def test():
env = nnpu.get_env()
a = tvm.placeholder((4, 16), env.cfg['dtype_w'], 'a')
sph = ScheduleProcHelper()
a_buf, a_dram = nnpu.utils.CopyHtoBuf(a, 'a', sph)
k = tvm.reduce_axis((0, 16), 'k')
c_buf = tvm.compute((4, 1), lambda i, j: tvm.sum(a_buf[i,k], axis=k), 'c_buf')
sph.MarkScope(c_buf)
c_host, c_dram = nnpu.utils.CopyBufToH(c_buf, 'c', sph)
k1 = tvm.reduce_axis((0, 16), 'k1')
max_buf = tvm.compute((4, 1), lambda i, j: tvm.max(a_buf[i,k1], axis=k1), 'max_buf')
sph.MarkScope(max_buf)
max_host, max_dram = nnpu.utils.CopyBufToH(max_buf, 'max', sph)
k2 = tvm.reduce_axis((0, 16), 'k2')
min_buf = tvm.compute((4, 1), lambda i, j: tvm.min(a_buf[i,k2], axis=k2), 'min_buf')
sph.MarkScope(min_buf)
min_host, min_dram = nnpu.utils.CopyBufToH(min_buf, 'min', sph)
# create schedule and tensorize
s = tvm.create_schedule([c_host.op, max_host.op, min_host.op])
sph.Transform(s)
s[c_buf].tensorize(s[c_buf].op.axis[1], env.intrins.get('VReduceSum', mode='w'))
s[max_buf].tensorize(s[max_buf].op.axis[1], env.intrins.get('VReduceMax', mode='w'))
s[min_buf].tensorize(s[min_buf].op.axis[1], env.intrins.get('VReduceMin', mode='w'))
# build
print(nnpu.lower(s, [a, c_host, max_host, min_host], simple_mode=True))
func = nnpu.build(s, [a, c_host, max_host, min_host], 'nnpu', 'llvm', name='nnpu_func')
# create data and run
ctx = tvm.nd.TVMContext(13, 0)
a_np = np.random.randint(size=(4, 16), dtype=a.dtype, low = 0, high = 64)
#a_np = np.random.random(size=shape).astype(a_host.dtype)
a_nd = tvm.nd.array(a_np, ctx)
c_nd = tvm.nd.array(np.zeros((4, 1)).astype(c_host.dtype), ctx)
max_nd = tvm.nd.array(np.zeros((4, 1)).astype(c_host.dtype), ctx)
min_nd = tvm.nd.array(np.zeros((4, 1)).astype(c_host.dtype), ctx)
func(a_nd, c_nd, max_nd, min_nd)
# check results
gt = np.sum(a_np, axis=(1,), keepdims=True)
np.testing.assert_allclose(c_nd.asnumpy(), gt)
np.testing.assert_allclose(max_nd.asnumpy(), np.max(a_np, axis=(1,), keepdims=True))
np.testing.assert_allclose(min_nd.asnumpy(), np.min(a_np, axis=(1,), keepdims=True))
print('test passed')
def test():
env = nnpu.get_env()
a = tvm.placeholder((16,16), env.cfg['dtype_n'], 'a')
sph = ScheduleProcHelper()
Imm = tvm.const(7, env.cfg['dtype_n'])
a_buf, a_dram = nnpu.utils.CopyHtoBuf(a, 'a', sph)
add_buf = tvm.compute((16,16), lambda i,j: Imm+a_buf[i][j], 'add_buf')
sph.MarkScope(add_buf)
add_host, add_dram = nnpu.utils.CopyBufToH(add_buf, 'add', sph)
dtype_w = env.cfg['dtype_w']
mul_buf = tvm.compute((16,16), lambda i,j: a_buf[i][j].astype(dtype_w) * Imm.astype(dtype_w),
'mul_buf')
sph.MarkScope(mul_buf)
mul_host, mul_dram = nnpu.utils.CopyBufToH(mul_buf, 'mul', sph)
rsub_buf = tvm.compute((16,16), lambda i,j: Imm-a_buf[i][j], 'rsub_buf')
sph.MarkScope(rsub_buf)
rsub_host, rsub_dram = nnpu.utils.CopyBufToH(rsub_buf, 'rsub', sph)
s = tvm.create_schedule([add_host.op,mul_host.op,rsub_host.op])
sph.Transform(s)
s[add_buf].tensorize(s[add_buf].op.axis[0], env.intrins.get('MAddI',
shape=(16,16), imm_value=Imm.value, mode='n'))
s[mul_buf].tensorize(s[mul_buf].op.axis[0], env.intrins.get('MMulI',
shape=(16,16), imm_value=Imm.value, mode='inc'))
s[rsub_buf].tensorize(s[rsub_buf].op.axis[0], env.intrins.get('ISubM',
shape=(16,16), imm_value=Imm.value, mode='n'))
print(nnpu.lower(s, [a,add_host,mul_host,rsub_host], simple_mode=True))
func = nnpu.build(s, [a,add_host,mul_host,rsub_host], 'nnpu', 'llvm', name='nnpu_vmuli')
ctx = tvm.nd.TVMContext(13, 0)
a_np = np.random.randint(size=(16,16), dtype=a.dtype, low = 3, high = 100)
a_nd = tvm.nd.array(a_np, ctx)
add_nd = tvm.nd.array(np.zeros((16,16)).astype(add_host.dtype), ctx)
mul_nd = tvm.nd.array(np.zeros((16,16)).astype(mul_host.dtype), ctx)
rsub_nd = tvm.nd.array(np.zeros((16,16)).astype(rsub_host.dtype), ctx)
func(a_nd, add_nd,mul_nd,rsub_nd)
print(a_nd.asnumpy())
print('add result is: ')
print(add_nd.asnumpy())
np.testing.assert_allclose(add_nd.asnumpy(), a_np + Imm.value)
print('mul result is: ')
print(mul_nd.asnumpy())
np.testing.assert_allclose(mul_nd.asnumpy(), a_np.astype(dtype_w) * Imm.value)
print('rsub result is: ')
print(rsub_nd.asnumpy())
np.testing.assert_allclose(rsub_nd.asnumpy(), Imm.value - a_np )
print('test passed')
def test():
env = nnpu.get_env()
shape = (8, 16)
a = tvm.placeholder(shape, env.cfg['dtype_n'], 'a')
b = tvm.placeholder(shape, env.cfg['dtype_n'], 'b')
sph = ScheduleProcHelper()
a_buf, a_dram = nnpu.utils.CopyHtoBuf(a, 'a', sph)
b_buf, b_dram = nnpu.utils.CopyHtoBuf(b, 'b', sph)
dtype_w = env.cfg['dtype_w']
k = tvm.reduce_axis((0, 16), 'k')
dot_buf = tvm.compute((8, ), lambda i: tvm.sum(
a_buf[i, k].astype(dtype_w) * b_buf[i, k].astype(dtype_w), k),
'dot_buf')
sph.MarkScope(dot_buf)
dot_host, dot_dram = nnpu.utils.CopyBufToH(dot_buf, 'sum', sph)
s = tvm.create_schedule(dot_host.op)
sph.Transform(s)
s[dot_buf].tensorize(s[dot_buf].op.axis[0],
env.intrins.get('MRowDot', shape=shape, mode='inc'))
print(nnpu.lower(s, [a, b, dot_host], simple_mode=True))
func = nnpu.build(s, [a, b, dot_host], 'nnpu', 'llvm', name='nnpu_func')
print('------------------- device module 1 llvm IR: ')
print(func.imported_modules[0].get_source('ll'))
print('------------------- device module 1 asm code: ')
print(func.imported_modules[0].get_source('asm'))
ctx = tvm.nd.TVMContext(13, 0)
a_np = np.random.randint(size=(8, 16), dtype=a.dtype, low=-32, high=32)
#a_np = np.random.random(size=shape).astype(a_host.dtype)
a_nd = tvm.nd.array(a_np, ctx)
b_np = np.random.randint(size=(8, 16), dtype=b.dtype, low=-32, high=32)
b_nd = tvm.nd.array(b_np, ctx)
c_nd = tvm.nd.array(np.zeros((8, )).astype(dot_host.dtype), ctx)
func(a_nd, b_nd, c_nd)
#print('a = ')
#print(a_np)
#print('b = ')
#print(b_np)
print(c_nd.asnumpy())
print('ground truth is')
gt = np.multiply(a_np, b_np, dtype=dot_host.dtype)
gt = np.sum(gt, axis=1)
print(gt)
np.testing.assert_allclose(c_nd.asnumpy(), gt)
def test():
env = nnpu.get_env()
shape = (16, 16)
flatten_shape = (shape[0] * shape[1],)
a = tvm.placeholder(flatten_shape, env.cfg['dtype_n'], 'a')
b = tvm.placeholder(flatten_shape, env.cfg['dtype_n'], 'b')
sph = ScheduleProcHelper()
a_buf, a_dram = nnpu.utils.CopyHtoBuf(a, 'a', sph)
b_buf, b_dram = nnpu.utils.CopyHtoBuf(b, 'b', sph)
sum_buf = tvm.compute(flatten_shape, lambda i: a_buf[i] + b_buf[i], 'sum_buf')
sph.MarkScope(sum_buf)
sum_host, sum_dram = nnpu.utils.CopyBufToH(sum_buf, 'sum', sph)
s = tvm.create_schedule([sum_host.op])
sph.Transform(s)
xo, xi = s[sum_buf].split(sum_buf.op.axis[0], 16)
s[sum_buf].tensorize(xo, env.intrins.get('MAddM', shape=shape, mode='n'))
print(nnpu.lower(s, [a, b, sum_host], simple_mode=True))
func = nnpu.build(s, [a, b, sum_host], 'nnpu', 'llvm', name='nnpu_exp')
print('------------------- device module 1 llvm IR: ')
print(func.imported_modules[0].get_source('ll'))
print('------------------- device module 1 asm code: ')
print(func.imported_modules[0].get_source('asm'))
ctx = tvm.nd.TVMContext(13, 0)
a_np = np.random.randint(size=flatten_shape, dtype=a.dtype, low = 0, high = 23)
#a_np = np.random.random(size=shape).astype(a_host.dtype)
a_nd = tvm.nd.array(a_np, ctx)
b_np = np.random.randint(size=flatten_shape, dtype=b.dtype, low = 0, high = 23)
b_nd = tvm.nd.array(b_np, ctx)
c_nd = tvm.nd.array(np.zeros(flatten_shape).astype(sum_host.dtype), ctx)
func(a_nd, b_nd, c_nd)
print('a = ')
print(a_np)
print('b = ')
print(b_np)
print('a + b = ')
print(c_nd.asnumpy())
print("numpy ground truth is")
print(a_np + b_np)
np.testing.assert_allclose(c_nd.asnumpy(), a_np + b_np)
def test():
env = nnpu.get_env()
nnpu.set_device(env)
shape = (16, )
bigshape = (4, 64)
dtype_n, dtype_w = env.cfg['dtype_n'], env.cfg['dtype_w']
sph = ScheduleProcHelper()
a = tvm.placeholder(bigshape, dtype_n, 'a')
a_buf, a_dram = nnpu.utils.CopyHtoBuf(a, 'a', sph)
str_op = 'VAddMerge'
k = tvm.reduce_axis((0, 4), 'k')
c_buf = tvm.compute((64, ), lambda i: tvm.sum(a_buf[k, i], axis=k),
'c_buf')
sph.MarkScope(c_buf)
c_host, c_dram = nnpu.utils.CopyBufToH(c_buf, 'c', sph)
s = tvm.create_schedule(c_host.op)
sph.Transform(s)
#tensorize
ko, ki = s[c_buf].split(c_buf.op.reduce_axis[0], factor=1)
xo, xi = s[c_buf].split(c_buf.op.axis[0], factor=shape[0])
s[c_buf].reorder(xo, ko, ki, xi)
#s[c_buf].tensorize(ki, env.intrins.get(str_op, mode='n'))
print(nnpu.lower(s, [a, c_host], simple_mode=True))
exit()
func = nnpu.build(s, [a, c_host], 'nnpu', 'llvm', name='nnpu_func')
ctx = tvm.nd.TVMContext(13, 0)
a_np = np.random.randint(size=bigshape, dtype=a.dtype, low=-4, high=4)
a_nd = tvm.nd.array(a_np, ctx)
c_nd = tvm.nd.array(np.zeros((64, ), dtype=c_host.dtype), ctx)
func(a_nd, c_nd)
print(str_op)
print(c_nd.asnumpy())
gt = np.sum(a_np, axis=0, dtype=dtype_w)
print('ground truth=')
print(gt)
np.testing.assert_allclose(c_nd.asnumpy(), gt)
def test():
env = nnpu.get_env()
shape = (16, )
bigshape = (128, )
dtype_n, dtype_w = env.cfg['dtype_n'], env.cfg['dtype_w']
assert bigshape[0] % shape[0] == 0, 'the big vctr size is wrong'
n_sheet = bigshape[0] // shape[0]
sph = ScheduleProcHelper()
a = tvm.placeholder(bigshape, dtype_n, 'a')
b = tvm.placeholder(bigshape, dtype_n, 'b')
a_buf, a_dram = nnpu.utils.CopyHtoBuf(a, 'a', sph)
b_buf, b_dram = nnpu.utils.CopyHtoBuf(b, 'b', sph)
strop = 'VAddV'
c_buf = tvm.compute(bigshape, lambda *i: a_buf(*i) + b_buf(*i), 'c_buf')
sph.MarkScope(c_buf)
c_host, c_dram = nnpu.utils.CopyBufToH(c_buf, 'sum', sph)
s = tvm.create_schedule(c_host.op)
sph.Transform(s)
#tensorize
xo, xi = s[c_buf].split(c_buf.op.axis[0], factor=shape[0])
s[c_buf].reorder(xo, xi)
s[c_buf].tensorize(xi, env.intrins.get(strop, mode='n'))
print(nnpu.lower(s, [a, b, c_host], simple_mode=True))
func = nnpu.build(s, [a, b, c_host], 'nnpu', 'llvm', name='nnpu_func')
ctx = tvm.nd.TVMContext(13, 0)
a_np = np.random.randint(size=bigshape, dtype=a.dtype, low=-4, high=4)
a_nd = tvm.nd.array(a_np, ctx)
b_np = np.random.randint(size=bigshape, dtype=b.dtype, low=-4, high=4)
b_nd = tvm.nd.array(b_np, ctx)
c_nd = tvm.nd.array(np.zeros(bigshape, dtype=c_host.dtype), ctx)
func(a_nd, b_nd, c_nd)
print(strop)
print(c_nd.asnumpy())
gt = a_np + b_np
np.testing.assert_allclose(c_nd.asnumpy(), gt)
def test():
env = nnpu.get_env()
a = tvm.placeholder((4, 16), 'int16', 'a')
b = tvm.placeholder((16, ), 'int16', 'b')
sph = ScheduleProcHelper()
a_buf, a_dram = nnpu.utils.CopyHtoBuf(a, 'a', sph)
b_buf, b_dram = nnpu.utils.CopyHtoBuf(b, 'b', sph)
k = tvm.reduce_axis((0, 16), 'k')
c_buf = tvm.compute(
(4, 1), lambda i, j: tvm.sum(a_buf[i, k] * b_buf[k], axis=k), 'c_buf')
sph.MarkScope(c_buf)
c_host, c_dram = nnpu.utils.CopyBufToH(c_buf, 'c', sph)
s = tvm.create_schedule(c_host.op)
sph.Transform(s)
print(s[c_buf])
s[c_buf].tensorize(s[c_buf].op.axis[1], env.intrins.get('VDotV', mode='w'))
print(nnpu.lower(s, [a, b, c_host], simple_mode=True))
func = nnpu.build(s, [a, b, c_host], 'nnpu', 'llvm', name='nnpu_func')
print('------------------- device module 1 llvm IR: ')
print(func.imported_modules[0].get_source('ll'))
print('------------------- device module 1 asm code: ')
print(func.imported_modules[0].get_source('asm'))
ctx = tvm.nd.TVMContext(13, 0)
a_np = np.random.randint(size=(4, 16), dtype=a.dtype, low=0, high=64)
#a_np = np.random.random(size=shape).astype(a_host.dtype)
a_nd = tvm.nd.array(a_np, ctx)
b_np = np.random.randint(size=(16, ), dtype=b.dtype, low=0, high=64)
b_nd = tvm.nd.array(b_np, ctx)
c_nd = tvm.nd.array(np.zeros((4, 1)).astype(c_host.dtype), ctx)
func(a_nd, b_nd, c_nd)
print(c_nd.asnumpy())
print("numpy ground truth is")
print(np.dot(a_np, b_np))
def test():
env = nnpu.get_env()
nnpu.set_device(env)
a = tvm.placeholder((4, 4, 16), 'int16', 'a')
#b = tvm.placeholder((16, ), 'int16', 'b')
sph = ScheduleProcHelper()
a_buf, a_dram = nnpu.utils.CopyHtoBuf(a, 'a', sph)
#b_buf, b_dram = nnpu.utils.CopyHtoBuf(b, 'b', sph)
k = tvm.reduce_axis((0, 4), 'k0')
c_buf = tvm.compute((4, 16), lambda i, j: tvm.sum(a_buf[k, i, j], axis=k),
'c_buf')
sph.MarkScope(c_buf)
c_host, c_dram = nnpu.utils.CopyBufToH(c_buf, 'c', sph)
s = tvm.create_schedule(c_host.op)
sph.Transform(s)
ko, ki = s[c_buf].split(c_buf.op.reduce_axis[0], factor=1)
s[c_buf].reorder(c_buf.op.axis[0], ko, ki, c_buf.op.axis[1])
s[c_buf].tensorize(ki, env.intrins.get('VAddMerge', mode='w', nDim=3))
print(nnpu.lower(s, [a, c_host], simple_mode=True))
func = nnpu.build(s, [a, c_host], 'nnpu', 'llvm', name='nnpu_exp')
ctx = tvm.nd.TVMContext(13, 0)
a_np = np.random.randint(size=(4, 4, 16),
dtype=a.dtype,
low=-4000,
high=4000)
#a_np = np.random.random(size=shape).astype(a_host.dtype)
a_nd = tvm.nd.array(a_np, ctx)
c_nd = tvm.nd.array(np.zeros((4, 16)).astype(c_host.dtype), ctx)
func(a_nd, c_nd)
print(c_nd.asnumpy())
print("numpy ground truth is")
gt = np.sum(a_np, axis=0)
print(gt)
def test():
env = nnpu.get_env()
dtype_n, dtype_w = env.cfg['dtype_n'], env.cfg['dtype_w']
a = tvm.placeholder((64, ), dtype_n, 'a')
b = tvm.placeholder((1, ), dtype_n, 'b')
sph = ScheduleProcHelper()
a_buf, a_dram = nnpu.utils.CopyHtoBuf(a, 'a', sph)
b_buf, b_dram = nnpu.utils.CopyHtoBuf(b, 'b', sph)
c_buf = tvm.compute((64, ), lambda i: a_buf[i] + b_buf[0], 'c_buf')
sph.MarkScope(c_buf)
c_host, _ = nnpu.utils.CopyBufToH(c_buf, 'c', sph)
sub_buf = tvm.compute((64, ), lambda i: a_buf[i] - b_buf[0], 'sub_buf')
sph.MarkScope(sub_buf)
sub_host, _ = nnpu.utils.CopyBufToH(sub_buf, 'sub', sph)
rsub_buf = tvm.compute((64, ), lambda i: b_buf[0] - a_buf[i], 'rsub_buf')
sph.MarkScope(rsub_buf)
rsub_host, _ = nnpu.utils.CopyBufToH(rsub_buf, 'rsub', sph)
mul_buf = tvm.compute(
(64, ), lambda i: a_buf[i].astype(dtype_w) * b_buf[0].astype(dtype_w),
'mul_buf')
sph.MarkScope(mul_buf)
mul_host, _ = nnpu.utils.CopyBufToH(mul_buf, 'mul', sph)
div_buf = tvm.compute((64, ), lambda i: a_buf[i] / b_buf[0], 'div_buf')
sph.MarkScope(div_buf)
div_host, _ = nnpu.utils.CopyBufToH(div_buf, 'div', sph)
rdiv_buf = tvm.compute((64, ), lambda i: b_buf[0] / a_buf[i], 'rdiv_buf')
sph.MarkScope(rdiv_buf)
rdiv_host, _ = nnpu.utils.CopyBufToH(rdiv_buf, 'rdiv', sph)
gtm_buf = tvm.compute((64, ), lambda i: tvm.max(a_buf[i], b_buf[0]),
'gtm_buf')
sph.MarkScope(gtm_buf)
gtm_host, gtm_dram = nnpu.utils.CopyBufToH(gtm_buf, 'gtm', sph)
s = tvm.create_schedule([
c_host.op, sub_host.op, mul_host.op, rsub_host.op, div_host.op,
rdiv_host.op, gtm_host.op
])
sph.Transform(s)
xo, xi = s[c_buf].split(c_buf.op.axis[0], 16)
s[c_buf].tensorize(xi, env.intrins.get('VAddS', mode='n'))
xo, xi = s[sub_buf].split(sub_buf.op.axis[0], 16)
s[sub_buf].tensorize(xi, env.intrins.get('VSubS', mode='n'))
xo, xi = s[rsub_buf].split(rsub_buf.op.axis[0], 16)
s[rsub_buf].tensorize(xi, env.intrins.get('SSubV', mode='n'))
xo, xi = s[mul_buf].split(mul_buf.op.axis[0], 16)
s[mul_buf].tensorize(xi, env.intrins.get('VMulS', mode='inc'))
xo, xi = s[div_buf].split(div_buf.op.axis[0], 16)
s[div_buf].tensorize(xi, env.intrins.get('VDivS', mode='n'))
xo, xi = s[rdiv_buf].split(rdiv_buf.op.axis[0], 16)
s[rdiv_buf].tensorize(xi, env.intrins.get('SDivV', mode='n'))
xo, xi = s[gtm_buf].split(gtm_buf.op.axis[0], 16)
s[gtm_buf].tensorize(xi, env.intrins.get('VGTMS', mode='n'))
print(
nnpu.lower(s, [
a, b, c_host, sub_host, mul_host, rsub_host, div_host, rdiv_host,
gtm_host
],
simple_mode=True))
func = nnpu.build(s, [
a, b, c_host, sub_host, mul_host, rsub_host, div_host, rdiv_host,
gtm_host
],
'nnpu',
'llvm',
name='nnpu_exp')
ctx = tvm.nd.TVMContext(13, 0)
a_np = np.random.randint(size=(64, ), dtype=a.dtype, low=1, high=63)
#a_np = np.random.random(size=shape).astype(a_host.dtype)
a_nd = tvm.nd.array(a_np, ctx)
b_np = np.random.randint(size=(1, ), dtype=b.dtype, low=2, high=31)
b_nd = tvm.nd.array(b_np, ctx)
c_nd = tvm.nd.array(np.zeros((64, )).astype(c_host.dtype), ctx)
sub_nd = tvm.nd.array(np.zeros((64, )).astype(sub_host.dtype), ctx)
rsub_nd = tvm.nd.array(np.zeros((64, )).astype(rsub_host.dtype), ctx)
mul_nd = tvm.nd.array(np.zeros((64, )).astype(mul_host.dtype), ctx)
div_nd = tvm.nd.array(np.zeros((64, )).astype(div_host.dtype), ctx)
rdiv_nd = tvm.nd.array(np.zeros((64, )).astype(rdiv_host.dtype), ctx)
gtm_nd = tvm.nd.array(np.zeros((64, )).astype(gtm_host.dtype), ctx)
print('------------------- device module 1 llvm IR: ')
print(func.imported_modules[0].get_source('ll'))
print('------------------- device module 1 asm code: ')
print(func.imported_modules[0].get_source('asm'))
func(a_nd, b_nd, c_nd, sub_nd, mul_nd, rsub_nd, div_nd, rdiv_nd, gtm_nd)
print('a = ')
print(a_np)
print('b = ')
print(b_np)
print('a + b =')
print(c_nd.asnumpy())
print('numpy ground truth =')
gt = a_np + b_np
print(gt)
np.testing.assert_allclose(c_nd.asnumpy(), gt)
print('a - b =')
print(sub_nd.asnumpy())
np.testing.assert_allclose(sub_nd.asnumpy(), a_np - b_np)
print('b - a =')
print(rsub_nd.asnumpy())
np.testing.assert_allclose(rsub_nd.asnumpy(), b_np - a_np)
print('a * b =')
print(mul_nd.asnumpy())
np.testing.assert_allclose(mul_nd.asnumpy(), a_np * b_np.astype(dtype_w))
print('a / b =')
print(div_nd.asnumpy())
# numpy always round down, while in c, the numerator will be rounded to zero.
#np.testing.assert_allclose(div_nd.asnumpy(), a_np / b_np)
print('b / a =')
print(rdiv_nd.asnumpy())
print('max(a, b)=')
print(gtm_nd.asnumpy())
def test():
env = nnpu.get_env()
dtype_n, dtype_w = env.cfg['dtype_n'], env.cfg['dtype_w']
shape = (4, 16)
a = tvm.placeholder(shape, dtype_n, 'a')
b = tvm.placeholder((16, ), dtype_n, 'b')
sph = ScheduleProcHelper()
a_buf, _ = nnpu.utils.CopyHtoBuf(a, 'a', sph)
b_buf, _ = nnpu.utils.CopyHtoBuf(b, 'b', sph)
sum_buf = tvm.compute(shape, lambda i, j: a_buf[i, j] + b_buf[j],
'sum_buf')
sph.MarkScope(sum_buf)
sum_host, _ = nnpu.utils.CopyBufToH(sum_buf, 'sum', sph)
sub_buf = tvm.compute(shape, lambda i, j: a_buf[i, j] - b_buf[j],
'sub_buf')
sph.MarkScope(sub_buf)
sub_host, _ = nnpu.utils.CopyBufToH(sub_buf, 'sub', sph)
mul_buf = tvm.compute(
shape,
lambda i, j: a_buf[i, j].astype(dtype_w) * b_buf[j].astype(dtype_w),
'sub_buf')
sph.MarkScope(mul_buf)
mul_host, _ = nnpu.utils.CopyBufToH(mul_buf, 'mul', sph)
s = tvm.create_schedule([sum_host.op, sub_host.op, mul_host.op])
sph.Transform(s)
s[sum_buf].pragma(sum_buf.op.axis[0], 'nnpu.vector',
str({
'code': 'matrix-vector',
'shape': shape
}))
s[sub_buf].pragma(sub_buf.op.axis[0], 'nnpu.vector',
str({
'code': 'matrix-vector',
'shape': shape
}))
s[mul_buf].pragma(mul_buf.op.axis[0], 'nnpu.vector',
str({
'code': 'matrix-vector',
'shape': shape
}))
print(nnpu.lower(s, [a, b, sum_host, sub_host, mul_host],
simple_mode=True))
func = nnpu.build(s, [a, b, sum_host, sub_host, mul_host],
'nnpu',
'llvm',
name='nnpu_func')
print('------------------- device module 1 llvm IR: ')
print(func.imported_modules[0].get_source('ir'))
print('------------------- device module 1 uop code: ')
print(func.imported_modules[0].get_source('uop'))
ctx = tvm.nd.TVMContext(13, 0)
a_np = np.random.randint(size=(4, 16), dtype=a.dtype, low=0, high=64)
#a_np = np.random.random(size=shape).astype(a_host.dtype)
a_nd = tvm.nd.array(a_np, ctx)
b_np = np.random.randint(size=(16, ), dtype=b.dtype, low=0, high=64)
b_nd = tvm.nd.array(b_np, ctx)
sum_nd = tvm.nd.array(np.zeros(shape).astype(sum_host.dtype), ctx)
sub_nd = tvm.nd.array(np.zeros(shape).astype(sub_host.dtype), ctx)
mul_nd = tvm.nd.array(np.zeros(shape).astype(mul_host.dtype), ctx)
func(a_nd, b_nd, sum_nd, sub_nd, mul_nd)
gt = a_np + b_np
np.testing.assert_allclose(sum_nd.asnumpy(), gt)
gt = a_np - b_np
np.testing.assert_allclose(sub_nd.asnumpy(), gt)
gt = a_np.astype(dtype_w) * b_np
np.testing.assert_allclose(mul_nd.asnumpy(), gt)
print('test passed')
def test():
env = nnpu.get_env()
dtype_n, dtype_w = env.cfg['dtype_n'], env.cfg['dtype_w']
shape = (4, 64)
# nvctr_unit = env.cfg['vector_unit']['size']
nvctr_unit = 32
# assert shape[0] % nvctr_unit == 0, 'error'
a = tvm.placeholder(shape, dtype_n, 'a')
b = tvm.placeholder(shape, dtype_n, 'b')
sph = ScheduleProcHelper()
b_scope = 'buffer0'
a_buf, a_dram = nnpu.utils.CopyHtoBuf(a, 'a', sph)
b_buf, b_dram = nnpu.utils.CopyHtoBuf(b, 'b', sph, dst_scope=b_scope)
c_buf = tvm.compute(shape, lambda *i: a_buf(*i) + b_buf(*i), 'c_buf')
sph.MarkScope(c_buf)
c_host, c_dram = nnpu.utils.CopyBufToH(c_buf, 'c', sph)
mul_buf = tvm.compute(
shape,
lambda *i: a_buf(*i).astype(dtype_w) * b_buf(*i).astype(dtype_w),
'mul_buf')
sph.MarkScope(mul_buf)
mul_host, mul_dram = nnpu.utils.CopyBufToH(mul_buf, 'mul', sph)
gtm_buf = tvm.compute(shape, lambda *i: tvm.max(a_buf(*i), b_buf(*i)),
'gtm_buf')
sph.MarkScope(gtm_buf)
gtm_host, gtm_dram = nnpu.utils.CopyBufToH(gtm_buf, 'gtm', sph)
s = tvm.create_schedule([c_host.op, mul_host.op, gtm_host.op])
sph.Transform(s)
# x = s[c_buf].fuse(*c_buf.op.axis)
# xo, xi = s[c_buf].split(x, factor=nvctr_unit)
params = dict()
params['code'] = 'binary'
params['size'] = nvctr_unit
x = s[c_buf].fuse(*c_buf.op.axis)
xo, xi = s[c_buf].split(x, factor=nvctr_unit)
s[c_buf].pragma(xi, 'nnpu.vector', str(params))
x = s[mul_buf].fuse(*mul_buf.op.axis)
xo, xi = s[mul_buf].split(x, factor=nvctr_unit)
s[mul_buf].pragma(xi, 'nnpu.vector', str(params))
x = s[gtm_buf].fuse(*gtm_buf.op.axis)
xo, xi = s[gtm_buf].split(x, factor=nvctr_unit)
s[gtm_buf].pragma(xi, 'nnpu.vector', str(params))
print(tvm.lower(s, [a, b, c_host, mul_host, gtm_host], simple_mode=True))
print(nnpu.lower(s, [a, b, c_host, mul_host, gtm_host], simple_mode=True))
# exit()
func = nnpu.build(s, [a, b, c_host, mul_host, gtm_host],
'nnpu',
'llvm',
name='nnpu_exp')
print('------------------- device module 1 IR: ')
print(func.imported_modules[0].get_source('ir'))
print('------------------- device module 1 micro code: ')
print(func.imported_modules[0].get_source('uop'))
# exit()
ctx = tvm.nd.TVMContext(13, 0)
a_np = np.random.randint(size=shape, dtype=a.dtype, low=-64, high=63)
#a_np = np.random.random(size=shape).astype(a_host.dtype)
a_nd = tvm.nd.array(a_np, ctx)
b_np = np.random.randint(size=shape, dtype=b.dtype, low=-64, high=63)
b_nd = tvm.nd.array(b_np, ctx)
c_nd = tvm.nd.array(np.zeros(shape).astype(c_host.dtype), ctx)
mul_nd = tvm.nd.array(np.zeros(shape).astype(mul_host.dtype), ctx)
gtm_nd = tvm.nd.array(np.zeros(shape).astype(gtm_host.dtype), ctx)
# print('------------------- device module 1 llvm IR: ')
# print(func.imported_modules[0].get_source('ll'))
# print('------------------- device module 1 asm code: ')
# print(func.imported_modules[0].get_source('asm'))
func(a_nd, b_nd, c_nd, mul_nd, gtm_nd)
gt = a_np + b_np
np.testing.assert_allclose(c_nd.asnumpy(), gt)
gt = np.multiply(a_np, b_np, dtype=mul_host.dtype)
np.testing.assert_allclose(mul_nd.asnumpy(), gt)
gt = np.maximum(a_np, b_np)
np.testing.assert_allclose(gtm_nd.asnumpy(), gt)
print('test passed!!')
def test():
env = nnpu.get_env()
dtype_n, dtype_w = env.cfg['dtype_n'], env.cfg['dtype_w']
shape = (4, 16)
a = tvm.placeholder(shape, dtype_n, 'a')
b = tvm.placeholder((16, ), dtype_n, 'b')
sph = ScheduleProcHelper()
a_buf, a_dram = nnpu.utils.CopyHtoBuf(a, 'a', sph)
b_buf, b_dram = nnpu.utils.CopyHtoBuf(b, 'b', sph)
sum_buf = tvm.compute(shape, lambda i, j: a_buf[i, j] + b_buf[j],
'sum_buf')
sph.MarkScope(sum_buf)
sum_host, sum_dram = nnpu.utils.CopyBufToH(sum_buf, 'sum', sph)
sub_buf = tvm.compute(shape, lambda i, j: a_buf[i, j] - b_buf[j],
'sub_buf')
sph.MarkScope(sub_buf)
sub_host, sub_dram = nnpu.utils.CopyBufToH(sub_buf, 'sub', sph)
mul_buf = tvm.compute(
shape,
lambda i, j: a_buf[i, j].astype(dtype_w) * b_buf[j].astype(dtype_w),
'sub_buf')
sph.MarkScope(mul_buf)
mul_host, mul_dram = nnpu.utils.CopyBufToH(mul_buf, 'mul', sph)
s = tvm.create_schedule([sum_host.op, sub_host.op, mul_host.op])
sph.Transform(s)
s[sum_buf].tensorize(s[sum_buf].op.axis[0],
env.intrins.get('MAddV', shape=(4, 16), mode='n'))
s[sub_buf].tensorize(s[sub_buf].op.axis[0],
env.intrins.get('MSubV', shape=(4, 16), mode='n'))
s[mul_buf].tensorize(s[mul_buf].op.axis[0],
env.intrins.get('MMulV', shape=(4, 16), mode='inc'))
print(nnpu.lower(s, [a, b, sum_host, sub_host, mul_host],
simple_mode=True))
func = nnpu.build(s, [a, b, sum_host, sub_host, mul_host],
'nnpu',
'llvm',
name='nnpu_func')
print('------------------- device module 1 llvm IR: ')
print(func.imported_modules[0].get_source('ll'))
print('------------------- device module 1 asm code: ')
print(func.imported_modules[0].get_source('asm'))
ctx = tvm.nd.TVMContext(13, 0)
a_np = np.random.randint(size=(4, 16), dtype=a.dtype, low=0, high=64)
#a_np = np.random.random(size=shape).astype(a_host.dtype)
a_nd = tvm.nd.array(a_np, ctx)
b_np = np.random.randint(size=(16, ), dtype=b.dtype, low=0, high=64)
b_nd = tvm.nd.array(b_np, ctx)
sum_nd = tvm.nd.array(np.zeros(shape).astype(sum_host.dtype), ctx)
sub_nd = tvm.nd.array(np.zeros(shape).astype(sub_host.dtype), ctx)
mul_nd = tvm.nd.array(np.zeros(shape).astype(mul_host.dtype), ctx)
func(a_nd, b_nd, sum_nd, sub_nd, mul_nd)
print('a = ')
print(a_np)
print('b = ')
print(b_np)
print('sum result is ')
print(sum_nd.asnumpy())
print("numpy ground truth is")
gt = a_np + b_np
print(gt)
np.testing.assert_allclose(sum_nd.asnumpy(), gt)
print('sub result is ')
print(sub_nd.asnumpy())
np.testing.assert_allclose(sub_nd.asnumpy(), a_np - b_np)
print('mul result is ')
print(mul_nd.asnumpy())
np.testing.assert_allclose(mul_nd.asnumpy(), a_np.astype(dtype_w) * b_np)
print('test passed')
def test():
env = nnpu.get_env()
a = tvm.placeholder((16, ), env.cfg['dtype_w'], 'a')
sph = ScheduleProcHelper()
Imm = tvm.const(5, env.cfg['dtype_w'])
a_buf, a_dram = nnpu.utils.CopyHtoBuf(a, 'a', sph)
#c_buf = tvm.compute((16, ), lambda i: tvm.select(a_buf[i]>Imm,a_buf[i],Imm), 'c_buf')
c_buf = tvm.compute((16, ), lambda i: Imm+a_buf[i], 'c_buf')
sph.MarkScope(c_buf)
c_host, c_dram = nnpu.utils.CopyBufToH(c_buf, 'c', sph)
sub_buf = tvm.compute((16, ), lambda i: a_buf[i] - Imm , 'sub_buf')
sph.MarkScope(sub_buf)
sub_host, sub_dram = nnpu.utils.CopyBufToH(sub_buf, 'sub', sph)
mul_buf = tvm.compute((16, ), lambda i: a_buf[i] * Imm, 'mul_buf')
sph.MarkScope(mul_buf)
mul_host, mul_dram = nnpu.utils.CopyBufToH(mul_buf, 'mul', sph)
div_buf = tvm.compute((16, ), lambda i: a_buf[i] / Imm, 'rdiv_buf')
sph.MarkScope(div_buf)
div_host, div_dram = nnpu.utils.CopyBufToH(div_buf, 'rdiv', sph)
gtm_buf = tvm.compute((16, ), lambda i: tvm.max(a_buf[i], Imm), 'gtm_buf')
sph.MarkScope(gtm_buf)
gtm_host, gtm_dram = nnpu.utils.CopyBufToH(gtm_buf, 'gtm', sph)
rsub_buf = tvm.compute((16, ), lambda i: Imm-a_buf[i], 'rsub_buf')
sph.MarkScope(rsub_buf)
rsub_host, rsub_dram = nnpu.utils.CopyBufToH(rsub_buf, 'rsub', sph)
s = tvm.create_schedule([c_host.op, sub_host.op, mul_host.op, div_host.op, gtm_host.op,rsub_host.op])
sph.Transform(s)
s[c_buf].tensorize(s[c_buf].op.axis[0], env.intrins.get('VAddI', imm_value=Imm.value,mode='w'))
s[sub_buf].tensorize(s[sub_buf].op.axis[0], env.intrins.get('VSubI', imm_value=Imm.value,mode='w'))
s[mul_buf].tensorize(s[mul_buf].op.axis[0], env.intrins.get('VMulI', imm_value=Imm.value,mode='w'))
s[div_buf].tensorize(s[div_buf].op.axis[0], env.intrins.get('VDivI', imm_value=Imm.value,mode='w'))
s[gtm_buf].tensorize(s[gtm_buf].op.axis[0], env.intrins.get('VGTMI', imm_value=Imm.value,mode='w'))
s[rsub_buf].tensorize(s[rsub_buf].op.axis[0], env.intrins.get('ISubV', imm_value=Imm.value,mode='w'))
print(nnpu.lower(s, [a,c_host,sub_host,mul_host,div_host,gtm_host,rsub_host], simple_mode=True))
func = nnpu.build(s, [a,c_host,sub_host,mul_host,div_host,gtm_host,rsub_host], 'nnpu', 'llvm', name='nnpu_vmuli')
print('------------------- device module 1 llvm IR: ')
print(func.imported_modules[0].get_source('ll'))
print('------------------- device module 1 asm code: ')
print(func.imported_modules[0].get_source('asm'))
ctx = tvm.nd.TVMContext(13, 0)
a_np = np.random.randint(size=(16, ), dtype=a.dtype, low = 3, high = 122)
#a_np = np.random.random(size=shape).astype(a_host.dtype)
a_nd = tvm.nd.array(a_np, ctx)
c_nd = tvm.nd.array(np.zeros((16, )).astype(c_host.dtype), ctx)
sub_nd = tvm.nd.array(np.zeros((16, )).astype(c_host.dtype), ctx)
mul_nd = tvm.nd.array(np.zeros((16, )).astype(c_host.dtype), ctx)
div_nd = tvm.nd.array(np.zeros((16, )).astype(c_host.dtype), ctx)
gtm_nd = tvm.nd.array(np.zeros((16, )).astype(c_host.dtype), ctx)
rsub_nd = tvm.nd.array(np.zeros((16, )).astype(c_host.dtype), ctx)
func(a_nd, c_nd, sub_nd, mul_nd, div_nd, gtm_nd,rsub_nd)
print('a = ')
print(a_nd.asnumpy())
print('a + {0} = '.format(Imm.value))
print(c_nd.asnumpy())
print('numpy ground truth =')
gt = a_np + Imm.value
print(gt)
np.testing.assert_allclose(c_nd.asnumpy(), gt)
print('a - {0} = '.format(Imm.value))
print(sub_nd.asnumpy())
np.testing.assert_allclose(sub_nd.asnumpy(), a_np - Imm.value)
print('a * {0} = '.format(Imm.value))
print(mul_nd.asnumpy())
np.testing.assert_allclose(mul_nd.asnumpy(), a_np * Imm.value)
print('a / {0} = '.format(Imm.value))
print(div_nd.asnumpy())
np.testing.assert_allclose(div_nd.asnumpy(), a_np / Imm.value)
print('a > {0} ? a : {0} = '.format(Imm.value))
print(gtm_nd.asnumpy())
#np.testing.assert_allclose(gtm_nd.asnumpy(), a_np Imm.value)
print('{0} - a = '.format(Imm.value))
print(rsub_nd.asnumpy())
np.testing.assert_allclose(rsub_nd.asnumpy(), Imm.value-a_np)
print('test passed')
def test():
env = nnpu.get_env()
nnpu.set_dump(False)
#==================================#
# ------ first define shapes ------
#==================================#
# input data layout: HWC
in_shape = (32, 32, 128)
# pooling windows size, height == width.
cell_shape = 4
# in this demo we don't do padding, so input data height and width must be divisible to pooling window size.
assert in_shape[0] % cell_shape == 0, 'error'
assert in_shape[1] % cell_shape == 0, 'error'
nvctr_unit = env.cfg['vector_unit']['size']
assert in_shape[2] % nvctr_unit == 0, 'channel not divisible to vector unit size'
out_shape = (in_shape[0] // cell_shape,in_shape[1] // cell_shape,in_shape[2])
dtype_n, dtype_w = env.cfg['dtype_n'], env.cfg['dtype_w']
sph = ScheduleProcHelper()
str_op = 'VGTMMerge'
#=================================================================#
# ------ after all shapes defined, begin compute describing. ------
#=================================================================#
a = tvm.placeholder(in_shape, dtype_w, 'a')
# first copy to scratchpad.
a_buf, _1 = nnpu.utils.CopyHtoBuf(a, 'a', sph)
# stage 1, find the maximum pixel in every pooling window.
# the extent of two reduction axes are sizes of pooling window.
k1 = tvm.reduce_axis((0,cell_shape), 'k1')
k2 = tvm.reduce_axis((0,cell_shape), 'k2')
pooling_buf = tvm.compute(out_shape,
lambda i,j,k:
tvm.max(a_buf[i * cell_shape + k1, j * cell_shape + k2, k],
axis=[k1, k2]),
'pooling_buf')
sph.MarkScope(pooling_buf, 'buffer1')
# copy back to host.
step2_host, step2_dram = nnpu.utils.CopyBufToH(pooling_buf, 'pooling',sph)
# ------ this ends the computation description. ------
#==================================#
# ------ begin scheduling ------
#==================================#
s = tvm.create_schedule(step2_host.op)
sph.Transform(s)
#tensorize
i, j, k = pooling_buf.op.axis
k1, k2 = pooling_buf.op.reduce_axis
# split the reduce_axis by factor 1, to produce a dummy reduce axis.
# this is a trick to enable tensorize, due to limitation of tvm's tensorize pattern matcher.
ko, ki = s[pooling_buf].split(k2, factor=1)
xo, xi = s[pooling_buf].split(k, factor=16)
# reorder axes.
# put xo right before ki to eliminate memory dependency between two consecutive VGTMV instruction
s[pooling_buf].reorder( i, j, k1, ko, xo, ki, xi)
s[pooling_buf].tensorize(ki, env.intrins.get(str_op, scope_out='buffer1', mode='w'))
# unroll
# s[pooling_buf].unroll(ko)
# s[pooling_buf].unroll(xo)
#==================================#
# ------ this ends the scheduling ------
#==================================#
print(nnpu.lower(s, [a, step2_host], simple_mode=True))
# exit()
func = nnpu.build(s, [a, step2_host], 'nnpu', 'llvm', name='nnpu_func')
ctx = tvm.nd.TVMContext(13, 0)
a_np = np.random.randint(size=in_shape, dtype=a.dtype, low = -128, high = 127)
a_nd = tvm.nd.array(a_np, ctx)
c_nd = tvm.nd.array(np.zeros(out_shape, dtype=step2_host.dtype), ctx)
func(a_nd, c_nd)
# print("pooling-max")
# print(c_nd.asnumpy())
# print("nppooling-max")
gt=max_pooling(in_shape,out_shape,cell_shape,a_np,a.dtype)
# print(gt)
np.testing.assert_allclose(c_nd.asnumpy(), gt)
print('test passed')
def test():
env = nnpu.get_env()
shape = (4, 16)
dtype_n, dtype_w = env.cfg['dtype_n'], env.cfg['dtype_w']
a = tvm.placeholder(shape, dtype_w, 'a')
sph = ScheduleProcHelper()
a_buf, a_dram = nnpu.utils.CopyHtoBuf(a, 'a', sph)
k = tvm.reduce_axis((0, 4), 'k')
add_buf = tvm.compute((16, ), lambda i: tvm.sum(a_buf[k, i], axis=k),
'add_buf')
sph.MarkScope(add_buf)
add_host, add_dram = nnpu.utils.CopyBufToH(add_buf, 'add', sph)
# k1 = tvm.reduce_axis((0, 4), 'k1')
# mul_buf = tvm.compute((16, ), lambda i: tvm.sum(a_buf[k1, i], axis=k1), 'mul_buf')
# sph.MarkScope(mul_buf)
# mul_host, mul_dram = nnpu.utils.CopyBufToH(mul_buf, 'mul', sph)
k2 = tvm.reduce_axis((0, 4), 'k2')
gtm_buf = tvm.compute((16, ), lambda i: tvm.max(a_buf[k2, i], axis=k2),
'gtm_buf')
sph.MarkScope(gtm_buf)
gtm_host, gtm_dram = nnpu.utils.CopyBufToH(gtm_buf, 'gtm', sph)
s = tvm.create_schedule([add_host.op, gtm_host.op])
sph.Transform(s)
ko, ki = s[add_buf].split(add_buf.op.reduce_axis[0], factor=1)
s[add_buf].reorder(ko, ki, s[add_buf].op.axis[0])
s[add_buf].tensorize(ki, env.intrins.get('VAddMerge', mode='w'))
# ko1, ki1 = s[mul_buf].split(mul_buf.op.reduce_axis[0], factor=1)
# s[mul_buf].reorder(ko1, ki1, s[mul_buf].op.axis[0])
# s[mul_buf].tensorize(ki1, env.intrins.get('VMulMerge', mode='w'))
ko2, ki2 = s[gtm_buf].split(gtm_buf.op.reduce_axis[0], factor=1)
s[gtm_buf].reorder(ko2, ki2, s[gtm_buf].op.axis[0])
s[gtm_buf].tensorize(ki2, env.intrins.get('VGTMMerge', mode='w'))
print(nnpu.lower(s, [a, add_host, gtm_host], simple_mode=True))
func = nnpu.build(s, [a, add_host, gtm_host],
'nnpu',
'llvm',
name='nnpu_func')
#exit()
ctx = tvm.nd.TVMContext(13, 0)
a_np = np.random.randint(size=(4, 16), dtype=a.dtype, low=-16, high=16)
a_nd = tvm.nd.array(a_np, ctx)
add_nd = tvm.nd.array(np.zeros((16, )).astype(add_host.dtype), ctx)
# mul_nd = tvm.nd.array(np.zeros((16,)).astype(mul_host.dtype), ctx)
gtm_nd = tvm.nd.array(np.zeros((16, )).astype(gtm_host.dtype), ctx)
print('------------------- device module 1 IR code: ')
print(func.imported_modules[0].get_source('ir'))
func(a_nd, add_nd, gtm_nd)
print('a = ')
print(a_np)
print('reduce sum row = ')
print(add_nd.asnumpy())
print('ground truth is: ')
gt = np.sum(a_np, axis=0)
print(gt)
np.testing.assert_allclose(add_nd.asnumpy(), gt)
# print('reduce mul row = ')
# print(mul_nd.asnumpy())
# gt = np.multiply.reduce(a_np ,axis=0,dtype = a.dtype)
# print(gt)
# np.testing.assert_allclose(mul_nd.asnumpy(), gt)
print('reduce max row = ')
print(gtm_nd.asnumpy())
gt = np.max(a_np, axis=0)
print(gt)
np.testing.assert_allclose(gtm_nd.asnumpy(), gt)
def test():
env = nnpu.get_env()
nnpu.set_device(env)
shape = (2, 2, 16)
dtype_n, dtype_w = env.cfg['dtype_n'], env.cfg['dtype_w']
a = tvm.placeholder(shape, dtype_w, 'a')
sph = ScheduleProcHelper()
a_buf, a_dram = nnpu.utils.CopyHtoBuf(a, 'a', sph)
k = tvm.reduce_axis((0, 2), 'k')
add_buf = tvm.compute(
(2, 16), lambda i, j: tvm.sum(a_buf[k, i, j], axis=k), 'add_buf')
sph.MarkScope(add_buf)
add_host, add_dram = nnpu.utils.CopyBufToH(add_buf, 'add', sph)
k1 = tvm.reduce_axis((0, 2), 'k1')
mul_buf = tvm.compute(
(2, 16), lambda i, j: tvm.sum(a_buf[k1, i, j], axis=k1), 'mul_buf')
sph.MarkScope(mul_buf)
mul_host, mul_dram = nnpu.utils.CopyBufToH(mul_buf, 'mul', sph)
s = tvm.create_schedule([add_host.op, mul_host.op])
sph.Transform(s)
ko, ki = s[add_buf].split(add_buf.op.reduce_axis[0], factor=1)
s[add_buf].reorder(ko, ki, *(s[add_buf].op.axis))
s[add_buf].tensorize(ki, env.intrins.get('MAddMerge',
shape=shape,
mode='w'))
ko1, ki1 = s[mul_buf].split(mul_buf.op.reduce_axis[0], factor=1)
s[mul_buf].reorder(ko1, ki1, *(s[mul_buf].op.axis))
s[mul_buf].tensorize(ki1,
env.intrins.get('MMulMerge', shape=shape, mode='w'))
print(nnpu.lower(s, [a, add_host, mul_host], simple_mode=True))
func = nnpu.build(s, [a, add_host, mul_host],
'nnpu',
'llvm',
name='nnpu_func')
#exit()
ctx = tvm.nd.TVMContext(13, 0)
a_np = np.random.randint(size=(2, 2, 16), dtype=a.dtype, low=-16, high=16)
a_nd = tvm.nd.array(a_np, ctx)
add_nd = tvm.nd.array(np.zeros((2, 16)).astype(add_host.dtype), ctx)
mul_nd = tvm.nd.array(np.zeros((2, 16)).astype(mul_host.dtype), ctx)
func(a_nd, add_nd, mul_nd)
print('a = ')
print(a_np)
print('reduce sum row = ')
print(add_nd.asnumpy())
print('ground truth is: ')
gt = np.sum(a_np, axis=0)
print(gt)
np.testing.assert_allclose(add_nd.asnumpy(), gt)
print('reduce mul row = ')
print(mul_nd.asnumpy())
gt = np.multiply.reduce(a_np, axis=0, dtype=a.dtype)
print(gt)
np.testing.assert_allclose(mul_nd.asnumpy(), gt)
def test_ib():
print('aaaa')
env = nnpu.get_env()
nnpu.set_device(env)
shape = (16, )
dtype_n, dtype_w = env.cfg['dtype_n'], env.cfg['dtype_w']
a = tvm.placeholder(shape, dtype_w, name='a')
w = shape[0]
e = 16
def build_nms_ir(ten_in, ten_out):
ib = tvm.ir_builder.create()
imm_value = 10
ib.scope_attr(env.nnpu_axis, "coproc_scope", 0)
p_in = ib.buffer_ptr(ten_in[0])
p_out = ib.buffer_ptr(ten_out[0])
#with ib.for_range(0,w, name="k") as k:
with ib.for_range(0, w / e, name="i") as i:
ib.emit(
make_intrin_call(
"void", 'VAddI', ten_out[0].access_ptr("w", 'uint32') +
i * dtype_bytes(dtype_w),
ten_in[0].access_ptr("r", 'uint32') +
i * dtype_bytes(dtype_w), tvm.const(imm_value, 'float64'),
env.cfg['vector_unit']['size'], 3))
stmt = ib.get()
return stmt
sph = ScheduleProcHelper()
a_buf, a_dram = nnpu.utils.CopyHtoBuf(a, 'a', sph)
sph.MarkScope(a_buf)
out = tvm.extern(a_buf.shape, [a_buf],
build_nms_ir,
in_buffers=[
tvm.decl_buffer(a_buf.shape,
dtype_w,
data_alignment=dtype_bytes(dtype_w),
scope='local.nnpu_scratchpad0')
],
out_buffers=[
tvm.decl_buffer(a_buf.shape,
dtype_w,
data_alignment=dtype_bytes(dtype_w),
scope='local.nnpu_scratchpad0')
],
dtype=dtype_w,
name="test_ir")
sph.MarkScope(out)
out_host, out_dram = nnpu.utils.CopyBufToH(out, 'out', sph)
s = tvm.create_schedule([out_host.op])
sph.Transform(s)
print(tvm.lower(s, [a, out_host], simple_mode=True))
print(nnpu.lower(s, [a, out_host], simple_mode=True))
# exit(0)
func = nnpu.build(s, [a, out_host], 'nnpu', 'llvm', name='nnpu_test')
ctx = tvm.nd.TVMContext(13, 0)
a_np = np.random.randint(size=(16, ), dtype=a.dtype, low=0, high=127)
a_nd = tvm.nd.array(a_np, ctx)
b_nd = tvm.nd.array(np.zeros(16, ).astype(out_host.dtype), ctx)
func(a_nd, b_nd)
print('a = ')
print(a_np)
print('xjb sum = ')
print(b_nd.asnumpy())
return
def test():
env = nnpu.get_env()
nnpu.set_dump(False)
#==================================#
# ------ first define shapes ------
#==================================#
# input data layout: HWC
in_shape = (32, 32, 128)
# pooling windows size, height == width.
cell_shape = 4
# in this demo we don't do padding, so input data height and width must be divisible to pooling window size.
assert in_shape[0] % cell_shape == 0, 'error'
assert in_shape[1] % cell_shape == 0, 'error'
nvctr_unit = env.cfg['vector_unit']['size']
assert in_shape[2] % nvctr_unit == 0, 'channel not divisible to vector unit size'
out_shape = (in_shape[0] // cell_shape, in_shape[1] // cell_shape, in_shape[2])
dtype_n, dtype_w = env.cfg['dtype_n'], env.cfg['dtype_w']
sph = ScheduleProcHelper()
#=================================================================#
# ------ after all shapes defined, begin compute describing. ------
#=================================================================#
a = tvm.placeholder(in_shape, dtype_w, 'a')
# first copy to scratchpad.
a_buf, _1 = nnpu.utils.CopyHtoBuf(a, 'a', sph)
# stage 1, sum up the pixels in every pooling window.
# the extent of two reduction axes are sizes of pooling window.
k1 = tvm.reduce_axis((0,cell_shape), 'k1')
k2 = tvm.reduce_axis((0,cell_shape), 'k2')
pooling_buf = tvm.compute(out_shape,
lambda i, j, k:
tvm.sum(a_buf[i * cell_shape + k1, j * cell_shape + k2, k],
axis=[k1, k2]),
'pooling_buf')
sph.MarkScope(pooling_buf)
sum_host, _ = nnpu.utils.CopyBufToH(pooling_buf, 'step3', sph)
# stage 2, divide by cell_shape^2, to compute average.
Imm = tvm.const(cell_shape*cell_shape, env.cfg['dtype_w'])
step3_buf = tvm.compute(out_shape,
lambda i, j, k:
pooling_buf[i,j,k]/Imm,
'step3_buf')
sph.MarkScope(step3_buf)
# copy back to host.
step3_host, step3_dram = nnpu.utils.CopyBufToH(step3_buf, 'step3',sph)
# ------ this ends the computation description. ------
#==================================#
# ------ begin scheduling ------
#==================================#
s = tvm.create_schedule([step3_host.op, sum_host.op])
sph.Transform(s)
#tensorize
i, j, k = pooling_buf.op.axis
k1, k2 = pooling_buf.op.reduce_axis
# split the reduce_axis by factor 1, to produce a dummy reduce axis.
# this is a trick to enable tensorize, due to limitation of tvm's tensorize pattern matcher.
ko, ki = s[pooling_buf].split(k2, factor=1)
xo, xi = s[pooling_buf].split(k, factor=nvctr_unit)
# reorder axes.
# put xo right before ki to eliminate memory dependency between two consecutive VAddV instruction
s[pooling_buf].reorder( i, j, k1, ko, xo, ki, xi)
s[pooling_buf].tensorize(ki, env.intrins.get('VAddMerge', mode='w'))
# unroll
# s[pooling_buf].unroll(xo)
# s[pooling_buf].unroll(ko)
# split and tensorize.
xo2, xi2 = s[step3_buf].split(step3_buf.op.axis[2], factor=nvctr_unit)
s[step3_buf].reorder( step3_buf.op.axis[0], step3_buf.op.axis[1], xo2, xi2)
s[step3_buf].tensorize(xi2, env.intrins.get('VDivI',imm_value=Imm.value, mode='w'))
# s[step3_buf].unroll(xo2)
#==================================#
# ------ this ends the scheduling ------
#==================================#
print(nnpu.lower(s, [a, sum_host, step3_host], simple_mode=True))
# exit()
func = nnpu.build(s, [a, sum_host, step3_host], 'nnpu', 'llvm', name='nnpu_func')
print('------------------- device module 1 TVM IR: ')
print(func.imported_modules[0].get_source('ir'))
print('------------------- device module 1 uop: ')
print(func.imported_modules[0].get_source('uop'))
ctx = tvm.nd.TVMContext(13, 0)
a_np = np.random.randint(size=in_shape, dtype=a.dtype, low = -128, high = 127)
a_nd = tvm.nd.array(a_np, ctx)
c_nd = tvm.nd.array(np.zeros(out_shape, dtype=step3_host.dtype), ctx)
s_nd = tvm.nd.array(np.zeros(out_shape, dtype=step3_host.dtype), ctx)
func(a_nd, s_nd, c_nd)
# gt = mean_pooling_sum(in_shape, out_shape, cell_shape, a_np, a.dtype)
# np.testing.assert_allclose(s_nd.asnumpy(), gt)
# print('sum is ok')
gt=mean_pooling(in_shape,out_shape,cell_shape,a_np,a.dtype)
np.testing.assert_allclose(c_nd.asnumpy(), gt)
print('test passed')