Signed-off-by: coleramos425 <colramos@amd.com>


[ROCm/rocprofiler-compute commit: 8c173446d2]
Этот коммит содержится в:
coleramos425
2023-05-08 11:56:49 -05:00
родитель 2be3eaca2e
Коммит 26ef8b672b
+53 -27
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@@ -86,35 +86,61 @@ perfmon_config = {
},
}
# joins disparate runs less dumbly than rocprof
def join_prof(workload_dir, out):
files = glob.glob(workload_dir + "/" + "pmc_perf_*.csv")
df = None
for i, file in enumerate(files):
#_df = parse_rocprof_kernels(file)
# _df = parse_rocprof_kernels(file)
_df = pd.read_csv(file)
key = _df.groupby("KernelName").cumcount()
_df['key'] = _df.KernelName + ' - ' + key.astype(str)
_df["key"] = _df.KernelName + " - " + key.astype(str)
if df is None:
df = _df
else:
# join by unique index of kernel
df = pd.merge(df, _df, how='inner', on='key', suffixes=('', f'_{i}'))
df = pd.merge(df, _df, how="inner", on="key", suffixes=("", f"_{i}"))
# now, we can:
#   A) throw away any of the "boring" duplicats
df = df[[k for k in df.keys() if not any(
check in k for check in [
'gpu', 'queue-id', 'queue-index', 'pid', 'tid', 'grd', 'wgr',
'lds', 'scr', 'vgpr', 'sgpr', 'fbar', 'sig', 'obj'])]]
#   B) any timestamps that are _not_ the duration, which is the one we care
#   about
df = df[[k for k in df.keys() if not any(
check in k for check in [
'DispatchNs', 'CompleteNs'])]]
#   C) sanity check the name and key
namekeys = [k for k in df.keys() if 'KernelName' in k]
#   A) throw away any of the "boring" duplicats
df = df[
[
k
for k in df.keys()
if not any(
check in k
for check in [
"gpu",
"queue-id",
"queue-index",
"pid",
"tid",
"grd",
"wgr",
"lds",
"scr",
"vgpr",
"sgpr",
"fbar",
"sig",
"obj",
]
)
]
]
#   B) any timestamps that are _not_ the duration, which is the one we care
#   about
df = df[
[
k
for k in df.keys()
if not any(check in k for check in ["DispatchNs", "CompleteNs"])
]
]
#   C) sanity check the name and key
namekeys = [k for k in df.keys() if "KernelName" in k]
assert len(namekeys)
for k in namekeys[1:]:
assert (df[namekeys[0]] == df[k]).all()
@@ -123,26 +149,27 @@ def join_prof(workload_dir, out):
bkeys = []
ekeys = []
for k in df.keys():
if 'Begin' in k:
if "Begin" in k:
bkeys.append(k)
if 'End' in k:
if "End" in k:
ekeys.append(k)
# compute mean begin and end timestamps
endNs = df[ekeys].mean(axis=1)
beginNs = df[bkeys].mean(axis=1)
# and replace
df= df.drop(columns=bkeys)
df= df.drop(columns=ekeys)
df['BeginNs'] = beginNs
df['EndNs'] = endNs
df = df.drop(columns=bkeys)
df = df.drop(columns=ekeys)
df["BeginNs"] = beginNs
df["EndNs"] = endNs
# finally, join the drop key
df = df.drop(columns=['key'])
df = df.drop(columns=["key"])
# and save to file
df.to_csv(out, index=False)
# and delete old file(s)
for file in files:
os.remove(file)
def pmc_perf_split(workload_dir):
workload_perfmon_dir = workload_dir + "/perfmon"
lines = open(workload_perfmon_dir + "/pmc_perf.txt", "r").read().splitlines()
@@ -160,7 +187,7 @@ def pmc_perf_split(workload_dir):
m = re.match(mpattern, stext)
if m is None:
continue
# Create separate file for each line
fd = open(workload_perfmon_dir + "/pmc_perf_" + str(i) + ".txt", "w")
fd.write(stext + "\n\n")
@@ -170,12 +197,11 @@ def pmc_perf_split(workload_dir):
fd.close()
i += 1
# Remove old pmc_perf.txt input from perfmon dir
os.remove(workload_perfmon_dir + "/pmc_perf.txt")
def perfmon_coalesce(pmc_files_list, workload_dir, soc):
workload_perfmon_dir = workload_dir + "/perfmon"