| commit | fbf407383c93774d10bd7c45cd66788a070b0e07 | [log] [tgz] |
|---|---|---|
| author | Nick Kreeger <kreeger@google.com> | Fri Jun 19 10:40:30 2020 -0700 |
| committer | TensorFlower Gardener <gardener@tensorflow.org> | Fri Jun 19 10:45:51 2020 -0700 |
| tree | 44d2dcea78d6bf606aafee5b415d86b3afb012da | |
| parent | e0780ef031fc27f4f2a71d745004d859a711c90a [diff] |
Reduce the size of TfLiteTensor for the TF Micro runtime. This change uses the existing micro-specific build flag (TF_LITE_STATIC_MEMORY) to reduce the size of TfLiteTensor. In this build setting, only the minimum number of fields required for preparing and initializing a model in TFLM are used. This build define is opt-in only for internal builds and continues to be enabled by default in Makefile builds./ All TFLM internal targets can be built with this flag by adding '--copt=-DTF_LITE_STATIC_MEMORY'. This change reduces the sizeof(TfLiteTensor) to 64 bytes (64bit systems) down from 112 bytes (64 bit systems). TfLiteTensor struct reduced by 1.75x (~43% reduction) Tail allocation reduced by: 2,592kb (~12.5% reduction) Total allocation reduced by: 2,592kb (~12% reduction) Optimized results from memory_arena_threshold_test: Keyword Model: -------------- [RecordingMicroAllocator] Arena allocation total 18448 bytes [RecordingMicroAllocator] Arena allocation head 672 bytes [RecordingMicroAllocator] Arena allocation tail 17776 bytes [RecordingMicroAllocator] 'TfLiteTensor struct' used 3456 bytes with alignment overhead (requested 3456 bytes for 54 tensors) [RecordingMicroAllocator] 'TfLiteTensor quantization data' used 1728 bytes with alignment overhead (requested 1728 bytes for 108 allocations) [RecordingMicroAllocator] 'TfLiteTensor variable buffer data' used 10240 bytes with alignment overhead (requested 10240 bytes for 7 allocations) [RecordingMicroAllocator] 'NodeAndRegistration struct' used 1200 bytes with alignment overhead (requested 1200 bytes for 15 NodeAndRegistration structs) [RecordingMicroAllocator] 'Operator runtime data' used 148 bytes with alignment overhead (requested 148 bytes for 13 OpData structs) Test Conv Model: ---------------- [RecordingMicroAllocator] Arena allocation total 10960 bytes [RecordingMicroAllocator] Arena allocation head 7744 bytes [RecordingMicroAllocator] Arena allocation tail 3216 bytes [RecordingMicroAllocator] 'TfLiteTensor struct' used 960 bytes with alignment overhead (requested 960 bytes for 15 tensors) [RecordingMicroAllocator] 'TfLiteTensor quantization data' used 768 bytes with alignment overhead (requested 752 bytes for 24 allocations) [RecordingMicroAllocator] 'TfLiteTensor variable buffer data' used 0 bytes with alignment overhead (requested 0 bytes for 0 allocations) [RecordingMicroAllocator] 'NodeAndRegistration struct' used 560 bytes with alignment overhead (requested 560 bytes for 7 NodeAndRegistration structs) [RecordingMicroAllocator] 'Operator runtime data' used 136 bytes with alignment overhead (requested 136 bytes for 5 OpData structs) PiperOrigin-RevId: 317335359 Change-Id: Ic3d4d2c3e62249f072ece8f621f9ef94eaa28589
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TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.
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| Build Type | Status | Artifacts |
|---|---|---|
| Linux CPU |  | PyPI |
| Linux GPU |  | PyPI |
| Linux XLA |  | TBA |
| macOS |  | PyPI |
| Windows CPU |  | PyPI |
| Windows GPU |  | PyPI |
| Android |  |  |
| Raspberry Pi 0 and 1 |  | Py3 |
| Raspberry Pi 2 and 3 |  | Py3 |
| Libtensorflow MacOS CPU |  | GCS |
| Libtensorflow Linux CPU |  | GCS |
| Libtensorflow Linux GPU |  | GCS |
| Libtensorflow Windows CPU |  | GCS |
| Libtensorflow Windows GPU |  | GCS |
| Build Type | Status | Artifacts |
|---|---|---|
| Linux AMD ROCm GPU Nightly | Nightly | |
| Linux AMD ROCm GPU Stable Release | Release 1.15 / 2.x | |
| Linux s390x Nightly | Nightly | |
| Linux s390x CPU Stable Release | Release | |
| Linux ppc64le CPU Nightly | Nightly | |
| Linux ppc64le CPU Stable Release | Release 1.15 / 2.x | |
| Linux ppc64le GPU Nightly | Nightly | |
| Linux ppc64le GPU Stable Release | Release 1.15 / 2.x | |
| Linux CPU with Intel® MKL-DNN Nightly | Nightly | |
| Linux CPU with Intel® MKL-DNN Stable Release | Release 1.15 / 2.x | |
| Red Hat® Enterprise Linux® 7.6 CPU & GPU Python 2.7, 3.6 | 1.13.1 PyPI |
Learn more about the TensorFlow community and how to contribute.