| import tilelang |
| import torch |
| from tilelang import language as T |
|
|
|
|
| @tilelang.jit |
| def expand_to_mhc_fwd_tl(hidden: int, mhc_mult: int) -> tilelang.JITKernel: |
| n = T.dynamic('num_tokens') |
| h = hidden |
| mhc = mhc_mult |
|
|
| blk_n = 32 |
| blk_h = 128 |
|
|
| @T.prim_func |
| def expand_to_mhc_fwd_kernel( |
| x: T.Tensor[(n, h), T.bfloat16], |
| o: T.Tensor[(n, mhc, h), T.bfloat16], |
| ) -> None: |
| with T.Kernel(T.ceildiv(n, blk_n), T.ceildiv(h, blk_h)) as (pid_i, pid_j): |
| if n > 0: |
| xl = T.alloc_fragment((blk_n, blk_h), T.bfloat16) |
| T.copy(x[pid_i * blk_n, pid_j * blk_h], xl) |
| for m in T.serial(mhc): |
| for ti, tj in T.Parallel(blk_n, blk_h): |
| i = pid_i * blk_n + ti |
| j = pid_j * blk_h + tj |
| if i < n and j < h: |
| o[i, m, j] = xl[ti, tj] |
|
|
| return expand_to_mhc_fwd_kernel |
|
|
|
|
| @tilelang.jit |
| def expand_to_mhc_bwd_tl(hidden: int, mhc_mult: int) -> tilelang.JITKernel: |
| n = T.dynamic('num_tokens') |
| h = hidden |
| mhc = mhc_mult |
|
|
| blk_n = 32 |
| blk_h = 128 |
|
|
| @T.prim_func |
| def expand_to_mhc_bwd_kernel( |
| o_grad: T.Tensor[(n, mhc, h), T.bfloat16], |
| x_grad: T.Tensor[(n, h), T.bfloat16], |
| ) -> None: |
| with T.Kernel(T.ceildiv(n, blk_n), T.ceildiv(h, blk_h)) as (pid_i, pid_j): |
| if n > 0: |
| xgl = T.alloc_fragment((blk_n, blk_h), T.float32) |
| T.fill(xgl, 0) |
| for m in T.serial(mhc): |
| for ti, tj in T.Parallel(blk_n, blk_h): |
| i = pid_i * blk_n + ti |
| j = pid_j * blk_h + tj |
| if i < n and j < h: |
| xgl[ti, tj] += o_grad[i, m, j] |
| T.copy(xgl, x_grad[pid_i * blk_n, pid_j * blk_h]) |
|
|
| return expand_to_mhc_bwd_kernel |
|
|
