Added support F.one_hot (#128)

thunder/executors/torchex.py

@@ -1202,6 +1202,7 @@ def _take_along_axis_prim_transform(a: TensorProxy, /, index: TensorProxy, dim:
 dropout = _register_torch_operation("dropout", module=torch.nn.functional)
 embedding = _register_torch_operation("embedding", module=torch.nn.functional)
 embedding_backward = _register_torch_operation("torch.ops.aten.embedding_backward", like=ltorch.embedding_backward)
+one_hot = _register_torch_operation("one_hot", module=torch.nn.functional)
 group_norm = _register_torch_operation("group_norm", module=torch.nn.functional)
 interpolate = _register_torch_operation("interpolate", module=torch.nn.functional)
 linear = _register_torch_operation("linear", module=torch.nn.functional)
@@ -1447,6 +1448,7 @@ def _pad_prim_impl(
 _register_implementation(ltorch.dropout, dropout, checker=_always_executable)
 _register_implementation(ltorch.embedding, embedding, checker=_always_executable)
 _register_implementation(ltorch.embedding_backward, embedding_backward, checker=_always_executable)
+_register_implementation(ltorch.one_hot, one_hot, checker=_always_executable)
 _register_implementation(ltorch.group_norm, group_norm, checker=_always_executable)
 _register_implementation(ltorch.interpolate, interpolate, checker=_interpolate_checker)
 _register_implementation(ltorch.linear, linear, checker=_always_executable)

thunder/tests/opinfos.py

@@ -6152,6 +6152,33 @@ def sample_generator(op, device, dtype, requires_grad, **kwargs):
 nn_ops.append(max_pool3d_opinfo)
 
 
+def one_hot_sample_generator(op, device, dtype, requires_grad, **kwargs):
+    make = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad)
+
+    test_shapes = [
+        (0, 512),
+        (10,),
+        (5, 10),
+        (3, 5, 10),
+    ]
+
+    max_value = 9
+    for shape in test_shapes:
+        for num_classes in range(1, max_value + 1):
+            a = make(shape, low=0, high=num_classes - 1)  # use non-negative integers
+
+            yield SampleInput(a, num_classes=num_classes)
+
+
+one_hot_opinfo = OpInfo(
+    ltorch.one_hot,
+    sample_input_generator=one_hot_sample_generator,
+    torch_reference=torch.nn.functional.one_hot,
+    dtypes=(datatypes.int64,),  # akin to torch.long. F.one_hot expects input LongTensor
+)
+nn_ops.append(one_hot_opinfo)
+
+
 def group_norm_sample_generator(op, device, dtype, requires_grad, **kwargs):
     # NOTE: we set low/high to -+ 1 to avoid numerical issues with reduced float types.
     make = partial(make_tensor, low=-1, high=+1, device=device, dtype=dtype, requires_grad=requires_grad)

thunder/torch/__init__.py

@@ -3489,6 +3489,24 @@ def embedding_backward(grad, indices, num_weights, padding_idx, scale_grad_by_fr
     return result
 
 
+@torchsymbol(torch.nn.functional.one_hot, id="torch.nn.functional.one_hot", is_method=False)
+def one_hot(a: TensorLike, /, num_classes: int) -> TensorLike:
+    # TODO: refactor when we're ready to support auto-inference for `num_classes = -1` using `.item()`
+    utils.check(
+        num_classes >= 1,
+        lambda: f"Currently supports only positive input for num_classes, got num_classes={num_classes}",
+        exception_type=NotImplementedError,
+    )
+    # TODO: would we want to implement this check in the future?
+    #  utils.check(a.any() >= 0, lambda f"input tensor should have non-negative values", exception_type=ValueError)
+
+    canvas = zeros(*a.shape, num_classes, device=a.device, dtype=dtypes.int64)
+    index = a.unsqueeze(-1)
+    src = ones_like(index, device=a.device, dtype=dtypes.int64)
+
+    return scatter_add(canvas, dim=-1, index=index, src=src)
+
+
 @torchsymbol(torch.group_norm, torch.nn.functional.group_norm, id="torch.nn.functional.group_norm", is_method=False)
 def group_norm(
     a: TensorProxy,