diff options
Diffstat (limited to 'tensorflow/contrib/lite')
99 files changed, 7967 insertions, 2018 deletions
diff --git a/tensorflow/contrib/lite/BUILD b/tensorflow/contrib/lite/BUILD index f3ebe3b245..787a85644c 100644 --- a/tensorflow/contrib/lite/BUILD +++ b/tensorflow/contrib/lite/BUILD @@ -4,6 +4,7 @@ package(default_visibility = [ licenses(["notice"]) # Apache 2.0 +load("//tensorflow:tensorflow.bzl", "tf_cc_test") load("//tensorflow/contrib/lite:build_def.bzl", "tflite_copts", "gen_selected_ops") exports_files(glob([ @@ -165,10 +166,6 @@ cc_library( "stderr_reporter.h", ], copts = tflite_copts(), - defines = select({ - ":with_tflite_flex": ["TFLITE_FLEX"], - "//conditions:default": [], - }), linkopts = [ ] + select({ "//tensorflow:android": [ @@ -276,6 +273,7 @@ cc_test( "testdata/0_subgraphs.bin", "testdata/2_subgraphs.bin", "testdata/empty_model.bin", + "testdata/multi_add_flex.bin", "testdata/test_model.bin", "testdata/test_model_broken.bin", ], @@ -283,6 +281,26 @@ cc_test( ":framework", "//tensorflow/contrib/lite/c:c_api_internal", "//tensorflow/contrib/lite/core/api", + "//tensorflow/contrib/lite/kernels:builtin_ops", + "//tensorflow/contrib/lite/testing:util", + "@com_google_googletest//:gtest", + ], +) + +# Test model framework with the flex library linked into the target. +tf_cc_test( + name = "model_flex_test", + size = "small", + srcs = ["model_flex_test.cc"], + data = [ + "testdata/multi_add_flex.bin", + ], + tags = ["no_windows"], # TODO(b/116667551): No weak symbols with MSVC. + deps = [ + ":framework", + "//tensorflow/contrib/lite/core/api", + "//tensorflow/contrib/lite/delegates/flex:delegate", + "//tensorflow/contrib/lite/kernels:builtin_ops", "//tensorflow/contrib/lite/testing:util", "@com_google_googletest//:gtest", ], diff --git a/tensorflow/contrib/lite/delegates/flex/BUILD b/tensorflow/contrib/lite/delegates/flex/BUILD index 9dd38958e5..9b89ed4f84 100644 --- a/tensorflow/contrib/lite/delegates/flex/BUILD +++ b/tensorflow/contrib/lite/delegates/flex/BUILD @@ -2,7 +2,7 @@ # This is a TF Lite delegate that is powered by TensorFlow's Eager. # package(default_visibility = [ - "//visibility:public", + "//visibility:private", ]) licenses(["notice"]) # Apache 2.0 @@ -50,6 +50,7 @@ cc_library( hdrs = [ "delegate.h", ], + visibility = ["//visibility:public"], deps = [ ":buffer_map", ":delegate_data", @@ -66,6 +67,7 @@ cc_library( "//tensorflow/core:lib", ], }), + alwayslink = 1, ) tf_cc_test( diff --git a/tensorflow/contrib/lite/delegates/flex/delegate.cc b/tensorflow/contrib/lite/delegates/flex/delegate.cc index ba065a8ff5..c72b0cf513 100644 --- a/tensorflow/contrib/lite/delegates/flex/delegate.cc +++ b/tensorflow/contrib/lite/delegates/flex/delegate.cc @@ -83,6 +83,15 @@ TfLiteStatus CopyFromBufferHandle(TfLiteContext* context, } // namespace delegate } // namespace flex +// Corresponding weak declaration found in lite/model.cc. +std::unique_ptr<TfLiteDelegate, void (*)(TfLiteDelegate*)> +AcquireFlexDelegate() { + return std::unique_ptr<TfLiteDelegate, void (*)(TfLiteDelegate*)>( + tflite::FlexDelegate::Create().release(), [](TfLiteDelegate* delegate) { + delete reinterpret_cast<tflite::FlexDelegate*>(delegate); + }); +} + std::unique_ptr<FlexDelegate> FlexDelegate::Create() { std::unique_ptr<flex::DelegateData> delegate_data; if (!flex::DelegateData::Create(&delegate_data).ok()) { diff --git a/tensorflow/contrib/lite/experimental/micro/BUILD b/tensorflow/contrib/lite/experimental/micro/BUILD new file mode 100644 index 0000000000..df1036bc8b --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/BUILD @@ -0,0 +1,76 @@ +package( + default_visibility = ["//visibility:public"], +) + +licenses(["notice"]) # Apache 2.0 + +load( + "//tensorflow/contrib/lite/experimental/micro/testing:micro_test.bzl", + "tflite_micro_cc_test", +) + +cc_library( + name = "micro_framework", + srcs = [ + "micro_error_reporter.cc", + "micro_interpreter.cc", + "micro_mutable_op_resolver.cc", + "simple_tensor_allocator.cc", + ], + hdrs = [ + "compatibility.h", + "micro_error_reporter.h", + "micro_interpreter.h", + "micro_mutable_op_resolver.h", + "simple_tensor_allocator.h", + ], + deps = [ + "//tensorflow/contrib/lite:schema_fbs_version", + "//tensorflow/contrib/lite/c:c_api_internal", + "//tensorflow/contrib/lite/core/api", + "//tensorflow/contrib/lite/schema:schema_fbs", + ], +) + +tflite_micro_cc_test( + name = "micro_error_reporter_test", + srcs = [ + "micro_error_reporter_test.cc", + ], + deps = [ + ":micro_framework", + ], +) + +tflite_micro_cc_test( + name = "micro_mutable_op_resolver_test", + srcs = [ + "micro_mutable_op_resolver_test.cc", + ], + deps = [ + ":micro_framework", + "//tensorflow/contrib/lite/experimental/micro/testing:micro_test", + ], +) + +tflite_micro_cc_test( + name = "micro_interpreter_test", + srcs = [ + "micro_interpreter_test.cc", + ], + deps = [ + ":micro_framework", + "//tensorflow/contrib/lite/experimental/micro/testing:micro_test", + ], +) + +tflite_micro_cc_test( + name = "simple_tensor_allocator_test", + srcs = [ + "simple_tensor_allocator_test.cc", + ], + deps = [ + ":micro_framework", + "//tensorflow/contrib/lite/experimental/micro/testing:micro_test", + ], +) diff --git a/tensorflow/contrib/lite/experimental/micro/README.md b/tensorflow/contrib/lite/experimental/micro/README.md new file mode 100644 index 0000000000..414cafde4d --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/README.md @@ -0,0 +1,114 @@ +# TensorFlow Lite for Microcontrollers + +This an experimental port of TensorFlow Lite aimed at micro controllers and other devices with only kilobytes of memory. It doesn't require any operating system support, any standard C or C++ libraries, or dynamic memory allocation, so it's designed to be portable even to 'bare metal' systems. The core runtime fits in 16KB on a Cortex M3, and with enough operators to run a speech keyword detection model, takes up a total of 22KB. + +The design goals are for the framework to be: + +- **Readable**: We want embedded software engineers to be able to understand what's required to run ML inference without having to study research papers. We've tried to keep the code base small, modular, and have reference implementations of all operations to help with this. + +- **Easy to modify**: We know that there are a lot of different platforms and requirements in the embedded world, and we don't expect to cover all of them in one framework. Instead, we're hoping that it can be a good starting point for developers to build on top of to meet their own needs. For example, we tried to make it easy to replace the implementations of key computational operators that are often crucial for performance, without having to touch the data flow and other runtime code. We want it to make more sense to use our workflow to handle things like model import and less-important operations, and customize the parts that matter, rather than having to reimplement everything in your own engine. + +- **Well-tested**: If you're modifying code, you need to know if your changes are correct. Having an easy way to test lets you develop much faster. To help there, we've written tests for all the components, and we've made sure that the tests can be run on almost any platform, with no dependencies apart from the ability to log text to a debug console somewhere. We also provide an easy way to run all the tests on-device as part of an automated test framework, and we use qemu/Renode emulation so that tests can be run even without physical devices present. + +- **Easy to integrate**: We want to be as open a system as possible, and use the best code available for each platform. To do that, we're going to rely on projects like [CMSIS-NN](https://www.keil.com/pack/doc/CMSIS/NN/html/index.html), [uTensor](https://github.com/uTensor/uTensor), and other vendor libraries to handle as much performance-critical code as possible. We know that there are an increasing number of options to accelerate neural networks on microcontrollers, so we're aiming to be a good host for deploying those hardware technologies too. + +- **Compatible**: We're using the same file schema, interpreter API, and kernel interface as regular TensorFlow Lite, so we leverage the large existing set of tools, documentation, and examples for the project. The biggest barrier to deploying ML models is getting them from a training environment into a form that's easy to run inference on, so we see reusing this rich ecosystem as being crucial to being easily usable. We also hope to integrate this experimental work back into the main codebase in the future. + +To meet those goals, we've made some tradeoffs: + +- **Simple C++**: To help with readability, our code is written in a modern version of C++, but we generally treat it as a "better C", rather relying on more complex features such as template meta-programming. As mentioned earlier, we avoid any use of dynamic memory allocation (new/delete) or the standard C/C++ libraries, so we believe this should still be fairly portable. It does mean that some older devices with C-only toolchains won't be supported, but we're hoping that the reference operator implementations (which are simple C-like functions) can still be useful in those cases. The interfaces are also designed to be C-only, so it should be possible to integrate the resulting library with pure C projects. + +- **Interpreted**: Code generation is a popular pattern for embedded code, because it gives standalone code that's easy to modify and step through, but we've chosen to go with an interpreted approach. In our internal microcontroller work we've found that using an extremely stripped-down interpreter with almost no dependencies gives us a lot of the same advantages, but is easier to maintain. For example, when new updates come out for the underlying library, you can just merge your local modifications in a single step, rather than having to regenerate new code and then patch in any changes you subsequently made. The coarse granularity of the interpreted primitives means that each operation call typically takes hundreds of thousands of instruction cycles at least, so we don't see noticeable performance gains from avoiding what's essentially a single switch statement at the interpreter level to call each operation. We're still working on improving the packaging though, for example we're considering having the ability to snapshot all the source files and headers used for a particular model, being able to compile the code and data together as a library, and then access it through a minimal set of C interface calls which hide the underlying complexity. + +- **Flatbuffers**: We represent our models using [the standard flatbuffer schema used by the rest of TensorFlow Lite](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/schema/schema.fbs), with the difference that we always keep it in read-only program memory (typically flash) rather than relying on having a file system to read it from. This is a good fit because flatbuffer's serialized format is designed to be mapped into memory without requiring any extra memory allocations or modifications to access it. All of the functions to read model values work directly on the serialized bytes, and large sections of data like weights are directly accessible as sequential C-style arrays of their data type, with no strides or unpacking needed. We do get a lot of value from using flatbuffers, but there is a cost in complexity. The flat buffer library code is all inline [inside the main headers](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/schema/schema_generated.h), but it isn't straightforward to inspect their implementations, and the model data structures aren't easy to comprehend from the debugger. The header for the schema itself also has to be periodically updated when new information is added to the file format, though we try to handle that transparently for most developers by checking in a pre-generated version. + +- **Code Duplication**: Some of the code in this prototype largely duplicates the logic in other parts of the TensorFlow Lite code base, for example the operator wrappers. We've tried to keep share as much as we can between the two interpreters, but there are some assumptions built into the original runtime that make this difficult. We'll be working on modularizing the main interpreter so that we can move to an entirely shared system. + +This initial preview release is designed to get early feedback, and is not intended to be a final product. It only includes enough operations to run a simple keyword recognition model, and the implementations are not optimized. We're hoping this will be a good way to get feedback and collaborate to improve the framework. + +## Getting Started + +Building requires a Linux or OS X machine. + + - Open a terminal + - Download the TensorFlow source with `git clone https://github.com/tensorflow` + - Enter the source root directory by running `cd tensorflow` + - Download the dependencies by running `tensorflow/contrib/lite/experimental/micro/tools/make/download_dependencies.sh`. This may take a few minutes + - Build and test the library with `make -f tensorflow/contrib/lite/experimental/micro/tools/make/Makefile test` + +You should see a series of compilation steps, followed by "~~~ALL TESTS PASSED~~~" for the various tests of the code that it will run. If there's an error, you should get an informative message from make about what went wrong. + +These tests are all built as simple binaries with few dependencies, so you can run them manually. For example, here's how to run the depthwise convolution test, and its output: + +``` +tensorflow/contrib/lite/experimental/micro/tools/make/gen/linux_x86_64/bin/tensorflow/contrib/lite/experimental/micro/kernels/depthwise_conv_test + +Testing SimpleTest +Testing SimpleTestQuantized +Testing SimpleTestRelu +Testing SimpleTestReluQuantized +4/4 tests passed +~ALL TESTS PASSED~~~ +``` + +Looking at the [depthwise_conv_test.cc](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/experimental/micro/kernels/depthwise_conv_test.cc) code, you'll see a sequence that looks like this: + +``` +... +TF_LITE_MICRO_TESTS_BEGIN + +TF_LITE_MICRO_TEST(SimpleTest) { +... +} +... +TF_LITE_MICRO_TESTS_END +``` + +These macros work a lot like [the Google test framework](https://github.com/google/googletest), but they don't require any dependencies and just write results to stderr, rather than aborting the program. If all the tests pass, then "~~~ALL TESTS PASSED~~~" is output, and the test harness that runs the binary during the make process knows that everything ran correctly. If there's an error, the lack of the expected string lets the harness know that the test failed. + +So, why are we running tests in this complicated way? So far, we've been building binaries that run locally on the Mac OS or Linux machine you're building on, but this approach becomes important when we're targeting simple micro controller devices. + +## Building for the "Blue Pill" STM32F103 + +The goal of this library is to enable machine learning on resource-constrained micro controllers and DSPs, and as part of that we've targeted the ["Blue Pill" STM32F103-compatible development board](https://github.com/google/googletest) as a cheap and popular platform. It only has 20KB of RAM and 64KB of flash, so it's a good device to ensure we can run efficiently on small chips. + +It's fairly easy to [buy and wire up a physical board](https://github.com/google/stm32_bare_lib#wiring-up-your-blue-pill), but even if you don't have an actual device, the [Renode project](https://renode.io/) makes it easy to run a faithful emulation on your desktop machine. You'll need [Docker](https://www.docker.com/) installed, but once you have that set up, try running the following command: + +`make -f tensorflow/contrib/lite/experimental/micro/tools/make/Makefile TARGET=bluepill test` + +You should see a similar set of outputs as you did in the previous section, with the addition of some extra Docker logging messages. These are because we're using Docker to run the Renode micro controller emulation tool, and the tests themselves are being run on a simulated STM32F103 device. The communication channels between an embedded device and the host are quite limited, so the test harness looks at the output of the debug log to see if tests have passed, just as it did in the previous section. This makes it a very flexible way to run cross-platform tests, even when a platform has no operating system facilities, as long as it can output debugging text logs. + +To understand what's happening here, try running the same depthwise convolution test, but through the emulated device test harness, with the following command: + +``` +tensorflow/contrib/lite/experimental/micro/testing/test_bluepill_binary.sh \ +tensorflow/contrib/lite/experimental/micro/tools/make/gen/bluepill_cortex-m3/bin/tensorflow/contrib/lite/experimental/micro/kernels/depthwise_conv_test + +``` + +You should see output that looks something like this: + +``` +Sending build context to Docker daemon 21.5kB +Step 1/2 : FROM antmicro/renode:latest + ---> 1b670a243e8f +Step 2/2 : LABEL maintainer="Pete Warden <petewarden@google.com>" + ---> Using cache + ---> 3afcd410846d +Successfully built 3afcd410846d +Successfully tagged renode_bluepill:latest +LOGS: +... +03:27:32.4340 [INFO] machine-0: Machine started. +03:27:32.4790 [DEBUG] cpu.uartSemihosting: [+0.22s host +0s virt 0s virt from start] Testing SimpleTest +03:27:32.4812 [DEBUG] cpu.uartSemihosting: [+2.21ms host +0s virt 0s virt from start] Testing SimpleTestQuantized +03:27:32.4833 [DEBUG] cpu.uartSemihosting: [+2.14ms host +0s virt 0s virt from start] Testing SimpleTestRelu +03:27:32.4834 [DEBUG] cpu.uartSemihosting: [+0.18ms host +0s virt 0s virt from start] Testing SimpleTestReluQuantized +03:27:32.4838 [DEBUG] cpu.uartSemihosting: [+0.4ms host +0s virt 0s virt from start] 4/4 tests passed +03:27:32.4839 [DEBUG] cpu.uartSemihosting: [+41µs host +0s virt 0s virt from start] ~~~ALL TESTS PASSED~~~ +03:27:32.4839 [DEBUG] cpu.uartSemihosting: [+5µs host +0s virt 0s virt from start] +... +tensorflow/contrib/lite/experimental/micro/tools/make/gen/bluepill_cortex-m3/bin/tensorflow/contrib/lite/experimental/micro/kernels/depthwise_conv_test: PASS +``` + +There's a lot of output here, but you should be able to see that the same tests that were covered when we ran locally on the development machine show up in the debug logs here, along with the magic string "~~~ALL TESTS PASSED~~~". This is the exact same code as before, just compiled and run on the STM32F103 rather than your desktop. We hope that the simplicity of this testing approach will help make adding support for new platforms as easy as possible. diff --git a/tensorflow/contrib/lite/experimental/micro/compatibility.h b/tensorflow/contrib/lite/experimental/micro/compatibility.h new file mode 100644 index 0000000000..4f0fd9f312 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/compatibility.h @@ -0,0 +1,32 @@ +/* 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_CONTRIB_LITE_EXPERIMENTAL_MICRO_COMPATIBILITY_H_ +#define TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_MICRO_COMPATIBILITY_H_ + +// C++ will automatically create class-specific delete operators for virtual +// objects, which by default call the global delete function. For embedded +// applications we want to avoid this, and won't be calling new/delete on these +// objects, so we need to override the default implementation with one that does +// nothing to avoid linking in ::delete(). +// This macro needs to be included in all subclasses of a virtual base class in +// the private section. +#ifdef TF_LITE_STATIC_MEMORY +#define TF_LITE_REMOVE_VIRTUAL_DELETE \ + void operator delete(void* p) {} +#else +#define TF_LITE_REMOVE_VIRTUAL_DELETE +#endif + +#endif // TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_MICRO_COMPATIBILITY_H_ diff --git a/tensorflow/contrib/lite/experimental/micro/examples/micro_speech/BUILD b/tensorflow/contrib/lite/experimental/micro/examples/micro_speech/BUILD new file mode 100644 index 0000000000..dad58b6c1c --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/examples/micro_speech/BUILD @@ -0,0 +1,31 @@ +# Description: +# TensorFlow Lite microcontroller example. + +package(default_visibility = ["//visibility:public"]) + +licenses(["notice"]) # Apache 2.0 + +load( + "//tensorflow/contrib/lite/experimental/micro/testing:micro_test.bzl", + "tflite_micro_cc_test", +) + +tflite_micro_cc_test( + name = "micro_speech_test", + srcs = [ + "micro_speech_test.cc", + "tiny_conv_model_data.cc", + "tiny_conv_model_data.h", + ], + tags = [ + "nomsan", + ], + deps = [ + "//tensorflow/contrib/lite:schema_fbs_version", + "//tensorflow/contrib/lite/experimental/micro:micro_framework", + "//tensorflow/contrib/lite/experimental/micro/kernels:all_ops_resolver", + "//tensorflow/contrib/lite/experimental/micro/kernels:micro_ops", + "//tensorflow/contrib/lite/experimental/micro/testing:micro_test", + "//tensorflow/contrib/lite/schema:schema_fbs", + ], +) diff --git a/tensorflow/contrib/lite/experimental/micro/examples/micro_speech/micro_speech_test.cc b/tensorflow/contrib/lite/experimental/micro/examples/micro_speech/micro_speech_test.cc new file mode 100644 index 0000000000..86cd056a72 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/examples/micro_speech/micro_speech_test.cc @@ -0,0 +1,55 @@ +/* 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. +==============================================================================*/ + +#include "tensorflow/contrib/lite/experimental/micro/examples/micro_speech/tiny_conv_model_data.h" +#include "tensorflow/contrib/lite/experimental/micro/kernels/all_ops_resolver.h" +#include "tensorflow/contrib/lite/experimental/micro/micro_error_reporter.h" +#include "tensorflow/contrib/lite/experimental/micro/micro_interpreter.h" +#include "tensorflow/contrib/lite/experimental/micro/testing/micro_test.h" +#include "tensorflow/contrib/lite/schema/schema_generated.h" +#include "tensorflow/contrib/lite/version.h" + +TF_LITE_MICRO_TESTS_BEGIN + +TF_LITE_MICRO_TEST(TestInvoke) { + tflite::MicroErrorReporter micro_error_reporter; + tflite::ErrorReporter* error_reporter = µ_error_reporter; + + const tflite::Model* model = ::tflite::GetModel(g_tiny_conv_model_data); + if (model->version() != TFLITE_SCHEMA_VERSION) { + error_reporter->Report( + "Model provided is schema version %d not equal " + "to supported version %d.\n", + model->version(), TFLITE_SCHEMA_VERSION); + } + tflite::ops::micro::AllOpsResolver resolver; + + const int tensor_arena_size = 10 * 1024; + uint8_t tensor_arena[tensor_arena_size]; + tflite::SimpleTensorAllocator tensor_allocator(tensor_arena, + tensor_arena_size); + + tflite::MicroInterpreter interpreter(model, resolver, &tensor_allocator, + error_reporter); + TfLiteStatus invoke_status = interpreter.Invoke(); + if (invoke_status != kTfLiteOk) { + error_reporter->Report("Invoke failed\n"); + } + TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, invoke_status); + + error_reporter->Report("Ran successfully\n"); +} + +TF_LITE_MICRO_TESTS_END diff --git a/tensorflow/contrib/lite/experimental/micro/examples/micro_speech/tiny_conv_model_data.cc b/tensorflow/contrib/lite/experimental/micro/examples/micro_speech/tiny_conv_model_data.cc new file mode 100644 index 0000000000..f1f9e0e219 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/examples/micro_speech/tiny_conv_model_data.cc @@ -0,0 +1,1672 @@ +/* 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. +==============================================================================*/ + +// Automatically created from a TensorFlow Lite flatbuffer using the command: +// xxd -i tiny_conv.tflite > tiny_conv_model_data.cc + +#include "tensorflow/contrib/lite/experimental/micro/examples/micro_speech/tiny_conv_model_data.h" + +const unsigned char g_tiny_conv_model_data[] = { + 0x18, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x00, 0x00, 0x0e, 0x00, + 0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x14, 0x00, + 0x0e, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x08, 0x4d, 0x00, 0x00, + 0x0c, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0xf4, 0x47, 0x00, 0x00, 0x0f, 0x00, 0x00, 0x00, + 0x54, 0x4f, 0x43, 0x4f, 0x20, 0x43, 0x6f, 0x6e, 0x76, 0x65, 0x72, 0x74, + 0x65, 0x64, 0x2e, 0x00, 0x09, 0x00, 0x00, 0x00, 0xd4, 0x47, 0x00, 0x00, + 0x04, 0x03, 0x00, 0x00, 0xfc, 0x02, 0x00, 0x00, 0xf4, 0x02, 0x00, 0x00, + 0x64, 0x00, 0x00, 0x00, 0x44, 0x00, 0x00, 0x00, 0x3c, 0x00, 0x00, 0x00, + 0x0c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xb8, 0xb3, 0xff, 0xff, + 0x16, 0xb4, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x20, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0xd7, 0x02, 0x00, 0x00, 0x2f, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xe8, 0xb3, 0xff, 0xff, + 0x46, 0xb4, 0xff, 0xff, 0x04, 0x00, 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0x00, 0x00, 0x00, 0x00, 0x80, 0x3b, + 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7f, 0x3f, 0x01, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x18, 0x00, 0x08, 0x00, + 0x07, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x14, 0x00, 0x0e, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x03, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, + 0x14, 0x00, 0x00, 0x00, 0x54, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, + 0x04, 0x00, 0x00, 0x00, 0x30, 0x11, 0x00, 0x00, 0x31, 0x00, 0x00, 0x00, + 0x77, 0x65, 0x69, 0x67, 0x68, 0x74, 0x73, 0x5f, 0x71, 0x75, 0x61, 0x6e, + 0x74, 0x5f, 0x31, 0x2f, 0x46, 0x61, 0x6b, 0x65, 0x51, 0x75, 0x61, 0x6e, + 0x74, 0x57, 0x69, 0x74, 0x68, 0x4d, 0x69, 0x6e, 0x4d, 0x61, 0x78, 0x56, + 0x61, 0x72, 0x73, 0x2f, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x70, 0x6f, 0x73, + 0x65, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x14, 0x00, 0x04, 0x00, 0x08, 0x00, + 0x0c, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x2c, 0x00, 0x00, 0x00, + 0x20, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x49, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x31, 0x83, 0xce, 0x3a, 0x01, 0x00, 0x00, 0x00, + 0x4d, 0x97, 0x92, 0x3e, 0x01, 0x00, 0x00, 0x00, 0x84, 0x75, 0xec, 0xbd, + 0x03, 0x00, 0x00, 0x00, 0xb4, 0x00, 0x00, 0x00, 0x5c, 0x00, 0x00, 0x00, + 0x04, 0x00, 0x00, 0x00, 0xc0, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x09, + 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, + 0x24, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, + 0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x08, 0x00, 0x04, 0x00, + 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x80, 0x3f, 0x14, 0x00, 0x1c, 0x00, + 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x07, 0x00, 0x14, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x18, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, + 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, + 0x28, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, + 0x02, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, + 0x01, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, + 0x04, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00, 0x14, 0x00, 0x18, 0x00, + 0x00, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x07, 0x00, 0x10, 0x00, 0x00, 0x00, + 0x00, 0x00, 0x14, 0x00, 0x14, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, + 0x10, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00, 0x30, 0x00, 0x00, 0x00, + 0x1c, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, + 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, + 0x02, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x10, 0x00, + 0x00, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x0c, 0x00, 0x00, 0x00, + 0x02, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, + 0x03, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x14, 0x00, 0x00, 0x00, + 0x04, 0x00, 0x00, 0x00, 0xfa, 0xff, 0xff, 0xff, 0x00, 0x19, 0x06, 0x00, + 0x06, 0x00, 0x05, 0x00, 0x06, 0x00, 0x00, 0x00, 0x00, 0x09, 0x06, 0x00, + 0x08, 0x00, 0x07, 0x00, 0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x04}; +const int g_tiny_conv_model_data_len = 19800; diff --git a/tensorflow/contrib/lite/experimental/micro/examples/micro_speech/tiny_conv_model_data.h b/tensorflow/contrib/lite/experimental/micro/examples/micro_speech/tiny_conv_model_data.h new file mode 100644 index 0000000000..2953cc852d --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/examples/micro_speech/tiny_conv_model_data.h @@ -0,0 +1,27 @@ +/* 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. +==============================================================================*/ + +// This is a standard TensorFlow Lite model file that has been converted into a +// C data array, so it can be easily compiled into a binary for devices that +// don't have a file system. It was created using the command: +// xxd -i tiny_conv.tflite > tiny_conv_model_data.cc + +#ifndef TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_TINY_CONV_MODEL_DATA_H_ +#define TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_TINY_CONV_MODEL_DATA_H_ + +extern const unsigned char g_tiny_conv_model_data[]; +extern const int g_tiny_conv_model_data_len; + +#endif // TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_MICRO_EXAMPLES_MICRO_SPEECH_TINY_CONV_MODEL_DATA_H_ diff --git a/tensorflow/contrib/lite/experimental/micro/kernels/BUILD b/tensorflow/contrib/lite/experimental/micro/kernels/BUILD new file mode 100644 index 0000000000..a012f950e6 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/kernels/BUILD @@ -0,0 +1,107 @@ +package(default_visibility = [ + "//visibility:public", +]) + +licenses(["notice"]) # Apache 2.0 + +load("//tensorflow/contrib/lite:build_def.bzl", "tflite_copts") +load( + "//tensorflow/contrib/lite/experimental/micro/testing:micro_test.bzl", + "tflite_micro_cc_test", +) + +cc_library( + name = "micro_ops", + srcs = [ + "depthwise_conv.cc", + "fully_connected.cc", + "softmax.cc", + ], + hdrs = [ + ], + copts = tflite_copts(), + deps = [ + "//tensorflow/contrib/lite/c:c_api_internal", + "//tensorflow/contrib/lite/experimental/micro:micro_framework", + "//tensorflow/contrib/lite/kernels:kernel_util", + "//tensorflow/contrib/lite/kernels:op_macros", + "//tensorflow/contrib/lite/kernels:padding", + "//tensorflow/contrib/lite/kernels/internal:quantization_util", + "//tensorflow/contrib/lite/kernels/internal:reference_base", + "//tensorflow/contrib/lite/kernels/internal:tensor", + ], +) + +cc_library( + name = "all_ops_resolver", + srcs = [ + "all_ops_resolver.cc", + ], + hdrs = [ + "all_ops_resolver.h", + ], + copts = tflite_copts(), + deps = [ + ":micro_ops", + "//tensorflow/contrib/lite/c:c_api_internal", + "//tensorflow/contrib/lite/experimental/micro:micro_framework", + ], +) + +cc_library( + name = "test_utils", + srcs = [ + ], + hdrs = [ + "test_utils.h", + ], + copts = tflite_copts(), + deps = [ + "//tensorflow/contrib/lite/c:c_api_internal", + "//tensorflow/contrib/lite/core/api", + "//tensorflow/contrib/lite/experimental/micro:micro_framework", + "//tensorflow/contrib/lite/experimental/micro/testing:micro_test", + ], +) + +tflite_micro_cc_test( + name = "depthwise_conv_test", + srcs = [ + "depthwise_conv_test.cc", + ], + deps = [ + ":all_ops_resolver", + ":test_utils", + "//tensorflow/contrib/lite/c:c_api_internal", + "//tensorflow/contrib/lite/experimental/micro:micro_framework", + "//tensorflow/contrib/lite/experimental/micro/testing:micro_test", + ], +) + +tflite_micro_cc_test( + name = "fully_connected_test", + srcs = [ + "fully_connected_test.cc", + ], + deps = [ + ":all_ops_resolver", + ":test_utils", + "//tensorflow/contrib/lite/c:c_api_internal", + "//tensorflow/contrib/lite/experimental/micro:micro_framework", + "//tensorflow/contrib/lite/experimental/micro/testing:micro_test", + ], +) + +tflite_micro_cc_test( + name = "softmax_test", + srcs = [ + "softmax_test.cc", + ], + deps = [ + ":all_ops_resolver", + ":test_utils", + "//tensorflow/contrib/lite/c:c_api_internal", + "//tensorflow/contrib/lite/experimental/micro:micro_framework", + "//tensorflow/contrib/lite/experimental/micro/testing:micro_test", + ], +) diff --git a/tensorflow/contrib/lite/experimental/micro/kernels/all_ops_resolver.cc b/tensorflow/contrib/lite/experimental/micro/kernels/all_ops_resolver.cc new file mode 100644 index 0000000000..bd0a37badb --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/kernels/all_ops_resolver.cc @@ -0,0 +1,43 @@ +/* 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. +==============================================================================*/ + +#include "tensorflow/contrib/lite/experimental/micro/kernels/all_ops_resolver.h" + +namespace tflite { +namespace ops { +namespace micro { + +TfLiteRegistration* Register_DEPTHWISE_CONV_2D(); +TfLiteRegistration* Micro_Register_DEPTHWISE_CONV_2D() { + return Register_DEPTHWISE_CONV_2D(); +} + +TfLiteRegistration* Register_FULLY_CONNECTED(); +TfLiteRegistration* Micro_Register_FULLY_CONNECTED() { + return Register_FULLY_CONNECTED(); +} + +TfLiteRegistration* Register_SOFTMAX(); +TfLiteRegistration* Micro_Register_SOFTMAX() { return Register_SOFTMAX(); } + +AllOpsResolver::AllOpsResolver() { + AddBuiltin(BuiltinOperator_DEPTHWISE_CONV_2D, + Micro_Register_DEPTHWISE_CONV_2D()); + AddBuiltin(BuiltinOperator_FULLY_CONNECTED, Micro_Register_FULLY_CONNECTED(), + /* min_version */ 1, + /* max_version */ 2); + AddBuiltin(BuiltinOperator_SOFTMAX, Micro_Register_SOFTMAX()); +} + +} // namespace micro +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/experimental/micro/kernels/all_ops_resolver.h b/tensorflow/contrib/lite/experimental/micro/kernels/all_ops_resolver.h new file mode 100644 index 0000000000..f836064a3f --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/kernels/all_ops_resolver.h @@ -0,0 +1,34 @@ +/* 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_CONTRIB_LITE_EXPERIMENTAL_MICRO_KERNELS_ALL_OPS_RESOLVER_H_ +#define TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_MICRO_KERNELS_ALL_OPS_RESOLVER_H_ + +#include "tensorflow/contrib/lite/experimental/micro/compatibility.h" +#include "tensorflow/contrib/lite/experimental/micro/micro_mutable_op_resolver.h" + +namespace tflite { +namespace ops { +namespace micro { + +class AllOpsResolver : public MicroMutableOpResolver { + public: + AllOpsResolver(); + + private: + TF_LITE_REMOVE_VIRTUAL_DELETE +}; + +} // namespace micro +} // namespace ops +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_MICRO_KERNELS_ALL_OPS_RESOLVER_H_ diff --git a/tensorflow/contrib/lite/experimental/micro/kernels/depthwise_conv.cc b/tensorflow/contrib/lite/experimental/micro/kernels/depthwise_conv.cc new file mode 100644 index 0000000000..4f17263181 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/kernels/depthwise_conv.cc @@ -0,0 +1,208 @@ +/* Copyright 2017 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. +==============================================================================*/ + +#include "tensorflow/contrib/lite/c/builtin_op_data.h" +#include "tensorflow/contrib/lite/c/c_api_internal.h" +#include "tensorflow/contrib/lite/kernels/internal/common.h" +#include "tensorflow/contrib/lite/kernels/internal/quantization_util.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor_ctypes.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/padding.h" + +#include "tensorflow/contrib/lite/kernels/internal/reference/depthwiseconv_float.h" +#include "tensorflow/contrib/lite/kernels/internal/reference/depthwiseconv_uint8.h" + +namespace tflite { +namespace ops { +namespace micro { +namespace depthwise_conv { +namespace { + +constexpr int kInputTensor = 0; +constexpr int kFilterTensor = 1; +constexpr int kBiasTensor = 2; +constexpr int kOutputTensor = 0; + +struct OpData { + TfLitePaddingValues padding; + // The scaling factor from input to output (aka the 'real multiplier') can + // be represented as a fixed point multiplier plus a left shift. + int32_t output_multiplier; + int output_shift; + // The range of the fused activation layer. For example for kNone and + // uint8_t these would be 0 and 255. + int32_t output_activation_min; + int32_t output_activation_max; +}; + +TfLiteStatus CalculateOpData(TfLiteContext* context, TfLiteNode* node, + TfLiteDepthwiseConvParams* params, int width, + int height, int filter_width, int filter_height, + int out_width, int out_height, + const TfLiteType data_type, OpData* data) { + data->padding.height = ComputePadding(params->stride_height, 1, height, + filter_height, out_height); + data->padding.width = + ComputePadding(params->stride_width, 1, width, filter_width, out_width); + + // Note that quantized inference requires that all tensors have their + // parameters set. This is usually done during quantized training. + if (data_type != kTfLiteFloat32) { + const TfLiteTensor* input = GetInput(context, node, kInputTensor); + const TfLiteTensor* filter = GetInput(context, node, kFilterTensor); + const TfLiteTensor* bias = + GetOptionalInputTensor(context, node, kBiasTensor); + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + + double real_multiplier = 0.0; + TF_LITE_ENSURE_STATUS(GetQuantizedConvolutionMultipler( + context, input, filter, bias, output, &real_multiplier)); + int exponent; + QuantizeMultiplier(real_multiplier, &data->output_multiplier, &exponent); + data->output_shift = -exponent; + CalculateActivationRangeUint8(params->activation, output, + &data->output_activation_min, + &data->output_activation_max); + } + return kTfLiteOk; +} + +} // namespace + +void* Init(TfLiteContext* context, const char* buffer, size_t length) { + return nullptr; +} + +void Free(TfLiteContext* context, void* buffer) {} + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + return kTfLiteOk; +} + +void EvalFloat(TfLiteContext* context, TfLiteNode* node, + TfLiteDepthwiseConvParams* params, OpData* data, + const TfLiteTensor* input, const TfLiteTensor* filter, + const TfLiteTensor* bias, TfLiteTensor* output) { + float output_activation_min, output_activation_max; + CalculateActivationRange(params->activation, &output_activation_min, + &output_activation_max); + + tflite::DepthwiseParams op_params; + // Padding type is ignored, but still set. + op_params.padding_type = PaddingType::kSame; + op_params.padding_values.width = data->padding.width; + op_params.padding_values.height = data->padding.height; + op_params.stride_width = params->stride_width; + op_params.stride_height = params->stride_height; + op_params.dilation_width_factor = 1; + op_params.dilation_height_factor = 1; + op_params.depth_multiplier = params->depth_multiplier; + op_params.float_activation_min = output_activation_min; + op_params.float_activation_max = output_activation_max; + + tflite::reference_ops::DepthwiseConv( + op_params, GetTensorShape(input), GetTensorData<float>(input), + GetTensorShape(filter), GetTensorData<float>(filter), + GetTensorShape(bias), GetTensorData<float>(bias), GetTensorShape(output), + GetTensorData<float>(output)); +} + +void EvalQuantized(TfLiteContext* context, TfLiteNode* node, + TfLiteDepthwiseConvParams* params, OpData* data, + const TfLiteTensor* input, const TfLiteTensor* filter, + const TfLiteTensor* bias, TfLiteTensor* output) { + const int32_t input_offset = -input->params.zero_point; + const int32_t filter_offset = -filter->params.zero_point; + const int32_t output_offset = output->params.zero_point; + + tflite::DepthwiseParams op_params; + // Padding type is ignored, but still set. + op_params.padding_type = PaddingType::kSame; + op_params.padding_values.width = data->padding.width; + op_params.padding_values.height = data->padding.height; + op_params.stride_width = params->stride_width; + op_params.stride_height = params->stride_height; + op_params.dilation_width_factor = 1; + op_params.dilation_height_factor = 1; + op_params.depth_multiplier = params->depth_multiplier; + op_params.quantized_activation_min = data->output_activation_min; + op_params.quantized_activation_max = data->output_activation_max; + op_params.input_offset = input_offset; + op_params.weights_offset = filter_offset; + op_params.output_offset = output_offset; + op_params.output_multiplier = data->output_multiplier; + // Legacy ops used mixed left and right shifts. Now all are +ve-means-left. + op_params.output_shift = -data->output_shift; + + tflite::reference_ops::DepthwiseConv( + op_params, GetTensorShape(input), GetTensorData<uint8_t>(input), + GetTensorShape(filter), GetTensorData<uint8_t>(filter), + GetTensorShape(bias), GetTensorData<int32_t>(bias), + GetTensorShape(output), GetTensorData<uint8_t>(output)); +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + auto* params = + reinterpret_cast<TfLiteDepthwiseConvParams*>(node->builtin_data); + + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + const TfLiteTensor* input = GetInput(context, node, kInputTensor); + const TfLiteTensor* filter = GetInput(context, node, kFilterTensor); + const TfLiteTensor* bias = + (NumInputs(node) == 3) ? GetInput(context, node, kBiasTensor) : nullptr; + + const TfLiteType data_type = input->type; + int width = SizeOfDimension(input, 2); + int height = SizeOfDimension(input, 1); + int filter_width = SizeOfDimension(filter, 2); + int filter_height = SizeOfDimension(filter, 1); + int out_width = ComputeOutSize(params->padding, width, filter_width, + params->stride_width); + int out_height = ComputeOutSize(params->padding, height, filter_height, + params->stride_height); + OpData local_data_object; + OpData* data = &local_data_object; + TF_LITE_ENSURE_STATUS(CalculateOpData(context, node, params, width, height, + filter_width, filter_height, out_width, + out_height, data_type, data)); + + // TODO(aselle): Consider whether float conv and quantized conv should be + // separate ops to avoid dispatch overhead here. + switch (input->type) { // Already know in/out types are same. + case kTfLiteFloat32: + EvalFloat(context, node, params, data, input, filter, bias, output); + break; + case kTfLiteUInt8: + EvalQuantized(context, node, params, data, input, filter, bias, output); + break; + default: + context->ReportError(context, "Type %d not currently supported.", + input->type); + return kTfLiteError; + } + return kTfLiteOk; +} + +} // namespace depthwise_conv + +TfLiteRegistration* Register_DEPTHWISE_CONV_2D() { + static TfLiteRegistration r = {depthwise_conv::Init, depthwise_conv::Free, + depthwise_conv::Prepare, depthwise_conv::Eval}; + return &r; +} + +} // namespace micro +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/experimental/micro/kernels/depthwise_conv_test.cc b/tensorflow/contrib/lite/experimental/micro/kernels/depthwise_conv_test.cc new file mode 100644 index 0000000000..169899c471 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/kernels/depthwise_conv_test.cc @@ -0,0 +1,406 @@ +/* Copyright 2017 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. +==============================================================================*/ + +#include "tensorflow/contrib/lite/c/builtin_op_data.h" +#include "tensorflow/contrib/lite/c/c_api_internal.h" +#include "tensorflow/contrib/lite/experimental/micro/kernels/all_ops_resolver.h" +#include "tensorflow/contrib/lite/experimental/micro/kernels/test_utils.h" +#include "tensorflow/contrib/lite/experimental/micro/simple_tensor_allocator.h" +#include "tensorflow/contrib/lite/experimental/micro/testing/micro_test.h" + +namespace tflite { +namespace testing { +namespace { + +void TestDepthwiseConvFloat(std::initializer_list<int> input_dims_data, + std::initializer_list<float> input_data, + std::initializer_list<int> filter_dims_data, + std::initializer_list<float> filter_data, + std::initializer_list<int> bias_dims_data, + std::initializer_list<float> bias_data, + std::initializer_list<float> expected_output_data, + std::initializer_list<int> output_dims_data, + TfLiteFusedActivation activation, + float* output_data) { + TfLiteIntArray* input_dims = IntArrayFromInitializer(input_dims_data); + TfLiteIntArray* filter_dims = IntArrayFromInitializer(filter_dims_data); + TfLiteIntArray* bias_dims = IntArrayFromInitializer(bias_dims_data); + TfLiteIntArray* output_dims = IntArrayFromInitializer(output_dims_data); + const int output_dims_count = ElementCount(*output_dims); + + constexpr int inputs_size = 3; + constexpr int outputs_size = 1; + constexpr int tensors_size = inputs_size + outputs_size; + TfLiteTensor tensors[tensors_size] = { + CreateFloatTensor(input_data, input_dims, "input_tensor"), + CreateFloatTensor(filter_data, filter_dims, "filter_tensor"), + CreateFloatTensor(bias_data, bias_dims, "bias_tensor"), + CreateFloatTensor(output_data, output_dims, "output_tensor"), + }; + + TfLiteContext context; + PopulateContext(tensors, tensors_size, &context); + + ::tflite::ops::micro::AllOpsResolver resolver; + const TfLiteRegistration* registration = + resolver.FindOp(tflite::BuiltinOperator_DEPTHWISE_CONV_2D, 1); + TF_LITE_MICRO_EXPECT_NE(nullptr, registration); + + int input_depth = input_dims->data[3]; + int output_depth = filter_dims->data[3]; + int depth_mul = output_depth / input_depth; + TfLiteDepthwiseConvParams builtin_data = { + kTfLitePaddingValid, 1, 1, depth_mul, activation, + }; + const char* init_data = reinterpret_cast<const char*>(&builtin_data); + size_t init_data_size = 0; + void* user_data = nullptr; + if (registration->init) { + user_data = registration->init(&context, init_data, init_data_size); + } + int inputs_array_data[] = {3, 0, 1, 2}; + TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data); + int outputs_array_data[] = {1, 3}; + TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data); + int temporaries_array_data[] = {0}; + TfLiteIntArray* temporaries_array = IntArrayFromInts(temporaries_array_data); + + TfLiteNode node; + node.inputs = inputs_array; + node.outputs = outputs_array; + node.temporaries = temporaries_array; + node.user_data = user_data; + node.builtin_data = reinterpret_cast<void*>(&builtin_data); + node.custom_initial_data = nullptr; + node.custom_initial_data_size = 0; + node.delegate = nullptr; + if (registration->prepare) { + TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->prepare(&context, &node)); + } + TF_LITE_MICRO_EXPECT_NE(nullptr, registration->invoke); + TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->invoke(&context, &node)); + if (registration->free) { + registration->free(&context, user_data); + } + for (int i = 0; i < output_dims_count; ++i) { + TF_LITE_MICRO_EXPECT_NEAR(expected_output_data.begin()[i], output_data[i], + 1e-5f); + } +} + +void TestDepthwiseConvQuantized( + std::initializer_list<int> input_dims_data, + std::initializer_list<uint8_t> input_data, float input_min, float input_max, + std::initializer_list<int> filter_dims_data, + std::initializer_list<uint8_t> filter_data, float filter_min, + float filter_max, std::initializer_list<int> bias_dims_data, + std::initializer_list<int32_t> bias_data, float bias_min, float bias_max, + std::initializer_list<uint8_t> expected_output_data, + std::initializer_list<int> output_dims_data, float output_min, + float output_max, TfLiteFusedActivation activation, uint8_t* output_data) { + TfLiteIntArray* input_dims = IntArrayFromInitializer(input_dims_data); + TfLiteIntArray* filter_dims = IntArrayFromInitializer(filter_dims_data); + TfLiteIntArray* bias_dims = IntArrayFromInitializer(bias_dims_data); + TfLiteIntArray* output_dims = IntArrayFromInitializer(output_dims_data); + const int output_dims_count = ElementCount(*output_dims); + + constexpr int inputs_size = 3; + constexpr int outputs_size = 1; + constexpr int tensors_size = inputs_size + outputs_size; + TfLiteTensor tensors[tensors_size] = { + CreateQuantizedTensor(input_data, input_dims, "input_tensor", input_min, + input_max), + CreateQuantizedTensor(filter_data, filter_dims, "filter_tensor", + filter_min, filter_max), + CreateQuantized32Tensor(bias_data, bias_dims, "bias_tensor", bias_min, + bias_max), + CreateQuantizedTensor(output_data, output_dims, "output_tensor", + output_min, output_max), + }; + + TfLiteContext context; + PopulateContext(tensors, tensors_size, &context); + + ::tflite::ops::micro::AllOpsResolver resolver; + const TfLiteRegistration* registration = + resolver.FindOp(tflite::BuiltinOperator_DEPTHWISE_CONV_2D, 1); + TF_LITE_MICRO_EXPECT_NE(nullptr, registration); + + int input_depth = input_dims->data[3]; + int output_depth = filter_dims->data[3]; + int depth_mul = output_depth / input_depth; + TfLiteDepthwiseConvParams builtin_data = { + kTfLitePaddingValid, 1, 1, depth_mul, activation, + }; + const char* init_data = reinterpret_cast<const char*>(&builtin_data); + size_t init_data_size = 0; + void* user_data = nullptr; + if (registration->init) { + user_data = registration->init(&context, init_data, init_data_size); + } + + int inputs_array_data[] = {3, 0, 1, 2}; + TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data); + int outputs_array_data[] = {1, 3}; + TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data); + int temporaries_array_data[] = {0}; + TfLiteIntArray* temporaries_array = IntArrayFromInts(temporaries_array_data); + + TfLiteNode node; + node.inputs = inputs_array; + node.outputs = outputs_array; + node.temporaries = temporaries_array; + node.user_data = user_data; + node.builtin_data = reinterpret_cast<void*>(&builtin_data); + node.custom_initial_data = nullptr; + node.custom_initial_data_size = 0; + node.delegate = nullptr; + + if (registration->prepare) { + TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->prepare(&context, &node)); + } + TF_LITE_MICRO_EXPECT_NE(nullptr, registration->invoke); + TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->invoke(&context, &node)); + if (registration->free) { + registration->free(&context, user_data); + } + for (int i = 0; i < output_dims_count; ++i) { + TF_LITE_MICRO_EXPECT_EQ(expected_output_data.begin()[i], output_data[i]); + } +} + +} // namespace +} // namespace testing +} // namespace tflite + +TF_LITE_MICRO_TESTS_BEGIN + +TF_LITE_MICRO_TEST(SimpleTest) { + const int output_dims_count = 8; + float output_data[output_dims_count]; + tflite::testing::TestDepthwiseConvFloat( // + {4, 1, 3, 2, 2}, // Input shape. + { + 1, 2, 7, 8, // Input values. + 3, 4, 9, 10, // + 5, 6, 11, 12, // + }, + {4, 1, 2, 2, 4}, // Filters shape. + { + 1, 2, 3, 4, // Filters values. + -9, 10, -11, 12, // + 5, 6, 7, 8, // + 13, -14, 15, -16, // + }, + {1, 4}, // Bias shape. + { + 1, 2, 3, 4, // Bias values. + }, + { + 71, -34, 99, -20, // Expected results. + 91, -26, 127, -4, // + }, + {4, 1, 2, 1, 4}, // Output shape. + kTfLiteActNone, output_data); +} + +TF_LITE_MICRO_TEST(SimpleTestQuantized) { + using tflite::testing::F2Q; + using tflite::testing::F2Q32; + + const float input_min = -63.5f; + const float input_max = 64.0f; + const float filter_min = -63.5f; + const float filter_max = 64.0f; + const float bias_min = 0.0f; + const float bias_max = 64.0f * (1 << 24); + const float output_min = -127.0f; + const float output_max = 128.0f; + const int output_dims_count = 8; + uint8_t output_data[output_dims_count]; + + tflite::testing::TestDepthwiseConvQuantized( // + {4, 1, 3, 2, 2}, // Input shape. + { + // Input values. + F2Q(1, input_min, input_max), + F2Q(2, input_min, input_max), + F2Q(7, input_min, input_max), + F2Q(8, input_min, input_max), + F2Q(3, input_min, input_max), + F2Q(4, input_min, input_max), + F2Q(9, input_min, input_max), + F2Q(10, input_min, input_max), + F2Q(5, input_min, input_max), + F2Q(6, input_min, input_max), + F2Q(11, input_min, input_max), + F2Q(12, input_min, input_max), + }, + input_min, input_max, // Input quantization range. + {4, 1, 2, 2, 4}, // Filter shape. + { + // Filter values. + F2Q(1, filter_min, filter_max), + F2Q(2, filter_min, filter_max), + F2Q(3, filter_min, filter_max), + F2Q(4, filter_min, filter_max), + F2Q(-9, filter_min, filter_max), + F2Q(10, filter_min, filter_max), + F2Q(-11, filter_min, filter_max), + F2Q(12, filter_min, filter_max), + F2Q(5, filter_min, filter_max), + F2Q(6, filter_min, filter_max), + F2Q(7, filter_min, filter_max), + F2Q(8, filter_min, filter_max), + F2Q(13, filter_min, filter_max), + F2Q(-14, filter_min, filter_max), + F2Q(15, filter_min, filter_max), + F2Q(-16, filter_min, filter_max), + }, + filter_min, filter_max, // Filter quantization range. + {1, 4}, // Bias shape. + { + // Bias values. + F2Q32(1, bias_min, bias_max), + F2Q32(2, bias_min, bias_max), + F2Q32(3, bias_min, bias_max), + F2Q32(4, bias_min, bias_max), + }, + bias_min, bias_max, // Bias quantization range. + { + // Expected results. + F2Q(71, output_min, output_max), + F2Q(-34, output_min, output_max), + F2Q(99, output_min, output_max), + F2Q(-20, output_min, output_max), + F2Q(91, output_min, output_max), + F2Q(-26, output_min, output_max), + F2Q(127, output_min, output_max), + F2Q(-4, output_min, output_max), + }, + {4, 1, 2, 1, 4}, // Output shape. + output_min, output_max, // Output quantization range. + kTfLiteActNone, output_data); +} + +TF_LITE_MICRO_TEST(SimpleTestRelu) { + const int output_dims_count = 8; + float output_data[output_dims_count]; + tflite::testing::TestDepthwiseConvFloat( // + {4, 1, 3, 2, 2}, // Input shape. + { + 1, 2, 7, 8, // Input values. + 3, 4, 9, 10, // + 5, 6, 11, 12, // + }, + {4, 1, 2, 2, 4}, // Filters shape. + { + 1, 2, 3, 4, // Filters values. + -9, 10, -11, 12, // + 5, 6, 7, 8, // + 13, -14, 15, -16, // + }, + {1, 4}, // Bias shape. + { + 1, 2, 3, 4, // Bias values. + }, + { + 71, 0, 99, 0, // Expected results. + 91, 0, 127, 0, // + }, + {4, 1, 2, 1, 4}, // Output shape. + kTfLiteActRelu, output_data); +} + +TF_LITE_MICRO_TEST(SimpleTestReluQuantized) { + using tflite::testing::F2Q; + using tflite::testing::F2Q32; + + const float input_min = -63.5f; + const float input_max = 64.0f; + const float filter_min = -63.5f; + const float filter_max = 64.0f; + const float bias_min = 0.0f; + const float bias_max = 64.0f * (1 << 24); + const float output_min = -127.0f; + const float output_max = 128.0f; + const int output_dims_count = 8; + uint8_t output_data[output_dims_count]; + + tflite::testing::TestDepthwiseConvQuantized( // + {4, 1, 3, 2, 2}, // Input shape. + { + // Input values. + F2Q(1, input_min, input_max), + F2Q(2, input_min, input_max), + F2Q(7, input_min, input_max), + F2Q(8, input_min, input_max), + F2Q(3, input_min, input_max), + F2Q(4, input_min, input_max), + F2Q(9, input_min, input_max), + F2Q(10, input_min, input_max), + F2Q(5, input_min, input_max), + F2Q(6, input_min, input_max), + F2Q(11, input_min, input_max), + F2Q(12, input_min, input_max), + }, + input_min, input_max, // Input quantization range. + {4, 1, 2, 2, 4}, // Filter shape. + { + // Filter values. + F2Q(1, filter_min, filter_max), + F2Q(2, filter_min, filter_max), + F2Q(3, filter_min, filter_max), + F2Q(4, filter_min, filter_max), + F2Q(-9, filter_min, filter_max), + F2Q(10, filter_min, filter_max), + F2Q(-11, filter_min, filter_max), + F2Q(12, filter_min, filter_max), + F2Q(5, filter_min, filter_max), + F2Q(6, filter_min, filter_max), + F2Q(7, filter_min, filter_max), + F2Q(8, filter_min, filter_max), + F2Q(13, filter_min, filter_max), + F2Q(-14, filter_min, filter_max), + F2Q(15, filter_min, filter_max), + F2Q(-16, filter_min, filter_max), + }, + filter_min, filter_max, // Filter quantization range. + {1, 4}, // Bias shape. + { + // Bias values. + F2Q32(1, bias_min, bias_max), + F2Q32(2, bias_min, bias_max), + F2Q32(3, bias_min, bias_max), + F2Q32(4, bias_min, bias_max), + }, + bias_min, bias_max, // Bias quantization range. + { + // Expected results. + F2Q(71, output_min, output_max), + F2Q(0, output_min, output_max), + F2Q(99, output_min, output_max), + F2Q(0, output_min, output_max), + F2Q(91, output_min, output_max), + F2Q(0, output_min, output_max), + F2Q(127, output_min, output_max), + F2Q(0, output_min, output_max), + }, + {4, 1, 2, 1, 4}, // Output shape. + output_min, output_max, // Output quantization range. + kTfLiteActRelu, output_data); +} + +TF_LITE_MICRO_TESTS_END diff --git a/tensorflow/contrib/lite/experimental/micro/kernels/fully_connected.cc b/tensorflow/contrib/lite/experimental/micro/kernels/fully_connected.cc new file mode 100644 index 0000000000..1e9e54cafb --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/kernels/fully_connected.cc @@ -0,0 +1,184 @@ +/* Copyright 2017 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. +==============================================================================*/ + +#include "tensorflow/contrib/lite/kernels/internal/reference/fully_connected.h" +#include "tensorflow/contrib/lite/c/builtin_op_data.h" +#include "tensorflow/contrib/lite/c/c_api_internal.h" +#include "tensorflow/contrib/lite/kernels/internal/common.h" +#include "tensorflow/contrib/lite/kernels/internal/quantization_util.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor_ctypes.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" + +namespace tflite { +namespace ops { +namespace micro { +namespace fully_connected { +namespace { + +struct OpData { + // The scaling factor from input to output (aka the 'real multiplier') can + // be represented as a fixed point multiplier plus a left shift. + int32_t output_multiplier; + int output_shift; + // The range of the fused activation layer. For example for kNone and + // uint8_t these would be 0 and 255. + int32_t output_activation_min; + int32_t output_activation_max; + // The index of the temporary tensor where the quantized inputs are cached. + int input_quantized_index; +}; + +constexpr int kInputTensor = 0; +constexpr int kWeightsTensor = 1; +constexpr int kBiasTensor = 2; +constexpr int kOutputTensor = 0; + +TfLiteStatus CalculateOpData(TfLiteContext* context, + TfLiteFullyConnectedParams* params, + TfLiteType data_type, const TfLiteTensor* input, + const TfLiteTensor* filter, + const TfLiteTensor* bias, TfLiteTensor* output, + OpData* data) { + TfLiteStatus status = kTfLiteOk; + if (data_type != kTfLiteFloat32) { + double real_multiplier = 0.0; + TF_LITE_ENSURE_STATUS(GetQuantizedConvolutionMultipler( + context, input, filter, bias, output, &real_multiplier)); + int exponent; + QuantizeMultiplier(real_multiplier, &data->output_multiplier, &exponent); + data->output_shift = -exponent; + TF_LITE_ENSURE_STATUS(CalculateActivationRangeQuantized( + context, params->activation, output, &data->output_activation_min, + &data->output_activation_max)); + } + return status; +} + +} // namespace + +void* Init(TfLiteContext* context, const char* buffer, size_t length) { + return nullptr; +} + +void Free(TfLiteContext* context, void* buffer) {} + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + return kTfLiteOk; +} + +TfLiteStatus EvalQuantized(TfLiteContext* context, TfLiteNode* node, + TfLiteFullyConnectedParams* params, OpData* data, + const TfLiteTensor* input, + const TfLiteTensor* filter, const TfLiteTensor* bias, + TfLiteTensor* output) { + const int32_t input_offset = -input->params.zero_point; + const int32_t filter_offset = -filter->params.zero_point; + const int32_t output_offset = output->params.zero_point; + + tflite::FullyConnectedParams op_params; + op_params.input_offset = input_offset; + op_params.weights_offset = filter_offset; + op_params.output_offset = output_offset; + op_params.output_multiplier = data->output_multiplier; + // Legacy ops used mixed left and right shifts. Now all are +ve-means-left. + op_params.output_shift = -data->output_shift; + op_params.quantized_activation_min = data->output_activation_min; + op_params.quantized_activation_max = data->output_activation_max; + +#define TF_LITE_FULLY_CONNECTED(output_data_type) \ + reference_ops::FullyConnected( \ + op_params, GetTensorShape(input), GetTensorData<uint8_t>(input), \ + GetTensorShape(filter), GetTensorData<uint8_t>(filter), \ + GetTensorShape(bias), GetTensorData<int32_t>(bias), \ + GetTensorShape(output), GetTensorData<output_data_type>(output), \ + nullptr) + switch (output->type) { + case kTfLiteUInt8: + TF_LITE_FULLY_CONNECTED(uint8_t); + break; + case kTfLiteInt16: + TF_LITE_FULLY_CONNECTED(int16_t); + break; + default: + context->ReportError( + context, + "Quantized FullyConnected expects output data type uint8 or int16"); + return kTfLiteError; + } + + return kTfLiteOk; +} + +TfLiteStatus EvalFloat(TfLiteContext* context, TfLiteNode* node, + TfLiteFullyConnectedParams* params, OpData* data, + const TfLiteTensor* input, const TfLiteTensor* filter, + const TfLiteTensor* bias, TfLiteTensor* output) { + float output_activation_min, output_activation_max; + CalculateActivationRange(params->activation, &output_activation_min, + &output_activation_max); + tflite::FullyConnectedParams op_params; + op_params.float_activation_min = output_activation_min; + op_params.float_activation_max = output_activation_max; + tflite::reference_ops::FullyConnected( + op_params, GetTensorShape(input), GetTensorData<float>(input), + GetTensorShape(filter), GetTensorData<float>(filter), + GetTensorShape(bias), GetTensorData<float>(bias), GetTensorShape(output), + GetTensorData<float>(output)); + return kTfLiteOk; +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + auto* params = + reinterpret_cast<TfLiteFullyConnectedParams*>(node->builtin_data); + + const TfLiteTensor* input = GetInput(context, node, kInputTensor); + const TfLiteTensor* filter = GetInput(context, node, kWeightsTensor); + const TfLiteTensor* bias = GetOptionalInputTensor(context, node, kBiasTensor); + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + + TfLiteType data_type = input->type; + OpData local_data_object; + OpData* data = &local_data_object; + TF_LITE_ENSURE_STATUS(CalculateOpData(context, params, data_type, input, + filter, bias, output, data)); + + switch (filter->type) { // Already know in/out types are same. + case kTfLiteFloat32: + return EvalFloat(context, node, params, data, input, filter, bias, + output); + case kTfLiteUInt8: + return EvalQuantized(context, node, params, data, input, filter, bias, + output); + + default: + context->ReportError(context, "Type %d not currently supported.", + filter->type); + return kTfLiteError; + } + return kTfLiteOk; +} + +} // namespace fully_connected + +TfLiteRegistration* Register_FULLY_CONNECTED() { + static TfLiteRegistration r = {fully_connected::Init, fully_connected::Free, + fully_connected::Prepare, + fully_connected::Eval}; + return &r; +} + +} // namespace micro +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/experimental/micro/kernels/fully_connected_test.cc b/tensorflow/contrib/lite/experimental/micro/kernels/fully_connected_test.cc new file mode 100644 index 0000000000..b42bf4c3bc --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/kernels/fully_connected_test.cc @@ -0,0 +1,643 @@ +/* Copyright 2017 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. +==============================================================================*/ + +#include "tensorflow/contrib/lite/c/builtin_op_data.h" +#include "tensorflow/contrib/lite/c/c_api_internal.h" +#include "tensorflow/contrib/lite/experimental/micro/kernels/all_ops_resolver.h" +#include "tensorflow/contrib/lite/experimental/micro/kernels/test_utils.h" +#include "tensorflow/contrib/lite/experimental/micro/simple_tensor_allocator.h" +#include "tensorflow/contrib/lite/experimental/micro/testing/micro_test.h" + +namespace tflite { +namespace testing { +namespace { + +void TestFullyConnectedFloat(std::initializer_list<int> input_dims_data, + std::initializer_list<float> input_data, + std::initializer_list<int> weights_dims_data, + std::initializer_list<float> weights_data, + std::initializer_list<int> bias_dims_data, + std::initializer_list<float> bias_data, + std::initializer_list<float> expected_output_data, + std::initializer_list<int> output_dims_data, + TfLiteFusedActivation activation, + float* output_data) { + TfLiteIntArray* input_dims = IntArrayFromInitializer(input_dims_data); + TfLiteIntArray* weights_dims = IntArrayFromInitializer(weights_dims_data); + TfLiteIntArray* bias_dims = IntArrayFromInitializer(bias_dims_data); + TfLiteIntArray* output_dims = IntArrayFromInitializer(output_dims_data); + const int output_dims_count = ElementCount(*output_dims); + + constexpr int inputs_size = 3; + constexpr int outputs_size = 1; + constexpr int tensors_size = inputs_size + outputs_size; + TfLiteTensor tensors[tensors_size] = { + CreateFloatTensor(input_data, input_dims, "input_tensor"), + CreateFloatTensor(weights_data, weights_dims, "weights_tensor"), + CreateFloatTensor(bias_data, bias_dims, "bias_tensor"), + CreateFloatTensor(output_data, output_dims, "output_tensor"), + }; + + TfLiteContext context; + PopulateContext(tensors, tensors_size, &context); + + ::tflite::ops::micro::AllOpsResolver resolver; + const TfLiteRegistration* registration = + resolver.FindOp(tflite::BuiltinOperator_FULLY_CONNECTED, 1); + TF_LITE_MICRO_EXPECT_NE(nullptr, registration); + + TfLiteFullyConnectedParams builtin_data = { + activation, + kTfLiteFullyConnectedWeightsFormatDefault, + }; + const char* init_data = reinterpret_cast<const char*>(&builtin_data); + size_t init_data_size = 0; + void* user_data = nullptr; + if (registration->init) { + user_data = registration->init(&context, init_data, init_data_size); + } + int inputs_array_data[] = {3, 0, 1, 2}; + TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data); + int outputs_array_data[] = {1, 3}; + TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data); + int temporaries_array_data[] = {0}; + TfLiteIntArray* temporaries_array = IntArrayFromInts(temporaries_array_data); + + TfLiteNode node; + node.inputs = inputs_array; + node.outputs = outputs_array; + node.temporaries = temporaries_array; + node.user_data = user_data; + node.builtin_data = reinterpret_cast<void*>(&builtin_data); + node.custom_initial_data = nullptr; + node.custom_initial_data_size = 0; + node.delegate = nullptr; + if (registration->prepare) { + TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->prepare(&context, &node)); + } + TF_LITE_MICRO_EXPECT_NE(nullptr, registration->invoke); + TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->invoke(&context, &node)); + if (registration->free) { + registration->free(&context, user_data); + } + for (int i = 0; i < output_dims_count; ++i) { + TF_LITE_MICRO_EXPECT_NEAR(expected_output_data.begin()[i], output_data[i], + 1e-5f); + } +} + +void TestFullyConnectedQuantized( + std::initializer_list<int> input_dims_data, + std::initializer_list<uint8_t> input_data, float input_min, float input_max, + std::initializer_list<int> weights_dims_data, + std::initializer_list<uint8_t> weights_data, float weights_min, + float weights_max, std::initializer_list<int> bias_dims_data, + std::initializer_list<int32_t> bias_data, float bias_min, float bias_max, + std::initializer_list<uint8_t> expected_output_data, + std::initializer_list<int> output_dims_data, float output_min, + float output_max, TfLiteFusedActivation activation, uint8_t* output_data) { + TfLiteIntArray* input_dims = IntArrayFromInitializer(input_dims_data); + TfLiteIntArray* weights_dims = IntArrayFromInitializer(weights_dims_data); + TfLiteIntArray* bias_dims = IntArrayFromInitializer(bias_dims_data); + TfLiteIntArray* output_dims = IntArrayFromInitializer(output_dims_data); + const int output_dims_count = ElementCount(*output_dims); + + constexpr int inputs_size = 3; + constexpr int outputs_size = 1; + constexpr int tensors_size = inputs_size + outputs_size; + TfLiteTensor tensors[tensors_size] = { + CreateQuantizedTensor(input_data, input_dims, "input_tensor", input_min, + input_max), + CreateQuantizedTensor(weights_data, weights_dims, "weights_tensor", + weights_min, weights_max), + CreateQuantized32Tensor(bias_data, bias_dims, "bias_tensor", bias_min, + bias_max), + CreateQuantizedTensor(output_data, output_dims, "output_tensor", + output_min, output_max), + }; + + TfLiteContext context; + PopulateContext(tensors, tensors_size, &context); + + ::tflite::ops::micro::AllOpsResolver resolver; + const TfLiteRegistration* registration = + resolver.FindOp(tflite::BuiltinOperator_FULLY_CONNECTED, 1); + TF_LITE_MICRO_EXPECT_NE(nullptr, registration); + + TfLiteFullyConnectedParams builtin_data = { + activation, + kTfLiteFullyConnectedWeightsFormatDefault, + }; + const char* init_data = reinterpret_cast<const char*>(&builtin_data); + size_t init_data_size = 0; + void* user_data = nullptr; + if (registration->init) { + user_data = registration->init(&context, init_data, init_data_size); + } + + int inputs_array_data[] = {3, 0, 1, 2}; + TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data); + int outputs_array_data[] = {1, 3}; + TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data); + int temporaries_array_data[] = {0}; + TfLiteIntArray* temporaries_array = IntArrayFromInts(temporaries_array_data); + + TfLiteNode node; + node.inputs = inputs_array; + node.outputs = outputs_array; + node.temporaries = temporaries_array; + node.user_data = user_data; + node.builtin_data = reinterpret_cast<void*>(&builtin_data); + node.custom_initial_data = nullptr; + node.custom_initial_data_size = 0; + node.delegate = nullptr; + + if (registration->prepare) { + TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->prepare(&context, &node)); + } + TF_LITE_MICRO_EXPECT_NE(nullptr, registration->invoke); + TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->invoke(&context, &node)); + if (registration->free) { + registration->free(&context, user_data); + } + for (int i = 0; i < output_dims_count; ++i) { + TF_LITE_MICRO_EXPECT_EQ(expected_output_data.begin()[i], output_data[i]); + } +} + +} // namespace +} // namespace testing +} // namespace tflite + +TF_LITE_MICRO_TESTS_BEGIN + +TF_LITE_MICRO_TEST(SimpleTest) { + const int output_dims_count = 6; + float output_data[output_dims_count]; + tflite::testing::TestFullyConnectedFloat( // + {2, 2, 10}, // Input shape. + { + 1, 2, 3, 4, 5, 6, 7, 8, -9, -10, // b = 0 + 1, 2, 3, 4, 5, 6, 7, -8, 9, -10, // b = 1 + }, + {2, 3, 10}, // Weights shape. + { + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 0 + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 1 + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 2 + }, + {1, 3}, // Bias shape. + { + 1, 2, 3, // Bias values. + }, + { + 24, 25, 26, 58, 59, 60, // Expected results. + }, + {2, 2, 3}, // Output shape. + kTfLiteActNone, output_data); +} + +TF_LITE_MICRO_TEST(SimpleTest2) { + const int output_dims_count = 6; + float output_data[output_dims_count]; + tflite::testing::TestFullyConnectedFloat( // + {2, 2, 2}, // Input shape. + { + 1, 2, // b = 0 + 2, 1, // b = 1 + }, + {2, 1, 2}, // Weights shape. + { + 2, 4, // u = 0 + }, + {1, 1}, // Bias shape. + { + 1, // Bias values. + }, + { + 11, 9, // Expected results. + }, + {2, 2, 1}, // Output shape. + kTfLiteActNone, output_data); +} + +TF_LITE_MICRO_TEST(SimpleTestRelu) { + const int output_dims_count = 6; + float output_data[output_dims_count]; + tflite::testing::TestFullyConnectedFloat( // + {2, 2, 10}, // Input shape. + { + 1, 2, 3, 4, 5, 6, 7, 8, -9, -10, // b = 0 + 1, 2, 3, 4, 5, 6, 7, -8, 9, -10, // b = 1 + }, + {2, 3, 10}, // Weights shape. + { + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 0 + -1, -2, -3, -4, -5, -6, -7, -8, -9, -10, // u = 1 + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 2 + }, + {1, 3}, // Bias shape. + { + 1, -2, 3, // Bias values. + }, + { + 24, 0, 26, 58, 0, 60, // Expected results. + }, + {2, 2, 3}, // Output shape. + kTfLiteActRelu, output_data); +} + +TF_LITE_MICRO_TEST(SimpleTestQuantized) { + using tflite::testing::F2Q; + using tflite::testing::F2Q32; + + const float input_min = -63.5f; + const float input_max = 64.0f; + const float weights_min = -63.5f; + const float weights_max = 64.0f; + const float bias_min = 0.0f; + const float bias_max = 64.0f * (1 << 24); + const float output_min = -127.0f; + const float output_max = 128.0f; + const int output_dims_count = 6; + uint8_t output_data[output_dims_count]; + tflite::testing::TestFullyConnectedQuantized( // + {2, 2, 10}, // Input shape. + { + // Input values. + F2Q(1, input_min, input_max), F2Q(2, input_min, input_max), + F2Q(3, input_min, input_max), F2Q(4, input_min, input_max), + F2Q(5, input_min, input_max), F2Q(6, input_min, input_max), + F2Q(7, input_min, input_max), F2Q(8, input_min, input_max), + F2Q(-9, input_min, input_max), F2Q(-10, input_min, input_max), + F2Q(1, input_min, input_max), F2Q(2, input_min, input_max), + F2Q(3, input_min, input_max), F2Q(4, input_min, input_max), + F2Q(5, input_min, input_max), F2Q(6, input_min, input_max), + F2Q(7, input_min, input_max), F2Q(-8, input_min, input_max), + F2Q(9, input_min, input_max), F2Q(-10, input_min, input_max), + }, + input_min, input_max, // Input quantization range. + {2, 3, 10}, // Weights shape. + { + // Weight values. + F2Q(1, weights_min, weights_max), F2Q(2, weights_min, weights_max), + F2Q(3, weights_min, weights_max), F2Q(4, weights_min, weights_max), + F2Q(5, weights_min, weights_max), F2Q(6, weights_min, weights_max), + F2Q(7, weights_min, weights_max), F2Q(8, weights_min, weights_max), + F2Q(9, weights_min, weights_max), F2Q(10, weights_min, weights_max), + F2Q(1, weights_min, weights_max), F2Q(2, weights_min, weights_max), + F2Q(3, weights_min, weights_max), F2Q(4, weights_min, weights_max), + F2Q(5, weights_min, weights_max), F2Q(6, weights_min, weights_max), + F2Q(7, weights_min, weights_max), F2Q(8, weights_min, weights_max), + F2Q(9, weights_min, weights_max), F2Q(10, weights_min, weights_max), + F2Q(1, weights_min, weights_max), F2Q(2, weights_min, weights_max), + F2Q(3, weights_min, weights_max), F2Q(4, weights_min, weights_max), + F2Q(5, weights_min, weights_max), F2Q(6, weights_min, weights_max), + F2Q(7, weights_min, weights_max), F2Q(8, weights_min, weights_max), + F2Q(9, weights_min, weights_max), F2Q(10, weights_min, weights_max), + }, + weights_min, weights_max, // Weights quantization range. + {1, 3}, // Bias shape. + { + F2Q32(1, bias_min, bias_max), + F2Q32(2, bias_min, bias_max), + F2Q32(3, bias_min, bias_max), + }, + bias_min, bias_max, // Bias quantization range. + { + // Expected results. + F2Q(24, output_min, output_max), + F2Q(25, output_min, output_max), + F2Q(26, output_min, output_max), + F2Q(58, output_min, output_max), + F2Q(59, output_min, output_max), + F2Q(60, output_min, output_max), + }, + {2, 2, 3}, // Output shape. + output_min, output_max, // Output quantization range. + kTfLiteActNone, output_data); +} + +TF_LITE_MICRO_TEST(SimpleTestQuantizedRelu) { + using tflite::testing::F2Q; + using tflite::testing::F2Q32; + + const float input_min = -63.5f; + const float input_max = 64.0f; + const float weights_min = -63.5f; + const float weights_max = 64.0f; + const float bias_min = 0.0f; + const float bias_max = 64.0f * (1 << 24); + const float output_min = -127.0f; + const float output_max = 128.0f; + const int output_dims_count = 6; + uint8_t output_data[output_dims_count]; + tflite::testing::TestFullyConnectedQuantized( // + {2, 2, 10}, // Input shape. + { + // Input values. + F2Q(1, input_min, input_max), F2Q(2, input_min, input_max), + F2Q(3, input_min, input_max), F2Q(4, input_min, input_max), + F2Q(5, input_min, input_max), F2Q(6, input_min, input_max), + F2Q(7, input_min, input_max), F2Q(8, input_min, input_max), + F2Q(-9, input_min, input_max), F2Q(-10, input_min, input_max), + F2Q(1, input_min, input_max), F2Q(2, input_min, input_max), + F2Q(3, input_min, input_max), F2Q(4, input_min, input_max), + F2Q(5, input_min, input_max), F2Q(6, input_min, input_max), + F2Q(7, input_min, input_max), F2Q(-8, input_min, input_max), + F2Q(9, input_min, input_max), F2Q(-10, input_min, input_max), + }, + input_min, input_max, // Input quantization range. + {2, 3, 10}, // Weights shape. + { + // Weight values. + F2Q(1, weights_min, weights_max), F2Q(2, weights_min, weights_max), + F2Q(3, weights_min, weights_max), F2Q(4, weights_min, weights_max), + F2Q(5, weights_min, weights_max), F2Q(6, weights_min, weights_max), + F2Q(7, weights_min, weights_max), F2Q(8, weights_min, weights_max), + F2Q(9, weights_min, weights_max), F2Q(10, weights_min, weights_max), + F2Q(-1, weights_min, weights_max), F2Q(-2, weights_min, weights_max), + F2Q(-3, weights_min, weights_max), F2Q(-4, weights_min, weights_max), + F2Q(-5, weights_min, weights_max), F2Q(-6, weights_min, weights_max), + F2Q(-7, weights_min, weights_max), F2Q(-8, weights_min, weights_max), + F2Q(-9, weights_min, weights_max), F2Q(-10, weights_min, weights_max), + F2Q(1, weights_min, weights_max), F2Q(2, weights_min, weights_max), + F2Q(3, weights_min, weights_max), F2Q(4, weights_min, weights_max), + F2Q(5, weights_min, weights_max), F2Q(6, weights_min, weights_max), + F2Q(7, weights_min, weights_max), F2Q(8, weights_min, weights_max), + F2Q(9, weights_min, weights_max), F2Q(10, weights_min, weights_max), + }, + weights_min, weights_max, // Weights quantization range. + {1, 3}, // Bias shape. + { + F2Q32(1, bias_min, bias_max), + F2Q32(0, bias_min, bias_max), + F2Q32(3, bias_min, bias_max), + }, + bias_min, bias_max, // Bias quantization range. + { + // Expected results. + F2Q(24, output_min, output_max), + F2Q(0, output_min, output_max), + F2Q(26, output_min, output_max), + F2Q(58, output_min, output_max), + F2Q(0, output_min, output_max), + F2Q(60, output_min, output_max), + }, + {2, 2, 3}, // Output shape. + output_min, output_max, // Output quantization range. + kTfLiteActRelu, output_data); +} + +TF_LITE_MICRO_TEST(SimpleTestQuantizedOutputMultiplierGreaterThan1) { + using tflite::testing::F2Q; + using tflite::testing::F2Q32; + + const float input_min = -127.0f; + const float input_max = 128.0f; + const float weights_min = -127.0f; + const float weights_max = 128.0f; + const float bias_min = 0.0f; + const float bias_max = 256.0f * (1 << 24); + const float output_min = -63.5f; + const float output_max = 64.0f; + const int output_dims_count = 6; + uint8_t output_data[output_dims_count]; + tflite::testing::TestFullyConnectedQuantized( // + {2, 2, 10}, // Input shape. + { + // Input values. + F2Q(1, input_min, input_max), F2Q(2, input_min, input_max), + F2Q(3, input_min, input_max), F2Q(4, input_min, input_max), + F2Q(5, input_min, input_max), F2Q(6, input_min, input_max), + F2Q(7, input_min, input_max), F2Q(8, input_min, input_max), + F2Q(-9, input_min, input_max), F2Q(-10, input_min, input_max), + F2Q(1, input_min, input_max), F2Q(2, input_min, input_max), + F2Q(3, input_min, input_max), F2Q(4, input_min, input_max), + F2Q(5, input_min, input_max), F2Q(6, input_min, input_max), + F2Q(7, input_min, input_max), F2Q(-8, input_min, input_max), + F2Q(9, input_min, input_max), F2Q(-10, input_min, input_max), + }, + input_min, input_max, // Input quantization range. + {2, 3, 10}, // Weights shape. + { + // Weight values. + F2Q(1, weights_min, weights_max), F2Q(2, weights_min, weights_max), + F2Q(3, weights_min, weights_max), F2Q(4, weights_min, weights_max), + F2Q(5, weights_min, weights_max), F2Q(6, weights_min, weights_max), + F2Q(7, weights_min, weights_max), F2Q(8, weights_min, weights_max), + F2Q(9, weights_min, weights_max), F2Q(10, weights_min, weights_max), + F2Q(1, weights_min, weights_max), F2Q(2, weights_min, weights_max), + F2Q(3, weights_min, weights_max), F2Q(4, weights_min, weights_max), + F2Q(5, weights_min, weights_max), F2Q(6, weights_min, weights_max), + F2Q(7, weights_min, weights_max), F2Q(8, weights_min, weights_max), + F2Q(9, weights_min, weights_max), F2Q(10, weights_min, weights_max), + F2Q(1, weights_min, weights_max), F2Q(2, weights_min, weights_max), + F2Q(3, weights_min, weights_max), F2Q(4, weights_min, weights_max), + F2Q(5, weights_min, weights_max), F2Q(6, weights_min, weights_max), + F2Q(7, weights_min, weights_max), F2Q(8, weights_min, weights_max), + F2Q(9, weights_min, weights_max), F2Q(10, weights_min, weights_max), + }, + weights_min, weights_max, // Weights quantization range. + {1, 3}, // Bias shape. + { + F2Q32(1, bias_min, bias_max), + F2Q32(2, bias_min, bias_max), + F2Q32(3, bias_min, bias_max), + }, + bias_min, bias_max, // Bias quantization range. + { + // Expected results. + F2Q(24, output_min, output_max), + F2Q(25, output_min, output_max), + F2Q(26, output_min, output_max), + F2Q(58, output_min, output_max), + F2Q(59, output_min, output_max), + F2Q(60, output_min, output_max), + }, + {2, 2, 3}, // Output shape. + output_min, output_max, // Output quantization range. + kTfLiteActNone, output_data); +} + +TF_LITE_MICRO_TEST(SimpleTest4DInput) { + const int output_dims_count = 6; + float output_data[output_dims_count]; + tflite::testing::TestFullyConnectedFloat( // + {4, 1, 1, 5, 1}, // Input shape. + { + 1, 2, 3, 4, 5, 6, 7, 8, -9, -10, // b = 0 + 1, 2, 3, 4, 5, 6, 7, -8, 9, -10, // b = 1 + }, + {2, 3, 10}, // Weights shape. + { + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 0 + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 1 + 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, // u = 2 + }, + {1, 3}, // Bias shape. + { + 1, 2, 3, // Bias values. + }, + { + 24, 25, 26, 58, 59, 60, // Expected results. + }, + {2, 2, 3}, // Output shape. + kTfLiteActNone, output_data); +} + +TF_LITE_MICRO_TEST(SimpleTest4DInputQuantized) { + using tflite::testing::F2Q; + using tflite::testing::F2Q32; + + const float input_min = -63.5f; + const float input_max = 64.0f; + const float weights_min = -63.5f; + const float weights_max = 64.0f; + const float bias_min = 0.0f; + const float bias_max = 64.0f * (1 << 24); + const float output_min = -127.0f; + const float output_max = 128.0f; + const int output_dims_count = 6; + uint8_t output_data[output_dims_count]; + tflite::testing::TestFullyConnectedQuantized( // + {4, 1, 1, 5, 1}, // Input shape. + { + // Input values. + F2Q(1, input_min, input_max), F2Q(2, input_min, input_max), + F2Q(3, input_min, input_max), F2Q(4, input_min, input_max), + F2Q(5, input_min, input_max), F2Q(6, input_min, input_max), + F2Q(7, input_min, input_max), F2Q(8, input_min, input_max), + F2Q(-9, input_min, input_max), F2Q(-10, input_min, input_max), + F2Q(1, input_min, input_max), F2Q(2, input_min, input_max), + F2Q(3, input_min, input_max), F2Q(4, input_min, input_max), + F2Q(5, input_min, input_max), F2Q(6, input_min, input_max), + F2Q(7, input_min, input_max), F2Q(-8, input_min, input_max), + F2Q(9, input_min, input_max), F2Q(-10, input_min, input_max), + }, + input_min, input_max, // Input quantization range. + {2, 3, 10}, // Weights shape. + { + // Weight values. + F2Q(1, weights_min, weights_max), F2Q(2, weights_min, weights_max), + F2Q(3, weights_min, weights_max), F2Q(4, weights_min, weights_max), + F2Q(5, weights_min, weights_max), F2Q(6, weights_min, weights_max), + F2Q(7, weights_min, weights_max), F2Q(8, weights_min, weights_max), + F2Q(9, weights_min, weights_max), F2Q(10, weights_min, weights_max), + F2Q(1, weights_min, weights_max), F2Q(2, weights_min, weights_max), + F2Q(3, weights_min, weights_max), F2Q(4, weights_min, weights_max), + F2Q(5, weights_min, weights_max), F2Q(6, weights_min, weights_max), + F2Q(7, weights_min, weights_max), F2Q(8, weights_min, weights_max), + F2Q(9, weights_min, weights_max), F2Q(10, weights_min, weights_max), + F2Q(1, weights_min, weights_max), F2Q(2, weights_min, weights_max), + F2Q(3, weights_min, weights_max), F2Q(4, weights_min, weights_max), + F2Q(5, weights_min, weights_max), F2Q(6, weights_min, weights_max), + F2Q(7, weights_min, weights_max), F2Q(8, weights_min, weights_max), + F2Q(9, weights_min, weights_max), F2Q(10, weights_min, weights_max), + }, + weights_min, weights_max, // Weights quantization range. + {1, 3}, // Bias shape. + { + F2Q32(1, bias_min, bias_max), + F2Q32(2, bias_min, bias_max), + F2Q32(3, bias_min, bias_max), + }, + bias_min, bias_max, // Bias quantization range. + { + // Expected results. + F2Q(24, output_min, output_max), + F2Q(25, output_min, output_max), + F2Q(26, output_min, output_max), + F2Q(58, output_min, output_max), + F2Q(59, output_min, output_max), + F2Q(60, output_min, output_max), + }, + {2, 2, 3}, // Output shape. + output_min, output_max, // Output quantization range. + kTfLiteActNone, output_data); +} + +TF_LITE_MICRO_TEST(SimpleTest4DInputQuantizedOutputMultiplierGreaterThan1) { + using tflite::testing::F2Q; + using tflite::testing::F2Q32; + + const float input_min = -127.0f; + const float input_max = 128.0f; + const float weights_min = -127.0f; + const float weights_max = 128.0f; + const float bias_min = 0.0f; + const float bias_max = 256.0f * (1 << 24); + const float output_min = -63.5f; + const float output_max = 64.0f; + const int output_dims_count = 6; + uint8_t output_data[output_dims_count]; + tflite::testing::TestFullyConnectedQuantized( // + {4, 1, 1, 5, 1}, // Input shape. + { + // Input values. + F2Q(1, input_min, input_max), F2Q(2, input_min, input_max), + F2Q(3, input_min, input_max), F2Q(4, input_min, input_max), + F2Q(5, input_min, input_max), F2Q(6, input_min, input_max), + F2Q(7, input_min, input_max), F2Q(8, input_min, input_max), + F2Q(-9, input_min, input_max), F2Q(-10, input_min, input_max), + F2Q(1, input_min, input_max), F2Q(2, input_min, input_max), + F2Q(3, input_min, input_max), F2Q(4, input_min, input_max), + F2Q(5, input_min, input_max), F2Q(6, input_min, input_max), + F2Q(7, input_min, input_max), F2Q(-8, input_min, input_max), + F2Q(9, input_min, input_max), F2Q(-10, input_min, input_max), + }, + input_min, input_max, // Input quantization range. + {2, 3, 10}, // Weights shape. + { + // Weight values. + F2Q(1, weights_min, weights_max), F2Q(2, weights_min, weights_max), + F2Q(3, weights_min, weights_max), F2Q(4, weights_min, weights_max), + F2Q(5, weights_min, weights_max), F2Q(6, weights_min, weights_max), + F2Q(7, weights_min, weights_max), F2Q(8, weights_min, weights_max), + F2Q(9, weights_min, weights_max), F2Q(10, weights_min, weights_max), + F2Q(1, weights_min, weights_max), F2Q(2, weights_min, weights_max), + F2Q(3, weights_min, weights_max), F2Q(4, weights_min, weights_max), + F2Q(5, weights_min, weights_max), F2Q(6, weights_min, weights_max), + F2Q(7, weights_min, weights_max), F2Q(8, weights_min, weights_max), + F2Q(9, weights_min, weights_max), F2Q(10, weights_min, weights_max), + F2Q(1, weights_min, weights_max), F2Q(2, weights_min, weights_max), + F2Q(3, weights_min, weights_max), F2Q(4, weights_min, weights_max), + F2Q(5, weights_min, weights_max), F2Q(6, weights_min, weights_max), + F2Q(7, weights_min, weights_max), F2Q(8, weights_min, weights_max), + F2Q(9, weights_min, weights_max), F2Q(10, weights_min, weights_max), + }, + weights_min, weights_max, // Weights quantization range. + {1, 3}, // Bias shape. + { + F2Q32(1, bias_min, bias_max), + F2Q32(2, bias_min, bias_max), + F2Q32(3, bias_min, bias_max), + }, + bias_min, bias_max, // Bias quantization range. + { + // Expected results. + F2Q(24, output_min, output_max), + F2Q(25, output_min, output_max), + F2Q(26, output_min, output_max), + F2Q(58, output_min, output_max), + F2Q(59, output_min, output_max), + F2Q(60, output_min, output_max), + }, + {2, 2, 3}, // Output shape. + output_min, output_max, // Output quantization range. + kTfLiteActNone, output_data); +} + +TF_LITE_MICRO_TESTS_END diff --git a/tensorflow/contrib/lite/experimental/micro/kernels/softmax.cc b/tensorflow/contrib/lite/experimental/micro/kernels/softmax.cc new file mode 100644 index 0000000000..a4019a067c --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/kernels/softmax.cc @@ -0,0 +1,213 @@ +/* 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. +==============================================================================*/ + +#include "tensorflow/contrib/lite/kernels/internal/reference/softmax.h" +#include "tensorflow/contrib/lite/c/builtin_op_data.h" +#include "tensorflow/contrib/lite/c/c_api_internal.h" +#include "tensorflow/contrib/lite/kernels/internal/common.h" +#include "tensorflow/contrib/lite/kernels/internal/quantization_util.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor_ctypes.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/op_macros.h" + +namespace tflite { +namespace ops { +namespace micro { +namespace activations { +namespace { + +struct OpData { + int32_t input_multiplier = 0; + int input_left_shift = 0; + int32_t input_range_radius = 0; + int diff_min = 0; +}; + +TfLiteStatus CalculateSoftmaxOpData(TfLiteContext* context, + const TfLiteTensor* input, + TfLiteTensor* output, + const TfLiteSoftmaxParams* params, + OpData* data) { + if (input->type == kTfLiteUInt8) { + TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0); + TF_LITE_ENSURE(context, output->params.scale == 1. / 256); + + static const int kScaledDiffIntegerBits = 5; + + tflite::PreprocessSoftmaxScaling( + params->beta, input->params.scale, kScaledDiffIntegerBits, + &data->input_multiplier, &data->input_left_shift); + data->diff_min = -1.0 * tflite::CalculateInputRadius( + kScaledDiffIntegerBits, data->input_left_shift); + } + return kTfLiteOk; +} + +} // namespace + +void* Init(TfLiteContext* context, const char* buffer, size_t length) { + return nullptr; +} + +void Free(TfLiteContext* context, void* buffer) {} + +TfLiteStatus SoftmaxPrepare(TfLiteContext* context, TfLiteNode* node) { + return kTfLiteOk; +} + +// Takes a 1D tensor and performs softmax along it. +void Softmax1DFloat(const TfLiteTensor* input, TfLiteTensor* output, + TfLiteSoftmaxParams* params) { + const int input_size = input->dims->data[0]; + tflite::reference_ops::Softmax(input->data.f, input_size, 1, params->beta, + output->data.f); +} + +// Takes a 2D tensor and perform softmax along the last dimension. +void Softmax2DFloat(const TfLiteTensor* input, TfLiteTensor* output, + TfLiteSoftmaxParams* params) { + const int batch_size = input->dims->data[0]; + const int input_size = input->dims->data[1]; + tflite::reference_ops::Softmax(input->data.f, input_size, batch_size, + params->beta, output->data.f); +} + +void Softmax1DQuantized(const TfLiteTensor* input, TfLiteTensor* output, + TfLiteSoftmaxParams* params, OpData* data) { + // TODO(ahentz): this is arguably a dirty trick. Since the implementation + // always traverses the last dimension of a 4D tensor, we will pretend our 1D + // tensor is 4D in a special way. We will convert a (Y) shape into a (1, + // 1, 1, Y) shape. + const int input_size = input->dims->data[0]; + const int32_t shape_data[4] = {1, 1, 1, input_size}; + RuntimeShape shape(4, shape_data); + SoftmaxParams op_params; + op_params.input_multiplier = data->input_multiplier; + op_params.input_left_shift = data->input_left_shift; + op_params.diff_min = data->diff_min; + tflite::reference_ops::Softmax(op_params, shape, + GetTensorData<uint8_t>(input), shape, + GetTensorData<uint8_t>(output)); +} + +void Softmax2DQuantized(const TfLiteTensor* input, TfLiteTensor* output, + TfLiteSoftmaxParams* params, OpData* data) { + // TODO(ahentz): this is arguably a dirty trick. Since the implementation + // always traverses the last dimension of a 4D tensor, we will pretend our 2D + // tensor is 4D in a special way. We will convert a (X, Y) shape into a (X, + // 1, 1, Y) shape. + const int batch_size = input->dims->data[0]; + const int input_size = input->dims->data[1]; + const int32_t shape_data[4] = {batch_size, 1, 1, input_size}; + RuntimeShape shape(4, shape_data); + SoftmaxParams op_params; + op_params.input_multiplier = data->input_multiplier; + op_params.input_left_shift = data->input_left_shift; + op_params.diff_min = data->diff_min; + tflite::reference_ops::Softmax(op_params, shape, + GetTensorData<uint8_t>(input), shape, + GetTensorData<uint8_t>(output)); +} + +// Takes a 4D tensor and perform softmax along the forth dimension. +void Softmax4DFloat(const TfLiteTensor* input, TfLiteTensor* output, + TfLiteSoftmaxParams* params) { + SoftmaxParams op_params; + op_params.beta = params->beta; + tflite::reference_ops::Softmax( + op_params, GetTensorShape(input), GetTensorData<float>(input), + GetTensorShape(output), GetTensorData<float>(output)); +} + +void Softmax4DQuantized(const TfLiteTensor* input, TfLiteTensor* output, + TfLiteSoftmaxParams* params, OpData* data) { + SoftmaxParams op_params; + op_params.input_multiplier = data->input_multiplier; + op_params.input_left_shift = data->input_left_shift; + op_params.diff_min = data->diff_min; + tflite::reference_ops::Softmax( + op_params, GetTensorShape(input), GetTensorData<uint8_t>(input), + GetTensorShape(output), GetTensorData<uint8_t>(output)); +} + +TfLiteStatus SoftmaxEval(TfLiteContext* context, TfLiteNode* node) { + auto* params = reinterpret_cast<TfLiteSoftmaxParams*>(node->builtin_data); + + const TfLiteTensor* input = GetInput(context, node, 0); + TfLiteTensor* output = GetOutput(context, node, 0); + + OpData local_data_object; + OpData* data = &local_data_object; + TF_LITE_ENSURE_STATUS( + CalculateSoftmaxOpData(context, input, output, params, data)); + + // TODO(ahentz): consider an implementation that works for many (all?) + // dimensions. + switch (input->type) { + case kTfLiteFloat32: { + if (NumDimensions(input) == 1) { + Softmax1DFloat(input, output, params); + return kTfLiteOk; + } + if (NumDimensions(input) == 2) { + Softmax2DFloat(input, output, params); + return kTfLiteOk; + } + if (NumDimensions(input) == 4) { + Softmax4DFloat(input, output, params); + return kTfLiteOk; + } + context->ReportError( + context, "Only 1D, 2D and 4D tensors supported currently, got %dD.", + NumDimensions(input)); + return kTfLiteError; + } + case kTfLiteUInt8: { + if (NumDimensions(input) == 1) { + Softmax1DQuantized(input, output, params, data); + return kTfLiteOk; + } + if (NumDimensions(input) == 2) { + Softmax2DQuantized(input, output, params, data); + return kTfLiteOk; + } + if (NumDimensions(input) == 4) { + Softmax4DQuantized(input, output, params, data); + return kTfLiteOk; + } + context->ReportError( + context, "Only 2D and 4D tensors supported currently, got %dD.", + NumDimensions(input)); + return kTfLiteError; + } + default: + context->ReportError( + context, "Only float32 and uint8_t supported currently, got %d.", + input->type); + return kTfLiteError; + } +} +} // namespace activations + +TfLiteRegistration* Register_SOFTMAX() { + static TfLiteRegistration r = {activations::Init, activations::Free, + activations::SoftmaxPrepare, + activations::SoftmaxEval}; + return &r; +} + +} // namespace micro +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/experimental/micro/kernels/softmax_test.cc b/tensorflow/contrib/lite/experimental/micro/kernels/softmax_test.cc new file mode 100644 index 0000000000..694456d8ac --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/kernels/softmax_test.cc @@ -0,0 +1,220 @@ +/* Copyright 2017 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. +==============================================================================*/ + +#include "tensorflow/contrib/lite/c/builtin_op_data.h" +#include "tensorflow/contrib/lite/c/c_api_internal.h" +#include "tensorflow/contrib/lite/experimental/micro/kernels/all_ops_resolver.h" +#include "tensorflow/contrib/lite/experimental/micro/kernels/test_utils.h" +#include "tensorflow/contrib/lite/experimental/micro/simple_tensor_allocator.h" +#include "tensorflow/contrib/lite/experimental/micro/testing/micro_test.h" + +namespace tflite { +namespace testing { +namespace { + +void TestSoftmaxFloat(std::initializer_list<int> input_dims_data, + std::initializer_list<float> input_data, + std::initializer_list<float> expected_output_data, + std::initializer_list<int> output_dims_data, + float* output_data) { + TfLiteIntArray* input_dims = IntArrayFromInitializer(input_dims_data); + TfLiteIntArray* output_dims = IntArrayFromInitializer(output_dims_data); + const int output_dims_count = ElementCount(*output_dims); + + constexpr int inputs_size = 2; + constexpr int outputs_size = 1; + constexpr int tensors_size = inputs_size + outputs_size; + TfLiteTensor tensors[tensors_size] = { + CreateFloatTensor(input_data, input_dims, "input_tensor"), + CreateFloatTensor(output_data, output_dims, "output_tensor"), + }; + + TfLiteContext context; + PopulateContext(tensors, tensors_size, &context); + + ::tflite::ops::micro::AllOpsResolver resolver; + const TfLiteRegistration* registration = + resolver.FindOp(tflite::BuiltinOperator_SOFTMAX, 1); + TF_LITE_MICRO_EXPECT_NE(nullptr, registration); + + TfLiteSoftmaxParams builtin_data = {1.0f}; + const char* init_data = reinterpret_cast<const char*>(&builtin_data); + size_t init_data_size = 0; + void* user_data = nullptr; + if (registration->init) { + user_data = registration->init(&context, init_data, init_data_size); + } + int inputs_array_data[] = {1, 0}; + TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data); + int outputs_array_data[] = {1, 1}; + TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data); + int temporaries_array_data[] = {0}; + TfLiteIntArray* temporaries_array = IntArrayFromInts(temporaries_array_data); + + TfLiteNode node; + node.inputs = inputs_array; + node.outputs = outputs_array; + node.temporaries = temporaries_array; + node.user_data = user_data; + node.builtin_data = reinterpret_cast<void*>(&builtin_data); + node.custom_initial_data = nullptr; + node.custom_initial_data_size = 0; + node.delegate = nullptr; + if (registration->prepare) { + TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->prepare(&context, &node)); + } + TF_LITE_MICRO_EXPECT_NE(nullptr, registration->invoke); + TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->invoke(&context, &node)); + if (registration->free) { + registration->free(&context, user_data); + } + for (int i = 0; i < output_dims_count; ++i) { + TF_LITE_MICRO_EXPECT_NEAR(expected_output_data.begin()[i], output_data[i], + 1e-5f); + } +} + +void TestSoftmaxQuantized(std::initializer_list<int> input_dims_data, + std::initializer_list<uint8_t> input_data, + float input_min, float input_max, + std::initializer_list<uint8_t> expected_output_data, + std::initializer_list<int> output_dims_data, + float output_min, float output_max, + uint8_t* output_data) { + TfLiteIntArray* input_dims = IntArrayFromInitializer(input_dims_data); + TfLiteIntArray* output_dims = IntArrayFromInitializer(output_dims_data); + const int output_dims_count = ElementCount(*output_dims); + + constexpr int inputs_size = 1; + constexpr int outputs_size = 1; + constexpr int tensors_size = inputs_size + outputs_size; + TfLiteTensor tensors[tensors_size] = { + CreateQuantizedTensor(input_data, input_dims, "input_tensor", input_min, + input_max), + CreateQuantizedTensor(output_data, output_dims, "output_tensor", + output_min, output_max), + }; + + TfLiteContext context; + PopulateContext(tensors, tensors_size, &context); + + ::tflite::ops::micro::AllOpsResolver resolver; + const TfLiteRegistration* registration = + resolver.FindOp(tflite::BuiltinOperator_SOFTMAX, 1); + TF_LITE_MICRO_EXPECT_NE(nullptr, registration); + + TfLiteSoftmaxParams builtin_data = {1.0f}; + const char* init_data = reinterpret_cast<const char*>(&builtin_data); + size_t init_data_size = 0; + void* user_data = nullptr; + if (registration->init) { + user_data = registration->init(&context, init_data, init_data_size); + } + + int inputs_array_data[] = {1, 0}; + TfLiteIntArray* inputs_array = IntArrayFromInts(inputs_array_data); + int outputs_array_data[] = {1, 1}; + TfLiteIntArray* outputs_array = IntArrayFromInts(outputs_array_data); + int temporaries_array_data[] = {0}; + TfLiteIntArray* temporaries_array = IntArrayFromInts(temporaries_array_data); + + TfLiteNode node; + node.inputs = inputs_array; + node.outputs = outputs_array; + node.temporaries = temporaries_array; + node.user_data = user_data; + node.builtin_data = reinterpret_cast<void*>(&builtin_data); + node.custom_initial_data = nullptr; + node.custom_initial_data_size = 0; + node.delegate = nullptr; + + if (registration->prepare) { + TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->prepare(&context, &node)); + } + TF_LITE_MICRO_EXPECT_NE(nullptr, registration->invoke); + TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->invoke(&context, &node)); + if (registration->free) { + registration->free(&context, user_data); + } + for (int i = 0; i < output_dims_count; ++i) { + TF_LITE_MICRO_EXPECT_EQ(expected_output_data.begin()[i], output_data[i]); + } +} + +} // namespace +} // namespace testing +} // namespace tflite + +TF_LITE_MICRO_TESTS_BEGIN + +TF_LITE_MICRO_TEST(SimpleTest) { + const int output_dims_count = 10; + float output_data[output_dims_count]; + tflite::testing::TestSoftmaxFloat( // + {2, 2, 5}, // Input shape. + { + 1.0, 2.0, 3.0, 4.0, 5.0, // b = 0 + -1.0, -2.0, -3.0, -4.0, -5.0, // b = 0 + }, + { + // Expected results. + 0.011656231, + 0.031684921, + 0.086128544, + 0.234121657, + 0.636408647, + 0.636408647, + 0.234121657, + 0.086128544, + 0.031684921, + 0.011656231, + }, + {2, 2, 5}, // Output shape. + output_data); +} + +TF_LITE_MICRO_TEST(SimpleTestQuantized) { + using tflite::testing::F2Q; + + const float input_min = -63.5f; + const float input_max = 64.0f; + const float output_min = 0.0f; + const float output_max = (255.0f / 256.0f); + const int output_dims_count = 5; + uint8_t output_data[output_dims_count]; + tflite::testing::TestSoftmaxQuantized( // + {2, 1, 5}, // Input shape. + { + F2Q(1.0, input_min, input_max), + F2Q(2.0, input_min, input_max), + F2Q(3.0, input_min, input_max), + F2Q(4.0, input_min, input_max), + F2Q(5.0, input_min, input_max), + }, + input_min, input_max, // Input quantized range. + { + // Expected results. + F2Q(0.011656231, output_min, output_max), + F2Q(0.031684921, output_min, output_max), + F2Q(0.086128544, output_min, output_max), + F2Q(0.234121657, output_min, output_max), + F2Q(0.636408647, output_min, output_max), + }, + {2, 1, 5}, // Output shape. + output_min, output_max, // Output quantized range. + output_data); +} + +TF_LITE_MICRO_TESTS_END diff --git a/tensorflow/contrib/lite/experimental/micro/kernels/test_utils.h b/tensorflow/contrib/lite/experimental/micro/kernels/test_utils.h new file mode 100644 index 0000000000..789a48ece8 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/kernels/test_utils.h @@ -0,0 +1,170 @@ +/* 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_CONTRIB_LITE_EXPERIMENTAL_MICRO_KERNELS_TEST_UTILS_H_ +#define TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_MICRO_KERNELS_TEST_UTILS_H_ + +#include <cstdarg> +#include <initializer_list> +#include <limits> + +#include "tensorflow/contrib/lite/c/builtin_op_data.h" +#include "tensorflow/contrib/lite/c/c_api_internal.h" +#include "tensorflow/contrib/lite/core/api/error_reporter.h" +#include "tensorflow/contrib/lite/experimental/micro/kernels/test_utils.h" +#include "tensorflow/contrib/lite/experimental/micro/testing/micro_test.h" + +namespace tflite { +namespace testing { + +// How many elements are in the array with this shape. +inline int ElementCount(const TfLiteIntArray& dims) { + int result = 1; + for (int i = 0; i < dims.size; ++i) { + result *= dims.data[i]; + } + return result; +} + +// Wrapper to forward kernel errors to the interpreter's error reporter. +inline void ReportOpError(struct TfLiteContext* context, const char* format, + ...) { + ErrorReporter* error_reporter = static_cast<ErrorReporter*>(context->impl_); + va_list args; + va_start(args, format); + error_reporter->Report(format, args); + va_end(args); +} + +// Derives the quantization scaling factor from a min and max range. +template <typename T> +inline float ScaleFromMinMax(const float min, const float max) { + return (max - min) / ((std::numeric_limits<T>::max() * 1.0) - + std::numeric_limits<T>::min()); +} + +// Derives the quantization zero point from a min and max range. +template <typename T> +inline int ZeroPointFromMinMax(const float min, const float max) { + return static_cast<int>((-min / ScaleFromMinMax<T>(min, max)) + 0.5f); +} + +// Converts a float value into an unsigned eight-bit quantized value. +inline uint8_t F2Q(const float value, const float min, const float max) { + int32_t result = ZeroPointFromMinMax<uint8_t>(min, max) + + (value / ScaleFromMinMax<uint8_t>(min, max)) + 0.5f; + if (result < 0) { + result = 0; + } + if (result > 256) { + result = 256; + } + return result; +} + +// Converts a float value into a signed thirty-two-bit quantized value. +inline uint8_t F2Q32(const float value, const float min, const float max) { + return static_cast<int32_t>((value - ZeroPointFromMinMax<int32_t>(min, max)) / + ScaleFromMinMax<int32_t>(min, max)); +} + +inline void PopulateContext(TfLiteTensor* tensors, int tensors_size, + TfLiteContext* context) { + context->tensors_size = tensors_size; + context->tensors = tensors; + context->impl_ = static_cast<void*>(micro_test::reporter); + context->GetExecutionPlan = nullptr; + context->ResizeTensor = nullptr; + context->ReportError = ReportOpError; + context->AddTensors = nullptr; + context->GetNodeAndRegistration = nullptr; + context->ReplaceSubgraphsWithDelegateKernels = nullptr; + context->recommended_num_threads = 1; + context->GetExternalContext = nullptr; + context->SetExternalContext = nullptr; +} + +inline TfLiteIntArray* IntArrayFromInts(const int* int_array) { + return const_cast<TfLiteIntArray*>( + reinterpret_cast<const TfLiteIntArray*>(int_array)); +} + +inline TfLiteIntArray* IntArrayFromInitializer( + std::initializer_list<int> int_initializer) { + return IntArrayFromInts(int_initializer.begin()); +} + +inline TfLiteTensor CreateFloatTensor(const float* data, TfLiteIntArray* dims, + const char* name) { + const size_t bytes = ElementCount(*dims) * sizeof(float); + return { + kTfLiteFloat32, {const_cast<int*>(reinterpret_cast<const int*>(data))}, + dims, {}, + kTfLiteMemNone, bytes, + nullptr, name}; +} + +inline TfLiteTensor CreateFloatTensor(std::initializer_list<float> data, + TfLiteIntArray* dims, const char* name) { + return CreateFloatTensor(data.begin(), dims, name); +} + +inline TfLiteTensor CreateQuantizedTensor(const uint8_t* data, + TfLiteIntArray* dims, + const char* name, float min, + float max) { + const size_t bytes = ElementCount(*dims) * sizeof(uint8_t); + const TfLiteQuantizationParams q_params = { + ScaleFromMinMax<uint8_t>(min, max), + ZeroPointFromMinMax<uint8_t>(min, max)}; + return { + kTfLiteUInt8, {const_cast<int*>(reinterpret_cast<const int*>(data))}, + dims, q_params, + kTfLiteMemNone, bytes, + nullptr, name}; +} + +inline TfLiteTensor CreateQuantizedTensor(std::initializer_list<uint8_t> data, + TfLiteIntArray* dims, + const char* name, float min, + float max) { + return CreateQuantizedTensor(data.begin(), dims, name, min, max); +} + +inline TfLiteTensor CreateQuantized32Tensor(const int32_t* data, + TfLiteIntArray* dims, + const char* name, float min, + float max) { + const size_t bytes = ElementCount(*dims) * sizeof(int32_t); + const TfLiteQuantizationParams q_params = { + ScaleFromMinMax<int32_t>(min, max), + ZeroPointFromMinMax<int32_t>(min, max)}; + return { + kTfLiteUInt8, {const_cast<int*>(reinterpret_cast<const int*>(data))}, + dims, q_params, + kTfLiteMemNone, bytes, + nullptr, name}; +} + +inline TfLiteTensor CreateQuantized32Tensor(std::initializer_list<int32_t> data, + TfLiteIntArray* dims, + const char* name, float min, + float max) { + return CreateQuantized32Tensor(data.begin(), dims, name, min, max); +} + +} // namespace testing +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_MICRO_KERNELS_TEST_UTILS_H_ diff --git a/tensorflow/contrib/lite/experimental/micro/micro_error_reporter.cc b/tensorflow/contrib/lite/experimental/micro/micro_error_reporter.cc new file mode 100644 index 0000000000..99dd883661 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/micro_error_reporter.cc @@ -0,0 +1,78 @@ +/* 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. +==============================================================================*/ + +#include "tensorflow/contrib/lite/experimental/micro/micro_error_reporter.h" + +#ifdef TF_LITE_MCU_DEBUG_LOG +#include <debug_log.h> +#else // TF_LITE_MCU_DEBUG_LOG +#include <cstdint> +#include <cstdio> +void DebugLog(const char* s) { fprintf(stderr, "%s", s); } +void DebugLogInt32(int32_t i) { fprintf(stderr, "%d", i); } +void DebugLogUInt32(uint32_t i) { fprintf(stderr, "%d", i); } +void DebugLogHex(uint32_t i) { fprintf(stderr, "0x%8x", i); } +void DebugLogFloat(float i) { fprintf(stderr, "%f", i); } +#endif // TF_LITE_MCU_DEBUG_LOG + +namespace tflite { +namespace { +void DebugLogPrintf(const char* format, va_list args) { + const int output_cache_size = 64; + char output_cache[output_cache_size + 1]; + int output_cache_index = 0; + const char* current = format; + while (*current != 0) { + if (*current == '%') { + const char next = *(current + 1); + if ((next == 'd') || (next == 's')) { + current += 1; + if (output_cache_index > 0) { + output_cache[output_cache_index] = 0; + DebugLog(output_cache); + output_cache_index = 0; + } + if (next == 'd') { + DebugLogInt32(va_arg(args, int)); + } else if (next == 's') { + DebugLog(va_arg(args, char*)); + } + } + } else { + output_cache[output_cache_index] = *current; + output_cache_index += 1; + } + if (output_cache_index >= output_cache_size) { + output_cache[output_cache_index] = 0; + DebugLog(output_cache); + output_cache_index = 0; + } + current += 1; + } + if (output_cache_index > 0) { + output_cache[output_cache_index] = 0; + DebugLog(output_cache); + output_cache_index = 0; + } + DebugLog("\n"); +} +} // namespace + +int MicroErrorReporter::Report(const char* format, va_list args) { + DebugLogPrintf(format, args); + return 0; +} + +} // namespace tflite diff --git a/tensorflow/contrib/lite/experimental/micro/micro_error_reporter.h b/tensorflow/contrib/lite/experimental/micro/micro_error_reporter.h new file mode 100644 index 0000000000..33e54f7990 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/micro_error_reporter.h @@ -0,0 +1,34 @@ +/* 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_CONTRIB_LITE_EXPERIMENTAL_MICRO_MICRO_ERROR_REPORTER_H_ +#define TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_MICRO_MICRO_ERROR_REPORTER_H_ + +#include "tensorflow/contrib/lite/core/api/error_reporter.h" +#include "tensorflow/contrib/lite/experimental/micro/compatibility.h" + +namespace tflite { + +class MicroErrorReporter : public ErrorReporter { + public: + ~MicroErrorReporter() {} + int Report(const char* format, va_list args) override; + + private: + TF_LITE_REMOVE_VIRTUAL_DELETE +}; + +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_MICRO_MICRO_ERROR_REPORTER_H_ diff --git a/tensorflow/contrib/lite/experimental/micro/micro_error_reporter_test.cc b/tensorflow/contrib/lite/experimental/micro/micro_error_reporter_test.cc new file mode 100644 index 0000000000..ef3c32050c --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/micro_error_reporter_test.cc @@ -0,0 +1,25 @@ +/* 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. +==============================================================================*/ + +#include "tensorflow/contrib/lite/experimental/micro/micro_error_reporter.h" + +int main(int argc, char** argv) { + tflite::MicroErrorReporter micro_error_reporter; + tflite::ErrorReporter* error_reporter = µ_error_reporter; + error_reporter->Report("Number: %d", 42); + error_reporter->Report("Badly-formed format string %"); + error_reporter->Report("Another % badly-formed %% format string"); + error_reporter->Report("~~~%s~~~", "ALL TESTS PASSED"); +} diff --git a/tensorflow/contrib/lite/experimental/micro/micro_interpreter.cc b/tensorflow/contrib/lite/experimental/micro/micro_interpreter.cc new file mode 100644 index 0000000000..0f38991bb0 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/micro_interpreter.cc @@ -0,0 +1,310 @@ +/* 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. +==============================================================================*/ +#include "tensorflow/contrib/lite/experimental/micro/micro_interpreter.h" + +#include "tensorflow/contrib/lite/core/api/flatbuffer_conversions.h" +#include "tensorflow/contrib/lite/experimental/micro/compatibility.h" + +namespace tflite { +namespace { +const int kStackDataAllocatorSize = 128; +class StackDataAllocator : public BuiltinDataAllocator { + public: + void* Allocate(size_t size) override { + if (size > kStackDataAllocatorSize) { + return nullptr; + } else { + return data_; + } + } + void Deallocate(void* data) override { + // Do nothing. + } + + private: + uint8_t data_[kStackDataAllocatorSize]; + + TF_LITE_REMOVE_VIRTUAL_DELETE +}; + +const char* OpNameFromRegistration(const TfLiteRegistration* registration) { + if (registration->builtin_code == BuiltinOperator_CUSTOM) { + return registration->custom_name; + } else { + return EnumNameBuiltinOperator(BuiltinOperator(registration->builtin_code)); + } +} + +void ReportOpError(struct TfLiteContext* context, const char* format, ...) { + MicroInterpreter* interpreter = + static_cast<MicroInterpreter*>(context->impl_); + va_list args; + va_start(args, format); + interpreter->error_reporter()->Report(format, args); + va_end(args); +} + +} // namespace + +MicroInterpreter::MicroInterpreter(const Model* model, + const OpResolver& op_resolver, + SimpleTensorAllocator* tensor_allocator, + ErrorReporter* error_reporter) + : model_(model), + op_resolver_(op_resolver), + tensor_allocator_(tensor_allocator), + error_reporter_(error_reporter), + initialization_status_(kTfLiteOk) { + const flatbuffers::Vector<flatbuffers::Offset<Buffer>>* buffers = + model->buffers(); + auto* subgraphs = model->subgraphs(); + if (subgraphs->size() != 1) { + error_reporter->Report("Only 1 subgraph is currently supported.\n"); + initialization_status_ = kTfLiteError; + return; + } + subgraph_ = (*subgraphs)[0]; + tensors_ = subgraph_->tensors(); + operators_ = subgraph_->operators(); + + context_.tensors_size = tensors_->Length(); + context_.tensors = + reinterpret_cast<TfLiteTensor*>(tensor_allocator_->AllocateMemory( + sizeof(TfLiteTensor) * context_.tensors_size)); + for (int i = 0; i < subgraph_->inputs()->Length(); ++i) { + const int tensor_index = subgraph_->inputs()->Get(i); + const auto* tensor = tensors_->Get(tensor_index); + initialization_status_ = tensor_allocator_->AllocateTensor( + *tensor, 0, operators_->Length(), buffers, error_reporter, + &context_.tensors[tensor_index]); + if (initialization_status_ != kTfLiteOk) { + return; + } + } + + int* first_created = reinterpret_cast<int*>( + tensor_allocator_->AllocateMemory(sizeof(int) * tensors_->Length())); + int* last_used = reinterpret_cast<int*>( + tensor_allocator_->AllocateMemory(sizeof(int) * tensors_->Length())); + for (int i = 0; i < tensors_->Length(); ++i) { + first_created[i] = -1; + last_used[i] = -1; + } + + for (int i = (operators_->Length() - 1); i >= 0; --i) { + const auto* op = operators_->Get(i); + for (int n = 0; n < op->inputs()->Length(); ++n) { + const int tensor_index = op->inputs()->Get(n); + if ((last_used[tensor_index] == -1) || (last_used[tensor_index] < i)) { + last_used[tensor_index] = i; + } + } + for (int n = 0; n < op->outputs()->Length(); ++n) { + const int tensor_index = op->outputs()->Get(n); + const int create_before = i; + int destroy_after = last_used[tensor_index]; + if (destroy_after == -1) { + destroy_after = operators_->Length(); + } + const auto* tensor = tensors_->Get(tensor_index); + if (!tensor->is_variable()) { + initialization_status_ = tensor_allocator_->AllocateTensor( + *tensor, create_before, destroy_after, buffers, error_reporter, + &context_.tensors[tensor_index]); + if (initialization_status_ != kTfLiteOk) { + return; + } + first_created[tensor_index] = i; + } + } + } + + for (int i = 0; i < tensors_->Length(); ++i) { + const auto* tensor = tensors_->Get(i); + const bool is_read_only = (first_created[i] == -1) && (last_used[i] != -1); + if (tensor->is_variable() || is_read_only) { + initialization_status_ = tensor_allocator_->AllocateTensor( + *tensor, 0, operators_->Length(), buffers, error_reporter, + &context_.tensors[i]); + if (initialization_status_ != kTfLiteOk) { + return; + } + } + } + context_.impl_ = static_cast<void*>(this); + context_.GetExecutionPlan = nullptr; + context_.ResizeTensor = nullptr; + context_.ReportError = ReportOpError; + context_.AddTensors = nullptr; + context_.GetNodeAndRegistration = nullptr; + context_.ReplaceSubgraphsWithDelegateKernels = nullptr; + context_.recommended_num_threads = 1; + context_.GetExternalContext = nullptr; + context_.SetExternalContext = nullptr; +} + +TfLiteStatus MicroInterpreter::Invoke() { + if (initialization_status_ != kTfLiteOk) { + error_reporter_->Report("Invoke() called after initialization failed\n"); + return kTfLiteError; + } + TfLiteStatus status = kTfLiteOk; + auto opcodes = model_->operator_codes(); + for (int i = 0; i < operators_->Length(); ++i) { + const auto* op = operators_->Get(i); + int index = op->opcode_index(); + if (index < 0 || index >= opcodes->size()) { + error_reporter_->Report("Missing registration for opcode_index %d\n", + index); + return kTfLiteError; + } + auto opcode = (*opcodes)[index]; + const TfLiteRegistration* registration = nullptr; + status = GetRegistrationFromOpCode(opcode, op_resolver_, error_reporter_, + ®istration); + if (status != kTfLiteOk) { + return status; + } + if (registration == nullptr) { + error_reporter_->Report("Skipping op for opcode_index %d\n", index); + return kTfLiteError; + } + BuiltinOperator op_type = + static_cast<BuiltinOperator>(registration->builtin_code); + + if (op_type != BuiltinOperator_CUSTOM && op->custom_options()) { + error_reporter_->Report( + "Found builtin operator %s with custom options.\n", + EnumNameBuiltinOperator(op_type)); + } + StackDataAllocator stack_data_allocator; + const char* custom_data = nullptr; + size_t custom_data_size = 0; + unsigned char* builtin_data = nullptr; + if (op->custom_options()) { + custom_data = reinterpret_cast<const char*>(op->custom_options()->data()); + custom_data_size = op->custom_options()->size(); + } else { + TF_LITE_ENSURE_STATUS(ParseOpData(op, op_type, error_reporter_, + &stack_data_allocator, + (void**)(&builtin_data))); + } + + const char* init_data; + size_t init_data_size; + if (registration->builtin_code == BuiltinOperator_CUSTOM) { + init_data = custom_data; + init_data_size = custom_data_size; + } else { + init_data = reinterpret_cast<const char*>(builtin_data); + init_data_size = 0; + } + void* user_data = nullptr; + if (registration->init) { + user_data = registration->init(&context_, init_data, init_data_size); + } + + const int kMaxInputs = 16; + int inputs_data[kMaxInputs + 1]; + TfLiteIntArray* inputs_array = + reinterpret_cast<TfLiteIntArray*>(inputs_data); + if (op->inputs()->Length() >= kMaxInputs) { + error_reporter_->Report("Too many inputs (%d)\n", op->inputs()->Length()); + return kTfLiteError; + } + inputs_array->size = op->inputs()->Length(); + for (int n = 0; n < op->inputs()->Length(); ++n) { + inputs_array->data[n] = op->inputs()->Get(n); + } + + const int kMaxOutputs = 16; + int outputs_data[kMaxOutputs + 1]; + TfLiteIntArray* outputs_array = + reinterpret_cast<TfLiteIntArray*>(outputs_data); + if (op->outputs()->Length() >= kMaxOutputs) { + error_reporter_->Report("Too many outputs (%d)\n", + op->outputs()->Length()); + return kTfLiteError; + } + outputs_array->size = op->outputs()->Length(); + for (int n = 0; n < op->outputs()->Length(); ++n) { + outputs_array->data[n] = op->outputs()->Get(n); + } + + const int kMaxTemporaries = 16; + int temporaries_data[kMaxTemporaries + 1]; + TfLiteIntArray* temporaries_array = + reinterpret_cast<TfLiteIntArray*>(temporaries_data); + temporaries_array->size = 0; + + TfLiteNode node; + node.inputs = inputs_array; + node.outputs = outputs_array; + node.temporaries = temporaries_array; + node.user_data = user_data; + node.builtin_data = reinterpret_cast<void*>(builtin_data); + node.custom_initial_data = custom_data; + node.custom_initial_data_size = custom_data_size; + node.delegate = nullptr; + if (registration->prepare) { + TfLiteStatus prepare_status = registration->prepare(&context_, &node); + if (prepare_status != kTfLiteOk) { + error_reporter_->Report( + "Node %s (number %d) failed to prepare with status %d", + OpNameFromRegistration(registration), i, prepare_status); + return kTfLiteError; + } + } + + if (registration->invoke) { + TfLiteStatus invoke_status = registration->invoke(&context_, &node); + if (invoke_status != kTfLiteOk) { + error_reporter_->Report( + "Node %s (number %d) failed to invoke with status %d", + OpNameFromRegistration(registration), i, invoke_status); + return kTfLiteError; + } + } + + if (registration->free) { + registration->free(&context_, user_data); + } + } + return status; +} + +TfLiteTensor* MicroInterpreter::input(int index) { + const flatbuffers::Vector<int32_t>* inputs = subgraph_->inputs(); + const size_t length = inputs->Length(); + if ((index < 0) || (index >= length)) { + error_reporter_->Report("Input index %d out of range (length is %d)", index, + length); + return nullptr; + } + return &(context_.tensors[inputs->Get(index)]); +} + +TfLiteTensor* MicroInterpreter::output(int index) { + const flatbuffers::Vector<int32_t>* outputs = subgraph_->outputs(); + const size_t length = outputs->Length(); + if ((index < 0) || (index >= outputs->Length())) { + error_reporter_->Report("Output index %d out of range (length is %d)", + index, length); + return nullptr; + } + return &(context_.tensors[outputs->Get(index)]); +} + +} // namespace tflite diff --git a/tensorflow/contrib/lite/experimental/micro/micro_interpreter.h b/tensorflow/contrib/lite/experimental/micro/micro_interpreter.h new file mode 100644 index 0000000000..a88514cde8 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/micro_interpreter.h @@ -0,0 +1,71 @@ +/* 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_CONTRIB_LITE_EXPERIMENTAL_MICRO_MICRO_INTERPRETER_H_ +#define TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_MICRO_MICRO_INTERPRETER_H_ + +#include "tensorflow/contrib/lite/c/c_api_internal.h" +#include "tensorflow/contrib/lite/core/api/error_reporter.h" +#include "tensorflow/contrib/lite/core/api/op_resolver.h" +#include "tensorflow/contrib/lite/experimental/micro/simple_tensor_allocator.h" +#include "tensorflow/contrib/lite/schema/schema_generated.h" + +namespace tflite { + +class MicroInterpreter { + public: + // The lifetime of the model, op resolver, allocator, and error reporter must + // be at least as long as that of the interpreter object, since the + // interpreter may need to access them at any time. This means that you should + // usually create them with the same scope as each other, for example having + // them all allocated on the stack as local variables through a top-level + // function. + // The interpreter doesn't do any deallocation of any of the pointed-to + // objects, ownership remains with the caller. + MicroInterpreter(const Model* model, const OpResolver& op_resolver, + SimpleTensorAllocator* tensor_allocator, + ErrorReporter* error_reporter); + + TfLiteStatus Invoke(); + + size_t tensors_size() const { return context_.tensors_size; } + TfLiteTensor* tensor(int tensor_index); + + TfLiteTensor* input(int index); + size_t inputs_size() const { return subgraph_->inputs()->Length(); } + + TfLiteTensor* output(int index); + size_t outputs_size() const { return subgraph_->outputs()->Length(); } + + TfLiteStatus initialization_status() const { return initialization_status_; } + + ErrorReporter* error_reporter() { return error_reporter_; } + + private: + const Model* model_; + const OpResolver& op_resolver_; + SimpleTensorAllocator* tensor_allocator_; + ErrorReporter* error_reporter_; + + TfLiteStatus initialization_status_; + const flatbuffers::Vector<flatbuffers::Offset<Tensor>>* tensors_; + const flatbuffers::Vector<flatbuffers::Offset<Operator>>* operators_; + TfLiteContext context_; + + const SubGraph* subgraph_; +}; + +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_MICRO_MICRO_INTERPRETER_H_ diff --git a/tensorflow/contrib/lite/experimental/micro/micro_interpreter_test.cc b/tensorflow/contrib/lite/experimental/micro/micro_interpreter_test.cc new file mode 100644 index 0000000000..251e5f7203 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/micro_interpreter_test.cc @@ -0,0 +1,197 @@ +/* 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. +==============================================================================*/ + +#include "tensorflow/contrib/lite/experimental/micro/micro_interpreter.h" + +#include "tensorflow/contrib/lite/experimental/micro/testing/micro_test.h" + +namespace tflite { +namespace { +void* MockInit(TfLiteContext* context, const char* buffer, size_t length) { + // Do nothing. + return nullptr; +} + +void MockFree(TfLiteContext* context, void* buffer) { + // Do nothing. +} + +TfLiteStatus MockPrepare(TfLiteContext* context, TfLiteNode* node) { + return kTfLiteOk; +} + +TfLiteStatus MockInvoke(TfLiteContext* context, TfLiteNode* node) { + const TfLiteTensor* input = &context->tensors[node->inputs->data[0]]; + const int32_t* input_data = input->data.i32; + const TfLiteTensor* weight = &context->tensors[node->inputs->data[1]]; + const uint8_t* weight_data = weight->data.uint8; + TfLiteTensor* output = &context->tensors[node->outputs->data[0]]; + int32_t* output_data = output->data.i32; + output_data[0] = input_data[0] + weight_data[0]; + return kTfLiteOk; +} + +class MockOpResolver : public OpResolver { + public: + const TfLiteRegistration* FindOp(BuiltinOperator op, + int version) const override { + return nullptr; + } + const TfLiteRegistration* FindOp(const char* op, int version) const override { + if (strcmp(op, "mock_custom") == 0) { + static TfLiteRegistration r = {MockInit, MockFree, MockPrepare, + MockInvoke}; + return &r; + } else { + return nullptr; + } + } +}; + +class StackAllocator : public flatbuffers::Allocator { + public: + StackAllocator() : data_(data_backing_), data_size_(0) {} + + uint8_t* allocate(size_t size) override { + if ((data_size_ + size) > kStackAllocatorSize) { + // TODO(petewarden): Add error reporting beyond returning null! + return nullptr; + } + uint8_t* result = data_; + data_ += size; + data_size_ += size; + return result; + } + + void deallocate(uint8_t* p, size_t) override {} + + static StackAllocator& instance() { + // Avoid using true dynamic memory allocation to be portable to bare metal. + static char inst_memory[sizeof(StackAllocator)]; + static StackAllocator* inst = new (inst_memory) StackAllocator; + return *inst; + } + + static constexpr int kStackAllocatorSize = 4096; + + private: + uint8_t data_backing_[kStackAllocatorSize]; + uint8_t* data_; + int data_size_; +}; + +const Model* BuildMockModel() { + using flatbuffers::Offset; + flatbuffers::FlatBufferBuilder builder(StackAllocator::kStackAllocatorSize, + &StackAllocator::instance()); + constexpr size_t buffer_data_size = 1; + const uint8_t buffer_data[buffer_data_size] = {21}; + constexpr size_t buffers_size = 2; + const Offset<Buffer> buffers[buffers_size] = { + CreateBuffer(builder), + CreateBuffer(builder, + builder.CreateVector(buffer_data, buffer_data_size))}; + constexpr size_t tensor_shape_size = 1; + const int32_t tensor_shape[tensor_shape_size] = {1}; + constexpr size_t tensors_size = 3; + const Offset<Tensor> tensors[tensors_size] = { + CreateTensor(builder, + builder.CreateVector(tensor_shape, tensor_shape_size), + TensorType_INT32, 0, + builder.CreateString("test_input_tensor"), 0, false), + CreateTensor(builder, + builder.CreateVector(tensor_shape, tensor_shape_size), + TensorType_UINT8, 1, + builder.CreateString("test_weight_tensor"), 0, false), + CreateTensor(builder, + builder.CreateVector(tensor_shape, tensor_shape_size), + TensorType_INT32, 0, + builder.CreateString("test_output_tensor"), 0, false), + }; + constexpr size_t inputs_size = 1; + const int32_t inputs[inputs_size] = {0}; + constexpr size_t outputs_size = 1; + const int32_t outputs[outputs_size] = {2}; + constexpr size_t operator_inputs_size = 2; + const int32_t operator_inputs[operator_inputs_size] = {0, 1}; + constexpr size_t operator_outputs_size = 1; + const int32_t operator_outputs[operator_outputs_size] = {2}; + constexpr size_t operators_size = 1; + const Offset<Operator> operators[operators_size] = {CreateOperator( + builder, 0, builder.CreateVector(operator_inputs, operator_inputs_size), + builder.CreateVector(operator_outputs, operator_outputs_size), + BuiltinOptions_NONE)}; + constexpr size_t subgraphs_size = 1; + const Offset<SubGraph> subgraphs[subgraphs_size] = { + CreateSubGraph(builder, builder.CreateVector(tensors, tensors_size), + builder.CreateVector(inputs, inputs_size), + builder.CreateVector(outputs, outputs_size), + builder.CreateVector(operators, operators_size), + builder.CreateString("test_subgraph"))}; + constexpr size_t operator_codes_size = 1; + const Offset<OperatorCode> operator_codes[operator_codes_size] = { + CreateOperatorCodeDirect(builder, BuiltinOperator_CUSTOM, "mock_custom", + 0)}; + const Offset<Model> model_offset = CreateModel( + builder, 0, builder.CreateVector(operator_codes, operator_codes_size), + builder.CreateVector(subgraphs, subgraphs_size), + builder.CreateString("test_model"), + builder.CreateVector(buffers, buffers_size)); + FinishModelBuffer(builder, model_offset); + void* model_pointer = builder.GetBufferPointer(); + const Model* model = flatbuffers::GetRoot<Model>(model_pointer); + return model; +} + +} // namespace +} // namespace tflite + +TF_LITE_MICRO_TESTS_BEGIN + +TF_LITE_MICRO_TEST(TestInterpreter) { + const tflite::Model* model = tflite::BuildMockModel(); + TF_LITE_MICRO_EXPECT_NE(nullptr, model); + tflite::MockOpResolver mock_resolver; + constexpr size_t allocator_buffer_size = 1024; + uint8_t allocator_buffer[allocator_buffer_size]; + tflite::SimpleTensorAllocator simple_tensor_allocator(allocator_buffer, + allocator_buffer_size); + tflite::MicroInterpreter interpreter( + model, mock_resolver, &simple_tensor_allocator, micro_test::reporter); + TF_LITE_MICRO_EXPECT_EQ(1, interpreter.inputs_size()); + TF_LITE_MICRO_EXPECT_EQ(1, interpreter.outputs_size()); + + TfLiteTensor* input = interpreter.input(0); + TF_LITE_MICRO_EXPECT_NE(nullptr, input); + TF_LITE_MICRO_EXPECT_EQ(kTfLiteInt32, input->type); + TF_LITE_MICRO_EXPECT_EQ(1, input->dims->size); + TF_LITE_MICRO_EXPECT_EQ(1, input->dims->data[0]); + TF_LITE_MICRO_EXPECT_EQ(4, input->bytes); + TF_LITE_MICRO_EXPECT_NE(nullptr, input->data.i32); + input->data.i32[0] = 21; + + TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, interpreter.Invoke()); + + TfLiteTensor* output = interpreter.output(0); + TF_LITE_MICRO_EXPECT_NE(nullptr, output); + TF_LITE_MICRO_EXPECT_EQ(kTfLiteInt32, output->type); + TF_LITE_MICRO_EXPECT_EQ(1, output->dims->size); + TF_LITE_MICRO_EXPECT_EQ(1, output->dims->data[0]); + TF_LITE_MICRO_EXPECT_EQ(4, output->bytes); + TF_LITE_MICRO_EXPECT_NE(nullptr, output->data.i32); + TF_LITE_MICRO_EXPECT_EQ(42, output->data.i32[0]); +} + +TF_LITE_MICRO_TESTS_END diff --git a/tensorflow/contrib/lite/experimental/micro/micro_mutable_op_resolver.cc b/tensorflow/contrib/lite/experimental/micro/micro_mutable_op_resolver.cc new file mode 100644 index 0000000000..40c21c6448 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/micro_mutable_op_resolver.cc @@ -0,0 +1,80 @@ +/* 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. +==============================================================================*/ + +#include "tensorflow/contrib/lite/experimental/micro/micro_mutable_op_resolver.h" + +namespace tflite { + +const TfLiteRegistration* MicroMutableOpResolver::FindOp( + tflite::BuiltinOperator op, int version) const { + for (int i = 0; i < registrations_len_; ++i) { + const TfLiteRegistration& registration = registrations_[i]; + if ((registration.builtin_code == op) && + (registration.version == version)) { + return ®istration; + } + } + return nullptr; +} + +const TfLiteRegistration* MicroMutableOpResolver::FindOp(const char* op, + int version) const { + for (int i = 0; i < registrations_len_; ++i) { + const TfLiteRegistration& registration = registrations_[i]; + if ((registration.builtin_code == -1) && + (strcmp(registration.custom_name, op) == 0) && + (registration.version == version)) { + return ®istration; + } + } + return nullptr; +} + +void MicroMutableOpResolver::AddBuiltin(tflite::BuiltinOperator op, + TfLiteRegistration* registration, + int min_version, int max_version) { + for (int version = min_version; version <= max_version; ++version) { + if (registrations_len_ >= TFLITE_REGISTRATIONS_MAX) { + // TODO(petewarden) - Add error reporting hooks so we can report this! + return; + } + TfLiteRegistration* new_registration = ®istrations_[registrations_len_]; + registrations_len_ += 1; + + *new_registration = *registration; + new_registration->builtin_code = op; + new_registration->version = version; + } +} + +void MicroMutableOpResolver::AddCustom(const char* name, + TfLiteRegistration* registration, + int min_version, int max_version) { + for (int version = min_version; version <= max_version; ++version) { + if (registrations_len_ >= TFLITE_REGISTRATIONS_MAX) { + // TODO(petewarden) - Add error reporting hooks so we can report this! + return; + } + TfLiteRegistration* new_registration = ®istrations_[registrations_len_]; + registrations_len_ += 1; + + *new_registration = *registration; + new_registration->builtin_code = -1; + new_registration->custom_name = name; + new_registration->version = version; + } +} + +} // namespace tflite diff --git a/tensorflow/contrib/lite/experimental/micro/micro_mutable_op_resolver.h b/tensorflow/contrib/lite/experimental/micro/micro_mutable_op_resolver.h new file mode 100644 index 0000000000..f3750a2484 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/micro_mutable_op_resolver.h @@ -0,0 +1,46 @@ +/* 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_CONTRIB_LITE_EXPERIMENTAL_MICRO_MICRO_MUTABLE_OP_RESOLVER_H_ +#define TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_MICRO_MICRO_MUTABLE_OP_RESOLVER_H_ + +#include "tensorflow/contrib/lite/core/api/op_resolver.h" +#include "tensorflow/contrib/lite/experimental/micro/compatibility.h" + +#ifndef TFLITE_REGISTRATIONS_MAX +#define TFLITE_REGISTRATIONS_MAX (128) +#endif + +namespace tflite { + +class MicroMutableOpResolver : public OpResolver { + public: + const TfLiteRegistration* FindOp(tflite::BuiltinOperator op, + int version) const override; + const TfLiteRegistration* FindOp(const char* op, int version) const override; + void AddBuiltin(tflite::BuiltinOperator op, TfLiteRegistration* registration, + int min_version = 1, int max_version = 1); + void AddCustom(const char* name, TfLiteRegistration* registration, + int min_version = 1, int max_version = 1); + + private: + TfLiteRegistration registrations_[TFLITE_REGISTRATIONS_MAX]; + int registrations_len_ = 0; + + TF_LITE_REMOVE_VIRTUAL_DELETE +}; + +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_MICRO_MICRO_MUTABLE_OP_RESOLVER_H_ diff --git a/tensorflow/contrib/lite/experimental/micro/micro_mutable_op_resolver_test.cc b/tensorflow/contrib/lite/experimental/micro/micro_mutable_op_resolver_test.cc new file mode 100644 index 0000000000..5420a33e87 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/micro_mutable_op_resolver_test.cc @@ -0,0 +1,83 @@ +/* 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. +==============================================================================*/ + +#include "tensorflow/contrib/lite/experimental/micro/micro_mutable_op_resolver.h" + +#include "tensorflow/contrib/lite/experimental/micro/testing/micro_test.h" + +namespace tflite { +namespace { +void* MockInit(TfLiteContext* context, const char* buffer, size_t length) { + // Do nothing. + return nullptr; +} + +void MockFree(TfLiteContext* context, void* buffer) { + // Do nothing. +} + +TfLiteStatus MockPrepare(TfLiteContext* context, TfLiteNode* node) { + return kTfLiteOk; +} + +TfLiteStatus MockInvoke(TfLiteContext* context, TfLiteNode* node) { + return kTfLiteOk; +} +} // namespace +} // namespace tflite + +TF_LITE_MICRO_TESTS_BEGIN + +TF_LITE_MICRO_TEST(TestOperations) { + using tflite::BuiltinOperator_CONV_2D; + using tflite::BuiltinOperator_RELU; + using tflite::MicroMutableOpResolver; + using tflite::OpResolver; + + static TfLiteRegistration r = {tflite::MockInit, tflite::MockFree, + tflite::MockPrepare, tflite::MockInvoke}; + + MicroMutableOpResolver micro_mutable_op_resolver; + micro_mutable_op_resolver.AddBuiltin(BuiltinOperator_CONV_2D, &r, 0, 2); + micro_mutable_op_resolver.AddCustom("mock_custom", &r, 0, 3); + OpResolver* resolver = µ_mutable_op_resolver; + + const TfLiteRegistration* registration = + resolver->FindOp(BuiltinOperator_CONV_2D, 0); + TF_LITE_MICRO_EXPECT_NE(nullptr, registration); + TF_LITE_MICRO_EXPECT_EQ(nullptr, registration->init(nullptr, nullptr, 0)); + TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->prepare(nullptr, nullptr)); + TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->invoke(nullptr, nullptr)); + + registration = resolver->FindOp(BuiltinOperator_CONV_2D, 10); + TF_LITE_MICRO_EXPECT_EQ(nullptr, registration); + + registration = resolver->FindOp(BuiltinOperator_RELU, 0); + TF_LITE_MICRO_EXPECT_EQ(nullptr, registration); + + registration = resolver->FindOp("mock_custom", 0); + TF_LITE_MICRO_EXPECT_NE(nullptr, registration); + TF_LITE_MICRO_EXPECT_EQ(nullptr, registration->init(nullptr, nullptr, 0)); + TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->prepare(nullptr, nullptr)); + TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->invoke(nullptr, nullptr)); + + registration = resolver->FindOp("mock_custom", 10); + TF_LITE_MICRO_EXPECT_EQ(nullptr, registration); + + registration = resolver->FindOp("nonexistent_custom", 0); + TF_LITE_MICRO_EXPECT_EQ(nullptr, registration); +} + +TF_LITE_MICRO_TESTS_END diff --git a/tensorflow/contrib/lite/experimental/micro/simple_tensor_allocator.cc b/tensorflow/contrib/lite/experimental/micro/simple_tensor_allocator.cc new file mode 100644 index 0000000000..8c090a20a5 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/simple_tensor_allocator.cc @@ -0,0 +1,149 @@ +/* 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. +==============================================================================*/ + +#include "tensorflow/contrib/lite/experimental/micro/simple_tensor_allocator.h" + +#include "tensorflow/contrib/lite/core/api/flatbuffer_conversions.h" + +namespace tflite { +namespace { + +TfLiteStatus TfLiteTypeSizeOf(TfLiteType type, size_t* size, + ErrorReporter* reporter) { + switch (type) { + case kTfLiteFloat32: + *size = sizeof(float); + break; + case kTfLiteInt16: + *size = sizeof(int16_t); + break; + case kTfLiteInt32: + *size = sizeof(int32_t); + break; + case kTfLiteUInt8: + *size = sizeof(uint8_t); + break; + case kTfLiteInt64: + *size = sizeof(int64_t); + break; + case kTfLiteBool: + *size = sizeof(bool); + break; + case kTfLiteComplex64: + *size = sizeof(float) * 2; + break; + default: + reporter->Report( + "Only float32, int16, int32, int64, uint8, bool, complex64 " + "supported currently."); + return kTfLiteError; + } + return kTfLiteOk; +} + +TfLiteStatus BytesRequired(const tflite::Tensor& flatbuffer_tensor, + size_t dims_size, size_t* bytes, + ErrorReporter* error_reporter) { + TfLiteType tf_lite_type; + TF_LITE_ENSURE_STATUS(ConvertTensorType(flatbuffer_tensor.type(), + &tf_lite_type, error_reporter)); + size_t type_size; + TF_LITE_ENSURE_STATUS( + TfLiteTypeSizeOf(tf_lite_type, &type_size, error_reporter)); + *bytes = dims_size * type_size; + return kTfLiteOk; +} + +} // namespace + +TfLiteStatus SimpleTensorAllocator::AllocateTensor( + const tflite::Tensor& flatbuffer_tensor, int create_before, + int destroy_after, + const flatbuffers::Vector<flatbuffers::Offset<Buffer>>* buffers, + ErrorReporter* error_reporter, TfLiteTensor* result) { + TF_LITE_ENSURE_STATUS(ConvertTensorType(flatbuffer_tensor.type(), + &result->type, error_reporter)); + result->is_variable = flatbuffer_tensor.is_variable(); + + result->data.raw = nullptr; + result->bytes = 0; + if (auto* buffer = (*buffers)[flatbuffer_tensor.buffer()]) { + if (auto* array = buffer->data()) { + if (size_t array_size = array->size()) { + result->data.raw = + const_cast<char*>(reinterpret_cast<const char*>(array->data())); + TF_LITE_ENSURE_STATUS(BytesRequired(flatbuffer_tensor, array_size, + &result->bytes, error_reporter)); + } + } + } + if (result->data.raw) { + result->allocation_type = kTfLiteMmapRo; + } else { + int data_size = 1; + for (int n = 0; n < flatbuffer_tensor.shape()->Length(); ++n) { + data_size *= flatbuffer_tensor.shape()->Get(n); + } + TF_LITE_ENSURE_STATUS(BytesRequired(flatbuffer_tensor, data_size, + &result->bytes, error_reporter)); + result->data.raw = reinterpret_cast<char*>(AllocateMemory(result->bytes)); + if (result->data.raw == nullptr) { + const char* tensor_name = flatbuffer_tensor.name()->c_str(); + if (tensor_name == nullptr) { + tensor_name = "<None>"; + } + error_reporter->Report( + "Couldn't allocate memory for tensor '%s', wanted %d bytes but only " + "%d were available", + tensor_name, result->bytes, (data_size_max_ - data_size_)); + return kTfLiteError; + } + result->allocation_type = kTfLiteArenaRw; + } + result->dims = reinterpret_cast<TfLiteIntArray*>( + AllocateMemory(sizeof(int) * (flatbuffer_tensor.shape()->Length() + 1))); + result->dims->size = flatbuffer_tensor.shape()->Length(); + for (int n = 0; n < flatbuffer_tensor.shape()->Length(); ++n) { + result->dims->data[n] = flatbuffer_tensor.shape()->Get(n); + } + if (flatbuffer_tensor.quantization()) { + result->params.scale = flatbuffer_tensor.quantization()->scale()->Get(0); + result->params.zero_point = + flatbuffer_tensor.quantization()->zero_point()->Get(0); + } + result->allocation = nullptr; + if (flatbuffer_tensor.name()) { + result->name = flatbuffer_tensor.name()->c_str(); + } else { + result->name = "<No name>"; + } + result->delegate = nullptr; + result->buffer_handle = 0; + result->data_is_stale = false; + return kTfLiteOk; +} + +uint8_t* SimpleTensorAllocator::AllocateMemory(size_t size) { + if ((data_size_ + size) > data_size_max_) { + // TODO(petewarden): Add error reporting beyond returning null! + return nullptr; + } + uint8_t* result = data_; + data_ += size; + data_size_ += size; + return result; +} + +} // namespace tflite diff --git a/tensorflow/contrib/lite/experimental/micro/simple_tensor_allocator.h b/tensorflow/contrib/lite/experimental/micro/simple_tensor_allocator.h new file mode 100644 index 0000000000..4f16a9d0e5 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/simple_tensor_allocator.h @@ -0,0 +1,51 @@ +/* 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_CONTRIB_LITE_EXPERIMENTAL_MICRO_SIMPLE_TENSOR_ALLOCATOR_H_ +#define TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_MICRO_SIMPLE_TENSOR_ALLOCATOR_H_ + +#include "tensorflow/contrib/lite/c/c_api_internal.h" +#include "tensorflow/contrib/lite/core/api/error_reporter.h" +#include "tensorflow/contrib/lite/schema/schema_generated.h" + +namespace tflite { + +// TODO(petewarden): This allocator never frees up or reuses any memory, even +// though we have enough information about lifetimes of the tensors to do so. +// This makes it pretty wasteful, so we should use a more intelligent method. +class SimpleTensorAllocator { + public: + SimpleTensorAllocator(uint8_t* buffer, int buffer_size) + : data_size_(0), data_size_max_(buffer_size), data_(buffer) {} + + TfLiteStatus AllocateTensor( + const tflite::Tensor& flatbuffer_tensor, int create_before, + int destroy_after, + const flatbuffers::Vector<flatbuffers::Offset<Buffer>>* buffers, + ErrorReporter* error_reporter, TfLiteTensor* result); + + uint8_t* AllocateMemory(size_t size); + + int GetDataSize() const { return data_size_; } + + private: + int data_size_; + int data_size_max_; + uint8_t* data_; +}; + +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_MICRO_SIMPLE_TENSOR_ALLOCATOR_H_ diff --git a/tensorflow/contrib/lite/experimental/micro/simple_tensor_allocator_test.cc b/tensorflow/contrib/lite/experimental/micro/simple_tensor_allocator_test.cc new file mode 100644 index 0000000000..c835427243 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/simple_tensor_allocator_test.cc @@ -0,0 +1,144 @@ +/* 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. +==============================================================================*/ + +#include "tensorflow/contrib/lite/experimental/micro/micro_interpreter.h" + +#include "tensorflow/contrib/lite/experimental/micro/testing/micro_test.h" + +namespace tflite { +namespace { +class StackAllocator : public flatbuffers::Allocator { + public: + StackAllocator() : data_(data_backing_), data_size_(0) {} + + uint8_t* allocate(size_t size) override { + if ((data_size_ + size) > kStackAllocatorSize) { + // TODO(petewarden): Add error reporting beyond returning null! + return nullptr; + } + uint8_t* result = data_; + data_ += size; + data_size_ += size; + return result; + } + + void deallocate(uint8_t* p, size_t) override {} + + static StackAllocator& instance() { + // Avoid using true dynamic memory allocation to be portable to bare metal. + static char inst_memory[sizeof(StackAllocator)]; + static StackAllocator* inst = new (inst_memory) StackAllocator; + return *inst; + } + + static constexpr int kStackAllocatorSize = 4096; + + private: + uint8_t data_backing_[kStackAllocatorSize]; + uint8_t* data_; + int data_size_; +}; + +flatbuffers::FlatBufferBuilder* BuilderInstance() { + static char inst_memory[sizeof(flatbuffers::FlatBufferBuilder)]; + static flatbuffers::FlatBufferBuilder* inst = + new (inst_memory) flatbuffers::FlatBufferBuilder( + StackAllocator::kStackAllocatorSize, &StackAllocator::instance()); + return inst; +} + +const Tensor* Create1dTensor(int size) { + using flatbuffers::Offset; + flatbuffers::FlatBufferBuilder* builder = BuilderInstance(); + constexpr size_t tensor_shape_size = 1; + const int32_t tensor_shape[tensor_shape_size] = {size}; + const Offset<Tensor> tensor_offset = CreateTensor( + *builder, builder->CreateVector(tensor_shape, tensor_shape_size), + TensorType_INT32, 0, builder->CreateString("test_tensor"), 0, false); + builder->Finish(tensor_offset); + void* tensor_pointer = builder->GetBufferPointer(); + const Tensor* tensor = flatbuffers::GetRoot<Tensor>(tensor_pointer); + return tensor; +} + +const flatbuffers::Vector<flatbuffers::Offset<Buffer>>* CreateBuffers() { + using flatbuffers::Offset; + flatbuffers::FlatBufferBuilder* builder = BuilderInstance(); + constexpr size_t buffers_size = 1; + const Offset<Buffer> buffers[buffers_size] = { + CreateBuffer(*builder), + }; + const flatbuffers::Offset<flatbuffers::Vector<flatbuffers::Offset<Buffer>>> + buffers_offset = builder->CreateVector(buffers, buffers_size); + builder->Finish(buffers_offset); + void* buffers_pointer = builder->GetBufferPointer(); + const flatbuffers::Vector<flatbuffers::Offset<Buffer>>* result = + flatbuffers::GetRoot<flatbuffers::Vector<flatbuffers::Offset<Buffer>>>( + buffers_pointer); + return result; +} + +} // namespace +} // namespace tflite + +TF_LITE_MICRO_TESTS_BEGIN + +TF_LITE_MICRO_TEST(TestAllocateTensor) { + constexpr size_t arena_size = 1024; + uint8_t arena[arena_size]; + tflite::SimpleTensorAllocator allocator(arena, arena_size); + + const tflite::Tensor* tensor = tflite::Create1dTensor(100); + const flatbuffers::Vector<flatbuffers::Offset<tflite::Buffer>>* buffers = + tflite::CreateBuffers(); + + TfLiteTensor allocated_tensor; + TF_LITE_MICRO_EXPECT_EQ( + kTfLiteOk, + allocator.AllocateTensor(*tensor, 0, 1, buffers, micro_test::reporter, + &allocated_tensor)); + TF_LITE_MICRO_EXPECT_EQ(kTfLiteInt32, allocated_tensor.type); + TF_LITE_MICRO_EXPECT_EQ(1, allocated_tensor.dims->size); + TF_LITE_MICRO_EXPECT_EQ(100, allocated_tensor.dims->data[0]); + TF_LITE_MICRO_EXPECT_EQ(400, allocated_tensor.bytes); + TF_LITE_MICRO_EXPECT_NE(nullptr, allocated_tensor.data.i32); +} + +TF_LITE_MICRO_TEST(TestTooLarge) { + constexpr size_t arena_size = 1024; + uint8_t arena[arena_size]; + tflite::SimpleTensorAllocator allocator(arena, arena_size); + + const tflite::Tensor* tensor = tflite::Create1dTensor(10000); + const flatbuffers::Vector<flatbuffers::Offset<tflite::Buffer>>* buffers = + tflite::CreateBuffers(); + + TfLiteTensor allocated_tensor; + TF_LITE_MICRO_EXPECT_NE( + kTfLiteOk, + allocator.AllocateTensor(*tensor, 0, 1, buffers, micro_test::reporter, + &allocated_tensor)); +} + +TF_LITE_MICRO_TEST(TestJustFits) { + constexpr size_t arena_size = 1024; + uint8_t arena[arena_size]; + tflite::SimpleTensorAllocator allocator(arena, arena_size); + + uint8_t* result = allocator.AllocateMemory(arena_size); + TF_LITE_MICRO_EXPECT_NE(nullptr, result); +} + +TF_LITE_MICRO_TESTS_END diff --git a/tensorflow/contrib/lite/experimental/micro/testing/BUILD b/tensorflow/contrib/lite/experimental/micro/testing/BUILD new file mode 100644 index 0000000000..0d23be5712 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/testing/BUILD @@ -0,0 +1,17 @@ +package( + default_visibility = ["//visibility:public"], +) + +licenses(["notice"]) # Apache 2.0 + +exports_files(["test_linux_binary.sh"]) + +cc_library( + name = "micro_test", + hdrs = [ + "micro_test.h", + ], + deps = [ + "//tensorflow/contrib/lite/experimental/micro:micro_framework", + ], +) diff --git a/tensorflow/contrib/lite/experimental/micro/testing/Dockerfile.bluepill b/tensorflow/contrib/lite/experimental/micro/testing/Dockerfile.bluepill new file mode 100644 index 0000000000..7d6d81af0f --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/testing/Dockerfile.bluepill @@ -0,0 +1,21 @@ +# 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. +# ============================================================================== + +# This docker configuration file lets you emulate a Blue Pill board +# on an x86 desktop or laptop, which can be useful for debugging and +# automated testing. +FROM antmicro/renode:latest + +LABEL maintainer="Pete Warden <petewarden@google.com>"
\ No newline at end of file diff --git a/tensorflow/contrib/lite/experimental/micro/testing/bluepill.resc b/tensorflow/contrib/lite/experimental/micro/testing/bluepill.resc new file mode 100644 index 0000000000..9333dc42bf --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/testing/bluepill.resc @@ -0,0 +1,36 @@ +# 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. +# ============================================================================== + +using sysbus + +mach create +machine LoadPlatformDescription @platforms/cpus/stm32f103.repl + +# These lines are needed to show the results of DebugLog calls in the output. +machine LoadPlatformDescriptionFromString "uartSemihosting: UART.SemihostingUart @ cpu" +showAnalyzer cpu.uartSemihosting Antmicro.Renode.Analyzers.LoggingUartAnalyzer + +logFile @/tmp/renode_bluepill_log.txt + +macro reset +""" + sysbus LoadELF $bin +""" + +runMacro $reset + +emulation RunFor @1 + +quit
\ No newline at end of file diff --git a/tensorflow/contrib/lite/experimental/micro/testing/micro_test.bzl b/tensorflow/contrib/lite/experimental/micro/testing/micro_test.bzl new file mode 100644 index 0000000000..916e3eeac3 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/testing/micro_test.bzl @@ -0,0 +1,67 @@ +"""Rules for simple testing without dependencies by parsing output logs.""" + +def tflite_micro_cc_test( + name, + expected_in_logs = "~~~ALL TESTS PASSED~~~", + srcs = [], + includes = [], + defines = [], + copts = [], + nocopts = "", + linkopts = [], + deps = [], + tags = [], + visibility = None): + """Tests a C/C++ binary without testing framework dependencies`. + + Runs a C++ binary, and tests that the output logs contain the + expected value. This is a deliberately spartan way of testing, to match + what's available when testing microcontroller binaries. + + Args: + name: a unique name for this rule. + expected_in_logs: A regular expression that is required to be + present in the binary's logs for the test to pass. + srcs: sources to compile (C, C++, ld scripts). + includes: include paths to add to this rule and its dependents. + defines: list of `VAR` or `VAR=VAL` to pass to CPP for this rule and + its dependents. + copts: gcc compilation flags for this rule only. + nocopts: list of gcc compilation flags to remove for this rule + only. No regexp like for `cc_library`. + linkopts: `gcc` flags to add to the linking phase. For "pure" ld flags, + prefix them with the `-Wl,` prefix here. + deps: dependencies. only `tflite_bare_metal_cc_library()` dependencies + allowed. + visibility: visibility. + """ + native.cc_binary( + name = name + "_binary", + srcs = srcs, + includes = includes, + defines = defines, + copts = copts, + nocopts = nocopts, + linkopts = linkopts, + deps = deps, + tags = tags, + visibility = visibility, + ) + native.sh_test( + name = name, + size = "medium", + srcs = [ + "//tensorflow/contrib/lite/experimental/micro/testing:test_linux_binary.sh", + ], + args = [ + native.package_name() + "/" + name + "_binary", + "'" + expected_in_logs + "'", + ], + data = [ + name + "_binary", + # Internal test dependency placeholder + ], + deps = [ + ], + tags = tags, + ) diff --git a/tensorflow/contrib/lite/experimental/micro/testing/micro_test.h b/tensorflow/contrib/lite/experimental/micro/testing/micro_test.h new file mode 100644 index 0000000000..104509c9dc --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/testing/micro_test.h @@ -0,0 +1,138 @@ +/* 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. +==============================================================================*/ + +// An ultra-lightweight testing framework designed for use with microcontroller +// applications. Its only dependency is on TensorFlow Lite's ErrorReporter +// interface, where log messages are output. This is designed to be usable even +// when no standard C or C++ libraries are available, and without any dynamic +// memory allocation or reliance on global constructors. +// +// To build a test, you use syntax similar to gunit, but with some extra +// decoration to create a hidden 'main' function containing each of the tests to +// be run. Your code should look something like: +// ---------------------------------------------------------------------------- +// #include "path/to/this/header" +// +// TF_LITE_MICRO_TESTS_BEGIN +// +// TF_LITE_MICRO_TEST(SomeTest) { +// TF_LITE_LOG_EXPECT_EQ(true, true); +// } +// +// TF_LITE_MICRO_TESTS_END +// ---------------------------------------------------------------------------- +// If you compile this for your platform, you'll get a normal binary that you +// should be able to run. Executing it will output logging information like this +// to stderr (or whatever equivalent is available and written to by +// ErrorReporter): +// ---------------------------------------------------------------------------- +// Testing SomeTest +// 1/1 tests passed +// ~~~ALL TESTS PASSED~~~ +// ---------------------------------------------------------------------------- +// This is designed to be human-readable, so you can just run tests manually, +// but the string "~~~ALL TESTS PASSED~~~" should only appear if all of the +// tests do pass. This makes it possible to integrate with automated test +// systems by scanning the output logs and looking for that magic value. +// +// This framework is intended to be a rudimentary alternative to no testing at +// all on systems that struggle to run more conventional approaches, so use with +// caution! + +#ifndef TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_MICRO_TESTING_MICRO_TEST_H_ +#define TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_MICRO_TESTING_MICRO_TEST_H_ + +#include "tensorflow/contrib/lite/experimental/micro/micro_error_reporter.h" + +namespace micro_test { +extern int tests_passed; +extern int tests_failed; +extern bool is_test_complete; +extern bool did_test_fail; +extern tflite::ErrorReporter* reporter; +} // namespace micro_test + +#define TF_LITE_MICRO_TESTS_BEGIN \ + namespace micro_test { \ + int tests_passed; \ + int tests_failed; \ + bool is_test_complete; \ + bool did_test_fail; \ + tflite::ErrorReporter* reporter; \ + } \ + \ + int main(int argc, char** argv) { \ + micro_test::tests_passed = 0; \ + micro_test::tests_failed = 0; \ + tflite::MicroErrorReporter error_reporter; \ + micro_test::reporter = &error_reporter; + +#define TF_LITE_MICRO_TESTS_END \ + micro_test::reporter->Report( \ + "%d/%d tests passed", micro_test::tests_passed, \ + (micro_test::tests_failed + micro_test::tests_passed)); \ + if (micro_test::tests_failed == 0) { \ + micro_test::reporter->Report("~~~ALL TESTS PASSED~~~\n"); \ + } else { \ + micro_test::reporter->Report("~~~SOME TESTS FAILED~~~\n"); \ + } \ + } + +// TODO(petewarden): I'm going to hell for what I'm doing to this poor for loop. +#define TF_LITE_MICRO_TEST(name) \ + micro_test::reporter->Report("Testing %s", #name); \ + for (micro_test::is_test_complete = false, \ + micro_test::did_test_fail = false; \ + !micro_test::is_test_complete; micro_test::is_test_complete = true, \ + micro_test::tests_passed += (micro_test::did_test_fail) ? 0 : 1, \ + micro_test::tests_failed += (micro_test::did_test_fail) ? 1 : 0) + +#define TF_LITE_MICRO_EXPECT(x) \ + do { \ + if (!(x)) { \ + micro_test::reporter->Report(#x " failed at %s:%d", __FILE__, __LINE__); \ + micro_test::did_test_fail = true; \ + } \ + } while (false) + +#define TF_LITE_MICRO_EXPECT_EQ(x, y) \ + do { \ + if ((x) != (y)) { \ + micro_test::reporter->Report(#x " == " #y " failed at %s:%d", __FILE__, \ + __LINE__); \ + micro_test::did_test_fail = true; \ + } \ + } while (false) + +#define TF_LITE_MICRO_EXPECT_NE(x, y) \ + do { \ + if ((x) == (y)) { \ + micro_test::reporter->Report(#x " != " #y " failed at %s:%d", __FILE__, \ + __LINE__); \ + micro_test::did_test_fail = true; \ + } \ + } while (false) + +#define TF_LITE_MICRO_EXPECT_NEAR(x, y, epsilon) \ + do { \ + auto delta = ((x) > (y)) ? ((x) - (y)) : ((y) - (x)); \ + if (delta > epsilon) { \ + micro_test::reporter->Report(#x " near " #y " failed at %s:%d", \ + __FILE__, __LINE__); \ + micro_test::did_test_fail = true; \ + } \ + } while (false) + +#endif // TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_MICRO_TESTING_MICRO_TEST_H_ diff --git a/tensorflow/contrib/lite/experimental/micro/testing/test_bluepill_binary.sh b/tensorflow/contrib/lite/experimental/micro/testing/test_bluepill_binary.sh new file mode 100755 index 0000000000..07742a8262 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/testing/test_bluepill_binary.sh @@ -0,0 +1,54 @@ +#!/bin/bash -e +# 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. +# ============================================================================== +# +# Tests a 'bluepill' STM32F103 ELF by parsing the log output of Renode emulation. +# +# First argument is the ELF location. +# Second argument is a regular expression that's required to be in the output logs +# for the test to pass. + +declare -r ROOT_DIR=`pwd` +declare -r TEST_TMPDIR=/tmp/test_bluepill_binary/ +declare -r MICRO_LOG_PATH=${TEST_TMPDIR} +declare -r MICRO_LOG_FILENAME=${MICRO_LOG_PATH}/logs.txt +mkdir -p ${MICRO_LOG_PATH} + +docker build -t renode_bluepill \ + -f ${ROOT_DIR}/tensorflow/contrib/lite/experimental/micro/testing/Dockerfile.bluepill \ + ${ROOT_DIR}/tensorflow/contrib/lite/experimental/micro/testing/ + +docker run \ + --log-driver=none -a stdout -a stderr \ + -v ${ROOT_DIR}:/workspace \ + -v /tmp:/tmp \ + -it renode_bluepill \ + /bin/bash -c "renode -P 5000 --disable-xwt -e ' +\$bin?=@/workspace/$1 +s @/workspace/tensorflow/contrib/lite/experimental/micro/testing/bluepill.resc +' 2>&1 >${MICRO_LOG_FILENAME}" + +echo "LOGS:" +cat ${MICRO_LOG_FILENAME} + +if grep -q "$2" ${MICRO_LOG_FILENAME} +then + echo "$1: PASS" + exit 0 +else + echo "$1: FAIL - '$2' not found in logs." + exit 1 +fi + diff --git a/tensorflow/contrib/lite/experimental/micro/testing/test_linux_binary.sh b/tensorflow/contrib/lite/experimental/micro/testing/test_linux_binary.sh new file mode 100755 index 0000000000..24131a6d2d --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/testing/test_linux_binary.sh @@ -0,0 +1,39 @@ +#!/bin/bash -e +# Copyright 2018 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +# +# Tests a Linux binary by parsing the log output. +# +# First argument is the binary location. +# Second argument is a regular expression that's required to be in the output logs +# for the test to pass. + +declare -r ROOT_DIR=`pwd` +declare -r TEST_TMPDIR=/tmp/test_bluepill_binary/ +declare -r MICRO_LOG_PATH=${TEST_TMPDIR}/$1 +declare -r MICRO_LOG_FILENAME=${MICRO_LOG_PATH}/logs.txt +mkdir -p ${MICRO_LOG_PATH} + +$1 2>&1 | tee ${MICRO_LOG_FILENAME} + +if grep -q "$2" ${MICRO_LOG_FILENAME} +then + echo "$1: PASS" + exit 0 +else + echo "$1: FAIL - '$2' not found in logs." + exit 1 +fi + diff --git a/tensorflow/contrib/lite/experimental/micro/tools/make/Makefile b/tensorflow/contrib/lite/experimental/micro/tools/make/Makefile new file mode 100644 index 0000000000..880bb4763c --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/tools/make/Makefile @@ -0,0 +1,166 @@ +MAKEFILE_DIR := tensorflow/contrib/lite/experimental/micro/tools/make + +# Try to figure out the host system +HOST_OS := +ifeq ($(OS),Windows_NT) + HOST_OS = windows +else + UNAME_S := $(shell uname -s) + ifeq ($(UNAME_S),Linux) + HOST_OS := linux + endif + ifeq ($(UNAME_S),Darwin) + HOST_OS := osx + endif +endif + +HOST_ARCH := $(shell if [[ $(shell uname -m) =~ i[345678]86 ]]; then echo x86_32; else echo $(shell uname -m); fi) + +# Override these on the make command line to target a specific architecture. For example: +# make -f tensorflow/contrib/lite/Makefile TARGET=rpi TARGET_ARCH=armv7l +TARGET := $(HOST_OS) +TARGET_ARCH := $(HOST_ARCH) + +INCLUDES := \ +-I. \ +-I$(MAKEFILE_DIR)/../../../../../ \ +-I$(MAKEFILE_DIR)/../../../../../../ \ +-I$(MAKEFILE_DIR)/downloads/ \ +-I$(MAKEFILE_DIR)/downloads/gemmlowp \ +-I$(MAKEFILE_DIR)/downloads/flatbuffers/include \ +-I$(OBJDIR) +# This is at the end so any globally-installed frameworks like protobuf don't +# override local versions in the source tree. +INCLUDES += -I/usr/local/include + +TEST_SCRIPT := tensorflow/contrib/lite/experimental/micro/testing/test_linux_binary.sh + +MICROLITE_LIBS := -lm + +# There are no rules for compiling objects for the host system (since we don't +# generate things like the protobuf compiler that require that), so all of +# these settings are for the target compiler. +CXXFLAGS := -O3 -DNDEBUG +CXXFLAGS += --std=c++11 -g -DTF_LITE_STATIC_MEMORY +CCFLAGS := -DNDEBUG -g -DTF_LITE_STATIC_MEMORY +LDOPTS := -L/usr/local/lib +ARFLAGS := -r +TARGET_TOOLCHAIN_PREFIX := +CC_PREFIX := + +# This library is the main target for this makefile. It will contain a minimal +# runtime that can be linked in to other programs. +MICROLITE_LIB_NAME := libtensorflow-microlite.a + +# Test binary for the microcontroller speech model. +MICRO_SPEECH_TEST_SRCS := \ +tensorflow/contrib/lite/experimental/micro/examples/micro_speech/micro_speech_test.cc \ +tensorflow/contrib/lite/experimental/micro/examples/micro_speech/tiny_conv_model_data.cc + +MICROLITE_TEST_SRCS := \ +$(wildcard tensorflow/contrib/lite/experimental/micro/*test.cc) \ +$(wildcard tensorflow/contrib/lite/experimental/micro/kernels/*test.cc) + +MICROLITE_CC_BASE_SRCS := \ +$(wildcard tensorflow/contrib/lite/experimental/micro/*.cc) \ +$(wildcard tensorflow/contrib/lite/experimental/micro/kernels/*.cc) \ +tensorflow/contrib/lite/c/c_api_internal.c \ +tensorflow/contrib/lite/core/api/error_reporter.cc \ +tensorflow/contrib/lite/core/api/flatbuffer_conversions.cc \ +tensorflow/contrib/lite/core/api/op_resolver.cc \ +tensorflow/contrib/lite/kernels/kernel_util.cc \ +tensorflow/contrib/lite/kernels/internal/quantization_util.cc +MICROLITE_CC_SRCS := $(filter-out $(MICROLITE_TEST_SRCS), $(MICROLITE_CC_BASE_SRCS)) + +# These target-specific makefiles should modify or replace options like +# CXXFLAGS or LIBS to work for a specific targetted architecture. All logic +# based on platforms or architectures should happen within these files, to +# keep this main makefile focused on the sources and dependencies. +include $(wildcard $(MAKEFILE_DIR)/targets/*_makefile.inc) + +ALL_SRCS := \ + $(MICRO_SPEECH_TEST_SRCS) \ + $(MICROLITE_CC_SRCS) \ + $(MICROLITE_TEST_SRCS) + +# Where compiled objects are stored. +GENDIR := $(MAKEFILE_DIR)/gen/$(TARGET)_$(TARGET_ARCH)/ +OBJDIR := $(GENDIR)obj/ +BINDIR := $(GENDIR)bin/ +LIBDIR := $(GENDIR)lib/ + +MICROLITE_LIB_PATH := $(LIBDIR)$(MICROLITE_LIB_NAME) + +MICRO_SPEECH_TEST_BINARY := $(BINDIR)micro_speech_test + +CXX := $(CC_PREFIX)${TARGET_TOOLCHAIN_PREFIX}g++ +CC := $(CC_PREFIX)${TARGET_TOOLCHAIN_PREFIX}gcc +AR := $(CC_PREFIX)${TARGET_TOOLCHAIN_PREFIX}ar + +MICRO_SPEECH_TEST_OBJS := $(addprefix $(OBJDIR), \ +$(patsubst %.cc,%.o,$(patsubst %.c,%.o,$(MICRO_SPEECH_TEST_SRCS)))) + +MICROLITE_LIB_OBJS := $(addprefix $(OBJDIR), \ +$(patsubst %.cc,%.o,$(patsubst %.c,%.o,$(MICROLITE_CC_SRCS)))) + +MICROLITE_TEST_TARGETS := $(addprefix $(BINDIR), \ +$(patsubst %_test.cc,%.test_target,$(MICROLITE_TEST_SRCS))) + +# For normal manually-created TensorFlow C++ source files. +$(OBJDIR)%.o: %.cc + @mkdir -p $(dir $@) + $(CXX) $(CXXFLAGS) $(INCLUDES) -c $< -o $@ + +# For normal manually-created TensorFlow C source files. +$(OBJDIR)%.o: %.c + @mkdir -p $(dir $@) + $(CC) $(CCFLAGS) $(INCLUDES) -c $< -o $@ + +# The target that's compiled if there's no command-line arguments. +all: $(MICROLITE_LIB_PATH) $(MICRO_SPEECH_TEST_BINARY) + +microlite: $(MICROLITE_LIB_PATH) + +# Hack for generating schema file bypassing flatbuffer parsing +tensorflow/contrib/lite/schema/schema_generated.h: + @cp -u tensorflow/contrib/lite/schema/schema_generated.h.OPENSOURCE tensorflow/contrib/lite/schema/schema_generated.h + +# Gathers together all the objects we've compiled into a single '.a' archive. +$(MICROLITE_LIB_PATH): tensorflow/contrib/lite/schema/schema_generated.h $(MICROLITE_LIB_OBJS) + @mkdir -p $(dir $@) + $(AR) $(ARFLAGS) $(MICROLITE_LIB_PATH) $(MICROLITE_LIB_OBJS) + +$(MICRO_SPEECH_TEST_BINARY): $(MICRO_SPEECH_TEST_OBJS) $(MICROLITE_LIB_PATH) + @mkdir -p $(dir $@) + $(CXX) $(CXXFLAGS) $(INCLUDES) \ + -o $(MICRO_SPEECH_TEST_BINARY) $(MICRO_SPEECH_TEST_OBJS) \ + $(LIBFLAGS) $(MICROLITE_LIB_PATH) $(LDFLAGS) $(MICROLITE_LIBS) + +micro_speech_test: $(MICRO_SPEECH_TEST_BINARY) +micro_speech_test_bin: $(MICRO_SPEECH_TEST_BINARY).bin + +test_micro_speech: $(MICRO_SPEECH_TEST_BINARY) + $(TEST_SCRIPT) $(MICRO_SPEECH_TEST_BINARY) '~~~ALL TESTS PASSED~~~' + +$(BINDIR)%_test : $(OBJDIR)%_test.o $(MICROLITE_LIB_PATH) + @mkdir -p $(dir $@) + $(CXX) $(CXXFLAGS) $(INCLUDES) \ + -o $@ $< \ + $(LIBFLAGS) $(MICROLITE_LIB_PATH) $(LDFLAGS) $(MICROLITE_LIBS) + +$(BINDIR)%.test_target: $(BINDIR)%_test + $(TEST_SCRIPT) $< '~~~ALL TESTS PASSED~~~' + +$(info $(MICROLITE_TEST_TARGETS)) + +test: test_micro_speech $(MICROLITE_TEST_TARGETS) + +# Gets rid of all generated files. +clean: + rm -rf $(MAKEFILE_DIR)/gen + +$(DEPDIR)/%.d: ; +.PRECIOUS: $(DEPDIR)/%.d +.PRECIOUS: $(BINDIR)%_test + +-include $(patsubst %,$(DEPDIR)/%.d,$(basename $(ALL_SRCS))) diff --git a/tensorflow/contrib/lite/experimental/micro/tools/make/download_dependencies.sh b/tensorflow/contrib/lite/experimental/micro/tools/make/download_dependencies.sh new file mode 100755 index 0000000000..4c2ff8545d --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/tools/make/download_dependencies.sh @@ -0,0 +1,73 @@ +#!/bin/bash +# Copyright 2017 The TensorFlow Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== + +set -e + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +cd "$SCRIPT_DIR/../../../../../../.." + +DOWNLOADS_DIR=tensorflow/contrib/lite/experimental/micro/tools/make/downloads +BZL_FILE_PATH=tensorflow/workspace.bzl + +# Ensure it is being run from repo root +if [ ! -f $BZL_FILE_PATH ]; then + echo "Could not find ${BZL_FILE_PATH}": + echo "Likely you are not running this from the root directory of the repository."; + exit 1; +fi + +GEMMLOWP_URL="https://github.com/google/gemmlowp/archive/719139ce755a0f31cbf1c37f7f98adcc7fc9f425.zip" +FLATBUFFERS_URL="https://github.com/google/flatbuffers/archive/1f5eae5d6a135ff6811724f6c57f911d1f46bb15.tar.gz" +CMSIS_URL="https://github.com/ARM-software/CMSIS_5/archive/5.4.0.zip" +STM32_BARE_LIB_URL="https://github.com/google/stm32_bare_lib/archive/50e0da307a2821bb54af1f57b969e6b76cb89d32.zip" + +download_and_extract() { + local usage="Usage: download_and_extract URL DIR" + local url="${1:?${usage}}" + local dir="${2:?${usage}}" + echo "downloading ${url}" >&2 + mkdir -p "${dir}" + if [[ "${url}" == *gz ]]; then + curl -Ls "${url}" | tar -C "${dir}" --strip-components=1 -xz + elif [[ "${url}" == *zip ]]; then + tempdir=$(mktemp -d) + tempdir2=$(mktemp -d) + + curl -L ${url} > ${tempdir}/zipped.zip + unzip ${tempdir}/zipped.zip -d ${tempdir2} + + # If the zip file contains nested directories, extract the files from the + # inner directory. + if ls ${tempdir2}/*/* 1> /dev/null 2>&1; then + # unzip has no strip components, so unzip to a temp dir, and move the + # files we want from the tempdir to destination. + cp -R ${tempdir2}/*/* ${dir}/ + else + cp -R ${tempdir2}/* ${dir}/ + fi + rm -rf ${tempdir2} ${tempdir} + fi + + # Delete any potential BUILD files, which would interfere with Bazel builds. + find "${dir}" -type f -name '*BUILD' -delete +} + +download_and_extract "${GEMMLOWP_URL}" "${DOWNLOADS_DIR}/gemmlowp" +download_and_extract "${FLATBUFFERS_URL}" "${DOWNLOADS_DIR}/flatbuffers" +download_and_extract "${CMSIS_URL}" "${DOWNLOADS_DIR}/cmsis" +download_and_extract "${STM32_BARE_LIB_URL}" "${DOWNLOADS_DIR}/stm32_bare_lib" + +echo "download_dependencies.sh completed successfully." >&2 diff --git a/tensorflow/contrib/lite/experimental/micro/tools/make/targets/bluepill_makefile.inc b/tensorflow/contrib/lite/experimental/micro/tools/make/targets/bluepill_makefile.inc new file mode 100644 index 0000000000..022a8422dc --- /dev/null +++ b/tensorflow/contrib/lite/experimental/micro/tools/make/targets/bluepill_makefile.inc @@ -0,0 +1,65 @@ +# Settings for Blue Pill platforms. +ifeq ($(TARGET), bluepill) + TARGET_ARCH := cortex-m3 + TARGET_TOOLCHAIN_PREFIX := arm-none-eabi- + + PLATFORM_FLAGS = \ + -DGEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK \ + -DTF_LITE_STATIC_MEMORY \ + -DTF_LITE_MCU_DEBUG_LOG \ + -fno-rtti \ + -fmessage-length=0 \ + -fno-exceptions \ + -fno-unwind-tables \ + -fno-builtin \ + -ffunction-sections \ + -fdata-sections \ + -funsigned-char \ + -MMD \ + -mcpu=cortex-m3 \ + -mthumb \ + -std=gnu++11 \ + -Wvla \ + -Wall \ + -Wextra \ + -Wno-unused-parameter \ + -Wno-missing-field-initializers \ + -Wno-write-strings \ + -Wno-sign-compare \ + -fno-delete-null-pointer-checks \ + -fomit-frame-pointer \ + -fpermissive \ + -nostdlib \ + -g \ + -Os + CXXFLAGS += $(PLATFORM_FLAGS) + CCFLAGS += $(PLATFORM_FLAGS) + LDFLAGS += \ + -T $(MAKEFILE_DIR)/downloads/stm32_bare_lib/stm32_linker_layout.lds \ + -Wl,-Map=$(MAKEFILE_DIR)/gen/$(TARGET).map,--cref \ + -Wl,--gc-sections + BUILD_TYPE := micro + MICROLITE_LIBS := \ + -lm + INCLUDES += \ + -isystem$(MAKEFILE_DIR)/downloads/cmsis/CMSIS/Core/Include/ \ + -I$(MAKEFILE_DIR)/downloads/stm32_bare_lib/include + MICROLITE_CC_SRCS += \ + $(wildcard $(MAKEFILE_DIR)/downloads/stm32_bare_lib/source/*.c) \ + $(wildcard $(MAKEFILE_DIR)/downloads/stm32_bare_lib/source/*.cc) + TEST_SCRIPT := tensorflow/contrib/lite/experimental/micro/testing/test_bluepill_binary.sh + # These are tests that don't currently work on the blue pill. + EXCLUDED_TESTS := \ + tensorflow/contrib/lite/experimental/micro/micro_interpreter_test.cc \ + tensorflow/contrib/lite/experimental/micro/simple_tensor_allocator_test.cc + MICROLITE_TEST_SRCS := $(filter-out $(EXCLUDED_TESTS), $(MICROLITE_TEST_SRCS)) + +# These are microcontroller-specific rules for converting the ELF output +# of the linker into a binary image that can be loaded directly. +OBJCOPY := $(TARGET_TOOLCHAIN_PREFIX)objcopy + +$(BINDIR)/%.bin: $(BINDIR)/% + @mkdir -p $(dir $@) + $(OBJCOPY) $< $@ -O binary + +endif
\ No newline at end of file diff --git a/tensorflow/contrib/lite/g3doc/tfmobile/android_build.md b/tensorflow/contrib/lite/g3doc/tfmobile/android_build.md index b0f32a8d6c..2eb776d10c 100644 --- a/tensorflow/contrib/lite/g3doc/tfmobile/android_build.md +++ b/tensorflow/contrib/lite/g3doc/tfmobile/android_build.md @@ -1,6 +1,22 @@ - # Building TensorFlow on Android +Warning: We expect to deprecate TensorFlow Mobile in early 2019 + +<div class="caution"> + <p> + <a href="../">TensorFlow Lite</a> is our main mobile and embedded offering. We are + working hard to close the feature gap between TensorFlow Mobile and + TensorFlow Lite. We expect to deprecate TensorFlow Mobile in early 2019. We + will give ample notice to our users when we get to that point and will + provide help and support to ensure easy migrations. + </p> + <p> + In the meantime, please use TensorFlow Lite. If you have a feature request, + such as a missing op, please post to our <a + href="https://github.com/tensorflow/tensorflow/issues">GitHub</a>. + </p> +</div> + To get you started working with TensorFlow on Android, we'll walk through two ways to build our TensorFlow mobile demos and deploying them on an Android device. The first is Android Studio, which lets you build and deploy in an diff --git a/tensorflow/contrib/lite/g3doc/tfmobile/index.md b/tensorflow/contrib/lite/g3doc/tfmobile/index.md index 49ad35d4e6..15f0fd3961 100644 --- a/tensorflow/contrib/lite/g3doc/tfmobile/index.md +++ b/tensorflow/contrib/lite/g3doc/tfmobile/index.md @@ -1,6 +1,22 @@ - # Overview +Warning: We expect to deprecate TensorFlow Mobile in early 2019 + +<div class="caution"> + <p> + <a href="../">TensorFlow Lite</a> is our main mobile and embedded offering. We are + working hard to close the feature gap between TensorFlow Mobile and + TensorFlow Lite. We expect to deprecate TensorFlow Mobile in early 2019. We + will give ample notice to our users when we get to that point and will + provide help and support to ensure easy migrations. + </p> + <p> + In the meantime, please use TensorFlow Lite. If you have a feature request, + such as a missing op, please post to our <a + href="https://github.com/tensorflow/tensorflow/issues">GitHub</a>. + </p> +</div> + TensorFlow was designed to be a good deep learning solution for mobile platforms. Currently we have two solutions for deploying machine learning applications on mobile and embedded devices: TensorFlow for Mobile and diff --git a/tensorflow/contrib/lite/g3doc/tfmobile/ios_build.md b/tensorflow/contrib/lite/g3doc/tfmobile/ios_build.md index be8b4100c8..d922907cdc 100644 --- a/tensorflow/contrib/lite/g3doc/tfmobile/ios_build.md +++ b/tensorflow/contrib/lite/g3doc/tfmobile/ios_build.md @@ -1,6 +1,22 @@ - # Building TensorFlow on iOS +Warning: We expect to deprecate TensorFlow Mobile in early 2019 + +<div class="caution"> + <p> + <a href="../">TensorFlow Lite</a> is our main mobile and embedded offering. We are + working hard to close the feature gap between TensorFlow Mobile and + TensorFlow Lite. We expect to deprecate TensorFlow Mobile in early 2019. We + will give ample notice to our users when we get to that point and will + provide help and support to ensure easy migrations. + </p> + <p> + In the meantime, please use TensorFlow Lite. If you have a feature request, + such as a missing op, please post to our <a + href="https://github.com/tensorflow/tensorflow/issues">GitHub</a>. + </p> +</div> + ## Using CocoaPods The simplest way to get started with TensorFlow on iOS is using the CocoaPods diff --git a/tensorflow/contrib/lite/g3doc/tfmobile/linking_libs.md b/tensorflow/contrib/lite/g3doc/tfmobile/linking_libs.md index 4d4bb3bc08..fd0e322c93 100644 --- a/tensorflow/contrib/lite/g3doc/tfmobile/linking_libs.md +++ b/tensorflow/contrib/lite/g3doc/tfmobile/linking_libs.md @@ -1,6 +1,22 @@ - # Integrating TensorFlow libraries +Warning: We expect to deprecate TensorFlow Mobile in early 2019 + +<div class="caution"> + <p> + <a href="../">TensorFlow Lite</a> is our main mobile and embedded offering. We are + working hard to close the feature gap between TensorFlow Mobile and + TensorFlow Lite. We expect to deprecate TensorFlow Mobile in early 2019. We + will give ample notice to our users when we get to that point and will + provide help and support to ensure easy migrations. + </p> + <p> + In the meantime, please use TensorFlow Lite. If you have a feature request, + such as a missing op, please post to our <a + href="https://github.com/tensorflow/tensorflow/issues">GitHub</a>. + </p> +</div> + Once you have made some progress on a model that addresses the problem you’re trying to solve, it’s important to test it out inside your application immediately. There are often unexpected differences between your training data diff --git a/tensorflow/contrib/lite/g3doc/tfmobile/optimizing.md b/tensorflow/contrib/lite/g3doc/tfmobile/optimizing.md index 7436594fd8..59ff8e774c 100644 --- a/tensorflow/contrib/lite/g3doc/tfmobile/optimizing.md +++ b/tensorflow/contrib/lite/g3doc/tfmobile/optimizing.md @@ -1,6 +1,22 @@ - # Optimizing for mobile +Warning: We expect to deprecate TensorFlow Mobile in early 2019 + +<div class="caution"> + <p> + <a href="../">TensorFlow Lite</a> is our main mobile and embedded offering. We are + working hard to close the feature gap between TensorFlow Mobile and + TensorFlow Lite. We expect to deprecate TensorFlow Mobile in early 2019. We + will give ample notice to our users when we get to that point and will + provide help and support to ensure easy migrations. + </p> + <p> + In the meantime, please use TensorFlow Lite. If you have a feature request, + such as a missing op, please post to our <a + href="https://github.com/tensorflow/tensorflow/issues">GitHub</a>. + </p> +</div> + There are some special issues that you have to deal with when you’re trying to ship on mobile or embedded devices, and you’ll need to think about these as you’re developing your model. diff --git a/tensorflow/contrib/lite/g3doc/tfmobile/prepare_models.md b/tensorflow/contrib/lite/g3doc/tfmobile/prepare_models.md index d1c67d4c61..1d373251dd 100644 --- a/tensorflow/contrib/lite/g3doc/tfmobile/prepare_models.md +++ b/tensorflow/contrib/lite/g3doc/tfmobile/prepare_models.md @@ -1,6 +1,22 @@ - # Preparing models for mobile deployment +Warning: We expect to deprecate TensorFlow Mobile in early 2019 + +<div class="caution"> + <p> + <a href="../">TensorFlow Lite</a> is our main mobile and embedded offering. We are + working hard to close the feature gap between TensorFlow Mobile and + TensorFlow Lite. We expect to deprecate TensorFlow Mobile in early 2019. We + will give ample notice to our users when we get to that point and will + provide help and support to ensure easy migrations. + </p> + <p> + In the meantime, please use TensorFlow Lite. If you have a feature request, + such as a missing op, please post to our <a + href="https://github.com/tensorflow/tensorflow/issues">GitHub</a>. + </p> +</div> + The requirements for storing model information during training are very different from when you want to release it as part of a mobile app. This section covers the tools involved in converting from a training model to something diff --git a/tensorflow/contrib/lite/interpreter.h b/tensorflow/contrib/lite/interpreter.h index 7ef736d01b..651a97e9dc 100644 --- a/tensorflow/contrib/lite/interpreter.h +++ b/tensorflow/contrib/lite/interpreter.h @@ -349,6 +349,10 @@ class Interpreter { return context_.allow_fp32_relax_to_fp16; } + // Owning handle to a TfLiteDelegate instance. + using TfLiteDelegatePtr = + std::unique_ptr<TfLiteDelegate, void (*)(TfLiteDelegate*)>; + // Allow a delegate to look at the graph and modify the graph to handle // parts of the graph themselves. After this is called, the graph may // contain new nodes that replace 1 more nodes. @@ -574,19 +578,11 @@ class Interpreter { TfLiteExternalContextType type, TfLiteExternalContext* ctx); - using TfLiteDelegatePtr = - std::unique_ptr<TfLiteDelegate, void (*)(TfLiteDelegate*)>; - // Variant of the public ModifyGraphWithDelegate method that additionally // Assumes ownership of the provided delegate. // WARNING: This is an experimental API and subject to change. - template <typename Delegate> - TfLiteStatus ModifyGraphWithDelegate(std::unique_ptr<Delegate> typed_delegate, + TfLiteStatus ModifyGraphWithDelegate(TfLiteDelegatePtr delegate, bool allow_dynamic_tensors = false) { - TfLiteDelegatePtr delegate(typed_delegate.release(), - [](TfLiteDelegate* delegate) { - delete static_cast<Delegate*>(delegate); - }); // Note that we retain ownership of the delegate even if graph modification // fails, as delegate use will be in an indeterminate state at that point. owned_delegates_.push_back(std::move(delegate)); @@ -676,6 +672,7 @@ class Interpreter { // List of delegates that have been installed and are owned by this // interpreter instance. Useful if client delegate ownership is burdensome. // WARNING: This is an experimental API and subject to change. + // TODO(b/116667551): Use TfLiteExternalContext for storing state. std::vector<TfLiteDelegatePtr> owned_delegates_; std::unique_ptr<MemoryPlanner> memory_planner_; diff --git a/tensorflow/contrib/lite/interpreter_test.cc b/tensorflow/contrib/lite/interpreter_test.cc index cdede430e2..6c71d5a8d7 100644 --- a/tensorflow/contrib/lite/interpreter_test.cc +++ b/tensorflow/contrib/lite/interpreter_test.cc @@ -30,7 +30,11 @@ class InterpreterTest : public ::testing::Test { template <typename Delegate> static TfLiteStatus ModifyGraphWithDelegate( Interpreter* interpreter, std::unique_ptr<Delegate> delegate) { - return interpreter->ModifyGraphWithDelegate(std::move(delegate)); + Interpreter::TfLiteDelegatePtr tflite_delegate( + delegate.release(), [](TfLiteDelegate* delegate) { + delete reinterpret_cast<Delegate*>(delegate); + }); + return interpreter->ModifyGraphWithDelegate(std::move(tflite_delegate)); } protected: diff --git a/tensorflow/contrib/lite/java/BUILD b/tensorflow/contrib/lite/java/BUILD index 098ba7e773..e68cd26f81 100644 --- a/tensorflow/contrib/lite/java/BUILD +++ b/tensorflow/contrib/lite/java/BUILD @@ -11,6 +11,10 @@ load("//tensorflow/java:build_defs.bzl", "JAVACOPTS") load("//tensorflow/contrib/lite:build_def.bzl", "tflite_jni_binary") load("//tensorflow/contrib/lite/java:aar_with_jni.bzl", "aar_with_jni") +JAVA_SRCS = glob([ + "src/main/java/org/tensorflow/lite/*.java", +]) + # Building tensorflow-lite.aar including 4 variants of .so # To build an aar for release, run below command: # bazel build --cxxopt='--std=c++11' -c opt --fat_apk_cpu=x86,x86_64,arm64-v8a,armeabi-v7a \ @@ -20,28 +24,38 @@ aar_with_jni( android_library = ":tensorflowlite", ) +# EXPERIMENTAL: AAR target that supports TensorFlow op execution with TFLite. +aar_with_jni( + name = "tensorflow-lite-flex", + android_library = ":tensorflowlite_flex", +) + android_library( name = "tensorflowlite", - srcs = glob( - [ - "src/main/java/org/tensorflow/lite/*.java", - ], - ), + srcs = JAVA_SRCS, + manifest = "AndroidManifest.xml", + visibility = ["//visibility:public"], + deps = [ + ":tensorflowlite_native", + "@org_checkerframework_qual", + ], +) + +# EXPERIMENTAL: Android target that supports TensorFlow op execution with TFLite. +android_library( + name = "tensorflowlite_flex", + srcs = JAVA_SRCS, manifest = "AndroidManifest.xml", visibility = ["//visibility:public"], deps = [ - ":tflite_runtime", + ":tensorflowlite_native_flex", "@org_checkerframework_qual", ], ) android_library( name = "tensorflowlite_java", - srcs = glob( - [ - "src/main/java/org/tensorflow/lite/*.java", - ], - ), + srcs = JAVA_SRCS, visibility = ["//visibility:public"], deps = [ "@org_checkerframework_qual", @@ -50,16 +64,23 @@ android_library( java_library( name = "tensorflowlitelib", - srcs = glob( - [ - "src/main/java/org/tensorflow/lite/*.java", - ], - ), + srcs = JAVA_SRCS, javacopts = JAVACOPTS, visibility = ["//visibility:public"], deps = [ ":libtensorflowlite_jni.so", - "//tensorflow/contrib/lite/java/src/main/native", + "@org_checkerframework_qual", + ], +) + +# EXPERIMENTAL: Java target that supports TensorFlow op execution with TFLite. +java_library( + name = "tensorflowlitelib_flex", + srcs = JAVA_SRCS, + javacopts = JAVACOPTS, + visibility = ["//visibility:public"], + deps = [ + ":libtensorflowlite_flex_jni.so", "@org_checkerframework_qual", ], ) @@ -72,7 +93,6 @@ java_test( tags = ["no_oss"], test_class = "org.tensorflow.lite.TensorFlowLiteTest", deps = [ - ":libtensorflowlite_jni.so", ":tensorflowlitelib", "@com_google_truth", "@junit", @@ -87,7 +107,6 @@ java_test( tags = ["no_oss"], test_class = "org.tensorflow.lite.DataTypeTest", deps = [ - ":libtensorflowlite_jni.so", ":tensorflowlitelib", "@com_google_truth", "@junit", @@ -110,7 +129,6 @@ java_test( tags = ["no_oss"], test_class = "org.tensorflow.lite.NativeInterpreterWrapperTest", deps = [ - ":libtensorflowlite_jni.so", ":tensorflowlitelib", "@com_google_truth", "@junit", @@ -125,13 +143,13 @@ java_test( data = [ "src/testdata/add.bin", "src/testdata/mobilenet.tflite.bin", + "//tensorflow/contrib/lite:testdata/multi_add_flex.bin", ], javacopts = JAVACOPTS, tags = ["no_oss"], test_class = "org.tensorflow.lite.InterpreterTest", visibility = ["//visibility:private"], deps = [ - ":libtensorflowlite_jni.so", ":tensorflowlitelib", "@com_google_truth", "@junit", @@ -139,6 +157,24 @@ java_test( ) java_test( + name = "InterpreterFlexTest", + size = "small", + srcs = ["src/test/java/org/tensorflow/lite/InterpreterFlexTest.java"], + data = [ + "//tensorflow/contrib/lite:testdata/multi_add_flex.bin", + ], + javacopts = JAVACOPTS, + tags = ["no_oss"], + test_class = "org.tensorflow.lite.InterpreterFlexTest", + visibility = ["//visibility:private"], + deps = [ + ":tensorflowlitelib_flex", + "@com_google_truth", + "@junit", + ], +) + +java_test( name = "TensorTest", size = "small", srcs = ["src/test/java/org/tensorflow/lite/TensorTest.java"], @@ -164,14 +200,29 @@ filegroup( ) cc_library( - name = "tflite_runtime", + name = "tensorflowlite_native", srcs = ["libtensorflowlite_jni.so"], visibility = ["//visibility:public"], ) +cc_library( + name = "tensorflowlite_native_flex", + srcs = ["libtensorflowlite_flex_jni.so"], + visibility = ["//visibility:public"], +) + tflite_jni_binary( name = "libtensorflowlite_jni.so", deps = [ "//tensorflow/contrib/lite/java/src/main/native", ], ) + +# EXPERIMENTAL: Native target that supports TensorFlow op execution with TFLite. +tflite_jni_binary( + name = "libtensorflowlite_flex_jni.so", + deps = [ + "//tensorflow/contrib/lite/delegates/flex:delegate", + "//tensorflow/contrib/lite/java/src/main/native", + ], +) diff --git a/tensorflow/contrib/lite/java/aar_with_jni.bzl b/tensorflow/contrib/lite/java/aar_with_jni.bzl index 9d2aead266..360d622b1b 100644 --- a/tensorflow/contrib/lite/java/aar_with_jni.bzl +++ b/tensorflow/contrib/lite/java/aar_with_jni.bzl @@ -30,7 +30,10 @@ EOF # In some platforms we don't have an Android SDK/NDK and this target # can't be built. We need to prevent the build system from trying to # use the target in that case. - tags = ["manual"], + tags = [ + "manual", + "no_cuda_on_cpu_tap", + ], ) native.genrule( diff --git a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/TensorFlowLite.java b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/TensorFlowLite.java index 711638a9f9..d5447b3bf8 100644 --- a/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/TensorFlowLite.java +++ b/tensorflow/contrib/lite/java/src/main/java/org/tensorflow/lite/TensorFlowLite.java @@ -18,7 +18,8 @@ package org.tensorflow.lite; /** Static utility methods loading the TensorFlowLite runtime. */ public final class TensorFlowLite { - private static final String LIBNAME = "tensorflowlite_jni"; + private static final String PRIMARY_LIBNAME = "tensorflowlite_jni"; + private static final String FALLBACK_LIBNAME = "tensorflowlite_flex_jni"; private TensorFlowLite() {} @@ -29,13 +30,24 @@ public final class TensorFlowLite { * Load the TensorFlowLite runtime C library. */ static boolean init() { + Throwable primaryLibException; try { - System.loadLibrary(LIBNAME); + System.loadLibrary(PRIMARY_LIBNAME); return true; } catch (UnsatisfiedLinkError e) { - System.err.println("TensorFlowLite: failed to load native library: " + e.getMessage()); - return false; + primaryLibException = e; } + + try { + System.loadLibrary(FALLBACK_LIBNAME); + return true; + } catch (UnsatisfiedLinkError e) { + // If the fallback fails, log the error for the primary load instead. + System.err.println( + "TensorFlowLite: failed to load native library: " + primaryLibException.getMessage()); + } + + return false; } static { diff --git a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterFlexTest.java b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterFlexTest.java new file mode 100644 index 0000000000..2791c3864b --- /dev/null +++ b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterFlexTest.java @@ -0,0 +1,46 @@ +/* Copyright 2017 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. +==============================================================================*/ + +package org.tensorflow.lite; + +import static com.google.common.truth.Truth.assertThat; + +import java.io.File; +import org.junit.Test; +import org.junit.runner.RunWith; +import org.junit.runners.JUnit4; + +/** + * Unit tests for {@link org.tensorflow.lite.Interpreter} that validate execution with models that + * have TensorFlow ops. + */ +@RunWith(JUnit4.class) +public final class InterpreterFlexTest { + + private static final File FLEX_MODEL_FILE = + new File("tensorflow/contrib/lite/testdata/multi_add_flex.bin"); + + /** Smoke test validating that flex model loading works when the flex delegate is linked. */ + @Test + public void testFlexModel() throws Exception { + try (Interpreter interpreter = new Interpreter(FLEX_MODEL_FILE)) { + assertThat(interpreter.getInputTensorCount()).isEqualTo(4); + assertThat(interpreter.getInputTensor(0).dataType()).isEqualTo(DataType.FLOAT32); + assertThat(interpreter.getOutputTensorCount()).isEqualTo(4); + assertThat(interpreter.getOutputTensor(0).dataType()).isEqualTo(DataType.FLOAT32); + interpreter.run(new float[1], new float[1]); + } + } +} diff --git a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java index a98fca0132..f8b73c7cf3 100644 --- a/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java +++ b/tensorflow/contrib/lite/java/src/test/java/org/tensorflow/lite/InterpreterTest.java @@ -43,6 +43,9 @@ public final class InterpreterTest { private static final File MOBILENET_MODEL_FILE = new File("tensorflow/contrib/lite/java/src/testdata/mobilenet.tflite.bin"); + private static final File FLEX_MODEL_FILE = + new File("tensorflow/contrib/lite/testdata/multi_add_flex.bin"); + @Test public void testInterpreter() throws Exception { Interpreter interpreter = new Interpreter(MODEL_FILE); @@ -345,4 +348,15 @@ public final class InterpreterTest { interpreter.close(); interpreter.close(); } + + /** Smoke test validating that flex model loading fails when the flex delegate is not linked. */ + @Test + public void testFlexModel() throws Exception { + try { + new Interpreter(FLEX_MODEL_FILE); + fail(); + } catch (IllegalStateException e) { + // Expected failure. + } + } } diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD index 95e387814d..d2d8073abd 100644 --- a/tensorflow/contrib/lite/kernels/BUILD +++ b/tensorflow/contrib/lite/kernels/BUILD @@ -234,11 +234,11 @@ cc_library( ":activation_functor", ":eigen_support", ":kernel_util", + ":lstm_eval", ":op_macros", ":padding", "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite:string_util", - "//tensorflow/contrib/lite:util", "//tensorflow/contrib/lite/c:c_api_internal", "//tensorflow/contrib/lite/kernels:gemm_support", "//tensorflow/contrib/lite/kernels/internal:audio_utils", @@ -255,6 +255,18 @@ cc_library( ) cc_library( + name = "lstm_eval", + srcs = ["lstm_eval.cc"], + hdrs = ["lstm_eval.h"], + deps = [ + ":op_macros", + "//tensorflow/contrib/lite/c:c_api_internal", + "//tensorflow/contrib/lite/kernels/internal:kernel_utils", + "//tensorflow/contrib/lite/kernels/internal:tensor_utils", + ], +) + +cc_library( name = "builtin_ops", srcs = ["register.cc"], hdrs = ["register.h"], diff --git a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc index 0532528f52..a326827b1e 100644 --- a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc +++ b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc @@ -26,6 +26,7 @@ limitations under the License. #include "tensorflow/contrib/lite/kernels/internal/kernel_utils.h" #include "tensorflow/contrib/lite/kernels/internal/tensor_utils.h" #include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/lstm_eval.h" #include "tensorflow/contrib/lite/kernels/op_macros.h" namespace tflite { @@ -694,330 +695,6 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { return kTfLiteOk; } -TfLiteStatus EvalFloat( - const TfLiteTensor* input, const TfLiteTensor* input_to_input_weights, - const TfLiteTensor* input_to_forget_weights, - const TfLiteTensor* input_to_cell_weights, - const TfLiteTensor* input_to_output_weights, - const TfLiteTensor* recurrent_to_input_weights, - const TfLiteTensor* recurrent_to_forget_weights, - const TfLiteTensor* recurrent_to_cell_weights, - const TfLiteTensor* recurrent_to_output_weights, - const TfLiteTensor* cell_to_input_weights, - const TfLiteTensor* cell_to_forget_weights, - const TfLiteTensor* cell_to_output_weights, const TfLiteTensor* aux_input, - const TfLiteTensor* aux_input_to_input_weights, - const TfLiteTensor* aux_input_to_forget_weights, - const TfLiteTensor* aux_input_to_cell_weights, - const TfLiteTensor* aux_input_to_output_weights, - const TfLiteTensor* input_gate_bias, const TfLiteTensor* forget_gate_bias, - const TfLiteTensor* cell_bias, const TfLiteTensor* output_gate_bias, - const TfLiteTensor* projection_weights, const TfLiteTensor* projection_bias, - const TfLiteLSTMParams* params, bool forward_sequence, int output_offset, - TfLiteTensor* scratch_buffer, TfLiteTensor* activation_state, - TfLiteTensor* cell_state, TfLiteTensor* output) { - const int max_time = input->dims->data[0]; - const int n_batch = input->dims->data[1]; - const int n_input = input->dims->data[2]; - const int aux_input_size = (aux_input) ? aux_input->dims->data[2] : 0; - - // n_cell and n_output will be the same size when there is no projection. - const int n_cell = input_to_output_weights->dims->data[0]; - const int n_output = recurrent_to_output_weights->dims->data[1]; - - // Since we have already checked that weights are all there or none, we can - // check the existense of only one to the get the condition. - const bool use_cifg = (input_to_input_weights == nullptr); - const bool use_peephole = (cell_to_output_weights != nullptr); - - // Index the scratch buffers pointers to the global scratch buffer. - float* input_gate_scratch = nullptr; - float* cell_scratch = nullptr; - float* forget_gate_scratch = nullptr; - float* output_gate_scratch = nullptr; - if (use_cifg) { - cell_scratch = scratch_buffer->data.f; - forget_gate_scratch = scratch_buffer->data.f + n_cell * n_batch; - output_gate_scratch = scratch_buffer->data.f + 2 * n_cell * n_batch; - } else { - input_gate_scratch = scratch_buffer->data.f; - cell_scratch = scratch_buffer->data.f + n_cell * n_batch; - forget_gate_scratch = scratch_buffer->data.f + 2 * n_cell * n_batch; - output_gate_scratch = scratch_buffer->data.f + 3 * n_cell * n_batch; - } - - // Check optional tensors, the respective pointers can be null. - const float* input_to_input_weights_ptr = - (use_cifg) ? nullptr : input_to_input_weights->data.f; - const float* recurrent_to_input_weights_ptr = - (use_cifg) ? nullptr : recurrent_to_input_weights->data.f; - const float* input_gate_bias_ptr = - (use_cifg) ? nullptr : input_gate_bias->data.f; - const float* cell_to_input_weights_ptr = - (use_peephole && !use_cifg) ? cell_to_input_weights->data.f : nullptr; - const float* cell_to_forget_weights_ptr = - (use_peephole) ? cell_to_forget_weights->data.f : nullptr; - const float* cell_to_output_weights_ptr = - (use_peephole) ? cell_to_output_weights->data.f : nullptr; - const float* projection_weights_ptr = - (projection_weights == nullptr) ? nullptr : projection_weights->data.f; - const float* projection_bias_ptr = - (projection_bias == nullptr) ? nullptr : projection_bias->data.f; - - float* aux_input_ptr = nullptr; - float* aux_input_to_input_weights_ptr = nullptr; - float* aux_input_to_forget_weights_ptr = nullptr; - float* aux_input_to_cell_weights_ptr = nullptr; - float* aux_input_to_output_weights_ptr = nullptr; - if (aux_input_size > 0) { - aux_input_ptr = aux_input->data.f; - aux_input_to_input_weights_ptr = aux_input_to_input_weights->data.f; - aux_input_to_forget_weights_ptr = aux_input_to_forget_weights->data.f; - aux_input_to_cell_weights_ptr = aux_input_to_cell_weights->data.f; - aux_input_to_output_weights_ptr = aux_input_to_output_weights->data.f; - } - - // Loop through the sequence. - const int input_step = n_batch * n_input; - const int output_step = n_batch * output->dims->data[2]; - for (int t = 0; t < max_time; t++) { - // If this is the forward_sequence, step forward, otherwise step backwards. - const int t_rel = forward_sequence ? t : max_time - t - 1; - const float* input_ptr = input->data.f + t_rel * input_step; - float* output_ptr_time = - output->data.f + t_rel * output_step + output_offset; - - kernel_utils::LstmStepWithAuxInput( - input_ptr, input_to_input_weights_ptr, input_to_forget_weights->data.f, - input_to_cell_weights->data.f, input_to_output_weights->data.f, - aux_input_ptr, aux_input_to_input_weights_ptr, - aux_input_to_forget_weights_ptr, aux_input_to_cell_weights_ptr, - aux_input_to_output_weights_ptr, recurrent_to_input_weights_ptr, - recurrent_to_forget_weights->data.f, recurrent_to_cell_weights->data.f, - recurrent_to_output_weights->data.f, cell_to_input_weights_ptr, - cell_to_forget_weights_ptr, cell_to_output_weights_ptr, - input_gate_bias_ptr, forget_gate_bias->data.f, cell_bias->data.f, - output_gate_bias->data.f, projection_weights_ptr, projection_bias_ptr, - params, n_batch, n_cell, n_input, aux_input_size, n_output, - activation_state->data.f, cell_state->data.f, input_gate_scratch, - forget_gate_scratch, cell_scratch, output_gate_scratch, - output_ptr_time); - } - return kTfLiteOk; -} - -TfLiteStatus EvalHybrid( - const TfLiteTensor* input, const TfLiteTensor* input_to_input_weights, - const TfLiteTensor* input_to_forget_weights, - const TfLiteTensor* input_to_cell_weights, - const TfLiteTensor* input_to_output_weights, - const TfLiteTensor* recurrent_to_input_weights, - const TfLiteTensor* recurrent_to_forget_weights, - const TfLiteTensor* recurrent_to_cell_weights, - const TfLiteTensor* recurrent_to_output_weights, - const TfLiteTensor* cell_to_input_weights, - const TfLiteTensor* cell_to_forget_weights, - const TfLiteTensor* cell_to_output_weights, const TfLiteTensor* aux_input, - const TfLiteTensor* aux_input_to_input_weights, - const TfLiteTensor* aux_input_to_forget_weights, - const TfLiteTensor* aux_input_to_cell_weights, - const TfLiteTensor* aux_input_to_output_weights, - const TfLiteTensor* input_gate_bias, const TfLiteTensor* forget_gate_bias, - const TfLiteTensor* cell_bias, const TfLiteTensor* output_gate_bias, - const TfLiteTensor* projection_weights, const TfLiteTensor* projection_bias, - const TfLiteLSTMParams* params, bool forward_sequence, int output_offset, - TfLiteTensor* scratch_buffer, TfLiteTensor* scaling_factors, - TfLiteTensor* prod_scaling_factors, TfLiteTensor* recovered_cell_weights, - TfLiteTensor* input_quantized, TfLiteTensor* aux_input_quantized, - TfLiteTensor* output_state_quantized, TfLiteTensor* cell_state_quantized, - TfLiteTensor* output_state, TfLiteTensor* cell_state, - TfLiteTensor* output) { - const int max_time = input->dims->data[0]; - const int n_batch = input->dims->data[1]; - const int n_input = input->dims->data[2]; - const int aux_input_size = (aux_input) ? aux_input->dims->data[2] : 0; - // n_cell and n_output will be the same size when there is no projection. - const int n_cell = input_to_output_weights->dims->data[0]; - const int n_output = recurrent_to_output_weights->dims->data[1]; - - // Since we have already checked that weights are all there or none, we can - // check the existence of only one to get the condition. - const bool use_cifg = (input_to_input_weights == nullptr); - const bool use_peephole = (cell_to_output_weights != nullptr); - - float* input_gate_scratch = nullptr; - float* cell_scratch = nullptr; - float* forget_gate_scratch = nullptr; - float* output_gate_scratch = nullptr; - if (use_cifg) { - cell_scratch = scratch_buffer->data.f; - forget_gate_scratch = scratch_buffer->data.f + n_cell * n_batch; - output_gate_scratch = scratch_buffer->data.f + 2 * n_cell * n_batch; - } else { - input_gate_scratch = scratch_buffer->data.f; - cell_scratch = scratch_buffer->data.f + n_cell * n_batch; - forget_gate_scratch = scratch_buffer->data.f + 2 * n_cell * n_batch; - output_gate_scratch = scratch_buffer->data.f + 3 * n_cell * n_batch; - } - - // Check optional tensors, the respective pointers can be null. - int8_t* input_to_input_weights_ptr = nullptr; - float input_to_input_weights_scale = 1.0f; - int8_t* recurrent_to_input_weights_ptr = nullptr; - float recurrent_to_input_weights_scale = 1.0f; - float* input_gate_bias_ptr = nullptr; - if (!use_cifg) { - input_to_input_weights_ptr = - reinterpret_cast<int8_t*>(input_to_input_weights->data.uint8); - recurrent_to_input_weights_ptr = - reinterpret_cast<int8_t*>(recurrent_to_input_weights->data.uint8); - input_gate_bias_ptr = input_gate_bias->data.f; - input_to_input_weights_scale = input_to_input_weights->params.scale; - recurrent_to_input_weights_scale = recurrent_to_input_weights->params.scale; - } - - int8_t* cell_to_input_weights_ptr = nullptr; - int8_t* cell_to_forget_weights_ptr = nullptr; - int8_t* cell_to_output_weights_ptr = nullptr; - float cell_to_input_weights_scale = 1.0f; - float cell_to_forget_weights_scale = 1.0f; - float cell_to_output_weights_scale = 1.0f; - if (use_peephole) { - if (!use_cifg) { - cell_to_input_weights_ptr = - reinterpret_cast<int8_t*>(cell_to_input_weights->data.uint8); - cell_to_input_weights_scale = cell_to_input_weights->params.scale; - } - cell_to_forget_weights_ptr = - reinterpret_cast<int8_t*>(cell_to_forget_weights->data.uint8); - cell_to_output_weights_ptr = - reinterpret_cast<int8_t*>(cell_to_output_weights->data.uint8); - cell_to_forget_weights_scale = cell_to_forget_weights->params.scale; - cell_to_output_weights_scale = cell_to_output_weights->params.scale; - } - - const int8_t* projection_weights_ptr = - (projection_weights == nullptr) - ? nullptr - : reinterpret_cast<int8_t*>(projection_weights->data.uint8); - const float projection_weights_scale = - (projection_weights == nullptr) ? 1.0f : projection_weights->params.scale; - const float* projection_bias_ptr = - (projection_bias == nullptr) ? nullptr : projection_bias->data.f; - - // Required tensors, pointers are non-null. - const int8_t* input_to_forget_weights_ptr = - reinterpret_cast<int8_t*>(input_to_forget_weights->data.uint8); - const float input_to_forget_weights_scale = - input_to_forget_weights->params.scale; - const int8_t* input_to_cell_weights_ptr = - reinterpret_cast<int8_t*>(input_to_cell_weights->data.uint8); - const float input_to_cell_weights_scale = input_to_cell_weights->params.scale; - const int8_t* input_to_output_weights_ptr = - reinterpret_cast<int8_t*>(input_to_output_weights->data.uint8); - const float input_to_output_weights_scale = - input_to_output_weights->params.scale; - const int8_t* recurrent_to_forget_weights_ptr = - reinterpret_cast<int8_t*>(recurrent_to_forget_weights->data.uint8); - const float recurrent_to_forget_weights_scale = - recurrent_to_forget_weights->params.scale; - const int8_t* recurrent_to_cell_weights_ptr = - reinterpret_cast<int8_t*>(recurrent_to_cell_weights->data.uint8); - const float recurrent_to_cell_weights_scale = - recurrent_to_cell_weights->params.scale; - const int8_t* recurrent_to_output_weights_ptr = - reinterpret_cast<int8_t*>(recurrent_to_output_weights->data.uint8); - const float recurrent_to_output_weights_scale = - recurrent_to_output_weights->params.scale; - const float* forget_gate_bias_ptr = forget_gate_bias->data.f; - const float* cell_bias_ptr = cell_bias->data.f; - const float* output_gate_bias_ptr = output_gate_bias->data.f; - - float* output_state_ptr = output_state->data.f; - float* cell_state_ptr = cell_state->data.f; - - // Temporary storage for quantized values and scaling factors. - int8_t* quantized_input_ptr = - reinterpret_cast<int8_t*>(input_quantized->data.uint8); - int8_t* quantized_aux_input_ptr = - (aux_input_quantized == nullptr) - ? nullptr - : reinterpret_cast<int8_t*>(aux_input_quantized->data.uint8); - int8_t* quantized_output_state_ptr = - reinterpret_cast<int8_t*>(output_state_quantized->data.uint8); - int8_t* quantized_cell_state_ptr = - reinterpret_cast<int8_t*>(cell_state_quantized->data.uint8); - float* scaling_factors_ptr = scaling_factors->data.f; - float* prod_scaling_factors_ptr = prod_scaling_factors->data.f; - float* recovered_cell_weights_ptr = recovered_cell_weights->data.f; - - // Auxiliary input and weights. - float* aux_input_ptr = nullptr; - int8_t* aux_input_to_input_weights_ptr = nullptr; - int8_t* aux_input_to_forget_weights_ptr = nullptr; - int8_t* aux_input_to_cell_weights_ptr = nullptr; - int8_t* aux_input_to_output_weights_ptr = nullptr; - float aux_input_to_input_weights_scale = 0.0f; - float aux_input_to_forget_weights_scale = 0.0f; - float aux_input_to_cell_weights_scale = 0.0f; - float aux_input_to_output_weights_scale = 0.0f; - if (aux_input_size > 0) { - aux_input_ptr = aux_input->data.f; - aux_input_to_input_weights_ptr = - reinterpret_cast<int8_t*>(aux_input_to_input_weights->data.uint8); - aux_input_to_forget_weights_ptr = - reinterpret_cast<int8_t*>(aux_input_to_forget_weights->data.uint8); - aux_input_to_cell_weights_ptr = - reinterpret_cast<int8_t*>(aux_input_to_cell_weights->data.uint8); - aux_input_to_output_weights_ptr = - reinterpret_cast<int8_t*>(aux_input_to_output_weights->data.uint8); - aux_input_to_input_weights_scale = aux_input_to_input_weights->params.scale; - aux_input_to_forget_weights_scale = - aux_input_to_forget_weights->params.scale; - aux_input_to_cell_weights_scale = aux_input_to_cell_weights->params.scale; - aux_input_to_output_weights_scale = - aux_input_to_output_weights->params.scale; - } - - // Feed the sequence into the LSTM step-by-step. - const int input_step = n_batch * n_input; - const int output_step = n_batch * output->dims->data[2]; - for (int t = 0; t < max_time; t++) { - // If this is the forward_sequence, step forward, otherwise step backwards. - const int t_rel = forward_sequence ? t : max_time - t - 1; - const float* input_ptr = input->data.f + t_rel * input_step; - float* output_ptr = output->data.f + t_rel * output_step + output_offset; - - kernel_utils::LstmStepWithAuxInput( - input_ptr, input_to_input_weights_ptr, input_to_input_weights_scale, - input_to_forget_weights_ptr, input_to_forget_weights_scale, - input_to_cell_weights_ptr, input_to_cell_weights_scale, - input_to_output_weights_ptr, input_to_output_weights_scale, - aux_input_ptr, aux_input_to_input_weights_ptr, - aux_input_to_input_weights_scale, aux_input_to_forget_weights_ptr, - aux_input_to_forget_weights_scale, aux_input_to_cell_weights_ptr, - aux_input_to_cell_weights_scale, aux_input_to_output_weights_ptr, - aux_input_to_output_weights_scale, recurrent_to_input_weights_ptr, - recurrent_to_input_weights_scale, recurrent_to_forget_weights_ptr, - recurrent_to_forget_weights_scale, recurrent_to_cell_weights_ptr, - recurrent_to_cell_weights_scale, recurrent_to_output_weights_ptr, - recurrent_to_output_weights_scale, cell_to_input_weights_ptr, - cell_to_input_weights_scale, cell_to_forget_weights_ptr, - cell_to_forget_weights_scale, cell_to_output_weights_ptr, - cell_to_output_weights_scale, input_gate_bias_ptr, forget_gate_bias_ptr, - cell_bias_ptr, output_gate_bias_ptr, projection_weights_ptr, - projection_weights_scale, projection_bias_ptr, params, n_batch, n_cell, - n_input, aux_input_size, n_output, input_gate_scratch, - forget_gate_scratch, cell_scratch, output_gate_scratch, - scaling_factors_ptr, prod_scaling_factors_ptr, - recovered_cell_weights_ptr, quantized_input_ptr, - quantized_aux_input_ptr, quantized_output_state_ptr, - quantized_cell_state_ptr, output_state_ptr, cell_state_ptr, output_ptr); - } - - return kTfLiteOk; -} - // The LSTM Op engine. TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { const auto* params = reinterpret_cast<TfLiteBidirectionalSequenceLSTMParams*>( @@ -1157,7 +834,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { switch (fw_input_to_output_weights->type) { case kTfLiteFloat32: { - TfLiteStatus fw_pass_status = EvalFloat( + TfLiteStatus fw_pass_status = lstm_eval::EvalFloat( input, fw_input_to_input_weights, fw_input_to_forget_weights, fw_input_to_cell_weights, fw_input_to_output_weights, fw_recurrent_to_input_weights, fw_recurrent_to_forget_weights, @@ -1172,7 +849,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { fw_activation_state, fw_cell_state, fw_output); TF_LITE_ENSURE_OK(context, fw_pass_status); - TfLiteStatus bw_pass_status = EvalFloat( + TfLiteStatus bw_pass_status = lstm_eval::EvalFloat( input, bw_input_to_input_weights, bw_input_to_forget_weights, bw_input_to_cell_weights, bw_input_to_output_weights, bw_recurrent_to_input_weights, bw_recurrent_to_forget_weights, @@ -1208,7 +885,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { TfLiteTensor* recovered_cell_weights = GetTemporary(context, node, kRecoveredCellWeights); - TfLiteStatus fw_pass_status = EvalHybrid( + TfLiteStatus fw_pass_status = lstm_eval::EvalHybrid( input, fw_input_to_input_weights, fw_input_to_forget_weights, fw_input_to_cell_weights, fw_input_to_output_weights, fw_recurrent_to_input_weights, fw_recurrent_to_forget_weights, @@ -1226,7 +903,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { fw_output); TF_LITE_ENSURE_OK(context, fw_pass_status); - TfLiteStatus bw_pass_status = EvalHybrid( + TfLiteStatus bw_pass_status = lstm_eval::EvalHybrid( input, bw_input_to_input_weights, bw_input_to_forget_weights, bw_input_to_cell_weights, bw_input_to_output_weights, bw_recurrent_to_input_weights, bw_recurrent_to_forget_weights, diff --git a/tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn.cc b/tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn.cc index 9f62ac3f2c..c22a457a71 100644 --- a/tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn.cc +++ b/tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn.cc @@ -113,6 +113,7 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { // input configuration. TF_LITE_ENSURE_EQ(context, input->type, kTfLiteFloat32); + TF_LITE_ENSURE_EQ(context, input->dims->size, 3); const int batch_size = input->dims->data[0]; const int max_time = input->dims->data[1]; const int fw_num_units = fw_input_weights->dims->data[0]; diff --git a/tensorflow/contrib/lite/kernels/comparisons.cc b/tensorflow/contrib/lite/kernels/comparisons.cc index f765235e04..3926af5b97 100644 --- a/tensorflow/contrib/lite/kernels/comparisons.cc +++ b/tensorflow/contrib/lite/kernels/comparisons.cc @@ -66,31 +66,25 @@ TfLiteStatus ComparisonPrepare(TfLiteContext* context, TfLiteNode* node) { if (input1->type == kTfLiteUInt8) { \ auto input1_offset = -input1->params.zero_point; \ auto input2_offset = -input2->params.zero_point; \ - const int left_shift = 20; \ - const double twice_max_input_scale = \ - 2 * std::max(input1->params.scale, input2->params.scale); \ - const double real_input1_multiplier = \ - input1->params.scale / twice_max_input_scale; \ - const double real_input2_multiplier = \ - input2->params.scale / twice_max_input_scale; \ + const int left_shift = 8; \ \ int32 input1_multiplier; \ int input1_shift; \ - QuantizeMultiplierSmallerThanOneExp(real_input1_multiplier, \ + QuantizeMultiplierSmallerThanOneExp(input1->params.scale, \ &input1_multiplier, &input1_shift); \ int32 input2_multiplier; \ int input2_shift; \ - QuantizeMultiplierSmallerThanOneExp(real_input2_multiplier, \ + QuantizeMultiplierSmallerThanOneExp(input2->params.scale, \ &input2_multiplier, &input2_shift); \ \ ComparisonParams op_params; \ op_params.left_shift = left_shift; \ op_params.input1_offset = input1_offset; \ op_params.input1_multiplier = input1_multiplier; \ - op_params.input1_shift = -input1_shift; \ + op_params.input1_shift = input1_shift; \ op_params.input2_offset = input2_offset; \ op_params.input2_multiplier = input2_multiplier; \ - op_params.input2_shift = -input2_shift; \ + op_params.input2_shift = input2_shift; \ if (requires_broadcast) { \ reference_ops::Broadcast4DSlow##opname##WithScaling( \ op_params, GetTensorShape(input1), GetTensorData<uint8_t>(input1), \ diff --git a/tensorflow/contrib/lite/kernels/comparisons_test.cc b/tensorflow/contrib/lite/kernels/comparisons_test.cc index 67a91c17fd..04c8bf2e30 100644 --- a/tensorflow/contrib/lite/kernels/comparisons_test.cc +++ b/tensorflow/contrib/lite/kernels/comparisons_test.cc @@ -402,6 +402,17 @@ TEST(ComparisonsTest, GreaterQuantized) { EXPECT_THAT(model.GetOutput(), ElementsAre(false, true, true, false)); } +TEST(ComparisonsTest, GreaterQuantizedSmallRange) { + ComparisonOpModel model({TensorType_UINT8, {1, 2, 2, 1}, 0.0, 1.0}, + {TensorType_UINT8, {1, 2, 2, 1}, 0.0, 2.0}, + TensorType_UINT8, BuiltinOperator_GREATER); + model.QuantizeAndPopulate<uint8_t>(model.input1(), {1.0, 0.5, 0.35, 0.1}); + model.QuantizeAndPopulate<uint8_t>(model.input2(), {1.01, 0.25, 0.3, 0.4}); + model.Invoke(); + + EXPECT_THAT(model.GetOutput(), ElementsAre(false, true, true, false)); +} + TEST(ComparisonsTest, GreaterEqualQuantized) { const float kMin = -1.f; const float kMax = 128.f; diff --git a/tensorflow/contrib/lite/kernels/internal/compatibility.h b/tensorflow/contrib/lite/kernels/internal/compatibility.h index b87cf2b60d..7c176e0fa1 100644 --- a/tensorflow/contrib/lite/kernels/internal/compatibility.h +++ b/tensorflow/contrib/lite/kernels/internal/compatibility.h @@ -84,4 +84,27 @@ using uint16 = std::uint16_t; using int32 = std::int32_t; using uint32 = std::uint32_t; +// TFLITE_DEPRECATED() +// +// Duplicated from absl/base/macros.h to avoid pulling in that library. +// Marks a deprecated class, struct, enum, function, method and variable +// declarations. The macro argument is used as a custom diagnostic message (e.g. +// suggestion of a better alternative). +// +// Example: +// +// class TFLITE_DEPRECATED("Use Bar instead") Foo {...}; +// TFLITE_DEPRECATED("Use Baz instead") void Bar() {...} +// +// Every usage of a deprecated entity will trigger a warning when compiled with +// clang's `-Wdeprecated-declarations` option. This option is turned off by +// default, but the warnings will be reported by clang-tidy. +#if defined(__clang__) && __cplusplus >= 201103L +#define TFLITE_DEPRECATED(message) __attribute__((deprecated(message))) +#endif + +#ifndef TFLITE_DEPRECATED +#define TFLITE_DEPRECATED(message) +#endif + #endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_COMPATIBILITY_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc b/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc index 56e9367878..083e5839bd 100644 --- a/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/kernel_utils.cc @@ -169,603 +169,5 @@ void RnnBatchStep( hidden_state_ptr_batch); } -void LstmStep( - const float* input_ptr_batch, const float* input_to_input_weights_ptr, - const float* input_to_forget_weights_ptr, - const float* input_to_cell_weights_ptr, - const float* input_to_output_weights_ptr, - const float* recurrent_to_input_weights_ptr, - const float* recurrent_to_forget_weights_ptr, - const float* recurrent_to_cell_weights_ptr, - const float* recurrent_to_output_weights_ptr, - const float* cell_to_input_weights_ptr, - const float* cell_to_forget_weights_ptr, - const float* cell_to_output_weights_ptr, const float* input_gate_bias_ptr, - const float* forget_gate_bias_ptr, const float* cell_bias_ptr, - const float* output_gate_bias_ptr, const float* projection_weights_ptr, - const float* projection_bias_ptr, const TfLiteLSTMParams* params, - int n_batch, int n_cell, int n_input, int n_output, float* output_state_ptr, - float* cell_state_ptr, float* input_gate_scratch, - float* forget_gate_scratch, float* cell_scratch, float* output_gate_scratch, - float* output_ptr_batch) { - LstmStepWithAuxInput( - input_ptr_batch, input_to_input_weights_ptr, input_to_forget_weights_ptr, - input_to_cell_weights_ptr, input_to_output_weights_ptr, - /*aux_input_ptr_batch=*/nullptr, - /*aux_input_to_input_weights_ptr=*/nullptr, - /*aux_input_to_forget_weights_ptr=*/nullptr, - /*aux_input_to_cell_weights_ptr=*/nullptr, - /*aux_input_to_output_weights_ptr=*/nullptr, - recurrent_to_input_weights_ptr, recurrent_to_forget_weights_ptr, - recurrent_to_cell_weights_ptr, recurrent_to_output_weights_ptr, - cell_to_input_weights_ptr, cell_to_forget_weights_ptr, - cell_to_output_weights_ptr, input_gate_bias_ptr, forget_gate_bias_ptr, - cell_bias_ptr, output_gate_bias_ptr, projection_weights_ptr, - projection_bias_ptr, params, n_batch, n_cell, n_input, /*n_aux_input=*/0, - n_output, output_state_ptr, cell_state_ptr, input_gate_scratch, - forget_gate_scratch, cell_scratch, output_gate_scratch, output_ptr_batch); -} - -void LstmStepWithAuxInput( - const float* input_ptr_batch, const float* input_to_input_weights_ptr, - const float* input_to_forget_weights_ptr, - const float* input_to_cell_weights_ptr, - const float* input_to_output_weights_ptr, const float* aux_input_ptr_batch, - const float* aux_input_to_input_weights_ptr, - const float* aux_input_to_forget_weights_ptr, - const float* aux_input_to_cell_weights_ptr, - const float* aux_input_to_output_weights_ptr, - const float* recurrent_to_input_weights_ptr, - const float* recurrent_to_forget_weights_ptr, - const float* recurrent_to_cell_weights_ptr, - const float* recurrent_to_output_weights_ptr, - const float* cell_to_input_weights_ptr, - const float* cell_to_forget_weights_ptr, - const float* cell_to_output_weights_ptr, const float* input_gate_bias_ptr, - const float* forget_gate_bias_ptr, const float* cell_bias_ptr, - const float* output_gate_bias_ptr, const float* projection_weights_ptr, - const float* projection_bias_ptr, const TfLiteLSTMParams* params, - int n_batch, int n_cell, int n_input, int n_aux_input, int n_output, - float* output_state_ptr, float* cell_state_ptr, float* input_gate_scratch, - float* forget_gate_scratch, float* cell_scratch, float* output_gate_scratch, - float* output_ptr_batch) { - // Since we have already checked that weights are all there or none, we can - // check the existense of only one to the get the condition. - const bool use_cifg = (input_to_input_weights_ptr == nullptr); - const bool use_peephole = (cell_to_output_weights_ptr != nullptr); - // Initialize scratch buffers with bias. - if (!use_cifg) { - tensor_utils::VectorBatchVectorAssign(input_gate_bias_ptr, n_cell, n_batch, - input_gate_scratch); - } - tensor_utils::VectorBatchVectorAssign(forget_gate_bias_ptr, n_cell, n_batch, - forget_gate_scratch); - tensor_utils::VectorBatchVectorAssign(cell_bias_ptr, n_cell, n_batch, - cell_scratch); - tensor_utils::VectorBatchVectorAssign(output_gate_bias_ptr, n_cell, n_batch, - output_gate_scratch); - - // For each batch and cell: compute input_weight * input. - if (!use_cifg) { - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_input_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch, - input_gate_scratch, /*result_stride=*/1); - } - - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_forget_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch, - forget_gate_scratch, /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_cell_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch, - cell_scratch, /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_output_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch, - output_gate_scratch, /*result_stride=*/1); - - // If auxiliary input is available then compute aux_input_weight * aux_input - if (aux_input_ptr_batch != nullptr) { - if (!use_cifg) { - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - aux_input_to_input_weights_ptr, n_cell, n_aux_input, - aux_input_ptr_batch, n_batch, input_gate_scratch, - /*result_stride=*/1); - } - - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - aux_input_to_forget_weights_ptr, n_cell, n_aux_input, - aux_input_ptr_batch, n_batch, forget_gate_scratch, /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - aux_input_to_cell_weights_ptr, n_cell, n_aux_input, aux_input_ptr_batch, - n_batch, cell_scratch, /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - aux_input_to_output_weights_ptr, n_cell, n_aux_input, - aux_input_ptr_batch, n_batch, output_gate_scratch, /*result_stride=*/1); - } - - // For each batch and cell: compute recurrent_weight * output_state. - if (!use_cifg) { - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_input_weights_ptr, n_cell, n_output, output_state_ptr, - n_batch, input_gate_scratch, /*result_stride=*/1); - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_forget_weights_ptr, n_cell, n_output, output_state_ptr, - n_batch, forget_gate_scratch, - /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_cell_weights_ptr, n_cell, n_output, output_state_ptr, - n_batch, cell_scratch, /*result_stride=*/1); - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_output_weights_ptr, n_cell, n_output, output_state_ptr, - n_batch, output_gate_scratch, - /*result_stride=*/1); - - // For each batch and cell: update input gate. - if (!use_cifg) { - if (use_peephole) { - tensor_utils::VectorBatchVectorCwiseProductAccumulate( - cell_to_input_weights_ptr, n_cell, cell_state_ptr, n_batch, - input_gate_scratch); - } - tensor_utils::ApplySigmoidToVector(input_gate_scratch, n_cell * n_batch, - input_gate_scratch); - } - - // For each batch and cell: update forget gate. - if (use_peephole) { - tensor_utils::VectorBatchVectorCwiseProductAccumulate( - cell_to_forget_weights_ptr, n_cell, cell_state_ptr, n_batch, - forget_gate_scratch); - } - tensor_utils::ApplySigmoidToVector(forget_gate_scratch, n_cell * n_batch, - forget_gate_scratch); - - // For each batch and cell: update the cell. - tensor_utils::VectorVectorCwiseProduct(forget_gate_scratch, cell_state_ptr, - n_batch * n_cell, cell_state_ptr); - tensor_utils::ApplyActivationToVector(cell_scratch, n_batch * n_cell, - params->activation, cell_scratch); - if (use_cifg) { - tensor_utils::Sub1Vector(forget_gate_scratch, n_batch * n_cell, - forget_gate_scratch); - tensor_utils::VectorVectorCwiseProductAccumulate( - cell_scratch, forget_gate_scratch, n_batch * n_cell, cell_state_ptr); - } else { - tensor_utils::VectorVectorCwiseProductAccumulate( - cell_scratch, input_gate_scratch, n_batch * n_cell, cell_state_ptr); - } - if (params->cell_clip > 0.0) { - tensor_utils::ClipVector(cell_state_ptr, n_batch * n_cell, - params->cell_clip, cell_state_ptr); - } - - // For each batch and cell: update the output gate. - if (use_peephole) { - tensor_utils::VectorBatchVectorCwiseProductAccumulate( - cell_to_output_weights_ptr, n_cell, cell_state_ptr, n_batch, - output_gate_scratch); - } - tensor_utils::ApplySigmoidToVector(output_gate_scratch, n_batch * n_cell, - output_gate_scratch); - tensor_utils::ApplyActivationToVector(cell_state_ptr, n_batch * n_cell, - params->activation, cell_scratch); - tensor_utils::VectorVectorCwiseProduct(output_gate_scratch, cell_scratch, - n_batch * n_cell, output_gate_scratch); - - // For each batch: update the projection and output_state. - const bool use_projection_weight = (projection_weights_ptr != nullptr); - const bool use_projection_bias = (projection_bias_ptr != nullptr); - if (use_projection_weight) { - if (use_projection_bias) { - tensor_utils::VectorBatchVectorAssign(projection_bias_ptr, n_output, - n_batch, output_ptr_batch); - } else { - tensor_utils::ZeroVector(output_ptr_batch, n_batch * n_output); - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - projection_weights_ptr, n_output, n_cell, output_gate_scratch, n_batch, - output_ptr_batch, /*result_stride=*/1); - if (params->proj_clip > 0.0) { - tensor_utils::ClipVector(output_ptr_batch, n_batch * n_output, - params->proj_clip, output_ptr_batch); - } - } else { - tensor_utils::CopyVector(output_gate_scratch, n_batch * n_output, - output_ptr_batch); - } - tensor_utils::CopyVector(output_ptr_batch, n_batch * n_output, - output_state_ptr); -} - -void LstmStep( - const float* input_ptr_batch, const int8_t* input_to_input_weights_ptr, - float input_to_input_weights_scale, - const int8_t* input_to_forget_weights_ptr, - float input_to_forget_weights_scale, - const int8_t* input_to_cell_weights_ptr, float input_to_cell_weights_scale, - const int8_t* input_to_output_weights_ptr, - float input_to_output_weights_scale, - const int8_t* recurrent_to_input_weights_ptr, - float recurrent_to_input_weights_scale, - const int8_t* recurrent_to_forget_weights_ptr, - float recurrent_to_forget_weights_scale, - const int8_t* recurrent_to_cell_weights_ptr, - float recurrent_to_cell_weights_scale, - const int8_t* recurrent_to_output_weights_ptr, - float recurrent_to_output_weights_scale, - const int8_t* cell_to_input_weights_ptr, float cell_to_input_weights_scale, - const int8_t* cell_to_forget_weights_ptr, - float cell_to_forget_weights_scale, - const int8_t* cell_to_output_weights_ptr, - float cell_to_output_weights_scale, const float* input_gate_bias_ptr, - const float* forget_gate_bias_ptr, const float* cell_bias_ptr, - const float* output_gate_bias_ptr, const int8_t* projection_weights_ptr, - float projection_weights_scale, const float* projection_bias_ptr, - const TfLiteLSTMParams* params, int n_batch, int n_cell, int n_input, - int n_output, float* input_gate_scratch, float* forget_gate_scratch, - float* cell_scratch, float* output_gate_scratch, float* scaling_factors, - float* product_scaling_factors, float* recovered_cell_weights, - int8_t* quantized_input_ptr_batch, int8_t* quantized_output_state_ptr, - int8_t* quantized_cell_state_ptr, float* output_state_ptr, - float* cell_state_ptr, float* output_ptr_batch) { - LstmStepWithAuxInput( - input_ptr_batch, input_to_input_weights_ptr, input_to_input_weights_scale, - input_to_forget_weights_ptr, input_to_forget_weights_scale, - input_to_cell_weights_ptr, input_to_cell_weights_scale, - input_to_output_weights_ptr, input_to_output_weights_scale, - /*aux_input_ptr_batch=*/nullptr, - /*aux_input_to_input_weights_ptr=*/nullptr, - /*aux_input_to_input_weights_scale=*/0.0f, - /*aux_input_to_forget_weights_ptr=*/nullptr, - /*aux_input_to_forget_weights_scale=*/0.0f, - /*aux_input_to_cell_weights_ptr=*/nullptr, - /*aux_input_to_cell_weights_scale=*/0.0f, - /*aux_input_to_output_weights_ptr=*/nullptr, - /*aux_input_to_output_weights_scale=*/0.0f, - recurrent_to_input_weights_ptr, recurrent_to_input_weights_scale, - recurrent_to_forget_weights_ptr, recurrent_to_forget_weights_scale, - recurrent_to_cell_weights_ptr, recurrent_to_cell_weights_scale, - recurrent_to_output_weights_ptr, recurrent_to_output_weights_scale, - cell_to_input_weights_ptr, cell_to_input_weights_scale, - cell_to_forget_weights_ptr, cell_to_forget_weights_scale, - cell_to_output_weights_ptr, cell_to_output_weights_scale, - input_gate_bias_ptr, forget_gate_bias_ptr, cell_bias_ptr, - output_gate_bias_ptr, projection_weights_ptr, projection_weights_scale, - projection_bias_ptr, params, n_batch, n_cell, n_input, - /*n_aux_input=*/0, n_output, input_gate_scratch, forget_gate_scratch, - cell_scratch, output_gate_scratch, scaling_factors, - product_scaling_factors, recovered_cell_weights, - quantized_input_ptr_batch, - /*quantized_aux_input_ptr_batch=*/nullptr, quantized_output_state_ptr, - quantized_cell_state_ptr, output_state_ptr, cell_state_ptr, - output_ptr_batch); - } - - void LstmStepWithAuxInput( - const float* input_ptr_batch, const int8_t* input_to_input_weights_ptr, - float input_to_input_weights_scale, - const int8_t* input_to_forget_weights_ptr, - float input_to_forget_weights_scale, - const int8_t* input_to_cell_weights_ptr, - float input_to_cell_weights_scale, - const int8_t* input_to_output_weights_ptr, - float input_to_output_weights_scale, const float* aux_input_ptr_batch, - const int8_t* aux_input_to_input_weights_ptr, - float aux_input_to_input_weights_scale, - const int8_t* aux_input_to_forget_weights_ptr, - float aux_input_to_forget_weights_scale, - const int8_t* aux_input_to_cell_weights_ptr, - float aux_input_to_cell_weights_scale, - const int8_t* aux_input_to_output_weights_ptr, - float aux_input_to_output_weights_scale, - const int8_t* recurrent_to_input_weights_ptr, - float recurrent_to_input_weights_scale, - const int8_t* recurrent_to_forget_weights_ptr, - float recurrent_to_forget_weights_scale, - const int8_t* recurrent_to_cell_weights_ptr, - float recurrent_to_cell_weights_scale, - const int8_t* recurrent_to_output_weights_ptr, - float recurrent_to_output_weights_scale, - const int8_t* cell_to_input_weights_ptr, - float cell_to_input_weights_scale, - const int8_t* cell_to_forget_weights_ptr, - float cell_to_forget_weights_scale, - const int8_t* cell_to_output_weights_ptr, - float cell_to_output_weights_scale, const float* input_gate_bias_ptr, - const float* forget_gate_bias_ptr, const float* cell_bias_ptr, - const float* output_gate_bias_ptr, const int8_t* projection_weights_ptr, - float projection_weights_scale, const float* projection_bias_ptr, - const TfLiteLSTMParams* params, int n_batch, int n_cell, int n_input, - int n_aux_input, int n_output, float* input_gate_scratch, - float* forget_gate_scratch, float* cell_scratch, - float* output_gate_scratch, float* scaling_factors, - float* product_scaling_factors, float* recovered_cell_weights, - int8_t* quantized_input_ptr_batch, - int8_t* quantized_aux_input_ptr_batch, - int8_t* quantized_output_state_ptr, int8_t* quantized_cell_state_ptr, - float* output_state_ptr, float* cell_state_ptr, - float* output_ptr_batch) { - // Since we have already checked that weights are all there or none, we - // can check the existense of only one to the get the condition. - const bool use_cifg = (input_to_input_weights_ptr == nullptr); - const bool use_peephole = (cell_to_output_weights_ptr != nullptr); - // Initialize scratch buffers with bias. - if (!use_cifg) { - tensor_utils::VectorBatchVectorAssign(input_gate_bias_ptr, n_cell, - n_batch, input_gate_scratch); - } - tensor_utils::VectorBatchVectorAssign(forget_gate_bias_ptr, n_cell, - n_batch, forget_gate_scratch); - tensor_utils::VectorBatchVectorAssign(cell_bias_ptr, n_cell, n_batch, - cell_scratch); - tensor_utils::VectorBatchVectorAssign(output_gate_bias_ptr, n_cell, - n_batch, output_gate_scratch); - - if (!tensor_utils::IsZeroVector(input_ptr_batch, n_batch * n_input)) { - // Save quantization and matmul computation for all zero input. - float unused_min, unused_max; - for (int b = 0; b < n_batch; ++b) { - const int offset = b * n_input; - tensor_utils::SymmetricQuantizeFloats( - input_ptr_batch + offset, n_input, - quantized_input_ptr_batch + offset, &unused_min, &unused_max, - &scaling_factors[b]); - } - // For each batch and cell: compute input_weight * input. - if (!use_cifg) { - for (int b = 0; b < n_batch; ++b) { - product_scaling_factors[b] = - scaling_factors[b] * input_to_input_weights_scale; - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_input_weights_ptr, n_cell, n_input, - quantized_input_ptr_batch, product_scaling_factors, n_batch, - input_gate_scratch, /*result_stride=*/1); - } - - for (int b = 0; b < n_batch; ++b) { - product_scaling_factors[b] = - scaling_factors[b] * input_to_forget_weights_scale; - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_forget_weights_ptr, n_cell, n_input, - quantized_input_ptr_batch, product_scaling_factors, n_batch, - forget_gate_scratch, - /*result_stride=*/1); - - for (int b = 0; b < n_batch; ++b) { - product_scaling_factors[b] = - scaling_factors[b] * input_to_cell_weights_scale; - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_cell_weights_ptr, n_cell, n_input, - quantized_input_ptr_batch, product_scaling_factors, n_batch, - cell_scratch, /*result_stride=*/1); - - for (int b = 0; b < n_batch; ++b) { - product_scaling_factors[b] = - scaling_factors[b] * input_to_output_weights_scale; - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - input_to_output_weights_ptr, n_cell, n_input, - quantized_input_ptr_batch, product_scaling_factors, n_batch, - output_gate_scratch, - /*result_stride=*/1); - } - - if (aux_input_ptr_batch != nullptr && - !tensor_utils::IsZeroVector(aux_input_ptr_batch, n_batch * n_input)) { - // Save quantization and matmul computation for all zero input. - float unused_min, unused_max; - for (int b = 0; b < n_batch; ++b) { - const int offset = b * n_input; - tensor_utils::SymmetricQuantizeFloats( - aux_input_ptr_batch + offset, n_input, - quantized_aux_input_ptr_batch + offset, &unused_min, &unused_max, - &scaling_factors[b]); - } - // For each batch and cell: compute input_weight * input. - if (!use_cifg) { - for (int b = 0; b < n_batch; ++b) { - product_scaling_factors[b] = - scaling_factors[b] * aux_input_to_input_weights_scale; - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - aux_input_to_input_weights_ptr, n_cell, n_input, - quantized_aux_input_ptr_batch, product_scaling_factors, n_batch, - input_gate_scratch, /*result_stride=*/1); - } - - for (int b = 0; b < n_batch; ++b) { - product_scaling_factors[b] = - scaling_factors[b] * aux_input_to_forget_weights_scale; - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - aux_input_to_forget_weights_ptr, n_cell, n_input, - quantized_aux_input_ptr_batch, product_scaling_factors, n_batch, - forget_gate_scratch, /*result_stride=*/1); - - for (int b = 0; b < n_batch; ++b) { - product_scaling_factors[b] = - scaling_factors[b] * aux_input_to_cell_weights_scale; - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - aux_input_to_cell_weights_ptr, n_cell, n_input, - quantized_aux_input_ptr_batch, product_scaling_factors, n_batch, - cell_scratch, /*result_stride=*/1); - - for (int b = 0; b < n_batch; ++b) { - product_scaling_factors[b] = - scaling_factors[b] * aux_input_to_output_weights_scale; - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - aux_input_to_output_weights_ptr, n_cell, n_input, - quantized_aux_input_ptr_batch, product_scaling_factors, n_batch, - output_gate_scratch, /*result_stride=*/1); - } - - if (!tensor_utils::IsZeroVector(output_state_ptr, n_batch * n_output)) { - // Save quantization and matmul computation for all zero input. - float unused_min, unused_max; - for (int b = 0; b < n_batch; ++b) { - const int offset = b * n_output; - tensor_utils::SymmetricQuantizeFloats( - output_state_ptr + offset, n_output, - quantized_output_state_ptr + offset, &unused_min, &unused_max, - &scaling_factors[b]); - } - // For each batch and cell: compute recurrent_weight * output_state. - if (!use_cifg) { - for (int b = 0; b < n_batch; ++b) { - product_scaling_factors[b] = - scaling_factors[b] * recurrent_to_input_weights_scale; - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_input_weights_ptr, n_cell, n_output, - quantized_output_state_ptr, product_scaling_factors, n_batch, - input_gate_scratch, /*result_stride=*/1); - } - - for (int b = 0; b < n_batch; ++b) { - product_scaling_factors[b] = - scaling_factors[b] * recurrent_to_forget_weights_scale; - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_forget_weights_ptr, n_cell, n_output, - quantized_output_state_ptr, product_scaling_factors, n_batch, - forget_gate_scratch, /*result_stride=*/1); - - for (int b = 0; b < n_batch; ++b) { - product_scaling_factors[b] = - scaling_factors[b] * recurrent_to_cell_weights_scale; - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_cell_weights_ptr, n_cell, n_output, - quantized_output_state_ptr, product_scaling_factors, n_batch, - cell_scratch, /*result_stride=*/1); - - for (int b = 0; b < n_batch; ++b) { - product_scaling_factors[b] = - scaling_factors[b] * recurrent_to_output_weights_scale; - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - recurrent_to_output_weights_ptr, n_cell, n_output, - quantized_output_state_ptr, product_scaling_factors, n_batch, - output_gate_scratch, /*result_stride=*/1); - } - - // Save quantization and matmul computation for all zero input. - bool is_cell_state_all_zeros = - tensor_utils::IsZeroVector(cell_state_ptr, n_batch * n_cell); - - // For each batch and cell: update input gate. - if (!use_cifg) { - if (use_peephole && !is_cell_state_all_zeros) { - tensor_utils::VectorScalarMultiply(cell_to_input_weights_ptr, n_cell, - cell_to_input_weights_scale, - recovered_cell_weights); - tensor_utils::VectorBatchVectorCwiseProductAccumulate( - recovered_cell_weights, n_cell, cell_state_ptr, n_batch, - input_gate_scratch); - } - tensor_utils::ApplySigmoidToVector(input_gate_scratch, n_cell * n_batch, - input_gate_scratch); - } - - // For each batch and cell: update forget gate. - if (use_peephole && !is_cell_state_all_zeros) { - tensor_utils::VectorScalarMultiply(cell_to_forget_weights_ptr, n_cell, - cell_to_forget_weights_scale, - recovered_cell_weights); - tensor_utils::VectorBatchVectorCwiseProductAccumulate( - recovered_cell_weights, n_cell, cell_state_ptr, n_batch, - forget_gate_scratch); - } - tensor_utils::ApplySigmoidToVector(forget_gate_scratch, n_cell * n_batch, - forget_gate_scratch); - - // For each batch and cell: update the cell. - tensor_utils::VectorVectorCwiseProduct(forget_gate_scratch, - cell_state_ptr, n_batch * n_cell, - cell_state_ptr); - tensor_utils::ApplyActivationToVector(cell_scratch, n_batch * n_cell, - params->activation, cell_scratch); - if (use_cifg) { - tensor_utils::Sub1Vector(forget_gate_scratch, n_batch * n_cell, - forget_gate_scratch); - tensor_utils::VectorVectorCwiseProductAccumulate( - cell_scratch, forget_gate_scratch, n_batch * n_cell, - cell_state_ptr); - } else { - tensor_utils::VectorVectorCwiseProductAccumulate( - cell_scratch, input_gate_scratch, n_batch * n_cell, cell_state_ptr); - } - if (params->cell_clip > 0.0) { - tensor_utils::ClipVector(cell_state_ptr, n_batch * n_cell, - params->cell_clip, cell_state_ptr); - } - - is_cell_state_all_zeros = - tensor_utils::IsZeroVector(cell_state_ptr, n_batch * n_cell); - // For each batch and cell: update the output gate. - if (use_peephole && !is_cell_state_all_zeros) { - tensor_utils::VectorScalarMultiply(cell_to_output_weights_ptr, n_cell, - cell_to_output_weights_scale, - recovered_cell_weights); - tensor_utils::VectorBatchVectorCwiseProductAccumulate( - recovered_cell_weights, n_cell, cell_state_ptr, n_batch, - output_gate_scratch); - } - tensor_utils::ApplySigmoidToVector(output_gate_scratch, n_batch * n_cell, - output_gate_scratch); - tensor_utils::ApplyActivationToVector(cell_state_ptr, n_batch * n_cell, - params->activation, cell_scratch); - tensor_utils::VectorVectorCwiseProduct(output_gate_scratch, cell_scratch, - n_batch * n_cell, - output_gate_scratch); - - // For each batch: update the projection and output_state. - const bool use_projection_weight = (projection_weights_ptr != nullptr); - const bool use_projection_bias = (projection_bias_ptr != nullptr); - if (use_projection_weight) { - if (use_projection_bias) { - tensor_utils::VectorBatchVectorAssign(projection_bias_ptr, n_output, - n_batch, output_ptr_batch); - } else { - tensor_utils::ZeroVector(output_ptr_batch, n_batch * n_output); - } - if (!tensor_utils::IsZeroVector(output_gate_scratch, - n_batch * n_cell)) { - // Save quantization and matmul computation for all zero input. - float unused_min, unused_max; - for (int b = 0; b < n_batch; ++b) { - const int offset = b * n_cell; - tensor_utils::SymmetricQuantizeFloats( - output_gate_scratch + offset, n_cell, - quantized_cell_state_ptr + offset, &unused_min, &unused_max, - &scaling_factors[b]); - } - for (int b = 0; b < n_batch; ++b) { - product_scaling_factors[b] = - scaling_factors[b] * projection_weights_scale; - } - tensor_utils::MatrixBatchVectorMultiplyAccumulate( - projection_weights_ptr, n_output, n_cell, - quantized_cell_state_ptr, product_scaling_factors, n_batch, - output_ptr_batch, - /*result_stride=*/1); - } - if (params->proj_clip > 0.0) { - tensor_utils::ClipVector(output_ptr_batch, n_batch * n_output, - params->proj_clip, output_ptr_batch); - } - } else { - tensor_utils::CopyVector(output_gate_scratch, n_batch * n_output, - output_ptr_batch); - } - tensor_utils::CopyVector(output_ptr_batch, n_batch * n_output, - output_state_ptr); - } - } // namespace kernel_utils } // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/internal/kernel_utils.h b/tensorflow/contrib/lite/kernels/internal/kernel_utils.h index b5558cce55..74e0a4a53d 100644 --- a/tensorflow/contrib/lite/kernels/internal/kernel_utils.h +++ b/tensorflow/contrib/lite/kernels/internal/kernel_utils.h @@ -76,190 +76,6 @@ void RnnBatchStep( int8_t* quantized_hidden_state_ptr_batch, float* scaling_factors, float* hidden_state_ptr_batch, float* output_ptr_batch); -// Performs an LSTM batch inference step for input specified by input_ptr_batch. -// The LSTM cell is specified by the pointers to its weights (*_weights_ptr) and -// biases (*_bias_ptr), and buffers (*_scratch), along with additional -// parameters: -// - params: various LSTM params including activation, clipping, etc., -// - n_batch: size of batch, -// - n_cell: number of cells (or units), -// - n_input: the input size, -// - n_output: the output size. -// -// The pointers to the cell and output state and the output are updated. -// -// The pointers with the suffix "_batch" point to data aligned in batch_major -// order, and each step processes batch_size many inputs from input_ptr_batch, -// and updates batch_size many cell and output states. -void LstmStep( - const float* input_ptr_batch, const float* input_to_input_weights_ptr, - const float* input_to_forget_weights_ptr, - const float* input_to_cell_weights_ptr, - const float* input_to_output_weights_ptr, - const float* recurrent_to_input_weights_ptr, - const float* recurrent_to_forget_weights_ptr, - const float* recurrent_to_cell_weights_ptr, - const float* recurrent_to_output_weights_ptr, - const float* cell_to_input_weights_ptr, - const float* cell_to_forget_weights_ptr, - const float* cell_to_output_weights_ptr, const float* input_gate_bias_ptr, - const float* forget_gate_bias_ptr, const float* cell_bias_ptr, - const float* output_gate_bias_ptr, const float* projection_weights_ptr, - const float* projection_bias_ptr, const TfLiteLSTMParams* params, - int n_batch, int n_cell, int n_input, int n_output, float* output_state_ptr, - float* cell_state_ptr, float* input_gate_scratch, - float* forget_gate_scratch, float* cell_scratch, float* output_gate_scratch, - float* output_ptr_batch); - -// Same as above but includes an auxiliary input with the corresponding weights. -void LstmStepWithAuxInput( - const float* input_ptr_batch, const float* input_to_input_weights_ptr, - const float* input_to_forget_weights_ptr, - const float* input_to_cell_weights_ptr, - const float* input_to_output_weights_ptr, const float* aux_input_ptr_batch, - const float* aux_input_to_input_weights_ptr, - const float* aux_input_to_forget_weights_ptr, - const float* aux_input_to_cell_weights_ptr, - const float* aux_input_to_output_weights_ptr, - const float* recurrent_to_input_weights_ptr, - const float* recurrent_to_forget_weights_ptr, - const float* recurrent_to_cell_weights_ptr, - const float* recurrent_to_output_weights_ptr, - const float* cell_to_input_weights_ptr, - const float* cell_to_forget_weights_ptr, - const float* cell_to_output_weights_ptr, const float* input_gate_bias_ptr, - const float* forget_gate_bias_ptr, const float* cell_bias_ptr, - const float* output_gate_bias_ptr, const float* projection_weights_ptr, - const float* projection_bias_ptr, const TfLiteLSTMParams* params, - int n_batch, int n_cell, int n_input, int n_aux_input, int n_output, - float* output_state_ptr, float* cell_state_ptr, float* input_gate_scratch, - float* forget_gate_scratch, float* cell_scratch, float* output_gate_scratch, - float* output_ptr_batch); - -// Same as above but with quantized weight matrices. In detail: -// Input of size 'n_batch * n_input': -// input_ptr_batch -// -// LSTM weights: -// Quantized input weights of size 'n_cell * n_input': -// input_to_input_weights - optional (can be nullptr) -// input_to_forget_weights -// input_to_cell_weights -// input_to_input_weights -// Quantized recurrent weights of size 'n_cell * n_output': -// recurrent_to_input_weights - optional -// recurrent_to_forget_weights -// recurrent_to_cell_weights -// recurrent_to_input_weights -// Quantized peephole weights of size 'n_cell', representing diagonal matrices. -// cell_to_input_weights - optional -// cell_to_cell_weights - optional -// cell_to_output_weights - optional -// Quantized projection weights of size 'n_output * n_cell' -// projection_weights_ptr - optional -// Weight scales (scalars) for each of the weights above. -// input_to_input_weights_scale - optional -// input_to_forget_weights_scale -// input_to_cell_weights_scale -// input_to_output_weights_scale -// recurrent_to_input_weights_scale - optional -// recurrent_to_forget_weights_scale -// recurrent_to_cell_weights_scale -// recurrent_to_output_weights_scale -// cell_to_input_weights_scale, -// cell_to_forget_weights_scale, -// cell_to_output_weights_scale, -// projection_weights_scale - optional -// Gate biases of size 'n_cell': -// input_gate_bias_ptr - optional -// forget_gate_bias_ptr -// cell_gate_bias_ptr -// output_gate_bias_ptr -// -// Temporary pre-allocated storage for quantized values: -// quantized_input_ptr_batch (same size as input_ptr_batch) -// quantized_output_state_ptr (same size as output_state_ptr) -// quantized_cell_state_ptr (same size as cell_state_ptr) -// Temporary pre-allocated storage for recovered values: -// recovered_cell_weights (same size as cell_to_*_weights) -// -// Outputs: -// output_state_ptr - size 'n_batch * n_output' -// cell_state_ptr - size 'n_batch * n_cell' -// output_ptr_batch - size 'n_batch * n_output' -void LstmStep( - const float* input_ptr_batch, const int8_t* input_to_input_weights_ptr, - float input_to_input_weights_scale, - const int8_t* input_to_forget_weights_ptr, - float input_to_forget_weights_scale, - const int8_t* input_to_cell_weights_ptr, float input_to_cell_weights_scale, - const int8_t* input_to_output_weights_ptr, - float input_to_output_weights_scale, - const int8_t* recurrent_to_input_weights_ptr, - float recurrent_to_input_weights_scale, - const int8_t* recurrent_to_forget_weights_ptr, - float recurrent_to_forget_weights_scale, - const int8_t* recurrent_to_cell_weights_ptr, - float recurrent_to_cell_weights_scale, - const int8_t* recurrent_to_output_weights_ptr, - float recurrent_to_output_weights_scale, - const int8_t* cell_to_input_weights_ptr, float cell_to_input_weights_scale, - const int8_t* cell_to_forget_weights_ptr, - float cell_to_forget_weights_scale, - const int8_t* cell_to_output_weights_ptr, - float cell_to_output_weights_scale, const float* input_gate_bias_ptr, - const float* forget_gate_bias_ptr, const float* cell_bias_ptr, - const float* output_gate_bias_ptr, const int8_t* projection_weights_ptr, - float projection_weights_scale, const float* projection_bias_ptr, - const TfLiteLSTMParams* params, int n_batch, int n_cell, int n_input, - int n_output, float* input_gate_scratch, float* forget_gate_scratch, - float* cell_scratch, float* output_gate_scratch, float* scaling_factors, - float* product_scaling_factors, float* recovered_cell_weights, - int8_t* quantized_input_ptr_batch, int8_t* quantized_output_state_ptr, - int8_t* quantized_cell_state_ptr, float* output_state_ptr, - float* cell_state_ptr, float* output_ptr_batch); - -void LstmStepWithAuxInput( - const float* input_ptr_batch, const int8_t* input_to_input_weights_ptr, - float input_to_input_weights_scale, - const int8_t* input_to_forget_weights_ptr, - float input_to_forget_weights_scale, - const int8_t* input_to_cell_weights_ptr, float input_to_cell_weights_scale, - const int8_t* input_to_output_weights_ptr, - float input_to_output_weights_scale, const float* aux_input_ptr_batch, - const int8_t* aux_input_to_input_weights_ptr, - float aux_input_to_input_weights_scale, - const int8_t* aux_input_to_forget_weights_ptr, - float aux_input_to_forget_weights_scale, - const int8_t* aux_input_to_cell_weights_ptr, - float aux_input_to_cell_weights_scale, - const int8_t* aux_input_to_output_weights_ptr, - float aux_input_to_output_weights_scale, - const int8_t* recurrent_to_input_weights_ptr, - float recurrent_to_input_weights_scale, - const int8_t* recurrent_to_forget_weights_ptr, - float recurrent_to_forget_weights_scale, - const int8_t* recurrent_to_cell_weights_ptr, - float recurrent_to_cell_weights_scale, - const int8_t* recurrent_to_output_weights_ptr, - float recurrent_to_output_weights_scale, - const int8_t* cell_to_input_weights_ptr, float cell_to_input_weights_scale, - const int8_t* cell_to_forget_weights_ptr, - float cell_to_forget_weights_scale, - const int8_t* cell_to_output_weights_ptr, - float cell_to_output_weights_scale, const float* input_gate_bias_ptr, - const float* forget_gate_bias_ptr, const float* cell_bias_ptr, - const float* output_gate_bias_ptr, const int8_t* projection_weights_ptr, - float projection_weights_scale, const float* projection_bias_ptr, - const TfLiteLSTMParams* params, int n_batch, int n_cell, int n_input, - int n_aux_input, int n_output, float* input_gate_scratch, - float* forget_gate_scratch, float* cell_scratch, float* output_gate_scratch, - float* scaling_factors, float* product_scaling_factors, - float* recovered_cell_weights, int8_t* quantized_input_ptr_batch, - int8_t* quantized_aux_input_ptr_batch, int8_t* quantized_output_state_ptr, - int8_t* quantized_cell_state_ptr, float* output_state_ptr, - float* cell_state_ptr, float* output_ptr_batch); - } // namespace kernel_utils } // namespace tflite #endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_KERNEL_UTILS_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc b/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc index 14281f25c6..25ea72b886 100644 --- a/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc +++ b/tensorflow/contrib/lite/kernels/internal/quantization_util_test.cc @@ -259,7 +259,7 @@ TEST(QuantizationUtilTest, IntegerFrExpVersusDouble) { EXPECT_EQ(double_shift, 1); result = IntegerFrExp(123.45, &shift); - EXPECT_NEAR(result, (0.964453 * (1L << 31)), 1000); + EXPECT_NEAR(result, (0.964453 * (1LL << 31)), 1000); EXPECT_EQ(shift, 7); double_result = std::frexp(123.45, &double_shift); EXPECT_NEAR(double_result, 0.964453, 1e-5); diff --git a/tensorflow/contrib/lite/kernels/internal/types.h b/tensorflow/contrib/lite/kernels/internal/types.h index b39347758a..c6bc6074d4 100644 --- a/tensorflow/contrib/lite/kernels/internal/types.h +++ b/tensorflow/contrib/lite/kernels/internal/types.h @@ -18,7 +18,6 @@ limitations under the License. #include <algorithm> #include <cstring> -#include "absl/base/macros.h" #include "tensorflow/contrib/lite/kernels/internal/compatibility.h" namespace tflite { @@ -269,8 +268,9 @@ class RuntimeShape { // This creates a shape padded to the desired size with the specified value. RuntimeShape(int new_shape_size, const RuntimeShape& shape, int pad_value) : size_(0) { + // If the following check fails, it is likely because a 4D-only kernel is + // being used with an array of larger dimension count. TFLITE_CHECK_GE(new_shape_size, shape.DimensionsCount()); - TFLITE_CHECK_LE(new_shape_size, kMaxSmallSize); Resize(new_shape_size); const int size_increase = new_shape_size - shape.DimensionsCount(); for (int i = 0; i < size_increase; ++i) { @@ -441,7 +441,7 @@ inline int FlatSize(const Dims<N>& dims) { return flat_size; } -ABSL_DEPRECATED("Prefer FlatSize.") +TFLITE_DEPRECATED("Prefer FlatSize.") inline int RequiredBufferSizeForDims(const Dims<4>& dims) { return FlatSize(dims); } diff --git a/tensorflow/contrib/lite/kernels/lstm.cc b/tensorflow/contrib/lite/kernels/lstm.cc index 5b996d00bc..16d67a1a93 100644 --- a/tensorflow/contrib/lite/kernels/lstm.cc +++ b/tensorflow/contrib/lite/kernels/lstm.cc @@ -29,6 +29,7 @@ limitations under the License. #include "tensorflow/contrib/lite/kernels/internal/tensor.h" #include "tensorflow/contrib/lite/kernels/internal/tensor_utils.h" #include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/lstm_eval.h" #include "tensorflow/contrib/lite/kernels/op_macros.h" namespace tflite { @@ -424,263 +425,6 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { return kTfLiteOk; } -// The LSTM Op engine. -TfLiteStatus EvalFloat( - const TfLiteTensor* input, const TfLiteTensor* input_to_input_weights, - const TfLiteTensor* input_to_forget_weights, - const TfLiteTensor* input_to_cell_weights, - const TfLiteTensor* input_to_output_weights, - const TfLiteTensor* recurrent_to_input_weights, - const TfLiteTensor* recurrent_to_forget_weights, - const TfLiteTensor* recurrent_to_cell_weights, - const TfLiteTensor* recurrent_to_output_weights, - const TfLiteTensor* cell_to_input_weights, - const TfLiteTensor* cell_to_forget_weights, - const TfLiteTensor* cell_to_output_weights, - const TfLiteTensor* input_gate_bias, const TfLiteTensor* forget_gate_bias, - const TfLiteTensor* cell_bias, const TfLiteTensor* output_gate_bias, - const TfLiteTensor* projection_weights, const TfLiteTensor* projection_bias, - const TfLiteLSTMParams* params, TfLiteTensor* scratch_buffer, - TfLiteTensor* activation_state, TfLiteTensor* cell_state, - TfLiteTensor* output) { - const int n_batch = input->dims->data[0]; - const int n_input = input->dims->data[1]; - // n_cell and n_output will be the same size when there is no projection. - const int n_cell = input_to_output_weights->dims->data[0]; - const int n_output = recurrent_to_output_weights->dims->data[1]; - - // Since we have already checked that weights are all there or none, we can - // check the existence of only one to get the condition. - const bool use_cifg = (input_to_input_weights == nullptr); - const bool use_peephole = (cell_to_output_weights != nullptr); - - float* input_gate_scratch = nullptr; - float* cell_scratch = nullptr; - float* forget_gate_scratch = nullptr; - float* output_gate_scratch = nullptr; - if (use_cifg) { - cell_scratch = scratch_buffer->data.f; - forget_gate_scratch = scratch_buffer->data.f + n_cell * n_batch; - output_gate_scratch = scratch_buffer->data.f + 2 * n_cell * n_batch; - } else { - input_gate_scratch = scratch_buffer->data.f; - cell_scratch = scratch_buffer->data.f + n_cell * n_batch; - forget_gate_scratch = scratch_buffer->data.f + 2 * n_cell * n_batch; - output_gate_scratch = scratch_buffer->data.f + 3 * n_cell * n_batch; - } - - // Check optional tensors, the respective pointers can be null. - const float* input_to_input_weights_ptr = - (use_cifg) ? nullptr : input_to_input_weights->data.f; - const float* recurrent_to_input_weights_ptr = - (use_cifg) ? nullptr : recurrent_to_input_weights->data.f; - const float* input_gate_bias_ptr = - (use_cifg) ? nullptr : input_gate_bias->data.f; - const float* cell_to_input_weights_ptr = - (use_peephole && !use_cifg) ? cell_to_input_weights->data.f : nullptr; - const float* cell_to_forget_weights_ptr = - (use_peephole) ? cell_to_forget_weights->data.f : nullptr; - const float* cell_to_output_weights_ptr = - (use_peephole) ? cell_to_output_weights->data.f : nullptr; - const float* projection_weights_ptr = - (projection_weights == nullptr) ? nullptr : projection_weights->data.f; - const float* projection_bias_ptr = - (projection_bias == nullptr) ? nullptr : projection_bias->data.f; - - // Required tensors, pointers are non-null. - const float* input_ptr_batch = input->data.f; - const float* input_to_forget_weights_ptr = input_to_forget_weights->data.f; - const float* input_to_cell_weights_ptr = input_to_cell_weights->data.f; - const float* input_to_output_weights_ptr = input_to_output_weights->data.f; - const float* recurrent_to_forget_weights_ptr = - recurrent_to_forget_weights->data.f; - const float* recurrent_to_cell_weights_ptr = - recurrent_to_cell_weights->data.f; - const float* recurrent_to_output_weights_ptr = - recurrent_to_output_weights->data.f; - const float* forget_gate_bias_ptr = forget_gate_bias->data.f; - const float* cell_bias_ptr = cell_bias->data.f; - const float* output_gate_bias_ptr = output_gate_bias->data.f; - - float* activation_state_ptr = activation_state->data.f; - float* cell_state_ptr = cell_state->data.f; - float* output_ptr_batch = output->data.f; - - kernel_utils::LstmStep( - input_ptr_batch, input_to_input_weights_ptr, input_to_forget_weights_ptr, - input_to_cell_weights_ptr, input_to_output_weights_ptr, - recurrent_to_input_weights_ptr, recurrent_to_forget_weights_ptr, - recurrent_to_cell_weights_ptr, recurrent_to_output_weights_ptr, - cell_to_input_weights_ptr, cell_to_forget_weights_ptr, - cell_to_output_weights_ptr, input_gate_bias_ptr, forget_gate_bias_ptr, - cell_bias_ptr, output_gate_bias_ptr, projection_weights_ptr, - projection_bias_ptr, params, n_batch, n_cell, n_input, n_output, - activation_state_ptr, cell_state_ptr, input_gate_scratch, - forget_gate_scratch, cell_scratch, output_gate_scratch, output_ptr_batch); - - return kTfLiteOk; -} - -TfLiteStatus EvalHybrid( - const TfLiteTensor* input, const TfLiteTensor* input_to_input_weights, - const TfLiteTensor* input_to_forget_weights, - const TfLiteTensor* input_to_cell_weights, - const TfLiteTensor* input_to_output_weights, - const TfLiteTensor* recurrent_to_input_weights, - const TfLiteTensor* recurrent_to_forget_weights, - const TfLiteTensor* recurrent_to_cell_weights, - const TfLiteTensor* recurrent_to_output_weights, - const TfLiteTensor* cell_to_input_weights, - const TfLiteTensor* cell_to_forget_weights, - const TfLiteTensor* cell_to_output_weights, - const TfLiteTensor* input_gate_bias, const TfLiteTensor* forget_gate_bias, - const TfLiteTensor* cell_bias, const TfLiteTensor* output_gate_bias, - const TfLiteTensor* projection_weights, const TfLiteTensor* projection_bias, - const TfLiteLSTMParams* params, TfLiteTensor* scratch_buffer, - TfLiteTensor* scaling_factors, TfLiteTensor* prod_scaling_factors, - TfLiteTensor* recovered_cell_weights, TfLiteTensor* input_quantized, - TfLiteTensor* activation_state_quantized, - TfLiteTensor* cell_state_quantized, TfLiteTensor* activation_state, - TfLiteTensor* cell_state, TfLiteTensor* output) { - const int n_batch = input->dims->data[0]; - const int n_input = input->dims->data[1]; - // n_cell and n_output will be the same size when there is no projection. - const int n_cell = input_to_output_weights->dims->data[0]; - const int n_output = recurrent_to_output_weights->dims->data[1]; - - // Since we have already checked that weights are all there or none, we can - // check the existence of only one to get the condition. - const bool use_cifg = (input_to_input_weights == nullptr); - const bool use_peephole = (cell_to_output_weights != nullptr); - - float* input_gate_scratch = nullptr; - float* cell_scratch = nullptr; - float* forget_gate_scratch = nullptr; - float* output_gate_scratch = nullptr; - if (use_cifg) { - cell_scratch = scratch_buffer->data.f; - forget_gate_scratch = scratch_buffer->data.f + n_cell * n_batch; - output_gate_scratch = scratch_buffer->data.f + 2 * n_cell * n_batch; - } else { - input_gate_scratch = scratch_buffer->data.f; - cell_scratch = scratch_buffer->data.f + n_cell * n_batch; - forget_gate_scratch = scratch_buffer->data.f + 2 * n_cell * n_batch; - output_gate_scratch = scratch_buffer->data.f + 3 * n_cell * n_batch; - } - - // Check optional tensors, the respective pointers can be null. - int8_t* input_to_input_weights_ptr = nullptr; - float input_to_input_weights_scale = 1.0f; - int8_t* recurrent_to_input_weights_ptr = nullptr; - float recurrent_to_input_weights_scale = 1.0f; - float* input_gate_bias_ptr = nullptr; - if (!use_cifg) { - input_to_input_weights_ptr = - reinterpret_cast<int8_t*>(input_to_input_weights->data.uint8); - recurrent_to_input_weights_ptr = - reinterpret_cast<int8_t*>(recurrent_to_input_weights->data.uint8); - input_gate_bias_ptr = input_gate_bias->data.f; - input_to_input_weights_scale = input_to_input_weights->params.scale; - recurrent_to_input_weights_scale = recurrent_to_input_weights->params.scale; - } - - int8_t* cell_to_input_weights_ptr = nullptr; - int8_t* cell_to_forget_weights_ptr = nullptr; - int8_t* cell_to_output_weights_ptr = nullptr; - float cell_to_input_weights_scale = 1.0f; - float cell_to_forget_weights_scale = 1.0f; - float cell_to_output_weights_scale = 1.0f; - if (use_peephole) { - if (!use_cifg) { - cell_to_input_weights_ptr = - reinterpret_cast<int8_t*>(cell_to_input_weights->data.uint8); - cell_to_input_weights_scale = cell_to_input_weights->params.scale; - } - cell_to_forget_weights_ptr = - reinterpret_cast<int8_t*>(cell_to_forget_weights->data.uint8); - cell_to_output_weights_ptr = - reinterpret_cast<int8_t*>(cell_to_output_weights->data.uint8); - cell_to_forget_weights_scale = cell_to_forget_weights->params.scale; - cell_to_output_weights_scale = cell_to_output_weights->params.scale; - } - - const int8_t* projection_weights_ptr = - (projection_weights == nullptr) - ? nullptr - : reinterpret_cast<int8_t*>(projection_weights->data.uint8); - const float projection_weights_scale = - (projection_weights == nullptr) ? 1.0f : projection_weights->params.scale; - const float* projection_bias_ptr = - (projection_bias == nullptr) ? nullptr : projection_bias->data.f; - - // Required tensors, pointers are non-null. - const float* input_ptr_batch = input->data.f; - const int8_t* input_to_forget_weights_ptr = - reinterpret_cast<int8_t*>(input_to_forget_weights->data.uint8); - const float input_to_forget_weights_scale = - input_to_forget_weights->params.scale; - const int8_t* input_to_cell_weights_ptr = - reinterpret_cast<int8_t*>(input_to_cell_weights->data.uint8); - const float input_to_cell_weights_scale = input_to_cell_weights->params.scale; - const int8_t* input_to_output_weights_ptr = - reinterpret_cast<int8_t*>(input_to_output_weights->data.uint8); - const float input_to_output_weights_scale = - input_to_output_weights->params.scale; - const int8_t* recurrent_to_forget_weights_ptr = - reinterpret_cast<int8_t*>(recurrent_to_forget_weights->data.uint8); - const float recurrent_to_forget_weights_scale = - recurrent_to_forget_weights->params.scale; - const int8_t* recurrent_to_cell_weights_ptr = - reinterpret_cast<int8_t*>(recurrent_to_cell_weights->data.uint8); - const float recurrent_to_cell_weights_scale = - recurrent_to_cell_weights->params.scale; - const int8_t* recurrent_to_output_weights_ptr = - reinterpret_cast<int8_t*>(recurrent_to_output_weights->data.uint8); - const float recurrent_to_output_weights_scale = - recurrent_to_output_weights->params.scale; - const float* forget_gate_bias_ptr = forget_gate_bias->data.f; - const float* cell_bias_ptr = cell_bias->data.f; - const float* output_gate_bias_ptr = output_gate_bias->data.f; - - float* activation_state_ptr = activation_state->data.f; - float* cell_state_ptr = cell_state->data.f; - float* output_ptr_batch = output->data.f; - - // Temporary storage for quantized values and scaling factors. - int8_t* quantized_input_ptr = - reinterpret_cast<int8_t*>(input_quantized->data.uint8); - int8_t* quantized_activation_state_ptr = - reinterpret_cast<int8_t*>(activation_state_quantized->data.uint8); - int8_t* quantized_cell_state_ptr = - reinterpret_cast<int8_t*>(cell_state_quantized->data.uint8); - float* scaling_factors_ptr = scaling_factors->data.f; - float* prod_scaling_factors_ptr = prod_scaling_factors->data.f; - float* recovered_cell_weights_ptr = recovered_cell_weights->data.f; - - kernel_utils::LstmStep( - input_ptr_batch, input_to_input_weights_ptr, input_to_input_weights_scale, - input_to_forget_weights_ptr, input_to_forget_weights_scale, - input_to_cell_weights_ptr, input_to_cell_weights_scale, - input_to_output_weights_ptr, input_to_output_weights_scale, - recurrent_to_input_weights_ptr, recurrent_to_input_weights_scale, - recurrent_to_forget_weights_ptr, recurrent_to_forget_weights_scale, - recurrent_to_cell_weights_ptr, recurrent_to_cell_weights_scale, - recurrent_to_output_weights_ptr, recurrent_to_output_weights_scale, - cell_to_input_weights_ptr, cell_to_input_weights_scale, - cell_to_forget_weights_ptr, cell_to_forget_weights_scale, - cell_to_output_weights_ptr, cell_to_output_weights_scale, - input_gate_bias_ptr, forget_gate_bias_ptr, cell_bias_ptr, - output_gate_bias_ptr, projection_weights_ptr, projection_weights_scale, - projection_bias_ptr, params, n_batch, n_cell, n_input, n_output, - input_gate_scratch, forget_gate_scratch, cell_scratch, - output_gate_scratch, scaling_factors_ptr, prod_scaling_factors_ptr, - recovered_cell_weights_ptr, quantized_input_ptr, - quantized_activation_state_ptr, quantized_cell_state_ptr, - activation_state_ptr, cell_state_ptr, output_ptr_batch); - - return kTfLiteOk; -} - TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { const auto* params = reinterpret_cast<TfLiteLSTMParams*>(node->builtin_data); OpData* op_data = reinterpret_cast<OpData*>(node->user_data); @@ -738,15 +482,21 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { // TODO(mirkov): add a check that weights are all uint8s or all floats. switch (input_to_output_weights->type) { case kTfLiteFloat32: { - return EvalFloat(input, input_to_input_weights, input_to_forget_weights, - input_to_cell_weights, input_to_output_weights, - recurrent_to_input_weights, recurrent_to_forget_weights, - recurrent_to_cell_weights, recurrent_to_output_weights, - cell_to_input_weights, cell_to_forget_weights, - cell_to_output_weights, input_gate_bias, - forget_gate_bias, cell_bias, output_gate_bias, - projection_weights, projection_bias, params, - scratch_buffer, activation_state, cell_state, output); + return lstm_eval::EvalFloat( + input, input_to_input_weights, input_to_forget_weights, + input_to_cell_weights, input_to_output_weights, + recurrent_to_input_weights, recurrent_to_forget_weights, + recurrent_to_cell_weights, recurrent_to_output_weights, + cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, + /*aux_input=*/nullptr, + /*aux_input_to_input_weights=*/nullptr, + /*aux_input_to_forget_weights=*/nullptr, + /*aux_input_to_cell_weights=*/nullptr, + /*aux_input_to_output_weights=*/nullptr, input_gate_bias, + forget_gate_bias, cell_bias, output_gate_bias, projection_weights, + projection_bias, params, /*forward_sequence=*/true, + /*output_offset=*/0, scratch_buffer, activation_state, cell_state, + output); } case kTfLiteUInt8: { TfLiteTensor* input_quantized = GetTemporary(context, node, /*index=*/1); @@ -759,17 +509,23 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { GetTemporary(context, node, /*index=*/5); TfLiteTensor* recovered_cell_weights = GetTemporary(context, node, /*index=*/6); - return EvalHybrid( + return lstm_eval::EvalHybrid( input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, - input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias, - projection_weights, projection_bias, params, scratch_buffer, - scaling_factors, prod_scaling_factors, recovered_cell_weights, - input_quantized, activation_state_quantized, cell_state_quantized, - activation_state, cell_state, output); + /*aux_input=*/nullptr, + /*aux_input_to_input_weights=*/nullptr, + /*aux_input_to_forget_weights=*/nullptr, + /*aux_input_to_cell_weights=*/nullptr, + /*aux_input_to_output_weights=*/nullptr, input_gate_bias, + forget_gate_bias, cell_bias, output_gate_bias, projection_weights, + projection_bias, params, /*forward_sequence=*/true, + /*output_offset=*/0, scratch_buffer, scaling_factors, + prod_scaling_factors, recovered_cell_weights, input_quantized, + /*aux_input_quantized=*/nullptr, activation_state_quantized, + cell_state_quantized, activation_state, cell_state, output); } default: context->ReportError(context, "Type %d is not currently supported.", diff --git a/tensorflow/contrib/lite/kernels/lstm_eval.cc b/tensorflow/contrib/lite/kernels/lstm_eval.cc new file mode 100644 index 0000000000..20a4e30009 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/lstm_eval.cc @@ -0,0 +1,912 @@ +/* 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. +==============================================================================*/ +#include "tensorflow/contrib/lite/kernels/lstm_eval.h" + +#include <stdint.h> + +#include "tensorflow/contrib/lite/kernels/internal/kernel_utils.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor_utils.h" +#include "tensorflow/contrib/lite/kernels/op_macros.h" + +namespace tflite { +namespace ops { +namespace builtin { +namespace lstm_eval { + +namespace { + +// Performs an LSTM batch inference step for input specified by input_ptr_batch. +// The LSTM cell is specified by the pointers to its weights (*_weights_ptr) and +// biases (*_bias_ptr), and buffers (*_scratch), along with additional +// parameters: +// - params: various LSTM params including activation, clipping, etc., +// - n_batch: size of batch, +// - n_cell: number of cells (or units), +// - n_input: the input size, +// - n_output: the output size. +// +// The pointers to the cell and output state and the output are updated. +// +// The pointers with the suffix "_batch" point to data aligned in batch_major +// order, and each step processes batch_size many inputs from input_ptr_batch, +// and updates batch_size many cell and output states. +inline void LstmStepWithAuxInput( + const float* input_ptr_batch, const float* input_to_input_weights_ptr, + const float* input_to_forget_weights_ptr, + const float* input_to_cell_weights_ptr, + const float* input_to_output_weights_ptr, const float* aux_input_ptr_batch, + const float* aux_input_to_input_weights_ptr, + const float* aux_input_to_forget_weights_ptr, + const float* aux_input_to_cell_weights_ptr, + const float* aux_input_to_output_weights_ptr, + const float* recurrent_to_input_weights_ptr, + const float* recurrent_to_forget_weights_ptr, + const float* recurrent_to_cell_weights_ptr, + const float* recurrent_to_output_weights_ptr, + const float* cell_to_input_weights_ptr, + const float* cell_to_forget_weights_ptr, + const float* cell_to_output_weights_ptr, const float* input_gate_bias_ptr, + const float* forget_gate_bias_ptr, const float* cell_bias_ptr, + const float* output_gate_bias_ptr, const float* projection_weights_ptr, + const float* projection_bias_ptr, const TfLiteLSTMParams* params, + int n_batch, int n_cell, int n_input, int n_aux_input, int n_output, + float* output_state_ptr, float* cell_state_ptr, float* input_gate_scratch, + float* forget_gate_scratch, float* cell_scratch, float* output_gate_scratch, + float* output_ptr_batch) { + // Since we have already checked that weights are all there or none, we can + // check the existense of only one to the get the condition. + const bool use_cifg = (input_to_input_weights_ptr == nullptr); + const bool use_peephole = (cell_to_output_weights_ptr != nullptr); + // Initialize scratch buffers with bias. + if (!use_cifg) { + tensor_utils::VectorBatchVectorAssign(input_gate_bias_ptr, n_cell, n_batch, + input_gate_scratch); + } + tensor_utils::VectorBatchVectorAssign(forget_gate_bias_ptr, n_cell, n_batch, + forget_gate_scratch); + tensor_utils::VectorBatchVectorAssign(cell_bias_ptr, n_cell, n_batch, + cell_scratch); + tensor_utils::VectorBatchVectorAssign(output_gate_bias_ptr, n_cell, n_batch, + output_gate_scratch); + + // For each batch and cell: compute input_weight * input. + if (!use_cifg) { + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + input_to_input_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch, + input_gate_scratch, /*result_stride=*/1); + } + + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + input_to_forget_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch, + forget_gate_scratch, /*result_stride=*/1); + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + input_to_cell_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch, + cell_scratch, /*result_stride=*/1); + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + input_to_output_weights_ptr, n_cell, n_input, input_ptr_batch, n_batch, + output_gate_scratch, /*result_stride=*/1); + + // If auxiliary input is available then compute aux_input_weight * aux_input + if (aux_input_ptr_batch != nullptr) { + if (!use_cifg) { + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + aux_input_to_input_weights_ptr, n_cell, n_aux_input, + aux_input_ptr_batch, n_batch, input_gate_scratch, + /*result_stride=*/1); + } + + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + aux_input_to_forget_weights_ptr, n_cell, n_aux_input, + aux_input_ptr_batch, n_batch, forget_gate_scratch, /*result_stride=*/1); + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + aux_input_to_cell_weights_ptr, n_cell, n_aux_input, aux_input_ptr_batch, + n_batch, cell_scratch, /*result_stride=*/1); + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + aux_input_to_output_weights_ptr, n_cell, n_aux_input, + aux_input_ptr_batch, n_batch, output_gate_scratch, /*result_stride=*/1); + } + + // For each batch and cell: compute recurrent_weight * output_state. + if (!use_cifg) { + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + recurrent_to_input_weights_ptr, n_cell, n_output, output_state_ptr, + n_batch, input_gate_scratch, /*result_stride=*/1); + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + recurrent_to_forget_weights_ptr, n_cell, n_output, output_state_ptr, + n_batch, forget_gate_scratch, + /*result_stride=*/1); + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + recurrent_to_cell_weights_ptr, n_cell, n_output, output_state_ptr, + n_batch, cell_scratch, /*result_stride=*/1); + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + recurrent_to_output_weights_ptr, n_cell, n_output, output_state_ptr, + n_batch, output_gate_scratch, + /*result_stride=*/1); + + // For each batch and cell: update input gate. + if (!use_cifg) { + if (use_peephole) { + tensor_utils::VectorBatchVectorCwiseProductAccumulate( + cell_to_input_weights_ptr, n_cell, cell_state_ptr, n_batch, + input_gate_scratch); + } + tensor_utils::ApplySigmoidToVector(input_gate_scratch, n_cell * n_batch, + input_gate_scratch); + } + + // For each batch and cell: update forget gate. + if (use_peephole) { + tensor_utils::VectorBatchVectorCwiseProductAccumulate( + cell_to_forget_weights_ptr, n_cell, cell_state_ptr, n_batch, + forget_gate_scratch); + } + tensor_utils::ApplySigmoidToVector(forget_gate_scratch, n_cell * n_batch, + forget_gate_scratch); + + // For each batch and cell: update the cell. + tensor_utils::VectorVectorCwiseProduct(forget_gate_scratch, cell_state_ptr, + n_batch * n_cell, cell_state_ptr); + tensor_utils::ApplyActivationToVector(cell_scratch, n_batch * n_cell, + params->activation, cell_scratch); + if (use_cifg) { + tensor_utils::Sub1Vector(forget_gate_scratch, n_batch * n_cell, + forget_gate_scratch); + tensor_utils::VectorVectorCwiseProductAccumulate( + cell_scratch, forget_gate_scratch, n_batch * n_cell, cell_state_ptr); + } else { + tensor_utils::VectorVectorCwiseProductAccumulate( + cell_scratch, input_gate_scratch, n_batch * n_cell, cell_state_ptr); + } + if (params->cell_clip > 0.0) { + tensor_utils::ClipVector(cell_state_ptr, n_batch * n_cell, + params->cell_clip, cell_state_ptr); + } + + // For each batch and cell: update the output gate. + if (use_peephole) { + tensor_utils::VectorBatchVectorCwiseProductAccumulate( + cell_to_output_weights_ptr, n_cell, cell_state_ptr, n_batch, + output_gate_scratch); + } + tensor_utils::ApplySigmoidToVector(output_gate_scratch, n_batch * n_cell, + output_gate_scratch); + tensor_utils::ApplyActivationToVector(cell_state_ptr, n_batch * n_cell, + params->activation, cell_scratch); + tensor_utils::VectorVectorCwiseProduct(output_gate_scratch, cell_scratch, + n_batch * n_cell, output_gate_scratch); + + // For each batch: update the projection and output_state. + const bool use_projection_weight = (projection_weights_ptr != nullptr); + const bool use_projection_bias = (projection_bias_ptr != nullptr); + if (use_projection_weight) { + if (use_projection_bias) { + tensor_utils::VectorBatchVectorAssign(projection_bias_ptr, n_output, + n_batch, output_ptr_batch); + } else { + tensor_utils::ZeroVector(output_ptr_batch, n_batch * n_output); + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + projection_weights_ptr, n_output, n_cell, output_gate_scratch, n_batch, + output_ptr_batch, /*result_stride=*/1); + if (params->proj_clip > 0.0) { + tensor_utils::ClipVector(output_ptr_batch, n_batch * n_output, + params->proj_clip, output_ptr_batch); + } + } else { + tensor_utils::CopyVector(output_gate_scratch, n_batch * n_output, + output_ptr_batch); + } + tensor_utils::CopyVector(output_ptr_batch, n_batch * n_output, + output_state_ptr); +} + +// Same as above but with quantized weight matrices. In detail: +// Input of size 'n_batch * n_input': +// input_ptr_batch +// +// LSTM weights: +// Quantized input weights of size 'n_cell * n_input': +// input_to_input_weights - optional (can be nullptr) +// input_to_forget_weights +// input_to_cell_weights +// input_to_input_weights +// Quantized recurrent weights of size 'n_cell * n_output': +// recurrent_to_input_weights - optional +// recurrent_to_forget_weights +// recurrent_to_cell_weights +// recurrent_to_input_weights +// Quantized peephole weights of size 'n_cell', representing diagonal matrices. +// cell_to_input_weights - optional +// cell_to_cell_weights - optional +// cell_to_output_weights - optional +// Quantized projection weights of size 'n_output * n_cell' +// projection_weights_ptr - optional +// Weight scales (scalars) for each of the weights above. +// input_to_input_weights_scale - optional +// input_to_forget_weights_scale +// input_to_cell_weights_scale +// input_to_output_weights_scale +// recurrent_to_input_weights_scale - optional +// recurrent_to_forget_weights_scale +// recurrent_to_cell_weights_scale +// recurrent_to_output_weights_scale +// cell_to_input_weights_scale, +// cell_to_forget_weights_scale, +// cell_to_output_weights_scale, +// projection_weights_scale - optional +// Gate biases of size 'n_cell': +// input_gate_bias_ptr - optional +// forget_gate_bias_ptr +// cell_gate_bias_ptr +// output_gate_bias_ptr +// +// Temporary pre-allocated storage for quantized values: +// quantized_input_ptr_batch (same size as input_ptr_batch) +// quantized_output_state_ptr (same size as output_state_ptr) +// quantized_cell_state_ptr (same size as cell_state_ptr) +// Temporary pre-allocated storage for recovered values: +// recovered_cell_weights (same size as cell_to_*_weights) +// +// Outputs: +// output_state_ptr - size 'n_batch * n_output' +// cell_state_ptr - size 'n_batch * n_cell' +// output_ptr_batch - size 'n_batch * n_output' +inline void LstmStepWithAuxInput( + const float* input_ptr_batch, const int8_t* input_to_input_weights_ptr, + float input_to_input_weights_scale, + const int8_t* input_to_forget_weights_ptr, + float input_to_forget_weights_scale, + const int8_t* input_to_cell_weights_ptr, float input_to_cell_weights_scale, + const int8_t* input_to_output_weights_ptr, + float input_to_output_weights_scale, const float* aux_input_ptr_batch, + const int8_t* aux_input_to_input_weights_ptr, + float aux_input_to_input_weights_scale, + const int8_t* aux_input_to_forget_weights_ptr, + float aux_input_to_forget_weights_scale, + const int8_t* aux_input_to_cell_weights_ptr, + float aux_input_to_cell_weights_scale, + const int8_t* aux_input_to_output_weights_ptr, + float aux_input_to_output_weights_scale, + const int8_t* recurrent_to_input_weights_ptr, + float recurrent_to_input_weights_scale, + const int8_t* recurrent_to_forget_weights_ptr, + float recurrent_to_forget_weights_scale, + const int8_t* recurrent_to_cell_weights_ptr, + float recurrent_to_cell_weights_scale, + const int8_t* recurrent_to_output_weights_ptr, + float recurrent_to_output_weights_scale, + const int8_t* cell_to_input_weights_ptr, float cell_to_input_weights_scale, + const int8_t* cell_to_forget_weights_ptr, + float cell_to_forget_weights_scale, + const int8_t* cell_to_output_weights_ptr, + float cell_to_output_weights_scale, const float* input_gate_bias_ptr, + const float* forget_gate_bias_ptr, const float* cell_bias_ptr, + const float* output_gate_bias_ptr, const int8_t* projection_weights_ptr, + float projection_weights_scale, const float* projection_bias_ptr, + const TfLiteLSTMParams* params, int n_batch, int n_cell, int n_input, + int n_aux_input, int n_output, float* input_gate_scratch, + float* forget_gate_scratch, float* cell_scratch, float* output_gate_scratch, + float* scaling_factors, float* product_scaling_factors, + float* recovered_cell_weights, int8_t* quantized_input_ptr_batch, + int8_t* quantized_aux_input_ptr_batch, int8_t* quantized_output_state_ptr, + int8_t* quantized_cell_state_ptr, float* output_state_ptr, + float* cell_state_ptr, float* output_ptr_batch) { + // Since we have already checked that weights are all there or none, we + // can check the existense of only one to the get the condition. + const bool use_cifg = (input_to_input_weights_ptr == nullptr); + const bool use_peephole = (cell_to_output_weights_ptr != nullptr); + // Initialize scratch buffers with bias. + if (!use_cifg) { + tensor_utils::VectorBatchVectorAssign(input_gate_bias_ptr, n_cell, n_batch, + input_gate_scratch); + } + tensor_utils::VectorBatchVectorAssign(forget_gate_bias_ptr, n_cell, n_batch, + forget_gate_scratch); + tensor_utils::VectorBatchVectorAssign(cell_bias_ptr, n_cell, n_batch, + cell_scratch); + tensor_utils::VectorBatchVectorAssign(output_gate_bias_ptr, n_cell, n_batch, + output_gate_scratch); + + if (!tensor_utils::IsZeroVector(input_ptr_batch, n_batch * n_input)) { + // Save quantization and matmul computation for all zero input. + float unused_min, unused_max; + for (int b = 0; b < n_batch; ++b) { + const int offset = b * n_input; + tensor_utils::SymmetricQuantizeFloats( + input_ptr_batch + offset, n_input, quantized_input_ptr_batch + offset, + &unused_min, &unused_max, &scaling_factors[b]); + } + // For each batch and cell: compute input_weight * input. + if (!use_cifg) { + for (int b = 0; b < n_batch; ++b) { + product_scaling_factors[b] = + scaling_factors[b] * input_to_input_weights_scale; + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + input_to_input_weights_ptr, n_cell, n_input, + quantized_input_ptr_batch, product_scaling_factors, n_batch, + input_gate_scratch, /*result_stride=*/1); + } + + for (int b = 0; b < n_batch; ++b) { + product_scaling_factors[b] = + scaling_factors[b] * input_to_forget_weights_scale; + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + input_to_forget_weights_ptr, n_cell, n_input, quantized_input_ptr_batch, + product_scaling_factors, n_batch, forget_gate_scratch, + /*result_stride=*/1); + + for (int b = 0; b < n_batch; ++b) { + product_scaling_factors[b] = + scaling_factors[b] * input_to_cell_weights_scale; + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + input_to_cell_weights_ptr, n_cell, n_input, quantized_input_ptr_batch, + product_scaling_factors, n_batch, cell_scratch, /*result_stride=*/1); + + for (int b = 0; b < n_batch; ++b) { + product_scaling_factors[b] = + scaling_factors[b] * input_to_output_weights_scale; + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + input_to_output_weights_ptr, n_cell, n_input, quantized_input_ptr_batch, + product_scaling_factors, n_batch, output_gate_scratch, + /*result_stride=*/1); + } + + if (aux_input_ptr_batch != nullptr && + !tensor_utils::IsZeroVector(aux_input_ptr_batch, n_batch * n_input)) { + // Save quantization and matmul computation for all zero input. + float unused_min, unused_max; + for (int b = 0; b < n_batch; ++b) { + const int offset = b * n_input; + tensor_utils::SymmetricQuantizeFloats( + aux_input_ptr_batch + offset, n_input, + quantized_aux_input_ptr_batch + offset, &unused_min, &unused_max, + &scaling_factors[b]); + } + // For each batch and cell: compute input_weight * input. + if (!use_cifg) { + for (int b = 0; b < n_batch; ++b) { + product_scaling_factors[b] = + scaling_factors[b] * aux_input_to_input_weights_scale; + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + aux_input_to_input_weights_ptr, n_cell, n_input, + quantized_aux_input_ptr_batch, product_scaling_factors, n_batch, + input_gate_scratch, /*result_stride=*/1); + } + + for (int b = 0; b < n_batch; ++b) { + product_scaling_factors[b] = + scaling_factors[b] * aux_input_to_forget_weights_scale; + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + aux_input_to_forget_weights_ptr, n_cell, n_input, + quantized_aux_input_ptr_batch, product_scaling_factors, n_batch, + forget_gate_scratch, /*result_stride=*/1); + + for (int b = 0; b < n_batch; ++b) { + product_scaling_factors[b] = + scaling_factors[b] * aux_input_to_cell_weights_scale; + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + aux_input_to_cell_weights_ptr, n_cell, n_input, + quantized_aux_input_ptr_batch, product_scaling_factors, n_batch, + cell_scratch, /*result_stride=*/1); + + for (int b = 0; b < n_batch; ++b) { + product_scaling_factors[b] = + scaling_factors[b] * aux_input_to_output_weights_scale; + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + aux_input_to_output_weights_ptr, n_cell, n_input, + quantized_aux_input_ptr_batch, product_scaling_factors, n_batch, + output_gate_scratch, /*result_stride=*/1); + } + + if (!tensor_utils::IsZeroVector(output_state_ptr, n_batch * n_output)) { + // Save quantization and matmul computation for all zero input. + float unused_min, unused_max; + for (int b = 0; b < n_batch; ++b) { + const int offset = b * n_output; + tensor_utils::SymmetricQuantizeFloats(output_state_ptr + offset, n_output, + quantized_output_state_ptr + offset, + &unused_min, &unused_max, + &scaling_factors[b]); + } + // For each batch and cell: compute recurrent_weight * output_state. + if (!use_cifg) { + for (int b = 0; b < n_batch; ++b) { + product_scaling_factors[b] = + scaling_factors[b] * recurrent_to_input_weights_scale; + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + recurrent_to_input_weights_ptr, n_cell, n_output, + quantized_output_state_ptr, product_scaling_factors, n_batch, + input_gate_scratch, /*result_stride=*/1); + } + + for (int b = 0; b < n_batch; ++b) { + product_scaling_factors[b] = + scaling_factors[b] * recurrent_to_forget_weights_scale; + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + recurrent_to_forget_weights_ptr, n_cell, n_output, + quantized_output_state_ptr, product_scaling_factors, n_batch, + forget_gate_scratch, /*result_stride=*/1); + + for (int b = 0; b < n_batch; ++b) { + product_scaling_factors[b] = + scaling_factors[b] * recurrent_to_cell_weights_scale; + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + recurrent_to_cell_weights_ptr, n_cell, n_output, + quantized_output_state_ptr, product_scaling_factors, n_batch, + cell_scratch, /*result_stride=*/1); + + for (int b = 0; b < n_batch; ++b) { + product_scaling_factors[b] = + scaling_factors[b] * recurrent_to_output_weights_scale; + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + recurrent_to_output_weights_ptr, n_cell, n_output, + quantized_output_state_ptr, product_scaling_factors, n_batch, + output_gate_scratch, /*result_stride=*/1); + } + + // Save quantization and matmul computation for all zero input. + bool is_cell_state_all_zeros = + tensor_utils::IsZeroVector(cell_state_ptr, n_batch * n_cell); + + // For each batch and cell: update input gate. + if (!use_cifg) { + if (use_peephole && !is_cell_state_all_zeros) { + tensor_utils::VectorScalarMultiply(cell_to_input_weights_ptr, n_cell, + cell_to_input_weights_scale, + recovered_cell_weights); + tensor_utils::VectorBatchVectorCwiseProductAccumulate( + recovered_cell_weights, n_cell, cell_state_ptr, n_batch, + input_gate_scratch); + } + tensor_utils::ApplySigmoidToVector(input_gate_scratch, n_cell * n_batch, + input_gate_scratch); + } + + // For each batch and cell: update forget gate. + if (use_peephole && !is_cell_state_all_zeros) { + tensor_utils::VectorScalarMultiply(cell_to_forget_weights_ptr, n_cell, + cell_to_forget_weights_scale, + recovered_cell_weights); + tensor_utils::VectorBatchVectorCwiseProductAccumulate( + recovered_cell_weights, n_cell, cell_state_ptr, n_batch, + forget_gate_scratch); + } + tensor_utils::ApplySigmoidToVector(forget_gate_scratch, n_cell * n_batch, + forget_gate_scratch); + + // For each batch and cell: update the cell. + tensor_utils::VectorVectorCwiseProduct(forget_gate_scratch, cell_state_ptr, + n_batch * n_cell, cell_state_ptr); + tensor_utils::ApplyActivationToVector(cell_scratch, n_batch * n_cell, + params->activation, cell_scratch); + if (use_cifg) { + tensor_utils::Sub1Vector(forget_gate_scratch, n_batch * n_cell, + forget_gate_scratch); + tensor_utils::VectorVectorCwiseProductAccumulate( + cell_scratch, forget_gate_scratch, n_batch * n_cell, cell_state_ptr); + } else { + tensor_utils::VectorVectorCwiseProductAccumulate( + cell_scratch, input_gate_scratch, n_batch * n_cell, cell_state_ptr); + } + if (params->cell_clip > 0.0) { + tensor_utils::ClipVector(cell_state_ptr, n_batch * n_cell, + params->cell_clip, cell_state_ptr); + } + + is_cell_state_all_zeros = + tensor_utils::IsZeroVector(cell_state_ptr, n_batch * n_cell); + // For each batch and cell: update the output gate. + if (use_peephole && !is_cell_state_all_zeros) { + tensor_utils::VectorScalarMultiply(cell_to_output_weights_ptr, n_cell, + cell_to_output_weights_scale, + recovered_cell_weights); + tensor_utils::VectorBatchVectorCwiseProductAccumulate( + recovered_cell_weights, n_cell, cell_state_ptr, n_batch, + output_gate_scratch); + } + tensor_utils::ApplySigmoidToVector(output_gate_scratch, n_batch * n_cell, + output_gate_scratch); + tensor_utils::ApplyActivationToVector(cell_state_ptr, n_batch * n_cell, + params->activation, cell_scratch); + tensor_utils::VectorVectorCwiseProduct(output_gate_scratch, cell_scratch, + n_batch * n_cell, output_gate_scratch); + + // For each batch: update the projection and output_state. + const bool use_projection_weight = (projection_weights_ptr != nullptr); + const bool use_projection_bias = (projection_bias_ptr != nullptr); + if (use_projection_weight) { + if (use_projection_bias) { + tensor_utils::VectorBatchVectorAssign(projection_bias_ptr, n_output, + n_batch, output_ptr_batch); + } else { + tensor_utils::ZeroVector(output_ptr_batch, n_batch * n_output); + } + if (!tensor_utils::IsZeroVector(output_gate_scratch, n_batch * n_cell)) { + // Save quantization and matmul computation for all zero input. + float unused_min, unused_max; + for (int b = 0; b < n_batch; ++b) { + const int offset = b * n_cell; + tensor_utils::SymmetricQuantizeFloats( + output_gate_scratch + offset, n_cell, + quantized_cell_state_ptr + offset, &unused_min, &unused_max, + &scaling_factors[b]); + } + for (int b = 0; b < n_batch; ++b) { + product_scaling_factors[b] = + scaling_factors[b] * projection_weights_scale; + } + tensor_utils::MatrixBatchVectorMultiplyAccumulate( + projection_weights_ptr, n_output, n_cell, quantized_cell_state_ptr, + product_scaling_factors, n_batch, output_ptr_batch, + /*result_stride=*/1); + } + if (params->proj_clip > 0.0) { + tensor_utils::ClipVector(output_ptr_batch, n_batch * n_output, + params->proj_clip, output_ptr_batch); + } + } else { + tensor_utils::CopyVector(output_gate_scratch, n_batch * n_output, + output_ptr_batch); + } + tensor_utils::CopyVector(output_ptr_batch, n_batch * n_output, + output_state_ptr); +} +} // namespace + +TfLiteStatus EvalFloat( + const TfLiteTensor* input, const TfLiteTensor* input_to_input_weights, + const TfLiteTensor* input_to_forget_weights, + const TfLiteTensor* input_to_cell_weights, + const TfLiteTensor* input_to_output_weights, + const TfLiteTensor* recurrent_to_input_weights, + const TfLiteTensor* recurrent_to_forget_weights, + const TfLiteTensor* recurrent_to_cell_weights, + const TfLiteTensor* recurrent_to_output_weights, + const TfLiteTensor* cell_to_input_weights, + const TfLiteTensor* cell_to_forget_weights, + const TfLiteTensor* cell_to_output_weights, const TfLiteTensor* aux_input, + const TfLiteTensor* aux_input_to_input_weights, + const TfLiteTensor* aux_input_to_forget_weights, + const TfLiteTensor* aux_input_to_cell_weights, + const TfLiteTensor* aux_input_to_output_weights, + const TfLiteTensor* input_gate_bias, const TfLiteTensor* forget_gate_bias, + const TfLiteTensor* cell_bias, const TfLiteTensor* output_gate_bias, + const TfLiteTensor* projection_weights, const TfLiteTensor* projection_bias, + const TfLiteLSTMParams* params, bool forward_sequence, int output_offset, + TfLiteTensor* scratch_buffer, TfLiteTensor* activation_state, + TfLiteTensor* cell_state, TfLiteTensor* output) { + TF_LITE_ASSERT(input->dims->size >= 2 && input->dims->size <= 3); + const int max_time = (input->dims->size == 2) ? 1 : input->dims->data[0]; + const int n_batch = input->dims->data[input->dims->size - 2]; + const int n_input = input->dims->data[input->dims->size - 1]; + const int aux_input_size = + (aux_input) ? aux_input->dims->data[aux_input->dims->size - 1] : 0; + + // n_cell and n_output will be the same size when there is no projection. + const int n_cell = input_to_output_weights->dims->data[0]; + const int n_output = recurrent_to_output_weights->dims->data[1]; + + // Since we have already checked that weights are all there or none, we can + // check the existense of only one to the get the condition. + const bool use_cifg = (input_to_input_weights == nullptr); + const bool use_peephole = (cell_to_output_weights != nullptr); + + // Index the scratch buffers pointers to the global scratch buffer. + float* input_gate_scratch = nullptr; + float* cell_scratch = nullptr; + float* forget_gate_scratch = nullptr; + float* output_gate_scratch = nullptr; + if (use_cifg) { + cell_scratch = scratch_buffer->data.f; + forget_gate_scratch = scratch_buffer->data.f + n_cell * n_batch; + output_gate_scratch = scratch_buffer->data.f + 2 * n_cell * n_batch; + } else { + input_gate_scratch = scratch_buffer->data.f; + cell_scratch = scratch_buffer->data.f + n_cell * n_batch; + forget_gate_scratch = scratch_buffer->data.f + 2 * n_cell * n_batch; + output_gate_scratch = scratch_buffer->data.f + 3 * n_cell * n_batch; + } + + // Check optional tensors, the respective pointers can be null. + const float* input_to_input_weights_ptr = + (use_cifg) ? nullptr : input_to_input_weights->data.f; + const float* recurrent_to_input_weights_ptr = + (use_cifg) ? nullptr : recurrent_to_input_weights->data.f; + const float* input_gate_bias_ptr = + (use_cifg) ? nullptr : input_gate_bias->data.f; + const float* cell_to_input_weights_ptr = + (use_peephole && !use_cifg) ? cell_to_input_weights->data.f : nullptr; + const float* cell_to_forget_weights_ptr = + (use_peephole) ? cell_to_forget_weights->data.f : nullptr; + const float* cell_to_output_weights_ptr = + (use_peephole) ? cell_to_output_weights->data.f : nullptr; + const float* projection_weights_ptr = + (projection_weights == nullptr) ? nullptr : projection_weights->data.f; + const float* projection_bias_ptr = + (projection_bias == nullptr) ? nullptr : projection_bias->data.f; + + float* aux_input_ptr = nullptr; + float* aux_input_to_input_weights_ptr = nullptr; + float* aux_input_to_forget_weights_ptr = nullptr; + float* aux_input_to_cell_weights_ptr = nullptr; + float* aux_input_to_output_weights_ptr = nullptr; + if (aux_input_size > 0) { + aux_input_ptr = aux_input->data.f; + aux_input_to_input_weights_ptr = aux_input_to_input_weights->data.f; + aux_input_to_forget_weights_ptr = aux_input_to_forget_weights->data.f; + aux_input_to_cell_weights_ptr = aux_input_to_cell_weights->data.f; + aux_input_to_output_weights_ptr = aux_input_to_output_weights->data.f; + } + + // Loop through the sequence. + const int input_step = n_batch * n_input; + const int output_step = n_batch * output->dims->data[output->dims->size - 1]; + for (int t = 0; t < max_time; t++) { + // If this is the forward_sequence, step forward, otherwise step backwards. + const int t_rel = forward_sequence ? t : max_time - t - 1; + const float* input_ptr = input->data.f + t_rel * input_step; + float* output_ptr_time = + output->data.f + t_rel * output_step + output_offset; + + LstmStepWithAuxInput( + input_ptr, input_to_input_weights_ptr, input_to_forget_weights->data.f, + input_to_cell_weights->data.f, input_to_output_weights->data.f, + aux_input_ptr, aux_input_to_input_weights_ptr, + aux_input_to_forget_weights_ptr, aux_input_to_cell_weights_ptr, + aux_input_to_output_weights_ptr, recurrent_to_input_weights_ptr, + recurrent_to_forget_weights->data.f, recurrent_to_cell_weights->data.f, + recurrent_to_output_weights->data.f, cell_to_input_weights_ptr, + cell_to_forget_weights_ptr, cell_to_output_weights_ptr, + input_gate_bias_ptr, forget_gate_bias->data.f, cell_bias->data.f, + output_gate_bias->data.f, projection_weights_ptr, projection_bias_ptr, + params, n_batch, n_cell, n_input, aux_input_size, n_output, + activation_state->data.f, cell_state->data.f, input_gate_scratch, + forget_gate_scratch, cell_scratch, output_gate_scratch, + output_ptr_time); + } + return kTfLiteOk; +} + +TfLiteStatus EvalHybrid( + const TfLiteTensor* input, const TfLiteTensor* input_to_input_weights, + const TfLiteTensor* input_to_forget_weights, + const TfLiteTensor* input_to_cell_weights, + const TfLiteTensor* input_to_output_weights, + const TfLiteTensor* recurrent_to_input_weights, + const TfLiteTensor* recurrent_to_forget_weights, + const TfLiteTensor* recurrent_to_cell_weights, + const TfLiteTensor* recurrent_to_output_weights, + const TfLiteTensor* cell_to_input_weights, + const TfLiteTensor* cell_to_forget_weights, + const TfLiteTensor* cell_to_output_weights, const TfLiteTensor* aux_input, + const TfLiteTensor* aux_input_to_input_weights, + const TfLiteTensor* aux_input_to_forget_weights, + const TfLiteTensor* aux_input_to_cell_weights, + const TfLiteTensor* aux_input_to_output_weights, + const TfLiteTensor* input_gate_bias, const TfLiteTensor* forget_gate_bias, + const TfLiteTensor* cell_bias, const TfLiteTensor* output_gate_bias, + const TfLiteTensor* projection_weights, const TfLiteTensor* projection_bias, + const TfLiteLSTMParams* params, bool forward_sequence, int output_offset, + TfLiteTensor* scratch_buffer, TfLiteTensor* scaling_factors, + TfLiteTensor* prod_scaling_factors, TfLiteTensor* recovered_cell_weights, + TfLiteTensor* input_quantized, TfLiteTensor* aux_input_quantized, + TfLiteTensor* output_state_quantized, TfLiteTensor* cell_state_quantized, + TfLiteTensor* output_state, TfLiteTensor* cell_state, + TfLiteTensor* output) { + TF_LITE_ASSERT(input->dims->size >= 2 && input->dims->size <= 3); + const int max_time = (input->dims->size == 2) ? 1 : input->dims->data[0]; + const int n_batch = input->dims->data[input->dims->size - 2]; + const int n_input = input->dims->data[input->dims->size - 1]; + const int aux_input_size = + (aux_input) ? aux_input->dims->data[aux_input->dims->size - 1] : 0; + // n_cell and n_output will be the same size when there is no projection. + const int n_cell = input_to_output_weights->dims->data[0]; + const int n_output = recurrent_to_output_weights->dims->data[1]; + + // Since we have already checked that weights are all there or none, we can + // check the existence of only one to get the condition. + const bool use_cifg = (input_to_input_weights == nullptr); + const bool use_peephole = (cell_to_output_weights != nullptr); + + float* input_gate_scratch = nullptr; + float* cell_scratch = nullptr; + float* forget_gate_scratch = nullptr; + float* output_gate_scratch = nullptr; + if (use_cifg) { + cell_scratch = scratch_buffer->data.f; + forget_gate_scratch = scratch_buffer->data.f + n_cell * n_batch; + output_gate_scratch = scratch_buffer->data.f + 2 * n_cell * n_batch; + } else { + input_gate_scratch = scratch_buffer->data.f; + cell_scratch = scratch_buffer->data.f + n_cell * n_batch; + forget_gate_scratch = scratch_buffer->data.f + 2 * n_cell * n_batch; + output_gate_scratch = scratch_buffer->data.f + 3 * n_cell * n_batch; + } + + // Check optional tensors, the respective pointers can be null. + int8_t* input_to_input_weights_ptr = nullptr; + float input_to_input_weights_scale = 1.0f; + int8_t* recurrent_to_input_weights_ptr = nullptr; + float recurrent_to_input_weights_scale = 1.0f; + float* input_gate_bias_ptr = nullptr; + if (!use_cifg) { + input_to_input_weights_ptr = + reinterpret_cast<int8_t*>(input_to_input_weights->data.uint8); + recurrent_to_input_weights_ptr = + reinterpret_cast<int8_t*>(recurrent_to_input_weights->data.uint8); + input_gate_bias_ptr = input_gate_bias->data.f; + input_to_input_weights_scale = input_to_input_weights->params.scale; + recurrent_to_input_weights_scale = recurrent_to_input_weights->params.scale; + } + + int8_t* cell_to_input_weights_ptr = nullptr; + int8_t* cell_to_forget_weights_ptr = nullptr; + int8_t* cell_to_output_weights_ptr = nullptr; + float cell_to_input_weights_scale = 1.0f; + float cell_to_forget_weights_scale = 1.0f; + float cell_to_output_weights_scale = 1.0f; + if (use_peephole) { + if (!use_cifg) { + cell_to_input_weights_ptr = + reinterpret_cast<int8_t*>(cell_to_input_weights->data.uint8); + cell_to_input_weights_scale = cell_to_input_weights->params.scale; + } + cell_to_forget_weights_ptr = + reinterpret_cast<int8_t*>(cell_to_forget_weights->data.uint8); + cell_to_output_weights_ptr = + reinterpret_cast<int8_t*>(cell_to_output_weights->data.uint8); + cell_to_forget_weights_scale = cell_to_forget_weights->params.scale; + cell_to_output_weights_scale = cell_to_output_weights->params.scale; + } + + const int8_t* projection_weights_ptr = + (projection_weights == nullptr) + ? nullptr + : reinterpret_cast<int8_t*>(projection_weights->data.uint8); + const float projection_weights_scale = + (projection_weights == nullptr) ? 1.0f : projection_weights->params.scale; + const float* projection_bias_ptr = + (projection_bias == nullptr) ? nullptr : projection_bias->data.f; + + // Required tensors, pointers are non-null. + const int8_t* input_to_forget_weights_ptr = + reinterpret_cast<int8_t*>(input_to_forget_weights->data.uint8); + const float input_to_forget_weights_scale = + input_to_forget_weights->params.scale; + const int8_t* input_to_cell_weights_ptr = + reinterpret_cast<int8_t*>(input_to_cell_weights->data.uint8); + const float input_to_cell_weights_scale = input_to_cell_weights->params.scale; + const int8_t* input_to_output_weights_ptr = + reinterpret_cast<int8_t*>(input_to_output_weights->data.uint8); + const float input_to_output_weights_scale = + input_to_output_weights->params.scale; + const int8_t* recurrent_to_forget_weights_ptr = + reinterpret_cast<int8_t*>(recurrent_to_forget_weights->data.uint8); + const float recurrent_to_forget_weights_scale = + recurrent_to_forget_weights->params.scale; + const int8_t* recurrent_to_cell_weights_ptr = + reinterpret_cast<int8_t*>(recurrent_to_cell_weights->data.uint8); + const float recurrent_to_cell_weights_scale = + recurrent_to_cell_weights->params.scale; + const int8_t* recurrent_to_output_weights_ptr = + reinterpret_cast<int8_t*>(recurrent_to_output_weights->data.uint8); + const float recurrent_to_output_weights_scale = + recurrent_to_output_weights->params.scale; + const float* forget_gate_bias_ptr = forget_gate_bias->data.f; + const float* cell_bias_ptr = cell_bias->data.f; + const float* output_gate_bias_ptr = output_gate_bias->data.f; + + float* output_state_ptr = output_state->data.f; + float* cell_state_ptr = cell_state->data.f; + + // Temporary storage for quantized values and scaling factors. + int8_t* quantized_input_ptr = + reinterpret_cast<int8_t*>(input_quantized->data.uint8); + int8_t* quantized_aux_input_ptr = + (aux_input_quantized == nullptr) + ? nullptr + : reinterpret_cast<int8_t*>(aux_input_quantized->data.uint8); + int8_t* quantized_output_state_ptr = + reinterpret_cast<int8_t*>(output_state_quantized->data.uint8); + int8_t* quantized_cell_state_ptr = + reinterpret_cast<int8_t*>(cell_state_quantized->data.uint8); + float* scaling_factors_ptr = scaling_factors->data.f; + float* prod_scaling_factors_ptr = prod_scaling_factors->data.f; + float* recovered_cell_weights_ptr = recovered_cell_weights->data.f; + + // Auxiliary input and weights. + float* aux_input_ptr = nullptr; + int8_t* aux_input_to_input_weights_ptr = nullptr; + int8_t* aux_input_to_forget_weights_ptr = nullptr; + int8_t* aux_input_to_cell_weights_ptr = nullptr; + int8_t* aux_input_to_output_weights_ptr = nullptr; + float aux_input_to_input_weights_scale = 0.0f; + float aux_input_to_forget_weights_scale = 0.0f; + float aux_input_to_cell_weights_scale = 0.0f; + float aux_input_to_output_weights_scale = 0.0f; + if (aux_input_size > 0) { + aux_input_ptr = aux_input->data.f; + aux_input_to_input_weights_ptr = + reinterpret_cast<int8_t*>(aux_input_to_input_weights->data.uint8); + aux_input_to_forget_weights_ptr = + reinterpret_cast<int8_t*>(aux_input_to_forget_weights->data.uint8); + aux_input_to_cell_weights_ptr = + reinterpret_cast<int8_t*>(aux_input_to_cell_weights->data.uint8); + aux_input_to_output_weights_ptr = + reinterpret_cast<int8_t*>(aux_input_to_output_weights->data.uint8); + aux_input_to_input_weights_scale = aux_input_to_input_weights->params.scale; + aux_input_to_forget_weights_scale = + aux_input_to_forget_weights->params.scale; + aux_input_to_cell_weights_scale = aux_input_to_cell_weights->params.scale; + aux_input_to_output_weights_scale = + aux_input_to_output_weights->params.scale; + } + + // Feed the sequence into the LSTM step-by-step. + const int input_step = n_batch * n_input; + const int output_step = n_batch * output->dims->data[output->dims->size - 1]; + for (int t = 0; t < max_time; t++) { + // If this is the forward_sequence, step forward, otherwise step backwards. + const int t_rel = forward_sequence ? t : max_time - t - 1; + const float* input_ptr = input->data.f + t_rel * input_step; + float* output_ptr = output->data.f + t_rel * output_step + output_offset; + + LstmStepWithAuxInput( + input_ptr, input_to_input_weights_ptr, input_to_input_weights_scale, + input_to_forget_weights_ptr, input_to_forget_weights_scale, + input_to_cell_weights_ptr, input_to_cell_weights_scale, + input_to_output_weights_ptr, input_to_output_weights_scale, + aux_input_ptr, aux_input_to_input_weights_ptr, + aux_input_to_input_weights_scale, aux_input_to_forget_weights_ptr, + aux_input_to_forget_weights_scale, aux_input_to_cell_weights_ptr, + aux_input_to_cell_weights_scale, aux_input_to_output_weights_ptr, + aux_input_to_output_weights_scale, recurrent_to_input_weights_ptr, + recurrent_to_input_weights_scale, recurrent_to_forget_weights_ptr, + recurrent_to_forget_weights_scale, recurrent_to_cell_weights_ptr, + recurrent_to_cell_weights_scale, recurrent_to_output_weights_ptr, + recurrent_to_output_weights_scale, cell_to_input_weights_ptr, + cell_to_input_weights_scale, cell_to_forget_weights_ptr, + cell_to_forget_weights_scale, cell_to_output_weights_ptr, + cell_to_output_weights_scale, input_gate_bias_ptr, forget_gate_bias_ptr, + cell_bias_ptr, output_gate_bias_ptr, projection_weights_ptr, + projection_weights_scale, projection_bias_ptr, params, n_batch, n_cell, + n_input, aux_input_size, n_output, input_gate_scratch, + forget_gate_scratch, cell_scratch, output_gate_scratch, + scaling_factors_ptr, prod_scaling_factors_ptr, + recovered_cell_weights_ptr, quantized_input_ptr, + quantized_aux_input_ptr, quantized_output_state_ptr, + quantized_cell_state_ptr, output_state_ptr, cell_state_ptr, output_ptr); + } + + return kTfLiteOk; +} + +} // namespace lstm_eval +} // namespace builtin +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/lstm_eval.h b/tensorflow/contrib/lite/kernels/lstm_eval.h new file mode 100644 index 0000000000..adf8cf0f64 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/lstm_eval.h @@ -0,0 +1,79 @@ +/* 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_CONTRIB_LITE_KERNELS_LSTM_EVAL_H_ +#define TENSORFLOW_CONTRIB_LITE_KERNELS_LSTM_EVAL_H_ + +#include "tensorflow/contrib/lite/c/builtin_op_data.h" +#include "tensorflow/contrib/lite/c/c_api_internal.h" + +namespace tflite { +namespace ops { +namespace builtin { +namespace lstm_eval { + +TfLiteStatus EvalFloat( + const TfLiteTensor* input, const TfLiteTensor* input_to_input_weights, + const TfLiteTensor* input_to_forget_weights, + const TfLiteTensor* input_to_cell_weights, + const TfLiteTensor* input_to_output_weights, + const TfLiteTensor* recurrent_to_input_weights, + const TfLiteTensor* recurrent_to_forget_weights, + const TfLiteTensor* recurrent_to_cell_weights, + const TfLiteTensor* recurrent_to_output_weights, + const TfLiteTensor* cell_to_input_weights, + const TfLiteTensor* cell_to_forget_weights, + const TfLiteTensor* cell_to_output_weights, const TfLiteTensor* aux_input, + const TfLiteTensor* aux_input_to_input_weights, + const TfLiteTensor* aux_input_to_forget_weights, + const TfLiteTensor* aux_input_to_cell_weights, + const TfLiteTensor* aux_input_to_output_weights, + const TfLiteTensor* input_gate_bias, const TfLiteTensor* forget_gate_bias, + const TfLiteTensor* cell_bias, const TfLiteTensor* output_gate_bias, + const TfLiteTensor* projection_weights, const TfLiteTensor* projection_bias, + const TfLiteLSTMParams* params, bool forward_sequence, int output_offset, + TfLiteTensor* scratch_buffer, TfLiteTensor* activation_state, + TfLiteTensor* cell_state, TfLiteTensor* output); + +TfLiteStatus EvalHybrid( + const TfLiteTensor* input, const TfLiteTensor* input_to_input_weights, + const TfLiteTensor* input_to_forget_weights, + const TfLiteTensor* input_to_cell_weights, + const TfLiteTensor* input_to_output_weights, + const TfLiteTensor* recurrent_to_input_weights, + const TfLiteTensor* recurrent_to_forget_weights, + const TfLiteTensor* recurrent_to_cell_weights, + const TfLiteTensor* recurrent_to_output_weights, + const TfLiteTensor* cell_to_input_weights, + const TfLiteTensor* cell_to_forget_weights, + const TfLiteTensor* cell_to_output_weights, const TfLiteTensor* aux_input, + const TfLiteTensor* aux_input_to_input_weights, + const TfLiteTensor* aux_input_to_forget_weights, + const TfLiteTensor* aux_input_to_cell_weights, + const TfLiteTensor* aux_input_to_output_weights, + const TfLiteTensor* input_gate_bias, const TfLiteTensor* forget_gate_bias, + const TfLiteTensor* cell_bias, const TfLiteTensor* output_gate_bias, + const TfLiteTensor* projection_weights, const TfLiteTensor* projection_bias, + const TfLiteLSTMParams* params, bool forward_sequence, int output_offset, + TfLiteTensor* scratch_buffer, TfLiteTensor* scaling_factors, + TfLiteTensor* prod_scaling_factors, TfLiteTensor* recovered_cell_weights, + TfLiteTensor* input_quantized, TfLiteTensor* aux_input_quantized, + TfLiteTensor* output_state_quantized, TfLiteTensor* cell_state_quantized, + TfLiteTensor* output_state, TfLiteTensor* cell_state, TfLiteTensor* output); + +} // namespace lstm_eval +} // namespace builtin +} // namespace ops +} // namespace tflite +#endif // TENSORFLOW_CONTRIB_LITE_KERNELS_LSTM_EVAL_H_ diff --git a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc index 63817bd886..ec9cf38b83 100644 --- a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc +++ b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc @@ -26,6 +26,7 @@ limitations under the License. #include "tensorflow/contrib/lite/kernels/internal/kernel_utils.h" #include "tensorflow/contrib/lite/kernels/internal/tensor_utils.h" #include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/lstm_eval.h" #include "tensorflow/contrib/lite/kernels/op_macros.h" namespace tflite { @@ -429,273 +430,6 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { return kTfLiteOk; } -// The LSTM Op engine. -TfLiteStatus EvalFloat( - const TfLiteTensor* input, const TfLiteTensor* input_to_input_weights, - const TfLiteTensor* input_to_forget_weights, - const TfLiteTensor* input_to_cell_weights, - const TfLiteTensor* input_to_output_weights, - const TfLiteTensor* recurrent_to_input_weights, - const TfLiteTensor* recurrent_to_forget_weights, - const TfLiteTensor* recurrent_to_cell_weights, - const TfLiteTensor* recurrent_to_output_weights, - const TfLiteTensor* cell_to_input_weights, - const TfLiteTensor* cell_to_forget_weights, - const TfLiteTensor* cell_to_output_weights, - const TfLiteTensor* input_gate_bias, const TfLiteTensor* forget_gate_bias, - const TfLiteTensor* cell_bias, const TfLiteTensor* output_gate_bias, - const TfLiteTensor* projection_weights, const TfLiteTensor* projection_bias, - const TfLiteLSTMParams* params, TfLiteTensor* scratch_buffer, - TfLiteTensor* activation_state, TfLiteTensor* cell_state, - TfLiteTensor* output) { - const int max_time = input->dims->data[0]; - const int n_batch = input->dims->data[1]; - const int n_input = input->dims->data[2]; - // n_cell and n_output will be the same size when there is no projection. - const int n_cell = input_to_output_weights->dims->data[0]; - const int n_output = recurrent_to_output_weights->dims->data[1]; - - // Since we have already checked that weights are all there or none, we can - // check the existence of only one to get the condition. - const bool use_cifg = (input_to_input_weights == nullptr); - const bool use_peephole = (cell_to_output_weights != nullptr); - - float* input_gate_scratch = nullptr; - float* cell_scratch = nullptr; - float* forget_gate_scratch = nullptr; - float* output_gate_scratch = nullptr; - if (use_cifg) { - cell_scratch = scratch_buffer->data.f; - forget_gate_scratch = scratch_buffer->data.f + n_cell * n_batch; - output_gate_scratch = scratch_buffer->data.f + 2 * n_cell * n_batch; - } else { - input_gate_scratch = scratch_buffer->data.f; - cell_scratch = scratch_buffer->data.f + n_cell * n_batch; - forget_gate_scratch = scratch_buffer->data.f + 2 * n_cell * n_batch; - output_gate_scratch = scratch_buffer->data.f + 3 * n_cell * n_batch; - } - - // Check optional tensors, the respective pointers can be null. - const float* input_to_input_weights_ptr = - (use_cifg) ? nullptr : input_to_input_weights->data.f; - const float* recurrent_to_input_weights_ptr = - (use_cifg) ? nullptr : recurrent_to_input_weights->data.f; - const float* input_gate_bias_ptr = - (use_cifg) ? nullptr : input_gate_bias->data.f; - const float* cell_to_input_weights_ptr = - (use_peephole && !use_cifg) ? cell_to_input_weights->data.f : nullptr; - const float* cell_to_forget_weights_ptr = - (use_peephole) ? cell_to_forget_weights->data.f : nullptr; - const float* cell_to_output_weights_ptr = - (use_peephole) ? cell_to_output_weights->data.f : nullptr; - const float* projection_weights_ptr = - (projection_weights == nullptr) ? nullptr : projection_weights->data.f; - const float* projection_bias_ptr = - (projection_bias == nullptr) ? nullptr : projection_bias->data.f; - - // Required tensors, pointers are non-null. - const float* input_to_forget_weights_ptr = input_to_forget_weights->data.f; - const float* input_to_cell_weights_ptr = input_to_cell_weights->data.f; - const float* input_to_output_weights_ptr = input_to_output_weights->data.f; - const float* recurrent_to_forget_weights_ptr = - recurrent_to_forget_weights->data.f; - const float* recurrent_to_cell_weights_ptr = - recurrent_to_cell_weights->data.f; - const float* recurrent_to_output_weights_ptr = - recurrent_to_output_weights->data.f; - const float* forget_gate_bias_ptr = forget_gate_bias->data.f; - const float* cell_bias_ptr = cell_bias->data.f; - const float* output_gate_bias_ptr = output_gate_bias->data.f; - - float* activation_state_ptr = activation_state->data.f; - float* cell_state_ptr = cell_state->data.f; - - // Feed the sequence into the LSTM step-by-step. - for (int t = 0; t < max_time; t++) { - const float* input_ptr_batch = input->data.f + t * n_batch * n_input; - float* output_ptr_batch = output->data.f + t * n_batch * n_output; - - kernel_utils::LstmStep( - input_ptr_batch, input_to_input_weights_ptr, - input_to_forget_weights_ptr, input_to_cell_weights_ptr, - input_to_output_weights_ptr, recurrent_to_input_weights_ptr, - recurrent_to_forget_weights_ptr, recurrent_to_cell_weights_ptr, - recurrent_to_output_weights_ptr, cell_to_input_weights_ptr, - cell_to_forget_weights_ptr, cell_to_output_weights_ptr, - input_gate_bias_ptr, forget_gate_bias_ptr, cell_bias_ptr, - output_gate_bias_ptr, projection_weights_ptr, projection_bias_ptr, - params, n_batch, n_cell, n_input, n_output, activation_state_ptr, - cell_state_ptr, input_gate_scratch, forget_gate_scratch, cell_scratch, - output_gate_scratch, output_ptr_batch); - } - return kTfLiteOk; -} - -TfLiteStatus EvalHybrid( - const TfLiteTensor* input, const TfLiteTensor* input_to_input_weights, - const TfLiteTensor* input_to_forget_weights, - const TfLiteTensor* input_to_cell_weights, - const TfLiteTensor* input_to_output_weights, - const TfLiteTensor* recurrent_to_input_weights, - const TfLiteTensor* recurrent_to_forget_weights, - const TfLiteTensor* recurrent_to_cell_weights, - const TfLiteTensor* recurrent_to_output_weights, - const TfLiteTensor* cell_to_input_weights, - const TfLiteTensor* cell_to_forget_weights, - const TfLiteTensor* cell_to_output_weights, - const TfLiteTensor* input_gate_bias, const TfLiteTensor* forget_gate_bias, - const TfLiteTensor* cell_bias, const TfLiteTensor* output_gate_bias, - const TfLiteTensor* projection_weights, const TfLiteTensor* projection_bias, - const TfLiteLSTMParams* params, TfLiteTensor* scratch_buffer, - TfLiteTensor* scaling_factors, TfLiteTensor* prod_scaling_factors, - TfLiteTensor* recovered_cell_weights, TfLiteTensor* input_quantized, - TfLiteTensor* activation_state_quantized, - TfLiteTensor* cell_state_quantized, TfLiteTensor* activation_state, - TfLiteTensor* cell_state, TfLiteTensor* output) { - const int max_time = input->dims->data[0]; - const int n_batch = input->dims->data[1]; - const int n_input = input->dims->data[2]; - // n_cell and n_output will be the same size when there is no projection. - const int n_cell = input_to_output_weights->dims->data[0]; - const int n_output = recurrent_to_output_weights->dims->data[1]; - - // Since we have already checked that weights are all there or none, we can - // check the existence of only one to get the condition. - const bool use_cifg = (input_to_input_weights == nullptr); - const bool use_peephole = (cell_to_output_weights != nullptr); - - float* input_gate_scratch = nullptr; - float* cell_scratch = nullptr; - float* forget_gate_scratch = nullptr; - float* output_gate_scratch = nullptr; - if (use_cifg) { - cell_scratch = scratch_buffer->data.f; - forget_gate_scratch = scratch_buffer->data.f + n_cell * n_batch; - output_gate_scratch = scratch_buffer->data.f + 2 * n_cell * n_batch; - } else { - input_gate_scratch = scratch_buffer->data.f; - cell_scratch = scratch_buffer->data.f + n_cell * n_batch; - forget_gate_scratch = scratch_buffer->data.f + 2 * n_cell * n_batch; - output_gate_scratch = scratch_buffer->data.f + 3 * n_cell * n_batch; - } - - // Check optional tensors, the respective pointers can be null. - int8_t* input_to_input_weights_ptr = nullptr; - float input_to_input_weights_scale = 1.0f; - int8_t* recurrent_to_input_weights_ptr = nullptr; - float recurrent_to_input_weights_scale = 1.0f; - float* input_gate_bias_ptr = nullptr; - if (!use_cifg) { - input_to_input_weights_ptr = - reinterpret_cast<int8_t*>(input_to_input_weights->data.uint8); - recurrent_to_input_weights_ptr = - reinterpret_cast<int8_t*>(recurrent_to_input_weights->data.uint8); - input_gate_bias_ptr = input_gate_bias->data.f; - input_to_input_weights_scale = input_to_input_weights->params.scale; - recurrent_to_input_weights_scale = recurrent_to_input_weights->params.scale; - } - - int8_t* cell_to_input_weights_ptr = nullptr; - int8_t* cell_to_forget_weights_ptr = nullptr; - int8_t* cell_to_output_weights_ptr = nullptr; - float cell_to_input_weights_scale = 1.0f; - float cell_to_forget_weights_scale = 1.0f; - float cell_to_output_weights_scale = 1.0f; - if (use_peephole) { - if (!use_cifg) { - cell_to_input_weights_ptr = - reinterpret_cast<int8_t*>(cell_to_input_weights->data.uint8); - cell_to_input_weights_scale = cell_to_input_weights->params.scale; - } - cell_to_forget_weights_ptr = - reinterpret_cast<int8_t*>(cell_to_forget_weights->data.uint8); - cell_to_output_weights_ptr = - reinterpret_cast<int8_t*>(cell_to_output_weights->data.uint8); - cell_to_forget_weights_scale = cell_to_forget_weights->params.scale; - cell_to_output_weights_scale = cell_to_output_weights->params.scale; - } - - const int8_t* projection_weights_ptr = - (projection_weights == nullptr) - ? nullptr - : reinterpret_cast<int8_t*>(projection_weights->data.uint8); - float projection_weights_scale = - (projection_weights == nullptr) ? 1.0f : projection_weights->params.scale; - const float* projection_bias_ptr = - (projection_bias == nullptr) ? nullptr : projection_bias->data.f; - - // Required tensors, pointers are non-null. - const int8_t* input_to_forget_weights_ptr = - reinterpret_cast<int8_t*>(input_to_forget_weights->data.uint8); - const float input_to_forget_weights_scale = - input_to_forget_weights->params.scale; - const int8_t* input_to_cell_weights_ptr = - reinterpret_cast<int8_t*>(input_to_cell_weights->data.uint8); - const float input_to_cell_weights_scale = input_to_cell_weights->params.scale; - const int8_t* input_to_output_weights_ptr = - reinterpret_cast<int8_t*>(input_to_output_weights->data.uint8); - const float input_to_output_weights_scale = - input_to_output_weights->params.scale; - const int8_t* recurrent_to_forget_weights_ptr = - reinterpret_cast<int8_t*>(recurrent_to_forget_weights->data.uint8); - const float recurrent_to_forget_weights_scale = - recurrent_to_forget_weights->params.scale; - const int8_t* recurrent_to_cell_weights_ptr = - reinterpret_cast<int8_t*>(recurrent_to_cell_weights->data.uint8); - const float recurrent_to_cell_weights_scale = - recurrent_to_cell_weights->params.scale; - const int8_t* recurrent_to_output_weights_ptr = - reinterpret_cast<int8_t*>(recurrent_to_output_weights->data.uint8); - const float recurrent_to_output_weights_scale = - recurrent_to_output_weights->params.scale; - const float* forget_gate_bias_ptr = forget_gate_bias->data.f; - const float* cell_bias_ptr = cell_bias->data.f; - const float* output_gate_bias_ptr = output_gate_bias->data.f; - - float* activation_state_ptr = activation_state->data.f; - float* cell_state_ptr = cell_state->data.f; - - // Temporary storage for quantized values and scaling factors. - int8_t* quantized_input_ptr = - reinterpret_cast<int8_t*>(input_quantized->data.uint8); - int8_t* quantized_activation_state_ptr = - reinterpret_cast<int8_t*>(activation_state_quantized->data.uint8); - int8_t* quantized_cell_state_ptr = - reinterpret_cast<int8_t*>(cell_state_quantized->data.uint8); - float* scaling_factors_ptr = scaling_factors->data.f; - float* prod_scaling_factors_ptr = prod_scaling_factors->data.f; - float* recovered_cell_weights_ptr = recovered_cell_weights->data.f; - - // Feed the sequence into the LSTM step-by-step. - for (int t = 0; t < max_time; t++) { - const float* input_ptr_batch = input->data.f + t * n_batch * n_input; - float* output_ptr_batch = output->data.f + t * n_batch * n_output; - - kernel_utils::LstmStep( - input_ptr_batch, input_to_input_weights_ptr, - input_to_input_weights_scale, input_to_forget_weights_ptr, - input_to_forget_weights_scale, input_to_cell_weights_ptr, - input_to_cell_weights_scale, input_to_output_weights_ptr, - input_to_output_weights_scale, recurrent_to_input_weights_ptr, - recurrent_to_input_weights_scale, recurrent_to_forget_weights_ptr, - recurrent_to_forget_weights_scale, recurrent_to_cell_weights_ptr, - recurrent_to_cell_weights_scale, recurrent_to_output_weights_ptr, - recurrent_to_output_weights_scale, cell_to_input_weights_ptr, - cell_to_input_weights_scale, cell_to_forget_weights_ptr, - cell_to_forget_weights_scale, cell_to_output_weights_ptr, - cell_to_output_weights_scale, input_gate_bias_ptr, forget_gate_bias_ptr, - cell_bias_ptr, output_gate_bias_ptr, projection_weights_ptr, - projection_weights_scale, projection_bias_ptr, params, n_batch, n_cell, - n_input, n_output, input_gate_scratch, forget_gate_scratch, - cell_scratch, output_gate_scratch, scaling_factors_ptr, - prod_scaling_factors_ptr, recovered_cell_weights_ptr, - quantized_input_ptr, quantized_activation_state_ptr, - quantized_cell_state_ptr, activation_state_ptr, cell_state_ptr, - output_ptr_batch); - } - return kTfLiteOk; -} - TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { auto* params = reinterpret_cast<TfLiteLSTMParams*>(node->builtin_data); const TfLiteTensor* input = GetInput(context, node, kInputTensor); @@ -750,15 +484,21 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { switch (input_to_output_weights->type) { case kTfLiteFloat32: { - return EvalFloat(input, input_to_input_weights, input_to_forget_weights, - input_to_cell_weights, input_to_output_weights, - recurrent_to_input_weights, recurrent_to_forget_weights, - recurrent_to_cell_weights, recurrent_to_output_weights, - cell_to_input_weights, cell_to_forget_weights, - cell_to_output_weights, input_gate_bias, - forget_gate_bias, cell_bias, output_gate_bias, - projection_weights, projection_bias, params, - scratch_buffer, activation_state, cell_state, output); + return lstm_eval::EvalFloat( + input, input_to_input_weights, input_to_forget_weights, + input_to_cell_weights, input_to_output_weights, + recurrent_to_input_weights, recurrent_to_forget_weights, + recurrent_to_cell_weights, recurrent_to_output_weights, + cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, + /*aux_input=*/nullptr, + /*aux_input_to_input_weights=*/nullptr, + /*aux_input_to_forget_weights=*/nullptr, + /*aux_input_to_cell_weights=*/nullptr, + /*aux_input_to_output_weights=*/nullptr, input_gate_bias, + forget_gate_bias, cell_bias, output_gate_bias, projection_weights, + projection_bias, params, /*forward_sequence=*/true, + /*output_offset=*/0, scratch_buffer, activation_state, cell_state, + output); } case kTfLiteUInt8: { TfLiteTensor* input_quantized = GetTemporary(context, node, /*index=*/1); @@ -771,17 +511,23 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { GetTemporary(context, node, /*index=*/5); TfLiteTensor* recovered_cell_weights = GetTemporary(context, node, /*index=*/6); - return EvalHybrid( + return lstm_eval::EvalHybrid( input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_input_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, - input_gate_bias, forget_gate_bias, cell_bias, output_gate_bias, - projection_weights, projection_bias, params, scratch_buffer, - scaling_factors, prod_scaling_factors, recovered_cell_weights, - input_quantized, activation_state_quantized, cell_state_quantized, - activation_state, cell_state, output); + /*aux_input=*/nullptr, + /*aux_input_to_input_weights=*/nullptr, + /*aux_input_to_forget_weights=*/nullptr, + /*aux_input_to_cell_weights=*/nullptr, + /*aux_input_to_output_weights=*/nullptr, input_gate_bias, + forget_gate_bias, cell_bias, output_gate_bias, projection_weights, + projection_bias, params, /*forward_sequence=*/true, + /*output_offset=*/0, scratch_buffer, scaling_factors, + prod_scaling_factors, recovered_cell_weights, input_quantized, + /*aux_input_quantized=*/nullptr, activation_state_quantized, + cell_state_quantized, activation_state, cell_state, output); } default: context->ReportError(context, "Type %d is not currently supported.", diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index d50c345194..d7b109ac1a 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -27,9 +27,6 @@ limitations under the License. #ifndef TFLITE_MCU #include "tensorflow/contrib/lite/nnapi_delegate.h" #endif -#if defined(TFLITE_FLEX) -#include "tensorflow/contrib/lite/delegates/flex/delegate.h" -#endif #include "tensorflow/contrib/lite/version.h" namespace tflite { @@ -43,6 +40,25 @@ ErrorReporter* ValidateErrorReporter(ErrorReporter* e) { const char* kEmptyTensorName = ""; +// Normally we'd use ABSL_HAVE_ATTRIBUTE_WEAK and ABSL_ATTRIBUTE_WEAK, but +// we avoid the absl dependency for binary size reasons. +#ifdef __has_attribute +#define TFLITE_HAS_ATTRIBUTE(x) __has_attribute(x) +#else +#define TFLITE_HAS_ATTRIBUTE(x) 0 +#endif + +#if TFLITE_HAS_ATTRIBUTE(weak) || (defined(__GNUC__) && !defined(__clang__)) +// Using weak symbols for the flex delegate allows automatic injection of the +// delegate simply by adding it as a dependency. See also the strong override in +// lite/delegates/flex/delegate.cc. +__attribute__((weak)) Interpreter::TfLiteDelegatePtr AcquireFlexDelegate() { + return Interpreter::TfLiteDelegatePtr(nullptr, [](TfLiteDelegate*) {}); +} +#else +Interpreter::TfLiteDelegatePtr (*AcquireFlexDelegate)() = nullptr; +#endif + #ifndef TFLITE_MCU // Loads a model from `filename`. If `mmap_file` is true then use mmap, // otherwise make a copy of the model in a buffer. @@ -450,13 +466,14 @@ TfLiteStatus InterpreterBuilder::operator()( } (**interpreter).SetVariables(std::move(variables)); -#if defined(TFLITE_FLEX) - if (auto delegate = FlexDelegate::Create()) { - (**interpreter) - .ModifyGraphWithDelegate(std::move(delegate), - /*allow_dynamic_tensors=*/true); + // TODO(b/116667551): Only create the flex delegate if the model has flex ops. + if (AcquireFlexDelegate != nullptr) { + if (auto flex_delegate = AcquireFlexDelegate()) { + (**interpreter) + .ModifyGraphWithDelegate(std::move(flex_delegate), + /*allow_dynamic_tensors=*/true); + } } -#endif return kTfLiteOk; } diff --git a/tensorflow/contrib/lite/model_flex_test.cc b/tensorflow/contrib/lite/model_flex_test.cc new file mode 100644 index 0000000000..52e76bee49 --- /dev/null +++ b/tensorflow/contrib/lite/model_flex_test.cc @@ -0,0 +1,45 @@ +/* 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. +==============================================================================*/ +#include "tensorflow/contrib/lite/model.h" + +#include <gtest/gtest.h> +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/testing/util.h" + +namespace tflite { + +// Ensures that a model with TensorFlow ops can be imported as long as the +// appropriate delegate is linked into the client. +TEST(FlexModel, WithFlexDelegate) { + auto model = FlatBufferModel::BuildFromFile( + "tensorflow/contrib/lite/testdata/multi_add_flex.bin"); + ASSERT_TRUE(model); + + std::unique_ptr<Interpreter> interpreter; + ASSERT_EQ(InterpreterBuilder(*model, + ops::builtin::BuiltinOpResolver{})(&interpreter), + kTfLiteOk); + ASSERT_TRUE(interpreter); + + ASSERT_EQ(interpreter->AllocateTensors(), kTfLiteOk); +} + +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/model_test.cc b/tensorflow/contrib/lite/model_test.cc index ec7d46af7c..b969bea5dc 100644 --- a/tensorflow/contrib/lite/model_test.cc +++ b/tensorflow/contrib/lite/model_test.cc @@ -24,6 +24,7 @@ limitations under the License. #include <gtest/gtest.h> #include "tensorflow/contrib/lite/core/api/error_reporter.h" +#include "tensorflow/contrib/lite/kernels/register.h" #include "tensorflow/contrib/lite/testing/util.h" // Comparison for TfLiteRegistration. Since TfLiteRegistration is a C object, @@ -193,6 +194,27 @@ TEST(BasicFlatBufferModel, TestModelInInterpreter) { } } +// Test that loading a model with TensorFlow ops fails when the flex delegate is +// not linked into the target. +TEST(FlexModel, FailureWithoutFlexDelegate) { + auto model = FlatBufferModel::BuildFromFile( + "tensorflow/contrib/lite/testdata/multi_add_flex.bin"); + ASSERT_TRUE(model); + + // Note that creation will succeed when using the BuiltinOpResolver, but + // unless the appropriate delegate is linked into the target or the client + // explicitly installs the delegate, execution will fail. + std::unique_ptr<Interpreter> interpreter; + ASSERT_EQ(InterpreterBuilder(*model, + ops::builtin::BuiltinOpResolver{})(&interpreter), + kTfLiteOk); + ASSERT_TRUE(interpreter); + + // As the flex ops weren't resolved implicitly by the flex delegate, runtime + // allocation and execution will fail. + ASSERT_EQ(interpreter->AllocateTensors(), kTfLiteError); +} + // This tests on a flatbuffer that defines a shape of 2 to be a memory mapped // buffer. But the buffer is provided to be only 1 element. TEST(BasicFlatBufferModel, TestBrokenMmap) { diff --git a/tensorflow/contrib/lite/testdata/multi_add_flex.bin b/tensorflow/contrib/lite/testdata/multi_add_flex.bin Binary files differnew file mode 100644 index 0000000000..9aac2155fe --- /dev/null +++ b/tensorflow/contrib/lite/testdata/multi_add_flex.bin diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_binary.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_binary.cc index 3e57d3f467..f7e5aa6609 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_binary.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_binary.cc @@ -191,14 +191,6 @@ void EvaluateBinaryOperatorOnConstantInputs(Model* model, bool ResolveConstantBinaryOperator::Run(Model* model, std::size_t op_index) { const auto binary_it = model->operators.begin() + op_index; const auto* binary_op = binary_it->get(); - - // If the output of this op is a non-discardable array such as an input_array - // or a state array of the model, then this is a job for RemoveUnusedOp, not - // for constants-propagation. - if (!IsDiscardableArray(*model, binary_op->outputs[0])) { - return false; - } - // Test for binary ops of types that we know how to resolve if (binary_op->type != OperatorType::kAdd && binary_op->type != OperatorType::kMul && diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_concatenation.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_concatenation.cc index c6c5035a51..d916ae0ddf 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_concatenation.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_concatenation.cc @@ -144,13 +144,6 @@ bool ResolveConstantConcatenation::Run(Model* model, std::size_t op_index) { const auto* concat_op = static_cast<const ConcatenationOperator*>(concat_base_op); - // If the output of this op is a non-discardable array such as an input_array - // or a state array of the model, then this is a job for RemoveUnusedOp, not - // for constants-propagation. - if (!IsDiscardableArray(*model, concat_op->outputs[0])) { - return false; - } - for (const string& input_name : concat_op->inputs) { // We only expect constant unquantized arrays as input, otherwise we return. // We also make sure the shapes of the input arrays are known and they are diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_fake_quant.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_fake_quant.cc index 3d797533c9..f5f2f77460 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_fake_quant.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_fake_quant.cc @@ -69,13 +69,6 @@ bool ResolveConstantFakeQuant::Run(Model* model, std::size_t op_index) { const auto* fakequant_op = static_cast<const FakeQuantOperator*>(fakequant_base_op); - // If the output of this op is a non-discardable array such as an input_array - // or a state array of the model, then this is a job for RemoveUnusedOp, not - // for constants-propagation. - if (!IsDiscardableArray(*model, fakequant_op->outputs[0])) { - return false; - } - // Yield until the fakequant MinMax has been resolved. if (!fakequant_op->minmax) { return false; diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_fill.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_fill.cc index 2cb1e64f3a..f6f95481b5 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_fill.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_fill.cc @@ -52,13 +52,6 @@ bool ResolveConstantFill::Run(Model* model, std::size_t op_index) { CHECK_EQ(op->inputs.size(), 2); CHECK_EQ(op->outputs.size(), 1); - // If the output of this op is a non-discardable array such as an input_array - // or a state array of the model, then this is a job for RemoveUnusedOp, not - // for constants-propagation. - if (!IsDiscardableArray(*model, op->outputs[0])) { - return false; - } - auto& output_array = model->GetArray(op->outputs[0]); if (output_array.data_type == ArrayDataType::kNone) { // Yield until the output type has been set by PropagateArrayDataTypes diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_gather.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_gather.cc index 4dfe203a25..36d7dad0ce 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_gather.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_gather.cc @@ -71,14 +71,6 @@ bool ResolveConstantGather::Run(Model* model, std::size_t op_index) { CHECK_GE(op->inputs.size(), 2); CHECK_EQ(op->outputs.size(), 1); - - // If the output of this op is a non-discardable array such as an input_array - // or a state array of the model, then this is a job for RemoveUnusedOp, not - // for constants-propagation. - if (!IsDiscardableArray(*model, op->outputs[0])) { - return false; - } - auto& output_array = model->GetArray(op->outputs[0]); if (output_array.data_type == ArrayDataType::kNone) { // Yield until the output type has been set by PropagateArrayDataTypes. diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_pack.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_pack.cc index 6f44025dd4..e86616574d 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_pack.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_pack.cc @@ -59,14 +59,6 @@ bool ResolveConstantPack::Run(Model* model, std::size_t op_index) { CHECK_GE(op->inputs.size(), 1); CHECK_EQ(op->outputs.size(), 1); - - // If the output of this op is a non-discardable array such as an input_array - // or a state array of the model, then this is a job for RemoveUnusedOp, not - // for constants-propagation. - if (!IsDiscardableArray(*model, op->outputs[0])) { - return false; - } - auto& output_array = model->GetArray(op->outputs[0]); if (output_array.data_type == ArrayDataType::kNone) { // Yield until the output type has been set by PropagateArrayDataTypes diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_random_uniform.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_random_uniform.cc index c9f2b95d09..88d06d7dc7 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_random_uniform.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_random_uniform.cc @@ -70,13 +70,6 @@ bool ResolveConstantRandomUniform::Run(Model* model, std::size_t op_index) { CHECK_EQ(op->inputs.size(), 1); CHECK_EQ(op->outputs.size(), 1); - // If the output of this op is a non-discardable array such as an input_array - // or a state array of the model, then this is a job for RemoveUnusedOp, not - // for constants-propagation. - if (!IsDiscardableArray(*model, op->outputs[0])) { - return false; - } - auto& output_array = model->GetArray(op->outputs[0]); if (output_array.data_type == ArrayDataType::kNone) { // Yield until the output type has been set by PropagateArrayDataTypes diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_range.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_range.cc index e347286dd4..1a0ba9e2bc 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_range.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_range.cc @@ -28,14 +28,6 @@ bool ResolveConstantRange::Run(Model* model, std::size_t op_index) { auto* op = static_cast<RangeOperator*>(base_op); CHECK_EQ(op->inputs.size(), 3); - - // If the output of this op is a non-discardable array such as an input_array - // or a state array of the model, then this is a job for RemoveUnusedOp, not - // for constants-propagation. - if (!IsDiscardableArray(*model, op->outputs[0])) { - return false; - } - const auto& start_array = model->GetArray(op->inputs[0]); if (!start_array.has_shape()) { // Yield until all input dims have been resolved. diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_reshape.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_reshape.cc index bfdaa8aafd..a6f665b5f0 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_reshape.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_reshape.cc @@ -33,13 +33,6 @@ bool ResolveConstantReshape::Run(Model* model, std::size_t op_index) { CHECK_EQ(op->inputs.size(), 2); CHECK_EQ(op->outputs.size(), 1); - // If the output of this op is a non-discardable array such as an input_array - // or a state array of the model, then this is a job for RemoveUnusedOp, not - // for constants-propagation. - if (!IsDiscardableArray(*model, op->outputs[0])) { - return false; - } - // We require constant inputs. if (!IsConstantParameterArray(*model, op->inputs[0]) || !IsConstantParameterArray(*model, op->inputs[1])) { diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_select.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_select.cc index 3a95d39cd4..e880a3f44d 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_select.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_select.cc @@ -37,14 +37,6 @@ bool ResolveConstantSelect::Run(Model* model, std::size_t op_index) { CHECK_GE(op->inputs.size(), 3); CHECK_EQ(op->outputs.size(), 1); - - // If the output of this op is a non-discardable array such as an input_array - // or a state array of the model, then this is a job for RemoveUnusedOp, not - // for constants-propagation. - if (!IsDiscardableArray(*model, op->outputs[0])) { - return false; - } - auto& output_array = model->GetArray(op->outputs[0]); if (output_array.data_type == ArrayDataType::kNone) { // Yield until the output type has been set by PropagateArrayDataTypes. diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_shape_or_rank.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_shape_or_rank.cc index 452bef1f16..8a0e3e8995 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_shape_or_rank.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_shape_or_rank.cc @@ -27,14 +27,6 @@ bool ResolveConstantShapeOrRank::Run(Model* model, std::size_t op_index) { } CHECK_EQ(op->outputs.size(), 1); - - // If the output of this op is a non-discardable array such as an input_array - // or a state array of the model, then this is a job for RemoveUnusedOp, not - // for constants-propagation. - if (!IsDiscardableArray(*model, op->outputs[0])) { - return false; - } - auto& output_array = model->GetArray(op->outputs[0]); if (output_array.data_type == ArrayDataType::kNone) { // Yield until the output type has been resolved diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_slice.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_slice.cc index 58d6797e1c..b35c3e19c4 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_slice.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_slice.cc @@ -96,14 +96,6 @@ bool ResolveConstantSlice::Run(Model* model, std::size_t op_index) { const SliceOperator* op = static_cast<const SliceOperator*>(base_op); CHECK_EQ(op->outputs.size(), 1); - - // If the output of this op is a non-discardable array such as an input_array - // or a state array of the model, then this is a job for RemoveUnusedOp, not - // for constants-propagation. - if (!IsDiscardableArray(*model, op->outputs[0])) { - return false; - } - auto& output_array = model->GetArray(op->outputs[0]); if (output_array.data_type == ArrayDataType::kNone) { // Yield until the output type has been set by PropagateArrayDataTypes. diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_strided_slice.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_strided_slice.cc index e275447a0c..8853ed87e6 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_strided_slice.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_strided_slice.cc @@ -114,14 +114,6 @@ bool ResolveConstantStridedSlice::Run(Model* model, std::size_t op_index) { static_cast<const StridedSliceOperator*>(base_op); CHECK_EQ(op->outputs.size(), 1); - - // If the output of this op is a non-discardable array such as an input_array - // or a state array of the model, then this is a job for RemoveUnusedOp, not - // for constants-propagation. - if (!IsDiscardableArray(*model, op->outputs[0])) { - return false; - } - auto& output_array = model->GetArray(op->outputs[0]); if (output_array.data_type == ArrayDataType::kNone) { // Yield until the output type has been set by PropagateArrayDataTypes diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_tile.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_tile.cc index 378a38f14b..5cfa1a5582 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_tile.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_tile.cc @@ -105,13 +105,6 @@ bool ResolveConstantTile::Run(Model* model, std::size_t op_index) { } const auto* op = static_cast<const TensorFlowTileOperator*>(base_op); - // If the output of this op is a non-discardable array such as an input_array - // or a state array of the model, then this is a job for RemoveUnusedOp, not - // for constants-propagation. - if (!IsDiscardableArray(*model, op->outputs[0])) { - return false; - } - CHECK_GE(op->inputs.size(), 2); CHECK_EQ(op->outputs.size(), 1); auto& output_array = model->GetArray(op->outputs[0]); diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_transpose.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_transpose.cc index 5d3f4a6240..fe15dfa06f 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_transpose.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_transpose.cc @@ -111,14 +111,6 @@ bool ResolveConstantTranspose::Run(Model* model, std::size_t op_index) { CHECK_EQ(op->inputs.size(), 2); CHECK_EQ(op->outputs.size(), 1); - - // If the output of this op is a non-discardable array such as an input_array - // or a state array of the model, then this is a job for RemoveUnusedOp, not - // for constants-propagation. - if (!IsDiscardableArray(*model, op->outputs[0])) { - return false; - } - auto& output_array = model->GetArray(op->outputs[0]); if (output_array.data_type == ArrayDataType::kNone) { // Yield until the output type has been set by PropagateArrayDataTypes. diff --git a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc index e35ed0898b..c698a9567a 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/resolve_constant_unary.cc @@ -48,14 +48,6 @@ bool CopyMinMaxFromFirstInput(const Operator& op, Model* model) { bool ResolveConstantUnaryOperator::Run(Model* model, std::size_t op_index) { const auto unary_it = model->operators.begin() + op_index; const auto* unary_op = unary_it->get(); - - // If the output of this op is a non-discardable array such as an input_array - // or a state array of the model, then this is a job for RemoveUnusedOp, not - // for constants-propagation. - if (!IsDiscardableArray(*model, unary_op->outputs[0])) { - return false; - } - // Test for unary ops of types that we know how to resolve. switch (unary_op->type) { case OperatorType::kCast: diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc index 5eaf6e27fc..133ef79a34 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc @@ -477,6 +477,30 @@ string CreateConstArray(Model* model, string const& name, return array_name; } +// Retain TensorFlow NodeDef in Toco Operator. +// +// If an op is supported by Toco but not supported by TFLite, TFLite exporter +// will use the retained NodeDef to populate a Flex op when Flex mode is +// enabled. +// +// This can't be easily applied to all operations, because a TensorFlow node +// may become multiple Toco operators. Thus we need to call this function in +// operator conversion functions one by one whenever feasible. +// +// This may cause problems if a graph transformation rule changes parameters +// of the node. When calling this function, please check if any existing +// graph transformation rule will change an existing operator with the same +// type. +// +// This provides a route to handle Toco-supported & TFLite-unsupported ops +// in Flex mode. However it's not a solid solution. Eventually we should +// get rid of this. +// TODO(b/117327937): Implement all Toco-supported ops in TFLite, and remove +// this function. +void RetainTensorFlowNodeDef(const NodeDef& node, Operator* op) { + node.SerializeToString(&op->tensorflow_node_def); +} + tensorflow::Status ConvertConstOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { @@ -990,6 +1014,10 @@ tensorflow::Status ConvertBatchMatMulOperator( auto* batch_matmul = new BatchMatMulOperator; batch_matmul->inputs = {node.input(0), node.input(1)}; batch_matmul->outputs = {node.name()}; + + // For Flex mode. Please read the comments of the function. + RetainTensorFlowNodeDef(node, batch_matmul); + model->operators.emplace_back(batch_matmul); return tensorflow::Status::OK(); } @@ -1081,7 +1109,10 @@ tensorflow::Status ConvertUnsupportedOperator( auto* op = new TensorFlowUnsupportedOperator; op->tensorflow_op = node.op(); - node.SerializeToString(&op->tensorflow_node_def); + + // For Flex mode. Please read the comments of the function. + RetainTensorFlowNodeDef(node, op); + model->operators.emplace_back(op); // Parse inputs. @@ -1605,6 +1636,10 @@ tensorflow::Status ConvertRangeOperator( op->inputs.push_back(node.input(1)); op->inputs.push_back(node.input(2)); op->outputs.push_back(node.name()); + + // For Flex mode. Please read the comments of the function. + RetainTensorFlowNodeDef(node, op); + model->operators.emplace_back(op); return tensorflow::Status::OK(); } diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h index 6e207fdf54..61f1f095e9 100644 --- a/tensorflow/contrib/lite/toco/model.h +++ b/tensorflow/contrib/lite/toco/model.h @@ -376,6 +376,13 @@ struct Operator { // looks unused. bool unresolved_outputs = false; + // A serialized tensorflow::NodeDef string. + // The field is filled only when importing from TensorFlow. + // It's guaranteed to be filled for `TensorFlowUnsupportedOperator`. + // It's not guaranteed to be filled for other ops. Ops created by graph + // transformations won't have TensorFlow NodeDef. + string tensorflow_node_def; + protected: // Constructor used by subclasses for specific OperatorType's. explicit Operator(OperatorType t) @@ -1535,8 +1542,6 @@ struct TensorFlowUnsupportedOperator : Operator { // The original TF operation type. Used for diagnostic purposes. string tensorflow_op; - // A serialized tensorflow::NodeDef string. - string tensorflow_node_def; // A boolean indicating if the unsupported op should be treated as quantized. bool quantized = false; // A boolean indicating if the unsupported op output should allow float values diff --git a/tensorflow/contrib/lite/toco/tflite/export.cc b/tensorflow/contrib/lite/toco/tflite/export.cc index 45ca7f7f0c..3b34cd6285 100644 --- a/tensorflow/contrib/lite/toco/tflite/export.cc +++ b/tensorflow/contrib/lite/toco/tflite/export.cc @@ -63,21 +63,21 @@ bool IsControlFlowOp(const string& tensorflow_op) { return false; } -details::OperatorKey GetOperatorKey( - const ::toco::Operator& op, - const std::map<OperatorType, std::unique_ptr<BaseOperator>>& ops_by_type, - bool allow_flex_ops) { - string custom_code; - if (op.type == OperatorType::kUnsupported) { - const TensorFlowUnsupportedOperator& unsupported_op = - static_cast<const TensorFlowUnsupportedOperator&>(op); - custom_code = unsupported_op.tensorflow_op; - } - int version = 1; - if (ops_by_type.count(op.type) != 0) { - version = ops_by_type.at(op.type)->GetVersion(op); +// Map from operator name to TF Lite enum value, for all builtins. +const std::map<string, BuiltinOperator>& GetBuiltinOpsMap() { + static std::map<string, BuiltinOperator>* builtin_ops = nullptr; + if (builtin_ops == nullptr) { + builtin_ops = new std::map<string, BuiltinOperator>(); + + for (int i = BuiltinOperator_MIN; i <= BuiltinOperator_MAX; ++i) { + BuiltinOperator op = static_cast<BuiltinOperator>(i); + string name = EnumNameBuiltinOperator(op); + if (op != BuiltinOperator_CUSTOM && !name.empty()) { + (*builtin_ops)[name] = op; + } + } } - return details::OperatorKey(op.type, custom_code, version, allow_flex_ops); + return *builtin_ops; } void WriteModelToString(const flatbuffers::FlatBufferBuilder& builder, @@ -91,27 +91,70 @@ void WriteModelToString(const flatbuffers::FlatBufferBuilder& builder, namespace details { -OperatorKey::OperatorKey(OperatorType type, const std::string& custom_code, - int version, bool allow_flex_ops) { - this->type = type; - this->custom_code = custom_code; - this->version = version; +OperatorKey GetOperatorKey( + const ::toco::Operator& op, + const std::map<OperatorType, std::unique_ptr<BaseOperator>>& ops_by_type, + bool allow_flex_ops) { + // Get the op name (by Toco definition). + string name = HelpfulOperatorTypeName(op); + + bool is_builtin = false; + OperatorKey key; + + const auto& builtin_ops = GetBuiltinOpsMap(); + if (ops_by_type.count(op.type) != 0) { + key.version = ops_by_type.at(op.type)->GetVersion(op); + name = ops_by_type.at(op.type)->name(); + is_builtin = (builtin_ops.count(name) > 0); + } + + if (is_builtin) { + // For TFLite supported builtin ops, find out its BuiltinOperator enum used + // in FlatBuffer. + key.type = builtin_ops.at(name); + return key; + } + + // The logic below is all for custom ops. + key.is_custom_op = true; + key.type = BuiltinOperator_CUSTOM; + + if (op.type == OperatorType::kUnsupported) { + const TensorFlowUnsupportedOperator& unsupported_op = + static_cast<const TensorFlowUnsupportedOperator&>(op); + const auto tensorflow_op = unsupported_op.tensorflow_op; - if (type == OperatorType::kUnsupported) { // TODO(b/113715895): When `allow_flex_ops` is on, for now there's no way // to populate a regular custom op. We need to find a way to fix this. if (allow_flex_ops) { - // Memorize the original TensorFlow op name. - this->flex_tensorflow_op = custom_code; - // Prefix the custom code of the flex op. - this->custom_code = string(::tflite::kFlexCustomCodePrefix) + custom_code; - this->is_flex_op = true; - - if (IsControlFlowOp(this->flex_tensorflow_op)) { - is_unsupported_flex_op = true; - } + key.is_flex_op = true; + key.flex_tensorflow_op = tensorflow_op; + key.custom_code = + string(::tflite::kFlexCustomCodePrefix) + key.flex_tensorflow_op; + } else { + key.custom_code = tensorflow_op; + } + } else if (allow_flex_ops && !op.tensorflow_node_def.empty()) { + // For Toco-supported/TFLite-unsupported ops, if the TensorFlow NodeDef + // is retained in the Toco Operator, we produce a Flex op if Flex mode + // is enabled. + key.is_flex_op = true; + key.flex_tensorflow_op = name; + key.custom_code = + string(::tflite::kFlexCustomCodePrefix) + key.flex_tensorflow_op; + } else { + // If Flex is disabled or the original TensorFlow NodeDef isn't available, + // we produce a custom op. This gives developers a chance to implemenr + // custom ops. + key.custom_code = name; + } + + if (key.is_flex_op) { + if (IsControlFlowOp(key.flex_tensorflow_op)) { + key.is_unsupported_flex_op = true; } } + return key; } void LoadTensorsMap(const Model& model, TensorsMap* tensors_map) { @@ -145,6 +188,7 @@ void LoadOperatorsMap( ++index; } } + } // namespace details Offset<Vector<Offset<Tensor>>> ExportTensors( @@ -230,7 +274,7 @@ Offset<Vector<Offset<OperatorCode>>> ExportOperatorCodes( const Model& model, const std::map<OperatorType, std::unique_ptr<BaseOperator>>& ops_by_type, const details::OperatorsMap& operators_map, FlatBufferBuilder* builder, - std::set<string>* unsupported_ops, const ExportParams& params) { + const ExportParams& params) { // Map from operator name to TF Lite enum value, for all builtins. std::map<string, BuiltinOperator> builtin_ops; for (int i = BuiltinOperator_MIN; i <= BuiltinOperator_MAX; ++i) { @@ -247,37 +291,16 @@ Offset<Vector<Offset<OperatorCode>>> ExportOperatorCodes( for (const auto& op : model.operators) { const details::OperatorKey operator_key = - GetOperatorKey(*op, ops_by_type, params.allow_flex_ops); + details::GetOperatorKey(*op, ops_by_type, params.allow_flex_ops); int op_index = operators_map.at(operator_key); - int op_version = operator_key.version; - string name = HelpfulOperatorTypeName(*op); - bool is_builtin = false; - if (ops_by_type.count(op->type) != 0) { - name = ops_by_type.at(op->type)->name(); - is_builtin = (builtin_ops.count(name) > 0); + flatbuffers::Offset<flatbuffers::String> custom_code = 0; + if (!operator_key.custom_code.empty()) { + custom_code = builder->CreateString(operator_key.custom_code); } - if (is_builtin) { - ordered_opcodes[op_index] = - CreateOperatorCode(*builder, builtin_ops[name], 0, op_version); - } else { - // This could be a kUnsupported, in which case we should be - // able to retrieve the original Tensorflow name from the OperatorKey, or - // this could be a proper TOCO operator that is completely unknown to TF - // Lite. - if (!operator_key.custom_code.empty()) { - name = operator_key.custom_code; - } - // Either way, this is an operator that is not supported by TF Lite, - // so we output it as a custom op and add it to the error summary. - if (unsupported_ops) { - unsupported_ops->insert(name); - } - ordered_opcodes[op_index] = - CreateOperatorCode(*builder, BuiltinOperator_CUSTOM, - builder->CreateString(name), op_version); - } + ordered_opcodes[op_index] = CreateOperatorCode( + *builder, operator_key.type, custom_code, operator_key.version); } std::vector<Offset<OperatorCode>> opcode_vector; @@ -311,8 +334,9 @@ Offset<Vector<Offset<Operator>>> ExportOperators( outputs.push_back(tensors_map.at(output)); } - int op_index = operators_map.at( - GetOperatorKey(*op, ops_by_type, params.allow_flex_ops)); + const auto key = + details::GetOperatorKey(*op, ops_by_type, params.allow_flex_ops); + int op_index = operators_map.at(key); auto tflite_op_it = ops_by_type.find(op->type); BaseOperator* tflite_op = tflite_op_it == ops_by_type.end() @@ -337,6 +361,11 @@ Offset<Vector<Offset<Operator>>> ExportOperators( variable_tensor_indices->insert(variable_tensor_index); } } + } else if (key.is_flex_op && !op->tensorflow_node_def.empty()) { + auto fbb = WriteFlexOpOptions(op->tensorflow_node_def); + if (fbb) { + options = Options::Custom(builder->CreateVector(fbb->GetBuffer())); + } } // The only supported CustomOptionFormat is FLEXBUFFERS now. op_vector.push_back(CreateOperator( @@ -386,9 +415,8 @@ void Export( Array empty_array; buffers_to_write.push_back(&empty_array); - std::set<string> unsupported_ops; - auto op_codes = ExportOperatorCodes(model, ops_by_type, operators_map, - &builder, &unsupported_ops, params); + auto op_codes = + ExportOperatorCodes(model, ops_by_type, operators_map, &builder, params); for (const auto& op : model.operators) { if (op->type == OperatorType::kFakeQuant) { @@ -398,7 +426,20 @@ void Export( "for --std_values and --mean_values."; } } - if (!unsupported_ops.empty()) { + + std::set<string> custom_ops; + std::set<string> unsupported_flex_ops; + for (const auto& it : operators_map) { + const details::OperatorKey& key = it.first; + if (key.is_custom_op) { + custom_ops.insert(key.custom_code); + } + if (key.is_unsupported_flex_op) { + unsupported_flex_ops.insert(key.flex_tensorflow_op); + } + } + + if (!custom_ops.empty()) { if (!params.allow_custom_ops) { // Remove ExpandDims and ReorderAxes from unimplemented list unless they // compose the list. Both ops are removed during graph transformations. @@ -406,14 +447,14 @@ void Export( // transformation is unable to run because the output shape is not // defined. This causes unnecessary confusion during model conversion // time. - std::set<string> unsupported_ops_final; - for (const auto& op_type : unsupported_ops) { + std::set<string> custom_ops_final; + for (const auto& op_type : custom_ops) { if (op_type != "ReorderAxes" && op_type != "ExpandDims") { - unsupported_ops_final.insert(op_type); + custom_ops_final.insert(op_type); } } - if (unsupported_ops_final.empty()) { - unsupported_ops_final = unsupported_ops; + if (custom_ops_final.empty()) { + custom_ops_final = custom_ops; } LOG(QFATAL) @@ -423,13 +464,13 @@ void Export( "--allow_custom_ops, or by setting allow_custom_ops=True " "when calling tf.contrib.lite.TFLiteConverter(). Here is a list " "of operators for which you will need custom implementations: " - << absl::StrJoin(unsupported_ops_final, ", ") << "."; + << absl::StrJoin(custom_ops_final, ", ") << "."; } std::set<string> unsupported_control_flow_ops; // Check if unsupported ops contains control flow ops. It's impossible // to implement these ops as custom ops at the moment. - for (const auto& op : unsupported_ops) { + for (const auto& op : custom_ops) { if (IsControlFlowOp(op)) { unsupported_control_flow_ops.insert(op); } @@ -441,14 +482,6 @@ void Export( } } - std::set<string> unsupported_flex_ops; - for (const auto& it : operators_map) { - const details::OperatorKey& key = it.first; - if (key.is_unsupported_flex_op) { - unsupported_flex_ops.insert(key.custom_code); - } - } - if (!unsupported_flex_ops.empty()) { LOG(QFATAL) << "Some of the operators in the model are not supported by " "TensorFlow Flex runtime: " diff --git a/tensorflow/contrib/lite/toco/tflite/export.h b/tensorflow/contrib/lite/toco/tflite/export.h index 9efb282c6c..c627f48086 100644 --- a/tensorflow/contrib/lite/toco/tflite/export.h +++ b/tensorflow/contrib/lite/toco/tflite/export.h @@ -81,16 +81,20 @@ using TensorsMap = std::unordered_map<string, int>; // Only when `type` is `kUnsupported`, `custom_code` is filled to // identify which operation is used. struct OperatorKey { - OperatorKey(OperatorType type, const std::string& custom_code, int version, - bool allow_flex_ops = false); + OperatorKey() {} + OperatorKey(::tflite::BuiltinOperator type, const std::string& custom_code, + int version) + : type(type), custom_code(custom_code), version(version) {} // Only `type`, `custom_code` and `version` is used to compute hash and // identity. - OperatorType type; + ::tflite::BuiltinOperator type = ::tflite::BuiltinOperator_CUSTOM; std::string custom_code; - int version; + int version = 1; - // THe fields below are not used to compute hash and identity. + // The fields below are not used to compute hash and identity. + // TODO(ycling): Consider to change these fields to accessor functions. + bool is_custom_op = false; bool is_flex_op = false; bool is_unsupported_flex_op = false; // The original TensorFlow op name for the flex op. Filled only when @@ -124,6 +128,11 @@ struct OperatorKey { }; }; +OperatorKey GetOperatorKey( + const ::toco::Operator& op, + const std::map<OperatorType, std::unique_ptr<BaseOperator>>& ops_by_type, + bool allow_flex_ops); + // A maps from operator type to its final position in the TF Lite buffer. using OperatorsMap = std::unordered_map<OperatorKey, int, OperatorKey::Hash>; diff --git a/tensorflow/contrib/lite/toco/tflite/export_test.cc b/tensorflow/contrib/lite/toco/tflite/export_test.cc index a71a64d56f..eda1aa78a3 100644 --- a/tensorflow/contrib/lite/toco/tflite/export_test.cc +++ b/tensorflow/contrib/lite/toco/tflite/export_test.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/contrib/lite/toco/tflite/builtin_operator.h" #include "tensorflow/contrib/lite/toco/tflite/operator.h" #include "tensorflow/contrib/lite/toco/tflite/types.h" +#include "tensorflow/core/framework/node_def.pb.h" namespace toco { namespace tflite { @@ -105,13 +106,15 @@ TEST_F(ExportTest, LoadOperatorsMap) { details::OperatorsMap operators; const auto ops_by_type = BuildOperatorByTypeMap(); - // TODO(ycling): Add a test for allow_flex_ops. details::LoadOperatorsMap(input_model_, &operators, ops_by_type, false); - EXPECT_EQ(0, operators[details::OperatorKey(OperatorType::kAdd, "", 1)]); - EXPECT_EQ(1, operators[details::OperatorKey(OperatorType::kConv, "", 1)]); - EXPECT_EQ(2, operators[details::OperatorKey(OperatorType::kSub, "", 1)]); - EXPECT_EQ(3, operators[details::OperatorKey(OperatorType::kUnsupported, + EXPECT_EQ( + 0, operators[details::OperatorKey(::tflite::BuiltinOperator_ADD, "", 1)]); + EXPECT_EQ(1, operators[details::OperatorKey(::tflite::BuiltinOperator_CONV_2D, + "", 1)]); + EXPECT_EQ(2, operators[details::OperatorKey(::tflite::BuiltinOperator_CUSTOM, "MyCrazyOp", 1)]); + EXPECT_EQ( + 3, operators[details::OperatorKey(::tflite::BuiltinOperator_SUB, "", 1)]); } TEST_F(ExportTest, Export) { @@ -133,7 +136,7 @@ TEST_F(ExportTest, Export) { } EXPECT_THAT(names, ElementsAre("builtin:ADD", "builtin:CONV_2D", - "builtin:SUB", "custom:MyCrazyOp")); + "custom:MyCrazyOp", "builtin:SUB")); std::vector<uint32_t> indices; auto operators = (*model->subgraphs())[0]->operators(); @@ -142,7 +145,7 @@ TEST_F(ExportTest, Export) { indices.push_back(op->opcode_index()); } - EXPECT_THAT(indices, ElementsAre(1, 0, 3, 2)); + EXPECT_THAT(indices, ElementsAre(1, 0, 2, 3)); } TEST_F(ExportTest, QuantizeWeights) { @@ -257,7 +260,8 @@ TEST_F(VersionedOpExportTest, LoadOperatorsMapWithOpV1) { details::LoadOperatorsMap(input_model_, &operators, ops_by_type, false); EXPECT_EQ(1, operators.size()); - EXPECT_EQ(0, operators.at(details::OperatorKey(OperatorType::kConv, "", 1))); + EXPECT_EQ(0, operators.at(details::OperatorKey( + ::tflite::BuiltinOperator_CONV_2D, "", 1))); } TEST_F(VersionedOpExportTest, LoadOperatorsMapWithOpV2) { @@ -268,7 +272,8 @@ TEST_F(VersionedOpExportTest, LoadOperatorsMapWithOpV2) { details::LoadOperatorsMap(input_model_, &operators, ops_by_type, false); EXPECT_EQ(1, operators.size()); - EXPECT_EQ(0, operators.at(details::OperatorKey(OperatorType::kConv, "", 2))); + EXPECT_EQ(0, operators.at(details::OperatorKey( + ::tflite::BuiltinOperator_CONV_2D, "", 2))); } TEST_F(VersionedOpExportTest, LoadOperatorsMapWithBothVersions) { @@ -280,8 +285,10 @@ TEST_F(VersionedOpExportTest, LoadOperatorsMapWithBothVersions) { details::LoadOperatorsMap(input_model_, &operators, ops_by_type, false); EXPECT_EQ(2, operators.size()); - EXPECT_EQ(0, operators.at(details::OperatorKey(OperatorType::kConv, "", 1))); - EXPECT_EQ(1, operators.at(details::OperatorKey(OperatorType::kConv, "", 2))); + EXPECT_EQ(0, operators.at(details::OperatorKey( + ::tflite::BuiltinOperator_CONV_2D, "", 1))); + EXPECT_EQ(1, operators.at(details::OperatorKey( + ::tflite::BuiltinOperator_CONV_2D, "", 2))); } TEST_F(VersionedOpExportTest, Export) { @@ -314,38 +321,61 @@ TEST_F(VersionedOpExportTest, Export) { } TEST(OperatorKeyTest, TestBuiltinOp) { - details::OperatorKey key(OperatorType::kConv, "", 2); - EXPECT_EQ(key.type, OperatorType::kConv); + auto op = absl::make_unique<ConvOperator>(); + + const auto ops_by_type = BuildOperatorByTypeMap(); + const auto key = details::GetOperatorKey(*op, ops_by_type, false); + + EXPECT_EQ(key.type, ::tflite::BuiltinOperator_CONV_2D); EXPECT_EQ(key.custom_code, ""); - EXPECT_EQ(key.version, 2); + EXPECT_EQ(key.version, 1); +} + +TEST(OperatorKeyTest, TestCustomOp) { + auto op = absl::make_unique<TensorFlowUnsupportedOperator>(); + op->tensorflow_op = "MyCrazyCustomOp"; + + const auto ops_by_type = BuildOperatorByTypeMap(); + const auto key = details::GetOperatorKey(*op, ops_by_type, false); + + EXPECT_EQ(key.type, ::tflite::BuiltinOperator_CUSTOM); + EXPECT_EQ(key.custom_code, "MyCrazyCustomOp"); + EXPECT_EQ(key.version, 1); } TEST(OperatorKeyTest, TestFlexOp) { + auto op = absl::make_unique<TensorFlowUnsupportedOperator>(); + op->tensorflow_op = "BatchMatMul"; + + const auto ops_by_type = BuildOperatorByTypeMap(); { - details::OperatorKey key(OperatorType::kUnsupported, "SomeUnsupportedOp", 1, - false); - EXPECT_EQ(key.type, OperatorType::kUnsupported); + const auto key = details::GetOperatorKey(*op, ops_by_type, false); // It shouldn't be converted to Flex op if `allow_flex_op` is false. - EXPECT_EQ(key.custom_code, "SomeUnsupportedOp"); + EXPECT_EQ(key.type, ::tflite::BuiltinOperator_CUSTOM); + EXPECT_EQ(key.custom_code, "BatchMatMul"); EXPECT_EQ(key.version, 1); EXPECT_FALSE(key.is_flex_op); } { - details::OperatorKey key(OperatorType::kUnsupported, "SomeUnsupportedOp", 1, - true); - EXPECT_EQ(key.type, OperatorType::kUnsupported); // Verify that the custom op name is prefixed by "Flex" and `is_flex_op` // is true. - EXPECT_EQ(key.custom_code, "FlexSomeUnsupportedOp"); + const auto key = details::GetOperatorKey(*op, ops_by_type, true); + EXPECT_EQ(key.type, ::tflite::BuiltinOperator_CUSTOM); + EXPECT_EQ(key.custom_code, "FlexBatchMatMul"); EXPECT_EQ(key.version, 1); EXPECT_TRUE(key.is_flex_op); } } TEST(OperatorKeyTest, TestFlexWithControlFlowOp) { - details::OperatorKey key(OperatorType::kUnsupported, "Merge", 1, true); - EXPECT_EQ(key.type, OperatorType::kUnsupported); + auto op = absl::make_unique<TensorFlowUnsupportedOperator>(); + op->tensorflow_op = "Merge"; + + const auto ops_by_type = BuildOperatorByTypeMap(); + const auto key = details::GetOperatorKey(*op, ops_by_type, true); + + EXPECT_EQ(key.type, ::tflite::BuiltinOperator_CUSTOM); EXPECT_EQ(key.custom_code, "FlexMerge"); EXPECT_EQ(key.version, 1); EXPECT_TRUE(key.is_flex_op); @@ -353,6 +383,39 @@ TEST(OperatorKeyTest, TestFlexWithControlFlowOp) { EXPECT_TRUE(key.is_unsupported_flex_op); } +TEST(OperatorKeyTest, TestFlexWithPartiallySupportedOps) { + // Test Toco-supported/TFLite-unsupported operators. + // TODO(ycling): The test will be broken if Range is implemented in TFLite. + // Find a more robust way to test the fallback logic. + auto op = absl::make_unique<RangeOperator>(); + + const auto ops_by_type = BuildOperatorByTypeMap(); + + { + // If NodeDef isn't retained in the Toco op, a regular custom op + // will be exported. + const auto key = details::GetOperatorKey(*op, ops_by_type, true); + EXPECT_EQ(key.type, ::tflite::BuiltinOperator_CUSTOM); + EXPECT_EQ(key.custom_code, "Range"); + EXPECT_EQ(key.version, 1); + EXPECT_FALSE(key.is_flex_op); + } + + ::tensorflow::NodeDef node_def; + node_def.set_name("Range"); + node_def.set_op("Range"); + node_def.SerializeToString(&op->tensorflow_node_def); + + { + // If NodeDef is retained in the Toco op, a Flex op will be exported. + const auto key = details::GetOperatorKey(*op, ops_by_type, true); + EXPECT_EQ(key.type, ::tflite::BuiltinOperator_CUSTOM); + EXPECT_EQ(key.custom_code, "FlexRange"); + EXPECT_EQ(key.version, 1); + EXPECT_TRUE(key.is_flex_op); + } +} + // TODO(ahentz): tests for tensors, inputs, outputs, opcodes and operators. } // namespace diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc index 9addbb81e7..ed37535fe0 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator.cc @@ -1157,6 +1157,25 @@ class Unpack : public BuiltinOperator<UnpackOperator, ::tflite::UnpackOptions, int GetVersion(const Operator& op) const override { return 1; } }; +std::unique_ptr<flexbuffers::Builder> WriteFlexOpOptions( + const string& tensorflow_node_def) { + auto fbb = absl::make_unique<flexbuffers::Builder>(); + + ::tensorflow::NodeDef node_def; + if (!node_def.ParseFromString(tensorflow_node_def)) { + LOG(ERROR) << "Failed to parse TensorFlow NodeDef"; + return {}; + } + + fbb->Vector([&]() { + fbb->String(node_def.op()); + fbb->String(tensorflow_node_def); + }); + fbb->Finish(); + LOG(INFO) << "Writing flex op: " << node_def.op(); + return std::unique_ptr<flexbuffers::Builder>(fbb.release()); +} + class TensorFlowUnsupported : public BaseOperator { public: TensorFlowUnsupported(const string& name, OperatorType type, @@ -1192,6 +1211,9 @@ class TensorFlowUnsupported : public BaseOperator { std::unique_ptr<flexbuffers::Builder> WriteOptions( const TensorFlowUnsupportedOperator& op) const { + if (allow_flex_ops_) { + return WriteFlexOpOptions(op.tensorflow_node_def); + } auto fbb = absl::make_unique<flexbuffers::Builder>(); ::tensorflow::NodeDef node_def; @@ -1200,16 +1222,6 @@ class TensorFlowUnsupported : public BaseOperator { return std::unique_ptr<flexbuffers::Builder>(); } - if (allow_flex_ops_) { - fbb->Vector([&]() { - fbb->String(node_def.op()); - fbb->String(op.tensorflow_node_def); - }); - fbb->Finish(); - LOG(INFO) << "Writing flex op: " << node_def.op(); - return std::unique_ptr<flexbuffers::Builder>(fbb.release()); - } - bool has_valid_attr = false; size_t map_start = fbb->StartMap(); for (const auto& pair : node_def.attr()) { diff --git a/tensorflow/contrib/lite/toco/tflite/operator.h b/tensorflow/contrib/lite/toco/tflite/operator.h index 13d9f6c49a..6e4e0a16d1 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.h +++ b/tensorflow/contrib/lite/toco/tflite/operator.h @@ -16,6 +16,7 @@ limitations under the License. #define TENSORFLOW_CONTRIB_LITE_TOCO_TFLITE_OPERATOR_H_ #include "flatbuffers/flatbuffers.h" +#include "flatbuffers/flexbuffers.h" #include "tensorflow/contrib/lite/schema/schema_generated.h" #include "tensorflow/contrib/lite/toco/model.h" @@ -36,6 +37,11 @@ std::map<string, std::unique_ptr<BaseOperator>> BuildOperatorByNameMap( std::map<OperatorType, std::unique_ptr<BaseOperator>> BuildOperatorByTypeMap( bool allow_flex_ops = false); +// Write the custom option FlexBuffer with a serialized TensorFlow NodeDef +// for a Flex op. +std::unique_ptr<flexbuffers::Builder> WriteFlexOpOptions( + const string& tensorflow_node_def); + // These are the flatbuffer types for custom and builtin options. using CustomOptions = flatbuffers::Vector<uint8_t>; using BuiltinOptions = void; diff --git a/tensorflow/contrib/lite/tools/benchmark/BUILD b/tensorflow/contrib/lite/tools/benchmark/BUILD index 502e181139..71bf61657e 100644 --- a/tensorflow/contrib/lite/tools/benchmark/BUILD +++ b/tensorflow/contrib/lite/tools/benchmark/BUILD @@ -40,7 +40,7 @@ cc_binary( srcs = [ "benchmark_main.cc", ], - copts = common_copts + ["-DTFLITE_FLEX"], + copts = common_copts, linkopts = tflite_linkopts() + select({ "//tensorflow:android": [ "-pie", # Android 5.0 and later supports only PIE @@ -49,8 +49,9 @@ cc_binary( "//conditions:default": [], }), deps = [ - ":benchmark_tflite_model_plus_flex_lib", + ":benchmark_tflite_model_lib", ":logging", + "//tensorflow/contrib/lite/delegates/flex:delegate", ], ) @@ -111,25 +112,6 @@ cc_library( ) cc_library( - name = "benchmark_tflite_model_plus_flex_lib", - srcs = [ - "benchmark_tflite_model.cc", - "logging.h", - ], - hdrs = ["benchmark_tflite_model.h"], - copts = common_copts + ["-DTFLITE_FLEX"], - deps = [ - ":benchmark_model_lib", - ":logging", - "//tensorflow/contrib/lite:framework", - "//tensorflow/contrib/lite:string_util", - "//tensorflow/contrib/lite/delegates/flex:delegate", - "//tensorflow/contrib/lite/kernels:builtin_ops", - "//tensorflow/contrib/lite/profiling:profile_summarizer", - ], -) - -cc_library( name = "benchmark_params", srcs = [ "benchmark_params.cc", diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.cc b/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.cc index 463d5993f4..2a3df7f289 100644 --- a/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.cc +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.cc @@ -23,9 +23,6 @@ limitations under the License. #include <unordered_set> #include <vector> -#ifdef TFLITE_FLEX -#include "tensorflow/contrib/lite/delegates/flex/delegate.h" -#endif // TFLITE_FLEX #include "tensorflow/contrib/lite/kernels/register.h" #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/op_resolver.h" @@ -305,15 +302,6 @@ void BenchmarkTfLiteModel::Init() { interpreter->UseNNAPI(use_nnapi); -#ifdef TFLITE_FLEX - TFLITE_LOG(INFO) << "Instantiating Flex Delegate"; - delegate_ = FlexDelegate::Create(); - if (delegate_) { - interpreter->ModifyGraphWithDelegate(delegate_.get(), - /*allow_dynamic_tensors=*/true); - } -#endif // TFLITE_FLEX - auto interpreter_inputs = interpreter->inputs(); if (!inputs.empty()) { diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h b/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h index b091e18a29..25a302b2aa 100644 --- a/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h @@ -20,9 +20,6 @@ limitations under the License. #include <string> #include <vector> -#ifdef TFLITE_FLEX -#include "tensorflow/contrib/lite/delegates/flex/delegate.h" -#endif // TFLITE_FLEX #include "tensorflow/contrib/lite/model.h" #include "tensorflow/contrib/lite/profiling/profile_summarizer.h" #include "tensorflow/contrib/lite/tools/benchmark/benchmark_model.h" @@ -73,9 +70,6 @@ class BenchmarkTfLiteModel : public BenchmarkModel { void PrepareInputsAndOutputs() override; private: -#ifdef TFLITE_FLEX - std::unique_ptr<FlexDelegate> delegate_; -#endif // TFLITE_FLEX std::unique_ptr<tflite::FlatBufferModel> model; std::unique_ptr<tflite::Interpreter> interpreter; std::vector<InputLayerInfo> inputs; |