def test_papi(target, dev):
target = tvm.target.Target(target)
if str(target.kind) == "llvm":
metric = "PAPI_FP_OPS"
elif str(target.kind) == "cuda":
metric = "cuda:::event:shared_load:device=0"
else:
pytest.skip(f"Target {target.kind} not supported by this test")
mod, params = mlp.get_workload(1)
exe = relay.vm.compile(mod, target, params=params)
vm = profiler_vm.VirtualMachineProfiler(exe, dev)
data = tvm.nd.array(np.random.rand(1, 1, 28, 28).astype("float32"),
device=dev)
report = vm.profile(
[data],
func_name="main",
collectors=[
tvm.runtime.profiling.PAPIMetricCollector({dev: [metric]})
],
)
assert metric in str(report)
csv = read_csv(report)
assert metric in csv.keys()
assert any([float(x) > 0 for x in csv[metric]])
def test_vm(target, dev):
dtype = "float32"
x = relay.var("x", shape=(relay.Any(), relay.Any()), dtype=dtype)
y = relay.var("y", shape=(relay.Any(), relay.Any()), dtype=dtype)
mod = tvm.IRModule()
mod["main"] = relay.Function([x, y], relay.add(x, y))
exe = relay.vm.compile(mod, target)
vm = profiler_vm.VirtualMachineProfiler(exe, dev)
data = np.random.rand(28, 28).astype("float32")
report = vm.profile(data, data, func_name="main")
assert "fused_add" in str(report)
assert "Total" in str(report)
assert "AllocTensorReg" in str(report)
assert "AllocStorage" in str(report)
csv = read_csv(report)
assert "Hash" in csv.keys()
# Ops should have a duration greater than zero.
assert all([
float(dur) > 0 for dur, name in zip(csv["Duration (us)"], csv["Name"])
if name[:5] == "fused"
])
# AllocTensor or AllocStorage may be cached, so their duration could be 0.
assert all([
float(dur) >= 0 for dur, name in zip(csv["Duration (us)"], csv["Name"])
if name[:5] != "fused"
])
def updateFollowing(csv):
f = 0
friends = api.friends()
csv = readCSV(csv)
dicty = csv.copy()
for friend in friends:
if friend.screen_name not in csv.keys():
dicty[friend.screen_name] = 0
f += 1
writeCSV(dicty)
print(f'{f} following updated')
def test_vm(target, dev):
mod, params = mlp.get_workload(1)
exe = relay.vm.compile(mod, target, params=params)
vm = profiler_vm.VirtualMachineProfiler(exe, dev)
data = np.random.rand(1, 1, 28, 28).astype("float32")
report = vm.profile(data, func_name="main")
assert "fused_nn_softmax" in str(report)
assert "Total" in str(report)
csv = read_csv(report)
assert "Hash" in csv.keys()
assert all([float(x) > 0 for x in csv["Duration (us)"]])
def test_vm(target, dev):
dtype = "float32"
x = relay.var("x", shape=(relay.Any(), relay.Any()), dtype=dtype)
y = relay.var("y", shape=(relay.Any(), relay.Any()), dtype=dtype)
mod = tvm.IRModule()
mod["main"] = relay.Function([x, y], relay.add(x, y))
exe = relay.vm.compile(mod, target)
vm = profiler_vm.VirtualMachineProfiler(exe, dev)
data = np.random.rand(28, 28).astype("float32")
report = vm.profile(data, data, func_name="main")
assert "fused_add" in str(report)
assert "Total" in str(report)
assert "AllocTensorReg" in str(report)
assert "AllocStorage" in str(report)
csv = read_csv(report)
assert "Hash" in csv.keys()
assert all([float(x) > 0 for x in csv["Duration (us)"]])
def build_csv(d_list):
"""
build a csv from a list of dictionnaries
"""
#get one leveled dicitonnaries
for d in d_list:
parse_nested_objects(d)
csv = {}
#get the header columns of the csv
for d in d_list:
for k in d.keys():
if not k in csv:
csv[k] = []
# get the values of the csv
for d in d_list:
for k in csv.keys():
if k in d:
csv[k].append(d[k])
else:
csv[k].append(None)
return csv
def writeCsv(self, csv, filename):
with open(filename, "w") as csvfile:
for st in csv.keys():
csvfile.write("%s %d\n" % (st, csv[st][0]))