Add log_sigmoid_backward forward-AD

test/functorch/test_ops.py

@@ -1368,13 +1368,11 @@ def get_vjp(cotangents, *primals):
         xfail('grid_sampler_2d', ''),  # NYI: forward AD for grid_sampler_2d
         xfail('nn.functional.hardsigmoid', ''),  # NYI: forward AD for hardsigmoid_backward
         xfail('nn.functional.huber_loss', ''),  # NYI: forward AD for huber_loss_backward
-        xfail('nn.functional.logsigmoid', ''),  # not differentiable w.r.t. buffer
         xfail('NumpyCubeNotComposableAutogradFunction'),  # not composable
         xfail('renorm', ''),  # NYI: forward AD for renorm
         xfail('ormqr', ''),  # NYI: forward AD for ormqr
         xfail('nn.functional.multilabel_margin_loss', ''),  # NYI: multilabel_margin_loss_forward
-        xfail('nn.functional.multilabel_soft_margin_loss', ''),  # NYI: log_sigmoid_backward
-        xfail('nn.functional.soft_margin_loss', ''),  # NYI: forward-AD for log_sigmoid_backward
+        xfail('nn.functional.soft_margin_loss', ''),  # NYI: forward-AD for soft_margin_loss_backward
         xfail('nn.functional.ctc_loss', ''),  # NYI: forward-AD for _ctc_loss
         xfail('nn.functional.pdist', ''),  # NYI: forward-AD with _pdist_forward
         skip('nn.functional.scaled_dot_product_attention', device_type='cuda'),
@@ -1518,14 +1516,12 @@ def reference(primals, cotangents, primals_tangents, cotangents_tangents):
         # running_mean or running_var, which will be updated in place,
         # were not batched.
         xfail('nn.functional.instance_norm'),
-        xfail('nn.functional.logsigmoid'),  # Forward AD not implemented and no decomposition
         # NYI: Tensor.clone(memory_format) inside vmap is only supported with
         # memory_format torch.preserve_format or torch.contiguous_format (got ChannelsLast)
         xfail('nn.functional.max_unpool2d'),
         xfail('nn.functional.max_unpool2d', 'grad'),
         xfail('nn.functional.multi_margin_loss'),  # Forward AD not implemented and no decomposition
         xfail('nn.functional.multilabel_margin_loss'),  # Forward AD not implemented and no decomposition
-        xfail('nn.functional.multilabel_soft_margin_loss'),  # Forward AD not implemented and no decomposition
         xfail('nn.functional.pdist'),  # Forward AD not implemented and no decomposition
         xfail('nn.functional.rrelu'),  # vmap: we do not yet support aten::rrelu_with_noise.
         xfail('nn.functional.soft_margin_loss'),  # Forward AD not implemented and no decomposition

tools/autograd/derivatives.yaml

@@ -1998,6 +1998,7 @@
 - name: log_sigmoid_forward(Tensor self) -> (Tensor output, Tensor buffer)
   self: log_sigmoid_backward(grad, self, buffer)
   output: log_sigmoid_backward(self_t.conj(), self_p, buffer).conj()
+  output_differentiability: [True, False]
 
 - name: _log_softmax(Tensor self, int dim, bool half_to_float) -> Tensor
   self: _log_softmax_backward_data(grad, result, dim, self.scalar_type())
@@ -2326,6 +2327,7 @@
 - name: log_sigmoid_backward(Tensor grad_output, Tensor self, Tensor buffer) -> Tensor
   grad_output: log_sigmoid_backward(grad, self, buffer)
   self: log_sigmoid_double_backward(grad * grad_output, self)
+  result: log_sigmoid_backward(grad_output_t, self_p, buffer) + log_sigmoid_double_backward(self_t * grad_output_p, self_p)
 
 - name: _log_softmax_backward_data(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype) -> Tensor
   grad_output: grad.to(output.dtype()) - (grad.to(output.dtype()) * output.exp()).sum(dim, true)

torch/testing/_internal/common_methods_invocations.py

@@ -12526,6 +12526,7 @@ def reference_flatten(input, start_dim=0, end_dim=-1):
         dtypesIfCUDA=floating_types_and(torch.float16, torch.bfloat16),
         sample_inputs_func=sample_inputs_multilabel_soft_margin_loss,
         supports_forward_ad=True,
+        supports_fwgrad_bwgrad=True,
         decorators=(
             DecorateInfo(
                 toleranceOverride({torch.float32: tol(atol=1e-4, rtol=1e-4)}),
@@ -13132,6 +13133,7 @@ def reference_flatten(input, start_dim=0, end_dim=-1):
         supports_autograd=True,
         assert_autodiffed=False,
         supports_forward_ad=True,
+        supports_fwgrad_bwgrad=True,
         supports_gradgrad=True,
         # autodiff_nonfusible_nodes=["aten::log_sigmoid"],
         decorators=[