def test_dense():
shape = (16, 1024)
weight_shape = (256, 1024)
bias_shape = (256, )
inputs = nnvm.symbol.Variable("inputs")
weights = nnvm.symbol.Variable("weights")
bias = nnvm.symbol.Variable("bias")
env = nnpu.get_env()
target_host = "llvm"
device = "nnpu"
target = tvm.target.create("llvm -device={}".format(device))
z = nnvm.symbol.dense(data=inputs, weight=weights, use_bias=0, units=256)
z1 = nnvm.symbol.relu(z)
compute_graph = nnvm.graph.create(z1)
with nnvm.compiler.build_config(opt_level=1):
if target.device_name != "nnpu":
deploy_graph, lib, params = nnvm.compiler.build(
compute_graph,
target,
shape={
"inputs": shape,
"weights": weight_shape
},
dtype="float32",
target_host=target_host)
else:
with ScheduleProcHelper():
with nnpu.build_config():
nnpu.set_device(nnpu.get_env(), type='SC')
deploy_graph, lib, params = nnvm.compiler.build(
compute_graph,
target,
shape={
"inputs": shape,
"weights": weight_shape
},
dtype="float32",
target_host=target_host)
ctx = tvm.context(str("nnpu"), 0) if device == "nnpu" else tvm.context(
str("llvm"), 0)
m = runtime.create(deploy_graph, lib, ctx)
a_np = np.random.random(size=shape)
b_np = np.random.random(size=weight_shape)
m.set_input(**{"inputs": a_np, "weights": b_np})
m.run()
gt = a_np.dot(b_np.transpose())
out = m.get_output(0, out=tvm.nd.empty((16, 256)))
np.testing.assert_allclose(out.asnumpy(), gt, rtol=5e-5)
print("tests")
print(out)
print(compute_graph.ir())
print(deploy_graph.ir())
def test_elemwise_mul():
env = nnpu.get_env()
device = "nnpu"
target_host = "llvm"
target = tvm.target.create("llvm -device={}".format(device))
inputs1 = nnvm.symbol.Variable("inputs1")
inputs2 = nnvm.symbol.Variable("inputs2")
shape = (16, 6, 16)
z = nnvm.symbol.elemwise_mul(inputs1, inputs2)
compute_graph = nnvm.graph.create(z)
with nnvm.compiler.build_config(opt_level=0):
if target.device_name != "nnpu":
deploy_graph, lib, params = nnvm.compiler.build(
compute_graph,
target,
shape={
"inputs1": shape,
"inputs2": shape
},
dtype="float32",
target_host=target_host)
else:
with ScheduleProcHelper():
with nnpu.build_config():
nnpu.set_device(nnpu.get_env(), type='S0')
deploy_graph, lib, params = nnvm.compiler.build(
compute_graph,
target,
shape={
"inputs1": shape,
"inputs2": shape
},
dtype="float32",
target_host=target_host)
ctx = tvm.context(str("nnpu"), 0) if device == "nnpu" else tvm.context(
str("llvm"), 0)
m = runtime.create(deploy_graph, lib, ctx)
a_np = np.random.random((16, 6, 16))
b_np = np.random.random((16, 6, 16))
print("a_np : ")
print(a_np)
print("b_np : ")
print(b_np)
m.set_input(**{"inputs1": a_np, "inputs2": b_np})
gt = (a_np.astype("float32") *
b_np.astype("float32")).astype("float32")
m.run()
out = m.get_output(0, out=tvm.nd.empty((16, 6, 16)))
np.testing.assert_allclose(out.asnumpy(), gt)
print("elemwise_mul tests success")
print(out)
def test_batch_norm():
input_shape = (1, 4, 4, 16)
target_host = "llvm"
device = "nnpu"
target = tvm.target.create("llvm -device={}".format(device))
inputs1 = nnvm.symbol.Variable("inputs1")
inputs2 = nnvm.symbol.Variable("inputs2")
z1 = nnvm.symbol.relu(inputs1)
# z2 = nnvm.symbol.relu(z1)
compute_graph = nnvm.graph.create(z1)
with nnvm.compiler.build_config(opt_level=0):
if target.device_name != "nnpu":
deploy_graph, lib, params = nnvm.compiler.build(compute_graph, target, shape =
{"inputs1" : input_shape}, dtype = "float32", target_host = target_host)
else:
with ScheduleProcHelper():
with nnpu.build_config():
nnpu.set_device(nnpu.get_env(), type = 'S0')
deploy_graph, lib, params = nnvm.compiler.build(compute_graph, target, shape =
{"inputs1" : input_shape}, dtype = "float32", target_host = target_host)
ctx = tvm.context(str("nnpu"), 0) if device == "nnpu" else tvm.context(str("llvm"), 0)
module = runtime.create(deploy_graph, lib, ctx)
a_np = np.random.uniform(size = (1, 4, 4, 16), low = -32, high = 32).astype(np.float32)
b_np = np.random.uniform(size = (1, 16), low = -32, high = 32).astype(np.float32)
print(a_np)
module.set_input(inputs1 = a_np)
module.run()
out = module.get_output(0, out = tvm.nd.empty((1, 4, 4, 16)))
print(out.asnumpy)
print(compute_graph.ir())
print(deploy_graph.ir())
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()
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_log():
env = nnpu.get_env()
shape = (1, 22, 22, 16)
device = "nnpu"
target_host = "llvm"
target = tvm.target.create("llvm -device={}".format(device))
inputs = nnvm.symbol.Variable("inputs")
z = nnvm.symbol.log(inputs)
z1 = nnvm.symbol.exp(z)
compute_graph = nnvm.graph.create(z1)
with nnvm.compiler.build_config(opt_level=1):
if target.device_name != "nnpu":
deploy_graph, lib, params = nnvm.compiler.build(
compute_graph,
target,
shape={"inputs": shape},
dtype="float32",
target_host=target_host)
else:
with ScheduleProcHelper():
with nnpu.build_config():
nnpu.set_device(nnpu.get_env(), type='S0')
deploy_graph, lib, params = nnvm.compiler.build(
compute_graph,
target,
shape={"inputs": shape},
dtype="float32",
target_host=target_host)
ctx = tvm.context(str("nnpu"), 0) if device == "nnpu" else tvm.context(
str("llvm"), 0)
m = runtime.create(deploy_graph, lib, ctx)
a_np = np.random.random(shape)
print(a_np)
m.set_input(**{"inputs": a_np})
m.run()
out = m.get_output(0, out=tvm.nd.empty(shape))
gt = np.exp(np.log(
a_np.astype("float32")).astype("float32")).astype("float32")
print(out)
np.testing.assert_allclose(out.asnumpy(), gt)
print("log tests success")
print(compute_graph.ir())
print(deploy_graph.ir())
def test_relu():
shape = (2, 16)
inputs = nnvm.symbol.Variable("inputs")
env = nnpu.get_env()
target_host = "llvm"
device = "nnpu"
target = tvm.target.create("llvm -device={}".format(device))
z = nnvm.symbol.relu(inputs)
compute_graph = nnvm.graph.create(z)
with nnvm.compiler.build_config(opt_level=0):
if target.device_name != "nnpu":
deploy_graph, lib, params = nnvm.compiler.build(
compute_graph,
target,
shape={"inputs": shape},
dtype="float32",
target_host=target_host)
else:
with ScheduleProcHelper():
with nnpu.build_config():
nnpu.set_device(nnpu.get_env(), type='S0')
deploy_graph, lib, params = nnvm.compiler.build(
compute_graph,
target,
shape={"inputs": shape},
dtype="float32",
target_host=target_host)
ctx = tvm.context(str("nnpu"), 0) if device == "nnpu" else tvm.context(
str("llvm"), 0)
m = runtime.create(deploy_graph, lib, ctx)
a_np = np.random.random(size=(2, 16)).astype("float32") - 0.5
m.set_input(**{'inputs': a_np})
m.run()
out = m.get_output(0, out=tvm.nd.empty((2, 16)))
print(a_np)
print(out.dtype)
print(out)
np.testing.assert_allclose(out.asnumpy(), np.maximum(a_np, 0))
print("tests")
print(compute_graph.ir())
print(deploy_graph.ir())
def test_conv2d():
input_shape = (1, 16, 10, 64)
target_host = "llvm"
device = "nnpu"
target = tvm.target.create("llvm -device={}".format(device))
inputs = nnvm.symbol.Variable("inputs")
inputs1 = nnvm.symbol.Variable("inputs1")
z1 = nnvm.symbol.conv2d(data=inputs,
channels=64,
kernel_size=(3, 3),
padding=(0, 0),
use_bias=False,
layout='NHWC',
kernel_layout='HWOI')
z2 = nnvm.symbol.sigmoid(z1)
z = nnvm.symbol.elemwise_add(z2, inputs1)
compute_graph = nnvm.graph.create(z)
with nnvm.compiler.build_config(opt_level=1):
if target.device_name != "nnpu":
deploy_graph, lib, params = nnvm.compiler.build(
compute_graph,
target,
shape={
"inputs": input_shape,
"inputs1": (1, 14, 8, 64)
},
dtype="float32",
target_host=target_host)
else:
with ScheduleProcHelper():
with nnpu.build_config():
nnpu.set_device(nnpu.get_env(), type='SC')
deploy_graph, lib, params = nnvm.compiler.build(
compute_graph,
target,
shape={"inputs": input_shape},
dtype="float32",
target_host=target_host)
ctx = tvm.context(str("nnpu"), 0) if device == "nnpu" else tvm.context(
str("llvm"), 0)
module = runtime.create(deploy_graph, lib, ctx)
a_np = np.random.uniform(size=input_shape, low=-32,
high=32).astype(np.float32)
b_np = np.random.uniform(size=(1, 14, 8, 64), low=-32,
high=32).astype(np.float32)
module.set_input(inputs=a_np)
module.run()
print(deploy_graph.ir())
out = module.get_output(0, out=tvm.nd.empty((1, 14, 8, 64)))
def test_onemore():
shape = (1, 32, 32, 16)
inputs = nnvm.symbol.Variable("inputs")
env = nnpu.get_env()
target_host = "llvm"
device = "nnpu"
target = tvm.target.create("llvm -device={}".format(device))
z1 = nnvm.symbol.relu(inputs)
z = nnvm.symbol.sqrt(z1)
compute_graph = nnvm.graph.create(z)
with nnvm.compiler.build_config(opt_level=0):
if target.device_name != "nnpu":
deploy_graph, lib, params = nnvm.compiler.build(
compute_graph,
target,
shape={"inputs": shape},
dtype="float32",
target_host=target_host)
else:
with ScheduleProcHelper():
with nnpu.build_config():
nnpu.set_device(nnpu.get_env(), type='S0')
deploy_graph, lib, params = nnvm.compiler.build(
compute_graph,
target,
shape={"inputs": shape},
dtype="float32",
target_host=target_host)
ctx = tvm.context(str("nnpu"), 0) if device == "nnpu" else tvm.context(
str("llvm"), 0)
m = runtime.create(deploy_graph, lib, ctx)
a_np = np.random.random(size=(1, 32, 32, 16))
m.set_input(**{'inputs': a_np})
m.run()
out = m.get_output(0, out=tvm.nd.empty((1, 32, 32, 16)))
print(out)
print(compute_graph.ir())
print(deploy_graph.ir())
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()
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 = (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_max_pool2d():
device = "nnpu"
target = tvm.target.create("llvm -device={}".format(device))
target_host = "llvm"
inputs = nnvm.symbol.Variable("inputs")
shape = (1, 224, 224, 16)
kernels = nnvm.symbol.Variable("kernels")
kernel_shape = (2, 2)
z = nnvm.symbol.avg_pool2d(inputs,
pool_size=(2, 2),
strides=(1, 1),
layout="NHWC")
compute_graph = nnvm.graph.create(z)
with nnvm.compiler.build_config(opt_level=0):
if target.device_name != "nnpu":
deploy_graph, lib, params = nnvm.compiler.build(
compute_graph,
target,
shape={"inputs": shape},
dtype="float32")
else:
with ScheduleProcHelper():
with nnpu.build_config():
nnpu.set_device(nnpu.get_env(), type='S0')
deploy_graph, lib, params = nnvm.compiler.build(
compute_graph,
target,
shape={"inputs": shape},
dtype="float32")
ctx = tvm.context(str("nnpu"), 0) if device == "nnpu" else tvm.context(
str("llvm"), 0)
m = runtime.create(deploy_graph, lib, ctx)
a_np = np.random.random(size=(1, 224, 224, 16))
m.set_input(**{"inputs": a_np})
m.run()
out = m.get_output(0, out=tvm.nd.empty((1, 223, 223, 16)))
gt = avg_pooling((1, 224, 224, 16), (1, 223, 223, 16), (2, 2), a_np,
(1, 1), "float32")
np.testing.assert_allclose(out.asnumpy(), gt, rtol=5e-7)
print("max_pool2d tests success")
print(gt)
print(out)
print("end")
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()
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']
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():
pass
if (False):
print('-----')
with ScheduleProcHelper():
env = nnpu.get_env()
shape = (16, 64)
a_host = tvm.placeholder(shape, env.cfg['dtype_n'], 'a_host')
a_buf, _ = nnpu.utils.CopyHtoBuf(a_host, 'a')
vctr_shape = (64, )
b_host = tvm.placeholder(vctr_shape, env.cfg['dtype_n'], 'b_host')
b_buf, _ = nnpu.utils.CopyHtoBuf(b_host, 'b')
dtype_w = env.cfg['dtype_w']
out_shape = (4, 16)
k = tvm.reduce_axis((0, 16), 'k')
c_buf = tvm.compute(
out_shape, lambda j, i: tvm.sum(a_buf[i, j * 16 + k].astype(
dtype_w) * b_buf[j * 16 + k].astype(dtype_w),
axis=k))
utils.MarkScope(c_buf)
c_host, _ = utils.CopyBufToH(c_buf, 'c')
s = nnpu.create_schedule(c_host.op)
# mark variable scopes
# tensorize
s[c_buf].tensorize(
s[c_buf].op.axis[1],
env.intrins.get('GEMM', shape=(16, 16, 1), mode='inc',
reduce=True))
# build
print(tvm.lower(s, [a_host, b_host, c_host], simple_mode=True))
print(nnpu.lower(s, [a_host, b_host, c_host], simple_mode=True))
#exit()
func = nnpu.build(s, [a_host, b_host, c_host],
'nnpu',
'llvm',
name='nnpu_exp')
print('function built: ')
print('------------------- device module 1 asm code: ')
print(func.imported_modules[0].get_source('asm'))
#print(func.get_source())
# prepare data
ctx = tvm.nd.TVMContext(13, 0)
a_np = np.random.randint(size=shape,
dtype=a_host.dtype,
low=-32,
high=32)
# a_np = np.ones(shape).astype(a_host.dtype)
a_nd = tvm.nd.array(a_np, ctx)
b_np = np.random.randint(size=vctr_shape,
dtype=b_host.dtype,
low=-16,
high=16)
# b_np = np.ones(vctr_shape).astype(b_host.dtype)
b_nd = tvm.nd.array(b_np, ctx)
out_nd = tvm.nd.array(np.zeros(out_shape).astype(c_host.dtype), ctx)
# run
func(a_nd, b_nd, out_nd)
print('run finished')
print('a=')
print(a_np)
print('b=')
print(b_np)
print('out=')
out_np = out_nd.asnumpy()
out_np = np.sum(out_np, axis=0)
print(out_np)
print('numpy ground truth is: ')
gt = np.dot(a_np.astype(dtype_w), b_np.astype(dtype_w))
#gt = np.greater(np.dot(a_np.astype(dtype_w), b_np.astype(dtype_w)), bias_np)
print(gt)
np.testing.assert_allclose(out_np, gt)
def test():
env = nnpu.get_env()
nnpu.set_device(env)
shape = (2, 16)
a_host = tvm.placeholder(shape, env.cfg['dtype_n'], 'a_host')
print('a host ' + str(a_host))
a = tvm.compute(shape, lambda *i: a_host(*i), name='a')
a_buf = tvm.compute(shape, lambda *i: a(*i), name='a_buf')
b_buf = tvm.compute(
shape,
lambda i, j: tvm.log(a_buf[i, j].astype(env.cfg['dtype_w'])),
name='b_buf')
b = tvm.compute(shape, lambda *i: b_buf(*i), name='b')
b_host = tvm.compute(shape, lambda *i: b(*i), name='b_host')
s = tvm.create_schedule(b_host.op)
# mark variable scopes
s[a].set_scope(env.dram_scope)
s[b].set_scope(env.dram_scope)
s[a_buf].set_scope(env.uni_scratchpad_scope)
s[b_buf].set_scope(env.uni_scratchpad_scope)
#print
# (dir(s[b].op.body))
# mark compiler pragmas
s[a].pragma(s[a].op.axis[0], env.dma_copy_pragma)
s[b_host].pragma(s[b_host].op.axis[0], env.dma_copy_pragma)
s[a_buf].pragma(s[a_buf].op.axis[0], env.scratchpad_ls)
s[b].pragma(s[b].op.axis[0], env.scratchpad_ls)
s[a_buf].compute_at(s[b_buf], b_buf.op.axis[0])
# tensorize
s[b_buf].tensorize(s[b_buf].op.axis[1], env.intrins.get('VLOG',
mode='inc'))
# build
print(tvm.lower(s, [a_host, b_host], simple_mode=True))
print(nnpu.lower(s, [a_host, b_host], simple_mode=True))
#exit()
func = nnpu.build(s, [a_host, b_host], 'nnpu', 'llvm', name='nnpu_log')
print('function built: ')
#print(func.get_source())
# prepare data
ctx = tvm.nd.TVMContext(13, 0) #???
print('i want to know:')
print(ctx.exist)
a_np = np.random.randint(size=shape, dtype=a_host.dtype, low=1, high=20)
a_nd = tvm.nd.array(a_np, ctx)
b_nd = tvm.nd.array(np.zeros(shape).astype(b_host.dtype), ctx)
# run
func(a_nd, b_nd)
print('run finished')
b_np = b_nd.asnumpy()
print('a=')
print(a_np)
print('b=')
print(b_np)
print('ground truth =')
gt = np.log(a_np, dtype=b_host.dtype)
print(gt)
np.testing.assert_allclose(b_np, gt)
import tvm
import topi
from nnpu.utils import ScheduleProcHelper
import numpy as np
import argparse
parser = argparse.ArgumentParser(description='test of NNPU Op')
parser.add_argument('--sim',
type=str,
help='the simulator to use',
default='S0',
choices=['S0', 'S1', 'SC'])
args = parser.parse_args()
env = nnpu.get_env()
assert env.cfg['multi_core'], 'multi core test need multi_core switch on'
nnpu.set_device(env, type=args.sim)
with ScheduleProcHelper():
env = nnpu.get_env()
shape1 = (8, 128) # (8, 128) reshaped & transpoased to (16, 8, 8)
shape2 = (128, 128) # (128, 128) tiled to (16, 128, 8)
gemm_shape = (8, 8, 8)
factor = gemm_shape[1]
assert shape1[1] == shape2[1], \
'gemm do dot product between rows, so the shape[1] of inputs should match'
assert shape1[0] % gemm_shape[
0] == 0, 'gemm insn require size of input 1 be x{0}'.format(
gemm_shape[0])
assert shape2[0] % gemm_shape[
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_inception_v3():
def Conv(data,
num_filter,
kernel=(1, 1),
stride=(1, 1),
pad=(0, 0),
name=None,
suffix=''):
if pad[0] != 0 or pad[1] != 0:
data = sym.pad(data=data,
pad_width=((0, 0), (pad[0], pad[0]),
(pad[1], pad[1]), (0, 0)))
conv = sym.conv2d(data=data,
channels=num_filter,
kernel_size=kernel,
strides=stride,
padding=(0, 0),
use_bias=False,
layout='NHWC',
kernel_layout='HWOI',
name="%s%s_conv2d" % (name, suffix))
bn = sym.batch_norm(data=conv,
name="%s%s_batchnorm" % (name, suffix),
epsilon=2e-5,
axis=3)
act = sym.relu(data=bn, name="%s%s_relu" % (name, suffix))
return act
def Pooling(data, kernel, stride, pad, pool_type, name):
if pad[0] != 0 or pad[1] != 0:
data = sym.pad(data=data,
pad_width=((0, 0), (pad[0], pad[0]),
(pad[1], pad[1]), (0, 0)))
if pool_type == 'max':
return sym.max_pool2d(data=data,
pool_size=kernel,
strides=stride,
name=name,
layout='NHWC')
if pool_type == 'avg':
return sym.avg_pool2d(data=data,
pool_size=kernel,
strides=stride,
name=name,
layout='NHWC')
raise ValueError("Invalid pooling type: " + pool_type)
def Inception7A(data, num_1x1, num_3x3_red, num_3x3_1, num_3x3_2,
num_5x5_red, num_5x5, pool, proj, name):
# num_1x1 = 64
# num_3x3_red = 64
# num_3x3_1 = 96
# num_3x3_2 = 96
# num_5x5_red = 48
# num_5x5 = 64
tower_1x1 = Conv(data, num_1x1, name=('%s_conv' % name))
tower_5x5 = Conv(data,
num_5x5_red,
name=('%s_tower' % name),
suffix='_conv')
tower_5x5 = Conv(tower_5x5,
num_5x5,
kernel=(5, 5),
pad=(2, 2),
name=('%s_tower' % name),
suffix='_conv_1')
tower_3x3 = Conv(data,
num_3x3_red,
name=('%s_tower_1' % name),
suffix="_conv")
tower_3x3 = Conv(tower_3x3,
num_3x3_1,
kernel=(3, 3),
pad=(1, 1),
name=('%s_tower_1' % name),
suffix='_conv_1')
tower_3x3 = Conv(tower_3x3,
num_3x3_2,
kernel=(3, 3),
pad=(1, 1),
name=('%s_tower_1' % name),
suffix='_conv_2')
pooling = Pooling(data,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
pool_type=pool,
name=('%s_pool_%s_pool' % (pool, name)))
cproj = Conv(pooling, proj, name=('%s_tower_2' % name), suffix='_conv')
concat = sym.concatenate(*[tower_1x1, tower_5x5, tower_3x3, cproj],
axis=3,
name='ch_concat_%s_chconcat' % name)
return concat
def Inception7B(data, num_3x3, num_d3x3_red, num_d3x3_1, num_d3x3_2, pool,
name):
tower_3x3 = Conv(data,
num_3x3,
kernel=(3, 3),
pad=(0, 0),
stride=(2, 2),
name=('%s_conv' % name))
tower_d3x3 = Conv(data,
num_d3x3_red,
name=('%s_tower' % name),
suffix='_conv')
tower_d3x3 = Conv(tower_d3x3,
num_d3x3_1,
kernel=(3, 3),
pad=(1, 1),
stride=(1, 1),
name=('%s_tower' % name),
suffix='_conv_1')
tower_d3x3 = Conv(tower_d3x3,
num_d3x3_2,
kernel=(3, 3),
pad=(0, 0),
stride=(2, 2),
name=('%s_tower' % name),
suffix='_conv_2')
pooling = Pooling(data=data,
kernel=(3, 3),
stride=(2, 2),
pad=(0, 0),
pool_type="max",
name=('max_pool_%s_pool' % name))
concat = sym.concatenate(*[tower_3x3, tower_d3x3, pooling],
axis=3,
name='ch_concat_%s_chconcat' % name)
return concat
def Inception7C(data, num_1x1, num_d7_red, num_d7_1, num_d7_2, num_q7_red,
num_q7_1, num_q7_2, num_q7_3, num_q7_4, pool, proj, name):
tower_1x1 = Conv(data=data,
num_filter=num_1x1,
kernel=(1, 1),
name=('%s_conv' % name))
tower_d7 = Conv(data=data,
num_filter=num_d7_red,
name=('%s_tower' % name),
suffix='_conv')
tower_d7 = Conv(data=tower_d7,
num_filter=num_d7_1,
kernel=(1, 7),
pad=(0, 3),
name=('%s_tower' % name),
suffix='_conv_1')
tower_d7 = Conv(data=tower_d7,
num_filter=num_d7_2,
kernel=(7, 1),
pad=(3, 0),
name=('%s_tower' % name),
suffix='_conv_2')
tower_q7 = Conv(data=data,
num_filter=num_q7_red,
name=('%s_tower_1' % name),
suffix='_conv')
tower_q7 = Conv(data=tower_q7,
num_filter=num_q7_1,
kernel=(7, 1),
pad=(3, 0),
name=('%s_tower_1' % name),
suffix='_conv_1')
tower_q7 = Conv(data=tower_q7,
num_filter=num_q7_2,
kernel=(1, 7),
pad=(0, 3),
name=('%s_tower_1' % name),
suffix='_conv_2')
tower_q7 = Conv(data=tower_q7,
num_filter=num_q7_3,
kernel=(7, 1),
pad=(3, 0),
name=('%s_tower_1' % name),
suffix='_conv_3')
tower_q7 = Conv(data=tower_q7,
num_filter=num_q7_4,
kernel=(1, 7),
pad=(0, 3),
name=('%s_tower_1' % name),
suffix='_conv_4')
pooling = Pooling(data=data,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
pool_type=pool,
name=('%s_pool_%s_pool' % (pool, name)))
cproj = Conv(data=pooling,
num_filter=proj,
kernel=(1, 1),
name=('%s_tower_2' % name),
suffix='_conv')
concat = sym.concatenate(*[tower_1x1, tower_d7, tower_q7, cproj],
axis=3,
name='ch_concat_%s_chconcat' % name)
return concat
def Inception7D(data, num_3x3_red, num_3x3, num_d7_3x3_red, num_d7_1,
num_d7_2, num_d7_3x3, pool, name):
tower_3x3 = Conv(data=data,
num_filter=num_3x3_red,
name=('%s_tower' % name),
suffix='_conv')
tower_3x3 = Conv(data=tower_3x3,
num_filter=num_3x3,
kernel=(3, 3),
pad=(0, 0),
stride=(2, 2),
name=('%s_tower' % name),
suffix='_conv_1')
tower_d7_3x3 = Conv(data=data,
num_filter=num_d7_3x3_red,
name=('%s_tower_1' % name),
suffix='_conv')
tower_d7_3x3 = Conv(data=tower_d7_3x3,
num_filter=num_d7_1,
kernel=(1, 7),
pad=(0, 3),
name=('%s_tower_1' % name),
suffix='_conv_1')
tower_d7_3x3 = Conv(data=tower_d7_3x3,
num_filter=num_d7_2,
kernel=(7, 1),
pad=(3, 0),
name=('%s_tower_1' % name),
suffix='_conv_2')
tower_d7_3x3 = Conv(data=tower_d7_3x3,
num_filter=num_d7_3x3,
kernel=(3, 3),
stride=(2, 2),
name=('%s_tower_1' % name),
suffix='_conv_3')
pooling = Pooling(data=data,
kernel=(3, 3),
stride=(2, 2),
pool_type=pool,
pad=(0, 0),
name=('%s_pool_%s_pool' % (pool, name)))
concat = sym.concatenate(*[tower_3x3, tower_d7_3x3, pooling],
axis=3,
name='ch_concat_%s_chconcat' % name)
return concat
def Inception7E(data, num_1x1, num_d3_red, num_d3_1, num_d3_2,
num_3x3_d3_red, num_3x3, num_3x3_d3_1, num_3x3_d3_2, pool,
proj, name):
tower_1x1 = Conv(data=data,
num_filter=num_1x1,
kernel=(1, 1),
name=('%s_conv' % name))
tower_d3 = Conv(data=data,
num_filter=num_d3_red,
name=('%s_tower' % name),
suffix='_conv')
tower_d3_a = Conv(data=tower_d3,
num_filter=num_d3_1,
kernel=(1, 3),
pad=(0, 1),
name=('%s_tower' % name),
suffix='_mixed_conv')
tower_d3_b = Conv(data=tower_d3,
num_filter=num_d3_2,
kernel=(3, 1),
pad=(1, 0),
name=('%s_tower' % name),
suffix='_mixed_conv_1')
tower_3x3_d3 = Conv(data=data,
num_filter=num_3x3_d3_red,
name=('%s_tower_1' % name),
suffix='_conv')
tower_3x3_d3 = Conv(data=tower_3x3_d3,
num_filter=num_3x3,
kernel=(3, 3),
pad=(1, 1),
name=('%s_tower_1' % name),
suffix='_conv_1')
tower_3x3_d3_a = Conv(data=tower_3x3_d3,
num_filter=num_3x3_d3_1,
kernel=(1, 3),
pad=(0, 1),
name=('%s_tower_1' % name),
suffix='_mixed_conv')
tower_3x3_d3_b = Conv(data=tower_3x3_d3,
num_filter=num_3x3_d3_2,
kernel=(3, 1),
pad=(1, 0),
name=('%s_tower_1' % name),
suffix='_mixed_conv_1')
pooling = Pooling(data=data,
kernel=(3, 3),
stride=(1, 1),
pad=(1, 1),
pool_type=pool,
name=('%s_pool_%s_pool' % (pool, name)))
cproj = Conv(data=pooling,
num_filter=proj,
kernel=(1, 1),
name=('%s_tower_2' % name),
suffix='_conv')
concat = sym.concatenate(*[
tower_1x1, tower_d3_a, tower_d3_b, tower_3x3_d3_a, tower_3x3_d3_b,
cproj
],
axis=3,
name='ch_concat_%s_chconcat' % name)
return concat
def get_symbol(data, num_classes=16, **kwargs):
conv = Conv(data, 32, kernel=(3, 3), stride=(2, 2), name="conv")
conv_1 = Conv(conv, 32, kernel=(3, 3), name="conv_1")
conv_2 = Conv(conv_1, 64, kernel=(3, 3), pad=(1, 1), name="conv_2")
pool = Pooling(data=conv_2,
kernel=(3, 3),
stride=(2, 2),
pool_type="max",
pad=(0, 0),
name="pool")
conv_3 = Conv(pool, 80, kernel=(1, 1), name="conv_3")
conv_4 = Conv(conv_3, 192, kernel=(3, 3), name="conv_4")
pool1 = Pooling(data=conv_4,
kernel=(3, 3),
stride=(2, 2),
pool_type="max",
pad=(0, 0),
name="pool1")
in3a = Inception7A(pool1, 64, 64, 96, 96, 48, 64, "avg", 32, "mixed")
in3b = Inception7A(in3a, 64, 64, 96, 96, 48, 64, "avg", 64, "mixed_1")
in3c = Inception7A(in3b, 64, 64, 96, 96, 48, 64, "avg", 64, "mixed_2")
in3d = Inception7B(in3c, 384, 64, 96, 96, "max", "mixed_3")
in4a = Inception7C(in3d, 192, 128, 128, 192, 128, 128, 128, 128, 192,
"avg", 192, "mixed_4")
in4b = Inception7C(in4a, 192, 160, 160, 192, 160, 160, 160, 160, 192,
"avg", 192, "mixed_5")
in4c = Inception7C(in4b, 192, 160, 160, 192, 160, 160, 160, 160, 192,
"avg", 192, "mixed_6")
in4d = Inception7C(in4c, 192, 192, 192, 192, 192, 192, 192, 192, 192,
"avg", 192, "mixed_7")
in4e = Inception7D(in4d, 192, 320, 192, 192, 192, 192, "max",
"mixed_8")
in5a = Inception7E(in4e, 320, 384, 384, 384, 448, 384, 384, 384, "avg",
192, "mixed_9")
in5b = Inception7E(in5a, 320, 384, 384, 384, 448, 384, 384, 384, "max",
192, "mixed_10")
pool = Pooling(data=in5b,
kernel=(8, 8),
stride=(1, 1),
pool_type="avg",
pad=(0, 0),
name="global_pool")
flatten = sym.flatten(data=pool, name="flatten")
fc1 = sym.dense(data=flatten, units=num_classes, name="fc1")
softmax = sym.softmax(data=fc1, name="softmax")
return softmax
input_shape = (1, 299, 299, 16)
target_host = "llvm"
device = "nnpu"
data = nnvm.symbol.Variable(name="data")
target = tvm.target.create("llvm -device={}".format(device))
print("ok")
num_runs = 3
z = get_symbol(data=data, num_classes=16)
compute_graph = nnvm.graph.create(z)
with nnvm.compiler.build_config(opt_level=1):
if target.device_name != "nnpu":
deploy_graph, lib, params = nnvm.compiler.build(
compute_graph,
target,
shape={"data": input_shape},
dtype="float32",
target_host=target_host)
else:
with ScheduleProcHelper():
with nnpu.build_config():
nnpu.set_device(nnpu.get_env(), type='S0')
deploy_graph, lib, params = nnvm.compiler.build(
compute_graph,
target,
shape={"data": input_shape},
dtype="float32",
target_host=target_host)
ctx = tvm.context(str("nnpu"), 0) if device == "nnpu" else tvm.context(
str("llvm"), 0)
module = runtime.create(deploy_graph, lib, ctx)
a_np = np.random.uniform(size=(1, 299, 299, 16), low=-32,
high=32).astype(np.float32)
print(a_np)
module.set_input(data=a_np)
ftimer = module.module.time_evaluator("run",
ctx,
number=num_runs,
repeat=1)
module.run()
out = module.get_output(0, out=tvm.nd.empty((1, 16)))
print(out.asnumpy)
print(deploy_graph.ir())
print(ftimer().mean * 10)
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()
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():
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()
shape = (16, 16)
a_host = tvm.placeholder(shape, env.cfg['dtype_n'], 'a_host')
a = tvm.compute(shape, lambda *i: a_host(*i), name='a')
a_buf = tvm.compute(shape, lambda *i: a(*i), name='a_buf')
vctr_shape = (1, 16)
b_host = tvm.placeholder(vctr_shape, env.cfg['dtype_n'], 'b_host')
b = tvm.compute(vctr_shape, lambda *i: b_host(*i), name='b')
b_buf = tvm.compute(vctr_shape, lambda *i: b(*i), name='b_buf')
dtype_w = env.cfg['dtype_w']
mul_shape = (1, 16)
k = tvm.reduce_axis((0, 16), 'k')
c_buf = tvm.compute(
mul_shape, lambda i, j: tvm.sum(
b_buf[i, k].astype(dtype_w) * a_buf[j, k].astype(dtype_w), axis=k))
out_shape = (16, )
bias_host = tvm.placeholder(out_shape, env.cfg['dtype_w'], 'bias_host')
bias = tvm.compute(out_shape, lambda *i: bias_host(*i), 'bias')
bias_buf = tvm.compute(out_shape, lambda *i: bias(*i), 'bias_buf')
#c = tvm.compute(out_shape, lambda *i: c_buf(*i), name='c')
#c_host = tvm.compute(out_shape, lambda *i: c(*i), name='c_host')
out_buf = tvm.compute(out_shape, lambda i: c_buf[0, i] + bias_buf[i],
'out_buf')
out = tvm.compute(out_shape, lambda *i: out_buf(*i), 'out')
out_host = tvm.compute(out_shape, lambda *i: out(*i), 'out_host')
s = tvm.create_schedule(out_host.op)
# mark variable scopes
s[a].set_scope(env.dram_scope)
s[b].set_scope(env.dram_scope)
s[bias].set_scope(env.dram_scope)
s[out].set_scope(env.dram_scope)
s[a_buf].set_scope(env.uni_scratchpad_scope)
s[b_buf].set_scope(env.uni_scratchpad_scope)
s[c_buf].set_scope(env.uni_scratchpad_scope)
s[bias_buf].set_scope(env.uni_scratchpad_scope)
s[out_buf].set_scope(env.uni_scratchpad_scope)
#print(dir(s[b].op.body))
# mark compiler pragmas
s[a].pragma(s[a].op.axis[0], env.dma_copy_pragma)
s[b].pragma(s[b].op.axis[0], env.dma_copy_pragma)
s[bias].pragma(s[bias].op.axis[0], env.dma_copy_pragma)
s[out_host].pragma(s[out_host].op.axis[0], env.dma_copy_pragma)
s[a_buf].pragma(s[a_buf].op.axis[0], env.scratchpad_ls)
s[b_buf].pragma(s[b_buf].op.axis[0], env.scratchpad_ls)
s[bias_buf].pragma(s[bias_buf].op.axis[0], env.scratchpad_ls)
s[out].pragma(s[out].op.axis[0], env.scratchpad_ls)
#s[a_buf].compute_at(s[b_buf], b_buf.op.axis[0])
# tensorize
#s[b_buf].tensorize(s[b_buf].op.axis[1], env.intrins.get('VEXP', mode='inc'))
s[c_buf].tensorize(s[c_buf].op.axis[0],
env.intrins.get('GEMM', shape=(1, 16, 16), mode='inc'))
#outer, inner = out_buf.op.axis
#s[out_buf].reorder(inner, outer)
#print(outer)
#print(tvm.lower(s, [a_host, b_host, bias_host, out_host], simple_mode=True))
s[out_buf].tensorize(s[out_buf].op.axis[0],
env.intrins.get('VAddV', mode='w'))
# build
print(tvm.lower(s, [a_host, b_host, bias_host, out_host],
simple_mode=True))
print(
nnpu.lower(s, [a_host, b_host, bias_host, out_host], simple_mode=True))
#exit()
func = nnpu.build(s, [a_host, b_host, bias_host, out_host],
'nnpu',
'llvm',
name='nnpu_exp')
print('function built: ')
#print(func.get_source())
# prepare data
ctx = tvm.nd.TVMContext(13, 0)
a_np = np.random.randint(size=shape, dtype=a_host.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=vctr_shape,
dtype=b_host.dtype,
low=0,
high=64)
#b_np = np.random.random(size=vctr_shape).astype(b_host.dtype)
b_nd = tvm.nd.array(b_np, ctx)
bias_np = np.random.randint(size=out_shape,
dtype=bias_host.dtype,
low=0,
high=64)
#bias_np = np.random.random(size=out_shape).astype(bias_host.dtype)
bias_nd = tvm.nd.array(bias_np, ctx)
out_nd = tvm.nd.array(np.zeros(out_shape).astype(out_host.dtype), ctx)
# run
func(a_nd, b_nd, bias_nd, out_nd)
print('run finished')
print('a=')
print(a_np)
print('b=')
print(b_np)
print('bias=')
print(bias_np)
print('out=')
print(out_nd.asnumpy())
print('numpy ground truth is: ')
gt = np.dot(b_np.astype(dtype_w),
a_np.astype(dtype_w).transpose((1, 0))).reshape(
(16, )) + bias_np
print(gt)
np.testing.assert_allclose(out_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_vgg():
def get_feature(internel_layer, layers, filters, batch_norm=False):
"""
Get VGG feature body as stacks of convoltions.
layers : [1, 1, 2, 2, 2]
filters : [64, 128, 256, 512, 512]
"""
for i, num in enumerate(layers):
"""
i = 0, num = 1
i = 1, num = 1
i = 2, num = 2
i = 3, num = 2
i = 4, num = 2
"""
for j in range(num):
internel_layer = sym.pad(data=internel_layer,
pad_width=((0, 0), (1, 1), (1, 1),
(0, 0)))
internel_layer = sym.conv2d(data=internel_layer,
kernel_size=(3, 3),
channels=filters[i],
layout='NHWC',
kernel_layout='HWOI',
name="conv%s_%s" % (i + 1, j + 1))
if batch_norm:
internel_layer = sym.batch_norm(data=internel_layer,
axis=3,
name="bn%s_%s" %
(i + 1, j + 1))
internel_layer = sym.relu(data=internel_layer,
name="relu%s_%s" % (i + 1, j + 1))
internel_layer = sym.max_pool2d(data=internel_layer,
pool_size=(2, 2),
strides=(2, 2),
layout="NHWC",
name="pool%s" % (i + 1))
return internel_layer
def get_classifier(input_data, num_classes):
"""
Get VGG classifier layers as fc layers.
"""
flatten = sym.flatten(data=input_data, name="flatten")
fc1 = sym.dense(data=flatten, units=32, name="fc1")
relu1 = sym.relu(data=fc1, name="relu1")
drop1 = sym.dropout(data=relu1, rate=0.5, name="drop1")
fc2 = sym.dense(data=drop1, units=32, name="fc2")
relu2 = sym.relu(data=fc2, name="relu2")
drop2 = sym.dropout(data=relu2, rate=0.5, name="drop2")
fc3 = sym.dense(data=drop2, units=num_classes, name="fc3")
return fc3
def get_symbol(datas, num_classes, num_layers=11, batch_norm=False):
"""
Parameters
------------
num_classes : int, default 16
Number of classification classes
num_layers : int
Number of layers for the variant of vgg. Options are 11, 13, 16, 19
batch_norm : bool, default False
Use batch normalization.
"""
vgg_spec = {
11: ([1, 1, 2, 2, 2], [64, 128, 256, 512, 512]),
13: ([2, 2, 2, 2, 2], [64, 128, 256, 512, 512]),
16: ([2, 2, 3, 3, 3], [64, 128, 256, 512, 512]),
19: ([2, 2, 4, 4, 4], [64, 128, 256, 512, 512])
}
if num_layers not in vgg_spec:
raise ValueError(
"Invalide num_layers {}. Choices are 11, 13, 16, 19.".format(
num_layers))
layers, filters = vgg_spec[num_layers]
feature = get_feature(datas, layers, filters, batch_norm)
classifier = get_classifier(feature, num_classes)
symbol = sym.softmax(data=classifier, name="softmax")
return symbol
input_shape = (1, 224, 224, 16)
target_host = "llvm"
device = "nnpu"
data = nnvm.symbol.Variable(name="data")
target = tvm.target.create("llvm -device={}".format(device))
print("ok")
num_runs = 1
z = get_symbol(datas=data, num_classes=16)
compute_graph = nnvm.graph.create(z)
print(compute_graph.ir())
with nnvm.compiler.build_config(opt_level=0):
if target.device_name != "nnpu":
deploy_graph, lib, params = nnvm.compiler.build(
compute_graph,
target,
shape={"data": input_shape},
dtype="float32",
target_host=target_host)
else:
nnpu.set_device(nnpu.get_env(), type='SC')
with ScheduleProcHelper():
with nnpu.build_config():
deploy_graph, lib, params = nnvm.compiler.build(
compute_graph,
target,
shape={"data": input_shape},
dtype="float32",
target_host=target_host)
ctx = tvm.context(str("nnpu"), 0) if device == "nnpu" else tvm.context(
str("llvm"), 0)
module = runtime.create(deploy_graph, lib, ctx)
a_np = np.random.uniform(size=input_shape, low=-32,
high=32).astype(np.float32)
print(a_np)
module.set_input(data=a_np)
ftimer = module.module.time_evaluator("run",
ctx,
number=num_runs,
repeat=1)
# module.run()
out = module.get_output(0, out=tvm.nd.empty((1, 16)))
print(out.asnumpy)
print(deploy_graph.ir())
print(ftimer().mean * 10)
