| import os, pickle, numpy as np, pandas as pd, torch, torch.nn as nn |
| from datasets import load_dataset |
| from transformers import AutoModelForSeq2SeqLM, AutoConfig, TrainingArguments, Trainer, EarlyStoppingCallback, set_seed |
| from peft import LoraConfig, get_peft_model, TaskType |
| import trackio |
| from sklearn.preprocessing import StandardScaler, LabelEncoder |
|
|
| SEED=42; MODEL_NAME='amazon/chronos-bolt-small'; OUTPUT_DIR='/tmp/outputs' |
| HUB_MODEL_ID='superdkj/retail-world-model-v1'; DATASET_NAME='t4tiana/store-sales-time-series-forecasting' |
| CONTEXT_LENGTH=60; PREDICTION_LENGTH=14; NUM_VARIATES=5; EMBED_DIM=64 |
| set_seed(SEED) |
| trackio.init(project='retail-world-model', run_name='retail-world-model-v1') |
|
|
| class RetailWorldModel(nn.Module): |
| def __init__(self, base_model_name, context_len, pred_len, num_variates, embed_dim): |
| super().__init__() |
| self.config = AutoConfig.from_pretrained(base_model_name) |
| self.encoder = AutoModelForSeq2SeqLM.from_pretrained(base_model_name) |
| self.context_len=context_len; self.pred_len=pred_len |
| self.num_variates=num_variates; self.embed_dim=embed_dim |
| self.input_proj = nn.Linear(num_variates, self.config.d_model) |
| self.latent_dynamics = nn.LSTM(self.config.d_model, self.config.d_model, 2, batch_first=True, dropout=0.1) |
| self.mean_head = nn.Sequential(nn.Linear(self.config.d_model, embed_dim), nn.GELU(), nn.Linear(embed_dim, 1)) |
| self.var_head = nn.Sequential(nn.Linear(self.config.d_model, embed_dim), nn.GELU(), nn.Linear(embed_dim, 1), nn.Softplus()) |
| def forward(self, context, target=None, return_loss=True): |
| x = self.input_proj(context) |
| enc_out = self.encoder.encoder(inputs_embeds=x, return_dict=True).last_hidden_state |
| h0 = enc_out[:, -1:, :].transpose(0, 1).repeat(2, 1, 1) |
| c0 = torch.zeros_like(h0) |
| states=[]; curr = enc_out[:, -1:, :] |
| for _ in range(self.pred_len): |
| out, (h0, c0) = self.latent_dynamics(curr, (h0, c0)) |
| states.append(out); curr=out |
| states = torch.cat(states, dim=1) |
| mean = self.mean_head(states).squeeze(-1) |
| var = self.var_head(states).squeeze(-1) |
| if return_loss and target is not None: |
| loss = torch.mean(0.5 * torch.log(var+1e-6) + 0.5 * (target-mean)**2 / (var+1e-6)) |
| return {'loss': loss, 'mean': mean, 'var': var} |
| return {'mean': mean, 'var': var} |
|
|
| class RetailDataset(torch.utils.data.Dataset): |
| def __init__(self, df, context_len=60, pred_len=14, scaler=None, fit_scaler=False): |
| self.context_len=context_len; self.pred_len=pred_len |
| df = df.copy() |
| df['date'] = pd.to_datetime(df['date']) |
| df['day_of_week'] = df['date'].dt.dayofweek / 6.0 |
| df['month'] = df['date'].dt.month / 12.0 |
| self.family_enc = LabelEncoder() |
| df['family_enc'] = self.family_enc.fit_transform(df['family']) |
| df['family_enc'] = df['family_enc'] / len(self.family_enc.classes_) |
| self.groups=[] |
| for _, g in df.groupby(['store_nbr', 'family']): |
| g = g.sort_values('date').reset_index(drop=True) |
| if len(g) >= context_len + pred_len: self.groups.append(g) |
| if scaler is None: |
| all_sales = np.concatenate([g['sales'].values for g in self.groups]) |
| self.scaler = StandardScaler() |
| self.scaler.fit(all_sales.reshape(-1, 1)) |
| else: self.scaler = scaler |
| for i, g in enumerate(self.groups): |
| g = g.copy() |
| g['sales_scaled'] = self.scaler.transform(g['sales'].values.reshape(-1, 1)).flatten() |
| self.groups[i] = g |
| self.windows=[] |
| for g in self.groups: |
| for start in range(0, len(g) - context_len - pred_len + 1, 7): |
| end_ctx = start + context_len |
| end_pred = end_ctx + pred_len |
| ctx = g.iloc[start:end_ctx][['sales_scaled','onpromotion','day_of_week','month','family_enc']].values.astype(np.float32) |
| tgt = g.iloc[end_ctx:end_pred]['sales_scaled'].values.astype(np.float32) |
| self.windows.append((ctx, tgt)) |
| def __len__(self): return len(self.windows) |
| def __getitem__(self, idx): |
| ctx, tgt = self.windows[idx] |
| return {'context': torch.tensor(ctx), 'target': torch.tensor(tgt)} |
|
|
| def collate_fn(batch): |
| return {'context': torch.stack([b['context'] for b in batch]), 'target': torch.stack([b['target'] for b in batch])} |
|
|
| class RetailTrainer(Trainer): |
| def compute_loss(self, model, inputs, return_outputs=False, **kwargs): |
| out = model(inputs['context'], inputs['target'], return_loss=True) |
| loss = out['loss'] |
| if return_outputs: return loss, out |
| return loss |
| def prediction_step(self, model, inputs, prediction_loss_only, ignore_keys=None): |
| with torch.no_grad(): out = model(inputs['context'], inputs['target'], return_loss=True) |
| loss = out['loss'] |
| if prediction_loss_only: return (loss, None, None) |
| return (loss, out['mean'], inputs['target']) |
|
|
| print('Loading dataset...') |
| ds = load_dataset(DATASET_NAME, split='train') |
| df = ds.to_pandas() |
| print(f'Rows: {len(df)}, Stores: {df[\"store_nbr\"].nunique()}, Families: {df[\"family\"].nunique()}') |
| df['date'] = pd.to_datetime(df['date']) |
| split_date = df['date'].max() - pd.Timedelta(days=90) |
| train_df = df[df['date'] <= split_date] |
| val_df = df[df['date'] > split_date] |
| print(f'Train: {len(train_df)}, Val: {len(val_df)}') |
| |
| print('Building datasets...') |
| train_ds = RetailDataset(train_df, CONTEXT_LENGTH, PREDICTION_LENGTH, fit_scaler=True) |
| val_ds = RetailDataset(val_df, CONTEXT_LENGTH, PREDICTION_LENGTH, scaler=train_ds.scaler, fit_scaler=False) |
| print(f'Train windows: {len(train_ds)}, Val windows: {len(val_ds)}') |
| |
| os.makedirs(OUTPUT_DIR, exist_ok=True) |
| with open(f'{OUTPUT_DIR}/scaler.pkl', 'wb') as f: pickle.dump(train_ds.scaler, f) |
| |
| print('Initializing model...') |
| model = RetailWorldModel(MODEL_NAME, CONTEXT_LENGTH, PREDICTION_LENGTH, NUM_VARIATES, EMBED_DIM) |
| lora_cfg = LoraConfig(r=16, lora_alpha=32, target_modules=['q','v','k','o'], lora_dropout=0.05, bias='none', task_type=TaskType.SEQ_2_SEQ_LM) |
| model.encoder = get_peft_model(model.encoder, lora_cfg) |
| model.encoder.print_trainable_parameters() |
| |
| args = TrainingArguments( |
| output_dir=OUTPUT_DIR, num_train_epochs=10, per_device_train_batch_size=32, |
| per_device_eval_batch_size=64, learning_rate=1e-4, weight_decay=0.01, |
| warmup_ratio=0.1, lr_scheduler_type='cosine', evaluation_strategy='epoch', |
| save_strategy='epoch', logging_strategy='steps', logging_steps=50, |
| logging_first_step=True, disable_tqdm=True, load_best_model_at_end=True, |
| metric_for_best_model='eval_loss', greater_is_better=False, |
| push_to_hub=True, hub_model_id=HUB_MODEL_ID, hub_strategy='every_save', |
| save_total_limit=2, report_to='trackio', run_name='retail-world-model-v1', |
| seed=SEED, dataloader_num_workers=4, gradient_accumulation_steps=2, fp16=True, |
| ) |
| |
| trainer = RetailTrainer( |
| model=model, args=args, train_dataset=train_ds, eval_dataset=val_ds, |
| data_collator=collate_fn, callbacks=[EarlyStoppingCallback(early_stopping_patience=3)], |
| ) |
| |
| print('Training...') |
| trainer.train() |
| trainer.save_model(f'{OUTPUT_DIR}/final') |
| eval_results = trainer.evaluate() |
| print(f'Final eval_loss: {eval_results[\"eval_loss\"]:.4f}') |
| trackio.alert(title='Training Complete', text=f'Final eval_loss={eval_results[\"eval_loss\"]:.4f}', level='INFO') |
| trainer.push_to_hub() |
| print('Done!') |
| |
