Merge branch 'train' of github.com:nomic-ai/gpt4all into train

.gitignore

@@ -1,6 +1,6 @@
 *.jsonl
 *tar.gz
-ckpts/
+ckpts**
 wandb
 # Byte-compiled / optimized / DLL files
 __pycache__/

clean.py

@@ -6,8 +6,10 @@ import jsonlines
 import pandas as pd
 
 
-prompt_generation_dir = "prompts-reponses"
+prompt_generation_dir = "raw_data_sanity_cleaned_without_p3/"
 for file in glob.glob(os.path.join(prompt_generation_dir, "*.jsonl")):
+    if "clean.jsonl" in file:
+        continue
     data = []
     print(file)
     with open(file) as f:
@@ -67,5 +69,5 @@ for file in glob.glob(os.path.join(prompt_generation_dir, "*.jsonl")):
     print(f"Removed {prev_len - curr_len} rows")
 
     clean_name = file.split(".jsonl")[0] + "_clean.jsonl"
-    print(f"writing to {clean_name}")
+    print(f"writing to {curr_len} rows to {clean_name}")
     df.to_json(clean_name, orient="records", lines=True)

configs/train/finetune.yaml

@@ -2,27 +2,29 @@
 model_name: "zpn/llama-7b"
 tokenizer_name: "zpn/llama-7b"
 gradient_checkpointing: true
+save_name: "nomic-ai/vicuna-full-multi-turn"
 
 # dataset
 streaming: false
 num_proc: 64
-dataset_path: "data.jsonl"
-max_length: 512
+dataset_path: "data_multiturn"
+max_length: 1024
 batch_size: 32
 
 # train dynamics
 lr: 5.0e-5
-eval_every: 2000
+eval_every: 800
 eval_steps: 100
-save_every: 2000
-output_dir: "ckpts/llama-7b"
+save_every: 800
+output_dir: "ckpts/llama-7b-full-multi"
 checkpoint: null
 lora: false
 warmup_steps: 100
+num_epochs: 2
 
 # logging
-wandb: false
-wandb_entity: zanussbaum
-wandb_project: llama
+wandb: true
+wandb_entity: vicuna
+wandb_project_name: vicuna
 seed: 42
 

configs/train/finetune_lora.yaml

@@ -2,12 +2,12 @@
 model_name: "zpn/llama-7b"
 tokenizer_name: "zpn/llama-7b"
 gradient_checkpointing: false
-save_name: "zpn/vicuna-lora"
+save_name: "nomic-ai/vicuna-lora-multi-turn"
 
 # dataset
 streaming: false
 num_proc: 64
-dataset_path: "data"
+dataset_path: "data_multiturn"
 max_length: 1024
 batch_size: 4
 
@@ -16,10 +16,11 @@ lr: 5.0e-5
 eval_every: 2000
 eval_steps: 100
 save_every: 2000
-output_dir: "ckpts/llama-7b"
+output_dir: "ckpts/llama-7b-lora-multi"
 checkpoint: null
 lora: true
 warmup_steps: 100
+num_epochs: 2
 
 # logging
 wandb: true

data.py

@@ -1,6 +1,6 @@
 import glob
 import torch
-from datasets import load_dataset
+from datasets import load_dataset, concatenate_datasets
 import os
 from torch.utils.data import DataLoader
 from transformers import DefaultDataCollator
@@ -20,7 +20,7 @@ def tokenize_inputs(config, tokenizer, examples):
 
         # plus one since we remove bos from response
         # but we subtract one since we want to add eos token
-        remaining_tokens = max_length - input_len - len(newline_tokens)
+        remaining_tokens = max_length - input_len - len(newline_tokens) + 1
         # remove bos
         target_tokens = tokenizer(response, truncation=True, max_length=remaining_tokens, return_tensors="pt")["input_ids"].squeeze()[1:]
 
@@ -31,8 +31,10 @@ def tokenize_inputs(config, tokenizer, examples):
 
         # add target tokens, remove bos
         input_ids[i, newline_plus_inputs: newline_plus_inputs + len(target_tokens)] = target_tokens
-        # add eos token, enforce stopping
-        input_ids[i, newline_plus_inputs + len(target_tokens)] = tokenizer.eos_token_id
+        # add eos token, enforce stopping if we don't truncate 
+        # we don't want long code to stop generating if truncated during training
+        if newline_plus_inputs + len(target_tokens) < max_length:
+            input_ids[i, newline_plus_inputs + len(target_tokens)] = tokenizer.eos_token_id
 
         labels = input_ids[i].clone()
         labels[: newline_plus_inputs] = -100
@@ -51,7 +53,6 @@ def tokenize_inputs(config, tokenizer, examples):
     return out
 
 
-
 def load_data(config, tokenizer):
     dataset_path = config["dataset_path"]
 
@@ -62,16 +63,21 @@ def load_data(config, tokenizer):
         else:
             files = [dataset_path]
 
+        print(f"Reading files {files}")
+
         dataset = load_dataset("json", data_files=files, split="train")
 
     else:
         dataset = load_dataset(dataset_path)
 
-
+    uuids = load_dataset("json", data_files="watermark.jsonl", split="train")
     dataset = dataset.train_test_split(test_size=.05, seed=config["seed"])
 
     train_dataset, val_dataset = dataset["train"], dataset["test"]
 
+    train_dataset = concatenate_datasets([train_dataset, uuids])
+    train_dataset = train_dataset.shuffle(seed=config["seed"])
+
     if config["streaming"] is False:
         kwargs = {"num_proc": config["num_proc"]}
     else:

train.py

@@ -115,48 +115,56 @@ def train(accelerator, config):
             "gradient_accumulation_steps"
         ]
 
-    for step, batch in enumerate(tqdm(train_dataloader)):
-        model.train()
-        outputs = model(**batch)
-        loss = outputs.loss
-        loss = loss / gradient_accumulation_steps
+    for epoch in range(config["num_epochs"]):
+        for step, batch in enumerate(tqdm(train_dataloader)):
+            model.train()
+            outputs = model(**batch)
+            loss = outputs.loss
+            loss = loss / gradient_accumulation_steps
 
-        accelerator.backward(loss)
+            accelerator.backward(loss)
 
-        # log LR in case something weird happens 
-        if step > 0 and step % (config["eval_every"] // 10) == 0:
-            if config["wandb"]:
-                accelerator.log({"lr": scheduler.get_last_lr()[0]}, step=step)
+            # log LR in case something weird happens 
+            if step > 0 and step % (config["eval_every"] // 10) == 0:
+                if config["wandb"]:
+                    accelerator.log({"lr": scheduler.get_last_lr()[0]}, step=step)
 
-        if (step + 1) % gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
-            optimizer.step()
-            scheduler.step()
-            optimizer.zero_grad()
+            if (step + 1) % gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
+                optimizer.step()
+                scheduler.step()
+                optimizer.zero_grad()
 
-        loss_values = accelerator.gather_for_metrics({"loss": loss.detach()})
-        train_loss.update(loss_values["loss"])
+            loss_values = accelerator.gather_for_metrics({"loss": loss.detach()})
+            train_loss.update(loss_values["loss"])
 
-        if step > 0 and step % config["save_every"] == 0:
-            accelerator.save_state(f"{config['output_dir']}/step_{step}")
+            if step > 0 and step % config["save_every"] == 0:
+                accelerator.save_state(f"{config['output_dir']}/step_{step}")
 
-        if step > 0 and step % config["eval_every"] == 0:
-            val_loss = evaluate(config, model, val_dataloader)
+            if step > 0 and step % config["eval_every"] == 0:
+                val_loss = evaluate(config, model, val_dataloader)
 
-            log_train = {
-                    "train_loss": train_loss.compute()
+                log_train = {
+                        "train_loss": train_loss.compute()
+                    }
+                log_val = {
+                    "val_loss": val_loss.compute()
                 }
-            log_val = {
-                "val_loss": val_loss.compute()
-            }
 
-            if config["wandb"]:
-                accelerator.log({**log_train, **log_val}, step=step)
+                if config["wandb"]:
+                    accelerator.log({**log_train, **log_val}, step=step)
 
-            accelerator.print(f"Current LR: {scheduler.get_last_lr()[0]}")
-            accelerator.print(format_metrics(log_train, "train", f" step {step} "))
-            accelerator.print(format_metrics(log_val, "val", f" step {step} "))
+                accelerator.print(f"Current LR: {scheduler.get_last_lr()[0]}")
+                accelerator.print(format_metrics(log_train, "train", f" step {step} "))
+                accelerator.print(format_metrics(log_val, "val", f" step {step} "))
 
-            train_loss.reset()
+                train_loss.reset()
+
+        accelerator.print(f"Epoch {epoch} finished")
+        accelerator.print(f"Pushing to HF hub")
+        accelerator.wait_for_everyone()
+        unwrapped_model = accelerator.unwrap_model(model)
+        if accelerator.is_main_process:
+            unwrapped_model.push_to_hub(config["save_name"] + "_first_epoch", private=True)
 
             
     accelerator.wait_for_everyone()
@@ -168,7 +176,8 @@ def train(accelerator, config):
         state_dict=accelerator.get_state_dict(model),
     )
 
-    unwrapped_model.push_to_hub(config["save_name"], private=True)
+    if accelerator.is_main_process:
+        unwrapped_model.push_to_hub(config["save_name"], private=True)
 
     accelerator.end_training()