chore: import upstream snapshot with attribution

tensorflow/lite/tools/optimize/quantize_model.h

@@ -0,0 +1,139 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+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 TENSORFLOW_LITE_TOOLS_OPTIMIZE_QUANTIZE_MODEL_H_
+#define TENSORFLOW_LITE_TOOLS_OPTIMIZE_QUANTIZE_MODEL_H_
+
+#include <memory>
+#include <unordered_set>
+
+#include "tensorflow/lite/context.h"
+#include "tensorflow/lite/core/api/error_reporter.h"
+#include "tensorflow/lite/core/model.h"
+#include "tensorflow/lite/schema/schema_generated.h"
+#include "tensorflow/lite/util.h"
+
+namespace tflite {
+namespace optimize {
+
+// Quantizes input_model and populates the provided builder with the new model.
+// input_model is required to have min/max information populated in its
+// quantization params.
+//
+// Inputs and output types default to float instead of a quantized type.
+//
+// Note: This is a private API, subject to change.
+TfLiteStatus QuantizeModel(flatbuffers::FlatBufferBuilder* builder,
+                           ModelT* input_model, ErrorReporter* error_reporter);
+
+// Same as above, but the types of quantized inputs and outputs are
+// configurable.
+//
+// Note: This is a private API, subject to change.
+TfLiteStatus QuantizeModel(flatbuffers::FlatBufferBuilder* builder,
+                           ModelT* input_model, const TensorType& input_type,
+                           const TensorType& output_type,
+                           ErrorReporter* error_reporter);
+
+// Same as above, but can enable allowing float intermediate operations for ops
+// that do not yet support quantizable.
+//
+// Note: This is a private API, subject to change.
+TfLiteStatus QuantizeModel(flatbuffers::FlatBufferBuilder* builder,
+                           ModelT* input_model, const TensorType& input_type,
+                           const TensorType& output_type, bool allow_float,
+                           ErrorReporter* error_reporter);
+
+// Same as above but with added option of disabling per channel quantization
+//
+// Note: This is a private API, subject to change.
+TfLiteStatus QuantizeModel(
+    flatbuffers::FlatBufferBuilder* builder, ModelT* input_model,
+    const TensorType& input_type, const TensorType& output_type,
+    bool allow_float, bool disable_per_channel,
+    bool disable_per_channel_quantization_for_dense_layers,
+    ErrorReporter* error_reporter);
+
+// Same as above but with added option of handling quantization of external
+// state tensors. This assumes first input and output tensors are ouputs and
+// rest are state tensors which are quantized later with type as
+// activation type (hence no fake quant ops).
+// Note: This is a private API, subject to change.
+TfLiteStatus QuantizeModel(
+    flatbuffers::FlatBufferBuilder* builder, ModelT* input_model,
+    const TensorType& input_type, const TensorType& output_type,
+    bool allow_float, bool disable_per_channel,
+    bool disable_per_channel_quantization_for_dense_layers,
+    ErrorReporter* error_reporter, bool handle_external_state);
+
+// Same as above, but enables only quantizing an allowlist of operations,
+// specified by their operator output name.
+//
+// Note: This is a private API, subject to change.
+TfLiteStatus QuantizeModel(flatbuffers::FlatBufferBuilder* builder,
+                           ModelT* input_model, const TensorType& input_type,
+                           const TensorType& output_type, bool allow_float,
+                           const std::unordered_set<string>& operator_names,
+                           ErrorReporter* error_reporter);
+
+// Same as above, but enables to provide activation type, which
+// could be TensorType_INT16 or TensorType_INT8.
+//
+// Note: This is a private API, subject to change.
+TfLiteStatus QuantizeModel(flatbuffers::FlatBufferBuilder* builder,
+                           ModelT* model, const TensorType& input_type,
+                           const TensorType& output_type, bool allow_float,
+                           const std::unordered_set<string>& operator_names,
+                           const TensorType& activations_type,
+                           const TensorType& bias_type,
+                           ErrorReporter* error_reporter);
+
+// Same as above, but all operators supporting quantization are quantized.
+//
+// Note: This is a private API, subject to change.
+TfLiteStatus QuantizeModelAllOperators(
+    flatbuffers::FlatBufferBuilder* builder, ModelT* model,
+    const TensorType& input_type, const TensorType& output_type,
+    bool allow_float, const TensorType& activations_type,
+    const TensorType& bias_type, ErrorReporter* error_reporter);
+
+// Same as above, but allows disabling per channel quantization.
+//
+// Note: This is a private API, subject to change.
+TfLiteStatus QuantizeModelAllOperators(
+    flatbuffers::FlatBufferBuilder* builder, ModelT* model,
+    const TensorType& input_type, const TensorType& output_type,
+    bool allow_float, const TensorType& activations_type,
+    const TensorType& bias_type, bool disable_per_channel,
+    bool disable_per_channel_quantization_for_dense_layers,
+    ErrorReporter* error_reporter);
+
+// Quantizes input_model and populates the provided builder with the new model
+// with all possible input parameters including disabling per_channel
+// quantization.
+//
+// All functions above call this function underneath.
+TfLiteStatus QuantizeModel(
+    flatbuffers::FlatBufferBuilder* builder, ModelT* model,
+    const TensorType& input_type, const TensorType& output_type,
+    bool allow_float, const std::unordered_set<string>& operator_names,
+    const TensorType& activations_type, const TensorType& bias_type,
+    bool disable_per_channel,
+    bool disable_per_channel_quantization_for_dense_layers,
+    ErrorReporter* error_reporter, bool handle_external_state);
+
+}  // namespace optimize
+}  // namespace tflite
+
+#endif  // TENSORFLOW_LITE_TOOLS_OPTIMIZE_QUANTIZE_MODEL_H_