Mojo function
advanced_indexing_getitem
advanced_indexing_getitem[input_rank: Int, index_rank: Int, input_type: DType, index_type: DType, //, start_axis: Int, num_index_tensors: Int, target: StringSlice[StaticConstantOrigin], single_thread_blocking_override: Bool, trace_description: StringSlice[StaticConstantOrigin], input_tensor_fn: fn[Int](Index[input_rank]) capturing -> SIMD[input_type, $0], indices_fn: fn[Int](Index[index_rank]) capturing -> SIMD[index_type, 1]](out_tensor: NDBuffer[input_type, ((num_index_tensors * -1) + index_rank + input_rank), origin], in_tensor_strides: Index[input_rank], ctx: DeviceContextPtr)
Implement basic numpy-style advanced indexing.
This is designed to be fused with other view-producing operations to implement full numpy-indexing semantics.
This assumes the dimensions in input_tensor
not indexed by index tensors
are ":", ie selecting all indices along the slice. For example in numpy:
# rank(indices1) == 3
# rank(indices2) == 3
out_tensor = input_tensor[:, :, :, indices1, indices2, :, :]
# rank(indices1) == 3
# rank(indices2) == 3
out_tensor = input_tensor[:, :, :, indices1, indices2, :, :]
We calculate the following for all valid valued indexing variables:
out_tensor[a, b, c, i, j, k, d, e] = input_tensor[
a, b, c,
indices1[i, j, k],
indices2[i, j, k],
d, e
]
out_tensor[a, b, c, i, j, k, d, e] = input_tensor[
a, b, c,
indices1[i, j, k],
indices2[i, j, k],
d, e
]
In this example start_axis = 3
and num_index_tensors = 2
.
TODO(GEX-1951): Support boolean tensor mask support TODO(GEX-1952): Support non-contiguous indexing tensor case TODO(GEX-1953): Support fusion (especially view-fusion)
Parameters:
- input_rank (
Int
): The rank of the input tensor. - index_rank (
Int
): The rank of the indexing tensors. - input_type (
DType
): The dtype of the input tensor. - index_type (
DType
): The dtype of the indexing tensors. - start_axis (
Int
): The first dimension in input where the indexing tensors are applied. It is assumed the indexing tensors are applied in consecutive dimensions. - num_index_tensors (
Int
): The number of indexing tensors. - target (
StringSlice[StaticConstantOrigin]
): The target architecture to operation on. - single_thread_blocking_override (
Bool
): If True, then the operation is run synchronously using a single thread. - trace_description (
StringSlice[StaticConstantOrigin]
): For profiling, the trace name the operation will appear under. - input_tensor_fn (
fn[Int](Index[input_rank]) capturing -> SIMD[input_type, $0]
): Fusion lambda for the input tensor. - indices_fn (
fn[Int](Index[index_rank]) capturing -> SIMD[index_type, 1]
): Fusion lambda for the indices tensors.
Args:
- out_tensor (
NDBuffer[input_type, ((num_index_tensors * -1) + index_rank + input_rank), origin]
): The output tensor to write to. - in_tensor_strides (
Index[input_rank]
): The strides of the input tensor. - ctx (
DeviceContextPtr
): The DeviceContextPtr as prepared by the graph compiler.
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