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The BlockDropout implementation already provides very complete logic for
generating random numbers and executing dropout for the P tensor after
first attention Gemm with capability to support both Warp-Gemm 32x32 and
16x16 as well as to run on both wave32 and wave64 arch.

But in some situation, we only need the block-layer process to generate
random numbers, rather than simultaneously execute dropout in real-time
on the vgpr tile. For example, xformers'
`test_mem_eff_attention.py::test_dropout_ck` requires the host reference
implementation of `attention forward with dropout` to use the same
random numbers to compare & verify the device side implementation of
`attention forward with dropout`, so a standalone kernel to generate
random numbers only is required.

This PR will enable xformers's random_val generating kernel (in file
`ck_tiled_rand_uniform_kernel.h`) to depend on BlockDropout's `Run()`
operator completely to generate random numbers for a `[MPerBlock,
NPerBlock]` tile during the tile iteration, no need to replicate the
logic of BlockDropout in the xformers kernel