Adds OpInfos for l1_loss and smooth_l1_loss

Per title
Pull Request resolved: pytorch#75877
Approved by: https://github.com/ngimel

torch/testing/_internal/common_methods_invocations.py

@@ -8021,6 +8021,24 @@ def sample_inputs_allclose(op_info, device, dtype, requires_grad, **kwargs):
 
     return samples
 
+def sample_inputs_l1_loss(op_info, device, dtype, requires_grad, **kwargs):
+    yield from sample_inputs_loss(op_info, device, dtype, requires_grad, **kwargs)
+
+    # In addition to the regular test cases, we add two for mixed floating point and complex inputs
+    if dtype.is_complex:
+        make = partial(make_tensor, (), device=device, requires_grad=requires_grad)
+        yield SampleInput(make(dtype=dtype), args=(make(dtype=torch.double),))
+        yield SampleInput(make(dtype=torch.double), args=(make(dtype=dtype),))
+
+def sample_inputs_smooth_l1_loss(op_info, device, dtype, requires_grad, **kwargs):
+    yield from sample_inputs_loss(op_info, device, dtype, requires_grad, **kwargs)
+
+    make = partial(make_tensor, (S, S), device=device, dtype=dtype, requires_grad=requires_grad)
+
+    # This test case always triggers the smooth condition, since absolute difference of input and target
+    # is smaller than beta
+    yield SampleInput(make(low=0, high=2), args=(make(low=-2, high=0),), kwargs=dict(beta=5))
+    yield SampleInput(make(), args=(make(),), kwargs=dict(beta=0))
 
 def sample_inputs_kl_div(op_info, device, dtype, requires_grad, **kwargs):
     make = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad)
@@ -15401,6 +15419,38 @@ def generate_std_var_kwargs(t: torch.Tensor, **kwargs):
                        DecorateInfo(unittest.skip("Skipped!"), 'TestUnaryUfuncs', 'test_reference_numerics_normal',
                                     active_if=TEST_SCIPY and LooseVersion(scipy.__version__) < "1.4.0"),
                    )),
+    OpInfo("nn.functional.smooth_l1_loss",
+           ref=reference_smooth_l1_loss,
+           sample_inputs_func=sample_inputs_smooth_l1_loss,
+           dtypes=floating_types_and(torch.float16, torch.bfloat16),
+           backward_dtypesIfCPU=floating_types_and(torch.bfloat16),
+           dtypesIfCUDA=floating_types_and(torch.float16),
+           backward_dtypesIfCUDA=floating_types_and(torch.float16),
+           supports_out=False,
+           supports_forward_ad=True,
+           skips=(
+               # RuntimeError: input->type()->kind() == TypeKind::OptionalTypeINTERNAL ASSERT FAILED
+               # at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":270, please report a bug to PyTorch.
+               DecorateInfo(unittest.expectedFailure, "TestJit", "test_variant_consistency_jit"),)),
+    OpInfo(
+        "nn.functional.l1_loss",
+        ref=loss_reference_reduction_wrapper(lambda input, target: np.abs(input - target)),
+        sample_inputs_func=sample_inputs_l1_loss,
+        dtypes=floating_and_complex_types_and(torch.float16, torch.bfloat16),
+        backward_dtypes=all_types_and(torch.float16, torch.bfloat16),
+        supports_out=False,
+        supports_forward_ad=True,
+        skips=(
+            # RuntimeError: input->type()->kind() == TypeKind::OptionalTypeINTERNAL ASSERT FAILED
+            # at "../torch/csrc/jit/passes/utils/check_alias_annotation.cpp":270, please report a bug to PyTorch.
+            DecorateInfo(
+                unittest.expectedFailure,
+                "TestJit",
+                "test_variant_consistency_jit",
+                dtypes=(torch.float32,),
+            ),
+        ),
+    ),
     UnaryUfuncInfo('lgamma',
                    ref=reference_lgamma if TEST_SCIPY else _NOTHING,
                    aliases=('special.gammaln', ),