// Copyright 2018 The Chromium Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.

// Datatypes and interfaces of models for the Machine Learning API.

// NOTE: This mojom exists in two places and must be kept in sync:
//       Chromium:  //chromeos/services/machine_learning/public/mojom/
//       Chrome OS: src/platform2/ml/mojom/
// Example: A backwards-compatible mojom change (and corresponding
// implementation change) can be made in Chrome OS first, then replicated to the
// client (Chromium) later.

module chromeos.machine_learning.mojom;

// NOTE: The base directory for 'import' statements is expected to differ
//       between Chromium and Chrome OS versions of this file.
import "ml/mojom/graph_executor.mojom";

// These values are persisted to logs. Entries should not be renumbered and
// numeric values should never be reused.
// ModelIds prefixed with UNSUPPORTED_ are no longer supported. Attempts to load
// them will produce an error.
enum BuiltinModelId {
  // Unknown ML model. It is marked as unsupported.
  UNSUPPORTED_UNKNOWN = 0,
  // Test ML model.
  TEST_MODEL = 1,
  // The Smart Dim (20181115) ML model.
  SMART_DIM_20181115 = 2,
  // The Smart Dim (20190221) ML model.
  SMART_DIM_20190221 = 3,
  // The Top Cat (20190722) ML model.
  TOP_CAT_20190722 = 4,
  // The Smart Dim (20190521) ML model.
  SMART_DIM_20190521 = 5,
  // The Search Ranker (20190923) ML model.
  SEARCH_RANKER_20190923 = 6,
};

// These values are persisted to logs. Entries should not be renumbered and
// numeric values should never be reused.
enum CreateGraphExecutorResult {
  OK = 0,
  MODEL_INTERPRETATION_ERROR = 1,
  MEMORY_ALLOCATION_ERROR = 2,
};

// Model specification for builtin models.
// Because ml-service can retrieve a builtin model's content and metadata, only
// an |id| is needed to specify it.
struct BuiltinModelSpec {
  BuiltinModelId id;
};

// Model specification for downloaded models.
// For a downloaded model, both of the model content and metadata must be
// specified.
struct FlatBufferModelSpec {
  // The content of the model's tflite model file.
  string model_string;
  // A map from input nodes' names to their indices.
  map<string, int32> inputs;
  // A map from output nodes' names to their indices.
  map<string, int32> outputs;
  // Used in naming the UMA metric histograms of the model. An example of the
  // names of the histograms is:
  //
  // MachineLearningService.|metrics_model_name|.ExecuteResult.CpuTimeMicrosec
  //
  // This variable must NOT be empty.
  string metrics_model_name;
};

// The lifetime of the cached model is tied to the lifetime of the Model
// interface pipe. The Model interface pipe can be used to acquire multiple
// separate GraphExecutor instances.
interface Model {
  CreateGraphExecutor(GraphExecutor& request) =>
      (CreateGraphExecutorResult result);
};