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feat(orttraining): add class_axis parameter to CrossEntropyLoss #25807

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feat(orttraining): add class_axis parameter to CrossEntropyLoss #25807

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20 changes: 17 additions & 3 deletions orttraining/orttraining/python/training/onnxblock/loss/loss.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,7 +61,7 @@ class CrossEntropyLoss(blocks.Block):
contribute to the input gradient.
"""

def __init__(self, weight=None, reduction: str = "mean", ignore_index: int | None = None):
def __init__(self, weight=None, reduction: str = "mean", ignore_index: int | None = None, class_axis: int = -1):
super().__init__()

if reduction not in ["mean", "sum", "none"]:
Expand All @@ -70,6 +70,7 @@ def __init__(self, weight=None, reduction: str = "mean", ignore_index: int | Non
self._weight = weight
self._reduction = reduction
self._ignore_index = ignore_index
self._class_axis = class_axis

def build(self, scores_input_name: str, labels_name: str = "labels"):
"""Adds a CrossEntropyLoss subgraph on top of an onnx model.
Expand All @@ -92,10 +93,23 @@ def build(self, scores_input_name: str, labels_name: str = "labels"):
labels_input = copy.deepcopy(_graph_utils.get_output_from_output_name(self.base, scores_input_name))
labels_input.name = labels_name
labels_input.type.tensor_type.elem_type = onnx.TensorProto.INT64
# Assumes classes is the last dimension
# Remove the class dimension to create the labels shape
# By default, assumes classes is the last dimension (class_axis=-1)
# e.g., predictions: (num_examples, num_classes) -> labels: (num_examples,)
# or predictions: (batch_size, seq_len, vocab) -> labels: (batch_size, seq_len)
del labels_input.type.tensor_type.shape.dim[-1]
# For channel-last formats, user can specify class_axis to correctly identify the class dimension
if self._class_axis < 0:
# Handle negative indexing
class_dim_index = len(labels_input.type.tensor_type.shape.dim) + self._class_axis
else:
class_dim_index = self._class_axis

# Validate class dimension index
if 0 <= class_dim_index < len(labels_input.type.tensor_type.shape.dim):
del labels_input.type.tensor_type.shape.dim[class_dim_index]
else:
# Fallback to original behavior if invalid class_axis specified
del labels_input.type.tensor_type.shape.dim[-1]
self.base.graph.input.append(labels_input)

loss_node_input_names = [scores_input_name, labels_name]
Expand Down