fix: index_put converter to handle multi-shape slicing with None

Author: chohk88

py/torch_tensorrt/dynamo/conversion/impl/select.py

@@ -501,7 +501,6 @@ def index_put_converter(
     F = [i for i in range(rank) if indices[i] is None]  # Free dimensions
     I = [i for i in range(rank) if indices[i] is not None]  # Indexed dimensions
     K = len(I)
-
     # Determine the maximum size 'N' among the index tensors
     if K > 0:
         index_shapes = [tensor.shape[0] for tensor in indices if tensor is not None]
@@ -684,16 +683,6 @@ def index_put_converter(
         values_reshaped = impl.shuffle.reshape(
             ctx, target, source_ir, f"{name}_reshape_scalar", values, (1,)
         )
-        num_dims = len(expected_shape)
-        ones_shape = tuple([1] * num_dims)
-        values_reshaped = impl.shuffle.reshape(
-            ctx,
-            target,
-            source_ir,
-            f"{name}_reshape_to_ones",
-            values_reshaped,
-            ones_shape,
-        )
         values_expanded = impl.slice.expand(
             ctx,
             target,
@@ -704,40 +693,79 @@ def index_put_converter(
         )
     else:  # Non-scalar case
         values_shape = list(values.shape)
-
-        # Pad dimensions if necessary
-        if len(values_shape) < len(expected_shape):
-            values_shape = [1] * (
-                len(expected_shape) - len(values_shape)
-            ) + values_shape
-
-        # Calculate a broadcastable shape
-        broadcast_shape = []
-        for exp_dim, val_dim in zip(expected_shape, values_shape):
-            if val_dim == 1:
-                broadcast_shape.append(exp_dim)
-            elif val_dim == exp_dim:
-                broadcast_shape.append(val_dim)
+        if K > 0 and N in values_shape:
+            n_idx = values_shape.index(N)
+            permute_order = [n_idx] + [
+                i for i in range(len(values_shape)) if i != n_idx
+            ]
+            values_permuted = impl.permutation.permute(
+                ctx, target, source_ir, f"{name}_permute_values", values, permute_order
+            )
+            remaining_shape = [
+                values_shape[i] for i in range(len(values_shape)) if i != n_idx
+            ]
+            target_f_dims = len(F)
+            current_f_dims = len(remaining_shape)
+            if current_f_dims < target_f_dims:
+                values_expanded_shape = (
+                    [N] + [1] * (target_f_dims - current_f_dims) + remaining_shape
+                )
             else:
-                raise ValueError(f"Cannot broadcast {values_shape} to {expected_shape}")
-
-        # Reshape and then expand
-        values_reshaped = impl.shuffle.reshape(
-            ctx,
-            target,
-            source_ir,
-            f"{name}_reshape_values",
-            values,
-            tuple(broadcast_shape),
-        )
-        values_expanded = impl.slice.expand(
-            ctx,
-            target,
-            source_ir,
-            f"{name}_expand_values",
-            values_reshaped,
-            expected_shape,
-        )
+                values_expanded_shape = [N] + remaining_shape[:target_f_dims]
+            values_expanded = impl.shuffle.reshape(
+                ctx,
+                target,
+                source_ir,
+                f"{name}_unsqueeze_values",
+                values_permuted,
+                tuple(values_expanded_shape),
+            )
+            broadcast_shape = []
+            for exp_dim, val_dim in zip(expected_shape, values_expanded_shape):
+                if val_dim == 1:
+                    broadcast_shape.append(exp_dim)
+                elif val_dim == exp_dim:
+                    broadcast_shape.append(val_dim)
+                else:
+                    raise ValueError(
+                        f"Cannot broadcast {values_expanded_shape} to {expected_shape}"
+                    )
+            values_expanded = impl.slice.expand(
+                ctx,
+                target,
+                source_ir,
+                f"{name}_expand_values",
+                values_expanded,
+                tuple(broadcast_shape),
+            )
+        else:
+            values_shape_padded = [1] * (
+                len(expected_shape) - len(values.shape)
+            ) + list(values.shape)
+            broadcast_shape = []
+            for exp_dim, val_dim in zip(expected_shape, values_shape_padded):
+                if val_dim == 1 or exp_dim == val_dim:
+                    broadcast_shape.append(exp_dim)
+                else:
+                    raise ValueError(
+                        f"Cannot broadcast {values.shape} to {expected_shape}"
+                    )
+            values_reshaped = impl.shuffle.reshape(
+                ctx,
+                target,
+                source_ir,
+                f"{name}_reshape_values",
+                values,
+                tuple(broadcast_shape),
+            )
+            values_expanded = impl.slice.expand(
+                ctx,
+                target,
+                source_ir,
+                f"{name}_expand_values",
+                values_reshaped,
+                expected_shape,
+            )
 
     # Flatten values to (N * F_volume,)
     flattened_values = impl.shuffle.reshape(
@@ -749,6 +777,7 @@ def index_put_converter(
         (N * F_volume,),
     )
 
+    indices_cat = cast_trt_tensor(ctx, indices_cat, trt.int32, f"{name}_idx_int32")
     # Perform Scatter ND operation
     scatter_layer = ctx.net.add_scatter(
         input_tensor,

tests/py/dynamo/conversion/test_index_put_aten.py

@@ -194,6 +194,12 @@ class TestIndexPutConverter(DispatchTestCase):
                     dtype=torch.int32,
                 ),
             ),
+            param(
+                test_name="4d_indices_none_none_multiple_idx_broadcast_error",
+                source_tensor=torch.zeros([1, 2, 5, 3], dtype=torch.float32),
+                indices_tensor=(None, None, torch.tensor([0, 1, 2], dtype=torch.int64)),
+                value_tensor=torch.randn([2, 3, 3], dtype=torch.float32),
+            ),
             # param(
             #     test_name="2d_indices_accumulate_True",
             #     source_tensor=torch.zeros([5, 5], dtype=torch.int32),