add logsigmoid op

thunder/core/transforms.py

@@ -1435,6 +1435,31 @@ def _copy_with_setitem_grad(a: TensorProxy, index, value: Number | TensorProxy):
 
 register_grad(pids.COPY_WITH_SETITEM, _copy_with_setitem_grad)
 
+
+def _log_sigmoid_grad(
+    a: TensorProxy,
+) -> TensorProxy:
+    from thunder.torch import abs, exp, log_sigmoid_backward, logsigmoid
+
+    fwd = logsigmoid(a)
+
+    g = get_grad(fwd)
+    if a.device.type == "cpu":
+        # NOTE PyTorch's CPU computation for logsigmoid's grad uses an additional "buffer" tensor, see
+        # https://github.com/pytorch/pytorch/blob/7667235a23e2ffca4d32e6e16aa60a683418e159/torch/_decomp/decompositions.py#L332
+        z = exp(-abs(a))
+        a_grad = log_sigmoid_backward(g, a, z)
+    else:
+        # Here a placeholder tensor is provided.
+        a_grad = log_sigmoid_backward(g, a, full((1,), fill_value=0, device=a.device))
+    put_grad(a, a_grad)
+
+    return fwd
+
+
+register_grad("torch.nn.functional.logsigmoid", _log_sigmoid_grad)
+
+
 #
 # Phantom grad transform helpers
 #

thunder/executors/torchex.py

@@ -835,11 +835,15 @@ def _erfcinv_impl(a: torch.Tensor) -> torch.Tensor:
 celu = _register_torch_operation("celu", module=torch.nn.functional)
 elu = _register_torch_operation("elu", module=torch.nn.functional)
 gelu = _register_torch_operation("gelu", module=torch.nn.functional)
+hardshrink = _register_torch_operation("hardshrink", module=torch.nn.functional)
+hardswish = _register_torch_operation("hardswish", module=torch.nn.functional)
 leaky_relu = _register_torch_operation("leaky_relu", module=torch.nn.functional)
+logsigmoid = _register_torch_operation("logsigmoid", module=torch.nn.functional)
+log_sigmoid_backward = _register_torch_operation(
+    "torch.ops.aten.log_sigmoid_backward", like=ltorch.log_sigmoid_backward
+)
 relu = _register_torch_operation("relu", module=torch.nn.functional)
 relu6 = _register_torch_operation("relu6", module=torch.nn.functional)
-hardshrink = _register_torch_operation("hardshrink", module=torch.nn.functional)
-hardswish = _register_torch_operation("hardswish", module=torch.nn.functional)
 selu = _register_torch_operation("selu", module=torch.nn.functional)
 silu = _register_torch_operation("silu", module=torch.nn.functional)
 
@@ -851,11 +855,15 @@ def _elementwise_unary_with_inplace_checker(a: TensorProxy, /, inplace: bool = F
 _register_elementwise_unary_implementation(ltorch.elu, elu, checker=_always_executable)
 _register_elementwise_unary_implementation(ltorch.celu, celu, checker=_always_executable)
 _register_elementwise_unary_implementation(ltorch.gelu, gelu, checker=_always_executable)
+_register_elementwise_unary_implementation(ltorch.hardshrink, hardshrink, checker=_always_executable)
+_register_elementwise_unary_implementation(ltorch.hardswish, hardswish, checker=_elementwise_unary_with_inplace_checker)
 _register_elementwise_unary_implementation(ltorch.leaky_relu, leaky_relu, checker=_always_executable)
+_register_elementwise_unary_implementation(
+    ltorch.log_sigmoid_backward, log_sigmoid_backward, checker=_always_executable
+)
+_register_elementwise_unary_implementation(ltorch.logsigmoid, logsigmoid)
 _register_elementwise_unary_implementation(ltorch.relu, relu, checker=_elementwise_unary_with_inplace_checker)
 _register_elementwise_unary_implementation(ltorch.relu6, relu6, checker=_elementwise_unary_with_inplace_checker)
-_register_elementwise_unary_implementation(ltorch.hardshrink, hardshrink, checker=_always_executable)
-_register_elementwise_unary_implementation(ltorch.hardswish, hardswish, checker=_elementwise_unary_with_inplace_checker)
 _register_elementwise_unary_implementation(ltorch.selu, selu, checker=_elementwise_unary_with_inplace_checker)
 _register_elementwise_unary_implementation(ltorch.silu, silu, checker=_always_executable)
 

thunder/tests/opinfos.py

@@ -1685,6 +1685,17 @@ def gen(op, device, dtype, requires_grad):
 elementwise_unary_ops.append(leaky_relu_opinfo)
 
 
+logsigmoid_opinfo = OpInfo(
+    ltorch.logsigmoid,
+    dtypes=(datatypes.floating,),
+    sample_input_generator=elementwise_unary_generator,
+    torch_reference=torch.nn.functional.logsigmoid,
+    domain=(-1, 1),
+    test_directives=(),
+)
+elementwise_unary_ops.append(logsigmoid_opinfo)
+
+
 relu_opinfo = OpInfo(
     ltorch.relu,
     sample_input_generator=elementwise_unary_generator,

thunder/torch/__init__.py

@@ -1812,6 +1812,19 @@ def leaky_relu(a: TensorProxy, /, negative_slope: float = 0.01, inplace: bool =
 _inplace_to_out_of_place[leaky_relu] = leaky_relu, 2
 
 
+@torchsymbol(torch.nn.functional.logsigmoid, is_method=False)
+def logsigmoid(a: TensorProxy, /) -> TensorLike:
+    return where(a > 0, -log1p(exp(-a)), a - log1p(exp(a)))
+
+
+@torchsymbol("log_sigmoid_backward", id="log_sigmoid_backward")
+def log_sigmoid_backward(g: TensorProxy, a: TensorProxy, buffer: TensorProxy) -> TensorLike:
+    # buffer is used by PyTorch in cpu-based calculations.  See
+    # https://github.com/pytorch/pytorch/blob/7667235a23e2ffca4d32e6e16aa60a683418e159/torch/_decomp/decompositions.py#L332
+    # This is addressed in the custom grad fn thunder.core.transforms._log_sigmoid_grad.
+    return g * where(a > 0, exp(-a) / (1 + exp(-a)), 1 - exp(a) / (1 + exp(a)))
+
+
 # TODO Should this use clamp? -- Would that propagate NaNs properly?
 @torchsymbol(torch.relu, torch.nn.functional.relu, id="torch.relu", is_method=True)
 def relu(a: TensorLike, /, inplace: bool = False) -> TensorLike:
@@ -1858,9 +1871,6 @@ def hardshrink(a: TensorProxy, /, lambd: float = 0.5) -> TensorLike:
     return where(abs(a) <= lambd, 0, a)
 
 
-_inplace_to_out_of_place[hardshrink] = hardshrink, -1
-
-
 @torchsymbol(torch.nn.functional.hardswish, id="torch.hardswish", is_method=False)
 def hardswish(a: TensorProxy, /, inplace: bool = False) -> TensorLike:
     utils.check(

thunder/torch/default_torch_ops.py

@@ -354,7 +354,6 @@
         torch.nn.functional.kl_div,
         torch.nn.functional.l1_loss,
         torch.nn.functional.local_response_norm,
-        torch.nn.functional.logsigmoid,
         torch.nn.functional.lp_pool1d,
         torch.nn.functional.lp_pool2d,
         torch.nn.functional.lp_pool3d,