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Adapt ones_like dtype for torch 2.8.0 #2598

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Adapt ones_like dtype for torch 2.8.0 #2598

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26 changes: 16 additions & 10 deletions coremltools/converters/mil/frontend/torch/ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5505,7 +5505,7 @@ def _parse_keyword_args(context, node, dtype) -> Var:

@register_torch_op
def ones_like(context, node):
def _parse_positional_args(context, node) -> Tuple[Var, Optional[Var]]:
def _parse_positional_args(context, node) -> Tuple[Var, Optional[str]]:
inputs = _get_inputs(
context,
node,
Expand All @@ -5516,22 +5516,28 @@ def _parse_positional_args(context, node) -> Tuple[Var, Optional[Var]]:
dtype = None
if len(inputs) > 1 and inputs[1] is not None:
dtype = inputs[1]
if dtype is None:
dtype = _get_kwinputs(context, node, "dtype", default=[dtype])[0]
if dtype is None and node.meta is not None:
dtype = TORCH_DTYPE_TO_NUM[node.meta['tensor_meta'].dtype]
if isinstance(dtype, Var): dtype = dtype.val.item()
if isinstance(dtype, int): dtype = NUM_TO_DTYPE_STRING[dtype]
if dtype is None: dtype = types.builtin_to_string(x.dtype)
return x, dtype

def _parse_keyword_args(context, node, dtype) -> Var:
dtype = _get_kwinputs(context, node, "dtype", default=[dtype])[0]
return dtype

x, dtype = _parse_positional_args(context, node)
dtype = _parse_keyword_args(context, node, dtype)

if is_current_opset_version_compatible_with(target.iOS16):
res = mb.fill_like(ref_tensor=x, value=1.0)
v = {
"fp16": np.float16(1.0),
"fp32": np.float32(1.0),
"int32": np.int32(1),
"bool": np.bool_(True),
}.get(dtype, 1.0)
res = mb.fill_like(ref_tensor=x, value=v)
else:
res = mb.fill(shape=mb.shape(x=x), value=1.0)
# By default use input x's dtype.
dtype_str = NUM_TO_DTYPE_STRING[dtype.val] if dtype is not None else types.builtin_to_string(x.dtype)
res = _cast_to(res, dtype_str, node.name)
res = _cast_to(res, dtype, node.name)
context.add(res, node.name)


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