diff --git a/hannah/nas/performance_prediction/nn_meter/predictor.py b/hannah/nas/performance_prediction/nn_meter/predictor.py index d4da5261..56d6be6b 100644 --- a/hannah/nas/performance_prediction/nn_meter/predictor.py +++ b/hannah/nas/performance_prediction/nn_meter/predictor.py @@ -41,12 +41,12 @@ def __init__(self, hardware_name, predictor_version: Optional[float] = None): hardware_name, predictor_version ) - def predict(self, model): + def predict(self, model, input=None): self.tmp_dir = TemporaryDirectory() tmp_dir = Path(self.tmp_dir.name) logging.info("transfering model to onnx") - dummy_input = model.example_input_array + dummy_input = model.example_input_array if input is None else input torch.onnx.export(model, dummy_input, tmp_dir / "model.onnx", verbose=False) logging.info("Creating onnxrt-model") onnx_model = onnx.load(tmp_dir / "model.onnx") diff --git a/hannah/nas/performance_prediction/simple.py b/hannah/nas/performance_prediction/simple.py index 112bfa84..e55fa72b 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nvsmi = "^0.4.2" libsvm="^3.23.0.4" fairscale = "^0.4.5" -numpy = {version = "^1.26.4", optional = true} scikit-learn = "~1.2.2" hydra-colorlog = "^1.2.0" z3-solver = "^4.11.2.0" @@ -68,6 +67,7 @@ tensorboard = "^2.17.1" pymoo = {version = "^0.6.1.3", optional = true} zigzag-dse = {version = "^3.6.1", optional = true, python = ">=3.11"} nn-meter = {version = "^2.0", optional = true} +numpy = "1.26.4" [tool.poetry.dev-dependencies] pytest = ">=7.2.0" @@ -94,6 +94,7 @@ vision = ["pycocotools", "albumentations", "imagecorruptions", "timm", "gdown", optimizers = ["pymoo"] performancemodel = ["zigzag-dse", "nn-meter"] + [tool.poetry.scripts] hannah-train = 'hannah.tools.train:main' hannah-characterize = 'hannah.tools.characterize:main' diff --git a/tutorials/automlschool24/hannah_handson_automlschool24.ipynb b/tutorials/automlschool24/hannah_handson_automlschool24.ipynb index 3c8adfdd..c3ef3f6b 100644 --- a/tutorials/automlschool24/hannah_handson_automlschool24.ipynb +++ b/tutorials/automlschool24/hannah_handson_automlschool24.ipynb @@ -21,12 +21,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 143, "id": "5b13300d", "metadata": {}, "outputs": [], "source": [ - "#!pip install hannah[vision,models]@git+https://github.com/ekut-es/hannah.git" + "!pip install hannah[vision,models]@git+https://github.com/ekut-es/hannah.git@pub/automlschool2024\n", + "!pip install mlc-ai -f https://mlc.ai/wheels" ] }, { @@ -39,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 144, "id": "3c12b2a6", "metadata": {}, "outputs": [], @@ -50,6 +51,9 @@ "import os\n", "from contextlib import contextmanager\n", "import sys\n", + "import nn_meter\n", + "import tvm\n", + "from IPython.display import Code, display\n", "\n", "\n", "\n", @@ -75,7 +79,9 @@ " finally:\n", " os.chdir(current)\n", " \n", - " \n" + "# Setup logging\n", + "import logging\n", + "logging.basicConfig(level=logging.INFO)\n" ] }, { @@ -93,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 145, "id": "07617fb3", "metadata": {}, "outputs": [ @@ -183,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 146, "id": "6fe5a4a5", "metadata": {}, "outputs": [], @@ -204,22 +210,6 @@ "OmegaConf.save(config=cfg, f=\"model/cnn_search_space.yaml\")\n" ] }, - { - "cell_type": "markdown", - "id": "a706118e", - "metadata": {}, - "source": [ - "### Define Hardware Description" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "34e587f9", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "f5e57dec", @@ -254,10 +244,32 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 147, "id": "02c94b94", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:hannah.utils.utils:Environment info:\n", + "INFO:hannah.utils.utils: Number of GPUs: 2\n", + "INFO:hannah.utils.utils: CUDA version: 12.1\n", + "INFO:hannah.utils.utils: CUDNN version: 8907\n", + "INFO:hannah.utils.utils: Kernel: 4.18.0-553.16.1.el8_10.x86_64\n", + "INFO:hannah.utils.utils: Python: 3.11.9 (main, Jun 24 2024, 06:19:13) [GCC 8.5.0 20210514 (Red Hat 8.5.0-22)]\n", + "INFO:hannah.utils.utils: PyTorch: 2.2.2+cu121\n", + "INFO:hannah.utils.utils: Pytorch Lightning: 2.4.0\n", + "INFO:hannah.utils.utils: Numpy: 1.26.4\n", + "INFO:hannah.utils.utils: Hannah version info:\n", + "INFO:hannah.utils.utils: Cannot find a Git repository. You probably downloaded an archive\n", + "INFO:hannah.utils.utils: Command line: /local/gerum/.cache/pypoetry/virtualenvs/hannah-8Yp6PnCx-py3.11/lib/python3.11/site-packages/ipykernel_launcher.py --f=/home/gerum/.local/share/jupyter/runtime/kernel-v2-2090420HcS9vqNt563N.json\n", + "INFO:hannah.utils.utils: \n", + "INFO:hannah.nas.search.sampler.base_sampler:Loaded 5 points from history\n", + "INFO:hannah.callbacks.optimization:Monitoring the following values for optimization\n", + "INFO:hannah.callbacks.optimization: - val_error direction: minimize(1)\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -284,7 +296,7 @@ " name: cnn_search_space\n", " num_classes: 10\n", " max_channels: 32\n", - " max_blocks: 1\n", + " max_blocks: 2\n", "scheduler:\n", " _target_: torch.optim.lr_scheduler.OneCycleLR\n", " max_lr: 0.001\n", @@ -367,7 +379,7 @@ " n_jobs: 10\n", " total_candidates: 5\n", " num_selected_candidates: 10\n", - " presample: true\n", + " presample: false\n", " bounds:\n", " val_error: 0.1\n", " total_macs: 128000000\n", @@ -382,7033 +394,7 @@ "seed:\n", "- 1234\n", "validate_output: false\n", - 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"Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 8382848.00000000\n", - " total_weights: 43088.00000000\n", - " total_act: 19674.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [12.01553293]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 16884544.00000000\n", - " total_weights: 84064.00000000\n", - " total_act: 63578.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [10.11959996]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 247984.00000000\n", - " total_weights: 4096.00000000\n", - " total_act: 9466.00000000\n", - " est_act: 6272.00000000\n", - "0.0 [11.44941533]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 12663488.00000000\n", - " total_weights: 63128.00000000\n", - " total_act: 51034.00000000\n", - 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" total_weights: 19576.00000000\n", - " total_act: 44730.00000000\n", - " est_act: 37632.00000000\n", - "0.0 [11.11861039]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 2540400.00000000\n", - " total_weights: 7368.00000000\n", - " total_act: 47882.00000000\n", - " est_act: 37632.00000000\n", - "0.0 [11.53090198]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 1762752.00000000\n", - " total_weights: 28512.00000000\n", - " total_act: 16538.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [9.76112943]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 341984.00000000\n", - " total_weights: 6704.00000000\n", - " total_act: 8666.00000000\n", - " est_act: 6272.00000000\n", - "0.0 [10.8918677]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - 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"Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 1157504.00000000\n", - " total_weights: 6224.00000000\n", - " total_act: 19674.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [10.98751226]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 3725808.00000000\n", - " total_weights: 4992.00000000\n", - " total_act: 63562.00000000\n", - " est_act: 37632.00000000\n", - "0.0 [11.80802776]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 30425792.00000000\n", - " total_weights: 39128.00000000\n", - " total_act: 88666.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [11.61354731]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 13422320.00000000\n", - " total_weights: 64016.00000000\n", - " total_act: 60426.00000000\n", - 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"Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 16414144.00000000\n", - " total_weights: 84064.00000000\n", - " total_act: 25946.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [11.33359546]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 33129024.00000000\n", - " total_weights: 42576.00000000\n", - " total_act: 76122.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [11.00410316]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 14921248.00000000\n", - " total_weights: 19192.00000000\n", - " total_act: 63546.00000000\n", - " est_act: 37632.00000000\n", - "0.0 [10.35873793]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 6327040.00000000\n", - " total_weights: 32440.00000000\n", - " total_act: 16506.00000000\n", - 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" total_weights: 10624.00000000\n", - " total_act: 9466.00000000\n", - " est_act: 6272.00000000\n", - "0.0 [10.31868415]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 3730432.00000000\n", - " total_weights: 19192.00000000\n", - " total_act: 16506.00000000\n", - " est_act: 9408.00000000\n", - "0.0 [10.58433091]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 748176.00000000\n", - " total_weights: 14856.00000000\n", - " total_act: 12602.00000000\n", - " est_act: 9408.00000000\n", - "0.0 [10.31992142]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 719872.00000000\n", - " total_weights: 3832.00000000\n", - " total_act: 16506.00000000\n", - " est_act: 9408.00000000\n", - "0.0 [11.01492961]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - 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"0.0 [10.23825703]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 831280.00000000\n", - " total_weights: 4480.00000000\n", - " total_act: 16522.00000000\n", - " est_act: 9408.00000000\n", - "0.0 [10.6303116]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 1764240.00000000\n", - " total_weights: 30408.00000000\n", - " total_act: 12602.00000000\n", - " est_act: 9408.00000000\n", - "0.0 [10.21417414]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 512896.00000000\n", - " total_weights: 9976.00000000\n", - " total_act: 11802.00000000\n", - " est_act: 9408.00000000\n", - "0.0 [10.59536308]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 10148256.00000000\n", - " total_weights: 13104.00000000\n", - " total_act: 51002.00000000\n", - 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"Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 608704.00000000\n", - " total_weights: 10336.00000000\n", - " total_act: 16538.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [10.1976889]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 705840.00000000\n", - " total_weights: 3840.00000000\n", - " total_act: 16522.00000000\n", - " est_act: 9408.00000000\n", - "0.0 [10.26361743]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 1303168.00000000\n", - " total_weights: 20920.00000000\n", - " total_act: 11802.00000000\n", - " est_act: 9408.00000000\n", - "0.0 [11.76868657]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 29974208.00000000\n", - " total_weights: 38552.00000000\n", - " total_act: 88666.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [11.94159409]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 30733040.00000000\n", - " total_weights: 39440.00000000\n", - " total_act: 88650.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [10.37696544]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 1058560.00000000\n", - " total_weights: 5560.00000000\n", - " total_act: 16506.00000000\n", - " est_act: 9408.00000000\n", - "0.0 [10.08153558]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 1602736.00000000\n", - " total_weights: 31744.00000000\n", - " total_act: 9466.00000000\n", - " est_act: 6272.00000000\n", - "0.0 [10.54982164]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 9947552.00000000\n", - " total_weights: 12848.00000000\n", - " total_act: 51002.00000000\n", - 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"Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 5224736.00000000\n", - " total_weights: 26816.00000000\n", - " total_act: 19642.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [11.22102558]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 7884144.00000000\n", - " total_weights: 40464.00000000\n", - " total_act: 22794.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [10.22974907]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 588160.00000000\n", - " total_weights: 10360.00000000\n", - " total_act: 11802.00000000\n", - " est_act: 9408.00000000\n", - "0.0 [10.93806569]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 1217088.00000000\n", - " total_weights: 5328.00000000\n", - " total_act: 38490.00000000\n", - " est_act: 25088.00000000\n", - "0.0 [11.04215175]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 3293040.00000000\n", - " total_weights: 15240.00000000\n", - " total_act: 47882.00000000\n", - " est_act: 37632.00000000\n", - "0.0 [12.37475462]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 41370432.00000000\n", - " total_weights: 53088.00000000\n", - " total_act: 101210.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [10.17275177]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 1367456.00000000\n", - " total_weights: 6704.00000000\n", - " total_act: 32186.00000000\n", - " est_act: 25088.00000000\n", - "0.0 [10.65154227]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 10449312.00000000\n", - " total_weights: 13488.00000000\n", - " total_act: 51002.00000000\n", - " est_act: 25088.00000000\n", - "0.0 [11.20945146]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 8755872.00000000\n", - " total_weights: 42432.00000000\n", - " total_act: 57274.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [11.9000994]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 15680240.00000000\n", - " total_weights: 20240.00000000\n", - " total_act: 88650.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [10.17275177]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 1367456.00000000\n", - " total_weights: 6704.00000000\n", - " total_act: 32186.00000000\n", - " est_act: 25088.00000000\n", - "0.0 [9.84713445]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 1282784.00000000\n", - 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"Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 3716400.00000000\n", - " total_weights: 19200.00000000\n", - " total_act: 16522.00000000\n", - " est_act: 9408.00000000\n", - "0.0 [10.62531881]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 2763136.00000000\n", - " total_weights: 14416.00000000\n", - " total_act: 19674.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [11.09886271]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 12574112.00000000\n", - " total_weights: 64472.00000000\n", - " total_act: 22810.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [9.84713445]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 1282784.00000000\n", - " total_weights: 6704.00000000\n", - " total_act: 13370.00000000\n", - 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" total_weights: 19200.00000000\n", - " total_act: 63562.00000000\n", - " est_act: 37632.00000000\n", - "0.0 [10.22964951]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 1754752.00000000\n", - " total_weights: 32440.00000000\n", - " total_act: 11802.00000000\n", - " est_act: 9408.00000000\n", - "0.0 [10.28382577]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 550528.00000000\n", - " total_weights: 5560.00000000\n", - " total_act: 11802.00000000\n", - " est_act: 9408.00000000\n", - "0.0 [10.21814038]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 1039744.00000000\n", - " total_weights: 19576.00000000\n", - " total_act: 11802.00000000\n", - " est_act: 9408.00000000\n", - "0.0 [9.95902517]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 4318432.00000000\n", - " total_weights: 22192.00000000\n", - " total_act: 13370.00000000\n", - " est_act: 6272.00000000\n", - "0.0 [10.66934894]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 2352160.00000000\n", - " total_weights: 10360.00000000\n", - " total_act: 44730.00000000\n", - " est_act: 37632.00000000\n", - "0.0 [11.57543947]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 4390640.00000000\n", - " total_weights: 20240.00000000\n", - " total_act: 60426.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [11.13841313]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 846960.00000000\n", - " total_weights: 9744.00000000\n", - " total_act: 15738.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [11.0897206]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 3355760.00000000\n", - " total_weights: 64016.00000000\n", - " total_act: 15738.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [10.6891828]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 9262416.00000000\n", - " total_weights: 47496.00000000\n", - " total_act: 19658.00000000\n", - " est_act: 9408.00000000\n", - "0.0 [11.19349927]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 12349808.00000000\n", - " total_weights: 63248.00000000\n", - " total_act: 22794.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [12.28362905]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 7451456.00000000\n", - " total_weights: 9824.00000000\n", - " total_act: 101210.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [10.22927958]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 211840.00000000\n", - " total_weights: 3832.00000000\n", - " total_act: 11802.00000000\n", - " est_act: 9408.00000000\n", - "0.0 [11.61478361]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 16313792.00000000\n", - " total_weights: 83552.00000000\n", - " total_act: 25946.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [11.1953217]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 7432560.00000000\n", - " total_weights: 38160.00000000\n", - " total_act: 22794.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [10.26067188]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 1066400.00000000\n", - " total_weights: 3248.00000000\n", - " total_act: 32186.00000000\n", - " est_act: 25088.00000000\n", - "0.0 [11.29544383]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 10650176.00000000\n", - " total_weights: 13904.00000000\n", - " total_act: 76122.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [11.50055594]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 2615744.00000000\n", - " total_weights: 51296.00000000\n", - " total_act: 16538.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [9.77204711]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 141280.00000000\n", - " total_weights: 2608.00000000\n", - " total_act: 8666.00000000\n", - " est_act: 6272.00000000\n", - "0.0 [10.17275177]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 1367456.00000000\n", - " total_weights: 6704.00000000\n", - " total_act: 32186.00000000\n", - " est_act: 25088.00000000\n", - "0.0 [10.21417414]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 512896.00000000\n", - " total_weights: 9976.00000000\n", - " total_act: 11802.00000000\n", - " est_act: 9408.00000000\n", - "0.0 [12.30453373]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 7852864.00000000\n", - " total_weights: 10336.00000000\n", - " total_act: 101210.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [11.53090198]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 1762752.00000000\n", - " total_weights: 28512.00000000\n", - " total_act: 16538.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [9.86686777]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 530144.00000000\n", - " total_weights: 2864.00000000\n", - " total_act: 13370.00000000\n", - " est_act: 6272.00000000\n", - "0.0 [11.09962707]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 1472672.00000000\n", - " total_weights: 7832.00000000\n", - " total_act: 22810.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [11.34924385]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 4629056.00000000\n", - " total_weights: 6224.00000000\n", - " total_act: 76122.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [10.87400936]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 14864880.00000000\n", - " total_weights: 19200.00000000\n", - " total_act: 63562.00000000\n", - " est_act: 37632.00000000\n", - "0.0 [11.78610914]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 6341312.00000000\n", - " total_weights: 8408.00000000\n", - " total_act: 88666.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [10.20958016]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 1641856.00000000\n", - " total_weights: 31864.00000000\n", - " total_act: 11802.00000000\n", - " est_act: 9408.00000000\n", - "0.0 [11.49725973]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 10261152.00000000\n", - " total_weights: 13248.00000000\n", - " total_act: 76090.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [10.49598508]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 379776.00000000\n", - " total_weights: 5712.00000000\n", - " total_act: 10266.00000000\n", - " est_act: 6272.00000000\n", - "0.0 [11.15188299]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 1129120.00000000\n", - " total_weights: 5056.00000000\n", - " total_act: 57274.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [10.69401343]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 1386272.00000000\n", - " total_weights: 26048.00000000\n", - " total_act: 14938.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [11.07977842]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 3104880.00000000\n", - " total_weights: 62736.00000000\n", - " total_act: 15738.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [10.19322297]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 2571680.00000000\n", - " total_weights: 12848.00000000\n", - " total_act: 32186.00000000\n", - " est_act: 25088.00000000\n", - "0.0 [10.1794739]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 3716400.00000000\n", - " total_weights: 19200.00000000\n", - " total_act: 16522.00000000\n", - " est_act: 9408.00000000\n", - "0.0 [10.55620753]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 4995968.00000000\n", - " total_weights: 25808.00000000\n", - " total_act: 19674.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [10.68919778]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 2189088.00000000\n", - " total_weights: 42432.00000000\n", - " total_act: 14938.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [11.49725973]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 10261152.00000000\n", - " total_weights: 13248.00000000\n", - " total_act: 76090.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [11.52346994]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 10662560.00000000\n", - " total_weights: 13760.00000000\n", - " total_act: 76090.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [12.05574756]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 11866944.00000000\n", - " total_weights: 53088.00000000\n", - " total_act: 63578.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [11.02928079]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 9314160.00000000\n", - " total_weights: 47112.00000000\n", - " total_act: 47882.00000000\n", - " est_act: 37632.00000000\n", - "0.0 [12.30453373]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 7852864.00000000\n", - " total_weights: 10336.00000000\n", - " total_act: 101210.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [11.646251]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 34546336.00000000\n", - " total_weights: 44224.00000000\n", - " total_act: 76090.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [10.47770868]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 304512.00000000\n", - " total_weights: 5328.00000000\n", - " total_act: 10266.00000000\n", - " est_act: 6272.00000000\n", - "0.0 [11.20686106]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 3819888.00000000\n", - " total_weights: 19728.00000000\n", - " total_act: 22794.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [11.18289925]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 3136160.00000000\n", - " total_weights: 13760.00000000\n", - " total_act: 57274.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [12.05574756]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 11866944.00000000\n", - " total_weights: 53088.00000000\n", - " total_act: 63578.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [11.48993342]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 4221376.00000000\n", - " total_weights: 84064.00000000\n", - " total_act: 16538.00000000\n", - " est_act: 12544.00000000\n", - "0.0 [11.60470286]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 8605424.00000000\n", - " total_weights: 39440.00000000\n", - " total_act: 60426.00000000\n", - " est_act: 50176.00000000\n", - "0.0 [9.89578879]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 1408224.00000000\n", - " total_weights: 7344.00000000\n", - " total_act: 13370.00000000\n", - " est_act: 6272.00000000\n", - "0.0 [10.37696544]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 1058560.00000000\n", - " total_weights: 5560.00000000\n", - " total_act: 16506.00000000\n", - " est_act: 9408.00000000\n", - "0.0 [9.85975842]\n", - "Predicted metrics for gcn:\n", - " val_error: 0.00000000\n", - "Predicted metrics for macs:\n", - " total_macs: 367072.00000000\n", - " total_weights: 3760.00000000\n", - " total_act: 8666.00000000\n", - " est_act: 6272.00000000\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[28], line 10\u001b[0m\n\u001b[1;32m 7\u001b[0m cfg \u001b[38;5;241m=\u001b[39m compose(config_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconfig\u001b[39m\u001b[38;5;124m\"\u001b[39m, overrides\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel=cnn_search_space\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnas=random_nas\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrainer.max_epochs=1\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdataset=mnist\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdataset.data_folder=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mwd\u001b[38;5;250m \u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m+trainer.enable_progress_bar=false\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodule.num_workers=8\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m change_wd(wd \u001b[38;5;241m/\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun1\u001b[39m\u001b[38;5;124m\"\u001b[39m, create\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, clean\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 10\u001b[0m \u001b[43mmain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcfg\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/local/gerum/.cache/pypoetry/virtualenvs/hannah-8Yp6PnCx-py3.11/lib/python3.11/site-packages/hydra/main.py:83\u001b[0m, in \u001b[0;36mmain..main_decorator..decorated_main\u001b[0;34m(cfg_passthrough)\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(task_function)\n\u001b[1;32m 81\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorated_main\u001b[39m(cfg_passthrough: Optional[DictConfig] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m 82\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cfg_passthrough \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 83\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtask_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcfg_passthrough\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 85\u001b[0m args_parser \u001b[38;5;241m=\u001b[39m get_args_parser()\n", - "File \u001b[0;32m/local/gerum/hannah/hannah/tools/train.py:42\u001b[0m, in \u001b[0;36mmain\u001b[0;34m(config)\u001b[0m\n\u001b[1;32m 40\u001b[0m handle_dataset(config)\n\u001b[1;32m 41\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m config\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnas\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 42\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mnas\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 43\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m train(config)\n", - "File \u001b[0;32m/local/gerum/hannah/hannah/train.py:266\u001b[0m, in \u001b[0;36mnas\u001b[0;34m(config)\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[38;5;28mprint\u001b[39m(OmegaConf\u001b[38;5;241m.\u001b[39mto_yaml(config))\n\u001b[1;32m 265\u001b[0m nas_trainer \u001b[38;5;241m=\u001b[39m instantiate(config\u001b[38;5;241m.\u001b[39mnas, parent_config\u001b[38;5;241m=\u001b[39mconfig, _recursive_\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m--> 266\u001b[0m \u001b[43mnas_trainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/local/gerum/hannah/hannah/nas/search/search.py:79\u001b[0m, in \u001b[0;36mNASBase.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 78\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbefore_search()\n\u001b[0;32m---> 79\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msearch\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 80\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mafter_search()\n", - "File \u001b[0;32m/local/gerum/hannah/hannah/nas/search/search.py:179\u001b[0m, in \u001b[0;36mDirectNAS.search\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnew_points \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 178\u001b[0m \u001b[38;5;66;03m# first batch of candidates\u001b[39;00m\n\u001b[0;32m--> 179\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minit_candidates\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 181\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msampler\u001b[38;5;241m.\u001b[39mhistory) \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbudget:\n\u001b[1;32m 182\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mworklist \u001b[38;5;241m=\u001b[39m []\n", - "File \u001b[0;32m/local/gerum/hannah/hannah/nas/search/search.py:167\u001b[0m, in \u001b[0;36mDirectNAS.init_candidates\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcandidates \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 166\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m remaining_candidates \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 167\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcandidates \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample_candidates\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 168\u001b[0m \u001b[43m \u001b[49m\u001b[43mremaining_candidates\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 169\u001b[0m \u001b[43m \u001b[49m\u001b[43mremaining_candidates\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 170\u001b[0m \u001b[43m \u001b[49m\u001b[43mpresample\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpresample\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 171\u001b[0m \u001b[43m \u001b[49m\u001b[43mconstrain\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 172\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/local/gerum/hannah/hannah/nas/search/search.py:257\u001b[0m, in \u001b[0;36mDirectNAS.sample_candidates\u001b[0;34m(self, num_total, num_candidates, sort_key, presample, constrain)\u001b[0m\n\u001b[1;32m 255\u001b[0m parameters \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msample(constrain)\n\u001b[1;32m 256\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuild_model(parameters)\n\u001b[0;32m--> 257\u001b[0m estimated_metrics, satisfied_bounds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestimate_metrics\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 258\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdeepcopy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 259\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 260\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m presample:\n\u001b[1;32m 261\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpresampler\u001b[38;5;241m.\u001b[39mcheck(model, estimated_metrics):\n", - "File \u001b[0;32m/local/gerum/hannah/hannah/nas/search/search.py:374\u001b[0m, in \u001b[0;36mDirectNAS.estimate_metrics\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m 372\u001b[0m estimated_metrics \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m 373\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, predictor \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictors\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m--> 374\u001b[0m metrics \u001b[38;5;241m=\u001b[39m \u001b[43mpredictor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexample_input_array\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 376\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPredicted metrics for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 377\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m metrics\u001b[38;5;241m.\u001b[39mitems():\n", - "File \u001b[0;32m/local/gerum/hannah/hannah/nas/performance_prediction/simple.py:130\u001b[0m, in \u001b[0;36mGCNPredictor.predict\u001b[0;34m(self, model, input)\u001b[0m\n\u001b[1;32m 127\u001b[0m nx_graph\u001b[38;5;241m.\u001b[39mnodes[n][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfeatures\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m fea\u001b[38;5;241m.\u001b[39miloc[i]\u001b[38;5;241m.\u001b[39mto_numpy()\n\u001b[1;32m 128\u001b[0m dgl_graph \u001b[38;5;241m=\u001b[39m to_dgl_graph(nx_graph)\n\u001b[0;32m--> 130\u001b[0m result, std_dev \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredictor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdgl_graph\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 132\u001b[0m \u001b[38;5;28mprint\u001b[39m(result, std_dev)\n\u001b[1;32m 134\u001b[0m metrics \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mval_error\u001b[39m\u001b[38;5;124m\"\u001b[39m: result\u001b[38;5;241m.\u001b[39mitem()}\n", - "File \u001b[0;32m/local/gerum/hannah/hannah/nas/performance_prediction/gcn/predictor.py:395\u001b[0m, in \u001b[0;36mGaussianProcessPredictor.predict\u001b[0;34m(self, X, return_std)\u001b[0m\n\u001b[1;32m 393\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(X, dgl\u001b[38;5;241m.\u001b[39mDGLGraph):\n\u001b[1;32m 394\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m X\u001b[38;5;241m.\u001b[39mbatch_size \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 395\u001b[0m embeddings \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_embedding\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mnumpy()\n\u001b[1;32m 396\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 397\u001b[0m embeddings \u001b[38;5;241m=\u001b[39m []\n", - "File \u001b[0;32m/local/gerum/hannah/hannah/nas/performance_prediction/gcn/predictor.py:372\u001b[0m, in \u001b[0;36mGaussianProcessPredictor.get_embedding\u001b[0;34m(self, graph)\u001b[0m\n\u001b[1;32m 369\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_feature_size \u001b[38;5;241m=\u001b[39m shape[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 370\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minit_embeddings()\n\u001b[0;32m--> 372\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_embedding\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgraph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgraph\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mndata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfea_name\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/local/gerum/hannah/hannah/nas/performance_prediction/gcn/model.py:73\u001b[0m, in \u001b[0;36mGCNEmbedding.get_embedding\u001b[0;34m(self, g, in_feat)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_embedding\u001b[39m(\u001b[38;5;28mself\u001b[39m, g, in_feat):\n\u001b[0;32m---> 73\u001b[0m h \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_feat\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 74\u001b[0m h \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39mrelu(h)\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m conv \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvs:\n", - "File \u001b[0;32m/local/gerum/.cache/pypoetry/virtualenvs/hannah-8Yp6PnCx-py3.11/lib64/python3.11/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/local/gerum/.cache/pypoetry/virtualenvs/hannah-8Yp6PnCx-py3.11/lib64/python3.11/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1518\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1519\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1523\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m/local/gerum/.cache/pypoetry/virtualenvs/hannah-8Yp6PnCx-py3.11/lib64/python3.11/site-packages/dgl/nn/pytorch/conv/graphconv.py:460\u001b[0m, in \u001b[0;36mGraphConv.forward\u001b[0;34m(self, graph, feat, weight, edge_weight)\u001b[0m\n\u001b[1;32m 458\u001b[0m rst \u001b[38;5;241m=\u001b[39m graph\u001b[38;5;241m.\u001b[39mdstdata[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 459\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m weight \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 460\u001b[0m rst \u001b[38;5;241m=\u001b[39m \u001b[43mth\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmatmul\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 462\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_norm \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mright\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mboth\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m 463\u001b[0m degs \u001b[38;5;241m=\u001b[39m graph\u001b[38;5;241m.\u001b[39min_degrees()\u001b[38;5;241m.\u001b[39mto(feat_dst)\u001b[38;5;241m.\u001b[39mclamp(\u001b[38;5;28mmin\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + "\n" ] } ], @@ -7419,9 +405,9 @@ "wd = pathlib.Path.cwd()\n", "\n", "with initialize_config_module(\"hannah.conf\", version_base=\"1.2\"):\n", - " cfg = compose(config_name=\"config\", overrides=[\"model=cnn_search_space\", \"nas=random_nas\", \"trainer.max_epochs=1\", \"dataset=mnist\", f\"dataset.data_folder={wd / 'data'}\", \"+trainer.enable_progress_bar=false\", \"module.num_workers=8\"])\n", + " cfg = compose(config_name=\"config\", overrides=[\"model=cnn_search_space\", \"nas=random_nas\", \"trainer.max_epochs=1\", \"dataset=mnist\", f\"dataset.data_folder={wd / 'data'}\", \"+trainer.enable_progress_bar=false\", \"module.num_workers=8\", \"nas.presample=false\"])\n", " \n", - " with change_wd(wd / \"run1\", create=True, clean=True):\n", + " with change_wd(wd / \"run1\", create=True, clean=False):\n", " main(cfg)" ] }, @@ -7437,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 148, "id": "60134d78", "metadata": {}, "outputs": [ @@ -7473,63 +459,63 @@ " \n", " \n", " 0\n", - " 100352.0\n", - " 0.292995\n", - " 138874.0\n", - " 72153568.0\n", - " 363808.0\n", - " 0.052310\n", + " 12544.0\n", + " 0.028927\n", + " 16778.0\n", + " 2011328.0\n", + " 45152.0\n", + " 0.109545\n", " \n", " \n", " 1\n", - " 21952.0\n", - " 0.167889\n", - " 51098.0\n", - " 51642720.0\n", - " 288312.0\n", - " 0.074898\n", + " 37632.0\n", + " 0.011582\n", + " 44730.0\n", + " 2201632.0\n", + " 5560.0\n", + " 0.348166\n", " \n", " \n", " 2\n", - " 87808.0\n", - " 0.502111\n", - " 201626.0\n", - " 207622656.0\n", - " 289656.0\n", - " 0.072520\n", + " 50176.0\n", + " 0.018490\n", + " 57274.0\n", + " 2734752.0\n", + " 13248.0\n", + " 0.521909\n", " \n", " \n", " 3\n", - " 21952.0\n", - " 0.325369\n", - " 44730.0\n", - " 49857856.0\n", - " 254536.0\n", - " 0.065048\n", + " 9408.0\n", + " 0.056489\n", + " 19674.0\n", + " 6766240.0\n", + " 44440.0\n", + " 0.111073\n", " \n", " \n", " 4\n", - " 6272.0\n", - " 0.070293\n", - " 17386.0\n", - " 12955456.0\n", - " 261136.0\n", - " 0.057575\n", + " 12544.0\n", + " 0.027557\n", + " 38490.0\n", + " 12149184.0\n", + " 62304.0\n", + " 0.080673\n", " \n", " \n", "\n", "" ], "text/plain": [ - " est_act ff total_act total_macs total_weights val_error\n", - "0 100352.0 0.292995 138874.0 72153568.0 363808.0 0.052310\n", - "1 21952.0 0.167889 51098.0 51642720.0 288312.0 0.074898\n", - "2 87808.0 0.502111 201626.0 207622656.0 289656.0 0.072520\n", - "3 21952.0 0.325369 44730.0 49857856.0 254536.0 0.065048\n", - "4 6272.0 0.070293 17386.0 12955456.0 261136.0 0.057575" + " est_act ff total_act total_macs total_weights val_error\n", + "0 12544.0 0.028927 16778.0 2011328.0 45152.0 0.109545\n", + "1 37632.0 0.011582 44730.0 2201632.0 5560.0 0.348166\n", + "2 50176.0 0.018490 57274.0 2734752.0 13248.0 0.521909\n", + "3 9408.0 0.056489 19674.0 6766240.0 44440.0 0.111073\n", + "4 12544.0 0.027557 38490.0 12149184.0 62304.0 0.080673" ] }, - "execution_count": 22, + "execution_count": 148, "metadata": {}, "output_type": "execute_result" } @@ -7546,18 +532,803 @@ }, { "cell_type": "markdown", - "id": "d5cced93-a8fd-4e98-8e37-d48a4e3b3bf4", + "id": "8b6e7268", + "metadata": {}, + "source": [ + "# Getting Hardware into the Picture" + ] + }, + { + "cell_type": "markdown", + "id": "ff3b0453", + "metadata": {}, + "source": [ + "## Hardware aware NAS\n", + "\n", + "Many approaches to integrate hardware properties into the optimization loop use hardware measurements of inference latency or energy consumption as target functions for the optimization loop. We can do the same, by using\n", + "\n", + "1. By directly measuring the neural network`s exection time using a compilation backend\n", + "2. By using a prefitted estimator that predicts the performance measurements\n", + "3. By using a hardware model that estimates harware metrics using analytical or functional modeling" + ] + }, + { + "cell_type": "markdown", + "id": "8291e260", + "metadata": {}, + "source": [ + "## Hardware Aware-NAS\n", + "\n", + "In this case we use a pretrained performance estimator [nn-meter](https://github.com/microsoft/nn-Meter). \n", + "\n", + "The performance estimator has been pretrained for the following devices and inference frameworks:\n", + "\n", + "| Device | Framework | Processor | Hardware name |\n", + "| :-----------------: | :------------: | :------------: | :----------------------: |\n", + "| Pixel4 | TFLite v2.1 | CortexA76 CPU | cortexA76cpu_tflite21 |\n", + "| Mi9 | TFLite v2.1 | Adreno 640 GPU | adreno640gpu_tflite21 |\n", + "| Pixel3XL | TFLite v2.1 | Adreno 630 GPU | adreno630gpu_tflite21 |\n", + "| Intel Movidius NCS2 | OpenVINO2019R2 | Myriad VPU | myriadvpu_openvino2019r2 |\n", + "\n", + "`nn-meter` has been integrated into hannah and can be used as a performance estimator by adding it to the configuration. \n", + "\n", + "Using the overwrites: `nas/predictor=nn-meter nas.predictor.nn_meter.hardware_name=\n", + "\n", + "The following adds the `nn-meter` predictor to the list of predictors, and configures the multiobjective optimization to also consider the reported duration per inference, searching for models with an uper execution time bound of 0.1 ms on the given target hardware. " + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "id": "4ac4c2dd", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:hannah.utils.utils:Environment info:\n", + "INFO:hannah.utils.utils: Number of GPUs: 2\n", + "INFO:hannah.utils.utils: CUDA version: 12.1\n", + "INFO:hannah.utils.utils: CUDNN version: 8907\n", + "INFO:hannah.utils.utils: Kernel: 4.18.0-553.16.1.el8_10.x86_64\n", + "INFO:hannah.utils.utils: Python: 3.11.9 (main, Jun 24 2024, 06:19:13) [GCC 8.5.0 20210514 (Red Hat 8.5.0-22)]\n", + "INFO:hannah.utils.utils: PyTorch: 2.2.2+cu121\n", + "INFO:hannah.utils.utils: Pytorch Lightning: 2.4.0\n", + "INFO:hannah.utils.utils: Numpy: 1.26.4\n", + "INFO:hannah.utils.utils: Hannah version info:\n", + "INFO:hannah.utils.utils: Cannot find a Git repository. You probably downloaded an archive\n", + "INFO:hannah.utils.utils: Command line: /local/gerum/.cache/pypoetry/virtualenvs/hannah-8Yp6PnCx-py3.11/lib/python3.11/site-packages/ipykernel_launcher.py --f=/home/gerum/.local/share/jupyter/runtime/kernel-v2-2090420HcS9vqNt563N.json\n", + "INFO:hannah.utils.utils: \n", + "INFO:hannah.nas.search.sampler.base_sampler:Loaded 5 points from history\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " ** **\n", + "/** /**\n", + "/** ****** ******* ******* ****** /**\n", + "/****** //////** //**///**//**///** //////** /******\n", + "/**///** ******* /** /** /** /** ******* /**///**\n", + "/** /** **////** /** /** /** /** **////** /** /**\n", + "/** /**//******** *** /** *** /**//********/** /**\n", + "// // //////// /// // /// // //////// // //\n", + "\n", + "dataset:\n", + " data_folder: /local/gerum/hannah/tutorials/automlschool24/data\n", + " cls: hannah.datasets.vision.MNISTDataset\n", + " dataset: mnist\n", + " val_percent: 0.1\n", + "features:\n", + " _target_: torch.nn.Identity\n", + "model:\n", + " _target_: space.cnn_search_space\n", + " name: cnn_search_space\n", + " num_classes: 10\n", + " max_channels: 32\n", + " max_blocks: 2\n", + "scheduler:\n", + " _target_: torch.optim.lr_scheduler.OneCycleLR\n", + " max_lr: 0.001\n", + " pct_start: 0.3\n", + " anneal_strategy: cos\n", + " cycle_momentum: true\n", + " base_momentum: 0.85\n", + " max_momentum: 0.95\n", + " div_factor: 25.0\n", + " final_div_factor: 10000.0\n", + " last_epoch: -1\n", + "optimizer:\n", + " _target_: torch.optim.adamw.AdamW\n", + " lr: 0.001\n", + " betas:\n", + " - 0.9\n", + " - 0.999\n", + " eps: 1.0e-08\n", + " weight_decay: 1.0e-05\n", + " amsgrad: false\n", + "module:\n", + " _target_: hannah.modules.vision.ImageClassifierModule\n", + " num_workers: 8\n", + " batch_size: 128\n", + " shuffle_all_dataloaders: false\n", + "trainer:\n", + " _target_: pytorch_lightning.trainer.Trainer\n", + " accelerator: auto\n", + " devices: 1\n", + " limit_train_batches: 1.0\n", + " limit_val_batches: 1.0\n", + " limit_test_batches: 1.0\n", + " max_epochs: 1\n", + " default_root_dir: .\n", + " fast_dev_run: false\n", + " overfit_batches: 0.0\n", + " benchmark: false\n", + " deterministic: warn\n", + " gradient_clip_val: 0\n", + " accumulate_grad_batches: 1\n", + " plugins: null\n", + " strategy: auto\n", + " reload_dataloaders_every_n_epochs: 0\n", + " precision: 32\n", + " enable_progress_bar: false\n", + "checkpoint:\n", + " _target_: pytorch_lightning.callbacks.ModelCheckpoint\n", + " dirpath: checkpoints\n", + " save_top_k: 1\n", + " verbose: true\n", + " monitor: val_error\n", + " mode: min\n", + " save_last: true\n", + "nas:\n", + " sampler:\n", + " _target_: hannah.nas.search.sampler.random_sampler.RandomSampler\n", + " model_trainer:\n", + " _target_: hannah.nas.search.model_trainer.simple_model_trainer.SimpleModelTrainer\n", + " per_process_memory_fraction: null\n", + " constraint_model:\n", + " _target_: hannah.nas.constraints.constraint_model.ConstraintModel\n", + " method: naive\n", + " fix_strategy: last\n", + " timeout: 300\n", + " predictor:\n", + " nn_meter:\n", + " _target_: hannah.nas.performance_prediction.nn_meter.NNMeterPredictor\n", + " hardware_name: adreno630gpu_tflite21\n", + " predictor_version: null\n", + " macs:\n", + " _target_: hannah.nas.performance_prediction.simple.MACPredictor\n", + " predictor: fx\n", + " gcn:\n", + " _target_: hannah.nas.performance_prediction.simple.GCNPredictor\n", + " model:\n", + " _target_: hannah.nas.performance_prediction.gcn.predictor.GaussianProcessPredictor\n", + " input_feature_size: 35\n", + " presampler:\n", + " _target_: hannah.nas.search.presampler.simple.SingleRangeChecker\n", + " min: 250000\n", + " max: 550000\n", + " _target_: hannah.nas.search.search.DirectNAS\n", + " budget: 5\n", + " n_jobs: 10\n", + " total_candidates: 5\n", + " num_selected_candidates: 10\n", + " presample: false\n", + " bounds:\n", + " val_error: 0.1\n", + " total_macs: 128000000\n", + " total_weights: 500000\n", + "experiment_id: test\n", + "output_dir: trained_models\n", + "auto_lr: false\n", + "resume: false\n", + "fx_mac_summary: true\n", + "skip_test: false\n", + "skip_val: false\n", + "seed:\n", + "- 1234\n", + "validate_output: false\n", + "hannah:\n", + " nas:\n", + " bounds:\n", + " duration: 0.2\n", + "\n", + "(nn-Meter) checking local kernel predictors at /local/gerum/.nn_meter/data/predictor/adreno630gpu_tflite21\n", + "(nn-Meter) load predictor /local/gerum/.nn_meter/data/predictor/adreno630gpu_tflite21/concat.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:py.warnings:/local/gerum/.cache/pypoetry/virtualenvs/hannah-8Yp6PnCx-py3.11/lib64/python3.11/site-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator DecisionTreeRegressor from version 0.23.1 when using version 1.2.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n", + "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n", + " warnings.warn(\n", + "\n", + "WARNING:py.warnings:/local/gerum/.cache/pypoetry/virtualenvs/hannah-8Yp6PnCx-py3.11/lib64/python3.11/site-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator RandomForestRegressor from version 0.23.1 when using version 1.2.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n", + "https://scikit-learn.org/stable/model_persistence.html#security-maintainability-limitations\n", + " warnings.warn(\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(nn-Meter) load predictor /local/gerum/.nn_meter/data/predictor/adreno630gpu_tflite21/dwconv-bn-relu.pkl\n", + "(nn-Meter) load predictor /local/gerum/.nn_meter/data/predictor/adreno630gpu_tflite21/relu.pkl\n", + "(nn-Meter) load predictor /local/gerum/.nn_meter/data/predictor/adreno630gpu_tflite21/maxpool.pkl\n", + "(nn-Meter) load predictor /local/gerum/.nn_meter/data/predictor/adreno630gpu_tflite21/hswish.pkl\n", + "(nn-Meter) load predictor /local/gerum/.nn_meter/data/predictor/adreno630gpu_tflite21/se.pkl\n", + "(nn-Meter) load predictor /local/gerum/.nn_meter/data/predictor/adreno630gpu_tflite21/avgpool.pkl\n", + "(nn-Meter) load predictor /local/gerum/.nn_meter/data/predictor/adreno630gpu_tflite21/bn.pkl\n", + "(nn-Meter) load predictor /local/gerum/.nn_meter/data/predictor/adreno630gpu_tflite21/conv-bn-relu.pkl\n", + "(nn-Meter) load predictor /local/gerum/.nn_meter/data/predictor/adreno630gpu_tflite21/bnrelu.pkl\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:hannah.callbacks.optimization:Monitoring the following values for optimization\n", + "INFO:hannah.callbacks.optimization: - val_error direction: minimize(1)\n" + ] + } + ], + "source": [ + "from hannah.tools.train import main\n", + "from hydra import compose, initialize_config_module\n", + "\n", + "wd = pathlib.Path.cwd()\n", + "\n", + "with initialize_config_module(\"hannah.conf\", version_base=\"1.2\"):\n", + " cfg = compose(config_name=\"config\", overrides=[\"nas/predictor=[nn-meter,macs,gcn]\", \"+hannah.nas.bounds.duration=0.2\", \"model=cnn_search_space\", \"nas=random_nas\", \"trainer.max_epochs=1\", \"dataset=mnist\", f\"dataset.data_folder={wd / 'data'}\", \"+trainer.enable_progress_bar=false\", \"module.num_workers=8\", \"nas.presample=false\"])\n", + " \n", + " with change_wd(wd / \"run_nn_meter\", create=True, clean=False):\n", + " main(cfg)" + ] + }, + { + "cell_type": "markdown", + "id": "4078b475", + "metadata": {}, + "source": [ + "The search yielded the following results:" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "id": "78c6aeca", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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durationest_actfftotal_acttotal_macstotal_weightsval_error
00.5101699408.00.10172014170.02827344.052104.00.082371
10.54097225088.00.13167551002.017273248.022192.00.175102
20.2797796272.00.05060311290.01115520.039120.00.084749
30.4413239408.00.03011914154.01924176.059016.00.072690
40.51802237632.00.06066188634.017775008.022832.00.253397
\n", + "
" + ], + "text/plain": [ + " duration est_act ff total_act total_macs total_weights \\\n", + "0 0.510169 9408.0 0.101720 14170.0 2827344.0 52104.0 \n", + "1 0.540972 25088.0 0.131675 51002.0 17273248.0 22192.0 \n", + "2 0.279779 6272.0 0.050603 11290.0 1115520.0 39120.0 \n", + "3 0.441323 9408.0 0.030119 14154.0 1924176.0 59016.0 \n", + "4 0.518022 37632.0 0.060661 88634.0 17775008.0 22832.0 \n", + "\n", + " val_error \n", + "0 0.082371 \n", + "1 0.175102 \n", + "2 0.084749 \n", + "3 0.072690 \n", + "4 0.253397 " + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd \n", + "import yaml\n", + "\n", + "history = yaml.unsafe_load((wd / \"run_nn_meter\" / \"history.yml\").read_text())\n", + "\n", + "df = pd.DataFrame((item.result for item in history))\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "ae5a16e8", + "metadata": {}, + "source": [ + "As the number of multiply accumulate operations is often used as a first order approximation we can also have a look at a comparison of the number of MAC operations in the networks versus their actual runtimes. " + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "id": "052a845f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "df.plot(x='total_macs', y='duration', kind='scatter')\n", + "plt.xlabel('Total MACs')\n", + "plt.ylabel('Duration')\n", + "plt.title('Duration vs Total MACs')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b68c6721", + "metadata": {}, + "source": [ + "## Taking hardware-deployment into consideration\n", + "\n", + "The neural architecture search so far has been hardware-aware but did not consider any specific hardware-optimizations. Going one step-further we do not only optimize over the neural network architecture space but also over the neural network architectures. For this purpose we make use of the neural network compilation framework tvm. \n", + "\n", + "In TVM neural network operators can be represented using a lower level of abstraction than a graph of machine learning operators, it is more closely related to a lower level programming language like C but the compiler toolchain allows to expose mapping decisions to an external optimizer like a neural architecture search. \n", + "\n", + "For demonstration purposes we consider the operator in TVMs internal representation TIR. " + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "id": "6cbe0cfe", "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
# from tvm.script import tir as T\n",
+       "\n",
+       "@T.prim_func\n",
+       "def main(input: T.Buffer((1, 8, 32, 32), "float32"), filter: T.Buffer((16, 8, 3, 3), "float32"), compute: T.Buffer((1, 16, 32, 32), "float32")):\n",
+       "    T.func_attr({"tir.noalias": T.bool(True)})\n",
+       "    # with T.block("root"):\n",
+       "    pad_temp = T.alloc_buffer((1, 8, 34, 34))\n",
+       "    conv2d_nchw = T.alloc_buffer((1, 16, 32, 32))\n",
+       "    for i0, i1, i2, i3 in T.grid(1, 8, 34, 34):\n",
+       "        with T.block("pad_temp"):\n",
+       "            v_i0, v_i1, v_i2, v_i3 = T.axis.remap("SSSS", [i0, i1, i2, i3])\n",
+       "            T.reads(input[v_i0, v_i1, v_i2 - 1, v_i3 - 1])\n",
+       "            T.writes(pad_temp[v_i0, v_i1, v_i2, v_i3])\n",
+       "            pad_temp[v_i0, v_i1, v_i2, v_i3] = T.if_then_else(1 <= v_i2 and v_i2 < 33 and 1 <= v_i3 and v_i3 < 33, input[v_i0, v_i1, v_i2 - 1, v_i3 - 1], T.float32(0))\n",
+       "    for nn, ff, yy, xx, rc, ry, rx in T.grid(1, 16, 32, 32, 8, 3, 3):\n",
+       "        with T.block("conv2d_nchw"):\n",
+       "            v_nn, v_ff, v_yy, v_xx, v_rc, v_ry, v_rx = T.axis.remap("SSSSRRR", [nn, ff, yy, xx, rc, ry, rx])\n",
+       "            T.reads(pad_temp[v_nn, v_rc, v_yy + v_ry, v_xx + v_rx], filter[v_ff, v_rc, v_ry, v_rx])\n",
+       "            T.writes(conv2d_nchw[v_nn, v_ff, v_yy, v_xx])\n",
+       "            with T.init():\n",
+       "                conv2d_nchw[v_nn, v_ff, v_yy, v_xx] = T.float32(0)\n",
+       "            conv2d_nchw[v_nn, v_ff, v_yy, v_xx] = conv2d_nchw[v_nn, v_ff, v_yy, v_xx] + pad_temp[v_nn, v_rc, v_yy + v_ry, v_xx + v_rx] * filter[v_ff, v_rc, v_ry, v_rx]\n",
+       "    for i0, i1, i2, i3 in T.grid(1, 16, 32, 32):\n",
+       "        with T.block("compute"):\n",
+       "            v_i0, v_i1, v_i2, v_i3 = T.axis.remap("SSSS", [i0, i1, i2, i3])\n",
+       "            T.reads(conv2d_nchw[v_i0, v_i1, v_i2, v_i3])\n",
+       "            T.writes(compute[v_i0, v_i1, v_i2, v_i3])\n",
+       "            compute[v_i0, v_i1, v_i2, v_i3] = T.max(conv2d_nchw[v_i0, v_i1, v_i2, v_i3], T.float32(0))\n",
+       "
\n" + ], + "text/latex": [ + "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", + "\\PY{c+c1}{\\PYZsh{} from tvm.script import tir as T}\n", + "\n", + "\\PY{n+nd}{@T}\\PY{o}{.}\\PY{n}{prim\\PYZus{}func}\n", + "\\PY{k}{def} \\PY{n+nf}{main}\\PY{p}{(}\\PY{n+nb}{input}\\PY{p}{:} \\PY{n}{T}\\PY{o}{.}\\PY{n}{Buffer}\\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{p}{,} \\PY{l+m+mi}{8}\\PY{p}{,} \\PY{l+m+mi}{32}\\PY{p}{,} \\PY{l+m+mi}{32}\\PY{p}{)}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{float32}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,} \\PY{n+nb}{filter}\\PY{p}{:} \\PY{n}{T}\\PY{o}{.}\\PY{n}{Buffer}\\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{16}\\PY{p}{,} \\PY{l+m+mi}{8}\\PY{p}{,} \\PY{l+m+mi}{3}\\PY{p}{,} \\PY{l+m+mi}{3}\\PY{p}{)}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{float32}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,} \\PY{n}{compute}\\PY{p}{:} \\PY{n}{T}\\PY{o}{.}\\PY{n}{Buffer}\\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{p}{,} \\PY{l+m+mi}{16}\\PY{p}{,} \\PY{l+m+mi}{32}\\PY{p}{,} 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\\PY{n}{T}\\PY{o}{.}\\PY{n}{reads}\\PY{p}{(}\\PY{n}{conv2d\\PYZus{}nchw}\\PY{p}{[}\\PY{n}{v\\PYZus{}i0}\\PY{p}{,} \\PY{n}{v\\PYZus{}i1}\\PY{p}{,} \\PY{n}{v\\PYZus{}i2}\\PY{p}{,} \\PY{n}{v\\PYZus{}i3}\\PY{p}{]}\\PY{p}{)}\n", + " \\PY{n}{T}\\PY{o}{.}\\PY{n}{writes}\\PY{p}{(}\\PY{n}{compute}\\PY{p}{[}\\PY{n}{v\\PYZus{}i0}\\PY{p}{,} \\PY{n}{v\\PYZus{}i1}\\PY{p}{,} \\PY{n}{v\\PYZus{}i2}\\PY{p}{,} \\PY{n}{v\\PYZus{}i3}\\PY{p}{]}\\PY{p}{)}\n", + " \\PY{n}{compute}\\PY{p}{[}\\PY{n}{v\\PYZus{}i0}\\PY{p}{,} \\PY{n}{v\\PYZus{}i1}\\PY{p}{,} \\PY{n}{v\\PYZus{}i2}\\PY{p}{,} \\PY{n}{v\\PYZus{}i3}\\PY{p}{]} \\PY{o}{=} \\PY{n}{T}\\PY{o}{.}\\PY{n}{max}\\PY{p}{(}\\PY{n}{conv2d\\PYZus{}nchw}\\PY{p}{[}\\PY{n}{v\\PYZus{}i0}\\PY{p}{,} \\PY{n}{v\\PYZus{}i1}\\PY{p}{,} \\PY{n}{v\\PYZus{}i2}\\PY{p}{,} \\PY{n}{v\\PYZus{}i3}\\PY{p}{]}\\PY{p}{,} \\PY{n}{T}\\PY{o}{.}\\PY{n}{float32}\\PY{p}{(}\\PY{l+m+mi}{0}\\PY{p}{)}\\PY{p}{)}\n", + "\\end{Verbatim}\n" + ], + "text/plain": [ + "# from tvm.script import tir as T\n", + "\n", + "@T.prim_func\n", + "def main(input: T.Buffer((1, 8, 32, 32), \"float32\"), filter: T.Buffer((16, 8, 3, 3), \"float32\"), compute: T.Buffer((1, 16, 32, 32), \"float32\")):\n", + " T.func_attr({\"tir.noalias\": T.bool(True)})\n", + " # with T.block(\"root\"):\n", + " pad_temp = T.alloc_buffer((1, 8, 34, 34))\n", + " conv2d_nchw = T.alloc_buffer((1, 16, 32, 32))\n", + " for i0, i1, i2, i3 in T.grid(1, 8, 34, 34):\n", + " with T.block(\"pad_temp\"):\n", + " v_i0, v_i1, v_i2, v_i3 = T.axis.remap(\"SSSS\", [i0, i1, i2, i3])\n", + " T.reads(input[v_i0, v_i1, v_i2 - 1, v_i3 - 1])\n", + " T.writes(pad_temp[v_i0, v_i1, v_i2, v_i3])\n", + " pad_temp[v_i0, v_i1, v_i2, v_i3] = T.if_then_else(1 <= v_i2 and v_i2 < 33 and 1 <= v_i3 and v_i3 < 33, input[v_i0, v_i1, v_i2 - 1, v_i3 - 1], T.float32(0))\n", + " for nn, ff, yy, xx, rc, ry, rx in T.grid(1, 16, 32, 32, 8, 3, 3):\n", + " with T.block(\"conv2d_nchw\"):\n", + " v_nn, v_ff, v_yy, v_xx, v_rc, v_ry, v_rx = T.axis.remap(\"SSSSRRR\", [nn, ff, yy, xx, rc, ry, rx])\n", + " T.reads(pad_temp[v_nn, v_rc, v_yy + v_ry, v_xx + v_rx], filter[v_ff, v_rc, v_ry, v_rx])\n", + " T.writes(conv2d_nchw[v_nn, v_ff, v_yy, v_xx])\n", + " with T.init():\n", + " conv2d_nchw[v_nn, v_ff, v_yy, v_xx] = T.float32(0)\n", + " conv2d_nchw[v_nn, v_ff, v_yy, v_xx] = conv2d_nchw[v_nn, v_ff, v_yy, v_xx] + pad_temp[v_nn, v_rc, v_yy + v_ry, v_xx + v_rx] * filter[v_ff, v_rc, v_ry, v_rx]\n", + " for i0, i1, i2, i3 in T.grid(1, 16, 32, 32):\n", + " with T.block(\"compute\"):\n", + " v_i0, v_i1, v_i2, v_i3 = T.axis.remap(\"SSSS\", [i0, i1, i2, i3])\n", + " T.reads(conv2d_nchw[v_i0, v_i1, v_i2, v_i3])\n", + " T.writes(compute[v_i0, v_i1, v_i2, v_i3])\n", + " compute[v_i0, v_i1, v_i2, v_i3] = T.max(conv2d_nchw[v_i0, v_i1, v_i2, v_i3], T.float32(0))" + ] + }, + "execution_count": 175, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "![](comparison_accuracy_macs.png)" + "import tvm\n", + "from tvm import topi\n", + "from tvm import te\n", + "from tvm import tir\n", + "\n", + "# Define the input tensor shape\n", + "batch_size = 1\n", + "in_channels = 8\n", + "in_height = 32\n", + "in_width = 32\n", + "\n", + "# Define the filter shape\n", + "out_channels = 16\n", + "kernel_height = 3\n", + "kernel_width = 3\n", + "\n", + "# Define the stride and padding\n", + "stride = 1\n", + "padding = 1\n", + "dilation = 1\n", + "\n", + "input_tensor = te.placeholder((batch_size, in_channels, in_height, in_width), name='input')\n", + "filter_tensor = te.placeholder((out_channels, in_channels, kernel_height, kernel_width), name='filter')\n", + "\n", + "# Perform the 2D convolution using topi\n", + "convolution = topi.nn.conv2d(input_tensor, filter_tensor, strides=stride, padding=padding, dilation=1)\n", + "\n", + "te_expression = topi.nn.relu(convolution) # Apply the ReLU activation function\n", + "tir_code = te.create_prim_func([input_tensor, filter_tensor, te_expression]) # Create a schedule from the expression \n", + "\n", + "Code(tir_code.script(), language='python')" ] }, { "cell_type": "markdown", - "id": "7f3942fd-4f3a-4869-a755-6be78cbed83b", + "id": "6de17d5a", "metadata": {}, "source": [ - "![](comparison_accuracy_memory.png)" + "In TVM these programs can be transformed using a specialized DSL, to allow a better mapping of tensor programs to the hardware accelerators.\n", + "\n", + "For the example applications we are using the following subset of transformations. \n", + "\n", + "1. *loop tiling*: loop tiling splits the generated loops in two or more subloops\n", + "2. *loop reordering*: reordering the loops, in combination with loop tiling allows us to iterate over small subspace of the hardware\n", + "3. *vectorization*: gives a vectorization hint to the compiler backends, wether this hint is followed, mostly depends on properties of the target hardware\n", + "\n", + "A simple schedule space can be generated using the following schedule function:" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "id": "4ca3b374", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[]\n", + "[]\n", + "8\n", + "3\n", + "3\n", + "[T.iter_var(v_rc, T.Range(0, 8), \"CommReduce\", \"\")]\n", + "[]\n", + "[]\n", + "[]\n" + ] + }, + { + "data": { + "text/plain": [ + "tir.Schedule(0x5628f3b44f58)" + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class BlockInfo:\n", + " def __init__(self, block, iter_vars):\n", + " self.name = block.name_hint\n", + " self.block = block\n", + " self.iter_vars = iter_vars\n", + "\n", + " def reduction_vars(self):\n", + " return [iv for iv in self.iter_vars if iv.iter_type == tir.IterVar.CommReduce]\n", + " \n", + " @property\n", + " def is_complex(self):\n", + " return len(self.reduction_vars) > 0 \n", + "\n", + "def schedule_tir(input_tir, vector_len = 8):\n", + " schedule = tir.Schedule(input_tir)\n", + "\n", + " gvar = schedule.func_working_on\n", + " function = schedule.mod[gvar]\n", + " \n", + " worklist = []\n", + " def visitor(stmt):\n", + " if isinstance(stmt, tir.Block):\n", + " if stmt.name_hint != \"root\":\n", + " iter_vars = stmt.iter_vars\n", + " \n", + " info = BlockInfo(stmt, iter_vars)\n", + " worklist.append(info)\n", + " \n", + " return True\n", + " \n", + " tir.stmt_functor.pre_order_visit(function.body, visitor) \n", + " \n", + " for block_info in worklist:\n", + " block_rv = schedule.get_block(block_info.name)\n", + " loops_rv = schedule.get_loops(block_rv)\n", + "\n", + "\n", + " candidates_reduce = []\n", + " candidates_parallel = [] \n", + " for v in block_info.iter_vars:\n", + " if v.iter_type == tir.IterVar.CommReduce:\n", + " dom = v.dom\n", + " extent = dom.extent\n", + " print(extent)\n", + " if extent % vector_len == 0:\n", + " candidates_reduce.append(v)\n", + " \n", + " print(candidates_reduce)\n", + " print(candidates_parallel)\n", + " \n", + " \n", + " return schedule \n", + " \n", + "schedule_tir(tir_code)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca01ffc6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# from tvm.script import tir as T\n", + "\n", + "@T.prim_func\n", + "def main(A: T.Buffer((128, 128), \"float32\"), B: T.Buffer((128, 128), \"float32\"), C: T.Buffer((128, 128), \"float32\")):\n", + " T.func_attr({\"tir.noalias\": T.bool(True)})\n", + " # with T.block(\"root\"):\n", + " for x, y, k in T.grid(128, 128, 128):\n", + " with T.block(\"C\"):\n", + " v_x, v_y, v_k = T.axis.remap(\"SSR\", [x, y, k])\n", + " T.reads(A[v_x, v_k], B[v_y, v_k])\n", + " T.writes(C[v_x, v_y])\n", + " with T.init():\n", + " C[v_x, v_y] = T.float32(0)\n", + " C[v_x, v_y] = C[v_x, v_y] + A[v_x, v_k] * B[v_y, v_k]\n" + ] + } + ], + "source": [ + "import tvm\n", + "from tvm import te\n", + "from tvm.te import create_prim_func\n", + "import tvm.script\n", + "\n", + "A = te.placeholder((128, 128), name=\"A\")\n", + "B = te.placeholder((128, 128), name=\"B\")\n", + "k = te.reduce_axis((0, 128), \"k\")\n", + "C = te.compute((128, 128), lambda x, y: te.sum(A[x, k] * B[y, k], axis=k), name=\"C\")\n", + "func = create_prim_func([A, B, C])\n", + "print(func.script())" ] } ],