const { + rj::Document document; + document.SetObject(); + rj::Document::AllocatorType& allocator = document.GetAllocator(); + + rj::Value shape(rj::kArrayType); + for (auto v : shape_) { + shape.PushBack(v, allocator); + } + document.AddMember(rj::Value("shape", allocator), shape, allocator); + + if (!permutation_.empty()) { + rj::Value permutation(rj::kArrayType); + for (auto v : permutation_) { + permutation.PushBack(v, allocator); + } + document.AddMember(rj::Value("permutation", allocator), permutation, allocator); + } + + if (!dim_names_.empty()) { + rj::Value dim_names(rj::kArrayType); + for (std::string v : dim_names_) { + dim_names.PushBack(rj::Value{}.SetString(v.c_str(), allocator), allocator); + } + document.AddMember(rj::Value("dim_names", allocator), dim_names, allocator); + } + + rj::StringBuffer buffer; + rj::Writer<rj::StringBuffer> writer(buffer); + document.Accept(writer); + return buffer.GetString(); +} + +Result<std::shared_ptr<DataType>> FixedShapeTensorType::Deserialize( + std::shared_ptr<DataType> storage_type, const std::string& serialized_data) const { + if (storage_type->id() != Type::FIXED_SIZE_LIST) { + return Status::Invalid("Expected FixedSizeList storage type, got ", + storage_type->ToString()); + } + auto value_type = + internal::checked_pointer_cast<FixedSizeListType>(storage_type)->value_type(); + rj::Document document; + if (document.Parse(serialized_data.data(), serialized_data.length()).HasParseError() || + !document.HasMember("shape") || !document["shape"].IsArray()) { + return Status::Invalid("Invalid serialized JSON data: ", serialized_data); + } + + std::vector<int64_t> shape; + for (auto& x : document["shape"].GetArray()) { + shape.emplace_back(x.GetInt64()); + } + std::vector<int64_t> permutation; + if (document.HasMember("permutation")) { + for (auto& x : document["permutation"].GetArray()) { + permutation.emplace_back(x.GetInt64()); + } + if (shape.size() != permutation.size()) { + return Status::Invalid("Invalid permutation"); + } + } + std::vector<std::string> dim_names; + if (document.HasMember("dim_names")) { + for (auto& x : document["dim_names"].GetArray()) { + dim_names.emplace_back(x.GetString()); + } + if (shape.size() != dim_names.size()) { + return Status::Invalid("Invalid dim_names"); + } + } + + return fixed_shape_tensor(value_type, shape, permutation, dim_names); +} + +std::shared_ptr<Array> FixedShapeTensorType::MakeArray( + std::shared_ptr<ArrayData> data) const { + return std::make_shared<ExtensionArray>(data); +} + +Result<std::shared_ptr<Array>> FixedShapeTensorType::MakeArray( + std::shared_ptr<Tensor> tensor) const { + auto permutation = internal::ArgSort(tensor->strides()); + std::reverse(permutation.begin(), permutation.end()); + if (permutation[0] != 0) { + return Status::Invalid( + "Only first-major tensors can be zero-copy converted to arrays"); + } + + auto cell_shape = tensor->shape(); + cell_shape.erase(cell_shape.begin()); + if (cell_shape != shape_) { + return Status::Invalid("Expected cell shape does not match input tensor shape"); + } + + permutation.erase(permutation.begin()); + for (auto& x : permutation) { + x--; + } + + auto ext_type = internal::checked_pointer_cast<ExtensionType>( + fixed_shape_tensor(tensor->type(), cell_shape, permutation, tensor->dim_names())); + + std::shared_ptr<FixedSizeListArray> arr; + std::shared_ptr<Array> value_array; + switch (tensor->type_id()) { + case Type::UINT8: { + value_array = std::make_shared<UInt8Array>(tensor->size(), tensor->data()); + break; + } + case Type::INT8: { + value_array = std::make_shared<Int8Array>(tensor->size(), tensor->data()); + break; + } + case Type::UINT16: { + value_array = std::make_shared<UInt16Array>(tensor->size(), tensor->data()); + break; + } + case Type::INT16: { + value_array = std::make_shared<Int16Array>(tensor->size(), tensor->data()); + break; + } + case Type::UINT32: { + value_array = std::make_shared<UInt32Array>(tensor->size(), tensor->data()); + break; + } + case Type::INT32: { + value_array = std::make_shared<Int32Array>(tensor->size(), tensor->data()); + break; + } + case Type::UINT64: { + value_array = std::make_shared<Int64Array>(tensor->size(), tensor->data()); + break; + } + case Type::INT64: { + value_array = std::make_shared<Int64Array>(tensor->size(), tensor->data()); + break; + } + case Type::HALF_FLOAT: { + value_array = std::make_shared<HalfFloatArray>(tensor->size(), tensor->data()); + break; + } + case Type::FLOAT: { + value_array = std::make_shared<FloatArray>(tensor->size(), tensor->data()); + break; + } + case Type::DOUBLE: { + value_array = std::make_shared<DoubleArray>(tensor->size(), tensor->data()); + break; + } + default: { + return Status::NotImplemented("Unsupported tensor type: ", + tensor->type()->ToString()); + } + } + arr = std::make_shared<FixedSizeListArray>(ext_type->storage_type(), tensor->shape()[0], + value_array); + auto ext_data = arr->data(); + ext_data->type = ext_type; + return MakeArray(ext_data); +} + +Result<std::shared_ptr<Tensor>> FixedShapeTensorType::ToTensor( + std::shared_ptr<Array> arr) { + // To convert an array of n dimensional tensors to a n+1 dimensional tensor we + // interpret the array's length as the first dimension the new tensor. Further, we + // define n+1 dimensional tensor's strides by front appending a new stride to the n + // dimensional tensor's strides. + + ARROW_CHECK(is_tensor_supported(this->value_type_->id())); + + ARROW_DCHECK_EQ(arr->null_count(), 0) << "Null values not supported in tensors."; + auto ext_arr = internal::checked_pointer_cast<FixedSizeListArray>( + internal::checked_pointer_cast<ExtensionArray>(arr)->storage()); + + std::vector<int64_t> shape = this->shape(); + shape.insert(shape.begin(), 1, arr->length()); + + std::vector<int64_t> tensor_strides = this->strides(); + tensor_strides.insert(tensor_strides.begin(), 1, arr->length() * tensor_strides[0]); + + std::shared_ptr<Buffer> buffer = ext_arr->values()->data()->buffers[1]; + return *Tensor::Make(ext_arr->value_type(), buffer, shape, tensor_strides, dim_names()); +} + +Result<std::shared_ptr<DataType>> FixedShapeTensorType::Make( Review Comment: We could probably refactor `internal::ValidateTensorParams` into multiple functions so we can re-use some of `Tensor`'s validation logic for type, shape, strides - which is currently more extensive. If it's ok with you, I can try contributing to this more directly. I'm pretty sure I have push access to your fork... ########## cpp/src/arrow/extension/fixed_shape_tensor.cc: ########## @@ -0,0 +1,299 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#include "arrow/extension/fixed_shape_tensor.h" + +#include "arrow/array/array_nested.h" +#include "arrow/array/array_primitive.h" +#include "arrow/json/rapidjson_defs.h" // IWYU pragma: keep +#include "arrow/tensor.h" +#include "arrow/util/logging.h" +#include "arrow/util/sort.h" + +#include <rapidjson/document.h> +#include <rapidjson/writer.h> + +namespace rj = arrow::rapidjson; + +namespace arrow { +namespace extension { + +bool FixedShapeTensorType::ExtensionEquals(const ExtensionType& other) const { + if (extension_name() != other.extension_name()) { + return false; + } + const auto& other_ext = static_cast<const FixedShapeTensorType&>(other); + + auto is_permutation_trivial = [](const std::vector<int64_t>& permutation) { + for (size_t i = 1; i < permutation.size(); ++i) { + if (permutation[i - 1] + 1 != permutation[i]) { + return false; + } + } + return true; + }; + const bool permutation_equivalent = + (permutation_ == other_ext.permutation()) || + ((permutation_.empty() && is_permutation_trivial(other_ext.permutation())) && + (is_permutation_trivial(permutation_) || other_ext.permutation().empty())); + + return storage_type()->Equals(other_ext.storage_type()) && + shape_ == other_ext.shape() && dim_names_ == other_ext.dim_names() && + permutation_equivalent; +} + +std::string FixedShapeTensorType::Serialize() const { + rj::Document document; + document.SetObject(); + rj::Document::AllocatorType& allocator = document.GetAllocator(); + + rj::Value shape(rj::kArrayType); + for (auto v : shape_) { + shape.PushBack(v, allocator); + } + document.AddMember(rj::Value("shape", allocator), shape, allocator); + + if (!permutation_.empty()) { + rj::Value permutation(rj::kArrayType); + for (auto v : permutation_) { + permutation.PushBack(v, allocator); + } + document.AddMember(rj::Value("permutation", allocator), permutation, allocator); + } + + if (!dim_names_.empty()) { + rj::Value dim_names(rj::kArrayType); + for (std::string v : dim_names_) { + dim_names.PushBack(rj::Value{}.SetString(v.c_str(), allocator), allocator); + } + document.AddMember(rj::Value("dim_names", allocator), dim_names, allocator); + } + + rj::StringBuffer buffer; + rj::Writer<rj::StringBuffer> writer(buffer); + document.Accept(writer); + return buffer.GetString(); +} + +Result<std::shared_ptr<DataType>> FixedShapeTensorType::Deserialize( + std::shared_ptr<DataType> storage_type, const std::string& serialized_data) const { + if (storage_type->id() != Type::FIXED_SIZE_LIST) { + return Status::Invalid("Expected FixedSizeList storage type, got ", + storage_type->ToString()); + } + auto value_type = + internal::checked_pointer_cast<FixedSizeListType>(storage_type)->value_type(); + rj::Document document; + if (document.Parse(serialized_data.data(), serialized_data.length()).HasParseError() || + !document.HasMember("shape") || !document["shape"].IsArray()) { + return Status::Invalid("Invalid serialized JSON data: ", serialized_data); + } + + std::vector<int64_t> shape; + for (auto& x : document["shape"].GetArray()) { + shape.emplace_back(x.GetInt64()); + } + std::vector<int64_t> permutation; + if (document.HasMember("permutation")) { + for (auto& x : document["permutation"].GetArray()) { + permutation.emplace_back(x.GetInt64()); + } + if (shape.size() != permutation.size()) { + return Status::Invalid("Invalid permutation"); + } + } + std::vector<std::string> dim_names; + if (document.HasMember("dim_names")) { + for (auto& x : document["dim_names"].GetArray()) { + dim_names.emplace_back(x.GetString()); + } + if (shape.size() != dim_names.size()) { + return Status::Invalid("Invalid dim_names"); + } + } + + return fixed_shape_tensor(value_type, shape, permutation, dim_names); +} + +std::shared_ptr<Array> FixedShapeTensorType::MakeArray( + std::shared_ptr<ArrayData> data) const { + return std::make_shared<ExtensionArray>(data); +} + +Result<std::shared_ptr<Array>> FixedShapeTensorType::MakeArray( + std::shared_ptr<Tensor> tensor) const { + auto permutation = internal::ArgSort(tensor->strides()); + std::reverse(permutation.begin(), permutation.end()); + if (permutation[0] != 0) { + return Status::Invalid( + "Only first-major tensors can be zero-copy converted to arrays"); + } + + auto cell_shape = tensor->shape(); + cell_shape.erase(cell_shape.begin()); + if (cell_shape != shape_) { + return Status::Invalid("Expected cell shape does not match input tensor shape"); + } + + permutation.erase(permutation.begin()); + for (auto& x : permutation) { + x--; + } + + auto ext_type = internal::checked_pointer_cast<ExtensionType>( + fixed_shape_tensor(tensor->type(), cell_shape, permutation, tensor->dim_names())); + + std::shared_ptr<FixedSizeListArray> arr; + std::shared_ptr<Array> value_array; + switch (tensor->type_id()) { + case Type::UINT8: { + value_array = std::make_shared<UInt8Array>(tensor->size(), tensor->data()); + break; + } + case Type::INT8: { + value_array = std::make_shared<Int8Array>(tensor->size(), tensor->data()); + break; + } + case Type::UINT16: { + value_array = std::make_shared<UInt16Array>(tensor->size(), tensor->data()); + break; + } + case Type::INT16: { + value_array = std::make_shared<Int16Array>(tensor->size(), tensor->data()); + break; + } + case Type::UINT32: { + value_array = std::make_shared<UInt32Array>(tensor->size(), tensor->data()); + break; + } + case Type::INT32: { + value_array = std::make_shared<Int32Array>(tensor->size(), tensor->data()); + break; + } + case Type::UINT64: { + value_array = std::make_shared<Int64Array>(tensor->size(), tensor->data()); + break; + } + case Type::INT64: { + value_array = std::make_shared<Int64Array>(tensor->size(), tensor->data()); + break; + } + case Type::HALF_FLOAT: { + value_array = std::make_shared<HalfFloatArray>(tensor->size(), tensor->data()); + break; + } + case Type::FLOAT: { + value_array = std::make_shared<FloatArray>(tensor->size(), tensor->data()); + break; + } + case Type::DOUBLE: { + value_array = std::make_shared<DoubleArray>(tensor->size(), tensor->data()); + break; + } + default: { + return Status::NotImplemented("Unsupported tensor type: ", + tensor->type()->ToString()); + } + } + arr = std::make_shared<FixedSizeListArray>(ext_type->storage_type(), tensor->shape()[0], + value_array); + auto ext_data = arr->data(); + ext_data->type = ext_type; + return MakeArray(ext_data); +} + +Result<std::shared_ptr<Tensor>> FixedShapeTensorType::ToTensor( Review Comment: This function returns a `Result` but none of the potential errors are propagated (my guess is the signature changed at some point) -- This is an automated message from the Apache Git Service. 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