add elu op

thunder/executors/torchex.py

@@ -803,6 +803,7 @@ def _erfcinv_impl(a: torch.Tensor) -> torch.Tensor:
 
 # nn.functional elementwise unary
 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)
 relu = _register_torch_operation("relu", module=torch.nn.functional)
 relu6 = _register_torch_operation("relu6", module=torch.nn.functional)
@@ -815,6 +816,7 @@ def _elementwise_unary_with_inplace_checker(a: TensorProxy, /, inplace: bool = F
     return isinstance(a, TensorProxy) and not inplace
 
 
+_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.relu, relu, checker=_elementwise_unary_with_inplace_checker)

thunder/tests/opinfos.py

@@ -1633,7 +1633,7 @@ def _abs_torch(x: torch.Tensor | Number):
 elementwise_unary_ops.append(reciprocal_opinfo)
 
 
-def celu_sample_generator(op, device, dtype, requires_grad):
+def elementwise_unary_with_alpha_generator(op, device, dtype, requires_grad):
     alphas = (None, -1.0, 0.5)
     samples = elementwise_unary_generator(op, device, dtype, requires_grad)
     for alpha, sample in itertools.product(alphas, samples):
@@ -1646,13 +1646,25 @@ def celu_sample_generator(op, device, dtype, requires_grad):
 celu_opinfo = OpInfo(
     ltorch.celu,
     dtypes=(datatypes.floating,),
-    sample_input_generator=celu_sample_generator,
+    sample_input_generator=elementwise_unary_with_alpha_generator,
     torch_reference=_elementwise_unary_torch(torch.celu),
     test_directives=(),
 )
 elementwise_unary_ops.append(celu_opinfo)
 
 
+elu_opinfo = OpInfo(
+    ltorch.elu,
+    dtypes=(datatypes.floating,),
+    sample_input_generator=elementwise_unary_with_alpha_generator,
+    torch_reference=torch.nn.functional.elu,
+    # fdm.jvp, which is used in test_vjp_correctness, behaves badly on (-1e-6, 1e-6) for this function
+    singularity_fn=lambda x: x,
+    test_directives=(),
+)
+elementwise_unary_ops.append(elu_opinfo)
+
+
 relu_opinfo = OpInfo(
     ltorch.relu,
     sample_input_generator=elementwise_unary_generator,

thunder/tests/test_grad.py

@@ -281,7 +281,7 @@ def check_vjp(f, *primals, comp, executor="torch", set_compile_data: bool = Fals
     """
     # Let f be a function from vectors of size n to vectors of size m.
     # Its Jacobian is a matrix J of size m x n.
-    # The adjoint property is J^* J = I, where J^* is the conjugate transpose (adjoint) of J.
+    # Represent by J^* the conjugate transpose (adjoint) of J.
     # J^* is a matrix of size n x m.
     # For any vector v of size m, J^* v is a vector of size n.
     # For any vector u of size n, J u is a vector of size m.
@@ -296,7 +296,7 @@ def check_vjp(f, *primals, comp, executor="torch", set_compile_data: bool = Fals
 
     u = tree_map(make, primals)
 
-    comp_f = thunder.jit(f)
+    comp_f = thunder.jit(f, disable_torch_autograd=True)
 
     outs_p, J_u = numerical_jvp(comp_f)(primals, u)
 

thunder/torch/__init__.py

@@ -1774,6 +1774,18 @@ def celu(a: TensorLike, /, alpha: float = 1.0, inplace: bool = False) -> TensorL
 _inplace_to_out_of_place[celu] = celu, 2
 
 
+@torchsymbol(torch.nn.functional.elu, is_method=False)
+def elu(a: TensorProxy, /, alpha: float = 1.0, inplace: bool = False) -> TensorLike:
+    negative_domain_value = alpha * expm1(a)
+    out = where(a > 0, a, negative_domain_value)
+    if inplace:
+        return prims.copy_(out, a)
+    return out
+
+
+_inplace_to_out_of_place[elu] = elu, 2
+
+
 @torchsymbol(torch.nn.functional.gelu, is_method=False)
 def gelu(a: TensorProxy, /, *, approximate: str = "none") -> TensorLike:
     if approximate == "none":

thunder/torch/default_torch_ops.py

@@ -338,7 +338,6 @@
         torch.nn.functional.dropout1d,
         torch.nn.functional.dropout2d,
         torch.nn.functional.dropout3d,
-        torch.nn.functional.elu,
         torch.nn.functional.embedding_bag,
         torch.nn.functional.feature_alpha_dropout,
         torch.nn.functional.fold,