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