Adds binary_cross_entropy opinfo

Per title.
Pull Request resolved: pytorch#75876
Approved by: https://github.com/ngimel

torch/testing/_internal/common_methods_invocations.py

@@ -8016,6 +8016,30 @@ def sample_inputs_pixel_unshuffle(op_info, device, dtype, requires_grad, **kwarg
         for downscale_factor in (1, 3)
     ]
 
+def sample_inputs_binary_cross_entropy(op_info, device, dtype, requires_grad, logits=False, **kwargs):
+    make = partial(make_tensor, device=device, dtype=dtype)
+    make_prob = partial(make, low=0, high=1)
+
+    reductions = ("mean", "sum", "none")
+
+    shapes_and_kwargs = [
+        *[(shape, None) for shape in ((), (1,), (S,), (S, S), (S, S, S))],
+        *[((S, S), dict(reduction=reduction)) for reduction in reductions],
+        *[((S, S), dict(reduction=reduction, weight=make((S, S)))) for reduction in reductions],
+    ]
+
+    if logits:
+        shapes_and_kwargs.extend(
+            [((S, S), dict(reduction=reduction, pos_weight=make((S,), low=0))) for reduction in reductions]
+        )
+
+    for shape, kwargs in shapes_and_kwargs:
+        yield SampleInput(
+            (make if logits else make_prob)(shape, requires_grad=requires_grad),
+            args=(make_prob(shape, requires_grad=requires_grad),),
+            kwargs=kwargs,
+        )
+
 def sample_inputs_allclose(op_info, device, dtype, requires_grad, **kwargs):
     samples = []
     sample_shapes = [(), (S), (S, S, S)]
@@ -12640,6 +12664,55 @@ def generate_std_var_kwargs(t: torch.Tensor, **kwargs):
                DecorateInfo(unittest.skip("We don't want to differentiate wrt running mean / std"),
                             "TestCommon", "test_floating_inputs_are_differentiable"),),
            sample_inputs_func=sample_inputs_batch_norm),
+    OpInfo(
+        "nn.functional.binary_cross_entropy",
+        sample_inputs_func=sample_inputs_binary_cross_entropy,
+        dtypes=floating_types(),
+        dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16),
+        supports_out=False,
+        gradcheck_fast_mode=False,
+        decorators=(
+            # RuntimeError: expected int at position 0, but got: Tensor
+            DecorateInfo(
+                unittest.skip("Skipped!"),
+                "TestCudaFuserOpInfo",
+                "test_nvfuser_correctness",
+            ),
+            # RuntimeError: expected int at position 0, but got: Tensor
+            DecorateInfo(
+                unittest.skip("Skipped!"),
+                "TestNNCOpInfo",
+                "test_nnc_correctness",
+            ),
+            DecorateInfo(
+                toleranceOverride({torch.float32: tol(atol=1e-3, rtol=1e-3)}),
+                "TestJit",
+                "test_variant_consistency_jit",
+            ),
+        ),
+        skips=(
+            # RuntimeError: expected int at position 0, but got: Tensor
+            DecorateInfo(
+                unittest.expectedFailure,
+                "TestJit",
+                "test_variant_consistency_jit",
+            ),
+            # NotImplementedError: the derivative for 'binary_cross_entropy_backward wrt `target`' is not implemented.
+            DecorateInfo(
+                unittest.expectedFailure,
+                "TestGradients",
+                "test_fn_gradgrad",
+            ),
+            # AssertionError: Found a sampled tensor of floating-point dtype torch.float32 sampled with
+            # requires_grad=False.
+            # `weight` input does not support gradient.
+            DecorateInfo(
+                unittest.expectedFailure,
+                "TestCommon",
+                "test_floating_inputs_are_differentiable",
+            ),
+        ),
+    ),
     # We have to add 2 OpInfo entry for `igamma` and `igammac`.First is the
     # standard entry, second is to run gradcheck tests on the second argument.
     BinaryUfuncInfo('igamma',