From 30b864c3a11e94fd431109baa4692c5c24c83f3e Mon Sep 17 00:00:00 2001 From: Robert Muchsel <16564465+rotx-maxim@users.noreply.github.com> Date: Wed, 11 Oct 2023 10:30:57 -0500 Subject: [PATCH 01/12] Upgrade to Python 3.11, PyTorch 2.2 with MPS/ROCm support; add YAMLwriter Co-authored-by: Gorkem Ulkar <38257119+MaximGorkem@users.noreply.github.com> --- .flake8 | 1 - .github/linters/.python-lint | 2 +- .gitignore | 1 - .isort.cfg | 6 - .pylintrc | 19 - .python-version | 2 +- README.md | 184 +++-- README.pdf | Bin 6738271 -> 6553858 bytes ai8x-training.code-workspace | 9 - ai8x.py | 159 +++-- check_cuda.py | 16 +- datasets/imagenet.py | 12 +- distiller | 2 +- docs/shap.png | Bin 186419 -> 0 bytes mypy.ini | 4 - nas/parse_nas_yaml.py | 7 +- parse_qat_yaml.py | 7 +- parsecmd.py | 65 +- policies/qat_policy.yaml | 2 +- pyproject.toml | 94 ++- requirements-cu11.txt | 3 - requirements-win-cu11.txt | 3 - requirements.txt | 45 +- run_nas_network_search.py | 7 +- scripts/distributed.sh | 2 + test/test_quantile.py | 81 +++ train.py | 1096 ++++++++++-------------------- utils/augmentation_utils.py | 2 +- utils/model_wrapper.py | 28 + yamlwriter.py | 1238 ++++++++++++++++++++++++++++++++++ 30 files changed, 2069 insertions(+), 1028 deletions(-) delete mode 100644 .isort.cfg delete mode 100644 .pylintrc delete mode 100644 docs/shap.png delete mode 100644 mypy.ini delete mode 100644 requirements-cu11.txt delete mode 100644 requirements-win-cu11.txt create mode 100755 scripts/distributed.sh create mode 100755 test/test_quantile.py create mode 100644 utils/model_wrapper.py create mode 100644 yamlwriter.py diff --git a/.flake8 b/.flake8 index 2a2f9ebca..f9671c625 100644 --- a/.flake8 +++ b/.flake8 @@ -1,5 +1,4 @@ [flake8] max-line-length = 99 per-file-ignores = - range_linear_ai84.py:E501,E128 train.py:E402 diff --git a/.github/linters/.python-lint b/.github/linters/.python-lint index 2c02be588..c4f4a6f6c 100644 --- a/.github/linters/.python-lint +++ b/.github/linters/.python-lint @@ -9,7 +9,7 @@ max-line-length = 99 max-locals=100 max-statements=350 min-public-methods=1 -max-branches=120 +max-branches=130 max-module-lines=5000 max-args=20 max-returns=10 diff --git a/.gitignore b/.gitignore index 061b59e12..a9380d39a 100644 --- a/.gitignore +++ b/.gitignore @@ -19,5 +19,4 @@ **/__pycache__/ /super-linter.log /super-linter.report -/venv*/ /typings/ diff --git a/.isort.cfg b/.isort.cfg deleted file mode 100644 index 99b46845a..000000000 --- a/.isort.cfg +++ /dev/null @@ -1,6 +0,0 @@ -[settings] -profile= -line_length=99 -known_third_party=distiller -known_math=numpy,torch,torchvision,torchaudio,tensorboard,tensorflow -sections=FUTURE,STDLIB,MATH,THIRDPARTY,FIRSTPARTY,LOCALFOLDER diff --git a/.pylintrc b/.pylintrc deleted file mode 100644 index 26cbe5d6a..000000000 --- a/.pylintrc +++ /dev/null @@ -1,19 +0,0 @@ -[MESSAGES CONTROL] -disable=invalid-name, consider-using-with, duplicate-code, not-callable -[TYPECHECK] -ignored-modules = numpy, matplotlib.cm, torch, distiller -ignored-classes = ModelProto -[FORMAT] -max-line-length = 99 -[DESIGN] -max-locals=100 -max-statements=350 -min-public-methods=1 -max-branches=120 -max-module-lines=5000 -max-args=20 -max-returns=10 -max-attributes=25 -max-nested-blocks=10 -[MASTER] -init-hook="from pylint.config import find_default_config_files; import os, sys; sys.path.append(os.path.dirname(next(find_default_config_files())))" diff --git a/.python-version b/.python-version index 73bb444b2..b6d8b7612 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.8.11 +3.11.8 diff --git a/README.md b/README.md index b5d0eb314..efce7a937 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,8 @@ March 8, 2024 +**Note: The pytorch-2 branch is in development. Not all features are available/functional yet.** + ADI’s MAX78000/MAX78002 project is comprised of five repositories: 1. **Start here**: @@ -62,8 +64,8 @@ PyTorch operating system and hardware support are constantly evolving. This docu Full support and documentation are provided for the following platform: * CPU: 64-bit amd64/x86_64 “PC” with [Ubuntu Linux 20.04 LTS or 22.04 LTS](https://ubuntu.com/download/server) -* GPU for hardware acceleration (optional but highly recommended): Nvidia with [CUDA 11](https://developer.nvidia.com/cuda-toolkit-archive) -* [PyTorch 1.8.1 (LTS)](https://pytorch.org/get-started/locally/) on Python 3.8.x +* GPU for hardware acceleration (optional but highly recommended): Nvidia with [CUDA 12.1](https://developer.nvidia.com/cuda-toolkit-archive) or later +* [PyTorch 2.2.1](https://pytorch.org/get-started/locally/) on Python 3.11.x Limited support and advice for using other hardware and software combinations is available as follows. @@ -85,7 +87,7 @@ If WSL2 is not available, it is also possible (but not recommended due to inhere ##### macOS -The software works on macOS, but model training suffers from the lack of hardware acceleration. +The software works on macOS and uses MPS acceleration on Apple Silicon. On Intel CPUs, model training suffers from the lack of hardware acceleration. ##### Virtual Machines (Unsupported) @@ -97,20 +99,18 @@ This software also works inside Docker containers. However, CUDA support inside #### PyTorch and Python -The officially supported version of [PyTorch is 1.8.1 (LTS)](https://pytorch.org/get-started/locally/) running on Python 3.8.x. Newer versions will typically work, but are not covered by support, documentation, and installation scripts. +The officially supported version of [PyTorch is 2.2.1](https://pytorch.org/get-started/locally/) running on Python 3.11.x. Newer versions will typically work, but are not covered by support, documentation, and installation scripts. #### Hardware Acceleration -When going beyond simple models, model training does not work well without CUDA hardware acceleration. The network loader (“izer”) does not require CUDA, and very simple models can also be trained on systems without CUDA. - -* CUDA requires Nvidia GPUs. - -* There is a PyTorch pre-release with ROCm acceleration for certain AMD GPUs on Linux ([see blog entry](https://pytorch.org/blog/pytorch-for-amd-rocm-platform-now-available-as-python-package/)), but this is not currently covered by the installation instructions in this document, and it is not supported. - -* At this time, there is neither CUDA nor ROCm nor Neural Engine support on macOS, and therefore no hardware acceleration (there is a pre-release version of PyTorch with M1 acceleration on macOS 12.3 or later, and M1 acceleration will be supported in a future release of these tools). +When going beyond simple models, model training does not work well without hardware acceleration – Nvidia CUDA, AMD ROCm, or Apple Silicon MPS. The network loader (“izer”) does not require hardware acceleration, and very simple models can also be trained on systems without hardware acceleration. +* CUDA requires modern Nvidia GPUs. This is the most compatible, and best supported hardware accelerator. +* ROCm requires certain AMD GPUs, see [blog entry](https://pytorch.org/blog/pytorch-for-amd-rocm-platform-now-available-as-python-package/). +* MPS requires Apple Silicon (M1 or newer) and macOS 12.3 or newer. * PyTorch does not include CUDA support for aarch64/arm64 systems. *Rebuilding PyTorch from source is not covered by this document.* + ##### Using Multiple GPUs When using multiple GPUs (graphics cards), the software will automatically use all available GPUs and distribute the workload. To prevent this (for example, when the GPUs are not balanced), set the `CUDA_VISIBLE_DEVICES` environment variable. Use the `--gpus` command line argument to set the default GPU. @@ -171,13 +171,13 @@ Some additional system packages are required, and installation of these addition ##### macOS -On macOS (no CUDA support available) use: +On macOS use: ```shell $ brew install libomp libsndfile tcl-tk ``` -##### Linux (Ubuntu), including WSL2 +##### Linux (Ubuntu), including WSL2) ```shell $ sudo apt-get install -y make build-essential libssl-dev zlib1g-dev \ @@ -207,11 +207,11 @@ $ sudo dnf install openssl-devel zlib-devel \ libsndfile libsndfile-devel portaudio-devel ``` -#### Python 3.8 +#### Python 3.11 -*The software in this project uses Python 3.8.11 or a later 3.8.x version.* +*The software in this project uses Python 3.11.8 or a later 3.11.x version.* -First, check whether there is a default Python interpreter and whether it is version 3.8.x: +First, check whether there is a default Python interpreter and whether it is version 3.11.x: ```shell $ python --version @@ -225,17 +225,17 @@ Python 2.7.18 # wrong version, pyenv required ``` -Python 2 **will not function correctly** with the MAX78000/MAX78002 tools. If the result is Python **3.8**.x, skip ahead to [git Environment](#git-environment). For *any* other version (for example, 2.7, 3.7, 3.9, 3.10), or no version, continue here. +Python 2 **will not function correctly** with the MAX78000/MAX78002 tools. If the result is Python **3.11**.x, skip ahead to [git Environment](#git-environment). For *any* other version (for example, 2.7, 3.7, 3.8, 3.9, 3.10), or no version, continue here. -*Note: For the purposes of the MAX78000/MAX78002 tools, “python3” is not a substitute for “python”. Please install pyenv when `python --version` does not return version 3.8.x, even if “python3” is available.* +*Note: For the purposes of the MAX78000/MAX78002 tools, “python3” is not a substitute for “python”. Please install pyenv when `python --version` does not return version 3.11.x, even if “python3” is available.* -*Note for advanced users: `sudo apt-get install python-is-python3` on Ubuntu 20.04 will install Python 3 as the default Python version; however, it may not be version 3.8.x.* +*Note for advanced users: `sudo apt-get install python-is-python3` on Ubuntu 20.04 will install Python 3 as the default Python version; however, it may not be version 3.11.x.* ##### pyenv -It is not necessary to install Python 3.8 system-wide, or to rely on the system-provided Python. To manage Python versions, instead use `pyenv` (). This allows multiple Python versions to co-exist on the same system without interfering with the system or with one another. +It is not necessary to install Python 3.11 system-wide, or to rely on the system-provided Python. To manage Python versions, instead use `pyenv` (). This allows multiple Python versions to co-exist on the same system without interfering with the system or with one another. -On macOS (no CUDA support available): +On macOS: ```shell $ brew install pyenv pyenv-virtualenv @@ -277,7 +277,7 @@ $ ~/.pyenv/bin/pyenv init *Note: Installing both conda and pyenv in parallel may cause issues. Ensure that the pyenv initialization tasks are executed before any conda related tasks.* -Next, close the Terminal, open a new Terminal and install Python 3.8.11. +Next, close the Terminal, open a new Terminal and install Python 3.11.8. On macOS: @@ -289,13 +289,13 @@ $ env \ PKG_CONFIG_PATH="$(brew --prefix tcl-tk)/lib/pkgconfig" \ CFLAGS="-I$(brew --prefix tcl-tk)/include" \ PYTHON_CONFIGURE_OPTS="--with-tcltk-includes='-I$(brew --prefix tcl-tk)/include' --with-tcltk-libs='-L$(brew --prefix tcl-tk)/lib -ltcl8.6 -ltk8.6'" \ - pyenv install 3.8.11 + pyenv install 3.11.8 ``` On Linux, including WSL2: ```shell -$ pyenv install 3.8.11 +$ pyenv install 3.11.8 ``` #### git Environment @@ -331,39 +331,40 @@ $ cd ai8x-training The default branch is “develop” which is updated most frequently. If you want to use the “main” branch instead, switch to “main” using `git checkout main`. -If using pyenv, set the local directory to use Python 3.8.11. +If using pyenv, set the local directory to use Python 3.11.8. ```shell -$ pyenv local 3.8.11 +$ pyenv local 3.11.8 ``` -In all cases, verify that a 3.8.x version of Python is used: +In all cases, verify that a 3.11.x version of Python is used: ```shell $ python --version -Python 3.8.11 +Python 3.11.8 ``` -If this does *not* return version 3.8.x, please install and initialize [pyenv](#python-38). +If this does *not* return version 3.11.x, please install and initialize [pyenv](#python-311). Then continue with the following: ```shell -$ python -m venv venv --prompt ai8x-training +$ python -m venv .venv --prompt ai8x-training +$ echo "*" > .venv/.gitignore ``` -If this command returns an error message similar to *“The virtual environment was not created successfully because ensurepip is not available,”* please install and initialize [pyenv](#python-38). +If this command returns an error message similar to *“The virtual environment was not created successfully because ensurepip is not available,”* please install and initialize [pyenv](#python-311). On macOS and Linux, including WSL2, activate the environment using ```shell -$ source venv/bin/activate +$ source .venv/bin/activate ``` On native Windows, instead use: ```shell -$ source venv/Scripts/activate +$ source .venv/Scripts/activate ``` Then continue with @@ -372,21 +373,21 @@ Then continue with (ai8x-training) $ pip3 install -U pip wheel setuptools ``` -The next step differs depending on whether the system uses CUDA 11.x, or not. +The next step differs depending on whether the system uses CUDA 12.1+, or not. -For CUDA 11.x on Linux, including WSL2: +For CUDA 12: ```shell -(ai8x-training) $ pip3 install -r requirements-cu11.txt +(ai8x-training) $ pip3 install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu121 ``` -For CUDA 11.x on native Windows: +For ROCm 5.7: ```shell -(ai8x-training) $ pip3 install -r requirements-win-cu11.txt +(ai8x-training) $ pip3 install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/rocm5.7 ``` -For all other systems, including macOS, and CUDA 10.2 on Linux: +For all other systems, including macOS: ```shell (ai8x-training) $ pip3 install -r requirements.txt @@ -410,7 +411,7 @@ After a small delay of typically a day, a “Release” tag is created on GitHub In addition to code updated in the repository itself, **submodules and Python libraries may have been updated as well**. -Major upgrades (such as updating from PyTorch 1.7 to PyTorch 1.8) are best done by removing all installed wheels. This can be achieved most easily by creating a new folder and starting from scratch at [Upstream Code](#upstream-code). Starting from scratch is also recommended when upgrading the Python version. +Major upgrades (such as updating from PyTorch 1.8 to PyTorch 2.0) are best done by removing all installed wheels. This can be achieved most easily by creating a new folder and starting from scratch at [Upstream Code](#upstream-code). Starting from scratch is also recommended when upgrading the Python version. For minor updates, pull the latest code and install the updated wheels: @@ -427,11 +428,11 @@ Please *also* update the MSDK or use the Maintenance Tool as documented in the [ ##### Python Version Updates -Updating Python may require updating `pyenv` first. Should `pyenv install 3.8.11` fail, +Updating Python may require updating `pyenv` first. Should `pyenv install 3.11.8` fail, ```shell -$ pyenv install 3.8.11 -python-build: definition not found: 3.8.11 +$ pyenv install 3.11.8 +python-build: definition not found: 3.11.8 ``` then `pyenv` must be updated. On macOS, use: @@ -454,19 +455,19 @@ $ The update should now succeed: ```shell -$ pyenv install 3.8.11 -Downloading Python-3.8.11.tar.xz... --> https://www.python.org/ftp/python/3.8.11/Python-3.8.11.tar.xz -Installing Python-3.8.11... +$ pyenv install 3.11.8 +Downloading Python-3.11.8.tar.xz... +-> https://www.python.org/ftp/python/3.11.8/Python-3.11.8.tar.xz +Installing Python-3.11.8... ... -$ pyenv local 3.8.11 +$ pyenv local 3.11.8 ``` #### Synthesis Project -The `ai8x-synthesis` project does not require CUDA. +The `ai8x-synthesis` project does not require hardware acceleration. Start by deactivating the `ai8x-training` environment if it is active. @@ -486,34 +487,35 @@ If you want to use the main branch, switch to “main” using the optional comm If using pyenv, run: ```shell -$ pyenv local 3.8.11 +$ pyenv local 3.11.8 ``` -In all cases, make sure Python 3.8.x is the active version: +In all cases, make sure Python 3.11.x is the active version: ```shell $ python --version -Python 3.8.11 +Python 3.11.8 ``` -If this does *not* return version 3.8.x, please install and initialize [pyenv](#python-38). +If this does *not* return version 3.11.x, please install and initialize [pyenv](#python-311). Then continue: ```shell -$ python -m venv venv --prompt ai8x-synthesis +$ python -m venv .venv --prompt ai8x-synthesis +$ echo "*" > .venv/.gitignore ``` Activate the virtual environment. On macOS and Linux (including WSL2), use ```shell -$ source venv/bin/activate +$ source .venv/bin/activate ``` On native Windows, instead use ```shell -$ source venv/Scripts/activate +$ source .venv/Scripts/activate ``` For all systems, continue with: @@ -1224,30 +1226,31 @@ The example shows a fractionally-strided convolution with a stride of 2, a pad o If hardware acceleration is not available, skip the following two steps and continue with [Training Script](#training-script). -1. Before the first training session, check that CUDA hardware acceleration is available using `nvidia-smi -q`: +Before the first training session, check that hardware acceleration is available and recognized by PyTorch: - ```shell - (ai8x-training) $ nvidia-smi -q - ... - Driver Version : 470.57.02 - CUDA Version : 11.4 + ```shell + (ai8x-training) $ python check_cuda.py + System: linux + Python version: 3.11.8 (main, Mar 4 2024, 15:29:36) [GCC 11.4.0] + PyTorch version: 2.2.1+cu121 + CUDA/ROCm acceleration: available in PyTorch + MPS acceleration: NOT available in PyTorch + ``` - Attached GPUs : 1 - GPU 00000000:01:00.0 - Product Name : NVIDIA TITAN RTX - Product Brand : Titan - ... - ``` +CUDA can be diagnosed using `nvidia-smi -q`: -2. Verify that PyTorch recognizes CUDA: +```shell +(ai8x-training) $ nvidia-smi -q +... +Driver Version : 545.23.06 +CUDA Version : 12.3 - ```shell - (ai8x-training) $ python check_cuda.py - System: linux - Python version: 3.8.11 (default, Jul 14 2021, 12:46:05) [GCC 9.3.0] - PyTorch version: 1.8.1+cu111 - CUDA acceleration: available in PyTorch - ``` +Attached GPUs : 2 +GPU 00000000:01:00.0 + Product Name : NVIDIA TITAN RTX + Product Brand : Titan +... +``` ### Training Script @@ -1257,11 +1260,15 @@ The `models/` folder contains models that fit into the MAX78000 or MAX78002’s To train the FP32 model for MNIST on MAX78000 or MAX78002, run `scripts/train_mnist.sh` from the `ai8x-training` project. This script will place checkpoint files into the log directory. Training makes use of the Distiller framework, but the `train.py` software has been modified slightly to improve it and add some MAX78000/MAX78002 specifics. +#### Distributed Training + +On systems with multiple GPUs, the training script supports `DistributedDataParallel`. To use distributed training, prefix the training script with `scripts/distributed.sh`. For example, run `scripts/distributed.sh scripts/train_mnist.sh`. Note that (at this time) distributed training is only supported locally. + Since training can take a significant amount of time, the training script does not overwrite any weights previously produced. Results are placed in sub-directories under `logs/` named with the date and time when training began. The latest results are always soft-linked to by `latest-log_dir` and `latest_log_file`. #### Troubleshooting -1. If the training script returns `ModuleNotFoundError: No module named 'numpy'`, please activate the virtual environment using `source venv/bin/activate`, or on native Windows without WSL2, `source venv/scripts/activate`. +1. If the training script returns `ModuleNotFoundError: No module named 'numpy'`, please activate the virtual environment using `source .venv/bin/activate`, or on native Windows without WSL2, `source .venv/scripts/activate`. 2. If the training script crashes, or if it returns an internal error (such as `CUDNN_STATUS_INTERNAL_ERROR`), it may be necessary to limit the number of PyTorch workers to 1 (this has been observed running on native Windows). Add `--workers=1` when running any training script, for example; @@ -1312,7 +1319,9 @@ Since training can take a significant amount of time, the training script does n Training might succeed after reducing the batch size, reducing image dimensions, or pruning the dataset. Unfortunately, the only real fix for this issue is more system RAM. In the example, `kinetics_get_datasets()` from `datasets/kinetics.py` states “The current implementation of using 2000 training and 150 test examples per class at 240×240 resolution and 5 frames per second requires around 50 GB of RAM.” - +6. On CUDA-capable machines, the training script by default uses PyTorch 2.0’s [`torch.compile()` feature](https://pytorch.org/docs/stable/generated/torch.compile.html) which improves execution speed. However, some models may not support this feature. It can be disabled using the command line option + `--compiler-mode none` + Disabling `torch.compile()` may also be necessary when using AMD ROCm acceleration. ### Example Training Session @@ -1456,6 +1465,7 @@ The following table describes the most important command line arguments for `tra | `--nas` | Enable network architecture search | | | `--nas-policy` | Define NAS policy in YAML file | `--nas-policy nas/nas_policy.yaml` | | `--regression` | Select regression instead of classification (changes Loss function, and log output) | | +| `--compiler-mode` | Select [TorchDynamo optimization mode](https://pytorch.org/docs/stable/generated/torch.compile.html) (default: enabled on CUDA capable machines) | `--compiler-mode none` | | `--dr` | Set target embedding dimensionality for dimensionality reduction |`--dr 64` | | `--scaf-lr` | Initial learning rate for sub-center ArcFace loss optimizer | | | `--scaf-scale` |Scale hyperparameter for sub-center ArcFace loss | | @@ -1616,7 +1626,7 @@ Quantization-aware training (QAT) is enabled by default. QAT is controlled by a * `weight_bits` describes the number of bits available for weights. * `overrides` allows specifying the `weight_bits` on a per-layer basis. -By default, weights are quantized to 8-bits after 10 epochs as specified in `policies/qat_policy.yaml`. A more refined example that specifies weight sizes for individual layers can be seen in `policies/qat_policy_cifar100.yaml`. +By default, weights are quantized to 8-bits after 30 epochs as specified in `policies/qat_policy.yaml`. A more refined example that specifies weight sizes for individual layers can be seen in `policies/qat_policy_cifar100.yaml`. Quantization-aware training can be disabled by specifying `--qat-policy None`. @@ -1688,20 +1698,6 @@ When using PuTTY, port forwarding is achieved as follows: -#### SHAP — SHapely Additive exPlanations - -The training software integrates code to generate SHAP plots (see ). This can help with feature attribution for input images. - -The `train.py` program can create plots using the `--shap` command line argument in combination with `--evaluate`: - -```shell -$ python train.py --model ai85net5 --dataset CIFAR10 --confusion --evaluate --device MAX78000 --exp-load-weights-from logs/CIFAR-new/best.pth.tar --shap 3 -``` - -This will create a plot with a random selection of 3 test images. The plot shows ten outputs (the ten classes) for the three different input images on the left. Red pixels increase the model’s output while blue pixels decrease the output. The sum of the SHAP values equals the difference between the expected model output (averaged over the background dataset) and the current model output. - -shap - ### BatchNorm Fusing Batchnorm fusing (see [Batch Normalization](#batch-normalization)) is needed as a separate step only when both the following are true: @@ -2933,7 +2929,7 @@ For RGB image inputs, there are three channels. For example, a 3×80×60 (C×H× ```shell (ai8x-synthesis) $ deactivate $ cd ../ai8x-training - $ source venv/bin/activate + $ source .venv/bin/activate ``` 2. 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forward(self, x, s): # pylint: disable=arguments-differ return x.mul(s) +class ID3(nn.Module): + """ + ID forward function with 3 arguments + """ + def forward(self, x, _): # pylint: disable=arguments-differ + """Forward prop""" + return x + + class RoundQat(nn.Module): """ Round function for AvgPool in QAT mode @@ -408,16 +417,55 @@ def quantize_clamp_parameters(weight_bits, bias_bits): return quantize_weight, quantize_bias, clamp_weight, clamp_bias -class OutputShiftSqueeze(nn.Module): +class OutputShiftPassthrough(nn.Module): """ Return output_shift when not using quantization-aware training. """ def forward(self, _, x): # pylint: disable=arguments-differ """Forward prop""" - return x.squeeze(0) + return x -class OutputShift(nn.Module): +def interp(x, xp, fp, method='linear'): + """ + Simple PyTorch implementation of `np.interp`. + 1D data only, length must be 2 or greater. + `method` must be "linear" or "lower". + """ + # Find the index + n = len(xp) - 1 + if n == 0: + return fp[0] + if x == 1.: + return fp[-1] + i = torch.clip(torch.searchsorted(xp, x, side='right').unsqueeze(0), 1, n) - 1 + # Calculate fractional index + if method == 'linear': + g = x * n - i + else: + assert method == 'lower' + g = .0 + # Interpolate result + return fp[i] + g * (fp[i + 1] - fp[i]) + + +def quantile(x, q, method='linear'): + """ + Ersatz quantile function in PyTorch that works with torch.compile(). + 1D data only, len(x) must be 2 or greater. + `method` must be "linear" or "lower". + """ + x = x.flatten() + n = len(x) + return interp( + q, + torch.linspace(1 / (2 * n), (2 * n - 1) / (2 * n), n, device=x.device), + torch.sort(x)[0], + method, + ).squeeze(0) + + +class OutputShiftLimit(nn.Module): """ Calculate the clamped output shift when adjusting during quantization-aware training. """ @@ -427,7 +475,7 @@ def __init__(self, shift_quantile=1.0): def forward(self, x, _): # pylint: disable=arguments-differ """Forward prop""" - limit = torch.quantile(x.abs(), self.shift_quantile) + limit = quantile(x.abs(), self.shift_quantile) return -(1./limit).log2().floor().clamp(min=-15., max=15.) @@ -555,40 +603,52 @@ def __init__( self.pool = pool self.op = op + if op is not None and not hasattr(self, '_conv_forward'): + self._conv_forward = op._conv_forward # pylint: disable=protected-access self.bn = bn self.pooling = pooling self.output_shift = nn.Parameter(torch.tensor([0.]), requires_grad=False) self.init_module(weight_bits, bias_bits, quantize_activation, shift_quantile) - def init_module(self, weight_bits, bias_bits, quantize_activation, shift_quantile): + def init_module( + self, + weight_bits, + bias_bits, + quantize_activation, + shift_quantile, + export=False, + ): """Initialize model parameters""" if weight_bits is None and bias_bits is None and not quantize_activation: - self.weight_bits = nn.Parameter(torch.tensor([0]), requires_grad=False) - self.bias_bits = nn.Parameter(torch.tensor([0]), requires_grad=False) - self.quantize_activation = nn.Parameter(torch.tensor([False]), requires_grad=False) - self.adjust_output_shift = nn.Parameter(torch.tensor([False]), requires_grad=False) + if not export: + self.weight_bits = nn.Parameter(torch.tensor([0]), requires_grad=False) + self.bias_bits = nn.Parameter(torch.tensor([0]), requires_grad=False) + self.quantize_activation = nn.Parameter(torch.tensor([False]), requires_grad=False) + self.adjust_output_shift = nn.Parameter(torch.tensor([False]), requires_grad=False) elif weight_bits in [1, 2, 4, 8] and bias_bits in [1, 2, 4, 8] and quantize_activation: self.weight_bits = nn.Parameter(torch.tensor([weight_bits]), requires_grad=False) - self.bias_bits = nn.Parameter(torch.tensor([bias_bits]), requires_grad=False) - self.quantize_activation = nn.Parameter(torch.tensor([True]), requires_grad=False) - self.adjust_output_shift = nn.Parameter(torch.tensor([not dev.simulate]), - requires_grad=False) + if not export: + self.bias_bits = nn.Parameter(torch.tensor([bias_bits]), requires_grad=False) + self.quantize_activation = nn.Parameter(torch.tensor([True]), requires_grad=False) + self.adjust_output_shift = nn.Parameter(torch.tensor([not dev.simulate]), + requires_grad=False) else: assert False, f'Undefined mode with weight_bits: {weight_bits}, ' \ f'bias_bits: {bias_bits}, ' \ f'quantize_activation: {quantize_activation}' - self.shift_quantile = nn.Parameter(torch.tensor([shift_quantile]), requires_grad=False) - self.set_functions() + if not export: + self.shift_quantile = nn.Parameter(torch.tensor([shift_quantile]), requires_grad=False) + self.set_functions() def set_functions(self): """Set functions to be used wrt the model parameters""" if self.adjust_output_shift.detach(): - self.calc_out_shift = OutputShift(self.shift_quantile.detach().item()) + self.calc_out_shift = OutputShiftLimit(self.shift_quantile.detach().item()) self.calc_weight_scale = WeightScale() else: - self.calc_out_shift = OutputShiftSqueeze() + self.calc_out_shift = OutputShiftPassthrough() self.calc_weight_scale = One() self.scale = Scaler() @@ -618,21 +678,12 @@ def forward(self, x): # pylint: disable=arguments-differ weight_scale = self.calc_weight_scale(out_shift) out_scale = self.calc_out_scale(out_shift) - self.output_shift.data = out_shift.unsqueeze(0) - - weights = self.op.weight.data - self.op.weight.data = \ - self.clamp_weight(self.quantize_weight(self.op.weight.mul(weight_scale))) - if self.op.bias is not None: - biases = self.op.bias.data - self.op.bias.data = \ - self.clamp_bias(self.quantize_bias(self.op.bias.mul(weight_scale))) - - x = self.op(x) - - self.op.weight.data = weights - if self.op.bias is not None: - self.op.bias.data = biases + x = self._conv_forward( # pylint: disable=protected-access + x, + self.clamp_weight(self.quantize_weight(self.op.weight.mul(weight_scale))), + None if self.op.bias is None + else self.clamp_bias(self.quantize_bias(self.op.bias.mul(weight_scale))), + ) if self.bn is not None: x = self.bn(x).div(4.) @@ -1023,6 +1074,15 @@ class ConvTranspose2d(Conv2d): def __init__(self, *args, **kwargs): super().__init__(*args, op='ConvTranspose2d', **kwargs) + def _conv_forward(self, x, weight, bias): # pylint: disable=method-hidden + output_padding = self.op._output_padding( # pylint: disable=protected-access + x, None, self.op.stride, self.op.padding, # type: ignore[arg-type] + self.op.kernel_size, 2, self.op.dilation) # type: ignore[arg-type] + + return nn.functional.conv_transpose2d( # pylint: disable=method-hidden + x, weight, bias, self.op.stride, self.op.padding, + output_padding, self.op.groups, self.op.dilation) + class FusedConvTranspose2dReLU(ConvTranspose2d): """ @@ -1273,6 +1333,9 @@ def __init__( self.op.dilation = None self.op.groups = None + def _conv_forward(self, x, weight, bias): # pylint: disable=method-hidden + return nn.functional.linear(x, weight, bias) + class FusedLinearReLU(Linear): """ @@ -1750,31 +1813,28 @@ def forward(self, x): # pylint: disable=arguments-differ return x.mul(factor).round().div(factor) -def initiate_qat(m, qat_policy): +def initiate_qat(m, qat_policy, export=False): """ - Modify model `m` to start quantization aware training. + Modify the (unwrapped) model `m` to start quantization aware training. """ - if isinstance(m, nn.DataParallel): - m = m.module - for name, module in m.named_modules(): if isinstance(module, QuantizationAwareModule) and hasattr(module, 'weight_bits'): if 'shift_quantile' in qat_policy: module.init_module(qat_policy['weight_bits'], qat_policy['weight_bits'], - True, qat_policy['shift_quantile']) + True, qat_policy['shift_quantile'], export) else: module.init_module(qat_policy['weight_bits'], - qat_policy['weight_bits'], True, 1.0) + qat_policy['weight_bits'], True, 1.0, export) if 'overrides' in qat_policy: if name in qat_policy['overrides']: weight_field = qat_policy['overrides'][name]['weight_bits'] if 'shift_quantile' in qat_policy: module.init_module(weight_field, weight_field, - True, qat_policy['shift_quantile']) + True, qat_policy['shift_quantile'], export) else: module.init_module(weight_field, - weight_field, True, 1.0) + weight_field, True, 1.0, export) def update_model(m): @@ -1829,6 +1889,7 @@ def update_optimizer(m, optimizer): if key != 'params': new_state_dict['param_groups'][x][key] = \ old_state_dict['param_groups'][x][key] + optimizer.load_state_dict(new_state_dict) return optimizer @@ -1871,7 +1932,7 @@ def _fuse_bn_layers(m): m.apply(_fuse_bn_layers) -def onnx_export_prep(m, simplify=False): +def onnx_export_prep(m, simplify=False, remove_clamp=False): """ Prepare model `m` for ONNX export. When `simplify` is True, remove several quantization related operators from the model graph. @@ -1890,7 +1951,7 @@ def _onnx_export_prep(m): setattr(m, attr_str, FloorQatONNX()) elif isinstance(target_attr, RoundQat): setattr(m, attr_str, RoundQatONNX()) - elif isinstance(target_attr, OutputShift): + elif isinstance(target_attr, OutputShiftLimit): setattr(m, attr_str, OutputShiftONNX()) elif isinstance(target_attr, Scaler): setattr(m, attr_str, ScalerONNX()) @@ -1901,10 +1962,16 @@ def _onnx_export_prep(m): elif isinstance(target_attr, (Quantize, Clamp, Round, AvgPoolFloor, Floor, FloorQat, RoundQat)): setattr(m, attr_str, Empty()) - elif isinstance(target_attr, OutputShift): - setattr(m, attr_str, OutputShiftONNX()) + elif isinstance(target_attr, OutputShiftLimit): + if remove_clamp: + setattr(m, attr_str, ID3()) + else: + setattr(m, attr_str, OutputShiftONNX()) elif isinstance(target_attr, Scaler): - setattr(m, attr_str, ScalerONNX()) + if remove_clamp: + setattr(m, attr_str, ID3()) + else: + setattr(m, attr_str, ScalerONNX()) m.apply(_onnx_export_prep) diff --git a/check_cuda.py b/check_cuda.py index 5341b4328..b5c63684b 100755 --- a/check_cuda.py +++ b/check_cuda.py @@ -1,6 +1,6 @@ #!/usr/bin/env python3 ################################################################################################### -# Copyright (C) Maxim Integrated Products, Inc. All Rights Reserved. +# Copyright (C) 2019-2023 Maxim Integrated Products, Inc. All Rights Reserved. # # Maxim Integrated Products, Inc. Default Copyright Notice: # https://www.maximintegrated.com/en/aboutus/legal/copyrights.html @@ -27,12 +27,18 @@ def signal_handler( if __name__ == '__main__': signal.signal(signal.SIGINT, signal_handler) - print("System: ", sys.platform) - print("Python version: ", sys.version.replace('\n', '')) - print("PyTorch version: ", torch.__version__) - print("CUDA acceleration: ", end='') + print("System: ", sys.platform) + print("Python version: ", sys.version.replace('\n', '')) + print("PyTorch version: ", torch.__version__) + print("CUDA/ROCm acceleration: ", end='') if not torch.cuda.is_available(): print("NOT available in PyTorch") else: print("available in PyTorch") + + print("MPS acceleration: ", end='') + if not torch.backends.mps.is_available(): + print("NOT available in PyTorch") + else: + print("available in PyTorch") diff --git a/datasets/imagenet.py b/datasets/imagenet.py index a1185f4e1..4e77633e5 100644 --- a/datasets/imagenet.py +++ b/datasets/imagenet.py @@ -45,7 +45,7 @@ def imagenet_get_datasets(data, load_train=True, load_test=True, if augment_data: if load_train: train_transform = transforms.Compose([ - transforms.RandomResizedCrop(input_size), + transforms.RandomResizedCrop(input_size, antialias=True), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), @@ -68,7 +68,7 @@ def imagenet_get_datasets(data, load_train=True, load_test=True, if load_test: test_transform = transforms.Compose([ - transforms.Resize(int(input_size / 0.875)), + transforms.Resize(int(input_size / 0.875), antialias=True), # type: ignore transforms.CenterCrop(input_size), transforms.ToTensor(), transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), @@ -88,14 +88,14 @@ def imagenet_get_datasets(data, load_train=True, load_test=True, ) if args.truncate_testset: - test_dataset.data = test_dataset.data[:1] + test_dataset.data = test_dataset.data[:1] # type: ignore # .data exists else: test_dataset = None else: if load_train: train_transform = transforms.Compose([ - transforms.RandomResizedCrop(input_size), + transforms.RandomResizedCrop(input_size, antialias=True), transforms.ToTensor(), ai8x.normalize(args=args), ]) @@ -116,7 +116,7 @@ def imagenet_get_datasets(data, load_train=True, load_test=True, if load_test: test_transform = transforms.Compose([ - transforms.RandomResizedCrop(input_size), + transforms.RandomResizedCrop(input_size, antialias=True), transforms.ToTensor(), ai8x.normalize(args=args), ]) @@ -134,7 +134,7 @@ def imagenet_get_datasets(data, load_train=True, load_test=True, ) if args.truncate_testset: - test_dataset.data = test_dataset.data[:1] + test_dataset.data = test_dataset.data[:1] # type: ignore # .data exists else: test_dataset = None diff --git a/distiller b/distiller index 0477a66ef..84c0bf0c9 160000 --- a/distiller +++ b/distiller @@ -1 +1 @@ -Subproject commit 0477a66ef0ace09f5572f27c0178ea422ed9bf4e +Subproject commit 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+pyffmpeg==2.4.2.18.1 +pytorch-metric-learning==2.4.1 +pytube==15.0.0 +scipy==1.12.0 +tensorboard==2.16.2 +tk==0.1.0 +torchmetrics==1.3.1 +torchnet==0.0.4 -e distiller diff --git a/run_nas_network_search.py b/run_nas_network_search.py index 2f8a583f5..8b5f565fc 100755 --- a/run_nas_network_search.py +++ b/run_nas_network_search.py @@ -229,8 +229,11 @@ def main(): args = parse_args(model_names, dataset_names) args.truncate_testset = False - use_cuda = torch.cuda.is_available() - args.device = torch.device("cuda:0" if use_cuda else "cpu") + args.device = torch.device( + "cuda" if torch.cuda.is_available() + else "mps" if torch.backends.mps.is_available() + else "cpu" + ) args.act_mode_8bit = False # Get policy for once for all training policy diff --git a/scripts/distributed.sh b/scripts/distributed.sh new file mode 100755 index 000000000..d0170d0a7 --- /dev/null +++ b/scripts/distributed.sh @@ -0,0 +1,2 @@ +#!/bin/sh +OMP_NUM_THREADS=1 torchrun --nnodes=1 --nproc_per_node=auto --no-python "$@" diff --git a/test/test_quantile.py b/test/test_quantile.py new file mode 100755 index 000000000..2303ce5d2 --- /dev/null +++ b/test/test_quantile.py @@ -0,0 +1,81 @@ +#!/usr/bin/env python3 +################################################################################################### +# +# Copyright (C) 2023 Maxim Integrated Products, Inc. All Rights Reserved. +# +# Maxim Integrated Products, Inc. Default Copyright Notice: +# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html +# +################################################################################################### +""" +Test routine for quantile() +""" +import os +import sys + +import torch + +# Allow test to run outside of pytest +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) + +import ai8x # noqa: E402 pylint: disable=wrong-import-position + + +def calc_quantile(q, x): + """ + Test ai8x.quantile against torch.quantile() with `num` 1D values. + """ + def test_method(quantile, values, method): + q_pytorch = torch.quantile(values, quantile, interpolation=method) + q_ai8x = ai8x.quantile(values, quantile, method=method) + + print(f'{method+":":10}{q_ai8x}') + assert torch.isclose(q_ai8x, q_pytorch) + + n = len(x.flatten()) + print(f'\n====== q={q} len={n} ', end='') + if n < 10: + print(f'{x.numpy()} ', end='') + print('======\n' + 'PyTorch:\n' + f'linear: {torch.quantile(x, q, interpolation="linear")}\n' + f'lower: {torch.quantile(x, q, interpolation="lower")}\n' + f'higher: {torch.quantile(x, q, interpolation="higher")}\n' + f'nearest: {torch.quantile(x, q, interpolation="nearest")}\n' + f'midpoint: {torch.quantile(x, q, interpolation="midpoint")}\n') + + print('ai8x:') + test_method(q, x, 'linear') + test_method(q, x, 'lower') + + +def test_quantile(): + """Main program to test ai8x.quantile.""" + calc_quantile(.5, torch.tensor([[1., 2., 3.]])) + calc_quantile(.5, torch.tensor([[1., 2., 3., 4.]])) + calc_quantile(.0, torch.tensor([[1., 2., 3.]])) + calc_quantile(.0, torch.tensor([[1., 2., 3., 4.]])) + calc_quantile(1., torch.tensor([[1., 2., 3.]])) + calc_quantile(1., torch.tensor([[1., 2., 3., 4.]])) + calc_quantile(.75, torch.tensor([[1., 2., 3., 4.]])) + calc_quantile(.75, torch.tensor([[1., 2., 3.]])) + calc_quantile(.5, torch.rand((1, 1))) + calc_quantile(.75, torch.rand((1, 1))) + calc_quantile(.75, torch.rand((1, 2))) + calc_quantile(.75, torch.rand((1, 3))) + calc_quantile(.88, torch.rand((1, 4))) + calc_quantile(.75, torch.rand((1, 5))) + calc_quantile(.88, torch.tensor([[1., 2., 3., 4., 5., 6., 7.]])) + calc_quantile(.88, torch.tensor([[1., 2., 3., 4., 5., 6., 7., 8.]])) + calc_quantile(.5, torch.rand((1, 100))) + calc_quantile(.75, torch.rand((1, 10000))) + calc_quantile(.43, torch.rand((1, 99999))) + calc_quantile(.992, torch.rand((1, 1000000))) + # batch size + calc_quantile(.75, torch.rand((16, 5))) + calc_quantile(.66, torch.rand((32, 512, 8, 8))) + print('\n\n*** PASS ***') + + +if __name__ == '__main__': + test_quantile() diff --git a/train.py b/train.py index c05e90814..34102de41 100644 --- a/train.py +++ b/train.py @@ -52,6 +52,7 @@ import logging import operator import os +import re # pylint: disable=wrong-import-position if os.name == 'posix': @@ -63,13 +64,16 @@ import time import traceback from collections import OrderedDict -from functools import partial from pydoc import locate import numpy as np import matplotlib +# pylint: disable=wrong-import-position +os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" # Set this before importing PyTorch +# pylint: enable=wrong-import-position + # TensorFlow 2.x compatibility try: import tensorboard # pylint: disable=import-error @@ -79,30 +83,25 @@ pass import torch +import torch.distributed import torch.optim import torch.utils.data from torch import nn from torch.backends import cudnn +from torch.nn.parallel import DistributedDataParallel # pylint: disable=wrong-import-order import distiller -import shap import torchnet.meter as tnt from distiller import apputils, model_summaries # type: ignore[attr-defined] from distiller.data_loggers import PythonLogger, TensorBoardLogger -# pylint: disable=no-name-in-module -from distiller.data_loggers.collector import (QuantCalibrationStatsCollector, - RecordsActivationStatsCollector, - SummaryActivationStatsCollector, collectors_context) -from distiller.quantization.range_linear import PostTrainLinearQuantizer from pytorch_metric_learning import losses as pml_losses from pytorch_metric_learning import testers from pytorch_metric_learning.distances import CosineSimilarity from pytorch_metric_learning.utils.accuracy_calculator import AccuracyCalculator from pytorch_metric_learning.utils.inference import CustomKNN -from torchmetrics.detection.map import MAP as MeanAveragePrecision +from torchmetrics.detection import MeanAveragePrecision -# pylint: enable=no-name-in-module import ai8x import ai8x_nas import datasets @@ -110,12 +109,11 @@ import parse_qat_yaml import parsecmd import sample +import yamlwriter from losses.dummyloss import DummyLoss from losses.multiboxloss import MultiBoxLoss from nas import parse_nas_yaml -from utils import kd_relationbased, object_detection_utils, parse_obj_detection_yaml - -# from range_linear_ai84 import PostTrainLinearQuantizerAI84 +from utils import kd_relationbased, model_wrapper, object_detection_utils, parse_obj_detection_yaml matplotlib.use("pgf") @@ -141,12 +139,17 @@ def main(): model_names = [] dataset_names = [] - if os.name == 'posix': - # Check file descriptor limits - nfiles = resource.getrlimit(resource.RLIMIT_NOFILE)[0] - if nfiles < 4096: - print(f'WARNING: The open file limit is {nfiles}. ' - 'Please raise the limit (see documentation).') + local_rank = None + local_world_size = 1 + try: + local_rank = os.environ['LOCAL_RANK'] + local_world_size = os.environ['LOCAL_WORLD_SIZE'] + except KeyError: + pass + finally: + local_rank = int(local_rank) if local_rank is not None else -1 + if local_world_size is not None: + local_world_size = int(local_world_size) # Dynamically load models for _, _, files in sorted(os.walk('models')): @@ -177,6 +180,50 @@ def main(): # Parse arguments args = parsecmd.get_parser(model_names, dataset_names).parse_args() + args.local_world_size = local_world_size + + if local_rank <= 0: # not DistributedDataParallel or rank 0 + msglogger = apputils.config_pylogger(os.path.join(script_dir, 'logging.conf'), args.name, + args.output_dir) + else: + msglogger = logging.getLogger() + msglogger.log_filename = 'none' + msglogger.setLevel(logging.CRITICAL) + pattern = re.compile(r'.*Profiler function .* will be ignored') + logging.getLogger('torch._dynamo.variables.torch').addFilter( + lambda record: not pattern.search(record.getMessage()) + ) + + # Redirect 'print' + class StdStreamLogger(): + """Stream object to redirect sys.stdout to Python logger""" + def __init__(self, logger, level): + self._logger = logger + self._level = level + self._buf = '' + + def write(self, msg): + """write()""" + self._buf = self._buf + msg + while '\n' in self._buf: + pos = self._buf.find('\n') + self._logger.log(self._level, self._buf[:pos]) + self._buf = self._buf[pos + 1:] + + def flush(self): + """flush()""" + if self._buf != '': + self._logger.log(self._level, self._buf) + self._buf = '' + + sys.stdout = StdStreamLogger(msglogger, logging.INFO) + + if os.name == 'posix': + # Check file descriptor limits + nfiles = resource.getrlimit(resource.RLIMIT_NOFILE)[0] + if nfiles < 4096: + msglogger.warning('The open file limit is %d. ' + 'Please raise the limit (see documentation).', nfiles) # Set hardware device ai8x.set_device(args.device, args.act_mode_8bit, args.avg_pool_rounding) @@ -184,39 +231,34 @@ def main(): if args.epochs is None: args.epochs = 90 - if not os.path.exists(args.output_dir): + if not os.path.exists(args.output_dir) and local_rank <= 0: # not DDP or rank 0 os.makedirs(args.output_dir) - if args.shap > 0: - args.batch_size = 100 + args.shap - if args.optimizer is None: - if not args.evaluate: - print('WARNING: --optimizer not set, selecting SGD.') args.optimizer = 'SGD' + if not args.evaluate: + msglogger.warning('--optimizer not set, selecting %s.', args.optimizer) if args.lr is None: - if not args.evaluate: - print('WARNING: Initial learning rate (--lr) not set, selecting 0.1.') args.lr = 0.1 + if not args.evaluate: + msglogger.warning('Initial learning rate (--lr) not set, selecting %f.', args.lr) if args.generate_sample is not None and not args.act_mode_8bit: - print('WARNING: Cannot save sample in training mode, ignoring --save-sample option. ' - 'Use with --evaluate instead.') - - msglogger = apputils.config_pylogger(os.path.join(script_dir, 'logging.conf'), args.name, - args.output_dir) + msglogger.warning('Cannot save sample in training mode, ignoring --save-sample option. ' + 'Use with --evaluate instead.') # Log various details about the execution environment. It is sometimes useful # to refer to past experiment executions and this information may be useful. - apputils.log_execution_env_state(args.compress, msglogger.logdir) + if local_rank <= 0: # not DistributedDataParallel or rank 0 + apputils.log_execution_env_state(args.compress, msglogger.logdir) msglogger.debug("Distiller: %s", distiller.__version__) start_epoch = 0 ending_epoch = args.epochs perf_scores_history = [] - if args.evaluate and args.shap == 0: + if args.evaluate: args.deterministic = True if args.deterministic: # torch.set_deterministic(True) @@ -230,16 +272,19 @@ def main(): # https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936/3 cudnn.benchmark = True - if args.cpu or not torch.cuda.is_available(): + if args.cpu or (not torch.cuda.is_available() and not torch.backends.mps.is_available()): if not args.cpu: # Print warning if no hardware acceleration - print("WARNING: CUDA hardware acceleration is not available, training will be slow") + msglogger.warning('No CUDA, ROCm, or MPS hardware acceleration, training will be slow') # Set GPU index to -1 if using CPU args.device = 'cpu' args.gpus = -1 - else: + elif torch.cuda.is_available(): args.device = 'cuda' - if args.gpus is not None: + if local_rank >= 0: # DistributedDataParallel + torch.cuda.set_device(local_rank) + args.gpus = local_rank + elif args.gpus is not None: try: args.gpus = [int(s) for s in args.gpus.split(',')] except ValueError as exc: @@ -252,12 +297,11 @@ def main(): f'{available_gpus} devices available') # Set default device in case the first one on the list != 0 torch.cuda.set_device(args.gpus[0]) - - if args.earlyexit_thresholds: - args.num_exits = len(args.earlyexit_thresholds) + 1 - args.loss_exits = [0] * args.num_exits - args.losses_exits = [] - args.exiterrors = [] + elif os.uname().release < '22.3.0': + msglogger.warning('mps disabled, update macOS to Ventura 13.4 or later for mps support') + args.device = 'cpu' + else: + args.device = 'mps' selected_source = next((item for item in supported_sources if item['name'] == args.dataset)) args.labels = selected_source['output'] @@ -293,17 +337,12 @@ def main(): model = create_model(supported_models, dimensions, args) - # if args.add_logsoftmax: - # model = nn.Sequential(model, nn.LogSoftmax(dim=1)) - # if args.add_softmax: - # model = nn.Sequential(model, nn.Softmax(dim=1)) - compression_scheduler = None # Create a couple of logging backends. TensorBoardLogger writes log files in a format # that can be read by Google's Tensor Board. PythonLogger writes to the Python logger. pylogger = PythonLogger(msglogger, log_1d=True) all_loggers = [pylogger] - if args.tblog: + if args.tblog and local_rank <= 0: # not DistributedDataParallel or rank 0 tflogger = TensorBoardLogger(msglogger.logdir, log_1d=True, comment='_'+args.dataset) tflogger.tblogger.writer.add_text('Command line', str(args)) @@ -320,10 +359,6 @@ def main(): tflogger = None all_tbloggers = [] - # Capture thresholds for early-exit training - if args.earlyexit_thresholds: - msglogger.info('=> using early-exit threshold values of %s', args.earlyexit_thresholds) - # Get policy for quantization aware training qat_policy = parse_qat_yaml.parse(args.qat_policy) \ if args.qat_policy.lower() != "none" else None @@ -340,40 +375,31 @@ def main(): optimizer = None loss_optimizer = None if args.resumed_checkpoint_path: - update_old_model_params(args.resumed_checkpoint_path, model) if qat_policy is not None: checkpoint = torch.load(args.resumed_checkpoint_path, map_location=lambda storage, loc: storage) - # pylint: disable=unsubscriptable-object if checkpoint.get('epoch', None) >= qat_policy['start_epoch']: ai8x.fuse_bn_layers(model) if args.name: args.name = f'{args.name}_qat' else: args.name = 'qat' - # pylint: enable=unsubscriptable-object model, compression_scheduler, optimizer, start_epoch = apputils.load_checkpoint( model, args.resumed_checkpoint_path, model_device=args.device) - ai8x.update_model(model) elif args.load_model_path: - update_old_model_params(args.load_model_path, model) if qat_policy is not None: checkpoint = torch.load(args.load_model_path, map_location=lambda storage, loc: storage) - # pylint: disable=unsubscriptable-object if checkpoint.get('epoch', None) >= qat_policy['start_epoch']: ai8x.fuse_bn_layers(model) if args.name: args.name = f'{args.name}_qat' else: args.name = 'qat' - # pylint: enable=unsubscriptable-object model = apputils.load_lean_checkpoint(model, args.load_model_path, model_device=args.device) - ai8x.update_model(model) - if not args.load_serialized and args.gpus != -1 and torch.cuda.device_count() > 1: - model = nn.DataParallel(model, device_ids=args.gpus).to(args.device) + ai8x.update_model(model) if args.reset_optimizer: start_epoch = 0 @@ -384,18 +410,11 @@ def main(): # Define loss function (criterion) if args.obj_detection: - - # .module is added to model for access in multi GPU environments - # as https://github.com/pytorch/pytorch/issues/16885 has not been merged yet - m = model.module if isinstance(model, nn.DataParallel) else model - - criterion = MultiBoxLoss(priors_cxcy=m.priors_cxcy, + criterion = MultiBoxLoss(priors_cxcy=model.priors_cxcy, alpha=obj_detection_params['multi_box_loss']['alpha'], neg_pos_ratio=obj_detection_params['multi_box_loss'] ['neg_pos_ratio'], device=args.device).to(args.device) - elif args.dr: - criterion = pml_losses.SubCenterArcFaceLoss(num_classes=args.num_classes, embedding_size=args.dr, margin=args.scaf_margin, @@ -414,17 +433,14 @@ def main(): custom_knn = CustomKNN(distance_fn, batch_size=args.batch_size) accuracy_calculator = AccuracyCalculator(knn_func=custom_knn, include=("precision_at_1",), k=1) - + elif args.regression: + criterion = nn.MSELoss().to(args.device) + elif 'weight' in selected_source: + criterion = nn.CrossEntropyLoss( + torch.tensor(selected_source['weight'], dtype=torch.float) + ).to(args.device) else: - if not args.regression: - if 'weight' in selected_source: - criterion = nn.CrossEntropyLoss( - torch.tensor(selected_source['weight'], dtype=torch.float) - ).to(args.device) - else: - criterion = nn.CrossEntropyLoss().to(args.device) - else: - criterion = nn.MSELoss().to(args.device) + criterion = nn.CrossEntropyLoss().to(args.device) # Override criterion with dummy loss when student weight is 0 if args.kd_student_wt == 0: @@ -440,23 +456,47 @@ def main(): if args.summary: return summarize_model(model, args.dataset, which_summary=args.summary, filename=args.summary_filename) + if args.yaml_template is not None: + return yamlwriter.create( + model, + args.dataset, + args.cnn, + filename=args.yaml_template, + qat_policy=qat_policy, + ) + + if local_rank >= 0: # DistributedDataParallel + torch.distributed.init_process_group(backend='nccl' if args.device == 'cuda' else 'gloo') + model = DistributedDataParallel( + torch.nn.SyncBatchNorm.convert_sync_batchnorm(model), + device_ids=[local_rank] if args.device == 'cuda' else None, + output_device=local_rank if args.device == 'cuda' else None, + ) - activations_collectors = create_activation_stats_collectors(model, *args.activation_stats) + if local_rank >= 0: # DistributedDataParallel + # Auto-divide batch size + args.batch_size //= local_world_size + # Create semaphore + tensor = torch.zeros(1).to(args.device) + if local_rank > 0: # DistributedDataParallel and rank > 0 + # Wait for rank 0 to take advantage of disk caching and downloading + torch.distributed.broadcast(tensor, src=0) # Load the datasets - train_loader, val_loader, test_loader, _ = apputils.get_data_loaders( + train_loader, val_loader, test_loader, _, train_sampler = apputils.get_data_loaders( args.datasets_fn, (os.path.expanduser(args.data), args), args.batch_size, args.workers, args.validation_split, args.deterministic, - args.effective_train_size, args.effective_valid_size, args.effective_test_size, - test_only=args.evaluate, collate_fn=args.collate_fn, cpu=args.device == 'cpu') + 1., 1., 1., # effective sizes 100% + test_only=args.evaluate, collate_fn=args.collate_fn, cpu=args.device == 'cpu', + distributed=local_rank >= 0, rank=local_rank, world_size=local_world_size) assert args.evaluate or train_loader is not None and val_loader is not None, \ "No training and/or validation data in train mode" assert not args.evaluate or test_loader is not None, "No test data in eval mode" + assert local_rank < 0 or train_sampler is not None - if args.sensitivity is not None: - sensitivities = np.arange(args.sensitivity_range[0], args.sensitivity_range[1], - args.sensitivity_range[2]) - return sensitivity_analysis(model, criterion, test_loader, pylogger, args, sensitivities) + if local_rank == 0: # DistributedDataParallel rank 0 + # Notify the other ranks but don't wait + torch.distributed.broadcast(tensor, src=0, async_op=True) if args.compress: # The main use-case for this sample application is CNN compression. Compression @@ -472,21 +512,6 @@ def main(): # Model is re-transferred to GPU in case parameters were added (e.g. PACTQuantizer) model.to(args.device) - if args.thinnify: - # zeros_mask_dict = distiller.create_model_masks_dict(model) - assert args.resumed_checkpoint_path is not None, \ - "You must use --resume-from to provide a checkpoint file to thinnify" - distiller.remove_filters(model, compression_scheduler.zeros_mask_dict, args.cnn, - args.dataset, optimizer=None) - apputils.save_checkpoint(0, args.cnn, model, optimizer=None, - scheduler=compression_scheduler, - name=f'{args.resumed_checkpoint_path.replace(".pth.tar", "")}' - '_thinned', - dir=msglogger.logdir) - print("Note: your model may have collapsed to random inference, " - "so you may want to fine-tune") - return None - args.kd_policy = None if args.kd_teacher: teacher = create_model(supported_models, dimensions, args, mode='kd_teacher') @@ -520,10 +545,8 @@ def main(): 'OnceForAllModel interface for NAS training!' if nas_policy: args.nas_stage_transition_list = create_nas_training_stage_list(model, nas_policy) - # pylint: disable=unsubscriptable-object args.nas_kd_params = nas_policy['kd_params'] \ if nas_policy and 'kd_params' in nas_policy else None - # pylint: enable=unsubscriptable-object if args.nas_kd_resume_from == '': args.nas_kd_policy = None else: @@ -535,10 +558,25 @@ def main(): args.epochs) create_nas_kd_policy(model, compression_scheduler, start_epoch, kd_end_epoch, args) + if args.compiler_mode.lower() != 'none': + if local_rank >= 0 or args.device == 'cuda' \ + and (torch.cuda.device_count() == 1 or args.gpus is not None and len(args.gpus) <= 1): + model = torch.compile(model, mode=args.compiler_mode, + backend=args.compiler_backend) + msglogger.info( + 'torch.compile() successful, mode=%s, cache limit=%d', + args.compiler_mode, + torch._dynamo.config.cache_size_limit, # pylint: disable=protected-access + ) + else: + msglogger.info('torch.compile() not available, using "eager" mode') + if args.device == 'cuda' and torch.cuda.device_count() > 1: + msglogger.info('Use distributed training to enable torch.compile() ' + 'with multiple GPUs') + if args.evaluate: msglogger.info('Dataset sizes:\n\ttest=%d', len(test_loader.sampler)) - return evaluate_model(model, criterion, test_loader, pylogger, activations_collectors, - args, compression_scheduler) + return evaluate_model(model, criterion, test_loader, pylogger, args, compression_scheduler) assert train_loader and val_loader msglogger.info('Dataset sizes:\n\ttraining=%d\n\tvalidation=%d\n\ttest=%d', @@ -546,11 +584,14 @@ def main(): vloss = 10**6 for epoch in range(start_epoch, ending_epoch): - # pylint: disable=unsubscriptable-object + if local_rank >= 0: # DistributedDataParallel + train_sampler.set_epoch(epoch) + if qat_policy is not None and epoch > 0 and epoch == qat_policy['start_epoch']: - msglogger.info('\n') msglogger.info('Initiating quantization aware training (QAT)...') + model, dynamo, ddp = model_wrapper.unwrap(model) + # Fuse the BN parameters into conv layers before Quantization Aware Training (QAT) ai8x.fuse_bn_layers(model) @@ -571,13 +612,29 @@ def main(): # Model is re-transferred to GPU in case parameters were added model.to(args.device) + if ddp: + model = DistributedDataParallel( + model, + device_ids=[local_rank] if args.device == 'cuda' else None, + output_device=local_rank if args.device == 'cuda' else None, + ) + + if dynamo: + torch._dynamo.reset() # pylint: disable=protected-access + model = torch.compile(model, mode=args.compiler_mode, + backend=args.compiler_backend) + msglogger.info( + 'torch.compile() successful, mode=%s, cache limit=%d', + args.compiler_mode, + torch._dynamo.config.cache_size_limit, # pylint: disable=protected-access + ) + # Empty the performance scores list for QAT operation perf_scores_history = [] if args.name: args.name = f'{args.name}_qat' else: args.name = 'qat' - # pylint: enable=unsubscriptable-object # This is the main training loop. msglogger.info('\n') @@ -585,22 +642,13 @@ def main(): compression_scheduler.on_epoch_begin(epoch, metrics=vloss) # Train for one epoch - with collectors_context(activations_collectors["train"]) as collectors: - train(train_loader, model, criterion, optimizer, epoch, compression_scheduler, - loggers=all_loggers, args=args, loss_optimizer=loss_optimizer) - - # distiller.log_weights_sparsity(model, epoch, loggers=all_loggers) - distiller.log_activation_statistics(epoch, "train", loggers=all_tbloggers, - collector=collectors["sparsity"]) - if args.masks_sparsity: - msglogger.info(distiller.masks_sparsity_tbl_summary(model, compression_scheduler)) + train(train_loader, model, criterion, optimizer, epoch, compression_scheduler, + loggers=all_loggers, args=args, loss_optimizer=loss_optimizer) # evaluate on validation set - # pylint: disable=unsubscriptable-object run_validation = not args.nas or (args.nas and (epoch < nas_policy['start_epoch'])) run_nas_validation = args.nas and (epoch >= nas_policy['start_epoch']) and \ ((epoch+1) % nas_policy['validation_freq'] == 0) - # pylint: enable=unsubscriptable-object if run_validation or run_nas_validation: checkpoint_name = args.name @@ -614,27 +662,23 @@ def main(): else: checkpoint_name = f'nas_stg{stage}_lev{level}' - with collectors_context(activations_collectors["valid"]) as collectors: - if not args.dr: - top1, top5, vloss, mAP = validate(val_loader, model, criterion, [pylogger], - args, epoch, tflogger) - else: - top1, top5, vloss, mAP = scaf_test(val_loader, model, accuracy_calculator) - distiller.log_activation_statistics(epoch, "valid", loggers=all_tbloggers, - collector=collectors["sparsity"]) - save_collectors_data(collectors, msglogger.logdir) + if not args.dr: + top1, top5, vloss, mAP = validate(val_loader, model, criterion, [pylogger], + args, epoch, tflogger) + else: + top1, top5, vloss, mAP = scaf_test(val_loader, model, accuracy_calculator) if args.obj_detection: - stats = ('Performance/Validation/', OrderedDict([('Loss', vloss), ('mAP', mAP)])) + stats = ('Performance/Validation/', OrderedDict([('Loss', vloss), + ('mAP', mAP)])) + elif args.regression: + stats = ('Performance/Validation/', OrderedDict([('Loss', vloss), + ('MSE', top1)])) else: - if not args.regression: - stats = ('Performance/Validation/', OrderedDict([('Loss', vloss), - ('Top1', top1)])) - if args.num_classes > 5 and not args.dr: - stats[1]['Top5'] = top5 - else: - stats = ('Performance/Validation/', OrderedDict([('Loss', vloss), - ('MSE', top1)])) + stats = ('Performance/Validation/', OrderedDict([('Loss', vloss), + ('Top1', top1)])) + if args.num_classes > 5 and not args.dr: + stats[1]['Top5'] = top5 distiller.log_training_progress(stats, None, epoch, steps_completed=0, total_steps=1, log_freq=1, loggers=all_tbloggers) @@ -659,21 +703,24 @@ def main(): checkpoint_extras['loss_weights'] = criterion.W checkpoint_extras['loss_optimizer_state_dict'] = loss_optimizer.state_dict() - apputils.save_checkpoint(epoch, args.cnn, model, optimizer=optimizer, - scheduler=compression_scheduler, extras=checkpoint_extras, - is_best=is_best, name=checkpoint_name, - dir=msglogger.logdir) + if local_rank <= 0: # not DistributedDataParallel or rank 0 + m, _, _ = model_wrapper.unwrap(model) + apputils.save_checkpoint(epoch, args.cnn, m, optimizer=optimizer, + scheduler=compression_scheduler, extras=checkpoint_extras, + is_best=is_best, name=checkpoint_name, + dir=msglogger.logdir) if compression_scheduler: compression_scheduler.on_epoch_end(epoch, optimizer) # Finally run results on the test set if not args.dr: - test(test_loader, model, criterion, [pylogger], activations_collectors, args=args) + test(test_loader, model, criterion, [pylogger], args=args) - if args.copy_output_folder: + if args.copy_output_folder and local_rank <= 0: msglogger.info('Copying output folder to: %s', args.copy_output_folder) shutil.copytree(msglogger.logdir, args.copy_output_folder, dirs_exist_ok=True) + return None @@ -683,25 +730,26 @@ def main(): def create_model(supported_models, dimensions, args, mode='default'): """Create the model""" - if mode == 'default': - module = next(item for item in supported_models if item['name'] == args.cnn) - elif mode == 'kd_teacher': + if mode == 'kd_teacher': module = next(item for item in supported_models if item['name'] == args.kd_teacher) + else: # including 'default' + module = next(item for item in supported_models if item['name'] == args.cnn) # Override distiller's input shape detection. This is not a very clean way to do it since # we're replacing a protected member. distiller.utils._validate_input_shape = ( # pylint: disable=protected-access lambda _a, _b: (1, ) + dimensions[:module['dim'] + 1] ) - if mode == 'default': - Model = locate(module['module'] + '.' + args.cnn) - if not Model: - raise RuntimeError("Model " + args.cnn + " not found\n") - elif mode == 'kd_teacher': + if mode == 'kd_teacher': Model = locate(module['module'] + '.' + args.kd_teacher) if not Model: raise RuntimeError("Model " + args.kd_teacher + " not found\n") + else: # including 'default' + Model = locate(module['module'] + '.' + args.cnn) + if not Model: + raise RuntimeError("Model " + args.cnn + " not found\n") + if args.dr and ('dr' not in module or not module['dr']): raise ValueError("Dimensionality reduction is not supported for this model") @@ -751,6 +799,7 @@ def create_optimizer(model, args): optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay) else: + assert msglogger is not None msglogger.info('Unknown optimizer type: %s. SGD is set as optimizer!!!', args.optimizer) optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay) @@ -768,6 +817,7 @@ def create_nas_kd_policy(model, compression_scheduler, epoch, next_state_start_e compression_scheduler.add_policy(args.nas_kd_policy, starting_epoch=epoch, ending_epoch=next_state_start_epoch, frequency=1) + assert msglogger is not None msglogger.info('\nStudent-Teacher knowledge distillation enabled for NAS:') msglogger.info('\tStart Epoch: %d, End Epoch: %d', epoch, next_state_start_epoch) msglogger.info('\tTemperature: %s', args.nas_kd_params['temperature']) @@ -788,20 +838,12 @@ def train(train_loader, model, criterion, optimizer, epoch, batch_time = tnt.AverageValueMeter() data_time = tnt.AverageValueMeter() - # For Early Exit, we define statistics for each exit - # So exiterrors is analogous to classerr for the non-Early Exit case - if args.earlyexit_lossweights: - args.exiterrors = [] - for exitnum in range(args.num_exits): - if not args.regression: - args.exiterrors.append(tnt.ClassErrorMeter(accuracy=True, topk=(1, 5))) - else: - args.exiterrors.append(tnt.MSEMeter()) - total_samples = len(train_loader.sampler) batch_size = train_loader.batch_size steps_per_epoch = (total_samples + batch_size - 1) // batch_size - msglogger.info('Training epoch: %d samples (%d per mini-batch)', total_samples, batch_size) + assert msglogger is not None + msglogger.info('Training epoch: %d samples (%d per mini-batch, world size: %d)', + total_samples, batch_size, args.local_world_size) if args.nas: if args.nas_stage_transition_list is not None: @@ -869,29 +911,23 @@ def train(train_loader, model, criterion, optimizer, epoch, output = ai8x.unfold_batch(output, args.out_fold_ratio) loss = criterion(output, target) - # TODO Early exit mechanism for Object Detection case is NOT implemented yet if not args.obj_detection and not args.dr and not args.kd_relationbased: - if not args.earlyexit_lossweights: - # Measure accuracy if the conditions are set. For `Last Batch` only accuracy - # calculation last two batches are used as the last batch might include just a few - # samples. - if args.show_train_accuracy == 'full' or \ - (args.show_train_accuracy == 'last_batch' - and train_step >= len(train_loader)-2): - if len(output.data.shape) <= 2 or args.regression: - classerr.add(output.data, target) - else: - classerr.add(output.data.permute(0, 2, 3, 1).flatten(start_dim=0, - end_dim=2), - target.flatten()) - if not args.regression: - acc_stats.append([classerr.value(1), - classerr.value(min(args.num_classes, 5))]) - else: - acc_stats.append([classerr.value()]) - else: - # Measure accuracy and record loss - loss = earlyexit_loss(output, target, criterion, args) + # Measure accuracy if the conditions are set. For `Last Batch` only accuracy + # calculation last two batches are used as the last batch might include just a few + # samples. + if args.show_train_accuracy == 'full' or \ + (args.show_train_accuracy == 'last_batch' + and train_step >= len(train_loader)-2): + if len(output.data.shape) <= 2 or args.regression: + classerr.add(output.data, target) + else: + classerr.add(output.data.permute(0, 2, 3, 1).flatten(start_dim=0, end_dim=2), + target.flatten()) + if not args.regression: + acc_stats.append([classerr.value(1), + classerr.value(min(args.num_classes, 5))]) + else: + acc_stats.append([classerr.value()]) # Record loss losses[OBJECTIVE_LOSS_KEY].add(loss.item()) @@ -944,29 +980,19 @@ def train(train_loader, model, criterion, optimizer, epoch, if steps_completed % args.print_freq == 0 or steps_completed == steps_per_epoch: # Log some statistics errs = OrderedDict() - if not args.earlyexit_lossweights: - if not args.regression: - if classerr.n != 0: - errs['Top1'] = classerr.value(1) - if args.num_classes > 5: - errs['Top5'] = classerr.value(5) - else: - errs['Top1'] = None - errs['Top5'] = None + if not args.regression: + if classerr.n != 0: + errs['Top1'] = classerr.value(1) + if args.num_classes > 5: + errs['Top5'] = classerr.value(5) else: - if classerr.n != 0: - errs['MSE'] = classerr.value() - else: - errs['MSE'] = None + errs['Top1'] = None + errs['Top5'] = None else: - # for Early Exit case, the Top1 and Top5 stats are computed for each exit. - for exitnum in range(args.num_exits): - if not args.regression: - errs['Top1_exit' + str(exitnum)] = args.exiterrors[exitnum].value(1) - if args.num_classes > 5: - errs['Top5_exit' + str(exitnum)] = args.exiterrors[exitnum].value(5) - else: - errs['MSE_exit' + str(exitnum)] = args.exiterrors[exitnum].value() + if classerr.n != 0: + errs['MSE'] = classerr.value() + else: + errs['MSE'] = None stats_dict = OrderedDict() for loss_name, meter in losses.items(): @@ -1017,6 +1043,7 @@ def scaf_test(val_loader, model, accuracy_calculator): def validate(val_loader, model, criterion, loggers, args, epoch=-1, tflogger=None): """Model validation""" + assert msglogger is not None if epoch > -1: msglogger.info('--- validate (epoch=%d)-----------', epoch) else: @@ -1024,65 +1051,12 @@ def validate(val_loader, model, criterion, loggers, args, epoch=-1, tflogger=Non return _validate(val_loader, model, criterion, loggers, args, epoch, tflogger) -def test(test_loader, model, criterion, loggers, activations_collectors, args): +def test(test_loader, model, criterion, loggers, args): """Model Test""" + assert msglogger is not None msglogger.info('--- test ---------------------') - if activations_collectors is None: - activations_collectors = create_activation_stats_collectors(model, None) - with collectors_context(activations_collectors["test"]) as collectors: - top1, top5, vloss, mAP = _validate(test_loader, model, criterion, loggers, args) - distiller.log_activation_statistics(-1, "test", loggers, collector=collectors['sparsity']) - - if args.kernel_stats: - print("==> Kernel Stats") - with torch.no_grad(): - global weight_min, weight_max, weight_count # pylint: disable=global-statement - global weight_sum, weight_stddev, weight_mean # pylint: disable=global-statement - weight_min = torch.tensor(float('inf'), device=args.device) - weight_max = torch.tensor(float('-inf'), device=args.device) - weight_count = torch.tensor(0, dtype=torch.int, device=args.device) - weight_sum = torch.tensor(0.0, device=args.device) - weight_stddev = torch.tensor(0.0, device=args.device) - - def traverse_pass1(m): - """ - Traverse model to build weight stats - """ - global weight_min, weight_max # pylint: disable=global-statement - global weight_count, weight_sum # pylint: disable=global-statement - if isinstance(m, nn.Conv2d): - weight_min = torch.min(torch.min(m.weight), weight_min) - weight_max = torch.max(torch.max(m.weight), weight_max) - weight_count += len(m.weight.flatten()) - weight_sum += m.weight.flatten().sum() - if hasattr(m, 'bias') and m.bias is not None: - weight_min = torch.min(torch.min(m.bias), weight_min) - weight_max = torch.max(torch.max(m.bias), weight_max) - weight_count += len(m.bias.flatten()) - weight_sum += m.bias.flatten().sum() - - def traverse_pass2(m): - """ - Traverse model to build weight stats - """ - global weight_stddev # pylint: disable=global-statement - if isinstance(m, nn.Conv2d): - weight_stddev += ((m.weight.flatten() - weight_mean) ** 2).sum() - if hasattr(m, 'bias') and m.bias is not None: - weight_stddev += ((m.bias.flatten() - weight_mean) ** 2).sum() - - model.apply(traverse_pass1) - - weight_mean = weight_sum / weight_count - - model.apply(traverse_pass2) - - weight_stddev = torch.sqrt(weight_stddev / weight_count) - - print(f"Total 2D kernel weights: {weight_count} --> min: {weight_min}, " - f"max: {weight_max}, stddev: {weight_stddev}") - - save_collectors_data(collectors, msglogger.logdir) + top1, top5, vloss, mAP = _validate(test_loader, model, criterion, loggers, args) + return top1, top5, vloss, mAP @@ -1093,10 +1067,10 @@ def _validate(data_loader, model, criterion, loggers, args, epoch=-1, tflogger=N if args.obj_detection: map_calculator = MeanAveragePrecision( - # box_format='xyxy', # Enable in torchmetrics > 0.6 - # iou_type='bbox', # Enable in torchmetrics > 0.6 + box_format='xyxy', + iou_type='bbox', class_metrics=False, - # iou_thresholds=[0.5], # Enable in torchmetrics > 0.6 + iou_thresholds=[0.5], ).to(args.device) mAP = 0.00 if not args.regression: @@ -1104,49 +1078,13 @@ def _validate(data_loader, model, criterion, loggers, args, epoch=-1, tflogger=N else: classerr = tnt.MSEMeter() - def save_tensor(t, f, regression=True): - """ Save tensor `t` to file handle `f` in CSV format """ - if t.dim() > 1: - if not regression: - t = nn.functional.softmax(t, dim=1) - np.savetxt(f, t.reshape(t.shape[0], t.shape[1], -1).cpu().numpy().mean(axis=2), - delimiter=",") - else: - if regression: - np.savetxt(f, t.cpu().numpy(), delimiter=",") - else: - for i in t: - f.write(f'{args.labels[i.int()]}\n') - - if args.csv_prefix is not None: - with open(f'{args.csv_prefix}_ytrue.csv', mode='w', encoding='utf-8') as f_ytrue: - f_ytrue.write('truth\n') - with open(f'{args.csv_prefix}_ypred.csv', mode='w', encoding='utf-8') as f_ypred: - f_ypred.write(','.join(args.labels) + '\n') - with open(f'{args.csv_prefix}_x.csv', mode='w', encoding='utf-8') as f_x: - for i in range(args.dimensions[0]-1): - f_x.write(f'x_{i}_mean,') - f_x.write(f'x_{args.dimensions[0]-1}_mean\n') - - if args.earlyexit_thresholds: - # for Early Exit, we have a list of errors and losses for each of the exits. - args.exiterrors = [] - args.losses_exits = [] - for exitnum in range(args.num_exits): - if not args.regression: - args.exiterrors.append(tnt.ClassErrorMeter(accuracy=True, - topk=(1, min(args.num_classes, 5)))) - else: - args.exiterrors.append(tnt.MSEMeter()) - args.losses_exits.append(tnt.AverageValueMeter()) - args.exit_taken = [0] * args.num_exits - batch_time = tnt.AverageValueMeter() total_samples = len(data_loader.sampler) batch_size = data_loader.batch_size if args.display_confusion: confusion = tnt.ConfusionMeter(args.num_classes) total_steps = (total_samples + batch_size - 1) // batch_size + assert msglogger is not None msglogger.info('%d samples (%d per mini-batch)', total_samples, batch_size) # Switch to evaluation mode @@ -1164,12 +1102,13 @@ def save_tensor(t, f, regression=True): mAP = 0.0 have_mAP = False with torch.no_grad(): - m = model.module if isinstance(model, nn.DataParallel) else model + m = model.module if isinstance(model, DistributedDataParallel) else model for validation_step, (inputs, target) in enumerate(data_loader): if args.obj_detection: if not object_detection_utils.check_target_exists(target): - print(f'No target in batch. Ep: {epoch}, validation_step: {validation_step}') + msglogger.info('No target in batch. Ep: %d, ' + 'validation_step: %d', epoch, validation_step) continue boxes_list = [elem[0].to(args.device) for elem in target] @@ -1200,8 +1139,9 @@ def save_tensor(t, f, regression=True): output = (output_boxes, output_conf) if boxes_list: - # .module is added to model for access in multi GPU environments - # as https://github.com/pytorch/pytorch/issues/16885 has not been merged yet + m, _, _ = model_wrapper.unwrap(model) + + assert m.detect_objects is not None det_boxes_batch, det_labels_batch, det_scores_batch = \ m.detect_objects(output_boxes, output_conf, min_score=obj_detection_params['nms']['min_score'], @@ -1265,32 +1205,23 @@ def save_tensor(t, f, regression=True): args.dataset, False, args.slice_sample) sample_saved = True - if args.csv_prefix is not None: - save_tensor(inputs, f_x) - save_tensor(output, f_ypred, regression=args.regression) - save_tensor(target, f_ytrue, regression=args.regression) - - if not args.earlyexit_thresholds: - # compute loss - loss = criterion(output, target) - if args.kd_relationbased: - agg_loss = args.kd_policy.before_backward_pass(None, None, None, None, - loss, None) - losses[OVERALL_LOSS_KEY].add(agg_loss.overall_loss.item()) - # measure accuracy and record loss - losses[OBJECTIVE_LOSS_KEY].add(loss.item()) - - if not args.obj_detection and not args.kd_relationbased: - if len(output.data.shape) <= 2 or args.regression: - classerr.add(output.data, target) - else: - classerr.add(output.data.permute(0, 2, 3, 1).flatten(start_dim=0, - end_dim=2), - target.flatten()) - if args.display_confusion: - confusion.add(output.data, target) - else: - earlyexit_validate_loss(output, target, criterion, args) + # compute loss + loss = criterion(output, target) + if args.kd_relationbased: + agg_loss = args.kd_policy.before_backward_pass(None, None, None, None, + loss, None) + losses[OVERALL_LOSS_KEY].add(agg_loss.overall_loss.item()) + # measure accuracy and record loss + losses[OBJECTIVE_LOSS_KEY].add(loss.item()) + + if not args.obj_detection and not args.kd_relationbased: + if len(output.data.shape) <= 2 or args.regression: + classerr.add(output.data, target) + else: + classerr.add(output.data.permute(0, 2, 3, 1).flatten(start_dim=0, end_dim=2), + target.flatten()) + if args.display_confusion: + confusion.add(output.data, target) # measure elapsed time batch_time.add(time.time() - end) @@ -1305,60 +1236,30 @@ def save_tensor(t, f, regression=True): class_probs.append(class_probs_batch) class_preds.append(class_preds_batch) - if not args.earlyexit_thresholds: - if args.kd_relationbased: - stats = ( - '', - OrderedDict([('Loss', losses[OBJECTIVE_LOSS_KEY].mean), - ('Overall Loss', losses[OVERALL_LOSS_KEY].mean)]) - ) - elif args.obj_detection: - # Only run compute() if there is at least one new update() - if have_mAP: - # Remove [0] in new torchmetrics - mAP = map_calculator.compute()['map_50'][0] - have_mAP = False - stats = ( - '', - OrderedDict([('Loss', losses[OBJECTIVE_LOSS_KEY].mean), - ('mAP', mAP)]) - ) - else: - if not args.regression: - stats = ( - '', - OrderedDict([('Loss', losses[OBJECTIVE_LOSS_KEY].mean), - ('Top1', classerr.value(1))]) - ) - if args.num_classes > 5: - stats[1]['Top5'] = classerr.value(5) - else: - stats = ( - '', - OrderedDict([('Loss', losses[OBJECTIVE_LOSS_KEY].mean), - ('MSE', classerr.value())]) - ) + if args.obj_detection: + # Only run compute() if there is at least one new update() + if have_mAP: + mAP = map_calculator.compute()['map_50'] + have_mAP = False + stats = ( + '', + OrderedDict([('Loss', losses[OBJECTIVE_LOSS_KEY].mean), + ('mAP', mAP)]) + ) + elif args.regression: + stats = ( + '', + OrderedDict([('Loss', losses[OBJECTIVE_LOSS_KEY].mean), + ('MSE', classerr.value())]) + ) else: - stats_dict = OrderedDict() - stats_dict['Test'] = validation_step - for exitnum in range(args.num_exits): - la_string = 'LossAvg' + str(exitnum) - stats_dict[la_string] = args.losses_exits[exitnum].mean - # Because of the nature of ClassErrorMeter, if an exit is never taken - # during the batch, then accessing the value(k) will cause a divide by - # zero. So we'll build the OrderedDict accordingly and we will not print - # for an exit error when that exit is never taken. - if args.exit_taken[exitnum]: - if not args.regression: - t1 = 'Top1_exit' + str(exitnum) - stats_dict[t1] = args.exiterrors[exitnum].value(1) - if args.num_classes > 5: - t5 = 'Top5_exit' + str(exitnum) - stats_dict[t5] = args.exiterrors[exitnum].value(5) - else: - t1 = 'MSE_exit' + str(exitnum) - stats_dict[t1] = args.exiterrors[exitnum].value() - stats = ('Performance/Validation/', stats_dict) + stats = ( + '', + OrderedDict([('Loss', losses[OBJECTIVE_LOSS_KEY].mean), + ('Top1', classerr.value(1))]) + ) + if args.num_classes > 5: + stats[1]['Top5'] = classerr.value(5) distiller.log_training_progress(stats, None, epoch, steps_completed, total_steps, args.print_freq, loggers) @@ -1396,68 +1297,52 @@ def select_n_random(data, labels, features, n=100): tag='verification/embedding' ) - if args.csv_prefix is not None: - f_ytrue.close() - f_ypred.close() - f_x.close() - - if not args.earlyexit_thresholds: - - if args.kd_relationbased: - - msglogger.info('==> Overall Loss: %.3f\n', - losses[OVERALL_LOSS_KEY].mean) - - return 0, 0, losses[OVERALL_LOSS_KEY].mean, 0 - - if args.obj_detection: - - msglogger.info('==> mAP: %.5f Loss: %.3f\n', - mAP, - losses[OBJECTIVE_LOSS_KEY].mean) + if args.obj_detection: + msglogger.info('==> mAP: %.5f Loss: %.3f\n', mAP, + losses[OBJECTIVE_LOSS_KEY].mean) + return 0, 0, losses[OBJECTIVE_LOSS_KEY].mean, mAP - return 0, 0, losses[OBJECTIVE_LOSS_KEY].mean, mAP + if args.kd_relationbased: + msglogger.info('==> Overall Loss: %.3f\n', + losses[OVERALL_LOSS_KEY].mean) + return 0, 0, losses[OVERALL_LOSS_KEY].mean, 0 + + if args.regression: + msglogger.info('==> MSE: %.5f Loss: %.3f\n', + classerr.value(), losses[OBJECTIVE_LOSS_KEY].mean) + return classerr.value(), .0, losses[OBJECTIVE_LOSS_KEY].mean, 0 + + if args.num_classes > 5: + msglogger.info('==> Top1: %.3f Top5: %.3f Loss: %.3f\n', + classerr.value()[0], classerr.value()[1], + losses[OBJECTIVE_LOSS_KEY].mean) + else: + msglogger.info('==> Top1: %.3f Loss: %.3f\n', + classerr.value()[0], losses[OBJECTIVE_LOSS_KEY].mean) - if not args.regression: - if args.num_classes > 5: - msglogger.info('==> Top1: %.3f Top5: %.3f Loss: %.3f\n', - classerr.value()[0], classerr.value()[1], - losses[OBJECTIVE_LOSS_KEY].mean) - else: - msglogger.info('==> Top1: %.3f Loss: %.3f\n', - classerr.value()[0], losses[OBJECTIVE_LOSS_KEY].mean) - else: - msglogger.info('==> MSE: %.5f Loss: %.3f\n', - classerr.value(), losses[OBJECTIVE_LOSS_KEY].mean) - return classerr.value(), .0, losses[OBJECTIVE_LOSS_KEY].mean, 0 - - if args.display_confusion: - msglogger.info('==> Confusion:\n%s\n', str(confusion.value())) - if tflogger is not None: - cf = nnplot.confusion_matrix(confusion.value(), args.labels) - tflogger.tblogger.writer.add_image('Validation/ConfusionMatrix', cf, epoch, - dataformats='HWC') - if not args.regression: - return classerr.value(1), classerr.value(min(args.num_classes, 5)), \ - losses[OBJECTIVE_LOSS_KEY].mean, 0 - # else: - total_top1, total_top5, losses_exits_stats = earlyexit_validate_stats(args) - return total_top1, total_top5, losses_exits_stats[args.num_exits-1], 0 + if args.display_confusion: + msglogger.info('==> Confusion:\n%s\n', str(confusion.value())) + if tflogger is not None: + cf = nnplot.confusion_matrix(confusion.value(), args.labels) + tflogger.tblogger.writer.add_image('Validation/ConfusionMatrix', cf, epoch, + dataformats='HWC') + return classerr.value(1), classerr.value(min(args.num_classes, 5)), \ + losses[OBJECTIVE_LOSS_KEY].mean, 0 def update_training_scores_history(perf_scores_history, model, top1, top5, mAP, vloss, epoch, args): """ Update the list of top training scores achieved so far, and log the best scores so far""" - model_sparsity, _, params_nnz_cnt = distiller.model_params_stats(model, param_dims=[2, 3, 4]) + _, _, params_nnz_cnt = distiller.model_params_stats(model, param_dims=[2, 3, 4]) perf_scores_history.append( - distiller.MutableNamedTuple({'params_nnz_cnt': -params_nnz_cnt, 'sparsity': model_sparsity, + distiller.MutableNamedTuple({'params_nnz_cnt': -params_nnz_cnt, 'top1': top1, 'top5': top5, 'mAP': mAP, 'vloss': -vloss, 'epoch': epoch})) + assert msglogger is not None if args.kd_relationbased: - # Keep perf_scores_history sorted from best to worst based on overall loss # overall_loss = student_loss*student_weight + distillation_loss*distillation_weight if not args.sparsity_perf: @@ -1471,165 +1356,46 @@ def update_training_scores_history(perf_scores_history, model, top1, top5, mAP, -score.vloss, score.epoch) elif args.obj_detection: - # Keep perf_scores_history sorted from best to worst - if not args.sparsity_perf: - # Sort by mAP as main sort key, then sort by vloss and epoch - perf_scores_history.sort(key=operator.attrgetter('mAP', 'vloss', 'epoch'), - reverse=True) - else: - perf_scores_history.sort(key=operator.attrgetter('params_nnz_cnt', 'mAP', - 'vloss', 'epoch'), - reverse=True) + # Sort by mAP as main sort key, then sort by vloss and epoch + perf_scores_history.sort(key=operator.attrgetter('mAP', 'vloss', 'epoch'), + reverse=True) for score in perf_scores_history[:args.num_best_scores]: - msglogger.info('==> Best [mAP: %f vloss: %f Sparsity:%.2f ' + msglogger.info('==> Best [mAP: %f vloss: %f ' 'Params: %d on epoch: %d]', - score.mAP, -score.vloss, score.sparsity, -score.params_nnz_cnt, + score.mAP, -score.vloss, -score.params_nnz_cnt, score.epoch) - else: - - if not args.regression: - - # Keep perf_scores_history sorted from best to worst - if not args.sparsity_perf: - # Sort by top1 as main sort key, then sort by top5 and epoch - perf_scores_history.sort(key=operator.attrgetter('top1', 'top5', 'epoch'), - reverse=True) - else: - # Sort by sparsity as main sort key, then sort by top1, top5 and epoch - perf_scores_history.sort(key=operator.attrgetter('params_nnz_cnt', 'top1', - 'top5', 'epoch'), - reverse=True) - for score in perf_scores_history[:args.num_best_scores]: - if args.num_classes > 5: - msglogger.info('==> Best [Top1: %.3f Top5: %.3f Sparsity:%.2f ' - 'Params: %d on epoch: %d]', - score.top1, score.top5, score.sparsity, -score.params_nnz_cnt, - score.epoch) - else: - msglogger.info('==> Best [Top1: %.3f Sparsity:%.2f ' - 'Params: %d on epoch: %d]', - score.top1, score.sparsity, -score.params_nnz_cnt, - score.epoch) - else: - - # Sort by MSE as main sort key, then sort by epoch - perf_scores_history.sort(key=operator.attrgetter('epoch'), reverse=True) - perf_scores_history.sort(key=operator.attrgetter('top1'), reverse=False) + elif args.regression: + # Sort by MSE as main sort key, then sort by epoch + perf_scores_history.sort(key=operator.attrgetter('epoch'), reverse=True) + perf_scores_history.sort(key=operator.attrgetter('top1'), reverse=False) - if args.sparsity_perf: - # Sort by sparsity as main sort key - perf_scores_history.sort(perf_scores_history, - key=operator.attrgetter('params_nnz_cnt'), - reverse=True) + for score in perf_scores_history[:args.num_best_scores]: + msglogger.info('==> Best [Top 1 (MSE): %.5f ' + 'Params: %d on epoch: %d]', + score.top1, -score.params_nnz_cnt, + score.epoch) - for score in perf_scores_history[:args.num_best_scores]: - msglogger.info('==> Best [Top 1 (MSE): %.5f Sparsity:%.2f ' + else: + # Keep perf_scores_history sorted from best to worst + # Sort by top1 as main sort key, then sort by top5 and epoch + perf_scores_history.sort(key=operator.attrgetter('top1', 'top5', 'epoch'), + reverse=True) + for score in perf_scores_history[:args.num_best_scores]: + if args.num_classes > 5: + msglogger.info('==> Best [Top1: %.3f Top5: %.3f ' 'Params: %d on epoch: %d]', - score.top1, score.sparsity, -score.params_nnz_cnt, + score.top1, score.top5, -score.params_nnz_cnt, score.epoch) - - -def earlyexit_loss(output, target, criterion, args): - """earlyexit_loss""" - loss = 0 - sum_lossweights = 0 - for exitnum in range(args.num_exits-1): - loss += (args.earlyexit_lossweights[exitnum] * criterion(output[exitnum], target)) - sum_lossweights += args.earlyexit_lossweights[exitnum] - args.exiterrors[exitnum].add(output[exitnum].data, target) - # handle final exit - loss += (1.0 - sum_lossweights) * criterion(output[args.num_exits-1], target) - args.exiterrors[args.num_exits-1].add(output[args.num_exits-1].data, target) - return loss - - -def earlyexit_validate_loss(output, target, _criterion, args): - """ - We need to go through each sample in the batch itself - in other words, we are - not doing batch processing for exit criteria - we do this as though it were batchsize of 1 - but with a grouping of samples equal to the batch size. - Note that final group might not be a full batch - so determine actual size. - """ - this_batch_size = target.size()[0] - earlyexit_validate_criterion = nn.CrossEntropyLoss(reduce=False).to(args.device) - - for exitnum in range(args.num_exits): - # calculate losses at each sample separately in the minibatch. - args.loss_exits[exitnum] = earlyexit_validate_criterion( # pylint: disable=not-callable - output[exitnum], target - ) - # for batch_size > 1, we need to reduce this down to an average over the batch - args.losses_exits[exitnum].add(torch.mean(args.loss_exits[exitnum]).cpu()) - - for batch_index in range(this_batch_size): - earlyexit_taken = False - # take the exit using CrossEntropyLoss as confidence measure (lower is more confident) - for exitnum in range(args.num_exits - 1): - if args.loss_exits[exitnum][batch_index] < args.earlyexit_thresholds[exitnum]: - # take the results from early exit since lower than threshold - args.exiterrors[exitnum].add( - torch.tensor( - np.array(output[exitnum].data[batch_index].cpu(), ndmin=2), - dtype=torch.float - ), - torch.full([1], target[batch_index], dtype=torch.long)) - args.exit_taken[exitnum] += 1 - earlyexit_taken = True - break # since exit was taken, do not affect the stats of subsequent exits - # this sample does not exit early and therefore continues until final exit - if not earlyexit_taken: - exitnum = args.num_exits - 1 - args.exiterrors[exitnum].add( - torch.tensor( - np.array(output[exitnum].data[batch_index].cpu(), ndmin=2), - dtype=torch.float - ), - torch.full([1], target[batch_index], dtype=torch.long)) - args.exit_taken[exitnum] += 1 - - -def earlyexit_validate_stats(args): - """Print some interesting summary stats for number of data points that could exit early""" - top1k_stats = [0] * args.num_exits - top5k_stats = [0] * args.num_exits - losses_exits_stats = [0] * args.num_exits - sum_exit_stats = 0 - for exitnum in range(args.num_exits): - if args.exit_taken[exitnum]: - sum_exit_stats += args.exit_taken[exitnum] - msglogger.info("Exit %d: %d", exitnum, args.exit_taken[exitnum]) - if not args.regression: - top1k_stats[exitnum] += args.exiterrors[exitnum].value(1) - top5k_stats[exitnum] += args.exiterrors[exitnum].value( - min(args.num_classes, 5) - ) else: - top1k_stats[exitnum] += args.exiterrors[exitnum].value() - losses_exits_stats[exitnum] += args.losses_exits[exitnum].mean - for exitnum in range(args.num_exits): - if args.exit_taken[exitnum]: - msglogger.info("Percent Early Exit %d: %.3f", exitnum, - (args.exit_taken[exitnum]*100.0) / sum_exit_stats) - total_top1 = 0 - total_top5 = 0 - for exitnum in range(args.num_exits): - total_top1 += (top1k_stats[exitnum] * (args.exit_taken[exitnum] / sum_exit_stats)) - if not args.regression: - total_top5 += (top5k_stats[exitnum] * (args.exit_taken[exitnum] / sum_exit_stats)) - msglogger.info("Accuracy Stats for exit %d: top1 = %.3f, top5 = %.3f", exitnum, - top1k_stats[exitnum], top5k_stats[exitnum]) - else: - msglogger.info("Accuracy Stats for exit %d: top1 = %.3f", exitnum, - top1k_stats[exitnum]) - msglogger.info("Totals for entire network with early exits: top1 = %.3f, top5 = %.3f", - total_top1, total_top5) - return total_top1, total_top5, losses_exits_stats + msglogger.info('==> Best [Top1: %.3f ' + 'Params: %d on epoch: %d]', + score.top1, -score.params_nnz_cnt, + score.epoch) -def evaluate_model(model, criterion, test_loader, loggers, activations_collectors, args, - scheduler=None): +def evaluate_model(model, criterion, test_loader, loggers, args, scheduler=None, local_rank=-1): """ This sample application can be invoked to evaluate the accuracy of your model on the test dataset. @@ -1642,52 +1408,19 @@ def evaluate_model(model, criterion, test_loader, loggers, activations_collector if not isinstance(loggers, list): loggers = [loggers] - if args.quantize_eval: - model.cpu() - # if args.ai84: - # quantizer = PostTrainLinearQuantizerAI84.from_args(model, args) - # else: - quantizer = PostTrainLinearQuantizer.from_args(model, args) - quantizer.prepare_model() - model.to(args.device) - - top1, _, _, mAP = test(test_loader, model, criterion, loggers, activations_collectors, - args=args) - - if args.shap > 0: - matplotlib.use('TkAgg') - print("Generating plot...") - images, _ = iter(test_loader).next() - background = images[:100] - test_images = images[100:100 + args.shap] - - # pylint: disable=protected-access - shap.explainers._deep.deep_pytorch.op_handler['Clamp'] = \ - shap.explainers._deep.deep_pytorch.passthrough - shap.explainers._deep.deep_pytorch.op_handler['Empty'] = \ - shap.explainers._deep.deep_pytorch.passthrough - shap.explainers._deep.deep_pytorch.op_handler['Floor'] = \ - shap.explainers._deep.deep_pytorch.passthrough - shap.explainers._deep.deep_pytorch.op_handler['Quantize'] = \ - shap.explainers._deep.deep_pytorch.passthrough - shap.explainers._deep.deep_pytorch.op_handler['Scaler'] = \ - shap.explainers._deep.deep_pytorch.passthrough - # pylint: enable=protected-access - e = shap.DeepExplainer(model.to(args.device), background.to(args.device)) - shap_values = e.shap_values(test_images.to(args.device)) - shap_numpy = [np.swapaxes(np.swapaxes(s, 1, -1), 1, 2) for s in shap_values] - test_numpy = np.swapaxes(np.swapaxes(test_images.numpy(), 1, -1), 1, 2) - # Plot the feature attributions - shap.image_plot(shap_numpy, -test_numpy) - - if args.quantize_eval: + top1, _, _, mAP = test(test_loader, model, criterion, loggers, args=args) + + if args.quantize_eval and local_rank <= 0: # not DistributedDataParallel or rank 0 checkpoint_name = 'quantized' if args.obj_detection: extras = {'quantized_mAP': mAP} else: extras = {'quantized_top1': top1} - apputils.save_checkpoint(0, args.cnn, model, optimizer=None, scheduler=scheduler, + assert msglogger is not None + + m, _, _ = model_wrapper.unwrap(model) + apputils.save_checkpoint(0, args.cnn, m, optimizer=None, scheduler=scheduler, name='_'.join([args.name, checkpoint_name]) if args.name else checkpoint_name, dir=msglogger.logdir, extras=extras) @@ -1696,6 +1429,8 @@ def evaluate_model(model, criterion, test_loader, loggers, activations_collector def summarize_model(model, dataset, which_summary, filename='model'): """summarize_model""" if which_summary.startswith('png'): + if which_summary == 'png_simplified': + ai8x.onnx_export_prep(model, simplify=True, remove_clamp=True) model_summaries.draw_img_classifier_to_file(model, filename + '.png', dataset, which_summary == 'png_w_params') elif which_summary in ['onnx', 'onnx_simplified']: @@ -1705,36 +1440,15 @@ def summarize_model(model, dataset, which_summary, filename='model'): filename + '.onnx', dataset, add_softmax=False, - opset_version=13, ) else: distiller.model_summary(model, which_summary, dataset, log_1d=True) -def sensitivity_analysis(model, criterion, data_loader, loggers, args, sparsities): - """ - This sample application can be invoked to execute Sensitivity Analysis on your - model. The output is saved to CSV and PNG. - """ - msglogger.info("Running sensitivity tests") - if not isinstance(loggers, list): - loggers = [loggers] - test_fnc = partial(test, test_loader=data_loader, criterion=criterion, - loggers=loggers, args=args, - activations_collectors=create_activation_stats_collectors(model)) - which_params = [param_name for param_name, _ in model.named_parameters()] - sensitivity = distiller.perform_sensitivity_analysis(model, - net_params=which_params, - sparsities=sparsities, - test_func=test_fnc, - group=args.sensitivity) - distiller.sensitivities_to_png(sensitivity, 'sensitivity.png') - distiller.sensitivities_to_csv(sensitivity, 'sensitivity.csv') - - def create_nas_training_stage_list(model, nas_policy): """Create list to define NAS stage transition epochs""" stage_transition_list = [] + assert msglogger is not None msglogger.info('NAS Policy: %s', nas_policy) stage_transition_list.append((nas_policy['start_epoch'], 0, 0)) @@ -1802,102 +1516,10 @@ def __missing__(self, key): return None # note, does *not* set self[key] - we don't want defaultdict's behavior -def create_activation_stats_collectors(model, *phases): - """Create objects that collect activation statistics. - - This is a utility function that creates two collectors: - 1. Fine-grade sparsity levels of the activations - 2. L1-magnitude of each of the activation channels - - Args: - model - the model on which we want to collect statistics - phases - the statistics collection phases: train, valid, and/or test - - WARNING! Enabling activation statsitics collection will significantly slow down training! - """ - distiller.utils.assign_layer_fq_names(model) - - genCollectors = lambda: missingdict({ # noqa E731 pylint: disable=unnecessary-lambda-assignment - "sparsity": SummaryActivationStatsCollector(model, "sparsity", - lambda t: - 100 * distiller.utils.sparsity(t)), - "l1_channels": SummaryActivationStatsCollector(model, "l1_channels", - distiller.utils.activation_channels_l1), - "apoz_channels": SummaryActivationStatsCollector(model, "apoz_channels", - distiller.utils.activation_channels_apoz), - "mean_channels": SummaryActivationStatsCollector(model, "mean_channels", - distiller.utils. - activation_channels_means), - "records": RecordsActivationStatsCollector(model, classes=[nn.Conv2d]) - }) - - return {k: (genCollectors() if k in phases else missingdict()) - for k in ('train', 'valid', 'test')} - - -def create_quantization_stats_collector(model): - """create_quantization_stats_collector""" - distiller.utils.assign_layer_fq_names(model) - return {'test': missingdict({ - 'quantization_stats': QuantCalibrationStatsCollector(model, classes=None, - inplace_runtime_check=True, - disable_inplace_attrs=True)})} - - -def save_collectors_data(collectors, directory): - """Utility function that saves all activation statistics to Excel workbooks - """ - for name, collector in collectors.items(): - workbook = os.path.join(directory, name) - msglogger.info("Generating %s", workbook) - collector.save(workbook) - - -def update_old_model_params(model_path, model_new): - """Adds missing model parameters added with default values. - This is mainly due to the saved checkpoints from previous versions of the repo. - New model is saved to `model_path` and the old model copied into the same file_path with - `__obsolete__` prefix.""" - is_model_old = False - model_old = torch.load(model_path, - map_location=lambda storage, loc: storage) - # Fix up any instances of DataParallel - old_dict = model_old['state_dict'].copy() - for k in old_dict: - if k.startswith('module.'): - model_old['state_dict'][k[7:]] = old_dict[k] - for new_key, new_val in model_new.state_dict().items(): - if new_key not in model_old['state_dict'] and 'bn' not in new_key: - is_model_old = True - model_old['state_dict'][new_key] = new_val - if 'compression_sched' in model_old: - if 'masks_dict' in model_old['compression_sched']: - model_old['compression_sched']['masks_dict'][new_key] = None - - if is_model_old: - dir_path, file_name = os.path.split(model_path) - new_file_name = '__obsolete__' + file_name - old_model_path = os.path.join(dir_path, new_file_name) - os.rename(model_path, old_model_path) - torch.save(model_old, model_path) - msglogger.info('Model `%s` is old. Missing parameters added with default values!', - model_path) - - if __name__ == '__main__': try: np.set_printoptions(threshold=sys.maxsize, linewidth=190) torch.set_printoptions(threshold=sys.maxsize, linewidth=190) - - # import builtins, sys - # print(distiller.config.__name__) - # print(distiller.config.__builtins__) - # # print(distiller.config.__import__) - # builtins.QuantAwareTrainRangeLinearQuantizerAI84 = \ - # range_linear_ai84.QuantAwareTrainRangeLinearQuantizerAI84 - # globals()['range_linear_ai84'] = __import__('range_linear_ai84') - # sys.path.append('/home/robertmuchsel/Documents/Source/ai84') - # site.addsitedir("/home/robertmuchsel/Documents/Source/ai84") main() except KeyboardInterrupt: print("\n-- KeyboardInterrupt --") diff --git a/utils/augmentation_utils.py b/utils/augmentation_utils.py index 60df39f6e..ce92ae424 100644 --- a/utils/augmentation_utils.py +++ b/utils/augmentation_utils.py @@ -252,7 +252,7 @@ def resize(image, boxes, dims=(300, 300), return_percent_coords=True): in which case they remain the same) """ # Resize image - new_image = FT.resize(image, dims) + new_image = FT.resize(image, dims, antialias=True) # Resize bounding boxes # old_dims = diff --git a/utils/model_wrapper.py b/utils/model_wrapper.py new file mode 100644 index 000000000..12a36bbff --- /dev/null +++ b/utils/model_wrapper.py @@ -0,0 +1,28 @@ +################################################################################################### +# Copyright (C) 2023 Maxim Integrated Products, Inc. All Rights Reserved. +# +# Maxim Integrated Products, Inc. Default Copyright Notice: +# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html +# +################################################################################################### +""" +Common code for wrapped models +""" +from torch.nn.parallel import DistributedDataParallel + + +def unwrap( + model, +): + """ + Unwrap a model from torch.compile() and DistributedDataParallel + """ + dynamo = hasattr(model, "_orig_mod") + if dynamo: + model = model._orig_mod # pylint: disable=protected-access + + ddp = isinstance(model, DistributedDataParallel) + if ddp: + model = model.module + + return model, dynamo, ddp diff --git a/yamlwriter.py b/yamlwriter.py new file mode 100644 index 000000000..acdb7860e --- /dev/null +++ b/yamlwriter.py @@ -0,0 +1,1238 @@ +################################################################################################### +# +# Copyright (C) 2022-2024 Maxim Integrated Products, Inc. All Rights Reserved. +# +# Maxim Integrated Products, Inc. Default Copyright Notice: +# https://www.maximintegrated.com/en/aboutus/legal/copyrights.html +# +################################################################################################### +""" +Generate YAML template from PyTorch model + +ALPHA version + +TODO: Implement smart processor and data memory allocator (out_offset, in_offset) +TODO: Implement dilation +TODO: Testing + +NOTE: This code partially depends on ai8x.py: + - Quantization information is expected in '.weight_bits'. +""" +import os +import warnings +from typing import Any, Callable, Dict, List, Optional, OrderedDict, Tuple, Union + +import distiller + +import ai8x +import devices + + +def allocate_offset( + _layer_name: str, + _processor_map: int, + prev_offset: int = 0, +) -> int: + """ + Find a data memory offset for the given layer. Currently just uses "ping-pong" from 0 to + half of the data memory. + TODO: Implement an algorithm that interacts with weight memories and processor selection, + and understands element-wise data. + """ + assert ai8x.dev is not None + HALF_DATA = 0x4000 if ai8x.dev.device == 85 else 0xa000 + + return HALF_DATA if prev_offset == 0 else 0 + + +# pylint: disable=too-many-branches, too-many-statements, too-many-locals +def create( + model: Any, + dataset: str, + arch: str, + hwc: bool = False, + use_fifos: bool = False, + move_l0: bool = False, + filename: str = 'template.yaml', + qat_policy: Optional[str] = None, + verbose=True, +) -> None: + """ + Create YAML template + """ + assert ai8x.dev is not None + if ai8x.dev.device == 85: + MAX_PIXELS = 8192 + elif ai8x.dev.device == 87: + MAX_PIXELS = 20480 + else: + print(f'Unknown device {ai8x.dev.device}') + return + + # Filter warnings + s0 = 'The shape inference of prim::Constant type is missing, so it may result in wrong '\ + 'shape inference for the exported graph. Please consider adding it in symbolic function.' + warnings.filterwarnings(action='ignore', message=s0) + s1 = 'Constant folding - Only steps=1 can be constant folded for opset >= 10 ' \ + 'onnx::Slice op. Constant folding not applied.' + warnings.filterwarnings(action='ignore', message=s1) + + MAX_PROC = 64 + + model = distiller.make_non_parallel_copy(model) # type: ignore[attr-defined] + + # Replace unsupported BitwiseOr and BitwiseXor with an Add() and record the locations. + # After ONNX conversion, put them back. This is necessary until PyTorch ONNX export supports + # BitwiseOr/BitwiseXor. + bitwise_replacements: List[Tuple[str, str]] = [] + + def replace_bitwise(m): + for attr_str in dir(m): + target_attr = getattr(m, attr_str) + if isinstance(target_attr, (ai8x.BitwiseXor, ai8x.BitwiseOr)): + print(attr_str) + source = 'bitwise_or' if isinstance(target_attr, ai8x.BitwiseOr) else 'bitwise_xor' + bitwise_replacements.append((attr_str, source)) + setattr(m, attr_str, ai8x.Add()) + + model.apply(replace_bitwise) + + # Apply the QAT policy early to set weight_bits + ai8x.fuse_bn_layers(model) + if qat_policy is not None: + ai8x.initiate_qat(model, qat_policy, export=True) + ai8x.onnx_export_prep(model, simplify=False, remove_clamp=False) + + dummy_input = distiller.get_dummy_input( # type: ignore[attr-defined] + dataset=None, + device=distiller.model_device(model), # type: ignore[attr-defined] + input_shape=None, + ) + g = distiller.SummaryGraph(model, dummy_input, True, '#') # type: ignore[attr-defined] + + # Get the input/output dimensions + shapes: Dict[str, Tuple] = {} + for i, param in g.params.items(): + shape = param['shape'] + if len(shape) > 1: + shapes[i] = shape[1:] # Remove batch dimension + + # all_ops is the main dictionary for the layers + all_ops: OrderedDict[str, Dict[str, Any]] = g.ops + + # Put the bitwise operations back + for (attr_str, bitwise_op) in bitwise_replacements: + assert all_ops[attr_str]['type'] == 'Add' + all_ops[attr_str]['type'] = 'BitwiseOr' if bitwise_op == 'bitwise_or' else 'BitwiseXor' + + # ONNX operators for convolution: + convolution_ops: Tuple[str, ...] = ('Conv', 'ConvTranspose', 'Gemm', 'MatMul') + + def canonical_name( + s: str, + ) -> str: + """ + Return the canonical name for this layer + """ + separator = s.rfind('_MatMul_1') + if separator > 0: + s = s[:separator] + separator = s.rfind('.') + if separator > 0: + if s[separator + 1:] == 'op': + return s[:separator] + return s + + def ignore_layer( + _name: str, + layer: Dict[str, Any], + ) -> bool: + """ + Remove unnecessary layers from the graph + """ + if not layer['inputs'] or not layer['outputs']: + return True # Not consuming or producing anything, useless layer + return layer['type'] not in convolution_ops and layer['type'] not in ( + 'Add', 'Sub', 'BitwiseOr', 'BitwiseXor', # element-wise + 'MaxPool', 'AveragePool', + 'Abs', 'Relu', + ) + + def follow_input( + trace_in: List[str], + condition: Callable[[str], bool], + replace: Tuple[str, str], + ) -> List[str]: + """ + Trace a list of inputs back upstream and stop when condition is met + """ + results: List[str] = [] + while len(trace_in) > 0: + next_trace: List[str] = [] + for t in trace_in: + if condition(t): + results.append(t.replace(replace[0], replace[1])) + else: + for n in all_ops: + if t in all_ops[n]['outputs']: + next_trace += all_ops[n]['inputs'] + trace_in = next_trace + + return results + + # 1 - Set output_width to 32 where needed + # Trace all "-32768" constants to the next 'Min' operation (there may be more than one) + results: List[str] = [] + + for n in all_ops: + if all_ops[n]['type'] in convolution_ops: + # Ignore weights/biases in the input list + # These operations have 2 or three arguments: data/weights/biases(optional) + all_ops[n]['weights'] = follow_input([all_ops[n]['inputs'][1]], + lambda s: s in shapes, ('#', '.')) + if len(all_ops[n]['inputs']) == 3: + all_ops[n]['biases'] = follow_input([all_ops[n]['inputs'][2]], + lambda s: s in shapes, ('#', '.')) + # Remove weights and biases from the inputs for this operation + all_ops[n]['inputs'] = [all_ops[n]['inputs'][0]] + + if all_ops[n]['type'] != 'Constant' or 'value' not in all_ops[n]['attrs'] \ + or all_ops[n]['attrs']['value'].dim() != 0 or all_ops[n]['attrs']['value'] != -32768: + continue + trace_out: List[str] = all_ops[n]['outputs'] + while len(trace_out) > 0: + next_trace: List[str] = [] + for t in trace_out: + for p in all_ops: + if t in all_ops[p]['inputs']: + if all_ops[p]['type'] in ('Min', 'Clip'): + results.append(p) + else: + next_trace += all_ops[p]['outputs'] + trace_out = next_trace + # Trace the inputs of these layers back to the previous convolution + for n in results: + trace_in: List[str] = all_ops[n]['inputs'] + while len(trace_in) > 0: + next_trace = [] + for t in trace_in: + for p in all_ops: + if t in all_ops[p]['outputs']: + if all_ops[p]['type'] in convolution_ops: + all_ops[p]['wide'] = True + else: + next_trace += all_ops[p]['inputs'] + trace_in = next_trace + + # 2 - Remove inputs with zero dimensions + remove_list: List[str] = [] + + for n in all_ops: + s: List[str] = all_ops[n]['inputs'] + all_ops[n]['original_inputs'] = s + remove: bool = False + new: List[str] = [] + for i in s: + if i in shapes: + new.append(i) + else: + remove = True + remove_list.append(i) + if remove: + all_ops[n]['inputs'] = new + + s = all_ops[n]['outputs'] + remove = False + new = [] + for i in s: + if i in shapes: + new.append(i) + else: + remove = True + remove_list.append(i) + if remove: + all_ops[n]['outputs'] = new + + # 3 - Remove layers + ignore_layers: List[str] = [] + + for n in all_ops: + if ignore_layer(n, all_ops[n]): + ignore_layers.append(n) + + def follow_chain( + layer: str, + remove: str, + replacements: List[str], + which: str, + ) -> None: + for n in all_ops: + if layer == n: + continue + for an_output in remove: + new_inputs: List[str] = [] + for an_input in all_ops[n][which]: + if an_output == an_input: + new_inputs += replacements + else: + new_inputs.append(an_input) + + filtered: List[str] = [] + for ie in new_inputs: + if ie not in filtered: + filtered.append(ie) + if filtered != all_ops[n][which]: + all_ops[n][which] = filtered + + # Fix up inputs after removing a layer + for layer in ignore_layers: + # Look for layer's outputs in the inputs of all other layers and replace + follow_chain( + layer, + remove=all_ops[layer]['outputs'], + replacements=all_ops[layer]['inputs'], + which='inputs', + ) + + # 4 - Associate inputs and outputs + input_layer: Dict[str, str] = {} + output_layer: Dict[str, str] = {} + final_layer: str = '' + + for name in all_ops: + for ie in all_ops[name]['inputs']: + if ie != '': + input_layer[ie] = name + + for ie in all_ops[name]['outputs']: + if ie != '': + output_layer[ie] = name + + final_layer = name + + # 5 - Collect inputs and outputs + inputs: Dict[str, List[str]] = {} + outputs: Dict[str, List[str]] = {} + + for name in all_ops: + if ignore_layer(name, all_ops[name]): + continue + + # Get input names from first of the layer group + if name not in inputs: + inputs[name] = [] + for ie in all_ops[name]['inputs']: + if ie != '' and (ie not in output_layer or output_layer[ie] != name): + inputs[name].append(ie) + + if name not in outputs: + outputs[name] = [] + for ie in all_ops[name]['outputs']: + if ie != '' and (ie not in input_layer or input_layer[ie] != name): + outputs[name].append(ie) + + # 6 - Collect ops + prev_op_name: str = '' + layers: Dict[str, Dict[str, Any]] = {} + input_hwc: bool = False + input_processors: int = 0 + + for name in all_ops: + if ignore_layer(name, all_ops[name]): + continue + + this_layer: Dict[str, Any] = {} + + this_layer['name'] = name + ins = inputs[name] + + # Mark output layers (the final layer is always an output layer) + if name != final_layer and any(x not in input_layer for x in outputs[name]): + this_layer['output'] = 'true' + + this_layer['op'] = main_op = 'Passthrough' + operands: int = 1 + + op = all_ops[name]['type'] + + if op in ('Add', 'Sub', 'BitwiseOr', 'BitwiseXor'): + operands = len(ins) + if op.startswith('Bitwise'): + op = op[7:] + this_layer['eltwise'] = op + this_layer['operands'] = operands + elif op in ('Gemm', 'MatMul'): + this_layer['op'] = main_op = 'Linear' + this_layer['activate'] = 'None' + elif op in ('Conv', 'ConvTranspose'): + kernel_size = all_ops[name]['attrs']['kernel_shape'] + this_layer['op'] = f'{op}{len(kernel_size)}d' + this_layer['activate'] = 'None' + main_op = op + elif op in ('MaxPool', 'AveragePool'): + shape = all_ops[name]['attrs']['kernel_shape'] + if len(shape) == 1 or shape[0] == shape[1]: + shape = shape[0] + if all_ops[name]['type'] == 'MaxPool': + this_layer['max_pool'] = shape + else: + this_layer['avg_pool'] = shape + this_layer['pool_stride'] = all_ops[name]['attrs']['strides'][0] + if 'dilations' in all_ops[name]['attrs']: + this_layer['pool_dilation'] = all_ops[name]['attrs']['dilations'] + elif op in ('Abs', 'Relu'): + this_layer['activate'] = op + else: + this_layer['op'] = f'Unknown ({op})' + + this_layer['main_op'] = main_op + + if op in ('Conv', 'ConvTranspose'): + if len(kernel_size) == 1: + this_layer['kernel_size'] = str(kernel_size[0]) + else: + this_layer['kernel_size'] = f'{kernel_size[0]}x{kernel_size[1]}' + pad = all_ops[name]['attrs']['pads'] + this_layer['pad'] = pad[0] + groups = all_ops[name]['attrs']['group'] + if groups != 1: + this_layer['groups'] = groups + + # Quantization uses hard-coded name from ai8x.py + quantization = 8 + try: + quantization = int(model.get_parameter(name + '.weight_bits')) + except AttributeError: + try: + quantization = int(model.get_parameter(canonical_name(name) + '.weight_bits')) + except AttributeError: + pass + if quantization not in (0, 8): + assert quantization in (1, 2, 4), f'ERROR: {name}: quantization={quantization}' + this_layer['quantization'] = quantization + + # Check for biases and 32-bit output using the data recorded earlier + if op in convolution_ops: + if 'wide' in all_ops[name] and all_ops[name]['wide']: + this_layer['output_width'] = 32 + this_layer['have_bias'] = 'biases' in all_ops[name] + this_layer['weight_count'] = int(all_ops[name]['attrs']['weights_vol']) + + # Check whether inputs need to be flattened (when they are not in 1x1 dimensions) + flatten: bool = False + if op in ('Gemm', 'MatMul'): + for ie in inputs[name]: + if ie in shapes: + mult: int = 1 + for x in shapes[ie]: + mult *= x + if shapes[ie][0] != mult: + flatten = True + if flatten: + this_layer['flatten'] = 'true' + + in_dim: Optional[Union[Tuple[int, ...], List[int]]] = None + prev_dim: Optional[Union[Tuple[int, ...], List[int]]] = None + + # Check whether dimensions need to change + if not flatten: + mult = 1 + for n in inputs[name]: + if n in shapes: + for x in shapes[n]: + mult *= x + if len(shapes[n]) > 1 and mult != shapes[n][0]: + in_dim = shapes[n][1:] + else: + in_dim = shapes[n] + break + mult = 1 + for n in all_ops[name]['original_inputs']: + if n in shapes: + for x in shapes[n]: + mult *= x + if len(shapes[n]) > 1 and mult != shapes[n][0]: + prev_dim = shapes[n][1:] + else: + prev_dim = shapes[n] + break + if in_dim is not None and prev_dim is not None: + if in_dim != prev_dim \ + and (in_dim[0] != prev_dim[0] or len(prev_dim) > 1 or mult != prev_dim[0]): + if len(prev_dim) > 0: + prev_dim = list(prev_dim) + this_layer['in_dim'] = prev_dim + + # Find number of processors needed + processors: int = 0 + for ie in inputs[name]: + if ie in shapes: + processors += shapes[ie][0] + if operands > 1: # Element-wise: data is always interleaved + processors //= operands + this_layer['proc_count'] = max(1, processors) + + # Inner layers and more than 16 channels are always HWC + hwc = hwc or (processors > 16) or (prev_op_name != '') + + if prev_op_name == '': + # Show input dimensions and data format for input layers + this_layer['data_format'] = hwc + input_hwc = hwc + input_processors = this_layer['proc_count'] + else: + # Don't set in_sequences when using strictly sequential single inputs + ins = [output_layer[x] for x in ins] + if len(ins) > 1 or (len(ins) > 0 and ins[0] != prev_op_name): + if operands > 1: + ins.reverse() # Reverse the list since PyTorch does it backwards + this_layer['in_sequences'] = ins + + prev_op_name = name + layers[name] = this_layer + + del input_layer + del output_layer + + prev_name: str = '' + pop_list: List[Tuple[str, str]] = [] + veto: bool = False + prev: Dict[str, Any] = {} + + # 7a - Merge (fuse) conv and activation layers + for count, (name, ll) in enumerate(layers.items()): + if ll['main_op'] == 'Passthrough' and 'activate' in ll and prev_name != '': + prev = layers[prev_name] + if prev['main_op'] not in ('Conv', 'ConvTranspose', 'Linear'): + print(f'ERROR: Activation layer {name} does not follow ' + f'Conv/Linear layer {prev_name}!') + prev_name = name + continue + # Check that no layer other than the activation layer uses the intermediate output of + # the conv layer as an input + veto = False + for (other_name, ol) in layers.items(): + if other_name != name and other_name != prev_name and 'in_sequences' in ol: + if prev_name in ol['in_sequences']: + veto = True + break + if veto: + print(f'ERROR: Activation layer {name} has inputs that are not from a directly ' + f'preceding Conv/Linear layer {prev_name}!') + prev_name = name + continue + # Combine both layers + if 'comment' not in prev: + prev['comment'] = f'{prev_name} fused with {name}' + else: + prev['comment'] += f' and {name}' + pop_list.append((prev_name, name)) # Mark second layer for deletion + + # Copy over convolution operation and keep the element-wise operation in place + outputs[prev_name] = outputs[name] + + prev['activate'] = ll['activate'] + if 'output' in ll: + prev['output'] = ll['output'] + if 'quantization' in ll: + prev['quantization'] = ll['quantization'] + if 'output_width' in ll: + prev['output_width'] = ll['output_width'] + + prev_name = name + + # Delete the conv layers that were fused into the eltwise layer + for (prev_name, name) in pop_list: + # Change any dangling input sequences to the fused layer + for (other_name, ol) in layers.items(): + if other_name != name and other_name != prev_name and 'in_sequences' in ol: + for i, e in enumerate(ol['in_sequences']): + if e == name: + ol['in_sequences'][i] = prev_name + + # Delete the conv portion + layers.pop(name) + + # 7b - Merge (fuse) pooling and conv layers + prev_name = '' + pop_list = [] + veto = False + prev = {} + + for count, (name, ll) in enumerate(layers.items()): + if ll['main_op'] in ('Conv', 'ConvTranspose', 'Linear') and prev_name != '': + prev = layers[prev_name] + if prev['main_op'] != 'Passthrough' or ( + 'max_pool' not in prev and 'avg_pool' not in prev + ): + prev_name = name + continue + # Check that no layer other than the activation layer uses the intermediate output of + # the conv layer as an input + veto = False + for (other_name, ol) in layers.items(): + if other_name != name and other_name != prev_name and 'in_sequences' in ol: + if prev_name in ol['in_sequences']: + veto = True + break + if veto: + prev_name = name + continue + # Combine both layers + if 'comment' not in ll: + ll['comment'] = f'{prev_name} fused with {name}' + else: + ll['comment'] = f'{prev_name} and ' + ll['comment'] + pop_list.append((prev_name, name)) # Mark first layer for deletion + + # Copy the pooling information into the convolution layer + inputs[name] = inputs[prev_name] + + if 'in_sequences' in prev: + ll['in_sequences'] = prev['in_sequences'] + if 'avg_pool' in prev: + ll['avg_pool'] = prev['avg_pool'] + if 'max_pool' in prev: + ll['max_pool'] = prev['max_pool'] + if 'pool_stride' in prev: + ll['pool_stride'] = prev['pool_stride'] + if 'pool_dilation' in prev: + ll['pool_dilation'] = prev['pool_dilation'] + if 'data_format' in prev: + ll['data_format'] = prev['data_format'] + + prev_name = name + + # Delete the pooling layers that were fused into the conv layer + for (prev_name, _) in pop_list: + # Delete the pooling portion + layers.pop(prev_name) + + # 7c - Merge (fuse) element-wise and convolution layers + prev_name = '' + pop_list = [] + for count, (name, ll) in enumerate(layers.items()): + if ll['main_op'] in ('Conv', 'ConvTranspose') and prev_name != '': + prev = layers[prev_name] + # Only one pooling operation possible + pool_count: int = 0 + if 'max_pool' in prev: + pool_count += 1 + if 'avg_pool' in prev: + pool_count += 1 + if 'max_pool' in ll: + pool_count += 1 + if 'avg_pool' in ll: + pool_count += 1 + # Check that no layer other than the conv layer uses the intermediate output of the + # element-wise layer as an input + veto = False + for (other_name, ol) in layers.items(): + if other_name != name and other_name != prev_name and 'in_sequences' in ol: + if prev_name in ol['in_sequences']: + veto = True + break + if veto or 'in_sequences' in ll or 'in_dim' in ll or 'flatten' in ll: + prev_name = name + continue + if prev['main_op'] != 'Passthrough' or 'operands' not in prev \ + or prev['operands'] == 1 or pool_count > 1: + prev_name = name + continue + # MAX78002 - avoid element-wise with bias and convolution when using multi-pass + if ai8x.dev.device == 87 and ll['have_bias'] and prev['proc_count'] > MAX_PROC: + prev_name = name + continue + # Combine both layers + if 'comment' not in ll: + ll['comment'] = f'{prev_name} fused with {name}' + else: + ll['comment'] = f'{prev_name} and ' + ll['comment'] + pop_list.append((prev_name, name)) # Mark first layer for deletion + + # Copy over convolution operation and keep the element-wise operation in place + inputs[name] = inputs[prev_name] + + if 'in_sequences' in prev: + ll['in_sequences'] = prev['in_sequences'] + if 'max_pool' in prev: + ll['max_pool'] = prev['max_pool'] + ll['pool_first'] = 'true' + if 'avg_pool' in prev: + ll['avg_pool'] = prev['avg_pool'] + ll['pool_first'] = 'true' + if 'max_pool' in ll or 'avg_pool' in ll: + ll['pool_first'] = 'false' + if 'pool_stride' in prev: + ll['pool_stride'] = prev['pool_stride'] + if 'pool_dilation' in prev: + ll['pool_dilation'] = prev['pool_dilation'] + if 'data_format' in prev: + ll['data_format'] = prev['data_format'] + ll['eltwise'] = prev['eltwise'] + ll['operands'] = prev['operands'] + + prev_name = name + + # Delete the element-wise layers that were fused into the convolution layer + for (prev_name, _) in pop_list: + # Delete the element-wise portion + layers.pop(prev_name) + + # 8 - Insert passthrough layers for write_gap + write_gap_list: List[Tuple[str, int]] = [] + insert_list: List[Tuple[str, int]] = [] + source_list: List[Tuple[str, str]] = [] + + prev_name = '' + for (name, ll) in layers.items(): + if 'in_sequences' not in ll: + if prev_name == '': + prev_name = name + continue + sources: List[str] = [prev_name] + else: + sources = ll['in_sequences'] + operands = ll['operands'] if 'operands' in ll else len(sources) + if operands < 2: + prev_name = name + continue + + # For each input, check whether anybody else is using the input. If yes, insert a dummy + # layer that creates a write_gap version of the data. If no, add the write_gap to the + # producer. + for source in sources: + must_insert: bool = False + prev_name_inner = '' + for (other_name, ol) in layers.items(): + if other_name not in (name, source): + if 'in_sequences' in ol: + for e in ol['in_sequences']: + if e == source: + must_insert = True + elif prev_name_inner == source: + must_insert = True + # Break the sequence + ol['in_sequences'] = [source] + prev_name_inner = other_name + if not must_insert: + # The source is used only by the element-wise layer, so we can insert the write gap + # directly + write_gap_list.append((source, operands)) + else: + insert_list.append((source, operands)) + # Replace source with source_gap in layers[name]['in_sequences'] + source_list.append((name, source)) + new_name = 'gap_' + source + # ...and insert shaope information (input and output are both the same as the + # original layer's output) + inputs[new_name] = [source + '_data'] + outputs[new_name] = [name + '_data'] + for ie in outputs[source]: + if ie in shapes: + shapes[name + '_data'] = shapes[ie] + shapes[source + '_data'] = shapes[ie] + + prev_name = name + + # Insert simple write gaps + for (name, operands) in write_gap_list: + layers[name]['write_gap'] = operands - 1 + # Break sequence + for (name, source) in source_list: + seq = layers[name]['in_sequences'] + for i, s in enumerate(seq): + if s == source: + seq[i] = 'gap_' + source + # Insert additional layers + for (name, operands) in insert_list: + new_layer: Dict[str, Any] = {} + new_name = 'gap_' + name + new_layer['name'] = new_name + processors = 0 + for ie in inputs[new_name]: + if ie in shapes: + processors += shapes[ie][0] + new_layer['proc_count'] = max(1, processors) + new_layer['op'] = new_layer['main_op'] = 'Passthrough' + new_layer['write_gap'] = operands - 1 + + # Insert into dict via list + insert_pos: int = list(layers.keys()).index(name) + 1 + layers_list: List[Any] = list(layers.items()) + layers_list.insert(insert_pos, (new_name, new_layer)) + layers = dict(layers_list) + + # 9 - Record actual used processors for all layers and weight cost for all conv layers + # Add all_inputs (either in_sequence or the previous layer's name if not defined) + all_inputs: Dict[str, List[str]] = {} # Input LAYERS to the key layer (vs. shapes in inputs) + all_outputs: Dict[str, List[str]] = {} # Output LAUYERS to th key layer (vs. shapes) + + prev_name = '' + for (name, ll) in layers.items(): + val: Optional[List[str]] = None + if 'in_sequences' not in ll: + val = [prev_name] if prev_name != '' else None + else: + val = ll['in_sequences'] + if val is not None: + all_inputs[name] = val + for ie in val: + if ie in all_outputs: + all_outputs[ie].append(name) + else: + all_outputs[ie] = [name] + prev_name = name + + # There are two cases: element-wise operations ('operands' defined and > 1), and conv + # operations with multi-pass (> 64 input channels). In either case, in_sequences has + # more than one member. + if 'in_sequences' not in ll or len(ll['in_sequences']) < 2: + continue + operands = ll['operands'] if 'operands' in ll else 1 + if operands == 1: + operands = len(ll['in_sequences']) + # Concat instead of interleave for small channel counts + if ll['proc_count'] <= MAX_PROC: + ll['concat'] = True + for i, ie in enumerate(ll['in_sequences']): + lin = layers[ie] + lin['concat_source'] = max(i, lin['concat_source']) \ + if 'concat_source' in lin else i + else: + # Interleave the inputs in the channels + ll['proc_count'] //= operands + + # Mark the size of all concat outputs + for (name, ll) in layers.items(): + if 'concat_source' not in ll: + continue + + if name not in outputs or len(outputs[name]) != 1 or outputs[name][0] not in shapes: + print('ERROR: Cannot derive output shape(s) for layer', name) + oshape = shapes[outputs[name][0]] + ll['concat_shape'] = oshape[0] + + def calculate_processors(processors: int) -> Tuple[int, int]: + """ + Given a channel count, return the multi-pass adjusted processor count + """ + multipass: int = 1 + if processors > MAX_PROC: # Multi-pass processor count + multipass = (processors + MAX_PROC - 1) // MAX_PROC + processors = (processors + multipass - 1) // multipass # Rounded up + remainder: int = processors % 4 + if remainder != 0: + remainder = 4 - remainder + processors += remainder # To next multiple of 4 + return processors, multipass + + # cost_list: processors/weights per layer for Conv layers with 60 or fewer processors + cost_list: List[Tuple[int, int, str]] = [] + + for (name, ll) in layers.items(): + hwc = ll['data_format'] if 'data_format' in ll else True + processors = ll['proc_count'] + multipass: int = 1 + # Calculate the final number of used processors + if hwc: + processors, multipass = calculate_processors(processors) + assert processors <= MAX_PROC + else: + assert processors <= MAX_PROC // 4 + ll['proc_used'] = processors + ll['multipass'] = multipass + + weights_per_processor: int = 0 + if ll['main_op'] in ('Conv', 'ConvTranspose', 'Linear'): + # Account for kernel size for convolution operations + weights_per_processor = ll['weight_count'] // processors + if 'quantization' in ll: + weights_per_processor //= 8 // ll['quantization'] + ll['weight_cost'] = weights_per_processor + + if processors <= 60: + # Only add to this list if we're using less than the full processor count + # (for 64 processors, there are no options and hence no optimizations) + cost_list.append((weights_per_processor, processors, name)) + else: + # Append even those layers that use 64 processors, because they may be 'concat' layer + # or it may be a layer 0 and therefore, the source may need to be arranged properly. + # But use zero cost to get them to move to the end (or ignored in the cost + # calculation). + cost_list.append((0, processors, name)) + + # print('\nFINAL cost_list', cost_list, '\n\n') + + # Layer 0 is not the output of anything, so add it in from the beginning + bucket_groups: List[List[Tuple[int, int, str]]] = [[cost_list[0]]] + bucket: Dict[str, int] = {} + bucket[cost_list[0][2]] = 0 + + # Find layers where the output is used as input of more than one layer. Those layers then + # must use the same processors. This is achieved by "combining the rectangles". + for (name, ll) in layers.items(): + if name not in all_outputs: + continue # This shouldn't ever happen except for the final layer + + # print(name, 'grouping everything in', all_outputs[name]) + # FIXME What is the output shape of this layer? May need the processor_count + merge_buckets: List[int] = [] + for i, (_, _, n) in enumerate(cost_list): + if n in all_outputs[name]: + if n in bucket: # We already have a bucket that contains n. + # Merge the bucket contents + merge_buckets.append(bucket[n]) + else: + # Create a new empty bucket and add the layer to it. Also add the new bucket + # to the merge list. + bucket_number = len(bucket_groups) + bucket[n] = bucket_number + merge_buckets.append(bucket_number) + bucket_groups.append([cost_list[i]]) + if len(merge_buckets) > 1: + target: int = merge_buckets[0] + for i in merge_buckets[1:]: + bucket_groups[target] += bucket_groups[i] + bucket_groups[i] = [] # Rather than deleting, we just set invalid values + + # For the remaining buckets, combine the weights, check the processors, and make a layer list + # print('FINAL bucket_groups', bucket_groups) + + del cost_list # No longer needed + + # Same as cost_list, but now with multiple entries + group_list: List[Tuple[int, int, int, int, List[Tuple[int, int, str]]]] = [] + + for b in bucket_groups: + if not b: + continue + # print('============ bucket group', b) + group_weights: int = 0 + group_procs: int = -1 + group_item: List[Tuple[int, int, str]] = [] + min_shift = 0 + last_processor = MAX_PROC - 1 + for (weights_per_processor, processors, name) in b: + # print('examining - weights', weights_per_processor, 'procs', processors, 'name', + # name, 'concat', 'YES' if 'concat' in layers[name] else 'NO') + group_weights += weights_per_processor # Add weights + # print('processors', processors, 'vs', group_procs) + if 'concat' in layers[name]: # For concatenation, we need partials + assert group_procs < 0 or group_procs == processors \ + or group_procs == processors // len(all_inputs[name]) + # FIXME: a straight divide may not be correct + # FIXME: If there's a concat target, make sure to leave the extra space + # print('Found a concat, should set min_shift') + # min_shift = max(min_shift, processors // len(all_inputs[name])) + else: + # print(layers[name]) + assert group_procs < 0 or group_procs == processors + group_procs = max(processors, group_procs) + group_item.append((weights_per_processor, processors, name)) + if group_procs >= 0: + group_list.append((group_weights, group_procs, + min_shift, last_processor, group_item)) + + # print('group_list', group_list, '\n') + + # 10 - Assign processors + # TODO: Real algorithm for output_offset + weights_used: List[int] = [0] * MAX_PROC + + def allocate_processors( + weight_cost: int, + count: int, + min_shift: int, + data: List[Tuple[int, int, str]], + hwc: bool = True, + ) -> Tuple[List[int], int]: + """ + Allocate a given count of processors and return the bit map. + TODO: This function should be expanded to evenly distribute resources, not only based on + weights but also data memory utilization. + Additionally, the weight allocator could instead use a box packing algorithm, but the + expected improvements are modest. + """ + min_cost: Tuple[int, int] = (2**63-1, -1) + shifted_map: List[int] = [0] * len(data) + processor_map: List[int] = shifted_map + + # Move processor map left if needed to achieve the lowest combined 'weights_used' + for shift in range(min_shift, MAX_PROC - count + 1, 4): + for d, (item_cost, item_procs, _) in enumerate(data): + if hwc: + # Pick "count" processors starting from 0 + shifted_map[d] = ((1 << item_procs) - 1) << shift + else: + # Pick the first for every quadrant (FIFO compatible) or one every 4 + mult = 16 if count <= 4 else 4 + shifted_map[d] = 0 + for i in range(item_procs): + shifted_map[d] |= 1 << mult * i + shifted_map[d] <<= shift + + if weight_cost == 0: + min_cost = (0, min_shift) + break + + cost: List[int] = weights_used.copy() + + for p in range(MAX_PROC): + for d, (item_cost, item_procs, _) in enumerate(data): + if shifted_map[d] & (1 << p): + cost[p] += item_cost + + max_cost = max(cost) # High water mark + + if max_cost < min_cost[0]: # Better option? + min_cost = (max_cost, shift) + processor_map = shifted_map.copy() + + if weight_cost > 0: + # Accounting based on the result + for p in range(MAX_PROC): + proc_used: bool = False + for d, _ in enumerate(data): + if processor_map[d] & (1 << p): + proc_used = True + if proc_used: + weights_used[p] = min_cost[0] + + return processor_map, min_cost[1] + + # When using CHW inputs with more than 1 processor, or we use fifos, we leave layer 0 alone + # and don't move processors. Technically, there would be a few more options for these layers + # but they are not considered since the potential gain is minimal. + l0: Optional[Tuple[int, int, int, int, List[Tuple[int, int, str]]]] = None + if not move_l0 or not input_hwc or input_processors != 1 or use_fifos: + l0 = group_list[0] + del group_list[0] + + # Sort by height and then width for the row allocator + group_list.sort(reverse=True) # Sort by weight depth, then processor count + if l0 is not None: + group_list.insert(0, l0) # Put layer 0 back at the start + + # print('SORTED FINAL group_list', group_list, '\n\n') + + # Allocate and record processors for the conv layers, and follow back to the producing layers + # and record the 'output_processors'. + for (weights_per_processor, processors, min_shift, _stop, data) in group_list: + # print('\n', weights_per_processor, processors, min_shift, _stop, data) + + # Grab the first item. This should be the input layer for the very first item in the list. + ll = layers[data[0][2]] + assert processors > 0 + hwc = ll['data_format'] if 'data_format' in ll else True + + processor_map, _ = \ + allocate_processors( + weights_per_processor, + processors, + min_shift, + data, + hwc=hwc, + ) + + # print(data) + + # When an output is used as an input to both a regular layer and a concatenation + intersected_map: int = 0xffffffffffffffff + for i, (_, _, name) in enumerate(data): + # print(name, f'0x{processor_map[i]:016x}') + layers[name]['processors'] = processor_map[i] + intersected_map &= processor_map[i] + # print(f'processor_map[{i}] 0x{processor_map[i]:016x}') + + # print(f'intersected_map 0x{intersected_map:016x}') + # Number of 1-bits in the processor map, used for shifting concatenated inputs + shift_count: int = 0 + while intersected_map & 1 == 0: + shift_count += 1 + intersected_map >>= 1 + + for i, (_, _, name) in enumerate(data): + ll = layers[name] + + if name not in all_inputs: + continue + + if 'concat' not in ll: + # Interleaved (write_gap) outputs must share the same output processors. + for seq in all_inputs[name]: + layers[seq]['output_processors'] = processor_map[i] + else: + # Concatenation: Create partial maps + shift: int = shift_count + for seq in all_inputs[name]: + # print('layer', seq, 'concat_shape', layers[seq]['concat_shape'], + # 'concat_source', layers[seq]['concat_source'], end='') + item_procs: int = layers[seq]['concat_shape'] + layers[seq]['output_processors'] = ((1 << item_procs) - 1) << shift + # print(f' map 0x{layers[seq]["output_processors"]:016x}, item_procs', + # item_procs) + if item_procs % 4 != 0: + item_procs += 4 - item_procs % 4 + shift += item_procs + + # 11 - Set output_offset. TODO: Use real algorithm + out_offset: int = 0 # Start at 0 (default input offset) + + for (name, ll) in layers.items(): + out_offset = allocate_offset(name, ll['processors'], out_offset) + ll['out_offset'] = out_offset + + # 12 - Sanity check + for (name, ll) in layers.items(): + if name not in all_inputs: + continue + if 'concat' in ll: + processor_union: int = 0 + for ie in all_inputs[name]: + processor_union |= layers[ie]['output_processors'] + if ll['processors'] != processor_union: + print(f'ERROR: Layer {name} processors 0x{ll["processors"]:016x} does not match ' + f'the contributing output_processors union 0x{processor_union:016x} of ' + f'the inputs {all_inputs[name]}') + else: + for ie in all_inputs[name]: + if ll['processors'] != layers[ie]['output_processors']: + print(f'ERROR: Layer {name} processors 0x{ll["processors"]:016x} does not ' + f'match the output_processors 0x{layers[ie]["output_processors"]} of ' + f'the input {ie}') + + # 13 - Clean up unnecessary output_processors + prev_name = '' + for (name, ll) in layers.items(): + if prev_name != '': + prev = layers[prev_name] + if 'output_processors' in prev and prev['output_processors'] == ll['processors']: + del prev['output_processors'] + prev_name = name + + # 14 - Print + target_dir: str = os.path.dirname(filename) + if target_dir != '': + os.makedirs(target_dir, exist_ok=True) + with open(filename, mode='w', encoding='utf-8') as f: + f.write( + '---\n' + '# YAML template -- requires manual editing, particularly with regard to out_offset ' + 'and processors\n' + f'# Generated for {devices.partnum(ai8x.dev.device)} with input format ' + f'{"HWC" if input_hwc else "CHW"}\n\n' + f'arch: {arch}\n' + f'dataset: {dataset}\n' + '\n' + 'layers:' + ) + + prev_name = '' + for count, (name, ll) in enumerate(layers.items()): + f.write('\n' + f' # Layer {count}\n' + f" - name: {canonical_name(ll['name'])}") + if 'comment' in ll: + f.write(f" # {ll['comment']}") + f.write('\n') + + hwc = ll['data_format'] if 'data_format' in ll else True + + # Check input sequences and dimensions + warn_dim = False + print_dim = verbose or prev_name == '' + if print_dim: + f.write(' # input shape: ') + i = 0 + max_pixels = MAX_PIXELS if hwc else 4 * MAX_PIXELS + for ie in inputs[name]: + if ie in shapes: + if i > 0 and print_dim: + f.write(', ') + if print_dim: + f.write(str(shapes[ie])) + pixels = 1 + for x in range(1, len(shapes[ie])): + pixels *= shapes[ie][x] + if pixels > max_pixels: + warn_dim = True + i += 1 + if print_dim: + f.write('\n') + if warn_dim: + f.write(f' # dimensions ({pixels} pixels) may require streaming or folding\n') + + if 'data_format' in ll: + f.write(f' data_format: {"HWC" if hwc else "CHW"}\n') + if 'in_sequences' in ll: + f.write(' in_sequences: [') + ins = ll['in_sequences'] + for i, ie in enumerate(ins): + if i > 0: + f.write(', ') + f.write(canonical_name(ie)) + f.write(']\n') + if 'in_dim' in ll: + f.write(f" in_dim: {ll['in_dim']}\n") + show_output = verbose + if 'output' in ll: + f.write(f" output: {ll['output']}\n") + show_output = True + processors = ll['processors'] + if processors == 0: + f.write(' processors: unknown\n') + else: + f.write(f' processors: 0x{processors:016x}\n') + f.write(f" out_offset: 0x{ll['out_offset']:04x}\n") + if 'quantization' in ll: + f.write(f" quantization: {ll['quantization']}\n") + if 'output_width' in ll: + f.write(f" output_width: {ll['output_width']}\n") + f.write(f" op: {ll['op']}\n") + if 'eltwise' in ll: + f.write(f" eltwise: {ll['eltwise']}\n") + if 'operands' in ll: + f.write(f" operands: {ll['operands']}\n") + if 'kernel_size' in ll: + f.write(f" kernel_size: {ll['kernel_size']}\n") + if 'pad' in ll: + f.write(f" pad: {ll['pad']}\n") + if 'groups' in ll: + f.write(f" groups: {ll['groups']}\n") + if 'flatten' in ll: + f.write(f" flatten: {ll['flatten']}\n") + if 'activate' in ll: + f.write(f" activate: {ll['activate']}\n") + if 'pool_first' in ll: + f.write(f" pool_first: {ll['pool_first']}\n") + if 'max_pool' in ll: + f.write(f" max_pool: {ll['max_pool']}\n") + if 'avg_pool' in ll: + f.write(f" avg_pool: {ll['avg_pool']}\n") + if 'pool_stride' in ll: + f.write(f" pool_stride: {ll['pool_stride']}\n") + if 'pool_dilation' in ll: + f.write(f" pool_dilation: {ll['pool_dilation']}\n") + if 'write_gap' in ll: + f.write(f" write_gap: {ll['write_gap']}\n") + if 'output_processors' in ll: + processors = ll['output_processors'] + if processors == 0: + f.write(' output_processors: unknown\n') + else: + f.write(f' output_processors: 0x{processors:016x}\n') + + # Show output dimensions for all output layers + if name == final_layer or show_output: + f.write(' # output shape: ') + i = 0 + for ie in outputs[name]: + if ie in shapes: + if i > 0: + f.write(', ') + f.write(str(shapes[ie])) + i += 1 + f.write('\n') + + prev_name = name From 2d6c1045176e9780ffd6ef3889d9a3057d698775 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?O=C4=9Fuzhan=20B=C3=BCy=C3=BCksolak?= Date: Wed, 13 Mar 2024 16:14:29 +0300 Subject: [PATCH 02/12] Fix eval ckpt naming for ai87netmobilefacenet_112 (#298) (cherry picked from commit 89abd5cd9f1ca29a778a62fa41ac2ad5b51b52d1) --- scripts/evaluate_mobilefacenet_112.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/evaluate_mobilefacenet_112.sh b/scripts/evaluate_mobilefacenet_112.sh index d1fa87f71..332e2b938 100755 --- a/scripts/evaluate_mobilefacenet_112.sh +++ b/scripts/evaluate_mobilefacenet_112.sh @@ -1,2 +1,2 @@ #!/bin/sh -python train.py --model ai87netmobilefacenet_112 --dataset VGGFace2_FaceID --kd-student-wt 0 --kd-distill-wt 1 --kd-teacher ir_152 --kd-resume pretrained/ir152_dim64/best.pth.tar --kd-relationbased --evaluate --device MAX78002 --exp-load-weights-from ../ai8x-synthesis/trained/ai87-mobilefacenet_112_qat_best-q.pth.tar -8 --use-bias --save-sample 10 --slice-sample "$@" +python train.py --model ai87netmobilefacenet_112 --dataset VGGFace2_FaceID --kd-student-wt 0 --kd-distill-wt 1 --kd-teacher ir_152 --kd-resume pretrained/ir152_dim64/best.pth.tar --kd-relationbased --evaluate --device MAX78002 --exp-load-weights-from ../ai8x-synthesis/trained/ai87-mobilefacenet-112-qat-q.pth.tar -8 --use-bias --save-sample 10 --slice-sample "$@" From 4599a45c2f8c4ec555b9e04084bfe33fc44539d5 Mon Sep 17 00:00:00 2001 From: Robert Muchsel <16564465+rotx-maxim@users.noreply.github.com> Date: Mon, 18 Mar 2024 13:25:21 -0500 Subject: [PATCH 03/12] Improve documentation, remove deprecated VScode project settings (#300) (cherry picked from commit 398214cab5a83b34466ed20505556a597d416875) --- README.md | 8 +++++++- README.pdf | Bin 6553858 -> 6556293 bytes ai8x-training.code-workspace | 12 ++++++++++++ 3 files changed, 19 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index efce7a937..ed49f2a87 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # ADI MAX78000/MAX78002 Model Training and Synthesis -March 8, 2024 +March 13, 2024 **Note: The pytorch-2 branch is in development. Not all features are available/functional yet.** @@ -3244,7 +3244,13 @@ ERROR: Layer 6: 64 input channels (before flattening) using 1 pass, and 1 operan *In this example, each dimension was half the expected size, so the expected processor count was off by a factor of 4. To resolve the error, a properly dimensioned sample input had to be provided.* +##### “new-lines” Configuration File Parsing Error +```shell +ERROR: invalid config: option "type" of "new-lines" should be in ('unix', 'dos') +``` + +This message indicates that an outdated version of `yamllint` is installed in the search path. Either update `yamllint` to version 1.27.0 or later, or uninstall `yamllint`, or edit `.yamllint` to remove `new-lines: type: platform`, or use `--yamllint none`. #### Energy Measurement diff --git a/README.pdf b/README.pdf index 3f9a5b7f01203d272a62be150359d42e347bf64c..5c8f8329e522bb2a53b08200af61c5a5f0f48e5f 100644 GIT binary patch delta 456648 zcmaHTbzD^K^R{&764DJzvqdP4Al=>FT`GtxUD9y?0civT=_N%#P#Q!8DG8A_P{2ao zC7wrrkAB}jZuu@9SXWL3kab+I-HJ z5k(Pl@1g9X60aNlP;HTFKqc4sQEZ|G^?GO-Q0A`Vrq^h@4oe$6ezW9DDiY7ts;0{K zQV1ejJONB5;6};Z9UB=d>p!k@8EI^M>uUE1>NT9^I$ z?qh4JHyg~NlN%-b;y(#-%WgrPfVW9JWfqlzACOW%j2?9GYYL5|rGfb0rTKj#(~}Ot z_D$fuCj(`Dk+a8fh$Gl9E!P`A9xm0DKObI{s}TJyYK*7E9}5Qrrdkt<6xE)0uu+S9 z9Z7ok!`E3qtC&sDLB|R(Y4j+G_w@(0#l6nb^_TBTEb}Xcov_u8;XFtLOaApV=_}MY zoJ7bukCA~_($(q>CN8Bnzs}v=jvtvb@?yzOWBH*z{$yNO+sb|@lJ;Vo~+M=pNPOI{IQ+%+aY+thvW)Tq7b6BH0=lk*O1@}&n0 z&rdqFrk_DT_QQe05X6I00)}Jz@6ReP1a+1nkmKGzWz7mI2E2v7L|D99Ex)=bdW)!; zC#PQX)jXX`%*?6?*tCV`z{7cZ-1&wCg)jT}$ikU@#ZS|qSHe%n6I&~<+1UOR(t7TC 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Mar 2024 12:10:25 -0500 Subject: [PATCH 04/12] Update to PyTorch 2.2.2; update requirements.txt --- requirements.txt | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/requirements.txt b/requirements.txt index a84dcb48e..25d90905c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,16 +1,16 @@ pip>=24.0 -torch==2.2.1 -torchvision==0.17.1 -torchaudio==2.2.1 +torch==2.2.2 +torchvision==0.17.2 +torchaudio==2.2.2 GitPython==3.1.42 Pillow==10.2.0 PyYAML==6.0.1 -albumentations==1.4.0 +albumentations==1.4.2 faiss-cpu==1.8.0 face-detection==0.2.2 h5py==3.10.0 imutils==0.5.4 -kornia==0.7.1 +kornia==0.7.2 librosa==0.10.1 numpy==1.26.4 opencv-python==4.9.0.80 @@ -21,6 +21,6 @@ pytube==15.0.0 scipy==1.12.0 tensorboard==2.16.2 tk==0.1.0 -torchmetrics==1.3.1 +torchmetrics==1.3.2 torchnet==0.0.4 -e distiller From b503fecb3942376a5726de16fce2ebc7b13ce678 Mon Sep 17 00:00:00 2001 From: Robert Muchsel <16564465+rotx-maxim@users.noreply.github.com> Date: Fri, 29 Mar 2024 09:58:11 -0500 Subject: [PATCH 05/12] Sort directory listings where necessary; minor documentation improvements (#302) --- README.md | 18 +++++++++--------- README.pdf | Bin 6556293 -> 6534159 bytes datasets/aisegment.py | 8 ++++---- datasets/msnoise.py | 2 +- datasets/samplemotordatalimerick.py | 22 ++++++++++++++-------- datasets/speechcom.py | 9 ++++----- 6 files changed, 32 insertions(+), 27 deletions(-) mode change 100755 => 100644 datasets/samplemotordatalimerick.py diff --git a/README.md b/README.md index ed49f2a87..6e5534c1a 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # ADI MAX78000/MAX78002 Model Training and Synthesis -March 13, 2024 +April 5, 2024 **Note: The pytorch-2 branch is in development. Not all features are available/functional yet.** @@ -951,7 +951,7 @@ Because each processor has its own dedicated weight memory, this will introduce For each layer, a set of active processors must be specified. The number of input channels for the layer must be equal to, or be a multiple of, the active processors, and the input data for that layer must be located in data memory instances accessible to the selected processors. It is possible to specify a relative offset into the data memory instance that applies to all processors. -_Example:_ Assuming HWC data format, specifying the offset as 16384 bytes (or 0x4000) will cause processors 0-3 to read their input from the second half of data memory 0, processors 4-7 will read from the second half of data memory instance 1, etc. +_Example:_ Assuming HWC data format, specifying the offset as 16,384 bytes (or 0x4000) will cause processors 0-3 to read their input from the second half of data memory 0, processors 4-7 will read from the second half of data memory instance 1, etc. For most simple networks with limited data sizes, it is easiest to ping-pong between the first and second halves of the data memories – specify the data offset as 0 for the first layer, 0x4000 for the second layer, 0 for the third layer, etc. This strategy avoids overlapping inputs and outputs when a given processor is used in two consecutive layers. @@ -1068,7 +1068,7 @@ The MAX78000 hardware does not support arbitrary network parameters. Specificall * Streaming mode: - * When using data greater than 90×91 (8,192 pixels per channel in HWC mode), or 181×181 (32,768 pixels in CHW mode), and [Data Folding](#data-folding) techniques are not used, then `streaming` mode must be used. + * When using data greater than 8192 pixels per channel (approximately 90×90 when width = height) in HWC mode, or 32,768 pixels per channel (181×181 when width = height) in CHW mode, and [Data Folding](#data-folding) techniques are not used, then `streaming` mode must be used. * When using `streaming` mode, the product of any layer’s input width, input height, and input channels divided by 64 rounded up must not exceed 2^21: $width * height * ⌈\frac{channels}{64}⌉ < 2^{21}$; _width_ and _height_ must not exceed 1023. * Streaming is limited to 8 consecutive layers or fewer, and is limited to four FIFOs (up to 4 input channels in CHW and up to 16 channels in HWC format), see [FIFOs](#fifos). * For streaming layers, bias values may not be added correctly in all cases. @@ -1080,7 +1080,7 @@ The MAX78000 hardware does not support arbitrary network parameters. Specificall When using more than 64 input or output channels, weight memory is shared, and effective capacity decreases proportionally (for example, 128 input channels require twice as much space as 64 input channels, and a layer with both 128 input and 128 output channels requires four times as much space as a layer with only 64 input channels and 64 output channels). Weights must be arranged according to specific rules detailed in [Layers and Weight Memory](#layers-and-weight-memory). -* There are 16 instances of 32 KiB data memory ([for a total of 512 KiB](docs/AHBAddresses.md)). When not using streaming mode, any data channel (input, intermediate, or output) must completely fit into one memory instance. This limits the first-layer input to 181×181 pixels per channel in the CHW format. However, when using more than one input channel, the HWC format may be preferred, and all layer outputs are in HWC format as well. In those cases, it is required that four channels fit into a single memory instance — or 91×90 pixels per channel. +* There are 16 instances of 32 KiB data memory ([for a total of 512 KiB](docs/AHBAddresses.md)). When not using streaming mode, any data channel (input, intermediate, or output) must completely fit into one memory instance. This limits the first-layer input to 32,768 pixels per channel in the CHW format (181×181 when width = height). However, when using more than one input channel, the HWC format may be preferred, and all layer outputs are in HWC format as well. In those cases, it is required that four channels fit into a single memory instance — or 8192 pixels per channel (approximately 90×90 when width = height). Note that the first layer commonly creates a wide expansion (i.e., a large number of output channels) that needs to fit into data memory, so the input size limit is mostly theoretical. In many cases, [Data Folding](#data-folding) (distributing the input data across multiple channels) can effectively increase both the input dimensions as well as improve model performance. * The hardware supports 1D and 2D convolution layers, 2D transposed convolution layers (upsampling), element-wise addition, subtraction, binary OR, binary XOR as well as fully connected layers (`Linear`), which are implemented using 1×1 convolutions on 1×1 data: @@ -1088,7 +1088,7 @@ The MAX78000 hardware does not support arbitrary network parameters. Specificall * `Flatten` functionality is available to convert 2D input data for use by fully connected layers, see [Fully Connected Layers](#fully-connected-linear-layers). - * When “flattening” two-dimensional data, the input dimensions (C×H×W) must satisfy C×H×W ≤ 16384, and H×W ≤ 256. Pooling cannot be used at the same time as flattening. + * When “flattening” two-dimensional data, the input dimensions (C×H×W) must satisfy C×H×W ≤ 16,384, and H×W ≤ 256. Pooling cannot be used at the same time as flattening. * Element-wise operators support from 2 up to 16 inputs. @@ -1165,7 +1165,7 @@ The MAX78002 hardware does not support arbitrary network parameters. Specificall * Streaming mode: - * When using data greater than 143×143 (20,480 pixels per channel in HWC mode), or 286×286 (81,920 pixels in CHW mode), and [Data Folding](#data-folding) techniques are not used, then `streaming` mode must be used. + * When using data greater than 20,480 pixels per channel in HWC mode (143×143 when height = width), or 81,920 pixels in CHW mode (286×286 when height = width), and [Data Folding](#data-folding) techniques are not used, then `streaming` mode must be used. * When using `streaming` mode, the product of any layer’s input width, input height, and input channels divided by 64 rounded up must not exceed 2^21: $width * height * ⌈\frac{channels}{64}⌉ < 2^{21}$; _width_ and _height_ must not exceed 2047. * Streaming is limited to 8 consecutive layers or fewer, and is limited to four FIFOs (up to 4 input channels in CHW and up to 16 channels in HWC format), see [FIFOs](#fifos). * Layers that use 1×1 kernels without padding are automatically replaced with equivalent layers that use 3×3 kernels with padding. @@ -1176,14 +1176,14 @@ The MAX78002 hardware does not support arbitrary network parameters. Specificall When using more than 64 input or output channels, weight memory is shared, and effective capacity decreases. Weights must be arranged according to specific rules detailed in [Layers and Weight Memory](#layers-and-weight-memory). -* The total of [1,280 KiB of data memory](docs/AHBAddresses.md) is split into 16 sections of 80 KiB each. When not using streaming mode, any data channel (input, intermediate, or output) must completely fit into one memory instance. This limits the first-layer input to 286×286 pixels per channel in the CHW format. However, when using more than one input channel, the HWC format may be preferred, and all layer outputs are in HWC format as well. In those cases, it is required that four channels fit into a single memory section — or 143×143 pixels per channel. +* The total of [1,280 KiB of data memory](docs/AHBAddresses.md) is split into 16 sections of 80 KiB each. When not using streaming mode, any data channel (input, intermediate, or output) must completely fit into one memory instance. This limits the first-layer input to 81,920 pixels per channel in CHW format (286×286 when height = width). However, when using more than one input channel, the HWC format may be preferred, and all layer outputs are in HWC format as well. In those cases, it is required that four channels fit into a single memory section — or 20,480 pixels per channel (143×143 when height = width). Note that the first layer commonly creates a wide expansion (i.e., a large number of output channels) that needs to fit into data memory, so the input size limit is mostly theoretical. In many cases, [Data Folding](#data-folding) (distributing the input data across multiple channels) can effectively increase both the input dimensions as well as improve model performance. * The hardware supports 1D and 2D convolution layers, 2D transposed convolution layers (upsampling), element-wise addition, subtraction, binary OR, binary XOR as well as fully connected layers (`Linear`), which are implemented using 1×1 convolutions on 1×1 data: * The maximum number of input neurons is 1024, and the maximum number of output neurons is 1024 (16 each per processor used). * `Flatten` functionality is available to convert 2D input data for use by fully connected layers, see [Fully Connected Layers](#fully-connected-linear-layers). - * When “flattening” two-dimensional data, the input dimensions (C×H×W) must satisfy C×H×W ≤ 16384, and H×W ≤ 256. Pooling cannot be used at the same time as flattening. + * When “flattening” two-dimensional data, the input dimensions (C×H×W) must satisfy C×H×W ≤ 16,384, and H×W ≤ 256. Pooling cannot be used at the same time as flattening. * Element-wise operators support from 2 up to 16 inputs. * Element-wise operators can be chained in-flight with pooling and 2D convolution (where the order of pooling and element-wise operations can be swapped). * For convenience, a `Softmax` operator is supported in software. @@ -1206,7 +1206,7 @@ The MAX78002 hardware does not support arbitrary network parameters. Specificall m×n fully connected layers can be realized in hardware by “flattening” 2D input data of dimensions C×H×W into m=C×H×W channels of 1×1 input data. The hardware will produce n channels of 1×1 output data. When chaining multiple fully connected layers, the flattening step is omitted. The following picture shows 2D data, the equivalent flattened 1D data, and the output. -For MAX78000/MAX78002, the product C×H×W must not exceed 16384. +For MAX78000/MAX78002, the product C×H×W must not exceed 16,384. ![MLP](docs/MLP.png) diff --git a/README.pdf b/README.pdf index 5c8f8329e522bb2a53b08200af61c5a5f0f48e5f..c47a080d106344d97fa92428cec8abbca08b42eb 100644 GIT binary patch delta 2032598 zcmY(KQ*b80)~;jQwr$&XCbn(ki*0ja+nHEjG_j3|olNXAd;g~nF1psmdb_&%qHDeV z^y={RB=E%31n?vgQ0`<=O2A8sgu-Pt4>Ct@Yg&|UZZRf*LAJ&hHFTz15H7=nft*^C zXEZ2Yd|#d;2g}Fc$Ha%R?FVu0ih{gdz1Ok){iuM?Q#&iRlj6ZwfA8AUFL&_n`3G^$ z10%DDJ}&ibtPWJ3xm6`ko35su45GznGtMcu&D^5gnm!v#b|!gFF2JIBv|&z{uI=vr z9QW;-Vz3B*$MV)RBTfABmg)JZ&>4%#{;B(QL0MLS{8`+hzT#t}1Yg_1GOOW|COq61 zbP0*{b7Qs?b=}yV77NQ`>aR|=ej)ZNwYJck{eIIU0~b$=pLzK@E(HcT{n>xd$dl|8 zUO$pSi&oMKI+?9mp8&9ULhyoX_carOG?TpUl}@kzVsafSEWu)`jm?jAo2ph06VhnSFkHW&G6ZPWAPv@5@7F_}tF>-0}B6R(!+J#7JVW z@8Gn;j%;O{wk%8k3!HUwG`!yF^L-!B`y8HyP(=6yYiXz{Is=q@23gkFrCx%T_Z-z!b1bI?uDcS3vC!aGXtR%81?R;kNPma zdZiwO`oiYO%T673dT>VtDKJvSs*nVW^Jgim8=j-<%@FnZM&77c6;n^+UgFsrT$gHZ zoWm?J*={7Bn}87yzZm3yLz%cSL8IS1xXF9oPB3V&LFS)n!B)QvXjv^@YgMNP$rw_0 zVU~(e*aNWOa1%B**VUO|$iPO8DMDICe~rx~;pnaE7VG@H8=8dqo31Jit%+Vn(fRN* ziG&WX^`iv6DD0O-b(fEAZkS{}e#%ibuKnQMpfNuemxII`XWTf%W*Kw+)Y7^_VZ6h! z$410f3qD^frP=b+f`VBa$M29d4mJCXdfO|Kb`ya6LizlJc3ZCmo?pZDUl?1D$UK+y zco5o|(;G2XU1rWF8)q~zY|@N=cqQIF-cLme$e+Rs(xxMK<>aSngCpZ0%6DJ%zNdtu zi#bGqgbC9G=RDA=z(&^`m3L5mYHE7O0lz)mnOF!KFsR_U>3*>SH)b?nWGOogRQqQ~ z=xG4Y<%W8h*bpJbDHVFc7rS+KV=1&zW-#1c{?6A2a`5PCqF7?0wfvOKI8&z7pCL=3 z!n4ysLxi7;<~&)-6huhJlk8Ce9q@{h*@$o{?)=cysA{Ck+sY;$=eVWXG(S~l7C?^} zm7cczD=8~7n#%k5J*eE6@@3eHa|PEmF~k6H2P@((O`#OOBk<`4n*m^8#1UDr^(}JJ zms0M}O7w-bgU%|KL0iS_1Tryfph3k(28 zx;y!Sa!E&e&*s9uJeCDqkVYJaqLHG5U=BU|tR}w+JZIyX$HJ1PJU>zmPAHDkW(;8U zxJ>8G*03==F0?^`9ZA#4jtq9NR1qV!2^L6hssOS;0kMhGLII^xONS~#JE=QV!8F z7c|kE`4M4Ki)X&2>aQ@8Fmy9P8H;axZ8Iz%do8=@%OU#!0~+?-PFs-hkt&_GP_@Gez~4IM*^(D}6?cwP{l zmQ!Ap)8*w>`y6|;deqU~felz~=vxo4GwfyMW;W{O+`VQF?Ahr3)ij^R-{yzQ16Dt# z&0hNXKn&98*6bzbDbiLUV9(HY6uot1DAo8tjY?{+A>uN*%?>Ba2L#{iKP~)100YS#33$iawW?0 zye8+PvSRv!ogM%f?N!&$DThF>fOP{m;4!bvSdVqhA06I=*{@SI+!K@CLM*tppLKi_-qixxLkb-GqiODkjt)|P2YIHvk-Zn{2&4~w!JWJ&T`$FvJ_kh0-j^JCUcAlI7-$t zv&)gobvFOeM$UxgL8IB_#?UYPv|2`V+Aype`6R(79lsPx%v2bP$$25xr-0o7SFmXP zB-fR`B<`$Yy@fLF85n|>UoD<-v$uA`9@R|thjmEvhrg%CuYRheD3bGqgrD^CCCse4 z0+brHO*~m%k5ZtbUAtc*UYbqiLQNXz*hnP%Wy%TAo(A?J&EK7zM_gN>$WMr^2PVZJhlwZYa?u03B$X)HJO_|A*UYc=tuDSxnheUPrskF45qPexN~awiooo^)5zvqjzS$)D`z9OFy|xFZecMDPfr&O+xhz zKqI4Ngh8S&5l`Nkps1~(gF$}SZn4Tnfj__jc(X)uSAU1SAt*I$9Zlz!~NdXHAy%G0Qeq{~=GO?WVQGus&cJ z^+~}DenV1f0D=mDFa#6W`k%U3?EP>}Ey#H0LO4aVQ;|^ZTRj>wx=Q`-6&nttH8e0)d`B5<8(P4(& znM**qt&J~1Y8sC@o8jrae*@*oG?#|T3_a$F&%_D7F4>rUy%oVJz7nyCxjovLAZO05 zoQQe4mUpC^r7tHLMPWmN1vgj4Kgp<-E9_AwG~cqRtR++DAmbWmRt00DQwBBQh<~H^ zLoQ4e?3QexdR)Zlj+1;uP@frTb@9jzff^3aUS-XffqAL$mcj9*J@p=u5&(+z^GLwC z2ZD|V$&9q|+2=bttOPuL0ty|;8 zZgsa;OZ@rLag>V$ZWZpO2LL`6sDpZ(NV*`PHBH85@Il+>zslM9)zle6JH&ULM?R>9 z-yFZjrIEk8a1QLPzjF|R)oCQ^P))>eK`(9a^DCd?0`ag%2MQ%9(wHW)tS~Ov#!K-u zZXpq><;$?zpaaeA#ZZ%W3b%4^-7K7AXZqkW(Bc$}GvgFa_r-`I01@1?Bk_z%1NfJI z$+j*0q;1irY+OYzEVrWeh04SyGr|3CAkPH$TI2hnAmXTnJZk$v0x+Qv)0a8U7I&Cl zT@a39jm`p4^R8*9fUAb8YDT#HUh`H|QP+FDi-*X{&xG+d#1@z6WI;x@NQ>acH~+C; zb(F8RwJXjA3R~-HYf2M8z#}EAQ0gsg)usm~fMnQWRaou7SKvN=@+Bdu_i%M0JSm;ax3h)4Ek%S+}RyP8mE)km|$01Yv| zS%)wZw7efR%$t`KRi~#)XdaA57qKI>pclWd0xLwbk$94EDvDbUB5+7Y+rmG?R|bvR3nq|2)0`RHIKsJ9N>wI?c5Z6*Ev6-*PbUM!jso9@P_6o?6H%^y z(a()P-}!npQRYckg==ZaL^v6Y)qwl|C7Zew!?)fq2E-Z`Vs8uMs`&$==eY_dCSySpB2Ajo2c~$S;+M)?=xCQ#!thdk@xISv1)K>zeS_HQ%oV3ILBsg{3#

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