def record_params(self, parameters_dict):
if os.environ.get("use_auto_diff", '1') == '0':
return
rid = self.rid
self.rid += 1
global has_error
pps = {}
for k, v in parameters_dict.items():
if k.endswith("num_batches_tracked"):
continue
pps[k] = v
ps = {name: convert(param) for name, param in pps.items()}
fpath = os.path.join(self.base_path, f"{rid}-params.pkl")
if os.path.isfile(fpath):
with open(fpath, 'rb') as f:
prev_ps = pickle.load(f)
if len(prev_ps) != len(ps):
has_error += 1
LOG.e(f"Params len not match {len(prev_ps)} != {len(ps)}")
for k in ps:
a = ps[k]
if k not in prev_ps:
has_error += 1
LOG.e(f"prev param <{k}> not found.")
continue
b = prev_ps[k]
if a.shape != b.shape:
has_error += 1
LOG.e(
f"Params <{k}> shape not match {a.shape} != {b.shape}")
continue
std_a, mean_a = a.std(), a.mean()
std_b, mean_b = b.std(), b.mean()
n = a.size
# law of large number
std_mean_a = (std_a + std_b) / 2 / np.sqrt(n) + 1e-6
std_std_a = (std_a + std_b) / 2 / np.sqrt((n - 1) / 2) + 1e-6
x = 4
if np.abs(mean_a - mean_b) > x * std_mean_a:
has_error += 1
LOG.e(
f"param mean not match, mean_a:{mean_a}, mean_b:{mean_b}, acceptable range:({mean_a - x * std_mean_a}, {mean_a + x * std_mean_a}) name:{k} shape:{a.shape}"
)
elif np.abs(std_a - std_b) > x * std_std_a:
has_error += 1
LOG.e(
f"param std not match, std_a:{std_a}, std_b:{std_b}, acceptable range:({std_a - x * std_std_a}, {std_a + x * std_std_a}) name:{k} shape:{a.shape}"
)
else:
LOG.i(f"check param ok: <{k}> shape:{a.shape}")
var = pps[k]
if hasattr(var, "copy_"):
import torch
var.data.copy_(torch.from_numpy(b))
else:
var.assign(b)
else:
with open(fpath, 'wb') as f:
pickle.dump(ps, f)
LOG.i(f"save params ok")
def check_cache_compile():
files = [
"src/utils/cache_compile.cc",
"src/utils/log.cc",
"src/utils/tracer.cc",
"src/utils/jit_utils.cc",
"src/utils/str_utils.cc",
]
if os.name == 'nt':
files = [x.replace('/', '\\') for x in files]
global jit_utils_core_files
jit_utils_core_files = files
recompile = compile(
cc_path, cc_flags + f" {opt_flags} ", files,
jit_utils.cache_path + '/jit_utils_core' + extension_suffix, True)
if recompile and jit_utils.cc:
LOG.e("jit_utils updated, please rerun your command.")
sys.exit(0)
if not jit_utils.cc:
with jit_utils.import_scope(import_flags):
jit_utils.try_import_jit_utils_core()
assert jit_utils.cc
# recompile, generate cache key
compile(cc_path, cc_flags + f" {opt_flags} ", files,
jit_utils.cache_path + '/jit_utils_core' + extension_suffix,
True)
def check(self, name, pre_data, data):
global has_error
if type(pre_data) != type(data):
LOG.e(
f"type not match, {pre_data.__class__.__name__}!={data.__class__.__name__}, name: {name}"
)
has_error += 1
return
if isinstance(pre_data, (list, tuple)):
if len(pre_data) != len(data):
has_error += 1
LOG.e(
f"Name <{name}> len not match, {len(pre_data)} != {len(data)}"
)
n = max(len(pre_data), len(data))
for i in range(n):
a = pre_data[i] if i < len(pre_data) else "None"
b = data[i] if i < len(data) else "None"
self.check(name + f".{i}", a, b)
elif isinstance(pre_data, np.ndarray):
if pre_data.shape != data.shape:
has_error += 1
LOG.e(f"Ndarray shape <{name}> not match")
return
self.check_array(name, pre_data, data)
elif isinstance(pre_data, dict):
if len(pre_data) != len(data):
has_error += 1
LOG.w(
f"Dict Name <{name}> len not match, {len(pre_data)} != {len(data)}"
)
for k in pre_data:
pv = pre_data[k]
if k not in data:
has_error += 1
msg = f"Key <{k}> not in data, Name <{name}>"
if isinstance(pv, np.ndarray):
LOG.e(msg)
else:
LOG.w(msg)
continue
self.check(name + f".{k}", pre_data[k], data[k])
else:
if pre_data != data:
has_error += 1
LOG.e(
f"Type: {type(pre_data).__name__} Name <{name}> not match {pre_data} != {data}"
)
def check_cache_compile():
files = [
"src/utils/cache_compile.cc",
"src/utils/log.cc",
"src/utils/tracer.cc",
"src/utils/jit_utils.cc",
]
global jit_utils_core_files
jit_utils_core_files = files
recompile = compile(cc_path, cc_flags+f" {opt_flags} ", files, 'jit_utils_core'+extension_suffix, True)
if recompile and jit_utils.cc:
LOG.e("jit_utils updated, please restart jittor.")
sys.exit(0)
if not jit_utils.cc:
with jit_utils.import_scope(import_flags):
jit_utils.try_import_jit_utils_core()
assert jit_utils.cc
# recompile, generate cache key
compile(cc_path, cc_flags+f" {opt_flags} ", files, 'jit_utils_core'+extension_suffix, True)
def check_array(self, name, a, b):
rtol = self.rtol
atol = self.atol
global has_error
err = np.abs(a-b)
tol = atol + rtol * np.abs(b)
is_error = np.logical_or( err > tol, (a>=-1e-5)!=(b>=-1e-5))
index = np.where(is_error)
assert len(index)>0
if len(index[0]) == 0:
return
has_error += 1
LOG.w(f"Ndarray <{name}> not match, shape:{a.shape}")
i = tuple( i[0] for i in index )
err_rate = is_error.mean()
LOG.w(f"error index at [{i}], a({a[i]}) b({b[i]}) err({err[i]}) > tol({tol[i]}), err_rate:{err_rate*100:.3f}% amean({a.mean()}) bmean({b.mean()}) astd({a.std()}) bstd({b.std()}) ")
if err_rate > 0.01:
LOG.e("!"*10+"Very HIGH err rate"+"!"*10)
def record(self, name, data, ex_name=""):
if os.environ.get("use_auto_diff", '1') == '0':
return
rid = self.rid
self.rid += 1
fpath = os.path.join(self.base_path, f"{rid}.pkl")
data = convert(data)
if os.path.isfile(fpath):
with open(fpath, 'rb') as f:
pre_name, pre_data = pickle.load(f)
if pre_name != name:
global has_error
has_error += 1
LOG.e(f"The {rid} result name not match, {pre_name} != {name}")
self.rid -= 1
return
LOG.i(f"check {rid}:<{ex_name}{name}> ...")
self.check(ex_name + name, pre_data, data)
else:
with open(fpath, 'wb') as f:
pickle.dump((name, data), f)
LOG.i(f"save {rid}:<{name}> ok")
def record(self, name, data, ex_name=""):
if os.environ.get("use_auto_diff", '1') == '0':
return
self.record_status[name] += 1
fpath = os.path.join(self.base_path,
f"{name}-{self.record_status[name]}.pkl")
data = convert(data)
self.rid += 1
if self.mode == 'check':
if os.path.isfile(fpath):
with open(fpath, 'rb') as f:
pre_name, pre_data = pickle.load(f)
LOG.i(f"check {self.rid}:<{ex_name}{name}> ...")
self.check(ex_name + name, pre_data, data)
else:
global has_error
has_error += 1
LOG.e(f"No previous result found: {name}")
return
else:
with open(fpath, 'wb') as f:
pickle.dump((name, data), f)
LOG.i(f"save {self.rid}:<{name}> ok")
