[WIP] Add 2d and 3d indirect indexing support #593
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jansel
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Nov 25, 2025
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| def get_tile_index_tensor_block_id(self, tensor: torch.Tensor) -> int | None: | ||
| """Return the originating ``tile.index`` block id if present.""" | ||
| return getattr(tensor, "_tile_index_block_id", None) |
Contributor
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What is going on here? We should either have an attribute or not -- this getattr stuff is hacky. I think there should be a cleaner way to get this.
| output_size.extend(k.size()) | ||
| elif isinstance(k, torch.Tensor): | ||
| base_dim = input_size.popleft() | ||
| if broadcast_shape is None: |
Contributor
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Why do we need two cases here?
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| def tensor_index_source_and_mask( | ||
| index_elem: torch.Tensor, | ||
| index_var: str, | ||
| pos: int, | ||
| ) -> tuple[str, int | None]: | ||
| tile_block_id = env.get_tile_index_tensor_block_id(index_elem) | ||
| index_source = ( | ||
| state.codegen.index_var(tile_block_id) | ||
| if tile_block_id is not None | ||
| else index_var | ||
| ) | ||
| mask_block_id = tile_block_id or ( | ||
| env.get_block_id(output_size[pos]) if pos < len(output_size) else None | ||
| ) | ||
| return index_source, mask_block_id | ||
|
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| def handle_cartesian_tensor( | ||
| position: int, index_elem: torch.Tensor, index_var: str | ||
| ) -> bool: | ||
| nonlocal first_tensor_idx, output_idx, tensor_count, k_index | ||
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| assert broadcast_shape is not None | ||
| dims = len(broadcast_shape) | ||
| is_cartesian = ( | ||
| dims >= 2 | ||
| and len(tensor_shapes) == dims | ||
| and all( | ||
| len(shape) == 1 | ||
| or sum(1 for dim in shape if env.size_hint(dim) != 1) <= 1 | ||
| for shape in tensor_shapes | ||
| ) | ||
| ) | ||
| if not is_cartesian: | ||
| return False | ||
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| original_length = _get_padded_iota_original_length(state, position) | ||
| if tensor_count == 0: | ||
| first_tensor_idx = output_idx | ||
| output_idx += dims | ||
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| axis_pos = first_tensor_idx + tensor_count | ||
| expand_axis = tile_strategy.expand_str(output_size, axis_pos) | ||
| index_values.append(f"({index_var}){expand_axis}") | ||
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| if original_length is not None: | ||
| mask_values.setdefault( | ||
| f"(({index_var} < {original_length}){expand_axis})" | ||
| ) | ||
|
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| tensor_count += 1 | ||
| k_index += 1 | ||
| return True | ||
|
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| def handle_broadcast_tensor(index_elem: torch.Tensor, index_var: str) -> bool: | ||
| nonlocal first_tensor_idx, output_idx, tensor_count, k_index | ||
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| assert broadcast_shape is not None | ||
| if tensor_count == 0: | ||
| first_tensor_idx = output_idx | ||
| output_idx += len(broadcast_shape) | ||
|
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| shape = ( | ||
| tensor_shapes[tensor_count] | ||
| if tensor_count < len(tensor_shapes) | ||
| else [1] | ||
| ) | ||
| shape_size = len(shape) | ||
| non_bcast_dims = sum(1 for dim in shape if env.size_hint(dim) != 1) | ||
| is_single_dim = non_bcast_dims <= 1 | ||
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| offset = max(0, len(broadcast_shape) - shape_size) | ||
| non_one_positions = [ | ||
| i for i, dim in enumerate(shape) if env.size_hint(dim) != 1 | ||
| ] | ||
| expand_pos = first_tensor_idx + offset | ||
| if is_single_dim and shape_size > 0: | ||
| rel_pos = non_one_positions[0] if non_one_positions else shape_size - 1 | ||
| expand_pos += rel_pos | ||
|
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| if output_size: | ||
| expand_pos = max(0, min(expand_pos, len(output_size) - 1)) | ||
| else: | ||
| expand_pos = 0 | ||
|
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| # Number of dimensions to process for tensor indexing expansion | ||
| width = ( | ||
| 1 | ||
| if is_single_dim | ||
| else min(shape_size, max(0, len(output_size) - expand_pos)) | ||
| ) | ||
|
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| if width <= 1: | ||
| expand_pos_str = ( | ||
| tile_strategy.expand_str(output_size, expand_pos) | ||
| if index_elem.ndim == 1 | ||
| else "" | ||
| ) | ||
| index_source, mask_block_id = tensor_index_source_and_mask( | ||
| index_elem, | ||
| index_var, | ||
| expand_pos, | ||
| ) | ||
| expand = ( | ||
| expand_pos_str | ||
| if expand_pos_str is not None | ||
| else tile_strategy.expand_str(output_size, expand_pos) | ||
| ) | ||
| index_values.append(f"({index_source}){expand}") | ||
| if tensor_count == 0 and mask_block_id is not None: | ||
| mask_var = state.codegen.mask_var(mask_block_id) | ||
| if mask_var and not _is_size_one( | ||
| fake_value.size(len(index_values) - 1) | ||
| ): | ||
| mask_values.setdefault(f"({mask_var}){expand}") | ||
| else: | ||
| index_values.append(f"({index_var})") | ||
|
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| if tensor_count == 0: | ||
| for pos in [expand_pos + d for d in range(width)]: | ||
| if pos >= len(output_size): | ||
| continue | ||
| block_idx = env.get_block_id(output_size[pos]) | ||
| if block_idx is None: | ||
| continue | ||
| expand_str = tile_strategy.expand_str(output_size, pos) | ||
| mask_var = state.codegen.mask_var(block_idx) | ||
| if mask_var and not _is_size_one( | ||
| fake_value.size(len(index_values) - 1) | ||
| ): | ||
| mask_values.setdefault(f"({mask_var}){expand_str}") | ||
|
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| tensor_count += 1 | ||
| k_index += 1 | ||
| return True | ||
|
|
Contributor
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The changes here complicate this function a lot. Is there a reason it needs to be so complex? Are there really this many different cases?
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Fixes #546.