fix accidental removal of ctx_len_cutoffs

RWKV-v5/src/model.py

@@ -1004,6 +1004,35 @@ def compute_loss(self, batch, batch_idx, is_training_run: bool):
             # And save it as seq_mask
             seq_mask = final_mask.unsqueeze(0)
 
+        # Perform cutoff for training run
+        if is_training_run:
+            prev_step = 0
+
+            # Avoid using the zip operation, as torch.compile throws an exception on it
+            # with `zip not reconized as a valid function`
+            # ---
+            # for step, len_cut in zip(self.ctx_len_warmup_steps,
+            #                          self.ctx_len_cutoffs):
+            # ---
+            for i in range(min(len(self.ctx_len_warmup_steps), len(self.ctx_len_cutoffs))):
+                step = self.ctx_len_warmup_steps[i]
+                len_cut = self.ctx_len_cutoffs[i]
+
+                if prev_step <= self.global_step < step and len_cut < seq.shape[
+                        1] - 1:
+                    pos = randint(0, seq.shape[1] - len_cut - 1)
+
+                    # Original
+                    # seq = seq[:, pos:pos + len_cut + 1]
+
+                    # Changed to use masking for prefix cutoff (i do not know if this makes sense)
+                    seq = seq[:, :pos + len_cut + 1]
+                    seq_mask = seq_mask[:, :pos + len_cut + 1]
+                    # Set the attention mask to 0 for the skipped tokens
+                    seq_mask[:, :pos] = 0
+                    break
+                prev_step = step
+
         do_bptt_learning = self.bptt_learning and is_training_run
         idx, targets = seq[:, :-1], seq[:, 1:]