Tests create difference logs calculated from a baseline
Signed-off-by: JoseSantosAMD <Jose.Santos@amd.com>
[ROCm/rocprofiler-compute commit: 279552c03f]
This commit is contained in:
JoseSantosAMD
committad av
Karl W. Schulz
Karl W. Schulz
förälder
84a94a3e3e
incheckning
71bd81d92d
@@ -8,7 +8,7 @@ import subprocess
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import re
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import shutil
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omniperf = SourceFileLoader("omniperf", "src/omniperf").load_module()
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# omniperf = SourceFileLoader("omniperf", "src/omniperf").load_module()
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workload_1 = os.path.realpath("workload")
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kernel_name_1 = "vecCopy(double*, double*, double*, int, int) [clone .kd]"
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app_1 = ["./sample/vcopy", "1048576", "256"]
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@@ -70,6 +70,56 @@ ALL_CSVS_MI200 = [
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ROOF_ONLY_CSVS = ['pmc_perf.csv', 'pmc_perf_0.csv', 'pmc_perf_1.csv', 'pmc_perf_2.csv', 'roofline.csv', 'sysinfo.csv', 'timestamps.csv']
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Baseline_dir = os.path.realpath("Baseline_mi100")
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def metric_compare(errors_pd, baseline_df, run_df, threshold = 1):
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#iterate data one row at a time
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for idx_1, idx_2 in zip(baseline_df.index, run_df.index):
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row_1 = baseline_df.iloc[idx_1]
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row_2 = run_df.iloc[idx_2]
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kernel_name=""
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differences={}
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if "KernelName" in row_2.index:
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kernel_name=row_2["KernelName"]
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differences ={}
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for pmc_counter in row_2.index:
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if "Ns" in pmc_counter or "id" in pmc_counter or "[" in pmc_counter:
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# print("skipping "+pmc_counter)
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continue
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# assert 0
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if not pmc_counter in list(baseline_df.columns):
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print("error: pmc mismatch! "+pmc_counter+" is not in baseline_df")
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continue
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data_1 = row_1[pmc_counter]
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data_2 = row_2[pmc_counter]
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if isinstance( data_1, str) and isinstance( data_2, str):
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if data_1 not in data_2:
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print(data_2)
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else:
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# relative difference
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if not data_1 == 0:
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diff = round(100* abs(data_2 - data_1)/data_1,2)
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if diff > threshold:
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print("["+pmc_counter+"] diff is :"+str(diff)+"%")
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if pmc_counter not in differences.keys():
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print("["+pmc_counter+"] not found in ", list(differences.keys()))
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differences[pmc_counter] = diff
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else:
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# Why are we here?
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print("Why did we get here?!?!? errors_pd[idx_1]:", list(differences.keys()))
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differences[pmc_counter].append(diff)
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else:
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# if 0 show absolute difference
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diff = round(data_2 - data_1, 2)
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if diff > threshold:
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print(idx_1+"["+pmc_counter+"] diff is :"+str(diff,2))
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errors_pd = pd.concat([errors_pd,pd.DataFrame(differences,index=[kernel_name])])
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return errors_pd
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def run(cmd):
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p = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
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@@ -107,26 +157,26 @@ soc = gpu_soc()
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def test_path():
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if os.path.exists(workload_1):
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shutil.rmtree(workload_1)
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with pytest.raises(SystemExit) as e:
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with patch(
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"sys.argv",
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[
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"omniperf",
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"profile",
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"-n",
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"app_1",
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"-VVV",
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"--path",
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workload_1,
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"--",
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]
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+ app_1,
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):
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omniperf.main()
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# assert successful run
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assert e.value.code == 0
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# if os.path.exists(workload_1):
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# shutil.rmtree(workload_1)
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# with pytest.raises(SystemExit) as e:
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# with patch(
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# "sys.argv",
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# [
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# "omniperf",
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# "profile",
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# "-n",
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# "app_1",
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# "-VVV",
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# "--path",
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# workload_1,
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# "--",
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# ]
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# + app_1,
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# ):
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# omniperf.main()
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# # assert successful run
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# assert e.value.code == 0
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files_in_workload = os.listdir(workload_1)
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@@ -134,7 +184,7 @@ def test_path():
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file_dict = {}
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for file in files_in_workload:
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if file.endswith(".csv"):
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file_dict[file] = pd.read_csv(workload_1 + "/" + file)
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file_dict[file] = pd.read_csv(workload_1 + "/" + file,index_col=0)
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print("length is: ", len(file_dict[file].index))
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print(file_dict[file])
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assert len(file_dict[file].index)
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@@ -144,7 +194,23 @@ def test_path():
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assert sorted(list(file_dict.keys())) == ALL_CSVS_MI200
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else:
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assert sorted(list(file_dict.keys())) == ALL_CSVS
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for file in file_dict.keys():
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if file == "pmc_perf.csv" or "SQ" in file:
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print(file)
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# read file in Baseline
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df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
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# get corresponding file from current test run
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df_2 = file_dict[file]
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errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
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if not errors.empty:
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if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
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error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
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new_error_log = pd.concat([error_log, errors])
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new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
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else:
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errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
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def test_kernel():
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if os.path.exists(workload_1):
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@@ -307,6 +373,23 @@ def test_ipblocks_SQ():
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]
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assert sorted(list(file_dict.keys())) == expected_csvs
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for file in file_dict.keys():
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if file == "pmc_perf.csv" or "SQ" in file:
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print(file)
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# read file in Baseline
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df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
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# get corresponding file from current test run
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df_2 = file_dict[file]
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errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
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if not errors.empty:
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if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
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error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
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new_error_log = pd.concat([error_log, errors])
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new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
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else:
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errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
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def test_ipblocks_SQC():
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@@ -356,6 +439,23 @@ def test_ipblocks_SQC():
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expected_csvs.insert(5, "roofline.csv")
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assert sorted(list(file_dict.keys())) == expected_csvs
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for file in file_dict.keys():
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if file == "pmc_perf.csv" or "SQ" in file:
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print(file)
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# read file in Baseline
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df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
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# get corresponding file from current test run
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df_2 = file_dict[file]
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errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
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if not errors.empty:
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if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
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error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
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new_error_log = pd.concat([error_log, errors])
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new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
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else:
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errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
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def test_ipblocks_TA():
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@@ -411,6 +511,23 @@ def test_ipblocks_TA():
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expected_csvs.insert(9, "roofline.csv")
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assert sorted(list(file_dict.keys())) == expected_csvs
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for file in file_dict.keys():
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if file == "pmc_perf.csv" or "SQ" in file:
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print(file)
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# read file in Baseline
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df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
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# get corresponding file from current test run
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df_2 = file_dict[file]
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errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
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if not errors.empty:
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if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
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error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
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new_error_log = pd.concat([error_log, errors])
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new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
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else:
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errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
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def test_ipblocks_TD():
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@@ -470,6 +587,23 @@ def test_ipblocks_TD():
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]
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assert sorted(list(file_dict.keys())) == expected_csvs
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for file in file_dict.keys():
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if file == "pmc_perf.csv" or "SQ" in file:
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print(file)
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# read file in Baseline
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df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
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# get corresponding file from current test run
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df_2 = file_dict[file]
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errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
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if not errors.empty:
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if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
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error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
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new_error_log = pd.concat([error_log, errors])
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new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
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else:
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errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
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def test_ipblocks_TCP():
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@@ -526,6 +660,23 @@ def test_ipblocks_TCP():
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expected_csvs.insert(11, "roofline.csv")
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assert sorted(list(file_dict.keys())) == expected_csvs
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for file in file_dict.keys():
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if file == "pmc_perf.csv" or "SQ" in file:
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print(file)
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# read file in Baseline
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df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
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# get corresponding file from current test run
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df_2 = file_dict[file]
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errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
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if not errors.empty:
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if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
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error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
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new_error_log = pd.concat([error_log, errors])
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new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
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else:
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errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
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def test_ipblocks_TCC():
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@@ -583,6 +734,23 @@ def test_ipblocks_TCC():
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expected_csvs.insert(12, "roofline.csv")
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assert sorted(list(file_dict.keys())) == expected_csvs
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for file in file_dict.keys():
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if file == "pmc_perf.csv" or "SQ" in file:
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print(file)
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# read file in Baseline
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df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
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# get corresponding file from current test run
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df_2 = file_dict[file]
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errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
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if not errors.empty:
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if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
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error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
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new_error_log = pd.concat([error_log, errors])
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new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
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else:
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errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
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def test_ipblocks_SPI():
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@@ -638,6 +806,23 @@ def test_ipblocks_SPI():
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expected_csvs.insert(10, "roofline.csv")
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assert sorted(list(file_dict.keys())) == expected_csvs
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for file in file_dict.keys():
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if file == "pmc_perf.csv" or "SQ" in file:
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print(file)
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# read file in Baseline
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df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
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# get corresponding file from current test run
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df_2 = file_dict[file]
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errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
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if not errors.empty:
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if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
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error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
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new_error_log = pd.concat([error_log, errors])
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new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
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else:
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errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
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def test_ipblocks_CPC():
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@@ -688,6 +873,23 @@ def test_ipblocks_CPC():
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if soc == "mi200":
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expected_csvs.insert(7, "roofline.csv")
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assert sorted(list(file_dict.keys())) == expected_csvs
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for file in file_dict.keys():
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if file == "pmc_perf.csv" or "SQ" in file:
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print(file)
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# read file in Baseline
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df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
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# get corresponding file from current test run
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df_2 = file_dict[file]
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errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
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if not errors.empty:
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if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
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error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
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new_error_log = pd.concat([error_log, errors])
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new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
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else:
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errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
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def test_ipblocks_CPF():
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@@ -736,6 +938,23 @@ def test_ipblocks_CPF():
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if soc == "mi200":
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expected_csvs.insert(5, "roofline.csv")
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assert sorted(list(file_dict.keys())) == expected_csvs
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for file in file_dict.keys():
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if file == "pmc_perf.csv" or "SQ" in file:
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print(file)
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# read file in Baseline
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df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
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# get corresponding file from current test run
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df_2 = file_dict[file]
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errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
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if not errors.empty:
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if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
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error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
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new_error_log = pd.concat([error_log, errors])
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new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
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else:
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errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
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def test_ipblocks_SQ_CPC():
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@@ -820,6 +1039,23 @@ def test_ipblocks_SQ_CPC():
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]
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assert sorted(list(file_dict.keys())) == expected_csvs
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for file in file_dict.keys():
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if file == "pmc_perf.csv" or "SQ" in file:
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print(file)
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# read file in Baseline
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df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
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# get corresponding file from current test run
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df_2 = file_dict[file]
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errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
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if not errors.empty:
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if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
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error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
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new_error_log = pd.concat([error_log, errors])
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new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
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else:
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errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
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def test_ipblocks_SQ_TA():
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@@ -903,6 +1139,23 @@ def test_ipblocks_SQ_TA():
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"timestamps.csv",
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]
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assert sorted(list(file_dict.keys())) == expected_csvs
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|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_ipblocks_SQ_SPI():
|
||||
@@ -986,6 +1239,23 @@ def test_ipblocks_SQ_SPI():
|
||||
"timestamps.csv",
|
||||
]
|
||||
assert sorted(list(file_dict.keys())) == expected_csvs
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_ipblocks_SQ_SQC_TCP_CPC():
|
||||
@@ -1072,6 +1342,23 @@ def test_ipblocks_SQ_SQC_TCP_CPC():
|
||||
"timestamps.csv",
|
||||
]
|
||||
assert sorted(list(file_dict.keys())) == expected_csvs
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_ipblocks_SQ_SPI_TA_TCC_CPF():
|
||||
@@ -1160,6 +1447,23 @@ def test_ipblocks_SQ_SPI_TA_TCC_CPF():
|
||||
"timestamps.csv",
|
||||
]
|
||||
assert sorted(list(file_dict.keys())) == expected_csvs
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_dispatch_0():
|
||||
@@ -1200,6 +1504,23 @@ def test_dispatch_0():
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS_MI200
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_dispatch_0_1():
|
||||
@@ -1241,6 +1562,23 @@ def test_dispatch_0_1():
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS_MI200
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_dispatch_2():
|
||||
@@ -1281,6 +1619,23 @@ def test_dispatch_2():
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS_MI200
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_kernel_verbose_0():
|
||||
@@ -1321,6 +1676,23 @@ def test_kernel_verbose_0():
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS_MI200
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_kernel_verbose_1():
|
||||
@@ -1361,6 +1733,23 @@ def test_kernel_verbose_1():
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS_MI200
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_kernel_verbose_2():
|
||||
@@ -1401,6 +1790,23 @@ def test_kernel_verbose_2():
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS_MI200
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_kernel_verbose_3():
|
||||
@@ -1441,6 +1847,23 @@ def test_kernel_verbose_3():
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS_MI200
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_kernel_verbose_4():
|
||||
@@ -1481,6 +1904,23 @@ def test_kernel_verbose_4():
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS_MI200
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_kernel_verbose_5():
|
||||
@@ -1521,6 +1961,23 @@ def test_kernel_verbose_5():
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS_MI200
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_join_type_grid():
|
||||
@@ -1561,6 +2018,23 @@ def test_join_type_grid():
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS_MI200
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_join_type_kernel():
|
||||
@@ -1601,6 +2075,23 @@ def test_join_type_kernel():
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS_MI200
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_device_0():
|
||||
@@ -1641,6 +2132,23 @@ def test_device_0():
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS_MI200
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_no_roof():
|
||||
@@ -1706,6 +2214,23 @@ def test_no_roof():
|
||||
]
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_sort_dispatches():
|
||||
@@ -1753,6 +2278,23 @@ def test_sort_dispatches():
|
||||
assert sorted(list(file_dict.keys())) == ROOF_ONLY_CSVS
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_sort_kernels():
|
||||
@@ -1799,6 +2341,23 @@ def test_sort_kernels():
|
||||
assert sorted(list(file_dict.keys())) == ROOF_ONLY_CSVS
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_mem_levels_HBM():
|
||||
@@ -1846,6 +2405,23 @@ def test_mem_levels_HBM():
|
||||
assert sorted(list(file_dict.keys())) == ROOF_ONLY_CSVS
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_mem_levels_L2():
|
||||
@@ -1893,6 +2469,23 @@ def test_mem_levels_L2():
|
||||
assert sorted(list(file_dict.keys())) == ROOF_ONLY_CSVS
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_mem_levels_vL1D():
|
||||
@@ -1939,6 +2532,23 @@ def test_mem_levels_vL1D():
|
||||
assert sorted(list(file_dict.keys())) == ROOF_ONLY_CSVS
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_mem_levels_LDS():
|
||||
@@ -1985,6 +2595,23 @@ def test_mem_levels_LDS():
|
||||
assert sorted(list(file_dict.keys())) == ROOF_ONLY_CSVS
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_mem_levels_HBM_LDS():
|
||||
@@ -2032,6 +2659,23 @@ def test_mem_levels_HBM_LDS():
|
||||
assert sorted(list(file_dict.keys())) == ROOF_ONLY_CSVS
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_mem_levels_vL1D_LDS():
|
||||
@@ -2079,6 +2723,23 @@ def test_mem_levels_vL1D_LDS():
|
||||
assert sorted(list(file_dict.keys())) == ROOF_ONLY_CSVS
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_mem_levels_L2_vL1D_LDS():
|
||||
@@ -2126,6 +2787,23 @@ def test_mem_levels_L2_vL1D_LDS():
|
||||
assert sorted(list(file_dict.keys())) == ROOF_ONLY_CSVS
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
|
||||
def test_kernel_names():
|
||||
@@ -2171,3 +2849,20 @@ def test_kernel_names():
|
||||
assert sorted(list(file_dict.keys())) == ROOF_ONLY_CSVS
|
||||
else:
|
||||
assert sorted(list(file_dict.keys())) == ALL_CSVS
|
||||
|
||||
for file in file_dict.keys():
|
||||
if file == "pmc_perf.csv" or "SQ" in file:
|
||||
print(file)
|
||||
# read file in Baseline
|
||||
df_1 = pd.read_csv(Baseline_dir +"/" +file, index_col=0)
|
||||
# get corresponding file from current test run
|
||||
df_2 = file_dict[file]
|
||||
|
||||
errors = metric_compare(pd.DataFrame(), df_1, df_2, 1)
|
||||
if not errors.empty:
|
||||
if os.path.exists(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv"):
|
||||
error_log = pd.read_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv")
|
||||
new_error_log = pd.concat([error_log, errors])
|
||||
new_error_log.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
else:
|
||||
errors.to_csv(Baseline_dir +"/" + file.split(".")[0] +"_error_log.csv", index = False)
|
||||
|
||||
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