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Convert n_items type from torch.Tensor to int #139

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Convert n_items type from torch.Tensor to int #139

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facf34d
Updated device of num_items_in_batch and n_items (same one) to loss w…
May 26, 2025
f78ca0a
Updated device of num_items_in_batch and n_items (same one) to loss w…
MoonRainy21 May 26, 2025
0a51436
Merge branch 'fix/num_batch_items-type' of https://github.com/MoonRai…
May 26, 2025
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12 changes: 9 additions & 3 deletions unsloth_zoo/compiler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -623,7 +623,7 @@ def _compiled_loss_function(
logit_scale_divide : float = 0,
logit_softcapping : float = 0,
vocab_size : int = 0,
n_items : int = 0,
n_items : int | torch.Tensor = 0,
):
device = output_logits.device
if logit_scale_multiply != 0:
Expand Down Expand Up @@ -658,6 +658,8 @@ def _compiled_loss_function(
)
pass
if n_items != 0:
if torch.is_tensor(n_items):
n_items = n_items.to(loss.device)
loss = loss / n_items
else:
loss = loss / (shift_labels != -100).sum()
Expand Down Expand Up @@ -743,7 +745,7 @@ def _compiled_loss_function(
logit_scale_divide : float = 0,
logit_softcapping : float = 0,
vocab_size : int = 0,
n_items : int = 0,
n_items : int | torch.Tensor = 0,
):
device = output_logits.device
if logit_scale_multiply != 0:
Expand Down Expand Up @@ -778,6 +780,8 @@ def _compiled_loss_function(
)
pass
if n_items != 0:
if torch.is_tensor(n_items):
n_items = n_items.to(loss.device)
loss = loss / n_items
else:
loss = loss / (shift_labels != -100).sum()
Expand Down Expand Up @@ -851,7 +855,7 @@ def _compiled_loss_function(
logit_scale_divide : float = 0,
logit_softcapping : float = 0,
vocab_size : int = 0,
n_items : int = 0,
n_items : int | torch.Tensor = 0,
):
device = output_logits.device
if logit_scale_multiply != 0:
Expand Down Expand Up @@ -886,6 +890,8 @@ def _compiled_loss_function(
)
pass
if n_items != 0:
if torch.is_tensor(n_items):
n_items = n_items.to(loss.device)
loss = loss / n_items
else:
loss = loss / (shift_labels != -100).sum()
Expand Down
23 changes: 15 additions & 8 deletions unsloth_zoo/loss_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -83,13 +83,16 @@ def unsloth_fixed_cross_entropy(source, target, num_items_in_batch: int = None,
ignore_index = ignore_index,
reduction = reduction,
)
if reduction == "sum": loss = loss / num_items_in_batch
if reduction == "sum":
if torch.is_tensor(num_items_in_batch):
num_items_in_batch = num_items_in_batch.to(loss.device)
loss = loss / num_items_in_batch
return loss
pass

# Causal LM loss
def UnslothForCausalLMLoss(
logits, labels, vocab_size: int, num_items_in_batch: int = None, ignore_index: int = -100, **kwargs
logits, labels, vocab_size, num_items_in_batch= None, ignore_index = -100, **kwargs
):
if labels is None: return None
shift_logits = logits
Expand Down Expand Up @@ -159,7 +162,7 @@ def fused_linear_cross_entropy(
hidden_states : torch.Tensor,
lm_weight : torch.Tensor,
labels : torch.Tensor,
num_items_in_batch : int = None,
num_items_in_batch : int | torch.Tensor = None,
ignore_index : int = -100,
reduction : str = "mean",
logit_softcapping : float = 0,
Expand All @@ -179,7 +182,10 @@ def fused_linear_cross_entropy(
shift = True,
filter_eps = accuracy_threshold,
)
if num_items_in_batch is not None: loss = loss / num_items_in_batch
if num_items_in_batch is not None:
if torch.is_tensor(num_items_in_batch):
num_items_in_batch = num_items_in_batch.to(loss.device)
loss = loss / num_items_in_batch
return loss
pass

Expand All @@ -188,7 +194,7 @@ def fast_linear_cross_entropy(
hidden_states : torch.Tensor,
lm_head : torch.nn.Linear,
labels : torch.Tensor,
num_items_in_batch : int = None,
num_items_in_batch : int | torch.Tensor = None,
ignore_index : int = -100,
reduction : str = "mean",
logit_softcapping : float = 0,
Expand Down Expand Up @@ -218,7 +224,10 @@ def fast_linear_cross_entropy(
chunk_size = 512,
attention_mask = attention_mask,
)
if num_items_in_batch is not None: loss = loss / num_items_in_batch
if num_items_in_batch is not None:
if torch.is_tensor(num_items_in_batch):
num_items_in_batch = num_items_in_batch.to(loss.device)
loss = loss / num_items_in_batch
return loss
pass

Expand Down Expand Up @@ -292,8 +301,6 @@ def _unsloth_get_batch_samples(self, epoch_iterator, num_batches, device = None,

if self.args.average_tokens_across_devices:
num_items_in_batch = self.accelerator.gather(num_items_in_batch).sum()
if device is not None and torch.is_tensor(num_items_in_batch):
num_items_in_batch = num_items_in_batch.to(device)
except Exception as exception:
raise RuntimeError(exception)
pass
Expand Down