[ExecuTorch] Support Several Indexing Ops

coremltools/converters/mil/frontend/torch/ops.py

@@ -1676,6 +1676,14 @@ def view(context, node):
     x = inputs[0]
     shape = inputs[1]
 
+    if np.prod(shape.shape) == 0:
+        # Reshape to empty shape (works only for scalar) is a no op
+        assert (
+            np.prod(x.shape) <= 1
+        ), "Reshape to empty shape works only for scalar and single-element tensor"
+        context.add(mb.identity(x=x, name=node.name))
+        return
+
     if isinstance(shape, ListVar):
         length = mb.list_length(ls=shape)
         indices = mb.range_1d(start=0, end=length, step=1)
@@ -3759,39 +3767,180 @@ def _internal_op_tensor_inplace_fill(context, node):
 
 
 @register_torch_op
-def index_put(context, node):
+def select_scatter(context, node):
     inputs = _get_inputs(context, node, expected=4)
     x = inputs[0]
+    updates = inputs[1]
+    dim = inputs[2].val
+    if dim is None:
+        raise ValueError("Only compile time known dim supported yet")
+    index = inputs[3]
+
+    # mb.torch_tensor_assign handles multi-dim slicing
+    # so we need to create slice specifications for all other dimensions
+    begin = [0] * x.rank
+    begin[dim] = index
+    begin = mb.concat(values=begin, axis=0)
+    end = x.shape
+    # and squeeze dim to do pure indexing on it
+    squeeze_mask = [False] * x.rank
+    squeeze_mask[dim] = True
+
+    updated_x = _translate_torch_tensor_assign(
+        x=x,
+        updates=updates,
+        begin=begin,
+        end=end,
+        stride=None,
+        begin_mask=None,
+        end_mask=None,
+        squeeze_mask=squeeze_mask,
+        name=node.name,
+    )
+    context.add(updated_x)
+
+
+@register_torch_op
+def slice_scatter(context, node):
+    inputs = _get_inputs(context, node, min_expected=2)
+    x, updates = promote_input_dtypes(inputs[0:2])
+    dim = 0 if len(inputs) <= 2 else inputs[2].val
+    if dim is None:
+        raise ValueError("Only compile time known dim supported yet")
+    start = 0 if len(inputs) <= 3 else inputs[3]
+    end = x.shape[dim] if len(inputs) <= 4 else mb.minimum(x=inputs[4], y=x.shape[dim])
+    step = 1 if len(inputs) <= 5 else inputs[5]
+
+    assert dim is not None, "slice dim must be known at compile time"
+    assert 0 <= dim and dim < x.rank
+
+    # mb.torch_tensor_assign handles multi-dim slicing
+    # so we need to pad start, end, step from scalar to x.rank
+    starts = [0] * x.rank
+    starts[dim] = start
+    starts = mb.concat(values=starts, axis=0)
+    ends = list(x.shape)
+    ends[dim] = end
+    ends = mb.concat(values=ends, axis=0)
+    steps = [1] * x.rank
+    steps[dim] = step
+    steps = mb.concat(values=steps, axis=0)
+
+    updated_x = _translate_torch_tensor_assign(
+        x=x,
+        updates=updates,
+        begin=starts,
+        end=ends,
+        stride=steps,
+        begin_mask=None,
+        end_mask=None,
+        squeeze_mask=None,
+        name=node.name,
+    )
+    context.add(updated_x)
+
+
+@register_torch_op
+def index_put(context, node):
+    inputs = _get_inputs(context, node, min_expected=3)
+    x = inputs[0]
     indices = inputs[1]
     values = inputs[2]
-    accumulate = inputs[3].val
-    rank = x.rank
+    accumulate = False if len(inputs) < 4 else inputs[3].val
     mode = "add" if accumulate else "update"
 
-    indices_type = indices[0].sym_type.get_primitive()
+    assert isinstance(indices, list), "indices must be a list of tensors"
+    # Usually indices is a list of non-None tensors, so we stack them and feed to mb.scatter_nd
+    # However, when there exists a whole slice (i.e. :), that index is represented as None
+    if any(map(lambda index: index is None, indices)):
+        # We have 2 ways to translate such torch.index_put, both have pros and cons
+        # 1. mb.scatter_nd
+        #    * pro: can handle accumulate or update
+        #    * con: can only have whole slice at last dimensions
+        # 2. mb.torch_tensor_assign
+        #    * pro: can have whole slice at arbitrary dimension
+        #    * con: can only handle update
+        # Here we use mb.torch_tensor_assign
+        # TODO: explore how can we cover as many torch.index_put cases as possible
+        if accumulate:
+            raise NotImplementedError(
+                "If there existed any whole slice (e.g. : in x[:, 0]), "
+                "only torch.index_put(..., accumulate=False) handled yet"
+            )
+
+        begin = [0] * x.rank
+        end = list(x.shape)
+        stride = [1] * x.rank
+        begin_mask = [True] * x.rank
+        end_mask = [True] * x.rank
+        # note: in torch slice, an indexed dim becomes size 1, rather than squeezed, e.g.
+        #     x = torch.zeros((2, 3))
+        #     y = x[:, 1]
+        # we will get y.shape as (2, 1)
+        is_dim_unity = [False] * x.rank
+        for dim, index in enumerate(indices):
+            if index is not None:
+                if len(index.shape) > 0:
+                    index = mb.squeeze(x=index)
+                begin[dim] = index
+                end[dim] = mb.add(x=index, y=1)
+                begin_mask[dim] = False
+                end_mask[dim] = False
+                is_dim_unity[dim] = True
+        begin = mb.concat(values=begin, axis=0)
+        end = mb.concat(values=end, axis=0)
+
+        expected_values_shape = []
+        for dim in range(x.rank):
+            expected_values_shape.append(1 if is_dim_unity[dim] else x.shape[dim])
+        expected_values_shape = tuple(expected_values_shape)
+
+        if values.shape != expected_values_shape:
+            values = _broadcast(values.name + "_broadcasted", values, expected_values_shape)
+
+        updated_x = _translate_torch_tensor_assign(
+            x=x,
+            updates=values,
+            begin=begin,
+            end=end,
+            stride=stride,
+            begin_mask=begin_mask,
+            end_mask=end_mask,
+            squeeze_mask=[False] * x.rank,
+            name=node.name,
+        )
+        context.add(updated_x)
+        return
 
+    indices_type = indices[0].sym_type.get_primitive()
     if types.is_bool(indices_type):
+        # indices
         assert len(indices) == 1, "Unsupported index_put_ usage."
         indices = indices[0]
         assert (
             indices.shape == x.shape
         ), "indices shape must equal to input shape for index put operation."
         indices = mb.cast(x=indices, dtype="int32")
         indices = mb.non_zero(x=indices)
-
-    if types.is_int(indices_type):
+        # values
+        if values.shape == ():
+            values = mb.expand_dims(x=values, axes=[0])
+        if values.rank == 1 and values.shape[0] == 1:
+            reps = value_at(mb.shape(x=indices), 0)
+            reps = mb.expand_dims(x=reps, axes=[0])
+            values = mb.tile(x=values, reps=reps)
+    elif types.is_int(indices_type):
+        # indices
         if len(indices) > 1:
             indices = mb.stack(values=indices, axis=indices[0].rank)
         else:
             indices = mb.expand_dims(x=indices[0], axes=[-1])
-
-    if len(values.shape) == 0:
-        values = mb.expand_dims(x=values, axes=[0])
-
-    if values.rank == 1 and values.shape[0] == 1:
-        reps = value_at(mb.shape(x=indices), 0)
-        reps = mb.expand_dims(x=reps, axes=[0])
-        values = mb.tile(x=values, reps=reps)
+        # values
+        expected_values_shape = indices.shape[:-1] + x.shape[indices.shape[-1] :]
+        if values.shape != expected_values_shape:
+            values = _broadcast(values.name + "_broadcasted", values, expected_values_shape)
+    else:
+        raise ValueError(f"Only bool and int index handled yet, but got {indices_type}")
 
     if is_current_opset_version_compatible_with(target.iOS17):
         # IOS17 `scatter_nd` behaviour is undefined for negative indices.
@@ -5598,7 +5747,20 @@ def std(context, node):
 @register_torch_op
 def copy(context, node):
     inputs = _get_inputs(context, node, expected=[2, 3])
-    context.add(mb.identity(x=inputs[0], name=node.name))
+    assert (
+        context.frontend != TorchFrontend.TORCHSCRIPT
+    ), (
+        "In torch script frontend, by graph pass `generate_tensor_assignment_ops`, "
+        "`torch.copy_` should have been replaced with `_internal_op_tensor_inplace_copy`"
+    )
+    if context.frontend == TorchFrontend.EXIR:
+        src = inputs[1]
+        if inputs[0].shape != src.shape:
+            _, src = _broadcast_tensors(inputs[: 2])
+        result = mb.identity(x=src, name=node.name)
+    else:
+        raise ValueError(f"Invalid PyTorch frontend {context.frontend}")
+    context.add(result)
 
 
 @register_torch_op