pitrou commented on code in PR #38008:
URL: https://github.com/apache/arrow/pull/38008#discussion_r2817423624
##########
python/pyarrow/tests/test_extension_type.py:
##########
@@ -1482,6 +1482,17 @@ def test_tensor_class_methods(np_type_str):
assert result.to_tensor().shape == (1, 3, 2, 2)
assert result.to_tensor().strides == (12 * bw, 1 * bw, 6 * bw, 2 * bw)
+ tensor_type = pa.fixed_shape_tensor(arrow_type, [2, 2, 3], permutation=[2,
1, 0])
Review Comment:
I suppose it's unrelated but it's testing something useful anyway?
##########
cpp/src/arrow/compute/kernels/CMakeLists.txt:
##########
@@ -22,7 +22,8 @@ arrow_install_all_headers("arrow/compute/kernels")
# Define arrow_compute_kernels_testing object library for common test files
if(ARROW_TESTING)
- add_library(arrow_compute_kernels_testing OBJECT test_util_internal.cc)
+ add_library(arrow_compute_kernels_testing OBJECT
+ test_util_internal.cc
../../extension/tensor_extension_array_test.cc)
Review Comment:
Why do we need this? This doesn't sound logical.
##########
python/pyarrow/tests/test_extension_type.py:
##########
@@ -1712,7 +1723,7 @@ def test_tensor_type_is_picklable(pickle_module):
'fixed_shape_tensor[value_type=int64, shape=[2,2,3],
dim_names=[C,H,W]]'
)
])
-def test_tensor_type_str(tensor_type, text):
+def test_tensor_type_str(tensor_type, text, pickle_module):
Review Comment:
This change doesn't seem useful?
##########
cpp/src/arrow/extension/tensor_internal.cc:
##########
@@ -0,0 +1,86 @@
+// 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/tensor_internal.h"
+
+#include "arrow/tensor.h"
+#include "arrow/util/checked_cast.h"
+#include "arrow/util/int_util_overflow.h"
+#include "arrow/util/sort_internal.h"
+
+#include "arrow/status.h"
+#include "arrow/util/print_internal.h"
+
+namespace arrow::internal {
+
+Status IsPermutationValid(const std::vector<int64_t>& permutation) {
+ const auto size = static_cast<int64_t>(permutation.size());
+ std::vector<uint8_t> dim_seen(size, 0);
+
+ for (const auto p : permutation) {
+ if (p < 0 || p >= size || dim_seen[p] != 0) {
+ return Status::Invalid(
+ "Permutation indices for ", size,
+ " dimensional tensors must be unique and within [0, ", size - 1,
+ "] range. Got: ", ::arrow::internal::PrintVector{permutation, ","});
+ }
+ dim_seen[p] = 1;
+ }
+ return Status::OK();
+}
+
+Status ComputeStrides(const std::shared_ptr<DataType>& value_type,
+ const std::vector<int64_t>& shape,
+ const std::vector<int64_t>& permutation,
+ std::vector<int64_t>* strides) {
+ auto fixed_width_type =
internal::checked_pointer_cast<FixedWidthType>(value_type);
+ if (permutation.empty()) {
+ return internal::ComputeRowMajorStrides(*fixed_width_type.get(), shape,
strides);
Review Comment:
This one is doing the exact same thing except no permutation, please
reconcile the two functions instead of duplicating code.
##########
cpp/src/arrow/extension/tensor_internal.h:
##########
@@ -20,25 +20,17 @@
#include <cstdint>
#include <vector>
-#include "arrow/status.h"
-#include "arrow/util/print_internal.h"
+#include "arrow/array/array_nested.h"
namespace arrow::internal {
-inline Status IsPermutationValid(const std::vector<int64_t>& permutation) {
- const auto size = static_cast<int64_t>(permutation.size());
- std::vector<uint8_t> dim_seen(size, 0);
+ARROW_EXPORT
+Status IsPermutationValid(const std::vector<int64_t>& permutation);
- for (const auto p : permutation) {
- if (p < 0 || p >= size || dim_seen[p] != 0) {
- return Status::Invalid(
- "Permutation indices for ", size,
- " dimensional tensors must be unique and within [0, ", size - 1,
- "] range. Got: ", ::arrow::internal::PrintVector{permutation, ","});
- }
- dim_seen[p] = 1;
- }
- return Status::OK();
-}
+ARROW_EXPORT
+Status ComputeStrides(const std::shared_ptr<DataType>& value_type,
Review Comment:
Could return `Result<std::vector<int64_t>>` for better ergonomics
##########
cpp/src/arrow/extension/tensor_internal.h:
##########
@@ -20,25 +20,17 @@
#include <cstdint>
#include <vector>
-#include "arrow/status.h"
-#include "arrow/util/print_internal.h"
+#include "arrow/array/array_nested.h"
namespace arrow::internal {
-inline Status IsPermutationValid(const std::vector<int64_t>& permutation) {
- const auto size = static_cast<int64_t>(permutation.size());
- std::vector<uint8_t> dim_seen(size, 0);
+ARROW_EXPORT
+Status IsPermutationValid(const std::vector<int64_t>& permutation);
Review Comment:
These helper functions all could take `std::span<const int64_t>` as inputs,
though not important either.
##########
cpp/src/arrow/extension/variable_shape_tensor.cc:
##########
@@ -0,0 +1,314 @@
+// 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 <set>
+#include <sstream>
+
+#include "arrow/extension/tensor_internal.h"
+#include "arrow/extension/variable_shape_tensor.h"
+
+#include "arrow/array/array_primitive.h"
+#include "arrow/json/rapidjson_defs.h" // IWYU pragma: keep
+#include "arrow/scalar.h"
+#include "arrow/tensor.h"
+#include "arrow/util/int_util_overflow.h"
+#include "arrow/util/logging_internal.h"
+#include "arrow/util/print_internal.h"
+#include "arrow/util/sort_internal.h"
+#include "arrow/util/string.h"
+
+#include <rapidjson/document.h>
+#include <rapidjson/writer.h>
+
+namespace rj = arrow::rapidjson;
+
+namespace arrow::extension {
+
+bool VariableShapeTensorType::ExtensionEquals(const ExtensionType& other)
const {
+ if (extension_name() != other.extension_name()) {
+ return false;
+ }
+ const auto& other_ext = internal::checked_cast<const
VariableShapeTensorType&>(other);
+ if (this->ndim() != other_ext.ndim()) {
+ return false;
+ }
+
+ 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())) &&
+ (dim_names_ == other_ext.dim_names()) &&
+ (uniform_shape_ == other_ext.uniform_shape()) &&
permutation_equivalent;
+}
+
+std::string VariableShapeTensorType::ToString(bool show_metadata) const {
+ std::stringstream ss;
+ ss << "extension<" << this->extension_name()
+ << "[value_type=" << value_type_->ToString() << ", ndim=" << ndim_;
+
+ if (!permutation_.empty()) {
+ ss << ", permutation=" << ::arrow::internal::PrintVector{permutation_,
","};
+ }
+ if (!dim_names_.empty()) {
+ ss << ", dim_names=[" << internal::JoinStrings(dim_names_, ",") << "]";
+ }
+ if (!uniform_shape_.empty()) {
+ std::vector<std::string> uniform_shape;
+ for (const auto& v : uniform_shape_) {
+ if (v.has_value()) {
+ uniform_shape.emplace_back(std::to_string(v.value()));
+ } else {
+ uniform_shape.emplace_back("null");
+ }
+ }
+ ss << ", uniform_shape=[" << internal::JoinStrings(uniform_shape, ",") <<
"]";
+ }
+ ss << "]>";
+ return ss.str();
+}
+
+std::string VariableShapeTensorType::Serialize() const {
+ rj::Document document;
+ document.SetObject();
+ rj::Document::AllocatorType& allocator = document.GetAllocator();
+
+ 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);
+ }
+
+ if (!uniform_shape_.empty()) {
+ rj::Value uniform_shape(rj::kArrayType);
+ for (auto v : uniform_shape_) {
+ if (v.has_value()) {
+ uniform_shape.PushBack(v.value(), allocator);
+ } else {
+ uniform_shape.PushBack(rj::Value{}.SetNull(), allocator);
+ }
+ }
+ document.AddMember(rj::Value("uniform_shape", allocator), uniform_shape,
allocator);
+ }
+
+ rj::StringBuffer buffer;
+ rj::Writer<rj::StringBuffer> writer(buffer);
+ document.Accept(writer);
+ return buffer.GetString();
+}
+
+Result<std::shared_ptr<DataType>> VariableShapeTensorType::Deserialize(
+ std::shared_ptr<DataType> storage_type, const std::string&
serialized_data) const {
+ if (storage_type->id() != Type::STRUCT) {
+ return Status::Invalid("Expected Struct storage type, got ",
+ storage_type->ToString());
+ }
+ if (storage_type->num_fields() != 2) {
+ return Status::Invalid("Expected Struct storage type with 2 fields, got ",
+ storage_type->num_fields());
+ }
+ if (storage_type->field(0)->type()->id() != Type::LIST) {
+ return Status::Invalid("Expected List storage type, got ",
+ storage_type->field(0)->type()->ToString());
+ }
+ if (storage_type->field(1)->type()->id() != Type::FIXED_SIZE_LIST) {
+ return Status::Invalid("Expected FixedSizeList storage type, got ",
+ storage_type->field(1)->type()->ToString());
+ }
+ if (internal::checked_cast<const
FixedSizeListType&>(*storage_type->field(1)->type())
+ .value_type() != int32()) {
+ return Status::Invalid("Expected FixedSizeList value type int32, got ",
+ storage_type->field(1)->type()->ToString());
+ }
+
+ const auto value_type = storage_type->field(0)->type()->field(0)->type();
+ const uint32_t ndim =
+ internal::checked_cast<const
FixedSizeListType&>(*storage_type->field(1)->type())
+ .list_size();
+
+ rj::Document document;
+ if (document.Parse(serialized_data.data(),
serialized_data.length()).HasParseError()) {
+ return Status::Invalid("Invalid serialized JSON data: ", serialized_data);
+ }
+
+ std::vector<int64_t> permutation;
+ if (document.HasMember("permutation")) {
+ permutation.reserve(ndim);
+ for (const auto& x : document["permutation"].GetArray()) {
+ permutation.emplace_back(x.GetInt64());
+ }
+ }
+ std::vector<std::string> dim_names;
+ if (document.HasMember("dim_names")) {
+ dim_names.reserve(ndim);
+ for (const auto& x : document["dim_names"].GetArray()) {
+ dim_names.emplace_back(x.GetString());
+ }
+ }
+
+ std::vector<std::optional<int64_t>> uniform_shape;
+ if (document.HasMember("uniform_shape")) {
+ uniform_shape.reserve(ndim);
+ for (const auto& x : document["uniform_shape"].GetArray()) {
+ if (x.IsNull()) {
+ uniform_shape.emplace_back(std::nullopt);
+ } else {
+ uniform_shape.emplace_back(x.GetInt64());
+ }
+ }
+ }
+
+ return VariableShapeTensorType::Make(value_type, ndim, permutation,
dim_names,
+ uniform_shape);
+}
+
+std::shared_ptr<Array> VariableShapeTensorType::MakeArray(
+ std::shared_ptr<ArrayData> data) const {
+ DCHECK_EQ(data->type->id(), Type::EXTENSION);
+ DCHECK_EQ("arrow.variable_shape_tensor",
+ internal::checked_cast<const
ExtensionType&>(*data->type).extension_name());
+ return std::make_shared<ExtensionArray>(data);
+}
+
+Result<std::shared_ptr<Tensor>> VariableShapeTensorType::MakeTensor(
+ const std::shared_ptr<ExtensionScalar>& scalar) {
+ const auto& tensor_scalar = internal::checked_cast<const
StructScalar&>(*scalar->value);
+ const auto& ext_type =
+ internal::checked_cast<const VariableShapeTensorType&>(*scalar->type);
+
+ ARROW_ASSIGN_OR_RAISE(const auto data_scalar, tensor_scalar.field(0));
+ ARROW_ASSIGN_OR_RAISE(const auto shape_scalar, tensor_scalar.field(1));
+ ARROW_CHECK(tensor_scalar.is_valid);
+ const auto data_array =
+ internal::checked_pointer_cast<BaseListScalar>(data_scalar)->value;
+ const auto shape_array = internal::checked_pointer_cast<Int32Array>(
+
internal::checked_pointer_cast<FixedSizeListScalar>(shape_scalar)->value);
+
+ const auto& value_type =
+ internal::checked_cast<const FixedWidthType&>(*ext_type.value_type());
+
+ if (data_array->null_count() > 0) {
+ return Status::Invalid("Cannot convert data with nulls to Tensor.");
+ }
+
+ auto permutation = ext_type.permutation();
+ if (permutation.empty()) {
+ permutation.resize(ext_type.ndim());
+ std::iota(permutation.begin(), permutation.end(), 0);
+ }
+
+ ARROW_CHECK_EQ(shape_array->length(), ext_type.ndim());
+ std::vector<int64_t> shape;
+ shape.reserve(ext_type.ndim());
+ for (int64_t j = 0; j < static_cast<int64_t>(ext_type.ndim()); ++j) {
+ const auto size_value = shape_array->Value(j);
+ if (size_value < 0) {
+ return Status::Invalid("shape must have non-negative values");
+ }
+ shape.push_back(std::move(size_value));
+ }
+
+ std::vector<std::string> dim_names = ext_type.dim_names();
+ if (!dim_names.empty()) {
+ internal::Permute<std::string>(permutation, &dim_names);
+ }
+
+ std::vector<int64_t> strides;
+ ARROW_RETURN_NOT_OK(
+ internal::ComputeStrides(ext_type.value_type(), shape, permutation,
&strides));
+ internal::Permute<int64_t>(permutation, &shape);
+
+ const auto byte_width = value_type.byte_width();
+ const auto start_position = data_array->offset() * byte_width;
+ const auto size = std::accumulate(shape.begin(), shape.end(),
static_cast<int64_t>(1),
+ std::multiplies<>());
+ ARROW_CHECK_EQ(size * byte_width, data_array->length() * byte_width);
Review Comment:
This can be evidently simplified.
##########
cpp/src/arrow/extension/variable_shape_tensor.cc:
##########
@@ -0,0 +1,314 @@
+// 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 <set>
+#include <sstream>
+
+#include "arrow/extension/tensor_internal.h"
+#include "arrow/extension/variable_shape_tensor.h"
+
+#include "arrow/array/array_primitive.h"
+#include "arrow/json/rapidjson_defs.h" // IWYU pragma: keep
+#include "arrow/scalar.h"
+#include "arrow/tensor.h"
+#include "arrow/util/int_util_overflow.h"
+#include "arrow/util/logging_internal.h"
+#include "arrow/util/print_internal.h"
+#include "arrow/util/sort_internal.h"
+#include "arrow/util/string.h"
+
+#include <rapidjson/document.h>
+#include <rapidjson/writer.h>
+
+namespace rj = arrow::rapidjson;
+
+namespace arrow::extension {
+
+bool VariableShapeTensorType::ExtensionEquals(const ExtensionType& other)
const {
+ if (extension_name() != other.extension_name()) {
+ return false;
+ }
+ const auto& other_ext = internal::checked_cast<const
VariableShapeTensorType&>(other);
+ if (this->ndim() != other_ext.ndim()) {
+ return false;
+ }
+
+ auto is_permutation_trivial = [](const std::vector<int64_t>& permutation) {
Review Comment:
Again, please don't duplicate code like this, factor out into common helpers.
##########
cpp/src/arrow/extension/variable_shape_tensor.cc:
##########
@@ -0,0 +1,314 @@
+// 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 <set>
+#include <sstream>
+
+#include "arrow/extension/tensor_internal.h"
+#include "arrow/extension/variable_shape_tensor.h"
+
+#include "arrow/array/array_primitive.h"
+#include "arrow/json/rapidjson_defs.h" // IWYU pragma: keep
+#include "arrow/scalar.h"
+#include "arrow/tensor.h"
+#include "arrow/util/int_util_overflow.h"
+#include "arrow/util/logging_internal.h"
+#include "arrow/util/print_internal.h"
+#include "arrow/util/sort_internal.h"
+#include "arrow/util/string.h"
+
+#include <rapidjson/document.h>
+#include <rapidjson/writer.h>
+
+namespace rj = arrow::rapidjson;
+
+namespace arrow::extension {
+
+bool VariableShapeTensorType::ExtensionEquals(const ExtensionType& other)
const {
+ if (extension_name() != other.extension_name()) {
+ return false;
+ }
+ const auto& other_ext = internal::checked_cast<const
VariableShapeTensorType&>(other);
+ if (this->ndim() != other_ext.ndim()) {
+ return false;
+ }
+
+ 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())) &&
+ (dim_names_ == other_ext.dim_names()) &&
+ (uniform_shape_ == other_ext.uniform_shape()) &&
permutation_equivalent;
+}
+
+std::string VariableShapeTensorType::ToString(bool show_metadata) const {
+ std::stringstream ss;
+ ss << "extension<" << this->extension_name()
+ << "[value_type=" << value_type_->ToString() << ", ndim=" << ndim_;
+
+ if (!permutation_.empty()) {
+ ss << ", permutation=" << ::arrow::internal::PrintVector{permutation_,
","};
+ }
+ if (!dim_names_.empty()) {
+ ss << ", dim_names=[" << internal::JoinStrings(dim_names_, ",") << "]";
+ }
+ if (!uniform_shape_.empty()) {
+ std::vector<std::string> uniform_shape;
+ for (const auto& v : uniform_shape_) {
+ if (v.has_value()) {
+ uniform_shape.emplace_back(std::to_string(v.value()));
+ } else {
+ uniform_shape.emplace_back("null");
+ }
+ }
+ ss << ", uniform_shape=[" << internal::JoinStrings(uniform_shape, ",") <<
"]";
+ }
+ ss << "]>";
+ return ss.str();
+}
+
+std::string VariableShapeTensorType::Serialize() const {
+ rj::Document document;
+ document.SetObject();
+ rj::Document::AllocatorType& allocator = document.GetAllocator();
+
+ 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);
+ }
+
+ if (!uniform_shape_.empty()) {
+ rj::Value uniform_shape(rj::kArrayType);
+ for (auto v : uniform_shape_) {
+ if (v.has_value()) {
+ uniform_shape.PushBack(v.value(), allocator);
+ } else {
+ uniform_shape.PushBack(rj::Value{}.SetNull(), allocator);
+ }
+ }
+ document.AddMember(rj::Value("uniform_shape", allocator), uniform_shape,
allocator);
+ }
+
+ rj::StringBuffer buffer;
+ rj::Writer<rj::StringBuffer> writer(buffer);
+ document.Accept(writer);
+ return buffer.GetString();
+}
+
+Result<std::shared_ptr<DataType>> VariableShapeTensorType::Deserialize(
+ std::shared_ptr<DataType> storage_type, const std::string&
serialized_data) const {
+ if (storage_type->id() != Type::STRUCT) {
+ return Status::Invalid("Expected Struct storage type, got ",
+ storage_type->ToString());
+ }
+ if (storage_type->num_fields() != 2) {
+ return Status::Invalid("Expected Struct storage type with 2 fields, got ",
+ storage_type->num_fields());
+ }
+ if (storage_type->field(0)->type()->id() != Type::LIST) {
+ return Status::Invalid("Expected List storage type, got ",
+ storage_type->field(0)->type()->ToString());
+ }
+ if (storage_type->field(1)->type()->id() != Type::FIXED_SIZE_LIST) {
+ return Status::Invalid("Expected FixedSizeList storage type, got ",
+ storage_type->field(1)->type()->ToString());
+ }
+ if (internal::checked_cast<const
FixedSizeListType&>(*storage_type->field(1)->type())
+ .value_type() != int32()) {
+ return Status::Invalid("Expected FixedSizeList value type int32, got ",
+ storage_type->field(1)->type()->ToString());
+ }
+
+ const auto value_type = storage_type->field(0)->type()->field(0)->type();
+ const uint32_t ndim =
+ internal::checked_cast<const
FixedSizeListType&>(*storage_type->field(1)->type())
+ .list_size();
+
+ rj::Document document;
+ if (document.Parse(serialized_data.data(),
serialized_data.length()).HasParseError()) {
+ return Status::Invalid("Invalid serialized JSON data: ", serialized_data);
+ }
+
+ std::vector<int64_t> permutation;
+ if (document.HasMember("permutation")) {
+ permutation.reserve(ndim);
+ for (const auto& x : document["permutation"].GetArray()) {
+ permutation.emplace_back(x.GetInt64());
+ }
Review Comment:
What happens if "permutation" is not an array of ints?
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