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This commit is contained in:
Phil Tillet
2023-01-09 16:29:45 -08:00
parent 137e866bd2
commit 3fefcd78d4
1 changed files with 27 additions and 27 deletions
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54
python/tutorials/06-fused-attention.py
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@@ -191,7 +191,7 @@ def _bwd_kernel(
tl.store(dv_ptrs, dv) tl.store(dv_ptrs, dv)
tl.store(dk_ptrs, dk) tl.store(dk_ptrs, dk)
# _bwd_kernel = triton.compile("./slow.ttgir", num_warps=8) _bwd_kernel = triton.compile("./slow.ttgir", num_warps=8)
# _bwd_kernel = triton.compile("./unoptimized.ttgir", num_warps=8) # _bwd_kernel = triton.compile("./unoptimized.ttgir", num_warps=8)
# _bwd_kernel = triton.compile("./bwd.ttgir", num_warps=8) # _bwd_kernel = triton.compile("./bwd.ttgir", num_warps=8)
# _fwd_kernel = triton.compile("./fails.ptx", num_warps=4, shared=18432) # _fwd_kernel = triton.compile("./fails.ptx", num_warps=4, shared=18432)
@@ -260,34 +260,34 @@ class _attention(torch.autograd.Function):
BLOCK_M=ctx.BLOCK, D_HEAD=ctx.BLOCK_DMODEL, BLOCK_M=ctx.BLOCK, D_HEAD=ctx.BLOCK_DMODEL,
) )
# _bwd_kernel[(ctx.grid[1],1,1)]( _bwd_kernel[(ctx.grid[1],1,1)](
# q.data_ptr(), k.data_ptr(), v.data_ptr(), ctx.sm_scale, q.data_ptr(), k.data_ptr(), v.data_ptr(), ctx.sm_scale,
# o.data_ptr(), do_scaled.data_ptr(), o.data_ptr(), do_scaled.data_ptr(),
# dq.data_ptr(), dk.data_ptr(), dv.data_ptr(), dq.data_ptr(), dk.data_ptr(), dv.data_ptr(),
# l.data_ptr(), m.data_ptr(), l.data_ptr(), m.data_ptr(),
# delta.data_ptr(), delta.data_ptr(),
# q.stride(0), q.stride(1), q.stride(2), q.stride(0), q.stride(1), q.stride(2),
# k.stride(0), k.stride(1), k.stride(2), k.stride(0), k.stride(1), k.stride(2),
# v.stride(0), v.stride(1), v.stride(2), v.stride(0), v.stride(1), v.stride(2),
# q.shape[0], q.shape[1], q.shape[2],
# ctx.grid[0]
# )
pgm = _bwd_kernel[(ctx.grid[1],)](
q, k, v, ctx.sm_scale,
o, do_scaled,
dq, dk, dv,
l, m,
delta,
q.stride(0), q.stride(1), q.stride(2), q.stride(3),
k.stride(0), k.stride(1), k.stride(2), k.stride(3),
v.stride(0), v.stride(1), v.stride(2), v.stride(3),
q.shape[0], q.shape[1], q.shape[2], q.shape[0], q.shape[1], q.shape[2],
ctx.grid[0], ctx.grid[0]
BLOCK_M=ctx.BLOCK, BLOCK_N=ctx.BLOCK,
BLOCK_DMODEL=ctx.BLOCK_DMODEL, num_warps=8,
num_stages=1,
) )
# pgm = _bwd_kernel[(ctx.grid[1],)](
# q, k, v, ctx.sm_scale,
# o, do_scaled,
# dq, dk, dv,
# l, m,
# delta,
# q.stride(0), q.stride(1), q.stride(2), q.stride(3),
# k.stride(0), k.stride(1), k.stride(2), k.stride(3),
# v.stride(0), v.stride(1), v.stride(2), v.stride(3),
# q.shape[0], q.shape[1], q.shape[2],
# ctx.grid[0],
# BLOCK_M=ctx.BLOCK, BLOCK_N=ctx.BLOCK,
# BLOCK_DMODEL=ctx.BLOCK_DMODEL, num_warps=8,
# num_stages=1,
# )
# print(pgm.asm["ttgir"]) # print(pgm.asm["ttgir"])
# exit() # exit()
return dq, dk, dv, None return dq, dk, dv, None