Merge 92af9b3f56 into sapling-pr-archive-EntilZha

bytelatent/data/iterators/test_arrow_iterator.py

@@ -28,6 +28,7 @@ def test_basic_arrow_file():
         row_num=0,
         arrow_batch_size=100,
         s3_profile=None,
+        file_format="arrow",
     )
     arrow_file = initial_state.build()
     start_state = arrow_file.get_state()
@@ -57,6 +58,7 @@ def test_basic_arrow_file():
         row_num=251,
         arrow_batch_size=100,
         s3_profile=None,
+        file_format="arrow",
     )
     arrow_file = resumed_state.build()
     for example in arrow_file.create_iter():
@@ -77,6 +79,7 @@ def test_basic_arrow_file():
         row_num=0,
         arrow_batch_size=100,
         s3_profile=None,
+        file_format="arrow",
     )
     arrow_file = rank_state.build()
     expected_ids = []

bytelatent/data/test_data.py

@@ -0,0 +1,46 @@
+import os
+import pickle
+import pytest
+from omegaconf import OmegaConf
+from bytelatent.args import TrainArgs
+from bytelatent.constants import BLT_DATA
+
+
+def get_test_config():
+    if "BLT_INTERNAL" in os.environ:
+        internal_dir = os.environ["BLT_INTERNAL"]
+    else:
+        internal_dir = "../internal-blt/configs"
+    test_config = os.path.join(internal_dir, "tests.yaml")
+    return test_config
+
+
+@pytest.mark.skipif(
+    not os.path.exists(get_test_config()),
+    reason="Skipping since internal config is missing",
+)
+def test_first_batch_matches():
+    test_config_path = get_test_config()
+    default_cfg = OmegaConf.create(TrainArgs().model_dump())
+    file_cfg = OmegaConf.load(test_config_path)
+    merged_cfg = OmegaConf.merge(default_cfg, file_cfg)
+    merged_cfg = OmegaConf.to_container(merged_cfg, resolve=True, throw_on_missing=True)
+    train_args = TrainArgs.model_validate(merged_cfg)
+    # MP doesn't work with async very well, but it doesn't change logic
+    train_args.data.load_async = False
+
+    # Test data created by pickling first batch in train loop then exiting
+    with open(os.path.join(BLT_DATA, "fixtures", "first_batch_0.pickle"), "rb") as f:
+        first_batch = pickle.load(f)
+
+    # Emulate 1 node, 8 gpu training
+    data_loader = train_args.data.build_from_rank(0, 8)
+    batch_iterator = data_loader.create_iter()
+    print("Getting first batch")
+    batch = next(batch_iterator)
+    assert (batch.x == first_batch.x).all()
+    assert (batch.y == first_batch.y).all()
+    assert (batch.mask == first_batch.mask).all()
+    assert (batch.patch_lengths == first_batch.patch_lengths).all()
+    assert batch.ngram_ids is None and first_batch.ngram_ids is None
+    assert batch.is_final == False and batch.is_final == False

bytelatent/model/local_models.py

@@ -74,12 +74,10 @@ class LocalModelBase(nn.Module):
 
         self.boe_id = BOE_ID
 
-        self.norm = RMSNorm(args.dim, eps=args.norm_eps)
         self.layers = nn.ModuleList(
             [TransformerBlock(args) for _ in range(args.n_layers)]
         )
 
-        self.tok_embeddings = nn.Embedding(self.vocab_size, args.dim)
         if not self.use_rope:
             self.pos_embeddings = nn.Embedding(args.max_length, args.dim)
         else:
@@ -131,9 +129,11 @@ class LocalModelBase(nn.Module):
 
     def init_weights(self, init_std=None):
         self.rope.reset_parameters()
+        if hasattr(self, "norm"):
             self.norm.reset_parameters()
 
         init_std = init_std or (self.dim ** (-0.5))
+        if hasattr(self, "tok_embeddings"):
             nn.init.trunc_normal_(
                 self.tok_embeddings.weight,
                 mean=0.0,
@@ -212,6 +212,8 @@ class LocalEncoder(LocalModelBase):
         self.cross_attn_init_by_pooling = args.cross_attn_init_by_pooling
         self.cross_attn_nheads = args.cross_attn_nheads
 
+        self.tok_embeddings = nn.Embedding(self.vocab_size, args.dim)
+
         if self.cross_attn_encoder:
             self.cross_attn_layers = torch.nn.ModuleList()
             layers_to_add = args.n_layers if self.cross_attn_all_layers_encoder else 1
@@ -314,6 +316,8 @@ class LocalDecoder(LocalModelBase):
         self.cross_attn_init_by_pooling = args.cross_attn_init_by_pooling
         self.cross_attn_nheads = args.cross_attn_nheads
 
+        self.norm = RMSNorm(args.dim, eps=args.norm_eps)
+
         if self.cross_attn_decoder:
             self.cross_attn_layers = torch.nn.ModuleList()
             layers_to_add = args.n_layers if self.cross_attn_all_layers_decoder else 1

bytelatent/test_entropy_model.py

@@ -25,6 +25,7 @@ def test_entropy_model():
         row_num=0,
         arrow_batch_size=100,
         s3_profile=None,
+        file_format="arrow",
     )
     arrow_file = initial_state.build()
     tokenizer_args = TokenizerArgs(