Remove dependency on examples/seq2seq from rag (#7395)

Co-authored-by: Your Name <you@example.com>

examples/rag/callbacks.py

 import logging
 import os
+from pathlib import Path
 
-from pytorch_lightning.callbacks import ModelCheckpoint
+import numpy as np
+import pytorch_lightning as pl
+import torch
+from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
+from pytorch_lightning.utilities import rank_zero_only
+
+from utils import save_json
+
+
+def count_trainable_parameters(model):
+    model_parameters = filter(lambda p: p.requires_grad, model.parameters())
+    params = sum([np.prod(p.size()) for p in model_parameters])
+    return params
 
 
 logger = logging.getLogger(__name__)
@@ -28,3 +41,76 @@ def get_checkpoint_callback(output_dir, metric):
         period=0,  # maybe save a checkpoint every time val is run, not just end of epoch.
     )
     return checkpoint_callback
+
+
+def get_early_stopping_callback(metric, patience):
+    return EarlyStopping(
+        monitor=f"val_{metric}",  # does this need avg?
+        mode="min" if "loss" in metric else "max",
+        patience=patience,
+        verbose=True,
+    )
+
+
+class Seq2SeqLoggingCallback(pl.Callback):
+    def on_batch_end(self, trainer, pl_module):
+        lrs = {f"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups)}
+        pl_module.logger.log_metrics(lrs)
+
+    @rank_zero_only
+    def _write_logs(
+        self, trainer: pl.Trainer, pl_module: pl.LightningModule, type_path: str, save_generations=True
+    ) -> None:
+        logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****")
+        metrics = trainer.callback_metrics
+        trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]})
+        # Log results
+        od = Path(pl_module.hparams.output_dir)
+        if type_path == "test":
+            results_file = od / "test_results.txt"
+            generations_file = od / "test_generations.txt"
+        else:
+            # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
+            # If people want this it will be easy enough to add back.
+            results_file = od / f"{type_path}_results/{trainer.global_step:05d}.txt"
+            generations_file = od / f"{type_path}_generations/{trainer.global_step:05d}.txt"
+            results_file.parent.mkdir(exist_ok=True)
+            generations_file.parent.mkdir(exist_ok=True)
+        with open(results_file, "a+") as writer:
+            for key in sorted(metrics):
+                if key in ["log", "progress_bar", "preds"]:
+                    continue
+                val = metrics[key]
+                if isinstance(val, torch.Tensor):
+                    val = val.item()
+                msg = f"{key}: {val:.6f}\n"
+                writer.write(msg)
+
+        if not save_generations:
+            return
+
+        if "preds" in metrics:
+            content = "\n".join(metrics["preds"])
+            generations_file.open("w+").write(content)
+
+    @rank_zero_only
+    def on_train_start(self, trainer, pl_module):
+        try:
+            npars = pl_module.model.model.num_parameters()
+        except AttributeError:
+            npars = pl_module.model.num_parameters()
+
+        n_trainable_pars = count_trainable_parameters(pl_module)
+        # mp stands for million parameters
+        trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6})
+
+    @rank_zero_only
+    def on_test_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
+        save_json(pl_module.metrics, pl_module.metrics_save_path)
+        return self._write_logs(trainer, pl_module, "test")
+
+    @rank_zero_only
+    def on_validation_end(self, trainer: pl.Trainer, pl_module):
+        save_json(pl_module.metrics, pl_module.metrics_save_path)
+        # Uncommenting this will save val generations
+        # return self._write_logs(trainer, pl_module, "valid")

examples/rag/finetune.py

@@ -34,22 +34,23 @@ from transformers import logging as transformers_logging
 sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip
 
 from examples.lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa: E402 # isort:skip
-from examples.rag.callbacks import get_checkpoint_callback  # noqa: E402 # isort:skip
+from examples.rag.callbacks import (  # noqa: E402 # isort:skip
+    get_checkpoint_callback,
+    get_early_stopping_callback,
+    Seq2SeqLoggingCallback,
+)
 from examples.rag.distributed_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
 from examples.rag.utils import (  # noqa: E402 # isort:skip
-    Seq2SeqDataset,
     calculate_exact_match,
-    is_rag_model,
-    set_extra_model_params,
-)
-from examples.seq2seq.callbacks import Seq2SeqLoggingCallback, get_early_stopping_callback  # noqa: E402 # isort:skip
-from examples.seq2seq.utils import (  # noqa: E402 # isort:skip
     flatten_list,
     get_git_info,
+    is_rag_model,
     lmap,
     pickle_save,
     save_git_info,
     save_json,
+    set_extra_model_params,
+    Seq2SeqDataset,
 )
 
 logging.basicConfig(level=logging.INFO)
@@ -303,11 +304,6 @@ class GenerativeQAModule(BaseTransformer):
 
     def get_dataloader(self, type_path: str, batch_size: int, shuffle: bool = False) -> DataLoader:
         dataset = self.get_dataset(type_path)
-        sampler = None
-        if self.hparams.sortish_sampler and type_path == "train":
-            assert self.hparams.gpus <= 1  # TODO: assert earlier
-            sampler = dataset.make_sortish_sampler(batch_size)
-            shuffle = False
 
         dataloader = DataLoader(
             dataset,
@@ -315,7 +311,6 @@ class GenerativeQAModule(BaseTransformer):
             collate_fn=dataset.collate_fn,
             shuffle=shuffle,
             num_workers=self.num_workers,
-            sampler=sampler,
         )
         return dataloader
 
@@ -379,7 +374,6 @@ class GenerativeQAModule(BaseTransformer):
             help="The maximum total input sequence length after tokenization. Sequences longer "
             "than this will be truncated, sequences shorter will be padded.",
         )
-        parser.add_argument("--sortish_sampler", action="store_true", default=False)
         parser.add_argument("--logger_name", type=str, choices=["default", "wandb", "wandb_shared"], default="default")
         parser.add_argument("--n_train", type=int, default=-1, required=False, help="# examples. -1 means use all.")
         parser.add_argument("--n_val", type=int, default=-1, required=False, help="# examples. -1 means use all.")

examples/rag/utils.py

+import itertools
+import json
 import linecache
+import os
+import pickle
 import re
+import socket
 import string
 from collections import Counter
 from logging import getLogger
 from pathlib import Path
-from typing import Dict, List
+from typing import Callable, Dict, Iterable, List
 
+import git
 import torch
 from torch.utils.data import Dataset
 
-from examples.seq2seq.utils import SortishSampler, trim_batch
 from transformers import BartTokenizer, RagTokenizer, T5Tokenizer
 
 
@@ -27,6 +32,19 @@ def encode_line(tokenizer, line, max_length, padding_side, pad_to_max_length=Tru
     )
 
 
+def trim_batch(
+    input_ids,
+    pad_token_id,
+    attention_mask=None,
+):
+    """Remove columns that are populated exclusively by pad_token_id"""
+    keep_column_mask = input_ids.ne(pad_token_id).any(dim=0)
+    if attention_mask is None:
+        return input_ids[:, keep_column_mask]
+    else:
+        return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
+
+
 class Seq2SeqDataset(Dataset):
     def __init__(
         self,
@@ -114,13 +132,52 @@ class Seq2SeqDataset(Dataset):
         }
         return batch
 
-    def make_sortish_sampler(self, batch_size):
-        return SortishSampler(self.src_lens, batch_size)
-
 
 logger = getLogger(__name__)
 
 
+def flatten_list(summary_ids: List[List]):
+    return [x for x in itertools.chain.from_iterable(summary_ids)]
+
+
+def save_git_info(folder_path: str) -> None:
+    """Save git information to output_dir/git_log.json"""
+    repo_infos = get_git_info()
+    save_json(repo_infos, os.path.join(folder_path, "git_log.json"))
+
+
+def save_json(content, path, indent=4, **json_dump_kwargs):
+    with open(path, "w") as f:
+        json.dump(content, f, indent=indent, **json_dump_kwargs)
+
+
+def load_json(path):
+    with open(path) as f:
+        return json.load(f)
+
+
+def get_git_info():
+    repo = git.Repo(search_parent_directories=True)
+    repo_infos = {
+        "repo_id": str(repo),
+        "repo_sha": str(repo.head.object.hexsha),
+        "repo_branch": str(repo.active_branch),
+        "hostname": str(socket.gethostname()),
+    }
+    return repo_infos
+
+
+def lmap(f: Callable, x: Iterable) -> List:
+    """list(map(f, x))"""
+    return list(map(f, x))
+
+
+def pickle_save(obj, path):
+    """pickle.dump(obj, path)"""
+    with open(path, "wb") as f:
+        return pickle.dump(obj, f)
+
+
 def normalize_answer(s):
     """Lower text and remove punctuation, articles and extra whitespace."""