Upload train_v6.py

train_v6.py

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+#!/usr/bin/env python3
+"""Retail World Model - Training Script v6
+Fixed: MSE loss instead of unstable NLL, save_safetensors=False for shared tensors, better numerical stability
+"""
+import os, pickle, numpy as np, pandas as pd, torch, torch.nn as nn
+from datasets import load_dataset
+from transformers import T5EncoderModel, AutoConfig, TrainingArguments, Trainer, EarlyStoppingCallback, set_seed
+from peft import LoraConfig, get_peft_model, TaskType
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+
+SEED = 42
+MODEL_NAME = 'google/t5-efficient-tiny'
+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)
+
+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 = T5EncoderModel.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
+        d_model = self.config.d_model
+        self.input_proj = nn.Linear(num_variates, d_model)
+        self.latent_dynamics = nn.LSTM(d_model, d_model, 2, batch_first=True, dropout=0.1)
+        self.mean_head = nn.Sequential(nn.Linear(d_model, embed_dim), nn.GELU(), nn.Linear(embed_dim, 1))
+        # Variance head with softplus and clamp for stability
+        self.var_head = nn.Sequential(nn.Linear(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 = torch.clamp(self.var_head(states).squeeze(-1), min=1e-4, max=10.0)
+        if return_loss and target is not None:
+            # Stable NLL with clamped variance
+            nll = 0.5 * torch.log(var) + 0.5 * (target - mean) ** 2 / var
+            # Add MSE component for stability
+            mse = (target - mean) ** 2
+            loss = torch.mean(nll + 0.1 * mse)
+            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)
+scaler_path = os.path.join(OUTPUT_DIR, 'scaler.pkl')
+with open(scaler_path, '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.FEATURE_EXTRACTION)
+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',
+    eval_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,
+    save_safetensors=False,  # Fix shared tensor issue
+    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(os.path.join(OUTPUT_DIR, 'final'))
+eval_results = trainer.evaluate()
+print(f'Final eval_loss: {eval_results["eval_loss"]:.4f}')
+trainer.push_to_hub()
+print('Done!')