housekeeping: remove unused code and fix tests

litgpt/finetune/full.py

@@ -6,7 +6,6 @@
 from pathlib import Path
 from pprint import pprint
 from typing import Dict, List, Literal, Optional, Tuple, Union
-import warnings
 
 import lightning as L
 import torch

litgpt/model.py

@@ -541,11 +541,10 @@ def batched_index_copy_(t, dim, idx, val):
             return t.index_copy_(dim, idx, val)
 
         assert idx.dim() == 2, f"multiple batch dims not yet {idx.shape=}"
-        assert dim != 0, f"cannot index batch dim"
+        assert dim != 0, f"cannot index batch dim {dim=}"
         batch_size, idx_size = idx.shape
         assert batch_size == t.size(0)
         assert batch_size == val.size(0)
-        t_indexed_dim = t.size(dim)
 
         # if we can view the batch and indexed dimensions together, we could
         # do index trickery. This is, sadly, not the case for kvcache so we

litgpt/pretrain.py

@@ -7,7 +7,6 @@
 from functools import partial
 from pathlib import Path
 from typing import Optional, Tuple, Union, Dict
-import warnings
 
 import lightning as L
 import torch

tests/test_api.py

@@ -130,7 +130,7 @@ def test_llm_load_hub_init(tmp_path):
 
     text_2 = llm.generate("text", max_new_tokens=10, top_k=1, stream=True)
     text_2 = "".join(list(text_2))
-    assert text_1 == text_2, (text1, text_2)
+    assert text_1 == text_2, (text_1, text_2)
 
 
 def test_model_not_initialized(tmp_path):
@@ -174,14 +174,14 @@ def test_more_than_1_device_for_sequential_gpu(tmp_path):
             model=model_name,
         )
 
-    with pytest.raises(NotImplementedError, match=f"Support for multiple devices is currently only implemented for generate_strategy='sequential'|'tensor_parallel'."):
+    with pytest.raises(NotImplementedError, match="Support for multiple devices is currently only implemented for generate_strategy='sequential'|'tensor_parallel'."):
         llm.distribute(devices=2)
 
     llm.distribute(devices=2, generate_strategy="sequential")
     assert isinstance(llm.generate("What do llamas eat?"), str)
     assert str(llm.model.transformer.h[0].mlp.fc.weight.device) == "cuda:0"
     last_layer_idx = len(llm.model.transformer.h) - 1
-    assert str(llm.model.transformer.h[last_layer_idx].mlp.fc.weight.device) == f"cuda:1"
+    assert str(llm.model.transformer.h[last_layer_idx].mlp.fc.weight.device) == "cuda:1"
 
     # Also check with default (devices="auto") setting
     llm.distribute(generate_strategy="sequential")
@@ -270,7 +270,7 @@ def test_fixed_kv_cache(tmp_path):
 
     # Request too many tokens
     with pytest.raises(NotImplementedError, match="max_seq_length 512 needs to be >= 9223372036854775809"):
-        output_text = llm.generate("hello world", max_new_tokens=2**63)
+        _ = llm.generate("hello world", max_new_tokens=2**63)
 
 
 def test_invalid_accelerator(tmp_path):

tests/test_chat.py

@@ -6,14 +6,13 @@
 from io import StringIO
 from itertools import repeat
 from pathlib import Path
-from unittest.mock import ANY, MagicMock, Mock, call, patch
+from unittest.mock import MagicMock, Mock, patch
 import sys
-from typing import Iterable
+from typing import Iterable, Iterator
 
 import pytest
 import torch
 import yaml
-from lightning.fabric import Fabric
 
 import litgpt.chat.base as chat
 import litgpt.generate.base as generate

tests/test_generate.py

@@ -6,7 +6,6 @@
 from contextlib import redirect_stderr, redirect_stdout
 from io import StringIO
 import os
-from pathlib import Path
 from unittest import mock
 from unittest.mock import ANY, Mock, call
 

tests/test_generate_adapter.py

@@ -6,7 +6,6 @@
 from contextlib import redirect_stderr, redirect_stdout
 from io import StringIO
 import os
-from pathlib import Path
 from unittest.mock import ANY, Mock, call
 
 import pytest