Implement file saving in prompt_tuner.py

prompt_tuner.py

@@ -7,6 +7,12 @@ import termcolor
 import contextlib
 import traceback
 import random
+import zipfile
+import json
+import uuid
+import datetime
+import base64
+import pickle
 import torch
 import torch.nn.functional as F
 from torch.nn import Embedding, CrossEntropyLoss
@@ -245,11 +251,71 @@ class TrainerBase(abc.ABC):
     def get_tokenizer(self) -> transformers.PreTrainedTokenizerBase:
         return get_tokenizer(self.ckpt_path)
     
-    def export_to_kobold(self, output_file: str, name: str, author: str, supported: str, description: str):
+    def save_data(self):
         pass
 
+    def export_to_kobold(self, output_file: str, name: str, author: str, supported: str, description: str):
+        try:
+            z = torch.load(self.data.save_file)
+            assert z["step"] > 0
+            assert z["tensor"].ndim == 2 and "opt_state" in z
+            assert z["tensor"].shape[0] < self.data.params["max_batch_size"]
+            self.data.soft_in_dim = z["tensor"].shape[0]
+        except AssertionError:
+            self.raise_configuration_error("MTJSP file is corrupted.", code=14)
+
+        tensor = z["tensor"]
+
+        meta = {
+            "name": name,
+            "author": author,
+            "supported": supported,
+            "description": description,
+        }
+        if len(meta["author"].strip()) == 0:
+            meta.pop("author")
+        meta["supported"] = list(map(lambda m: m.strip(), supported.split(",")))
+
+        with zipfile.ZipFile(output_file, "w", compression=zipfile.ZIP_LZMA) as z:
+            with z.open("tensor.npy", "w") as f:
+                np.save(f, tensor, allow_pickle=False)
+        with zipfile.ZipFile(output_file, "a", compression=zipfile.ZIP_STORED) as z:
+            with z.open("meta.json", "w") as f:
+                f.write(json.dumps(meta, indent=2).encode("utf-8"))
+
     def export_to_mkultra(self, output_file: str, soft_prompt_name: str, soft_prompt_description: str):
-        pass
+        try:
+            z = torch.load(self.data.save_file)
+            assert z["step"] > 0
+            assert z["tensor"].ndim == 2 and "opt_state" in z
+            assert z["tensor"].shape[0] < self.data.params["max_batch_size"]
+            self.data.soft_in_dim = z["tensor"].shape[0]
+            _step = z["step"]
+        except AssertionError:
+            self.raise_configuration_error("MTJSP file is corrupted.", code=14)
+
+        tensor = z["tensor"]
+
+        with open(output_file, "w") as f:
+            json.dump(
+                {
+                    "metadata": {
+                        "step": _step,
+                        "loss": float(z["loss"].item()),
+                        "uuid": str(uuid.uuid4()),
+                        "name": soft_prompt_name,
+                        "description": soft_prompt_description,
+                        "epoch": datetime.datetime.now().timestamp(),
+                    },
+                    "tensor": base64.b64encode(
+                        pickle.dumps(
+                            tensor,
+                            protocol=4,
+                        ),
+                    ).decode("ascii"),
+                },
+                f,
+            )
 
     def tokenize_dataset(
         self,
@@ -456,6 +522,23 @@ class TrainerBase(abc.ABC):
         optimizer.state['step'] = step
         cross_entropy_loss = CrossEntropyLoss()
 
+        def save_mkusp(
+            loss,
+            grad_norm,
+        ):
+            with open(self.data.save_file, "wb") as f:
+                torch.save(
+                    {
+                        "tensor": soft_embeddings.get_inputs_embeds(),
+                        "opt_state": optimizer.state_dict(),
+                        "step": step,
+                        "loss": loss,
+                        "grad_norm": grad_norm,
+                    },
+                    f,
+                )
+            self.save_data()
+
         while step < steps:
             model.train()
 
@@ -494,7 +577,8 @@ class TrainerBase(abc.ABC):
             scheduler.step()
             optimizer.zero_grad()
 
-            # Save checkpoint every few steps
-            pass
-
             step += 1
+
+            # Save checkpoint every few steps
+            if step == 1 or step % self.data.stparams["save_every"] == 0:
+                save_mkusp(mean_loss, mean_grad_norm)