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Mojo function

dispatch_1x1x1_matmul_conv3d

def dispatch_1x1x1_matmul_conv3d[input_type: DType, filter_type: DType, output_type: DType, //, filter_is_fcrs: Bool = False, maybe_epilogue_func: Optional[def[dtype: DType, rank: Int, width: SIMDLength, alignment: Int = Int(1)](IndexList[rank], SIMD[dtype, width]) capturing thin -> None] = None](input: TileTensor[input_type, Storage=input.Storage, address_space=input.address_space, linear_idx_type=input.linear_idx_type], filter: TileTensor[filter_type, Storage=filter.Storage, address_space=filter.address_space, linear_idx_type=filter.linear_idx_type], output: TileTensor[output_type, Storage=output.Storage, address_space=output.address_space, linear_idx_type=output.linear_idx_type], stride: IndexList[Int(3)], dilation: IndexList[Int(3)], symmetric_padding: IndexList[Int(3)], num_groups: Int, ctx: DeviceContext) -> Bool

Try to dispatch a 1x1x1 3D conv directly as a single _matmul_gpu.

Returns True if the conv was handled; False if the caller should fall back to another implementation.

Skips on: non-bf16 dtype, grouped conv, dilation != 1, stride != 1, non-zero padding, kernel size other than 1x1x1, and K (= C_in) below the matmul's minimum useful size.

Parameters:

input_type (DType): Element type of the input tensor (inferred).
filter_type (DType): Element type of the filter tensor (inferred).
output_type (DType): Element type of the output tensor (inferred).
filter_is_fcrs (Bool): Whether the filter is in FCQRS layout (True) or QRSCF layout (False) (defaults to False).
maybe_epilogue_func (Optional[def[dtype: DType, rank: Int, width: SIMDLength, alignment: Int = Int(1)](IndexList[rank], SIMD[dtype, width]) capturing thin -> None]): Optional SIMD elementwise epilogue invoked per output element in 5D coordinates (defaults to None).

Returns:

Bool