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Input preprocessing library to support vocab-dimension mini-batching.
Currently only PMAP is supported for simplicity. JAX support will be added later. PiperOrigin-RevId: 716024250
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88 changes: 88 additions & 0 deletions
88
jax_tpu_embedding/sparsecore/lib/core/input_preprocessing_py_util.cc
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| Original file line number | Diff line number | Diff line change |
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| // Copyright 2024 The JAX SC Authors. | ||
| // | ||
| // Licensed 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 "jax_tpu_embedding/sparsecore/lib/core/input_preprocessing_py_util.h" | ||
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| #include <algorithm> | ||
| #include <cmath> | ||
| #include <string> | ||
| #include <utility> | ||
| #include <vector> | ||
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| #include "absl/container/flat_hash_map.h" // from @com_google_absl | ||
| #include "absl/log/check.h" // from @com_google_absl | ||
| #include "jax_tpu_embedding/sparsecore/lib/core/input_preprocessing_util.h" | ||
| #include "pybind11/cast.h" // from @pybind11 | ||
| #include "pybind11/gil.h" // from @pybind11 | ||
| #include "pybind11/numpy.h" // from @pybind11 | ||
| #include "pybind11/pybind11.h" // from @pybind11 | ||
| #include "pybind11/pytypes.h" // from @pybind11 | ||
| #include "tsl/profiler/lib/traceme.h" // from @tsl | ||
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| namespace jax_sc_embedding { | ||
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| namespace py = ::pybind11; | ||
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| absl::flat_hash_map<std::string, std::vector<StackedTableMetadata>> | ||
| GetStackedTableMetadata(const py::list& feature_specs, const int batch_size) { | ||
| tsl::profiler::TraceMe t([] { return "GetStackedTableMetadata"; }); | ||
| absl::flat_hash_map<std::string, std::vector<StackedTableMetadata>> | ||
| stacked_table_metadata; | ||
| for (int i = 0; i < feature_specs.size(); ++i) { | ||
| const py::object& feature_spec = feature_specs[i]; | ||
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| const py::object& feature_transformation = | ||
| feature_spec.attr("_id_transformation"); | ||
| const py::object& table_spec = feature_spec.attr("table_spec"); | ||
| const py::object& stacked_table_spec = | ||
| table_spec.attr("stacked_table_spec"); | ||
| const std::string stacked_table_name = py::cast<std::string>( | ||
| table_spec.attr("_setting_in_stack").attr("stack_name")); | ||
| int col_shift = 0; | ||
| int col_offset = 0; | ||
| int row_offset = 0; | ||
| const int max_ids_per_partition = | ||
| py::cast<int>(stacked_table_spec.attr("max_ids_per_partition")); | ||
| const int max_unique_ids_per_partition = | ||
| py::cast<int>(stacked_table_spec.attr("max_unique_ids_per_partition")); | ||
| const int vocab_size = | ||
| py::cast<int>(stacked_table_spec.attr("stack_vocab_size")); | ||
| if (!feature_transformation.is_none()) { | ||
| row_offset = py::cast<int>(feature_transformation.attr("row_offset")); | ||
| col_shift = py::cast<int>(feature_transformation.attr("col_shift")); | ||
| col_offset = py::cast<int>(feature_transformation.attr("col_offset")); | ||
| } | ||
| stacked_table_metadata[stacked_table_name].emplace_back( | ||
| i, max_ids_per_partition, max_unique_ids_per_partition, row_offset, | ||
| col_offset, col_shift, | ||
| /*batch_size=*/batch_size, vocab_size); | ||
| } | ||
| // Sort the stacked tables by row_offset. | ||
| for (auto& [_, t] : stacked_table_metadata) { | ||
| std::sort(t.begin(), t.end(), | ||
| [](const StackedTableMetadata& a, const StackedTableMetadata& b) { | ||
| return a.row_offset < b.row_offset; | ||
| }); | ||
| } | ||
| return stacked_table_metadata; | ||
| } | ||
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| absl::flat_hash_map<std::string, std::vector<StackedTableMetadata>> | ||
| GetStackedTableMetadata(const py::list& feature_specs, | ||
| const py::list& features) { | ||
| tsl::profiler::TraceMe t([] { return "GetStackedTableMetadata"; }); | ||
| int batch_size = features[0].cast<py::array>().shape(0); | ||
| return GetStackedTableMetadata(feature_specs, batch_size); | ||
| } | ||
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| } // namespace jax_sc_embedding |
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jax_tpu_embedding/sparsecore/lib/core/input_preprocessing_py_util.h
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,40 @@ | ||
| // Copyright 2024 The JAX SC Authors. | ||
| // | ||
| // Licensed 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. | ||
| #ifndef JAX_TPU_EMBEDDING_SPARSECORE_LIB_CORE_INPUT_PREPROCESSING_H_ | ||
| #define JAX_TPU_EMBEDDING_SPARSECORE_LIB_CORE_INPUT_PREPROCESSING_H_ | ||
| #include <string> | ||
| #include <vector> | ||
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| #include "absl/container/flat_hash_map.h" // from @com_google_absl | ||
| #include "jax_tpu_embedding/sparsecore/lib/core/input_preprocessing_util.h" | ||
| #include "pybind11/numpy.h" // from @pybind11 | ||
| #include "pybind11/pytypes.h" // from @pybind11 | ||
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| namespace jax_sc_embedding { | ||
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| namespace py = ::pybind11; | ||
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| // Copy information from feature_specs to StackedTableMetadata. | ||
| // The features argument is only used to get the batch size. | ||
| absl::flat_hash_map<std::string, std::vector<StackedTableMetadata>> | ||
| GetStackedTableMetadata(const py::list& feature_specs, | ||
| const py::list& features); | ||
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| // Copy information from feature_specs to StackedTableMetadata. | ||
| absl::flat_hash_map<std::string, std::vector<StackedTableMetadata>> | ||
| GetStackedTableMetadata(const py::list& feature_specs, int batch_size); | ||
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| } // namespace jax_sc_embedding | ||
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| #endif // JAX_TPU_EMBEDDING_SPARSECORE_LIB_CORE_INPUT_PREPROCESSING_H_ |
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