[ADD] Support CrossEntropyLoss in KFAC

curvlinops/kfac.py

@@ -16,9 +16,10 @@
 from math import sqrt
 from typing import Dict, Iterable, List, Tuple, Union
 
+from einops import rearrange
 from numpy import ndarray
 from torch import Generator, Tensor, einsum, randn
-from torch.nn import Linear, Module, MSELoss, Parameter
+from torch.nn import CrossEntropyLoss, Linear, Module, MSELoss, Parameter
 from torch.utils.hooks import RemovableHandle
 
 from curvlinops._base import _LinearOperator
@@ -66,7 +67,7 @@ class KFACLinearOperator(_LinearOperator):
         _SUPPORTED_MODULES: Tuple of supported layers.
     """
 
-    _SUPPORTED_LOSSES = (MSELoss,)
+    _SUPPORTED_LOSSES = (MSELoss, CrossEntropyLoss)
     _SUPPORTED_MODULES = (Linear,)
 
     def __init__(
@@ -279,6 +280,23 @@ def draw_label(self, output: Tensor) -> Tensor:
                 generator=self._generator,
             )
             return output.clone().detach() + perturbation
+
+        elif isinstance(self._loss_func, CrossEntropyLoss):
+            # TODO For output.ndim > 2, the scale of the 'would-be' gradient resulting
+            # from these labels might be off
+            if output.ndim != 2:
+                raise NotImplementedError(
+                    "Only 2D output is supported for CrossEntropyLoss for now."
+                )
+            probs = output.softmax(dim=1)
+            # each row contains a vector describing a categorical
+            probs_as_mat = rearrange(probs, "n c ... -> (n ...) c")
+            labels = probs_as_mat.multinomial(
+                num_samples=1, generator=self._generator
+            ).squeeze(-1)
+            label_shape = output.shape[:1] + output.shape[2:]
+            return labels.reshape(label_shape)
+
         else:
             raise NotImplementedError
 

setup.cfg

@@ -39,6 +39,7 @@ install_requires =
     torch>=2.0
     scipy>=1.7.1,<2.0.0
     tqdm>=4.61.0,<5.0.0
+    einops
 # Require a specific Python version, e.g. Python 2.7 or >= 3.4
 python_requires = >=3.8
 

test/conftest.py

@@ -1,7 +1,7 @@
 """Contains pytest fixtures that are visible by other files."""
 
 from test.cases import ADJOINT_CASES, CASES, NON_DETERMINISTIC_CASES
-from test.kfac_cases import KFAC_EXPAND_EXACT_CASES
+from test.kfac_cases import KFAC_EXPAND_EXACT_CASES, KFAC_EXPAND_EXACT_ONE_DATUM_CASES
 from typing import Callable, Dict, Iterable, List, Tuple
 
 from numpy import random
@@ -70,5 +70,19 @@ def kfac_expand_exact_case(
         A neural network, the mean-squared error function, a list of parameters, and
         a data set.
     """
-    kfac_case = request.param
-    yield initialize_case(kfac_case)
+    case = request.param
+    yield initialize_case(case)
+
+
+@fixture(params=KFAC_EXPAND_EXACT_ONE_DATUM_CASES)
+def kfac_expand_exact_one_datum_case(
+    request,
+) -> Tuple[Module, Module, List[Tensor], Iterable[Tuple[Tensor, Tensor]],]:
+    """Prepare a test case for which KFAC-expand equals the GGN and one datum is used.
+
+    Yields:
+        A neural network, loss function, a list of parameters, and
+        a data set with a single datum.
+    """
+    case = request.param
+    yield initialize_case(case)

test/kfac_cases.py

@@ -1,10 +1,10 @@
 """Contains test cases for the KFAC linear operator."""
 
 from functools import partial
-from test.utils import get_available_devices, regression_targets
+from test.utils import classification_targets, get_available_devices, regression_targets
 
 from torch import rand
-from torch.nn import Linear, MSELoss, Sequential
+from torch.nn import CrossEntropyLoss, Linear, MSELoss, Sequential
 
 # Add test cases here, devices and loss function with different reductions will be
 # added automatically below
@@ -42,3 +42,33 @@
                 "loss_func": partial(MSELoss, reduction=reduction),
             }
             KFAC_EXPAND_EXACT_CASES.append(case_with_device_and_loss_func)
+
+
+# Add test cases here, devices will be added automatically below
+KFAC_EXPAND_EXACT_ONE_DATUM_CASES_NO_DEVICE = [
+    ###############################################################################
+    #                                CLASSIFICATION                               #
+    ###############################################################################
+    # deep linear network with vector output (both reductions)
+    {
+        "model_func": lambda: Sequential(Linear(5, 4), Linear(4, 3)),
+        "loss_func": lambda: CrossEntropyLoss(reduction="mean"),
+        "data": lambda: [(rand(1, 5), classification_targets((1,), 3))],
+        "seed": 0,
+    },
+    {
+        "model_func": lambda: Sequential(Linear(5, 4), Linear(4, 3)),
+        "loss_func": lambda: CrossEntropyLoss(reduction="sum"),
+        "data": lambda: [(rand(1, 5), classification_targets((1,), 3))],
+        "seed": 0,
+    },
+]
+
+KFAC_EXPAND_EXACT_ONE_DATUM_CASES = []
+for case in KFAC_EXPAND_EXACT_ONE_DATUM_CASES_NO_DEVICE:
+    for device in get_available_devices():
+        case_with_device = {
+            **case,
+            "device": device,
+        }
+        KFAC_EXPAND_EXACT_ONE_DATUM_CASES.append(case_with_device)

test/test_kfac.py

@@ -32,3 +32,25 @@ def test_kfac(
     rtol = {"sum": 2e-2, "mean": 2e-2}[loss_func.reduction]
 
     report_nonclose(ggn, kfac_mat, rtol=rtol, atol=atol)
+
+
+def test_kfac_one_datum(
+    kfac_expand_exact_one_datum_case: Tuple[
+        Module, MSELoss, List[Parameter], Iterable[Tuple[Tensor, Tensor]]
+    ]
+):
+    model, loss_func, params, data = kfac_expand_exact_one_datum_case
+
+    ggn_blocks = []  # list of per-parameter GGNs
+    for param in params:
+        ggn = GGNLinearOperator(model, loss_func, [param], data)
+        ggn_blocks.append(ggn @ eye(ggn.shape[1]))
+    ggn = block_diag(*ggn_blocks)
+
+    kfac = KFACLinearOperator(model, loss_func, params, data, mc_samples=10_000)
+    kfac_mat = kfac @ eye(kfac.shape[1])
+
+    atol = {"sum": 1e-3, "mean": 1e-3}[loss_func.reduction]
+    rtol = {"sum": 3e-2, "mean": 3e-2}[loss_func.reduction]
+
+    report_nonclose(ggn, kfac_mat, rtol=rtol, atol=atol)