mirror of
https://github.com/wassname/pytorch-transformer-ts.git
synced 2026-06-27 16:46:28 +08:00
Kashif Rasul
472 lines
437 KiB
Plaintext
472 lines
437 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"from matplotlib import pyplot as plt\n",
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"import matplotlib.dates as mdates\n",
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"\n",
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"from itertools import islice"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from gluonts.evaluation import make_evaluation_predictions, Evaluator\n",
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"from gluonts.dataset.repository.datasets import get_dataset\n",
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"\n",
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"from estimator import PerceiverAREstimator"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"dataset = get_dataset(\"electricity\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"estimator = PerceiverAREstimator(\n",
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" depth=2,\n",
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" heads=2,\n",
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" freq=dataset.metadata.freq,\n",
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" prediction_length=dataset.metadata.prediction_length,\n",
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" context_length=dataset.metadata.prediction_length*10,\n",
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" num_feat_static_cat=1,\n",
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" cardinality=[321],\n",
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" embedding_dimension=[3],\n",
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"\n",
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" batch_size=128,\n",
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" num_batches_per_epoch=100,\n",
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" trainer_kwargs=dict(max_epochs=50, accelerator='gpu', devices=1,),\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/kashif/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/utilities/parsing.py:261: UserWarning: Attribute 'model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['model'])`.\n",
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" rank_zero_warn(\n",
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"Using bfloat16 Automatic Mixed Precision (AMP)\n",
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"GPU available: True (cuda), used: True\n",
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"TPU available: False, using: 0 TPU cores\n",
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"IPU available: False, using: 0 IPUs\n",
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"HPU available: False, using: 0 HPUs\n",
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"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
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"\n",
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" | Name | Type | Params\n",
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"-------------------------------------------\n",
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"0 | model | PerceiverARModel | 55.3 K\n",
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"-------------------------------------------\n",
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"55.3 K Trainable params\n",
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"0 Non-trainable params\n",
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"55.3 K Total params\n",
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"0.221 Total estimated model params size (MB)\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "f50989f4d05147ba8278e32a3d94d84f",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Sanity Checking: 0it [00:00, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "65e5193ce4324e88924bbf38ff431d0e",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Training: 0it [00:00, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "13ba258d93644aa9b31fed205132f148",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Validation: 0it [00:00, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Epoch 0, global step 100: 'val_loss' reached 5.44867 (best 5.44867), saving model to '/mnt/scratch/kashif/pytorch-transformer-ts/perceiverar/lightning_logs/version_32/checkpoints/epoch=0-step=100.ckpt' as top 1\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "17eb1bee55b2466fa8d7c806264bdf00",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Validation: 0it [00:00, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Epoch 1, global step 200: 'val_loss' reached 5.35806 (best 5.35806), saving model to '/mnt/scratch/kashif/pytorch-transformer-ts/perceiverar/lightning_logs/version_32/checkpoints/epoch=1-step=200.ckpt' as top 1\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "af700c7c2002408eb804fa6ab03a4293",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Validation: 0it [00:00, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Epoch 2, global step 300: 'val_loss' was not in top 1\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "a24a9928028241b8b8cabb45177e1b71",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Validation: 0it [00:00, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Epoch 3, global step 400: 'val_loss' was not in top 1\n"
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]
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},
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{
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"ename": "ValueError",
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"evalue": "Expected parameter df (Tensor of shape (128, 24)) of distribution Chi2() to satisfy the constraint GreaterThan(lower_bound=0.0), but found invalid values:\ntensor([[nan, nan, nan, ..., nan, nan, nan],\n [nan, nan, nan, ..., nan, nan, nan],\n [nan, nan, nan, ..., nan, nan, nan],\n ...,\n [nan, nan, nan, ..., nan, nan, nan],\n [nan, nan, nan, ..., nan, nan, nan],\n [nan, nan, nan, ..., nan, nan, nan]], device='cuda:0',\n grad_fn=<MulBackward0>)",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m/home/kashif/scratch/pytorch-transformer-ts/perceiverar/perceiverar.ipynb Cell 5\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Brirs7fpdysrvpdl8.myfritz.net/home/kashif/scratch/pytorch-transformer-ts/perceiverar/perceiverar.ipynb#W4sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m predictor \u001b[39m=\u001b[39m estimator\u001b[39m.\u001b[39;49mtrain(\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2Brirs7fpdysrvpdl8.myfritz.net/home/kashif/scratch/pytorch-transformer-ts/perceiverar/perceiverar.ipynb#W4sdnNjb2RlLXJlbW90ZQ%3D%3D?line=1'>2</a>\u001b[0m training_data\u001b[39m=\u001b[39;49mdataset\u001b[39m.\u001b[39;49mtrain,\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2Brirs7fpdysrvpdl8.myfritz.net/home/kashif/scratch/pytorch-transformer-ts/perceiverar/perceiverar.ipynb#W4sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m validation_data\u001b[39m=\u001b[39;49mdataset\u001b[39m.\u001b[39;49mtest,\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2Brirs7fpdysrvpdl8.myfritz.net/home/kashif/scratch/pytorch-transformer-ts/perceiverar/perceiverar.ipynb#W4sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3'>4</a>\u001b[0m num_workers\u001b[39m=\u001b[39;49m\u001b[39m8\u001b[39;49m,\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2Brirs7fpdysrvpdl8.myfritz.net/home/kashif/scratch/pytorch-transformer-ts/perceiverar/perceiverar.ipynb#W4sdnNjb2RlLXJlbW90ZQ%3D%3D?line=4'>5</a>\u001b[0m shuffle_buffer_length\u001b[39m=\u001b[39;49m\u001b[39m1024\u001b[39;49m\n\u001b[1;32m <a href='vscode-notebook-cell://ssh-remote%2Brirs7fpdysrvpdl8.myfritz.net/home/kashif/scratch/pytorch-transformer-ts/perceiverar/perceiverar.ipynb#W4sdnNjb2RlLXJlbW90ZQ%3D%3D?line=5'>6</a>\u001b[0m )\n",
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"File \u001b[0;32m~/gluon-ts-PR/src/gluonts/torch/model/estimator.py:230\u001b[0m, in \u001b[0;36mPyTorchLightningEstimator.train\u001b[0;34m(self, training_data, validation_data, num_workers, shuffle_buffer_length, cache_data, ckpt_path, **kwargs)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mtrain\u001b[39m(\n\u001b[1;32m 221\u001b[0m \u001b[39mself\u001b[39m,\n\u001b[1;32m 222\u001b[0m training_data: Dataset,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 228\u001b[0m \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs,\n\u001b[1;32m 229\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m PyTorchPredictor:\n\u001b[0;32m--> 230\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrain_model(\n\u001b[1;32m 231\u001b[0m training_data,\n\u001b[1;32m 232\u001b[0m validation_data,\n\u001b[1;32m 233\u001b[0m num_workers\u001b[39m=\u001b[39;49mnum_workers,\n\u001b[1;32m 234\u001b[0m shuffle_buffer_length\u001b[39m=\u001b[39;49mshuffle_buffer_length,\n\u001b[1;32m 235\u001b[0m cache_data\u001b[39m=\u001b[39;49mcache_data,\n\u001b[1;32m 236\u001b[0m ckpt_path\u001b[39m=\u001b[39;49mckpt_path,\n\u001b[1;32m 237\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs,\n\u001b[1;32m 238\u001b[0m )\u001b[39m.\u001b[39mpredictor\n",
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"File \u001b[0;32m~/gluon-ts-PR/src/gluonts/torch/model/estimator.py:197\u001b[0m, in \u001b[0;36mPyTorchLightningEstimator.train_model\u001b[0;34m(self, training_data, validation_data, num_workers, shuffle_buffer_length, cache_data, ckpt_path, **kwargs)\u001b[0m\n\u001b[1;32m 194\u001b[0m trainer_kwargs \u001b[39m=\u001b[39m {\u001b[39m*\u001b[39m\u001b[39m*\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer_kwargs, \u001b[39m\"\u001b[39m\u001b[39mcallbacks\u001b[39m\u001b[39m\"\u001b[39m: callbacks}\n\u001b[1;32m 195\u001b[0m trainer \u001b[39m=\u001b[39m pl\u001b[39m.\u001b[39mTrainer(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mtrainer_kwargs)\n\u001b[0;32m--> 197\u001b[0m trainer\u001b[39m.\u001b[39;49mfit(\n\u001b[1;32m 198\u001b[0m model\u001b[39m=\u001b[39;49mtraining_network,\n\u001b[1;32m 199\u001b[0m train_dataloaders\u001b[39m=\u001b[39;49mtraining_data_loader,\n\u001b[1;32m 200\u001b[0m val_dataloaders\u001b[39m=\u001b[39;49mvalidation_data_loader,\n\u001b[1;32m 201\u001b[0m ckpt_path\u001b[39m=\u001b[39;49mckpt_path,\n\u001b[1;32m 202\u001b[0m )\n\u001b[1;32m 204\u001b[0m logger\u001b[39m.\u001b[39minfo(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mLoading best model from \u001b[39m\u001b[39m{\u001b[39;00mcheckpoint\u001b[39m.\u001b[39mbest_model_path\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 205\u001b[0m best_model \u001b[39m=\u001b[39m training_network\u001b[39m.\u001b[39mload_from_checkpoint(\n\u001b[1;32m 206\u001b[0m checkpoint\u001b[39m.\u001b[39mbest_model_path\n\u001b[1;32m 207\u001b[0m )\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:696\u001b[0m, in \u001b[0;36mTrainer.fit\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 677\u001b[0m \u001b[39mr\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 678\u001b[0m \u001b[39mRuns the full optimization routine.\u001b[39;00m\n\u001b[1;32m 679\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 693\u001b[0m \u001b[39m datamodule: An instance of :class:`~pytorch_lightning.core.datamodule.LightningDataModule`.\u001b[39;00m\n\u001b[1;32m 694\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 695\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstrategy\u001b[39m.\u001b[39mmodel \u001b[39m=\u001b[39m model\n\u001b[0;32m--> 696\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_and_handle_interrupt(\n\u001b[1;32m 697\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path\n\u001b[1;32m 698\u001b[0m )\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:650\u001b[0m, in \u001b[0;36mTrainer._call_and_handle_interrupt\u001b[0;34m(self, trainer_fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 648\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstrategy\u001b[39m.\u001b[39mlauncher\u001b[39m.\u001b[39mlaunch(trainer_fn, \u001b[39m*\u001b[39margs, trainer\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m 649\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 650\u001b[0m \u001b[39mreturn\u001b[39;00m trainer_fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 651\u001b[0m \u001b[39m# TODO(awaelchli): Unify both exceptions below, where `KeyboardError` doesn't re-raise\u001b[39;00m\n\u001b[1;32m 652\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyboardInterrupt\u001b[39;00m \u001b[39mas\u001b[39;00m exception:\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:737\u001b[0m, in \u001b[0;36mTrainer._fit_impl\u001b[0;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[1;32m 733\u001b[0m ckpt_path \u001b[39m=\u001b[39m ckpt_path \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mresume_from_checkpoint\n\u001b[1;32m 734\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_ckpt_path \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__set_ckpt_path(\n\u001b[1;32m 735\u001b[0m ckpt_path, model_provided\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, model_connected\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlightning_module \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 736\u001b[0m )\n\u001b[0;32m--> 737\u001b[0m results \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_run(model, ckpt_path\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mckpt_path)\n\u001b[1;32m 739\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstate\u001b[39m.\u001b[39mstopped\n\u001b[1;32m 740\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtraining \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:1168\u001b[0m, in \u001b[0;36mTrainer._run\u001b[0;34m(self, model, ckpt_path)\u001b[0m\n\u001b[1;32m 1164\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_checkpoint_connector\u001b[39m.\u001b[39mrestore_training_state()\n\u001b[1;32m 1166\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_checkpoint_connector\u001b[39m.\u001b[39mresume_end()\n\u001b[0;32m-> 1168\u001b[0m results \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_run_stage()\n\u001b[1;32m 1170\u001b[0m log\u001b[39m.\u001b[39mdetail(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m: trainer tearing down\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 1171\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_teardown()\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:1254\u001b[0m, in \u001b[0;36mTrainer._run_stage\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1252\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpredicting:\n\u001b[1;32m 1253\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_run_predict()\n\u001b[0;32m-> 1254\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_run_train()\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:1285\u001b[0m, in \u001b[0;36mTrainer._run_train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1282\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfit_loop\u001b[39m.\u001b[39mtrainer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\n\u001b[1;32m 1284\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mautograd\u001b[39m.\u001b[39mset_detect_anomaly(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_detect_anomaly):\n\u001b[0;32m-> 1285\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfit_loop\u001b[39m.\u001b[39;49mrun()\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/loops/loop.py:200\u001b[0m, in \u001b[0;36mLoop.run\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 199\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mon_advance_start(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m--> 200\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49madvance(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 201\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mon_advance_end()\n\u001b[1;32m 202\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_restarting \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py:270\u001b[0m, in \u001b[0;36mFitLoop.advance\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 266\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_data_fetcher\u001b[39m.\u001b[39msetup(\n\u001b[1;32m 267\u001b[0m dataloader, batch_to_device\u001b[39m=\u001b[39mpartial(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39m_call_strategy_hook, \u001b[39m\"\u001b[39m\u001b[39mbatch_to_device\u001b[39m\u001b[39m\"\u001b[39m, dataloader_idx\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m)\n\u001b[1;32m 268\u001b[0m )\n\u001b[1;32m 269\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39mprofiler\u001b[39m.\u001b[39mprofile(\u001b[39m\"\u001b[39m\u001b[39mrun_training_epoch\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m--> 270\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mepoch_loop\u001b[39m.\u001b[39;49mrun(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_data_fetcher)\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/loops/loop.py:200\u001b[0m, in \u001b[0;36mLoop.run\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 199\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mon_advance_start(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m--> 200\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49madvance(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 201\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mon_advance_end()\n\u001b[1;32m 202\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_restarting \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py:203\u001b[0m, in \u001b[0;36mTrainingEpochLoop.advance\u001b[0;34m(self, data_fetcher)\u001b[0m\n\u001b[1;32m 200\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbatch_progress\u001b[39m.\u001b[39mincrement_started()\n\u001b[1;32m 202\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39mprofiler\u001b[39m.\u001b[39mprofile(\u001b[39m\"\u001b[39m\u001b[39mrun_training_batch\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m--> 203\u001b[0m batch_output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbatch_loop\u001b[39m.\u001b[39;49mrun(kwargs)\n\u001b[1;32m 205\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbatch_progress\u001b[39m.\u001b[39mincrement_processed()\n\u001b[1;32m 207\u001b[0m \u001b[39m# update non-plateau LR schedulers\u001b[39;00m\n\u001b[1;32m 208\u001b[0m \u001b[39m# update epoch-interval ones only when we are at the end of training epoch\u001b[39;00m\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/loops/loop.py:200\u001b[0m, in \u001b[0;36mLoop.run\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 199\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mon_advance_start(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m--> 200\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49madvance(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 201\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mon_advance_end()\n\u001b[1;32m 202\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_restarting \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py:87\u001b[0m, in \u001b[0;36mTrainingBatchLoop.advance\u001b[0;34m(self, kwargs)\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39mlightning_module\u001b[39m.\u001b[39mautomatic_optimization:\n\u001b[1;32m 84\u001b[0m optimizers \u001b[39m=\u001b[39m _get_active_optimizers(\n\u001b[1;32m 85\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39moptimizers, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39moptimizer_frequencies, kwargs\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mbatch_idx\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m0\u001b[39m)\n\u001b[1;32m 86\u001b[0m )\n\u001b[0;32m---> 87\u001b[0m outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptimizer_loop\u001b[39m.\u001b[39;49mrun(optimizers, kwargs)\n\u001b[1;32m 88\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 89\u001b[0m outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmanual_loop\u001b[39m.\u001b[39mrun(kwargs)\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/loops/loop.py:200\u001b[0m, in \u001b[0;36mLoop.run\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 199\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mon_advance_start(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m--> 200\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49madvance(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 201\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mon_advance_end()\n\u001b[1;32m 202\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_restarting \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py:201\u001b[0m, in \u001b[0;36mOptimizerLoop.advance\u001b[0;34m(self, optimizers, kwargs)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39madvance\u001b[39m(\u001b[39mself\u001b[39m, optimizers: List[Tuple[\u001b[39mint\u001b[39m, Optimizer]], kwargs: OrderedDict) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m: \u001b[39m# type: ignore[override]\u001b[39;00m\n\u001b[1;32m 199\u001b[0m kwargs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_build_kwargs(kwargs, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moptimizer_idx, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_hiddens)\n\u001b[0;32m--> 201\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_run_optimization(kwargs, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_optimizers[\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptim_progress\u001b[39m.\u001b[39;49moptimizer_position])\n\u001b[1;32m 202\u001b[0m \u001b[39mif\u001b[39;00m result\u001b[39m.\u001b[39mloss \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 203\u001b[0m \u001b[39m# automatic optimization assumes a loss needs to be returned for extras to be considered as the batch\u001b[39;00m\n\u001b[1;32m 204\u001b[0m \u001b[39m# would be skipped otherwise\u001b[39;00m\n\u001b[1;32m 205\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_outputs[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39moptimizer_idx] \u001b[39m=\u001b[39m result\u001b[39m.\u001b[39masdict()\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py:248\u001b[0m, in \u001b[0;36mOptimizerLoop._run_optimization\u001b[0;34m(self, kwargs, optimizer)\u001b[0m\n\u001b[1;32m 240\u001b[0m closure()\n\u001b[1;32m 242\u001b[0m \u001b[39m# ------------------------------\u001b[39;00m\n\u001b[1;32m 243\u001b[0m \u001b[39m# BACKWARD PASS\u001b[39;00m\n\u001b[1;32m 244\u001b[0m \u001b[39m# ------------------------------\u001b[39;00m\n\u001b[1;32m 245\u001b[0m \u001b[39m# gradient update with accumulated gradients\u001b[39;00m\n\u001b[1;32m 246\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 247\u001b[0m \u001b[39m# the `batch_idx` is optional with inter-batch parallelism\u001b[39;00m\n\u001b[0;32m--> 248\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_optimizer_step(optimizer, opt_idx, kwargs\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mbatch_idx\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m0\u001b[39;49m), closure)\n\u001b[1;32m 250\u001b[0m result \u001b[39m=\u001b[39m closure\u001b[39m.\u001b[39mconsume_result()\n\u001b[1;32m 252\u001b[0m \u001b[39mif\u001b[39;00m result\u001b[39m.\u001b[39mloss \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 253\u001b[0m \u001b[39m# if no result, user decided to skip optimization\u001b[39;00m\n\u001b[1;32m 254\u001b[0m \u001b[39m# otherwise update running loss + reset accumulated loss\u001b[39;00m\n\u001b[1;32m 255\u001b[0m \u001b[39m# TODO: find proper way to handle updating running loss\u001b[39;00m\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py:358\u001b[0m, in \u001b[0;36mOptimizerLoop._optimizer_step\u001b[0;34m(self, optimizer, opt_idx, batch_idx, train_step_and_backward_closure)\u001b[0m\n\u001b[1;32m 355\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moptim_progress\u001b[39m.\u001b[39moptimizer\u001b[39m.\u001b[39mstep\u001b[39m.\u001b[39mincrement_ready()\n\u001b[1;32m 357\u001b[0m \u001b[39m# model hook\u001b[39;00m\n\u001b[0;32m--> 358\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrainer\u001b[39m.\u001b[39;49m_call_lightning_module_hook(\n\u001b[1;32m 359\u001b[0m \u001b[39m\"\u001b[39;49m\u001b[39moptimizer_step\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m 360\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrainer\u001b[39m.\u001b[39;49mcurrent_epoch,\n\u001b[1;32m 361\u001b[0m batch_idx,\n\u001b[1;32m 362\u001b[0m optimizer,\n\u001b[1;32m 363\u001b[0m opt_idx,\n\u001b[1;32m 364\u001b[0m train_step_and_backward_closure,\n\u001b[1;32m 365\u001b[0m on_tpu\u001b[39m=\u001b[39;49m\u001b[39misinstance\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrainer\u001b[39m.\u001b[39;49maccelerator, TPUAccelerator),\n\u001b[1;32m 366\u001b[0m using_native_amp\u001b[39m=\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrainer\u001b[39m.\u001b[39;49mamp_backend \u001b[39m==\u001b[39;49m AMPType\u001b[39m.\u001b[39;49mNATIVE),\n\u001b[1;32m 367\u001b[0m using_lbfgs\u001b[39m=\u001b[39;49mis_lbfgs,\n\u001b[1;32m 368\u001b[0m )\n\u001b[1;32m 370\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m should_accumulate:\n\u001b[1;32m 371\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moptim_progress\u001b[39m.\u001b[39moptimizer\u001b[39m.\u001b[39mstep\u001b[39m.\u001b[39mincrement_completed()\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:1552\u001b[0m, in \u001b[0;36mTrainer._call_lightning_module_hook\u001b[0;34m(self, hook_name, pl_module, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1549\u001b[0m pl_module\u001b[39m.\u001b[39m_current_fx_name \u001b[39m=\u001b[39m hook_name\n\u001b[1;32m 1551\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprofiler\u001b[39m.\u001b[39mprofile(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m[LightningModule]\u001b[39m\u001b[39m{\u001b[39;00mpl_module\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m{\u001b[39;00mhook_name\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m):\n\u001b[0;32m-> 1552\u001b[0m output \u001b[39m=\u001b[39m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1554\u001b[0m \u001b[39m# restore current_fx when nested context\u001b[39;00m\n\u001b[1;32m 1555\u001b[0m pl_module\u001b[39m.\u001b[39m_current_fx_name \u001b[39m=\u001b[39m prev_fx_name\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/core/module.py:1666\u001b[0m, in \u001b[0;36mLightningModule.optimizer_step\u001b[0;34m(self, epoch, batch_idx, optimizer, optimizer_idx, optimizer_closure, on_tpu, using_native_amp, using_lbfgs)\u001b[0m\n\u001b[1;32m 1584\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39moptimizer_step\u001b[39m(\n\u001b[1;32m 1585\u001b[0m \u001b[39mself\u001b[39m,\n\u001b[1;32m 1586\u001b[0m epoch: \u001b[39mint\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1593\u001b[0m using_lbfgs: \u001b[39mbool\u001b[39m \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m,\n\u001b[1;32m 1594\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 1595\u001b[0m \u001b[39mr\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 1596\u001b[0m \u001b[39m Override this method to adjust the default way the :class:`~pytorch_lightning.trainer.trainer.Trainer` calls\u001b[39;00m\n\u001b[1;32m 1597\u001b[0m \u001b[39m each optimizer.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1664\u001b[0m \n\u001b[1;32m 1665\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1666\u001b[0m optimizer\u001b[39m.\u001b[39;49mstep(closure\u001b[39m=\u001b[39;49moptimizer_closure)\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py:168\u001b[0m, in \u001b[0;36mLightningOptimizer.step\u001b[0;34m(self, closure, **kwargs)\u001b[0m\n\u001b[1;32m 165\u001b[0m \u001b[39mraise\u001b[39;00m MisconfigurationException(\u001b[39m\"\u001b[39m\u001b[39mWhen `optimizer.step(closure)` is called, the closure should be callable\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 167\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_strategy \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m--> 168\u001b[0m step_output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_strategy\u001b[39m.\u001b[39;49moptimizer_step(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_optimizer, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_optimizer_idx, closure, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 170\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_on_after_step()\n\u001b[1;32m 172\u001b[0m \u001b[39mreturn\u001b[39;00m step_output\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py:216\u001b[0m, in \u001b[0;36mStrategy.optimizer_step\u001b[0;34m(self, optimizer, opt_idx, closure, model, **kwargs)\u001b[0m\n\u001b[1;32m 206\u001b[0m \u001b[39m\"\"\"Performs the actual optimizer step.\u001b[39;00m\n\u001b[1;32m 207\u001b[0m \n\u001b[1;32m 208\u001b[0m \u001b[39mArgs:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 213\u001b[0m \u001b[39m **kwargs: Any extra arguments to ``optimizer.step``\u001b[39;00m\n\u001b[1;32m 214\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 215\u001b[0m model \u001b[39m=\u001b[39m model \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlightning_module\n\u001b[0;32m--> 216\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprecision_plugin\u001b[39m.\u001b[39;49moptimizer_step(model, optimizer, opt_idx, closure, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/native_amp.py:80\u001b[0m, in \u001b[0;36mNativeMixedPrecisionPlugin.optimizer_step\u001b[0;34m(self, model, optimizer, optimizer_idx, closure, **kwargs)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39moptimizer_step\u001b[39m(\n\u001b[1;32m 71\u001b[0m \u001b[39mself\u001b[39m,\n\u001b[1;32m 72\u001b[0m model: Optional[Union[\u001b[39m\"\u001b[39m\u001b[39mpl.LightningModule\u001b[39m\u001b[39m\"\u001b[39m, Module]],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs: Any,\n\u001b[1;32m 77\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Any:\n\u001b[1;32m 78\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mscaler \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 79\u001b[0m \u001b[39m# skip scaler logic, as bfloat16 does not require scaler\u001b[39;00m\n\u001b[0;32m---> 80\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49moptimizer_step(model, optimizer, optimizer_idx, closure, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 81\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(optimizer, LBFGS):\n\u001b[1;32m 82\u001b[0m \u001b[39mraise\u001b[39;00m MisconfigurationException(\n\u001b[1;32m 83\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mNative AMP and the LBFGS optimizer are not compatible (optimizer \u001b[39m\u001b[39m{\u001b[39;00moptimizer_idx\u001b[39m}\u001b[39;00m\u001b[39m).\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 84\u001b[0m )\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py:153\u001b[0m, in \u001b[0;36mPrecisionPlugin.optimizer_step\u001b[0;34m(self, model, optimizer, optimizer_idx, closure, **kwargs)\u001b[0m\n\u001b[1;32m 151\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(model, pl\u001b[39m.\u001b[39mLightningModule):\n\u001b[1;32m 152\u001b[0m closure \u001b[39m=\u001b[39m partial(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_wrap_closure, model, optimizer, optimizer_idx, closure)\n\u001b[0;32m--> 153\u001b[0m \u001b[39mreturn\u001b[39;00m optimizer\u001b[39m.\u001b[39;49mstep(closure\u001b[39m=\u001b[39;49mclosure, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/torch/optim/optimizer.py:113\u001b[0m, in \u001b[0;36mOptimizer._hook_for_profile.<locals>.profile_hook_step.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 111\u001b[0m profile_name \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mOptimizer.step#\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m.step\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(obj\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m)\n\u001b[1;32m 112\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mautograd\u001b[39m.\u001b[39mprofiler\u001b[39m.\u001b[39mrecord_function(profile_name):\n\u001b[0;32m--> 113\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/torch/autograd/grad_mode.py:27\u001b[0m, in \u001b[0;36m_DecoratorContextManager.__call__.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m 25\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdecorate_context\u001b[39m(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m 26\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mclone():\n\u001b[0;32m---> 27\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/torch/optim/adam.py:118\u001b[0m, in \u001b[0;36mAdam.step\u001b[0;34m(self, closure)\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[39mif\u001b[39;00m closure \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 117\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39menable_grad():\n\u001b[0;32m--> 118\u001b[0m loss \u001b[39m=\u001b[39m closure()\n\u001b[1;32m 120\u001b[0m \u001b[39mfor\u001b[39;00m group \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mparam_groups:\n\u001b[1;32m 121\u001b[0m params_with_grad \u001b[39m=\u001b[39m []\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py:138\u001b[0m, in \u001b[0;36mPrecisionPlugin._wrap_closure\u001b[0;34m(self, model, optimizer, optimizer_idx, closure)\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_wrap_closure\u001b[39m(\n\u001b[1;32m 126\u001b[0m \u001b[39mself\u001b[39m,\n\u001b[1;32m 127\u001b[0m model: \u001b[39m\"\u001b[39m\u001b[39mpl.LightningModule\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 130\u001b[0m closure: Callable[[], Any],\n\u001b[1;32m 131\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Any:\n\u001b[1;32m 132\u001b[0m \u001b[39m\"\"\"This double-closure allows makes sure the ``closure`` is executed before the\u001b[39;00m\n\u001b[1;32m 133\u001b[0m \u001b[39m ``on_before_optimizer_step`` hook is called.\u001b[39;00m\n\u001b[1;32m 134\u001b[0m \n\u001b[1;32m 135\u001b[0m \u001b[39m The closure (generally) runs ``backward`` so this allows inspecting gradients in this hook. This structure is\u001b[39;00m\n\u001b[1;32m 136\u001b[0m \u001b[39m consistent with the ``PrecisionPlugin`` subclasses that cannot pass ``optimizer.step(closure)`` directly.\u001b[39;00m\n\u001b[1;32m 137\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 138\u001b[0m closure_result \u001b[39m=\u001b[39m closure()\n\u001b[1;32m 139\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_after_closure(model, optimizer, optimizer_idx)\n\u001b[1;32m 140\u001b[0m \u001b[39mreturn\u001b[39;00m closure_result\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py:146\u001b[0m, in \u001b[0;36mClosure.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs: Any, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs: Any) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Optional[Tensor]:\n\u001b[0;32m--> 146\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mclosure(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 147\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_result\u001b[39m.\u001b[39mloss\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py:132\u001b[0m, in \u001b[0;36mClosure.closure\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 131\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mclosure\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs: Any, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs: Any) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m ClosureResult:\n\u001b[0;32m--> 132\u001b[0m step_output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_step_fn()\n\u001b[1;32m 134\u001b[0m \u001b[39mif\u001b[39;00m step_output\u001b[39m.\u001b[39mclosure_loss \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 135\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mwarning_cache\u001b[39m.\u001b[39mwarn(\u001b[39m\"\u001b[39m\u001b[39m`training_step` returned `None`. If this was on purpose, ignore this warning...\u001b[39m\u001b[39m\"\u001b[39m)\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/optimizer_loop.py:407\u001b[0m, in \u001b[0;36mOptimizerLoop._training_step\u001b[0;34m(self, kwargs)\u001b[0m\n\u001b[1;32m 398\u001b[0m \u001b[39m\"\"\"Performs the actual train step with the tied hooks.\u001b[39;00m\n\u001b[1;32m 399\u001b[0m \n\u001b[1;32m 400\u001b[0m \u001b[39mArgs:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 404\u001b[0m \u001b[39m A ``ClosureResult`` containing the training step output.\u001b[39;00m\n\u001b[1;32m 405\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 406\u001b[0m \u001b[39m# manually capture logged metrics\u001b[39;00m\n\u001b[0;32m--> 407\u001b[0m training_step_output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrainer\u001b[39m.\u001b[39;49m_call_strategy_hook(\u001b[39m\"\u001b[39;49m\u001b[39mtraining_step\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m*\u001b[39;49mkwargs\u001b[39m.\u001b[39;49mvalues())\n\u001b[1;32m 408\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39mstrategy\u001b[39m.\u001b[39mpost_training_step()\n\u001b[1;32m 410\u001b[0m model_output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainer\u001b[39m.\u001b[39m_call_lightning_module_hook(\u001b[39m\"\u001b[39m\u001b[39mtraining_step_end\u001b[39m\u001b[39m\"\u001b[39m, training_step_output)\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py:1706\u001b[0m, in \u001b[0;36mTrainer._call_strategy_hook\u001b[0;34m(self, hook_name, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1703\u001b[0m \u001b[39mreturn\u001b[39;00m\n\u001b[1;32m 1705\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprofiler\u001b[39m.\u001b[39mprofile(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m[Strategy]\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstrategy\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m{\u001b[39;00mhook_name\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m):\n\u001b[0;32m-> 1706\u001b[0m output \u001b[39m=\u001b[39m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1708\u001b[0m \u001b[39m# restore current_fx when nested context\u001b[39;00m\n\u001b[1;32m 1709\u001b[0m pl_module\u001b[39m.\u001b[39m_current_fx_name \u001b[39m=\u001b[39m prev_fx_name\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py:358\u001b[0m, in \u001b[0;36mStrategy.training_step\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprecision_plugin\u001b[39m.\u001b[39mtrain_step_context():\n\u001b[1;32m 357\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39misinstance\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel, TrainingStep)\n\u001b[0;32m--> 358\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmodel\u001b[39m.\u001b[39;49mtraining_step(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
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"File \u001b[0;32m/mnt/scratch/kashif/pytorch-transformer-ts/perceiverar/lightning_module.py:87\u001b[0m, in \u001b[0;36mPerceiverARLightningModule.training_step\u001b[0;34m(self, batch, batch_idx)\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mtraining_step\u001b[39m(\u001b[39mself\u001b[39m, batch, batch_idx: \u001b[39mint\u001b[39m): \u001b[39m# type: ignore\u001b[39;00m\n\u001b[1;32m 84\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 85\u001b[0m \u001b[39m Execute training step.\u001b[39;00m\n\u001b[1;32m 86\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 87\u001b[0m train_loss \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_compute_loss(batch)\n\u001b[1;32m 88\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlog(\n\u001b[1;32m 89\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mtrain_loss\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 90\u001b[0m train_loss,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 93\u001b[0m prog_bar\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[1;32m 94\u001b[0m )\n\u001b[1;32m 95\u001b[0m \u001b[39mreturn\u001b[39;00m train_loss\n",
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"File \u001b[0;32m/mnt/scratch/kashif/pytorch-transformer-ts/perceiverar/lightning_module.py:64\u001b[0m, in \u001b[0;36mPerceiverARLightningModule._compute_loss\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m 53\u001b[0m future_observed_values \u001b[39m=\u001b[39m batch[\u001b[39m\"\u001b[39m\u001b[39mfuture_observed_values\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m 55\u001b[0m params, scale, _, _ \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel\u001b[39m.\u001b[39mlagged_perciever(\n\u001b[1;32m 56\u001b[0m feat_static_cat,\n\u001b[1;32m 57\u001b[0m feat_static_real,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 62\u001b[0m future_target,\n\u001b[1;32m 63\u001b[0m )\n\u001b[0;32m---> 64\u001b[0m distr \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmodel\u001b[39m.\u001b[39;49moutput_distribution(params, scale)\n\u001b[1;32m 66\u001b[0m \u001b[39m# context_target = past_target[:, -self.model.context_length + 1 :]\u001b[39;00m\n\u001b[1;32m 67\u001b[0m \u001b[39m# target = torch.cat(\u001b[39;00m\n\u001b[1;32m 68\u001b[0m \u001b[39m# (context_target, future_target),\u001b[39;00m\n\u001b[1;32m 69\u001b[0m \u001b[39m# dim=1,\u001b[39;00m\n\u001b[1;32m 70\u001b[0m \u001b[39m# )\u001b[39;00m\n\u001b[1;32m 71\u001b[0m loss_values \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mloss(distr, future_target)\n",
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"File \u001b[0;32m/mnt/scratch/kashif/pytorch-transformer-ts/perceiverar/module.py:457\u001b[0m, in \u001b[0;36mPerceiverARModel.output_distribution\u001b[0;34m(self, params, scale, trailing_n)\u001b[0m\n\u001b[1;32m 455\u001b[0m \u001b[39mif\u001b[39;00m trailing_n \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 456\u001b[0m sliced_params \u001b[39m=\u001b[39m [p[:, \u001b[39m-\u001b[39mtrailing_n:] \u001b[39mfor\u001b[39;00m p \u001b[39min\u001b[39;00m params]\n\u001b[0;32m--> 457\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdistr_output\u001b[39m.\u001b[39;49mdistribution(sliced_params, scale\u001b[39m=\u001b[39;49mscale)\n",
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"File \u001b[0;32m~/gluon-ts-PR/src/gluonts/torch/distributions/distribution_output.py:138\u001b[0m, in \u001b[0;36mDistributionOutput.distribution\u001b[0;34m(self, distr_args, loc, scale)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdistribution\u001b[39m(\n\u001b[1;32m 118\u001b[0m \u001b[39mself\u001b[39m,\n\u001b[1;32m 119\u001b[0m distr_args,\n\u001b[1;32m 120\u001b[0m loc: Optional[torch\u001b[39m.\u001b[39mTensor] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m,\n\u001b[1;32m 121\u001b[0m scale: Optional[torch\u001b[39m.\u001b[39mTensor] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m,\n\u001b[1;32m 122\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Distribution:\n\u001b[1;32m 123\u001b[0m \u001b[39mr\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 124\u001b[0m \u001b[39m Construct the associated distribution, given the collection of\u001b[39;00m\n\u001b[1;32m 125\u001b[0m \u001b[39m constructor arguments and, optionally, a scale tensor.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[39m batch_shape+event_shape of the resulting distribution.\u001b[39;00m\n\u001b[1;32m 137\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 138\u001b[0m distr \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_base_distribution(distr_args)\n\u001b[1;32m 139\u001b[0m \u001b[39mif\u001b[39;00m loc \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m scale \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 140\u001b[0m \u001b[39mreturn\u001b[39;00m distr\n",
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"File \u001b[0;32m~/gluon-ts-PR/src/gluonts/torch/distributions/distribution_output.py:115\u001b[0m, in \u001b[0;36mDistributionOutput._base_distribution\u001b[0;34m(self, distr_args)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_base_distribution\u001b[39m(\u001b[39mself\u001b[39m, distr_args):\n\u001b[0;32m--> 115\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdistr_cls(\u001b[39m*\u001b[39;49mdistr_args)\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/torch/distributions/studentT.py:50\u001b[0m, in \u001b[0;36mStudentT.__init__\u001b[0;34m(self, df, loc, scale, validate_args)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\u001b[39mself\u001b[39m, df, loc\u001b[39m=\u001b[39m\u001b[39m0.\u001b[39m, scale\u001b[39m=\u001b[39m\u001b[39m1.\u001b[39m, validate_args\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[1;32m 49\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdf, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mloc, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mscale \u001b[39m=\u001b[39m broadcast_all(df, loc, scale)\n\u001b[0;32m---> 50\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_chi2 \u001b[39m=\u001b[39m Chi2(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdf)\n\u001b[1;32m 51\u001b[0m batch_shape \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdf\u001b[39m.\u001b[39msize()\n\u001b[1;32m 52\u001b[0m \u001b[39msuper\u001b[39m(StudentT, \u001b[39mself\u001b[39m)\u001b[39m.\u001b[39m\u001b[39m__init__\u001b[39m(batch_shape, validate_args\u001b[39m=\u001b[39mvalidate_args)\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/torch/distributions/chi2.py:22\u001b[0m, in \u001b[0;36mChi2.__init__\u001b[0;34m(self, df, validate_args)\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\u001b[39mself\u001b[39m, df, validate_args\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m---> 22\u001b[0m \u001b[39msuper\u001b[39;49m(Chi2, \u001b[39mself\u001b[39;49m)\u001b[39m.\u001b[39;49m\u001b[39m__init__\u001b[39;49m(\u001b[39m0.5\u001b[39;49m \u001b[39m*\u001b[39;49m df, \u001b[39m0.5\u001b[39;49m, validate_args\u001b[39m=\u001b[39;49mvalidate_args)\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/torch/distributions/gamma.py:52\u001b[0m, in \u001b[0;36mGamma.__init__\u001b[0;34m(self, concentration, rate, validate_args)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 51\u001b[0m batch_shape \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconcentration\u001b[39m.\u001b[39msize()\n\u001b[0;32m---> 52\u001b[0m \u001b[39msuper\u001b[39;49m(Gamma, \u001b[39mself\u001b[39;49m)\u001b[39m.\u001b[39;49m\u001b[39m__init__\u001b[39;49m(batch_shape, validate_args\u001b[39m=\u001b[39;49mvalidate_args)\n",
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"File \u001b[0;32m~/.env/pytorch/lib/python3.10/site-packages/torch/distributions/distribution.py:55\u001b[0m, in \u001b[0;36mDistribution.__init__\u001b[0;34m(self, batch_shape, event_shape, validate_args)\u001b[0m\n\u001b[1;32m 53\u001b[0m valid \u001b[39m=\u001b[39m constraint\u001b[39m.\u001b[39mcheck(value)\n\u001b[1;32m 54\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m valid\u001b[39m.\u001b[39mall():\n\u001b[0;32m---> 55\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 56\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mExpected parameter \u001b[39m\u001b[39m{\u001b[39;00mparam\u001b[39m}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 57\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m(\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mtype\u001b[39m(value)\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m of shape \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mtuple\u001b[39m(value\u001b[39m.\u001b[39mshape)\u001b[39m}\u001b[39;00m\u001b[39m) \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 58\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mof distribution \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mrepr\u001b[39m(\u001b[39mself\u001b[39m)\u001b[39m}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 59\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mto satisfy the constraint \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mrepr\u001b[39m(constraint)\u001b[39m}\u001b[39;00m\u001b[39m, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 60\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mbut found invalid values:\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m{\u001b[39;00mvalue\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m 61\u001b[0m )\n\u001b[1;32m 62\u001b[0m \u001b[39msuper\u001b[39m(Distribution, \u001b[39mself\u001b[39m)\u001b[39m.\u001b[39m\u001b[39m__init__\u001b[39m()\n",
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"\u001b[0;31mValueError\u001b[0m: Expected parameter df (Tensor of shape (128, 24)) of distribution Chi2() to satisfy the constraint GreaterThan(lower_bound=0.0), but found invalid values:\ntensor([[nan, nan, nan, ..., nan, nan, nan],\n [nan, nan, nan, ..., nan, nan, nan],\n [nan, nan, nan, ..., nan, nan, nan],\n ...,\n [nan, nan, nan, ..., nan, nan, nan],\n [nan, nan, nan, ..., nan, nan, nan],\n [nan, nan, nan, ..., nan, nan, nan]], device='cuda:0',\n grad_fn=<MulBackward0>)"
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]
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}
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],
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"source": [
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"predictor = estimator.train(\n",
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" training_data=dataset.train,\n",
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" validation_data=dataset.test,\n",
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" num_workers=8,\n",
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" shuffle_buffer_length=4000,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"forecast_it, ts_it = make_evaluation_predictions(\n",
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" dataset=dataset.test,\n",
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" predictor=predictor\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"forecasts = list(forecast_it)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"tss = list(ts_it)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\n",
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"Running evaluation: 100%|██████████| 2247/2247 [00:00<00:00, 4768.58it/s]\n",
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"/home/kashif/.env/pytorch/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1181: UserWarning: Warning: converting a masked element to nan.\n",
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" return arr.astype(dtype, copy=True)\n"
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]
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}
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],
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"source": [
|
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"evaluator = Evaluator()\n",
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"agg_metrics, ts_metrics = evaluator(iter(tss), iter(forecasts), num_series=len(dataset.test))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'MSE': 2632277.5637280988,\n",
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" 'abs_error': 8510987.796981812,\n",
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" 'abs_target_sum': 128632956.0,\n",
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" 'abs_target_mean': 2385.272140631954,\n",
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" 'seasonal_error': 189.49338196116761,\n",
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" 'MASE': 0.6778296899315452,\n",
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" 'MAPE': 0.08927172247730361,\n",
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" 'sMAPE': 0.09760858887603995,\n",
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" 'MSIS': 5.672968862545339,\n",
|
|
" 'QuantileLoss[0.1]': 3457375.6345648617,\n",
|
|
" 'Coverage[0.1]': 0.08778371161548731,\n",
|
|
" 'QuantileLoss[0.2]': 5534597.8590742415,\n",
|
|
" 'Coverage[0.2]': 0.17855288532858626,\n",
|
|
" 'QuantileLoss[0.3]': 6996813.588530006,\n",
|
|
" 'Coverage[0.3]': 0.2765353805073431,\n",
|
|
" 'QuantileLoss[0.4]': 7983532.04939911,\n",
|
|
" 'Coverage[0.4]': 0.3759827918706424,\n",
|
|
" 'QuantileLoss[0.5]': 8510987.873387918,\n",
|
|
" 'Coverage[0.5]': 0.48075211392968403,\n",
|
|
" 'QuantileLoss[0.6]': 8669924.372969786,\n",
|
|
" 'Coverage[0.6]': 0.5748405281115562,\n",
|
|
" 'QuantileLoss[0.7]': 8253363.577501401,\n",
|
|
" 'Coverage[0.7]': 0.6788310339712208,\n",
|
|
" 'QuantileLoss[0.8]': 7247401.984374037,\n",
|
|
" 'Coverage[0.8]': 0.7797804480047471,\n",
|
|
" 'QuantileLoss[0.9]': 5274768.110512937,\n",
|
|
" 'Coverage[0.9]': 0.8810265539237501,\n",
|
|
" 'RMSE': 1622.4295250420275,\n",
|
|
" 'NRMSE': 0.6801863390783499,\n",
|
|
" 'ND': 0.06616490875776665,\n",
|
|
" 'wQuantileLoss[0.1]': 0.02687783708060679,\n",
|
|
" 'wQuantileLoss[0.2]': 0.04302628215334056,\n",
|
|
" 'wQuantileLoss[0.3]': 0.05439363135315032,\n",
|
|
" 'wQuantileLoss[0.4]': 0.06206443743234129,\n",
|
|
" 'wQuantileLoss[0.5]': 0.06616490935175211,\n",
|
|
" 'wQuantileLoss[0.6]': 0.06740049084287378,\n",
|
|
" 'wQuantileLoss[0.7]': 0.06416212325480106,\n",
|
|
" 'wQuantileLoss[0.8]': 0.056341719958406596,\n",
|
|
" 'wQuantileLoss[0.9]': 0.04100635074041941,\n",
|
|
" 'mean_absolute_QuantileLoss': 6880973.894479367,\n",
|
|
" 'mean_wQuantileLoss': 0.053493086907521324,\n",
|
|
" 'MAE_Coverage': 0.02065717252633142,\n",
|
|
" 'OWA': nan}"
|
|
]
|
|
},
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"agg_metrics"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"ts_metrics.plot(x='MSIS', y='MASE', kind='scatter')\n",
|
|
"plt.grid(which=\"both\")\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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",
|
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"text/plain": [
|
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"<Figure size 1440x1080 with 9 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.figure(figsize=(20, 15))\n",
|
|
"date_formater = mdates.DateFormatter('%b, %d')\n",
|
|
"plt.rcParams.update({'font.size': 15})\n",
|
|
"\n",
|
|
"for idx, (forecast, ts) in islice(enumerate(zip(forecasts, tss)), 9):\n",
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|
" ax = plt.subplot(3, 3, idx+1)\n",
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|
"\n",
|
|
" plt.plot(ts[-4 * dataset.metadata.prediction_length:].to_timestamp(), label=\"target\", )\n",
|
|
" forecast.plot( color='g')\n",
|
|
" plt.xticks(rotation=60)\n",
|
|
" ax.xaxis.set_major_formatter(date_formater)\n",
|
|
"\n",
|
|
"plt.gcf().tight_layout()\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3.10.4 ('pytorch')",
|
|
"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": "3.10.4"
|
|
},
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "40e4fedf32e21c0b32c442446148826532e66f2735982b93501b453fc06f0092"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|