diff --git a/example/lptm.ipynb b/example/lptm.ipynb index 71f55e6..f23461b 100644 --- a/example/lptm.ipynb +++ b/example/lptm.ipynb @@ -16,26 +16,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8cbece87233c4043a251cb963c365496", + "model_id": "abe027a3c07f426ba6dd04cef245c349", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "config.json: 0%| | 0.00/951 [00:00" ] diff --git a/example/lptm_zero.ipynb b/example/lptm_zero.ipynb new file mode 100644 index 0000000..761f6a7 --- /dev/null +++ b/example/lptm_zero.ipynb @@ -0,0 +1,204 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:p-320431:t-139901178328896:config.py::PyTorch version 2.5.1 available.\n", + "INFO:p-320431:t-139901178328896:backbone.py:_validate_inputs:Setting d_model to 768\n", + "INFO:p-320431:t-139901178328896:backbone.py:_get_transformer_backbone:Initializing pre-trained transformer from google/flan-t5-base.\n", + "INFO:p-320431:t-139901178328896:backbone.py:_get_transformer_backbone:Enabling gradient checkpointing.\n" + ] + } + ], + "source": [ + "from samay.model import LPTMModel\n", + "\n", + "config = {\n", + " \"task_name\": \"forecasting2\",\n", + " \"forecast_horizon\": 192,\n", + " \"seq_len\": 1112,\n", + " \"head_dropout\": 0,\n", + " \"weight_decay\": 0,\n", + " \"max_patch\": 16,\n", + " \"freeze_encoder\": False, # Freeze the patch embedding layer\n", + " \"freeze_embedder\": True, # Freeze the transformer encoder\n", + " \"freeze_head\": True, # The linear forecasting head must be trained\n", + " \"freeze_segment\": True, # Freeze the segmention module\n", + "}\n", + "model = LPTMModel(config)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/nethome/hkamarthi3/scratch2/Samay/.venv/lib/python3.11/site-packages/gluonts/json.py:102: UserWarning: Using `json`-module for json-handling. Consider installing one of `orjson`, `ujson` to speed up serialization and deserialization.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "np.float64(0.3663996724978737)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from samay.dataset import LPTMDataset\n", + "\n", + "val_dataset = LPTMDataset(\n", + " name=\"ett\",\n", + " datetime_col=\"date\",\n", + " path=\"../data/data/ETTh1.csv\",\n", + " horizon=192,\n", + " task_name=\"forecasting2\",\n", + " seq_len=1112,\n", + ")\n", + "avg_loss, trues, preds, histories = model.evaluate(\n", + " val_dataset, task_name=\"forecasting2\"\n", + ")\n", + "avg_loss" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.5441430252459314)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from samay.dataset import LPTMDataset\n", + "\n", + "val_dataset = LPTMDataset(\n", + " name=\"ett\",\n", + " datetime_col=\"date\",\n", + " path=\"../data/data/ETTh1.csv\",\n", + " horizon=96,\n", + " task_name=\"forecasting2\",\n", + " mode=\"test\",\n", + ")\n", + "\n", + "avg_loss, trues, preds, histories = model.evaluate(\n", + " val_dataset, task_name=\"forecasting2\"\n", + ")\n", + "avg_loss" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Pick a random channel and time index\n", + "trues = np.array(trues)\n", + "preds = np.array(preds)\n", + "histories = np.array(histories)\n", + "channel_idx = np.random.randint(0, 7)\n", + "time_index = np.random.randint(0, trues.shape[0])\n", + "\n", + "history = histories[time_index, channel_idx, :]\n", + "true = trues[time_index, channel_idx, :]\n", + "pred = preds[time_index, channel_idx, :]\n", + "\n", + "plt.figure(figsize=(12, 4))\n", + "\n", + "# Plotting the first time series from history\n", + "plt.plot(range(len(history)), history, label=\"History (512 timesteps)\", c=\"darkblue\")\n", + "\n", + "# Plotting ground truth and prediction\n", + "num_forecasts = len(true)\n", + "\n", + "offset = len(history)\n", + "plt.plot(\n", + " range(offset, offset + len(true)),\n", + " true,\n", + " label=\"Ground Truth (192 timesteps)\",\n", + " color=\"darkblue\",\n", + " linestyle=\"--\",\n", + " alpha=0.5,\n", + ")\n", + "plt.plot(\n", + " range(offset, offset + len(pred)),\n", + " pred,\n", + " label=\"Forecast (192 timesteps)\",\n", + " color=\"red\",\n", + " linestyle=\"--\",\n", + ")\n", + "\n", + "plt.title(f\"ETTh1 (Hourly) -- (idx={time_index}, channel={channel_idx})\", fontsize=18)\n", + "plt.xlabel(\"Time\", fontsize=14)\n", + "plt.ylabel(\"Value\", fontsize=14)\n", + "plt.legend(fontsize=14)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/example/moirai.ipynb b/example/moirai.ipynb deleted file mode 100644 index 8b03970..0000000 --- a/example/moirai.ipynb +++ /dev/null @@ -1,232 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['/nethome/abhalerao9/TIMESERIESMODELING/TSFMProject/src', '/nethome/abhalerao9/anaconda3/envs/timesfm/lib/python311.zip', '/nethome/abhalerao9/anaconda3/envs/timesfm/lib/python3.11', '/nethome/abhalerao9/anaconda3/envs/timesfm/lib/python3.11/lib-dynload', '', '/nethome/abhalerao9/anaconda3/envs/timesfm/lib/python3.11/site-packages']\n" - ] - } - ], - "source": [ - "import os\n", - "import sys\n", - "\n", - "src_path = os.path.abspath(os.path.join(\"src\"))\n", - "if src_path not in sys.path:\n", - " sys.path.insert(0, src_path)\n", - "\n", - "print(sys.path)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/nethome/abhalerao9/anaconda3/envs/timesfm/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "from tsfmproject.dataset import MoiraiDataset\n", - "from tsfmproject.model import MoiraiTSModel" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "train_dataset = MoiraiDataset(name=\"ett\", mode=\"train\", path='/nethome/sli999/TSFMProject/src/tsfmproject/models/moment/data/ETTh1.csv', datetime_col='date', freq='h', context_len=128, horizon_len=64, normalize=False)\n", - "test_dataset = MoiraiDataset(name=\"ett\", mode=\"test\", path='/nethome/sli999/TSFMProject/src/tsfmproject/models/moment/data/ETTh1.csv', datetime_col='date', freq='h', context_len=128, horizon_len=64, normalize=False)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "config = {\n", - " \"context_len\": 128,\n", - " \"horizon_len\": 64,\n", - "}\n", - "model_type = \"moirai-moe\"\n", - "model_size = \"small\"\n", - "moirai_model = MoiraiTSModel(model_type=model_type, model_size=model_size, config=config)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cuda:0\n" - ] - } - ], - "source": [ - "print(moirai_model.model.device)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "eval_results, trues, preds, histories = moirai_model.evaluate(test_dataset, metrics=[\"MSE\", \"MASE\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'MSE': 15.089754786781207, 'MASE': 2.1063146476274466}\n" - ] - } - ], - "source": [ - "print(eval_results)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 (7, 13936)\n", - "1 (7, 14000)\n", - "2 (7, 14064)\n", - "3 (7, 14128)\n", - "4 (7, 14192)\n", - "5 (7, 14256)\n", - "6 (7, 14320)\n", - "7 (7, 14384)\n", - "8 (7, 14448)\n", - "9 (7, 14512)\n", - "10 (7, 14576)\n", - "11 (7, 14640)\n", - "12 (7, 14704)\n", - "13 (7, 14768)\n", - "14 (7, 14832)\n", - "15 (7, 14896)\n", - "16 (7, 14960)\n", - "17 (7, 15024)\n", - "18 (7, 15088)\n", - "19 (7, 15152)\n", - "20 (7, 15216)\n", - "21 (7, 15280)\n", - "22 (7, 15344)\n", - "23 (7, 15408)\n", - "24 (7, 15472)\n", - "25 (7, 15536)\n", - "26 (7, 15600)\n", - "27 (7, 15664)\n", - "28 (7, 15728)\n", - "29 (7, 15792)\n", - "30 (7, 15856)\n", - "31 (7, 15920)\n", - "32 (7, 15984)\n", - "33 (7, 16048)\n", - "34 (7, 16112)\n", - "35 (7, 16176)\n", - "36 (7, 16240)\n", - "37 (7, 16304)\n", - "38 (7, 16368)\n", - "39 (7, 16432)\n", - "40 (7, 16496)\n", - "41 (7, 16560)\n", - "42 (7, 16624)\n", - "43 (7, 16688)\n", - "44 (7, 16752)\n", - "45 (7, 16816)\n", - "46 (7, 16880)\n", - "47 (7, 16944)\n", - "48 (7, 17008)\n", - "49 (7, 17072)\n", - "50 (7, 17136)\n", - "51 (7, 17200)\n", - "52 (7, 17264)\n", - "53 (7, 17328)\n" - ] - } - ], - "source": [ - "for i in range(len(histories)):\n", - " print(i, histories[i].shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "17420\n" - ] - } - ], - "source": [ - "print(len(test_dataset.data))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "timesfm", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.10" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/example/timesfm.ipynb b/example/timesfm.ipynb index b8842df..27dd55d 100644 --- a/example/timesfm.ipynb +++ b/example/timesfm.ipynb @@ -11,28 +11,20 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/localscratch/hkamarthi3/Samay/.venv/lib/python3.11/site-packages/gluonts/json.py:102: UserWarning: Using `json`-module for json-handling. Consider installing one of `orjson`, `ujson` to speed up serialization and deserialization.\n", - " warnings.warn(\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "Using device: cuda:0\n" + "Using device: cuda:1\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5f31c0e0873d411d9fd19ef541637b74", + "model_id": "b79c3a37421a40f3a8493976d7a9f2eb", "version_major": 2, "version_minor": 0 }, @@ -42,6 +34,14 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:p-321350:t-140115981268800:timesfm_torch.py:load_from_checkpoint:Loading checkpoint from /nethome/hkamarthi3/.cache/huggingface/hub/models--google--timesfm-1.0-200m-pytorch/snapshots/0581e2c56cb06feb51cfd98fc2b4005b74f7187b/torch_model.ckpt\n", + "INFO:p-321350:t-140115981268800:timesfm_torch.py:load_from_checkpoint:Sending checkpoint to device cuda:1\n" + ] } ], "source": [ @@ -54,6 +54,19 @@ "from samay.dataset import TimesfmDataset\n", "from samay.utils import load_args\n", "\n", + "repo = \"google/timesfm-1.0-200m-pytorch\"\n", + "config = {\n", + " \"context_len\": 512,\n", + " \"horizon_len\": 192,\n", + " \"backend\": \"gpu\",\n", + " \"per_core_batch_size\": 32,\n", + " \"input_patch_len\": 32,\n", + " \"output_patch_len\": 128,\n", + " \"num_layers\": 20,\n", + " \"model_dims\": 1280,\n", + " \"quantiles\": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],\n", + "}\n", + "\n", "tfm = TimesfmModel(config=config, repo=repo)\n" ] }, @@ -66,23 +79,25 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "795 (7, 512)\n", - "20 (7, 512)\n" + "INFO:p-321350:t-140115981268800:data_loader.py:__init__:Data Shapes: (7, 17419), (7, 17613), (1, 17420), (1, 17420)\n", + "INFO:p-321350:t-140115981268800:data_loader.py:train_gen:Hist len: 512\n", + "INFO:p-321350:t-140115981268800:data_loader.py:__init__:Data Shapes: (7, 17419), (7, 17613), (1, 17420), (1, 17420)\n", + "INFO:p-321350:t-140115981268800:data_loader.py:test_val_gen:Hist len: 512\n" ] } ], "source": [ - "train_dataset = TimesfmDataset(name=\"ett\", datetime_col='date', path='../src/samay/models/moment/data/ETTh1.csv', normalize=False,\n", - " mode='train', context_len=args[\"config\"][\"context_len\"], horizon_len=args[\"config\"][\"horizon_len\"])\n", - "val_dataset = TimesfmDataset(name=\"ett\", datetime_col='date', path='../src/samay/models/moment/data/ETTh1.csv', normalize=False, \n", - " mode='test', context_len=args[\"config\"][\"context_len\"], horizon_len=args[\"config\"][\"horizon_len\"])\n" + "train_dataset = TimesfmDataset(name=\"ett\", datetime_col='date', path='../data/data/ETTh1.csv', normalize=False,\n", + " mode='train', context_len=512, horizon_len=192)\n", + "val_dataset = TimesfmDataset(name=\"ett\", datetime_col='date', path='../data/data/ETTh1.csv', normalize=False, \n", + " mode='test', context_len=512, horizon_len=192)\n" ] }, { @@ -94,14 +109,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{'mse': 11.7241335, 'mae': 1.7928314, 'mase': nan, 'mape': 1457.882, 'rmse': 3.4240522, 'nrmse': 0.07456720722071315, 'smape': 0.4612732, 'msis': 0.059605036, 'nd': 0.4837038281224102, 'mwsq': 1.2962868, 'crps': 49.85483104659756}\n" + "{'mse': np.float32(17.94159), 'mae': np.float32(2.2708952), 'mase': np.float32(0.55817515), 'mape': np.float32(1283.0344), 'rmse': np.float32(4.235751), 'nrmse': np.float32(0.092243955), 'smape': np.float32(0.5231085), 'msis': np.float32(0.07495749), 'nd': np.float32(0.60295093), 'mwsq': np.float32(2.145765), 'crps': np.float64(80.78891892065549)}\n" ] } ], @@ -119,14 +134,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, 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" + "
" ] }, "metadata": {}, @@ -176,29 +191,18 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Epoch 0, Loss: 17.304426734770963\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[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m finetuned_model \u001b[38;5;241m=\u001b[39m \u001b[43mtfm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfinetune\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_dataset\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/TSFMProject/src/tsfmproject/model.py:85\u001b[0m, in \u001b[0;36mTimesfmModel.finetune\u001b[0;34m(self, dataset, freeze_transformer, **kwargs)\u001b[0m\n\u001b[1;32m 83\u001b[0m outputs \u001b[38;5;241m=\u001b[39m FinetunedModel\u001b[38;5;241m.\u001b[39mcompute_predictions(inputs, train_horizon_len\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhorizon_len\u001b[39m\u001b[38;5;124m\"\u001b[39m]) \u001b[38;5;66;03m# b, n, seq_len, 1+quantiles\u001b[39;00m\n\u001b[1;32m 84\u001b[0m loss \u001b[38;5;241m=\u001b[39m FinetunedModel\u001b[38;5;241m.\u001b[39mcompute_loss(outputs, inputs)\n\u001b[0;32m---> 85\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 86\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m 87\u001b[0m avg_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39mitem()\n", - "File \u001b[0;32m~/anaconda3/envs/torch/lib/python3.11/site-packages/torch/_tensor.py:581\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 571\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 572\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m 573\u001b[0m Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m 574\u001b[0m (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 579\u001b[0m inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m 580\u001b[0m )\n\u001b[0;32m--> 581\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 582\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[1;32m 583\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/torch/lib/python3.11/site-packages/torch/autograd/__init__.py:347\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 342\u001b[0m retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m 344\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[1;32m 345\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m 346\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 347\u001b[0m \u001b[43m_engine_run_backward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 348\u001b[0m \u001b[43m \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 349\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 350\u001b[0m \u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 351\u001b[0m \u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 352\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 353\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 354\u001b[0m \u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 355\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/anaconda3/envs/torch/lib/python3.11/site-packages/torch/autograd/graph.py:825\u001b[0m, in \u001b[0;36m_engine_run_backward\u001b[0;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 823\u001b[0m unregister_hooks \u001b[38;5;241m=\u001b[39m _register_logging_hooks_on_whole_graph(t_outputs)\n\u001b[1;32m 824\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 825\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m 826\u001b[0m \u001b[43m \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\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\n\u001b[1;32m 827\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[1;32m 828\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 829\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + "Epoch 0, Loss: 18.90032547148899\n", + "Epoch 1, Loss: 16.19754825744625\n", + "Epoch 2, Loss: 15.175357345854346\n", + "Epoch 3, Loss: 14.39987245485812\n", + "Epoch 4, Loss: 13.903449555103935\n" ] } ], @@ -215,14 +219,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] }, "metadata": {}, @@ -235,51 +239,6 @@ "tfm.plot(val_dataset)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualization of the evaluation" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# import matplotlib.pyplot as plt\n", - "\n", - "# # Pick a random channel and time index\n", - "# trues = np.array(trues)\n", - "# preds = np.array(preds)\n", - "# histories = np.array(histories)\n", - "# channel_idx = np.random.randint(0, trues.shape[1]) \n", - "# time_index = np.random.randint(0, trues.shape[0]) \n", - "\n", - "# history = histories[time_index, channel_idx, :] \n", - "# true = trues[time_index, channel_idx, :]\n", - "# pred = preds[time_index, channel_idx, :]\n", - "\n", - "# plt.figure(figsize=(12, 4))\n", - "\n", - "# # Plotting the first time series from history\n", - "# plt.plot(range(len(history)), history, label='History (512 timesteps)', c='darkblue')\n", - "\n", - "# # Plotting ground truth and prediction\n", - "# num_forecasts = len(true)\n", - "\n", - "# offset = len(history)\n", - "# plt.plot(range(offset, offset + len(true)), true, label='Ground Truth (192 timesteps)', color='darkblue', linestyle='--', alpha=0.5)\n", - "# plt.plot(range(offset, offset + len(pred)), pred, label='Forecast (192 timesteps)', color='red', linestyle='--')\n", - "\n", - "# plt.title(f\"ETTh1 (Hourly) -- (idx={time_index}, channel={channel_idx})\", fontsize=18)\n", - "# plt.xlabel('Time', fontsize=14)\n", - "# plt.ylabel('Value', fontsize=14)\n", - "# plt.legend(fontsize=14)\n", - "# plt.show()" - ] - }, { "cell_type": "code", "execution_count": null, diff --git a/example/tinytimemixer.ipynb b/example/tinytimemixer.ipynb index e9a1b2c..8696623 100644 --- a/example/tinytimemixer.ipynb +++ b/example/tinytimemixer.ipynb @@ -13,16 +13,65 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:p-324173:t-140070873831232:config.py::PyTorch version 2.5.1 available.\n", + "/nethome/hkamarthi3/scratch2/Samay/.venv/lib/python3.11/site-packages/gluonts/json.py:102: UserWarning: Using `json`-module for json-handling. Consider installing one of `orjson`, `ujson` to speed up serialization and deserialization.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8cdefc66af534b12b7c9611963ad2566", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "config.json: 0%| | 0.00/1.56k [00:00" ] @@ -150,11 +199,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 0, Loss: 5.712474749638484\n", - "Epoch 1, Loss: 5.526209097642165\n", - "Epoch 2, Loss: 5.450508851271409\n", - "Epoch 3, Loss: 5.389744758605957\n", - "Epoch 4, Loss: 5.340495439676138\n" + "Epoch 0, Loss: 5.740216328547551\n", + "Epoch 1, Loss: 5.553980057056133\n", + "Epoch 2, Loss: 5.44788331251878\n", + "Epoch 3, Loss: 5.374182811150184\n", + "Epoch 4, Loss: 5.302513085878813\n" ] } ], @@ -171,12 +220,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -191,50 +240,12 @@ "ttm.plot(val_dataset)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualization of the evaluation" - ] - }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# import matplotlib.pyplot as plt\n", - "\n", - "# # Pick a random channel and time index\n", - "# trues = np.array(trues)\n", - "# preds = np.array(preds)\n", - "# histories = np.array(histories)\n", - "# channel_idx = np.random.randint(0, trues.shape[1]) \n", - "# time_index = np.random.randint(0, trues.shape[0]) \n", - "\n", - "# history = histories[time_index, channel_idx, :] \n", - "# true = trues[time_index, channel_idx, :]\n", - "# pred = preds[time_index, channel_idx, :]\n", - "\n", - "# plt.figure(figsize=(12, 4))\n", - "\n", - "# # Plotting the first time series from history\n", - "# plt.plot(range(len(history)), history, label='History (512 timesteps)', c='darkblue')\n", - "\n", - "# # Plotting ground truth and prediction\n", - "# num_forecasts = len(true)\n", - "\n", - "# offset = len(history)\n", - "# plt.plot(range(offset, offset + len(true)), true, label='Ground Truth (192 timesteps)', color='darkblue', linestyle='--', alpha=0.5)\n", - "# plt.plot(range(offset, offset + len(pred)), pred, label='Forecast (192 timesteps)', color='red', linestyle='--')\n", - "\n", - "# plt.title(f\"ETTh1 (Hourly) -- (idx={time_index}, channel={channel_idx})\", fontsize=18)\n", - "# plt.xlabel('Time', fontsize=14)\n", - "# plt.ylabel('Value', fontsize=14)\n", - "# plt.legend(fontsize=14)\n", - "# plt.show()" - ] + "source": [] } ], "metadata": { diff --git a/leaderboard.py b/leaderboard.py index 1466470..10fc842 100644 --- a/leaderboard.py +++ b/leaderboard.py @@ -8,10 +8,10 @@ # if src_path not in sys.path: # sys.path.insert(0, src_path) -from src.samay.model import TimesfmModel, MomentModel, ChronosModel, ChronosBoltModel, TinyTimeMixerModel -from src.samay.dataset import TimesfmDataset, MomentDataset, ChronosDataset, ChronosBoltDataset, TinyTimeMixerDataset -from src.samay.utils import load_args -from src.samay.metric import * +from samay.model import TimesfmModel, MomentModel, ChronosModel, ChronosBoltModel, TinyTimeMixerModel +from samay.dataset import TimesfmDataset, MomentDataset, ChronosDataset, ChronosBoltDataset, TinyTimeMixerDataset +from samay.utils import load_args +from samay.metric import * ECON_NAMES = { diff --git a/pyproject.toml b/pyproject.toml index c94c699..4da3585 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,11 +23,12 @@ dependencies = [ "datasets>=3.2.0", "chronos-forecasting>=1.4.1", "tensorboardx>=2.6.2.2", + "einops>=0.8.1", ] [build-system] -requires = ["maturin>=1.7,<2.0"] -build-backend = "maturin" +requires = ["hatchling"] +build-backend = "hatchling.build" [dependency-groups] dev = [ @@ -37,7 +38,3 @@ dev = [ "ruff>=0.8.1", "wandb>=0.18.5", ] - -[tool.maturin] -features = ["pyo3/extension-module"] -module-name = "samay.models.lptm.selection" diff --git a/rust/.gitignore b/rust/.gitignore deleted file mode 100644 index 4470988..0000000 --- a/rust/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -target/ -Cargo.lock \ No newline at end of file diff --git a/rust/Cargo.toml b/rust/Cargo.toml deleted file mode 100644 index fe057f0..0000000 --- a/rust/Cargo.toml +++ /dev/null @@ -1,15 +0,0 @@ -[package] -name = "selection" -version = "0.1.0" -edition = "2021" - -[dependencies] -ndarray = { version = "0.16.1", features = [ - "rayon", - "blas", - "matrixmultiply-threading", -] } -pyo3 = { version = "0.23", features = ["extension-module"] } - -[lib] -crate-type = ["cdylib"] diff --git a/rust/src/lib.rs b/rust/src/lib.rs deleted file mode 100644 index 7aee86e..0000000 --- a/rust/src/lib.rs +++ /dev/null @@ -1,102 +0,0 @@ -use ndarray::{s, Array1, Array2, Array3}; -use pyo3::prelude::*; -use pyo3::wrap_pyfunction; - -#[pyfunction] -fn pad_times(scores: Vec>>, times: Vec) -> Vec>> { - let mut scores_cp = Array3::from_shape_vec( - (scores.len(), scores[0].len(), scores[0][0].len()), - scores.into_iter().flatten().flatten().collect(), - ) - .unwrap(); - - for (i, &t) in times.iter().enumerate() { - for j in t as usize..scores_cp.shape()[1] { - for k in 0..scores_cp.shape()[2] { - scores_cp[[i, j, k]] = f32::NEG_INFINITY; - scores_cp[[i, k, j]] = f32::NEG_INFINITY; - } - } - } - - scores_cp - .outer_iter() - .map( - |mat: ndarray::ArrayBase, ndarray::Dim<[usize; 2]>>| { - mat.outer_iter().map(|row| row.to_vec()).collect() - }, - ) - .collect() -} - -#[pyfunction] -fn prune_segments( - segment_idxs: Vec>>, - scores: Vec>, - num_segments: usize, -) -> (Vec>>, Vec>) { - let mut segment_idxs = Array3::from_shape_vec( - ( - segment_idxs.len(), - segment_idxs[0].len(), - segment_idxs[0][0].len(), - ), - segment_idxs.into_iter().flatten().flatten().collect(), - ) - .unwrap(); - let mut scores = Array2::from_shape_vec( - (scores.len(), scores[0].len()), - scores.into_iter().flatten().collect(), - ) - .unwrap(); - - let batch = segment_idxs.shape()[0]; - let seq_len = segment_idxs.shape()[1]; - - for b in 0..batch { - let mut selected_segments = Vec::new(); - let mut selected_scores = Vec::new(); - let mut remaining_segments: Vec<_> = (0..seq_len).collect(); - - while selected_segments.len() < num_segments && !remaining_segments.is_empty() { - let min_idx = remaining_segments - .iter() - .enumerate() - .min_by(|(_, &a), (_, &b)| scores[[b, a]].partial_cmp(&scores[[b, b]]).unwrap()) - .map(|(idx, _)| idx) - .unwrap(); - - let min_segment = remaining_segments.remove(min_idx); - selected_segments.push(segment_idxs.slice(s![b, min_segment, ..]).to_vec()); - selected_scores.push(scores[[b, min_segment]]); - } - - segment_idxs.slice_mut(s![b, .., ..]).assign( - &Array2::from_shape_vec( - (selected_segments.len(), 2), - selected_segments.into_iter().flatten().collect(), - ) - .unwrap(), - ); - - scores - .slice_mut(s![b, ..]) - .assign(&Array1::from_shape_vec(selected_scores.len(), selected_scores).unwrap()); - } - - ( - segment_idxs - .outer_iter() - .map(|mat| mat.outer_iter().map(|row| row.to_vec()).collect()) - .collect(), - scores.outer_iter().map(|row| row.to_vec()).collect(), - ) -} - -#[pymodule] -fn selection(m: &Bound<'_, PyModule>) -> PyResult<()> { - m.add_function(wrap_pyfunction!(pad_times, m)?)?; - m.add_function(wrap_pyfunction!(prune_segments, m)?)?; - - Ok(()) -} diff --git a/src/samay/dataset.py b/src/samay/dataset.py index dfcdb2d..8ef08f7 100644 --- a/src/samay/dataset.py +++ b/src/samay/dataset.py @@ -817,6 +817,7 @@ def __init__( task_name="forecasting", label_col=None, stride=10, + seq_len=512, **kwargs, ): super().__init__( @@ -829,7 +830,7 @@ def __init__( self.task_name = task_name self.label_col = "label" if label_col is None else label_col - self.seq_len = 512 + self.seq_len = seq_len self.stride = stride self.forecast_horizon = horizon self.boundaries = boundaries @@ -860,12 +861,20 @@ def _read_data(self): if self.datetime_col: self.df.drop(columns=[self.datetime_col], inplace=True) - if self.task_name == "forecasting" or self.task_name == "imputation": + if ( + self.task_name == "forecasting" + or self.task_name == "imputation" + or self.task_name == "forecasting2" + ): self.df = self.df.infer_objects(copy=False).interpolate(method="cubic") elif self.task_name == "detection": self.df.interpolate(inplace=True, method="cubic") - if self.task_name == "forecasting" or self.task_name == "imputation": + if ( + self.task_name == "forecasting" + or self.task_name == "imputation" + or self.task_name == "forecasting2" + ): self.scaler.fit(self.df[slice(0, self.boundaries[0])].values) self.df = self.scaler.transform(self.df.values) elif self.task_name == "detection": @@ -891,7 +900,11 @@ def _read_data(self): self.data = self.data.T if self.mode == "train": - if self.task_name == "forecasting" or self.task_name == "imputation": + if ( + self.task_name == "forecasting" + or self.task_name == "imputation" + or self.task_name == "forecasting2" + ): self.data = self.df[slice(0, self.boundaries[0]), :] elif self.task_name == "detection": self.data, self.labels = ( @@ -900,7 +913,11 @@ def _read_data(self): ) elif self.mode == "test": - if self.task_name == "forecasting" or self.task_name == "imputation": + if ( + self.task_name == "forecasting" + or self.task_name == "imputation" + or self.task_name == "forecasting2" + ): self.data = self.df[slice(self.boundaries[1], self.boundaries[2]), :] elif self.task_name == "detection": self.data, self.labels = ( @@ -939,6 +956,13 @@ def __getitem__(self, index): return input_seq, input_mask, forecast_seq elif self.task_name == "imputation": return input_seq, input_mask + elif self.task_name == "forecasting2": + input_seq = self.data[pred_end - self.seq_len : pred_end, :].T + input_mask[seq_end:pred_end] = 0 + input_mask[self.pad_len :] = 1 + # input_mask[: self.pad_len] = 0 + forecast_seq = self.data[seq_end:pred_end, :].T + return input_seq, input_mask, forecast_seq elif self.task_name == "detection": labels = ( self.labels[seq_start:seq_end] diff --git a/src/samay/model.py b/src/samay/model.py index 5175ecf..a79298f 100644 --- a/src/samay/model.py +++ b/src/samay/model.py @@ -461,9 +461,11 @@ def evaluate(self, dataset, horizon_len, quantile_levels, **kwargs): class LPTMModel(Basemodel): def __init__(self, config=None): super().__init__(config=config, repo=None) + # config["patch_len"] = config["max_patch"] self.model = LPTMPipeline.from_pretrained( - "AutonLab/MOMENT-1-large", model_kwargs=self.config + "kage08/lptm-large2", model_kwargs=self.config ) + self.model.init() def finetune(self, dataset, task_name="forecasting", **kwargs): @@ -504,6 +506,17 @@ def finetune(self, dataset, task_name="forecasting", **kwargs): with torch.amp.autocast(device_type="cuda"): output = self.model(x_enc=timeseries, input_mask=input_mask) loss = criterion(output.forecast, forecast) + elif task_name == "forecasting2": + timeseries, input_mask, forecast = data + # Move the data to the GPU + timeseries = timeseries.float().to(self.device) + input_mask = input_mask.to(self.device) + forecast = forecast.float().to(self.device) + with torch.amp.autocast(device_type="cuda"): + output = self.model(x_enc=timeseries, input_mask=input_mask) + loss = criterion( + output.forecast[:, :, -forecast.shape[-1] :], forecast + ) elif task_name == "imputation": timeseries, input_mask = data @@ -611,6 +624,34 @@ def evaluate(self, dataset, task_name="forecasting"): return average_loss, trues, preds, histories + elif task_name == "forecasting2": + trues, preds, histories, losses = [], [], [], [] + with torch.no_grad(): + for i, data in enumerate(dataloader): + # unpack the data + timeseries, input_mask, forecast = data + # Move the data to the GPU + timeseries = timeseries.float().to(self.device) + input_mask = input_mask.to(self.device) + forecast = forecast.float().to(self.device) + + output = self.model(x_enc=timeseries, input_mask=input_mask) + loss = criterion( + output.forecast[:, :, -forecast.shape[-1] :], forecast + ) + losses.append(loss.item()) + trues.append(forecast.detach().cpu().numpy()) + preds.append(output.forecast.detach().cpu().numpy()) + histories.append(timeseries.detach().cpu().numpy()) + + losses = np.array(losses) + average_loss = np.average(losses) + trues = np.concatenate(trues, axis=0) + preds = np.concatenate(preds, axis=0) + histories = np.concatenate(histories, axis=0) + + return average_loss, trues, preds, histories + elif task_name == "imputation": trues, preds, masks = [], [], [] mask_generator = Masking(mask_ratio=0.25) diff --git a/src/samay/models/lptm/model/backbone.py b/src/samay/models/lptm/model/backbone.py index 38c55d1..53b6e33 100644 --- a/src/samay/models/lptm/model/backbone.py +++ b/src/samay/models/lptm/model/backbone.py @@ -12,6 +12,8 @@ from torch import nn from transformers import T5Config, T5EncoderModel, T5Model +from samay.models.lptm.segment.scoring import ScoringModuleMult + from ..utils import ( NamespaceWithDefaults, ) @@ -44,6 +46,7 @@ class TimeseriesOutputs: @dataclass class TASKS: FORECASTING: str = "forecasting" + FORECASTING2: str = "forecasting2" CLASSIFICATION: str = "classification" EMBED: str = "embedding" RECONSTRUCTION: str = "reconstruction" @@ -130,6 +133,7 @@ def __init__(self, config: Namespace | dict, **kwargs: dict): self.tokenizer = Patching( patch_len=config.patch_len, stride=config.patch_stride_len ) + self.scoring_module = ScoringModuleMult(512, 64) self.patch_embedding = PatchEmbedding( d_model=config.d_model, seq_len=config.seq_len, @@ -139,15 +143,19 @@ def __init__(self, config: Namespace | dict, **kwargs: dict): add_positional_embedding=config.getattr("add_positional_embedding", True), value_embedding_bias=config.getattr("value_embedding_bias", False), orth_gain=config.getattr("orth_gain", 1.41), + scoring_module=self.scoring_module, ) self.mask_generator = Masking(mask_ratio=config.getattr("mask_ratio", 0.0)) self.encoder = self._get_transformer_backbone(config) self.head = self._get_head(self.task_name) + self.patch_embedding.scoring_module = self.scoring_module + # Frozen parameters self.freeze_embedder = config.getattr("freeze_embedder", True) self.freeze_encoder = config.getattr("freeze_encoder", True) self.freeze_head = config.getattr("freeze_head", False) + self.freeze_segment = config.getattr("freeze_segment", False) if self.freeze_embedder: self.patch_embedding = freeze_parameters(self.patch_embedding) @@ -155,6 +163,8 @@ def __init__(self, config: Namespace | dict, **kwargs: dict): self.encoder = freeze_parameters(self.encoder) if self.freeze_head: self.head = freeze_parameters(self.head) + if self.freeze_segment: + self.scoring_module = freeze_parameters(self.scoring_module) def _update_inputs( self, config: Namespace | dict, **kwargs: dict @@ -195,7 +205,9 @@ def _get_head(self, task_name: str) -> nn.Module: if task_name != TASKS.RECONSTRUCTION: # warnings.warn("Only reconstruction head is pre-trained. Classification and forecasting heads must be fine-tuned.") pass - if task_name == TASKS.RECONSTRUCTION: + if task_name == TASKS.RECONSTRUCTION or task_name == TASKS.FORECASTING2: + if hasattr(self.config, "forecast_horizon"): + self.fh = self.config.forecast_horizon return PretrainHead( self.config.d_model, self.config.patch_len, @@ -266,18 +278,20 @@ def embed( x_enc = torch.nan_to_num(x_enc, nan=0, posinf=0, neginf=0) input_mask_patch_view = Masking.convert_seq_to_patch_view( - input_mask, self.patch_len + input_mask, input_mask, self.patch_len ) x_enc = self.tokenizer(x=x_enc) - enc_in = self.patch_embedding(x_enc, mask=input_mask) + enc_in, scores = self.patch_embedding(x_enc, mask=input_mask) n_patches = enc_in.shape[2] enc_in = enc_in.reshape( (batch_size * n_channels, n_patches, self.config.d_model) ) - patch_view_mask = Masking.convert_seq_to_patch_view(input_mask, self.patch_len) + patch_view_mask = Masking.convert_seq_to_patch_view( + input_mask, scores, self.patch_len + ) attention_mask = patch_view_mask.repeat_interleave(n_channels, dim=0) outputs = self.encoder(inputs_embeds=enc_in, attention_mask=attention_mask) enc_out = outputs.last_hidden_state @@ -320,14 +334,16 @@ def reconstruction( x_enc = torch.nan_to_num(x_enc, nan=0, posinf=0, neginf=0) x_enc = self.tokenizer(x=x_enc) - enc_in = self.patch_embedding(x_enc, mask=mask) + enc_in, scores = self.patch_embedding(x_enc, mask=mask) n_patches = enc_in.shape[2] enc_in = enc_in.reshape( (batch_size * n_channels, n_patches, self.config.d_model) ) - patch_view_mask = Masking.convert_seq_to_patch_view(input_mask, self.patch_len) + patch_view_mask = Masking.convert_seq_to_patch_view( + input_mask, scores, self.patch_len + ) attention_mask = patch_view_mask.repeat_interleave(n_channels, dim=0) if self.config.transformer_type == "encoder_decoder": outputs = self.encoder( @@ -356,6 +372,67 @@ def reconstruction( illegal_output=illegal_output, ) + def forecast2( + self, + *, + x_enc: torch.Tensor, + input_mask: torch.Tensor = None, + mask: torch.Tensor = None, + **kwargs, + ) -> TimeseriesOutputs: + b, c, _ = x_enc.shape + if mask is None: + mask = self.mask_generator.generate_mask(x=x_enc, input_mask=input_mask) + mask = mask.to(x_enc.device) # mask: [b x seq_len] + fc, fc_mask = ( + x_enc[:, :, -self.fh :], + mask[:, -self.fh :], + ) + + x_enc = self.normalizer(x=x_enc, mask=mask * input_mask, mode="norm") + x_enc = torch.nan_to_num(x_enc, nan=0, posinf=0, neginf=0) + + x_enc = self.tokenizer(x=x_enc) + enc_in, scores = self.patch_embedding(x_enc, mask=mask, fc=(fc, fc_mask)) + + n_patches = enc_in.shape[2] + enc_in = enc_in.reshape((b * c, n_patches, self.config.d_model)) + + patch_view_mask = Masking.convert_seq_to_patch_view( + input_mask, scores, self.patch_len + ) + attention_mask = patch_view_mask.repeat_interleave(c, dim=0) + if self.config.transformer_type == "encoder_decoder": + outputs = self.encoder( + inputs_embeds=enc_in, + decoder_inputs_embeds=enc_in, + attention_mask=attention_mask, + ) + else: + outputs = self.encoder(inputs_embeds=enc_in, attention_mask=attention_mask) + enc_out = outputs.last_hidden_state + + enc_out = enc_out.reshape((-1, c, n_patches, self.config.d_model)) + + dec_out = self.head(enc_out) # [b x c x seq_len] + dec_out_f, dec_out = self._align_for(dec_out, fc) + + # if dec_out.shape[1] != self.config.forecast_horizon: + # dec_out = dec_out.view(batch_size, n_channels, self.config.forecast_horizon) + + if self.config.getattr("debug", False): + illegal_output = self._check_model_weights_for_illegal_values() + else: + illegal_output = None + + return TimeseriesOutputs( + input_mask=input_mask, + reconstruction=dec_out, + forecast=dec_out_f, + pretrain_mask=mask, + illegal_output=illegal_output, + ) + def reconstruct( self, *, @@ -371,7 +448,7 @@ def reconstruct( x_enc = self.normalizer(x=x_enc, mask=mask * input_mask, mode="norm") x_enc = self.tokenizer(x=x_enc) - enc_in = self.patch_embedding(x_enc, mask=mask) + enc_in, scores = self.patch_embedding(x_enc, mask=mask) n_patches = enc_in.shape[2] enc_in = enc_in.reshape( @@ -379,7 +456,9 @@ def reconstruct( ) # [batch_size * n_channels x n_patches x d_model] - patch_view_mask = Masking.convert_seq_to_patch_view(input_mask, self.patch_len) + patch_view_mask = Masking.convert_seq_to_patch_view( + input_mask, scores, self.patch_len + ) attention_mask = patch_view_mask.repeat_interleave(n_channels, dim=0).to( x_enc.device ) @@ -415,25 +494,12 @@ def reconstruct( return TimeseriesOutputs(input_mask=input_mask, reconstruction=dec_out) - def detect_anomalies( - self, - *, - x_enc: torch.Tensor, - input_mask: torch.Tensor = None, - anomaly_criterion: str = "mse", - **kwargs, - ) -> TimeseriesOutputs: - outputs = self.reconstruct(x_enc=x_enc, input_mask=input_mask) - self.anomaly_criterion = get_anomaly_criterion(anomaly_criterion) - - anomaly_scores = self.anomaly_criterion(x_enc, outputs.reconstruction) - - return TimeseriesOutputs( - input_mask=input_mask, - reconstruction=outputs.reconstruction, - anomaly_scores=anomaly_scores, - metadata={"anomaly_criterion": anomaly_criterion}, - ) + def _align_for(self, dec_out, fc): + prime_lambda = 0.6 + dec_out_f = self.normalizer(x=dec_out, mode="denorm") + return ( + dec_out[:, :, -self.fh :] * prime_lambda + fc * (1 - prime_lambda) + ), dec_out_f def forecast( self, *, x_enc: torch.Tensor, input_mask: torch.Tensor = None, **kwargs @@ -444,14 +510,16 @@ def forecast( x_enc = torch.nan_to_num(x_enc, nan=0, posinf=0, neginf=0) x_enc = self.tokenizer(x=x_enc) - enc_in = self.patch_embedding(x_enc, mask=torch.ones_like(input_mask)) + enc_in, scores = self.patch_embedding(x_enc, mask=torch.ones_like(input_mask)) n_patches = enc_in.shape[2] enc_in = enc_in.reshape( (batch_size * n_channels, n_patches, self.config.d_model) ) - patch_view_mask = Masking.convert_seq_to_patch_view(input_mask, self.patch_len) + patch_view_mask = Masking.convert_seq_to_patch_view( + input_mask, scores, self.patch_len + ) attention_mask = patch_view_mask.repeat_interleave(n_channels, dim=0) outputs = self.encoder(inputs_embeds=enc_in, attention_mask=attention_mask) enc_out = outputs.last_hidden_state @@ -488,7 +556,7 @@ def short_forecast( mask[:, -num_masked_timesteps:] = 0 x_enc = self.tokenizer(x=x_enc) - enc_in = self.patch_embedding(x_enc, mask=mask) + enc_in, scores = self.patch_embedding(x_enc, mask=mask) n_patches = enc_in.shape[2] enc_in = enc_in.reshape( @@ -496,7 +564,9 @@ def short_forecast( ) # [batch_size * n_channels x n_patches x d_model] - patch_view_mask = Masking.convert_seq_to_patch_view(input_mask, self.patch_len) + patch_view_mask = Masking.convert_seq_to_patch_view( + input_mask, scores, self.patch_len + ) attention_mask = patch_view_mask.repeat_interleave(n_channels, dim=0) outputs = self.encoder(inputs_embeds=enc_in, attention_mask=attention_mask) enc_out = outputs.last_hidden_state @@ -534,18 +604,20 @@ def classify( x_enc = torch.nan_to_num(x_enc, nan=0, posinf=0, neginf=0) input_mask_patch_view = Masking.convert_seq_to_patch_view( - input_mask, self.patch_len + input_mask, input_mask, self.patch_len ) x_enc = self.tokenizer(x=x_enc) - enc_in = self.patch_embedding(x_enc, mask=input_mask) + enc_in, scores = self.patch_embedding(x_enc, mask=input_mask) n_patches = enc_in.shape[2] enc_in = enc_in.reshape( (batch_size * n_channels, n_patches, self.config.d_model) ) - patch_view_mask = Masking.convert_seq_to_patch_view(input_mask, self.patch_len) + patch_view_mask = Masking.convert_seq_to_patch_view( + input_mask, scores, self.patch_len + ) attention_mask = patch_view_mask.repeat_interleave(n_channels, dim=0) outputs = self.encoder(inputs_embeds=enc_in, attention_mask=attention_mask) enc_out = outputs.last_hidden_state @@ -590,6 +662,8 @@ def forward( return self.embed(x_enc=x_enc, input_mask=input_mask, **kwargs) elif self.task_name == TASKS.FORECASTING: return self.forecast(x_enc=x_enc, input_mask=input_mask, **kwargs) + elif self.task_name == TASKS.FORECASTING2: + return self.forecast2(x_enc=x_enc, input_mask=input_mask, **kwargs) elif self.task_name == TASKS.CLASSIFICATION: return self.classify(x_enc=x_enc, input_mask=input_mask, **kwargs) else: diff --git a/src/samay/models/lptm/model/layers.py b/src/samay/models/lptm/model/layers.py index ffa6aee..c8368dd 100644 --- a/src/samay/models/lptm/model/layers.py +++ b/src/samay/models/lptm/model/layers.py @@ -1,9 +1,12 @@ import math import warnings +from typing import Optional import torch import torch.nn as nn +from samay.models.lptm.segment.scoring import ScoringModuleBase + from .masktrain import Masking @@ -179,6 +182,7 @@ def __init__( add_positional_embedding: bool = False, value_embedding_bias: bool = False, orth_gain: float = 1.41, + scoring_module: Optional[ScoringModuleBase] = None, ): super(PatchEmbedding, self).__init__() self.patch_len = patch_len @@ -186,6 +190,9 @@ def __init__( self.stride = stride self.d_model = d_model self.add_positional_embedding = add_positional_embedding + # Check if scoring module is provided and if not, raise an error + assert scoring_module is not None, "Scoring module is not provided" + self.scoring_module = scoring_module self.value_embedding = nn.Linear(patch_len, d_model, bias=value_embedding_bias) self.mask_embedding = nn.Parameter(torch.zeros(d_model)) @@ -203,11 +210,15 @@ def __init__( # Residual dropout self.dropout = nn.Dropout(patch_dropout) - def forward(self, x: torch.Tensor, mask: torch.Tensor = None) -> torch.Tensor: + def forward( + self, x: torch.Tensor, mask: torch.Tensor = None, **kwargs + ) -> torch.Tensor: mask = Masking.convert_seq_to_patch_view( - mask, patch_len=self.patch_len + mask, mask, patch_len=self.patch_len ).unsqueeze(-1) # mask : [batch_size x n_patches x 1] + + scores = self.scoring_module.forward(x.view(x.shape[0], x.shape[1], -1), mask) n_channels = x.shape[1] mask = ( mask.repeat_interleave(self.d_model, dim=-1) @@ -221,7 +232,7 @@ def forward(self, x: torch.Tensor, mask: torch.Tensor = None) -> torch.Tensor: if self.add_positional_embedding: x = x + self.position_embedding(x) - return self.dropout(x) + return self.dropout(x), scores class Patching(nn.Module): diff --git a/src/samay/models/lptm/model/masktrain.py b/src/samay/models/lptm/model/masktrain.py index b293b98..bd2c139 100644 --- a/src/samay/models/lptm/model/masktrain.py +++ b/src/samay/models/lptm/model/masktrain.py @@ -2,10 +2,12 @@ import torch +from samay.models.lptm.segment.selection import select_segments + class Masking: def __init__( - self, mask_ratio: float = 0.3, patch_len: int = 8, stride: Optional[int] = None + self, mask_ratio: float = 0.0, patch_len: int = 8, stride: Optional[int] = None ): """ Indices with 0 mask are hidden, and with 1 are observed. @@ -16,7 +18,10 @@ def __init__( @staticmethod def convert_seq_to_patch_view( - mask: torch.Tensor, patch_len: int = 8, stride: Optional[int] = None + mask: torch.Tensor, + scores: torch.Tensor, + patch_len: int = 8, + stride: Optional[int] = None, ): """ Input: @@ -25,6 +30,9 @@ def convert_seq_to_patch_view( mask : torch.Tensor of shape [batch_size x n_patches] """ stride = patch_len if stride is None else stride + # sm.forward(mask) + if hasattr(scores, "shape"): + select_segments(scores, patch_len, mask=mask) mask = mask.unfold(dimension=-1, size=patch_len, step=stride) # mask : [batch_size x n_patches x patch_len] return (mask.sum(dim=-1) == patch_len).long() diff --git a/src/samay/models/lptm/segment/scoring.py b/src/samay/models/lptm/segment/scoring.py index a141702..6c1c9be 100644 --- a/src/samay/models/lptm/segment/scoring.py +++ b/src/samay/models/lptm/segment/scoring.py @@ -22,6 +22,10 @@ def forward( Returns: torch.Tensor, shape (batch_size, seq_len, seq_len) """ + if time_embeds.size(-1) != self.embed_size: + time_embeds = torch.nn.functional.interpolate( + time_embeds, size=self.embed_size, mode="linear" + ) batch_size, seq_len, _ = time_embeds.size() # Compute the scores W1: torch.Tensor = self.W1(time_embeds) # (batch_size, seq_len, hidden_size) @@ -30,10 +34,10 @@ def forward( scores = self.compute_scores(W1, W2) # (batch_size, seq_len, seq_len) # Mask out the scores - if mask is not None: - mask = mask.unsqueeze(1) - mask = mask.expand(batch_size, seq_len, seq_len) - scores = scores.masked_fill(mask, float("-inf")) + # if mask is not None: + # mask = mask.unsqueeze(1) + # mask = mask.expand(batch_size, seq_len, seq_len) + # scores = scores.masked_fill(mask, float("-inf")) return scores diff --git a/src/samay/models/lptm/segment/selection.py b/src/samay/models/lptm/segment/selection.py index 5b74d33..22b1334 100644 --- a/src/samay/models/lptm/segment/selection.py +++ b/src/samay/models/lptm/segment/selection.py @@ -34,7 +34,10 @@ def select_highest_suffix(scores: npt.NDArray[np.float32]): # Make all values of the diagonal and below -inf mask = np.tri(scores.shape[1], dtype=bool) mask = einops.repeat(mask, "n m -> b n m", b=scores.shape[0]) - scores_cp[mask] = -np.inf + if scores_cp.dtype == np.int64: + pass + else: + scores_cp[mask] = -np.inf first_idx = np.arange(scores.shape[1] - 1) first_idx = einops.repeat( first_idx, "n -> b n", b=scores.shape[0] @@ -115,7 +118,7 @@ def prune_segments_all( return segments, out_scores -def select_segments(scores: npt.NDArray[np.float32], num_segments: int): +def select_segments(scores: npt.NDArray[np.float32], num_segments: int, **kwargs): """Select the highest scoring segments Args: @@ -126,5 +129,13 @@ def select_segments(scores: npt.NDArray[np.float32], num_segments: int): npt.NDArray[np.int32]: selected segments start and end indices [batch, num_segments, 2] npt.NDArray[np.float32]: selected segments scores [batch, num_segments] """ - idxs, scores = select_highest_suffix(scores) - return prune_segments_all(idxs, scores, num_segments) + # If scores is pytorch tensor, convert it to numpy array + if hasattr(scores, "detach"): + scores_ = scores.cpu().detach().numpy() + else: + scores_ = scores + # If scores_ is [batch, seq_len] convert it to [batch, seq_len, seq_len] (batch = 1) + if scores_.ndim == 2: + scores_ = einops.repeat(scores_, "b n -> b n m", m=scores_.shape[1]) + idxs, scores_ = select_highest_suffix(scores_) + return prune_segments_all(idxs, scores_, num_segments) diff --git a/src/samay/models/lptm/selection.pyi b/src/samay/models/lptm/selection.pyi deleted file mode 100644 index 3fc9551..0000000 --- a/src/samay/models/lptm/selection.pyi +++ /dev/null @@ -1,39 +0,0 @@ -from typing import Tuple - -import numpy as np -import numpy.typing as npt - -def pad_times( - scores: npt.NDArray[np.float32], times: npt.NDArray[np.int32] -) -> npt.NDArray[np.float32]: - """ - Pads the scores array with negative infinity based on the times array. - - Parameters: - scores (np.ndarray): A 3D array of shape (batch_size, seq_len, seq_len) containing scores. - times (np.ndarray): A 1D array of shape (batch_size,) containing time indices. - - Returns: - np.ndarray: A 3D array of shape (batch_size, seq_len, seq_len) with padded scores. - """ - ... - -def prune_segments( - segment_idxs: npt.NDArray[np.int32], - scores: npt.NDArray[np.float32], - num_segments: int, -) -> Tuple[npt.NDArray[np.int32], npt.NDArray[np.float32]]: - """ - Prunes the segments to keep only the top scoring ones. - - Parameters: - segment_idxs (np.ndarray): A 3D array of shape (batch_size, seq_len, 2) containing segment indices. - scores (np.ndarray): A 2D array of shape (batch_size, seq_len) containing scores. - num_segments (int): The number of segments to keep. - - Returns: - Tuple[np.ndarray, np.ndarray]: A tuple containing: - - A 3D array of shape (batch_size, num_segments, 2) with pruned segment indices. - - A 2D array of shape (batch_size, num_segments) with pruned scores. - """ - ... diff --git a/src/samay/models/timesfm/timesfm/__init__.py b/src/samay/models/timesfm/timesfm/__init__.py index a5ba46c..9b8f71b 100644 --- a/src/samay/models/timesfm/timesfm/__init__.py +++ b/src/samay/models/timesfm/timesfm/__init__.py @@ -15,7 +15,7 @@ # print( # "TimesFM v1.2.0. See https://github.com/google-research/timesfm/blob/master/README.md for updated APIs." # ) -from src.samay.models.timesfm.timesfm.timesfm_base import (TimesFmCheckpoint, +from samay.models.timesfm.timesfm.timesfm_base import (TimesFmCheckpoint, TimesFmHparams, TimesFmBase, freq_map, @@ -26,4 +26,4 @@ # from timesfm.src.timesfm import data_loader # except Exception as _: # print("Loaded PyTorch TimesFM.") -from src.samay.models.timesfm.timesfm.timesfm_torch import TimesFmTorch as TimesFm +from samay.models.timesfm.timesfm.timesfm_torch import TimesFmTorch as TimesFm diff --git a/src/samay/models/timesfm/timesfm/patched_decoder.py b/src/samay/models/timesfm/timesfm/patched_decoder.py deleted file mode 100644 index 2e4e0a8..0000000 --- a/src/samay/models/timesfm/timesfm/patched_decoder.py +++ /dev/null @@ -1,541 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Pax ML model for patched time-series decoder. - -The file implements Residual MLPs, Patched Decoder layers and PAX ML models. -""" - -import dataclasses -from typing import Optional, Tuple - -import einshape as es -from jax import lax -import jax.numpy as jnp -from praxis import base_layer -from praxis import base_model -from praxis import layers -from praxis import pax_fiddle -from praxis import py_utils -from praxis import pytypes -from praxis.layers import activations -from praxis.layers import embedding_softmax -from praxis.layers import linears -from praxis.layers import normalizations -from praxis.layers import stochastics -from praxis.layers import transformers - -# PAX shortcuts -NestedMap = py_utils.NestedMap -JTensor = pytypes.JTensor - -LayerTpl = pax_fiddle.Config[base_layer.BaseLayer] -template_field = base_layer.template_field - -PAD_VAL = 1123581321.0 -DEFAULT_QUANTILES = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] - -# NestedMap keys -_INPUT_TS = "input_ts" -_TARGET_FUTURE = "actual_ts" -_INPUT_PADDING = "input_padding" -_OUTPUT_TS = "output_ts" -_FREQ = "freq" -_OUTPUT_TOKENS = "output_tokens" -_STATS = "stats" - -# Small numerical value. -_TOLERANCE = 1e-7 - - -def _shift_padded_seq(mask: JTensor, seq: JTensor) -> JTensor: - """Shifts rows of seq based on the first 0 in each row of the mask.""" - num = seq.shape[1] - - # Find the index of the first 0 in each row of the mask - first_zero_idx = jnp.argmin(mask, axis=1) - - # Create a range array for indexing - idx_range = jnp.arange(num) - - def shift_row(carry, x): - seq_row, shift = x - shifted_idx = (idx_range - shift) % num - shifted_row = seq_row[shifted_idx] - return carry, shifted_row - - # Use lax.scan to shift each row of seq based on the corresponding - # first_zero_idx. - _, shifted_seq = lax.scan(shift_row, None, (seq, first_zero_idx)) - - return shifted_seq - - -class ResidualBlock(base_layer.BaseLayer): - """Simple feedforward block with residual connection. - - Attributes: - input_dims: input dimension. - hidden_dims: hidden dimension. - output_dims: output dimension. - dropout_prob: dropout probability. - layer_norm: whether to use layer norm or not. - dropout_tpl: config for dropout. - ln_tpl: config for layer norm. - act_tpl: config for activation in hidden layer. - """ - - input_dims: int = 0 - hidden_dims: int = 0 - output_dims: int = 0 - dropout_prob: float = 0.0 - layer_norm: bool = False - dropout_tpl: LayerTpl = template_field(stochastics.Dropout) - ln_tpl: LayerTpl = template_field(normalizations.LayerNorm) - act_tpl: LayerTpl = template_field(activations.Swish) - - def setup(self): - lnorm_tpl = self.ln_tpl.clone() - lnorm_tpl.dim = self.output_dims - self.create_child("ln_layer", lnorm_tpl) - - dropout_tpl = self.dropout_tpl.clone() - dropout_tpl.keep_prob = 1.0 - self.dropout_prob - self.create_child("dropout", dropout_tpl) - - self.create_child( - "hidden_layer", - pax_fiddle.Config( - linears.FeedForward, - input_dims=self.input_dims, - output_dims=self.hidden_dims, - activation_tpl=self.act_tpl.clone(), - ), - ) - - self.create_child( - "output_layer", - pax_fiddle.Config( - linears.FeedForward, - input_dims=self.hidden_dims, - output_dims=self.output_dims, - activation_tpl=pax_fiddle.Config(activations.Identity), - ), - ) - - self.create_child( - "residual_layer", - pax_fiddle.Config( - linears.FeedForward, - input_dims=self.input_dims, - output_dims=self.output_dims, - activation_tpl=pax_fiddle.Config(activations.Identity), - ), - ) - - def __call__(self, inputs: JTensor) -> JTensor: - hidden = self.hidden_layer(inputs) - output = self.output_layer(hidden) - output = self.dropout(output) - residual = self.residual_layer(inputs) - if self.layer_norm: - return self.ln_layer(output + residual) - else: - return output + residual - - -def _masked_mean_std(inputs: JTensor, - padding: JTensor) -> Tuple[JTensor, JTensor]: - """Calculates mean and standard deviation of arr across axis 1. - - It should exclude values where pad is 1. - - Args: - inputs: A JAX array of shape [b, n, p]. - padding: A JAX array of shape [b, n, p] with values 0 or 1. - - Returns: - A tuple containing the mean and standard deviation of arr. We return the - statistics of the first patch with more than three non-padded values. - """ - # Selecting the first pad with more than 3 unpadded values. - pad_sum = jnp.sum(1 - padding, axis=2) - - def _get_patch_index(arr: JTensor): - indices = jnp.argmax(arr >= 3, axis=1) - row_sum = (arr >= 3).sum(axis=1) - return jnp.where(row_sum == 0, arr.shape[1] - 1, indices) - - patch_indices = _get_patch_index(pad_sum) - bidxs = jnp.arange(inputs.shape[0]) - - arr = inputs[bidxs, patch_indices, :] - pad = padding[bidxs, patch_indices, :] - - # Create a mask where P is 0 - mask = 1 - pad - - # Calculate the number of valid elements - num_valid_elements = jnp.sum(mask, axis=1) - - num_valid_elements = jnp.where(num_valid_elements == 0, 1, num_valid_elements) - - # Calculate the masked sum and squared sum of M - masked_sum = jnp.sum(arr * mask, axis=1) - masked_squared_sum = jnp.sum((arr * mask)**2, axis=1) - - # Calculate the masked mean and standard deviation - masked_mean = masked_sum / num_valid_elements - masked_var = masked_squared_sum / num_valid_elements - masked_mean**2 - masked_var = jnp.where(masked_var < 0.0, 0.0, masked_var) - masked_std = jnp.sqrt(masked_var) - - return masked_mean, masked_std - - -def _create_quantiles() -> list[float]: - """Returns the quantiles for forecasting.""" - return DEFAULT_QUANTILES - - -class PatchedTimeSeriesDecoder(base_layer.BaseLayer): - """Patch decoder layer for time-series foundation model. - - Attributes: - patch_len: length of input patches. - horizon_len: length of output patches. Referred to as `output_patch_len` - during inference. - model_dims: model dimension of stacked transformer layer. - hidden_dims: hidden dimensions in fully connected layers. - quantiles: list of quantiles for non prob model. - residual_block_tpl: config for residual block. - stacked_transformer_params_tpl: config for stacked transformer. - use_freq: whether to use frequency encoding. - - In all of what followed, except specified otherwise, B is batch size, T is - sequence length of time-series. N is the number of input patches that can be - obtained from T. P is the input patch length and H is the horizon length. Q is - number of output logits. D is model dimension. - """ - - patch_len: int = 0 - horizon_len: int = 0 - model_dims: int = 0 - hidden_dims: int = 0 - quantiles: list[float] = dataclasses.field(default_factory=_create_quantiles) - residual_block_tpl: LayerTpl = template_field(ResidualBlock) - stacked_transformer_params_tpl: LayerTpl = template_field( - transformers.StackedTransformer) - use_freq: bool = True - use_pos_emb: bool = True - - def setup(self) -> None: - """Construct the model.""" - num_outputs = len(self.quantiles) + 1 - - stl = self.stacked_transformer_params_tpl.clone() - stl.model_dims = self.model_dims - stl.hidden_dims = self.hidden_dims - stl.mask_self_attention = True - - self.create_child("stacked_transformer_layer", stl) - - input_resl = self.residual_block_tpl.clone() - ff_in_dims = 2 * self.patch_len - input_resl.input_dims = ff_in_dims - input_resl.hidden_dims = self.hidden_dims - input_resl.output_dims = self.model_dims - self.create_child( - "input_ff_layer", - input_resl, - ) - - horizon_resl = self.residual_block_tpl.clone() - horizon_resl.input_dims = self.model_dims - horizon_resl.hidden_dims = self.hidden_dims - horizon_resl.output_dims = self.horizon_len * num_outputs - self.create_child( - "horizon_ff_layer", - horizon_resl, - ) - - self.create_child( - "position_emb", - pax_fiddle.Config(layers.PositionalEmbedding, - embedding_dims=self.model_dims), - ) - - if self.use_freq: - self.create_child( - "freq_emb", - pax_fiddle.Config( - embedding_softmax.Embedding, - num_classes=3, - input_dims=self.model_dims, - ), - ) - - def transform_decode_state( - self, transform_fn: base_layer.DecodeStateTransformFn) -> None: - """Transforms all decode state variables based on transform_fn.""" - self.stacked_transformer_layer.transform_decode_state(transform_fn) - - def _forward_transform( - self, inputs: JTensor, - patched_pads: JTensor) -> Tuple[JTensor, Tuple[JTensor, JTensor]]: - """Input is of shape [B, N, P].""" - mu, sigma = _masked_mean_std(inputs, patched_pads) - sigma = jnp.where(sigma < _TOLERANCE, 1.0, sigma) - # Normalize each patch. - outputs = (inputs - mu[:, None, None]) / sigma[:, None, None] - outputs = jnp.where( - jnp.abs(inputs - PAD_VAL) < _TOLERANCE, PAD_VAL, outputs) - return outputs, (mu, sigma) - - def _reverse_transform(self, outputs: JTensor, - stats: Tuple[JTensor, JTensor]) -> JTensor: - """Output is of shape [B, N, P, Q].""" - mu, sigma = stats - return outputs * sigma[:, None, None, None] + mu[:, None, None, None] - - def _preprocess_input( - self, - input_ts: JTensor, - input_padding: JTensor, - pos_emb: Optional[JTensor] = None, - ) -> Tuple[JTensor, JTensor, Optional[Tuple[JTensor, JTensor]], JTensor]: - """Preprocess input for stacked transformer.""" - # Reshape into patches. - patched_inputs = es.jax_einshape("b(np)->bnp", input_ts, p=self.patch_len) - patched_pads = es.jax_einshape("b(np)->bnp", - input_padding, - p=self.patch_len) - patched_inputs = jnp.where( - jnp.abs(patched_pads - 1.0) < _TOLERANCE, 0.0, patched_inputs) - patched_pads = jnp.where( - jnp.abs(patched_inputs - PAD_VAL) < _TOLERANCE, 1, patched_pads) - patched_inputs, stats = self._forward_transform(patched_inputs, - patched_pads) - - # B x N x D - patched_inputs = patched_inputs * (1.0 - patched_pads) - concat_inputs = jnp.concatenate([patched_inputs, patched_pads], axis=-1) - model_input = self.input_ff_layer(concat_inputs) - # A patch should not be padded even if there is at least one zero. - patched_padding = jnp.min(patched_pads, axis=-1) - - if use_pos_emb: - if pos_emb is None: - position_emb = self.position_emb(seq_length=model_input.shape[1]) - else: - position_emb = pos_emb - if self.do_eval: - if position_emb.shape[0] != model_input.shape[0]: - position_emb = jnp.repeat(position_emb, model_input.shape[0], axis=0) - position_emb = _shift_padded_seq(patched_padding, position_emb) - model_input += position_emb - - return model_input, patched_padding, stats, patched_inputs - - def _postprocess_output( - self, - model_output: JTensor, - num_outputs: int, - stats: Tuple[JTensor, JTensor], - ) -> JTensor: - """Postprocess output of stacked transformer.""" - # B x N x (H.Q) - output_ts = self.horizon_ff_layer(model_output) - output_ts = es.jax_einshape("bn(hq)->bnhq", - output_ts, - q=num_outputs, - h=self.horizon_len) - return self._reverse_transform(output_ts, stats) - - def __call__(self, inputs: NestedMap) -> NestedMap: - """PatchTST call. - - Args: - inputs: A NestedMap containing (1) input_ts: input sequence of shape [B, - T] where T must be multiple of patch_length; (2) input_padding: that - contains padding map. - - Returns: - A nested map with two keys: - (1) 'output_tokens' of shape [B, N, D]. - (2) 'output_ts' of shape [B, N, H, Q] - (3) 'stats' a Tuple of statistics for renormalization. - """ - input_ts, input_padding = inputs[_INPUT_TS], inputs[_INPUT_PADDING] - num_outputs = len(self.quantiles) + 1 - model_input, patched_padding, stats, _ = self._preprocess_input( - input_ts=input_ts, - input_padding=input_padding, - ) - if self.use_freq: - freq = inputs[_FREQ].astype(jnp.int32) - f_emb = self.freq_emb(freq) # B x 1 x D - f_emb = jnp.repeat(f_emb, model_input.shape[1], axis=1) - model_input += f_emb - model_output = self.stacked_transformer_layer(model_input, patched_padding) - - output_ts = self._postprocess_output(model_output, num_outputs, stats) - return NestedMap({ - _OUTPUT_TOKENS: model_output, - _OUTPUT_TS: output_ts, - _STATS: stats - }) - - def decode( - self, - inputs: NestedMap, - horizon_len: int, - output_patch_len: Optional[int] = None, - max_len: int = 512, - return_forecast_on_context: bool = False, - ) -> tuple[JTensor, JTensor]: - """Auto-regressive decoding without caching. - - Args: - inputs: input time-series and paddings. Time-series shape B x C, padding - shape shape B x (C + H) where H is the prediction length. - horizon_len: prediction length. - output_patch_len: output length to be fetched from one step of - auto-regressive decoding. - max_len: maximum training context length. - return_forecast_on_context: whether to return the model forecast on the - context except the first input patch. - - Returns: - Tuple of two forecasting results: - - Point (mean) output predictions as a tensor with shape B x H'. - - Full predictions (mean and quantiles) as a tensor with shape - B x H' x (1 + # quantiles). - In particular, if return_forecast_on_context is True, H' is H plus - the forecastable context length, i.e. context_len - (first) patch_len. - """ - final_out = inputs[_INPUT_TS] - context_len = final_out.shape[1] - paddings = inputs[_INPUT_PADDING] - if self.use_freq: - freq = inputs[_FREQ].astype(jnp.int32) - else: - freq = jnp.zeros([final_out.shape[0], 1], dtype=jnp.int32) - full_outputs = [] - if paddings.shape[1] != final_out.shape[1] + horizon_len: - raise ValueError( - "Length of paddings must match length of input + horizon_len:" - f" {paddings.shape[1]} != {final_out.shape[1]} + {horizon_len}") - if output_patch_len is None: - output_patch_len = self.horizon_len - num_decode_patches = (horizon_len + output_patch_len - - 1) // output_patch_len - for step_index in range(num_decode_patches): - current_padding = paddings[:, 0:final_out.shape[1]] - input_ts = final_out[:, -max_len:] - input_padding = current_padding[:, -max_len:] - model_input = NestedMap( - input_ts=input_ts, - input_padding=input_padding, - freq=freq, - ) - fprop_outputs = self(model_input)[_OUTPUT_TS] - if return_forecast_on_context and step_index == 0: - # For the first decodings step, collect the model forecast on the - # context except the unavailable first input batch forecast. - new_full_ts = fprop_outputs[:, :-1, :self.patch_len, :] - new_full_ts = es.jax_einshape("bnph->b(np)h", new_full_ts) - - full_outputs.append(new_full_ts) - - # (full batch, last patch, output_patch_len, index of mean forecast = 0) - new_ts = fprop_outputs[:, -1, :output_patch_len, 0] - new_full_ts = fprop_outputs[:, -1, :output_patch_len, :] - # (full batch, last patch, output_patch_len, all output indices) - full_outputs.append(new_full_ts) - final_out = jnp.concatenate([final_out, new_ts], axis=-1) - - if return_forecast_on_context: - # `full_outputs` indexing starts at after the first input patch. - full_outputs = jnp.concatenate(full_outputs, - axis=1)[:, :(context_len - self.patch_len + - horizon_len), :] - else: - # `full_outputs` indexing starts at the forecast horizon. - full_outputs = jnp.concatenate(full_outputs, axis=1)[:, 0:horizon_len, :] - - return (full_outputs[:, :, 0], full_outputs) - - -class PatchedDecoderFinetuneModel(base_model.BaseModel): - """Model class for finetuning patched time-series decoder. - - Attributes: - core_layer_tpl: config for core layer. - freq: freq to finetune on. - """ - - core_layer_tpl: LayerTpl = template_field(PatchedTimeSeriesDecoder) - freq: int = 0 - - def setup(self) -> None: - self.create_child("core_layer", self.core_layer_tpl) - - def compute_predictions(self, input_batch: NestedMap) -> NestedMap: - input_ts = input_batch[_INPUT_TS] - input_padding = jnp.zeros_like(input_ts) - context_len = input_ts.shape[1] - input_patch_len = self.core_layer_tpl.patch_len - context_pad = ((context_len + input_patch_len - 1) // - input_patch_len) * input_patch_len - context_len - - input_ts = jnp.pad(input_ts, [(0, 0), (context_pad, 0)]) - input_padding = jnp.pad(input_padding, [(0, 0), (context_pad, 0)], - constant_values=1) - freq = jnp.ones([input_ts.shape[0], 1], dtype=jnp.int32) * self.freq - new_input_batch = NestedMap( - input_ts=input_ts, - input_padding=input_padding, - freq=freq, - ) - return self.core_layer(new_input_batch) - - def _quantile_loss(self, pred: JTensor, actual: JTensor, - quantile: float) -> JTensor: - """Calculates quantile loss. - - Args: - pred: B x T - actual: B x T - quantile: quantile at which loss is computed. - - Returns: - per coordinate loss. - """ - dev = actual - pred - loss_first = dev * quantile - loss_second = -dev * (1.0 - quantile) - return 2 * jnp.where(loss_first >= 0, loss_first, loss_second) - - def compute_loss(self, prediction_output: NestedMap, - input_batch: NestedMap) -> Tuple[NestedMap, NestedMap]: - output_ts = prediction_output[_OUTPUT_TS] - actual_ts = input_batch[_TARGET_FUTURE] - pred_ts = output_ts[:, -1, 0:actual_ts.shape[1], :] - loss = jnp.square(pred_ts[:, :, 0] - actual_ts) - for i, quantile in enumerate(self.core_layer.quantiles): - loss += self._quantile_loss(pred_ts[:, :, i + 1], actual_ts, quantile) - loss = loss.mean() - loss_weight = jnp.array(1.0, dtype=jnp.float32) - per_example_out = NestedMap() - return {"avg_qloss": (loss, loss_weight)}, per_example_out diff --git a/src/samay/models/timesfm/timesfm/timesfm_jax.py b/src/samay/models/timesfm/timesfm/timesfm_jax.py deleted file mode 100644 index 3d01d8f..0000000 --- a/src/samay/models/timesfm/timesfm/timesfm_jax.py +++ /dev/null @@ -1,358 +0,0 @@ -# Copyright 2024 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""TimesFM JAX forecast API for inference.""" - -import logging -import multiprocessing -import time -from os import path -from typing import Any, Sequence - -import einshape as es -import jax -import jax.numpy as jnp -import numpy as np -from huggingface_hub import snapshot_download - -from paxml import checkpoints, tasks_lib -from praxis import base_hyperparams, base_layer, pax_fiddle, py_utils, pytypes -from praxis.layers import normalizations, transformers -from timesfm import timesfm_base -from timesfm import patched_decoder - -instantiate = base_hyperparams.instantiate -NestedMap = py_utils.NestedMap -JTensor = pytypes.JTensor - -_TOL = 1e-6 - - -class TimesFmJax(timesfm_base.TimesFmBase): - """TimesFM forecast API for inference. - - This class is the scaffolding for calling TimesFM forecast. To properly use: - 1. Create an instance with the correct hyperparameters of a TimesFM model. - 2. Call `load_from_checkpoint` to load a compatible checkpoint. - 3. Call `forecast` for inference. - - Given the model size, this API does not shard the model weights for SPMD. All - parallelism happens on the data dimension. - - Compilation happens during the first time `forecast` is called and uses the - `per_core_batch_size` to set and freeze the input signature. Subsequent calls - to `forecast` reflect the actual inference latency. - """ - - def _get_sample_inputs(self): - return { - "input_ts": - jnp.zeros( - ( - self.per_core_batch_size, - self.context_len + self.output_patch_len, - ), - dtype=jnp.float32, - ), - "input_padding": - jnp.zeros( - ( - self.per_core_batch_size, - self.context_len + self.output_patch_len, - ), - dtype=jnp.float32, - ), - "freq": - jnp.zeros( - ( - self.per_core_batch_size, - 1, - ), - dtype=jnp.int32, - ), - } - - def __post_init__(self): - self.num_cores = jax.local_device_count(self.backend) - self.global_batch_size = self.per_core_batch_size * self.num_cores - self._eval_context = base_layer.JaxContext.HParams(do_eval=True) - self._pmapped_decode = None - self._model = None - self._train_state = None - - def load_from_checkpoint( - self, - checkpoint: timesfm_base.TimesFmCheckpoint, - ) -> None: - """Loads a checkpoint and compiles the decoder.""" - checkpoint_type = (checkpoints.CheckpointType.FLAX - if checkpoint.type is None else checkpoint.type) - checkpoint_path = checkpoint.path - step = checkpoint.step - repo_id = checkpoint.huggingface_repo_id - if checkpoint_path is None: - checkpoint_path = path.join(snapshot_download(repo_id), "checkpoints") - # Rewrite the devices for Jax. - self.mesh_shape = [1, self.num_cores, 1] - self.mesh_name = ["replica", "data", "mdl"] - - self.model_p = pax_fiddle.Config( - patched_decoder.PatchedTimeSeriesDecoder, - name="patched_decoder", - horizon_len=self.output_patch_len, - patch_len=self.input_patch_len, - model_dims=self.model_dims, - hidden_dims=self.model_dims, - residual_block_tpl=pax_fiddle.Config(patched_decoder.ResidualBlock), - quantiles=self.quantiles, - use_freq=True, - stacked_transformer_params_tpl=pax_fiddle.Config( - transformers.StackedTransformer, - num_heads=self.num_heads, - num_layers=self.num_layers, - transformer_layer_params_tpl=pax_fiddle.Config( - transformers.Transformer, - ln_tpl=pax_fiddle.Config(normalizations.RmsNorm,), - ), - ), - ) - - self._key1, self._key2 = jax.random.split(jax.random.PRNGKey(42)) - self._model = None - self._train_state = None - self._pmapped_decode = None - self._eval_context = base_layer.JaxContext.HParams(do_eval=True) - try: - multiprocessing.set_start_method("spawn") - except RuntimeError: - print("Multiprocessing context has already been set.") - # Download the checkpoint from Hugging Face Hub if not given - - # Initialize the model weights. - self._logging("Constructing model weights.") - start_time = time.time() - self._model = instantiate(self.model_p) - var_weight_hparams = self._model.abstract_init_with_metadata( - self._get_sample_inputs(), do_eval=True) - train_state_partition_specs = tasks_lib.create_state_partition_specs( - var_weight_hparams, - mesh_shape=self.mesh_shape, - mesh_axis_names=self.mesh_name, - discard_opt_states=True, - learners=None, - ) - train_state_local_shapes = tasks_lib.create_state_unpadded_shapes( - var_weight_hparams, - discard_opt_states=True, - learners=None, - ) - self._logging( - f"Constructed model weights in {time.time() - start_time:.2f} seconds.") - - # Load the model weights. - self._logging(f"Restoring checkpoint from {checkpoint_path}.") - start_time = time.time() - self._train_state = checkpoints.restore_checkpoint( - train_state_local_shapes, - checkpoint_dir=checkpoint_path, - checkpoint_type=checkpoint_type, - state_specs=train_state_partition_specs, - step=step, - ) - self._logging( - f"Restored checkpoint in {time.time() - start_time:.2f} seconds.") - self.jit_decode() - - def jit_decode(self): - """Jitting decoding function.""" - - # Initialize and jit the decode fn. - def _decode(inputs): - assert self._model is not None - assert self._train_state is not None - return self._model.apply( - self._train_state.mdl_vars, - inputs, - horizon_len=self.horizon_len, - output_patch_len=self.output_patch_len, - max_len=self.context_len, - return_forecast_on_context=True, - rngs={ - base_layer.PARAMS: self._key1, - base_layer.RANDOM: self._key2, - }, - method=self._model.decode, - ) - - self._logging("Jitting decoding.") - start_time = time.time() - self._pmapped_decode = jax.pmap( - _decode, - axis_name="batch", - devices=jax.devices(self.backend), - backend=self.backend, - axis_size=self.num_cores, - ) - with base_layer.JaxContext.new_context(hparams=self._eval_context): - _ = self._pmapped_decode( - NestedMap({ - "input_ts": - jnp.zeros( - ( - self.num_cores, - self.per_core_batch_size, - self.context_len, - ), - dtype=jnp.float32, - ), - "input_padding": - jnp.zeros( - ( - self.num_cores, - self.per_core_batch_size, - self.context_len + self.horizon_len, - ), - dtype=jnp.float32, - ), - "date_features": - None, - "freq": - jnp.zeros( - (self.num_cores, self.per_core_batch_size, 1), - dtype=jnp.int32, - ), - })) - self._logging(f"Jitted decoding in {time.time() - start_time:.2f} seconds.") - - def forecast( - self, - inputs: Sequence[Any], - freq: Sequence[int] | None = None, - window_size: int | None = None, - forecast_context_len: int | None = None, - return_forecast_on_context: bool = False, - truncate_negative: bool = False, - ) -> tuple[np.ndarray, np.ndarray]: - """Forecasts on a list of time series. - - Args: - inputs: list of time series forecast contexts. Each context time series - should be in a format convertible to JTensor by `jnp.array`. - freq: frequency of each context time series. 0 for high frequency - (default), 1 for medium, and 2 for low. Notice this is different from - the `freq` required by `forecast_on_df`. - window_size: window size of trend + residual decomposition. If None then - we do not do decomposition. - forecast_context_len: optional max context length. - return_forecast_on_context: True to return the forecast on the context - when available, i.e. after the first input patch. - truncate_negative: truncate to only non-negative values if all the contexts - have non-negative values. - - Returns: - A tuple for JTensors: - - the mean forecast of size (# inputs, # forecast horizon), - - the full forecast (mean + quantiles) of size - (# inputs, # forecast horizon, 1 + # quantiles). - - Raises: - ValueError: If the checkpoint is not properly loaded. - """ - if not self._train_state or not self._model: - raise ValueError( - "Checkpoint not loaded. Call `load_from_checkpoint` before" - " `forecast`.") - if forecast_context_len is None: - fcontext_len = self.context_len - else: - fcontext_len = forecast_context_len - inputs = [np.array(ts)[-fcontext_len:] for ts in inputs] - inp_min = np.min([np.min(ts) for ts in inputs]) - - if window_size is not None: - new_inputs = [] - for ts in inputs: - new_inputs.extend(timesfm_base.moving_average(ts, window_size)) - inputs = new_inputs - - if freq is None: - logging.info("No frequency provided via `freq`. Default to high (0).") - freq = [0] * len(inputs) - - input_ts, input_padding, inp_freq, pmap_pad = self._preprocess(inputs, freq) - with base_layer.JaxContext.new_context(hparams=self._eval_context): - mean_outputs = [] - full_outputs = [] - assert input_ts.shape[0] % self.global_batch_size == 0 - for i in range(input_ts.shape[0] // self.global_batch_size): - input_ts_in = jnp.array(input_ts[i * self.global_batch_size:(i + 1) * - self.global_batch_size]) - input_padding_in = jnp.array( - input_padding[i * self.global_batch_size:(i + 1) * - self.global_batch_size],) - inp_freq_in = jnp.array( - inp_freq[i * self.global_batch_size:(i + 1) * - self.global_batch_size, :], - dtype=jnp.int32, - ) - pmapped_inputs = NestedMap({ - "input_ts": - es.jax_einshape( - "(db)...->db...", - input_ts_in, - d=self.num_cores, - ), - "input_padding": - es.jax_einshape( - "(db)...->db...", - input_padding_in, - d=self.num_cores, - ), - "date_features": - None, - "freq": - es.jax_einshape( - "(db)...->db...", - inp_freq_in, - d=self.num_cores, - ), - }) - mean_output, full_output = self._pmapped_decode(pmapped_inputs) - if not return_forecast_on_context: - mean_output = mean_output[:, :, self._horizon_start:, ...] - full_output = full_output[:, :, self._horizon_start:, ...] - mean_output = es.jax_einshape("db...->(db)...", - mean_output, - d=self.num_cores) - full_output = es.jax_einshape("db...->(db)...", - full_output, - d=self.num_cores) - mean_output = np.array(mean_output) - full_output = np.array(full_output) - mean_outputs.append(mean_output) - full_outputs.append(full_output) - - mean_outputs = np.concatenate(mean_outputs, axis=0) - full_outputs = np.concatenate(full_outputs, axis=0) - - if pmap_pad > 0: - mean_outputs = mean_outputs[:-pmap_pad, ...] - full_outputs = full_outputs[:-pmap_pad, ...] - - if window_size is not None: - mean_outputs = mean_outputs[0::2, ...] + mean_outputs[1::2, ...] - full_outputs = full_outputs[0::2, ...] + full_outputs[1::2, ...] - if inp_min >= 0 and truncate_negative: - mean_outputs = np.maximum(mean_outputs, 0.0) - full_outputs = np.maximum(full_outputs, 0.0) - return mean_outputs, full_outputs diff --git a/src/samay/models/timesfm/timesfm/timesfm_torch.py b/src/samay/models/timesfm/timesfm/timesfm_torch.py index bbd3003..fd1eea2 100644 --- a/src/samay/models/timesfm/timesfm/timesfm_torch.py +++ b/src/samay/models/timesfm/timesfm/timesfm_torch.py @@ -21,9 +21,9 @@ import torch from huggingface_hub import snapshot_download -from src.samay.models.timesfm.timesfm import pytorch_patched_decoder as ppd -from src.samay.models.timesfm.timesfm import timesfm_base -from src.samay.utils import get_least_used_gpu +from samay.models.timesfm.timesfm import pytorch_patched_decoder as ppd +from samay.models.timesfm.timesfm import timesfm_base +from samay.utils import get_least_used_gpu _TOL = 1e-6 diff --git a/src/samay/models/timesfm/timesfm/xreg_lib.py b/src/samay/models/timesfm/timesfm/xreg_lib.py index e32b8f9..a77316d 100644 --- a/src/samay/models/timesfm/timesfm/xreg_lib.py +++ b/src/samay/models/timesfm/timesfm/xreg_lib.py @@ -17,8 +17,7 @@ import math from typing import Any, Iterable, Literal, Mapping, Sequence -import jax -import jax.numpy as jnp + import numpy as np from sklearn import preprocessing @@ -38,19 +37,6 @@ def _repeat(elements: Iterable[Any], counts: Iterable[int]) -> np.ndarray: ) -def _to_padded_jax_array(x: np.ndarray) -> jax.Array: - if x.ndim == 1: - (i,) = x.shape - di = 2 ** math.ceil(math.log2(i)) - i - return jnp.pad(x, ((0, di),), mode="constant", constant_values=0.0) - elif x.ndim == 2: - i, j = x.shape - di = 2 ** math.ceil(math.log2(i)) - i - dj = 2 ** math.ceil(math.log2(j)) - j - return jnp.pad(x, ((0, di), (0, dj)), mode="constant", constant_values=0.0) - else: - raise ValueError(f"Unsupported array shape: {x.shape}") - class BatchedInContextXRegBase: """Helper class for in-context regression covariate formatting. @@ -403,117 +389,3 @@ def fit(self) -> Any: raise NotImplementedError("Fit is not implemented.") -class BatchedInContextXRegLinear(BatchedInContextXRegBase): - """Linear in-context regression model.""" - - def fit( - self, - ridge: float = 0.0, - one_hot_encoder_drop: str | None = "first", - use_intercept: bool = True, - force_on_cpu: bool = False, - max_rows_per_col: int = 0, - max_rows_per_col_sample_seed: int = 42, - debug_info: bool = False, - assert_covariates: bool = False, - assert_covariate_shapes: bool = False, - ) -> ( - list[np.ndarray] - | tuple[list[np.ndarray], list[np.ndarray], jax.Array, jax.Array, jax.Array] - ): - """Fits a linear model for in-context regression. - - Args: - ridge: A non-negative value for specifying the ridge regression penalty. - If 0 is provided, fallback to ordinary least squares. Note this penalty - is added to the normalized covariate matrix. - one_hot_encoder_drop: Which drop strategy to use for the one hot encoder. - use_intercept: Whether to prepare an intercept (all 1) column in the - matrices. - force_on_cpu: Whether to force execution on cpu for accelerator machines. - max_rows_per_col: How many rows to subsample per column. 0 for no - subsampling. This is for speeding up model fitting. - max_rows_per_col_sample_seed: The seed for the subsampling if needed by - `max_rows_per_col`. - debug_info: Whether to return debug info. - assert_covariates: Whether to assert the validity of the covariate inputs. - assert_covariate_shapes: Whether to assert the shapes of the covariate - inputs when `assert_covariates` is True. - - Returns: - If `debug_info` is False: - The linear fits on the horizon. - If `debug_info` is True: - A tuple of: - - the linear fits on the horizon, - - the linear fits on the context, - - the flattened target vector, - - the covariate matrix for the context, and - - the covariate matrix for the horizon. - """ - flat_targets, x_train_raw, x_test = self.create_covariate_matrix( - one_hot_encoder_drop=one_hot_encoder_drop, - use_intercept=use_intercept, - assert_covariates=assert_covariates, - assert_covariate_shapes=assert_covariate_shapes, - ) - - x_train = x_train_raw.copy() - if max_rows_per_col: - nrows, ncols = x_train.shape - if nrows > (w := ncols * max_rows_per_col): - subsample = jax.random.choice( - jax.random.PRNGKey(max_rows_per_col_sample_seed), - nrows, - (w,), - replace=False, - ) - x_train = x_train[subsample] - flat_targets = flat_targets[subsample] - - device = jax.devices("cpu")[0] if force_on_cpu else None - # Runs jitted version of the solvers which are quicker at the cost of - # running jitting during the first time calling. Re-jitting happens whenever - # new (padded) shapes are encountered. - # Ocassionally it helps with the speed and the accuracy if we force single - # thread execution on cpu for accelerator machines: - # 1. Avoid moving data to accelarator memory. - # 2. Avoid precision loss if any. - with jax.default_device(device): - x_train_raw = _to_padded_jax_array(x_train_raw) - x_train = _to_padded_jax_array(x_train) - flat_targets = _to_padded_jax_array(flat_targets) - x_test = _to_padded_jax_array(x_test) - beta_hat = ( - jnp.linalg.pinv( - x_train.T @ x_train + ridge * jnp.eye(x_train.shape[1]), - hermitian=True, - ) - @ x_train.T - @ flat_targets - ) - y_hat = x_test @ beta_hat - y_hat_context = x_train_raw @ beta_hat if debug_info else None - - outputs = [] - outputs_context = [] - - # Reconstruct the ragged 2-dim batched forecasts from flattened linear fits. - train_index, test_index = 0, 0 - for train_index_delta, test_index_delta in zip(self.train_lens, self.test_lens): - outputs.append( - np.array(y_hat[test_index : (test_index + test_index_delta)]) - ) - if debug_info: - outputs_context.append( - np.array( - y_hat_context[train_index : (train_index + train_index_delta)] - ) - ) - train_index += train_index_delta - test_index += test_index_delta - - if debug_info: - return outputs, outputs_context, flat_targets, x_train, x_test - else: - return outputs diff --git a/transform_monash.py b/transform_monash.py index 89d1e4a..20da8bf 100644 --- a/transform_monash.py +++ b/transform_monash.py @@ -1,4 +1,4 @@ -from src.samay.utils import arrow_to_csv +from samay.utils import arrow_to_csv import os import pandas as pd diff --git a/uv.lock b/uv.lock index 2c8e025..639f8d4 100644 --- a/uv.lock +++ b/uv.lock @@ -1,4 +1,5 @@ version = 1 +revision = 1 requires-python = ">=3.11" resolution-markers = [ "python_full_version >= '3.12' and sys_platform == 'linux'", @@ -573,6 +574,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/f5/e8/f6bd1eee09314e7e6dee49cbe2c5e22314ccdb38db16c9fc72d2fa80d054/docker_pycreds-0.4.0-py2.py3-none-any.whl", hash = "sha256:7266112468627868005106ec19cd0d722702d2b7d5912a28e19b826c3d37af49", size = 8982 }, ] +[[package]] +name = "einops" +version = "0.8.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/e5/81/df4fbe24dff8ba3934af99044188e20a98ed441ad17a274539b74e82e126/einops-0.8.1.tar.gz", hash = "sha256:de5d960a7a761225532e0f1959e5315ebeafc0cd43394732f103ca44b9837e84", size = 54805 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/87/62/9773de14fe6c45c23649e98b83231fffd7b9892b6cf863251dc2afa73643/einops-0.8.1-py3-none-any.whl", hash = "sha256:919387eb55330f5757c6bea9165c5ff5cfe63a642682ea788a6d472576d81737", size = 64359 }, +] + [[package]] name = "einshape" version = "1.0" @@ -1682,7 +1692,7 @@ name = "nvidia-cudnn-cu12" version = "9.1.0.70" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-cublas-cu12" }, + { name = "nvidia-cublas-cu12", marker = "sys_platform == 'linux'" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/9f/fd/713452cd72343f682b1c7b9321e23829f00b842ceaedcda96e742ea0b0b3/nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl", hash = "sha256:165764f44ef8c61fcdfdfdbe769d687e06374059fbb388b6c89ecb0e28793a6f", size = 664752741 }, @@ -1693,7 +1703,7 @@ name = "nvidia-cufft-cu12" version = "11.2.1.3" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-nvjitlink-cu12" }, + { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/27/94/3266821f65b92b3138631e9c8e7fe1fb513804ac934485a8d05776e1dd43/nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl", hash = "sha256:f083fc24912aa410be21fa16d157fed2055dab1cc4b6934a0e03cba69eb242b9", size = 211459117 }, @@ -1712,9 +1722,9 @@ name = "nvidia-cusolver-cu12" version = "11.6.1.9" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-cublas-cu12" }, - { name = "nvidia-cusparse-cu12" }, - { name = "nvidia-nvjitlink-cu12" }, + { name = "nvidia-cublas-cu12", marker = "sys_platform == 'linux'" }, + { name = "nvidia-cusparse-cu12", marker = "sys_platform == 'linux'" }, + { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/3a/e1/5b9089a4b2a4790dfdea8b3a006052cfecff58139d5a4e34cb1a51df8d6f/nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl", hash = "sha256:19e33fa442bcfd085b3086c4ebf7e8debc07cfe01e11513cc6d332fd918ac260", size = 127936057 }, @@ -1725,7 +1735,7 @@ name = "nvidia-cusparse-cu12" version = "12.3.1.170" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-nvjitlink-cu12" }, + { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/db/f7/97a9ea26ed4bbbfc2d470994b8b4f338ef663be97b8f677519ac195e113d/nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl", hash = "sha256:ea4f11a2904e2a8dc4b1833cc1b5181cde564edd0d5cd33e3c168eff2d1863f1", size = 207454763 }, @@ -2576,6 +2586,7 @@ dependencies = [ { name = "absl-py" }, { name = "chronos-forecasting" }, { name = "datasets" }, + { name = "einops" }, { name = "einshape" }, { name = "gluonts" }, { name = "huggingface-hub" }, @@ -2605,6 +2616,7 @@ requires-dist = [ { name = "absl-py", specifier = ">=2.1.0" }, { name = "chronos-forecasting", specifier = ">=1.4.1" }, { name = "datasets", specifier = ">=3.2.0" }, + { name = "einops", specifier = ">=0.8.1" }, { name = "einshape", specifier = ">=1.0" }, { name = "gluonts", specifier = ">=0.16.0" }, { name = "huggingface-hub", specifier = ">=0.26.2" }, @@ -3036,7 +3048,7 @@ name = "triton" version = "3.1.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "filelock" }, + { name = "filelock", marker = "sys_platform == 'linux'" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/86/17/d9a5cf4fcf46291856d1e90762e36cbabd2a56c7265da0d1d9508c8e3943/triton-3.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0f34f6e7885d1bf0eaaf7ba875a5f0ce6f3c13ba98f9503651c1e6dc6757ed5c", size = 209506424 },