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test_non_perfect_loop.py
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test_non_perfect_loop.py
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import tvm
def intrin_vadd(n):
x = tvm.placeholder((n,), name='vx')
y = tvm.placeholder((n,), name='vy')
z = tvm.compute(x.shape, lambda i: x[i] + y[i], name='z')
def intrin_func(ins, outs):
xx, yy = ins
zz = outs[0]
return tvm.call_packed("vadd", xx, yy, zz)
with tvm.build_config(offset_factor=16):
return tvm.decl_tensor_intrin(z.op, intrin_func)
def intrin_gemv(m, n):
w = tvm.placeholder((m, n), name='w')
x = tvm.placeholder((n,), name='x')
k = tvm.reduce_axis((0, n), name='k')
z = tvm.compute((m,), lambda i:
tvm.sum(w[i, k] * x[k], axis=k), name='z')
Wb = tvm.decl_buffer(w.shape, w.dtype,
name="W",
offset_factor=16,
strides=[tvm.var('ldw'), 1])
def intrin_func(ins, outs):
ww, xx = ins
zz = outs[0]
ww_ptr = ww.access_ptr("r")
xx_ptr = xx.access_ptr("r")
zz_ptr = zz.access_ptr("w")
body = tvm.call_packed(
"gemv", ww_ptr, xx_ptr, zz_ptr, n, ww.strides[0])
reset = tvm.call_packed(
"fill_zero", zz_ptr, n)
update = tvm.call_packed(
"gemv_add", ww_ptr, xx_ptr, zz_ptr, n, ww.strides[0])
return body, reset, update
with tvm.build_config(data_alignment=16,
offset_factor=16):
return tvm.decl_tensor_intrin(z.op, intrin_func,
binds={w: Wb})
def intrin_gemv_no_reset(m, n):
w = tvm.placeholder((m, n), name='w')
x = tvm.placeholder((n,), name='x')
k = tvm.reduce_axis((0, n), name='k')
z = tvm.compute((m,), lambda i:
tvm.sum(w[i, k] * x[k], axis=k), name='z')
Wb = tvm.decl_buffer(w.shape, w.dtype,
name="W",
offset_factor=16,
strides=[tvm.var('ldw'), 1])
def intrin_func(ins, outs):
ww, xx = ins
zz = outs[0]
ww_ptr = ww.access_ptr("r")
xx_ptr = xx.access_ptr("r")
zz_ptr = zz.access_ptr("w")
body = tvm.call_packed(
"gemv", ww_ptr, xx_ptr, zz_ptr, n, ww.strides[0])
update = tvm.call_packed(
"gemv_add", ww_ptr, xx_ptr, zz_ptr, n, ww.strides[0])
return body, None, update
with tvm.build_config(data_alignment=16,
offset_factor=16):
return tvm.decl_tensor_intrin(z.op, intrin_func,
binds={w: Wb})
def test_tensorize_vadd():
m = 128
x = tvm.placeholder((m,m), name='x')
y = tvm.placeholder((m,m), name='y')
z = tvm.compute(x.shape, lambda i, j: x[i,j] + y[i,j], name='z')
def check(factor):
s = tvm.create_schedule(z.op)
xo, xi, yo, yi = s[z].tile(z.op.axis[0],z.op.axis[1], factor,factor)
yfuse = s[z].fuse(yo,yi)
print(tvm.lower(s, [x, y, z],simple_mode=True))
vadd = intrin_vadd(factor*factor)
s[z].tensorize(yfuse, vadd)
s = s.normalize()
dom_map = tvm.schedule.InferBound(s)
finfer = tvm.get_global_func("test.op.InferTensorizeRegion")
out_dom, in_dom = finfer(s[z], dom_map)
assert tvm.ir_pass.Equal(out_dom[z.op.axis[0]].extent, factor)
assert tvm.ir_pass.Equal(out_dom[z.op.axis[0]].min, xo * factor)
assert tvm.ir_pass.Equal(in_dom.items()[0][1][0].extent, factor)
fmatch = tvm.get_global_func("test.op.MatchTensorizeBody")
body = fmatch(s[z], out_dom, in_dom, vadd)
assert tvm.ir_pass.Equal(tvm.ir_pass.CanonicalSimplify(body[0]),
tvm.ir_pass.CanonicalSimplify(vadd.op.body[0]))
stmt = tvm.schedule.ScheduleOps(s, dom_map)
print(tvm.lower(s, [x, y, z],simple_mode=True))
#check(16)
check(15)
def test_tensorize_matmul():
n = 1024
m = n
l = n
A = tvm.placeholder((n, l), name='A')
B = tvm.placeholder((m, l), name='B')
k = tvm.reduce_axis((0, l), name='k')
C = tvm.compute((n, m), lambda i, j:
tvm.sum(B[j, k] * A[i, k], axis=k), name='C')
def check(factor):
s = tvm.create_schedule(C.op)
x, y = C.op.axis
yo, yi = s[C].split(y, factor=factor)
gemv = intrin_gemv(factor, l)
s[C].tensorize(yi, gemv)
s = s.normalize()
dom_map = tvm.schedule.InferBound(s)
finfer = tvm.get_global_func("test.op.InferTensorizeRegion")
out_dom, in_dom = finfer(s[C], dom_map)
assert tvm.ir_pass.Equal(out_dom[x].extent, 1)
assert tvm.ir_pass.Equal(out_dom[y].extent, factor)
assert tvm.ir_pass.Equal(out_dom[y].min, yo * factor)
fmatch = tvm.get_global_func("test.op.MatchTensorizeBody")
body = fmatch(s[C], out_dom, in_dom, gemv)
assert tvm.ir_pass.Equal(tvm.ir_pass.CanonicalSimplify(body[0]),
tvm.ir_pass.CanonicalSimplify(gemv.op.body[0]))
stmt = tvm.schedule.ScheduleOps(s, dom_map)
tvm.lower(s, [A, B, C])
def check_rfactor(factor, rfactor):
s = tvm.create_schedule(C.op)
x, y = C.op.axis
rk = C.op.reduce_axis[0]
yo, yi = s[C].split(y, factor=factor)
ro, ri = s[C].split(rk, factor=rfactor)
s[C].reorder(yo, ro, yi, ri)
gemv = intrin_gemv(factor, rfactor)
s[C].tensorize(yi, gemv)
s = s.normalize()
dom_map = tvm.schedule.InferBound(s)
finfer = tvm.get_global_func("test.op.InferTensorizeRegion")
out_dom, in_dom = finfer(s[C], dom_map)
assert tvm.ir_pass.Equal(out_dom[x].extent, 1)
assert tvm.ir_pass.Equal(out_dom[y].extent, factor)
assert tvm.ir_pass.Equal(out_dom[y].min, yo * factor)
fmatch = tvm.get_global_func("test.op.MatchTensorizeBody")
body = fmatch(s[C], out_dom, in_dom, gemv)
assert tvm.ir_pass.Equal(tvm.ir_pass.CanonicalSimplify(body[0]),
tvm.ir_pass.CanonicalSimplify(gemv.op.body[0]))
stmt = tvm.schedule.ScheduleOps(s, dom_map)
tvm.lower(s, [A, B, C])
def check_rfactor_no_reset(factor, rfactor):
s = tvm.create_schedule(C.op)
x, y = C.op.axis
rk = C.op.reduce_axis[0]
yo, yi = s[C].split(y, factor=factor)
ro, ri = s[C].split(rk, factor=rfactor)
s[C].reorder(yo, ro, yi, ri)
gemv = intrin_gemv_no_reset(factor, rfactor)
s[C].tensorize(yi, gemv)
s = s.normalize()
dom_map = tvm.schedule.InferBound(s)
finfer = tvm.get_global_func("test.op.InferTensorizeRegion")
out_dom, in_dom = finfer(s[C], dom_map)
assert tvm.ir_pass.Equal(out_dom[x].extent, 1)
assert tvm.ir_pass.Equal(out_dom[y].extent, factor)
assert tvm.ir_pass.Equal(out_dom[y].min, yo * factor)
fmatch = tvm.get_global_func("test.op.MatchTensorizeBody")
body = fmatch(s[C], out_dom, in_dom, gemv)
assert tvm.ir_pass.Equal(tvm.ir_pass.CanonicalSimplify(body[0]),
tvm.ir_pass.CanonicalSimplify(gemv.op.body[0]))
stmt = tvm.schedule.ScheduleOps(s, dom_map)
tvm.lower(s, [A, B, C])
def check_rfactor_no_reset_multi_reduction(factor, rfactor):
s = tvm.create_schedule(C.op)
x, y = C.op.axis
rk = C.op.reduce_axis[0]
yo, yi = s[C].split(y, factor=factor)
ro, ri = s[C].split(rk, factor=rfactor)
roo, roi = s[C].split(ro, factor=2)
s[C].reorder(yo, roo, roi, yi, ri)
gemv = intrin_gemv_no_reset(factor, rfactor)
s[C].tensorize(yi, gemv)
s = s.normalize()
dom_map = tvm.schedule.InferBound(s)
finfer = tvm.get_global_func("test.op.InferTensorizeRegion")
out_dom, in_dom = finfer(s[C], dom_map)
assert tvm.ir_pass.Equal(out_dom[x].extent, 1)
assert tvm.ir_pass.Equal(out_dom[y].extent, factor)
assert tvm.ir_pass.Equal(out_dom[y].min, yo * factor)
fmatch = tvm.get_global_func("test.op.MatchTensorizeBody")
body = fmatch(s[C], out_dom, in_dom, gemv)
assert tvm.ir_pass.Equal(tvm.ir_pass.CanonicalSimplify(body[0]),
tvm.ir_pass.CanonicalSimplify(gemv.op.body[0]))
stmt = tvm.schedule.ScheduleOps(s, dom_map)
tvm.lower(s, [A, B, C])
check(16)
check_rfactor(16, 16)
check_rfactor_no_reset(16, 16)
check_rfactor_no_reset_multi_reduction(16, 16)
# This tests whether algorithm and intrinsics expressions are simplified
# as much as possible first and then checked for equality. See Issue #696
def test_tensorize_op():
def op_intrin():
bh = 9
bw = 9
x = tvm.placeholder((5, 5), name='A')
y = tvm.compute((bh, bw), lambda i,j: x[j/3 + i%3, j%3+ i/3])
def intrin_func(ins, outs):
xx, = ins
zz = outs[0]
return tvm.call_packed("op", xx, zz)
with tvm.build_config(offset_factor=2):
return tvm.decl_tensor_intrin(y.op, intrin_func)
A = tvm.placeholder((5, 5), name='A')
B = tvm.compute((9,9), lambda i, j: A[j/3 + i%3, j%3 + i/3])
bt = op_intrin()
s = tvm.create_schedule(B.op)
x,y = B.op.axis
s[B].tensorize(x, bt)
s = s.normalize()
tvm.lower(s, [A, B])
if __name__ == "__main__":
test_tensorize_vadd()
test_tensorize_matmul()
test_tensorize_op()