ca3e7f55d9
This CL adds experimental enable_numeric_verify in TFLite converter.mlir_quantize() for quantization numeric verification. This pass adds NumericVerify ops for debugging during full-integer quantization, and NumericVerify op will output the differences between the original float & the dequantized values of the quantized values. The main characteristic of NumericVerify op is the output tensor name has the following format:
NumericVerify/{tensor_name}:{tensor_id}
where tensor_name and tensor_id are from the original quantized op's activation tensor (the first input tensor of the NumericVerify op) for the purpose of debugging. It provides users the easy way to search for tensors in the debugging tools.
If you turn on log_if_failed in NumericVerify op, then it will output logs & throw errors when there exist any errors > tolerance.
PiperOrigin-RevId: 348740984
Change-Id: Ifb965d2237e827a747d1888cbd21af311d42e4a1
92 lines
3.8 KiB
C++
92 lines
3.8 KiB
C++
/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "pybind11/pybind11.h"
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#include "tensorflow/lite/toco/python/toco_python_api.h"
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#include "tensorflow/python/lib/core/pybind11_lib.h"
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namespace py = pybind11;
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PYBIND11_MODULE(_pywrap_toco_api, m) {
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m.def(
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"TocoConvert",
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[](py::object model_flags_proto_txt_raw,
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py::object toco_flags_proto_txt_raw, py::object input_contents_txt_raw,
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bool extended_return, py::object debug_info_txt_raw,
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bool enable_mlir_converter) {
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return tensorflow::PyoOrThrow(toco::TocoConvert(
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model_flags_proto_txt_raw.ptr(), toco_flags_proto_txt_raw.ptr(),
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input_contents_txt_raw.ptr(), extended_return,
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debug_info_txt_raw.ptr(), enable_mlir_converter));
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},
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py::arg("model_flags_proto_txt_raw"), py::arg("toco_flags_proto_txt_raw"),
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py::arg("input_contents_txt_raw"), py::arg("extended_return") = false,
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py::arg("debug_info_txt_raw") = py::none(),
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py::arg("enable_mlir_converter") = false,
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R"pbdoc(
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Convert a model represented in `input_contents`. `model_flags_proto`
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describes model parameters. `toco_flags_proto` describes conversion
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parameters (see relevant .protos for more information). Returns a string
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representing the contents of the converted model. When extended_return
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flag is set to true returns a dictionary that contains string representation
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of the converted model and some statistics like arithmetic ops count.
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`debug_info_str` contains the `GraphDebugInfo` proto. When
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`enable_mlir_converter` is True, tuse MLIR-based conversion instead of
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TOCO conversion.
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)pbdoc");
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m.def(
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"TocoGetPotentiallySupportedOps",
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[]() {
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return tensorflow::PyoOrThrow(toco::TocoGetPotentiallySupportedOps());
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},
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R"pbdoc(
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Returns a list of names of all ops potentially supported by tflite.
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)pbdoc");
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m.def(
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"ExperimentalMlirQuantizeModel",
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[](py::object input_contents_txt_raw, bool disable_per_channel,
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bool fully_quantize, int inference_type, bool enable_numeric_verify) {
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return tensorflow::PyoOrThrow(toco::MlirQuantizeModel(
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input_contents_txt_raw.ptr(), disable_per_channel, fully_quantize,
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inference_type, enable_numeric_verify));
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},
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py::arg("input_contents_txt_raw"), py::arg("disable_per_channel") = false,
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py::arg("fully_quantize") = true, py::arg("inference_type") = 9,
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py::arg("enable_numeric_verify") = false,
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R"pbdoc(
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Returns a quantized model.
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)pbdoc");
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m.def(
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"ExperimentalMlirSparsifyModel",
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[](py::object input_contents_txt_raw) {
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return tensorflow::PyoOrThrow(
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toco::MlirSparsifyModel(input_contents_txt_raw.ptr()));
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},
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py::arg("input_contents_txt_raw"),
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R"pbdoc(
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Returns a sparsified model.
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)pbdoc");
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m.def(
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"RegisterCustomOpdefs",
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[](py::object custom_opdefs_txt_raw) {
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return tensorflow::PyoOrThrow(
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toco::RegisterCustomOpdefs(custom_opdefs_txt_raw.ptr()));
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},
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py::arg("custom_opdefs_txt_raw"),
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R"pbdoc(
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Registers the given custom opdefs to the TensorFlow global op registry.
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)pbdoc");
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}
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