refactor so we can add test

src/axolotl/monkeypatch/loss/chunked.py

@@ -71,7 +71,7 @@ class CEWithChunkedOutputLoss(torch.nn.Module):
         return total_loss / total_elements
 
 
-def build_chunked_ce_loss_fn(num_output_chunks: int = 8, ignore_index: int = -100):
+def _build_chunked_ce_loss_fn(num_output_chunks: int = 8, ignore_index: int = -100):
     loss_fn_ce = CEWithChunkedOutputLoss(num_output_chunks, ignore_index)
     loss_fn_ce.compute_cross_entropy = torch.compile(
         loss_fn_ce.compute_cross_entropy, backend="inductor"
@@ -79,10 +79,8 @@ def build_chunked_ce_loss_fn(num_output_chunks: int = 8, ignore_index: int = -10
     return loss_fn_ce
 
 
-def patch_chunked_ce_loss_fn(num_output_chunks: int = 8, ignore_index: int = -100):
+def get_causal_lm_loss(num_output_chunks: int = 8, ignore_index: int = -100):
-    import transformers.loss.loss_utils
+    loss_fn_ce = _build_chunked_ce_loss_fn(num_output_chunks, ignore_index)
-
-    loss_fn_ce = build_chunked_ce_loss_fn(num_output_chunks, ignore_index)
 
     def chunked_fix_cross_entropy(
         source,
@@ -103,7 +101,7 @@ def patch_chunked_ce_loss_fn(num_output_chunks: int = 8, ignore_index: int = -10
     def for_causal_lm_chunked_loss(
         logits,
         labels,
-        vocab_size: int,  # pylint: disable=unused-argument
+        vocab_size: int = None,  # pylint: disable=unused-argument
         num_items_in_batch: Optional[int] = None,
         ignore_index: int = -100,
         shift_labels: Optional[torch.Tensor] = None,
@@ -123,6 +121,13 @@ def patch_chunked_ce_loss_fn(num_output_chunks: int = 8, ignore_index: int = -10
         )
         return loss
 
+    return for_causal_lm_chunked_loss
+
+
+def patch_chunked_ce_loss_fn(num_output_chunks: int = 8, ignore_index: int = -100):
+    import transformers.loss.loss_utils
+
+    for_causal_lm_chunked_loss = get_causal_lm_loss(num_output_chunks, ignore_index)
     transformers.loss.loss_utils.ForCausalLMLoss = for_causal_lm_chunked_loss
     transformers.loss.loss_utils.LOSS_MAPPING["ForCausalLM"] = (
         for_causal_lm_chunked_loss

tests/test_chunked_xentropy.py

@@ -0,0 +1,40 @@
+"""
+test suite for chunked cross entropy
+"""
+
+import pytest
+import torch
+from torch import nn
+
+from axolotl.monkeypatch.loss.chunked import get_causal_lm_loss
+
+
+@pytest.fixture
+def chunked_fixtures():
+    model_dim = 512
+    vocab_size = 1024 * 256
+    seq_len = 2048
+    batch_size = 1
+
+    lm_head = nn.Linear(model_dim, vocab_size)
+    hidden_state = torch.randn(batch_size, seq_len, model_dim)
+    labels = torch.randint(low=0, high=vocab_size, size=(batch_size, seq_len))
+    return lm_head, hidden_state, labels, vocab_size
+
+
+def test_chunked_forward(chunked_fixtures):  # pylint: disable=redefined-outer-name
+    lm_head, hidden_state, labels, vocab_size = chunked_fixtures
+    lm_loss = get_causal_lm_loss()
+
+    logits = lm_head(hidden_state)
+
+    chunked_lm_loss = lm_loss(logits, labels)
+
+    logits_flattened = logits.view(-1, vocab_size)
+    labels_flattened = labels.view(-1)
+
+    loss = nn.functional.cross_entropy(
+        logits_flattened.float(), labels_flattened, reduction="mean"
+    )
+
+    assert torch.allclose(chunked_lm_loss, loss, atol=1e-2, rtol=1e-2)