diff --git a/ignite/metrics/loss.py b/ignite/metrics/loss.py
index 0e9bf9e0..a8ddac9f 100644
--- a/ignite/metrics/loss.py
+++ b/ignite/metrics/loss.py
@@ -8,9 +8,21 @@ class Loss(Metric):
     """
     Calculates the average loss according to the passed loss_fn.
 
-    - `loss_fn` must return the average loss over all observations in the batch.
-    - `update` must receive output of the form `(y_pred, y)`.
+    Args:
+        loss_fn (callable): a callable taking a prediction tensor, a target
+            tensor, optionally other arguments, and returns the average loss
+            over all observations in the batch.
+        output_transform (callable): a callable that is used to transform the
+            :class:`ignite.engine.Engine`'s `process_function`'s output into the
+            form expected by the metric.
+            This can be useful if, for example, you have a multi-output model and
+            you want to compute the metric with respect to one of the outputs.
+            The output is is expected to be a tuple (prediction, target) or
+            (prediction, target, kwargs) where kwargs is a dictionary of extra
+            keywords arguments.
+
     """
+
     def __init__(self, loss_fn, output_transform=lambda x: x):
         super(Loss, self).__init__(output_transform)
         self._loss_fn = loss_fn
@@ -20,8 +32,12 @@ class Loss(Metric):
         self._num_examples = 0
 
     def update(self, output):
-        y_pred, y = output
-        average_loss = self._loss_fn(y_pred, y)
+        if len(output) == 2:
+            y_pred, y = output
+            kwargs = {}
+        else:
+            y_pred, y, kwargs = output
+        average_loss = self._loss_fn(y_pred, y, **kwargs)
         assert len(average_loss.shape) == 0, '`loss_fn` did not return the average loss'
         self._sum += average_loss.item() * y.shape[0]
         self._num_examples += y.shape[0]

[Feature Request] More general metrics
I find the metrics to be a bit limited as one might want to pass additional options (even tensors) to the loss.
For instance in recurrent models with different sequence lengths, one would use a mask to avoid counting errors on padded time steps.
The mask is necessary in the loss to know which outputs to use in the final averaging/ loss.
diff --git a/tests/ignite/metrics/test_loss.py b/tests/ignite/metrics/test_loss.py
index cc494d75..b5e7c59b 100644
--- a/tests/ignite/metrics/test_loss.py
+++ b/tests/ignite/metrics/test_loss.py
@@ -50,6 +50,15 @@ def test_non_averaging_loss():
         loss.update((y_pred, y))
 
 
+def test_kwargs_loss():
+    loss = Loss(nll_loss)
+
+    y_pred = torch.Tensor([[0.1, 0.4, 0.5], [0.1, 0.7, 0.2]]).log()
+    y = torch.LongTensor([2, 2])
+    loss.update((y_pred, y, {"weight": torch.Tensor([0, 0, 0])}))
+    assert_almost_equal(loss.compute(), 0)
+
+
 def test_reset():
     loss = Loss(nll_loss)
 

pip install -e .
parse_log_pytest
pytest
numpy mock pytest codecov pytest-cov tqdm scikit-learn visdom torchvision tensorboardX gym
apt-get update apt-get install -y gcc
3.6
pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning
attrs @ file:///opt/conda/conda-bld/attrs_1642510447205/work
certifi==2021.5.30
charset-normalizer==2.0.12
cloudpickle==2.2.1
codecov==2.1.13
coverage==6.2
dataclasses==0.8
decorator==4.4.2
gym==0.26.2
gym-notices==0.0.8
idna==3.10
importlib-metadata @ file:///tmp/build/80754af9/importlib-metadata_1631916693255/work
importlib-resources==5.4.0
iniconfig @ file:///home/linux1/recipes/ci/iniconfig_1610983019677/work
joblib==1.1.1
jsonpatch==1.32
jsonpointer==2.3
mock==5.2.0
more-itertools @ file:///tmp/build/80754af9/more-itertools_1637733554872/work
networkx==2.5.1
numpy==1.19.5
packaging @ file:///tmp/build/80754af9/packaging_1637314298585/work
Pillow==8.4.0
pluggy @ file:///tmp/build/80754af9/pluggy_1615976315926/work
protobuf==4.21.0
py @ file:///opt/conda/conda-bld/py_1644396412707/work
pyparsing @ file:///tmp/build/80754af9/pyparsing_1635766073266/work
pytest==6.2.4
pytest-cov==4.0.0
-e git+https://github.com/pytorch/ignite.git@a582e2d5a1317d96889d17aa0fd11790a376e22b#egg=pytorch_ignite
requests==2.27.1
scikit-learn==0.24.2
scipy==1.5.4
six==1.17.0
tensorboardX==2.6.2.2
threadpoolctl==3.1.0
toml @ file:///tmp/build/80754af9/toml_1616166611790/work
tomli==1.2.3
torch==1.10.1
torchvision==0.11.2
tornado==6.1
tqdm==4.64.1
typing_extensions @ file:///opt/conda/conda-bld/typing_extensions_1647553014482/work
urllib3==1.26.20
visdom==0.2.4
websocket-client==1.3.1
zipp @ file:///tmp/build/80754af9/zipp_1633618647012/work

name: ignite
channels:
  - defaults
  - https://repo.anaconda.com/pkgs/main
  - https://repo.anaconda.com/pkgs/r
  - conda-forge
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=5.1=1_gnu
  - attrs=21.4.0=pyhd3eb1b0_0
  - ca-certificates=2025.2.25=h06a4308_0
  - certifi=2021.5.30=py36h06a4308_0
  - importlib-metadata=4.8.1=py36h06a4308_0
  - importlib_metadata=4.8.1=hd3eb1b0_0
  - iniconfig=1.1.1=pyhd3eb1b0_0
  - ld_impl_linux-64=2.40=h12ee557_0
  - libffi=3.3=he6710b0_2
  - libgcc-ng=11.2.0=h1234567_1
  - libgomp=11.2.0=h1234567_1
  - libstdcxx-ng=11.2.0=h1234567_1
  - more-itertools=8.12.0=pyhd3eb1b0_0
  - ncurses=6.4=h6a678d5_0
  - openssl=1.1.1w=h7f8727e_0
  - packaging=21.3=pyhd3eb1b0_0
  - pip=21.2.2=py36h06a4308_0
  - pluggy=0.13.1=py36h06a4308_0
  - py=1.11.0=pyhd3eb1b0_0
  - pyparsing=3.0.4=pyhd3eb1b0_0
  - pytest=6.2.4=py36h06a4308_2
  - python=3.6.13=h12debd9_1
  - readline=8.2=h5eee18b_0
  - setuptools=58.0.4=py36h06a4308_0
  - sqlite=3.45.3=h5eee18b_0
  - tk=8.6.14=h39e8969_0
  - toml=0.10.2=pyhd3eb1b0_0
  - typing_extensions=4.1.1=pyh06a4308_0
  - wheel=0.37.1=pyhd3eb1b0_0
  - xz=5.6.4=h5eee18b_1
  - zipp=3.6.0=pyhd3eb1b0_0
  - zlib=1.2.13=h5eee18b_1
  - pip:
      - charset-normalizer==2.0.12
      - cloudpickle==2.2.1
      - codecov==2.1.13
      - coverage==6.2
      - dataclasses==0.8
      - decorator==4.4.2
      - gym==0.26.2
      - gym-notices==0.0.8
      - idna==3.10
      - importlib-resources==5.4.0
      - joblib==1.1.1
      - jsonpatch==1.32
      - jsonpointer==2.3
      - mock==5.2.0
      - networkx==2.5.1
      - numpy==1.19.5
      - pillow==8.4.0
      - protobuf==4.21.0
      - pytest-cov==4.0.0
      - requests==2.27.1
      - scikit-learn==0.24.2
      - scipy==1.5.4
      - six==1.17.0
      - tensorboardx==2.6.2.2
      - threadpoolctl==3.1.0
      - tomli==1.2.3
      - torch==1.10.1
      - torchvision==0.11.2
      - tornado==6.1
      - tqdm==4.64.1
      - urllib3==1.26.20
      - visdom==0.2.4
      - websocket-client==1.3.1
prefix: /opt/conda/envs/ignite

tests/ignite/metrics/test_loss.py::test_kwargs_loss
tests/ignite/metrics/test_loss.py::test_zero_div tests/ignite/metrics/test_loss.py::test_compute tests/ignite/metrics/test_loss.py::test_compute_on_criterion tests/ignite/metrics/test_loss.py::test_non_averaging_loss tests/ignite/metrics/test_loss.py::test_reset
ignite/metrics/loss.py
ignite/metrics/loss.py