add leaky_relu op

thunder/executors/torchex.py

@@ -836,6 +836,7 @@ 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)
+leaky_relu = _register_torch_operation("leaky_relu", module=torch.nn.functional)
 relu = _register_torch_operation("relu", module=torch.nn.functional)
 relu6 = _register_torch_operation("relu6", module=torch.nn.functional)
 hardswish = _register_torch_operation("hardswish", module=torch.nn.functional)
@@ -850,6 +851,7 @@ 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.leaky_relu, leaky_relu, checker=_always_executable)
 _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.hardswish, hardswish, checker=_elementwise_unary_with_inplace_checker)

thunder/tests/opinfos.py

@@ -1633,20 +1633,24 @@ def _abs_torch(x: torch.Tensor | Number):
 elementwise_unary_ops.append(reciprocal_opinfo)
 
 
-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):
-        if alpha is None:
-            yield sample
-        else:
-            yield SampleInput(*sample.args, alpha=alpha, **sample.kwargs)
+def get_elementwise_unary_with_alpha_generator():
+    kwargs_list = [{}, {"alpha": -1.0}, {"alpha": 0.5}]
+    return get_elementwise_unary_with_kwargs_generator(kwargs_list)
+
+
+def get_elementwise_unary_with_kwargs_generator(kwargs_list):
+    def gen(op, device, dtype, requires_grad):
+        samples = elementwise_unary_generator(op, device, dtype, requires_grad)
+        for kwargs, sample in itertools.product(kwargs_list, samples):
+            yield SampleInput(*sample.args, **kwargs, **sample.kwargs)
+
+    return gen
 
 
 celu_opinfo = OpInfo(
     ltorch.celu,
     dtypes=(datatypes.floating,),
-    sample_input_generator=elementwise_unary_with_alpha_generator,
+    sample_input_generator=get_elementwise_unary_with_alpha_generator(),
     torch_reference=_elementwise_unary_torch(torch.celu),
     test_directives=(),
 )
@@ -1656,7 +1660,7 @@ def elementwise_unary_with_alpha_generator(op, device, dtype, requires_grad):
 elu_opinfo = OpInfo(
     ltorch.elu,
     dtypes=(datatypes.floating,),
-    sample_input_generator=elementwise_unary_with_alpha_generator,
+    sample_input_generator=get_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,
@@ -1665,6 +1669,18 @@ def elementwise_unary_with_alpha_generator(op, device, dtype, requires_grad):
 elementwise_unary_ops.append(elu_opinfo)
 
 
+leaky_relu_opinfo = OpInfo(
+    ltorch.leaky_relu,
+    dtypes=(datatypes.floating,),
+    sample_input_generator=get_elementwise_unary_with_kwargs_generator([{}, {"negative_slope": 0.5}]),
+    torch_reference=torch.nn.functional.leaky_relu,
+    # 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(leaky_relu_opinfo)
+
+
 relu_opinfo = OpInfo(
     ltorch.relu,
     sample_input_generator=elementwise_unary_generator,

thunder/torch/__init__.py

@@ -1801,6 +1801,17 @@ def gelu(a: TensorProxy, /, *, approximate: str = "none") -> TensorLike:
         raise ValueError(f"gelu does not support the approximate={approximate} argument")
 
 
+@torchsymbol(torch.nn.functional.leaky_relu, is_method=False)
+def leaky_relu(a: TensorProxy, /, negative_slope: float = 0.01, inplace: bool = False) -> TensorLike:
+    out = where(a > 0, a, a * negative_slope)
+    if inplace:
+        return prims.copy_(out, a)
+    return out
+
+
+_inplace_to_out_of_place[leaky_relu] = leaky_relu, 2
+
+
 # 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:

thunder/torch/default_torch_ops.py

@@ -356,7 +356,6 @@
         torch.nn.functional.instance_norm,
         torch.nn.functional.kl_div,
         torch.nn.functional.l1_loss,
-        torch.nn.functional.leaky_relu,
         torch.nn.functional.local_response_norm,
         torch.nn.functional.logsigmoid,
         torch.nn.functional.lp_pool1d,