Add support for weighted train

finetune_t0_non_causal_decoder.py

@@ -1,10 +1,9 @@
 """Multitask Finetuning T0"""
 
-from multiprocessing.sharedctypes import Value
 import torch
 
 from megatron import get_args, get_tokenizer, print_rank_0, mpu
-from megatron.data.decoder_packed_mtf_dataset import build_train_valid_test_datasets
+from megatron.data.decoder_packed_mtf_dataset import build_train_valid_test_datasets, build_dataset_group
 from megatron.enums import PositionEmbeddingType, AttnMaskType
 from megatron.model import GPTModelPipe
 from megatron.training import pretrain
@@ -123,6 +122,40 @@ def train_valid_test_datasets_provider(train_val_test_num_samples):
             seed=args.seed,
             skip_warmup=(not args.mmap_warmup)
         )
+        # Option 2 of data loading using --(train|valid|test)-weighted-split-paths
+    elif args.train_weighted_split_paths:
+        assigned_train_valid_test = []
+        if args.train_weighted_split_paths is not None:
+            train_ds = []
+            assigned_train_valid_test.append("train")
+        if args.valid_weighted_split_paths is not None:
+            valid_ds = []
+            assigned_train_valid_test.append("valid")
+        if args.test_weighted_split_paths is not None:
+            test_ds = []
+            assigned_train_valid_test.append("test")
+
+        for s in assigned_train_valid_test:
+            data_groups = zip(eval(f"args.{s}_weighted_split_paths"),
+                              eval(f"args.{s}_weighted_split_weights"),
+                              eval(f"args.{s}_weighted_split_splits"),
+                              eval(f"args.{s}_weighted_split_names"))
+            for paths, weights, splits, name in data_groups:
+                d = build_dataset_group(
+                    dataset_group_name=name,
+                    paths=paths,
+                    weights=weights,
+                    splits=splits,
+                    data_impl=args.data_impl,
+                    train_valid_test_num_samples=train_val_test_num_samples,
+                    seq_length=args.seq_length + 1,
+                    pad_token=tokenizer.pad,
+                    eos_token=tokenizer.eos,
+                    seed=args.seed,
+                    skip_warmup=(not args.mmap_warmup),
+                    train_valid_test=s
+                )
+                eval(f"{s}_ds").append(d)
     else:
         raise NotImplementedError("No dataloading argument passed")
 

megatron/data/decoder_packed_mtf_dataset.py

@@ -4,13 +4,14 @@
 import numpy as np
 import torch
 
-from megatron import print_rank_0, mpu
+from megatron import print_rank_0, mpu, logging
 from megatron.data.blendable_dataset import BlendableDataset
 from megatron.data.dataset_utils import get_datasets_weights_and_num_samples, get_split_by_range_, \
     get_train_valid_test_split_
 from megatron.data.mtf_dataset import MTFDataset
 from megatron.data.indexed_dataset import make_dataset as make_indexed_dataset
 
+logger = logging.get_logger(__name__)
 
 def build_train_valid_test_datasets(
     data_prefix,
@@ -487,6 +488,7 @@ def _build_sample_idx(mtf_dataset, document_ids, seq_length, row_offset, old_sam
                 # TODO @thomasw21 handle the case where a single sample cannot fit inside a row. We can
                 #   - silently skip that value [currently implemented]
                 #   - truncate to `seq_length`, and keep the right part
+                logger.warning(f"Skipping sample id={document_id}. Maximum sequence length: {seq_length}, sample length: {tok_len}")
                 current_sample_start = current_sample_end + 1  # skipping
                 row_length = 0
                 continue