def check(dim, size, backward=False):
n = 64
a = jt.random((n, n, n, n))
a.sync()
m = n // size
arr = []
for i in range(m):
arr.append(
a.getitem((slice(None), ) * dim +
(slice(i * size, i * size + size), )))
b = concat2(arr, dim)
if backward:
loss = b * a
b = jt.grad(loss, a)
with jt.profile_scope(1, 0) as rep:
b.sync()
# print(rep)
i = rep[0].index("TotalTime")
stime = 0
for r in rep[1:]:
stime += float(r[i])
bw = 4 * 64**4 * 2 * 2 / stime
# sizeof(float) * numel * (split and concat) * (read and write)
print(f"{dim} {size} {stime/1e6}ms, {bw}GB/s")
return bw
def test_case(box_num, out_size, time_limit):
boxes = []
for i in range(box_num):
t = [
random.random() * 0.9,
random.random() * 0.9,
random.random() * 0.9,
random.random() * 0.9
]
t2 = [
min(t[0], t[2]),
min(t[1], t[3]),
max(t[0], t[2]) + 0.1,
max(t[1], t[3]) + 0.1
]
boxes.append(t2)
img = jt.random([121, 121, 3])
out = resize_and_crop(img,
jt.array(boxes),
interpolation='bilinear',
out_size=out_size)
with jt.profile_scope() as rep:
our_out = out.data
t = 0
fused_op_num = 0
for i in range(1, len(rep)):
t += float(rep[i][3]) / 1e9
name = rep[i][0]
if name.startswith('[') and (not '[graph:]' in name):
fused_op_num += 1
assert fused_op_num == 1, fused_op_num
assert t <= time_limit, t
def check(self, use_int32):
n = 1024
a = jt.random((n, n * 8))
b = jt.random((n * 8, ))
a.data, b.data
with jt.profile_scope(compile_options={
"compile_shapes": 1,
"parallel": 1,
"split1": n,
"order1": 1
},
try_use_32bit_index=use_int32) as rep:
c = a - b
# def func(a, b, c, tid, num):
# for i in range(tid*1024, 1024*8, num*1024):
# for j in range(n):
# for k in range(n):
# c[j*1024*8 + i+k] = a[j*1024*8 + i+k] - b[i+k]
nc = c.data
assert len(rep) == 2
assert (a.data - b.data == nc).all()
fname = rep[1][1]
with open(fname) as f:
src = f.read()
assert "thread_id" in src
with open(fname.replace(".cc", ".s")) as f:
asm = SimpleAsmParser(f.read())
func_name = "run"
ca = asm.count_instructions(func_name, "vmova")
cu = asm.count_instructions(func_name, "vmovu")
return ca, cu
def check(self, use_int32):
n = 1024
a = jt.random((n, n))
b = jt.random((n, n))
a.data, b.data
with jt.profile_scope(compile_options={
"compile_shapes": 1,
"parallel": 2,
"try_use_32bit_index": use_int32
},
try_use_32bit_index=use_int32) as rep:
c = a + b
nc = c.data
assert len(rep) == 2
assert (a.data + b.data == nc).all()
fname = rep[1][1]
with open(fname) as f:
src = f.read()
assert "thread_id" in src
with open(fname.replace(".cc", ".s")) as f:
asm = SimpleAsmParser(f.read())
func_name = "run"
ca = asm.count_instructions(func_name, "vmova")
cu = asm.count_instructions(func_name, "vmovu")
return ca, cu
def reduce_check(self, ndim, depth, tdim, rdim, has_atomic, order=[], split=[], **args):
shape = [8]*ndim
a = jt.random(shape)
a.sync()
config = {
"parallel":1, "max_parallel_depth":depth, "merge_loop_var": self.merge_loop_var
}
for k in args:
config[k] = args[k]
if not isinstance(rdim, list):
rdim = [rdim]
rdim = tuple(rdim)
nshape = [1024, 256, 128][len(rdim)]
for d in rdim: shape[d] = nshape
for i,o in enumerate(order):
config[f"order{i}"] = o
for i,o in enumerate(split):
config[f"split{i}"] = o
with jt.profile_scope(
compile_options = config,
enable_tuner = 0
) as rep:
b = a.sum(rdim).data
assert len(rep) == 2
fname = rep[1][1]
with open(fname) as f:
src = f.read()
for i in range(tdim):
assert f"tnum{i}" in src
assert f"tnum{tdim}" not in src, f"tnum{tdim}"
src_has_atomic = "atomic_add" in src or "atomicAdd" in src
assert has_atomic == src_has_atomic
assert np.allclose(a.data.sum(rdim), b), (b.sum(), a.data.sum())
def test_fuse_transpose5(self):
with jt.profile_scope() as rep:
a = jt.rand((10, 11, 6, 7))
c = jt.rand((10, 11, 6, 7))
b = (a + c).fuse_transpose((1, 0, 2, 3))
np.testing.assert_allclose((a.data + c.data).transpose(
(1, 0, 2, 3)), b.data)
assert len(rep) == 3
def test_fuse_transpose3(self):
with jt.profile_scope() as rep:
a = jt.rand((10, 11, 12))
c = jt.rand((11, 12, 10))
b = a.fuse_transpose((1, 2, 0)) + c
np.testing.assert_allclose(
a.data.transpose((1, 2, 0)) + c.data, b.data)
assert len(rep) == 3
def test2(self):
a = jt.ones([10, 10, 10, 10])
a.sync()
with jt.profile_scope() as rep:
b = a + 1
b.sync()
with open(rep[1][1]) as f:
src = f.read()
assert "range0123" in src
def test_resnet_train_profile(self):
with jt.profile_scope(trace_py_var=1):
resnet18 = resnet.Resnet18()
opt = jt.optim.SGD(resnet18.parameters(), 0.1)
x = jt.float32(np.random.rand(2, 3, 224, 224))
y = resnet18(x)
opt.step(y**2)
jt.sync_all()
def test_scalar_fuse_unary(self):
with jt.profile_scope() as rep:
a = jt.array([1])
b = -a
a = a.clone()
b = b.clone()
jt.sync([a, b])
assert a.data == 1
assert b.data == -1
assert len(rep) == 2
def test5(self):
a = jt.ones([10, 10, 10, 10])
a.sync()
with jt.profile_scope() as rep:
b = a.sum([1])
b.sync()
with open(rep[1][1]) as f:
src = f.read()
assert "range01" not in src
assert "range23" in src
def test4(self):
# don't optimize reindex like op yet
a = jt.ones([10, 10, 10, 10])
a.sync()
with jt.profile_scope() as rep:
b = a.reindex_reduce("add", [10, 10], ["i0", "i1"])
b.sync()
with open(rep[1][1]) as f:
src = f.read()
assert "range23" not in src
def test3(self):
a = jt.ones([10, 10, 10, 10])
x = jt.ones([1, 10, 1, 1])
a.sync(), x.sync()
with jt.profile_scope() as rep:
b = a + x
b.sync()
with open(rep[1][1]) as f:
src = f.read()
assert "range23" in src
def setUpClass(self):
n, m, k = 2, 6, 16
a = jt.random((n, m, 1))
b = jt.random((1, m, k))
jt.fetch_sync([a, b])
with jt.profile_scope(compile_options={"jtune": 1}) as rep:
c = (a * b).sum(1)
c.sync()
assert len(rep) == 2
self.fname = rep[1][1]
self.jtune_path = os.path.join(jt.flags.jittor_path, "utils/jtune.py")
def test(self):
a = jt.array([1,2,3])
a.sync()
assert a.compile_options=={}
a.compile_options = {"compile_shapes":1}
assert a.compile_options=={"compile_shapes":1}
b = a+a
assert b.compile_options=={}
with jt.flag_scope(compile_options={"compile_shapes":1}):
c = a+b
assert c.compile_options=={"compile_shapes":1}
with jt.profile_scope() as report:
c.sync()
assert len(report)==2 and "compile_shapes:1" in report[1][0]
def check(ndim, depth, tdim):
a = jt.random([16] * ndim)
a.sync()
compile_options = {"parallel": 1}
if depth is not None:
compile_options["max_parallel_depth"] = depth
with jt.profile_scope(compile_options=compile_options) as rep:
b = (a + a).data
assert np.allclose(a.data * 2, b)
assert len(rep) == 2
fname = rep[1][1]
with open(fname) as f:
src = f.read()
for i in range(tdim):
assert f"tnum{i}" in src
assert f"tnum{tdim}" not in src
def test_stop_fuse2(self):
with jt.profile_scope() as report:
a = jt.float32(0).stop_fuse()
c = jt.float32(0).stop_fuse()
bs = [c]
for i in range(2000):
b = jt.float32(i) * 2 * c
bs.append(b)
a += b
a = a * 2
dbs = jt.grad(a, bs)
jt.sync(dbs + [a])
for a in report[1:]:
assert len(a[0].split("opkey")) < 8
def test_stop_fuse(self):
with jt.profile_scope() as report:
a = jt.float32(0).stop_fuse()
c = jt.float32(0)
bs = [c]
for i in range(2000):
b = jt.float32(i) * 2 * c
bs.append(b)
a += b
a = a * 2
dbs = jt.grad(a, bs)
jt.sync(dbs + [a])
for a in report[1:]:
# origin is 50
# after update queue, increase to 102
assert len(a[0].split("opkey")) < 110, len(a[0].split("opkey"))
def test_reduce_opt(self):
a = jt.random((16, 512, 38, 38))
b = jt.random((16, 512, 38, 38))
jt.sync([a, b])
with jt.profile_scope(rerun=10, warmup=10) as rep:
norm = a.sqr().sum(1, keepdims=True).sqrt()
c = a / norm
da = jt.grad(c * b, a)
jt.sync([c, da])
gpu_c = c.numpy()
gpu_da = da.numpy()
with jt.flag_scope(use_cuda=0):
norm = a.sqr().sum(1, keepdims=True).sqrt()
c = a / norm
da = jt.grad(c * b, a)
assert np.allclose(gpu_c, c.data, 1e-3)
assert (np.abs(gpu_da - da.data).max() < 1e-6)
assert float(rep[1][3]) < 15e6, float(rep[1][3]) # 15ms(about 8ms)
def check(n, m, reduce_dim, cache_report_, error_rate_threshold):
a = jt.random([n, m])
a.sync()
with jt.profile_scope(
compile_options={
"check_cache": 1,
"replace_strategy": 1,
"page_size": 4 << 10, #2 << 20
"vtop": 0,
"tlb_size": 64,
"tlb_ways": 4,
"tlb_line_size": 1,
"L1_size": 32 << 10,
"L1_ways": 8,
"L1_line_size": 64,
"L2_size": 256 << 10,
"L2_ways": 8,
"L2_line_size": 64,
"L3_size": 15 << 20,
"L3_ways": 20,
"L3_line_size": 64
},
enable_tuner=0) as report:
c = a.sum(reduce_dim)
c.sync()
check_cache_code(report[1][1])
cache_report = report[-1][-5:]
for i in range(len(cache_report)):
cache_report[i] = int(cache_report[i])
for i in range(len(cache_report)):
assert abs(cache_report[i] - cache_report_[i]) <= int(
cache_report_[i] *
error_rate_threshold), "cache report error: " + report[-2][
-(len(cache_report) - i)] + " error, " + str(
cache_report[i]) + "!=" + str(cache_report_[i])
def test(name, model_name, bs):
print("hello", name, model_name, bs)
import numpy as np
import time
is_train = False
_model_name = model_name
if model_name.startswith("train_"):
is_train = True
model_name = model_name[6:]
if name == "torch":
import torch
import torchvision.models as tcmodels
from torch import optim
from torch import nn
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = True
model = tcmodels.__dict__[model_name]()
model = model.cuda()
else:
import jittor as jt
from jittor import optim
from jittor import nn
jt.flags.use_cuda = 1
jt.cudnn.set_algorithm_cache_size(10000)
import jittor.models as jtmodels
model = jtmodels.__dict__[model_name]()
if (model == "resnet152" or model == "resnet101") and bs == 128 and is_train:
jt.cudnn.set_max_workspace_ratio(0.05)
if is_train:
model.train()
else:
model.eval()
img_size = 224
if model_name == "inception_v3":
img_size = 300
test_img = np.random.random((bs, 3, img_size, img_size)).astype("float32")
if is_train:
label = (np.random.random((bs,)) * 1000).astype("int32")
if name == "torch":
test_img = torch.Tensor(test_img).cuda()
if is_train:
label = torch.LongTensor(label).cuda()
opt = optim.SGD(model.parameters(), 0.001)
sync = lambda: torch.cuda.synchronize()
jt = torch
else:
test_img = jt.array(test_img).stop_grad()
if is_train:
label = jt.array(label).stop_grad()
opt = optim.SGD(model.parameters(), 0.001)
sync = lambda: jt.sync_all(True)
sync()
use_profiler = os.environ.get("use_profiler", "0") == "1"
if hasattr(jt, "nograd"):
ng = jt.no_grad()
ng.__enter__()
def iter():
x = model(test_img)
if isinstance(x, tuple):
x = x[0]
if is_train:
loss = nn.CrossEntropyLoss()(x, label)
if name == "jittor":
opt.step(loss)
else:
opt.zero_grad()
loss.backward()
opt.step()
else:
x.sync()
sync()
for i in time_iter():
iter()
sync()
for i in time_iter():
iter()
sync()
if use_profiler:
if name == "torch":
prof = torch.autograd.profiler.profile(use_cuda=True)
else:
prof = jt.profile_scope()
prof.__enter__()
if name == "jittor":
if hasattr(jt.flags, "use_parallel_op_compiler"):
jt.flags.use_parallel_op_compiler = 0
start = time.time()
for i in time_iter(10):
iter()
sync()
end = time.time()
if use_profiler:
prof.__exit__(None,None,None)
if name == "torch":
print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=30))
total_iter = i+1
print("duration:", end-start, "FPS:", total_iter*bs/(end-start))
fpath = f"{home_path}/.cache/jittor/{name}-{_model_name}-{bs}.txt"
with open(fpath, 'w') as f:
f.write(f"duration: {end-start} FPS: {total_iter*bs/(end-start)}")
os.chmod(fpath, 0x666)
def test_fuse_transpose2(self):
with jt.profile_scope() as rep:
a = jt.rand((10, 11, 12))
b = (a + 1).fuse_transpose((1, 2, 0))
np.testing.assert_allclose(a.data.transpose((1, 2, 0)) + 1, b.data)
assert len(rep) == 3
