Skip to content

[TOPI] Allow batch matmul to be fused into injective ops #4537

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
kevinthesun merged 1 commit into from
Dec 18, 2019
Merged

[TOPI] Allow batch matmul to be fused into injective ops #4537

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
45 changes: 26 additions & 19 deletions topi/python/topi/x86/batch_matmul.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -92,33 +92,40 @@ def schedule_batch_matmul(cfg, outs):
def _callback(op):
if "batch_matmul" in op.tag:
C = op.output(0)
A, B = s[C].op.input_tensors
A, B = op.input_tensors
_, M, K = get_const_tuple(A.shape)
_, _, N = get_const_tuple(C.shape)

if op not in s.outputs:
s[C].compute_inline()
O = outs[0]
else:
O = C

CC = s.cache_write(C, "global")

# create tuning space
cfg.define_split("tile_y", M, num_outputs=2)
cfg.define_split("tile_x", N, num_outputs=2)
cfg.define_split("tile_k", K, num_outputs=2)

k, = s[C].op.reduce_axis

ko, ki = cfg["tile_k"].apply(s, C, k)
CC = s.rfactor(C, ki)

b, y, x = s[C].op.axis
yo, yi = cfg["tile_y"].apply(s, C, y)
xo, xi = cfg["tile_x"].apply(s, C, x)
s[C].reorder(b, yo, xo, yi, xi)
bxyo = s[C].fuse(b, yo, xo)
s[C].parallel(bxyo)
s[C].fuse(yi, xi)

s[CC].compute_at(s[C], bxyo)
_, _, y, x = s[CC].op.axis
s[CC].fuse(y, x)
s[CC].vectorize(s[CC].op.axis[0])
s[C].pragma(bxyo, 'auto_unroll_max_step', 16)
b, y, x = s[O].op.axis
yo, yi = cfg["tile_y"].apply(s, O, y)
xo, xi = cfg["tile_x"].apply(s, O, x)
s[O].reorder(b, yo, xo, yi, xi)
bxyo = s[O].fuse(b, yo, xo)
s[O].parallel(bxyo)

s[CC].compute_at(s[O], bxyo)
k, = s[CC].op.reduce_axis
ko, ki = cfg["tile_k"].apply(s, CC, k)

Crf = s.rfactor(CC, ki)
s[Crf].compute_at(s[CC], s[CC].op.axis[0])
_, _, y, x = s[Crf].op.axis
s[Crf].fuse(y, x)
s[Crf].vectorize(s[Crf].op.axis[0])
s[O].pragma(bxyo, 'auto_unroll_max_step', 16)

traverse_inline(s, outs[0].op, _callback)
return s
Expand Down