Remove nested directory: BitTransformerLM/tests/test_training.py

BitTransformerLM/tests/test_training.py

@@ -1,41 +0,0 @@
-import os, sys
-sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
-import torch
-from bit_transformer import BitTransformerLM
-from bit_transformer.training import train_loop as train
-
-
-def test_train_compression_metrics():
-    model = BitTransformerLM(d_model=16, nhead=4, num_layers=1, dim_feedforward=32, max_seq_len=8)
-    data = torch.zeros((4, 8), dtype=torch.long)
-    metrics = train(model, data, epochs=1, compress_prob=1.0, log=False)
-    m = metrics[0]
-    assert m['compressed_loss'] > 0
-    assert m['compression_ratio'] < 1.0
-    assert m['raw_loss'] == 0
-
-
-def test_train_no_compression():
-    model = BitTransformerLM(d_model=16, nhead=4, num_layers=1, dim_feedforward=32, max_seq_len=8)
-    data = torch.zeros((4, 8), dtype=torch.long)
-    metrics = train(model, data, epochs=1, compress_prob=0.0, log=False)
-    m = metrics[0]
-    assert m['raw_loss'] > 0
-    assert m['compressed_loss'] == 0
-
-
-def test_train_direct_compression():
-    model = BitTransformerLM(d_model=16, nhead=4, num_layers=1, dim_feedforward=32, max_seq_len=8)
-    data = torch.zeros((4, 8), dtype=torch.long)
-    metrics = train(model, data, epochs=1, compress_prob=0.0, direct_prob=1.0, log=False)
-    m = metrics[0]
-    assert m['direct_loss'] > 0
-
-
-def test_diffusion_training_loop():
-    model = BitTransformerLM(d_model=16, nhead=4, num_layers=1, dim_feedforward=32, max_seq_len=8)
-    data = torch.randint(0, 2, (4, 8), dtype=torch.long)
-    metrics = train(model, data, epochs=1, diffusion=True, log=False)
-    m = metrics[0]
-    assert m['raw_loss'] > 0
-